1
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Kundu P, Beura S, Mondal S, Das AK, Ghosh A. Machine learning for the advancement of genome-scale metabolic modeling. Biotechnol Adv 2024; 74:108400. [PMID: 38944218 DOI: 10.1016/j.biotechadv.2024.108400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 05/13/2024] [Accepted: 06/23/2024] [Indexed: 07/01/2024]
Abstract
Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.
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Affiliation(s)
- Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Satyajit Beura
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Suman Mondal
- P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Kumar Das
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
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2
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Nemkov T, Stephenson D, Earley EJ, Keele GR, Hay A, Key A, Haiman ZB, Erickson C, Dzieciatkowska M, Reisz JA, Moore A, Stone M, Deng X, Kleinman S, Spitalnik SL, Hod EA, Hudson KE, Hansen KC, Palsson BO, Churchill GA, Roubinian N, Norris PJ, Busch MP, Zimring JC, Page GP, D'Alessandro A. Biological and genetic determinants of glycolysis: Phosphofructokinase isoforms boost energy status of stored red blood cells and transfusion outcomes. Cell Metab 2024:S1550-4131(24)00232-8. [PMID: 38964323 DOI: 10.1016/j.cmet.2024.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 01/04/2024] [Accepted: 06/07/2024] [Indexed: 07/06/2024]
Abstract
Mature red blood cells (RBCs) lack mitochondria and thus exclusively rely on glycolysis to generate adenosine triphosphate (ATP) during aging in vivo or storage in blood banks. Here, we leveraged 13,029 volunteers from the Recipient Epidemiology and Donor Evaluation Study to identify associations between end-of-storage levels of glycolytic metabolites and donor age, sex, and ancestry-specific genetic polymorphisms in regions encoding phosphofructokinase 1, platelet (detected in mature RBCs); hexokinase 1 (HK1); and ADP-ribosyl cyclase 1 and 2 (CD38/BST1). Gene-metabolite associations were validated in fresh and stored RBCs from 525 Diversity Outbred mice and via multi-omics characterization of 1,929 samples from 643 human RBC units during storage. ATP and hypoxanthine (HYPX) levels-and the genetic traits linked to them-were associated with hemolysis in vitro and in vivo, both in healthy autologous transfusion recipients and in 5,816 critically ill patients receiving heterologous transfusions, suggesting their potential as markers to improve transfusion outcomes.
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Affiliation(s)
- Travis Nemkov
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver - Anschutz Medical Campus, Aurora, CO, USA; Omix Technologies Inc., Aurora, CO, USA
| | - Daniel Stephenson
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver - Anschutz Medical Campus, Aurora, CO, USA
| | | | | | - Ariel Hay
- Department of Pathology, University of Virginia, Charlottesville, VA, USA
| | - Alicia Key
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver - Anschutz Medical Campus, Aurora, CO, USA
| | - Zachary B Haiman
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Christopher Erickson
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver - Anschutz Medical Campus, Aurora, CO, USA
| | - Monika Dzieciatkowska
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver - Anschutz Medical Campus, Aurora, CO, USA
| | - Julie A Reisz
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver - Anschutz Medical Campus, Aurora, CO, USA
| | | | - Mars Stone
- Vitalant Research Institute, San Francisco, CA, USA; Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Xutao Deng
- Vitalant Research Institute, San Francisco, CA, USA; Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | | | - Steven L Spitalnik
- Department of Pathology & Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Eldad A Hod
- Department of Pathology & Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Krystalyn E Hudson
- Department of Pathology & Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Kirk C Hansen
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver - Anschutz Medical Campus, Aurora, CO, USA; Omix Technologies Inc., Aurora, CO, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | | | - Nareg Roubinian
- Vitalant Research Institute, San Francisco, CA, USA; Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA; Kaiser Permanente Northern California Division of Research, Oakland, CA, USA
| | - Philip J Norris
- Vitalant Research Institute, San Francisco, CA, USA; Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Michael P Busch
- Vitalant Research Institute, San Francisco, CA, USA; Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - James C Zimring
- Department of Pathology, University of Virginia, Charlottesville, VA, USA
| | | | - Angelo D'Alessandro
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver - Anschutz Medical Campus, Aurora, CO, USA; Omix Technologies Inc., Aurora, CO, USA.
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3
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Nemkov T, Stephenson D, Earley EJ, Keele GR, Hay A, Key A, Haiman Z, Erickson C, Dzieciatkowska M, Reisz JA, Moore A, Stone M, Deng X, Kleinman S, Spitalnik SL, Hod EA, Hudson KE, Hansen KC, Palsson BO, Churchill GA, Roubinian N, Norris PJ, Busch MP, Zimring JC, Page GP, D'Alessandro A. Biological and Genetic Determinants of Glycolysis: Phosphofructokinase Isoforms Boost Energy Status of Stored Red Blood Cells and Transfusion Outcomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.11.557250. [PMID: 38260479 PMCID: PMC10802247 DOI: 10.1101/2023.09.11.557250] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Mature red blood cells (RBCs) lack mitochondria, and thus exclusively rely on glycolysis to generate adenosine triphosphate (ATP) during aging in vivo or storage in the blood bank. Here we leveraged 13,029 volunteers from the Recipient Epidemiology and Donor Evaluation Study to identify an association between end-of-storage levels of glycolytic metabolites and donor age, sex, and ancestry-specific genetic polymorphisms in regions encoding phosphofructokinase 1, platelet (detected in mature RBCs), hexokinase 1, ADP-ribosyl cyclase 1 and 2 (CD38/BST1). Gene-metabolite associations were validated in fresh and stored RBCs from 525 Diversity Outbred mice, and via multi-omics characterization of 1,929 samples from 643 human RBC units during storage. ATP and hypoxanthine levels - and the genetic traits linked to them - were associated with hemolysis in vitro and in vivo, both in healthy autologous transfusion recipients and in 5,816 critically ill patients receiving heterologous transfusions, suggesting their potential as markers to improve transfusion outcomes. eTOC and Highlights Highlights Blood donor age and sex affect glycolysis in stored RBCs from 13,029 volunteers;Ancestry, genetic polymorphisms in PFKP, HK1, CD38/BST1 influence RBC glycolysis;Modeled PFKP effects relate to preventing loss of the total AXP pool in stored RBCs;ATP and hypoxanthine are biomarkers of hemolysis in vitro and in vivo.
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4
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Turanli B, Gulfidan G, Aydogan OO, Kula C, Selvaraj G, Arga KY. Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models. Mol Omics 2024; 20:234-247. [PMID: 38444371 DOI: 10.1039/d3mo00152k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.
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Affiliation(s)
- Beste Turanli
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Gizem Gulfidan
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
| | - Ozge Onluturk Aydogan
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
| | - Ceyda Kula
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Gurudeeban Selvaraj
- Concordia University, Centre for Research in Molecular Modeling & Department of Chemistry and Biochemistry, Quebec, Canada
- Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha Dental College and Hospital, Department of Biomaterials, Bioinformatics Unit, Chennai, India
| | - Kazim Yalcin Arga
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
- Marmara University, Genetic and Metabolic Diseases Research and Investigation Center, Istanbul, Turkey
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Jin X, Zhang Y, Wang D, Zhang X, Li Y, Wang D, Liang Y, Wang J, Zheng L, Song H, Zhu X, Liang J, Ma J, Gao J, Tong J, Shi L. Metabolite and protein shifts in mature erythrocyte under hypoxia. iScience 2024; 27:109315. [PMID: 38487547 PMCID: PMC10937114 DOI: 10.1016/j.isci.2024.109315] [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] [Indexed: 03/17/2024] Open
Abstract
As the only cell type responsible for oxygen delivery, erythrocytes play a crucial role in supplying oxygen to hypoxic tissues, ensuring their normal functions. Hypoxia commonly occurs under physiological or pathological conditions, and understanding how erythrocytes adapt to hypoxia is fundamental for exploring the mechanisms of hypoxic diseases. Additionally, investigating acute and chronic mountain sickness caused by plateaus, which are naturally hypoxic environments, will aid in the study of hypoxic diseases. In recent years, increasingly developed proteomics and metabolomics technologies have become powerful tools for studying mature enucleated erythrocytes, which has significantly contributed to clarifying how hypoxia affects erythrocytes. The aim of this article is to summarize the composition of the cytoskeleton and cytoplasmic proteins of hypoxia-altered erythrocytes and explore the impact of hypoxia on their essential functions. Furthermore, we discuss the role of microRNAs in the adaptation of erythrocytes to hypoxia, providing new perspectives on hypoxia-related diseases.
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Affiliation(s)
- Xu Jin
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
| | - Yingnan Zhang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
| | - Ding Wang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
| | - Xiaoru Zhang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
| | - Yue Li
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
| | - Di Wang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
| | - Yipeng Liang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
| | - Jingwei Wang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
| | - Lingyue Zheng
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
| | - Haoze Song
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
| | - Xu Zhu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
| | - Jing Liang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
| | - Jinfa Ma
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
| | - Jie Gao
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
| | - Jingyuan Tong
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
| | - Lihong Shi
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
- Tianjin Institutes of Health Science, Tianjin 301600, China
- CAMS Center for Stem Cell Medicine, PUMC Department of Stem Cell and Regenerative Medicine, Tianjin 300020, China
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6
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Chen Y, Gustafsson J, Yang J, Nielsen J, Kerkhoven EJ. Single-cell omics analysis with genome-scale metabolic modeling. Curr Opin Biotechnol 2024; 86:103078. [PMID: 38359604 DOI: 10.1016/j.copbio.2024.103078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/19/2024] [Indexed: 02/17/2024]
Abstract
Single-cell technologies have been widely used in biological studies and generated a plethora of single-cell data to be interpreted. Due to the inclusion of the priori metabolic network knowledge as well as gene-protein-reaction associations, genome-scale metabolic models (GEMs) have been a powerful tool to integrate and thereby interpret various omics data mostly from bulk samples. Here, we first review two common ways to leverage bulk omics data with GEMs and then discuss advances on integrative analysis of single-cell omics data with GEMs. We end by presenting our views on current challenges and perspectives in this field.
