The integration of artificial intelligence(AI)and multiomics has transformed clinical and life sciences,enabling precision medicine and redefining disease understanding.Scientific publications grew significantly from ...The integration of artificial intelligence(AI)and multiomics has transformed clinical and life sciences,enabling precision medicine and redefining disease understanding.Scientific publications grew significantly from 2.1 million in 2012 to 3.3 million in 2022,with AI research tripling during this period.Multiomics fields,including genomics and proteomics,also advanced,exemplified by the Human Proteome Project achieving a 90%complete blueprint by 2021.This growth highlights opportunities and challenges in integrating AI and multiomics into clinical reporting.A review of studies and case reports was conducted to evaluate AI and multiomics integration.Key areas analyzed included diagnostic accuracy,predictive modeling,and personalized treatment approaches driven by AI tools.Case examples were studied to assess impacts on clinical decision-making.AI and multiomics enhanced data integration,predictive insights,and treatment personalization.Fields like radiomics,genomics,and proteomics improved diagnostics and guided therapy.For instance,the“AI radiomics,geno-mics,oncopathomics,and surgomics project”combined radiomics and genomics for surgical decision-making,enabling preoperative,intraoperative,and post-operative interventions.AI applications in case reports predicted conditions like postoperative delirium and monitored cancer progression using genomic and imaging data.AI and multiomics enable standardized data analysis,dynamic updates,and predictive modeling in case reports.Traditional reports often lack objectivity,but AI enhances reproducibility and decision-making by processing large datasets.Challenges include data standardization,biases,and ethical concerns.Overcoming these barriers is vital for optimizing AI applications and advancing personalized medicine.AI and multiomics integration is revolutionizing clinical research and practice.Standardizing data reporting and addressing challenges in ethics and data quality will unlock their full potential.Emphasizing collaboration and transparency is essential for leveraging these tools to improve patient care and scientific communication.展开更多
Objective This study was aimed to explore the prolonged therapeutic profile and underlying mechanisms of Yiqi Zishen Formula(YZF)in chronic obstructive pulmonary disease(COPD)management.Methods A COPD rat model was es...Objective This study was aimed to explore the prolonged therapeutic profile and underlying mechanisms of Yiqi Zishen Formula(YZF)in chronic obstructive pulmonary disease(COPD)management.Methods A COPD rat model was established through exposure to tobacco smoke and Klebsiella pneumoniae infections from weeks 1 to 8,followed by treatment with YZF from weeks 9 to 20.No treatment was administered from weeks 21 to 31.At week 32,all rats were euthanized,and lung tissue samples and blood specimens were collected for subsequent analyses.Then,comprehensive multiomics profiling—encompassing transcriptomics,proteomics,andmetabolomics—was conducted to identify differentially expressed molecules in lung tissues and elucidate the underlying molecular mechanisms.Results By week 32,sustained therapeutic efficacy became apparent,characterized by diminished inflammatory cytokine expression,mitigation of protease–antiprotease dysregulation,and reduced collagen deposition.These differentially expressed molecules were predominantly enriched in pathways related to oxidoreductase activity,antioxidant homeostasis,focal adhesion,tight junction formation,adherens junction dynamics,and lipid metabolism regulation.Integrative analysis of predicted targets,transcriptomic,proteomic,and metabolomic datasets revealed that differentially expressed molecules in YZF-treated rats and YZF-targeted proteins collectively participated in lipid metabolism,inflammatory responses,oxidative stress,and focal adhesion pathways.Conclusion YZF provides sustained therapeutic benefits in COPD rat models,potentially through systemic regulation of lipid metabolism,inflammatory responses,oxidative stress,and focal adhesion pathways.展开更多
Maize kernel moisture content(KMC)at harvest greatly affects mechanical harvesting,transport and storage.KMC is correlated with kernel dehydration rate(KDR)before and after physiological maturity.KMC and KDR are compl...Maize kernel moisture content(KMC)at harvest greatly affects mechanical harvesting,transport and storage.KMC is correlated with kernel dehydration rate(KDR)before and after physiological maturity.KMC and KDR are complex traits governed by multiple quantitative trait loci(QTL).Their genetic architecture is incompletely understood.We used a multiomics integration approach with an association panel to identify genes influencing KMC and KDR.A genome-wide association study using time-series KMC data from 7 to 70 days after pollination and their transformed KDR data revealed respectively 98and 279 loci significantly associated with KMC and KDR.Time-series transcriptome and proteome datasets were generated to construct KMC correlation networks,from which respectively 3111 and 759 module genes and proteins were identified as highly associated with KMC.Integrating multiomics analysis,several promising candidate genes for KMC and KDR,including Zm00001d047799 and Zm00001d035920,were identified.Further mutant experiments showed that Zm00001d047799,a gene encoding heat shock 70 kDa protein 5,reduced KMC in the late stage of kernel development.Our study provides resources for the identification of candidate genes influencing maize KMC and KDR,shedding light on the genetic architecture of dynamic changes in maize KMC.展开更多
Genetic,epigenetic,and metabolic alterations are all hallmarks of cancer.However,the epigenome and metabolome are both highly complex and dynamic biological networks in vivo.The interplay between the epigenome and met...Genetic,epigenetic,and metabolic alterations are all hallmarks of cancer.However,the epigenome and metabolome are both highly complex and dynamic biological networks in vivo.The interplay between the epigenome and metabolome contributes to a biological system that is responsive to the tumor microenvironment and possesses a wealth of unknown biomarkers and targets of cancer therapy.From this perspective,we first review the state of high-throughput biological data acquisition(i.e.multiomics data)and analysis(i.e.computational tools)and then propose a conceptual in silico metabolic and epigenetic regulatory network(MER-Net)that is based on these current high-throughput methods.The conceptual MER-Net is aimed at linking metabolomic and epigenomic networks through observation of biological processes,omics data acquisition,analysis of network information,and integration with validated database knowledge.Thus,MER-Net could be used to reveal new potential biomarkers and therapeutic targets using deep learning models to integrate and analyze large multiomics networks.We propose that MER-Net can serve as a tool to guide integrated metabolomics and epigenomics research or can be modified to answer other complex biological and clinical questions using multiomics data.展开更多
The meticulous examination of the genomic,transcriptomic,epigenomic,and proteomic landscapes,conducted at the precise resolution of single cells,has emerged as an indispensable instrument for comprehending the inheren...The meticulous examination of the genomic,transcriptomic,epigenomic,and proteomic landscapes,conducted at the precise resolution of single cells,has emerged as an indispensable instrument for comprehending the inherent mechanisms governing cellular heterogeneity.These methodologies have provided unprecedented insights into the intrinsic and extrinsic factors that underlie cellular morphological characteristics and differentiated functions.Within this field,multimodal techniques that concurrently analyze the epigenetic features of chromatin or cellular proteins and gene expression within an identical cell delineate intricate gene regulatory networks and phenotypes,thereby enhancing our understanding of cellular states during differentiation or pathological conditions.These techniques can be applied to identify cell subpopulations,infer cell developmental trajectories,and analyze patterns of cell-to-cell communication.In this context,we initiate by delineating the singular cell separation techniques employed in single-cell multiomics.Subsequently,we narrow our focus to methodologies amalgamating epigenetic features with gene expression at single-cell resolution.The epigenetic features entail DNA methylation,chromatin accessibility,histone modifications,chromatin conformation,and transcription factors.Following this,we discuss techniques for the conjoint analysis of cell surface and intracellular proteins in tandem with the transcriptome.Finally,we discuss the challenges and opportunities that manifest within this field,contributing to its continued advancement and exploration.展开更多
