Understanding the molecular responses of tea leaves to mechanical stress is crucial for elucidating the mechanisms of post-harvest quality formation during oolong tea processing.This study employed an integrated multi...Understanding the molecular responses of tea leaves to mechanical stress is crucial for elucidating the mechanisms of post-harvest quality formation during oolong tea processing.This study employed an integrated multi-omics strategy to characterize the changes and interactions among metabolomic(MB),transcriptomic(TX),and proteomic(PT)profiles in mechanically stressed tea leaves.Mechanical stress initially activated damage-associated molecular patterns(DAMPs),including Ca^(2+)signaling,jasmonic acid signaling,and glutathione metabolism pathways.These processes subsequently induced quality-related metabolic pathways(QRMPs),particularly α-linolenic acid and phenylalanine metabolism.Upregulated expression of LOX,ADH1,and PAR genes,together with the increased abundance of their encoded proteins,respectively promoted the accumulation of jasmine lactone,benzyl alcohol,and 2-phenylethanol.These findings indicate that mechanical stress influences the metabolite biosynthesis in tea leaves through coordinated molecular responses.This study provides new insights into the molecular mechanisms underlying tea leaf responses to mechanical stress and a foundation for future investigations into how early molecular events may contribute to post-harvest metabolic changes during oolong tea processing.展开更多
Background:Giant cell arteritis(GCA),the most common systemic vasculitis affecting elderly individuals,currently lacks specific therapies.This study aimed to systematically identify therapeutic targets for GCA through...Background:Giant cell arteritis(GCA),the most common systemic vasculitis affecting elderly individuals,currently lacks specific therapies.This study aimed to systematically identify therapeutic targets for GCA through integration of large-scale multi-omics datasets.Methods:We constructed a multi-stage analytical framework encompassing 32 proteomic datasets(covering 2914 unique plasma proteins)and 6 transcriptomic datasets.Multi-omics integration strategies,including two-sample Mendelian randomization,colocalization analysis,and functional enrichment analysis,were employed to identify and validate causal relationships between candidate targets and GCA risk across 4 independent European-ancestry GCA cohorts.Single-cell RNA sequencing analysis of peripheral blood mononuclear cells from untreated GCA patients was performed to characterize hub gene-immune cell relationships.Results:We identified 43 plasma proteins causally associated with GCA[false discovery rate(FDR)<0.05],with 17 representing novel therapeutic targets.Through dual validation using proteome-wide association studies and transcriptome-wide association studies,we identified 13 high-confidence candidate targets with distinct tissue-specific expression patterns.Unc-51 like kinase 3(ULK3)emerged as the strongest protective factor(odds ratio=0.47,95%confidence interval:0.37–0.71)through autophagy regulation,while SLAMF7 represents an immediate drug repositioning opportunity as the target of food and drug administration-approved elotuzumab.Five targets have existing approved drugs(SLAMF7,ICAM1,IL18,IL6ST,CTSS).Single-cell analysis revealed profound disruption of hub gene-immune cell relationships in untreated GCA patients,with cell-type-specific alterations in inflammatory gene expression,and TYMP as the most critical hub gene.Conclusions:This study provides a clinically-actionable atlas of 43 potential therapeutic targets in GCA,identifying novel mechanisms including autophagy modulation and metabolic reprogramming,with immediate drug repositioning opportunities and precision medicine strategies based on tissue-specific and cell-type-specific expression patterns.These findings require experimental validation before clinical translation.展开更多
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
Bamei pigs,an indigenous Chinese breed,yield meat with a delectable flavor and boast higher carcass fat content compared to commercial breeds,making them a rich food source for humans.However,the differences in lipid ...Bamei pigs,an indigenous Chinese breed,yield meat with a delectable flavor and boast higher carcass fat content compared to commercial breeds,making them a rich food source for humans.However,the differences in lipid and nutrient components between the adipose tissue of Bamei pigs and commercial pigs are still unclear.The study employed UPLC-MS/MS to quantify the composition of lipids and metabolites in the backfat of both Bamei and Large White pigs.A total of 428 lipids and 193 metabolites were significantly different between the 2 groups.Specifically,Bamei pig backfat exhibited altered levels of various lipids and metabolites that may potentially contribute to nutritional and flavor differences,including unsaturated triglycerides,free fatty acids,medium-chain triglycerides,essential amino acids,vitamins and antioxidants,while maintaining reduced cholesterol levels.Furthermore,we delved into the molecular mechanisms underlying these nutritional differences by analyzing significantly different 431 m RNAs and 865 proteins and integrating the regulatory network of protein-metabolite-lipid pathway.Importantly,in the pyruvate metabolic pathway of Bamei pigs,the bioprocess of lactate production was inhibited but the acetyl-Co A production was activated,suggesting the possibility that energy allocation favors the biogenesis of lipid precursors.These findings may contribute to guiding industrial food producers in enhancing the quality of lard at the genetic and molecular levels.展开更多
