Integrative COVID-19 Biological Network Inference with Probabilistic Core Decomposition

The SARS-CoV-2 coronavirus is responsible for millions of deaths around the world. To help contribute to the understanding of crucial knowledge and to further generate new hypotheses relevant to SARS-CoV-2 and human protein interactions, we make use of the information abundant Biomine probabilistic database and extend the experimentally identified SARS-CoV-2-human protein-protein interaction (PPI) network in silico. We generate an extended network by integrating information from the Biomine database and the PPI network. To generate novel hypotheses, we focus on the high-connectivity sub-communities that overlap most with the PPI network in the extended network. Therefore, we propose a new data analysis pipeline that can efficiently compute core decomposition on the extended network and identify dense subgraphs. We then evaluate the identified dense subgraph and the generated hypotheses in three contexts: literature validation for uncovered virus targeting genes and proteins, gene function enrichment analysis on subgraphs, and literature support on drug repurposing for identified tissues and diseases related to COVID-19. The majority types of the generated hypotheses are proteins with their encoding genes and we rank them by sorting their connections to known PPI network nodes. In addition, we compile a comprehensive list of novel genes, and proteins potentially related to COVID-19, as well as novel diseases which might be comorbidities. Together with the generated hypotheses, our results provide novel knowledge relevant to COVID-19 for further validation.

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A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing

10.1101/2020.03.22.002386 ◽

2020 ◽

Cited By ~ 73

Author(s):

David E. Gordon ◽

Gwendolyn M. Jang ◽

Mehdi Bouhaddou ◽

Jiewei Xu ◽

Kirsten Obernier ◽

...

Keyword(s):

Protein Interactions ◽

Drug Targets ◽

Affinity Purification ◽

Drug Repurposing ◽

Human Protein ◽

Protein Protein Interaction ◽

Human Proteins ◽

Interaction Map ◽

Novel Coronavirus ◽

Approved Drugs

ABSTRACTAn outbreak of the novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 290,000 people since the end of 2019, killed over 12,000, and caused worldwide social and economic disruption1,2. There are currently no antiviral drugs with proven efficacy nor are there vaccines for its prevention. Unfortunately, the scientific community has little knowledge of the molecular details of SARS-CoV-2 infection. To illuminate this, we cloned, tagged and expressed 26 of the 29 viral proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), which identified 332 high confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 existing FDA-approved drugs, drugs in clinical trials and/or preclinical compounds, that we are currently evaluating for efficacy in live SARS-CoV-2 infection assays. The identification of host dependency factors mediating virus infection may provide key insights into effective molecular targets for developing broadly acting antiviral therapeutics against SARS-CoV-2 and other deadly coronavirus strains.

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IHP-PING—generating integrated human protein–protein interaction networks on-the-fly

Briefings in Bioinformatics ◽

10.1093/bib/bbaa277 ◽

2020 ◽

Author(s):

Gaston K Mazandu ◽

Christopher Hooper ◽

Kenneth Opap ◽

Funmilayo Makinde ◽

Victoria Nembaware ◽

...

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

High Throughput Sequencing ◽

Current Knowledge ◽

Interaction Network ◽

Human Protein ◽

Online Resources ◽

Ppi Network ◽

Genomic Context ◽

Protein Protein Interaction

Abstract Advances in high-throughput sequencing technologies have resulted in an exponential growth of publicly accessible biological datasets. In the ‘big data’ driven ‘post-genomic’ context, much work is being done to explore human protein–protein interactions (PPIs) for a systems level based analysis to uncover useful signals and gain more insights to advance current knowledge and answer specific biological and health questions. These PPIs are experimentally or computationally predicted, stored in different online databases and some of PPI resources are updated regularly. As with many biological datasets, such regular updates continuously render older PPI datasets potentially outdated. Moreover, while many of these interactions are shared between these online resources, each resource includes its own identified PPIs and none of these databases exhaustively contains all existing human PPI maps. In this context, it is essential to enable the integration of or combining interaction datasets from different resources, to generate a PPI map with increased coverage and confidence. To allow researchers to produce an integrated human PPI datasets in real-time, we introduce the integrated human protein–protein interaction network generator (IHP-PING) tool. IHP-PING is a flexible python package which generates a human PPI network from freely available online resources. This tool extracts and integrates heterogeneous PPI datasets to generate a unified PPI network, which is stored locally for further applications.

