1
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Ruiz-Perez D, Gimon I, Sazal M, Mathee K, Narasimhan G. Unfolding and de-confounding: biologically meaningful causal inference from longitudinal multi-omic networks using METALICA. mSystems 2024:e0130323. [PMID: 39240096 DOI: 10.1128/msystems.01303-23] [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: 12/19/2023] [Accepted: 07/10/2024] [Indexed: 09/07/2024] Open
Abstract
A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state of the art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery, and network inference algorithms were applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases. IMPORTANCE We have developed a suite of tools and techniques capable of inferring interactions between microbiome entities. METALICA introduces novel techniques called unrolling and de-confounding that are employed to uncover multi-omic entities considered to be confounders for some of the relationships that may be inferred using standard causal inferencing tools. To evaluate our method, we conducted tests on the inflammatory bowel disease (IBD) dataset from the iHMP longitudinal study, which we pre-processed in accordance with our previous work. From this dataset, we generated various subsets, encompassing different combinations of metagenomics, metabolomics, and metatranscriptomics datasets. Using these multi-omics datasets, we demonstrate how the unrolling process aids in the identification of putative intermediaries (genes and/or metabolites) to explain the interactions between microbes. Additionally, the de-confounding process identifies potential common causes that may give rise to spurious relationships to be inferred. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.
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Affiliation(s)
- Daniel Ruiz-Perez
- Bioinformatics Research Group (BioRG), Florida International University, Miami, Florida, USA
| | - Isabella Gimon
- Bioinformatics Research Group (BioRG), Florida International University, Miami, Florida, USA
| | - Musfiqur Sazal
- Bioinformatics Research Group (BioRG), Florida International University, Miami, Florida, USA
| | - Kalai Mathee
- Florida International University, Miami, Florida, USA
- Biomolecular Sciences Institute, Florida International University, Miami, Florida, USA
| | - Giri Narasimhan
- Bioinformatics Research Group (BioRG), Florida International University, Miami, Florida, USA
- Biomolecular Sciences Institute, Florida International University, Miami, Florida, USA
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2
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Dasari MR, Roche KE, Jansen D, Anderson J, Alberts SC, Tung J, Gilbert JA, Blekhman R, Mukherjee S, Archie EA. Social and environmental predictors of gut microbiome age in wild baboons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.02.605707. [PMID: 39131274 PMCID: PMC11312535 DOI: 10.1101/2024.08.02.605707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Understanding why some individuals age faster than others is essential to evolutionary biology and geroscience, but measuring variation in biological age is difficult. One solution may lie in measuring gut microbiome composition because microbiota change with many age-related factors (e.g., immunity and behavior). Here we create a microbiome-based age predictor using 13,563 gut microbial profiles from 479 wild baboons collected over 14 years. The resulting "microbiome clock" predicts host chronological age. Deviations from the clock's predictions are linked to demographic and socio-environmental factors that predict baboon health and survival: animals who appear old-for-age tend to be male, sampled in the dry season (for females), and high social status (both sexes). However, an individual's "microbiome age" does not predict the attainment of developmental milestones or lifespan. Hence, the microbiome clock accurately reflects age and some social and environmental conditions, but not the pace of development or mortality risk.
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Affiliation(s)
- Mauna R. Dasari
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA
- California Academy of Sciences, San Francisco, CA, USA
| | - Kimberly E. Roche
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, USA
| | - David Jansen
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA
| | - Jordan Anderson
- Department of Evolutionary Anthropology, Duke University, Durham, NC, USA
| | - Susan C. Alberts
- Department of Evolutionary Anthropology, Duke University, Durham, NC, USA
- Department of Biology, Duke University, Durham, NC, USA
- Duke University Population Research Institute, Duke University, Durham, NC, USA
| | - Jenny Tung
- Department of Evolutionary Anthropology, Duke University, Durham, NC, USA
- Department of Biology, Duke University, Durham, NC, USA
- Duke University Population Research Institute, Duke University, Durham, NC, USA
- Department of Primate Behavior and Evolution, Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig, Germany
- Canadian Institute for Advanced Research, Toronto, Ontario, Canada
- Faculty of Life Sciences, Institute of Biology, Leipzig University, Leipzig, Germany
| | - Jack A. Gilbert
- Department of Pediatrics and the Scripps Institution of Oceanography, University of California, San Diego, San Diego, CA, USA
| | - Ran Blekhman
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Sayan Mukherjee
- Departments of Statistical Science, Mathematics, Computer Science, and Bioinformatics & Biostatistics, Duke University, Durham, NC, USA
- Center for Scalable Data Analytics and Artificial Intelligence, University of Leipzig, Leipzig Germany
- Max Planck Institute for Mathematics in the Natural Sciences, Leipzig, Germany
| | - Elizabeth A. Archie
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA
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3
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Bogale AT, Braun M, Bernhardt J, Zühlke D, Schiefelbein U, Bog M, Scheidegger C, Zengerer V, Becher D, Grube M, Riedel K, Bengtsson MM. The microbiome of the lichen Lobaria pulmonaria varies according to climate on a subcontinental scale. ENVIRONMENTAL MICROBIOLOGY REPORTS 2024; 16:e13289. [PMID: 38923181 PMCID: PMC11194104 DOI: 10.1111/1758-2229.13289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Accepted: 05/03/2024] [Indexed: 06/28/2024]
Abstract
The Lobaria pulmonaria holobiont comprises algal, fungal, cyanobacterial and bacterial components. We investigated L. pulmonaria's bacterial microbiome in the adaptation of this ecologically sensitive lichen species to diverse climatic conditions. Our central hypothesis posited that microbiome composition and functionality aligns with subcontinental-scale (a stretch of ~1100 km) climatic parameters related to temperature and precipitation. We also tested the impact of short-term weather dynamics, sampling season and algal/fungal genotypes on microbiome variation. Metaproteomics provided insights into compositional and functional changes within the microbiome. Climatic variables explained 41.64% of microbiome variation, surpassing the combined influence of local weather and sampling season at 31.63%. Notably, annual mean temperature and temperature seasonality emerged as significant climatic drivers. Microbiome composition correlated with algal, not fungal genotype, suggesting similar environmental recruitment for the algal partner and microbiome. Differential abundance analyses revealed distinct protein compositions in Sub-Atlantic Lowland and Alpine regions, indicating differential microbiome responses to contrasting environmental/climatic conditions. Proteins involved in oxidative and cellular stress were notably different. Our findings highlight microbiome plasticity in adapting to stable climates, with limited responsiveness to short-term fluctuations, offering new insights into climate adaptation in lichen symbiosis.
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Affiliation(s)
| | - Maria Braun
- Institute of MicrobiologyUniversity of GreifswaldGreifswaldGermany
| | - Jörg Bernhardt
- Institute of MicrobiologyUniversity of GreifswaldGreifswaldGermany
| | - Daniela Zühlke
- Institute of MicrobiologyUniversity of GreifswaldGreifswaldGermany
| | - Ulf Schiefelbein
- Landscape EcologyUniversity of Rostock, Botanical GardenRostockGermany
| | - Manuela Bog
- Institute of Botany and Landscape EcologyUniversity of GreifswaldGreifswaldGermany
| | - Christoph Scheidegger
- Swiss Federal Institute for Forest, Snow and Landscape Research WSLBirmensdorfSwitzerland
| | - Veronika Zengerer
- Swiss Federal Institute for Forest, Snow and Landscape Research WSLBirmensdorfSwitzerland
| | - Dörte Becher
- Institute of MicrobiologyUniversity of GreifswaldGreifswaldGermany
| | - Martin Grube
- Karl‐Franzens‐Universität Graz, Institut für BiologieGrazAustria
| | - Katharina Riedel
- Institute of MicrobiologyUniversity of GreifswaldGreifswaldGermany
| | - Mia M. Bengtsson
- Institute of MicrobiologyUniversity of GreifswaldGreifswaldGermany
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4
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Sankaran K, Jeganathan P. mbtransfer: Microbiome intervention analysis using transfer functions and mirror statistics. PLoS Comput Biol 2024; 20:e1012196. [PMID: 38875277 PMCID: PMC11210883 DOI: 10.1371/journal.pcbi.1012196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 06/27/2024] [Accepted: 05/27/2024] [Indexed: 06/16/2024] Open
Abstract
Time series studies of microbiome interventions provide valuable data about microbial ecosystem structure. Unfortunately, existing models of microbial community dynamics have limited temporal memory and expressivity, relying on Markov or linearity assumptions. To address this, we introduce a new class of models based on transfer functions. These models learn impulse responses, capturing the potentially delayed effects of environmental changes on the microbial community. This allows us to simulate trajectories under hypothetical interventions and select significantly perturbed taxa with False Discovery Rate guarantees. Through simulations, we show that our approach effectively reduces forecasting errors compared to strong baselines and accurately pinpoints taxa of interest. Our case studies highlight the interpretability of the resulting differential response trajectories. An R package, mbtransfer, and notebooks to replicate the simulation and case studies are provided.
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Affiliation(s)
- Kris Sankaran
- Department of Statistics, University of Wisconsin - Madison, Madison, Wisconsin, United States of America
| | - Pratheepa Jeganathan
- Department of Mathematics & Statistics, McMaster University, Hamilton, Ontario, Canada
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5
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Nodari R, Arghittu M, Bailo P, Cattaneo C, Creti R, D’Aleo F, Saegeman V, Franceschetti L, Novati S, Fernández-Rodríguez A, Verzeletti A, Farina C, Bandi C. Forensic Microbiology: When, Where and How. Microorganisms 2024; 12:988. [PMID: 38792818 PMCID: PMC11123702 DOI: 10.3390/microorganisms12050988] [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: 03/07/2024] [Revised: 04/30/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024] Open
Abstract
Forensic microbiology is a relatively new discipline, born in part thanks to the development of advanced methodologies for the detection, identification and characterization of microorganisms, and also in relation to the growing impact of infectious diseases of iatrogenic origin. Indeed, the increased application of medical practices, such as transplants, which require immunosuppressive treatments, and the growing demand for prosthetic installations, associated with an increasing threat of antimicrobial resistance, have led to a rise in the number of infections of iatrogenic origin, which entails important medico-legal issues. On the other hand, the possibility of detecting minimal amounts of microorganisms, even in the form of residual traces (e.g., their nucleic acids), and of obtaining gene and genomic sequences at contained costs, has made it possible to ask new questions of whether cases of death or illness might have a microbiological origin, with the possibility of also tracing the origin of the microorganisms involved and reconstructing the chain of contagion. In addition to the more obvious applications, such as those mentioned above related to the origin of iatrogenic infections, or to possible cases of infections not properly diagnosed and treated, a less obvious application of forensic microbiology concerns its use in cases of violence or violent death, where the characterization of the microorganisms can contribute to the reconstruction of the case. Finally, paleomicrobiology, e.g., the reconstruction and characterization of microorganisms in historical or even archaeological remnants, can be considered as a sister discipline of forensic microbiology. In this article, we will review these different aspects and applications of forensic microbiology.
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Affiliation(s)
- Riccardo Nodari
- Department of Pharmacological and Biomolecular Sciences (DiSFeB), University of Milan, 20133 Milan, Italy
| | - Milena Arghittu
- Analysis Laboratory, ASST Melegnano e Martesana, 20077 Vizzolo Predabissi, Italy
| | - Paolo Bailo
- Section of Legal Medicine, School of Law, University of Camerino, 62032 Camerino, Italy
| | - Cristina Cattaneo
- LABANOF, Laboratory of Forensic Anthropology and Odontology, Section of Forensic Medicine, Department of Biomedical Sciences for Health, University of Milan, 20133 Milan, Italy
| | - Roberta Creti
- Antibiotic Resistance and Special Pathogens Unit, Department of Infectious Diseases, Istituto Superiore di Sanità, 00161 Rome, Italy
| | - Francesco D’Aleo
- Microbiology and Virology Laboratory, GOM—Grande Ospedale Metropolitano, 89124 Reggio Calabria, Italy
| | - Veroniek Saegeman
- Microbiology and Infection Control, Vitaz Hospital, 9100 Sint-Niklaas, Belgium
| | - Lorenzo Franceschetti
- LABANOF, Laboratory of Forensic Anthropology and Odontology, Section of Forensic Medicine, Department of Biomedical Sciences for Health, University of Milan, 20133 Milan, Italy
| | - Stefano Novati
- Department of Infectious Diseases, Fondazione IRCCS Policlinico San Matteo, University of Pavia, 27100 Pavia, Italy
| | - Amparo Fernández-Rodríguez
- Microbiology Department, Biology Service, Instituto Nacional de Toxicología y Ciencias Forenses, 41009 Madrid, Spain
| | - Andrea Verzeletti
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health University of Brescia, 25123 Brescia, Italy
| | - Claudio Farina
- Microbiology and Virology Laboratory, ASST Papa Giovanni XXIII, 24127 Bergamo, Italy
| | - Claudio Bandi
- Romeo ed Enrica Invernizzi Paediatric Research Centre, Department of Biosciences, University of Milan, 20133 Milan, Italy
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6
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Borrego-Ruiz A, Borrego JJ. Human gut microbiome, diet, and mental disorders. Int Microbiol 2024:10.1007/s10123-024-00518-6. [PMID: 38561477 DOI: 10.1007/s10123-024-00518-6] [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: 02/03/2024] [Revised: 03/15/2024] [Accepted: 03/22/2024] [Indexed: 04/04/2024]
Abstract
Diet is one of the most important external factor shaping the composition and metabolic activities of the gut microbiome. The gut microbiome plays a crucial role in host health, including immune system development, nutrients metabolism, and the synthesis of bioactive molecules. In addition, the gut microbiome has been described as critical for the development of several mental disorders. Nutritional psychiatry is an emerging field of research that may provide a link between diet, microbial function, and brain health. In this study, we have reviewed the influence of different diet types, such as Western, Mediterranean, vegetarian, and ketogenic, on the gut microbiota composition and function, and their implication in various neuropsychiatric and psychological disorders.