Meniscal injury presents a formidable challenge and often leads to functional impairment and osteoarthritic progression.Meniscus tissue engineering(MTE)is a promising solution,as conventional strategies for modu-latin...Meniscal injury presents a formidable challenge and often leads to functional impairment and osteoarthritic progression.Meniscus tissue engineering(MTE)is a promising solution,as conventional strategies for modu-lating local immune responses and generating a conducive microenvironment for effective tissue repair are lacking.Recently,magnesium-containing bioactive glass nanospheres(Mg-BGNs)have shown promise in tissue regeneration.However,few studies have explored the ability of Mg-BGNs to promote meniscal regeneration.First,we verified the anti-inflammatory and fibrochondrogenic abilities of Mg-BGNs in vitro.A comprehensive in vivo evaluation of a rabbit critical-size meniscectomy model revealed that Mg-BGNs have multiple effects on meniscal reconstruction and effectively promote fibrochondrogenesis,collagen deposition,and cartilage pro-tection.Multiomics analysis was subsequently performed to further explore the mechanism by which Mg-BGNs regulate the regenerative microenvironment.Mechanistically,Mg-BGNs first activate the TRPM7 ion channel through the PI3K/AKT signaling pathway to promote the cellular function of synovium-derived mesenchymal stem cells and then activate the PPARγ/NF-κB axis to modulate macrophage polarization and inflammatory reactions.We demonstrated that Mg^(2+)is critical for the crosstalk among biomaterials,immune cells,and effector cells in Mg-BGN-mediated tissue regeneration.This study provides a theoretical basis for the application of Mg-BGNs as nanomedicines to achieve in situ tissue regeneration in complex intrajoint pathological microenvironments.展开更多
The journal Genomics,Proteomics&Bioinformatics(GPB)is interested in submissions across all areas of life science,biology,and biomedicine,focusing on large data acquisition,analysis,and curation.GPB is inviting sub...The journal Genomics,Proteomics&Bioinformatics(GPB)is interested in submissions across all areas of life science,biology,and biomedicine,focusing on large data acquisition,analysis,and curation.GPB is inviting submissions for a special issue on the topic of"Spatial Multiomics"(to be published in the Winter of 2025),which will aim to explore methodological advancements,computational data analyses,and applications of spatial multiomics in biological and medical research.展开更多
The journal Genomics,Proteomics&Bioinformatics(GPB)is interested in submissions across all areas of life science,biology,and biomedicine,focusing on large data acquisition,analysis,and curation.GPB is inviting sub...The journal Genomics,Proteomics&Bioinformatics(GPB)is interested in submissions across all areas of life science,biology,and biomedicine,focusing on large data acquisition,analysis,and curation.GPB is inviting submissions for a special issue on the topic of"Spatial Multiomics"(to be published in the Winter of 2025),which will aim to explore methodological advancements,computational data analyses,and applications of spatial multiomics in biological and medical research.展开更多
Background:Intrahepatic cholangiocarcinoma(iCCA)is a highly heteroge-neous and lethal hepatobiliary tumor with few therapeutic strategies.The metabolic reprogramming of tumor cells plays an essential role in the devel...Background:Intrahepatic cholangiocarcinoma(iCCA)is a highly heteroge-neous and lethal hepatobiliary tumor with few therapeutic strategies.The metabolic reprogramming of tumor cells plays an essential role in the develop-ment of tumors,while the metabolic molecular classification of iCCA is largely unknown.Here,we performed an integrated multiomics analysis and metabolic classification to depict differences in metabolic characteristics of iCCA patients,hoping to provide a novel perspective to understand and treat iCCA.Methods:We performed integrated multiomics analysis in 116 iCCA samples,including whole-exome sequencing,bulk RNA-sequencing and proteome anal-ysis.Based on the non-negative matrix factorization method and the protein abundance of metabolic genes in human genome-scale metabolic models,the metabolic subtype of iCCA was determined.Survival and prognostic gene analy-ses were used to compare overall survival(OS)differences between metabolic subtypes.Cell proliferation analysis,5-ethynyl-2’-deoxyuridine(EdU)assay,colony formation assay,RNA-sequencing and Western blotting were performed to investigate the molecular mechanisms of diacylglycerol kinaseα(DGKA)in iCCA cells.Results:Three metabolic subtypes(S1-S3)with subtype-specific biomarkers of iCCA were identified.These metabolic subtypes presented with distinct prog-noses,metabolic features,immune microenvironments,and genetic alterations.The S2 subtype with the worst survival showed the activation of some special metabolic processes,immune-suppressed microenvironment and Kirsten ratsar-coma viral oncogene homolog(KRAS)/AT-rich interactive domain 1A(ARID1A)mutations.Among the S2 subtype-specific upregulated proteins,DGKA was further identified as a potential drug target for iCCA,which promoted cell proliferation by enhancing phosphatidic acid(PA)metabolism and activating mitogen-activated protein kinase(MAPK)signaling.Conclusion:Viamultiomics analyses,we identified three metabolic subtypes of iCCA,revealing that the S2 subtype exhibited the poorest survival outcomes.We further identified DGKA as a potential target for the S2 subtype.展开更多
Despite the success of antiretroviral therapy,human immunodeficiency virus(HIV)cannot be cured because of a reservoir of latently infected cells that evades therapy.To understand the mechanisms of HIV latency,we emplo...Despite the success of antiretroviral therapy,human immunodeficiency virus(HIV)cannot be cured because of a reservoir of latently infected cells that evades therapy.To understand the mechanisms of HIV latency,we employed an integrated single-cell RNA sequencing(scRNA-seq)and single-cell assay for transposase-accessible chromatin with sequencing(scATAC-seq)approach to simultaneously profile the transcriptomic and epigenomic characteristics of~125,000 latently infected primary CD4^(+)T cells after reactivation using three different latency reversing agents.Differentially expressed genes and differentially accessible motifs were used to examine transcriptional pathways and transcription factor(TF)activities across the cell population.We identified cellular transcripts and TFs whose expression/activity was correlated with viral reactivation and demonstrated that a machine learning model trained on these data was 75%-79%accurate at predicting viral reactivation.Finally,we validated the role of two candidate HIV-regulating factors,FOXP1 and GATA3,in viral transcription.These data demonstrate the power of integrated multimodal single-cell analysis to uncover novel relationships between host cell factors and HIV latency.展开更多
During the process of carcinogenesis and tumor progression,various molecular alternations occur in different omics levels.In recent years,multiomics approaches including genomics,epigenetics,transcriptomics,proteomics...During the process of carcinogenesis and tumor progression,various molecular alternations occur in different omics levels.In recent years,multiomics approaches including genomics,epigenetics,transcriptomics,proteomics,metabolomics,single-cell omics,and spatial omics have been applied in mapping diverse omics profiles of cancers.The development of high-throughput technologies such as sequencing and mass spectrometry has revealed different omics levels of tumor cells or tissues separately.While focusing on a single omics level results in a lack of accuracy,joining multiple omics approaches together undoubtedly benefits accurate molecular subtyping and precision medicine for cancer patients.With the deepening of tumor research in recent years,taking pathological classification as the only criterion of diagnosis and predicting prognosis and treatment response is found to be not accurate enough.Therefore,identifying precise molecular subtypes by exploring the molecular alternations during tumor occurrence and development is of vital importance.The review provides an overview of the advanced technologies and recent progress in multiomics applied in cancer molecular subtyping and detailedly explains the application of multiomics in identifying cancer driver genes and metastasis-related genes,exploring tumor microenvironment,and selecting liquid biopsy biomarkers and potential therapeutic targets.展开更多
