yeast competition experiments

Research article
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Published: 05 January 2017

Competition assays and physiological experiments of soil and phyllosphere yeasts identify Candida subhashii as a novel antagonist of filamentous fungi

Maja Hilber-Bodmer 1 ,
Michael Schmid 1 , 2 ,
Christian H. Ahrens 1 , 2 &
Florian M. Freimoser 1

BMC Microbiology volume 17 , Article number: 4 ( 2017 ) Cite this article

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While recent advances in next generation sequencing technologies have enabled researchers to readily identify countless microbial species in soil, rhizosphere, and phyllosphere microbiomes, the biological functions of the majority of these species are unknown. Functional studies are therefore urgently needed in order to characterize the plethora of microorganisms that are being identified and to point out species that may be used for biotechnology or plant protection. Here, we used a dual culture assay and growth analyses to characterise yeasts (40 different isolates) and their antagonistic effect on 16 filamentous fungi; comprising plant pathogens, antagonists, and saprophytes.

Overall, this competition screen of 640 pairwise combinations revealed a broad range of outcomes, ranging from small stimulatory effects of some yeasts up to a growth inhibition of more than 80% by individual species. On average, yeasts isolated from soil suppressed filamentous fungi more strongly than phyllosphere yeasts and the antagonistic activity was a species-/isolate-specific property and not dependent on the filamentous fungus a yeast was interacting with. The isolates with the strongest antagonistic activity were Metschnikowia pulcherrima , Hanseniaspora sp. , Cyberlindnera sargentensis , Aureobasidium pullulans , Candida subhashii , and Pichia kluyveri . Among these, the soil yeasts ( C. sargentensis, A. pullulans, C. subhashii ) assimilated and/or oxidized more di-, tri- and tetrasaccharides and organic acids than yeasts from the phyllosphere. Only the two yeasts C. subhashii and M. pulcherrima were able to grow with N-acetyl-glucosamine as carbon source.

Conclusions

The competition assays and physiological experiments described here identified known antagonists that have been implicated in the biological control of plant pathogenic fungi in the past, but also little characterised species such as C. subhashii . Overall, soil yeasts were more antagonistic and metabolically versatile than yeasts from the phyllosphere. Noteworthy was the strong antagonistic activity of the soil yeast C. subhashii , which had so far only been described from a clinical sample and not been studied with respect to biocontrol. Based on binary competition assays and growth analyses (e.g., on different carbon sources, growth in root exudates), C. subhashii was identified as a competitive and antagonistic soil yeast with potential as a novel biocontrol agent against plant pathogenic fungi.

The fungal kingdom includes important plant pathogens that cause a plethora of diseases in all crops worldwide. Of particular concern are rot and wilt diseases caused by soilborne fungi, fungal spots, blights and blotches, rusts, mildews, cankers and anthracnoses, as well as postharvest decay of fruits and vegetables [ 1 , 2 ]. Infestations by aggressive, fungal pathogens can severely constrain agricultural production and often the only resort is crop rotation, fallow, or even an abandonment of the cropland [ 3 , 4 ].

Soil, roots, and the phyllosphere harbour complex microbiomes consisting of thousands of bacterial and fungal species that may suppress diseases, act as pathogens, or affect plant health and growth by various other mechanisms [ 5 – 8 ]. Yet, microbiomes are still a largely untapped resource for protecting crop plants against pathogens and for increasing agricultural productivity [ 9 , 10 ]. Considerable efforts are therefore undertaken to harness and use microbiota for novel applications in agriculture [ 11 – 14 ]. Microbiomes of plants, rhizosphere, or soil have been elucidated by large-scale, DNA sequencing-based metagenomics approaches [ 7 , 15 , 16 ], but the contributions and functions of the large majority of the species are still mostly unknown. Microbiota thus consist predominantly of yet uncharacterized bacteria and fungi, tritagonists, that regulate microbial interactions [ 17 ].

Yeast-like fungi inhabit all aerobic environments; from the arctic and glaciers to the tropics or even the desert and from dry to saline and high-sugar habitats [ 18 – 24 ]. Many yeast species are particularly well known for their biotechnological applications or medical relevance. In agriculture, yeasts have been identified as powerful antagonists of fungal pathogens causing postharvest and storage diseases and of microorganisms attacking flowers and leaves [ 25 – 31 ]. Few yeast species have reached the market as commercial products for the postharvest control of pathogens (e.g., Aureobasidium pullulans as BoniProtect, Candida oleophila strain 1-182 as Aspire TM , Candida sake as Candifruit, Metschnikowia fructicola as Shemer, or Cryptococcus albidus as YieldPlus) or against fireblight (e.g., A. pullulans as BlossomProtect); some of which are not marketed anymore or only registered locally [ 32 – 37 ]. Yeasts suppressing soilborne pathogens have been described rarely and a commercial application has not been considered yet. Candida valida , Rhodotorula glutinis and Trichosporon asahii protected sugar beet against the soil pathogen Rhizoctonia solani [ 38 ]. In another study, Saccharomyces unispora and Candida steatolytica antagonised Fusarium oxysporum causing wilt disease in kidney beans [ 39 ] and Saccharomyces cerevisiae controlled a Fusarium infection of sugar beet [ 40 ]. In a successful example of postharvest biocontrol, M. fructicola has been employed as part of a combined strategy to control the soilborne pathogen Thielaviopsis basicola on carrots [ 41 ]. These examples clearly document the potential of yeasts to suppress and antagonise soilborne pathogens, but also highlight the limited knowledge on their biological functions in soil.

The genus Candida comprises several species that have been studied extensively with respect to biotechnological applications, biocontrol, but also as human pathogens. Candida guilliermondii is a ubiquitously present, saprophytic yeast that has received particularly broad attention because of its presence in clinical samples, the biotechnological production of metabolites and enzymes, applications in bioremediation, or the control of plant pathogenic fungi [ 42 ]. The antagonistic potential of C. guilliermondii against diverse fungal pathogens (e.g., Botrytis cinerea , Colletotrichum capsici , Penicillium expansum , Penicillium digitatum , Rhizopus stolonifer ) has been demonstrated in various cultures such as apple, citrus, nectarine, peach, or tomato ([ 42 ], and references therein). Other Candida species have also been studied for their biocontrol potential and as commercial plant protection agents against postharvest decay of fruits, based on Candida species, have been developed (see above) [ 29 , 43 , 44 ].

In the course of the work described here, we used binary competition assays to determine the antagonistic activity of soil and phyllosphere yeasts from Switzerland against a range of pathogenic and saprophytic filamentous fungi. Among the six most antagonistic yeasts out of a collection of 40 different isolates ( A. pullulans, Candida subhashii, Cyberlindnera sargentensis, Hanseniaspora sp., Metschnikowia pulcherrima , and Pichia kluyveri ), C. subhashii was the only one that has so far not been studied with respect to biocontrol. This species has only been reported from a patient suffering from peritonitis during a long-term peritoneal dialysis treatment and an isolate highly similar to this type strain (99.8% identity in the 26S rDNA D1/D2 domain, 1.3% sequence difference for the 5.8S-ITS region) was obtained from a soil sample from East Japan [ 45 , 46 ]. Except for these two reports, only one additional publication reporting the mitochondrial genome of C. subhashii has appeared [ 47 ]. Here, we describe C. subhashii as a common and frequent soil fungus that has broad metabolic capabilities, grows in root exudates, and that strongly antagonizes a wide range of filamentous fungi (all species tested in this study, including notorious plant pathogens, saprophytes, but also other antagonists of the genus Trichoderma ). Since it has not been experimentally confirmed that C. subhashii is indeed a pathogen, and based on its broad distribution in different soils and the apparent adaptations to the soil environment, it is concluded that C. subhashii is a competitive soil fungus and potential candidate for the biological control of soilborne fungal pathogens.

Isolation and cultivation of fungi

Soil or plant material (e.g., apple leaves, flowers, bark, skin) was diluted 10-fold ( w/v ) with peptone water (1 g/L Bacto Peptone) [ 48 ], vigorously mixed, and shaken (20 min, 25 °C, 250 rpm, on an orbital shaker). The resulting suspensions were diluted and different dilutions (usually 1:50 and 1:100) were plated on Difco TM potato dextrose agar (PDA; Becton, Dickinson and Company, Le Pont de Claix, France) supplemented with 5 ml chloramphenicol and tetracycline HCl (5 mg/ml in ethanol or water, respectively), and incubated at 22 °C for 2–4 days. Single fungal colonies were transferred to PDA agar plates without antibiotics and repeatedly streaked out until pure cultures were obtained. Isolates were maintained on PDA agar plates and stored in 15% ( v/v ) glycerol at −80 °C.

Identification of fungal isolates

Species identification was first attempted by MALDI-TOF as previously described [ 49 ]. In cases where MALDI-TOF did not allow species identification, the fungal ITS region was amplified with primers ITS1f [ 50 ] and ITS4 [ 51 ], PCR products were directly used for sequencing, and all isolates were assigned a species hypothesis according to the UNITE database [ 52 , 53 ] (see also Table 1 ). Crude protein extracts of isolates that were identified based on their ITS sequence were used to generate reference MALDI-TOF spectra for future identifications of the same species [ 49 ]. All isolates generated in the course of this study have been deposited and are available at the Culture Collection of Switzerland (CCoS; https://www.ccos.ch ; Table 1 ).

Quantification of yeast antagonism against filamentous fungi in vitro

Yeasts were collected from a PDA plate (less than 2 weeks old), diluted in water, and adjusted to an OD 600 of 0.1. Fifteen microlitre of this suspension was plated on PDA plates (5.5 cm in diameter) in quadruples. Conidia of filamentous fungi were collected in water, diluted (OD 600 = 0.1), and 5 μl were inoculated in the centre of the plates (previously overlaid with yeasts or fresh PDA plates as a control). Plates were incubated at 22 °C for 3 to 15 days depending on the fungal species. Growth of the filamentous fungus was quantified before it reached the edge of the control plate (plate without yeasts) with the help of a planimeter (Planix 5, Tamaya Technics Inc., Tokyo, Japan). The average of the relative growth (growth in presence of yeast/growth on control plate) of four replicates for each of the 640 combinations was calculated, log 2 -transformed, and all data were clustered using EPCLUST ( http://www.bioinf.ebc.ee/EP/EP/EPCLUST/ ) for visualisation (correlation measure based distance (uncentered), complete linkage).

Growth analysis of yeasts at different temperatures

Yeasts were collected from a PDA plate, resuspended in sterile water, adjusted to an OD 600 of 1, and 10-fold dilutions were prepared in a microtiter plate. The dilutions were spotted onto PDA plates with a multi-blot replicator (delivered volume approx. 3 μl) (V & P Scientific, Inc., San Diego, USA). The plates were incubated at temperatures ranging from 15 to 37 °C and the maximal dilution to which the yeast grew was recorded. Each experiment was performed at least twice for each isolate and the average fold-dilution is indicated as reflective of the growth.

Microarray phenotype analysis

Overnight liquid cultures were grown in Difco TM potato dextrose broth (PDB; Becton, Dickinson and Company, Le Pont de Claix, France). Cells were pelleted by centrifugation (4 °C, 10 min, 650 g), the supernatant was discarded, and the cells were washed twice with sterile water. For each yeast isolate, a suspension with an OD 600 of 1 was prepared and 100 μl of this solution were inoculated in each well of a Biolog YT MicroPlate TM (Endotell AG, Allschwil, Switzerland) [ 54 ]. The absorption at 590 nm was determined in a plate reader (Infinite® 200 Pro; Tecan Group Ltd., Switzerland) daily for 3 days. All data were normalized with the corresponding water control and growth was expressed relative to the initial measurement at day 0. The maximal relative growth at any of the three time-points was recorded (rounded to the first integer). For each yeast, the experiment was performed twice and the average of the two measurements is shown. Substrates that did not lead to detectable growth for any of the yeasts are not shown. For four carbon sources (glucose, maltose, N-acetyl-glucosamine, melezitose), the microarray phenotype results were confirmed by performing growth analyses in defined medium. Yeast nitrogen base (with amino acids and ammonium sulphate) was supplemented with glucose, maltose, N-acetylglucosamine or melezitose (stock solutions were filter sterilized, final concentration 10 g/L) and growth was followed by measuring the OD 600 in a plate reader (Infinite® 200 Pro; Tecan Group Ltd., Switzerland). The final measurement (mean of five replicates and standard error) after 42 h is shown.

Growth in root exudates

Mung bean ( Vigna radiata ) root exudates were collected according to Barbour et al. [ 55 ] and used at a final concentration of 0.1 mg/ml. Yeasts were inoculated to an initial OD 600 of 0.1 and growth was measured in a plate reader (Infinite® 200 Pro; Tecan Group Ltd., Switzerland) for 3 days. The mean of six replicates and the standard error are shown.

Sequencing and analysis of the C. subhashii FGA 2.2 mitochondrial genome

Candida subhashii strain FGA 2.2 genomic DNA was extracted using the Qiagen DNeasy Plant Mini Kit and sequenced on the PacBio RS II platform (performed at the Functional Genomics Center Zurich). Subsequent de novo genome assembly and resequencing were performed using PacBio SMRT Portal 2.3.0 [ 56 ]. Assembly was generated using protocol RS_HGAP_Assembly.3. The contig corresponding to the mitochondrial genome revealed a linear DNA molecule. Manual curation was performed to extend both telomeres to their full length of 729 bp, resulting in a mitochondrial DNA (mtDNA) assembly of 29,930 bp. One additional resequencing step was performed using SMRT portal protocol RS_Resequencing.1, which resulted in a mean coverage depth of 567-fold. The C. subhashii strain FGA 2.2 mitochondrial genome was annotated by reference to the C. subhashii type strain CBS10753 [ 47 ].

To construct a phylogenetic tree, the mtDNA sequences of 22 diverse yeast species, selected based on previous studies and the availability of complete and annotated mitochondrial genomes [ 47 ], were obtained from NCBI (Table 2 ). The amino acid sequences of the conserved proteins Atp6, Atp8, Atp9, Cob, Cox1, Cox2 and Cox3 were extracted from the downloaded sequences as well as from the C. subhashii mtDNA assembly. Multiple sequence alignment (MSA) using MUSCLE 3.8 [ 57 ] and trimming of overhanging sequences ensured that the amino acid sequences of all genes and all 22 strains were of similar length. The amino acid sequences of all proteins were concatenated for every strain and a final MSA with MUSCLE was performed. The resulting alignment of 1743 amino acids was used to create a phylogenetic tree by RAxML 8.1 applying the JTT + Γ model [ 58 ]. The phylogeny was tested by performing 100 bootstrap replicates.

The antagonistic activity of naturally occurring yeasts against filamentous fungi in vitro

From a collection of yeasts naturally occurring in agricultural environments, a subset of 40 species was selected (Table 1 ). These isolates represented the taxonomic diversity in our collection and mostly originated from soil samples (agricultural soil, orchard soil, potting soil, compost; 18 isolates), and the apple phyllosphere (flowers, leaves, fruits, bark; 19 isolates) (Table 1 ). In addition, one isolate each from irrigation water or a Drosophila species (collected in Wädenswil, Switzerland) was included. Finally, for comparison, a reference strain of Saccharomyces cerevisiae (BY4741) [ 59 ] was included. The antagonistic activity of these 40 yeasts against 16 fungal test strains (a broad selection of commonly isolated, pathogenic, antagonistic, or saprophytic filamentous fungi) (Table 1 ) was quantified by determining the relative growth of each filamentous fungus in the presence of each yeast (relative to the growth in the absence of yeasts) (Fig. 1 ; Additional file 1 ).

Example of the binary competition assay that was used to quantify the interactions of 40 yeast isolates with 16 filamentous test fungi. Competition assays were performed by quantifying the growth area of a filamentous fungus (e.g., the plant pathogen Gibberella fujikuroi BC 8.14) on control plates ( left ) and in the presence of a yeast isolate (e.g., C. subhashii , right ). Overall, 640 competition assays were carried out and quantified (in quadruples)

All data were clustered based on the outcome of the pairwise interactions of all filamentous fungi with each yeast isolate (Fig. 2a ). Overall, the majority of yeast isolates reduced the growth of filamentous fungi, but in a few interactions a small stimulatory effect of a yeast isolate was detected (Fig. 2a ; Additional file 1 ). Based on their growth profiles in the presence of all 40 yeast isolates, the three Trichoderma isolates were clustered together with the two Mucor isolates, while nine plant pathogenic species (six Fusarium isolates, Alternaria eichhorniae , Mycosphaerella tassiana , Monilinia fructicola ) formed a second, broad cluster (Fig. 2a ). The growth profiles of R. solani and Metarhizium brunneum in the presence of yeasts strongly differed from each other and from all other filamentous fungi. Clustering of the different yeasts based on their effect on the growth of all 16 filamentous fungi lead to a clear separation of isolates obtained from the apple phyllosphere and those isolated from soil (Fig. 2a ).

Binary competition assays identify strongly antagonistic yeasts with potential for biocontrol applications. a The average relative growth (four replicates) of 16 filamentous fungi in the presence of 40 different yeasts was log 2 -transformed and all data were clustered (correlation measure based distance (uncentered), complete linkage). Colours (see legend) range from strong inhibition (−8; dark blue ), via no effect ( white ) to strong growth promotion (8; dark pink ). Missing data are indicated by grey squares . b The overall average relative growth of filamentous fungi (over all 16 test strains used in this study) in the presence of each yeast isolate. The strain S. cerevisiae BY4741 is included as a reference. c The average relative growth of each filamentous fungus (average of relative growth in presence all apple or soil yeast isolates). Data obtained with yeasts that were isolated from the apple phyllosphere or from soil are marked in red and yellow , respectively

The overall average relative growth of filamentous fungi (over all 16 isolates used in this study) in the presence of each yeast isolate revealed a broad spectrum of responses (Fig. 2b ). While, on average, some yeast isolates (e.g., APC 18.3) exhibited no detectable effect on filamentous fungi, others (e.g., APC 1.2) reduced their growth by more than 80%. The variance of this measure, for each yeast, was small and similar over the entire range of overall relative growth, suggesting that the average antagonistic activity, against a broad range of filamentous fungi, is an inherent property of a particular yeast isolate. The same effect was documented by ranking all 40 yeasts according to their effect on the relative growth of all 16 filamentous fungi (most antagonistic yeast ranked as “1”; least antagonistic isolated as “40”) (not shown). Based on both measures, the overall average relative growth and the average rank for all filamentous fungi, the same six yeast isolates were identified as having the highest antagonistic activity (APC 1.2: Metschnikowia pulcherrima , APC 12.1: Hanseniaspora sp., SHA 17.2: Cyberlindnera sargentensis , NBB 7.2.1: A. pullulans , FGA 2.2: C. subhashii , APC 10.11 B: Pichia kluyveri ). In 10 interactions with filamentous fungi, M. pulcherrima (APC 1.2) was the most antagonistic yeast isolate among those tested here (average relative growth of 0.1, average rank of 1.9). Although the two most antagonistic yeast isolates were obtained from apple (APC 1.2 and APC 12.1), overall the results indicated weaker antagonism of yeasts isolated from apple as compared to the isolates obtained from soil samples (Fig. 2b ). Comparing the average relative growth of each filamentous fungus in the presence of yeasts from soil (17 isolates) or from apple (19 isolates) documented this finding: as compared to the apple yeasts, soil yeasts more strongly reduced the growth of all tested filamentous fungi (Fig. 2c ). The overall relative growth of the 16 filamentous fungi ranged from 0.3 to 0.9 (average 0.6) and above-ground plant pathogens (e.g., M. tassiana , F. gramineaurm , F. poae , M. fructicola , F. langsethiae , F. crookwellense , A. eichhorniae ) were generally more sensitive to inhibition by yeasts than soil fungi (Fig. 2c ). Fast-growing, saprophytic soil fungi such as Mucor circinelloides , Mucor moelleri , and the soil pathogen R. solani were least inhibited in their growth by yeasts.

Physiological characteristics of strongly antagonistic yeasts from soil or apple

The six overall strongest antagonists comprised three yeasts from apple (APC 1.2: M. pulcherrima ; APC 12.1: Hanseniaspora sp. ; APC 10.11 B: P. kluyveri ) and soil each (SHA 17.2: C. sargentensis ; NBB 7.2.1: A. pullulans ; FGA 2.2: C. subhashii ). In order to identify common and distinguishing characteristics that may affect the potential as biocontrol agents, these six most antagonistic yeasts were further characterized with respect to their growth requirements.

All six yeast isolates grew well at temperatures up to 30 °C and two isolates, one isolate each from apple and soil (APC 11.10 B: P. kluyveri ; FGA 2.2: C. subhashii , respectively), were able to multiply at 37 °C (Fig. 3a ). Microarray phenotype analysis, using the Biolog YT MicroPlate TM , revealed a broader metabolic versatility of the three soil yeasts as compared to the three isolates obtained from the apple phyllosphere (Fig. 3b ). Most noteworthy were a number of di-, tri- and tetrasaccharides (e.g., maltose, melebiose, palatinose, sucrose, maltotriose, melezitose, raffinose, stachyose) that were assimilated and/or oxidized by at least one soil yeast, while none of these carbon sources were utilized by any of the three yeast isolates obtained from the apple phyllosphere. In particular the two yeasts A. pullulans (NBB 7.2.1) and C. subhashii (FGA 2.2) assimilated and/or oxidized a large number of compounds (34 and 20, respectively), including different acids (e.g., acetic, formic, aspartic, fumaric, malic acids) (Fig. 3b ). In contrast, P. kluyveri (APC 11.10 B) only grew with glucose and M. pulcherrima (APC 1.2) and Hanseniaspora sp. (APC 12.1) only showed detectable growth with 9 and 11 carbon sources, respectively. Interestingly, however, the phyllosphere yeast M. pulcherrima , as well as C. subhashii and A. pullulans , were able to utilize N-acetyl-glucosamine (GlcNac), a component of bacterial and fungal cell walls and insect exoskeletons. The broad metabolic versatility observed, for example for A. pullulans , did not go along with the ability to grow with root exudates as the sole source of nutrients (Fig. 3c ). Aureobasidium pullulans (NBB 7.2.1) and Hanseniaspora sp. (APC 12.1) did not grow solely in root exudates (0.1%). In contrast, P. kluyveri (APC 11.10 B), which grew only in the presence of glucose in the phenotype microarray analysis, was able to multiply in 0.1% ( w/w ) mung bean root exudate. Of the six yeast isolates tested here, the soil isolate SHA 17.2 of C. sargentensis grew best in root exudates.

Physiological characteristics of strongly antagonistic yeasts. The six most strongly antagonistic yeasts were characterized by determining their growth at different temperatures ( a ), the assimilation and oxidation of different carbon sources ( b ) and the growth with selected sugars ( c ) or with root exudates (growth at days 0, 1, 2, 3, and 6 is depicted) ( d ). All experiments were repeated at least twice and the mean and standard errors are shown

Candida subhashii is an abundant soil fungus

One of the overall strongest antagonists was C. subhashii , a species that has previously only been described in a patient sample in Canada and was considered a human pathogen [ 45 ]. During our collection of fungal isolates from Swiss agricultural samples, C. subhashii was repeatedly isolated from agricultural soil and from commercially available potting substrates. In one of the latter, C. subhashii constituted approx. 50,000 CFU per gram of soil and was the fourth most frequent taxon based on ITS barcode sequencing (data not shown). To further confirm that the C. subhashii soil isolate was indeed the same species as the clinical isolate, the mitochondrial genome of the Swiss C. subhashii isolate was sequenced (available at NCBI under the accession number KX781248) and phylogenetic analyses were performed.

The mitochondrial genome sequence of the C. subhashii soil isolate FGA 2.2 was identical to the C. subhashii type strain (FR 392/CBS 10753), except that the former had an insertion of 135 bp in a non-coding region between two genes (bases 15,872 to 16,006 in the assembly of FGA 2.2). Consequently, the FGA 2.2 mitochondrial genome exhibited the same peculiarities as the corresponding genome of the type strain: exceptionally high GC content (52.7%), a lack of introns in coding sequences, and telomere-like termini of the linear molecules. A maximum likelihood phylogenetic tree of seven mitochondrial proteins (Atp6, Atp8, Atp9, Cob, Cox1, Cox2, Cox3) revealed the C. subhashii sequences as a group basal to the C. parapsilosis/C. albicans/C. tropicalis cluster, within the CTG clade. The CTG clade comprises the majority of Candida species and forms a monophyletic group of yeasts that exhibit a genetic code transition, causing the codon CTG to be translated as serine instead of leucine [ 60 – 62 ] (Fig. 4 ). Based on these results it was concluded that the two C. subhashii isolates indeed belong to the same species, are virtually identical despite the vastly different sources of origin, and that soil is a natural habitat of C. subhashii .

Maximum likelihood phylogeny constructed using a concatenated alignment of conserved mitochondrial protein sequences. The concatenated amino acid alignment of the conserved protein coding genes Atp6, Atp8, Atp9, Cob, Cox1, Cox2 and Cox3 was used to construct a phylogenetic tree. The isolate C. subhashii FGA 2.2 is shown in bold and the CTG clade is marked in green. Yarrowia lipolytica was used as the outgroup. As the concatenated protein sequences encoded by the seven genes of the previously published and the Swiss C. subhashii strains were identical, the branch length on the respective node was zero (in red ). Bootstrap scores for all nodes are shown (percentage of 100 bootstrap runs). The bar represents the number of amino acid substitutions per site. Further information about the selected sequences is reported in Table 2

Soil yeasts are generally more antagonistic and metabolically versatile than apple phyllosphere yeasts

Our competition experiments indicated that, on average and under the in vitro conditions tested here, yeasts isolated from soil suppress filamentous fungi more strongly than phyllosphere yeasts. This was the case irrespective of whether the filamentous fungus was isolated from soil or the phyllosphere, or if it was a pathogen or saprophyte. Furthermore, the comparison of three strongly antagonistic yeasts from soil and from the apple phyllosphere suggested a higher metabolic diversity of soil yeasts.

Due to rapidly fluctuating temperatures, low humidity, scarce nutrient availability, and UV irradiation, the phyllosphere is considered a harsh environment [ 63 ], but likely features a lower niche complexity as compared to soil. Consequently, interspecific competition between phyllosphere microorganisms is strong and favours the evolution of antagonistic activities to ward off competing microbes. Soil, in contrast, is a highly heterogeneous and rich habitat with a plethora of niches and thus hosts a complex microbiome [ 64 ]. In addition to environmental factors and interspecific competition, plants release root exudates and thereby also shape the microbial community in the rhizosphere [ 6 , 65 , 66 ]. The ability to metabolize root exudates may thus indicate adaptation of the corresponding yeast to soil. Indeed, soil yeasts were able to grow in the presence of various sugars and organic acids (e.g., maltose, sucrose, raffinose, acetic, formic, aspartic, fumaric, malic acids) that have been detected in root exudates of higher plants [ 67 ], while the tested, strongly antagonistic phyllosphere yeasts were unable to utilize these substrates. Nevertheless, the two phyllosphere yeasts M. pulcherrima and P. kluyveri were both able to multiply in root exudates, suggesting that on one hand plants likely release factors that allow these species to grow and that on the other hand M. pulcherrima and P. kluyveri may have the potential to colonize the rhizosphere, even though they were usually isolated from the phyllosphere. This finding is particularly relevant with respect to potential biocontrol applications against soilborne fungal pathogens, where rhizosphere competence is a factor that can contribute to a successful control [ 68 – 70 ].

Binary competition assays identify strongly antagonistic yeasts with potential for biocontrol

The dual culture assays employed here revealed the antagonistic activity of 40 yeast isolates against 16 filamentous fungi. The level of inhibition ranged from no effect at all (even slight stimulatory activities were detected in some interactions) to a growth reduction of more than 80% as compared to growth on the control plates (in the absence of yeasts). The most strongly inhibitory yeasts were M. pulcherrima , Hanseniaspora sp. , C. sargentensis , A. pullulans , C. subhashii , and P. kluyveri . Except for C. subhashii , these species, or close relatives thereof, are known antagonists and have been implicated in the biological control of plant pathogenic fungi in the past. The general nature of the antagonistic activity observed under the experimental conditions used here suggests that yeasts inhibited filamentous fungi based on their strong competitiveness for micro- and/or macro-nutrients or due to indirect effects, which is an advantageous property for a potential biocontrol agent. Further studies will have to reveal the mode of antagonism and to decipher the contribution of competition, indirect effects of metabolites, or specific antagonistic factors, in each interaction, in more detail.

In the experiments described here, M. pulcherrima was the overall most strongly antagonistic yeast. Pulcherrimin, an iron-binding pigment produced by M. pulcherrima , is believed to mediate this antagonistic activity against other fungi [ 71 – 74 ]. In the past, M. pulcherrima has been studied as an antagonist of fruit rot diseases (for example caused by Alternaria alternata , B. cinerea , P. expansum ) [ 31 , 75 , 76 ] and a related species, M. fructicola , is being used for postharvest biocontrol applications against storage diseases of sweet potatoes and carrots [ 77 ]. A strong antagonistic activity against soilborne fungal pathogens and species of Fusarium has not been reported. Hanseniaspora species are widespread and frequent in the environment, mostly studied with respect to their occurrence on grapes and winemaking, and their antagonistic activity against green mould of citrus or B. cinerea was shown [ 78 – 82 ]. Cyberlindnera sargentensis (synonym Williopsis sargentensis ) belongs to a genus of yeasts that have been shown to promote plant growth, produce volatile sulphur compounds, and kill other fungi or bacteria via killer proteins [ 83 – 88 ]. The basidiomycetous yeast A. pullulans is a cosmopolitan species that is used in biotechnology and acts as an antagonist against fungal and bacterial plant pathogens such as postharvest diseases or fire blight [ 25 , 89 – 95 ]. Pichia kluyveri and related species (e.g., Wickerhamomyces anomalus , P. fermentans , etc.) are widely studied with respect to wine fermentation as well as biological control, mostly of fungal postharvest diseases of fruits [ 30 , 96 – 101 ].

Besides the identification of known antagonists (as well as at least one new antagonist; C. subhashii ), this study also identified soilborne pathogens and several Fusarium species as new, potential targets of antagonistic yeasts. The results presented also suggest that yeast antagonism is an isolate-/species-specific property and little dependent on the target organism: a strongly antagonistic yeast exhibits this activity against a broad range of fungi. This finding has important implications for using and studying such yeasts with respect to their application in biocontrol. For example, it may be more promising to optimize the activity of demonstrably strong antagonists than isolating “new” antagonists for each pathogenic fungus to be controlled. An intriguing possibility for optimizing the activity of biocontrol organisms are communities of compatible strains that may achieve better control of plant pathogens than single strains. Initial experiments with mixtures of weakly antagonistic yeasts show that such synergistic effects can indeed be observed (data not shown). With respect to research, these results emphasize the need to study and reveal modes of antagonism that will enable translating strong antagonistic activity in the laboratory to an effective and reliable control in the field. Reliable, biological assays, but also 3rd generation DNA sequencing technologies and bioinformatics tools that have become available as of late, are the foundations for characterizing potential biocontrol strains and for identifying modes of antagonism goal-oriented and rapidly.

Candida subhashii is an antagonistic soil fungus

Among the strongly antagonistic yeasts, C. subhashii was the least studied species and not described as an antagonist of saprophytic and pathogenic, filamentous fungi. In fact, C. subhashii was considered a human pathogen because it has been isolated from a patient sample [ 45 ]. Nevertheless, it must be noted that only one case report of a C. subhashii infection exists: a patient on a long-term peritoneal dialysis treatment developed a peritonitis that was ascribed to a C. subhashii infection and successfully treated with fluconazole, ampicillin, and amoxicillin [ 45 ]. Whether or not C. subhashii can indeed colonise and cause symptoms in a healthy mammalian host has not been tested. The thermotolerance (growth at 37 °C) of C. subhashii , also observed for the isolates described here and for an isolate similar to C. subhashii described from Japan [ 46 ], is a requirement for human pathogenicity, but many isolates exhibiting this property, more frequently found within the Ascomycota than the Basidiomycota, have not yet been described as mammalian, let alone human pathogens [ 102 ].

Here, C. subhashii was repeatedly isolated from soil samples and from commercially available potting substrates, where it occurred in large concentrations (among the most frequent fungi in potting substrate: approx. 50,000 CFU per gram of substrate, the fourth most frequent taxon based on ITS barcode sequencing (data not shown)). In addition, C. subhashii was highly competitive against different soil fungi, metabolized carbohydrates commonly found in the rhizosphere, and grew in root exudates as well as on roots and in soil. The metabolic profile of the Swiss C. subhashii isolate FGA 2.2 was comparable to the one of Candida sp. NY7122, a pentose-fermenting soil yeast that is similar to C. subhashii and that was isolated from a Japanese soil. However, the latter isolate was able to assimilate L-arabinose and D-xylose [ 46 ], which was not the case for C. subhashii FGA 2.2 under the conditions tested here. Based on these results it was concluded that soil is the natural habitat for C. subhashii , where this species is a common and competitive organism. Specifically, the particular large number of C. subhashii cells in potting substrate, comprised of white and black peat (of European origin), suggests that either or both of these components are a natural reservoir of this antagonistic soil yeast.

The extremely high similarity of the mitochondrial genomes of the Swiss and clinical (Canadian) C. subhashii isolates is surprising and unexpected, particularly when considering the vastly different origins of the two isolates. However, identical or almost identical mitochondrial genomes have also been reported, for example, in Penicillium isolates from Spain and China, respectively, and may indicate a rapid, global spread of one particular isolate of C. subhashii [ 103 ]. On the other hand, the identical mitochondrial genomes are in contrast to studies reporting considerable intra-species variation in size, intron content, and recombination in fungal mitochondrial genomes [ 104 – 106 ]. At present, it is not clear why the C. subhashii mitochondrial genome is so conserved and future studies will have to address the conservation and evolution of the mitochondrial genome in more detail as well as reveal the entire genome sequence of C subhashii as a basis for identifying genes mediating antagonistic functions.

The work presented here combines a broad screening of the antagonistic activity of naturally occurring yeasts against saprophytic and pathogenic filamentous fungi with growth analyses to compare the metabolic potential of the most antagonistic yeasts. Among the most strongly antagonistic yeasts were M. pulcherrima , A. pullulans , Hanseniaspora sp. , C. sargentensis , P. kluyveri and C. subhashii . Competition assays indicated that the antagonistic activity of yeasts is an inherent property of particular yeast isolates and species and little dependent on the interacting filamentous fungus. Among the strongly antagonistic yeasts, soil yeasts were generally more antagonistic and metabolically versatile as compared to yeasts isolated from the phyllosphere. The identification of C. subhashii as a strongly antagonistic soil yeast is particularly noteworthy, because previously the natural habitat of this species was unknown and it was described, in one publication, as a human pathogen. The results presented here thus define C. subhashii as a common and competitive soil yeast.

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Acknowledgements

Kenneth H. Wolfe and Geraldine Butler are greatly acknowledged for the annotation of the C. subhashii mitochondrial genome and for helpful comments during the planning and preparation of this manuscript. Ina Schlathölter and Daniel Prata provided experimental support. Interesting discussions and conceptual input by Jürg E. Frey, Eduard Holliger and Mitja Remus-Emsermann contributed to and improved this work. Susanne Vogelgsang, Klaus Marschall, and Matthias Lutz provided fungal strains that were used in this study.

This research was supported by Agroscope.

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All strains obtained in the course of this study are deposited at the Culture Collection of Switzerland (CCOS; https://www.ccos.ch ) and the corresponding accession numbers are listed in Table 1 of this manuscript. The sequence of the Swiss C. subhashii isolate FGA 2.2 is deposited at NCBI under the accession number KX781248.

Authors’ contributions

MHB and FMF performed the experimental work. FMF planned the study and wrote the manuscript. MS and CHA sequenced and assembled the C. subhashii mitochondrial genome and wrote the corresponding sections of the manuscript. All authors have read the final manuscript.

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Additional file 1:.

The average relative growth (four replicates) of all binary competition experiments (16 filamentous fungi in the presence of 40 different yeasts) performed in the course of this study. (XLSX 21 kb)

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Hilber-Bodmer, M., Schmid, M., Ahrens, C.H. et al. Competition assays and physiological experiments of soil and phyllosphere yeasts identify Candida subhashii as a novel antagonist of filamentous fungi. BMC Microbiol 17 , 4 (2017). https://doi.org/10.1186/s12866-016-0908-z

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Competition Experiments Coupled with High-Throughput Analyses for Functional Genomics Studies in Yeast

First Online: 01 January 2011

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Daniela Delneri 3

Part of the book series: Methods in Molecular Biology ((MIMB,volume 759))

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Competition experiments are an effective way to provide a measurement of the fitness of yeast strains. The availability of the Saccharomyces cerevisiae yeast knock-out (YKO) deletion collection allows scientists to retrieve fitness data for the ~6,000 S. cerevisiae genes at the same time in a given environment. The molecular barcodes, characterizing each yeast mutant, serve as strain identifiers, which can be detected in a single microarray analysis. Competition experiments in continuous culture using chemically defined media allow a more specific discrimination of the strains based on their fitness profile. With this high-throughput approach, a series of genes that, when one allele is missing, result in either defective (haplo-insufficient) or favored (haplo-proficient) growth phenotype have been discovered, for each nutrient-limiting condition tested. While haplo-insufficient genes seemed to overlap largely across all the media used, the haplo-proficient ones seem to be more environment specific. For example, genes involved in the protein secretion pathway were highly haplo-insufficient in all the contexts, whereas most of the genes encoding for proteasome components showed a haplo-proficient phenotype specific to nitrogen-limiting conditions. In this chapter, the method used for implementation of competition experiments for high-throughput studies in yeast is presented.

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Daniela Delneri

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, Department of Biochemistry, University of Cambridge, Tennis Court Road 80, Cambridge, CB2 1GA, United Kingdom

Juan I. Castrillo

Stephen G. Oliver

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Delneri, D. (2011). Competition Experiments Coupled with High-Throughput Analyses for Functional Genomics Studies in Yeast. In: Castrillo, J., Oliver, S. (eds) Yeast Systems Biology. Methods in Molecular Biology, vol 759. Humana Press. https://doi.org/10.1007/978-1-61779-173-4_16

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Yeast-bacteria competition induced new metabolic traits through large-scale genomic rearrangements in Lachancea kluyveri

Affiliations.