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Affiliation(s)
- Alejandro Borrego-Ruiz
- Departamento de Psicología Social y de las Organizaciones, Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain
| | - Juan J Borrego
- Departamento de Microbiología, Universidad de Málaga. Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina BIONAND, Málaga, Spain.
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7
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Narla AV, Hwa T, Murugan A. Dynamic coexistence driven by physiological transitions in microbial communities. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.10.575059. [PMID: 38260536 PMCID: PMC10802591 DOI: 10.1101/2024.01.10.575059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Microbial ecosystems are commonly modeled by fixed interactions between species in steady exponential growth states. However, microbes often modify their environments so strongly that they are forced out of the exponential state into stressed or non-growing states. Such dynamics are typical of ecological succession in nature and serial-dilution cycles in the laboratory. Here, we introduce a phenomenological model, the Community State model, to gain insight into the dynamic coexistence of microbes due to changes in their physiological states. Our model bypasses specific interactions (e.g., nutrient starvation, stress, aggregation) that lead to different combinations of physiological states, referred to collectively as "community states", and modeled by specifying the growth preference of each species along a global ecological coordinate, taken here to be the total community biomass density. We identify three key features of such dynamical communities that contrast starkly with steady-state communities: increased tolerance of community diversity to fast growth rates of species dominating different community states, enhanced community stability through staggered dominance of different species in different community states, and increased requirement on growth dominance for the inclusion of late-growing species. These features, derived explicitly for simplified models, are proposed here to be principles aiding the understanding of complex dynamical communities. Our model shifts the focus of ecosystem dynamics from bottom-up studies based on idealized inter-species interaction to top-down studies based on accessible macroscopic observables such as growth rates and total biomass density, enabling quantitative examination of community-wide characteristics.
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Affiliation(s)
| | - Terence Hwa
- Department of Physics, University of California, San Diego
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8
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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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9
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Ruiz-Perez D, Gimon I, Sazal M, Mathee K, Narasimhan G. Unfolding and De-confounding: Biologically meaningful causal inference from longitudinal multi-omic networks using METALICA. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.12.571384. [PMID: 38168315 PMCID: PMC10760167 DOI: 10.1101/2023.12.12.571384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
A key challenge in the analysis of microbiome data is the integration of multi-omic datasets and the discovery of interactions between microbial taxa, their expressed genes, and the metabolites they consume and/or produce. In an effort to improve the state-of-the-art in inferring biologically meaningful multi-omic interactions, we sought to address some of the most fundamental issues in causal inference from longitudinal multi-omics microbiome data sets. We developed METALICA, a suite of tools and techniques that can infer interactions between microbiome entities. METALICA introduces novel unrolling and de-confounding techniques used to uncover multi-omic entities that are believed to act as confounders for some of the relationships that may be inferred using standard causal inferencing tools. The results lend support to predictions about biological models and processes by which microbial taxa interact with each other in a microbiome. The unrolling process helps to identify putative intermediaries (genes and/or metabolites) to explain the interactions between microbes; the de-confounding process identifies putative common causes that may lead to spurious relationships to be inferred. METALICA was applied to the networks inferred by existing causal discovery and network inference algorithms applied to a multi-omics data set resulting from a longitudinal study of IBD microbiomes. The most significant unrollings and de-confoundings were manually validated using the existing literature and databases.
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Affiliation(s)
- Daniel Ruiz-Perez
- Bioinformatics Research Group (BioRG), Florida International University, Miami, FL 33199, USA
| | - Isabella Gimon
- Bioinformatics Research Group (BioRG), Florida International University, Miami, FL 33199, USA
| | - Musfiqur Sazal
- Bioinformatics Research Group (BioRG), Florida International University, Miami, FL 33199, USA
| | - Kalai Mathee
- Florida International University, Miami, FL 33199, USA
- Biomolecular Sciences Institute, Florida International University, Miami, FL 33199, USA
| | - Giri Narasimhan
- Bioinformatics Research Group (BioRG), Florida International University, Miami, FL 33199, USA
- Biomolecular Sciences Institute, Florida International University, Miami, FL 33199, USA
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10
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Schneider A, Zeiler M, Kopp K, Wagner G, Karwautz A. [The Therapeutic Potential of Prebiotics and Probiotics in Child and Adolescent Psychiatric Disorders]. ZEITSCHRIFT FUR KINDER- UND JUGENDPSYCHIATRIE UND PSYCHOTHERAPIE 2023; 51:441-450. [PMID: 37070434 DOI: 10.1024/1422-4917/a000930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2023]
Abstract
The Therapeutic Potential of Prebiotics and Probiotics in Child and Adolescent Psychiatric Disorders Abstract: This short review summarizes the literature available on therapeutic interventions with prebiotics and probiotics and their potential use in psychiatric disorders in childhood, adolescence, and adulthood. Most studies of children and adolescents are done on ADHD and autism spectrum disorders, whereas single reports exist largely on positive effects on cognitive symptoms and quality of life. Initial studies regarding anorexia nervosa point to a potential effect of weight gain and reduction of gastrointestinal symptoms. To date, the effects of prebiotics and probiotics in depression, bipolar disorder, anxiety disorders, and schizophrenia have been mainly investigated in adults. The best reported evidence exists for depression, whereas the effects on depressive symptomatology are small. Positive effects are seen on gastrointestinal symptoms in these disorders. Given these positive effects, the mixed literature reports may result from very heterogeneous study designs. Nevertheless, the high potential of prebiotics and probiotics may be seen for minors with mental health problems. Further studies that include child and adolescent psychiatric populations and reflect the complexity of the gut-brain axis are urgently needed.
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Affiliation(s)
- Andrea Schneider
- Universitätsklinik für Kinder- und Jugendpsychiatrie, Medizinische Universität Wien, Österreich
| | - Michael Zeiler
- Universitätsklinik für Kinder- und Jugendpsychiatrie, Medizinische Universität Wien, Österreich
| | - Konstantin Kopp
- Universitätsklinik für Kinder- und Jugendpsychiatrie, Medizinische Universität Wien, Österreich
| | - Gudrun Wagner
- Universitätsklinik für Kinder- und Jugendpsychiatrie, Medizinische Universität Wien, Österreich
| | - Andreas Karwautz
- Universitätsklinik für Kinder- und Jugendpsychiatrie, Medizinische Universität Wien, Österreich
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11
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Romero R, Theis KR, Gomez-Lopez N, Winters AD, Panzer JJ, Lin H, Galaz J, Greenberg JM, Shaffer Z, Kracht DJ, Chaiworapongsa T, Jung E, Gotsch F, Ravel J, Peddada SD, Tarca AL. The Vaginal Microbiota of Pregnant Women Varies with Gestational Age, Maternal Age, and Parity. Microbiol Spectr 2023; 11:e0342922. [PMID: 37486223 PMCID: PMC10434204 DOI: 10.1128/spectrum.03429-22] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 06/25/2023] [Indexed: 07/25/2023] Open
Abstract
The composition of the vaginal microbiota is heavily influenced by pregnancy and may factor into pregnancy complications, including spontaneous preterm birth. However, results among studies have been inconsistent due, in part, to variation in sample sizes and ethnicity. Thus, an association between the vaginal microbiota and preterm labor continues to be debated. Yet, before assessing associations between the composition of the vaginal microbiota and preterm labor, a robust and in-depth characterization of the vaginal microbiota throughout pregnancy in the specific study population under investigation is required. Here, we report a large longitudinal study (n = 474 women, 1,862 vaginal samples) of a predominantly African-American cohort-a population that experiences a relatively high rate of pregnancy complications-evaluating associations between individual identity, gestational age, and other maternal characteristics with the composition of the vaginal microbiota throughout gestation resulting in term delivery. The principal factors influencing the composition of the vaginal microbiota in pregnancy are individual identity and gestational age at sampling. Other factors are maternal age, parity, obesity, and self-reported Cannabis use. The general pattern across gestation is for the vaginal microbiota to remain or transition to a state of Lactobacillus dominance. This pattern can be modified by maternal parity and obesity. Regardless, network analyses reveal dynamic associations among specific bacterial taxa within the vaginal ecosystem, which shift throughout the course of pregnancy. This study provides a robust foundational understanding of the vaginal microbiota in pregnancy and sets the stage for further investigation of this microbiota in obstetrical disease. IMPORTANCE There is debate regarding links between the vaginal microbiota and pregnancy complications, especially spontaneous preterm birth. Inconsistencies in results among studies are likely due to differences in sample sizes and cohort ethnicity. Ethnicity is a complicating factor because, although all bacterial taxa commonly inhabiting the vagina are present among all ethnicities, the frequencies of these taxa vary among ethnicities. Therefore, an in-depth characterization of the vaginal microbiota throughout pregnancy in the specific study population under investigation is required prior to evaluating associations between the vaginal microbiota and obstetrical disease. This initial investigation is a large longitudinal study of the vaginal microbiota throughout gestation resulting in a term delivery in a predominantly African-American cohort, a population that experiences disproportionally negative maternal-fetal health outcomes. It establishes the magnitude of associations between maternal characteristics, such as age, parity, body mass index, and self-reported Cannabis use, on the vaginal microbiota in pregnancy.
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Affiliation(s)
- Roberto Romero
- Pregnancy Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland, USA
- Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, Michigan, USA
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan, USA
| | - Kevin R. Theis
- Pregnancy Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland, USA
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, Michigan, USA
- Department of Biochemistry, Microbiology, and Immunology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Nardhy Gomez-Lopez
- Pregnancy Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland, USA
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, Michigan, USA
- Department of Biochemistry, Microbiology, and Immunology, Wayne State University School of Medicine, Detroit, Michigan, USA
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Andrew D. Winters
- Pregnancy Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland, USA
- Department of Biochemistry, Microbiology, and Immunology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Jonathan J. Panzer
- Pregnancy Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland, USA
- Department of Biochemistry, Microbiology, and Immunology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Huang Lin
- Biostatistics and Bioinformatics Branch, National Institute of Child Health and Human Development, Bethesda, Maryland, USA
| | - Jose Galaz
- Pregnancy Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland, USA
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, Michigan, USA
- Division of Obstetrics and Gynecology, School of Medicine, Faculty of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Jonathan M. Greenberg
- Pregnancy Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland, USA
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Zachary Shaffer
- Pregnancy Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland, USA
- Department of Physiology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - David J. Kracht
- Pregnancy Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland, USA
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Tinnakorn Chaiworapongsa
- Pregnancy Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland, USA
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Eunjung Jung
- Pregnancy Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland, USA
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, Michigan, USA
- Department of Obstetrics and Gynecology, Busan Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Francesca Gotsch
- Pregnancy Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland, USA
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Jacques Ravel
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, USA
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Shyamal D. Peddada
- Biostatistics and Bioinformatics Branch, National Institute of Child Health and Human Development, Bethesda, Maryland, USA
| | - Adi L. Tarca
- Pregnancy Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, Maryland, USA
- Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, Michigan, USA
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, Michigan, USA
- Department of Computer Science, Wayne State University College of Engineering, Detroit, Michigan, USA
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12
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Heck N, Freudenthal J, Dumack K. Microeukaryotic predators shape the wastewater microbiome. WATER RESEARCH 2023; 242:120293. [PMID: 37421865 DOI: 10.1016/j.watres.2023.120293] [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: 04/05/2023] [Revised: 06/26/2023] [Accepted: 06/29/2023] [Indexed: 07/10/2023]
Abstract
The physicochemical parameters that shape the prokaryotic community composition in wastewater have been extensively studied. In contrast, it is poorly understood whether and how biotic interactions affect the prokaryotic community composition in wastewater. We used metatranscriptomics data from a bioreactor sampled weekly over 14 months to investigate the wastewater microbiome, including often neglected microeukaryotes. Our analysis revealed that while prokaryotes are unaffected by seasonal changes in water temperature, they are impacted by a seasonal, temperature-induced change in the microeukaryotic community. Our findings suggest that selective predation pressure exerted by microeukaryotes is a significant factor shaping the prokaryotic community in wastewater. This study underscores the importance of investigating the entire wastewater microbiome to develop a comprehensive understanding of wastewater treatment.
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Affiliation(s)
- Nils Heck
- Terrestrial Ecology, Institute of Zoology, University of Cologne, Zülpicher Str. 47b, Köln 50674, Germany
| | - Jule Freudenthal
- Terrestrial Ecology, Institute of Zoology, University of Cologne, Zülpicher Str. 47b, Köln 50674, Germany
| | - Kenneth Dumack
- Terrestrial Ecology, Institute of Zoology, University of Cologne, Zülpicher Str. 47b, Köln 50674, Germany.
