The rapid development of multiome(transcriptome,proteome,cistrome,imaging,and regulome)-wide association study methods have opened new avenues for biologists to understand the susceptibility genes underlying complex d...The rapid development of multiome(transcriptome,proteome,cistrome,imaging,and regulome)-wide association study methods have opened new avenues for biologists to understand the susceptibility genes underlying complex diseases.Thorough comparisons of these methods are essential for selecting the most appropriate tool for a given research objective.This review provides a detailed categorization and summary of the statistical models,use cases,and advantages of recent multiome-wide association studies.In addition,to illustrate gene-disease association studies based on transcriptome-wide association study(TWAS),we collected 478 disease entries across 22 categories from 235 manually reviewed publications.Our analysis reveals that mental disorders are the most frequently studied diseases by TWAS,indicating its potential to deepen our understanding of the genetic architecture of complex diseases.In summary,this review underscores the importance of multiome-wide association studies in elucidating complex diseases and highlights the significance of selecting the appropriate method for each study.展开更多
The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This...The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This era integrates biotechnology,artificial intelligence(AI),and big data information technology.In contrast,China is still in a transition period between stages 2.0 and 3.0,which primarily relies on conventional selection and molecular breeding.In the context of increasingly complex international situations,accurately identifying core issues in China's seed industry innovation and seizing the frontier of international seed technology are strategically important.These efforts are essential for ensuring food security and revitalizing the seed industry.This paper systematically analyzes the characteristics of crop breeding data from artificial selection to intelligent design breeding.It explores the applications and development trends of AI and big data in modern crop breeding from several key perspectives.These include highthroughput phenotype acquisition and analysis,multiomics big data database and management system construction,AI-based multiomics integrated analysis,and the development of intelligent breeding software tools based on biological big data and AI technology.Based on an in-depth analysis of the current status and challenges of China's seed industry technology development,we propose strategic goals and key tasks for China's new generation of AI and big data-driven intelligent design breeding.These suggestions aim to accelerate the development of an intelligent-driven crop breeding engineering system that features large-scale gene mining,efficient gene manipulation,engineered variety design,and systematized biobreeding.This study provides a theoretical basis and practical guidance for the development of China's seed industry technology.展开更多
The integration of artificial intelligence(AI)and multiomics has transformed clinical and life sciences,enabling precision medicine and redefining disease understanding.Scientific publications grew significantly from ...The integration of artificial intelligence(AI)and multiomics has transformed clinical and life sciences,enabling precision medicine and redefining disease understanding.Scientific publications grew significantly from 2.1 million in 2012 to 3.3 million in 2022,with AI research tripling during this period.Multiomics fields,including genomics and proteomics,also advanced,exemplified by the Human Proteome Project achieving a 90%complete blueprint by 2021.This growth highlights opportunities and challenges in integrating AI and multiomics into clinical reporting.A review of studies and case reports was conducted to evaluate AI and multiomics integration.Key areas analyzed included diagnostic accuracy,predictive modeling,and personalized treatment approaches driven by AI tools.Case examples were studied to assess impacts on clinical decision-making.AI and multiomics enhanced data integration,predictive insights,and treatment personalization.Fields like radiomics,genomics,and proteomics improved diagnostics and guided therapy.For instance,the“AI radiomics,geno-mics,oncopathomics,and surgomics project”combined radiomics and genomics for surgical decision-making,enabling preoperative,intraoperative,and post-operative interventions.AI applications in case reports predicted conditions like postoperative delirium and monitored cancer progression using genomic and imaging data.AI and multiomics enable standardized data analysis,dynamic updates,and predictive modeling in case reports.Traditional reports often lack objectivity,but AI enhances reproducibility and decision-making by processing large datasets.Challenges include data standardization,biases,and ethical concerns.Overcoming these barriers is vital for optimizing AI applications and advancing personalized medicine.AI and multiomics integration is revolutionizing clinical research and practice.Standardizing data reporting and addressing challenges in ethics and data quality will unlock their full potential.Emphasizing collaboration and transparency is essential for leveraging these tools to improve patient care and scientific communication.展开更多
Pu-erh tea,a traditional Chinese beverage,performs an anti-obesity function,but the correlation between its components and efficacy remains unknown.Here,we screened two Pu-erh teas with significant anti-obesity effica...Pu-erh tea,a traditional Chinese beverage,performs an anti-obesity function,but the correlation between its components and efficacy remains unknown.Here,we screened two Pu-erh teas with significant anti-obesity efficacies from 11 teas.In vitro experiments revealed that lipid accumulation in L02 cells and lipid synthesis in 3T3-L1 cells were significantly better inhibited by Tea-B than Tea-A.Further in vivo experiments using model mice revealed that the differences in chemical components generated two pathways in the anti-obesity efficacy and mechanism of Pu-erh teas.Tea-A changes the histomorphology of brown adipose tissue(BAT)and increases the abundance of Coriobacteriaceae_UCG_002 and cyclic AMP in guts through high chemical contents of cyclopentasiloxane,decamethyl,tridecane and 1,2,3-trimethoxybenzene,eventually increasing BAT activation and fat browning gene expression;the high content of hexadecane and 1,2-dimethoxybenzene in Tea-B reduces white adipose tissue(WAT)accumulation and the process of fatty liver,increases the abundance of Odoribacter and sphinganine 1-phosphate,inhibits the expression of lipid synthesis and transport genes.These mechanistic findings on the association of the representative bioactive components in Pu-erh teas with the anti-obesity phenotypes,gut microbes,gut metabolite structure and anti-obesity pathways,which were obtained for the first time,provide foundations for developing functional Pu-erh tea.展开更多
Alcohol-related liver disease(ARLD)remains a major public health concern,often diagnosed at advanced stages with limited treatment options.Early identification of high-risk individuals is crucial for timely interventi...Alcohol-related liver disease(ARLD)remains a major public health concern,often diagnosed at advanced stages with limited treatment options.Early identification of high-risk individuals is crucial for timely intervention and improved patient outcomes.Artificial intelligence(AI)has emerged as a powerful tool for predicting ARLD,leveraging multi-omics data,machine learning algorithms,and non-invasive biomarkers.This review explores the current advancements in AIdriven ARLD prediction,highlighting key methodologies such as multi-omics data integration,gut microbiome-based modeling,and predictive analytics using machine learning techniques.AI models incorporating transcriptomics,proteomics,and clinical data have demonstrated high diagnostic accuracy,with some achieving an area under the curve exceeding 0.90.Furthermore,non-invasive biomarkers,including liver stiffness measurements and circulating proteomic panels,have been successfully integrated into AI frameworks for early detection and risk stratification.Despite these advancements,challenges such as data heterogeneity,model generalizability,and ethical considerations remain.Future directions include the development of advanced biomarker discovery,wearable and point-of-care AI-integrated technologies,and precision medicine approaches tailored to individual risk profiles.AI-driven models hold significant potential in transforming ARLD prediction and management,ultimately contributing to early diagnosis and improved clinical outcomes.展开更多
