Project description: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.
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≠µg/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.
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.
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.
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.
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.
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.
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.
Project description:BACKGROUND:Genome-phenome studies have identified thousands of variants that are statistically associated with disease or traits; however, their functional roles are largely unclear. A comprehensive investigation of regulatory mechanisms and the gene regulatory networks between phenome-wide association study (PheWAS) and genome-wide association study (GWAS) is needed to identify novel regulatory variants contributing to risk for human diseases. METHODS:In this study, we developed an integrative functional genomics framework that maps 215,107 significant single nucleotide polymorphism (SNP) traits generated from the PheWAS Catalog and 28,870 genome-wide significant SNP traits collected from the GWAS Catalog into a global human genome regulatory map via incorporating various functional annotation data, including transcription factor (TF)-based motifs, promoters, enhancers, and expression quantitative trait loci (eQTLs) generated from four major functional genomics databases: FANTOM5, ENCODE, NIH Roadmap, and Genotype-Tissue Expression (GTEx). In addition, we performed a tissue-specific regulatory circuit analysis through the integration of the identified regulatory variants and tissue-specific gene expression profiles in 7051 samples across 32 tissues from GTEx. RESULTS:We found that the disease-associated loci in both the PheWAS and GWAS Catalogs were significantly enriched with functional SNPs. The integration of functional annotations significantly improved the power of detecting novel associations in PheWAS, through which we found a number of functional associations with strong regulatory evidence in the PheWAS Catalog. Finally, we constructed tissue-specific regulatory circuits for several complex traits: mental diseases, autoimmune diseases, and cancer, via exploring tissue-specific TF-promoter/enhancer-target gene interaction networks. We uncovered several promising tissue-specific regulatory TFs or genes for Alzheimer's disease (e.g. ZIC1 and STX1B) and asthma (e.g. CSF3 and IL1RL1). CONCLUSIONS:This study offers powerful tools for exploring the functional consequences of variants generated from genome-phenome association studies in terms of their mechanisms on affecting multiple complex diseases and traits.
Project description:Analyses of the interrelationships between RNA structure and function are increasingly important components of genomic studies. The SHAPE-MaP strategy enables accurate RNA structure probing and realistic structure modeling of kilobase-length noncoding RNAs and mRNAs. Existing tools for visualizing RNA structure models are not suitable for efficient analysis of long, structurally heterogeneous RNAs. In addition, structure models are often advantageously interpreted in the context of other experimental data and gene annotation information, for which few tools currently exist. We have developed a module within the widely used and well supported open-source Integrative Genomics Viewer (IGV) that allows visualization of SHAPE and other chemical probing data, including raw reactivities, data-driven structural entropies, and data-constrained base-pair secondary structure models, in context with linear genomic data tracks. We illustrate the usefulness of visualizing RNA structure in the IGV by exploring structure models for a large viral RNA genome, comparing bacterial mRNA structure in cells with its structure under cell- and protein-free conditions, and comparing a noncoding RNA structure modeled using SHAPE data with a base-pairing model inferred through sequence covariation analysis.