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13
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Gihawi A, Cooper CS, Brewer DS. Caution regarding the specificities of pan-cancer microbial structure. Microb Genom 2023; 9:mgen001088. [PMID: 37555750 PMCID: PMC10483429 DOI: 10.1099/mgen.0.001088] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 07/25/2023] [Indexed: 08/10/2023] Open
Abstract
Results published in an article by Poore et al. (Nature. 2020;579:567-574) suggested that machine learning models can almost perfectly distinguish between tumour types based on their microbial composition using machine learning models. Whilst we believe that there is the potential for microbial composition to be used in this manner, we have concerns with the paper that make us question the certainty of the conclusions drawn. We believe there are issues in the areas of the contribution of contamination, handling of batch effects, false positive classifications and limitations in the machine learning approaches used. This makes it difficult to identify whether the authors have identified true biological signal and how robust these models would be in use as clinical biomarkers. We commend Poore et al. on their approach to open data and reproducibility that has enabled this analysis. We hope that this discourse assists the future development of machine learning models and hypothesis generation in microbiome research.
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Affiliation(s)
- Abraham Gihawi
- Bob Champion Research & Education Building, Norwich Medical School, University of East Anglia, Norwich NR4 7UQ, UK
| | - Colin S. Cooper
- Bob Champion Research & Education Building, Norwich Medical School, University of East Anglia, Norwich NR4 7UQ, UK
| | - Daniel S. Brewer
- Bob Champion Research & Education Building, Norwich Medical School, University of East Anglia, Norwich NR4 7UQ, UK
- Earlham Institute, Norwich Research Park, Colney Lane, Norwich NR4 7UG, UK
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14
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Schluter J, Djukovic A, Taylor BP, Yan J, Duan C, Hussey GA, Liao C, Sharma S, Fontana E, Amoretti LA, Wright RJ, Dai A, Peled JU, Taur Y, Perales MA, Siranosian BA, Bhatt AS, van den Brink MRM, Pamer EG, Xavier JB. The TaxUMAP atlas: Efficient display of large clinical microbiome data reveals ecological competition in protection against bacteremia. Cell Host Microbe 2023; 31:1126-1139.e6. [PMID: 37329880 PMCID: PMC10527165 DOI: 10.1016/j.chom.2023.05.027] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 09/28/2022] [Accepted: 05/24/2023] [Indexed: 06/19/2023]
Abstract
Longitudinal microbiome data provide valuable insight into disease states and clinical responses, but they are challenging to mine and view collectively. To address these limitations, we present TaxUMAP, a taxonomically informed visualization for displaying microbiome states in large clinical microbiome datasets. We used TaxUMAP to chart a microbiome atlas of 1,870 patients with cancer during therapy-induced perturbations. Bacterial density and diversity were positively associated, but the trend was reversed in liquid stool. Low-diversity states (dominations) remained stable after antibiotic treatment, and diverse communities had a broader range of antimicrobial resistance genes than dominations. When examining microbiome states associated with risk for bacteremia, TaxUMAP revealed that certain Klebsiella species were associated with lower risk for bacteremia localize in a region of the atlas that is depleted in high-risk enterobacteria. This indicated a competitive interaction that was validated experimentally. Thus, TaxUMAP can chart comprehensive longitudinal microbiome datasets, enabling insights into microbiome effects on human health.
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Affiliation(s)
- Jonas Schluter
- Institute for Systems Genetics, Department of Microbiology, New York University Grossman School of Medicine, New York, NY, USA.
| | - Ana Djukovic
- Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Bradford P Taylor
- Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jinyuan Yan
- Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Caichen Duan
- Institute for Systems Genetics, Department of Microbiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Grant A Hussey
- Institute for Systems Genetics, Department of Microbiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Chen Liao
- Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Sneh Sharma
- Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Emily Fontana
- Department of Immunology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Luigi A Amoretti
- Department of Immunology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Roberta J Wright
- Department of Immunology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Anqi Dai
- Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jonathan U Peled
- Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Weill Cornell Medical College, New York, NY, USA
| | - Ying Taur
- Department of Immunology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Miguel-Angel Perales
- Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Weill Cornell Medical College, New York, NY, USA
| | | | - Ami S Bhatt
- Department of Genetics, Stanford University, Stanford, CA, USA; Department of Medicine, Division of Hematology, Stanford University, Stanford, CA, USA; Department of Medicine, Division of Blood and Marrow Transplantation and Cellular Therapy, Stanford University School of Medicine, Stanford, CA, USA
| | - Marcel R M van den Brink
- Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Weill Cornell Medical College, New York, NY, USA
| | - Eric G Pamer
- Duchossois Family Institute, University of Chicago, Chicago, IL, USA
| | - Joao B Xavier
- Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.
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15
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Larsen PE, Dai Y. Modeling interaction networks between host, diet, and bacteria predicts obesogenesis in a mouse model. Front Mol Biosci 2022; 9:1059094. [PMID: 36458093 PMCID: PMC9705962 DOI: 10.3389/fmolb.2022.1059094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 10/31/2022] [Indexed: 09/10/2024] Open
Abstract
Host-microbiome interactions are known to have substantial effects on human health, but the diversity of the human microbiome makes it difficult to definitively attribute specific microbiome features to a host phenotype. One approach to overcoming this challenge is to use animal models of host-microbiome interaction, but it must be determined that relevant aspects of host-microbiome interactions are reflected in the animal model. One such experimental validation is an experiment by Ridura et al. In that experiment, transplanting a microbiome from a human into a mouse also conferred the human donor's obesity phenotype. We have aggregated a collection of previously published host-microbiome mouse-model experiments and combined it with thousands of sequenced and annotated bacterial genomes and metametabolomic pathways. Three computational models were generated, each model reflecting an aspect of host-microbiome interactions: 1) Predict the change in microbiome community structure in response to host diet using a community interaction network, 2) Predict metagenomic data from microbiome community structure, and 3) Predict host obesogenesis from modeled microbiome metagenomic data. These computationally validated models were combined into an integrated model of host-microbiome-diet interactions and used to replicate the Ridura experiment in silico. The results of the computational models indicate that network-based models are significantly more predictive than similar but non-network-based models. Network-based models also provide additional insight into the molecular mechanisms of host-microbiome interaction by highlighting metabolites and metabolic pathways proposed to be associated with microbiome-based obesogenesis. While the models generated in this study are likely too specific to the animal models and experimental conditions used to train our models to be of general utility in a broader understanding of obesogenesis, the approach detailed here is expected to be a powerful tool of investigating multiple types of host-microbiome interactions.
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Affiliation(s)
- Peter E. Larsen
- Loyola Genomics Facility, Loyola University at Chicago Health Science Campus, Maywood, IL, United States
| | - Yang Dai
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States
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16
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MDITRE: Scalable and Interpretable Machine Learning for Predicting Host Status from Temporal Microbiome Dynamics. mSystems 2022; 7:e0013222. [PMID: 36069455 PMCID: PMC9600536 DOI: 10.1128/msystems.00132-22] [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] [Indexed: 12/24/2022] Open
Abstract
Longitudinal microbiome data sets are being generated with increasing regularity, and there is broad recognition that these studies are critical for unlocking the mechanisms through which the microbiome impacts human health and disease. However, there is a dearth of computational tools for analyzing microbiome time-series data. To address this gap, we developed an open-source software package, Microbiome Differentiable Interpretable Temporal Rule Engine (MDITRE), which implements a new highly efficient method leveraging deep-learning technologies to derive human-interpretable rules that predict host status from longitudinal microbiome data. Using semi-synthetic and a large compendium of publicly available 16S rRNA amplicon and metagenomics sequencing data sets, we demonstrate that in almost all cases, MDITRE performs on par with or better than popular uninterpretable machine learning methods, and orders-of-magnitude faster than the prior interpretable technique. MDITRE also provides a graphical user interface, which we show through case studies can be used to derive biologically meaningful interpretations linking patterns of microbiome changes over time with host phenotypes. IMPORTANCE The human microbiome, or collection of microbes living on and within us, changes over time. Linking these changes to the status of the human host is crucial to understanding how the microbiome influences a variety of human diseases. Due to the large scale and complexity of microbiome data, computational methods are essential. Existing computational methods for linking changes in the microbiome to the status of the human host are either unable to scale to large and complex microbiome data sets or cannot produce human-interpretable outputs. We present a new computational method and software package that overcomes the limitations of previous methods, allowing researchers to analyze larger and more complex data sets while producing easily interpretable outputs. Our method has the potential to enable new insights into how changes in the microbiome over time maintain health or lead to disease in humans and facilitate the development of diagnostic tests based on the microbiome.
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17
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Wojciechowski S, Majchrzak-Górecka M, Biernat P, Odrzywołek K, Pruss Ł, Zych K, Jan Majta, Milanowska-Zabel K. Machine learning on the road to unlocking microbiota's potential for boosting immune checkpoint therapy. Int J Med Microbiol 2022; 312:151560. [PMID: 36113358 DOI: 10.1016/j.ijmm.2022.151560] [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: 08/30/2021] [Revised: 07/15/2022] [Accepted: 08/31/2022] [Indexed: 10/14/2022] Open
Abstract
The intestinal microbiota is a complex and diverse ecological community that fulfills multiple functions and substantially impacts human health. Despite its plasticity, unfavorable conditions can cause perturbations leading to so-called dysbiosis, which have been connected to multiple diseases. Unfortunately, understanding the mechanisms underlying the crosstalk between those microorganisms and their host is proving to be difficult. Traditionally used bioinformatic tools have difficulties to fully exploit big data generated for this purpose by modern high throughput screens. Machine Learning (ML) may be a potential means of solving such problems, but it requires diligent application to allow for drawing valid conclusions. This is especially crucial as gaining insight into the mechanistic basis of microbial impact on human health is highly anticipated in numerous fields of study. This includes oncology, where growing amounts of studies implicate the gut ecosystems in both cancerogenesis and antineoplastic treatment outcomes. Based on these reports and first signs of clinical benefits related to microbiota modulation in human trials, hopes are rising for the development of microbiome-derived diagnostics and therapeutics. In this mini-review, we're inspecting analytical approaches used to uncover the role of gut microbiome in immune checkpoint therapy (ICT) with the use of shotgun metagenomic sequencing (SMS) data.
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Affiliation(s)
| | | | | | - Krzysztof Odrzywołek
- Ardigen, Podole 76, 30-394 Kraków, Poland; Institute of Computer Science, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, Mickiewicza 30, 30-059 Kraków, Poland
| | - Łukasz Pruss
- Ardigen, Podole 76, 30-394 Kraków, Poland; Department of Biochemistry, Molecular Biology and Biotechnology, Faculty of Chemistry, Wroclaw University of Science and Technology, 50-373 Wroclaw, Poland
| | | | - Jan Majta
- Ardigen, Podole 76, 30-394 Kraków, Poland; Department of Computational Biophysics and Bioinformatics, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Krakow, Poland
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18
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Liu Q, Lee B, Xie L. Small molecule modulation of microbiota: a systems pharmacology perspective. BMC Bioinformatics 2022; 23:403. [PMID: 36175827 PMCID: PMC9523894 DOI: 10.1186/s12859-022-04941-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 09/16/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Microbes are associated with many human diseases and influence drug efficacy. Small-molecule drugs may revolutionize biomedicine by fine-tuning the microbiota on the basis of individual patient microbiome signatures. However, emerging endeavors in small-molecule microbiome drug discovery continue to follow a conventional "one-drug-one-target-one-disease" process. A systematic pharmacology approach that would suppress multiple interacting pathogenic species in the microbiome, could offer an attractive alternative solution. RESULTS We construct a disease-centric signed microbe-microbe interaction network using curated microbe metabolite information and their effects on host. We develop a Signed Random Walk with Restart algorithm for the accurate prediction of effect of microbes on human health and diseases. With a survey on the druggable and evolutionary space of microbe proteins, we find that 8-10% of them can be targeted by existing drugs or drug-like chemicals and that 25% of them have homologs to human proteins. We demonstrate that drugs for diabetes can be the lead compounds for development of microbiota-targeted therapeutics. We further show that the potential drug targets that specifically exist in pathogenic microbes are periplasmic and cellular outer membrane proteins. CONCLUSION The systematic studies of the polypharmacological landscape of the microbiome network may open a new avenue for the small-molecule drug discovery of the microbiome. We believe that the application of systematic method on the polypharmacological investigation could lead to the discovery of novel drug therapies.
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Affiliation(s)
- Qiao Liu
- Department of Computer Science, Hunter College, The City University of New York, New York, NY, USA
| | - Bohyun Lee
- Ph.D. Program in Computer Science, The City University of New York, New York, NY, USA
| | - Lei Xie
- Department of Computer Science, Hunter College, The City University of New York, New York, NY, USA.
- Ph.D. Program in Computer Science, The City University of New York, New York, NY, USA.
- Ph.D. Program in Biochemistry and Biology, The City University of New York, New York, NY, USA.
- Helen and Robert Appel Alzheimer's Disease Research Institute, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, Cornell University, New York, NY, USA.