BACKGROUND Hepatocellular carcinoma(HCC)is notorious for its aggressive progression and dismal prognosis,with chromatin accessibility dynamics emerging as pivotal yet poorly understood drivers.AIM To dissect how multi...BACKGROUND Hepatocellular carcinoma(HCC)is notorious for its aggressive progression and dismal prognosis,with chromatin accessibility dynamics emerging as pivotal yet poorly understood drivers.AIM To dissect how multilayered chromatin regulation sustains oncogenic transcription and tumor-stroma crosstalk in HCC,we combined multiomics single cell analysis.METHODS We integrated single-cell RNA sequencing and paired single-cell assay for transposase-accessible chromatin with sequencing data of HCC samples,complemented by bulk RNA sequencing validation across The Cancer Genome Atlas,Liver Cancer Institute,and GSE25907 cohorts.Cell type-specific chromatin architectures were resolved via ArchR,with regulatory hubs identified through peak-to-gene linkages and coaccessibility networks.Functional validation employed A485-mediated histone 3 lysine 27 acetylation suppression and small interfering RNA targeting DGAT1.RESULTS Malignant hepatocytes exhibited expanded chromatin accessibility profiles,characterized by increased numbers of accessible peaks and larger physical regions despite reduced peak intensity.Enhancer-like peaks enriched in malignant regulation,forming long-range hubs.Eighteen enhancer-like peak-related genes showed tumor-specific overexpression and diagnostic accuracy,correlating with poor prognosis.Intercellular coaccessibility analysis revealed tumor-stroma symbiosis via shared chromatin states.Pharmacological histone 3 lysine 27 acetylation inhibition paradoxically downregulated DGAT1,the hub gene most strongly regulated by chromatin accessibility.DGAT1 knockdown suppressed cell proliferation.CONCLUSION Multilayered chromatin reprogramming sustains HCC progression through tumor-stroma crosstalk and DGAT1-related oncogenic transcription,defining targetable epigenetic vulnerabilities.展开更多
Salinity poses a significant challenge to global agricultural productivity,impacting plant growth,yield,soil fertility,and the composition of soil microbial communities.Moreover,salinity has a significant impact in sh...Salinity poses a significant challenge to global agricultural productivity,impacting plant growth,yield,soil fertility,and the composition of soil microbial communities.Moreover,salinity has a significant impact in shifting soil microbial communities and their functional profiles.Therefore,we explored and analyzed the intricate relationships among plant-associated microbes/microbiome,including plant growth-promoting bacteria,arbuscular mycorrhizal fungi(AMF),archaea,and viruses in alleviating salinity stress in plants.In this review,we have highlighted that salinity stress selectively enhances the growth of certain microbes such as Gammaproteobacteria,Bacteroidetes,Firmicutes,Acidobacteria,Euryarchaeota,Thaumarchaeota,Crenarchaeota,and lysogenic viruses,while decreasing the abundances of others(Alphaproteobacteria and Betaproteobacteria)and AMF root colonization.These microbes regulate water and nutrient uptake,decrease ionic and osmotic toxicity,enhance the syntheses of antioxidant enzymes(catalase and glutathione S-transferases)and osmolytes(erythrose and galactinol),increase phytohormone(indole-3 acetic acid)production,and activate salinity stress tolerance genes(SOD,APX,and SKOR)in plants.Furthermore,we meticulously examined the significance of soil microbiome and the need for multidisciplinary omics studies on the changes in soil microbiome composition and the relationships of synergistic holobiont in mitigating salinity stress in plants.Such studies will provide insights into the use of microbial components as a sustainable and eco-friendly approach to modulate salinity stress and enhance agricultural productivity.展开更多
Hepatocellular carcinoma(HCC),a leading cause of cancer mortality,faces diagnostic and therapeutic challenges due to its histopathological complexity and clinical heterogeneity.Pathomics,an emerging discipline that in...Hepatocellular carcinoma(HCC),a leading cause of cancer mortality,faces diagnostic and therapeutic challenges due to its histopathological complexity and clinical heterogeneity.Pathomics,an emerging discipline that integrates artificial intelligence(AI)with quantitative pathology image analysis,aims to decode disease heterogeneity by extracting high-dimensional features from histopathological specimens.This review highlights how AI-driven pathomics has revolutionized liver cancer management through automated analysis of whole-slide images.Pathomics integrates deep learning with histopathological features to enable precise tumour classification(e.g.,HCC vs cholangiocarcinoma),microvascular invasion(MVI)detection,recurrence risk stratification,and survival prediction.Advanced frameworks such as MVI-AI diagnostic model and CHOWDER demonstrate high accuracy in identifying prognostic biomarkers,whereas multiomics integration links morphometric patterns to molecular signatures(e.g.,EZH2 expression and immune infiltration).Despite these breakthroughs,critical bottlenecks persist,including limited multicentre validation studies,"black box"model interpretability,and clinical workflow integration.Future studies should emphasize AI-enhanced multimodal fusion(radiogenomics and liquid biopsy)and standardized platforms to bridge computational pathology and precision oncology,ultimately improving personalized therapeutic strategies for liver malignancies.This synthesis aims to guide research translation and advance personalized therapeutic strategies for liver malignancies.展开更多
Objective This study was aimed to explore the prolonged therapeutic profile and underlying mechanisms of Yiqi Zishen Formula(YZF)in chronic obstructive pulmonary disease(COPD)management.Methods A COPD rat model was es...Objective This study was aimed to explore the prolonged therapeutic profile and underlying mechanisms of Yiqi Zishen Formula(YZF)in chronic obstructive pulmonary disease(COPD)management.Methods A COPD rat model was established through exposure to tobacco smoke and Klebsiella pneumoniae infections from weeks 1 to 8,followed by treatment with YZF from weeks 9 to 20.No treatment was administered from weeks 21 to 31.At week 32,all rats were euthanized,and lung tissue samples and blood specimens were collected for subsequent analyses.Then,comprehensive multiomics profiling—encompassing transcriptomics,proteomics,andmetabolomics—was conducted to identify differentially expressed molecules in lung tissues and elucidate the underlying molecular mechanisms.Results By week 32,sustained therapeutic efficacy became apparent,characterized by diminished inflammatory cytokine expression,mitigation of protease–antiprotease dysregulation,and reduced collagen deposition.These differentially expressed molecules were predominantly enriched in pathways related to oxidoreductase activity,antioxidant homeostasis,focal adhesion,tight junction formation,adherens junction dynamics,and lipid metabolism regulation.Integrative analysis of predicted targets,transcriptomic,proteomic,and metabolomic datasets revealed that differentially expressed molecules in YZF-treated rats and YZF-targeted proteins collectively participated in lipid metabolism,inflammatory responses,oxidative stress,and focal adhesion pathways.Conclusion YZF provides sustained therapeutic benefits in COPD rat models,potentially through systemic regulation of lipid metabolism,inflammatory responses,oxidative stress,and focal adhesion pathways.展开更多
