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Thalén NB, Barzadd MM, Lundqvist M, Rodhe J, Andersson M, Bidkhori G, Possner D, Su C, Nilsson J, Eisenhut P, Malm M, Karlsson A, Vestin J, Forsberg J, Nordling E, Mardinoglu A, Volk AL, Sandegren A, Rockberg J. Tuning of CHO secretional machinery improve activity of secreted therapeutic sulfatase 150-fold. Metab Eng 2024; 81:157-166. [PMID: 38081506 DOI: 10.1016/j.ymben.2023.12.003] [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: 12/11/2022] [Revised: 10/12/2023] [Accepted: 12/01/2023] [Indexed: 12/18/2023]
Abstract
Rare diseases are, despite their name, collectively common and millions of people are affected daily of conditions where treatment often is unavailable. Sulfatases are a large family of activating enzymes related to several of these diseases. Heritable genetic variations in sulfatases may lead to impaired activity and a reduced macromolecular breakdown within the lysosome, with several severe and lethal conditions as a consequence. While therapeutic options are scarce, treatment for some sulfatase deficiencies by recombinant enzyme replacement are available. The recombinant production of such sulfatases suffers greatly from both low product activity and yield, further limiting accessibility for patient groups. To mitigate the low product activity, we have investigated cellular properties through computational evaluation of cultures with varying media conditions and comparison of two CHO clones with different levels of one active sulfatase variant. Transcriptome analysis identified 18 genes in secretory pathways correlating with increased sulfatase production. Experimental validation by upregulation of a set of three key genes improved the specific enzymatic activity at varying degree up to 150-fold in another sulfatase variant, broadcasting general production benefits. We also identified a correlation between product mRNA levels and sulfatase activity that generated an increase in sulfatase activity when expressed with a weaker promoter. Furthermore, we suggest that our proposed workflow for resolving bottlenecks in cellular machineries, to be useful for improvements of cell factories for other biologics as well.
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Affiliation(s)
- Niklas Berndt Thalén
- Dept. of Protein science, KTH - Royal Institute of Technology, Stockholm, SE-106 91, Sweden
| | - Mona Moradi Barzadd
- Dept. of Protein science, KTH - Royal Institute of Technology, Stockholm, SE-106 91, Sweden
| | - Magnus Lundqvist
- Dept. of Protein science, KTH - Royal Institute of Technology, Stockholm, SE-106 91, Sweden
| | | | | | - Gholamreza Bidkhori
- Science for Life Laboratory, KTH - Royal Institute of Technology, Solna, 171 65, Sweden; AIVIVO Ltd. Unit 25, Bio-innovation centre, Cambridge Science park, Cambridge, UK
| | | | - Chao Su
- SOBI AB, Tomtebodavägen 23A, Stockholm, Sweden
| | | | - Peter Eisenhut
- ACIB - Austrian Centre of Industrial Biotechnology, Krenngasse 37, 8010 Graz, Austria; BOKU - University of Natural Resources and Life Sciences, Department of Biotechnology, Vienna, 1190, Austria
| | - Magdalena Malm
- Dept. of Protein science, KTH - Royal Institute of Technology, Stockholm, SE-106 91, Sweden
| | - Alice Karlsson
- Dept. of Protein science, KTH - Royal Institute of Technology, Stockholm, SE-106 91, Sweden
| | | | | | | | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Solna, 171 65, Sweden
| | - Anna-Luisa Volk
- Dept. of Protein science, KTH - Royal Institute of Technology, Stockholm, SE-106 91, Sweden
| | | | - Johan Rockberg
- Dept. of Protein science, KTH - Royal Institute of Technology, Stockholm, SE-106 91, Sweden.
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2
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Clasen F, Nunes PM, Bidkhori G, Bah N, Boeing S, Shoaie S, Anastasiou D. Systematic diet composition swap in a mouse genome-scale metabolic model reveals determinants of obesogenic diet metabolism in liver cancer. iScience 2023; 26:106040. [PMID: 36844450 PMCID: PMC9947310 DOI: 10.1016/j.isci.2023.106040] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 09/08/2022] [Accepted: 01/20/2023] [Indexed: 01/26/2023] Open
Abstract
Dietary nutrient availability and gene expression, together, influence tissue metabolic activity. Here, we explore whether altering dietary nutrient composition in the context of mouse liver cancer suffices to overcome chronic gene expression changes that arise from tumorigenesis and western-style diet (WD). We construct a mouse genome-scale metabolic model and estimate metabolic fluxes in liver tumors and non-tumoral tissue after computationally varying the composition of input diet. This approach, called Systematic Diet Composition Swap (SyDiCoS), revealed that, compared to a control diet, WD increases production of glycerol and succinate irrespective of specific tissue gene expression patterns. Conversely, differences in fatty acid utilization pathways between tumor and non-tumor liver are amplified with WD by both dietary carbohydrates and lipids together. Our data suggest that combined dietary component modifications may be required to normalize the distinctive metabolic patterns that underlie selective targeting of tumor metabolism.
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Affiliation(s)
- Frederick Clasen
- Cancer Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London SE1 9RT, UK
| | - Patrícia M. Nunes
- Cancer Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Gholamreza Bidkhori
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London SE1 9RT, UK
| | - Nourdine Bah
- Bioinformatics and Biostatistics Science Technology Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Stefan Boeing
- Bioinformatics and Biostatistics Science Technology Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London SE1 9RT, UK
- Science for Life Laboratory (SciLifeLab), KTH - Royal Institute of Technology, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden
| | - Dimitrios Anastasiou
- Cancer Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
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3
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Ezzamouri B, Rosario D, Bidkhori G, Lee S, Uhlen M, Shoaie S. Metabolic modelling of the human gut microbiome in type 2 diabetes patients in response to metformin treatment. NPJ Syst Biol Appl 2023; 9:2. [PMID: 36681701 PMCID: PMC9867701 DOI: 10.1038/s41540-022-00261-6] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 11/08/2022] [Indexed: 01/22/2023] Open
Abstract
The human gut microbiome has been associated with several metabolic disorders including type 2 diabetes mellitus. Understanding metabolic changes in the gut microbiome is important to elucidate the role of gut bacteria in regulating host metabolism. Here, we used available metagenomics data from a metformin study, together with genome-scale metabolic modelling of the key bacteria in individual and community-level to investigate the mechanistic role of the gut microbiome in response to metformin. Individual modelling predicted that species that are increased after metformin treatment have higher growth rates in comparison to species that are decreased after metformin treatment. Gut microbial enrichment analysis showed prior to metformin treatment pathways related to the hypoglycemic effect were enriched. Our observations highlight how the key bacterial species after metformin treatment have commensal and competing behavior, and how their cellular metabolism changes due to different nutritional environment. Integrating different diets showed there were specific microbial alterations between different diets. These results show the importance of the nutritional environment and how dietary guidelines may improve drug efficiency through the gut microbiota.
