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Sulaiman JE, Thompson J, Qian Y, Vivas EI, Diener C, Gibbons SM, Safdar N, Venturelli OS. Elucidating human gut microbiota interactions that robustly inhibit diverse Clostridioides difficile strains across different nutrient landscapes. Nat Commun 2024; 15:7416. [PMID: 39198411 PMCID: PMC11358386 DOI: 10.1038/s41467-024-51062-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: 04/12/2024] [Accepted: 07/25/2024] [Indexed: 09/01/2024] Open
Abstract
The human gut pathogen Clostridioides difficile displays substantial inter-strain genetic variability and confronts a changeable nutrient landscape in the gut. We examined how human gut microbiota inter-species interactions influence the growth and toxin production of various C. difficile strains across different nutrient environments. Negative interactions influencing C. difficile growth are prevalent in an environment containing a single highly accessible resource and sparse in an environment containing C. difficile-preferred carbohydrates. C. difficile toxin production displays significant community-context dependent variation and does not trend with growth-mediated inter-species interactions. C. difficile strains exhibit differences in interactions with Clostridium scindens and the ability to compete for proline. Further, C. difficile shows substantial differences in transcriptional profiles in co-culture with C. scindens or Clostridium hiranonis. C. difficile exhibits massive alterations in metabolism and other cellular processes in co-culture with C. hiranonis, reflecting their similar metabolic niches. C. hiranonis uniquely inhibits the growth and toxin production of diverse C. difficile strains across different nutrient environments and robustly ameliorates disease severity in mice. In sum, understanding the impact of C. difficile strain variability and nutrient environments on inter-species interactions could help improve the effectiveness of anti-C. difficile strategies.
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Affiliation(s)
- Jordy Evan Sulaiman
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Jaron Thompson
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
- Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Yili Qian
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Eugenio I Vivas
- Gnotobiotic Animal Core Facility, University of Wisconsin-Madison, Madison, WI, USA
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
| | - Christian Diener
- Institute for Systems Biology, Seattle, WA, USA
- Diagnostic and Research Institute of Hygiene, Microbiology and Environmental Medicine, Medical University of Graz, Graz, Austria
| | - Sean M Gibbons
- Institute for Systems Biology, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- eScience Institute, University of Washington, Seattle, WA, USA
| | - Nasia Safdar
- Division of Infectious Disease, Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
- Department of Medicine, William S. Middleton Veterans Hospital Madison, Madison, WI, USA
| | - Ophelia S Venturelli
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA.
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2
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Sulaiman JE, Thompson J, Cheung PLK, Qian Y, Mill J, James I, Vivas EI, Simcox J, Venturelli O. Human gut microbiota interactions shape the long-term growth dynamics and evolutionary adaptations of Clostridioides difficile. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.15.603560. [PMID: 39071283 PMCID: PMC11275832 DOI: 10.1101/2024.07.15.603560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Clostridioides difficile can transiently or persistently colonize the human gut, posing a risk factor for infections. This colonization is influenced by complex molecular and ecological interactions with human gut microbiota. By investigating C. difficile dynamics in human gut communities over hundreds of generations, we show patterns of stable coexistence, instability, or competitive exclusion. Lowering carbohydrate concentration shifted a community containing C. difficile and the prevalent human gut symbiont Phocaeicola vulgatus from competitive exclusion to coexistence, facilitated by increased cross-feeding. In this environment, C. difficile adapted via single-point mutations in key metabolic genes, altering its metabolic niche from proline to glucose utilization. These metabolic changes substantially impacted inter-species interactions and reduced disease severity in the mammalian gut. In sum, human gut microbiota interactions are crucial in shaping the long-term growth dynamics and evolutionary adaptations of C. difficile, offering key insights for developing anti-C. difficile strategies.
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Affiliation(s)
- Jordy Evan Sulaiman
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Jaron Thompson
- Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Yili Qian
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Jericha Mill
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Isabella James
- Integrated Program in Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Eugenio I. Vivas
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
- Gnotobiotic Animal Core Facility, University of Wisconsin-Madison, Madison, WI, USA
| | - Judith Simcox
- Howard Hughes Medical Institute, Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Ophelia Venturelli
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
- Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
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3
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Sulaiman JE, Thompson J, Qian Y, Vivas EI, Diener C, Gibbons SM, Safdar N, Venturelli OS. Elucidating human gut microbiota interactions that robustly inhibit diverse Clostridioides difficile strains across different nutrient landscapes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.13.589383. [PMID: 38659900 PMCID: PMC11042340 DOI: 10.1101/2024.04.13.589383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
The human gut pathogen Clostridioides difficile displays extreme genetic variability and confronts a changeable nutrient landscape in the gut. We mapped gut microbiota inter-species interactions impacting the growth and toxin production of diverse C. difficile strains in different nutrient environments. Although negative interactions impacting C. difficile are prevalent in environments promoting resource competition, they are sparse in an environment containing C. difficile-preferred carbohydrates. C. difficile strains display differences in interactions with Clostridium scindens and the ability to compete for proline. C. difficile toxin production displays substantial community-context dependent variation and does not trend with growth-mediated inter-species interactions. C. difficile shows substantial differences in transcriptional profiles in the presence of the closely related species C. hiranonis or C. scindens. In co-culture with C. hiranonis, C. difficile exhibits massive alterations in metabolism and other cellular processes, consistent with their high metabolic overlap. Further, Clostridium hiranonis inhibits the growth and toxin production of diverse C. difficile strains across different nutrient environments and ameliorates the disease severity of a C. difficile challenge in a murine model. In sum, strain-level variability and nutrient environments are major variables shaping gut microbiota interactions with C. difficile.