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19
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Pinacho-Guendulain B, Montiel-Castro AJ, Ramos-Fernández G, Pacheco-López G. Social complexity as a driving force of gut microbiota exchange among conspecific hosts in non-human primates. Front Integr Neurosci 2022; 16:876849. [PMID: 36110388 PMCID: PMC9468716 DOI: 10.3389/fnint.2022.876849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 05/16/2022] [Indexed: 11/13/2022] Open
Abstract
The emergent concept of the social microbiome implies a view of a highly connected biological world, in which microbial interchange across organisms may be influenced by social and ecological connections occurring at different levels of biological organization. We explore this idea reviewing evidence of whether increasing social complexity in primate societies is associated with both higher diversity and greater similarity in the composition of the gut microbiota. By proposing a series of predictions regarding such relationship, we evaluate the existence of a link between gut microbiota and primate social behavior. Overall, we find that enough empirical evidence already supports these predictions. Nonetheless, we conclude that studies with the necessary, sufficient, explicit, and available evidence are still scarce. Therefore, we reflect on the benefit of founding future analyses on the utility of social complexity as a theoretical framework.
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Affiliation(s)
- Braulio Pinacho-Guendulain
- Doctorado en Ciencias Biológicas y de la Salud, Universidad Autónoma Metropolitana (UAM), Ciudad de México, Mexico
| | - Augusto Jacobo Montiel-Castro
- Department of Health Sciences, Metropolitan Autonomous University (UAM), Lerma, Mexico
- *Correspondence: Augusto Jacobo Montiel-Castro,
| | - Gabriel Ramos-Fernández
- Institute for Research on Applied Mathematics and Systems (IIMAS), National Autonomous University of Mexico (UNAM), Mexico City, Mexico
- Center for Complexity Sciences, National Autonomous University of Mexico, Mexico City, Mexico
| | - Gustavo Pacheco-López
- Department of Health Sciences, Metropolitan Autonomous University (UAM), Lerma, Mexico
- Gustavo Pacheco-López,
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20
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Boyraz A, Pawlowsky-Glahn V, Egozcue JJ, Acar AC. Principal microbial groups: compositional alternative to phylogenetic grouping of microbiome data. Brief Bioinform 2022; 23:6675749. [PMID: 36007229 DOI: 10.1093/bib/bbac328] [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: 02/01/2022] [Revised: 07/19/2022] [Accepted: 07/20/2022] [Indexed: 11/13/2022] Open
Abstract
Statistical and machine learning techniques based on relative abundances have been used to predict health conditions and to identify microbial biomarkers. However, high dimensionality, sparsity and the compositional nature of microbiome data represent statistical challenges. On the other hand, the taxon grouping allows summarizing microbiome abundance with a coarser resolution in a lower dimension, but it presents new challenges when correlating taxa with a disease. In this work, we present a novel approach that groups Operational Taxonomical Units (OTUs) based only on relative abundances as an alternative to taxon grouping. The proposed procedure acknowledges the compositional data making use of principal balances. The identified groups are called Principal Microbial Groups (PMGs). The procedure reduces the need for user-defined aggregation of $\textrm{OTU}$s and offers the possibility of working with coarse group of $\textrm{OTU}$s, which are not present in a phylogenetic tree. PMGs can be used for two different goals: (1) as a dimensionality reduction method for compositional data, (2) as an aggregation procedure that provides an alternative to taxon grouping for construction of microbial balances afterward used for disease prediction. We illustrate the procedure with a cirrhosis study data. PMGs provide a coherent data analysis for the search of biomarkers in human microbiota. The source code and demo data for PMGs are available at: https://github.com/asliboyraz/PMGs.
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Affiliation(s)
- Aslı Boyraz
- Department of Computer Programming, Recep Tayyip Erdoğan University, Ardeşen Vocational School, Rize, 53400, Turkey
| | - Vera Pawlowsky-Glahn
- Department of Computer Sciences, Applied Mathematics and Statistics, University of Girona, Campus Montilivi, 17003 Girona, Spain
| | - Juan José Egozcue
- Department of Civil and Environmental Engineering, Universitat Politécnica de Catalunya, Barcelona, 08034, Spain
| | - Aybar Can Acar
- Department of Medical Informatics, Middle East Technical University, Ankara Turkey
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21
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Rashidi A, Ebadi M, Rehman TU, Elhusseini H, Halaweish H, Kaiser T, Holtan SG, Khoruts A, Weisdorf DJ, Staley C. Compilation of longitudinal gut microbiome, serum metabolome, and clinical data in acute myeloid leukemia. Sci Data 2022; 9:468. [PMID: 35918343 PMCID: PMC9346123 DOI: 10.1038/s41597-022-01600-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 07/27/2022] [Indexed: 12/03/2022] Open
Abstract
Induction chemotherapy for patients with acute myeloid leukemia (AML) is a unique clinical scenario. These patients spend several weeks in the hospital, receiving multiple antibiotics, experiencing gastrointestinal mucosal damage, and suffering severe impairments in their immune system and nutrition. These factors cause major disruptions to the gut microbiota to a level rarely seen in other clinical conditions. Thus, the study of the gut microbiota in these patients can reveal novel aspects of microbiota-host relationships. When combined with the circulating metabolome, such studies could shed light on gut microbiota contribution to circulating metabolites. Collectively, gut microbiota and circulating metabolome are known to regulate host physiology. We have previously deposited amplicon sequences from 566 fecal samples from 68 AML patients. Here, we provide sample-level details and a link, using de-identified patient IDs, to additional data including serum metabolomics (260 samples from 36 patients) and clinical metadata. The detailed information provided enables comprehensive multi-omics analysis. We validate the technical quality of these data through 3 examples and demonstrate a method for integrated analysis.
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Affiliation(s)
- Armin Rashidi
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN, USA.
| | - Maryam Ebadi
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Tauseef Ur Rehman
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Heba Elhusseini
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Hossam Halaweish
- Department of Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Thomas Kaiser
- Department of Surgery, University of Minnesota, Minneapolis, MN, USA
| | - Shernan G Holtan
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Alexander Khoruts
- Division of Gastroenterology, Hepatology, and Nutrition, Department of Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Daniel J Weisdorf
- Division of Hematology, Oncology, and Transplantation, Department of Medicine, University of Minnesota, Minneapolis, MN, USA
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22
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Microbiota succession throughout life from the cradle to the grave. Nat Rev Microbiol 2022; 20:707-720. [PMID: 35906422 DOI: 10.1038/s41579-022-00768-z] [Citation(s) in RCA: 70] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/21/2022] [Indexed: 11/08/2022]
Abstract
Associations between age and the human microbiota are robust and reproducible. The microbial composition at several body sites can predict human chronological age relatively accurately. Although it is largely unknown why specific microorganisms are more abundant at certain ages, human microbiota research has elucidated a series of microbial community transformations that occur between birth and death. In this Review, we explore microbial succession in the healthy human microbiota from the cradle to the grave. We discuss the stages from primary succession at birth, to disruptions by disease or antibiotic use, to microbial expansion at death. We address how these successions differ by body site and by domain (bacteria, fungi or viruses). We also review experimental tools that microbiota researchers use to conduct this work. Finally, we discuss future directions for studying the microbiota's relationship with age, including designing consistent, well-powered, longitudinal studies, performing robust statistical analyses and improving characterization of non-bacterial microorganisms.
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23
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Kodikara S, Ellul S, Lê Cao KA. Statistical challenges in longitudinal microbiome data analysis. Brief Bioinform 2022; 23:bbac273. [PMID: 35830875 PMCID: PMC9294433 DOI: 10.1093/bib/bbac273] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 05/28/2022] [Accepted: 06/12/2022] [Indexed: 11/13/2022] Open
Abstract
The microbiome is a complex and dynamic community of microorganisms that co-exist interdependently within an ecosystem, and interact with its host or environment. Longitudinal studies can capture temporal variation within the microbiome to gain mechanistic insights into microbial systems; however, current statistical methods are limited due to the complex and inherent features of the data. We have identified three analytical objectives in longitudinal microbial studies: (1) differential abundance over time and between sample groups, demographic factors or clinical variables of interest; (2) clustering of microorganisms evolving concomitantly across time and (3) network modelling to identify temporal relationships between microorganisms. This review explores the strengths and limitations of current methods to fulfill these objectives, compares different methods in simulation and case studies for objectives (1) and (2), and highlights opportunities for further methodological developments. R tutorials are provided to reproduce the analyses conducted in this review.
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Affiliation(s)
- Saritha Kodikara
- Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Royal Parade, 3052, Victoria, Australia
| | - Susan Ellul
- Murdoch Children’s Research Institute and Department of Paediatrics, University of Melbourne, Bouverie Street, 3052, Victoria, Australia
| | - Kim-Anh Lê Cao
- Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Royal Parade, 3052, Victoria, Australia
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24
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Kaiser T, Jahansouz C, Staley C. Network-based approaches for the investigation of microbial community structure and function using metagenomics-based data. Future Microbiol 2022; 17:621-631. [PMID: 35360922 DOI: 10.2217/fmb-2021-0219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Network-based approaches offer a powerful framework to evaluate microbial community organization and function as it relates to a variety of environmental processes. Emerging studies are exploring network theory as a method for data integration that is likely to be critical for the integration of 'omics' data using systems biology approaches. Intricacies of network theory and methodological and computational complexities in network construction, however, impede the use of these tools for translational science. We provide a perspective on the methods of network construction, interpretation and emerging uses for these techniques in understanding host-microbiota interactions.
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Affiliation(s)
- Thomas Kaiser
- Department of Surgery, University of Minnesota, Minneapolis, MN 55455, USA.,Biotechnology Institute, University of Minnesota, Saint Paul, MN 55108, USA
| | - Cyrus Jahansouz
- Department of Surgery, University of Minnesota, Minneapolis, MN 55455, USA
| | - Christopher Staley
- Department of Surgery, University of Minnesota, Minneapolis, MN 55455, USA.,Biotechnology Institute, University of Minnesota, Saint Paul, MN 55108, USA
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25
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Sowah SA, Milanese A, Schübel R, Wirbel J, Kartal E, Johnson TS, Hirche F, Grafetstätter M, Nonnenmacher T, Kirsten R, López-Nogueroles M, Lahoz A, Schwarz KV, Okun JG, Ulrich CM, Nattenmüller J, von Eckardstein A, Müller D, Stangl GI, Kaaks R, Kühn T, Zeller G. Calorie restriction improves metabolic state independently of gut microbiome composition: a randomized dietary intervention trial. Genome Med 2022; 14:30. [PMID: 35287713 PMCID: PMC8919571 DOI: 10.1186/s13073-022-01030-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 02/14/2022] [Indexed: 12/12/2022] Open
Abstract
Background The gut microbiota has been suggested to play a significant role in the development of overweight and obesity. However, the effects of calorie restriction on gut microbiota of overweight and obese adults, especially over longer durations, are largely unexplored. Methods Here, we longitudinally analyzed the effects of intermittent calorie restriction (ICR) operationalized as the 5:2 diet versus continuous calorie restriction (CCR) on fecal microbiota of 147 overweight or obese adults in a 50-week parallel-arm randomized controlled trial, the HELENA Trial. The primary outcome of the trial was the differential effects of ICR versus CCR on gene expression in subcutaneous adipose tissue. Changes in the gut microbiome, which are the focus of this publication, were defined as exploratory endpoint of the trial. The trial comprised a 12-week intervention period, a 12-week maintenance period, and a final follow-up period of 26 weeks. Results Both diets resulted in ~5% weight loss. However, except for Lactobacillales being enriched after ICR, post-intervention microbiome composition did not significantly differ between groups. Overall weight loss was associated with significant metabolic improvements, but not with changes in the gut microbiome. Nonetheless, the abundance of the Dorea genus at baseline was moderately predictive of subsequent weight loss (AUROC of 0.74 for distinguishing the highest versus lowest weight loss quartiles). Despite the lack of consistent intervention effects on microbiome composition, significant study group-independent co-variation between gut bacterial families and metabolic biomarkers, anthropometric measures, and dietary composition was detectable. Our analysis in particular revealed associations between insulin sensitivity (HOMA-IR) and Akkermansiaceae, Christensenellaceae, and Tanerellaceae. It also suggests the possibility of a beneficial modulation of the latter two intestinal taxa by a diet high in vegetables and fiber, and low in processed meat. Conclusions Overall, our results suggest that the gut microbiome remains stable and highly individual-specific under dietary calorie restriction. Trial registration The trial, including the present microbiome component, was prospectively registered at ClinicalTrials.govNCT02449148 on May 20, 2015. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-022-01030-0.