High-throughput technologies for multiomics or molecular phenomics profiling have been extensively adopted in biomedical research and clinical applications,offering a more comprehensive understanding of biological pro...High-throughput technologies for multiomics or molecular phenomics profiling have been extensively adopted in biomedical research and clinical applications,offering a more comprehensive understanding of biological processes and diseases.Omics reference materials play a pivotal role in ensuring the accuracy,reliability,and comparability of laboratory measurements and analyses.However,the current application of omics reference materials has revealed several issues,including inappropriate selection and underutilization,leading to inconsistencies across laboratories.This review aims to address these concerns by emphasizing the importance of well-characterized reference materials at each level of omics,encompassing(epi-)genomics,transcriptomics,proteomics,and metabolomics.By summarizing their characteristics,advantages,and limitations along with appropriate performance metrics pertinent to study purposes,we provide an overview of how omics reference materials can enhance data quality and data integration,thus fostering robust scientific investigations with omics technologies.展开更多
Identifying cancer driver genes has paramount significance in elucidating the intricate mechanisms underlying cancer development,progression,and therapeutic interventions.Abundant omics data and interactome networks p...Identifying cancer driver genes has paramount significance in elucidating the intricate mechanisms underlying cancer development,progression,and therapeutic interventions.Abundant omics data and interactome networks provided by numerous extensive databases enable the application of graph deep learning techniques that incorporate network structures into the deep learning framework.However,most existing models primarily focus on individual network,inevitably neglecting the incompleteness and noise of interactions.Moreover,samples with imbalanced classes in driver gene identification hamper the performance of models.To address this,we propose a novel deep learning framework MMGN,which integrates multiplex networks and pan-cancer multiomics data using graph neural networks combined with negative sample inference to discover cancer driver genes,which not only enhances gene feature learning based on the mutual information and the consensus regularizer,but also achieves balanced class of positive and negative samples for model training.The reliability of MMGN has been verified by the Area Under the Receiver Operating Characteristic curves(AUROC)and the Area Under the Precision-Recall Curves(AUPRC).We believe MMGN has the potential to provide new prospects in precision oncology and may find broader applications in predicting biomarkers for other intricate diseases.展开更多
Background Moyamoya disease(MMD)is a rare and complex cerebrovascular disorder characterized by the progressive narrowing of the internal carotid arteries and the formation of compensatory collateral vessels.The etiol...Background Moyamoya disease(MMD)is a rare and complex cerebrovascular disorder characterized by the progressive narrowing of the internal carotid arteries and the formation of compensatory collateral vessels.The etiology of MMD remains enigmatic,making diagnosis and management challenging.The MOYAOMICS project was initiated to investigate the molecular underpinnings of MMD and explore potential diagnostic and therapeutic strategies.Methods The MOYAOMICS project employs a multidisciplinary approach,integrating various omics technologies,including genomics,transcriptomics,proteomics,and metabolomics,to comprehensively examine the molecular signatures associated with MMD pathogenesis.Additionally,we will investigate the potential influence of gut microbiota and brain-gut peptides on MMD development,assessing their suitability as targets for therapeutic strategies and dietary interventions.Radiomics,a specialized field in medical imaging,is utilized to analyze neuroimaging data for early detection and characterization of MMD-related brain changes.Deep learning algorithms are employed to differentiate MMD from other conditions,automating the diagnostic process.We also employ single-cellomics and mass cytometry to precisely study cellular heterogeneity in peripheral blood samples from MMD patients.Conclusions The MOYAOMICS project represents a significant step toward comprehending MMD’s molecular underpinnings.This multidisciplinary approach has the potential to revolutionize early diagnosis,patient stratification,and the development of targeted therapies for MMD.The identification of blood-based biomarkers and the integration of multiple omics data are critical for improving the clinical management of MMD and enhancing patient outcomes for this complex disease.展开更多
Advanced chronic liver disease(ACLD)can lead to fibrosis,cirrhosis,and eventually hepatocellular carcinoma(HCC)[1].Hepatic fibrosis is characterized by formation of a fibrous scar because of accumulation of extracellu...Advanced chronic liver disease(ACLD)can lead to fibrosis,cirrhosis,and eventually hepatocellular carcinoma(HCC)[1].Hepatic fibrosis is characterized by formation of a fibrous scar because of accumulation of extracellular matrix proteins,predominantly crosslinked type I and type III collagens,which replace the damaged tissue[2].展开更多
The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This...The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This era integrates biotechnology,artificial intelligence(AI),and big data information technology.In contrast,China is still in a transition period between stages 2.0 and 3.0,which primarily relies on conventional selection and molecular breeding.In the context of increasingly complex international situations,accurately identifying core issues in China's seed industry innovation and seizing the frontier of international seed technology are strategically important.These efforts are essential for ensuring food security and revitalizing the seed industry.This paper systematically analyzes the characteristics of crop breeding data from artificial selection to intelligent design breeding.It explores the applications and development trends of AI and big data in modern crop breeding from several key perspectives.These include highthroughput phenotype acquisition and analysis,multiomics big data database and management system construction,AI-based multiomics integrated analysis,and the development of intelligent breeding software tools based on biological big data and AI technology.Based on an in-depth analysis of the current status and challenges of China's seed industry technology development,we propose strategic goals and key tasks for China's new generation of AI and big data-driven intelligent design breeding.These suggestions aim to accelerate the development of an intelligent-driven crop breeding engineering system that features large-scale gene mining,efficient gene manipulation,engineered variety design,and systematized biobreeding.This study provides a theoretical basis and practical guidance for the development of China's seed industry technology.展开更多
