The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities...The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.展开更多
Lactic acid bacteria and the fermentation environment interact to form an intertwined system.Lactic acid bacteria are constantly evolving to adapt to different fermentation environments,causing changes in their physio...Lactic acid bacteria and the fermentation environment interact to form an intertwined system.Lactic acid bacteria are constantly evolving to adapt to different fermentation environments,causing changes in their physiological processes.To achieve a targeted improvement of their adaptability to various environments,a detail analysis of their evolutionary physiological processes is required.While several studies have been carried out in the past by using single-omics techniques to investigate their response to environmental stress,most researchers are now using a multi-omics approach to explore more detail in the biological regulatory networks and molecular mechanisms of lactic acid bacteria in response to environmental stress,thereby overcoming the limitations of single-omics analysis.In this review,we describe the various single-omics approaches that have been used to study environmental stress in lactic acid bacteria,present the advantages of various multi-omics combined analysis approaches,and discuss the potential and practicality of applying emerging single-cell transcriptomics and single-cell metabolomics techniques to the molecular mechanism study of microbes response to environmental stress.Multi-omics approaches enable the accurate identification of complex microbial physiological processes in different environments,allow people to comprehensively reveal the molecular mechanisms of microbes response to stress from different perspectives.Single-cell omics techniques,analyze the targeted regulation of microbial functions in a multi-dimensional space,provides a new perspective on understanding microbes responses environment stress.展开更多
Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single ...Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single target”presents significant challenges due to its holistic approach.Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks,overcoming the limitations of reductionist research models and showing considerable value in TCM research.Recent integration of network target computational and experimental methods with artificial intelligence(AI)and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology.The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles.This review,centered on network targets,examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships,alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae,syndromes,and toxicity.Looking forward,network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics,potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.展开更多
Objective:Triple-negative breast cancer(TNBC)is a highly aggressive subtype that lacks targeted therapies,leading to a poorer prognosis.However,some patients achieve long-term recurrence-free survival(RFS),offering va...Objective:Triple-negative breast cancer(TNBC)is a highly aggressive subtype that lacks targeted therapies,leading to a poorer prognosis.However,some patients achieve long-term recurrence-free survival(RFS),offering valuable insights into tumor biology and potential treatment strategies.Methods:We conducted a comprehensive multi-omics analysis of 132 patients with American Joint Committee on Cancer(AJCC)stage III TNBC,comprising 36 long-term survivors(RFS≥8 years),62 moderate-term survivors(RFS:3-8 years),and 34 short-term survivors(RFS<3 years).Analyses investigated clinicopathological factors,whole-exome sequencing,germline mutations,copy number alterations(CNAs),RNA sequences,and metabolomic profiles.Results:Long-term survivors exhibited fewer metastatic regional lymph nodes,along with tumors showing reduced stromal fibrosis and lower Ki67 index.Molecularly,these tumors exhibited multiple alterations in genes related to homologous recombination repair,with higher frequencies of germline mutations and somatic CNAs.Additionally,tumors from long-term survivors demonstrated significant downregulation of the RTK-RAS signaling pathway.Metabolomic profiling revealed decreased levels of lipids and carbohydrate,particularly those involved in glycerophospholipid,fructose,and mannose metabolism,in long-term survival group.Multivariate Cox analysis identified fibrosis[hazard ratio(HR):12.70,95%confidence interval(95%CI):2.19-73.54,P=0.005]and RAC1copy number loss/deletion(HR:0.22,95%CI:0.06-0.83,P=0.026)as independent predictors of RFS.Higher fructose/mannose metabolism was associated with worse overall survival(HR:1.30,95%CI:1.01-1.68,P=0.045).Our findings emphasize the association between biological determinants and prolonged survival in patients with TNBC.Conclusions:Our study systematically identified the key molecular and metabolic features associated with prolonged survival in AJCC stage III TNBC,suggesting potential therapeutic targets to improve patient outcomes.展开更多
Diabetes mellitus(DM)comprises distinct subtypes-including type 1 DM,type 2 DM,and gestational DM-all characterized by chronic hyperglycemia and sub-stantial morbidity.Conventional diagnostic and therapeutic strategie...Diabetes mellitus(DM)comprises distinct subtypes-including type 1 DM,type 2 DM,and gestational DM-all characterized by chronic hyperglycemia and sub-stantial morbidity.Conventional diagnostic and therapeutic strategies often fall short in addressing the complex,multifactorial nature of DM.This review ex-plores how multi-omics integration enhances our mechanistic understanding of DM and informs emerging personalized therapeutic approaches.We consolidated genomic,transcriptomic,proteomic,metabolomic,and microbiomic data from major databases and peer-reviewed publications(2015-2025),with an emphasis on clinical relevance.Multi-omics investigations have identified convergent mole-cular networks underlyingβ-cell dysfunction,insulin resistance,and diabetic complications.The combination of metabolomics and microbiomics highlights critical interactions between metabolic intermediates and gut dysbiosis.Novel biomarkers facilitate early detection of DM and its complications,while single-cell multi-omics and machine learning further refine risk stratification.By dissecting DM heterogeneity more precisely,multi-omics integration enables targeted in-terventions and preventive strategies.Future efforts should focus on data har-monization,ethical considerations,and real-world validation to fully leverage multi-omics in addressing the global DM burden.展开更多
BACKGROUND Gastrointestinal(GI)malignancies,including gastric and colorectal cancers,remain one of the primary contributors to cancer-related illness and death globally.Despite the availability of conventional diagnos...BACKGROUND Gastrointestinal(GI)malignancies,including gastric and colorectal cancers,remain one of the primary contributors to cancer-related illness and death globally.Despite the availability of conventional diagnostic tools,early detection and personalized treatment remain significant clinical challenges.Integrated multi-omics methods encompassing genomic,transcriptomic,proteomic,metabolomic,and microbiome profiles have emerged as powerful tools for advancing precision oncology,improving diagnostic accuracy,and informing therapeutic strategies.AIM To investigate the application of multi-omics approaches in the early detection,risk stratification,treatment optimization,and biomarker discovery of GI malignancies.METHODS The systematic review process was conducted in accordance with the PRISMA 2020 guidelines.Five databases,PubMed,ScienceDirect,Scopus,ProQuest,and Web of Science,were searched for studies published in English from 2015 onwards.Eligible studies involved human subjects and focused on multi-omics integration in GI cancers,including biomarker identification,tumor microenvironment analysis,tumor heterogeneity,organoid modeling,and artificial intelligence(AI)-driven analytics.Data extraction included study characteristics,omics modalities,clinical applications,and evaluation of study quality conducted with the Cochrane risk of bias 2.0 instrument.RESULTS A total of 17196 initially identified articles,20 met the inclusion criteria.The findings highlight the superiority of multi-omics platforms over traditional biomarkers(e.g.,carcinoembryonic antigen and carbohydrate antigen 19-9 in detecting early stage GI cancers.Key applications include the identification of circulating tumor DNA,extracellular vesicles,lipidomic and proteomic signatures,and the adoption of AI algorithms to enhance diagnostic precision.Multi-omics analysis has also revealed the mechanisms of immune modulation,tumor microenvironment regulation,metastatic behavior,and drug resistance.Organoid models and microbiota profiling have contributed to personalized therapeutic strategies and immunotherapy optimization.CONCLUSION Multi-omics approaches offer significant advancements in the early diagnosis,prognostic evaluation,and personalized treatment of GI malignancies.Their integration with AI analytics,organoid biobanking,and microbiota modulation provides a pathway for precision oncology research.展开更多
