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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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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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3
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Chen K, Wang F. Cell-specific genome-scale metabolic modeling of SARS-CoV-2-infected lung to identify antiviral enzymes. FEBS Open Bio 2023; 13:2172-2186. [PMID: 37734920 PMCID: PMC10699103 DOI: 10.1002/2211-5463.13710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 08/09/2023] [Accepted: 09/19/2023] [Indexed: 09/23/2023] Open
Abstract
Computational systems biology plays a key role in the discovery of suitable antiviral targets. We designed a cell-specific, constraint-based modeling technique for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected lungs. We used the gene sequence of the alpha variant of SARS-CoV-2 to build a viral biomass reaction (VBR). We also used the mass proportion of lipids between the viral biomass and its host cell to estimate the stoichiometric coefficients of viral lipids in the reaction. We then integrated the VBR, the gene expression of the alpha variant of SARS-CoV-2, and the generic human metabolic network Recon3D to reconstruct a cell-specific genome-scale metabolic model. An antiviral target discovery (AVTD) platform was introduced using this model to identify therapeutic drug targets for combating COVID-19. The AVTD platform not only identified antiviral genes for eliminating viral replication but also predicted side effects of treatments. Our computational results revealed that knocking out dihydroorotate dehydrogenase (DHODH) might reduce the synthesis rate of cytidine-5'-triphosphate and uridine-5'-triphosphate, which terminate the viral building blocks of DNA and RNA for SARS-CoV-2 replication. Our results also indicated that DHODH is a promising antiviral target that causes minor side effects, which is consistent with the results of recent reports. Moreover, we discovered that the genes that participate in the de novo biosynthesis of glycerophospholipids and ceramides become unidentifiable if the VBR does not involve the stoichiometry of lipids.
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Affiliation(s)
- Ke‐Lin Chen
- Department of Chemical EngineeringNational Chung Cheng UniversityChiayiTaiwan
| | - Feng‐Sheng Wang
- Department of Chemical EngineeringNational Chung Cheng UniversityChiayiTaiwan
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4
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Ray A, Kundu P, Ghosh A. Reconstruction of a Genome-Scale Metabolic Model of Scenedesmus obliquus and Its Application for Lipid Production under Three Trophic Modes. ACS Synth Biol 2023; 12:3463-3481. [PMID: 37852251 DOI: 10.1021/acssynbio.3c00516] [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] [Indexed: 10/20/2023]
Abstract
Green microalgae have emerged as beneficial feedstocks for biofuel production. A systems-level understanding of the biochemical network is needed to harness the microalgal metabolic capacity for bioproduction. Genome-scale metabolic modeling (GEM) showed immense potential in rational metabolic engineering, utilizing biochemical flux distribution analysis. Here, we report the first GEM for the green microalga, Scenedesmus obliquus (iAR632), a promising biodiesel feedstock with high lipid-storing capability. iAR632 comprises 1467 reactions, 734 metabolites, and 632 genes distributed among 7 compartments. The model was optimized under three different trophic modes of microalgal cultivation, i.e., autotrophy, mixotrophy, and heterotrophy. The robustness of the reconstructed network was confirmed by analyzing its sensitivity to the biomass components. Pathway-level flux profiles were analyzed, and significant flux space expansion was noticed majorly in reactions associated with lipid biosynthesis. In agreement with the experimental observation, iAR632 predicted about 3.8-fold increased biomass and almost 4-fold higher lipid under mixotrophy than the other trophic modes. Thus, the assessment of the condition-specific metabolic flux distribution of iAR632 suggested that mixotrophy is the preferred cultivation condition for improved microalgal growth and lipid production. Overall, the reconstructed GEM and subsequent analyses will provide a systematic framework for developing model-driven strategies to improve microalgal bioproduction.