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Decoding Protein-protein Interactions: An Overview

Current Topics in Medicinal Chemistry ◽

10.2174/1568026620666200226105312 ◽

2020 ◽

Vol 20 (10) ◽

pp. 855-882

Author(s):

Olivia Slater ◽

Bethany Miller ◽

Maria Kontoyianni

Keyword(s):

Drug Discovery ◽

Protein Interactions ◽

Drug Repurposing ◽

Protein Docking ◽

Target Space ◽

Protein Protein Interactions ◽

X Ray Crystallography ◽

Protein Protein Interaction ◽

Interaction Sites ◽

Long Time

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

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Decoding the molecular mechanism of parthenocarpy in Musa spp. through protein–protein interaction network

Scientific Reports ◽

10.1038/s41598-021-93661-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Suthanthiram Backiyarani ◽

Rajendran Sasikala ◽

Simeon Sharmiladevi ◽

Subbaraya Uma

Keyword(s):

Candidate Genes ◽

Protein Interaction ◽

Target Genes ◽

Interaction Network ◽

Enrichment Analysis ◽

Auxin Signaling ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Protein Protein Interaction ◽

The Difference

AbstractBanana, one of the most important staple fruit among global consumers is highly sterile owing to natural parthenocarpy. Identification of genetic factors responsible for parthenocarpy would facilitate the conventional breeders to improve the seeded accessions. We have constructed Protein–protein interaction (PPI) network through mining differentially expressed genes and the genes used for transgenic studies with respect to parthenocarpy. Based on the topological and pathway enrichment analysis of proteins in PPI network, 12 candidate genes were shortlisted. By further validating these candidate genes in seeded and seedless accession of Musa spp. we put forward MaAGL8, MaMADS16, MaGH3.8, MaMADS29, MaRGA1, MaEXPA1, MaGID1C, MaHK2 and MaBAM1 as possible target genes in the study of natural parthenocarpy. In contrary, expression profile of MaACLB-2 and MaZEP is anticipated to highlight the difference in artificially induced and natural parthenocarpy. By exploring the PPI of validated genes from the network, we postulated a putative pathway that bring insights into the significance of cytokinin mediated CLAVATA(CLV)–WUSHEL(WUS) signaling pathway in addition to gibberellin mediated auxin signaling in parthenocarpy. Our analysis is the first attempt to identify candidate genes and to hypothesize a putative mechanism that bridges the gaps in understanding natural parthenocarpy through PPI network.

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Comparison of Computational Tools for Protein-Protein Interaction (PPI) Mapping and Analysis

Jurnal Teknologi ◽

10.11113/jt.v63.1226 ◽

2013 ◽

Vol 63 (1) ◽

Cited By ~ 1

Author(s):

Geok Wei Leong ◽

Sheau Chen Lee ◽

Cher Chien Lau ◽

Peter Klappa ◽

Mohd Shahir Shamsir Omar

Keyword(s):

Protein Interactions ◽

Network Visualization ◽

Database Integration ◽

File Format ◽

Output File ◽

Ppi Network ◽

Protein Protein Interactions ◽

Computational Tools ◽

Protein Protein Interaction ◽

Visualization Tools

Several visualization tools for the mapping of protein-protein interactions have been developed in recent years. However, a systematic comparison of the virtues and limitations of different PPI visualization tools has not been carried out so far. In this study, we compare seven commonly used visualization tools, based on input and output file format, layout algorithm, database integration, Gene Ontology annotation and accessibility of each tool. The assessment was carried out based on brain disease datasets. Our suggested tools, NAViGaTOR, Cytoscape and Gephi perform competitively as PPI network visualization tools, can be a reference for future researches on PPI mapping and analysis.

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Identification of Key miRNAs in the Treatment of Dabrafenib-Resistant Melanoma

BioMed Research International ◽

10.1155/2021/5524486 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Guangyu Gao ◽

Zhen Yao ◽

Jiaofeng Shen ◽

Yulong Liu

Keyword(s):

Drug Resistance ◽

Molecular Mechanism ◽

Target Genes ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Protein Protein Interaction ◽

Obvious Association

Dabrafenib resistance is a significant problem in melanoma, and its underlying molecular mechanism is still unclear. The purpose of this study is to research the molecular mechanism of drug resistance of dabrafenib and to explore the key genes and pathways that mediate drug resistance in melanoma. GSE117666 was downloaded from the Gene Expression Omnibus (GEO) database and 492 melanoma statistics were also downloaded from The Cancer Genome Atlas (TCGA) database. Besides, differentially expressed miRNAs (DEMs) were identified by taking advantage of the R software and GEO2R. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) and FunRich was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to identify potential pathways and functional annotations linked with melanoma chemoresistance. 9 DEMs and 872 mRNAs were selected after filtering. Then, target genes were uploaded to Metascape to construct protein-protein interaction (PPI) network. Also, 6 hub mRNAs were screened after performing the PPI network. Furthermore, a total of 4 out of 9 miRNAs had an obvious association with the survival rate ( P < 0.05 ) and showed a good power of risk prediction model of over survival. The present research may provide a deeper understanding of regulatory genes of dabrafenib resistance in melanoma.

