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
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 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
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
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
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
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
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
Accurate genomic information is essential for advancing genetic breeding research in specific rice varieties.This study presented a gapless genome assembly of the indica rice cultivar Zhonghui 8015(ZH8015)using Pac Bi...Accurate genomic information is essential for advancing genetic breeding research in specific rice varieties.This study presented a gapless genome assembly of the indica rice cultivar Zhonghui 8015(ZH8015)using Pac Bio HiFi,Hi-C,and ONT(Oxford Nanopore Technologies)ultra-long sequencing technologies,annotating 43037 gene structures.Subsequently,utilizing this genome along with transcriptomic and metabolomic techniques,we explored ZH8015's response to brown planthopper(BPH)infestation.Continuous transcriptomic sampling indicated significant changes in gene expression levels around 48 h after BPH feeding.Enrichment analysis revealed particularly significant alterations in genes related to reactive oxygen species scavenging and cell wall formation.Metabolomic results demonstrated marked increases in levels of several monosaccharides,which are components of the cell wall and dramatic changes in flavonoid contents.Omics association analysis identified differentially expressed genes associated with key metabolites,shedding light on ZH8015's response to BPH infestation.In summary,this study constructed a reliable genome sequence resource for ZH8015,and the preliminary multi-omics results will guide future insect-resistant breeding research.展开更多
Gastric cancer(GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development;therefore, systematic re...Gastric cancer(GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development;therefore, systematic research in this area should improve understanding of the biological mechanisms that initiate GC development and promote cancer hallmarks. Here, we summarize biological knowledge regarding gastric inflammation-induced tumorigenesis, and characterize the multi-omics data and systems biology methods for investigating GC development. Of note, we highlight pioneering studies in multi-omics data and state-of-the-art network-based algorithms used for dissecting the features of gastric inflammation-induced tumorigenesis, and we propose translational applications in early GC warning biomarkers and precise treatment strategies. This review offers integrative insights for GC research, with the goal of paving the way to novel paradigms for GC precision oncology and prevention.展开更多
Metabolic network construction plays a pivotal role in unraveling the regulatory mechanism of biological activities,although it often proves to be challenging and labor-intensive,particularly with non-model organisms....Metabolic network construction plays a pivotal role in unraveling the regulatory mechanism of biological activities,although it often proves to be challenging and labor-intensive,particularly with non-model organisms.In this study,we develop a computational approach that employs reaction models based on the structure-guided chemical modification and related compounds to construct a metabolic network in wheat.This construction results in a comprehensive structure-guided network,including 625 identified metabolites and additional 333 putative reactions compared with the Kyoto Encyclopedia of Genes and Genomes database.Using a combination of gene annotation,reaction classification,structure similarity,and correlations from transcriptome and metabolome analysis,a total of 229 potential genes related to these reactions are identified within this network.To validate the network,the functionality of a hydroxycinnamoyltransferase(TraesCS3D01G314900)for the synthesis of polyphenols and a rhamnosyltransferase(TraesCS2D01G078700)for the modification of flavonoids are verified through in vitro enzymatic studies and wheat mutant tests,respectively.Our research thus supports the utility of structure-guided chemical modification as an effective tool in identifying causal candidate genes for constructing metabolic networks and further in metabolomic genetic studies.展开更多
Background:Physiological and biochemical processes across tissues of the body are regulated in response to the high demands of intense physical activity in several occupations,such as firefighting,law enforcement,mili...Background:Physiological and biochemical processes across tissues of the body are regulated in response to the high demands of intense physical activity in several occupations,such as firefighting,law enforcement,military,and sports.A better understanding of such processes can ultimately help improve human performance and prevent illnesses in the work environment.Methods:To study regulatory processes in intense physical activity simulating real-life conditions,we performed a multi-omics analysis of 3 biofluids(blood plasma,urine,and saliva)collected from 11 wildland firefighters before and after a 45 min,intense exercise regimen.Omics profiles post-vs.pre-exercise were compared by Student’s t-test followed by pathway analysis and comparison between the different omics modalities.Results:Our multi-omics analysis identified and quantified 3835 proteins,730 lipids and 182 metabolites combining the 3 different types of samples.The blood plasma analysis revealed signatures of tissue damage and acute repair response accompanied by enhanced carbon metabolism to meet energy demands.The urine analysis showed a strong,concomitant regulation of 6 out of 8 identified proteins from the renin-angiotensin system supporting increased excretion of catabolites,reabsorption of nutrients and maintenance of fluid balance.In saliva,we observed a decrease in 3 pro-inflammatory cytokines and an increase in 8 antimicrobial peptides.A systematic literature review identified 6 papers that support an altered susceptibility to respiratory infection.Conclusions:This study shows simultaneous regulatory signatures in biofluids indicative of homeostatic maintenance during intense physical activity with possible effects on increased infection susceptibility,suggesting that caution against respiratory diseases could benefit workers on highly physical demanding jobs.展开更多
