Publications by authors named "Mehdi Mirzaie"

35 Publications

Serum metabolomics study of women with different annual decline rates of anti-Müllerian hormone: an untargeted gas chromatography-mass spectrometry-based study.

Hum Reprod 2021 Feb;36(3):721-733

Reproductive Endocrinology Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Study Question: Which metabolites are associated with varying rates of ovarian aging, measured as annual decline rates of anti-Müllerian hormone (AMH) concentrations?

Summary Answer: Higher serum concentrations of metabolites of phosphate, N-acetyl-d-glucosamine, branched chained amino acids (BCAAs), proline, urea and pyroglutamic acid were associated with higher odds of fast annual decline rate of AMH.

What Is Known Already: Age-related rate of ovarian follicular loss varies among women, and the factors underlying such inter-individual variations are mainly unknown. The rate of ovarian aging is clinically important due to its effects on both reproduction and health of women. Metabolomics, a global investigation of metabolites in biological samples, provides an opportunity to study metabolites or metabolic pathways in relation to a physiological/pathophysiological condition. To date, no metabolomics study has been conducted regarding the differences in the rates of ovarian follicular loss.

Study Design, Size, Duration: This prospective study was conducted on 186 reproductive-aged women with regular menstrual cycles and history of natural fertility, randomly selected using random case selection option in SPSS from the Tehran Lipid and Glucose Study.

Participants/materials, Setting, Methods: AMH concentrations were measured at baseline (1999-2001) and the fifth follow-up examination (2014-2017), after a median follow-up of 16 years, by immunoassay using Gen II kit. The annual decline rate of AMH was calculated by dividing the AMH decline rate by the follow-up duration (percent/year). The women were categorized based on the tertiles of the annual decline rates. Untargeted metabolomics analysis of the fasting-serum samples collected during the second follow-up examination cycle (2005-2008) was performed using gas chromatography-mass spectrometry. A combination of univariate and multivariate approaches was used to investigate the associations between metabolites and the annual decline rates of AMH.

Main Results And The Role Of Chance: After adjusting the baseline values of age, AMH and BMI, 29 metabolites were positively correlated with the annual AMH decline rates. The comparisons among the tertiles of the annual decline rate of AMH revealed an increase in the relative abundance of 15 metabolites in the women with a fast decline (tertile 3), compared to those with a slow decline (tertile 1). There was no distinct separation between women with slow and fast decline rates while considering 41 metabolites simultaneously using the principal component analysis and the partial least-squares discriminant analysis models. The odds of fast AMH decline was increased with higher serum metabolites of phosphate, N-acetyl-d-glucosamine, BCAAs, proline, urea and pyroglutamic acid. Amino sugar and nucleotide sugar metabolism, BCAAs metabolism and aminoacyl tRNA biosynthesis were among the most significant pathways associated with the fast decline rate of AMH.

Limitations, Reasons For Caution: Estimating the annual decline rates of AMH using the only two measures of AMH is the main limitation of the study which assumes a linear fixed reduction in AMH during the study. Since using the two-time points did not account for the variability in the decline rate of AMH, the annual decline rates estimated in this study may not accurately show the trend of the reduction in AMH. In addition, despite the longitudinal nature of the study and statistical adjustment of the participants' ages, it is difficult to distinguish the AMH-related metabolites observed in this study can accelerate ovarian aging or they are reflections of different rates of the aging process.

Wider Implications Of The Findings: Some metabolite features related to the decline rates of AMH have been suggested in this study; further prospective studies with multiple measurements of AMH are needed to confirm the findings of this study and to better understand the molecular process underlying variations in ovarian aging.

Study Funding/competing Interest(s): This study, as a part of PhD thesis of Ms Nazanin Moslehi, was supported by Shahid Beheshti University of Medical Sciences (10522-4). There were no competing interests.

Trial Registration Number: N/A.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/humrep/deaa279DOI Listing
February 2021

Rps27a might act as a controller of microglia activation in triggering neurodegenerative diseases.

PLoS One 2020 17;15(9):e0239219. Epub 2020 Sep 17.

Skull Base Research Center, The Five Senses Health Institute, Iran University of Medical Sciences, Tehran, Iran.