BACKGROUND Autoimmune liver diseases,including primary biliary cholangitis(PBC),autoi-mmune hepatitis(AIH),and their overlap syndrome(OS),involve immune-mediated liver injury,with OS occurring in 1.2%-25%of PBC patien...BACKGROUND Autoimmune liver diseases,including primary biliary cholangitis(PBC),autoi-mmune hepatitis(AIH),and their overlap syndrome(OS),involve immune-mediated liver injury,with OS occurring in 1.2%-25%of PBC patients.OS carries a higher risk of cirrhosis,hepatocellular carcinoma,and reduced survival.While its pathogenesis remains unclear,gut microbiota dysbiosis and serum metabolite alterations may play key roles.This study uses 16S rRNA sequencing and liquid chromatography-mass spec-trometry(LC-MS)metabolomics to compare gut microbiota and serum metabolites among PBC,AIH,and OS patients,and explores their associations with liver function.AIM To differentiate OS from PBC and AIH based on gut microbiota,serum metabolites,and liver function.METHODS Gut microbiota profiles were analyzed using 16S rRNA sequencing,while untargeted serum metabolomics was conducted via LC-MS.Comparative analyses were performed to identify differences in microbial composition and serum metabolite levels among PBC,AIH,and OS groups.Correlation analyses and network visualization tech-niques were applied to elucidate the interactions among liver function parameters,gut microbiota,and serum metabolites in OS patients.RESULTS Compared to patients with PBC or AIH,OS patients demonstrated significantly reduced microbial diversity and richness.Notable taxonomic shifts included decreased abundances of Firmicutes,Bacteroidetes,and Actinobacteria,alongside increased levels of Proteobacteria and Verrucomicrobia.Distinct serum metabolites,such as pentadecanoic acid and aminoimidazole carboxamide ribonucleotide,were identified in OS patients.Correlation analysis revealed that aspartate aminotransferase(AST)levels were negatively associated with the bacterial genus Fusicatenibacter and the metabolite L-Tyrosine.A microbial-metabolite network diagram further confirmed a strong association between Fusicatenibacter and L-Tyrosine in OS patients.CONCLUSION OS patients show decreased gut microbiota diversity and unique serum metabolites.Multi-omics linked AST,Fusicatenibacter,and L-Tyrosine,revealing OS mechanisms and diagnostic potential.展开更多
Cancer rates are increasing globally,making it more urgent than ever to enhance research and treatment strategies.This study aims to investigate how innovative technology and integrated multi-omics techniques could he...Cancer rates are increasing globally,making it more urgent than ever to enhance research and treatment strategies.This study aims to investigate how innovative technology and integrated multi-omics techniques could help improve cancer diagnosis,knowledge,and therapy.A complete literature search was undertaken using PubMed,Elsevier,Google Scholar,ScienceDirect,Embase,and NCBI.This review examined the articles published from 2010 to 2025.Relevant articles were found using keywords and selected using inclusion criteria New sequencing methods,like next-generation sequencing and single-cell analysis,have transformed our ability to study tumor complexity and genetic mutations,paving the way for more precise,personalized treatments.At the same time,imaging technologies such as Positron Emission Tomography(PET)and Magnetic Resonance Imaging(MRI)have made detecting tumors early and tracking treatment progress easier,all while improving patient comfort.Artificial intelligence(AI)and machine learning(ML)are having a significant impact by helping to analyze large volumes of data more efficiently and enhancing diagnostic accuracy.Meanwhile,Clustered Regulatory Interspaced Short Palindromic Repeats(CRISPR/Cas9)gene editing is emerging as a promising tool for directly targeting genes related to cancer,providing new possibilities for treatment.By integrating genomic,transcriptomic,proteomic,and metabolomic data,multi-omics approaches provide researchers with a more comprehensive understanding of the molecular mechanisms driving cancer,thereby facilitating the discovery of novel biomarkers and therapeutic targets.Despite these advancements,additional challenges persist,such as data integration,elevated costs,standardisation concerns,and the intricacies of translating findings into clinical practice,which might prevent wider implementation.Research needs to concentrate on improving these developments and encouraging multidisciplinary cooperation going forward to maximize their possibilities.Personalized cancer therapies will become more successful with ongoing developments,therefore enhancing patient outcomes and quality of life.展开更多
Objective To map the research hotspots,developmental trends,and existing challenges in the integration of artificial intelligence(AI)with multi-omics in traditional Chinese medicine(TCM)through comprehensive bibliomet...Objective To map the research hotspots,developmental trends,and existing challenges in the integration of artificial intelligence(AI)with multi-omics in traditional Chinese medicine(TCM)through comprehensive bibliometric analysis.Methods China National Knowledge Infrastructure(CNKI),Wanfang Data,China Science and Technology Journal Database(VIP),Chaoxing Journal Database,PubMed,and Web of Science were searched to collect literature on the theme of AI in TCM multi-omics research from the inception of each database to December 31,2024.Eligible records were required to simultaneously address AI,TCM,and multi-omics.Quantitative and visual analyses of publication growth,core authorship networks,institutional collaboration patterns,and keyword co-occurrence were performed using Microsoft Excel 2021,NoteExpress v4.0.0,and Cite-Space 6.3.R1.AI application modes in TCM multi-omics research were also categorized and summarized.Results A total of 1106 articles were enrolled(932 Chinese and 174 English).Publication output has increased continuously since 2010 and accelerated after 2016.Region-specific collaboration clusters were identified,dominated by Beijing University of Chinese Medicine,China Academy of Chinese Medical Sciences,Shanghai University of Traditional Chinese Medicine,and Nanjing University of Chinese Medicine.Keyword co-occurrence analysis revealed that current AI applications predominantly centered on metabolomics and algorithms such as cluster analysis and data mining.Research foci mainly ranked as follows:single herbs,herbal formulae,and disease-syndrome differentiation.Conclusion Machine learning methods are the predominant integrative modality of AI in the realm of TCM multi-omics research at present,utilized for processing omics data and uncovering latent patterns therein.The domain of TCM,in addition to investigating omics information procured through high-throughput technologies,also integrates data on traditional Chinese medicinal substances and clinical phenotypes,progressing towards joint analysis of multi-omics,high-dimensionality of data,and multi-modality of information.Deep learning approaches represent an emerging trend in the field.展开更多
