1
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Leonidou N, Ostyn L, Coenye T, Crabbé A, Dräger A. Genome-scale model of Rothia mucilaginosa predicts gene essentialities and reveals metabolic capabilities. Microbiol Spectr 2024; 12:e0400623. [PMID: 38652457 DOI: 10.1128/spectrum.04006-23] [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: 11/28/2023] [Accepted: 03/20/2024] [Indexed: 04/25/2024] Open
Abstract
Cystic fibrosis (CF), an inherited genetic disorder caused by mutations in the cystic fibrosis transmembrane conductance regulator gene, results in sticky and thick mucosal fluids. This environment facilitates the colonization of various microorganisms, some of which can cause acute and chronic lung infections, while others may positively impact the disease. Rothia mucilaginosa, an oral commensal, is relatively abundant in the lungs of CF patients. Recent studies have unveiled its anti-inflammatory properties using in vitro three-dimensional lung epithelial cell cultures and in vivo mouse models relevant to chronic lung diseases. Apart from this, R. mucilaginosa has been associated with severe infections. However, its metabolic capabilities and genotype-phenotype relationships remain largely unknown. To gain insights into its cellular metabolism and genetic content, we developed the first manually curated genome-scale metabolic model, iRM23NL. Through growth kinetics and high-throughput phenotypic microarray testings, we defined its complete catabolic phenome. Subsequently, we assessed the model's effectiveness in accurately predicting growth behaviors and utilizing multiple substrates. We used constraint-based modeling techniques to formulate novel hypotheses that could expedite the development of antimicrobial strategies. More specifically, we detected putative essential genes and assessed their effect on metabolism under varying nutritional conditions. These predictions could offer novel potential antimicrobial targets without laborious large-scale screening of knockouts and mutant transposon libraries. Overall, iRM23NL demonstrates a solid capability to predict cellular phenotypes and holds immense potential as a valuable resource for accurate predictions in advancing antimicrobial therapies. Moreover, it can guide metabolic engineering to tailor R. mucilaginosa's metabolism for desired performance.IMPORTANCECystic fibrosis (CF) is a genetic disorder characterized by thick mucosal secretions, leading to chronic lung infections. Rothia mucilaginosa is a common bacterium found in various parts of the human body, acting as a normal part of the flora. In people with weakened immune systems, it can become an opportunistic pathogen, while it is prevalent and active in CF airways. Recent studies have highlighted its anti-inflammatory properties in the lower pulmonary system, indicating the intricate relationship between microbes and human health. Herein, we have developed the first manually curated metabolic model of R. mucilaginosa. Our study examined the previously unknown relationships between the bacterium's genotype and phenotype and identified essential genes that impact the metabolism under various conditions. With this, we opt for paving the way for developing new strategies in antimicrobial therapy and metabolic engineering, leading to enhanced therapeutic outcomes in cystic fibrosis and related conditions.
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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 Karl University of Tübingen, Tübingen, Germany
- Department of Computer Science, Eberhard Karl University of Tübingen, Tübingen, Germany
- Cluster of Excellence 'Controlling Microbes to Fight Infections', Eberhard Karl University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), partner site Tübingen, Tübingen, Germany
- Quantitative Biology Center (QBiC), Eberhard Karl University of Tübingen, Tübingen, Germany
| | - Lisa Ostyn
- Laboratory of Pharmaceutical Microbiology (LPM), Ghent University, Ghent, Belgium
| | - Tom Coenye
- Laboratory of Pharmaceutical Microbiology (LPM), Ghent University, Ghent, Belgium
| | - Aurélie Crabbé
- Laboratory of Pharmaceutical Microbiology (LPM), Ghent University, Ghent, Belgium
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen, Tübingen, Germany
- German Center for Infection Research (DZIF), partner site Tübingen, Tübingen, Germany
- Data Analytics and Bioinformatics, Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
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2
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Omar MN, Minggu MM, Nor Muhammad NA, Abdul PM, Zhang Y, Ramzi AB. Towards consolidated bioprocessing of biomass and plastic substrates for semi-synthetic production of bio-poly(ethylene furanoate) (PEF) polymer using omics-guided construction of artificial microbial consortia. Enzyme Microb Technol 2024; 177:110429. [PMID: 38537325 DOI: 10.1016/j.enzmictec.2024.110429] [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: 11/28/2023] [Revised: 02/20/2024] [Accepted: 03/14/2024] [Indexed: 04/29/2024]
Abstract
Poly(ethylene furanoate) (PEF) plastic is a 100% renewable polyester that is currently being pursued for commercialization as the next-generation bio-based plastic. This is in line with growing demand for circular bioeconomy and new plastics economy that is aimed at minimizing plastic waste mismanagement and lowering carbon footprint of plastics. However, the current catalytic route for the synthesis of PEF is impeded with technical challenges including high cost of pretreatment and catalyst refurbishment. On the other hand, the semi-biosynthetic route of PEF plastic production is of increased biotechnological interest. In particular, the PEF monomers (Furan dicarboxylic acid and ethylene glycol) can be synthesized via microbial-based biorefinery and purified for subsequent catalyst-mediated polycondensation into PEF. Several bioengineering and bioprocessing issues such as efficient substrate utilization and pathway optimization need to be addressed prior to establishing industrial-scale production of the monomers. This review highlights current advances in semi-biosynthetic production of PEF monomers using consolidated waste biorefinery strategies, with an emphasis on the employment of omics-driven systems biology approaches in enzyme discovery and pathway construction. The roles of microbial protein transporters will be discussed, especially in terms of improving substrate uptake and utilization from lignocellulosic biomass, as well as from depolymerized plastic waste as potential bio-feedstock. The employment of artificial bioengineered microbial consortia will also be highlighted to provide streamlined systems and synthetic biology strategies for bio-based PEF monomer production using both plant biomass and plastic-derived substrates, which are important for circular and new plastics economy advances.
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Affiliation(s)
- Mohd Norfikri Omar
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), UKM, Bangi, Selangor 43600, Malaysia
| | - Matthlessa Matthew Minggu
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), UKM, Bangi, Selangor 43600, Malaysia
| | - Nor Azlan Nor Muhammad
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), UKM, Bangi, Selangor 43600, Malaysia
| | - Peer Mohamed Abdul
- Department of Chemical and Process Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia; Centre for Sustainable Process Technology (CESPRO), Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia
| | - Ying Zhang
- BBSRC/EPSRC Synthetic Biology Research Centre (SBRC), School of Life Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK
| | - Ahmad Bazli Ramzi
- Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), UKM, Bangi, Selangor 43600, Malaysia.
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3
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Silva-Lance F, Montejano-Montelongo I, Bautista E, Nielsen LK, Johansson PI, Marin de Mas I. Integrating Genome-Scale Metabolic Models with Patient Plasma Metabolome to Study Endothelial Metabolism In Situ. Int J Mol Sci 2024; 25:5406. [PMID: 38791446 PMCID: PMC11121795 DOI: 10.3390/ijms25105406] [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: 03/01/2024] [Revised: 04/30/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024] Open
Abstract
Patient blood samples are invaluable in clinical omics databases, yet current methodologies often fail to fully uncover the molecular mechanisms driving patient pathology. While genome-scale metabolic models (GEMs) show promise in systems medicine by integrating various omics data, having only exometabolomic data remains a limiting factor. To address this gap, we introduce a comprehensive pipeline integrating GEMs with patient plasma metabolome. This pipeline constructs case-specific GEMs using literature-based and patient-specific metabolomic data. Novel computational methods, including adaptive sampling and an in-house developed algorithm for the rational exploration of the sampled space of solutions, enhance integration accuracy while improving computational performance. Model characterization involves task analysis in combination with clustering methods to identify critical cellular functions. The new pipeline was applied to a cohort of trauma patients to investigate shock-induced endotheliopathy using patient plasma metabolome data. By analyzing endothelial cell metabolism comprehensively, the pipeline identified critical therapeutic targets and biomarkers that can potentially contribute to the development of therapeutic strategies. Our study demonstrates the efficacy of integrating patient plasma metabolome data into computational models to analyze endothelial cell metabolism in disease contexts. This approach offers a deeper understanding of metabolic dysregulations and provides insights into diseases with metabolic components and potential treatments.
