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

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 Microarray Analysis Identifies Key Differentially Expressed Circular RNAs in Aged Mice With Postoperative Cognitive Dysfunction

2021 ◽  
Vol 13 ◽  
Author(s):  
Yu-Qing Wu ◽  
Qiang Liu ◽  
Hai-Bi Wang ◽  
Chen Chen ◽  
Hui Huang ◽  
...  
Keyword(s):  
Cognitive Dysfunction ◽  
Protein Interactions ◽  
Expression Profiles ◽  
Postoperative Cognitive Dysfunction ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Circular Rnas ◽  
Protein Protein Interactions ◽  
Hub Genes ◽  
Aged Mice

Postoperative cognitive dysfunction (POCD) is a common complication in elderly patients. Circular RNAs (circRNAs) may contribute to neurodegenerative diseases. However, the role of circRNAs in POCD in aged mice has not yet been reported. This study aimed to explore the potential circRNAs in a POCD model. First, a circRNA microarray was used to analyze the expression profiles. Differentially expressed circRNAs were validated using quantitative real-time polymerase chain reaction. A bioinformatics analysis was then used to construct a competing endogenous RNA (ceRNA) network. The database for annotation, visualization, and integrated discovery was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of circRNA-related genes. Moreover, protein-protein interactions were analyzed to predict the circRNA-regulated hub genes using the STRING and molecular complex detection plug-in of Cytoscape. Microarray screen 124 predicted circRNAs in the POCD of aged mice. We found that the up/downregulated circRNAs were involved in multiple signaling pathways. Hub genes, including Egfr and Prkacb, were identified and may be regulated by ceRNA networks. These results suggest that circRNAs are dysexpressed in the hippocampus and may contribute to POCD in aged mice.

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 H2V, a Database for Human Proteins and Genes in Response to SARS-CoV-2, SARS-CoV, and MERS-CoV Infection

2020 ◽  
Author(s):  
Nan Zhou ◽  
Jinku Bao ◽  
Yuping Ning
Keyword(s):  
Differentially Expressed Genes ◽  
Protein Interactions ◽  
Viral Infections ◽  
Antiviral Agents ◽  
Differentially Expressed ◽  
Easy Access ◽  
Differentially Expressed Proteins ◽  
Protein Protein Interactions ◽  
Associated Proteins ◽  
Human Proteins

Abstract The ongoing COVID-19 pandemic in the world is caused by SARS-CoV-2, a new coronavirus firstly discovered in the end of 2019. It has led to more than 10 million confirmed cases and more than 500,000 confirmed deaths across 216 countries by 1 July 2020, according to WHO statistics. SARS-CoV-2, SARS-CoV, and MERS-CoV are alike, killing people, impairing economy, and inflicting long-term impacts on the society. However, no specific drug or vaccine has been approved as a cure for these viruses. The efforts to develop antiviral measures are hampered by insufficient understanding of molecular responses of human to viral infections. In this study, we collected experimentally validated human proteins that interact with SARS-CoV-2 proteins, human proteins whose expression, translation and phosphorylation levels experience significantly changes after SARS-CoV-2 or SARS-CoV infection, human proteins that correlate with COVID-19 severity, and human genes whose expression levels significantly changed upon SARS-CoV-2 or MERS-CoV infection. A database, H2V, was then developed for easy access to these data. Currently H2V includes: 332 human-SARS-CoV-2 protein-protein interactions; 65 differentially expressed proteins, 232 differentially translated proteins, 1298 differentially phosphorylated proteins, 204 severity associated proteins, and 4012 differentially expressed genes responding to SARS-CoV-2 infection; 66 differentially expressed proteins responding to SARS-CoV infection; and 6981 differentially expressed genes responding to MERS-CoV infection. H2V can help to understand the cellular responses associated with SARS-CoV-2, SARS-CoV and MERS-CoV infection. It is expected to speed up the development of antiviral agents and shed light on the preparation for potential coronavirus emergency in the future.Database url: http://www.zhounan.org/h2v

scholarly journals Discovery, development and application of drugs targeting BCL-2 pro-survival proteins in cancer

2021 ◽  
Author(s):  
Erinna F. Lee ◽  
W. Douglas Fairlie
Keyword(s):  
Structural Biology ◽  
Protein Interactions ◽  
Basic Science ◽  
Great Success ◽  
Protein Protein Interactions ◽  
New Class ◽  
Success Stories ◽  
History Of ◽  
Approved Drugs ◽  
Close Relatives

The discovery of a new class of small molecule compounds that target the BCL-2 family of anti-apoptotic proteins is one of the great success stories of basic science leading to translational outcomes in the last 30 years. The eponymous BCL-2 protein was identified over 30 years ago due to its association with cancer. However, it was the unveiling of the biochemistry and structural biology behind it and its close relatives’ mechanism(s)-of-action that provided the inspiration for what are now known as ‘BH3-mimetics’, the first clinically approved drugs designed to specifically inhibit protein–protein interactions. Herein, we chart the history of how these drugs were discovered, their evolution and application in cancer treatment.