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Affiliation(s)
- Jordy Evan Sulaiman
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Jaron Thompson
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
- Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Yili Qian
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Eugenio I. Vivas
- Gnotobiotic Animal Core Facility, University of Wisconsin-Madison, Madison, WI, USA
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Sean M. Gibbons
- Institute for Systems Biology, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- eScience Institute, University of Washington, Seattle, WA, USA
| | - Nasia Safdar
- Division of Infectious Disease, Department of Medicine, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
- Department of Medicine, William S. Middleton Veterans Hospital Madison, Madison, WI, USA
| | - Ophelia S. Venturelli
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
- Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
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4
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Liu Y, Cheng YY, Thompson J, Zhou Z, Vivas EI, Warren MF, Rey FE, Anantharaman K, Venturelli OS. Shaping human gut community assembly and butyrate production by controlling the arginine dihydrolase pathway. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.01.10.523442. [PMID: 37986770 PMCID: PMC10659395 DOI: 10.1101/2023.01.10.523442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
The arginine dihydrolase pathway (arc operon) present in a subset of diverse human gut species enables arginine catabolism. This specialized metabolic pathway can alter environmental pH and nitrogen availability, which in turn could shape gut microbiota inter-species interactions. By exploiting synthetic control of gene expression, we investigated the role of the arc operon in probiotic Escherichia coli Nissle 1917 on human gut community assembly and health-relevant metabolite profiles in vitro and in the murine gut. By stabilizing environmental pH, the arc operon reduced variability in community composition across different initial pH perturbations. The abundance of butyrate producing bacteria were altered in response to arc operon activity and butyrate production was enhanced in a physiologically relevant pH range. While the presence of the arc operon altered community dynamics, it did not impact production of short chain fatty acids. Dynamic computational modeling of pH-mediated interactions reveals the quantitative contribution of this mechanism to community assembly. In sum, our framework to quantify the contribution of molecular pathways and mechanism modalities on microbial community dynamics and functions could be applied more broadly.
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Affiliation(s)
- Yiyi Liu
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison WI 53706
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706
| | - Yu-Yu Cheng
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706
| | - Jaron Thompson
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison WI 53706
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706
| | - Zhichao Zhou
- Department of Bacteriology, University of Wisconsin-Madison, WI 53706
| | - Eugenio I. Vivas
- Department of Bacteriology, University of Wisconsin-Madison, WI 53706
- Gnotobiotic Animal Core Facility, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Matthew F. Warren
- Department of Bacteriology, University of Wisconsin-Madison, WI 53706
| | - Federico E. Rey
- Department of Bacteriology, University of Wisconsin-Madison, WI 53706
| | | | - Ophelia S. Venturelli
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison WI 53706
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706
- Department of Bacteriology, University of Wisconsin-Madison, WI 53706
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5
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Wu L, Wang XW, Tao Z, Wang T, Zuo W, Zeng Y, Liu YY, Dai L. Data-driven prediction of colonization outcomes for complex microbial communities. Nat Commun 2024; 15:2406. [PMID: 38493186 PMCID: PMC10944475 DOI: 10.1038/s41467-024-46766-y] [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: 07/07/2023] [Accepted: 03/08/2024] [Indexed: 03/18/2024] Open
Abstract
Microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse mechanisms governing microbial dynamics. Here, we propose a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validate this approach using synthetic data, finding that machine learning models can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conduct colonization experiments for commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approaches can predict the colonization outcomes in experiments. Furthermore, we find that while most resident species are predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., Enterococcus faecalis inhibits the invasion of E. faecium invasion. The presented results suggest that the data-driven approaches are powerful tools to inform the ecology and management of microbial communities.
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Affiliation(s)
- Lu Wu
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xu-Wen Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Zining Tao
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shandong Agricultural University, Tai'an, China
| | - Tong Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Wenlong Zuo
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yu Zeng
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
| | - Lei Dai
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- University of Chinese Academy of Sciences, Beijing, China.
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6
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Geesink P, ter Horst J, Ettema TJG. More than the sum of its parts: uncovering emerging effects of microbial interactions in complex communities. FEMS Microbiol Ecol 2024; 100:fiae029. [PMID: 38444203 PMCID: PMC10950044 DOI: 10.1093/femsec/fiae029] [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: 11/15/2023] [Revised: 03/01/2024] [Accepted: 03/04/2024] [Indexed: 03/07/2024] Open
Abstract
Microbial communities are not only shaped by the diversity of microorganisms and their individual metabolic potential, but also by the vast amount of intra- and interspecies interactions that can occur pairwise interactions among microorganisms, we suggest that more attention should be drawn towards the effects on the entire microbiome that emerge from individual interactions between community members. The production of certain metabolites that can be tied to a specific microbe-microbe interaction might subsequently influence the physicochemical parameters of the habitat, stimulate a change in the trophic network of the community or create new micro-habitats through the formation of biofilms, similar to the production of antimicrobial substances which might negatively affect only one microorganism but cause a ripple effect on the abundance of other community members. Here, we argue that combining established as well as innovative laboratory and computational methods is needed to predict novel interactions and assess their secondary effects. Such efforts will enable future microbiome studies to expand our knowledge on the dynamics of complex microbial communities.
