seed imbibition experiment

• Biology Article
• Study Of Imbibition In Seeds

Study of Imbibition In Seeds or Raisins

Table of Contents

Aim of the Experiment

Materials required, procedure of the experiment, observations, calculations, precautions.

To study and demonstrate the amount of water that is imbibed by raisins.

What is imbibition?

It is the phenomenon wherein water adsorption takes place by substances without solution formation. Example: When seeds are immersed in water, they swell due to imbibition . This swelling causes a temporary increase in the volume of the cell and does not require energy as materials are transported passively.

Raisins increase in size due to the imbibition of water. The quantity of water imbibed can be obtained by considering the difference in the mass between the dry raisins and the swollen raisins.

How is water transported to different parts of the plants?

It is transported through imbibition, diffusion, and osmosis.

What are imbibants and adsorbents?

They are the substances that imbibe water. Example of imbibants – Starch. The adsorbent is the liquid that is imbibed.

Why does Imbibition occur?

It primarily occurs due to the presence of lyophilic and hydrophilic colloids. Through the sub-microscopic capillaries that are located on the body’s surface water is imbibed.

The imbibition of water continues until a dynamic equilibrium is established which causes an increase in the imbibant’s volume resulting in imbibitional pressure which is of enormous magnitude.

How is imbibition affected?

Some factors that can affect the imbibition rate are:

• Temperature – Imbibition rate is directly affected by the temperature
• Imbibant’s nature – Starch has lesser capacity while proteins have a great imbibing capacity
• Solute’s concentration – It is indirectly proportional to the rate of imbibition. An increase in the rate of imbibition is observed when the concentration of solute is less
• Imbibition rate is directly related to the surface area of the imbibant. The greater the surface area the more the rate of imbibition

Significance Of Imbibition

• Germination of seed is inducted by imbibition
• The force generated by imbibition is required for the cohesion of water to the elements of xylem
• Retention of water by fruits is due to imbibition
• Ripening of ovules into seeds is due to imbibition as it causes water movement to the ovules

Also Refer:  Germination of seed

• Blotting paper
• Distilled water
• Electronic balance
• 25-30 raisins with their stalks intact
• Take a clean and dried small beaker and add distilled water
• Take around 20 clean, dried and fresh raisins with their stalks
• Weigh the selected 20 raisins on an electronic balance
• Make note of the measurement
• Now add the weight 20 raisins into the beaker with distilled water
• Leave the raisins undisturbed and allow it to soak in water for around 2 to 3 hours
• Take a clean and moisture-free petri dish and cover spread the blotting paper
• Now slowly remove the soaked 20 raisins into a petri dish with the help of a spatula
• Gently pat dry the raisins
• Now again weigh the soaked and dried 20 raisins on an electronic balance
• Now calculate the difference between the two readings
• Dry raisins weigh x_________ gm
• Swollen raisins weigh y ___________ gm
• The water absorbed by the raisins weigh = (y – x) gm
• The water absorbed by the raisins in percentage = (x-y)/x

Raisins that are used in the experiment should be ensured of being dry and clean and having their stalks intact.

Explore more biological concepts and other experiments on imbibition, and transpiration by registering at BYJU’S.

Further Reading

Put your understanding of this concept to test by answering a few MCQs. Click ‘Start Quiz’ to begin!

Select the correct answer and click on the “Finish” button Check your score and answers at the end of the quiz

Visit BYJU’S for all Biology related queries and study materials

Your result is as below

Request OTP on Voice Call

Leave a Comment Cancel reply

Your Mobile number and Email id will not be published. Required fields are marked *

Post My Comment

Register with BYJU'S & Download Free PDFs

Register with byju's & watch live videos.

PlantPropagation.org

How To Propagate and Clone Plants

The Imbibition Process: Understanding Seed Water Uptake and Germination

Seed germination is a fascinating process that marks the beginning of a plant’s life cycle. At the core of this process lies the imbibition phenomenon, a critical step that enables seeds to absorb water and initiate germination. Imbibition plays a pivotal role in seed hydration, triggering a series of physiological and biochemical changes that culminate in seedling emergence. In this article, we delve into the imbibition process, exploring its key mechanisms and significance in seed germination.

What is Imbibition?

Definition and overview.

Imbibition refers to the process by which dry seeds absorb water, leading to the swelling and softening of their structures. It is an essential prerequisite for germination, providing the necessary hydration to reactivate the dormant seed and initiate metabolic activities.

The Role of Water in Imbibition

Water acts as the driving force behind imbibition. Seeds, typically in a dehydrated state, possess a low water content, and their cellular structures are imbued with hydrophilic molecules. When placed in a suitable environment with adequate moisture, water molecules are drawn into the seed’s structures, driven by osmosis, capillary action, and diffusion. As water enters the seed, it activates a cascade of biochemical processes, kickstarting the germination process.

Imbibition Mechanisms

Hydration of seed coat.

The imbibition process begins with the absorption of water by the seed coat. The seed coat, composed of protective layers, undergoes a swelling and softening effect upon water uptake. This facilitates the penetration of water into the seed’s internal structures.

Activation of Enzymes and Metabolic Pathways

As water enters the seed, it triggers the activation of various enzymes and metabolic pathways that were in a dormant state during seed dormancy. Enzymes, such as amylase and protease, become reactivated, initiating the breakdown of stored nutrients, such as starch and proteins, into simpler forms that can be utilized by the developing seedling.

Expansion and Mobilization of Cellular Structures

The imbibition process causes the expansion and swelling of cells within the embryo and endosperm. This expansion exerts pressure on the seed coat, eventually leading to its rupture or cracking, allowing the emerging seedling to break free.

Significance of Imbibition in Seed Germination

Water uptake for metabolic activities.

Imbibition enables seeds to absorb water, which is essential for various metabolic processes vital for germination. These include enzyme activation, respiration, DNA replication, and protein synthesis, which collectively drive seedling growth.

Breakdown and Mobilization of Stored Nutrients

The imbibition-triggered activation of enzymes facilitates the breakdown and mobilization of stored nutrients, such as carbohydrates, lipids, and proteins, from the endosperm or cotyledons. These nutrients serve as a source of energy and building blocks for the developing seedling until it establishes an independent photosynthetic system.

Embryo Expansion and Seedling Emergence

The imbibition-induced expansion of cells within the embryo promotes the elongation of the embryonic axis, leading to the emergence of the radicle (primary root) and the plumule (shoot). This process is crucial for seedling establishment and the successful transition from the dormant seed state to an actively growing plant.

The imbibition process serves as the gateway to seed germination, allowing seeds to absorb water, activate metabolic activities, and initiate seedling growth. Through the imbibition phenomenon, seeds undergo structural changes, enzyme activation, and nutrient mobilization, culminating in the emergence of a new plant. Understanding the imbibition process provides valuable insights into the mechanisms underlying successful seed germination and can inform practices related to seed propagation, agriculture, and horticulture.

• Search Menu
• Sign in through your institution
• Advance articles
• Darwin Reviews
• Special Issues
• Expert View
• Flowering Newsletter Reviews
• Technical Innovations
• Editor's Choice
• Virtual Issues
• Community Resources
• Reasons to submit
• Author Guidelines
• Peer Reviewers
• Submission Site
• Open Access
• About Journal of Experimental Botany
• About the Society for Experimental Biology
• Editorial Board
• Advertising and Corporate Services
• Journals Career Network
• Permissions
• Self-Archiving Policy
• Dispatch Dates
• Journal metrics
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

Introduction, the dry seed stage: moisture content, after-ripening, and the stored transcriptome, physical, morphological, and physiological aspects of imbibition and testa rupture, hormonal and temperature regulation of early gene expression in imbibed seeds, reactivation of metabolism: transcription and translation, reactivation of metabolism: energy production, novel directions and techniques for studying early seed germination.

• < Previous

First off the mark: early seed germination

These authors contributed equally to this work.

• Article contents
• Figures & tables
• Supplementary Data

Karin Weitbrecht, Kerstin Müller, Gerhard Leubner-Metzger, First off the mark: early seed germination, Journal of Experimental Botany , Volume 62, Issue 10, June 2011, Pages 3289–3309, https://doi.org/10.1093/jxb/err030

• Permissions Icon Permissions

Most plant seeds are dispersed in a dry, mature state. If these seeds are non-dormant and the environmental conditions are favourable, they will pass through the complex process of germination. In this review, recent progress made with state-of-the-art techniques including genome-wide gene expression analyses that provided deeper insight into the early phase of seed germination, which includes imbibition and the subsequent plateau phase of water uptake in which metabolism is reactivated, is summarized. The physiological state of a seed is determined, at least in part, by the stored mRNAs that are translated upon imbibition. Very early upon imbibition massive transcriptome changes occur, which are regulated by ambient temperature, light conditions, and plant hormones. The hormones abscisic acid and gibberellins play a major role in regulating early seed germination. The early germination phase of Arabidopsis thaliana culminates in testa rupture, which is followed by the late germination phase and endosperm rupture. An integrated view on the early phase of seed germination is provided and it is shown that it is characterized by dynamic biomechanical changes together with very early alterations in transcript, protein, and hormone levels that set the stage for the later events. Early seed germination thereby contributes to seed and seedling performance important for plant establishment in the natural and agricultural ecosystem.

‘… I have had one experiment some little time in progress which will, I think, be interesting, namely, seeds in salt water, immersed in water of 32°–33° […] I have in small bottles out of doors, exposed to variation of temperature, cress, radish, cabbages, lettuces, carrots, and celery, and onion seed—four great families. These, after immersion for exactly one week, have all germinated, which I did not in the least expect (and thought how you would sneer at me); for the water of nearly all, and of the cress especially, smelt very badly, and the cress seed emitted a wonderful quantity of mucus (the ‘Vestiges’ would have expected them to turn into tadpoles), so as to adhere in a mass; but these seeds germinated and grew splendidly. The germination of all (especially cress and lettuces) has been accelerated, except the cabbages, which have come up very irregularly, and a good many, I think, dead. One would have thought, from their native habitat, that the cabbage would have stood well. The Umbelliferae and onions seem to stand the salt well.’ (April 13th, 1855, cited from: Darwin, 1887 ).

Charles Darwin's interest in seed germination was a focus within his wider interest in plant development. He published several papers about the above findings in the Gardeners’ Chronicle and Agricultural Gazette (including Darwin, 1855 a , b , c , d ). His interest in seed germination was indeed well founded: seed germination is a crucial process in the seed plant life cycle. It determines when plants enter natural or agricultural ecosystems and is the basis for crop production. This review deals with the early events during this important life cycle transition. Early seed germination is defined here as imbibition plus the early plateau phase of water uptake. It is thus positioned between the dry state of the seed and the late phase of germination. Germination is completed by visible radicle protrusion through the seed covering layers, and followed by seedling establishment ( Fig. 1 ). Late germination has been the focus of seed research for many decades (summarized in recent reviews, e.g. Finch-Savage and Leubner-Metzger, 2006 ; Holdsworth et al. , 2008 ; Nonogaki et al. , 2007 ; North et al. , 2010 ). It is believed that unravelling the mechanisms underlying germination requires the integration of all of its facets including early events.

Comparison of morphological and physiological key processes during the germination of typical endospermic (e.g. Arabidopsis thaliana , Lepidium sativum , and tobacco) eudicot seeds. (A) Morphology of a mature seed of A. thaliana with a single layer of endosperm between the testa (seed coat) and the embryo. (B) Typical moisture sorption isotherm of an oil-seed at room temperature. Region 1 represents strongly bound water (monolayer) which is unavailable for water-dependent biochemical reactions. Region 2 represents weakly bound, multilayered water, which leads to a limited availability for water-dependent biochemical reactions. Only water represented in region 3 is freely available and may allow molecular biochemical events that occur during seed imbibition. (C) Visible events during two-step germination: testa and endosperm rupture. Abscisic acid (ABA) inhibits endosperm rupture, but not testa rupture, of after-ripened seeds. The seed image is from Müller et al. (2006) with permission of the publisher. The moisture sorption isotherm diagram is based on quantitative data of Hay et al. (2003) and Manz et al. (2005) .

Most mature angiosperm seeds consist of an embryo surrounded by covering layers such as the maternal testa (seed coat) and the triploid endosperm. Seeds exhibit species-specific differences in their structure and the composition of their storage compounds ( Obroucheva and Antipova, 1997 ; Linkies et al. , 2010 ). Interestingly, Charles Darwin already worked with some species that would later become model species in seed biology, namely lettuce, on which the red/far red light-induced reversibility of phytochrome effects was discovered ( Borthwick et al. , 1952 ), and cress, radish, and cabbages, which are members of the Brassicaceae family for which the first plant genome was sequenced ( Arabidopsis thaliana ; Koornneef and Meinke, 2010 ). This review will focus on a range of orthodox eudicot model systems of seed germination. Pea ( Pisum sativum , Fabaceae, Fig. 2A ) seeds store mainly proteins and starch in the embryo's storage cotyledons; mature pea seeds have no endosperm ( Obroucheva and Antipova, 1997 ; Melkus et al. , 2009 ). The Brassicaceae oil-seeds of Arabidopsis ( Fig. 1A ) and garden cress ( Lepidium sativum , ‘cress’) contain a thin endosperm layer ( Haughn and Chaudhury, 2005 ; Müller et al. , 2006 ), while the oil-seeds of tobacco ( Nicotiana tabacum , Solanaceae) contain a thicker endosperm layer ( Leubner-Metzger, 2003 ; Manz et al. , 2005 ). This review will follow the physiological timeline of events during early seed germination, from the dry seed to fully reactivated metabolism. This is supported by corresponding figures and in addition by Supplementary material available at JXB online.

Comparison of morphological and physiological key processes during the germination of typical endospermless (e.g. Brassica napus , pea, and many other legumes) eudicot seeds. (A) Morphology of a mature pea seed which is endospermless. (B) Time courses of B. napus seed water uptake, testa rupture, radicle growth >2 mm, and the effect of abscisic acid (ABA); control without added hormone (CON). (C) Visible events during one-step germination typical for endospermless species. Seed images (A, C) are from Finch-Savage and Leubner-Metzger (2006) with permission of the publishe. Diagram (B) is based on quantitative data by Schopfer and Plachy (1984) .

Seed maturation and desiccation were recently reviewed ( Holdsworth et al. , 2008 ; Angelovici et al. , 2010 ). This discussion will start at the end-point of these reviews: with the biochemical properties of the desiccated mature orthodox seed, which constitutes a desiccation-tolerant state of the sporophyte with typical average water contents of ∼10%. This ‘dry’ seed state is therefore in fact a ‘low-hydrated’ state, and dry seeds are not completely metabolically inert. The physiological state of dry seeds changes during after-ripening (i.e. a prolonged period of dry storage at room temperature of freshly harvested, mature seeds). After-ripening storage is associated with a loss of dormancy, although dormancy release and after-ripening may be separate pathways ( Carrera et al. , 2008 ; Holdsworth et al. , 2008 ). After-ripening depends on temperature and seed moisture content. The optimal moisture content for after-ripening is lower for oil-storing compared with starchy seeds, but in general after-ripening takes place at seed moisture contents between 8% and 15% ( Probert, 2000 ; Bazin et al. , 2011 ). Moisture sorption isotherms of seeds ( Fig. 1B ) show that water molecules are weakly bound at this water content, which means its availability for biochemical reactions is limited. The shape of the moisture sorption isotherm curves is similar for oil-seeds (e.g. tobacco and Arabidopsis ; Hay et al. , 2003 ; Manz et al. , 2005 ) and starchy seeds (e.g. pea; Chen, 2003 ); even though absolute values differ slightly. However, water distribution is inhomogeneous within seeds, and seed tissues differ in their moisture sorption isotherms (see references in Hay et al. , 2003 ; Manz et al. , 2005 ; Wojtyla et al. , 2006 ). 1 H-nuclear magnetic resonance (NMR) spectroscopic imaging of tobacco seeds suggests that there are local pockets of higher hydration, in which water may be freely available for biochemical reactions. This may lead to differing biochemical capabilities between seed organs and tissues. Although evidence is still fragmentary, there are several reports that indicate that low-level transcription, post-transcriptional processing, and translation may be possible during seed after-ripening of tobacco ( Leubner-Metzger, 2002 , 2005 ), Arabidopsis ( Müller et al. , 2009 ), barley ( Leymarie et al. , 2007 ), and other species ( Holdsworth et al. , 2008 ). Holdsworth et al. (2008) also provide a critical review of this issue which is recommended for further reading. Future experiments to elucidate these highly debated findings are required and could include the use of novel imaging techniques as well as tissue-specific transcriptome and proteome analyses during after-ripening storage.

There are indications that after-ripening includes protein oxidation by reactive oxygen species (ROS). ROS are formed in the dry state as can be shown by the redox state of dry seeds shifting towards a more oxidized cellular environment ( Kranner et al. , 2006 , 2010 b ). Antioxidant enzyme activity will be limited or impossible in most parts of the seeds due to the lack of available water. The seeds therefore rely on small antioxidant molecules for their protection from oxidative damage, with the glutathione (GSH) system probably playing a major role. ROS oxidize GSH to its dimer GSSG, which accumulates during seed storage ( Kranner and Grill, 1993 ). In addition, the lipophilic antioxidant tocopherol, which protects membranes from lipid peroxidation, is essential for seed longevity and germination characteristics, as was shown in a mutant approach in Arabidopsis ( Sattler et al. , 2004 ; Mene-Saffrane et al. , 2010 ). A third major antioxidant, ascorbate, is only present in small amounts in dry seeds and therefore probably plays a minor role in regulating the redox situation in the dry state ( Wojtyla et al. , 2006 ; Dowdle et al. , 2007 ).

Oracz et al. (2007 , 2009) proposed a causal role for ROS in sunflower embryo dormancy release, and Müller et al. (2009) showed that the Arabidopsis mutant atrbohB which does not after-ripen has an altered pattern of protein oxidation in the dry seeds. In this context, the concept of an oxidative window has been suggested which assumes that oxidative processes in seeds first lead to after-ripening and loss of dormancy, but later tip the scale towards oxidative damage, deterioration, and loss of viability ( Bailly, 2004 ).

The processes that take place in dry seeds and lead to after-ripening or deterioration are an important aspect of seed biology and the topic of active research. As even small changes in overall seed moisture content influence storability and longevity of seeds ( Buitink et al. , 2000 ; Finch-Savage and Leubner-Metzger, 2006 ), understanding these processes is an issue of economic importance and a major concern of seed banks ( Nagel and Börner, 2010 ). New non-invasive imaging approaches (see last section) will be very helpful in elucidating this stage of the plant life cycle.

Dry seeds contain mRNAs stored during maturation, also called long-lived transcripts to indicate that they survived desiccation ( Rajjou et al. , 2004 ). Over 10 000 different stored mRNAs have been identified in transcriptome analyses of Arabidopsis ( Nakabayashi et al. , 2005 ; Kimura and Nambara, 2010 ; Okamoto et al. , 2010 ). Similar numbers were found in barley and rice ( Howell et al. , 2009 ; Sreenivasulu et al. , 2010 ). In Arabidopsis , abscisic acid (ABA)-responsive elements are over-represented in the promoters of genes whose transcripts are stored ( Nakabayashi et al. , 2005 ), in accordance with the major role of ABA during seed maturation ( Nambara et al. , 2010 ; Radchuk et al. , 2010 ). So far the published transcriptomes are from whole dry seeds, but it is known that the different seed compartments, for example the endosperm and embryo, accumulate different transcripts during seed development ( Le et al. , 2010 ). Kimura and Nambara (2010) showed that major portions of the dry seed transcriptomes of the non-dormant Arabidopsis ecotype Columbia (Col) and the dormant ecotype Cape Verde Island (Cvi) are very similar. The majority of stored mRNAs are of the LEA (‘late embryogenesis abundant’) group or transcripts of storage proteins, supporting the view that the dry seed transcriptome mirrors the process of seed maturation as well as prepares the seed for the following germination. The transcriptomes of the two ecotypes differ in an over-representation of heat shock proteins and ROS-related transcripts in Col and of phosphate and lipid metabolism as well as cytoskeleton-associated transcripts in Cvi. Larger transcriptome differences between these dormant and non-dormant seeds only develop after imbibition. A comparison between the dry seed transcriptomes of near isogenic lines (NILs) representing ‘Delay of Germination’ (DOG) quantitative trait loci (QTLs) of Arabidopsis that differ in after-ripening and/or dormancy suggests that natural variations for these traits are mainly controlled by additive genetic and molecular pathways, rather than epistatic interactions ( Bentsink et al. , 2010 ). It will be interesting to see if, as is the case for the comparison of Col and Cvi ( Kimura and Nambara, 2010 ), the differences between the transcriptomes of the NILs become larger during imbibition and if these distinct DOG pathways remain independent. This review will come back to the dry seed transcriptome and its changes upon imbibition when metabolism is explored in a later section.

Seed germination begins when the dry seeds come into contact with water under favourable conditions. It comprises three phases of water uptake. Dry seeds have very low water potentials ( Woodstock, 1988 ; Obroucheva and Antipova, 1997 ) which cause rapid water influx during phase I (imbibition, Figs 2B , 3 ). As this process is driven by the matrix potential, it also occurs in dead seeds ( Krishnan et al. , 2004 ). During imbibition the seed rapidly swells and changes in size and shape (e.g. Robert et al. , 2008 ; Preston et al. , 2009 ). 1 H-NMR image analyses of imbibition with pea, tobacco, and other species demonstrate that there are major entry points for water uptake such as the micropyle, and that the progress of imbibition differs between seed tissues (e.g. Manz et al. , 2005 ; Wojtyla et al. , 2006 ). In Arabidopsis the shape of the imbibed wild-type seed approximates a prolate spheroid during this period. Robert et al. (2008) showed that ethylene mutants differ in seed shape and imbibition behaviour from wild-type seeds. These differences in changes in size and shape of imbibing seeds could in the future be used for large-scale mutant screens, as computational approaches facilitate the high-throughput analysis of image time series (e.g. Joosen et al. , 2010 ).

Key processes during the germination of typical endospermic eudicot seeds with separate testa and endosperm rupture (two-step germination). Time courses of Arabidopsis thaliana seed water uptake, testa and endosperm rupture, and the effect of abscisic acid (ABA) on these processes; control without added hormone (CON). Important biophysical, biochemical, and cellular events during seed germination are triggered, at least in part, by water uptake and are depicted in the diagram. The diagram is based on quantitative data by Preston et al. (2009) , Vander Willigen et al. (2006) , and Manz et al. (2005) . Events were added based on Bewley, (1997), Nonogaki et al. (2007) , and Obroucheva and Antipova (1997) .

The Arabidopsis testa contains volcano-shaped cell wall structures on the seed surface known as columellae ( Fig. 4A ). Upon first contact with water of Arabidopsis and other mucilaginous seeds, mucilage is released quickly from the columellae—the ‘wonderful quantity of mucus’ that Darwin observed on his cress seeds is a further example. Arabidopsis mucilage is composed mainly of rhamnogalacturonan pectins and cellulose arranged in an outer water-soluble layer and an inner layer covalently bound to the testa by cellulose microfibrils ( Windsor et al. , 2000 ; Macquet et al. , 2007 ). Possible functions of the mucilage are the adherence to surfaces and animals for seed dispersal ( Mummenhoff et al. , 2004 ) and aiding germination in osmotically and saline-stressful environments ( Yang et al. , 2010 ) as the mucilage is very hydrophilic and delays water loss.

Scanning electron microscopy (SEM) and environmental scanning electron microscopy (eSEM) images of Arabidopsis thaliana seed germination. (A) Air-dry seed of Arabidopsis (SEM) showing the hexagonal testa cells on the surface with the mucilage packed into the middle elevation resulting in the columella. (B) Imbibed Arabidopsis seed (eSEM) in testa rupture state; the micropylar endosperm covering the radicle is visible. (C) Arabidopsis seed (eSEM) in endosperm rupture state; the emerged radicle is visible and designates the completion of germination. eSEM works without freezing, coating, fixing, or embedding and in a relative humidity of ≥90 %. It can thus be used to image a living organism with high magnification ( Windsor et al. , 2000 , Muscariello et al. , 2005 ). Images taken by Dr Ralf Thomann, Freiburg Materials Research Center (FMF).

Initial imbibition is often accompanied by a massive leakage of cellular solutes. Similar phenomena can be observed in resurrection plants and pollen that rapidly return from a dry quiescent state to a fully hydrated state ( Hoekstra et al. , 1999 ). While leakage can accelerate germination by lowering inhibitor concentrations within seeds ( Matilla et al. , 2005 ), it is also a sign of damage to membranes and cellular compartments caused by fast and/or inhomogeneous rehydration ( Powell and Matthews, 1978 ). In order to deal with the damage imposed during dehydration, storage, and, most significantly, rehydration, seeds activate a number of repair mechanisms during imbibition ( Fig. 3 ). This includes the repair of membranes as well as of proteins in which aspartyl residues were damaged by conversion to isoaspartyl. The latter can be reversed by isoaspartyl methyltransferase ( Oge et al. , 2008 ).

Damage to genomic DNA includes progressive loss of telomeric sequences during prolonged dry storage ( Boubriak et al. , 2007 ) as well as strand breaks and other types of DNA damage that result from cumulative effects of temperature, moisture, oxygen, and ROS levels (reviewed by Bray and West, 2005 ). The accumulation of chromosomal damage and/or an inability to repair such damage during the imbibition period appear to be significant factors contributing to loss of seed viability during storage. Seed dehydration during maturation and rehydration during imbibition lead to the appearance of a large number of DNA single-strand breaks in maize seeds, most of which can be attributed to imbibitional damage. This includes the appearance and repair of apurinic/apyrimidinic sites in DNA during early germination. DNA damage, which would obviously be a major obstacle during germination, can be repaired by DNA ligases. DNA ligase expression is activated quickly upon imbibition of Arabidopsis seeds, and high levels of de novo DNA synthesis have been observed in the absence of nuclear DNA replication or cell division, indicating a role in DNA repair. In aged seeds, which have suffered more severe damage during storage, enhanced early DNA synthesis has been observed ( Bray and West, 2005 ). Insertional knock-out mutants of two DNA ligases, AtLIG4 and the plant-specific AtLIG6, consequently showed a delay in germination under optimal conditions which was aggravated under cold stress conditions and in the presence of ROS ( Waterworth et al. , 2010 ). Bray and West (2005) state that the seed provides an ideal ‘model’ system for investigating the effects of a variety of endogenous DNA-damaging agents and environmental stresses on genome integrity.

The permeability of the testa, being the part of the seed that comes into contact with the ambient water in most seeds, plays a central role in the rate of water uptake ( Chachalis and Smith, 2000 ; Wojtyla et al. , 2006 ; Koizumi et al. , 2008 ). Brassica seeds with different testa morphology show altered germination characteristics ( Zeng et al. , 2004 ; Matilla et al. , 2005 ). Arabidopsis testa mutant seeds with reduced pigmentation are more permeable to tetrazolium salts than the wild type, and the seeds show a lower dormancy and differ in hormone sensitivities during germination ( Debeaujon and Koornneef, 2000 ; North et al. , 2010 ).

Once the rate of water uptake and changes in seed size and shape start to stagnate, germinating seeds move into water uptake phase II, during which the water content remains stable and which can vary widely in duration. In species with a ‘two-step germination’ process such as Arabidopsis , cress, and tobacco ( Liu et al. , 2005 ; Manz et al. , 2005 ; Müller et al. , 2006 ), phase II encompasses testa rupture ( Figs 1C, 3, 4B ). This is followed by phase III water uptake, endosperm rupture, and radicle protrusion; that is, the completion of germination sensu stricto ( Fig. 4C ). Phase III water uptake continues during the transition to seedling growth ( Figs 1C , 3 ). Endospermless species such as pea and Brassica napus ( Brassica ) enter phase III after testa rupture (‘one-step germination’, Fig. 2 ). In non-dormant seeds, exogenous ABA inhibits the transition from water uptake phase II to III and late embryo cell expansion, but does not affect phase I and II and testa rupture ( Figs 2B , 3 ) (e.g. Schopfer and Plachy, 1984 ; da Silva et al. , 2004 ; Manz et al. , 2005 ; Müller et al. , 2006 ). The presence of endosperm in mature seeds provides an additional target tissue for regulating the completion of germination by ABA and environmental factors. Its visibility as a two-step process appears to be a phylogenetically widespread trait determined by the anatomy of the seed-covering layers ( Petruzzelli et al. , 2003 ; Linkies et al. , 2010 ).

Aquaporins, small membrane proteins that can transport water as well as non-polar small molecules, facilitate cell–cell water transport and may contribute to spatial distribution of water within seed tissues during imbibition as well as to the timing of testa rupture in tobacco (Schuurmans et al. , 2003; Maurel et al. , 2009 ). Vander Willingen et al. (2006) analysed the involvement of aquaporins in the germination of cold-stratified Arabidopsis seeds and found that tonoplast intrinsic proteins (TIPs) show a germination-related shift of protein accumulation from TIP3 to TIP1 at the later stages of germination and found a similar pattern for RNA transcription levels. In contrast to TIPs, they did not find strong indications for the involvement of plasma membrane intrinsic proteins (PIP) as neither protein nor transcript presence and changes were detected. A different conclusion for the PIP-type aquaporins comes from evidence obtained with knock-down tobacco mutants of the plasma membrane aquaporins PIP1 and PIP2, for which testa rupture was affected differentially ( Ernst, 2007 ): testa rupture occurred earlier in pip2 mutant seeds, while it was delayed in pip1 . The time period between testa rupture and the completion of germination was not altered in the mutants compared with the wild type. Taken together, these contrasting results in tobacco and Arabidopsis clearly show that further research in this area is needed.

While Arabidopsis seed coat development including the genes and hormones involved in this process has been studied in detail ( Haughn and Chaudhury, 2005 ), little is known about the changes of the seed coat's mechanical and biochemical properties that ultimately lead to testa rupture. In tobacco, testa rupture starts near the micropylar seed end that covers the radicle and spreads along the ridges on the testa ( Leubner-Metzger, 2003 ). Progression of tobacco testa rupture is facilitated by channel-like structures underlying the ridges, suggesting pre-determined breaking points. Arabidopsis testa rupture ( Fig. 4B ) also starts at the micropylar seed end, but it is unknown if pre-determined breaking points exist. Future experiments concerning a potential enzymatic weakening, spatial water redistribution in connection with cell elongation, and transcriptomic activity before testa rupture are required as they might shed light on how testa rupture is controlled. Tissue-specific transgenic approaches might help to elucidate the role of the embryo, endosperm, and integument layers in testa rupture.

Embryo cells elongate prior to the completion of seed germination of Arabidopsis , Brassica , Medicago , and other species; cell division is not evident in the embryos of these seeds during germination ( Barroco et al. , 2005 ; Gimeno-Gilles et al. , 2009 ; Sliwinska et al. , 2009 ). After the initial swelling is completed, all changes in seed size and shape during germination are caused by cell expansion. Expanding plant cells adjust the extensibility of their cell walls by remodelling the major components of the wall, the cellulose microfibrils and/or the pectin/hemicellulose matrix. Loosening of the wall allows water influx which drives cell expansion and generates cellular turgor pressure ( Schopfer, 2006 ). This led to the model that embryo growth during germination depends primarily on changes in cell wall extensibility. These changes are accompanied by progressing vacuolation during late phase II of water uptake. Embryo and endosperm cells are not fully vacuolated during early phase II, but display many small vacuoles ( Bethke et al. , 2007 ). Sliwinska et al. (2009) proposed that Arabidopsis embryo elongation occurs in a distinct and confined elongation zone between the radicle and the lower hypocotyl.

Hormone contents, signalling, and interactions play important roles in determining the physiological state of the seed and in regulating the germination process ( Kucera et al. , 2005 ). The endogenous ABA contents of non-dormant and dormant seeds rapidly decline upon imbibition during the early phase of germination (within 6–12 h, Fig. 5 ; Chiwocha et al. , 2005 ; Nakabayashi et al. , 2005 ; Hermann et al. , 2007 ; Linkies et al. , 2009 ; Preston et al. , 2009 ). However, in dormant and thermoinhibited seeds (i.e. seeds in which high temperatures inhibit germination) this decrease stops, and de novo ABA synthesis in the imbibed state causes higher ABA contents which are required for dormancy maintenance and for inhibiting germination ( Nambara et al. , 2010 ). A sufficient decrease in endogenous ABA content during imbibition and early phase II is thus a major prerequisite for the completion of germination. Exogenous treatment of after-ripened Arabidopsis or cress seeds with ABA does not affect the kinetics of testa rupture, but inhibits endosperm weakening and rupture ( Fig. 6A and Müller et al. , 2006 ). Nine- cis -epoxycarotenoid dioxygenaes (NCEDs) and ABA 8'-hydroxylases (CYP707As) are the major key regulatory enzymes for ABA biosynthesis and degradation, respectively ( Fig. 5A ). NCEDs and CYP707As are encoded by multigene families, and their tissue- and environment-specific regulation determines the ABA contents ( Seo et al. , 2006 ; Toh et al. , 2008 ). CYP707A2 transcripts are expressed in the radicle upon seed imbibition ( Okamoto et al. , 2006 ). Changes in hormone contents during the early germination phase are also evident in Arabidopsis seeds for jasmonic acid, whose content decreases, and indole acetic acid, whose content increases ( Preston et al. , 2009 ).

Abscisic acid (ABA) contents in germinating Arabidopsis thaliana seeds and the effect of moist cold stratification. (A) Important steps of ABA biosyntheses, degradation, and signalling; see main text for details. (B) Endogenous contents of ABA in germinating seeds and the effect of moist cold stratification. After-ripened seeds were incubated at 4 °C in the dark which inhibits germination (cold stratification). Germination occurs during subsequent incubation at 22 °C in the light and is completed by endosperm rupture. Data on seed ABA contents used to draw the diagram are from Chiwocha et al. (2005) .

Gibberellins (GAs), abscisic acid (ABA), and cold stratification as related to α-expansin (EXPA) transcript expression during early seed germination of Arabidopsis thaliana . (A) ABA inhibits endosperm rupture, but does not alter the kinetics of testa rupture of after-ripened seeds imbibed in the light without cold stratification. (B) α-Expansin transcript accumulation during early seed germination. (C) The effect of cold stratification on α-expansin transcript accumulation. After-ripened seeds were incubated in the dark which inhibits germination either at 22 °C or at 4 °C. Relative transcript levels were compared at 96 h. (D) The effect of cold stratification, GAs, and ABA on the transcript level ratios (fold induction). For cold stratification the ratios 4 °C/22 °C were calculated from the values in C. For hormones the ratios were calculated by comparison of hormone-treated seeds with the untreated seeds at 6 h and 24 h for GAs and ABA, respectively. Wild-type seeds were used, except for the GA response which was studied in GA-deficient ga1-3 seeds. Results are from (A) Müller et al. (2006) , (B–D) Arabidopsis transcriptome analysis available via the seed-specific eFP-browser at www.bar.utoronto.ca ( Winter et al. , 2007 ; Bassel et al. , 2008 ) based on experiments for non-dormant, non-stratified after-ripened wild-type seeds ( Nakabayashi et al. , 2005 ; Preston et al. , 2009 ), cold-stratified wild-type seeds ( Yamauchi et al. , 2004 ), ABA-treated wild-type, and GA-treated ga1-3 seeds (RIKEN transcriptome sets).

The germination-inhibiting effect of ABA is counteracted by gibberellins (GAs) and by ethylene. The effects of these hormones on the late germination process have been extensively reviewed ( Kucera et al. , 2005 ; Holdsworth et al. , 2008 ; Linkies et al. , 2009 ; North et al. , 2010 ) and their interaction with each other and with light has been studied (e.g. Debeaujon and Koornneef, 2000 ; Seo et al. , 2008; Piskurewicz et al. , 2009 ; North et al. , 2010 ). Ethylene has important roles during the late phase of germination and counteracts the ABA inhibition by interfering with ABA signalling, but it does not affect ABA contents ( Linkies et al. , 2009 ). In contrast, GAs are important during the early and the late phase of germination and counteract the ABA inhibition. Bioactive GA 4 was already present in physiologically relevant amounts in the dry, after-ripened seeds that Ogawa et al. (2003) used for their transcriptome analysis ( Fig. 7B ), and a further increase in GA 4 contents occurs during late germination. GA20 and GA3 oxidases (GA20ox and GA3ox) are the major key regulatory enzymes for GA biosynthesis, while GA2 oxidases mediate GA degradation ( Fig. 7A ). Transcripts of GA20ox and GA3ox accumulate during early germination. Ogawa et al. (2003) demonstrated that GA biosynthesis localizes to the radicle, hypocotyl, and micropylar endosperm during germination. Due to the rapid ABA degradation, the GA/ABA ratio increases ∼3-fold during early germination and ∼10-fold during late germination (compare Figs 5B and 7B ). While for the early germination phase Ogawa et al. (2003) did not find altered ABA contents upon treatment of GA-deficient ga1-3 Arabidopsis seeds with exogenous GA, Yano et al. (2009) found that GA 4 contents and GA3ox1 transcript levels were decreased in ABA-overproducing cyp707a2 Arabidopsis seeds. This is most probably due to the increased ABA contents and it therefore seems that ABA can inhibit GA biosynthesis during early germination.

