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Karimifard SA, Salehzadeh-Yazdi A, Taghizadeh-Tabarsi R, Akbari-Birgani S. Mechanical effects modulate drug resistance in MCF-7-derived organoids: Insights into the wnt/β-catenin pathway. Biochem Biophys Res Commun 2024; 695:149420. [PMID: 38154263 DOI: 10.1016/j.bbrc.2023.149420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 11/29/2023] [Accepted: 12/20/2023] [Indexed: 12/30/2023]
Abstract
Addressing drug resistance poses a significant challenge in cancer treatment, as cancer cells develop diverse mechanisms to evade chemotherapy drugs, leading to treatment failure and disease relapse. Three-dimensional (3D) cell culture has emerged as a valuable model for studying drug resistance, although the underlying mechanisms remain elusive. By obtaining a better understanding of drug resistance within the 3D culture environment, we can develop more effective strategies to overcome it and improve the success of cancer treatments. Notably, the physical structure undergoes notable changes in 3D culture, with mechanical effects believed to play a pivotal role in drug resistance. Hence, our study aimed to explore the influence of mechanical effects on drug resistance by analyzing data related to "drug resistance" and "mechanobiology". Through this analysis, we identified β-catenin and JNK1 as potential factors, which were further examined in MCF-7 cells cultivated under both 2D and 3D culture conditions. Our findings demonstrate that β-catenin is activated through canonical and non-canonical pathways and associated with the drug resistance, particularly in organoids obtained under 3D culture.
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Affiliation(s)
- Seyed Ali Karimifard
- Department of Biological Sciences, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, 45137-66731, Iran
| | | | - Reza Taghizadeh-Tabarsi
- Department of Biological Sciences, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, 45137-66731, Iran
| | - Shiva Akbari-Birgani
- Department of Biological Sciences, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, 45137-66731, Iran; Research Center for Basic Sciences and Modern Technologies (RBST), Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, 45137-66731, Iran.
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2
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Jalili M, Scharm M, Wolkenhauer O, Salehzadeh-Yazdi A. Metabolic function-based normalization improves transcriptome data-driven reduction of genome-scale metabolic models. NPJ Syst Biol Appl 2023; 9:15. [PMID: 37210409 DOI: 10.1038/s41540-023-00281-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 05/10/2023] [Indexed: 05/22/2023] Open
Abstract
Genome-scale metabolic models (GEMs) are extensively used to simulate cell metabolism and predict cell phenotypes. GEMs can also be tailored to generate context-specific GEMs, using omics data integration approaches. To date, many integration approaches have been developed, however, each with specific pros and cons; and none of these algorithms systematically outperforms the others. The key to successful implementation of such integration algorithms lies in the optimal selection of parameters, and thresholding is a crucial component in this process. To improve the predictive accuracy of context-specific models, we introduce a new integration framework that improves the ranking of related genes and homogenizes the expression values of those gene sets using single-sample Gene Set Enrichment Analysis (ssGSEA). In this study, we coupled ssGSEA with GIMME and validated the advantages of the proposed framework to predict the ethanol formation of yeast grown in the glucose-limited chemostats, and to simulate metabolic behaviors of yeast growth in four different carbon sources. This framework enhances the predictive accuracy of GIMME which we demonstrate for predicting the yeast physiology in nutrient-limited cultures.
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Affiliation(s)
- Mahdi Jalili
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
- Stellenbosch University, Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre, Stellenbosch, South Africa
- Leibniz Institute for Food Systems Biology at the Technical University Munich, Freising, Germany
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Khalafiyan A, Emadi-Baygi M, Wolfien M, Salehzadeh-Yazdi A, Nikpour P. Construction of a three-component regulatory network of transcribed ultraconserved regions for the identification of prognostic biomarkers in gastric cancer. J Cell Biochem 2023; 124:396-408. [PMID: 36748954 DOI: 10.1002/jcb.30373] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 01/04/2023] [Accepted: 01/09/2023] [Indexed: 02/08/2023]
Abstract
Altered expression and functional roles of the transcribed ultraconserved regions (T-UCRs), as genomic sequences with 100% conservation between the genomes of human, mouse, and rat, in the pathophysiology of neoplasms has already been investigated. Nevertheless, the relevance of the functions for T-UCRs in gastric cancer (GC) is still the subject of inquiry. In the current study, we first used a genome-wide profiling approach to analyze the expression of T-UCRs in GC patients. Then, we constructed a three-component regulatory network and investigated potential diagnostic and prognostic values of the T-UCRs. The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) dataset was used as a resource for the RNA-sequencing data. FeatureCounts was utilized to quantify the number of reads mapped to each T-UCR. Differential expression analysis was then conducted using DESeq2. In the following, interactions between T-UCRs, microRNAs (miRNAs), and messenger RNAs (mRNAs) were combined into a three-component network. Enrichment analyses were performed and a protein-protein interaction (PPI) network was constructed. The R Survival package was utilized to identify survival-related significantly differentially expressed T-UCRs (DET-UCRs). Using an in-house cohort of GC tissues, expression of two DET-UCRs was furthermore experimentally verified. Our results showed that several T-UCRs were dysregulated in TCGA-STAD tumoral samples compared to nontumoral counterparts. The three-component network was constructed which composed of DET-UCRs, miRNAs, and mRNAs nodes. Functional enrichment and PPI network analyses revealed important enriched signaling pathways and gene ontologies such as "pathway in cancer" and regulation of cell proliferation and apoptosis. Five T-UCRs were significantly correlated with the overall survival of GC patients. While no expression of uc.232 was observed in our in-house cohort of GC tissues, uc.343 showed an increased expression, although not statistically significant, in gastric tumoral tissues. The constructed three-component regulatory network of T-UCRs in GC presents a comprehensive understanding of the underlying gene expression regulation processes involved in tumor development and can serve as a basis to investigate potential prognostic biomarkers and therapeutic targets.
