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Integrating Large-Scale Functional Genomic Data to Dissect the Complexity of Yeast Regulatory Networks

ABSTRACT: A major goal of biology is the construction of networks that predict complex system behavior. We combine multiple types of molecular data, including genotypic, expression, transcription factor binding site (TFBS), and protein-protein interaction (PPI) data previously generated from a number of yeast experiments in order to reconstruct causal gene networks. Networks based on different types of data are compared using metrics devised to assess the predictive power of a network. A network reconstructed by integrating genotypic, TFBS and PPI data is shown to be the most predictive. This network is used to predict causal regulators responsible for hot spots of gene expression activity in a segregating yeast population. The network is also shown to elucidate the mechanisms by which causal regulators give rise to larger-scale changes in gene expression activity. Predictions are prospectively validated to provide direct experimental evidence that predictive networks can be constructed by integrating multiple, appropriate data types. Keywords: allele replacement

ORGANISM(S): Saccharomyces cerevisiae

PROVIDER: GSE11111 | GEO | 2008/06/15

SECONDARY ACCESSION(S): PRJNA107001

REPOSITORIES: GEO

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Integrating Large-Scale Functional Genomic Data to Dissect the Complexity of Yeast Regulatory Networks

Project description:A major goal of biology is the construction of networks that predict complex system behavior. We combine multiple types of molecular data, including genotypic, expression, transcription factor binding site (TFBS), and protein-protein interaction (PPI) data previously generated from a number of yeast experiments in order to reconstruct causal gene networks. Networks based on different types of data are compared using metrics devised to assess the predictive power of a network. A network reconstructed by integrating genotypic, TFBS and PPI data is shown to be the most predictive. This network is used to predict causal regulators responsible for hot spots of gene expression activity in a segregating yeast population. The network is also shown to elucidate the mechanisms by which causal regulators give rise to larger-scale changes in gene expression activity. Predictions are prospectively validated to provide direct experimental evidence that predictive networks can be constructed by integrating multiple, appropriate data types. Keywords: allele replacement Allele replacement strains were created in two backgrounds: BY (BY4724 and BY4742), and RM11-1a, and compared to the parental type (BY4716 and RM11-1a). BY7g, BY9g, BY11g are BY4716, while RM7g, RM9g, and RM11g are RM11-1a. YAD350 is a MKT1 D30G replacement strain in BY4742 background. YLK804 is a SAL1-RM allele in BY4724 and YLK806 is a SAL1-BY allele in RM11-1a. All strains were grown in YNB + 2% glucose, with leucine, lysine, and uracil. YAD350 was also grown with histidine.

2008-06-15 | E-GEOD-11111 | biostudies-arrayexpress

Neuron-specific protein network mapping of autism risk genes identifies shared biological mechanisms and disease relevant pathologies

Project description:There are hundreds of risk genes for autism spectrum disorder (ASD), but signaling networks at the protein level remain unexplored. We used neuron-specific proximity-labeling proteomics (BioID) to identify protein-protein interaction (PPI) networks for 41 ASD-risk genes. Neuron-specific PPI networks included synaptic transmission proteins, which are disrupted by de novo missense variants. The PPI network map revealed convergent pathways including mitochondrial/metabolic processes, Wnt signaling, ion channel activity and MAPK signaling. CRISPR knockout validations revealed an association between mitochondrial activity and ASD-risk genes. The PPI network showed an enrichment of 112 additional ASD-risk genes and differentially expressed genes from post-mortem ASD patients. Clustering of risk genes based on PPI networks identified gene groups corresponding to clinical behavior score severity. Our data reveal that cell type-specific PPI networks can identify previously unknown individual and convergent ASD signaling networks, provide a method to assess patient variants, and reveal biological insight into disease mechanisms and sub-cohorts of ASD.

