scholarly journals Correlation between gene expression profiles and protein-protein interactions within and across genomes

2005 ◽  
Vol 21 (11) ◽  
pp. 2730-2738 ◽  
Author(s):  
N. Bhardwaj ◽  
H. Lu
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Protein Protein Interactions

scholarly journals Distinct p53 acetylation cassettes differentially influence gene-expression patterns and cell fate

2006 ◽  
Vol 173 (4) ◽  
pp. 533-544 ◽  
Author(s):  
Chad D. Knights ◽  
Jason Catania ◽  
Simone Di Giovanni ◽  
Selen Muratoglu ◽  
Ricardo Perez ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Fate ◽  
Protein Interactions ◽  
Posttranslational Modifications ◽  
Biological Activities ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
P53 Gene ◽  
Protein Protein Interactions

The activity of the p53 gene product is regulated by a plethora of posttranslational modifications. An open question is whether such posttranslational changes act redundantly or dependently upon one another. We show that a functional interference between specific acetylated and phosphorylated residues of p53 influences cell fate. Acetylation of lysine 320 (K320) prevents phosphorylation of crucial serines in the NH2-terminal region of p53; only allows activation of genes containing high-affinity p53 binding sites, such as p21/WAF; and promotes cell survival after DNA damage. In contrast, acetylation of K373 leads to hyperphosphorylation of p53 NH2-terminal residues and enhances the interaction with promoters for which p53 possesses low DNA binding affinity, such as those contained in proapoptotic genes, leading to cell death. Further, acetylation of each of these two lysine clusters differentially regulates the interaction of p53 with coactivators and corepressors and produces distinct gene-expression profiles. By analogy with the “histone code” hypothesis, we propose that the multiple biological activities of p53 are orchestrated and deciphered by different “p53 cassettes,” each containing combination patterns of posttranslational modifications and protein–protein interactions.

scholarly journals Intra- and intergenerational changes in the cortical DNA methylome in response to therapeutic intermittent hypoxia in mice

2020 ◽  
Vol 52 (1) ◽  
pp. 20-34 ◽  
Author(s):  
Krystal Courtney D. Belmonte ◽  
Jarrod C. Harman ◽  
Nicholas A. Lanson ◽  
Jeffrey M. Gidday
Keyword(s):  
Gene Expression ◽  
Prefrontal Cortex ◽  
Protein Interactions ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Protein Protein Interactions ◽  
Dna Methylome ◽  
Methylated Dna ◽  
Modified Dna ◽  
F1 Generation

Recent evidence from our laboratory documents functional resilience to retinal ischemic injury in untreated mice derived from parents exposed to repetitive hypoxic conditioning (RHC) before breeding. To begin to understand the epigenetic basis of this intergenerational protection, we used methylated DNA immunoprecipitation and sequencing to identify genes with differentially methylated promoters (DMGPs) in the prefrontal cortex of mice treated directly with the same RHC stimulus (F0-RHC) and in the prefrontal cortex of their untreated F1-generation offspring (F1-*RHC). Subsequent bioinformatic analyses provided key mechanistic insights into how changes in gene expression secondary to promoter hypo- and hypermethylation might afford such protection within and across generations. We found extensive changes in DNA methylation in both generations consistent with the expression of many survival-promoting genes, with twice the number of DMGPs in the cortex of F1*RHC mice relative to their F0 parents that were directly exposed to RHC. In contrast to our hypothesis that similar epigenetic modifications would be realized in the cortices of both F0-RHC and F1-*RHC mice, we instead found relatively few DMGPs common to both generations; in fact, each generation manifested expected injury resilience via distinctly unique gene expression profiles. Whereas in the cortex of F0-RHC mice, predicted protein-protein interactions reflected activation of an anti-ischemic phenotype, networks activated in F1-*RHC cortex comprised networks indicative of a much broader cytoprotective phenotype. Altogether, our results suggest that the intergenerational transfer of an acquired phenotype to offspring does not necessarily require the faithful recapitulation of the conditioning-modified DNA methylome of the parent.

scholarly journals Molecular Characterization and Elucidation of Pathways to Identify Novel Therapeutic Targets in Pulmonary Arterial Hypertension

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaoting Yao ◽  
Tian Jing ◽  
Tianxing Wang ◽  
Chenxin Gu ◽  
Xi Chen ◽  
...  
Keyword(s):  
Pulmonary Arterial Hypertension ◽  
Arterial Hypertension ◽  
Lung Tissue ◽  
Protein Interactions ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Signatures ◽  
Protein Protein Interactions ◽  
Pulmonary Arterial ◽  
Microarray Datasets

