Objective:Polycystic ovary syndrome(PCOS)is a common endocrine disorder that affects women’s health.This study aims to investigate gene and transcription factor(TF)expression differences between PCOS patients and hea...Objective:Polycystic ovary syndrome(PCOS)is a common endocrine disorder that affects women’s health.This study aims to investigate gene and transcription factor(TF)expression differences between PCOS patients and healthy individuals using bioinformatics approaches,and to verify the function of key transcription factors,with the goal of providing new insights into the pathogenesis of PCOS.Methods:Differentially expressed genes(DEGs)and differentially expressed transcription factors(DETFs)between PCOS patients and controls were identified from the RNA sequencing dataset GSE168404 using bioinformatics methods.Functional enrichment analysis was performed using Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)databases.The expression and function of core transcription factors were further validated in ovarian tissues of PCOS model mice and control mice using Western blotting and reverse transcription quantitative polymerase chain reaction(RTqPCR).Results:A total of 332 DEGs were identified between PCOS patients and controls,including 259 upregulated and 73 downregulated genes in the PCOS group.19 DETFs were further screened,of which 16 were upregulated and 3 were downregulated in PCOS.The upregulated DETFs(including TFCP2L1,DACH1,ESR2,AFF3,SMAD9,ZNF331,HOPX,ATOH8,HIF3α,DPF3,HOXC4,HES1,ID1,JDP2,SOX4,and ID3)were primarily associated with lipid metabolism,development,and cell adhesion.Protein and mRNA expression analysis in PCOS model mice revealed significantly decreased levels of hypoxia-inducible factor(HIF)1αand HIF2α,and significantly increased expression of HIF3αcompared to control mice(all P<0.001).Conclusion:Significant differences in gene and TF expression exist between PCOS patients and healthy individuals.HIF-3αmay play a crucial role in PCOS and could serve as a novel biomarker for diagnosis and a potential therapeutic target.展开更多
Climate deterioration,water shortages,and abiotic stress are the main threats worldwide that seriously affect cotton growth,yield,and fiber quality.Therefore,research on improving cotton yield and tolerance to biotic ...Climate deterioration,water shortages,and abiotic stress are the main threats worldwide that seriously affect cotton growth,yield,and fiber quality.Therefore,research on improving cotton yield and tolerance to biotic and abiotic stresses is of great importance.The NAC proteins are crucial and plant-specific transcription factors(TFs)that are involved in cotton growth,development,and stress responses.The comprehensive utilization of cotton NAC TFs in the improvement of cotton varieties through novel biotechnological methods is feasible.Based on cotton genomic data,genome-wide identification and analyses have revealed potential functions of cotton NAC genes.Here,we comprehensively summarize the recent progress in understanding cotton NAC TFs roles in regulating responses to drought,salt,and Verticillium wilt-related stresses,as well as leaf senescence and the development of fibers,xylem,and glands.The detailed regulatory network of NAC proteins in cotton is also elucidated.Cotton NAC TFs directly bind to the promoters of genes associated with ABA biosynthesis and secondary cell-wall formation,participate in several biological processes by interacting with related proteins,and regulate the expression of downstream genes.Studies have shown that the overexpression of NAC TF genes in cotton and other model plants improve their drought or salt tolerance.This review elucidates the latest findings on the functions and regulation of cotton NAC proteins,broadens our understanding of cotton NAC TFs,and lays a fundamental foundation for further molecular breeding research in cotton.展开更多
转录调控网络一直是系统生物学和生物信息学领域的一个研究热点。构建转录调控网络为揭示细胞内的生化反应机制提供了重要的手段。目前该领域的研究存在生物数据利用不充分,基因转录调控网络构建精度低等问题,尤其是在比较大的数据集上...转录调控网络一直是系统生物学和生物信息学领域的一个研究热点。构建转录调控网络为揭示细胞内的生化反应机制提供了重要的手段。目前该领域的研究存在生物数据利用不充分,基因转录调控网络构建精度低等问题,尤其是在比较大的数据集上。针对以上问题,充分利用基因表达数据、基因序列数据和基因注释数据,提出了基于深度自编码器的XGBoost和逻辑回归组合模型DAXL(combined model with XGBoost and logistic regression based on deep Auto Encoder)。最后,在拟南芥数据集上进行了实验,结果表明DAXL方法提高了转录调控网络的预测精度,并且较对比方法优势明显。展开更多
基金supported by the Natural Science Foundation of Hunan Province,China(2022JJ30886).