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Affiliation(s)
- Yu Chen
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
| | - Johan Gustafsson
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, SE-405 30 Gothenburg, Sweden; Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Jingyu Yang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jens Nielsen
- Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; BioInnovation Institute, DK-2200 Copenhagen, Denmark
| | - Eduard J Kerkhoven
- Department of Life Sciences, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technology University of Denmark, DK-2800 Kgs. Lyngby, Denmark; SciLifeLab, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.
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Chatzinikolaou PN, Margaritelis NV, Paschalis V, Theodorou AA, Vrabas IS, Kyparos A, D'Alessandro A, Nikolaidis MG. Erythrocyte metabolism. Acta Physiol (Oxf) 2024; 240:e14081. [PMID: 38270467 DOI: 10.1111/apha.14081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 12/11/2023] [Accepted: 01/01/2024] [Indexed: 01/26/2024]
Abstract
Our aim is to present an updated overview of the erythrocyte metabolism highlighting its richness and complexity. We have manually collected and connected the available biochemical pathways and integrated them into a functional metabolic map. The focus of this map is on the main biochemical pathways consisting of glycolysis, the pentose phosphate pathway, redox metabolism, oxygen metabolism, purine/nucleoside metabolism, and membrane transport. Other recently emerging pathways are also curated, like the methionine salvage pathway, the glyoxalase system, carnitine metabolism, and the lands cycle, as well as remnants of the carboxylic acid metabolism. An additional goal of this review is to present the dynamics of erythrocyte metabolism, providing key numbers used to perform basic quantitative analyses. By synthesizing experimental and computational data, we conclude that glycolysis, pentose phosphate pathway, and redox metabolism are the foundations of erythrocyte metabolism. Additionally, the erythrocyte can sense oxygen levels and oxidative stress adjusting its mechanics, metabolism, and function. In conclusion, fine-tuning of erythrocyte metabolism controls one of the most important biological processes, that is, oxygen loading, transport, and delivery.
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Affiliation(s)
- Panagiotis N Chatzinikolaou
- Department of Physical Education and Sports Science at Serres, Aristotle University of Thessaloniki, Serres, Greece
| | - Nikos V Margaritelis
- Department of Physical Education and Sports Science at Serres, Aristotle University of Thessaloniki, Serres, Greece
| | - Vassilis Paschalis
- School of Physical Education and Sport Science, National and Kapodistrian University of Athens, Athens, Greece
| | - Anastasios A Theodorou
- Department of Life Sciences, School of Sciences, European University Cyprus, Nicosia, Cyprus
| | - Ioannis S Vrabas
- Department of Physical Education and Sports Science at Serres, Aristotle University of Thessaloniki, Serres, Greece
| | - Antonios Kyparos
- Department of Physical Education and Sports Science at Serres, Aristotle University of Thessaloniki, Serres, Greece
| | - Angelo D'Alessandro
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Michalis G Nikolaidis
- Department of Physical Education and Sports Science at Serres, Aristotle University of Thessaloniki, Serres, Greece
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Jóhannsson F, Yurkovich JT, Guðmundsson S, Sigurjónsson ÓE, Rolfsson Ó. Temperature Dependence of Platelet Metabolism. Metabolites 2024; 14:91. [PMID: 38392983 PMCID: PMC10890334 DOI: 10.3390/metabo14020091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/16/2024] [Accepted: 01/19/2024] [Indexed: 02/25/2024] Open
Abstract
Temperature plays a fundamental role in biology, influencing cellular function, chemical reaction rates, molecular structures, and interactions. While the temperature dependence of many biochemical reactions is well defined in vitro, the effect of temperature on metabolic function at the network level is poorly understood, and it remains an important challenge in optimizing the storage of cells and tissues at lower temperatures. Here, we used time-course metabolomic data and systems biology approaches to characterize the effects of storage temperature on human platelets (PLTs) in a platelet additive solution. We observed that changes to the metabolome with storage time do not simply scale with temperature but instead display complex temperature dependence, with only a small subset of metabolites following an Arrhenius-type relationship. Investigation of PLT energy metabolism through integration with computational modeling revealed that oxidative metabolism is more sensitive to temperature changes than glycolysis. The increased contribution of glycolysis to ATP turnover at lower temperatures indicates a stronger glycolytic phenotype with decreasing storage temperature. More broadly, these results demonstrate that the temperature dependence of the PLT metabolic network is not uniform, suggesting that efforts to improve the health of stored PLTs could be targeted at specific pathways.
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Affiliation(s)
- Freyr Jóhannsson
- Center for Systems Biology, University of Iceland, Sturlugata 8, 102 Reykjavik, Iceland
- School of Health Sciences, Medical Department, University of Iceland, Sturlugata 8, 102 Reykjavik, Iceland
| | - James T Yurkovich
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
- Phenome Health, Seattle, WA 98109, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA 94945, USA
| | - Steinn Guðmundsson
- Center for Systems Biology, University of Iceland, Sturlugata 8, 102 Reykjavik, Iceland
- Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Dunhagi 3, 107 Reykjavik, Iceland
| | - Ólafur E Sigurjónsson
- The Blood Bank, Landspitali-University Hospital, Snorrabraut 60, 101 Reykjavik, Iceland
- School of Science and Engineering, Reykjavik University, Menntavegur 1, 102 Reykjavik, Iceland
| | - Óttar Rolfsson
- Center for Systems Biology, University of Iceland, Sturlugata 8, 102 Reykjavik, Iceland
- School of Health Sciences, Medical Department, University of Iceland, Sturlugata 8, 102 Reykjavik, Iceland
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9
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Key A, Haiman Z, Palsson BO, D’Alessandro A. Modeling Red Blood Cell Metabolism in the Omics Era. Metabolites 2023; 13:1145. [PMID: 37999241 PMCID: PMC10673375 DOI: 10.3390/metabo13111145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 10/23/2023] [Accepted: 11/08/2023] [Indexed: 11/25/2023] Open
Abstract
Red blood cells (RBCs) are abundant (more than 80% of the total cells in the human body), yet relatively simple, as they lack nuclei and organelles, including mitochondria. Since the earliest days of biochemistry, the accessibility of blood and RBCs made them an ideal matrix for the characterization of metabolism. Because of this, investigations into RBC metabolism are of extreme relevance for research and diagnostic purposes in scientific and clinical endeavors. The relative simplicity of RBCs has made them an eligible model for the development of reconstruction maps of eukaryotic cell metabolism since the early days of systems biology. Computational models hold the potential to deepen knowledge of RBC metabolism, but also and foremost to predict in silico RBC metabolic behaviors in response to environmental stimuli. Here, we review now classic concepts on RBC metabolism, prior work in systems biology of unicellular organisms, and how this work paved the way for the development of reconstruction models of RBC metabolism. Translationally, we discuss how the fields of metabolomics and systems biology have generated evidence to advance our understanding of the RBC storage lesion, a process of decline in storage quality that impacts over a hundred million blood units transfused every year.
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Affiliation(s)
- Alicia Key
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO 80045, USA;
| | - Zachary Haiman
- Department of Bioengineering, University of California, San Diego, CA 92093, USA (B.O.P.)
- Bioinformatics and Systems Biology Program, University of California, San Diego, CA 92093, USA
- Department of Pediatrics, University of California, San Diego, CA 92161, USA
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, CA 92093, USA (B.O.P.)
- Bioinformatics and Systems Biology Program, University of California, San Diego, CA 92093, USA
- Department of Pediatrics, University of California, San Diego, CA 92161, USA
| | - Angelo D’Alessandro
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO 80045, USA;
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10
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Wendering P, Nikoloski Z. Model-driven insights into the effects of temperature on metabolism. Biotechnol Adv 2023; 67:108203. [PMID: 37348662 DOI: 10.1016/j.biotechadv.2023.108203] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 05/22/2023] [Accepted: 06/18/2023] [Indexed: 06/24/2023]
Abstract
Temperature affects cellular processes at different spatiotemporal scales, and identifying the genetic and molecular mechanisms underlying temperature responses paves the way to develop approaches for mitigating the effects of future climate scenarios. A systems view of the effects of temperature on cellular physiology can be obtained by focusing on metabolism since: (i) its functions depend on transcription and translation and (ii) its outcomes support organisms' development, growth, and reproduction. Here we provide a systematic review of modelling efforts directed at investigating temperature effects on properties of single biochemical reactions, system-level traits, metabolic subsystems, and whole-cell metabolism across different prokaryotes and eukaryotes. We compare and contrast computational approaches and theories that facilitate modelling of temperature effects on key properties of enzymes and their consideration in constraint-based as well as kinetic models of metabolism. In addition, we provide a summary of insights from computational approaches, facilitating integration of omics data from temperature-modulated experiments with models of metabolic networks, and review the resulting biotechnological applications. Lastly, we provide a perspective on how different types of metabolic modelling can profit from developments in machine learning and models of different cellular layers to improve model-driven insights into the effects of temperature relevant for biotechnological applications.
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Affiliation(s)
- Philipp Wendering
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany; Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, 14476 Potsdam, Germany; Systems Biology and Mathematical Modeling, Max Planck Institute of Molecular Plant Physiology, 14476 Potsdam, Germany.
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11
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Karlsen ST, Rau MH, Sánchez BJ, Jensen K, Zeidan AA. From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry. FEMS Microbiol Rev 2023; 47:fuad030. [PMID: 37286882 PMCID: PMC10337747 DOI: 10.1093/femsre/fuad030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/31/2023] [Accepted: 06/06/2023] [Indexed: 06/09/2023] Open
Abstract
When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.