Background: Pulmonary arterial hypertension (PAH) is a life-threatening chronic cardiopulmonary disease. However, there are limited studies reflecting the available biomarkers from separate gene expression profiles in PAH. This study explored two microarray datasets by an integrative analysis to estimate the molecular signatures in PAH.Methods: Two microarray datasets (GSE53408 and GSE113439) were exploited to compare lung tissue transcriptomes of patients and controls with PAH and to estimate differentially expressed genes (DEGs). According to common DEGs of datasets, gene and protein overrepresentation analyses, protein–protein interactions (PPIs), DEG–transcription factor (TF) interactions, DEG–microRNA (miRNA) interactions, drug–target protein interactions, and protein subcellular localizations were conducted in this study.Results: We obtained 38 common DEGs for these two datasets. Integration of the genome transcriptome datasets with biomolecular interactions revealed hub genes (HSP90AA1, ANGPT2, HSPD1, HSPH1, TTN, SPP1, SMC4, EEA1, and DKC1), TFs (FOXC1, FOXL1, GATA2, YY1, and SRF), and miRNAs (hsa-mir-17-5p, hsa-mir-26b-5p, hsa-mir-122-5p, hsa-mir-20a-5p, and hsa-mir-106b-5p). Protein–drug interactions indicated that two compounds, namely, nedocromil and SNX-5422, affect the identification of PAH candidate biomolecules. Moreover, the molecular signatures were mostly localized in the extracellular and nuclear areas.Conclusions: In conclusion, several lung tissue-derived molecular signatures, highlighted in this study, might serve as novel evidence for elucidating the essential mechanisms of PAH. The potential drugs associated with these molecules could thus contribute to the development of diagnostic and therapeutic strategies to ameliorate PAH.

scholarly journals Malignant Peritoneal Mesothelioma Interactome with 417 Novel Protein-Protein Interactions

2021 ◽  
Author(s):  
Kalyani B. Karunakaran ◽  
Madhavi K. Ganapathiraju
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Protein Interactions ◽  
Cellular Localization ◽  
Expression Profiles ◽  
Abdominal Cavity ◽  
Peritoneal Mesothelioma ◽  
Pleural Mesothelioma ◽  
Malignant Peritoneal Mesothelioma ◽  
Protein Protein Interactions

Abstract Malignant peritoneal mesothelioma (MPeM) is an aggressive cancer affecting the peritoneal lining of the abdominal cavity and intra-abdominal organs, with a median survival of ~2.5 years. We constructed an ‘MPeM interactome’ with over 400 computationally predicted protein-protein interactions (PPIs) and over 4,700 known PPIs of 59 literature-curated genes whose activity affects MPeM. Known PPIs of the 59 MPeM-associated genes were derived from BioGRID and HPRD databases. Novel PPIs were predicted by applying the HiPPIP algorithm, which computes features of protein pairs such as cellular localization, molecular function, biological process membership, genomic location of the gene, and gene expression in microarray experiments, and classifies the pairwise features as interacting or non-interacting based on a random forest model. 75.6% of the interactome and 65% of the novel interactors in it were supported by transcriptomic evidence in rodent and human peritoneal mesothelioma/mesothelial cell lines and tumor specimens. 152 drugs targeted 427 proteins in the MPeM interactome. Comparative transcriptome analysis of peritoneal mesothelioma-associated versus drug-induced gene expression profiles revealed 39 repurposable drugs, out of which 29 were effective against peritoneal/pleural mesothelioma and/or peritoneal metastasis/primary peritoneal cancer in clinical trials, animal models or cell lines. Functional modules of chromosomal segregation, transcriptional deregulation, positive regulation of IL-6 production and hematopoiesis were identified from the interactome. Genes with tissue-specific expression in 2 sites of extramedullary hematopoiesis (spleen and thymus) and those correlated with unfavorable prognosis in liver, renal, pancreatic and lung cancers were noted. MPeM interactome showed extensive overlap with the malignant pleural mesothelioma (MPM) interactome and MPM cell line expression profiles. Our findings demonstrate the utility of the MPeM interactome in discovering systems-level functional links among MPeM genes and generating clinically translatable results such as repurposed drugs.

scholarly journals Identification of hub genes and pathways in nonalcoholic steatohepatitis by integrated bioinformatics analysis

2021 ◽  
Author(s):  
Pegah Einaliyan ◽  
Ali Owfi ◽  
Mohammadamin Mahmanzar ◽  
Taha Aghajanzadeh ◽  
Morteza Hadizadeh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Expression Profiles ◽  
Principal Component ◽  
Gene Expression Omnibus ◽  
P Value ◽  
Protein Protein Interactions ◽  
Hub Genes ◽  
Alcoholic Fatty Liver ◽  
Critical Pathways