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Affiliation(s)
- Anis Khalafiyan
- Department of Genetics and Molecular Biology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Modjtaba Emadi-Baygi
- Department of Genetics, Faculty of Basic Sciences, Shahrekord University, Shahrekord, Iran
| | - Markus Wolfien
- Department of System Biology and Bioinformatics, University of Rostock, Rostock, Germany
- Center for Medical Informatics, Dresden, Germany
| | - Ali Salehzadeh-Yazdi
- Department of Life Sciences and Chemistry, Jacobs University Bremen, Bremen, Germany
| | - Parvaneh Nikpour
- Department of Genetics and Molecular Biology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
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Collin CB, Gebhardt T, Golebiewski M, Karaderi T, Hillemanns M, Khan FM, Salehzadeh-Yazdi A, Kirschner M, Krobitsch S, Kuepfer L. Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation. J Pers Med 2022; 12:jpm12020166. [PMID: 35207655 PMCID: PMC8879572 DOI: 10.3390/jpm12020166] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/14/2022] [Accepted: 01/20/2022] [Indexed: 12/12/2022] Open
Abstract
The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.
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Affiliation(s)
- Catherine Bjerre Collin
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark; (C.B.C.); (T.K.)
| | - Tom Gebhardt
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies gGmbH, 69118 Heidelberg, Germany;
| | - Tugce Karaderi
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark; (C.B.C.); (T.K.)
- Center for Health Data Science, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark
| | - Maximilian Hillemanns
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | - Faiz Muhammad Khan
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | | | - Marc Kirschner
- Forschungszentrum Jülich GmbH, Project Management Jülich, 52425 Jülich, Germany; (M.K.); (S.K.)
| | - Sylvia Krobitsch
- Forschungszentrum Jülich GmbH, Project Management Jülich, 52425 Jülich, Germany; (M.K.); (S.K.)
| | | | - Lars Kuepfer
- Institute for Systems Medicine with Focus on Organ Interaction, University Hospital RWTH Aachen, 52074 Aachen, Germany
- Correspondence: ; Tel.: +49-241-8085900
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Salahshouri P, Emadi-Baygi M, Jalili M, Khan FM, Wolkenhauer O, Salehzadeh-Yazdi A. A Metabolic Model of Intestinal Secretions: The Link between Human Microbiota and Colorectal Cancer Progression. Metabolites 2021; 11:metabo11070456. [PMID: 34357350 PMCID: PMC8303431 DOI: 10.3390/metabo11070456] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 07/12/2021] [Accepted: 07/13/2021] [Indexed: 12/22/2022] Open
Abstract
The human gut microbiota plays a dual key role in maintaining human health or inducing disorders, for example, obesity, type 2 diabetes, and cancers such as colorectal cancer (CRC). High-throughput data analysis, such as metagenomics and metabolomics, have shown the diverse effects of alterations in dynamic bacterial populations on the initiation and progression of colorectal cancer. However, it is well established that microbiome and human cells constantly influence each other, so it is not appropriate to study them independently. Genome-scale metabolic modeling is a well-established mathematical framework that describes the dynamic behavior of these two axes at the system level. In this study, we created community microbiome models of three conditions during colorectal cancer progression, including carcinoma, adenoma and health status, and showed how changes in the microbial population influence intestinal secretions. Conclusively, our findings showed that alterations in the gut microbiome might provoke mutations and transform adenomas into carcinomas. These alterations include the secretion of mutagenic metabolites such as H2S, NO compounds, spermidine and TMA (trimethylamine), as well as the reduction of butyrate. Furthermore, we found that the colorectal cancer microbiome can promote inflammation, cancer progression (e.g., angiogenesis) and cancer prevention (e.g., apoptosis) by increasing and decreasing certain metabolites such as histamine, glutamine and pyruvate. Thus, modulating the gut microbiome could be a promising strategy for the prevention and treatment of CRC.
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Affiliation(s)
- Pejman Salahshouri
- Department of Genetics, Faculty of Basic Sciences, Shahrekord University, Shahrekord 8818634141, Iran; (P.S.); (M.E.-B.)
| | - Modjtaba Emadi-Baygi
- Department of Genetics, Faculty of Basic Sciences, Shahrekord University, Shahrekord 8818634141, Iran; (P.S.); (M.E.-B.)