Background Backfat thickness(BFT)is a vital economic trait in pigs,reflecting subcutaneous fat levels that affect meat quality and production efficiency.As a complex trait shaped by multiple genetic factors,BFT has be...Background Backfat thickness(BFT)is a vital economic trait in pigs,reflecting subcutaneous fat levels that affect meat quality and production efficiency.As a complex trait shaped by multiple genetic factors,BFT has been studied using genome-wide association studies(GWAS)and linkage analyses to locate fat-related quantitative trait loci(QTLs),but pinpointing causal variants and genes is hindered by linkage disequilibrium and limited regulatory data.This study aimed to dissect the QTLs affecting BFT on Sus scrofa chromosome 1(SSC1),elucidating regulatory variants,effector genes,and the cell types involved.Results Using whole-genome genotyping data from 3,578 pigs and phenotypic data for five BFT traits,we identified a 630.6 kb QTL on SSC1 significantly associated with these traits via GWAS and fine-mapping,pinpointing 34 candidate causal variants.Using deep convolutional neural networks to predict regulatory activity from sequence data integrated with detailed pig epigenetic profiles,we identified five SNPs potentially affecting enhancer activity in specific tissues.Notably,rs342950505(SSC1:161,123,588)influences weak enhancer activity across multiple tissues,including the brain.High-throughput chromosome conformation capture(Hi-C)analysis identified that rs342950505 interacts with eight genes.Chromatin state annotations confirmed enhancer activity at this QTL in the cerebellum.Leveraging these insights,single-cell ATAC-seq revealed a chromatin accessibility peak encompassing rs342950505 that regulates PMAIP1 expression in inhibitory neurons via enhancer-mediated mechanisms,with an adjacent peak modulating CCBE1 expression in neuroblasts and granule cells.Transcriptome-wide association studies(TWAS)confirmed PMAIP1's role in the hypothalamus,and Mendelian randomization(MR)validated PMAIP1 and CCBE1 as key brain expression quantitative trait locus(eQTL)effectors.We propose that the variant rs342950505,located within a regulatory peak,modulates PMAIP1 expression in inhibitory neurons,potentially influencing energy homeostasis via hypothalamic regulation.Similarly,CCBE1 may contribute to this process.Conclusions Our results,through systematic dissection of pleiotropic BFT-associated loci,provide a framework to elucidate regulatory mechanisms of complex traits,offering insights into polygenic control through lipid metabolism and neural signaling pathways.展开更多
Osmanthus fragrans Lour.is a well-known aromatic plant widely used as a food ingredient due to its unique floral fragrance and bioactive compounds.To fully utilize O.fragrans resources,we established an O.fragrans mul...Osmanthus fragrans Lour.is a well-known aromatic plant widely used as a food ingredient due to its unique floral fragrance and bioactive compounds.To fully utilize O.fragrans resources,we established an O.fragrans multi-omics database called the O.fragrans Information Resource(OfIR:http://yanglab.hzau.edu.cn/OfIR/home/).OfIR is a convenient and comprehensive multi-omics database that efficiently integrates phenotype and genetic variation from 127 O.fragrans cultivars,and provides many easy-to-use analysis tools,including primer design,sequence extraction,multi-sequence alignment,GO and KEGG enrichment analysis,variation annotation,and electronic PCR.Two case studies were used to demonstrate its power to mine candidate genetic variation sites or genes associated with specific traits or regulatory networks.In summary,the multi-omics database OfIR provides a convenient and user-friendly platform for researchers in mining functional genes and contributes to the genetic breeding of O.fragrans.展开更多
Autoimmune liver disease overlap syndrome(OS)is a rare and clinically significant condition that has received limited attention in microbiome research.In their recent study,Wang et al combined 16S rRNA sequencing with...Autoimmune liver disease overlap syndrome(OS)is a rare and clinically significant condition that has received limited attention in microbiome research.In their recent study,Wang et al combined 16S rRNA sequencing with untargeted metabolomics to characterize the gut-liver axis in OS,identifying shared features of dysbiosis in autoimmune hepatitis(AIH)and primary biliary cholangitis(PBC),and unique signatures,including enrichment of Klebsiella and Escherichia and depletion of aromatic amino acids.In this letter,we critically appraise these findings,emphasizing that OS should be considered a distinct immunometabolic phenotype rather than a simple mixture of AIH and PBC.We discuss the potential mechanistic relevance of the Fusicatenibacter-tyrosine relationship,highlight the clinical implications of integrating microbiota-metabolite analyses,and outline the limitations that future studies must address.展开更多
Background As an indigenous livestock species on the Tibetan Plateau,Tibetan sheep exhibit remarkable adaptability to low temperatures and nutrient-scarce environments.During the cold season,Tibetan sheep are typicall...Background As an indigenous livestock species on the Tibetan Plateau,Tibetan sheep exhibit remarkable adaptability to low temperatures and nutrient-scarce environments.During the cold season,Tibetan sheep are typically managed under two feeding regimes:barn feeding(BF)and traditional grazing(TG).However,the molecular mechanisms underlying their adaptation to these distinct management strategies remain unclear.This study aimed to investigate the adaptive strategies of rumen function in Tibetan sheep to cold-season feeding regimes by integrating analyses of rumen morphology,microbiome,metabolome,and transcriptome.Twelve healthy Tibetan sheep with similar body weights were assigned into two groups(BF vs.TG).At the end of the experiment,rumen tissues were subjected to histological observation.Multi-omics techniques were employed to evaluate the effects of cold-season feeding regimes on rumen function in Tibetan sheep.Results The ruminal papilla height,width,and muscular thickness were significantly higher in BF group.The relative abundances of Actinobacteria and Succiniclasticum were significantly elevated in the rumen of BF group,whereas Rikenellaceae,Gracilibacteria,and Lachnospiraceae showed higher abundances in the TG group.Metabolomic analysis identified 19 differential metabolites between the two groups,including upregulated compounds in BF group(fumaric acid,maltose,L-phenylalanine,and L-alanine)and TG group(e.g.,phenylacetic acid,salicyluric acid and ferulic acid).These metabolites were predominantly enriched in phenylalanine metabolism,alanine,aspartate and glutamate metabolism,and phenylalanine,tyrosine and tryptophan biosynthesis pathways.Additionally,210 differentially expressed genes(DEGs)were identified in rumen epithelium:100 upregulated DEGs in the BF group were enriched in nutrient metabolism-related pathways(e.g.,fatty acid degradation and PPAR signaling pathway),while 110 upregulated DEGs in the TG group were associated with immune-related pathways(e.g.,p53 signaling pathway and glutathione metabolism).Conclusions Among these,we observed distinct rumen functional responses to different cold-season feeding regimes in Tibetan sheep and revealed energy allocation strategies mediated by host-microbe interactions.In the BF group,Tibetan sheep adopted a"metabolic efficiency-priority"strategy,driving rumen microbiota to maximize energy capture from high-nutrient diets to support host growth.In contrast,the TG group exhibited an"environmental adaptation-priority"strategy,where rumen microbiota prioritized cellulose degradation and anti-inflammatory functions,reallocating energy toward homeostasis maintenance at the expense of rumen development and growth performance.展开更多
In this editorial,we discuss the findings reported by Wang et al in the latest issue of the World Journal of Gastrointestinal Oncology.Various research methodologies,including microbiome analysis,assert that the Tzu-C...In this editorial,we discuss the findings reported by Wang et al in the latest issue of the World Journal of Gastrointestinal Oncology.Various research methodologies,including microbiome analysis,assert that the Tzu-Chi Cancer-Antagonizing and Life-Protecting II Decoction of Chinese herbal compounds mitigates inflammatory responses by inhibiting the NF-κB signaling pathway.This action helps maintain the dynamic equilibrium of the intestinal microecology and lessens chemotherapy-induced gastrointestinal damage.The efficacy of these compounds is intimately linked to the composition of intestinal microbes.These compounds regulate intestinal microecology by virtue of their specific compatibility and effectiveness,thereby enhancing the overall therapeutic outcomes of cancer chemotherapy.Nonetheless,the exact mechanisms underlying these effects warrant further investigation.Multi-omics technologies offer a systematic approach to elucidate the mechanisms and effectiveness of Chinese herbal compounds in vivo.This manuscript reviews the application of multi-omics technologies to Chinese herbal compounds and explores their potential role in modulating the gastrointestinal microenvironment following cancer chemotherapy,thus providing a theoretical foundation for their continued use in adjunct cancer treatment.展开更多