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Affiliation(s)
- Fernando Silva-Lance
- Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
| | | | - Eric Bautista
- Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
| | - Lars K. Nielsen
- Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
- CAG Center for Endotheliomics, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
| | - Pär I. Johansson
- CAG Center for Endotheliomics, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Igor Marin de Mas
- Novo Nordisk Foundation Center for Biosustainability, Danish Technical University, 2800 Lyngby, Denmark
- CAG Center for Endotheliomics, Copenhagen University Hospital, Rigshospitalet, 2100 Copenhagen, Denmark
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4
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Santangelo BE, Apgar M, Colorado ASB, Martin CG, Sterrett J, Wall E, Joachimiak MP, Hunter LE, Lozupone CA. Integrating biological knowledge for mechanistic inference in the host-associated microbiome. Front Microbiol 2024; 15:1351678. [PMID: 38638909 PMCID: PMC11024261 DOI: 10.3389/fmicb.2024.1351678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/26/2024] [Indexed: 04/20/2024] Open
Abstract
Advances in high-throughput technologies have enhanced our ability to describe microbial communities as they relate to human health and disease. Alongside the growth in sequencing data has come an influx of resources that synthesize knowledge surrounding microbial traits, functions, and metabolic potential with knowledge of how they may impact host pathways to influence disease phenotypes. These knowledge bases can enable the development of mechanistic explanations that may underlie correlations detected between microbial communities and disease. In this review, we survey existing resources and methodologies for the computational integration of broad classes of microbial and host knowledge. We evaluate these knowledge bases in their access methods, content, and source characteristics. We discuss challenges of the creation and utilization of knowledge bases including inconsistency of nomenclature assignment of taxa and metabolites across sources, whether the biological entities represented are rooted in ontologies or taxonomies, and how the structure and accessibility limit the diversity of applications and user types. We make this information available in a code and data repository at: https://github.com/lozuponelab/knowledge-source-mappings. Addressing these challenges will allow for the development of more effective tools for drawing from abundant knowledge to find new insights into microbial mechanisms in disease by fostering a systematic and unbiased exploration of existing information.
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Affiliation(s)
- Brook E. Santangelo
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Madison Apgar
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | | | - Casey G. Martin
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - John Sterrett
- Department of Integrative Physiology, University of Colorado, Boulder, CO, United States
| | - Elena Wall
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Marcin P. Joachimiak
- Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Biosystems Data Science Department, Berkeley, CA, United States
| | - Lawrence E. Hunter
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Catherine A. Lozupone
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, United States
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5
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Herbst K, Wang T, Forchielli EJ, Thommes M, Paschalidis IC, Segrè D. Multi-Attribute Subset Selection enables prediction of representative phenotypes across microbial populations. Commun Biol 2024; 7:407. [PMID: 38570615 PMCID: PMC10991586 DOI: 10.1038/s42003-024-06093-w] [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: 07/05/2022] [Accepted: 03/22/2024] [Indexed: 04/05/2024] Open
Abstract
The interpretation of complex biological datasets requires the identification of representative variables that describe the data without critical information loss. This is particularly important in the analysis of large phenotypic datasets (phenomics). Here we introduce Multi-Attribute Subset Selection (MASS), an algorithm which separates a matrix of phenotypes (e.g., yield across microbial species and environmental conditions) into predictor and response sets of conditions. Using mixed integer linear programming, MASS expresses the response conditions as a linear combination of the predictor conditions, while simultaneously searching for the optimally descriptive set of predictors. We apply the algorithm to three microbial datasets and identify environmental conditions that predict phenotypes under other conditions, providing biologically interpretable axes for strain discrimination. MASS could be used to reduce the number of experiments needed to identify species or to map their metabolic capabilities. The generality of the algorithm allows addressing subset selection problems in areas beyond biology.
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Affiliation(s)
- Konrad Herbst
- Bioinformatics Program, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
| | - Taiyao Wang
- Division of Systems Engineering, Boston University, Boston, MA, USA
| | - Elena J Forchielli
- Biological Design Center, Boston University, Boston, MA, USA
- Department of Biology, Boston University, Boston, MA, USA
| | - Meghan Thommes
- Biological Design Center, Boston University, Boston, MA, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Ioannis Ch Paschalidis
- Division of Systems Engineering, Boston University, Boston, MA, USA.
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
- Faculty of Computing and Data Science, Boston University, Boston, MA, USA.
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA.
| | - Daniel Segrè
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Biological Design Center, Boston University, Boston, MA, USA.
- Department of Biology, Boston University, Boston, MA, USA.
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
- Faculty of Computing and Data Science, Boston University, Boston, MA, USA.
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6
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Chan D, Cromar GL, Taj B, Parkinson J. Cell4D: a general purpose spatial stochastic simulator for cellular pathways. BMC Bioinformatics 2024; 25:121. [PMID: 38515063 PMCID: PMC10956314 DOI: 10.1186/s12859-024-05739-0] [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: 09/13/2023] [Accepted: 03/11/2024] [Indexed: 03/23/2024] Open
Abstract
BACKGROUND With the generation of vast compendia of biological datasets, the challenge is how best to interpret 'omics data alongside biochemical and other small-scale experiments to gain meaningful biological insights. Key to this challenge are computational methods that enable domain-users to generate novel hypotheses that can be used to guide future experiments. Of particular interest are flexible modeling platforms, capable of simulating a diverse range of biological systems with low barriers of adoption to those with limited computational expertise. RESULTS We introduce Cell4D, a spatial-temporal modeling platform combining a robust simulation engine with integrated graphics visualization, a model design editor, and an underlying XML data model capable of capturing a variety of cellular functions. Cell4D provides an interactive visualization mode, allowing intuitive feedback on model behavior and exploration of novel hypotheses, together with a non-graphics mode, compatible with high performance cloud compute solutions, to facilitate generation of statistical data. To demonstrate the flexibility and effectiveness of Cell4D, we investigate the dynamics of CEACAM1 localization in T-cell activation. We confirm the importance of Ca2+ microdomains in activating calmodulin and highlight a key role of activated calmodulin on the surface expression of CEACAM1. We further show how lymphocyte-specific protein tyrosine kinase can help regulate this cell surface expression and exploit spatial modeling features of Cell4D to test the hypothesis that lipid rafts regulate clustering of CEACAM1 to promote trans-binding to neighbouring cells. CONCLUSIONS Through demonstrating its ability to test and generate hypotheses, Cell4D represents an effective tool to help integrate knowledge across diverse, large and small-scale datasets.
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Affiliation(s)
- Donny Chan
- Program in Molecular Medicine, Hospital for Sick Children, Toronto, M5G 0A4, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, M5S 1A8, Canada
| | - Graham L Cromar
- Program in Molecular Medicine, Hospital for Sick Children, Toronto, M5G 0A4, Canada
| | - Billy Taj
- Program in Molecular Medicine, Hospital for Sick Children, Toronto, M5G 0A4, Canada
| | - John Parkinson
- Program in Molecular Medicine, Hospital for Sick Children, Toronto, M5G 0A4, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, M5S 1A8, Canada.
- Department of Biochemistry, University of Toronto, Toronto, M5S 1A8, Canada.
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7
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Thumsi A, Martínez D, Swaminathan SJ, Esrafili A, Suresh AP, Jaggarapu MMC, Lintecum K, Halim M, Mantri SV, Sleiman Y, Appel N, Gu H, Curtis M, Zuniga C, Acharya AP. Inverse-Vaccines for Rheumatoid Arthritis Re-establish Metabolic and Immunological Homeostasis in Joint Tissues. Adv Healthc Mater 2024:e2303995. [PMID: 38469995 DOI: 10.1002/adhm.202303995] [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: 11/14/2023] [Revised: 03/06/2024] [Indexed: 03/13/2024]
Abstract
Rheumatoid arthritis (RA) causes immunological and metabolic imbalances in tissue, exacerbating inflammation in affected joints. Changes in immunological and metabolic tissue homeostasis at different stages of RA are not well understood. Herein, the changes in the immunological and metabolic profiles in different stages in collagen induced arthritis (CIA), namely, early, intermediate, and late stage is examined. Moreover, the efficacy of the inverse-vaccine, paKG(PFK15+bc2) microparticle, to restore tissue homeostasis at different stages is also investigated. Immunological analyses of inverse-vaccine-treated group revealed a significant decrease in the activation of pro-inflammatory immune cells and remarkable increase in regulatory T-cell populations in the intermediate and late stages compared to no treatment. Also, glycolysis in the spleen is normalized in the late stages of CIA in inverse-vaccine-treated mice, which is similar to no-disease tissues. Metabolomics analyses revealed that metabolites UDP-glucuronic acid and L-Glutathione oxidized are significantly altered between treatment groups, and thus might provide new druggable targets for RA treatment. Flux metabolic modeling identified amino acid and carnitine pathways as the central pathways affected in arthritic tissue with CIA progression. Overall, this study shows that the inverse-vaccines initiate early re-establishment of homeostasis, which persists through the disease span.
