human in vivo experimental study

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The term in vivo refers to a type of experiment that is carried out within a whole, living organism , such as a plant or animal.

In vivo means “within the living” in Latin, which aptly fits its modern definition.

In vivo refers to a specific type of experimentation that involves living animals. Living animals are very complex, which provides both benefits and detriments to experimentation. On one hand, in vivo experiments are much more complex than experiments carried out in a test tube – so the results can be less revealing and may inspire more questions instead of providing concrete answers.

A laboratory mouse is a commonly used animal for in vivo experimentation, and several breeds of lab mice have been created with different traits.

On the other hand, in vivo experiments are a very important part of understanding the complexities of life. Whether it comes to understanding the brain, testing a new drug, or understanding how behavior works, these processes are not possible in a test tube because the level of complexity found in a living animal cannot be replicated easily. While some researchers are working on advanced computer modeling to replace in vivo testing, these models are not yet as robust or complex as a living organism.

In vivo vs. In vitro

So we know that in vivo refers to experiments carried out on a living organism, but what’s the difference between this form of experimentation and in vitro experimentation?

In vitro is pretty much the exact opposite. Instead of complex, living organisms, in vitro experiments refer to techniques that use biological components such as cells or biological molecules and are carried out outside of a living organism , whether that be in a test tube, culture dish, or so on.

Tests using in vitro methods are often used to observe things such as bacterial or animal cells in a more controlled environment , which is one of their main advantages over in vivo testing.

In vitro methods also have other advantages including the fact that they:

Are usually cheaper
Can be used for large-scale production
Reduce the amount of animal testing, which is more ethical

Photo of scientist working with cells on agar plate - a type of in vitro experimentation

In vivo vs. Ex vivo

The difference between in vivo and ex vivo is simple.

Ex vivo means ‘outside of a living body’ in Latin and refers to methods wherein living tissues are taken directly from a living organism rather than created artificially, and testing is carried out on them with very minimal changes to the tissue’s natural state.

This varies slightly from in vitro , where things such as cells are separated and purified from their natural surroundings and are tested on in very controlled environments.

A scientists looks at cells through a microscope - a tool often used in ex vivo experimentation.

Advantages of in vivo experiments

There are several advantages to using living organisms over in vitro or ex vivo methods. These include:

Evaluation of the effects of certain substances is more accurate in a complex model
You can easily view all the side effects that a substance produces in all parts of the body
The procedure may be easier as fewer variables need to be (or can be) controlled
They are more clinically relevant

Disadvantages of in vivo experiments

While using in vivo methods in studies has its advantages, every scientific method comes with its own drawbacks. In vivo experimentation is no exception.

Some of the disadvantages of in vivo studies include:

Whole, living organisms are used, which can prove to be unethical if harm or distress is caused
It is a lot harder to control every variable , so the results may not be reliable nor applicable to a wider population
Can sometimes be expensive

Rabbits are often used for in vivo testing of pharmaceuticals, beauty products, and other things that must be tested for safety before human consumption.

Examples of in vivo experiments

In vivo experiments have been around for a long time, and were one of the earliest methods of investigation.

However, even though technology has advanced so much over the past few decades, we still use live animals in experiments very frequently to this day.

Here are some examples:

Clinical trials

In vivo experiments are the best way through which clinical trials are carried out.

First of all, this is because it’s important to test the effects of a particular substance on the body as a whole , rather than in one localized area or on one particular biological process.

These clinical trials help us to see all of the side effects that a certain drug has on other parts of the body, so we can better evaluate how safe and effective it is.

For instance, though a substance may seem to be highly effective during an initial in vitro study, it may not be so effective in an actual animal due to unforeseen factors. For example, the drug might not be absorbed when it passes through the stomach.

This is why it’s very important to carry out an in vivo experiment after an in vitro one – before releasing a drug to the public.

A research draws blood from a mouse during an experimentation.

Animal studies

In vivo methods are also used during animal studies , which often come before the clinical trials used on humans.

Animal studies are a useful way of gathering data on the effects of a substance on a living body, without risking human life first.

Things that are often tested on animals include beauty products such as shampoos and soaps, as well as drugs for human consumption.

Some of the most common animals used in animal studies include mice , rabbits , non-human primates , and rats .

For example, non-human primates were used extensively in animal testing to find vaccines for polio , which has since lead to two out of three wild poliovirus strains being pretty much completely eradicated!

On the other hand, other studies have shown that animal testing is not always predictive of human outcomes. In fact, up to 90% of drugs that pass in vivo animal trials fail in subsequent human trials !

A macaque sits in a cage during a testing trial.

1. Which of the following describes an in vivo experiment?

2. Are in vivo experiments always the best?

3. Which of these organisms is NOT a common laboratory animal used in in vivo experimentation?

4. A clinical trial that aims to test the safety of a new vaccine represents which type of experimentation?

5. Which of the following forms of experimentation has the fewest uncontrollable variables ?

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In Vivo vs. In Vitro: What Are the Differences?

Both can advance medical knowledge but have unique limitations

Definitions

In Vitro Medical Studies

In vivo clinical trials.

The terms "in vivo" and "in vitro" describe different types of scientific research. "In vivo" means research done on a living organism, while "in vitro" means research done in a laboratory dish or test tube.

Both types of studies are used by medical researchers developing drugs or studying diseases. Each type has benefits and drawbacks.

In Vivo vs. In Vitro: Definitions

In vitro : The term in vitro comes from the Latin "in glass." It refers to a medical study or experiment that is done in the laboratory within the confines of a test tube or laboratory dish. This means tissue, cells, or other parts of an organism are removed and placed in a laboratory dish. Researchers may use these samples to test the action of a drug or study a disease process.

In vivo : The term in vivo comes from the Latin "in (something) living." It refers to a medical test, experiment, or procedure that is done on (or in) a living organism, such as a laboratory animal or human.

Clinical trials or medical studies may be performed either in vivo or in vitro. These approaches are similar in that they are both done in order to make advances in the knowledge and treatment of illness and disease as well as understanding "wellness" and normal bodily functions.

But there are also many important differences in how in vivo and in vitro studies are conducted, how they can be interpreted, and the practical applications of any discoveries which are made.

Medical studies (such as looking at the ability of a drug to treat cancer) are often first performed in vitro—either in a test tube or laboratory dish. An example would be growing cancer cells in a dish outside of the body to study them and possible treatments.

Studies are usually done in vitro first for ethical reasons. In vitro studies allow a substance to be studied safely, without subjecting humans or animals to the possible side effects or toxicity of a new drug.

Researchers learn as much as possible about a drug before exposing humans to potential negative effects. If a chemotherapy drug , for example, does not work on cancer cells grown in a dish, it would be unethical to have humans use the drug and risk the potential toxicity.

In vitro studies are important in that they allow more rapid development of new treatments—many drugs can be studied at one time (and they can be studied in a large number of samples of cells) and only those that appear to be efficacious go on to human studies.

An absence of biokinetics (how the body transports and metabolized drugs and toxins) is one of the significant drawbacks of in vitro studies. This, as well as several other factors, can make it very difficult to extrapolate the results of in vitro tests. Thus, it's hard to know what might be expected when the drug is used in vivo.

In contrast to in vitro studies, in vivo studies are needed to see how the body as a whole will respond to a particular substance.

In some cases in vitro studies of a drug will be promising, but subsequent in vivo studies fail to show any efficacy (or, on the other hand, find a drug to be unsafe) when used within the multiple metabolic processes that are continually taking place in the body.

An example of how in vivo studies are needed to evaluate drugs is with respect to drug absorption in the body. A new drug may appear to work in a dish, but not in the human body. It could be that the drug is not absorbed when it passes through the stomach, so it has little effect on humans.

In other cases, even if a drug is given intravenously , the drug might break down through continuous body reactions. Therefore, the drug would not be effective when used directly in humans.

It's important to note that oftentimes in vivo studies are first done in non-human animals such as mice. These studies allow researchers an opportunity to see how a drug works amid other bodily processes.

Mice and humans have important differences. Sometimes a drug that is effective in mice will not be effective in humans (and vice versa) due to inherent differences in the species.

A Word From Verywell

When you look at studies done to evaluate cancer treatments—or any other treatments—checking to see which kind of study it is (in vivo vs in vitro) is an important first step.

In vitro studies are extremely important and lay the groundwork for further research, but many of these studies declare findings that are interesting—but will not affect you as an individual for quite some time to come.

In contrast, in vivo studies are looking at the actual effect on an organism—whether a laboratory animal or a human.

Saeidnia, S., Manayi, A., and M. Abdollahi. From in vitro experiments to in vivo and clinical studies; Pros and cons . Current Drug Discovery Technologies . 2015. 12(4):218-24. doi:10.2174/1570163813666160114093140

Kilkenny C, Browne W, Cuthill IC, Emerson M, Altman DG; NC3Rs Reporting Guidelines Working Group. Animal research: Reporting in vivo experiments: the ARRIVE guidelines . Br J Pharmacol . 2010;160(7):1577–1579. doi:10.1111/j.1476-5381.2010.00872.x

By Lynne Eldridge, MD Lynne Eldrige, MD, is a lung cancer physician, patient advocate, and award-winning author of "Avoiding Cancer One Day at a Time."

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Transcription Factors Regulation in Human Peripheral White Blood Cells during Hypobaric Hypoxia Exposure: an in-vivo experimental study

Affiliations.

1 Institute of Mountain Emergency Medicine, Eurac Research, Bolzano, Italy. [email protected].
2 Department of Biomedical Sciences, University of Padova, Padova, Italy. [email protected].
3 Department of Medicine-DIMED, Campus Biomedico Pietro D'Abano, University of Padova, Padova, Italy.
4 Department of Biomedical Sciences, University of Padova, Padova, Italy.
5 Institute of Bioimaging and Molecular Physiology, National Council of Research, Segrate (Milan), Italy.
6 Institute of Mountain Emergency Medicine, Eurac Research, Bolzano, Italy.
7 Department of Neurology, General Hospital of Bolzano, Bolzano, Italy.
PMID: 31289332
PMCID: PMC6617471
DOI: 10.1038/s41598-019-46391-6

High altitude is a natural laboratory, within which the clinical study of human physiological response to hypobaric hypoxia (HH) is possible. Failure in the response results in progressive hypoxemia, inflammation and increased tissue oxidative stress (OxS). Thus, investigating temporal changes in key transcription factors (TFs) HIF-1α, HIF-2α, NF-κB and NRF2 mRNA levels, relative to OxS and inflammatory markers, may reveal molecular targets which contrast deleterious effects of hypoxia. Biological samples and clinical data from 15 healthy participants were collected at baseline and after rapid, passive ascent to 3830 m (24 h and 72 h). Gene expression was assessed by qPCR and ROS generation was determined by EPR spectroscopy. Oxidative damage and cytokine levels were estimated by immuno or enzymatic methods. Hypoxia transiently enhanced HIF-1α mRNA levels over time reaching a peak after 24 h. Whereas, HIF-2α and NRF2 mRNA levels increased over time. In contrast, the NF-κB mRNA levels remained unchanged. Plasma levels of IL-1β and IL-6 also remained within normal ranges. ROS production rate and markers of OxS damage were significantly increased over time. The analysis of TF-gene expression suggests that HIF-1α is a lead TF during sub-acute HH exposure. The prolongation of the HH exposure led to a switch between HIF-1α and HIF-2α/NRF2, suggesting the activation of new pathways. These results provide new insights regarding the temporal regulation of TFs, inflammatory state, and ROS homeostasis involved in human hypoxic response, potentially also relevant to the mediation of diseases that induce a hypoxic state.

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Conflict of interest statement

The authors declare no competing interests.

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pregnancy health center / pregnancy a-z list / why is in vivo better than in vitro article

Why Is In Vivo Better Than In Vitro?

Medical Author: Pallavi Suyog Uttekar, MD
Medical Reviewer: Shaziya Allarakha, MD

What are in vivo and in vitro?

Comments **COMMENTSTAGLIST**
More **OTHERTAGLIST**

While the terms in vivo and in vitro sound very similar, their meanings are not. In vivo is Latin for “within the living.” It is a study model used for a process or procedure that is conducted “on” a living being rather than in cell samples. The in vivo method simulates biological conditions found in a living subject. In vivo studies may be conducted in animals and humans.

In vitro is Latin for “in the glass.” It is a study model in which a procedure is conducted in labs. Interventions may be done on a specific cell culture to grow a virus, bacterium, or fungus, or to test a new drug. Because we look at specific cells during an “in vitro” procedure and not the whole organism, these experiments are more suited for research and analysis.

Given that an in vivo model involves the internal environment of a living being, the results of in vivo studies are considered more reliable or more relevant than those of in vitro studies. This does not mean that an in vitro model is in any way less valuable.

For cases in which normal conception or in vivo fertilization fails, the doctor may advise the couple to try in vitro fertilization (IVF). In vitro fertilization happens when the ovum (female egg cell) and sperm (male reproductive cell) are united outside the body or in a “glass” petri dish.

In the world of research, “in vivo” and “in vitro” models are used in clinical trials , scientific studies, and up-and-coming medical procedures. Scientists use both these models to understand the way a drug (or intervention) affects the body (pharmacodynamic profile) and the way the body affects/breaks down the drug (pharmacokinetic profile). Both these studies serve to create a safer and effective drug profile before they are approved for use by the general population.

Let us look at an example. Suppose scientists want to study a new anti-epilepsy drug. This study goes through various stages. Initially, in vitro studies are conducted in specialized acute seizure models that only need slices of the brain tissue. These models create electroencephalography ( EEG ) signals (brain signals) that are identical to those seen in a patient having a seizure.

During initial testing of the drug, scientists prefer acute seizure models (in vitro) over time-consuming human trials that often involve high levels of individual variation and ethical concerns. Only those drug molecules that show promising results in the in vitro stage will be tested in human clinical trials (in vivo). Here, scientists will see if trends observed in preclinical data (in vitro model) still hold in human volunteers consistently and if the results are reproducible. Thus, drug development needs both in vivo and in vitro models before it is U.S. Food and Drug Administration (FDA)-approved.

Both in vitro and in vivo studies have their own set of advantages and disadvantages, which are explained in the table below.


Category	In vivo method	In vitro model
Cost and preparation
Time
Result
Testing regulations
Applications

In vitro models do not put live subjects at risk. However, they cannot capture the inherent complexity of organ systems and the internal environment of the human body . For example, in vitro cell culture may not account for interactions between various body procedures and cellular biochemistry. Therefore, in vitro studies are often followed by animal studies (in vivo).

Drugs may be tested on animals such as rabbits, hamsters, mice, rats, guinea pigs, dogs , and primates (including monkeys, gibbons, and chimpanzees). Scientists can better evaluate the safety, toxicity, and efficacy of a drug candidate in a complex model (animal). The majority of animals in laboratories are purpose-bred (bred specifically to be used in experiments).

Besides ethical concerns over the use of animals for experiments, the problem of translatability persists because there are considerable physiological differences between humans and animals, and drug absorption, distribution, and excretion may differ.

Only those drugs that appear safe and effective in the in vitro stage eventually go on to the clinical trial stage or in vivo stage.

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Open access
Published: 09 September 2024

Overall survival prediction of gastric cancer using the gene signature of CT-detected extramural venous invasion combined with M2 macrophages infiltration

Hao Yang 1 na1 ,
Xinyi Gou 2 na1 ,
Caizhen Feng 2 ,
Yuanyuan Zhang 3 ,
Boshi Sun 4 ,
Peng Peng 6 ,
Yi Wang 2 ,
Nan Hong 2 ,
Yingjiang Ye 5 ,
Jin Cheng 2 &

Journal of Translational Medicine volume 22 , Article number: 829 ( 2024 ) Cite this article

Metrics details

CT-detected Extramural venous invasion (EMVI) is known as an independent risk factor for distant metastasis in patients with advanced gastric cancer (GC). However, the molecular basis is not clear. In colorectal cancer, M2 macrophages plays a vital role in determining EMVI. This study aimed to investigate the relationship between CT-detected EMVI and the M2 macrophages as well as prognosis predictionusing a radiogenomic approach.

We utilized EMVI-related genes (from mRNA sequencing of 13 GC samples correlated with EMVI score by spearman analysis, P < 0.01) to overlap the co-expression genes of WGCNA module and M2 macrophages related genes (from mRNA data of 371 GC patients in TCGA database), generating a total of 136 genes. An EMVI-M2-prognosis-related hub gene signature was constructed by COX and least absolute shrinkage and selection operator (LASSO) analysis from a training cohort TCGA database ( n = 371) and validated it in a validation cohort from GEO database ( n = 357). High- and low-risk groups were divided by hub gene ( EGFLAM and GNG11 ) signature-derived risk scores. We assessed its predictive ability through Kaplan-Meier (K-M) curve and COX analysis. Furthermore, we utilized ESTIMATE to detect tumor mutation burden (TMB) and evaluate sensitivity to immune checkpoint inhibitors (ICIs). Expression of hub genes was tested using western blotting and immunohistochemistry (IHC) analysis.

The overall survival (OS) was significantly reduced in the high-risk group (Training/Validation: AUC = 0.701/0.620; P < 0.001/0.003). Furthermore, the risk score was identified as an independent predictor of OS in multivariate COX regression analyses (Training/Validation: HR = 1.909/1.928; 95% CI: 1.225–2.974/1.308–2.844). The low-risk group exhibited significantly higher TMB levels ( P = 1.6e − 07 ) and greater sensitivity to ICIs. Significant higher expression of hub-genes was identified on multiple GC cell lines and original samples. Hub-genes knockdown in gastric cancer cell lines inhibited their proliferation, metastatic and invasive capacity to varying degrees. In vivo experiments indicate that EGFLAM, as one of the hub genes, its high expression can serve as a biomarker for low response to immunotherapy.

Our study demonstrated EMVI-M2 gene signature could effectively predict the prognosis of GC tissue, reflecting the relationship between EMVI and M2 macrophages.

Introduction

Gastric cancer (GC) is a highly heterogeneous disease and ranks among the leading causes of cancer-related death worldwide. The heterogeneous characteristics of GC result in significant variation in patient prognosis, even for patients with the same American Joint Committee on Cancer (AJCC) tumor, node, and metastasis (TNM) stage and similar treatment regimens [ 1 ]. Contrast enhanced computed tomography (CT), as the routine examination for GC patients, allows evaluation of the whole lesion characterization and assessment of disease progression. Among multiple macro imaging features derived from CT, extramural venous invasion (EMVI) has been identified as an independent poor prognostic factor in GC patients [ 2 , 3 ]. Notably, it is a strong biomarker correlated with distant metastasis [ 4 ].

Tumor invasion and metastasis are related to the immune status of the tumor microenvironment (TME) [ 5 , 6 , 7 ]. There is evidence suggesting that immune responses may be blunted in EMVI-positive tumors [ 8 ]. In our previous study, we also found that the EMVI-positive group exhibited lower microsatellite instability (MSI), tumor mutation burden (TMB) and response rate to immune checkpoint inhibitors (ICIs), but paradoxically had a higher immune escape status [ 9 ]. Among the various cell types infiltrating the GC stroma, tumor-associated macrophages (TAMs) with an M2 phenotype have attract attention associating with cancer metastasis and worse prognosis in patients [ 10 , 11 ]. M2 macrophages are known to promote tumor angiogenesis and contribute to the assembly of the intravasation sites [ 12 ], and It has been observed play a significant role in determining EMVI in colorectal cancer patients [ 8 ]. However, there is no evidence between M2 macrophages infiltration and the development of EMVI in GC.

In this study, we aimed to investigate the relationship between CT-detected EMVI and immune cell infiltration, particularly M2 macrophages, in GC patients by analyzing mRNA sequencing from GC specimens and the Cancer Genome Atlas (TCGA). Then to validate the findings using Gene Expression Omnibus (GEO) database.

Materials and methods

Research design.

The retrospective analysis was supported by the institutional review board (Approval number: 2020PHB395-01), and the requirement for informed consent was waived. We included a total of 13 pathologically confirmed T4aN + M0 GC patients who underwent preoperative contrast-enhanced multidetector CT (ceMDCT), standard D2 gastrectomy, and adjuvant chemotherapies. Frozen tumor samples were stored in the institute’s biobank. The study’s flow chart is shown in Fig. 1 . First, patients underwent preoperative abdominal ceMDCTs, and tissue samples were subjected to whole transcriptome sequencing. Second, CT-detected EMVI score-related genes were selected. Third, we utilized CIBERSORT and WGCNA analyses to identify M2 macrophage-related module and genes in all GC patients in TCGA database. In the fourth step, we identified the EMVI-M2-prognosis gene signature through the univariate COX regression and least absolute shrinkage and selection operator (LASSO) regression analyses. This gene signature was proposed using the TCGA database for training and validated externally using the GEO (GSE84433). In the fifth step, we compared gene mutation rate, immune cell infiltration status, ESTIMATE scores, and sensitivity to immune check point inhibitors (ICIs) treatment between high- and low-risk groups stratified by the EMVI-M2-prognosis gene signature. In the last step, we verified hub gene expression using western blotting and immunohistochemistry (IHC) on GC cell lines and original samples.

Flow chart of the study

CT detected EMVI scoring

EMVI statuses were reviewed on the preoperative ceMDCT images. Although in previous studies, CT-detected EMVI was usually identified as negative and positive, however, similar with the EMVI scoring system in rectal cancer [ 13 ], CT detected EMVI of GC could be scored from 0 to 4. The scoring of EMVI was defined as follows: Score 0- tumor outline is not nodular without adjacent vessel; Score 1- tumor outline is irregular or nodular, without adjacent vessel; Score 2- Stranding demonstrates in the vicinity of extramural normal calibre vessel, without tumor attenuation within vessel lumen; Score 3: tumor attenuation apparent within extramural vessel, the calibre of the vessel is slightly expanded; Score 4: Irregular vessel contour or nodular expansion of extramural vessel by tumor attenuation.

mRNA sequencing and EMVI related gene selecting

Frozen tumor tissue samples from 13 patients were subjected to quality control followed by global genome sequencing using Illumina HiSeq 4000 (Platforms: GPL20301). RNA quantification and quality assurance were evaluated by NanoDrop ND-1000. The quality control process ensures that the samples are of high quality and suitable for sequencing. The sequencing data had been uploaded to the GEO database (GSE182831). Based on sequencing data, we calculated the correlation between gene expression and EMVI score using R language. EMVI (extramural venous invasion) score is a measure of how far cancer cells have spread into blood vessels outside the wall of the colon. A score of 1–2 indicates a negative result for EMVI, while a score of 3–4 indicates a positive result for EMVI. EMVI-related genes, the differentially expressed genes between the EMVI-positive and EMVI-negative groups, were identified when P < 0.01. The ESTIMATE scores and p-values of 396 EMVI-related genes are described in the additional file 1 .

Coexpression gene selection of immune cell infiltration in WGCNA defined modules on TCGA database

We downloaded mRNA sequencing data and corresponding clinical information from all GC patients ( n = 371) on the TCGA database using official download tool Genomic Data Commons (data through January, 2022, used). Using the CIBERSORT algorithm, the levels of 22 types of immune cells in every GC patients were identified. The screening criterion for CIBERSORT analysis was P < 0.05. Heatmap of immune related genes and the clinical data was generated.

Co-expression analysis was used to identify WGCNA modules related to immune cell infiltration and related gene expressions, with correlation coefficients and P values calculated for all 22 kinds of immune cells and each WGCNA module. From multiple immune-related WGCNA modules, only the midnightblue module and its module-related genes were positively related to M2 macrophages ( P < 0.05). The midnightblue module was intersected with EMVI-related genes to obtain EMVI-M2 macrophage-related genes for subsequent analysis.

Construction and validation for EMVI-M2-prognosis related gene model

For construction for the EMVI-M2-prognosis related gene signature, the LASSO algorithm was used for variable selection and shrinkage with the “glmnet” R package. The independent variable in the regression was normalized expression matrix of candidate EMVI-M2-prognosis related genes, and the response variables were OS and status of patients in the training cohort of TCGA database. The penalty parameter (λ) for the model was determined using 10-fold cross-validation following the minimum criteria.

