Lung cancer is the leading cause of cancer-related mortality globally,including small-cell lung cancer and non-small-cell lung cancer.As the most prevalent histological subtype of non-small-cell lung cancer,lung adeno...Lung cancer is the leading cause of cancer-related mortality globally,including small-cell lung cancer and non-small-cell lung cancer.As the most prevalent histological subtype of non-small-cell lung cancer,lung adenocarcinoma(LUAD)accounts for approximately 40%of all lung cancer cases.1 Due to the heterogeneity of LUAD,accurate categorization is required to create a treatment plan for LUAD patients,while the existing paradigm does not adequately capture the enormously heterogeneous characteristics of LUAD.The rise of epigenetics has brought new perspectives for tumor heterogeneity exploration.Epigenetic modifications,such as aberrant DNA methylation and microRNA(miRNA),are essential in controlling gene expression,heterogeneity,and clinical implication.2 Meanwhile,epigenetic disruptions contribute to lung cancer tumorigenesis,the generation of a malignant phenotype and aggression,and chemoresistance,which could serve as credible biomarkers for lung cancer molecular categorization,early diagnosis,prognosis classification,and treatment efficacy prediction.3 Through integrative clustering of the gene expression profiles regulated by epigenetics,we determined and validated four lung adenocarcinoma epigenetic subtypes(LAESs)with distinct prognoses and biological peculiarities from four independent multi-center lung adenocarcinoma cohorts.展开更多
Osteoarthritis(OA)is a degenerative joint disease with significant clinical and societal impact.Traditional diagnostic methods,including subjective clinical assessments and imaging techniques such as X-rays and MRIs,a...Osteoarthritis(OA)is a degenerative joint disease with significant clinical and societal impact.Traditional diagnostic methods,including subjective clinical assessments and imaging techniques such as X-rays and MRIs,are often limited in their ability to detect early-stage OA or capture subtle joint changes.These limitations result in delayed diagnoses and inconsistent outcomes.Additionally,the analysis of omics data is challenged by the complexity and high dimensionality of biological datasets,making it difficult to identify key molecular mechanisms and biomarkers.Recent advancements in artificial intelligence(AI)offer transformative potential to address these challenges.This review systematically explores the integration of AI into OA research,focusing on applications such as AI-driven early screening and risk prediction from electronic health records(EHR),automated grading and morphological analysis of imaging data,and biomarker discovery through multi-omics integration.By consolidating progress across clinical,imaging,and omics domains,this review provides a comprehensive perspective on how AI is reshaping OA research.The findings have the potential to drive innovations in personalized medicine and targeted interventions,addressing longstanding challenges in OA diagnosis and management.展开更多
We are very pleased to announce a special issue, to be published in the spring of 2020, on "Single-cell Omics Analysis" in the journal Genomics, Proteomics & Bioinformatics(GPB). The cell has been primar...We are very pleased to announce a special issue, to be published in the spring of 2020, on "Single-cell Omics Analysis" in the journal Genomics, Proteomics & Bioinformatics(GPB). The cell has been primarily studied as a part of its bulk population for decades until recent展开更多
Dear Editor,Multi-omics association analysis is a key method in crop germplasm research,helping to elucidate the regulatory mechanisms of agronomic traits(Liu et al.,2020;Liang et al.,2021).However,most existing multi...Dear Editor,Multi-omics association analysis is a key method in crop germplasm research,helping to elucidate the regulatory mechanisms of agronomic traits(Liu et al.,2020;Liang et al.,2021).However,most existing multi-omics association studies focus on omics data under a single condition,posing challenges in identifying stress-related agronomically important genes.This difficultymainly arises fromthe increased complexity ofmulti-omics analyseswhen comparing control and stress conditions.展开更多
Background:Currently,the treatment of liver diseases remains an unsolved problem due to its complicated etiology and pathogenesis.Traditional Chinese medicine(TCM)has been used for liver disease treatment for thousand...Background:Currently,the treatment of liver diseases remains an unsolved problem due to its complicated etiology and pathogenesis.Traditional Chinese medicine(TCM)has been used for liver disease treatment for thousands of years.Disease treatment using TCM compounds conforms to the concept of“holism”,which coincides with the complicated pathogenic mechanisms of liver diseases.However,the mechanisms have not been clearly explained due to the complex components and multi-targets,which is a big obstacle TCM’s popularity and application.In recent years,studying the mechanisms and identifying the novel ingredients in herbal medicines are becoming a hot spot for many researchers.Therefore,we obtained literature in PubMed and summarized the progress of TCM’s active ingredients and formulas in treating various liver diseases in 2019.Based on the literature,flavonoids,polysaccharides,saponins,and alkaloids,as well as Chinese medicine formulas,such as Ba-Bao pill and Yin-Chen-Hao decoction,have attracted much attention.In addition,we also focused on the application of new omics analysis techniques,such as metabolomics,network pharmacology,and other omics analyses in the study of TCM formulas.展开更多