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Affiliation(s)
- Signe T Karlsen
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Martin H Rau
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Benjamín J Sánchez
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Kristian Jensen
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
| | - Ahmad A Zeidan
- Bioinformatics & Modeling, R&D Digital Innovation, Chr. Hansen A/S, Bøge Allé 10-12, 2970 Hørsholm, Denmark
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12
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D’Alessandro A, Anastasiadi AT, Tzounakas VL, Nemkov T, Reisz JA, Kriebardis AG, Zimring JC, Spitalnik SL, Busch MP. Red Blood Cell Metabolism In Vivo and In Vitro. Metabolites 2023; 13:793. [PMID: 37512500 PMCID: PMC10386156 DOI: 10.3390/metabo13070793] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 06/23/2023] [Accepted: 06/25/2023] [Indexed: 07/30/2023] Open
Abstract
Red blood cells (RBC) are the most abundant cell in the human body, with a central role in oxygen transport and its delivery to tissues. However, omics technologies recently revealed the unanticipated complexity of the RBC proteome and metabolome, paving the way for a reinterpretation of the mechanisms by which RBC metabolism regulates systems biology beyond oxygen transport. The new data and analytical tools also informed the dissection of the changes that RBCs undergo during refrigerated storage under blood bank conditions, a logistic necessity that makes >100 million units available for life-saving transfusions every year worldwide. In this narrative review, we summarize the last decade of advances in the field of RBC metabolism in vivo and in the blood bank in vitro, a narrative largely influenced by the authors' own journeys in this field. We hope that this review will stimulate further research in this interesting and medically important area or, at least, serve as a testament to our fascination with this simple, yet complex, cell.
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Affiliation(s)
- Angelo D’Alessandro
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (T.N.); (J.A.R.)
| | - Alkmini T. Anastasiadi
- Laboratory of Reliability and Quality Control in Laboratory Hematology (HemQcR), Department of Biomedical Sciences, School of Health & Caring Sciences, University of West Attica (UniWA), 12243 Egaleo, Greece; (A.T.A.); (A.G.K.)
- Department of Biochemistry, School of Medicine, University of Patras, 26504 Patras, Greece;
| | - Vassilis L. Tzounakas
- Department of Biochemistry, School of Medicine, University of Patras, 26504 Patras, Greece;
| | - Travis Nemkov
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (T.N.); (J.A.R.)
| | - Julie A. Reisz
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (T.N.); (J.A.R.)
| | - Anastsios G. Kriebardis
- Laboratory of Reliability and Quality Control in Laboratory Hematology (HemQcR), Department of Biomedical Sciences, School of Health & Caring Sciences, University of West Attica (UniWA), 12243 Egaleo, Greece; (A.T.A.); (A.G.K.)
| | - James C. Zimring
- Department of Pathology, University of Virginia, Charlottesville, VA 22903, USA;
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13
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D'Alessandro A. Red Blood Cell Omics and Machine Learning in Transfusion Medicine: Singularity Is Near. Transfus Med Hemother 2023; 50:174-183. [PMID: 37434999 PMCID: PMC10331163 DOI: 10.1159/000529744] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 02/14/2023] [Indexed: 07/30/2023] Open
Abstract
Background Blood transfusion is a life-saving intervention for millions of recipients worldwide. Over the last 15 years, the advent of high-throughput, affordable omics technologies - including genomics, proteomics, lipidomics, and metabolomics - has allowed transfusion medicine to revisit the biology of blood donors, stored blood products, and transfusion recipients. Summary Omics approaches have shed light on the genetic and non-genetic factors (environmental or other exposures) impacting the quality of stored blood products and efficacy of transfusion events, based on the current Food and Drug Administration guidelines (e.g., hemolysis and post-transfusion recovery for stored red blood cells). As a treasure trove of data accumulates, the implementation of machine learning approaches promises to revolutionize the field of transfusion medicine, not only by advancing basic science. Indeed, computational strategies have already been used to perform high-content screenings of red blood cell morphology in microfluidic devices, generate in silico models of erythrocyte membrane to predict deformability and bending rigidity, or design systems biology maps of the red blood cell metabolome to drive the development of novel storage additives. Key Message In the near future, high-throughput testing of donor genomes via precision transfusion medicine arrays and metabolomics of all donated products will be able to inform the development and implementation of machine learning strategies that match, from vein to vein, donors, optimal processing strategies (additives, shelf life), and recipients, realizing the promise of personalized transfusion medicine.
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Affiliation(s)
- Angelo D'Alessandro
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
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14
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Kuriya Y, Murata M, Yamamoto M, Watanabe N, Araki M. Prediction of Metabolic Flux Distribution by Flux Sampling: As a Case Study, Acetate Production from Glucose in Escherichia coli. Bioengineering (Basel) 2023; 10:636. [PMID: 37370567 DOI: 10.3390/bioengineering10060636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/21/2023] [Accepted: 05/22/2023] [Indexed: 06/29/2023] Open
Abstract
Omics data was acquired, and the development and research of metabolic simulation and analysis methods using them were also actively carried out. However, it was a laborious task to acquire such data each time the medium composition, culture conditions, and target organism changed. Therefore, in this study, we aimed to extract and estimate important variables and necessary numbers for predicting metabolic flux distribution as the state of cell metabolism by flux sampling using a genome-scale metabolic model (GSM) and its analysis. Acetic acid production from glucose in Escherichia coli with GSM iJO1366 was used as a case study. Flux sampling obtained by OptGP using 1000 pattern constraints on substrate, product, and growth fluxes produced a wider sample than the default case. The analysis also suggested that the fluxes of iron ions, O2, CO2, and NH4+, were important for predicting the metabolic flux distribution. Additionally, the comparison with the literature value of 13C-MFA using CO2 emission flux as an example of an important flux suggested that the important flux obtained by this method was valid for the prediction of flux distribution. In this way, the method of this research was useful for extracting variables that were important for predicting flux distribution, and as a result, the possibility of contributing to the reduction of measurement variables in experiments was suggested.
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Affiliation(s)
- Yuki Kuriya
- Artificial Intelligence Center for Health and Biomedical Research, National Institute of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Settsu 566-0002, Japan
| | - Masahiro Murata
- Graduate School of Science, Technology and Innovation, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan
| | - Masaki Yamamoto
- Artificial Intelligence Center for Health and Biomedical Research, National Institute of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Settsu 566-0002, Japan
| | - Naoki Watanabe
- Artificial Intelligence Center for Health and Biomedical Research, National Institute of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Settsu 566-0002, Japan
| | - Michihiro Araki
- Artificial Intelligence Center for Health and Biomedical Research, National Institute of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Settsu 566-0002, Japan
- Graduate School of Science, Technology and Innovation, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe 657-8501, Japan
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15
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Luo G, Wosinski P, Salazar-Noratto GE, Bensidhoum M, Bizios R, Marashi SA, Potier E, Sheng P, Petite H. Glucose Metabolism: Optimizing Regenerative Functionalities of Mesenchymal Stromal Cells Postimplantation. TISSUE ENGINEERING. PART B, REVIEWS 2023; 29:47-61. [PMID: 35754335 DOI: 10.1089/ten.teb.2022.0063] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Mesenchymal stromal cells (MSCs) are considered promising candidates for regenerative medicine applications. Their clinical performance postimplantation, however, has been disappointing. This lack of therapeutic efficacy is most likely due to suboptimal formulations of MSC-containing material constructs. Tissue engineers, therefore, have developed strategies addressing/incorporating optimized cell, microenvironmental, biochemical, and biophysical cues/stimuli to enhance MSC-containing construct performance. Such approaches have had limited success because they overlooked that maintenance of MSC viability after implantation for a sufficient time is necessary for MSCs to develop their regenerative functionalities fully. Following a brief overview of glucose metabolism and regulation in MSCs, the present literature review includes recent pertinent findings that challenge old paradigms and notions. We hereby report that glucose is the primary energy substrate for MSCs, provides precursors for biomass generation, and regulates MSC functions, including proliferation and immunosuppressive properties. More importantly, glucose metabolism is central in controlling in vitro MSC expansion, in vivo MSC viability, and MSC-mediated angiogenesis postimplantation when addressing MSC-based therapies. Meanwhile, in silico models are highlighted for predicting the glucose needs of MSCs in specific regenerative medicine settings, which will eventually enable tissue engineers to design viable and potent tissue constructs. This new knowledge should be incorporated into developing novel effective MSC-based therapies. Impact statement The clinical use of mesenchymal stromal cells (MSCs) has been unsatisfactory due to the inability of MSCs to survive and be functional after implantation for sufficient periods to mediate directly or indirectly a successful regenerative tissue response. The present review summarizes the endeavors in the past, but, most importantly, reports the latest findings that elucidate underlying mechanisms and identify glucose metabolism as the crucial parameter in MSC survival and the subsequent functions pertinent to new tissue formation of importance in tissue regeneration applications. These latest findings justify further basic research and the impetus for developing new strategies to improve the modalities and efficacy of MSC-based therapies.