1 Department of Biology, Lund University, Sölvegatan 35, 22362 Lund, Sweden.
2 Department of Biological Sciences and Biotechnology, Botswana International University of Science and Technology, P Bag 16, 00267 Palapye, Botswana.
3 Department of Biology and Biotechnology, University of Pavia, 27100 Pavia, Italy.
4 Carlsberg Laboratories, Gamle Carlsberg Vej 10, 1799 Copenhagen V, Denmark.
5 Department of Genetics, Genomics and Microbiology, University of Strasbourg, CNRS UMR7156, 67083 Strasbourg, France.
6 Institute of Molecular Biology, Academia Sinica, Taipei 11529, Taiwan.
7 Lund Protein Production Platform, Lund University, Sölvegatan 35, 22362 Lund, Sweden.
8 Department of Food, Environmental and Nutritional Sciences, University of Milan, Via Giovanni Celoria 2, 20133 Milan, Italy.
PMID: 28910985
DOI: 10.1093/femsyr/fox060

Large-scale chromosomal rearrangements are an important source of evolutionary novelty that may have reshaped the genomes of existing yeast species. They dramatically alter genome organization and gene expression fueling a phenotypic leap in response to environmental constraints. Although the emergence of such signatures of genetic diversity is thought to be associated with human exploitation of yeasts, less is known about the driving forces operating in natural habitats. Here we hypothesize that an ecological battlefield characteristic of every autumn when fruits ripen accounts for the genomic innovations in natural populations. We described a long-term cross-kingdom competition experiment between Lachancea kluyveri and five species of bacteria. Now, we report how we further subjected the same yeast to a sixth species of bacteria, Pseudomonas fluorescens, resulting in the appearance of a fixed and stably inherited large-scale genomic rearrangement in two out of three parallel evolution lines. The 'extra-banded' karyotype, characterized by a higher fitness and an elevated fermentative capacity, conferred the emergence of new metabolic traits in most carbon sources and osmolytes. We tracked down the event to a duplication and translocation event involving a 261-kb segment. Such an experimental setup described here is an attractive method for developing industrial strains without genetic engineering strategies.

Keywords: experimental evolution; genome evolution; large-scale genomic rearrangements; strain development; yeast–bacteria co-evolution.

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Published: 13 January 2012

The genetic control of growth rate: a systems biology study in yeast

Pınar Pir 1 ,
Alex Gutteridge 1 ,
Jian Wu 2 ,
Bharat Rash 2 ,
Douglas B Kell 3 ,
Nianshu Zhang 1 &
Stephen G Oliver 1

BMC Systems Biology volume 6 , Article number: 4 ( 2012 ) Cite this article

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Control of growth rate is mediated by tight regulation mechanisms in all free-living organisms since long-term survival depends on adaptation to diverse environmental conditions. The yeast, Saccharomyces cerevisiae , when growing under nutrient-limited conditions, controls its growth rate via both nutrient-specific and nutrient-independent gene sets. At slow growth rates, at least, it has been found that the expression of the genes that exert significant control over growth rate (high flux control or HFC genes) is not necessarily regulated by growth rate itself. It has not been determined whether the set of HFC genes is the same at all growth rates or whether it is the same in conditions of nutrient limitation or excess.

HFC genes were identified in competition experiments in which a population of hemizygous diploid yeast deletants were grown at, or close to, the maximum specific growth rate in either nutrient-limiting or nutrient-sufficient conditions. A hemizygous mutant is one in which one of any pair of homologous genes is deleted in a diploid, These HFC genes divided into two classes: a haploinsufficient (HI) set, where the hemizygous mutants grow slower than the wild type, and a haploproficient (HP) set, which comprises hemizygotes that grow faster than the wild type. The HI set was found to be enriched for genes involved in the processes of gene expression, while the HP set was enriched for genes concerned with the cell cycle and genome integrity.

A subset of growth-regulated genes have HFC characteristics when grown in conditions where there are few, or no, external constraints on the rate of growth that cells may attain. This subset is enriched for genes that participate in the processes of gene expression, itself ( i.e. transcription and translation). The fact that haploproficiency is exhibited by mutants grown at the previously determined maximum rate implies that the control of growth rate in this simple eukaryote represents a trade-off between the selective advantages of rapid growth and the need to maintain the integrity of the genome.

Appropriate control of the rate of cell growth is central to the long-term survival of species, particularly microorganisms. A fast growth rate is a competitive advantage when environmental conditions are favourable, while slow growth or even quiescence may allow survival under stress conditions such as rapid changes in the physicochemical environment, starvation, or exposure to toxins. Thus most organisms have evolved stringent controls over the rate of cell growth, and any disruption of these controls has severe consequences; an obvious example being the development of cancer as a result of uncontrolled cell division.

Our genome-scale approach to the investigation of growth rate control in the model eukaryote cell, S. cerevisiae , is based on the concepts of Metabolic Control Analysis (MCA; [ 1 ]). MCA defines the extent of control exerted over the flux through a pathway by its components. In our context, flux is the growth rate of cells, pathway components are the gene products, and control coefficients are the ratios of the fractional changes in growth rate to the fractional changes in the concentrations of gene products. Thus the term "control" has a special meaning in the context of MCA. Those cell components that exert control over flux are not necessarily regulatory molecules. Rather, they exert control because the flux through the pathway is sensitive to changes in the concentration (or activity) of the component. In this paper, we shall routinely use the term 'growth rate', rather than 'flux', since we have measured differences in growth rate consequent on reducing the copy number of individual genes from two to one in diploid yeast.

As part of our overall approach, we have designed experiments in two categories to elucidate the control coefficients of gene products for growth rate. In Category 1 experiments, we measured the change in the concentration of gene products as a response to a change in flux ([ 2 ];[ 3 ]). We report no new Category 1 experiments in the present study, but we shall refer to our earlier experiments in this Category. In Category 2 experiments, the concentration of gene products is altered and any impact on growth rate measured [ 4 ]; all new data reported in this paper relates to Category 2 experiments.

In our earlier Category 1 experiments, we grew a reference yeast strain in carbon-, nitrogen-, phosphorus-, or sulphur-limited conditions in chemostat culture at dilution rates, D = 0.07, 0.1 and 0.2 h -1 (equivalent to doubling times ~ 10, 7, and 3.5 h, respectively), and collected samples at each of the different steady states. We analysed our samples for changes in the levels of mRNAs, proteins, and metabolites with respect to dilution rate and identified a set of growth-rate-regulated (GR) genes; i.e. a set of genes that significantly changed their expression levels in response to the change in growth rate, irrespective of the specific nutrient whose rate of supply determined the rate of growth [ 2 ]. There is also a nutrient-regulated (NR) gene set, whose expression levels change according to the nutrient whose rate of supply is determining the growth rate; again, we have reported on these previously [ 3 ]. The level of expression of NR genes may vary in either a growth-rate-dependent or growth-rate-independent manner.

In the set of Category 2 experiments that we reported previously, we established a growth-rate competition between yeast deletion mutants (each hemizygous for just one of the organisms 5,800 protein-encoding genes [ 5 ]) under carbon, nitrogen, or phosphorus limitation in chemostats at D = 0.1 h -1 [ 4 ]. We classified all the mutants according to the sign of their relative growth rate, the mutants with negative relative growth rate (i.e. whose proportion in the population fell significantly overtime) were classified as haploinsufficient (HI) and the mutants with positive relative growth rate (i.e. whose proportion in the population significantly increased with time) were classified as haploproficient (HP). The set of haploinsufficient and haploproficient genes together form the set of high flux control (HFC) genes, i.e. those genes for which a reduction of their copy number in diploid cells from 2 to 1 results in a significant change in growth rate (flux).

Our earlier results showed that, under all three nutrient limitations and at a dilution rate of 0.1 h -1 , there was little overlap between HFC genes from Category 2 experiments and the GR genes defined in Category 1 experiments; i.e. the genes regulated by growth rate are not, themselves, regulators of growth. At the time [ 4 ], we noted that this result might, in the jargon of Systems Biology, represent a 'design rule' for the eukaryotic cell. However, since cells are the products of evolution rather than design, such rules may change according to the selection pressures to which an organism is exposed. Thus, while this rule about HFC genes holds for nutrient-limited environments, further studies are required to determine whether it has any greater generality. This paper reports these additional studies.

Since our previous set of Category 2 experiments was carried out at a low growth rate and under nutrient limitation, we decided to determine whether the lack of overlap between HFC and GR gene sets still held at high growth rates or under nutrient-unconstrained conditions. Chemostats tend to be unstable at dilution rates close to the cells' maximum specific growth rate (μ max ) and so, to establish a steady state at μ max , we used turbidostats to allow us to monitor competition between the pool of hemizygous yeast deletants growing in a complex synthetic medium (FPM [ 6 ]; see Methods section for modifications). Turbidostats are continuous cultures in which the cells, rather than the experimenter, control the growth rate [ 7 ] since the nutrient supply is determined by the biomass concentration in the growth vessel. Accordingly, we established a turbidostat in which the biomass concentration was held at a value equivalent to that of a mid-exponential phase batch culture. The turbidostat is unconstrained by the supply of nutrients and so equilibrates at the maximum specific growth rate of the yeast strain used. However, if the culture is a pool of hemizygous mutants of S. cerevisiae , and if the different mutants in the pool can have different maximum growth rates, those mutants that can achieve a μ max greater than the population average will increase in the population over time, while those with a μ max less than the population average will decrease in the population. (Those readers unfamiliar with chemostats and turbidostats will find a fuller explanation in Additional File 1 , Additional Text; also see reviews by Bull [ 8 ] and Pirt [ 7 ].)

Turbidostats, just like chemostats, represent a sensitive way of identifying haploinsufficient and haploproficient phenotypes, with the difference that (in a turbidostat) haploproficient mutants must be capable of growing at a rate greater than the previously recorded μ max . Thus, in this study, we have searched for sets of genes that exhibit either haploinsufficient or haploproficient phenotypes (these are both sub-sets of the class of HFC genes) in rapidly growing yeast cultures, either in nutrient-limited chemostats operated at D-values close to μ max or in the nutrient-unconstrained conditions of turbidostat culture. Further, we investigated subsets of HFC genes that have closely related cellular functions in order to elucidate the gene and protein features that mediate their high degree of control over growth rate. Finally, we propose an extended approach to growth rate (or flux) control, without limiting its definition to enzymes or haploinsufficiency.

Genes showing high growth-rate control during rapid growth in nutrient-unconstrained conditions

After monitoring the competitive growth of the mutants in the turbidostat for about 30 generations, we calculated the relative change in the log.-transformed abundance of mutants in the pool with respect to time:

The mutants were then classified according to the sign of their relative growth rate. 796 mutants were identified with a positive slope (FDR < 0.05). These grew faster than the population average and are defined as haploproficient. 1932 mutants grew slower than the population average (FDR < 0.05) and are defined as haploinsufficient. In total, 2728 HFC genes were found for which the changes in the relative concentrations of their hemizygotes were not significant (see Additional File 1 , Additional Text; Additional File 2 , Figure S1; Additional File 3 , Table S1).

We examined whether particular functional classes of genes were enriched among the HI and HP sets using GO terms [ 9 ] and logistic regression [ 10 ] (Table 1 ; see Additional File 4 , Table S2 for complete results for GO terms). Genes associated with the processes of transcription and translation, e.g. genes that encode cytosolic ribosomal proteins and the components of the RNA polymerase I, II, and III complexes were enriched in the HI gene set at a high level of significance (P < 10 -5 ). Apart from the functional categories, it was also noted that genes whose products are members of macromolecular complexes were highly enriched in the HI set ( P < 10 -9 ). This was true even if genes encoding ribosomal proteins were excluded from the HI set.

Analysis of the smaller set of HP genes from the turbidostat selection showed that the over-represented functional categories included the cell cycle ( e.g. spindle checkpoint genes) and macromolecule storage ( e.g. genes encoding proteins involved in trehalose metabolism). The fraction of HFC genes in various organelles, selected protein complexes and cellular processes is shown in Additional File 5 , Figure S2.

HFC at rapid growth in comparison to nutrient-limited chemostats at low growth rates

Results from the present study demonstrate that the functional distribution of HFC genes detected by competitions in turbidostats is very different from the HFC genes detected by our earlier Category 2 experiments [ 4 ] that involved competitions in nutrient-limited chemostats. However, it is not clear if this difference is a result of nutrient availability or growth rate, since the experiments by Delneri and co-workers were performed at a relatively slow growth rate (D = 0.1 h -1 ), while cells in a turbidostat grow at their maximum rate. To address this question, we carried out additional competition experiments aimed at distinguishing between the impact of fast growth and that of nutrient sufficiency (See Figure 1A for the experimental design).

Experimental design and correlation of growth profiles in different conditions . A : Competition in carbon-, nitrogen- or phosphorus-limited (F1) chemostats operating at 0.1 h -1 [ 4 ] were initially compared to competition in turbidostat cultures using complete synthetic medium (FPM, [ 6 ]; this work). Nitrogen-limited chemostat experiments at 0.2 h -1 and 0.3 h -1 (this work) and FPM fed chemostat at 0.3 h -1 (this work) were designed to investigate the differences between the competition in nutrient-limited chemostats operated at low growth rates to competition in the high growth-rate nutrient-sufficient conditions of the turbidostat. B: Correlation matrix based on the Spearman correlation coefficient of the relative growth rates of each hemizygote obtained from each experiment. The dendrograms are calculated using the Euclidean distance between rows (experiments) and hierarchical clustering.

First, a yeast culture in synthetic complete medium (FPM) was grown in chemostat mode at D = 0.3 h -1 . This means the same growth conditions were applied as in the turbidostat except that the cells were growing at a fixed dilution rate close to the maximum specific growth rate. The dilution rate in the turbidostats, and hence the maximum growth rate of the pool, stabilised at 0.32 h -1 . However, 0.3 h -1 was used in the chemostat, because operating at 0.32 h -1 would be at the limit of the stability for a chemostat culture of the pool of mutants. We found that the set of HFC genes detected under these conditions was very similar to the set detected in turbidostat competitions (Additional File 3 , Table S1). Thus, similar sets of HFC genes were found at similar growth rates despite the fact that one culture was nutrient-limited (the chemostat with FPM is leucine-limited), while the other was nutrient sufficient (turbidostat).

Next, we carried out competitions in ammonium-limited chemostats at higher dilution rates (D = 0.2 h -1 and 0.3 h -1 ). We will refer to these two conditions as N02 and N03, respectively, with a nitrogen-limited chemostat at D = 0.1 h -1 being termed N01; correspondingly cultures grown in FPM are termed FPMT, if run in turbidostatic mode, and FPM03, if run as a chemostat at D = 0.3 h -1 . First of all, the results demonstrated that the HFC genes detected under nitrogen limitation vary considerably at different dilution rates. The correlation of FCC's (Figure 1B ) from N01 and N02 is 0.54, indicating that most mutants behave similarly under the two conditions, however when dilution rate is increased further to 0.3, the similarity disappears. Competitions with different nutrient limitations (C01, N01 and P01) are highly correlated to each other at a slow growth rate, and cluster together on the dendrogram. The fast-growth rate experiments cluster together (FPMT, FPM03 and N03) and are highly correlated to each other. Hence the major difference between the competitions is the rate of growth achieved, rather than the nutrients being in plentiful or limiting supply.

Genetic control of growth rate is qualitatively, but not quantitatively, correlated with, growth rate regulation of gene expression

Previously, we have shown that genes involved in the processes of gene expression are highly up-regulated at high growth rates (GR genes from Category 1 experiments, [ 2 ]). These significantly up-regulated genes show extensive identity to genes identified as HI at high growth rates (hypergeometric p < 10 -5 ) and are highly underrepresented in the set of HP genes (hypergeometric p < 10 -32 ) (Figure 2A ). However, while the degree of overlap between the GR and HI gene sets is high, we find that (in quantitative terms) the fold change of up-regulation in gene expression is only weakly correlated to the FCC values (rho = -0.15) (Figure 2B ). This demonstrates that, although growth-regulated genes are, themselves, controllers of growth rate at the fastest growth rates, there is no simple relationship between the change in growth rate due to haploinsufficiency and the fold change in transcript level or protein concentration of those same HI genes at different growth rates.

Comparison of FCC's of genes in turbidostat competition to regulation by growth rate . Only the genes significantly regulated by growth rate are shown. A. The overlap between HFC genes identified by selection in turbidostats (this work) and up-regulation at high dilution rates [ 2 ] is significant. B. No significant correlation can be found between a gene's expression being regulated by growth rate and its FCC' value in turbidostat culture.

It should be noted that, although Category 1 experiments showed the transcript levels of genes encoding components of the mitochondrial ribosome to be up-regulated as a function of growth rate in chemostats, the competition experiments in turbidostats did not indicate that any growth-rate control was exerted by these components. Although mitochondrial activity has long been known to be an important factor that affects the growth rate in yeast ( e.g. respiratory-deficient petite mutants are slow-growing [ 11 ]), our competition experiments do not suggest that genes encoding mitochondrial proteins exert any significant growth-rate control. Expression of the mitochondrial ribosomal proteins were up-regulated in C-limited chemostats at high dilution rates [ 2 ], indicating high levels of respiration were taking place at higher dilution rates under carbon limitation. The lack of growth-rate control by mitochondria in our experiments could be an outcome of the high glucose concentration in both the turbidostat and the nitrogen-limited chemostat at D = 0.3 h -1 , where excess glucose would repress respiration and promote fermentation.

Range of flux control varies among genes within the same functional category

The control of growth rate by genes involved in large cellular components can be of different strength or even of negative directions. For example genes encoding the proteins located in nucleus or in mitochondrion can have a large range of FCC's, spanning from positive FCC's with large magnitude to negative FCC's with large magnitude, including the 'weak range', where FCC's are very close to zero but still are significant (Figure 3 ). However, even protein complexes specialized on a single function, like ribosomes, can have components with a large range of FCC's.

Range of FCC' values for HFC genes belonging to the categories shown in Additional Figure 2 . On the left, light blue bars show the range of FCC's of significantly HI genes with smaller magnitude than the average for the category, dark blue bars shown the range of FCC's of significantly HI genes with larger magnitude than the average for the category (the junction of the light and dark blue bars is the average FCC' of the category). On the right, light red bars show the range of FCC's of significantly HP genes with smaller magnitude than the average of the category, dark red bars show the range of FCC's of significantly HP genes with larger magnitude than the average of the complex (the junction of the light and dark red bars is the average FCC' of the complex). It should be noted that ranges of FCC' values for HI genes are larger than those for HP genes. The numbers in parentheses give the number of genes in the category with data from competition experiments.

Most protein complexes are essential for viability, though not every component of a given complex is essential. By analogy, it can be hypothesized that most protein complexes exert growth-rate control when cells are growing rapidly, though not every gene that encodes a component of a particular has a significant flux control coefficient (FCC'). We have selected a subset of protein complexes and cellular processes to exemplify the range of FCC' values in turbidostats (Figure 3 ; Additional File 5 , Figure S2). A highly haploinsufficient small protein complex like RNA polymerase I, can include a few HP genes with a small range of FCC's, while the genes encoding the rest of its subunits have a large range of negative growth-rate control coefficients. Moreover, the average FCC' values for members of different multiprotein complexes was found to vary in turbidostat competitions. For instance, genes encoding sub-units of the COPI vesicle coat and the exosome have high average positive FCC' values, while those encoding components of the RNA polymerase I complex and the chaperonin-containing T-complex have high average negative FCC's.

Genes with functions involved in the cell cycle and the maintenance of genome integrity are associated with haploproficiency

Genes involved in the cell cycle that determine the G1/S phase transition and progression through S phase were particularly enriched in our set of haploproficient genes. A subset of cell cycle related genes are expressed periodically as the cell cycle progresses [ 12 ] and regulated post-transcriptionally by a sequence of events [ 13 ] including 'just-in-time assembly' [ 14 ]. A reduction in the copy number of some of these genes in the results in growth rate above μ max in turbidostats (Additional File 3 , Table S1). This indicates that a reduction of controls that delay the cell cycle under normal conditions can, in fact, allow the cycle proceed more quickly in these HP hemizygotes.

Having observed that cell-cycle functions were associated with haploproficiency at higher growth rates, we measured (for 120 hemizygotes), the proportional distribution of cells in different phases of the cell cycle (Figure 4 ). Previously, wild-type cells growing in chemostats at different growth rates were reported to have the length of their unbudded G1 or G0 phase negatively correlated with their growth rate [ 15 ]. We compared the G1-phase enrichment of the 120 mutants to their flux control coefficients in turbidostat culture, and found no significant correlation (rho = -0.14, p-value = 0.14). This demonstrated that the haploproficiency of these mutants is not an immediate result of a shortened G1-phase.

The effect on of hemizygous deletions on progress through the cell cycle . The percentage of cells in G1 phase of the cell cycle during exponential growth is shown on the vertical axis for 120 heterozygous deletion mutants. The mean length of G1 as a percentage of the total length of the cell cycle is shown for all strains (bars show ± 1 standard deviation from the mean). The HO/ho mutant strain used as a control is shown in blue. Strains showing changes in the cell cycle > 1 sd and significant haploproficiency (P < 0.001) in one or more of the D > = 0.3 h -1 experiments are highlighted in red, along with cyclin gene CLB2 , which does not show haploproficiency, but shows a strong cell cycle phenotype.

Amongst those mutants that showed a large change in their cycle maps were deletants of CDC28 and two cyclin genes, CLB2 and CLN3 , whose products promote the G2/M and G1/S transitions, respectively [ 16 , 17 ]. Both CLN3 and CDC28 were HP at high growth rates, suggesting a link between altered progress through the cycle and faster growth. We have selected a subset of 9 genes that control the G 2 /M transition to investigate their effect on progress of cell cycle and phenotype in tetraploid deletion mutants (Alcasabas and co-workers, submitted). Other genes showing both haploproficiency and an altered cell cycle included DMA2 , whose human ortholog RNF8 regulates the G 2 /M DNA damage checkpoint [ 18 ], and EPL1 , a component of the NuA4 complex, which has also been linked to DNA repair [ 19 ]. We will now discuss in more detail the set of haploproficient genes involved in the maintenance of genome integrity.

Genome integrity related functions are tightly linked to cell cycle as replication of genome requires timely functioning of various protein and protein complexes regulated by cell cycle. 207 of the 384 genes annotated to the GO term 'chromosome organisation' are HFC genes. Many of these genes, as well as others, are directly involved in the maintenance of genome integrity and also show a haploproficient phenotype. These genes and their functions (some 42 of them are listed in Additional File 6 , Table S3). It is clear from the results of the turbidostat competition experiments that yeast is capable of growing at faster than maximum specific growth rate of the wild-type strain. However, the imperative of correctly replicating and segregating its chromosomes, repairing DNA damage, and generally maintaining its chromosome integrity has imposed restraints on the absolute rate of growth that can be sustained over evolutionary time.

Growth-Rate Control is a Complex Function of Protein Activity

The relationship between gene copy number and protein levels is an important factor that must be taken into consideration when investigating growth-rate control. Torres and co-workers [ 20 ] have reported that aneuploid yeast cells overexpress the genes on the extra chromosomes (93% of the genes on the extra chromosomes were found to be overexpressed by at least a factor of 1.3-fold compared to the wild-type haploid parent). Recently, it has also been reported that, for most genes tested, the average level of their protein product is proportional to gene copy number, which indicates that dosage compensation is a rare event [ 21 ].

Springer and co-workers [ 21 ] investigated 643 genes and 123 of them were found to have significant levels of dosage compensation when one copy was deleted from the genome, while 18 genes reduced their expression to less than half of the wild-type level, when one copy is deleted from the genome. We reasoned that more HI genes should be found in the set of genes that exhibit no dosage compensation and more HP or non-HFC genes in the set of genes that exhibit some dosage compensation. This is because a lack of dosage compensation is more likely to result in inadequate protein concentrations in the cell, and thus result in a reduction of fitness. Genes with dosage compensation would be expected to have less growth-rate control as the concentrations of their protein products in a hemizygote will be closer to those in the wild type. Furthermore, haploproficiency can be expected if dosage compensation leads to product levels that exceed those of the wild type. We have compared the dosage compensation values [ 21 ] (log2 transformed fold changes, 0 indicates no dosage compensation, 1 indicates dosage compensation that matches the wild-type level of the protein) to FCC' values from turbidostat competitions and the overlap of HFC genes to genes with or without significant dosage compensation.

The genome-wide frequency of HI genes identified in our turbidostats is 34%, while the frequency of HI genes in the sets of genes with and without dosage compensation is 25% and 33% (31/123 and 167/502), respectively (Figure 5A ), indicating that HI genes are only slightly underrepresented in the set of genes with dosage compensation (hypergeometric p-value = 0.0246), rather than being depleted. Thus, there is no significant correlation (Figure 5B ) between genes showing dosage compensation and those with an HFC phenotype in turbidostats or in any of the other conditions that we investigated. This indicates that there is no simple relationship between either a protein's level orthe dosage compensation of its cognate gene and the HFC characteristics of that gene.

Comparison of FCC's of genes in turbidostat competition to dosage compensation . Only the genes studied by Springer and co-workers [ 42 ] are shown. A. The overlap between HFC genes in turbidostats and genes with compensation or exacerbation is not significant. B. No significant correlation (Spearman rho = 0.016, p-value = 0.69) can be found between dosage compensation and FCC'. (Note: Correlation of FCC' to dosage compensation reported by Torres and co-workers' [ 20 ] is rho = 0.01, p = 0.83).

It was further reported by Springer and co-workers [ 21 ] that dosage compensation remains the same for a protein across different growth conditions. If dosage compensation (or the lack of it) was a simple indication of high growth-rate control, then we would expect to find a similar set of HFC genes in different conditions, and flux control coefficients to be (negatively) correlated to dosage compensation, both of which we failed to find. Hence, it should be realised that the flux control exerted by a protein is a function of its biological activity (which is probably context dependent), rather than simply on its molecular abundance. None of the high-throughput studies (including our own) measures biological activity, and so it might be anticipated that FCC' values are a complex function of protein abundance.

Few other factors were shown to link protein abundance to growth-rate control. For example, Sopko and co-workers [ 22 ] found that ca. 15% of the genes in yeast are toxic when overexpressed; that is, their overexpression reduced the growth rate. This set of toxic genes was enriched in cell cycle genes, particularly in genes that are expressed periodically. This finding supports the hypothesis that a protein can be toxic even at its wild-type levels and thus the expression of its cognate gene has to be carefully controlled. Such toxicity may increase as the protein abundance increases. It follows that a reduction in the concentration of a protein that acts as an inhibitor of growth at its wild-type concentration can have a positive impact on growth rate. Yoshikawa and co-workers [ 23 ] reported that deletion and overexpression mutants of more than 400 genes have growth defects. (Note that very few deletion or over-expression mutants that have a haploproficient phenotype were found -probably because of the low sensitivity of measurements in batch culture in microtitre plates). Another factor that should be considered is dosage imbalance as a result of deviation from wild-type protein levels ([ 24 – 27 ]).

Kacser and Burns [ 1 ] have described the relationship between enzyme activity and flux as a monotonic curve that converges to a saturation level (Figure 6 ). This curve implies that changes in enzyme activity in the 'undetectable range' do not have a major impact on flux, while small changes can be more easily detected within the 'detectable range'. However, this view of dosage versus flux is limited for two reasons: (i) Only enzymes are considered, hence increasing an enzyme's level either increases the flux or has no effect on it (monotonic). (ii) It was assumed that enzymes are unlikely to have a mutation that will increase their activity (and that, even if they did, the effect would be in the 'undetectable' range). Indeed, we have not found any enrichment of genes that encode enzymes in our set of HFC genes. Hence, a saturation curve will not explain the phenotypes observed in a genome-wide screening of HI and HP mutants.

The classical view of the enzyme - flux relationship (Kacser and Burns, 1981) . Reproduced with permission (copyright is retained by the Genetics Society of America).

In order to explain our results with a dosage v. flux curve, we have to extend its applicability to all the cases we observe and we should expect to find a different curve for each protein. For example, the lack of correlation between dosage compensation and high flux control demonstrates that the threshold lies at a different level of activity for each gene or protein. Further, haploproficiency cannot be explained by a curve that converges to a saturation level, as the maximum growth rate of a heterozygous mutant can be no higher than the maximum growth rate of the wild type on such a curve. Therefore, the flux and the control exerted on it by a protein have to be the outcome of multiple factors, each of which can be hypothesized to be a non-linear function of gene (protein) dosage/activity. The activity v. flux curve shown in Figure 6 has to be coupled with additional functions that explain the contributions or limitations imposed on the growth rate by the protein - either due to its toxic effects and functional inhibition on growth, or to the imbalances that result from changes in its abundance.

In conclusion, we hypothesize that a more realistic representation of the link between protein abundance and flux control can be achieved by a superimposition of curves, as shown in Figure 7A . We have extended the classical view shown on Figure 6 by including four more factors:

Phenotype-gene copy number relationships . A . Hypothetical relationship between phenotype and gene copy number. B-D Hypothetical examples of superimposed phenotype-gene copy number curves.

The possibility of non-zero flux, even if the protein concentration is zero, and the possibility of achieving higher fluxes as the protein concentration is increased to higher levels than in the wild type. The former possibility applies to non-essential genes in a diploid organism, and the latter to proteins that increase the flux when they are over-expressed.

Dosage imbalance: Components of the tubulin complex [ 24 ] and cell-cycle-related processes [ 27 ] have been shown to cause dosage imbalance if their concentration deviates from wild-type levels.

Inhibition by toxicity: Over-expression of hundreds of genes has been shown to cause toxicity [ 22 ] and [ 23 ]; the mechanism of toxicity is not known for most of these genes.

Functional inhibition: The function of many proteins is the inhibition or delay of processes that lead to cell growth. The timing and level of expression of genes encoding such proteins is usually fine-tuned by the cells to avoid unwanted inhibition of growth. Our results indicate that cell cycle checkpoint proteins may exert a significant inhibition on growth rate.

Hypothetical examples of such curves are given in Figures 7B-D . It is likely that genes with curves as shown in Figures 7C and 7D constitute a considerable fraction of the genome, rather than being exceptional cases. Revealing the superimposed fitness curve for each gene requires data on growth rates at various gene copy numbers (or protein concentrations), examples of which are starting to appear in the literature (Alcasabas and co-workers (submitted); [ 28 ]).

Two examples of applicability of these principles are shown on Figure 8 . Average slope of gene copy number - phenotype curve for genes encoding cytosolic ribosomal proteins is significantly positive in turbidostat selections; however, it is very close to zero in nitrogen-limited competitions. This indicates that cells with reduced numbers of ribosomes can still grow at the population average under nitrogen limitation, probably because competition is not for rapid synthesis of proteins. However, having reduced ribosomal protein gene copies in turbidostats caused growth deficiency, indicating that protein synthesis is rate-determining in turbidostats. Hence, on average, the protein components of cytosolic ribosomes have detectable growth-rate control under the turbidostatic conditions, but not under nitrogen limitation. The range of FCC' values for HFC genes encoding components of cytosolic ribosomes is shown in Figures 8B-C . Similarly, the average slope of the copy number - phenotype curve for genes specifying proteasomal proteins is significantly positive in turbidostat selections. However, the value of the slope is smaller in nitrogen-limited cultures at high growth rates and finally changes its sign to negative at D = 0.2 h -1 and to an even lower value at D = 0.1 h -1 (Figure 8D ). Both ribosomal and proteasomal proteins span a large range of negative FCC's while proteasomal proteins span the largest range of positive FCCs', indicating the variation of FCCs' within a protein complex and context dependency of flux control. Hence the dosage vs flux curve for a protein identifies how its function, toxicity, imbalance, abundance, or redundancy reflects on the growth rate.

Phenotype of genes encoding components of protein complexes as a function of copy number is context dependent . A . Cytosolic ribosomal proteins have positive slope in turbidostats B-C . The range of significant FCC's for cytosolic ribosomal proteins. D . Sign of slope of the phenotype-copy number curve for genes specifying proteasome subunits. E-F . Range of significant FCC's for proteasomal proteins.

Haploinsufficiency is the situation where the reduction in the copy number of a gene in a diploid organism from two to one results in a significant loss of fitness. It is assumed that the reduction in copy number results in a corresponding reduction in the concentration of the gene's protein product, such that there is insufficient of that protein present to sustain the wild-type phenotype. The connection of haploinsufficiency to diseases has long been known [ 29 – 31 ] and various hypotheses have been proposed to explain the mechanisms of haploinsufficiency ([ 26 , 25 ]). The kinetics of gene expression in connection to haploinsufficiency has also been studied [ 32 ]. Haploinsufficiency was initially identified in gene-by-gene phenotype screenings in humans and other organisms. However, the availability of a genome-wide collection of deletion mutants, as well as tag arrays for quantifying the proportions of individual mutants in a population, has enabled genome-wide screening for phenotypes of S. cerevisiae deletion mutants under various conditions ([ 5 , 33 , 4 ]). Previously, sets of HFC genes have been reported for the following cell populations grown in batch culture: (i) homozygous deletion mutant pool growing in YPD [ 5 ]; (ii) both homozygous and heterozygous pools growing on fermentable and non-fermentable carbon sources [ 34 ]; and (iii) heterozygous and homozygous pools growing in YPD and minimal medium [ 35 ]. Some of these pools have also been screened for haploinsufficiency when treated with drugs and sets of genes with drug sensitivity and resistance have been identified ([ 36 – 38 ]).

Haploproficiency under fast growth rate conditions has not been reported until recently [ 39 ] in genome-wide phenotype screenings of S. cerevisiae . However, it has been reported that it is possible to select for mutants with 5-50% increase in growth rate (as compared to wild type) under specific nutrient limitations [ 40 ]. Some of these evolved strains had transporter gene amplifications, together with other mutations not associated to related physiology. It was later reported that Kluyveromyces marxianus [ 41 ], allowed to evolve in a mineral medium, can attain higher growth rates than the mother strain. Changes in the morphology of these cells were found to be linked to the increased maximum growth rate, probably because a larger surface area (relative to cell volume) of the elongated mutants allows transport processes to take place more efficiently. See the review by Cakar and co-workers for recent approaches in evolutionary engineering of strains [ 42 ].

Although data on the evolution of strains have provided a valuable resource for elucidating the mechanisms of growth rate control, systematic screens of single-gene deletion mutants provide genome-wide profiling of all known genes. While mutants with severe growth defects can be easily detected in such screenings, it has been difficult to detect single-gene deletion mutants which grow faster than the wild type, as the difference in growth rate can be small and below the threshold of detection. This hindered the identification of haploproficient hemizygotes in many studies. We have shown that competition at slow growth rates in nutrient-limited chemostats can be used to identify growth advantages as well as growth deficiencies at high sensitivity [ 4 ]. Most of the genes only displayed haploproficiency under a particular nutrient limitation; for example, most genes encoding subunits of the 26S proteasome were HP under nitrogen limitation at D = 0.1 h -1 [ 4 ]. The reason for their haploproficiency under these conditions is unknown; however, it is likely to be a positive outcome of reducing the rate of protein turnover when there is competition for nitrogen resources.

In a recent study on Schizosaccharomyces pombe [ 39 ], 136 heterozygous deletion mutants in competition were shown to have faster growth rates than wild type when grown in batch on a rich medium. The same study also reported 183 haploproficient S. cerevisiae genes derived from a re-analysis of previously published data from a competition of heterozygous deletion pools under similar conditions [ 35 ]. Screening of a library of hypomorphic alleles of essential genes also revealed a small set of faster-growing mutants with a reduced concentration of essential proteins [ 43 ]. In this study, we have identified a large set of haploproficient as well as haploinsufficient hemizygotes using S. cerevisiae cultures growing at their maximum growth rates. Although these results are novel, it is not surprising that reducing the level of proteins that act as negative regulators of processes required for rapid growth, or that are toxic as monomers in the cytosol, can have a positive impact on growth rate. Although we might expect such growth-rate-limiting factors to be eliminated by evolution in order to attain yet higher growth rates and thus greater fitness, it is possible that these factors were optimized rather than eliminated, without compromising the basic function of the protein, which may be essential under sub-optimal conditions.

Our HI gene set includes 124 of the 186 HI genes reported by Deutschbauer and co-workers [ 35 ] (GR < 0.97) (our set had data on 166 of the 186), while 8 of the 166 are significantly HP in our set. Our HP gene set includes only 21 of the 181 HP genes reported by Deutschbauer and co-workers [ 35 ] (GR > 1.03), 178 of which exist in our data set. 46 of these genes are HI in our set, demonstrating that the results from the two experiments vary significantly with respect to the identification of the HP phenotype. There can be a number of reasons for the discrepancy between the two datasets. First, our classification criteria are based on goodness-of-fit rather than the percent change in growth rate - so mutants with small, but significant, changes are included in our HFC set, while mutants with large changes may have been excluded if their readings were noisy. Moreover, the experimental regimes used in the two studies are different since continuous turbidostats were used in this study as opposed to batch shake flasks. Finally, both experiments are expected to have some level of stochasticity in both the competition and sampling steps, and downstream processing of the samples may also introduce noise into the final results.

Features of genes and proteins are not globally correlated to flux control

Haploinsufficiency can be linked to a number of factors, and previous authors have found evidence for the HI phenotype of hemizygous deletants being the result of either the insufficient concentration of the protein encoded by the single-copy gene or of dosage imbalance [ 35 ]. Noise in gene expression has also been proposed as a reason for haploinsufficiency ([ 32 , 44 ]), while Veitia [ 45 ] suggested synergistic interactions between transcription factors (TF) as the cause of the haploinsufficiency of TF mutants. Accordingly, we compared our FCC' measurements to data from the literature to determine whether these, or any other factors, had an important impact on FCC' values in rapidly growing yeast.