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Affiliation(s)
- Abhirami Thumsi
- Department of Pathology, Case Western REserve University School of Medicine, Cleveland, OH, 44106, USA
| | - Diego Martínez
- Department of Biology, San Diego State University, San Diego, CA, 92182, USA
| | | | - Arezoo Esrafili
- Department of Chemical Engineering, School for the Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, 85281, USA
| | - Abhirami P Suresh
- Department of Pathology, Case Western REserve University School of Medicine, Cleveland, OH, 44106, USA
| | | | - Kelly Lintecum
- Department of Chemical Engineering, School for the Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, 85281, USA
| | - Michelle Halim
- Department of Chemical Engineering, School for the Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, 85281, USA
| | - Shivani V Mantri
- Department of Biomedical Engineering, School of Biological and Health System Engineering, Arizona State University, Tempe, AZ, 85281, USA
| | - Yasmine Sleiman
- Department of Biomedical Engineering, School of Biological and Health System Engineering, Arizona State University, Tempe, AZ, 85281, USA
| | - Nicole Appel
- Department of Chemical Engineering, School for the Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, 85281, USA
| | - Haiwei Gu
- College of Health Solutions, Arizona State University, Phoenix, AZ, 85281, USA
| | - Marion Curtis
- Department of Cancer Biology, Mayo Clinic, Scottsdale, AZ, 85259, USA
- College of Medicine and Science, Mayo Clinic, Scottsdale, AZ, 85259, USA
| | - Cristal Zuniga
- Department of Biology, San Diego State University, San Diego, CA, 92182, USA
| | - Abhinav P Acharya
- Department of Pathology, Case Western REserve University School of Medicine, Cleveland, OH, 44106, USA
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, 44106, USA
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio, 44106, USA
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8
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Ferdosnejad K, Zamani MS, Soroush E, Fateh A, Siadat SD, Tarashi S. Tuberculosis and lung cancer: metabolic pathways play a key role. NUCLEOSIDES, NUCLEOTIDES & NUCLEIC ACIDS 2024:1-20. [PMID: 38305273 DOI: 10.1080/15257770.2024.2308522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 01/14/2024] [Indexed: 02/03/2024]
Abstract
Despite the fact that some cases of tuberculosis (TB) are undiagnosed and untreated, it remains a serious global public health issue. In the diagnosis, treatment, and control of latent and active TB, there may be a lack of effectiveness. An understanding of metabolic pathways can be fundamental to treat latent TB infection and active TB disease. Rather than targeting Mycobacterium tuberculosis, the control strategies aim to strengthen host responses to infection and reduce chronic inflammation by effectively enhancing host resistance to infection. The pathogenesis and progression of TB are linked to several metabolites and metabolic pathways, and they are potential targets for host-directed therapies. Additionally, metabolic pathways can contribute to the progression of lung cancer in patients with latent or active TB. A comprehensive metabolic pathway analysis is conducted to highlight lung cancer development in latent and active TB. The current study aimed to emphasize the association between metabolic pathways of tumor development in patients with latent and active TB. Health control programs around the world are compromised by TB and lung cancer due to their special epidemiological and clinical characteristics. Therefore, presenting the importance of lung cancer progression through metabolic pathways occurring upon TB infection can open new doors to improving control of TB infection and active TB disease while stressing that further evaluations are required to uncover this correlation.
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Affiliation(s)
| | | | - Erfan Soroush
- Microbiology Research Center (MRC), Pasteur Institute of Iran, Tehran, Iran
| | - Abolfazl Fateh
- Microbiology Research Center (MRC), Pasteur Institute of Iran, Tehran, Iran
- Department of Mycobacteriology and Pulmonary Research, Pasteur Institute of Iran, Tehran, Iran
| | - Seyed Davar Siadat
- Microbiology Research Center (MRC), Pasteur Institute of Iran, Tehran, Iran
- Department of Mycobacteriology and Pulmonary Research, Pasteur Institute of Iran, Tehran, Iran
| | - Samira Tarashi
- Microbiology Research Center (MRC), Pasteur Institute of Iran, Tehran, Iran
- Department of Mycobacteriology and Pulmonary Research, Pasteur Institute of Iran, Tehran, Iran
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9
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Chew YH, Marucci L. Mechanistic Model-Driven Biodesign in Mammalian Synthetic Biology. Methods Mol Biol 2024; 2774:71-84. [PMID: 38441759 DOI: 10.1007/978-1-0716-3718-0_6] [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: 03/07/2024]
Abstract
Mathematical modeling plays a vital role in mammalian synthetic biology by providing a framework to design and optimize design circuits and engineered bioprocesses, predict their behavior, and guide experimental design. Here, we review recent models used in the literature, considering mathematical frameworks at the molecular, cellular, and system levels. We report key challenges in the field and discuss opportunities for genome-scale models, machine learning, and cybergenetics to expand the capabilities of model-driven mammalian cell biodesign.
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Affiliation(s)
- Yin Hoon Chew
- School of Mathematics, University of Birmingham, Birmingham, UK
| | - Lucia Marucci
- Department of Engineering Mathematics, University of Bristol, Bristol, UK.
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, UK.
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10
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Valadez-Cano C, Olivares-Hernández R, Espino-Vázquez AN, Partida-Martínez LP. Genome-Scale Model of Rhizopus microsporus: Metabolic integration of a fungal holobiont with its bacterial and viral endosymbionts. Environ Microbiol 2024; 26:e16551. [PMID: 38072824 DOI: 10.1111/1462-2920.16551] [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: 08/22/2023] [Accepted: 11/24/2023] [Indexed: 01/30/2024]
Abstract
Rhizopus microsporus often lives in association with bacterial and viral symbionts that alter its biology. This fungal model represents an example of the complex interactions established among diverse organisms in functional holobionts. We constructed a Genome-Scale Model (GSM) of the fungal-bacterial-viral holobiont (iHol). We employed a constraint-based method to calculate the metabolic fluxes to decipher the metabolic interactions of the symbionts with their host. Our computational analyses of iHol simulate the holobiont's growth and the production of the toxin rhizoxin. Analyses of the calculated fluxes between R. microsporus in symbiotic (iHol) versus asymbiotic conditions suggest that changes in the lipid and nucleotide metabolism of the host are necessary for the functionality of the holobiont. Glycerol plays a pivotal role in the fungal-bacterial metabolic interaction, as its production does not compromise fungal growth, and Mycetohabitans bacteria can efficiently consume it. Narnavirus RmNV-20S and RmNV-23S affected the nucleotide metabolism without impacting the fungal-bacterial symbiosis. Our analyses highlighted the metabolic stability of Mycetohabitans throughout its co-evolution with the fungal host. We also predicted changes in reactions of the bacterial metabolism required for the active production of rhizoxin. This iHol is the first GSM of a fungal holobiont.
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Affiliation(s)
- Cecilio Valadez-Cano
- Departamento de Ingeniería Genética, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav), Irapuato, Mexico
| | - Roberto Olivares-Hernández
- Departamento de Procesos y Tecnología, Universidad Autónoma Metropolitana, Unidad Cuajimalpa, Ciudad de México, Mexico
| | - Astrid N Espino-Vázquez
- Departamento de Ingeniería Genética, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav), Irapuato, Mexico
| | - Laila P Partida-Martínez
- Departamento de Ingeniería Genética, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav), Irapuato, Mexico
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11
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Angarita-Rodríguez A, González-Giraldo Y, Rubio-Mesa JJ, Aristizábal AF, Pinzón A, González J. Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets. Int J Mol Sci 2023; 25:365. [PMID: 38203536 PMCID: PMC10778851 DOI: 10.3390/ijms25010365] [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: 10/21/2023] [Revised: 12/01/2023] [Accepted: 12/19/2023] [Indexed: 01/12/2024] Open
Abstract
Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.