Liver is prone to viral infection.Viral hepatitis can be roughly divided into hepatitis A,B,C,D and E.Accurate diagnosis of viral hepatitis is crucial for accurate treatments.Different types of biomarkers,including no...Liver is prone to viral infection.Viral hepatitis can be roughly divided into hepatitis A,B,C,D and E.Accurate diagnosis of viral hepatitis is crucial for accurate treatments.Different types of biomarkers,including non-invasive biomarkers have been explored for the diagnosis of viral hepatitis.With the fast development of multi-omics technology,non-invasive biomarkers can be detected from blood,saliva,urine,stool,and other body fluids.The advantages of non-invasive biomarkers are:1)non-invasive;2)convenient to test and 3)repeatable.The application of non-invasive biomarkers significantly improves the diagnostic accuracy of viral hepatitis.The non-invasive biomarkers can be sugars,proteins,nucleic acids,and even microorganisms.In this review,we summarized recent advances in identifying non-invasive biomarkers using multi-omics technology and discussed their potential diagnostic values for viral hepatitis.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
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
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
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
Lysosomal diseases(LDs)are a group of rare inherited disorders belonging to inborn metabolism errors.LDs are characterized by the excessive storage of undegraded substrates,most often due to the enzymatic deficiency r...Lysosomal diseases(LDs)are a group of rare inherited disorders belonging to inborn metabolism errors.LDs are characterized by the excessive storage of undegraded substrates,most often due to the enzymatic deficiency resulting from disease-causing gene variants.LDs lead to dysregulated cellular pathways and imbalanced molecular homeostasis and can affect multiple organs and tissues.Despite being rare,LDs account for a significant incidence when considered collectively.Due to complex molecular and genetic fingerprints,considerable challenges in LD management must be overcome.Diagnosis can be signifi-cantly delayed due to the broad and nonspecific clinical manifestations and the lack of specific bio-markers.Available treatments fail to fully stop the disease progression and can alter the disease's typical phenotypes with novel manifestations.Therefore,a paradigm shift is crucial to better understand LDs and provide actionable insights.Herein,we comprehensively review the literature to demonstrate that multi-omics approaches are promising for pathophysiology elucidation,biomarker discovery,and pre-cision therapy in LDs.We recommend adopting longitudinal study designs integrated with a multi-omics-empowered framework to facilitate mechanistic delineation,biomarker discovery,and treat-ment development.Relevant approaches exploring the association between LDs and common neuro-degenerative disorders are also discussed,paving a potential path for improved therapeutic development and ultimately improving the patient's quality of life.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
Body weight is a polygenic trait with intricate inheritance patterns.Functional genomics enriched with multi-layer annotations offers essential resources for exploring the genetic architecture of complex traits.In thi...Body weight is a polygenic trait with intricate inheritance patterns.Functional genomics enriched with multi-layer annotations offers essential resources for exploring the genetic architecture of complex traits.In this study,we conducted an extensive characterization of regulatory variants associated with body weight-related traits in cattle using multi-omics analysis.First,we identified seven candidate genes by integrating selective sweep analysis and multiple genome-wide association study(GWAS)strategies using imputed whole-genome sequencing data from a population of 1577 individuals.Subsequently,we uncovered 3340 eGenes(genes whose expression levels are associated with genetic variants)across 227 muscle samples.Transcriptome-wide association studies(TWASs)further revealed a total of 532 distinct candidate genes associated with body weight-related traits.Colocalization analyses unveiled 44 genes shared between expression quantitative trait loci(eQTLs)and GWAS signals.Moreover,a comprehensive analysis by integrating GWAS,selective sweep,eQTL,TWAS,epigenomic profiling,and molecular validation highlighted a positively selected genomic region on Bos taurus autosome 6(BTA6).This locus harbors pleiotropic genes(LAP3,MED28,and NCAPG)and a prioritized functional variant involved in the complex regulation of body weight.Additionally,convergent evolution analysis and phenome-wide association studies underscored the conservation of this locus across species.Our study provides a comprehensive understanding of the genetic regulation of body weight through multi-omics analysis in cattle.Our findings contribute to unraveling the genetic mechanisms governing weight-related traits and shed valuable light on the genetic improvement of farm animals.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
基金supported by the National Key Research and Development Program of China(2022YFD2101101)the Earmarked Fund for CARS-19+2 种基金the National Natural Science Foundation of China(32402634)the Modern Agricultural(Tea)Industry Technology System of Fujian Province,China(2025 No.593)the Special Fund for Science and Technology Innovation of Fujian Zhang Tianfu Tea Development Foundation,China(FJZTF01)。
文摘Understanding the molecular responses of tea leaves to mechanical stress is crucial for elucidating the mechanisms of post-harvest quality formation during oolong tea processing.This study employed an integrated multi-omics strategy to characterize the changes and interactions among metabolomic(MB),transcriptomic(TX),and proteomic(PT)profiles in mechanically stressed tea leaves.Mechanical stress initially activated damage-associated molecular patterns(DAMPs),including Ca^(2+)signaling,jasmonic acid signaling,and glutathione metabolism pathways.These processes subsequently induced quality-related metabolic pathways(QRMPs),particularly α-linolenic acid and phenylalanine metabolism.Upregulated expression of LOX,ADH1,and PAR genes,together with the increased abundance of their encoded proteins,respectively promoted the accumulation of jasmine lactone,benzyl alcohol,and 2-phenylethanol.These findings indicate that mechanical stress influences the metabolite biosynthesis in tea leaves through coordinated molecular responses.This study provides new insights into the molecular mechanisms underlying tea leaf responses to mechanical stress and a foundation for future investigations into how early molecular events may contribute to post-harvest metabolic changes during oolong tea processing.