Patients were stratified into high-risk and low-risk groups based on the median value of the risk score. Kaplan-Meier curves were calculatedanalyses were also performed in training (TCGA) and validation set (GEO) to evaluate the impact of risk scores on the prognosis of gastric cancer patients. Independent prognostic parameters analysis was performed using univariate and multivariate COX regression analyses. The parameters included age, gender, tumor differentiation grade, T/N/disease stage, and risk score. The influence of the parameter on the outcome event was evaluated through computing the Hazard Ratio (HR) for this parameter. If the HR exceeds 1, it denotes that the variable acts as a risk factor, promoting mortality. An HR less than 1 indicates a protective role of the variable, hindering death. Lastly, an HR equal to 1 suggests that the variable exerts no impact on mortality.

Association of EMVI-M2-prognostic related signature with TMB, single mutation rate, immune cell infiltration and sensitivity to immune checkpoint inhibitors (ICIs) therapy

Based on the prognostic gene signature of EMVI-M2 macrophages, tumor mutation burden (TMB) was compared according to risk score and signature genes. Single-gene mutation rates were compared between high- and low-risk groups. K-M curves were compared according to EMVI-M2 macrophage-prognosis gene signature combined with TMB status. Immune cell infiltration and ESTIMATE analysis were compared according to risk score and signature gene expressions. Gene set variation analysis (GSVA) including KEGG and HALLAMRK were analyzed according to risk signature genes. Finally, sensitivity to ICIs according to risk score and signature genes were also analyzed.

Cell culture

GC cells (GES-1, AGS, HGC-27, KATO III, MKN-1, MKN-45 and MFC) were purchased from Procell Life Science & Technology (Wuhan, China), and the cells were cultured according to the manual instructions. The cell lines were cultured in RPMI-1640 medium (Gibco, USA) supplemented with 10% fetal bovine serum (FBS) (Gibco, USA) and 1% penicillin/streptomycin (Gibco, USA).

HGC-27, KATO III, MKN-45 and MFC cells (5 × 10 3 ) were cultured in 24-well plates and transfected with previously constructed RNA interference lentiviral vectors (Genechem, China) or a negative control (empty plasmid) for 24 h. Interference sequences of lentiviral are shown in additional file 2 . The medium was changed to complete medium, and cell culture was continued for 1 week. The medium was then changed to complete medium containing puromycin. After 72 h, the fluorescence intensity was observed under a fluorescence microscope, and the visible fluorescence of the cells indicated that the transfection was successful. The lentivirus was resistant to puromycin, and the stable expression lentiviral cell lines were screened by adding puromycin in the medium. In the process of culture, the cells were overgrown in 24-well plates, and gradually passed into 12-well plates and 6-well plates.

Colony formation assay

HGC-27, KATO III and MKN-45 were seeded in a six-well plate with 1000 cells per plate, and colony formation was visible after 7 days of incubation. Microscopically counting more than 50 cell clones counts as a colony. After fixation in 4% paraformaldehyde for 30 min, cells were stained with 0.5% crystal violet for 30 min.

Wound-healing assay

HGC-27, KATO III and MKN-45 (5 × 10 6 ) were cultured in six-well plates until full confluence was achieved, and then were starved by adding serum-free medium for 24 h. Plates were scratched using a 200 µL pipette tip, removing a line of cells. Photographs were taken at 0, 12, and 24 h under a microscope to observe the degree of wound healing.

Transwell assay

The Transwell assay was performed according to the manufacturer’s instructions. HGC-27, KATO III and MKN-45 (2 × 10 4 ) were inoculated into a Transwell chamber containing 200 µL serum-free medium. The upper chamber surface of the Transwell chamber was coated with Matrigel mix to determine the invasion ability of cells. When testing the cell migration ability, the bottom of the chamber was not coated with Matrigel. Medium containing 10% FBS was added to the lower culture plate. After 24 h of incubation, the chamber was removed and stained with crystal violet for 30 min.

Western blotting

Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE, Epizyme, China) was used for western blotting of gastric cancer cells. After lysing cells, the lysate was subjected to electrophoresis, membrane transfer, and blocking of non-specific antigens. This was then incubated overnight at 4 °C with primary antibodies specific for anti-EGFLAM (ThermoFisher, USA), anti-GNG11 (Affinity, USA), and GAPDH (ImmunoWay, USA). The following day, the membrane was incubated with secondary antibodies for 1 h at room temperature. After visualization of protein bands, grayscale analysis was performed using the ImageJ software (version 1.8.0). The grayscale of the target protein was divided by the grayscale of Actin to obtain the relative amount of the target protein in each protein sample. Then GraphPad Prism 8.0 software was used for statistical analysis of target protein levels between samples.

Immunohistochemistry and immunofluorescence staining

Formalin-fixed tissue was processed, embedded in paraffin and sliced into 5 μm sections. Immunohistochemical (IHC) staining was performed using antibodies anti-EGFLAM and anti-GNG11. Tissue sections were deparaffinized in xylene and rehydrated in graded ethanol. Antigen retrieval was performed by heating sections in boiling sodium citrate buffer (Sigma-Aldrich, C-9999) for 20 min. After blocking with 3% hydrogen peroxide and bovine serum albumin (BSA), the tissues were incubated with the primary antibody at 4 °C overnight. After washing, the tissues were incubated with corresponding horseradish peroxidase (HRP)-conjugated secondary antibodies. The color was developed using diaminobenzidine (DAB) substrate (Sigma-Aldrich, D-7304) and slides were counterstained with hematoxylin. Images of three random areas from each section were captured at 100x magnification for evaluation. Immunofluorescence staining was performed using primary antibodies anti-EGFLAM and anti-GNG11. Corresponding Alexa Fluor dyes were used for fluorescent detection. DAPI was used for nuclear counter staining. Images were captured on the Zeiss LSM780 laser scanning confocal microscope. Quantitative analysis of immunohistochemistry and immunofluorescence images was performed using ImageJ software by measuring mean optical density values, and differences were analyzed using the t-test.

Subcutaneous tumor xenograft nude mouse model

Establishment of xenograft model was approved by Peking University People’s Hospital Ethics Committee. BALB/c mice were anesthetized with 2% isoflurane, and the axillary skin was disinfected using sterile cotton balls. MFC gastric cancer cells were adjusted to a density of 1 × 10 6 /mL, and 100 µL of cell suspension was subcutaneously injected into the axilla using a 1 mL syringe. Mice were intraperitoneally administered anti-mouse PD-1 (Bio X Cell; BE0273; 3 mg/kg) or anti-mouse CTLA-4 (Bio X Cell; BE0131; 3 mg/kg) according to their grouping every 3 days. The tumor volume in each mouse was measured by vernier caliperevery 3 days. All mice were sacrificed after 18 days. Tumor tissues were harvested for measurement and weighing, the tumor volume was calculated, and growth curves were plotted. The tumor volume was calculated as volume (mm 3 ) = 0.5 × long diameter × short diameter 2 (mm 2 ).

Data were analyzed and visualized using the GraphPad Prism 8.0 software. The Student’s t-test was used to compare means between two groups, and one-way ANOVA was conducted to determine the significance of differences among multiple groups (> 2). Subcutaneous tumor growth curves were analyzed using two-way ANOVA. P < 0.05 was considered statistically significant.

Co-expression gene selection of immune cell infiltration in WGCNA defined modules on TCGA database

The CIBERSORT algorithm was used to calculated the immune cell infiltration score of each GC patient on TCGA database ( n = 371) (Fig. 2 a-c). Co-expression analysis was used between WGCNA identifying gene modules and immune cell infiltration status (Fig. 2 d-f). We screened for modules that showed positive and specific correlations with immune cells. The results indicated that Macrophages M2, Dendritic cells resting, Mast cells resting, and Eosinophils were significantly and positively correlated only with the midnight module, whereas Activated Dendritic cells and Activated Mast cells were significantly and positively correlated only with the cyan module (Fig. 2 f). Given that among these immune cells, only M2 macrophages have a high association with gastric cancer progression, we chose the midnightblue module as the central module for this study’s focus. Further analysis was then conducted on the 3568 genes encompassed within this module. CT-detected EMVI scores and the clinical characteristics of thirteen included patients were detailed in our previous work [ 14 ]. Based on CT-detected EMVI scores, we selected 396 genes that were significantly associated with EMVI ( P < 0.01) (additional file 1 ). By overlapping all EMVI related genes and M2 macrophages related genes of midnightblue module, and there were 136 genes selected for further analysis (Fig. 2 g).

Immune cell infiltration and WGCNA analysis in GC patients of TCGA. ( a ) Immune cell infiltration in each GC patients. ( b ) Correlation analysis of 22 types of immune cells in GC tissues. ( c ) Immune cell infiltration status of normal and cancerous gastric tissues. ( d ) Correlation of genes with each WGCNA module after clustering. Genes on the cluster tree are represented by different colors corresponding to distinct modules, where the color gray represents genes that could not be classified into any specific module. ( e ) Select the number of WGCNA modules according to the power value. ( f ) Co-expression of gene modules defined by WGCNA and immune cell infiltration. The vertical axis uses different colors to represent 13 functional modules, while the horizontal axis represents 22 types of immune infiltrating cells. ( g ) Gene overlapping of midnightblue module and EMVI related gene

We performed univariate COX analysis on the 136 target genes previously screened (with p < 0.001 as the screening condition), and we obtained prognostic-related genes EGFLAM and GNG11 (Fig. 3 a, additional file 3 ). A LASSO regression model was then constructed based on EGFLAM and GNG11 (Fig. 3 b, c). Then, we divided the Train Set cases and Test Set cases into high-risk group and low-risk group based on the median cutoff value. The high-risk group had significantly lower OS than the low-risk group (Fig. 3 d, e). The risk curve diagram shows that as the sample risk score increases, the expression levels of the risk genes also rise. Patients in the high-risk group have shorter survival times compared to those in the low-risk group, indicating a poorer prognosis for the high-risk group (Fig. 3 f, g). Univariate and multivariate COX regression analyses demonstrated that age (Train/Test HR = 1.034/1.023; 95% CI: 1.016–1.053/1.009–1.037) and risk score (Train/Test: HR = 1.909/1.928; 95% CI: 1.225–2.974/1.308–2.844) were independent predictors of OS (Fig. 3 h, i) in training and validation cohort at the same time. The larger the area under the curve (AUC), the greater the accuracy of the model constructed, indicating a better performance of the predictive model. AUC of risk score were 0.701 and 0.620 in training and validation cohorts, respectively (Fig. 3 j, k). AUC of Age were 0.606 and 0.543 in training and validation cohorts, respectively. This suggests that the risk score has greater specificity than age. Heat map of risk score and clinical characteristics was shown in additional file 4 a. Furthermore, there were statistical differences in tumor differential grade, disease stage and T stage between high- and low-risk groups (additional file 4 b).

a . Univariate COX analysis was performed on 136 target genes to screen for genes associated with patient prognosis, and the results were displayed using a forest plot; b . A LASSO cross-validation plot was drawn, where the dotted line on the left side of the plot represents lambda.min, and the dotted line on the right side represents lambda.1se. c . The LASSO coefficient path plot shows how the coefficients of EGFLAM and GNG11 change as the Log Lambda parameter increases. Patients in the high-risk group had a significantly lower OS and death events than those in the low-risk group in training ( d , f ) and validation ( e , g ) cohorts. Univariate and multivariate Cox regression analyses demonstrated that age were independent predictors of OS ( h , i ) in training and validation cohort at the same time. AUC of risk scorewere 0.701 and 0.620 in training and validation cohorts, respectively ( j , k )

Association of EMVI-M2-related signature with TMB, single-gene mutation rate, immune cell infiltration and sensitivity to immune therapy

Tumor mutation burden (TMB) was higher in high-risk group (Fig. 4 a, b), and patients with high expression of EGFLAM and GNG11(Fig. 4 c, d). Single-gene mutation (Fig. 4 e, f) rates were also higher in low-risk group than in high-risk group. According to the median value of TMB rate, there was significant survival difference in TCGA cohort (Fig. 4 g). Combined with the EMVI-related risk score and TMB rate, we found that the high-TMB/low-risk group had the best prognosis, while the low-TMB/high-risk group had the worst prognosis (Fig. 4 h). The survival curves were cross between low-mutation/low risk and high-mutation/high risk.

Box-plot ( a ) and scatter diagrams ( b ) show different tumor mutation burden (TMB) between low- and high-risk groups, as well as the different expression of EGFLAM ( c ) and GNG11 ( d ). Single mutations rate was different between low- ( e , 91.8%) and high-risk ( f , 84.36%) groups. K-M curves according to TMB status ( g ) and combined with risk score ( h )

Immune cell infiltration was positively correlated (Correlation coefficient > 0) with risk score according to multiple algorithms (CIBERSORT-ABS, CIBERSORT, MCPCOUNTER, EPC, XCELL, TIMER, QUANTISEQ) (Fig. 5 a). Associations between immune cells and two risk signature genes ( EGFLM and GNG11 ) were shown in Fig. 5 b and c. The two genes were positively correlated with M2 macrophages, fibroblasts and endothelial cells. ESTIMATE analysis shows that ImmuneScore, StromalScore and ESTIMATEScore were all significantly higher in high-risk groups than in low-risk groups (Fig. 6 a). Low risk group was shown better response to immune check point inhibitors (ICIs) of Cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) compared with high-risk groups, but there was no significant difference in response to programmed death 1 (PD-1) treatment (Fig. 6 b). EMVI-M2-prognosis related gene signature had positive correlation with multiple ICIs related genes (additional file 4 a). KEGG analysis showed that there were multiple oncology related pathways, such as WNT, VEGF, P53 and ERBB, et al., correlated with risk score and risk genes (additional file 4 b). Furthermore, risk score and corresponding genes were also correlated with oncology related characteristics, by HALLMARK analysis, including angiogenesis, P53 (additional file 4 c). We found that EGFLAM and GNG11 were highly expressed in tumors with high ImmuneScore, StromalScore and ESTIMATEScore by ESTIMATE analysis (Fig. 6 c, f). Both genes were positively correlated with M2 macrophages by using CIBERSORT, with P values of 0.008 and 0.013, respectively (Fig. 6 d, g). For ICIs therapy analysis, EGFLAM expression was a significant response factor (Fig. 6 e), but not GNG11 (Fig. 6 h). The results showed that patients with low expression of EGFLAM had a better response to anti-PD-1 or anti-CTLA-4 treatment compared to those with high expression. In contrast, there was no significant difference in treatment responsiveness between groups with high and low expression of GNG11. According to the pRRophetic algorithms, there were 12 ICIs drugs with higher sensitivity in the high-risk group (Fig. 7 ).

Immune cell infiltration were positively correlated (Correlation coefficient > 0) with risk score according to multiple algorithms (CIBERSORT-ABS、CIBERSORT、MCPCOUNTER、EPC、XCELL、TIMER、QUANTISEQ) ( a ). Scatter diagrams show associations of immune cells and two risk signature compromising genes, EGFLM ( b ) and GNG11( c )

Violin diagrams show that ImmuneScore, StromalS and ESTIMATEScore were all significantly higher in high-risk groups ( a ), high expression of EGFLAM ( c ), and GNG11 ( f ). Violin diagrams showed different responses to immune check point inhibitor (ICIs) according to risk-score (b), EGFLAM ( e ) and GNG11 ( h ) expressions. These two genes were both positive correlated with M2 macrophages ( d , g )

Box-plots show 12 kinds of ICI medicine more sensitive in high-risk groups

The expression analysis of EMVI-M2-related gene EGFLAM and GNG11

The result of IHC showed that the expression of EGFLAM and GNG11 in ceMDCT 3–4 EMVI score samples was significantly higher than that in ceMDCT 1–2 EMVI score samples ( P < 0.0001) (Fig. 8 a, additional file 5 a). We detected the expression of the EMVI-M2-related gene EGFLAM and GNG11 in gastirc mucosal epithelial cell line (GES-1) and 5 GC cell lines (AGS, HGC-27, KATO III, MKN-1 and MKN-45) through western blotting. The results showed that the expression of EGFLAM and GNG11 in cancer cell lines was higher than gastric mucosal epithelial cell line. EGFLAM has the highest expression in HGC-27 ( P < 0.01) and MKN-45 ( P < 0.001) cell lines. GNG11 has the highest expression in HGC-27 ( P < 0.01) and KATO III ( P < 0.01) cell lines (Fig. 8 b). The result of immunofluorescence was consistent with the result of western blotting (Fig. 8 c). The knockdown of EGFLAM and GNG11 was performed in the HGC-27 cell line. Western Blot analysis revealed that the shRNA constructs shRNA-10,011 and shRNA-10,008 exhibited the most efficient knockdown (additional file 5 b, c). Colony formation assays, scratch assays, and Transwell assays demonstrated that the knockdown of EGFLAM and GNG11 in gastric cancer cell lines (HGC-27, KATO III, and MKN-45) significantly inhibited their proliferation, migration, and invasion capabilities (Fig. 8 d-f). Subcutaneous tumor volumes in mice were notably reduced by anti-PD-1 treatment in vivo experiments. The reduction in subcutaneous tumor volume was even more pronounced in the group where EGFLAM was knocked down compared to those treated with alone. It suggests that EGFLAM may attenuate the sensitivity to anti-PD-1 treatment (Fig. 8 g-h). In contrast, there was no significant difference in subcutaneous tumor volume between the GNG11-KD group and the anti-PD-1 treatment group. Flow cytometry revealed that after anti-PD-1 treatment, BALB/c mice exhibited an increased population of CD8 + T cells, which contributes to the anti-tumor response in the mice. Notably, the EGFLAM-KD group had a higher number of sorted CD8 + T cells compared to the group treated with anti-PD-1 treatment alone (Fig. 8 i). Using the same methodology, we also investigated the therapeutic effects of anti-CTLA-4 treatment. Our findings confirmed that CTLA-4 is capable of reducing the volume of subcutaneous tumors in BALB/c mice. Moreover, the group with EGFLAM knockdown displayed even smaller subcutaneous tumor volumes and a higher production of CD8 + T cells compared to those treated with CTLA-4 alone (Fig. 8 j-l). The results are consistent with the previous bioinformatic analysis of immunotherapy responses, indicating that high expression of EGFLAM correlated with a lack of response to immunotherapy in gastric cancer. The knockdown of EGFLAM subsequently enhances the sensitivity to immunotherapy.

EGFLAM and GNG11 augment the proliferation, migration, and invasiveness of gastric cancer cell lines. Furthermore, in in vivo experiments, EGFLAM attenuates the sensitivity of mice to anti-PD-1 and anti-CTLA-4 treatments. a . The result of IHC showed that the expression of EGFLAM and GNG11 in ceMDCT 3–4 EMVI score samples ( n = 4) was significantly higher than that in ceMDCT 1–2 EMVI score samples ( n = 4). b . Western blotting showed that EGFLAM has the highest expression in HGC-27 and MKN-45 cell lines. GNG11 has the highest expression in HGC-27 and KATO III cell lines. c . The result of immunofluorescence was consistent with the result of western blotting. d . EGFLAM or GNG11 knockdown in gastric cancer cell lines (HGC-27, KATO III, and MKN-45) inhibited their proliferative capacity. e , f . Scratch assays and Transwell assays indicated that after the knockout of EGFLAM and GNG11 in gastric cancer cell lines, there was a notable inhibition of both migratory and invasive abilities; g , j . A subcutaneous tumor model was established in BALB/C mice using MFC cells, followed by treatment with anti-PD-1 and anti-CTLA-4; h, k. Growth curves of subcutaneous tumors in BALB/C mice were monitored over time. i , l. Flow cytometry was performed on the subcutaneous tumors to quantify the presence of CD8 + T cells and CD4 + T cells, providing insights into the immune cell infiltration within the tumor microenvironment. * P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001

In this radiogenomics-based study, we investigated the association of CT-detected EMVI related genes and M2 macrophages related genes from WGCNA constructed gene modules. Then we established a 2-gene ( EGFLAM and GNG11 ) model which divided GC patients into low- and high-risk groups. These two genes were both positively correlated with M2 macrophages. The high-risk group was with lower TMB/single mutation rate and worse response to ICIs and poorer prognosis with significant differences.

Among multiple kinds of immune cells, M2 macrophages promote tumor angiogenesis and aid in intravasating migration [ 12 ], leading to poor prognosis [ 10 , 11 ]. In Sonal et.al.’s study, EMVI positive colorectal cancer represented blunting immune response, demonstrating decreased expression PD-L1 positive macrophages. However, there was no significant difference in CD163, another M2 macrophages markers (PD-L1) between EMVI positive and negative groups [ 8 ]. They speculated that fibrosis and obliteration of the blood vessel caused by EMVI could inability of immune cells to reach inside the tumor and the “cold” environment elicits for tumorigenesis and subsequent generating EMVI. Different from colorectal cancer, GC is a characteristic inflammation related cancer but with high heterogeneity [ 15 ], and the tumor inflammatory microenvironment plays a crucial role in tumor progression and affecting the clinical benefit of ICIs therapy and prognosis [ 16 , 17 ]. In this study, we found that immune cell infiltration status was higher in high-risk group, which confirmed the complexity of the tumor immune microenvironment.

In our study, we focused on the intersected EMVI-related genes that coexpressed with gene modules as defined by WGCNA. This systemic unit of coexpression among the EMVI-M2 macrophage-related genes derived from the WGCNA-defined model allows us to estimate the function of the gene network at its most comprehensive level, moving beyond a mere list of individual genes. Based on univariate Cox and LASSO analyses, we developed a CT-detected EMVI-M2 prognostic gene signature along with a corresponding risk score model. This model effectively distinguished survival differences between low- and high-risk groups in both training and validation cohorts for GC patients, showing satisfactory AUC values. These results are consistent with the previously reported prognostic predictive ability of CT-detected EMVI [ 2 , 18 ].

Furthermore, these findings shed light on the development of EMVI, where cancer cells infiltrate and proliferate within the lumen of draining veins, potentially facilitated by the immune environment, including M2 macrophages and their associated cytokines [ 12 ]. Moreover, they enhance our understanding of the clinical significance of CT-detected EMVI, which serves as a critical predictor of distant metastasis and prognosis in GC patients [ 19 ]. According to the TCGA database, there was a significant difference in overall survival (OS) rates between high- and low-risk groups with respect to TMB. Previous literature has shown that patients with higher TMB exhibit significantly longer survival times, even among those with advanced disease stages [ 20 , 21 ]. The K-M curves overlapped for patients with high-TMB/low-risk score and low-TMB/high-risk score. Noteworthy that the risk score was positively associated with T stage, disease stage and tumor differential grade, which is consistent with the characteristics of EMVI [ 22 ]. In order to avoid the bias of different clinical characteristics with EMVI presentation, all GC patients for mRNA sequencing were in T4aN + and had poorly differentiated adenocarcinoma.

Multiple oncologic-related gene pathways and functions were found to be correlated with the risk score. Among them, the VEGF pathway and angiogenesis characteristics could be used to explain the development of CT-detected EMVI. According to the hub genes of the EMVI-M2-prognosis related signature, EGFLAM has been shown to have functions related to cell proliferation, migration, and invasion, and is correlated with poor prognosis in glioblastoma patients [ 23 ]. Meanwhile, GNG11 (G protein subunit gamma 11) had also been identified as playing a crucial role in the biological process of ovarian cancer by the Extracellular Matrix (ECM) receptor mutation pathway, potentially affecting patient prognosis [ 24 ]. We observed a positive correlation between both genes and M2 macrophages, fibroblasts and endothelial cells, suggesting that EGFLAM and GNG11 are involved in the immune microenvironment. The upregulation of risk genes may facilitate the tumor microenvironment, which promotes tumor proliferation and migration.

