Protein-protein interaction network analysis of cirrhosis liver disease
17 Mar 2016-Gastroenterology and hepatology from bed to bench (Gastroenterol Hepatol Bed Bench)-Vol. 9, Iss: 2, pp 114-123
TL;DR: The result indicates that regulation of lipid metabolism and cell survival are important biological processes involved in cirrhosis disease.
Abstract: Aim: Evaluation of biological characteristics of 13 identified proteins of patients with cirrhotic liver disease is the main aim of this research. Background: In clinical usage, liver biopsy remains the gold standard for diagnosis of hepatic fibrosis. Evaluation and confirmation of liver fibrosis stages and severity of chronic diseases require a precise and noninvasive biomarkers. Since the early detection of cirrhosis is a clinical problem, achieving a sensitive, specific and predictive novel method based on biomarkers is an important task. Methods: Essential analysis, such as gene ontology (GO) enrichment and protein-protein interactions (PPI) was undergone EXPASy, STRING Database and DAVID Bioinformatics Resources query. Results: Based on GO analysis, most of proteins are located in the endoplasmic reticulum lumen, intracellular organelle lumen, membrane-enclosed lumen, and extracellular region. The relevant molecular functions are actin binding, metal ion binding, cation binding and ion binding. Cell adhesion, biological adhesion, cellular amino acid derivative, metabolic process and homeostatic process are the related processes. Protein-protein interaction network analysis introduced five proteins (fibroblast growth factor receptor 4, tropomyosin 4, tropomyosin 2 (beta), lectin, Lectin galactoside-binding soluble 3 binding protein and apolipoprotein A-I) as hub and bottleneck proteins. Conclusion: Our result indicates that regulation of lipid metabolism and cell survival are important biological processes involved in cirrhosis disease. More investigation of above mentioned proteins will provide a better understanding of cirrhosis disease.
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TL;DR: In this review, it has been collected a heterogeneous set of metabolomics published studies to discovery of biomarkers in researches to introduce diagnostic biomarkers for early detection and the choice of patient-specific therapies.
Abstract: Metabolome analysis is used to evaluate the characteristics and interactions of low molecular weight metabolites under a specific set of conditions. In cirrhosis, hepatocellular carcinoma, non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatotic hepatitis (NASH) the liver does not function thoroughly due to long-term damage. Unfortunately the early detection of cirrhosis, HCC, NAFLD and NASH is a clinical problem and determining a sensitive, specific and predictive novel method based on biomarker discovery is an important task. On the other hand, metabolomics has been reported as a new and powerful technology in biomarker discovery and dynamic field that cause global comprehension of system biology. In this review, it has been collected a heterogeneous set of metabolomics published studies to discovery of biomarkers in researches to introduce diagnostic biomarkers for early detection and the choice of patient-specific therapies.
68 citations
Cites background from "Protein-protein interaction network..."
...Metabolomics, with other omics technologies help detailed understanding of biochemical viral events inside the cell and relationships with each other in the systems biology approach (5-11)....
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TL;DR: Chaperons have a bold presentation in curtail area in network therefore these key proteins beside the other hub-bottlneck proteins may be a suitable candidates biomarker panel for diagnosis, prognosis and treatment processes in celiac disease.
Abstract: Aim The aim of this study is to investigate the Protein-Protein Interaction Network of Celiac Disease. Background Celiac disease (CD) is an autoimmune disease with susceptibility of individuals to gluten of wheat, rye and barley. Understanding the molecular mechanisms and involved pathway may lead to the development of drug target discovery. The protein interaction network is one of the supportive fields to discover the pathogenesis biomarkers for celiac disease. Material and methods In the present study, we collected the articles that focused on the proteomic data in celiac disease. According to the gene expression investigations of these articles, 31 candidate proteins were selected for this study. The networks of related differentially expressed protein were explored using Cytoscape 3.3 and the PPI analysis methods such as MCODE and ClueGO. Results According to the network analysis Ubiquitin C, Heat shock protein 90kDa alpha (cytosolic and Grp94); class A, B and 1 member, Heat shock 70kDa protein, and protein 5 (glucose-regulated protein, 78kDa), T-complex, Chaperon in containing TCP1; subunit 7 (beta) and subunit 4 (delta) and subunit 2 (beta), have been introduced as hub-bottlnecks proteins. HSP90AA1, MKKS, EZR, HSPA14, APOB and CAD have been determined as seed proteins. Conclusion Chaperons have a bold presentation in curtail area in network therefore these key proteins beside the other hub-bottlneck proteins may be a suitable candidates biomarker panel for diagnosis, prognosis and treatment processes in celiac disease.