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Affiliation(s)
- Bouchra Ezzamouri
- grid.13097.3c0000 0001 2322 6764Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, SE1 9RT London, UK ,grid.420545.20000 0004 0489 3985Unit for Population-Based Dermatology, St John’s Institute of Dermatology, King’s College London and Guy’s and St Thomas’ NHS Foundation Trust, London, UK
| | - Dorines Rosario
- grid.13097.3c0000 0001 2322 6764Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, SE1 9RT London, UK
| | - Gholamreza Bidkhori
- grid.13097.3c0000 0001 2322 6764Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, SE1 9RT London, UK ,Present Address: AIVIVO Ltd. Unit 25, Bio-innovation centre, Cambridge Science Park, Cambridge, UK
| | - Sunjae Lee
- grid.13097.3c0000 0001 2322 6764Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, SE1 9RT London, UK
| | - Mathias Uhlen
- grid.5037.10000000121581746Science for Life Laboratory, KTH - Royal Institute of Technology, 171 21 Stockholm, Sweden
| | - Saeed Shoaie
- grid.13097.3c0000 0001 2322 6764Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, SE1 9RT London, UK ,grid.5037.10000000121581746Science for Life Laboratory, KTH - Royal Institute of Technology, 171 21 Stockholm, Sweden
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4
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Proffitt C, Bidkhori G, Lee S, Tebani A, Mardinoglu A, Uhlen M, Moyes DL, Shoaie S. Genome-scale metabolic modelling of the human gut microbiome reveals changes of the glyoxylate and dicarboxylate metabolism in metabolic disorders. iScience 2022; 25:104513. [PMID: 35754734 PMCID: PMC9213702 DOI: 10.1016/j.isci.2022.104513] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [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: 10/04/2021] [Revised: 03/14/2022] [Accepted: 05/27/2022] [Indexed: 11/20/2022] Open
Abstract
The human gut microbiome has been associated with metabolic disorders including obesity, type 2 diabetes, and atherosclerosis. Understanding the contribution of microbiome metabolic changes is important for elucidating the role of gut bacteria in regulating metabolism. We used available metagenomics data from these metabolic disorders, together with genome-scale metabolic modeling of key bacteria in the individual and community-level to investigate the mechanistic role of the gut microbiome in metabolic diseases. Modeling predicted increased levels of glutamate consumption along with the production of ammonia, arginine, and proline in gut bacteria common across the disorders. Abundance profiles and network-dependent analysis identified the enrichment of tartrate dehydrogenase in the disorders. Moreover, independent plasma metabolite levels showed associations between metabolites including proline and tyrosine and an increased tartrate metabolism in healthy obese individuals. We, therefore, propose that an increased tartrate metabolism could be a significant mediator of the microbiome metabolic changes in metabolic disorders. Metagenomic analysis highlights key common bacterial species across metabolic diseases Metabolic models showed higher levels of acetate produced by disease enriched bacteria Reaction analysis revealed increases in the glyoxylate and dicarboxylate pathway Metabolomics and modeling analysis showed the potential role of tartrate metabolism
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Affiliation(s)
- Ceri Proffitt
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
| | - Gholamreza Bidkhori
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
| | - Sunjae Lee
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
| | - Abdellah Tebani
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Adil Mardinoglu
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
| | - David L. Moyes
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
| | - Saeed Shoaie
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, SE1 9RT, UK
- Science for Life Laboratory, KTH–Royal Institute of Technology, Stockholm, Sweden
- Corresponding author
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5
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Rosario D, Bidkhori G, Lee S, Bedarf J, Hildebrand F, Le Chatelier E, Uhlen M, Ehrlich SD, Proctor G, Wüllner U, Mardinoglu A, Shoaie S. Systematic analysis of gut microbiome reveals the role of bacterial folate and homocysteine metabolism in Parkinson's disease. Cell Rep 2021; 34:108807. [PMID: 33657381 DOI: 10.1016/j.celrep.2021.108807] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [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: 03/31/2020] [Revised: 12/22/2020] [Accepted: 02/09/2021] [Indexed: 01/06/2023] Open
Abstract
Parkinson's disease (PD) is the most common progressive neurological disorder compromising motor functions. However, nonmotor symptoms, such as gastrointestinal (GI) dysfunction, precede those affecting movement. Evidence of an early involvement of the GI tract and enteric nervous system highlights the need for better understanding of the role of gut microbiota in GI complications in PD. Here, we investigate the gut microbiome of patients with PD using metagenomics and serum metabolomics. We integrate these data using metabolic modeling and construct an integrative correlation network giving insight into key microbial species linked with disease severity, GI dysfunction, and age of patients with PD. Functional analysis reveals an increased microbial capability to degrade mucin and host glycans in PD. Personalized community-level metabolic modeling reveals the microbial contribution to folate deficiency and hyperhomocysteinemia observed in patients with PD. The metabolic modeling approach could be applied to uncover gut microbial metabolic contributions to PD pathophysiology.
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Affiliation(s)
- Dorines Rosario
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT London, UK
| | - Gholamreza Bidkhori
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT London, UK
| | - Sunjae Lee
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT London, UK
| | - Janis Bedarf
- Department of Neurology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany; Gut Microbes & Health, Quadram Institute Bioscience, Norwich Research Park, Norwich, Norfolk NR4 7UA, UK
| | - Falk Hildebrand
- Gut Microbes & Health, Quadram Institute Bioscience, Norwich Research Park, Norwich, Norfolk NR4 7UA, UK; European Molecular Biology Laboratory, Structural and Computational Biology Unit, 69117 Heidelberg, Germany; Digital Biology, Earlham Institute, Norwich, Norwich Research Park, Norwich NR4 7UZ, Norfolk, UK
| | | | - Mathias Uhlen
- Science for Life Laboratory (SciLifeLab), KTH - Royal Institute of Technology, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden
| | | | - Gordon Proctor
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT London, UK
| | - Ullrich Wüllner
- Department of Neurology, University Hospital Bonn, Venusberg Campus 1, 53127 Bonn, Germany; German Centre for Neurodegenerative Disease Research (DZNE), 53127 Bonn, Germany
| | - Adil Mardinoglu
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT London, UK; Science for Life Laboratory (SciLifeLab), KTH - Royal Institute of Technology, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden.
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT London, UK; Science for Life Laboratory (SciLifeLab), KTH - Royal Institute of Technology, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden.
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6
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Harzandi A, Lee S, Bidkhori G, Saha S, Hendry BM, Mardinoglu A, Shoaie S, Sharpe CC. Acute kidney injury leading to CKD is associated with a persistence of metabolic dysfunction and hypertriglyceridemia. iScience 2021; 24:102046. [PMID: 33554059 PMCID: PMC7843454 DOI: 10.1016/j.isci.2021.102046] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [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: 10/01/2020] [Revised: 12/12/2020] [Accepted: 01/06/2021] [Indexed: 12/14/2022] Open
Abstract
Fibrosis is the pathophysiological hallmark of progressive chronic kidney disease (CKD). The kidney is a highly metabolically active organ, and it has been suggested that disruption in its metabolism leads to renal fibrosis. We developed a longitudinal mouse model of acute kidney injury leading to CKD and an in vitro model of epithelial to mesenchymal transition to study changes in metabolism, inflammation, and fibrosis. Using transcriptomics, metabolic modeling, and serum metabolomics, we observed sustained fatty acid metabolic dysfunction in the mouse model from early to late stages of CKD. Increased fatty acid biosynthesis and downregulation of catabolic pathways for triglycerides and diacylglycerides were associated with a marked increase in these lipids in the serum. We therefore suggest that the kidney may be the source of the abnormal lipid profile seen in patients with CKD, which may provide insights into the association between CKD and cardiovascular disease. Following AKI, markers of fibrosis and inflammation go up simultaneously AKI is associated with reduced fatty acid oxidation and oxidative phosphorylation Changes in metabolism persist as chronic kidney disease develops Changes in metabolism are associated with increased serum levels of triglycerides
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Affiliation(s)
- Azadeh Harzandi
- Renal Sciences, Department of Inflammation Biology, School of Immunology & Microbial Sciences, Faculty of Life Sciences and Medicine, King's College London, SE5 9NU London, UK
| | - Sunjae Lee
- School of Life Sciences, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea, 61005
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT London, UK
| | - Gholamreza Bidkhori
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT London, UK
| | - Sujit Saha
- Renal Sciences, Department of Inflammation Biology, School of Immunology & Microbial Sciences, Faculty of Life Sciences and Medicine, King's College London, SE5 9NU London, UK
| | - Bruce M. Hendry
- Renal Sciences, Department of Inflammation Biology, School of Immunology & Microbial Sciences, Faculty of Life Sciences and Medicine, King's College London, SE5 9NU London, UK
| | - Adil Mardinoglu
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT London, UK
- Science for Life Laboratory (SciLifeLab), KTH - Royal Institute of Technology, Tomtebodavägen 23, Solna, Stockholm 171 65, Sweden
- Corresponding author
| | - Saeed Shoaie
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, SE1 9RT London, UK
- Science for Life Laboratory (SciLifeLab), KTH - Royal Institute of Technology, Tomtebodavägen 23, Solna, Stockholm 171 65, Sweden
- Corresponding author
| | - Claire C. Sharpe
- Renal Sciences, Department of Inflammation Biology, School of Immunology & Microbial Sciences, Faculty of Life Sciences and Medicine, King's College London, SE5 9NU London, UK
- Corresponding author
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7
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Zhan C, Bidkhori G, Schwarz H, Malm M, Mebrahtu A, Field R, Sellick C, Hatton D, Varley P, Mardinoglu A, Rockberg J, Chotteau V. Low Shear Stress Increases Recombinant Protein Production and High Shear Stress Increases Apoptosis in Human Cells. iScience 2020; 23:101653. [PMID: 33145483 PMCID: PMC7593556 DOI: 10.1016/j.isci.2020.101653] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [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: 11/25/2019] [Revised: 08/07/2020] [Accepted: 10/05/2020] [Indexed: 11/30/2022] Open
Abstract
Human embryonic kidney cells HEK293 can be used for the production of therapeutic glycoproteins requiring human post-translational modifications. High cell density perfusion processes are advantageous for such production but are challenging due to the shear sensitivity of HEK293 cells. To understand the impact of hollow filter cell separation devices, cells were cultured in bioreactors operated with tangential flow filtration (TFF) or alternating tangential flow filtration (ATF) at various flow rates. The average theoretical velocity profile in these devices showed a lower shear stress for ATF by a factor 0.637 compared to TFF. This was experimentally validated and, furthermore, transcriptomic evaluation provided insights into the underlying cellular processes. High shear caused cellular stress leading to apoptosis by three pathways, i.e. endoplasmic reticulum stress, cytoskeleton reorganization, and extrinsic signaling pathways. Positive effects of mild shear stress were observed, with increased recombinant erythropoietin production and increased gene expression associated with transcription and protein phosphorylation.
