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Kutumova EO, Akberdin IR, Egorova VS, Kolesova EP, Parodi A, Pokrovsky VS, Zamyatnin, Jr AA, Kolpakov FA. Physiologically based pharmacokinetic model for predicting the biodistribution of albumin nanoparticles after induction and recovery from acute lung injury. Heliyon 2024; 10:e30962. [PMID: 38803942 PMCID: PMC11128879 DOI: 10.1016/j.heliyon.2024.e30962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 04/02/2024] [Accepted: 05/08/2024] [Indexed: 05/29/2024] Open
Abstract
The application of nanomedicine in the treatment of acute lung injury (ALI) has great potential for the development of new therapeutic strategies. To gain insight into the kinetics of nanocarrier distribution upon time-dependent changes in tissue permeability after ALI induction in mice, we developed a physiologically based pharmacokinetic model for albumin nanoparticles (ANP). The model was calibrated using data from mice treated with intraperitoneal LPS (6 mg/kg), followed by intravenous ANP (0.5 mg/mouse or about 20.8 mg/kg) at 0.5, 6, and 24 h. The simulation results reproduced the experimental observations and indicated that the accumulation of ANP in the lungs increased, reaching a peak 6 h after LPS injury, whereas it decreased in the liver, kidney, and spleen. The model predicted that LPS caused an immediate (within the first 30 min) dramatic increase in lung and kidney tissue permeability, whereas splenic tissue permeability gradually increased over 24 h after LPS injection. This information can be used to design new therapies targeting specific organs affected by bacterial infections and potentially by other inflammatory insults.
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Affiliation(s)
- Elena O. Kutumova
- Department of Computational Biology, Sirius University of Science and Technology, 354340, Sirius, Krasnodar Region, Russia
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, 630090, Novosibirsk, Russia
- Biosoft.Ru, Ltd., 630058, Novosibirsk, Russia
| | - Ilya R. Akberdin
- Department of Computational Biology, Sirius University of Science and Technology, 354340, Sirius, Krasnodar Region, Russia
- Biosoft.Ru, Ltd., 630058, Novosibirsk, Russia
- Department of Natural Sciences, Novosibirsk State University, 630090, Novosibirsk, Russia
| | - Vera S. Egorova
- Scientific Center for Translational Medicine, Sirius University of Science and Technology, 354340, Sirius, Krasnodar Region, Russia
| | - Ekaterina P. Kolesova
- Scientific Center for Translational Medicine, Sirius University of Science and Technology, 354340, Sirius, Krasnodar Region, Russia
| | - Alessandro Parodi
- Scientific Center for Translational Medicine, Sirius University of Science and Technology, 354340, Sirius, Krasnodar Region, Russia
| | - Vadim S. Pokrovsky
- N.N. Blokhin Medical Research Center of Oncology, 115522, Moscow, Russia
- Patrice Lumumba People's Friendship University, 117198, Moscow, Russia
| | - Andrey A. Zamyatnin, Jr
- Scientific Center for Translational Medicine, Sirius University of Science and Technology, 354340, Sirius, Krasnodar Region, Russia
- Faculty of Bioengineering and Bioinformatics and Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, 119234, Moscow, Russia
- Department of Biological Chemistry, Sechenov First Moscow State Medical University, 119991, Moscow, Russia
| | - Fedor A. Kolpakov
- Department of Computational Biology, Sirius University of Science and Technology, 354340, Sirius, Krasnodar Region, Russia
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, 630090, Novosibirsk, Russia
- Biosoft.Ru, Ltd., 630058, Novosibirsk, Russia
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Kutumova E, Kovaleva A, Sharipov R, Lifshits G, Kolpakov F. Mathematical modelling of the influence of ACE I/D polymorphism on blood pressure and antihypertensive therapy. Heliyon 2024; 10:e29988. [PMID: 38707445 PMCID: PMC11068647 DOI: 10.1016/j.heliyon.2024.e29988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 03/29/2024] [Accepted: 04/18/2024] [Indexed: 05/07/2024] Open
Abstract
The angiotensin-converting enzyme (ACE) gene (ACE) insertion/deletion (I/D) polymorphism raises the possibility of personalising ACE inhibitor therapy to optimise its efficiency and reduce side effects in genetically distinct subgroups. However, the extent of its influence among these subgroups is unknown. Therefore, we extended our computational model of blood pressure regulation to investigate the effect of the ACE I/D polymorphism on haemodynamic parameters in humans undergoing antihypertensive therapy. The model showed that the dependence of blood pressure on serum ACE activity is a function of saturation and therefore, the lack of association between ACE I/D and blood pressure levels may be due to high ACE activity in specific populations. Additionally, in an extended model simulating the effects of different classes of antihypertensive drugs, we explored the relationship between ACE I/D and the efficacy of inhibitors of the renin-angiotensin-aldosterone system. The model predicted that the response of cardiovascular and renal parameters to treatment directly depends on ACE activity. However, significant differences in parameter changes were observed only between groups with high and low ACE levels, while different ACE I/D genotypes within the same group had similar changes in absolute values. We conclude that a single genetic variant is responsible for only a small fraction of heredity in treatment success and its predictive value is limited.
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Affiliation(s)
- Elena Kutumova
- Department of Computational Biology, Sirius University of Science and Technology, Sirius, Krasnodar region, Russia
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia
- Biosoft.Ru, Ltd., Novosibirsk, Russia
| | - Anna Kovaleva
- Laboratory for Personalized Medicine, Center of New Medical Technologies, Institute of Chemical Biology and Fundamental Medicine SB RAS, Novosibirsk, Russia
| | - Ruslan Sharipov
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia
- Biosoft.Ru, Ltd., Novosibirsk, Russia
- Specialized Educational Scientific Center, Novosibirsk State University, Novosibirsk, Russia
| | - Galina Lifshits
- Laboratory for Personalized Medicine, Center of New Medical Technologies, Institute of Chemical Biology and Fundamental Medicine SB RAS, Novosibirsk, Russia
| | - Fedor Kolpakov
- Department of Computational Biology, Sirius University of Science and Technology, Sirius, Krasnodar region, Russia
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia
- Biosoft.Ru, Ltd., Novosibirsk, Russia
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Samad SS, Schwartz JM, Francavilla C. Functional selectivity of Receptor Tyrosine Kinases regulates distinct cellular outputs. Front Cell Dev Biol 2024; 11:1348056. [PMID: 38259512 PMCID: PMC10800419 DOI: 10.3389/fcell.2023.1348056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024] Open
Abstract
Functional selectivity refers to the activation of differential signalling and cellular outputs downstream of the same membrane-bound receptor when activated by two or more different ligands. Functional selectivity has been described and extensively studied for G-protein Coupled Receptors (GPCRs), leading to specific therapeutic options for dysregulated GPCRs functions. However, studies regarding the functional selectivity of Receptor Tyrosine Kinases (RTKs) remain sparse. Here, we will summarize recent data about RTK functional selectivity focusing on how the nature and the amount of RTK ligands and the crosstalk of RTKs with other membrane proteins regulate the specificity of RTK signalling. In addition, we will discuss how structural changes in RTKs upon ligand binding affects selective signalling pathways. Much remains to be known about the integration of different signals affecting RTK signalling specificity to orchestrate long-term cellular outcomes. Recent advancements in omics, specifically quantitative phosphoproteomics, and in systems biology methods to study, model and integrate different types of large-scale omics data have increased our ability to compare several signals affecting RTK functional selectivity in a global, system-wide fashion. We will discuss how such methods facilitate the exploration of important signalling hubs and enable data-driven predictions aiming at improving the efficacy of therapeutics for diseases like cancer, where redundant RTK signalling pathways often compromise treatment efficacy.
