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Letourneau J, Neubert BC, Dayal D, Carrion VM, Durand HK, Dallow EP, Jiang S, Kirtley M, Ginsburg GS, Doraiswamy PM, David LA. Weight, habitual fibre intake, and microbiome composition predict tolerance to fructan supplementation. Int J Food Sci Nutr 2024; 75:571-581. [PMID: 38982571 DOI: 10.1080/09637486.2024.2372590] [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: 12/14/2023] [Accepted: 06/21/2024] [Indexed: 07/11/2024]
Abstract
Fructans are commonly used as dietary fibre supplements for their ability to promote the growth of beneficial gut microbes. However, fructan consumption has been associated with various dosage-dependent side effects. We characterised side effects in an exploratory analysis of a randomised trial in healthy adults (n = 40) who consumed 18 g/day inulin or placebo. We found that individuals weighing more or habitually consuming higher fibre exhibited the best tolerance. Furthermore, we identified associations between gut microbiome composition and host tolerance. Specifically, higher levels of Christensenellaceae R-7 group were associated with gastrointestinal discomfort, and a machine-learning-based approach successfully predicted high levels of flatulence, with [Ruminococcus] torques group and (Oscillospiraceae) UCG-002 sp. identified as key predictive taxa. These data reveal trends that can help guide personalised recommendations for initial inulin dosage. Our results support prior ecological findings indicating that fibre supplementation has the greatest impact on individuals whose baseline fibre intake is lowest.
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Affiliation(s)
- Jeffrey Letourneau
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA
| | - Benjamin C Neubert
- Program in Computational Biology and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Diana Dayal
- School of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | | | - Heather K Durand
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA
| | - Eric P Dallow
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA
| | - Sharon Jiang
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA
| | - Michelle Kirtley
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA
| | - Geoffrey S Ginsburg
- Duke Center for Applied Genomics and Precision Medicine, Duke University Health System, Durham, NC, USA
- Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - P Murali Doraiswamy
- Duke Center for Applied Genomics and Precision Medicine, Duke University Health System, Durham, NC, USA
- Department of Medicine, Duke University School of Medicine, Durham, NC, USA
- Department of Psychiatry, Duke University School of Medicine, Durham, NC, USA
- Duke Microbiome Center, Duke University School of Medicine, Durham, NC, USA
| | - Lawrence A David
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA
- Program in Computational Biology and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
- Duke Microbiome Center, Duke University School of Medicine, Durham, NC, USA
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2
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Baran AM, Patil AH, Aparicio-Puerta E, Halushka MK, McCall MN. miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.03.592274. [PMID: 39071300 PMCID: PMC11275874 DOI: 10.1101/2024.05.03.592274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
MicroRNA-seq data is produced by aligning small RNA sequencing reads of different miRNA transcript isoforms, called isomiRs, to known microRNAs. Aggregation to microRNA-level counts discards information and violates core assumptions of differential expression (DE) methods developed for mRNA-seq data. We establish miRglmm, a DE method for microRNA-seq data, that uses a generalized linear mixed model of isomiR-level counts, facilitating detection of miRNA with differential expression or differential isomiR usage. We demonstrate that miRglmm outperforms current DE methods in estimating DE for miRNA, whether or not there is significant isomiR variability, and simultaneously provides estimates of isomiR-level DE.
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Affiliation(s)
- Andrea M Baran
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, 265 Crittenden Blvd., Box 630, Rochester, NY 14642, USA
| | - Arun H Patil
- Lieber Institute for Brain Development, Johns Hopkins University, 855 North Wolfe St. Suite 300, Baltimore, MD 21205, USA
| | - Ernesto Aparicio-Puerta
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, 265 Crittenden Blvd., Box 630, Rochester, NY 14642, USA
| | - Marc K Halushka
- Department of Pathology, Cleveland Clinic, 9500 Euclid Ave., Cleveland, OH 44195, USA
| | - Matthew N McCall
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, 265 Crittenden Blvd., Box 630, Rochester, NY 14642, USA
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3
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Alessandri G, Rizzo SM, Mancabelli L, Fontana F, Longhi G, Turroni F, van Sinderen D, Ventura M. Impact of cryoprotective agents on human gut microbes and in vitro stabilized artificial gut microbiota communities. Microb Biotechnol 2024; 17:e14509. [PMID: 38878269 PMCID: PMC11179620 DOI: 10.1111/1751-7915.14509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 05/29/2024] [Accepted: 06/03/2024] [Indexed: 06/18/2024] Open
Abstract
The availability of microbial biobanks for the storage of individual gut microbiota members or their derived and artificially assembled consortia has become fundamental for in vitro investigation of the molecular mechanisms behind microbe-microbe and/or microbe-host interactions. However, to preserve bacterial viability, adequate storage and processing technologies are required. In this study, the effects on cell viability of seven different combinations of cryoprotective agents were evaluated by flow cytometry for 53 bacterial species representing key members of the human gut microbiota after one and 3 months of cryopreservation at -80°C. The obtained results highlighted that no universal cryoprotectant was identified capable of guaranteeing effective recovery of intact cells after cryopreservation for all tested bacteria. However, the presence of inulin or skimmed milk provided high levels of viability protection during cryoexposure. These results were further corroborated by cryopreserving 10 artificial gut microbiota produced through in vitro continuous fermentation system technology. Indeed, in this case, the inclusion of inulin or skimmed milk resulted in a high recovery of viable cells, while also allowing consistent and reliable preservation of the artificial gut microbiota biodiversity. Overall, these results suggest that, although the efficacy of various cryoprotective agents is species-specific, some cryoprotectants based on glycerol and the addition of inulin or skimmed milk are preferable to retain viability and biodiversity for both single bacterial species and artificial gut microbiota.
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Affiliation(s)
- Giulia Alessandri
- Laboratory of Probiogenomics, Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parma, Italy
| | - Sonia Mirjam Rizzo
- Laboratory of Probiogenomics, Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parma, Italy
| | - Leonardo Mancabelli
- Department of Medicine and Surgery, University of Parma, Parma, Italy
- Microbiome Research Hub, University of Parma, Parma, Italy
| | - Federico Fontana
- Laboratory of Probiogenomics, Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parma, Italy
| | - Giulia Longhi
- Laboratory of Probiogenomics, Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parma, Italy
| | - Francesca Turroni
- Laboratory of Probiogenomics, Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parma, Italy
- Microbiome Research Hub, University of Parma, Parma, Italy
| | - Douwe van Sinderen
- APC Microbiome Institute and School of Microbiology, Bioscience Institute, National University of Ireland, Cork, Ireland
| | - Marco Ventura
- Laboratory of Probiogenomics, Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parma, Italy
- Microbiome Research Hub, University of Parma, Parma, Italy
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4
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Young AM, Van Buren S, Rashid NU. Differential transcript usage analysis incorporating quantification uncertainty via compositional measurement error regression modeling. Biostatistics 2024; 25:559-576. [PMID: 37040757 PMCID: PMC11017126 DOI: 10.1093/biostatistics/kxad008] [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: 07/01/2021] [Revised: 12/22/2022] [Accepted: 02/06/2023] [Indexed: 04/13/2023] Open
Abstract
Differential transcript usage (DTU) occurs when the relative expression of multiple transcripts arising from the same gene changes between different conditions. Existing approaches to detect DTU often rely on computational procedures that can have speed and scalability issues as the number of samples increases. Here we propose a new method, CompDTU, that uses compositional regression to model the relative abundance proportions of each transcript that are of interest in DTU analyses. This procedure leverages fast matrix-based computations that make it ideally suited for DTU analysis with larger sample sizes. This method also allows for the testing of and adjustment for multiple categorical or continuous covariates. Additionally, many existing approaches for DTU ignore quantification uncertainty in the expression estimates for each transcript in RNA-seq data. We extend our CompDTU method to incorporate quantification uncertainty leveraging common output from RNA-seq expression quantification tool in a novel method CompDTUme. Through several power analyses, we show that CompDTU has excellent sensitivity and reduces false positive results relative to existing methods. Additionally, CompDTUme results in further improvements in performance over CompDTU with sufficient sample size for genes with high levels of quantification uncertainty, while also maintaining favorable speed and scalability. We motivate our methods using data from the Cancer Genome Atlas Breast Invasive Carcinoma data set, specifically using RNA-seq data from primary tumors for 740 patients with breast cancer. We show greatly reduced computation time from our new methods as well as the ability to detect several novel genes with significant DTU across different breast cancer subtypes.
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Affiliation(s)
- Amber M Young
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
| | - Scott Van Buren
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
| | - Naim U Rashid
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC, 27599, USA and Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, 450 West Drive, Chapel Hill, NC, 27599, USA
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5
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Srinivasan S, Jnana A, Murali TS. Modeling Microbial Community Networks: Methods and Tools for Studying Microbial Interactions. MICROBIAL ECOLOGY 2024; 87:56. [PMID: 38587642 PMCID: PMC11001700 DOI: 10.1007/s00248-024-02370-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Accepted: 03/28/2024] [Indexed: 04/09/2024]
Abstract
Microbial interactions function as a fundamental unit in complex ecosystems. By characterizing the type of interaction (positive, negative, neutral) occurring in these dynamic systems, one can begin to unravel the role played by the microbial species. Towards this, various methods have been developed to decipher the function of the microbial communities. The current review focuses on the various qualitative and quantitative methods that currently exist to study microbial interactions. Qualitative methods such as co-culturing experiments are visualized using microscopy-based techniques and are combined with data obtained from multi-omics technologies (metagenomics, metabolomics, metatranscriptomics). Quantitative methods include the construction of networks and network inference, computational models, and development of synthetic microbial consortia. These methods provide a valuable clue on various roles played by interacting partners, as well as possible solutions to overcome pathogenic microbes that can cause life-threatening infections in susceptible hosts. Studying the microbial interactions will further our understanding of complex less-studied ecosystems and enable design of effective frameworks for treatment of infectious diseases.
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Affiliation(s)
- Shanchana Srinivasan
- Department of Public Health Genomics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Apoorva Jnana
- Department of Public Health Genomics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Thokur Sreepathy Murali
- Department of Public Health Genomics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, 576104, India.
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6
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Letourneau J, Carrion VM, Zeng J, Jiang S, Osborne OW, Holmes ZC, Fox A, Epstein P, Tan CY, Kirtley M, Surana NK, David LA. Interplay between particle size and microbial ecology in the gut microbiome. THE ISME JOURNAL 2024; 18:wrae168. [PMID: 39214074 PMCID: PMC11406467 DOI: 10.1093/ismejo/wrae168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 07/30/2024] [Accepted: 08/28/2024] [Indexed: 09/04/2024]
Abstract
Physical particles can serve as critical abiotic factors that structure the ecology of microbial communities. For non-human vertebrate gut microbiomes, fecal particle size (FPS) has been known to be shaped by chewing efficiency and diet. However, little is known about what drives FPS in the human gut. Here, we analyzed FPS by laser diffraction across a total of 76 individuals and found FPS to be strongly individualized. Contrary to our initial hypothesis, a behavioral intervention with 41 volunteers designed to increase chewing efficiency did not impact FPS. Dietary patterns could also not be associated with FPS. Instead, we found evidence that human and mouse gut microbiomes shaped FPS. Fecal samples from germ-free and antibiotic-treated mice exhibited increased FPS relative to colonized mice. In humans, markers of longer transit time were correlated with smaller FPS. Gut microbiota diversity and composition were also associated with FPS. Finally, ex vivo culture experiments using human fecal microbiota from distinct donors showed that differences in microbiota community composition can drive variation in particle size. Together, our results support an ecological model in which the human gut microbiome plays a key role in reducing the size of food particles during digestion. This finding has important implications for our understanding of energy extraction and subsequent uptake in gastrointestinal tract. FPS may therefore be viewed as an informative functional readout, providing new insights into the metabolic state of the gut microbiome.
