Time series integrative analysis of RNA-Seq and miRNA expression data reveals key biologic pathways during keloid formation
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ABSTRACT:
ORGANISM(S): synthetic construct Homo sapiens
PROVIDER: GSE113621 | GEO | 2018/06/29
REPOSITORIES: GEO
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Project description:Keloids represent a common form of exaggerated wound scarring that cause considerable morbidity. Moreover, there are limited data on molecular mechanisms underlying keloids and effective therapies are lacking. To gain new insight in the transcriptomic alterations of wound healing in keloid-prone individuals, we followed an integrative approach of RNA-Seq and miRNA expression data analysis in serial skin biopsies of the same site (baseline and six weeks after wounding) in keloid-prone (n=8) and healthy matched control individuals (n=6). Bioinformatic analysis identified 37 miRNAs and 1449 genes that are differentially expressed specifically in keloid-prone individuals during wound healing. Pathway enrichment analysis was undertaken in the RNA-Seq data and identified NOTCH signaling, MAPK signaling, and Toll-like receptor pathways to be altered in keloid-prone individuals after wounding. In addition, dysregulation of DNA repair, p53 signalling and metabolic pathways (RNA, protein, fructose, mannose and glycerophospholipid metabolism) was highlighted during keloid formation. Gene association network analysis demonstrated divergent average expression profiles of cytokine signaling genes, as well as lipid metabolism genes between keloid-prone and healthy individuals during wound healing. In summary, our study provides a comprehensive and integrative analysis of the keloid transcriptome and miRNAome and highlights biological pathways that feature during keloid formation.
2018-06-29 | GSE113620 | GEO
Project description:Keloids represent a common form of exaggerated wound scarring that cause considerable morbidity. Moreover, there are limited data on molecular mechanisms underlying keloids and effective therapies are lacking. To gain new insight in the transcriptomic alterations of wound healing in keloid-prone individuals, we followed an integrative approach of RNA-Seq and miRNA expression data analysis in serial skin biopsies of the same site (baseline and six weeks after wounding) in keloid-prone (n=8) and healthy matched control individuals (n=6). Bioinformatic analysis identified 37 miRNAs and 1449 genes that are differentially expressed specifically in keloid-prone individuals during wound healing. Pathway enrichment analysis was undertaken in the RNA-Seq data and identified NOTCH signaling, MAPK signaling, and Toll-like receptor pathways to be altered in keloid-prone individuals after wounding. In addition, dysregulation of DNA repair, p53 signalling and metabolic pathways (RNA, protein, fructose, mannose and glycerophospholipid metabolism) was highlighted during keloid formation. Gene association network analysis demonstrated divergent average expression profiles of cytokine signaling genes, as well as lipid metabolism genes between keloid-prone and healthy individuals during wound healing. In summary, our study provides a comprehensive and integrative analysis of the keloid transcriptome and miRNAome and highlights biological pathways that feature during keloid formation.
2018-06-29 | GSE113619 | GEO
Project description:Time series integrative analysis of RNA-Seq and miRNA expression data reveals key biologic pathways during keloid formation [miRNA]
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Project description:Time series integrative analysis of RNA-Seq and miRNA expression data reveals key biologic pathways during keloid formation
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Project description:Analysis of regulatory networks is a powerful framework for identification and quantification of intracellular interactions. We introduce miRGTF-net, a novel tool for construction of miRNA-gene-TF networks. We consider multiple transcriptional and post-transcriptional interaction types, including regulation of gene and miRNA expression by transcription factors, gene silencing by miRNAs, and co-expression of host genes with their intronic miRNAs. The underlying algorithm uses information on experimentally validated interactions as well as integrative miRNA/mRNA expression profiles in a given set of samples. The latter ensures simultaneous tissue-specificity and biological validity of interactions. We applied miRGTF-net to paired miRNA/mRNA-sequencing data of breast cancer samples from The Cancer Genome Atlas (TCGA). Together with topological analysis of the constructed network we showed that considered players can form reliable prognostic gene signatures for ER-positive breast cancer. A number of signatures demonstrated remarkably high accuracy on transcriptomic data obtained by both microarrays and RNA sequencing from several independent patient cohorts. Furthermore, an essential part of prognostic genes were identified as direct targets of transcription factor E2F1. The putative interplay between estrogen receptor alpha and E2F1 was suggested as a potential recurrence factor in patients treated with tamoxifen. Source codes of miRGTF-net are available at GitHub (https://github.com/s-a-nersisyan/miRGTF-net).