文摘Objective:Polycystic ovary syndrome(PCOS)is a common endocrine disorder that affects women’s health.This study aims to investigate gene and transcription factor(TF)expression differences between PCOS patients and healthy individuals using bioinformatics approaches,and to verify the function of key transcription factors,with the goal of providing new insights into the pathogenesis of PCOS.Methods:Differentially expressed genes(DEGs)and differentially expressed transcription factors(DETFs)between PCOS patients and controls were identified from the RNA sequencing dataset GSE168404 using bioinformatics methods.Functional enrichment analysis was performed using Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)databases.The expression and function of core transcription factors were further validated in ovarian tissues of PCOS model mice and control mice using Western blotting and reverse transcription quantitative polymerase chain reaction(RTqPCR).Results:A total of 332 DEGs were identified between PCOS patients and controls,including 259 upregulated and 73 downregulated genes in the PCOS group.19 DETFs were further screened,of which 16 were upregulated and 3 were downregulated in PCOS.The upregulated DETFs(including TFCP2L1,DACH1,ESR2,AFF3,SMAD9,ZNF331,HOPX,ATOH8,HIF3α,DPF3,HOXC4,HES1,ID1,JDP2,SOX4,and ID3)were primarily associated with lipid metabolism,development,and cell adhesion.Protein and mRNA expression analysis in PCOS model mice revealed significantly decreased levels of hypoxia-inducible factor(HIF)1αand HIF2α,and significantly increased expression of HIF3αcompared to control mice(all P<0.001).Conclusion:Significant differences in gene and TF expression exist between PCOS patients and healthy individuals.HIF-3αmay play a crucial role in PCOS and could serve as a novel biomarker for diagnosis and a potential therapeutic target.
基金supported by the Young Scientists Fund of the National Natural Science Foundation of China(32101797)Central Public-interest Scientific Institution Basal Research Fund(No.1610162023020)。
文摘Climate deterioration,water shortages,and abiotic stress are the main threats worldwide that seriously affect cotton growth,yield,and fiber quality.Therefore,research on improving cotton yield and tolerance to biotic and abiotic stresses is of great importance.The NAC proteins are crucial and plant-specific transcription factors(TFs)that are involved in cotton growth,development,and stress responses.The comprehensive utilization of cotton NAC TFs in the improvement of cotton varieties through novel biotechnological methods is feasible.Based on cotton genomic data,genome-wide identification and analyses have revealed potential functions of cotton NAC genes.Here,we comprehensively summarize the recent progress in understanding cotton NAC TFs roles in regulating responses to drought,salt,and Verticillium wilt-related stresses,as well as leaf senescence and the development of fibers,xylem,and glands.The detailed regulatory network of NAC proteins in cotton is also elucidated.Cotton NAC TFs directly bind to the promoters of genes associated with ABA biosynthesis and secondary cell-wall formation,participate in several biological processes by interacting with related proteins,and regulate the expression of downstream genes.Studies have shown that the overexpression of NAC TF genes in cotton and other model plants improve their drought or salt tolerance.This review elucidates the latest findings on the functions and regulation of cotton NAC proteins,broadens our understanding of cotton NAC TFs,and lays a fundamental foundation for further molecular breeding research in cotton.
文摘转录调控网络一直是系统生物学和生物信息学领域的一个研究热点。构建转录调控网络为揭示细胞内的生化反应机制提供了重要的手段。目前该领域的研究存在生物数据利用不充分,基因转录调控网络构建精度低等问题,尤其是在比较大的数据集上。针对以上问题,充分利用基因表达数据、基因序列数据和基因注释数据,提出了基于深度自编码器的XGBoost和逻辑回归组合模型DAXL(combined model with XGBoost and logistic regression based on deep Auto Encoder)。最后,在拟南芥数据集上进行了实验,结果表明DAXL方法提高了转录调控网络的预测精度,并且较对比方法优势明显。