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Affiliation(s)
- Patricia Geesink
- Laboratory of Microbiology, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
| | - Jolanda ter Horst
- Laboratory of Microbiology, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
| | - Thijs J G Ettema
- Laboratory of Microbiology, Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
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7
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Carr A, Baliga NS, Diener C, Gibbons SM. Personalized Clostridioides difficile engraftment risk prediction and probiotic therapy assessment in the human gut. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.28.538771. [PMID: 37162960 PMCID: PMC10168307 DOI: 10.1101/2023.04.28.538771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Clostridioides difficile colonizes up to 30-40% of community-dwelling adults without causing disease. C. difficile infections (CDIs) are the leading cause of antibiotic-associated diarrhea in the U.S. and typically develop in individuals following disruption of the gut microbiota due to antibiotic or chemotherapy treatments. Further treatment of CDI with antibiotics is not always effective and can lead to antibiotic resistance and recurrent infections (rCDI). The most effective treatment for rCDI is the reestablishment of an intact microbiota via fecal microbiota transplants (FMTs). However, the success of FMTs has been difficult to generalize because the microbial interactions that prevent engraftment and facilitate the successful clearance of C. difficile are still only partially understood. Here we show how microbial community-scale metabolic models (MCMMs) accurately predicted known instances of C. difficile colonization susceptibility or resistance in vitro and in vivo. MCMMs provide detailed mechanistic insights into the ecological interactions that govern C. difficile engraftment, like cross-feeding or competition involving metabolites like succinate, trehalose, and ornithine, which differ from person to person. Indeed, three distinct C. difficile metabolic niches emerge from our MCMMs, two associated with positive growth rates and one that represents non-growth, which are consistently observed across 15,204 individuals from five independent cohorts. Finally, we show how MCMMs can predict personalized engraftment and C. difficile growth suppression for a probiotic cocktail (VE303) designed to replace FMTs for the treatment rCDI. Overall, this powerful modeling approach predicts personalized C. difficile engraftment risk and can be leveraged to assess probiotic treatment efficacy. MCMMs could be extended to understand the mechanistic underpinnings of personalized engraftment of other opportunistic bacterial pathogens, beneficial probiotic organisms, or more complex microbial consortia.
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Affiliation(s)
- Alex Carr
- Institute for Systems Biology, Seattle, WA, USA
- Molecular Engineering Program, University of Washington, Seattle, WA, USA
| | - Nitin S. Baliga
- Institute for Systems Biology, Seattle, WA, USA
- Molecular Engineering Program, University of Washington, Seattle, WA, USA
- Departments of Biology and Microbiology, University of Washington, Seattle, WA, USA
- Lawrence Berkeley National Lab, Berkeley, CA, USA
| | - Christian Diener
- Institute for Systems Biology, Seattle, WA, USA
- Diagnostic and Research Institute of Hygiene, Microbiology and Environmental Medicine, Medical University of Graz, Graz, Austria
| | - Sean M. Gibbons
- Institute for Systems Biology, Seattle, WA, USA
- Molecular Engineering Program, University of Washington, Seattle, WA, USA
- Departments of Bioengineering and Genome Sciences, University of Washington, Seattle, WA, USA
- eScience Institute, University of Washington, Seattle, WA, USA
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8
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Sun X, Xie J, Zheng D, Xia R, Wang W, Xun W, Huang Q, Zhang R, Kovács ÁT, Xu Z, Shen Q. Metabolic interactions affect the biomass of synthetic bacterial biofilm communities. mSystems 2023; 8:e0104523. [PMID: 37971263 PMCID: PMC10734490 DOI: 10.1128/msystems.01045-23] [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: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 11/19/2023] Open
Abstract
IMPORTANCE Co-occurrence network analysis is an effective tool for predicting complex networks of microbial interactions in the natural environment. Using isolates from a rhizosphere, we constructed multi-species biofilm communities and investigated co-occurrence patterns between microbial species in genome-scale metabolic models and in vitro experiments. According to our results, metabolic exchanges and resource competition may partially explain the co-occurrence network analysis results found in synthetic bacterial biofilm communities.
