Publications by authors named "Mohieddin Jafari"

33 Publications

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Mol Pharmacol 2021 Feb 25. Epub 2021 Feb 25.

Helsinki University, Finland

Celecoxib is one of the most common medicines for treating inflammatory diseases. Recently, it has been shown that celecoxib is associated with implications in complex diseases such as Alzheimer's disease and cancer, as well as with cardiovascular risk assessment and toxicity, suggesting that celecoxib may affect multiple unknown targets. In this project, we detected targets of celecoxib within the nervous system using a label-free TPP (Thermal Proteome Profiling) method. First, proteins of the rat hippocampus were treated with multiple drug concentrations and temperatures. Next, we separated the soluble proteins from the denatured and sedimented total protein load by ultracentrifugation. Subsequently, the soluble proteins were analyzed by nano-liquid chromatography-mass spectrometry to determine the identity of the celecoxib targeted proteins based on structural changes by thermal stability variation of targeted proteins towards higher solubility in the higher temperatures. In the analysis of the soluble protein extract at 67 centigrade, 44 proteins were uniquely detected in drug-treated samples out of all 478 identified proteins at this temperature. Rab4a, one out of these 44 proteins, has previously been reported as one of the celecoxib off-targets in the rat CNS. Furthermore, we provide more molecular details through biomedical enrichment analysis to explore the potential role of all detected proteins in the biological systems. We show that the determined proteins play a role in the signaling pathways related to neurodegenerative disease - and cancer pathways. Finally, we fill out molecular supporting evidence for using celecoxib towards the drug repurposing approach by exploring drug targets. In this study, we determined forty-four off-target proteins of celecoxib, a non-steroidal anti-inflammatory, and one of the most common medicines for treating inflammatory diseases. We showed that these proteins play a role in the signaling pathways related to neurodegenerative disease and cancer pathways. Finally, we provided molecular supporting evidence for using celecoxib towards the drug repurposing approach by exploring drug targets.
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http://dx.doi.org/10.1124/molpharm.120.000210DOI Listing
February 2021

Re-evaluating experimental validation in the Big Data Era: a conceptual argument.

Genome Biol 2021 Feb 24;22(1):71. Epub 2021 Feb 24.

Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7LF, UK.

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http://dx.doi.org/10.1186/s13059-021-02292-4DOI Listing
February 2021

Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine.

Front Pharmacol 2020 26;11:1319. Epub 2020 Aug 26.

Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.

The ultimate goal of precision medicine is to determine right treatment for right patients based on precise diagnosis. To achieve this goal, correct stratification of patients using molecular features and clinical phenotypes is crucial. During the long history of medical science, our understanding on disease classification has been improved greatly by chemistry and molecular biology. Nowadays, we gain access to large scale patient-derived data by high-throughput technologies, generating a greater need for data science including unsupervised learning and network modeling. Unsupervised learning methods such as clustering could be a better solution to stratify patients when there is a lack of predefined classifiers. In network modularity analysis, clustering methods can be also applied to elucidate the complex structure of biological and disease networks at the systems level. In this review, we went over the main points of clustering analysis and network modeling, particularly in the context of Traditional Chinese medicine (TCM). We showed that this approach can provide novel insights on the rationale of classification for TCM herbs. In a case study, using a modularity analysis of multipartite networks, we illustrated that the TCM classifications are associated with the chemical properties of the herb ingredients. We concluded that multipartite network modeling may become a suitable data integration tool for understanding the mechanisms of actions of traditional medicine.
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http://dx.doi.org/10.3389/fphar.2020.01319DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7479204PMC
August 2020

Can We Assume the Gene Expression Profile as a Proxy for Signaling Network Activity?

Biomolecules 2020 06 3;10(6). Epub 2020 Jun 3.

Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00270 Helsinki, Finland.

Studying relationships among gene products by expression profile analysis is a common approach in systems biology. Many studies have generalized the outcomes to the different levels of central dogma information flow and assumed a correlation of transcript and protein expression levels. However, the relation between the various types of interaction (i.e., activation and inhibition) of gene products to their expression profiles has not been widely studied. In fact, looking for any perturbation according to differentially expressed genes is the common approach, while analyzing the effects of altered expression on the activity of signaling pathways is often ignored. In this study, we examine whether significant changes in gene expression necessarily lead to dysregulated signaling pathways. Using four commonly used and comprehensive databases, we extracted all relevant gene expression data and all relationships among directly linked gene pairs. We aimed to evaluate the ratio of coherency or sign consistency between the expression level as well as the causal relationships among the gene pairs. Through a comparison with random unconnected gene pairs, we illustrate that the signaling network is incoherent, and inconsistent with the recorded expression profile. Finally, we demonstrate that, to infer perturbed signaling pathways, we need to consider the type of relationships in addition to gene-product expression data, especially at the transcript level. We assert that identifying enriched biological processes via differentially expressed genes is limited when attempting to infer dysregulated pathways.
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http://dx.doi.org/10.3390/biom10060850DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7355924PMC
June 2020

UNaProd: A Universal Natural Product Database for of Iranian Traditional Medicine.

Evid Based Complement Alternat Med 2020 13;2020:3690781. Epub 2020 May 13.

Department of Traditional Medicine, School of Persian Medicine, Tehran University of Medical Sciences, Tehran, Iran.

