3β-hydroxy-5-ene steroid dehydrogenase gene expression regulation in porcine granulosa cells. I: FSH- and LH-mediated transcriptional activation

Endocrine ◽  
1995 ◽  
Vol 3 (3) ◽  
pp. 195-199 ◽  
Author(s):  
P. Jorge Chedrese ◽  
Gheorghe T. Braileanu ◽  
Rebecca Salmon
Keyword(s):  
Gene Expression ◽  
Granulosa Cells ◽  
Transcriptional Activation ◽  
Gene Expression Regulation ◽  
Expression Regulation ◽  
Porcine Granulosa Cells ◽  
Dehydrogenase Gene ◽  
Steroid Dehydrogenase

scholarly journals Adult-specific trimethylation of histone H3 lysine 4 is prone to dynamic changes with aging in C. elegans somatic cells

2017 ◽  
Author(s):  
Mintie Pu ◽  
Minghui Wang ◽  
Wenke Wang ◽  
Satheeja Santhi Velayudhan ◽  
Siu Sylvia Lee
Keyword(s):  
Gene Expression ◽  
Transcriptional Activation ◽  
Developmental Stages ◽  
Gene Expression Regulation ◽  
Histone H3 ◽  
Expression Regulation ◽  
Rna Expression ◽  
Dynamic Changes ◽  
C Elegans ◽  
Expression Of Genes

AbstractTri-methylation on histone H3 lysine 4 (H3K4me3) is associated with active gene expression but its regulatory role in transcriptional activation is unclear. Here we used Caenorhabditis elegans to investigate the connection between H3K4me3 and gene expression regulation during aging. We uncovered around 30% of H3K4me3 enriched regions to show significant and reproducible changes with age. We further showed that these age-dynamic H3K4me3 regions largely mark gene-bodies and are acquired during adult stages. We found that these adult-specific age-dynamic H3K4me3 regions are correlated with gene expression changes with age. In contrast, H3K4me3 marking established during developmental stages remained largely stable with age, even when the H3K4me3 associated genes exhibited RNA expression changes during aging. Moreover, we found that global reduction of H3K4me3 levels results in significantly decreased RNA expression of genes that acquire H3K4me3 marking in their gene-bodies during adult stage, suggesting that altered H3K4me3 levels with age could result in age-dependent gene expression changes. Interestingly, the genes with dynamic changes in H3K4me3 and RNA levels with age are enriched for those involved in fatty acid metabolism, oxidation-reduction, and stress response. Therefore, our findings revealed divergent roles of H3K4me3 in gene expression regulation during aging, with important implications on physiological relevance.

Gerea: Prediction Of Gene Expression Regulators From Transcriptome Profiling Data To Transition Networks

2021 ◽  
Vol 16 ◽  
Author(s):  
Min Yao ◽  
Caiyun Jiang ◽  
Chenglong Li ◽  
Yongxia Li ◽  
Shan Jiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Target Gene ◽  
Gene Expression Regulation ◽  
Transcriptome Profiling ◽  
Expression Regulation ◽  
Mouse Gene ◽  
Mouse Gene Expression ◽  
Causality Inference ◽  
Identify Gene Expression

Background: Mammalian genes are regulated at the transcriptional and post-transcriptional levels. These mechanisms may involve the direct promotion or inhibition of transcription via a regulator or post-transcriptional regulation through factors such as micro (mi)RNAs. Objective: This study aimed to construct gene regulation relationships modulated by causality inference-based miRNA-(transition factor)-(target gene) networks and analyze gene expression data to identify gene expression regulators. Methods: Mouse gene expression regulation relationships were manually curated from literature using a text mining method which was then employed to generate miRNA-(transition factor)-(target gene) networks. An algorithm was then introduced to identify gene expression regulators from transcriptome profiling data by applying enrichment analysis to these networks. Results: A total of 22,271 mouse gene expression regulation relationships were curated for 4,018 genes and 242 miRNAs. GEREA software was developed to perform the integrated analyses. We applied the algorithm to transcriptome data for synthetic miR-155 oligo-treated mouse CD4+ T-cells and confirmed that miR-155 is an important network regulator. The software was also tested on publicly available transcriptional profiling data for Salmonella infection, resulting in the identification of miR-125b as an important regulator. Conclusion: The causality inference-based miRNA-(transition factor)-(target gene) networks serve as a novel resource for gene expression regulation research, and GEREA is an effective and useful adjunct to the currently available methods. The regulatory networks and the algorithm implemented in the GEREA software package are available under a free academic license at website : http://www.thua45.cn/gerea.

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