Gibberellin (GA) contents in germinating Arabidopsis thaliana seeds and the effect of moist cold stratification. (A) Important steps of GA biosyntheses, degradation, and signalling. (B) Endogenous contents of bioactive GA 4 and GA 1 in non-stratified germinating seeds incubated in the light at 22 °C. (C) The effect of moist cold stratification on the endogenous contents of bioactive GA 4 and GA 1 . GA values of seeds imbibed in darkness (which inhibits germination) for 96 h are compared for 4 °C and 22 °C. Results are from Ogawa et al. (2003) (B) and Yamauchi et al. (2004) (C).

GA20ox and GA3ox are induced by red light and cold stratification ( Supplementary Fig. S1 at JXB online; Yamauchi et al. , 2004 ; Kucera et al. , 2005 ). Moist cold stratification of Arabidopsis (i.e. incubation of imbibed seeds at 4 °C in darkness usually for 1–4 d) is often used to break dormancy and promote subsequent germination in the light. Yamauchi et al. (2004) have demonstrated that cold stratification is associated with increased contents of bioactive GAs ( Fig. 7C ) and by the accumulation of GA20 and GA3 oxidase transcripts ( Supplementary Fig. S1B ). Furthermore, cold stratification induced a spatial change in GA3ox1 transcript expression; in addition to the radicle it strongly accumulated in the micropylar endosperm ( Yamauchi et al. , 2004 ). The seed-specific eFP-browser and the eNorthern tool at www.bar.utoronto.ca visualize transcript expression patterns based on global transcriptome analyses during Arabidopsis seed germination ( Winter et al. , 2007 ; Bassel et al. , 2008 ). These tools were used for the purpose of this review to analyse early temporal transcript changes and their regulation by GAs and ABA or upon cold stratification. As discussed later, the transcriptional changes of key metabolic enzymes were analysed during the early phase of germination (0–24 h) of non-dormant, non-stratified Arabidopsis seeds ( Nakabayashi et al. , 2005 ; Preston et al. , 2009 ) to test and generate hypotheses for the activation of seed metabolism ( Fig. 8 ). Selected transcript changes of non-stratified and stratified seeds were also compared in order to determine the effects of cold stratification ( Yamauchi et al. , 2004 ), and GA and ABA treatments (RIKEN transcriptome sets at www.bar.utoronto.ca ). This approach is obviously limited as changes in transcript level do not necessarily correspond to similar changes in protein level and/or enzyme activity. The authors are aware of the descriptive nature of this approach and its limitations, but it is believed that this approach is useful to generate hypotheses that can be tested in subsequent experiments on the physiological, protein, and activity level and by mutant approaches.

Reactivation of the primary metabolic pathways and energy production during early seed germination and its possible relationship to early transcriptome changes in Arabidopsis thaliana . Glycolysis, aerobic (TCA cycle) and anaerobic (fermentation) respiration are the commonly used pathways for ATP production. The diagrams provide early transcript expression patterns for key genes representing these metabolic pathways as evident from the transcriptome analysis of non-dormant, non-stratified after-ripened Arabidopsis Columbia seeds ( Nakabayashi et al. , 2005 ; Preston et al. , 2009 ) available via the seed-specific eFP-browser at www.bar.utoronto.ca ( Winter et al. , 2007 ; Bassel et al. , 2008 ). Transcript levels are only presented if an early up-regulation of at least 2-fold was evident (0–24 h). Key enzymes and pathways for which transcript levels are up-regulated are labelled in red, while others are labelled in black. Fermentation-related transcripts [pyruvate dehydrogenase (PDC1) and lactate dehydrogenase (LDH)] are transiently up-regulated during early Arabidopsis seed germination. The transcript expression patterns in Arabidopsis seeds support the early up-regulation of glycolysis [phosphofructokinase (PFK) (irreversible step), pyrophosphate-dependent phosphofructokinase (PFP), and pyruvate kinase (PK)], the TCA cycle [succinate dehydrogenase (SDH), succinyl-CoA ligase (SCoS), and malate dehydrogenase (MDH)], and the OPPP [provides NADPH and precursors, 6-phosphogluconate dehydrogenase (6PGDH)], but not of gluconeogenesis [e.g. phosphoenolpyruvate carboxykinase (PEPCK)], sucrose production [sucrose synthase (SUS1–6)], fatty acid transport [comatose (CTS)], fatty acid β-oxidation [acyl-CoA oxidase (ACX)], the glyoxylate cycle [isocitrate lyase (ICL)], and the γ-aminobutyric acid (GABA) shunt pathway [glutamate decarboxylase (GAD)]. Seed-specific routes that may contribute to ATP production include (1) the ‘seed glycerol shunt pathway’ (TAG lipases, GLI1/NOH1, and G3PDHc) for which it is proposed that AtTGL-type TAG lipases are involved and (2) ‘Perl's pathway’ [PEP carboxylase (PEPC), MDHc, and PK] which includes amino acid aminotransferases (AspAT and AlaAT). The pathways presented describe metabolic routes in the cytosol and mitochondria. Note that transcriptome results of cold-stratified seeds may differ and that early up-regulation of transcript levels provides hypotheses, but is not necessarily associated with the accumulation of protein and activity of the corresponding enzymes.

Here, this approach is applied to investigate the transcript expression patterns of α-expansins and ABA-related genes in Arabidopsis seeds. α-Expansins are known to be induced in the endosperm upon imbibition ( Penfield et al. , 2006 ; Carrera et al. , 2008 ; Linkies et al. , 2009 ). They are a group of proteins proposed to be involved in cell wall remodelling important for cell expansion growth, and they exhibit extensive regulation during early germination ( Fig. 6B–D ). During the early phase of Arabidopsis seed germination, transcripts of EXPA1, 2, 3, 8, 9, 15, and 20 accumulate 100- to 500-fold from 0 h to 12 h in whole unstratified seeds imbibed in the light ( Fig. 6B ). This induction upon imbibition is also evident during moist cold stratification (4 °C in the dark), but not if seeds are imbibed at 22 °C in the dark ( Fig. 6C ). When the 4 °C/22 °C transcript ratios are compared, an ∼30-fold cold induction was evident for EXPA1 and EXPA2 ( Fig. 6D ). Many α-expansin genes are GA inducible, as shown by the +GA/–GA transcript ratios obtained with GA-deficient ga1-3 mutant seeds; EXPA2 accumulates >200-fold upon GA treatment ( Fig. 6D ). The cold induction of α-expansin expression could therefore be mediated by GAs as cold stratification is associated with the induction of GA biosynthesis and increased contents of bioactive GAs ( Fig. 7C , and Supplementary Fig. S1B at JXB online). Cold stratification is also associated with enhanced GA biosynthesis in the micropylar endosperm ( Yamauchi et al. , 2004 ) where α-expansin is localized. Cold stratification also promoted the decline in ABA contents and, associated with this, caused earlier completion of germination ( Fig. 5B ). In contrast to GAs, ABA treatment did not affect the α-expansin transcript expression (+ABA/–ABA ratios, Fig. 6D ). ABA also did not affect the kinetics of testa rupture of after-ripened Arabidopsis seeds ( Fig. 6A ). Taken together, these temporal, hormonal, and cold-inducible transcript expression patterns of EXPA2 and other α-expansins in the micropylar endosperm are in agreement with the hypothesis that they could have roles in endosperm-mediated processes during early germination that lead to and control testa rupture.

Recent advances in Arabidopsis molecular genetics have revealed the core ABA signalling pathways ( Nambara et al. , 2010 ). Group A members of the protein phosphatase 2C (PP2C) family of genes ( Supplementary Fig. S1C at JXB online), including ABA-INSENSITIVE1 ( ABI1 ), seem to act as negative regulators of seed germination ( Kucera et al. , 2005 ; Nishimura et al. , 2007 ). The PYR1/PYL1/RCAR family of START proteins is a family of ABA receptors and may have a prominent function in seed ABA responsiveness through regulation of PP2C activity in an ABA-dependent manner ( Nambara et al. , 2010 ; Nishimura et al. , 2010 ). Targets of the PP2C are members of the SNF1-related protein kinase subfamily 2 (SnRK2) that act as positive regulators of ABA signalling in activating ABRE-binding transcription factors such as ABI5 ( Nakashima et al. , 2009 ). The SnRK2s become active when they are de-repressed from their inhibition by PP2Cs. Changes in the phosphorylation status of >50 proteins have been demonstrated in 12-day-old Arabidopsis plants after the addition of 50 μM ABA to the growth medium ( Kline et al. , 2010 ). This included an increase in phosphorylation of four SnRK2s after 30 min of treatment. Transcription is important for seed ABA responsiveness and is mediated, at least in part, by the transcription factors ABI5, ABI4, and ABI3 ( Holdsworth et al. , 2008 ; Nambara et al. , 2010 ). ABA degradation ( Fig. 5B ) combined with a decrease in ABA sensitivity, for example by targeted proteolysis of ABI3 and ABI5 via the N-end rule pathway ( Holman et al. , 2009 ), promotes seed germination. Cold stratification not only induces a decline in ABA contents, but also affects the transcript expression of several ABA signalling components including PYL6, ABI4, ABI5, and several PP2Cs and SnRK2s ( Supplementary Fig. S1C ). In contrast to the group A PP2Cs mentioned above that act as negative regulators of ABA signalling, PP2C5 was found to be a positive regulator of ABA signalling ( Brock et al. , 2010 ). ATHB20 is a transcription factor involved in ABA sensitivity that is induced in the micropylar endosperm during early germination ( Barrero et al. , 2010 ). The MFT ( MOTHER OF FT AND TFL1 ) gene serves as a mediator in response to ABA and GA signals, and regulates seed germination through a negative feedback loop modulating ABA signalling in Arabidopsis ( Xi et al. , 2010 ). Interestingly, the ABA-inducible expression of MFT is confined to the embryo elongation zone identified by Sliwinska et al. (2009) . Based on these findings and the rapid decline of ABA contents upon imbibition, the seed tissue-specific regulation of ABA signalling is an emerging research field important for early seed germination.

A major decision for seed germination-related experiments is whether or not moist (cold) stratification should be used to release dormancy and achieve fast and uniform germination. The stratification treatment not only releases dormancy, but also promotes germination, and it is often hard to draw a clear line between the two interconnected processes. While a homogenous population is desirable, stratified seeds will already have gone through many processes that are important in early germination, and these will be lost to the subsequent observations. This also implies that early germination is hard to study in deeply dormant seeds such as many conifers which might need multiple months of moist chilling before they are able to germinate ( Zeng et al. , 2003 ). The important point is that early germination differs between non-stratified and stratified seeds, and if moist stratification is used it cannot simply be regarded as a technical treatment. It is suggested that if stratification is used, sampling during the stratification period should be included as part of the experimental investigations. In the following sections, the Arabidopsis transcriptome will be used to describe the activation of metabolism during early germination of non-dormant, non-stratified seeds. In addition, how ABA, GAs, and cold stratification affect the transcriptome responses regarding metabolism during early seed germination will be addressed.

During water uptake phases I and II, large metabolic changes take place in seeds which set the course for subsequent radicle protrusion. Metabolism is reactivated with enzymes that were stored in the seed during maturation. This has been shown in proteomic approaches in Arabidopsis , where a large number of enzymes involved in the major metabolic pathways were found in dry seeds and remained stable or even accumulated further during early germination ( www.seed-proteome.com ; Gallardo et al. , 2001 ; Rajjou et al. , 2004 ; Fu et al. , 2005 ). Proteomic evidence for this includes enzymes from energy production pathways in dry Arabidopsis seeds ( Supplementary Table S1 at JXB online): glycolysis [6-phosphofructokinase (PFK), phosphoglycerate kinase (PGK)], gluconeogenesis [PEP carboxykinase (PEPCK)], fermentation [alcohol dehydrogenase (ADH)], pyruvate dehydrogenase (PDH), the tricarboxylic acid (TCA) cycle [succinate dehydrogenase, succinyl-CoA ligase, malate dehydrogenase (MDH)], the glyoxylate cycle (isocitrate lyase), and the amino acid aminotransferases. However, the number of proteins detected by proteome analyses of seeds of Arabidopsis (see above), cress ( Müller et al. , 2010 ), sugar beet ( Catusse et al. , 2008 ), Medicago ( Boudet et al. , 2006 ), barley ( Sreenivasulu et al. , 2010 ), and rice ( Yang et al. , 2007 ) is limited (<1000) and therefore does not allow a complete comparison with the genome-wide transcriptome analyses. In Supplementary Table S1 the demonstrated presence of these proteins by proteome analysis of dry Arabidopsis seeds has been compared with the dry seed transcriptome. In almost all the cases where proteome analyses demonstrated a protein to be present in dry seeds, the corresponding transcript is abundant in the dry seed transcriptome. For example, the proteins of one cytosolic PGK and one cytosolic MDH were detected by proteome analysis. In each of these cases there are three genes, only one of which shows high transcript abundance in the dry seed. The proteins detected correspond to these highly abundant transcripts. It seems clear, therefore, that many of the abundant dry seed transcripts simply reflect translation during seed maturation. This conclusion is further supported by the fact that in many cases the most abundant transcripts in dry seeds are rapidly degraded upon imbibition, whereas several transcripts with lower abundance in the dry state accumulate during the early germination phase (for examples see the next section).

Upon imbibition, dramatic changes in the transcriptome can be observed after as little as 1–3 h, that is in phase I of water uptake ( Howell et al. , 2009 ; Preston et al. , 2009 ; Okamoto et al. , 2010 ). Some of these changes have been shown to be tissue specific ( Okamoto et al. , 2010 ). Transcripts displaying strong changes in abundance and following similar expression patterns during early germination have been found to share common cis -acting elements in their gene promoters. 3' Untranslated regions (UTRs) with motifs associated with RNA stability are enriched in transcripts with a strong regulation between 0 h and 3 h in rice ( Nakabayashi et al. , 2005 ; Howell et al. , 2009 ; Preston et al. , 2009 ), possibly indicating that many changes are due to degradation of stored mRNAs. It would therefore be particularly interesting to perpetuate earlier work ( Marcus and Feeley, 1964 ; Dure and Waters, 1965 ; Comai and Harada, 1990 ) and investigate polysome-associated transcripts in addition to the general transcriptome in order to get a clearer picture of the actual translational activity of the many RNAs found in seeds during early germination.

A radiolabelling approach in Arabidopsis showed that de novo protein synthesis already took place in the first 8 h after imbibition and peaked between 8 h and 16 h ( Rajjou et al. , 2006 ), which corresponds to early phase II. All components of the transcriptional machinery are stored in dry seeds and are quickly activated upon imbibition, as has been demonstrated by the fact that the addition of the translation inhibitor cycloheximide does not alter early transcript up-regulation in Arabidopsis ( Kimura and Nambara, 2010 ). Interestingly, cycloheximide disrupted early down-regulation of transcripts. Translation thus seems to be necessary to activate mRNA degradation mechanisms. In addition, ribosomal proteins are among the first to be transcribed in Arabidopsis ( Tatematsu et al. , 2008 ) and maize ( Beltran-Pena et al. , 1995 ). Translation is required for successful germination, while inhibition of transcription delays, but does not block, the completion of germination of Arabidopsis ( Rajjou et al. , 2004 ). Tobacco, on the other hand, can proceed to testa rupture but not to endosperm rupture in the presence of a transcriptional inhibitor ( Arcila and Mohapatra, 1992 ; Leubner-Metzger, 2003 ). In agreement with a high ABA content during imbibition and early phase II of Arabidopsis seeds (i.e. before the ABA levels decline, Fig. 5B ), the first 8 h comprise a short window of time in which ABA-induced genes that belong to the set of seed maturation genes that are typically expressed in an ABA-inducible manner during seed maturation are transcribed and translated ( Lopez-Molina et al. , 2002 ; Rajjou et al. , 2006 ). From the work of Rajjou et al. (2004 , 2008) it is clear that translation from stored mRNAs can differ depending on the physiological seed state, but which of the stored transcripts are absolutely required for the completion of germination is still an unsolved question.

Minutes after the start of imbibition, a sharp increase in oxygen uptake and carbon dioxide release can be observed ( Botha et al. , 1992 ; Bewley, 1997 ). Gases may be released from colloidal adsorption or simply pushed out from gas-filled spaces by the water rushing in ( Cloetens et al. , 2006 ). Oxygen uptake then stagnates or increases only slowly; this lasts until the end of phase II ( Botha et al. , 1992 ; Bewley, 1997 ).

Dry seeds contain only low amounts of ATP, but a rapid production is initiated upon cellular hydration in association with the gas exchange ( Botha et al. , 1992 ; Spoelstra et al. , 2002 ; Benamar et al. , 2008 ). It is clear that respiratory pathways operate in imbibed seeds ( Fig. 8 ), but their relative contribution and the substrates for ATP production during germination are still a matter of debate. Most of our knowledge about metabolic pathways at the level of enzyme activities and their products during the early phase of seed germination comes from studies of species with large seeds such as pea ( Obroucheva and Antipova, 1997 ; Macherel et al. , 2007 ; Benamar et al. , 2008 ; Smiri et al. , 2009 ). Most of the pathways presented in Fig. 8 operate in the cytosol. Mitochondria in dry seeds need repair and differentiation before contributing significantly to ATP production by oxidative phosphorylation. However, substrate oxidation of succinate by mitochondria extracted from dry pea seeds is possible ( Morohashi and Bewley, 1980 ; Botha et al. , 1992 ). Mitochondrial enzymes and membranes in starchy seeds such as pea seem to be protected by LEA proteins, and repair of pre-existing mitochondria takes place upon imbibition ( Grelet et al. , 2005 ; Tolleter et al. , 2010 ). In contrast, biogenesis of new mitochondria is more important in oil-seeds ( Morohashi et al. , 1981 ; Morohashi, 1986 ). It is known that many key enzyme activities of the TCA cycle accumulate during early germination.

Oxygen-sensitive microsensors have been used to investigate the spatial and temporal oxygen status of germinating seeds of legumes, cereals, sunflower, and oilseed rape. The results point toward a limitation of oxygen uptake by seed-covering layers leading to hypoxia in seeds. In pea seeds, the internal oxygen content dropped during imbibition to anoxic levels while the respiration rate increased continuously ( Obroucheva and Antipova, 1997 ; Benamar et al. , 2008 ; Rolletschek et al. , 2009 ). The ratio between fermentation and aerobic respiration varies between species and during the progression of germination, as does the sensitivity of seeds to oxygen availability ( Benamar et al. , 2008 ; Rolletschek et al. , 2009 ). The adenylate energy charge of pea seeds can be increased and the accumulation of fermentation products decreased by providing the imbibing seeds with additional oxygen, which again points to a limiting role for oxygen rather than enzyme availability. Additional oxygen does not, however, increase the final germination percentage of pea, pointing to the fact that the seeds are adapted to germinate under hypoxic conditions. The outcome of Darwin's experiment described in the Introduction, which imposed salt stress and hypoxia during germination, testifies to the amazing ability of seeds to germinate under conditions of oxygen deprivation.

At the level of enzyme activities far less is known about the early germination of the small seeds of Arabidopsis , but the dynamics of the proteome (e.g. Gallardo et al. , 2001 ; Fu et al. , 2005 ) and the transcriptome (e.g. Nakabayashi et al. , 2005 ; Preston et al. , 2009 ) have been investigated. Temporal transcript expression patterns of key metabolic enzymes during the early phase of germination (0–24 h, water phase I and II) of non-dormant, non-stratified Arabidopsis seeds ( Nakabayashi et al. , 2005 ; Preston et al. , 2009 ) were therefore used to test and generate hypotheses for the activation of seed metabolism ( Fig. 4 ) and to determine the effects of moist cold stratification (incubation of Col seeds in darkness for 96 h at 4 °C compared with 22 °C; Yamauchi et al. , 2004 ), ABA (L er seeds at 24 h without and with ABA added), and GA (GA-deficient ga1-3 seeds at 6 h without and with GA added) treatments (RIKEN transcriptome sets at www.bar.utoronto.ca ). It should be kept in mind that while the approach with ga1-3 has been very informative, the results may have been influenced by the fact that a mutant was used and might not be completely applicable to the wild type. The dry seed transcriptome for energy metabolism was also compared with its counterpart during early germination (imbibed for 6 h) to highlight the similarities and differences in transcript abundances ( Fig. 8 , and Supplementary Table S1 at JXB online). It was presumed that an at least 2-fold increase in transcript abundance is an indication for the activation of the encoded enzyme. As mentioned above, this approach is obviously limited as an effect on the transcript level does not necessarily correspond to a similar change on the protein and activity level. Where possible, information available from proteome and mutant work with Arabidopsis is therefore included.

Based on an at least 2-fold increase in transcript levels, the Arabidopsis seed transcriptome during early germination supports the view that glycolysis, fermentation, the TCA cycle and the oxidative pentose phosphate pathway (OPPP) are activated during early germination ( Fig. 8 , Supplementary Fig. S2 at JXB online). This is in agreement with evidence from enzyme activity measurements (e.g. Bettey and Finch-Savage, 1996 ; Wakao et al. , 2008 ; Smiri et al. , 2009 ) and proteome analyses (e.g. Gallardo et al. , 2001 ; Fu et al. , 2005 ; Müller et al. , 2010 ). These activations are, however, not simply evident for each gene of a particular pathway, but seem to be a complex combination of stored proteins, stored transcripts, and de novo transcription and translation. Ethanolic fermentation by pyruvate decarboxylase (PDC) and ADH is a good example of this. In the dry seed transcriptome, PDC2 and ADH are the most abundant transcripts of the fermentation pathway, while the abundance of PDC1 and others is low. However, upon imbibition, the PDC2 and ADH transcript levels rapidly decline (3- to 5-fold within 6 h; Fig. 8 , and Supplementary Table S1 and Supplementary Data ). Proteome analysis, however, demonstrated that ADH protein levels remain constant; that is, ADH activity depends on stored ADH protein that accumulated during seed maturation and/or on newly synthesized ADH that replaced stored ADH subjected to protein degradation ( Gallardo et al. , 2001 ). The high abundance of ADH transcripts in dry seeds combined with its rapid degradation would then simply be a remnant from seed maturation. On the other hand, de novo translation and ADH protein accumulation have also been described ( Fu et al. , 2005 ; Rajjou et al. , 2006 ). ADH transcript expression is induced by cold stratification, but not regulated by GA ( ga1-3 seeds ±GA) or ABA (wild-type seeds ±ABA) (Supplementary Fig. S2). While PDC2 is not inducible by these factors, PDC1 transcripts accumulate upon imbibition, cold stratification, and GA treatment ( Supplementary Fig. S2 ). This example demonstrates that activation of energy metabolism during early germination is complex and cannot simply be predicted from the most abundant transcripts in the dry seed transcriptome.

Supplementary Fig. S2 also shows that imbibition itself and cold stratification are the most important factors for up-regulating transcripts of the sugar-related metabolic pathways mentioned above; neither up-regulation by GA nor down-regulation by ABA appears to be of major importance. However, other mechanisms for hormonal regulation exist; for example, ABA induces AtPirin1 ( Lapik and Kaufman, 2003 ), which is known to regulate pyruvate catabolism by inhibiting the PDH complex ( Soo et al. , 2007 ). Cold stratification does not cause an increase in the levels of the TCA cycle metabolites citrate, malate, and succinate, but subsequent incubation in the light causes increases in their levels ( Angelovici et al. , 2011 ).

In contrast to the pathways for glycolysis, fermentation, the TCA cycle, and the OPPP mentioned above, transcript expression for key genes of gluconeogenesis, sucrose synthesis, and the peroxisomal pathways (fatty acid β-oxidation and the glyoxylate cycle) is not up-regulated during early germination ( Fig. 8 , and Supplementary Table S1 , Supplementary Data at JXB online). Transcripts of the fatty acid β-oxidation enzyme 3-ketoacyl-CoA thiolase (KAT2/PED1) and the glyoxylate cycle enzyme isocitrate lyase (ICL) are among the top 100 most highly expressed transcripts in dry Arabidopsis seeds ( Kimura and Nambara, 2010 ). However, the ICL transcript levels decline 25-fold (6 h/dry) upon imbibition and remain low until the late germination phase for which ICL protein accumulation has been shown ( Supplementary Table S1 , Supplementary Data ; Gallardo et al. , 2001 ). Gluconeogenesis, sucrose synthesis, and the peroxisomal pathways are most important for post-germinative seedling establishment (e.g. Penfield et al. , 2004 ; Holdsworth et al. , 2008 ; Holman et al. , 2009 ). Seedling arrest, but not germination phenotypes were evident for single-gene knock-out mutants of these pathways ( Penfield et al. , 2005 ). In contrast, recent evidence from double mutants for the peroxisomal pathways (the glyoxylate cycle and fatty acid β-oxidation, e.g. Pinfield-Wells et al. , 2005 ; Pracharoenwattana et al. , 2005 , 2010 ) and careful consideration of the physiological seed state combined with distinct germination conditions (distinct media, sucrose addition, cold stratification; Footitt et al. , 2006 ) demonstrates that the peroxisomal β-oxidation determines germination potential. This includes the ABC transporter COMATOSE (CTS, also known as PED3) required for the import of substrates for peroxisomal β-oxidation ( Russell et al. , 2000 ), for which a complex interaction with ABA has been proposed ( Footitt et al. , 2006 ; Kanai et al. , 2010 ). The recent results of Kanai et al. (2010) suggest that CTS/PED3 promotes seed germination by suppressing the ABA-mediated inhibition of pectin degradation in the seed-covering layers.

Cleavage of triacylglycerol (TAG, seed oil) by TAG lipases at the water–oil interface of oil bodies provides glycerol and fatty acids. The Arabidopsis early germination transcriptome suggests that a glycerol shunt pathway is activated that feeds into glycolysis via dihydroxyacetone phosphate (DHAP) and involves glycerol-3-phosphate dehydrogenase (G3PDc), glycerol kinase (Gycerol-insensitive1/Nonhost1, GLI1/NOH1), and TAG lipases ( Fig. 8 ). GLI1/NOH1 transcript levels increase >20-fold, the corresponding mutants have a germination phenotype ( Eastmond, 2004 ), and in seedlings a metabolic connection to DHAP involving GLI1/NOH1 and G3PDc is known ( Chanda et al. , 2008 ). Transcripts of several AtTLG -type TAG lipase genes accumulate dramatically (e.g. AtTGL1 transcripts >300-fold) ( Fig. 8 , and Supplementary Table S1 , Supplementary Data at JXB online). TAG lipase enzyme activity of AtTGL1 has been demonstrated, and the corresponding mutants show delayed germination which could be overcome by sucrose treatment ( Körner, 2005 ). It is therefore hypothesized that glycerol released by AtTLG-type TAG lipases together with a seed glycerol shunt pathway ( Fig. 8 ) could provide energy during early oil-seed germination. In addition, several of the AtTLG -type TAG lipase transcripts accumulate upon cold stratification, AtTGL1 is GA induced, but none of the AtTLG -type TAG lipase transcripts is affected by ABA ( Supplementary Fig. S2 ). Other types of TAG lipase transcripts also accumulate upon imbibition, but none of them as strongly as AtTGL1 and AtTGL8 .

Interestingly, fatty acid metabolism is repressed by ABA in the embryo, but not in the endosperm ( Manz et al. , 2005 ; Penfield et al. , 2006 ). It has been proposed that ABA inhibits Arabidopsis seed germination by limiting the availability of energy and nutrients by preventing seed storage protein degradation ( Garciarrubio et al. , 1997 ), but not by inhibiting storage lipid mobilization ( Penfield et al. , 2005 ).

Stored proteins in seeds are not only an important source of amino acids during early germination, but are also important for energy production ( Angelovici et al. , 2011 ). Their activation is already prepared in the dry seeds: stored proteinases mobilize storage proteins in legume radicles (reviewed by Müntz et al. , 2001 ; Müntz, 2007 ). Early degradation of protein bodies also occurs in the micropylar endosperm of Arabidopsis ( Bethke et al. , 2007 ). Aspartate and glutamate are among the most abundant amino acids in seed storage proteins. They are substrates for aspartate and alanine aminotransferases (AspAT and AlaAT) that are activated during imbibition and thought to participate in respiratory pathways ( Fig. 8 ) ( Obroucheva and Antipova, 1997 ; Miyashita et al. , 2007 ; Rocha et al. , 2010 ). AspAT could also contribute with oxaloacetate production to a unique system to explain ATP synthesis in seeds, termed ‘Perl's pathway’ ( Perl, 1986 ; Botha et al. , 1992 ). It depends on the fact that cytosolic malate dehydrogenase (MDHc) and PEPCK activities are already high in some seeds during the early phase of germination. In this ATP-synthesizing system MDHc provides NADH which is split by NADH-pyrophosphorylase yielding ADP. The latter is converted to ATP by pyruvate kinase (PK; Fig. 8 ). That PEPCK activity increases in germinating seeds is known from Arabidopsis ( Penfield et al. 2004 ) and several other species ( Botha et al. , 1992 ; Ratajczak et al. , 1998 ). MDHc and PEPCK protein also accumulate in Arabidopsis seeds ( Supplementary Table S1 , and references therein). In support of ‘Perl's pathway’, transcripts of MDHc, PK, and AspAT accumulate during early germination in Arabidopsis seeds ( Fig. 8 ). Further research is needed to elucidate the possible role of this pathway in ATP production during seed germination. Cold stratification induces aspartate accumulation, but accumulation of TCA cycle metabolites derived from it is only evident upon subsequent incubation at 21 °C in the light ( Angelovici et al. , 2011 ). ABA induces the expression of glutamate decarboxylase (GAD; Supplementary Table S1 and Supplementary Data ) which produces γ-aminobutyric acid (GABA) associated with stress responses, and the GABA shunt for energy production is also evident in seeds ( Shelp et al. , 1995 ; Bouche et al. , 2003 ).

Seeds store not only protein, oil, and starch, but also essential metals such as iron (Fe). In an innovative approach, Lanquar et al. (2005) identified the importance of vacuolar metal storage and activation during early germination in Arabidopsis seeds. Metal, in particular Fe, is mobilized during early germination by the redundant broad-range metal transporters NRAMP3 and 4. Germination of the double mutant is inhibited under conditions of Fe deficiency, as the seeds fail to retrieve Fe from the vacuole even though they contain as much Fe as the wild type. Fe, zinc, provitamin A (‘Golden Rice’), and folate are the most important micronutrients for which malnutrition can be improved by biofortification ( Mayer et al. , 2008 ). Research and breeding programmes are underway to enrich these compounds in crop seeds and depend on understanding seed metabolic engineering.

In the coming years, novel methods will lead to significant advances in our understanding of seed biology and plant evolution. New technologies situated at the interface of biology and disciplines such as material sciences, physical chemistry, and engineering offer the possibility to tackle new questions with interdisciplinary approaches.

Huge advances have been made in the area of imaging. Environmental scanning electron microscopy (eSEM) offers the opportunity to take high magnification images of living seeds ( Fig. 4 ) ( Muscariello et al. , 2005 ; Windsor et al. , 2000 ). These tools (eSEM and similar high resolution imaging techniques) can be used to tackle questions surrounding the structure of seeds and changes in these during germination in different seed types. eSEM is an excellent method to show the diversity in seed structures in different species and through the observation link back to questions concerning the evolution of morphologically different seed types.

New tools based on 1 H-NMR imaging technology can be used not only to visualize and quantify water uptake ( Manz et al. , 2005 ; Wojtyla et al. , 2006 ; Koizumi et al. , 2008 ), but also for non-invasive imaging of seed oils ( Neuberger et al. , 2009 ). This enables scientists to assess spatial water distribution and oil content. Microsensors can be used to provide spatial and temporal oxygen maps of seeds ( Rolletschek et al. , 2009 ) which can be an important hint to the answer to the question of where and when fermentation processes occur in seeds. Kranner et al. (2010 a ) applied non-invasive infrared thermography to seeds and demonstrated that viability and even biochemical processes such as the dissolution of low molecular weight compounds could be assessed. This promising method is able to link biophysical with biochemical parameters and seed viability and could, for example, be used to distinguish different seed types by their thermographic behaviour during germination. MALDI-MS (matrix-assisted laser desorption/ionization mass spectrometry) imaging involves the visualization of the spatial distribution of proteins, peptides, metabolites, biomarkers, or other chemicals within thin tissue sections and might be a powerful tool for exploring the spatial distribution of nutrients in seeds. It has been used to visualize GABA in eggplant fruit sections where it localizes to the seeds ( Goto-Inoue et al. , 2010 ). 1 H-NMR imaging, microsensors, MALDI-MS imaging, and thermography all can be used to quantify global changes in seeds in a spatial and temporal manner.

Bringing together confocal microscopy and computer-based image analyses, Sliwinska et al. (2009) created informative 3D images of an elongating embryo, which led to the localization of an elongation zone of the embryo. This could be further used for a cross-species approach to investigate conservation and biodiversity of embryo elongation zones in combination with established methods that can identify genes and proteins involved in cell expansion growth during germination. Quantitative phase tomography was used to elucidate structural details of Arabidopsis seeds ( Cloetens et al. , 2006 ). This technique uses a synchrotron-based approach to generate 3D, high-resolution images of a specimen to the cell level.

There have been great advances in the area of sequencing and epigenetics, which will greatly enhance our knowledge of germination in a wide array of species. Combined epigenetic (ChIP-seq) and transcriptome (RNA-seq) analyses with next-generation sequencing technologies will make it possible to analyse plants without a sequenced genome on a genomic scale ( Bräutigam and Gowik, 2010 ). A combination of high-throughput sequencing with more classical methods can greatly advance our knowledge about developmental processes. Recently such a combined approach was used to study the developmental dynamics in maize leafs and identified 180 transcription factors for which now functional genomics studies would be interesting ( Li et al. , 2010 ). To describe dynamic networks from such results ‘Systems Biology Graphical Notation’ can be used, and corresponds to an engineer's view on regulation as it was published for seed development ( Junker et al. , 2010 ).

In addition, new databases and platforms designed specifically to collect and analyse information from high-throughput approaches to seed germination are now in place or being developed. The seed-specific gene ontology system TAGGIT facilitates the identification and visualization of the germination signature ( Holdsworth et al. , 2008 ), and the seed-specific eFP-browser and the eNorthern tool at the Toronto bar website ( www.bar.utoronto.ca ) visualizes Arabidopsis transcript expression patterns in seeds ( Winter et al. , 2007 ; Bassel et al. , 2008 ). The co-expression tool (CORNET), similar to the Genemania browser from the Toronto bar website, provides an easy to use and helpful tool in finding interactions either in already published experiments or in user-supplied data, and thus helps to handle the generated amount of data better ( De Bodt et al. , 2010 ). These tools can help to generate hypotheses by supplying in silico data from already published experiments and help curate the massive amount of data generated by high-throughput analyses.

Seeds are starting to be used in cross-species systems biology approaches and interdisciplinary collaborations such as the European ‘virtual SEED’ network project ( www.vseed.eu ) where a molecular approach is combined with biophysical and morphological data, enabling the assembly of a more comprehensive model of seed germination. Understanding this process as a whole from the very beginning—from early seed germination to the establishment of the seedling—can help engineer and select for better and more robust crop species, thus increasing crop yield and quality.

Plant species developed a huge morphological and physiological diversity in seed types and states to match local environmental demands for germination timing. Darwin was aware of what he called the ‘vitality of seeds’ and made a connection to plant evolution: ‘The power in seeds of retaining their vitality when buried in damp soil may well be an element in preserving the species, and therefore seeds may be specially endowed with this capacity’ ( Darwin, 1855 a ) . Cross-species approaches in seed science and other areas of plant science will further increase our understanding of the evolution of plants. ‘Appropriate germination responses to environmental factors are the first requirement for successful growth and adaptation in any life-history trait; no subsequent life-history trait can even be expressed if the plant does not first survive past the germination stage. As such, germination timing can be a stringent selective sieve, determining which genotypes can establish in particular conditions.’ ( Donohue, 2005 ). Seed germination and dormancy are indeed the most important early life-history traits.

We thank Dr Ralf Thomann (FMF, Freiburger Materialforschungszentrum, University of Freiburg, Germany, www.polymermicroscopy.com ) for expert advice and the seed eSEM images, and our colleague Kai Gräber for his critical reading and helpful comments. Our work is funded by the ERA-NET Plant Genomics grant vSEED (grant no. DFG LE720/8) to GLM and by a postdoctoral fellowship of the Deutsche Forschungsgemeinschaft to KM (grant no. MU3114/1-1); these are gratefully acknowledged.

Google Scholar

Google Preview

Author notes

Supplementary data.

Email alerts

Citing articles via.

• Recommend to your Library

Affiliations

• Online ISSN 1460-2431
• Print ISSN 0022-0957
• Copyright © 2024 Society for Experimental Biology
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

• Petroleum Engineering

Imbibition in plant seeds

• Auburn University
• This person is not on ResearchGate, or hasn't claimed this research yet.

