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Lee KW, Shin JS, Lee CM, Han HY, O Y, Kim HW, Cho TJ. Gut-on-a-Chip for the Analysis of Bacteria-Bacteria Interactions in Gut Microbial Community: What Would Be Needed for Bacterial Co-Culture Study to Explore the Diet-Microbiota Relationship? Nutrients 2023; 15:nu15051131. [PMID: 36904133 PMCID: PMC10005057 DOI: 10.3390/nu15051131] [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: 01/31/2023] [Revised: 02/17/2023] [Accepted: 02/21/2023] [Indexed: 02/26/2023] Open
Abstract
Bacterial co-culture studies using synthetic gut microbiomes have reported novel research designs to understand the underlying role of bacterial interaction in the metabolism of dietary resources and community assembly of complex microflora. Since lab-on-a-chip mimicking the gut (hereafter "gut-on-a-chip") is one of the most advanced platforms for the simulative research regarding the correlation between host health and microbiota, the co-culture of the synthetic bacterial community in gut-on-a-chip is expected to reveal the diet-microbiota relationship. This critical review analyzed recent research on bacterial co-culture with perspectives on the ecological niche of commensals, probiotics, and pathogens to categorize the experimental approaches for diet-mediated management of gut health as the compositional and/or metabolic modulation of the microbiota and the control of pathogens. Meanwhile, the aim of previous research on bacterial culture in gut-on-a-chip has been mainly limited to the maintenance of the viability of host cells. Thus, the integration of study designs established for the co-culture of synthetic gut consortia with various nutritional resources into gut-on-a-chip is expected to reveal bacterial interspecies interactions related to specific dietary patterns. This critical review suggests novel research topics for co-culturing bacterial communities in gut-on-a-chip to realize an ideal experimental platform mimicking a complex intestinal environment.
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Affiliation(s)
- Ki Won Lee
- Department of Food and Biotechnology, College of Science and Technology, Korea University, 2511, Sejong-ro, Sejong 30019, Republic of Korea
| | - Jin Song Shin
- Department of Food Regulatory Science, College of Science and Technology, Korea University, 2511, Sejong-ro, Sejong 30019, Republic of Korea
| | - Chan Min Lee
- Department of Food and Biotechnology, College of Science and Technology, Korea University, 2511, Sejong-ro, Sejong 30019, Republic of Korea
| | - Hea Yeon Han
- Department of Food and Biotechnology, College of Science and Technology, Korea University, 2511, Sejong-ro, Sejong 30019, Republic of Korea
| | - Yun O
- Department of Food Regulatory Science, College of Science and Technology, Korea University, 2511, Sejong-ro, Sejong 30019, Republic of Korea
| | - Hye Won Kim
- Department of Animal Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Tae Jin Cho
- Department of Food and Biotechnology, College of Science and Technology, Korea University, 2511, Sejong-ro, Sejong 30019, Republic of Korea
- Department of Food Regulatory Science, College of Science and Technology, Korea University, 2511, Sejong-ro, Sejong 30019, Republic of Korea
- Correspondence: ; Tel.: +82-44-860-1433
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Sieow BFL, De Sotto R, Seet ZRD, Hwang IY, Chang MW. Synthetic Biology Meets Machine Learning. Methods Mol Biol 2023; 2553:21-39. [PMID: 36227537 DOI: 10.1007/978-1-0716-2617-7_2] [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: 06/16/2023]
Abstract
This chapter outlines the myriad applications of machine learning (ML) in synthetic biology, specifically in engineering cell and protein activity, and metabolic pathways. Though by no means comprehensive, the chapter highlights several prominent computational tools applied in the field and their potential use cases. The examples detailed reinforce how ML algorithms can enhance synthetic biology research by providing data-driven insights into the behavior of living systems, even without detailed knowledge of their underlying mechanisms. By doing so, ML promises to increase the efficiency of research projects by modeling hypotheses in silico that can then be tested through experiments. While challenges related to training dataset generation and computational costs remain, ongoing improvements in ML tools are paving the way for smarter and more streamlined synthetic biology workflows that can be readily employed to address grand challenges across manufacturing, medicine, engineering, agriculture, and beyond.
