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
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
Acute myeloid leukemia(AML)is an aggressive hematologic malignancy characterized by poor clinical outcomes,frequently exacerbated by mutations in the FMS-like tyrosine kinase 3(FLT3)gene.Although FLT3 inhibitors(FLT3i...Acute myeloid leukemia(AML)is an aggressive hematologic malignancy characterized by poor clinical outcomes,frequently exacerbated by mutations in the FMS-like tyrosine kinase 3(FLT3)gene.Although FLT3 inhibitors(FLT3i)have emerged as promising therapeutic agents,the absence of molecular biomarkers to predict FLT3i response remains a critical limitation in clinical practice.In this study,we performed a comprehensive multi-omics analysis integrating transcriptomic,proteomic,and pharmacogenomic data from the Beat AML cohort,the Cancer Cell Line Encyclopedia(CCLE),and the PXD023201 repository to elucidate the molecular consequences of FLT3 mutations in AML.Our analysis revealed significant differences in RNA and protein expression profiles between FLT3-mutant and wild-type AML cases,with a particularly striking association between FLT3 mutations and immune suppression.We further evaluated the drug sensitivity of FLT3-mutant patients to 3 FDA-approved FLT3i,gilteritinib,midostaurin,and quizartinib,and observed heightened sensitivity in FLT3-mutant cohorts,accompanied by the activation of immune-related pathways in treatment-responsive groups.These findings suggest a potential synergy between FLT3i efficacy and immune activation.Through rigorous bioinformatic analysis,we identified 3 candidate biomarkers:CD36,SASH1,and NIBAN2,associated with FLT3i sensitivity.These biomarkers were consistently upregulated in favorable prognostic subgroups and demonstrated strong correlations with immune activation pathways.The identification of CD36,SASH1,and NIBAN2 as predictive biomarkers offers a novel toolset for stratifying FLT3i response and prognosis.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
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
Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technol...Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technology overcomes the limitations of traditional single-organ models,providing a novel platform for investigating complex disease mechanisms and evaluating drug efficacy and toxicity.Although it demonstrates broad application prospects,its development still faces critical bottlenecks,including inadequate physiological coupling between organs,short functional maintenance durations,and limited real-time monitoring capabilities.Contemporary research is advancing along three key directions,including functional coupling,sensor integration,and full-process automation systems,to propel the technology toward enhanced levels of physiological relevance and predictive accuracy.展开更多
China is carving out a distinctive development path which features urban-rural integration.This approach has not only yielded tangible results domestically but also drawn the attention of other countries.
Camellia oil extracted from the seeds of Camellia oleifera Abel.is a popular and high-quality edible oil,but its yield is limited by seed setting,which is mainly caused by self-incompatibility(SI).One of the obvious b...Camellia oil extracted from the seeds of Camellia oleifera Abel.is a popular and high-quality edible oil,but its yield is limited by seed setting,which is mainly caused by self-incompatibility(SI).One of the obvious biological features of SI plants is the inhibition of self-pollen tubes;however,the underlying mechanism of this inhibition in C.oleifera is poorly understood.In this study,we constructed a semi-in vivo pollen tube growth test(SIV-PGT)system that can screen for substances that inhibit self-pollen tubes without interference from the genetic background.Combined with multi-omics analysis,the results revealed the important role of galloylated catechins in self-pollen tube inhibition,and a possible molecular regulatory network mediated by UDP-glycosyltransferase(UGT)and serine carboxypeptidase-like(SCPL)was proposed.In summary,galloylation of catechins and high levels of galloylated catechins are specifically involved in pollen tube inhibition under self-pollination rather than cross-pollination,which provides a new understanding of SI in C.oleifera.These results will contribute to sexual reproduction research on C.oleifera and provide theoretical support for improving Camellia oil yield in production.展开更多