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Affiliation(s)
- Sakim S. Samad
- Division of Molecular and Cellular Functions, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom
- Division of Evolution, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom
| | - Jean-Marc Schwartz
- Division of Evolution, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom
| | - Chiara Francavilla
- Division of Molecular and Cellular Functions, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom
- Section of Protein Science and Biotherapeutics, Department of Bioengineering and Biomedicine, Danish Technical University, Lyngby, Denmark
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Biswas A, Salvucci M, Connor K, Düssmann H, Carberry S, Fichtner M, King E, Murphy B, O'Farrell AC, Cryan J, Beausang A, Heffernan J, Cremona M, Hennessy BT, Clerkin J, Sweeney KJ, MacNally S, Brett F, O'Halloran P, Bacon O, Furney S, Verreault M, Quissac E, Bielle F, Ahmed MH, Idbaih A, Leenstra S, Ntafoulis I, Fabro F, Lamfers M, Golebiewska A, Hertel F, Niclou SP, Yen RTC, Kremer A, Dilcan G, Lodi F, Arijs I, Lambrechts D, Purushothama MK, Kel A, Byrne AT, Prehn JHM. Comparative analysis of deeply phenotyped GBM cohorts of 'short-term' and 'long-term' survivors. J Neurooncol 2023:10.1007/s11060-023-04341-3. [PMID: 37237151 PMCID: PMC10322749 DOI: 10.1007/s11060-023-04341-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 05/11/2023] [Indexed: 05/28/2023]
Abstract
BACKGROUND Glioblastoma (GBM) is an aggressive brain cancer that typically results in death in the first 15 months after diagnosis. There have been limited advances in finding new treatments for GBM. In this study, we investigated molecular differences between patients with extremely short (≤ 9 months, Short term survivors, STS) and long survival (≥ 36 months, Long term survivors, LTS). METHODS Patients were selected from an in-house cohort (GLIOTRAIN-cohort), using defined inclusion criteria (Karnofsky score > 70; age < 70 years old; Stupp protocol as first line treatment, IDH wild type), and a multi-omic analysis of LTS and STS GBM samples was performed. RESULTS Transcriptomic analysis of tumour samples identified cilium gene signatures as enriched in LTS. Moreover, Immunohistochemical analysis confirmed the presence of cilia in the tumours of LTS. Notably, reverse phase protein array analysis (RPPA) demonstrated increased phosphorylated GAB1 (Y627), SRC (Y527), BCL2 (S70) and RAF (S338) protein expression in STS compared to LTS. Next, we identified 25 unique master regulators (MR) and 13 transcription factors (TFs) belonging to ontologies of integrin signalling and cell cycle to be upregulated in STS. CONCLUSION Overall, comparison of STS and LTS GBM patients, identifies novel biomarkers and potential actionable therapeutic targets for the management of GBM.
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Affiliation(s)
- Archita Biswas
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, 123 St Stephen's Green, Dublin 2, Dublin, D02 YN77, Ireland
| | - Manuela Salvucci
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, 123 St Stephen's Green, Dublin 2, Dublin, D02 YN77, Ireland
| | - Kate Connor
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, 123 St Stephen's Green, Dublin 2, Dublin, D02 YN77, Ireland
| | - Heiko Düssmann
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, 123 St Stephen's Green, Dublin 2, Dublin, D02 YN77, Ireland
| | - Steven Carberry
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, 123 St Stephen's Green, Dublin 2, Dublin, D02 YN77, Ireland
| | - Michael Fichtner
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, 123 St Stephen's Green, Dublin 2, Dublin, D02 YN77, Ireland
| | - Ellen King
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, 123 St Stephen's Green, Dublin 2, Dublin, D02 YN77, Ireland
| | - Brona Murphy
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, 123 St Stephen's Green, Dublin 2, Dublin, D02 YN77, Ireland
| | - Alice C O'Farrell
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, 123 St Stephen's Green, Dublin 2, Dublin, D02 YN77, Ireland
| | - Jane Cryan
- Department of Neuropathology, Beaumont Hospital, Dublin 9, Dublin, Ireland
| | - Alan Beausang
- Department of Neuropathology, Beaumont Hospital, Dublin 9, Dublin, Ireland
| | | | - Mattia Cremona
- Department of Medicine, Royal College of Surgeons in Ireland and Beaumont Hospital, Dublin 9, Dublin, Ireland
| | - Bryan T Hennessy
- Department of Medicine, Royal College of Surgeons in Ireland and Beaumont Hospital, Dublin 9, Dublin, Ireland
| | - James Clerkin
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, 123 St Stephen's Green, Dublin 2, Dublin, D02 YN77, Ireland
- Department of Neurosurgery, Beaumont Hospital, Dublin 9, Dublin, Ireland
| | - Kieron J Sweeney
- Department of Neurosurgery, Beaumont Hospital, Dublin 9, Dublin, Ireland
| | - Steve MacNally
- Department of Neurosurgery, Beaumont Hospital, Dublin 9, Dublin, Ireland
| | - Francesca Brett
- Department of Neuropathology, Beaumont Hospital, Dublin 9, Dublin, Ireland
| | - Philip O'Halloran
- Department of Neurosurgery, Beaumont Hospital, Dublin 9, Dublin, Ireland
| | - Orna Bacon
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, 123 St Stephen's Green, Dublin 2, Dublin, D02 YN77, Ireland
| | - Simon Furney
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, 123 St Stephen's Green, Dublin 2, Dublin, D02 YN77, Ireland