Cold tumors,defined by insufficient immune cell infiltration and a highly immunosuppressive tumor microenvironment(TME),exhibit limited responsiveness to conventional immunotherapies.This reviewsystematically summariz...Cold tumors,defined by insufficient immune cell infiltration and a highly immunosuppressive tumor microenvironment(TME),exhibit limited responsiveness to conventional immunotherapies.This reviewsystematically summarizes the mechanisms of immune evasion and the therapeutic strategies for cold tumors as revealed by multiomics technologies.By integrating genomic,transcriptomic,proteomic,metabolomic,and spatialmulti-omics data,the review elucidates key immune evasionmechanisms,including activation of the WNT/β-catenin pathway,transforming growth factor-β(TGF-β)–mediated immunosuppression,metabolic reprogramming(e.g.,lactate accumulation),and aberrant expression of immune checkpoint molecules.Furthermore,this review proposes multi-dimensional therapeutic strategies,such as targeting immunosuppressive pathways(e.g.,programmed death-1(PD-1)/programmed death-ligand 1(PD-L1)inhibitors combined with TGF-βblockade),reshaping the TME through chemokine-based therapies,oncolytic viruses,and vascular normalization,and metabolic interventions(e.g.,inhibition of lactate dehydrogenase A(LDHA)or glutaminase(GLS)).In addition,personalized neoantigen vaccines and engineered cell therapies(e.g.,T cell receptor-engineered T(TCR-T)and natural killer(NK)cells)show promising potential.Emerging evidence also highlights the role of epigenetic regulation(e.g.,histone deacetylase(HDAC)inhibitors)and N6-Methyladenosine(m6A)RNA modifications in reversing immune evasion.Despite the promising insights offered by multi-omics integration in guiding precision immunotherapy,challenges remain in clinical translation,including data heterogeneity,target-specific toxicity,and limitations in preclinical models.Future efforts should focus on coupling dynamic multi-omics technologies with intelligent therapeutic design to convert cold tumors into immunologically active(“hot”)microenvironments,ultimately facilitating breakthroughs in personalized immunotherapy.展开更多
Increasing evidence implicates disruptions in testicular fatty acid metabolism as a contributing factor in nonobstructive azoospermia(NOA),a severe form of male infertility.However,the precise mechanisms linking fatty...Increasing evidence implicates disruptions in testicular fatty acid metabolism as a contributing factor in nonobstructive azoospermia(NOA),a severe form of male infertility.However,the precise mechanisms linking fatty acid metabolism to NOA pathogenesis have not yet been fully elucidated.Multi-omics analyses,including microarray analysis,single-cell RNA sequencing(scRNA-seq),and metabolomics,were utilized to investigate disruptions in fatty acid metabolism associated with NOA using data from public databases.Results identified ACSL6,ACSBG2,and OLAH as key genes linked to fatty acid metabolism dysregulation,suggesting their potential causative roles in NOA.A marked reduction in omega-3 polyunsaturated fatty acids,especially docosahexaenoic acid(DHA),was observed,potentially contributing to the pathological process of NOA.Sertoli cells in NOA patients exhibited apparent fatty acid metabolic dysfunction,with PPARG identified as a key transcription factor(TF)regulating this process.Functional analyses demonstrated that PPARG is crucial for maintaining blood-testis barrier(BTB)integrity and promoting spermatogenesis via regulation of fatty acid metabolism.These findings reveal the pivotal role of fatty acid metabolism in NOA and identify PPARG as a potential therapeutic target.展开更多
Objective Pneumoconiosis,a lung disease caused by irreversible fibrosis,represents a significant public health burden.This study investigates the causal relationships between gut microbiota,gene methylation,gene expre...Objective Pneumoconiosis,a lung disease caused by irreversible fibrosis,represents a significant public health burden.This study investigates the causal relationships between gut microbiota,gene methylation,gene expression,protein levels,and pneumoconiosis using a multi-omics approach and Mendelian randomization(MR).Methods We analyzed gut microbiota data from MiBioGen and Esteban et al.to assess their potential causal effects on pneumoconiosis subtypes(asbestosis,silicosis,and inorganic pneumoconiosis)using conventional and summary-data-based MR(SMR).Gene methylation and expression data from Genotype-Tissue Expression and eQTLGen,along with protein level data from deCODE and UK Biobank Pharma Proteomics Project,were examined in relation to pneumoconiosis data from FinnGen.To validate our findings,we assessed self-measured gut flora from a pneumoconiosis cohort and performed fine mapping,drug prediction,molecular docking,and Phenome-Wide Association Studies to explore relevant phenotypes of key genes.Results Three core gut microorganisms were identified:Romboutsia(OR=0.249)as a protective factor against silicosis,Pasteurellaceae(OR=3.207)and Haemophilus parainfluenzae(OR=2.343)as risk factors for inorganic pneumoconiosis.Additionally,mapping and quantitative trait loci analyses revealed that the genes VIM,STX8,and MIF were significantly associated with pneumoconiosis risk.Conclusions This multi-omics study highlights the associations between gut microbiota and key genes(VIM,STX8,MIF)with pneumoconiosis,offering insights into potential therapeutic targets and personalized treatment strategies.展开更多
Aging and regeneration represent complex biological phenomena that have long captivated the scientific community.To fully comprehend these processes,it is essential to investigate molecular dynamics through a lens tha...Aging and regeneration represent complex biological phenomena that have long captivated the scientific community.To fully comprehend these processes,it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions.Conventional omics methodologies,such as genomics and transcriptomics,have been instrumental in identifying critical molecular facets of aging and regeneration.However,these methods are somewhat limited,constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations.The advent of emerging spatiotemporal multi-omics approaches,encompassing transcriptomics,proteomics,metabolomics,and epigenomics,furnishes comprehensive insights into these intricate molecular dynamics.These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells,tissues,and organs,thereby offering an in-depth understanding of the fundamental mechanisms at play.This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research.It underscores how these methodologies augment our comprehension of molecular dynamics,cellular interactions,and signaling pathways.Initially,the review delineates the foundational principles underpinning these methods,followed by an evaluation of their recent applications within the field.The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field.Indubitably,spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration,thus charting a course toward potential therapeutic innovations.展开更多
Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information,with its application to neuroscience termed neuroinformatics.Da...Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information,with its application to neuroscience termed neuroinformatics.Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms,which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases.Importantly,integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile.In this review,we first summarize data mining studies utilizing datasets from the individual type of omics analysis,including epigenetics/epigenomics,transcriptomics,proteomics,metabolomics,lipidomics,and spatial omics,pertaining to Alzheimer's disease,Parkinson's disease,and multiple sclerosis.We then discuss multi-omics integration approaches,including independent biological integration and unsupervised integration methods,for more intuitive and informative interpretation of the biological data obtained across different omics layers.We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks.Finally,we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery,therapeutic development,and elucidation of disease mechanisms.We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.展开更多
文摘The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.
基金supported by the National Natural Science Foundation of China(32160578)the Ningxia Hui Autonomous Region Key Research and Develoment Program(2023BCF01027).
