Analysis of ARID3A regulated genes by RNA-seq
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens (human)
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PROVIDER: S-BSST957 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:As the main product of livestock, muscle itself plays an irreplaceable role in maintaining animal body movement and regulating metabolism. Therefore, it is of great significance to explore its growth, development and regeneration to improve the meat yield and quality of livestock. In this study, we attempted to use RNA-seq and ATAC-seq techniques to identify differentially expressed genes (DEGs) specifically expressed in bovine skeletal muscle as potential candidates for studying the regulatory mechanisms of muscle development. Microarray data from 8 tissue samples were selected from the GEO database for analysis. First, we obtained gene modules related to each tissue through WGCNA analysis. Through Gene Ontology (GO) functional annotation, the module of lightyellow (MElightyellow) was closely related to muscle development, and 213 hub genes were screened as follow-up research targets. Further, the difference analysis showed that, except for PREB, all other candidate hub genes were up-regulated (muscle group vs. other-group). ATAC-seq analysis showed that muscle-specific accessible chromatin regions were mainly located in promoter of genes related to muscle structure development (GO:0061061), muscle cell development (GO:0055001) and muscle system process (GO:0003012), which were involved in cAMP, CGMP-PKG, MAPK, and other signaling pathways. Next, we integrated the results of RNA-seq and ATAC-seq analysis, and 54 of the 212 candidate hub genes were identified as key regulatory genes in skeletal muscle development. Finally, through motif analysis, 22 of the 54 key genes were found to be potential target genes of transcription factor MEF2C. Including CAPN3, ACTN2, MB, MYOM3, SRL, CKM, ALPK3, MAP3K20, UBE2G1, NEURL2, CAND2, DOT1L, HRC, MAMSTR, FSD2, LRRC2, LSMEM1, SLC29A2, FHL3, KLHL41, ATXN7L2, and PDRG1. This provides a potential reference for studying the molecular mechanism of skeletal muscle development in mammals.
| S-EPMC9404375 | biostudies-literature
Project description:Glucocorticoids such as dexamethasone (Dex) are widely used to treat both acute and chronic inflammatory conditions. They regulate immune responses by dampening cell-mediated immunity in a glucocorticoid receptor (GR)-dependent manner, by suppressing the expression of pro-inflammatory cytokines and chemokines and by stimulating the expression of anti-inflammatory mediators. Despite its evident clinical benefit, the mechanistic underpinnings of the gene regulatory networks transcriptionally controlled by GR in a context-specific manner remain mysterious. Next generation sequencing methods such mRNA sequencing (RNA-seq) and Ribosome profiling (ribo-seq) provide tools to investigate the transcriptional and post-transcriptional mechanisms that govern gene expression. Here, we integrate matched RNA-seq data with ribo-seq data from human acute monocytic leukemia (THP-1) cells treated with the TLR4 ligand lipopolysaccharide (LPS) and with Dex, to investigate the global transcriptional and translational regulation (translational efficiency, ΔTE) of Dex-responsive genes. We find that the expression of most of the Dex-responsive genes are regulated at both the transcriptional and the post-transcriptional level, with the transcriptional changes intensified on the translational level. Overrepresentation pathway analysis combined with STRING protein network analysis and manual functional exploration, identified these genes to encode immune effectors and immunomodulators that contribute to macrophage-mediated immunity and to the maintenance of macrophage-mediated immune homeostasis. Further research into the translational regulatory network underlying the GR anti-inflammatory response could pave the way for the development of novel immunomodulatory therapeutic regimens with fewer undesirable side effects.
| S-EPMC9582734 | biostudies-literature
Project description:BackgroundAlthough different quality controls have been applied at different stages of the sample preparation and data analysis to ensure both reproducibility and reliability of RNA-seq results, there are still limitations and bias on the detectability for certain differentially expressed genes (DEGs). Whether the transcriptional dynamics of a gene can be captured accurately depends on experimental design/operation and the following data analysis processes. The workflow of subsequent data processing, such as reads alignment, transcript quantification, normalization, and statistical methods for ultimate identification of DEGs can influence the accuracy and sensitivity of DEGs analysis, producing a certain number of false-positivity or false-negativity. Machine learning (ML) is a multidisciplinary field that employs computer science, artificial intelligence, computational statistics and information theory to construct algorithms that can learn from existing data sets and to make predictions on new data set. ML-based differential network analysis has been applied to predict stress-responsive genes through learning the patterns of 32 expression characteristics of known stress-related genes. In addition, the epigenetic regulation plays critical roles in gene expression, therefore, DNA and histone methylation data has been shown to be powerful for ML-based model for prediction of gene expression in many systems, including lung cancer cells. Therefore, it is promising that ML-based methods could help to identify the DEGs that are not identified by traditional RNA-seq method.ResultsWe identified the top 23 most informative features through assessing the performance of three different feature selection algorithms combined with five different classification methods on training and testing data sets. By comprehensive comparison, we found that the model based on InfoGain feature selection and Logistic Regression classification is powerful for DEGs prediction. Moreover, the power and performance of ML-based prediction was validated by the prediction on ethylene regulated gene expression and the following qRT-PCR.ConclusionsOur study shows that the combination of ML-based method with RNA-seq greatly improves the sensitivity of DEGs identification.