• Technical University of Denmark

Abstract and Figures

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations

• Norikazu Nakayama
• Setsuko Komatsu
• POWDER TECHNOL
• G Leubner-Metzger
• B G Swanson
• H P Rasmussen
• S Abu-Shakra
• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

Physical Review E

Covering statistical, nonlinear, biological, and soft matter physics.

• Collections
• Editorial Team

Imbibition in plant seeds

Jean-françois louf, yi zheng, aradhana kumar, tomas bohr, carsten gundlach, jesper harholt, henning friis poulsen, and kaare h. jensen, phys. rev. e 98 , 042403 – published 4 october 2018.

• Citing Articles (14)
• INTRODUCTION
• EXPERIMENTS
• THEORY OF IMBIBITION IN PLANT SEEDS
• DISCUSSION AND CONCLUSION
• ACKNOWLEDGMENTS

We describe imbibition in real and artificial plant seeds, using a combination of experiments and theory. In both systems, our experiments demonstrate that liquid permeates the substrate at a rate which decreases gradually over time. Tomographic imaging of soy seeds is used to confirmed this by observation of the permeating liquid using an iodine stain. To rationalize the experimental data, we propose a model based on capillary action which predicts the temporal evolution of the radius of the wet front and the seed mass. The depth of the wetting front initially evolves as t 1 / 2 in accord with the Lucas-Washburn law. At later times, when the sphere is almost completely filled, the front radius scales as ( 1 − t / t max ) 1 / 2 where t max is the time required to complete imbibition. The data obtained on both natural and artificial seeds collapse onto a single curve that agrees well with our model, suggesting that capillary phenomena contribute to moisture uptake in soy seeds.

• Received 13 June 2018

DOI: https://doi.org/10.1103/PhysRevE.98.042403

©2018 American Physical Society

Physics Subject Headings (PhySH)

• Research Areas

Authors & Affiliations

• 1 Department of Physics, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
• 2 Carlsberg Research Laboratory, J.C. Jacobsens Gade 4, DK-1799, Copenhagen V, Denmark
• * [email protected]

Article Text (Subscription Required)

References (subscription required).

Vol. 98, Iss. 4 — October 2018

Access Options

• Buy Article »
• Log in with individual APS Journal Account »
• Log in with a username/password provided by your institution »
• Get access through a U.S. public or high school library »

Authorization Required

Other options.

• Buy Article »
• Find an Institution with the Article »

Download & Share

Imbibition in plants seeds. (a) Photograph of soy and biomimetic seeds. (b) Schematic of the experimental setup and (c) zoom-in on the imbibition into the seed of radius a . The blue arrows indicate the liquid flow speed v into the porous seed, thus gradually reducing the dry front position r f . (d) X-ray tomography images of the imbibition of water into a soy seed. The change in grayscale intensity indicates the binding of an iodine stain to the starch in the seed. The approximate rotational symmetry of the scans indicate that the imbibition process in soy seeds is homogenous. See additional details of the experimental methods in the text.

Imbibition kinetics in real and artificial seeds. (a) Plot of the mass m of soy seeds as a function of time t . Prior to the experiments, the seeds were stored at temperatures 20 , 50 , and 80 ∘ C for 24 h (see legend). (b) Plot of the mass m of a biomimetic PDMS seed as a function of time t . Both seeds exhibit similar behavior: the mass increases gradually until it reaches a plateau corresponding to a fully wetted sphere.

Theoretical values of the normalized radius R [Eq. ( 13 ), dashed line] and mass M [Eq. ( 18 ), solid line] plotted as as a function of the normalized time T = t / t max .

Quantitative comparison between theory and experiment. The normalized mass M = [ m ( t ) − m 0 ] / ( m max − m 0 ) plotted as a function of normalized time T = T / T max for real and artificial seeds (points connected by lines). The thick solid line shows the theoretical prediction [Eq. ( 18 )]. We observe reasonable agreement between theory and experiment, except for early times T ∼ 10 − 3 where the imbibition process is dominated by the hydration of the seed coat. The data in Fig.  2 were fitted to Eq. ( 18 ) using least squares to determine the best estimates of the parameters t max , m max , and m 0 .

Sign up to receive regular email alerts from Physical Review E

• Forgot your username/password?
• Create an account

Article Lookup

Paste a citation or doi, enter a citation.

• BiologyDiscussion.com
• Follow Us On:
• Google Plus
• Publish Now

Imbibition in Plants: Meaning and Factors

ADVERTISEMENTS:

In this article we will discuss about the meaning and factors of imbibition.

Meaning of Imbibition:

Place a few grams of air-dried seeds in a flask with water to which a capillary tube is attached.

Initially, the water level increases but as the imbibition continues; this rise is followed by a fall. Water molecules adsorbed by the colloidal particles surface are held relatively tight and consequently are packed together. As a result the volume of the system decreases.

The uptake or absorption of liquids by the solid particles of a substance without forming a solution is called imbibition.

Here, the net movement of water is along a diffusion gradient. Imbibition is the capacity of a gel or any other colloidal material to take up relatively large quantities of water and swell e.g. absorption of water by cell wall, swelling of seed coats, starch, glue, cellulose, agar, gelatin, swelling of doors, and wood work during the rainy season.

As a result of imbibition the volume of imbibant increases even though the total volume of the system (volume of water, in which imbibant is submerged + volume of the imbibant) is always less after imbibition than initially.

There is always a rise in temperature as a result of imbibition since some of the kinetic energy of the molecules is lost.

During the swelling of an imbibant, pressure of enormous magnitude develops. In practice, this is used in the breaking up of rocks by pouring water on the wooden wedges driven into them and also in pulling apart of the bones of a skull.

A substance imbibes only those liquids for which its molecules have affinity e.g., dry plant materials imbibe water but not ether. Similarly, rubber imbibes ether but not water.

Imbibition pressure (IP) is analogous to osmotic potential and represents the potential maximum pressure that a substance develops when immersed in pure water.

The actual pressure which develops following water imbibition by a substance is considered as pressure potential. Thus Ψ = IP r + Ψ – TP.

However, no pressure potential develops in an unconfined imbibant and, accordingly, the above expression is stated as follows:

Ψ = Ψ r = IP

The imbibition pressure (IP) of air-dried seeds of Xanthium is nearly 1000 atm. If these seeds are immersed in pure water, the Ψ of water in the dry seeds would be nearly 1000 atm. at equilibrium.

After imbibition stops, the Ψ of the external and the internal water is nil. Temperature and osmotic potential of the substance affect the rate and extent of imbibition.

An increase in temperature increases the rate of imbibition but not the amount of water imbibed.

Both the amount of water imbibed and the rate of imbibition are affected by the osmotic potential of the liquid to be imbibed.

Factors Affecting Imbibition:

Imbibition is influenced by several factors and some of these are discussed below:

i. Temperature:

With an increase in temperature the rate of imbibition increases. Perhaps the increased temperature increases the kinetic energy of the system. The situation is reverse at low temperature.

ii. Texture of imbibant:

The cohesion of molecules of the imbibant largely affects the amount of water imbibed. Thus a closely packed imbibant imbibes less amount of water compared to loosely packed one.

iii. Pressure:

It may be remembered that imbibition pressure comprises hundreds of atmospheres. Thus colloidal particles can imbibe water against lot of pressure.

Imbibition is also dependent upon the acidity and alkalinity or pH of the medium. Thus cellulose which is negatively charged colloid imbibes maximum in alkaline medium while it will absorb least in the acidic medium.

In case of positively charged colloids, a reverse situation exists. However, proteins being amphoteric are exceptions.

v. Electrolytes:

Inorganic solutes also influence imbibition by neutralising the charge of imbibing colloids and production of osmotic pressure.

Different ions have variable power to reduce imbibition. Accordingly some of the electrolytes are most effective in influencing imbibition than the others.

It is a common experience that during the rainy season, wooden frames of the doors and windows imbibe water and expand. Similarly when the seeds are sown watering is immediately seen to affect imbibition. This affects the seed germination and emergence of radicle.

The seed coat is made up of cellulose which imbibes enormous amount of water and facilitates the breakage of seed coat and the embryo is unaffected because it is made up of proteins, lipids and starch.

In the young cells, the wall is made up of hydrophillic colloids and thus water absorption is brought about through imbibition. These colloids also provide protection to the seeds against desiccation in a dry environment and freezing temperature.

Some of the lower plants like mosses and Selaginella remain green even after low level of precipitation. These plants possess hydrophilic colloids which help absorbance of water from slight precipitation.

Related Articles:

• Imbibition | Plant Physiology
• Imbibition: Conditions, Characteristics, Factors, Role and Significance | Botany

Biology , Plant Physiology , Movement of Substances , Consuming Liquid , Imbibition

• Anybody can ask a question
• Anybody can answer
• The best answers are voted up and rise to the top

Forum Categories

• Animal Kingdom
• Biodiversity
• Biological Classification
• Biology An Introduction 11
• Biology An Introduction
• Biology in Human Welfare 175
• Biomolecules
• Biotechnology 43
• Body Fluids and Circulation
• Breathing and Exchange of Gases
• Cell- Structure and Function
• Chemical Coordination
• Digestion and Absorption
• Diversity in the Living World 125
• Environmental Issues
• Excretory System
• Flowering Plants
• Food Production
• Genetics and Evolution 110
• Human Health and Diseases
• Human Physiology 242
• Human Reproduction
• Immune System
• Living World
• Locomotion and Movement
• Microbes in Human Welfare
• Mineral Nutrition
• Molecualr Basis of Inheritance
• Neural Coordination
• Organisms and Population
• Photosynthesis
• Plant Growth and Development
• Plant Kingdom
• Plant Physiology 261
• Principles and Processes
• Principles of Inheritance and Variation
• Reproduction 245
• Reproduction in Animals
• Reproduction in Flowering Plants
• Reproduction in Organisms
• Reproductive Health
• Respiration
• Structural Organisation in Animals
• Transport in Plants
• Trending 14

Privacy Overview

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 03 August 2024

Comprehensive mapping and modelling of the rice regulome landscape unveils the regulatory architecture underlying complex traits

• Tao Zhu   ORCID: orcid.org/0000-0001-9667-498X 1 , 2   na1 ,
• Chunjiao Xia 3   na1 ,
• Ranran Yu 1 ,
• Xinkai Zhou 1 ,
• Xingbing Xu 3 ,
• Lin Wang 1 ,
• Zhanxiang Zong   ORCID: orcid.org/0000-0001-5287-420X 3 ,
• Junjiao Yang 3 ,
• Yinmeng Liu 3 ,
• Luchang Ming   ORCID: orcid.org/0000-0002-4361-717X 3 ,
• Yuxin You 1 ,
• Dijun Chen   ORCID: orcid.org/0000-0002-7456-2511 1 , 2 &
• Weibo Xie   ORCID: orcid.org/0000-0002-2768-3572 3 , 4 , 5  

Nature Communications volume  15 , Article number:  6562 ( 2024 ) Cite this article

14 Accesses

1 Altmetric

Metrics details

• Bioinformatics
• Data mining
• Functional genomics
• Plant genetics

Unraveling the regulatory mechanisms that govern complex traits is pivotal for advancing crop improvement. Here we present a comprehensive regulome atlas for rice ( Oryza sativa ), charting the chromatin accessibility across 23 distinct tissues from three representative varieties. Our study uncovers 117,176 unique open chromatin regions (OCRs), accounting for ~15% of the rice genome, a notably higher proportion compared to previous reports in plants. Integrating RNA-seq data from matched tissues, we confidently predict 59,075 OCR-to-gene links, with enhancers constituting 69.54% of these associations, including many known enhancer-to-gene links. Leveraging this resource, we re-evaluate genome-wide association study results and discover a previously unknown function of OsbZIP06 in seed germination, which we subsequently confirm through experimental validation. We optimize deep learning models to decode regulatory grammar, achieving robust modeling of tissue-specific chromatin accessibility. This approach allows to predict cross-variety regulatory dynamics from genomic sequences, shedding light on the genetic underpinnings of cis-regulatory divergence and morphological disparities between varieties. Overall, our study establishes a foundational resource for rice functional genomics and precision molecular breeding, providing valuable insights into regulatory mechanisms governing complex traits.

Similar content being viewed by others

Integrative analysis of reference epigenomes in 20 rice varieties

Transcriptome-wide association analyses reveal the impact of regulatory variants on rice panicle architecture and causal gene regulatory networks

ChIP-Hub provides an integrative platform for exploring plant regulome

Introduction.

Rice ( Oryza sativa ) is not only one of the most important crops in the world but also an outstanding model species for studying plant growth and development. Over the past two decades, tremendous efforts have been made to understand the genetic basis of important agronomic traits in rice 1 . Genome-wide association studies (GWAS) have played a pivotal role in this pursuit, helping to link genetic variations to phenotypic diversity. These studies have identified a large number of candidate genes that hold promise for trait improvement 2 , 3 , 4 , 5 . However, despite these advances, our understanding of the regulatory mechanisms governing complex traits in rice remains incomplete.

Gene regulatory networks (GRNs) are largely dictated by cis -regulatory DNA sequences, such as promoters and enhancers, which are bound by specific transcription factors (TFs) 6 . Deciphering the regulatory code within these regulatory sequences and linking the regulatory sequences to target genes are crucial for rewiring GRNs for crop improvement and trait optimization 6 . Nonetheless, efforts to profile the regulome, encompassing the entirety of regulatory elements in the genome, remain constrained in rice. These efforts often concentrate on specific tissues, neglecting the comprehensive landscape across developmental stages and tissues 7 , 8 . Similarly, endeavors to establish links between regulatory regions and their target genes in rice are also limited 8 .

Meanwhile, many functional genetic variants associated with agronomic traits in rice reside within noncoding regulatory regions (e.g., qSH1 9 , DROT1 10 , and FZP 11 ), which makes their interpretation challenging and underscores the necessity for a systematic dissection of regulatory sequences. Given that diverse traits manifest across distinct developmental stages and tissues, systematic annotation of noncoding regulatory variants in rice is currently hindered by the lack of a comprehensive epigenome map across various tissues and growth stages.

To bridge these gaps, we systematically mapped chromatin accessibility profiles in various tissues across the life cycle of three representative rice cultivars using the UMI-ATAC-seq method 12 , a modified ATAC-seq (assay for transposase accessible chromatin-sequencing) protocol developed in our lab. Through analysis of 145 ATAC-seq datasets, we obtained a total of 117,176 unique open chromatin regions (OCRs), accounting for ~ 15% of the rice genome. By integration of RNA-seq data from matched tissues, we predicted potential target genes for OCRs based on the correlation of gene expression and adjacent chromatin accessibility across tissues. Through TF footprinting analysis, we inferred tissue- or stage-specific regulatory networks and identified cultivar-polymorphic/trait-associated OCRs by comparing the regulatory landscapes between indica and japonica rice subspecies. Notably, our analysis unveiled a preference for GWAS-associated variants within tissue-specific OCRs, enabling the identification of causal associations between 209 complex agronomic traits and noncoding regulatory variants using this OCR landscape. Utilizing optimized deep learning models, we decoded the regulatory grammar through modeling of tissue-specific chromatin accessibility and across-variety predictions from sequences. The modeling approach sheds light on the key genetic alterations contributing to cis -regulatory divergence. Overall, these data not only serve as a cornerstone resource for the plant research community but also provide valuable regulatory variants for precision molecular breeding.

Charting a reference atlas of chromatin accessibility in rice

To generate a comprehensive landscape of accessible chromatin in rice ( Oryza sativa ), we took advantage of an improved ATAC-seq protocol (UMI-ATAC-seq 12 , which incorporates unique molecular identifiers to the regular ATAC-seq technique for accurate quantification and footprinting) to perform chromatin accessibility profiling in 23 tissues/organs spanning the entire life cycle of rice. The representative tissues include callus, radicle, plumule, leaf, leaf sheath, root, apical meristem (AM1/AM2), dormant buds (DBuds), shoot apical meristem (SAM1/SAM2/SAM3), panicle neck node (PNN), stem, young panicle (Panicle1/Panicle2/Panicle3/Panicle4), lemma, palea, pistil, stamen and seed coat (Seed1/Seed2/Seed3). The experiments were conducted in three representative rice varieties, namely Nipponbare (NIP; japonica subspecies), Minghui 63 (MH63; indica subspecies type II), and Zhenshan 97 (ZS97; indica subspecies type I), with each experiment consisting of at least two biological replicates (Fig.  1a and Supplementary Data  1 ). In total, 145 genome-wide chromatin accessibility datasets with high sequencing depth (~30.7 M reads on average) were generated. We applied the ENCODE standards 13 , 14 to establish the analysis pipeline (see Methods ). Compared to published ATAC-seq datasets in the plants as deposited in the ChIP-Hub database 14 , our data exhibited a significantly higher signal-to-noise ratio (Supplementary Fig.  1 ). Through data analysis using the corresponding reference genomes of the three cultivars 15 , 16 , we identified on average of 40,676 (ranging from 28,991 to 49,737) reproducible OCRs (with an Irreproducible Discovery Rate [IDR] 17  < 0.05) per experiment (Fig.  1b ). As expected, the identified OCRs from all experiments predominantly located either in the proximal upstream regions of the transcription start site (TSS) or the distal intergenic regions (Fig.  1b, f , Supplementary Fig.  2 and Supplementary Data  2 ), resembling promoters or enhancers, respectively 18 . Of note, OCRs from intragenic regions accounted for a relatively small proportion (about 15.7%), while most of these OCRs originated from intronic regions (Supplementary Fig.  2b ). These observations indicate that the vast majority of OCRs originate from noncoding regions of the rice genome.

a ATAC-seq and RNA-seq experiments were conducted in three varieties (Nipponbare, Minghui 63, and Zhenshan 97) of rice in various tissues across the entire life. See Supplementary Data  1 for detailed descriptions of sample collection. Consistent tissue color code is used throughout the figure. b Bar plot showing the number of reproducible OCRs identified from each tissue in the three rice varieties. The OCRs are classified into three categories based on the distance of the OCR summit to its closest transcription start site (TSS): distal (>1 kb), proximal (<=1 kb), and intragenic. No data from the tissues of SAM3 (NIP), Seed1 (ZS97), and Stem (ZS97). c The proportion of the rice genome annotated as open chromatin regions (OCRs) in our study. d The accumulative number of unique OCRs in each tissue, calculated by excluding OCRs that overlap with the OCR superset. e Density plot showing the enrichment of TF binding sites (TFBSs) around the OCRs in Nipponbare (NIP). TFBSs were predicted either by ChIP-seq datasets for 56 distinct TFs (left) or DNA motifs for 458 TFs (right), which were obtained from the ChIP-Hub database 14 . The flanking area on both sides is 1 kb. f The distribution of the distance of OCR summit to its closest TSS in the three rice varieties. Published open chromatin data 14 in rice (NIP) were included for comparison. Based on the distribution, a cutoff of 1 kb (dashed line) was used to distinguish the proximal and distal regulatory OCRs. g The distribution of the conservation PhastCons score 19 around the NIP OCRs. h The t-SNE plot showing an unsupervised clustering analysis of chromatin accessibility across different samples. Each dot represents one replicate. Color code as in a . i Boxplot showing the distribution of tissue specificity score of intragenic ( n  = 14239), proximal (n = 29524) and distal ( n  = 57153) OCRs (left) or the median score in each tissue. P1  = 4.01e-39, p2 = 2.13e-96, p3 = 1.23e-95. All p-values were calculated by two-sided Mann–Whitney U test between proximal and distal OCRs in terms of specificity. Tissue color code as in a . Boxplot shows the median (horizontal line), second to third quartiles (box), and Tukey-style whiskers (beyond the box). Source data are provided as a Source Data file.

We estimated that approximate 15% of the rice genome could be annotated as OCRs, with a consistent pattern observed across each variety (Fig.  1c ), and the estimation appeared to have reached saturation in rice (Fig.  1d ). OCRs contain multiple TF binding sites and are responsible for regulating the expression of target genes 6 , 18 . We collected publicly available ChIP-seq data for 56 distinct TFs (Supplementary Data  3 ) and predicted DNA motifs for 458 TFs in rice from the ChIP-Hub database 14 , and showed that OCRs were significantly enriched for TF binding sites (Fig.  1e ). Furthermore, we found that OCRs are highly evolutionarily constrained compared to flanking genomic regions (Fig.  1g ), supporting previous findings that conserved noncoding sequences (CNSs) are predictive of OCRs in plants 19 , 20 .

We next assessed the overall similarities and differences of chromatin accessibility across varieties and tissues. We quantified all datasets based on the merged OCRs ( n  = 117,176) called from the same reference genome (i.e., Nipponbare) and visualized their global patterns using t-distributed stochastic neighbor embedding (t-SNE) 21 . While dimension 1 and 2 of t-SNE results generally reflected differences between the indica (MH63 and ZS97) and japonica (NIP) subspecies, dimensions 2 and 3 primarily delineated distinct clusters among tissue types (Fig.  1h ). For instance, the chromatin accessibility patterns of vegetative and productive tissues of NIP were separated into distinct clusters, whereas young panicles and callus tissues exhibited similar patterns regardless of their variety origin. We further calculated the tissue specificity of each OCR based on the Jensen-Shannon divergence (JSD) index. Obviously, distal OCRs showed significantly higher specificity scores than proximal OCRs (Fig.  1i and Supplementary Fig.  3a, b ), consistent with previous findings 14 , 18 , 22 .

In short, the comprehensive accessible chromatin landscape in rice represents a value resource for crop functional genomic studies.

Linking open chromatin regions to target genes

To decipher which genes these OCRs may regulate, we generated matched RNA-seq datasets for the investigated tissues in each rice variety (Supplementary Fig.  3c and Supplementary Data  4 ). We adopted a strategy 23 to predict OCR-to-gene links based on correlation analysis between the OCR accessibility and gene expression across all samples (Fig.  2a ; see Methods ). Genes can be regulated by multiple OCRs (including promoters and enhancers) through chromatin interactions, which are supposed to occur within topologically associated domains (TADs). Since the size of TADs in the rice genome was estimated to be ranging from 35 kilobase pair (kb) to 45 kb based on Hi-C data 24 , 25 , we restricted our analysis to 40 kb (i.e., from 20 kb upstream to 20 kb downstream of the TSS) to predict target genes of OCRs. Using a cutoff of absolute Pearson correlation coefficient | R |>= 0.4 and P  < 0.05, we obtained a total of 59,075 unique links between OCRs ( n  = 38,437, 32.8% of all OCRs) and genes ( n  = 18,781, 48.1% of annotated genes; Supplementary Fig.  4a, b and Supplementary Data  5 ). As expected, the OCR-to-gene links tended to occur more frequently in the proximal OCRs, and consequently the correlation between the gene expression and chromatin accessibility is higher for proximal links (Supplementary Fig.  4c–f ).

a Schematic diagram illustrating the correlation-based approach to link ATAC-seq OCRs to target genes based on correlation analysis between chromatin accessibility and gene expression. b Heatmap showing the tissue-specific OCR-to-gene links ( R  > = 0.4, P  < 0.05, two-tailed Z -test). Each row in the left panel is a unique OCR. Each row in the middle panel is a gene, corresponding to target genes for OCRs in the left panel. Representative genes are shown on the right. c Examples of tissue-specific OCRs (in the dashed box) regulating dynamic expression of the corresponding target genes. The orange lines indicate the OCR-to-gene links, and the deeper the line the higher the correlation between the chromatin accessibility and gene expression. d Enrichment of biological processes gene ontology (GO) terms for target genes in each OCR cluster in b . The asterisk (*) denotes P  < 0.05 ( P -values were calculated by Hypergeometric test after Bonferroni correction). e Bar plot showing the percentage of OCRs from different categories based on the genomic location. Source data are provided as a Source Data file.

Genetic variants within OCRs can contribute to changes in gene expression levels through expression quantitative trait loci (eQTL). We colocalized the identified OCR-to-gene links from our study with published eQTL data in rice 26 , and we found a significant overlap (Chi-squared test, P  < 1.55e − 06) between OCR-to-gene links and eQTL-gene pairs (Supplementary Fig.  4g ). As expected, the correlation coefficients of colocalized OCR-to-gene links with eQTLs are significantly higher than those without colocalization (Wilcoxon test, P  = 4.11e−38; Supplementary Fig.  4h ). We identified numerous known regulatory variants that influence the expression of genes associated with agronomic traits. To name a few, a variant within a distal regulatory region ( ~ 12 kb upstream) of qSH1 modulates its expression dynamics, leading to change the seed shattering in rice 9 . Accordingly, there is a positive correlation ( R  = 0.47, P  < 0.013) between the accessibility of this enhancer and the expression of qSH1 in various tissues, particularly in SAM where gene expression increases (Supplementary Fig.  4i, l ). Similarly, OsLG1 is tightly linked to upstream regulatory regions that colocalize with a strong QTL associated with the panicle shape trait 27 (Supplementary Fig.  4j, l ). IPA1 showed significantly positive correlation ( R  = 0.84, P  < 2.95e−8) between its enhancer activity and gene expression, with increased expression in yield-related tissues (Supplementary Fig.  4k, l ), confirming an important role of IPA1 to shape rice ideal plant architecture (IPA) and thus to enhance grain yield 28 .

Taken together, the predicted OCR-to-gene links provide regulatory insights into agronomic trait development in rice and highlight targetable OCRs of important genes for genome editing.

Dissecting tissue-specific and stage-specific regulatory grammar

The comprehensive chromatin accessibility landscape of representative tissues gave us an opportunity to uncover tissue-specific regulatory grammar. We quantified the tissue-specificity of OCRs by utilizing the JSD score, which enables the discrimination of target genes from housekeeping (e.g., GAPDH 29 and OsGOGAT1 30 ) to tissue-specific (e.g., OsYABBY5 31 and OsWRKY47 32 ) according to the above predicted OCR-to-gene links (Supplementary Fig.  5 and Supplementary Data  6 ). We have specifically focused on analyzing highly tissue-specific OCRs ( n  = 6686 with a cutoff of JSD > 0.08, ~ 7% of all OCRs) as they may encode the tissue-specific regulatory grammar. These OCRs were further annotated as promoters ( n  = 2322) or enhancers ( n  = 4364) according to the genomic distance to the TSS. By performing joint clustering analysis of chromatin accessibility and target gene expression using OCR-to-gene links, we identified 20 distinct clusters of OCRs (Fig.  2b and Supplementary Data  7 ). Each cluster had 200~500 OCR-to-gene links that were highly activated in specific tissues, and showed a high degree of consistency with the known biological characteristics of the corresponding tissues (Fig.  2b–d ). For instance, the palea- and lemma-specific links in cluster 5 (C5) contained promoter-enhancer interactions at the locus of GW8 , which is a known gene controlling grain weight in rice 33 (Fig.  2c ). Accordingly, GW8 was highly expressed in pistil, lemma, and palea. Gene ontology (GO) enrichment analysis using genes from C5 revealed that biological processes such as ‘pollen−pistil interaction’ and ‘pollination’ were overrepresented (Fig.  2d ). Similarly, we identified a number of OCRs in C19 that were highly and specifically accessible in meristem-like tissues (including young panicle and shoot apical meristem), and the associated target genes showed significant enrichment for functions related to ‘reproductive system development’, ‘flower development’, and ‘shoot system development’ (Fig.  2b, d ). Notably, RFL , a crucial regulator for plant architecture and flowering time 34 , 35 , was among these target genes (Fig.  2c ). Interestingly, we observed that a higher proportion (28.9%) of tissue-specific OCRs originated from distal intergenic regions compared to constitutive OCRs (12.3%). In contrast, approximately 85% of constitutive OCRs were derived from the proximal-promoter regions. (Fig.  2e ).

To delineate the TFs that may bind to these tissue-specific OCRs, we used GimmeMotifs 36 , a versatile tool can detect tissue-specific TF binding motifs by comparing TF binding activity across multiple experiments. We restricted our analysis to the top 2,500 OCRs in each tissue, as determined by their specificity measurement (SPM) score 37 . The predicted regulatory motifs showed significant enrichments in a tissue-specific manner in matching tissue types (Supplementary Fig  6 and Supplementary Data  8 ). We narrowed our focus to the top enriched regulators in each tissue type, and found many of the inferred links correspond to known regulatory relationships (Fig.  3a ). For example, OsIDS1 , a gene that plays a vital role in shaping inflorescence structure and establishing floral meristems 38 , 39 , exhibited relatively high activity in the panicle. OsbZIP72 , enriched in plumule tissue, has been found to regulate plumule length by modulating abscisic acid (ABA) signaling and promote seed germination 40 , 41 . Notably, the tissues of seed and pistil demonstrated a co-enrichment pattern of crucial regulators involved in flower and seed development, including MFO1 and MADS63 42 , 43 , 44 from the MADS gene family (Fig.  3a ). For each tissue type, we performed a systematic analysis to calculate the relative preference of regulators within TF families. Our analysis revealed distinct tissue-specific TF binding patterns, indicating clear preferences for specific regulators in different tissues (Fig.  3b ). For instance, the TCP TF family showed a preference for enrichment in stem, stamen, and panicle neck node (PNN) tissues. This observation aligns with the known biological function of TCP genes, specifically their role in regulating cell proliferation in developing tissues 45 .

a Enrichment of TF motifs in tissue-specific OCRs. Only top 5 enriched TFs in each tissue are shown. See Supplementary Data  8 for the full list. The thickness of edges is proportion to the corresponding enrichment score. b The relative preference of regulators within TF families in each tissue type. Only the top 100 TF motifs in each tissue were used for analysis. c The scatter plot showing the distribution of the Pearson correlation coefficient between TF footprint score and its expression. Only absolute values of correlation coefficients greater than 0.5 are marked. d The scatter plot showing the distribution of TF footprint score and its gene expression in NIP, MH63, and ZS97(left). The error bands indicate 95% confidence intervals. Distribution of Tn5 cuts around the footprint of DL and OsSPL9 at different stages of young panicle (right). Source data are provided as a Source Data file.

Analyzing temporal ATAC-seq data through footprinting could assist in identifying key regulators, such as pioneer factors, that control developmental progression and transition 46 . We generated temporal open chromatin data from the young panicle, which is a crucial organ determining the yield of rice 47 , 48 , across four successive developmental stages (<1 mm, 1–2 mm, 3–5 mm, and 5–10 mm; Fig.  1a ). We endeavored to identify regulatory motifs that exhibited either positive or negative correlation with the young panicle developmental stage in terms of enrichment, using dynamically changing OCRs ( n  = 9244; Fig.  3c , Supplementary Fig.  7a and Supplementary Data  9 ). The regulators that were most enriched displayed predominantly positive correlations, indicating their function as transcriptional activators. Conversely, a subset of factors exhibited negative correlations, suggesting a repressive role. In this regard, DL (encoding OsYABBY 49 ), OsSPL9 50 , and OsSPL14 51 were identified as representative positive regulators, during the development of young panicles in rice (Fig.  3d and Supplementary Fig.  7b ). However, further experimental data is necessary to validate the potential involvement of these TFs in young panicle development.

Overall, the above results provide a valuable resource that can help guide studies of candidate key regulators for tissue-specific gene regulation.

Systemic localization of GWAS variants in tissue-specific regulatory DNA

Genome-wide association studies (GWAS) have identified numerous natural variations linked to various agronomic traits in rice 3 . To systematically colocalize GWAS-associated variants with the above annotated regulatory elements, especially those from noncoding regulatory regions, we compiled a comprehensive rice GWAS catalog from recent genome-wide association meta-analysis studies 2 , 52 , 53 , 54 as well as the NGDC GWAS Atlas database 55 . In total, we collected 4831 significant ( P  < 1e−5) and representative (only considering lead SNP) associations for 209 distinct quantitative traits which can be classified into seven major categories 56 : morphological characteristics, physiological features, yield components, grain quality, resistance, coloration, and others (Fig.  4a and Supplementary Data  10 ). In a nutshell, these GWAS SNPs dominantly located in intergenic noncoding regions (Fig.  4b and Supplementary Fig.  8a ) and 24.5% of them were either situated within a noncoding OCR (21.1%) or located in linkage disequilibrium (LD) with SNPs in a neighboring OCR (3.4%) (Fig.  4c ). Moreover, OCRs revealed significantly higher enrichment of GWAS SNPs than protein-coding sequences (Fig.  4d ), highlighting the crucial function of regulatory variants in determining phenotypic characteristics.

a Categorical proportions of lead SNP in each GWAS. The inner circle indicates the proportions of the seven major categories, and the outer circle indicates the subcategories contained in each major category. Only high proportions are marked in the outer circle. b Distribution of curated lead SNPs by genomic context. All lead SNPs are the same as in a . c Overlap proportions of lead SNPs and sets of SNPs with strong linkage disequilibrium (LD > 0.8) with lead SNPs with ATAC-seq OCRs, ChIP-seq peaks and footprints identified by NIP ATAC-seq, respectively. d The barplot showing the SNP density of OCR and CDS regions at different GWAS P -value thresholds. The error bars are the standard deviations of the SNP densities in the six GWAS catalogs from a . Data represents the mean ± SD of 6 independent GWAS catalogs. The P values were calculated by two-tailed Student’s t -test. e Boxplots showing the tissue-specificity score distribution of OCRs that overlap with grain width 53 and leaf blade width 52 GWAS SNPs. For grain width, the sample sizes for the “with” and “without” groups are 896 and 4480, respectively. For leaf blade width, the sample sizes for the “with” and “without” groups are 2864 and 5728, respectively. Boxplot shows the median (horizontal line), second to third quartiles (box), and Tukey-style whiskers (beyond the box). The P -values were calculated by two-tailed Student’s t -test. f The enrichment of GWAS SNPs 2 in OCRs with different GWAS P -value threshold. g Manhattan plot showing the GWAS signal distribution of vg0724670482 and the LD distribution of its surrounding SNPs. The track plot demonstrates that the OCR where this SNP is located has a higher accessibility in palea tissue. “O2G” represents OCR-to-gene links. h Same meaning as g , except that vg0431203743 has a higher accessibility in SAM and young panicle. Source data are provided as a Source Data file.

Furthermore, our findings demonstrated that OCRs containing GWAS SNPs exhibited greater tissue specificity (Fig.  4e, f and Supplementary Fig.  8b-d ). For instance, one of the OCRs containing a GWAS lead variant vg0724671055 53 (C/T, GWAS P  < 9.27e−8) significantly associated to panicle number. This OCR was found to be highly accessible specifically to young panicle tissues and its accessibility showed a positive OCR-to-gene link with the expression of GW7 ( R  = 0.59, P  < 9.14e−5; Fig.  4g ). In another example, the GWAS lead variant vg0431427332 is significantly associated to leaf blade width 52 ( P  < 1.58e−8), which was located in a SAM/Panicle-specific OCR to positively regulate the expression of NAL1 ( R  = 0.72, P  < 1.16e−6) (Fig.  4h ). The previous studies have shown that NAL1 is not only associated with leaf width but also with yield 52 and has natural variations in expression levels 26 . More examples of validated OCR-related associations are presented in Supplementary Fig.  8e .

Tissue-specific regulatory variants explain agronomic trait associations

The variation in DNA sequences within OCRs plays a significant role in driving phenotypic innovation through altering chromatin state and gene expression patterns, which usually occurs in a tissue-specific manner. To investigate the relationship between genetic variations associated with agronomic traits and tissue-specific OCRs, we calculated the enrichment of genetic variations within OCRs in a tissue-specific manner. It turned out that significant GWAS SNPs were frequently enriched in OCRs of trait-relevant tissues (Fig.  4f and Supplementary Fig.  8d ). For example, GWAS variants associated with spikelet traits were highly enriched in OCRs specific to the tissues of SAM1, pistil and panicle. Motivated by this observation, we performed an enrichment analysis of GWAS-identified SNPs in OCRs from various tissues, using a SNP enrichment method termed CHEERS 57 (Supplementary Fig.  9 ). Of the 209 curated GWAS-related traits, ~78% (163 of 209) phenotypic traits showed GWAS SNP enrichment in at least one tissue (Supplementary Fig.  10 and Supplementary Data  11 ). The observed enrichment of agronomic trait-associated variants in regulatory elements was highly specific to tissue types, and the association is largely compatible with our current understanding of the tissue function (Fig.  5a ). For example, in various GWAS studies, regulatory variants associated with plant height was enriched in stem-related tissues; while genetic associations for grain-related traits (such as grain thickness, grain width, grain length, blighted grains per plant, and filled grains per plant) were highly enriched in OCRs specific to the tissues of seed, lemma, pistil, and stamen (Fig.  5a ). Meanwhile, we found that variants associated with root length were predominantly enriched in the root tissue. Specifically, a significant SNP (vg0806201957 58 , P  < 3.98e-8) located in a root-specific enhancer of OsHAK12 , which has been shown to be involved in K + uptake in roots 59 (Supplementary Fig.  11a ).

a GWAS SNPs enrichments for ATAC-seq OCRs of different tissues. The heatmap showing the significant tissue-specific enrichment results. The values are transformed by -log10( P ) and then normalized by row. Those marked with an asterisk represent P  < 0.05 for this result. The P values were calculated by Kolmogorov-Smirnov test. Only tissue data for the NIP variety were used for this analysis. The full list for GWAS enrichment result could be accessed by Supplementary Data  11 . b One representative examples of genomic tracks at loci OsbZIP06 showing that GWAS lead SNP is located in tissue-specific OCRs. The GWAS study name and SNP location (denoted by red dashed line) are shown at the top of panel c . Haplotype distribution of vg0131729028 in the population. This result was obtained from the RiceVarMap 2.0 database 7 . d Identification of mutation information of two OsbZIP06 mutants based on Sanger sequencing. e The images show seed germination rates of wild type and mutants of OsbZIP06 . f The line graph showing the germination rates of different mutants osbzip06 at different days of imbibition. “OE” represents overexpression. g Boxplot showing the enrichment results of proximal and distal OCRs with 209 GWAS results respectively. Only results where GWAS was significantly enriched with at least one of proximal and distal OCRs are shown. The sample size of each group is 764. The P value was calculated by Student’s t -test. Boxplot shows the median (horizontal line), second to third quartiles (box), and Tukey-style whiskers (beyond the box). h Venn plot showing the number of results significantly enriched ( P  < 0.05, Kolmogorov-Smirnov test) by proximal and distal OCRs. i Enrichment of GWAS SNPs in TSS proximal and distal OCRs. The names of the GWAS are marked at the top of the panel. The grey dashed line indicates the P -value threshold of 0.05. The P values were calculated by Kolmogorov-Smirnov test. Source data are provided as a Source Data file.