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Affiliation(s)
- Xinli Sun
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
- Bacterial Interactions and Evolution Group, DTU Bioengineering, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Jiyu Xie
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
- Bacterial Interactions and Evolution Group, DTU Bioengineering, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Daoyue Zheng
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Riyan Xia
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Wei Wang
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Weibing Xun
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Qiwei Huang
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Ruifu Zhang
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Ákos T. Kovács
- Bacterial Interactions and Evolution Group, DTU Bioengineering, Technical University of Denmark, Kongens Lyngby, Denmark
- Institute of Biology Leiden, Leiden University, Leiden, the Netherlands
| | - Zhihui Xu
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
| | - Qirong Shen
- Key lab of organic-based fertilizers of China and Jiangsu provincial key lab for solid organic waste utilization, Nanjing Agricultural University, Nanjing, Jiangsu, China
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9
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Connors BM, Thompson J, Ertmer S, Clark RL, Pfleger BF, Venturelli OS. Control points for design of taxonomic composition in synthetic human gut communities. Cell Syst 2023; 14:1044-1058.e13. [PMID: 38091992 PMCID: PMC10752370 DOI: 10.1016/j.cels.2023.11.007] [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: 08/03/2022] [Revised: 06/22/2023] [Accepted: 11/20/2023] [Indexed: 12/23/2023]
Abstract
Microbial communities offer vast potential across numerous sectors but remain challenging to systematically control. We develop a two-stage approach to guide the taxonomic composition of synthetic microbiomes by precisely manipulating media components and initial species abundances. By combining high-throughput experiments and computational modeling, we demonstrate the ability to predict and design the diversity of a 10-member synthetic human gut community. We reveal that critical environmental factors governing monoculture growth can be leveraged to steer microbial communities to desired states. Furthermore, systematically varied initial abundances drive variation in community assembly and enable inference of pairwise inter-species interactions via a dynamic ecological model. These interactions are overall consistent with conditioned media experiments, demonstrating that specific perturbations to a high-richness community can provide rich information for building dynamic ecological models. This model is subsequently used to design low-richness communities that display low or high temporal taxonomic variability over an extended period. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Bryce M Connors
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Jaron Thompson
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Sarah Ertmer
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Ryan L Clark
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Brian F Pfleger
- Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Ophelia S Venturelli
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Bacteriology, University of Wisconsin-Madison, Madison, WI 53706, USA.
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10
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Thompson JC, Zavala VM, Venturelli OS. Integrating a tailored recurrent neural network with Bayesian experimental design to optimize microbial community functions. PLoS Comput Biol 2023; 19:e1011436. [PMID: 37773951 PMCID: PMC10540976 DOI: 10.1371/journal.pcbi.1011436] [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: 12/23/2022] [Accepted: 08/16/2023] [Indexed: 10/01/2023] Open
Abstract
Microbiomes interact dynamically with their environment to perform exploitable functions such as production of valuable metabolites and degradation of toxic metabolites for a wide range of applications in human health, agriculture, and environmental cleanup. Developing computational models to predict the key bacterial species and environmental factors to build and optimize such functions are crucial to accelerate microbial community engineering. However, there is an unknown web of interactions that determine the highly complex and dynamic behavior of these systems, which precludes the development of models based on known mechanisms. By contrast, entirely data-driven machine learning models can produce physically unrealistic predictions and often require significant amounts of experimental data to learn system behavior. We develop a physically-constrained recurrent neural network that preserves model flexibility but is constrained to produce physically consistent predictions and show that it can outperform existing machine learning methods in the prediction of certain experimentally measured species abundance and metabolite concentrations. Further, we present a closed-loop, Bayesian experimental design algorithm to guide data collection by selecting experimental conditions that simultaneously maximize information gain and target microbial community functions. Using a bioreactor case study, we demonstrate how the proposed framework can be used to efficiently navigate a large design space to identify optimal operating conditions. The proposed methodology offers a flexible machine learning approach specifically tailored to optimize microbiome target functions through the sequential design of informative experiments that seek to explore and exploit community functions.
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Affiliation(s)
- Jaron C. Thompson
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Victor M. Zavala
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Ophelia S. Venturelli
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Bacteriology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
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11
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Lührmann A, Palmini A, Hellmich J, Belik V, Zentek J, Vahjen W. Antimicrobial resistance- and pathogen patterns in the fecal microbiota of sows and their offspring in German commercial pig farms. PLoS One 2023; 18:e0290554. [PMID: 37616234 PMCID: PMC10449214 DOI: 10.1371/journal.pone.0290554] [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: 01/19/2023] [Accepted: 08/09/2023] [Indexed: 08/26/2023] Open
Abstract
Reducing antibiotic use is one of the biggest challenges in pig farming, as antibiotics have been used for years to control typical problems such as newborn or post-weaning diarrhea. The pressure a one health approach has created on animal production regarding antimicrobial resistance is an opportunity to find other strategies against enterobacterial pathogens in suckling and weaned piglets. A farm-specific approach could have a good success due to the individual farm structures in Germany and other countries. In this study, non-metric multidimensional scaling, hierarchical clustering, and latent class analysis were used to determine the impact of antibiotic use on antibiotic resistance patterns and pathogen prevalence in 20 German pig farms. This may help to develop individualized health strategies. 802 fresh fecal samples were collected from sows and piglets from 20 piglet production and rearing farms at different production times (sows antepartum and postpartum, suckling piglets, weaned piglets). In addition, the use of antibiotics was recorded. DNA extracts were subjected to quantitative real-time qPCR with primers specific for antibiotic resistance genes (int1, sul1-3, dfrA1, mcr-1, blaCTX-M), and virulence factors of relevant bacteria (C. difficile, C. perfringens, Salmonella, Escherichia/Shigella/Hafnia, E. coli). Linear and logistic regression models were used to analyze the relationship between different antibiotics and the major genes contributing to the clustering of observations for the different animal groups. Clustering revealed different farm clusters for sows, suckling piglets, and weaned piglets, with the most remarkable diversity in antibiotic use among weaned piglets. Amoxicillin, lincomycin, and enrofloxacin were identified as the most probable cause of increased odds of the presence of relevant antibiotic resistance genes (mcr1, dfrA1, blaCTX-M). Still, direct effects of a specific antibiotic on its associated resistance gene were rare. Enrofloxacin and florfenicol favored the occurrence of C. difficile in sows. The E. coli fimbriae genes were less affected by antibiotic use in sows and piglets, but the F4 fimbriae gene could be associated with the integrase 1 gene in piglets. The results confirm that multidrug-resistant enterobacteria are widespread in German pig farms and give awareness of the impact of current antibiotic use while searching for alternative health strategies.