INFERRING PROTEIN-PROTEIN INTERACTIONS FROM MESSENGER RNA EXPRESSION PROFILES WITH SVM

2005 ◽  
Vol 13 (03) ◽  
pp. 287-298 ◽  
Author(s):  
JUN CAI ◽  
YING HUANG ◽  
LIANG JI ◽  
YANDA LI
Keyword(s):  
High Throughput ◽  
Protein Interactions ◽  
Messenger Rna ◽  
Expression Profiles ◽  
Support Vector ◽  
Svm Classifier ◽  
Good Prediction ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
High Throughput Experiments

In post-genomic biology, researchers in the field of proteome focus their attention on the networks of protein interactions that control the lives of cells and organisms. Protein-protein interactions play a useful role in dynamic cellular machinery. In this paper, we developed a method to infer protein-protein interactions based on the theory of support vector machine (SVM). For a given pair of proteins, a new strategy of calculating cross-correlation function of mRNA expression profiles was used to encode SVM vectors. We compared the performance with other methods of inferring protein-protein interaction. Results suggested that, through five-fold cross validation, our SVM model achieved a good prediction. It enables us to show that expression profiles in transcription level can be used to distinguish physical or functional interactions of proteins as well as sequence contents. Lastly, we applied our SVM classifier to evaluate data quality of interaction data sets from four high-throughput experiments. The results show that high-throughput experiments sacrifice some accuracy in determination of interactions because of limitation of experiment technologies.

scholarly journals Analysis of IFITM-IFITM Interactions by a Flow Cytometry-Based FRET Assay

2019 ◽  
Vol 20 (16) ◽  
pp. 3859 ◽  
Author(s):  
Michael Winkler ◽  
Florian Wrensch ◽  
Pascale Bosch ◽  
Maike Knoth ◽  
Michael Schindler ◽  
...  
Keyword(s):  
Antiviral Activity ◽  
Protein Interactions ◽  
Viral Entry ◽  
Cellular Localization ◽  
Fluorescent Protein ◽  
Fluorescent Proteins ◽  
Yellow Fluorescent Protein ◽  
Resonance Energy Transfer ◽  
Resonance Energy ◽  
Protein Protein Interactions

The interferon-induced transmembrane proteins 1–3 (IFITM1–3) inhibit host cell entry of several viruses. However, it is incompletely understood how IFITM1–3 exert antiviral activity. Two phenylalanine residues, F75 and F78, within the intramembrane domain 1 (IM1) were previously shown to be required for IFITM3/IFITM3 interactions and for inhibition of viral entry, suggesting that IFITM/IFITM interactions might be pivotal to antiviral activity. Here, we employed a fluorescence resonance energy transfer (FRET) assay to analyze IFITM/IFITM interactions. For assay calibration, we equipped two cytosolic, non-interacting proteins, super yellow fluorescent protein (SYFP) and super cyan fluorescent protein (SCFP), with signals that target proteins to membrane rafts and also analyzed a SCFP-SYFP fusion protein. This strategy allowed us to discriminate background signals resulting from colocalization of proteins at membrane subdomains from signals elicited by protein–protein interactions. Coexpression of IFITM1–3 and IFITM5 fused to fluorescent proteins elicited strong FRET signals, and mutation of F75 and F78 in IFITM3 (mutant IFITM3-FF) abrogated antiviral activity, as expected, but did not alter cellular localization and FRET signals. Moreover, IFITM3-FF co-immunoprecipitated efficiently with wild type (wt) IFITM3, lending further support to the finding that lack of antiviral activity of IFITM3-FF was not due to altered membrane targeting or abrogated IFITM3-IFITM3 interactions. Collectively, we report an assay that allows quantifying IFITM/IFITM interactions. Moreover, we confirm residues F75 and F78 as critical for antiviral activity but also show that these residues are dispensable for IFITM3 membrane localization and IFITM3/IFITM3 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 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.

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