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Repurposing novel therapeutic candidate drugs for coronavirus disease-19 based on protein-protein interaction network analysis

BMC Biotechnology ◽

10.1186/s12896-021-00680-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Masoumeh Adhami ◽

Balal Sadeghi ◽

Ali Rezapour ◽

Ali Akbar Haghdoost ◽

Habib MotieGhader

Keyword(s):

Network Analysis ◽

Protein Interaction ◽

Target Therapy ◽

Interaction Network ◽

Drug Repurposing ◽

Ppi Network ◽

Protein Protein Interaction ◽

Candidate Drug ◽

Starting Point ◽

Experimental Validations

Abstract Background The coronavirus disease-19 (COVID-19) emerged in Wuhan, China and rapidly spread worldwide. Researchers are trying to find a way to treat this disease as soon as possible. The present study aimed to identify the genes involved in COVID-19 and find a new drug target therapy. Currently, there are no effective drugs targeting SARS-CoV-2, and meanwhile, drug discovery approaches are time-consuming and costly. To address this challenge, this study utilized a network-based drug repurposing strategy to rapidly identify potential drugs targeting SARS-CoV-2. To this end, seven potential drugs were proposed for COVID-19 treatment using protein-protein interaction (PPI) network analysis. First, 524 proteins in humans that have interaction with the SARS-CoV-2 virus were collected, and then the PPI network was reconstructed for these collected proteins. Next, the target miRNAs of the mentioned module genes were separately obtained from the miRWalk 2.0 database because of the important role of miRNAs in biological processes and were reported as an important clue for future analysis. Finally, the list of the drugs targeting module genes was obtained from the DGIDb database, and the drug-gene network was separately reconstructed for the obtained protein modules. Results Based on the network analysis of the PPI network, seven clusters of proteins were specified as the complexes of proteins which are more associated with the SARS-CoV-2 virus. Moreover, seven therapeutic candidate drugs were identified to control gene regulation in COVID-19. PACLITAXEL, as the most potent therapeutic candidate drug and previously mentioned as a therapy for COVID-19, had four gene targets in two different modules. The other six candidate drugs, namely, BORTEZOMIB, CARBOPLATIN, CRIZOTINIB, CYTARABINE, DAUNORUBICIN, and VORINOSTAT, some of which were previously discovered to be efficient against COVID-19, had three gene targets in different modules. Eventually, CARBOPLATIN, CRIZOTINIB, and CYTARABINE drugs were found as novel potential drugs to be investigated as a therapy for COVID-19. Conclusions Our computational strategy for predicting repurposable candidate drugs against COVID-19 provides efficacious and rapid results for therapeutic purposes. However, further experimental analysis and testing such as clinical applicability, toxicity, and experimental validations are required to reach a more accurate and improved treatment. Our proposed complexes of proteins and associated miRNAs, along with discovered candidate drugs might be a starting point for further analysis by other researchers in this urgency of the COVID-19 pandemic.

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NCOR2 Mutations Are Associated with IMiD Resistance in Human Myeloma Cells

Blood ◽

10.1182/blood-2020-141968 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 4-4

Author(s):

Tomoaki Mori ◽

Cristina Panaroni ◽

Chukwuamaka Onyewadume ◽

Noopur S. Raje

Keyword(s):