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Affiliation(s)
- Guotian Luo
- Université Paris Cité, CNRS, INSERM, B3OA, Paris, France.,École Nationale Vétérinaire d'Alfort, B3OA, Maisons-Alfort, France
| | - Pauline Wosinski
- Université Paris Cité, CNRS, INSERM, B3OA, Paris, France.,École Nationale Vétérinaire d'Alfort, B3OA, Maisons-Alfort, France
| | - Giuliana E Salazar-Noratto
- Université Paris Cité, CNRS, INSERM, B3OA, Paris, France.,École Nationale Vétérinaire d'Alfort, B3OA, Maisons-Alfort, France
| | - Morad Bensidhoum
- Université Paris Cité, CNRS, INSERM, B3OA, Paris, France.,École Nationale Vétérinaire d'Alfort, B3OA, Maisons-Alfort, France
| | - Rena Bizios
- Department of Biomedical Engineering, The University of Texas at San Antonio, San Antonio, Texas, USA
| | - Sayed-Amir Marashi
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran
| | - Esther Potier
- Université Paris Cité, CNRS, INSERM, B3OA, Paris, France.,École Nationale Vétérinaire d'Alfort, B3OA, Maisons-Alfort, France
| | - Puyi Sheng
- Department of Joint Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Hervé Petite
- Université Paris Cité, CNRS, INSERM, B3OA, Paris, France.,École Nationale Vétérinaire d'Alfort, B3OA, Maisons-Alfort, France
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16
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Hartmann FSF, Udugama IA, Seibold GM, Sugiyama H, Gernaey KV. Digital models in biotechnology: Towards multi-scale integration and implementation. Biotechnol Adv 2022; 60:108015. [PMID: 35781047 DOI: 10.1016/j.biotechadv.2022.108015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 06/03/2022] [Accepted: 06/27/2022] [Indexed: 12/28/2022]
Abstract
Industrial biotechnology encompasses a large area of multi-scale and multi-disciplinary research activities. With the recent megatrend of digitalization sweeping across all industries, there is an increased focus in the biotechnology industry on developing, integrating and applying digital models to improve all aspects of industrial biotechnology. Given the rapid development of this field, we systematically classify the state-of-art modelling concepts applied at different scales in industrial biotechnology and critically discuss their current usage, advantages and limitations. Further, we critically analyzed current strategies to couple cell models with computational fluid dynamics to study the performance of industrial microorganisms in large-scale bioprocesses, which is of crucial importance for the bio-based production industries. One of the most challenging aspects in this context is gathering intracellular data under industrially relevant conditions. Towards comprehensive models, we discuss how different scale-down concepts combined with appropriate analytical tools can capture intracellular states of single cells. We finally illustrated how the efforts could be used to develop digitals models suitable for both cell factory design and process optimization at industrial scales in the future.
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Affiliation(s)
- Fabian S F Hartmann
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads, Building 223, 2800 Kgs. Lyngby, Denmark
| | - Isuru A Udugama
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan; Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 228 A, 2800 Kgs. Lyngby, Denmark.
| | - Gerd M Seibold
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads, Building 223, 2800 Kgs. Lyngby, Denmark
| | - Hirokazu Sugiyama
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan
| | - Krist V Gernaey
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 228 A, 2800 Kgs. Lyngby, Denmark.
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17
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Sampaio M, Rocha M, Dias O. Exploring synergies between plant metabolic modelling and machine learning. Comput Struct Biotechnol J 2022; 20:1885-1900. [PMID: 35521559 PMCID: PMC9052043 DOI: 10.1016/j.csbj.2022.04.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 04/08/2022] [Accepted: 04/11/2022] [Indexed: 11/03/2022] Open
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18
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Galvão Ferrarini M, Ziska I, Andrade R, Julien-Laferrière A, Duchemin L, César RM, Mary A, Vinga S, Sagot MF. Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations. Front Genet 2022; 13:815476. [PMID: 35281848 PMCID: PMC8905348 DOI: 10.3389/fgene.2022.815476] [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: 11/15/2021] [Accepted: 01/24/2022] [Indexed: 11/16/2022] Open
Abstract
Motivation: The increasing availability of metabolomic data and their analysis are improving the understanding of cellular mechanisms and how biological systems respond to different perturbations. Currently, there is a need for novel computational methods that facilitate the analysis and integration of increasing volume of available data. Results: In this paper, we present Totoro a new constraint-based approach that integrates quantitative non-targeted metabolomic data of two different metabolic states into genome-wide metabolic models and predicts reactions that were most likely active during the transient state. We applied Totoro to real data of three different growth experiments (pulses of glucose, pyruvate, succinate) from Escherichia coli and we were able to predict known active pathways and gather new insights on the different metabolisms related to each substrate. We used both the E. coli core and the iJO1366 models to demonstrate that our approach is applicable to both smaller and larger networks. Availability:Totoro is an open source method (available at https://gitlab.inria.fr/erable/totoro) suitable for any organism with an available metabolic model. It is implemented in C++ and depends on IBM CPLEX which is freely available for academic purposes.
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Affiliation(s)
- Mariana Galvão Ferrarini
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France.,Univ Lyon, INRAE, INSA-Lyon, BF2I, UMR 203, Villeurbanne, France
| | - Irene Ziska
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France.,INRIA Grenoble Rhône-Alpes, Villeurbanne, France
| | - Ricardo Andrade
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France.,Institute of Mathematics and Statistics (IME), University of São Paulo, São Paulo, Brazil
| | | | - Louis Duchemin
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France
| | | | - Arnaud Mary
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France.,INRIA Grenoble Rhône-Alpes, Villeurbanne, France
| | - Susana Vinga
- INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - Marie-France Sagot
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France.,INRIA Grenoble Rhône-Alpes, Villeurbanne, France
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19
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Catala A, Stone M, Busch MP, D'Alessandro A. Reprogramming of red blood cell metabolism in Zika virus–infected donors. Transfusion 2022; 62:1045-1064. [PMID: 35285520 PMCID: PMC9086146 DOI: 10.1111/trf.16851] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 02/24/2022] [Accepted: 02/24/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND Diseases caused by arthropod-borne viruses remain a burden to global health; in particular, Zika virus (ZIKV) has been reported in 87 countries and territories. In healthy blood donors, ZIKV RNA can be detected in red blood cells (RBCs) months after infection, clearance of detectable nucleic acid in plasma, and seroconversion. However, little information is available on the impact of ZIKV infection to metabolism. STUDY DESIGN AND METHODS We applied mass spectrometry-based metabolomics and lipidomics approaches to investigate the impact of ZIKV infection on RBCs over the course of infection. ZIKV-infected blood donors (n = 25) were identified through molecular and serologic methods, which included nucleic acid amplification testing and real-time polymerase chain reaction (PCR) for detection of ZIKV RNA and enzyme-linked immunosorbent assay (ELISA) for detection of flavivirus-specific IgM and IgG. RESULTS In ZIKV RNA-positive donors, we observed lower glucose and lactate levels, and higher levels of ribose phosphate, suggestive of the activation of the pentose phosphate pathway. The top pathways altered in RBCs from ZIKV-IgM-positive donors include amino acid metabolism and biosynthesis, fatty acid metabolism and biosynthesis, linoleic acid and arachidonate metabolism and glutathione metabolism. RBCs from ZIKV-infected donors had increased levels of early glycolytic metabolites, and higher levels of metabolites of the pentose phosphate pathway. Alterations in acyl-carnitine and fatty acid metabolism are consistent with impaired membrane lipid homeostasis in RBCs from ZIKV IgM positive donors. CONCLUSION RBC from healthy blood donors who had been infected by ZIKV are characterized by long-lasting metabolic alterations even months after infection has resolved.
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Affiliation(s)
- Alexis Catala
- Department of Biochemistry and Molecular Genetics University of Colorado Anschutz Medical Campus Aurora Colorado USA
- Program in Structural Biology and Biochemistry University of Colorado Anschutz Medical Campus Aurora Colorado USA
| | - Mars Stone
- Vitalant Research Institute San Francisco California USA
- Department of Laboratory Medicine University of California San Francisco San Francisco California USA
| | - Michael P. Busch
- Vitalant Research Institute San Francisco California USA
- Department of Laboratory Medicine University of California San Francisco San Francisco California USA
| | - Angelo D'Alessandro
- Department of Biochemistry and Molecular Genetics University of Colorado Anschutz Medical Campus Aurora Colorado USA
- Program in Structural Biology and Biochemistry University of Colorado Anschutz Medical Campus Aurora Colorado USA
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20
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Li X, Yilmaz LS, Walhout AJ. Compartmentalization of metabolism between cell types in multicellular organisms: a computational perspective. CURRENT OPINION IN SYSTEMS BIOLOGY 2022; 29:100407. [PMID: 35224313 PMCID: PMC8865431 DOI: 10.1016/j.coisb.2021.100407] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
In multicellular organisms, metabolism is compartmentalized at many levels, including tissues and organs, different cell types, and subcellular compartments. Compartmentalization creates a coordinated homeostatic system where each compartment contributes to the production of energy and biomolecules the organism needs to carrying out specific metabolic tasks. Experimentally studying metabolic compartmentalization and metabolic interactions between cells and tissues in multicellular organisms is challenging at a systems level. However, recent progress in computational modeling provides an alternative approach to this problem. Here we discuss how integrating metabolic network modeling with omics data offers an opportunity to reveal metabolic states at the level of organs, tissues and, ultimately, individual cells. We review the current status of genome-scale metabolic network models in multicellular organisms, methods to study metabolic compartmentalization in silico, and insights gained from computational analyses. We also discuss outstanding challenges and provide perspectives for the future directions of the field.
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21
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Purohit V, Wagner A, Yosef N, Kuchroo VK. Systems-based approaches to study immunometabolism. Cell Mol Immunol 2022; 19:409-420. [PMID: 35121805 PMCID: PMC8891302 DOI: 10.1038/s41423-021-00783-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 09/17/2021] [Indexed: 02/06/2023] Open
Abstract
Technical advances at the interface of biology and computation, such as single-cell RNA-sequencing (scRNA-seq), reveal new layers of complexity in cellular systems. An emerging area of investigation using the systems biology approach is the study of the metabolism of immune cells. The diverse spectra of immune cell phenotypes, sparsity of immune cell numbers in vivo, limitations in the number of metabolites identified, dynamic nature of cellular metabolism and metabolic fluxes, tissue specificity, and high dependence on the local milieu make investigations in immunometabolism challenging, especially at the single-cell level. In this review, we define the systemic nature of immunometabolism, summarize cell- and system-based approaches, and introduce mathematical modeling approaches for systems interrogation of metabolic changes in immune cells. We close the review by discussing the applications and shortcomings of metabolic modeling techniques. With systems-oriented studies of metabolism expected to become a mainstay of immunological research, an understanding of current approaches toward systems immunometabolism will help investigators make the best use of current resources and push the boundaries of the discipline.