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Affiliation(s)
- Caijuan Zhan
- KTH - Cell Technology Group (CETEG), Department of Industrial Biotechnology, 106 91, Stockholm, Sweden
- Wallenberg Centre for Protein Research (WCPR), 106 91 Stockholm, Sweden
- AdBIOPRO, Competence Centre for Advanced Bioproduction by Continuous Processing, Stockholm, Sweden
| | - Gholamreza Bidkhori
- Science for Life Laboratory, KTH - Royal Institute of Technology, 171 21, Stockholm, Sweden
| | - Hubert Schwarz
- KTH - Cell Technology Group (CETEG), Department of Industrial Biotechnology, 106 91, Stockholm, Sweden
- Wallenberg Centre for Protein Research (WCPR), 106 91 Stockholm, Sweden
- AdBIOPRO, Competence Centre for Advanced Bioproduction by Continuous Processing, Stockholm, Sweden
| | - Magdalena Malm
- KTH - Royal Institute of Technology, Department of Protein Science, 106 91 Stockholm, Sweden
- Wallenberg Centre for Protein Research (WCPR), 106 91 Stockholm, Sweden
| | - Aman Mebrahtu
- KTH - Royal Institute of Technology, Department of Protein Science, 106 91 Stockholm, Sweden
- Wallenberg Centre for Protein Research (WCPR), 106 91 Stockholm, Sweden
| | - Ray Field
- BioPharmaceutical Development, AstraZeneca, Cambridge, UK
| | | | - Diane Hatton
- BioPharmaceutical Development, AstraZeneca, Cambridge, UK
| | - Paul Varley
- BioPharmaceutical Development, AstraZeneca, Cambridge, UK
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, 171 21, Stockholm, Sweden
| | - Johan Rockberg
- KTH - Royal Institute of Technology, Department of Protein Science, 106 91 Stockholm, Sweden
- Wallenberg Centre for Protein Research (WCPR), 106 91 Stockholm, Sweden
- AdBIOPRO, Competence Centre for Advanced Bioproduction by Continuous Processing, Stockholm, Sweden
| | - Veronique Chotteau
- KTH - Cell Technology Group (CETEG), Department of Industrial Biotechnology, 106 91, Stockholm, Sweden
- Wallenberg Centre for Protein Research (WCPR), 106 91 Stockholm, Sweden
- AdBIOPRO, Competence Centre for Advanced Bioproduction by Continuous Processing, Stockholm, Sweden
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Vaga S, Lee S, Ji B, Andreasson A, Talley NJ, Agréus L, Bidkhori G, Kovatcheva-Datchary P, Park J, Lee D, Proctor G, Ehrlich SD, Nielsen J, Engstrand L, Shoaie S. Compositional and functional differences of the mucosal microbiota along the intestine of healthy individuals. Sci Rep 2020; 10:14977. [PMID: 32917913 PMCID: PMC7486370 DOI: 10.1038/s41598-020-71939-2] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 08/17/2020] [Indexed: 12/14/2022] Open
Abstract
Gut mucosal microbes evolved closest to the host, developing specialized local communities. There is, however, insufficient knowledge of these communities as most studies have employed sequencing technologies to investigate faecal microbiota only. This work used shotgun metagenomics of mucosal biopsies to explore the microbial communities' compositions of terminal ileum and large intestine in 5 healthy individuals. Functional annotations and genome-scale metabolic modelling of selected species were then employed to identify local functional enrichments. While faecal metagenomics provided a good approximation of the average gut mucosal microbiome composition, mucosal biopsies allowed detecting the subtle variations of local microbial communities. Given their significant enrichment in the mucosal microbiota, we highlight the roles of Bacteroides species and describe the antimicrobial resistance biogeography along the intestine. We also detail which species, at which locations, are involved with the tryptophan/indole pathway, whose malfunctioning has been linked to pathologies including inflammatory bowel disease. Our study thus provides invaluable resources for investigating mechanisms connecting gut microbiota and host pathophysiology.
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Affiliation(s)
- Stefania Vaga
- Centre for Host-Microbiome Interactions, Dental Institute, King's College London, London, UK
| | - Sunjae Lee
- Centre for Host-Microbiome Interactions, Dental Institute, King's College London, London, UK
| | - Boyang Ji
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Anna Andreasson
- Stress Research Institute, Stockholm University, Stockholm, Sweden
- Department of Psychology, Macquarie University, Macquarie Park, NSW, Australia
- Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | | | - Lars Agréus
- Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Gholamreza Bidkhori
- Centre for Host-Microbiome Interactions, Dental Institute, King's College London, London, UK
| | - Petia Kovatcheva-Datchary
- Wallenberg Laboratory, Department of Molecular and Clinical Medicine, University of Gothenburg, 41345, Gothenburg, Sweden
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Scientific Research Center for Translational Medicine, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China
| | - Junseok Park
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Doheon Lee
- Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea
| | - Gordon Proctor
- Centre for Host-Microbiome Interactions, Dental Institute, King's College London, London, UK
| | | | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
- BioInnovation Institute, Ole Maaløes Vej 3, 2200, Copenhagen N, Denmark.
| | - Lars Engstrand
- Centre for Translational Microbiome Research (CTMR), Department of Microbiology, Tumor and Cell Biology, & Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden.
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Dental Institute, King's College London, London, UK.
- Science for Life Laboratory, KTH-Royal Institute of Technology, Tomtebodavägen 23A, 17165, Solna, Sweden.
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Proffitt C, Bidkhori G, Moyes D, Shoaie S. Disease, Drugs and Dysbiosis: Understanding Microbial Signatures in Metabolic Disease and Medical Interventions. Microorganisms 2020; 8:microorganisms8091381. [PMID: 32916966 PMCID: PMC7565856 DOI: 10.3390/microorganisms8091381] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [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: 07/16/2020] [Revised: 09/03/2020] [Accepted: 09/08/2020] [Indexed: 02/06/2023] Open
Abstract
Since the discovery of the potential role for the gut microbiota in health and disease, many studies have gone on to report its impact in various pathologies. These studies have fuelled interest in the microbiome as a potential new target for treating disease Here, we reviewed the key metabolic diseases, obesity, type 2 diabetes and atherosclerosis and the role of the microbiome in their pathogenesis. In particular, we will discuss disease associated microbial dysbiosis; the shift in the microbiome caused by medical interventions and the altered metabolite levels between diseases and interventions. The microbial dysbiosis seen was compared between diseases including Crohn’s disease and ulcerative colitis, non-alcoholic fatty liver disease, liver cirrhosis and neurodegenerative diseases, Alzheimer’s and Parkinson’s. This review highlights the commonalities and differences in dysbiosis of the gut between diseases, along with metabolite levels in metabolic disease vs. the levels reported after an intervention. We identify the need for further analysis using systems biology approaches and discuss the potential need for treatments to consider their impact on the microbiome.
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Affiliation(s)
- Ceri Proffitt
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London SE1 9RT, UK; (G.B.); (D.M.)
- Correspondence: (C.P.); (S.S.)
| | - Gholamreza Bidkhori
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London SE1 9RT, UK; (G.B.); (D.M.)
| | - David Moyes
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London SE1 9RT, UK; (G.B.); (D.M.)
| | - Saeed Shoaie
- Centre for Host–Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London SE1 9RT, UK; (G.B.); (D.M.)
- Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, 114 17 Stockholm, Sweden
- Correspondence: (C.P.); (S.S.)