Neurodegenerative diseases (NDDs) are increasing serious menaces to human health in the recent years. Despite exhibiting different clinical phenotypes and selective neuronal loss, there are certain common features in these disorders, suggesting the presence of commonly dysregulated pathways. Identifying causal genes and dysregulated pathways can be helpful in providing effective treatment in these diseases. Interestingly, in spite of the considerable researches on NDDs, to the best of our knowledge, no dysregulated genes and/or pathways were reported in common across all the major NDDs so far. In this study, for the first time, we have applied the three-way interaction model, as an approach to unravel sophisticated gene interactions, to trace switch genes and significant pathways that are involved in six major NDDs. Subsequently, a gene regulatory network was constructed to investigate the regulatory communication of statistically significant triplets. Finally, KEGG pathway enrichment analysis was applied to find possible common pathways. Because of the central role of neuroinflammation and immune system responses in both pathogenic and protective mechanisms in the NDDs, we focused on immune genes in this study. Our results suggest that "cytokine-cytokine receptor interaction" pathway is enriched in all of the studied NDDs, while "osteoclast differentiation" and "natural killer cell mediated cytotoxicity" pathways are enriched in five of the NDDs each. The results of this study indicate that three pathways that include "osteoclast differentiation", "natural killer cell mediated cytotoxicity" and "cytokine-cytokine receptor interaction" are common in five, five and six NDDs, respectively. Additionally, our analysis showed that Rps27a as a switch gene, together with the gene pair {Il-18, Cx3cl1} form a statistically significant and biologically relevant triplet in the major NDDs. More specifically, we suggested that Cx3cl1 might act as a potential upstream regulator of Il-18 in microglia activation, and in turn, might be controlled with Rps27a in triggering NDDs.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0239219PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7498011PMC
November 2020

Optimal control and differential game solutions for social distancing in response to epidemics of infectious diseases on networks.

Optim Control Appl Methods 2020 Aug 2. Epub 2020 Aug 2.

Department of Applied Mathematics, Faculty of Mathematical Sciences Tarbiat Modares University Tehran Iran.

In this paper, the problem of social distancing in the spread of infectious diseases in the human network is extended by optimal control and differential game approaches. Hear, SEAIR model on simulation network is used. Total costs for both approaches are formulated as objective functions. SEAIR dynamics for group that contacts with individuals including susceptible, exposed, asymptomatically infected, symptomatically infected and improved or safe individuals is modeled. A novel random model including the concept of social distancing and relative risk of infection using Markov process is proposed. For each group, an aggregate investment is derived and computed using adjoint equations and maximum principle. Results show that for each group, investments in the differential game are less than investments in an optimal control approach. Although individuals' participation in investment for social distancing causes to reduce the epidemic cost, the epidemic cost according to the second approach is too much less than the first approach.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/oca.2650DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7435580PMC
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.
View Article and Find Full Text PDF

Download full-text PDF

Source
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/.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1155/2020/3690781DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7243028PMC
May 2020

Proteomics Analysis of Trastuzumab Toxicity in the H9c2 Cardiomyoblast Cell Line and its Inhibition by Carvedilol.

Curr Pharm Biotechnol 2020 ;21(13):1377-1385

Protein Chemistry and Proteomics Laboratory, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran

Objective: Heart dysfunctions are the major complications of trastuzumab in patients with Human Epidermal growth factor Receptor-2 (HER2)-positive breast cancers.

Methods: In this study, the cytotoxicity of trastuzumab on H9c2 cardiomyoblasts was demonstrated, and the proteome changes of cells were investigated by a tandem mass tagging quantitative approach. The Differentially Abundant Proteins (DAPs) were identified and functionally enriched.

Results: We determined that carvedilol, a non-selective beta-blocker, could effectively inhibit trastuzumab toxicity when administrated in a proper dose and at the same time. The proteomics analysis of carvedilol co-treated cardiomyoblasts showed complete or partial reversion in expressional levels of trastuzumab-induced DAPs.

Conclusion: Downregulation of proteins involved in the translation biological process is one of the most important changes induced by trastuzumab and reversed by carvedilol. These findings provide novel insights to develop new strategies for the cardiotoxicity of trastuzumab.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.2174/1389201021666200515135548DOI Listing
December 2020

The Effect of ACE Inhibitor on the Quality of Life amongst Patients with Cancer Cachexia.

Asian Pac J Cancer Prev 2020 Feb 1;21(2):325-330. Epub 2020 Feb 1.

Hematology Research Center, Department of Hematology and Medical Oncology, Shiraz University of Medical Sciences, Shiraz, Iran.

Background: ACEI (Angiotensin Converting Enzyme Inhibitors) inhibits tumor growth and development. Different mechanisms have been proposed for this matter, including the inhibition of enzymes that are involved in extracellular matrix degradation, matrix metalloproteinase (MMP) and etc. The present study was designed with the aim to investigate the effects of low dose ACEI on the Quality of Life (QoL) of non-hospitalized gastric cancer patients with cachexia.