Background:Non-small cell lung cancer(NSCLC)involves complex alterations in the epidermal growth factor receptor(EGFR)signaling pathway.This study aims to integrate multimodal omics analyses to evaluate and enhance EG...Background:Non-small cell lung cancer(NSCLC)involves complex alterations in the epidermal growth factor receptor(EGFR)signaling pathway.This study aims to integrate multimodal omics analyses to evaluate and enhance EGFR-targeted therapies.Methods:We reviewed and synthesized omics data—including genomics,transcriptomics,proteomics,epigenomics,and metabolomics data—related to the EGFR pathway in NSCLC,examined the clinical outcomes of current therapies and proposed new treatment strategies.Results:Integrated omics analyses revealed the multifaceted role of EGFR in NSCLC.Transcriptomic analysis revealed gene expression alterations due to EGFR mutations,with upregulation of oncogenes and downregulation of tumor suppressors.Proteomics revealed complex interactions within the EGFR network,revealing cross-talk with other receptors.Epigenomics highlighted the impact of DNA methylation and histone modifications on EGFR and its downstream genes,whereas metabolomics demonstrated shifts in metabolic patterns essential for tumor growth.Conclusion:This study highlights the critical role of multimodal omics in understanding the molecular landscape of NSCLC,offering insights into more effective,personalized therapies.Future advancements in omic technologies and analysis are expected to significantly enhance NSCLC diagnosis and treatment.展开更多
Objective:Recurrence continues to be a pivotal challenge among hormone receptor-positive(HR^(+))/human epidermal growth factor receptor 2^(−)negative(HER2^(−))breast cancers.In the international consensus guidelines,H...Objective:Recurrence continues to be a pivotal challenge among hormone receptor-positive(HR^(+))/human epidermal growth factor receptor 2^(−)negative(HER2^(−))breast cancers.In the international consensus guidelines,HR^(+)/HER2^(−)breast cancer relapse patterns are divided into three distinct types:primary resistant,secondary resistant,and endocrine sensitive.However,owing to the lack of cohorts with treatment and follow-up data,the heterogeneity among different recurrence patterns remains uncharted.Current treatments still lack precision.Methods:This analysis included data from a large-scale multiomics study of a HR^(+)/HER2^(−)breast cancer cohort(n=314).Through the analysis of transcriptomics(n=312),proteomics(n=124),whole-exome sequencing(n=290),metabolomics(n=217),and digital pathology(n=228)data,we explored distinctive molecular features and identified putative therapeutic targets for patients experiencing recurrence.Results:We explored distinct clinicopathological characteristics,biological heterogeneity,and potential therapeutic strategies for recurrence.Based on a shared relapse signature,we stratified patients into high-and lowrecurrence-risk groups.Patients with different relapse patterns presented unique molecular features in primary tumors.Specifically,receptor tyrosine kinase(RTK)pathway activation in the primary resistant group suggested the utility of RTK inhibitors,whereas mammalian target of rapamycin(mTOR)and cell cycle pathway activation in the secondary resistant group highlighted the potential of mTOR and CDK4/6 inhibitors.Interestingly,the endocrine-sensitive group displayed a quiescent state and high genomic instability,suggesting that targeting quiescent cells and using poly-ADP-ribose polymerase(PARP)inhibitors could be effective strategies.Conclusions:These findings illuminate the clinicopathological and molecular landscape of HR^(+)/HER2^(−)breast cancer patients with distinct recurrence patterns,highlighting potential targeted therapies.展开更多
Metabolic-associated fatty liver disease(MAFLD),formerly known as nonalcoho-lic fatty liver disease,is an increasing global health challenge with substantial implications for metabolic and cardiovascular health(CVH).A...Metabolic-associated fatty liver disease(MAFLD),formerly known as nonalcoho-lic fatty liver disease,is an increasing global health challenge with substantial implications for metabolic and cardiovascular health(CVH).A recent study by Fu et al investigated the relationship between CVH metrics,specifically Life’s Simple 7 and Life’s Essential 8,and the prevalence of MAFLD.While this study offered important insights into the relationship between CVH and MAFLD,several me-thodological limitations,unaddressed confounding factors,and potential biases that could impact the interpretation of their findings should be considered.The study’s cross-sectional nature restricted the ability to draw causal conclusions,and it did not fully account for potential confounding factors such as dietary habits,genetic predispositions,and medication use.Furthermore,relying on tran-sient elastography to diagnose MAFLD introduces certain diagnostic limitations.Longitudinal study designs,advanced statistical modeling techniques,and diverse population groups should be utilized to strengthen future research.Exploring the mechanistic pathways that link CVH metrics to MAFLD through multi-omics approaches and interventional studies will be essential in formulating targeted prevention and treatment strategies.Structural equation modeling and machine learning techniques could provide a more refined analysis of these interrelated factors.Additionally,future research should employ longitudinal study designs and explore genetic and epigenetic influences to enhance our un-derstanding of CVH and MAFLD interactions.展开更多
Background Hepatic steatosis is a prevalent manifestation of fatty liver, that has detrimental effect on the health and productivity of laying hens, resulting in economic losses to the poultry industry. Here, we aimed...Background Hepatic steatosis is a prevalent manifestation of fatty liver, that has detrimental effect on the health and productivity of laying hens, resulting in economic losses to the poultry industry. Here, we aimed to systematically investigate the genetic regulatory mechanisms of hepatic steatosis in laying hens.Methods Ninety individuals with the most prominent characteristics were selected from 686 laying hens according to the accumulation of lipid droplets in the liver, and were graded into three groups, including the control, mild hepatic steatosis and severe hepatic steatosis groups. A combination of transcriptome, proteome, acetylome and lipidome analyses, along with bioinformatics analysis were used to screen the key biological processes, modifications and lipids associated with hepatic steatosis.Results The rationality of the hepatic steatosis grouping was verified through liver biochemical assays and RNA-seq. Hepatic steatosis was characterized by increased lipid deposition and multiple metabolic abnormalities. Integration of proteome and acetylome revealed that differentially expressed proteins(DEPs) interacted with differentially acetylated proteins(DAPs) and were involved in maintaining the metabolic balance in the liver. Acetylation alterations mainly occurred in the progression from mild to severe hepatic steatosis, i.e., the enzymes in the fatty acid oxidation and bile acid synthesis pathways were significantly less acetylated in severe hepatic steatosis group than that in mild group(P < 0.05). Lipidomics detected a variety of sphingolipids(SPs) and glycerophospholipids(GPs) were negatively correlated with hepatic steatosis(r ≤-0.5, P < 0.05). Furthermore, the severity of hepatic steatosis was associated with a decrease in cholesterol and bile acid synthesis and an increase in exogenous cholesterol transport.Conclusions In addition to acquiring a global and thorough picture of hepatic steatosis in laying hens, we were able to reveal the role of acetylation in hepatic steatosis and depict the changes in hepatic cholesterol metabolism. The findings provides a wealth of information to facilitate a deeper understanding of the pathophysiology of fatty liver and contributes to the development of therapeutic strategies.展开更多