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Affiliation(s)
- Brendan Fu-Long Sieow
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- NUS Graduate School for Integrative Sciences and Engineering Programme, National University of Singapore, Singapore, Singapore
| | - Ryan De Sotto
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Zhi Ren Darren Seet
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - In Young Hwang
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Matthew Wook Chang
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore.
- Synthetic Biology Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
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Bi T, Zhang L, Zhan L, Feng R, Zhao T, Ren W, Hang T, Zhou W, Lu X. Integrated Analyses of Microbiomics and Metabolomics Explore the Effect of Gut Microbiota Transplantation on Diabetes-Associated Cognitive Decline in Zucker Diabetic Fatty Rats. Front Aging Neurosci 2022; 14:913002. [PMID: 35721013 PMCID: PMC9204715 DOI: 10.3389/fnagi.2022.913002] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 05/18/2022] [Indexed: 11/13/2022] Open
Abstract
Diabetes-associated cognitive decline (DACD), one of the complications of type 2 diabetes (T2DM), correlates significantly with the disorder in glycolipid metabolism, insulin/leptin resistance, and accumulation of β-amyloid (Aβ). Although gut microbiota transplantation (GMT), a novel non-invasive physiotherapy strategy, has been a promising intervention to alleviate the symptoms of T2DM, its protective effect on progressive cognitive decline remains elusive. Here, we transplanted the gut microbiota of healthy or cognitive decline donor rats into ZDF or LZ rats, and integrated microbiomics and metabolomics to evaluate the directional effect of the gut microbiota on the recipient rats. The basal metabolism phenotype changed in ZDF rats instead of in LZ rats. One possible mechanism is that the microbiota and metabolites alter the structure of the intestinal tract, stimulate the brain insulin and leptin signaling pathways, and regulate the deposition of Aβ in the brain. It is worth noting that 10 species of genera, such as Parabacteroides, Blautia, and Lactobacillus, can regulate 20 kinds of metabolites, such as propanoic acid, acetic acid, and citramalic acid, and having a significant improvement on the cognitive behavior of ZDF rats. In addition, the correlation analysis indicated the gut microbiota and metabolites are highly associated with host phenotypes affected by GMT. In summary, our study indicates that altering the microbiota-gut-brain axis by reshaping the composition of gut microbiota is a viable strategy that has great potential for improving cognitive function and combatting DACD.
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Affiliation(s)
- Tingting Bi
- School of Traditional Chinese Medicine and School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Lijing Zhang
- School of Traditional Chinese Medicine and School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Libin Zhan
- Center for Innovative Engineering Technology in Traditional Chinese Medicine, Liaoning University of Traditional Chinese Medicine, Shenyang, China
- Key Laboratory of Ministry of Education for TCM Viscera-State Theory and Applications, Liaoning University of Traditional Chinese Medicine, Shenyang, China
- *Correspondence: Libin Zhan,
| | - Ruiqi Feng
- School of Traditional Chinese Medicine and School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Tian Zhao
- School of Traditional Chinese Medicine and School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Weiming Ren
- School of Traditional Chinese Medicine and School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Tianyi Hang
- School of Traditional Chinese Medicine and School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Wen Zhou
- School of Traditional Chinese Medicine and School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Xiaoguang Lu
- Department of Emergency Medicine, Zhongshan Hospital, Dalian University, Dalian, China
- Xiaoguang Lu,
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Chowdhury S, Fong SS. Leveraging genome-scale metabolic models for human health applications. Curr Opin Biotechnol 2020; 66:267-276. [PMID: 33120253 DOI: 10.1016/j.copbio.2020.08.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 08/27/2020] [Accepted: 08/31/2020] [Indexed: 02/07/2023]
Abstract
Genome-scale metabolic modeling is a scalable and extensible computational method for analyzing and predicting biological function. With the ongoing improvements in computational methods and experimental capabilities, genome-scale metabolic models (GEMs) are demonstrating utility in addressing human health applications. The initial areas of highest impact are likely to be health applications where disease states involve metabolic changes. In this review, we focus on recent application of GEMs to studying cancer and the human microbiome by describing the enabling methodologies and outcomes of these studies. We conclude with proposing some areas of research that are likely to arise as a result of recent methodological advances.