| | - Maite Verreault
- DMU Neurosciences, Service de Neurologie 2-Mazarin, Sorbonne Université, AP-HP, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Inserm, F-75013, Paris, France
| | - Emie Quissac
- DMU Neurosciences, Service de Neurologie 2-Mazarin, Sorbonne Université, AP-HP, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Inserm, F-75013, Paris, France
| | - Franck Bielle
- DMU Neurosciences, Service de Neurologie 2-Mazarin, Sorbonne Université, AP-HP, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Inserm, F-75013, Paris, France
| | - Mohammed H Ahmed
- DMU Neurosciences, Service de Neurologie 2-Mazarin, Sorbonne Université, AP-HP, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Inserm, F-75013, Paris, France
| | - Ahmed Idbaih
- DMU Neurosciences, Service de Neurologie 2-Mazarin, Sorbonne Université, AP-HP, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Inserm, F-75013, Paris, France
| | - Sieger Leenstra
- Dept of Neurosurgery Brain Tumor Center, Erasmus University Medical Center, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands
| | - Ioannis Ntafoulis
- Dept of Neurosurgery Brain Tumor Center, Erasmus University Medical Center, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands
| | - Federica Fabro
- Dept of Neurosurgery Brain Tumor Center, Erasmus University Medical Center, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands
| | - Martine Lamfers
- Dept of Neurosurgery Brain Tumor Center, Erasmus University Medical Center, Wytemaweg 80, 3015 CN, Rotterdam, The Netherlands
| | - Anna Golebiewska
- NORLUX Neuro-Oncology laboratory, Department of Cancer Research, Luxembourg Institute of Health, 6A, Rue Nicolas-Ernest Barblé, L-1210, Luxembourg, Luxembourg
| | - Frank Hertel
- NORLUX Neuro-Oncology laboratory, Department of Cancer Research, Luxembourg Institute of Health, 6A, Rue Nicolas-Ernest Barblé, L-1210, Luxembourg, Luxembourg
- Faculty of Sciences, Technology and Medicine, University of Luxembourg, L-4365, Esch-sur-Alzette, Luxembourg
| | - Simone P Niclou
- NORLUX Neuro-Oncology laboratory, Department of Cancer Research, Luxembourg Institute of Health, 6A, Rue Nicolas-Ernest Barblé, L-1210, Luxembourg, Luxembourg
- Faculty of Sciences, Technology and Medicine, University of Luxembourg, L-4365, Esch-sur-Alzette, Luxembourg
| | - Romain Tching Chi Yen
- Information Technology for Translational Medicine, 27, Rue Henri Koch - House of BioHealth, L-4354, Esch-sur-Alzette, Luxembourg
| | - Andreas Kremer
- Information Technology for Translational Medicine, 27, Rue Henri Koch - House of BioHealth, L-4354, Esch-sur-Alzette, Luxembourg
| | - Gonca Dilcan
- VIB-KU Leuven Cancer for Cancer Biology, Onderwijs en Navorsing 5, Herestraat, 49, 3000, Leuven, Belgium
| | - Francesca Lodi
- VIB-KU Leuven Cancer for Cancer Biology, Onderwijs en Navorsing 5, Herestraat, 49, 3000, Leuven, Belgium
| | - Ingrid Arijs
- VIB-KU Leuven Cancer for Cancer Biology, Onderwijs en Navorsing 5, Herestraat, 49, 3000, Leuven, Belgium
| | - Diether Lambrechts
- VIB-KU Leuven Cancer for Cancer Biology, Onderwijs en Navorsing 5, Herestraat, 49, 3000, Leuven, Belgium
| | | | - Alexander Kel
- geneXplain GmbH, Am Exer 19b, 38302, Wolfenbüttel, Germany
| | - Annette T Byrne
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, 123 St Stephen's Green, Dublin 2, Dublin, D02 YN77, Ireland
| | - Jochen H M Prehn
- Department of Physiology and Medical Physics, Centre for Systems Medicine, Royal College of Surgeons in Ireland, 123 St Stephen's Green, Dublin 2, Dublin, D02 YN77, Ireland.
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Kolpakov F, Akberdin I, Kiselev I, Kolmykov S, Kondrakhin Y, Kulyashov M, Kutumova E, Pintus S, Ryabova A, Sharipov R, Yevshin I, Zhatchenko S, Kel A. BioUML-towards a universal research platform. Nucleic Acids Res 2022; 50:W124-W131. [PMID: 35536253 PMCID: PMC9252820 DOI: 10.1093/nar/gkac286] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 04/04/2022] [Accepted: 04/13/2022] [Indexed: 12/12/2022] Open
Abstract
BioUML (https://www.biouml.org)—is a web-based integrated platform for systems biology and data analysis. It supports visual modelling and construction of hierarchical biological models that allow us to construct the most complex modular models of blood pressure regulation, skeletal muscle metabolism, COVID-19 epidemiology. BioUML has been integrated with git repositories where users can store their models and other data. We have also expanded the capabilities of BioUML for data analysis and visualization of biomedical data: (i) any programs and Jupyter kernels can be plugged into the BioUML platform using Docker technology; (ii) BioUML is integrated with the Galaxy and Galaxy Tool Shed; (iii) BioUML provides two-way integration with R and Python (Jupyter notebooks): scripts can be executed on the BioUML web pages, and BioUML functions can be called from scripts; (iv) using plug-in architecture, specialized viewers and editors can be added. For example, powerful genome browsers as well as viewers for molecular 3D structure are integrated in this way; (v) BioUML supports data analyses using workflows (own format, Galaxy, CWL, BPMN, nextFlow). Using these capabilities, we have initiated a new branch of the BioUML development—u-science—a universal scientific platform that can be configured for specific research requirements.