Multiple clinical trials had proven that GC patients can benefit from immune therapy, primarily through the use of programmed cell death 1 (PD-1)/ programmed cell death ligand 1 (PD-L1) inhibitors [ 25 , 26 ]. Additionally, several studies have demonstrated the anticancer effects of anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) antibodies in GC [ 27 , 28 ]. In a previous study, targeting CTLA-4 could decrease M2 macrophages and promotes T cell activation [ 29 ], potentially explaining the significant difference in sensitivity to anti-CTLA-4 between low- and high-risk groups. Furthermore, in this study we found that risk score and corresponding genes were positively correlated with multiple immune checkpoint genes, suggesting the potential for targeted therapy of relevant ICIs in GC patients. Using the pRRophetic algorithm, we predicted sensitivities to 12 ICIs drugs and confirmed that the sensitivities of high-risk groups were significantly higher than those of low-risk groups. These new medicines could be suitable for high-risk or advanced GC patients. For example, PD0325901, as an ERK inhibitor could enhance the efficiency of PD-1 inhibitor in non-small cell lung carcinoma [ 30 ].

In recent years, TMB has received considerable attention in research related to immune checkpoint inhibitors (ICIs). Studies have shown that patients with high tumor mutational burdens are more likely to benefit from ICI treatments. For patients with TMB-high (TMB-H) tumors, their cancer cells produce neoantigens. Once these neoantigens are presented on the surface of tumor cells, they can be recognized by corresponding immune cells, leading to the activation of immune cell-mediated killing of the tumors [ 31 ]. Some studies have shown that TMB is associated with monotherapy or combination therapy using Immune Checkpoint Inhibitors (ICIs) for various tumors, and it has been proven to serve as a predictive biomarker for the efficacy of pan-cancer immunotherapy [ 32 , 33 ]. Xishan Hao and colleagues found that the TMB-H subtype displayed a markedly immunologically active phenotype, as determined through transcriptomic analysis and further validated in the TCGA GC cohort. GC patients with TMB-H showed a significantly improved survival rate ( P = 0.047) [ 34 ]. Similar research has found that in pancreatic cancer, patients with TMB-H tend to have longer survival times [ 35 ]. This is consistent with the findings of this article, where patients in the low-risk group were shown to have higher TMB status, alongside lower expression levels of EGFLAM and GNG11 in the same group. In the analysis of responsiveness to ICI treatments, the low EGFLAM group demonstrated improved responsiveness to PD-1 and CTLA-4 blockade. In vivo experiments showed that after knocking down EGFLAM, the volume of subcutaneous tumors in mice was significantly reduced.

The study has several limitations. First, the sample size was relatively small. However, our study had sufficient samples (6:7) for target genes mining. Secondly, most GC patients in TCGA database are white, African, or Latino; however, the model we constructed showed satisfactory predictive ability.

Conclusions

CT-detected EMVI-related genes were closely correlated with M2 macrophages infiltration in GC tissue, highlighting the relationship between EMVI and poor prognosis.

Data availability

The original contributions presented in the study are publicly available. This data can be found in the GEO database, accession number: GSE182831.

Abbreviations

Gastric cancer

Tumor, node, and metastasis

Computed tomography

Extramural venous invasion

Tumor microenvironment

Tumor mutation burden

Immune checkpoint inhibitors

Tumor-associated macrophages

The Cancer Genome Atlas

Gene Expression Omnibus

Contrast-enhanced multidetector CT

Least absolute shrinkage and selection operator

Immunohistochemistry

Receiver operating curve

Gene set variation analysis

Area under the curve

Cytotoxic T-lymphocyte-associated antigen 4

Programmed death 1

Programmed cell death ligand 1

Overall survival

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under grant number 81901819 and Peking University People’s Hospital Research and Development Funds under grant number RS2021-08.

Author information

Hao Yang and Xinyi Gou contributed equally to this work and share first authorship.

Authors and Affiliations

Department of Oncology Surgery, Harbin Medical University Cancer Hospital, Harbin, China

Department of Radiology, Peking University People’s Hospital, 11 Xizhimen South St, Beijing, 100044, China

Xinyi Gou, Caizhen Feng, Yi Wang, Nan Hong & Jin Cheng

Department of Pathology, Peking University People’s Hospital, Beijing, China

Yuanyuan Zhang

Department of General Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China

Department of Gastrointestinal Surgery, Peking University People’s Hospital, Beijing, China

Yingjiang Ye

Department of Hernia and Abdominal Wall Surgery, Peking University People’s Hospital, Beijing, 100044, China

Peng Peng & Bo Gao

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HY: bioinformatics analysis, molecular biology experiment, manuscript writing and graphical visualization. XG: bioinformatics analysis and manuscript writing. CF: CT imaging analysis. YZ: molecular biology experiment and pathological analysis. BS: molecular biology experiment. PP: conceptual design and manuscript revision. NH: clinical data collection. YY: providing gastric cancer clinical samples. YW: CT imaging analysis, gene sequencing and study supervision. BG: conceptual design, bioinformatics analysis, manuscript revision and providing fund. JC: CT imaging analysis, gene sequencing, manuscript revision and providing fund. All authors contributed to the article and approved the submitted version.

Corresponding authors

Correspondence to Jin Cheng or Bo Gao .

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Yang, H., Gou, X., Feng, C. et al. Overall survival prediction of gastric cancer using the gene signature of CT-detected extramural venous invasion combined with M2 macrophages infiltration. J Transl Med 22 , 829 (2024). https://doi.org/10.1186/s12967-024-05628-3

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The Experimental Design Assistant

* E-mail: [email protected]

Affiliation National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs), London, United Kingdom

Affiliation Certus Technology Associates Ltd, Exeter, United Kingdom

Affiliation GlaxoSmithKline, Stevenage, United Kingdom

Affiliation University of Oxford, Oxford, United Kingdom

Affiliation Envigo, Alconbury, United Kingdom

Affiliation University of Bristol, Bristol, United Kingdom

Affiliation University of Manchester, Manchester, United Kingdom

Affiliations Wellcome Trust Sanger Institute, Hinxton, United Kingdom, Quantitative Biology IMED, AstraZeneca R&D, Cambridge, United Kingdom

Affiliation Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom

Affiliation King’s College London, London, United Kingdom

Affiliation University College London, London, United Kingdom

Nathalie Percie du Sert,
Ian Bamsey,
Simon T. Bate,
Manuel Berdoy,
Robin A. Clark,
Innes Cuthill,
Derek Fry,
Natasha A. Karp,
Malcolm Macleod,

Published: September 28, 2017

https://doi.org/10.1371/journal.pbio.2003779
Reader Comments

Addressing the common problems that researchers encounter when designing and analysing animal experiments will improve the reliability of in vivo research. In this article, the Experimental Design Assistant (EDA) is introduced. The EDA is a web-based tool that guides the in vivo researcher through the experimental design and analysis process, providing automated feedback on the proposed design and generating a graphical summary that aids communication with colleagues, funders, regulatory authorities, and the wider scientific community. It will have an important role in addressing causes of irreproducibility.

Citation: Percie du Sert N, Bamsey I, Bate ST, Berdoy M, Clark RA, Cuthill I, et al. (2017) The Experimental Design Assistant. PLoS Biol 15(9): e2003779. https://doi.org/10.1371/journal.pbio.2003779

Copyright: © 2017 du Sert et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors received no specific funding for this work.

Competing interests: The EDA was developed as part of an NC3Rs programme. All authors were involved in developing the EDA and NPdS currently works at the NC3Rs.

Abbreviations: EDA, Experimental Design Assistant; NC3Rs, UK National Centre for the Replacement, Refinement and Reduction of Animals in Research; ARRIVE, Animal research: Reporting of in vivo experiments

Provenance: Commissioned; externally peer reviewed.

Introduction

The poor reproducibility of findings from animal research has received much attention over the last few years [ 1 ], not least because of the impact it has on translation, scientific progress, and the use of resources. It has been estimated that over half of preclinical research is irreproducible (see [ 2 ]). There are many reasons for this, aside from the complication that reproducibility can be defined in different ways [ 3 ], but flawed experimental design, inappropriate statistical analysis, and inadequate reporting have been flagged as major concerns [ 4 – 6 ]. There is considerable scope for improving the way animal research is designed, conducted, analysed, and reported.

As a starting point, the UK National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs) developed the Animal research: Reporting of in vivo experiments (ARRIVE) guidelines to improve the reporting of animal experiments [ 7 , 8 ]. As of 2016, compliance with these guidelines was recommended by over 600 leading journals in the biomedical sciences [ 9 ]; with more advocating their use each year, this number has now increased to over 1,000 [ 7 ]. Compliance should ensure that published articles contain sufficient information to assess the reliability of the findings and enable the experiments to be adequately replicated [ 8 ]. Improved reporting should also increase the quality of retrospective studies, such as systematic reviews. However, in order to increase the reliability of findings, the design, conduct, and analysis of individual experiments needs to be improved. Here, we present the Experimental Design Assistant (EDA), which was launched to support the scientific community with this process [ 10 , 11 ].

The EDA ( https://eda.nc3rs.org.uk ) is a web-based application with an integrated website, which guides researchers through the process of designing animal experiments; the output includes a diagram that improves the transparency of the experimental plan. The resource is freely available and was developed by the authors as an NC3Rs-led collaboration between in vivo researchers and statisticians from academia and industry and a team of software designers who specialise in innovative solutions for the life sciences ( http://www.certus-tech.com/ ). The EDA enables researchers to build a stepwise, schematic representation of an experiment—the EDA diagram—and uses computer-based logical reasoning to provide feedback and advice on the experimental plan. The system’s main features are presented in Table 1 .

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https://doi.org/10.1371/journal.pbio.2003779.t001

The EDA improves the reliability of experimental results and analysis

The majority of published in vivo research provides no indication that basic precautions have been taken to obtain reliable findings [ 4 , 12 ]. High internal validity can only be achieved by minimising systematic bias so that observed differences can be confidently associated with the treatment of interest. For example, many publications include no information on randomisation and blinding [ 4 , 12 ]. This is not considered to be just a reporting issue; studies have shown that publications of animal experiments that report the use of such measures also tend to report lower effect sizes compared to those that do not [ 13 ]. This implies that experiments are generally not designed and conducted to the highest standards, and the results may not be reliable. Random allocation and blinding have 2 benefits: first, they help meet a key assumption of the statistical analysis, namely, that different groups are drawn from the same background population using random sampling. Second, applying these techniques minimises systematic differences between the treatment groups during the conduct of the experiment, assessment of the results, and data analysis. Such differences can be caused by researchers subconsciously influencing the animals’ allocation to treatment groups, the animals’ behaviour [ 14 ], or the handling of the data (e.g., removal of outliers).

Another common concern regarding the reliability of animal experiments is that they are underpowered, using too few animals to yield dependable results. Button, Ioannidis and colleagues [ 15 ] estimated the average power in neuroscience animal studies to be around 20%. This constitutes a high risk of missing a genuine effect (a false negative), because only 1 in 5 experiments would have a chance of detecting an effect of the magnitude reported. Conversely, the use of sample sizes that are too small also reduces the reliability of the conclusions from an individual experiment and of the published literature as a whole. When a statistically significant effect is detected, it is less likely to be genuine and its magnitude more likely to be overestimated [ 15 , 16 ]. Indeed, justification for the choice of sample sizes is rarely included in published papers [ 4 , 12 ].

The EDA helps avoid such pitfalls when designing in vivo experiments and improves the reliability of the results—and ultimately, their reproducibility. The system generates a randomisation sequence for the experiment, which takes into account any blocking factors included in the design and provides dedicated functionalities, such as support for blinding and sample size calculation, to assist researchers in following best practice (see Fig 1 ). A tailored critique provides suggestions on optimising the experimental plan. For example, it helps researchers to identify variables that could confound the outcome and provides advice on how to include them in the randomisation and the statistical analysis. Finally, once the researcher has addressed the feedback and is satisfied with the design, the system advises on which methods of statistical analysis are most appropriate. Designing experiments with the EDA encourages researchers to consider the sources of bias at the design stages of the experiment before the data are collected, ensuring a rigorous design that is more likely to yield robust findings that can be reproduced. The EDA can also be used as a teaching resource, thereby promoting a better understanding of the principles of experimental design at an early stage of the research training process. The process of building an EDA diagram familiarises students with the different components of the design and how they are connected. The visual representation of abstract concepts, such as the experimental unit, the independent variables, or data transformations, brings clarity that enables a detailed discussion of the experimental plan.

The workflow is not fixed and different users might prefer to do some steps in a different order. A potential workflow using the different functionalities of the EDA is described as follows: (1) The user starts by drawing a diagram (with nodes and links) representing the experiment they are planning. Assistance is provided in the form of examples, templates, and video tutorials. (2) Information is added into the node properties, providing more details about the specific step of the process represented by each node. (3) The “Critique” functionality (see Table 2 ) enables the researcher to obtain feedback on the diagram and the design it represents. The feedback might prompt a change in their plans or the addition of missing information. This is an iterative process and the user might go through the first 3 steps a number of times. (4) Once feedback from the critique has been addressed and the user is satisfied with the final design, the analysis method suggested by the system can be reviewed (see Table 2 ). (5) Depending on how the data will be analysed, a suitable sample size can be calculated using one of the calculators provided within the system. (6) Once the number of animals needed per group is known, the EDA can generate the randomisation sequence. The spreadsheet detailing the group allocation for each animal can be sent directly to a third party nominated by the user, thus blinding the allocation. This enables the researcher to remain unaware of the group allocation until the data have been collected and analysed. (7) Diagrams can be safely shared with colleagues and collaborators at any stage of the process. (8) The user can export a PDF report, which contains key information about the internal validity of the experiment, a summary of the feedback from the system, and the EDA diagram itself. This report can be submitted as part of a grant application, as part of the ethical review process, or, later on, with a journal manuscript. Alternatively, the diagram data can be exported (as an.eda file) and saved locally or used to register the protocol before the experiment is conducted. (9) Once the planning is complete, the experiment is carried out. (10) The diagram can be updated after data collection to enable the user to keep an accurate record (e.g., to record the number of animals analysed if some failed to complete the experiment or if data are missing for other reasons).

https://doi.org/10.1371/journal.pbio.2003779.g001

The EDA offers a new standard notation for describing experiments

It is difficult to find a technical discipline that has not adopted a schematic, diagrammatic, or symbolic notation to improve communications and the recording of methodological detail. However, there are no universally accepted standards to describe the different components of an experimental design. Different terms can be used to describe the same things; for example, the outcome measure is also known as the dependent variable, the response variable, the outcome variable, or the variable of interest, which can easily be confused with the independent variable of interest (also known as the factor of interest or the predictor of interest). By contrast, the same terms can be used to describe different settings; for example, a ‘repeated measure design’ can imply a situation in which animals receive multiple treatments in a different order (sometimes described as a crossover design). However, it could also refer to a situation in which the response to a given treatment in each animal is measured over time or to a situation in which multiple responses are measured for each animal [ 17 ]. The EDA resolves this problem by helping the user generate unambiguous representations of these different designs using EDA diagrams (see Fig 2 and S1 Fig ) and hence does not require knowledge or understanding of labels such as ‘repeated measure design’.

EDA diagrams are composed of nodes and links to represent an entire experimental plan. Each node contains properties where specific details are captured (properties are not shown in this picture, but in the EDA they are accessible by clicking on the specific node). This particular example is a simple 2-group comparison. The grey nodes contain high-level information about the experiment, such as the null and alternative hypotheses, the effect of interest (via the experiment node), the experimental unit, or the animal characteristics. The blue and purple nodes represent the practical steps carried out in the laboratory, such as the allocation to groups (allocation node) and the group sizes and role in the experiment (group nodes), the treatments (via the intervention nodes), and the measurements taken (measurement nodes). The green and red nodes represent the analysis, the outcome measures, and the independent variables.

https://doi.org/10.1371/journal.pbio.2003779.g002

A central feature of the EDA is the development of standards for communicating experimental design. This has required the careful definition of the concepts used in experimental design together with an associated vocabulary of preferred terms. This was developed using an iterative approach and tested using a wide range of experimental plans from the published literature. The result is an ontology that supports the capture of every element of an experimental plan, from high-level information such as the hypotheses, effect of interest, and animal characteristics, to the practical steps carried out in the laboratory, as well as details on the variables included in the design and statistical analysis. This ontology has underpinned the development of a computer-aided design tool to support the experimental design process. The tool helps users develop EDA diagrams consistent with the ontology. The diagrams are unambiguous and more explicit than the text descriptions normally included in grant applications or journal publications. Novel ideas and intellectual property contained within the diagrams are protected; a summary of the security measures in place is included on the website: https://eda.nc3rs.org.uk/security . EDA diagrams are also computer interpretable, allowing automated critiquing of designs against recognised best practice without constraining the experimental design process or the plans themselves.

The EDA enables an effective assessment of the experimental plans

For researchers who have limited access to statistical support, the critical feedback provided by the EDA will be particularly pertinent, as it provides users with information that is specific to the experiment they are planning. The system is not designed to replace specialist statistical advice but can facilitate it. The process of building a design using the EDA emulates the initial fact-finding discussion a researcher might have with a statistician. It helps the researcher to identify much of the information that a statistician would need in order to provide expert advice and presents it in an explicit and standardised format, which can be made available to funding bodies, ethical review committees, journal editors, and peer reviewers.

Users also have the option to share their designs with team members and collaborators, and anecdotal evidence shows that diagrams are extremely useful when discussing the experimental plans within a research team. This is partly because the visual representation enables an efficient critical appraisal of the plans, such as questioning the role of and need for each experimental group, defining variables, identifying potential sources of bias, or debating the type of outcome measures. This detailed scrutiny before the experiment is carried out, or even before the plans are reviewed beyond the laboratory, enables researchers to identify potential pitfalls based on what they know about the science and experimental environment and perhaps follow up with more advanced questions to a statistician.

In addition to problems with the design, analysis, and reporting of scientific research, there are 2 practices that are widely encountered and further compromise the reliability of published results: ‘p-hacking’ (running multiple statistical tests on the same data and choosing the one with the lowest p value) and selective outcome reporting (measuring different outcomes, or the same outcome in different ways, and only reporting the ones that reach statistical significance) [ 18 ]. These issues effectively represent a post hoc choice of outcome and analysis plan and would be prevented by formalising a clear protocol and plan for the statistical analysis before collecting the data. EDA diagrams are ideal for this purpose. EDA diagrams and the nonvisual information they contain (e.g., prespecified primary outcome measure, chosen method of randomisation) can be registered before the experiment is carried out, on specific platforms such as the Open Science Framework ( https://osf.io/ ) or more generic platforms such as Figshare ( https://figshare.com ). This provides evidence that a study was planned as it has been reported and confirms that the primary outcome measure has not been changed during the course of the experiment and that any additional results reported should be treated with caution.

The EDA promotes better understanding of experimental design and analysis

The EDA is not a ‘black box’ that instructs researchers on what design they should use. Instead, it promotes better understanding of experimental design and raises awareness about problems caused by a lack of randomisation and blinding, underpowered experiments, or inappropriate statistical analysis. The feedback provided by the system (see Table 2 ) enables users to learn about the implications of different design choices and helps them make informed decisions about the most appropriate one to adopt.

https://doi.org/10.1371/journal.pbio.2003779.t002

Animal studies often use suboptimal statistical analysis, such as failing to use factorial or block designs when appropriate, treating repeated measures as being independent, or failing to account for multiple testing [ 4 , 19 – 22 ]. The EDA encourages scientists to spend time planning their experiments and optimising the design interactively. It prompts researchers to evaluate carefully their experimental plan and to consider the data to be collected. It also helps to identify sources of variability by providing examples that are commonly encountered in animal research, such as the day of the experiment (if animals are used over several days); the time of day when the experiment is performed; the piece of equipment used to record measurements; the litter or cage mates; the location of cages in the room; or baseline variables, such as the animals’ weight or locomotor activity. Such sources of variability, termed ‘nuisance variables’, can then be accounted for in the design and analysis of the experiment (e.g., as covariates or blocking factors), or they can be standardised. The choice depends on the characteristics of the specific variable, for example, whether it can be treated as a continuous or categorical variable, and the extent of its likely impact on the variability of the response and on how far the conclusions of the experiment can be generalised. It also helps the user to identify independent variables that are repeated factors and warrant a repeated measure analysis, thereby ensuring that users are provided with enough information to avoid common pitfalls.

The EDA is a novel tool bringing together machine-readable flow diagrams and computer-based logical reasoning to assist the robust and reproducible design of animal experiments. It ensures that the experimental plans are explicit and transparent, thus allowing greater scrutiny before and after data are collected and a meaningful dialogue between researchers and statisticians. It encourages improvements on the design by providing researchers with critical feedback and targeted information. Future development of the system will continue to incorporate user feedback to ensure that the EDA continues to support the needs of the research community. Together with comprehensive reporting and a better understanding of the factors that impact on the reliability and integrity of research findings, the EDA forms part of the solution identified by NC3Rs and others to improve the quality of animal research.

Supporting information

S1 fig. here, we present 4 experimental design assistant (eda) diagrams depicting ‘repeated measure designs’..

Diagram 1 shows an experiment in which animals receive multiple treatments in a different order for each animal, and each animal is used as its own control. Diagram 2 shows an experiment in which different groups of animals receive different treatments, 1 treatment per group, and the response to these treatments is measured over time, with each time point included in the analysis. Diagram 3 shows an experiment in which different groups of animals receive different treatments, 1 treatment per group, and the response to these treatments is measured over time, but a summary measure is taken for each animal. Diagram 4 shows an experiment in which multiple responses are measured for each animal.

https://doi.org/10.1371/journal.pbio.2003779.s001

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7. NC3Rs. ARRIVE: Animal Research Reporting In Vivo Experiments 2017 [14 September 2017]. Available from: https://www.nc3rs.org.uk/arrive-animal-research-reporting-vivo-experiments .
17. Bate ST, Clark RA. The Design and Statistical Analysis of Animal Experiments: Cambridge University Press; 2014.

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Published: 08 September 2024

Quercetin inhibited LPS-induced cytokine storm by interacting with the AKT1-FoxO1 and Keap1-Nrf2 signaling pathway in macrophages

Jingyi Xu 1 na1 ,
Yue Li 1 na1 ,
Xi Yang 2 na1 ,
Hong Li 1 ,
Xi Xiao 1 ,
Jia You 2 ,
Huawei Li 3 ,
Lingnan Zheng 2 ,
Cheng Yi 2 ,
Zhaojun Li 4 &
Ying Huang 1

Scientific Reports volume 14 , Article number: 20913 ( 2024 ) Cite this article

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Inflammation

Cytokine storm (CS) emerges as an exacerbated inflammatory response triggered by various factors such as pathogens and excessive immunotherapy, posing a significant threat to life if left unchecked. Quercetin, a monomer found in traditional Chinese medicine, exhibits notable anti-inflammatory and antiviral properties. This study endeavors to explore whether quercetin intervention could mitigate CS through a combination of network pharmacology analysis and experimental validation. First, common target genes and potential mechanisms affected by quercetin and CS were identified through network pharmacology, and molecular docking experiments confirmed quercetin and core targets. Subsequently, in vitro experiments of Raw264.7 cells stimulated by lipopolysaccharide (LPS) showed that quercetin could effectively inhibit the overexpression of pro-inflammatory mediators and regulate the AKT1-FoxO1 signaling pathway. At the same time, quercetin can reduce ROS through the Keap1-Nrf2 signaling pathway. In addition, in vivo studies of C57BL/6 mice injected with LPS further confirmed quercetin's inhibitory effect on CS. In conclusion, this investigation elucidated novel target genes and signaling pathways implicated in the therapeutic effects of quercetin on CS. Moreover, it provided compelling evidence supporting the efficacy of quercetin in reversing LPS-induced CS, primarily through the regulation of the AKT1-FoxO1 and Keap1-Nrf2 signaling pathways.

Molecular mechanism of the anti-inflammatory effects of Sophorae Flavescentis Aiton identified by network pharmacology

Network pharmacology integrated with experimental validation revealed the anti-inflammatory effects of Andrographis paniculata

Network pharmacology and experimental validation to identify the potential mechanism of Hedyotis diffusa Willd against rheumatoid arthritis

Introduction.