Cell Lines ◽

Protein Interaction ◽

Enrichment Analysis ◽

Myeloma Cell ◽

Resistant Cell ◽

Ppi Network ◽

Myeloma Cells ◽

Knock Out ◽

Protein Protein Interaction ◽

Bet Inhibitor

The immunomodulatory drug thalidomide, and its analogs, lenalidomide, and pomalidomide (IMiDs) have significantly changed the treatment paradigm of multiple myeloma (MM). Despite this progress, IMiD resistance develops in the majority of patients resulting in the development of refractory disease. Cereblon (CRBN), a direct target, has been implicated in IMiD resistance. However, alternate mechanisms of IMiD resistance independent of CRBN remain largely unknown. To understand and study the mechanisms responsible for the development of IMiD resistance, we created lenalidomide-resistant (Len-R) and pomalidomide-resistant (Pom-R) human myeloma MM.1s cell lines, by continuous culture in the presence of lenalidomide or pomalidomide for 3 months. Whole genome sequencing of these 2 resistant cell lines compared with parental MM.1s revealed 172 genes with exonic mutations in both Len-R and Pom-R myeloma cells. Furthermore, a protein-protein interaction (PPI) network was constructed based on Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The PPI network demonstrated 8 genes that scored a high degree of protein-protein interaction. Among these genes, we identified NCOR2, a corepressor that negatively regulates gene expression, as a downregulated gene in resistant cell lines. To study this further, we created NCOR2 knock out MM.1s cell lines using CRISPR/cas9 gene modification. Our data demonstrates that depletion of NCOR2 confers IMiD resistance independent to CRBN. Interestingly, Len-R, Pom-R and NCOR2 knock out MM.1s showed increased MYC protein expression, which is essential for myeloma cell survival and proliferation. A BET inhibitor, known to disrupt the binding of BRD4 to chromatin, inhibited the proliferation of Len-R and Pom-R and NCOR2 knock out MM.1s by completely suppressing MYC expression. These results indicate that NCOR2 down regulation in IMiD resistant cells induces MYC upregulation which may in part result in IMiD resistance. Our findings reveal a novel molecular mechanism associated with IMiD resistance, independent of CRBN and suggest that NCOR2-MYC pathway may be a new target for IMiD refractory patients. Disclosures Raje: Celgene: Consultancy.

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Quantitative proteomics analysis of FFPE tumor samples reveals the influences of novel targeting nanobubbles conjugated with NET-1 siRNA on tetraspanin protein involved in HCC

10.21203/rs.3.rs-20923/v1 ◽

2020 ◽

Author(s):

Bolin Wu ◽

Haitao Shang ◽

Xitian Liang ◽

Huajing Yang Huajing Yang ◽

Hui Jing ◽

...

Keyword(s):

Protein Interactions ◽

Interaction Analysis ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Differentially Expressed Proteins ◽

Label Free ◽

Protein Protein Interaction ◽

Study Results ◽

Ffpe Tumor

Abstract Background: Hepatocellular carcinoma (HCC) poses a severe threat to human health. The NET-1 protein has been proved to be strongly associated with HCC proliferation and metastasis in our previous study. Methods: Here, we developed a label-free proteome mass spectrometry workflow to analyze formalin-fixed and paraffin-embedded HCC xenograft samples collected in our previous study. Results: The result showed that 78 proteins were differentially expressed after NET-1 protein inhibited. Among them, the expression of 61 proteins up-regulated and the expression of 17 proteins were significantly down-regulated. Of the differentially expressed proteins, the vast majority of Gene Ontology enrichment terms belong to the biological process. The KEGG pathway enrichment analysis showed that the 78 differentially expressed proteins significantly enriched in 45 pathways. We concluded that the function of the NET-1 gene is not only to regulate HCC but also to participate in a variety of biochemical metabolic pathways in the human body. Furthermore, the protein-protein interaction analysis indicated that the interactions of differentially expressed proteins are incredibly sophisticated. All the protein-protein interactions happened after the NET-1 gene has been silenced. Conclusions: Finally, our study also provides a useful proposal for targeted therapy based on tetraspanin proteins to treat HCC, and further mechanism investigations are needed to reveal a more detailed mechanism of action for NET-1 protein regulation of HCC.

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In Silico Integration Approach Reveals Key MicroRNAs and Their Target Genes in Follicular Thyroid Carcinoma

BioMed Research International ◽

10.1155/2019/2725192 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9

Author(s):

Shengqing Hu ◽

Yunfei Liao ◽

Juan Zheng ◽

Luoning Gou ◽

Anita Regmi ◽

...

Keyword(s):

Thyroid Carcinoma ◽

Target Genes ◽

Follicular Thyroid Carcinoma ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Biological Targets ◽

Protein Protein Interaction ◽

Bioinformatic Tools ◽

The Common

To better understand the molecular mechanism for the pathogenesis of follicular thyroid carcinoma (FTC), this study aimed at identifying key miRNAs and their target genes associated with FTC, as well as analyzing their interactions. Based on the gene microarray data GSE82208 and microRNA dataset GSE62054, the differentially expressed genes (DEGs) and microRNAs (DEMs) were obtained using R and SAM software. The common DEMs from R and SAM were fed to three different bioinformatic tools, TargetScan, miRDB, and miRTarBase, respectively, to predict their biological targets. With DEGs intersected with target genes of DEMs, the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed through the DAVID database. Then a protein-protein interaction (PPI) network was constructed by STRING. Finally, the module analysis for PPI network was performed by MCODE and BiNGO. A total of nine DEMs were identified, and their function might work through regulating hub genes in the PPI network especially KIT and EGFR. KEGG analysis showed that intersection genes were enriched in the PI3K-Akt signaling pathway and microRNAs in cancer. In conclusion, the study of miRNA-mRNA network would offer molecular support for differential diagnosis between malignant FTC and benign FTA, providing new insights into the potential targets for follicular thyroid carcinoma diagnosis and treatment.

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