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Affiliation(s)
- Jeffrey Letourneau
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC 27710, United States
| | - Verónica M Carrion
- Duke Office of Clinical Research, Duke University School of Medicine, Durham, NC 27710, United States
| | - Jun Zeng
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC 27710, United States
| | - Sharon Jiang
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC 27710, United States
| | - Olivia W Osborne
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC 27710, United States
| | - Zachary C Holmes
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC 27710, United States
| | - Aiden Fox
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC 27710, United States
| | - Piper Epstein
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC 27710, United States
| | - Chin Yee Tan
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC 27710, United States
- Department of Pediatrics, Duke University School of Medicine, Durham, NC 27710, United States
| | - Michelle Kirtley
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC 27710, United States
| | - Neeraj K Surana
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC 27710, United States
- Department of Pediatrics, Duke University School of Medicine, Durham, NC 27710, United States
- Department of Integrative Immunobiology, Duke University School of Medicine, Durham, NC 27710, United States
- Duke Microbiome Center, Duke University School of Medicine, Durham, NC 27710, United States
| | - Lawrence A David
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC 27710, United States
- Duke Microbiome Center, Duke University School of Medicine, Durham, NC 27710, United States
- Program in Computational Biology and Bioinformatics, Duke University School of Medicine, Durham, NC 27710, United States
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7
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Lyu R, Qu Y, Divaris K, Wu D. Methodological Considerations in Longitudinal Analyses of Microbiome Data: A Comprehensive Review. Genes (Basel) 2023; 15:51. [PMID: 38254941 PMCID: PMC11154524 DOI: 10.3390/genes15010051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 12/22/2023] [Accepted: 12/26/2023] [Indexed: 01/24/2024] Open
Abstract
Biological processes underlying health and disease are inherently dynamic and are best understood when characterized in a time-informed manner. In this comprehensive review, we discuss challenges inherent in time-series microbiome data analyses and compare available approaches and methods to overcome them. Appropriate handling of longitudinal microbiome data can shed light on important roles, functions, patterns, and potential interactions between large numbers of microbial taxa or genes in the context of health, disease, or interventions. We present a comprehensive review and comparison of existing microbiome time-series analysis methods, for both preprocessing and downstream analyses, including differential analysis, clustering, network inference, and trait classification. We posit that the careful selection and appropriate utilization of computational tools for longitudinal microbiome analyses can help advance our understanding of the dynamic host-microbiome relationships that underlie health-maintaining homeostases, progressions to disease-promoting dysbioses, as well as phases of physiologic development like those encountered in childhood.
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Affiliation(s)
- Ruiqi Lyu
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA;
| | - Yixiang Qu
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;
| | - Kimon Divaris
- Division of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Di Wu
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;
- Division of Oral and Craniofacial Health Sciences, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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8
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Knez M, Stangoulis JCR. Dietary Zn deficiency, the current situation and potential solutions. Nutr Res Rev 2023; 36:199-215. [PMID: 37062532 DOI: 10.1017/s0954422421000342] [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: 11/07/2022]
Abstract
Zinc (Zn) deficiency is a worldwide problem, and this review presents an overview of the magnitude of Zn deficiency with a particular emphasis on present global challenges, current recommendations for Zn intake, and factors that affect dietary requirements. The challenges of monitoring Zn status are clarified together with the discussion of relevant Zn bioaccessibility and bioavailability issues. Modern lifestyle factors that may exacerbate Zn deficiency and new strategies of reducing its effects are presented. Biofortification, as a potentially useful strategy for improving Zn status in sensitive populations, is discussed. The review proposes potential actions that could deliver promising results both in terms of monitoring dietary and physiological Zn status as well as in alleviating dietary Zn deficiency in affected populations.
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Affiliation(s)
- Marija Knez
- College of Science and Engineering, Flinders University, GPO Box 2100, AdelaideSA5001, Australia
- Center of Research Excellence in Nutrition and Metabolism, University of Belgrade, Institute for Medical Research, National Institute of the Republic of Serbia, 11000Belgrade, Serbia
| | - James C R Stangoulis
- College of Science and Engineering, Flinders University, GPO Box 2100, AdelaideSA5001, Australia
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9
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Čeprnja M, Hadžić E, Oros D, Melvan E, Starcevic A, Zucko J. Current Viewpoint on Female Urogenital Microbiome-The Cause or the Consequence? Microorganisms 2023; 11:1207. [PMID: 37317181 PMCID: PMC10224287 DOI: 10.3390/microorganisms11051207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/01/2023] [Accepted: 05/03/2023] [Indexed: 06/16/2023] Open
Abstract
An increasing amount of evidence implies that native microbiota is a constituent part of a healthy urinary tract (UT), making it an ecosystem on its own. What is still not clear is whether the origin of the urinary microbial community is the indirect consequence of the more abundant gut microbiota or a more distinct separation exists between these two systems. Another area of uncertainty is the existence of a link between the shifts in UT microbial composition and both the onset and persistence of cystitis symptoms. Cystitis is one of the most common reasons for antimicrobial drugs prescriptions in primary and secondary care and an important contributor to the problem of antimicrobial resistance. Despite this fact, we still have trouble distinguishing whether the primary cause of the majority of cystitis cases is a single pathogen overgrowth or a systemic disorder affecting the entire urinary microbiota. There is an increasing trend in studies monitoring changes and dynamics of UT microbiota, but this field of research is still in its infancy. Using NGS and bioinformatics, it is possible to obtain microbiota taxonomic profiles directly from urine samples, which can provide a window into microbial diversity (or the lack of) underlying each patient's cystitis symptoms. However, while microbiota refers to the living collection of microorganisms, an interchangeably used term microbiome referring to the genetic material of the microbiota is more often used in conjunction with sequencing data. It is this vast amount of sequences, which are truly "Big Data", that allow us to create models that describe interactions between different species contributing to an UT ecosystem, when coupled with machine-learning techniques. Although in a simplified predator-prey form these multi-species interaction models have the potential to further validate or disprove current beliefs; whether it is the presence or the absence of particular key players in a UT microbial ecosystem, the exact cause or consequence of the otherwise unknown etiology in the majority of cystitis cases. These insights might prove to be vital in our ongoing struggle against pathogen resistance and offer us new and promising clinical markers.
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Affiliation(s)
- Marina Čeprnja
- Biochemical Laboratory, Special Hospital Agram, Polyclinic Zagreb, 10000 Zagreb, Croatia
| | - Edin Hadžić
- Department of Biochemical Engineering, Faculty of Food Technology and Biotechnology, Zagreb University, 10000 Zagreb, Croatia
| | - Damir Oros
- Department of Biochemical Engineering, Faculty of Food Technology and Biotechnology, Zagreb University, 10000 Zagreb, Croatia
| | - Ena Melvan
- Department of Biological Science, Faculty of Science, Macquarie University, Sydney, NSW 2109, Australia
| | - Antonio Starcevic
- Department of Biochemical Engineering, Faculty of Food Technology and Biotechnology, Zagreb University, 10000 Zagreb, Croatia
| | - Jurica Zucko
- Department of Biochemical Engineering, Faculty of Food Technology and Biotechnology, Zagreb University, 10000 Zagreb, Croatia
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10
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García Mendez D, Sanabria J, Wist J, Holmes E. Effect of Operational Parameters on the Cultivation of the Gut Microbiome in Continuous Bioreactors Inoculated with Feces: A Systematic Review. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:6213-6225. [PMID: 37070710 PMCID: PMC10143624 DOI: 10.1021/acs.jafc.2c08146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 01/27/2023] [Accepted: 01/27/2023] [Indexed: 05/03/2023]
Abstract
Since the early 1980s, multiple researchers have contributed to the development of in vitro models of the human gastrointestinal system for the mechanistic interrogation of the gut microbiome ecology. Using a bioreactor for simulating all the features and conditions of the gastrointestinal system is a massive challenge. Some conditions, such as temperature and pH, are readily controlled, but a more challenging feature to simulate is that both may vary in different regions of the gastrointestinal tract. Promising solutions have been developed for simulating other functionalities, such as dialysis capabilities, peristaltic movements, and biofilm growth. This research field is under constant development, and further efforts are needed to drive these models closer to in vivo conditions, thereby increasing their usefulness for studying the gut microbiome impact on human health. Therefore, understanding the influence of key operational parameters is fundamental for the refinement of the current bioreactors and for guiding the development of more complex models. In this review, we performed a systematic search for operational parameters in 229 papers that used continuous bioreactors seeded with human feces. Despite the reporting of operational parameters for the various bioreactor models being variable, as a result of a lack of standardization, the impact of specific operational parameters on gut microbial ecology is discussed, highlighting the advantages and limitations of the current bioreactor systems.
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Affiliation(s)
- David
Felipe García Mendez
- Australian
National Phenome Centre and Computational and Systems Medicine, Health
Futures Institute, Murdoch University, Harry Perkins Building, Perth, Australia WA6150
| | - Janeth Sanabria
- Australian
National Phenome Centre and Computational and Systems Medicine, Health
Futures Institute, Murdoch University, Harry Perkins Building, Perth, Australia WA6150
- Environmental
Microbiology and Biotechnology Laboratory, Engineering School of Environmental
& Natural Resources, Engineering Faculty, Universidad del Valle—Sede Meléndez, Cali, Colombia 76001
| | - Julien Wist
- Australian
National Phenome Centre and Computational and Systems Medicine, Health
Futures Institute, Murdoch University, Harry Perkins Building, Perth, Australia WA6150
- Chemistry
Department, Universidad del Valle, 76001, Cali, Colombia
| | - Elaine Holmes
- Australian
National Phenome Centre and Computational and Systems Medicine, Health
Futures Institute, Murdoch University, Harry Perkins Building, Perth, Australia WA6150
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11
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Calle ML, Pujolassos M, Susin A. coda4microbiome: compositional data analysis for microbiome cross-sectional and longitudinal studies. BMC Bioinformatics 2023; 24:82. [PMID: 36879227 PMCID: PMC9990256 DOI: 10.1186/s12859-023-05205-3] [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: 07/21/2022] [Accepted: 02/22/2023] [Indexed: 03/08/2023] Open
Abstract
BACKGROUND One of the main challenges of microbiome analysis is its compositional nature that if ignored can lead to spurious results. Addressing the compositional structure of microbiome data is particularly critical in longitudinal studies where abundances measured at different times can correspond to different sub-compositions. RESULTS We developed coda4microbiome, a new R package for analyzing microbiome data within the Compositional Data Analysis (CoDA) framework in both, cross-sectional and longitudinal studies. The aim of coda4microbiome is prediction, more specifically, the method is designed to identify a model (microbial signature) containing the minimum number of features with the maximum predictive power. The algorithm relies on the analysis of log-ratios between pairs of components and variable selection is addressed through penalized regression on the "all-pairs log-ratio model", the model containing all possible pairwise log-ratios. For longitudinal data, the algorithm infers dynamic microbial signatures by performing penalized regression over the summary of the log-ratio trajectories (the area under these trajectories). In both, cross-sectional and longitudinal studies, the inferred microbial signature is expressed as the (weighted) balance between two groups of taxa, those that contribute positively to the microbial signature and those that contribute negatively. The package provides several graphical representations that facilitate the interpretation of the analysis and the identified microbial signatures. We illustrate the new method with data from a Crohn's disease study (cross-sectional data) and on the developing microbiome of infants (longitudinal data). CONCLUSIONS coda4microbiome is a new algorithm for identification of microbial signatures in both, cross-sectional and longitudinal studies. The algorithm is implemented as an R package that is available at CRAN ( https://cran.r-project.org/web/packages/coda4microbiome/ ) and is accompanied with a vignette with a detailed description of the functions. The website of the project contains several tutorials: https://malucalle.github.io/coda4microbiome/.