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Affiliation(s)
- Fedor Kolpakov
- Sirius University of Science and Technology, Sochi 354340, Russian Federation.,Federal Research Center for Information and Computational Technologies, Novosibirsk 630090, Russian Federation.,Budker Institute of Nuclear Physics SB RAS, Novosibirsk 630090, Russian Federation
| | - Ilya Akberdin
- Sirius University of Science and Technology, Sochi 354340, Russian Federation.,Biosoft.ru, LLC, Novosibirsk 630058, Russian Federation.,Novosibirsk State University, Novosibirsk 630090, Russian Federation
| | - Ilya Kiselev
- Sirius University of Science and Technology, Sochi 354340, Russian Federation.,Federal Research Center for Information and Computational Technologies, Novosibirsk 630090, Russian Federation.,Budker Institute of Nuclear Physics SB RAS, Novosibirsk 630090, Russian Federation
| | - Semyon Kolmykov
- Sirius University of Science and Technology, Sochi 354340, Russian Federation.,Biosoft.ru, LLC, Novosibirsk 630058, Russian Federation
| | - Yury Kondrakhin
- Federal Research Center for Information and Computational Technologies, Novosibirsk 630090, Russian Federation.,Biosoft.ru, LLC, Novosibirsk 630058, Russian Federation
| | | | - Elena Kutumova
- Sirius University of Science and Technology, Sochi 354340, Russian Federation.,Federal Research Center for Information and Computational Technologies, Novosibirsk 630090, Russian Federation
| | - Sergey Pintus
- Sirius University of Science and Technology, Sochi 354340, Russian Federation
| | - Anna Ryabova
- Sirius University of Science and Technology, Sochi 354340, Russian Federation
| | - Ruslan Sharipov
- Sirius University of Science and Technology, Sochi 354340, Russian Federation.,Biosoft.ru, LLC, Novosibirsk 630058, Russian Federation.,Novosibirsk State University, Novosibirsk 630090, Russian Federation
| | - Ivan Yevshin
- Sirius University of Science and Technology, Sochi 354340, Russian Federation.,Biosoft.ru, LLC, Novosibirsk 630058, Russian Federation
| | - Sergey Zhatchenko
- Sirius University of Science and Technology, Sochi 354340, Russian Federation.,Biosoft.ru, LLC, Novosibirsk 630058, Russian Federation
| | - Alexander Kel
- Biosoft.ru, LLC, Novosibirsk 630058, Russian Federation.,geneXplain GmbH, Wolfenbüttel 38302, Germany
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Gather F, Ihrig-Biedert I, Kohlhas P, Krutenko T, Peitz M, Brüstle O, Pautz A, Kleinert H. A specific, non-immune system-related isoform of the human inducible nitric oxide synthase is expressed during differentiation of human stem cells into various cell types. Cell Commun Signal 2022; 20:47. [PMID: 35392923 PMCID: PMC8991583 DOI: 10.1186/s12964-022-00855-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 03/03/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND NOS2 expression is mostly found in bacteria-exposed or cytokine-treated tissues and is mostly connected to innate immune reactions. There are three isoforms of NOS2 (NOS2-1 to -3). In RNA-seq data sets, analyzing inflammatory gene expression, only expression of the NOS2-1 mRNA isoform is detected. However, the expression of NOS2 in differentiating human pluripotent stems (hPSCs) has not been analyzed yet. METHODS Public available RNA-seq databases were screened for data of hPSCs during differentiation to different target cells. An isoform specific algorithm was used to analyze NOS2 mRNA isoform expression. In addition, we differentiated four different human iPSC cell lines toward cortical neurons and analyzed NOS2 mRNA expression by qRT-PCR and 5'-RACE. The functionality of the NOS2-2 protein was analyzed by transient transfection of expression clones in human DLD1 cells and nitrate measurement in the supernatant of these cells. RESULTS In RNA-seq databases we detected a transient expression of the NOS2 mRNA during the differentiation of hPSCs to cardiomyocytes, chondrocytes, mesenchymal stromal cells, neurons, syncytiotrophoblast cells, and trophoblasts. NOS2 mRNA isoform specific analyses showed, that the transiently expressed NOS2 mRNA in differentiating hPSC (NOS2-2; "diff-iNOS") differ remarkably from the already described NOS2 transcript found in colon or induced islets (NOS2-1; "immuno-iNOS"). Also, analysis of the NOS2 mRNA- and protein expression during the differentiation of four different hiPSC lines towards cortical neurons showed a transient expression of the NOS2 mRNA and NOS2 protein on day 18 of the differentiation course. 5'-RACE experiments and isoform specific qRT-PCR analyses revealed that only the NOS2-2 mRNA isoform was expressed in these experiments. To analyze the functionality of the NOS2-2 protein, we transfected human DLD-1 cells with tetracycline inducible expression clones encoding the NOS2-1- or -2 coding sequence. After induction of the NOS2-1 or -2 mRNA expression by tetracycline a similar nitrate production was measured proofing the functionality of the NOS2-2 protein isoform. CONCLUSIONS Our data show that a differentiation specific NOS2 isoform (NOS2-2) is transiently expressed during differentiation of hPSC. Video Abstract.
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Affiliation(s)
- Fabian Gather
- Department of Pharmacology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131, Mainz, Germany.,Department of Molecular Embryology, Institute for Anatomy and Cell Biology, Freiburg, Germany
| | - Irmgard Ihrig-Biedert
- Department of Pharmacology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Paul Kohlhas
- Department of Pharmacology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Tamara Krutenko
- Institute of Reconstructive Neurobiology, University of Bonn Medical Faculty & University Hospital Bonn, Bonn, Germany
| | - Michael Peitz
- Cell Programming Core Facility, Institute of Reconstructive Neurobiology, University of Bonn Medical Faculty & University Hospital Bonn, Bonn, Germany.,Institute of Reconstructive Neurobiology, University of Bonn Medical Faculty & University Hospital Bonn, Bonn, Germany
| | - Oliver Brüstle
- Institute of Reconstructive Neurobiology, University of Bonn Medical Faculty & University Hospital Bonn, Bonn, Germany
| | - Andrea Pautz
- Department of Pharmacology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131, Mainz, Germany.
| | - Hartmut Kleinert
- Department of Pharmacology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131, Mainz, Germany.
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Modular Representation of Physiologically Based Pharmacokinetic Models: Nanoparticle Delivery to Solid Tumors in Mice as an Example. MATHEMATICS 2022. [DOI: 10.3390/math10071176] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Here we describe a toolkit for presenting physiologically based pharmacokinetic (PBPK) models in a modular graphical view in the BioUML platform. Firstly, we demonstrate the BioUML capabilities for PBPK modeling tested on an existing model of nanoparticles delivery to solid tumors in mice. Secondly, we provide guidance on the conversion of the PBPK model code from a text modeling language like Berkeley Madonna to a visual modular diagram in the BioUML. We give step-by-step explanations of the model transformation and demonstrate that simulation results from the original model are exactly the same as numerical results obtained for the transformed model. The main advantage of the proposed approach is its clarity and ease of perception. Additionally, the modular representation serves as a simplified and convenient base for in silico investigation of the model and reduces the risk of technical errors during its reuse and extension by concomitant biochemical processes. In summary, this article demonstrates that BioUML can be used as an alternative and robust tool for PBPK modeling.
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Kutumova E, Kiselev I, Sharipov R, Lifshits G, Kolpakov F. Thoroughly Calibrated Modular Agent-Based Model of the Human Cardiovascular and Renal Systems for Blood Pressure Regulation in Health and Disease. Front Physiol 2021; 12:746300. [PMID: 34867451 PMCID: PMC8632703 DOI: 10.3389/fphys.2021.746300] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 10/18/2021] [Indexed: 11/13/2022] Open
Abstract
Here we present a modular agent-based mathematical model of the human cardiovascular and renal systems. It integrates the previous models primarily developed by A. C. Guyton, F. Karaaslan, K. M. Hallow, and Y. V. Solodyannikov. We performed the model calibration to find an equilibrium state within the normal vital sign ranges for a healthy adult. We verified the model's abilities to reproduce equilibrium states with abnormal physiological values related to different combinations of cardiovascular diseases (such as systemic hypertension, chronic heart failure, pulmonary hypertension, etc.). For the model creation and validation, we involved over 200 scientific studies covering known models of the human cardiovascular and renal functions, biosimulation platforms, and clinical measurements of physiological quantities in normal and pathological conditions. We compiled detailed documentation describing all equations, parameters and variables of the model with justification of all formulas and values. The model is implemented in BioUML and available in the web-version of the software.