In the case of seed germination percentage, GWAS SNPs were most significantly enriched in plumule-specific OCRs (Fig.  5a ). We noted a lead SNP (vg0131729028 60 , A/G, P  < 8.4e−8) localized within an intronic OCR of OsbZIP06 , where the intronic OCR and OsbZIP06 formed a positive OCR-to-gene link ( R  = 0.82, P  < 2.55e-7) with high tissue specificity in plumule and radicle (Fig.  5b ). The minor allele (G) of vg0131729028 was present in a very small proportion (0.3%) in the XI population, but in 65.80% of the Aus population (Fig.  5c ). We mutated the coding region (mainly 1st exon) of OsbZIP06 with CRISPR/Cas9 and found that the germination rate was higher in two frameshift mutations (osbzip06-1 and osbzip06-2) than in the wild type (Fig.  5d-f and Supplementary Data  12 ). In contrast, overexpression of the OsbZIP06 resulted in a lower germination rate (Fig. 5e, f ). Therefore, the integration of publish GWAS data and our chromatin landscape can greatly facilitate the identification of candidate genes and the functional annotation of noncoding variants.

Furthermore, when we divided the OCRs into proximal (<3 kb from the TSS, 60,006 OCRs) and distal OCRs (>3 kb from TSS, 35,691 OCRs) before using CHEERS to do enrichment analysis. We observed that the proximal OCRs are more enriched in GWAS SNPs (Fig.  5g–i and Supplementary Fig.  11b ). This implies that the enrichment above is mainly driven by the OCR close to the TSS and this result is consistent with previous studies 57 , 61 .

Deep learning models accurately predict differences in chromatin accessibility between tissues and unveil common regulatory grammar among varieties

We further investigated whether the tissue- and stage-specific regulatory grammar can be modelled. Deep learning has been successfully utilized to learn and identify essential features in genomic sequences, such as the identification of cis -elements 62 , 63 . Our previous study demonstrated that the Basenji deep learning framework 64 is powerful for modelling epigenomic data in rice, such as the ability to accurately predict chromatin accessibility and to assess the impacts of variants 7 . Therefore, we optimized the Basenji framework to effectively model our ATAC-seq datasets from multiple tissues (Supplementary Fig.  12a,b ). Three distinct models were trained for the varieties of NIP, MH63, and ZS97, demonstrating high accuracy with the mean AUROC values of 0.931, 0.921, and 0.928, respectively (Fig.  6a and Supplementary Fig.  12c ). We observed that the Pearson’s correlation coefficient between the predicted and observed values of chromatin accessibility at different locations on the genome reached approximately 0.81, with the best prediction at the location of <1 kb upstream regions (Fig. 6b and Supplementary Fig.  12d ). This implies that the regulatory syntax patterns within promoter regions could carry more significant information encoded in sequences, which can be effectively captured by deep learning models. Furthermore, the predicted signals from the test sets exhibit the ability to discern between distinct tissues and closely align with the clustering results of the actual values (Fig.  6c ). For example, the root-specific expressed gene RCc3 , responsible for regulating lateral root growth 65 , exhibits distinct chromatin accessibility patterns specifically in root (Fig.  6d and Supplementary Fig.  13 ).

a Receiver operating characteristic curves for different tissues in the NIP cultivar. The average AUORC value was 0.931. b Distribution of Pearson correlation coefficients between predicted and true signal values for different genomic regions using NIP model. Each point represents one tissue ( n  = 24). Data are displayed as mean ± SD. c Comparison of clustering results based on predicted and true signal values using NIP model. d The genomic tracks show the signal values predicted by NIP model versus the true signal values for Panicle1, PNN and Root, respectively. The shaded area is labelled with the gene region of RCc3 . The heatmap below the tracks show the expression of the RCc3 in NIP varieties. e The boxplot showing the distribution of Pearson’s correlation coefficients for the models of NIP, MH63 and ZS97 tested separately using sequences from the other two varieties. The red dashed line represents a correlation coefficient at 0.80. Each sample consists of 24 observations. Boxplot shows the median (horizontal line), second to third quartiles (box), and Tukey-style whiskers (beyond the box). f The genomic tracks showing the signal values predicted with the ZS97 model for NIP, MH63 and ZS97 sequences versus the true signal values in Stamen and Stem tissues, respectively. The shaded area represents the orthologous region of GSE9 in NIP, MH63 and ZS97 varieties. The heatmap below the tracks show the expression of the GSE9 in NIP, MH63, and ZS97 varieties. g Comparison of OCRs in the three rice cultivars (NIP, MH63, and ZS97). For each cultivar, OCRs from all tissues were merged and then compared based on whole genome sequence alignments. h Ternary plot showing the chromatin accessibility of orthologous OCRs among the three rice cultivars with Panicle1 tissue. i Comparison of the SNP density within the balanced ( n  = 19793) and unbalanced ( n  = 8385) orthologous OCRs. The P value was calculated by two-tailed Student’s t test. Boxplot shows the median (horizontal line), second to third quartiles (box), and Tukey-style whiskers (beyond the box). j Sankey diagram showing the true chromatin accessibility difference and the chromatin accessibility difference predicted by the deep learning model for orthologous OCRs in NIP, MH63, and ZS97. The color representation is categorized in the same way as in h . Source data are provided as a Source Data file.

Subsequently, for each variety-specific model, we used test sets from the remaining two varieties to evaluate the model’s capacity for making predictions across different varieties. Our analysis revealed high Pearson correlation coefficients (about 0.8) between the predicted and observed signals (Fig.  6e ). Notably, in the GSE9 promoter region, there is divergence between indica and japonica rice, marked by a 9 bp deletion and several SNPs in MH63 when compared to the sequences of NIP and ZS97 66 . The ZS97 model predicted the chromatin accessibility of this region in MH63 with weak signals. Contrarily, the ZS97 model accurately predicted the chromatin accessibility in NIP and ZS97, showing strong signals (Fig. 6f and Supplementary Fig.  14 ). These results suggest that the deep learning model can effectively make accurate predictions across varieties, implying that shared regulatory grammar across rice varieties.

We next performed comparative analyses on ATAC-seq data of 22 matched tissues/organs in both japonica rice (NIP) and indica rice (MH63 and ZS97), utilizing their respective reference genomes (Fig.  1a, b ). We found that roughly 60% (60,764 out of 95,697) of OCRs were shared across all three cultivars (Fig.  6g and Supplementary Data  13 ). The indica varieties MH63 and ZS97 exhibited a higher proportion of shared OCRs compared to NIP from different subspecies (Fig.  6g ). We next sought to compare chromatin accessibility dynamics of the 1:1:1 orthologous OCRs across the three varieties (referred to as triads; see Methods ). To investigate the accessible bias of orthologous OCRs, we compared the chromatin accessibility of orthologous OCRs in each individual tissue (Fig.  6h ). Orthologous OCRs were assigned into seven categories on the ternary plot based on their relative accessibility, including a balanced category and six dominated or suppressed categories in specific cultivars (Supplementary Fig.  15 ). The proportion of OCR triads assigned to unbalanced categories varied among different tissues, ranging from 3.2% in plumule to 24.8% in AM1 (Fig.  6h and Supplementary Fig.  16a ). While promoters generally display balanced OCRs, indicating consistent accessibility across different cultivars, enhancers frequently exhibit unbalanced OCRs, reflecting cultivar-specific regulation (Supplementary Fig.  16b ). Interestingly, unbalanced OCRs harbored more genotypic variations in terms of SNPs (Fig.  6i ). This observation led us to suppose whether sequence variation among different varieties caused the differences in chromatin accessibility of these OCR orthologs. Therefore, we used NIP-based deep learning model to predict the chromatin accessibility signals of sequences from orthologous OCRs in NIP, MH63 and ZS97, respectively, and then compare these predictions. The results showed that about 50% of the differences in orthologous OCRs could be successfully resolved in terms of sequence variation (Fig.  6j and Supplementary Fig.  17 ).

In summary, the above results illustrate that deep learning models could accurately predict chromatin accessibility across tissues and varieties. The high accuracy of the models also indicates the high quality of our data.

Elucidate key genetic changes underlying cis -regulatory divergence by deep learning models

Genetic variants and de novo mutations in regulatory regions may lead to cis -regulatory divergence and thus changes in gene expression and organismal phenotypes 67 . We systematically dissected the cis -regulatory divergence due to genomic sequence changes (e.g., SNPs) in regulatory regions, which could be inferred from ATAC-seq data. To measure the effect of the variant on chromatin accessibility, we extracted variants that differed in the three varieties. The effect of different alleles of each variant on chromatin accessibility was evaluated using the deep learning models. We found that unbalanced OCRs had a higher absolute effect score than the balanced OCRs (Supplementary Fig.  18a ) and these large-effect loci were significantly enriched for eQTLs 26 , 68 (Supplementary Fig.  18b ). This observation suggests that these putative large-effect variants are associated with changes in chromatin accessibility and gene expression. Meanwhile, we performed separate OCR-to-gene correlation analysis for each of the three varieties. We then identified conserved OCR-to-gene links and compared the correlation coefficients between them (Fig.  7a ). Notably, OCRs with significant differences in correlation coefficients exhibited higher SNP density (Fig.  7b ), and the OCR-to-gene links with large differences in correlation coefficients between MH63 and ZS97 were significantly enriched for differential cis -eQTL between MH63 and ZS97 (Fisher’s exact test, odds ratio = 1.81 and P  < 1.83e−28) 69 . These suggesting that regulatory sequence variations among different varieties could influence gene expression. For instance, we observed that a SNP (vg0336150781, G/A) located in the GNP1 promoter region control grain number and plant height 70 . Among the OCR-to-gene links we inferred, the allele in NIP (‘G’ at this SNP) correlated with GNP1 ( R  = 0.59, P  < 6.48e−04), whereas the allele (‘A’ at this SNP) did not show OCR-to-gene correlation in MH63 ( R  = 0.01, P  = 0.99) and ZS97 ( R  = 0.17, P  = 0.34) (Fig.  7c ). In addition, eGWAS also demonstrated that this SNP affects GNP1 expression (Fig.  7d ). When we evaluated the effects of this SNP with the deep learning model, we found that mutation of this SNP from “G” to “A” in Panicle2 significantly reduced chromatin accessibility (Fig.  7e ). We also found that this variant overlaps with the footprint of OsSPL10 identified in Panicle2, and its binding site shows the typical “GTAC” motif of the SBP TF family. These results suggest that mutations control gene expression by affecting TF binding to alter chromatin accessibility.

a Density plot showing the difference in Pearson correlation coefficients ( R ) between the OCR-to-gene of NIP, MH63, and ZS97, respectively. The R of OCR-to-gene is not less than 0.4 we consider large differences while R located between −0.05 and 0.05 we consider no difference. b Boxplots showing the density of SNP differences between big and small difference groups. Comparisons are made by two-tailed Student’s t-test . Sample sizes for each group are labeled above their respective boxes. Boxplot shows the median (horizontal line), second to third quartiles (box), and Tukey-style whiskers (beyond the box). c The dot plot demonstrates that the GNP1 gene associates to an OCR (chr3:36150374-36152039) in NIP, but not in MH63 and ZS97 due to the presence of a variant (vg0336150781, G/A). Pearson’s correlation coefficient is used for the test. The error bands indicate 95% confidence intervals. The P -values were calculated by a two-tailed Z -test. d Manhattan plot showing local eGWAS results for GNP1 . The eGWAS results were obtained from Ming et al. 68 . e Changes in chromatin accessibility using deep learning models for mutations of 100 bp each on the left and right of vg0336150781. “Loss” represents reduced chromatin accessibility after the mutation compared to before the mutation, and “gain” represents increased. The figure shows the change in chromatin accessibility before and after the mutation in Panicle2. f The treemap showing the proportion and composition of OCRs without structural variants (SV) and OCRs with SV. Here we only consider deletions (DEL), inversions (INV), and duplications (DUP) for SV. OCRs were considered SV-related when it overlaps with DEL, DUP, and INV by at least 1 bp. g The heatmap showing the 12,313 OCR-to-gene links ( R  > = 0.4, P  < 0.05, two-tailed Z -test) associated with SV. They were grouped into 6 clusters based on their chromatin accessibility. The number of OCRs in each cluster and the number of target genes are labeled on the right side of the heatmap. h The doughnut showing the proportion of DEL, DUP, and INV in each cluster. i Scatter plot demonstrates Pearson correlation coefficients ( R  = 0.83, P  < 6.94e−09) between tissues for the accessibility of OCR associated with deletion and the expression of target genes ( Oshsp18.0-CII ). The error bands indicate 95% confidence intervals. The P -values were calculated by two-tailed Z -test. j Genome Browser showing ATAC-seq signal distribution in the vicinity of gene Oshsp18.0-CII . The gray dashed bracket represents the absence of this OCR in MH63 and ZS97 due to the deletion of this sequence. The barplot on the right shows the expression of the gene in each tissue. Source data are provided as a Source Data file.

Besides point mutations, small genomic alterations (including short insertions/deletions, inversions, and duplications) may abolish OCRs and thus confer an important avenue of regulatory divergence. We quantified all OCRs based on the NIP reference genome and investigated whether their regulatory activity dynamics were associated with short genomic alterations, which were determined by whole genome comparison across different cultivars (see Methods ). In total, we found that nearly one third (26.6%) of the OCRs harbored small alterations (Fig.  7f ). The regulatory activity of these mutation-associated OCRs is positively correlated with their surrounding gene expression patterns in a cultivar-specific manner (Fig.  7g, h ), as exemplified at the loci of Oshsp18.0-CII and MAG2 (Fig.  7i, j and Supplementary Fig.  19a ). Notably, GO analysis showed that these genes were highly enriched for various ‘response’ related functions (Supplementary Fig.  19b and Supplementary Data  14 ). Further investigation revealed that the identified mutation-embedded OCRs were significantly overlapped with transposable elements (TEs) (Supplementary Fig.  19c ). The above results indicate that TEs may contribute to modification of regulatory sequences, fine-tuning gene expression networks and driving new functions 71 .

Despite substantial progress, a complete catalog of regulatory sequences within the rice genome remains elusive, limiting the understanding of tissue-specific regulatory dynamics and GRNs. Our study presents a comprehensive exploration of rice genome regulation using the UMI-ATAC-seq technique 12 , providing insights into tissue-specific regulatory elements and their influence on complex agronomic traits. Of note, the identified OCRs in rice encompass approximately 15% of the genome, a notably higher proportion compared to previous reports in plants such as Arabidopsis (~4%) 72 and maize (~4%) 73 . This expanded coverage underscores the importance of sampling depth in characterizing the regulatory complexity in plants and highlights the need for further comparative analyses to elucidate species-specific regulatory features.

Predicting OCR-to-gene links presents a significant challenge due to the intricate regulatory mechanisms governing gene expression. By integrating RNA-seq data from matched tissues, we predicted 59,075 OCR-to-gene links, including many reported enhancer-to-gene links. This analysis offers a holistic view of how changes in chromatin accessibility directly impact gene expression patterns, underscoring the significance of regulatory elements in shaping the rice transcriptome. The identified associations between enhancers and target genes provide guidelines for dissecting complex regulatory mechanisms and gene editing in non-coding regions. The approach for predicting OCR-to-gene links based on multi-omics data is versatile and transferable to other plant species. Despite our efforts to predict OCR-to-gene links, less than half of the protein-coding genes exhibit a relatively strong correlation (Pearson correlation coefficient | R |≥ 0.4 and P  < 0.05) with OCRs. The complexities of dynamic and context-dependent regulation, coupled with long-range and indirect regulatory mechanisms, introduce additional layers of complexity to OCR-to-gene link prediction beyond the capabilities of linear models aimed at directly mapping OCRs to their target genes. These factors likely contribute to the weaker correlations observed for certain genes. Moreover, tissue-specific and housekeeping genes are difficult to correlate through linear models due to the small variation in expression levels between tissues (Supplementary Fig. 20 ).

Deep learning has emerged as a potent tool for interpreting the genomic and epigenomic data 62 , 63 , but its application in rice is hindered by the scarcity of high-quality epigenomic datasets. Our study addressed this gap and successfully modelled the chromatin accessibility of three rice varieties. The highly accurate models enable the prediction of chromatin accessibility variation across varieties using sequences, providing a reference for scientists to explore the functional effects of rare variants or new variants across different tissues.

Moreover, our comparative analysis across varieties revealed cis -regulatory divergence that could largely be predicted using deep learning models based on sequences, highlighting the genetic diversity of rice varieties and its impact on regulatory architecture. By integrating GWAS data, we localized significant variants within noncoding regulatory regions, demonstrating that these variants are preferentially located in tissue-specific OCRs, thus providing insights into the influence of regulatory variations on phenotypic outcomes. A notable achievement of our study is the identification of OsbZIP06’s role in seed germination, demonstrating the potential of integrating GWAS data with chromatin accessibility to uncover the genetic basis of complex traits.

In summary, our extensive chromatin accessibility atlas and the deep learning models we have constructed not only enhance our understanding of regulatory elements in rice but also serve as a versatile resource for gene editing and breeding strategies targeting non-coding regions. Nevertheless, there are several limitations associated with our study. Firstly, our map solely encompasses data from normal conditions, omitting insights into responses to biotic or abiotic stresses, mutants, and diverse environmental circumstances. Secondly, the inferred associations between OCRs and genes require experimental validation to confirm their regulatory relationships. Thirdly, our study primarily employed the NIP reference genome, thereby excluding sequences that were not available in the NIP genome. Furthermore, the advent of single-cell technologies has opened avenues for studying cis -elements at a single-cell resolution 73 , 74 . In the future, incorporating single-cell data will be crucial for further characterizing the heterogeneity among different cell types. These advancements will collectively contribute to a more comprehensive understanding of the regulatory landscape in rice and beyond.

Plant materials, ATAC-seq, and RNA-seq experiments

Three rice varieties, Nipponbare, Zhenshan 97 and Minghui 63, were planted in a field in Wuhan, China in the summer of 2020 and were used to obtain most of the tissues or organs used in this study. Details of the sampling are listed in Supplementary Data  1 . We followed our previously established method to perform UMI-ATAC-seq experiments 12 . RNA was isolated using TRIzol reagent (Invitrogen Life Technologies), and sequencing libraries were prepared using the MGIEasy RNA Library Preparation Kit. The libraries were subsequently sequenced on the MGISEQ-2000.

ATAC-seq data analysis

For the pre-processing of ATAC-seq data, we follow the workflow of ChIP-Hub 14 and cisDynet 75 . The raw reads were first trimmed by Trimmomatic (v.0.36) 76 to remove sequencing adapters. The trimmed reads were aligned to the Oryza sativa L.ssp.japonica (cv. Nipponbare) reference genome (v.7.0) 16 using Bowtie2 77 with the following parameters”-q—no-unal—threads 8—sensitive”. All reads mapped to mitochondrial and chloroplast DNA were removed. After sorting mapped reads with SAMtools 78 (version 0.1.19), we only used properly paired reads with high mapping quality (MAPQ score > 30) for the subsequent analysis. The PCR duplicates were removed using the MarkDuplicates function from Picard tools (version 2.60; http://broadinstitute.github.io/picard/ ). The “callpeak” function in MACS2 79 (version 2.1.0) was used to call peaks with the following parameters: “-g 3.0e8 --nomodel --keep-dup 1 -B --SPMR --call-summits”. The “-shift” used in the model was determined by the analysis of cross-correlation scores using the phantompeakqualtools package ( https://code.google.com/archive/p/phantompeakqualtools/ ).

RNA-seq data analysis

RNA-seq reads were aligned to the Nipponbare reference genome 16 using STAR 80 (version 2.7.1a). The expression of annotated genes was measured by RSEM 81 (version 1.2.22) and normalized with transcripts per million (TPM).

Linking OCRs to target genes

To assign OCRs to genes, we used an approach similar to the previous study 23 , 82 . First, we prepared the ATAC-seq quantification matrix, with each row representing a merged OCR and each column representing a sample. After merging replicates, 66 tissues with both ATAC-seq data and RNA-seq data were taken as independent samples for the analysis. For the gene expression quantification matrix, we removed possible noise by considering only those genes whose TPM of each row added up to > 1.5. For each of the remaining 29,571 genes, we screened the OCRs that might regulate the gene within 20 kb upstream and downstream of the TSS of that gene separately. Then we calculated the Pearson correlation coefficients between the chromatin accessibility of these OCRs and the expression of that gene. Then we randomly generated pseudo-peak sets of the same length and number as these OCRs on other chromosomes, repeated the process 10,000 times, and used Z-test ( z.test function from the R package ‘TeachDemos’) to calculate P values. Finally, we considered that absolute Pearson correlation coefficients (| R |) >= 0.4, and P  < 0.05 were significant OCR-to-gene links. For the identification of OCR-to-gene links of NIP, MH63, and ZS97, we used the same strategy except that we used ATAC-seq and RNA-seq samples of the corresponding varieties.

Tissue-specific OCRs analysis

We merged the peaks with NIP tissues, counted the number of Tn5 cuts of these peaks in different tissues, normalized them, and then used the Jensen-Shannon Divergence (JSD) from the philentropy R package ( https://github.com/drostlab/philentropy ) to screen tissue-specific OCRs. Here we considered OCRs with JSD score > 0.08 (except > 0.1 for young panicle) as tissue-specific OCRs. For tissue-specific OCR, we performed Z-score transformation by row for visualization. To identify the top tissue-specific OCRs in each tissue, we employed a scoring metric known as Specificity Measurement (SPM), as detailed in the method provided at https://github.com/apcamargo/tspex . Subsequently, we sorted the OCRs within each tissue based on their SPM scores to select the top 2500 tissue-specific OCRs in each tissue.

Motif enrichment analysis

For motif enrichment analysis of tissue-specific OCRs, we first calculated the Tau index score using SPM metric for each tissue’s OCRs and selected the top 2500 OCRs of each tissue for motif enrichment analysis according to the ranking of Tau index scores. Then we used GimmeMotifs 36 with maelstrom function to determine the tissue-specific motifs enrichment. We set the “--filter-cutoff” to 0.4. The input Position weight matrix (PWM) was downloaded from the JASPAR 83 database ( https://jaspar.genereg.net/ ). We combined the enrichment results of three methods (Lasso, Bayesian ridge regression, and boosted trees regression) to get the final motif enrichment lists.

ChIP-seq enrichment analysis

The public ChIP-seq data used in this study are provided in Supplementary Data  3 . We downloaded the narrow Peak files of these TFs from the ChIP-Hub database ( https://biobigdata.nju.edu.cn/ChIPHub/ ), and then used BEDTools 84 (version 2.29.1) fisher function to calculate the enrichment level with the OCRs.

TF motif and footprinting analysis

For the TF motif enrichment analysis, we used the SEA program from the MEME suite and used constitutive OCRs as background. We considered motifs with P -value < 1e−5 to be significantly enriched. For genome-wide TF potential binding sites, we used the FIMO program in MEME to identify them and also used P -value < 1e−5 as the cutoff.

TF footprints were calculated by TOBIAS (version 0.13.1) 85 . We first used TOBIAS ATACCorrect function to correct the Tn5 inherent insertion bias. Then we calculated the footprint score in OCRs using FootprintScores function with default parameters. Finally, we used BINDetect function to predict the transcription factor binding footprint for each sample, which were matched to curated list of JASPAR 83 motifs ( https://jaspar.genereg.net/ ).

Cross-variety comparisons of OCRs

We first aligned the whole genome sequences of NIP, MH63, and ZS97 to each other. The strategy used for the whole-genome alignment was similar to the previously described method 23 . The results were further filtered to obtain more reliable conserved sequences following the default process of “Reciprocal Best” ( http://genomewiki.ucsc.edu/index.php/HowTo:_Syntenic_Net_or_Reciprocal_Best ). We obtained three superset OCRs by merging OCRs of tissues shared by three varieties s ( n  = 22). Then we used the bnMapper.py script in bx-python ( https://github.com/bxlab/bx-python ) to convert the OCRs coordinates of MH63, ZS97 to the corresponding coordinates of the NIP. We then considered the OCRs of MH63, and ZS97 with at least a 50% overlap with the OCRs of NIP to be conserved OCRs for the three varieties. To obtain a quantitative matrix of conserved OCRs, we first quantified all OCRs for each variety and divided the length of the corresponding OCRs by the CPM strategy, and then extracted the conserved OCRs for each variety for subsequent analysis. We then refer to it to classify conservative OCRs into seven categories (NIP dominant, MH63 dominant, ZS97 dominant, NIP suppressed, MH63 suppressed, ZS97 suppressed, and balanced).

Deep learning model analysis

We used the Basenji 64 deep learning framework with modifications to accommodate the relatively small rice genome for deep learning model training. We first use the bam_cov.py script to convert the bam files into bigwig files. We then used the basenji_data.py script to prepare the input files for the deep learning model according to the following parameters: “-d 1.0 -s 0.1 –local -t 1 -v 4”. The “-c, -l, -w” of these parameters are shown in the Supplementary Data  15 . Data from chromosome 1 was used as the test sets and data from chromosome 2 was used as the validation sets. Next, we used the basenji_train.py script to train the model on a NVIDIA GTX 3090. The basenji_test.py script (default parameters) was used to perform the model performance test. We found differences in the training performance for different parameter settings for rice, with “-l 32768 -c 2048 -w 128” being the best, and subsequent analyses were based on models trained with this parameter. To measure the effect of variation in OCRs, we used the basenji_sat_bed.py script to perform base mutations at this locus and calculated the difference in signals between the reference and the mutation as the variation effect value. To predict the chromatin accessibility of orthologous OCRs, we extended the center of the OCR by 16,384 bp left and right to make a total length of 32,768 bp. Sequences exceeding the length of the corresponding chromosome were removed, and then sequences of the corresponding varieties were extracted using BEDTools getfasta function, and then basenji-predict_bed.py was modified to enable it to use fasta as input.

Analysis of structural variants and transposable elements

We downloaded deletions, duplications and inversions for MH63 (CX145), ZS97 (B156) in Rice SNP-Seek Database ( https://snp-seek.irri.org/ ). Since this database provides large structural variants with a minimum length of 10 bp, we also integrated a series of variants with reference to this workflow 7 . Briefly, we selected Leaf ATAC-seq data from NIP, MH63, and ZS97 varieties, then aligned them to the Nipponbare reference genome using BWA-MEM 78 (version 0.7.12-r1039) and identified INDELs using GATK 86 (version 3.3-0-g37228af). The annotation files of transposable elements (TEs) were downloaded from Phytozome database. We use the default parameters of the BEDtools 84 (version 2.29.1) intersect function to identify OCRs that overlap with structural variants and TEs.

GO enrichment analysis

All GO enrichment analysis was done in the Rice Gene Index database ( https://riceome.hzau.edu.cn/ ) 87 using default parameters. We considered FDR < 0.05 as a significantly enriched pathway.

GWAS data processing

The genotype and phenotype data used in this study were downloaded from four published cohorts. We refer to this reference 3 to name them as 529 rice accessions 2 , 1,275 Chinese rice accessions 53 , 176 Japanese rice accessions 52 , and 3 K rice accessions 54 , respectively. GWAS was performed separately for each cohort by GCTA 88 (version 7.93.2) with a mixed linear model. To determine the significant SNPs cutoff, we first used Genetic type 1 error calculator (GEC 89 , version 0.2) to evaluate the effective numbers of independent SNPs ( N ) and approximated by 0.05/ N to estimate the cutoff. The threshold for significant SNPs varied by cohorts, we set the thresholds to 1 × 10 −6 , 1 × 10 −4 , 1 × 10 −5 , and 1 × 10 −6 for 3 K rice accessions, 176 Japanese rice accessions, 529 rice accessions, and 1,275 Chinese rice accessions, respectively. Variants with a minor allele frequency (MAF) that was <5% were excluded. For the lead SNP identification, we used PLINK 90 (version 1.9) and set the parameter “--clump-p1” to the threshold we defined above, “--clump-p2 0.05 --clump-r2 0.6 --clump-kb 1000” for the first round of parameters. Then we set the second round of “--clump-r2” to 0.1, other parameters are unchanged. We used PLINK with the following parameters “--ld-window-kb 1000 --ld-window 99999 --ld-window-r2 0.8” to calculate the SNPs with strong linkage disequilibrium ( r 2  > 0.8) with lead SNPs.

Enrichment analysis of GWAS-associated SNPs of different P -values with OCRs

The enrichment in the OCRs of tissue at a given threshold of different P -values was calculated as the fraction of SNPs with P -values below this threshold that overlap with the OCRs (merged all NIP tissues’ OCRs), divided by the fraction of all noncoding SNPs that overlap with the OCRs in the study. Enrichment was performed at P -value thresholds ranging from 1e-1 to 1e-7. The smallest threshold had at least 50 SNPs in their study to ensure a sufficient sample size.

GWAS SNPs enrichments

We first merged the peaks from all tissues in Nipponbare and used this peak superset to quantify each tissue. To make sure that our analysis was not interfered by low confident peaks, we dropped the peaks in the tenth percentile of the lowest Tn5 cuts coverage, yielding 86,011 ATAC-seq peaks finally. Then we performed the normalization with CHEERS_normalize.py from CHEERS 57 (Chromatin Element Enrichment Ranking by Specificity) software ( https://github.com/TrynkaLab/CHEERS/tree/python3 ). The normalized quantification matrix was next transformed to tissue-specificity score with range 0–1. To do the enrichment analysis, we used the set of lead SNPs and SNPs with strong linkage disequilibrium ( r 2  > 0.8) with the lead SNPs computed separately for the corresponding cohort from the 209 GWAS above as the input to CHEERS_computeEnrichment.py . The enrichment P -values were transformed by -log10 and normalized by row with Z-score for visualization. For the proximal and distal GWAS SNPs enrichment analysis, we first divided OCRs into proximal and distal according to its summit distance from the nearest TSS. All other steps are the same as described above.

Generation of transgenic rice plants

To obtain overexpression lines of OsbZIP06 , the cDNA of OsbZIP06 was cloned using primers OsbZIP06-OE-F and OsbZIP06-OE-R and inserted into the Kpn1-BamH1 site of the pCAMBIA1301 vector and fused with the maize Ubiquitin promoter and three FLAG tags at its C-terminus using the ClonExpress II One Step Cloning Kit (Vazyme). The construct was then transformed into ZhongHua11 (ZH11) by Biogle GeneTech. Primers used to clone OsbZIP06 are listed in Supplementary Data  16 .

For the OsbZIP06 mutant strain, T1 generation seeds produced using the CRISPR-Cas9 system were purchased from Biogle GeneTech. The sgRNA sequence OsbZIP06-CR-gRNA is listed in Supplementary Data  16 .

Seed germination experiments

Seed germination experiments were performed as previously described 60 . Seeds of Zhonghua 11, OsbZIP06 mutant in the Zhonghua 11 background were used for germination experiments.

Statistics and reproducibility

If not specified, all statistical analyses and data visualization were done in R (version 4.0.0) or Python (version 3.8.9). R packages (e.g. ggplot2 and plotly) and Python packages (e.g. Seaborn) are heavily used for graphics. All the sources data for each figure can be found in the Supplementary Information. Specific tests used to determine statistical analyses are noted in each figure legend.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The sequencing data from ATAC-seq and RNA-seq generated in this study have been deposited in the NCBI BioProject database under accession code PRJNA940508 . All public ChIP-seq used in this study are download from ChIP-Hub database ( https://biobigdata.nju.edu.cn/ChIPHub/ ). The accession number are provided in the Supplementary Data  3 . Some critical analysis results about this study can be accessed in the CART database ( https://biobigdata.nju.edu.cn/cart/ ).

Code availability

The code related to figures is available at https://github.com/compbioNJU/CART .

Wang, C. & Han, B. Twenty years of rice genomics research: From sequencing and functional genomics to quantitative genomics. Mol. Plant. 15 , 593–619 (2022).

Article   PubMed   Google Scholar  

Xie, W. et al. Breeding signatures of rice improvement revealed by a genomic variation map from a large germplasm collection. Proc. Natl Acad. Sci. 112 , E5411–E5419 (2015).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Wei, X. et al. A quantitative genomics map of rice provides genetic insights and guides breeding. Nat. Genet. 53 , 243–253 (2021).

Article   CAS   PubMed   Google Scholar  

Purugganan, M. D. & Jackson, S. A. Advancing crop genomics from lab to field. Nat. Genet. 53 , 595–601 (2021).

Li, Y. et al. Rice functional genomics research: past decade and future. Mol. Plant 11 , 359–380 (2018).

Schmitz, R. J., Grotewold, E. & Stam, M. Cis-regulatory sequences in plants: Their importance, discovery, and future challenges. Plant Cell 34 , 718–741 (2022).

Zhao, H. et al. An inferred functional impact map of genetic variants in rice. Mol. Plant 14 , 1584–1599 (2021).

Zhang, W. et al. High-resolution mapping of open chromatin in the rice genome. Genome Res. 22 , 151–162 (2012).

Konishi, S. et al. An SNP caused loss of seed shattering during rice domestication. Science 312 , 1392–1396 (2006).

Article   ADS   CAS   PubMed   Google Scholar  

Sun, X. et al. Natural variation of DROT1 confers drought adaptation in upland rice. Nat. Commun. 13 , 4265 (2022).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Bai, X. et al. Duplication of an upstream silencer of FZP increases grain yield in rice. Nat. Plants 3 , 885–893 (2017).

Zhu, T., Liao, K., Zhou, R., Xia, C. & Xie, W. ATAC-seq with unique molecular identifiers improves quantification and footprinting. Commun. Biol. 3 , 675 (2020).

Landt, S. G. et al. ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 22 , 1813–1831 (2012).

Fu, L.-Y. et al. ChIP-Hub provides an integrative platform for exploring plant regulome. Nat. Commun. 13 , 3413 (2022).

Zhang, J. et al. Extensive sequence divergence between the reference genomes of two elite indica rice varieties Zhenshan 97 and Minghui 63. Proc. Natl Acad. Sci. 113 , E5163–E5171 (2016).

CAS   PubMed   PubMed Central   Google Scholar  

Kawahara, Y. et al. Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data. Rice 6 , 4 (2013).

Article   PubMed   PubMed Central   Google Scholar  

Qunhua, L., James, B. B., Haiyan, H. & Peter, J. B. Measuring reproducibility of high-throughput experiments. Ann. Appl. Stat. 5 , 1752–1779 (2011).

MathSciNet   Google Scholar  

Yan, W. et al. Dynamic control of enhancer activity drives stage-specific gene expression during flower morphogenesis. Nat. Commun. 10 , 1705 (2019).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Zhou, X. et al. Systematic annotation of conservation states provides insights into regulatory regions in rice. J. Genet. Genomics 49 , 1127–1137 (2022).

Van de Velde, J., Van Bel, M., Vaneechoutte, D. & Vandepoele, K. A collection of conserved noncoding sequences to study gene regulation in flowering plants. Plant Physiol. 171 , 2586–2598 (2016).

Linderman, G. C., Rachh, M., Hoskins, J. G., Steinerberger, S. & Kluger, Y. Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data. Nat. Methods 16 , 243–245 (2019).

Villar, D. et al. Enhancer evolution across 20 mammalian species. Cell 160 , 554–566 (2015).

Corces, M. R. et al. The chromatin accessibility landscape of primary human cancers. Science 362 , eaav1898 (2018).

Liu, C., Cheng, Y.-J., Wang, J.-W. & Weigel, D. Prominent topologically associated domains differentiate global chromatin packing in rice from Arabidopsis. Nat. Plants 3 , 742–748 (2017).