Cytokine storm (CS) is a systemic inflammatory syndrome with excessive hyperactivation of immune cells characterized by increased cytokine release, including interleukin-6 (IL-6), tumor necrosis factor α (TNF-α) and monocyte chemotactic protein 1 (MCP-1), which causes severe pathologic complications, such as sepsis, tissue damage, multiple organ failure, and ultimately, death 1 . CS might be stimulated by multiple factors such as pathogens, auto-inflammation, monogenic, or therapeutic intervention and the lungs are the main organ to be affected by CS 2 .

Macrophages play a pivotal role in infection and inflammation as the principal innate immune cells, exerting crucial regulatory functions in pathological inflammation. Within the tissue microenvironment, they exhibit polarization into either the classically activated M1 phenotype, characterized by pro-inflammatory properties, or the alternatively activated M2 phenotype, which demonstrates anti-inflammatory characteristics. Dysregulation in macrophage phenotypes can result in unchecked inflammatory responses, thereby precipitating CS and subsequent tissue damage 3 . Considering the pivotal role of macrophages in CS progression, modulating macrophage overactivation emerges as a promising strategy for CS intervention.

Quercetin, a flavonoid compound, possesses a spectrum of biological properties, including antioxidant, anti-inflammatory, antiviral, and neuroprotective effects 4 , 5 , 6 , 7 . Research indicated that quercetin exerted its anti-inflammatory effects by targeting Syk/Src/IRAK-1 to inhibit LPS-induced macrophage activation, while also preventing LPS-induced oxidative stress and inflammation through pathways NOX2/ROS/NF-κB 8 , 9 . However, the specific targets and signaling pathways through which quercetin regulates CS remain elusive. Therefore, we embarked on an exploration of new targets and potential mechanisms of quercetin for CS treatment, employing network pharmacology, molecular docking, and experimental validation techniques.

The Forkhead box O (FoxO) family of transcription factors assumes pivotal roles in diverse cellular processes encompassing cell growth, metabolism, survival, and inflammation 10 , 11 , 12 . Nonetheless, FoxO1’s nuclear export or phosphorylation culminates in its inactivation, abrogating its capacity to engage with target regulatory elements 13 . Notably, studies have underscored that elevated FoxO1 expression post-inflammatory injury prompts macrophages to unleash an array of inflammatory mediators, thereby exacerbating inflammatory damage 14 , 15 . In LPS-treated mice, macrophages exhibited heightened FoxO1 levels; transfection of FoxO1 into Raw264.7 cells markedly upregulated interleukin-1β (IL-1β) and concurrently downregulated interleukin-10 (IL-10) expression 16 . Furthermore, FoxO1 serves as a direct substrate of AKT, and its activity hinges on AKT phosphorylation. Notably, AKT inhibition in macrophages abolishes FoxO1 phosphorylation and nuclear exclusion, signifying AKT phosphorylation as a pivotal regulatory event governing FoxO1 activity 17 . Conversely, the Keap1-Nrf2 pathway constitutes a principal defense mechanism safeguarding cells and tissues against oxidative stress while upholding homeostasis. Kelch-like ECH-associated protein 1 (Keap1) serves as an electrophilic reagent and sensor of redox damage, whereas Nuclear factor erythroid 2-related factor (Nrf2) acts as a transcription factor modulating various cytoprotective genes. Oxidative stress prompts Keap1 modification, resulting in its inactivation and disassociation from Nrf2. Consequently, stabilized Nrf2 translocates to the nucleus, where it acts as a transcription factor, activating oxidative stress-responsive genes, thereby exerting antioxidant effects 18 .

In this study, we found that quercetin could effectively inhibit inflammatory responses and oxidative stress in vitro and exhibited anti-inflammatory activity in mice model. Likewise, the AKT1-FoxO1 and Keap1-Nrf2 signaling pathways may be involved in quercetin-mediated anti-inflammatory and antioxidant activities.

Materials and methods

Screening for target genes of quercetin and cs.

The two-dimensional (2D) molecular structure and SMILES of quercetin were downloaded from PubChem ( https://pubchem.ncbi.nlm.nih.gov/ ), the world's largest database of chemical information. To predict potential quercetin targets, 2D structures or SMILES were imported into the Swiss Target Prediction Database ( http://swisstargetprediction.ch/ ). The target genes associated with CS were acquired from the OMIM ( https://www.omim.org ), GeneCards ( https://www.genecards.org ), and PharmGKB ( https://www.pharmgkb.org ) databases by using the following keywords; “cytokine storm” and “cytokine release syndrome.” Subsequently, to analyze and screen common target genes of CS and quercetin, the Venny.2.1.0 e-mapping tool ( https://bioinfogp.cnb.csic.es/tools/venny/ ) was used, and then a Venn diagram was drawn.

To acquire a protein–protein interaction network (PPI), the overlapping genes were submitted to the STRING database ( https://cn.string-db.org/ ); the species restriction was “Homo sapiens,” and the confidence level was > 4.0 for exploring their relationship.

The acquired data were imported to Cytoscape 3.9.1 software for visualization. The core target genes were screened via CytoNCA plug-in using the closeness centrality, betweenness centrality, degree centrality, eigenvector centrality, network centrality and local average connectivity.

GO function and KEGG pathway enrichment analyses

The CS and quercetin target genes intersection were converted to the corresponding gene IDs for gene ontology (GO) functional and Kyoto Protocol Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses in DAVID database ( https://david.ncifcrf.gov/ ). GO comprises biological processes (BP), molecular function (MF), and cellular components (CC). KEGG enrichment analysis can forecast some potential signaling pathways involved in biological processes 19 , 20 , 21 . Subsequently, for analyzing the GO and KEGG data, the Bioinformatics ( http://www.bioinformatics.com.cn/ ) platform was employed.

Molecular docking

Molecular docking can predict the binding potential between drugs and targets. Quercetin and the eight core targets were subjected to molecular docking via SYBYL-X2.1.1 software. First, the crystal structures of the protease were retrieved in the RCSB Protein Data Bank (PDB, http://www.rcsb.org/ ) database as the receptors; then the ligand was docked with the receptor by first extraction the ligand, removing the water molecules, modifying the terminal residues, and hydrogenating to generate the active pocket. Lastly, the total score was used to record the strength of the interaction between the small molecule and the target.

Cell viability assay

Murine macrophage cell line Raw264.7 (SC-6005, ATCC) was cultured in 96-well plates (1 × 10 4 cells/well) in DMEM medium with 10% fetal bovine serum and 1% antibiotics (100 unit/ml penicillin and 100 μg/ml streptomycin) at 37 °C and 5% CO 2 overnight. The next day, cells were treated with different concentrations (2.5, 5, 10, 20, and 40 μg/ml) of quercetin for 24 h. Subsequently, cell viability was tested by CCK-8 kit assay (MA0218, meilunbio), per the kits’ instructions.

Enzyme-linked immunosorbent assay (ELISA)

Raw264.7 cells were seeded in 48-well plates at a density of 5 × 10 4 cells overnight; then, in the Control and LPS groups, the media was replaced with 500 μl fresh complete medium, whereas in the drug groups, 500 μl medium containing the corresponding drug concentrations was added. The dexamethasone (Dex) group represented a positive control. Furthermore, LPS was added to each group except the Control to achieve a final concentration of 1 μg/ml. After 24 h of co-culture, the cell supernatant was collected, and several inflammatory mediators, including IL-6, TNF-α, IL-1β, and MCP-1, were measured using ELISA (Dakowei Biotechnology Ltd.), per the manufacturers’ instructions.

Flow cytometry analysis

Raw264.7 cells were propagated and treated as mentioned above, collected after 24 h, and co-stained after probing with CD11b (101207, BioLegend), CD40 (124612, BioLegend), CD80 (104733, BioLegend) antibodies for 20 min at room temperature, while blank and positive controls (single stained tubes for each antibody) were prepared. The cells were then washed with PBS, resuspended in 500 μl PBS, gated, and then analyzed using a flow cytometer.

Detection of nitric oxide (NO)

Raw264.7 was propagated overnight at the density 1 × 10 5 in 24-well plates before receiving the corresponding treatment based on the experimental groups. After 24 and 48 h of co-incubation with the drug and LPS, the cell supernatant was obtained, and the expression level of NO was detected by the Griess method using NO kit (S0021S, Beyotime).

Detection of intracellular reactive oxygen species (ROS)

Raw264.7 (1 × 10 6 /well) were inoculated in 6-well plates and co-culture with LPS and drug for 24 and 48 h before collecting their supernatants. ROS kit (CA1420, Solarbio) detected intracellular ROS levels. The probe DCFH-DA was added to the cell precipitates and incubated at 37 °C for 20 min. DCFH-DA is a non-fluorescent substance, and the kit uses the principle that the probe can enter cells, where it will be subsequently hydrolyzed into DCFH by esterase, and the intracellular ROS will then oxidize non-fluorescent DCFH to produce fluorescent DCF. Flow cytometry and fluorescence microscopy measured intracellular ROS's fluorescence intensity.

Immunofluorescence assay

Raw264.7 cells were propagated and treated as mentioned above, Raw264.7 cells were fixed with 4% paraformaldehyde for 20 min, permeabilized with 0.5% Triton X-100 for 20 min, and after being closed with 5% BSA for 2 h at room temperature, the cells were incubated with anti-FoxO1 antibody (1:100) at 4 °C overnight. After incubation with Alexa Fluor 488-labeled secondary antibody (1:800 dilution,HA1121, HUABIO), the cellular localization of the cells to FoxO1 was assessed using a confocal microscope (Leica, German).

RNA extraction and quantitative Real-time PCR (qRT-PCR)

The total RNA of treated Raw264.7 cells was extracted using a total RNA extraction kit (RE-03111, FOREGENE). The cDNA was synthesized using the RT Easy™II (With gDNase) kit (RT-01032, FOREGENE), which was then amplified via a Real-Time PCR Easy™-SYBR Green kit (QP-01014, FOREGENE). The relative expression levels of mRNA were calculated by the 2 − △ △ ct method.

Western blot

Cellular proteins were extracted using the RIPA lysis buffer (E-BC-R327, Elabscience), a protease inhibitor (GRF101, epizyme), and a phosphatase inhibitor (GRF102, epizyme). The proteins were quantified using the BCA protein assay kit (P0010, Beyotime), then mixed with sample loading buffer (P0295, Beyotime) and boiled for 10 min. Proteins were separated on 10% SDS–polyacrylamide gels (PG112, epizyme), transferred to PVDF membranes (IPVH00010, Millipore), which were then blocked with 5% skimmed milk at room temperature, and incubated overnight with primary antibodies at 4 °C. The primary antibodies were as follows: anti-TLR2 (ab209216, Abcam), anti-TLR4 (14358, CST), anti-MyD88 (4283, CST), anti-AKT1 (ET1609-51, HUABIO), anti-phospho-AKT1 (ET1607-73, HUABIO), anti-FoxO1 (ET1608-25, HUABIO), anti-Keap1 (10503-2-AP, Proteintech), anti-Nrf2 (16396-1-AP, Proteintech). Subsequently, the membranes were washed and then incubated with a secondary antibody (RS0002, Immunoway) for 1 h. The proteins were visualized by a supersensitive ECL kit (PD203, Oriscience) and a chemiluminescent imaging system.

Anti-CS in vivo

All procedures were conducted following ARRIVE guidelines. The Ethics Committee of West China Hospital has approved this study and confirmed the statement that all methods were performed in accordance with relevant guidelines and regulations. Male C57BL/6J mice (8 weeks old, 23 ± 2 g) were purchased from Beijing Huafukang Biotechnology Co., Ltd. The mice were first acclimatized with the environment for a week and then categorized into six groups: control, LPS, high quercetin dose (100 mg/kg), medium quercetin dose (50 mg/kg), low quercetin dose (25 mg/kg) and Dex (5 mg/kg). The mice were in 12 h fast condition before the experiment; then, four drug groups were treated with quercetin and Dex at corresponding concentrations by gavage and intraperitoneal injection, respectively. The Control and LPS groups received the same volume of normal saline. After 2 h, mice were anesthetized with an intraperitoneal injection of 0.3% pentobarbital (55 mg/kg), then 50 μl of 5 mg/kg LPS was intratracheally administered in each group (except the Control) to establish the CS model. 4 h after LPS treatment, the mice were killed, the skin on the front of the neck was cut open, the trachea was separated and exposed, the indentation needle was inserted into the trachea and fixed, and the irrigation solution was irrigated with normal saline three times, 0.6 ml each time, with a recovery rate of 80–90%. The BALF was collected for the detection of cytokines.

Histological analysis

To observe the lung's histopathological alterations, mice were sacrificed 24 h after LPS stimulation; lung tissues were dissected, fixed with 4% formaldehyde, embedded in paraffin, sectionalized, and finally stained with hematoxylin–eosin (HE) for visual analysis.

Statistical analysis

All the statistical measurements were performed using GraphPad Prism 9.0, and the acquired data are expressed as means ± standard deviation (SD). Differences between the two groups were assessed using One-way ANOVA analysis and were considered significant at P < 0.05.

Acquisition of quercetin targets against CS

The 2D molecular structure (Fig. 1 A) and SMILES [C1=CC(=C(C=C1C2=C(C(=O) C3=C(C=C(C=C3O2) O) O) O) O) O)] of quercetin were downloaded from PubChem. The PubChem CID of quercetin is 5280343, and the molecular formula and weight are C15H10O7 and 302.23. Subsequently, this structure was uploaded to the Swiss Targets Prediction Database, and 100 genes were identified as quercetin targets. Furthermore, 8390 CS target genes were obtained from three disease databases, including GeneCards, OMIM, and PharmGKB, after screening for disease and removing the duplication. Venn’s diagram (Fig. 1 B) indicated the potential 90 CS target genes selected after matching drugs to target genes.

Acquisition of target genes for quercetin action on CS. ( A ) 2D molecule structure of quercetin. ( B ) Venn diagram of quercetin and CS target genes. ( C ) PPI network analysis in the common target of quercetin and CS. ( D ) Screening of quercetin and CS core genes by CytoNCA Plug-in.

The PPI network (Fig. 1 C), constructed using STRING, consisted of 90 nodes and 375 edges, with PPI enrichment p-value < 1.0e−16. The nodes represent target proteins, and the edges represent predicted and confirmed interactions between proteins. This network was visualized with Cytoscape 9.0 to identify core targets, which were then screened using the CytoNCA plug-in. Based on six parameters-betweenness centrality, closeness centrality, degree centrality, rigenvector centrality, network centrality, and local average connectivity, 8 core target genes, including AKT1, EGFR, SRC, MMP9, KDR, PIK3R1, CDK1, and MMP2 were filtered. These were significant genes associated with the quercetin mechanism, which regulates the occurrence and development of CS (Fig. 1 D).

Potential mechanism and signal pathways of quercetin in regulating CS

GO and KEGG enrichment analyses were performed through the DAVID database to further explore the BP and potential mechanisms of 90 target genes involved in CS. The result of the GO enrichment analysis (Fig. 2 A) displayed 262 BP, 54 CC, and 112 MF. The major BP included protein phosphorylation, negative regulation of the apoptotic process, and protein autophosphorylation. The target genes were mainly associated with the following determined CC: cytosol, plasma membrane, cytoplasm, nucleus, etc. Moreover, MF included ATP binding, protein serine/threonine/tyrosine kinase activity, protein kinase activity, protein serine/threonine kinase activity, etc.

GO and KEGG enrichment analysis. ( A , B ) Analysis of GO enrichment and KEGG potential signaling pathway enrichment for targets of action. ( C ) The PI3K-AKT signaling pathway was identified as the key KEGG pathway for quercetin action on CS. The above KEGG data were obtained from the KEGG database.

The KEGG enrichment assay indicated the possible signaling pathways via which quercetin improves CS, revealing the therapeutic mechanisms of CS (Fig. 2 B). It identified the CS-related key signal pathways involved in the PI3K-AKT, FoxO and ErbB. The specific signal pathways of PI3K-AKT are listed in Fig. 2 C.

Molecular docking results

The interaction of eight core targets-AKT1, KDR, CDK1, EFGR, MMP2, MMP9, SRC, and PIK3R1 with quercetin in the generated active pocket regions was assessed. The higher the total score, the more stable the binding activity. The binding activity is extremely high when the total score is > 7. The docking results revealed that among the 8 core targets, the interaction between the quercetin and AKT1 was the strongest, with a total score of 8.35 (Fig. 3 A–E).

Molecular docking of quercetin with target genes. ( A – D ) The molecular docking of quercetin with AK1, KDR, CDK1, EFGR, respectively. ( E ) The total score for molecular docking of quercetin to the four core targets showed the highest total score for binding to AKT1.

Quercetin attenuated LPS-induced expression of proinflammatory factors in Raw264.7 cells

The effects of quercetin and drug solvent dimethyl sulphoxide (DMSO) on cell viability were assessed by CCK8 assay, which indicated that 2.5, 5, and 10 μg/ml concentrations do not affect cell survival (Fig. 4 A), therefore, these concentrations were selected as low, medium and high doses for subsequent experiments. LPS is an essential component of the Gram-negative bacterial cell wall and induces inflammation, allowing its application in various in vivo and in vitro experiments 22 . Under inflammatory conditions, LPS exposure activates macrophages to produce diverse cytokines and also promotes oxidative stress 23 , 24 . Consequently, a CS model was established utilizing 1 μg/ml LPS(Fig. 4 B). As shown in Fig. 4 C, quercetin significantly inhibited LPS-induced proinflammatory cytokines (IL-6, TNF-α, IL-1β) and MCP-1 in a dose-dependent manner.

Quercetin inhibited LPS-induced inflammatory factors in vitro. ( A ) Survival of Raw264.7 cells after 24 h intervention with different concentrations of quercetin. ( B ) A scheme for quercetin intervention in LPS-induced macrophage activation. ( C ) Quercetin reduced the concentration of IL-6, TNF-α, IL-1β and MCP-1 released by LPS-activated macrophages in a concentration-dependent manner. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, compared with the LPS group.

Upon exposure to LPS, activated M1 macrophages secrete a plethora of cytokines 25 . In addition, the surface markers CD40 and CD80, indicative of M1 macrophage activation, undergo alterations (Fig. 5 A). To scrutinize these changes, we conducted flow cytometry analysis. As illustrated in Fig. 5 B, C, the proportion of CD40 + CD80 + cells constituted approximately 70% in the LPS-untreated group, which exhibited a reduction following quercetin treatment. This observation finds validation in quantitative polymerase chain reaction (qPCR) results (Fig. 5 D), wherein mRNA levels of CD40 and CD80 were augmented upon LPS stimulation, a response mitigated by quercetin intervention in a dose-dependent manner. These findings suggested that quercetin could hold promise in ameliorating LPS-induced macrophage polarization.

The ability of quercetin to modulate macrophage polarization in vitro. ( A ) Schematic representation of LPS-stimulated macrophage polarization. ( B ) Flow cytometry results indicated that quercetin treatment decreased the expression of CD40, CD80, surface markers of M1 phenotype macrophages. ( C ) Statistical analysis of flow cytometry results in ( B ). ( D ) Quercetin treatment also decreased mRNA expression of CD40 and CD80. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, compared with the LPS group.

Quercetin inhibited LPS-induced NO production in Raw264.7 cells

Nitric oxide (NO) has been implicated in various cellular responses to external stimuli such as ischemia and LPS stimulation 26 . Our NO detection assays unveiled an absence of NO in the resting state at 24 and 48 h, but its significant induction following LPS exposure. Notably, quercetin exhibited a dose-dependent reduction in NO release in the cell supernatant, with the most pronounced effect observed at 10 μg/ml (Fig. 6 A). Inducible nitric oxide synthase (iNOS) is exclusively present under inflammatory conditions and is responsible for sustained NO production. Therefore, we delved deeper into the expression of NOS2 mRNA (encoding iNOS protein) and iNOS protein. Both quantitative polymerase chain reaction (qPCR) and western blot analyses demonstrated that quercetin downregulated the expression of both in a dose-dependent manner compared to the LPS group at 24 and 48 h (Fig. 6 B–F, Supplementary Fig. 2A, B ).

Effects of quercetin on LPS-induced NOS2 mRNA, iNOS protein expression and NO release in Raw264.7 cells. ( A ) At both 24 and 48 h, quercetin reduced LPS-induced NO release in a concentration-dependent manner. ( B ) NOS2 mRNA levels also decreased with increasing drug concentrations after 24 and 48 h of quercetin treatment. ( C , D ) After 24 and 48 h of LPS attack, iNOS expression at the protein level was down-regulated in a concentration-dependent manner in response to quercetin intervention. ( E , F ) The iNOS expression levels at 24 and 48 h were normalized to GAPDH. Original blots are presented in Supplementary Fig. 2A, B . * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, compared with the LPS group.

Quercetin regulated the AKT1-FoxO1 signaling pathway

The molecular docking results revealed a high binding score of quercetin to AKT1, suggesting its potential to regulate AKT1 activity. In comparison to the LPS-treated group, quercetin administration led to an increase in AKT1 phosphorylation without altering its total protein levels (Fig. 7 A, B, Supplementary Fig. 2C ). Following LPS stimulation, the expression of FoxO1, a transcription factor, and its nuclear translocation are augmented. However, FoxO1 is subject to negative regulation by AKT, as AKT phosphorylation prompts its exclusion from the nucleus, thereby mitigating inflammation 16 , 27 . Immunofluorescence experiments depicted an intensified nuclear fluorescence upon LPS treatment, which significantly decreased following quercetin intervention (Fig. 7 C, Supplementary Fig. 1A ). Correspondingly, western blot analysis illustrated that LPS augmented total FoxO1 expression, a trend reversed by quercetin treatment (Fig. 7 D, E, Supplementary Fig. 2D ). Additionally, quercetin exhibited a dose-dependent downregulation of Toll-like receptor 2 (TLR2), Toll-like receptor 4 (TLR4), and MyD88 expression following LPS stimulation ( Supplementary Fig. 1B–G , Supplementary Fig. 2G–I ). In summary, our findings suggested that quercetin could induce AKT1 activation, leading to subsequent FoxO1 inactivation.

Effects of quercetin on AKT1 and FoxO1 expression levels in response to LPS stimulation. ( A ) The level of phosphorylated AKT1 gradually up-regulated after quercetin intervention. ( B ) p-AKT1 levels were normalized to total AKT1 levels. ( C ) Immunofluorescence assessment of FoxO1 intranuclear expression levels in Raw264.7 cells after LPS and quercetin treatment. The FoxO1 was stained as green granular dots, while the nucleus was stained with blue. The highest expression of FoxO1 in the nucleus was observed in the LPS group, and the fluorescence of FoxO1 in the nucleus gradually decreased with the increase of quercetin concentration. ( D ) Western blotting analysis similarly confirmed that quercetin down-regulated FoxO1 expression in Raw264.7 cells. ( E ) FoxO1 levels were normalized to GAPDH. Original blots are presented in Supplementary Fig. 2C, D . * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, compared with the LPS group.

Quercetin activated Keap1-Nrf2 signaling pathway to mediate antioxidant response

Oxidative stress, emblematic of the imbalance between reactive oxygen species (ROS) and antioxidant defenses, can potentiate inflammatory responses and exacerbate tissue damage 28 . To confirm whether the quercetin-induced antioxidative effect involves ROS inhibition, intracellular ROS levels were assessed through flow cytometry analysis. Results revealed a gradual increase in cellular ROS levels over time in the untreated LPS cells compared to the control group, reaching approximately 70% and 90% at 24 and 48 h, respectively. Moreover, quercetin demonstrated an augmented ability to scavenge ROS with increasing time and drug dosage (Fig. 8 A, B). Green fluorescence, indicative of intracellular ROS content, exhibited a progressive decline with escalating drug concentrations (Fig. 8 C, D). Both flow cytometry and fluorescence microscopy analyses indicated that quercetin alleviated the high ROS levels induced by LPS. Subsequently, we investigated the expression of key factors in the Keap1-Nrf2 axis. Our results showed low or negligible expression of Nrf2 in the control group, with Nrf2 accumulation increasing with higher quercetin doses following LPS stimulation and quercetin intervention. Conversely, Keap1 expression exhibited an inverse trend compared to Nrf2 (Fig. 8 E–G, Supplementary Fig. 2E, F ). Hence, our findings suggested that quercetin could mitigate oxidative stress by activating the Keap1-Nrf2 signaling pathway.