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Affiliation(s)
- Elena Kutumova
- Department of Computational Biology, Sirius University of Science and Technology, Sochi, Russia
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia
- Biosoft.Ru, Ltd., Novosibirsk, Russia
| | - Ilya Kiselev
- Department of Computational Biology, Sirius University of Science and Technology, Sochi, Russia
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia
- Biosoft.Ru, Ltd., Novosibirsk, Russia
| | - Ruslan Sharipov
- Department of Computational Biology, Sirius University of Science and Technology, Sochi, Russia
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia
- Biosoft.Ru, Ltd., Novosibirsk, Russia
- Specialized Educational Scientific Center, Novosibirsk State University, Novosibirsk, Russia
| | - Galina Lifshits
- Laboratory for Personalized Medicine, Center of New Medical Technologies, Institute of Chemical Biology and Fundamental Medicine SB RAS, Novosibirsk, Russia
| | - Fedor Kolpakov
- Department of Computational Biology, Sirius University of Science and Technology, Sochi, Russia
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, Novosibirsk, Russia
- Biosoft.Ru, Ltd., Novosibirsk, Russia
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9
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Akberdin IR, Kiselev IN, Pintus SS, Sharipov RN, Vertyshev AY, Vinogradova OL, Popov DV, Kolpakov FA. A Modular Mathematical Model of Exercise-Induced Changes in Metabolism, Signaling, and Gene Expression in Human Skeletal Muscle. Int J Mol Sci 2021; 22:10353. [PMID: 34638694 PMCID: PMC8508736 DOI: 10.3390/ijms221910353] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/04/2021] [Accepted: 09/22/2021] [Indexed: 11/29/2022] Open
Abstract
Skeletal muscle is the principal contributor to exercise-induced changes in human metabolism. Strikingly, although it has been demonstrated that a lot of metabolites accumulating in blood and human skeletal muscle during an exercise activate different signaling pathways and induce the expression of many genes in working muscle fibres, the systematic understanding of signaling-metabolic pathway interrelations with downstream genetic regulation in the skeletal muscle is still elusive. Herein, a physiologically based computational model of skeletal muscle comprising energy metabolism, Ca2+, and AMPK (AMP-dependent protein kinase) signaling pathways and the expression regulation of genes with early and delayed responses was developed based on a modular modeling approach and included 171 differential equations and more than 640 parameters. The integrated modular model validated on diverse including original experimental data and different exercise modes provides a comprehensive in silico platform in order to decipher and track cause-effect relationships between metabolic, signaling, and gene expression levels in skeletal muscle.
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Affiliation(s)
- Ilya R. Akberdin
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (I.N.K.); (S.S.P.); (R.N.S.); (F.A.K.)
- BIOSOFT.RU, LLC, 630090 Novosibirsk, Russia
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Federal Research Center Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
| | - Ilya N. Kiselev
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (I.N.K.); (S.S.P.); (R.N.S.); (F.A.K.)
- BIOSOFT.RU, LLC, 630090 Novosibirsk, Russia
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, 633010 Novosibirsk, Russia
| | - Sergey S. Pintus
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (I.N.K.); (S.S.P.); (R.N.S.); (F.A.K.)
- BIOSOFT.RU, LLC, 630090 Novosibirsk, Russia
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, 633010 Novosibirsk, Russia
| | - Ruslan N. Sharipov
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (I.N.K.); (S.S.P.); (R.N.S.); (F.A.K.)
- BIOSOFT.RU, LLC, 630090 Novosibirsk, Russia
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, 633010 Novosibirsk, Russia
| | | | - Olga L. Vinogradova
- Institute of Biomedical Problems of the Russian Academy of Sciences, 123007 Moscow, Russia;
| | - Daniil V. Popov
- Institute of Biomedical Problems of the Russian Academy of Sciences, 123007 Moscow, Russia;
| | - Fedor A. Kolpakov
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (I.N.K.); (S.S.P.); (R.N.S.); (F.A.K.)
- BIOSOFT.RU, LLC, 630090 Novosibirsk, Russia
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, 633010 Novosibirsk, Russia
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Sekiguchi T, Hamada H, Okamoto M. WinBEST-KIT: Biochemical Reaction Simulator for Analyzing Multi-Layered Metabolic Pathways. Bioengineering (Basel) 2021; 8:bioengineering8080114. [PMID: 34436117 PMCID: PMC8389272 DOI: 10.3390/bioengineering8080114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 08/01/2021] [Accepted: 08/05/2021] [Indexed: 11/16/2022] Open
Abstract
We previously developed the biochemical reaction simulator WinBEST-KIT. In recent years, research interest has shifted from analysis of individual biochemical reactions to analysis of metabolic pathways as systems. These large-scale and complicated metabolic pathways can be considered as characteristic multi-layered structures, which, for convenience, are separated from whole biological systems according to their specific roles. These pathways include reactants having the same name but with unique stoichiometric coefficients arranged across many different places and connected between arbitrary layers. Accordingly, in this study, we have developed a new version of WinBEST-KIT that allows users (1) to utilize shortcut symbols that can be arranged with multiple reactants having the same name but with unique stoichiometric coefficients, thereby providing a layout that is similar to metabolic pathways depicted in biochemical textbooks; (2) to create layers that divide large-scale and complicated metabolic pathways according to their specific roles; (3) to connect the layers by using shortcut symbols; and (4) to analyze the interactions between these layers. These new and existing features allow users to create and analyze such multi-layered metabolic pathways efficiently. Furthermore, WinBEST-KIT supports SBML, making it possible for users to utilize these new and existing features to create and publish SBML models.
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Affiliation(s)
- Tatsuya Sekiguchi
- Department of Life Sciences and Informatics, Faculty of Engineering, Maebashi Institute of Technology, 460-1, Kamisatori-cho, Maebashi 371-0816, Japan
- Correspondence:
| | - Hiroyuki Hamada
- Department of Bioscience and Biotechnology, Faculty of Agriculture, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka 819-0395, Japan;
| | - Masahiro Okamoto
- Graduate School of Systems Life Sciences, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka 819-0395, Japan;
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Pintus SS, Akberdin IR, Yevshin I, Makhnovskii P, Tyapkina O, Nigmetzyanov I, Nurullin L, Devyatiyarov R, Shagimardanova E, Popov D, Kolpakov FA, Gusev O, Gazizova GR. Genome-Wide Atlas of Promoter Expression Reveals Contribution of Transcribed Regulatory Elements to Genetic Control of Disuse-Mediated Atrophy of Skeletal Muscle. BIOLOGY 2021; 10:biology10060557. [PMID: 34203013 PMCID: PMC8235325 DOI: 10.3390/biology10060557] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 06/10/2021] [Accepted: 06/15/2021] [Indexed: 12/05/2022]
Abstract
Simple Summary The genetic process underlying the control of skeletal muscle homeostasis is a key factor in methods that develop technologies to prevent age and immobility-driven atrophy. In the current paper, using advanced methods for the whole-genome profiling of transcription starting sites in fast and slow muscle in rats, we developed an integrative database of transcribed regulatory elements. Employing methods of comparative transcriptomics, we demonstrate that cis-regulatory elements are actively involved in the control of atrophy and recovery, and that the differential use of promoters and enhancers is the one of the key mechanisms that distinguishes between specific processes in slow and fast skeletal muscles. Abstract The prevention of muscle atrophy carries with it clinical significance for the control of increased morbidity and mortality following physical inactivity. While major transcriptional events associated with muscle atrophy-recovery processes are the subject of active research on the gene level, the contribution of non-coding regulatory elements and alternative promoter usage is a major source for both the production of alternative protein products and new insights into the activity of transcription factors. We used the cap-analysis of gene expression (CAGE) to create a genome-wide atlas of promoter-level transcription in fast (m. EDL) and slow (m. soleus) muscles in rats that were subjected to hindlimb unloading and subsequent recovery. We found that the genetic regulation of the atrophy-recovery cycle in two types of muscle is mediated by different pathways, including a unique set of non-coding transcribed regulatory elements. We showed that the activation of “shadow” enhancers is tightly linked to specific stages of atrophy and recovery dynamics, with the largest number of specific regulatory elements being transcriptionally active in the muscles on the first day of recovery after a week of disuse. The developed comprehensive database of transcription of regulatory elements will further stimulate research on the gene regulation of muscle homeostasis in mammals.