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Affiliation(s)
- Andrea Angarita-Rodríguez
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
- Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Yeimy González-Giraldo
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
| | - Juan J. Rubio-Mesa
- Departamento de Estadística, Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Andrés Felipe Aristizábal
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
| | - Andrés Pinzón
- Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia, Bogotá 111321, Colombia;
| | - Janneth González
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Edf. Carlos Ortiz, Oficina 107, Cra. 7 40-62, Bogotá 110231, Colombia; (A.A.-R.); (Y.G.-G.); (A.F.A.)
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12
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Kaizu K, Takahashi K. Technologies for whole-cell modeling: Genome-wide reconstruction of a cell in silico. Dev Growth Differ 2023; 65:554-564. [PMID: 37856476 DOI: 10.1111/dgd.12897] [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: 11/20/2022] [Revised: 09/06/2023] [Accepted: 10/14/2023] [Indexed: 10/21/2023]
Abstract
With advances in high-throughput, large-scale in vivo measurement and genome modification techniques at the single-nucleotide level, there is an increasing demand for the development of new technologies for the flexible design and control of cellular systems. Computer-aided design is a powerful tool to design new cells. Whole-cell modeling aims to integrate various cellular subsystems, determine their interactions and cooperative mechanisms, and predict comprehensive cellular behaviors by computational simulations on a genome-wide scale. It has been applied to prokaryotes, yeasts, and higher eukaryotic cells, and utilized in a wide range of applications, including production of valuable substances, drug discovery, and controlled differentiation. Whole-cell modeling, consisting of several thousand elements with diverse scales and properties, requires innovative model construction, simulation, and analysis techniques. Furthermore, whole-cell modeling has been extended to multiple scales, including high-resolution modeling at the single-nucleotide and single-amino acid levels and multicellular modeling of tissues and organs. This review presents an overview of the current state of whole-cell modeling, discusses the novel computational and experimental technologies driving it, and introduces further developments toward multihierarchical modeling on a whole-genome scale.
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Affiliation(s)
- Kazunari Kaizu
- RIKEN Center for Biosystems Dynamics Research, Osaka, Japan
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13
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Pettersen JP, Castillo S, Jouhten P, Almaas E. Genome-scale metabolic models reveal determinants of phenotypic differences in non-Saccharomyces yeasts. BMC Bioinformatics 2023; 24:438. [PMID: 37990145 PMCID: PMC10664357 DOI: 10.1186/s12859-023-05506-7] [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: 05/09/2023] [Accepted: 09/29/2023] [Indexed: 11/23/2023] Open
Abstract
BACKGROUND Use of alternative non-Saccharomyces yeasts in wine and beer brewing has gained more attention the recent years. This is both due to the desire to obtain a wider variety of flavours in the product and to reduce the final alcohol content. Given the metabolic differences between the yeast species, we wanted to account for some of the differences by using in silico models. RESULTS We created and studied genome-scale metabolic models of five different non-Saccharomyces species using an automated processes. These were: Metschnikowia pulcherrima, Lachancea thermotolerans, Hanseniaspora osmophila, Torulaspora delbrueckii and Kluyveromyces lactis. Using the models, we predicted that M. pulcherrima, when compared to the other species, conducts more respiration and thus produces less fermentation products, a finding which agrees with experimental data. Complex I of the electron transport chain was to be present in M. pulcherrima, but absent in the others. The predicted importance of Complex I was diminished when we incorporated constraints on the amount of enzymatic protein, as this shifts the metabolism towards fermentation. CONCLUSIONS Our results suggest that Complex I in the electron transport chain is a key differentiator between Metschnikowia pulcherrima and the other yeasts considered. Yet, more annotations and experimental data have the potential to improve model quality in order to increase fidelity and confidence in these results. Further experiments should be conducted to confirm the in vivo effect of Complex I in M. pulcherrima and its respiratory metabolism.
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Affiliation(s)
- Jakob P Pettersen
- Department of Biotechnology and Food Science, NTNU-Norwegian University of Science and Technology, Trondheim, Norway.
| | | | - Paula Jouhten
- Department of Bioproducts and Biosystems, Aalto University, Espoo, Finland
| | - Eivind Almaas
- Department of Biotechnology and Food Science, NTNU-Norwegian University of Science and Technology, Trondheim, Norway.
- Department of Public Health and General Practice, K.G. Jebsen Center for Genetic Epidemiology, NTNU- Norwegian University of Science and Technology, Trondheim, Norway.
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14
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Wu P, Yuan Q, Cheng T, Han Y, Zhao W, Liao X, Wang L, Cai J, He Q, Guo Y, Zhang X, Lu F, Wang J, Ma H, Huang Z. Genome sequencing and metabolic network reconstruction of a novel sulfur-oxidizing bacterium Acidithiobacillus Ameehan. Front Microbiol 2023; 14:1277847. [PMID: 38053556 PMCID: PMC10694236 DOI: 10.3389/fmicb.2023.1277847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 11/01/2023] [Indexed: 12/07/2023] Open
Abstract
Sulfur-oxidizing bacteria play a crucial role in various processes, including mine bioleaching, biodesulfurization, and treatment of sulfur-containing wastewater. Nevertheless, the pathway involved in sulfur oxidation is highly intricate, making it complete comprehension a formidable and protracted undertaking. The mechanisms of sulfur oxidation within the Acidithiobacillus genus, along with the process of energy production, remain areas that necessitate further research and elucidation. In this study, a novel strain of sulfur-oxidizing bacterium, Acidithiobacillus Ameehan, was isolated. Several physiological characteristics of the strain Ameehan were verified and its complete genome sequence was presented in the study. Besides, the first genome-scale metabolic network model (AMEE_WP1377) was reconstructed for Acidithiobacillus Ameehan to gain a comprehensive understanding of the metabolic capacity of the strain.The characteristics of Acidithiobacillus Ameehan included morphological size and an optimal growth temperature range of 37-45°C, as well as an optimal growth pH range of pH 2.0-8.0. The microbe was found to be capable of growth when sulfur and K2O6S4 were supplied as the energy source and electron donor for CO2 fixation. Conversely, it could not utilize Na2S2O3, FeS2, and FeSO4·7H2O as the energy source or electron donor for CO2 fixation, nor could it grow using glucose or yeast extract as a carbon source. Genome annotation revealed that the strain Ameehan possessed a series of sulfur oxidizing genes that enabled it to oxidize elemental sulfur or various reduced inorganic sulfur compounds (RISCs). In addition, the bacterium also possessed carbon fixing genes involved in the incomplete Calvin-Benson-Bassham (CBB) cycle. However, the bacterium lacked the ability to oxidize iron and fix nitrogen. By implementing a constraint-based flux analysis to predict cellular growth in the presence of 71 carbon sources, 88.7% agreement with experimental Biolog data was observed. Five sulfur oxidation pathways were discovered through model simulations. The optimal sulfur oxidation pathway had the highest ATP production rate of 14.81 mmol/gDW/h, NADH/NADPH production rate of 5.76 mmol/gDW/h, consumed 1.575 mmol/gDW/h of CO2, and 1.5 mmol/gDW/h of sulfur. Our findings provide a comprehensive outlook on the most effective cellular metabolic pathways implicated in sulfur oxidation within Acidithiobacillus Ameehan. It suggests that the OMP (outer-membrane proteins) and SQR enzymes (sulfide: quinone oxidoreductase) have a significant impact on the energy production efficiency of sulfur oxidation, which could have potential biotechnological applications.