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Affiliation(s)
- M. Luz Calle
- Biosciences Department, Faculty of Sciences, Technology and Engineering, University of Vic - Central University of Catalonia, Carrer de La Laura, 13, 08500 Vic, Spain
| | - Meritxell Pujolassos
- Biosciences Department, Faculty of Sciences, Technology and Engineering, University of Vic - Central University of Catalonia, Carrer de La Laura, 13, 08500 Vic, Spain
| | - Antoni Susin
- Mathematical Department, UPC-Barcelona Tech, Barcelona, Spain
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12
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Sun Z, Lee-Sarwar K, Kelly RS, Lasky-Su JA, Litonjua AA, Weiss ST, Liu YY. Revealing the importance of prenatal gut microbiome in offspring neurodevelopment in humans. EBioMedicine 2023; 90:104491. [PMID: 36868051 PMCID: PMC9996363 DOI: 10.1016/j.ebiom.2023.104491] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 02/01/2023] [Accepted: 02/09/2023] [Indexed: 03/05/2023] Open
Abstract
BACKGROUND It has been widely recognized that a critical time window for neurodevelopment occurs in early life and the host's gut microbiome plays an important role in neurodevelopment. Following recent demonstrations that the maternal prenatal gut microbiome influences offspring brain development in murine models, we aim to explore whether the critical time window for the association between the gut microbiome and neurodevelopment is prenatal or postnatal for human. METHODS Here we leverage a large-scale human study and compare the associations between the gut microbiota and metabolites from mothers during pregnancy and their children with the children's neurodevelopment. Specifically, using multinomial regression integrated in Songbird, we assessed the discriminating power of the maternal prenatal and child gut microbiome for children's neurodevelopment at early life as measured by the Ages & Stages Questionnaires (ASQ). FINDINGS We show that the maternal prenatal gut microbiome is more relevant than the children's gut microbiome to the children's neurodevelopment in the first year of life (maximum Q2 = 0.212 and 0.096 separately using the taxa at the class level). Moreover, we found that Fusobacteriia is more associated with high fine motor skills in ASQ in the maternal prenatal gut microbiota but become more associated with low fine motor skills in the infant gut microbiota (rank = 0.084 and -0.047 separately), suggesting the roles of the same taxa with respect to neurodevelopment can be opposite at the two stages of fetal neurodevelopment. INTERPRETATION These findings shed light, especially in terms of timing, on potential therapeutic interventions to prevent neurodevelopmental disorders. FUNDING This work was supported by the National Institutes of Health (grant numbers: R01AI141529, R01HD093761, RF1AG067744, UH3OD023268, U19AI095219, U01HL089856, R01HL141826, K08HL148178, K01HL146980), and the Charles A. King Trust Postdoctoral Fellowship.
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Affiliation(s)
- Zheng Sun
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Kathleen Lee-Sarwar
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Rachel S Kelly
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Jessica A Lasky-Su
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Augusto A Litonjua
- Division of Pediatric Pulmonary Medicine, Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, 14642, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA; Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute of Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, 61801, USA.
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13
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Rahman G, Morton JT, Martino C, Sepich-Poore GD, Allaband C, Guccione C, Chen Y, Hakim D, Estaki M, Knight R. BIRDMAn: A Bayesian differential abundance framework that enables robust inference of host-microbe associations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.30.526328. [PMID: 36778470 PMCID: PMC9915500 DOI: 10.1101/2023.01.30.526328] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Quantifying the differential abundance (DA) of specific taxa among experimental groups in microbiome studies is challenging due to data characteristics (e.g., compositionality, sparsity) and specific study designs (e.g., repeated measures, meta-analysis, cross-over). Here we present BIRDMAn (Bayesian Inferential Regression for Differential Microbiome Analysis), a flexible DA method that can account for microbiome data characteristics and diverse experimental designs. Simulations show that BIRDMAn models are robust to uneven sequencing depth and provide a >20-fold improvement in statistical power over existing methods. We then use BIRDMAn to identify antibiotic-mediated perturbations undetected by other DA methods due to subject-level heterogeneity. Finally, we demonstrate how BIRDMAn can construct state-of-the-art cancer-type classifiers using The Cancer Genome Atlas (TCGA) dataset, with substantial accuracy improvements over random forests and existing DA tools across multiple sequencing centers. Collectively, BIRDMAn extracts more informative biological signals while accounting for study-specific experimental conditions than existing approaches.
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Affiliation(s)
- Gibraan Rahman
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | - James T Morton
- Biostatistics & Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA
| | - Cameron Martino
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | | | - Celeste Allaband
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Caitlin Guccione
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA
| | - Yang Chen
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Department of Dermatology, University of California San Diego, La Jolla, CA, USA
- Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA
| | - Daniel Hakim
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | - Mehrbod Estaki
- Department of Physiology & Pharmacology, University of Calgary, Calgary, Canada
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California, USA
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14
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Park DJ, Plantinga AM. Impact of Data and Study Characteristics on Microbiome Volatility Estimates. Genes (Basel) 2023; 14:genes14010218. [PMID: 36672959 PMCID: PMC9859452 DOI: 10.3390/genes14010218] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 01/10/2023] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
The human microbiome is a dynamic community of bacteria, viruses, fungi, and other microorganisms. Both the composition of the microbiome (the microbes that are present and their relative abundances) and the temporal variability of the microbiome (the magnitude of changes in their composition across time, called volatility) has been associated with human health. However, the effect of unbalanced sampling intervals and differential read depth on the estimates of microbiome volatility has not been thoroughly assessed. Using four publicly available gut and vaginal microbiome time series, we subsampled the datasets to several sampling intervals and read depths and then compared additive, multiplicative, centered log ratio (CLR)-based, qualitative, and distance-based measures of microbiome volatility between the conditions. We find that longer sampling intervals are associated with larger quantitative measures of change (particularly for common taxa), but not with qualitative measures of change or distance-based volatility quantification. A lower sequencing read depth is associated with smaller multiplicative, CLR-based, and qualitative measures of change (particularly for less common taxa). Strategic subsampling may serve as a useful sensitivity analysis in unbalanced longitudinal studies investigating clinical associations with microbiome volatility.
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Affiliation(s)
| | - Anna M. Plantinga
- Department of Mathematics and Statistics, Williams College, Williamstown, MA 01267, USA
- Correspondence:
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15
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van de Velde C, Joseph C, Simoens K, Raes J, Bernaerts K, Faust K. Technical versus biological variability in a synthetic human gut community. Gut Microbes 2023; 15:2155019. [PMID: 36580382 PMCID: PMC9809966 DOI: 10.1080/19490976.2022.2155019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Synthetic communities grown in well-controlled conditions are an important tool to decipher the mechanisms driving community dynamics. However, replicate time series of synthetic human gut communities in chemostats are rare, and it is thus still an open question to what extent stochasticity impacts gut community dynamics. Here, we address this question with a synthetic human gut bacterial community using an automated fermentation system that allows for a larger number of biological replicates. We collected six biological replicates for a community initially consisting of five common gut bacterial species that fill different metabolic niches. After an initial 12 hours in batch mode, we switched to chemostat mode and observed the community to stabilize after 2-3 days. Community profiling with 16S rRNA resulted in high variability across replicate vessels and high technical variability, while the variability across replicates was significantly lower for flow cytometric data. Both techniques agree on the decrease in the abundance of Bacteroides thetaiotaomicron, accompanied by an initial increase in Blautia hydrogenotrophica. These changes occurred together with reproducible metabolic shifts, namely a fast depletion of glucose and trehalose concentration in batch followed by a decrease in formic acid and pyruvic acid concentrations within the first 12 hours after the switch to chemostat mode. In conclusion, the observed variability in the synthetic bacterial human gut community, as assessed with 16S rRNA gene sequencing, is largely due to technical variability. The low variability seen in HPLC and flow cytometry data suggests a highly deterministic system.
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Affiliation(s)
- Charlotte van de Velde
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, LeuvenB-3000, Belgium
| | - Clémence Joseph
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, LeuvenB-3000, Belgium
| | - Kenneth Simoens
- KU Leuven, Department of Chemical Engineering, Chemical and Biochemical Reactor Engineering and Safety (CREaS), LeuvenB-3001, Belgium
| | - Jeroen Raes
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, LeuvenB-3000, Belgium,Center for Microbiology, VIB-KU Leuven, Leuven, Belgium
| | - Kristel Bernaerts
- KU Leuven, Department of Chemical Engineering, Chemical and Biochemical Reactor Engineering and Safety (CREaS), LeuvenB-3001, Belgium
| | - Karoline Faust
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, LeuvenB-3000, Belgium,CONTACT Karoline Faust Rega institute, Herestraat 49, Leuven3000, Belgium
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16
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Letourneau J, Holmes ZC, Dallow EP, Durand HK, Jiang S, Carrion VM, Gupta SK, Mincey AC, Muehlbauer MJ, Bain JR, David LA. Ecological memory of prior nutrient exposure in the human gut microbiome. THE ISME JOURNAL 2022; 16:2479-2490. [PMID: 35871250 PMCID: PMC9563064 DOI: 10.1038/s41396-022-01292-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 07/05/2022] [Accepted: 07/07/2022] [Indexed: 04/20/2023]
Abstract
Many ecosystems have been shown to retain a memory of past conditions, which in turn affects how they respond to future stimuli. In microbial ecosystems, community disturbance has been associated with lasting impacts on microbiome structure. However, whether microbial communities alter their response to repeated stimulus remains incompletely understood. Using the human gut microbiome as a model, we show that bacterial communities retain an "ecological memory" of past carbohydrate exposures. Memory of the prebiotic inulin was encoded within a day of supplementation among a cohort of human study participants. Using in vitro gut microbial models, we demonstrated that the strength of ecological memory scales with nutrient dose and persists for days. We found evidence that memory is seeded by transcriptional changes among primary degraders of inulin within hours of nutrient exposure, and that subsequent changes in the activity and abundance of these taxa are sufficient to enhance overall community nutrient metabolism. We also observed that ecological memory of one carbohydrate species impacts microbiome response to other carbohydrates, and that an individual's habitual exposure to dietary fiber was associated with their gut microbiome's efficiency at digesting inulin. Together, these findings suggest that the human gut microbiome's metabolic potential reflects dietary exposures over preceding days and changes within hours of exposure to a novel nutrient. The dynamics of this ecological memory also highlight the potential for intra-individual microbiome variation to affect the design and interpretation of interventions involving the gut microbiome.