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Affiliation(s)
- Solomon A Sowah
- German Cancer Research Center (DKFZ), Division of Cancer Epidemiology, Heidelberg, Germany.,Medical Faculty, Heidelberg University, Heidelberg, Germany.,European Molecular Biology Laboratory (EMBL), Structural and Computational Biology Unit, Heidelberg, Germany
| | - Alessio Milanese
- European Molecular Biology Laboratory (EMBL), Structural and Computational Biology Unit, Heidelberg, Germany
| | - Ruth Schübel
- German Cancer Research Center (DKFZ), Division of Cancer Epidemiology, Heidelberg, Germany
| | - Jakob Wirbel
- European Molecular Biology Laboratory (EMBL), Structural and Computational Biology Unit, Heidelberg, Germany
| | - Ece Kartal
- European Molecular Biology Laboratory (EMBL), Structural and Computational Biology Unit, Heidelberg, Germany.,Molecular Medicine Partnership Unit, Heidelberg, Germany
| | - Theron S Johnson
- German Cancer Research Center (DKFZ), Division of Cancer Epidemiology, Heidelberg, Germany
| | - Frank Hirche
- Institute of Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Mirja Grafetstätter
- German Cancer Research Center (DKFZ), Division of Cancer Epidemiology, Heidelberg, Germany
| | - Tobias Nonnenmacher
- Heidelberg University Hospital, Diagnostic and Interventional Radiology, Heidelberg, Germany
| | - Romy Kirsten
- Biobank of the National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany
| | - Marina López-Nogueroles
- Analytical Unit, Biomarkers and Precision Medicine Unit, Health Research Institute Hospital La Fe, Valencia, Spain
| | - Agustín Lahoz
- Analytical Unit, Biomarkers and Precision Medicine Unit, Health Research Institute Hospital La Fe, Valencia, Spain
| | - Kathrin V Schwarz
- Department of General Paediatrics, Division of Neuropediatrics and Metabolic Medicine, University Hospital Heidelberg, Dietmar-Hopp Metabolic Center, Heidelberg, Germany
| | - Jürgen G Okun
- Department of General Paediatrics, Division of Neuropediatrics and Metabolic Medicine, University Hospital Heidelberg, Dietmar-Hopp Metabolic Center, Heidelberg, Germany
| | - Cornelia M Ulrich
- Huntsman Cancer Institute and Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Johanna Nattenmüller
- Heidelberg University Hospital, Diagnostic and Interventional Radiology, Heidelberg, Germany
| | | | - Daniel Müller
- Institute of Clinical Chemistry (IGFS), University Hospital Zurich, Zurich, Switzerland
| | - Gabriele I Stangl
- Institute of Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Rudolf Kaaks
- German Cancer Research Center (DKFZ), Division of Cancer Epidemiology, Heidelberg, Germany
| | - Tilman Kühn
- German Cancer Research Center (DKFZ), Division of Cancer Epidemiology, Heidelberg, Germany. .,Institute for Global Food Security, Queen's University Belfast, Belfast, Northern Ireland, UK. .,Heidelberg Institute of Global Health (HIGH), Faculty of Medicine and University Hospital, Heidelberg, Germany.
| | - Georg Zeller
- European Molecular Biology Laboratory (EMBL), Structural and Computational Biology Unit, Heidelberg, Germany.
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26
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He L, Wang C, Hu J, Gao Z, Falcone E, Holland SM, Blaser MJ, Li H. ARZIMM: A Novel Analytic Platform for the Inference of Microbial Interactions and Community Stability from Longitudinal Microbiome Study. Front Genet 2022; 13:777877. [PMID: 35281829 PMCID: PMC8914110 DOI: 10.3389/fgene.2022.777877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 01/31/2022] [Indexed: 11/13/2022] Open
Abstract
Dynamic changes of microbiome communities may play important roles in human health and diseases. The recent rise in longitudinal microbiome studies calls for statistical methods that can model the temporal dynamic patterns and simultaneously quantify the microbial interactions and community stability. Here, we propose a novel autoregressive zero-inflated mixed-effects model (ARZIMM) to capture the sparse microbial interactions and estimate the community stability. ARZIMM employs a zero-inflated Poisson autoregressive model to model the excessive zero abundances and the non-zero abundances separately, a random effect to investigate the underlining dynamic pattern shared within the group, and a Lasso-type penalty to capture and estimate the sparse microbial interactions. Based on the estimated microbial interaction matrix, we further derive the estimate of community stability, and identify the core dynamic patterns through network inference. Through extensive simulation studies and real data analyses we evaluate ARZIMM in comparison with the other methods.
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Affiliation(s)
- Linchen He
- Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States
| | - Chan Wang
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, East Hanover, NY, United States
| | - Jiyuan Hu
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, East Hanover, NY, United States
| | - Zhan Gao
- Center for Advanced Biotechnology and Medicine, Rutgers University, New Brunswick, NJ, United States
| | - Emilia Falcone
- Division of Intramural Research, Immunopathogenesis Section, NIAID, NIH, Bethesda, MD, United States
| | - Steven M. Holland
- Division of Intramural Research, Immunopathogenesis Section, NIAID, NIH, Bethesda, MD, United States
| | - Martin J. Blaser
- Center for Advanced Biotechnology and Medicine, Rutgers University, New Brunswick, NJ, United States
| | - Huilin Li
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, East Hanover, NY, United States
- *Correspondence: Huilin Li,
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27
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Kodera SM, Das P, Gilbert JA, Lutz HL. Conceptual strategies for characterizing interactions in microbial communities. iScience 2022; 25:103775. [PMID: 35146390 PMCID: PMC8819398 DOI: 10.1016/j.isci.2022.103775] [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: 11/17/2022] Open
Abstract
Understanding the sets of inter- and intraspecies interactions in microbial communities is a fundamental goal of microbial ecology. However, the study and quantification of microbial interactions pose several challenges owing to their complexity, dynamic nature, and the sheer number of unique interactions within a typical community. To overcome such challenges, microbial ecologists must rely on various approaches to distill the system of study to a functional and conceptualizable level, allowing for a practical understanding of microbial interactions in both simplified and complex systems. This review broadly addresses the role of several conceptual approaches available for the microbial ecologist’s arsenal, examines specific tools used to accomplish such approaches, and describes how the assumptions, expectations, and philosophies underlying these tools change across scales of complexity.
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Affiliation(s)
- Sho M Kodera
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92037, USA
| | - Promi Das
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA 92093, USA.,Department of Pediatrics, University of California San Diego, La Jolla, CA 92161, USA
| | - Jack A Gilbert
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92037, USA.,Center for Microbiome Innovation, University of California San Diego, La Jolla, CA 92093, USA.,Department of Pediatrics, University of California San Diego, La Jolla, CA 92161, USA
| | - Holly L Lutz
- Center for Microbiome Innovation, University of California San Diego, La Jolla, CA 92093, USA.,Department of Pediatrics, University of California San Diego, La Jolla, CA 92161, USA.,Negaunee Integrative Collections Center, Field Museum of Natural History, Chicago, IL 60605, USA
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28
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Haindl R, Totzauer L, Kulozik U. Preservation by lyophilization of a human intestinal microbiota: influence of the cultivation pH on the drying outcome and re‐establishment ability. Microb Biotechnol 2022; 15:886-900. [PMID: 35124900 PMCID: PMC8913864 DOI: 10.1111/1751-7915.14007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 01/05/2022] [Indexed: 11/28/2022] Open
Affiliation(s)
- Regina Haindl
- Chair of Food and Bioprocess Engineering TUM School of Life Sciences ZIEL‐Institute for Food and Health Technical University of Munich Weihenstephaner Berg 1 Freising‐Weihenstephan Germany
| | - Lisa Totzauer
- Chair of Food and Bioprocess Engineering TUM School of Life Sciences ZIEL‐Institute for Food and Health Technical University of Munich Weihenstephaner Berg 1 Freising‐Weihenstephan Germany
| | - Ulrich Kulozik
- Chair of Food and Bioprocess Engineering TUM School of Life Sciences ZIEL‐Institute for Food and Health Technical University of Munich Weihenstephaner Berg 1 Freising‐Weihenstephan Germany
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29
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Chen B, Xu W. Functional response regression model on correlated longitudinal microbiome sequencing data. Stat Methods Med Res 2021; 31:361-371. [PMID: 34866471 PMCID: PMC8829735 DOI: 10.1177/09622802211061634] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Functional regression has been widely used on longitudinal data, but it is not clear how to apply functional regression to microbiome sequencing data. We propose a novel functional response regression model analyzing correlated longitudinal microbiome sequencing data, which extends the classic functional response regression model only working for independent functional responses. We derive the theory of generalized least squares estimators for predictors' effects when functional responses are correlated, and develop a data transformation technique to solve the computational challenge for analyzing correlated functional response data using existing functional regression method. We show by extensive simulations that our proposed method provides unbiased estimations for predictors' effect, and our model has accurate type I error and power performance for correlated functional response data, compared with classic functional response regression model. Finally we implement our method to a real infant gut microbiome study to evaluate the relationship of clinical factors to predominant taxa along time.
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Affiliation(s)
- Bo Chen
- Department of Biostatistics, Princess Margaret Cancer Centre, 7989University Health Network, Toronto, Ontario, Canada
| | - Wei Xu
- Department of Biostatistics, Princess Margaret Cancer Centre, 7989University Health Network, Toronto, Ontario, Canada.,Dalla Lana School of Public Health, 7938University of Toronto, Toronto, Ontario, Canada
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30
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Wang C, Hu J, Blaser MJ, Li H. Microbial trend analysis for common dynamic trend, group comparison, and classification in longitudinal microbiome study. BMC Genomics 2021; 22:667. [PMID: 34525957 PMCID: PMC8442444 DOI: 10.1186/s12864-021-07948-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 08/25/2021] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND The human microbiome is inherently dynamic and its dynamic nature plays a critical role in maintaining health and driving disease. With an increasing number of longitudinal microbiome studies, scientists are eager to learn the comprehensive characterization of microbial dynamics and their implications to the health and disease-related phenotypes. However, due to the challenging structure of longitudinal microbiome data, few analytic methods are available to characterize the microbial dynamics over time. RESULTS We propose a microbial trend analysis (MTA) framework for the high-dimensional and phylogenetically-based longitudinal microbiome data. In particular, MTA can perform three tasks: 1) capture the common microbial dynamic trends for a group of subjects at the community level and identify the dominant taxa; 2) examine whether or not the microbial overall dynamic trends are significantly different between groups; 3) classify an individual subject based on its longitudinal microbial profiling. Our extensive simulations demonstrate that the proposed MTA framework is robust and powerful in hypothesis testing, taxon identification, and subject classification. Our real data analyses further illustrate the utility of MTA through a longitudinal study in mice. CONCLUSIONS The proposed MTA framework is an attractive and effective tool in investigating dynamic microbial pattern from longitudinal microbiome studies.
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Affiliation(s)
- Chan Wang
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, 10016 NY USA
| | - Jiyuan Hu
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, 10016 NY USA
| | - Martin J. Blaser
- Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, 08854-8021 NJ USA
| | - Huilin Li
- Division of Biostatistics, Department of Population Health, New York University School of Medicine, New York, 10016 NY USA
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31
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Qian Y, Lan F, Venturelli OS. Towards a deeper understanding of microbial communities: integrating experimental data with dynamic models. Curr Opin Microbiol 2021; 62:84-92. [PMID: 34098512 PMCID: PMC8286325 DOI: 10.1016/j.mib.2021.05.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/03/2021] [Accepted: 05/06/2021] [Indexed: 12/15/2022]
Abstract
Microbial communities and their functions are shaped by complex networks of interactions among microbes and with their environment. While the critical roles microbial communities play in numerous environments have become increasingly appreciated, we have a very limited understanding of their interactions and how these interactions combine to generate community-level behaviors. This knowledge gap hinders our ability to predict community responses to perturbations and to design interventions that manipulate these communities to our benefit. Dynamic models are promising tools to address these questions. We review existing modeling techniques to construct dynamic models of microbial communities at different scales and suggest ways to leverage multiple types of models and data to facilitate our understanding and engineering of microbial communities.
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Affiliation(s)
- Yili Qian
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, United States
| | - Freeman Lan
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, United States
| | - Ophelia S Venturelli
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI 53706, United States; Department of Bacteriology, University of Wisconsin-Madison, Madison, WI 53706, United States; Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, United States.
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32
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Sharma D, Xu W. phyLoSTM: a novel deep learning model on disease prediction from longitudinal microbiome data. Bioinformatics 2021; 37:3707-3714. [PMID: 34213529 DOI: 10.1093/bioinformatics/btab482] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 05/24/2021] [Accepted: 06/30/2021] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Research shows that human microbiome is highly dynamic on longitudinal timescales, changing dynamically with diet, or due to medical interventions. In this paper, we propose a novel deep learning framework "phyLoSTM", using a combination of Convolutional Neural Networks and Long Short Term Memory Networks (LSTM) for feature extraction and analysis of temporal dependency in longitudinal microbiome sequencing data along with host's environmental factors for disease prediction. Additional novelty in terms of handling variable timepoints in subjects through LSTMs, as well as, weight balancing between imbalanced cases and controls is proposed. RESULTS We simulated 100 datasets across multiple time points for model testing. To demonstrate the model's effectiveness, we also implemented this novel method into two real longitudinal human microbiome studies: (i) DIABIMMUNE three country cohort with food allergy outcomes (Milk, Egg, Peanut and Overall) (ii) DiGiulio study with preterm delivery as outcome. Extensive analysis and comparison of our approach yields encouraging performance with an AUC of 0.897 (increased by 5%) on simulated studies and AUCs of 0.762 (increased by 19%) and 0.713 (increased by 8%) on the two real longitudinal microbiome studies respectively, as compared to the next best performing method, Random Forest. The proposed methodology improves predictive accuracy on longitudinal human microbiome studies containing spatially correlated data, and evaluates the change of microbiome composition contributing to outcome prediction. AVAILABILITY AND IMPLEMENTATION https://github.com/divya031090/phyLoSTM.
