Detecting TF-miRNA-gene network based modules for 5hmC and 5mC brain samples: a intra- and inter-species case-study between human and rhesus

Abstract

BackgroundStudy of epigenetics is currently a high-impact research topic. Multi stage methylation is also an area of high-dimensional prospect. In this article, we provide a new study (intra and inter-species study) on brain tissue between human and rhesus on two methylation cytosine variants based data-profiles (viz., 5-hydroxymethylcytosine (5hmC) and 5-methylcytosine (5mC) samples) through TF-miRNA-gene network based module detection.ResultsFirst of all, we determine differentially 5hmC methylated genes for human as well as rhesus for intra-species analysis, and differentially multi-stage methylated genes for inter-species analysis. Thereafter, we utilize weighted topological overlap matrix (TOM) measure and average linkage clustering consecutively on these genesets for intra- and inter-species study.We identify co-methylated and multi-stage co-methylated gene modules by using dynamic tree cut, for intra-and inter-species cases, respectively. Each module is represented by individual color in the dendrogram. Gene Ontology and KEGG pathway based analysis are then performed to identify biological functionalities of the identified modules. Finally, top ten regulator TFs and targeter miRNAs that are associated with the maximum number of gene modules, are determined for both intra-and inter-species analysis.ConclusionsThe novel TFs and miRNAs obtained from the analysis are: MYST3 and ZNF771 as TFs (for human intra-species analysis), BAZ2B, RCOR3 and ATF1 as TFs (for rhesus intra-species analysis), and mml-miR-768-3p and mml-miR-561 as miRs (for rhesus intra-species analysis); and MYST3 and ZNF771 as miRs(for inter-species study). Furthermore, the genes/TFs/miRNAs that are already found to be liable for several brain-related dreadful diseases as well as rare neglected diseases (e.g., wolf Hirschhorn syndrome, Joubarts Syndrome, Huntington’s disease, Simian Immunodeficiency Virus(SIV) mediated enchaphilits, Parkinsons Disease, Bipolar disorder and Schizophenia etc.) are mentioned.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-017-0574-7) contains supplementary material, which is available to authorized users.

DNA methylation differences at birth after conception through assisted reproductive technologies

“…Indeed, several CpGs can be linked to genes with roles in brain function or (early) brain development. ZNF771 (cg06450634), as a transcription factor (TF), is a driving factor behind a large gene network in the brain (40) while CYFIP1 (cg13051607) (41), GABRB3 (cg01251603, cg15066197,cg14859324) (39) and SHANK1 (cg14560133) (42) are all autism spectrum disorder candidate genes, a late life phenotype associated with ART (1).…”

Epigenetic Modulation of CD8+ T Cell Function in Lentivirus Infections: A Review

“…Although the SIV model has not been utilized to characterize the epigenetic landscape in response to SIV infection, recent reports highlight the importance of studying this model to further elucidate the molecular events driving CD8 + T cell dysfunction. A recent study identified the differentially methylated transcription factors and microRNAs in inter and intra-species analysis between humans and rhesus macaques [ 134 ]. MicroRNA mml-miR-338-5p is differentially methylated and modulated by SIV infection in the brain after treatment with cannabinoids, frequently prescribed to people living with HIV/AIDS (PLWHA) [ 135 ].…”

Multi-Objective Optimized Fuzzy Clustering for Detecting Cell Clusters from Single-Cell Expression Profiles

“…In the literature, there are many articles about the identification of gene signature [25,26,27,28] or, biomarker [29,30,31] or, gene module [26,32,33] or, transcriptome analysis [34,35] for microarray/RNA-seq data and the integration of multi-omics data [36,37,38,39,40]. Among them, there are a few papers using fuzzy clustering for the microarray/RNA-seq data [41,42].…”