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Affiliation(s)
- Anja Lührmann
- Institute of Animal Nutrition, Freie Universität Berlin, Berlin, Germany
| | - Andrea Palmini
- System Modeling Group, Institute of Veterinary Epidemiology and Biostatistics, Freie Universität Berlin, Berlin, Germany
| | - Justinus Hellmich
- Institute of Animal Nutrition, Freie Universität Berlin, Berlin, Germany
| | - Vitaly Belik
- System Modeling Group, Institute of Veterinary Epidemiology and Biostatistics, Freie Universität Berlin, Berlin, Germany
| | - Jürgen Zentek
- Institute of Animal Nutrition, Freie Universität Berlin, Berlin, Germany
| | - Wilfried Vahjen
- Institute of Animal Nutrition, Freie Universität Berlin, Berlin, Germany
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12
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Martin AJ, Serebrinsky-Duek K, Riquelme E, Saa PA, Garrido D. Microbial interactions and the homeostasis of the gut microbiome: the role of Bifidobacterium. MICROBIOME RESEARCH REPORTS 2023; 2:17. [PMID: 38046822 PMCID: PMC10688804 DOI: 10.20517/mrr.2023.10] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 04/17/2023] [Accepted: 04/24/2023] [Indexed: 12/05/2023]
Abstract
The human gut is home to trillions of microorganisms that influence several aspects of our health. This dense microbial community targets almost all dietary polysaccharides and releases multiple metabolites, some of which have physiological effects on the host. A healthy equilibrium between members of the gut microbiota, its microbial diversity, and their metabolites is required for intestinal health, promoting regulatory or anti-inflammatory immune responses. In contrast, the loss of this equilibrium due to antibiotics, low fiber intake, or other conditions results in alterations in gut microbiota composition, a term known as gut dysbiosis. This dysbiosis can be characterized by a reduction in health-associated microorganisms, such as butyrate-producing bacteria, enrichment of a small number of opportunistic pathogens, or a reduction in microbial diversity. Bifidobacterium species are key species in the gut microbiome, serving as primary degraders and contributing to a balanced gut environment in various ways. Colonization resistance is a fundamental property of gut microbiota for the prevention and control of infections. This community competes strongly with foreign microorganisms, such as gastrointestinal pathogens, antibiotic-resistant bacteria, or even probiotics. Resistance to colonization is based on microbial interactions such as metabolic cross-feeding, competition for nutrients, or antimicrobial-based inhibition. These interactions are mediated by metabolites and metabolic pathways, representing the inner workings of the gut microbiota, and play a protective role through colonization resistance. This review presents a rationale for how microbial interactions provide resistance to colonization and gut dysbiosis, highlighting the protective role of Bifidobacterium species.
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Affiliation(s)
- Alberto J.M. Martin
- Laboratorio de Redes Biológicas, Centro Científico y Tecnológico de Excelencia Ciencia & Vida, Fundación Ciencia & Vida, Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Santiago 8580702, Chile
| | - Kineret Serebrinsky-Duek
- Department of Chemical and Bioprocess Engineering, Pontificia Universidad Católica de Chile, Santiago 833115, Chile
| | - Erick Riquelme
- Department of Respiratory Diseases, School of Medicine, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
| | - Pedro A. Saa
- Department of Chemical and Bioprocess Engineering, Pontificia Universidad Católica de Chile, Santiago 833115, Chile
- Institute for Mathematical and Computational Engineering, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
| | - Daniel Garrido
- Department of Chemical and Bioprocess Engineering, Pontificia Universidad Católica de Chile, Santiago 833115, Chile
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13
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Hromada S, Venturelli OS. Gut microbiota interspecies interactions shape the response of Clostridioides difficile to clinically relevant antibiotics. PLoS Biol 2023; 21:e3002100. [PMID: 37167201 PMCID: PMC10174544 DOI: 10.1371/journal.pbio.3002100] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 03/30/2023] [Indexed: 05/13/2023] Open
Abstract
In the human gut, the growth of the pathogen Clostridioides difficile is impacted by a complex web of interspecies interactions with members of human gut microbiota. We investigate the contribution of interspecies interactions on the antibiotic response of C. difficile to clinically relevant antibiotics using bottom-up assembly of human gut communities. We identify 2 classes of microbial interactions that alter C. difficile's antibiotic susceptibility: interactions resulting in increased ability of C. difficile to grow at high antibiotic concentrations (rare) and interactions resulting in C. difficile growth enhancement at low antibiotic concentrations (common). Based on genome-wide transcriptional profiling data, we demonstrate that metal sequestration due to hydrogen sulfide production by the prevalent gut species Desulfovibrio piger increases the minimum inhibitory concentration (MIC) of metronidazole for C. difficile. Competition with species that display higher sensitivity to the antibiotic than C. difficile leads to enhanced growth of C. difficile at low antibiotic concentrations due to competitive release. A dynamic computational model identifies the ecological principles driving this effect. Our results provide a deeper understanding of ecological and molecular principles shaping C. difficile's response to antibiotics, which could inform therapeutic interventions.