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Affiliation(s)
- Divya Sharma
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada
| | - Wei Xu
- Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada.,Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
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33
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Remien CH, Eckwright MJ, Ridenhour BJ. Structural identifiability of the generalized Lotka-Volterra model for microbiome studies. ROYAL SOCIETY OPEN SCIENCE 2021; 8:201378. [PMID: 34295510 PMCID: PMC8292772 DOI: 10.1098/rsos.201378] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 06/21/2021] [Indexed: 05/13/2023]
Abstract
Population dynamic models can be used in conjunction with time series of species abundances to infer interactions. Understanding microbial interactions is a prerequisite for numerous goals in microbiome research, including predicting how populations change over time, determining how manipulations of microbiomes affect dynamics and designing synthetic microbiomes to perform tasks. As such, there is great interest in adapting population dynamic theory for microbial systems. Despite the appeal, numerous hurdles exist. One hurdle is that the data commonly obtained from DNA sequencing yield estimates of relative abundances, while population dynamic models such as the generalized Lotka-Volterra model track absolute abundances or densities. It is not clear whether relative abundance data alone can be used to infer parameters of population dynamic models such as the Lotka-Volterra model. We used structural identifiability analyses to determine the extent to which a time series of relative abundances can be used to parametrize the generalized Lotka-Volterra model. We found that only with absolute abundance data to accompany relative abundance estimates from sequencing can all parameters be uniquely identified. However, relative abundance data alone do contain information on relative interaction strengths, which is sufficient for many studies where the goal is to estimate key interactions and their effects on dynamics. Using synthetic data of a simple community for which we know the underlying structure, local practical identifiability analysis showed that modest amounts of both process and measurement error do not fundamentally affect these identifiability properties.
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Affiliation(s)
- Christopher H. Remien
- Department of Mathematics and Statistical Science, University of Idaho, Moscow, ID, USA
| | - Mariah J. Eckwright
- Bioinformatics and Computational Biology Program, University of Idaho, Moscow, ID, USA
| | - Benjamin J. Ridenhour
- Department of Mathematics and Statistical Science, University of Idaho, Moscow, ID, USA
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34
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Suzuki K, Nakaoka S, Fukuda S, Masuya H. Energy landscape analysis elucidates the multistability of ecological communities across environmental gradients. ECOL MONOGR 2021. [DOI: 10.1002/ecm.1469] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Kenta Suzuki
- Integrated Bioresource Information Division BioResource Research Center RIKEN 3‐1‐1 Koyadai Tsukuba Ibaraki 305‐0074 Japan
| | - Shinji Nakaoka
- Laboratory of Mathematical Biology Faculty of Advanced Life Science Hokkaido University Kita‐10 Nishi‐8Kita‐ku Sapporo Hokkaido 060‐0819 Japan
- PRESTO Japan Science and Technology Agency 4‐1‐8 Honcho Kawaguchi Saitama 332‐0012 Japan
| | - Shinji Fukuda
- PRESTO Japan Science and Technology Agency 4‐1‐8 Honcho Kawaguchi Saitama 332‐0012 Japan
- Institute for Advanced Biosciences Keio University 246‐2 MizukamiKakuganji Tsuruoka Yamagata 997‐0052 Japan
- Intestinal Microbiota Project Kanagawa Institute of Industrial Science and Technology 3‐25‐13 TonomachiKawasaki‐ku Kawasaki Kanagawa 210‐0821 Japan
- Transborder Medical Research Center University of Tsukuba 1‐1‐1 Tennodai Tsukuba Ibaraki 305‐8575 Japan
| | - Hiroshi Masuya
- Integrated Bioresource Information Division BioResource Research Center RIKEN 3‐1‐1 Koyadai Tsukuba Ibaraki 305‐0074 Japan
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35
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Martínez Arbas S, Busi SB, Queirós P, de Nies L, Herold M, May P, Wilmes P, Muller EEL, Narayanasamy S. Challenges, Strategies, and Perspectives for Reference-Independent Longitudinal Multi-Omic Microbiome Studies. Front Genet 2021; 12:666244. [PMID: 34194470 PMCID: PMC8236828 DOI: 10.3389/fgene.2021.666244] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 04/30/2021] [Indexed: 12/21/2022] Open
Abstract
In recent years, multi-omic studies have enabled resolving community structure and interrogating community function of microbial communities. Simultaneous generation of metagenomic, metatranscriptomic, metaproteomic, and (meta) metabolomic data is more feasible than ever before, thus enabling in-depth assessment of community structure, function, and phenotype, thus resulting in a multitude of multi-omic microbiome datasets and the development of innovative methods to integrate and interrogate those multi-omic datasets. Specifically, the application of reference-independent approaches provides opportunities in identifying novel organisms and functions. At present, most of these large-scale multi-omic datasets stem from spatial sampling (e.g., water/soil microbiomes at several depths, microbiomes in/on different parts of the human anatomy) or case-control studies (e.g., cohorts of human microbiomes). We believe that longitudinal multi-omic microbiome datasets are the logical next step in microbiome studies due to their characteristic advantages in providing a better understanding of community dynamics, including: observation of trends, inference of causality, and ultimately, prediction of community behavior. Furthermore, the acquisition of complementary host-derived omics, environmental measurements, and suitable metadata will further enhance the aforementioned advantages of longitudinal data, which will serve as the basis to resolve drivers of community structure and function to understand the biotic and abiotic factors governing communities and specific populations. Carefully setup future experiments hold great potential to further unveil ecological mechanisms to evolution, microbe-microbe interactions, or microbe-host interactions. In this article, we discuss the challenges, emerging strategies, and best-practices applicable to longitudinal microbiome studies ranging from sampling, biomolecular extraction, systematic multi-omic measurements, reference-independent data integration, modeling, and validation.
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Affiliation(s)
- Susana Martínez Arbas
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Susheel Bhanu Busi
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Pedro Queirós
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Laura de Nies
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Malte Herold
- Department of Environmental Research and Innovation, Luxembourg Institute of Science and Technology, Belvaux, Luxembourg
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Paul Wilmes
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Emilie E. L. Muller
- Université de Strasbourg, UMR 7156 CNRS, Génétique Moléculaire, Génomique, Microbiologie, Strasbourg, France
| | - Shaman Narayanasamy
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
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Johnson D, Thurairajasingam S, Letchumanan V, Chan KG, Lee LH. Exploring the Role and Potential of Probiotics in the Field of Mental Health: Major Depressive Disorder. Nutrients 2021; 13:nu13051728. [PMID: 34065187 PMCID: PMC8161395 DOI: 10.3390/nu13051728] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/17/2021] [Accepted: 05/18/2021] [Indexed: 12/21/2022] Open
Abstract
The field of probiotic has been exponentially expanding over the recent decades with a more therapeutic-centered research. Probiotics mediated microbiota modulation within the microbiota–gut–brain axis (MGBA) have been proven to be beneficial in various health domains through pre-clinical and clinical studies. In the context of mental health, although probiotic research is still in its infancy stage, the promising role and potential of probiotics in various mental disorders demonstrated via in-vivo and in-vitro studies have laid a strong foundation for translating preclinical models to humans. The exploration of the therapeutic role and potential of probiotics in major depressive disorder (MDD) is an extremely noteworthy field of research. The possible etio-pathological mechanisms of depression involving inflammation, neurotransmitters, the hypothalamic–pituitary–adrenal (HPA) axis and epigenetic mechanisms potentially benefit from probiotic intervention. Probiotics, both as an adjunct to antidepressants or a stand-alone intervention, have a beneficial role and potential in mitigating anti-depressive effects, and confers some advantages compared to conventional treatments of depression using anti-depressants.
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Affiliation(s)
- Dinyadarshini Johnson
- Novel Bacteria and Drug Discovery Research Group (NBDD), Microbiome and Bioresource Research Strength (MBRS), Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway 47500, Malaysia;
| | - Sivakumar Thurairajasingam
- Clinical School Johor Bahru, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Johor Bahru 80100, Malaysia;
| | - Vengadesh Letchumanan
- Novel Bacteria and Drug Discovery Research Group (NBDD), Microbiome and Bioresource Research Strength (MBRS), Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway 47500, Malaysia;
- Correspondence: (V.L.); (K.-G.C.); or (L.-H.L.); Tel.: +60-355-146-261 (V.L.); +60-379-677-748 (K.-G.C.); +60-355-145-887 (L.-H.L.)
| | - Kok-Gan Chan
- Division of Genetics and Molecular Biology, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur 50603, Malaysia
- International Genome Centre, Jiangsu University, Zhenjiang 212013, China
- Correspondence: (V.L.); (K.-G.C.); or (L.-H.L.); Tel.: +60-355-146-261 (V.L.); +60-379-677-748 (K.-G.C.); +60-355-145-887 (L.-H.L.)
| | - Learn-Han Lee
- Novel Bacteria and Drug Discovery Research Group (NBDD), Microbiome and Bioresource Research Strength (MBRS), Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway 47500, Malaysia;
- Correspondence: (V.L.); (K.-G.C.); or (L.-H.L.); Tel.: +60-355-146-261 (V.L.); +60-379-677-748 (K.-G.C.); +60-355-145-887 (L.-H.L.)
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Kim ET, Lee SJ, Kim TY, Lee HG, Atikur RM, Gu BH, Kim DH, Park BY, Son JK, Kim MH. Dynamic Changes in Fecal Microbial Communities of Neonatal Dairy Calves by Aging and Diarrhea. Animals (Basel) 2021; 11:1113. [PMID: 33924525 PMCID: PMC8070554 DOI: 10.3390/ani11041113] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 04/08/2021] [Accepted: 04/09/2021] [Indexed: 12/17/2022] Open
Abstract
Microbiota plays a critical role in the overall growth performance and health status of dairy cows, especially during their early life. Several studies have reported that fecal microbiome of neonatal calves is shifted by various factors such as diarrhea, antibiotic treatment, or environmental changes. Despite the importance of gut microbiome, a lack of knowledge regarding the composition and functions of microbiota impedes the development of new strategies for improving growth performance and disease resistance during the neonatal calf period. In this study, we utilized next-generation sequencing to monitor the time-dependent dynamics of the gut microbiota of dairy calves before weaning (1-8 weeks of age) and further investigated the microbiome changes caused by diarrhea. Metagenomic analysis revealed that continuous changes, including increasing gut microbiome diversity, occurred from 1 to 5 weeks of age. However, the composition and diversity of the fecal microbiome did not change after 6 weeks of age. The most prominent changes in the fecal microbiome composition caused by aging at family level were a decreased abundance of Bacteroidaceae and Enterobacteriaceae and an increased abundance of Prevotellaceae. Phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) analysis indicated that the abundance of microbial genes associated with various metabolic pathways changed with aging. All calves with diarrhea symptoms showed drastic microbiome changes and about a week later returned to the microbiome of pre-diarrheal stage regardless of age. At phylum level, abundance of Bacteroidetes was decreased (p = 0.09) and that of Proteobacteria increased (p = 0.07) during diarrhea. PICRUSt analysis indicated that microbial metabolism-related genes, such as starch and sucrose metabolism, sphingolipid metabolism, alanine aspartate, and glutamate metabolism were significantly altered in diarrheal calves. Together, these results highlight the important implications of gut microbiota in gut metabolism and health status of neonatal dairy calves.
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Affiliation(s)
- Eun-Tae Kim
- Dairy Science Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000, Korea; (E.-T.K.); (D.-H.K.); (J.-K.S.)
| | - Sang-Jin Lee
- Department of Animal Science, College of Natural Resources & Life Science, Pusan National University, Miryang 50463, Korea; (S.-J.L.); (T.-Y.K.); (H.-G.L.); (R.M.A.)
| | - Tae-Yong Kim
- Department of Animal Science, College of Natural Resources & Life Science, Pusan National University, Miryang 50463, Korea; (S.-J.L.); (T.-Y.K.); (H.-G.L.); (R.M.A.)
| | - Hyo-Gun Lee
- Department of Animal Science, College of Natural Resources & Life Science, Pusan National University, Miryang 50463, Korea; (S.-J.L.); (T.-Y.K.); (H.-G.L.); (R.M.A.)
| | - Rahman M. Atikur
- Department of Animal Science, College of Natural Resources & Life Science, Pusan National University, Miryang 50463, Korea; (S.-J.L.); (T.-Y.K.); (H.-G.L.); (R.M.A.)
| | - Bon-Hee Gu
- Life and Industry Convergence Research Institute, Pusan National University, Miryang 50463, Korea;
| | - Dong-Hyeon Kim
- Dairy Science Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000, Korea; (E.-T.K.); (D.-H.K.); (J.-K.S.)
| | - Beom-Young Park
- National Institute of Animal Science, Rural Development Administration, Wanju 55365, Korea;
| | - Jun-Kyu Son
- Dairy Science Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000, Korea; (E.-T.K.); (D.-H.K.); (J.-K.S.)
| | - Myung-Hoo Kim
- Department of Animal Science, College of Natural Resources & Life Science, Pusan National University, Miryang 50463, Korea; (S.-J.L.); (T.-Y.K.); (H.-G.L.); (R.M.A.)
- Life and Industry Convergence Research Institute, Pusan National University, Miryang 50463, Korea;
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Abstract
A key challenge in the analysis of longitudinal microbiome data is the inference of temporal interactions between microbial taxa, their genes, the metabolites that they consume and produce, and host genes. To address these challenges, we developed a computational pipeline, a pipeline for the analysis of longitudinal multi-omics data (PALM), that first aligns multi-omics data and then uses dynamic Bayesian networks (DBNs) to reconstruct a unified model. Our approach overcomes differences in sampling and progression rates, utilizes a biologically inspired multi-omic framework, reduces the large number of entities and parameters in the DBNs, and validates the learned network. Applying PALM to data collected from inflammatory bowel disease patients, we show that it accurately identifies known and novel interactions. Targeted experimental validations further support a number of the predicted novel metabolite-taxon interactions. IMPORTANCE While a number of large consortia collect and profile several different types of microbiome and genomic time series data, very few methods exist for joint modeling of multi-omics data sets. We developed a new computational pipeline, PALM, which uses dynamic Bayesian networks (DBNs) and is designed to integrate multi-omics data from longitudinal microbiome studies. When used to integrate sequence, expression, and metabolomics data from microbiome samples along with host expression data, the resulting models identify interactions between taxa, their genes, and the metabolites that they produce and consume, as well as their impact on host expression. We tested the models both by using them to predict future changes in microbiome levels and by comparing the learned interactions to known interactions in the literature. Finally, we performed experimental validations for a few of the predicted interactions to demonstrate the ability of the method to identify novel relationships and their impact.