Recently developed technologies to generate single-cell genomic data have made a revolutionary impact in the field of biology.Multi-omics assays offer even greater opportunities to understand cellular states and biolo...Recently developed technologies to generate single-cell genomic data have made a revolutionary impact in the field of biology.Multi-omics assays offer even greater opportunities to understand cellular states and biological processes.The problem of integrating different omics data with very different dimensionality and statistical properties remains,however,quite challenging.A growing body of computational tools is being developed for this task,leveraging ideas ranging from machine translation to the theory of networks,and represents another frontier on the interface of biology and data science.Our goal in this review is to provide a comprehensive,up-to-date survey of computational techniques for the integration of single-cell multi-omics data,while making the concepts behind each algorithm approachable to a non-expert audience.展开更多
The recently developed technologies that allow the analysis of each single omics have provided an unbiased insight into ongoing disease processes.However,it remains challenging to specify the study design for the subs...The recently developed technologies that allow the analysis of each single omics have provided an unbiased insight into ongoing disease processes.However,it remains challenging to specify the study design for the subsequent integration strategies that can associate sepsis pathophysiology and clinical outcomes.Here,we conducted a time-dependent multi-omics integration(TDMI)in a sepsis-associated liver dysfunction(SALD)model.We successfully deduced the relation of the Toll-like receptor 4(TLR4)pathway with SALD.Although TLR4 is a critical factor in sepsis progression,it is not specified in single-omics analyses but only in the TDMI analysis.This finding indicates that the TDMI-based approach is more advantageous than single-omics analyses in terms of exploring the underlying pathophysiological mechanism of SALD.Furthermore,TDMI-based approach can be an ideal paradigm for insightful biological interpretations of multi-omics datasets that will potentially reveal novel insights into basic biology,health,and diseases,thus allowing the identification of promising candidates for therapeutic strategies.展开更多
T-cell acute lymphoblastic leukemia(T-ALL),a heterogeneous hematological malignancy,is caused by the developmental arrest of normal T-cell progenitors.The development of targeted therapeutic regimens is impeded by poo...T-cell acute lymphoblastic leukemia(T-ALL),a heterogeneous hematological malignancy,is caused by the developmental arrest of normal T-cell progenitors.The development of targeted therapeutic regimens is impeded by poor knowledge of the stage-specific aberrances in this disease.In this study,we performed multi-omics integration analysis,which included mRNA expression,chromatin accessibility,and gene-dependency database analyses,to identify potential stage-specific druggable targets and repositioned drugs for this disease.This multi-omics integration helped identify 29 potential pathological genes for T-ALL.These genes exhibited tissue-specific expression profiles and were enriched in the cell cycle,hematopoietic stem cell differentiation,and the AMPK signaling pathway.Of these,four known druggable targets(CDK6,TUBA1A,TUBB,and TYMS)showed dysregulated and stage-specific expression in malignant T cells and may serve as stage-specific targets in T-ALL.The TUBA1A expression level was higher in the early T cell precursor(ETP)-ALL cells,while TUBB and TYMS were mainly highly expressed in malignant T cells arrested at the CD4 and CD8 double-positive or single-positive stage.CDK6 exhibited a U-shaped expression pattern in malignant T cells along the naıve to maturation stages.Furthermore,mebendazole and gemcitabine,which target TUBA1A and TYMS,respectively,exerted stage-specific inhibitory effects on T-ALL cell lines,indicating their potential stage-specific antileukemic role in T-ALL.Collectively,our findings might aid in identifying potential stage-specific druggable targets and are promising for achieving more precise therapeutic strategies for T-ALL.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
Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.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
Sesquiterpene valencene is dominant in flavedo tissues of sweet oranges and imparts a unique woody aroma.However,the interaction between the biosynthetic pathways of valencene and other nutritional compounds is less s...Sesquiterpene valencene is dominant in flavedo tissues of sweet oranges and imparts a unique woody aroma.However,the interaction between the biosynthetic pathways of valencene and other nutritional compounds is less studied.Sesquiterpenoids were significantly accumulated in a previously reported glossy mutant of orange(MT)than the wild type(WT),especially valencene and caryophyllene.In addition,we identified several other pathways with variations at both the transcriptional and metabolic levels in MT.It’s interesting to found those upregulated metabolites in MT,such as eukaryotic lipids,kaempferol and proline also showed strong positive correlation with valencene along with fruit maturation while those down-regulated metabolites,such as phenylpropanoid coumarins and most of the modified flavonoids exhibited negative correlation.We then categorized these shifted pathways into the‘sesquitepenoid-identical shunt’and the sesquitepenoid-opposite shunt’and confirmed the classification result at transcriptional level.Our results provide important insights into the connections between various fruit quality-related properties.展开更多