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10
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Zhang C, Bjornson E, Arif M, Tebani A, Lovric A, Benfeitas R, Ozcan M, Juszczak K, Kim W, Kim JT, Bidkhori G, Ståhlman M, Bergh P, Adiels M, Turkez H, Taskinen M, Bosley J, Marschall H, Nielsen J, Uhlén M, Borén J, Mardinoglu A. The acute effect of metabolic cofactor supplementation: a potential therapeutic strategy against non-alcoholic fatty liver disease. Mol Syst Biol 2020; 16:e9495. [PMID: 32337855 PMCID: PMC7184219 DOI: 10.15252/msb.209495] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [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] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 03/04/2020] [Accepted: 03/10/2020] [Indexed: 12/21/2022] Open
Abstract
The prevalence of non-alcoholic fatty liver disease (NAFLD) continues to increase dramatically, and there is no approved medication for its treatment. Recently, we predicted the underlying molecular mechanisms involved in the progression of NAFLD using network analysis and identified metabolic cofactors that might be beneficial as supplements to decrease human liver fat. Here, we first assessed the tolerability of the combined metabolic cofactors including l-serine, N-acetyl-l-cysteine (NAC), nicotinamide riboside (NR), and l-carnitine by performing a 7-day rat toxicology study. Second, we performed a human calibration study by supplementing combined metabolic cofactors and a control study to study the kinetics of these metabolites in the plasma of healthy subjects with and without supplementation. We measured clinical parameters and observed no immediate side effects. Next, we generated plasma metabolomics and inflammatory protein markers data to reveal the acute changes associated with the supplementation of the metabolic cofactors. We also integrated metabolomics data using personalized genome-scale metabolic modeling and observed that such supplementation significantly affects the global human lipid, amino acid, and antioxidant metabolism. Finally, we predicted blood concentrations of these compounds during daily long-term supplementation by generating an ordinary differential equation model and liver concentrations of serine by generating a pharmacokinetic model and finally adjusted the doses of individual metabolic cofactors for future human clinical trials.
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Affiliation(s)
- Cheng Zhang
- Science for Life LaboratoryKTH—Royal Institute of TechnologyStockholmSweden
- School of Pharmaceutical SciencesZhengzhou UniversityZhengzhouChina
| | - Elias Bjornson
- Department of Molecular and Clinical MedicineUniversity of Gothenburg and Sahlgrenska University Hospital GothenburgGothenburgSweden
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Muhammad Arif
- Science for Life LaboratoryKTH—Royal Institute of TechnologyStockholmSweden
| | - Abdellah Tebani
- Science for Life LaboratoryKTH—Royal Institute of TechnologyStockholmSweden
| | - Alen Lovric
- Science for Life LaboratoryKTH—Royal Institute of TechnologyStockholmSweden
- Present address:
Division of Clinical PhysiologyDepartment of Laboratory MedicineKarolinska InstitutetKarolinska University HospitalStockholmSweden
- Present address:
Unit of Clinical PhysiologyKarolinska University HospitalStockholmSweden
| | - Rui Benfeitas
- Science for Life LaboratoryKTH—Royal Institute of TechnologyStockholmSweden
- Present address:
Science for Life LaboratoryDepartment of Biochemistry and BiophysicsNational Bioinformatics Infrastructure Sweden (NBIS)Stockholm UniversityStockholmSweden
| | - Mehmet Ozcan
- Science for Life LaboratoryKTH—Royal Institute of TechnologyStockholmSweden
| | - Kajetan Juszczak
- Science for Life LaboratoryKTH—Royal Institute of TechnologyStockholmSweden
| | - Woonghee Kim
- Science for Life LaboratoryKTH—Royal Institute of TechnologyStockholmSweden
| | - Jung Tae Kim
- Science for Life LaboratoryKTH—Royal Institute of TechnologyStockholmSweden
| | - Gholamreza Bidkhori
- Centre for Host‐Microbiome InteractionsFaculty of Dentistry, Oral & Craniofacial SciencesKing's College LondonLondonUK
| | - Marcus Ståhlman
- Department of Molecular and Clinical MedicineUniversity of Gothenburg and Sahlgrenska University Hospital GothenburgGothenburgSweden
| | - Per‐Olof Bergh
- Department of Molecular and Clinical MedicineUniversity of Gothenburg and Sahlgrenska University Hospital GothenburgGothenburgSweden
| | - Martin Adiels
- Department of Molecular and Clinical MedicineUniversity of Gothenburg and Sahlgrenska University Hospital GothenburgGothenburgSweden
| | - Hasan Turkez
- Department of Medical BiologyFaculty of MedicineAtatürk UniversityErzurumTurkey
| | - Marja‐Riitta Taskinen
- Research Programs Unit, Diabetes and ObesityDepartment of Internal MedicineHelsinki University HospitalUniversity of HelsinkiHelsinkiFinland
| | | | - Hanns‐Ulrich Marschall
- Department of Molecular and Clinical MedicineUniversity of Gothenburg and Sahlgrenska University Hospital GothenburgGothenburgSweden
| | - Jens Nielsen
- Department of Biology and Biological EngineeringChalmers University of TechnologyGothenburgSweden
| | - Mathias Uhlén
- Science for Life LaboratoryKTH—Royal Institute of TechnologyStockholmSweden
| | - Jan Borén
- Department of Molecular and Clinical MedicineUniversity of Gothenburg and Sahlgrenska University Hospital GothenburgGothenburgSweden
| | - Adil Mardinoglu
- Science for Life LaboratoryKTH—Royal Institute of TechnologyStockholmSweden
- Centre for Host‐Microbiome InteractionsFaculty of Dentistry, Oral & Craniofacial SciencesKing's College LondonLondonUK
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11
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Rajkumar AP, Bidkhori G, Shoaie S, Clarke E, Morrin H, Hye A, Williams G, Ballard C, Francis P, Aarsland D. Postmortem Cortical Transcriptomics of Lewy Body Dementia Reveal Mitochondrial Dysfunction and Lack of Neuroinflammation. Am J Geriatr Psychiatry 2020; 28:75-86. [PMID: 31327631 DOI: 10.1016/j.jagp.2019.06.007] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 06/11/2019] [Accepted: 06/20/2019] [Indexed: 01/13/2023]
Abstract
OBJECTIVE Prevalence of Lewy body dementias (LBD) is second only to Alzheimer's disease (AD) among people with neurodegenerative dementia. LBD cause earlier mortality, more intense neuropsychiatric symptoms, more caregivers' burden, and higher costs than AD. The molecular mechanisms underlying LBD are largely unknown. As advancing molecular level mechanistic understanding is essential for identifying reliable peripheral biomarkers and novel therapeutic targets for LBD, the authors aimed to identify differentially expressed genes (DEG), and dysfunctional molecular networks in postmortem LBD brains. METHODS The authors investigated the transcriptomics of postmortem anterior cingulate and dorsolateral prefrontal cortices of people with pathology-verified LBD using next-generation RNA-sequencing. The authors verified the identified DEG using high-throughput quantitative polymerase chain reactions. Functional implications of identified DEG and the consequent metabolic reprogramming were evaluated by Ingenuity pathway analyses, genome-scale metabolic modeling, reporter metabolite analyses, and in silico gene silencing. RESULTS The authors identified and verified 12 novel DEGs (MPO, SELE, CTSG, ALPI, ABCA13, GALNT6, SST, RBM3, CSF3, SLC4A1, OXTR, and RAB44) in LBD brains with genome-wide statistical significance. The authors documented statistically significant down-regulation of several cytokine genes. Identified dysfunctional molecular networks highlighted the contributions of mitochondrial dysfunction, oxidative stress, and immunosenescence toward neurodegeneration in LBD. CONCLUSION Our findings support that chronic microglial activation and neuroinflammation, well-documented in AD, are notably absent in LBD. The lack of neuroinflammation in LBD brains was corroborated by statistically significant down-regulation of several inflammatory markers. Identified DEGs, especially down-regulated inflammatory markers, may aid distinguishing LBD from AD, and their biomarker potential warrant further investigation.
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Affiliation(s)
- Anto P Rajkumar
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK; Mental Health of Older Adults and Dementia Clinical Academic Group, South London and Maudsley NHS foundation Trust, London, UK.
| | - Gholamreza Bidkhori
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK
| | - Emily Clarke
- Wolfson Centre for Age-Related Diseases, King's College London, London, UK
| | | | - Abdul Hye
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK; NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS foundation trust, London, UK
| | - Gareth Williams
- Wolfson Centre for Age-Related Diseases, King's College London, London, UK
| | - Clive Ballard
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK; The Medical School, Exeter University, Exeter, UK
| | - Paul Francis
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK; Wolfson Centre for Age-Related Diseases, King's College London, London, UK
| | - Dag Aarsland
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK; Mental Health of Older Adults and Dementia Clinical Academic Group, South London and Maudsley NHS foundation Trust, London, UK
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12
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Lee S, Zhang C, Arif M, Liu Z, Benfeitas R, Bidkhori G, Deshmukh S, Al Shobky M, Lovric A, Boren J, Nielsen J, Uhlen M, Mardinoglu A. TCSBN: a database of tissue and cancer specific biological networks. Nucleic Acids Res 2019; 46:D595-D600. [PMID: 29069445 PMCID: PMC5753183 DOI: 10.1093/nar/gkx994] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [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: 08/15/2017] [Accepted: 10/12/2017] [Indexed: 12/15/2022] Open
Abstract
Biological networks provide new opportunities for understanding the cellular biology in both health and disease states. We generated tissue specific integrated networks (INs) for liver, muscle and adipose tissues by integrating metabolic, regulatory and protein-protein interaction networks. We also generated human co-expression networks (CNs) for 46 normal tissues and 17 cancers to explore the functional relationships between genes as well as their relationships with biological functions, and investigate the overlap between functional and physical interactions provided by CNs and INs, respectively. These networks can be employed in the analysis of omics data, provide detailed insight into disease mechanisms by identifying the key biological components and eventually can be used in the development of efficient treatment strategies. Moreover, comparative analysis of the networks may allow for the identification of tissue-specific targets that can be used in the development of drugs with the minimum toxic effect to other human tissues. These context-specific INs and CNs are presented in an interactive website http://inetmodels.com without any limitation.