Epilepsy affects approximately 70 million people worldwide.Yet scientists have a partial understanding of the disease pathophysiology due to its heterogenic nature.About 70%of cases of epilepsy are treatable with FDA-...Epilepsy affects approximately 70 million people worldwide.Yet scientists have a partial understanding of the disease pathophysiology due to its heterogenic nature.About 70%of cases of epilepsy are treatable with FDA-approved anti-epileptic drugs while temporal lobe epilepsy with hippocampus sclerosis(TLE-HS)is drug resistant.Numerous herbs have been noted for their potential anti-convulsant properties.Yet,due to the scarcity of experimental data,there is an urgent need to conduct thorough investigations into these herbs for their practical use in treating TLE-HS.In-depth multi-omics research is needed for targeted TLE-HS therapy,focusing on cornu ammonis subregions,dentate gyrus,and also genetically glutamate,andγ-aminobutyric acid receptors.Animal models,due to the lack of human brain tissue,enable homogeneous sample selection,comparable groups,and ample tissue for in-vitro and ex-vivo studies.Consequently,it becomes feasible to examine the effectiveness of herbs on individual brain regions at the molecular level,paving the way for the potential development of drug interventions to treat TLE-HS.展开更多
The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against cancer.These neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-relat...The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against cancer.These neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related fatalities.The accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal treatments.Addressing this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of accuracy.The MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN model.The outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST cases.The source code for this groundbreaking study is readily accessible for download at https://github.com/bigone1/MGTCN.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
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.
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.
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.
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.
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 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 the Chinese Academy of Agricultural Sciences Innovation Project(Grant No.CAASASTIP-2013CNRRI)Fundamental Research Funds for Central Public Welfare Research Institutes of Chinese Rice Research Institute(Grant No.CPSIBRF-CNRRI-202102)。
文摘Accurate genomic information is essential for advancing genetic breeding research in specific rice varieties.This study presented a gapless genome assembly of the indica rice cultivar Zhonghui 8015(ZH8015)using Pac Bio HiFi,Hi-C,and ONT(Oxford Nanopore Technologies)ultra-long sequencing technologies,annotating 43037 gene structures.Subsequently,utilizing this genome along with transcriptomic and metabolomic techniques,we explored ZH8015's response to brown planthopper(BPH)infestation.Continuous transcriptomic sampling indicated significant changes in gene expression levels around 48 h after BPH feeding.Enrichment analysis revealed particularly significant alterations in genes related to reactive oxygen species scavenging and cell wall formation.Metabolomic results demonstrated marked increases in levels of several monosaccharides,which are components of the cell wall and dramatic changes in flavonoid contents.Omics association analysis identified differentially expressed genes associated with key metabolites,shedding light on ZH8015's response to BPH infestation.In summary,this study constructed a reliable genome sequence resource for ZH8015,and the preliminary multi-omics results will guide future insect-resistant breeding research.
基金supported by funds from the National Natural Science Foundation of China (Grant No. T2341008)。
文摘Gastric cancer(GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development;therefore, systematic research in this area should improve understanding of the biological mechanisms that initiate GC development and promote cancer hallmarks. Here, we summarize biological knowledge regarding gastric inflammation-induced tumorigenesis, and characterize the multi-omics data and systems biology methods for investigating GC development. Of note, we highlight pioneering studies in multi-omics data and state-of-the-art network-based algorithms used for dissecting the features of gastric inflammation-induced tumorigenesis, and we propose translational applications in early GC warning biomarkers and precise treatment strategies. This review offers integrative insights for GC research, with the goal of paving the way to novel paradigms for GC precision oncology and prevention.
基金supported by the Young Top-notch Talent Cultivation Program of Hubei Province,the Natural Science Foundation for Distinguished Young Scientists of Hubei Province(2021CFA058)the First-Class Discipline Construction Funds of College of Plant Science and Technology,Huazhong Agricultural University(2023ZKPY005).