Effects of quercetin on intracellular ROS levels under LPS induction at 24 and 48 h. ( A, B ) Flow cytometry results showed that quercetin enhanced the scavenging of intracellular ROS with increasing concentration and time under LPS induction. ( C, D ) The intracellular ROS content was observed by fluorescence microscopy, and green fluorescence represents ROS. Magnification is 20×. ( E ) Western blotting analysis revealed that Keap1 protein expression was down-regulated and Nrf2 protein expression was up-regulated in Raw264.7 cells treated with LPS and quercetin. ( F, G ) The expression levels of Keap1 and Nrf2 were normalized to their respective GAPDH at 24 and 48 h. Original blots are presented in Supplementary Fig. 2E, F . * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, compared with the LPS group.

Quercetin prevented LPS-induced CS in mice

To assess the in vivo anti-inflammatory activity of quercetin, four inflammatory factors in BALF, including IL-6, TNF-α, IL-1β, and MCP-1, were measured by ELISA, which indicated their rapid upregulation after LPS stimulation and quercetin reversed this effect in a concentration-dependent manner under preconditioning (Fig. 9 A, B).

Quercetin inhibited CS in vivo studies. ( A ) Scheme for in vivo induction and intervention of CS. ( B ) Quercetin suppressed the LPS-induced elevation of IL-6, TNF-α, IL-1β and MCP-1 in BALF. ( C ) Histological study of the protective effect of quercetin against LPS-induced CS lung injury. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, compared with the LPS group.

As H&E-staining indicates (Fig. 9 C), the control group indicated normal lung structure and clear alveolar morphology, while LPS exposure distinctly caused lung tissue congestion, edema, and extensive inflammatory cell infiltration, destroying the lung structure and preventing normal lung function. Pretreated quercetin mice had improved histopathologic changes induced by LPS in a concentration-dependent manner. The examination of pathological changes in lung tissue and BALF inflammatory factors demonstrated that quercetin could effectively protect mice from LPS attacks.

CS is a systemic immune overreaction. Under normal circumstances, pro-inflammatory and anti-inflammatory factors are in a state of mutual balance, which is disrupted when pathogens invade or medical intervention, resulting in the excessive emission of the cytokines and inducing CS. If not treated properly, it can lead to systemic damage, multi-organ failure, or even death 29 . In CS caused by immune-related pneumonitis and viral infection, activated macrophages produce excessive amounts of IL-6, TNF-α, and IL-1β accompanied by elevated chemokines. IL-6 is a crucial target for CS treatment and is a risk factor for assessing the severity of COVID-19 as it is associated with a high mortality rate 30 , 31 , 32 , 33 , 34 . Corticosteroids and cytokine monoclonal antibodies are essential for CS treatments. However, the optimal dose and duration of corticosteroids in immune-related pneumonia remains undetermined and could exacerbate the risk of opportunistic infections 35 , 36 , 37 . In COVID-19, despite the benefits of corticosteroids, there is some variation in different patients 38 . Furthermore, it has been studied that they are associated with high mortality, hyperglycemia, and infection 39 . Monoclonal cytokines antibodies, such as IL-6R monoclonal antibodies, TNF inhibitors, and IL-1 antagonists, are specific for specific cytokines, and unfortunately, CS comprises multiple cytokines. Some clinical trials have shown that monoclonal antibodies are only effective in some people 40 , 41 .

LPS used to model pneumonia is a classical approach that activates macrophages and monocytes to produce high levels of inflammatory cytokines (IL-6, TNF-α, IL-1β, etc.) while eliciting oxidative stress, with literature suggesting that it can also mimic the CS that occurs in the lungs 42 , 43 , 44 , 45 , 46 . Pneumonia frequently ensues as a consequence of CS and often stems from viral infections. In our investigation, we successfully established in vitro and in vivo CS models utilizing LPS, with in vivo modeling achieved through tracheal infusion of LPS. The groups treated solely with LPS exhibited elevated levels of inflammatory factors (IL-6, TNF-α, IL-1β, MCP-1) alongside inflammatory cell infiltration in lung tissue. Our findings highlighted quercetin's capacity to modulate the inflammatory response induced by LPS-activated macrophages, effectively suppressing the release of inflammatory factors and thereby exerting an anti-inflammatory effect.

The findings from network pharmacology unveiled quercetin's capacity to modulate CS primarily by targeting AKT1, EGFR, SRC, MMP9, KDR, PIK3R1, CDK1, and MMP2, with AKT1 being the most significantly regulated. Moreover, KEGG enrichment analysis indicated a potential association between quercetin's mechanism of action against CS and the PI3K-AKT signaling pathway. AKT1, an intracellular kinase, governs various biological processes such as cell growth, survival, and metabolism, serving as a pivotal signaling node in various tissues and cellular inflammatory responses 47 , 56 , 49 . Within macrophages, AKT1 represents the sole subtype. Macrophages lacking AKT1 exhibit heightened responsiveness to LPS and display a robust pro-inflammatory reaction, while AKT1 ablation induces the production of M1-type macrophages 50 . Furthermore, AKT1 phosphorylates and deactivates downstream GSK3β, thereby diminishing NF-κB activation and fostering the expression of the anti-inflammatory cytokine IL-10 51 . These findings suggest that AKT1 may play a pivotal anti-inflammatory role in inflammation. The PI3K-AKT signaling pathway indicates that AKT1 might mediate the inflammatory response through the downstream FoxO1 signaling pathway. Multiple studies have implicated FoxO1 in promoting inflammatory signaling 16 , 17 , 52 . It has been observed that the TLR4/MyD88/MD2-NF-κB signaling pathway is markedly activated following FoxO1 overexpression, whereas silencing of FoxO1 downregulates levels of inflammatory pathway proteins 15 . However, AKT-mediated phosphorylation of FoxO1 leads to its nuclear exclusion and inhibition of its activity. Sun et al. 53 demonstrated that Schisandrin substantially reversed the high expression of total FoxO1 protein in the nucleus and upregulated AKT phosphorylation following LPS stimulation. Consistent with these findings, our study revealed that LPS stimulation increased FoxO1 protein levels in Raw264.7 cells. Quercetin targeted AKT1 and significantly phosphorylated it, thereby inhibiting the entry of FoxO1 into the nucleus and reducing the expression of pro-inflammatory genes. This inhibitory effect of quercetin on FoxO1 was further confirmed using immunofluorescence assays.

CS can induce severe oxidative stress, leading to heightened production of ROS and subsequent damage to crucial organs. The Keap1-Nrf2 pathway serves as a primary defense mechanism within cells, safeguarding against oxidative stress and preserving homeostasis. Upon encountering ROS, Keap1 undergoes modification, dissociating from Nrf2. This allows Nrf2 to translocate to the nucleus, where it accumulates and counteracts oxidative stress, thereby protecting cells 18 , 54 . Curcumin, a bioactive compound found in turmeric, has been shown to scavenge ROS generated in macrophages, shielding them from oxidative stress by activating the Keap1-Nrf2 pathway 55 . Similarly, studies by Liu et al. 56 demonstrated that Mollugin activated the Keap1-Nrf2 pathway, mitigating oxidative stress in Raw264.7 cells. Additionally, astaxanthin has been found to safeguard against LPS-induced cellular inflammation and acute lung injury in mice by suppressing iron-induced cell death through the Keap1-Nrf2 pathway 57 . In our investigation, we observed a quercetin dose-dependent decrease in Keap1 levels and an increase in Nrf2 protein levels, significantly inhibiting LPS-induced ROS production. This finding suggests a potential contribution of quercetin to antioxidant stress effects.

In summary, the present study demonstrated that quercetin could inhibit LPS-induced inflammation and alleviate cytokine storm in vitro and in vivo. Mechanistically, quercetin exerted its protective effects by regulating AKT-FoxO1 and Keap1-Nrf2 pathway.

This study employed network pharmacology and molecular docking technology to identify the effective target genes of quercetin against CS. It also preliminarily revealed that quercetin might act against CS through signaling pathways such as PI3K-AKT and FoxO. In addition, The in vitro and in vivo experiments confirmed that quercetin could play an anti-inflammatory role. Collectively, it could regulate AKT1-FoxO1 and Keap1-Nrf2 signaling pathways.

Data availability

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

Abbreviations

Bronchoalveolar lavage fluid

Biological process

Cellular composition

Coronavirus disease 2019

Cytokine storm

Dexamethasone

Dimethyl sulphoxide

Forkhead box proteins O

Gene ontology

Interleukin-6

Inducible nitric oxide synthase

Kelch-like ECH-associated protein 1

Kyoto encyclopedia of genes and genomes

Lipopolysaccharides

Monocyte chemotactic protein 1

Molecular function

Nitric oxide

Nuclear factor erythroid 2-related factor

Protein–protein interaction network

Reactive oxygen species

Traditional Chinese medicine

Tumor necrosis factor α

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Acknowledgements

The authors thank Ping Lin, Jie Zhang and Qin Lin from the lab of experimental oncology for their great help in this study. The authors gratefully appreciate BioRender's modifications to the figures. The authors would like to thank all the reviewers who participated in the review and MJEditor ( www.mjeditor.com ) for their linguistic assistance during the preparation of this manuscript.

This study was supported by the National Natural Science Foundation of China (NO.82260490), Sichuan Provincial Nature Science Foundation (2022NSFSC1379); Sichuan Science and Technology Programme (2022YFSY0054) and Technology Innovation Project of Chengdu Science and Technology (2020-YF05-00059-SN); Natural Science Foundation of Hainan Province (NO.821QN394); Science and technology research project on novel corona-virus pneumonia outbreak, West China Hospital, Sichuan University (HX-2019-nCoV-069).

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These authors contributed equally: Jingyi Xu, Yue Li and Xi Yang.

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West China School of Basic Medical Science and Forensic Medicine, Sichuan University, No.17, Section3, Renmin South Road, Chengdu, 610044, People’s Republic of China

Jingyi Xu, Yue Li, Hong Li, Xi Xiao & Ying Huang

Department of Medical Oncology, West China Hospital, Cancer Center, Sichuan University, No.37 Guoxue Lane, Chengdu, 610041, China

Xi Yang, Jia You, Lingnan Zheng & Cheng Yi

Department of Integrated Traditional Chinese and Western Medicine, School of Medicine, Cancer Hospital, University of Electronic Science and Technology of China, Chengdu, 610041, China

Department of Radiation Oncology, Hainan Affiliated Hospital of Hainan Medical University (Hainan General Hospital), No.31, Longhua Road, Haikou, 570100, China

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Xu, Li, Yang, Li, Xiao, You, Li, Zheng, Li, Yi, and Huang contributed to this study. Xu, Li,ang contributed equally to this study. Yi, Li andHuang directed the design of this study, supervised its implementation and revised draft. Xu, Li, Yang, Li, Xiao participated in the specific experimental process, data analysis and paper writing. You, Li, Zheng were involved in the charting of the paper. All authors have read and approved the final draft.

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Correspondence to Cheng Yi , Zhaojun Li or Ying Huang .

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Xu, J., Li, Y., Yang, X. et al. Quercetin inhibited LPS-induced cytokine storm by interacting with the AKT1-FoxO1 and Keap1-Nrf2 signaling pathway in macrophages. Sci Rep 14 , 20913 (2024). https://doi.org/10.1038/s41598-024-71569-y

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DOI : https://doi.org/10.1038/s41598-024-71569-y

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Investigation of the lipid-lowering activity and mechanism of three extracts from astragalus membranaceus , hippophae rhamnoides l., and taraxacum mongolicum hand. mazz based on network pharmacology and in vitro and in vivo experiments.

1. Introduction

2. materials and methods, 2.1. chemicals and reagents, 2.2. lc-ms analysis conditions, 2.3. aht chemical composition screening and target prediction, 2.4. prediction of targets for hyperlipidemia, 2.5. construction of drug target network and pathways, 2.6. pancreatic lipase inhibition test and combined index analysis, 2.7. cell culture and mtt assay, 2.8. establishment of high-fat model and administration regimen, 2.9. oil red o staining, 2.10. determination of lipid-lowering levels in hepg2 cells by aht, 2.11. western blot analysis, 2.12. animal experiments, 2.13. determination of serum biochemical indicators, 2.14. statistical analysis, 3.1. analysis of aht active ingredients, 3.2. potential target prediction of aht and hlp, 3.3. target network construction for aht and hlp, 3.4. construction of ppi network, 3.5. enrichment analysis of go and kegg pathways, 3.6. determination of aht compounding ratio, 3.7. combination index analysis of aht, ame, hre, and tme, 3.8. the effects of aht, ame, hre, and tme on hepg2 cell proliferation and oleic acid staining, 3.9. the lipid-lowering effect of aht on oleic acid-induced hepg2 cells, 3.10. the effect of aht on the lipid-lowering mechanism induced by oleic acid in hepg2 cells, 3.11. the effect of aht on body weight, food intake, and organ index in mice, 3.12. the effect of aht on serum biochemical indicators in high-fat mice, 4. discussion, 5. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest, list of abbreviations.

ALT	Alanine aminotransferase
AST	Aspartate aminotransferase
AME	Astragalus membranaceus extract
AHT	Astragalus membranaceus extract: Hippophae rhamnoides L. extract: Taraxacum mongolicum Hand. Mazz extract = 3:1:2
HDL-C	High-density lipoprotein cholesterol
HRE	Hippophae rhamnoides L. extract
LC-MS	Liquid chromatography–mass spectrometry
LDL-C	Low-density lipoprotein cholesterol
OMIM	Online Mendelian Inheritance in Man
PPI	Protein–protein interaction
SOD	Superoxide dismutase
TME	Taraxacum mongolicum Hand. Mazz extract
TCMSP	Traditional Chinese Medicine System Pharmacology Analysis Platform
T-AOC	Total antioxidant capacity
TC	Total cholesterol
TG	Triglycerides

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Click here to enlarge figure

No.	Name	Molecule ID	Molecule Name	OB (%)	DL
1	Luteolin	MOL000006	Luteolin	36.16	0.25
2	Quercetin	MOL000098	Quercetin	46.43	0.28
3	Isorhamnetin	MOL000354	Isorhamnetin	49.6	0.31
4	Kaempferol	MOL000422	Kaempferol	41.88	0.24
5	Phaseollidin	MOL000457	Phaseollidin	52.04	0.53
6	Cholesterol	MOL000953	CLR	37.87	0.68
7	Ellagic acid	MOL001002	Ellagic acid	43.06	0.43
8	Pelargonidin	MOL001004	Pelargonidin	37.99	0.21
9	Dihydrochelerythrine	MOL001461	Dihydrochelerythrine	32.73	0.81
10	Dihydrosanguinarine	MOL001463	Dihydrosanguinarine	59.31	0.86
11	Sanguinarine	MOL001474	Sanguinarine	37.81	0.86
12	Chelerythrine	MOL001478	Toddaline	25.99	0.81
13	Scopolamine	MOL001554	Scopolamine	67.97	0.27
14	(+)-Sesamin	MOL001558	Sesamin	56.55	0.83
15	Acacetin	MOL001689	Acacetin	34.97	0.24
16	Diphyllin	MOL001699	Diphyllin	36.23	0.75
17	Podofilox	MOL001714	Podophyllotoxin	59.94	0.86
18	Linarin	MOL001790	Linarin	39.84	0.71
19	Scopolin	MOL002218	Scopolin	56.45	0.39
20	Hesperetin	MOL002341	Hesperetin	70.31	0.27
21	Medicarpin	MOL002565	Medicarpin	49.22	0.34
22	Baicalein	MOL002714	Baicalein	33.52	0.21
23	Melilotoside	MOL004101	Melilotoside	36.85	0.26
24	Corydaline	MOL004195	CORYDALINE	65.84	0.68
25	Kaempferide	MOL004564	Kaempferid	73.41	0.27
26	Glabranin	MOL004910	Glabranin	52.9	0.31
27	Artemetin	MOL005229	Artemetin	49.55	0.48
28	Arachidonic acid	MOL005320	Arachidonate	45.57	0.2
29	Citromitin	MOL005815	Citromitin	86.9	0.51
30	Papaverine	MOL006980	Papaverine	64.04	0.38
31	Codeine	MOL006982	Codeine	45.48	0.56
32	Cirsimaritin	MOL007274	Skrofulein	30.35	0.3
33	Artemisinin	MOL007424	Artemisinin	49.88	0.31
34	Corynoline	MOL008636	Corynoline	30.53	0.85
35	Camptothecin	MOL009830	EHD	61.04	0.81
36	Leucocyanidin	MOL010489	Resivit	30.84	0.27
37	Estrone	MOL010921	Estrone	53.56	0.32
38	Lobelanine	MOL012208	Lobelanine	54.13	0.32
39	Uridine 5’-monophosphate	MOL012820	5’-Uridylic acid	40.25	0.2
40	Fustin	MOL013296	Fustin	50.91	0.24
41	Garbanzol	MOL013305	Garbanzol	83.67	0.21

Peak No.	Proposed Compound	RT/s	Precursor m/z	Error (ppm)	Formula
1	Luteolin	290	285.0761	2.606	C H O
2	Quercetin	287	303.0498	0.420	C H O
3	Isorhamnetin	303.7	315.0515	1.512	C H O
4	Kaempferol	282	285.0391	4.770	C H O
5	Phaseollidin	224	325.1379	17.003	C H O
6	CLR	412.7	369.3588	19.607	C H O
7	Ellagic acid	116.6	324.9999	13.574	C H O
8	Pelargonidin	57.5	543.1263	4.175	C H O
9	Dihydrochelerythrine	63.8	332.1285	1.169	C H NO
10	Dihydrosanguinarine	48.1	665.1907	3.352	C H NO
11	Sanguinarine	116.9	289.1103	1.990	C H NO
12	Toddaline	83.9	332.1315	10.197	C H NO
13	Scopolamine	301.9	285.1163	10.784	C H NO
14	Sesamin	48.5	337.1115	13.218	C H O
15	Acacetin	336.1	283.0606	2.098	C H O
16	Diphyllin	46.8	381.0936	8.594	C H O
17	Podophyllotoxin	58.8	829.2688	1.687	C H O
18	Linarin	240.2	653.184	17.888	C H O
19	Scopolin	81.8	335.0758	4.287	C H O
20	Hesperetin	241.6	301.071	2.518	C H O
21	Medicarpin	240.9	254.0728	11.289	C H O
22	Baicalein	297.6	269.0448	2.767	C H O
23	Melilotoside	81.8	371.0968	4.221	C H O
24	CORYDALINE	226.1	387.2221	14.781	C H NO
25	Kaempferid	302.1	299.0567	1.977	C H O
26	Glabranin	385.4	369.1471	4.907	C H O
27	Artemetin	61.3	345.1367	9.972	C H O
28	Arachidonate	447.9	303.2351	7.121	C H O
29	Citromitin	206.6	405.1339	18.738	C H O
30	Papaverine	66.5	322.127	4.678	C H NO
31	Codeine	219.3	300.1587	2.372	C H NO
32	Skrofulein	252	359.073	11.803	C H O
33	Artemisinin	260.9	343.1382	4.764	C H O
34	Corynoline	254.3	350.1458	3.569	C H NO
35	EHD	260.3	330.2363	11.189	C H 6N O
36	Resivit	267.9	288.0443	14.498	C H O
37	Estrone	292.4	269.079	10.881	C H O
38	Lobelanine	276	336.1978	5.963	C H NO
39	5’-Uridylic acid	41.1	323.0274	3.682	C H N O P
40	Fustin	326.2	333.0473	15.279	C H O
41	Garbanzol	252.4	273.0805	17.407	C H O

NO.	Symbol ID	Protein Name	Pathways
1	TP53	Tumor protein p53	hsa05417, hsa04010, hsa04151
2	PPARG	Peroxisome proliferator-activated receptor gamma	hsa05417
3	ESR1	Estrogen receptor	hsa05207
4	TNF	Tumor necrosis factor	hsa05417, hsa04933, hsa04010
5	CCL2	C-C motif chemokine 2	hsa05417, hsa04933, hsa05418
6	AKT1	Potassium channel AKT1	hsa05417, hsa04933, hsa04010, hsa04151
7	RELA	Transcription factor p65	hsa05417, hsa04933, hsa04010, hsa04151
8	MAPK1	Mitogen-activated protein kinase 1	hsa05417, hsa04933, hsa04010, hsa04151
9	IL6	Interleukin-6	hsa05417, hsa04933, hsa04151
10	CXCL8	Interleukin-8	hsa05417, hsa04933
11	IL1A	Interleukin-1 alpha	hsa04933, hsa04010, hsa05418
12	IL4	Interleukin-4	hsa04151
13	IL10	Interleukin-10	-
14	IL1B	Interleukin-1 beta	hsa05417, hsa04933, hsa04010
15	IFNG	Interferon gamma	hsa05418

ID	Description	p-Value	Gene ID	Count
hsa05200	Pathways in cancer	29.15	GSK3B, CXCL8, PTEN, CASP9, CASP8, CCND1, MYC, CASP3, AKT1, NCOA1, CHUK, PRKCB, MMP1, MMP2, FOS, MMP9, AR, IFNG, BIRC5, PPARG, RAF1, TP53, PPARD, PTGS2, HIF1A, EGFR, RELA, RXRB, MAPK8, RXRA, ERBB2, E2F1, HMOX1, MAPK1, RXRG, TGFB1, NOS2, CDKN2A, EGF, STAT1, IGF2, ESR1, ESR2, IL2, VEGFA, MAPK10, IL4, IL6, CDK4, BCL2, MDM2, BAX, NFE2L2	53
hsa05417	Lipid and atherosclerosis	30.27	GSK3B, CXCL8, TNF, CXCL2, RELA, ICAM1, CASP9, RXRB, PPP3CA, MAPK8, CASP8, CYP2B6, RXRA, CASP3, CCL2, AKT1, MAPK1, OLR1, RXRG, VCAM1, CHUK, MMP1, NOS3, MMP3, NFATC1, FOS, MAPK14, SELE, MMP9, MAPK10, IL6, CD40LG, IL1B, CYP1A1, BCL2, BAX, PPARG, TP53, NFE2L2	39
hsa04933	AGE-RAGE signaling pathway in diabetic complications	31.05	CXCL8, SERPINE1, TNF, RELA, ICAM1, THBD, MAPK8, CCND1, CASP3, CCL2, AKT1, MAPK1, TGFB1, VCAM1, PRKCB, NOS3, STAT1, MMP2, NFATC1, MAPK14, SELE, F3, VEGFA, MAPK10, IL1A, COL3A1, IL6, CDK4, IL1B, BCL2, BAX	31
hsa05167	Kaposi sarcoma-associated herpesvirus infection	20.65	GSK3B, CXCL8, PTGS2, HIF1A, CXCL2, RELA, PIK3CG, ICAM1, CASP9, PPP3CA, MAPK8, CASP8, CCND1, MYC, CASP3, E2F1, AKT1, MAPK1, CHUK, STAT1, NFATC1, FOS, MAPK14, VEGFA, MAPK10, IL6, CDK4, BAX, RAF1, TP53	30
hsa05207	Chemical carcinogenesis—receptor activation	19.50	NR1I3, ADRB1, AHR, ADRB2, CYP3A4, RELA, EGFR, RXRB, CYP2B6, RXRA, CCND1, MYC, E2F1, AKT1, MAPK1, RXRG, UGT1A1, PRKCB, EGF, FOS, ESR1, ESR2, VEGFA, AR, CYP1A1, BCL2, BIRC5, PGR, RAF1, PPARA	30
hsa05163	Human cytomegalovirus infection	17.81	GSK3B, CXCL8, PTGS2, TNF, RELA, EGFR, CASP9, PPP3CA, CASP8, CCND1, MYC, CASP3, E2F1, CCL2, AKT1, MAPK1, CHUK, CDKN2A, PRKCB, NFATC1, MAPK14, VEGFA, IL6, CDK4, IL1B, MDM2, BAX, RAF1, TP53	29
hsa04010	MAPK signaling pathway	12.69	HSPB1, TNF, RELA, EGFR, PPP3CA, MAPK8, MYC, CASP3, ERBB2, AKT1, MAPK1, TGFB1, CHUK, PRKCB, EGF, INSR, IGF2, NFATC1, FOS, MAPK14, VEGFA, MAPK10, IL1A, RASA1, IL1B, RAF1, TP53	27
hsa04151	PI3K-Akt signaling pathway	10.81	GSK3B, PTEN, RELA, EGFR, PIK3CG, CASP9, RXRA, CCND1, MYC, ERBB2, SPP1, AKT1, MAPK1, CHUK, EGF, NOS3, INSR, IGF2, IL2, VEGFA, IL4, IL6, CDK4, BCL2, MDM2, RAF1, TP53	27
hsa05418	Fluid shear stress and atherosclerosis	19.75	PLAT, TNF, RELA, ICAM1, THBD, MAPK8, CCL2, AKT1, HMOX1, VCAM1, CHUK, NOS3, CAV1, MMP2, FOS, MAPK14, SELE, MMP9, VEGFA, MAPK10, IL1A, IFNG, IL1B, BCL2, TP53, NFE2L2	26
hsa05161	Hepatitis B	18.15	CXCL8, TNF, RELA, CASP9, MAPK8, CASP8, MYC, CASP3, E2F1, AKT1, MAPK1, TGFB1, CHUK, PRKCB, STAT1, NFATC1, FOS, MAPK14, MMP9, MAPK10, IL6, BCL2, BAX, BIRC5, RAF1, TP53	26

