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.

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Metabolomic analysis of human cirrhosis, hepatocellular carcinoma, non-alcoholic fatty liver disease and non-alcoholic steatohepatitis diseases.

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.

https://journals.sbmu.ac.ir/ghfbb/index.php/ghfbb/article/download/984/602

Protein-protein interaction network of celiac disease.

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).

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Protein-protein interaction analysis of Alzheimer`s disease and NAFLD based on systems biology methods unhide common ancestor pathways.

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.

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In vitro inhibition of hepatic stellate cell activation by the autophagy-related lipid droplet protein ATG2A.

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.

http://journal.waocp.org/article_53179_8c7330929d819f74207bb973a4282ecb.pdf

Protein-Protein Interaction Network Analysis for a Biomarker Panel Related to Human Esophageal Adenocarcinoma

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.

https://journals.sbmu.ac.ir/ghfbb/index.php/ghfbb/article/view/896/568

Protein-protein interaction network analysis of cirrhosis liver disease

Pancreatic cancer (PC) is one of the most malignant tumors with a very poor prognosis. Late diagnosis and lack of effective therapeutic method is the weak point in pancreatic cancer therapy. For pancreatic cancer clustering analysis, thirty proteins were selected from Wiki pathway and sequence comparison was prepared by Needleman-Wunsch algorithm. Similarity scores were obtained by comparing of these proteins and the relevant dissimilarity matrix was constructed. Graphical illustrations of clustering data were performed by R software using package cluster to find subgraphs with maximal density. Finally the interactions of these proteins with their neighbors that was obtained from Mentha, Reactome-Fls Databases by the application of PSICQUIC source in Cytoscape software are presented. The result of this study showed that CREB-binding protein and epidermal growth factor receptor are reported in Wiki Pathway Androgen receptor and most significant hub that involved in pancreatic cancer, but not in the high close relationship. In conclusion cell cycle and signaling pathway have highlighted its importance in pancreatic cancer, as well as its potential as a therapeutic target in PC.

http://arvandj.abadanums.ac.ir/article_43216_a08388e34897b63840014bc6612b63ec.pdf

Afsaneh Arefi Oskouei

Papers

Protein-protein interaction network analysis of cirrhosis liver disease

Evaluation of protein clustering of pancreatic cancer