| S-EPMC6053725 | biostudies-literature
Project description:Learning and memory formation are known to require dynamic CpG (de)methylation and gene expression changes. Here, we aimed at establishing a genome-wide DNA methylation map of the zebra finch genome, a model organism in neuroscience, as well as identifying putatively epigenetically regulated genes. RNA- and MethylCap-seq experiments were performed on two zebra finch cell lines in presence or absence of 5-aza-2'-deoxycytidine induced demethylation. First, the MethylCap-seq methodology was validated in zebra finch by comparison with RRBS-generated data. To assess the influence of (variable) methylation on gene expression, RNA-seq experiments were performed as well. Comparison of RNA-seq and MethylCap-seq results showed that at least 357 of the 3,457 AZA-upregulated genes are putatively regulated by methylation in the promoter region, for which a pathway analysis showed remarkable enrichment for neurological networks. A subset of genes was validated using Exon Arrays, quantitative RT-PCR and CpG pyrosequencing on bisulfite-treated samples. To our knowledge, this study provides the first genome-wide DNA methylation map of the zebra finch genome as well as a comprehensive set of genes of which transcription is under putative methylation control.
| S-EPMC4750092 | biostudies-literature
Project description:Many recent RNA-seq studies were focused mainly on detecting the differentially expressed genes (DEGs) between two or more conditions. In contrast, only a few attempts have been made to detect genes associated with quantitative traits, such as obesity index and milk yield, on RNA-seq experiment with large number of biological replicates. This study illustrates the linear model application on trait associated genes (TAGs) detection in two real RNA-seq datasets: 89 replicated human obesity related data and 21 replicated Holsteins' milk production related RNA-seq data. Based on these two datasets, the performance between suggesting methods, such as ordinary regression and robust regression, and existing methods: DESeq2 and Voom, were compared. The results indicate that suggesting methods have much lower false discoveries compared to the precedent two group comparisons based approaches in our simulation study and qRT-PCR experiment. In particular, the robust regression outperforms existing DEG finding method as well as ordinary regression in terms of precision. Given the current trend in RNA-seq pricing, we expect our methods to be successfully applied in various RNA-seq studies with numerous biological replicates that handle continuous response traits.
| S-EPMC4829873 | biostudies-literature
Project description:For identifying the genes that are regulated by a transcription factor (TF), we have established an analytical pipeline that combines genomic systematic evolution of ligands by exponential enrichment (gSELEX)-Seq and RNA-Seq. Here, SELEX was used to select DNA fragments from an Aspergillus nidulans genomic library that bound specifically to AmyR, a TF from A. nidulans. High-throughput sequencing data were obtained for the DNAs enriched through the selection, following which various in silico analyses were performed. Mapping reads to the genome revealed the binding motifs including the canonical AmyR-binding motif, CGGN8CGG, as well as the candidate promoters controlled by AmyR. In parallel, differentially expressed genes related to AmyR were identified by using RNA-Seq analysis with samples from A. nidulans WT and amyR deletant. By obtaining the intersecting set of genes detected using both gSELEX-Seq and RNA-Seq, the genes directly regulated by AmyR in A. nidulans can be identified with high reliability. This analytical pipeline is a robust platform for comprehensive genome-wide identification of the genes that are regulated by a target TF.