- Biotechnology Research Institute, Shahrekord University, Shahrekord 8818634141, Iran
| | - Mahdi Jalili
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran 14114, Iran;
| | - Faiz M. Khan
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany; (F.M.K.); (O.W.)
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany; (F.M.K.); (O.W.)
| | - Ali Salehzadeh-Yazdi
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany; (F.M.K.); (O.W.)
- Correspondence:
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Henze L, Walter U, Murua Escobar H, Junghanss C, Jaster R, Köhling R, Lange F, Salehzadeh-Yazdi A, Wolkenhauer O, Hamed M, Barrantes I, Palmer D, Möller S, Kowald A, Heussen N, Fuellen G. Towards biomarkers for outcomes after pancreatic ductal adenocarcinoma and ischaemic stroke, with focus on (co)-morbidity and ageing/cellular senescence (SASKit): protocol for a prospective cohort study. BMJ Open 2020; 10:e039560. [PMID: 33334830 PMCID: PMC7747584 DOI: 10.1136/bmjopen-2020-039560] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
INTRODUCTION Ageing-related processes such as cellular senescence are believed to underlie the accumulation of diseases in time, causing (co)morbidity, including cancer, thromboembolism and stroke. Interfering with these processes may delay, stop or reverse morbidity. The aim of this study is to investigate the link between (co)morbidity and ageing by exploring biomarkers and molecular mechanisms of disease-triggered deterioration in patients with pancreatic ductal adenocarcinoma (PDAC) and (thromboembolic) ischaemic stroke (IS). METHODS AND ANALYSIS We will recruit 50 patients with PDAC, 50 patients with (thromboembolic) IS and 50 controls at Rostock University Medical Center, Germany. We will gather routine blood data, clinical performance measurements and patient-reported outcomes at up to seven points in time, alongside in-depth transcriptomics and proteomics at two of the early time points. Aiming for clinically relevant biomarkers, the primary outcome is a composite of probable sarcopenia, clinical performance (described by ECOG Performance Status for patients with PDAC and the Modified Rankin Scale for patients with stroke) and quality of life. Further outcomes cover other aspects of morbidity such as cognitive decline and of comorbidity such as vascular or cancerous events. The data analysis is comprehensive in that it includes biostatistics and machine learning, both following standard role models and additional explorative approaches. Prognostic and predictive biomarkers for interventions addressing senescence may become available if the biomarkers that we find are specifically related to ageing/cellular senescence. Similarly, diagnostic biomarkers will be explored. Our findings will require validation in independent studies, and our dataset shall be useful to validate the findings of other studies. In some of the explorative analyses, we shall include insights from systems biology modelling as well as insights from preclinical animal models. We anticipate that our detailed study protocol and data analysis plan may also guide other biomarker exploration trials. ETHICS AND DISSEMINATION The study was approved by the local ethics committee (Ethikkommission an der Medizinischen Fakultät der Universität Rostock, A2019-0174), registered at the German Clinical Trials Register (DRKS00021184), and results will be published following standard guidelines.
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Affiliation(s)
- Larissa Henze
- Department of Medicine, Clinic III, Hematology, Oncology, Palliative Medicine, Rostock University Medical Center and Research Focus Oncology, Rostock, Germany
| | - Uwe Walter
- Department of Neurology, Rostock University Medical Center and Centre for Transdisciplinary Neurosciences Rostock, Rostock, Germany
| | - Hugo Murua Escobar
- Department of Medicine, Clinic III, Hematology, Oncology, Palliative Medicine, Rostock University Medical Center and Research Focus Oncology, Rostock, Germany
| | - Christian Junghanss
- Department of Medicine, Clinic III, Hematology, Oncology, Palliative Medicine, Rostock University Medical Center and Research Focus Oncology, Rostock, Germany
| | - Robert Jaster
- Department of Gastroenterology, Rostock University Medical Center and Research Focus Oncology, Rostock, Germany
| | - Rüdiger Köhling
- Oscar Langendorff Institute of Physiology, Rostock University Medical Center and Centre for Transdisciplinary Neurosciences Rostock and Ageing of Individuals and Society, Interdisciplinary Faculty, Rostock University, Rostock, Germany
| | - Falko Lange
- Oscar Langendorff Institute of Physiology, Rostock University Medical Center, Rostock, Germany
| | - Ali Salehzadeh-Yazdi
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock and Centre for Transdisciplinary Neurosciences Rostock, Rostock University Medical Center, Rostock, Germany
| | - Mohamed Hamed
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center and Research Focus Oncology, Rostock, Germany
| | - Israel Barrantes
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center and Research Focus Oncology, Rostock, Germany
| | - Daniel Palmer
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
| | - Steffen Möller
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
| | - Axel Kowald
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
| | - Nicole Heussen
- Department of Medical Statistics, RWTH Aachen, Aachen, Germany
| | - Georg Fuellen
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center and Centre for Transdisciplinary Neurosciences Rostock and Research Focus Oncology, Rostock and Ageing of Individuals and Society, Interdisciplinary Faculty, Rostock University, Rostock, Germany
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Scharm M, Wolkenhauer O, Jalili M, Salehzadeh-Yazdi A. GEMtractor: extracting views into genome-scale metabolic models. Bioinformatics 2020; 36:3281-3282. [PMID: 32003785 DOI: 10.1093/bioinformatics/btaa068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 01/17/2020] [Accepted: 01/24/2020] [Indexed: 11/13/2022] Open
Abstract
SUMMARY Computational metabolic models typically encode for graphs of species, reactions and enzymes. Comparing genome-scale models through topological analysis of multipartite graphs is challenging. However, in many practical cases it is not necessary to compare the full networks. The GEMtractor is a web-based tool to trim models encoded in SBML. It can be used to extract subnetworks, for example focusing on reaction- and enzyme-centric views into the model. AVAILABILITY AND IMPLEMENTATION The GEMtractor is licensed under the terms of GPLv3 and developed at github.com/binfalse/GEMtractor-a public version is available at sbi.uni-rostock.de/gemtractor.
