Biology subjects

Mirzaie, M.

Publications and source records attributed to Mirzaie, M..

11 recordsLinked to original sources

bioRxiv2025

A Fast Approach for Structural and Evolutionary Analysis Based on Energetic Profile Protein Comparison

In structural bioinformatics, the efficiency of predicting protein similarity, function, and evolutionary relationships is crucial. Our approach proposed herein leverages protein energy profiles derived from a knowledge-based potential, deviating from traditional methods relying on structural alignment or atomic distances. This method assigns unique energy profiles to individual proteins, facilitating rapid comparative analysis for both structural similarities and evolutionary relationships across various hierarchical levels. Our study demonstrates that energy profiles contain substantial information about protein structure at class, fold, superfamily, and family levels. Notably, these profiles accurately distinguish proteins across species, illustrated by the classification of coronavirus spike glycoproteins and bacteriocin proteins. Introducing a separation measure based on energy profile similarity, our method shows significant correlation with a network-based approach, emphasizing the potential of energy profiles as efficient predictors for drug combinations with faster computational requirements. Our key insight is that the sequence-based energy profile strongly correlates with structure-derived energy, enabling rapid and efficient protein comparisons based solely on sequences.

bioinformatics↗

bioRxiv2023

Designing patient-oriented combination therapies for acute myeloid leukemia based on efficacy/toxicity integration and bipartite network modeling

Acute myeloid leukemia (AML), a heterogeneous and aggressive blood cancer, does not respond well to single-drug therapy. A combination of drugs is required to effectively treat this disease. Computational models are critical for combination therapy discovery due to the tens of thousands of two-drug combinations, even with approved drugs. While predicting synergistic drugs is the focus of current methods, few consider drug efficacy and potential toxicity, which are crucial for treatment success. To find effective new drug candidates, we constructed a bipartite network using patient-derived tumor samples and drugs. The network is based on drug-response screening and summarizes all treatment response heterogeneity as drug response weights. This bipartite network is then projected onto the drug part, resulting in the drug similarity network. Distinct drug clusters were identified using community detection methods, each targeting different biological processes and pathways as revealed by enrichment and pathway analysis of the drugs protein targets. Four drugs with the highest efficacy and lowest toxicity from each cluster were selected and tested for drug sensitivity using cell viability assays on various samples. Results show that the combinations of ruxolitinib-ulixertinib and sapanisertib-LY3009120 are the most effective with the least toxicity and best synergistic effects on blasts. These findings lay the foundation for personalized and successful AML therapies, ultimately leading to the development of drug combinations that can be used alongside standard first-line AML treatment. Key PointsO_LIRuxolitinib-ulixertinib and sapanisertib-LY3009120 have the best synergistic effects on AML, with the least toxicity. C_LIO_LIThis studys combinations destroy blasts without harming other healthy cells, unlike standard chemotherapy, which is less specific. C_LI

cancer biology↗

bioRxiv2022

Monitoring Functional Post-Translational Modifications Using a Data-Driven Proteome Informatic Pipeline Based on PEIMAN2

Post-translational modifications (PTMs) are of significant interest in molecular biomedicine due to their crucial role in signal transduction across various cellular and organismal processes. Characterizing PTMs, distinguishing between functional and inert modifications, quantifying their occupancies, and understanding PTM crosstalk are challenging tasks in any biosystem. Studying each PTM often requires a specific, labor- intensive experimental design. Here, we present a PTM-centric proteome informatic pipeline for predicting relevant PTMs in mass spectrometry-based proteomics data without prior information. Once predicted, these in silico identified PTMs can be incorporated into a refined database search and compared to measured data. As a practical application, we demonstrate how this pipeline can be used to study glycoproteomics in oral squamous cell carcinoma based on the proteome profile of primary tumors. Subsequently, we experimentally identified cellular proteins that are differentially expressed in cells treated with multikinase inhibitors dasatinib and staurosporine using mass spectrometry-based proteomics. Computational enrichment analysis was then employed to determine the potential PTMs of differentially expressed proteins induced by both drugs. Finally, we conducted an additional round of database search with the predicted PTMs. Our pipeline successfully analyzed the enriched PTMs, and detected proteins not identified in the initial search. Our findings support the effectiveness of PTM-centric searching of MS data in proteomics based on computational enrichment analysis, and we propose integrating this approach into future proteomics search engines.

