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
Background:Ketamine demonstrates therapeutic potential but also high abuse liability.The neurobiological mechanisms underlying its addiction remain unclear.This study employs integrated metabolomics and proteomics to ...Background:Ketamine demonstrates therapeutic potential but also high abuse liability.The neurobiological mechanisms underlying its addiction remain unclear.This study employs integrated metabolomics and proteomics to investigate alterations in endogenous metabolites and proteins across brain regions and plasma in a rat model of ketamine addiction.Materials and Methods:A ketamine addiction model was established by repeated administration(20 mg/kg)in Sprague–Dawley rats,with behavioral validation through conditioned place preference(CPP).Prefrontal cortex(PFC),striatum,and plasma samples were analyzed using ultra-high-performance liquid chromatography-Q-Orbitrap mass spectrometry-based metabolomics and TMT-based quantitative proteomics.Multivariate statistics and joint multiomics pathway analysis were applied.Results:Ketamine administration induced significant CPP(P<0.05).Proteomics revealed 245 differentially expressed proteins in the PFC,enriched in dopaminergic,glutamatergic,and GABAergic synapses,and 188 in the striatum related to Alzheimer’s disease,retrograde endocannabinoid,and cAMP signaling.Plasma proteomics showed 156 differential proteins,primarily involved in complement,coagulation cascades,glycolysis,and gluconeogenesis.In the PFC,60 metabolites were altered,like amino sugar and nucleotide sugar metabolism,while 132 metabolites in the striatum were linked to retrograde endocannabinoid signaling(ECS),dopaminergic synapse,and purine metabolism.Plasma metabolomics identified significant changes,such as arginine and proline metabolism.Joint multiomics pathway analysis highlighted consistent disruptions in glutamate/glutamine metabolism,retrograde ECS,purine metabolism,etc.,across tissues.Conclusion:Ketamine addiction induces system-wide alterations in energy metabolism and neurotransmitter systems,with pronounced effects in the PFC and striatum,and detectable changes in plasma.These findings elucidate key enriched and interconnected metabolic pathways,advancing our mechanistic understanding of ketamine addiction and revealing potential targets for intervention.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
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.
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
Heavy metal pollution severely impacts plant health by inhibiting growth,photosynthesis,enzyme activities,and causing oxidative stress.Plants respond to such stress by activating complex defense mechanisms involving r...Heavy metal pollution severely impacts plant health by inhibiting growth,photosynthesis,enzyme activities,and causing oxidative stress.Plants respond to such stress by activating complex defense mechanisms involving reactive oxygen species and different signaling pathways.These pathways are pivotal in triggering plant defense responses and are currently a major focus of research.Understanding the complex mechanisms of heavy metal uptake,transport,chelation,and signaling can guide strategies to improve plant resilience and stress tolerance.In this review,we aim to highlight the key heavy metals found in soil and the environment,along with their mechanisms of accumulation in plants.We also explore the defense responses of plants through various signaling pathways such as calcium(Ca^(2+)),MAP kinase,and hormone signaling.Additionally,we emphasize the importance of understanding advanced omics technologies,including transcriptomics,metabolomics,and bioinformatic tools,in enhancing our knowledge of plant resilience and stress tolerance.Highlights·Heavy metals are major environmental concern with consequences on plant and public health.·Heavy metal pollution has increased globally decreasing agricultural growth and productivity.·Calcium functions as a messenger in both the regular physiology and against various stressors.·Phytohormones have antagonistic and synergistic effects that control response to stressors.·Bioinformatic tools aid in processing,analyzing,and interpreting data in stress 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
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
Male breast cancer(MBC)is rare,representing 0.5%–1%of all breast cancers,but its incidence is increasing due to improved diagnostics and awareness.MBC typically presents in older men,is human epidermal growth factor ...Male breast cancer(MBC)is rare,representing 0.5%–1%of all breast cancers,but its incidence is increasing due to improved diagnostics and awareness.MBC typically presents in older men,is human epidermal growth factor receptor 2(HER2)-negative and estrogen receptor(ER)-positive,and lacks routine screening,leading to delayed diagnosis and advanced disease.Major risk factors include hormonal imbalance,radiation exposure,obesity,alcohol use,and Breast Cancer Gene 1 and 2(BRCA1/2)mutations.Clinically,it may resemble gynecomastia but usually appears as a unilateral,painless mass or nipple discharge.Advances in imaging and liquid biopsy have enhanced early detection.Molecular mechanisms involve hormonal signaling,HER2/epidermal growth factor receptor(EGFR)pathways,tumor suppressor gene alterations,and epigenetic changes.While standard treatments mirror those for female breast cancer,emerging options such as cyclin-dependent kinase 4 and 6(CDK4/6),and poly(ADP-ribose)polymerase(PARP)inhibitors,immunotherapy,and precision medicine are reshaping management.Incorporating artificial intelligence,molecular profiling,and male-specific clinical trials is essential to improve outcomes and bridge current diagnostic and therapeutic gaps.展开更多