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Affiliation(s)
- Jeffrey Letourneau
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA
| | - Zachary C Holmes
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA
| | - Eric P Dallow
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA
| | - Heather K Durand
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA
| | - Sharon Jiang
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA
| | - Verónica M Carrion
- Duke Office of Clinical Research, Duke University School of Medicine, Durham, NC, USA
| | - Savita K Gupta
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA
| | - Adam C Mincey
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
| | - Michael J Muehlbauer
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
| | - James R Bain
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
- Department of Medicine (Endocrinology), Duke University School of Medicine, Durham, NC, USA
| | - Lawrence A David
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA.
- Program in Computational Biology and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
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17
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Zhang T, Li D, Zhu X, Zhang M, Guo J, Chen J. Nano-Al 2O 3 particles affect gut microbiome and resistome in an in vitro simulator of the human colon microbial ecosystem. JOURNAL OF HAZARDOUS MATERIALS 2022; 439:129513. [PMID: 35870212 DOI: 10.1016/j.jhazmat.2022.129513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 06/12/2022] [Accepted: 06/29/2022] [Indexed: 06/15/2023]
Abstract
Nano-Al2O3 has been widely used in various consumer products and water treatment processes because of its unique physicochemical properties. The probability of human exposure to nano-Al2O3 increases significantly, of which oral ingestion is an important route. However, effects and underlying mechanisms of nano-Al2O3 on gut microbiota and resistome are still not well delineated. Here, we systematically investigated the effects of nano-Al2O3 on the human gut microbiome by an in vitro simulator of human colon microbial ecosystem. Results indicated that nano-Al2O3 interfered with the gut microbiota, and significantly suppressed the short-chain fatty acids metabolism, which might pose adverse effects on the host. More seriously, high level of nano-Al2O3 (50 mg/L) was more destructive to the gut flora, though the damage might be temporary. In addition, sub-inhibitory low-dose of nano-Al2O3 (0.1 mg/L) significantly enhanced the abundance of antibiotic resistance genes (ARGs) after 7-day exposure. This is attributed to that low concentration of nano-Al2O3 can promote horizontal transfer of ARGs by increasing cell membrane permeability and relative abundance of transposase (e.g. tnpA, IS613, and Tp614). Our findings confirmed the adverse effects of nano-Al2O3 on the human gut resistome and emphasized the necessity to assess potential risks of nanomaterials on the human gut health.
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Affiliation(s)
- Tingting Zhang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan Tyndall Centre, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China; Academy of Environmental Planning & Design, Co., Ltd. Nanjing University, Nanjing 210093, China
| | - Dan Li
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan Tyndall Centre, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China.
| | - Xuan Zhu
- School of Food Science and Bioengineering, Zhejiang Gongshang University, Hangzhou 310018, China
| | - Minglu Zhang
- State Environmental Protection Key Laboratory of Food Chain Pollution Control,Beijing Technology and Business University, Beijing 100048, China
| | - Jianhua Guo
- Australian Centre for Water and Environmental Biotechnology (ACWEB, formerly AWMC), The University of Queensland, St. Lucia, Queensland 4072, Australia
| | - Jianmin Chen
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan Tyndall Centre, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
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18
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Dynamic metabolic interactions and trophic roles of human gut microbes identified using a minimal microbiome exhibiting ecological properties. THE ISME JOURNAL 2022; 16:2144-2159. [PMID: 35717467 PMCID: PMC9381525 DOI: 10.1038/s41396-022-01255-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 04/30/2022] [Accepted: 05/25/2022] [Indexed: 11/08/2022]
Abstract
AbstractMicrobe–microbe interactions in the human gut are influenced by host-derived glycans and diet. The high complexity of the gut microbiome poses a major challenge for unraveling the metabolic interactions and trophic roles of key microbes. Synthetic minimal microbiomes provide a pragmatic approach to investigate their ecology including metabolic interactions. Here, we rationally designed a synthetic microbiome termed Mucin and Diet based Minimal Microbiome (MDb-MM) by taking into account known physiological features of 16 key bacteria. We combined 16S rRNA gene-based composition analysis, metabolite measurements and metatranscriptomics to investigate community dynamics, stability, inter-species metabolic interactions and their trophic roles. The 16 species co-existed in the in vitro gut ecosystems containing a mixture of complex substrates representing dietary fibers and mucin. The triplicate MDb-MM’s followed the Taylor’s power law and exhibited strikingly similar ecological and metabolic patterns. The MDb-MM exhibited resistance and resilience to temporal perturbations as evidenced by the abundance and metabolic end products. Microbe-specific temporal dynamics in transcriptional niche overlap and trophic interaction network explained the observed co-existence in a competitive minimal microbiome. Overall, the present study provides crucial insights into the co-existence, metabolic niches and trophic roles of key intestinal microbes in a highly dynamic and competitive in vitro ecosystem.
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19
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Holmes ZC, Villa MM, Durand HK, Jiang S, Dallow EP, Petrone BL, Silverman JD, Lin PH, David LA. Microbiota responses to different prebiotics are conserved within individuals and associated with habitual fiber intake. MICROBIOME 2022; 10:114. [PMID: 35902900 PMCID: PMC9336045 DOI: 10.1186/s40168-022-01307-x] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 06/15/2022] [Indexed: 05/12/2023]
Abstract
BACKGROUND Short-chain fatty acids (SCFAs) derived from gut bacteria are associated with protective roles in diseases ranging from obesity to colorectal cancers. Intake of microbially accessible dietary fibers (prebiotics) lead to varying effects on SCFA production in human studies, and gut microbial responses to nutritional interventions vary by individual. It is therefore possible that prebiotic therapies will require customizing to individuals. RESULTS Here, we explored prebiotic personalization by conducting a three-way crossover study of three prebiotic treatments in healthy adults. We found that within individuals, metabolic responses were correlated across the three prebiotics. Individual identity, rather than prebiotic choice, was also the major determinant of SCFA response. Across individuals, prebiotic response was inversely related to basal fecal SCFA concentration, which, in turn, was associated with habitual fiber intake. Experimental measures of gut microbial SCFA production for each participant also negatively correlated with fiber consumption, supporting a model in which individuals' gut microbiota are limited in their overall capacity to produce fecal SCFAs from fiber. CONCLUSIONS Our findings support developing personalized prebiotic regimens that focus on selecting individuals who stand to benefit, and that such individuals are likely to be deficient in fiber intake. Video Abstract.
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Affiliation(s)
- Zachary C. Holmes
- Department of Molecular Genetics and Microbiology, Duke University, 3 Genome Court, Durham, NC 27705 USA
| | - Max M. Villa
- Department of Molecular Genetics and Microbiology, Duke University, 3 Genome Court, Durham, NC 27705 USA
- Center for Genomic and Computational Biology, Duke University, 3 Genome Court, Durham, NC 27705 USA
| | - Heather K. Durand
- Department of Molecular Genetics and Microbiology, Duke University, 3 Genome Court, Durham, NC 27705 USA
- Center for Genomic and Computational Biology, Duke University, 3 Genome Court, Durham, NC 27705 USA
| | - Sharon Jiang
- Department of Molecular Genetics and Microbiology, Duke University, 3 Genome Court, Durham, NC 27705 USA
- Center for Genomic and Computational Biology, Duke University, 3 Genome Court, Durham, NC 27705 USA
| | - Eric P. Dallow
- Department of Molecular Genetics and Microbiology, Duke University, 3 Genome Court, Durham, NC 27705 USA
- Center for Genomic and Computational Biology, Duke University, 3 Genome Court, Durham, NC 27705 USA
| | - Brianna L. Petrone
- Department of Molecular Genetics and Microbiology, Duke University, 3 Genome Court, Durham, NC 27705 USA
- Medical Scientist Training Program, Duke University, 3 Genome Court, Durham, NC 27705 USA
| | - Justin D. Silverman
- College of Information Science and Technology, Penn State University, Westgate Bldg, University Park, PA 16802 USA
- Department of Medicine, Penn State University, Hershey, Westgate Bldg, University Park, PA 16802 USA
- Institute for Computational and Data Science, Penn State University, Westgate Bldg, University Park, PA 16802 USA
| | - Pao-Hwa Lin
- Duke Molecular Physiology Institute, Duke University, Stedman Nutrition Ctr, 3475 Erwin Rd, Durham, NC 27705 USA
- Department of Medicine, Duke University Medical Center, Stedman Nutrition Ctr, 3475 Erwin Rd, Durham, NC 27705 USA
| | - Lawrence A. David
- Department of Molecular Genetics and Microbiology, Duke University, 3 Genome Court, Durham, NC 27705 USA
- Center for Genomic and Computational Biology, Duke University, 3 Genome Court, Durham, NC 27705 USA
- Program in Computational Biology and Bioinformatics, Duke University, 3 Genome Court, Durham, NC 27705 USA
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20
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Mouse Subcutaneous BCG Vaccination and Mycobacterium tuberculosis Infection Alter the Lung and Gut Microbiota. Microbiol Spectr 2022; 10:e0169321. [PMID: 35652642 PMCID: PMC9241886 DOI: 10.1128/spectrum.01693-21] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
The objective of this study was to characterize the effect of Bacillus Calmette-Guérin (BCG) vaccination and M. tuberculosis infection on gut and lung microbiota of C57BL/6 mice, a well-characterized mouse model of tuberculosis. BCG vaccination and infection with M. tuberculosis altered the relative abundance of Firmicutes and Bacteroidetes phyla in the lung compared with control group. Vaccination and infection changed the alpha- and beta-diversity in both the gut and the lung. However, lung diversity was the most affected organ after BCG vaccination and M. tuberculosis infection. Focusing on the gut-lung axis, a multivariate regression approach was used to compare profile evolution of gut and lung microbiota. More genera have modified relative abundances associated with BCG vaccination status at gut level compared with lung. Conversely, genera with modified relative abundances associated with M. tuberculosis infection were numerous at lung level. These results indicated that the host local response against infection impacted the whole microbial flora, while the immune response after vaccination modified mainly the gut microbiota. This study showed that a subcutaneous vaccination with a live attenuated microorganism induced both gut and lung dysbiosis that may play a key role in the immunopathogenesis of tuberculosis. IMPORTANCE The microbial communities in gut and lung are important players that may modulate the immunity against tuberculosis or other infections as well as impact the vaccine efficacy. We discovered that vaccination through the subcutaneous route affect the composition of gut and lung bacteria, and this might influence susceptibility and defense mechanisms against tuberculosis. Through these studies, we can identify microbial communities that can be manipulated to improve vaccine response and develop treatment adjuvants.