Background: Iranian traditional medicine (ITM) is a holistic medical system that uses a wide range of medicinal substances to treat disease. Reorganization and standardization of the data on ITM concepts is a necessity for optimal use of this rich source. In an initial step towards this goal, we created a database of ITM . . Primarily based on Makhzan al-Advieh, which is the most recent encyclopedia of in ITM with the largest number of monographs, a database of natural medicinal substances was created using both text mining methods and manual editing. UNaProd, a Universal Natural Product database for of ITM, is currently host to 2696 monographs, from herbal to animal to mineral compounds in 16 diverse attributes such as origin and scientific name. Currently, systems biology, and more precisely systems medicine and pharmacology, can be an aid in providing rationalizations for many traditional medicines and elucidating a great deal of knowledge they can offer to guide future research in medicine.

Conclusions: A database of is a stepping stone in creating a systems pharmacology platform of ITM that encompasses the relationships between the drugs, their targets, and diseases. UNaProd is hyperlinked to IrGO and CMAUP databases for and molecular features, respectively, and it is freely available at http://jafarilab.com/unaprod/.
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http://dx.doi.org/10.1155/2020/3690781DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7243028PMC
May 2020

Rigosertib potently protects against colitis-associated intestinal fibrosis and inflammation by regulating PI3K/AKT and NF-κB signaling pathways.

Life Sci 2020 May 2;249:117470. Epub 2020 Mar 2.

Department of Medical Biochemistry, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.; Metabolic syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. Electronic address:

Aims: Rigosertib (RGS) is a PI3K inhibitor that exerts protective effects against tumor progression and cancer-related inflammation. This study was aimed to explore the regulatory effects of RGS on proliferative, pro-fibrotic and inflammatory factors in DSS- induced colitis mice model.

Materials And Methods: The present study integrates systems and molecular biology approaches to investigate the therapeutic potency of RGS in an experimental model of colitis specifically examining its effects on the PI3K/AKT and NF-κB signaling pathways.

Key Findings: Analysis of time-resolved proteome profiling showed that PI3K-AKT inhibitors regulate expression of many proteins in all stages of inflammation, fibrogenesis and extracellular matrix remodeling. Consistent with our in-silico findings, RGS improved colitis disease activity as assessed by changes in body weight, degree of stool consistency, rectal bleeding and prolapse. RGS also reduced oxidative stress markers and colon histopathological score by decreasing inflammatory responses in colon tissues. Moreover, expression of pro-fibrotic and pro-inflammatory factors including Acta 2, Col 1a1, Col 1a2, IL-1β, TNF-α, INF-γ, and MCP-1 were suppressed in the mice treated with RGS compared to the control group. The protective effects of RGS were mediated by inactivation of PI3K/AKT and NF-kB signaling pathways.

Significance: This study clearly demonstrates the anti-proliferative, anti-inflammatory and anti-fibrotic effects of RGS in colitis that may have implications for the treatment of colitis and colitis-associated cancer.
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http://dx.doi.org/10.1016/j.lfs.2020.117470DOI Listing
May 2020

Intraperitoneal Administration of Telmisartan Prevents Postsurgical Adhesion Band Formation.

J Surg Res 2020 04 7;248:171-181. Epub 2020 Jan 7.

Department of Clinical Biochemistry, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran; Metabolic syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. Electronic address:

Background: Angiotensin II receptor blockers (ARBs) have a potential role in reducing inflammation and fibrosis. We have integrated systems and molecular biology approaches to investigate the therapeutic potential of ARBs in preventing postsurgical adhesion band formation.

Material And Methods: we have followed the ARRIVE guidelines point by point during experimental studies. Telmisartan (1 and 9 mg/kg), valsartan (1 and 9 mg/kg), and losartan (1 and 10 mg/kg) were administered intraperitoneally in different groups of male albino Wistar rat. After 7 d of treatment, macroscopic evidence and score of fibrotic bands based on scaling methods was performed. Moreover, the anti-inflammatory and antifibrosis effects of telmisartan on reduction of fibrotic bands were investigated by using histopathology, ELISA, and real-time polymerase chain reaction methods.

Results: Telmisartan, but not losartan or valsartan, prevented the frequency as well as the stability of adhesion bands. Telmisartan appears to elicit anti-inflammatory responses by attenuating submucosal edema, suppressing proinflammatory cytokines, decreasing proinflammatory cell infiltration, and inhibiting oxidative stress at the site of peritoneal surgery. We also showed that telmisartan prevents fibrotic adhesion band formation by reducing excessive collagen deposition and suppression of profibrotic genes expression at the peritoneum adhesion tissues.

Conclusions: These results support the potential application of telmisartan in preventing postsurgical adhesion band formation by inhibiting key pathologic responses of inflammation and fibrosis in postsurgery patients.
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http://dx.doi.org/10.1016/j.jss.2019.10.029DOI Listing
April 2020

An insight to HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) pathogenesis; evidence from high-throughput data integration and meta-analysis.

Retrovirology 2019 12 30;16(1):46. Epub 2019 Dec 30.

Department of Virology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.

Background: Human T-lymphotropic virus 1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a progressive disease of the central nervous system that significantly affected spinal cord, nevertheless, the pathogenesis pathway and reliable biomarkers have not been well determined. This study aimed to employ high throughput meta-analysis to find major genes that are possibly involved in the pathogenesis of HAM/TSP.