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Affiliation(s)
- Ayusmita Ray
- P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Pritam Kundu
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
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Chu SW, Wang FS. Fuzzy optimization for identifying antiviral targets for treating SARS-CoV-2 infection in the heart. BMC Bioinformatics 2023; 24:364. [PMID: 37759157 PMCID: PMC10537911 DOI: 10.1186/s12859-023-05487-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023] Open
Abstract
In this paper, a fuzzy hierarchical optimization framework is proposed for identifying potential antiviral targets for treating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the heart. The proposed framework comprises four objectives for evaluating the elimination of viral biomass growth and the minimization of side effects during treatment. In the application of the framework, Dulbecco's modified eagle medium (DMEM) and Ham's medium were used as uptake nutrients on an antiviral target discovery platform. The prediction results from the framework reveal that most of the antiviral enzymes in the aforementioned media are involved in fatty acid metabolism and amino acid metabolism. However, six enzymes involved in cholesterol biosynthesis in Ham's medium and three enzymes involved in glycolysis in DMEM are unable to eliminate the growth of the SARS-CoV-2 biomass. Three enzymes involved in glycolysis, namely BPGM, GAPDH, and ENO1, in DMEM combine with the supplemental uptake of L-cysteine to increase the cell viability grade and metabolic deviation grade. Moreover, six enzymes involved in cholesterol biosynthesis reduce and fail to reduce viral biomass growth in a culture medium if a cholesterol uptake reaction does not occur and occurs in this medium, respectively.
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Affiliation(s)
- Sz-Wei Chu
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, 621301, Taiwan
| | - Feng-Sheng Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, 621301, Taiwan.
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6
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Walakira A, Skubic C, Nadižar N, Rozman D, Režen T, Mraz M, Moškon M. Integrative computational modeling to unravel novel potential biomarkers in hepatocellular carcinoma. Comput Biol Med 2023; 159:106957. [PMID: 37116239 DOI: 10.1016/j.compbiomed.2023.106957] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 03/17/2023] [Accepted: 04/16/2023] [Indexed: 04/30/2023]
Abstract
Hepatocellular carcinoma (HCC) is a major health problem around the world. The management of this disease is complicated by the lack of noninvasive diagnostic tools and the few treatment options available. Better clinical outcomes can be achieved if HCC is detected early, but unfortunately, clinical signs appear when the disease is in its late stages. We aim to identify novel genes that can be targeted for the diagnosis and therapy of HCC. We performed a meta-analysis of transcriptomics data to identify differentially expressed genes and applied network analysis to identify hub genes. Fatty acid metabolism, complement and coagulation cascade, chemical carcinogenesis and retinol metabolism were identified as key pathways in HCC. Furthermore, we integrated transcriptomics data into a reference human genome-scale metabolic model to identify key reactions and subsystems relevant in HCC. We conclude that fatty acid activation, purine metabolism, vitamin D, and E metabolism are key processes in the development of HCC and therefore need to be further explored for the development of new therapies. We provide the first evidence that GABRP, HBG1 and DAK (TKFC) genes are important in HCC in humans and warrant further studies.
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Affiliation(s)
- Andrew Walakira
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
| | - Cene Skubic
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Nejc Nadižar
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Tadeja Režen
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Mraz
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Moškon
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
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Lee G, Lee SM, Kim HU. A contribution of metabolic engineering to addressing medical problems: Metabolic flux analysis. Metab Eng 2023; 77:283-293. [PMID: 37075858 DOI: 10.1016/j.ymben.2023.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 03/20/2023] [Accepted: 04/12/2023] [Indexed: 04/21/2023]
Abstract
Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA.
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Affiliation(s)
- GaRyoung Lee
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Sang Mi Lee
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea.