Golicz, A. A., Bhalla, P. L., Edwards, D. & Singh, M. B. Rice 3D chromatin structure correlates with sequence variation and meiotic recombination rate. Commun. Biol. 3 , 235 (2020).

Liu, C. et al. eQTLs play critical roles in regulating gene expression and identifying key regulators in rice. Plant Biotechnol. J. n/a , https://doi.org/10.1111/pbi.13912 (2022).

Ishii, T. et al. OsLG1 regulates a closed panicle trait in domesticated rice. Nat. Genet. 45 , 462–465 (2013).

Zhang, L. et al. A natural tandem array alleviates epigenetic repression of IPA1 and leads to superior yielding rice. Nat. Commun. 8 , 14789 (2017).

Zhang, X.-H., Rao, X.-L., Shi, H.-T., Li, R.-J. & Lu, Y.-T. Overexpression of a cytosolic glyceraldehyde-3-phosphate dehydrogenase gene OsGAPC3 confers salt tolerance in rice. Plant Cell Tissue Organ Cult. (PCTOC) 107 , 1–11 (2011).

Article   CAS   Google Scholar  

Lee, S. et al. Concurrent activation of OsAMT1;2 and OsGOGAT1 in rice leads to enhanced nitrogen use efficiency under nitrogen limitation. Plant J. 103 , 7–20 (2020).

Tanaka, W. et al. The YABBY Gene TONGARI-BOUSHI1 is involved in lateral organ development and maintenance of meristem organization in the rice spikelet. Plant Cell 24 , 80–95 (2012).

Raineri, J., Wang, S., Peleg, Z., Blumwald, E. & Chan, R. L. The rice transcription factor OsWRKY47 is a positive regulator of the response to water deficit stress. Plant Mol. Biol. 88 , 401–413 (2015).

Wang, S. et al. The OsSPL16-GW7 regulatory module determines grain shape and simultaneously improves rice yield and grain quality. Nat. Genet. 47 , 949–954 (2015).

Rao, N. N., Prasad, K., Kumar, P. R. & Vijayraghavan, U. Distinct regulatory role for RFL, the rice LFY homolog, in determining flowering time and plant architecture. Proc. Natl Acad. Sci. 105 , 3646–3651 (2008).

Huang, L. et al. The LARGE2-APO1/APO2 regulatory module controls panicle size and grain number in rice. Plant Cell 33 , 1212–1228 (2021).

van Heeringen, S. J. & Veenstra, G. J. C. GimmeMotifs: a de novo motif prediction pipeline for ChIP-sequencing experiments. Bioinformatics 27 , 270–271 (2011).

Xiao, S.-J., Zhang, C., Zou, Q. & Ji, Z.-L. TiSGeD: a database for tissue-specific genes. Bioinformatics 26 , 1273–1275 (2010).

Lee, D.-Y. & An, G. Two AP2 family genes, SUPERNUMERARY BRACT (SNB) and OsINDETERMINATE SPIKELET 1 (OsIDS1), synergistically control inflorescence architecture and floral meristem establishment in rice. Plant J. 69 , 445–461 (2012).

Lee, Y.-S., Lee, D.-Y., Cho, L.-H. & An, G. Rice miR172 induces flowering by suppressing OsIDS1 and SNB, two AP2 genes that negatively regulate expression of Ehd1 and florigens. Rice 7 , 31 (2014).

Baoxiang, W. et al. OsbZIP72 is involved in transcriptional gene-regulation pathway of abscisic acid signal transduction by activating rice high-affinity potassium transporter OsHKT1;1. Rice Sci. 28 , 257–267 (2021).

Article   Google Scholar  

Wang, Y. et al. Abscisic acid promotes jasmonic acid biosynthesis via a ‘SAPK10-bZIP72-AOC’ pathway to synergistically inhibit seed germination in rice (Oryza sativa). N. Phytol. 228 , 1336–1353 (2020).

Yu, X. et al. Abnormal flower and grain 1 encodes OsMADS6 and determines palea identity and affects rice grain yield and quality. Sci. China Life Sci. 63 , 228–238 (2020).

Li, H. et al. Rice MADS6 interacts with the floral homeotic genes SUPERWOMAN1, MADS3, MADS58, MADS13, and drooping leaf in specifying floral organ identities and meristem fate. Plant Cell 23 , 2536–2552 (2011).

Li, H. et al. The AGL6-like gene OsMADS6 regulates floral organ and meristem identities in rice. Cell Res. 20 , 299–313 (2010).

Martín-Trillo, M. & Cubas, P. TCP genes: a family snapshot ten years later. Trends Plant Sci. 15 , 31–39 (2010).

Zaret, K. S. & Mango, S. E. Pioneer transcription factors, chromatin dynamics, and cell fate control. Curr. Opin. Genet Dev. 37 , 76–81 (2016).

Xing, Y. & Zhang, Q. Genetic and molecular bases of rice yield. Annu. Rev. Plant Biol. 61 , 421–442 (2010).

Li, G., Zhang, H., Li, J., Zhang, Z. & Li, Z. Genetic control of panicle architecture in rice. Crop J. 9 , 590–597 (2021).

Yamaguchi, T. et al. The YABBY Gene DROOPING LEAF regulates carpel specification and midrib development in Oryza sativa [W]. Plant Cell 16 , 500–509 (2004).

Hu, L. et al. OsSPL9 regulates grain number and grain yield in rice. Front Plant Sci. 12 , 682018 (2021).

Song, X. et al. Targeting a gene regulatory element enhances rice grain yield by decoupling panicle number and size. Nat. Biotechnol. 40 , 1403–1411 (2022).

Yano, K. et al. Genome-wide association study using whole-genome sequencing rapidly identifies new genes influencing agronomic traits in rice. Nat. Genet. 48 , 927–934 (2016).

Li, X. et al. Analysis of genetic architecture and favorable allele usage of agronomic traits in a large collection of Chinese rice accessions. Sci. China Life Sci. 63 , 1688–1702 (2020).

Wang, W. et al. Genomic variation in 3,010 diverse accessions of Asian cultivated rice. Nature 557 , 43–49 (2018).

Liu, X. et al. GWAS Atlas: an updated knowledgebase integrating more curated associations in plants and animals. Nucleic Acids Res. 51 , D969–D976 (2023).

Huang, X. et al. Genome-wide association studies of 14 agronomic traits in rice landraces. Nat. Genet. 42 , 961–967 (2010).

Soskic, B. et al. Chromatin activity at GWAS loci identifies T cell states driving complex immune diseases. Nat. Genet. 51 , 1486–1493 (2019).

Zhao, Y. et al. Genetic basis and network underlying synergistic roots and shoots biomass accumulation revealed by genome-wide association studies in rice. Sci. Rep. 11 , 13769 (2021).

Bañuelos, M. A., Garciadeblas, B., Cubero, B. & Rodríguez-Navarro, A. Inventory and functional characterization of the HAK potassium transporters of rice. Plant Physiol. 130 , 784–795 (2002).

Magwa, R. A., Zhao, H. & Xing, Y. Genome-wide association mapping revealed a diverse genetic basis of seed dormancy across subpopulations in rice (Oryza sativa L.). BMC Genet. 17 , 28 (2016).

Chen, K. M., Wong, A. K., Troyanskaya, O. G. & Zhou, J. A sequence-based global map of regulatory activity for deciphering human genetics. Nat. Genet. 54 , 940–949 (2022).

Zhao, H. et al. PlantDeepSEA, a deep learning-based web service to predict the regulatory effects of genomic variants in plants. Nucleic Acids Res. 49 , W523–W529 (2021).

Maslova, A. et al. Deep learning of immune cell differentiation. Proc. Natl Acad. Sci. 117 , 25655–25666 (2020).

Kelley, D. R. et al. Sequential regulatory activity prediction across chromosomes with convolutional neural networks. Genome Res. 28 , 739–750 (2018).

Li, X. et al. Overexpression of RCc3 improves root system architecture and enhances salt tolerance in rice. Plant Physiol. Biochem. 130 , 566–576 (2018).

Chen, R. et al. A de novo evolved gene contributes to rice grain shape difference between indica and japonica. Nat. Commun. 14 , 5906 (2023).

Wittkopp, P. J. & Kalay, G. Cis-regulatory elements: molecular mechanisms and evolutionary processes underlying divergence. Nat. Rev. Genet. 13 , 59–69 (2012).

Ming, L. et al. Transcriptome-wide association analyses reveal the impact of regulatory variants on rice panicle architecture and causal gene regulatory networks. Nat. Commun. 14 , 7501 (2023).

Wang, J. et al. An expression quantitative trait loci-guided co-expression analysis for constructing regulatory network using a rice recombinant inbred line population. J. Exp. Bot. 65 , 1069–1079 (2014).

Wu, Y. et al. The QTL GNP1 encodes GA20ox1, which increases grain number and yield by increasing cytokinin activity in rice Panicle Meristems. PLoS Genet. 12 , e1006386 (2016).

Bourque, G. et al. Ten things you should know about transposable elements. Genome Biol. 19 , 199 (2018).

Sullivan, A. M. et al. Mapping and dynamics of regulatory DNA and transcription factor networks in A. thaliana. Cell Rep. 8 , 2015–2030 (2014).

Marand, A. P., Chen, Z., Gallavotti, A. & Schmitz, R. J. A cis-regulatory atlas in maize at single-cell resolution. Cell 184 , 3041–3055.e3021 (2021).

Cuperus, J. T. Single-cell genomics in plants: current state, future directions, and hurdles to overcome. Plant Physiol. 188 , 749–755 (2022).

Zhu, T. et al. cisDynet: An integrated platform for modeling gene-regulatory dynamics and networks. iMeta 2 , e152 (2023).

Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30 , 2114–2120 (2014).

Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9 , 357–359 (2012).

Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25 , 1754–1760 (2009).

Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9 , R137 (2008).

Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29 , 15–21 (2013).

Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinforma. 12 , 323 (2011).

Sheffield, N. C. et al. Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions. Genome Res 23 , 777–788 (2013).

Castro-Mondragon, J. A. et al. JASPAR 2022: the 9th release of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 50 , D165–D173 (2022).

Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26 , 841–842 (2010).

Bentsen, M. et al. ATAC-seq footprinting unravels kinetics of transcription factor binding during zygotic genome activation. Nat. Commun. 11 , 4267 (2020).

McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20 , 1297–1303 (2010).

Yu, Z. et al. Rice Gene Index (RGI): a comprehensive pan-genome database for comparative and functional genomics of Asian rice. Mol. Plant, https://doi.org/10.1016/j.molp.2023.03.012 (2023).

Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: A tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88 , 76–82 (2011).

Li, M.-X., Yeung, J. M. Y., Cherny, S. S. & Sham, P. C. Evaluating the effective numbers of independent tests and significant p-value thresholds in commercial genotyping arrays and public imputation reference datasets. Hum. Genet. 131 , 747–756 (2012).

Purcell, S. et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81 , 559–575 (2007).

Download references

Acknowledgements

This work is supported by grants from STI2030-Major Projects (2023ZD04076), the National Natural Science Foundation of China (No. 32070656, U23A20189, 31922065), the Hubei Provincial Natural Science Foundation of China (2023AFA043), the Earmarked Fund for the China Agriculture Research System (CARS-01-01), the Fundamental Research Funds for the Central Universities (2662023PY002), the Foundation of Hubei Hongshan Laboratory (2021hszd005) and HZAU-AGIS Cooperation Fund (SZYJY2023003). The authors acknowledge the Center for Information Technology and the High-Performance Computing Center of Nanjing University and the bioinformatics computing platform of the National Key Laboratory of Crop Genetic Improvement at Huazhong Agricultural University for providing high performance computing (HPC) resources.

Author information

These authors contributed equally: Tao Zhu, Chunjiao Xia.

Authors and Affiliations

State Key Laboratory of Pharmaceutical Biotechnology, Department of Gastroenterology, Nanjing Drum Tower Hospital, National Resource Center for Mutant Mice, School of Life Sciences, Nanjing University, Nanjing, 210023, China

Tao Zhu, Ranran Yu, Xinkai Zhou, Lin Wang, Yuxin You & Dijun Chen

Chemistry and Biomedicine Innovation Center, Nanjing University, Nanjing, 210023, China

Tao Zhu & Dijun Chen

National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China

Chunjiao Xia, Xingbing Xu, Zhanxiang Zong, Junjiao Yang, Yinmeng Liu, Luchang Ming & Weibo Xie

Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan, 430070, China

Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China

You can also search for this author in PubMed   Google Scholar

Contributions

D.C. and W.X. conceived and designed the project. T.Z., C.X., X.X., Y.L., and J.Y. performed experiments. T.Z., D.C., X.Z., R.Y., L.W., Z.Z., L.M. and Y.Y. conducted the bioinformatics analysis. D.C., W.X. and T.Z. wrote the paper. All the authors reviewed and approved the paper.

Corresponding authors

Correspondence to Dijun Chen or Weibo Xie .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Communications thanks Xuehui Huang and the other anonymous reviewer for their contribution to the peer review of this work. A peer review file is available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information, description of additional supplementary files, supplementary data 1, supplementary data 2, supplementary data 3, supplementary data 4, supplementary data 5, supplementary data 6, supplementary data 7, supplementary data 8, supplementary data 9, supplementary data 10, supplementary data 11, supplementary data 12, supplementary data 13, supplementary data 14, supplementary data 15, supplementary data 16, reporting summary, peer review file, source data, source data, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Zhu, T., Xia, C., Yu, R. et al. Comprehensive mapping and modelling of the rice regulome landscape unveils the regulatory architecture underlying complex traits. Nat Commun 15 , 6562 (2024). https://doi.org/10.1038/s41467-024-50787-y

Download citation

Received : 08 September 2023

Accepted : 19 July 2024

Published : 03 August 2024

DOI : https://doi.org/10.1038/s41467-024-50787-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

The physiological and molecular mechanisms of exogenous melatonin promote the seed germination of maize ( zea mays l.) under salt stress.

1. Introduction

2.1. effect of exogenous mt on emergence traits of maize under salt stress, 2.2. effect of mt on oxidative damage and osmoregulation in seeds, 2.3. effect of mt on endogenous hormone levels, 2.4. transcriptome data quality assessment and analysis of degs, 2.5. go and kegg enrichment analysis of degs, 2.6. time series analysis of expression patterns of degs, 2.7. gene set enrichment analysis of s-vs-sm50 in different germination stages, 2.8. effect of mt on phytohormone signal transduction pathways during seed germination under salt stress, 2.9. effect of mt on starch and sucrose metabolic pathways during seed germination under salt stress, 2.10. effect of mt on transcription factors during seed germination under salt stress, 3. discussion, 3.1. mt alleviates salt stress-induced oxidative damage by enhancing the ability to scavenge ros, 3.2. mt improves seed germination under salt stress by regulating phytohormone levels and phytohormone signal transduction pathways, 3.3. mt promotes seed germination under salt stress by regulating starch and sucrose metabolic pathways during seed germination, 3.4. mt regulates the expression of different functional genes at different seed germination stages to promote seed germination under salt stress, 4. materials and methods, 4.1. materials and treatments, 4.2. salt stress concentration screening, 4.3. optimal mt concentration screening, 4.4. endogenous hormone content and transcriptome sequencing, 4.5. measurement of germination and morphological indicators, 4.6. measurement of antioxidant enzyme activities, 4.7. measurement of mda and soluble sugar content, 4.8. reactive oxygen content measurement, 4.9. measurement of proline content, 4.10. measurement of endogenous hormone content, 4.11. transcriptome sequencing and analysis, 4.12. quantitative real-time pcr assays, 4.13. data processing and analysis, 5. conclusions, supplementary materials, author contributions, data availability statement, conflicts of interest.

• Mahajan, S.; Tuteja, N. Cold, salinity and drought stresses: An overview. Arch. Biochem. Biophys. 2005 , 444 , 139–158. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhang, Z.; Liu, L.; Li, H.; Zhang, S.; Fu, X.; Zhai, X.; Li, D. Exogenous melatonin promotes the salt tolerance by removing active oxygen and maintaining ion balance in wheat ( Triticum aestivum L.). Front. Plant Sci. 2022 , 12 , 787062. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kolář, J.; Johnson, C.H.; Macháčková, I. Exogenously applied melatonin (N-acetyl-5-methoxytryptamine) affects flowering of the short-day plant Chenopodium rubrum . Physiol. Plant 2003 , 118 , 605–612. [ Google Scholar ] [ CrossRef ]
• Hernández-Ruiz, J.; Cano, A.; Arnao, M.B. Melatonin acts as a growth-stimulating compound in some monocot species. J. Pineal Res. 2005 , 39 , 137–142. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Munns, R.; Tester, M. Mechanisms of salinity tolerance. Annu. Rev. Plant Biol. 2008 , 59 , 651–681. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yang, Y.; Guo, Y. Unraveling salt stress signaling in plants. J. Integr. Plant Biol. 2018 , 60 , 796–804. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Li, S.; Wang, Y.; Gao, X.; Lan, J.; Fu, B. Comparative physiological and transcriptome analysis reveal the molecular mechanism of melatonin in regulating salt tolerance in alfalfa ( Medicago sativa L.). Front. Plant Sci. 2002 , 13 , 919177. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Park, H.S.; Kazerooni, E.A.; Kang, S.M.; Al-Sadi, A.M.; Lee, I.J. Melatonin enhances the tolerance and recovery mechanisms in Brassica juncea (L.) Czern. Under saline conditions. Front. Plant Sci. 2021 , 12 , 593717. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ismail, A.M.; Horie, T. Genomics, physiology, and molecular breeding approaches for improving salt tolerance. Annu. Rev. Plant Biol. 2017 , 68 , 405–434. [ Google Scholar ] [ CrossRef ]
• Chen, M.; Yang, Z.; Liu, J.; Zhu, T.; Wei, X.; Fan, H.; Wang, B. Adaptation mechanism of salt excluders under saline conditions and its applications. Int. J. Mol. Sci. 2018 , 19 , 3668. [ Google Scholar ] [ CrossRef ]
• Zhu, J.K. Genetic analysis of plant salt tolerance using Arabidopsis. Plant Physiol. 2000 , 124 , 941–948. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhu, J.K. Plant salt tolerance. Trends Plant Sci. 2001 , 6 , 66–71. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhao, S.; Zhang, Q.; Liu, M.; Zhou, H.; Ma, C.; Wang, P. Regulation of plant responses to salt stress. Int. J. Mol. Sci. 2021 , 22 , 4609. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ahmad, P.; Jaleel, C.A.; Salem, M.A.; Nabi, G.; Sharma, S. Roles of enzymatic and nonenzymatic antioxidants in plants during abiotic stress. Crit. Rev. Biotechnol. 2010 , 30 , 161–175. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Bressan, R.A.; Hasegawa, P.M.; Pardo, J.M. Plants use calcium to resolve salt stress. Trends Plant Sci. 1998 , 3 , 411–412. [ Google Scholar ] [ CrossRef ]
• Singh, P.; Choudhary, K.K.; Chaudhary, N.; Gupta, S.; Sahu, M.; Tejaswini, B.; Sarkar, S. Salt stress resilience in plants mediated through osmolyte accumulation and its crosstalk mechanism with phytohormones. Front. Plant Sci. 2022 , 13 , 1006617. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ashraf, M.; Foolad, M.R. Roles of glycine betaine and proline in improving plant abiotic stress resistance. Environ. Exp. Bot. 2007 , 59 , 206–216. [ Google Scholar ] [ CrossRef ]
• Wong, C.E.; Li, Y.; Labbe, A.; Guevara, D.; Nuin, P.; Whitty, B.; Moffatt, B.A. Transcriptional profiling implicates novel interactions between abiotic stress and hormonal responses in Thellungiella, a close relative of Arabidopsis. Plant Physiol. 2006 , 140 , 1437–1450. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Alvarez, M.E.; Savouré, A.; Szabados, L. Proline metabolism as regulatory hub. Trends Plant Sci. 2022 , 27 , 39–55. [ Google Scholar ] [ CrossRef ]
• Alnusairi, G.S.; Mazrou, Y.S.; Qari, S.H.; Elkelish, A.A.; Soliman, M.H.; Eweis, M.; ElNahhas, N. Exogenous nitric oxide reinforces photosynthetic efficiency, osmolyte, mineral uptake, antioxidant, expression of stress-responsive genes and ameliorates the effects of salinity stress in wheat. Plants 2021 , 10 , 1693. [ Google Scholar ] [ CrossRef ]
• Ke, Q.; Ye, J.; Wang, B.; Ren, J.; Yin, L.; Deng, X.; Wang, S. Melatonin mitigates salt stress in wheat seedlings by modulating polyamine metabolism. Front. Plant Sci. 2018 , 9 , 914. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Iwaniuk, P.; Kaczyński, P.; Pietkun, M.; Łozowicka, B. Evaluation of titanium and silicon role in mitigation of fungicides toxicity in wheat expressed at the level of biochemical and antioxidant profile. Chemosphere 2022 , 308 , 136284. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Santosh, K.B.; Prianka, H. Melatonin plays multifunctional role in horticultural crops against environmental stresses: A review. Environ. Exp. Bot. 2020 , 176 , 104063. [ Google Scholar ] [ CrossRef ]
• Hattori, A.; Migitaka, H.; Iigo, M.; Itoh, M.; Yamamoto, K.; Ohtani-Kaneko, R.; Reiter, R.J. Identification of melatonin in plants and its effects on plasma melatonin levels and binding to melatonin receptors in vertebrates. Biochem. Mol. Biol. Int. 1995 , 35 , 627–634. [ Google Scholar ] [ PubMed ]
• Xu, T.; Chen, Y.; Kang, H. Melatonin is a potential target for improving post-harvest preservation of fruits and vegetables. Front. Plant Sci. 2019 , 10 , 488368. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wei, W.; Li, Q.T.; Chu, Y.N.; Reiter, R.J.; Yu, X.M.; Zhu, D.H.; Chen, S.Y. Melatonin enhances plant growth and abiotic stress tolerance in soybean plants. J. Exp. Bot. 2015 , 66 , 695–707. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Li, J.; Xie, J.; Yu, J.; Lv, J.; Zhang, J.; Ding, D.; Gao, F. Melatonin enhanced low-temperature combined with low-light tolerance of pepper ( Capsicum annuum L.) seedlings by regulating root growth, antioxidant defense system, and osmotic adjustment. Front. Plant Sci. 2022 , 13 , 998293. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kurt-Celebi, A.; Colak, N.; Torun, H.; Dosedělová, V.; Tarkowski, P.; Ayaz, F.A. Exogenous melatonin ameliorates ionizing radiation-induced damage by modulating growth, osmotic adjustment and photosynthetic capacity in wheat seedlings. Plant Physiol. Biochem. 2022 , 187 , 67–76. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Lou, J.; Wu, C.; Wang, H.; Cao, S.; Wei, Y.; Chen, Y.; Xu, F. Melatonin treatment delays postharvest senescence of broccoli with regulation of carotenoid metabolism. Food Chem. 2023 , 408 , 135185. [ Google Scholar ] [ CrossRef ]
• Wang, J.; Lv, P.; Yan, D.; Zhang, Z.; Xu, X.; Wang, T.; Li, R. Exogenous melatonin improves seed germination of wheat ( Triticum aestivum L.) under salt stress. Int. J. Mol. Sci. 2022 , 23 , 8436. [ Google Scholar ] [ CrossRef ]
• Hamayun, M.; Khan, S.A.; Khan, A.L.; Shin, J.H.; Ahmad, B.; Shin, D.H.; Lee, I.J. Exogenous gibberellic acid reprograms soybean to higher growth and salt stress tolerance. J. Agric. Food Chem. 2010 , 58 , 7226–7232. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ahmad, S.; Cui, W.; Kamran, M.; Ahmad, I.; Meng, X.; Wu, X.; Han, Q. Exogenous application of melatonin induces tolerance to salt stress by improving the photosynthetic efficiency and antioxidant defense system of maize seedling. J. Plant Growth Regul. 2021 , 40 , 1270–1283. [ Google Scholar ] [ CrossRef ]
• Wei, L.; Zhao, H.; Wang, B.; Wu, X.; Lan, R.; Huang, X.; Zheng, Q. Exogenous melatonin improves the growth of rice seedlings by regulating redox balance and ion homeostasis under salt stress. J. Plant Growth Regul. 2022 , 41 , 2108–2121. [ Google Scholar ] [ CrossRef ]
• Reiter, R.J.; Mayo, J.C.; Tan, D.X.; Sainz, R.M.; Alatorre-Jimenez, M.L.; Qin, L. Melatonin as an antioxidant: Under promises but over delivers. J. Pineal Res. 2016 , 61 , 253–278. [ Google Scholar ] [ CrossRef ]
• Arnao, M.B.; Hernández-Ruiz, J. Melatonin: A new plant hormone and/or a plant master regulator? Trends Plant Sci. 2019 , 24 , 38–48. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhang, H.J.; Zhang, N.A.; Yang, R.C.; Wang, L.; Sun, Q.Q.; Li, D.B.; Guo, Y.D. Melatonin promotes seed germination under high salinity by regulating antioxidant systems, ABA and GA4 interaction in cucumber ( Cucumis sativus L.). J. Pineal Res. 2014 , 57 , 269–279. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhang, Y.; Fan, Y.; Rui, C.; Zhang, H.; Xu, N.; Dai, M.; Ye, W. Melatonin improves cotton salt tolerance by regulating ROS scavenging system and Ca 2+ signal transduction. Front. Plant Sci. 2021 , 12 , 693690. [ Google Scholar ] [ CrossRef ]
• Li, Y.; Zhang, Y.; Feng, F.; Liang, D.; Cheng, L.; Ma, F.; Shi, S. Overexpression of a Malus vacuolar Na + /H + antiporter gene (MdNHX1) in apple rootstock M. 26 and its influence on salt tolerance. Plant Cell Tissue Organ Cult. 2010 , 102 , 337–345. [ Google Scholar ] [ CrossRef ]
• Shao, Q.; Han, N.; Ding, T.; Zhou, F.; Wang, B. SsHKT1; 1 is a potassium transporter of the C3 halophyte Suaeda salsa that is involved in salt tolerance. Funct. Plant Biol. 2014 , 41 , 790–802. [ Google Scholar ] [ CrossRef ]
• Chen, L.; Lu, B.; Liu, L.; Duan, W.; Jiang, D.; Li, J.; Bai, Z. Melatonin promotes seed germination under salt stress by regulating ABA and GA3 in cotton ( Gossypium hirsutum L.). Plant Physiol. Biochem. 2021 , 162 , 506–516. [ Google Scholar ] [ CrossRef ]
• Lee, H.Y.; Byeon, Y.; Back, K. Melatonin as a signal molecule triggering defense responses against pathogen attack in Arabidopsis and tobacco. J. Pineal Res. 2014 , 57 , 262–268. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Lee, H.Y.; Byeon, Y.; Tan, D.X.; Reiter, R.J.; Back, K. Arabidopsis serotonin N-acetyltransferase knockout mutant plants exhibit decreased melatonin and salicylic acid levels resulting in susceptibility to an avirulent pathogen. J. Pineal Res. 2015 , 58 , 291–299. [ Google Scholar ] [ CrossRef ]
• Shi, H.; Jiang, C.; Ye, T.; Tan, D.X.; Reiter, R.J.; Zhang, H.; Chan, Z. Comparative physiological, metabolomic, and transcriptomic analyses reveal mechanisms of improved abiotic stress resistance in bermudagrass ( Cynodon dactylon L. Pers.) by exogenous melatonin. J. Exp. Bot. 2015 , 66 , 681–694. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Cao, Y.; Zhou, X.; Song, H.; Zhang, M.; Jiang, C. Advances in deciphering salt tolerance mechanism in maize. Crop J. 2023 , 11 , 1001–1010. [ Google Scholar ] [ CrossRef ]
• Zhang, N.; Zhang, H.J.; Sun, Q.Q.; Cao, Y.Y.; Li, X.; Zhao, B.; Guo, Y.D. Proteomic analysis reveals a role of melatonin in promoting cucumber seed germination under high salinity by regulating energy production. Sci. Rep. 2017 , 7 , 503. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Li, J.; Liu, J.; Zhu, T.; Zhao, C.; Li, L.; Chen, M. The role of melatonin in salt stress responses. Int. J. Mol. Sci. 2019 , 20 , 1735. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Jain, M.; Khurana, J.P. Transcript profiling reveals diverse roles of auxin-responsive genes during reproductive development and abiotic stress in rice. FEBS J. 2009 , 276 , 3148–3162. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wei, T.; Sun, Y.; Li, H.; Hua, L.; Ren, X.; Jia, H.; Guo, J. Methyl jasmonate triggers cadmium detoxification in tomato plants via depressing Cd 2+ influx, altering the subcellular distribution, and chemical forms of cadmium. Water Air Soil Pollut. 2022 , 233 , 460. [ Google Scholar ] [ CrossRef ]
• Zhao, Y. Essential roles of local auxin biosynthesis in plant development and in adaptation to environmental changes. Annu. Rev. Plant Biol. 2018 , 69 , 417–435. [ Google Scholar ] [ CrossRef ]
• Zhang, L.; Gao, M.; Hu, J.; Zhang, X.; Wang, K.; Ashraf, M. Modulation role of abscisic acid (ABA) on growth, water relations and glycinebetaine metabolism in two maize ( Zea mays L.) cultivars under drought stress. Int. J. Mol. Sci. 2012 , 13 , 3189–3202. [ Google Scholar ] [ CrossRef ]
• Yang, J.; Duan, G.; Li, C.; Liu, L.; Han, G.; Zhang, Y.; Wang, C. The crosstalks between jasmonic acid and other plant hormone signaling highlight the involvement of jasmonic acid as a core component in plant response to biotic and abiotic stresses. Front. Plant Sci. 2019 , 10 , 1349. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhang, H.; Zhu, J.; Gong, Z.; Zhu, J.K. Abiotic stress responses in plants. Nat. Rev. Genet. 2022 , 23 , 104–119. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yang, X.; Zhang, Y.; Liu, T.; Shi, J.; Qi, M.; Liu, Y.; Li, T. Integrated physiological, transcriptomic, and proteomic analyses reveal the regulatory role of melatonin in tomato plants’ response to low night temperature. Antioxidants 2022 , 11 , 2060. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yan, D.; Wang, J.; Lu, Z.; Liu, R.; Hong, Y.; Su, B.; Li, R. Melatonin-Mediated Enhancement of Photosynthetic Capacity and Photoprotection Improves Salt Tolerance in Wheat. Plants 2023 , 12 , 3984. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Talaat, N.B.; Todorova, D. Antioxidant machinery and glyoxalase system regulation confers salt stress tolerance to wheat ( Triticum aestivum L.) plants treated with melatonin and salicylic Acid. J. Soil Sci. Plant Nutr. 2022 , 22 , 3527–3540. [ Google Scholar ] [ CrossRef ]
• Liang, C.; Zheng, G.; Li, W.; Wang, Y.; Hu, B.; Wang, H.; Chu, C. Melatonin delays leaf senescence and enhances salt stress tolerance in rice. J. Pineal Res. 2015 , 59 , 91–101. [ Google Scholar ] [ CrossRef ]
• Alharbi, B.M.; Elhakem, A.H.; Alnusairi, G.S.; Soliman, M.H.; Hakeem, K.R.; Hasan, M.M.; Abdelhamid, M.T. Exogenous application of melatonin alleviates salt stress-induced decline in growth and photosynthesis in Glycine max (L.) seedlings by improving mineral uptake, antioxidant and glyoxalase system. Plant Soil Environ. 2021 , 67 , 208–220. [ Google Scholar ] [ CrossRef ]
• Altaf, M.A.; Shahid, R.; Ren, M.X.; Altaf, M.M.; Khan, L.U.; Shahid, S.; Jahan, M.S. Melatonin alleviates salt damage in tomato seedling: A root architecture system, photosynthetic capacity, ion homeostasis, and antioxidant enzymes analysis. Sci. Hortic. 2021 , 285 , 110145. [ Google Scholar ] [ CrossRef ]
• Miransari, M.; Smith, D.L. Plant hormones and seed germination. Environ. Exp. Bot. 2014 , 99 , 110–121. [ Google Scholar ] [ CrossRef ]
• Waadt, R. Phytohormone signaling mechanisms and genetic methods for their modulation and detection. Curr. Opin. Plant Biol. 2020 , 57 , 31–40. [ Google Scholar ] [ CrossRef ]
• Luo, Y.; Li, W.; Huang, C.; Yang, J.; Jin, M.; Chen, J.; Wang, Z. Exogenous abscisic acid coordinating leaf senescence and transport of assimilates into wheat grains under drought stress by regulating hormones homeostasis. Crop J. 2021 , 9 , 901–914. [ Google Scholar ] [ CrossRef ]
• Jiang, Z.; Zhu, H.; Zhu, H.; Tao, Y.; Liu, C.; Liu, J.; Li, M. Exogenous ABA enhances the antioxidant defense system of maize by regulating the AsA-GSH cycle under drought stress. Sustainability 2022 , 14 , 3071. [ Google Scholar ] [ CrossRef ]
• He, J.; Jin, Y.; Palta, J.A.; Liu, H.Y.; Chen, Z.; Li, F.M. Exogenous ABA induces osmotic adjustment, improves leaf water relations and water use efficiency, but not yield in soybean under water stress. Agronomy 2019 , 9 , 395. [ Google Scholar ] [ CrossRef ]
• Zhang, Y.; Li, Y.; Hassan, M.J.; Li, Z.; Peng, Y. Indole-3-acetic acid improves drought tolerance of white clover via activating auxin, abscisic acid and jasmonic acid related genes and inhibiting senescence genes. BMC Plant Biol. 2020 , 20 , 150. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhao, Y.; Song, C.; Qi, S.; Lin, Q.; Duan, Y. Jasmonic acid and salicylic acid induce the accumulation of sucrose and increase resistance to chilling injury in peach fruit. J. Sci. Food Agric. 2021 , 101 , 4250–4255. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, Y.; Shi, J.; Ren, Z.; Fan, X.; Li, R.; Yu, C.; Duan, L. Physiological and transcriptomic analyses of the effects of coronatine on drought tolerance in Carex leucochlora . Environ. Exp. Bot. 2023 , 206 , 105184. [ Google Scholar ] [ CrossRef ]
• Jing, T.; Liu, K.; Wang, Y.; Ai, X.; Bi, H. Melatonin positively regulates both dark-and age-induced leaf senescence by reducing ROS accumulation and modulating abscisic acid and auxin biosynthesis in cucumber plants. Int. J. Mol. Sci. 2022 , 23 , 3576. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Shi, H.; Reiter, R.J.; Tan, D.X.; Chan, Z. INDOLE-3-ACETIC ACID INDUCIBLE 17 positively modulates natural leaf senescence through melatonin-mediated pathway in Arabidopsis. J. Pineal Res. 2015 , 58 , 26–33. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Keunen, E.L.S.; Peshev, D.; Vangronsveld, J.; Van Den Ende, W.I.M.; Cuypers, A.N.N. Plant sugars are crucial players in the oxidative challenge during abiotic stress: Extending the traditional concept. Plant Cell Environ. 2013 , 36 , 1242–1255. [ Google Scholar ] [ CrossRef ]
• Liu, Y.; Zhang, W.; Elango, D.; Liu, H.; Jin, D.; Wang, X.; Wu, Y. Metabolome and transcriptome analysis reveals molecular mechanisms of watermelon under salt stress. Environ. Exp. Bot. 2023 , 206 , 105200. [ Google Scholar ] [ CrossRef ]
• Thalmann, M.; Santelia, D. Starch as a determinant of plant fitness under abiotic stress. New Phytol. 2017 , 214 , 943–951. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Mahmoud, L.M.; Stanton, D.; Amin, B.H.; Grosser, J.W.; Dutt, M. Overexpression of the Arabidopsis NPR1 gene confers enhanced salt tolerance by regulating antioxidant and starch accumulation in citrus. Plant Cell Tissue Organ Cult. 2022 , 150 , 695–707. [ Google Scholar ] [ CrossRef ]
• Punia, H.; Tokas, J.; Mor, V.S.; Bhuker, A.; Malik, A.; Singh, N.; Hefft, D.I. Deciphering reserve mobilization, antioxidant potential, and expression analysis of starch synthesis in sorghum seedlings under salt stress. Plants 2021 , 10 , 2463. [ Google Scholar ] [ CrossRef ]
• Li, X.; Wang, C.; Cheng, J.; Zhang, J.; da Silva, J.A.T.; Liu, X.; Sun, H. Transcriptome analysis of carbohydrate metabolism during bulblet formation and development in Lilium davidii var. unicolor. BMC Plant Biol. 2014 , 14 , 358. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Abdallah, M.S.; Abdelgawad, Z.A.; El-Bassiouny, H.M.S. Alleviation of the adverse effects of salinity stress using trehalose in two rice varieties. S. Afr. J. Bot. 2016 , 103 , 275–282. [ Google Scholar ] [ CrossRef ]
• Wang, Y.; Zhao, H.; Xu, L.; Zhang, H.; Xing, H.; Fu, Y.; Zhu, L. PUB30-mediated downregulation of the HB24-SWEET11 module is involved in root growth inhibition under salt stress by attenuating sucrose supply in Arabidopsis. New Phytol. 2023 , 237 , 1667–1683. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhou, Z.; Liu, J.; Meng, W.; Sun, Z.; Tan, Y.; Liu, Y.; Tan, M.; Wang, B.; Yang, J. Integrated Analysis of Transcriptome and Metabolome Reveals Molecular Mechanisms of Rice with Different Salinity Tolerances. Plants 2023 , 12 , 3359. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• He, D.; Yang, P. Proteomics of rice seed germination. Front. Plant Sci. 2013 , 4 , 246. [ Google Scholar ] [ CrossRef ]
• Rajora, N.; Vats, S.; Raturi, G.; Thakral, V.; Kaur, S.; Rachappanavar, V.; Kumar, M.; Kesarwani, A.K.; Sonah, H.; Sharma, T.R.; et al. Seed priming with melatonin: A promising approach to combat abiotic stress in plants. Plant Stress 2022 , 4 , 100071. [ Google Scholar ] [ CrossRef ]
• Giannopolitis, C.N.; Ries, S.K. Superoxide dismutases, I. Occurrence in higher plants. Plant Physiol. 1977 , 59 , 309–314. [ Google Scholar ] [ CrossRef ]
• Lu, X.; Min, W.; Shi, Y.; Tian, L.; Li, P.; Ma, T.; Luo, C. Exogenous melatonin alleviates alkaline stress by removing reactive oxygen species and promoting antioxidant defence in rice seedlings. Front. Plant Sci. 2022 , 13 , 849553. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zhao, C.; Guo, H.; Wang, J.; Wang, Y.; Zhang, R. Melatonin enhances drought tolerance by regulating leaf stomatal behavior, carbon and nitrogen metabolism, and related gene expression in maize plants. Front. Plant Sci. 2021 , 12 , 779382. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Livak, K.J.; Schmittgen, T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2 −ΔΔCT method. Methods 2001 , 25 , 402–408. [ Google Scholar ] [ CrossRef ] [ PubMed ]

Click here to enlarge figure

Share and Cite

Wang, J.; Yan, D.; Liu, R.; Wang, T.; Lian, Y.; Lu, Z.; Hong, Y.; Wang, Y.; Li, R. The Physiological and Molecular Mechanisms of Exogenous Melatonin Promote the Seed Germination of Maize ( Zea mays L.) under Salt Stress. Plants 2024 , 13 , 2142. https://doi.org/10.3390/plants13152142

Wang J, Yan D, Liu R, Wang T, Lian Y, Lu Z, Hong Y, Wang Y, Li R. The Physiological and Molecular Mechanisms of Exogenous Melatonin Promote the Seed Germination of Maize ( Zea mays L.) under Salt Stress. Plants . 2024; 13(15):2142. https://doi.org/10.3390/plants13152142

Wang, Jiajie, Di Yan, Rui Liu, Ting Wang, Yijia Lian, Zhenzong Lu, Yue Hong, Ye Wang, and Runzhi Li. 2024. "The Physiological and Molecular Mechanisms of Exogenous Melatonin Promote the Seed Germination of Maize ( Zea mays L.) under Salt Stress" Plants 13, no. 15: 2142. https://doi.org/10.3390/plants13152142

Article Metrics

Article access statistics, supplementary material.