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Affiliation(s)
- Vinee Purohit
- Evergrande Center for Immunologic Diseases and Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, 02141, USA
| | - Allon Wagner
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, 94720, USA
- Center for Computational Biology, University of California, Berkeley, CA, 94720, USA
| | - Nir Yosef
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, 94720, USA
- Center for Computational Biology, University of California, Berkeley, CA, 94720, USA
| | - Vijay K Kuchroo
- Evergrande Center for Immunologic Diseases and Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, 02141, USA.
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22
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Bauermeister A, Mannochio-Russo H, Costa-Lotufo LV, Jarmusch AK, Dorrestein PC. Mass spectrometry-based metabolomics in microbiome investigations. Nat Rev Microbiol 2022; 20:143-160. [PMID: 34552265 PMCID: PMC9578303 DOI: 10.1038/s41579-021-00621-9] [Citation(s) in RCA: 150] [Impact Index Per Article: 75.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/03/2021] [Indexed: 02/08/2023]
Abstract
Microbiotas are a malleable part of ecosystems, including the human ecosystem. Microorganisms affect not only the chemistry of their specific niche, such as the human gut, but also the chemistry of distant environments, such as other parts of the body. Mass spectrometry-based metabolomics is one of the key technologies to detect and identify the small molecules produced by the human microbiota, and to understand the functional role of these microbial metabolites. This Review provides a foundational introduction to common forms of untargeted mass spectrometry and the types of data that can be obtained in the context of microbiome analysis. Data analysis remains an obstacle; therefore, the emphasis is placed on data analysis approaches and integrative analysis, including the integration of microbiome sequencing data.
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Affiliation(s)
- Anelize Bauermeister
- Institute of Biomedical Science, Universidade de São Paulo, São Paulo, SP, Brazil,Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA
| | - Helena Mannochio-Russo
- Department of Biochemistry and Organic Chemistry, Institute of Chemistry, São Paulo State University, Araraquara, SP, Brazil,Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA
| | | | - Alan K. Jarmusch
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA
| | - Pieter C. Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA.,Department of Pediatrics, University of California, San Diego, CA, USA.,Center for Microbiome Innovation, University of California, San Diego, CA, USA
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23
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Di Filippo M, Pescini D, Galuzzi BG, Bonanomi M, Gaglio D, Mangano E, Consolandi C, Alberghina L, Vanoni M, Damiani C. INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation. PLoS Comput Biol 2022; 18:e1009337. [PMID: 35130273 PMCID: PMC8853556 DOI: 10.1371/journal.pcbi.1009337] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 02/17/2022] [Accepted: 01/13/2022] [Indexed: 12/12/2022] Open
Abstract
Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. On the one hand, the expression level of the catalyzing enzyme sets the maximal theoretical flux level (i.e., the net rate of the reaction) for each enzyme-controlled reaction. On the other hand, metabolic regulation controls the metabolic flux through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. High-throughput data, such as metabolomics and transcriptomics data, if analyzed separately, do not accurately characterize the hierarchical regulation of metabolism outlined above. They must be integrated to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we propose INTEGRATE, a computational pipeline that integrates metabolomics and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomics data and use constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients.
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Affiliation(s)
- Marzia Di Filippo
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
| | - Dario Pescini
- Department of Statistics and Quantitative Methods, University of Milan-Bicocca, Milan, Italy
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
| | - Bruno Giovanni Galuzzi
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
| | - Marcella Bonanomi
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
| | - Daniela Gaglio
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council (CNR), Segrate, Italy
| | - Eleonora Mangano
- Institute for Biomedical Technologies (ITB), National Research Council (CNR), Segrate, Italy
| | - Clarissa Consolandi
- Institute for Biomedical Technologies (ITB), National Research Council (CNR), Segrate, Italy
| | - Lilia Alberghina
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
| | - Marco Vanoni
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
| | - Chiara Damiani
- ISBE/SYSBIO Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milan-Bicocca, Milan, Italy
- * E-mail:
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24
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Multi-Omics Analysis of Multiple Glucose-Sensing Receptor Systems in Yeast. Biomolecules 2022; 12:biom12020175. [PMID: 35204676 PMCID: PMC8961648 DOI: 10.3390/biom12020175] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/14/2022] [Accepted: 01/18/2022] [Indexed: 12/13/2022] Open
Abstract
The yeast Saccharomyces cerevisiae has long been used to produce alcohol from glucose and other sugars. While much is known about glucose metabolism, relatively little is known about the receptors and signaling pathways that indicate glucose availability. Here, we compare the two glucose receptor systems in S. cerevisiae. The first is a heterodimer of transporter-like proteins (transceptors), while the second is a seven-transmembrane receptor coupled to a large G protein (Gpa2) that acts in coordination with two small G proteins (Ras1 and Ras2). Through comprehensive measurements of glucose-dependent transcription and metabolism, we demonstrate that the two receptor systems have distinct roles in glucose signaling: the G-protein-coupled receptor directs carbohydrate and energy metabolism, while the transceptors regulate ancillary processes such as ribosome, amino acids, cofactor and vitamin metabolism. The large G-protein transmits the signal from its cognate receptor, while the small G-protein Ras2 (but not Ras1) integrates responses from both receptor pathways. Collectively, our analysis reveals the molecular basis for glucose detection and the earliest events of glucose-dependent signal transduction in yeast.
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25
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OUP accepted manuscript. FEMS Microbiol Rev 2022; 46:6585976. [DOI: 10.1093/femsre/fuac020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 05/05/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
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26
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Systems Biology Approaches to Decipher the Underlying Molecular Mechanisms of Glioblastoma Multiforme. Int J Mol Sci 2021; 22:ijms222413213. [PMID: 34948010 PMCID: PMC8706582 DOI: 10.3390/ijms222413213] [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: 11/04/2021] [Revised: 11/30/2021] [Accepted: 12/04/2021] [Indexed: 11/29/2022] Open
Abstract
Glioblastoma multiforme (GBM) is one of the most malignant central nervous system tumors, showing a poor prognosis and low survival rate. Therefore, deciphering the underlying molecular mechanisms involved in the progression of the GBM and identifying the key driver genes responsible for the disease progression is crucial for discovering potential diagnostic markers and therapeutic targets. In this context, access to various biological data, development of new methodologies, and generation of biological networks for the integration of multi-omics data are necessary for gaining insights into the appearance and progression of GBM. Systems biology approaches have become indispensable in analyzing heterogeneous high-throughput omics data, extracting essential information, and generating new hypotheses from biomedical data. This review provides current knowledge regarding GBM and discusses the multi-omics data and recent systems analysis in GBM to identify key biological functions and genes. This knowledge can be used to develop efficient diagnostic and treatment strategies and can also be used to achieve personalized medicine for GBM.
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27
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Wang J, Delfarah A, Gelbach PE, Fong E, Macklin P, Mumenthaler SM, Graham NA, Finley SD. Elucidating tumor-stromal metabolic crosstalk in colorectal cancer through integration of constraint-based models and LC-MS metabolomics. Metab Eng 2021; 69:175-187. [PMID: 34838998 PMCID: PMC8818109 DOI: 10.1016/j.ymben.2021.11.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 10/07/2021] [Accepted: 11/09/2021] [Indexed: 12/28/2022]
Abstract
Colorectal cancer (CRC) is a major cause of morbidity and mortality in the United States. Tumor-stromal metabolic crosstalk in the tumor microenvironment promotes CRC development and progression, but exactly how stromal cells, in particular cancer-associated fibroblasts (CAFs), affect the metabolism of tumor cells remains unknown. Here we take a data-driven approach to investigate the metabolic interactions between CRC cells and CAFs, integrating constraint-based modeling and metabolomic profiling. Using metabolomics data, we perform unsteady-state parsimonious flux balance analysis to infer flux distributions for central carbon metabolism in CRC cells treated with or without CAF-conditioned media. We find that CAFs reprogram CRC metabolism through stimulation of glycolysis, the oxidative arm of the pentose phosphate pathway (PPP), and glutaminolysis, as well as inhibition of the tricarboxylic acid cycle. To identify potential therapeutic targets, we simulate enzyme knockouts and find that CAF-treated CRC cells are especially sensitive to inhibitions of hexokinase and glucose-6-phosphate, the rate limiting steps of glycolysis and oxidative PPP. Our work gives mechanistic insights into the metabolic interactions between CRC cells and CAFs and provides a framework for testing hypotheses towards CRC-targeted therapies.
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Affiliation(s)
- Junmin Wang
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Alireza Delfarah
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
| | - Patrick E Gelbach
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Emma Fong
- Lawrence J. Ellison Institute for Transformative Medicine, Los Angeles, CA, 90064, USA
| | - Paul Macklin
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, 46202, USA
| | - Shannon M Mumenthaler
- Lawrence J. Ellison Institute for Transformative Medicine, Los Angeles, CA, 90064, USA; Division of Medical Oncology, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90033, USA
| | - Nicholas A Graham
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
| | - Stacey D Finley
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA; Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, 90089, USA.
