scholarly journals Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms

Science ◽  
2020 ◽  
Vol 370 (6521) ◽  
pp. eabe9403 ◽  
Author(s):  
David E. Gordon ◽  
Joseph Hiatt ◽  
Mehdi Bouhaddou ◽  
Veronica V. Rezelj ◽  
Svenja Ulferts ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Global Economy ◽  
Molecular Mechanisms ◽  
Protein Localization ◽  
Viral Disease ◽  
Host Factors ◽  
Protein Protein Interaction ◽  
Chaperone Protein ◽  
Cryo Electron Microscopy ◽  
Drug Treatments

The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a grave threat to public health and the global economy. SARS-CoV-2 is closely related to the more lethal but less transmissible coronaviruses SARS-CoV-1 and Middle East respiratory syndrome coronavirus (MERS-CoV). Here, we have carried out comparative viral-human protein-protein interaction and viral protein localization analyses for all three viruses. Subsequent functional genetic screening identified host factors that functionally impinge on coronavirus proliferation, including Tom70, a mitochondrial chaperone protein that interacts with both SARS-CoV-1 and SARS-CoV-2 ORF9b, an interaction we structurally characterized using cryo–electron microscopy. Combining genetically validated host factors with both COVID-19 patient genetic data and medical billing records identified molecular mechanisms and potential drug treatments that merit further molecular and clinical study.

scholarly journals Structural basis for activation of trimeric Gi proteins by multiple growth factor receptors via GIV/Girdin

2014 ◽  
Vol 25 (22) ◽  
pp. 3654-3671 ◽  
Author(s):  
Changsheng Lin ◽  
Jason Ear ◽  
Krishna Midde ◽  
Inmaculada Lopez-Sanchez ◽  
Nicolas Aznar ◽  
...  
Keyword(s):  
Growth Factor ◽  
G Proteins ◽  
Protein Interaction ◽  
Cross Talk ◽  
Tyrosine Kinases ◽  
Molecular Mechanisms ◽  
Growth Factor Receptors ◽  
Structural Basis ◽  
Nucleotide Exchange ◽  
Protein Protein Interaction

A long-standing issue in the field of signal transduction is to understand the cross-talk between receptor tyrosine kinases (RTKs) and heterotrimeric G proteins, two major and distinct signaling hubs that control eukaryotic cell behavior. Although stimulation of many RTKs leads to activation of trimeric G proteins, the molecular mechanisms behind this phenomenon remain elusive. We discovered a unifying mechanism that allows GIV/Girdin, a bona fide metastasis-related protein and a guanine-nucleotide exchange factor (GEF) for Gαi, to serve as a direct platform for multiple RTKs to activate Gαi proteins. Using a combination of homology modeling, protein–protein interaction, and kinase assays, we demonstrate that a stretch of ∼110 amino acids within GIV C-terminus displays structural plasticity that allows folding into a SH2-like domain in the presence of phosphotyrosine ligands. Using protein–protein interaction assays, we demonstrated that both SH2 and GEF domains of GIV are required for the formation of a ligand-activated ternary complex between GIV, Gαi, and growth factor receptors and for activation of Gαi after growth factor stimulation. Expression of a SH2-deficient GIV mutant (Arg 1745→Leu) that cannot bind RTKs impaired all previously demonstrated functions of GIV—Akt enhancement, actin remodeling, and cell migration. The mechanistic and structural insights gained here shed light on the long-standing questions surrounding RTK/G protein cross-talk, set a novel paradigm, and characterize a unique pharmacological target for uncoupling GIV-dependent signaling downstream of multiple oncogenic RTKs.

A Pathway and Network Oriented Approach to Enlighten Molecular Mechanisms of Type 2 Diabetes Using Multiple Association Studies

2020 ◽  
Author(s):  
Burcu Bakir-Gungor ◽  
Miray Unlu Yazici ◽  
Gokhan Goy ◽  
Mustafa Temiz
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Protein Interaction ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Oriented Approach