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Affiliation(s)
- Peng Wu
- College of Bioengineering, Tianjin University of Science and Technology, Tianjin, China
- Tianjin Key Laboratory for Industrial Biological Systems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Qianqian Yuan
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Tingting Cheng
- College of Bioengineering, Tianjin University of Science and Technology, Tianjin, China
- Tianjin Key Laboratory for Industrial Biological Systems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Yifan Han
- Tianjin Key Laboratory for Industrial Biological Systems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Wei Zhao
- Tianjin Key Laboratory for Industrial Biological Systems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Xiaoping Liao
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Lu Wang
- College of Bioengineering, Tianjin University of Science and Technology, Tianjin, China
- Tianjin Key Laboratory for Industrial Biological Systems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Jingyi Cai
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Qianqian He
- Tianjin Key Laboratory for Industrial Biological Systems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Ying Guo
- Tianjin Key Laboratory for Industrial Biological Systems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Xiaoxia Zhang
- Tianjin Key Laboratory for Industrial Biological Systems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Fuping Lu
- College of Bioengineering, Tianjin University of Science and Technology, Tianjin, China
| | - Jingjing Wang
- Tianjin Key Laboratory for Industrial Biological Systems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
| | - Hongwu Ma
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
- Biodesign Center, Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
| | - Zhiyong Huang
- Tianjin Key Laboratory for Industrial Biological Systems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China
- National Technology Innovation Center of Synthetic Biology, Tianjin, China
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15
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Jotshi A, Sukla KK, Haque MM, Bose C, Varma B, Koppiker CB, Joshi S, Mishra R. Exploring the human microbiome - A step forward for precision medicine in breast cancer. Cancer Rep (Hoboken) 2023; 6:e1877. [PMID: 37539732 PMCID: PMC10644338 DOI: 10.1002/cnr2.1877] [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: 03/08/2023] [Revised: 06/24/2023] [Accepted: 07/22/2023] [Indexed: 08/05/2023] Open
Abstract
BACKGROUND The second most frequent cancer in the world and the most common malignancy in women is breast cancer. Breast cancer is a significant health concern in India with a high mortality-to-incidence ratio and presentation at a younger age. RECENT FINDINGS Recent studies have identified gut microbiota as a significant factor that can have an influence on the development, treatment, and prognosis of breast cancer. This review article aims to describe the influence of microbial dysbiosis on breast cancer occurrence and the possible interactions between oncobiome and specific breast cancer molecular subtypes. The review further also discusses the role of epigenetics and diet/nutrition in the regulation of the gut and breast microbiome and its association with breast cancer prevention, therapy, and recurrence. Additionally, the recent technological advances in microbiome research, including next-generation sequencing (NGS) technologies, genome sequencing, single-cell sequencing, and microbial metabolomics along with recent advances in artificial intelligence (AI) have also been reviewed. This is an attempt to present a comprehensive status of the microbiome as a key cancer biomarker. CONCLUSION We believe that correlating microbiome and carcinogenesis is important as it can provide insights into the mechanisms by which microbial dysbiosis can influence cancer development and progression, leading to the potential use of the microbiome as a tool for prognostication and personalized therapy.
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Affiliation(s)
- Asmita Jotshi
- Centre for Translational Cancer Research: A Joint Initiative of Indian Institute of Science Education and Research (IISER) Pune and Prashanti Cancer Care Mission (PCCM)PuneIndia
| | | | | | - Chandrani Bose
- Life Sciences R&D, TCS Research, Tata Consultancy Services LimitedPuneIndia
| | - Binuja Varma
- TCS Genomics Lab, Tata Consultancy Services LimitedNew DelhiIndia
| | - C. B. Koppiker
- Centre for Translational Cancer Research: A Joint Initiative of Indian Institute of Science Education and Research (IISER) Pune and Prashanti Cancer Care Mission (PCCM)PuneIndia
- Prashanti Cancer Care Mission, Pune, India and Orchids Breast Health Centre, a PCCM initiativePuneIndia
| | - Sneha Joshi
- Centre for Translational Cancer Research: A Joint Initiative of Indian Institute of Science Education and Research (IISER) Pune and Prashanti Cancer Care Mission (PCCM)PuneIndia
| | - Rupa Mishra
- Centre for Translational Cancer Research: A Joint Initiative of Indian Institute of Science Education and Research (IISER) Pune and Prashanti Cancer Care Mission (PCCM)PuneIndia
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16
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Neal M, Thiruppathy D, Zengler K. Genome-scale metabolic modeling of the human gut bacterium Bacteroides fragilis strain 638R. PLoS Comput Biol 2023; 19:e1011594. [PMID: 37903176 PMCID: PMC10635569 DOI: 10.1371/journal.pcbi.1011594] [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: 03/12/2023] [Revised: 11/09/2023] [Accepted: 10/11/2023] [Indexed: 11/01/2023] Open
Abstract
Bacteroides fragilis is a universal member of the dominant commensal gut phylum Bacteroidetes. Its fermentation products and abundance have been linked to obesity, inflammatory bowel disease, and other disorders through its effects on host metabolic regulation and the immune system. As of yet, there has been no curated systems-level characterization of B. fragilis' metabolism that provides a comprehensive analysis of the link between human diet and B. fragilis' metabolic products. To address this, we developed a genome-scale metabolic model of B. fragilis strain 638R. The model iMN674 contains 1,634 reactions, 1,362 metabolites, three compartments, and reflects the strain's ability to utilize 142 metabolites. Predictions made with this model include its growth rate and efficiency on these substrates, the amounts of each fermentation product it produces under different conditions, and gene essentiality for each biomass component. The model highlights and resolves gaps in knowledge of B. fragilis' carbohydrate metabolism and its corresponding transport proteins. This high quality model provides the basis for rational prediction of B. fragilis' metabolic interactions with its environment and its host.
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Affiliation(s)
- Maxwell Neal
- Department of Bioengineering, University of California, San Diego, California, United States of America
| | - Deepan Thiruppathy
- Department of Bioengineering, University of California, San Diego, California, United States of America
| | - Karsten Zengler
- Department of Bioengineering, University of California, San Diego, California, United States of America
- Department of Pediatrics, University of California, San Diego, California, United States of America
- Center for Microbiome Innovation, University of California, San Diego, California, United States of America
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17
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Scott WT, Benito-Vaquerizo S, Zimmermann J, Bajić D, Heinken A, Suarez-Diez M, Schaap PJ. A structured evaluation of genome-scale constraint-based modeling tools for microbial consortia. PLoS Comput Biol 2023; 19:e1011363. [PMID: 37578975 PMCID: PMC10449394 DOI: 10.1371/journal.pcbi.1011363] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 08/24/2023] [Accepted: 07/17/2023] [Indexed: 08/16/2023] Open
Abstract
Harnessing the power of microbial consortia is integral to a diverse range of sectors, from healthcare to biotechnology to environmental remediation. To fully realize this potential, it is critical to understand the mechanisms behind the interactions that structure microbial consortia and determine their functions. Constraint-based reconstruction and analysis (COBRA) approaches, employing genome-scale metabolic models (GEMs), have emerged as the state-of-the-art tool to simulate the behavior of microbial communities from their constituent genomes. In the last decade, many tools have been developed that use COBRA approaches to simulate multi-species consortia, under either steady-state, dynamic, or spatiotemporally varying scenarios. Yet, these tools have not been systematically evaluated regarding their software quality, most suitable application, and predictive power. Hence, it is uncertain which tools users should apply to their system and what are the most urgent directions that developers should take in the future to improve existing capacities. This study conducted a systematic evaluation of COBRA-based tools for microbial communities using datasets from two-member communities as test cases. First, we performed a qualitative assessment in which we evaluated 24 published tools based on a list of FAIR (Findability, Accessibility, Interoperability, and Reusability) features essential for software quality. Next, we quantitatively tested the predictions in a subset of 14 of these tools against experimental data from three different case studies: a) syngas fermentation by C. autoethanogenum and C. kluyveri for the static tools, b) glucose/xylose fermentation with engineered E. coli and S. cerevisiae for the dynamic tools, and c) a Petri dish of E. coli and S. enterica for tools incorporating spatiotemporal variation. Our results show varying performance levels of the best qualitatively assessed tools when examining the different categories of tools. The differences in the mathematical formulation of the approaches and their relation to the results were also discussed. Ultimately, we provide recommendations for refining future GEM microbial modeling tools.