ZIP-Document (ZIP, 362 KiB)

Further Information

Mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

The interactive effect of seed spacing and orientation influences the time to germination and physiological response in tomato ( Lycopersicon esculentum Mill.)

• Original Article
• Published: 02 August 2024

Cite this article

• V. K. Varsha 1 ,
• N. Kruthika 1 ,
• B. K. Brindha Shree 1 ,
• B. A. Mahesh 1 ,
• Cinny Gaurav Singh 1 &
• M. N. Jithesh   ORCID: orcid.org/0000-0002-0355-0425 1  

Explore all metrics

Several environmental stimuli are recognized to impact seed germination, However, very little is known about how different stimuli interact to optimize germination over time. In this study, we conducted an in vitro investigation in agar media to examine the combined effect of seed density and different seed orientation conditions on the relative seedling emergence of Lycopersicon esculentum Mill. Plants sense gravity as a directional environmental cue for controlling growth orientation and growth architecture for survival. We observed that the seedling emergence was faster in horizontally oriented seeds under limiting seed spacing conditions and, in vertical orientation with greater spacing conditions. We also found that the radicle length was comparatively higher in seeds oriented vertically than horizontally. This study also revealed that seedlings under limiting spacing conditions showed higher MDA, a physiological indicator of elevated stress. From this study, it can be concluded that seed spacing and the gravitational stimulus influence seed germination and seedling growth characteristics in tomato.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Abdul-Baki, A. A., & Anderson, J. D. (1973). Vigor determination in soybean seed by multiple criteria 1. Crop Science, 13 (6), 630–633.

Article   Google Scholar  

Anjum, N. A., Sofo, A., Scopa, A., Roychoudhury, A., Gill, S. S., Iqbal, M., & Ahmad, I. (2015). Lipids and proteins—major targets of oxidative modifications in abiotic stressed plants. Environmental Science and Pollution Research, 22 , 4099–4121.

Article   CAS   PubMed   Google Scholar  

Aycan, M., Kayan, M., & Yildiz, M. (2014). The effect of invitro spacing competition on shoot regeneration from cotyledon node explants of Lathyruschrysanthus Boiss . Australian Journal of Crop Science, 8 , 1019–1023.

Google Scholar  

Benech Arnold, R. L., Fenner, M., & Edwards, P. J. (1991). Changes in germinability, ABA content and ABA embryonic sensitivity in developing seeds of Sorghum bicolor (L.) Moench. induced by water stress during grain filling. New Phytologist, 118 (2), 339–347.

Bewley, J. D. (1997). Seed germination and dormancy. The Plant Cell, 9 (7), 1055.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Bor, M., Özdemir, F., & Türkan, I. (2003). The effect of salt stress on lipid peroxidation and antioxidants in leaves of sugar beet Beta vulgaris L. and wild beet Beta maritima L. Plant Science, 164 (1), 77–84.

Article   CAS   Google Scholar  

Borchert, M. I., Davis, F. W., Michaelsen, J., & Oyler, L. D. (1989). Interactions of factors affecting seedling recruitment of blue oak ( Quercus douglasii ) in California. Ecology, 70 (2), 389–404.

Bosy, J., & Aarssen, L. W. (1995). The effect of seed orientation on germination in a uniform environment: differential success without genetic or environmental variation. Journal of Ecology, 83 , 769–773.

Classic Murashige, T., & Skoog, F. A. (1962). revised medium for rapid growth and bioassays with tobacco tissue cultures. Plant Physiology, 15 , 473–497.

Craine, J. M., & Dybzinski, R. (2013). Mechanisms of plant competition for nutrients, water and light. Functional Ecology, 27 , 833–840.

Daudi, A., Cheng, Z., O’Brien, J. A., Mammarella, N., Khan, S., Ausubel, F. M., & Bolwell, G. P. (2012). The apoplastic oxidative burst peroxidase in Arabidopsis is a major component of pattern-triggered immunity. The Plant Cell, 24 , 275–287.

Demidchik, V., Straltsova, D., Medvedev, S. S., Pozhvanov, A. G., Sokolik, A., & Yurin, V. (2014). Stress-induced electrolyte leakage: the role of K+- permeable channels and involvement in programmed cell death and metabolic adjustment. Journal of Experimental Biology, 65 (5), 1259–1270.

CAS   Google Scholar  

Deng, J., Ran, J., Wang, Z., Fan, Z., Wang, G., Ji, M., & Brown, J. H. (2012). Models and tests of optimal density and maximal yield for crop plants. Proceedings of the National Academy of Sciences, 109 (39), 15823–15828.

Donohue, K., Rubio de Casas, R., Burghardt, L., Kovach, K., & Willis, C. G. (2010). Germination, post germination adaptation, and species ecological ranges. Annual Review of Ecology, Evolution and Systematics, 41 , 293–319.

Dyer, A. R., Fenech, A., & Rice, K. J. (2000). Accelerated seedling emergence in interspecific competitive neighbourhoods. Ecology Letters, 3 (6), 523–529.

El Aou-ouad, H., Medrano, H., Lamarti, A., & Gulías, J. (2014). Seed germination at different temperatures and seedling emergence at different depths of Rhamnus spp. Central European Journal of Biology, 9 , 569–578.

Esmon, C. A., Pedmale, U. V., & Liscum, E. (2004). Plant tropisms: providing the power of movement to a sessile organism. International Journal of Developmental Biology, 49 (5–6), 665–674.

Fankhauser, C., & Christie, J. M. (2015). Plant phototropic growth. Current Biology, 25 (9), R384–R389.

Gioria, M., & Osborne, B. A. (2014). Resource competition in plant invasions: Emerging patterns and research needs. Frontiers in Plant Science, 5 , 501.

Article   PubMed   PubMed Central   Google Scholar  

Gioria, M., Pyšek, P., & Osborne, B. A. (2018). Timing is everything: Does early and late germination favor invasions by herbaceous alien plants? Journal of Plant Ecology, 11 (1), 4–16.

Gómez-Maqueo, X., Soriano, D., Velázquez-Rosas, N., Alvarado-López, S., Jiménez-Durán, K., Garciadiego, M. D. M., & Gamboa-deBuen, A. (2020). The seed water content as a time-independent physiological trait during germination in wild tree species such as Ceiba aesculifolia . Science and Reports, 10 (1), 10429.

Guo, C. R., Wang, Z. L., & Lu, J. Q. (2010). Seed germination and seedling development of Prunus armeniaca under different burial depths in soil. Journal of Forest Research, 21 (4), 492–496.

Guo, Y., Jia, W., Song, J., Wang, D., Chen, M., & Wang, B. (2012). Thellungilla halophila is more adaptive to salinity than Arabidopsis thaliana at stages of seed germination and seedling establishment. Acta Physiologiae Plantarum, 34 , 1287–1294.

Haizel, K. A., & Harper, J. L. (1973). The effects of density and the timing of removal on interference between barley, white mustard and wild oats. Journal of Applied Ecology, 10 (1), 23–31.

Heath, R. L., & Packer, L. (1968). Photoperoxidation in isolated chloroplasts: I. Kinetics and stoichiometry of fatty acid peroxidation. Archives of Biochemistry and Biophysics, 125 (1), 189–198.

Hodge, A., Berta, G., Doussan, C., Merchan, F., & Crespi, M. (2009). Plant root growth, architecture and function. Plant and Soil, 321 , 153–187.

Hogenbirk, J. C., & Reader, R. J. (1989). Biotic versus abiotic control of plant density: studies of Medicago lupulina L. on a topographic gradient. Journal of Biogeography, 16 , 269–277.

Hoson, T., Soga, K., Mori, R., Saiki, M., Wakabayash, K., Kamisaka, S., & Yamashita, M. (1999). Morphogenesis of rice and Arabidopsis seedlings in space. Journal of Plant Research, 112 , 477–486.

Huang, S. Q., Liu, G. X., & Han, J. G. (2007). Effect of seed mass and sowing depth on seedling establishment. Pratacultural Sci, 24 (6), 44–49.

Humphries, T., Chauhan, B. S., & Florentine, S. K. (2018). Environmental factors effecting the germination and seedling emergence of two populations of an aggressive agricultural weed; Nassella trichotoma . PLoS ONE, 13 (7), e0199491.

Ievinsh, G. (2023). Water content of plant tissues: So simple that almost forgotten? Plants, 12 , 1238.

International Seed Testing Association (ISTA). (2015). International rules for seed testing . Switzerland: International Seed Testing Association Bassersdorf.

Izzo, L. G., & Aronne, G. (2021). Root tropisms: New insights leading the growth direction of the hidden half. Plants (basel), 10 , 220.

CAS   PubMed   Google Scholar  

Izzo, L. G., Romano, L. E., De Pascale, S., Mele, G., Gargiulo, L., & Aronne, G. (2019). Chemotropic vs hydrotropic stimuli for root growth orientation in microgravity. Frontiers in Plant Science, 10 , 1547.

Javaid, M. M., Mahmood, A., Alshaya, D. S., AlKahtani, M. D., Waheed, H., Wasaya, A., & Fiaz, S. (2022). Influence of environmental factors on seed germination and seedling characteristics of perennial ryegrass ( Lolium perenne L.). Science and Reports, 12 (1), 9522.

Joo, J. H., Bae, Y. S., & Lee, J. S. (2001). Role of auxin-induced reactive oxygen species in root gravitropism. Plant Physiology, 126 (3), 1055–1060.

Karlova, R., Boer, D., Hayes, S., & Testerink, C. (2021). Root plasticity under abiotic stress. Plant Physiology, 187 (3), 1057–1070.

Kawamoto, N., & Morita, M. T. (2022). Gravity sensing and responses in the coordination of the shoot gravitropic setpoint angle. New Phytologist, 236 (5), 1637–1654.

Kevin, K. K., Bernard, N. K., Laurent, K. K., Ignace, K. K., Pierre, B. J., & Arsene, Z. B. I. (2015). Effects of seed orientation and sowing depths on germination, seedling vigor and yield in oleaginous type of bottle gourd, Lagenaria siceraria (Molina Standl). Internation Journal of Scientific Research in  Biological Sciences, 4 (12), 46–53.

Kumari, A., Nongmaithem, S., Devulapalli, S., Sreelakshmi, Y., & Sharma, R. (2021). Tomato root senses horizontal/vertical mechanical impedance and correspondingly modulates root/shoot metabolome. Environmental and Experimental Botany, 191 , 104608.

Kumari, S., PanjabiNeeSabharwal, V., Kushwaha, H. R., Sopory, S. K., Singla-Pareek, S. L., & Pareek, A. (2009). Transcriptome map for seedling stage specific salinity stress response indicates a specific set of genes as candidate for saline tolerance in Oryza sativa L. Functional & Integrative Genomics, 9 , 109–123.

Lynch, J. (1995). Root architecture and plant productivity. Plant Physiology, 109 (1), 7.

Lyon, C. J., & Yokoyama, K. (1966). Orientation of wheat seedling organs in relation to gravity. Plant Physiology, 41 (6), 1065–1073.

Ma, Z., & Hasenstein, K. H. (2006). The onset of gravisensitivity in the embryonic root of flax. Plant Physiology, 140 (1), 159–166.

Maffei, M. E., Mithöfer, A., & Boland, W. (2007). Before gene expression: early events in plant–insect interaction. Trends in Plant Science, 12 (7), 310–316.

Marangoni, A. G., Palma, T., & Stanley, D. W. (1996). Membrane effects in postharvest physiology. Postharvest Biology and Technology, 7 , 193–217.

Massa, G. D., & Gilroy, S. (2003). Touch modulates gravity sensing to regulate the growth of primary roots of Arabidopsis thaliana . The Plant Journal, 33 , 435–445.

Article   PubMed   Google Scholar  

Muthert, L. W. F., Izzo, L. G., Van Zanten, M., & Aronne, G. (2020). Root tropisms: investigations on earth and in space to unravel plant growth direction. Frontiers in Plant Science, 10 , 1807.

Nejadsadeghi, L., Maali-Amiri, R., Zeinali, H., Ramezanpour, S., & Sadeghzade, B. (2015). Membrane fatty acid compositions and cold-induced responses in tetraploid and hexaploid wheat. Molecular Biology Reports, 42 , 363–372.

Okamoto, T., Takatani, S., Noutoshi, Y., Motose, H., & Takahashi, T. (2018). Omeprazole enhances mechanical stress-induced root growth reduction in Arabidopsis thaliana . Plant and Cell Physiology, 59 (8), 1581–1591.

Orrock, J. L., & Christopher, C. C. (2010). Density of intraspecific competitors determines the occurrence and benefits of accelerated germination. American Journal of Botany, 97 , 694–699.

Paul, A. L., Amalfitano, C. E., & Ferl, R. J. (2012). Plant growth strategies are remodeled by spaceflight. BMC Plant Biology, 12 , 1–15.

Prusiński, J., & Borowska, M. (2022). Effect of planting density and row spacing on the yielding and morphological features of pea ( Pisum sativum L.). Agronomy, 12 (3), 715.

Rees, M. (1997). Seed dormancy. In: Crawley MJ (ed) Plant ecology , 2nd edn., pp 214–238.

Ross, M. A., & Harper, J. L. (1972). Occupation of biological space during seedling establishment. Journal of Ecology, 60 , 77–88.

Ryosuke, T., & Yoichiro, K. (2013). A quick method to estimate root length in each diameter class using freeware ImageJ. Plant Production Sciences, 16 , 9–11.

Sajib, S. A., Kandel, M., Prity, S. A., Oukacine, C., Gakière, B., & Merendino, L. (2023). Role of plastids and mitochondria in the early development of seedlings in dark growth conditions. Frontiers in Plant Science, 14 , 1272822.

Takahashi, H. (1997). Hydrotropism: the current state of our knowledge. Journal of Plant Research, 110 , 163–169.

Takahashi, N., Yamazaki, Y., Kobayashi, A., Higashitani, A., & Takahashi, H. (2003). Hydrotropism interacts with gravitropism by degrading amyloplasts in seedling roots of Arabidopsis and radish. Plant Physiology, 132 (2), 805–810.

Tanaka, K., Gilroy, S., Jones, A. M., & Stacey, G. (2010). Extracellular ATP signaling in plants. Trends in Cell Biology, 20 (10), 601–608.

Tarnawa, Á., Kende, Z., Sghaier, A. H., Kovács, G. P., Gyuricza, C., & Khaeim, H. (2023). Effect of abiotic stresses from drought, temperature, and density on germination and seedling growth of barley ( Hordeum vulgare L.). Plants, 12 (9), 179.

Tielbörger, K., & Prasse, R. (2009). Do seeds sense each other? Testing for density-dependent germination in desert perennial plants. Oikos, 118 , 792–800.

Verma, K., Mehta, S. K., & Shekhawat, G. S. (2013). Nitric oxide (NO) counteracts cadmium induced cytotoxic processes mediated by reactive oxygen species (ROS) in Brassica juncea : cross-talk between ROS, NO and antioxidant responses. BioMetals, 26 , 255–269.

Villacampa, A., Sora, L., Herranz, R., Medina, F. J., & Ciska, M. (2021). Analysis of graviresponse and biological effects of vertical and horizontal clinorotation in Arabidopsis thaliana root tip. Plants, 10 (4), 734.

Wei, N., Kwok, S. F., Von Arnim, A. G., Lee, A., McNellis, T. W., Piekos, B., & Deng, X. W. (1994). Arabidopsis COP8, COP10, and COP11 genes are involved in repression of photomorphogenic development in darkness. The Plant Cell, 6 (5), 629–643.

CAS   PubMed   PubMed Central   Google Scholar  

Zhang, Y., Xu, Z., Li, J., & Wang, R. (2021). Optimum planting density improves resource use efficiency and yield stability of rainfed maize in semiarid climate. Frontiers in Plant Science, 12 , 752606.

Download references

Acknowledgements

We are grateful to Jain University (Deemed-to-be University) for the infrastructure and facilities provided to the authors. The authors would like to thank the Indian Institute of Horticultural Research (Hessarghata, Bengaluru, India) for providing us with tomato seeds. We also thank the anonymous reviewers for their helpful suggestions and comments.

Authors are grateful to Jain University (Deemed-to-be University) (Karnataka, India) for providing us with financial support.

Author information

Authors and affiliations.

Department of Biotechnology and Genetics, School of Sciences, Jain (Deemed-to-Be University), # 34, 1st Cross, JC Road, Bengaluru, Karnataka, 560 027, India

V. K. Varsha, N. Kruthika, B. K. Brindha Shree, B. A. Mahesh, Cinny Gaurav Singh & M. N. Jithesh

You can also search for this author in PubMed   Google Scholar

Contributions

VVK conducted and repeated the experiments mentioned in the manuscript. KN helped with the statistical analysis. VVK and BSBK wrote the initial draft. JMN reviewed the draft and all authors have agreed on the final draft.

Corresponding author

Correspondence to M. N. Jithesh .

Ethics declarations

Conflict of interest.

The authors declare that they do not have a conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (DOCX 44286 KB)

Rights and permissions.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Varsha, V.K., Kruthika, N., Brindha Shree, B.K. et al. The interactive effect of seed spacing and orientation influences the time to germination and physiological response in tomato ( Lycopersicon esculentum Mill.). Plant Physiol. Rep. (2024). https://doi.org/10.1007/s40502-024-00810-7

Download citation

Received : 08 February 2024

Accepted : 13 July 2024

Published : 02 August 2024

DOI : https://doi.org/10.1007/s40502-024-00810-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Seedling emergence
• Seed spacing
• Gravisensing
• Lycopersicon esculentum Mill.
• Agar medium
• Find a journal
• Publish with us
• Track your research

• Open access
• Published: 31 July 2024

Individual or successiveseed priming with nitric oxide and calcium toward enhancing salt tolerance of wheat crop through early ROS detoxification and activation of antioxidant defense

• Rasha M. El-Shazoly 1 ,
• H. M. A. Hamed 2 &
• Mahmoud M. El-Sayed 2  

BMC Plant Biology volume  24 , Article number:  730 ( 2024 ) Cite this article

69 Accesses

Metrics details

Despite the considerable efforts reported so far to enhance seed priming, novel ideas are still needed to be suggested to this sustainable sector of agri-seed industry. This could be the first study addressing the effect of nitric oxide (NO) under open field conditions. The impacts of seed redox-priming using sodium nitroprusside (SNP) and osmo-priming with calcium chloride (CaCl 2 ), both applied individually or successively, were investigated under salinity stress conditions on wheat plants ( Triticum aestivum L. ). Various parameters, including water relations, growth, yield, photosynthetic pigments, and antioxidant activities (enzymatic and non-enzymatic), were recorded to assess the outcomes of these priming agents on mitigating the negative impacts of salinity stress on wheat plants. Water consumptive use (ETa) and irrigation water applied (IWA) decreased with seeds priming. Successive priming with SNP + CaCl 2 induced the greatest values of crop water productivity (CWP), irrigation water productivity (IWP), seed index, grain yield and grain nitrogen content.Under salinity stress, the dry weight of plants was decreased. However, hydro-priming and successive chemical priming agents using combinations of calcium chloride and sodium nitroprusside (CaCl 2  + SNP & SNP + CaCl 2 ) preserved growth under salinity stress.Individual priming with sodium nitroprusside (SNP) and calcium chloride (CaCl 2 ) resulted in the lowest recorded content of sodium in the shoot, with a value of 2 ppm. On the other hand, successive priming using CaCl 2  + SNP or SNP + CaCl 2 induced the contents of potassium in the shoot, with values of 40 ppm and 39 ppm, respectively. Malondialdehyde decreased in shoot significantly withapplicationof priming agents. Successive priming with CaCl 2  + SNP induced the highest proline contents in shoot (6 µg/ g FW). The highest value of phenolics and total antioxidants contents in shoot were recorded under successive priming using CaCl 2  + SNP and SNP + CaCl 2 .Priming agents improved the activities of ascorbate peroxidase and catalase enzymes. The successive priming improved water relations (ETa, IWA, CWP and IWP) and wheat growth and productivity under salinity stress more than individual priming treatments.

Peer Review reports

Introduction

Recently, climate change, the eco-toxicological practices, and environmental stressors have posed many threats to agriculture sustainability all over the globe. They have generated serious challenges to biodiversity, food security, and sustainable agriculture resources [ 1 , 2 ]. Recent global monitoring of climate changes has revealed a serious disturbing trend of buildup of extreme weather consequences, including the appearance of more severe and frequent periods of drought. Salt stress affects more than 20% of cultivated land worldwide as a consequence of the growing use of poor-quality water for irrigation and hence soil salinization [ 3 ]. The recorded increase in the concentration of atmospheric greenhouse gases, particularly CO 2 , and climate warming is unequivocal [ 4 ]. While elevated CO 2 levels may offer benefits for plants, they indirectly pose threats of heat stress, drought, and salinity [ 5 ]. The challenges projected from future climate change and the resulting impacts on global sustainable agriculture propose negative impacts on crop yields averages and yield variability [ 5 ]. On a global scale, it is obvious that among all abiotic stressors, salinity and drought are the main limiting factors of both growth and productivity of crops [ 6 ]. The most sensitive stages for crop development affected by abiotic stresses are seed sprouting and seedling establishment. Disturbance in plants water absorption occur as a result of hyperosmotic stress and salinity [ 7 ]. Seed germination and seedling sprouting time are negatively hindered due to high salt concentration, delayed germination is associated with high salt concentrations, and this correlation is also influenced by whether the salt concentration is low or high. Low salt concentration (i.e., below the optimum level) induces dormancy, whereas high salt concentrations above the optimum level reduce the percentage of germination and hamper the process of sprouting due to water loss as a consequence of increased transpiration and high salt accumulation around the plant roots [ 8 ]. Many molecular and physiological mechanisms are adapted by the plants to manage the stress condition such as ionic tolerance, osmotic tolerance, and tissue tolerance [ 9 , 10 ]. Kranner et al. [ 11 ] characterized the applications of stress in seed biology. The authors discussed the seed life cycle in light of the eustress-distress concept. In the field of seed and plant science, the concept of stress has been adopted from biomedical sciences and is divided into two types. The first type, known as eustress, activates a positive effect and stimulates a response. The second type, distress, causes a negative effect. According to this classification, seed priming (as an artificial process) could be considered eustress rather than distress as priming application shows similarity with the natural process of hydration-rehydration cycles, which seeds undergo after sowing in the soil [ 12 ]. Sustaining crop productivity and the ability to adapt to frequent stress impacts is a top urgent issue [ 13 ]. Thus, the priming technique is a very promising tactic used to enhance plant stress tolerance [ 14 ]. In brief, priming refers to the pre-exposure of seeds to stimulating factors, which helps plants develop tolerance towards future abiotic or biotic stressors [ 15 ]. It is considered as a potential technique to enhance stress tolerance, and it is connected to alert mode or “plant stress memory” [ 15 ]. As it is considered a cost-efficient approach [ 16 ], it is recently a prominent strategy for climate change scenarios [ 17 ]. There are many types of priming techniques and their mechanisms; hydro-priming, Osmo priming, Chemo-priming, and Redox priming [ 18 , 19 , 20 ]. Redox priming is a technique that utilizes natural and/or synthetic redox compounds, including antioxidants such as ascorbic acid, glutathione, and tocopherol, as well as hydrogen peroxide and sodium nitroprusside (SNP). This approach has been proved to improve seed germination and seedling establishment, both under stressed and unstressed conditions [ 21 , 22 ]. Exogenous application of gaseous nitric oxide (NO) in the form of SNP has gained importance. Nitric oxide has volatile and lipophilic nature and acts as a free radical [ 23 ]. Besides its regulatory roles in plants in improving seed germination and seedling growth [ 24 ], it also plays a protective role against different abiotic stresses particularly salinity [ 25 ]. Earlier studies have suggested its role in improving salt tolerance in many plants such as tomato, rice and wheat [ 26 , 27 , 28 ].

In plants, if large amount of calcium applied in the field it could promote calcification of soil, particularly, in the alkali saline soil [ 29 ]. Therefore, CaCl 2 as seed priming agent can be used as a sustainable agriculture and environmentally friendly tool to enhance crop tolerance. Moreover, seed priming with CaCl 2 offers prominent economic advantages when compared to within-crop spray treatments, as it can easily be applied by growers or seed distributors. The application of calcium as priming agent can increase the concentration of Ca 2+ in plants, particularly upon activation of resistance [ 30 ]. The effect of seed osmopriming (with CaCl 2 ) led to establishment of early tolerance mechanisms on wheat plant, which resulted in increased yield and crop allometry and improved leaf area index, crop growth rate and productivity under drought stress [ 31 ]. Wang et al. [ 32 ] found that pretreatment of seeds with CaCl 2 enhanced tolerance to salt and cold stress. The results obtained from this study indicate the role of CaCl 2 as priming agent in activating resistance mechanisms in rice seedlings. Hence, developing a cost effective and economically feasible technique to overcome salt or drought stresses is a challenge [ 33 , 34 ]. Globally, among various studies that have been carried out to deal with the osmotic stress, seed priming is a very promising strategy possessing the ability to improve crops yields and yield quality through alleviating salinity and drought stresses unfavorable outcomes.

Wheat is very important cereal crops; it is the main source of carbohydrates and a major staple food around the globe [ 35 , 36 ]. Under unfavorable conditions of water deficit and salinity stresses, wheat seed germination and seedling establishment also experience the aforementioned negative impacts on physiological and biochemical attributes and vast metabolic processes [ 37 ]. Subsequently, it seriously reduces the percentage of germination, growth, biomass production and grain yield in wheat plant [ 38 ]. In the human diet, wheat is considered a source of over 20% of calories [ 39 ]. Globally, it covers about a fifth of the total cereals-cultivated land [ 40 ]. As the demand for cereals is expected to expand by 2050 to reach about 3 billion tonnes, wheat cultivation has increased [ 41 ]. However, as a consequence of climate change, it is expected that its global production may decrease by about 1.9% in the second half of this century, and the negative impact of climate change will be more obvious in Southern Asian and African countries, with a predicted decline in yield of about16% and 15%, respectively, by 2050 [ 42 ]. Similarly, it is predicted that per every degree Celsius rise in temperature, there will be a reduction in wheat production by about 6% globally [ 43 ]. The continued scenarios of climate change, particularly salinity and drought, are predominant factors challenging the global wheat production.

Considering the aforesaid factors, working on anti-salinity and anti-drought techniques and strategies confronting the growing abiotic stress projected from climatic change is crucial to achieving sustainable food security. The present study hypnotized that different seed priming techniques (hydro, redox and chemical) and their application individually or successively can provoke regulated priming memory permanent till plant maturity, a much-expected technique for achieving sustainable agriculture under the anticipated scenario of climate change.

This study examined the effect of different priming methods (individually or successively) on wheat water relations, yield, wheat photosynthetic pigments, enzymatic and non-enzymatic antioxidant activities, and their related traits under changing scenarios of the climate. This study could be the first study addressing the effect of NO under open field conditions.

The following questions were addressed: 1) Do priming agents improve plant growth and production performance beyond the germination stage under field conditions? 2) Do priming agents improve salinity tolerance and yield production? 4) which priming agent(s) can improve the salinity tolerance of the test plant? 5) Are the beneficial effects resulting from chemical priming parallel to those from redox priming or hydro-priming? 6) Are the beneficial effects resulting from individual chemical priming or redox priming parallel to those from successive priming?

Materials and methods

The experimental site.

A field experiment was carried out at a privet farm in Eneiybes, Juhayna, Sohag Governorate, Egypt, which is located at 26° 73- 67 = N latitude and 31° 47- 56 = E longitude during two consecutive growing winter seasons of 2019/20 and 2020/21 to assess the effect of seed priming on wheat yield and water productivity under salinity stress.

Climate conditions

The climate condition of the studied area represented the Sohag Governorate (Upper Egypt). Monthly average agro-meteorological data at the experimental site and reference evapo-transpiration (ETo) values for the two seasons were obtained from the meteorological station in Sohag, Egypt, and are presented in Table  1 .

Soil analysis of the experimental site

Soil samples were taken from two close sites representing normal soil (unsaline soil) and nearby saline soil with a 15- cm increment and down to 60-cm soil depth using a spiral auger. In the laboratory, the collected samples were air- dried, ground, and sieved (particle size < 2 mm). The prepared samples were subjected to chemical and physical analysis according to Klute [ 44 ] and Page et al. [ 45 ]. The data for soil analysis were presented in Tables 2 and 3 . Also, undisturbed soil samples were taken using the core method technique.

Experimental design: salinity stress

The tested salinity stress treatments (Seven treatments), with three replicates, were arranged in a completely randomized design. The field experiment was conducted using treatment “no priming” (under unsaline soil) as control and six treatments under saline soil conditions as follows: no priming, hydro-priming, individual osmo-priming calcium chloride CaCl 2 , individual redox-priming sodium nitroprsside SNP, successive priming CaCl 2  + SNP, and successive priming SNP + CaCl 2 .

Growth conditions and treatments

Well-selected grains of wheat ( Triticum aestivum L .) were rinsed thoroughly in distilled water and then soaked in the priming agents Hydro H 2 O, CaCl 2 (15 mM), and SNP (0.5 mM). The concentrations of CaCl 2 and SNP applied in this experiment were selected according to preliminary experiments (data not shown).Grains were soaked for 12 h, then air-dried and sowed in the soil in the case of individual priming or re-soaked in an alternate successive priming agent for additional 12 h in the case of successive priming.

Agronomic practices

All the agriculture practices were carried out according to the given recommendations by the Egyptian Ministry of Agriculture and applied as commonly used for wheat plantations. Wheat plants were harvest after 160 days of planting. Ammonium nitrate (33.5% N) was used as nitrogen fertilizer and applied in two equal doses at a level of 240 kg ha −1 . The first dose was applied before post-planting irrigation, and the second one was applied before the second irrigation, particularly at the stage of tillering. Calcium superphosphate was used as phosphorus fertilizer (15.5% P 2 O 5 ). It was added to the soil at a level of 200 kg ha −1 . A single dose was added during the preparation of the soil. potassium sulphate was used as the source of potassium fertilizer (48% K 2 O). It was applied to the soil in two equal doses at a level of 100 kg ha −1 , concurrent with the addition of nitrogen fertilizer.

Water consumptive use (ETa) and irrigation water applied (IWA)

Actual evapotranspiration of the wheat crop was estimated by the soil sampling method to calculate soil moisture according to the method of Israelsen and Hansen [ 46 ] using the following formula:

CU = (θ2—θ1) Bd * ERZ where CU is the amount of consumptive water use (mm),

θ2 is the soil moisture percentage after irrigation, θ1 is soil moisture percentage before the following irrigation, Bd is bulk density (g. Cm −3 ), and ERZ is the effective root zone.

The experimental plots of 60 cm soil depth received an amount of water to boost the moisture up to field capacity. The irrigation water applied (IWA) in each irrigation treatment was calculated to be equal to the difference between moisture at the field capacity and the soil moisture content before irrigation.

Irrigation water productivity and crop water productivity

The Irrigation water use efficiency (IWUE) was calculated according to Du et al. [ 47 ] using the following equation: IWP (kg/ m 3 ) = Y/ I, Where Y is the grain yield (kg ha −1 ) and I is the irrigation water applied (m 3 ha −1 ).

Crop water productivity (CWP) describes the efficiency of the water applied for yield production. It was calculated, as described by Zwart and Bastianssen [ 48 ] as follows:

CWP (kg m −3 ) = Y/ Eta [ETa is the seasonal actual water consumptive use (m 3 ha −1 )].

Determination of photosynthetic pigments

Chlorophylls and carotenoids concentrations were conducted using equations as cited by Lichtenthaler [ 49 ]. To extract pigments, fresh leaf samples were suspended in 10 ml of 95% ethyl alcohol at 60ºC, until colorless. Absorbance readings were determined spectrophotometercaly.

Preparation of plant extract

Fresh plant samples were extracted according to Padmaja et al. [ 50 ]. The resultant supernatant was used for determination of antioxidant enzymes (catalase and peroxidase), non-enzymatic antioxidants (free phenolic and total antioxidant [DPPH]), and metabolites (soluble proteins). While proline and MDA are determined in shoots only and have their own extraction method.

Shoot stress markers

Determination of membrane damage.

To assess the membrane damage in shoot samples, lipid peroxidation (MDA) was conducted according to Hodges et al. [ 51 ]. The results were expressed as μM MDA g −1 FW.

Determination of proline

Free proline was extracted and measured as reported by Bates et al. [ 52 ]. Proline concentration was expressed as mg proline g −1 FW.

Total antioxidant activity (DPPH) and free phenolics

DPPH-stable free radical scavenging activity was determined by the method of Blois [ 53 ]. The inhibition percentage (I) was calculated as radical scavenging activity as follows I = (Abs control-Abs sample)/Abs control X 100.

The determination of Phenolics was conducted according to Kofalvi and Nassuth [ 54 ], and its concentration was expressed as µg g −1 FW.

Catalase (EC 1.11.1.6)

Catalase (CAT) activity was conducted by following the method of Aebi, (1984) [ 55 ].

Peroxidase (EC 1.11.1.7)

Peroxidase (POD) activity was determinedfollowing the method described by Tatiana et al. [ 56 ].

Assay of metabolites: soluble proteins

Protein contents in the shoot samples were measuredas described by Lowry et al. [ 57 ].

Ionic analysis

The plant material extractions were conducted by the mixed acid digestion procedure, as reported by Allen [ 58 ]. The determination of cations (Na + and K + ) assessed using Carl Zeiss flame photometer due to the high sensitivity of the flame emission method for cations [ 59 ].

Crop measurements

At the end of experimental time (harvest stage), ten random plants were chosen from a square meter from each treatment in order to estimate the following parameters: grain yield, seed index (weight as g/1000 grains), straw yield, and nitrogen percentage in grain. The estimation of grain and straw yield was assessed by collecting data from the centric area of each treatment. Four square meters (2 m x 2 m) were used, and the data were converted to yield/ ha.