Materials And Methods: This study was a single-blinded randomized controlled clinical trial conducted in clinics affiliated with Shiraz University of Medical Sciences (SUMS). All participants were patients with gastric cancer in cancer cachexia step aged 40-80 years old who had referred to our clinics from October 2013 to April 2014. In the intervention group, patients were assigned to receive ACEI (Captopril) and the placebo group served as control and received placebo during the same time course. They were asked questions in order to fill out QLQ-C30 (Persian Version) questionnaire 3 times; baseline, 1 and 2 months after their first visit.

Results: The mean age of patients was 60.55 ± 12.07 (range 31-80) years and the mean BMI of the patients was 17.21 ± 2.31. In the ACEI group, physical functioning and fatigue score changes were significant 1 and 2 months after treatment. The mean of fatigue score decreased significantly in the placebo group. Overall, global health status scores significantly increased in both groups, but other items of QoL did not change significantly.

Conclusion: Overall, our results showed that Captopril does not have a significant positive effect on QoL of patients with cancer cachexia.
.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.31557/APJCP.2020.21.2.325DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7332150PMC
February 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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12977-019-0508-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6937958PMC
December 2019

A mathematical model of honey bee colony dynamics to predict the effect of pollen on colony failure.

PLoS One 2019 22;14(11):e0225632. Epub 2019 Nov 22.

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

The decline in colony populations of the honey bee, known as the Colony Collapse Disorder (CCD), is a global concern. Numerous studies have reported possible causes, including pesticides, parasites, and nutritional stress. Poor nutrition affects the immune system at both the individual and colony level, amplifying effects of other stress factors. Pollen is the only source of ten amino acids that are essential to honey bee development, brood rearing and reproduction. This paper presents a new mathematical model to explore the effect of pollen on honey bee colony dynamics. In this model, we considered pollen and nectar as the required food for the colony. The effect of pollen and nectar collected by foragers was evaluated at different mortality rates of pupa, pollen and nectar foragers.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0225632PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6874302PMC
March 2020

Identification of native protein structures captured by principal interactions.

Authors:
Mehdi Mirzaie

BMC Bioinformatics 2019 Nov 21;20(1):604. Epub 2019 Nov 21.

Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University, Jalal Ale Ahmad Highway, P.O.Box: 14115-134, Tehran, Iran.

Background: Evaluation of protein structure is based on trustworthy potential function. The total potential of a protein structure is approximated as the summation of all pair-wise interaction potentials. Knowledge-based potentials (KBP) are one type of potential functions derived by known experimentally determined protein structures. Although several KBP functions with different methods have been introduced, the key interactions that capture the total potential have not studied yet.

Results: In this study, we seek the interaction types that preserve as much of the total potential as possible. We employ a procedure based on the principal component analysis (PCA) to extract the significant and key interactions in native protein structures. We call these interactions as principal interactions and show that the results of the model that considers only these interactions are very close to the full interaction model that considers all interactions in protein fold recognition. In fact, the principal interactions maintain the discriminative power of the full interaction model. This method was evaluated on 3 KBPs with different contact definitions and thresholds of distance and revealed that their corresponding principal interactions are very similar and have a lot in common. Additionally, the principal interactions consisted of 20 % of the full interactions on average, and they are between residues, which are considered important in protein folding.

Conclusions: This work shows that all interaction types are not equally important in discrimination of native structure. The results of the reduced model based on principal interactions that were very close to the full interaction model suggest that a new strategy is needed to capture the role of remaining interactions (non-principal interactions) to improve the power of knowledge-based potential functions.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-019-3186-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6873546PMC
November 2019

Discrimination power of knowledge-based potential dictated by the dominant energies in native protein structures.

Authors:
Mehdi Mirzaie

Amino Acids 2019 Jul 16;51(7):1029-1038. Epub 2019 May 16.