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Affiliation(s)
- Susan Hromada
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Microbiology Doctoral Training Program, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Ophelia S. Venturelli
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Bacteriology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
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14
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Newton DP, Ho PY, Huang KC. Modulation of antibiotic effects on microbial communities by resource competition. Nat Commun 2023; 14:2398. [PMID: 37100773 PMCID: PMC10133249 DOI: 10.1038/s41467-023-37895-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 04/03/2023] [Indexed: 04/28/2023] Open
Abstract
Antibiotic treatment significantly impacts the human gut microbiota, but quantitative understanding of how antibiotics affect community diversity is lacking. Here, we build on classical ecological models of resource competition to investigate community responses to species-specific death rates, as induced by antibiotic activity or other growth-inhibiting factors such as bacteriophages. Our analyses highlight the complex dependence of species coexistence that can arise from the interplay of resource competition and antibiotic activity, independent of other biological mechanisms. In particular, we identify resource competition structures that cause richness to depend on the order of sequential application of antibiotics (non-transitivity), and the emergence of synergistic and antagonistic effects under simultaneous application of multiple antibiotics (non-additivity). These complex behaviors can be prevalent, especially when generalist consumers are targeted. Communities can be prone to either synergism or antagonism, but typically not both, and antagonism is more common. Furthermore, we identify a striking overlap in competition structures that lead to non-transitivity during antibiotic sequences and those that lead to non-additivity during antibiotic combination. In sum, our results establish a broadly applicable framework for predicting microbial community dynamics under deleterious perturbations.
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Affiliation(s)
- Daniel P Newton
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Physics, Stanford University, Stanford, CA, USA
| | - Po-Yi Ho
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
| | - Kerwyn Casey Huang
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
- Chan Zuckerberg Biohub, San Francisco, CA, 94158, USA.
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15
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Wu L, Wang XW, Tao Z, Wang T, Zuo W, Zeng Y, Liu YY, Dai L. Data-driven prediction of colonization outcomes for complex microbial communities. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.19.537502. [PMID: 37131715 PMCID: PMC10153232 DOI: 10.1101/2023.04.19.537502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Complex microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. Here, we proposed a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validated this approach using synthetic data, finding that machine learning models (including Random Forest and neural ODE) can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conducted colonization experiments for two commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approach can successfully predict the colonization outcomes. Furthermore, we found that while most resident species were predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., the presence of Enterococcus faecalis inhibits the invasion of E. faecium . The presented results suggest that the data-driven approach is a powerful tool to inform the ecology and management of complex microbial communities.
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16
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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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17
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Celis AI, Relman DA, Huang KC. The impact of iron and heme availability on the healthy human gut microbiome in vivo and in vitro. Cell Chem Biol 2023; 30:110-126.e3. [PMID: 36603582 PMCID: PMC9913275 DOI: 10.1016/j.chembiol.2022.12.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 07/12/2022] [Accepted: 12/14/2022] [Indexed: 01/06/2023]
Abstract
Responses of the indigenous human gut commensal microbiota to iron are poorly understood because of an emphasis on in vitro studies of pathogen iron sensitivity. In a study of iron supplementation in healthy humans, we identified gradual microbiota shifts in some participants correlated with bacterial iron internalization. To identify direct effects due to taxon-specific iron sensitivity, we used participant stool samples to derive diverse in vitro communities. Iron supplementation of these communities caused small compositional shifts, mimicking those in vivo, whereas iron deprivation dramatically inhibited growth with irreversible, cumulative reduction in diversity and replacement of dominant species. Sensitivity of individual species to iron deprivation in axenic culture generally predicted iron dependency in a community. Finally, exogenous heme acted as a source of inorganic iron to prevent depletion of some species. Our results highlight the complementarity of in vivo and in vitro studies in understanding how environmental factors affect gut microbiotas.
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Affiliation(s)
- Arianna I Celis
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - David A Relman
- Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Infectious Diseases Section, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA.
| | - Kerwyn Casey Huang
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA.