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Affiliation(s)
- Sergey S. Pintus
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, 630090 Novosibirsk, Russia
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia
- BIOSOFT.RU LLC, 630058 Novosibirsk, Russia; (S.S.P.); (I.R.A.); (I.Y.)
| | - Ilya R. Akberdin
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, 630090 Novosibirsk, Russia
- BIOSOFT.RU LLC, 630058 Novosibirsk, Russia; (S.S.P.); (I.R.A.); (I.Y.)
- Faculty of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Ivan Yevshin
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, 630090 Novosibirsk, Russia
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia
- BIOSOFT.RU LLC, 630058 Novosibirsk, Russia; (S.S.P.); (I.R.A.); (I.Y.)
| | - Pavel Makhnovskii
- Institute of Biomedical Problems of the Russian Academy of Sciences, Moscow 123007, Russia; (P.M.); (D.P.)
| | - Oksana Tyapkina
- Kazan Institute of Biochemistry and Biophysics FRC Kazan Scientific Center of RAS, 420007 Kazan, Russia; (O.T.); (L.N.)
- Department of Biology, Kazan State Medical University, 420012 Kazan, Russia
| | - Islam Nigmetzyanov
- Extreme Biology Laboratory, Institute of Fundamental Medicine and Biology, Kazan Federal University, 420009 Kazan, Russia; (I.N.); (R.D.); (E.S.)
| | - Leniz Nurullin
- Kazan Institute of Biochemistry and Biophysics FRC Kazan Scientific Center of RAS, 420007 Kazan, Russia; (O.T.); (L.N.)
- Department of Biology, Kazan State Medical University, 420012 Kazan, Russia
| | - Ruslan Devyatiyarov
- Extreme Biology Laboratory, Institute of Fundamental Medicine and Biology, Kazan Federal University, 420009 Kazan, Russia; (I.N.); (R.D.); (E.S.)
| | - Elena Shagimardanova
- Extreme Biology Laboratory, Institute of Fundamental Medicine and Biology, Kazan Federal University, 420009 Kazan, Russia; (I.N.); (R.D.); (E.S.)
| | - Daniil Popov
- Institute of Biomedical Problems of the Russian Academy of Sciences, Moscow 123007, Russia; (P.M.); (D.P.)
| | - Fedor A. Kolpakov
- Laboratory of Bioinformatics, Federal Research Center for Information and Computational Technologies, 630090 Novosibirsk, Russia
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia
- BIOSOFT.RU LLC, 630058 Novosibirsk, Russia; (S.S.P.); (I.R.A.); (I.Y.)
- Correspondence: or (F.A.K.); (O.G.); (G.R.G.)
| | - Oleg Gusev
- Extreme Biology Laboratory, Institute of Fundamental Medicine and Biology, Kazan Federal University, 420009 Kazan, Russia; (I.N.); (R.D.); (E.S.)
- RIKEN Center for Integrative Medical Sciences, RIKEN, Yokohama, Kanagawa 230-0045, Japan
- Department of Functional Transcriptomics for Medical Genetic Diagnostics, Graduate School of Medicine, Juntendo University, Tokyo 113-8421, Japan
- Correspondence: or (F.A.K.); (O.G.); (G.R.G.)
| | - Guzel R. Gazizova
- Extreme Biology Laboratory, Institute of Fundamental Medicine and Biology, Kazan Federal University, 420009 Kazan, Russia; (I.N.); (R.D.); (E.S.)
- Correspondence: or (F.A.K.); (O.G.); (G.R.G.)
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12
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Kalya M, Kel A, Wlochowitz D, Wingender E, Beißbarth T. IGFBP2 Is a Potential Master Regulator Driving the Dysregulated Gene Network Responsible for Short Survival in Glioblastoma Multiforme. Front Genet 2021; 12:670240. [PMID: 34211498 PMCID: PMC8239365 DOI: 10.3389/fgene.2021.670240] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Accepted: 04/06/2021] [Indexed: 01/01/2023] Open
Abstract
Only 2% of glioblastoma multiforme (GBM) patients respond to standard therapy and survive beyond 36 months (long-term survivors, LTS), while the majority survive less than 12 months (short-term survivors, STS). To understand the mechanism leading to poor survival, we analyzed publicly available datasets of 113 STS and 58 LTS. This analysis revealed 198 differentially expressed genes (DEGs) that characterize aggressive tumor growth and may be responsible for the poor prognosis. These genes belong largely to the Gene Ontology (GO) categories “epithelial-to-mesenchymal transition” and “response to hypoxia.” In this article, we applied an upstream analysis approach that involves state-of-the-art promoter analysis and network analysis of the dysregulated genes potentially responsible for short survival in GBM. Binding sites for transcription factors (TFs) associated with GBM pathology like NANOG, NF-κB, REST, FRA-1, PPARG, and seven others were found enriched in the promoters of the dysregulated genes. We reconstructed the gene regulatory network with several positive feedback loops controlled by five master regulators [insulin-like growth factor binding protein 2 (IGFBP2), vascular endothelial growth factor A (VEGFA), VEGF165, platelet-derived growth factor A (PDGFA), adipocyte enhancer-binding protein (AEBP1), and oncostatin M (OSMR)], which can be proposed as biomarkers and as therapeutic targets for enhancing GBM prognosis. A critical analysis of this gene regulatory network gives insights into the mechanism of gene regulation by IGFBP2 via several TFs including the key molecule of GBM tumor invasiveness and progression, FRA-1. All the observations were validated in independent cohorts, and their impact on overall survival has been investigated.