Metabolic-associated fatty liver disease(MAFLD),formerly known as nonalcoho-lic fatty liver disease,is an increasing global health challenge with substantial implications for metabolic and cardiovascular health(CVH).A...Metabolic-associated fatty liver disease(MAFLD),formerly known as nonalcoho-lic fatty liver disease,is an increasing global health challenge with substantial implications for metabolic and cardiovascular health(CVH).A recent study by Fu et al investigated the relationship between CVH metrics,specifically Life’s Simple 7 and Life’s Essential 8,and the prevalence of MAFLD.While this study offered important insights into the relationship between CVH and MAFLD,several me-thodological limitations,unaddressed confounding factors,and potential biases that could impact the interpretation of their findings should be considered.The study’s cross-sectional nature restricted the ability to draw causal conclusions,and it did not fully account for potential confounding factors such as dietary habits,genetic predispositions,and medication use.Furthermore,relying on tran-sient elastography to diagnose MAFLD introduces certain diagnostic limitations.Longitudinal study designs,advanced statistical modeling techniques,and diverse population groups should be utilized to strengthen future research.Exploring the mechanistic pathways that link CVH metrics to MAFLD through multi-omics approaches and interventional studies will be essential in formulating targeted prevention and treatment strategies.Structural equation modeling and machine learning techniques could provide a more refined analysis of these interrelated factors.Additionally,future research should employ longitudinal study designs and explore genetic and epigenetic influences to enhance our un-derstanding of CVH and MAFLD interactions.展开更多
Pu-erh tea,a traditional Chinese beverage,performs an anti-obesity function,but the correlation between its components and efficacy remains unknown.Here,we screened two Pu-erh teas with significant anti-obesity effica...Pu-erh tea,a traditional Chinese beverage,performs an anti-obesity function,but the correlation between its components and efficacy remains unknown.Here,we screened two Pu-erh teas with significant anti-obesity efficacies from 11 teas.In vitro experiments revealed that lipid accumulation in L02 cells and lipid synthesis in 3T3-L1 cells were significantly better inhibited by Tea-B than Tea-A.Further in vivo experiments using model mice revealed that the differences in chemical components generated two pathways in the anti-obesity efficacy and mechanism of Pu-erh teas.Tea-A changes the histomorphology of brown adipose tissue(BAT)and increases the abundance of Coriobacteriaceae_UCG_002 and cyclic AMP in guts through high chemical contents of cyclopentasiloxane,decamethyl,tridecane and 1,2,3-trimethoxybenzene,eventually increasing BAT activation and fat browning gene expression;the high content of hexadecane and 1,2-dimethoxybenzene in Tea-B reduces white adipose tissue(WAT)accumulation and the process of fatty liver,increases the abundance of Odoribacter and sphinganine 1-phosphate,inhibits the expression of lipid synthesis and transport genes.These mechanistic findings on the association of the representative bioactive components in Pu-erh teas with the anti-obesity phenotypes,gut microbes,gut metabolite structure and anti-obesity pathways,which were obtained for the first time,provide foundations for developing functional Pu-erh tea.展开更多
Alcohol-related liver disease(ARLD)remains a major public health concern,often diagnosed at advanced stages with limited treatment options.Early identification of high-risk individuals is crucial for timely interventi...Alcohol-related liver disease(ARLD)remains a major public health concern,often diagnosed at advanced stages with limited treatment options.Early identification of high-risk individuals is crucial for timely intervention and improved patient outcomes.Artificial intelligence(AI)has emerged as a powerful tool for predicting ARLD,leveraging multi-omics data,machine learning algorithms,and non-invasive biomarkers.This review explores the current advancements in AIdriven ARLD prediction,highlighting key methodologies such as multi-omics data integration,gut microbiome-based modeling,and predictive analytics using machine learning techniques.AI models incorporating transcriptomics,proteomics,and clinical data have demonstrated high diagnostic accuracy,with some achieving an area under the curve exceeding 0.90.Furthermore,non-invasive biomarkers,including liver stiffness measurements and circulating proteomic panels,have been successfully integrated into AI frameworks for early detection and risk stratification.Despite these advancements,challenges such as data heterogeneity,model generalizability,and ethical considerations remain.Future directions include the development of advanced biomarker discovery,wearable and point-of-care AI-integrated technologies,and precision medicine approaches tailored to individual risk profiles.AI-driven models hold significant potential in transforming ARLD prediction and management,ultimately contributing to early diagnosis and improved clinical outcomes.展开更多
BACKGROUND Hepatocellular carcinoma(HCC)is notorious for its aggressive progression and dismal prognosis,with chromatin accessibility dynamics emerging as pivotal yet poorly understood drivers.AIM To dissect how multi...BACKGROUND Hepatocellular carcinoma(HCC)is notorious for its aggressive progression and dismal prognosis,with chromatin accessibility dynamics emerging as pivotal yet poorly understood drivers.AIM To dissect how multilayered chromatin regulation sustains oncogenic transcription and tumor-stroma crosstalk in HCC,we combined multiomics single cell analysis.METHODS We integrated single-cell RNA sequencing and paired single-cell assay for transposase-accessible chromatin with sequencing data of HCC samples,complemented by bulk RNA sequencing validation across The Cancer Genome Atlas,Liver Cancer Institute,and GSE25907 cohorts.Cell type-specific chromatin architectures were resolved via ArchR,with regulatory hubs identified through peak-to-gene linkages and coaccessibility networks.Functional validation employed A485-mediated histone 3 lysine 27 acetylation suppression and small interfering RNA targeting DGAT1.RESULTS Malignant hepatocytes exhibited expanded chromatin accessibility profiles,characterized by increased numbers of accessible peaks and larger physical regions despite reduced peak intensity.Enhancer-like peaks enriched in malignant regulation,forming long-range hubs.Eighteen enhancer-like peak-related genes showed tumor-specific overexpression and diagnostic accuracy,correlating with poor prognosis.Intercellular coaccessibility analysis revealed tumor-stroma symbiosis via shared chromatin states.Pharmacological histone 3 lysine 27 acetylation inhibition paradoxically downregulated DGAT1,the hub gene most strongly regulated by chromatin accessibility.DGAT1 knockdown suppressed cell proliferation.CONCLUSION Multilayered chromatin reprogramming sustains HCC progression through tumor-stroma crosstalk and DGAT1-related oncogenic transcription,defining targetable epigenetic vulnerabilities.展开更多
文摘The integration of artificial intelligence(AI)and multiomics has transformed clinical and life sciences,enabling precision medicine and redefining disease understanding.Scientific publications grew significantly from 2.1 million in 2012 to 3.3 million in 2022,with AI research tripling during this period.Multiomics fields,including genomics and proteomics,also advanced,exemplified by the Human Proteome Project achieving a 90%complete blueprint by 2021.This growth highlights opportunities and challenges in integrating AI and multiomics into clinical reporting.A review of studies and case reports was conducted to evaluate AI and multiomics integration.Key areas analyzed included diagnostic accuracy,predictive modeling,and personalized treatment approaches driven by AI tools.Case examples were studied to assess impacts on clinical decision-making.AI and multiomics enhanced data integration,predictive insights,and treatment personalization.Fields like radiomics,genomics,and proteomics improved diagnostics and guided therapy.For instance,the“AI radiomics,geno-mics,oncopathomics,and surgomics project”combined radiomics and genomics for surgical decision-making,enabling preoperative,intraoperative,and post-operative interventions.AI applications in case reports predicted conditions like postoperative delirium and monitored cancer progression using genomic and imaging data.AI and multiomics enable standardized data analysis,dynamic updates,and predictive modeling in case reports.Traditional reports often lack objectivity,but AI enhances reproducibility and decision-making by processing large datasets.Challenges include data standardization,biases,and ethical concerns.Overcoming these barriers is vital for optimizing AI applications and advancing personalized medicine.AI and multiomics integration is revolutionizing clinical research and practice.Standardizing data reporting and addressing challenges in ethics and data quality will unlock their full potential.Emphasizing collaboration and transparency is essential for leveraging these tools to improve patient care and scientific communication.