Results: High-throughput statistical analyses identified 832, 49, and 22 differentially expressed genes for normal vs. ACs, normal vs. HAM/TSP, and ACs vs. HAM/TSP groups, respectively. The protein-protein interactions between DEGs were identified in STRING and further network analyses highlighted 24 and 6 hub genes for normal vs. HAM/TSP and ACs vs. HAM/TSP groups, respectively. Moreover, four biologically meaningful modules including 251 genes were identified for normal vs. ACs. Biological network analyses indicated the involvement of hub genes in many vital pathways like JAK-STAT signaling pathway, interferon, Interleukins, and immune pathways in the normal vs. HAM/TSP group and Metabolism of RNA, Viral mRNA Translation, Human T cell leukemia virus 1 infection, and Cell cycle in the normal vs. ACs group. Moreover, three major genes including STAT1, TAP1, and PSMB8 were identified by network analysis. Real-time PCR revealed the meaningful down-regulation of STAT1 in HAM/TSP samples than AC and normal samples (P = 0.01 and P = 0.02, respectively), up-regulation of PSMB8 in HAM/TSP samples than AC and normal samples (P = 0.04 and P = 0.01, respectively), and down-regulation of TAP1 in HAM/TSP samples than those in AC and normal samples (P = 0.008 and P = 0.02, respectively). No significant difference was found among three groups in terms of the percentage of T helper and cytotoxic T lymphocytes (P = 0.55 and P = 0.12).

Conclusions: High-throughput data integration disclosed novel hub genes involved in important pathways in virus infection and immune systems. The comprehensive studies are needed to improve our knowledge about the pathogenesis pathways and also biomarkers of complex diseases.
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http://dx.doi.org/10.1186/s12977-019-0508-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6937958PMC
December 2019

Predicting Meridian in Chinese traditional medicine using machine learning approaches.

PLoS Comput Biol 2019 11 25;15(11):e1007249. Epub 2019 Nov 25.

Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.

Plant-derived nature products, known as herb formulas, have been commonly used in Traditional Chinese Medicine (TCM) for disease prevention and treatment. The herbs have been traditionally classified into different categories according to the TCM Organ systems known as Meridians. Despite the increasing knowledge on the active components of the herbs, the rationale of Meridian classification remains poorly understood. In this study, we took a machine learning approach to explore the classification of Meridian. We determined the molecule features for 646 herbs and their active components including structure-based fingerprints and ADME properties (absorption, distribution, metabolism and excretion), and found that the Meridian can be predicted by machine learning approaches with a top accuracy of 0.83. We also identified the top compound features that were important for the Meridian prediction. To the best of our knowledge, this is the first time that molecular properties of the herb compounds are associated with the TCM Meridians. Taken together, the machine learning approach may provide novel insights for the understanding of molecular evidence of Meridians in TCM.
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http://dx.doi.org/10.1371/journal.pcbi.1007249DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6876772PMC
November 2019

DrugComb: an integrative cancer drug combination data portal.

Nucleic Acids Res 2019 07;47(W1):W43-W51

Institute for Molecular Medicine Finland, Helsinki Life Science Institute, University of Helsinki, Finland.

Drug combination therapy has the potential to enhance efficacy, reduce dose-dependent toxicity and prevent the emergence of drug resistance. However, discovery of synergistic and effective drug combinations has been a laborious and often serendipitous process. In recent years, identification of combination therapies has been accelerated due to the advances in high-throughput drug screening, but informatics approaches for systems-level data management and analysis are needed. To contribute toward this goal, we created an open-access data portal called DrugComb (https://drugcomb.fimm.fi) where the results of drug combination screening studies are accumulated, standardized and harmonized. Through the data portal, we provided a web server to analyze and visualize users' own drug combination screening data. The users can also effectively participate a crowdsourcing data curation effect by depositing their data at DrugComb. To initiate the data repository, we collected 437 932 drug combinations tested on a variety of cancer cell lines. We showed that linear regression approaches, when considering chemical fingerprints as predictors, have the potential to achieve high accuracy of predicting the sensitivity of drug combinations. All the data and informatics tools are freely available in DrugComb to enable a more efficient utilization of data resources for future drug combination discovery.
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http://dx.doi.org/10.1093/nar/gkz337DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6602441PMC
July 2019

IMMAN: an R/Bioconductor package for Interolog protein network reconstruction, mapping and mining analysis.

BMC Bioinformatics 2019 Feb 12;20(1):73. Epub 2019 Feb 12.

School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.

Background: Reconstruction of protein-protein interaction networks (PPIN) has been riddled with controversy for decades. Particularly, false-negative and -positive interactions make this progress even more complicated. Also, lack of a standard PPIN limits us in the comparison studies and results in the incompatible outcomes. Using an evolution-based concept, i.e. interolog which refers to interacting orthologous protein sets, pave the way toward an optimal benchmark.

Results: Here, we provide an R package, IMMAN, as a tool for reconstructing Interolog Protein Network (IPN) by integrating several Protein-protein Interaction Networks (PPINs). Users can unify different PPINs to mine conserved common networks among species. IMMAN is designed to retrieve IPNs with different degrees of conservation to engage prediction analysis of protein functions according to their networks.