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8
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Kundu P, Ghosh A. Genome-scale community modeling for deciphering the inter-microbial metabolic interactions in fungus-farming termite gut microbiome. Comput Biol Med 2023; 154:106600. [PMID: 36739820 DOI: 10.1016/j.compbiomed.2023.106600] [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: 09/27/2022] [Revised: 12/27/2022] [Accepted: 01/22/2023] [Indexed: 01/27/2023]
Abstract
Specialized microbial communities in the fungus-farming termite gut and fungal comb microbiome help maintain host nutrition through interactive biochemical activities of complex carbohydrate degradation. Numerous research studies have been focused on identifying the microbial species in the termite gut and fungal comb microbiota, but the community-wide metabolic interaction patterns remain obscure. The inter-microbial metabolic interactions in the community environment are essential for executing biochemical processes like complex carbohydrate degradation and maintaining the host's physicochemical homeostasis. Recent progress in high-throughput sequencing techniques and mathematical modeling provides suitable platforms for constructing multispecies genome-scale community metabolic models that can render sound knowledge about microbial metabolic interaction patterns. Here, we have implemented the genome-scale metabolic modeling strategy to map the relationship between genes, proteins, and reactions of 12 key bacterial species from fungal cultivating termite gut and fungal comb microbiota. The resulting individual genome-scale metabolic models (GEMs) have been analyzed using flux balance analysis (FBA) to optimize the metabolic flux distribution pattern. Further, these individual GEMs have been integrated into genome-scale community metabolic models where a heuristics-based computational procedure has been employed to track the inter-microbial metabolic interactions. Two separate genome-scale community metabolic models were reconstructed for the O. badius gut and fungal comb microbiome. Analysis of the community models showed up to ∼167% increased flux range in lignocellulose degradation, amino acid biosynthesis, and nucleotide metabolism pathways. The inter-microbial metabolic exchange of amino acids, SCFAs, and small sugars was also upregulated in the multispecies community for maximum biomass formation. The flux variability analysis (FVA) has also been performed to calculate the feasible flux range of metabolic reactions. Furthermore, based on the calculated metabolic flux values, newly defined parameters, i.e., pairwise metabolic assistance (PMA) and community metabolic assistance (CMA) showed that the microbial species are getting up to 15% higher metabolic benefits in the multispecies community compared to pairwise growth. Assessment of the inter-microbial metabolic interaction patterns through pairwise growth support index (PGSI) indicated an increased mutualistic interaction in the termite gut environment compared to the fungal comb. Thus, this genome-scale community modeling study provides a systematic methodology to understand the inter-microbial interaction patterns with several newly defined parameters like PMA, CMA, and PGSI. The microbial metabolic assistance and interaction patterns derived from this computational approach will enhance the understanding of combinatorial microbial activities and may help develop effective synergistic microcosms to utilize complex plant polymers.
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Affiliation(s)
- Pritam Kundu
- School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal, 721302, India
| | - Amit Ghosh
- 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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9
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Leonidou N, Renz A, Mostolizadeh R, Dräger A. New workflow predicts drug targets against SARS-CoV-2 via metabolic changes in infected cells. PLoS Comput Biol 2023; 19:e1010903. [PMID: 36952396 PMCID: PMC10035753 DOI: 10.1371/journal.pcbi.1010903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 01/30/2023] [Indexed: 03/25/2023] Open
Abstract
COVID-19 is one of the deadliest respiratory diseases, and its emergence caught the pharmaceutical industry off guard. While vaccines have been rapidly developed, treatment options for infected people remain scarce, and COVID-19 poses a substantial global threat. This study presents a novel workflow to predict robust druggable targets against emerging RNA viruses using metabolic networks and information of the viral structure and its genome sequence. For this purpose, we implemented pymCADRE and PREDICATE to create tissue-specific metabolic models, construct viral biomass functions and predict host-based antiviral targets from more than one genome. We observed that pymCADRE reduces the computational time of flux variability analysis for internal optimizations. We applied these tools to create a new metabolic network of primary bronchial epithelial cells infected with SARS-CoV-2 and identified enzymatic reactions with inhibitory effects. The most promising reported targets were from the purine metabolism, while targeting the pyrimidine and carbohydrate metabolisms seemed to be promising approaches to enhance viral inhibition. Finally, we computationally tested the robustness of our targets in all known variants of concern, verifying our targets' inhibitory effects. Since laboratory tests are time-consuming and involve complex readouts to track processes, our workflow focuses on metabolic fluxes within infected cells and is applicable for rapid hypothesis-driven identification of potentially exploitable antivirals concerning various viruses and host cell types.