Haploinsufficiency is often described as a direct consequence of inadequate concentrations of proteins [ 35 ], so it can be hypothesized that the proteins with higher abundance will have higher flux control when their gene copy number is reduced - particularly if dosage compensation in hemizygous mutants is rare, as reported by Springer and co-workers [ 21 ]. However, we found no significant correlation between protein abundance [ 46 ] and high flux control at rapid growth. It was also hypothesized that proteins with noisy expression are more likely to fall below the threshold [ 31 ] that will cause irreversible growth defects. We have compared modified flux control coefficients to noise in mRNA expression (DV values from [ 47 ]). There was no genome-wide correlation between high growth-rate control and noise in mRNA expression, nor were the rates of mRNA and protein decay or half-lives [ 48 – 50 ] correlated to growth-rate control. In a recent study on haploinsufficiency in humans [ 51 ], genetic interaction network features were identified as the best predictors of haploinsufficiency. However, the correlation between the number of genetic interactions of genes and their FCC' value in our turbidostat study were insignificant (rho = -0.049 for positive interactions, -0.055 for negative interactions, -0.015 for synthetic lethal, rho = -0.065 for all physical and genetic interactions of yeast from BioGrid, [ 52 ]). We have also demonstrated that, although growth-regulated genes (from our Category 1 experiments) are, themselves, controllers of flux at the fastest growth rates, there is no simple relationship between the change in growth rate due to haploinsufficiency and the fold change in transcript level or protein concentration.

Haploinsufficiency has been linked to the stoichiometry of protein complexes as there is evidence that dosage imbalance has a profound effect on phenotype in some cases ([ 25 , 29 ]). Previous competition experiments on homozygous and heterozygous deletion mutant pools grown in batch on YPD have also shown that genes encoding the components of protein complexes are enriched among the HI genes [ 35 ]. Our HFC genes are also enriched in genes encoding components of protein complexes (hypergeometric distribution, p-value = 1.5e -7 for HI genes, p-value = 0.034 for HP genes; the genes assigned to GO Term 'protein complex' were used in the analysis (Saccharomyces Genome Database, SGD, http://www.yeastgenome.org ). Moreover, HI genes and their protein products are involved in more genetic or protein-protein interactions than expected by chance (p-value = 8.7e -7 for HI as opposed to p-value = 0.22 for HP, data from Biogrid [ 52 ]). Both HI and HP genes are more likely to be essential than the average for the genome (hypergeometric distribution, p-value = 0.0051 for HI genes and p-value = 0.039 for HP genes; list of essential genes downloaded from SGD). These results demonstrate that protein complexes, or proteins that function in interaction with others, are more likely to be involved in high flux control when compared to proteins that act as singletons in the cell.

Conclusions

Our method has identified many more HI and HP genes than have been reported previously. Our results cover 124 of the 166 HI genes previously identified in YPD [ 35 ]. The relatively poor overlap with previous data on HP genes indicates systematic differences between experimental design and data analysis regimes in the two different approaches.

We find that translation and transcription are the bottlenecks for fast growth when the nutrients are not limited. The enrichment of cell cycle genes in the set of genes that are toxic upon over-expression supports our hypothesis that reduction in protein levels increases growth rate as wild-type levels limit the growth rate by 'functional inhibition'. Accumulation of cells in the G1 phase may be an indication of its high flux control, although no direct link exists between the two. Genes involved in the maintenance of genome integrity also show a haploproficient phenotype under conditions of rapid growth. Thus the checks and balances that the yeast cell imposes on itself to ensure that its genome integrity is maintained and that the genome is faithfully replicated and segregated during the cell cycle limit the absolute rate of growth which is compatible with the organism's long-term survival.

Proteins may have different levels of flux control under different growth conditions; their flux control can even be in opposite directions depending on the nature of the competition. Hence, high flux control is context-dependent and is not a direct consequence of any single factor, such as protein abundance or growth-rate regulation of gene expression. It is more likely to be a complex function of protein activity, which can be hypothesised as a superimposition of functions set by multiple factors.

Competition Experiments

A 1 ml aliquot of the heterozygous deletion mutant (BY4743, MAT a /MATα his3Δ1/his3 Δ1 leu2Δ0/leu2Δ0 lys2Δ0/LYS2 MET15/met15Δ 0 ura3Δ 0 /ura3 Δ0) pool (obtained from the Yeast Genome Deletion Project library and prepared as described in Additional File 1 , Additional Methods) were grown in 100 ml of YPD media overnight at 30°C with shaking at 180 rpm. 10 ml of this preculture was used to inoculate 1L of the appropriate nitrogen-limited or FPM growth media in 2L fermentors. Cultures were grown overnight at 30°C with aeration and stirring at 750 rpm. After 24 hours the fermentors were switched to continuous growth with pH control (set point = 4.5) and either a fixed dilution rate (chemostat) or dilution rate determined by biomass (turbidostats). In turbidostat mode [ 53 ], the biomass corresponding to approximately half of the maximum reading from batch phase (corresponding to mid-exponential growth). 20 ml samples were taken from the preculture and overnight batch culture followed by at least four at approximately 24-hour intervals from the continuous cultures. Experiments were made with at least two biological replicates for each fermentation and two technical replicates for each sample. For further details on fermentations including instrumentation and sampling, see the Additional File 1 , Additional Methods section.

DNA extraction, tag amplification, hybridization

Total DNA was isolated from each of the samples using a Promega Wizard Genomic DNA purification kit, according to the protocol provided together with the kit. Universal primers were then used to amplify the up-tag and down-tag sequences with separate PCR reactions as described previously [ 4 ]. The PCR products were purified, quantified and hybridized on custom Affymetrix Tag3/4 arrays as described previously ([ 4 , 54 ]).

Data analysis

Tag3 data were normalised and modelled as described previously [ 4 ]. Tag4 data were normalized as described [ 54 ] and modelled as described previously [ 4 ]. In both cases a relative growth rate and multiple testing corrected P-value (null hypothesis that relative growth rate is equal to 0) were obtained for each strain. The 182 deletion mutants listed in [ 55 ] were excluded from further analysis. The relative growth rates were used to calculate the Spearman correlation coefficients between experiments shown in Figure 1B . In Figure 1B the dendrogram showing the relationship between different experiments was constructed using the Euclidean distance between rows (experiments) and complete linkage hierarchical clustering. The functional analysis was made using Gene Ontology [ 56 ] as described in the Additional File 1 , Additional Methods. All statistical methods were implemented either in R/Bioconductor [ 57 ] using RSRuby [ http://rubyforge.org/projects/rsruby/ ] or Matlab [The Mathworks, Inc]. For further details on sample processing and data analysis, see the Additional File 1 , Additional Methods section.

Derivation of modified flux control coefficients

Equation 1 shows the change in abundance of a mutant in a chemostat (or turbidostat) as a function of time:

Where X denotes hemizygote concentration (abundance in terms of number of cells, in arbitrary units) in the chemostat, t denotes time, μ denotes the growth rate of the mutant and D is the dilution rate, which is equal to the average growth rate of the population. After rearranging Equation 1 , a linear relationship between the change of log.-transformed hemizygote concentration and change in time can be derived (Eq.2). Thus, linear regression of log.-transformed hemizygote content of the samples with respect to time allows us to calculate the relative difference between the growth rate of the hemizygote and the population average (D)

Each mutant in the heterozygous deletion pool has half the wild-type copy number of one of its genes (reduction from two copies to one), and the change in the gene copy number introduces a deviation from D, so the relative growth rate of a mutant linearly relates to the 'alternative deviation index' (an approximation to the flux control coefficient, or FCC, for large perturbations; [ 58 ]) of the gene copy number of the corresponding gene:

where A is the scaling factor (initial copy number/D).

Since A is a constant that applies to all mutants in the pool, the relative growth rates of the mutants can be used to represent the FCC of the corresponding gene with the reduced copy number. We will call this quantity the "modified FCC" or FCC':

Note that the FCC' value of a gene depends on the local slope of a protein concentration vs fitness curve at the range of interest (Figures 6 and 7 give examples of such curves). For example, the FCC' of an essential gene can be very high in the range of 0 copies to 1 copy (change in flux in heterozygous deletion mutant with respect to the inviable null mutant), or the FCC' of a gene whose protein product is at a concentration higher than needed in the wild-type cell (i.e. a level beyond saturation) would be close to zero at proximity of the wild-type level. In this study, the relative growth rate (or FCC') gives the negative average slope of the protein concentration vs fitness curve between 1 copy and 2 copies of each gene, i.e. our HI genes with negative FCC's indicate a positive flux control by the gene when its copy number is increased from 1 to 2, and similarly our HP genes indicate a negative flux control. These principles apply both for gene copy number and for protein abundance, based on the assumption that diploid cells with only one copy of a gene always have a lower concentration of its protein product than the cells with two copies.

Cell cycle analysis

Cell cycle profiles were determined using the method described in Haase and co-workers [ 59 ]. Briefly, cultures were grown to exponential phase before fixation in 70% v/v ethanol, followed by RNase A and protease treatment. Washed samples were then mixed with 1 μM Sytox Green and analysed for DNA content in a Beckman Coulter CyanADP flow cytometer. 20,000 events were captured for each sample with three biological replicates (separate cultures) for each strain. Two peaks were fit to the count histogram of DNA content using the Dean-Jett-Fox method [ 60 ] and the percentage of the total cells under the G1 peak was recorded for each sample.

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Acknowledgements

We are grateful to Rachael Walker for assistance with the flow cytometry measurements, and thank Balázs Papp and Daniela Delneri for their comments on the manuscript. This work was supported by BBSRC grant BB/C505140/2 and a contract from the European Commission under the FP7 Collaborative Programme, UNICELLSYS (both to SGO).

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PP, AG, JW, BH and NZ designed and built the experimental setup; and carried out the experimental work. PP, AG and SGO performed the data analysis. SGO and DBK designed the study; SGO supervised the work. PP, AG and SGO wrote the manuscript. All authors read and approved the manuscript.

Electronic supplementary material

12918_2012_852_moesm1_esm.doc.

Additional file 1: Additional Text and Methods . Additional text on results and details on methods are provided. (DOC 95 KB)

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Additional file 2: Figure S1. Histogram of relative growth rates (FCC') of HFC genes and all genes in turbidostats . HI: Haploinsufficient, HP: Haploproficient (FDR < 0.05 is the threshold for significant HI and HP genes). FCC's of 5713 genes (genome), 1932 HI genes and 796 HP genes were binned into 50 intervals each. (PPT 122 KB)

12918_2012_852_MOESM3_ESM.XLS

Additional file 3: Table S1: Relative growth rates from the experiments . Abbreviations: C, N, P: Carbon-, Nitrogen- or Phosphorus-limited F1 media 01, 02, 03: Dilution rates in chemostats of 0.1, 0.2 or 0.3 h -1 , respectively Turb: Turbidostat FPM: Footprinting media Columns: 1: Systematic name of the ORF, one copy of which has been deleted in the hemizygous diploids All columns denoted as .FCC': Relative growth rate (modified control coefficient) of the mutant in the corresponding experiment. All columns denoted as .P: P-value for rejecting the hypothesis that GR ≠ 0. All columns denoted as .FDR: False discovery rate at the specified P-value. (XLS 2 MB)

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Additional file 4: Table S2: GO analysis of the growth rates . Each experiment is given on a separate spreadsheet. Abbreviations: C, N, P: Carbon-, Nitrogen- or Phosphorus-limited F1 media 01, 02, 03: Dilution rates in chemostats of 0.1, 0.2 or 0.3 h -1 , respectively Turb: Turbidostat FPM: Footprinting media Columns: 1: Internal database row number. 2: GO term ID 3: Name of the GO term 4: Class of the GO term (MF: Molecular Function, BP: Biological Process, CC: Cellular Compartment) 5: Number of genes annotated to the GO term 6: Coeff: Logistic regression coefficient β of the GO term. Positive values indicate enrichment of a GO term among the haploproficient genes, negative values indicate enrichment among the haploinsufficient genes. 7: Odd.ratio: Log odds ratio of the GO term. 8: P-value: P-value for rejecting the null hypothesis that β≠0 9: FDR: False Discovery Rate at specified P-value. (XLS 154 KB)

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Additional file 5: Figure S2. Fraction of HFC genes related to selected organelles, protein complexes, and cellular processes . Only genes showing an HFC phenotype in turbidostat culture are considered. The key on the top left gives the colour code used in the chart: Dark blue gives the percent of significantly HI (FDR < 0.05) and dark red gives the percent of significantly HP (FDR < 0.05). (PPT 1 MB)

12918_2012_852_MOESM6_ESM.XLS

Additional file 6: Table S3: Haploproficient genes involved in the maintenance of genome integrity . Columns: 1. ORF name 2. Gene name 3. Gene description 4. Relative growth rates (FCC') 5. FDR: False Discovery Rate. (XLS 32 KB)

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Pir, P., Gutteridge, A., Wu, J. et al. The genetic control of growth rate: a systems biology study in yeast. BMC Syst Biol 6 , 4 (2012). https://doi.org/10.1186/1752-0509-6-4

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Article Contents

Introduction, crabtree effect, ecology perspective and yeast lifestyle, ability to grow anaerobically, how to deduce the yeast evolutionary history, the origin of the long-term crabtree effect, short-term crabtree effect: a strengthened glycolytic flow, hypothesis on the original driving forces, conclusions, acknowledgements.

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Why, when, and how did yeast evolve alcoholic fermentation?

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Sofia Dashko, Nerve Zhou, Concetta Compagno, Jure Piškur, Why, when, and how did yeast evolve alcoholic fermentation?, FEMS Yeast Research , Volume 14, Issue 6, September 2014, Pages 826–832, https://doi.org/10.1111/1567-1364.12161

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The origin of modern fruits brought to microbial communities an abundant source of rich food based on simple sugars. Yeasts, especially Saccharomyces cerevisiae , usually become the predominant group in these niches. One of the most prominent and unique features and likely a winning trait of these yeasts is their ability to rapidly convert sugars to ethanol at both anaerobic and aerobic conditions. Why, when, and how did yeasts remodel their carbon metabolism to be able to accumulate ethanol under aerobic conditions and at the expense of decreasing biomass production? We hereby review the recent data on the carbon metabolism in Saccharomycetaceae species and attempt to reconstruct the ancient environment, which could promote the evolution of alcoholic fermentation. We speculate that the first step toward the so-called fermentative lifestyle was the exploration of anaerobic niches resulting in an increased metabolic capacity to degrade sugar to ethanol. The strengthened glycolytic flow had in parallel a beneficial effect on the microbial competition outcome and later evolved as a “new” tool promoting the yeast competition ability under aerobic conditions. The basic aerobic alcoholic fermentation ability was subsequently “upgraded” in several lineages by evolving additional regulatory steps, such as glucose repression in the S. cerevisiae clade, to achieve a more precise metabolic control.

The authors review the recent data on the carbon metabolism in Saccharomycetaceae species and attempt to reconstruct the ancient environment which could promote the evolution of alcoholic fermentation.

Yeast fermentation of different plant carbohydrate sources is one of the oldest human technologies, and its origins date back to the Neolithic period. Even nowadays, yeasts are essential for many biotechnological processes, such as beer, wine, and biofuel fermentations. However, the complexity of gene expression regulatory networks behind the alcoholic fermentation is still far from being completely understood (reviewed in Compagno et al ., ). Similarly, the origin and the driving forces in nature determining the path and outcomes of the yeast evolutionary history, and the present day evolutionary trends, are still rather unclear. It is the main aim of this review to speculate on and propose evolutionary pathways, trends, and driving forces, which operated during yeast evolutionary history and resulted in the present aerobic fermentative capacity of Saccharomyces yeasts and a new lifestyle of these yeasts.

One of the most prominent features of the baker's yeast Saccharomyces cerevisiae is its ability to rapidly convert sugars to ethanol and carbon dioxide at both anaerobic and aerobic conditions. Under aerobic conditions, respiration is possible with oxygen as the final electron acceptor, but S. cerevisiae exhibits alcoholic fermentation until the sugar reaches a low level. This phenomenon is called the Crabtree effect (De Deken, ), and the yeasts expressing this trait called Crabtree-positive yeasts. In contrast, “Crabtree-negative” yeasts lack fermentative products, and under aerobic conditions, biomass and carbon dioxide are the sole products. However, it is possible to obtain pure respiratory utilization of glucose by S. cerevisiae under aerobic conditions if the glucose concentration is kept very low, for example using a glucose-limited continuous culture operating below a certain strain-specific threshold value (called “critical” dilution rate) or using fed-batch cultivations (Postma et al ., ). This glucose repression phenomenon in S. cerevisiae involves different signal transduction pathways activated by extracellular and intracellular levels of glucose and its related metabolites and/or their fluxes through the involved metabolic pathways (reviewed in Johnston, ; Westergaard et al ., ). However, the complexity of glucose repression regulatory networks is still far from being completely understood. Some of the regulatory activities operate at the transcriptional level, and some others operate directly on the involved enzymes. Important to note, so far it is not clear yet if the glucose repression mechanism was the original step to promote evolution of the Crabtree effect, or it has been “added” later during the evolution of some yeast lineages.

Different physiological and molecular approaches have been used as the background for the current definition of the Crabtree effect. The most accepted definition explains the long-term Crabtree effect as aerobic alcoholic fermentation under steady-state conditions at high growth rates. When S. cerevisiae is cultivated under glucose-limited conditions, the long-term effect appears when the dilution rate (or in other words: the glucose uptake rate) exceeds the strain-specific threshold value. The same effect is observed also when yeast cells are cultivated in batch cultivations. The molecular background for the long-term Crabtree effect has been explained as a limited respiratory capacity due to the repression of the corresponding respiration-associated genes (Postma et al ., ; Alexander & Jeffries, ). On the other hand, the short-term Crabtree effect is the immediate appearance of aerobic alcoholic fermentation upon addition of excess sugar to sugar-limited and respiratory cultures. This effect has been explained as an overflow in the sugar metabolism and could be associated directly with the biochemical properties of some of the respiration-associated enzymes and their regulators (Pronk et al ., ; Vemuri et al ., ). However, it is still unclear if the regulatory molecular mechanisms operating during the long-term and short-term Crabtree effect are indeed different from each other. A very interesting aspect is also the evolutionary and ecological background for the development of these regulatory mechanisms (Piskur et al ., ; Rozpędowska et al ., ).

Every autumn, when fruits ripen, a fierce competition for the fruit sugars starts within microbial communities. Yeasts, especially S. cerevisiae and its close relatives, usually become the predominant group in niches with freely available mono- and oligosaccharides. The fast sugar consumption, ethanol production, and tolerance, and the ability to propagate without oxygen, are likely some of the “winning” traits responsible for the competition outcome (Piskur et al ., ). However, a great majority of yeasts, which we find in nature, has been only poorly studied in laboratory so far or even in their environmental context.

At least three lineages (Fig. 1 ), including budding and fission ( Schizosaccharoymces pombe ) yeasts, have apparently independently evolved the metabolic ability to produce ethanol in the presence of oxygen and excess of glucose (reviewed in Piskur et al ., ; Rozpędowska et al ., ; Rhind et al ., ). This metabolic »invention« (Crabtree effect) represents in nature a strong tool to outcompete other microorganisms. Both groups of ethanol-producing budding yeast, including S. cerevisiae and Dekkera bruxellensis , can also efficiently catabolize ethanol, and therefore, their corresponding lifestyle has been named as the “make-accumulate-consume (ethanol)” strategy (Thomson et al ., ; Piskur et al ., ; Rozpędowska et al ., ). On the other hand, S. pombe can grow only poorly on ethanol as sole carbon source. In short, this life strategy is based on that yeasts can consume very fast more sugar than other species, convert it to ethanol to inhibit the growth of other species, especially bacteria, and then consume the remaining carbon once they have established competitive dominance in the niche.

Phylogenetic relationship among some yeasts. Note that some of the shown yeast lineages separated from each other many million years ago and have therefore accumulated several molecular and physiological changes regarding their carbon metabolism. However, during the evolutionary history, there have also been parallel events. Apparently, at least three lineages, Saccharomyces , Dekkera , and Schizosaccharomyces , have evolved (1) the ability to ferment in the presence of oxygen and (2) to proliferate under anaerobic conditions. This figure was adopted from Compagno et al . ( ).

The availability of oxygen varies among different niches. One of the main problems an organism faces under anaerobic conditions is the lack of the final electron acceptor in the respiratory chain. This reduces or completely eliminates the activity of Krebs cycle, respiratory chain, and mitochondrial ATP generation. As a response to hypoxic and anaerobic conditions, organisms have developed several processes to optimize the utilization of oxygen and even reduce the dependence on the presence of oxygen. According to their dependence on oxygen during the life cycle, yeasts are classified as: (1) obligate aerobes displaying exclusively respiratory metabolism, (2) facultative fermentatives (or facultative anaerobes), displaying both respiratory and fermentative metabolism, and (3) obligate fermentatives (or obligate anaerobes) (Merico et al ., ).

The ability of yeasts to grow under oxygen-limited conditions seems to be strictly dependent on the ability to perform alcoholic fermentation. In other words, enough ATP should be generated during glycolysis to support the yeast growth, and NADH generated during glycolysis gets re-oxidized. Apart from the energy and NADH/NAD redox problems, under anaerobic conditions, yeasts must also find a way to run various reactions independent of the respiratory chain and a normal Krebs cycle. In other words, substrates (intermediates) for de novo reactions, for example for the amino acid synthetic pathways, need to originate from a modified metabolic network. On the other hand, in yeast some compounds, such as unsaturated fatty acids and sterols, cannot be synthesized in the cell under anaerobiosis and must originate from the medium or from previous aerobic growth.

Apparently, the progenitor of Saccharomycetaceae was an aerobic organism, strictly dependent on oxygen. It seems that later several yeast lineages (Fig. 1 ) have evolved the ability to grow anaerobically, or at least can grow partially independently of oxygen. S. cerevisae and a majority of post-WGD yeasts, as well as some lower Saccharomycetaceae branches, such as the Lachancea yeasts, show a clear ability to proliferate without oxygen. Interestingly, two other lineages, D. bruxellensis (Rozpędowska et al ., ) and S. pombe (Visser et al ., ) have apparently also evolved the ability to propagate under anaerobic conditions. However, they need some extra supplements in the medium to be able to propagate without oxygen. It is interesting to point out, that the same three lineages, which can perform alcoholic fermentation under aerobic conditions can also proliferate in the absence of oxygen.

The onset of yeast genomics (Goffeau et al ., ) has provided a tool to reconstruct several molecular events, which have reshaped the budding yeasts during their evolutionary history (reviewed in Dujon, ). Several molecular events have left a clear fingerprint in the modern genomes (Fig. 2 ), while the origin of more complex traits, such as the Crabtree effect, is often not easy to determine using only a genome analysis approach.

The Saccharomycetaceae family covers over 200 million years of the yeast evolutionary history and includes six post-whole-genome duplication (post-WGD) genera, Saccharomyces , Kazachstania , Naumovia , Nakaseomyces , Tetrapisispora , and Vanderwaltozyma ; and six non-WGD genera, Zygosaccharomyces , Zygotorulaspora , Torulaspora , Lachancea , Kluyveromyces , and Eremothecium . Hereby, we show a rough phylogenetic relationship among these genera. Two evolutionary events are shown, WGD, which took place app. 100 million years ago and the loss of Respiratory Complex I (which took place app. 150 million years ago). This figure was adopted from Hagman et al . ( ).

The Saccharomycetaceae family covers over 200 million years of the yeast evolutionary history and includes six post-whole-genome duplication (post-WGD) genera, Saccharomyces , Kazachstania , Naumovia , Nakaseomyces , Tetrapisispora , and Vanderwaltozyma ; and six non-WGD genera, Zygosaccharomyces , Zygotorulaspora , Torulaspora , Lachancea , Kluyveromyces , and Eremothecium (Kurtzman & Robnett, ; Casaregola et al ., ) (Fig. 2 ). The phylogenetic relationship among these genera is now relatively well understood. However, only a very few species are reported in literature for their carbon metabolism (Merico et al ., ).

We have recently studied over forty yeast species, which in nature occupy similar niches and rely on glucose as the »preferred« substrate (Kurtzman et al ., ) and analyzed their carbon metabolism using uniform experimental conditions all along the fully controlled growth in fermentors (Hagman et al ., ).

The studied yeasts belonged to the Saccharomycotina family, including six WGD genera and six non-WGD genera, thus covering 200 million years of evolution (Fig. 2 ). The observed extent of the Crabtree effect in each species corresponds to its position on the yeast phylogenetic tree. In addition, the observed Crabtree effect is much more pronounced in a majority of WGD yeasts than in the ethanol-producing non-WGD species, suggesting at least a two-step »invention«. On the other hand, carbon metabolism in the »lower« branches of Saccharomycetaceae yeasts, belonging to modern Kluyveromyces and Eremothecium , is similar to other Saccharomycotina yeasts, such as Candida albicans , Yarrowia lipolytica , and Pichia pastoris , which are Crabtree-negative yeasts. Therefore, the origin of the “make-accumulate-consume” strategy/Crabtree effect could take place within the time interval spanning the origin of the ability to grow under anaerobic conditions, and the loss of respiratory chain Complex I, after the split of the Saccharomyces - Lachancea and Kluyveromyces - Eremothecium lineages, approximately 125 million years ago. On the other hand, the second step, leading toward even a more pronounced Crabtree effect, occurred relatively close to the WGD event (Wolfe and Shields, 1996), the settlement of rewiring of the promoters involved in the respiratory part of the carbon metabolism (Ihmels et al ., ), and the settlement of the petite-positive character (Merico et al ., ). There are also some other possible scenarios, which can be “deduced” from the Hagman et al . ( ) results. The origin of the long-term Crabtree effect could took place much before, coinciding with the loss of respiratory chain Complex I, but this trait was later lost in some lineages, such as Kluyveromyces - Eremothecium . The long-term effect may have even originated independently in several Saccharomycetaceae lineages. To clarify this point, in the near future, one would need to focus on some of the “early Crabtree positive” branches, such as Lachancea , and perform detailed carbon metabolism studies on these yeasts, including gene expression profiling, to deduce which regulatory circuits are already present in Lachancea , and which ones only in Saccharomyces .

The origin of modern plants with fruits, at the end of the Cretaceous age, more than 125 mya (Sun et al ., ), brought to microbial communities a new larger and increasingly abundant source of food based on simple sugars. On the other hand, ancient yeasts could hardly produce the same amount of new biomass as bacteria during the same time interval and could therefore be out-competed. We speculate that slower growth rate could in principle be counter-acted by production of compounds that could inhibit the growth rate of bacteria, such as ethanol and acetate. However, what were the initial molecular mechanisms that promoted the evolution of the new “lifestyle” and rewiring of the carbon metabolism? Was competition between yeast and bacteria indeed the original driving force to promote evolution of the aerobic alcoholic fermentation?

The Crabtree effect, which is the background for the yeast »make-accumulate-consume« strategy, results in a lower biomass production because a fraction of sugar is converted into ethanol. This means that more glucose has to be consumed to achieve the same yield of cells (Fig. 3 ). Because only a fraction of sugar is used for the biomass and energy production this could theoretically result in a lower growth rate in Crabtree-positive yeasts. In nature, a lower growth rate would have a negative effect for the yeast during the competition with different yeasts species and between yeasts and bacteria. However, an increased glycolytic flow (achieved by increased uptake of glucose and its faster conversion to pyruvate and final fermentation products) could in principle compensate for the Crabtree effect and balance the growth rate providing the same number of cells during the same time interval. Just much more glucose would be consumed in this case (Fig. 3 ). What could be the original driving force that increased the flow through the glycolytic pathway?

$Crabtree effect results in lower biomass production because a fraction of sugar is converted into ethanol. This means that more glucose has to be consumed to achieve the same yield of cells if comparing with Crabtree-negative yeasts. Because only a fraction of sugar is used for the biomass and energy production, this could theoretically result in lower growth rate in Crabtree-positive yeasts and these could then simply be out-competed by Crabtree-negative yeasts and other microorganisms. However, ethanol could be used as a tool to slow down and control the proliferation of other competitive microorganisms.$

Crabtree effect results in lower biomass production because a fraction of sugar is converted into ethanol. This means that more glucose has to be consumed to achieve the same yield of cells if comparing with Crabtree-negative yeasts. Because only a fraction of sugar is used for the biomass and energy production, this could theoretically result in lower growth rate in Crabtree-positive yeasts and these could then simply be out-competed by Crabtree-negative yeasts and other microorganisms. However, ethanol could be used as a tool to slow down and control the proliferation of other competitive microorganisms.

The short-term Crabtree effect is defined as the immediate appearance of aerobic alcoholic fermentation upon a pulse of excess sugar to sugar-limited yeast cultures. In a recent follow-up (Hagman, ; Hagman et al ., ) of the above study, ten different yeast species, having a clearly defined phylogenetic relationship, have been characterized for short-term Crabtree effect. These species very roughly cover the phylogenetic span of yeasts, which have been studied in the long-term experiments. Yeasts have been cultivated as continuous cultures under glucose-limited conditions, and upon a glucose pulse, their general carbon metabolism analyzed (Hagman et al ., , Hagman, ). In pulse experiments, yeasts belonging to Pichia , Debaryomyces , Eremothecium , and Kluyveromyces marxianus have not exhibited any significant ethanol formation (just like they also do not show long-term Crabtree effect), while Kluyveromyces lactis behaved, surprisingly, as intermediate yeast. The Lachancea , Torulaspora , Vandervaltozyma , and Saccharomyces yeasts have, upon a glucose pulse, exhibited rapid ethanol accumulation. These yeasts are also long-term positive species (Hagman et al ., ).

Roughly, long-term positive species in glucose pulse experiments behaved also as short-term positive. However, the results suggest that Kluyveromyces yeasts can be considered as intermediate in both phylogenetic position and their carbon metabolism. K. lactis is in fact on one hand a short-term Crabtree positive, but on the other hand a long-term Crabtree negative. Even if the number of studied yeasts is limited (only ten), one could still speculate that the time of origin of the short-term Crabtree effect and the time of origin of the long-term Crabtree effect seem to be very close to each other and may even overlap, coinciding with the horizontal transfer of URA1 and the ability to proliferate anaerobically (Merico et al ., ).

However, one of the most surprising observations has been that when S. cerevisiae and its Crabtree-positive relatives grow in continuous culture below a sugar threshold with a respiratory metabolism, their fermentative pathways are fully expressed, whereas the respiration-associated parts are repressed when the sugar level overcomes a certain level. In other words, it seems that these yeast cells have all the time the capacity to ferment “switched-on”, while the respiration ability is strictly related to the amount of sugar availability. On the other hand, in several Crabtree-negative yeasts, it has been demonstrated that the fermentative pathway is “switched-on” only when oxygen becomes limiting (Kiers et al ., ; Jeffries, ; Baumann et al ., ). Interestingly, in Crabtree-negative yeasts, the flow through glycolysis matches the respiration associated one, whereas in Crabtree-positive yeasts, the glycolytic flow is apparently “over-dimensioned”.

Why would the progenitor yeast initially benefit from the strengthened glycolytic and fermentative flow? It is apparent from previous studies that the ability to proliferate under anaerobic conditions originated at approximately the same time as the origin of the first modern fruits and aerobic alcoholic fermentation, upon the split of the Kluyveromyces-Eremothecium and Lachancea-Saccharomyces lineages (Hagman et al ., ). It could be that regular exposure to poorly aerobic niches represented a selection pressure which promoted yeast “mutant” lineages with strengthened glycolytic and fermentation pathways, as well as having improved resistance toward ethanol. In these cells, up-regulation of the glycolytic-fermentative capacity should have provided sufficient carbon flow and energy yield even in the absence of oxygen. In other words, exploration of anaerobic niches could be a driving force to build up a carbon metabolism network, which is better adapted to ferment. However, in principle these yeasts could still alternate between aerobic and anaerobic niches.

At the same time, the yeast progenitor could also get remodeled several general metabolic pathways, not only the energy yielding ones, to be able to proliferate also under anaerobic conditions. For example, the fourth step of the de novo pyrimidine synthesis became, upon horizontal transfer of the URA1 gene, less dependent on the functional respiratory chain (Gojković et al ., ). Note, that this step toward independence of oxygen occurred before the separation of the Kluyveromyces and Lachancea-Saccharomyces lineages and therefore likely before the origin of the aerobic fermentative-respiratory lifestyle.

Fast consumption of glucose through an increased uptake would simply “starve-out” other microbial competitors. The duplication of glucose transporter genes in the progenitor of the Lachancea-Saccharomyces lineages could represent one of the molecular backgrounds for the initial increased ability to consume glucose. However, the increased carbon flow through glycolysis generated an overflow and resulted in synthesis of fermentation products. When these metabolites, especially ethanol, were accumulated at high concentrations, they could impair the growth of other competing microorganisms. The fermentation products could thus become a new weapon to out-compete other microorganisms. At this point, the driving biological force for optimizing the ethanol fermentation pathway, even in the presence of oxygen, could be “to kill” other competitors. In parallel, yeasts had also to evolve the ability to better tolerate ethanol. Further on, the origin of glucose repression of the respiratory pathways under fully aerobic conditions, in the S. cerevisiae lineage, could represent a fine tuning mechanism, which increased the efficiency of ethanol production.

During the recent years, there has been more and more research focus on nonconventional yeasts, especially many of these yeasts got their genomes sequenced. These sequence data now help to deduce a reliable phylogenetic relationship among yeasts and provide us with a possibility to reveal evolution fingerprints, which have remained preserved in the genome. These data should now be complemented with physiology and molecular genetic studies on a variety of yeast species. This will open additional avenues in biotechnology and evolution research. In this review, we attempted to analyze the most recent results on yeast carbon metabolism and develop a hypothesis on the evolution of alcoholic fermentation. We speculate that the exploration of anaerobic niches and later on the competition with other microorganisms were the driving forces behind the remodeling of the yeast carbon metabolism.

The authors thank Swedish Research Council, EU ITN Cornucopia, ARRS (Slovenia) and Crafoord, Fysiografen, Lindström, and Soerensen Foundations for their financial support.

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Peer-reviewed

Research Article

Modelling of Yeast Mating Reveals Robustness Strategies for Cell-Cell Interactions

Affiliation Department of Mathematics, University of California, Irvine, Irvine, California, United States of America

* E-mail: [email protected] (TMY); [email protected] (CSC)

Affiliation Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, California, United States of America

Affiliation Department of Mathematics, The Ohio State University, Columbus, Ohio, United States of America

Weitao Chen,
Qing Nie,
Tau-Mu Yi,
Ching-Shan Chou

Published: July 12, 2016
https://doi.org/10.1371/journal.pcbi.1004988
Reader Comments

Mating of budding yeast cells is a model system for studying cell-cell interactions. Haploid yeast cells secrete mating pheromones that are sensed by the partner which responds by growing a mating projection toward the source. The two projections meet and fuse to form the diploid. Successful mating relies on precise coordination of dynamic extracellular signals, signaling pathways, and cell shape changes in a noisy background. It remains elusive how cells mate accurately and efficiently in a natural multi-cell environment. Here we present the first stochastic model of multiple mating cells whose morphologies are driven by pheromone gradients and intracellular signals. Our novel computational framework encompassed a moving boundary method for modeling both a -cells and α-cells and their cell shape changes, the extracellular diffusion of mating pheromones dynamically coupled with cell polarization, and both external and internal noise. Quantification of mating efficiency was developed and tested for different model parameters. Computer simulations revealed important robustness strategies for mating in the presence of noise. These strategies included the polarized secretion of pheromone, the presence of the α-factor protease Bar1, and the regulation of sensing sensitivity; all were consistent with data in the literature. In addition, we investigated mating discrimination, the ability of an a -cell to distinguish between α-cells either making or not making α-factor, and mating competition, in which multiple a -cells compete to mate with one α-cell. Our simulations were consistent with previous experimental results. Moreover, we performed a combination of simulations and experiments to estimate the diffusion rate of the pheromone a -factor. In summary, we constructed a framework for simulating yeast mating with multiple cells in a noisy environment, and used this framework to reproduce mating behaviors and to identify strategies for robust cell-cell interactions.

Author Summary

One of the riddles of Nature is how cells interact with one another to create complex cellular networks such as the neural networks in the brain. Forming precise connections between irregularly shaped cells is a challenge for biology. We developed computational methods for simulating these complex cell-cell interactions. We applied these methods to investigate yeast mating in which two yeast cells grow projections that meet and fuse guided by pheromone attractants. The simulations described molecules both inside and outside of the cell, and represented the continually changing shapes of the cells. We found that positioning the secretion and sensing of pheromones at the same location on the cell surface was important. Other key factors for robust mating included secreting a protein that removed excess pheromone from outside of the cell so that the signal would not be too strong. An important advance was being able to simulate as many as five cells in complex mating arrangements. Taken together we used our novel computational methods to describe in greater detail the yeast mating process, and more generally, interactions among cells changing their shapes in response to their neighbors.

Citation: Chen W, Nie Q, Yi T-M, Chou C-S (2016) Modelling of Yeast Mating Reveals Robustness Strategies for Cell-Cell Interactions. PLoS Comput Biol 12(7): e1004988. https://doi.org/10.1371/journal.pcbi.1004988

Editor: Leah Edelstein-Keshet, University of British Columbia, CANADA

Received: January 5, 2016; Accepted: May 16, 2016; Published: July 12, 2016

Copyright: © 2016 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: CSC was supported by NSF DMS1253481. QN was supported by NIH grants R01GM107264 and P50GM76516 and NSF grant DMS1161621 and DMS1562176. TMY was supported by NSF DMS1001006. The URL for the NSF is http://www.nsf.gov/ and the URL for the NIH is http://www.nih.gov/ . The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Cell-to-cell signaling via diffusible molecules is an important mode of communication between cells in many mammalian systems such as neuron axon guidance [ 1 ], immune cell recognition [ 2 ], and angiogenesis [ 3 ]. These interactions involve sensing an attractant from the partner and responding by moving or growing in the appropriate direction (i.e. chemo-taxis/tropism), while secreting signaling molecules in a reciprocal fashion. This behavior is conserved in eukaryotes from fungi to humans [ 4 , 5 ].