Natural products, and especially the active ingredients found in traditional Chinese medicine(TCM), have a thousand-year-long history of clinical use and a strong theoretical basis in TCM. As such,traditional remedies...Natural products, and especially the active ingredients found in traditional Chinese medicine(TCM), have a thousand-year-long history of clinical use and a strong theoretical basis in TCM. As such,traditional remedies provide shortcuts for the development of original new drugs in China, and increasing numbers of natural products are showing great therapeutic potential in various diseases. This paper reviews the molecular mechanisms of action of natural products from different sources used in the treatment of inflammatory diseases and cancer, introduces the methods and newly emerging technologies used to identify and validate the targets of natural active ingredients, enumerates the expansive list of TCM used to treat inflammatory diseases and cancer, and summarizes the patterns of action of emerging technologies such as single-cell multiomics, network pharmacology, and artificial intelligence in the pharmacological studies of natural products to provide insights for the development of innovative natural product-based drugs. Our hope is that we can make use of advances in target identification and singlecell multiomics to obtain a deeper understanding of actions of mechanisms of natural products that will allow innovation and revitalization of TCM and its swift industrialization and internationalization.展开更多
Ubiquitin(Ub)and ubiquitin-like(Ubl)pathways are critical post-translational modifications that determine whether functional proteins are degraded or activated/inactivated.To date,>600 associated enzymes have been ...Ubiquitin(Ub)and ubiquitin-like(Ubl)pathways are critical post-translational modifications that determine whether functional proteins are degraded or activated/inactivated.To date,>600 associated enzymes have been reported that comprise a hierarchical task network(e.g.,E1–E2–E3 cascade enzymatic reaction and deubiquitination)to modulate substrates,including enormous oncoproteins and tumor-suppressive proteins.Several strategies,such as classical biochemical approaches,multiomics,and clinical sample analysis,were combined to elucidate the functional relations between these enzymes and tumors.In this regard,the fundamental advances and follow-on drug discoveries have been crucial in providing vital information concerning contemporary translational efforts to tailor individualized treatment by targeting Ub and Ubl pathways.Correspondingly,emphasizing the current progress of Ub-related pathways as therapeutic targets in cancer is deemed essential.In the present review,we summarize and discuss the functions,clinical significance,and regulatory mechanisms of Ub and Ubl pathways in tumorigenesis as well as the current progress of small-molecular drug discovery.In particular,multiomics analyses were integrated to delineate the complexity of Ub and Ubl modifications for cancer therapy.The present review will provide a focused and up-to-date overview for the researchers to pursue further studies regarding the Ub and Ubl pathways targeted anticancer strategies.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
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
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
The emergence of single-cell genomic and transcriptomic sequencing accelerates the development of single-cell epigenomic technologies,providing an unprecedented opportunity for decoding cell fate decisions largely enc...The emergence of single-cell genomic and transcriptomic sequencing accelerates the development of single-cell epigenomic technologies,providing an unprecedented opportunity for decoding cell fate decisions largely encoded in the epigenome.Recent advances in single-cell multimodality epigenomic technologies facilitate directly interrogating the reg-ulatory relationship between multi-layer molecular information in the same cell.In this review,we discuss recent progress in development of single-cell multimodality epigenomic technologies and applications in elucidating cellular diversifications in development and diseases,with a focus on protein-DNA interactomics and regulatory links between epigenome and tran-scriptome.Further,we provide perspective on the future direction of single-cell multiomics tool development as well as challenges facing ahead.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
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 Natural Science Foundation of Sichuan Province(Grant No.2024NSFSC0531)the National Natural Science Foundation of China(Grant numbers 82030057 and 82072111).