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Affiliation(s)
- Nantia Leonidou
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karls University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karls University of Tübingen, Tübingen, Germany
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Alina Renz
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karls University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karls University of Tübingen, Tübingen, Germany
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Reihaneh Mostolizadeh
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karls University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karls University of Tübingen, Tübingen, Germany
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, Eberhard Karls University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), partner site Tübingen, Germany
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karls University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karls University of Tübingen, Tübingen, Germany
- Cluster of Excellence ‘Controlling Microbes to Fight Infections’, Eberhard Karls University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), partner site Tübingen, Germany
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Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures. Metabolites 2023; 13:metabo13010126. [PMID: 36677051 PMCID: PMC9866716 DOI: 10.3390/metabo13010126] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/27/2022] [Accepted: 01/10/2023] [Indexed: 01/19/2023] Open
Abstract
Genome-scale metabolic models (GEMs) have found numerous applications in different domains, ranging from biotechnology to systems medicine. Herein, we overview the most popular algorithms for the automated reconstruction of context-specific GEMs using high-throughput experimental data. Moreover, we describe different datasets applied in the process, and protocols that can be used to further automate the model reconstruction and validation. Finally, we describe recent COVID-19 applications of context-specific GEMs, focusing on the analysis of metabolic implications, identification of biomarkers and potential drug targets.
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11
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Zhao Q, Yu Z, Zhang S, Shen XR, Yang H, Xu Y, Liu Y, Yang L, Zhang Q, Chen J, Lu M, Luo F, Hu M, Gong Y, Xie C, Zhou P, Wang L, Su L, Zhang Z, Cheng L. Metabolic modeling of single bronchoalveolar macrophages reveals regulators of hyperinflammation in COVID-19. iScience 2022; 25:105319. [PMID: 36246577 PMCID: PMC9549388 DOI: 10.1016/j.isci.2022.105319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 08/31/2022] [Accepted: 10/06/2022] [Indexed: 11/25/2022] Open
Abstract
SARS-CoV-2 infection induces imbalanced immune response such as hyperinflammation in patients with severe COVID-19. Here, we studied the immunometabolic regulatory mechanisms for the pathogenesis of COVID-19. We depicted the metabolic landscape of immune cells, especially macrophages, from bronchoalveolar lavage fluid of patients with COVID-19 at single-cell level. We found that most metabolic processes were upregulated in macrophages from lungs of patients with mild COVID-19 compared to cells from healthy controls, whereas macrophages from severe COVID-19 showed downregulation of most of the core metabolic pathways including glutamate metabolism, fatty acid oxidation, citrate cycle, and oxidative phosphorylation, and upregulation of a few pathways such as glycolysis. Rewiring cellular metabolism by amino acid supplementation, glycolysis inhibition, or PPARγ stimulation reduces inflammation in macrophages stimulated with SARS-CoV-2. Altogether, this study demonstrates that metabolic imbalance of bronchoalveolar macrophages may contribute to hyperinflammation in patients with severe COVID-19 and provides insights into treating COVID-19 by immunometabolic modulation.
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Affiliation(s)
- Qiuchen Zhao
- Department of Radiation and Medical Oncology, Medical Research Institute, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan 430071, China
- Frontier Science Center for Immunology and Metabolism, Department of Pulmonary and Critical Care Medicine, Zhongnan Hospital of Wuhan University, State Key Laboratory of Virology, Wuhan University, Wuhan 430071, China
- School of Life Science, Wuhan University, Wuhan 430071, China
| | - Zhenyang Yu
- Department of Radiation and Medical Oncology, Medical Research Institute, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan 430071, China
- Frontier Science Center for Immunology and Metabolism, Department of Pulmonary and Critical Care Medicine, Zhongnan Hospital of Wuhan University, State Key Laboratory of Virology, Wuhan University, Wuhan 430071, China
| | - Shengyuan Zhang
- Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People’s Hospital, the Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen 518112, China
| | - Xu-Rui Shen
- CAS Key Laboratory of Special Pathogens and State Key Laboratory of Virology, Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy of Sciences, Wuhan, China