AbstractDiabetes Mellitus (DM) is a group of metabolic disorder that is characterized by pancreatic dysfunction in insulin producing beta cells, glucagon secreting alpha cells, and insulin resistance or insulin in-functionality related hyperglycemia. Type 2 Diabetes Mellitus (T2D), which constitutes 90% of the diabetes cases, is a complex multifactorial disease. In the last decade, genome-wide association studies (GWASs) for type 2 diabetes (T2D) successfully pinpointed the genetic variants (typically single nucleotide polymorphisms, SNPs) that associate with disease risk. However, traditional GWASs focus on the ‘the tip of the iceberg’ SNPs, and the SNPs with mild effects are discarded. In order to diminish the burden of multiple testing in GWAS, researchers attempted to evaluate the collective effects of interesting variants. In this regard, pathway-based analyses of GWAS became popular to discover novel multi-genic functional associations. Still, to reveal the unaccounted 85 to 90% of T2D variation, which lies hidden in GWAS datasets, new post-GWAS strategies need to be developed. In this respect, here we reanalyze three meta-analysis data of GWAS in T2D, using the methodology that we have developed to identify disease-associated pathways by combining nominally significant evidence of genetic association with the known biochemical pathways, protein-protein interaction (PPI) networks, and the functional information of selected SNPs. In this research effort, to enlighten the molecular mechanisms underlying T2D development and progress, we integrated different in-silico approaches that proceed in top-down manner and bottom-up manner, and hence presented a comprehensive analysis at protein subnetwork, pathway, and pathway subnetwork levels. Our network and pathway-oriented approach is based on both the significance level of an affected pathway and its topological relationship with its neighbor pathways. Using the mutual information based on the shared genes, the identified protein subnetworks and the affected pathways of each dataset were compared. While, most of the identified pathways recapitulate the pathophysiology of T2D, our results show that incorporating SNP functional properties, protein-protein interaction networks into GWAS can dissect leading molecular pathways, which cannot be picked up using traditional analyses. We hope to bridge the knowledge gap from sequence to consequence.

scholarly journals HMNPPID—human malignant neoplasm protein–protein interaction database

2019 ◽  
Vol 13 (S1) ◽  
Author(s):  
Qingqing Li ◽  
Zhihao Yang ◽  
Zhehuan Zhao ◽  
Ling Luo ◽  
Zhiheng Li ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Malignant Neoplasm ◽  
Biomedical Literature ◽  
Malignant Neoplasms ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Interaction Database ◽  
Protein Interaction Database

Abstract Background Protein–protein interaction (PPI) information extraction from biomedical literature helps unveil the molecular mechanisms of biological processes. Especially, the PPIs associated with human malignant neoplasms can unveil the biology behind these neoplasms. However, such PPI database is not currently available. Results In this work, a database of protein–protein interactions associated with 171 kinds of human malignant neoplasms named HMNPPID is constructed. In addition, a visualization program, named VisualPPI, is provided to facilitate the analysis of the PPI network for a specific neoplasm. Conclusions HMNPPID can hopefully become an important resource for the research on PPIs of human malignant neoplasms since it provides readily available data for healthcare professionals. Thus, they do not need to dig into a large amount of biomedical literatures any more, which may accelerate the researches on the PPIs of malignant neoplasms.

scholarly journals A Dual Controllability Analysis of Influenza Virus-Host Protein-Protein Interaction Networks for Antiviral Drug Target Discovery

2018 ◽  
Author(s):  
Emily E. Ackerman ◽  
John F. Alcorn ◽  
Takeshi Hase ◽  
Jason E. Shoemaker
Keyword(s):  
Influenza Virus ◽  
Protein Interaction ◽  
Drug Target ◽  
Influenza A ◽  
Antiviral Drug ◽  
Host Factors ◽  
Host Protein ◽  
Drug Target Discovery ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks

ABSTRACTHost factors of influenza virus replication are often found in key topological positions within protein-protein interaction networks. This work explores how protein states can be manipulated through controllability analysis: the determination of the minimum manipulation needed to drive the cell system to any desired state. Here, we complete a two-part controllability analysis of two protein networks: a host network representing the healthy cell state and an influenza A virus-host network representing the infected cell state. This knowledge can be utilized to understand disease dynamics and isolate proteins for study as drug target candidates. Both topological and controllability analyses provide evidence of wide-reaching network effects stemming from the addition of viral-host protein interactions. Virus interacting and driver host proteins are significant both topologically and in controllability, therefore playing important roles in cell behavior during infection. 24 proteins are identified as holding regulatory roles specific to the infected cell by measures of topology, controllability, and functional role. These proteins are recommended for further study as potential antiviral drug targets.ImportanceSeasonal outbreaks of influenza A virus are a major cause of illness and death around the world each year, with a constant threat of pandemic infection. Even so, the FDA has only approved four treatments, two of which are unsuited for at risk groups such as children and those with breathing complications. This research aims to increase the efficiency of antiviral drug target discovery using existing protein-protein interaction data and network analysis methods. Controllability analyses identify key regulating host factors of the infected cell’s progression, findings which are supported by biological context. These results are beneficial to future studies of influenza virus, both experimental and computational.