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Affiliation(s)
- Manasa Kalya
- Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany.,geneXplain GmbH, Wolfenbüttel, Germany
| | - Alexander Kel
- geneXplain GmbH, Wolfenbüttel, Germany.,Institute of Chemical Biology and Fundamental Medicine SB RAS, Novosibirsk, Russia
| | - Darius Wlochowitz
- Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany
| | | | - Tim Beißbarth
- Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany
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13
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Kolmykov S, Yevshin I, Kulyashov M, Sharipov R, Kondrakhin Y, Makeev VJ, Kulakovskiy IV, Kel A, Kolpakov F. GTRD: an integrated view of transcription regulation. Nucleic Acids Res 2021; 49:D104-D111. [PMID: 33231677 PMCID: PMC7778956 DOI: 10.1093/nar/gkaa1057] [Citation(s) in RCA: 126] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/18/2020] [Accepted: 11/03/2020] [Indexed: 12/24/2022] Open
Abstract
The Gene Transcription Regulation Database (GTRD; http://gtrd.biouml.org/) contains uniformly annotated and processed NGS data related to gene transcription regulation: ChIP-seq, ChIP-exo, DNase-seq, MNase-seq, ATAC-seq and RNA-seq. With the latest release, the database has reached a new level of data integration. All cell types (cell lines and tissues) presented in the GTRD were arranged into a dictionary and linked with different ontologies (BRENDA, Cell Ontology, Uberon, Cellosaurus and Experimental Factor Ontology) and with related experiments in specialized databases on transcription regulation (FANTOM5, ENCODE and GTEx). The updated version of the GTRD provides an integrated view of transcription regulation through a dedicated web interface with advanced browsing and search capabilities, an integrated genome browser, and table reports by cell types, transcription factors, and genes of interest.
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Affiliation(s)
- Semyon Kolmykov
- BIOSOFT.RU, LLC, Novosibirsk 630090, Russian Federation
- Federal Research Center for Information and Computational Technologies, Novosibirsk 630090, Russian Federation
- Federal Research Center Institute of Cytology and Genetics SB RAS, Novosibirsk 630090, Russian Federation
| | - Ivan Yevshin
- BIOSOFT.RU, LLC, Novosibirsk 630090, Russian Federation
- Federal Research Center for Information and Computational Technologies, Novosibirsk 630090, Russian Federation
| | - Mikhail Kulyashov
- BIOSOFT.RU, LLC, Novosibirsk 630090, Russian Federation
- Federal Research Center for Information and Computational Technologies, Novosibirsk 630090, Russian Federation
- Novosibirsk State University, Novosibirsk 630090, Russian Federation
| | - Ruslan Sharipov
- BIOSOFT.RU, LLC, Novosibirsk 630090, Russian Federation
- Federal Research Center for Information and Computational Technologies, Novosibirsk 630090, Russian Federation
- Novosibirsk State University, Novosibirsk 630090, Russian Federation
| | - Yury Kondrakhin
- BIOSOFT.RU, LLC, Novosibirsk 630090, Russian Federation
- Federal Research Center for Information and Computational Technologies, Novosibirsk 630090, Russian Federation
| | - Vsevolod J Makeev
- Vavilov Institute of General Genetics RAS, Moscow 119991, Russian Federation
- Moscow Institute of Physics and Technology (State University), Dolgoprudny 141700, Russian Federation
- NRC «Kurchatov Institute» - GOSNIIGENETIKA, Kurchatov Genomic Center, Moscow 123182, Russian Federation
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russian Federation
| | - Ivan V Kulakovskiy
- Vavilov Institute of General Genetics RAS, Moscow 119991, Russian Federation
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow 119991, Russian Federation
- Institute of Protein Research, Russian Academy of Sciences, Pushchino 142290, Russian Federation
| | - Alexander Kel
- BIOSOFT.RU, LLC, Novosibirsk 630090, Russian Federation
- geneXplain GmbH, 38302 Wolfenbüttel, Germany
- Institute of Chemical Biology and Fundamental Medicine SB RAS, Novosibirsk 630090, Russian Federation
| | - Fedor Kolpakov
- BIOSOFT.RU, LLC, Novosibirsk 630090, Russian Federation
- Federal Research Center for Information and Computational Technologies, Novosibirsk 630090, Russian Federation
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Sekiguchi T, Hamada H, Okamoto M. WinBEST-KIT: Biochemical reaction simulator that can define and customize algebraic equations and events as GUI components. J Bioinform Comput Biol 2020; 17:1950036. [PMID: 32019416 DOI: 10.1142/s0219720019500367] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
We previously developed Windows-based Biochemical Engineering System analyzing Tool-KIT (WinBEST-KIT), a biochemical reaction simulator for analyzing large-scale and complicated biochemical reaction networks. One particularly notable feature is the ability for users to define original mathematical equations for representing unknown kinetic mechanisms and customize them as GUI components for representing reaction steps. Many simulators support System Biology Markup Language SBML; however, since the definition of the algebraic equations (AssignmentRule) and the events are made through an interface that is distinct from the definition of the reaction steps, there are tough works to define them. Accordingly, we have developed a new version of WinBEST-KIT that allows users to define the algebraic equations and the events through the same interface as those used in the definition of the reaction steps and customize them as GUI components appearing in the symbol selection area. The customized algebraic equations and events can thus be visually arranged at any time and any place. It also allows users to easily understand the roles of the algebraic equations and the events. We have also implemented other useful features, including importing/exporting of SBML format files, exporting to MATLAB, and merging the existing models into the model currently being created. The current version of WinBEST-KIT is freely available at http://winbest-kit.org/.
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Affiliation(s)
- Tatsuya Sekiguchi
- Department of Life Sciences and Informatics, Faculty of Engineering, Maebashi Institute of Technology, 460-1, Kamisatori-cho, Maebashi, Gunma 371-0816, Japan
| | - Hiroyuki Hamada
- Graduate School of Systems Life Sciences, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka 819-0395, Japan
| | - Masahiro Okamoto
- Graduate School of Systems Life Sciences, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka 819-0395, Japan
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15
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Maggioli F, Mancini T, Tronci E. SBML2Modelica: integrating biochemical models within open-standard simulation ecosystems. Bioinformatics 2020; 36:2165-2172. [PMID: 31738386 DOI: 10.1093/bioinformatics/btz860] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Revised: 10/15/2019] [Accepted: 11/15/2019] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION SBML is the most widespread language for the definition of biochemical models. Although dozens of SBML simulators are available, there is a general lack of support to the integration of SBML models within open-standard general-purpose simulation ecosystems. This hinders co-simulation and integration of SBML models within larger model networks, in order to, e.g. enable in silico clinical trials of drugs, pharmacological protocols, or engineering artefacts such as biomedical devices against Virtual Physiological Human models. Modelica is one of the most popular existing open-standard general-purpose simulation languages, supported by many simulators. Modelica models are especially suited for the definition of complex networks of heterogeneous models from virtually all application domains. Models written in Modelica (and in 100+ other languages) can be readily exported into black-box Functional Mock-Up Units (FMUs), and seamlessly co-simulated and integrated into larger model networks within open-standard language-independent simulation ecosystems. RESULTS In order to enable SBML model integration within heterogeneous model networks, we present SBML2Modelica, a software system translating SBML models into well-structured, user-intelligible, easily modifiable Modelica models. SBML2Modelica is SBML Level 3 Version 2-compliant and succeeds on 96.47% of the SBML Test Suite Core (with a few rare, intricate and easily avoidable combinations of constructs unsupported and cleanly signalled to the user). Our experimental campaign on 613 models from the BioModels database (with up to 5438 variables) shows that the major open-source (general-purpose) Modelica and FMU simulators achieve performance comparable to state-of-the-art specialized SBML simulators. AVAILABILITY AND IMPLEMENTATION SBML2Modelica is written in Java and is freely available for non-commercial use at https://bitbucket.org/mclab/sbml2modelica.