Conclusions: IPN consists of evolutionarily conserved nodes and their related edges regarding low false positive rates, which can be considered as a gold standard network in the contexts of biological network analysis regarding to those PPINs which is derived from.
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http://dx.doi.org/10.1186/s12859-019-2659-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6373071PMC
February 2019

CINNA: an R/CRAN package to decipher Central Informative Nodes in Network Analysis.

Bioinformatics 2019 04;35(8):1436-1437

Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran.

Motivation: Centrality analysis involves a series of ambiguities in that there are numerous well-known centrality measures with differing algorithms for establishing which nodes in a network are essential. There is no clearly preferred measure or means of deciding which measure is most germane to a given network with respect to node essentiality vis-à-vis topological features. Our aim here was to develop an instrument that enables comparisons among potentially appropriate centrality measures to be made with respect to network structure and thereby to support the identification of the most informative measure according to dimensional reduction methods.

Methods: The Central Informative Nodes in Network Analysis (CINNA) package introduced herein gathers all required functions for centrality analysis in weighted/unweighted and directed/undirected networks. Then, it compares, assorts and visualizes centrality measures to select which best describes the node importance.

Availability And Implementation: CINNA is available in CRAN, including a tutorial. URL: https://cran.r-project.org/web/packages/CINNA/index.html.
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http://dx.doi.org/10.1093/bioinformatics/bty819DOI Listing
April 2019

Why, When and How to Adjust Your P Values?

Cell J 2019 Jan 1;20(4):604-607. Epub 2018 Aug 1.

Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran. Electronic Address:

Currently, numerous papers are published reporting analysis of biological data at different omics levels by making statistical inferences. Of note, many studies, as those published in this Journal, report association of gene(s) at the genomic and transcriptomic levels by undertaking appropriate statistical tests. For instance, genotype, allele or haplotype frequencies at the genomic level or normalized expression levels at the transcriptomic level are compared between the case and control groups using the Chi-square/Fisher's exact test or independent (i.e. two-sampled) t-test respectively, with this culminating into a single numeric, namely the P value (or the degree of the false positive rate), which is used to make or break the outcome of the association test. This approach has flaws but nevertheless remains a standard and convenient approach in association studies. However, what becomes a critical issue is that the same cut-off is used when 'multiple' tests are undertaken on the same case-control (or any pairwise) comparison. Here, in brevity, we present what the P value represents, and why and when it should be adjusted. We also show, with worked examples, how to adjust P values for multiple testing in the R environment for statistical computing (http://www.R-project.org).
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http://dx.doi.org/10.22074/cellj.2019.5992DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6099145PMC
January 2019

A systematic survey of centrality measures for protein-protein interaction networks.

BMC Syst Biol 2018 07 31;12(1):80. Epub 2018 Jul 31.

Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, P.O. Box 13164, Tehran, Iran.

Background: Numerous centrality measures have been introduced to identify "central" nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures.

Results: We used yeast PPINs to compare 27 common of centrality measures. The measures characterize and assort influential nodes of the networks. We applied principal component analysis (PCA) and hierarchical clustering and found that the most informative measures depend on the network's topology. Interestingly, some measures had a high level of contribution in comparison to others in all PPINs, namely Latora closeness, Decay, Lin, Freeman closeness, Diffusion, Residual closeness and Average distance centralities.

Conclusions: The choice of a suitable set of centrality measures is crucial for inferring important functional properties of a network. We concluded that undertaking data reduction using unsupervised machine learning methods helps to choose appropriate variables (centrality measures). Hence, we proposed identifying the contribution proportions of the centrality measures with PCA as a prerequisite step of network analysis before inferring functional consequences, e.g., essentiality of a node.
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http://dx.doi.org/10.1186/s12918-018-0598-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6069823PMC
July 2018

Valproic acid inhibits the protective effects of stromal cells against chemotherapy in breast cancer: Insights from proteomics and systems biology.

J Cell Biochem 2018 11 28;119(11):9270-9283. Epub 2018 Jun 28.

Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran.

Interaction between tumor and stromal cells is beginning to be decoded as a contributor to chemotherapy resistance. Here, we aim to take a system-level approach to explore a mechanism by which stromal cells induce chemoresistance in cancer cells and subsequently identify a drug that can inhibit such interaction. Using a proteomic dataset containing quantitative data on secretome of stromal cells, we performed multivariate analyses and found that bone-marrow mesenchymal stem cells (BM-MSCs) play the most protective role against chemotherapeutics. Pathway enrichment tests showed that secreted cytokines from BM-MSCs activated 4 signaling pathways including Janus kinase-signal transducer and activator of transcription, phosphatidylinositol 3-kinase-protein kinase B, and mitogen-activated protein kinase, transforming growth factor-β in cancer cells collectively leading to nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) transcription factor activation. Based on the data from integrated Library of Integrated Network-Based Cellular Signatures (iLINCs) program, we found that among different drugs, valproic acid (VA) affected the expression of 34 genes within the identified pathways that are activated by stromal cells. Our in vitro experiments confirmed that VA inhibits NF-kB activation in cancer cells. In addition, analyzing gene expression data in patients taking oral VA showed that this drug decreased expression of antioxidant enzymes culminating in increased oxidative stress in tumor cells. These results suggest that VA confines the protective role of stromal cells by inhibiting the adaptation mechanisms toward oxidative stress which is potentiated by stromal cells. Since VA is an already prescribed drug manifesting anticancer effects, this study provides a mechanistic insight for combination of VA with chemotherapy in the clinical setting.
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http://dx.doi.org/10.1002/jcb.27196DOI Listing
November 2018

Integrated use of bioinformatic resources reveals that co-targeting of histone deacetylases, IKBK and SRC inhibits epithelial-mesenchymal transition in cancer.