At the level of in vitro drug screening,the development of a phenotypic analysis system with highcontent screening at the core provides a strong platform to support high-throughput drug screening.There are few systema...At the level of in vitro drug screening,the development of a phenotypic analysis system with highcontent screening at the core provides a strong platform to support high-throughput drug screening.There are few systematic reports on brain organoids,as a new three-dimensional in vitro model,in terms of model stability,key phenotypic fingerprint,and drug screening schemes,and particula rly rega rding the development of screening strategies for massive numbers of traditional Chinese medicine monomers.This paper reviews the development of brain organoids and the advantages of brain organoids over induced neurons or cells in simulated diseases.The paper also highlights the prospects from model stability,induction criteria of brain organoids,and the screening schemes of brain organoids based on the characteristics of brain organoids and the application and development of a high-content screening system.展开更多
T helper 9(Th9)cells are interleukin 9(IL-9)-producing cells that have diverse functions ranging from antitumor immune responses to allergic inflammation.Th9 cells differentiate from naïve CD4+T cells in the pres...T helper 9(Th9)cells are interleukin 9(IL-9)-producing cells that have diverse functions ranging from antitumor immune responses to allergic inflammation.Th9 cells differentiate from naïve CD4+T cells in the presence of IL-4 and transforming growth factor-beta(TGF-β);however,our understanding of the molecular basis of their differentiation remains incomplete.Previously,we reported that the differentiation of another subset of TGF-β–driven T helper cells,Th17 cells,is highly dependent on de novo lipid biosynthesis.On the basis of these findings,we hypothesized that lipid metabolism may also be important for Th9 cell differentiation.We therefore investigated the differentiation and function of mouse and human Th9 cells in vitro under conditions of pharmacologically or genetically induced deficiency of the intracellular fatty acid content and in vivo in mice genetically deficient in acetyl-CoA carboxylase 1(ACC1),an important enzyme for fatty acid biosynthesis.Both the inhibition of de novo fatty acid biosynthesis and the deprivation of environmental lipids augmented differentiation and IL-9 production in mouse and human Th9 cells.Mechanistic studies revealed that the increase in Th9 cell differentiation was mediated by the retinoic acid receptor and the TGF-β–SMAD signaling pathways.Upon adoptive transfer,ACC1-inhibited Th9 cells suppressed tumor growth in murine models of melanoma and adenocarcinoma.Together,our findings highlight a novel role of fatty acid metabolism in controlling the differentiation and in vivo functions of Th9 cells.展开更多
The convergence of artificial intelligence(AI)and microbial therapeutics offers promising avenues for novel discoveries and therapeutic interventions.With the exponential growth of omics datasets and rapid advancement...The convergence of artificial intelligence(AI)and microbial therapeutics offers promising avenues for novel discoveries and therapeutic interventions.With the exponential growth of omics datasets and rapid advancements in AI technology,the next generation of AI is increasingly prevalent in microbiology research.In microbial research,AI is instrumental in the classification and functional annotation of microorganisms.Machine learning algorithms facilitate efficient and accurate categorization of microbial taxa,enabling the identification of functional traits and metabolic pathways within microbial communities.Additionally,AI-driven protein design strategies hold promise for engineering enzymes with enhanced catalytic activities and stabilities.By predicting protein structures,functions,and interactions,AI algorithms enable the rational design of proteins and enzymes tailored for specific applications.AI systems are already present in clinical microbiology laboratories in the form of expert rules used by some automated susceptibility testing and identification systems.In the future,microbiology technologists will rely more heavily on AI for initial screening,allowing them to focus on diagnostic challenges and complex technical interpretations.AI-driven approaches hold immense promise in advancing our understanding of microbial ecosystems,accelerating drug discovery processes,and fostering the development of groundbreaking therapeutic interventions.This review aims to summarize common algorithms in AI and their applications within microbiology and synthetic biology.We provide a comprehensive evaluation of AI’s utility in microbial research,discussing both its advantages and challenges.Finally,we explore future research directions and the bottlenecks faced by AI in the microbial field.展开更多