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Affiliation(s)
- William T. Scott
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen, the Netherlands
| | - Sara Benito-Vaquerizo
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Johannes Zimmermann
- Christian-Albrechts-University Kiel, Institute of Experimental Medicine, Research Group Medical Systems Biology, Kiel, Germany
| | - Djordje Bajić
- Department of Biotechnology, Delft University of Technology, Delft, the Netherlands
| | - Almut Heinken
- Inserm U1256 Laboratoire nGERE, Université de Lorraine, Nancy, France
| | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
| | - Peter J. Schaap
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, the Netherlands
- UNLOCK, Wageningen University & Research and Delft University of Technology, Wageningen, the Netherlands
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18
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Tec-Campos D, Posadas C, Tibocha-Bonilla JD, Thiruppathy D, Glonek N, Zuñiga C, Zepeda A, Zengler K. The genome-scale metabolic model for the purple non-sulfur bacterium Rhodopseudomonas palustris Bis A53 accurately predicts phenotypes under chemoheterotrophic, chemoautotrophic, photoheterotrophic, and photoautotrophic growth conditions. PLoS Comput Biol 2023; 19:e1011371. [PMID: 37556472 PMCID: PMC10441798 DOI: 10.1371/journal.pcbi.1011371] [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: 12/02/2022] [Revised: 08/21/2023] [Accepted: 07/18/2023] [Indexed: 08/11/2023] Open
Abstract
The purple non-sulfur bacterium Rhodopseudomonas palustris is recognized as a critical microorganism in the nitrogen and carbon cycle and one of the most common members in wastewater treatment communities. This bacterium is metabolically extremely versatile. It is capable of heterotrophic growth under aerobic and anaerobic conditions, but also able to grow photoautotrophically as well as mixotrophically. Therefore R. palustris can adapt to multiple environments and establish commensal relationships with other organisms, expressing various enzymes supporting degradation of amino acids, carbohydrates, nucleotides, and complex polymers. Moreover, R. palustris can degrade a wide range of pollutants under anaerobic conditions, e.g., aromatic compounds such as benzoate and caffeate, enabling it to thrive in chemically contaminated environments. However, many metabolic mechanisms employed by R. palustris to breakdown and assimilate different carbon and nitrogen sources under chemoheterotrophic or photoheterotrophic conditions remain unknown. Systems biology approaches, such as metabolic modeling, have been employed extensively to unravel complex mechanisms of metabolism. Previously, metabolic models have been reconstructed to study selected capabilities of R. palustris under limited experimental conditions. Here, we developed a comprehensive metabolic model (M-model) for R. palustris Bis A53 (iDT1294) consisting of 2,721 reactions, 2,123 metabolites, and comprising 1,294 genes. We validated the model using high-throughput phenotypic, physiological, and kinetic data, testing over 350 growth conditions. iDT1294 achieved a prediction accuracy of 90% for growth with various carbon and nitrogen sources and close to 80% for assimilation of aromatic compounds. Moreover, the M-model accurately predicts dynamic changes of growth and substrate consumption rates over time under nine chemoheterotrophic conditions and demonstrated high precision in predicting metabolic changes between photoheterotrophic and photoautotrophic conditions. This comprehensive M-model will help to elucidate metabolic processes associated with the assimilation of multiple carbon and nitrogen sources, anoxygenic photosynthesis, aromatic compound degradation, as well as production of molecular hydrogen and polyhydroxybutyrate.
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Affiliation(s)
- Diego Tec-Campos
- Facultad de Ingeniería Química, Universidad Autónoma de Yucatán, Mérida, Yucatán, México
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
| | - Camila Posadas
- Facultad de Ingeniería Química, Universidad Autónoma de Yucatán, Mérida, Yucatán, México
| | - Juan D. Tibocha-Bonilla
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, California, United States of America
| | - Deepan Thiruppathy
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
- Department of Bioengineering, University of California, San Diego, La Jolla California, United States of America
| | - Nathan Glonek
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
| | - Cristal Zuñiga
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
| | - Alejandro Zepeda
- Facultad de Ingeniería Química, Universidad Autónoma de Yucatán, Mérida, Yucatán, México
| | - Karsten Zengler
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
- Department of Bioengineering, University of California, San Diego, La Jolla California, United States of America
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, California, United States of America
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Gomez Romero S, Boyle N. Systems biology and metabolic modeling for cultivated meat: A promising approach for cell culture media optimization and cost reduction. Compr Rev Food Sci Food Saf 2023; 22:3422-3443. [PMID: 37306528 DOI: 10.1111/1541-4337.13193] [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: 02/10/2023] [Revised: 05/07/2023] [Accepted: 05/22/2023] [Indexed: 06/13/2023]
Abstract
The cultivated meat industry, also known as cell-based meat, cultured meat, lab-grown meat, or meat alternatives, is a growing field that aims to generate animal tissues ex-vivo in a cost-effective manner that achieves price parity with traditional agricultural products. However, cell culture media costs account for 55%-90% of production costs. To address this issue, efforts are aimed at optimizing media composition. Systems biology-driven approaches have been successfully used to improve the biomass and productivity of multiple bioproduction platforms, like Chinese hamster ovary cells, by accelerating the development of cell line-specific media and reducing research and development and production costs related to cell media and its optimization. In this review, we summarize systems biology modeling approaches, methods for cell culture media and bioprocess optimization, and metabolic studies done in animals of interest to the cultivated meat industry. More importantly, we identify current gaps in knowledge that prevent the identification of metabolic bottlenecks. These include the lack of genome-scale metabolic models for some species (pigs and ducks), a lack of accurate biomass composition studies for different growth conditions, and 13 C-metabolic flux analysis (MFA) studies for many of the species of interest for the cultivated meat industry (only shrimp and duck cells have been subjected to 13 C-MFA). We also highlight the importance of characterizing the metabolic requirements of cells at the organism, breed, and cell line-specific levels, and we outline future steps that this nascent field needs to take to achieve price parity and production efficiency similar to those of other bioproduction platforms. Practical Application: Our article summarizes systems biology techniques for cell culture media design and bioprocess optimization, which may be used to significantly reduce cell-based meat production costs. We also present the results of experimental studies done on some of the species of interest to the cultivated meat industry and highlight why modeling approaches are required for multiple species, cell-types, and cell lines.
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Affiliation(s)
- Sandra Gomez Romero
- Quantitative Biosciences and Engineering, Colorado School of Mines, Golden, Colorado, USA
| | - Nanette Boyle
- Chemical and Biological Engineering, Colorado School of Mines, Golden, Colorado, USA
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20
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Behera SS, Nivedita S, Das S, Behera HT, Mojumdar A, Ray L. Whole genome sequencing of a novel chitinolytic Streptomyces sp. RB7AG reveals it's chitosan production potential: optimization of the process through Taguchi experimental design. 3 Biotech 2023; 13:198. [PMID: 37215370 PMCID: PMC10192506 DOI: 10.1007/s13205-023-03613-z] [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: 05/31/2022] [Accepted: 05/06/2023] [Indexed: 05/24/2023] Open
Abstract
This study reports about the De novo whole genome sequencing and analysis of a bacterial isolate Streptomyces sp. Strain. RB7AG, isolated from the sediments of Chilika Lake, Odisha, India. The genome report in this paper highlights a size of 7,708,681 bp and a GC content of 72.3%. It also consists of 7274 coding sequence, 66 tRNA and 4 rRNA. Furthermore, carbohydrate active enzyme analysis revealed that the strain RB7AG has 127 glycoside hydrolase family genes, which is well known for hydrolysis of glycosidic bond in complex sugars. Thus, exploiting these microorganisms for the production of chitosan can be an appropriate waste disposal method of choice. Chitosan being an important biomolecule that has various industrial applications. Hence, the study also sought to improve the culture conditions for the Streptomyces sp. strain RB7AG for generation, recovery, and characterization of chitosan. Utilizing the isolate, various low-cost nitrogen sources, including peptone, yeast extract, ammonium chloride, urea along with pH, media, metal ions and surfactant were assessed for chitosan synthesis. In this context, traditional methods such as One Factor One Time are more time consuming and expensive too. The current work aims to establish a methodology to optimize the degradation of chitin by the chitinolytic Streptomyces sp. strain RB7AG, isolated from lake sediment for the production of chitosan. More than one factor was considered at the time of experiments, and the knowledge was integrated into Taguchi statistical design to determine the contribution of the most important factors required to achieve the desired end product i.e. chitosan. Highest chitosan production (2.188 µg/ml) was observed in MSM media, 1.0% NaCl (w/v), 0.5% Yeast extract, 1% Ca2+ and 0.1% Tween 80 at pH 9. The whole genome analysis of RB7AG would help in determining the mechanism involved in the breakdown activity. Supplementary Information The online version contains supplementary material available at 10.1007/s13205-023-03613-z.