文摘Lactic acid bacteria and the fermentation environment interact to form an intertwined system.Lactic acid bacteria are constantly evolving to adapt to different fermentation environments,causing changes in their physiological processes.To achieve a targeted improvement of their adaptability to various environments,a detail analysis of their evolutionary physiological processes is required.While several studies have been carried out in the past by using single-omics techniques to investigate their response to environmental stress,most researchers are now using a multi-omics approach to explore more detail in the biological regulatory networks and molecular mechanisms of lactic acid bacteria in response to environmental stress,thereby overcoming the limitations of single-omics analysis.In this review,we describe the various single-omics approaches that have been used to study environmental stress in lactic acid bacteria,present the advantages of various multi-omics combined analysis approaches,and discuss the potential and practicality of applying emerging single-cell transcriptomics and single-cell metabolomics techniques to the molecular mechanism study of microbes response to environmental stress.Multi-omics approaches enable the accurate identification of complex microbial physiological processes in different environments,allow people to comprehensively reveal the molecular mechanisms of microbes response to stress from different perspectives.Single-cell omics techniques,analyze the targeted regulation of microbial functions in a multi-dimensional space,provides a new perspective on understanding microbes responses environment stress.
文摘Traditional Chinese medicine(TCM)demonstrates distinctive advantages in disease prevention and treatment.However,analyzing its biological mechanisms through the modern medical research paradigm of“single drug,single target”presents significant challenges due to its holistic approach.Network pharmacology and its core theory of network targets connect drugs and diseases from a holistic and systematic perspective based on biological networks,overcoming the limitations of reductionist research models and showing considerable value in TCM research.Recent integration of network target computational and experimental methods with artificial intelligence(AI)and multi-modal multi-omics technologies has substantially enhanced network pharmacology methodology.The advancement in computational and experimental techniques provides complementary support for network target theory in decoding TCM principles.This review,centered on network targets,examines the progress of network target methods combined with AI in predicting disease molecular mechanisms and drug-target relationships,alongside the application of multi-modal multi-omics technologies in analyzing TCM formulae,syndromes,and toxicity.Looking forward,network target theory is expected to incorporate emerging technologies while developing novel approaches aligned with its unique characteristics,potentially leading to significant breakthroughs in TCM research and advancing scientific understanding and innovation in TCM.
基金supported by grants from the Medical Engineering Jiont Fund of the Fudan University(No.IDH2310117)。
文摘Objective:Triple-negative breast cancer(TNBC)is a highly aggressive subtype that lacks targeted therapies,leading to a poorer prognosis.However,some patients achieve long-term recurrence-free survival(RFS),offering valuable insights into tumor biology and potential treatment strategies.Methods:We conducted a comprehensive multi-omics analysis of 132 patients with American Joint Committee on Cancer(AJCC)stage III TNBC,comprising 36 long-term survivors(RFS≥8 years),62 moderate-term survivors(RFS:3-8 years),and 34 short-term survivors(RFS<3 years).Analyses investigated clinicopathological factors,whole-exome sequencing,germline mutations,copy number alterations(CNAs),RNA sequences,and metabolomic profiles.Results:Long-term survivors exhibited fewer metastatic regional lymph nodes,along with tumors showing reduced stromal fibrosis and lower Ki67 index.Molecularly,these tumors exhibited multiple alterations in genes related to homologous recombination repair,with higher frequencies of germline mutations and somatic CNAs.Additionally,tumors from long-term survivors demonstrated significant downregulation of the RTK-RAS signaling pathway.Metabolomic profiling revealed decreased levels of lipids and carbohydrate,particularly those involved in glycerophospholipid,fructose,and mannose metabolism,in long-term survival group.Multivariate Cox analysis identified fibrosis[hazard ratio(HR):12.70,95%confidence interval(95%CI):2.19-73.54,P=0.005]and RAC1copy number loss/deletion(HR:0.22,95%CI:0.06-0.83,P=0.026)as independent predictors of RFS.Higher fructose/mannose metabolism was associated with worse overall survival(HR:1.30,95%CI:1.01-1.68,P=0.045).Our findings emphasize the association between biological determinants and prolonged survival in patients with TNBC.Conclusions:Our study systematically identified the key molecular and metabolic features associated with prolonged survival in AJCC stage III TNBC,suggesting potential therapeutic targets to improve patient outcomes.
文摘Diabetes mellitus(DM)comprises distinct subtypes-including type 1 DM,type 2 DM,and gestational DM-all characterized by chronic hyperglycemia and sub-stantial morbidity.Conventional diagnostic and therapeutic strategies often fall short in addressing the complex,multifactorial nature of DM.This review ex-plores how multi-omics integration enhances our mechanistic understanding of DM and informs emerging personalized therapeutic approaches.We consolidated genomic,transcriptomic,proteomic,metabolomic,and microbiomic data from major databases and peer-reviewed publications(2015-2025),with an emphasis on clinical relevance.Multi-omics investigations have identified convergent mole-cular networks underlyingβ-cell dysfunction,insulin resistance,and diabetic complications.The combination of metabolomics and microbiomics highlights critical interactions between metabolic intermediates and gut dysbiosis.Novel biomarkers facilitate early detection of DM and its complications,while single-cell multi-omics and machine learning further refine risk stratification.By dissecting DM heterogeneity more precisely,multi-omics integration enables targeted in-terventions and preventive strategies.Future efforts should focus on data har-monization,ethical considerations,and real-world validation to fully leverage multi-omics in addressing the global DM burden.
文摘BACKGROUND Gastrointestinal(GI)malignancies,including gastric and colorectal cancers,remain one of the primary contributors to cancer-related illness and death globally.Despite the availability of conventional diagnostic tools,early detection and personalized treatment remain significant clinical challenges.Integrated multi-omics methods encompassing genomic,transcriptomic,proteomic,metabolomic,and microbiome profiles have emerged as powerful tools for advancing precision oncology,improving diagnostic accuracy,and informing therapeutic strategies.AIM To investigate the application of multi-omics approaches in the early detection,risk stratification,treatment optimization,and biomarker discovery of GI malignancies.METHODS The systematic review process was conducted in accordance with the PRISMA 2020 guidelines.Five databases,PubMed,ScienceDirect,Scopus,ProQuest,and Web of Science,were searched for studies published in English from 2015 onwards.Eligible studies involved human subjects and focused on multi-omics integration in GI cancers,including biomarker identification,tumor microenvironment analysis,tumor heterogeneity,organoid modeling,and artificial intelligence(AI)-driven analytics.Data extraction included study characteristics,omics modalities,clinical applications,and evaluation of study quality conducted with the Cochrane risk of bias 2.0 instrument.RESULTS A total of 17196 initially identified articles,20 met the inclusion criteria.The findings highlight the superiority of multi-omics platforms over traditional biomarkers(e.g.,carcinoembryonic antigen and carbohydrate antigen 19-9 in detecting early stage GI cancers.Key applications include the identification of circulating tumor DNA,extracellular vesicles,lipidomic and proteomic signatures,and the adoption of AI algorithms to enhance diagnostic precision.Multi-omics analysis has also revealed the mechanisms of immune modulation,tumor microenvironment regulation,metastatic behavior,and drug resistance.Organoid models and microbiota profiling have contributed to personalized therapeutic strategies and immunotherapy optimization.CONCLUSION Multi-omics approaches offer significant advancements in the early diagnosis,prognostic evaluation,and personalized treatment of GI malignancies.Their integration with AI analytics,organoid biobanking,and microbiota modulation provides a pathway for precision oncology research.
基金Supported by WBE Liver Foundation,No.WBE20220182022 Young and Middle-aged Talents Incubation Project(Youth Innovation)of Beijing Youan Hospital,Capital Medical University,No.BJYAYY-YN-2022-092023 Young and Middle-aged Talents Incubation Project(Youth Innovation)of Beijing Youan Hospital,Capital Medical University,No.BJYAYYYN2023-14.