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18
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Midani FS, David LA. Tracking defined microbial communities by multicolor flow cytometry reveals tradeoffs between productivity and diversity. Front Microbiol 2023; 13:910390. [PMID: 36687598 PMCID: PMC9849913 DOI: 10.3389/fmicb.2022.910390] [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: 04/01/2022] [Accepted: 11/29/2022] [Indexed: 01/07/2023] Open
Abstract
Cross feeding between microbes is ubiquitous, but its impact on the diversity and productivity of microbial communities is incompletely understood. A reductionist approach using simple microbial communities has the potential to detect cross feeding interactions and their impact on ecosystem properties. However, quantifying abundance of more than two microbes in a community in a high throughput fashion requires rapid, inexpensive assays. Here, we show that multicolor flow cytometry combined with a machine learning-based classifier can rapidly quantify species abundances in simple, synthetic microbial communities. Our approach measures community structure over time and detects the exchange of metabolites in a four-member community of fluorescent Bacteroides species. Notably, we quantified species abundances in co-cultures and detected evidence of cooperation in polysaccharide processing and competition for monosaccharide utilization. We also observed that co-culturing on simple sugars, but not complex sugars, reduced microbial productivity, although less productive communities maintained higher community diversity. In summary, our multicolor flow cytometric approach presents an economical, tractable model system for microbial ecology using well-studied human bacteria. It can be extended to include additional species, evaluate more complex environments, and assay response of communities to a variety of disturbances.
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Affiliation(s)
- Firas S. Midani
- Center for Genomic and Computational Biology, Duke University, Durham, NC, United States
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, United States
| | - Lawrence A. David
- Center for Genomic and Computational Biology, Duke University, Durham, NC, United States
- Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, United States
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19
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Ostrem Loss E, Thompson J, Cheung PLK, Qian Y, Venturelli OS. Carbohydrate complexity limits microbial growth and reduces the sensitivity of human gut communities to perturbations. Nat Ecol Evol 2023; 7:127-142. [PMID: 36604549 DOI: 10.1038/s41559-022-01930-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 10/10/2022] [Indexed: 01/07/2023]
Abstract
Dietary fibre impacts the growth dynamics of human gut microbiota, yet we lack a detailed and quantitative understanding of how these nutrients shape microbial interaction networks and responses to perturbations. By building human gut communities coupled with computational modelling, we dissect the effects of fibres that vary in chemical complexity and each of their constituent sugars on community assembly and response to perturbations. We demonstrate that the degree of chemical complexity across different fibres limits microbial growth and the number of species that can utilize these nutrients. The prevalence of negative interspecies interactions is reduced in the presence of fibres compared with their constituent sugars. Carbohydrate chemical complexity enhances the reproducibility of community assembly and resistance of the community to invasion. We demonstrate that maximizing or minimizing carbohydrate competition between resident and invader species enhances resistance to invasion. In sum, the quantitative effects of carbohydrate chemical complexity on microbial interaction networks could be exploited to inform dietary and bacterial interventions to modulate community resistance to perturbations.
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Affiliation(s)
- Erin Ostrem Loss
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Jaron Thompson
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA.,Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Yili Qian
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Ophelia S Venturelli
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA. .,Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA. .,Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA. .,Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA.
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20
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Wang J, Lang H, Zhang W, Zhai Y, Zheng L, Chen H, Liu Y, Zheng H. Stably transmitted defined microbial community in honeybees preserves Hafnia alvei inhibition by regulating the immune system. Front Microbiol 2022; 13:1074153. [PMID: 36532452 PMCID: PMC9751035 DOI: 10.3389/fmicb.2022.1074153] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 11/14/2022] [Indexed: 12/08/2023] Open
Abstract
The gut microbiota of honeybees is highly diverse at the strain level and essential to the proper function and development of the host. Interactions between the host and its gut microbiota, such as specific microbes regulating the innate immune system, protect the host against pathogen infections. However, little is known about the capacity of these strains deposited in one colony to inhibit pathogens. In this study, we assembled a defined microbial community based on phylogeny analysis, the 'Core-20' community, consisting of 20 strains isolated from the honeybee intestine. The Core-20 community could trigger the upregulation of immune gene expressions and reduce Hafnia alvei prevalence, indicating immune priming underlies the microbial protective effect. Functions related to carbohydrate utilization and the phosphoenolpyruvate-dependent sugar phosphotransferase system (PTS systems) are represented in genomic analysis of the defined community, which might be involved in manipulating immune responses. Additionally, we found that the defined Core-20 community is able to colonize the honeybee gut stably through passages. In conclusion, our findings highlight that the synthetic gut microbiota could offer protection by regulating the host immune system, suggesting that the strain collection can yield insights into host-microbiota interactions and provide solutions to protect honeybees from pathogen infections.