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

Extracting a well-designed energy function is important for protein structure evaluation. Knowledge-based potential functions are one type of the energy functions which can be obtained from known protein structures. The pairwise potential between atom types is approximated using Boltzmann's law which relates the frequency of atom types to its potential. The total energy is approximated as a summation of pairwise potential between the atomic pairs. In the present study, the performance of knowledge-based potential function was assessed based on the strength of interaction between groups of amino acids. The dominant energies involved in the pairwise potentials were revealed by eigenvalue analysis of the matrix, the elements of which represent the energy between amino acids. For this purpose, the matrix including the mean of the energies of residue-residue interaction types was constructed using 500 native protein structures. The matrix has a dominant eigenvalue and amino acids, with LEU, VAL, ILE, PHE, TYR, ALA and TRP having high values along the dominant eigenvector. The results show that the ranking of amino acids is consistent with the power of amino acids in discriminating native structures using K-alphabet reduced model. In the reduced interactions, only amino acids from a subset of all 20 amino acids, along with their interactions are considered to assess the energy. In the K-alphabet reduced model, the reduced structures are constructed based on only the K-amino acid types. The dominant K-alphabet reduced model derived for the k-first amino acids in the list [LEU, VAL, PHE, ILE, TYR, ALA, TRP] of amino acids has the best discrimination of native structure among all possible K-alphabet reduced models. Knowledge-based potentials might be improved with a new strategy.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00726-019-02743-0DOI Listing
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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-019-2659-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6373071PMC
February 2019

Protein complex prediction: A survey.

Genomics 2020 01 17;112(1):174-183. Epub 2019 Jan 17.

School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran.

Protein complexes are one of the most important functional units for deriving biological processes within the cell. Experimental methods have provided valuable data to infer protein complexes. However, these methods have inherent limitations. Considering these limitations, many computational methods have been proposed to predict protein complexes, in the last decade. Almost all of these in-silico methods predict protein complexes from the ever-increasing protein-protein interaction (PPI) data. These computational approaches usually use the PPI data in the format of a huge protein-protein interaction network (PPIN) as input and output various sub-networks of the given PPIN as the predicted protein complexes. Some of these methods have already reached a promising efficiency in protein complex detection. Nonetheless, there are challenges in prediction of other types of protein complexes, specially sparse and small ones. New methods should further incorporate the knowledge of biological properties of proteins to improve the performance. Additionally, there are several challenges that should be considered more effectively in designing the new complex prediction algorithms in the future. This article not only reviews the history of computational protein complex prediction but also provides new insight for improvement of new methodologies. In this article, most important computational methods for protein complex prediction are evaluated and compared. In addition, some of the challenges in the reconstruction of the protein complexes are discussed. Finally, various tools for protein complex prediction and PPIN analysis as well as the current high-throughput databases are reviewed.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ygeno.2019.01.011DOI Listing
January 2020

Three-way interaction model with switching mechanism as an effective strategy for tracing functionally-related genes.

Expert Rev Proteomics 2019 02 24;16(2):161-169. Epub 2018 Dec 24.

a Department of Basic Sciences, Faculty of Paramedical Sciences , Shahid Beheshti University of Medical Sciences , Tehran , Iran.

: Identification of functionally-related genes is an important step in understanding biological systems. The most popular strategy to infer functional dependence is to study pairwise correlations between gene expression levels. However, certain functionally-related genes may have a low expression correlation due to their nonlinear interactions. The use of a three-way interaction (3WI) model with switching mechanism (SM) is a relatively new strategy to trace functionally-related genes. The 3WI model traces the dynamic and nonlinear nature of the co-expression relationship of two genes by introducing their link to the expression level of a third gene. : In this paper, we reviewed a variety of existing methods for tracing the 3WIs. Furthermore, we provide a comprehensive review of the previous biological studies based on 3WI models. : Comparison of features of these methods indicates that the modified liquid association algorithm has the best efficiency for tracing 3WI between others. The limited number of biological studies based on the 3WI suggests that high computational demand of the available algorithms is a major challenge to apply this approach for analyzing high-throughput omics data.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1080/14789450.2019.1559734DOI Listing
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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/bty819DOI Listing
April 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.
View Article and Find Full Text PDF

Download full-text PDF

Source
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.
View Article and Find Full Text PDF

Download full-text PDF

Source
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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1093/bib/bby030DOI Listing
March 2019

Hydrophobic residues can identify native protein structures.

Authors:
Mehdi Mirzaie

Proteins 2018 04 5;86(4):467-474. Epub 2018 Feb 5.