biochemistry↗

bioRxiv2022

NIMAA: an R/CRAN package to accomplish NomInal data Mining AnAlysis

SummaryNominal data is data that has been "labeled" and can be designated into a number of non-overlapping unordered groups. The analysis of this type of data is often trivial because it is not feasible to conduct extensive numerical methods on this type of data. Graphs or networks, on the other hand, are comprised of sets of nodes and edges that can also be considered as nominal variables. By integrating graph theory and data mining approaches, we offer the R package NIMAA to define a nominal data-mining pipeline to explore more information. Using nominal variables in a dataset, NIMAA provides functions for constructing weighted and unweighted bipartite graphs, analysing the similarity of labels in nominal variables, clustering labels or categories to super-labels, validating clustering results, predicting bipartite edges by missing weight imputation, and providing a variety of visualization tools. Here, we also indicated the application of nominal data mining in a biological dataset with well-riched nominal variables. AvailabilityNIMAAs official release and the beta update are available on CRAN and Github, respectively. URLs: https://CRAN.R-project.org/package=NIMAA and https://github.com/jafarilab/NIMAA Contactmohieddin.jafari@helsinki.fi; jing.tang@helisnki.fi ContributionsMJ conceived the study and developed the models, MJ and CC adopted and implemented the methods, MM improved the methods, JT provided the funding, MJ, CC, MM and JT wrote the paper.

bioinformatics↗

bioRxiv2021

Facilitating the design of combination therapy in cancer using multipartite network models: Emphasis on acute myeloid leukemia

From the drug discovery perspective, combination therapy is recommended in cancer due to efficiency and safety compared to the common cytotoxic and single-targeted monotherapies. However, identifying effective drug combinations is time- and cost-consuming. Here, we offer a novel strategy of predicting potential drug combinations and patient subclasses by constructing multipartite networks using drug response data on patient samples. In the present study, we used Beat AML and GDSC, two comprehensive datasets based on patient-derived and cell line-based samples, to show the potential of multipartite network modeling in cancer combinatorial therapy. We used the median values of cell viability to compare drug potency and reconstruct a weighted bipartite network, which models the interaction of drugs and biological samples. Then, clusters of network communities were identified in two projected networks based on the topological structure of networks. Chemical structures, drug-target networks, protein-protein interactions, and signaling networks were used to corroborate the intra-cluster homogeneity. We further leveraged the community structures within the drug-based multipartite networks to discover effective multi-targeted drug combinations, and the synergy levels which were supported with more evidence using the DrugComb and the ALMANAC databases. Furthermore, we confirmed the potency of selective combinations of drugs against monotherapy in vitro experiment using three acute myeloid leukemia (AML) cell lines. Taken together, this study presents an innovative data-driven strategy based on multipartite networks to suggest potential drug combinations to improve treatment of AML.

systems biology↗

bioRxiv2021

Metabolomic Signature of Amino Acids in Plasma of Patients with non-segmental Vitiligo

IntroductionVitiligo is an asymptomatic disorder that results from the loss of pigments (melanin), causing skin or mucosal depigmentation and impairs beauty. ObjectiveDue to the complexity of the pathogenesis of this disease and various theories including self-safety theory, oxidative stress, neurological theory and internal defects of melanocytes behind it, and finally, the vast role of amino acids in body metabolism and various activities of the body, amino acids targeted based metabolomics was set up to follow any fluctuation inside this disease. MethodologyThe study of amino acid profiles in plasma of people with non-segmental vitiligo using a liquid chromatography equipped with fluorescent detector was performed to find remarkable biomarkers for the diagnosis and evaluation of disease severity of patients with vitiligo. Twenty-two amino acids derivatized with o-phthalaldehyde (OPA) and fluorylmethyloxycarbonyl chloride (FMOC), were precisely determined. Next, the concentrations of these twenty-two amino acids and their corresponding molar ratios were calculated in 37 patients (including 18 females and 19 males) and corresponding 34 healthy individuals (18 females and 16 males). Using R programing, the data were completely analyzed between the two groups of patients and healthy to find suitable and reliable biomarkers. ResultsInterestingly, comparing the two groups, in the patient group, tyrosine, cysteine, the ratio of tyrosine to lysine and the ratio of cysteine to ornithine were increased while, arginine, lysine, ornithine and glycine ratios to cysteine have been decreased. These amino acids were selected for identification of patients with accuracy of detection of approximately 0.95 using the assessment of logistic regression. ConclusionThese results indicate a disruption of the production of melanin, increased immune activity and oxidative stress, which are also involved in the effects of vitiligo. Therefore, these amino acids can be used as biomarker for the evaluation of risk, prevention of complications in individuals at risk and monitoring of treatment process.