The budding yeast Saccharomyces cerevisiae , undergoes a mating response that has served as a model system for studying cell-to-cell communication [ 6 ]. Yeast cells have two haploid mating types, a and α. By sensing the pheromone molecules (α-factor and a -factor), a - and α-cells detect the presence of a mating partner. These secreted peptides form a spatial gradient, bind to the pheromone-specific receptors, and elicit a response that includes cell-cycle arrest, gene expression, and formation of a mating projection (“shmoo”). Ultimately, the mating response results in the fusion of the two cells and nuclei to create an a /α diploid cell (reviewed in [ 7 ]).

Mathematical modeling has provided a useful tool for studying cell-cell interactions. Previously, moving interface models have been used to investigate deforming the shape of eukaryotic cells. In [ 8 ], a 1D continuum model of cell motility in amoeboid cells based on a viscoelastic description of the cytoplasm was developed, and in [ 9 ], cells in a 2D domain were treated as a two-phase fluid. The immerse boundary and finite element based approach was developed to model the actin network and cell morphogenesis in [ 10 ], an evolving surface finite element method modeled cell motility and chemotaxis in [ 11 ], and the boundary tracking Lagrangian framework was used in [ 12 , 13 ]. Other models used agent-based frameworks such as the Potts model, which takes into account detailed chemical networks and moving cells [ 14 ]. Level set approaches have also been adopted [ 15 , 16 ] to simulate the cell membrane deformation coupled to chemistry reaction dynamics. Previous studies focused on the relationship between morphogenesis and its underlying biochemical or mechanical machinery. In this work, we extend this concept by including the molecular dynamics within the extracellular space to study multi-cell interactions.

In laboratory yeast mating assays, wild-type cells mate with approximately 100% efficiency [ 17 ]. Genetic screens have identified mutants that mate at reduced efficiency [ 18 ]. One class of mutants prevents mating altogether. In addition, Hartwell and colleagues have modified the basic assay to investigate “three-way” mating between an a -cell that can mate with either an α-cell that makes α-factor or an α-cell that does not [ 19 , 20 ]. In this mating discrimination test, wild-type a -cells mate almost exclusively with α-factor producers. Mutations that affect the sensitivity of the system, such as the deletion of SST2 (a gene which downregulates signaling via the heterotrimeric G-protein) or the deletion of BAR1 (which encodes for an α-factor protease), dramatically reduce both mating efficiency and mating discrimination [ 20 ].

The communication between mating cells is mediated by the mating pheromones which bind their cognate G-protein-coupled receptors turning them on. Active receptor catalyzes the conversion of heterotrimeric G-protein into Gα-GTP and free Gβγ. The resulting Gβγ subunit can then recruit Cdc24 to the membrane where it activates Cdc42. Active Cdc42 is a master regulator of the cell polarity response orchestrating the cytoskeleton, exo/endocytosis, and signaling complexes [ 21 , 22 ]. All of these processes involve noise due to Brownian motion, stochasticity in gene expression or other intracellular fluctuations [ 23 – 26 ], which may affect cell assessment of signals and their responses [ 27 ]. In particular, the diffusion of ligand into the local neighborhood of the cell and the binding of ligand to receptor are thought to introduce significant stochasticity to gradient-sensing systems [ 24 , 28 ]. Therefore, it is necessary to consider the effects of noise when exploring cell behavior during mating.

There has been extensive mathematical modeling of the yeast pheromone response system. The early models were non-spatial and emphasized signaling dynamics [ 29 – 31 ]. More recent modeling efforts have incorporated spatial dynamics, both deterministic [ 32 – 34 ] and stochastic [ 35 – 37 ]. Models have ranged from simple generic formulations to detailed mechanistic descriptions. Finally, we and others have modeled pheromone-induced morphological changes to cell shape [ 12 , 38 ]. In related research, Diener et al. employed a combination of image processing and computational modeling to describe the extracellular α-factor dynamics in a population of mating cells, and how those dynamics were altered by the protease Bar1 [ 39 ]. However, missing from the literature is modeling of the yeast mating process itself involving both a- and α-cells.

In this paper, the goal was to construct the computational infrastructure for simulating the mating of two or more yeast cells, and then to investigate the factors responsible for robust mating behavior. We want to use our models to understand in greater detail the spatial dynamics that ensure efficient mating, and provide quantitative explanations and predictions on how perturbing these dynamics (e.g. mutants) disrupts the cell-cell interactions during mating. We succeeded in developing numerical methods for simulating yeast mating. Key elements include modeling the shape of the cell described by a moving boundary technique, and the extracellular diffusion dynamics of the pheromone ligand. Using this framework, we explored different model structures and parameters in a systematic fashion using generic models. We were able to simulate the high efficiency of mating among wild-type cells, and their ability to discriminate among partners that synthesized mating pheromone at different levels. Our simulations suggested that two critical factors ensuring robust mating under noisy conditions were the polarized secretion of mating pheromone, and the presence of the Bar1 protease. In addition, we demonstrated that supersensitive mutants disrupted both mating efficiency and discrimination, reproducing experimental data. More generally, this work makes progress toward the goal of a more detailed description of cell-cell interactions.

1. A stochastic model with dynamic cell shapes for multi-cell mating systems

In this section, we describe the stochastic model for multi-cell mating systems in two-dimensional space. Cell shape is represented by a level set formulation to capture the deforming plasma membrane induced by pheromone signaling.

As described in the Introduction, mating occurs when an a -cell and α-cell are in close proximity ( Fig 1A ). They sense the pheromone gradient generated by the partner and project toward the source. In Fig 1A , cells are labeled with a marker for the polarisome ( a : Spa2-GFP or α: Spa2-mCherry), a cellular structure at the tip of the mating projection. From a simulation standpoint, this process can be broken down into a series of steps ( Fig 1B ) from the secretion and diffusion of pheromones to the resulting growth in the mating projection.

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(A) Time-lapse microscopy of mating yeast cells. Wild-type bar1Δ a -cells (left) and α-cells (right) were imaged over a two-hour period starting at t = 90m. The polarisome was labeled with Spa2-GFP ( a , first row, cells outlined in green) and Spa2-mCherry (α, second row, cells outlined in red). The GFP and mCherry channels are merged (third row) with the three a -cells and four α-cells labeled. The DIC images are shown in the fourth row. The pairs a 1 -α 1 and a 3 -α 2 mated successfully. The presence of Bar1 over time will degrade the α-factor in a mating mix, and so to maximize the mating response we employed a bar1Δ strain. Scale bar = 5 μm. (B) Schematic diagram of yeast mating simulations. At the start, two cells are separated by 4 microns. The two mating pheromones ( a -factor and α-factor) diffuse from their respective sources ( a -cell and α-cell) which are sensed by the respective partners. The spatial dynamics of the biochemical reaction network are triggered resulting in the polarization of the membrane species. The boundary of the cell moves in response to the concentration of the polarized species resulting in the growth of a mating projection. Mating ends when the tips of the projections contact one another.

https://doi.org/10.1371/journal.pcbi.1004988.g001

Describing the mating process between two cells requires solving diffusion equations for the ligands in the extracellular space, which evolve according to the shifting positions of the pheromone sources. These sources in turn depend on the sensing of the ligand input and the morphological response. Thus, the cell boundary is evolved together with the molecular dynamics associated with the membrane for each cell.

Unlike our previous model of a single polarizing cell which solves surface reaction-diffusion equations in Lagrangian coordinates to capture deformation of the cell membrane [ 12 ], here we apply the level set method [ 40 ], which can track the moving curve front implicitly by solving a Hamilton-Jacobi equation. In this way, it is easier to study the interactions of multiple cells, and it allows a straightforward extension to the case of multicellular interactions by introducing level set functions for each different cell, and inclusion of the surface diffusions for molecules on the cell membrane. With this methodology, we can distinguish between the intracellular and extracellular space, and couple extracellular pheromone diffusion with the intracellular reaction-diffusion dynamics. The numerical scheme is described in the Methods section.

For simplicity, the cell is modeled as a two-dimensional (2D) circle with radius of 1 μm; the actual yeast cell is a three-dimensional (3D) sphere with radius 2 μm. The experimental mating assay involves placing the cells on a surface (i.e. paper filter) so that the mating reaction is effectively in two dimensions. The time unit is 100 seconds to approximate within an order of magnitude the growth velocity observed in experiments.

In this model, the mating pheromone is denoted by f , which is the external cue of cell polarization. Two membrane-associated species, u 1 and u 2 , initially are uniformly distributed and then undergo polarization upon sensing the pheromone signal. The system forms a two-stage cascade in which the output of the first stage ( u 1 ) is the input to the second stage whose output is u 2 . The species v 1 and v 2 provide negative feedback (integral feedback) to regulate u 1 and u 2 (see S1 Text ). The cell grows in the direction determined by u 2 . This model is a generic model of the mating system and abstracts away the mechanistic details of yeast mating.

As studied in the previous model for the two-stage yeast cell polarity system on a single cell [ 41 ], u 1 represents the protein Gβγ, which is the output of the heterotrimeric G-protein system and the input to the Cdc42 system, and u 2 represents active Cdc42, which is the master regulator of yeast cell polarization. Finally, the peak of the u 2 distribution represents the polarisome which directs new secretion driving mating projection growth.

The default initial conditions for the simulations are two cells (one a -cell and one α-cell) whose centers are separated by 4 μm in which the membrane species u i is uniformly distributed on the cell surface, and thus no polarisome is formed in the beginning and initial pheromone secretion will be isotropic. Unless otherwise stated, no Bar1 (α-factor protease) is present, i.e., cells are considered bar1Δ; there is no background α-factor source.

At this point we note some of the limitations of the model which we expand upon in the Discussion. First the model is a generic representation of the system that lacks mechanistic detail. Second we employed a quasi-steady-state approximation of α-factor spatial dynamics to speed up the simulations. Third there was not rigorous fitting of the parameters to the experimental data but rather a sampling of different regions of parameter space that produced experimentally observed behaviors.

2. Noise disrupts mating alignment between two cells

In this section, we investigated the impact of noise in the context of exploring one specific parameter in the simulations, the a -factor diffusion constant.

2.1. Varying the diffusion rate of a-factor without noise.

The diffusion constants for the pheromones α-factor and a -factor are known to be different. Because α-factor is water-soluble, its diffusion constant can be estimated to be D α ~ 100 μm 2 /sec based on its molecular weight [ 42 ]. The diffusion coefficient for a- factor is thought to be lower than α-factor because of its hydrophobic tail, but the value is not known [ 43 ]. We investigated a range of values for this parameter, using our two-cell model in the absence of noise. As shown in Fig 2A , the diffusion rate D a was varied to be 100, 10, 1 and 0.1 μm 2 /s. Overall the mating behaviors were similar (cells projected toward one another) although the different diffusion coefficients of a -factor give rise to different morphologies for the α-cell.

(A) Varying a -factor diffusion rates under no-noise simulation conditions. The diffusion constant for a -factor D a was set to 0.1, 1, 10, and 100 μm 2 /s; the diffusion constant for α-factor was D α = 100 μm 2 /s. The cell centers were separated by 4 μm at the start. In all cases the cells were able to grow toward each other successfully. (B) External and internal noise disrupt mating. External ( κ 1 ) and internal ( κ 2 ) noises were added to the simulations using a range of values (0, 5, 10, 50 for κ 1 ; 0, 3, 5 for κ 2 ). 10 simulations were run for each combination, and an example simulation is shown for each specified pair of values ( κ 1 , κ 2 ). (C) Varying a -factor diffusion rates in the presence of noise. Simulations performed as in (A) except in the presence of noise ( κ 1 = 5, κ 2 = 3). All simulations produced mating except D a = 0.1.

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2.2. Varying the external and internal noise amplitude.

We investigated different values for the external ( κ 1 ) and internal ( κ 2 ) noise in the simulations. For simplicity, we set the value of noise on the negative feedback term to be a constant ( κ 3 = 0.1) in all simulations in order to focus on κ 1 and κ 2 . In Fig 2B , we show a table of typical simulations in which κ 1 was varied from 0 to 50, and κ 2 was varied from 0 to 5. D a was set to a test value of 10 μm 2 /s to reflect the fact that the lipid-modified a -factor is expected to diffuse slower than α-factor (which has no lipid modification) but faster than a lipid-modified membrane protein (0.1–1 μm 2 /s). The sample simulation indicates the effect of a given level of noise and is representative of at least 10 trial simulations.

Both types of noise make mating more challenging. For κ 1 (external), low levels are similar to no noise. At intermediate levels of κ 1 we observe less accurate mating. At high levels, the cells are unable to polarization. The same trend is observed with the internal noise parameter κ 2 albeit with an even bigger effect. Combining the two noise terms further decreases mating and polarization. Based on these results, we selected intermediate values for both κ parameters, and set the noise amplitudes to be κ 1 = 5, κ 2 = 3 in the stochastic simulations that follow unless otherwise specified. This level of noise disrupts mating alignment but does not prevent mating. These values are consistent with previous theoretical work [ 25 ], which when applied to the yeast system have led to estimates of external noise arising from ligand diffusion and internal noise arising from ligand-receptor interaction to be in the range from 1 to 10 [ 28 , 41 ].

2.3. Varying the diffusion rate of a-factor with noise.

We re-tested the different values for the diffusion coefficient of a -factor in the presence of noise ( Fig 2C ) using the noise parameters κ 1 = 5, κ 2 = 3. Compared to the no-noise simulations ( Fig 2A ), we observed that mating did not occur for the lowest value of D a = 0.1 μm 2 /s. One explanation is that at very low values of D a then a -factor does not diffuse far enough to influence mating during the time period. On the other hand, values of 100, 10, and 1 all resulted in mating; we chose a default value of 10 μm 2 /s to maintain the asymmetry between a -factor and α-factor while avoiding low values that hinder mating.

3. Mating Efficiency of Two Cells

3.1. defining mating efficiency..

A standard laboratory test of yeast mating is the mating efficiency assay [ 17 ]. Populations of a - and α-cells are mixed together and the percent of successful matings is calculated, i.e., percent of a -cells that have mated with α-cells to form diploids divided by the total number of a -cells. Below we attempt to reproduce this assay using the two-cell mating simulations. This approach is a simplification because it ignores the influence of surrounding cells. Later, we describe three- and five-cell simulations that take into account more cells.

In the simulations, two cells were started with their centers 4 μm apart to leave sufficient room for the cells to grow while the mating occurs in a reasonable amount of time which is set to be 1800 seconds in our simulations. A successful mating was defined as the focal region of u 2 (i.e. polarisome) from each cell coming into close contact ( Fig 3A , see Methods ). Snapshots of simulations at different time are provided in Fig F in S1 Text .

(A) Schematic of determining mating efficiency. In the simulation on the left, the tips marked by gray and black dots (polarisomes) of the two projections fall within a distance threshold (see Methods ) so that the mating is considered successful. In the simulation on the right, the tips do not pass close enough to one another by the end of the simulation, and so the mating is deemed unsuccessful. (B) Faster and slower boundary velocities yielded similar mating trajectories. We ran simulations at two different boundary velocities ( V amp = 0.0001 and V amp = 0.0002 μm/s). A plot of the distance between polarisomes of mating partners as a function of time is shown for a sample simulation. The plots are similar except the slower velocity took approximately twice as long to mate. (C) Direction plots for different boundary velocities and shorter cell-to-cell distance. In this plot each data circle represents one mating simulation. The average direction of each projection is plotted on the x -axis for the α-cell, and y -axis for the a-cell. The projections are toward one another when the data point lies along the diagonal line (i.e. top right and bottom left quadrants). We show the direction plots for the default simulation parameters (V = 0.0002 μm/s, left), slow boundary simulation (V = 0.0001 μm/s, middle), and close-cell positions (cell-to-cell distance = 2.5 μm instead of 4 μm, right). The mating efficiencies were similar for all three simulations. (D) Average mating time of successful matings under different simulation conditions same as in (C). Each bar represents the average time (± standard deviation) for successful mating. We performed 20 simulations for each condition, and the numbers of successful matings for default, slow and close parameters are 15, 17 and 16 respectively.

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As a negative control, we performed simulations in the absence of pheromone secretion in which pheromone was added exogenously to create a uniform distribution. In this case, the cells did not mate (see S1 Text ) consistent with the low mating efficiency of pheromoneless cells observed experimentally [ 44 ].

3.2. Varying velocity only affects the time of cell mating without changing efficiency.

3.3. Shorter cell-cell distance yields similar results.

The distance between two cells can affect their interactions. We compared the default distance of 4 μm between the cell centers with a shorter distance of 2.5 μm (0.5 μm between cell boundaries). Snapshots of simulations at different time are provided in Fig H in S1 Text . The mating efficiency was 15/20 for the 4 μm distance and 16/20 for the 2.5 μm distance, which were very similar (p-value is 1 by Fisher’s exact test). Although the direction vectors showed increased variability reflecting a more scattered distribution around the diagonal at the shorter distance ( Fig 3C ), ultimately the two projections were able to find each other. Thus, at the shorter distance, mating efficiency was approximately the same as at the default distance. When we measured the mating time for successful matings ( Fig 3D ), we saw that the mating time was approximately inversely proportional to the membrane velocity and proportional to the distance between cell membranes.

In summary we observed a mating efficiency of approximately 75% in two-cell simulations at two different distances. This mating efficiency is slightly lower than the 90–100% mating efficiency observed in experiments [ 19 , 45 ].

4. Robustness Strategies for Optimizing Mating Efficiency

In the natural environment, yeast mating is efficient and robust to a variety of perturbations. In this section, we explored how features of the mating process could promote robust and efficient mating; we compared different mating scenarios by modifying the model parameters.

4.1. Polarized pheromone source distribution increases mating efficiency.

One important variable is the spatial distribution of the pheromone source. We consider two possibilities with respect to the pheromone source: isotropic or non-isotropic (polarized) secretion. In the isotropic scenario, pheromone is secreted uniformly from all points on the cell surface. In the non-isotropic scenario, the source would be polarized to the front ( Fig 4A ). Intuitively, one may imagine that the polarized source distribution would contribute to accurate mating by helping the projections find each other, and in the simulations described above we used the polarized source as the default. However we wished to compare these two possibilities quantitatively as follows.

(A) Schematic diagram of isotropic versus polarized (non-isotropic) pheromone source. Top row indicates in black shading the spatial distribution of the pheromone source function. The bottom row depicts the a -factor diffusion profile shown as a concentration contour plot for the isotropic source (left) and the polarized source (right). (B) Polarization plots of u 2 showing mating cells at end of simulation. Four sample simulations each from the isotropic source group and from the polarized source group are shown. The normalized level of u 2 is color coded on the surface of the cell according to the colormap on right. The polarisome is denoted by the black or gray dot at the projection tip. The polarized source produces higher mating efficiencies; the 1 or 0 indicates a successful or unsuccessful mating. The polarization plots, distance plots, and direction plots are color coded (blue, red, green, brown) for a particular simulation. (C) Distance plots for each of the four simulations. These plots show the distance between polarisomes of the mating partners as a function of time. With the isotropic source, the distances do not converge to 0 for some of the simulations. The green isotropic source simulation was terminated early because it did not meet the distance/direction threshold. (D) Direction plots for polarized source and isotropic source simulations. Each data point represents the averaged direction of the projection from each cell during mating. Axes are described in the legend to Fig 3C . Mating is more likely if the projections are in the same direction i.e. along the diagonal in the top right or bottom left quadrants. The average distance from the diagonal is 0.26 radians for the isotropic source compared to 0.12 for the polarized source matings. Colored filled circles correspond to simulations shown in (B) and (C). (E) Average mating time of successful matings with isotropic and polarized sources. Each bar represents the average time (± standard deviation) for successful matings. We performed 20 simulations for both conditions, and the numbers of successful matings for isotropic and polarized sources are 6 and 15 respectively.

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At the start of the simulations both cells secrete isotropically. Once the polarization is activated, the secretion becomes localized and is confined around the growth tip by being a function of u 2 in the formulation of the polarized (non-isotropic) source distribution, whereas the isotropic function does not depend on u 2 (see Methods ).

As expected the polarized pheromone source matings produced higher mating efficiency (ME = 15/20) than the isotropic source matings (ME = 6/20). By carrying out Fisher’s exact test on the mating efficiency, p-value is 0.01, therefore the difference between the two ME scores is significant. The effect could be observed in four sample matings for each scenario. Snapshots of more examples at different times are provided in Fig I in S1 Text . A picture of the mating cells ( Fig 4B ) shows how with the polarized source the projections tend to meet, whereas with the isotropic source, they sometimes miss. The distance plots measure the distance between the polarisomes over the course of the simulation ( Fig 4C ). With the isotropic matings, sometimes the distance stops decreasing and begins increasing as the projections go past each other. With the polarized source matings, the projections tend to go toward each other so that the distance steadily decreases.

In addition, the direction plots ( Fig 4D ) show that compared to the polarized source case, the isotropic matings possess projection directions that are not always toward one another (i.e. points farther off-the-diagonal; 0.26 versus 0.12 radians). The polarized source mating projections are all in the two quadrants along the diagonal in which the projections are heading in the correct direction. However, when we measured the average mating time of successful matings for isotropic and polarized sources ( Fig 4E ), we did not observe a significant mating time difference if the mating is successful.

4.2. Supersensitive cells exhibit decreased mating efficiency.

For yeast cells, one challenge is keeping the ligand concentration in the proper range so that the cell can detect spatial differences. In mutant cells (e.g. sst2Δ ) that are overly sensitive to pheromone (supersensitive), the signaling system becomes easily saturated and the cell cannot determine the concentration. As a result, they cannot detect the correct gradient direction and fail to mate [ 19 ]. We represented supersensitivity in our model by increasing the parameter β 1 , the reciprocal of the value achieving half-maximal activation in the term modeling external stimuli.

We tested the case β 1 = 2.5 ( β 1 = 0.92 is default value) for both cells in two-cell mating in a noisy environment ( Fig 5 ). In the presence of noise, both cells can successfully make a projection. Although the α-cell can detect the gradient and grow toward the source, the a -cell cannot. The growth of the a -cell is triggered by noise fluctuations, so that the cell picks a random direction which may not be correlated with the gradient. As a result, no matings are observed in the supersensitive simulations (ME = 0/20, p-value = 7.7E-07 by Fisher’s exact test). Snapshots of more simulations at different time points are provided in Fig J in S1 Text .

(A) Polarization plots of u 2 showing four pairs of supersensitive mating cells at the end time point. The spatial distribution of u 2 is represented according to the normalized color map on the right. The 0’s indicate that none of the matings were successful. Both cells had β 2 = 2.5. (B) Distance plot showing trajectories from four sample supersensitive mating simulations. The distance between polarisomes was plotted as a function of time. The distances did not steadily decrease as was observed in the normal sensitivity simulations. The four plots are color-coded to match with the polarization plots. (C) Direction plot for supersensitive cell mating simulations. Many of the data points lie far off the diagonal indicating that the cells are not pointing toward each other; the average distance from the diagonal is 0.68 radians compared to 0.12 for the normal sensitivity matings.

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In summary, we observed dramatically reduced mating efficiency in supersensitive cells with ME = 0% in the simulations compared to the 75% in cells possessing normal sensitivity. Experimentally, past data from this lab showed a decrease in mating efficiency from 96% in wild-type cells to 28% in sst2Δ supersensitive cells [ 45 ].

4.3. The presence of the α-factor protease Bar1 improves mating efficiency.

The a -cell can secrete a protease, Bar1, to degrade α-factor in its vicinity during mating. Up to this point, we have not tried to model Bar1; in effect, the a -cells have been bar1Δ mutants. It is thought that cells without Bar1 mate less efficiently than cells with Bar1 [ 46 ]. One explanation is that the background level of α-factor increases without the presence of Bar1. This background level can saturate the sensing apparatus in a similar fashion experienced by the supersensitive mutants preventing gradient detection.

In the previous work [ 12 , 38 ], the authors suggest that Bar1 is necessary for efficient mating by reshaping the local pheromone concentration and avoiding nonproductive cell-cell interactions. To investigate this process, we compared the behavior of Bar1+ and bar1Δ cells at different background pheromone production rates.

https://doi.org/10.1371/journal.pcbi.1004988.t001

We then tested for the effect of the presence of Bar1. When C = 0, the bar1Δ and Bar1+ cells displayed approximately the same mating efficiency. For the bar1Δ simulations, as we increased C from 0 to 100, there was a progressive decline in mating efficiency from 15/20 to 2/20. For the Bar1+ simulations, there was also a decline but it was more gradual. At C = 10, the mating efficiency was still 85% and at C = 100 it was 80%, which was higher than the corresponding bar1Δ values. To test whether the mating efficiency of Bar1+ is significantly greater than that of bar1Δ, we performed Fisher’s exact test on H 0 (null hypothesis): ME Bar1+ = ME bar1Δ , versus H A (alternative hypothesis): ME Bar1+ > ME bar1Δ for the different production rates of background α-factor. At different values of C , the p-values are all less than 0.05 so that we can reject H 0 at the 95% confidence level. Thus, in the simulations, Bar1 improves mating efficiency at all levels of background α-factor especially at higher production rates. The trend that increasing background α-factor decreases mating efficiency especially in bar1Δ cells is consistent with past experimental observations [ 19 , 44 ].

5. Mating competition among three cells: Bar1 adjusts pheromone gradient to attract mating partner

A natural extension of two-cell mating simulations is three-cell mating simulations. In three-cell simulations, the set-up can be either two α-cells and one a -cell, or two a -cells and one α-cell. In the former case, if the two α-cells are equidistant from the a -cell, we found that in the absence of noise, the a -cell projected toward the middle in between the two α-cells. Interestingly, if the two α-cells are slightly offset (i.e. the a -cell is located 0.1 microns below the middle line of two α-cells so that one is closer), then the a -cell still projected toward the middle ( Fig 6A ). If the a -cell is Bar1+, then the a -cell is able to gradually reorient to the closer mating partner. However adding noise to the simulations, the Bar1+ a -cell projected toward one or the other α-cell in a random fashion whether or not the cells were offset. Although the no-noise case is somewhat artificial, it indicates how Bar1 can improve the ability to detect the gradient direction in this idealized scenario.

(A) Bar1 helps a -cell distinguish closer α-cell. Two α-cells and one a -cell were positioned approximately at the vertices of a triangle with one of the two α-cells slightly closer to the a -cell than the other. We tested whether a Bar1+ or a bar1Δ a -cell could distinguish between the two α-cells in simulations performed in the absence of noise. The Bar1+ cell projected toward the closer α-cell, whereas the bar1Δ cell projected toward the middle between the two α-cells. (B) Mating competition simulations in which two a -cells compete for a single α-cell. In these three-cell simulations, one a -cell is Bar1+ and the other is bar1Δ. In 20/20 simulations, the Bar1+ cell mated with the α-cell, and two sample simulations are shown. At the top are snapshots with the projections in contact. In the middle are the α-factor profiles from the two simulations, which show how the high concentration of α-factor in the absence of Bar1 precludes gradient detection. At the bottom is the α-factor distribution along the cross-section between the α-cell and a -cell. In both cases, the steeper gradient is observed with the Bar1+ a -cell.

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Alternatively, the simulation can be between one α-cell and two a -cells. If the two a -cells have different genotypes, then there is a competition between the two for the single α-cell. This corresponds to mating competition experiments, a second important type of mating assay [ 47 ], in which one mixes two a -cell genotypes with a limiting quantity of α-cells. We tested the importance of Bar1 using mating competition. In mating competition simulations between Bar1+ and bar1Δ cells we found that the Bar1+ cells mated with the single α-cell partner 20/20 times ( Fig 6B ). Snapshots of more simulations are provided in Fig K in S1 Text .

Greater insight on why the Bar1+ cell has the advantage can be provided by the α-factor profiles for two sample simulations ( Fig 6B , lower). The Bar1 helps to remove the excess α-factor so that the Bar1+ a -cell is able to sense the gradient from the α-cell. The bar1Δ cell is stuck in a region of high α-factor in which the gradient is shallower.

6. Mating discrimination among multiple cells

The third mating arrangement is having a single a -cell choose between two α-cells of different genotypes. One specific scenario is having one α-cell make α-factor whereas the other α-cell makes less or no α-factor. Experimentally this simulation corresponds to a third important type of mating assay: mating discrimination in which the a -cell must discriminate between the α-cell mating partner secreting α-factor from α-cell decoys that do not [ 20 , 47 ]. This assay measures the ability of an a -cell to sense and respond accurately to a pheromone gradient.

6.1 Mating discrimination for three-cell mating: The a-cell chooses the mating partner producing more pheromone.

The first arrangement we tested was to have one a -cell (bar1Δ) and two different α-cells; one α-cell makes α-factor and the other does not. In the 20/20 simulations in which a successful mating occurred, the a -cell mated with the correct partner thus exhibiting perfect mating discrimination ( Fig 7A ). Snapshots of more simulations are provided in Fig L in S1 Text .

(A) Three-cell mating discrimination simulations. One a -cell and two α-cells were arranged so that the a -cell was equidistant from the α-cells. One α-cell makes α-factor (α-factor producer, green) and the other α-cell does not (α-factor non-producer, blue). 20 simulations were run to determine the ratio at which the a -cell would mate with the α-factor producer versus the non-producer. Two sample simulations are presented. The left panel shows an a -cell with wild-type sensitivity, and the right panel shows a supersensitive a -cell. ME indicates mating efficiency; MD indicates mating discrimination. (B) Five-cell mating discrimination simulations. Four α-cells are arranged in a square with one bar1Δ a -cell in the center. One α-cell makes α-factor (α-factor producer, green) and the other three cells α-cell do not (α-factor non-producers, blue). 20 simulations were run to determine mating discrimination, and two sample simulations are presented. The left panel shows an a -cell with wild-type sensitivity, and the right panel shows a supersensitive a -cell. (C) Mating location plots for a -cells possessing normal sensitivity (WT, green) or supersensitivity (SS, red) in five-cell mating discrimination simulations. Each dot (correct MD) or cross (incorrect MD) symbol represents the polarisome location of the a -cell at the time of mating. The α-cell producing α-factor was in the top-right quadrant. The cells possessing normal sensitivity showed significantly better mating discrimination (MD) than the supersensitive cells (p < 0.0001, Fisher’s Exact Test).

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Experimentally it is known that supersensitive cells exhibit reduced mating discrimination along with lower mating efficiency. We tested the scenario in which the a -cell is supersensitive in the three-cell simulations. 11/20 simulations exhibited successful matings, and from the 11 matings, the a -cell correctly mated with the α-cell making pheromone 5/11 times ( Fig 7A ), which is close to the random (50%) mating discrimination score observed in experiments [ 19 ]. The p-values (Fisher’s exact test) for comparing normal sensitive cells versus supersensitive cells for mating efficiency (0.0012) and mating discrimination (0.00063) indicated significant differences between the two sets of simulations.

6.2. Defective mating discrimination by supersensitive cells in five-cell simulations.

To create a more competitive mating situation, we extended the three-cell simulations to five-cell mating discrimination simulations ( Fig 7B ). In this scenario, a single a -cell was surrounded on 4 sides by four α-cells (3 non-producers, and 1 producer). The a -cell possessing normal sensitvity was able to mate efficiently (ME = 19/20) and with almost perfect mating discrimination (MD = 18/19). When the a -cell was supersensitive, we observed slightly increased mating efficiency compared to the three-cell simulations (ME = 12/20); however the mating discrimination remained poor (MD = 3/12). The latter represents random mating with no regard to which α-cell is secreting α-factor ( Fig 7C ). The p-values obtained in Fisher’s exact test were 0.02 for ME and 9.6E-05 for MD, indicating significant differences between normal sensitive cells and supersensitive cells. In summary, we observed nearly perfect mating discrimination in wild-type cells, whereas supersensitive cells exhibited random mating discrimination. These results match experimental data from wild-type and supersensitive sst2Δ cells [ 20 , 45 ].

6.3. Bar1 improves mating discrimination with background α-factor.

In the five-cell mating discrimination simulations described above, normal sensitivity cells exhibited good mating discrimination in the absence of Bar1. Experimentally, it has been observed that bar1Δ cells are indeed capable of mating discrimination, but only at low mating mixture densities [ 19 ]. At high cell densities, bar1Δ cells show poor mating discrimination (nearly random), whereas Bar1+ cells are still capable of good mating discrimination. We interpreted these findings to mean that a background concentration of α-factor hindered mating discrimination. We tested this hypothesis by adding a high level of background α-factor to the five-cell mating discrimination simulations.

With background pheromone production rate C = 50, the simulated mating discrimination for bar1Δ cells was 1/11 compared to 18/19 when C = 0. By comparison, the Bar1+ strains exhibited superior performance with MD = 9/17 when C = 50 ( Fig 8 ). The p-value given by Fisher’s exact test was 0.041, indicating that mating discrimination was higher for the Bar1+ simulations. Thus, we show that Bar1 is important for both mating efficiency and mating discrimination in the presence of background α-factor. Indeed in previous experiments [ 19 ], mating discrimination for bar1Δ cells is sensitive to background α-factor concentrations, with discrimination perfect at low levels of α-factor but nearly random at high levels. Wild-type Bar1+ cells are much less sensitive to background α-factor concentrations consistent with the simulations.

(A) Five-cell mating discrimination simulations with background α-factor in the presence and absence of Bar1. Four α-cells are arranged in a square with one a -cell in the center. One α-cell makes α-factor (α-factor producer, top right corner) and the other three cells do not. Background α-factor source was set to C = 50. There are two sample simulations for bar1Δ a -cells (left), and two sample simulations for Bar1+ a -cells (right). ME is mating efficiency, and MD is mating discrimination. Bar1 was secreted in a polarized fashion. The second row shows the α-factor profiles for one sample (left) simulation from each group. The third row shows the α-factor profiles along the top-right to bottom-left diagonal for one example (yellow dotted line). There is an early (T = 50s) and late (T = 570s) time point for each simulation with α-factor concentration indicated by the shading (color bar). Pheromone profiles show a steeper gradient in Bar1+ a -cell simulations; troughs represent the cell body which excludes α-factor. (B) Mating location plots for Bar1+ a -cells (green) or bar1Δ (red) in five-cell mating discrimination simulations. Each circle (correct MD) or cross (incorrect MD) symbol represents the polarisome location of the a -cell at the time of mating. The α-cell producing α-factor was in the top-right quadrant. The Bar1+ cells showed significantly better mating discrimination (MD) than the bar1Δ cells (p < 0.05, Fisher’s Exact Test).

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Insight for this superior performance can be obtained by studying the α-factor profiles from the matings ( Fig 8A , lower). Examining both an earlier and later time point, one observes that in the absence of Bar1, the level of pheromone becomes very high preventing a significant gradient from being formed. In the presence of Bar1, the background α-factor is sufficiently degraded so that a steeper gradient is created.

7. Estimate of the a-factor diffusion constant

We imaged mating mixes using both Bar1+ and bar1Δ cells as well as a combination of the two. We found that mating was short-range when the a -cells were Bar1+, i.e., both a - and α-cells made short projections (see S1 Text ). With the bar1Δ a -cells, there was longer-range mating with only the a -cells forming longer projections. We hypothesized that degradation of α-factor by Bar1 resulting in short-range mating in the Bar1+ matings. The projection length in both simulations and experiments was defined by subtracting the initial cell radius from the distance between the center of the cell and the point that is farthest from the center on the cell membrane. The asymmetry in projections lengths in the bar1Δ matings was reminiscent of our simulations in which we varied the a -factor diffusion rate ( Fig 2 ). In particular, as the a -factor diffusion rates became slower, the α-cell projection became shorter (and the a -cell projection became longer). We attributed this difference to the reduced spread of a -factor from its source when its diffusion constant is lower.

To provide an estimate of the a -factor diffusion coefficient, we determined the relative length of the α-cell projection normalized by the total distance traveled by both projections, and plotted this α-cell length for both simulations and experiments in Fig 9 . In the simulations we varied the a -factor diffusion rate from 0.1 to 100. From this comparison we estimate that the a -factor diffusion rate is 1 μm 2 /s.

(A) Projection lengths in bar1Δ versus Bar1+ a -cells. In the presence of Bar1, we only observed short-range matings in which both a -cells and α-cells possessed short projections. In the absence of Bar1 (bar1Δ matings), we observed longer projections made by the bar1Δ a -cells, whereas the α-cell projections remained short. The top two panels are fluorescent images of Spa2-GFP ( a -cell) and Spa2-mCherry (α-cell) showing the adjacent/overlapping polarisomes indicating a successful mating. The bottom two panels are DIC images that depict the projection morphologies of the mating cells. Scale bars = 5 μm. (B) The relative projection lengths of α-cells versus a -cells in simulations compared to experiments. In the top bar graph, the α-cell projection length is presented as the fraction of the sum of the two projection lengths ( n = 25 matings for Exp.); the average and standard deviation (error bars) are shown. The two-cell simulations with noise were performed as described in Fig 2 for varying α-factor diffusion values: D a = 0.1, 1, 10, and 100 μm 2 /s. The average and standard deviation of the normalized α-cell projection length from 10 simulations are shown. In the bottom bar graph, the corresponding unnormalized a -cell and α-cell projection lengths (mean ± SD in μm) are shown. The a -cells in both experiments and simulations are bar1Δ. (C) Concentration profiles of a -factor for different diffusion constants. The a -factor distribution is color-coded using gray scale at T = 830s in one example simulation for different diffusion rates. With a diffusion constant of 0.1 μm 2 /s, a -factor is highly localized to its source and does not reach the mating partner. With the diffusion constant of 100 μm 2 /s, a -factor spreads widely and is almost homogeneous distributed.

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In this paper we performed computer simulations of the yeast mating process for the first time. The main advance was constructing a computational framework for yeast mating which we used to explore different model structures and parameters. We reproduced qualitatively the basic mating behaviors and calculated the simulated mating efficiency. In addition, we were able to model mating competition and mating discrimination which together with mating efficiency form the three basic assays of yeast mating [ 19 , 47 ].