2022-09-24 | GSE213899 | GEO

maxATAC: genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks

Project description:Transcription factors read the genome, fundamentally connecting DNA sequence to gene expression across diverse cell types. Determining how, where, and when TFs bind chromatin will advance our understanding of gene regulatory networks and cellular behavior. The 2017 ENCODE-DREAM in vivo Transcription-Factor Binding Site (TFBS) Prediction Challenge highlighted the value of chromatin accessibility data to TFBS prediction, establishing state-of-the-art methods. Yet, while Assay-for-Transposase-Accessible-Chromatin (ATAC)-seq datasets grow exponentially, suboptimal motif scanning is commonly used for TFBS prediction from ATAC-seq. Here, we present “maxATAC”, a suite of user-friendly, deep neural network models for genome-wide TFBS prediction from ATAC-seq in any cell type. With models available for 127 human TFs, maxATAC is the largest collection of state-of-the-art TFBS models to date. maxATAC performance extends to primary cells and single-cell ATAC-seq, enabling state-of-the-art TFBS prediction in vivo. We demonstrate maxATAC’s capabilities by identifying TFBS associated with allele-dependent chromatin accessibility at atopic dermatitis genetic risk loci.

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The Properties of Genome Conformation and Spatial Gene Interaction and Regulation Networks of Normal and Malignant Human Cell Types

Project description:In this work, we generated hundreds of millions of Hi-C paired end sequence reads for three different human cells (RL follicular lymphoma cell line, primary tumor B-cells from an acute lymphoblastic leukemia patient, and MHH-CALL-4 B-cell acute lymphoblastic leukemia cell line) using the Hi-C technique. An in-house Bioinformatics software pipeline was developed and applied to map sequence reads to the human reference genome, producing a large data set of high-quality and high-resolution chromosome contacts. Our computational analysis on these data reveal some interesting properties of human genome conformation, including conformational conservation and variation of the genomes of different cells, intra- and inter-chromosomal interactions, aberrant chromosomal translocation, spatial gene clusters, spatial gene-gene interactions, and spatial gene-regulatory-element interaction. Furthermore, we derived spatial interactions between functional elements (genes, transcription factor binding sites) from the chromosomal interaction data. The data were then used to generate chromosome-/genome-wide gene-gene interaction networks, transcription factor binding site (TFBS) â TFBS networks, and gene-TFBS networks. Remarkably, the connectivity in both networks shows the hallmark features of scale-free networks, suggesting that spatial interactions of gene-gene, gene-TFBS, and TFBS-TFBS in a genome are far from random. Three different human cells (RL follicular lymphoma cell line, primary tumor B-cells from an acute lymphoblastic leukemia patient, and MHH-CALL-4 B-cell acute lymphoblastic leukemia cell line) were analyzed

2015-11-01 | E-GEOD-73924 | biostudies-arrayexpress

The Properties of Genome Conformation and Spatial Gene Interaction and Regulation Networks of Normal and Malignant Human Cell Types

Project description:In this work, we generated hundreds of millions of Hi-C paired end sequence reads for three different human cells (RL follicular lymphoma cell line, primary tumor B-cells from an acute lymphoblastic leukemia patient, and MHH-CALL-4 B-cell acute lymphoblastic leukemia cell line) using the Hi-C technique. An in-house Bioinformatics software pipeline was developed and applied to map sequence reads to the human reference genome, producing a large data set of high-quality and high-resolution chromosome contacts. Our computational analysis on these data reveal some interesting properties of human genome conformation, including conformational conservation and variation of the genomes of different cells, intra- and inter-chromosomal interactions, aberrant chromosomal translocation, spatial gene clusters, spatial gene-gene interactions, and spatial gene-regulatory-element interaction. Furthermore, we derived spatial interactions between functional elements (genes, transcription factor binding sites) from the chromosomal interaction data. The data were then used to generate chromosome-/genome-wide gene-gene interaction networks, transcription factor binding site (TFBS) – TFBS networks, and gene-TFBS networks. Remarkably, the connectivity in both networks shows the hallmark features of scale-free networks, suggesting that spatial interactions of gene-gene, gene-TFBS, and TFBS-TFBS in a genome are far from random.

2015-11-01 | GSE73924 | GEO

In vivo mapping of protein-protein interactions of schizophrenia risk factors generates an interconnected disease network.