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Han Y, Baker C, Vogtmann E, Hua X, Shi J, Liu D. Modeling Longitudinal Microbiome Compositional Data: A Two-Part Linear Mixed Model with Shared Random Effects. STATISTICS IN BIOSCIENCES 2021. [DOI: 10.1007/s12561-021-09302-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Liao C, Taylor BP, Ceccarani C, Fontana E, Amoretti LA, Wright RJ, Gomes ALC, Peled JU, Taur Y, Perales MA, van den Brink MRM, Littmann E, Pamer EG, Schluter J, Xavier JB. Compilation of longitudinal microbiota data and hospitalome from hematopoietic cell transplantation patients. Sci Data 2021; 8:71. [PMID: 33654104 PMCID: PMC7925583 DOI: 10.1038/s41597-021-00860-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 02/12/2021] [Indexed: 02/06/2023] Open
Abstract
The impact of the gut microbiota in human health is affected by several factors including its composition, drug administrations, therapeutic interventions and underlying diseases. Unfortunately, many human microbiota datasets available publicly were collected to study the impact of single variables, and typically consist of outpatients in cross-sectional studies, have small sample numbers and/or lack metadata to account for confounders. These limitations can complicate reusing the data for questions outside their original focus. Here, we provide comprehensive longitudinal patient dataset that overcomes those limitations: a collection of fecal microbiota compositions (>10,000 microbiota samples from >1,000 patients) and a rich description of the "hospitalome" experienced by the hosts, i.e., their drug exposures and other metadata from patients with cancer, hospitalized to receive allogeneic hematopoietic cell transplantation (allo-HCT) at a large cancer center in the United States. We present five examples of how to apply these data to address clinical and scientific questions on host-associated microbial communities.
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Affiliation(s)
- Chen Liao
- grid.51462.340000 0001 2171 9952Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY USA
| | - Bradford P. Taylor
- grid.51462.340000 0001 2171 9952Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY USA
| | - Camilla Ceccarani
- grid.51462.340000 0001 2171 9952Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY USA ,grid.4708.b0000 0004 1757 2822Department of Health Sciences, Università degli Studi di Milano, Milan, Italy
| | - Emily Fontana
- grid.51462.340000 0001 2171 9952Infectious Disease Service, Department of Medicine, and Immunology Program, Sloan Kettering Institute, New York, NY USA
| | - Luigi A. Amoretti
- grid.51462.340000 0001 2171 9952Infectious Disease Service, Department of Medicine, and Immunology Program, Sloan Kettering Institute, New York, NY USA
| | - Roberta J. Wright
- grid.51462.340000 0001 2171 9952Infectious Disease Service, Department of Medicine, and Immunology Program, Sloan Kettering Institute, New York, NY USA
| | - Antonio L. C. Gomes
- grid.51462.340000 0001 2171 9952Department of Immunology, Memorial Sloan-Kettering Cancer Center, New York, NY USA
| | - Jonathan U. Peled
- grid.51462.340000 0001 2171 9952Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medical College, New York, NY USA
| | - Ying Taur
- grid.51462.340000 0001 2171 9952Infectious Disease Service, Department of Medicine, and Immunology Program, Sloan Kettering Institute, New York, NY USA
| | - Miguel-Angel Perales
- grid.51462.340000 0001 2171 9952Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medical College, New York, NY USA
| | - Marcel R. M. van den Brink
- grid.51462.340000 0001 2171 9952Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medical College, New York, NY USA
| | - Eric Littmann
- grid.170205.10000 0004 1936 7822Duchossois Family Institute, University of Chicago, Chicago, IL USA
| | - Eric G. Pamer
- grid.170205.10000 0004 1936 7822Duchossois Family Institute, University of Chicago, Chicago, IL USA
| | - Jonas Schluter
- grid.51462.340000 0001 2171 9952Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY USA
| | - Joao B. Xavier
- grid.51462.340000 0001 2171 9952Program for Computational and Systems Biology, Memorial Sloan-Kettering Cancer Center, New York, NY USA
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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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Bartenslager AC, Althuge ND, Loy JD, Hille MM, Spangler ML, Fernando SC. Longitudinal assessment of the bovine ocular bacterial community dynamics in calves. Anim Microbiome 2021; 3:16. [PMID: 33516260 PMCID: PMC7847012 DOI: 10.1186/s42523-021-00079-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 01/19/2021] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Infectious Bovine Keratoconjunctivitis (IBK), commonly known as pinkeye, is one of the most significant diseases of beef cattle. As such, IBK costs the US beef industry at least 150 million annually. However, strategies to prevent IBK are limited, with most cases resulting in treatment with antibiotics once the disease has developed. Longitudinal studies evaluating establishment of the ocular microbiota may identify critical risk periods for IBK outbreaks or changes in the microbiota that may predispose animals to IBK. RESULTS In an attempt to characterize the establishment and colonization patterns of the bovine ocular microbiota, we conducted a longitudinal study consisting of 227 calves and evaluated the microbiota composition over time using amplicon sequence variants (ASVs) based on 16S rRNA sequencing data and culture-based approaches. Beef calves on trial consisted of both male (intact) and females. Breeds were composed of purebred Angus and composites with varying percentages of Simmental, Angus, and Red Angus breeds. Average age at the start of the trial was 65 days ±15.02 and all calves remained nursing on their dam until weaning (day 139 of the study). The trial consisted of 139 days with four sampling time points on day 0, 21, 41, and 139. The experimental population received three different vaccination treatments (autogenous, commercial (both inactivated bacteria), and adjuvant placebo), to assess the effectiveness of different vaccines for IBK prevention. A significant change in bacterial community composition was observed across time periods sampled compared to the baseline (p < 0.001). However, no treatment effect of vaccine was detected within the ocular bacterial community. The bacterial community composition with the greatest time span between sampling time periods (98d span) was most similar to the baseline sample collected, suggesting re-establishment of the ocular microbiota to baseline levels over time after perturbation. The effect of IgA levels on the microbial community was investigated in a subset of cattle within the study. However, no significant effect of IgA was observed. Significant changes in the ocular microbiota were identified when comparing communities pre- and post-clinical signs of IBK. Additionally, dynamic changes in opportunistic pathogens Moraxella spp. were observed and confirmed using culture based methods. CONCLUSIONS Our results indicate that the bovine ocular microbiota is well represented by opportunistic pathogens such as Moraxella and Mycoplasma. Furthermore, this study characterizes the diversity of the ocular microbiota in calves and demonstrates the plasticity of the ocular microbiota to change. Additionally, we demonstrate the ocular microbiome in calves is similar between the eyes and the perturbation of one eye results in similar changes in the other eye. We also demonstrate the bovine ocular microbiota is slow to recover post perturbation and as a result provide opportunistic pathogens a chance to establish within the eye leading to IBK and other diseases. Characterizing the dynamic nature of the ocular microbiota provides novel opportunities to develop potential probiotic intervention to reduce IBK outbreaks in cattle.
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Affiliation(s)
| | - Nirosh D Althuge
- Department of Animal Science, University of Nebraska-Lincoln, Lincoln, NE, USA
- Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - John Dustin Loy
- School of Veterinary Medicine and Biomedical Sciences, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Matthew M Hille
- School of Veterinary Medicine and Biomedical Sciences, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Matthew L Spangler
- Department of Animal Science, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Samodha C Fernando
- Department of Animal Science, University of Nebraska-Lincoln, Lincoln, NE, USA.
- Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, NE, USA.
- School of Biological Sciences, University of Nebraska-Lincoln, Lincoln, NE, USA.
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Schluter J, Peled JU, Taylor BP, Markey KA, Smith M, Taur Y, Niehus R, Staffas A, Dai A, Fontana E, Amoretti LA, Wright RJ, Morjaria S, Fenelus M, Pessin MS, Chao NJ, Lew M, Bohannon L, Bush A, Sung AD, Hohl TM, Perales MA, van den Brink MRM, Xavier JB. The gut microbiota is associated with immune cell dynamics in humans. Nature 2020; 588:303-307. [PMID: 33239790 PMCID: PMC7725892 DOI: 10.1038/s41586-020-2971-8] [Citation(s) in RCA: 269] [Impact Index Per Article: 67.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 09/30/2020] [Indexed: 02/07/2023]
Abstract
The gut microbiota influences development1-3 and homeostasis4-7 of the mammalian immune system, and is associated with human inflammatory8 and immune diseases9,10 as well as responses to immunotherapy11-14. Nevertheless, our understanding of how gut bacteria modulate the immune system remains limited, particularly in humans, where the difficulty of direct experimentation makes inference challenging. Here we study hundreds of hospitalized-and closely monitored-patients with cancer receiving haematopoietic cell transplantation as they recover from chemotherapy and stem-cell engraftment. This aggressive treatment causes large shifts in both circulatory immune cell and microbiota populations, enabling the relationships between the two to be studied simultaneously. Analysis of observed daily changes in circulating neutrophil, lymphocyte and monocyte counts and more than 10,000 longitudinal microbiota samples revealed consistent associations between gut bacteria and immune cell dynamics. High-resolution clinical metadata and Bayesian inference allowed us to compare the effects of bacterial genera in relation to those of immunomodulatory medications, revealing a considerable influence of the gut microbiota-together and over time-on systemic immune cell dynamics. Our analysis establishes and quantifies the link between the gut microbiota and the human immune system, with implications for microbiota-driven modulation of immunity.
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Affiliation(s)
- Jonas Schluter
- Institute for Computational Medicine, NYU Langone Health, New York, NY, USA.
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Jonathan U Peled
- Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell Medical College, New York, NY, USA
| | - Bradford P Taylor
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kate A Markey
- Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell Medical College, New York, NY, USA
| | - Melody Smith
- Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell Medical College, New York, NY, USA
| | - Ying Taur
- Infectious Disease Service, Department of Medicine, and Immunology Program, Sloan Kettering Institute, New York, NY, USA
| | - Rene Niehus
- Harvard University, T. H. Chan School of Public Health, Boston, MA, USA
| | - Anna Staffas
- Sahlgrenska Cancer Center, Department of Microbiology and Immunology, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
| | - Anqi Dai
- Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Emily Fontana
- Infectious Disease Service, Department of Medicine, and Immunology Program, Sloan Kettering Institute, New York, NY, USA
| | - Luigi A Amoretti
- Infectious Disease Service, Department of Medicine, and Immunology Program, Sloan Kettering Institute, New York, NY, USA
| | - Roberta J Wright
- Infectious Disease Service, Department of Medicine, and Immunology Program, Sloan Kettering Institute, New York, NY, USA
| | - Sejal Morjaria
- Infectious Disease Service, Department of Medicine, and Immunology Program, Sloan Kettering Institute, New York, NY, USA
| | - Maly Fenelus
- Department of Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Melissa S Pessin
- Department of Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nelson J Chao
- Division of Hematologic Malignancies and Cellular Therapy, Duke University School of Medicine, Durham, NC, USA
| | - Meagan Lew
- Division of Hematologic Malignancies and Cellular Therapy, Duke University School of Medicine, Durham, NC, USA
| | - Lauren Bohannon
- Division of Hematologic Malignancies and Cellular Therapy, Duke University School of Medicine, Durham, NC, USA
| | - Amy Bush
- Division of Hematologic Malignancies and Cellular Therapy, Duke University School of Medicine, Durham, NC, USA
| | - Anthony D Sung
- Division of Hematologic Malignancies and Cellular Therapy, Duke University School of Medicine, Durham, NC, USA
| | - Tobias M Hohl
- Infectious Disease Service, Department of Medicine, and Immunology Program, Sloan Kettering Institute, New York, NY, USA
| | - Miguel-Angel Perales
- Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell Medical College, New York, NY, USA
| | - Marcel R M van den Brink
- Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Weill Cornell Medical College, New York, NY, USA
| | - Joao B Xavier
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Park SY, Ufondu A, Lee K, Jayaraman A. Emerging computational tools and models for studying gut microbiota composition and function. Curr Opin Biotechnol 2020; 66:301-311. [PMID: 33248408 PMCID: PMC7744364 DOI: 10.1016/j.copbio.2020.10.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 10/15/2020] [Accepted: 10/16/2020] [Indexed: 02/06/2023]
Abstract
The gut microbiota and its metabolites play critical roles in human health and disease. Advances in high-throughput sequencing, mass spectrometry, and other omics assay platforms have improved our ability to generate large volumes of data exploring the temporal variations in the compositions and functions of microbial communities. To elucidate mechanisms, methods and tools are needed that can rigorously model the dependencies within time-series data. Longitudinal data are often sparse and unevenly sampled, and nontrivial challenges remain in determining statistical significance, normalization across different data types, and model validation. In this review, we highlight recent developments in models and software tools for the analysis of time series microbiome and metabolome data, as well as integration of these data.