文摘BACKGROUND Autoimmune liver diseases,including primary biliary cholangitis(PBC),autoi-mmune hepatitis(AIH),and their overlap syndrome(OS),involve immune-mediated liver injury,with OS occurring in 1.2%-25%of PBC patients.OS carries a higher risk of cirrhosis,hepatocellular carcinoma,and reduced survival.While its pathogenesis remains unclear,gut microbiota dysbiosis and serum metabolite alterations may play key roles.This study uses 16S rRNA sequencing and liquid chromatography-mass spec-trometry(LC-MS)metabolomics to compare gut microbiota and serum metabolites among PBC,AIH,and OS patients,and explores their associations with liver function.AIM To differentiate OS from PBC and AIH based on gut microbiota,serum metabolites,and liver function.METHODS Gut microbiota profiles were analyzed using 16S rRNA sequencing,while untargeted serum metabolomics was conducted via LC-MS.Comparative analyses were performed to identify differences in microbial composition and serum metabolite levels among PBC,AIH,and OS groups.Correlation analyses and network visualization tech-niques were applied to elucidate the interactions among liver function parameters,gut microbiota,and serum metabolites in OS patients.RESULTS Compared to patients with PBC or AIH,OS patients demonstrated significantly reduced microbial diversity and richness.Notable taxonomic shifts included decreased abundances of Firmicutes,Bacteroidetes,and Actinobacteria,alongside increased levels of Proteobacteria and Verrucomicrobia.Distinct serum metabolites,such as pentadecanoic acid and aminoimidazole carboxamide ribonucleotide,were identified in OS patients.Correlation analysis revealed that aspartate aminotransferase(AST)levels were negatively associated with the bacterial genus Fusicatenibacter and the metabolite L-Tyrosine.A microbial-metabolite network diagram further confirmed a strong association between Fusicatenibacter and L-Tyrosine in OS patients.CONCLUSION OS patients show decreased gut microbiota diversity and unique serum metabolites.Multi-omics linked AST,Fusicatenibacter,and L-Tyrosine,revealing OS mechanisms and diagnostic potential.
文摘Cancer rates are increasing globally,making it more urgent than ever to enhance research and treatment strategies.This study aims to investigate how innovative technology and integrated multi-omics techniques could help improve cancer diagnosis,knowledge,and therapy.A complete literature search was undertaken using PubMed,Elsevier,Google Scholar,ScienceDirect,Embase,and NCBI.This review examined the articles published from 2010 to 2025.Relevant articles were found using keywords and selected using inclusion criteria New sequencing methods,like next-generation sequencing and single-cell analysis,have transformed our ability to study tumor complexity and genetic mutations,paving the way for more precise,personalized treatments.At the same time,imaging technologies such as Positron Emission Tomography(PET)and Magnetic Resonance Imaging(MRI)have made detecting tumors early and tracking treatment progress easier,all while improving patient comfort.Artificial intelligence(AI)and machine learning(ML)are having a significant impact by helping to analyze large volumes of data more efficiently and enhancing diagnostic accuracy.Meanwhile,Clustered Regulatory Interspaced Short Palindromic Repeats(CRISPR/Cas9)gene editing is emerging as a promising tool for directly targeting genes related to cancer,providing new possibilities for treatment.By integrating genomic,transcriptomic,proteomic,and metabolomic data,multi-omics approaches provide researchers with a more comprehensive understanding of the molecular mechanisms driving cancer,thereby facilitating the discovery of novel biomarkers and therapeutic targets.Despite these advancements,additional challenges persist,such as data integration,elevated costs,standardisation concerns,and the intricacies of translating findings into clinical practice,which might prevent wider implementation.Research needs to concentrate on improving these developments and encouraging multidisciplinary cooperation going forward to maximize their possibilities.Personalized cancer therapies will become more successful with ongoing developments,therefore enhancing patient outcomes and quality of life.
基金General Project of Scientific Research of Hunan Provincial Education Department (22C0191)General Project of University-level Scientific Research of Hunan University of Chinese Medicine (Z2023XJYB21)Hunan Provincial Degree and Graduate Education Reform Research Project(2024JGYB157)。
文摘Objective To map the research hotspots,developmental trends,and existing challenges in the integration of artificial intelligence(AI)with multi-omics in traditional Chinese medicine(TCM)through comprehensive bibliometric analysis.Methods China National Knowledge Infrastructure(CNKI),Wanfang Data,China Science and Technology Journal Database(VIP),Chaoxing Journal Database,PubMed,and Web of Science were searched to collect literature on the theme of AI in TCM multi-omics research from the inception of each database to December 31,2024.Eligible records were required to simultaneously address AI,TCM,and multi-omics.Quantitative and visual analyses of publication growth,core authorship networks,institutional collaboration patterns,and keyword co-occurrence were performed using Microsoft Excel 2021,NoteExpress v4.0.0,and Cite-Space 6.3.R1.AI application modes in TCM multi-omics research were also categorized and summarized.Results A total of 1106 articles were enrolled(932 Chinese and 174 English).Publication output has increased continuously since 2010 and accelerated after 2016.Region-specific collaboration clusters were identified,dominated by Beijing University of Chinese Medicine,China Academy of Chinese Medical Sciences,Shanghai University of Traditional Chinese Medicine,and Nanjing University of Chinese Medicine.Keyword co-occurrence analysis revealed that current AI applications predominantly centered on metabolomics and algorithms such as cluster analysis and data mining.Research foci mainly ranked as follows:single herbs,herbal formulae,and disease-syndrome differentiation.Conclusion Machine learning methods are the predominant integrative modality of AI in the realm of TCM multi-omics research at present,utilized for processing omics data and uncovering latent patterns therein.The domain of TCM,in addition to investigating omics information procured through high-throughput technologies,also integrates data on traditional Chinese medicinal substances and clinical phenotypes,progressing towards joint analysis of multi-omics,high-dimensionality of data,and multi-modality of information.Deep learning approaches represent an emerging trend in the field.
基金supported by the China Postdoctoral Science Foundation[Grant Number BX20240146 and 2024M761230]Key Project of Research and Development Plan in Jiangxi Province[Grant Number 20243BCC31001].
文摘Background Backfat thickness(BFT)is a vital economic trait in pigs,reflecting subcutaneous fat levels that affect meat quality and production efficiency.As a complex trait shaped by multiple genetic factors,BFT has been studied using genome-wide association studies(GWAS)and linkage analyses to locate fat-related quantitative trait loci(QTLs),but pinpointing causal variants and genes is hindered by linkage disequilibrium and limited regulatory data.This study aimed to dissect the QTLs affecting BFT on Sus scrofa chromosome 1(SSC1),elucidating regulatory variants,effector genes,and the cell types involved.Results Using whole-genome genotyping data from 3,578 pigs and phenotypic data for five BFT traits,we identified a 630.6 kb QTL on SSC1 significantly associated with these traits via GWAS and fine-mapping,pinpointing 34 candidate causal variants.Using deep convolutional neural networks to predict regulatory activity from sequence data integrated with detailed pig epigenetic profiles,we identified five SNPs potentially affecting enhancer activity in specific tissues.Notably,rs342950505(SSC1:161,123,588)influences weak enhancer activity across multiple tissues,including the brain.High-throughput chromosome conformation capture(Hi-C)analysis identified that rs342950505 interacts with eight genes.Chromatin state annotations confirmed enhancer activity at this QTL in the cerebellum.Leveraging these insights,single-cell ATAC-seq revealed a chromatin accessibility peak encompassing rs342950505 that regulates PMAIP1 expression in inhibitory neurons via enhancer-mediated mechanisms,with an adjacent peak modulating CCBE1 expression in neuroblasts and granule cells.Transcriptome-wide association studies(TWAS)confirmed PMAIP1's role in the hypothalamus,and Mendelian randomization(MR)validated PMAIP1 and CCBE1 as key brain expression quantitative trait locus(eQTL)effectors.We propose that the variant rs342950505,located within a regulatory peak,modulates PMAIP1 expression in inhibitory neurons,potentially influencing energy homeostasis via hypothalamic regulation.Similarly,CCBE1 may contribute to this process.Conclusions Our results,through systematic dissection of pleiotropic BFT-associated loci,provide a framework to elucidate regulatory mechanisms of complex traits,offering insights into polygenic control through lipid metabolism and neural signaling pathways.