| | - Hao Yang
- Department of Radiation and Medical Oncology, Medical Research Institute, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan 430071, China
| | - Yangyang Xu
- Department of Radiation and Medical Oncology, Medical Research Institute, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan 430071, China
| | - Yang Liu
- Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People’s Hospital, the Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen 518112, China
| | - Lin Yang
- Department of General Surgery, Xuzhou Mine Hospital, Xuzhou 221000, China
| | - Qing Zhang
- Cancer Institute, Xuzhou Medical University, Xuzhou 221000, China
| | - Jiaqi Chen
- School of Computer Sciences, Wuhan University, Wuhan 430071, China
| | - Mengmeng Lu
- Department of Radiation and Medical Oncology, Medical Research Institute, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan 430071, China
| | - Fei Luo
- Department of Radiation and Medical Oncology, Medical Research Institute, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan 430071, China
| | - Mingming Hu
- Frontier Science Center for Immunology and Metabolism, Department of Pulmonary and Critical Care Medicine, Zhongnan Hospital of Wuhan University, State Key Laboratory of Virology, Wuhan University, Wuhan 430071, China
| | - Yan Gong
- Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan 430071, China
| | - Conghua Xie
- Department of Radiation and Medical Oncology, Medical Research Institute, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan 430071, China
- Wuhan Research Center for Infectious Diseases and Cancer, Chinese Academy of Medical Sciences, Wuhan 430071, China
| | - Peng Zhou
- CAS Key Laboratory of Special Pathogens and State Key Laboratory of Virology, Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy of Sciences, Wuhan, China
| | - Li Wang
- Frontier Science Center for Immunology and Metabolism, Department of Pulmonary and Critical Care Medicine, Zhongnan Hospital of Wuhan University, State Key Laboratory of Virology, Wuhan University, Wuhan 430071, China
- Department of Cardiology, Institute of Myocardial Injury and Repair, Zhongnan Hospital of Wuhan University, Wuhan 430071, China
| | - Lishan Su
- Division of Virology, Pathogenesis and Cancer, Institute of Human Virology and Department of Pharmacology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Zheng Zhang
- Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People’s Hospital, the Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen 518112, China
| | - Liang Cheng
- Department of Radiation and Medical Oncology, Medical Research Institute, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan 430071, China
- Frontier Science Center for Immunology and Metabolism, Department of Pulmonary and Critical Care Medicine, Zhongnan Hospital of Wuhan University, State Key Laboratory of Virology, Wuhan University, Wuhan 430071, China
- Wuhan Research Center for Infectious Diseases and Cancer, Chinese Academy of Medical Sciences, Wuhan 430071, China
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12
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Beura S, Kundu P, Das AK, Ghosh A. Metagenome-scale community metabolic modelling for understanding the role of gut microbiota in human health. Comput Biol Med 2022; 149:105997. [DOI: 10.1016/j.compbiomed.2022.105997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 07/03/2022] [Accepted: 08/14/2022] [Indexed: 11/03/2022]
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13
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Agrawal P, Sambaturu N, Olgun G, Hannenhalli S. A Path-Based Analysis of Infected Cell Line and COVID-19 Patient Transcriptome Reveals Novel Potential Targets and Drugs Against SARS-CoV-2. Front Immunol 2022; 13:918817. [PMID: 35844595 PMCID: PMC9284228 DOI: 10.3389/fimmu.2022.918817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Most transcriptomic studies of SARS-CoV-2 infection have focused on differentially expressed genes, which do not necessarily reveal the genes mediating the transcriptomic changes. In contrast, exploiting curated biological network, our PathExt tool identifies central genes from the differentially active paths mediating global transcriptomic response. Here we apply PathExt to multiple cell line infection models of SARS-CoV-2 and other viruses, as well as to COVID-19 patient-derived PBMCs. The central genes mediating SARS-CoV-2 response in cell lines were uniquely enriched for ATP metabolic process, G1/S transition, leukocyte activation and migration. In contrast, PBMC response reveals dysregulated cell-cycle processes. In PBMC, the most frequently central genes are associated with COVID-19 severity. Importantly, relative to differential genes, PathExt-identified genes show greater concordance with several benchmark anti-COVID-19 target gene sets. We propose six novel anti-SARS-CoV-2 targets ADCY2, ADSL, OCRL, TIAM1, PBK, and BUB1, and potential drugs targeting these genes, such as Bemcentinib, Phthalocyanine, and Conivaptan.