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Affiliation(s)
- F Maggioli
- Computer Science Department, Sapienza University of Rome, Rome, Italy
| | - T Mancini
- Computer Science Department, Sapienza University of Rome, Rome, Italy
| | - E Tronci
- Computer Science Department, Sapienza University of Rome, Rome, Italy
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16
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Kulyashov M, Peltek SE, Akberdin IR. A Genome-Scale Metabolic Model of 2,3-Butanediol Production by Thermophilic Bacteria Geobacillus icigianus. Microorganisms 2020; 8:E1002. [PMID: 32635563 PMCID: PMC7409357 DOI: 10.3390/microorganisms8071002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 07/01/2020] [Accepted: 07/02/2020] [Indexed: 11/16/2022] Open
Abstract
The thermophilic strain of the genus Geobacillus, Geobacillus icigianus is a promising bacterial chassis for a wide range of biotechnological applications. In this study, we explored the metabolic potential of Geobacillus icigianus for the production of 2,3-butanediol (2,3-BTD), one of the cost-effective commodity chemicals. Here we present a genome-scale metabolic model iMK1321 for Geobacillus icigianus constructed using an auto-generating pipeline with consequent thorough manual curation. The model contains 1321 genes and includes 1676 reactions and 1589 metabolites, representing the most-complete and publicly available model of the genus Geobacillus. The developed model provides new insights into thermophilic bacterial metabolism and highlights new strategies for biotechnological applications of the strain. Our analysis suggests that Geobacillus icigianus has a potential for 2,3-butanediol production from a variety of utilized carbon sources, including glycerine, a common byproduct of biofuel production. We identified a set of solutions for enhancing 2,3-BTD production, including cultivation under anaerobic or microaerophilic conditions and decreasing the TCA flux to succinate via reducing citrate synthase activity. Both in silico predicted metabolic alternatives have been previously experimentally verified for closely related strains including the genus Bacillus.
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Affiliation(s)
- Mikhail Kulyashov
- Biosoft.ru, 630058 Novosibirsk, Russia;
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Department of Bioinformatics, Federal Research Center for Information and Computational Technologies, 630090 Novosibirsk, Russia
| | - Sergey E. Peltek
- Department of Molecular Biotechnology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia;
| | - Ilya R. Akberdin
- Biosoft.ru, 630058 Novosibirsk, Russia;
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Department of Molecular Biotechnology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia;
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Schaduangrat N, Lampa S, Simeon S, Gleeson MP, Spjuth O, Nantasenamat C. Towards reproducible computational drug discovery. J Cheminform 2020; 12:9. [PMID: 33430992 PMCID: PMC6988305 DOI: 10.1186/s13321-020-0408-x] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 01/02/2020] [Indexed: 12/11/2022] Open
Abstract
The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.
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Affiliation(s)
- Nalini Schaduangrat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, 10700, Bangkok, Thailand
| | - Samuel Lampa
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden
| | - Saw Simeon
- Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart University, 10900, Bangkok, Thailand
| | - Matthew Paul Gleeson
- Department of Biomedical Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, 10520, Bangkok, Thailand.
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden.
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, 10700, Bangkok, Thailand.
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Ortega OO, Lopez CF. Interactive Multiresolution Visualization of Cellular Network Processes. iScience 2019; 23:100748. [PMID: 31884165 PMCID: PMC6941861 DOI: 10.1016/j.isci.2019.100748] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 11/08/2019] [Accepted: 11/25/2019] [Indexed: 12/24/2022] Open
Abstract
Visualization plays a central role in the analysis of biochemical network models to identify patterns that arise from reaction dynamics and perform model exploratory analysis. To facilitate these analyses, we developed PyViPR, a visualization tool that generates static and dynamic representations of biochemical network processes within a Python-based environment. PyViPR embeds network visualizations within Jupyter notebooks, thus enabling integration with modeling, simulation, and analysis workflows. To present the capabilities of PyViPR, we explore execution mechanisms of extrinsic apoptosis in HeLa cells. We show that community-detection algorithms identify groups of molecular species that capture key biological functions and ease exploration of the apoptosis network. We then show how different kinetic parameter sets that fit the experimental data equally well exhibit significantly different signal-execution dynamics as the system progresses toward mitochondrial outer-membrane permeabilization. Therefore, PyViPR aids the conceptual understanding of dynamic network processes and accelerates hypothesis generation for further testing and validation.
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Affiliation(s)
- Oscar O Ortega
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, USA
| | - Carlos F Lopez
- Chemical and Physical Biology Program, Vanderbilt University, Nashville, TN, USA; Biochemistry Department, Vanderbilt University, Nashville, TN, USA.
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Population size estimation for quality control of ChIP-Seq datasets. PLoS One 2019; 14:e0221760. [PMID: 31465497 PMCID: PMC6715275 DOI: 10.1371/journal.pone.0221760] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 08/14/2019] [Indexed: 01/16/2023] Open
Abstract
Chromatin immunoprecipitation followed by sequencing, i.e. ChIP-Seq, is a widely used experimental technology for the identification of functional protein-DNA interactions. Nowadays, such databases as ENCODE, GTRD, ChIP-Atlas and ReMap systematically collect and annotate a large number of ChIP-Seq datasets. Comprehensive control of dataset quality is currently indispensable to select the most reliable data for further analysis. In addition to existing quality control metrics, we have developed two novel metrics that allow to control false positives and false negatives in ChIP-Seq datasets. For this purpose, we have adapted well-known population size estimate for determination of unknown number of genuine transcription factor binding regions. Determination of the proposed metrics was based on overlapping distinct binding sites derived from processing one ChIP-Seq experiment by different peak callers. Moreover, the metrics also can be useful for assessing quality of datasets obtained from processing distinct ChIP-Seq experiments by a given peak caller. We also have shown that these metrics appear to be useful not only for dataset selection but also for comparison of peak callers and identification of site motifs based on ChIP-Seq datasets. The developed algorithm for determination of the false positive control metric and false negative control metric for ChIP-Seq datasets was implemented as a plugin for a BioUML platform: https://ict.biouml.org/bioumlweb/chipseq_analysis.html.
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