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Affiliation(s)
- Subhransu Sekhar Behera
- School of Biotechnology, Kalinga Institute of Industrial Technology (KIIT), Deemed to be University, Odisha Bhubaneswar, India
| | - Suchismita Nivedita
- School of Biotechnology, Kalinga Institute of Industrial Technology (KIIT), Deemed to be University, Odisha Bhubaneswar, India
| | - Smrutiranjan Das
- School of Biotechnology, Kalinga Institute of Industrial Technology (KIIT), Deemed to be University, Odisha Bhubaneswar, India
| | - Himadri Tanaya Behera
- School of Biotechnology, Kalinga Institute of Industrial Technology (KIIT), Deemed to be University, Odisha Bhubaneswar, India
| | - Abhik Mojumdar
- School of Biotechnology, Kalinga Institute of Industrial Technology (KIIT), Deemed to be University, Odisha Bhubaneswar, India
| | - Lopamudra Ray
- School of Biotechnology, Kalinga Institute of Industrial Technology (KIIT), Deemed to be University, Odisha Bhubaneswar, India
- School of Law, KIIT Deemed to be University, Odisha Bhubaneswar, India
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21
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Alonso-Vásquez T, Fondi M, Perrin E. Understanding Antimicrobial Resistance Using Genome-Scale Metabolic Modeling. Antibiotics (Basel) 2023; 12:antibiotics12050896. [PMID: 37237798 DOI: 10.3390/antibiotics12050896] [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: 03/02/2023] [Revised: 04/28/2023] [Accepted: 05/06/2023] [Indexed: 05/28/2023] Open
Abstract
The urgent necessity to fight antimicrobial resistance is universally recognized. In the search of new targets and strategies to face this global challenge, a promising approach resides in the study of the cellular response to antimicrobial exposure and on the impact of global cellular reprogramming on antimicrobial drugs' efficacy. The metabolic state of microbial cells has been shown to undergo several antimicrobial-induced modifications and, at the same time, to be a good predictor of the outcome of an antimicrobial treatment. Metabolism is a promising reservoir of potential drug targets/adjuvants that has not been fully exploited to date. One of the main problems in unraveling the metabolic response of cells to the environment resides in the complexity of such metabolic networks. To solve this problem, modeling approaches have been developed, and they are progressively gaining in popularity due to the huge availability of genomic information and the ease at which a genome sequence can be converted into models to run basic phenotype predictions. Here, we review the use of computational modeling to study the relationship between microbial metabolism and antimicrobials and the recent advances in the application of genome-scale metabolic modeling to the study of microbial responses to antimicrobial exposure.
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Affiliation(s)
- Tania Alonso-Vásquez
- Department of Biology, University of Florence, Via Madonna del Piano 6, Sesto F.no, 50019 Florence, Italy
| | - Marco Fondi
- Department of Biology, University of Florence, Via Madonna del Piano 6, Sesto F.no, 50019 Florence, Italy
| | - Elena Perrin
- Department of Biology, University of Florence, Via Madonna del Piano 6, Sesto F.no, 50019 Florence, Italy
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22
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Bremer E, Calteau A, Danchin A, Harwood C, Helmann JD, Médigue C, Palsson BO, Sekowska A, Vallenet D, Zuniga A, Zuniga C. A model industrial workhorse:
Bacillus subtilis
strain 168 and its genome after a quarter of a century. Microb Biotechnol 2023; 16:1203-1231. [PMID: 37002859 DOI: 10.1111/1751-7915.14257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 03/20/2023] [Indexed: 04/04/2023] Open
Abstract
The vast majority of genomic sequences are automatically annotated using various software programs. The accuracy of these annotations depends heavily on the very few manual annotation efforts that combine verified experimental data with genomic sequences from model organisms. Here, we summarize the updated functional annotation of Bacillus subtilis strain 168, a quarter century after its genome sequence was first made public. Since the last such effort 5 years ago, 1168 genetic functions have been updated, allowing the construction of a new metabolic model of this organism of environmental and industrial interest. The emphasis in this review is on new metabolic insights, the role of metals in metabolism and macromolecule biosynthesis, functions involved in biofilm formation, features controlling cell growth, and finally, protein agents that allow class discrimination, thus allowing maintenance management, and accuracy of all cell processes. New 'genomic objects' and an extensive updated literature review have been included for the sequence, now available at the International Nucleotide Sequence Database Collaboration (INSDC: AccNum AL009126.4).
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Affiliation(s)
- Erhard Bremer
- Department of Biology, Laboratory for Microbiology and Center for Synthetic Microbiology (SYNMIKRO) Philipps‐University Marburg Marburg Germany
| | - Alexandra Calteau
- LABGeM, Génomique Métabolique, CEA, Genoscope, Institut de Biologie François Jacob Université d'Évry, Université Paris‐Saclay, CNRS Évry France
| | - Antoine Danchin
- School of Biomedical Sciences, Li KaShing Faculty of Medicine Hong Kong University Pokfulam SAR Hong Kong China
| | - Colin Harwood
- Centre for Bacterial Cell Biology, Biosciences Institute Newcastle University Baddiley Clark Building Newcastle upon Tyne UK
| | - John D. Helmann
- Department of Microbiology Cornell University Ithaca New York USA
| | - Claudine Médigue
- LABGeM, Génomique Métabolique, CEA, Genoscope, Institut de Biologie François Jacob Université d'Évry, Université Paris‐Saclay, CNRS Évry France
| | - Bernhard O. Palsson
- Department of Bioengineering University of California San Diego La Jolla USA
| | | | - David Vallenet
- LABGeM, Génomique Métabolique, CEA, Genoscope, Institut de Biologie François Jacob Université d'Évry, Université Paris‐Saclay, CNRS Évry France
| | - Abril Zuniga
- Department of Biology San Diego State University San Diego California USA
| | - Cristal Zuniga
- Bioinformatics and Medical Informatics Graduate Program San Diego State University San Diego California USA
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23
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Srivastava A, Vinod PK. Identification and Characterization of Metabolic Subtypes of Endometrial Cancer Using a Systems-Level Approach. Metabolites 2023; 13:metabo13030409. [PMID: 36984849 PMCID: PMC10054278 DOI: 10.3390/metabo13030409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 03/03/2023] [Accepted: 03/05/2023] [Indexed: 03/12/2023] Open
Abstract
Endometrial cancer (EC) is the most common gynecological cancer worldwide. Understanding metabolic adaptation and its heterogeneity in tumor tissues may provide new insights and help in cancer diagnosis, prognosis, and treatment. In this study, we investigated metabolic alterations of EC to understand the variations in metabolism within tumor samples. Integration of transcriptomics data of EC (RNA-Seq) and the human genome-scale metabolic network was performed to identify the metabolic subtypes of EC and uncover the underlying dysregulated metabolic pathways and reporter metabolites in each subtype. The relationship between metabolic subtypes and clinical variables was explored. Further, we correlated the metabolic changes occurring at the transcriptome level with the genomic alterations. Based on metabolic profile, EC patients were stratified into two subtypes (metabolic subtype-1 and subtype-2) that significantly correlated to patient survival, tumor stages, mutation, and copy number variations. We observed the co-activation of the pentose phosphate pathway, one-carbon metabolism, and genes involved in controlling estrogen levels in metabolic subtype-2, which is linked to poor survival. PNMT and ERBB2 are also upregulated in metabolic subtype-2 samples and present on the same chromosome locus 17q12, which is amplified. PTEN and TP53 mutations show mutually exclusive behavior between subtypes and display a difference in survival. This work identifies metabolic subtypes with distinct characteristics at the transcriptome and genome levels, highlighting the metabolic heterogeneity within EC.
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24
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Inwongwan S, Pekkoh J, Pumas C, Sattayawat P. Metabolic network reconstruction of Euglena gracilis: Current state, challenges, and applications. Front Microbiol 2023; 14:1143770. [PMID: 36937274 PMCID: PMC10018167 DOI: 10.3389/fmicb.2023.1143770] [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: 01/13/2023] [Accepted: 02/06/2023] [Indexed: 03/06/2023] Open
Abstract
A metabolic model, representing all biochemical reactions in a cell, is a prerequisite for several approaches in systems biology used to explore the metabolic phenotype of an organism. Despite the use of Euglena in diverse industrial applications and as a biological model, there is limited understanding of its metabolic network capacity. The unavailability of the completed genome data and the highly complex evolution of Euglena are significant obstacles to the reconstruction and analysis of its genome-scale metabolic model. In this mini-review, we discuss the current state and challenges of metabolic network reconstruction in Euglena gracilis. We have collated and present the available relevant data for the metabolic network reconstruction of E. gracilis, which could be used to improve the quality of the metabolic model of E. gracilis. Furthermore, we deliver the potential applications of the model in metabolic engineering. Altogether, it is supposed that this mini-review would facilitate the investigation of metabolic networks in Euglena and further lay out a direction for model-assisted metabolic engineering.