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Affiliation(s)
- Sunjae Lee
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Muhammad Arif
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Zhengtao Liu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Rui Benfeitas
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Gholamreza Bidkhori
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Sumit Deshmukh
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Mohamed Al Shobky
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Alen Lovric
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, SE-413 45, Sweden
| | - Jens Nielsen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden.,Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE-412 96, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden.,Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE-412 96, Sweden
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13
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Turanli B, Karagoz K, Bidkhori G, Sinha R, Gatza ML, Uhlen M, Mardinoglu A, Arga KY. Multi-Omic Data Interpretation to Repurpose Subtype Specific Drug Candidates for Breast Cancer. Front Genet 2019; 10:420. [PMID: 31134131 PMCID: PMC6514249 DOI: 10.3389/fgene.2019.00420] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [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: 11/23/2018] [Accepted: 04/17/2019] [Indexed: 12/20/2022] Open
Abstract
Triple-negative breast cancer (TNBC), which is largely synonymous with the basal-like molecular subtype, is the 5th leading cause of cancer deaths for women in the United States. The overall prognosis for TNBC patients remains poor given that few treatment options exist; including targeted therapies (not FDA approved), and multi-agent chemotherapy as standard-of-care treatment. TNBC like other complex diseases is governed by the perturbations of the complex interaction networks thereby elucidating the underlying molecular mechanisms of this disease in the context of network principles, which have the potential to identify targets for drug development. Here, we present an integrated "omics" approach based on the use of transcriptome and interactome data to identify dynamic/active protein-protein interaction networks (PPINs) in TNBC patients. We have identified three highly connected modules, EED, DHX9, and AURKA, which are extremely activated in TNBC tumors compared to both normal tissues and other breast cancer subtypes. Based on the functional analyses, we propose that these modules are potential drivers of proliferation and, as such, should be considered candidate molecular targets for drug development or drug repositioning in TNBC. Consistent with this argument, we repurposed steroids, anti-inflammatory agents, anti-infective agents, cardiovascular agents for patients with basal-like breast cancer. Finally, we have performed essential metabolite analysis on personalized genome-scale metabolic models and found that metabolites such as sphingosine-1-phosphate and cholesterol-sulfate have utmost importance in TNBC tumor growth.
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Affiliation(s)
- Beste Turanli
- Department of Bioengineering, Marmara University, Istanbul, Turkey.,Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.,Department of Bioengineering, Istanbul Medeniyet University, Istanbul, Turkey
| | - Kubra Karagoz
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States
| | - Gholamreza Bidkhori
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Raghu Sinha
- Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, PA, United States
| | - Michael L Gatza
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.,Faculty of Dentistry, Oral and Craniofacial Sciences, Centre for Host-Microbiome Interactions, King's College London, London, United Kingdom.,Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
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14
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Benfeitas R, Bidkhori G, Mukhopadhyay B, Klevstig M, Arif M, Zhang C, Lee S, Cinar R, Nielsen J, Uhlen M, Boren J, Kunos G, Mardinoglu A. Characterization of heterogeneous redox responses in hepatocellular carcinoma patients using network analysis. EBioMedicine 2018; 40:471-487. [PMID: 30606699 PMCID: PMC6412169 DOI: 10.1016/j.ebiom.2018.12.057] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2018] [Revised: 12/20/2018] [Accepted: 12/26/2018] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Redox metabolism is often considered a potential target for cancer treatment, but a systematic examination of redox responses in hepatocellular carcinoma (HCC) is missing. METHODS Here, we employed systems biology and biological network analyses to reveal key roles of genes associated with redox metabolism in HCC by integrating multi-omics data. FINDINGS We found that several redox genes, including 25 novel potential prognostic genes, are significantly co-expressed with liver-specific genes and genes associated with immunity and inflammation. Based on an integrative analysis, we found that HCC tumors display antagonistic behaviors in redox responses. The two HCC groups are associated with altered fatty acid, amino acid, drug and hormone metabolism, differentiation, proliferation, and NADPH-independent vs -dependent antioxidant defenses. Redox behavior varies with known tumor subtypes and progression, affecting patient survival. These antagonistic responses are also displayed at the protein and metabolite level and were validated in several independent cohorts. We finally showed the differential redox behavior using mice transcriptomics in HCC and noncancerous tissues and associated with hypoxic features of the two redox gene groups. INTERPRETATION Our integrative approaches highlighted mechanistic differences among tumors and allowed the identification of a survival signature and several potential therapeutic targets for the treatment of HCC.
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Affiliation(s)
- Rui Benfeitas
- Science for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
| | - Gholamreza Bidkhori
- Science for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
| | - Bani Mukhopadhyay
- Laboratory of Physiologic Studies, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA.
| | - Martina Klevstig
- Department of Molecular and Clinical Medicine, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden.
| | - Muhammad Arif
- Science for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
| | - Cheng Zhang
- Science for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
| | - Sunjae Lee
- Science for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
| | - Resat Cinar
- Laboratory of Physiologic Studies, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA.
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21 Stockholm, Sweden.
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden.
| | - George Kunos
- Laboratory of Physiologic Studies, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA.
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21 Stockholm, Sweden; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden; Centre for Host-Microbiome Interactions, Dental Institute, King's College London, London, UK.
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15
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Zhang C, Bidkhori G, Benfeitas R, Lee S, Arif M, Uhlén M, Mardinoglu A. ESS: A Tool for Genome-Scale Quantification of Essentiality Score for Reaction/Genes in Constraint-Based Modeling. Front Physiol 2018; 9:1355. [PMID: 30323767 PMCID: PMC6173058 DOI: 10.3389/fphys.2018.01355] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [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: 06/08/2018] [Accepted: 09/07/2018] [Indexed: 11/17/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are comprehensive descriptions of cell metabolism and have been extensively used to understand biological responses in health and disease. One such application is in determining metabolic adaptation to the absence of a gene or reaction, i.e., essentiality analysis. However, current methods do not permit efficiently and accurately quantifying reaction/gene essentiality. Here, we present Essentiality Score Simulator (ESS), a tool for quantification of gene/reaction essentialities in GEMs. ESS quantifies and scores essentiality of each reaction/gene and their combinations based on the stoichiometric balance using synthetic lethal analysis. This method provides an option to weight metabolic models which currently rely mostly on topologic parameters, and is potentially useful to investigate the metabolic pathway differences between different organisms, cells, tissues, and/or diseases. We benchmarked the proposed method against multiple network topology parameters, and observed that our method displayed higher accuracy based on experimental evidence. In addition, we demonstrated its application in the wild-type and ldh knock-out E. coli core model, as well as two human cell lines, and revealed the changes of essentiality in metabolic pathways based on the reactions essentiality score. ESS is available without any limitation at https://sourceforge.net/projects/essentiality-score-simulator.
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Affiliation(s)
- Cheng Zhang
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Gholamreza Bidkhori
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Rui Benfeitas
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Sunjae Lee
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Muhammad Arif
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Mathias Uhlén
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.,Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.,Centre for Host-Microbiome Interactions, Dental Institute, King's College London, London, United Kingdom
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16
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Bidkhori G, Benfeitas R, Elmas E, Kararoudi MN, Arif M, Uhlen M, Nielsen J, Mardinoglu A. Metabolic Network-Based Identification and Prioritization of Anticancer Targets Based on Expression Data in Hepatocellular Carcinoma. Front Physiol 2018; 9:916. [PMID: 30065658 PMCID: PMC6056771 DOI: 10.3389/fphys.2018.00916] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [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: 05/03/2018] [Accepted: 06/22/2018] [Indexed: 12/23/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is a deadly form of liver cancer with high mortality worldwide. Unfortunately, the large heterogeneity of this disease makes it difficult to develop effective treatment strategies. Cellular network analyses have been employed to study heterogeneity in cancer, and to identify potential therapeutic targets. However, the existing approaches do not consider metabolic growth requirements, i.e., biological network functionality, to rank candidate targets while preventing toxicity to non-cancerous tissues. Here, we developed an algorithm to overcome these issues based on integration of gene expression data, genome-scale metabolic models, network controllability, and dispensability, as well as toxicity analysis. This method thus predicts and ranks potential anticancer non-toxic controlling metabolite and gene targets. Our algorithm encompasses both objective-driven and-independent tasks, and uses network topology to finally rank the predicted therapeutic targets. We employed this algorithm to the analysis of transcriptomic data for 50 HCC patients with both cancerous and non-cancerous samples. We identified several potential targets that would prevent cell growth, including 74 anticancer metabolites, and 3 gene targets (PRKACA, PGS1, and CRLS1). The predicted anticancer metabolites showed good agreement with existing FDA-approved cancer drugs, and the 3 genes were experimentally validated by performing experiments in HepG2 and Hep3B liver cancer cell lines. Our observations indicate that our novel approach successfully identifies therapeutic targets for effective treatment of cancer. This approach may also be applied to any cancer type that has tumor and non-tumor gene or protein expression data.