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Affiliation(s)
- Piyush Agrawal
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Narmada Sambaturu
- IISc Mathematics Initiative, Indian Institute of Science, Bangalore, India
| | - Gulden Olgun
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Sridhar Hannenhalli
- Cancer Data Science Laboratory, National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
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14
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Režen T, Martins A, Mraz M, Zimic N, Rozman D, Moškon M. Integration of omics data to generate and analyse COVID-19 specific genome-scale metabolic models. Comput Biol Med 2022; 145:105428. [PMID: 35339845 PMCID: PMC8940269 DOI: 10.1016/j.compbiomed.2022.105428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 03/17/2022] [Accepted: 03/19/2022] [Indexed: 12/16/2022]
Abstract
COVID-19 presents a complex disease that needs to be addressed using systems medicine approaches that include genome-scale metabolic models (GEMs). Previous studies have used a single model extraction method (MEM) and/or a single transcriptomic dataset to reconstruct context-specific models, which proved to be insufficient for the broader biological contexts. We have applied four MEMs in combination with five COVID-19 datasets. Models produced by GIMME were separated by infection, while tINIT preserved the biological variability in the data and enabled the best prediction of the enrichment of metabolic subsystems. Vitamin D3 metabolism was predicted to be down-regulated in one dataset by GIMME, and in all by tINIT. Models generated by tINIT and GIMME predicted downregulation of retinol metabolism in different datasets, while downregulated cholesterol metabolism was predicted only by tINIT-generated models. Predictions are in line with the observations in COVID-19 patients. Our data indicated that GIMME and tINIT models provided the most biologically relevant results and should have a larger emphasis in further analyses. Particularly tINIT models identified the metabolic pathways that are a part of the host response and are potential antiviral targets. The code and the results of the analyses are available to download from https://github.com/CompBioLj/COVID_GEMs_and_MEMs.
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Affiliation(s)
- Tadeja Režen
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | | | - Miha Mraz
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Nikolaj Zimic
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Moškon
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
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15
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A genome-scale metabolic model of Cupriavidus necator H16 integrated with TraDIS and transcriptomic data reveals metabolic insights for biotechnological applications. PLoS Comput Biol 2022; 18:e1010106. [PMID: 35604933 PMCID: PMC9166356 DOI: 10.1371/journal.pcbi.1010106] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 06/03/2022] [Accepted: 04/14/2022] [Indexed: 11/29/2022] Open
Abstract
Exploiting biological processes to recycle renewable carbon into high value platform chemicals provides a sustainable and greener alternative to current reliance on petrochemicals. In this regard Cupriavidus necator H16 represents a particularly promising microbial chassis due to its ability to grow on a wide range of low-cost feedstocks, including the waste gas carbon dioxide, whilst also naturally producing large quantities of polyhydroxybutyrate (PHB) during nutrient-limited conditions. Understanding the complex metabolic behaviour of this bacterium is a prerequisite for the design of successful engineering strategies for optimising product yields. We present a genome-scale metabolic model (GSM) of C. necator H16 (denoted iCN1361), which is directly constructed from the BioCyc database to improve the readability and reusability of the model. After the initial automated construction, we have performed extensive curation and both theoretical and experimental validation. By carrying out a genome-wide essentiality screening using a Transposon-directed Insertion site Sequencing (TraDIS) approach, we showed that the model could predict gene knockout phenotypes with a high level of accuracy. Importantly, we indicate how experimental and computational predictions can be used to improve model structure and, thus, model accuracy as well as to evaluate potential false positives identified in the experiments. Finally, by integrating transcriptomics data with iCN1361 we create a condition-specific model, which, importantly, better reflects PHB production in C. necator H16. Observed changes in the omics data and in-silico-estimated alterations in fluxes were then used to predict the regulatory control of key cellular processes. The results presented demonstrate that iCN1361 is a valuable tool for unravelling the system-level metabolic behaviour of C. necator H16 and can provide useful insights for designing metabolic engineering strategies. Genome-scale metabolic models (GSMs) provide a tool for unravelling the complex metabolic behaviour of bacteria and how they adapt to changing environments and genetic perturbations, and thus offer invaluable insights for biotechnology applications. For a GSM to be used efficiently for strain development purposes, however, the model must be easily readable and reusable by other researchers, whilst being able to predict metabolic behaviour with a high level of accuracy. In this work, we developed a GSM for Cupriavidus necator H16 that is linked to the BioCyc database, which provides an efficient way of application, model update, integration of experimental data and network visualisation for other researchers. Using our model, we demonstrate how integrating experimental observations, including Transposon-directed Insertion site Sequencing (TraDIS) and omics data, can be used to compensate for the lack of regulatory, kinetic and thermodynamic information in GSMs, and thus improve model accuracy. Importantly, we found that TraDIS in vivo screening and GSM analysis are complementary approaches, which can be used in combination to provide reliable gene essentiality predictions. Overall, our results offer an informed strategy for the deliberate manipulation of C. necator H16 metabolic capabilities, towards its industrial application to convert greenhouse gases into biochemicals and biofuels.