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Affiliation(s)
- Seo-Young Park
- Department of Chemical and Biological Engineering, Tufts University, Medford, MA, USA
| | - Arinzechukwu Ufondu
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA
| | - Kyongbum Lee
- Department of Chemical and Biological Engineering, Tufts University, Medford, MA, USA.
| | - Arul Jayaraman
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA; Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA.
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Mota R, Pinto M, Palmeira J, Gonçalves D, Ferreira H. Multidrug-resistant bacteria as intestinal colonizers and evolution of intestinal colonization in healthy university students in Portugal. Access Microbiol 2020; 3:acmi000182. [PMID: 33997613 PMCID: PMC8115976 DOI: 10.1099/acmi.0.000182] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 11/02/2020] [Indexed: 12/18/2022] Open
Abstract
Multidrug-resistant bacteria have been increasingly described in healthcare institutions, however community resistance also seems to be emerging. Escherichia coli an intestinal commensal bacteria, is also a pathogen and represents an important intestinal reservoir of resistance. Our aim was the study of the intestinal colonization and of the persistence of antibiotic resistant intestinal bacteria in healthy university students of Porto, in the north of Portugal. Samples from 30 university students were collected and analysed. Two E. coli isolates were randomly obtained from each student and Gram-negative bacilli resistant to antibiotics were studied. In addition, we evaluated changes in the Gram-negative intestinal colonization of ten university students in a short period of time. Molecular characterization showed a high presence of bla TEM in commensal E. coli . Gram-negative bacteria with intrinsic and extrinsic resistance were isolated, namely Pseudomonas spp., Enterobacter spp. and Pantoea spp. We isolated three ESBL-producing E. coli from two students. These isolates showed bla CTX-M group 1 (n=1), bla CTX-M group 9 (n=2), bla TEM (n=2), bla SHV (n=1) and tetA (n=2) genes. Additionally, they showed specific virulence factors and conjugational transfer of antibiotic resistance and virulence genes. One Pseudomonas spp. isolate resistant to carbapenems was detected colonizing one student. Our results confirm that healthy young adults may be colonized with commensals showing clinically relevant antibiotic resistance mechanisms, creating a risk of silent spread of these bacteria in the community.
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Affiliation(s)
- Raquel Mota
- UCIBIO, Microbiology, Faculty of Pharmacy of University of Porto, Portugal.,Microbiology, Faculty of Pharmacy of University of Porto, Rua de Jorge Viterbo Ferreira 228, 4050-313 Porto, Portugal
| | - Marisa Pinto
- UCIBIO, Microbiology, Faculty of Pharmacy of University of Porto, Portugal.,Microbiology, Faculty of Pharmacy of University of Porto, Rua de Jorge Viterbo Ferreira 228, 4050-313 Porto, Portugal
| | - Josman Palmeira
- UCIBIO, Microbiology, Faculty of Pharmacy of University of Porto, Portugal.,Microbiology, Faculty of Pharmacy of University of Porto, Rua de Jorge Viterbo Ferreira 228, 4050-313 Porto, Portugal
| | - Daniela Gonçalves
- UCIBIO, Microbiology, Faculty of Pharmacy of University of Porto, Portugal.,Microbiology, Faculty of Pharmacy of University of Porto, Rua de Jorge Viterbo Ferreira 228, 4050-313 Porto, Portugal.,Instituto Superior de Saúde, Rua Castelo de Almourol, 4720-155 Amares, Portugal
| | - Helena Ferreira
- UCIBIO, Microbiology, Faculty of Pharmacy of University of Porto, Portugal.,Microbiology, Faculty of Pharmacy of University of Porto, Rua de Jorge Viterbo Ferreira 228, 4050-313 Porto, Portugal
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Zhang X, Guo B, Yi N. Zero-Inflated gaussian mixed models for analyzing longitudinal microbiome data. PLoS One 2020; 15:e0242073. [PMID: 33166356 PMCID: PMC7652264 DOI: 10.1371/journal.pone.0242073] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 10/26/2020] [Indexed: 01/01/2023] Open
Abstract
Motivation The human microbiome is variable and dynamic in nature. Longitudinal studies could explain the mechanisms in maintaining the microbiome in health or causing dysbiosis in disease. However, it remains challenging to properly analyze the longitudinal microbiome data from either 16S rRNA or metagenome shotgun sequencing studies, output as proportions or counts. Most microbiome data are sparse, requiring statistical models to handle zero-inflation. Moreover, longitudinal design induces correlation among the samples and thus further complicates the analysis and interpretation of the microbiome data. Results In this article, we propose zero-inflated Gaussian mixed models (ZIGMMs) to analyze longitudinal microbiome data. ZIGMMs is a robust and flexible method which can be applicable for longitudinal microbiome proportion data or count data generated with either 16S rRNA or shotgun sequencing technologies. It can include various types of fixed effects and random effects and account for various within-subject correlation structures, and can effectively handle zero-inflation. We developed an efficient Expectation-Maximization (EM) algorithm to fit the ZIGMMs by taking advantage of the standard procedure for fitting linear mixed models. We demonstrate the computational efficiency of our EM algorithm by comparing with two other zero-inflated methods. We show that ZIGMMs outperform the previously used linear mixed models (LMMs), negative binomial mixed models (NBMMs) and zero-inflated Beta regression mixed model (ZIBR) in detecting associated effects in longitudinal microbiome data through extensive simulations. We also apply our method to two public longitudinal microbiome datasets and compare with LMMs and NBMMs in detecting dynamic effects of associated taxa.
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Affiliation(s)
- Xinyan Zhang
- Department of Statistics and Data Analytics, Kennesaw State University, Kennesaw, GA, United States of America
| | - Boyi Guo
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, United States of America
| | - Nengjun Yi
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, United States of America
- * E-mail:
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O'Keeffe KR, Oppler ZJ, Brisson D. Evolutionary ecology of Lyme Borrelia. INFECTION, GENETICS AND EVOLUTION : JOURNAL OF MOLECULAR EPIDEMIOLOGY AND EVOLUTIONARY GENETICS IN INFECTIOUS DISEASES 2020; 85:104570. [PMID: 32998077 PMCID: PMC8349510 DOI: 10.1016/j.meegid.2020.104570] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 09/21/2020] [Accepted: 09/22/2020] [Indexed: 01/02/2023]
Abstract
The bacterial genus, Borrelia, is comprised of vector-borne spirochete species that infect and are transmitted from multiple host species. Some Borrelia species cause highly-prevalent diseases in humans and domestic animals. Evolutionary, ecological, and molecular research on many Borrelia species have resulted in tremendous progress toward understanding the biology and natural history of these species. Yet, many outstanding questions, such as how Borrelia populations will be impacted by climate and land-use change, will require an interdisciplinary approach. The evolutionary ecology research framework incorporates theory and data from evolutionary, ecological, and molecular studies while overcoming common assumptions within each field that can hinder integration across these disciplines. Evolutionary ecology offers a framework to evaluate the ecological consequences of evolved traits and to predict how present-day ecological processes may result in further evolutionary change. Studies of microbes with complex transmission cycles, like Borrelia, which interact with multiple vertebrate hosts and arthropod vectors, are poised to leverage the power of the evolutionary ecology framework to identify the molecular interactions involved in ecological processes that result in evolutionary change. Using existing data, we outline how evolutionary ecology theory can delineate how interactions with other species and the physical environment create selective forces or impact migration of Borrelia populations and result in micro-evolutionary changes. We further discuss the ecological and molecular consequences of those micro-evolutionary changes. While many of the currently outstanding questions will necessitate new experimental designs and additional empirical data, many others can be addressed immediately by integrating existing molecular and ecological data within an evolutionary ecology framework.
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Affiliation(s)
| | - Zachary J Oppler
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Dustin Brisson
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
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Van Cise AM, Wade PR, Goertz CEC, Burek-Huntington K, Parsons KM, Clauss T, Hobbs RC, Apprill A. Skin microbiome of beluga whales: spatial, temporal, and health-related dynamics. Anim Microbiome 2020; 2:39. [PMID: 33499987 PMCID: PMC7807513 DOI: 10.1186/s42523-020-00057-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 10/08/2020] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Host-specific microbiomes play an important role in individual health and ecology; in marine mammals, epidermal microbiomes may be a protective barrier between the host and its aqueous environment. Understanding these epidermal-associated microbial communities, and their ecological- or health-driven variability, is the first step toward developing health indices for rapid assessment of individual or population health. In Cook Inlet, Alaska, an endangered population of beluga whales (Delphinapterus leucas) numbers fewer than 300 animals and continues to decline, despite more than a decade of conservation effort. Characterizing the epidermal microbiome of this species could provide insight into the ecology and health of this endangered population and allow the development of minimally invasive health indicators based on tissue samples. RESULTS We sequenced the hypervariable IV region of bacterial and archaeal SSU rRNA genes from epidermal tissue samples collected from endangered Cook Inlet beluga whales (n = 33) and the nearest neighboring population in Bristol Bay (n = 39) between 2012 and 2018. We examined the sequences using amplicon sequence variant (ASV)-based analyses, and no ASVs were associated with all individuals, indicating a greater degree of epidermal microbiome variability among beluga whales than in previously studied cetacean species and suggesting the absence of a species-specific core microbiome. Epidermal microbiome composition differed significantly between populations and across sampling years. Comparing the microbiomes of Bristol Bay individuals of known health status revealed 11 ASVs associated with potential pathogens that differed in abundance between healthy individuals and those with skin lesions or dermatitis. Molting and non-molting individuals also differed significantly in microbial diversity and the abundance of potential pathogen-associated ASVs, indicating the importance of molting in maintaining skin health. CONCLUSIONS We provide novel insights into the dynamics of Alaskan beluga whale epidermal microbial communities. A core epidermal microbiome was not identified across all animals. We characterize microbial dynamics related to population, sampling year and health state including level of skin molting. The results of this study provide a basis for future work to understand the role of the skin microbiome in beluga whale health and to develop health indices for management of the endangered Cook Inlet beluga whales, and cetaceans more broadly.
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Affiliation(s)
- Amy M Van Cise
- Woods Hole Oceanographic Institution, Woods Hole, MA, USA.
- North Gulf Oceanic Society, Visiting Scientist at Northwest Fisheries Science Center, NOAA National Marine Fisheries Service, Seattle, WA, USA.
| | - Paul R Wade
- Marine Mammal Laboratory, Alaska Fisheries Science Center, NOAA National Marine Fisheries Service, Seattle, WA, USA
| | | | | | - Kim M Parsons
- Marine Mammal Laboratory, Alaska Fisheries Science Center, NOAA National Marine Fisheries Service, Seattle, WA, USA
- Conservation Biology Division, Northwest Fisheries Science Center, NOAA National Marine Fisheries Service, Seattle, WA, USA
| | - Tonya Clauss
- Animal & Environmental Heath, Georgia Aquarium, Atlanta, GA, USA
| | - Roderick C Hobbs
- Marine Mammal Laboratory (retired), Alaska Fisheries Science Center, NOAA National Marine Fisheries Service, Seattle, WA, USA
| | - Amy Apprill
- Woods Hole Oceanographic Institution, Woods Hole, MA, USA.
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Zhang X, Yi N. Fast zero-inflated negative binomial mixed modeling approach for analyzing longitudinal metagenomics data. Bioinformatics 2020; 36:2345-2351. [PMID: 31904815 DOI: 10.1093/bioinformatics/btz973] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 11/24/2019] [Accepted: 01/01/2020] [Indexed: 01/03/2023] Open
Abstract
MOTIVATION Longitudinal metagenomics data, including both 16S rRNA and whole-metagenome shotgun sequencing data, enhanced our abilities to understand the dynamic associations between the human microbiome and various diseases. However, analytic tools have not been fully developed to simultaneously address the main challenges of longitudinal metagenomics data, i.e. high-dimensionality, dependence among samples and zero-inflation of observed counts. RESULTS We propose a fast zero-inflated negative binomial mixed modeling (FZINBMM) approach to analyze high-dimensional longitudinal metagenomic count data. The FZINBMM approach is based on zero-inflated negative binomial mixed models (ZINBMMs) for modeling longitudinal metagenomic count data and a fast EM-IWLS algorithm for fitting ZINBMMs. FZINBMM takes advantage of a commonly used procedure for fitting linear mixed models, which allows us to include various types of fixed and random effects and within-subject correlation structures and quickly analyze many taxa. We found that FZINBMM remarkably outperformed in computational efficiency and was statistically comparable with two R packages, GLMMadaptive and glmmTMB, that use numerical integration to fit ZINBMMs. Extensive simulations and real data applications showed that FZINBMM outperformed other previous methods, including linear mixed models, negative binomial mixed models and zero-inflated Gaussian mixed models. AVAILABILITY AND IMPLEMENTATION FZINBMM has been implemented in the R package NBZIMM, available in the public GitHub repository http://github.com//nyiuab//NBZIMM. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xinyan Zhang
- Department of Biostatistics, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA 30458, USA
| | - Nengjun Yi
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
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An Approach to Analyze Longitudinal Zero-Inflated Microbiome Count Data Using Two-Stage Mixed Effects Models. STATISTICS IN BIOSCIENCES 2020. [DOI: 10.1007/s12561-020-09295-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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