基金supported by grants from the Fundamental Research Funds for the Central Universities(No.2025ZFJH03)the Central Guidance Fund for Local Science and Technology Development(No.2024ZY01054)the CAMS Innovation Fund for Medical Sciences(No.2019-I2M-5-045).
文摘Background:Giant cell arteritis(GCA),the most common systemic vasculitis affecting elderly individuals,currently lacks specific therapies.This study aimed to systematically identify therapeutic targets for GCA through integration of large-scale multi-omics datasets.Methods:We constructed a multi-stage analytical framework encompassing 32 proteomic datasets(covering 2914 unique plasma proteins)and 6 transcriptomic datasets.Multi-omics integration strategies,including two-sample Mendelian randomization,colocalization analysis,and functional enrichment analysis,were employed to identify and validate causal relationships between candidate targets and GCA risk across 4 independent European-ancestry GCA cohorts.Single-cell RNA sequencing analysis of peripheral blood mononuclear cells from untreated GCA patients was performed to characterize hub gene-immune cell relationships.Results:We identified 43 plasma proteins causally associated with GCA[false discovery rate(FDR)<0.05],with 17 representing novel therapeutic targets.Through dual validation using proteome-wide association studies and transcriptome-wide association studies,we identified 13 high-confidence candidate targets with distinct tissue-specific expression patterns.Unc-51 like kinase 3(ULK3)emerged as the strongest protective factor(odds ratio=0.47,95%confidence interval:0.37–0.71)through autophagy regulation,while SLAMF7 represents an immediate drug repositioning opportunity as the target of food and drug administration-approved elotuzumab.Five targets have existing approved drugs(SLAMF7,ICAM1,IL18,IL6ST,CTSS).Single-cell analysis revealed profound disruption of hub gene-immune cell relationships in untreated GCA patients,with cell-type-specific alterations in inflammatory gene expression,and TYMP as the most critical hub gene.Conclusions:This study provides a clinically-actionable atlas of 43 potential therapeutic targets in GCA,identifying novel mechanisms including autophagy modulation and metabolic reprogramming,with immediate drug repositioning opportunities and precision medicine strategies based on tissue-specific and cell-type-specific expression patterns.These findings require experimental validation before clinical translation.
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 Key Research and Development Program of China(2021YFF1000602)the National Natural Science Foundations(32202642)the earmarked fund for CARS-35-PIG.
文摘Bamei pigs,an indigenous Chinese breed,yield meat with a delectable flavor and boast higher carcass fat content compared to commercial breeds,making them a rich food source for humans.However,the differences in lipid and nutrient components between the adipose tissue of Bamei pigs and commercial pigs are still unclear.The study employed UPLC-MS/MS to quantify the composition of lipids and metabolites in the backfat of both Bamei and Large White pigs.A total of 428 lipids and 193 metabolites were significantly different between the 2 groups.Specifically,Bamei pig backfat exhibited altered levels of various lipids and metabolites that may potentially contribute to nutritional and flavor differences,including unsaturated triglycerides,free fatty acids,medium-chain triglycerides,essential amino acids,vitamins and antioxidants,while maintaining reduced cholesterol levels.Furthermore,we delved into the molecular mechanisms underlying these nutritional differences by analyzing significantly different 431 m RNAs and 865 proteins and integrating the regulatory network of protein-metabolite-lipid pathway.Importantly,in the pyruvate metabolic pathway of Bamei pigs,the bioprocess of lactate production was inhibited but the acetyl-Co A production was activated,suggesting the possibility that energy allocation favors the biogenesis of lipid precursors.These findings may contribute to guiding industrial food producers in enhancing the quality of lard at the genetic and molecular levels.