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21
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Using Community Ecology Theory and Computational Microbiome Methods To Study Human Milk as a Biological System. mSystems 2022; 7:e0113221. [PMID: 35103486 PMCID: PMC8805635 DOI: 10.1128/msystems.01132-21] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Human milk is a complex and dynamic biological system that has evolved to optimally nourish and protect human infants. Yet, according to a recent priority-setting review, “our current understanding of human milk composition and its individual components and their functions fails to fully recognize the importance of the chronobiology and systems biology of human milk in the context of milk synthesis, optimal timing and duration of feeding, and period of lactation” (P. Christian et al., Am J Clin Nutr 113:1063–1072, 2021, https://doi.org/10.1093/ajcn/nqab075). We attribute this critical knowledge gap to three major reasons as follows. (i) Studies have typically examined each subsystem of the mother-milk-infant “triad” in isolation and often focus on a single element or component (e.g., maternal lactation physiology or milk microbiome or milk oligosaccharides or infant microbiome or infant gut physiology). This undermines our ability to develop comprehensive representations of the interactions between these elements and study their response to external perturbations. (ii) Multiomics studies are often cross-sectional, presenting a snapshot of milk composition, largely ignoring the temporal variability during lactation. The lack of temporal resolution precludes the characterization and inference of robust interactions between the dynamic subsystems of the triad. (iii) We lack computational methods to represent and decipher the complex ecosystem of the mother-milk-infant triad and its environment. In this review, we advocate for longitudinal multiomics data collection and demonstrate how incorporating knowledge gleaned from microbial community ecology and computational methods developed for microbiome research can serve as an anchor to advance the study of human milk and its many components as a “system within a system.”
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22
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Saa P, Urrutia A, Silva-Andrade C, Martín AJ, Garrido D. Modeling approaches for probing cross-feeding interactions in the human gut microbiome. Comput Struct Biotechnol J 2021; 20:79-89. [PMID: 34976313 PMCID: PMC8685919 DOI: 10.1016/j.csbj.2021.12.006] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 12/03/2021] [Accepted: 12/04/2021] [Indexed: 12/16/2022] Open
Abstract
Microbial communities perform emergent activities that are essentially different from those carried by their individual members. The gut microbiome and its metabolites have a significant impact on the host, contributing to homeostasis or disease. Food molecules shape this community, being fermented through cross-feeding interactions of metabolites such as lactate, acetate, and amino acids, or products derived from macromolecule degradation. Mathematical and experimental approaches have been applied to understand and predict the interactions between microorganisms in complex communities such as the gut microbiota. Rational and mechanistic understanding of microbial interactions is essential to exploit their metabolic activities and identify keystone taxa and metabolites. The latter could be used in turn to modulate or replicate the metabolic behavior of the community in different contexts. This review aims to highlight recent experimental and modeling approaches for studying cross-feeding interactions within the gut microbiome. We focus on short-chain fatty acid production and fiber fermentation, which are fundamental processes in human health and disease. Special attention is paid to modeling approaches, particularly kinetic and genome-scale stoichiometric models of metabolism, to integrate experimental data under different diet and health conditions. Finally, we discuss limitations and challenges for the broad application of these modeling approaches and their experimental verification for improving our understanding of the mechanisms of microbial interactions.
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Affiliation(s)
- Pedro Saa
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Mathematical and Computational Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna, 4860 Santiago, Chile
| | - Arles Urrutia
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Claudia Silva-Andrade
- Laboratorio de Biología de Redes, Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, Chile
| | - Alberto J. Martín
- Laboratorio de Biología de Redes, Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Santiago, Chile
| | - Daniel Garrido
- Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
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23
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Li C, Av-Shalom TV, Tan JWG, Kwah JS, Chng KR, Nagarajan N. BEEM-Static: Accurate inference of ecological interactions from cross-sectional microbiome data. PLoS Comput Biol 2021; 17:e1009343. [PMID: 34495960 PMCID: PMC8452072 DOI: 10.1371/journal.pcbi.1009343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 09/20/2021] [Accepted: 08/11/2021] [Indexed: 11/19/2022] Open
Abstract
The structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized. With rapid adoption of next-generation sequencing for studying microbiomes, data-driven inference of microbial interactions based on abundance correlations is widely used, but with the drawback that ecological interpretations may not be possible. Leveraging cross-sectional microbiome datasets for unravelling ecological structure in a scalable manner thus remains an open problem. We present an expectation-maximization algorithm (BEEM-Static) that can be applied to cross-sectional datasets to infer interaction networks based on an ecological model (generalized Lotka-Volterra). The method exhibits robustness to violations in model assumptions by using statistical filters to identify and remove corresponding samples. Benchmarking against 10 state-of-the-art correlation based methods showed that BEEM-Static can infer presence and directionality of ecological interactions even with relative abundance data (AUC-ROC>0.85), a task that other methods struggle with (AUC-ROC<0.63). In addition, BEEM-Static can tolerate a high fraction of samples (up to 40%) being not at steady state or coming from an alternate model. Applying BEEM-Static to a large public dataset of human gut microbiomes (n = 4,617) identified multiple stable equilibria that better reflect ecological enterotypes with distinct carrying capacities and interactions for key species. Characterizing the ecological interactions among microbial members is an important step towards understanding the structure and function of diverse microbial communities. Widely used correlation based approaches for inferring interactions from cross-sectional microbiome sequencing data are not able to predict the directionality of interactions, and their results may not be interpretable. We developed an expectation-maximization algorithm (BEEM-Static) that can infer directed interaction networks from cross-sectional data based on an ecological model. Our benchmarking results showed that BEEM-Static inferred presence and directionality of interactions accurately, while correlation based methods had performance slightly better than random guesses. In addition, BEEM-Static was robust to various types of noises using statistical filters to identify and remove data points violating its assumptions. Applying BEEM-Static to a large public dataset of human gut microbiomes, we were able to identify multiple stable equilibria with distinct ecological properties.
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Affiliation(s)
- Chenhao Li
- Laboratory of Metagenomic Technologies and Microbial Systems, Genome Institute of Singapore, Singapore, Singapore
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- * E-mail: (CL); (NN)
| | - Tamar V. Av-Shalom
- Laboratory of Metagenomic Technologies and Microbial Systems, Genome Institute of Singapore, Singapore, Singapore
- Department of Cell & Systems Biology, University of Toronto, Toronto, Canada
| | - Jun Wei Gerald Tan
- Laboratory of Metagenomic Technologies and Microbial Systems, Genome Institute of Singapore, Singapore, Singapore
| | - Junmei Samantha Kwah
- Laboratory of Metagenomic Technologies and Microbial Systems, Genome Institute of Singapore, Singapore, Singapore
| | - Kern Rei Chng
- Laboratory of Metagenomic Technologies and Microbial Systems, Genome Institute of Singapore, Singapore, Singapore
| | - Niranjan Nagarajan
- Laboratory of Metagenomic Technologies and Microbial Systems, Genome Institute of Singapore, Singapore, Singapore
- School of Computing, National University of Singapore, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- * E-mail: (CL); (NN)
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24
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Sardelli L, Perottoni S, Tunesi M, Boeri L, Fusco F, Petrini P, Albani D, Giordano C. Technological tools and strategies for culturing human gut microbiota in engineered in vitro models. Biotechnol Bioeng 2021; 118:2886-2905. [PMID: 33990954 PMCID: PMC8361989 DOI: 10.1002/bit.27816] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 03/29/2021] [Accepted: 05/04/2021] [Indexed: 12/13/2022]
Abstract
The gut microbiota directly impacts the pathophysiology of different human body districts. Consequently, microbiota investigation is an hot topic of research and its in vitro culture has gained extreme interest in different fields. However, the high sensitivity of microbiota to external stimuli, such as sampling procedure, and the physicochemical complexity of the gut environment make its in vitro culture a challenging task. New engineered microfluidic gut-on-a-chip devices have the potential to model some important features of the intestinal structure, but they are usually unable to sustain culture of microbiota over an extended period of time. The integration of gut-on-a-chip devices with bioreactors for continuous bacterial culture would lead to fast advances in the study of microbiota-host crosstalk. In this review, we summarize the main technologies for the continuous culture of microbiota as upstream systems to be coupled with microfluidic devices to study bacteria-host cells communication. The engineering of integrated microfluidic platforms, capable of sustaining both anaerobic and aerobic cultures, would be the starting point to unveil complex biological phenomena proper of the microbiota-host crosstalks, paving to way to multiple research and technological applications.
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Affiliation(s)
- Lorenzo Sardelli
- Department of ChemistryMaterials and Chemical Engineering “Giulio Natta,” Politecnico di MilanoMilanItaly
| | - Simone Perottoni
- Department of ChemistryMaterials and Chemical Engineering “Giulio Natta,” Politecnico di MilanoMilanItaly
| | - Marta Tunesi
- Department of ChemistryMaterials and Chemical Engineering “Giulio Natta,” Politecnico di MilanoMilanItaly
| | - Lucia Boeri
- Department of ChemistryMaterials and Chemical Engineering “Giulio Natta,” Politecnico di MilanoMilanItaly
| | - Federica Fusco
- Department of ChemistryMaterials and Chemical Engineering “Giulio Natta,” Politecnico di MilanoMilanItaly
| | - Paola Petrini
- Department of ChemistryMaterials and Chemical Engineering “Giulio Natta,” Politecnico di MilanoMilanItaly
| | - Diego Albani
- Department of NeuroscienceIstituto di Ricerche Farmacologiche Mario Negri IRCCSMilanItaly
| | - Carmen Giordano
- Department of ChemistryMaterials and Chemical Engineering “Giulio Natta,” Politecnico di MilanoMilanItaly
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25
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Silverman JD, Bloom RJ, Jiang S, Durand HK, Dallow E, Mukherjee S, David LA. Measuring and mitigating PCR bias in microbiota datasets. PLoS Comput Biol 2021; 17:e1009113. [PMID: 34228723 PMCID: PMC8284789 DOI: 10.1371/journal.pcbi.1009113] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 07/16/2021] [Accepted: 05/25/2021] [Indexed: 12/16/2022] Open
Abstract
PCR amplification plays an integral role in the measurement of mixed microbial communities via high-throughput DNA sequencing of the 16S ribosomal RNA (rRNA) gene. Yet PCR is also known to introduce multiple forms of bias in 16S rRNA studies. Here we present a paired modeling and experimental approach to characterize and mitigate PCR NPM-bias (PCR bias from non-primer-mismatch sources) in microbiota surveys. We use experimental data from mock bacterial communities to validate our approach and human gut microbiota samples to characterize PCR NPM-bias under real-world conditions. Our results suggest that PCR NPM-bias can skew estimates of microbial relative abundances by a factor of 4 or more, but that this bias can be mitigated using log-ratio linear models. High-throughput DNA sequencing is often used to profile the species composition of host-associated microbial communities (microbiota). One important step in DNA sequencing is amplification where DNA from many different bacteria are repeatedly copied using a technique called Polymerase Chain Reaction (PCR). However, PCR is known to introduce multiple forms of bias as DNA from some bacteria are more efficiently copied than others. Here we introduce experimental and computational procedures that allows PCR NPM-bias (PCR bias from non-primer-mismatch sources) to be measured and mitigated in studies of microbial communities.