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28
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Quantifying the propagation of parametric uncertainty on flux balance analysis. Metab Eng 2021; 69:26-39. [PMID: 34718140 DOI: 10.1016/j.ymben.2021.10.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/21/2021] [Accepted: 10/24/2021] [Indexed: 12/27/2022]
Abstract
Flux balance analysis (FBA) and associated techniques operating on stoichiometric genome-scale metabolic models play a central role in quantifying metabolic flows and constraining feasible phenotypes. At the heart of these methods lie two important assumptions: (i) the biomass precursors and energy requirements neither change in response to growth conditions nor environmental/genetic perturbations, and (ii) metabolite production and consumption rates are equal at all times (i.e., steady-state). Despite the stringency of these two assumptions, FBA has been shown to be surprisingly robust at predicting cellular phenotypes. In this paper, we formally assess the impact of these two assumptions on FBA results by quantifying how uncertainty in biomass reaction coefficients, and departures from steady-state due to temporal fluctuations could propagate to FBA results. In the first case, conditional sampling of parameter space is required to re-weigh the biomass reaction so as the molecular weight remains equal to 1 g mmol-1, and in the second case, metabolite (and elemental) pool conservation must be imposed under temporally varying conditions. Results confirm the importance of enforcing the aforementioned constraints and explain the robustness of FBA biomass yield predictions.
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29
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Combining Kinetic and Constraint-Based Modelling to Better Understand Metabolism Dynamics. Processes (Basel) 2021. [DOI: 10.3390/pr9101701] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
To understand the phenotypic capabilities of organisms, it is useful to characterise cellular metabolism through the analysis of its pathways. Dynamic mathematical modelling of metabolic networks is of high interest as it provides the time evolution of the metabolic components. However, it also has limitations, such as the necessary mechanistic details and kinetic parameters are not always available. On the other hand, large metabolic networks exhibit a complex topological structure which can be studied rather efficiently in their stationary regime by constraint-based methods. These methods produce useful predictions on pathway operations. In this review, we present both modelling techniques and we show how they bring complementary views of metabolism. In particular, we show on a simple example how both approaches can be used in conjunction to shed some light on the dynamics of metabolic networks.
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30
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Kuriya Y, Inoue M, Yamamoto M, Murata M, Araki M. Knowledge extraction from literature and enzyme sequences complements FBA analysis in metabolic engineering. Biotechnol J 2021; 16:e2000443. [PMID: 34516717 DOI: 10.1002/biot.202000443] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 09/01/2021] [Accepted: 09/10/2021] [Indexed: 11/10/2022]
Abstract
Flux balance analysis (FBA) using genome-scale metabolic model (GSM) is a useful method for improving the bio-production of useful compounds. However, FBA often does not impose important constraints such as nutrients uptakes, by-products excretions and gases (oxygen and carbon dioxide) transfers. Furthermore, important information on metabolic engineering such as enzyme amounts, activities, and characteristics caused by gene expression and enzyme sequences is basically not included in GSM. Therefore, simple FBA is often not sufficient to search for metabolic manipulation strategies that are useful for improving the production of target compounds. In this study, we proposed a method using literature and enzyme search to complement the FBA-based metabolic manipulation strategies. As a case study, this method was applied to shikimic acid production by Corynebacterium glutamicum to verify its usefulness. As unique strategies in literature-mining, overexpression of the transcriptional regulator SugR and gene disruption related to by-products productions were complemented. In the search for alternative enzyme sequences, it was suggested that those candidates are searched for from various species based on features captured by deep learning, which are not simply homologous to amino acid sequences of the base enzymes.
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Affiliation(s)
- Yuki Kuriya
- Graduate School of Medicine, Kyoto University, Kyoto, Kyoto, Japan
| | - Mai Inoue
- Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Hyogo, Japan
| | - Masaki Yamamoto
- Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Hyogo, Japan
| | - Masahiro Murata
- Graduate School of Medicine, Kyoto University, Kyoto, Kyoto, Japan
| | - Michihiro Araki
- Graduate School of Medicine, Kyoto University, Kyoto, Kyoto, Japan.,Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Hyogo, Japan.,Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, Shinjuku-ku, Tokyo, Japan
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31
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How to Tackle Underdeterminacy in Metabolic Flux Analysis? A Tutorial and Critical Review. Processes (Basel) 2021. [DOI: 10.3390/pr9091577] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Metabolic flux analysis is often (not to say almost always) faced with system underdeterminacy. Indeed, the linear algebraic system formed by the steady-state mass balance equations around the intracellular metabolites and the equality constraints related to the measurements of extracellular fluxes do not define a unique solution for the distribution of intracellular fluxes, but instead a set of solutions belonging to a convex polytope. Various methods have been proposed to tackle this underdeterminacy, including flux pathway analysis, flux balance analysis, flux variability analysis and sampling. These approaches are reviewed in this article and a toy example supports the discussion with illustrative numerical results.
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32
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Sahu A, Blätke MA, Szymański JJ, Töpfer N. Advances in flux balance analysis by integrating machine learning and mechanism-based models. Comput Struct Biotechnol J 2021; 19:4626-4640. [PMID: 34471504 PMCID: PMC8382995 DOI: 10.1016/j.csbj.2021.08.004] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 08/03/2021] [Accepted: 08/03/2021] [Indexed: 02/08/2023] Open
Abstract
The availability of multi-omics data sets and genome-scale metabolic models for various organisms provide a platform for modeling and analyzing genotype-to-phenotype relationships. Flux balance analysis is the main tool for predicting flux distributions in genome-scale metabolic models and various data-integrative approaches enable modeling context-specific network behavior. Due to its linear nature, this optimization framework is readily scalable to multi-tissue or -organ and even multi-organism models. However, both data and model size can hamper a straightforward biological interpretation of the estimated fluxes. Moreover, flux balance analysis simulates metabolism at steady-state and thus, in its most basic form, does not consider kinetics or regulatory events. The integration of flux balance analysis with complementary data analysis and modeling techniques offers the potential to overcome these challenges. In particular machine learning approaches have emerged as the tool of choice for data reduction and selection of most important variables in big data sets. Kinetic models and formal languages can be used to simulate dynamic behavior. This review article provides an overview of integrative studies that combine flux balance analysis with machine learning approaches, kinetic models, such as physiology-based pharmacokinetic models, and formal graphical modeling languages, such as Petri nets. We discuss the mathematical aspects and biological applications of these integrated approaches and outline challenges and future perspectives.
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Affiliation(s)
- Ankur Sahu
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
| | - Mary-Ann Blätke
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
| | - Jędrzej Jakub Szymański
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
| | - Nadine Töpfer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstraße 3, 06466 Gatersleben, Germany
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33
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Natoli G, Pileri F, Gualdrini F, Ghisletti S. Integration of transcriptional and metabolic control in macrophage activation. EMBO Rep 2021; 22:e53251. [PMID: 34328708 DOI: 10.15252/embr.202153251] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 07/07/2021] [Accepted: 07/08/2021] [Indexed: 11/09/2022] Open
Abstract
Macrophages react to microbial and endogenous danger signals by activating a broad panel of effector and homeostatic responses. Such responses entail rapid and stimulus-specific changes in gene expression programs accompanied by extensive rewiring of metabolism, with alterations in chromatin modifications providing one layer of integration of transcriptional and metabolic regulation. A systematic and mechanistic understanding of the mutual influences between signal-induced metabolic changes and gene expression is still lacking. Here, we discuss current evidence, controversies, knowledge gaps, and future areas of investigation on how metabolic and transcriptional changes are dynamically integrated during macrophage activation. The cross-talk between metabolism and inflammatory gene expression is in part accounted for by alterations in the production, usage, and availability of metabolic intermediates that impact the macrophage epigenome. In addition, stimulus-inducible gene expression changes alter the production of inflammatory mediators, such as nitric oxide, that in turn modulate the activity of metabolic enzymes thus determining complex regulatory loops. Critical issues remain to be understood, notably whether and how metabolic rewiring can bring about gene-specific (as opposed to global) expression changes.
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Affiliation(s)
- Gioacchino Natoli
- Department of Experimental Oncology, European Institute of Oncology (IEO) IRCCS, Milan, Italy.,Humanitas University, Milan, Italy
| | - Francesco Pileri
- Department of Experimental Oncology, European Institute of Oncology (IEO) IRCCS, Milan, Italy
| | - Francesco Gualdrini
- Department of Experimental Oncology, European Institute of Oncology (IEO) IRCCS, Milan, Italy
| | - Serena Ghisletti
- Department of Experimental Oncology, European Institute of Oncology (IEO) IRCCS, Milan, Italy
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34
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Palsson BO. Genome‐Scale Models. Metab Eng 2021. [DOI: 10.1002/9783527823468.ch2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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35
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Zhou J, Zhuang Y, Xia J. Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions. Microb Cell Fact 2021; 20:125. [PMID: 34193117 PMCID: PMC8247156 DOI: 10.1186/s12934-021-01614-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 06/16/2021] [Indexed: 11/26/2022] Open
Abstract
Background Genome-scale metabolic model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level. Results Based on the basic GSMM model iHL1210 of Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints, termed a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO). The results show that enzyme constraints effectively improve the model’s phenotype prediction ability, and extended the model’s potential to guide target gene identification through predicting metabolic phenotype changes of A. niger by simulating gene knockout. In addition, enzyme constraints significantly reduced the solution space of the model, i.e., flux variability over 40.10% metabolic reactions were significantly reduced. The new model showed also versatility in other aspects, like estimating large-scale \documentclass[12pt]{minimal}
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\begin{document}$$k_{{cat}}$$\end{document}kcat values, predicting the differential expression of enzymes under different growth conditions. Conclusions This study shows that incorporating enzymes’ abundance information into GSMM is very effective for improving model performance with A. niger. Enzyme-constrained model can be used as a powerful tool for predicting the metabolic phenotype of A. niger by incorporating proteome data. In the foreseeable future, with the fast development of measurement techniques, and more precise and rich proteomics quantitative data being obtained for A. niger, the enzyme-constrained GSMM model will show greater application space on the system level. Supplementary Information The online version contains supplementary material available at 10.1186/s12934-021-01614-2.
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Affiliation(s)
- Jingru Zhou
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Yingping Zhuang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Jianye Xia
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, 200237, China. .,Tianjin Institute of Industrial Biotechnology, Chinese Academy of Science, Tianjin, 300308, China.