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Affiliation(s)
- Gholamreza Bidkhori
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Rui Benfeitas
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Ezgi Elmas
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | | | - Muhammad Arif
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden.,Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
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17
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Zamani L, Lundqvist M, Zhang Y, Aberg M, Edfors F, Bidkhori G, Lindahl A, Mie A, Mardinoglu A, Field R, Turner R, Rockberg J, Chotteau V. High Cell Density Perfusion Culture has a Maintained Exoproteome and Metabolome. Biotechnol J 2018; 13:e1800036. [DOI: 10.1002/biot.201800036] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 06/03/2018] [Indexed: 01/10/2023]
Affiliation(s)
- Leila Zamani
- Department Industrial Biotechnology; School of Engineering Sciences in Chemistry, Biotechnology, and Health; KTH-Royal Institute of Technology; 106 91 Stockholm Sweden
| | - Magnus Lundqvist
- School of Engineering Sciences in Chemistry, Biotechnology, and Health; Wallenberg Centre for Protein Research; KTH-Royal Institute of Technology; 106 91 Stockholm Sweden
- School of Engineering Sciences in Chemistry, Biotechnology, and Health; AdBIOPRO, Centre for Advanced Bioproduction by Continuous Processing; KTH-Royal Institute of Technology; 106 91 Stockholm Sweden
| | - Ye Zhang
- Department Industrial Biotechnology; School of Engineering Sciences in Chemistry, Biotechnology, and Health; KTH-Royal Institute of Technology; 106 91 Stockholm Sweden
- School of Engineering Sciences in Chemistry, Biotechnology, and Health; Wallenberg Centre for Protein Research; KTH-Royal Institute of Technology; 106 91 Stockholm Sweden
| | - Magnus Aberg
- Department of Analytical Chemistry; Stockholm University; 106 91 Stockholm Sweden
| | - Fredrik Edfors
- School of Engineering Sciences in Chemistry, Biotechnology, and Health; Science for Life Laboratory; KTH-Royal Institute of Technology; 171 65 Stockholm Sweden
| | - Gholamreza Bidkhori
- School of Engineering Sciences in Chemistry, Biotechnology, and Health; Science for Life Laboratory; KTH-Royal Institute of Technology; 171 65 Stockholm Sweden
| | - Anna Lindahl
- Department of Oncology-Pathology; Science for Life Laboratory; Karolinska Institutet; 171 65 Solna Sweden
| | - Axel Mie
- Department of Clinical Science and Education; Karolinska Institute; 118 83 Solna Sweden
| | - Adil Mardinoglu
- School of Engineering Sciences in Chemistry, Biotechnology, and Health; Science for Life Laboratory; KTH-Royal Institute of Technology; 171 65 Stockholm Sweden
| | - Raymond Field
- Department of Oncology-Pathology; Science for Life Laboratory; Karolinska Institutet; 171 65 Solna Sweden
| | - Richard Turner
- Department of Oncology-Pathology; Science for Life Laboratory; Karolinska Institutet; 171 65 Solna Sweden
| | - Johan Rockberg
- School of Engineering Sciences in Chemistry, Biotechnology, and Health; Wallenberg Centre for Protein Research; KTH-Royal Institute of Technology; 106 91 Stockholm Sweden
- School of Engineering Sciences in Chemistry, Biotechnology, and Health; AdBIOPRO, Centre for Advanced Bioproduction by Continuous Processing; KTH-Royal Institute of Technology; 106 91 Stockholm Sweden
| | - Veronique Chotteau
- Department Industrial Biotechnology; School of Engineering Sciences in Chemistry, Biotechnology, and Health; KTH-Royal Institute of Technology; 106 91 Stockholm Sweden
- School of Engineering Sciences in Chemistry, Biotechnology, and Health; Wallenberg Centre for Protein Research; KTH-Royal Institute of Technology; 106 91 Stockholm Sweden
- School of Engineering Sciences in Chemistry, Biotechnology, and Health; AdBIOPRO, Centre for Advanced Bioproduction by Continuous Processing; KTH-Royal Institute of Technology; 106 91 Stockholm Sweden
- Biopharmaceutical Development; MedImmune; CB21 6GH Cambridge United Kingdom
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18
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Rouhimoghadam M, Safarian S, Carroll JS, Sheibani N, Bidkhori G. Tamoxifen-Induced Apoptosis of MCF-7 Cells via GPR30/PI3K/MAPKs Interactions: Verification by ODE Modeling and RNA Sequencing. Front Physiol 2018; 9:907. [PMID: 30050469 PMCID: PMC6050429 DOI: 10.3389/fphys.2018.00907] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2018] [Accepted: 06/21/2018] [Indexed: 01/28/2023] Open
Abstract
Tamoxifen (Nolvadex) is one of the most widely used and effective therapeutic agent for breast cancer. It benefits nearly 75% of patients with estrogen receptor (ER)-positive breast cancer that receive this drug. Its effectiveness is mainly attributed to its capacity to function as an ER antagonist, blocking estrogen binding sites on the receptor, and inhibiting the proliferative action of the receptor-hormone complex. Although, tamoxifen can induce apoptosis in breast cancer cells via upregulation of pro-apoptotic factors, it can also promote uterine hyperplasia in some women. Thus, tamoxifen as a multi-functional drug could have different effects on cells based on the utilization of effective concentrations or availability of specific co-factors. Evidence that tamoxifen functions as a GPR30 (G-Protein Coupled Receptor 30) agonist activating adenylyl cyclase and EGFR (Epidermal Growth Factor Receptor) intracellular signaling networks, provides yet another means of explaining the multi-functionality of tamoxifen. Here ordinary differential equation (ODE) modeling, RNA sequencing and real time qPCR analysis were utilized to establish the necessary data for gene network mapping of tamoxifen-stimulated MCF-7 cells, which express the endogenous ER and GPR30. The gene set enrichment analysis and pathway analysis approaches were used to categorize transcriptionally upregulated genes in biological processes. Of the 2,713 genes that were significantly upregulated following a 48 h incubation with 250 μM tamoxifen, most were categorized as either growth-related or pro-apoptotic intermediates that fit into the Tp53 and/or MAPK signaling pathways. Collectively, our results display that the effects of tamoxifen on the breast cancer MCF-7 cell line are mediated by the activation of important signaling pathways including Tp53 and MAPKs to induce apoptosis.
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Affiliation(s)
- Milad Rouhimoghadam
- Department of Cell and Molecular Biology, School of Biology, College of Science, University of Tehran, Tehran, Iran
| | - Shahrokh Safarian
- Department of Cell and Molecular Biology, School of Biology, College of Science, University of Tehran, Tehran, Iran
| | - Jason S. Carroll
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Nader Sheibani
- Department of Ophthalmology and Visual Sciences, Biomedical Engineering, and Cell and Regenerative Biology, University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Gholamreza Bidkhori
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
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19
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Rosario D, Benfeitas R, Bidkhori G, Zhang C, Uhlen M, Shoaie S, Mardinoglu A. Understanding the Representative Gut Microbiota Dysbiosis in Metformin-Treated Type 2 Diabetes Patients Using Genome-Scale Metabolic Modeling. Front Physiol 2018; 9:775. [PMID: 29988585 PMCID: PMC6026676 DOI: 10.3389/fphys.2018.00775] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [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: 01/18/2018] [Accepted: 06/04/2018] [Indexed: 01/21/2023] Open
Abstract
Dysbiosis in the gut microbiome composition may be promoted by therapeutic drugs such as metformin, the world’s most prescribed antidiabetic drug. Under metformin treatment, disturbances of the intestinal microbes lead to increased abundance of Escherichia spp., Akkermansia muciniphila, Subdoligranulum variabile and decreased abundance of Intestinibacter bartlettii. This alteration may potentially lead to adverse effects on the host metabolism, with the depletion of butyrate producer genus. However, an increased production of butyrate and propionate was verified in metformin-treated Type 2 diabetes (T2D) patients. The mechanisms underlying these nutritional alterations and their relation with gut microbiota dysbiosis remain unclear. Here, we used Genome-scale Metabolic Models of the representative gut bacteria Escherichia spp., I. bartlettii, A. muciniphila, and S. variabile to elucidate their bacterial metabolism and its effect on intestinal nutrient pool, including macronutrients (e.g., amino acids and short chain fatty acids), minerals and chemical elements (e.g., iron and oxygen). We applied flux balance analysis (FBA) coupled with synthetic lethality analysis interactions to identify combinations of reactions and extracellular nutrients whose absence prevents growth. Our analyses suggest that Escherichia sp. is the bacteria least vulnerable to nutrient availability. We have also examined bacterial contribution to extracellular nutrients including short chain fatty acids, amino acids, and gasses. For instance, Escherichia sp. and S. variabile may contribute to the production of important short chain fatty acids (e.g., acetate and butyrate, respectively) involved in the host physiology under aerobic and anaerobic conditions. We have also identified pathway susceptibility to nutrient availability and reaction changes among the four bacteria using both FBA and flux variability analysis. For instance, lipopolysaccharide synthesis, nucleotide sugar metabolism, and amino acid metabolism are pathways susceptible to changes in Escherichia sp. and A. muciniphila. Our observations highlight important commensal and competing behavior, and their association with cellular metabolism for prevalent gut microbes. The results of our analysis have potential important implications for development of new therapeutic approaches in T2D patients through the development of prebiotics, probiotics, or postbiotics.