The rapid growth of artificial intelligence has accelerated data generation,which increasingly exposes the limitations faced by traditional computational architectures,particularly in terms of energy consumption and d...The rapid growth of artificial intelligence has accelerated data generation,which increasingly exposes the limitations faced by traditional computational architectures,particularly in terms of energy consumption and data latency.In contrast,data-centric computing that integrates processing and storage has the potential of reducing latency and energy usage.Organic optoelectronic synaptic transistors have emerged as one type of promising devices to implement the data-centric com-puting paradigm owing to their superiority of flexibility,low cost,and large-area fabrication.However,sophisticated functions including vector-matrix multiplication that a single device can achieve are limited.Thus,the fabrication and utilization of organic optoelectronic synaptic transistor arrays(OOSTAs)are imperative.Here,we summarize the recent advances in OOSTAs.Various strategies for manufacturing OOSTAs are introduced,including coating and casting,physical vapor deposition,printing,and photolithography.Furthermore,innovative applications of the OOSTA system integration are discussed,including neuromor-phic visual systems and neuromorphic computing systems.At last,challenges and future perspectives of utilizing OOSTAs in real-world applications are discussed.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
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
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
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 grants from the CAMS Innovation Fund for Medical Science(2023-I2M-3-014 to H.W.,2023-I2M-2-007 to H.W.,2021-I2M-1-073 to H.W.)the National Natural Science Foundation of China(82270236 to H.W.,82341080 to H.W.,82400271 to M.N.,82273217 to L.S.)+2 种基金the Fundamental Research Funds for the Central Universities,Peking Union Medical College(3332024200 to M.N.)the distinguished Young Scholars of Tianjin(22JCJQJC00090 to H.W.)the Tianjin Municipal Science and Technology Commission Grant(22JCQNJC00040 to L.S.).
文摘Acute myeloid leukemia(AML)is an aggressive hematologic malignancy characterized by poor clinical outcomes,frequently exacerbated by mutations in the FMS-like tyrosine kinase 3(FLT3)gene.Although FLT3 inhibitors(FLT3i)have emerged as promising therapeutic agents,the absence of molecular biomarkers to predict FLT3i response remains a critical limitation in clinical practice.In this study,we performed a comprehensive multi-omics analysis integrating transcriptomic,proteomic,and pharmacogenomic data from the Beat AML cohort,the Cancer Cell Line Encyclopedia(CCLE),and the PXD023201 repository to elucidate the molecular consequences of FLT3 mutations in AML.Our analysis revealed significant differences in RNA and protein expression profiles between FLT3-mutant and wild-type AML cases,with a particularly striking association between FLT3 mutations and immune suppression.We further evaluated the drug sensitivity of FLT3-mutant patients to 3 FDA-approved FLT3i,gilteritinib,midostaurin,and quizartinib,and observed heightened sensitivity in FLT3-mutant cohorts,accompanied by the activation of immune-related pathways in treatment-responsive groups.These findings suggest a potential synergy between FLT3i efficacy and immune activation.Through rigorous bioinformatic analysis,we identified 3 candidate biomarkers:CD36,SASH1,and NIBAN2,associated with FLT3i sensitivity.These biomarkers were consistently upregulated in favorable prognostic subgroups and demonstrated strong correlations with immune activation pathways.The identification of CD36,SASH1,and NIBAN2 as predictive biomarkers offers a novel toolset for stratifying FLT3i response and prognosis.
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.
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 the Shenzhen Medical Research Fund(Grant No.A2303049)Guangdong Basic and Applied Basic Research(Grant No.2023A1515010647)+1 种基金National Natural Science Foundation of China(Grant No.22004135)Shenzhen Science and Technology Program(Grant No.RCBS20210706092409020,GXWD20201231165807008,20200824162253002).