AbstractBackgroundCurrently, non-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases in the world. Forecasting the short-term, up to 2025, NASH due to fibrosis is one of the leading causes of liver transplantation. Cohort studies revealed that non-alcoholic steatohepatitis (NASH) has a higher risk of fibrosis progression among NAFLD patients. Identifying differentially expressed genes helps to determine NASH pathogenic pathways, make more accurate diagnoses, and prescribe appropriate treatment.Methods and ResultsIn this study, we found 11 NASH datasets by searching in the Gene Expression Omnibus (GEO) database. Subsequently, NASH datasets with low-quality control scores were excluded. Four datasets were analyzed with packages of R/Bioconductor. Then, all integrated genes were Imported into Cytoscape to illustrate the protein-protein interactions network. All hubs and nodes degree has been calculated to determine the hub genes with critical roles in networks.Possible correlations between expression profiles of mutual DEGs were identified employing Principal Component Analysis (PCA). Primary analyzed data were filtered based on gene expression (logFC > 1, logFC < −1) and adj-P-value (<0.05). Ultimately, among 379 DEGs, we selected the top 10 genes (MYC, JUN, EGR1, FOS, CCL2, IL1B, CXCL8, PTGS2, IL6, SERPINE1) as candidates among up and down regulated genes, and critical pathways such as IL-6, IL-17, TGF β, and TNFα were identified.ConclusionThe present study suggests an important DEGs, biological processes, and critical pathways involved in the pathogenesis of NASH disease. Further investigations are needed to clarify the exact mechanisms underlying the development and progression of NASH disease.

scholarly journals Identification of potential core genes in Kawasaki disease using bioinformatics analysis

2019 ◽  
Vol 47 (9) ◽  
pp. 4051-4058
Author(s):  
Wenbin Wu ◽  
Keyou You ◽  
Jinchan Zhong ◽  
Zhanwei Ruan ◽  
Bubu Wang
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Kawasaki Disease ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Humoral Response ◽  
Hub Genes ◽  
Potential Core

Objective The present study aimed to elucidate the underlying pathogenesis of Kawasaki disease (KD) and to identify potential biomarkers for KD. Methods Gene expression profiles for the GSE68004 dataset were downloaded from the Gene Expression Omnibus database. The pathways and functional annotations of differentially expressed genes (DEGs) in KD were examined by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses using the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool. Protein–protein interactions of the above-described DEGs were investigated using the Search Tool for the Retrieval of Interacting Genes (STRING). Results Gene Ontology analysis revealed that DEGs in KD were significantly enriched in biological processes, including the inflammatory response, innate immune response, defense response to Gram-positive bacteria, and antibacterial humoral response. In addition, 10 hub genes with high connectivity were selected from among these DEGs ( ITGAM, MPO, MAPK14, SLC11A1, HIST2H2BE, ELANE, CAMP, MMP9, NTS, and HIST2H2AC). Conclusion The identification of several novel hub genes in KD enhances our understanding of the molecular mechanisms underlying the progression of this disease. These genes may be potential diagnostic biomarkers and/or therapeutic molecular targets in patients with KD. ITGAM inhibitors in particular may be potential targets for KD therapy.

scholarly journals Comorbidities and Susceptibility to COVID-19: A Generalized Gene Set Meta-Analysis Approach

2020 ◽  
Author(s):  
Micaela F. Beckman ◽  
Chika K. Igba ◽  
Farah B. Mougeot ◽  
Jean-Luc Mougeot
Keyword(s):  
Protein Interactions ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Gene Expression Profiles ◽  
Gene Interaction ◽  
Snp Analysis ◽  
Nucleotide Polymorphisms ◽  
Protein Protein Interactions ◽  
Gene Expressions ◽  
Mrna Gene

Background The COVID-19 pandemic has led to over 820,000 deaths for almost 24 million confirmed cases worldwide, as of August 27th, 2020, per WHO report. Risk factors include pre-existing conditions such as cancer, cardiovascular disease, diabetes, obesity, and cancer. There are currently no effective treatments. Our objective was to complete a meta-analysis to identify comorbidity-associated single nucleotide polymorphisms (SNPs), potentially conferring increased susceptibility to SARS-CoV-2 infection using a computational approach. Results SNP datasets were downloaded from publicly available GWAS catalog for 141 of 258 candidate COVID-19 comorbidities. Gene-level SNP analysis was performed to identify significant pathways by using MAGMA program. SNP annotation program was used to analyze MAGMA-identified genes. COVID-19 comorbidities from six disease categories were found to have significant associated pathways, which were validated by Q-Q plots (p<0.05). The top 250 human mRNA gene expressions for SNP-affected pathways, extracted from publicly accessible gene expression profiles, were evaluated for significant pathways. Protein-protein interactions of identified differentially expressed genes, visualized with STRING program, were significant (p<0.05). Gene interaction networks were found to be relevant to SARS and influenza pathogenesis. Conclusion Pathways potentially affected by or affecting SARS-CoV-2 infection were identified in underlying medical conditions likely to confer susceptibility and/or severity to COVID-19. Our findings have implications in COVID-19 treatment development. Keywords: SARS-CoV-2, COVID-19, comorbidity, susceptibility, severity

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