Background:Tumor-derived exosomes are involved in tumor progression and immune invasion and might func-tion as promising noninvasive approaches for clinical management.However,there are few reports on exosom-based mar...Background:Tumor-derived exosomes are involved in tumor progression and immune invasion and might func-tion as promising noninvasive approaches for clinical management.However,there are few reports on exosom-based markers for predicting the progression and adjuvant therapy response rate among patients with clear cell renal cell carcinoma(ccRCC).Methods:The signatures differentially expressed in exosomes from tumor and normal tissues from ccRCC pa-tients were correspondingly deregulated in ccRCC tissues.We adopted a two-step strategy,including Lasso and bootstrapping,to construct a novel risk stratification system termed the TDERS(Tumor-Derived Exosome-Related Risk Score).During the testing and validation phases,we leveraged multiple external datasets containing over 2000 RCC cases from eight cohorts and one inhouse cohort to evaluate the accuracy of the TDERS.In addition,enrichment analysis,immune infiltration signatures,mutation landscape and therapy sensitivity between the high and low TDERS groups were compared.Finally,the impact of TDERS on the tumor microenvironment(TME)was also analysed in our single-cell datasets.Results:TDERS consisted of 12 mRNAs deregulated in both exosomes and tissues from patients with ccRCC.TDERS achieved satisfactory performance in both prognosis and immune checkpoint inhibitor(ICI)response across all ccRCC cohorts and other pathological types,since the average area under the curve(AUC)to predict 5-year overall survival(OS)was larger than 0.8 across the four cohorts.Patients in the TDERS high group were resistant to ICIs,while mercaptopurine might function as a promising agent for those patients.Patients with a high TDERS were characterized by coagulation and hypoxia,which induced hampered tumor antigen presentation and relative resistance to ICIs.In addition,single cells from 12 advanced samples validated this phenomenon since the interaction between dendritic cells and macrophages was limited.Finally,PLOD2,which is highly expressed in fibro-and epi-tissue,could be a potential therapeutic target for ccRCC patients since inhibiting PLOD2 altered the malignant phenotype of ccRCC in vitro.Conclusion:As a novel,non-invasive,and repeatable monitoring tool,the TDERS could work as a robust risk stratification system for patients with ccRCC and precisely inform treatment decisions about ICI therapy.展开更多
Nonalcoholic fatty liver disease(NAFLD)is a heterogeneous and complex disease that is imprecisely diagnosed by liver biopsy.NAFLD covers a spectrum that ranges from simple steatosis,nonalcoholic steatohepatitis(NASH)w...Nonalcoholic fatty liver disease(NAFLD)is a heterogeneous and complex disease that is imprecisely diagnosed by liver biopsy.NAFLD covers a spectrum that ranges from simple steatosis,nonalcoholic steatohepatitis(NASH)with varying degrees of fibrosis,to cirrhosis,which is a major risk factor for hepatocellular carcinoma.Lifestyle and eating habit changes during the last century have made NAFLD the most common liver disease linked to obesity,type 2 diabetes mellitus and dyslipidemia,with a global prevalence of 25%.NAFLD arises when the uptake of fatty acids(FA)and triglycerides(TG)from circulation and de novo lipogenesis saturate the rate of FAβ-oxidation and verylow density lipoprotein(VLDL)-TG export.Deranged lipid metabolism is also associated with NAFLD progression from steatosis to NASH,and therefore,alterations in liver and serum lipidomic signatures are good indicators of the disease’s development and progression.This review focuses on the importance of the classification of NAFLD patients into different subtypes,corresponding to the main alteration(s)in the major pathways that regulate FA homeostasis leading,in each case,to the initiation and progression of NASH.This concept also supports the targeted intervention as a key approach to maximize therapeutic efficacy and opens the door to the development of precise NASH treatments.展开更多
Inflammatory bowel disease(IBD)is a complex,immune-mediated gastrointestinal disorder with ill-defined etiology,multifaceted diagnostic criteria,and unpredictable treatment response.Innovations in IBD diagnostics,incl...Inflammatory bowel disease(IBD)is a complex,immune-mediated gastrointestinal disorder with ill-defined etiology,multifaceted diagnostic criteria,and unpredictable treatment response.Innovations in IBD diagnostics,including developments in genomic sequencing and molecular analytics,have generated tremendous interest in leveraging these large data platforms into clinically meaningful tools.Artificial intelligence,through machine learning facilitates the interpretation of large arrays of data,and may provide insight to improving IBD outcomes.While potential applications of machine learning models are vast,further research is needed to generate standardized models that can be adapted to target IBD populations.展开更多
Maize kernel moisture content(KMC)at harvest greatly affects mechanical harvesting,transport and storage.KMC is correlated with kernel dehydration rate(KDR)before and after physiological maturity.KMC and KDR are compl...Maize kernel moisture content(KMC)at harvest greatly affects mechanical harvesting,transport and storage.KMC is correlated with kernel dehydration rate(KDR)before and after physiological maturity.KMC and KDR are complex traits governed by multiple quantitative trait loci(QTL).Their genetic architecture is incompletely understood.We used a multiomics integration approach with an association panel to identify genes influencing KMC and KDR.A genome-wide association study using time-series KMC data from 7 to 70 days after pollination and their transformed KDR data revealed respectively 98and 279 loci significantly associated with KMC and KDR.Time-series transcriptome and proteome datasets were generated to construct KMC correlation networks,from which respectively 3111 and 759 module genes and proteins were identified as highly associated with KMC.Integrating multiomics analysis,several promising candidate genes for KMC and KDR,including Zm00001d047799 and Zm00001d035920,were identified.Further mutant experiments showed that Zm00001d047799,a gene encoding heat shock 70 kDa protein 5,reduced KMC in the late stage of kernel development.Our study provides resources for the identification of candidate genes influencing maize KMC and KDR,shedding light on the genetic architecture of dynamic changes in maize KMC.展开更多
Genetic,epigenetic,and metabolic alterations are all hallmarks of cancer.However,the epigenome and metabolome are both highly complex and dynamic biological networks in vivo.The interplay between the epigenome and met...Genetic,epigenetic,and metabolic alterations are all hallmarks of cancer.However,the epigenome and metabolome are both highly complex and dynamic biological networks in vivo.The interplay between the epigenome and metabolome contributes to a biological system that is responsive to the tumor microenvironment and possesses a wealth of unknown biomarkers and targets of cancer therapy.From this perspective,we first review the state of high-throughput biological data acquisition(i.e.multiomics data)and analysis(i.e.computational tools)and then propose a conceptual in silico metabolic and epigenetic regulatory network(MER-Net)that is based on these current high-throughput methods.The conceptual MER-Net is aimed at linking metabolomic and epigenomic networks through observation of biological processes,omics data acquisition,analysis of network information,and integration with validated database knowledge.Thus,MER-Net could be used to reveal new potential biomarkers and therapeutic targets using deep learning models to integrate and analyze large multiomics networks.We propose that MER-Net can serve as a tool to guide integrated metabolomics and epigenomics research or can be modified to answer other complex biological and clinical questions using multiomics data.展开更多
There is great heterogeneity among inflammatory bowel disease(IBD)patients in terms of pathogenesis,clinical manifestation,response to treatment,and prognosis,which requires the individualized and precision management...There is great heterogeneity among inflammatory bowel disease(IBD)patients in terms of pathogenesis,clinical manifestation,response to treatment,and prognosis,which requires the individualized and precision management of patients.Many studies have focused on prediction biomarkers and models for assessing IBD disease type,activity,severity,and prognosis.During the era of biologics,how to predict the response and side effects of patients to different treatments and how to quickly recognize the loss of response have also become important topics.Multiomics is a promising area for investigating the complex network of IBD pathogenesis.Integrating numerous amounts of data requires the use of artificial intelligence.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.62102068,62231013,61821002,81971750,81701833)the National Key R&D Program of China(Grant No.2017YFA0104300).