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36
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Ben Guebila M, Thiele I. Dynamic flux balance analysis of whole-body metabolism for type 1 diabetes. NATURE COMPUTATIONAL SCIENCE 2021; 1:348-361. [PMID: 38217214 DOI: 10.1038/s43588-021-00074-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 04/21/2021] [Indexed: 01/15/2024]
Abstract
Type 1 diabetes (T1D) mellitus is a systemic disease triggered by a local autoimmune inflammatory reaction in insulin-producing cells that induce organ-wide, long-term metabolic effects. Mathematical modeling of the whole-body regulatory bihormonal system has helped to identify therapeutic interventions but is limited to a coarse-grained representation of metabolism. To extend the depiction of T1D, we developed a whole-body model of organ-specific regulation and metabolism that highlighted chronic inflammation as a hallmark of the disease, identified processes related to neurodegenerative disorders and suggested calcium channel blockers as adjuvants for diabetes control. In addition, whole-body modeling of a patient population allowed for the assessment of between-individual variability to insulin and suggested that peripheral glucose levels are degenerate biomarkers of the internal metabolic state. Taken together, the organ-resolved, dynamic modeling approach enables modeling and simulation of metabolic disease at greater levels of coverage and precision and the generation of hypothesis from a molecular level up to the population level.
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Affiliation(s)
- Marouen Ben Guebila
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Ines Thiele
- School of Medicine, National University of Ireland, Galway, Ireland.
- Discipline of Microbiology, School of Natural Sciences, National University of Ireland, Galway, Galway, Ireland.
- APC Microbiome, Cork, Ireland.
- Ryan Institute, National University of Ireland, Galway, Ireland.
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37
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Kumar K, Venkatraman V, Bruheim P. Adaptation of central metabolite pools to variations in growth rate and cultivation conditions in Saccharomyces cerevisiae. Microb Cell Fact 2021; 20:64. [PMID: 33750414 PMCID: PMC7941957 DOI: 10.1186/s12934-021-01557-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 03/01/2021] [Indexed: 11/19/2022] Open
Abstract
Background Saccharomyces cerevisiae is a well-known popular model system for basic biological studies and serves as a host organism for the heterologous production of commercially interesting small molecules and proteins. The central metabolism is at the core to provide building blocks and energy to support growth and survival in normal situations as well as during exogenous stresses and forced heterologous protein production. Here, we present a comprehensive study of intracellular central metabolite pool profiling when growing S. cerevisiae on different carbon sources in batch cultivations and at different growth rates in nutrient-limited glucose chemostats. The latest versions of absolute quantitative mass spectrometry-based metabolite profiling methodology were applied to cover glycolytic and pentose phosphate pathway metabolites, tricarboxylic acid cycle (TCA), complete amino acid, and deoxy-/nucleoside phosphate pools. Results Glutamate, glutamine, alanine, and citrate were the four most abundant metabolites for most conditions tested. The amino acid is the dominant metabolite class even though a marked relative reduction compared to the other metabolite classes was observed for nitrogen and phosphate limited chemostats. Interestingly, glycolytic and pentose phosphate pathway (PPP) metabolites display the largest variation among the cultivation conditions while the nucleoside phosphate pools are more stable and vary within a closer concentration window. The overall trends for glucose and nitrogen-limited chemostats were increased metabolite pools with the increasing growth rate. Next, comparing the chosen chemostat reference growth rate (0.12 h−1, approximate one-fourth of maximal unlimited growth rate) illuminates an interesting pattern: almost all pools are lower in nitrogen and phosphate limited conditions compared to glucose limitation, except for the TCA metabolites citrate, isocitrate and α-ketoglutarate. Conclusions This study provides new knowledge-how the central metabolism is adapting to various cultivations conditions and growth rates which is essential for expanding our understanding of cellular metabolism and the development of improved phenotypes in metabolic engineering. Supplementary Information The online version contains supplementary material available at 10.1186/s12934-021-01557-8.
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Affiliation(s)
- Kanhaiya Kumar
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway
| | - Vishwesh Venkatraman
- Department of Chemistry, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway
| | - Per Bruheim
- Department of Biotechnology and Food Science, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway.
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Integration of relative metabolomics and transcriptomics time-course data in a metabolic model pinpoints effects of ribosome biogenesis defects on Arabidopsis thaliana metabolism. Sci Rep 2021; 11:4787. [PMID: 33637852 PMCID: PMC7910480 DOI: 10.1038/s41598-021-84114-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 01/11/2021] [Indexed: 01/30/2023] Open
Abstract
Ribosome biogenesis is tightly associated to plant metabolism due to the usage of ribosomes in the synthesis of proteins necessary to drive metabolic pathways. Given the central role of ribosome biogenesis in cell physiology, it is important to characterize the impact of different components involved in this process on plant metabolism. Double mutants of the Arabidopsis thaliana cytosolic 60S maturation factors REIL1 and REIL2 do not resume growth after shift to moderate 10 \documentclass[12pt]{minimal}
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\begin{document}$$^{\circ }\hbox {C}$$\end{document}∘C chilling conditions. To gain mechanistic insights into the metabolic effects of this ribosome biogenesis defect on metabolism, we developed TC-iReMet2, a constraint-based modelling approach that integrates relative metabolomics and transcriptomics time-course data to predict differential fluxes on a genome-scale level. We employed TC-iReMet2 with metabolomics and transcriptomics data from the Arabidopsis Columbia 0 wild type and the reil1-1 reil2-1 double mutant before and after cold shift. We identified reactions and pathways that are highly altered in a mutant relative to the wild type. These pathways include the Calvin–Benson cycle, photorespiration, gluconeogenesis, and glycolysis. Our findings also indicated differential NAD(P)/NAD(P)H ratios after cold shift. TC-iReMet2 allows for mechanistic hypothesis generation and interpretation of system biology experiments related to metabolic fluxes on a genome-scale level.
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Seaver SMD, Liu F, Zhang Q, Jeffryes J, Faria JP, Edirisinghe JN, Mundy M, Chia N, Noor E, Beber M, Best AA, DeJongh M, Kimbrel JA, D’haeseleer P, McCorkle SR, Bolton JR, Pearson E, Canon S, Wood-Charlson EM, Cottingham RW, Arkin AP, Henry CS. The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes. Nucleic Acids Res 2021; 49:D575-D588. [PMID: 32986834 PMCID: PMC7778927 DOI: 10.1093/nar/gkaa746] [Citation(s) in RCA: 88] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 08/25/2020] [Accepted: 09/24/2020] [Indexed: 12/31/2022] Open
Abstract
For over 10 years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical 'Rosetta Stone' to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org/biochem and KBase.
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Affiliation(s)
- Samuel M D Seaver
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Filipe Liu
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Qizhi Zhang
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - James Jeffryes
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - José P Faria
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Janaka N Edirisinghe
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Michael Mundy
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Nicholas Chia
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Elad Noor
- Department of Biology, Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule Zürich, CH-8093 Zürich, Switzerland
| | - Moritz E Beber
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, 2800, Denmark
| | - Aaron A Best
- Department of Biology, Hope College, Holland, MI 49423, USA
| | - Matthew DeJongh
- Department of Computer Science, Hope College, Holland, MI 49423, USA
| | - Jeffrey A Kimbrel
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Patrik D’haeseleer
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Sean R McCorkle
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Jay R Bolton
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Erik Pearson
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Shane Canon
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Elisha M Wood-Charlson
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Robert W Cottingham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Adam P Arkin
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Christopher S Henry
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
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Seaver SMD, Liu F, Zhang Q, Jeffryes J, Faria JP, Edirisinghe JN, Mundy M, Chia N, Noor E, Beber ME, Best AA, DeJongh M, Kimbrel JA, D'haeseleer P, McCorkle SR, Bolton JR, Pearson E, Canon S, Wood-Charlson EM, Cottingham RW, Arkin AP, Henry CS. The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes. Nucleic Acids Res 2021; 49:D1555. [PMID: 33179751 DOI: 10.1101/2020.03.31.018663] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/21/2023] Open
Abstract
ABSTRACTFor over ten years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions;; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical “Rosetta Stone” to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies, and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org and KBase.
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Affiliation(s)
- Samuel M D Seaver
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Filipe Liu
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Qizhi Zhang
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - James Jeffryes
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - José P Faria
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Janaka N Edirisinghe
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Michael Mundy
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Nicholas Chia
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Elad Noor
- Department of Biology, Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule Zürich, CH-8093 Zürich, Switzerland
| | - Moritz E Beber
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, 2800, Denmark
| | - Aaron A Best
- Department of Biology, Hope College, Holland, MI 49423, USA
| | - Matthew DeJongh
- Department of Computer Science, Hope College, Holland, MI 49423, USA
| | - Jeffrey A Kimbrel
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Patrik D'haeseleer
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Sean R McCorkle
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Jay R Bolton
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Erik Pearson
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Shane Canon
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Elisha M Wood-Charlson
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Robert W Cottingham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Adam P Arkin
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Christopher S Henry
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
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Wörheide MA, Krumsiek J, Kastenmüller G, Arnold M. Multi-omics integration in biomedical research - A metabolomics-centric review. Anal Chim Acta 2021; 1141:144-162. [PMID: 33248648 PMCID: PMC7701361 DOI: 10.1016/j.aca.2020.10.038] [Citation(s) in RCA: 101] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 10/09/2020] [Accepted: 10/19/2020] [Indexed: 02/07/2023]
Abstract
Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.
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Affiliation(s)
- Maria A Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jan Krumsiek
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.