scholarly journals The molecular mechanisms associated with PIN7, a protein-protein interaction network of seven pleiotropic proteins

2019 ◽  
Author(s):  
Jarmila Nahálková
Keyword(s):  
Signaling Pathway ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
P53 Signaling ◽  
Age Related ◽  
P53 Signaling Pathway ◽  
Protein Protein Interaction Network

The protein-protein interaction network of seven pleiotropic proteins (PIN7) contains proteins with multiple functions in the aging and age-related diseases (TPPII, CDK2, MYBBP1A, p53, SIRT6, SIRT7, and BSG). At the present work, the pathway enrichment, the gene function prediction and the protein node prioritization analysis were applied for the examination of main molecular mechanisms driving PIN7 and the extended network. Seven proteins of PIN7 were used as an input for the analysis by GeneMania, a Cytoscape application, which constructs the protein interaction network. The software also extends it using the interactions retrieved from databases of experimental and predicted protein-protein and genetic interactions. The analysis identified the p53 signaling pathway as the most dominant mediator of PIN7. The extended PIN7 was also analyzed by Cytohubba application, which showed that the top-ranked protein nodes belong to the group of histone acetyltransferases and histone deacetylases. These enzymes are involved in the reverse epigenetic regulation mechanisms linked to the regulation of PTK2, NFκB, and p53 signaling interaction subnetworks of the extended PIN7. The analysis emphasized the role of PTK2 signaling, which functions upstream of the p53 signaling pathway and its interaction network includes all members of the sirtuin family. Further, the analysis suggested the involvement of molecular mechanisms related to metastatic cancer (prostate cancer, small cell lung cancer), hemostasis, the regulation of the thyroid hormones and the cell cycle G1/S checkpoint. The additional data-mining analysis showed that the small protein interaction network MYBBP1A-p53-TPPII-SIRT6-CD147 controls Warburg effect and MYBBP1A-p53-TPPII-SIRT7-BSG influences mTOR signaling and autophagy. Further investigations of the detail mechanisms of these interaction networks would be beneficial for the development of novel treatments for aging and age-related diseases.

scholarly journals Protein–protein interaction in insulin signaling and the molecular mechanisms of insulin resistance

1999 ◽  
Vol 103 (7) ◽  
pp. 931-943 ◽  
Author(s):  
Antti Virkamäki ◽  
Kohjiro Ueki ◽  
C. Ronald Kahn
Keyword(s):  
Insulin Resistance ◽  
Protein Interaction ◽  
Insulin Signaling ◽  
Molecular Mechanisms ◽  
Protein Protein Interaction

scholarly journals Integrating protein networks and machine learning for disease stratification in the Hereditary Spastic Paraplegias

2021 ◽  
Author(s):  
Nikoleta Vavouraki ◽  
James E. Tomkins ◽  
Eleanna Kara ◽  
Henry Houlden ◽  
John Hardy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Molecular Mechanisms ◽  
Topological Analysis ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Hereditary Spastic Paraplegias ◽  
Core Proteins ◽  
Protein Protein Interaction Network

AbstractThe Hereditary Spastic Paraplegias are a group of neurodegenerative diseases characterized by spasticity and weakness in the lower body. Despite the identification of causative mutations in over 70 genes, the molecular aetiology remains unclear. Due to the combination of genetic diversity and variable clinical presentation, the Hereditary Spastic Paraplegias are a strong candidate for protein-protein interaction network analysis as a tool to understand disease mechanism(s) and to aid functional stratification of phenotypes. In this study, experimentally validated human protein-protein interactions were used to create a protein-protein interaction network based on the causative Hereditary Spastic Paraplegia genes. Network evaluation as a combination of both topological analysis and functional annotation led to the identification of core proteins in putative shared biological processes such as intracellular transport and vesicle trafficking. The application of machine learning techniques suggested a functional dichotomy linked with distinct sets of clinical presentations, suggesting there is scope to further classify conditions currently described under the same umbrella term of Hereditary Spastic Paraplegias based on specific molecular mechanisms of disease.

scholarly journals Local stabilization of subunit-subunit contacts causes global destabilization of Hepatitis B virus capsids

2020 ◽  
Author(s):  
Christopher John Schlicksup ◽  
Patrick Laughlin ◽  
Steven Dunkelbarger ◽  
Joseph Che-Yen Wang ◽  
Adam Zlotnick
Keyword(s):  
Electron Microscopy ◽  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Surface Area ◽  
Protein Interaction ◽  
Structural Defects ◽  
Protein Protein Interaction ◽  
Cryo Electron Microscopy ◽  
Virus Capsids ◽  
B Virus