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Affiliation(s)
- Martin Scharm
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany.,Stellenbosch Institute for Advanced Study, Wallenberg Research Centre at Stellenbosch University, Stellenbosch, 7602, South Africa
| | - Mahdi Jalili
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, 1416753955 Iran
| | - Ali Salehzadeh-Yazdi
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany
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Sulheim S, Kumelj T, van Dissel D, Salehzadeh-Yazdi A, Du C, van Wezel GP, Nieselt K, Almaas E, Wentzel A, Kerkhoven EJ. Enzyme-Constrained Models and Omics Analysis of Streptomyces coelicolor Reveal Metabolic Changes that Enhance Heterologous Production. iScience 2020; 23:101525. [PMID: 32942174 PMCID: PMC7501462 DOI: 10.1016/j.isci.2020.101525] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 07/19/2020] [Accepted: 08/31/2020] [Indexed: 02/06/2023] Open
Abstract
Many biosynthetic gene clusters (BGCs) require heterologous expression to realize their genetic potential, including silent and metagenomic BGCs. Although the engineered Streptomyces coelicolor M1152 is a widely used host for heterologous expression of BGCs, a systemic understanding of how its genetic modifications affect the metabolism is lacking and limiting further development. We performed a comparative analysis of M1152 and its ancestor M145, connecting information from proteomics, transcriptomics, and cultivation data into a comprehensive picture of the metabolic differences between these strains. Instrumental to this comparison was the application of an improved consensus genome-scale metabolic model (GEM) of S. coelicolor. Although many metabolic patterns are retained in M1152, we find that this strain suffers from oxidative stress, possibly caused by increased oxidative metabolism. Furthermore, precursor availability is likely not limiting polyketide production, implying that other strategies could be beneficial for further development of S. coelicolor for heterologous production of novel compounds.
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Affiliation(s)
- Snorre Sulheim
- Department of Biotechnology and Nanomedicine, SINTEF Industry, 7034 Trondheim, Norway
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Tjaša Kumelj
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Dino van Dissel
- Department of Biotechnology and Nanomedicine, SINTEF Industry, 7034 Trondheim, Norway
| | - Ali Salehzadeh-Yazdi
- Department of Systems Biology and Bioinformatics, Faculty of Computer Science and Electrical Engineering, University of Rostock, 18057 Rostock, Germany
| | - Chao Du
- Microbial Biotechnology, Institute of Biology, Leiden University, 2300 Leiden, the Netherlands
| | - Gilles P. van Wezel
- Microbial Biotechnology, Institute of Biology, Leiden University, 2300 Leiden, the Netherlands
| | - Kay Nieselt
- Integrative Transcriptomics, Center for Bioinformatics, University of Tübingen, 72070 Tübingen, Germany
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, 7491 Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, 7491 Trondheim, Norway
| | - Alexander Wentzel
- Department of Biotechnology and Nanomedicine, SINTEF Industry, 7034 Trondheim, Norway
| | - Eduard J. Kerkhoven
- Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, 412 96 Gothenburg, Sweden
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Scharm M, Gebhardt T, Touré V, Bagnacani A, Salehzadeh-Yazdi A, Wolkenhauer O, Waltemath D. Evolution of computational models in BioModels Database and the Physiome Model Repository. BMC Syst Biol 2018; 12:53. [PMID: 29650016 PMCID: PMC5898004 DOI: 10.1186/s12918-018-0553-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Accepted: 02/21/2018] [Indexed: 11/25/2022]
Abstract
BACKGROUND A useful model is one that is being (re)used. The development of a successful model does not finish with its publication. During reuse, models are being modified, i.e. expanded, corrected, and refined. Even small changes in the encoding of a model can, however, significantly affect its interpretation. Our motivation for the present study is to identify changes in models and make them transparent and traceable. METHODS We analysed 13734 models from BioModels Database and the Physiome Model Repository. For each model, we studied the frequencies and types of updates between its first and latest release. To demonstrate the impact of changes, we explored the history of a Repressilator model in BioModels Database. RESULTS We observed continuous updates in the majority of models. Surprisingly, even the early models are still being modified. We furthermore detected that many updates target annotations, which improves the information one can gain from models. To support the analysis of changes in model repositories we developed MoSt, an online tool for visualisations of changes in models. The scripts used to generate the data and figures for this study are available from GitHub https://github.com/binfalse/BiVeS-StatsGenerator and as a Docker image at https://hub.docker.com/r/binfalse/bives-statsgenerator/ . The website https://most.bio.informatik.uni-rostock.de/ provides interactive access to model versions and their evolutionary statistics. CONCLUSION The reuse of models is still impeded by a lack of trust and documentation. A detailed and transparent documentation of all aspects of the model, including its provenance, will improve this situation. Knowledge about a model's provenance can avoid the repetition of mistakes that others already faced. More insights are gained into how the system evolves from initial findings to a profound understanding. We argue that it is the responsibility of the maintainers of model repositories to offer transparent model provenance to their users.