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Affiliation(s)
- Justin D Silverman
- College of Information Science and Technology, Pennsylvania State University, State College, Pennsylvania, United States of America
- Institute for Computational and Data Science, Pennsylvania State University, State College, Pennsylvania, United States of America
- Department of Medicine, Pennsylvania State University, Hershey, Pennsylvania, United States of America
| | - Rachael J Bloom
- Center for Genomics and Computational Biology, Duke University, Durham, North Carolina, United States of America
- University Program for Genetics and Genomics, Duke University, Durham, North Carolina, United States of America
| | - Sharon Jiang
- Center for Genomics and Computational Biology, Duke University, Durham, North Carolina, United States of America
- Department of Molecular Genetics and Microbiology, Duke University, Durham, North Carolina, United States of America
| | - Heather K Durand
- Center for Genomics and Computational Biology, Duke University, Durham, North Carolina, United States of America
- Department of Molecular Genetics and Microbiology, Duke University, Durham, North Carolina, United States of America
| | - Eric Dallow
- Center for Genomics and Computational Biology, Duke University, Durham, North Carolina, United States of America
- Department of Molecular Genetics and Microbiology, Duke University, Durham, North Carolina, United States of America
| | - Sayan Mukherjee
- Departments of Statistical Science, Mathematics, Computer Science, Biostatistics & Bioinformatics, Duke University, Durham, North Carolina, United States of America
| | - Lawrence A David
- Center for Genomics and Computational Biology, Duke University, Durham, North Carolina, United States of America
- Department of Molecular Genetics and Microbiology, Duke University, Durham, North Carolina, United States of America
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26
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Dedrick S, Akbari MJ, Dyckman SK, Zhao N, Liu YY, Momeni B. Impact of Temporal pH Fluctuations on the Coexistence of Nasal Bacteria in an in silico Community. Front Microbiol 2021; 12:613109. [PMID: 33643241 PMCID: PMC7902723 DOI: 10.3389/fmicb.2021.613109] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 01/18/2021] [Indexed: 11/22/2022] Open
Abstract
To manipulate nasal microbiota for respiratory health, we need to better understand how this microbial community is assembled and maintained. Previous work has demonstrated that the pH in the nasal passage experiences temporal fluctuations. Yet, the impact of such pH fluctuations on nasal microbiota is not fully understood. Here, we examine how temporal fluctuations in pH might affect the coexistence of nasal bacteria in in silico communities. We take advantage of the cultivability of nasal bacteria to experimentally assess their responses to pH and the presence of other species. Based on experimentally observed responses, we formulate a mathematical model to numerically investigate the impact of temporal pH fluctuations on species coexistence. We assemble in silico nasal communities using up to 20 strains that resemble the isolates that we have experimentally characterized. We then subject these in silico communities to pH fluctuations and assess how the community composition and coexistence is impacted. Using this model, we then simulate pH fluctuations-varying in amplitude or frequency-to identify conditions that best support species coexistence. We find that the composition of nasal communities is generally robust against pH fluctuations within the expected range of amplitudes and frequencies. Our results also show that cooperative communities and communities with lower niche overlap have significantly lower composition deviations when exposed to temporal pH fluctuations. Overall, our data suggest that nasal microbiota could be robust against environmental fluctuations.
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Affiliation(s)
- Sandra Dedrick
- Department of Biology, Boston College, Chestnut Hill, MA, United States
| | - M. Javad Akbari
- Department of Biology, Boston College, Chestnut Hill, MA, United States
| | | | - Nannan Zhao
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Babak Momeni
- Department of Biology, Boston College, Chestnut Hill, MA, United States
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27
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Martino C, Shenhav L, Marotz CA, Armstrong G, McDonald D, Vázquez-Baeza Y, Morton JT, Jiang L, Dominguez-Bello MG, Swafford AD, Halperin E, Knight R. Context-aware dimensionality reduction deconvolutes gut microbial community dynamics. Nat Biotechnol 2021; 39:165-168. [PMID: 32868914 PMCID: PMC7878194 DOI: 10.1038/s41587-020-0660-7] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 08/03/2020] [Indexed: 11/27/2022]
Abstract
The translational power of human microbiome studies is limited by high interindividual variation. We describe a dimensionality reduction tool, compositional tensor factorization (CTF), that incorporates information from the same host across multiple samples to reveal patterns driving differences in microbial composition across phenotypes. CTF identifies robust patterns in sparse compositional datasets, allowing for the detection of microbial changes associated with specific phenotypes that are reproducible across datasets.
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Affiliation(s)
- Cameron Martino
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Liat Shenhav
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA
| | - Clarisse A Marotz
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - George Armstrong
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Daniel McDonald
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Yoshiki Vázquez-Baeza
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
- Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - James T Morton
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Lingjing Jiang
- Division of Biostatistics, University of California San Diego, La Jolla, CA, USA
| | - Maria Gloria Dominguez-Bello
- Department of Biochemistry and Microbiology, Rutgers University New Brunswick, New Brunswick, NJ, USA
- Department of Anthropology, Rutgers University, New Brunswick, NJ, USA
| | - Austin D Swafford
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA
| | - Eran Halperin
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA
- Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Institute of Precision Health, University of California, Los Angeles, CA, USA
| | - Rob Knight
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA, USA.
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
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28
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Application of OU processes to modelling temporal dynamics of the human microbiome, and calculating optimal sampling schemes. BMC Bioinformatics 2020; 21:450. [PMID: 33045987 PMCID: PMC7549249 DOI: 10.1186/s12859-020-03747-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Accepted: 09/11/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The vast majority of microbiome research so far has focused on the structure of the microbiome at a single time-point. There have been several studies that measure the microbiome from a particular environment over time. A few models have been developed by extending time series models to accomodate specific features in microbiome data to address questions of stability and interactions of the microbime time series. Most research has observed the stability and mean reversion for some microbiomes. However, little has been done to study the mean reversion rates of these stable microbes and how sampling frequencies are related to such conclusions. In this paper, we begin to rectify this situation. We analyse two widely studied microbial time series data sets on four healthy individuals. We choose to study healthy individuals because we are interested in the baseline temporal dynamics of the microbiome. RESULTS For this analysis, we focus on the temporal dynamics of individual genera, absorbing all interactions in a stochastic term. We use a simple stochastic differential equation model to assess the following three questions. (1) Does the microbiome exhibit temporal continuity? (2) Does the microbiome have a stable state? (3) To better understand the temporal dynamics, how frequently should data be sampled in future studies? We find that a simple Ornstein-Uhlenbeck model which incorporates both temporal continuity and reversion to a stable state fits the data for almost every genus better than a Brownian motion model that contains only temporal continuity. The Ornstein-Uhlenbeck model also fits the data better than modelling separate time points as independent. Under the Ornstein-Uhlenbeck model, we calculate the variance of the estimated mean reversion rate (the speed with which each genus returns to its stable state). Based on this calculation, we are able to determine the optimal sample schemes for studying temporal dynamics. CONCLUSIONS There is evidence of temporal continuity for most genera; there is clear evidence of a stable state; and the optimal sampling frequency for studying temporal dynamics is in the range of one sample every 0.8-3.2 days.
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29
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Silverman JD, Roche K, Mukherjee S, David LA. Naught all zeros in sequence count data are the same. Comput Struct Biotechnol J 2020; 18:2789-2798. [PMID: 33101615 PMCID: PMC7568192 DOI: 10.1016/j.csbj.2020.09.014] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 09/09/2020] [Accepted: 09/10/2020] [Indexed: 12/21/2022] Open
Abstract
Genomic studies feature multivariate count data from high-throughput DNA sequencing experiments, which often contain many zero values. These zeros can cause artifacts for statistical analyses and multiple modeling approaches have been developed in response. Here, we apply different zero-handling models to gene-expression and microbiome datasets and show models can disagree substantially in terms of identifying the most differentially expressed sequences. Next, to rationally examine how different zero handling models behave, we developed a conceptual framework outlining four types of processes that may give rise to zero values in sequence count data. Last, we performed simulations to test how zero handling models behave in the presence of these different zero generating processes. Our simulations showed that simple count models are sufficient across multiple processes, even when the true underlying process is unknown. On the other hand, a common zero handling technique known as "zero-inflation" was only suitable under a zero generating process associated with an unlikely set of biological and experimental conditions. In concert, our work here suggests several specific guidelines for developing and choosing state-of-the-art models for analyzing sparse sequence count data.
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Affiliation(s)
- Justin D Silverman
- College of Information Science and Technology, Pennsylvania State University, State College, PA 16802, United States
- Institute for Computational and Data Science, Pennsylvania State University, State College, PA 16802, United States
- Department of Medicine, Pennsylvania State University, Hershey, PA 17033, United States
| | - Kimberly Roche
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, United States
| | - Sayan Mukherjee
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, United States
- Departments of Statistical Science, Mathematics, Computer Science, Biostatistics & Bioinformatics, Duke University, Durham, NC 27708, United States
- Center for Genomic and Computational Biology, Duke University, Durham, NC 27708, United States
| | - Lawrence A David
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, United States
- Center for Genomic and Computational Biology, Duke University, Durham, NC 27708, United States
- Department of Molecular Genetics and Microbiology, Duke University, Durham, NC 27708, United States
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30
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Holmes ZC, Silverman JD, Dressman HK, Wei Z, Dallow EP, Armstrong SC, Seed PC, Rawls JF, David LA. Short-Chain Fatty Acid Production by Gut Microbiota from Children with Obesity Differs According to Prebiotic Choice and Bacterial Community Composition. mBio 2020; 11:e00914-20. [PMID: 32788375 PMCID: PMC7439474 DOI: 10.1128/mbio.00914-20] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 07/13/2020] [Indexed: 12/20/2022] Open
Abstract
Pediatric obesity remains a public health burden and continues to increase in prevalence. The gut microbiota plays a causal role in obesity and is a promising therapeutic target. Specifically, the microbial production of short-chain fatty acids (SCFA) from the fermentation of otherwise indigestible dietary carbohydrates may protect against pediatric obesity and metabolic syndrome. Still, it has not been demonstrated that therapies involving microbiota-targeting carbohydrates, known as prebiotics, will enhance gut bacterial SCFA production in children and adolescents with obesity (age, 10 to 18 years old). Here, we used an in vitro system to examine the SCFA production by fecal microbiota from 17 children with obesity when exposed to five different commercially available over-the-counter (OTC) prebiotic supplements. We found microbiota from all 17 patients actively metabolized most prebiotics. Still, supplements varied in their acidogenic potential. Significant interdonor variation also existed in SCFA production, which 16S rRNA sequencing supported as being associated with differences in the host microbiota composition. Last, we found that neither fecal SCFA concentration, microbiota SCFA production capacity, nor markers of obesity positively correlated with one another. Together, these in vitro findings suggest the hypothesis that OTC prebiotic supplements may be unequal in their ability to stimulate SCFA production in children and adolescents with obesity and that the most acidogenic prebiotic may differ across individuals.IMPORTANCE Pediatric obesity remains a major public health problem in the United States, where 17% of children and adolescents are obese, and rates of pediatric "severe obesity" are increasing. Children and adolescents with obesity face higher health risks, and noninvasive therapies for pediatric obesity often have limited success. The human gut microbiome has been implicated in adult obesity, and microbiota-directed therapies can aid weight loss in adults with obesity. However, less is known about the microbiome in pediatric obesity, and microbiota-directed therapies are understudied in children and adolescents. Our research has two important findings: (i) dietary prebiotics (fiber) result in the microbiota from adolescents with obesity producing more SCFA, and (ii) the effectiveness of each prebiotic is donor dependent. Together, these findings suggest that prebiotic supplements could help children and adolescents with obesity, but that these therapies may not be "one size fits all."