Statistical analysis

The data was collected in three replicates from six measurements from two independent experiments. The Analysis of variance (ANOVA) was conducted using the SPSS statistical 11.0 package. The comparison of the means for significant differences was performed using Duncan’s multiple range tests at p ≤ 0.05 as a posthoc test. All the assessed attributes were analyzed with Principal Component Analysis (PCA) variance regression ordination. The heatmap and scatter plot were generated using ggplot packages and visualization of corrplot, integrated into the R software (RStudio). The data (mean values) was normalized into a standard range of ± 1, in order to perform the analysis.

In the present investigation, an attempt was done to explore the effect of seed priming with nitric oxide or calcium chloride at different methods (individually or successively) on the performance of wheat grain germination, early seedling establishment, and crop production when germination occurs under salt stress under open field conditions. This study could be the first study addressing the effect of NO under field conditions. This work designed to further and deeper understand how primed seeds effectively take advantage from nitric oxide and calcium to downstream subsequent defense, the present investigation evaluated the events of oxidative stress, with focus on stress markers and antioxidant systems that may be activated after the exposure to salt stress.

Hydraulic conductivity (HC) and bulk density (Bd) of soil

Hydraulic conductivity as affected by salinity and seeds priming in the first season of 2019/20 and the second season 2020/21 is represented in Table  4 . The hydraulic conductivity was significantly increased due to the seeds priming.

The value hydraulic conductivity reached its peak (0.96 m day −1 ) with SNP + CaCl 2 during 1st season. The lowest value of hydraulic conductivity (0.61 m day −1 ) was observed with no priming in the 1st season. On the basis of average from both growing seasons, values of hydraulic conductivity were 0.65, 0.62, 0.66, 0.91, 0.86, 0.95 and 0.96 m day −1 at control, no priming, hydro, and CaCl 2 , SNP, CaCl 2  + SNP and SNP + CaCl 2 , respectively. The seed priming, regarding their effect on hydraulic conductivity, could be arranged descendingly in the following order: SNP + CaCl 2  > CaCl 2  + SNP > CaCl 2  > SNP > hydro > control > no priming for both seasons.

Bulk density as affected by salinity and seeds priming in the first season of 2019/20 and the second season of 2020/21 is represented in Table  4 . The bulk density was significantly increased due to the seeds priming. The bulk density value reached its peak (1.43 Mg m −3 ) with control during 2nd season. The lowest value of bulk density (1.23 Mg m −3 ) was observed with SNP + CaCl 2 in the 1st and 2nd season. On the basis of average from both growing seasons, values of bulk density were 1.43, 1.38, 1.37, 1.32, 1.30, 1.25, and 1.23 Mg m −3 at control, no priming, hydro and CaCl 2 , SNP, CaCl 2  + SNP and SNP + CaCl 2 , respectively. The seeds priming, regarding their effect on bulk density, could be arranged descendingly in the following order of: control > No > hydro > CaCl 2  > SNP > CaCl 2  + SNP > SNP + CaCl 2 for both seasons.

Wheat irrigation water applied (IWA) and water consumptive use (ETa)

Salinity greatly reduces the production of wheat crop in arid and semi-arid regions. The data of water consumptive use (ETa) and irrigation water applied (IWA) are shown in Table  5 for wheat that was affected by seeds priming and salinity during the two-growing season (2019/20 and 2020/21). Generally, the amount of water consumptive use (ETa) and irrigation water applied (IWA) decreased with seeds priming. The calculated amounts of applied irrigation water (IWA), on the basis of the average of two growing seasons, were 6476.45, 6493.53, 6487.01, 6464.48, 6436.63, 6423.14 and 6413.78 m3 ha −1 while water consumptive use (ETa) was 4859.65,4848.46, 4821.77, 4811.04, 4796.05, 4785.29 and 4792.71 m 3 ha −1 at control,no priming, hydro and CaCl 2 , SNP, CaCl 2  + SNP, and SNP + CaCl 2 , respectively.

It was observed that the amount IWA in the 2nd season was higher than that of the 1st one. The values ETa and IWA reached their peak under no priming (unsaline soil) and no priming (saline soil) treatments since they were 4875.35 and 6503.56 m 3 ha −1 , respectively, in the 2nd season (Table  5 ). The lowest values of ETa and IWA were attained under CaCl 2  + SNP and SNP + CaCl 2 treatments since they were 4776.76 and 6397.96 m 3 ha −1 , respectively, in the 1st season. The seeds priming agents could be arranged descendingly, following their effect on the Eta, in the following order: control > no priming > hydro > CaCl 2  > SNP > CaCl 2  + SNP > SNP + CaCl 2 , while IWA in the following order: no priming > hydro > control > CaCl 2  > SNP > CaCl 2  + SNP > SNP + CaCl 2 (for both seasons).

Crop water productivity (CWP) and irrigation water productivity (IWP)

CWP and IWP were affected by salinity and wheat grain priming in the winter season of 2019/20 and 2020/21, as presented in Table 6 . The CWP and IWP were significantly increased due to the grain priming. The highest values obtained of CWP and IWP were 1.64 and 1.23 kg m −3 , respectively, and were recorded at SNP + CaCl 2 in the 2nd season. On the other hands, the lowest values of CWP and IWP were found to be 0.70 and 0.52 kg m −3 , respectively, and recorded under no priming (saline soil) treatment in the 2nd growing season. The data from both seasons, on the basis of average, showed that CWP values were 1.51, 0.72, 0.96, 1.48, 1.51, 1.57 and 1.62 kg m −3 , while IWP values were 1.14,0.54, 0.72, 1.10, 1.12, 1.17 and 1.21kg m −3 at control, No, hydro and CaCl 2 , SNP, CaCl 2  + SNP and SNP + CaCl 2 , respectively. It was noticed that the amount of CWP and IWP was higher in the 2nd season than that of the 1st one in all treatments except the no priming in saline soil treatment (Table  6 ). The seeds priming could be arranged descendingly on the basis of their effect on the CWP and IWP in the following order: SNP + CaCl 2  > CaCl 2  + SNP > control > SNP > CaCl 2  > hydro > no priming in saline soil for both seasons.

Wheat traits and its yield

Wheat traits and their yield as affected by salinity and seeds priming in the winter season of 2019/20 and 2020/21 are presented in Tables (7&8).

Plant height

The plant height was significantly increased due to the seed priming. The highest value of plant height (108 cm) was recorded with SNP in the 2nd season. The lowest value of plant height (91.33 cm) was attained with no priming in saline soil in the 1st season. The data from both seasons, on the basis of average, Plant height recorded values were 103.88, 93.83, 97.83, 106.72, 107.98, 106.17 and107.33 cm at control, no priming in saline soil, hydro and CaCl 2 , SNP, CaCl 2  + SNP and SNP + CaCl 2 , respectively. The data showed that the plant height was greater in the 2nd growing season than the first one in all treatments (Table  7 ). The seeds priming treatments, regarding their effect on the plant height, could be arranged descendingly in the following order: SNP > SNP + CaCl 2  > CaCl 2  > CaCl 2  + SNP > control > hydro > no priming in saline soil, for both seasons.

The seed index was significantly increased due to the seeds priming. The highest value of seed index (48.33 g) was recorded with SNP + CaCl 2 in the 1st season. The lowest value of seed index (40.27g) was found with no priming in saline soil in the 1st season. On the basis of average from the two growing seasons, the obtained values of seed index were 41.36,40.47, 41.92, 44.10, 45.43, 45.15 and47.57g at control, no priming, hydro and CaCl 2 , SNP, CaCl 2  + SNP and SNP + CaCl 2 , respectively. The seeds priming treatments, regarding their effect on the seed index, could be arranged descendingly in the following order: SNP + CaCl 2  > SNP > CaCl 2  + SNP > CaCl 2  > hydro > control > no priming in saline soil, for both seasons.

Wheat grain yield

The grain yield was significantly increased due to the seeds priming. The highest value of grain yield (7.87 Mg ha −1 ) was recorded with SNP + CaCl 2 in the 2nd season. The lowest value of grain yield (3.40Mg ha −1 ) was observed with no priming in the 1st season. On the basis of average from both seasons, the obtained values of grain yield were 7.38,3.50, 4.64, 7.12, 7.23, 7.51 and7.75 Mg ha −1 at control, no priming, hydro and CaCl 2 , SNP, CaCl 2  + SNP and SNP + CaCl 2 , respectively. It was noticed in all treatments that the grain yield in the 2nd season was higher than the first one, as represented in Table 8 . The seeds priming treatments from both seasons,could be arranged descendingly regarding their effect on the grain yield according to the following order: SNP + CaCl 2  > CaCl 2  + SNP > control > SNP > CaCl 2  > hydro > no priming in saline soil.

Wheat straw yield

The straw yield was significantly increased due to the seeds priming. The highest value of straw yield (10.74Mg ha −1 ) was recorded with SNP + CaCl 2 in the 1st season. The lowest value of straw yield (6.62Mg ha −1 ) was found with no priming in the 2nd season. On the basis of average from both growing seasons, the obtained values of straw yield were10.64, 6.96, 8.91, 9.84, 9.85, 10.48 and10.65 Mg ha −1 at control, no priming, hydro and CaCl 2 , SNP, CaCl 2  + SNP and SNP + CaCl 2 , respectively (Table  8 ). The seeds priming could be arranged descendingly according to their effect on the straw yield in the following order: SNP + CaCl 2  > control > CaCl 2  + SNP > SNP > CaCl 2  > hydro > no priming in saline soil, for both seasons.

Grain nitrogen content

Grain nitrogen content as affected by salinity and seeds priming in the first season of 2019/20 and the second season 2020/21 is represented in Table  9 . The grain nitrogen content was significantly increased due to the seeds priming. The value of nitrogen content in grain reached its peak (2.90%) with SNP + CaCl 2 during 2nd season. The lowest value of grain nitrogen content (2.23%) was observed with no priming in the 2nd season. On the basis of average from both growing seasons, values of nitrogen content in grain were 2.39, 2.29, 2.49, 2.56, 2.61, 2.69 and2.88% at control, no priming, hydro and CaCl 2 , SNP, CaCl 2  + SNP and SNP + CaCl 2 , respectively. The seeds priming, regarding their effect on grain nitrogen content, could be arranged descendingly in the following order: SNP + CaCl 2  > CaCl 2  + SNP > SNP > CaCl 2  > hydro > control > no priming, for both seasons.

Shoot and root growth

The data represented in Fig.  1 a and b of shoot and root dry weight showed the significant inhibitory effect of salinity on wheat plant growth and dry matter gain. Shoot dry matter of wheat plants treated with hydro-priming or successive chemical priming agents (CaCl 2  + SNP & SNP + CaCl 2 ) counteracted salinity stress effect significantly and preserved growth rates up to control. On the other hand, exogenous application of osmo-priming (calcium chloride) individually or successively with sodium nitroprusside as redox priming (CaCl 2  + SNP) resulted in a significant increase in root dry weight compared to corresponding salinity stressed plants without priming treatments.

Dry weight of shoot ( a ) and root ( b ) as affected by salinity stress and seeds priming application (calcium chloride CaCl 2 , sodium nitroprusside SNP, and hydropriming H 2 O). Litters on bars indicate significance level of difference according to Duncan's test at p  < 0.05

Photosynthetic pigments

The biosynthesis of photosynthetic pigments (Chl. a, Chl. b and carot.) in the leaves of wheat plants that were grown after seed priming under salinity stress was analyzed and represented in Fig.  2 a, b and c. Generally, Salinity did not significantly affect chlorophyll a contents and carotenoids, while chlorophyll b reduced significantly under salinity stress. Also, it was observed that chlorophyll a and carotenoids contents of wheat plants that were treated with hydro-priming or individual osmo-primin (CaCl 2 ) were improved equal to or higher than those of the control plants. Slight induction in chl.a content was observed due to successive SNP + CaCl 2 application.

photosynthetic pigments Chl.a, ( a ) Chl.b ( b ) and carotenoids ( c ) as affected by salinity stress and seeds priming application (calcium chloride CaCl 2 , sodium nitroprusside SNP, and hydropriming H 2 O). Litters on bars indicate significance level of difference according to Duncan's test at p  < 0.05

Sodium and potassium

It was observed that all priming treatments diminished shoot sodium contents under soil salinity stress (Fig.  3 a). The lowest value (2 ppm) of sodium content in the shoot was recorded with individual osmo-priming (CaCl 2 ) and SNP. A similar trend was observed in roots under salinity stress (Fig.  3 b), where all the priming treatments decreased the sodium content in the roots except for successive priming with CaCl 2  + SNP, which showed the highest root sodium content (11 ppm).

Sodium (Na. + ) concentration in shoots (a) and roots (b) as affected by salinity stress and seeds priming application (calcium chloride CaCl 2 , sodium nitroprusside SNP, and hydropriming H 2 O). Litters on bars indicate significance level of difference according to Duncan's test at p  < 0.05

Potassium content in shoots decreased under salinity stress (Fig.  4 a). It was observed that successive priming with CaCl 2  + SNP and SNP + CaCl 2 enhanced potassium contents in shoot (40 ppm and 39 ppm, respectively). On the other hand, all priming treatments enhanced potassium contents in roots under salinity stress (Fig.  4 b). The highest potassium content in the root was recorded with individual osmo-priming CaCl 2 (49 ppm).

Potassium (K +) concentration in shoots ( a ) and roots ( b ) as affected by salinity stress and seeds priming application (calcium chloride CaCl 2 , sodium nitroprusside SNP, and hydropriming H 2 O). Litters on bars indicate significance level of difference according to Duncan's test at p  < 0.05

Malondialdehyde MDA

Malondialdehyde (MDA) contents in shoots were increased under salinity stress. All priming treatments significantly reduced MDA content in shoot except for hydro-priming, which showed a high MDA content in shoot (Fig.  5 ).

Shoot Malondialdehyde (MDA) as affected by salinity stress and seeds priming application (calcium chloride CaCl 2 , sodium nitroprusside SNP, and hydropriming H 2 O). Litters on bars indicate significance level of difference according to Duncan's test at p  < 0.05

Exogenous SNP and CaCl 2 application as priming agents (individually or successively) reduced the concentrations of MDA within the wheat shoot system exposed to salinity.

Proline contents in shoots decreased under salinity stress (Fig.  6 ). All priming treatments enhanced proline contents in the shoot; their effect on proline in the shoot could be arranged in ascending order as following: CaCl 2  < SNP < SNP + CaCl 2  < H 2 O < CaCl 2  + SNP. Successive priming with CaCl 2  + SNP induced the highest proline contents in shoot (6 µg/ g FW).

Shoot proline as affected by salinity stress and seeds priming application (calcium chloride CaCl 2 , sodium nitroprusside SNP, and hydropriming H 2 O). Litters on bars indicate significance level of difference according to Duncan's test at p  < 0.05

In general, all priming treatments could not change the contents of phenolics in shoot significantly under salinity stress (Fig.  7 ). Among priming treatments, successive priming with CaCl 2  + SNP and SNP + CaCl 2 showed the highest value of phenolics contents in shoot (0.38 and 0.36 µg/ g FW, respectively).

Shoot phenolics contents as affected by salinity stress and seeds priming application (calcium chloride CaCl 2 , sodium nitroprusside SNP, and hydropriming H 2 O). Litters on bars indicate significance level of difference according to Duncan's test at p  < 0.05

Total antioxidants (DPPH)

Successive priming with SNP + CaCl 2 and CaCl 2  + SNP recorded the highest value of total antioxidants contents in shoot under salinity stress (Fig.  8 ).

Shoot total antioxidants (DPPH) as affected by salinity stress and seeds priming application (calcium chloride CaCl 2 , sodium nitroprusside SNP, and hydropriming H 2 O). Litters on bars indicate significance level of difference according to Duncan's test at p  < 0.05

Enzymatic antioxidants

Catalase enzyme activities in wheat plant shoots decreased significantly under salinity stress. The priming treatments slightly improved catalase enzyme activities (Fig.  9 ). While ascorbate peroxidase activities in shoots were increased under most priming treatments under salinity stress (Fig.  10 ), their effect upon ascorbate peroxidase activities in the shoot could be arranged in ascending order as following: SNP < H 2 O < CaCl 2  < SNP + CaCl 2  < CaCl 2  + SNP. It was detected that successive priming with CaCl 2  + SNP, SNP + CaCl 2 and individual CaCl 2 showed the highest value of ascorbate peroxidase activity in the shoot.

Shoot Catalase enzyme (CAT) as affected by salinity stress and seeds priming application (calcium chloride CaCl 2 , sodium nitroprusside SNP, and hydropriming H 2 O). Litters on bars indicate significance level of difference according to Duncan's test at p  < 0.05

Shoot Ascorbate peroxidase enzyme (APX) as affected by salinity stress and seeds priming application (calcium chloride CaCl 2 , sodium nitroprusside SNP, and hydropriming H 2 O). Litters on bars indicate significance level of difference according to Duncan's test at p  < 0.05

Soluble proteins

All priming treatments under salinity stress increased the soluble proteins contents in shoot significantly (Fig.  11 ). Their effect on soluble proteins contents in the shoot could be arranged in ascending order as following: H 2 O < CaCl 2  + SNP < SNP + CaCl 2  < CaCl 2  < SNP. Individual priming with SNP showed the highest value of soluble proteins contents in the shoot (32.7 mg/ g FW).

Shoot soluble proteins as affected by salinity stress and seeds priming application (calcium chloride CaCl 2 , sodium nitroprusside SNP, and hydropriming H 2 O). Litters on bars indicate significance level of difference according to Duncan's test at p  < 0.0

Principle Component analysis (PCA)

Subjecting the original data of all assessed traits to the analysis of the principle component (PCA, Fig.  12 ) gives clear details for all possible negative and positive correlations among all measured traits. Thus, the PCA biplot indicated great contrariness between Eta, IWA and Bd (the right-hand half of Fig.  12 ) and the growth in addition to yield indicators (the left-hand half). PCA axis 1 captures about 38.3% of the cumulative percentage, followed by the second axis (27.7%). The right-hand half of Fig.  12 was greatly affected by the following treatments: control (No salinity-No priming), no priming in saline soil and salinity + hydro-priming. Meanwhile, the left-hand half was greatly affected with salinity + CaCl 2 priming, salinity + SNP priming, salinity + (CaCl 2  + SNP) priming, and salinity + (SNP + CaCl 2 ) priming treatments. On the first PCA axis, strong positive correlations were found among CWP & IWP and APX, proteins and ions (K + root) as well as growth parameters (plant height). Those are also positively correlated with different productivity attributes such as seed index, straw yield. All of these traits were arranged on the left-hand side half of PCA correlation biplot. Meanwhile, they were negatively correlated with IWA, Bd, Eta, antioxidants (CAT, proline, & free phenolics), stress marker (MDA) and ions (Na + root & shoot, K + shoot) and growth parameters such as dry weight of shoots & roots and photosynthetic pigments (ch.a, ch.b &carot). Second PCA axis showed another direction of trait correlation, i.e. some of the assessed growth and productivity parameters (root and shoot dry weight, plant height and straw yield) were arranged in the upper side half of the PCA correlation biplot and positively correlated with CWP, IWP, proline, CAT, total antioxidants, and K + shoot. Meanwhile, they were negatively correlated with soluble proteins, K + root, nitrogen content, MDA, and enzymatic and non-enzymatic antioxidants such as APX and phenolics. The quasi-trend of the assessed growth parameters showed clear negative correlations with MDA and Na + in the root. Most of the determined growth parameters (root and shoot dry weight, Ch.a, Ch.b) were positively correlated with K + and Na + in shoot, proline, CAT, Bd and Et;, all of them occupied the upper left-hand quadrate of the PCA biplot..

Loading plot of different studied attributes under salinity stress correlations to the first two Principal Component analysis (PCA) axes, Horizontal and vertical arrows indicate the rise-direction of salinity and priming treatments. a = control, b = salinity without priming, c = salinity + hydro priming, d = salinity + CaCl 2 priming, e = salinity + SNP priming, f = salinity + (CaCl 2  + SNP) priming, g = salinity + (SNP + CaCl 2 ) priming. parameters: A = hydraulic conductivity (HC), B = Bulk desity (Bd), C = water consumptive use (Eta), D = irrigation water applied (IWA), E = Crop water productivity (CWP), F = irrigation water productivity (IWP), G = Plant height, H = Seed index, I = Grain yield, J = Straw yield, K = Nitrogen content, L = MDA, M = Proline, N = Phenolics, O = protein, P  = total antioxidants DPPH, Q = ascorpate peroxidase APX, R = catalase CAT, S = chlorophyll a, T = chlorophyll b, U = carotenoids, V = Na + shoot, W = Na + root, X = K + shoot, Y = K + root, Z = Shoot D.Wt., AA = Root D.Wt

Correlation analysis

A visual plot of correlation analysis is used to find positive and negative correlations among multiple parameters under different treatments (Fig.  13 ). Strong negative correlations were observed between Bd, Eta, IWA from one side and SY, GY, CWP, IWP, SI, plant height and nitrogen content in grains from the other side. Another negative correlation can be observed between MDA from one side and CWP, IWP, SI and GY from the other side. A strong positive correlation could be noticed among all these parameters (SY, GY, CWP, IWP, SI, plant height and nitrogen content in grains). Another positive correlation was seen among IWA, Eta, and Bd.

Correlation matrix of the 27 measured traits of the studied parameters in shoot and root of wheat plants with priming agents (calcium chloride CaCl 2 , sodium nitroprusside SNP, and hydropriming H 2 O) under salinity stress. The increasing color intensities illustrate a higher correlation coefficient. parameters: N=nitrogen content, S.Y.= Straw yield, G.Y.=grain yield, S.I.=Seed index, Pl.H.=plant height, IWP=irrigation water productivity, CWP=crop water productivity, IWA=irrigation water applied, Eta=water consumptive use, Bd=bulk density, HC=hydraulic conductivity, R.D.Wt=root dry weight, S.D.Wt=shoot dry weight, KR=K + root, KS=K + shoot, Na R=Na + Root, Na S=Na + shoot, CAT= catalase, APX=ascorbate peroxidase, Carot.=carotenoids, Ch.a=chlorophyll a, Ch.b= chlorophyll b, DPPH= total antioxidants, Pro= proteins, Phen=phenolics, Prln=proline

Heat map analysis

As observed in Fig.  14 , hierarchical clustering analysis and a heat map clearly identified the significant differences between treatments on the left side and parameters on top. Priming agents CaCl 2 and SNP application individually or successively changed the response of all studied growth, physiological and yield attributes under salinity stress compared to salinity treatment without priming (Fig.  14 ). It was observed that growth and yield attributes clustered with antioxidant enzyme APX and proline, as observed in the heatmap and hierarchical cluster analysis (HCA) in Fig.  14 .

Loading plot of different studied attributes under salinity stress heatmap

Discussions

Through the higher plant life cycle, seed germination is considered the most decisive phase. A plethora of biochemical and physiological processes are activated inside seeds after rehydration, and water becomes sufficiently favorable for different metabolic activities, including respiration and protein synthesis [ 60 ]. Nevertheless, under salinity stress, germination performance is hindered by toxicity of Na + and Cl − , resulting in osmotic potential and ROS production [ 61 ]. The role of nitric oxide and calcium application in physiological processes is intensively reviewed in the literature [ 26 , 32 ]. However, little data is available on the effect of nitric oxide priming individually or in combination with calcium under field conditions.

The data revealed a reduction in the amount of water consumptive use (ETa) and irrigation water applied (IWA) with seeds priming. The lowest values of ETa and IWA were attained under CaCl 2  + SNP and SNP + CaCl 2 treatments. Semize tal. [ 62 ] reported that the soil salinity affects ETa due to the ion-specific toxicity and the decrease in both available water and photosynthetic activity. A similar trend was reported by Zhang et al. [ 63 ] who found that salinity treatments reduced ETa values in comparison with treatment without salinity application. Also, the reduction ratio of the yields was less than that of ETa.

In the present study seeds priming increased the CWP and IWP, with the highest values obtained at successive SNP + CaCl 2 in the 2nd season. Improving CWP can be achieved by increasing the production per unit of water consumed, or reducing the amount of water consumed per unit yield of production [ 64 ]. Increasing levels of subsoil NaCl salinity significantly depressed the water uptake with a depressing effect on water use efficiency. Also, there was a 21% decline in the water use efficiency of wheat when subsoil NaCl salinity was increased from S1 to S5 [ 65 ]. Conditions inducing stomatal closure, such as water stress and salinity, restrict the CO 2 supply to carboxylation sites that increases the intrinsic water use efficiency of the plant [ 66 ]. At Luancheng station, and based on field experiments during the period from 1987 to 2015, it was recorded that the average of CWP in winter wheat ranged from 1.36 to 2.07 kg m −3 [ 63 ]. Also, soil salinity affects CWP due to ion-specific toxicity and decrease both available water and photosynthetic activity [ 62 ].

The data obtained in this study revealed the positive effect of CaCl 2 and NO on alleviating salt stress on the water relations of wheat plants. During drought and salinity stress, water utilization is one of the most affected mechanisms of the plants [ 67 ]. Exogenous application of SNP improves water budgeting, leaf turgor and osmotic potentials of wheat plant under drought stress [ 68 ]. Relative water content, soluble sugar accumulation, and osmolyte were increased by CaCl 2 treatment for wheat genotypes under stress [ 69 ].

The data obtained herein revealed that seed priming enhanced plant height, seed index, grain yield, and straw yield. Successive SNP + CaCl 2 achieved the highest records. While Plant height, grain yield, and yield components were reduced significantly with the application of salinity. It has been previously documented that salinity stress causes plants to be exposed to three major challenges, including increasing osmotic pressure, misbalancing ion uptake, and oxidative stress [ 70 ]. Salinity stress induces the closure of stomata and a reduction in leaf expansion rate, which in turn restricts plant growth and yield production [ 71 ]. The increase in salt concentration in plant growth media severely reduces germination rate, seedling establishment, growth, development, and survival, which are critical parameters in determining plant productivity [ 72 ]. Under salinity stress, the wheat crop exhibits a slower growth rate, reduced tillering, and reduced grain yield [ 73 ]. In the winter wheat, Zhang et al. [ 63 ] detected that the yields average changed from 4160.7 to 7000.9 kg ha −1 during 1987–2015, based on field experiments at Luancheng station. Salinity stress reduced grain yields less than those without salinity. Elevated salt concentration in the growth medium imposes strong deleterious impacts on plant biomass [ 73 ]. Plant physiological functioning is negatively affected with soil salinity, which resulted in a major fraction of photosynthesis that divert or counter the negative effects of salinity instead of plant growth and development [ 74 ]. The exposure of seeds to salinity inhibits water imbibition, which in turn negatively affects the germination of seeds [ 75 ]. The improvement recorded in this study in plant height, seed index, the grain yield and straw yield as a result of seed priming treatments, particularly successive SNP + CaCl 2 , could be attributed to improved water relations and many biochemical alterations that consequently induced significant enhancement in wheat biomass and productivity under salt stress conditions. The enhancement of yield parameters indicates the activation of stress memory due to successive SNP + CaCl 2 priming treatment, which provoked salt stress resistance until the maturity of the wheat plant. Many previous studies regarding seed priming with SNP and CaCl 2 supported our finding under drought and salt stress [ 31 , 37 , 76 ].

This study showed that grain nitrogen content is negatively affected by salinity. Meanwhile, grain nitrogen content increased significantly due to seeds priming, particularly successive SNP + CaCl 2 during the 2nd season. A previous study reported that salinity reduced the contents of grain fats, proteins, and fibers significantly. According to Ashraf and Harris [ 77 ], salt tolerant cultivars of rice, sunflower, barley, and finger millet showed a higher content of soluble proteins. Maqsood et al. [ 78 ] reported that salt stress caused a reduction in the accumulation of fiber and protein content in maize grain. Similar findings are given by [ 79 , 80 ].

The data from this study showed the negative effect of salinity on wheat dry matter gain, while calcium chloride individually or successively CaCl 2  + SNP, significantly increased root dry weight. Our results are in accordance with the postulated hypothesis that reporting salinity stress can negatively disrupt the performance and normal functioning of plants by hampering plant growth in addition to biochemical processes [ 81 ]. The negative effects of salinity stress arise from reducing water retention and cell turgidity, the closure of stomata, and ultimately hampering plant growth and yield [ 82 ]. Salinity stress imparted a significant effect on plant vegetative stage and reduced dry biomass. The aforementioned negative effects of salinity stress on plant growth parameters may be attributed to the excessive accumulation of Na and Cl ions around the root system and accordingly, the toxic effect in plant cells. This result is in harmony with those obtained by Abdel Latef and Chaoxing, [ 83 ] on pepper and Mostofa et al. [ 84 ] on rice. The exogenous application of successive priming treatment (CaCl 2 &SNP) might help plants cope with the negative effects of salinity stresses through revamping of biochemical processes. The addition of SNP remarkably amended plants shoot and root growth under salt stress.

Changes in photosynthetic pigments are important for determining the level of plant stress tolerance. Many researchers have shown that salt stress leads to the disturbance of ion homeostasis with the excessive accumulation of toxic ions, which causes a great deleterious effect on critical metabolic processes like water status, nutrient uptake, photosynthetic pigments and efficiency [ 85 , 86 ]. Therefore, hydro-priming or osmo-priming seeds with CaCl 2 can be an effective and environmentally friendly tool to enhance crop resistance. Moreover, seed priming with CaCl 2 , when compared to crop spray, can offer great economic advantages as it can easily be applied by growers and seed distributors. Calcium exogenous application can increase the concentration of Ca 2+ in plants, particularly through resistance activation [ 30 ]. A slight induction in chl.a content was observed due to successive SNP + CaCl 2 application, which could be attributed to the dual effect of Ca 2+ and SNP. Increased photosynthetic pigments due to nitric oxide (SNP) had previously been reported in salt-stressed plants [ 14 , 87 ].

All priming treatments was observed to reduce shoot sodium contents under soil salinity stress. Gupta et al. [ 87 ] reported that toxic accumulation of Na + ions triggered by salinity leads to the generation of ROS, which may further disturb the homeostasis of cellular redox. Due to the higher accumulation of Na + in shoots than in roots, leaves are more vulnerable to Na + than roots [ 88 ]. Sodium (Na + ) is transported up to shoots through the rapid movement of the xylem transpiration stream, but Na + can return to roots only via the phloem. Regarding the high recirculation of Na + from shoot to root, there is very limited evidence, suggesting the transport of Na + is mostly unidirectional and results in high Na + accumulation as leaves age. The Na + -specific effects are superimposed on the osmotic effects of NaCl and, importantly, show greater variation within species than the osmotic effect. Na + -specific damage is associated with the accumulation of Na + in leaf tissues and results in the necrosis of older leaves. The disruption in protein synthesis appears to be an important cause of damage by Na + [ 89 ]. Many previous studies reported that SNP, under salinity stress, stimulated the expression of the plasma membrane H + -ATPase, indicating a NO role in sustaining a higher K + /Na + ratio [ 90 ]. According to Shaki et al. [ 91 ], redox priming with SA mitigated salinity adverse effect by enhancing the ability of cell membrane in root to hinder and reduce the entry of harmful ions, such as Cl‾ and Na + . Salinity negative effect was alleviated by SA application through up-regulation of Na + /H + antiporters (NHX1and SOS1) along with ion homeostasis regulation. This shows a comprehensive role of redox priming in mitigating salinity stress which can be used as a successful model for salinity tolerant cultivation [ 92 ].

Maintaining the ratio of K + /Na + in cellular compartments has been closely correlated with Salt tolerance, and correlated to low accumulation of Na + [ 93 ]. Concordantly, NO treatment reduced the toxic content of Na + ions in wheat plants under salinity stress. In addition, exogenous application of NO has been reported to induce the expression of H + -PPase and H + -ATPase. As a secondary messenger, NO treatment can activate secondary transporters to generate a very powerful electrochemical potential gradient and increase the exchange activity of Na + /H + [ 94 ].

Salinity stress negatively affected potassium content in wheat plants, while successive priming with CaCl 2  + SNP and SNP + CaCl 2 enhanced shoot potassium contents. Also, all priming treatments under salinity stress enhanced potassium contents in root. Potassium (K + ) has been reported to play a role in salinity stress remediation and osmotic stress, and many previous studies reported the restriction of K + influx by sodium chloride NaCl [ 95 ]. Tester and Davenport, [ 89 ] reported that high Na + concentration hampers the uptake of other nutrients by (1) interfering with transporters in the root plasma membrane, such as K + -selective ion channels, and (2) reducing root growth by high Na + concentration.

Sodium Na + can compete directly for K + - binding sites on enzymes, suggesting that the cytosolic K + to Na + ratio, rather than the absolute Na + concentration, is critical for tolerance [ 96 ]. Calcium (Ca +2 ) protective effect in salt-affected plants could be connected to its role in membrane integrity maintenance, because one of the salinity negative effects is membrane integrity disruption caused by displacing Ca +2 ions from the cell surface by Na + ions [ 97 ]. The results showed that CaCl 2 priming could sustain K + intake under salinity stress. A external priming agent (CaCl 2 ) could enhance Ca +2 content, thus increasing K + influx.

Many previous studies reported the positive effects of exogenous NO. The role of NO has been attributed to ionic homeostasis regulation (particularly K + /Na + ), activating antioxidant systems and restricting oxidative damage, regulating osmolytes concentration, and delaying leaf senescence [ 98 , 99 ], in addition to alterations in the cell wall due to indirect effects of auxin [ 100 ]. In light of previous findings, NO application as a priming agent has been found to induce plants tolerance against salinity stress through the modulation of interconnected stress-responsive pathways [ 101 ].

Among the most common injuries in plants induced by environmental stresses is ROS hyper-accumulation. Our data showed an enhancement in MDA contents in shoot under salinity stress. The membrane lipids are among the most cellular components oxidized and degraded by ROS, so, concentrations of MDA can increase, indicating injury in the plant cell membranous system [ 102 ]. This MDA increment could also induce antioxidants to come off to neutralize ROS production ensuing from salt stress.

Exogenous individually or successively, application of SNP and CaCl 2 as priming agents retarded the production of MDA within the wheat shoot system exposed to salinity. These findings were in accordance with many studies that reported the reduction in H 2 O 2 and MDA by SNP treatments throughout salt stress [ 87 , 103 , 104 ].

Under salt stress’s deleterious impacts, plant undergoes osmotic regulation through increased potential osmolyte synthesis, such as proline in the cytosol and organelles. Shoot system proline, a very important secondary metabolite, performs dual functions in plants as an osmo-protectant in addition to being an antioxidant [ 105 ]. Our data showed that Proline contents decreased in shoot under salinity stress. It is previously reported that under salinity stress, a high proline concentration acts as a substitute for water to stabilize and protect the cellular structures through their hydrogen bonding as well as hydrophobic interactions, which prevent the dehydration of membranes [ 106 ].

The accumulation of proline seems to be a strong defensive strategy against osmotic stress. It regulates the pH of the cytosol and scavenges free radicals by acting as a non-enzymatic antioxidant as well as an active osmolyte [ 107 ]. Increased accumulation of proline due to priming treatments might have boosted the antioxidative mechanisms by acting as a direct ROS scavenger or by playing an effective role as a signaling molecule [ 108 ]. In the present investigation, an increasing trend in cellular proline contents was observed when seed priming with SNP and CaCl 2 , particularly successive priming with CaCl 2  + SNP, which activated a higher protection for plants under environmental stresses. SNP and CaCl 2 could trigger the accumulation of proline, which may induce wheat plant salinity tolerance through the adjustment of osmotic stress by maintaining a greater cellular water content that cause better growth in wheat plants.

successive priming with CaCl 2  + SNP and SNP + CaCl 2 enhanced the content of phenolics in the shoot. Phenolic substances, through their ability to scavenge free radicals, may serve as potent antioxidants in addition to substrates for many antioxidant enzymes as well [ 109 ]. Under osmotic stress, plant possess a wide range of non-enzymatic antioxidants to quench ROS [ 110 ]. From the current data, the positive impact of successive priming could be observed.

Also, SNP + CaCl 2 and CaCl 2  + SNP successive priming achieved the highest value of total antioxidants contents in shoot under salinity stress, and this is another important indicator for the potential positive effect of the successive priming technique in enhancing antioxidant activity in plants under salinity stress. The results showed that applying successive priming agents stimulated the production of total antioxidants content in the leaves of wheat plants in relation to corresponding stressed treatments. These findings are in high accordance with our aforementioned results of phenolics, proline, and MDA. An enhancement of antioxidant capacity by the application of priming agents might protect plants under salinity stress conditions [ 111 , 112 ].