40 citations
Cites background from "Protein-protein interaction network..."
...In addition, it provides protein complex identification, (8) domain-domain interactions, (9) detection of proteins involved in disease pathways, (10) comparison between model organisms and humans (11) and introducing drug targets from network (12)....
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TL;DR: Systems biology methods, specifically PPI networks, can be useful for analyzing complicated related diseases and finding Hub and bottleneck proteins should be the goal of drug designing and introducing disease markers.
Abstract: Aim: Analysis reconstruction networks from two diseases, NAFLD and Alzheimer`s diseases and their relationship based on systems biology methods.Background: NAFLD and Alzheimer`s diseases are two complex diseases, with progressive prevalence and high cost for countries. There are some reports on relation and same spreading pathways of these two diseases. In addition, they have some similar risk factors, exclusively lifestyle such as feeding, exercises and so on. Therefore, systems biology approach can help to discover their relationship.Methods: DisGeNET and STRING databases were sources of disease genes and constructing networks. Three plugins of Cytoscape software, including ClusterONE, ClueGO and CluePedia, were used to analyze and cluster networks and enrichment of pathways. An R package used to define best centrality method. Finally, based on degree and Betweenness, hubs and bottleneck nodes were defined.Results: Common genes between NAFLD and Alzheimer`s disease were 190 genes that used construct a network with STRING database. The resulting network contained 182 nodes and 2591 edges and comprises from four clusters. Enrichment of these clusters separately lead to carbohydrate metabolism, long chain fatty acid and regulation of JAK-STAT and IL-17 signaling pathways, respectively. Also seven genes selected as hub-bottleneck include: IL6, AKT1, TP53, TNF, JUN, VEGFA and PPARG. Enrichment of these proteins and their first neighbors in network by OMIM database lead to diabetes and obesity as ancestors of NAFLD and AD.Conclusion: Systems biology methods, specifically PPI networks, can be useful for analyzing complicated related diseases. Finding Hub and bottleneck proteins should be the goal of drug designing and introducing disease markers.Keywords: Alzheimer`s disease (AD), Non-alcoholic fatty liver disease (NAFLD), Protein-protein interaction (PPI) network analysis, Hub-bottlenecks, Protein clusters.(Please cite as: Karbalaei R, Allahyari M, Rezaei-Tavirani M, Asadzadeh-Aghdaei H, Zali MR. Protein-protein interaction analysis of Alzheimer`s disease and NAFLD based on systems biology methods unhide common ancestor pathways. Gastroenterol Hepatol Bed Bench 2018;11(1):27-33).
39 citations
Cites background from "Protein-protein interaction network..."
...Nodes with high degree are called hubs and nodes that achieve top-ten, or top-five percent of betweenness centrality are called bottlenecks (both based on researcher’s definition) (21)....
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TL;DR: Lipid-droplet protein profile changes during the reversion of activated stellate cells might provide new insights into the molecular mechanisms linking lipid droplets to liver fibrosis, and ATG2A could represent a potential new drug target for hepatic fibrosis.
Abstract: Clinical studies have found that moderate intake of retinol or oleic acid can enlarge the lipid droplets of hepatic stellate cells and suppress their activation. However, the link between lipid droplets and cell activation is unknown. This study compared the dynamics of lipid droplet-associated protein expression between activated and reverted stellate cells. Reversion of the activated human stellate cell line LX-2 and inhibition of primary mouse stellate cell activation were induced by retinol or oleic acid, which resulted in larger lipid droplets and the downregulation of cell activation markers. Quantitative proteomics and immunoblotting were performed to compare lipid-droplet protein profiles between activated and reverted LX-2 cells. Compared to expression in activated cells, 50 lipid-droplet proteins were upregulated, whereas 28 were downregulated upon reversion. ATG2A was significantly enriched in lipid droplets of retinol/oleic acid-treated LX-2 cells and quiescent primary stellate cells. Reduced expression of α-SMA, increased expression of perilipin-3, enlarged lipid droplets, and suppression of autophagic flux were observed in ATG2A-deficient LX2 cells. Lipid-droplet protein profile changes during the reversion of activated stellate cells might provide new insights into the molecular mechanisms linking lipid droplets to liver fibrosis. ATG2A could represent a potential new drug target for hepatic fibrosis.
35 citations
TL;DR: The findings indicate nine crucial proteins could form a candidate biomarker panel for EAC, and main related terms to closely correspond with those for colorectal cancer.
