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 Malignant Pleural Mesothelioma Interactome with 364 Novel Protein-Protein Interactions

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1660
Author(s):  
Kalyani B. Karunakaran ◽  
Naveena Yanamala ◽  
Gregory Boyce ◽  
Michael J. Becich ◽  
Madhavi K. Ganapathiraju
Keyword(s):  
Malignant Pleural Mesothelioma ◽  
Protein Interactions ◽  
Cellular Localization ◽  
Expression Profiles ◽  
Reference Database ◽  
Pleural Mesothelioma ◽  
Cancer Genes ◽  
Protein Protein Interactions ◽  
Drug Induced

Malignant pleural mesothelioma (MPM) is an aggressive cancer affecting the outer lining of the lung, with a median survival of less than one year. We constructed an ‘MPM interactome’ with over 300 computationally predicted protein-protein interactions (PPIs) and over 2400 known PPIs of 62 literature-curated genes whose activity affects MPM. Known PPIs of the 62 MPM associated genes were derived from Biological General Repository for Interaction Datasets (BioGRID) and Human Protein Reference Database (HPRD). 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. We validated five novel predicted PPIs experimentally. The interactome is significantly enriched with genes differentially ex-pressed in MPM tumors compared with normal pleura and with other thoracic tumors, genes whose high expression has been correlated with unfavorable prognosis in lung cancer, genes differentially expressed on crocidolite exposure, and exosome-derived proteins identified from malignant mesothelioma cell lines. 28 of the interactors of MPM proteins are targets of 147 U.S. Food and Drug Administration (FDA)-approved drugs. By comparing disease-associated versus drug-induced differential expression profiles, we identified five potentially repurposable drugs, namely cabazitaxel, primaquine, pyrimethamine, trimethoprim and gliclazide. Preclinical studies may be con-ducted in vitro to validate these computational results. Interactome analysis of disease-associated genes is a powerful approach with high translational impact. It shows how MPM-associated genes identified by various high throughput studies are functionally linked, leading to clinically translatable results such as repurposed drugs. The PPIs are made available on a webserver with interactive user interface, visualization and advanced search capabilities.

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 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 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 Mesothelioma Interactome with 367 Novel Protein-Protein Interactions

2018 ◽  
Author(s):  
Kalyani B. Karunakaran ◽  
Naveena Yanamala ◽  
Gregory Boyce ◽  
Madhavi K. Ganapathiraju
Keyword(s):  
Protein Interactions ◽  
Cellular Localization ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Protein Protein Interactions ◽  
Advanced Search ◽  
Genomic Location ◽  
One Year ◽  
Approved Drugs ◽  
Novel Protein

AbstractMalignant pleural mesothelioma (MPM) is an aggressive cancer of the thorax with a median survival of one year. We constructed an ‘MPM interactome’ with over 300 computationally predicted PPIs and over 1300 known PPIs of 62 literature-curated genes whose activity affects MPM. Known PPIs of the 62 MPM 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, gene expression in microarray experiments, protein domains and tissue membership, and classifies the pairwise features asinteractingornon-interactingbased on a random forest model. To our satisfaction, the interactome is significantly enriched with genes differentially expressed in MPM tumors compared with normal pleura, and with other thoracic tumors. The interactome is also significantly enriched with genes whose high expression has been correlated with unfavorable prognosis in lung cancer, and with genes differentially expressed on crocidolite exposure. 28 of the interactors of MPM proteins are targets of 147 FDA-approved drugs. By comparing differential expression profiles induced by drug to profiles induced by MPM, potentially repurposable drugs are identified from this drug list. Development of PPIs of disease-specific set of genes is a powerful approach with high translational impact – the interactome is a vehicle to piece together an integrated view on how genes associated with MPM through various high throughput studies are functionally linked, leading to clinically translatable results such as clinical trials with repurposed drugs. The PPIs are made available on a webserver, calledWiki-Pi MPMathttp://severus.dbmi.pitt.edu/wiki-MPMwith advanced search capabilities.One Sentence SummaryMesothelioma Interactome with 367 novel protein-protein interactions may shed light on the mechanisms of cancer genesis and progression

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