AbstractDevelopment of antiviral molecules that bind virion is a strategy that remains in its infancy and the details of their mechanisms are poorly understood. Here we investigate the behavior of DBT1, a dibenzothiazapine, which specifically interacts with the capsid protein of Hepatitis B Virus (HBV). We found that DBT1 stabilizes protein-protein interaction, accelerates capsid assembly, and can induce formation of aberrant particles. Paradoxically, DBT1 can cause pre-formed capsids to dissociate. These activities may lead to (i) assembly of empty and defective capsids, inhibiting formation of new virus and (ii) disruption of mature viruses, which are metastable, to inhibit new infection. Using cryo-electron microscopy we observed that DBT1 led to asymmetric capsids where well-defined DBT1 density was bound at all inter-subunit contacts. These results suggest that DBT1 can support assembly by increasing buried surface area but induce disassembly of metastable capsids by favoring asymmetry to induce structural defects.

scholarly journals ACE2 Protein-Protein Interaction Networks Reveal Potential Druggable Targets For SARS-CoV-2

2021 ◽  
Author(s):  
Qiaoxi Xia ◽  
Xiao Zhou ◽  
Mantong Chen ◽  
Ling Lin ◽  
Yan Zhao ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Biological Response ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Host Cells ◽  
Interacting Proteins ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks

Abstract Background: The novel coronavirus SARS-CoV-2 pandemic has infected more than 130 million people, killed over 2.3 million so far. Currently, no effective drugs are available to treat this infectious disease, due to limited knowledge of the molecular mechanisms of SARS-CoV-2 infection. ACE2 (angiotensin I converting enzyme 2) has long been identified as the major receptor for coronavirus entry the host cells. Methods: In this study, we constructed the protein-protein interaction networks (PPIN) based on ACE2 and its interacting proteins, considering with the expression alternation and co-expression relationship. The potential drugs targeting the proteins in the PPIN were explored.Results: ACE2 and its interacting proteins AAMP and HRAS are obviously increased, and their PPIN show distinguishing expression patterns during the COVID-19 progression. At least six pathways are activated for the host cell in the response to the virus. Moreover, drug-target networks were built to provide important clues to block ACE2 and its interacting proteins. Except the reported four drugs for ACE2, its interacting protein CALM1 and HRAS are great potentially druggable. We also considered the path initiated from ACE2 to nucleus by cascades of interaction, especially for the transcription factors in the PPIN which are also druggable.Conclusion: In summary, this study provides new insight into the disruption of the biological response to virus mediated by ACE2, but also its cascade interacting proteins when considering of PPIN.

scholarly journals Exploring The Molecular Mechanisms of Classical Hodgkin Lymphoma Through Bioinformatics Analysis

2021 ◽  
Author(s):  
Zhu Lili ◽  
Zhu YuKun ◽  
Zhuangzhuang Tian ◽  
Yongsheng Li ◽  
Liyu Cao
Keyword(s):  
Hodgkin Lymphoma ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Molecular Mechanisms ◽  
Kegg Pathway ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Abstract Background Classic Hodgkin lymphoma (CHL) is the most common HL in the modern society. Although the treatment of cHL has made great progress, its molecular mechanisms have yet to be deciphered. Objectives The purpose of this study is to find out the crucial potential genes and pathways associated with cHL. Methods We downloaded the cHL microarray dataset (GSE12453) from Gene Expression Omnibus (GEO) database and to identify the differentially expressed genes (DEGs) between cHL samples and normal samples through the limma package in R. Then, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were carried out. Finally, we constructed the protein-protein interaction network to screen out the hub genes using Search Tool for the Retrieval of Interacting Genes (STRING) database. Results We screened out 788 DEGs in the cHL dataset, such as BATF3, IER3, RAB13 and FCRL2. GO functional enrichment analysis indicated that the DEGs were related with regulation of lymphocyte activation, secretory granule lumen and chemokine activity. KEGG pathway analysis showed that the genes enriched in Prion disease, Complement and coagulation cascades and Parkinson disease Coronavirus disease-COVID-19 pathway. Protein-protein interaction network construction identified 10 hub genes (IL6, ITGAM, CD86, FN1, MMP9, CXCL10, CCL5, CD19, IFNG, SELL, UBB) in the network. Conclusions In the present investigation, we identified several pathways and hub genes related to the occurrence and development of cHL, which may provide an important basis for further research and novel therapeutic targets and prognostic indicators for cHL.

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