The PENGUIN approach to reconstruct protein interactions at enhancer-promoter regions and its application to prostate cancer
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE235245 | GEO | 2023/09/30
REPOSITORIES: GEO
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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.
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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
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
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.
2015-09-01 | GSE54989 | GEO
Project description:Introduction Genome-wide association studies along with expression quantitative trait loci (eQTL) have identified hundreds of single nucleotide polymorphisms (SNPs) and their target genes in prostate cancer (PrCa). Although these genetic associations to PrCa have been widely reported, functional characterization of these risk loci remains challenging. Methods To screen for regulatory SNPs, we designed a library containing 9133 guide RNAs (gRNAs) to target 2,166 candidate SNP sites implicated in PrCa. We performed negative screening in dCas9-KRAB stable prostate cell lines and applied the RIGOR program to identify the essential SNPs for cell proliferation. We further characterized the regulatory role of a selected single nucleotide polymorphim (SNP, rs60464856) using luciferase reporter assay, ChIP-qPCR, and xCas9 base editing in prostate cells. Finally, we investigated the biological impact of the SNP-regulated gene RUVBL1 on cell proliferation and tumor growth via gene knockdown using in vitro and in vivo assays. Results From interference of 2,166 candidate SNPs via CRISPR interference screening, the RIGOR program identified 117 SNPs that could regulate genes to promote growth advantage in prostate cancer cell lines. Compared to unselected SNPs, the 117 candidates tended to reside near 5 kb flanking the transcription start sites (p = 0.01). To characterize the regulatory role of these SNPs, we selected one SNP (rs60464856) for detailed analysis. This SNP was covered by multiple gRNAs significantly depleted in the screening (FDR<0.05). Pooled SNP association analysis in the PRACTICAL cohort showed significantly higher PrCa risk for the G allele (pvalue=1.2E-16). eQTL analysis showed that the G allele is associated with an increased expression of RUVBL1 in multiple datasets. To further validate the CRISPR interference effect, we transfected a gRNA targeting the rs60464856 site in the dCas9 stable cell lines and observed significant inhibition of the RUVBL1 expression. We also applied the xCas9 adenine base editor to convert the rs60464856 A into G allele and observed an increased RUVBL1 expression in subclones carrying the rs60464856 G allele in prostate cell lines. To test if any protein showed allele-specific binding at rs60464856, we used SILAC-based DNA pull-down proteomics and observed that cohesin subunits (including SMC3) preferred the A allele. ChIP qPCR assays showed significant enrichment of CTCF and SMC3 signals at the rs60464856 site with preferential binding to the A allele. To evaluate the potential role of this locus in maintaining long-range chromatin structure, we analyzed a HiC dataset and found that the rs60464856 locus enriched consistent chromatin interactions in prostate cell lines. To determine the potential role of the rs60464856 target gene, we knocked down the RUVBL1 via shRNA and observed significant proliferation inhibition in prostate cell lines. We also tested PC3 cells with RUVBL1 knockdown in nude mice xenografts and observed reduced tumorigenesis. Gene set enrichment analysis showed that RUVBL1 expression was associated with the enrichment of cell cycle related pathways in both cell line and TCGA prostate cancer cohorts. Lastly, we showed that an increased RUVBL1 expression and its relevant pathway activation were associated with poor survival. Conclusion We applied the CRISPR interference screening at selected prostate cancer risk loci and identified over a hundred regulatory SNPs essential for prostate cell proliferation. Further analysis confirmed the important role of rs60464856 and its target gene RUVBL1 in prostate cell growth and tumorigenesis. To validate one of our candidate, we knocked down RUVBL1 gene in BPH1 and PC3 cell lines and observed the transcriptome alterations with RNA-seq
2023-07-05 | GSE224646 | GEO
Project description:Introduction Genome-wide association studies along with expression quantitative trait loci (eQTL) have identified hundreds of single nucleotide polymorphisms (SNPs) and their target genes in prostate cancer (PrCa). Although these genetic associations to PrCa have been widely reported, functional characterization of these risk loci remains challenging. Methods To screen for regulatory SNPs, we designed a library containing 9133 guide RNAs (gRNAs) to target 2,166 candidate SNP sites implicated in PrCa. We performed negative screening in dCas9-KRAB stable prostate cell lines and applied the RIGOR program to identify the essential SNPs for cell proliferation. We further characterized the regulatory role of a selected single nucleotide polymorphim (SNP, rs60464856) using luciferase reporter assay, ChIP-qPCR, and xCas9 base editing in prostate cells. Finally, we investigated the biological impact of the SNP-regulated gene RUVBL1 on cell proliferation and tumor growth via gene knockdown using in vitro and in vivo assays. Results From interference of 2,166 candidate SNPs via CRISPR interference screening, the RIGOR program identified 117 SNPs that could regulate genes to promote growth advantage in prostate cancer cell lines. Compared to unselected SNPs, the 117 candidates tended to reside near 5 kb flanking the transcription start sites (p = 0.01). To characterize the regulatory role of these SNPs, we selected one SNP (rs60464856) for detailed analysis. This SNP was covered by multiple gRNAs significantly depleted in the screening (FDR<0.05). Pooled SNP association analysis in the PRACTICAL cohort showed significantly higher PrCa risk for the G allele (pvalue=1.2E-16). eQTL analysis showed that the G allele is associated with an increased expression of RUVBL1 in multiple datasets. To further validate the CRISPR interference effect, we transfected a gRNA targeting the rs60464856 site in the dCas9 stable cell lines and observed significant inhibition of the RUVBL1 expression. We also applied the xCas9 adenine base editor to convert the rs60464856 A into G allele and observed an increased RUVBL1 expression in subclones carrying the rs60464856 G allele in prostate cell lines. To test if any protein showed allele-specific binding at rs60464856, we used SILAC-based DNA pull-down proteomics and observed that cohesin subunits (including SMC3) preferred the A allele. ChIP qPCR assays showed significant enrichment of CTCF and SMC3 signals at the rs60464856 site with preferential binding to the A allele. To evaluate the potential role of this locus in maintaining long-range chromatin structure, we analyzed a HiC dataset and found that the rs60464856 locus enriched consistent chromatin interactions in prostate cell lines. To determine the potential role of the rs60464856 target gene, we knocked down the RUVBL1 via shRNA and observed significant proliferation inhibition in prostate cell lines. We also tested PC3 cells with RUVBL1 knockdown in nude mice xenografts and observed reduced tumorigenesis. Gene set enrichment analysis showed that RUVBL1 expression was associated with the enrichment of cell cycle related pathways in both cell line and TCGA prostate cancer cohorts. Lastly, we showed that an increased RUVBL1 expression and its relevant pathway activation were associated with poor survival. Conclusion We applied the CRISPR interference screening at selected prostate cancer risk loci and identified over a hundred regulatory SNPs essential for prostate cell proliferation. Further analysis confirmed the important role of rs60464856 and its target gene RUVBL1 in prostate cell growth and tumorigenesis. To discover proliferative essential SNP loci, we used CRISPRi screening technology and DNA-seq to determine those functional candidate from previously published eQTL variants
2023-07-05 | GSE224653 | GEO
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2008-06-15 | E-GEOD-11111 | biostudies-arrayexpress
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
2008-06-15 | GSE11111 | GEO
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.
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