基金supported by research grants provided by the National Natural Science Foundation of China(Grant Nos.32101581,32271951,and 32372754)the Hubei Provincial Central Leading Local Special Project(Grant No.2022BGE263)+3 种基金the Key Research and Science and Technology Program of Hubei Province(Grant No.2021BBA098)the Hubei Province Natural Science Foundation(Grant Nos.2023AFB1063 and 2024AFB1057)the Innovation Team Project from Hubei University of Science and Technology(Grant No.2022T02)a PhD grant from the Hubei University of Science and Technology(Grant Nos.BK202002and BK202419).
文摘Osmanthus fragrans Lour.is a well-known aromatic plant widely used as a food ingredient due to its unique floral fragrance and bioactive compounds.To fully utilize O.fragrans resources,we established an O.fragrans multi-omics database called the O.fragrans Information Resource(OfIR:http://yanglab.hzau.edu.cn/OfIR/home/).OfIR is a convenient and comprehensive multi-omics database that efficiently integrates phenotype and genetic variation from 127 O.fragrans cultivars,and provides many easy-to-use analysis tools,including primer design,sequence extraction,multi-sequence alignment,GO and KEGG enrichment analysis,variation annotation,and electronic PCR.Two case studies were used to demonstrate its power to mine candidate genetic variation sites or genes associated with specific traits or regulatory networks.In summary,the multi-omics database OfIR provides a convenient and user-friendly platform for researchers in mining functional genes and contributes to the genetic breeding of O.fragrans.
基金Supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education,No.RS-2023-00237287.
文摘Autoimmune liver disease overlap syndrome(OS)is a rare and clinically significant condition that has received limited attention in microbiome research.In their recent study,Wang et al combined 16S rRNA sequencing with untargeted metabolomics to characterize the gut-liver axis in OS,identifying shared features of dysbiosis in autoimmune hepatitis(AIH)and primary biliary cholangitis(PBC),and unique signatures,including enrichment of Klebsiella and Escherichia and depletion of aromatic amino acids.In this letter,we critically appraise these findings,emphasizing that OS should be considered a distinct immunometabolic phenotype rather than a simple mixture of AIH and PBC.We discuss the potential mechanistic relevance of the Fusicatenibacter-tyrosine relationship,highlight the clinical implications of integrating microbiota-metabolite analyses,and outline the limitations that future studies must address.
基金funded by the Chief Scientist Program of Qinghai Province(2024-SF-102)the Joint Special Project of Sanjiangyuan National Park(LHZX-2023-02).
文摘Background As an indigenous livestock species on the Tibetan Plateau,Tibetan sheep exhibit remarkable adaptability to low temperatures and nutrient-scarce environments.During the cold season,Tibetan sheep are typically managed under two feeding regimes:barn feeding(BF)and traditional grazing(TG).However,the molecular mechanisms underlying their adaptation to these distinct management strategies remain unclear.This study aimed to investigate the adaptive strategies of rumen function in Tibetan sheep to cold-season feeding regimes by integrating analyses of rumen morphology,microbiome,metabolome,and transcriptome.Twelve healthy Tibetan sheep with similar body weights were assigned into two groups(BF vs.TG).At the end of the experiment,rumen tissues were subjected to histological observation.Multi-omics techniques were employed to evaluate the effects of cold-season feeding regimes on rumen function in Tibetan sheep.Results The ruminal papilla height,width,and muscular thickness were significantly higher in BF group.The relative abundances of Actinobacteria and Succiniclasticum were significantly elevated in the rumen of BF group,whereas Rikenellaceae,Gracilibacteria,and Lachnospiraceae showed higher abundances in the TG group.Metabolomic analysis identified 19 differential metabolites between the two groups,including upregulated compounds in BF group(fumaric acid,maltose,L-phenylalanine,and L-alanine)and TG group(e.g.,phenylacetic acid,salicyluric acid and ferulic acid).These metabolites were predominantly enriched in phenylalanine metabolism,alanine,aspartate and glutamate metabolism,and phenylalanine,tyrosine and tryptophan biosynthesis pathways.Additionally,210 differentially expressed genes(DEGs)were identified in rumen epithelium:100 upregulated DEGs in the BF group were enriched in nutrient metabolism-related pathways(e.g.,fatty acid degradation and PPAR signaling pathway),while 110 upregulated DEGs in the TG group were associated with immune-related pathways(e.g.,p53 signaling pathway and glutathione metabolism).Conclusions Among these,we observed distinct rumen functional responses to different cold-season feeding regimes in Tibetan sheep and revealed energy allocation strategies mediated by host-microbe interactions.In the BF group,Tibetan sheep adopted a"metabolic efficiency-priority"strategy,driving rumen microbiota to maximize energy capture from high-nutrient diets to support host growth.In contrast,the TG group exhibited an"environmental adaptation-priority"strategy,where rumen microbiota prioritized cellulose degradation and anti-inflammatory functions,reallocating energy toward homeostasis maintenance at the expense of rumen development and growth performance.
基金Supported by 2023 Government-funded Project of the Outstanding Talents Training Program in Clinical Medicine,No.ZF2023165Key Research and Development Projects of Hebei Province,No.18277731DNatural Science Foundation of Hebei Province,No.H202423105.
文摘In this editorial,we discuss the findings reported by Wang et al in the latest issue of the World Journal of Gastrointestinal Oncology.Various research methodologies,including microbiome analysis,assert that the Tzu-Chi Cancer-Antagonizing and Life-Protecting II Decoction of Chinese herbal compounds mitigates inflammatory responses by inhibiting the NF-κB signaling pathway.This action helps maintain the dynamic equilibrium of the intestinal microecology and lessens chemotherapy-induced gastrointestinal damage.The efficacy of these compounds is intimately linked to the composition of intestinal microbes.These compounds regulate intestinal microecology by virtue of their specific compatibility and effectiveness,thereby enhancing the overall therapeutic outcomes of cancer chemotherapy.Nonetheless,the exact mechanisms underlying these effects warrant further investigation.Multi-omics technologies offer a systematic approach to elucidate the mechanisms and effectiveness of Chinese herbal compounds in vivo.This manuscript reviews the application of multi-omics technologies to Chinese herbal compounds and explores their potential role in modulating the gastrointestinal microenvironment following cancer chemotherapy,thus providing a theoretical foundation for their continued use in adjunct cancer treatment.
基金The 75th Batch of China Postdoctoral Science Foundation projects(No.2024M754279)Natural Science Foundation of Jiangsu Province(No.BK20240738)+2 种基金Basic Science(Natural Science)Research Project in Universities of Jiangsu Province(No.24KJB360004)Jiangsu Province Chinese Medicine Science and Technology Development Plan Youth Talent Project(No.QN202206)Nanjing University of ChineseMedicine Luo Linxiu Teacher Development Fund Project(No.LLX202310).