文摘The rapid development of multiome(transcriptome,proteome,cistrome,imaging,and regulome)-wide association study methods have opened new avenues for biologists to understand the susceptibility genes underlying complex diseases.Thorough comparisons of these methods are essential for selecting the most appropriate tool for a given research objective.This review provides a detailed categorization and summary of the statistical models,use cases,and advantages of recent multiome-wide association studies.In addition,to illustrate gene-disease association studies based on transcriptome-wide association study(TWAS),we collected 478 disease entries across 22 categories from 235 manually reviewed publications.Our analysis reveals that mental disorders are the most frequently studied diseases by TWAS,indicating its potential to deepen our understanding of the genetic architecture of complex diseases.In summary,this review underscores the importance of multiome-wide association studies in elucidating complex diseases and highlights the significance of selecting the appropriate method for each study.
基金partially supported by the Construction of Collaborative Innovation Center of Beijing Academy of Agricultural and Forestry Sciences(KJCX20240406)the Beijing Natural Science Foundation(JQ24037)+1 种基金the National Natural Science Foundation of China(32330075)the Earmarked Fund for China Agriculture Research System(CARS-02 and CARS-54)。
文摘The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This era integrates biotechnology,artificial intelligence(AI),and big data information technology.In contrast,China is still in a transition period between stages 2.0 and 3.0,which primarily relies on conventional selection and molecular breeding.In the context of increasingly complex international situations,accurately identifying core issues in China's seed industry innovation and seizing the frontier of international seed technology are strategically important.These efforts are essential for ensuring food security and revitalizing the seed industry.This paper systematically analyzes the characteristics of crop breeding data from artificial selection to intelligent design breeding.It explores the applications and development trends of AI and big data in modern crop breeding from several key perspectives.These include highthroughput phenotype acquisition and analysis,multiomics big data database and management system construction,AI-based multiomics integrated analysis,and the development of intelligent breeding software tools based on biological big data and AI technology.Based on an in-depth analysis of the current status and challenges of China's seed industry technology development,we propose strategic goals and key tasks for China's new generation of AI and big data-driven intelligent design breeding.These suggestions aim to accelerate the development of an intelligent-driven crop breeding engineering system that features large-scale gene mining,efficient gene manipulation,engineered variety design,and systematized biobreeding.This study provides a theoretical basis and practical guidance for the development of China's seed industry technology.
文摘The integration of artificial intelligence(AI)and multiomics has transformed clinical and life sciences,enabling precision medicine and redefining disease understanding.Scientific publications grew significantly from 2.1 million in 2012 to 3.3 million in 2022,with AI research tripling during this period.Multiomics fields,including genomics and proteomics,also advanced,exemplified by the Human Proteome Project achieving a 90%complete blueprint by 2021.This growth highlights opportunities and challenges in integrating AI and multiomics into clinical reporting.A review of studies and case reports was conducted to evaluate AI and multiomics integration.Key areas analyzed included diagnostic accuracy,predictive modeling,and personalized treatment approaches driven by AI tools.Case examples were studied to assess impacts on clinical decision-making.AI and multiomics enhanced data integration,predictive insights,and treatment personalization.Fields like radiomics,genomics,and proteomics improved diagnostics and guided therapy.For instance,the“AI radiomics,geno-mics,oncopathomics,and surgomics project”combined radiomics and genomics for surgical decision-making,enabling preoperative,intraoperative,and post-operative interventions.AI applications in case reports predicted conditions like postoperative delirium and monitored cancer progression using genomic and imaging data.AI and multiomics enable standardized data analysis,dynamic updates,and predictive modeling in case reports.Traditional reports often lack objectivity,but AI enhances reproducibility and decision-making by processing large datasets.Challenges include data standardization,biases,and ethical concerns.Overcoming these barriers is vital for optimizing AI applications and advancing personalized medicine.AI and multiomics integration is revolutionizing clinical research and practice.Standardizing data reporting and addressing challenges in ethics and data quality will unlock their full potential.Emphasizing collaboration and transparency is essential for leveraging these tools to improve patient care and scientific communication.
基金The financial support received from the Shenzhen Science and Technology Innovation Commission(KCXFZ20201221173207022,WDZC20200821141349001)Shenzhen Bay Laboratory Startup Fund(21310041,S234602003)。
文摘Pu-erh tea,a traditional Chinese beverage,performs an anti-obesity function,but the correlation between its components and efficacy remains unknown.Here,we screened two Pu-erh teas with significant anti-obesity efficacies from 11 teas.In vitro experiments revealed that lipid accumulation in L02 cells and lipid synthesis in 3T3-L1 cells were significantly better inhibited by Tea-B than Tea-A.Further in vivo experiments using model mice revealed that the differences in chemical components generated two pathways in the anti-obesity efficacy and mechanism of Pu-erh teas.Tea-A changes the histomorphology of brown adipose tissue(BAT)and increases the abundance of Coriobacteriaceae_UCG_002 and cyclic AMP in guts through high chemical contents of cyclopentasiloxane,decamethyl,tridecane and 1,2,3-trimethoxybenzene,eventually increasing BAT activation and fat browning gene expression;the high content of hexadecane and 1,2-dimethoxybenzene in Tea-B reduces white adipose tissue(WAT)accumulation and the process of fatty liver,increases the abundance of Odoribacter and sphinganine 1-phosphate,inhibits the expression of lipid synthesis and transport genes.These mechanistic findings on the association of the representative bioactive components in Pu-erh teas with the anti-obesity phenotypes,gut microbes,gut metabolite structure and anti-obesity pathways,which were obtained for the first time,provide foundations for developing functional Pu-erh tea.
文摘Alcohol-related liver disease(ARLD)remains a major public health concern,often diagnosed at advanced stages with limited treatment options.Early identification of high-risk individuals is crucial for timely intervention and improved patient outcomes.Artificial intelligence(AI)has emerged as a powerful tool for predicting ARLD,leveraging multi-omics data,machine learning algorithms,and non-invasive biomarkers.This review explores the current advancements in AIdriven ARLD prediction,highlighting key methodologies such as multi-omics data integration,gut microbiome-based modeling,and predictive analytics using machine learning techniques.AI models incorporating transcriptomics,proteomics,and clinical data have demonstrated high diagnostic accuracy,with some achieving an area under the curve exceeding 0.90.Furthermore,non-invasive biomarkers,including liver stiffness measurements and circulating proteomic panels,have been successfully integrated into AI frameworks for early detection and risk stratification.Despite these advancements,challenges such as data heterogeneity,model generalizability,and ethical considerations remain.Future directions include the development of advanced biomarker discovery,wearable and point-of-care AI-integrated technologies,and precision medicine approaches tailored to individual risk profiles.AI-driven models hold significant potential in transforming ARLD prediction and management,ultimately contributing to early diagnosis and improved clinical outcomes.
基金Supported by the Science and Technology Planning Project of Guangzhou,No.2024A03J0102the Natural Science Foundation of Guangdong Province for Distinguished Young Scholar,No.2022B1515020024+1 种基金National Natural Science Foundation of China,No.82070574the Key Research and Development Program of Guangzhou,No.2023B03J1298.