α-Pinene is an important monoterpene,which is widely used as a flavoring agent and in fragrances,pharmaceuticals and biofuels.Although an evolved strain Escherichia coli YZFP,which had higher tolerance to pinene and ...α-Pinene is an important monoterpene,which is widely used as a flavoring agent and in fragrances,pharmaceuticals and biofuels.Although an evolved strain Escherichia coli YZFP,which had higher tolerance to pinene and titer,has been successfully used to produce high levels of pinene,the pinene titer is much lower than that of hemiterpene(isoprene)and sesquiterpenes(farnesene)to date.Moreover,the overall cellular physiological and metabolic changes caused by higher tolerance to pinene and overproduction of pinene remains unclear.To reveal the mechanism of Escherichia coli YZFP with the higher tolerance to pinene and titer,a comparative genomics and transcriptional level analyses combining with CRISPR activation(CRISPRa)and interference(CRISPRi)were carried out.The results show that the tolerance to pinene and the overproduction of pinene in E.coli may be associated with:1)the mutations of the DXP pathway genes,the rpoA and some membrane protein genes,and their upregulations of transcription levels;and 2)the mutations of some genes and their downregulation of transcriptional levels.These comparative omics analyses provided some genetic modification strategies to further improve pinene production.Overexpression of the mutated cbpA,tabA,pitA,rpoA,sufBCDS,mutS,ispH,oppF,dusB,dnaK,dxs,dxr and flgFGH genes further improved pinene production.This study also demonstrated that combining comparative omics analysis with CRISPRa and CRISPRi is an efficient technology to quickly find a new metabolic engineering strategy.展开更多
基金supported by Henan Provincial Key Laboratory of Medicine and Henan Provincial Clinical Medical Research Center for Respiratory Diseases.
文摘Lung cancer is the leading cause of cancer-related mortality globally,including small-cell lung cancer and non-small-cell lung cancer.As the most prevalent histological subtype of non-small-cell lung cancer,lung adenocarcinoma(LUAD)accounts for approximately 40%of all lung cancer cases.1 Due to the heterogeneity of LUAD,accurate categorization is required to create a treatment plan for LUAD patients,while the existing paradigm does not adequately capture the enormously heterogeneous characteristics of LUAD.The rise of epigenetics has brought new perspectives for tumor heterogeneity exploration.Epigenetic modifications,such as aberrant DNA methylation and microRNA(miRNA),are essential in controlling gene expression,heterogeneity,and clinical implication.2 Meanwhile,epigenetic disruptions contribute to lung cancer tumorigenesis,the generation of a malignant phenotype and aggression,and chemoresistance,which could serve as credible biomarkers for lung cancer molecular categorization,early diagnosis,prognosis classification,and treatment efficacy prediction.3 Through integrative clustering of the gene expression profiles regulated by epigenetics,we determined and validated four lung adenocarcinoma epigenetic subtypes(LAESs)with distinct prognoses and biological peculiarities from four independent multi-center lung adenocarcinoma cohorts.
基金supported by the National Natural Science Foundation of China(82302757)Shenzhen Science and Technology Program(JCY20240813145204006,SGDX20201103095600002,JCYJ20220818103417037,KJZD20230923115200002)+1 种基金Shenzhen Key Laboratory of Digital Surgical Printing Project(ZDSYS201707311542415)Shenzhen Development and Reform Program(XMHT20220106001).
文摘Osteoarthritis(OA)is a degenerative joint disease with significant clinical and societal impact.Traditional diagnostic methods,including subjective clinical assessments and imaging techniques such as X-rays and MRIs,are often limited in their ability to detect early-stage OA or capture subtle joint changes.These limitations result in delayed diagnoses and inconsistent outcomes.Additionally,the analysis of omics data is challenged by the complexity and high dimensionality of biological datasets,making it difficult to identify key molecular mechanisms and biomarkers.Recent advancements in artificial intelligence(AI)offer transformative potential to address these challenges.This review systematically explores the integration of AI into OA research,focusing on applications such as AI-driven early screening and risk prediction from electronic health records(EHR),automated grading and morphological analysis of imaging data,and biomarker discovery through multi-omics integration.By consolidating progress across clinical,imaging,and omics domains,this review provides a comprehensive perspective on how AI is reshaping OA research.The findings have the potential to drive innovations in personalized medicine and targeted interventions,addressing longstanding challenges in OA diagnosis and management.