文摘Liver is prone to viral infection.Viral hepatitis can be roughly divided into hepatitis A,B,C,D and E.Accurate diagnosis of viral hepatitis is crucial for accurate treatments.Different types of biomarkers,including non-invasive biomarkers have been explored for the diagnosis of viral hepatitis.With the fast development of multi-omics technology,non-invasive biomarkers can be detected from blood,saliva,urine,stool,and other body fluids.The advantages of non-invasive biomarkers are:1)non-invasive;2)convenient to test and 3)repeatable.The application of non-invasive biomarkers significantly improves the diagnostic accuracy of viral hepatitis.The non-invasive biomarkers can be sugars,proteins,nucleic acids,and even microorganisms.In this review,we summarized recent advances in identifying non-invasive biomarkers using multi-omics technology and discussed their potential diagnostic values for viral hepatitis.
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.
基金Graphical abstract and Figs.1-4 were createdwith BioRender.com.
文摘Lysosomal diseases(LDs)are a group of rare inherited disorders belonging to inborn metabolism errors.LDs are characterized by the excessive storage of undegraded substrates,most often due to the enzymatic deficiency resulting from disease-causing gene variants.LDs lead to dysregulated cellular pathways and imbalanced molecular homeostasis and can affect multiple organs and tissues.Despite being rare,LDs account for a significant incidence when considered collectively.Due to complex molecular and genetic fingerprints,considerable challenges in LD management must be overcome.Diagnosis can be signifi-cantly delayed due to the broad and nonspecific clinical manifestations and the lack of specific bio-markers.Available treatments fail to fully stop the disease progression and can alter the disease's typical phenotypes with novel manifestations.Therefore,a paradigm shift is crucial to better understand LDs and provide actionable insights.Herein,we comprehensively review the literature to demonstrate that multi-omics approaches are promising for pathophysiology elucidation,biomarker discovery,and pre-cision therapy in LDs.We recommend adopting longitudinal study designs integrated with a multi-omics-empowered framework to facilitate mechanistic delineation,biomarker discovery,and treat-ment development.Relevant approaches exploring the association between LDs and common neuro-degenerative disorders are also discussed,paving a potential path for improved therapeutic development and ultimately improving the patient's quality of life.
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supported by the National Key R&D Program of China(Grant No.2022YFF1000600)the National Natural Science Foundation of China(Grant No.31972554)+4 种基金supported by the Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences(Grant Nos.ASTIP-IAS-TS-16 and ASTIP-IAS03)the National Beef Cattle Industrial Technology System(Grant No.CARS-37)supported in part by the Agriculture and Food Research Initiative(AFRI)(Grant Nos.2019-67015-29321 and 2021-67015-33409)from the United States Department of Agriculture(USDA)National Institute of Food and Agriculture(NIFA)Animal Genome and Reproduction ProgramsLingyang Xu was supported by the Elite Youth Scientists Program of the Chinese Academy of Agricultural Sciences.
文摘Body weight is a polygenic trait with intricate inheritance patterns.Functional genomics enriched with multi-layer annotations offers essential resources for exploring the genetic architecture of complex traits.In this study,we conducted an extensive characterization of regulatory variants associated with body weight-related traits in cattle using multi-omics analysis.First,we identified seven candidate genes by integrating selective sweep analysis and multiple genome-wide association study(GWAS)strategies using imputed whole-genome sequencing data from a population of 1577 individuals.Subsequently,we uncovered 3340 eGenes(genes whose expression levels are associated with genetic variants)across 227 muscle samples.Transcriptome-wide association studies(TWASs)further revealed a total of 532 distinct candidate genes associated with body weight-related traits.Colocalization analyses unveiled 44 genes shared between expression quantitative trait loci(eQTLs)and GWAS signals.Moreover,a comprehensive analysis by integrating GWAS,selective sweep,eQTL,TWAS,epigenomic profiling,and molecular validation highlighted a positively selected genomic region on Bos taurus autosome 6(BTA6).This locus harbors pleiotropic genes(LAP3,MED28,and NCAPG)and a prioritized functional variant involved in the complex regulation of body weight.Additionally,convergent evolution analysis and phenome-wide association studies underscored the conservation of this locus across species.Our study provides a comprehensive understanding of the genetic regulation of body weight through multi-omics analysis in cattle.Our findings contribute to unraveling the genetic mechanisms governing weight-related traits and shed valuable light on the genetic improvement of farm animals.
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.