文摘Background:Ketamine demonstrates therapeutic potential but also high abuse liability.The neurobiological mechanisms underlying its addiction remain unclear.This study employs integrated metabolomics and proteomics to investigate alterations in endogenous metabolites and proteins across brain regions and plasma in a rat model of ketamine addiction.Materials and Methods:A ketamine addiction model was established by repeated administration(20 mg/kg)in Sprague–Dawley rats,with behavioral validation through conditioned place preference(CPP).Prefrontal cortex(PFC),striatum,and plasma samples were analyzed using ultra-high-performance liquid chromatography-Q-Orbitrap mass spectrometry-based metabolomics and TMT-based quantitative proteomics.Multivariate statistics and joint multiomics pathway analysis were applied.Results:Ketamine administration induced significant CPP(P<0.05).Proteomics revealed 245 differentially expressed proteins in the PFC,enriched in dopaminergic,glutamatergic,and GABAergic synapses,and 188 in the striatum related to Alzheimer’s disease,retrograde endocannabinoid,and cAMP signaling.Plasma proteomics showed 156 differential proteins,primarily involved in complement,coagulation cascades,glycolysis,and gluconeogenesis.In the PFC,60 metabolites were altered,like amino sugar and nucleotide sugar metabolism,while 132 metabolites in the striatum were linked to retrograde endocannabinoid signaling(ECS),dopaminergic synapse,and purine metabolism.Plasma metabolomics identified significant changes,such as arginine and proline metabolism.Joint multiomics pathway analysis highlighted consistent disruptions in glutamate/glutamine metabolism,retrograde ECS,purine metabolism,etc.,across tissues.Conclusion:Ketamine addiction induces system-wide alterations in energy metabolism and neurotransmitter systems,with pronounced effects in the PFC and striatum,and detectable changes in plasma.These findings elucidate key enriched and interconnected metabolic pathways,advancing our mechanistic understanding of ketamine addiction and revealing potential targets for intervention.
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.
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.
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Heavy metal pollution severely impacts plant health by inhibiting growth,photosynthesis,enzyme activities,and causing oxidative stress.Plants respond to such stress by activating complex defense mechanisms involving reactive oxygen species and different signaling pathways.These pathways are pivotal in triggering plant defense responses and are currently a major focus of research.Understanding the complex mechanisms of heavy metal uptake,transport,chelation,and signaling can guide strategies to improve plant resilience and stress tolerance.In this review,we aim to highlight the key heavy metals found in soil and the environment,along with their mechanisms of accumulation in plants.We also explore the defense responses of plants through various signaling pathways such as calcium(Ca^(2+)),MAP kinase,and hormone signaling.Additionally,we emphasize the importance of understanding advanced omics technologies,including transcriptomics,metabolomics,and bioinformatic tools,in enhancing our knowledge of plant resilience and stress tolerance.Highlights·Heavy metals are major environmental concern with consequences on plant and public health.·Heavy metal pollution has increased globally decreasing agricultural growth and productivity.·Calcium functions as a messenger in both the regular physiology and against various stressors.·Phytohormones have antagonistic and synergistic effects that control response to stressors.·Bioinformatic tools aid in processing,analyzing,and interpreting data in stress 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Male breast cancer(MBC)is rare,representing 0.5%–1%of all breast cancers,but its incidence is increasing due to improved diagnostics and awareness.MBC typically presents in older men,is human epidermal growth factor receptor 2(HER2)-negative and estrogen receptor(ER)-positive,and lacks routine screening,leading to delayed diagnosis and advanced disease.Major risk factors include hormonal imbalance,radiation exposure,obesity,alcohol use,and Breast Cancer Gene 1 and 2(BRCA1/2)mutations.Clinically,it may resemble gynecomastia but usually appears as a unilateral,painless mass or nipple discharge.Advances in imaging and liquid biopsy have enhanced early detection.Molecular mechanisms involve hormonal signaling,HER2/epidermal growth factor receptor(EGFR)pathways,tumor suppressor gene alterations,and epigenetic changes.While standard treatments mirror those for female breast cancer,emerging options such as cyclin-dependent kinase 4 and 6(CDK4/6),and poly(ADP-ribose)polymerase(PARP)inhibitors,immunotherapy,and precision medicine are reshaping management.Incorporating artificial intelligence,molecular profiling,and male-specific clinical trials is essential to improve outcomes and bridge current diagnostic and therapeutic gaps.