文摘BACKGROUND Hepatocellular carcinoma(HCC)is notorious for its aggressive progression and dismal prognosis,with chromatin accessibility dynamics emerging as pivotal yet poorly understood drivers.AIM To dissect how multilayered chromatin regulation sustains oncogenic transcription and tumor-stroma crosstalk in HCC,we combined multiomics single cell analysis.METHODS We integrated single-cell RNA sequencing and paired single-cell assay for transposase-accessible chromatin with sequencing data of HCC samples,complemented by bulk RNA sequencing validation across The Cancer Genome Atlas,Liver Cancer Institute,and GSE25907 cohorts.Cell type-specific chromatin architectures were resolved via ArchR,with regulatory hubs identified through peak-to-gene linkages and coaccessibility networks.Functional validation employed A485-mediated histone 3 lysine 27 acetylation suppression and small interfering RNA targeting DGAT1.RESULTS Malignant hepatocytes exhibited expanded chromatin accessibility profiles,characterized by increased numbers of accessible peaks and larger physical regions despite reduced peak intensity.Enhancer-like peaks enriched in malignant regulation,forming long-range hubs.Eighteen enhancer-like peak-related genes showed tumor-specific overexpression and diagnostic accuracy,correlating with poor prognosis.Intercellular coaccessibility analysis revealed tumor-stroma symbiosis via shared chromatin states.Pharmacological histone 3 lysine 27 acetylation inhibition paradoxically downregulated DGAT1,the hub gene most strongly regulated by chromatin accessibility.DGAT1 knockdown suppressed cell proliferation.CONCLUSION Multilayered chromatin reprogramming sustains HCC progression through tumor-stroma crosstalk and DGAT1-related oncogenic transcription,defining targetable epigenetic vulnerabilities.
基金supported by the Biological Materials Specialized Graduate Program through the Korea Environmental Industry&Technology Institute(KEITI)funded by the Ministry of Environment of Korea,the Cooperative Research Program for Agriculture Science&Technology Development(No.PJ017033)through the Rural Development Administration of Korea+2 种基金the Regional Researcher Program(No.NRF-2020R1I1A307452212)through the National Research Foundation(NRF)funded by the Ministry of Education of Koreathe Korea Basic Science Institute(National Research Facilities and Equipment Center)Grant(No.2021R1A6C101A416)funded by the Ministry of Education through the NGS Core Facility,Kyungpook National University for providing facility for data collection and management。
文摘Salinity poses a significant challenge to global agricultural productivity,impacting plant growth,yield,soil fertility,and the composition of soil microbial communities.Moreover,salinity has a significant impact in shifting soil microbial communities and their functional profiles.Therefore,we explored and analyzed the intricate relationships among plant-associated microbes/microbiome,including plant growth-promoting bacteria,arbuscular mycorrhizal fungi(AMF),archaea,and viruses in alleviating salinity stress in plants.In this review,we have highlighted that salinity stress selectively enhances the growth of certain microbes such as Gammaproteobacteria,Bacteroidetes,Firmicutes,Acidobacteria,Euryarchaeota,Thaumarchaeota,Crenarchaeota,and lysogenic viruses,while decreasing the abundances of others(Alphaproteobacteria and Betaproteobacteria)and AMF root colonization.These microbes regulate water and nutrient uptake,decrease ionic and osmotic toxicity,enhance the syntheses of antioxidant enzymes(catalase and glutathione S-transferases)and osmolytes(erythrose and galactinol),increase phytohormone(indole-3 acetic acid)production,and activate salinity stress tolerance genes(SOD,APX,and SKOR)in plants.Furthermore,we meticulously examined the significance of soil microbiome and the need for multidisciplinary omics studies on the changes in soil microbiome composition and the relationships of synergistic holobiont in mitigating salinity stress in plants.Such studies will provide insights into the use of microbial components as a sustainable and eco-friendly approach to modulate salinity stress and enhance agricultural productivity.
基金Supported by Wenzhou Municipal Science and Technology Bureau,No.Y20240109.
文摘Hepatocellular carcinoma(HCC),a leading cause of cancer mortality,faces diagnostic and therapeutic challenges due to its histopathological complexity and clinical heterogeneity.Pathomics,an emerging discipline that integrates artificial intelligence(AI)with quantitative pathology image analysis,aims to decode disease heterogeneity by extracting high-dimensional features from histopathological specimens.This review highlights how AI-driven pathomics has revolutionized liver cancer management through automated analysis of whole-slide images.Pathomics integrates deep learning with histopathological features to enable precise tumour classification(e.g.,HCC vs cholangiocarcinoma),microvascular invasion(MVI)detection,recurrence risk stratification,and survival prediction.Advanced frameworks such as MVI-AI diagnostic model and CHOWDER demonstrate high accuracy in identifying prognostic biomarkers,whereas multiomics integration links morphometric patterns to molecular signatures(e.g.,EZH2 expression and immune infiltration).Despite these breakthroughs,critical bottlenecks persist,including limited multicentre validation studies,"black box"model interpretability,and clinical workflow integration.Future studies should emphasize AI-enhanced multimodal fusion(radiogenomics and liquid biopsy)and standardized platforms to bridge computational pathology and precision oncology,ultimately improving personalized therapeutic strategies for liver malignancies.This synthesis aims to guide research translation and advance personalized therapeutic strategies for liver malignancies.
基金supported by the National Natural Science Fund of China(81130062).
文摘Objective This study was aimed to explore the prolonged therapeutic profile and underlying mechanisms of Yiqi Zishen Formula(YZF)in chronic obstructive pulmonary disease(COPD)management.Methods A COPD rat model was established through exposure to tobacco smoke and Klebsiella pneumoniae infections from weeks 1 to 8,followed by treatment with YZF from weeks 9 to 20.No treatment was administered from weeks 21 to 31.At week 32,all rats were euthanized,and lung tissue samples and blood specimens were collected for subsequent analyses.Then,comprehensive multiomics profiling—encompassing transcriptomics,proteomics,andmetabolomics—was conducted to identify differentially expressed molecules in lung tissues and elucidate the underlying molecular mechanisms.Results By week 32,sustained therapeutic efficacy became apparent,characterized by diminished inflammatory cytokine expression,mitigation of protease–antiprotease dysregulation,and reduced collagen deposition.These differentially expressed molecules were predominantly enriched in pathways related to oxidoreductase activity,antioxidant homeostasis,focal adhesion,tight junction formation,adherens junction dynamics,and lipid metabolism regulation.Integrative analysis of predicted targets,transcriptomic,proteomic,and metabolomic datasets revealed that differentially expressed molecules in YZF-treated rats and YZF-targeted proteins collectively participated in lipid metabolism,inflammatory responses,oxidative stress,and focal adhesion pathways.Conclusion YZF provides sustained therapeutic benefits in COPD rat models,potentially through systemic regulation of lipid metabolism,inflammatory responses,oxidative stress,and focal adhesion pathways.
基金funded by the Science and Technology Development Fund,Macao SAR(0098/2021/A2 and 0048/2023/AFJ)the Chinese Medicine Guangdong Laboratory(HQCML-C-2024006).