文摘Cold tumors,defined by insufficient immune cell infiltration and a highly immunosuppressive tumor microenvironment(TME),exhibit limited responsiveness to conventional immunotherapies.This reviewsystematically summarizes the mechanisms of immune evasion and the therapeutic strategies for cold tumors as revealed by multiomics technologies.By integrating genomic,transcriptomic,proteomic,metabolomic,and spatialmulti-omics data,the review elucidates key immune evasionmechanisms,including activation of the WNT/β-catenin pathway,transforming growth factor-β(TGF-β)–mediated immunosuppression,metabolic reprogramming(e.g.,lactate accumulation),and aberrant expression of immune checkpoint molecules.Furthermore,this review proposes multi-dimensional therapeutic strategies,such as targeting immunosuppressive pathways(e.g.,programmed death-1(PD-1)/programmed death-ligand 1(PD-L1)inhibitors combined with TGF-βblockade),reshaping the TME through chemokine-based therapies,oncolytic viruses,and vascular normalization,and metabolic interventions(e.g.,inhibition of lactate dehydrogenase A(LDHA)or glutaminase(GLS)).In addition,personalized neoantigen vaccines and engineered cell therapies(e.g.,T cell receptor-engineered T(TCR-T)and natural killer(NK)cells)show promising potential.Emerging evidence also highlights the role of epigenetic regulation(e.g.,histone deacetylase(HDAC)inhibitors)and N6-Methyladenosine(m6A)RNA modifications in reversing immune evasion.Despite the promising insights offered by multi-omics integration in guiding precision immunotherapy,challenges remain in clinical translation,including data heterogeneity,target-specific toxicity,and limitations in preclinical models.Future efforts should focus on coupling dynamic multi-omics technologies with intelligent therapeutic design to convert cold tumors into immunologically active(“hot”)microenvironments,ultimately facilitating breakthroughs in personalized immunotherapy.
基金supported by the National Natural Science Foundation of China (U22A20277,81971373)Jiangsu Provincial Medical Key Discipline Cultivation Unit (JSDW202215)+1 种基金333 High-level Personnel Training Project of Jiangsu Province (BRA2019109)Postgraduate Research&Practice Innovation Program of Jiangsu Province (KYCX22_1826)。
文摘Increasing evidence implicates disruptions in testicular fatty acid metabolism as a contributing factor in nonobstructive azoospermia(NOA),a severe form of male infertility.However,the precise mechanisms linking fatty acid metabolism to NOA pathogenesis have not yet been fully elucidated.Multi-omics analyses,including microarray analysis,single-cell RNA sequencing(scRNA-seq),and metabolomics,were utilized to investigate disruptions in fatty acid metabolism associated with NOA using data from public databases.Results identified ACSL6,ACSBG2,and OLAH as key genes linked to fatty acid metabolism dysregulation,suggesting their potential causative roles in NOA.A marked reduction in omega-3 polyunsaturated fatty acids,especially docosahexaenoic acid(DHA),was observed,potentially contributing to the pathological process of NOA.Sertoli cells in NOA patients exhibited apparent fatty acid metabolic dysfunction,with PPARG identified as a key transcription factor(TF)regulating this process.Functional analyses demonstrated that PPARG is crucial for maintaining blood-testis barrier(BTB)integrity and promoting spermatogenesis via regulation of fatty acid metabolism.These findings reveal the pivotal role of fatty acid metabolism in NOA and identify PPARG as a potential therapeutic target.
基金the Central Guidance for Regional Science and Technology Development Projects(YDZJSX2024B010)Research project of Shanxi Provincial Health Commission(2024067)。
文摘Objective Pneumoconiosis,a lung disease caused by irreversible fibrosis,represents a significant public health burden.This study investigates the causal relationships between gut microbiota,gene methylation,gene expression,protein levels,and pneumoconiosis using a multi-omics approach and Mendelian randomization(MR).Methods We analyzed gut microbiota data from MiBioGen and Esteban et al.to assess their potential causal effects on pneumoconiosis subtypes(asbestosis,silicosis,and inorganic pneumoconiosis)using conventional and summary-data-based MR(SMR).Gene methylation and expression data from Genotype-Tissue Expression and eQTLGen,along with protein level data from deCODE and UK Biobank Pharma Proteomics Project,were examined in relation to pneumoconiosis data from FinnGen.To validate our findings,we assessed self-measured gut flora from a pneumoconiosis cohort and performed fine mapping,drug prediction,molecular docking,and Phenome-Wide Association Studies to explore relevant phenotypes of key genes.Results Three core gut microorganisms were identified:Romboutsia(OR=0.249)as a protective factor against silicosis,Pasteurellaceae(OR=3.207)and Haemophilus parainfluenzae(OR=2.343)as risk factors for inorganic pneumoconiosis.Additionally,mapping and quantitative trait loci analyses revealed that the genes VIM,STX8,and MIF were significantly associated with pneumoconiosis risk.Conclusions This multi-omics study highlights the associations between gut microbiota and key genes(VIM,STX8,MIF)with pneumoconiosis,offering insights into potential therapeutic targets and personalized treatment strategies.
基金supported by the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang(2023R01002)the National Natural Science Foundation of China(82271629,82301790)。
文摘Aging and regeneration represent complex biological phenomena that have long captivated the scientific community.To fully comprehend these processes,it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions.Conventional omics methodologies,such as genomics and transcriptomics,have been instrumental in identifying critical molecular facets of aging and regeneration.However,these methods are somewhat limited,constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations.The advent of emerging spatiotemporal multi-omics approaches,encompassing transcriptomics,proteomics,metabolomics,and epigenomics,furnishes comprehensive insights into these intricate molecular dynamics.These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells,tissues,and organs,thereby offering an in-depth understanding of the fundamental mechanisms at play.This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research.It underscores how these methodologies augment our comprehension of molecular dynamics,cellular interactions,and signaling pathways.Initially,the review delineates the foundational principles underpinning these methods,followed by an evaluation of their recent applications within the field.The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field.Indubitably,spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration,thus charting a course toward potential therapeutic innovations.
基金supported by a Lee Kong Chian School of Medicine Dean’s Postdoctoral Fellowship(021207-00001)from Nanyang Technological University(NTU)Singapore and a Mistletoe Research Fellowship(022522-00001)from the Momental Foundation USA.Jialiu Zeng is supported by a Presidential Postdoctoral Fellowship(021229-00001)from NTU Singapore and an Open Fund Young Investigator Research Grant(OF-YIRG)(MOH-001147)from the National Medical Research Council(NMRC)SingaporeSu Bin Lim is supported by the National Research Foundation(NRF)of Korea(Grant Nos.:2020R1A6A1A03043539,2020M3A9D8037604,2022R1C1C1004756)a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),funded by the Ministry of Health&Welfare,Republic of Korea(Grant No.:HR22C1734).
文摘Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information,with its application to neuroscience termed neuroinformatics.Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms,which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases.Importantly,integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile.In this review,we first summarize data mining studies utilizing datasets from the individual type of omics analysis,including epigenetics/epigenomics,transcriptomics,proteomics,metabolomics,lipidomics,and spatial omics,pertaining to Alzheimer's disease,Parkinson's disease,and multiple sclerosis.We then discuss multi-omics integration approaches,including independent biological integration and unsupervised integration methods,for more intuitive and informative interpretation of the biological data obtained across different omics layers.We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks.Finally,we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery,therapeutic development,and elucidation of disease mechanisms.We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.