Brief Bioinform 2019 03;20(2):717-731

Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran.

With the advent of high-throughput technologies leading to big data generation, increasing number of gene signatures are being published to predict various features of diseases such as prognosis and patient survival. However, to use these signatures for identifying therapeutic targets, use of additional bioinformatic tools is indispensible part of research. Here, we have generated a pipeline comprised of nearly 15 bioinformatic tools and enrichment statistical methods to propose and validate a drug combination strategy from already approved drugs and present our approach using published pan-cancer epithelial-mesenchymal transition (EMT) signatures as a case study. We observed that histone deacetylases were critical targets to tune expression of multiple epithelial versus mesenchymal genes. Moreover, SRC and IKBK were the principal intracellular kinases regulating multiple signaling pathways. To confirm the anti-EMT efficacy of the proposed target combination in silico, we validated expression of targets in mesenchymal versus epithelial subtypes of ovarian cancer. Additionally, we inhibited the pinpointed proteins in vitro using an invasive lung cancer cell line. We found that whereas low-dose mono-therapy failed to limit cell dispersion from collagen spheroids in a microfluidic device as a metric of EMT, the combination fully inhibited dissociation and invasion of cancer cells toward cocultured endothelial cells. Given the approval status and safety profiles of the suggested drugs, the proposed combination set can be considered in clinical trials.
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http://dx.doi.org/10.1093/bib/bby030DOI Listing
March 2019

Phytosomal-curcumin antagonizes cell growth and migration, induced by thrombin through AMP-Kinase in breast cancer.

J Cell Biochem 2018 07 30;119(7):5996-6007. Epub 2018 Mar 30.

Faculty of Medicine, Department of Physiology, Mashhad University of Medical Sciences, Mashhad, Iran.

Here we explored the antitumor-activity of novel-formulated-form of curcumin (phytosomal-encapsulated-curcumin) or in combination with 5-FU in breast cancer. The antiproliferative activity was assessed in 2D and 3-dimensional cell-culture-model. The migratory-behaviors of the cells were determined by migration assay. The expression levels of CyclinD1,GSK3a/b, P-AMPK, MMP9, and E-cadherin were studied by qRT-PCR and/or Western blotting. The anti-inflammatory of nano-curcumin was assessed, while antioxidant activity was evaluated by malondialdehyde (MDA), superoxide dismutase (SOD), catalase (CAT), and total thiols (T-SH). To understand dynamic behavior of genes, we reconstructed a Boolean network, while the robustness of this model was evaluated by Hamming distance. phytosomal-curcumin suppressed cell-growth followed by tumor-shrinkage in 3D model through perturbation of AMP-activated protein kinase. Curcumin reduced the invasiveness of MCF-7 through perturbation of E-cadherin. Moreover, phytosomal-curcumin inhibited the tumor growth in xerograph model. Histological staining of tumor tissues revealed vascular disruption and RBC extravasation, necrosis, tumor stroma, and inflammation. Co-treatment of curcumin and 5-FU reduced the lipid-peroxidation and increased MDA/SOD level. Of note, curcumin reduced cyclinD-expression in breast cancer cell treated with thrombin, and activates AMPK in a time-dependent manner. Also suppression of AMPK abrogated inhibitory effect of phytosomal-curcumin on thrombin-induced cyclin D1 over-expression, suggesting that AMPK is essential for anti-proliferative effect of this agent in breast cancer. Our finding demonstrated that phytosomal-curcumin antagonizes cell growth and migration, induced by thrombin through AMP-Kinase in breast cancer, supporting further-investigations on the therapeutic potential of this novel anticancer agent in treatment of breast cancer.
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http://dx.doi.org/10.1002/jcb.26796DOI Listing
July 2018

A logic-based dynamic modeling approach to explicate the evolution of the central dogma of molecular biology.

PLoS One 2017;12(12):e0189922. Epub 2017 Dec 21.

Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University, Jalal Ale Ahmad Highway, Tehran, Iran.

It is nearly half a century past the age of the introduction of the Central Dogma (CD) of molecular biology. This biological axiom has been developed and currently appears to be all the more complex. In this study, we modified CD by adding further species to the CD information flow and mathematically expressed CD within a dynamic framework by using Boolean network based on its present-day and 1965 editions. We show that the enhancement of the Dogma not only now entails a higher level of complexity, but it also shows a higher level of robustness, thus far more consistent with the nature of biological systems. Using this mathematical modeling approach, we put forward a logic-based expression of our conceptual view of molecular biology. Finally, we show that such biological concepts can be converted into dynamic mathematical models using a logic-based approach and thus may be useful as a framework for improving static conceptual models in biology.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0189922PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5739447PMC
January 2018

Human T-lymphotropic virus 1 (HTLV-1) pathogenesis: A systems virology study.

J Cell Biochem 2018 05 19;119(5):3968-3979. Epub 2018 Jan 19.

Department of Virology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.