文摘Background:Non-small cell lung cancer(NSCLC)involves complex alterations in the epidermal growth factor receptor(EGFR)signaling pathway.This study aims to integrate multimodal omics analyses to evaluate and enhance EGFR-targeted therapies.Methods:We reviewed and synthesized omics data—including genomics,transcriptomics,proteomics,epigenomics,and metabolomics data—related to the EGFR pathway in NSCLC,examined the clinical outcomes of current therapies and proposed new treatment strategies.Results:Integrated omics analyses revealed the multifaceted role of EGFR in NSCLC.Transcriptomic analysis revealed gene expression alterations due to EGFR mutations,with upregulation of oncogenes and downregulation of tumor suppressors.Proteomics revealed complex interactions within the EGFR network,revealing cross-talk with other receptors.Epigenomics highlighted the impact of DNA methylation and histone modifications on EGFR and its downstream genes,whereas metabolomics demonstrated shifts in metabolic patterns essential for tumor growth.Conclusion:This study highlights the critical role of multimodal omics in understanding the molecular landscape of NSCLC,offering insights into more effective,personalized therapies.Future advancements in omic technologies and analysis are expected to significantly enhance NSCLC diagnosis and treatment.
基金supported by the National Key Research and Development Program of China (No. 2020YFA0112304)the National Natural Science Foundation of China (No. 82373167, 82341003 and 92159301)+4 种基金the Natural Science Foundation of Shanghai (No. 22ZR1479200)the Shanghai Key Laboratory of Breast Cancer (No. 12DZ2260100)the SHDC Municipal Project for Developing Emerging and Frontier Technology in Shanghai Hospitals (No. SHDC12 021103)Shanghai Medical Innovation Research Project (No. 22Y11912700)Shanghai Anticancer Association EYAS PROJECT (No. SACA-CY22A05)
文摘Objective:Recurrence continues to be a pivotal challenge among hormone receptor-positive(HR^(+))/human epidermal growth factor receptor 2^(−)negative(HER2^(−))breast cancers.In the international consensus guidelines,HR^(+)/HER2^(−)breast cancer relapse patterns are divided into three distinct types:primary resistant,secondary resistant,and endocrine sensitive.However,owing to the lack of cohorts with treatment and follow-up data,the heterogeneity among different recurrence patterns remains uncharted.Current treatments still lack precision.Methods:This analysis included data from a large-scale multiomics study of a HR^(+)/HER2^(−)breast cancer cohort(n=314).Through the analysis of transcriptomics(n=312),proteomics(n=124),whole-exome sequencing(n=290),metabolomics(n=217),and digital pathology(n=228)data,we explored distinctive molecular features and identified putative therapeutic targets for patients experiencing recurrence.Results:We explored distinct clinicopathological characteristics,biological heterogeneity,and potential therapeutic strategies for recurrence.Based on a shared relapse signature,we stratified patients into high-and lowrecurrence-risk groups.Patients with different relapse patterns presented unique molecular features in primary tumors.Specifically,receptor tyrosine kinase(RTK)pathway activation in the primary resistant group suggested the utility of RTK inhibitors,whereas mammalian target of rapamycin(mTOR)and cell cycle pathway activation in the secondary resistant group highlighted the potential of mTOR and CDK4/6 inhibitors.Interestingly,the endocrine-sensitive group displayed a quiescent state and high genomic instability,suggesting that targeting quiescent cells and using poly-ADP-ribose polymerase(PARP)inhibitors could be effective strategies.Conclusions:These findings illuminate the clinicopathological and molecular landscape of HR^(+)/HER2^(−)breast cancer patients with distinct recurrence patterns,highlighting potential targeted therapies.
文摘Metabolic-associated fatty liver disease(MAFLD),formerly known as nonalcoho-lic fatty liver disease,is an increasing global health challenge with substantial implications for metabolic and cardiovascular health(CVH).A recent study by Fu et al investigated the relationship between CVH metrics,specifically Life’s Simple 7 and Life’s Essential 8,and the prevalence of MAFLD.While this study offered important insights into the relationship between CVH and MAFLD,several me-thodological limitations,unaddressed confounding factors,and potential biases that could impact the interpretation of their findings should be considered.The study’s cross-sectional nature restricted the ability to draw causal conclusions,and it did not fully account for potential confounding factors such as dietary habits,genetic predispositions,and medication use.Furthermore,relying on tran-sient elastography to diagnose MAFLD introduces certain diagnostic limitations.Longitudinal study designs,advanced statistical modeling techniques,and diverse population groups should be utilized to strengthen future research.Exploring the mechanistic pathways that link CVH metrics to MAFLD through multi-omics approaches and interventional studies will be essential in formulating targeted prevention and treatment strategies.Structural equation modeling and machine learning techniques could provide a more refined analysis of these interrelated factors.Additionally,future research should employ longitudinal study designs and explore genetic and epigenetic influences to enhance our un-derstanding of CVH and MAFLD interactions.
基金funded in part by grants from the National Natural Science Foundation of China (No.31930105)National Key Research and Development Program of China (2022YFF1000204)China Agriculture Research Systems (CARS-40)。
文摘Background Hepatic steatosis is a prevalent manifestation of fatty liver, that has detrimental effect on the health and productivity of laying hens, resulting in economic losses to the poultry industry. Here, we aimed to systematically investigate the genetic regulatory mechanisms of hepatic steatosis in laying hens.Methods Ninety individuals with the most prominent characteristics were selected from 686 laying hens according to the accumulation of lipid droplets in the liver, and were graded into three groups, including the control, mild hepatic steatosis and severe hepatic steatosis groups. A combination of transcriptome, proteome, acetylome and lipidome analyses, along with bioinformatics analysis were used to screen the key biological processes, modifications and lipids associated with hepatic steatosis.Results The rationality of the hepatic steatosis grouping was verified through liver biochemical assays and RNA-seq. Hepatic steatosis was characterized by increased lipid deposition and multiple metabolic abnormalities. Integration of proteome and acetylome revealed that differentially expressed proteins(DEPs) interacted with differentially acetylated proteins(DAPs) and were involved in maintaining the metabolic balance in the liver. Acetylation alterations mainly occurred in the progression from mild to severe hepatic steatosis, i.e., the enzymes in the fatty acid oxidation and bile acid synthesis pathways were significantly less acetylated in severe hepatic steatosis group than that in mild group(P < 0.05). Lipidomics detected a variety of sphingolipids(SPs) and glycerophospholipids(GPs) were negatively correlated with hepatic steatosis(r ≤-0.5, P < 0.05). Furthermore, the severity of hepatic steatosis was associated with a decrease in cholesterol and bile acid synthesis and an increase in exogenous cholesterol transport.Conclusions In addition to acquiring a global and thorough picture of hepatic steatosis in laying hens, we were able to reveal the role of acetylation in hepatic steatosis and depict the changes in hepatic cholesterol metabolism. The findings provides a wealth of information to facilitate a deeper understanding of the pathophysiology of fatty liver and contributes to the development of therapeutic strategies.
基金funded by grants from the National Natural Science Foundation of China(grant numbers:82002664,81872074,81772740,82173345 and 82373154)the Hanghai Jiading District Health Commission Scientific Research Project Youth Fund(grant num-ber:2020-QN-02)the Meng Chao Talent Training Plan-Youth Re-search Talent Training Program of Eastern Hepatobiliary Surgery Hos-pital and the Foundation for Distinguished Youths of Jiangsu Province(grant number:BK20200006).