基金supported by the National Natural Science Fund of China(81130062).
文摘Objective This study was aimed to explore the prolonged therapeutic profile and underlying mechanisms of Yiqi Zishen Formula(YZF)in chronic obstructive pulmonary disease(COPD)management.Methods A COPD rat model was established through exposure to tobacco smoke and Klebsiella pneumoniae infections from weeks 1 to 8,followed by treatment with YZF from weeks 9 to 20.No treatment was administered from weeks 21 to 31.At week 32,all rats were euthanized,and lung tissue samples and blood specimens were collected for subsequent analyses.Then,comprehensive multiomics profiling—encompassing transcriptomics,proteomics,andmetabolomics—was conducted to identify differentially expressed molecules in lung tissues and elucidate the underlying molecular mechanisms.Results By week 32,sustained therapeutic efficacy became apparent,characterized by diminished inflammatory cytokine expression,mitigation of protease–antiprotease dysregulation,and reduced collagen deposition.These differentially expressed molecules were predominantly enriched in pathways related to oxidoreductase activity,antioxidant homeostasis,focal adhesion,tight junction formation,adherens junction dynamics,and lipid metabolism regulation.Integrative analysis of predicted targets,transcriptomic,proteomic,and metabolomic datasets revealed that differentially expressed molecules in YZF-treated rats and YZF-targeted proteins collectively participated in lipid metabolism,inflammatory responses,oxidative stress,and focal adhesion pathways.Conclusion YZF provides sustained therapeutic benefits in COPD rat models,potentially through systemic regulation of lipid metabolism,inflammatory responses,oxidative stress,and focal adhesion pathways.
基金supported by Natural Science Foundation of Shaanxi Province(S2021-JC-WT-006)the National Key Research and Development Program of China(2018YFD0100200)+1 种基金the China Postdoctoral Science Foundation(2018M633588)the China Agriculture Research System(CARS-02-77)。
文摘Maize kernel moisture content(KMC)at harvest greatly affects mechanical harvesting,transport and storage.KMC is correlated with kernel dehydration rate(KDR)before and after physiological maturity.KMC and KDR are complex traits governed by multiple quantitative trait loci(QTL).Their genetic architecture is incompletely understood.We used a multiomics integration approach with an association panel to identify genes influencing KMC and KDR.A genome-wide association study using time-series KMC data from 7 to 70 days after pollination and their transformed KDR data revealed respectively 98and 279 loci significantly associated with KMC and KDR.Time-series transcriptome and proteome datasets were generated to construct KMC correlation networks,from which respectively 3111 and 759 module genes and proteins were identified as highly associated with KMC.Integrating multiomics analysis,several promising candidate genes for KMC and KDR,including Zm00001d047799 and Zm00001d035920,were identified.Further mutant experiments showed that Zm00001d047799,a gene encoding heat shock 70 kDa protein 5,reduced KMC in the late stage of kernel development.Our study provides resources for the identification of candidate genes influencing maize KMC and KDR,shedding light on the genetic architecture of dynamic changes in maize KMC.
基金supported by the National Natural Science Foundation of China(81890994,31871343)National Key Research and Development Program of China(2017YFA0505503,2018YFB0704304,2018YFA0801402)+1 种基金the WBE Liver Fibrosis Foundation(CFHPC 2020021)the Beijing Dongcheng District outstanding talent funding project and the Beijing Undergraduate Training Programs for Innovation and Entrepreneurship(202010023046)。
文摘Genetic,epigenetic,and metabolic alterations are all hallmarks of cancer.However,the epigenome and metabolome are both highly complex and dynamic biological networks in vivo.The interplay between the epigenome and metabolome contributes to a biological system that is responsive to the tumor microenvironment and possesses a wealth of unknown biomarkers and targets of cancer therapy.From this perspective,we first review the state of high-throughput biological data acquisition(i.e.multiomics data)and analysis(i.e.computational tools)and then propose a conceptual in silico metabolic and epigenetic regulatory network(MER-Net)that is based on these current high-throughput methods.The conceptual MER-Net is aimed at linking metabolomic and epigenomic networks through observation of biological processes,omics data acquisition,analysis of network information,and integration with validated database knowledge.Thus,MER-Net could be used to reveal new potential biomarkers and therapeutic targets using deep learning models to integrate and analyze large multiomics networks.We propose that MER-Net can serve as a tool to guide integrated metabolomics and epigenomics research or can be modified to answer other complex biological and clinical questions using multiomics data.
基金supported by the National Natural Science Foundation of China(92253202 and 22177087 to X.W.)the Ministry of Science and Technology(2023YFC3402200)the Fundamental Research Funds for the Central Universities(2042023kfyq05).