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16
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Hallmarks of Metabolic Reprogramming and Their Role in Viral Pathogenesis. Viruses 2022; 14:v14030602. [PMID: 35337009 PMCID: PMC8955778 DOI: 10.3390/v14030602] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/09/2022] [Accepted: 03/10/2022] [Indexed: 02/07/2023] Open
Abstract
Metabolic reprogramming is a hallmark of cancer and has proven to be critical in viral infections. Metabolic reprogramming provides the cell with energy and biomass for large-scale biosynthesis. Based on studies of the cellular changes that contribute to metabolic reprogramming, seven main hallmarks can be identified: (1) increased glycolysis and lactic acid, (2) increased glutaminolysis, (3) increased pentose phosphate pathway, (4) mitochondrial changes, (5) increased lipid metabolism, (6) changes in amino acid metabolism, and (7) changes in other biosynthetic and bioenergetic pathways. Viruses depend on metabolic reprogramming to increase biomass to fuel viral genome replication and production of new virions. Viruses take advantage of the non-metabolic effects of metabolic reprogramming, creating an anti-apoptotic environment and evading the immune system. Other non-metabolic effects can negatively affect cellular function. Understanding the role metabolic reprogramming plays in viral pathogenesis may provide better therapeutic targets for antivirals.
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17
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Wang G, Xiao B, Deng J, Gong L, Li Y, Li J, Zhong Y. The Role of Cytochrome P450 Enzymes in COVID-19 Pathogenesis and Therapy. Front Pharmacol 2022; 13:791922. [PMID: 35185562 PMCID: PMC8847594 DOI: 10.3389/fphar.2022.791922] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 01/05/2022] [Indexed: 12/15/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19) has become a new public health crisis threatening the world. Dysregulated immune responses are the most striking pathophysiological features of patients with severe COVID-19, which can result in multiple-organ failure and death. The cytochrome P450 (CYP) system is the most important drug metabolizing enzyme family, which plays a significant role in the metabolism of endogenous or exogenous substances. Endogenous CYPs participate in the biosynthesis or catabolism of endogenous substances, including steroids, vitamins, eicosanoids, and fatty acids, whilst xenobiotic CYPs are associated with the metabolism of environmental toxins, drugs, and carcinogens. CYP expression and activity are greatly affected by immune response. However, changes in CYP expression and/or function in COVID-19 and their impact on COVID-19 pathophysiology and the metabolism of therapeutic agents in COVID-19, remain unclear. In this analysis, we review current evidence predominantly in the following areas: firstly, the possible changes in CYP expression and/or function in COVID-19; secondly, the effects of CYPs on the metabolism of arachidonic acid, vitamins, and steroid hormones in COVID-19; and thirdly, the effects of CYPs on the metabolism of therapeutic COVID-19 drugs.