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Affiliation(s)
- Zachary C Holmes
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Justin D Silverman
- Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina, USA
- Medical Scientist Training Program, Duke University School of Medicine, Durham, North Carolina, USA
| | - Holly K Dressman
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, North Carolina, USA
- Duke Microbiome Shared Resource, Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, USA
| | - Zhengzheng Wei
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Eric P Dallow
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Sarah C Armstrong
- Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA
| | - Patrick C Seed
- Division of Pediatric Infectious Diseases, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois, USA
| | - John F Rawls
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, USA
| | - Lawrence A David
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, North Carolina, USA
- Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina, USA
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, USA
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31
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Descheemaeker L, de Buyl S. Stochastic logistic models reproduce experimental time series of microbial communities. eLife 2020; 9:55650. [PMID: 32687052 PMCID: PMC7410486 DOI: 10.7554/elife.55650] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 07/20/2020] [Indexed: 11/25/2022] Open
Abstract
We analyze properties of experimental microbial time series, from plankton and the human microbiome, and investigate whether stochastic generalized Lotka-Volterra models could reproduce those properties. We show that this is the case when the noise term is large and a linear function of the species abundance, while the strength of the self-interactions varies over multiple orders of magnitude. We stress the fact that all the observed stochastic properties can be obtained from a logistic model, that is, without interactions, even the niche character of the experimental time series. Linear noise is associated with growth rate stochasticity, which is related to changes in the environment. This suggests that fluctuations in the sparsely sampled experimental time series may be caused by extrinsic sources.
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Affiliation(s)
- Lana Descheemaeker
- Applied Physics Research Group, Physics Department, Vrije Universiteit Brussel, Brussel, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Vrije Universiteit Brussel Université Libre de Bruxelles, Brussels, Belgium
| | - Sophie de Buyl
- Applied Physics Research Group, Physics Department, Vrije Universiteit Brussel, Brussel, Belgium.,Interuniversity Institute of Bioinformatics in Brussels, Vrije Universiteit Brussel Université Libre de Bruxelles, Brussels, Belgium
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32
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Interindividual Variation in Dietary Carbohydrate Metabolism by Gut Bacteria Revealed with Droplet Microfluidic Culture. mSystems 2020; 5:5/3/e00864-19. [PMID: 32606031 PMCID: PMC7329328 DOI: 10.1128/msystems.00864-19] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Bacterial culture and assay are components of basic microbiological research, drug development, and diagnostic screening. However, community diversity can make it challenging to comprehensively perform experiments involving individual microbiota members. Here, we present a new microfluidic culture platform that makes it feasible to measure the growth and function of microbiota constituents in a single set of experiments. As a proof of concept, we demonstrate how the platform can be used to measure how hundreds of gut bacterial taxa drawn from different people metabolize dietary carbohydrates. Going forward, we expect this microfluidic technique to be adaptable to a range of other microbial assay needs. Culture and screening of gut bacteria enable testing of microbial function and therapeutic potential. However, the diversity of human gut microbial communities (microbiota) impedes comprehensive experimental studies of individual bacterial taxa. Here, we combine advances in droplet microfluidics and high-throughput DNA sequencing to develop a platform for separating and assaying growth of microbiota members in picoliter droplets (MicDrop). MicDrop enabled us to cultivate 2.8 times more bacterial taxa than typical batch culture methods. We then used MicDrop to test whether individuals possess similar abundances of carbohydrate-degrading gut bacteria, using an approach which had previously not been possible due to throughput limitations of traditional bacterial culture techniques. Single MicDrop experiments allowed us to characterize carbohydrate utilization among dozens of gut bacterial taxa from distinct human stool samples. Our aggregate data across nine healthy stool donors revealed that all of the individuals harbored gut bacterial species capable of degrading common dietary polysaccharides. However, the levels of richness and abundance of polysaccharide-degrading species relative to monosaccharide-consuming taxa differed by up to 2.6-fold and 24.7-fold, respectively. Additionally, our unique dataset suggested that gut bacterial taxa may be broadly categorized by whether they can grow on single or multiple polysaccharides, and we found that this lifestyle trait is correlated with how broadly bacterial taxa can be found across individuals. This demonstration shows that it is feasible to measure the function of hundreds of bacterial taxa across multiple fecal samples from different people, which should in turn enable future efforts to design microbiota-directed therapies and yield new insights into microbiota ecology and evolution. IMPORTANCE Bacterial culture and assay are components of basic microbiological research, drug development, and diagnostic screening. However, community diversity can make it challenging to comprehensively perform experiments involving individual microbiota members. Here, we present a new microfluidic culture platform that makes it feasible to measure the growth and function of microbiota constituents in a single set of experiments. As a proof of concept, we demonstrate how the platform can be used to measure how hundreds of gut bacterial taxa drawn from different people metabolize dietary carbohydrates. Going forward, we expect this microfluidic technique to be adaptable to a range of other microbial assay needs.
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33
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Joseph TA, Pasarkar AP, Pe'er I. Efficient and Accurate Inference of Mixed Microbial Population Trajectories from Longitudinal Count Data. Cell Syst 2020; 10:463-469.e6. [PMID: 32684275 DOI: 10.1016/j.cels.2020.05.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Revised: 03/18/2020] [Accepted: 03/19/2020] [Indexed: 11/15/2022]
Abstract
The recently completed second phase of the Human Microbiome Project has highlighted the relationship between dynamic changes in the microbiome and disease, motivating new microbiome study designs based on longitudinal sampling. Yet, analysis of such data is hindered by presence of technical noise, high dimensionality, and data sparsity. Here, we introduce LUMINATE (longitudinal microbiome inference and zero detection), a fast and accurate method for inferring relative abundances from noisy read count data. We demonstrate that LUMINATE is orders of magnitude faster than current approaches, with better or similar accuracy. We further show that LUMINATE can accurately distinguish biological zeros, when a taxon is absent from the community, from technical zeros, when a taxon is below the detection threshold. We conclude by demonstrating the utility of LUMINATE on a real dataset, showing that LUMINATE smooths trajectories observed from noisy data. LUMINATE is freely available from https://github.com/tyjo/luminate.
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Affiliation(s)
- Tyler A Joseph
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Amey P Pasarkar
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Itsik Pe'er
- Department of Computer Science, Columbia University, New York, NY 10027, USA; Department of Systems Biology, Columbia University, New York, NY 10027, USA; Data Science Institute, Columbia University, New York, NY 10027, USA.
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Lovell DR, Chua XY, McGrath A. Counts: an outstanding challenge for log-ratio analysis of compositional data in the molecular biosciences. NAR Genom Bioinform 2020; 2:lqaa040. [PMID: 33575593 PMCID: PMC7671413 DOI: 10.1093/nargab/lqaa040] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 05/08/2020] [Accepted: 06/16/2020] [Indexed: 12/21/2022] Open
Abstract
Thanks to sequencing technology, modern molecular bioscience datasets are often compositions of counts, e.g. counts of amplicons, mRNAs, etc. While there is growing appreciation that compositional data need special analysis and interpretation, less well understood is the discrete nature of these count compositions (or, as we call them, lattice compositions) and the impact this has on statistical analysis, particularly log-ratio analysis (LRA) of pairwise association. While LRA methods are scale-invariant, count compositional data are not; consequently, the conclusions we draw from LRA of lattice compositions depend on the scale of counts involved. We know that additive variation affects the relative abundance of small counts more than large counts; here we show that additive (quantization) variation comes from the discrete nature of count data itself, as well as (biological) variation in the system under study and (technical) variation from measurement and analysis processes. Variation due to quantization is inevitable, but its impact on conclusions depends on the underlying scale and distribution of counts. We illustrate the different distributions of real molecular bioscience data from different experimental settings to show why it is vital to understand the distributional characteristics of count data before applying and drawing conclusions from compositional data analysis methods.
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Affiliation(s)
| | - Xin-Yi Chua
- Queensland University of Technology, Australia
| | - Annette McGrath
- Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
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Joseph TA, Shenhav L, Xavier JB, Halperin E, Pe’er I. Compositional Lotka-Volterra describes microbial dynamics in the simplex. PLoS Comput Biol 2020; 16:e1007917. [PMID: 32469867 PMCID: PMC7325845 DOI: 10.1371/journal.pcbi.1007917] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 06/19/2020] [Accepted: 04/28/2020] [Indexed: 12/14/2022] Open
Abstract
Dynamic changes in microbial communities play an important role in human health and disease. Specifically, deciphering how microbial species in a community interact with each other and their environment can elucidate mechanisms of disease, a problem typically investigated using tools from community ecology. Yet, such methods require measurements of absolute densities, whereas typical datasets only provide estimates of relative abundances. Here, we systematically investigate models of microbial dynamics in the simplex of relative abundances. We derive a new nonlinear dynamical system for microbial dynamics, termed “compositional” Lotka-Volterra (cLV), unifying approaches using generalized Lotka-Volterra (gLV) equations from community ecology and compositional data analysis. On three real datasets, we demonstrate that cLV recapitulates interactions between relative abundances implied by gLV. Moreover, we show that cLV is as accurate as gLV in forecasting microbial trajectories in terms of relative abundances. We further compare cLV to two other models of relative abundance dynamics motivated by common assumptions in the literature—a linear model in a log-ratio transformed space, and a linear model in the space of relative abundances—and provide evidence that cLV more accurately describes community trajectories over time. Finally, we investigate when information about direct effects can be recovered from relative data that naively provide information about only indirect effects. Our results suggest that strong effects may be recoverable from relative data, but more subtle effects are challenging to identify. Dynamic changes in microbial communities play an important role in human health and disease. Specifically, deciphering how microbial species in a community interact with each other and their environment can elucidate mechanisms of disease, a problem typically investigated using tools from community ecology. Yet, such methods require measurements of absolute densities, whereas typical only provide estimates of relative abundances. We investigate methods for describing microbial dynamics in terms of relative abundances using approaches from machine learning and dynamical systems. Across three real datasets, we show that relative abundances are sufficient to describe compositional dynamics. Additionally, we show that models trained on relative abundances alone predict future compositions as well models trained on absolute abundances. Finally, we provide criteria for when direct effects, which typically can only be learned from absolute abundances, are recoverable for relative data. As a proof of concept, we recapitulate a previously proposed interaction network for C. difficile colonization.