Project description:Genetic analyses of Schizophrenia (SCZ) patients have identified thousands of risk factors. In silico protein-protein interaction (PPI) network analysis has provided strong evidence that disrupted PPI networks underlie SCZ pathogenesis. In this study, we performed in vivo PPI analysis of several SCZ risk factors in the rodent brain. Using endogenous antibody immunoprecipitations coupled to mass spectrometry (MS) analysis, we constructed a SCZ network comprising 1612 unique PPI with a 5% FDR. Over 90% of the PPI were novel, reflecting the lack of previous PPI MS studies in brain tissue. Our SCZ PPI network was enriched with known SCZ risk factors, which supports the hypothesis that an accumulation of disturbances in selected PPI networks underlies SCZ. We used Stable Isotope Labeling in Mammals (SILAM) to quantitate phencyclidine (PCP) perturbations in the SCZ network and found that PCP weakened most PPI but also led to some enhanced or new PPI. These findings demonstrate that quantitating PPI in perturbed biological states can reveal alterations to network biology.

2023-11-17 | MSV000093425 | MassIVE

A protein network of common susceptibility genes provides a link in the genetic architecture of chronic inflammation and cardiovascular disease

Project description:Genome-wide association studies (GWAS) have identified hundreds of susceptibility loci for chronic and inflammatory disease phenotypes in humans. There is increasing evidence that chronic inflammation is a crucial driver in the pathogenesis of cardiovascular diseases (CVD), which may be genetically determined. To understand the genetic architecture underlying chronic inflammation and CVD we performed a systematic analysis of (1) common risk alleles coming from published GWAS, (2) of protein-protein interaction (PPI) networks informed by (3) gene expression data with a defined molecular target involved in the inflammatory processes promoting CVD, MRP8. (4) through analysis of integrated haplotype scores (iHS) and FST values in HapMap phase 2 data, we investigated whether recent selection pressure acting upon inflammatory genes affected CVD susceptibility loci. Our findings provide significant evidence for a PPI network, which connects inflammatory and cardiovascular susceptibility genes, and establish a genetic framework of inflammatory CVD. 41.59% of PPI genes are associated with immune functions. 28.3% of integrated genes can be linked to both, an inflammatory and cardiovascular disease phenotype. Interestingly, CDKN2B, and CELSR2/PSRC1/MYBPHL/SORT1, unequivocally replicated CVD loci, are integrated within this network as are several SNPs located in transcription factor recognition sequences, i.e. NFKB1, STAT3, which are key factors in inflammation. Finally, we observed a significant enrichment of inflammatory variants within CVD cluster loci that are targets of selection. Overall, 32 genes exhibit traces of selection, 16 of which are part of the PPI, further suggesting that recent selective sweeps may have affected the genomic architecture underlying CVD. 6 samples, no replicates.

2015-09-01 | E-GEOD-54989 | biostudies-arrayexpress

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2023-09-30 | GSE235245 | GEO

Integrative Multi‑omics Analysis Reveals Candidate Biomarkers for Oral Squamous Cell Carcinoma

Project description:We analyzed differentially expressed genes (DEGs) associated with OSCC using RNA sequencing technology. Methylation‑regulated and differentially expressed genes (MeDEGs) of OSCC were further identified via an integrative approach by examining methylomic datasets together with our own transcriptomic data. Protein‑protein interaction (PPI) networks of MeDEGs were constructed and highly connected hub MeDEGs were identified from these PPI networks. A total of 56 upregulated MeDEGs and 170 downregulated MeDEGs were identified in OSCC. Eleven hub genes with high degree of connectivity were picked out from the protein‑protein interaction (PPI) networks constructed by those MeDEGs.

2022-10-31 | GSE186775 | GEO

Protein interaction networks in the vasculature prioritize genes and pathways underlying coronary artery disease (Whitehead data)

Project description:Population-based association studies have identified many genetic risk loci for coronary artery disease (CAD), but it is often unclear how genes within these loci are linked to CAD. Here, we performed interaction proteomics for 11 CAD-risk genes to map their protein-protein interactions (PPIs) in human vascular cells and elucidate their biological relevance in CAD pathogenesis. The PPI networks contain many protein interactions that are outside of known biology in the vasculature and are enriched for genes involved in immunity-related and arterial-wall-specific mechanisms. Furthermore, several PPI networks derived from smooth muscle cells are significantly enriched for genetic variants associated with CAD and related vascular phenotypes. Finally, our networks identified 61 genes that are found in genetic loci associated with risk of CAD, prioritizing them as the causal candidates within these loci. Together, our results indicate that the PPI networks we have generated are a rich resource for guiding future research into the molecular pathogenesis of CAD.

2023-02-27 | MSV000091373 | MassIVE

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