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42
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Haiman ZB, Zielinski DC, Koike Y, Yurkovich JT, Palsson BO. MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics. PLoS Comput Biol 2021; 17:e1008208. [PMID: 33507922 PMCID: PMC7872247 DOI: 10.1371/journal.pcbi.1008208] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 02/09/2021] [Accepted: 12/21/2020] [Indexed: 01/01/2023] Open
Abstract
Mathematical models of metabolic networks utilize simulation to study system-level mechanisms and functions. Various approaches have been used to model the steady state behavior of metabolic networks using genome-scale reconstructions, but formulating dynamic models from such reconstructions continues to be a key challenge. Here, we present the Mass Action Stoichiometric Simulation Python (MASSpy) package, an open-source computational framework for dynamic modeling of metabolism. MASSpy utilizes mass action kinetics and detailed chemical mechanisms to build dynamic models of complex biological processes. MASSpy adds dynamic modeling tools to the COnstraint-Based Reconstruction and Analysis Python (COBRApy) package to provide an unified framework for constraint-based and kinetic modeling of metabolic networks. MASSpy supports high-performance dynamic simulation through its implementation of libRoadRunner: the Systems Biology Markup Language (SBML) simulation engine. Three examples are provided to demonstrate how to use MASSpy: (1) a validation of the MASSpy modeling tool through dynamic simulation of detailed mechanisms of enzyme regulation; (2) a feature demonstration using a workflow for generating ensemble of kinetic models using Monte Carlo sampling to approximate missing numerical values of parameters and to quantify biological uncertainty, and (3) a case study in which MASSpy is utilized to overcome issues that arise when integrating experimental data with the computation of functional states of detailed biological mechanisms. MASSpy represents a powerful tool to address challenges that arise in dynamic modeling of metabolic networks, both at small and large scales.
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Affiliation(s)
- Zachary B. Haiman
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
| | - Daniel C. Zielinski
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
| | - Yuko Koike
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - James T. Yurkovich
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
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43
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Ferreira AE, Sousa Silva M, Cordeiro C. Metabolic Network Inference from Time Series. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11347-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
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Suthers PF, Foster CJ, Sarkar D, Wang L, Maranas CD. Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms. Metab Eng 2020; 63:13-33. [PMID: 33310118 DOI: 10.1016/j.ymben.2020.11.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/13/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022]
Abstract
Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. This review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.
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Affiliation(s)
- Patrick F Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA
| | - Charles J Foster
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Debolina Sarkar
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA.
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Baloni P, Funk CC, Yan J, Yurkovich JT, Kueider-Paisley A, Nho K, Heinken A, Jia W, Mahmoudiandehkordi S, Louie G, Saykin AJ, Arnold M, Kastenmüller G, Griffiths WJ, Thiele I, Kaddurah-Daouk R, Price ND. Metabolic Network Analysis Reveals Altered Bile Acid Synthesis and Metabolism in Alzheimer's Disease. CELL REPORTS MEDICINE 2020; 1:100138. [PMID: 33294859 PMCID: PMC7691449 DOI: 10.1016/j.xcrm.2020.100138] [Citation(s) in RCA: 92] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 06/26/2020] [Accepted: 10/19/2020] [Indexed: 12/12/2022]
Abstract
Increasing evidence suggests Alzheimer's disease (AD) pathophysiology is influenced by primary and secondary bile acids, the end product of cholesterol metabolism. We analyze 2,114 post-mortem brain transcriptomes and identify genes in the alternative bile acid synthesis pathway to be expressed in the brain. A targeted metabolomic analysis of primary and secondary bile acids measured from post-mortem brain samples of 111 individuals supports these results. Our metabolic network analysis suggests that taurine transport, bile acid synthesis, and cholesterol metabolism differ in AD and cognitively normal individuals. We also identify putative transcription factors regulating metabolic genes and influencing altered metabolism in AD. Intriguingly, some bile acids measured in brain tissue cannot be explained by the presence of enzymes responsible for their synthesis, suggesting that they may originate from the gut microbiome and are transported to the brain. These findings motivate further research into bile acid metabolism in AD to elucidate their possible connection to cognitive decline.
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Affiliation(s)
| | - Cory C Funk
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Jingwen Yan
- Indiana Alzheimer Disease Center and Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Alexandra Kueider-Paisley
- Department of Psychiatry and Behavioral Medicine, Duke Institute for Brain Sciences, Duke University, Durham, NC 27708, USA
| | - Kwangsik Nho
- Indiana Alzheimer Disease Center and Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Almut Heinken
- School of Medicine, National University of Ireland, Galway, Ireland
| | - Wei Jia
- Cancer Biology Program, The University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Siamak Mahmoudiandehkordi
- Department of Psychiatry and Behavioral Medicine, Duke Institute for Brain Sciences, Duke University, Durham, NC 27708, USA
| | - Gregory Louie
- Department of Psychiatry and Behavioral Medicine, Duke Institute for Brain Sciences, Duke University, Durham, NC 27708, USA
| | - Andrew J Saykin
- Indiana Alzheimer Disease Center and Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Matthias Arnold
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - William J Griffiths
- Swansea University Medical School, ILS1 Building, Singleton Park, Swansea SA2 8PP, UK
| | - Ines Thiele
- School of Medicine, National University of Ireland, Galway, Ireland.,Discipline of Microbiology, School of Natural Sciences, National University of Ireland, Galway, Ireland
| | | | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Medicine, Duke Institute for Brain Sciences, Duke University, Durham, NC 27708, USA
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Antonakoudis A, Barbosa R, Kotidis P, Kontoravdi C. The era of big data: Genome-scale modelling meets machine learning. Comput Struct Biotechnol J 2020; 18:3287-3300. [PMID: 33240470 PMCID: PMC7663219 DOI: 10.1016/j.csbj.2020.10.011] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 10/07/2020] [Accepted: 10/08/2020] [Indexed: 12/15/2022] Open
Abstract
With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling.
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Affiliation(s)
| | | | | | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
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Dahal S, Yurkovich JT, Xu H, Palsson BO, Yang L. Synthesizing Systems Biology Knowledge from Omics Using Genome-Scale Models. Proteomics 2020; 20:e1900282. [PMID: 32579720 PMCID: PMC7501203 DOI: 10.1002/pmic.201900282] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 06/13/2020] [Indexed: 12/18/2022]
Abstract
Omic technologies have enabled the complete readout of the molecular state of a cell at different biological scales. In principle, the combination of multiple omic data types can provide an integrated view of the entire biological system. This integration requires appropriate models in a systems biology approach. Here, genome-scale models (GEMs) are focused upon as one computational systems biology approach for interpreting and integrating multi-omic data. GEMs convert the reactions (related to metabolism, transcription, and translation) that occur in an organism to a mathematical formulation that can be modeled using optimization principles. A variety of genome-scale modeling methods used to interpret multiple omic data types, including genomics, transcriptomics, proteomics, metabolomics, and meta-omics are reviewed. The ability to interpret omics in the context of biological systems has yielded important findings for human health, environmental biotechnology, bioenergy, and metabolic engineering. The authors find that concurrent with advancements in omic technologies, genome-scale modeling methods are also expanding to enable better interpretation of omic data. Therefore, continued synthesis of valuable knowledge, through the integration of omic data with GEMs, are expected.
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Affiliation(s)
- Sanjeev Dahal
- Department of Chemical Engineering, Queen’s University, Kingston, Canada
| | | | - Hao Xu
- Department of Chemical Engineering, Queen’s University, Kingston, Canada
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Laurence Yang
- Department of Chemical Engineering, Queen’s University, Kingston, Canada
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Rana P, Berry C, Ghosh P, Fong SS. Recent advances on constraint-based models by integrating machine learning. Curr Opin Biotechnol 2020; 64:85-91. [DOI: 10.1016/j.copbio.2019.11.007] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Revised: 11/04/2019] [Accepted: 11/06/2019] [Indexed: 01/06/2023]
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Volkova S, Matos MRA, Mattanovich M, Marín de Mas I. Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis. Metabolites 2020; 10:E303. [PMID: 32722118 PMCID: PMC7465778 DOI: 10.3390/metabo10080303] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 07/08/2020] [Accepted: 07/22/2020] [Indexed: 01/05/2023] Open
Abstract
Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.
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Affiliation(s)
| | | | | | - Igor Marín de Mas
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark; (S.V.); (M.R.A.M.); (M.M.)
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Campit SE, Meliki A, Youngson NA, Chandrasekaran S. Nutrient Sensing by Histone Marks: Reading the Metabolic Histone Code Using Tracing, Omics, and Modeling. Bioessays 2020; 42:e2000083. [PMID: 32638413 DOI: 10.1002/bies.202000083] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/23/2020] [Indexed: 12/19/2022]
Abstract
Several metabolites serve as substrates for histone modifications and communicate changes in the metabolic environment to the epigenome. Technologies such as metabolomics and proteomics have allowed us to reconstruct the interactions between metabolic pathways and histones. These technologies have shed light on how nutrient availability can have a dramatic effect on various histone modifications. This metabolism-epigenome cross talk plays a fundamental role in development, immune function, and diseases like cancer. Yet, major challenges remain in understanding the interactions between cellular metabolism and the epigenome. How the levels and fluxes of various metabolites impact epigenetic marks is still unclear. Discussed herein are recent applications and the potential of systems biology methods such as flux tracing and metabolic modeling to address these challenges and to uncover new metabolic-epigenetic interactions. These systems approaches can ultimately help elucidate how nutrients shape the epigenome of microbes and mammalian cells.
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Affiliation(s)
- Scott E Campit
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Alia Meliki
- Center for Bioinformatics and Computational Medicine, Ann Arbor, MI, 48109, USA
| | - Neil A Youngson
- Institute of Hepatology, Foundation for Liver Research, London, SE5 9NT, UK.,Faculty of Life Sciences and Medicine, King's College London, London, WC2R 2LS, UK.,School of Medical Sciences, UNSW Sydney, Sydney, 2052, Australia
| | - Sriram Chandrasekaran
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI, 48109, USA.,Center for Bioinformatics and Computational Medicine, Ann Arbor, MI, 48109, USA.,Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.,Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
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