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Affiliation(s)
- Shomeek Chowdhury
- Integrative Life Sciences, Virginia Commonwealth University, 1000 West Main Street, Richmond, 23284, VA, USA
| | - Stephen S Fong
- Integrative Life Sciences, Virginia Commonwealth University, 1000 West Main Street, Richmond, 23284, VA, USA; Chemical and Life Science Engineering, Virginia Commonwealth University, 601 West Main Street, Richmond, 23284, VA, USA.
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Baquero F, Lanza VF, Baquero MR, Del Campo R, Bravo-Vázquez DA. Microcins in Enterobacteriaceae: Peptide Antimicrobials in the Eco-Active Intestinal Chemosphere. Front Microbiol 2019; 10:2261. [PMID: 31649628 PMCID: PMC6795089 DOI: 10.3389/fmicb.2019.02261] [Citation(s) in RCA: 77] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 09/17/2019] [Indexed: 12/31/2022] Open
Abstract
Microcins are low-molecular-weight, ribosomally produced, highly stable, bacterial-inhibitory molecules involved in competitive, and amensalistic interactions between Enterobacteriaceae in the intestine. These interactions take place in a highly complex chemical landscape, the intestinal eco-active chemosphere, composed of chemical substances that positively or negatively influence bacterial growth, including those originated from nutrient uptake, and those produced by the action of the human or animal host and the intestinal microbiome. The contribution of bacteria results from their effect on the host generated molecules, on food and digested food, and organic substances from microbial origin, including from bacterial degradation. Here, we comprehensively review the main chemical substances present in the human intestinal chemosphere, particularly of those having inhibitory effects on microorganisms. With this background, and focusing on Enterobacteriaceae, the most relevant human pathogens from the intestinal microbiota, the microcin’s history and classification, mechanisms of action, and mechanisms involved in microcin’s immunity (in microcin producers) and resistance (non-producers) are reviewed. Products from the chemosphere likely modulate the ecological effects of microcin activity. Several cross-resistance mechanisms are shared by microcins, colicins, bacteriophages, and some conventional antibiotics, which are expected to produce cross-effects. Double-microcin-producing strains (such as microcins MccM and MccH47) have been successfully used for decades in the control of pathogenic gut organisms. Microcins are associated with successful gut colonization, facilitating translocation and invasion, leading to bacteremia, and urinary tract infections. In fact, Escherichia coli strains from the more invasive phylogroups (e.g., B2) are frequently microcinogenic. A publicly accessible APD3 database http://aps.unmc.edu/AP/ shows particular genes encoding microcins in 34.1% of E. coli strains (mostly MccV, MccM, MccH47, and MccI47), and much less in Shigella and Salmonella (<2%). Some 4.65% of Klebsiella pneumoniae are microcinogenic (mostly with MccE492), and even less in Enterobacter or Citrobacter (mostly MccS). The high frequency and variety of microcins in some Enterobacteriaceae indicate key ecological functions, a notion supported by their dominance in the intestinal microbiota of biosynthetic gene clusters involved in the synthesis of post-translationally modified peptide microcins.
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Affiliation(s)
- Fernando Baquero
- Department of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Institute for Health Research (IRYCIS), Madrid, Spain
| | - Val F Lanza
- Bioinformatics Unit, Ramón y Cajal University Hospital, Ramón y Cajal Institute for Health Research (IRYCIS), Madrid, Spain
| | - Maria-Rosario Baquero
- Department of Microbiology, Alfonso X El Sabio University, Villanueva de la Cañada, Spain
| | - Rosa Del Campo
- Department of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Institute for Health Research (IRYCIS), Madrid, Spain
| | - Daniel A Bravo-Vázquez
- Department of Microbiology, Alfonso X El Sabio University, Villanueva de la Cañada, Spain
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