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Affiliation(s)
- Tyler A. Joseph
- Department of Computer Science, Columbia University, New York, New York, United States of America
| | - Liat Shenhav
- Department of Computer Science, UCLA, Los Angeles, California, United States of America
| | - Joao B. Xavier
- Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Eran Halperin
- Department of Computer Science, UCLA, Los Angeles, California, United States of America
- Department of Human Genetics, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Computational Medicine, UCLA, Los Angeles, California, United States of America
- Institute of Precision Health, UCLA, Los Angeles, California, United States of America
| | - Itsik Pe’er
- Department of Computer Science, Columbia University, New York, New York, United States of America
- Department of Systems Biology, Columbia University, New York, New York, United States of America
- Data Science Institute, Columbia University, New York, New York, United States of America
- * E-mail:
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Björk JR, Dasari M, Grieneisen L, Archie EA. Primate microbiomes over time: Longitudinal answers to standing questions in microbiome research. Am J Primatol 2019; 81:e22970. [PMID: 30941803 PMCID: PMC7193701 DOI: 10.1002/ajp.22970] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 02/05/2019] [Accepted: 03/07/2019] [Indexed: 12/16/2022]
Abstract
To date, most insights into the processes shaping vertebrate gut microbiomes have emerged from studies with cross-sectional designs. While this approach has been valuable, emerging time series analyses on vertebrate gut microbiomes show that gut microbial composition can change rapidly from 1 day to the next, with consequences for host physical functioning, health, and fitness. Hence, the next frontier of microbiome research will require longitudinal perspectives. Here we argue that primatologists, with their traditional focus on tracking the lives of individual animals and familiarity with longitudinal fecal sampling, are well positioned to conduct research at the forefront of gut microbiome dynamics. We begin by reviewing some of the most important ecological processes governing microbiome change over time, and briefly summarizing statistical challenges and approaches to microbiome time series analysis. We then introduce five questions of general interest to microbiome science where we think field-based primate studies are especially well positioned to fill major gaps: (a) Do early life events shape gut microbiome composition in adulthood? (b) Do shifting social landscapes cause gut microbial change? (c) Are gut microbiome phenotypes heritable across variable environments? (d) Does the gut microbiome show signs of host aging? And (e) do gut microbiome composition and dynamics predict host health and fitness? For all of these questions, we highlight areas where primatologists are uniquely positioned to make substantial contributions. We review preliminary evidence, discuss possible study designs, and suggest future directions.
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Affiliation(s)
- Johannes R Björk
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana
| | - Mauna Dasari
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana
| | - Laura Grieneisen
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, Minnesota
| | - Elizabeth A Archie
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana
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Li C, Chng KR, Kwah JS, Av-Shalom TV, Tucker-Kellogg L, Nagarajan N. An expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing data. MICROBIOME 2019; 7:118. [PMID: 31439018 PMCID: PMC6706891 DOI: 10.1186/s40168-019-0729-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 08/13/2019] [Indexed: 05/05/2023]
Abstract
BACKGROUND The dynamics of microbial communities is driven by a range of interactions from symbiosis to predator-prey relationships, the majority of which are poorly understood. With the increasing availability of high-throughput microbiome taxonomic profiling data, it is now conceivable to directly learn the ecological models that explicitly define microbial interactions and explain community dynamics. The applicability of these approaches is severely limited by the lack of accurate absolute cell density measurements (biomass). METHODS We present a new computational approach that resolves this key limitation in the inference of generalized Lotka-Volterra models (gLVMs) by coupling biomass estimation and model inference with an expectation-maximization algorithm (BEEM). RESULTS BEEM outperforms the state-of-the-art methods for inferring gLVMs, while simultaneously eliminating the need for additional experimental biomass data as input. BEEM's application to previously inaccessible public datasets (due to the lack of biomass data) allowed us to construct ecological models of microbial communities in the human gut on a per-individual basis, revealing personalized dynamics and keystone species. CONCLUSIONS BEEM addresses a key bottleneck in "systems analysis" of microbiomes by enabling accurate inference of ecological models from high throughput sequencing data without the need for experimental biomass measurements.
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Affiliation(s)
- Chenhao Li
- Computational and Systems Biology, Genome Institute of Singapore, Singapore, 138672 Singapore
- School of Computing, National University of Singapore, Singapore, 117543 Singapore
| | - Kern Rei Chng
- Computational and Systems Biology, Genome Institute of Singapore, Singapore, 138672 Singapore
| | - Junmei Samantha Kwah
- Computational and Systems Biology, Genome Institute of Singapore, Singapore, 138672 Singapore
| | - Tamar V. Av-Shalom
- Computational and Systems Biology, Genome Institute of Singapore, Singapore, 138672 Singapore
- Department of Microbiology and Immunology, University of British Columbia, Vancouver, V6T 1Z3 Canada
- Department of Computer Science, University of British Columbia, Vancouver, V6T 1Z4 Canada
| | - Lisa Tucker-Kellogg
- Centre for Computational Biology, Duke–NUS Graduate Medical School, Singapore, 169857 Singapore
| | - Niranjan Nagarajan
- Computational and Systems Biology, Genome Institute of Singapore, Singapore, 138672 Singapore
- School of Computing, National University of Singapore, Singapore, 117543 Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228 Singapore
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Schlomann BH, Parthasarathy R. Timescales of gut microbiome dynamics. Curr Opin Microbiol 2019; 50:56-63. [PMID: 31689582 PMCID: PMC6899164 DOI: 10.1016/j.mib.2019.09.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 09/19/2019] [Accepted: 09/23/2019] [Indexed: 02/07/2023]
Abstract
Vast communities of microorganisms inhabit the gastrointestinal tracts of humans and other animals. Understanding their initial development, fluctuations in composition, stability over long times, and responses to transient perturbations - in other words their dynamics - is important both for gaining basic insights into these ecosystems and for rationally manipulating them for therapeutic ends. Gut microbiome dynamics, however, remain poorly understood. We review here studies of gut microbiome dynamics in the presence and absence of external perturbations, noting especially the long timescales associated with overall stability and the short timescales associated with various underlying biological processes. Integrating these disparate timescales, we suggest, is an important goal for future work and is necessary for developing a predictive understanding of microbiome dynamics.
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Affiliation(s)
- Brandon H Schlomann
- Department of Physics, Materials Science Institute, and Institute of Molecular Biology, University of Oregon, Eugene, OR, United States
| | - Raghuveer Parthasarathy
- Department of Physics, Materials Science Institute, and Institute of Molecular Biology, University of Oregon, Eugene, OR, United States.
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Morton JT, Marotz C, Washburne A, Silverman J, Zaramela LS, Edlund A, Zengler K, Knight R. Establishing microbial composition measurement standards with reference frames. Nat Commun 2019; 10:2719. [PMID: 31222023 PMCID: PMC6586903 DOI: 10.1038/s41467-019-10656-5] [Citation(s) in RCA: 360] [Impact Index Per Article: 72.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 05/14/2019] [Indexed: 12/30/2022] Open
Abstract
Differential abundance analysis is controversial throughout microbiome research. Gold standard approaches require laborious measurements of total microbial load, or absolute number of microorganisms, to accurately determine taxonomic shifts. Therefore, most studies rely on relative abundance data. Here, we demonstrate common pitfalls in comparing relative abundance across samples and identify two solutions that reveal microbial changes without the need to estimate total microbial load. We define the notion of "reference frames", which provide deep intuition about the compositional nature of microbiome data. In an oral time series experiment, reference frames alleviate false positives and produce consistent results on both raw and cell-count normalized data. Furthermore, reference frames identify consistent, differentially abundant microbes previously undetected in two independent published datasets from subjects with atopic dermatitis. These methods allow reassessment of published relative abundance data to reveal reproducible microbial changes from standard sequencing output without the need for new assays.
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Affiliation(s)
- James T Morton
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
- Department of Computer Science & Engineering, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Clarisse Marotz
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Alex Washburne
- Department of Microbiology and Immunology, Montana State University, Bozeman, MT, 59717, USA
| | - Justin Silverman
- Program in Computational Biology and Bioinformatics, Duke University, Durham, 27708, USA
- Medical Scientist Training Program, Duke University, Durham, 27708, USA
- Center for Genomic and Computational Biology, Duke University, Durham, 27708, USA
| | - Livia S Zaramela
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Anna Edlund
- J. Craig Venter Institute, Genomic Medicine Group, La Jolla, CA, 92037, USA
| | - Karsten Zengler
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA.
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, 92093, USA.
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, 92093, USA.
| | - Rob Knight
- Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA.
- Department of Computer Science & Engineering, University of California, San Diego, La Jolla, CA, 92093, USA.
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, 92093, USA.
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40
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Gilbert JA, Lynch SV. Community ecology as a framework for human microbiome research. Nat Med 2019; 25:884-889. [PMID: 31133693 PMCID: PMC7410146 DOI: 10.1038/s41591-019-0464-9] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 04/23/2019] [Indexed: 02/06/2023]
Abstract
The field of human microbiome research has revealed the intimate co-association of humans with diverse communities of microbes in various habitats in the human body, and the necessity of these microbes for the maintenance of human health. Microbial heterogeneity between humans and across spatial and temporal gradients requires multidimensional datasets and a unifying set of theories and statistical tools to analyze the human microbiome and fully realize the potential of this field. Here we consider the utility of community ecology as a framework for the interrogation and interpretation of the human microbiome.
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Affiliation(s)
- Jack A Gilbert
- Department of Pediatrics and Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, USA.
| | - Susan V Lynch
- Division of Gastroenterology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
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41
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Silverman JD, Durand HK, Bloom RJ, Mukherjee S, David LA. Correction to: Dynamic linear models guide design and analysis of microbiota studies within artificial human guts. MICROBIOME 2018; 6:212. [PMID: 30482245 PMCID: PMC6260730 DOI: 10.1186/s40168-018-0601-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
AbstractFollowing publication of the original article [1], the authors noticed an error in the presentation of equations in the PDF version.
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Affiliation(s)
- Justin D Silverman
- Program in Computational Biology and Bioinformatics, Duke University, CIEMAS, Room 2171, 101 Science Drive, Box 3382, Durham, NC, 27708, USA
- Medical Scientist Training Program, Duke University, Durham, NC, 27708, USA
- Center for Genomic and Computational Biology, Duke University, Durham, NC, 27708, USA
| | - Heather K Durand
- Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, 27708, USA
| | - Rachael J Bloom
- University Program in Genetics and Genomics, Duke University, Durham, NC, 27708, USA
| | - Sayan Mukherjee
- Program in Computational Biology and Bioinformatics, Duke University, CIEMAS, Room 2171, 101 Science Drive, Box 3382, Durham, NC, 27708, USA
- Departments of Statistical Science, Mathematics, Computer Science, Biostatistics & Bioinformatics, Duke University, Durham, NC, 27708, USA
| | - Lawrence A David
- Program in Computational Biology and Bioinformatics, Duke University, CIEMAS, Room 2171, 101 Science Drive, Box 3382, Durham, NC, 27708, USA.
- Center for Genomic and Computational Biology, Duke University, Durham, NC, 27708, USA.
- University Program in Genetics and Genomics, Duke University, Durham, NC, 27708, USA.
- Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, 27708, USA.
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