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Affiliation(s)
- Martin Scharm
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18051 Germany
| | - Tom Gebhardt
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18051 Germany
| | - Vasundra Touré
- Department of Biology, Norwegian University of Science and Technology, Trondheim, 7491 Norway
| | - Andrea Bagnacani
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18051 Germany
| | - Ali Salehzadeh-Yazdi
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18051 Germany
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18051 Germany
- Stellenbosch Institute for Advanced Study, Wallenberg Research Centre at Stellenbosch University, Stellenbosch, 7600 South Africa
| | - Dagmar Waltemath
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18051 Germany
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Jalili M, Gebhardt T, Wolkenhauer O, Salehzadeh-Yazdi A. Unveiling network-based functional features through integration of gene expression into protein networks. Biochim Biophys Acta Mol Basis Dis 2018; 1864:2349-2359. [PMID: 29466699 DOI: 10.1016/j.bbadis.2018.02.010] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 01/31/2018] [Accepted: 02/13/2018] [Indexed: 02/02/2023]
Abstract
Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype-phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers.
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Affiliation(s)
- Mahdi Jalili
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, Iran; Hematologic Malignancies Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Tom Gebhardt
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany
| | - Ali Salehzadeh-Yazdi
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany.
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11
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Razavi SM, Sabbaghian M, Jalili M, Divsalar A, Wolkenhauer O, Salehzadeh-Yazdi A. Comprehensive functional enrichment analysis of male infertility. Sci Rep 2017; 7:15778. [PMID: 29150651 PMCID: PMC5693951 DOI: 10.1038/s41598-017-16005-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Accepted: 11/06/2017] [Indexed: 02/07/2023] Open
Abstract
Spermatogenesis is a multifactorial process that forms differentiated sperm cells in a complex microenvironment. This process involves the genome, epigenome, transcriptome, and proteome to ensure the stability of the spermatogonia and supporting cells. The identification of signaling pathways linked to infertility has been hampered by the inherent complexity and multifactorial aspects of spermatogenesis. Systems biology is a promising approach to unveil underlying signaling pathways and genes and identify putative biomarkers. In this study, we analyzed thirteen microarray libraries of infertile humans and mice, and different classes of male infertility were compared using differentially expressed genes and functional enrichment analysis. We found regulatory processes, immune response, glutathione transferase and muscle tissue development to be among the most common biological processes in up-regulated genes, and genes involved in spermatogenesis were down-regulated in maturation arrest (MArrest) and oligospermia cases. We also observed the overexpression of genes involved in steroid metabolism in post-meiotic and meiotic arrest. Furthermore, we found that the infertile mouse model most similar to human MArrest was the Dazap1 mutant mouse. The results of this study could help elucidate features of infertility etiology and provide the basis for diagnostic markers.
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Affiliation(s)
- Seyed Morteza Razavi
- Department of Cell and Molecular Biology, Faculty of Biological Sciences, Kharazmi University, Tehran, Iran
| | - Marjan Sabbaghian
- Department of Andrology, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran.
| | - Mahdi Jalili
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Adeleh Divsalar
- Department of Cell and Molecular Biology, Faculty of Biological Sciences, Kharazmi University, Tehran, Iran
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany
| | - Ali Salehzadeh-Yazdi
- Department of Systems Biology and Bioinformatics, University of Rostock, 18051, Rostock, Germany.