The priming agents increased the activity of APX and CAT enzymes in the leaves. It can be concluded that priming agents, particularly successive priming, could lower ROS generation on wheat plants under salinity stress through increasing APX and CAT activity, thus protecting against oxidative damage. CAT activity has been reported to be negatively correlated with the degree of damage to plasmalemma, chloroplast, and mitochondrial membrane systems and positively related to the indices of stress resistance [ 113 ]. According to Jaleel et al. [ 114 ] CaCl 2 -treated seedlings maintain higher levels of CAT activities and lower levels of lipid peroxidation and POX activity [ 114 ]. A previous study reported that plant pretreatment with SNP could increase antioxidant enzyme activity of POD, CAT, and APX in plant leaves and root [ 115 ]. Moreover, it was reported that the main role of NO is to enhance the antioxidation defense system of plants by inducing the antioxidant enzyme activities of CAT, SOD, POD, APX, as well as glutathione reductase [ 116 ]. These findings are in accordance with those of Mohsenzadeh and Zohrabi [ 117 ], who reported the induction of antioxidative enzymes SOD, POD, CAT and APX as a consequence of SNP application. It was assumed that the SNP role could be achieved by improving the capability of scavenging free radicals and mitigating oxidation damage, along with lower MDA contents. Jabeen et al. [ 118 ] stated that under salinity stress, the application of SNP increased the activities of SOD, CAT, POD and APX. External application of SNP may help plants withstand salt stress through stimulating gene expression associated with antioxidant enzymes [ 119 ].

Priming treatments in this study increased the shoot contents of soluble proteins under salinity stress. The accumulation of proteins in plants under salt stress conditions may support a re-utilized form of stored nitrogen that can be used later to play a crucial role in osmotic adjustment. Proteins may be saved constitutively at low concentrations or may be synthesized de novo as a consequence of salinity stress. Hasegawa et al. [ 120 ] concluded that a number of proteins induced by salinity are cytoplasmic, which in turn can cause alterations in the viscosity of the cell cytoplasm. Habib et al. [ 68 ] reported a similar increasing trend in total cellular content of soluble protein in two wheat cultivars as a result of external application SNP or SNP + H 2 O 2 as seed priming agents, which improved a greater protection under stressed conditions.

Pretreatment of wheat grains with SNP + CaCl 2 as successive priming treatment has shown potential in enhancing the tolerance of wheat plants to salinity stress by suppressing the burst of ROS. The control of oxidative stress is evident from the increased levels of phenolics, enzymatic antioxidants (CAT & APX), and total antioxidants. The successive priming with SNP + CaCl 2 has been found to improve water relations (CWP & IWP), increase potassium content in shoot dry weight, and consequently enhance plant productivity and yield quality, including seed index, grain yield, and grain nitrogen content. These findings fulfilled the study’s aims, as the results answered the aforementioned questions. Our data revealed that successive priming improved water relations (Eta, IWA, CWP& IWP) and wheat plant growth and productivity under salinity stress more than individual priming treatments. Successive priming enhanced stress memory of salt tolerance in wheat, relatively, when compared to unprimed state. However, different seed priming techniques still need to be investigated for precise and reliable applications of this approach.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

• Nitric oxide

Sodium nitroprusside

• Water consumptive use
• Irrigation water applied
• Irrigation water productivity

Astapati AD. Nath S The complex interplay between plant-microbe and virus interactions in sustainable agriculture: Harnessing phytomicrobiomes for enhanced soil health, designer plants, resource use efficiency, and food security. Crop Design. 2023;12(12):100028. https://doi.org/10.3390/plants12122307 .

Article   CAS   Google Scholar  

Teh SY, Koh HL. Climate change and soil salinization: impact on agriculture, water and food security. IJAFP. 2016;2:1–9.

Google Scholar  

Gupta B, Huang B. Mechanism of salinity tolerance in plants: physiological, biochemical, and molecular characterization. Int J Genomics. 2014;701596:1–18.

Article   Google Scholar  

IPCC. Climate change 2014: synthesis report. In: Pachauri RK, Meyer LA, editors. Contribution of Working Groups I. IPCC, Geneva Switzerland p: II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; 2014. p. 151r.

Laifa I, Hajji M, Farhat N, Elkhouni A, Smaoui A, M’nif A, Hamzaoui AH, Savoure A, Abdelly C, Zorrig W,. Beneficial effects of silicon (Si) on sea barley ( Hordeum marinum Huds.) under salt stress. SILICON. 2021;13:4501–17.

Bohnert HJ, Nelson DE, Jensen RG. Adaptations to environmental stresses. Plant Cell. 1995;7(7):1099–111. https://doi.org/10.1105/tpc.7.7.1099 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Ellouzi H, Rabhi M, Khedher S, Debez A, Abdelly C, Zorrig W. Silicon seed priming enhances salt tolerance of barley seedlings through early ROS detoxification and stimulation of antioxidant defence. SILICON. 2023;15(1):37–60. https://doi.org/10.1007/s12633-022-02001-1 .

Ding Z, Kheir AM, Ali OA, Hafez EM, ElShamey EA, Zhou Z, Wang B, Lin X, Ge Y, Fahmy AE, Seleiman MF. A vermicompost and deep tillage system to improve saline-sodic soil quality and wheat productivity. J Environ Manage. 2020;277: 111388. https://doi.org/10.1016/j.jenvman.2020.111388 .

Article   CAS   PubMed   Google Scholar  

Yi J, Li H, Zhao Y, Shao M, Zhang H,... Liu M,. Assessing soil water balance to optimize irrigation schedules of flood-irrigated maize fields with different cultivation histories in the arid region. Agric Water Manag. 2022;265: 107543. https://doi.org/10.1016/j.agwat.2022.107543 .

Hu Q, Zhao Y, Hu X, Qi J, Suo L, Pan Y,... Chen X,. Effect of saline land reclamation by constructing the “Raised Field -Shallow Trench” pattern on agroecosystems in Yellow River Delta. Agric Water Manag. 2022;261: 107345. https://doi.org/10.1016/j.agwat.2021.107345 .

Kranner I, Minibayeva FV, Beckett RP, Seal CE. What is stress? Concepts, definitions and applications in seed science. New Phytol. 2010;188(3):655–73. https://doi.org/10.1111/j.1469-8137.2010.03461.xr .

Wojtyla L, Lechowska K, Kubala S, Garnczarska M. Molecular processes induced in primed seeds—increasing the potential to stabilize crop yields under drought conditions. J Plant Physiol. 2016;203:116–26. https://doi.org/10.1016/j.jplph.2016.04.008 .

Jacques C, Salon C, Barnard RL, VernoudV PM. Drought stress memory at the plant cycle level: A review. Plants. 2021;10(9):1873. https://doi.org/10.3390/plants10091873 .

Ahmad P, Ahanger MA, Alyemeni MN, Wijaya L, Alam P, Ashraf M. Mitigation of sodium chloride toxicity in Solanum lycopersicum L. by supplementation of jasmonic acid and nitric oxide. J Plant Interact. 2018;13(1):64–72. https://doi.org/10.1080/17429145.2018.1424272 .

Bruce TJ, Matthes MC, Napier JA, Pickett JA. Stressful “memories” of plants: evidence and possible mechanisms. Plant Sci. 2007;173(6):603–8. https://doi.org/10.1016/j.plantsci.2007.09.002 .

Martinez-Medina A, Flors V, Heil M, Mauch-Mani B, Pieterse CM, Pozo MJ, Conrath U. Recognizing plant defense priming. Trends plant sci. 2016;21(10):818–22. https://doi.org/10.1016/j.tplants.2016.07.009 .

Xia H, Liu X, Wang Y, Lin Z, Deng H, Wang J, Lin L, Deng Q, Lv X, Xu K, Liang D. 24-Epibrassinolide and nitric oxide combined to improve the drought tolerance in kiwifruit seedlings by proline pathway and nitrogen metabolism. Sci Hortic. 2022;297: 110929. https://doi.org/10.1016/j.scienta.2021.110929 .

Lutts S, Benincasa P, Wojtyla L, Kubala S, Pace R, Lechowska K, Quinet M, Garnczarska M. Seed priming: new comprehensive approaches for an old empirical technique. New challenges in seed biology-basic and translational research driving seed technology. 2016;46(10.5772):4420.

Singh HRK, JassalJ Kang S, Sandhu H, Kang Grewal K. Seed priming techniques in field crops-A review. Agric Rev. 2015;36(4):251–64.

Jisha K, Vijayakumari K, Puthur JT. Seed priming for abiotic stress tolerance: an overview. Acta Physiol Plant. 2013;35(5):1381–96. https://doi.org/10.1007/s11738-012-1134-6 .

Sivanandhan G, Selvaraj N, Ganapathi A, Manickavasagam M. Effect of nitrogen and carbon sources on in vitro shoot multiplication, root induction and withanolides content in Withania somnifera (L.) Dunal. Acta Physiol Plant. 2015;37(2):1–10.

Basit F, Ulhassan Z, Mou Q, Nazir MM, Hu J, Hu W, Song W, Sheteiwy MS, Zhou W, Bhat JA, Jeddi K. Seed priming with nitric oxide and/or spermine mitigate the chromium toxicity in rice ( Oryza sativa ) seedlings by improving the carbon-assimilation and minimising the oxidative damages. Funct Plant Biol. 2022;50(2):121–35. https://doi.org/10.1071/FP21082 .

Hayat S, Hasan SA, Mori M, Fariduddin Q, Ahmad A. Nitric oxide: chemistry, biosynthesis, and physiological role. Nitric oxide plant physiol. 2010;69(14):1–15. https://doi.org/10.1016/j.niox.2009.10.009 .

Habib NOMAN, Ashraf MUHAMMAD, Ahmad MSA. Enhancement in seed germinability of rice (Oryza sativa L.) by pre-sowing seed treatment with nitric oxide (NO) under salt stress. Pak J Bot. 2010;42(6):4071–8. https://doi.org/10.1016/j.jplph.2016.04.008 .

Begara-Morales JC, Sánchez-Calvo B, Chaki M, Mata-Pérez C, Valderrama R, Padilla MN, Barroso JB. Differential molecular response of monodehydroascorbate reductase and glutathione reductase by nitration and S-nitrosylation. J Exp Bot. 2015;66(19):5983–96. https://doi.org/10.1093/jxb/erv301 .

Manai J, Gouia H, Corpas FJ. Redox and nitric oxide homeostasis are affected in tomato ( Solanum lycopersicum ) roots under salinity-induced oxidative stress. J Plant Physiol. 2014;171(12):1028–35. https://doi.org/10.1016/j.jplph.2014.03.014 .

Habib N, Ashraf M, Shahbaz M. Effect of exogenously applied nitric oxide on some key physiological attributes of rice ( Oryza sativa L .) plants under salt stress. Pak J Bot. 2013;45:1563–9. https://doi.org/10.1016/j.jplph.2016.04.008 .

Zheng C, Jiang D, Liu F, Dai T, Liu W, Jing Q, Cao W. Exogenous nitric oxide improves seed germination in wheat against mitochondrial oxidative damage induced by high salinity. Environ Exp Bot. 2009;67(1):222–7. https://doi.org/10.1016/j.envexpbot.2009.06.005 .

Adnan M, Shah Z, Fahad S, Arif M, Alam M, Khan IA, Mian IA, Basir A, Ullah H, Arshad M, Rahman IU, Saud S, Ihsan MZ, Jamal Y, Amanullah HM, Nasim Hammad W. Phosphate-solubilizing bacteria nullify the antagonistic effect of soil calcification on bioavailability of phosphorus in alkaline soils. Sci Rep. 2017;7(1):16131. https://doi.org/10.1038/s41598-017-16392-7 .

Article   PubMed   PubMed Central   Google Scholar  

Wang J, Song J, Wu XB, Deng QQ, Zhu ZY, Ren MJ, Ye M, Zeng RS. Seed priming with calcium chloride enhances wheat resistance against wheat aphid Schizaphis graminum Rondani. Pest Manag Sci. 2021;77(10):4709–18. https://doi.org/10.1002/ps.6743 .

Hussain M, Farooq M, Sattar A, Ijaz M, Sher A, Ul-Allah S. Mitigating the adverse effects of drought stress through seed priming and seed quality on wheat ( Triticum aestivum L. ) productivity. Pak J Agr Sci. 2018;55:313–9.

Wang Y, Shen C, Jiang Q, Wang Z, Gao C, Wang W. Seed priming with calcium chloride enhances stress tolerance in rice seedlings. Plant Sci. 2022;323: 111381. https://doi.org/10.1016/j.plantsci.2022.111381 .

Zhang T, Song B, Han G, Zhao H, Hu Q, Zhao, Y.,... Liu, H. Effects of coastal wetland reclamation on soil organic carbon, total nitrogen, and total phosphorus in China: A meta-analysis. Land Degrad Dev. 2023;34(11):3340–9. https://doi.org/10.1002/ldr.4687 .

Qiu S, Yang H, Zhang S, Huang S, Zhao S, Xu, X.,... Banwart, S. A. Carbon storage in an arable soil combining field measurements, aggregate turnover modeling and climate scenarios. CATENA. 2023;220: 106708. https://doi.org/10.1016/j.catena.2022.106708 .

Du K, Huang J, Wang W, Zeng Y, Li X, Zhao F. Monitoring Low-Temperature Stress in Winter Wheat Using TROPOMI Solar-Induced Chlorophyll Fluorescence. IEEE Trans Geosci Remote Sens. 2024;62:1–11. https://doi.org/10.1109/TGRS.2024.3351141 .

Yang T, Zhang Y, Guo L, Li D, Liu A, Bilal M, Wang P. Antifreeze Polysaccharides from Wheat Bran: The Structural Characterization and Antifreeze Mechanism. Biomacromolecules. 2024. https://doi.org/10.1021/acs.biomac.3c00958 .

Ali Q, Daud MK, Haider MZ, Ali S, Rizwan M, Aslam N, Zhu SJ. Seed priming by sodium nitroprusside improves salt tolerance in wheat ( Triticum aestivum L .) by enhancing physiological and biochemical parameters. Plant Physiol Biochem. 2017;119:50–8. https://doi.org/10.1016/j.plaphy.2017.08.010r .

Gui YW, Sheteiwy MS, Zhu SG, Batool A, Xiong YC. Differentiate effects of non-hydraulic and hydraulic root signaling on yield and water use efficiency in diploid and tetraploid wheat under drought stress. Environ Exp Bot. 2020;181(4): 104287.

Shiferaw B, Smale M, Braun HJ, Duveiller E, Reynolds M, Muricho G. Crops that feed the world 10 past successes and future challenges to the role played by wheat in global food security. Food Secur. 2013;5:291–317.

Kiss I. Significance of wheat production in the world economy and position of Hungary in it ABSTRACT: Applied Studies in Agribusiness and Commerce. 2011;5(1–2):115–20.

Alexandratos N, Bruinsma J. World agriculture towards 2030/2050: the 2012 revision. 2012.

Pequeno DN, Hernandez-Ochoa IM, Reynolds M, Sonder K, MoleroMilan A, Robertson RD, Asseng S. Climate impact and adaptation to heat and drought stress of regional and global wheat production. Environ Res Lett. 2021;16(5):054070. https://doi.org/10.1088/1748-9326/abf3f2 .

Asseng S, Ewert F, Martre P, Rötter RP, Lobell DB, Cammarano D, Kimball BA, Ottman MJ, Wall GW, White JW, Reynolds MP, Alderman PD, Prasad PVV, Aggarwal PK, Anothai J, Basso B, Biernath C, Challinor AJ, De Sanctis G, Doltra J, Fereres E, Garcia-Vila M, Gayler S, Hoogenboom G, Hunt LA, Izaurralde RC, Jabloun M, Jones CD, Kersebaum KC, Koehler A-K, Müller C, Naresh Kumar S, O’Leary Nendel C, G, Olesen JE, Palosuo T, Priesack E, Eyshi Rezaei E, Ruane AC, Semenov MA, Shcherbak I, Stöckle C, Stratonovitch P, Streck T, Supit I, Tao F, Thorburn PJ, Waha K, Wang E, Wallach D, Wolf J, Zhao Z, Zhu Y,. Rising temperatures reduce global wheat production. Nat Clim Change. 2015;5(2):143–7. https://doi.org/10.1038/nclimate2470 .

Klute A. Methods of soil analysis. Part 1: Physical and mineralogical methods (2nd edition). American Society of Agronomy Inc., Madison, Wisconsin, USA. 1986. p. 463–78.

Book   Google Scholar  

Page AI, Miller RH, Keeny DR. Methods of Soil Analysis. Part II. Chemical and Microbiological Methods, 2nd ed. American Society of Agronomy, Madison, WI, USA. 1982. p. 225–46.

Israelsen OW, Hansen VE. Irrigation Principles and Practices, 3 rd Edition, John Wiley and Sons Inc", New York, U.S.A. 1962.

Du YD, Cao HX, Liu SQ, Gu XB, Cao YX. Response of yield, quality, water and nitrogen use efficiency of tomato to different levels of water and nitrogen under drip irrigation in Northwestern China. J Integr Agric. 2017;16(5):1153–61.

Zwart SJ, Bastianssen WGM. Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize. Agric Water Manag. 2014;69(2):115–33.

Lichthenthale H. Chlorophylls end carotenoids: Pigments of photosynthetic bio membranes: Methods Enzymol. San Diego: Academic Press; 1987. p. 350–82.

Padmaja M, Sravanthi M, Hemalatha KPJ. Evaluation of antioxidant activity of two Indian medicinal plants. J Phytol. 2011;3:3.

Hodges DM, DeLong JM, Forney CF, Prange RK. Improving the thiobarbituric acid-reactive-substances assay for estimating lipid peroxidation in plant tissues containing anthocyanin and other interfering compounds. Planta. 1999;207:604–11. https://doi.org/10.1007/s004250050524 .

Bates LS, Waldren RP, Teare ID. Rapid determination of free proline for water-stress studies. Plant Soil. 1973;39:205–7.

Blois MS. Antioxidant determinations by the use of a stable free radical. Nature. 1958;181:1199.

Kofalvi S, Nassuth A. Influence of wheat streak mosaic virus infection on phenylpropanoid metabolism and the accumulation of phenolics and lignin in wheat. Physiol Mol Plant Pathol. 1995;47:365.

Aebi H. Catalase in vitro : Methods Enzymol. Academic Press. 1984. p. 121–6.

Tatiana Z, Yamashita K, Matsumoto H. Iron deficiency induced changes in ascorbate content and enzyme activities related to ascorbate metabolism in cucumber roots. Plant Cell Physiol. 1999;40:273–80.

Lowry OH, Rosebrough NJ, Farr AL, Randall RJ. Protein measurement with Folin phenol reagent. J Biol Chem. 1951;193:265.

Allen SE, Grimshaw HM, Parkinson JA, Quarmby C. Chemical analysis of ecological materials. Blackwell Scientific Publications. 1974.

Williams V, Twine S. Flame photometeric method for element analysis. Modern methods of plant analysis. 1960;5:3–5.

Alves RC, Nicolau CM, Checchio MV, Junior GSS, Oliveira FA, Prado RM, Grata PL. Salt stress alleviation by seed priming with silicon in lettuce seedlings: an approach based on enhancing antioxidant responses. Bragantia. 2019;79:19–29.

Yan K, Shao H, Shao C, Chen P, Zhao S, Brestic M, Chen X. Physiological adaptive mechanisms of plants grown in saline soil and implications for sustainable saline agriculture in coastal zone. Acta Physiol Plant. 2013;35:2867–78.

Semiz G, Suarez D, Ünlükara A, Yurtseven E. Interactive effects of salinity and N on pepper (Capsicum annuum L.) yield, water use efficiency and root zone and drainage salinity. J Plant Nutr. 2014;37:595–610.

Zhang WW, Meng JJ, Xing JY, Yang S, Guo F, Li XG, Wan SB. The K + /H + antiporter AhNHX1 improved tobacco tolerance to NaCl stress by enhancing K + retention. J Plant Biol. 2017;60:259–67.

Dong G, Guo J, Chen J, Sun G, Gao S, Hu L, Wang Y. Effects of spring drought on carbon sequestration, evapotranspiration and water use efficiency in the Songnen meadow steppe in north-east China. Ecohydrology. 2011;4(2):211–24.

Harsharn SG. Water uptake, water use efficiency, plant growth and ionic balance of wheat, barley, canola and chickpea plants on asodic vertosol with variable subsoil Na Cl salinity. Agric Water Manag. 2010;97(1):148–56. https://doi.org/10.1016/j.agwat.2009.09.002 .

Tcherkez G, Mahé HM. 12 C/ 13 C fraction at ions in plant primary metabolism. Trends Plant Sci. 2011;16(9):499–506. https://doi.org/10.1016/j.tplants.2011.05.006 .

Malko MM, Khanzada A, Wang X, Samo A, Li Q, Jiang D, Cai J. Chemical treatment refines drought tolerance in wheat and its implications in changing climate: A review. Plant Stress. 2022;6: 100118.

Habib N, Ali Q, Ali S, Javed MT, Haider MZ, Perveen R, Shahid MR, Rizwan M, Abdel-Daim MM, Elkelish A, Bin-Jumah M. Use of Nitric Oxide and Hydrogen Peroxide for Better Yield of Wheat (Triticum aestivum L) under Water Deficit Conditions: Growth, Osmoregulation, and Antioxidative Defense Mechanism. Plants. 2020;9:285. https://doi.org/10.3390/plants9030285 .

Khushboo BK, Singh P, Raina M, Sharma V, Kumar D. Exogenous application of calcium chloride in wheat genotypes alleviates negative effect of drought stress by modulating antioxidant machinery and enhanced osmolyte accumulation. In Vitro Cell Dev-Pl. 2018;54:495–507.

Tahjib-Ul-Arif M, Roy PR, Sohag AAM, Afrin S, Rady MM, Hossain MA. Exogenous calcium supplementation improves salinity tolerance in BRRI Dhan28; a salt susceptible high-yielding Oryza sativa cultivar. J Crop Sci Biotechnol. 2018;21(4):383–94. https://doi.org/10.1007/s12892-018-0194-6 .

Ashraf M, Harris PJC. Abiotic stresses: plant resistance through breeding and molecular approaches. New York: Haworth Press; 2005.

Tuteja N. Mechanisms of high salinity tolerance in plants. Meth Enzymol. 2007;428:419–38. https://doi.org/10.1016/S0076-6879(07)28023-9 .

Munns R, Tester M. Mechanisms of Salinity Tolerance. Annu Rev Plant Biol. 2008;59:651–81. https://doi.org/10.1146/annurev.arplant.59.032607.092911 .

Basyuni M, Wasilah M, Hasibuan PAZ, Sulistiyono N, Sumardi S, Bimantara Y, Hayati R, Sagami H, Oku H. Salinity and subsequent freshwater influences on the growth, biomass, and polyisoprenoids distribution of Rhizophora apiculata seedlings. Biodiversitas. 2019;20:288–95.

Munns R, Day DA, Fricke W, Watt M, Arsova B, Barkla BJ, Bose J, Byrt CS, Chen ZH, Foster KJ, Gilliham M, Henderson SW, Kronzucker CLD, Jenkins HJ, Miklavcic SJ, Plett D, Roy SJ, Shabala S, Shelden MC, Soole KL, Taylor NL, Tester M, Wege S, Wegner LH, Tyerman SD. Energy costs of salt tolerance in crop plants. New Phytol. 2020;225(3):1072–90.

Hameed A, Farooq T, Hameed A, Sheikh MA. Sodium nitroprusside mediated priming memory invokes water-deficit stress acclimation in wheat plants through physio-biochemical alterations. Plant Physiol Biochem. 2021;160:329–40.

Ashraf M, Harris PJC. Potential biochemical indicators of salinity tolerance in plants. Plant Sci. 2004;166:3–16.

Maqsood T, Akhtar J, Farooq MR, Haq MA, Saqib ZA. Biochemical attributes of salt tolerant and salt sensitive maize cultivars to salinity and potassium nutrition. Pak J Agric Sci. 2008;45(1):1–5.

Tian Y, Guan B, Zhou D, Yu J, Li G, Lou Y. Responses of Seed Germination, Seedling Growth, and Seed Yield Traits to Seed Pretreatment in Maize (Zea mays L.). Sci World J. 2014;2014(1):834630. https://doi.org/10.1155/2014/834630 .

Toklu F, Baloch FS, Karaköy T, Özkan H. Effects of different priming applications on seed germination and some agro-morphological characteristics of bread wheat ( Triticum aestivum L .)". Turk J Agric For. 2015;39:1005–13.

Ibrahim EA. Seed priming to alleviate salinity stress in germinating seeds. Plant Physiol. 2016;192:38–46. https://doi.org/10.1016/j.plaphy.2016.01.004 .

Garg N, Bharti A. Salicylic acid improves arbuscular mycorrhizal symbiosis, and chickpea growth and yield by modulating carbohydrate metabolism under salt stress. Mycorrhiza. 2018;28:727–46. https://doi.org/10.1007/s00572-018-0853-6 .

Abdel Latef AA, Chaoxing H. Does the inoculation with Glomus mosseae improves salt tolerance in pepper plants? J Plant Growth Regul. 2014;33:644–53. https://doi.org/10.1007/s00344-014-9416-3 .

Mostofa MG, Fujita M, Tran LSP. Nitric oxide mediates hydrogen peroxide-and salicylic acid-induced salt tolerance in rice ( Oryza sativa L.) seedlings. Plant Growth Regul. 2015;77:265–77. https://doi.org/10.1007/s10725-015-0069-6 .

Ahmad Z, Tahir S, Abid M, Amanullah M. Salt-induced variations in physiological parameters and nutrient concentrations of two wheat cultivars. Commun Soil Sci Plant Anal. 2014;45:29–41. https://doi.org/10.1080/00103624.2013.855840 .

Habib N, Ashraf M. Effect of exogenously applied nitric oxide on water relations and ionic composition of rice ( Oryza sativa L.) plants under salt stress. Pak J Bot. 2014;46:111–6.

Gupta P, Srivastava S, Shekhar Seth C. 24-epibrassinolide and sodium nitroprusside alleviate the salinity stress in Brassica juncea L . cv. Varuna through cross talk among proline, nitrogen metabolism and abscisic acid. Plant Soil. 2017;411:483–98. https://doi.org/10.1007/s11104-016-3023-2 .

Hanafy AAH, Mohamed HFY, Orabi IOA, EL-Hefny AM,. Influence of gamma rays, humic acid and sodium nitroprusside on growth, chemical constituents and fruit quality of snap bean plants under different soil salinity levels. Biosci Res. 2018;15(2):575–88.

Tester M, Davenport R. Na + tolerance and Na + transport in higher plants. Ann Bot. 2003;91:503–27. https://doi.org/10.1093/aob/mcg058r .

Chen J, Xiao Q, Wu F, Dong X, He J, Pei Z, Zheng H. Nitric oxide enhances salt secretion and Na + sequestration in a mangrove plant Avicennia marina through increasing the expression of H + -ATPase and Na + /H + antiporter under high salinity. Tree physiol. 2010;30:1570–85.

Shaki F, Maboud HE, Niknam V. Effects of salicylic acid on hormonal cross talk, fatty acids profile, and ions homeostasis from salt-stressed safflower. J Plant Interact. 2019;14(1):340–6.

Ahanger MA, Aziz U, Alsahli AA, Alyemeni MN, Ahmad P. Influence of exogenous salicylic acid and nitric oxide on growth, photosynthesis, and ascorbate-glutathione cycle in salt stressed Vigna angularis. Biomolecules. 2020;10(1):42.

Miranda RS, Gomes-Filho E, Prisco JT, Alvarez-Pizarro JC. Ammonium improves tolerance to salinity stress in Sorghum bicolor plants. Plant Growth Regul. 2016;78:121–31.

Zhang X, Pei D, Chen S, Sun H, Yang Y. Performance of double-cropped winter wheat-summer maize under minimum irrigation in the North China Plain. Agron J. 2006;98(6):1620–6.

Shirazi MU, Ashraf MY, Khan MA, Naqvi MH. Potassium induced salinity tolerance in wheat. Int J Environ Sci Technol. 2005;2:233–6.

Abdul Majid S, Asghar R, Murtaza G. Potassium- calcium interrelationship linked to drought tolerance in wheat (Triticum aestivum L.). Pak J Bot. 2007;39(5):1609–31.

Mahmood A. A new rapid and simple method of screening wheat plants at early stage of growth for salinity tolerance. Pak J Bot. 2009;41(1):255–62. https://doi.org/10.1007/s12633-021-00807-2 .

Lin Y, Liu Z, Shi Q, Wang X, Wei M, Yang F. Exogenous nitric oxide (NO)increased antioxidant capacity of cucumber hypocotyl and radicle under salts tress. Sci Hortic (Amsterdam). 2012;142:118–27. https://doi.org/10.1016/j.scienta.2012.04.032 .

Ahmad P, Abdel Latef AA, Hashem A, Abd-Allah EF, Gucel S, Tran LSP. Nitric oxide mitigates salt stress by regulating levels of osmolytes and antioxidant enzymes in chickpea. Front Plant Sci. 2016;7:1–11. https://doi.org/10.3389/fpls.2016.00347 .

Bõhm FMLZ, de Ferrarese MLL, Zanardo DIL, Magalhaes JR, Ferrarese-Filho O. Nitric oxide affecting root growth, lignification and related enzymes in soybean seedlings. Acta Physiol Plant. 2010;32:1039–46.

Hossain MA, Burritt DJ, Fujita M. Cross-stress tolerance in plants: molecular mechanisms and possible involvement of reactive oxygen species and methylglyoxal detoxification systems. In: Tuteja N, Gill SS, editors. Abiotic stress response in plants. New York: Wiley; 2016. p. 323–75.

Kaya C, Akram A, Ashraf M, Sonmez O. Exogenous application of humic acid mitigates salinity stress in maize (Zea mays L.) plants by improving some key physio-biochemical attributes. Cereal Res Commun. 2018;46(1):67–78r.

Akram N, Iqbal M, Muhammad A, Ashraf M, Al-Qurainy F, Shafiq S. Aminolevulinic acid and nitric oxide regulate oxidative defense and secondary metabolisms in canola ( Brassica napus L.) under drought stress. Protoplasma. 2018;225:163–74.

Hasanuzzaman M, Nahar K, Rahman A, Inafuku M, Oku H, Fujits M. Exogenous nitric oxide donor and arginine provide protection against short-term drought stress in wheat seedlings. Physiol Mol Biol Plants. 2018;24(6):993–1004.

Habib N, Akram MS, Javed MT, Azeem M, Ali Q, Shaheen HL, Ashraf M. Nitric oxide regulated improvement in growth and yield of rice plants grown under salinity: Antioxidant defense system. Appl Ecol Environ Res. 2016;14:91–105.

KaviKishor PB, Sangam S, Amrutha RN, Laxmi PS, Naidu KR, Rao KRSS, Rao S, Reddy KJ, Theriappan P, Sreenivasulu N. Regulation of proline biosynthesis, degradation, uptake and transport in higher plants: Its implications in plant growth and abiotic stress tolerance. Curr Sci. 2005;88:424–38.

Reddy PP. Bio-priming of seeds. Recent Advances in Crop Protection. India: Springer; 2013. p. 83–90.

Hossain MA, Hoque MA, Burritt DJ, Fujita M. Proline Protects Plants Against Abiotic Oxidative Stress: Biochemical and Molecular Mechanisms. In: Oxidative Damage to Plants; Academic Press: Cambridge. USA: MA; 2014. p. 477–522.

Chapter   Google Scholar  

Blokhina O, Violainen E, Agerstedt KV. Antioxidants, oxidative damage and oxygen deprivation stress: A review. Ann Bot. 2003;91:179–94.

Ghadakchiasl A, Mozafari AA, Ghaderi N. Mitigation by sodium nitroprusside of the effects of salinity on the morpho-physiological and biochemical characteristics of Rubusidaeus under in vitro conditions. Physiol Mol Biol Plants. 2017;23(1):73–83.

Cervilla LM, Blasco B, Rios JJ, Romero L, Ruiz JM. Oxidative stress and antioxidants in tomato ( Solanum lycopersicum ) plants subjected to boron toxicity. Ann Bot. 2007;100:747–56.

Koshiba T, Kobayashi M, Matoh T. Boron nutrition of tobacco BY-2 cells. V. Oxidative damage is the major cause of cell death induced by boron deprivation. Plant Cell Physiol. 2009;50:26–36. https://doi.org/10.1093/pcp/pcn184 .

Qureshi MI, Israr M, Abdin MZ, Iqbal M. Responses of Artemisia annua L. to lead and salt induced oxidative stress. Environ Exp Bot. 2005;53:185–93.

Jaleel CA, Riadh K, Gopi R, Manivannan P, Inès J, Al-Juburi HJ, ChangXing Z, Hong-Bo S, Panneerselvam R. Antioxidant defense responses: physiological plasticity in higher plants under abiotic constraints. Acta Physiol Plant. 2009;31:427–36.

Nejadalimoradi H, Nasibi F, Kalantari KM, Zanganeh R. Effect of seed priming with L-arginine and sodium nitroprusside on some physiological parameters and antioxidant enzymes of sunflower plants exposed to salt stress. 2014.

Kaya C, Ashraf M, Sonmez O. Exogenously applied nitric oxide confers tolerance to salinity-induced oxidative stress in two maize ( Zea mays , L.) cultivars differing in salinity tolerance. Turk J Agric For. 2015;39:909–19.

Mohsenzadeh S, Zohrabi M. Auxin and sodium nitroprusside effects on wheat antioxidants in salinity. Russ J Plant Physiol. 2018;65:651–7.

Jabeen Z, Fayyaz HA, Irshad F, Hussain N, Hassan MN, Li J, Alsubeie MS. Sodium nitroprusside application improves morphological and physiological attributes of soybean (Glycine max L) under salinity stress. Plos one. 2021;16(4):e0248207.

Ahmad P, Hashem A, Abd-Allah EF, Alqarawi AA, John R, Egamberdieva D, Gucel S. Role of Trichoderma harzianumin mitigating NaCl stress in Indian mustard through antioxidative defense system. Front Plant Sci. 2015;6:868.

Hasegawa PM, Bressan RA, Zhu JK, Bohnert HJ. Plant cellular and molecular responses to high salinity. Annu. rev. plant physiol. plant mol. Biol. 2000;51:463–99.

CAS   Google Scholar  

Download references

Acknowledgements

Not applicable

Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB). Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB). This research did not receive any grant from funding agencies in the public, commercial, or not-for-proft sectors.

Author information

Authors and affiliations.

Botany and Microbiology Department Faculty of Sciences, New Valley Univ, Al-Kharja, New Valley, 72511, Egypt

Rasha M. El-Shazoly

Soils and Water Science Department, Faculty of Agriculture, Al-Azhar Univ, Assiut, Egypt

H. M. A. Hamed & Mahmoud M. El-Sayed

You can also search for this author in PubMed   Google Scholar

Contributions

M.M.E. and R.M.E-S. conceived and designed the research. M.M.E., R.M.E. and H.H.M.A. conducted experiments. M.M.E., R.M.E. and H.H.M.A. analyzed the data and wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Rasha M. El-Shazoly .

Ethics declarations

Ethics approval and consent to participate.

Permission was obtained from the farm owner for collecting plant materials.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

El-Shazoly, R.M., Hamed, H.M.A. & El-Sayed, M.M. Individual or successiveseed priming with nitric oxide and calcium toward enhancing salt tolerance of wheat crop through early ROS detoxification and activation of antioxidant defense. BMC Plant Biol 24 , 730 (2024). https://doi.org/10.1186/s12870-024-05390-0

Download citation

Received : 22 February 2024

Accepted : 05 July 2024

Published : 31 July 2024

DOI : https://doi.org/10.1186/s12870-024-05390-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Stress alleviation
• Seed priming

BMC Plant Biology

ISSN: 1471-2229

• General enquiries: [email protected]

Irrigation termination has the potential to improve cotton yield and quality on different soil types

• Farhadi Machekposhti, Mabood
• Leib, Brian G.
• Xie, Shuhua
• Raper, Tyson B.
• Grant, Timothy James

Irrigation termination timing is challenging for cotton producers in humid regions, especially for fields with varying soil types. A field experiment was conducted in Jackson, TN, to investigate the best cotton irrigation termination on different soil types. The water management treatments consisted of rainfed conditions (RF) and terminating irrigation 2 weeks before the first crack boll (ITBC1 and ITBC2), at the first crack boll (ITC1 and ITC2), and 2 weeks after the first cracked boll (ITAC1 and ITAC2). The irrigation rates consisted of normal irrigation (2) and increased irrigation (1) during the 2 weeks prior to irrigation termination. Irrigation treatments were implemented on three soils: a low, an intermediate, and a high available water‑holding capacity (AWHC) soil. In sandy soil, seed yield increased by 127% in 2015 with the ITAC1 treatment and by 313% in 2016 with the ITC1 treatment, compared to the control (RF). These treatments were also found to be optimal for lint yield and irrigation water productivity in their respective years. The high AWHC soil did not require any irrigation in either growing season to optimize yield. In fact, irrigating at a high rate at every termination date caused yield loss in 2015. These results indicate that cotton can benefit from later termination and higher irrigation rates when soil water and rainfall are low at the end of the growing season or be harmed when the opposite is true.Core Ideas In sandy soil, applying ITC1 greatly impacted seed yield compared to the rainfed crops. S3 had the highest seed and lint yield among the three different soil types. The lint quality components were not adversely affected by any termination treatment.