文摘We are very pleased to announce a special issue, to be published in the spring of 2020, on "Single-cell Omics Analysis" in the journal Genomics, Proteomics & Bioinformatics(GPB). The cell has been primarily studied as a part of its bulk population for decades until recent
基金supported by the Biological Breeding-Major Projects(2023ZD04076)the Pinduoduo-China Agricultural University Research Fund(PC2023B01012)+1 种基金the 2115 Talent Development Program of China Agricultural University,the National Natural Science Foundation of China(32201718)the Science and Technology Demonstration Project of Shandong Province(2024SFGC0402).
文摘Dear Editor,Multi-omics association analysis is a key method in crop germplasm research,helping to elucidate the regulatory mechanisms of agronomic traits(Liu et al.,2020;Liang et al.,2021).However,most existing multi-omics association studies focus on omics data under a single condition,posing challenges in identifying stress-related agronomically important genes.This difficultymainly arises fromthe increased complexity ofmulti-omics analyseswhen comparing control and stress conditions.
基金This study was supported by Science and Technology Projects in Key Fields of Traditional Chinese Medicine of Tianjin Municipal Health Commission(No.2020006)Tianjin Administration of Traditional Chinese Medicine,Integrated Chinese and Western Medicine Scientific Research Project of Tianjin Municipal Health Commission(No.2017073).
文摘Background:Currently,the treatment of liver diseases remains an unsolved problem due to its complicated etiology and pathogenesis.Traditional Chinese medicine(TCM)has been used for liver disease treatment for thousands of years.Disease treatment using TCM compounds conforms to the concept of“holism”,which coincides with the complicated pathogenic mechanisms of liver diseases.However,the mechanisms have not been clearly explained due to the complex components and multi-targets,which is a big obstacle TCM’s popularity and application.In recent years,studying the mechanisms and identifying the novel ingredients in herbal medicines are becoming a hot spot for many researchers.Therefore,we obtained literature in PubMed and summarized the progress of TCM’s active ingredients and formulas in treating various liver diseases in 2019.Based on the literature,flavonoids,polysaccharides,saponins,and alkaloids,as well as Chinese medicine formulas,such as Ba-Bao pill and Yin-Chen-Hao decoction,have attracted much attention.In addition,we also focused on the application of new omics analysis techniques,such as metabolomics,network pharmacology,and other omics analyses in the study of TCM formulas.
基金supported by the National Natural Science Foundation of China,No.32000498the Startup Funding of Zhejiang University City College,No.210000-581849 (both to CG)National College Students’Innovative Entrepreneurial Training Plan Program,No.2021 13021024 (to JQZ)。
文摘At the level of in vitro drug screening,the development of a phenotypic analysis system with highcontent screening at the core provides a strong platform to support high-throughput drug screening.There are few systematic reports on brain organoids,as a new three-dimensional in vitro model,in terms of model stability,key phenotypic fingerprint,and drug screening schemes,and particula rly rega rding the development of screening strategies for massive numbers of traditional Chinese medicine monomers.This paper reviews the development of brain organoids and the advantages of brain organoids over induced neurons or cells in simulated diseases.The paper also highlights the prospects from model stability,induction criteria of brain organoids,and the screening schemes of brain organoids based on the characteristics of brain organoids and the application and development of a high-content screening system.
基金supervised by Osamu Ohara at the Department of Applied Genomics,Kazusa DNA Research Institute and was supported by grants from the Ministry of Education,Culture,Sports,Science and Technology of Japan(Grants-in-Aid:Grant-in-Aid for Scientific Research[B]#20H03455Challenging Research(Exploratory)#20K21618+18 种基金Early-Career Scientists#21K15476 and#22K15502and Young Scientists(Startup)#21K20766)AMED-CREST(JP22gm1810002)from the Japan Agency for Medical Research and DevelopmentFOREST(JPMJFR225X)from JSTTERUMO Life Science FoundationKato Memorial Bioscience FoundationHamaguchi Foundation for the Advancement of BiochemistryTakeda Science FoundationMochida Memorial Foundation for Medical and Pharmaceutical ResearchKishimoto FoundationUehara Memorial FoundationCell Science Research FoundationAstellas Foundation for Research on Metabolic DisordersMSD Life Science FoundationPublic Interest Incorporated FoundationNAGASE Science Technology FoundationCanon FoundationONO Medical Research FoundationPrincess Takamatsu Cancer Research Fund.