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Affiliation(s)
- Guyi Wang
- Department of Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Bing Xiao
- Department of Emergency, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Jiayi Deng
- Department of Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Linmei Gong
- Department of Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yi Li
- Department of Cardiology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Jinxiu Li
- Department of Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yanjun Zhong
- Department of Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, China
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18
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Wang FS, Chen KL, Chu SW. Human/SARS-CoV-2 Genome-Scale Metabolic Modeling to Discover Potential Antiviral Targets for COVID-19. J Taiwan Inst Chem Eng 2022; 133:104273. [PMID: 35186172 PMCID: PMC8843340 DOI: 10.1016/j.jtice.2022.104273] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 02/06/2022] [Accepted: 02/11/2022] [Indexed: 12/20/2022]
Affiliation(s)
- Feng-Sheng Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan
| | - Ke-Lin Chen
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan
| | - Sz-Wei Chu
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan
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19
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Al Bataineh MT, Henschel A, Mousa M, Daou M, Waasia F, Kannout H, Khalili M, Kayasseh MA, Alkhajeh A, Uddin M, Alkaabi N, Tay GK, Feng SF, Yousef AF, Alsafar HS. Gut Microbiota Interplay With COVID-19 Reveals Links to Host Lipid Metabolism Among Middle Eastern Populations. Front Microbiol 2021; 12:761067. [PMID: 34803986 PMCID: PMC8603808 DOI: 10.3389/fmicb.2021.761067] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 09/30/2021] [Indexed: 12/15/2022] Open
Abstract
The interplay between the compositional changes in the gastrointestinal microbiome, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) susceptibility and severity, and host functions is complex and yet to be fully understood. This study performed 16S rRNA gene-based microbial profiling of 143 subjects. We observed structural and compositional alterations in the gut microbiota of the SARS-CoV-2-infected group in comparison to non-infected controls. The gut microbiota composition of the SARS-CoV-2-infected individuals showed an increase in anti-inflammatory bacteria such as Faecalibacterium (p-value = 1.72 × 10-6) and Bacteroides (p-value = 5.67 × 10-8). We also revealed a higher relative abundance of the highly beneficial butyrate producers such as Anaerostipes (p-value = 1.75 × 10-230), Lachnospiraceae (p-value = 7.14 × 10-65), and Blautia (p-value = 9.22 × 10-18) in the SARS-CoV-2-infected group in comparison to the control group. Moreover, phylogenetic investigation of communities by reconstructing unobserved state (PICRUSt) functional prediction analysis of the 16S rRNA gene abundance data showed substantial differences in the enrichment of metabolic pathways such as lipid, amino acid, carbohydrate, and xenobiotic metabolism, in comparison between both groups. We discovered an enrichment of linoleic acid, ether lipid, glycerolipid, and glycerophospholipid metabolism in the SARS-CoV-2-infected group, suggesting a link to SARS-CoV-2 entry and replication in host cells. We estimate the major contributing genera to the four pathways to be Parabacteroides, Streptococcus, Dorea, and Blautia, respectively. The identified differences provide a new insight to enrich our understanding of SARS-CoV-2-related changes in gut microbiota, their metabolic capabilities, and potential screening biomarkers linked to COVID-19 disease severity.
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Affiliation(s)
- Mohammad Tahseen Al Bataineh
- Sharjah Institute for Medical Research, University of Sharjah, Sharjah, United Arab Emirates.,Department of Clinical Sciences, College of Medicine, University of Sharjah, Sharjah, United Arab Emirates.,Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.,Department of Genetics and Molecular Biology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Andreas Henschel
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.,Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Mira Mousa
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.,Nuffield Department of Women's and Reproduction Health, Oxford University, Oxford, United Kingdom
| | - Marianne Daou
- Department of Chemistry, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Fathimathuz Waasia
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Hussein Kannout
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Mariam Khalili
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Mohd Azzam Kayasseh
- Emirates Specialty Hospital, Dubai Healthcare City, Dubai, United Arab Emirates
| | - Abdulmajeed Alkhajeh
- Medical Education and Research Department, Dubai Health Authority, Dubai, United Arab Emirates
| | - Maimunah Uddin
- Department of Pediatric Infectious Disease, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates
| | - Nawal Alkaabi
- Department of Pediatric Infectious Disease, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates
| | - Guan K Tay
- Division of Psychiatry, Faculty of Health and Medical Sciences, The University of Western Australia, Crawley, WA, Australia.,School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - Samuel F Feng
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.,Department of Mathematics, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Ahmed F Yousef
- Department of Chemistry, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Habiba S Alsafar
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.,Department of Mathematics, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.,Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
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