文摘Multi-organ-on-a-chip(MOOC)technology represents a pivotal direction in the organ-on-a-chip field,seeking to emulate the complex interactions of multiple human organs in vitro through microfluidic systems.This technology overcomes the limitations of traditional single-organ models,providing a novel platform for investigating complex disease mechanisms and evaluating drug efficacy and toxicity.Although it demonstrates broad application prospects,its development still faces critical bottlenecks,including inadequate physiological coupling between organs,short functional maintenance durations,and limited real-time monitoring capabilities.Contemporary research is advancing along three key directions,including functional coupling,sensor integration,and full-process automation systems,to propel the technology toward enhanced levels of physiological relevance and predictive accuracy.
文摘China is carving out a distinctive development path which features urban-rural integration.This approach has not only yielded tangible results domestically but also drawn the attention of other countries.
基金Our work was supported by the National Key R&D Program of China(2018YFD1000603-1)the Natural Science Foundation of Hunan Province(2020JJ5968)+2 种基金Scientific Research Foundation for Advanced Talents of Central South University of Forestry and Technology(2018YJ002)Special Funds for the Construction of Innovative Provinces in Hunan(2021NK1007)the Key Program of Education Department of Hunan Province(grant no.20A524).
文摘Camellia oil extracted from the seeds of Camellia oleifera Abel.is a popular and high-quality edible oil,but its yield is limited by seed setting,which is mainly caused by self-incompatibility(SI).One of the obvious biological features of SI plants is the inhibition of self-pollen tubes;however,the underlying mechanism of this inhibition in C.oleifera is poorly understood.In this study,we constructed a semi-in vivo pollen tube growth test(SIV-PGT)system that can screen for substances that inhibit self-pollen tubes without interference from the genetic background.Combined with multi-omics analysis,the results revealed the important role of galloylated catechins in self-pollen tube inhibition,and a possible molecular regulatory network mediated by UDP-glycosyltransferase(UGT)and serine carboxypeptidase-like(SCPL)was proposed.In summary,galloylation of catechins and high levels of galloylated catechins are specifically involved in pollen tube inhibition under self-pollination rather than cross-pollination,which provides a new understanding of SI in C.oleifera.These results will contribute to sexual reproduction research on C.oleifera and provide theoretical support for improving Camellia oil yield in production.
基金supported by R01 (Grant Nos. LM012373 and LM012907) awarded by the National Library of MedicineR01 (Grant No. HD084633) awarded by the National Institute of Child Health and Human Development to Lana X. Garmire
文摘Recently developed technologies to generate single-cell genomic data have made a revolutionary impact in the field of biology.Multi-omics assays offer even greater opportunities to understand cellular states and biological processes.The problem of integrating different omics data with very different dimensionality and statistical properties remains,however,quite challenging.A growing body of computational tools is being developed for this task,leveraging ideas ranging from machine translation to the theory of networks,and represents another frontier on the interface of biology and data science.Our goal in this review is to provide a comprehensive,up-to-date survey of computational techniques for the integration of single-cell multi-omics data,while making the concepts behind each algorithm approachable to a non-expert audience.
基金supported by the National Research Foundation of Korea funded by the Korean government[Ministry of Science and ICT(MSIT)](Grant Nos.2021R1A6A3A01086425 and 2022R1A4A1018900).
文摘The recently developed technologies that allow the analysis of each single omics have provided an unbiased insight into ongoing disease processes.However,it remains challenging to specify the study design for the subsequent integration strategies that can associate sepsis pathophysiology and clinical outcomes.Here,we conducted a time-dependent multi-omics integration(TDMI)in a sepsis-associated liver dysfunction(SALD)model.We successfully deduced the relation of the Toll-like receptor 4(TLR4)pathway with SALD.Although TLR4 is a critical factor in sepsis progression,it is not specified in single-omics analyses but only in the TDMI analysis.This finding indicates that the TDMI-based approach is more advantageous than single-omics analyses in terms of exploring the underlying pathophysiological mechanism of SALD.Furthermore,TDMI-based approach can be an ideal paradigm for insightful biological interpretations of multi-omics datasets that will potentially reveal novel insights into basic biology,health,and diseases,thus allowing the identification of promising candidates for therapeutic strategies.
