Browse
Submit Data
Databases
API
Help

Dataset Information

19 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Integrative genomics positions MKRN1 as a novel ribonucleoprotein within the embryonic stem cell gene regulatory network.

ABSTRACT: In embryonic stem cells (ESCs), gene regulatory networks (GRNs) coordinate gene expression to maintain ESC identity; however, the complete repertoire of factors regulating the ESC state is not fully understood. Our previous temporal microarray analysis of ESC commitment identified the E3 ubiquitin ligase protein Makorin-1 (MKRN1) as a potential novel component of the ESC GRN. Here, using multilayered systems-level analyses, we compiled a MKRN1-centered interactome in undifferentiated ESCs at the proteomic and ribonomic level. Proteomic analyses in undifferentiated ESCs revealed that MKRN1 associates with RNA-binding proteins, and ensuing RIP-chip analysis determined that MKRN1 associates with mRNAs encoding functionally related proteins including proteins that function during cellular stress. Subsequent biological validation identified MKRN1 as a novel stress granule-resident protein, although MKRN1 is not required for stress granule formation, or survival of unstressed ESCs. Thus, our unbiased systems-level analyses support a role for the E3 ligase MKRN1 as a ribonucleoprotein within the ESC GRN.

SUBMITTER: Cassar PA

PROVIDER: S-EPMC4670460 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Integrative Genomics Positions MKRN1 as a Novel Ribonucleoprotein within the Embryonic Stem Cell Gene Regulatory Network

Project description:For identification of proteins that associate with Makorin1 (MKRN1) in RNA-dependent and RNA-independent manners, we affinity purified FLAG-tagged Makorin1 (MKRN1) from mouse embryonic stem cells constitutively expressing FLAG:MKRN1. Anti-FLAG control immunoprecipitations were performed from a FLAG vectrol control (FLAG:Ctrl) mouse embryonic stem cell line that did not express FLAG:MKRN1. Following FLAG immunoprecipitation, anti-FLAG beads from FLAG:MKRN1 and FLAG:Ctrl immunoprecipitations were split into separate tubes such that half of the beads were digested with 200Ã∞â≈ Ã≠Âµg/mL RNase A while the other half of the beads were undigested. RNase A-digested and undigested immunoprecipitates were subjected to LC-MS/MS analysis. Of the 48 RNA-related proteins previously identified to associate with FLAG:MKRN1, L1TD1, PABPC1, PABPC4, YBX1, IGF2BP1 and UPF1 were found to remain associated with FLAG:MKRN1 in the presence of RNase A.

2015-07-21 | PXD001291 | Pride

Integrative Genomics Positions MKRN1 as a Novel Ribonucleoprotein within the Embryonic Stem Cell Gene Regulatory Network

Project description:In this study we affinity purified FLAG-tagged MKRN1 from mouse embryonic stem cells constitutively expressing FLAG:MKRN1 that were pre-treated or untreated with the proteasome inhibitor MG132. FLAG:MKRN1 repeatedly co-immunoprecipitated with 48 proteins irrespective of MG132 treatment. Many of the MKRN1-associated proteins are well-characterized RNA-binding proteins, and post-translational regulators of gene expression.

2018-10-19 | PXD001191 | Pride

Integrative genomics positions MKRN1 as a novel ribonucleoprotein within the embryonic stem cell gene regulatory network

Project description:This SuperSeries is composed of the SubSeries listed below.

2015-07-15 | GSE59394 | GEO

Integrative genomics positions MKRN1 as a novel ribonucleoprotein within the embryonic stem cell gene regulatory network [expression]

Project description:In embryonic stem cell (ESCs), gene regulatory networks (GRNs) coordinate gene expression to maintain ESC identity; however, the complete repertoire of factors that regulate the ESC state are not fully understood. Our previous temporal microarray analysis of ESC commitment identified the E3 Ubiquitin Ligase Protein Makorin-1 (MKRN1) as a potential novel component of the ESC GRN. Here, using multilayered systems-level analyses we compiled a MKRN1-centered interactome in undifferentiated ESCs at the proteomic and ribonomic level. Proteomic analyses revealed that MKRN1 is a novel RNA-binding protein that exists within messenger ribonucleoprotein (mRNP) complexes in undifferentiated ESC populations. In accordance with its presence in mRNPs, MKRN1 is mobilized to stress granules (SG) upon arsenite-induced stress, yet MKRN1 is not required for SG formation. RIP-chip analysis revealed that MKRN1 associates with mRNAs encoding functionally related regulatory proteins involved in diverse processes such as cell differentiation, apoptosis, or secreted proteins. Thus, our unbiased systems level analyses supports a role for MKRN1 as a novel RNA-binding protein and a potential gene regulatory protein within the ESC GRN.

2015-07-15 | GSE59392 | GEO

Integrative genomics positions MKRN1 as a novel ribonucleoprotein within the embryonic stem cell gene regulatory network [RIP-chip]

2015-07-15 | GSE59393 | GEO

Dissecting the brown adipogenic regulatory network using integrative genomics.