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Affiliation(s)
- Jieni Wang
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China
| | - Haoyu Lang
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China
| | - Wenhao Zhang
- Faculty of Agriculture and Food, Kunming University of Science and Technology, Kunming, China
| | - Yifan Zhai
- Shandong Academy of Agricultural Sciences, Institute of Plant Protection, Jinan, China
- Key Laboratory of Natural Enemies Insects, Ministry of Agriculture and Rural Affairs, Jinan, China
| | - Li Zheng
- Shandong Academy of Agricultural Sciences, Institute of Plant Protection, Jinan, China
- Key Laboratory of Natural Enemies Insects, Ministry of Agriculture and Rural Affairs, Jinan, China
| | - Hao Chen
- Shandong Academy of Agricultural Sciences, Institute of Plant Protection, Jinan, China
- Key Laboratory of Natural Enemies Insects, Ministry of Agriculture and Rural Affairs, Jinan, China
| | - Yan Liu
- Shandong Academy of Agricultural Sciences, Institute of Plant Protection, Jinan, China
- Key Laboratory of Natural Enemies Insects, Ministry of Agriculture and Rural Affairs, Jinan, China
| | - Hao Zheng
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China
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21
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Baranwal M, Clark RL, Thompson J, Sun Z, Hero AO, Venturelli OS. Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics. eLife 2022; 11:e73870. [PMID: 35736613 PMCID: PMC9225007 DOI: 10.7554/elife.73870] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 05/22/2022] [Indexed: 12/26/2022] Open
Abstract
Predicting the dynamics and functions of microbiomes constructed from the bottom-up is a key challenge in exploiting them to our benefit. Current models based on ecological theory fail to capture complex community behaviors due to higher order interactions, do not scale well with increasing complexity and in considering multiple functions. We develop and apply a long short-term memory (LSTM) framework to advance our understanding of community assembly and health-relevant metabolite production using a synthetic human gut community. A mainstay of recurrent neural networks, the LSTM learns a high dimensional data-driven non-linear dynamical system model. We show that the LSTM model can outperform the widely used generalized Lotka-Volterra model based on ecological theory. We build methods to decipher microbe-microbe and microbe-metabolite interactions from an otherwise black-box model. These methods highlight that Actinobacteria, Firmicutes and Proteobacteria are significant drivers of metabolite production whereas Bacteroides shape community dynamics. We use the LSTM model to navigate a large multidimensional functional landscape to design communities with unique health-relevant metabolite profiles and temporal behaviors. In sum, the accuracy of the LSTM model can be exploited for experimental planning and to guide the design of synthetic microbiomes with target dynamic functions.
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Affiliation(s)
- Mayank Baranwal
- Department of Systems and Control Engineering, Indian Institute of TechnologyBombayIndia
- Division of Data & Decision Sciences, Tata Consultancy Services ResearchMumbaiIndia
| | - Ryan L Clark
- Department of Biochemistry, University of Wisconsin-MadisonMadisonUnited States
| | - Jaron Thompson
- Department of Chemical & Biological Engineering, University of Wisconsin-MadisonMadisonUnited States
| | - Zeyu Sun
- Department of Electrical Engineering & Computer Science, University of MichiganAnn ArborUnited States
| | - Alfred O Hero
- Department of Electrical Engineering & Computer Science, University of MichiganAnn ArborUnited States
- Department of Biomedical Engineering, University of MichiganAnn ArborUnited States
- Department of Statistics, University of MichiganAnn ArborUnited States
| | - Ophelia S Venturelli
- Department of Biochemistry, University of Wisconsin-MadisonMadisonUnited States
- Department of Chemical & Biological Engineering, University of Wisconsin-MadisonMadisonUnited States
- Department of Bacteriology, University of Wisconsin-MadisonMadisonUnited States
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22
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Fast growth can counteract antibiotic susceptibility in shaping microbial community resilience to antibiotics. Proc Natl Acad Sci U S A 2022; 119:e2116954119. [PMID: 35394868 PMCID: PMC9169654 DOI: 10.1073/pnas.2116954119] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
SignificanceAntibiotic exposure stands among the most used interventions to drive microbial communities away from undesired states. How the ecology of microbial communities shapes their recovery-e.g., posttreatment shifts toward Clostridioides difficile infections in the gut-after antibiotic exposure is poorly understood. We study community response to antibiotics using a model community that can reach two alternative states. Guided by theory, our experiments show that microbial growth following antibiotic exposure can counteract antibiotic susceptibility in driving transitions between alternative community states. This makes it possible to reverse the outcome of antibiotic exposure through modifying growth dynamics, including cooperative growth, of community members. Our research highlights the relevance of simple ecological models to better understand the long-term effects of antibiotic treatment.
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23
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Aguirre AM, Sorg JA. Gut associated metabolites and their roles in Clostridioides difficile pathogenesis. Gut Microbes 2022; 14:2094672. [PMID: 35793402 PMCID: PMC9450991 DOI: 10.1080/19490976.2022.2094672] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/21/2022] [Indexed: 02/04/2023] Open
Abstract
The nosocomial pathogen Clostridioides difficile is a burden to the healthcare system. Gut microbiome disruption, most commonly by broad-spectrum antibiotic treatment, is well established to generate a state that is susceptible to CDI. A variety of metabolites produced by the host and/or gut microbiota have been shown to interact with C. difficile. Certain bile acids promote/inhibit germination while other cholesterol-derived compounds and amino acids used in the Stickland metabolic pathway affect growth and CDI colonization. Short chain fatty acids maintain intestinal barrier integrity and a myriad of other metabolic compounds are used as nutritional sources or used by C. difficile to inhibit or outcompete other bacteria in the gut. As the move toward non-antibiotic CDI treatment takes place, a deeper understanding of interactions between C. difficile and the host's gut microbiome and metabolites becomes more relevant.
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Affiliation(s)
| | - Joseph A. Sorg
- Department of Biology, Texas A&M University, College Station, TX, USA
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