文摘The meticulous examination of the genomic,transcriptomic,epigenomic,and proteomic landscapes,conducted at the precise resolution of single cells,has emerged as an indispensable instrument for comprehending the inherent mechanisms governing cellular heterogeneity.These methodologies have provided unprecedented insights into the intrinsic and extrinsic factors that underlie cellular morphological characteristics and differentiated functions.Within this field,multimodal techniques that concurrently analyze the epigenetic features of chromatin or cellular proteins and gene expression within an identical cell delineate intricate gene regulatory networks and phenotypes,thereby enhancing our understanding of cellular states during differentiation or pathological conditions.These techniques can be applied to identify cell subpopulations,infer cell developmental trajectories,and analyze patterns of cell-to-cell communication.In this context,we initiate by delineating the singular cell separation techniques employed in single-cell multiomics.Subsequently,we narrow our focus to methodologies amalgamating epigenetic features with gene expression at single-cell resolution.The epigenetic features entail DNA methylation,chromatin accessibility,histone modifications,chromatin conformation,and transcription factors.Following this,we discuss techniques for the conjoint analysis of cell surface and intracellular proteins in tandem with the transcriptome.Finally,we discuss the challenges and opportunities that manifest within this field,contributing to its continued advancement and exploration.
基金grants from Natural Science Foundation of China(82272481,323B2043).
文摘Meniscal injury presents a formidable challenge and often leads to functional impairment and osteoarthritic progression.Meniscus tissue engineering(MTE)is a promising solution,as conventional strategies for modu-lating local immune responses and generating a conducive microenvironment for effective tissue repair are lacking.Recently,magnesium-containing bioactive glass nanospheres(Mg-BGNs)have shown promise in tissue regeneration.However,few studies have explored the ability of Mg-BGNs to promote meniscal regeneration.First,we verified the anti-inflammatory and fibrochondrogenic abilities of Mg-BGNs in vitro.A comprehensive in vivo evaluation of a rabbit critical-size meniscectomy model revealed that Mg-BGNs have multiple effects on meniscal reconstruction and effectively promote fibrochondrogenesis,collagen deposition,and cartilage pro-tection.Multiomics analysis was subsequently performed to further explore the mechanism by which Mg-BGNs regulate the regenerative microenvironment.Mechanistically,Mg-BGNs first activate the TRPM7 ion channel through the PI3K/AKT signaling pathway to promote the cellular function of synovium-derived mesenchymal stem cells and then activate the PPARγ/NF-κB axis to modulate macrophage polarization and inflammatory reactions.We demonstrated that Mg^(2+)is critical for the crosstalk among biomaterials,immune cells,and effector cells in Mg-BGN-mediated tissue regeneration.This study provides a theoretical basis for the application of Mg-BGNs as nanomedicines to achieve in situ tissue regeneration in complex intrajoint pathological microenvironments.
文摘The journal Genomics,Proteomics&Bioinformatics(GPB)is interested in submissions across all areas of life science,biology,and biomedicine,focusing on large data acquisition,analysis,and curation.GPB is inviting submissions for a special issue on the topic of"Spatial Multiomics"(to be published in the Winter of 2025),which will aim to explore methodological advancements,computational data analyses,and applications of spatial multiomics in biological and medical research.
文摘The journal Genomics,Proteomics&Bioinformatics(GPB)is interested in submissions across all areas of life science,biology,and biomedicine,focusing on large data acquisition,analysis,and curation.GPB is inviting submissions for a special issue on the topic of"Spatial Multiomics"(to be published in the Winter of 2025),which will aim to explore methodological advancements,computational data analyses,and applications of spatial multiomics in biological and medical research.
基金This project was supported by grants from the National Natural Science Foundation of China(82273387,82273386,82073217,32270711,82073218 and 82003084)the National Key Research and Develop-ment Program of China(2018YFC1312100)+3 种基金Beijing Nova Program(20220484230)Shanghai Municipal Science and Technology Major Project(2018SHZDZX05)Shanghai Municipal Key Clinical Specialty,CAMS Innovation Fund for Medical Sciences(CIFMS)(2019-I2M-5-058)the State Key Laboratory of Proteomics(SKLP-K202004).
文摘Background:Intrahepatic cholangiocarcinoma(iCCA)is a highly heteroge-neous and lethal hepatobiliary tumor with few therapeutic strategies.The metabolic reprogramming of tumor cells plays an essential role in the develop-ment of tumors,while the metabolic molecular classification of iCCA is largely unknown.Here,we performed an integrated multiomics analysis and metabolic classification to depict differences in metabolic characteristics of iCCA patients,hoping to provide a novel perspective to understand and treat iCCA.Methods:We performed integrated multiomics analysis in 116 iCCA samples,including whole-exome sequencing,bulk RNA-sequencing and proteome anal-ysis.Based on the non-negative matrix factorization method and the protein abundance of metabolic genes in human genome-scale metabolic models,the metabolic subtype of iCCA was determined.Survival and prognostic gene analy-ses were used to compare overall survival(OS)differences between metabolic subtypes.Cell proliferation analysis,5-ethynyl-2’-deoxyuridine(EdU)assay,colony formation assay,RNA-sequencing and Western blotting were performed to investigate the molecular mechanisms of diacylglycerol kinaseα(DGKA)in iCCA cells.Results:Three metabolic subtypes(S1-S3)with subtype-specific biomarkers of iCCA were identified.These metabolic subtypes presented with distinct prog-noses,metabolic features,immune microenvironments,and genetic alterations.The S2 subtype with the worst survival showed the activation of some special metabolic processes,immune-suppressed microenvironment and Kirsten ratsar-coma viral oncogene homolog(KRAS)/AT-rich interactive domain 1A(ARID1A)mutations.Among the S2 subtype-specific upregulated proteins,DGKA was further identified as a potential drug target for iCCA,which promoted cell proliferation by enhancing phosphatidic acid(PA)metabolism and activating mitogen-activated protein kinase(MAPK)signaling.Conclusion:Viamultiomics analyses,we identified three metabolic subtypes of iCCA,revealing that the S2 subtype exhibited the poorest survival outcomes.We further identified DGKA as a potential target for the S2 subtype.
基金supported by the following grants from the National Institutes of Health:the National Institute of Allergy and Infectious Diseases(NIAID)(Grant No.R01 AI143381)to Edward P.Brownethe NIAID(Grant No.UM1 AI164567)to David M.Murdoch,the National Institute on Drug Abuse(NIDA)(Grant No.R61 DA047023)to Edward P.Browne+2 种基金the NIAID(Grant No.T32 AI007419)to Jackson J.Petersonthe UNC-Chapel Hill Molecular Biology of Viral Diseases T32 to Jackson J.Peterson,the National Institute of General Medical Sciences(NIGMS)(Grant No.R35 GM138342)to Yuchao Jiangthe NIDA(Grant No.R01 DA054994)to Cynthia D.Rudin.
文摘Despite the success of antiretroviral therapy,human immunodeficiency virus(HIV)cannot be cured because of a reservoir of latently infected cells that evades therapy.To understand the mechanisms of HIV latency,we employed an integrated single-cell RNA sequencing(scRNA-seq)and single-cell assay for transposase-accessible chromatin with sequencing(scATAC-seq)approach to simultaneously profile the transcriptomic and epigenomic characteristics of~125,000 latently infected primary CD4^(+)T cells after reactivation using three different latency reversing agents.Differentially expressed genes and differentially accessible motifs were used to examine transcriptional pathways and transcription factor(TF)activities across the cell population.We identified cellular transcripts and TFs whose expression/activity was correlated with viral reactivation and demonstrated that a machine learning model trained on these data was 75%-79%accurate at predicting viral reactivation.Finally,we validated the role of two candidate HIV-regulating factors,FOXP1 and GATA3,in viral transcription.These data demonstrate the power of integrated multimodal single-cell analysis to uncover novel relationships between host cell factors and HIV latency.