From a computational perspective, we combined modeling the shape of the cell using a moving boundary technique with the extracellular diffusion of the pheromone ligands with a previously described minimal model of pheromone-induced cell polarity. The simulations were CPU intensive because of the multiple time-scales, the evolution of the level set function over the computational domain, and the calculation of the velocity field. Overall the simulation time depended on the number of cells, time step size, length of simulation, and α-factor diffusion rate.

We examined for the first time the coupling among ligand secretion, ligand diffusion, and ligand-induced receptor activation which revealed new cell-cell interaction dynamics that could not be captured in single-cell simulations. We identified key factors that contributed to the efficiency and robustness of mating. First polarized secretion of mating pheromone resulted in higher mating efficiency than isotropic secretion. This finding is consistent with experimental data in which a -factor secretion through the Ste6 transporter is highly polarized [ 48 ]. It is likely that α-factor is secreted in a polarized fashion given the polarization of the secretory pathway during mating [ 22 ].

A second critical factor is the proper modulation of the sensitivity of the system. In experimental matings, strains that are “supersensitive” show considerably reduced mating efficiency and mating discrimination because they are unable to determine the pheromone gradient direction. By increasing the value of the parameter β 1 in Eq ( 3 ), we were able to mimic the supersensitive phenotype, and the resulting mating simulations were defective.

Finally, the presence of Bar1 helped cells to mate in the presence of background α-factor. Bar1 has been implicated to play an important role in modulating the pheromone dynamics [ 38 , 39 ]. Our results are consistent with the conclusions in [ 38 ] that Bar1 helps to shape the α-factor gradient for optimal mating. More specifically, both results show that Bar1 can create an α-factor sink that amplifies the α-factor gradient promoting gradient-sensing. The simulations in this work incorporated stochastic effects, a generic description of intracellular signaling that drives the cell membrane, and polarized secretion of both pheromone and Bar1.

There are important limitations to this study. First, we did not attempt to present a detailed quantitative portrait of the mating process with mechanistic reactions. We employed a generic model of yeast cell polarity with a small number of variables for computational efficiency and to facilitate parameter exploration. Second, we employed a quasi-steady-state approximation of α-factor spatial dynamics, although we provide simulation data that this choice does not affect the basic results. Third, we employed mechanisms that only crudely approximate physical reality such as ligand normalization. Fourth, we have not attempted to fit the parameters to actual mating data; rather our approach was to test multiple parameters values to qualitatively explore different scenarios. We thus achieved our goal of constructing a computational framework that is capable of generating realistic-looking responses and reproducing basic behaviors.

From a technical standpoint, one important future challenge is speeding up the simulations so that the boundary velocity can be reduced to a more realistic value. One possibility is to employ a quasi-steady-state approximation for the fast α-factor dynamics. For the model with multiple cells, each cell would be assigned with a level set function and a velocity field in our framework, and so there is the potential to improve the efficiency by performing parallel computation for different level set functions or representing all cells by one level set function with a mixed velocity field. Current simulations are all restricted to two-dimensional space. Theoretically it is feasible to extend this framework into three dimensions, although the computation could be very expensive because the computational cost increases exponentially with respect to dimensionality. In addition, experimentally the mating reaction occurs on a surface (i.e paper filter) which is effectively two-dimensional [ 17 ].

Importantly this research helps to identify the key processes to focus on for future work. The generic framework is easily extended, and we can incorporate more sophisticated and detailed mechanistic models. Because of the absence of mechanistic details, the models in this work can be thought of as “general mating models”, providing a generic description of gradient tracking informed by the yeast mating system. For example, we plan to replace the normalized f term with pheromone-induced Bar1. In the future, an important goal is to replace the generic terms with more mechanistic terms.

With a more realistic mechanistic model of pheromone-induced cell polarity, we could attempt to simulate the mating defects of a variety of mutants. Numerous mutants have been isolated that affect mating efficiency and discrimination including fus1Δ , spa2Δ , etc. [ 49 ]. One goal would be to reproduce these mating phenotypes at a quantitative level; another goal would be to predict novel mutants that may affect mating.

1. Details of mathematical model

An overview of the model including model equations is presented in the main text in Section 1. Here we present additional details.

1.1. Pheromone source function.

1.2. Model parameters.

We used the standard parameters described in previous work that were slightly modified [ 41 ]. The default parameters are given in the S1 Text along with simulation results from an alternative parameter set.

1.3. Geometry of initial mating arrangement.

For mating of two cells of different types, we set up a 2D rectangular domain [−3.6, 3.6] × [−1.6, 1.6] with one cell centered at (-2, 0). and the other centered at (2, 0). Each cell is initially represented by a circle of radius 1. For mating of three cells, the one a -cell is centered at (-2, 0), one α-cell centered at (1, 2) and the other centered at (1, -2) on a rectangle [−3.6, 3.6] × [−2.8, 2.8]. For the five-cell mating discrimination the a -cell is located at (0, 0), and the 4 α-cells are located at (±2, ±2) on a square [−3.72, 3.72] × [−3.72, 3.72].

1.4. Definition of polarisome.

1.5. Definition of a successful mating.

Two cells of opposite mating type successfully mate if the projections are on average growing toward one another, and the minimum distance between their respective polarisomes is less than the distance threshold 0.04 (the mesh size).

2. Numerical methods

The time step is set to be 4 × 10 −4 for extracellular pheromone, and 0.01 for membrane-associated dynamics.

The simulations were performed with the authors’ original MATLAB codes, and they can be provided by the authors upon request.

Supporting Information

S1 text. supporting information and figures..

There are 7 sections and 14 figures (A–N) in the S1 Text.

https://doi.org/10.1371/journal.pcbi.1004988.s001

Author Contributions

Analyzed the data: WC TMY CSC. Wrote the paper: WC QN TMY CSC. Conceived and designed the modeling simulations and experiments: WC TMY CSC. Performed the lab experiments: TMY. Performed the modeling simulations WC. Wrote the algorithms and software for performing the simulations: WC CSC.

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27. Phillips R, Kondev J, Theriot J (2009) Physical biology of the cell. New York: Garland Science. xxiv, 807 p. p.

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Microorganisms

Yeast–Yeast Interactions: Mechanisms, Methodologies and Impact on Composition

Fanny bordet.

1 Univ. Bourgogne Franche-Comté, AgroSup Dijon, PAM UMR A 02.102, F-21000 Dijon, France-IUVV Equipe VAlMiS, rue Claude Ladrey, BP 27877, 21078 Dijon CEDEX, France

2 Lallemand SAS, 19, rue des Briquetiers, BP 59, 31702 Blagnac CEDEX, France

Alexis Joran

Géraldine klein, chloé roullier-gall, hervé alexandre, associated data.

During the winemaking process, alcoholic fermentation is carried out by a consortium of yeasts in which interactions occurs. The consequences of these interactions on the wine matrix have been widely described for several years with the aim of controlling the winemaking process as well as possible. In this review, we highlight the wide diversity of methodologies used to study these interactions, and their underlying mechanisms and consequences on the final wine composition and characteristics. The wide variety of matrix parameters, yeast couples, and culture conditions have led to contradictions between the results of the different studies considered. More recent aspects of modifications in the composition of the matrix are addressed through different approaches that have not been synthesized recently. Non-volatile and volatile metabolomics, as well as sensory analysis approaches are developed in this paper. The description of the matrix composition modification does not appear sufficient to explain interaction mechanisms, making it vital to take an integrated approach to draw definite conclusions on them.

1. Introduction

The transformation of grape must into wine is a complex process involving various microorganisms: yeasts, molds, and bacteria. The main step, alcoholic fermentation, is performed by yeasts. In natural fermentation, microflora comes from grape berries but also from winery equipment and surroundings. Yeast biodiversity on grape berries is governed by various biotic and abiotic factors such as grape variety, climatic conditions and viticultural practices [ 1 , 2 , 3 ]. Yeasts present on grapes are mainly from non- Saccharomyces genera (essentially Hanseniaspora , Candida , Kluyveromyces , Metschnikowia , Pichia , Cryptococcus and Rhodotorula [ 3 ]) while Saccharomyces genera are very rare. However, although non- Saccharomyces yeasts initiate fermentation and develop during the first hours, their population declines rapidly in favor of Saccharomyces cerevisiae ( S. cerevisiae ), which becomes the dominant species until the end of alcoholic fermentation. The evolution of yeast populations during fermentation seems to be linked to several modifications that make the medium more selective. The establishment of nutrient depletion, anaerobic conditions, increased acidity, the production of sulfur dioxide, and increasing levels of ethanol (up to 15%v/v) results in a drop in yeast diversity [ 2 ]. This modification of the matrix environment allows the survival of S. cerevisiae because of its overall better resistance to stress compared to non- Saccharomyces species [ 4 ].

Producers have used wine starters for many decades to ensure proper fermentation initiation and the quality and reproducibility of wine. Indeed, starter yeasts allow efficient fermentation management that limits contaminations and avoids deviations due to interrupted or sluggish fermentations [ 5 ]. These starter yeasts are selected for their specific metabolic properties: resistance to various stresses, fermentation capacity, or the presence of enzymatic activities [ 6 ]. The ability of S. cerevisiae to grow in a selective medium as described above, to carry out efficient and quick alcoholic fermentations, make this species a tool of choice as an oenological starter [ 5 ].

However, in recent years, non- Saccharomyces yeasts have been used for wine production since several yeast species have shown high oenological potential [ 7 , 8 ]. Indeed, yeasts like Saccharomyces non- cerevisiae [ 9 , 10 ], non- Saccharomyces [ 6 , 7 , 11 , 12 , 13 , 14 , 15 , 16 ], and even natural hybrids [ 17 , 18 , 19 , 20 , 21 ] are of interest, because their different metabolisms compared to S. cerevisiae brings diversity to quantitative and qualitative composition of final wine (for example, ethanol content, organic acids, aroma production) [ 3 , 22 , 23 ]. Nevertheless, all these studies show that the utilization of these yeasts, in combination with S. cerevisiae , as wine starters, is still a challenge, since the results are unpredictable and lack of reproducibility. The conduct of fermentations by managing the simultaneous or successive implantation of different strains to obtain the desired impacts on wine has not yet been mastered. It is therefore necessary to understand the phenomena involved in the evolution of the yeast ecosystem during alcoholic fermentation, to control these mixed cocultures more efficiently. In recent years, numerous researchers have furthered research in understanding interaction mechanisms between microorganisms, since these interactions impact not only the population dynamics but also the metabolism of each strain, with consequences on the compounds produced, and eventually on final wine quality.

Authors have shown the existence of different interaction mechanisms between yeasts: competition for nutrients, the production of inhibitory or toxic compounds, the modification of metabolism by a quorum-sensing answer or induced by cell-contact. But all these results highlight that yeast interactions during wine alcoholic fermentation are very complex because of the variations according to yeasts (species, strain), medium composition, and abiotic conditions (oxygen, temperature). These fickle results (as described in a recent review from Conacher et al. (2019) [ 24 ]) seem to be linked to the sheer diversity of the methodologies employed. Each team often works with different strains including commercial and indigenous strains, different types of culture media, various matrixes (synthetic must or musts from different grape varieties) and also different culture modalities Each of these factors can impact the population and fermentation dynamics, by tipping the balance of a fragile equilibrium between species one way or another. Despite the great need for an overview of these methodological differences, so far none is available, although it would lead to better comparisons of results, and provide a synthesis of standard protocols for newcomers in the field.

The objective of this review is to highlight recent scientific developments concerning yeast–yeast interactions. First, the different methodologies employed will be discussed, including recent contributions from transcriptomic and metabolomic approaches. The impact of interactions on volatile and non-volatile composition will then be considered. Finally, the consequences of interactions on wine sensory characteristics will be discussed in-depth.

2. Methodologies

2.1. parameters: inoculation and culture conditions.

Numerous studies have been performed on mixed cultures between S. cerevisiae and non- Saccharomyces to understand how yeast interactions can impact wine quality. Authors have monitored population dynamics, fermentation parameters, metabolite production, especially aroma compound production, and highlighted interaction mechanisms. But contradictory results can be found in this field, as shown in Table 1 and Table S1 , which include the conditions and results of experiments for several couples of yeasts, those most studied to improve wine quality.

Diversity of methodologies and results in S. cerevisiae / Lachancea thermotolerans interaction experiments.




210 (Glc)		200 (Glc)				210 (Glc)			200 (Glc)								200 (Glc 100 Fru 100)								162					160									231
																																							PAN 154/NH3 22
25		25				25			25								25								25					20									25
+/-		+/-		+		+/-			+								-	+			++		+++		+/-		+			+									-
0		0				0			0								0								0		0			0		24		48		72			0
1:1		1:1				1:1			1:1								1:10								1:1		1:1			1:1									1:1			1:100			1:10,000
2		1		1		2			1			1					3	3			3		3		2		2			1		5		7		7			2			4			15
+		=				+			+ 1			+ 1					+								+		+			+ 1		+ 2		+ 7		+ 12			+ 1			+ 10			+ 15
-		2.5		2.5		-			-			-					-	3			3		3		6		-			-		-		-		-			-			-			-
2		1		1		2			1			1					1	1			1		1		2		2			3		2		5		3			1			3			3
3		2.5		2.5		3-6			1			1					-	-			-		-		3		4			3		6		6		10			3			10			15
Low 6						Low 6-10			No 6			Low 6/Yes													Low 4		Low 6			No 7		No 8		Low 8 No 30		Low 30			No 15			No 22			No 22
10						10			6			5													10		10			+		+		+		+			+
																	=	- -			- -		- -							- -		- -		- -		=			- -
																	++	- -			- -		- -							=		++		++		++			++
																														++		++		++		++			++
																														- -		- -		- -		- -			- -
																														++		++		++		++

																	OX/SPE								OX					DEL									RAT/SPE
No TOX/No COMP/CCC		No TOX/No COMP/No QS/CCC				COMP			TOX/CCC								COMP								No TOX/COMP
[ ]		[ ]				[ ]			[ ]								[ ]								[ ]					[ ]									[ ]



254											222								249					226					280 (Glc 140 Fru 140)						245			226						236
PAN 160/NH 36																			PAN 167					PAN 241					YAN 170 (supp)						YAN 181			PAN 333						YAN 235
20							30				25								20					25					25									25						22
++											+								+					-					++						+			+						+
0											0				48				96					96					96						72			72						48
1:10											1:10				1:10				10:1					1:1					1:1						1:1			1:1						1:1
4							1				4				7				6					8
-
-							-				4				7				-					-					-									-						-
4							4				2				2				4					4
18							4				4				4				4					4					+						+			5						3
							No 7				Low 10				Low 10				No 8					Low 10					No 7						No 6									Low 5
24							13												12					10					17															14
- -							=				- -				- -				- -					-					=						- -			- -						- -
++							+				++				++				++					++											+			=						++
++							++				++				++
+							- -				++				++														++						= or ++
++							+				++				++									++														++						+/-
																			- -					- -														- -						++
TEMP/DEL/GRA/REAC																													SPE						SPE									SPE/NS

[ ]																			[ ]					[ ]					[ ]						[ ]			[ ]						[ ]



224					237			205								245						217				234		218					225				487						212
PAN 154/NH3 22					PAN 147											PAN 177						YAN 170 (supp)											YAN 250 (supp)										251
25					20			18								25						15				25							20				22						20
-								+								+						+				-		-			++		+				+						+
0	24		48		48			0		24			48	72		0				96		96				168		0					48				0						24
1:10	1:10		1:10		10:1			1:1								1:2.5				1:2		1:1				1:1.3		1:10					1:1				1:5			1:20	1:50		1:1
2	1		2		4			4								4				8								4			2		4										3
+ 11	-		-					+																													-			-	-		+
-	-		-		-			6		6						-				-								6			6		14										-
1	2		2		2															4								0					0				6			6	6		1
11					2			4		6						+				4		+				+ or -		4			6		4				-			-	-		5
Low 18					No 15			Low 7		Yes						No 6				No 8		No 14						No 6			No 8		No 6
+	-		-		+			7		9						10				14		19						10			12		+				-			-	-		16
- -	- -		- -		-			=		- -						-				- -		=				=		=			- -		=				- -			- -	- -		- -
+	++		++		++											-				++								=			=		++				- -			- -	- -
++	++		++					++		++																- -							=
- -	- -		- -					++		+++												- -				- -
++	++		++		+											++				+++						++							++
					=											=				- -		-						=			=		=				- -			- -	- -
TEMP/DEL/GRA/REAC					SPE			DEL														SPE				MED		OX/SPE					DEL				RAT
																												COMP															CCC/COMP
[ ]					[ ]			[ ]								[ ]						[ ]				[ ]		[ ]					[ ]				[ ]						[ ]

YAN = yeast assimilable nitrogen/ PAN = primary amino nitrogen. Sc = S. cerevisiae / NS = Non- Saccharomyces. TA = total acidity/ VA = volatile acidity/ LA = lactic acid/ AA = acetic acid. SGJ = Synthetic Grape Juice/ Glc = Glucose/ Fru = Fructose/ WYPD = Yeast Peptone Dextrose medium modified for wine fermentation/ Supp = with supplementation. Oxygen : - = anaerobia, +/- semi-anaerobia, low oxygenation, + = semi-anaerobia, ++ aerobia, +++ aerobia, with higher oxygenation. Population columns : Max = maximal population reached by day x/ Dominance : + = dominance of S. cerevisiae , = similar populations, “x” = dominance obtained after x days/ Decrease = decrease since day x/ Low = low population since day x/ No = population not detectable since day x/ Yes = population still detectable at day x. Fermentation completion : x = reached at day x, +/- = reached/not reached during experimentation. Ethanol, glycerol and organic acids are compared to Sc pure culture: +++ very high increase, ++ high increase, + increase, +/- slight decrease, = no change, - decrease, - - high decrease. Impacting factors : inoculation delay ( DEL ), inoculation ratio between S. cerevisiae and non- Saccharomyces yeast ( RAT ), yeast species ( SPE ), yeast strain S. cerevisiae ( SC ) or non -Saccharomyces ( NS ), medium composition ( MED ), grape nature ( GRA ), temperature ( TEMP ), oxygenation ( OX ), type of reactor (lab, pilot, industrial) ( REAC ). Interaction mechanisms : involvement of quorum sensing mechanisms ( QS ), toxic compounds (including ethanol, antimicrobial peptides) ( TOX ), competition for nutrient (including oxygen) ( COMP ), cell-cell contact mechanisms ( CCC )/ No = mechanism involvement has been ruled out by the study.

Variability in population dynamics results can be observed depending on the various studies. The S. cerevisiae population is not affected in most experiments by the presence of another yeast, even if some exceptions exist [ 12 , 29 , 39 , 46 , 47 ]. On the other hand, the presence of S. cerevisiae usually negatively impacts non- Saccharomyces growth and early decline and even early death are often observed, but some authors have observed the stability of non- Saccharomyces yeasts during a longer period [ 33 , 45 ]. Fermentation kinetics can also be different. Mixed cultures with non -Saccharomyces yeasts can lead either to complete fermentations (within different timeframes) [ 48 , 49 ], or to incomplete fermentation [ 12 , 33 ]. The production of metabolites such as glycerol, acids, and aroma compounds is also variable [ 31 , 33 ].

Yeasts are often inoculated at a cell count of 10 6 cells/mL since this corresponds to the conditions occurring in natural fermentation [ 50 ], in which there is dominance of non- Saccharomyces populations at the early stage, but inoculation density can vary between 5.10 4 [ 26 ] and 2.10 7 cells/mL [ 29 , 51 ].

The first hypothesis to explain this diversity of results is medium composition, which is known to impact yeast physiology, metabolism, and yeast interactions. Table 1 and Table S1 show that numerous authors choose to use real grape juice or must to approach winemaking conditions. But natural grape must is not standardized and its composition varies depending, for example, on the year, harvest time, and grape variety. Englezos et al. (2016) [ 12 ] and Nisiotou et al. (2018) [ 49 ] both conducted mixed fermentation with Starmerella bacillaris ( S. bacillaris ) in similar conditions (temperature and inoculation) but obtained different results. Indeed, contradictory results are reported in terms of non- Saccharomyces persistence and fermentation completion reflecting the influence of the matrix composition on yeast interactions. However, other differences (must sterilization, yeast strain) in methodology can also explain these discrepancies.

Initial sugar concentration can impact yeast growth but also the capacity of yeasts to interact with other yeasts. The ability to take up glucose varies with glucose concentration with a species-dependent effect. Outside the 160-190 g/L range, non- Saccharomyces yeasts are less able to take up glucose and become less able to compete with S. cerevisiae in mixed cultures [ 27 ]. In addition, initial sugar concentration and the amount of sugar metabolized by S. cerevisiae could have an impact on toxic compound production and further on population dynamics in mixed cultures: a delay in Hanseniaspora guillermondii ( H. guillermondii) death can be observed when the initial sugar concentration is 100 g/L, compared to a standard medium with 200 g/L [ 52 ]. These effects are not always verified since Lachancea thermotolerans (L. thermotolerans) can survive until the end of fermentation with 200 g/L of initial sugars [ 39 ] but not with 160 g/L of initial sugars [ 31 ], although other factors can be involved (total population, oxygenation, strain). At high sugar concentrations (up to 200-300 g/L), yeasts can delay their growth and have a lower growth rate, with a possible effect on population dynamics [ 53 ]. However, L. thermotolerans can be used in the mixed fermentation of sun-dried grapes with a very high sugar content and become the dominant strain if inoculated at a high ratio [ 45 ].

Other medium components are also important to manage since S. cerevisiae and non- Saccharomyces yeasts have different needs and do not metabolize nutrients in the same way. A key point is nitrogen source quality since ammonium and amino acids may be assimilated or not, with various rates according to the strain and culture conditions like temperature [ 54 , 55 , 56 , 57 ]. In addition, competition for nutrients can occur between strains and limiting nutrients concentrations can increase their interactions. For example, in sequential mixed cultures, an insufficient initial amount of assimilable nitrogen can be entirely consumed by non- Saccharomyces yeast before the inoculation of S. cerevisiae , resulting in incomplete fermentation [ 51 ]. Moreover, nitrogen availability has an impact on the ability of yeast to compete with other strains. When nitrogen is limited, indigenous S. cerevisiae strains are more competitive toward commercial S. cerevisiae and can co-dominate fermentation; they have higher nitrogen demand and can quickly remove nitrogen from the medium, which is then no longer available for commercial strains [ 58 ]. In addition, as the production of aromatic alcohols (as tyrosol, tryptophol), known as quorum sensing molecules, is linked to nitrogen metabolism, the amount of nitrogen can impact this production or the effects of these molecules [ 59 ].

All these differences in must composition (sugars, YAN) can cause variabilities in yeast interactions and complicate understanding of the mechanisms involved. Considering this must variability, some authors choose to standardize their fermentation medium by supplementing must with sugar or nitrogen sources. However, referring to Table 1 and Table S1 , it can be seen that the target level is not always the same, making comparisons between studies difficult. Moreover, Englezos et al. (2018) [ 60 ] observed that even with standardized sugar and YAN composition, grape variety still has a significant impact on non- Saccharomyces persistence during the fermentation process, hinting at the impossibility of effectively standardizing real must.

An alternative to these limits would be to use a synthetic medium with a fixed composition that can simulate natural must. Some researchers have chosen to use a quite simple medium such as a classical laboratory medium supplemented with sugars [ 26 , 28 ], while others have used compositions more similar to natural must, called “synthetic must” or “synthetic juice” [ 61 ]. Here, several key choices subsist, as some nutrients can impact yeast dynamics: the proportion of fructose in sugars [ 62 , 63 ], vitamins, and growth factors [ 64 ]. Authors have usually used this type of media to allow for effective standardization while studying the impact of a specific factor on interactions. For example, Wang et al. in 2016 [ 65 ] used synthetic must to show strain dependence in interactions between multiple Saccharomyces/ non -Saccharomyces couples. Shekhawat et al. (2017) [ 29 ] also used synthetic grape juice to study the impact of must oxygenation on mixed cultures of L. thermotolerans/S. cerevisiae .

The inoculation procedure is also an important parameter. Authors have conducted mixed fermentations with the simultaneous or sequential inoculation of yeasts, with various times between both inoculations. The addition of S. cerevisiae after non- Saccharomyces can delay their death and increase their influence on wine characteristics. L. thermotolerans can be present until the end of fermentation, when S. cerevisiae is added 24, 48, or 72 h afterwards [ 33 , 39 ], but its population decreases drastically when a longer delay is used (4 days) [ 35 , 41 ]. However, some authors have observed a decrease with inoculation performed after 24 h [ 46 ], 48 h [ 14 , 31 , 44 ], or 72 h [ 37 , 66 ] indicating that other factors (medium composition, temperature, oxygenation) can interact. Non- Saccharomyces can also, in sequential culture, become the dominant strain and negatively impact S. cerevisiae growth, as was shown for L. thermotolerans [ 39 ], and S. bacillaris [ 12 , 47 , 49 , 60 , 67 , 68 , 69 ].

Different inoculation modes can also be coupled with different inoculation ratios between the two strains present in mixed culture, which has great importance for population dynamics. In spontaneous fermentation conditions, the yeast population of freshly extracted must is overwhelmingly constituted by non- Saccharomyces , with S. cerevisiae accounting for less than 1% of the total yeast population [ 70 ]. To simulate natural conditions, some researchers have inoculated using a large amount of non- Saccharomyces compared to S. cerevisiae , with the objective of improving non- Saccharomyces persistence during mixed fermentation. Usually, a ratio favoring a specific yeast has a positive impact on the latter’s population dynamics; longer persistence, higher population, and dominance, as was shown for S. bacillaris [ 32 ], Saccharomyces kudriavzevii (S. kudriavzevii) [ 9 ], H. guilliermondii [ 52 ], L. thermotolerans [ 45 ]. However, adjusting inoculation ratios is not always enough to obtain the persistence of non- Saccharomyces yeasts [ 48 , 51 ]. The initial amount of yeasts can also have an impact since the death of non- Saccharomyces yeasts can be linked to the presence of the high cell density of S. cerevisiae ( H. guilliermondii declines when S. cerevisiae reaches 10 7 CFU/mL [ 52 ]).

The physiological state of yeast can also have an impact on interaction. Branco et al. (2017) [ 71 ] showed that S. cerevisiae induces the death of non- Saccharomyces yeasts in mixed cultures, by different mechanisms, depending on its physiological state when mixed culture begins: cell-contact is involved when S. cerevisiae is in stationary phase and not when it is in mid-exponential phase. A potential explanation could be the accumulation of antimicrobial peptides on the cell-surface during S. cerevisiae growth, according to these authors.

Other culture parameters such as temperature can also impact population dynamics and ecosystems, since yeasts have different optimal growth temperatures [ 72 ]. S. cerevisiae is better adapted to higher temperature and even modifies temperature through heat production during fermentation [ 73 ]. The application of low temperature can favor the growth, survival, and even dominance of non- cerevisiae species [ 74 , 75 , 76 ] and of non- Saccharomyces yeasts [ 77 ]. Temperature can also increase the competitive ability of non- Saccharomyces : L. thermotolerans has an inhibitory effect on S. cerevisiae growth at 20 °C, while at 30 °C, S. cerevisiae competes better and L. thermotolerans biomass declines after 4 days [ 33 ]. Maturano et al. (2016) [ 78 ] even showed that the temperature of cold maceration prior to alcoholic fermentation can positively impact interspecific distribution. Temperature can also impact yeast metabolism. The response of S. cerevisiae against the presence of another strain (coculture), by gene expression, is indeed dependent on temperature (transcriptional response higher at 12 °C than at 20 °C) [ 10 ]. The variation of fermentation temperature may be involved in the variability of the results obtained by various authors. Differences in fermentation kinetics observed with red and white must (complete at 17 and 24 days respectively) by Whitener et al. and Becker Whitener et al. can be partly explained by differences of temperature (25 and 15 °C) [ 36 , 41 ]. Englezos et al. also explained the different impacts on ethanol content in their works of 2016 and 2018 by temperature differences between protocols [ 79 ]. Bagheri et al. recently showed that the population dynamics in a multi-species yeast consortium were affected by temperature, influencing consequently aroma compounds production [ 80 ].

One other key point used to explain the differences observed in population dynamics is oxygen availability, induced by different conditions of oxygenation and agitation in various authors’ protocols. Oxygen is indeed also known to have impacts on yeast interactions. Non- Saccharomyces yeasts are usually less tolerant to low oxygen availability than S. cerevisiae [ 30 ]: oxygen can increase their survival in mixed culture without affecting S. cerevisiae , resulting in a species-dependent variation in population dynamics (persistence) [ 29 , 43 , 67 ].

On the contrary, removing oxygenation and agitation altogether and allowing fermentations to occur in static conditions makes it possible to get closer to standard vinification conditions. These conditions, besides limiting oxygen intake, also allow the natural sedimentation of yeasts to occur. This sedimentation leads to heterogenous cell distribution, with an increase of local cell density in the sediment and a decrease in the supernatant. Cell density, as shown by Nissen et al. (2003) [ 26 ] is a key factor in yeast interactions. Cell density can also favor cell–cell contacts and coaggregation mechanisms which both seem to be involved in the population dynamics and metabolic changes observed in mixed cultures [ 24 ].

Studies have also shown that yeast interactions are heavily strain-dependent. Perrone et al. (2013) [ 81 ] studied 99 strains of autochthonous S. cerevisiae in must and showed that their dominance behavior varies and is expressed only when S. cerevisiae senses other yeasts in the same environment. As far as interactions between non- Saccharomyces and S. cerevisiae are concerned, mechanisms can be influenced by the strain chosen for both species. Wang et al. (2016) [ 65 ] observed that culturability loss of non- Saccharomyces because of interactions with S. cerevisiae is species- and strain-dependent. On the other hand, Englezos et al. (2016, 2019) [ 12 , 69 ] showed that S. cerevisiae strain choice also has a key impact on population dynamics (S. bacillaris is more or less able to dominate various S. cerevisiae strains), sugar consumption, wine composition (ethanol, glycerol, acid production), wine volatile compounds (decrease or increase of aroma production depending on the S. cerevisiae strain).

2.2. Yeast Interactions: Understanding Population Dynamics

When yeasts are cultivated together in the same medium, as happens in natural must, different interactions occur, with visible and measurable impacts on population dynamics (dominance of one strain, decline or death of others) and cell physiology.

Researchers usually approach population monitoring quantitatively: using the methods described below, they manage to get an overview of general population dynamics. However, a more qualitative approach can supplement this, by giving more information on the physiological state of the yeast cells monitored.

Yeast populations in mixed fermentations are often quantified by traditional methods such as plating using colony morphology, media composition, selective additives, and/or differential growth optima which allow distinguishing between different species [ 14 , 60 , 69 , 82 , 83 , 84 ]. Yeast populations can also be discriminated by using a combination of selective and non-selective culture media [ 26 , 33 , 52 , 65 , 71 , 85 , 86 , 87 ]. The incubation of plates at different temperatures can also be the solution to determine the population of different strains, the main example of this being S. cerevisiae growing at 37 °C while non- cerevisiae and non- Saccharomyces yeasts do not [ 10 , 88 , 89 ].

These methods are well understood, efficient, and rather precise. Moreover, they allow accurate interspecies discrimination using phenotypic differences, and provide information on the cell cultivability of the populations studied. However, growth time on plates involves a delay in analysis which can prove impractical when monitoring wine fermentation in real time. In addition, these culture-dependent techniques can hardly be used to monitor complex ecosystems, since some strains overcome others in culture medium [ 90 ]. They also can overlook microorganisms that grow slowly on artificial media or are present in very small amounts [ 72 ]. Moreover, sometimes, no colonies can be observed on plates, since some yeasts are in a viable but non-culturable state (VBNC) as a result of stress induced either by interaction with other yeasts or culture conditions. To confirm this state and evaluate the capacity of yeasts to recover, they are transferred into fresh liquid nutritive medium and incubated for 24-48 h, once or twice: VBNC yeasts after these cultures in ideal conditions can be cultivated again [ 65 , 91 ].

The need for new or adapted analysis methods that shorten the delay in obtaining information has thus emerged. Zupan et al. (2013) [ 92 ] presented a quick method to monitor the number of viable yeasts during fermentations, using microscopy and image analysis software; yeasts are observed on a hemocytometer with three settings of the same microscope to count viable, non-viable and total cells.

Flow cytometry is also an interesting technique used to enumerate microbial populations by automating the counting process, and Longin et al. (2017) [ 93 ] showed its potentiality for monitoring yeast populations during wine fermentation. As with plating, discriminating between both species studied is essential to monitor populations and highlight yeast interactions. To differentiate various strains, modified strains expressing fluorescent proteins are often used, as in the recent study by Petitgonnet et al. (2019) [ 46 ], which makes use of a GFP-modified S. cerevisiae to show its capacity to inhibit L. thermotolerans by cell–cell contact linked mechanisms. In these cases, it is necessary to verify that these modified strains have the same behavior as wild strains. This allows managing the proportion of S. cerevisiae in a mixed culture [ 94 , 95 ]. Another strategy is to use fluorescence in situ hybridization (FISH), like Wang et al. in 2014 [ 96 ], who developed specific probes and optimized conditions to monitor S. cerevisiae and two non- Saccharomyces yeasts in mixed cultures. This method is simple, rapid, and sensitive, but it involves membrane permeabilization and does not give information in real time or on cell viability. Flow cytometry can also be used to obtain extensive information on cell physiological state, as discussed below.

Authors have also used quantitative PCR to monitor yeast populations: from the isolated total DNA, amplifying a gene with species-specific primers gives the proportion of each species, and then extrapolates the population of each species in the total population. This method is rapid and very sensitive [ 90 ]. Andorra et al. (2010) [ 72 ], Wang et al. (2015) [ 70 ] studied the total population present in natural must by different techniques and showed that qPCR can be used to analyze the dynamics during wine fermentation; its advantage over culture-dependent techniques is that it takes into account non culturable yeasts. This qPCR technique was used recently to study the impact of competition between S. cerevisiae and other Saccharomyces yeasts on growth fitness and to understand the impact of nitrogen on competition between different strains of S. cerevisiae [ 58 , 76 ]. Garcia et al. (2017) [ 97 ] applied this method to monitor five non- Saccharomyces strains in a mixed culture, with satisfying efficiency (good specificity, sensitivity down to 10 3 cells/mL, linearity). Another method, reverse transcription (RT)-qPCR can be used but this methodology underestimates the culturable population in wine due to the decrease of rRNA level in cells facing environmental stress (ethanol, nutrient depletion) [ 70 ]. These RNA/DNA-based methods have several advantages; they save time, they are interesting in the case of microorganisms difficult to cultivate (need for specific medium, VBNC) [ 70 , 90 ] and they provide precise discrimination between strains. However, these methods do not provide any information on cell physiological state or viability, since DNA from dead cells is also detected.

To study the physiological state of yeasts from mixed cultures, authors have used specific staining with different compounds and probes, allowing them to either assess viability, and even to study specific consequences on some aspects of cell physiological state. Coupled with flow cytometry (or epifluorescence microscopy), staining can provide rapid information, but choosing suitable dyes is complex and depends on both the microorganism and medium. Longin et al. (2017) [ 93 ] discussed these choices extensively in their review on flow cytometry applied to wine. For example, staining with propidium iodide (PI) can be used to evaluate the viable populations in mixed culture [ 46 ], or to study the impact of interaction mechanisms such as anti-microbial peptides on membrane permeability [ 91 , 98 ]. Another marker, fluorescein di-acetate (FDA) or carboxy-FDA (CFDA), can be used as an indicator of cellular vitality since it reflects enzymatic activity (esterase) [ 93 ]. This fluorophore is used by Gobert et al. (2017) [ 55 ] in cocultures involving S. bacillaris and S. cerevisiae , to monitor yeast viability during fermentations. Double stainings are often used to help discriminate live cells and assess viability during mixed fermentations [ 65 , 93 , 99 ].

Fluorophores can also be used to measure intracellular pH (pHi) since this important parameter influences metabolism and can lead to cell membrane disruption if it is modified [ 93 ]. Probes such as 5,6-carboxy-2′,7′-dichlorofluorescein diacetate (CDCF) or 5,6-carboxy fluorescein diacetate succimidyl ester (cFDA-SE) (for pH values between 3-4.5 and 4-7 respectively) combined with flow cytometry, epifluorescence microscopy or fluorescence ratio imaging microscopy (FRIM), give information on pHi [ 91 , 98 ], highlighting how exposure to anti-microbial peptides (AMPs) can induce a drop in pHi in non- Saccharomyces cells during fermentation.

Based on DNA-techniques, other methods have been developed to show a difference between live and dead cells, by using dyes able to enter cells with compromised membranes and bind to DNA, making them non amplifiable by PCR. Ethidium monoazide bromide (EMA) qPCR can then be used to monitor viable yeasts during must fermentation [ 70 , 100 ]. FISH can also be coupled with live/dead staining such as IP/DAPI [ 99 ], to assess the identity and viability of strains in mixed cultures at the same time.

Yeast interactions impact population dynamics and their metabolism with consequences for fermentation kinetics (more or less rapid and complete consumption of substrates (sugars, yeast assimilable nitrogen, oxygen), the production of ethanol) and for the production of other metabolites (differences in quality and content (glycerol, organic acids, aroma compounds)). Authors have usually used the same methods to monitor all these compounds: enzymatic techniques [ 58 , 65 , 84 ], or high-performance liquid chromatography HPLC [ 9 , 54 , 69 , 81 , 101 ]. Fourier transformed infrared spectroscopy FTIR can also be used since it is very convenient, simple, and rapid, but the results can lack precision. More recently, a new approach is to study more globally the metabolites produced by yeasts [ 46 , 84 , 102 ] to obtain information on the global metabolism of strains and better evaluate the role of each strain in imprinting its own metabolomic signature on the mixed culture medium.

2.3. Yeast Interactions: Understanding Mechanisms

Although most authors have studied population dynamics and metabolite production, some of them have focused on various mechanisms involved in yeast interactions. Interactions can be linked to modifications of medium during fermentation (decrease of nutrients or the production of inhibitory or toxic compounds) or to the direct action of a yeast on another one (with physical cell-contact, through molecules present on the cell surface). To understand these phenomena, authors can employ different strategies often used in parallel studying the impact of specific culture conditions on yeast populations and metabolisms (modification of medium composition, increasing cell contacts, suppressing cell contacts); highlighting the presence of cell contacts, modification of physiological state induced by interactions.