文摘Background:Tumor-derived exosomes are involved in tumor progression and immune invasion and might func-tion as promising noninvasive approaches for clinical management.However,there are few reports on exosom-based markers for predicting the progression and adjuvant therapy response rate among patients with clear cell renal cell carcinoma(ccRCC).Methods:The signatures differentially expressed in exosomes from tumor and normal tissues from ccRCC pa-tients were correspondingly deregulated in ccRCC tissues.We adopted a two-step strategy,including Lasso and bootstrapping,to construct a novel risk stratification system termed the TDERS(Tumor-Derived Exosome-Related Risk Score).During the testing and validation phases,we leveraged multiple external datasets containing over 2000 RCC cases from eight cohorts and one inhouse cohort to evaluate the accuracy of the TDERS.In addition,enrichment analysis,immune infiltration signatures,mutation landscape and therapy sensitivity between the high and low TDERS groups were compared.Finally,the impact of TDERS on the tumor microenvironment(TME)was also analysed in our single-cell datasets.Results:TDERS consisted of 12 mRNAs deregulated in both exosomes and tissues from patients with ccRCC.TDERS achieved satisfactory performance in both prognosis and immune checkpoint inhibitor(ICI)response across all ccRCC cohorts and other pathological types,since the average area under the curve(AUC)to predict 5-year overall survival(OS)was larger than 0.8 across the four cohorts.Patients in the TDERS high group were resistant to ICIs,while mercaptopurine might function as a promising agent for those patients.Patients with a high TDERS were characterized by coagulation and hypoxia,which induced hampered tumor antigen presentation and relative resistance to ICIs.In addition,single cells from 12 advanced samples validated this phenomenon since the interaction between dendritic cells and macrophages was limited.Finally,PLOD2,which is highly expressed in fibro-and epi-tissue,could be a potential therapeutic target for ccRCC patients since inhibiting PLOD2 altered the malignant phenotype of ccRCC in vitro.Conclusion:As a novel,non-invasive,and repeatable monitoring tool,the TDERS could work as a robust risk stratification system for patients with ccRCC and precisely inform treatment decisions about ICI therapy.
文摘Nonalcoholic fatty liver disease(NAFLD)is a heterogeneous and complex disease that is imprecisely diagnosed by liver biopsy.NAFLD covers a spectrum that ranges from simple steatosis,nonalcoholic steatohepatitis(NASH)with varying degrees of fibrosis,to cirrhosis,which is a major risk factor for hepatocellular carcinoma.Lifestyle and eating habit changes during the last century have made NAFLD the most common liver disease linked to obesity,type 2 diabetes mellitus and dyslipidemia,with a global prevalence of 25%.NAFLD arises when the uptake of fatty acids(FA)and triglycerides(TG)from circulation and de novo lipogenesis saturate the rate of FAβ-oxidation and verylow density lipoprotein(VLDL)-TG export.Deranged lipid metabolism is also associated with NAFLD progression from steatosis to NASH,and therefore,alterations in liver and serum lipidomic signatures are good indicators of the disease’s development and progression.This review focuses on the importance of the classification of NAFLD patients into different subtypes,corresponding to the main alteration(s)in the major pathways that regulate FA homeostasis leading,in each case,to the initiation and progression of NASH.This concept also supports the targeted intervention as a key approach to maximize therapeutic efficacy and opens the door to the development of precise NASH treatments.
文摘Inflammatory bowel disease(IBD)is a complex,immune-mediated gastrointestinal disorder with ill-defined etiology,multifaceted diagnostic criteria,and unpredictable treatment response.Innovations in IBD diagnostics,including developments in genomic sequencing and molecular analytics,have generated tremendous interest in leveraging these large data platforms into clinically meaningful tools.Artificial intelligence,through machine learning facilitates the interpretation of large arrays of data,and may provide insight to improving IBD outcomes.While potential applications of machine learning models are vast,further research is needed to generate standardized models that can be adapted to target IBD populations.
基金supported by Natural Science Foundation of Shaanxi Province(S2021-JC-WT-006)the National Key Research and Development Program of China(2018YFD0100200)+1 种基金the China Postdoctoral Science Foundation(2018M633588)the China Agriculture Research System(CARS-02-77)。
文摘Maize kernel moisture content(KMC)at harvest greatly affects mechanical harvesting,transport and storage.KMC is correlated with kernel dehydration rate(KDR)before and after physiological maturity.KMC and KDR are complex traits governed by multiple quantitative trait loci(QTL).Their genetic architecture is incompletely understood.We used a multiomics integration approach with an association panel to identify genes influencing KMC and KDR.A genome-wide association study using time-series KMC data from 7 to 70 days after pollination and their transformed KDR data revealed respectively 98and 279 loci significantly associated with KMC and KDR.Time-series transcriptome and proteome datasets were generated to construct KMC correlation networks,from which respectively 3111 and 759 module genes and proteins were identified as highly associated with KMC.Integrating multiomics analysis,several promising candidate genes for KMC and KDR,including Zm00001d047799 and Zm00001d035920,were identified.Further mutant experiments showed that Zm00001d047799,a gene encoding heat shock 70 kDa protein 5,reduced KMC in the late stage of kernel development.Our study provides resources for the identification of candidate genes influencing maize KMC and KDR,shedding light on the genetic architecture of dynamic changes in maize KMC.
基金supported by the National Natural Science Foundation of China(81890994,31871343)National Key Research and Development Program of China(2017YFA0505503,2018YFB0704304,2018YFA0801402)+1 种基金the WBE Liver Fibrosis Foundation(CFHPC 2020021)the Beijing Dongcheng District outstanding talent funding project and the Beijing Undergraduate Training Programs for Innovation and Entrepreneurship(202010023046)。
文摘Genetic,epigenetic,and metabolic alterations are all hallmarks of cancer.However,the epigenome and metabolome are both highly complex and dynamic biological networks in vivo.The interplay between the epigenome and metabolome contributes to a biological system that is responsive to the tumor microenvironment and possesses a wealth of unknown biomarkers and targets of cancer therapy.From this perspective,we first review the state of high-throughput biological data acquisition(i.e.multiomics data)and analysis(i.e.computational tools)and then propose a conceptual in silico metabolic and epigenetic regulatory network(MER-Net)that is based on these current high-throughput methods.The conceptual MER-Net is aimed at linking metabolomic and epigenomic networks through observation of biological processes,omics data acquisition,analysis of network information,and integration with validated database knowledge.Thus,MER-Net could be used to reveal new potential biomarkers and therapeutic targets using deep learning models to integrate and analyze large multiomics networks.We propose that MER-Net can serve as a tool to guide integrated metabolomics and epigenomics research or can be modified to answer other complex biological and clinical questions using multiomics data.
基金the Youth Program of National Natural Science Foundation of China,No.82000526the National High Level Hospital Clinical Research Funding,No.2022-PUMCH-A-072the National College Students’Innovation and Entrepreneurship Training Program,No.2022zglc06083.
文摘There is great heterogeneity among inflammatory bowel disease(IBD)patients in terms of pathogenesis,clinical manifestation,response to treatment,and prognosis,which requires the individualized and precision management of patients.Many studies have focused on prediction biomarkers and models for assessing IBD disease type,activity,severity,and prognosis.During the era of biologics,how to predict the response and side effects of patients to different treatments and how to quickly recognize the loss of response have also become important topics.Multiomics is a promising area for investigating the complex network of IBD pathogenesis.Integrating numerous amounts of data requires the use of artificial intelligence.