基金National Natural Science Foundation of China(82173332).
文摘During the process of carcinogenesis and tumor progression,various molecular alternations occur in different omics levels.In recent years,multiomics approaches including genomics,epigenetics,transcriptomics,proteomics,metabolomics,single-cell omics,and spatial omics have been applied in mapping diverse omics profiles of cancers.The development of high-throughput technologies such as sequencing and mass spectrometry has revealed different omics levels of tumor cells or tissues separately.While focusing on a single omics level results in a lack of accuracy,joining multiple omics approaches together undoubtedly benefits accurate molecular subtyping and precision medicine for cancer patients.With the deepening of tumor research in recent years,taking pathological classification as the only criterion of diagnosis and predicting prognosis and treatment response is found to be not accurate enough.Therefore,identifying precise molecular subtypes by exploring the molecular alternations during tumor occurrence and development is of vital importance.The review provides an overview of the advanced technologies and recent progress in multiomics applied in cancer molecular subtyping and detailedly explains the application of multiomics in identifying cancer driver genes and metastasis-related genes,exploring tumor microenvironment,and selecting liquid biopsy biomarkers and potential therapeutic targets.
基金supported in part by Shanghai Sailing Program(22YF1403500)the National Natural Science Foundation of China(32300536,31720103909 and 32170657)+2 种基金the National Key R&D Project of China(2018YFE0201603 and 2018YFE0201600)State Key Laboratory of Genetic Engineering(SKLGE-2117)the 111 Project(B13016).
文摘High-throughput technologies for multiomics or molecular phenomics profiling have been extensively adopted in biomedical research and clinical applications,offering a more comprehensive understanding of biological processes and diseases.Omics reference materials play a pivotal role in ensuring the accuracy,reliability,and comparability of laboratory measurements and analyses.However,the current application of omics reference materials has revealed several issues,including inappropriate selection and underutilization,leading to inconsistencies across laboratories.This review aims to address these concerns by emphasizing the importance of well-characterized reference materials at each level of omics,encompassing(epi-)genomics,transcriptomics,proteomics,and metabolomics.By summarizing their characteristics,advantages,and limitations along with appropriate performance metrics pertinent to study purposes,we provide an overview of how omics reference materials can enhance data quality and data integration,thus fostering robust scientific investigations with omics technologies.
基金supported in part by the National Natural Science Foundation of China(No.62202383)the Guangdong Basic and Applied Basic Research Foundation(No.2024A1515012602)the National Key Research and Development Program of China(No.2022YFD1801200).
文摘Identifying cancer driver genes has paramount significance in elucidating the intricate mechanisms underlying cancer development,progression,and therapeutic interventions.Abundant omics data and interactome networks provided by numerous extensive databases enable the application of graph deep learning techniques that incorporate network structures into the deep learning framework.However,most existing models primarily focus on individual network,inevitably neglecting the incompleteness and noise of interactions.Moreover,samples with imbalanced classes in driver gene identification hamper the performance of models.To address this,we propose a novel deep learning framework MMGN,which integrates multiplex networks and pan-cancer multiomics data using graph neural networks combined with negative sample inference to discover cancer driver genes,which not only enhances gene feature learning based on the mutual information and the consensus regularizer,but also achieves balanced class of positive and negative samples for model training.The reliability of MMGN has been verified by the Area Under the Receiver Operating Characteristic curves(AUROC)and the Area Under the Precision-Recall Curves(AUPRC).We believe MMGN has the potential to provide new prospects in precision oncology and may find broader applications in predicting biomarkers for other intricate diseases.
基金supported by the National Natural Science Foundation of China(82301451)the National Key Research and Development Program of China(2021YFC2500502).
文摘Background Moyamoya disease(MMD)is a rare and complex cerebrovascular disorder characterized by the progressive narrowing of the internal carotid arteries and the formation of compensatory collateral vessels.The etiology of MMD remains enigmatic,making diagnosis and management challenging.The MOYAOMICS project was initiated to investigate the molecular underpinnings of MMD and explore potential diagnostic and therapeutic strategies.Methods The MOYAOMICS project employs a multidisciplinary approach,integrating various omics technologies,including genomics,transcriptomics,proteomics,and metabolomics,to comprehensively examine the molecular signatures associated with MMD pathogenesis.Additionally,we will investigate the potential influence of gut microbiota and brain-gut peptides on MMD development,assessing their suitability as targets for therapeutic strategies and dietary interventions.Radiomics,a specialized field in medical imaging,is utilized to analyze neuroimaging data for early detection and characterization of MMD-related brain changes.Deep learning algorithms are employed to differentiate MMD from other conditions,automating the diagnostic process.We also employ single-cellomics and mass cytometry to precisely study cellular heterogeneity in peripheral blood samples from MMD patients.Conclusions The MOYAOMICS project represents a significant step toward comprehending MMD’s molecular underpinnings.This multidisciplinary approach has the potential to revolutionize early diagnosis,patient stratification,and the development of targeted therapies for MMD.The identification of blood-based biomarkers and the integration of multiple omics data are critical for improving the clinical management of MMD and enhancing patient outcomes for this complex disease.
基金supported by grants from the National Natural Science Foundation of China(No.82372834 and 82173129).
文摘Advanced chronic liver disease(ACLD)can lead to fibrosis,cirrhosis,and eventually hepatocellular carcinoma(HCC)[1].Hepatic fibrosis is characterized by formation of a fibrous scar because of accumulation of extracellular matrix proteins,predominantly crosslinked type I and type III collagens,which replace the damaged tissue[2].
基金partially supported by the Construction of Collaborative Innovation Center of Beijing Academy of Agricultural and Forestry Sciences(KJCX20240406)the Beijing Natural Science Foundation(JQ24037)+1 种基金the National Natural Science Foundation of China(32330075)the Earmarked Fund for China Agriculture Research System(CARS-02 and CARS-54)。
文摘The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This era integrates biotechnology,artificial intelligence(AI),and big data information technology.In contrast,China is still in a transition period between stages 2.0 and 3.0,which primarily relies on conventional selection and molecular breeding.In the context of increasingly complex international situations,accurately identifying core issues in China's seed industry innovation and seizing the frontier of international seed technology are strategically important.These efforts are essential for ensuring food security and revitalizing the seed industry.This paper systematically analyzes the characteristics of crop breeding data from artificial selection to intelligent design breeding.It explores the applications and development trends of AI and big data in modern crop breeding from several key perspectives.These include highthroughput phenotype acquisition and analysis,multiomics big data database and management system construction,AI-based multiomics integrated analysis,and the development of intelligent breeding software tools based on biological big data and AI technology.Based on an in-depth analysis of the current status and challenges of China's seed industry technology development,we propose strategic goals and key tasks for China's new generation of AI and big data-driven intelligent design breeding.These suggestions aim to accelerate the development of an intelligent-driven crop breeding engineering system that features large-scale gene mining,efficient gene manipulation,engineered variety design,and systematized biobreeding.This study provides a theoretical basis and practical guidance for the development of China's seed industry technology.