基金the National Natural Science Foundation of China(No.82070167,81870126,81900190,81802803)The Chongqing Science and Technology Bureau Major Project,Chongqing,China(No.cstc2020jcyjmsxmX0782).
文摘T-cell acute lymphoblastic leukemia(T-ALL),a heterogeneous hematological malignancy,is caused by the developmental arrest of normal T-cell progenitors.The development of targeted therapeutic regimens is impeded by poor knowledge of the stage-specific aberrances in this disease.In this study,we performed multi-omics integration analysis,which included mRNA expression,chromatin accessibility,and gene-dependency database analyses,to identify potential stage-specific druggable targets and repositioned drugs for this disease.This multi-omics integration helped identify 29 potential pathological genes for T-ALL.These genes exhibited tissue-specific expression profiles and were enriched in the cell cycle,hematopoietic stem cell differentiation,and the AMPK signaling pathway.Of these,four known druggable targets(CDK6,TUBA1A,TUBB,and TYMS)showed dysregulated and stage-specific expression in malignant T cells and may serve as stage-specific targets in T-ALL.The TUBA1A expression level was higher in the early T cell precursor(ETP)-ALL cells,while TUBB and TYMS were mainly highly expressed in malignant T cells arrested at the CD4 and CD8 double-positive or single-positive stage.CDK6 exhibited a U-shaped expression pattern in malignant T cells along the naıve to maturation stages.Furthermore,mebendazole and gemcitabine,which target TUBA1A and TYMS,respectively,exerted stage-specific inhibitory effects on T-ALL cell lines,indicating their potential stage-specific antileukemic role in T-ALL.Collectively,our findings might aid in identifying potential stage-specific druggable targets and are promising for achieving more precise therapeutic strategies for T-ALL.
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.
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
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.
基金financed by the National Natural Science Foundation of China (31772261)the Huazhong Agricultural University Scientific & Technological Self-Innovation Foundation (Program No.2017RC002) to Dr. Weiwei Wen
文摘Sesquiterpene valencene is dominant in flavedo tissues of sweet oranges and imparts a unique woody aroma.However,the interaction between the biosynthetic pathways of valencene and other nutritional compounds is less studied.Sesquiterpenoids were significantly accumulated in a previously reported glossy mutant of orange(MT)than the wild type(WT),especially valencene and caryophyllene.In addition,we identified several other pathways with variations at both the transcriptional and metabolic levels in MT.It’s interesting to found those upregulated metabolites in MT,such as eukaryotic lipids,kaempferol and proline also showed strong positive correlation with valencene along with fruit maturation while those down-regulated metabolites,such as phenylpropanoid coumarins and most of the modified flavonoids exhibited negative correlation.We then categorized these shifted pathways into the‘sesquitepenoid-identical shunt’and the sesquitepenoid-opposite shunt’and confirmed the classification result at transcriptional level.Our results provide important insights into the connections between various fruit quality-related properties.
基金supported by the National Key Research and Development Program of China(2021YFA1101303)the National Natural Science Foundation of China(62374115)the Innovation Program of Shanghai Municipal Education Commission(2021-01-07-00-07-E00096).
文摘The rapid growth of artificial intelligence has accelerated data generation,which increasingly exposes the limitations faced by traditional computational architectures,particularly in terms of energy consumption and data latency.In contrast,data-centric computing that integrates processing and storage has the potential of reducing latency and energy usage.Organic optoelectronic synaptic transistors have emerged as one type of promising devices to implement the data-centric com-puting paradigm owing to their superiority of flexibility,low cost,and large-area fabrication.However,sophisticated functions including vector-matrix multiplication that a single device can achieve are limited.Thus,the fabrication and utilization of organic optoelectronic synaptic transistor arrays(OOSTAs)are imperative.Here,we summarize the recent advances in OOSTAs.Various strategies for manufacturing OOSTAs are introduced,including coating and casting,physical vapor deposition,printing,and photolithography.Furthermore,innovative applications of the OOSTA system integration are discussed,including neuromor-phic visual systems and neuromorphic computing systems.At last,challenges and future perspectives of utilizing OOSTAs in real-world applications are discussed.
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 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.