These different methods are described below. In addition, they are nowadays supplemented by more recent techniques: metabolomic, transcriptomic, and genomic techniques, on which we will focus in part 2 of this review and further.

Nissen et al. (2003) [ 26 ] were among the first authors to develop a strategy to understand which mechanisms are involved in yeast interactions in wine. They used modifications of culture conditions (addition of live or dead cells, addition of medium from other cultures, addition of nutrients) to highlight or not different interaction hypotheses.

In mixed culture, when certain yeast populations decline or death is observed, the first possibility is that nutrient competition occurs between both strains. To check this eventuality, authors have studied the addition of fresh culture medium or the replacement of the depleted one [ 26 , 68 , 84 ] and their impact on population dynamics.

The second hypothesis is the production of specific compounds by a strain impacting the growth of another (whether by toxic properties or other mechanisms like quorum sensing). To include or rule out this possibility, yeasts can be cultivated in pure culture in supernatants obtained from mixed culture (potentially containing toxic or inhibiting compounds (ethanol, fatty acids, peptides)), with supplementation in nitrogen sources to avoid nutrient limitation [ 52 ], or without supplementation [ 25 , 26 , 68 ]. This modus operandi can also be used to test the antimicrobial activity of peptides produced by yeasts in pure or mixed culture: more or less purified supernatants are incubated with different microorganisms [ 5 , 91 , 103 ]. This strategy makes use of a cell/medium separation technique by centrifugation, that can induce stress in yeasts (as shown by Chlup et al. 2008 [ 104 ]) and perhaps impact their metabolism.

To highlight the involvement of physical contact between two different yeast populations, biomass behaviors can be compared between a pure culture and a culture with the addition of another strain in different states and densities. Researchers have observed that the growth of non- Saccharomyces yeasts is not impacted by the addition of cellular debris or dead cells of S. cerevisiae but is immediately stopped when viable cells are added [ 25 , 26 ]. Thus, they showed that the presence of viable S. cerevisiae is necessary to influence the growth of the second yeast. In addition, they observed that a sufficiently high cellular density is required to obtain this impact, indicating the possibility of competition for space or the implication of a cell-contact mechanism. By changing the cell concentration in pure culture fermentation or the ratio of non- Saccharomyces/S. cerevisiae in mixed cultures, Nissen and Arneborg (2003) [ 25 ] showed that early death is not solely a consequence of high cell density or a low ratio but also of different abilities to compete for space.

Therefore, to prove that physical contact is necessary to observe a modification of population dynamics, one strategy is to suppress these contacts artificially by separating both yeast populations by a semipermeable membrane that prevents contacts between the yeasts but allows the exchange of substrates and metabolites between the two compartments. If cell–cell contacts are involved in yeasts interactions, yeast behaviors will be different in these conditions from those in mixed fermentations. Some authors used simple systems (tube or flask with dialysis membrane) with different conditions: without agitation and with a cut-off of 12–14 kDa [ 9 , 25 , 26 , 46 ], with agitation and a cut-off of 3.5–5 kDa [ 28 ] or 1000 kDa [ 28 , 71 ]. To ensure homogeneity on both sides, some authors measured the concentrations of only a few substrates and metabolites (ethanol for Nissen et al. 2003 [ 26 ], glucose and ethanol for Kemsawasd et al. 2015 [ 28 ]). Nissen and Arneborg (2003) [ 25 ] specified that fouling of the membrane was observed after 4 days of fermentation, with a difference in composition (glucose, ethanol) in both compartments. Kemsawasd et al. (2015) [ 28 ] noticed that peptides and proteins were freely transported through a 1000 kDa membrane but that 3.5–5 kDa was slightly permeable to molecules larger than 5 kDa, so that AMPs could be present in both compartments (AMPs derived from GADPH are about 8 kDa according to Branco et al. 2014 [ 101 ], AMPs studied by Albergaria et al. 2013 [ 103 ] are 2–10 kDa). More recently, Petitgonnet et al. (2019) have evaluated metabolism changes more globally by using metabolomic techniques and highlighted a notable difference in exo-metabolomes between fermentations with and without physical contact [ 46 ]. This aspect will be further developed at a later stage.

To study competition between different strains of S. cerevisiae , Perrone et al. (2013) [ 81 ] chose to use a partitioned reactor that had already been used in studies of bacteria cocultures (Di Cagno et al. 2009) [ 105 ]; a double culture vessel apparatus with compartments separated by a 0.45 µm membrane, and which can be stirred. The membrane allows the transfer of medium compounds and composition homogeneity is verified by HPLC for sugars, alcohol, glycerol, acids; only one difference was observed for sugars but with a value close to instrumental reproducibility. Lopez et al. (2014) [ 106 ] and Taillandier et al. (2014) [ 51 ] used a reactor composed of two jars interconnected with a hollow fiber membrane (0.1 µm). They regulated the flow between both compartments using alternating pressurization, but they observed fouling after 21 h of culture. The system proposed by Renault et al. (2013) [ 107 ] seemed to be more efficient. They designed a new double-compartment reactor; separation was ensured by a 1 µm membrane, a pump circulated the medium between both compartments through a 0.45 µm filter to homogenize it without the transfer of yeasts; it was also equipped with automatic reversal of the pumping direction to avoid clogging. All these systems used different means to separate yeasts while ensuring metabolic homogeneity, but they did not seem to allow the immediate transfer of all the metabolites in both compartments. That is why other authors took a completely different approach to investigate the involvement of cell contact in yeast interactions. Rossouw et al. (2018) [ 108 ] used a genetic system (based on FLO gene family) to modify cell adhesion properties and show that interspecies contacts impact population dynamics as the mechanism was called cell–cell contact by other authors. They made use of a simple sedimentation rate measurement to assess interspecific coaggregation. This macroscopic approach to aggregation dynamics could then be supplemented by microscopic analysis.

Various microscopy techniques can also be used to study different types of contact between yeasts; cell–cell contact, aggregation, or coaggregation with other cells or solids. Fluorescence microscopy with cell staining was used by Rossouw et al. (2015) [ 109 ] to highlight the co-flocculation of S. cerevisiae and Hanseniaspora opuntiae ( H. opuntiae) in mixed cultures and to study the involvement of different FLO genes on flocculation. Cell staining can also be used with flow cytometry, as shown in Pérez-Torrado et al. (2017) [ 94 ], which makes use of sonication to highlight cell aggregation. Caldeira et al. (2019) [ 110 ] observed the surface of yeast in mixed cultures by atomic force microscopy and showed the existence of direct cell–cell contact. Scanning electron microscopy (SEM) can be used to highlight the aggregation of yeasts in wine with the same or other yeast species, or with solids (as shown by Govender et al. 2011 [ 111 ]).

Epifluorescence microscopy observations can also be used to study how anti-microbial peptides (AMPs) act on yeast. Branco et al. (2017) [ 86 ] used chemically synthesized AMPs, with fluorescent labelling, and added them to the medium culture of non- Saccharomyces yeasts. They noticed that these AMPs can enter cells and at the same time, cells that internalized these AMPs showed compromised cell membranes (PI-stained).

Other less conventional methods can also be used to study the interaction mechanisms. For example, Branco et al. (2017) [ 71 ] used immunologic testing to highlight the involvement of cell contact in AMP activity. After extracting and fractionating surface proteins from S. cerevisiae , they analyzed fractions by enzyme-linked immunosorbent assays (ELISA) using a specific antibody against GAPDH-derived AMPs. They showed that these AMPs accumulated on cell surfaces, suggesting a potential link between cell–cell contact mechanisms and these AMPs.

In addition to all the analysis techniques discussed previously, novel omics approaches are nowadays widely used to solve different problems and are used to describe an organism’s response to genetic or environmental changes. The impact of cell interactions on yeast metabolism is no exception, and proteomics and transcriptomics in case of yeast–yeast interactions in wine have been used extensively in recent years. Most of the studies performed to characterize the consequences of co-cultures and interactions between microorganisms on gene expression and protein synthesis have focused on comparisons with S. cerevisiae , an organism that has undergone extensive investigation [ 88 , 112 , 113 ]. The modulation of gene expression has been clearly observed during alcoholic fermentation [ 9 , 112 , 113 ]. Most of the genes whose expression is modified during co-cultures and interactions are involved in stress response, endocytosis, membrane biogenesis, nutrient uptake, and apoptosis [ 84 , 88 , 89 ]. Complete metabolic pathways are affected by altered gene expression, as shown by Sadoudi et al. [ 87 ], with a change in acetic acid and glycerol metabolism in S. cerevisiae in the presence of Metschnikowia pulcherrima (M. pulcherrima) . More specifically, in the case of direct cell contact between two populations of distinct species, a change in the expression of FLO genes has been described, leading to a modification of population dynamics [ 109 ]. We will not develop this aspect in our discussion, as the subject has recently been discussed and detailed by Conacher et al. [ 24 ].

3. Environmental Changes Related to Interactions and Sensory Impacts

Proteomics and transcriptomics provide insights into the impact of interactions on wine composition [ 24 , 89 , 112 , 114 ] but none of them has so far provided significant progress on the microbial interaction mechanisms involved. Metabolomics is a tool of choice for observing the impact of yeast interactions on the composition of the wine matrix and more interestingly, it can help unravel the as yet unknown mechanisms involved in these interactions. Analytical techniques developed for metabolomics studies allow screening hundreds of metabolites from various metabolic pathways with high-throughput techniques [ 115 ] that link the impact of yeast interactions to wine composition [ 102 ].

The literature includes various studies in which the specific composition of wine enables distinguishing between wines on the basis of fermentations with different yeast species and strain [ 116 , 117 , 118 , 119 ] and with single and co-cultures [ 46 , 102 , 120 , 121 ].

3.1. Metabolic Profiling

Non-targeted metabolomics studies provide a global vision of the modifications of the matrix. Through this approach, all the products from metabolic pathways affected by interactions with a second microbial population can be studied. Only a few studies aimed at understanding yeast–yeast interactions in wines have been carried out using this non-targeted metabolomic approach. Some studies have used FT-ICR-MS to explore metabolomes in wine [ 46 , 102 , 122 ]. In 2016, Liu et al. [ 122 ] studied fifteen strains of S. cerevisiae known to positively or negatively impact malolactic fermentation (MLF) through interaction with lactic acid bacteria. They identified a wide variety of markers such as oligopeptide and sulfur-containing peptide metabolites for each of the yeast phenotypes studied. Later, Petitgonnet et al. [ 46 ] highlighted changes in the exo-metabolome of wines from co-culture fermentation, depending on the presence or not of a physical barrier. The originality of this paper resided in the study of the physical separation of the two populations. Indeed, greater diversity of compounds was demonstrated in L. thermotolerans alone and contactless S. cerevisiae / L. thermotolerans modalities. Biomarkers specific to these modalities were mainly identified as involved in amino acid metabolism and carbon fixation. The general conclusion of the study shows that cell to cell yeast interaction does induce a significant change of diversity and variability in the intensity of metabolic compounds in final wine composition [ 46 ]. More recently, Roullier-Gall et al. [ 102 ] worked on the non-volatile metabolic fingerprint comparison of three different non- Saccharomyces species in single and co-cultures with S. cerevisiae . It was pointed out that the metabolite composition of wine from the co-culture did not match the assembly of two wines resulting from single yeast fermentation, an observation already made in previous studies [ 120 , 123 ] involving non-neutral interaction phenomena.

The majority of non-targeted works have focused on the metabolome at the end of alcoholic fermentation and therefore are not able to reveal at what stage of growth and fermentation metabolic changes occur. Fortunately, several papers have focused on the different stages of fermentation, including works from Richter’s team in 2015, who conducted alcoholic fermentation on Chardonnay must with S. cerevisiae [ 124 ]. Significant metabolic changes were identified at each stage of the fermentation studied. This contribution made it possible to attribute a certain regulation of yeast metabolism during fermentation to the efficiency of the glycolytic pathway, probably due to a reduced activity of several enzymes or to glucose transport. In 2018, Peng et al. [ 121 ] demonstrated the impact of bringing together two yeast populations of S. cerevisiae and L. thermotolerans at two key points of alcoholic fermentation: at the onset of early death of non- Saccharomyces yeast and at the end of this phase. Owing to NMR, a single culture of L. thermotolerans and a co-culture were discriminated based on metabolite composition variations. On the contrary, no changes could be identified when comparing the metabolome of the single culture of S. cerevisiae and co-culture. In addition, they highlighted that part of the metabolite composition disappeared at the end of fermentation, suggesting that metabolic changes of co-culture occur after the death of the non- Saccharomyces yeasts [ 121 ]. It also appears that at different sampling times, the diversity and concentration of metabolites is very different compared to previous works on single culture [ 124 , 125 ]. These works highlighted that sampling time is an essential point for understanding interaction phenomena. Furthermore, studies [ 124 , 126 ] began to explore the differences between the endometabolome and the exometabolome associated with microorganisms involved in fermentation processes to explain the mechanisms involved in interactions. However, it remains difficult to study the endometabolome because of the complexity of sampling [ 127 ]. Therefore, recent works have focused on modelling the composition of the endometabolome based on exometabolome measurements [ 126 ].

It should also be noted that the identification of compounds detected during the metabolic profiling of the non-volatile fraction of wine remains difficult at present. The complexity of wine has been widely described and still presents many shadowy areas. The databases giving the molecular compounds present in wine remain poorly supplied and do not allow identifying all the biomarkers [ 102 , 128 , 129 ].

Targeted analysis, often associated with hypothesis verification, involved detecting and quantifying known metabolites in the wine [ 130 , 131 , 132 ]. Targeted metabolomics is particularly used for studying the impact of microorganisms [ 118 , 133 ] and interactions [ 134 , 135 ] during winemaking. In the context of leavening different starters to carry out fermentations in the best possible way and with the objective of managing the final quality of Syrah wines, Minnaar et al. [ 134 ] were interested in understanding the microbial interactions involved. It would appear that microbial interactions affect the production of polyphenolic compounds including anthocyanins, flavonols, and phenolic acids. Different combinations of starters were studied, involving respectively a strain of S. cerevisiae with M. pulcherrima or Hanseniaspora uvarum (H. uvarum) , S. cerevisiae and one of the non- Saccharomyces with a LAB strain, and S. cerevisiae with a lactic acid bacteria (LAB) with a single starter of S. cerevisiae . They identified for the mixed starter of M. pulcherrima/S. cerevisiae a decrease in the amount of gallic acid and of caffeic acid for H. uvarum/S. cerevisiae . Similarly, Nardi et al. [ 135 ] studied the impact of a co-culture of S. cerevisiae with a strain of T. delbrueckii in combination or not with a strain of O. oeni . They were able to demonstrate that among the extracted compounds, involved in the discrimination of the different conditions, there was an increase in certain metabolites such as amino acids like alanine and threonine, and at the same time a decrease in deleterious compounds such as acetic acid. These works described the consequences of the presence of several yeast populations in co-cultures but did not describe the mechanisms involved in the underlying interactions.

3.2. Volatilome

The yeast metabolome does not consist entirely of compounds from the non-volatile fraction. In fact, a wide variety of volatile organic compounds (VOCs) are released during the winemaking process and enrich the total wine composition. These compounds produced by microorganisms are grouped together under the term “volatilome” [ 136 ]. The composition of the volatilome is related to the species [ 47 , 137 ] and the strains of microorganisms [ 79 , 138 ] used to conduct alcoholic fermentation. In addition, the interactions occurring in consortia or co-cultures also influence the VOC composition of wines [ 139 ]. Many VOCs participate in the aromatic profile of wines, as developed later in the discussion [ 140 , 141 , 142 ]. Unfortunately, as with non-volatile omics studies, most volatile studies focus on the impact of yeast interaction on the final VOC composition of co-cultured wines, but few explain the mechanisms of interactions and allow us only to hypothesize about the nature of yeast interactions.

VOCs can be produced by yeasts metabolizing sugars and amino acids and belong to different families of compounds including esters, higher alcohols, medium fatty acids, or aldehydes. Among these fermentation aromas, esters are widely represented, comprising acetate esters and ethyl esters. In the case of co-cultures between S. cerevisiae and non- Saccharomyces yeasts, the concentration of the major esters is mainly increased. For mixed crops in which M. pulcherrima . [ 47 , 143 ] and H. uvarum [ 144 ] are involved with S. cerevisiae , the concentration of esters increases, for example that of phenylethyl acetate. For the most part, mixed crops, including S. bacillaris, present higher concentrations of total esters [ 79 , 138 ], including ethyl octanoate or isoamyl acetate [ 14 ] compared to pure S. cerevisiae fermentation conditions. Conversely, when focusing on Muscat wort, Gobert et al. [ 55 ] showed a decrease in the production of isoamyl acetate in sequential fermentations of S. bacillaris and S. cerevisiae . Similarly, some esters such as isoamyl acetate and ethyl octanoate are detected in higher concentration in wines from co-fermented wort with S. cerevisiae and Torulaspora delbrueckii ( T. delbrueckii) [ 145 ] or L. thermotolerans [ 45 ] and in lower concentrations in other studies [ 39 , 146 ] ( Table 2 ). This different impact of co-culture on esters concentration can be explained by the use of various yeast strains and matrix [ 147 ]. In the same way, co-culture may increase the content of higher alcohols found in wines. This has been thoroughly described by Sadoudi et al. [ 47 ] for the mixed fermentations conducted with Candida zemplinina ( C. zemplinina) and S. cerevisiae , Escribano-Viana et al. [ 37 ] for T. delbrueckii and S. cerevisiae and Englezos et al. [ 79 , 138 ] for S. bacillaris and S. cerevisiae . However, others studies focusing on mixed-cultures with S. bacillaris and S. cerevisiae have shown a lower concentration of certain alcohols such as 2-phenylethanol and methyl butanol [ 55 , 148 ]. Fatty acids have been found in lower concentrations in most of the co-cultures studied using non- Saccharomyces [ 79 , 135 ] except for the couple with S. bacillaris [ 14 ] and C. zemplinina [ 36 ] ( Table 2 ). Varietal aromas can also be released by yeasts through the action of cleavage enzymes on odorless precursors present in the must such as terpenes, sulfur compounds, and volatile phenols [ 139 , 149 ]. The combination of yeast populations with a diversity of metabolism as enzymatic activity during fermentation can impact on their diversity and concentration [ 150 ]. Finally, one of the impacts studied most on VOC families in co-cultures are terpenes. The terpenes found in wines are mostly linalool, geraniol, and citronellol. In most cases they are found in higher concentrations in wines from mixed culture alcoholic fermentation [ 36 , 147 ].

Impact on volatile organic compounds of different mixed starters.

Mixed Culture	VOCs Impacted by Interactions	Impact	Inoculation Protocol	Mixed Culture Compared to	Matrix	SA	Ref.
	acetate esters	-	sim/blend	S	Sauvignon blanc	x	[ ]
	ethyl dodecanoate	+
	3-mercaptohexan-1-ol	+
	3-mercaptohexan-1-ol	+	sim	S	Sauvignon		[ ]
		-	seq	S and NS	Sauvignon		[ ]
		+	seq	S and NS	Sauvignon		[ ]
		-	seq	S and NS	Sauvignon		[ ]
		+	seq	S	Shiraz		[ ]
		+	seq	S	Shiraz		[ ]
		+	sim	S and NS	Pedro Ximenez		[ ]
			seq	S and NS	Pinot Grigio	x	[ ]
	1-propanol, methionol		seq	S	Tempranillo		[ ]
	2-methyl butanol, 3-methyl butanol, iso-butanol		sim/seq	S	Emir	x	[ ]
		-	seq	S and NS	Muscat		[ ]
	2-phenyl ethanol acetate, ethyl acetate, isoamyl acetate, isoamyl decanoate	+	sim	S and NS	Pedro Ximenez		[ ]
	ethyl octanoate		seq	S and NS	Pinot Grigio	x	[ ]
	2-phenylethyl acetate, isobutyl acetate, hexyl acetate		sim/seq		moschofilero	x	[ ]
			seq	S	Shiraz		[ ]
			sim/seq	S	Ecolly, Cabernet Sauvignon	x	[ ]
	isoamyl acetate	-	sim/seq	S	Emir	x	[ ]
			seq	S	Synthetic must with precursors		[ ]
			seq	S and NS	Muscat		[ ]
		+	seq	S and NS	Muscat		[ ]
		-	seq	S and NS	Pinot Grigio	x	[ ]
	ethanoic acid		sim	S	synthetic must		[ ]
	geraniol, citronellol	+	sim	S and NS	Pedro Ximenez		[ ]
			seq	S and NS	Pinot Grigio	x	[ ]
	geraniol, damascenone		sim/seq		moschofilero	x	[ ]
	geraniol, linalool, alpha terpinene		seq	S	Shiraz		[ ]
	linalool, geraniol		seq	S	Synthetic must with precursors		[ ]
		-	sim	S and NS	Pedro Ximenez		[ ]
		+	sim	S and NS	Pedro Ximenez		[ ]
		-	seq	S and NS	Rondo, Bolero, Regent	x	[ ]
			seq	S	Malbec		[ ]
		+	sim/seq	S	Ecolly, Cabernet Sauvignon	x	[ ]
	ethyl acetate, 2-phenylethyl acetate		seq	S and NS	Rondo, Bolero, Regent	x	[ ]
		-	seq	S	Malbec		[ ]
	2-phenyl ethanol, methionol	+	seq	S	Chardonnay, Soave, Vino Santo	x	[ ]
	2-phenyl ethanol, 1-butanol, methionol		seq	S	Tempranillo		[ ]
	2-phenyl ethanol		seq	S	Sauvignon		[ ]
			seq	S and NS	Pinot Grigio	x	[ ]
	phenlethyl alcohol		sim/seq	Sc	Cabernet sauvignon		[ ]
	acetate esters (2-phénylethyl acetate, isoamyl acetate, hexyl acetate, isobutyl acetate) and ethylesters (ethyl lactate, ethyl octanoate, ethyl hexanoate))	+	seq	S	Sauvignon, Syrah	x	[ ]
			sim/seq	S	Cabernet sauvignon		[ ]
	acetate esters		sim	S	Barbera	x	[ ]
	acetate esters (isoamyl acetate) and ethylesters (ethyl octanoate)	-	seq	S	Chardonnay, Soave, Vino Santo	x	[ ]
	acetate esters		seq	S	Sauvignon		[ ]
	hexanoic acid and octanoic acid	-	seq	S	Chardonnay, Soave, Vino Santo	x	[ ]
			seq	S and NS	Pinot Grigio	x	[ ]
			sim/seq	S	Cabernet sauvignon		[ ]
			sim	S	Barbera	x	[ ]
	geraniol, linalool, alpha terpinene	+	seq	S	Shiraz		[ ]
	linalool, geraniol		seq	S	Synthetic must with precursors		[ ]
	geraniol, nerolidol, farnesol		seq	S	Sauvignon		[ ]
			seq	S and NS	Pinot Grigio	x	[ ]
			sim/seq	S	Cabernet sauvignon		[ ]
	4-vinylphenol, 4-vinylguaiacol	-	seq	S	Chardonnay, Soave, Vino Santo	x	[ ]
			sim/seq	S	Cabernet sauvignon		[ ]
		+	seq	S	Chardonnay, Riesling, Muscat, Sauvignon blanc		[ ]
			seq	S	Barbera		[ ]
			seq	S and NS	Pinot Grigio	x	[ ]
	2-phenylethanol	-	sim	S and NS	Montepulciano	x	[ ]
			sim/seq	S	Kotsifali/Mandilari		[ ]
	isobutyl alcohol 1-hexanol increase, methyl butanol decrease	+/-	seq	S	Muscat		[ ]
		+	sim/seq	S	Kotsifali/Mandilari	x	[ ]
			sim	S and NS	Montepulciano	x	[ ]
			seq	S	Chardonnay, Riesling, Muscat, Sauvignon blanc		[ ]
			seq	S	Barbera		[ ]
	ethyl octanoate, isoamyl acetate		seq	S and NS	Pinot Grigio	x	[ ]
	isobutyl acetate phenylethyl acetate, isoamyl acetate	+/-	seq	S	Muscat		[ ]
		+	seq	S	Chardonnay, Riesling, Muscat, Sauvignon blanc		[ ]
		+	seq	S and NS	Pinot Grigio	x	[ ]
		-	seq	S and NS	Pinot Grigio	x	[ ]
	1-propanol, methionol	+	seq	S	Tempranillo		[ ]
			sim	S	Merlot	x	[ ]
			seq	S and NS	Pinot Grigio	x	[ ]
	phenylethyl alcohol, isobutyl alcohol		seq	S	Muscat		[ ]
		-	sim	NS	synthetic must		[ ]
		+	sim	S	Merlot	x	[ ]
			seq	S	Shiraz		[ ]
			seq	S and NS	Pinot Grigio	x	[ ]
			seq	S	Muscat		[ ]
			seq	S	Sauvignon		[ ]
		+	seq	S	Sauvignon		[ ]
		+	seq	S and NS	Pinot Grigio	x	[ ]
	geraniol, linalool, alpha terpinene		seq	S	Shiraz		[ ]
	linalool		seq	S	Sauvignon		[ ]
		-	seq	S and NS	Pinot Grigio	x	[ ]
	dimethylsulfide, ethanethiol, sulfure hydroxyde	+	sim	S	Merlot	x	[ ]
Mix of four non- and three		-	seq	S and NS	synthetic must	x	[ ]
Mix of four non- and three		=					[ ]
non		-	seq	S	Tempranillo		[ ]
Mix of five non			seq		synthetic must Sauvignon blanc	x	[ ]

Ref.: References S. cerevisiae : Saccharomyces cerevisiae C. zemplinina : Candida zemplinina L. thermotolerans : Lachancea thermotolerans H. uvarum : Hanseniaspora uvarum T. delbrueckii : Torulaspora delbrueckii S. bacillaris : Starmerella bacillaris M. pulcherrima : Metschnikowia pulcherrrima . Impact on VOCs concentration: -: decrease +: increase +/- : two cases are encountered =: no change sim : simultaneous/ seq : sequential S : S. cerevisiae / NS : non- Saccharomyces . SA : sensory analysis. VOCs : volatile organic compounds.

Although most studies describe the impact of interactions on VOC composition in fermentation in co-culture, some papers have tried to explain the interaction mechanisms associated with these compositional changes. Sadoudi et al. [ 47 ] highlighted interaction mechanisms for three couples of yeast by comparing VOC concentration in simple cultures and mixed cultures. The concentration of some terpenols such as 𝜷-damascenone doubled in the co-culture of M. pulcherrima/S. cerevisiae showed a positive interaction between both strains. The synergetic effects of T. delbrueckii/S. cerevisiae have also been revealed in co-culture, exhibiting an increase in the content of terpenols, C6 compounds and 2-phenylethanol, suggesting a cumulative effect of both yeast metabolisms related to biomass. Negative interactions for C. zemplinina/S. cerevisiae were highlighted by showing a decrease in the production of farnesol in co-culture, in comparison to a pure culture of C. zemplinina , which could be used as a modulator of gene expression. As mentioned above, later, in 2017, the same team showed that the presence of M. pulcherrima induced a change in the gene expressions involved in the metabolism of acetic acid of S. cerevisiae in co-cultures [ 87 ]. Later, Petitgonnet et al. [ 46 ] showed the importance of cell–cell contact in VOC composition in the case of S. cerevisiae and L. thermotolerans co-culture. The study compared VOCs from wines in pure culture, conventional contact co-culture, and co-culture physically separated by dialysis rod. It was pointed out that ester and fatty acid concentrations were higher in co-culture without cell contact. Similarly, other studies [ 12 , 55 ] have focused on nutrient sources as well as on competition for nutrients between species in the case of co-fermentation together with their impact on VOC composition. In sequential fermentations nutrient sources, such as nitrogen, are reduced at the time of S. cerevisiae inoculation. Gobert et al. [ 55 ] highlighted that, in the case of sequential fermentation, amino acids such as leucine are consumed by S. bacillaris before S. cerevisiae inoculation. However, leucine is the precursor of VOCs including isoamyl acetate and 2-methylbutanol mainly synthesized by S. cerevisiae . The depletion of leucine before S. cerevisiae inoculation would therefore lead to under-expression of these VOCs in wines. On the other hand, this paper showed a possible synergetic effect between S. bacillaris and S. cerevisiae for the synthesis of isobutyl alcohol [ 55 ].

Although most of the work has focused on the interaction between S. cerevisiae and non- Saccharomyces yeasts, few papers have explored other types of mixed fermentations. King and Capece studied the impact of co-cultures of different strains of S. cerevisiae on the volatilome [ 123 , 158 ]. In 2008, King found a positive variation in the thiol 3-mercaptohexan-1-ol (3MH) composition of Sauvignon Blanc wines in some of the co-cultures of S. cerevisiae strains studied [ 123 ]. Later, Escribano et al. [ 137 ] studied the co-culture of three yeasts in sequential fermentations including two non- Saccharomyces , T. delbrueckii and L. thermotolerans yeasts with S. cerevisiae yeast. Similarly, consortia including several non- Saccharomyces species have been considered [ 64 , 157 ]. Interestingly Padilla et al. [ 157 ] showed that a consortia of four non- Saccharomyces strains in mixed culture with three S. cerevisiae strains led to a decrease in total acids and did not affect the synthesis of other compounds in comparison to a pure fermentation with commercial S. cerevisiae .

Thus the yeast species [ 47 ] and the yeast strain [ 79 , 138 ] are the determining factors for the final wine VOC composition [ 159 ]. Nevertheless, the production of most of these VOCs also depends on different biotic and abiotic factors. It has been widely described that the composition of the matrix, and more particularly nitrogen sources [ 59 ], and the presence of VOC precursors have an impact on the production of VOCs [ 152 ]. In addition, the great variability in yeast inoculation protocols such as simultaneous and sequential fermentation [ 147 ], the time of adding S. cerevisiae in the case of sequential inoculation [ 39 ], population ratios [ 45 , 153 ], and environmental conditions [ 160 ] appears to play an essential role in yeast growing characteristics and subsequent VOC composition. Furthermore, the formation of these compounds occurs throughout the fermentation process. That is why some studies have aimed at identifying differences in VOC content based on the fermentation time [ 145 ]. They showed that the concentration of total esters increased by 40% from the beginning to the end of alcoholic fermentation. Another example was described by Escribano et al. [ 137 ] with the variation of the concentration of ethyl lactate for the ester formation due to an increase of higher alcohols in the medium. All of these factors induce additional variability between the different studies and observations [ 122 , 144 ].

Taken as a whole, the results of the various studies of volatile compounds show divergences. The approaches to data analysis are highly diverse, thus adding to the factors described previously that influence VOC composition. Most studies have attempted to describe differences between co-cultures and single fermentations of S. cerevisiae but some studies have compared the impact of these co-cultures with non- Saccharomyces single cultures, which may lead to differences in interpretation. Studies of VOCs in co-cultures have mainly focused on providing a descriptive approach and only a few of them have tried to explain the mechanisms leading to differences in VOC composition.

3.3. Sensory Impact

For many years, the impact of micro-organisms on the sensory component of wine has been addressed by many researchers and professionals in the wine sector. These studies have aimed at characterizing the individual impact of a S. cerevisiae or a non- Saccharomyces yeast strain on the aromatic [ 149 , 161 , 162 , 163 ], visual [ 164 ] and taste [ 165 ] profiles of wines. This approach is also increasingly applied to pairs or consortia of microorganisms involved in alcoholic fermentation [ 64 , 157 ]. In most cases the discrimination of wines fermented by more than one species of microorganism from those fermented by a single species is described. For example, Gobbi et al. [ 33 ] attributed more spicy and acidic notes to the co-culture in commercial white grape must compared to the single culture of S. cerevisiae . Barbera wine was studied after alcoholic fermentation assessed by T. delbrueckii and S. cerevisiae and showed a lower intensity in attributes such as floral and red fruit aromas, as well as a change in color, becoming more intense compared to S. cerevisiae in pure culture [ 135 ]. The same year, Varela et al. (2017) [ 156 ] showed that wines inoculated with M. pulcherrima and S. cerevisiae were closer to uninoculated wines and associated with high scores for positive attributes such as red fruit aroma and overall fruit aroma, which are found in a minority in wines from S. cerevisiae in pure culture.

More specifically, in various studies it appears that the sequential fermentation path has a greater impact on the sensory profiles of wines than simultaneous inoculation [ 33 , 145 , 148 ]). Benito et al. [ 40 ] showed in a comparison between sequential fermentation and simultaneous inoculation, S. cerevisiae and L. thermotolerans , that general acidity and overall impression were increased and described as characteristic of sequential fermentations. In 2015, the same research team also attempted to determine the impact of mixed starter wines with a non- Saccharomyces strain on the quality of Riesling wines [ 38 ]. As observed previously, the general impression was described as better in the case of sequential fermentations with respect to a single culture control of S. cerevisiae . The M. pulcherrima/S. cerevisiae pair was discriminated by the terms citrus/grapefruit and pear while L. thermotolerans/S. cerevisiae pair was associated with peach/apricot [ 38 ]. This last example shows that different aromatic notes are detected from the same matrix couples involving the same strain of S. cerevisiae, but with different non- Saccharomyces strains. Binati et al. [ 14 ] were also able to discriminate wines of Pinot Grigio from different sequential fermentations involving M. pulcherrima and S. bacillaris by different VOCs such as higher alcohols and esters. King et al. [ 123 ] highlighted a difference in sensory profiles between two co-inoculations of S. cerevisiae involving different strains characterized by box hedge and floral aromas and with different blends of two simple cultures of the same strain themselves described by the terms white vinegar and bruised apple. There is therefore an impact of co-cultivation and therefore interactions between populations on the sensory profile in addition to the strain effect. On the contrary, when studying two strains of T. delbrueckii in mixed culture with S. cerevisiae, Azzolini et al. [ 146 ] found no differences in the sensory profile with either pair. However, complexity and persistence were found to be increased in mixed cultures compared to the single culture of S. cerevisiae . Likewise, Liu et al. [ 122 ] mentioned that the impact of cultivar type was greater than the strain effect. Indeed, as pointed out earlier, the matrix plays an important role in the sensory profile. Hu et al. [ 144 ] confirmed the greater role of the matrix specifically the grape varieties with the observed decrease in the vegetal component in a Cabernet Sauvignon wine as opposed to an Ecolly wine fermented with the same mixed starter.

These latest works were carried out by studying VOC composition and the sensory aspect of wines in parallel; however, few of them focused on linking these two aspects. Hu et al. [ 144 ] established that the compounds that mainly contributed to tropical fruit and floral aspects of sequential fermentation that involved H. uvarum/S. cerevisiae , were C13 norisoprenoids, terpenes, and acetate esters, while the temperate fruit notes were generated mostly by ethyl esters. Similarly, Nisiotou et al. [ 49 ] identified an association between a higher concentration of ethyl ester and the fruity aroma of wine, as previously discussed by Lytra et al. [ 142 ] for simultaneous and sequential cultivation using L. thermotolerans. They likewise noted a correlation between acetate esters and the floral aroma descriptor. In the work by Renault et al. [ 145 ] on the sequential fermentation of T. delbrueckii and S. cerevisiae, the over-expression of four esters (ethyl propanoate, ethyl isobutanoate, ethyl dihydrocinnamate, and isobutyl acetate), described as minor, was highlighted. Moreover, the wine was characterized and differentiated from other modalities by fruity aromas and greater complexity. Then, they added at equal concentration the sequential condition and the single culture of S. cerevisiae to validate the impact of these esters on the wine sensory profile. This made it possible to highlight the sensory impact of these esters and attribute them the role of aromatic biomarker. However, it should be noted that only one of its esters was present at a concentration above its detection limit in wine. Therefore, it is suggested that the other esters also lead to aromatic modulation through different interaction phenomena. Among the four esters, ethyl propanoate and isobutyl acetate were described by several studies as enhancers of fruity notes [ 140 , 142 , 166 ].

The sensory aspect of the various studies mentioned above shows changes in the sensory profile, but they do not address or make it possible to understand or explain the mechanisms of interaction between the populations involved in alcoholic fermentation. Most authors have focused more on showing a change in the sensory profile of the wines in the case of co-culture. Indeed, sensory contribution remains complicated to integrate into a process of understanding the mechanisms of interaction, since no VOC or family of VOCs can explain the aromatic profile of a wine. It is still unclear what contribution they make to the aromatic notes. Despite trends and correlations, there is not always a mirror effect between chemical composition and sensory profiles due to the existence, among other things, of interactions between these volatile aromatic compounds and non-odorous volatile compounds to form aromas [ 141 , 167 , 168 ]. These interactions between VOCs were confirmed in a very recent publication by Mc Kay et al. [ 169 ]. Associated with these interactions, various factors can induce a mismatch between the volatile composition and the sensory profile of wines, such as the detection threshold [ 159 , 163 , 169 , 170 ], or the masking of certain flavors associated with volatile compounds by others, as suggested by Benito et al. [ 38 ] cited above. Higher alcohols have already been described as being able to mask these fruity notes [ 171 ]. Finally, in view of the diversity of volatile aromatic compounds, many of them participate in the same aromatic note [ 172 ]. Sensory evaluation also provides different information depending on the approach selected (description, comparison, preference, or determination of product quality), [ 160 ] and the panel of selected juries (expert or naive) [ 173 ]. Sensory analysis therefore remains an essential tool to qualify the impact of co-cultures on the final product, but it does not provide information on the interaction mechanisms that may occur.

4. Conclusion and Perspectives

This review presented the state-of-the-art of yeast–yeast interactions in wine and highlighted the difficulties of studying the mechanisms involved in these phenomena. The impact of co-culture on the final matrix is now well-known but little is understood about how this happens. Indeed, it appears that all the works presented distinct methodologies, mainly in terms of biological material with the use of different yeast species and strains, leading to a plethora of results specific to each pair. Therefore, understanding these mechanisms requires further studies at this level by combining the observation of a target mechanism with the use of different strains belonging to the same species in order to draw solid conclusions. Different matrices and the application of abiotic factors also remain a major source of diversity in the results. All these variations make it difficult to generate complementary and comparable results capable of leading to conclusions that unravel the mechanisms involved in these interactions.