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Project description:Metabolites, the biochemical products of the cellular process, can be used to measure alterations in biochemical pathways related to the pathogenesis of Alzheimer's disease (AD). However, the relationships between systemic abnormalities in metabolism and the pathogenesis of AD are poorly understood. In this study, we aim to identify AD-specific metabolomic changes and their potential upstream genetic and transcriptional regulators through an integrative systems biology framework for analyzing genetic, transcriptomic, metabolomic, and proteomic data in AD. Metabolite co-expression network analysis of the blood metabolomic data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) shows short-chain acylcarnitines/amino acids and medium/long-chain acylcarnitines are most associated with AD clinical outcomes, including episodic memory scores and disease severity. Integration of the gene expression data in both the blood from the ADNI and the brain from the Accelerating Medicines Partnership Alzheimer's Disease (AMP-AD) program reveals ABCA1 and CPT1A are involved in the regulation of acylcarnitines and amino acids in AD. Gene co-expression network analysis of the AMP-AD brain RNA-seq data suggests the CPT1A- and ABCA1-centered subnetworks are associated with neuronal system and immune response, respectively. Increased ABCA1 gene expression and adiponectin protein, a regulator of ABCA1, correspond to decreased short-chain acylcarnitines and amines in AD in the ADNI. In summary, our integrated analysis of large-scale multiomics data in AD systematically identifies novel metabolites and their potential regulators in AD and the findings pave a way for not only developing sensitive and specific diagnostic biomarkers for AD but also identifying novel molecular mechanisms of AD pathogenesis.
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Project description:Polarization of macrophages is regulated through complex signaling networks. Correlating miRNA and mRNA expression over time after macrophage polarization has not yet been investigated. We used paired RNA-Seq and miRNA-Seq experiments to measure the mRNA and miRNA expression in bone marrow-derived macrophages over a time-series of 8 hours. Bioinformatics analysis identified 31 differentially expressed miRNAs between M1 and M2 polarized macrophages. The top 4 M1 miRNAs (miR-155-3p, miR-155-5p, miR-147-3p and miR-9-5p) and top 4 M2 miRNAs (miR-27a-5p, let-7c-1-3p, miR-23a-5p and miR-23b-5p) were validated by qPCR. Interestingly, M1 specific miRNAs could be categorized to early- and late-response groups, in which three new miRNAs miR-1931, miR-3473e and miR-5128 were validated as early-response miRNAs. M1 polarization led to the enrichment of genes involved in immune responses and signal transduction, whereas M2 polarization enriched genes involved in cell cycle and metabolic processes. C2H2 zinc-finger family members are key targets of DE miRNAs. The integrative analysis between miRNAs and mRNAs demonstrates the regulations of miRNAs on nearly four thousand differentially expressed genes and most of the biological pathways enriched in macrophage polarization. In summary, this study elucidates the expression profiles of miRNAs and their potential targetomes during macrophage polarization.
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Project description:BackgroundExploratory analysis of multi-dimensional high-throughput datasets, such as microarray gene expression time series, may be instrumental in understanding the genetic programs underlying numerous biological processes. In such datasets, variations in the gene expression profiles are usually observed across replicates and time points. Thus mining the temporal expression patterns in such multi-dimensional datasets may not only provide insights into the key biological processes governing organs to grow and develop but also facilitate the understanding of the underlying complex gene regulatory circuits.ResultsIn this work we have developed an evolutionary multi-objective optimization for our previously introduced triclustering algorithm δ-TRIMAX. Its aim is to make optimal use of δ-TRIMAX in extracting groups of co-expressed genes from time series gene expression data, or from any 3D gene expression dataset, by adding the powerful capabilities of an evolutionary algorithm to retrieve overlapping triclusters. We have compared the performance of our newly developed algorithm, EMOA- δ-TRIMAX, with that of other existing triclustering approaches using four artificial dataset and three real-life datasets. Moreover, we have analyzed the results of our algorithm on one of these real-life datasets monitoring the differentiation of human induced pluripotent stem cells (hiPSC) into mature cardiomyocytes. For each group of co-expressed genes belonging to one tricluster, we identified key genes by computing their membership values within the tricluster. It turned out that to a very high percentage, these key genes were significantly enriched in Gene Ontology categories or KEGG pathways that fitted very well to the biological context of cardiomyocytes differentiation.ConclusionsEMOA- δ-TRIMAX has proven instrumental in identifying groups of genes in transcriptomic data sets that represent the functional categories constituting the biological process under study. The executable file can be found at http://www.bioinf.med.uni-goettingen.de/fileadmin/download/EMOA-delta-TRIMAX.tar.gz .
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