MiGut: A scalable in vitro platform for simulating the human gut microbiome—Development, validation and simulation of antibiotic‐induced dysbiosis
In vitro models of the human colon have been used extensively in understanding the human gut microbiome (GM) and evaluating how internal and external factors affect the residing bacterial populations. Such models have been shown to be highly predictive of in vivo outcomes and have a number of advant...
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Published in | Microbial biotechnology Vol. 16; no. 6; pp. 1312 - 1324 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
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United States
John Wiley & Sons, Inc
01.06.2023
John Wiley and Sons Inc Wiley |
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Abstract | In vitro models of the human colon have been used extensively in understanding the human gut microbiome (GM) and evaluating how internal and external factors affect the residing bacterial populations. Such models have been shown to be highly predictive of in vivo outcomes and have a number of advantages over animal models. The complexity required by in vitro models to closely mimic the physiology of the colon poses practical limits on their scalability. The scalable Mini Gut (MiGut) platform presented in this paper allows considerable expansion of model replicates and enables complex study design, without compromising on in vivo reflectiveness as is often the case with other model systems. MiGut has been benchmarked against a validated gut model in a demanding 9‐week study. MiGut showed excellent repeatability between model replicates and results were consistent with those of the benchmark system. The novel technology presented in this paper makes it conceivable that tens of models could be run simultaneously, allowing complex microbiome‐xenobiotic interactions to be explored in far greater detail, with minimal added resources or complexity. This platform expands the capacity to generate clinically relevant data to support our understanding of the cause‐effect relationships that govern the GM.
In vitro models of the human colon have been used extensively in developing an understanding of the human gut microbiome, but current technologies are extremely complex and have limited throughput. MiGut is a novel platform which addresses these shortcomings, allowing for considerable expansion of model runs without compromising on in vivo reflectiveness. The technology has been validated in a demanding 9‐week study where dysbiosis was simulated in vitro and has been benchmarked against a previously validated and extremely well understood triple‐stage gut model system. |
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AbstractList | Abstract
In vitro models of the human colon have been used extensively in understanding the human gut microbiome (GM) and evaluating how internal and external factors affect the residing bacterial populations. Such models have been shown to be highly predictive of in vivo outcomes and have a number of advantages over animal models. The complexity required by in vitro models to closely mimic the physiology of the colon poses practical limits on their scalability. The scalable Mini Gut (MiGut) platform presented in this paper allows considerable expansion of model replicates and enables complex study design, without compromising on in vivo reflectiveness as is often the case with other model systems. MiGut has been benchmarked against a validated gut model in a demanding 9‐week study. MiGut showed excellent repeatability between model replicates and results were consistent with those of the benchmark system. The novel technology presented in this paper makes it conceivable that tens of models could be run simultaneously, allowing complex microbiome‐xenobiotic interactions to be explored in far greater detail, with minimal added resources or complexity. This platform expands the capacity to generate clinically relevant data to support our understanding of the cause‐effect relationships that govern the GM. Abstract In vitro models of the human colon have been used extensively in understanding the human gut microbiome (GM) and evaluating how internal and external factors affect the residing bacterial populations. Such models have been shown to be highly predictive of in vivo outcomes and have a number of advantages over animal models. The complexity required by in vitro models to closely mimic the physiology of the colon poses practical limits on their scalability. The scalable Mini Gut (MiGut) platform presented in this paper allows considerable expansion of model replicates and enables complex study design, without compromising on in vivo reflectiveness as is often the case with other model systems. MiGut has been benchmarked against a validated gut model in a demanding 9‐week study. MiGut showed excellent repeatability between model replicates and results were consistent with those of the benchmark system. The novel technology presented in this paper makes it conceivable that tens of models could be run simultaneously, allowing complex microbiome‐xenobiotic interactions to be explored in far greater detail, with minimal added resources or complexity. This platform expands the capacity to generate clinically relevant data to support our understanding of the cause‐effect relationships that govern the GM. In vitro models of the human colon have been used extensively in understanding the human gut microbiome (GM) and evaluating how internal and external factors affect the residing bacterial populations. Such models have been shown to be highly predictive of in vivo outcomes and have a number of advantages over animal models. The complexity required by in vitro models to closely mimic the physiology of the colon poses practical limits on their scalability. The scalable Mini Gut (MiGut) platform presented in this paper allows considerable expansion of model replicates and enables complex study design, without compromising on in vivo reflectiveness as is often the case with other model systems. MiGut has been benchmarked against a validated gut model in a demanding 9-week study. MiGut showed excellent repeatability between model replicates and results were