During our research it became apparent that quorum sensing in yeast remains unexplained and unproven, making this gap in knowledge an important path of investigation, as discussed by Winters et al. 2019 [ 174 ]. Quorum sensing interactions have been proposed many times as a hypothesis by the authors [ 26 , 175 ], but neither have confirmed it. Although the direct impact of certain specific QS molecules on non- Saccharomyces growth has already been studied [ 59 ] when in high concentrations, experiments under conditions closer to those of winemaking have yet to be conducted. It also appears that the study of different types of interaction mechanisms such as cell–cell contact, for example, presents many contradictory results because of the use of different systems aimed at separating the different populations involved. The use of a double compartment bioreactor with a membrane making it possible to homogenize the surrounding medium in both compartments, while not denaturing the separation of microorganisms, seems to be a good strategy for understanding mechanisms. In addition, strategies at the molecular level can be considered to elucidate these mechanisms. For example, the creation of mutants of target genes that are presumed to be involved in mechanisms due to interactions such as cell–cell contact, and parietal genes, could be one avenue of investigation. Competition for nutrients is still difficult to assess when discriminating between the consumption of a nutrient by one or another of the populations involved. Monitoring this catabolic activity could be carried out, for example, by tagging amino acids for nitrogen competition. It was observed that, overall, the majority of the studies were essentially descriptive and failed to capture the interactions and their mechanisms. Technological deadlocks remain that must be overcome. The metabolomic approach is a real tool of choice and evokes metabolic pathways associated with changes in the composition of the metabolome, however, this requires further development of databases related to yeast metabolism in wine. With respect to representing the end result of all the metabolic and regulatory interactions that lead to metabolic changes, monitoring metabolic flows, currently called “fluxomics,” is one of the avenues to be considered with, for example, isotopic labelling of metabolites. In addition, a question arises as to the representativeness of targeted approaches that allow the quantification of target compounds in relation to the totality of the metabolites produced. A non-targeted approach, as suggested by Suklje et al. [ 176 ], may further explain the metabolic changes that occur. Transcriptomics, an indispensable approach for understanding gene expression under established environmental conditions, is still limited from the non- Saccharomyces perspective, as the genomes are poorly sequenced. Data mining is also of great importance since it can lead to the establishment of models of microbial behavior in response to different individual or combined parameters. An integrated approach combining different omics techniques is a strategy of choice that was recently described by Lawson et al. [ 177 ] with the objective of better understanding the mechanisms that direct interactions within a consortium of microorganisms. However, it should be taken into account that these approaches provide additional information and do not always lead to a general combined conclusion.

Supplementary Materials

The following are available online at https://www.mdpi.com/2076-2607/8/4/600/s1 , Table 1S: Diversity of methodologies and results in yeast interaction experiments.

This research was funded by the Regional Council of Bourgogne- Franche-Comté, the “Fonds Européen de Développement Régional (FEDER)

Conflicts of Interest

The authors declare no conflict of interest.

Competition between Paramecium species

The fourth example comes from the classic work of the great Russian ecologist G. F. Gause, who studied competition in laboratory experiments using three species of the protozoan Paramecium (Gause, 1934, 1935). All three species grew well alone, reaching stable carrying capacities in tubes of liquid medium. There, Paramecium consumed bacteria or yeast cells, which themselves lived on regularly replenished oatmeal (Figure 8.3a).

When Gause grew P. aurelia and P. caudatum together, P. caudatum always declined to the point of extinction, leaving P. aurelia as the victor (Figure 8.3b). P. caudatum would not normally have starved to death as quickly as it did, but Gause's experimental procedure involved the daily removal of 10% of the culture and animals. Thus, P. aurelia was successful in competition because near the point where its population size leveled off, it was still increasing by 10% per day (and able to counteract the enforced mortality), whilst P. caudatum was only increasing by 1.5% per day (Williamson, 1972).

By contrast, when P. caudatum and P. bursaria were grown together, neither species suffered a decline to the point of extinction - they coexisted. But, their stable densities were much lower than when grown alone (Figure 8.3c), indicating that they were in competition with one another (i.e. they 'suffered'). A closer

... between Paramecium species,...

Figure 8.3 Competition in Paramecium. (a) P. aurelia, P. caudatum and P. bursaria all establish populations when grown alone in culture medium. (b) When grown together, P. aurelia drives P. caudatum towards extinction. (c) When grown together, P. caudatum and P. bursaria coexist, although at lower densities than when alone. (After Clapham, 1973; from Gause, 1934.)

look, however, revealed that although they lived together in the same tubes, they were, like Taniguchi and Nakano's fish and Connell's barnacles, spatially separated. P. caudatum tended to live and feed on the bacteria suspended in the medium, whilst P. bursaria was concentrated on the yeast cells at the bottom of the tubes.

Continue reading here: Competition between diatoms

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Recommended Books

Gause, G.F. (1934). The Struggle for Existence.
Gause, G.F. (1935). The Principles of Genetics.
Clapham, A. (1973). Competition between Paramecium Species: Species Richness.
Williamson, M. (1972). The Evolution of Competition.

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Readers' Questions

Which of the following best describes how a swimmer moves through the water?

A swimmer moves through the water by applying thrust or force against the water with their arms and legs, propelling themselves forward. This is usually achieved through a combination of arm strokes and leg kicks. The swimmer's body position and technique play a significant role in minimizing drag and maximizing propulsion.

Which factorsmost influence the populations of paramecia when raised separately?

Several factors can most influence the populations of paramecia when raised separately. These factors include: Nutrient availability: The presence of sufficient nutrients in the environment is essential for the growth and reproduction of paramecia. The availability of food sources such as bacteria or organic matter can limit or promote their population size. Temperature: Paramecia are sensitive to temperature variations. Extreme temperatures, either too hot or too cold, can hinder their growth and reproduction. Optimal temperatures range between 20-30 degrees Celsius. pH levels: Paramecia prefer slightly alkaline or neutral pH levels (around 7-8). Drastic changes in pH can negatively impact their populations and may lead to death or reduced reproductive capabilities. Oxygen and carbon dioxide levels: Paramecia require a well-aerated environment to survive. High oxygen levels are necessary for their metabolic activities, while increased carbon dioxide levels can be detrimental to their population growth. Contaminants and toxins: Presence of pollutants, heavy metals, chemicals, or toxins can be harmful to paramecia populations. Exposure to such substances can lead to reduced reproductive rates or even death. Predation and competition: The presence of natural predators or competing species can impact paramecia populations when raised separately. For example, the presence of larger organisms like rotifers or ciliates that prey on paramecia can limit their population growth. It is essential to maintain optimal conditions for each of these factors to ensure healthy and sustainable paramecia populations when raised separately.

Why does p. aurelia beat p. caudatum?

Paramecium aurelia beats Paramecium caudatum due to its ability to move faster and exhibit more agile swimming behavior. This results in better maneuverability and the ability to catch and consume P. caudatum. Additionally, P. aurelia has a larger size, larger mouth, and longer cilia, which allow it to capture and ingest P. caudatum more effectively. It also has a more efficient food processing system, enabling it to extract nutrients more efficiently from the consumed prey. These advantages contribute to P. aurelia's ability to outcompete and beat P. caudatum for resources in their natural environment.

Which of the following statements best explains the relationship shown on the graph?

It seems that the graph was not provided. Please provide the graph or describe its relationship so that I can assist you accordingly.

Which statement best describes the relationship between network effects and innovation?

The statement that best describes the relationship between network effects and innovation is that network effects can drive innovation. Network effects refer to the increased value or utility that a product or service gains as more people use it. This, in turn, incentivizes companies to innovate and create better products or services to attract and retain users. As more users join a network, there are more opportunities for feedback, collaboration, and the emergence of new ideas, leading to continuous innovation. Additionally, network effects can also create barriers to entry for competitors, encouraging companies to invest in innovative solutions to maintain their competitive advantage.

Which of the following best describes an advanced center differential?

An advanced center differential is a component commonly found in all-wheel drive (AWD) systems. It is designed to distribute power between the front and rear axles, allowing for optimal traction and stability in various driving conditions. It is typically more technologically advanced than a traditional center differential, offering features such as torque vectoring, which can actively vary the power distribution between the axles and even individual wheels to enhance handling and performance.

Which of these statements regarding the industry life cycle is correct?

It is difficult to determine which statement regarding the industry life cycle is correct without knowing the specific statements provided. Could you please provide the statements you are referring to?

Which of the following best describes extinction?

Extinction refers to the complete disappearance or dying out of a species or group of organisms. It occurs when a species cannot adapt to changing environmental conditions, faces competition from other species, or experiences a catastrophic event that leads to its demise. Extinction can occur naturally or as a result of human activities such as habitat destruction, pollution, overhunting, or climate change.

Which of the following best illustrates a line flower?

Unfortunately, you have not provided any options for me to choose from. Could you please provide the options so that I can help you?

Which of these best describes how limited resources can lead to differential reproductive success?

The concept of limited resources relates to the fundamental principle of natural selection. Limited resources can lead to differential reproductive success by creating competition among individuals for access to those resources. In an environment with limited resources, individuals that are better adapted to acquire and utilize those resources have a higher chance of surviving and reproducing. The individuals with advantageous traits have a competitive edge over others, allowing them to outcompete and out-survive their peers. Through the process of natural selection, the advantageous traits become more prevalent in subsequent generations as they are passed down to offspring. This leads to a differential reproductive success, as individuals with these advantageous traits have a higher likelihood of producing more offspring that inherit the beneficial traits. For example, in a population of birds where food resources are scarce, those with longer beaks may be better suited to extract food from certain sources. These birds have a higher chance of survival and reproductive success compared to individuals with shorter beaks. Over time, the population may evolve to have longer beaks as the birds with these traits have a higher chance of passing them onto future generations. Overall, limited resources create selective pressures that favor individuals with traits that enhance their ability to acquire and utilize those resources. This results in differential reproductive success, shaping the traits and characteristics of populations over time.

When two species interact within a habitat, competitive exclusion can be observed if?

one of the species outcompetes the other and successfully occupies all available resources. This can lead to the local extinction of the weaker species. Competitive exclusion occurs when two species have similar ecological requirements and compete for the same resources such as food, space, or mates. If one species is more efficient at utilizing these resources or has a competitive advantage, it can outcompete and exclude the other species from the habitat.

Which of these statements regarding the industry life cycle is true?

It would be helpful to include the options you are referring to in order to provide an accurate response.

Which of the following statements are true concerning electromagnetic radiation fields?

To provide a comprehensive answer, I would need to know the specific statements you are referring to. However, I can provide some general information about electromagnetic radiation fields. Electromagnetic radiation fields are produced by electrically charged particles or changing magnetic fields. This is true. Electromagnetic radiation is created by the acceleration or oscillation of charged particles. Electromagnetic radiation fields consist of waves of electric and magnetic fields. This is true. Electromagnetic radiation consists of oscillating electric and magnetic fields that are perpendicular to each other and to the direction of propagation. Electromagnetic radiation fields can travel through a vacuum. This is true. Unlike sound waves, which require a medium for propagation, electromagnetic radiation can travel through a vacuum, such as empty space. Electromagnetic radiation fields can have different frequencies and wavelengths. This is true. Electromagnetic radiation spans a wide range of frequencies and wavelengths, from radio waves with long wavelengths to gamma rays with short wavelengths. Electromagnetic radiation fields can interact with matter. This is true. When electromagnetic radiation encounters matter, it can be absorbed, reflected, transmitted, or scattered depending on the properties of the material and the characteristics of the radiation. Electromagnetic radiation fields can be harmful to living organisms at certain intensities or frequencies. This is true. At high intensities or certain frequencies, electromagnetic radiation can have harmful effects on living organisms, such as causing tissue damage or increasing the risk of cancer. However, at lower intensities and within certain frequency ranges (e.g., visible light), electromagnetic radiation is generally not harmful. Remember that specific statements may have additional conditions or qualifications, so it is important to review the exact statements to determine their accuracy.

Which of the following statements about competition and rivalry is true?

It is impossible to provide a list of statements without knowing the options mentioned. Could you please provide the options or rephrase your question?

When species compete, they are competing to accomplish the goal of?

survival and reproduction.

Which of the following is a true statement concerning model organisms?

A true statement concerning model organisms is that they are commonly used in scientific research to study biological processes and diseases.

Which of the following statements regarding freshwater fish and saltwater fish is/are true?

Without knowing the statements provided, it is impossible to determine which ones are true. Could you please provide the statements?

Which of the following is a common strategy for successful intraspecific competition?

One common strategy for successful intraspecific competition is resource partitioning. Resource partitioning involves the division of limited resources, such as food, space, or mates, among individuals within the same species. This can be achieved through various mechanisms, including spatial segregation, temporal segregation, or morphological differentiation. By utilizing different resources or occupying different niches, individuals can reduce competition and optimize their chances of survival and reproduction.

Which of the following statements best describes protozoa?

Protozoa are single-celled organisms that belong to the kingdom Protista.

Which of the following protists has a mouthlike opening surrounded by cilia used to consume food?

The protist that has a mouthlike opening surrounded by cilia used to consume food is Paramecium.

Why are paramecium the most complex of the protists?

Paramecium is considered one of the most complex protists due to several reasons: Structure and cellular organization: Paramecium has a complex structure with cilia, or hair-like projections, covering its body. These cilia enable it to move and capture food. It also has a distinct organelle called the contractile vacuole, which helps regulate water balance within the cell. Its overall cellular organization, including specialized structures and organelles, is more advanced compared to other protists. Feeding mechanisms: Paramecium exhibits a sophisticated feeding mechanism. It has a specialized structure called the oral groove, lined with cilia, which helps sweep food particles into the mouth-like opening called the cytostome. It also possesses a food vacuole where digestion occurs. Reproduction: Paramecium has a complex reproductive system. It can reproduce asexually through binary fission, where the cell divides into two new individuals. Additionally, it can undergo sexual reproduction through a process called conjugation, where two individuals exchange genetic material. Behavioral responses: Paramecium is capable of complex behavioral responses, such as avoiding sources of light or harmful substances in their environment. They exhibit a coordinated response to stimuli known as taxis, which allows them to move towards favorable conditions and away from unfavorable ones. Genetic complexity: Paramecium possesses a relatively large and complex genome compared to other protists. It has multiple copies of some genes and exhibits gene regulation, allowing it to respond to different environmental conditions and adapt to changing circumstances. Overall, Paramecium showcases a higher level of structural complexity, specialized organelles, feeding mechanisms, reproductive strategies, and behavioral responses, making it one of the most complex protists.

How does a paramecium regulate its internal water concentration?

Paramecium is a single-celled organism that lives in freshwater environments. It needs to regulate its internal water concentration to maintain proper cell function and prevent excessive water influx or water loss. Paramecium primarily regulates its internal water concentration through a process called osmoregulation. Osmoregulation is the maintenance of a stable balance of water and solutes within the cell's cytoplasm, regardless of the external environment. The main mechanism of osmoregulation in paramecium involves the contractile vacuole. The contractile vacuole acts as a water pump, pumping excess water out of the cell to maintain the cell's internal osmotic pressure. - Water uptake: Paramecium takes in water through a process called endocytosis, where it opens its oral groove and engulfs food particles along with some water. This ingestion of water helps maintain an adequate water volume within the cell. - Contractile vacuole: Excess water that enters the cell is collected in the contractile vacuole. The contractile vacuole periodically fills with water from the surrounding cytoplasm. Once filled, the vacuole contracts and expels the excess water through a pore in the cell membrane called the cytoproct. The contractile vacuole functions as a water-regulating organelle, preventing the cell from filling up with too much water and potentially bursting. - Osmotic adjustment: Paramecium maintains the appropriate solute concentration within its cytoplasm to prevent excessive water influx or loss. It does this through various metabolic processes, such as active transport of ions and molecules across the cell membrane. This helps maintain the osmotic balance and water concentration within the cell. Overall, paramecium utilizes the contractile vacuole and osmotic adjustment mechanisms to regulate its internal water concentration. These processes ensure that the cell can function optimally and survive in its freshwater environment.

Which of the following statements concerning teams is true?

Unfortunately, you have not provided any options or statements to choose from. Could you please provide the statements so I can identify the true one?

What is the disease caused by paramecium?

Paramecium is a type of microscopic single-celled organism that is generally harmless to humans. However, it can be a potential cause of swimmer's itch, also known as cercarial dermatitis. This condition is caused by the larvae of certain parasitic worms that can be carried by paramecium and other similar organisms. When these larvae come into contact with human skin while swimming or wading in contaminated water, they can cause an itchy rash and other allergic reactions.

When yeast are grown on agar which term best describes the appearance of the colonies?

The term "colonial morphology" best describes the appearance of yeast colonies grown on agar.

Which one of these statements best describes the concept of line as it's used in design?

The statement "Line is a visual element that connects two points and creates a sense of movement, direction, and shape in design" best describes the concept of line as it is used in design.

What type of microscope would you use if you wanted to examine the movement of a freshwater protist?

If you want to examine the movement of a freshwater protist, you would typically use a compound microscope. The compound microscope is the most commonly used microscope in biology and allows you to view specimens using two or more lenses. This type of microscope provides high magnification and resolution, enabling you to observe the movement and structure of the protist in detail. You might also consider using a phase contrast microscope or a differential interference contrast (DIC) microscope, as these techniques can enhance the contrast and visibility of unstained protists.

Which of the following best describes a successful individual in evolutionary terms?

A successful individual in evolutionary terms is one that has a higher level of fitness.

What is a relationship between organisms that strive for the same limited resources?

The relationship between organisms that strive for the same limited resources is often referred to as competition. In this relationship, different individuals or species compete against each other for resources such as food, water, shelter, or mates. This competition can be intense, as each organism strives to outcompete others to secure the resources necessary for their survival and reproductive success. The competition can lead to adaptations, such as evolving techniques, physical attributes, or behaviors that enhance an organism's ability to access and utilize these limited resources.

Which statement best describes the evolutionary significance of mutualism?

The evolutionary significance of mutualism is that it provides benefits to both species involved, promoting coevolution and enhancing their chances of survival and reproduction.

Why is paramecium species used to investigate competition?

Paramecium species are commonly used to investigate competition in biology studies for several reasons: Easy to culture: Paramecium species, such as Paramecium aurelia and Paramecium caudatum, are relatively easy to culture in a laboratory setting. They can be maintained in simple growth media and replicated quickly, allowing for large-scale experiments. Rapid reproduction: Paramecium species have a short reproductive cycle, with a generation time of around 24 hours. This enables researchers to observe multiple generations within a reasonably short period, facilitating the study of long-term competition dynamics. High population density: Paramecium species can achieve high population densities in confined environments, making them suitable for studying competition. When resources become limited, Paramecium organisms can compete for availability, leading to observable effects on growth rates, population sizes, and other related parameters. Transparent body: Paramecium species possess a transparent body, which allows for easy microscopic observation of their internal structures and behaviors. This transparency is particularly advantageous when studying the interactions between individuals during competitive encounters. Amenable to manipulation: Paramecium species are amenable to various experimental manipulations, such as controlling their food availability, altering environmental conditions, or even genetically modifying them. These manipulations allow researchers to investigate specific aspects of competition and its underlying mechanisms. Combining these characteristics, Paramecium species serve as valuable model organisms to investigate competition and understand its implications in ecological dynamics and evolutionary processes.

Do paramecium caudatum can rapidly grow compared to the paramecium aurelia?

No, Paramecium aurelia can rapidly grow compared to Paramecium caudatum. Paramecium aurelia has a higher growth rate and reproduces more quickly than Paramecium caudatum. This is due to differences in their life cycle and reproductive strategies.

Which statement about competition is true?

There are multiple statements about competition that can be considered true, depending on the context. Here are a few possible true statements about competition: Competition can drive innovation and improve quality: When businesses or individuals compete with each other, it often leads to the development of new ideas, improvement in products or services, and higher standards within the industry. Competition can benefit consumers: Competition among businesses typically results in lower prices, increased variety of products or services, and better customer service, all of which benefit consumers. Competition can be both healthy and unhealthy: Healthy competition fosters growth, encourages individuals or businesses to strive for excellence, and adds excitement and inspiration to various fields. However, excessive or unfair competition can lead to detrimental effects such as market monopolies, unethical practices, and market imbalances. Competition is a natural part of human interaction: People naturally compete with one another in various aspects of life, including education, sports, career, and personal achievements. Ultimately, the true statement about competition depends on the specific context and the intended meaning behind it.

What do some paramecium have a symbiotic relationship with?

Some paramecium have a symbiotic relationship with certain species of bacteria. These bacteria live inside the paramecium and provide various benefits, such as helping with digestion, providing nutrients, and offering protection against harmful microorganisms. This mutualistic relationship benefits both the paramecium and the bacteria.

Which of the following best describes a symbiotic relationship between two organisms?

A symbiotic relationship is a close and long-term interaction between two different species that benefits at least one of the organisms involved. There are three types of symbiotic relationships: mutualism, commensalism, and parasitism. Mutualism: Both organisms benefit from the relationship. For example, the relationship between bees and flowers. Bees feed on nectar from flowers and, in the process, they transfer pollen from one flower to another, enabling plants to reproduce. Commensalism: One organism benefits while the other is neither harmed nor benefited. For example, a bird building its nest on a tree. The bird benefits from the shelter and stability provided by the tree, while the tree is unaffected. Parasitism: One organism benefits at the expense of the other. For example, ticks feeding on the blood of a host animal. The tick benefits from obtaining nutrients, while the host animal may experience adverse effects such as itching or the transmission of diseases. Therefore, the best answer would depend on the specific relationship being described.

Which of the following is a true statement about the effects of sugar?

a) Consuming excessive amounts of sugar can lead to weight gain and an increased risk of various health issues such as obesity, heart disease, and type 2 diabetes.

Which of the following best describes what it means to compete?

Competing generally refers to actively participating in a competition or contest in order to strive for victory or success. It often involves engaging in a rivalry or contest against others with the goal of outperforming them. Competing requires individuals or teams to demonstrate their skills, abilities, and strengths to differentiate themselves from others and achieve a competitive advantage. It may involve various aspects such as strategy, physical or mental prowess, adaptability, and resilience. Additionally, competition often implies a spirit of fair play, where individuals or teams follow and respect the rules and regulations governing the specific contest or competition.

Which of the following is not a characteristic of paramecium?

There are no options provided in the question, so I cannot determine which characteristic is not applicable to Paramecium. Please provide the options or specify the characteristic you are looking for.

Which of the following statements best describes carrying capacity?

Carrying capacity refers to the maximum number of individuals or amount of resources that an ecosystem or environment can sustainably support over a long period of time without causing detrimental effects to the population or the environment. It takes into consideration factors such as food availability, space, water, and other resources necessary for the survival and reproduction of a population. Essentially, it represents the limit at which an ecosystem's resources can support a population without causing environmental degradation or social disruption.

Which of the following statements describes the process of binary fission?

The correct statement describing the process of binary fission is: Binary fission is the method of asexual reproduction in which a single parent organism divides into two identical daughter cells, each with its own complete set of genetic material.

Which of the following is a true statement about the members of a species?

It would be helpful if you could provide the options you want me to choose from.

Does yeast finally have competition?

Yes, yeast finally has competition in the form of alternative sources of leavening agents or baking products. Some of the alternatives include: Sourdough starter: Sourdough bread is made using a naturally fermented starter, which contains wild yeasts and beneficial bacteria. It acts as a leavening agent, giving the bread its rise and flavor. Baking powder: Baking powder is a chemical leavening agent that consists of a combination of baking soda, cream of tartar, and sometimes cornstarch. It is commonly used in recipes that require a quick rise, like cakes and muffins. Baking soda: Baking soda, or sodium bicarbonate, is another chemical leavening agent that reacts with acidic ingredients like vinegar or lemon juice to produce carbon dioxide and create a rise in baked goods. Self-rising flour: Self-rising flour is a pre-mixed combination of flour, baking powder, and salt. It eliminates the need for yeast as a leavening agent and is commonly used for making biscuits and quick breads. Egg whites: Whipped egg whites can also act as a leavening agent by creating air pockets which expand during baking and give the baked goods a light and fluffy texture. These alternatives provide options for individuals with yeast allergies or those who prefer different flavors and textures in their baked goods. However, traditional yeast is still widely used and preferred for its specific flavor and texture it imparts to bread and other yeast-based products.

Which organelle is found in paramecia cells, but not plant cells?

Paramecium cells contain a variety of organelles, including some that are not found in plant cells, such as cilia, a contractile vacuole, and trichocysts.

Which of the following statements best describes the exclusion principle?

The Exclusion Principle states that no two electrons in an atom can have the same set of four quantum numbers. This means that each electron in an atom occupies its own unique energy level.

Which of the following statements concerning complex multicellular organisms is true?

Complex multicellular organisms consist of many different types of specialized cells that work together to form the organism's organs and other structures.

Which of the these is not a true statement of the war exclusion?

act The War Exclusion Act was a law that allowed citizens of certain countries to enter the United States.

Which of the following statements is true of a worm?

A worm is a type of malicious software that replicates itself in order to spread to other devices connected to a network.

Which of the following statements is true of both membrane potential responses shown in the graphs?

Both membrane potential responses show a gradual decrease in magnitude over time.

What is the scientific name for paramecium?

The scientific name for paramecium is Paramecium caudatum.

Which of the following statements is true about lakes?

All of the following statements are true about lakes: -Lakes are bodies of fresh or saltwater surrounded by land. -Lakes are important habitats for a variety of plants and animals. -Lakes can provide many recreational activities, including fishing, boating, and swimming. -Lakes can also provide drinking water for people and other animals.

Which of the following statements regarding protozoa is false?

Protozoa are multicellular organisms.

Which of the following statements describing cilia is false?

Cilia can be used to ingest food particles.

Which organelle, within the paramecium, controls sexual reproduction?

The macronucleus controls sexual reproduction in the paramecium.

Which of the following is a true statement regarding engineering controls?

Engineering controls are used to reduce or eliminate workplace hazards by changing the way the job is done or by isolating the worker from the hazard.

Which is not observed in paramecium?

Paramecium, a single-cell microorganism, does not have nervous tissue or a nervous system, so it does not have a brain. Therefore, it cannot experience consciousness, thought, emotion, or cognitive processes, which are usually associated with a brain.

What is the dependent variable responses volume volume days days p. caudatum p. caudatum p. aurelia?

The dependent variable is the p. caudatum and p. aurelia.

Why does parameceum audralias overcome paramecium caudata?

Paramecium audralias is able to survive and compete better than Paramecium caudata due to its ability to tolerate higher temperatures and greater salinity. This makes it better suited to live in the warmer and saltier environment in which it is found. Furthermore, Paramecium audralias has a greater reproductive rate which allows it to out-compete Paramecium caudata.

Which of the following is not a true statement regarding lakes?

"Lakes contain saltwater" is not a true statement regarding lakes. Freshwater lakes are made up of freshwater, not saltwater.

Where does digestion take place in many protozoa?

Digestion in many protozoa takes place inside the cytoplasm of the cell where the food particles are broken down into smaller molecules by hydrolysis and other metabolic processes.

What phylum does paramecium belong to?

Paramecium belongs to the Phylum Ciliophora.

Which of the following statements is true about hunting laws?

? Hunting laws vary by country, state, and local jurisdiction, and they can change from year to year.

What protists move by means of many short, hairlike projections?

called cilia? Ciliates. Examples of ciliates include Paramecium, Euplotes, and Stentor.

Which of the following statements describes the members of a population?

Population members are individuals or groups of individuals who possess certain characteristics in common, including origin, language, ethnicity, gender, age, and occupation.

What structure allows paramecium to move?

Paramecium move by means of cilia, which are tiny hairlike structures that line the surface of the organism. The cilia beat in a coordinated fashion, allowing the paramecium to move forwards, backwards, and turn in any direction.

Which statement describes a species that is at carrying capacity?

A species at carrying capacity is a species that has reached the maximum number of individuals of that species that the environment can sustain.

How do the structures of the paramecium help it survive?

The cilia of the paramecium help it move and find food. Their contractile vacuoles help balance the internal osmotic pressure of the paramecium by pumping excess water out of the cell. The macronucleus controls the behavior and metabolism of the paramecium. The micronucleus is responsible for the sexual reproduction and exchange of genetic material. The gullet, or cytostome, helps the paramecium to ingest food.

What is true about paramecium?

Paramecium is a single-celled organism that is commonly found in freshwater habitats. It is a protozoan, meaning that it is a unicellular organism that is not classified as a plant, animal, or fungus. Paramecium is able to move around, reproduce, and feed on its own. It has a large nucleus and various organelles, making it more complex than bacteria.

Which of the two protists is better adapted to competition?

The answer depends on the type of competition being considered. Some protists may be better adapted to competing for resources, while others may be better adapted to competing for mates.

What classification of protists would include paramecia?

Paramecia belongs to the phylum Ciliophora, which is classified as a type of protist.

How does the contractile vacuole help the paramecium survive in a freshwater environment?

The contractile vacuole helps the paramecium to survive in a freshwater environment by actively pumping and expelling excess water from the cell to maintain a stable osmotic balance. The contractile vacuole works in conjunction with the cell membrane to regulate the amount of water entering and leaving the cell. This helps the paramecium maintain a proper level of hydration, and prevents the cell from becoming over-saturated, which could cause it to burst.

Which of the following is a true statement regarding the cold war?

The Cold War was a period of tension and hostility between the United States and the Soviet Union, and their respective allies, that lasted from the late 1940s to the early 1990s.

Which is a true statement regarding nutrients and energy in an ecosystem?

Nutrients and energy are essential for an ecosystem to function properly and are cycled among the organisms within the system.

Which of the following statements describes true motility?

Motility is the ability of an organism to move spontaneously and actively, using metabolic energy. It is an important factor in a number of biological processes, including cell motility, the movement of organisms, and the transportation of substances within cells.

Which of the following statements describes a worm?

accurately A worm is a type of malicious software that is self-replicating and can spread from one computer to another without user intervention.

What are the defense mechanisms found in paramecia called?

The defense mechanisms found in paramecia are called cilia and mucus. Cilia are tiny hair-like structures on the surface of the cell that help it to move. They also help the paramecium to filter out food particles from its surrounding environment. Mucus is a slimy substance produced by the cell that acts as a barrier between it and any potential predators. Both cilia and mucus play an important role in protecting paramecia from harm.

Where does digestion occur in a paramecium?

Digestion occurs in a paramecium through a complex process of phagocytosis, where the cell engulfs particles of food and breaks them down inside a food vacuole.

Why paramacium aurelia outcompete paramacium caudatum when they are kept in same culture tube?

Paramacium aurelia has larger size, higher motility, and larger ingestion rate than Paramacium caudatum. These attributes enable Paramacium aurelia to outcompete Paramacium caudatum for food and space, allowing it to dominate the culture tube.

What are the three graphs with p aurelia and p caudatum communicating?

Interaction Graph: This graph displays the density of the interactions between P. aurelia and P. caudatum. It shows how the two species interact with each other, such as the type of interactions (predation, competition, etc.), their frequency and the extent to which the two species influence each other in the environment. Species-Specific Abundance Graph: This graph displays the species-specific abundance of P. aurelia and P. caudatum. It shows how the two species are distributed across a given environment, as well as how their individual populations vary over time. Genetic Compatibility Graph: This graph displays the amount of genetic compatibility between the two species. It shows the degree of genetic interchange between the two species and can indicate the potential for hybridization or other genetic mixing.

How to compare two paramecium with each other cultured in different media?

To compare two paramecium cultured in different media, scientists can observe certain biological characteristics, such as size and shape, swimming behavior, feeding behavior, and reproductive behavior. Additionally, scientists can observe and record the differences in their response to certain external stimuli such as light, chemicals, and predator cues. They can also analyze the differences in their ability to survive in different habitats and environments. Finally, scientists can measure the relative growth and lifespan of the two paramecium in different media, and compare the results.

How does nutrition take place in paramecium?

Nutrition in Paramecium occurs through a process of phagocytosis, which is the process of engulfing and taking in particles of food. Paramecium has an oral groove that helps to draw in the food particles, which are then encased in a vacuole, where digestion occurs. Nutrients are then released from the food particles, and are incorporated into the organism’s cells. The digestive vacuoles also act as storage for waste products to be eliminated from the organism.

How does food reach inside the paramecium?

Food reaches the inside of a paramecium by way of its cell membrane. This is a selectively permeable membrane that allows certain substances to pass through while blocking others. The process of food entering the paramecium includes the following steps: 1) The food is taken into the paramecium by a process called endocytosis. In this process, the membrane of the cell will engulf the food and create a vacuole around it. 2) The vacuole then moves towards the center of the cell and fuses with a lysosome, which is an organelle containing digestive enzymes. 3) The digestive enzymes break down the food into simpler molecules that can be absorbed by the cell. 4) The molecules are then transported across the cell membrane and into the cytoplasm. 5) Finally, the molecules are taken up by the cell for energy and used for various metabolic processes.

What is the method of motility of paramecium?

Paramecium uses a type of motility called ciliary locomotion. This involves beating of the cilia, which are hair-like projections on the organism's outer membrane. The cilia beat in a coordinated fashion and create a water current that moves the paramecium through the water.

What type of cell is a paramecium?

Paramecium is a type of single-celled organism classified as a protist. It is a ciliate protozoan, meaning it is covered in hairlike projections called cilia.

Which of the following statements describes a response of predators to prey?

Predators will adjust their hunting strategies in response to changes in the population size of their prey.

Which of the following is a true statement regarding species richness?

Species richness is a measure of the number of species found in a given area.

Which factors contribute most to intraspecific competition?

Availability of resources: The availability of resources such as food, shelter, and space is a major factor that contributes to intraspecific competition. When resources are limited, individuals of the same species are forced to compete for them, resulting in a struggle for survival. Aggression: Aggression can also be a factor in intraspecific competition, especially in animals that establish territories or hierarchies. Aggressive behavior is often used to claim resources or defend them from other individuals. Reproduction: Reproduction is another factor that contributes to intraspecific competition. By competition for a mate, individual reproductive success is ensured. This can result in competition for the best habitat and even fights between males of the same species. Interference: Interference competition can also occur when individuals directly interfere with the activities of other members of the same species. This can involve blocking resources or preventing access to them, as well as physically pushing or intimidating another individual. Mimicry: Mimicry is a form of intraspecific competition in which one individual mimics the behavior of another. This is often used to gain access to resources that the other individual has or to deceive the other individual in some way.

What is true of paramecia?

Paramecia are single-celled organisms that belong to the kingdom Protista. They are found in freshwater, brackish, and marine environments. They are usually about 0.25 to 0.5 millimeters in size and are easily visible under a microscope. Paramecia are free-living organisms that can move independently and feed on bacteria, algae, and other small organisms. They reproduce asexually by binary fission.

Why can't the paramecium change shape?

Paramecium are single-celled organisms, and as such their cell wall and structure is rigid and not able to change significantly. They are also bound by the laws of diffusion, which prevent them from altering their shape even if they wanted to.

When interspecific competition has an outcome called competitive exclusion, _________.?

one species will out-compete the other species and eventually drive the other species out of the environment.

What is the food source for the paramecium bacteria rice agar sugar?

Paramecium bacteria do not feed on rice agar or sugar. They feed primarily on microorganisms, such as bacteria, algae, and protozoa.

What was the food source the bacteria ate other bacteria weak paramecium rice nothing?

Nothing. Bacteria are not known to feed on other bacteria, weak paramecium, or rice.

How does paramecium obtain its food?

Paramecium obtains its food by a process called phagocytosis. In this process, the paramecium uses its oral groove or receptacle to draw in particles, such as bacteria, algae, and other small organisms. It then engulfs the particles with its cell membrane, forming a food vacuole. The cellular organelles within the paramecium, such as lysosomes and endoplasmic reticulum, work together to break down the particles and absorb their nutrients.

Where does paramecium live?

Paramecium live in aquatic environments, such as ponds, lakes, rivers, and oceans. They can also be found in sewage, soil, and other moist environments.

Is paramecium a bacteria?

No. Paramecium is a type of protozoa, not a type of bacteria. Bacteria are a different type of microorganism.

What is the relationship between the two species shown below?

The two species shown below are predator and prey. The predator is consuming the prey.

What happened to p. caudatum in the mixed population on day 14?

On day 14 of the mixed population, the numbers of P. caudatum had declined significantly, indicating that the species was being outcompeted by the other species.

When p. aurelia was grown alone, how long did it take to reach carrying capacity?

When grown alone, it took the protist Paramecium aurelia approximately 4 weeks to reach carrying capacity.

Which of the following could be true of two different species that have a competitive relationship?

One species may outcompete the other for resources. One species may dominate a certain environment while the other is relegated to less suitable habitats. One species may outlive the other due to its greater reproductive success. One species may consume the other as prey. Both species may experience reduced population growth due to the competition.

How does paramecium obtain energy?

Paramecium obtain energy through a process called chemoautotrophic nutrition. This involves taking in inorganic compounds from their environment and converting them into energy through biochemical mechanisms. They use their cilia to absorb inorganic compounds such as ammonia, sulfide, and nitrates which are then broken down by enzymes in the paramecium's cytoplasm. They then convert the products into a usable form of energy.

What can we conclude about p. caudatum from this experiment?

Based on this experiment, it can be concluded that Paramecium caudatum can reproduce asexually, as evidenced by the fact that the single organism cloned itself multiple times. This is a common form of reproduction among members of the Paramecium genus. It can also be concluded that this species has a short life cycle and can adapt to a wide range of environmental conditions.

What is g.f. gause's competitive exclusion principle?

Gause's Competitive Exclusion Principle states that two species competing for the same resources cannot coexist in the same environment if the resources are limited. This is because one species will outcompete the other for resources, and ultimately drive the other species to extinction. This principle states that in a given environment, the number of species will remain constant and that certain species are better adapted to survive than others due to their competitive advantage.

What is the food source for the paramecium?