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Affiliation(s)
- Sahutchai Inwongwan
- Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
- Research Center of Microbial Diversity and Sustainable Utilizations, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
| | - Jeeraporn Pekkoh
- Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
- Research Center of Microbial Diversity and Sustainable Utilizations, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
| | - Chayakorn Pumas
- Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
- Research Center in Bioresources for Agriculture, Industry and Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Pachara Sattayawat
- Department of Biology, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
- Research Center of Microbial Diversity and Sustainable Utilizations, Faculty of Science, Chiang Mai University, Chiang Mai, Thailand
- Research Center in Bioresources for Agriculture, Industry and Medicine, Chiang Mai University, Chiang Mai, Thailand
- *Correspondence: Pachara Sattayawat,
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25
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Predicting stress response and improved protein overproduction in Bacillus subtilis. NPJ Syst Biol Appl 2022; 8:50. [PMID: 36575180 PMCID: PMC9794813 DOI: 10.1038/s41540-022-00259-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 11/07/2022] [Indexed: 12/28/2022] Open
Abstract
Bacillus subtilis is a well-characterized microorganism and a model for the study of Gram-positive bacteria. The bacterium can produce proteins at high densities and yields, which has made it valuable for industrial bioproduction. Like other cell factories, metabolic modeling of B. subtilis has discovered ways to optimize its metabolism toward various applications. The first genome-scale metabolic model (M-model) of B. subtilis was published more than a decade ago and has been applied extensively to understand metabolism, to predict growth phenotypes, and served as a template to reconstruct models for other Gram-positive bacteria. However, M-models are ill-suited to simulate the production and secretion of proteins as well as their proteomic response to stress. Thus, a new generation of metabolic models, known as metabolism and gene expression models (ME-models), has been initiated. Here, we describe the reconstruction and validation of a ME model of B. subtilis, iJT964-ME. This model achieved higher performance scores on the prediction of gene essentiality as compared to the M-model. We successfully validated the model by integrating physiological and omics data associated with gene expression responses to ethanol and salt stress. The model further identified the mechanism by which tryptophan synthesis is upregulated under ethanol stress. Further, we employed iJT964-ME to predict amylase production rates under two different growth conditions. We analyzed these flux distributions and identified key metabolic pathways that permitted the increase in amylase production. Models like iJT964-ME enable the study of proteomic response to stress and the illustrate the potential for optimizing protein production in bacteria.
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26
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Tamasco G, Kumar M, Zengler K, Silva-Rocha R, da Silva RR. ChiMera: an easy to use pipeline for bacterial genome based metabolic network reconstruction, evaluation and visualization. BMC Bioinformatics 2022; 23:512. [PMID: 36451100 PMCID: PMC9710178 DOI: 10.1186/s12859-022-05056-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 11/14/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Genome-scale metabolic reconstruction tools have been developed in the last decades. They have helped to reconstruct eukaryotic and prokaryotic metabolic models, which have contributed to fields, e.g., genetic engineering, drug discovery, prediction of phenotypes, and other model-driven discoveries. However, the use of these programs requires a high level of bioinformatic skills. Moreover, the functionalities required to build models are scattered throughout multiple tools, requiring knowledge and experience for utilizing several tools. RESULTS Here we present ChiMera, which combines tools used for model reconstruction, prediction, and visualization. ChiMera uses CarveMe in the reconstruction module, generating a gap-filled draft reconstruction able to produce growth predictions using flux balance analysis for gram-positive and gram-negative bacteria. ChiMera also contains two modules for metabolic network visualization. The first module generates maps for the most important pathways, e.g., glycolysis, nucleotides and amino acids biosynthesis, fatty acid oxidation and biosynthesis and core-metabolism. The second module produces a genome-wide metabolic map, which can be used to retrieve KEGG pathway information for each compound in the model. A module to investigate gene essentiality and knockout is also present. CONCLUSIONS Overall, ChiMera uses automation algorithms to combine a variety of tools to automatically perform model creation, gap-filling, flux balance analysis (FBA), and metabolic network visualization. ChiMera models readily provide metabolic insights that can aid genetic engineering projects, prediction of phenotypes, and model-driven discoveries.
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Affiliation(s)
- Gustavo Tamasco
- grid.11899.380000 0004 1937 0722Ribeirão Preto School of Medicine (FMRP), University of São Paulo (USP), Ribeirão Preto, SP Brazil
| | - Manish Kumar
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760 USA
| | - Karsten Zengler
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0760 USA ,grid.266100.30000 0001 2107 4242Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093-0412 USA ,Center for Microbiome Innovation, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0403 USA
| | - Rafael Silva-Rocha
- grid.11899.380000 0004 1937 0722Ribeirão Preto School of Medicine (FMRP), University of São Paulo (USP), Ribeirão Preto, SP Brazil
| | - Ricardo Roberto da Silva
- grid.11899.380000 0004 1937 0722School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP Brazil
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27
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Big Data in Laboratory Medicine—FAIR Quality for AI? Diagnostics (Basel) 2022; 12:diagnostics12081923. [PMID: 36010273 PMCID: PMC9406962 DOI: 10.3390/diagnostics12081923] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/05/2022] [Accepted: 08/06/2022] [Indexed: 12/22/2022] Open
Abstract
Laboratory medicine is a digital science. Every large hospital produces a wealth of data each day—from simple numerical results from, e.g., sodium measurements to highly complex output of “-omics” analyses, as well as quality control results and metadata. Processing, connecting, storing, and ordering extensive parts of these individual data requires Big Data techniques. Whereas novel technologies such as artificial intelligence and machine learning have exciting application for the augmentation of laboratory medicine, the Big Data concept remains fundamental for any sophisticated data analysis in large databases. To make laboratory medicine data optimally usable for clinical and research purposes, they need to be FAIR: findable, accessible, interoperable, and reusable. This can be achieved, for example, by automated recording, connection of devices, efficient ETL (Extract, Transform, Load) processes, careful data governance, and modern data security solutions. Enriched with clinical data, laboratory medicine data allow a gain in pathophysiological insights, can improve patient care, or can be used to develop reference intervals for diagnostic purposes. Nevertheless, Big Data in laboratory medicine do not come without challenges: the growing number of analyses and data derived from them is a demanding task to be taken care of. Laboratory medicine experts are and will be needed to drive this development, take an active role in the ongoing digitalization, and provide guidance for their clinical colleagues engaging with the laboratory data in research.
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28
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Tanaya Behera H, Mojumdar A, Kumari K, Kumar Gouda S, Das S, Ray L. Exploration of genomic and functional features of chitinolytic bacterium Streptomyces chilikensis RC1830, isolated from Chilika Lake, India. 3 Biotech 2022; 12:120. [PMID: 35547016 PMCID: PMC9035197 DOI: 10.1007/s13205-022-03184-5] [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: 10/30/2021] [Accepted: 04/04/2022] [Indexed: 11/01/2022] Open
Abstract
Streptomyces chilikensis RC1830 was previously isolated as a novel chitinolytic streptomycete from Chilika Lake, Odisha, India. The strain RC1830 is a representative member of the soil-dwelling, filamentous Streptomyces group that produces the majority of natural antibiotics and secondary metabolites. The objective of this work was to assess the chitin degradation ability and whole-genome sequence of Streptomyces chilikensis RC1830. TLC analysis of the fermentation product revealed that strain RC1830 can convert shrimp shell colloidal chitin to N-acetylated chitooligosaccharides (N-AcCOS). A genome-wide investigation of RC1830 was also carried out to investigate the genetic basis for chitin breakdown. The result showed that the RC1830 genome possesses a chromosome with 7,121,774 bp (73.2% GC). The genome consists of 6807 coding sequences, 69 tRNA, and 3 rRNA genes. Furthermore, carbohydrate-active enzyme (CAZyme) analysis revealed that RC1830 has 89 glycoside hydrolase family genes, which could modulate the enzymes involved in the degradation of chitin ultimately producing industrially important COS. The whole-genome information of RC1830 could emphasize the mechanism involved in the RC1830's chitin breakdown activity, endowing RC1830 with a promising alternative for COS production. Supplementary Information The online version contains supplementary material available at 10.1007/s13205-022-03184-5.
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