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Affiliation(s)
- Dorines Rosario
- Science for Life Laboratory, Royal Institute of Technology, Stockholm, Sweden
| | - Rui Benfeitas
- Science for Life Laboratory, Royal Institute of Technology, Stockholm, Sweden
| | - Gholamreza Bidkhori
- Science for Life Laboratory, Royal Institute of Technology, Stockholm, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, Royal Institute of Technology, Stockholm, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, Royal Institute of Technology, Stockholm, Sweden
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Dental Institute, King's College London, London, United Kingdom.,Centre for Translational Microbiome Research, Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Stockholm, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, Royal Institute of Technology, Stockholm, Sweden.,Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
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20
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Uhlen M, Zhang C, Lee S, Sjöstedt E, Fagerberg L, Bidkhori G, Benfeitas R, Arif M, Liu Z, Edfors F, Sanli K, von Feilitzen K, Oksvold P, Lundberg E, Hober S, Nilsson P, Mattsson J, Schwenk JM, Brunnström H, Glimelius B, Sjöblom T, Edqvist PH, Djureinovic D, Micke P, Lindskog C, Mardinoglu A, Ponten F. A pathology atlas of the human cancer transcriptome. Science 2017; 357:357/6352/eaan2507. [PMID: 28818916 DOI: 10.1126/science.aan2507] [Citation(s) in RCA: 2015] [Impact Index Per Article: 287.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2017] [Revised: 06/02/2017] [Accepted: 07/14/2017] [Indexed: 12/11/2022]
Abstract
Cancer is one of the leading causes of death, and there is great interest in understanding the underlying molecular mechanisms involved in the pathogenesis and progression of individual tumors. We used systems-level approaches to analyze the genome-wide transcriptome of the protein-coding genes of 17 major cancer types with respect to clinical outcome. A general pattern emerged: Shorter patient survival was associated with up-regulation of genes involved in cell growth and with down-regulation of genes involved in cellular differentiation. Using genome-scale metabolic models, we show that cancer patients have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. All data are presented in an interactive open-access database (www.proteinatlas.org/pathology) to allow genome-wide exploration of the impact of individual proteins on clinical outcomes.
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Affiliation(s)
- Mathias Uhlen
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden. .,Center for Biosustainability, Danish Technical University, Copenhagen, Denmark.,School of Biotechnology, AlbaNova University Center, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Sunjae Lee
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Evelina Sjöstedt
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden.,Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Linn Fagerberg
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Gholamreza Bidkhori
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Rui Benfeitas
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Muhammad Arif
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Zhengtao Liu
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Fredrik Edfors
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Kemal Sanli
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Kalle von Feilitzen
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Per Oksvold
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Emma Lundberg
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Sophia Hober
- School of Biotechnology, AlbaNova University Center, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Peter Nilsson
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Johanna Mattsson
- Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Jochen M Schwenk
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden
| | - Hans Brunnström
- Division of Pathology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Bengt Glimelius
- Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Tobias Sjöblom
- Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Per-Henrik Edqvist
- Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Dijana Djureinovic
- Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Patrick Micke
- Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Cecilia Lindskog
- Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm, Sweden.,School of Biotechnology, AlbaNova University Center, KTH-Royal Institute of Technology, Stockholm, Sweden.,Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Fredrik Ponten
- Department of Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden
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21
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Mohammadi R, Fallah-Mehrabadi J, Bidkhori G, Zahiri J, Javad Niroomand M, Masoudi-Nejad A. A systems biology approach to reconcile metabolic network models with application to Synechocystis sp. PCC 6803 for biofuel production. Mol BioSyst 2016; 12:2552-61. [DOI: 10.1039/c6mb00119j] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Metabolic network models can be optimized for the production of desired materials like biofuels.
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Affiliation(s)
- Reza Mohammadi
- Laboratory of Systems Biology and Bioinformatics (LBB)
- Institute of Biochemistry and Biophysics
- University of Tehran
- Tehran
- Iran
| | | | | | - Javad Zahiri
- Bioinformatics and Computational Omics Lab (BioCOOL)
- Department of Biophysics
- Faculty of Biological Sciences
- Tarbiat Modares University
- Tehran
| | - Mohammad Javad Niroomand
- Learning Intelligent Systems Lab
- School of Electrical and Computer Engineering
- 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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22
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Masoudi-Nejad A, Meshkin A, Haji-Eghrari B, Bidkhori G. Retraction Note to: Candidate gene prioritization. Mol Genet Genomics 2015; 290:2357. [DOI: 10.1007/s00438-015-1117-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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23
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Khosraviani M, Saheb Zamani M, Bidkhori G. FogLight: an efficient matrix-based approach to construct metabolic pathways by search space reduction. Bioinformatics 2015; 32:398-408. [PMID: 26454274 DOI: 10.1093/bioinformatics/btv578] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2015] [Accepted: 10/02/2015] [Indexed: 12/31/2022] Open
Abstract
MOTIVATION A fundamental computational problem in the area of metabolic engineering is finding metabolic pathways between a pair of source and target metabolites efficiently. We present an approach, namely FogLight, for searching metabolic networks utilizing Boolean (AND-OR) operations represented in matrix notation to efficiently reduce the search space. This enables the enumeration of all pathways between metabolites that are too distant for the application of brute-force methods. RESULTS Benchmarking tests run with FogLight show that it can reduce the search space by up to 98%, after which the accelerated search for high accurate results is guaranteed. Using FogLight, several pathways between eight given pairs of metabolites are found of which the pathways from CO2 to ethanol are specifically discussed. Additionally, in comparison with three path-finding tools, namely PHT, FMM and RouteSearch, FogLight can find shorter and more pathways for attempted source-target metabolite pairs. CONTACT szamani@aut.ac.ir, gholamreza.bidkhori@vtt.fi SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mehrshad Khosraviani
- Department of Computer Engineering & IT, Amirkabir University of Technology, Tehran, Iran and
| | - Morteza Saheb Zamani
- Department of Computer Engineering & IT, Amirkabir University of Technology, Tehran, Iran and
| | - Gholamreza Bidkhori
- Department of Computer Engineering & IT, Amirkabir University of Technology, Tehran, Iran and
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24
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Najafi A, Bidkhori G, Bozorgmehr JH, Koch I, Masoudi-Nejad A. Genome scale modeling in systems biology: algorithms and resources. Curr Genomics 2014; 15:130-59. [PMID: 24822031 PMCID: PMC4009841 DOI: 10.2174/1389202915666140319002221] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [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: 08/19/2013] [Revised: 02/16/2014] [Accepted: 03/17/2014] [Indexed: 12/18/2022] Open
Abstract
In recent years, in silico studies and trial simulations have complemented experimental procedures. A model is a description of a system, and a system is any collection of interrelated objects; an object, moreover, is some elemental unit upon which observations can be made but whose internal structure either does not exist or is ignored. Therefore, any network analysis approach is critical for successful quantitative modeling of biological systems. This review highlights some of most popular and important modeling algorithms, tools, and emerging standards for representing, simulating and analyzing cellular networks in five sections. Also, we try to show these concepts by means of simple example and proper images and graphs. Overall, systems biology aims for a holistic description and understanding of biological processes by an integration of analytical experimental approaches along with synthetic computational models. In fact, biological networks have been developed as a platform for integrating information from high to low-throughput experiments for the analysis of biological systems. We provide an overview of all processes used in modeling and simulating biological networks in such a way that they can become easily understandable for researchers with both biological and mathematical backgrounds. Consequently, given the complexity of generated experimental data and cellular networks, it is no surprise that researchers have turned to computer simulation and the development of more theory-based approaches to augment and assist in the development of a fully quantitative understanding of cellular dynamics.