consistent with those of the benchmark system. The novel technology presented in this paper makes it conceivable that tens of models could be run simultaneously, allowing complex microbiome-xenobiotic interactions to be explored in far greater detail, with minimal added resources or complexity. This platform expands the capacity to generate clinically relevant data to support our understanding of the cause-effect relationships that govern the GM. In vitro models of the human colon have been used extensively in understanding the human gut microbiome (GM) and evaluating how internal and external factors affect the residing bacterial populations. Such models have been shown to be highly predictive of in vivo outcomes and have a number of advantages over animal models. The complexity required by in vitro models to closely mimic the physiology of the colon poses practical limits on their scalability. The scalable Mini Gut (MiGut) platform presented in this paper allows considerable expansion of model replicates and enables complex study design, without compromising on in vivo reflectiveness as is often the case with other model systems. MiGut has been benchmarked against a validated gut model in a demanding 9‐week study. MiGut showed excellent repeatability between model replicates and results were consistent with those of the benchmark system. The novel technology presented in this paper makes it conceivable that tens of models could be run simultaneously, allowing complex microbiome‐xenobiotic interactions to be explored in far greater detail, with minimal added resources or complexity. This platform expands the capacity to generate clinically relevant data to support our understanding of the cause‐effect relationships that govern the GM. In vitro models of the human colon have been used extensively in developing an understanding of the human gut microbiome, but current technologies are extremely complex and have limited throughput. MiGut is a novel platform which addresses these shortcomings, allowing for considerable expansion of model runs without compromising on in vivo reflectiveness. The technology has been validated in a demanding 9‐week study where dysbiosis was simulated in vitro and has been benchmarked against a previously validated and extremely well understood triple‐stage gut model system. |
Author | Davis Birch, William A. Kapur, Nikil Culmer, Peter R. Moura, Ines B. Wilcox, Mark H. Buckley, Anthony M. Ewin, Duncan J. |
AuthorAffiliation | 3 Microbiology Leeds Teaching Hospitals NHS Trust, Old Medical School, Leeds General Infirmary Leeds LS1 3EX UK 4 Microbiome and Nutritional Science Group, Faculty of Food Science and Nutrition, School of Food Science University of Leeds Leeds LS2 9JT UK 2 Healthcare‐Associated Infections Group Leeds Institute of Medical Research, Faculty of Medicine and Health, University of Leeds Leeds LS2 9JT UK 1 School of Mechanical Engineering University of Leeds Woodhouse Lane Leeds LS2 9JT UK |
AuthorAffiliation_xml | – name: 3 Microbiology Leeds Teaching Hospitals NHS Trust, Old Medical School, Leeds General Infirmary Leeds LS1 3EX UK – name: 2 Healthcare‐Associated Infections Group Leeds Institute of Medical Research, Faculty of Medicine and Health, University of Leeds Leeds LS2 9JT UK – name: 1 School of Mechanical Engineering University of Leeds Woodhouse Lane Leeds LS2 9JT UK – name: 4 Microbiome and Nutritional Science Group, Faculty of Food Science and Nutrition, School of Food Science University of Leeds Leeds LS2 9JT UK |
Author_xml | – sequence: 1 givenname: William A. orcidid: 0000-0002-3352-1721 surname: Davis Birch fullname: Davis Birch, William A. organization: University of Leeds – sequence: 2 givenname: Ines B. orcidid: 0000-0002-3019-7196 surname: Moura fullname: Moura, Ines B. organization: Leeds Institute of Medical Research, Faculty of Medicine and Health, University of Leeds – sequence: 3 givenname: Duncan J. orcidid: 0000-0001-6044-5368 surname: Ewin fullname: Ewin, Duncan J. organization: Leeds Institute of Medical Research, Faculty of Medicine and Health, University of Leeds – sequence: 4 givenname: Mark H. orcidid: 0000-0002-4565-2868 surname: Wilcox fullname: Wilcox, Mark H. organization: Leeds Teaching Hospitals NHS Trust, Old Medical School, Leeds General Infirmary – sequence: 5 givenname: Anthony M. orcidid: 0000-0002-2790-0717 surname: Buckley fullname: Buckley, Anthony M. organization: University of Leeds – sequence: 6 givenname: Peter R. orcidid: 0000-0003-2867-0420 surname: Culmer fullname: Culmer, Peter R. email: p.r.culmer@leeds.ac.uk organization: University of Leeds – sequence: 7 givenname: Nikil orcidid: 0000-0003-1041-8390 surname: Kapur fullname: Kapur, Nikil email: n.kapur@leeds.ac.uk organization: University of Leeds |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37035991$$D View this record in MEDLINE/PubMed |
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CitedBy_id | crossref_primary_10_1016_j_mex_2023_102393 crossref_primary_10_1002_bit_28636 |
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Snippet | In vitro models of the human colon have been used extensively in understanding the human gut microbiome (GM) and evaluating how internal and external factors... Abstract In vitro models of the human colon have been used extensively in understanding the human gut microbiome (GM) and evaluating how internal and external... Abstract In vitro models of the human colon have been used extensively in understanding the human gut microbiome (GM) and evaluating how internal and external... |
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SubjectTerms | Animal models Animals Anti-Bacterial Agents - adverse effects Antibiotics Bacteria - genetics Cause-effect relationships Colon Complexity Digestive system Dysbacteriosis Dysbiosis - chemically induced Dysbiosis - microbiology Gastrointestinal Microbiome Humans In vivo methods and tests Intestinal microflora Longitudinal studies Microbiomes Microbiota Pathogenesis Physiology Potassium |
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Title | MiGut: A scalable in vitro platform for simulating the human gut microbiome—Development, validation and simulation of antibiotic‐induced dysbiosis |
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