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Jalili M, Salehzadeh-Yazdi A, Mohammadi S, Yaghmaie M, Ghavamzadeh A, Alimoghaddam K. Meta-Analysis of Gene Expression Profiles in Acute Promyelocytic Leukemia Reveals Involved Pathways. Int J Hematol Oncol Stem Cell Res 2017; 11:1-12. [PMID: 28286608 PMCID: PMC5338275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Background: Acute promyelocytic leukemia (APL) is a unique subtype of acute leukemia. APL is a curable disease; however, drug resistance, early mortality, disease relapse and treatment-related complications remain challenges in APL patient management. One issue underlying these challenges is that the molecular mechanisms of the disease are not sufficiently understood. Materials and Methods: In this study, we performed a meta-analysis of gene expression profiles derived from microarray experiments and explored the background of disease by functional and pathway analysis. Results: Our analysis revealed a gene signature with 406 genes that are up or down-regulated in APL. The pathway analysis determined that MAPK pathway and its involved elements such as JUN gene and AP-1 play important roles in APL pathogenesis along with insulin-like growth factor-binding protein-7. Conclusion: The results of this meta-analysis could be useful for developing more effective therapy strategies and new targets for diagnosis and drugs.
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Affiliation(s)
- Mahdi Jalili
- Hematology-Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Salehzadeh-Yazdi
- Hematology-Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences, Tehran, Iran,Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany
| | - Saeed Mohammadi
- Hematology-Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Marjan Yaghmaie
- Hematology-Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Ardeshir Ghavamzadeh
- Hematology-Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Kamran Alimoghaddam
- Hematology-Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences, Tehran, Iran
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Jalili M, Salehzadeh-Yazdi A, Gupta S, Wolkenhauer O, Yaghmaie M, Resendis-Antonio O, Alimoghaddam K. Evolution of Centrality Measurements for the Detection of Essential Proteins in Biological Networks. Front Physiol 2016; 7:375. [PMID: 27616995 PMCID: PMC4999434 DOI: 10.3389/fphys.2016.00375] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Accepted: 08/12/2016] [Indexed: 02/02/2023] Open
Affiliation(s)
- Mahdi Jalili
- Hematology, Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences Tehran, Iran
| | - Ali Salehzadeh-Yazdi
- Hematology, Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical SciencesTehran, Iran; Department of Systems Biology and Bioinformatics, University of RostockRostock, Germany
| | - Shailendra Gupta
- Department of Systems Biology and Bioinformatics, University of RostockRostock, Germany; CSIR-Indian Institute of Toxicology ResearchLucknow, India
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock Rostock, Germany
| | - Marjan Yaghmaie
- Hematology, Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences Tehran, Iran
| | | | - Kamran Alimoghaddam
- Hematology, Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences Tehran, Iran
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14
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Jalili M, Salehzadeh-Yazdi A, Yaghmaie M, Ghavamzadeh A, Alimoghaddam K. Cancerome: A hidden informative subnetwork of the diseasome. Comput Biol Med 2016; 76:173-7. [PMID: 27468170 DOI: 10.1016/j.compbiomed.2016.07.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2016] [Revised: 06/30/2016] [Accepted: 07/18/2016] [Indexed: 11/18/2022]
Abstract
Neoplastic disorders are a leading cause of mortality and morbidity worldwide. Studying the relationships between different cancers using high throughput-generated data may elucidate undisclosed aspects of cancer etiology, diagnosis, and treatment. Several studies have described relationships between different diseases based on genes, proteins, pathways, gene ontology, comorbidity, symptoms, and other features. In this study, we first constructed an integrated human disease network based on nine different biological aspects, including molecular, functional, and clinical features. Next, we extracted the cancerome as a cancer-related subnetwork. Further investigation of cancerome could reveal hidden mechanisms of cancer and could be useful in developing new diagnostic tests and effective new drugs.
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Affiliation(s)
- Mahdi Jalili
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Salehzadeh-Yazdi
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, Iran; Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany
| | - Marjan Yaghmaie
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Ardeshir Ghavamzadeh
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Kamran Alimoghaddam
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, Iran.
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15
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von Meyenn F, Iurlaro M, Habibi E, Liu NQ, Salehzadeh-Yazdi A, Santos F, Petrini E, Milagre I, Yu M, Xie Z, Kroeze LI, Nesterova TB, Jansen JH, Xie H, He C, Reik W, Stunnenberg HG. Impairment of DNA Methylation Maintenance Is the Main Cause of Global Demethylation in Naive Embryonic Stem Cells. Mol Cell 2016; 62:983. [PMID: 27315559 PMCID: PMC4914604 DOI: 10.1016/j.molcel.2016.06.005] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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16
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von Meyenn F, Iurlaro M, Habibi E, Liu NQ, Salehzadeh-Yazdi A, Santos F, Petrini E, Milagre I, Yu M, Xie Z, Kroeze LI, Nesterova TB, Jansen JH, Xie H, He C, Reik W, Stunnenberg HG. Impairment of DNA Methylation Maintenance Is the Main Cause of Global Demethylation in Naive Embryonic Stem Cells. Mol Cell 2016; 62:848-861. [PMID: 27237052 PMCID: PMC4914828 DOI: 10.1016/j.molcel.2016.04.025] [Citation(s) in RCA: 143] [Impact Index Per Article: 17.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Revised: 04/04/2016] [Accepted: 04/21/2016] [Indexed: 12/20/2022]
Abstract