Paramecia typically feed on bacteria, algae, and small particles of organic matter. They can also absorb nutrients from the surrounding environment, or phagocytose small particles of food.

How does paramecium get energy?

Paramecium obtains energy by consuming bacteria, algae, and other small organic matter. They feed by using their cilia to create a whirlpool of water that pushes food particles into the cell. Once inside, the ingested particles are subjected to a process of digestion by vacuoles and lysosomes, which break down their contents into molecules that can be used by the cell.

Which of the following statements about intraspecific competition is false?

Intraspecific competition is always beneficial to the species.

How does paramecium grow?

Paramecium grows by a process known as binary fission. This is a form of asexual reproduction in which a single organism divides into two daughter cells. During binary fission, the cell replicates its genetic material and then splits into two identical daughter cells. The daughter cells are exact copies of the parent cell and are capable of performing the same functions. The whole process of binary fission takes anywhere from 15 minutes to an hour to complete.

What do paramecium use for defense?

Paramecium use cilia to defend against predators by forming a protective loricae- a curved, jelly-like coating around the cell's surface. This helps to decrease the speed of currents, making it more difficult for predacious organisms to capture them. Additionally, paramecium can use their rapid movement and light-sensing pigment to detect the presence of a potential predator and swim away.

What resource were the mixed paramecium competing for in this study food space water mates?

In the study, the mixed paramecium were competing for resources such as food, space, water, and mates.

When p. caudatum and p. bursaria are grown together:?

When P. caudatum and P. bursaria are grown together, they are known to form a symbiotic relationship. P. bursaria serves as the primary consumer, while P. caudatum feeds off of the organic compounds resulting from the digestion of the algal food source by P. bursaria. This symbiotic relationship allows both species to benefit from the relationship, as they are able to obtain resources they would not otherwise have access to, while also providing protection and shelter for the other species.

What is the relationship between paramecium aurelia and yeast?

Paramecium aurelia and yeast do not have a relationship. They are two distinct organisms from different taxonomic kingdoms. Paramecium aurelia is a single-celled protozoan organism and yeast is a single-celled fungus.

What prevents p caudatum from surviving with p aurelia?

P caudatum and P aurelia are different species which belong in separate genera. Thus, they are not able to physically interbreed, meaning that P caudatum is unable to survive with P aurelia.

Why interspecific competition has an effect on the relative population size of p.caudatum.?

Interspecific competition has an effect on the relative population size of P.caudatum because it competes with other species for the same resources. This competition can lead to a decrease in the size of P.caudatum's population due to decreased access to food, habitat, water, and other resources. Additionally, the competition may also lead to increased competition for other resources such as mates, which can further decrease the overall size of the P.caudatum population.

Why does p. aurelia population grows?

P. aurelia populations grow because of their ability to reproduce asexually through fission and because their environment often contains a variety of beneficial resources, including food and oxygen, which help them to survive and thrive.

Why are there more paramecium cells than didinium cells?

Paramecium cells reproduce much more quickly than didinium cells. Paramecium also has a larger population to start with, while didinium usually only exist in a few isolated locations. In addition, didinium tends to feed on much larger prey than paramecium, so it may be more difficult for didinium to find enough food to sustain its population.

Who is the dominant organism between p. aurelia and p. caudatum?

It is not possible to answer this question definitively as it depends on the specific environment and competition between the two species.

What competitive advantage does paramecium aurelia has over paramecium caudatum?

Paramecium aurelia has several competitive advantages over Paramecium caudatum: Speed: Paramecium aurelia is generally faster in terms of locomotion compared to Paramecium caudatum. This increased speed allows it to quickly move away from predators or capture prey more effectively. Reproduction rate: Paramecium aurelia has a higher reproductive rate, producing more offspring in a shorter period of time. This allows it to quickly repopulate and outcompete Paramecium caudatum in environments with limited resources. Genetic diversity: Paramecium aurelia has higher genetic diversity due to its ability to undergo both sexual and asexual reproduction. This genetic variability provides an advantage in adapting to changing environmental conditions and overcoming challenges. Predator resistance: Paramecium aurelia has developed various defensive mechanisms to protect itself from predators, including cilia movement and the ability to change direction rapidly. These adaptations make it harder for predators to capture and consume Paramecium aurelia compared to Paramecium caudatum. Overall, Paramecium aurelia's advantages in speed, reproduction rate, genetic diversity, and predator resistance give it a competitive edge over Paramecium caudatum in various ecological niches.

Are paramecium aurelia and paramecium caudatum two different species?

Yes, Paramecium aurelia and Paramecium caudatum are two different species. They belong to different genera and can be distinguished by their different shapes. Paramecium aurelia has a slipper-like form with a rounded anterior end and a pointed posterior end, while Paramecium caudatum has a more ovoid form with a tapered anterior end and a contractile vacuole at the posterior end. They also differ in the number of cilia they possess; Paramecium aurelia has a single row of cilia while Paramecium caudatum has two rows of cilia.

Do paramecium use exploitative competition?

No, paramecium do not use exploitative competition. Exploitative competition is a type of competition that occurs between organisms of different species.

What type of competition exist between paramecium aureria and paramecium caudatum?

Competition between Paramecium aureria and Paramecium caudatum exists in the form of resource competition. Both species compete for food, light, and space. They also compete for mates, and may utilize different mating strategies to outcompete one another.

Is p. caudatum and p.aurelia same species?

No, Paramecium caudatum and Paramecium aurelia are two different species within the Paramecium genus.

Why cant p. aurelia p. caudatum coexist?

P. aurelia and P. caudatum cannot coexist because they are competing for the same resources in the environment. P. aurelia is a more aggressive species that is better adapted to its environment, so it is able to outcompete P. caudatum for food and other resources.

Why are p.aurelia better adapted than p caudatum?

P. aurelia is better adapted than P. caudatum because it is more resilient to changes in its environment. P. aurelia has the ability to break down into multiple smaller organisms when there is a decrease in food or space or when the environment becomes hostile. This allows P. aurelia to survive in harsh conditions, whereas P. caudatum cannot break down into smaller organisms and therefore is less able to survive in difficult conditions. P. aurelia also is more tolerant to temperature fluctuations, whereas P. caudatum is more limited when it comes to temperature fluctuations.

What advantages does paramecium aurelia have over paramecium caudatum?

Paramecium aurelia has a number of advantages over Paramecium caudatum, including: Greater resistance to environmental stress. Increased metabolism. Better reproduction rates. More genetic variability. Ability to form diverse cyst types. Ability to form polycultures. Average body size is larger. More flagella per cell. Greater tolerance for crowding. Easier to culture in laboratory settings.

What was the outcome when paramecium caudatum and p. aurelia competed in the same test tube?

When Paramecium caudatum and P. aurelia competed in the same test tube, the P. caudatum population grew more quickly and eventually outcompeted the P. aurelia population.

Why do paramecium aurelia and caudatum have different carrying capacities?

The carrying capacity of a species is determined by a variety of environmental and physiological factors, such as the availability of food and suitable habitat, the number of predators, and the species' physiological limitations. Paramecium aurelia and caudatum may have different carrying capacities because they have different adaptations and tolerances to their environment. For example, Paramecium aurelia is better adapted to living in warmer, more acidic waters, while Paramecium caudatum can tolerate changes in temperature and pH levels more easily.

What is gause experiment of competition in paramaecium aurelia caudatum?

Gause’s experiment of competition in Paramecium aurelia caudatum is an example of how two closely related species may compete for resources in a given environment. Gause used two strains of Paramecium aurelia caudatum, one which was more active and the other which was less active, and placed them together in a flask. He found that the more active strain consistently outcompeted the less active strain and was able to use resources more efficiently. This experiment suggests that competition can influence the evolution of species if resources are limited.

How to determine whether p.aurelia and p.caudatum will be mixed together?

The only way to determine whether P. aurelia and P. caudatum will be mixed together is to conduct laboratory experiments to observe their behaviors in response to each other. Factors such as size, motility, and habitat preferences would need to be considered in order to determine whether the two species can coexist in the same environment. Additionally, studying the physical characteristics of each species and monitoring their interactions would provide useful insights into whether they can be mixed together.

What competition is demonstrated in the paramecium lab?

Competition in the paramecium lab is demonstrated between different species of paramecium. As they compete for resources, such as food and space, the species with the greatest reproductive success will survive and thrive while the other species may become extinct.

What does paramecium caudatum and paramecium aurelia feed on?

Paramecium caudatum and Paramecium aurelia feed on a variety of organic matter, including bacteria, algae, small pieces of plants, and even small protozoans. They also feed on certain inorganic materials, such as mineral particles and diatoms.

Which competition in paramecium cwote dom and paramecium aurelia?

Competition between Paramecium cwote dom and Paramecium aurelia occurs mainly when resources become limited in their environment. The competition is based on competition for food sources such as bacteria, yeasts, and other protists, as well as competition for space in the environment. This competition can be an important factor in determining which species thrives in a given environment.

What is the difference between paramecium aurelia?

Paramecium aurelia is a species of single-celled protists belonging to the genus Paramecium. It is a widely studied organism, used as a model organism in studies of cellular biology and genetics. There is a great deal of variation within the species, and two distinct forms of P. aurelia are recognized. The first form, P. aurelia form I, is commonly referred to as the “solitary” form, while the second form, P. aurelia form II, is commonly referred to as the “social” form. The primary difference between these two forms is that the solitary form lives as isolated cells while the social form lives as colonies that can contain up to several hundred cells. In addition, the social form is capable of asexual reproduction by transverse binary fission, whereas the solitary form cannot reproduce asexually.

How do paramecium Compete?

Paramecium compete for resources such as food, space, and mates. They typically compete with other protists, bacteria, and microorganisms for these resources. To compete, they use various strategies such as predation, outcompeting for resources, and co-existing with other species. They also take advantage of their ability to move around quickly and efficiently to outmaneuver their competitors.

More From Forbes

Starting small: how to successfully experiment with generative ai.

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CEO and Cofounder of AnswerRocket .

At this point, most enterprises are dabbling in generative AI or planning to leverage the technology soon. According to an October 2023 Gartner, Inc. survey , 45% of organizations are currently piloting generative AI, while 10% have deployed it in full production. Companies are eager to move from pilot to production and start seeing some real business results.

However, enterprises getting started with generative AI often run into a common stumbling block right out of the gate: They suffer analysis paralysis before they can even begin using the technology. There are tons of generative AI tools available today, both broad and highly specialized. Moreover, these tools can be leveraged for all sorts of professions and business purposes—sales, product development, finance, etc.

With so many choices and possibilities, enterprises often get stuck in the planning phase—debating where they should deploy generative AI first. Every business unit (and all of the business's key stakeholders) wants to own a part of the company's generative AI initiatives.

Things can get messy. To stay on track, businesses should follow these guidelines when experimenting with generative AI.

Focus On Specific Use Cases With Measurable Goals

Enterprises need to recognize that every part of the organization can benefit from generative AI—eventually. To get there, however, they need to get off the ground with a pilot project.

How do you decide where to get started? Keep it simple and identify a small, specific problem that exists today that can be improved with generative AI. Be practical. Choose an issue that's been challenging the business for a while, has been difficult to fix in the past and will make a visibly positive impact once resolved. Next, enterprises need to agree upon metrics and goals. The problem can't be too nebulous or vague; the impact of AI (success or failure) has to be easily measurable.

With that in mind, the pilot project should have a contained scope. The purpose is to demonstrate the real-world value of the technology, build support for it across the organization and then broaden adoption from there.

If organizations try to leverage AI in too many different ways and solve multiple problems, it'll cause the scope to grow out of control and make it impossible to complete the pilot within a reasonable timeframe. Ambition has to be balanced with practicality. Launching a massive pilot project that requires extensive resources and long timelines is a recipe for failure.

What's a good timeline for the pilot? It depends on the circumstances, of course. Generally speaking, however, it should only take a few weeks or a couple of months to execute, not multiple quarters or an entire year.

Start small, get something functional quickly and then iterate on it. This iterative approach allows for continuous learning and improvement, which is essential given the nascent state of generative AI technology.

Organizations must also be sure to keep humans in the loop from the very beginning of the experimentation phase. The rise of AI doesn't render human expertise obsolete; it amplifies it. As productivity and business benefits increase with generative AI, human employees become even more valuable as supervisors and validators of AI output. This is essential for maintaining control and building trust in AI. In addition, the pool of early participants will also help champion the technology throughout the organization once the enterprise is ready to deploy it widely.

Finally, once the project has begun, organizations have to stick with it until it's complete. Don't waste time starting over or shifting to other use cases prematurely. Just get going and stay the course. After that's been completed successfully, companies can expand their use of generative AI more broadly across the organization.

Choosing The Right Technology

The other major component of the experimentation phase is selecting the right vendor. With the generative AI market booming, it can seem impossible to tell the differences between one solution and another. Lots of noisy marketing only makes things more confusing.

The best way to cut through the noise is to identify the requirements that are most important to the organization (e.g., data security, governance, scalability, compatibility with existing infrastructure) and look for the vendor that best meets those needs.

It's extremely important to understand where vendors stand on each of these things early on to avoid the headache of discovering that they don't really check those boxes later. The only way to do that is by talking to the vendor (especially its sales engineering team) and seeing these capabilities demoed firsthand.

Get Ahead Of The Competition With A Strong Start

Within the next couple of years, I expect almost every enterprise will employ generative AI in production. Those wielding it effectively will get a leg up on their competition, while those struggling will be at risk of falling behind. Though the road may be uncharted, enterprises can succeed by focusing on contained, valuable projects, leveraging human expertise and selecting strategic technology partners.

Don't wait. Embrace this unique opportunity to innovate and take that crucial first step now.

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Cringy moves and a white b-girl’s durag prompt questions about Olympic breaking’s authenticity

Australia’s Rachael Gunn, known as B-Girl Raygun, competes during the Round Robin Battle at the breaking competition at La Concorde Urban Park at the 2024 Summer Olympics, Friday, Aug. 9, 2024, in Paris, France. (AP Photo/Frank Franklin)

Lithuania’s Dominika Banevic, known as B-Girl Nicka, competes during the B-Girls quarterfinals at the breaking competition at La Concorde Urban Park at the 2024 Summer Olympics, Friday, Aug. 9, 2024, in Paris, France. (AP Photo/Frank Franklin)

American artist Snoop Dogg stands on stage prior to the breaking competition at La Concorde Urban Park at the 2024 Summer Olympics, Friday, Aug. 9, 2024, in Paris, France. (AP Photo/Frank Franklin)

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PARIS (AP) — From the Australian b-girl with the meme-worthy “kangaroo” dance move to the silver-medal winning Lithuanian in a durag, breaking’s Olympic debut had a few moments that raised questions from viewers about whether the essence of the hip-hop art form was captured at the Paris Games.

Rachael Gunn, or “b-girl Raygun,” a 36-year-old professor from Sydney, Australia, quickly achieved internet fame, but not necessarily for Olympic-level skill. Competing against some b-girls half her age, she was swept out of the round-robin stage without earning a single point, and her unconventional moves landed flat while failing to match the skill level of her foes.

At one point, Gunn raised one leg while standing and leaned back with her arms bent toward her ears. At another, while laying on her side, she reached for her toes, flipped over and did it again in a move dubbed “the kangaroo.”

B-Girl Raygun competes during the Round Robin Battle on Friday, Aug. 9, 2024, in Paris, France. (AP Photo/Abbie Parr)

Gunn has a Ph.D. in cultural studies, and her LinkedIn page notes she is “interested in the cultural politics of breaking.”

“I was never going to beat these girls on what they do best — their power moves,” said Gunn. “What I bring is creativity.”

Clips of her routine have gone viral on TikTok and elsewhere, and many cringed at her moves platformed on the Olympic stage as a representation of hip-hop and breaking culture.

“It’s almost like they are mocking the genre,” wrote one user on X.

Some of it was ‘weird to see’

Many Black viewers, in particular, called out Lithuania’s silver medalist b-girl Nicka, (legally named Dominika Banevič) for donning a durag during each of her battles. Durags, once worn by enslaved Africans to tie up their hair for work, are still worn by Black people to protect and style their hair. They became a fashionable symbol of Black pride in the 1960s and 1970s and, in the 1990s and early 2000s, also became a popular element of hip-hop style. But when worn by those who aren’t Black, durags can be seen as cultural appropriation. Banevič is white.

Lithuania’s Dominika Banevic, known as B-Girl Nicka, competes during the B-Girls quarterfinals. (AP Photo/Frank Franklin)

2024 Paris Olympics:

What to know about the closing ceremony : A skydiving Tom Cruise and performances from Billie Eilish, the Red Hot Chili Peppers and Paris Olympics mainstay Snoop Dogg highlighted the French capital’s au revoir to the Olympics.
Indelible images : AP photographers pick their favorite images from the Paris Olympics .
Who won the 2024 Olympics?: See which countries tied for the most gold medals in Paris, and who exceeded expectations.
When are the next Summer Games? The Olympics will always have Paris . But next up for the Summer Games: Los Angeles 2028 . See how the City of Angels is preparing to follow the City of Light.

Actor Kevin Fredericks responded on Instagram to Banevič donning the headwear by saying it looked “weird to see somebody who don’t need it for protective style or waves to be rocking the durag.”

The 17-year-old breaker ultimately won the silver medal after losing in the final to Japan’s b-girl Ami (Ami Yuasa).

For her part, Banevič has credited the breakers from the 1970s in the Bronx — the OGs — or “original gangsters” in hip-hop who created the dance — for her own success and breaking style.

“It’s a huge responsibility to represent and raise the bar every time for breaking because they did an amazing job. Big respect for the OGs and the pioneers that invented all those moves. Without them, it wouldn’t be possible,” she said. “Without them, breaking wouldn’t be where it is today. So I’m grateful for them.”

Concerns over losing breaking’s roots

Friday night’s slips “may have alienated too many new viewers to garner the anticipated response from our Olympic premiere,” said Zack Slusser, vice president of Breaking for Gold USA and USA Dance, in a text message to the Associated Press.

“We need to change the narrative from yesterday’s first impression of breaking as Olympic sport. There were significant organizational and governance shortcomings that could have been easily reconciled but, unfortunately, negatively impacted Breaking’s first touching point to a new global audience.”

The challenge for Olympic organizers was to bring breaking and hip-hop culture to a mass audience, including many viewers who were skeptical about the dance form’s addition to the Olympic roster. Others feared the subculture being co-opted by officials, commercialized and put through a rigid judging structure, when the spirit of breaking has been rooted in local communities, centered around street battles, cyphers and block parties. Hip-hop was born as a youth culture within Black and brown communities in the Bronx as a way to escape strife and socio-economic struggles and make a statement of empowerment at a time when they were labeled as lost, lawless kids by New York politicians.

Refugee breaker Manizha Talash, or “b-girl Talash,” channeled that rebellious vibe by donning a “Free Afghan Women” cape during her pre-qualifier battle — a defiant and personal statement for a 21-year-old who fled her native Afghanistan to escape Taliban rule. Talash was quickly disqualified for violating the Olympics’ ban on political statements on the field of play.

Refugee Team’s Manizha Talash, known as Talash wears a cape which reads “free Afghan women.” (AP Photo/Frank Franklin)

Both American b-girls were eliminated in Friday’s round-robin phase, a blow to the country representing the birthplace of hip-hop in what could be the discipline’s only Games appearance. B-girl Logistx (legal name Logan Edra) and b-girl Sunny (Sunny Choi) both ranked in the top 12 internationally but came up short of the quarterfinals.

“Breaking for the Olympics has changed the way that some people are dancing,” said Choi, referring to some of the flashier moves and jam-packed routines. “Breaking changes over time. And maybe I’m just old-school and I don’t want to change. ... I think a lot of people in our community were a little bit afraid of that happening.”

The b-boys take the stage on Saturday to give Olympic breaking another chance at representing the culture.

Associated Press Race & Ethnicity Editor Aaron Morrison contributed to this report from New York.

AP Olympics: https://apnews.com/hub/2024-paris-olympic-games

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Keeping up with big tech on AI research ‘never about competition’

As tech giants spend staggering sums on artificial intelligence, the head of the engineering and physical sciences research council explains why it’s vital for academia to engage in this ‘hot’ area of research.

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With Microsoft and OpenAI building a $100 billion (£77.5 billion) supercomputer and technology giants set to invest more than $1 trillion in artificial intelligence, it is difficult to see how publicly funded bodies can keep up in this highly expensive field of research.

That is, however, the challenge that Charlotte Deane, who took over as executive chair of the UK’s Engineering and Physical Sciences Research Council (ESPRC) in January , faces – with the added pressure of a new Labour government keen to see economic returns from a technology with potential uses in science, business and beyond that may be enormous but are still unclear.

That Professor Deane is firmly rooted in the world of AI research may certainly help with this task. Prior to her arrival at the ESPRC – whose annual budget, including PhD fellowships, exceeds £1 billion – the University of Oxford professor of structural bioinformatics was chief scientist of biological AI at Exscientia, a leading British AI-led drug discovery firm.

Unusually, she has been engaged in this world since her student days at Oxford in the late 1990s.

“My friends would probably start my ‘Résumé for Researchers’ as ‘too clumsy to be a wet lab chemist’ – which is a factually correct statement – so I chose to do a research project in the computational area, as I’d always liked working with computers,” reflected Professor Deane.

On how AI has changed science in recent years, she reflected how computers have long been “a toy and tool to help us get better understanding or change the experiments we do” but “we now have some new toys like machine learning…which allow us to do things that five or six years ago we thought were impossible. It’s really exciting because you can see how it will change how we do science – it allows us to think about things in a different way and do experiments differently.”

Even as a relatively old hand in AI research, Professor Deane admitted she has been surprised by the rapid progress made in recent years, such as DeepMind’s AlphaFold 2 paper in 2020 , which used AI to solve the 50-year-old grand challenge of predicting the structure of proteins. “I’d worked on this problem and if you’d asked me if this problem would have been solved in my lifetime, I’d have said definitively yes. But was I surprised by the date it was solved? I was,” said Professor Deane.

While DeepMind can, however, spend tens of millions of dollars on experiments, that is not an option for the ESPRC. So how can university-based researchers keep up?

“It’s never about competition. Yes, we don’t have computers as big as they have but that’s OK, because there are different things that we’ll do in the public sector versus the private sector,” she said.

“In academia, we can play at the edge of what’s possible – I’m not going to try to build the perfect large language model…but we can think about things like uncertainty, explicability, decision-making…which are less important to DeepMind because they just want their model to get it right. I want to know why it got it right because that will change my way of doing science.”

Academics can also investigate areas that might prove highly valuable within a decade or so, continued Professor Deane. “These areas might not seem cool now but might be some day…quantum [technology] is a really good example. It’s worth about $9 billion and will be worth $90 billion in 10 years’ time. The reason this exists, and is a strength for the UK, is because – a long time ago – investment was put into something that still doesn’t sound that cool – which is photonics. AI too – it wasn’t that long ago that the idea you’d have companies in this area wasn’t such a good idea.”

There are, of course, challenges – not least attracting top AI talent when big tech is offering high six-figure salaries to AI engineers. “There is no point in pretending that, as the academic sector, we can compete on salaries for some of these things, but I have brilliant PhD students in my research group, and I see people make active choices on where they want to be,” she said.

Yet even with UKRI’s recent £100 million commitment to AI research , Professor Deane feels it is important that AI researchers can move more easily between academia and industry.

“It’s one of the things that I want to drive in UKRI – I’ve been able to work across academia and industry at the same time. Many companies want to have people with that freedom to do other things.”

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The Australian Olympian 'Raygun' went viral for her breaking moves. Now she's defending them.

Rachael Gunn, known as "Raygun," is an Australian B-girl (break-girl) who competed at the Olympics .
She lost three battles in the round-robin part of the competition, but her moves went viral online.
Gunn and sporting organizations are speaking out about harassment and misinformation after her performance.

Breaking made its debut at the 2024 Paris Olympics — and while she didn't earn a spot on the podium, the Australian breaker Rachael Gunn, known as Raygun, has received plenty of recognition online.

Gunn is a 36-year-old lecturer at Macquarie University in Sydney whose research focuses on the "cultural politics of breaking," according to her faculty profile .

But Gunn's time on the Olympic stage was short-lived. The B-girl was eliminated during the round-robin stage of the women's breaking competition, losing in one-on-one battles to the United States' Logistx, France's Syssy, and Lithuania's Nicka.

Raygun didn't earn a point in any of those battles, but as clips of her performance spread online, she got something else: instant meme status.

Here's what you need to know about Raygun now that the breaking competition is over.

Raygun is an academic who studies breaking — and she competes internationally

Before Gunn went to the Olympics, she approached the 2024 Games from an academic perspective.

With her coauthor, Lucas Marie, Gunn published an article in the June 2023 issue of Global Hip Hop Studies titled "The Australian breaking scene and the Olympic Games: The possibilities and politics of sportification." The article examined how the Olympics' institutionalization would affect the Australian breaking scene.

Alongside her academic career, Gunn is a competing B-girl. But before she got into breaking, she had experience with ballroom dancing, jazz, hip-hop, salsa, and tap, The Australian Women's Weekly reported. Gunn told The Sydney Morning Herald that her husband, Samuel Free, introduced her to breaking in 2008 while they were at university. Free is still her coach, she said.

Gunn told Women's Weekly that breaking "hooked" her in 2012, around the time that she began her doctoral program in cultural studies. She began competing more seriously in 2018 and eventually set her sights on the Olympics.

According to her university profile, she was the top-ranked B-girl of the Australian Breaking Association in 2020 and 2021, representing the country at the World DanceSport Federation Breaking Championships in 2021, 2022, and 2023. She also won the WDSF Oceania Breaking Championships in 2023.

"My bag always has two main things: It's like, my knee pads and my laptop," Gunn said on the podcast " The Female Athlete Project ." "Because I need my knee-pads to break. And then, yeah, just do some emails quickly. Or like, do some revisions on a chapter I submitted, or copyedit this article I did, or moderate those grades."

The athlete also told the Herald that she preferred to wear "baggy jeans and a baggy T-shirt" while breaking.

"I like the heaviness they bring," Gunn said. "Maybe it's my background in hip-hop, but having weight closer to the ground works for me, gets me in the right headspace."

Raygun's performances at the Olympics sparked memes and criticism

Raygun took the stage at the Olympics wearing a tracksuit in Australia's green and gold, breaking out moves that included hopping like a kangaroo. Her performances attracted attention online and memes that compared her moves to, among other things, dancing children.

Raygun qualified for the Olympics through the Oceania Breaking Championships

There were three ways to qualify for breaking at the Olympics, which the World Dance Sport Federation (WDSF) outlined in April 2022: at the WDSF championship in Belgium in September 2023, in a continental qualifier, or in an Olympic qualifier series held in 2024. Gunn qualified regionally by winning the WDSF Oceania Breaking Championships, which were held in Sydney in October 2023.

AUSBreaking organized the Oceania Breaking Championships, according to the WDSF .

AUSBreaking posted on Instagram about the Oceanic Olympic qualifying event on Instagram in September 2023, announcing in a September 25, 2023 post that competitor registration was open. The panel of judges was composed of 10 breakers from multiple countries, led by head judge Katsu One of Japan.

Per the Sydney Morning Herald, Gunn was the highest-scoring B-girl on day one of the championships. She won two battles on the second day to secure her title and a qualifying spot in the 2024 Olympic Games in Paris.

AUSBreaking released a statement on Instagram Monday about the selection process, saying that the qualifying event was "open to all interested participants in the Oceanic region," conducted in line with WDSF standards, and adjudicated by an international panel that used the same judging system as the 2024 Olympics.

"Ultimately, Rachael Gunn and Jeff Dunne emerged as the top performers in exactly the same process, securing their spots to represent Australia in Paris," the statement reads. "Their selection was based solely on their performance in their battles on that day."

Raygun and sporting organizations have spoken out about misinformation after her performance

Claims have circulated online that Gunn unfairly obtained her spot in the games. Posts online, as reported by the Australian Associated Press , claimed that Gunn's husband was one of the judges in her qualifying event. One petition hosted on Change.org claimed that she established the governing body that ran the selection process. That petition was eventually removed after it was placed under review, per an archived snapshot .

A representative for Change.org confirmed to Business Insider on Thursday that the petition had been flagged for misinformation, reviewed per the platform's community guidelines, and removed from the platform.

"Change.org maintains strict guidelines against content that constitutes harassment, bullying, or spreading false information. We take such matters seriously and remove any content that violates these standards to protect our users and uphold the integrity of our community," the rep said in an email statement to BI.

Despite the online claims, Free was not one of the judges at Gunn's qualifying event. And Gunn did not establish AUSBreaking. The organization said in a statement that it was founded by its president Lowe Napalan in 2019, and "at no point" was Gunn "the founder, an executive, committee member, or in any position of leadership."

The Australian Olympic Commission (AOC) also released a statement condemning the Change.org petition, and demanding its removal. It also said that by winning the Oceania championship, Gunn was "legitimately nominated" by DanceSport Australia to the AOC to represent Australia at the Olympics.

"The petition has stirred up public hatred without any factual basis. It's appalling," AOC chief executive officer Matt Carroll said in the statement. "No athlete who has represented their country at the Olympic Games should be treated in this way and we are supporting Dr. Gunn and Anna Meares at this time."

In a video uploaded to her personal Instagram account, Gunn said that she was "honored" to have represented Australia and breaking during its Olympic debut. But the "hate" that followed was "devastating," she said. When it came to misinformation around her qualification, Gunn referred viewers to previously issued statements from the AOC and AUSBreaking.

Raygun and breaking judges have defended her Olympic performance

At a press conference on Saturday, the day after Gunn's competition, Anna Meares, the head of the Australian team, responded to criticism of Gunn online.

"I love Rachael, and I think that what has occurred on social media with trolls and keyboard warriors, and taking those comments and giving them airtime, has been really disappointing," Meares said, per ESPN .

"Raygun is an absolutely loved member of this Olympic team. She has represented the Olympic team, the Olympic spirit with great enthusiasm. And I absolutely love her courage," Meares continued. "I love her character, and I feel very disappointed for her, that she has come under the attack that she has."

During a press conference on Sunday, Martin Gilian, the Olympic breaking head judge, defended Gunn's performance, saying breaking was "all about originality" and representing your roots, the Australian Broadcasting Corporation reported.

"This is exactly what Raygun was doing," Gilian said. "She got inspired by her surroundings, which in this case, for example, was a kangaroo."

Gunn said during the Saturday press conference that "all of my moves are original," ESPN reported. She told The Guardian that her biggest strength was "creativity."

"I was never going to beat these girls on what they do best, the dynamic and the power moves, so I wanted to move differently, be artistic and creative," Gunn told The Guardian, "because how many chances do you get that in a lifetime to do that on an international stage. I was always the underdog and wanted to make my mark in a different way."

This story was originally published on August 12, 2024, and has been updated to include the latest information and statements from those involved.

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Technical Report
Published: 12 February 2018

Yeast surface display platform for rapid discovery of conformationally selective nanobodies

Conor McMahon 1 ,
Alexander S. Baier ORCID: orcid.org/0000-0003-1647-9477 1 ,
Roberta Pascolutti 1 ,
Marcin Wegrecki ORCID: orcid.org/0000-0002-3275-9850 2 ,
Sanduo Zheng 1 ,
Janice X. Ong 1 ,
Sarah C. Erlandson 1 ,
Daniel Hilger 3 ,
Søren G. F. Rasmussen 2 ,
Aaron M. Ring 4 ,
Aashish Manglik ORCID: orcid.org/0000-0002-7173-3741 5 , 6 &
Andrew C. Kruse ORCID: orcid.org/0000-0002-1467-1222 1

Nature Structural & Molecular Biology volume 25 , pages 289–296 ( 2018 ) Cite this article

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Membrane proteins
Structural biology

Camelid single-domain antibody fragments (‘nanobodies’) provide the remarkable specificity of antibodies within a single 15-kDa immunoglobulin V HH domain. This unique feature has enabled applications ranging from use as biochemical tools to therapeutic agents. Nanobodies have emerged as especially useful tools in protein structural biology, facilitating studies of conformationally dynamic proteins such as G-protein-coupled receptors (GPCRs). Nearly all nanobodies available to date have been obtained by animal immunization, a bottleneck restricting many applications of this technology. To solve this problem, we report a fully in vitro platform for nanobody discovery based on yeast surface display. We provide a blueprint for identifying nanobodies, demonstrate the utility of the library by crystallizing a nanobody with its antigen, and most importantly, we utilize the platform to discover conformationally selective nanobodies to two distinct human GPCRs. To facilitate broad deployment of this platform, the library and associated protocols are freely available for nonprofit research.

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Acknowledgements

Financial support for this work was provided by the Vallee Foundation (A.C.K.), the Smith Family Foundation (A.C.K.), National Institutes of Health grants 5DP5OD021345 (A.C.K.), 1DP5OD023048 (A.M.), and 1DP5OD023088 (A.M.R.), the Lundbeck Foundation (grant no. R37-A3457 to S.G.F.R.), and the Danish Independent Research Council (grant no. 0602-02407B to S.G.F.R.).

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Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA

Conor McMahon, Alexander S. Baier, Roberta Pascolutti, Sanduo Zheng, Janice X. Ong, Sarah C. Erlandson & Andrew C. Kruse

Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark

Marcin Wegrecki & Søren G. F. Rasmussen

Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA, USA

Daniel Hilger

Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA

Aaron M. Ring

Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA

Aashish Manglik

Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, USA

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Contributions

A.C.K., A.M., and C.M. designed and generated the nanobody library. C.M., A.S.B., and A.C.K. performed quality control of the library. C.M., R.P., S.Z., J.X.O., D.H., and A.M. prepared antigens, performed selections, and isolated nanobody binders. C.M., R.P., and S.C.E. characterized nanobodies. A.M.R., A.M., and A.C.K. developed the modified yeast display system and associated expression vectors. M.W. and S.G.F.R. purified the A2A adenosine receptor. C.M., A.M., and A.C.K. wrote the manuscript with assistance and input from all coauthors.

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Correspondence to Aashish Manglik or Andrew C. Kruse .

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Integrated supplementary information

Supplementary figure 1 biochemical validation of nanobody clones.

(a–k ) Randomly chosen nanobodies were expressed and purified from E. coli , then analyzed by size exclusion chromatography to assess monodispersity. ( l ) SDS-PAGE analysis of nanobody purity following one-step nickel affinity purification.

Supplementary Figure 2 Design of display system

( a ) The display system was engineered using the high affinity SIRPα variant CV1 as a test protein, and its ligand CD47 ectodomain as the staining reagent. A biotin tag is schematized as a glowing red circle. ( b ) Length of the stalk region determines accessibility of a displayed protein as a function of molecular weight. ( c ) Analytical flow cytometry plots showing length dependence for two staining reagents: CD47 biotin and α-HA antibody. The 649 amino acid long stalk was used in all nanobody display experiments.

Supplementary Figure 3 Analysis of HSA-targeted nanobodies

( a ) Library design was assessed by monitoring the change in amino acid frequency in CDR3 throughout selection rounds with HSA as the antigen. Few changes were observed, with the only notable trend a modest increase in basic residue frequency and a decline in acidic residue frequency. ( b ) Assessment of Nb.b201 binding to human serum albumin by surface plasmon resonance, comparison with mouse serum albumin which shows no detectable binding. ( c ) 2Fo-Fc composite omit map contoured at 1.5 σ for antigen bound Nb.b201. The structure of both bound (yellow) and free (gray) forms of the nanobody are shown, highlighting structural divergence. ( d ) 2Fo-Fc composite omit map contoured at 1.5 σ for free Nb.b201.

Supplementary Figure 4 Discovery of nanobodies with nonpurified antigen

( a ) Conditioned medium containing adiponectin (left lane) was used for selection of nanobodies. It shows a complex mixture of proteins as assessed by SDS-PAGE. For reference, purified adiponectin is shown in the right lane. Adiponectin exists as a mix of 16-mers, hexamers, and trimers. ( b ) Schematic of selection process. Fluorescent anti-FLAG antibody was used to specifically mark those yeast cells that display adiponectin-binding nanobodies. ( c ) Flow cytometry analysis of final clone pool, showing that the library was highly enriched in adiponectin-binding clones. ( d ) Sequences of five selected clones showed highly diverse CDR3 sequence composition and length. ( e ) Binding assessed using in vitro pull-down with purified adiponectin globular domain. ( f ) Binding to adiponectin was further confirmed in vitro using surface plasmon resonance. Kinetic fit is shown for clone Nb.AQ103.

Supplementary Figure 5 Affinity of β 2 AR-binding nanobodies

On-yeast titration to estimate affinity of β 2 AR binding nanobodies. EC50 values are summarized in the lower right. Bottom panel shows measurement of conformational selectivity for selected clones as assessed by flow cytometry.

Supplementary information

Supplementary text and figures.

Supplementary Figures 1–5, Supplementary Tables 1 and 2 and Supplementary Note 1

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McMahon, C., Baier, A.S., Pascolutti, R. et al. Yeast surface display platform for rapid discovery of conformationally selective nanobodies. Nat Struct Mol Biol 25 , 289–296 (2018). https://doi.org/10.1038/s41594-018-0028-6

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DOI : https://doi.org/10.1038/s41594-018-0028-6

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Competition assays and physiological experiments of soil and phyllosphere yeasts identify Candida subhashii as a novel antagonist of filamentous fungi

Conclusions

Isolation and cultivation of fungi

Identification of fungal isolates

Quantification of yeast antagonism against filamentous fungi in vitro

Growth analysis of yeasts at different temperatures

Microarray phenotype analysis

Growth in root exudates

Sequencing and analysis of the C. subhashii FGA 2.2 mitochondrial genome

The antagonistic activity of naturally occurring yeasts against filamentous fungi in vitro

Physiological characteristics of strongly antagonistic yeasts from soil or apple

Candida subhashii is an abundant soil fungus

Soil yeasts are generally more antagonistic and metabolically versatile than apple phyllosphere yeasts

Binary competition assays identify strongly antagonistic yeasts with potential for biocontrol

Candida subhashii is an antagonistic soil fungus

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Why, when, and how did yeast evolve alcoholic fermentation?

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