The main mechanisms of interaction between Human T-lymphotropic virus type 1 (HTLV-1) and its hosts in the manifestation of the related disease including HTLV-1 associated myelopathy/tropical spastic paraparesis (HAM/TSP) and Adult T-cell leukemia/lymphoma (ATLL) are yet to be determined. It is pivotal to find out the changes in the genes expression toward an asymptomatic or symptomatic states. To this end, the systems virology analysis was performed. Firstly, the differentially expressed genes (DEGs) were taken pairwise among the four sample sets of Normal, Asymptomatic Carriers (ACs), ATLL, and HAM/TSP. Afterwards, the protein-protein interaction networks were reconstructed utilizing the hub genes. In conclusion, the pathways of cells proliferation and transformation were identified in the ACs state. In addition to immune pathways in ATLL, the inflammation and cancer pathways were discened in both diseases of ATLL and HAM/TSP. The outcomes can specify the genes involved in the pathogenesis and help to design the drugs in the future.
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http://dx.doi.org/10.1002/jcb.26546DOI Listing
May 2018

Systems Biomedicine of Rabies Delineates the Affected Signaling Pathways.

Front Microbiol 2016 7;7:1688. Epub 2016 Nov 7.

Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran Tehran, Iran.

The prototypical neurotropic virus, rabies, is a member of the Rhabdoviridae family that causes lethal encephalomyelitis. Although there have been a plethora of studies investigating the etiological mechanism of the rabies virus and many precautionary methods have been implemented to avert the disease outbreak over the last century, the disease has surprisingly no definite remedy at its late stages. The psychological symptoms and the underlying etiology, as well as the rare survival rate from rabies encephalitis, has still remained a mystery. We, therefore, undertook a systems biomedicine approach to identify the network of gene products implicated in rabies. This was done by meta-analyzing whole-transcriptome microarray datasets of the CNS infected by strain CVS-11, and integrating them with interactome data using computational and statistical methods. We first determined the differentially expressed genes (DEGs) in each study and horizontally integrated the results at the mRNA and microRNA levels separately. A total of 61 seed genes involved in signal propagation system were obtained by means of unifying mRNA and microRNA detected integrated DEGs. We then reconstructed a refined protein-protein interaction network (PPIN) of infected cells to elucidate the rabies-implicated signal transduction network (RISN). To validate our findings, we confirmed differential expression of randomly selected genes in the network using Real-time PCR. In conclusion, the identification of seed genes and their network neighborhood within the refined PPIN can be useful for demonstrating signaling pathways including interferon circumvent, toward proliferation and survival, and neuropathological clue, explaining the intricate underlying molecular neuropathology of rabies infection and thus rendered a molecular framework for predicting potential drug targets.
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http://dx.doi.org/10.3389/fmicb.2016.01688DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5098112PMC
November 2016

Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd.

Nat Commun 2016 Sep 26;7:12846. Epub 2016 Sep 26.

Department of Pharmacological Sciences, BD2K-LINCS Data Coordination and Integration Center, Illuminating the Druggable Genome Knowledge Management Center, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place Box 1215, New York, New York 10029, USA.

Gene expression data are accumulating exponentially in public repositories. Reanalysis and integration of themed collections from these studies may provide new insights, but requires further human curation. Here we report a crowdsourcing project to annotate and reanalyse a large number of gene expression profiles from Gene Expression Omnibus (GEO). Through a massive open online course on Coursera, over 70 participants from over 25 countries identify and annotate 2,460 single-gene perturbation signatures, 839 disease versus normal signatures, and 906 drug perturbation signatures. All these signatures are unique and are manually validated for quality. Global analysis of these signatures confirms known associations and identifies novel associations between genes, diseases and drugs. The manually curated signatures are used as a training set to develop classifiers for extracting similar signatures from the entire GEO repository. We develop a web portal to serve these signatures for query, download and visualization.
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http://dx.doi.org/10.1038/ncomms12846DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5052684PMC
September 2016

Proteomics of hot-wet and cold-dry temperaments proposed in Iranian traditional medicine: a Network-based Study.

Sci Rep 2016 07 25;6:30133. Epub 2016 Jul 25.

Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran 131694-3551, Iran.

Lack of molecular biology evidence has led clinical success of alternative and complementary medicine (CAM) to be marginalized. In turn, a large portion of life Science researchers could not communicate and help to develop therapeutic potential laid in these therapeutic approaches. In this study, we began to quantify descriptive classification theory in one of the CAM branches i.e. Iranian traditional medicine (ITM). Using proteomic tools and network analysis, the expressed proteins and their relationships were studied in mitochondrial lysate isolated from PBMCs from two different temperaments i.e. Hot-wet (HW) and Cold-dry (CD). The 82% of the identified proteins are over- or under-represented in distinct temperaments. Also, our result showed the different protein-protein interaction networks (PPIN) represented in these two temperaments using centrality and module finding analysis. Following the gene ontology and pathway enrichment analysis, we have found enriched biological terms in each group which are in conformity with the physiologically known evidence in ITM. In conclusion, we argued that the network biology which naturally consider life at the system level along with the different omics data will pave the way toward explicit delineation of the CAM activities.
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http://dx.doi.org/10.1038/srep30133DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4959000PMC
July 2016

Testis-Specific Y-Centric Protein-Protein Interaction Network Provides Clues to the Etiology of Severe Spermatogenic Failure.