Global demethylation is part of a conserved program of epigenetic reprogramming to naive pluripotency. The transition from primed hypermethylated embryonic stem cells (ESCs) to naive hypomethylated ones (serum-to-2i) is a valuable model system for epigenetic reprogramming. We present a mathematical model, which accurately predicts global DNA demethylation kinetics. Experimentally, we show that the main drivers of global demethylation are neither active mechanisms (Aicda, Tdg, and Tet1-3) nor the reduction of de novo methylation. UHRF1 protein, the essential targeting factor for DNMT1, is reduced upon transition to 2i, and so is recruitment of the maintenance methylation machinery to replication foci. Concurrently, there is global loss of H3K9me2, which is needed for chromatin binding of UHRF1. These mechanisms synergistically enforce global DNA hypomethylation in a replication-coupled fashion. Our observations establish the molecular mechanism for global demethylation in naive ESCs, which has key parallels with those operating in primordial germ cells and early embryos. Impaired DNA methylation maintenance is the cause of global demethylation in naive ESCs Loss of H3K9me2 and UHRF1 lead to impaired maintenance targeting to replication foci TET enzymes are not required for global demethylation Mathematical model accurately predicts global 5mC and 5hmC during epigenetic resetting
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Affiliation(s)
| | - Mario Iurlaro
- Epigenetics Programme, Babraham Institute, Cambridge CB22 3AT, UK
| | - Ehsan Habibi
- Department of Molecular Biology, Faculty of Science, Radboud University, 6525GA Nijmegen, the Netherlands
| | - Ning Qing Liu
- Department of Molecular Biology, Faculty of Science, Radboud University, 6525GA Nijmegen, the Netherlands
| | - Ali Salehzadeh-Yazdi
- Hematology-Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Fátima Santos
- Epigenetics Programme, Babraham Institute, Cambridge CB22 3AT, UK
| | - Edoardo Petrini
- Epigenetics Programme, Babraham Institute, Cambridge CB22 3AT, UK
| | - Inês Milagre
- Epigenetics Programme, Babraham Institute, Cambridge CB22 3AT, UK
| | - Miao Yu
- Department of Chemistry, Department of Biochemistry and Molecular Biology, and Institute for Biophysical Dynamics, The University of Chicago, 929 East 57th Street, Chicago, IL 60637, USA; Howard Hughes Medical Institute, The University of Chicago, 929 East 57th Street, Chicago, IL 60637, USA
| | - Zhenqing Xie
- Virginia Bioinformatics Institute and Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24060, USA
| | - Leonie I Kroeze
- Department of Laboratory Medicine, Laboratory of Hematology, Radboud University Nijmegen Medical Centre and Radboudumc Institute for Molecular Life Sciences (RIMLS), 6525GA Nijmegen, the Netherlands
| | - Tatyana B Nesterova
- Developmental Epigenetics Group, Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, UK
| | - Joop H Jansen
- Department of Laboratory Medicine, Laboratory of Hematology, Radboud University Nijmegen Medical Centre and Radboudumc Institute for Molecular Life Sciences (RIMLS), 6525GA Nijmegen, the Netherlands
| | - Hehuang Xie
- Virginia Bioinformatics Institute and Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24060, USA
| | - Chuan He
- Department of Chemistry, Department of Biochemistry and Molecular Biology, and Institute for Biophysical Dynamics, The University of Chicago, 929 East 57th Street, Chicago, IL 60637, USA; Howard Hughes Medical Institute, The University of Chicago, 929 East 57th Street, Chicago, IL 60637, USA
| | - Wolf Reik
- Epigenetics Programme, Babraham Institute, Cambridge CB22 3AT, UK; Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.
| | - Hendrik G Stunnenberg
- Department of Molecular Biology, Faculty of Science, Radboud University, 6525GA Nijmegen, the Netherlands.
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17
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Jalili M, Salehzadeh-Yazdi A, Asgari Y, Arab SS, Yaghmaie M, Ghavamzadeh A, Alimoghaddam K. CentiServer: A Comprehensive Resource, Web-Based Application and R Package for Centrality Analysis. PLoS One 2015; 10:e0143111. [PMID: 26571275 PMCID: PMC4646361 DOI: 10.1371/journal.pone.0143111] [Citation(s) in RCA: 66] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2015] [Accepted: 11/01/2015] [Indexed: 12/15/2022] Open
Abstract
Various disciplines are trying to solve one of the most noteworthy queries and broadly used concepts in biology, essentiality. Centrality is a primary index and a promising method for identifying essential nodes, particularly in biological networks. The newly created CentiServer is a comprehensive online resource that provides over 110 definitions of different centrality indices, their computational methods, and algorithms in the form of an encyclopedia. In addition, CentiServer allows users to calculate 55 centralities with the help of an interactive web-based application tool and provides a numerical result as a comma separated value (csv) file format or a mapped graphical format as a graph modeling language (GML) file. The standalone version of this application has been developed in the form of an R package. The web-based application (CentiServer) and R package (centiserve) are freely available at http://www.centiserver.org/
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Affiliation(s)
- Mahdi Jalili
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Salehzadeh-Yazdi
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Yazdan Asgari
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Seyed Shahriar Arab
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Marjan Yaghmaie
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Ardeshir Ghavamzadeh
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Kamran Alimoghaddam
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, Iran
- * E-mail:
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18
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Asgari Y, Zabihinpour Z, Salehzadeh-Yazdi A, Schreiber F, Masoudi-Nejad A. Alterations in cancer cell metabolism: the Warburg effect and metabolic adaptation. Genomics 2015; 105:275-81. [PMID: 25773945 DOI: 10.1016/j.ygeno.2015.03.001] [Citation(s) in RCA: 73] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2014] [Revised: 02/09/2015] [Accepted: 03/04/2015] [Indexed: 12/13/2022]
Abstract
The Warburg effect means higher glucose uptake of cancer cells compared to normal tissues, whereas a smaller fraction of this glucose is employed for oxidative phosphorylation. With the advent of high throughput technologies and computational systems biology, cancer cell metabolism has been reinvestigated over the last decades toward identifying various events underlying "how" and "why" a cancer cell employs aerobic glycolysis. Significant progress has been shaped to revise the Warburg effect. In this study, we have integrated the gene expression of 13 different cancer cells with the genome-scale metabolic network of human (Recon1) based on the E-Flux method, and analyzed them based on constraint-based modeling. Results show that regardless of significant up- and down-regulated metabolic genes, the distribution of metabolic changes is similar in different cancer types. These findings support the theory that the Warburg effect is a consequence of metabolic adaptation in cancer cells.