Project description:Brown adipocytes regulate energy expenditure via mitochondrial uncoupling, which makes them attractive therapeutic targets to tackle obesity. However, the regulatory mechanisms underlying brown adipogenesis are still poorly understood. To address this, we profiled the transcriptome and chromatin state during mouse brown fat cell differentiation, revealing extensive gene expression changes and chromatin remodeling, especially during the first day post-differentiation. To identify putatively causal regulators, we performed transcription factor binding site overrepresentation analyses in active chromatin regions and prioritized factors based on their expression correlation with the bona-fide brown adipogenic marker Ucp1 across multiple mouse and human datasets. Using loss-of-function assays, we evaluated both the phenotypic effect as well as the transcriptomic impact of several putative regulators on the differentiation process, uncovering ZFP467, HOXA4 and Nuclear Factor I A (NFIA) as novel transcriptional regulators. Of these, NFIA emerged as the regulator yielding the strongest molecular and cellular phenotypes. To examine its regulatory function, we profiled the genomic localization of NFIA, identifying it as a key early regulator of terminal brown fat cell differentiation.

| S-EPMC5299609 | biostudies-literature

Characterizing the role of miRNAs within gene regulatory networks using integrative genomics techniques.

Project description:Integrative genomics and genetics approaches have proven to be a useful tool in elucidating the complex relationships often found in gene regulatory networks. More importantly, a number of studies have provided the necessary experimental evidence confirming the validity of the causal relationships inferred using such an approach. By integrating messenger RNA (mRNA) expression data with microRNA (miRNA) (i.e. small non-coding RNA with well-established regulatory roles in a myriad of biological processes) expression data, we show how integrative genomics approaches can be used to characterize the role played by approximately a third of registered mouse miRNAs within the context of a liver gene regulatory network. Our analysis reveals that the transcript abundances of miRNAs are subject to regulatory control by many more loci than previously observed for mRNA expression. Moreover, our results indicate that miRNAs exist as highly connected hub-nodes and function as key sensors within the transcriptional network. We also provide evidence supporting the hypothesis that miRNAs can act cooperatively or redundantly to regulate a given pathway and that miRNAs play a subtle role by dampening expression of their target gene through the use of feedback loops.

| S-EPMC3130556 | biostudies-literature

Integrative genomics and transcriptomics analysis of human embryonic and induced pluripotent stem cells.

Project description:BackgroundHuman genomic variations, including single nucleotide polymorphisms (SNPs) and copy number variations (CNVs), are associated with several phenotypic traits varying from mild features to hereditary diseases. Several genome-wide studies have reported genomic variants that correlate with gene expression levels in various tissue and cell types.ResultsWe studied human embryonic stem cells (hESCs) and human induced pluripotent stem cells (hiPSCs) measuring the SNPs and CNVs with Affymetrix SNP 6 microarrays and expression values with Affymetrix Exon microarrays. We computed the linear relationships between SNPs and expression levels of exons, transcripts and genes, and the associations between gene CNVs and gene expression levels. Further, for a few of the resulted genes, the expression value was associated with both CNVs and SNPs. Our results revealed altogether 217 genes and 584 SNPs whose genomic alterations affect the transcriptome in the same cells. We analyzed the enriched pathways and gene ontologies within these groups of genes, and found out that the terms related to alternative splicing and development were enriched.ConclusionsOur results revealed that in the human pluripotent stem cells, the expression values of several genes, transcripts and exons were affected due to the genomic variation.

| S-EPMC4298950 | biostudies-literature

Protein interaction network topology uncovers melanogenesis regulatory network components within functional genomics datasets.

Project description:RNA-mediated interference (RNAi)-based functional genomics is a systems-level approach to identify novel genes that control biological phenotypes. Existing computational approaches can identify individual genes from RNAi datasets that regulate a given biological process. However, currently available methods cannot identify which RNAi screen "hits" are novel components of well-characterized biological pathways known to regulate the interrogated phenotype. In this study, we describe a method to identify genes from RNAi datasets that are novel components of known biological pathways. We experimentally validate our approach in the context of a recently completed RNAi screen to identify novel regulators of melanogenesis.In this study, we utilize a PPI network topology-based approach to identify targets within our RNAi dataset that may be components of known melanogenesis regulatory pathways. Our computational approach identifies a set of screen targets that cluster topologically in a human PPI network with the known pigment regulator Endothelin receptor type B (EDNRB). Validation studies reveal that these genes impact pigment production and EDNRB signaling in pigmented melanoma cells (MNT-1) and normal melanocytes.We present an approach that identifies novel components of well-characterized biological pathways from functional genomics datasets that could not have been identified by existing statistical and computational approaches.

| S-EPMC2904735 | biostudies-literature

A gene regulatory network in mouse embryonic stem cells.

Project description:We analyze new and existing expression and transcription factor-binding data to characterize gene regulatory relations in mouse ES cells (ESC). In addition to confirming the key roles of Oct4, Sox2, and Nanog, our analysis identifies several genes, such as Esrrb, Stat3, Tcf7, Sall4, and LRH-1, as statistically significant coregulators. The regulatory interactions among 15 core regulators are used to construct a gene regulatory network in ESC. The network encapsulates extensive cross-regulations among the core regulators, highlights how they may control epigenetic processes, and reveals the surprising roles of nuclear receptors. Our analysis also provides information on the regulation of a large number of putative target genes of the network.

| S-EPMC2034259 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data