J Proteome Res 2016 Mar 4;15(3):1011-22. Epub 2016 Feb 4.

Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran , Tehran 131694-3551, Iran.

Pinpointing causal genes for spermatogenic failure (SpF) on the Y chromosome has been an ever daunting challenge with setbacks during the past decade. Since complex diseases result from the interaction of multiple genes and also display considerable missing heritability, network analysis is more likely to explicate an etiological molecular basis. We therefore took a network medicine approach by integrating interactome (protein-protein interaction (PPI)) and transcriptome data to reconstruct a Y-centric SpF network. Two sets of seed genes (Y genes and SpF-implicated genes (SIGs)) were used for network reconstruction. Since no PPI was observed among Y genes, we identified their common immediate interactors. Interestingly, 81% (N = 175) of these interactors not only interacted directly with SIGs, but also they were enriched for differentially expressed genes (89.6%; N = 43). The SpF network, formed mainly by the dys-regulated interactors and the two seed gene sets, comprised three modules enriched for ribosomal proteins and nuclear receptors for sex hormones. Ribosomal proteins generally showed significant dys-regulation with RPL39L, thought to be expressed at the onset of spermatogenesis, strongly down-regulated. This network is the first global PPI network pertaining to severe SpF and if experimentally validated on independent data sets can lead to more accurate diagnosis and potential fertility recovery of patients.
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http://dx.doi.org/10.1021/acs.jproteome.5b01080DOI Listing
March 2016

Updates on drug-target network; facilitating polypharmacology and data integration by growth of DrugBank database.

Brief Bioinform 2016 11 21;17(6):1070-1080. Epub 2015 Oct 21.

Network pharmacology elucidates the relationship between drugs and targets. As the identified targets for each drug increases, the corresponding drug-target network (DTN) evolves from solely reflection of the pharmaceutical industry trend to a portrait of polypharmacology. The aim of this study was to evaluate the potentials of DrugBank database in advancing systems pharmacology. We constructed and analyzed DTN from drugs and targets associations in the DrugBank 4.0 database. Our results showed that in bipartite DTN, increased ratio of identified targets for drugs augmented density and connectivity of drugs and targets and decreased modular structure. To clear up the details in the network structure, the DTNs were projected into two networks namely, drug similarity network (DSN) and target similarity network (TSN). In DSN, various classes of Food and Drug Administration-approved drugs with distinct therapeutic categories were linked together based on shared targets. Projected TSN also showed complexity because of promiscuity of the drugs. By including investigational drugs that are currently being tested in clinical trials, the networks manifested more connectivity and pictured the upcoming pharmacological space in the future years. Diverse biological processes and protein-protein interactions were manipulated by new drugs, which can extend possible target combinations. We conclude that network-based organization of DrugBank 4.0 data not only reveals the potential for repurposing of existing drugs, also allows generating novel predictions about drugs off-targets, drug-drug interactions and their side effects. Our results also encourage further effort for high-throughput identification of targets to build networks that can be integrated into disease networks.
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http://dx.doi.org/10.1093/bib/bbv094DOI Listing
November 2016

Interlog protein network: an evolutionary benchmark of protein interaction networks for the evaluation of clustering algorithms.

BMC Bioinformatics 2015 Oct 5;16:319. Epub 2015 Oct 5.

National Institute of Genetic Engineering and Biotechnology (NIGEB), Pajoohesh Blvd, 17 Km Tehran-Karaj Highway, PO Box 161-14965, Tehran, Iran.

Background: In the field of network science, exploring principal and crucial modules or communities is critical in the deduction of relationships and organization of complex networks. This approach expands an arena, and thus allows further study of biological functions in the field of network biology. As the clustering algorithms that are currently employed in finding modules have innate uncertainties, external and internal validations are necessary.

Methods: Sequence and network structure alignment, has been used to define the Interlog Protein Network (IPN). This network is an evolutionarily conserved network with communal nodes and less false-positive links. In the current study, the IPN is employed as an evolution-based benchmark in the validation of the module finding methods. The clustering results of five algorithms; Markov Clustering (MCL), Restricted Neighborhood Search Clustering (RNSC), Cartographic Representation (CR), Laplacian Dynamics (LD) and Genetic Algorithm; to find communities in Protein-Protein Interaction networks (GAPPI) are assessed by IPN in four distinct Protein-Protein Interaction Networks (PPINs).

Results: The MCL shows a more accurate algorithm based on this evolutionary benchmarking approach. Also, the biological relevance of proteins in the IPN modules generated by MCL is compatible with biological standard databases such as Gene Ontology, KEGG and Reactome.

Conclusion: In this study, the IPN shows its potential for validation of clustering algorithms due to its biological logic and straightforward implementation.
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http://dx.doi.org/10.1186/s12859-015-0755-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4595048PMC
October 2015

Signaling network of lipids as a comprehensive scaffold for omics data integration in sputum of COPD patients.

Biochim Biophys Acta 2015 Oct 26;1851(10):1383-93. Epub 2015 Jul 26.