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Affiliation(s)
- Yazdan Asgari
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Zahra Zabihinpour
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ali Salehzadeh-Yazdi
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Falk Schreiber
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany; Clayton School of Information Technology, Monash University, Clayton, VIC, Australia.
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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Salehzadeh-Yazdi A, Asgari Y, Saboury AA, Masoudi-Nejad A. Computational analysis of reciprocal association of metabolism and epigenetics in the budding yeast: a genome-scale metabolic model (GSMM) approach. PLoS One 2014; 9:e111686. [PMID: 25365344 PMCID: PMC4218804 DOI: 10.1371/journal.pone.0111686] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2014] [Accepted: 10/07/2014] [Indexed: 12/13/2022] Open
Abstract
Metaboloepigenetics is a newly coined term in biological sciences that investigates the crosstalk between epigenetic modifications and metabolism. The reciprocal relation between biochemical transformations and gene expression regulation has been experimentally demonstrated in cancers and metabolic syndromes. In this study, we explored the metabolism-histone modifications crosstalk by topological analysis and constraint-based modeling approaches in the budding yeast. We constructed nine models through the integration of gene expression data of four mutated histone tails into a genome-scale metabolic model of yeast. Accordingly, we defined the centrality indices of the lowly expressed enzymes in the undirected enzyme-centric network of yeast by CytoHubba plug-in in Cytoscape. To determine the global effects of histone modifications on the yeast metabolism, the growth rate and the range of possible flux values of reactions, we used constraint-based modeling approach. Centrality analysis shows that the lowly expressed enzymes could affect and control the yeast metabolic network. Besides, constraint-based modeling results are in a good agreement with the experimental findings, confirming that the mutations in histone tails lead to non-lethal alterations in the yeast, but have diverse effects on the growth rate and reveal the functional redundancy.
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Affiliation(s)
- Ali Salehzadeh-Yazdi
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Yazdan Asgari
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ali Akbar Saboury
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
- * E-mail:
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Ahmadi H, Ahmadi A, Azimzadeh-Jamalkandi S, Shoorehdeli MA, Salehzadeh-Yazdi A, Bidkhori G, Masoudi-Nejad A. HomoTarget: a new algorithm for prediction of microRNA targets in Homo sapiens. Genomics 2012; 101:94-100. [PMID: 23174671 DOI: 10.1016/j.ygeno.2012.11.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2012] [Revised: 09/25/2012] [Accepted: 11/09/2012] [Indexed: 12/19/2022]
Abstract
MiRNAs play an essential role in the networks of gene regulation by inhibiting the translation of target mRNAs. Several computational approaches have been proposed for the prediction of miRNA target-genes. Reports reveal a large fraction of under-predicted or falsely predicted target genes. Thus, there is an imperative need to develop a computational method by which the target mRNAs of existing miRNAs can be correctly identified. In this study, combined pattern recognition neural network (PRNN) and principle component analysis (PCA) architecture has been proposed in order to model the complicated relationship between miRNAs and their target mRNAs in humans. The results of several types of intelligent classifiers and our proposed model were compared, showing that our algorithm outperformed them with higher sensitivity and specificity. Using the recent release of the mirBase database to find potential targets of miRNAs, this model incorporated twelve structural, thermodynamic and positional features of miRNA:mRNA binding sites to select target candidates.
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Affiliation(s)
- Hamed Ahmadi
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ali Ahmadi
- Department of Electrical and Computer Engineering, Khajeh-Nasir Toosi University, Tehran, Iran
| | - Sadegh Azimzadeh-Jamalkandi
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | | | - Ali Salehzadeh-Yazdi
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Gholamreza Bidkhori
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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