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Affiliation(s)
- Ali Najafi
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Iran
| | - Gholamreza Bidkhori
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Iran
| | - Joseph H. Bozorgmehr
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Iran
| | - Ina Koch
- Molecular Bioinformatics, Johann Wolfgang Goethe-University Frankfurt am Main, Germany
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Iran
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25
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Masoudi-Nejad A, Bidkhori G, Hosseini Ashtiani S, Najafi A, Bozorgmehr JH, Wang E. Cancer systems biology and modeling: microscopic scale and multiscale approaches. Semin Cancer Biol 2014; 30:60-9. [PMID: 24657638 DOI: 10.1016/j.semcancer.2014.03.003] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [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: 03/11/2014] [Accepted: 03/11/2014] [Indexed: 10/25/2022]
Abstract
Cancer has become known as a complex and systematic disease on macroscopic, mesoscopic and microscopic scales. Systems biology employs state-of-the-art computational theories and high-throughput experimental data to model and simulate complex biological procedures such as cancer, which involves genetic and epigenetic, in addition to intracellular and extracellular complex interaction networks. In this paper, different systems biology modeling techniques such as systems of differential equations, stochastic methods, Boolean networks, Petri nets, cellular automata methods and agent-based systems are concisely discussed. We have compared the mentioned formalisms and tried to address the span of applicability they can bear on emerging cancer modeling and simulation approaches. Different scales of cancer modeling, namely, microscopic, mesoscopic and macroscopic scales are explained followed by an illustration of angiogenesis in microscopic scale of the cancer modeling. Then, the modeling of cancer cell proliferation and survival are examined on a microscopic scale and the modeling of multiscale tumor growth is explained along with its advantages.
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Affiliation(s)
- Ali Masoudi-Nejad
- 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
| | - Saman Hosseini Ashtiani
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ali Najafi
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Joseph H Bozorgmehr
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Edwin Wang
- National Research Council Canada, Montreal, QC H4P 2R2, Canada; Center for Bioinformatics, McGill University, Montreal, QC H3G 0B1, Canada
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26
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Ghasemi M, Rahgozar M, Bidkhori G, Masoudi-Nejad A. C-element: a new clustering algorithm to find high quality functional modules in PPI networks. PLoS One 2013; 8:e72366. [PMID: 24039752 PMCID: PMC3764100 DOI: 10.1371/journal.pone.0072366] [Citation(s) in RCA: 0] [Impact Index Per Article: 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: 05/01/2013] [Accepted: 07/16/2013] [Indexed: 11/18/2022] Open
Abstract
Graph clustering algorithms are widely used in the analysis of biological networks. Extracting functional modules in protein-protein interaction (PPI) networks is one such use. Most clustering algorithms whose focuses are on finding functional modules try either to find a clique like sub networks or to grow clusters starting from vertices with high degrees as seeds. These algorithms do not make any difference between a biological network and any other networks. In the current research, we present a new procedure to find functional modules in PPI networks. Our main idea is to model a biological concept and to use this concept for finding good functional modules in PPI networks. In order to evaluate the quality of the obtained clusters, we compared the results of our algorithm with those of some other widely used clustering algorithms on three high throughput PPI networks from Sacchromyces Cerevisiae, Homo sapiens and Caenorhabditis elegans as well as on some tissue specific networks. Gene Ontology (GO) analyses were used to compare the results of different algorithms. Each algorithm's result was then compared with GO-term derived functional modules. We also analyzed the effect of using tissue specific networks on the quality of the obtained clusters. The experimental results indicate that the new algorithm outperforms most of the others, and this improvement is more significant when tissue specific networks are used.
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Affiliation(s)
- Mahdieh Ghasemi
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
- Database Research Group (DBRG), Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Maseud Rahgozar
- Database Research Group (DBRG), Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, 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
- * E-mail:
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27
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Bidkhori G, Narimani Z, Hosseini Ashtiani S, Moeini A, Nowzari-Dalini A, Masoudi-Nejad A. Reconstruction of an integrated genome-scale co-expression network reveals key modules involved in lung adenocarcinoma. PLoS One 2013; 8:e67552. [PMID: 23874428 PMCID: PMC3708931 DOI: 10.1371/journal.pone.0067552] [Citation(s) in RCA: 81] [Impact Index Per Article: 7.4] [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: 04/06/2013] [Accepted: 05/18/2013] [Indexed: 02/04/2023] Open
Abstract
Our goal of this study was to reconstruct a “genome-scale co-expression network” and find important modules in lung adenocarcinoma so that we could identify the genes involved in lung adenocarcinoma. We integrated gene mutation, GWAS, CGH, array-CGH and SNP array data in order to identify important genes and loci in genome-scale. Afterwards, on the basis of the identified genes a co-expression network was reconstructed from the co-expression data. The reconstructed network was named “genome-scale co-expression network”. As the next step, 23 key modules were disclosed through clustering. In this study a number of genes have been identified for the first time to be implicated in lung adenocarcinoma by analyzing the modules. The genes EGFR, PIK3CA, TAF15, XIAP, VAPB, Appl1, Rab5a, ARF4, CLPTM1L, SP4, ZNF124, LPP, FOXP1, SOX18, MSX2, NFE2L2, SMARCC1, TRA2B, CBX3, PRPF6, ATP6V1C1, MYBBP1A, MACF1, GRM2, TBXA2R, PRKAR2A, PTK2, PGF and MYO10 are among the genes that belong to modules 1 and 22. All these genes, being implicated in at least one of the phenomena, namely cell survival, proliferation and metastasis, have an over-expression pattern similar to that of EGFR. In few modules, the genes such as CCNA2 (Cyclin A2), CCNB2 (Cyclin B2), CDK1, CDK5, CDC27, CDCA5, CDCA8, ASPM, BUB1, KIF15, KIF2C, NEK2, NUSAP1, PRC1, SMC4, SYCE2, TFDP1, CDC42 and ARHGEF9 are present that play a crucial role in cell cycle progression. In addition to the mentioned genes, there are some other genes (i.e. DLGAP5, BIRC5, PSMD2, Src, TTK, SENP2, PSMD2, DOK2, FUS and etc.) in the modules.
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Affiliation(s)
- Gholamreza Bidkhori
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Zahra Narimani
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Saman Hosseini Ashtiani
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ali Moeini
- Department of Algorithms and Computation, College of Engineering, 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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28
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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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Bidkhori G, Moeini A, Masoudi-Nejad A. Modeling of tumor progression in NSCLC and intrinsic resistance to TKI in loss of PTEN expression. PLoS One 2012; 7:e48004. [PMID: 23133538 PMCID: PMC3483873 DOI: 10.1371/journal.pone.0048004] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [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: 05/05/2012] [Accepted: 09/19/2012] [Indexed: 11/18/2022] Open
Abstract
EGFR signaling plays a very important role in NSCLC. It activates Ras/ERK, PI3K/Akt and STAT activation pathways. These are the main pathways for cell proliferation and survival. We have developed two mathematical models to relate to the different EGFR signaling in NSCLC and normal cells in the presence or absence of EGFR and PTEN mutations. The dynamics of downstream signaling pathways vary in the disease state and activation of some factors can be indicative of drug resistance. Our simulation denotes the effect of EGFR mutations and increased expression of certain factors in NSCLC EGFR signaling on each of the three pathways where levels of pERK, pSTAT and pAkt are increased. Over activation of ERK, Akt and STAT3 which are the main cell proliferation and survival factors act as promoting factors for tumor progression in NSCLC. In case of loss of PTEN, Akt activity level is considerably increased. Our simulation results show that in the presence of erlotinib, downstream factors i.e. pAkt, pSTAT3 and pERK are inhibited. However, in case of loss of PTEN expression in the presence of erlotinib, pAkt level would not decrease which demonstrates that these cells are resistant to erlotinib.
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Affiliation(s)
- Gholamreza Bidkhori
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ali Moeini
- Department of Algorithms and Computation, College of Engineering, 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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Masoudi-Nejad A, Meshkin A, Haji-Eghrari B, Bidkhori G. RETRACTED ARTICLE: Candidate gene prioritization. Mol Genet Genomics 2012; 287:679-98. [DOI: 10.1007/s00438-012-0710-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2012] [Accepted: 07/12/2012] [Indexed: 01/16/2023]
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