Group	AME	HRE	TME	Inhibition Rate%
1	1	1	1	80.71 ± 0.83
2	1	2	2	79.84 ± 0.80
3	1	3	3	72.11 ± 1.94
4	2	1	2	94.25 ± 1.10
5	2	2	3	86.08 ± 0.86
6	2	3	1	89.64 ± 1.23
7	3	1	3	96.72 ± 1.30
8	3	2	1	95.87 ± 0.25
9	3	3	2	96.47 ± 0.32
k	77.55	90.56	88.74
k	89.99	87.26	90.19
k	96.35	86.07	84.97
R	18.80	4.49	5.13
Best combination	A H T

Group	NC	MC	PC	LD	MD	HD
Heart (%)	0.693 ± 0.11	0.672 ± 0.11	0.602 ± 0.10	0.707 ± 0.19	0.660 ± 0.09	0.613 ± 0.10
Liver (%)	4.554 ± 0.47	4.527 ± 0.35	3.847 ± 1.40	4.750 ± 0.67	4.483 ± 0.66	4.548 ± 0.74
Spleen (%)	0.327 ± 0.03	0.309 ± 0.05	0.253 ± 0.06	0.306 ± 0.08	0.282 ± 0.10	0.321 ± 0.09
Kidney (%)	1.462 ± 0.18	1.367 ± 0.13	1.357 ± 0.17	1.463 ± 0.19	1.334 ± 0.23	1.271 ± 0.14

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Yang, X.; Jia, M.; Luo, J.; An, Y.; Chen, Z.; Bao, Y. Investigation of the Lipid-Lowering Activity and Mechanism of Three Extracts from Astragalus membranaceus , Hippophae rhamnoides L., and Taraxacum mongolicum Hand. Mazz Based on Network Pharmacology and In Vitro and In Vivo Experiments. Foods 2024 , 13 , 2795. https://doi.org/10.3390/foods13172795

Yang X, Jia M, Luo J, An Y, Chen Z, Bao Y. Investigation of the Lipid-Lowering Activity and Mechanism of Three Extracts from Astragalus membranaceus , Hippophae rhamnoides L., and Taraxacum mongolicum Hand. Mazz Based on Network Pharmacology and In Vitro and In Vivo Experiments. Foods . 2024; 13(17):2795. https://doi.org/10.3390/foods13172795

Yang, Xue, Mingjie Jia, Jiayuan Luo, Yuning An, Zefu Chen, and Yihong Bao. 2024. "Investigation of the Lipid-Lowering Activity and Mechanism of Three Extracts from Astragalus membranaceus , Hippophae rhamnoides L., and Taraxacum mongolicum Hand. Mazz Based on Network Pharmacology and In Vitro and In Vivo Experiments" Foods 13, no. 17: 2795. https://doi.org/10.3390/foods13172795

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Why are in vivo Studies Important?

Animal research is important for drug development as it focuses on understanding the biology and genetics of living organisms with a high degree of accuracy and translatability. In biomedical studies, this type of analysis aims to understand disease etiology and progression. It is used to identify potential biochemical pathways that might serve as targets for drugs to cure or mitigate diseases. Without animal research, our medical science progress would not be nearly as advanced as it is today.

In vivo research achieves this by executing scientific studies in progressively higher order animal models , for example, rodents to swine to non-human primates . Early phase pharmaceutical and biomedical research strongly focus on drug metabolism, safety, and efficacy, using pharmacokinetic and pharmacodynamic techniques. Because the ultimate goal is to develop approved treatments for humans, there are strict requirements in pre-clinical in vivo research surrounding data collection , tracking, and integrity. Operational consistency and data management of pre-clinical in vivo studies are key to achieving IND approved research. In vivo protocols must be designed and conducted with a high degree of precision and recorded for future replication and data validation experiments. Time and process-sensitive tasks must be carefully tracked with complete chain of custody and audit trails covering all operations. Metadata must also be recorded for future reference and can be critical to identify disparities between validation studies.

In order to meet the stringent requirements for in vivo studies, modern labs rely on at least one laboratory information management system (LIMS). Drug discovery LIMS software is a vital investment for any biomedical or pharmaceutical organization conducting in vivo testing. Cloud-based LIMS are increasingly preferred as they promote collaboration between specialized teams throughout the development pipeline.

Climb 2.0 meets and exceeds the needs for drug discovery labs to undergo a digital transformation. This azure cloud-based platform is a fully auditable repository for experimental design, study execution, data capture, and animal and sample metadata recording. Climb 2.0 software also provides other key features to optimize in vivo based R&D such as exceptionally comprehensive animal colony management, API compatibility and permission-based access.

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Next-Generation in vivo Modeling of Human Cancers

Animal models of human cancers played a major role in our current understanding of tumor biology. In pre-clinical oncology, animal models empowered drug target and biomarker discovery and validation. In turn, this resulted in improved care for cancer patients. In the quest for understanding and treating a diverse spectrum of cancer types, technological breakthroughs in genetic engineering and single cell “omics” offer tremendous potential to enhance the informative value of pre-clinical models. Here, I review the state-of-the-art in modeling human cancers with focus on animal models for human malignant gliomas. The review highlights the use of glioma models in dissecting mechanisms of tumor initiation, in the retrospective identification of tumor cell-of-origin, in understanding tumor heterogeneity and in testing the potential of immuno-oncology. I build on the deep review of glioma models as a basis for a more general discussion of the potential ways in which transformative technologies may shape the next-generation of pre-clinical models. I argue that refining animal models along the proposed lines will benefit the success rate of translation for pre-clinical research in oncology.

Introduction

Modeling human tumors in animals has been the leading approach to translational research in oncology over the last three decades. Successes in the field include, among others, identifying druggable targets for aggressive subtypes of leukemia, breast cancer and melanoma. In the late 1990s, the retinoic acid (RA) was successful to induce full remission in 71–91% of patients with acute promyelocytic leukemia (APL) in clinical trials that compared this treatment with standard chemotherapy ( 1 ). The first transgenic murine models for human APL were generated by inserting the oncogene PML-RAR in promyelocytes downstream the Cathepsin G or hMPR8 regulatory elements ( 2 , 3 ). These models showed sensitivity to RA ( 4 ) confirming their value in the treatment of APL and making them the leading models to investigate responses to anti-cancer treatments at the cellular and molecular levels.

In solid tumors, Trastuzumab (clinical name Herceptin) was approved for treatment in Her2 positive breast cancer. Trastuzumab is an antibody binding to the EGF receptor Her2 and clinical trials showed benefit in Her2 positive breast cancer patients in terms of progression-free and overall survival ( 5 ). When the murine p185 antibody was tested in murine breast cancer xenograft models, it proved to be effective in counteracting tumor growth ( 6 ). Shortly afterwards, the murine Her2 antibody was humanized to allow its use in clinical trials ( 7 ).

Kinase inhibitors were considered the poster child of targeted therapies in the early 2000s and several were approved for treating different malignancies. Imatinib (clinical name, Gleevec) was approved to block the signaling activity of the BCR-ABL fusion protein oncogene in Chronic myeloid leukemia (CML) ( 8 ). In mice, the dependence for CML cells on BCR-ABL and the main features of response and resistance to Imatinib could be successfully demonstrated ( 9 ). In addition to mouse models, larger animal models such a spontaneous Canine B-cell Lymphoma have also been used to validate the therapeutic outlook for kinase inhibitors ( 10 ). The Ibrutinib, a Bruton's tyrosine kinase (BTK) inhibitor validated in this way, later showed a durable efficacy in relapsed or refractory mantle cell lymphoma patients as single agent ( 11 ), or in combination with an anti-CD20 antibody (clinical name, Rituximab) ( 12 ), which was later extended to chronic lymphocytic leukemia ( 13 ).

In the case of the specific BRAF V600E inhibitor Vemurafinib ( 14 ), preclinical models not only validated the response to BRAF inhibition but also revealed the RAF paradox, meaning that ERK signaling is amplified by RAF inhibition in BRAF -wild-type, RAS mutant tumors, despite RAF's position downstream RAS ( 15 ). In Non-Small Cell Lung cancer (NSCLC), Crizotinib showed great anti-tumor potential against EML4-ALK fusion positive carcinomas, and it has quickly gained momentum for treating a subset of NSCLC patients ( 16 ), after successful evaluation in a transgenic mouse model for lung adenocarcinoma ( 17 ).

Despite the great success of those compounds, which benefits many cancer patients, targeted therapies are accompanied by acquired resistance, when cancer cells experience gatekeeper mutations or activate alternative pro-tumorigenic pathways ( 18 , 19 ). To overcome this limitation, targeted therapies are combined with standard chemotherapy or combinations of targeted therapies are sought in appropriate pre-clinical settings ( 20 ).

In the clinics, drug combinations often aim at extending on-target toxicity by targeting the same mechanisms (i.e., unrestricted proliferation) with multiple drugs. For instance, the combination of four different chemotherapeutic agents Doxorubicin, Bleomycin, Vinblastine, and Dacarbazine (ABVD) is the standard of care in the treatment of Hodgkin lymphoma ( 21 ). However, the use of multiple drugs is accompanied by the increased burden of side effects for patients. Testing more sophisticated approaches, such as synthetic lethality in animal models represents therefore the ideal pre-clinical development. Synthetic lethality is a concept originated from yeast biology and reflects the observation that simultaneously hitting two closely related mechanisms can lead to significant toxicity, whereas single treatments are largely well-tolerated. This offers the possibility to target cancer cells bearing specific alterations, sparing normal cells from side effects. The paradigm of a synthetic-lethal treatment that has made its way into clinical application is the use of Poly-(ADP-ribose) polymerase (PARP) inhibitors in ovarian cancer and breast cancer ( 22 ). Targeting PARP proved to be synthetic lethal with concurrent alterations in homologous recombination (HR) DNA repair genes, such as BRCA1 and BRCA2 . In pre-clinical research, BRCA -mutant mouse models have been instrumental in highlighting the strength of these treatments and have illustrated potential ways to circumvent their limitations ( 23 ). Clinical trials have led to the FDA approval of a number of PARP inhibitors showing efficacy in treatments for ovarian, breast and prostate cancer. In a BRCA-competent triple negative breast cancer/TNBC) DDR-mediated PARP antitumor activity was reinforced by concurrent PI3K-AKT-mTor pathway inhibition ( 24 ). In general, predicting long-term responders to PARP inhibitors is a critical issue that has yet to be solved by future pre-clinical breakthroughs.

In addition to their roles as discovery platforms, accurate pre-clinical models can also predict patients' response to a given treatment. In a seminal study, Singh et al. retrospectively assessed targeted therapies either alone or in combination with standard-of-care treatments thereby replicating large-scale human clinical trials ( 25 ). This was achieved by applying a similar treatment protocol and evaluating the clinical endpoints overall survival (OS) and progression free survival (PFS) for in vivo studies. Animal models for pancreatic ductal adenocarcinoma (PDAC) and non-small cell lung cancer (NSCLC) were exploited as surrogate for KRAS G12D patients' response to Erlotinib and Bevacizumab, therapeutics targeting epidermal growth factor (EGFR) and vascular endothelial growth factor (VEGF), respectively. The standard of care (Carboplatin or Gemcitabine) was combined with targeted drugs thereby mimicking the original studies ( 25 ). Retrospectively, this post-clinical study obtained (largely negative) results comparable to those for human clinical trials. This set an excellent standard for future design of pre-clinical studies.

Currently, technological breakthrough in the field of genetic engineering and single cell genomics are enabling us to create ever-more sophisticated animal models of human cancers, and to exploit them in achieving a better translation of pre-clinical studies. Here, I focus on animal models for human malignant gliomas as an entry point for a retrospective review of the use of animal models in tumor biology and therapy. Thereafter, I review state-of-the-art technologies and offer future perspective to incorporating these in generating and exploiting animal models as pre-clinical tools for cancer biology and intervention, which can be valid for glioma and more in general for different types of human cancer.

Experimental models for high-grade gliomas

Autochthonous mouse models.

Autochthonous mouse models for human cancers are obtained by initiating tumors in a normal cell de novo and within the intact organism. The main advantage of these models is the pathophysiological relevance of the tumor initiation.

Mouse models for Glioblastoma Multiforme (GBM) have been systematically used to investigate tumor initiation and progression in the context of a living organism. The first example of genetically engineered mouse models (GEMMs) in modeling gliomas was developed in the Aguzzi lab. It was generated through the over-expression of the v-Src oncogene via the glial fibrillary acidic protein (GFAP) regulatory elements ( 26 ). Later, Holland and Varmus introduced to the scientific community a model based on avian retroviral gene transfer, the RCAS-TVA, which still counts as one of the most commonly used models for gliomas ( 27 ).

By far, the largest use for autochthonous models in GBM has been in the systematic dissection of the mechanisms leading to tumor initiation. Several studies have addressed the role of signaling pathways, validated the genetic dependencies on individual genes, and investigated the contribution of non-cell autonomous factors in GBM initiation (Table (Table1). 1 ). These studies have uncovered critical pathways in tumor initiation and subsequent genetic aberrations found in high-grade lesions. The unifying conclusion from gliomagenesis in autochthonous models is that GBM is primarily driven by an intricate mix of pro-oncogenic hits cooperating with inactivation of tumor suppressive pathways. On the one hand, hyper-activation of the AKT and MAPK pathways has to occur through either supra-physiological receptor tyrosine kinase (RTK) activity (e.g., EGFR, PDGFRA) or a loss of negative regulators, such as PTEN and NF1. On the other hand, the Rb and p53 pathways must be circumvented by either direct inactivation or through deletion of the INK4A / ARF locus (also known as CDKN2A ).

Autochthonous mouse models for glioma.


(gain)	GFAP	Astrocytes	Glioma formation driven by expression in astrocytes	Weissenberger et al. ( )
(gain); (loss)	GFAP, Nestin	Astrocytes and NPCs	Patient-specific alterations are capable of transformation; NPCs are more prone to transformation	Holland et al. ( )
12	Nestin	NPCs	RAS and AKT signaling cooperatively but not exclusively are capable of transforming NPCs.	Holland et al., ( )
(loss)	ubiquitous	Astrocytes and NPCs	Spontaneous gliomagenesis in animals with tumor suppressors deficient background; role for genotype in spontaneous tumor formation.	Reilly et al. ( )
12	GFAP	Astrocytes	Supraphysiological RAS activation in astrocytes can lead to pathologically-relevant alterations	Ding et al., ( )
( ; gain); (loss)	S100B	Glial cells	Overexpression of (EGFR) induces oligodendroglioma and GBM in combination with or deletion; spontaneous loss of chromosomal DNA synthenic to human chromosome 1p	Weiss et al., ( )
(loss)	GFAP KO+ubiquitous HET	Astrocytes	spontaneous optic nerve glioma formation in animals in which is biallelically deleted in astrocytes as well as heterozygously inactivated in the microenvironment	Bajenaru et al., ( )
(loss)	GFAP	NPCs	Complete penetrance for and mutant gliomas; evidence for NSC as cell of origin for gliomas	Zhu et al., ( )
(loss)	GFAP	NPCs	activation driven by loss coorelated with GBM grading in the mouse tumors	Kwon et al., ( )
( ); KO	S100B	Astrocytes and OPCs	Evidence for Side-population cancer stem-like cells in a mouse model for oligodentroglioma	Harris et al., ( )
	GFAP	Astrocytes and spinal cord	Tet-inducible model for spinal oligoastrocytoma	Hitoshi et al., ( )
; ;	GFAP	Astrocytes	stimulation and loss induce tumors in diferent parts of the brain	Hede et al., ( )
	GFAP	NPCs	Inactivation of different combinations of tumor suppressor genes in SVZ causes brain tumors with different phenotypes	Jacques et al. ( )
(loss)	GFAP, Nestin or NG2	NPCs and OPCs	OPCs can serve as cell of origin for gliomas	Liu et al., ( )
	retroviral activation	NPCs	stimulation and deletion of and p53 lead to a Proneural-like expression phenotype	Lei et al., ( )
12	lentiviral GFAP activation	Astrocytes and NPCs	Gliomagenesis is more effective in the hippocampus and the subventricular zone than in the cortex	Marumoto et al., ( )
12PTEN; INK4A/ARF; KRAS 12 p53; INK4A/ARF; KRAS 12	lentiviral CMV or GFAP activation	Astrocytes and NPCs	Higher penetrance and faster gliomagenesis in CMV- vs. GFAP-lenti-Cre activated mutations.	de Vries et al., ( )
, and	GFAP	Astrocytes and NPCs	or deletion drive somatic amplifications of genes in the PI3K or Rb pathways	Chow et al. ( )
(loss)	SynI-Cre, GFAP-Cre, Nes-Cre	Neurons, Astrocytes and NPCs	Neurons can be transformed by delivery of shRNAs targeting and . Dedifferentiation toward NPCs is pbserved in targeted matureastrocytes.	Friedmann Morvinski et al. ( )
	Nestin	NSCs	CSCs can exploit quiescence similar to adult neural stem cells (NSCs) to contribute to relapse after chemotherapy.
and or in and	CNP+ SVZ	OPCs	& or dictates astrocytic or oligodendroglial tumor development from OPCs	Lindberg et al. ( )
	Ascl1 or Ng2	Astrocytes, NPCs, or OPCs	The cell of origin emerges as a major determinant of GBM molecular subtype	Alcantara Llaguno et al. ( )
	electroporation delivery	NPCs	Cas9 and sgRNAs delivered to the cerebral verntricular zone lead to transformation	Zuckermann et al. ( )
	Hes5	NPCs	Notch signaling is tumor suppressive and contributes to the formation of primitive neuroectodermal-like lesions	Giachino et al. ( )
BCAN-NTRK1 fusion protein	adenoviral delivery	NPCs	Expression of the EML4-ALK fusion protein drives gliomagenesis	Cook et al. ( )
56 (brain) tumor suppressors	adenoviral delivery	astrocytes	mutations enhances resistance to therapy in mutated glioma	Chow et al. ( )
	GFAP+ SVZ, CNP+ SVZ, NES+ cortex progenitors	NSCs, OPCs, NPCs	A neural stem-cell-like origin produces higher malignancy and drug sensitivity	Jiang et al. ( )
; ; 600	GFAP or NES	astrocytes or NPCs	A diverse set of CRISPR-mediated genomic alterantions lead to tumorigenesis	Oldrini et al. ( )

The cellular origin of the disease is an additional intense area of research enabled by GEMM models. Mouse models are useful in this endeavor because the retrospective nature of this assessment makes it hard to precisely identify the cellular origin of cancer in humans. The identification of metastable GBM molecular subtypes and genetic biomarkers, however, provided indirect evidence that fully fledged tumors potentially bear a signature of their potential cell-of-origin ( 53 , 54 ). This has been now extended to several other cancers ( 55 ). Importantly, data in GEMMs are consistent with this view. Overall, from a formal literature review it emerged that: (i) different mutations appear to dictate the cellular phenotype of the resulting tumors; (ii) tumors with similar alterations but originating in different cellular compartments have private biological properties ( 38 , 43 , 45 , 46 , 48 , 56 ). Mouse models have produced overwhelming evidence that undifferentiated neural stem and progenitor cells can efficiently serve as cell-of-origin for the disease in the experimental models (Table (Table1). 1 ). Yet it is also evident that nearly every cell in the mouse brain, including post-mitotic neurons, has a potential for transformation, if the oncogenic pressure is significant enough ( 44 ). While these studies do not necessarily address the pathophysiological relevance of the models for the origins of human gliomas, two strong messages have emerged from this research. First, neural stem cells are significantly more prone to transformation than the differentiated cells composing the brain parenchyma. Consistently, sequencing of the human SVZ appears to suggest that pre-transformation clones tend to reside in the area of the human brain containing the most undifferentiated neural progenitors ( 57 ). Second, regardless of the cell targeted by the oncogenic signaling, the formation of a glial-like malignant progenitor population appears to be an obligatory step in the malignant transformation.

Despite the sophisticated and elegant approaches that were used to generate GEMMs for glioma, the implications of these findings for therapy have so far been limited. Cell cycle regulation emerged as a major predictor of therapeutic response ( 51 , 58 ), a finding that does not offer additional therapeutic options per se , but represents important ground for future glioma modeling. As discussed below, these studies provide enhanced confidence in transplantation models generated by transforming normal primary cells.

Recently, improved genetic engineering delivery and effectors, such as the CRISPR/Cas9 system have opened new routes to modeling human tumors, including gliomas. CRISPR/Cas9 operates via either non-homologous end joining (NHEJ) that generates genome deletions and insertions (indels), or by homology directed repair (HDR). This approach permits a direct genetic engineering of endogenous loci thereby avoiding random genome integration and potential genotoxicity, which is the intrinsic risk associated with retroviral delivery. For instance, in utero electroporation of gRNAs and Cas9 allowed the simultaneous in vivo deletion of the Trp53, Pten , and Nf1 tumor suppressor genes directly in the brain. This approach bypassed tedious modifications at zygote level ( 47 ). Considering that Trp53 and Nf1 are located in close proximity to each other in the genome, conventional breeding is unlikely to produce mutants through their separation onto different chromatids during chromosomal crossover without very time-consuming efforts. This illustrates one way for CRISPR/Cas9 to significantly speed up the pace at which disease models can be generated, including cancer (reviewed, among others, by Sanchez-Rivera and Jacks) ( 59 ). Most likely, other investigators will quickly adopt in vivo electroporation.