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

Evaluation of protein structures needs a trustworthy potential function. Although several knowledge-based potential functions exist, the impact of different types of amino acids in the scoring functions has not been studied yet. Previously, we have reported the importance of nonlocal interactions in scoring function (based on Delaunay tessellation) in discrimination of native structures. Then, we have questioned the structural impact of hydrophobic amino acids in protein fold recognition. Therefore, a Hydrophobic Reduced Model (HRM) was designed to reduce protein structure of FS (Full Structure) into RS (Reduced Structure). RS is considered as a reduced structure of only seven hydrophobic amino acids (L, V, F, I, A, W, Y) and all their interactions. The presented model was evaluated via four different performance metrics including the number of correctly identified natives, the Z-score of the native energy, the RMSD of the minimum score, and the Pearson correlation coefficient between the energy and the model quality. Results indicated that only nonlocal interactions between hydrophobic amino acids could be sufficient and accurate enough for protein fold recognition. Interestingly, the results of HRM is significantly close to the model that considers all amino acids (20-amino acid model) to discriminate the native structure of the proteins on eleven decoy sets. This indicates that the power of knowledge-based potential functions in protein fold recognition is mostly due to hydrophobic interactions. Hence, we suggest combining a different well-designed scoring function for non-hydrophobic interactions with HRM to achieve better performance in fold recognition.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/prot.25466DOI Listing
April 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.
View Article and Find Full Text PDF

Download full-text PDF

Source
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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/jcb.26546DOI Listing
May 2018

Three-way interaction model to trace the mechanisms involved in Alzheimer's disease transgenic mice.

PLoS One 2017 21;12(9):e0184697. Epub 2017 Sep 21.

Department of Basic Sciences, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Alzheimer's disease (AD) is the most common cause for dementia in human. Currently, more than 46 million people in the world suffer from AD and it is estimated that by 2050 this number increases to more than 131 million. AD is considered as a complex disease. Therefore, understanding the mechanism of AD is a universal challenge. Nowadays, a huge number of disease-related high-throughput "omics" datasets are freely available. Such datasets contain valuable information about disease-related pathways and their corresponding gene interactions. In the present work, a three-way interaction model is used as a novel approach to understand AD-related mechanisms. This model can trace the dynamic nature of co-expression relationship between two genes by introducing their link to a third gene. Apparently, such relationships cannot be traced by the classical two-way interaction model. Liquid association method was applied to capture the statistically significant triplets which are involved in three-way interaction. Subsequently, gene set enrichment analysis (GSEA) and gene regulatory network (GRN) inference were applied to analyze the biological relevance of the statistically significant triplets. The results of this study suggest that the innate immunity processes are important in AD. Specifically, our results suggest that H2-Ob as the switching gene and the gene pair {Csf1r, Milr1} form a statistically significant and biologically relevant triplet, which may play an important role in AD. We propose that the homeostasis-related link between mast cells and microglia is presumably controlled with H2-Ob expression levels as a switching gene.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0184697PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5608283PMC
October 2017

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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fmicb.2016.01688DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5098112PMC
November 2016

Investigating the importance of Delaunay-based definition of atomic interactions in scoring of protein-protein docking results.

J Mol Graph Model 2016 05 4;66:108-14. Epub 2016 Apr 4.

Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University, Jalal Ale Ahmad Highway, P.O. Box 14115-134, Tehran, Iran.

The approaches taken to represent and describe structural features of the macromolecules are of major importance when developing computational methods for studying and predicting their structures and interactions. This study attempts to explore the significance of Delaunay tessellation for the definition of atomic interactions by evaluating its impact on the performance of scoring protein-protein docking prediction. Two sets of knowledge-based scoring potentials are extracted from a training dataset of native protein-protein complexes. The potential of the first set is derived using atomic interactions extracted from Delaunay tessellated structures. The potential of the second set is calculated conventionally, that is, using atom pairs whose interactions were determined by their separation distances. The scoring potentials were tested against two different docking decoy sets and their performances were compared. The results show that, if properly optimized, the Delaunay-based scoring potentials can achieve higher success rate than the usual scoring potentials. These results and the results of a previous study on the use of Delaunay-based potentials in protein fold recognition, all point to the fact that Delaunay tessellation of protein structure can provide a more realistic definition of atomic interaction, and therefore, if appropriately utilized, may be able to improve the accuracy of pair potentials.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jmgm.2016.04.001DOI Listing
May 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.
View Article and Find Full Text PDF

Download full-text PDF

Source
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.
View Article and Find Full Text PDF

Download full-text PDF

Source
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.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.bbalip.2015.07.005DOI Listing
October 2015

Erratum: Bioactive Lipids in Emphysema: Decoding Fat to Reveal COPD Phenotypes.

Am J Respir Crit Care Med 2015 May;191(10):1210

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1164/rccm.19110erratum1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5469502PMC
May 2015

Bioactive lipids in emphysema. Decoding fat to reveal COPD phenotypes.

Am J Respir Crit Care Med 2015 Feb;191(3):241-3

Department of Computational Biology, Faculty of High Technologies, Tarbiat Modares University, Tehran, Iran; School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1164/rccm.201412-2264EDDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4351583PMC
February 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.

View Article and Find Full Text PDF

Download full-text PDF

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