Department of Industrial and Environmental Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran. Electronic address:

Chronic obstructive pulmonary disease (COPD) is a heterogeneous and progressive inflammatory condition that has been linked to the dysregulation of many metabolic pathways including lipid biosynthesis. How lipid metabolism could affect disease progression in smokers with COPD remains unclear. We cross-examined the transcriptomics, proteomics, metabolomics, and phenomics data available on the public domain to elucidate the mechanisms by which lipid metabolism is perturbed in COPD. We reconstructed a sputum lipid COPD (SpLiCO) signaling network utilizing active/inactive, and functional/dysfunctional lipid-mediated signaling pathways to explore how lipid-metabolism could promote COPD pathogenesis in smokers. SpLiCO was further utilized to investigate signal amplifiers, distributers, propagators, feed-forward and/or -back loops that link COPD disease severity and hypoxia to disruption in the metabolism of sphingolipids, fatty acids and energy. Also, hypergraph analysis and calculations for dependency of molecules identified several important nodes in the network with modular regulatory and signal distribution activities. Our systems-based analyses indicate that arachidonic acid is a critical and early signal distributer that is upregulated by the sphingolipid signaling pathway in COPD, while hypoxia plays a critical role in the elevated dependency to glucose as a major energy source. Integration of SpLiCo and clinical data shows a strong association between hypoxia and the upregulation of sphingolipids in smokers with emphysema, vascular disease, hypertension and those with increased risk of lung cancer.
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http://dx.doi.org/10.1016/j.bbalip.2015.07.005DOI Listing
October 2015

PEIMAN 1.0: Post-translational modification Enrichment, Integration and Matching ANalysis.

Database (Oxford) 2015 23;2015:bav037. Epub 2015 Apr 23.

Protein Chemistry & Proteomics Unit, Biotechnology Research Center, Pasteur Institute of Iran, 69, Pasteur St., 13164 Tehran, Iran, School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), P. O. Box 193955746, Tehran, Iran and Chronic Kidney Disease Research Center (CKDRC), Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Conventional proteomics has discovered a wide gap between protein sequences and biological functions. The third generation of proteomics was provoked to bridge this gap. Targeted and untargeted post-translational modification (PTM) studies are the most important parts of today's proteomics. Considering the expensive and time-consuming nature of experimental methods, computational methods are developed to study, analyze, predict, count and compute the PTM annotations on proteins. The enrichment analysis softwares are among the common computational biology and bioinformatic software packages. The focus of such softwares is to find the probability of occurrence of the desired biological features in any arbitrary list of genes/proteins. We introduce Post-translational modification Enrichment Integration and Matching Analysis (PEIMAN) software to explore more probable and enriched PTMs on proteins. Here, we also represent the statistics of detected PTM terms used in enrichment analysis in PEIMAN software based on the latest released version of UniProtKB/Swiss-Prot. These results, in addition to giving insight to any given list of proteins, could be useful to design targeted PTM studies for identification and characterization of special chemical groups. Database URL: http://bs.ipm.ir/softwares/PEIMAN/
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http://dx.doi.org/10.1093/database/bav037DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4408379PMC
December 2015

Proteomics and traditional medicine: new aspect in explanation of temperaments.

Forsch Komplementmed 2014 16;21(4):250-3. Epub 2014 Aug 16.

Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

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http://dx.doi.org/10.1159/000366118DOI Listing
July 2015

Atopic dermatitis-associated protein interaction network lead to new insights in chronic sulfur mustard skin lesion mechanisms.

Expert Rev Proteomics 2013 Oct;10(5):449-60

Chemical Injuries Research Center, Baqiyatallah University of Medical Sciences, Tehran,P.O. 1949613711, Iran.

Chronic sulfur mustard skin lesions (CSMSLs) are the most common complications of sulfur mustard exposure; however, its mechanism is not completely understood.According to clinical signs, there are similarities between CSMSL and atopic dermatitis (AD). In this study, proteomic results of AD were reviewed and the AD-associated protein-protein interaction network (PIN) was analyzed. According to centrality measurements, 16 proteins were designated as pivotal elements in AD mechanisms. Interestingly, most of these proteins had been reported in some sulfur mustard-related studies in late and acute phases separately. Based on the gene enrichment analysis, aging, cell response to stress, cancer, Toll- and NOD-like receptor and apoptosis signaling pathways have the greatest impact on the disease. By the analysis of directed protein interaction networks, it is concluded that TNF, IL-6, AKT1, NOS3 and CDKN1A are the most important proteins. It is possible that these proteins play role in the shared complications of AD and CSMSL including xerosis and itching.
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http://dx.doi.org/10.1586/14789450.2013.841548DOI Listing
October 2013

Evolutionarily conserved motifs and modules in mitochondrial protein-protein interaction networks.

Mitochondrion 2013 Nov 27;13(6):668-75. Epub 2013 Sep 27.

Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Electronic address:

Advances in organelle interactomics have led to new insights into organelle functions. In this study, we considered the common mitochondrial PIN of four evolutionarily distant eukaryotic species, namely Homo sapiens, Mus musculus, Drosophila melanogaster and Caenorhabditis elegans. By comparative interactomics analysis of mitochondrial PINs in these organisms, five conserved modules were identified. Modules comprise the main mitochondrial tasks, including proteins involved in translation process, mitochondrial import inner membrane proteins, TCA cycle enzymes, mitochondrial electron transport chain, and metabolic enzymes. Furthermore, we reemphasize that subgraphs of network, i.e., motifs and themes, may represent evolutionarily conserved topological units which are biologically significant.
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http://dx.doi.org/10.1016/j.mito.2013.09.006DOI Listing
November 2013