In addition to simplifying single gene modifications, CRISPR/Cas9 has permitted the modeling of complex karyotypes. For instance, CRISPR/Cas9 was instrumental to generate the mouse equivalent of fusion proteins previously discovered in human cancers ( 49 ). The intracranial adenoviral delivery of CRISPR/Cas9 directed microdeletions in the genomic loci of Bcan and Ntrk1 (Brevican and Neurotrophic Receptor Tyrosine Kinase 1, respectively), led to the generation of a mouse model for glioma in which tumorigenesis is driven by BCAN-NTRK1 fusion. This model demonstrated a good response to the kinase inhibitor Entrectinib, which preferentially targets tropomyosin receptor kinases, including NTRK1 ( 49 ). This report by the Ventura lab builds on their own pioneering work in generating an EML4-ALK fusion model for NSCLC ( 60 ), using CRISPR/Cas9 to systematically screen for fusions as oncogenic drivers in solid tumors. Using this approach, in addition to the chromosomal deletion required for the Bcan - Ntrk1 model, it has been possible to model the Myb - Qk chromosomal translocation as well as induce an equivalent of the human BRAF V600E point mutation by homology-directed-repair ( 52 ). The latter improvements in genetic engineering now make easier to generate more complex genotypes in autochthonous models. For instance, mutations in the Isocitrate dehydrogenase 1 (IDH1) are associated with a number of blood and solid tumors, including GBM. While a IDH1 R132H models were generated before using classic transgenesis ( 61 , 62 ), the ability of modifying single loci or create large chromosomal deletions paves the way to generate autochthonous models for human gliomas based on 1p/19q, IDH , and TERT Promoter Mutations, which represent specific entities in humans ( 63 , 64 ).

Collectively, these studies support GEMMs as invaluable tools in exposing the principles underlying glioma genetics and biology. Non-autochthonous models (described below) have a different set of advantages but clearly GEMMs stand to gain even more momentum in the CRISPR/Cas9 era.

Transplantation models for glioma

Transplantation models represent the most widely used alternative to GEMMs, and are essentially built using cells endowed with the ability to initiate tumors in secondary recipient animals. This modeling approach offers the flexibility of spatiotemporal control on tumor initiation and of the potential to experimentally manipulate individual cells. The impact of such manipulations, in turn, can be tested competitively during tumor growth or response to treatment.

The implantation can be carried out in either the tissue in which the disease originated (orthotopic), or more accessible locations, such as the flank of a recipient animal. The choice of the tumor cell and the recipient animal defines whether a model is to be considered syngeneic, homotypic, heterotypic or xenogeneic. For instance, the transplantation of GL261 mouse glioma cells in C57BL/6 recipient mice is considered syngeneic, because GL261 also have a C57BL/6 genetic background. If the background of the donor and recipient animas is not identical, the model is defined as homotypic. A transplantation mode is defined as xenotransplantation if the genotype of the donor is from humans, whereas it is heterotypic for genomes from every species other than humans. Transplantation-based models represent a trade-off between limitations on the pathophysiological relevance and an enhanced control over the temporal initiation of the disease as well as the unique feature of permitting perturbation experiments of various types. Since the first transplantation experiments in nude mice ( 65 , 66 ), transplantation has permitted testing the tumorigenic and developmental potential of glioma, dissecting its heterogeneity and characterizing underlying molecular mechanisms. Given the flexibility and scalability of this system, it is the best choice for individual target discovery and validation as well as for developing disease-relevant systematic discovery platforms in vivo .

Syngeneic transplantation models

Syngeneic tumor models have widely used GL261 cells, due to the fact that they exhibit key alterations in RAS, p53 and PI3K and other pathways which are commonly deregulated in human GBM. Tumors generated in this way share features with human GBM including the upregulation of VEGF and HIF-1α and a diffuse invasion pattern while retaining an intact immune system ( 67 ). Lately, syngeneic models have regained more attention with the increasing focus on cancer immunotherapy.

Evidence in cancer patients and mouse models have substantially supported that tumors can be immunogenic but also induce acquired immune tolerance ( 68 ). Thus, with the exception of tumors with high mutational and neoantigen load, such as melanoma and lung cancer ( 69 ), in heterogeneous solid tumors, the immune-checkpoint inhibitors are unlikely to be effective as single agents. Syngeneic models are well-positioned to evaluate the efficacy of combination therapies, which include the immunotherapy component. In gliomas, one viable combination therapy tested in syngeneic models is the efficacy of CAR T-cells (i.e., T-cells expressing chimeric antigen receptors) with the standard-of-care ( 70 ). In a transplantation setting, syngeneic splenocytes from C57BL/6 or VM/Dk were directed against GL-261 or SMA-497, SMA-540, and SMA-560 cells, respectively, by using the full-length NKG2D protein fused to CD3. This system has the advantage of targeting poorly expressed antigens, which is therefore better systemically tolerated. Moreover, NKG2D ligands are multiple antigens, therefore making it more difficult for the tumor to escape. Finally, NKG2D ligands expression appears to increase upon temozolomide and radiotherapy ( 70 , 71 ), making these targets particularly attractive in an adjuvant setting. Future preclinical testing should include metalloproteases inhibitors, since ADAM10 and ADAM17 expression by tumor cells appear to provide a simple solution to immunevasion by producing soluble NKG2D ( 72 ), thereby dampening the γδ T-cell adaptive response ( 71 ).

Training the immune system against tumor cells using vaccines could potentially induce a long-term immune response. In a syngeneic mouse model, dendritic cell vaccination using glioma stem-like cells (GSCs) lysate resulted in a measurable response in mice ( 73 ). This approach, however, is still being perfected in the setting of human vaccination against brain tumors, as witnessed by clinical trials that failed to show an objective clinical response ( 74 , 75 ).

Evidence from immunocompromised HIV/AIDS patients is compatible with the speculation that the adaptive immune system is most effective in controlling truly foreign antigens. In fact, HIV/AIDS patients largely develop virus-associated cancers with increased frequency but not other antigenically “colder” tumors ( 76 ). A compelling solution to selectively induce immune responses against foreign antigens in tumor cells is offered by oncolytic viruses (OVs). OVs with tropism for cancer cells can simultaneously act at different levels in the tumor microenvironment (TME). Local killing of tumor cells works as in situ vaccines, alerting antigen-presenting cells (APC) to multiple tumor-associated antigens (TAAs). To contribute to APCs maturation, this effect can be reinforced by OVs preloading with dedicated cargoes (e.g., GM-CSF gene as co-stimulatory treatment). OVs can promote intratumoral T-cell infiltration, for instance, by eliciting a type I interferon response. By inducing local acute inflammation, OVs can also reduce the impact of a suppressive TME. For a recent comprehensive review of the use for OVs as anti-cancer therapy, including a list of several clinical trials for OVs in cancer, I would refer the reader to Twumasi-Boateng et al. ( 77 ). Recently, Measles-based virotherapy has demonstrated synergistic activity with anti-PD-1 therapy in GBM treatment in a syngeneic C57BL/6 GL261 model ( 78 ). In a similar setting, intravenous delivery of GM-CSF/reovirus also showed synergistic activity with anti-PD-1. Moreover, intravenous reovirus delivery was also performed in human patients, and the viral payload was confirmed upon tumor resection, providing evidence of successful induction of the hallmarks of OVs activity ( 79 ). These examples are very important since checkpoint inhibition alone is insufficient to induce a response in GBM patients ( 80 , 81 ). Preliminary clinical trials on OVs approaches are now completed and report on some positive indicators onto which more clinical and pre-clinical research should be designed) ( 82 , 83 ), thereby underscoring the importance of animal models in testing strategies to awakening the immune system.

Thus, syngeneic models are becoming increasingly widespread. In addition to the mouse models, C6 glioma implantation in the fronto-parietal lobe of Whistar rats and 9L gliosarcoma in Fisher rats display features common to the human disease, such as proliferation, similar focal invasion as well as pseudopalisading necrosis surrounded by cells with great nuclear polymorphism ( 67 ). Interestingly, allogeneic 9L tumors in Wistar rats show a high infiltration of macrophages, microglia and CD4 + /CD8 + T-cells that coexist with tumor lesions ( 84 ). This model has been successfully used to prove that dendritic cell therapy leads to enhanced tumor infiltration by CD4 + /CD8 + T-cells and prolonged survival ( 85 ), and might represent a valid alternative to mouse models in the study of mechanisms of action for checkpoint inhibitors or therapeutic approaches targeting immune cells by other mechanisms, such as OVs.

Homotypic and xenogeneic transplantation models

Traditionally, transplantation-based models served the purpose of testing the genetic dependence of tumor cells on individual genes or pathways (Table (Table2 2 ).

Transplantation models for glioma.


GL261	3-methylcholanthrene into C57BL/6 mice	ic	C57BL/6	Syngeneic	pattern of expression matches human GBM	Zagzag et al., ( )
9L glioma cells	N-nitrosomethylurea-induced tumor in Wistar rats	ic	Wistar rats	Syngeneic	Well-defined tumor mass but adaptive immunological response starting after 14 days (CD4 T- and CD8 T-cells)	Stojiljkovic et al. ( )
C6	methylnitrosourea (MNU)-induced tumor in Wistar rats	ic	Sprague-Dawley rats	Homotypic	High take rate for secondary tumors formed from C6 migrated in the contralateral hemisphere in primary passage	Chicoine et al., ( )
12	Immortalization of Human Astrocytes	ic	Rnu/Rnu rats	Xenogeneic	and activation and loss of both p53 and pRb pathways are required for astrocyte transformation	Sonoda et al. ( )
	C57BL/6 NSCs or Astrocytes	ic	SCID mice	Homotypic	NSCs and astrocytes can both give rise to gliomagenesis with EGFR activation and loss	Bachoo et al. ( )
Patient-derived GBM	Human GBM	ic	NOD-SCID mice	Xenogeneic	CD133 cells propagate brain tumors with higher efficience than CD133	Singh et al. ( )
U87 and U251	Human glioma	ic, sc	SCID	Xenogeneic	Orthotopic implantation is superior to subcutaneous one in terms of imposing -specific gene expression	Camphausen et al. ( )
Patient-derived GBM	Human GBM	ic	Balb/c Nude	Xenogeneic	irradiation enhances tumorigeneicity of CD133 GSCs	Bao et al. ( )
Patient-derived GBM	Human GBM	ic	SCID	Xenogeneic	GBM cells grown in NSC media better represent patients' histology	Lee et al. ( )
GL261	3-methylcholanthrene into C57BL/6 mice	ic	C57BL/6	Syngeneic	DC loaded with lysates from glioma cells propagated as GSCs confer more robust vaccination	Pellegatta et al. ( )
I; ; ;	FVB NSCs or Astrocytes	ic	FVB or Balb/c nude	Homotypic	presence determines tumor growth rate, grading and phenotype.	Bruggeman et al. ( )
Patient-derived GBM	Human GBM	ic	NOD-SCID mice	Xenogeneic	Adherent cell lines preserve a more homogeneous undifferentiated profile	Pollard et al. ( )
9L glioma cells	N-nitrosomethylurea-induced tumor in Wistar rats	ic	Wistar rats	Syngeneic	Dendritic Cells loaded with tumor antigens induce intratumoral infiltration of CD8 and CD4 T-cells in a rat glioma	Liau et al. ( )
Patient-derived GBM	Human GBM	ic	NOD-SCID mice	Xenogeneic	CD44 Neurosphere cells propagate brain tumors with higher efficience than CD44	Anido et al. ( )
Patient-derived GBM	Human GBM	ic	CD1 nude	Xenogeneic	GBM Neurosphere cells propagate brain tumors regardless of marker expression but with different kinetics	Chen et al. ( )
Patient-derived GBM	Human GBM	ic	NSG	Xenogeneic	Anti-CD47 immunotherapy polarizes tumor-associated macrophages and increases survival	Zhang et al. ( )
Patient-derived GBM	Human GBM	ic	NSG	Xenogeneic	Lineage hierarchy is dominant to genetic and epigenetic heterogeneity in GBM propagation under homeostasis and therapeutic pressure	Lan et al. ( )
GL261	3-methylcholanthrene into C57BL/6 mice	ic	C57BL/6	Syngeneic	Oncolytic Measles cooperate with anti-PD1 immunotherapy	Hardcastle et al., ( )
GL261	3-methylcholanthrene into C57BL/6 mice	ic	C57BL/6	Syngeneic	NKG2D CAR T-cells prolonged survival benefit in mice and immunological memory against glioma	Weiss et al. ( )
GL261	3-methylcholanthrene into C57BL/6 mice	ic	C57BL/6	Syngeneic	Intravenous-injected GM-CSF/reovirus-reovirus accesses brain tumors in mice and sensitize to anti-PD-1 therapy.	Samson et al. ( )

While C6 tumors bear alterations in key tumor suppressors, such as the Cdkn2a ( Ink4a/Arf ) and Pten , in syngeneic as well as allogeneic transplantations, the transformation of astrocytes and neural stem cells derived from tumor suppressor mutant animals provides higher control of the tumor genotype ( 94 ), and represents a better setting for target discovery and validation ( 100 ).

A transplantation model based on immortalized astrocytes was instrumental in demonstrating that the loss of both the p53 and pRb pathways and gain of MAPK signaling and TERT-dependent telomeres protection are critical components in gliomagenesis ( 88 ). In the mouse, the minimal combination of the Ink4a/Arf locus deletion (i.e., p53 and pRb pathway inactivation) and constitutive EGFR activation by a glioma-specific mutant (i.e., MAPK activation) is dominant over the cell of origin ( 89 ). These seminal discoveries anticipated the demonstration that telomere protection can be reactivated by multiple means. TERT is reactivated, for example, in patient-derived glioma cells propagated under neural stem cell conditions, suggesting that upstream signaling can suffice ( 93 ). Moreover, there also exists a mechanism for the alternative lengthening of telomeres in GBM patients ( 101 ). Hence, while the control of telomere integrity is critical to gliomagenesis, TERT overexpression in itself may be a dispensable genetic manipulation in the process of glioma modeling in the mouse, despite promoter mutations define specific entities in human gliomas ( 63 , 64 ).

Despite the fact that transplantation-induced stress can impart clonal expansion and affect gene expression, orthotopic transplantation should be considered the best approximation for a transplantation setting. In fact, subcutaneous and orthotopic growth impart very different transcriptional responses to the glioma cells' in vivo gene expression profile and response to treatment ( 91 , 102 ). Consequently, orthotopic models for glioma also played a major role in the quest for potential epigenetic anti-cancer targets. The Polycomb group (PcG) gene Bmi1 was shown to have oncogenic functions in gliomagenesis as a negative regulator of the Ink4a/Arf tumor suppressor as well as Ink4a/Arf -independent functions, as found in both mouse and human cells ( 94 , 103 ). Likewise, the Polycomb repressive complex enzyme EZH2 also appeared to be required for glioma cell survival and proliferation in grafting experiments ( 104 ) and might be a good target reinforcing the adjuvant chemotherapeutic agent Temozolomide ( 99 ), even though context-dependent effects in the opposite direction were observed ( 105 ). Considering that Polycomb proteins contribute to coordinate the transcriptional response to converging pathways ( 100 ), the differential response to Polycomb inhibition may reflect the contribution of context-specific stress pathways within individual experimental settings. Alternatively, or in parallel, multiple cell populations composing the intra-tumoral mass may be differentially sensitive to Polycomb inhibitors ( 106 ), and their relative abundance may determine the overall response to the single agent.

To date, the genetic pathways that have been shown to prominently contribute to glioma growth in orthotopic mouse models have been largely validated by genomic studies in human patients ( 107 ), whereas targeting the epigenetic machinery as anti-cancer strategy still awaits the identification of effective combinations to account for compensatory mechanisms including adaptive responses and intra-tumoral heterogeneity.

Dissecting the tumorigenic potential of individual cellular populations that reflect this heterogeneity have also largely relied on the use of orthotopic models (Table (Table2). 2 ). Following pioneering studies in leukemia ( 108 ), Dirks and colleagues demonstrated that tumor initiating ability in Non-obese diabetic, severe combined immunodeficient (NOD-SCID) mice is restricted to a subset of brain tumor cells ( 90 ). These cells in human GBM could be prospectively isolated using CD133 as surface marker ( 90 ). The ability to form neurospheres in vitro ( 109 ), and to withstand ionizing radiation ( 92 ) are critical biological features associated with “cancer stem-like cells” from high-grade gliomas. Surface markers, such as CD15 and CD44 have also been used to positively enrich for brain tumor initiating cells ( 96 , 110 ). However, systematic comparisons of orthotopic grafts generated in CD1 Nude mice using glioma cell populations with different profiles of surface markers revealed that the non-enriched population also has tumor-initiating ability, with delayed growth kinetics ( 97 ). The difference for heterogeneous tumors, such as GBM may reflect the trans-differentiation ability of tumor cells into a wide range of lineages ( 111 – 114 ). Rather, the neural stem cell growth conditions of primary GBM cells proved to be a critical determinant in how accurately the orthotopic grafts resembled the patients' tumors ( 93 , 95 , 115 ). Collectively, these studies made enormous contributions to uncovering the cellular components of intra-tumor heterogeneity and highlighted the importance of orthotopic xenograft models.

Xenotransplantation is becoming increasingly the setting of choice for high-throughput target discovery and validation, a research area that recently evolved around the use of orthotopic models for cancer. Several pioneering studies in various allograft transplantation models have highlighted the importance of genetic screens in relevant physiological contexts ( 116 – 118 ). To cope with the numerical limitations imposed by the in vivo setting ( 119 ), genetic screens using RNAi in gliomagenesis ( 100 , 120 ) were performed in immunocompromised animals with small libraries, or a genome-wide CRISPR was performed first in vitro and followed later by a parallel in vivo / in vitro validation screen ( 121 ).

As inferred by these studies, the use of immunocompromised recipient animals remains an invaluable tool in investigating cell autonomous mechanisms in human cancer cells in vivo and the choice of cellular models is therefore critical. Tumor models using established tumor cell lines have enormously contributed to our knowledge of tumor biology but are increasingly being dismissed in gliomagenesis experiments ( 122 ). The state-of-the-art in the field of brain tumors is to propagate tumor cells under conditions supporting non-transformed the in vitro self-renewal of neural stem cells ( 93 ). Limitations to this approach include that: (i) ex vivo propagation remains anchored to the assumption that signaling, supplements and environmental conditions are known and can be delivered homogeneously, (ii) some low-grade tumors as well as specific genotypes systematically drop out in these conditions ( 123 ). Nonetheless, sophisticated ex vivo culture conditions represent the best approximation to preserve tumor identity while enabling experimental manipulations or in vitro screening endeavors ( 115 ). Indeed, when compared to patients' biopsies, conventional glioma cell lines fall short of representing patients' molecular profiles ( 53 ).

Patient-derived xenografts models

An elegant approach to bypass cultures while focusing on patient-oriented modeling is to generate Patient-derived xenografts (PDX) or Avatar models, created by patients' biopsies without applying ex vivo culturing prior to transplantation ( 124 ). PDX models propagate the complex cellular and genetic heterogeneity of the cell surviving in the host animal, and are therefore capable of modeling responses to standard, targeted or combination therapies without forcing assumptions on the tissue sample. A variant to preserving patients' biopsy tissue structure is dissecting the tissue prior to the transplantation of live cells. This is usually exploited to bypass the low take rate for some tumors, a drawback affecting low-grade or high complexity tumors. A similar approach is used to perform limited experimental manipulations followed by serial transplantation ( 99 ).

There is a general consensus that PDX models maintain some level of concordance between patients and PDX responses to therapy ( 125 , 126 ). This includes GBM, which partly preserves molecular profiles principles during xenotransplantation ( 53 , 127 ). Importantly, however, the response to anti-cancer treatment largely depends on the tumor cell genotype at the time of the treatment ( 127 ). The latter piece of evidence is relevant in that both ex vivo cellular passaging and PDX intrinsically suffer a drift toward genomic instability. In a recent large-scale study, the dynamics of copy number alterations (CNAs) in 1110 patient-derived samples of different cancer types during multiple rounds of in vivo propagation have been reported. A high rate of CNAs was observed in xenografts that artificially drift away from the human counterpart. For instance, glioblastoma patients acquire extra copies of chromosome 7 during tumor evolution, whereas PDX propagation in mice results in a loss of these extra copies. While the genetic drift is not surprising for malignant gliomas given their near-complete deficiency in DNA damage checkpoint control ( 128 ), these results raise awareness of the limitations associated with the use of PDX as patients' avatars to evaluate their responses to any given therapy and call for integrating this resource with more stable models ( 129 ). It is also critical to realize that responses to therapies in PDX models will be affected by the intra-cellular heterogeneity of the transplanted tumor. Using an elegant cellular barcoding strategy, the Dirks lab has recently demonstrated that a number of different cancer cells within a tumor can contribute to its homeostasis ( 99 ). Strikingly, however, this number can change from one tumor to the next, thereby affecting the reproducibility of hypothesis testing or target discovery and validation experiments.

In immune-oncology, transplantation models are the preferred choice when testing the impact of innate immune checkpoint inhibition and CAR T-cells. For instance, disruption of the CD47/SIRPα signaling affects leukemia, glioma, melanoma and hepatocellular carcinoma growth as xenografts ( 98 , 130 – 132 ). The efficacy of GD2-CAR T-cell against glioma cells was pre-clinically tested in NSG mice until the onset of graft-vs-host disease symptoms (GvHD; ~4 weeks), and GD2-CAR T-cells are currently tested in several clinical trials (among others, see NCT03252171).

These studies collectively highlight the continuing process of addressing the intrinsic challenges associated with transplantation models and expanding these to advanced pre-clinical settings, anticipating that this experimental system will retain its leading role in experimental medicine.

Next-generation autochthonous models

In mice, the CRISPR revolution will make the generation of complex animal models increasingly easier, faster and affordable (Figure (Figure1 1 ).

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Next-generation modeling of human cancers: longitudinal single-cell “omics” in autochthonous and transplantation-based models. Upper panel: genetic drivers of human cancer are combined with a genetic background of choice to give rise to homogenous, heterogeneous, or hypermutational tumors. Genetic engineering can use independent genetic and/or chemical switches to permit tissue-specific and temporal control. To increase tumor heterogeneity or test the contribution of mutations identified at recurrence upon tumor formation, intratumor injections of “steering” mutations using viral or other means can be used. These sophisticated cancer GEMMs permit testing complex dosing treatments. Lower panel: to test genetic dependences or perform genetic screens, patient-derived or ex vivo transformed tumor-initiating cells are genetically modified and/or barcoded and then transplanted into recipient animals. The host background can be chosen to favor tumor take and/or drug penetration. In this setting, a co-grafting of adaptive or innate immune cells or other microenvironment players (e.g., endothelial cells, pericytes, etc.) or serial transplantation can be implemented to study non-cell autonomous mechanisms and to exacerbate competition among cancer cells. Heterogeneity increases as a function of time and intra-tumor complexity. Both experimental models can be exploited in longitudinal follow-ups using live imaging or single cell “omics” (central panels). The latter approach can simultaneously generate spatiotemporal information on changes in cell cycle progression, apoptosis, cell fate decisions/microenvironment activation (i.e., biomarker variations) and immune cell composition. Right panel: single cell preps from GEMMs or orthotopic transplantation models can be transplanted (dashed line) in the indicated immunocompetent animals thereby creating a syngeneic/xenogeneic models, respectively. Potential applications for these models are indicated. T0, T1, and Tn = time points for longitudinal analyzes; pseudospatial complexity refers to the output of tSNE maps as a surrogate for spatial information. Iv, intravenous; Ic, intracranial.

Tumor heterogeneity and evolution is a major feature of human cancers ( 133 , 134 ), and these features have proven hard to model autochthonously. One important consideration for the design of future, next-generation mouse models is their genetic background. Most mouse strains are inbred and represent a great advantage in research to unravel specific targets or mechanisms in a certain type of cancer ( 135 ). Yet inbred strains are far from the real diverse setting of disease development in humans, and the genetic background of animals used as pre-clinical cancer models in research is critical to the outcome of the study. In gliomagenesis, Nf1 and p53 deletion leads to a tumor grading ranging from low-grade astrocytoma to GBM depending on the distinct mouse strain ( 29 ). Likewise, p53 heterozygosity leads to a spontaneous formation of mammary tumors in BALB/c mice but not in C57BL/6J ( 136 ), suggesting a distinct pro-tumorigenic genetic background in BALB/c mice. Moreover, the RF/J strain displays a high incidence of cancer owing to missense mutations in DNA damage repair and cancer-associated genes and may become the model of choice for genetically hypermutated tumors, such as smoking-driven lung cancer, UV-driven melanomas and DNA repair-deficient colon cancer. The choice of the background for future GEMMs may be driven by the clinical history of individual diseases and may exploit the Collaborative Cross Consortium 2012 benchmarking of several inbred mouse strains ( 137 ). In turn, this may also facilitate testing the contribution of natural variation and DNA repair in tumorigenesis and responses to treatment. This appears to be particularly important if one considers that genetic variation may not only affect tumor-intrinsic, but also and even more likely, the tumor-immune phenotype.