文摘Metabolic-associated fatty liver disease(MAFLD),formerly known as nonalcoho-lic fatty liver disease,is an increasing global health challenge with substantial implications for metabolic and cardiovascular health(CVH).A recent study by Fu et al investigated the relationship between CVH metrics,specifically Life’s Simple 7 and Life’s Essential 8,and the prevalence of MAFLD.While this study offered important insights into the relationship between CVH and MAFLD,several me-thodological limitations,unaddressed confounding factors,and potential biases that could impact the interpretation of their findings should be considered.The study’s cross-sectional nature restricted the ability to draw causal conclusions,and it did not fully account for potential confounding factors such as dietary habits,genetic predispositions,and medication use.Furthermore,relying on tran-sient elastography to diagnose MAFLD introduces certain diagnostic limitations.Longitudinal study designs,advanced statistical modeling techniques,and diverse population groups should be utilized to strengthen future research.Exploring the mechanistic pathways that link CVH metrics to MAFLD through multi-omics approaches and interventional studies will be essential in formulating targeted prevention and treatment strategies.Structural equation modeling and machine learning techniques could provide a more refined analysis of these interrelated factors.Additionally,future research should employ longitudinal study designs and explore genetic and epigenetic influences to enhance our un-derstanding of CVH and MAFLD interactions.
基金The financial support received from the Shenzhen Science and Technology Innovation Commission(KCXFZ20201221173207022,WDZC20200821141349001)Shenzhen Bay Laboratory Startup Fund(21310041,S234602003)。
文摘Pu-erh tea,a traditional Chinese beverage,performs an anti-obesity function,but the correlation between its components and efficacy remains unknown.Here,we screened two Pu-erh teas with significant anti-obesity efficacies from 11 teas.In vitro experiments revealed that lipid accumulation in L02 cells and lipid synthesis in 3T3-L1 cells were significantly better inhibited by Tea-B than Tea-A.Further in vivo experiments using model mice revealed that the differences in chemical components generated two pathways in the anti-obesity efficacy and mechanism of Pu-erh teas.Tea-A changes the histomorphology of brown adipose tissue(BAT)and increases the abundance of Coriobacteriaceae_UCG_002 and cyclic AMP in guts through high chemical contents of cyclopentasiloxane,decamethyl,tridecane and 1,2,3-trimethoxybenzene,eventually increasing BAT activation and fat browning gene expression;the high content of hexadecane and 1,2-dimethoxybenzene in Tea-B reduces white adipose tissue(WAT)accumulation and the process of fatty liver,increases the abundance of Odoribacter and sphinganine 1-phosphate,inhibits the expression of lipid synthesis and transport genes.These mechanistic findings on the association of the representative bioactive components in Pu-erh teas with the anti-obesity phenotypes,gut microbes,gut metabolite structure and anti-obesity pathways,which were obtained for the first time,provide foundations for developing functional Pu-erh tea.
文摘Alcohol-related liver disease(ARLD)remains a major public health concern,often diagnosed at advanced stages with limited treatment options.Early identification of high-risk individuals is crucial for timely intervention and improved patient outcomes.Artificial intelligence(AI)has emerged as a powerful tool for predicting ARLD,leveraging multi-omics data,machine learning algorithms,and non-invasive biomarkers.This review explores the current advancements in AIdriven ARLD prediction,highlighting key methodologies such as multi-omics data integration,gut microbiome-based modeling,and predictive analytics using machine learning techniques.AI models incorporating transcriptomics,proteomics,and clinical data have demonstrated high diagnostic accuracy,with some achieving an area under the curve exceeding 0.90.Furthermore,non-invasive biomarkers,including liver stiffness measurements and circulating proteomic panels,have been successfully integrated into AI frameworks for early detection and risk stratification.Despite these advancements,challenges such as data heterogeneity,model generalizability,and ethical considerations remain.Future directions include the development of advanced biomarker discovery,wearable and point-of-care AI-integrated technologies,and precision medicine approaches tailored to individual risk profiles.AI-driven models hold significant potential in transforming ARLD prediction and management,ultimately contributing to early diagnosis and improved clinical outcomes.
基金Supported by the Science and Technology Planning Project of Guangzhou,No.2024A03J0102the Natural Science Foundation of Guangdong Province for Distinguished Young Scholar,No.2022B1515020024+1 种基金National Natural Science Foundation of China,No.82070574the Key Research and Development Program of Guangzhou,No.2023B03J1298.
文摘BACKGROUND Hepatocellular carcinoma(HCC)is notorious for its aggressive progression and dismal prognosis,with chromatin accessibility dynamics emerging as pivotal yet poorly understood drivers.AIM To dissect how multilayered chromatin regulation sustains oncogenic transcription and tumor-stroma crosstalk in HCC,we combined multiomics single cell analysis.METHODS We integrated single-cell RNA sequencing and paired single-cell assay for transposase-accessible chromatin with sequencing data of HCC samples,complemented by bulk RNA sequencing validation across The Cancer Genome Atlas,Liver Cancer Institute,and GSE25907 cohorts.Cell type-specific chromatin architectures were resolved via ArchR,with regulatory hubs identified through peak-to-gene linkages and coaccessibility networks.Functional validation employed A485-mediated histone 3 lysine 27 acetylation suppression and small interfering RNA targeting DGAT1.RESULTS Malignant hepatocytes exhibited expanded chromatin accessibility profiles,characterized by increased numbers of accessible peaks and larger physical regions despite reduced peak intensity.Enhancer-like peaks enriched in malignant regulation,forming long-range hubs.Eighteen enhancer-like peak-related genes showed tumor-specific overexpression and diagnostic accuracy,correlating with poor prognosis.Intercellular coaccessibility analysis revealed tumor-stroma symbiosis via shared chromatin states.Pharmacological histone 3 lysine 27 acetylation inhibition paradoxically downregulated DGAT1,the hub gene most strongly regulated by chromatin accessibility.DGAT1 knockdown suppressed cell proliferation.CONCLUSION Multilayered chromatin reprogramming sustains HCC progression through tumor-stroma crosstalk and DGAT1-related oncogenic transcription,defining targetable epigenetic vulnerabilities.