| S-EPMC4943734 | biostudies-literature
Project description:RNA-Seq analysis carried out in three different backcrosses based on Iberian breed with Landrace, Pietrain and Duroc breeds
2017-10-31 | PRJEB23068 | EVA
Project description:ObjectiveTo identify novel DNA methylation-regulated differentially expressed genes (MeDEGs) in RA by integrated analysis of DNA methylation and RNA-Seq data.MethodsThe transcription and DNA methylation profiles of 9 RA and 15 OA synovial tissue were generated by RNA-Seq and Illumina 850K DNA methylation BeadChip. Gene set enrichment analysis (GSEA) and Weighted gene co-expression network analysis (WGCNA) were used to analyze methylation-regulated expressed genes by R software. The differentially expressed genes (DEGs), differentially methylated probes (DMPs), differentially methylated genes (DMGs) were analyzed by DESeq and ChAMP R package. The functional correlation of MeDEGs was analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The protein-protein interaction (PPI) network of MeDEGs was constructed by STRING and Reactome FI Cytoscape Plugin. Correlation analysis between methylation level and mRNA expression was conducted with R software.ResultsA total of 17,736 genes, 25,578 methylated genes and 755,852 methylation probes were detected. A total of 16,421 methylation-regulated expressed genes were obtained. The GSEA showed that these genes are associated with activation of immune response, adaptive immune response, Inflammatory response in C5 (ontology gene sets). For KEGG analysis, these genes are associated with rheumatoid arthritis, NF-kappa B signaling pathway, T cell receptor signaling pathway. The WGCNA showed that the turquoise module exhibited the strongest correlation with RA (R = 0.78, P = 1.27 × 10- 05), 660 genes were screened in the turquoise module. A total of 707 MeDEGs were obtained. GO analysis showed that MeDEGs were enriched in signal transduction, cell adhesion for BP, enriched in plasma membrane, integral component of membrane for CC, and enriched in identical protein binding, calcium ion binding for MF. The KEGG pathway analysis showed that the MeDEGs were enriched in calcium signaling pathway, T cell receptor signaling pathway, NF-kappa B signaling pathway, Rheumatoid arthritis. The PPI network containing 706 nodes and 882 edges, and the enrichment p value < 1.0 × 10- 16. With Cytoscape, based on the range of more than 10 genes, a total of 8 modules were screened out. Spearman correlation analysis showed RGS1(cg10718027), RGS1(cg02586212), RGS1(cg10861751) were significantly correlated with RA.ConclusionsRGS1 can be used as novel methylated biomarkers for RA.
| S-EPMC9588210 | biostudies-literature
Project description:The mechanisms that generate neural diversity during development remains largely unknown. Here, we use scRNA-seq methodology to discover new features of the Drosophila larval CNS across several key developmental timepoints. We identify multiple progenitor subtypes - both stem cell-like neuroblasts and intermediate progenitors - that change gene expression across larval development, and report on new candidate markers for each class of progenitors. We identify a pool of quiescent neuroblasts in newly hatched larvae and show that they are transcriptionally primed to respond to the insulin signaling pathway to exit from quiescence, including relevant pathway components in the adjacent glial signaling cell type. We identify candidate "temporal transcription factors" (TTFs) that are expressed at different times in progenitor lineages. Our work identifies many cell type specific genes that are candidates for functional roles, and generates new insight into the differentiation trajectory of larval neurons.
| S-EPMC9404614 | biostudies-literature
Project description:BackgroundNeuromuscular junctions (NMJs) are chemical synapses formed between motor neurons and skeletal muscle fibers and are essential for controlling muscle contraction. NMJ dysfunction causes motor disorders, muscle wasting, and even breathing difficulties. Increasing evidence suggests that many NMJ disorders are closely related to alterations in specific gene products that are highly concentrated in the synaptic region of the muscle. However, many of these proteins are still undiscovered. Thus, screening for NMJ-specific proteins is essential for studying NMJ and the pathogenesis of NMJ diseases.ResultsIn this study, synaptic regions (SRs) and nonsynaptic regions (NSRs) of diaphragm samples from newborn (P0) and adult (3-month-old) mice were used for RNA-seq. A total of 92 and 182 genes were identified as differentially expressed between the SR and NSR in newborn and adult mice, respectively. Meanwhile, a total of 1563 genes were identified as differentially expressed between the newborn SR and adult SR. Gene Ontology (GO) enrichment analyses, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and gene set enrichment analysis (GSEA) of the DEGs were performed. Protein-protein interaction (PPI) networks were constructed using STRING and Cytoscape. Further analysis identified some novel proteins and pathways that may be important for NMJ development, maintenance and maturation. Specifically, Sv2b, Ptgir, Gabrb3, P2rx3, Dlgap1 and Rims1 may play roles in NMJ development. Hcn1 may localize to the muscle membrane to regulate NMJ maintenance. Trim63, Fbxo32 and several Asb family proteins may regulate muscle developmental-related processes.ConclusionHere, we present a complete dataset describing the spatiotemporal transcriptome changes in synaptic genes and important synaptic pathways. The neuronal projection-related pathway, ion channel activity and neuroactive ligand-receptor interaction pathway are important for NMJ development. The myelination and voltage-gated ion channel activity pathway may be important for NMJ maintenance. These data will facilitate the understanding of the molecular mechanisms underlying the development and maintenance of NMJ and the pathogenesis of NMJ disorders.
| S-EPMC8088568 | biostudies-literature