基金supported by National Natural Science Foundation of China(Nos.81872877,81730100,91853109,82073975)School of Life Science(NJU)-Sipimo Joint Funds,Characteristic Innovation Project of Guangdong Provincial Education Department(Nos.2019GKTSCX039,2020KTSCX295,China),School-Level Scientific Research Project of Shenzhen Polytechnic(No.6021310023K,China)+1 种基金Natural Science Research of Jiangsu Higher Education Institutions of China(No.22KJB360005)Fundamental Research Funds for the Central Universities(No.020814380174,China).
文摘Natural products, and especially the active ingredients found in traditional Chinese medicine(TCM), have a thousand-year-long history of clinical use and a strong theoretical basis in TCM. As such,traditional remedies provide shortcuts for the development of original new drugs in China, and increasing numbers of natural products are showing great therapeutic potential in various diseases. This paper reviews the molecular mechanisms of action of natural products from different sources used in the treatment of inflammatory diseases and cancer, introduces the methods and newly emerging technologies used to identify and validate the targets of natural active ingredients, enumerates the expansive list of TCM used to treat inflammatory diseases and cancer, and summarizes the patterns of action of emerging technologies such as single-cell multiomics, network pharmacology, and artificial intelligence in the pharmacological studies of natural products to provide insights for the development of innovative natural product-based drugs. Our hope is that we can make use of advances in target identification and singlecell multiomics to obtain a deeper understanding of actions of mechanisms of natural products that will allow innovation and revitalization of TCM and its swift industrialization and internationalization.
基金National Natural Science Foundation of China (Grants 81820108022,82003297 and 22177076)Innovation Program of Shanghai Municipal Education Commission (2019-01-07-00-10-E00056,China)+2 种基金Shanghai Frontiers Science Center of Disease and Syndrome Biology of Inflammatory Cancer Transformation (2021KJ03-12,China)The Scientific and Technological Innovation Action Plan of Science and Technology Commission of Shanghai (20JC1411300,China)ChenGuang project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation (19CG49,China).
文摘Ubiquitin(Ub)and ubiquitin-like(Ubl)pathways are critical post-translational modifications that determine whether functional proteins are degraded or activated/inactivated.To date,>600 associated enzymes have been reported that comprise a hierarchical task network(e.g.,E1–E2–E3 cascade enzymatic reaction and deubiquitination)to modulate substrates,including enormous oncoproteins and tumor-suppressive proteins.Several strategies,such as classical biochemical approaches,multiomics,and clinical sample analysis,were combined to elucidate the functional relations between these enzymes and tumors.In this regard,the fundamental advances and follow-on drug discoveries have been crucial in providing vital information concerning contemporary translational efforts to tailor individualized treatment by targeting Ub and Ubl pathways.Correspondingly,emphasizing the current progress of Ub-related pathways as therapeutic targets in cancer is deemed essential.In the present review,we summarize and discuss the functions,clinical significance,and regulatory mechanisms of Ub and Ubl pathways in tumorigenesis as well as the current progress of small-molecular drug discovery.In particular,multiomics analyses were integrated to delineate the complexity of Ub and Ubl modifications for cancer therapy.The present review will provide a focused and up-to-date overview for the researchers to pursue further studies regarding the Ub and Ubl pathways targeted anticancer strategies.
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 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 the National Key Research and Development Program of China(2021YFA1100101 and 2017YFA0103402)the National Natural Science Foundation of China(32025015 and 31771607)the Peking-Tsinghua Center for Life Sciences.
文摘The emergence of single-cell genomic and transcriptomic sequencing accelerates the development of single-cell epigenomic technologies,providing an unprecedented opportunity for decoding cell fate decisions largely encoded in the epigenome.Recent advances in single-cell multimodality epigenomic technologies facilitate directly interrogating the reg-ulatory relationship between multi-layer molecular information in the same cell.In this review,we discuss recent progress in development of single-cell multimodality epigenomic technologies and applications in elucidating cellular diversifications in development and diseases,with a focus on protein-DNA interactomics and regulatory links between epigenome and tran-scriptome.Further,we provide perspective on the future direction of single-cell multiomics tool development as well as challenges facing ahead.
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.