The recent conclusion of a colossal genomic investigation in almost 10,000 genomes spanning 33 cancer types by the Cancer Genome Atlas (TCGA) has provided an enormous dataset of cancer driver genes ( 138 ). In vivo validation and hypotheses testing follow-ups are lagging behind. Simultaneously testing dozens of putative tumor suppressors within a native and immunocompetent microenvironment is now possible. In a medium throughput in vivo screening in gliomagenesis using the stereotactic injection of a sgRNA adenoviral library for CRISPR/Cas9 mediated knock-outs, the Sidi Chen lab reported on ability for PanCancer-defined significantly mutated genes to initiate brain tumor formation ( 50 ). The combination of regional viral delivery and GFAP-Cre conditional activation of Cas9 and GFP restricted the screen to a subset of glial cells. This type of screen follows the direct RNAi delivery pioneered by the Zender lab in hepatocellular carcinoma ( 139 ). While the inability of AAV to genetically integrate into the host genome requires extensive target sequencing as direct readout, compared to earlier studies, this provided a significant higher throughput in screening for combinatorial signaling dependencies during gliomagenesis ( 38 , 43 ). It also represented a more physiological setting for tumor suppressor discovery than transplantation models previously used in similar endeavors ( 100 ). Considering that adult GBM is a disease that often develops over several years and goes through regional and temporal evolution ( 140 , 141 ), future studies along these lines may benefit from integrating more sophisticated, spatiotemporally precise forms of editing. Examples of the sequential delivery of cancer driver mutations exploiting classic genetic recombination techniques were previously reported ( 142 ). To enable further spatiotemporal control, biochemical, chemical or optogenetic control of tumor progression may be employed. This would be particularly important in order to better mimic disease initiation and progression (Figure (Figure1 1 ).

An enhanced control of tumor progression using advanced genetic engineering methods may permit a validation of driver and passenger mutations as modifiers of tumor progression and responses to therapy or testing advanced genetics paradigms, such as genetic essentiality ( 22 ). To this end, while direct intracranial injection of RNP complexes has not yet been exploited in models of glioblastoma, it has been successfully applied to genome editing in the mouse brain ( 143 ). During brain tumor exposure to multimodal therapy, additional aberrations in core GBM driver pathways are acquired. Combining longitudinal intravital imaging and topical delivery of RNPs or in situ electroporation-inducing genetic engineering may help in elucidating the contribution of genetics to disease recurrence. Genetic alterations identified as critical regulators of tumor evolution could be defined as “steering” mutations, and would become an integral part of sequential modeling. In GBM modeling, two examples of such mutations may be Nf1 and Msh6 , both of which are associated with recurrent tumors and TMZ-induced hypermutations ( 144 , 145 ). Importantly, this approach would also permit the functional dissection of cell-autonomous mechanisms, such as tumor microtube formation ( 146 ), as well as non-cell autonomous processes, such as vessel dysmorphia ( 147 ).

Unpredictable adaptive responses to anti-cancer treatments are a hallmark of solid tumors. Cancer cells can evade chemotherapy by acquiring additional mutations, switching to a state of negligible growth, by activating survival pathways through changes in cell identity, and by other means. In modeling human cancer in mice, one should account for possible species-specific tumor genotype drifts that would not be representative of the patients'. Thus, next-generation GEM models that better represent primary tumors and their evolution at a genetic level (as depicted above) will be invaluable tools for testing complex chemotherapy-dosing schemes (Figure (Figure1 1 ).

Longitudinal studies in humans are revealing oncogenes dominant in recurrence. In GBM, mutations in NF1 and PI3KCA appear to drive disease progression ( 145 , 148 , 149 ), and may be temporally controlled to mimic disease progression. In such models, complex sequential drug combinations may be tested. For instance, switching off NF1 or PI3KCA, which reinforce the RAS pathway and confer sensitivity to a combination of BRD4 and MEK inhibition ( 150 ). A neoadjuvant-like multimodal therapy followed by targeted drugs, would be prototypic examples of testing complex targeting of acquired mutations.

Metronomic chemotherapy and planned drug holidays are complex dosing schemes that aim at reversing drug tolerance. In GBM, tumor cells evade adjuvant Temozolomide using MGMT reversal and cell cycle restriction. A Temozolomide holiday may be therefore alternated with targeted therapies, such as blood-brain-barrier penetrant PI3K inhibitors ( 151 ).

An emerging paradigm in cancer biology is to drive cancer cells to acquire specific addictions and then targeting such addictions with drugs. This approach may be important in exploiting metabolic ( 152 , 153 ) as well as epigenetic targets ( 154 ), or targeting collateral lethality associated with acquired resistance mechanisms [( 155 ) and our unpublished data]. This includes also extrinsic mechanisms, such as those targeted by adaptive immune checkpoint inhibitors. Indeed, studies in syngeneic models have shown promising results in a combination or neoadjuvant setting that induced PD-L1 expression ( 79 , 156 ).

While complex dosing schemes have been so far directly tested during clinical trials, the combination of accurate GEM models and complex readouts should make it standard practice at the pre-clinical level to test whether low-dose or sequential treatments are equally or even more effective than drug combinations, thereby sparing patients from the side effects of the added toxicity and possibly enhancing tumors' response. In this regard, once the specie-specific differences are accounted for, the autochthonous models are well-positioned to provide the most physiological setting for tumor initiation and organismal response to treatments.

Next-generation transplantation models

Transplantation models are currently developing on parallel research lines.

To avoid the complications of genetic drifts as well as patient-specific passenger mutations, PDX xenograft models are being complemented with de novo transforming human cells derived from normal tissue with relevant cancer mutations in the projected neoplasia. Transforming human astrocytes has been instrumental in formally testing the contributions of the main pathway required for efficient gliomagenesis in human cells. Recent work using human colon organoids has shown that combining an appropriate cell of origin and set of mutations is still insufficient to recapitulate some of the biological properties of the true tumors, such as metastasis ( 157 ). Future work in this direction should be aimed at precisely dissecting genotype-to-phenotype connections in human cancers using advanced genetic screening systems. In this regard, systematically transforming normal mouse and human cells to create next-generation avatar models is expected to help address some open questions. For instance, in numerous solid tumors, the role of copy number aberrations remains to be clarified. Whereas, human GBM is clearly a disease of copy number aberrations ( 158 ), studies in autochthonous models so far fall short of clarifying whether and how this feature contributes to the disease. Moreover, emphasis should be given to developing an understanding of molecular phenotype specifications for tumors including GBM in which there is limited evidence of genetically encoded subtypes. Overall, creating reliable and homogenous tumor-initiating cells in vitro may complement PDX-models and permit testing ad hoc biological questions as well as providing a more reproducible resource for target discovery and validation (Figure (Figure1 1 ).

The recipient animals are a critical determinant of the success of transplantation models. The informative value of such models is dependent on both cell-intrinsic and non-cell autonomous components. On the one hand, achieving the highest grafting potential through the use of severely immunocompromised animals is the essence of xenotransplantation ( 159 ). On the other, the discovery of adaptive checkpoint inhibitors and the need to identify the basis for responses to immunotherapy pose additional challenges to modeling human tumors using xenotransplantation; one solution is to reinstate the adaptive immune system in host recipients. Freshly isolated peripheral bone marrow cells (PBMCs) or specific immune cell subpopulations can be co-grafted with human cancer cells subcutaneously in immune deficient animals (e.g., NSG/NOG). Alternatively, PBMCs can be parentally infused (i.p. or i.v.) after subcutaneous or orthotopic tumor transplantation has taken place. This approach revealed, for instance, the immunogenic effect of a Carcinoembryonic Antigen CD3 T-Cell Bispecific Antibody (CEA-TCB) in promoting the infiltration of xenografts as well as adaptive PD-L1 over-expression, thereby suggesting the feasibility of combining its administration with adaptive immune-checkpoint inhibitors ( 160 ). Whereas this setting is limited to short-term studies given the potential for severe GvHD, it also enabled assessments of the efficacy of immunotherapies and the impact of T-cell subpopulations (e.g., regulatory T-cells; T-regs) or antigen-presenting cells (APC) on the activation of cancer cells ( 161 ).

The GvHD is not only a complication for the recipient animals. Immune cells that suddenly face a full-blown tumor to which they were previously naïve face an acute pathophysiological stimulation reflecting the actual tumor. Ideally, future models will feature intact innate and adaptive immune systems trained to ignore the grafting phase and triggered by pathophysiological stimuli to transiently allow grafting. An elegant example of this approach in glioma models has been achieved by blocking the T-cell activation by APC through CTL4A-IgG (clinical name: Abatacept) and anti-CD154. Immune-tolerant mice generated by this approach developed GBM lesions and these affected survival with similar kinetics as immune-deficient animals ( 162 ). As an alternative in immune-deficient backgrounds, co-grafted immune cells may be pre-exposed to tumor cells (e.g., DC loading with tumor cells lysate or RNA), thereby more closely mirroring the gradual rise of tumors in humans. Moreover, some innate immunity players may require specific co-grafting schemes. For instance, microglia cells, the resident macrophages of the central nervous system, are generated during embryonic development ( 163 ). These cells are associated with the full specification of the Mesenchymal GBM subtype identity ( 164 – 166 ), and most likely with responses to treatment ( 167 ). This situation calls for a reliable source of human microglia whether tumor-isolated or in vitro immortalized to serve as co-grafting partners in transplantation models.

The genetic engineering of recipient animals can also be exploited to selectively impair the tumor microenvironment. To determine the role of drug penetration and reabsorption in brain tumor models, for instance, the drug transporters ABCB1 and ABCG2 were deleted not only in wild-type glioma models, but also in immunodeficient recipients in xenotransplantation models ( 168 , 169 ).

In the neurosciences, the rat is the experimental and translational model of choice wherever possible. This species is considered superior for etiological, pathophysiological, pharmacological and behavioral studies ( 170 ). The discovery of culture conditions that facilitate the expansion of rat embryonic stem cells as well as the direct use of CRISPR during zygote formation may enable extending also to rats the generation of complex models for gliomas. This would allow extending the use of these animals from a limited pharmacodynamics setting to a fully-fledged advanced modeling system.

The future development of transplantation models at the levels of both tumor cells and recipient animals will enable more sophisticated experimental dissections of cell-intrinsic and non-cell autonomous mechanisms and a more effective platform for target discovery and validation.

Outlooks and perspectives for single cell “omics”

Interrogating cell cycle responses, apoptosis and cell identity drifts at the level of single cells is a transformational change in that it permits assessing the effects of a compound and simultaneously to build hypotheses based on possible combinatorial treatments or sequential treatments (Figure (Figure1 1 ).

Single-cell RNA sequencing has been significantly exploited in GBM to describe intratumor heterogeneity and the peculiarities of GBM subtypes and their microenvironments ( 164 – 166 ). This approach has also been instrumental in identifying genetic and transcriptional identities associated with tumor-specific biological properties, such as proximal and distal recurrence, infiltration and numbness to the fluorescence-guided probe for the resection of diseased tissue (5-Aminolevulinic acid, also known as 5-ALA) ( 149 ). DNA methylation is being currently implemented in clinical neuropathological practice for brain tumor classification ( 171 ). The availability of technologies enabling to simultaneously generate single cell RNA-seq (scRNA-seq) and bisulfite-converted DNA methylation (BS-seq) maps from the same cell ( 172 ) has a potential to allow the tracking of tumor responses to treatments. In turn, this will be a major driver in the development of targeted therapies in both autochthonous and transplantation models (Figure (Figure1 1 ).

I envision that the combination of multiple autochthonous models in parallel and scRNA-seq and BS-seq may be exploited to evaluate the way the core tumor and in the infiltrating margins respond to individual treatments. Importantly, metabolic labeling of RNA in vivo now enables identifying faster and more accurately the adaptive transcriptional changes ( 173 ).

To bridge preclinical and clinical testing, longitudinal scRNA-seq in GEMMs may help assessing and improving adjuvant and second-line treatments. To some extent, autochthonous models of aggressive human cancers may well represent low Karnofsky performance score (KPS) patients or very hard-to-resect tumors, in that surgery is discouraged. In these cases, single cell profiling will be very informative in testing the consequences of standard approaches, such as radiation followed by adjuvant treatments (e.g., in GBM, Temozolomide, Bevacizumab) and provide benchmarks for new treatments. This matter has so far been restricted to advanced clinical trials in patients and may be now repositioned at the preclinical stage.

Exceptionally, single cell “omics” and autochthonous models may also help testing next-generation probes for optically guided surgery. 5-ALA is currently used in fluorescent-guided surgery to delineate tumor margins for resection, which is extremely important in preventing local recurrence in the brain parenchyma, where surgeons need to be conservative. Recent studies in GBM at the single-cell level suggest that 5-ALA appears to mark Mesenchymal subtype-specific GBM cells, leaving behind Proneural-subtyped cells ( 149 ). It will be important to test this in autochthonous models in which the two states can be modeled ( 56 ), and to compare 5-ALA to novel fluorescent probes. By resecting a tumor margin pre-labeled with such probes, single cell profiling will reveal the identity of each cell that retain or miss the labeling.

The xenogeneic transplantation setting is well-versed for testing intrinsic cellular responses to novel treatments and detecting adaptive resistance, notably in cases in which tumor perfusion is homogeneous. Indeed, the response to intracellular targets for drugs are best assessed in human cells when on-target and off-target effects need to be accounted for. Moreover, applying CRISPR screens to transplantation models using scRNA-seq readouts, such as CROP-seq ( 174 ) may improve the resolution of in vivo functional screens, thereby increasing their throughput. In the brain tumor setting, these were limited so far to a few dozen targets ( 100 , 120 ), and CROP-seq may lead to more comprehensive screens without compromising the pathophysiology of the orthotopic transplantation setting ( 119 ). Importantly, even in the absence of functional perturbations, approaches like CROP-seq coupled with scRNA-seq will enable a next-generation of cellular barcoding experiments to trace in vivo tumor homeostasis and responses to treatments ( 99 ). In this area, PDX models should more systematically be complemented with de novo transforming human cells and with models better representing tumor molecular profiles.

Harnessing the therapeutic potential of the TME and immuno-oncology will significantly benefit of building experimental consensus within the community. In particular, it is critical to define the appropriateness of any given model in the assessment of the response to treatment of established tumors. Differences between animal models and humans specifically involve protein-coding genes and cis-regulatory DNA due to specie-specific adaptive selection, notably those controlling immunity ( 175 , 176 ). Moreover, the consistency in the response of human cells to mouse supplements and vice versa (e.g., growth factors, cytokines, etc.) are largely anecdotal. Nevertheless, whether GEMMs, syngeneic or xenogeneic models based on orthotopic transplantation may be used for testing of TME- and immune-therapies should be systematically assessed when appropriate readouts can be faithfully reproduced in both species. This has been possible, for instance, in the quest for identifying antigen-specific TCRs. In this case, comparing immune-deficient mice reconstituted with human hematopoietic progenitors (i.e., humanized mice) and mice transgenic for the human TCRα and TCRβ loci returned similar results ( 177 ).

Despite the acknowledged pathophysiological relevance of GEMMs, syngeneic models based on established cell lines have been preferred in investigating TME- and immune-therapies (Table (Table2). 2 ). To exploit the CRISPR/Cas9 potential in the future models, I anticipate that primary tumor cells from sophisticated GEMM models may soon replace cell lines in a secondary syngeneic orthotopic setting. This way, one could harness the genetic and spatiotemporal controls in an immune-tolerant setting (Figure (Figure1, 1 , right panel). This approach would be the ideal development for the preclinical dissection of intrinsic, acquired and non-cell autonomous resistance mechanisms. In the example provided by the combinatorial or neoadjuvant OVs treatment, syngeneic models may be used to uncover the mechanism conferring residual resistance, which is indicated by the incomplete penetrance of the sensitization to checkpoint inhibitors ( 79 , 156 ). Longitudinal scRNA-seq may reveal the optimal timing for starting the immune-checkpoint inhibition and whether cell intrinsic or other cells from the microenvironment are contributing to or preventing a fully penetrant response.

Whereas testing immunotherapy strategies in immunodeficient animals appears counterintuitive, the use of NSG mice emerges as the mainstream strain for studies involving sophisticated reagents eventually used in clinical trials. These include systems for T-cells engineering ( 178 , 179 ), and innate immune checkpoint inhibitors ( 98 , 130 – 132 ). While retaining a leading position in these experiments, a foreseeable evolution of this system will involve improving the recipient animals, including transplanting human cells into highly immunodeficient rats.

Transformative technologies, such as CRISPR/Cas9 and single-cell genomics have opened new avenues in the study of tumor biology. Here I propose that the longitudinal dissection of tumor responses in animal models can capitalize on single cell genomics approaches. The specific examples I have discussed here are cases that could clearly improve our current understanding of human cancers and their responses to treatment. Combining genetic engineering and single-cell genomics with the individual strengths of GEM and transplantation models is bringing about novel next-generation platforms for understanding tumor biology and for target discovery and validation.

Author contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Conflict of interest statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

I am grateful to Ulrike Brüning and to Andreas Göhrig for assistance with literature review, to Michela Serresi for critical reading and to Yuliia Dramaretska and Russel Hodge for proofreading and editing of the manuscript. I apologize for the original work I could not directly cite owing to space constraints. GG acknowledges funding from the European Research Council (ERC StG 714922), the Helmholtz Association and the MDC.

Exosomal Drug Delivery Systems: A Novel Therapy Targeting PD-1 in Septic-ALI

Published: 05 September 2024

Cite this article

Yuanlan Huang 1 na1 ,
Gang Li 1 na1 ,
Zeqi Chen 1 ,
Mengying Chen 1 ,
Weibin Zhai 1 ,
Dan Li 2 &
Qingqiang Xu ORCID: orcid.org/0009-0007-6872-8865 3 , 4

The cytokine storm triggered by sepsis can lead to the development of acute lung injury (ALI). Human umbilical cord Mesenchymal stem cells derived exosomes (HucMSCs-EXOs) have been demonstrated to possess immunosuppressive and anti-inflammatory properties. Programmed cell death receptor 1 (PD-1) plays a crucial role in maintaining the inflammatory immune homeostasis. The aim of this study is to investigate the synergistic therapeutic effect of EXOs loaded with anti-PD-1 peptide on septic-ALI.

This study prepares a novel EXOs-based drug, named MEP, by engineering modification of HucMSCs-EXOs, which are non-immunogenic extracellular vesicles, loaded with anti-PD-1 peptide. The therapeutic effect and potential mechanism of MEP on septic-ALI are elucidated through in vivo and in vitro experiments, providing experimental evidence for the treatment of septic acute lung injury with MEP.

We found that, compared to individual components (anti-PD-1 peptide or EXOs), MEP treatment can more effectively improve the lung injury index of septic-ALI mice, significantly reduce the expression levels of inflammatory markers CRP and PCT, as well as pro-inflammatory cytokines TNF-α and IL-1β in serum, decrease lung cell apoptosis, and significantly increase the expression of anti-inflammatory cytokine IL-10 and CD68 + macrophages. In vitro, MEP co-culture promotes the proliferation of CD206 + macrophages, increases the M2/M1 macrophage ratio, and attenuates the inflammatory response. GEO data analysis and qRT-PCR validation show that MEP reduces the expression of inflammasome-related genes and M1 macrophage marker iNOS.

In both in vitro and in vivo settings, MEP demonstrates superior therapeutic efficacy compared to individual components in the context of septic-ALI.

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MSC-NTF (NurOwn®) exosomes: a novel therapeutic modality in the mouse LPS-induced ARDS model

Exosomes Derived from hucMSCs Primed with IFN-γ Suppress the NF-κB Signal Pathway in LPS-Induced ALI by Modulating the miR-199b-5p/AFTPH Axis

Potential application of mesenchymal stem cells and their exosomes in lung injury: an emerging therapeutic option for COVID-19 patients

Data availability.

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

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Acknowledgements

We thank all members of our laboratory for their assistance.

This work was supported by the Shanghai Changning District Science and Technology Commission Key Project (CNKW2020Y55), Excellent Talent Project (21TPZY4801), National Key R&D Program of China(2022YFC2602900), National Natural Science Foundation of China (82273672), Natural Science Foundation of Shanghai (20ZR1470300), the Shanghai Municipal Health Commission-Outstanding Youth Foundation of Public Health (GWV-10.2-YQ48).

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Yuanlan Huang and Gang Li contributed equally to this work.

Authors and Affiliations

Department of Blood Transfusion, Naval Specialty Medical Center, Naval Medical University, Shanghai, 200050, People’s Republic of China

Yuanlan Huang, Gang Li, Zeqi Chen, Mengying Chen & Weibin Zhai

Special Food Equipment Research Laboratory, Naval Specialty Medical Center, Naval Medical University, Shanghai, 200050, People’s Republic of China

Lab of Toxicology and Pharmacology, Faculty of Naval Medicine, Naval Medical University, Shanghai, 200433, People’s Republic of China

Qingqiang Xu

Basic Medical Center for Pulmonary Disease, Naval Medical University, 800, Xiangyin Road, Shanghai, 200433, People’s Republic of China

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Contributions

YH and QX contributed to conceptualization; ZC and MC contributed to methodology; GL and WZ contributed to validation; GL and ZC contributed to formal analysis; YH and GL contributed to the investigation; YH and QX contributed to resources; YH and DL contributed to data curation; DL and QX contributed to supervision; GL and ZC contributed to visualization; YH and QX contributed to funding acquisition; DL and QX contributed to project administration; GL and ZC contributed to writing original draft; all authors contributed to reviewing & editing. All authors have read and approved the manuscript.

Corresponding authors

Correspondence to Dan Li or Qingqiang Xu .

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All the experimental procedures have been approved by the the Medical Ethics Management Committee of the People’s Liberation Army Navy Characteristic Medical Center in China (Approval No. 2023042401).

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The authors declare that they have no competing interests.

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Huang, Y., Li, G., Chen, Z. et al. Exosomal Drug Delivery Systems: A Novel Therapy Targeting PD-1 in Septic-ALI. Stem Cell Rev and Rep (2024). https://doi.org/10.1007/s12015-024-10784-6

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Title: can llms generate novel research ideas a large-scale human study with 100+ nlp researchers.

Abstract: Recent advancements in large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery, with a growing number of works proposing research agents that autonomously generate and validate new ideas. Despite this, no evaluations have shown that LLM systems can take the very first step of producing novel, expert-level ideas, let alone perform the entire research process. We address this by establishing an experimental design that evaluates research idea generation while controlling for confounders and performs the first head-to-head comparison between expert NLP researchers and an LLM ideation agent. By recruiting over 100 NLP researchers to write novel ideas and blind reviews of both LLM and human ideas, we obtain the first statistically significant conclusion on current LLM capabilities for research ideation: we find LLM-generated ideas are judged as more novel (p < 0.05) than human expert ideas while being judged slightly weaker on feasibility. Studying our agent baselines closely, we identify open problems in building and evaluating research agents, including failures of LLM self-evaluation and their lack of diversity in generation. Finally, we acknowledge that human judgements of novelty can be difficult, even by experts, and propose an end-to-end study design which recruits researchers to execute these ideas into full projects, enabling us to study whether these novelty and feasibility judgements result in meaningful differences in research outcome.

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Overall survival prediction of gastric cancer using the gene signature of CT-detected extramural venous invasion combined with M2 macrophages infiltration

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Association of EMVI-M2-related signature with TMB, single-gene mutation rate, immune cell infiltration and sensitivity to immune therapy

The expression analysis of EMVI-M2-related gene EGFLAM and GNG11

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