biochemistry↗

bioRxiv2019

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

Human T-lymphotropic virus 1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a progressive disease of the central nervous system that affected significantly spinal cord, nevertheless, the pathogenesis pathway. This study aimed to employ high throughput meta-analysis to find major genes involved in the pathogenesis of HAM/TSP. High-throughput statistical analyses identified 385, 49, and 22 differentially expressed genes for normal vs. ACs, normal vs. HAM/TSP and ACs vs. HAM/TSP groups, respectively. STRING and further network analyses highlighted 32, 29, and 13 hub genes for normal vs. ACs, normal vs. HAM/TSP, and ACs vs. HAM/TSP groups, respectively. Biological network analyses indicated the involvement of hub genes in the HAM/TSP group in many vital pathways like apoptosis and immune pathways. Moreover, the meta-analysis results disclosed three major genes including STAT1, TAP1, and PSMB8 which have function role in HAM/TSP progression. 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 percentage of T helper and cytotoxic T lymphocytes (P= 0.55 and P=0.12). Our results confirm that STAT1, TAP1, and PSMB8 are three important genes which their expressions levels were changed in three different groups. These proteins in association with other proteins can involve in the immune and apoptosis pathways.

systems biology↗

bioRxiv2019

Do signaling networks and whole-transcriptome gene expression profiles orchestrate the same symphony?

Studying relationships among gene-products by gene 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 correlation of transcript and protein expression levels. All these efforts partook in the current understanding of signaling network models and expanded the signaling databases. In fact, due to the unavailability or high-cost of the experiments, most of the studies do not usually look for direct interactions, and some parts of these networks are contradictory. Besides, it is now a standard step to accomplish enrichment analysis on biological annotations, to make claims about the potentially implicated biological pathways in any perturbation. Explicitly, upon identifying differentially expressed genes, they are spontaneously presumed the corresponding dysregulated pathways. Then, molecular mechanistic insights are proposed for disease etiology and drug discovery based on statistically enriched biological processes. In this study, using four common 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 and the causal relationships among the gene pairs. We illustrated that the signaling network was not more consistent or coherent with the recorded expression profile compared to the random relationships. Finally, we provided the pieces of evidence and concluded that gene-product expression data, especially at the transcript level, are not reliable or at least insufficient to infer causal biological relationships among genes and in turn, describe cellular behavior.

systems biology↗

bioRxiv2017

Revisiting the General Concept of Network Centralities: A Propose for Centrality Analysis in Network Science

BackgroundNumerous 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.\n\nResultsWe 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 networks 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.\n\nConclusionsThe 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.

systems biology↗

bioRxiv2017

A Rational Drug Combination Design Proves to Inhibit Epithelial-Mesenchymal Transition in a Three-Dimensional Microenvironment

Epithelial-Mesenchymal Transition (EMT) is a major player of tumor invasiveness whose inhibition is challenged by redundancy of multiple inducing factors. We applied a systems-pharmacology approach by integrating network-based analyses with multiple bioinformatic resources to design a drug combination regimen reversing EMT phenotype in aggressive cancers. 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 validate the anti-EMT efficacy of the target combinations, we inhibited the pinpointed proteins with already prescribed drugs and observed 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 co-cultured endothelial cells. Given the approval status and safety profiles of the suggested drugs, the proposed combination set can be considered in clinical trials.

systems biology↗

bioRxiv2017

The evolution of central dogma of molecular biology: a logic-based dynamic approach

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.

systems biology↗