文摘Metabolic network construction plays a pivotal role in unraveling the regulatory mechanism of biological activities,although it often proves to be challenging and labor-intensive,particularly with non-model organisms.In this study,we develop a computational approach that employs reaction models based on the structure-guided chemical modification and related compounds to construct a metabolic network in wheat.This construction results in a comprehensive structure-guided network,including 625 identified metabolites and additional 333 putative reactions compared with the Kyoto Encyclopedia of Genes and Genomes database.Using a combination of gene annotation,reaction classification,structure similarity,and correlations from transcriptome and metabolome analysis,a total of 229 potential genes related to these reactions are identified within this network.To validate the network,the functionality of a hydroxycinnamoyltransferase(TraesCS3D01G314900)for the synthesis of polyphenols and a rhamnosyltransferase(TraesCS2D01G078700)for the modification of flavonoids are verified through in vitro enzymatic studies and wheat mutant tests,respectively.Our research thus supports the utility of structure-guided chemical modification as an effective tool in identifying causal candidate genes for constructing metabolic networks and further in metabolomic genetic studies.
基金supported by the BRAVE Agile Investment from the PNNL
文摘Background:Physiological and biochemical processes across tissues of the body are regulated in response to the high demands of intense physical activity in several occupations,such as firefighting,law enforcement,military,and sports.A better understanding of such processes can ultimately help improve human performance and prevent illnesses in the work environment.Methods:To study regulatory processes in intense physical activity simulating real-life conditions,we performed a multi-omics analysis of 3 biofluids(blood plasma,urine,and saliva)collected from 11 wildland firefighters before and after a 45 min,intense exercise regimen.Omics profiles post-vs.pre-exercise were compared by Student’s t-test followed by pathway analysis and comparison between the different omics modalities.Results:Our multi-omics analysis identified and quantified 3835 proteins,730 lipids and 182 metabolites combining the 3 different types of samples.The blood plasma analysis revealed signatures of tissue damage and acute repair response accompanied by enhanced carbon metabolism to meet energy demands.The urine analysis showed a strong,concomitant regulation of 6 out of 8 identified proteins from the renin-angiotensin system supporting increased excretion of catabolites,reabsorption of nutrients and maintenance of fluid balance.In saliva,we observed a decrease in 3 pro-inflammatory cytokines and an increase in 8 antimicrobial peptides.A systematic literature review identified 6 papers that support an altered susceptibility to respiratory infection.Conclusions:This study shows simultaneous regulatory signatures in biofluids indicative of homeostatic maintenance during intense physical activity with possible effects on increased infection susceptibility,suggesting that caution against respiratory diseases could benefit workers on highly physical demanding jobs.
文摘Epilepsy affects approximately 70 million people worldwide.Yet scientists have a partial understanding of the disease pathophysiology due to its heterogenic nature.About 70%of cases of epilepsy are treatable with FDA-approved anti-epileptic drugs while temporal lobe epilepsy with hippocampus sclerosis(TLE-HS)is drug resistant.Numerous herbs have been noted for their potential anti-convulsant properties.Yet,due to the scarcity of experimental data,there is an urgent need to conduct thorough investigations into these herbs for their practical use in treating TLE-HS.In-depth multi-omics research is needed for targeted TLE-HS therapy,focusing on cornu ammonis subregions,dentate gyrus,and also genetically glutamate,andγ-aminobutyric acid receptors.Animal models,due to the lack of human brain tissue,enable homogeneous sample selection,comparable groups,and ample tissue for in-vitro and ex-vivo studies.Consequently,it becomes feasible to examine the effectiveness of herbs on individual brain regions at the molecular level,paving the way for the potential development of drug interventions to treat TLE-HS.
基金Science and Technology Innovation 2030-“New Generation Artificial Intelligence”Major Project,Grant/Award Number:2022ZD0116305Anhui Province Natural Science Funds for Distinguished Young Scholar,Grant/Award Number:2308085J02+3 种基金National Natural Science Foundation of China,Grant/Award Numbers:U2013601,U20A20225,42107112,32070670Innovation Leading Talent of Anhui Province TeZhi planCAAI-Huawei Mind Spore Open Fund,Grant/Award Number:CAAIXSJLJJ-2022-011ANatural Science Foundation of Hefei,China,Grant/Award Number:202321。
文摘The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against cancer.These neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related fatalities.The accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal treatments.Addressing this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of accuracy.The MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN model.The outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST cases.The source code for this groundbreaking study is readily accessible for download at https://github.com/bigone1/MGTCN.
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.