Abstract: Background: Esophageal adenocarcinoma (EAC) is one of the mostlethal cancers in the world with a very poor prognosis. Identification of molecular diagnostic methods is an important goal. Since protein-protein interaction (PPI) network analysis is a suitable method for molecular assessment, in the present research a PPI network related to EAC was targeted. Material and Method: Cytoscape software and its applications including STRING DB, Cluster ONE and ClueGO were applied to analyze the PPI network. Result: Among 182 EAC-related proteins which were identified, 129 were included in a main connected component. Proteins based on centrality analysis of characteristics such as degree, betweenness, closeness and stress were screened and key nodes were introduced. Two clusters were determined of which only one was significant statistically. Gene ontology revealed 50 terms in three groups associated with EAC. Conclusion: The findings indicate nine crucial proteins could form a candidate biomarker panel for EAC. Furthermore, an important cluster with 27 proteins related to the disease was identified. Gene ontology analysis of this cluster showed main related terms to closely correspond with those for colorectal cancer.
34 citations
Cites background from "Protein-protein interaction network..."
...These findings can be used in interpretation of played roles of the proteins in onset and development of any diseases (Goh et al., 2007; Barabási et al., 2011; Özgür et al, 2008; Safaei et al., 2016)....
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References
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TL;DR: By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
Abstract: DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
31,015 citations
TL;DR: The survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
Abstract: Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
13,102 citations
TL;DR: The update to version 9.1 of STRING is described, introducing several improvements, including extending the automated mining of scientific texts for interaction information, to now also include full-text articles, and providing users with statistical information on any functional enrichment observed in their networks.
Abstract: Complete knowledge of all direct and indirect interactions between proteins in a given cell would represent an important milestone towards a comprehensive description of cellular mechanisms and functions. Although this goal is still elusive, considerable progress has been made-particularly for certain model organisms and functional systems. Currently, protein interactions and associations are annotated at various levels of detail in online resources, ranging from raw data repositories to highly formalized pathway databases. For many applications, a global view of all the available interaction data is desirable, including lower-quality data and/or computational predictions. The STRING database (http://string-db.org/) aims to provide such a global perspective for as many organisms as feasible. Known and predicted associations are scored and integrated, resulting in comprehensive protein networks covering >1100 organisms. Here, we describe the update to version 9.1 of STRING, introducing several improvements: (i) we extend the automated mining of scientific texts for interaction information, to now also include full-text articles; (ii) we entirely re-designed the algorithm for transferring interactions from one model organism to the other; and (iii) we provide users with statistical information on any functional enrichment observed in their networks.
3,900 citations
TL;DR: The hepatic stellate cell has surprised and engaged physiologists, pathologists, and hepatologists for over 130 years, yet clear evidence of its role in hepatic injury and fibrosis only emerged following the refinement of methods for its isolation and characterization.
Abstract: The hepatic stellate cell has surprised and engaged physiologists, pathologists, and hepatologists for over 130 years, yet clear evidence of its role in hepatic injury and fibrosis only emerged following the refinement of methods for its isolation and characterization. The paradigm in liver injury of activation of quiescent vitamin A-rich stellate cells into proliferative, contractile, and fibrogenic myofibroblasts has launched an era of astonishing progress in understanding the mechanistic basis of hepatic fibrosis progression and regression. But this simple paradigm has now yielded to a remarkably broad appreciation of the cell's functions not only in liver injury, but also in hepatic development, regeneration, xenobiotic responses, intermediary metabolism, and immunoregulation. Among the most exciting prospects is that stellate cells are essential for hepatic progenitor cell amplification and differentiation. Equally intriguing is the remarkable plasticity of stellate cells, not only in their variable intermediate filament phenotype, but also in their functions. Stellate cells can be viewed as the nexus in a complex sinusoidal milieu that requires tightly regulated autocrine and paracrine cross-talk, rapid responses to evolving extracellular matrix content, and exquisite responsiveness to the metabolic needs imposed by liver growth and repair. Moreover, roles vital to systemic homeostasis include their storage and mobilization of retinoids, their emerging capacity for antigen presentation and induction of tolerance, as well as their emerging relationship to bone marrow-derived cells. As interest in this cell type intensifies, more surprises and mysteries are sure to unfold that will ultimately benefit our understanding of liver physiology and the diagnosis and treatment of liver disease.
2,419 citations
TL;DR: The DAVID Gene Functional Classification Tool uses a novel agglomeration algorithm to condense a list of genes or associated biological terms into organized classes of related genes or biology, called biological modules, for efficient interpretation of gene lists in a network context.
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2,067 citations