文摘T helper 9(Th9)cells are interleukin 9(IL-9)-producing cells that have diverse functions ranging from antitumor immune responses to allergic inflammation.Th9 cells differentiate from naïve CD4+T cells in the presence of IL-4 and transforming growth factor-beta(TGF-β);however,our understanding of the molecular basis of their differentiation remains incomplete.Previously,we reported that the differentiation of another subset of TGF-β–driven T helper cells,Th17 cells,is highly dependent on de novo lipid biosynthesis.On the basis of these findings,we hypothesized that lipid metabolism may also be important for Th9 cell differentiation.We therefore investigated the differentiation and function of mouse and human Th9 cells in vitro under conditions of pharmacologically or genetically induced deficiency of the intracellular fatty acid content and in vivo in mice genetically deficient in acetyl-CoA carboxylase 1(ACC1),an important enzyme for fatty acid biosynthesis.Both the inhibition of de novo fatty acid biosynthesis and the deprivation of environmental lipids augmented differentiation and IL-9 production in mouse and human Th9 cells.Mechanistic studies revealed that the increase in Th9 cell differentiation was mediated by the retinoic acid receptor and the TGF-β–SMAD signaling pathways.Upon adoptive transfer,ACC1-inhibited Th9 cells suppressed tumor growth in murine models of melanoma and adenocarcinoma.Together,our findings highlight a novel role of fatty acid metabolism in controlling the differentiation and in vivo functions of Th9 cells.
基金supported by the National Natural Science Foundation Projects of China(No.82350003,No.92049201).
文摘The convergence of artificial intelligence(AI)and microbial therapeutics offers promising avenues for novel discoveries and therapeutic interventions.With the exponential growth of omics datasets and rapid advancements in AI technology,the next generation of AI is increasingly prevalent in microbiology research.In microbial research,AI is instrumental in the classification and functional annotation of microorganisms.Machine learning algorithms facilitate efficient and accurate categorization of microbial taxa,enabling the identification of functional traits and metabolic pathways within microbial communities.Additionally,AI-driven protein design strategies hold promise for engineering enzymes with enhanced catalytic activities and stabilities.By predicting protein structures,functions,and interactions,AI algorithms enable the rational design of proteins and enzymes tailored for specific applications.AI systems are already present in clinical microbiology laboratories in the form of expert rules used by some automated susceptibility testing and identification systems.In the future,microbiology technologists will rely more heavily on AI for initial screening,allowing them to focus on diagnostic challenges and complex technical interpretations.AI-driven approaches hold immense promise in advancing our understanding of microbial ecosystems,accelerating drug discovery processes,and fostering the development of groundbreaking therapeutic interventions.This review aims to summarize common algorithms in AI and their applications within microbiology and synthetic biology.We provide a comprehensive evaluation of AI’s utility in microbial research,discussing both its advantages and challenges.Finally,we explore future research directions and the bottlenecks faced by AI in the microbial field.
基金We are grateful to the National Natural Science Foundation of China(Grant NO.201808248)the Natural Science Foundation of Guangdong Province(NO.2018A030310255)the Project of the Scientific and Technical Program of Guangzhou(No.201607010028)for their financial support.
文摘α-Pinene is an important monoterpene,which is widely used as a flavoring agent and in fragrances,pharmaceuticals and biofuels.Although an evolved strain Escherichia coli YZFP,which had higher tolerance to pinene and titer,has been successfully used to produce high levels of pinene,the pinene titer is much lower than that of hemiterpene(isoprene)and sesquiterpenes(farnesene)to date.Moreover,the overall cellular physiological and metabolic changes caused by higher tolerance to pinene and overproduction of pinene remains unclear.To reveal the mechanism of Escherichia coli YZFP with the higher tolerance to pinene and titer,a comparative genomics and transcriptional level analyses combining with CRISPR activation(CRISPRa)and interference(CRISPRi)were carried out.The results show that the tolerance to pinene and the overproduction of pinene in E.coli may be associated with:1)the mutations of the DXP pathway genes,the rpoA and some membrane protein genes,and their upregulations of transcription levels;and 2)the mutations of some genes and their downregulation of transcriptional levels.These comparative omics analyses provided some genetic modification strategies to further improve pinene production.Overexpression of the mutated cbpA,tabA,pitA,rpoA,sufBCDS,mutS,ispH,oppF,dusB,dnaK,dxs,dxr and flgFGH genes further improved pinene production.This study also demonstrated that combining comparative omics analysis with CRISPRa and CRISPRi is an efficient technology to quickly find a new metabolic engineering strategy.
