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Balasubramanian R, Schneider E, Gunnigle E, Cotter PD, Cryan JF. Fermented foods: Harnessing their potential to modulate the microbiota-gut-brain axis for mental health. Neurosci Biobehav Rev 2024; 158:105562. [PMID: 38278378 DOI: 10.1016/j.neubiorev.2024.105562] [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: 10/26/2023] [Revised: 01/18/2024] [Accepted: 01/21/2024] [Indexed: 01/28/2024]
Abstract
Over the past two decades, whole food supplementation strategies have been leveraged to target mental health. In addition, there has been increasing attention on the ability of gut microbes, so called psychobiotics, to positively impact behaviour though the microbiota-gut-brain axis. Fermented foods offer themselves as a combined whole food microbiota modulating intervention. Indeed, they contain potentially beneficial microbes, microbial metabolites and other bioactives, which are being harnessed to target the microbiota-gut-brain axis for positive benefits. This review highlights the diverse nature of fermented foods in terms of the raw materials used and type of fermentation employed, and summarises their potential to shape composition of the gut microbiota, the gut to brain communication pathways including the immune system and, ultimately, modulate the microbiota-gut-brain axis. Throughout, we identify knowledge gaps and challenges faced in designing human studies for investigating the mental health-promoting potential of individual fermented foods or components thereof. Importantly, we also suggest solutions that can advance understanding of the therapeutic merit of fermented foods to modulate the microbiota-gut-brain axis.
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Affiliation(s)
- Ramya Balasubramanian
- APC Microbiome Ireland, University College Cork, Cork, Ireland; Department of Anatomy and Neuroscience, University College Cork, Cork, Ireland; Food Biosciences Department, Teagasc Food Research Centre, Moorepark, Fermoy, P61C996, County Cork, Ireland
| | | | - Eoin Gunnigle
- APC Microbiome Ireland, University College Cork, Cork, Ireland
| | - Paul D Cotter
- APC Microbiome Ireland, University College Cork, Cork, Ireland; Food Biosciences Department, Teagasc Food Research Centre, Moorepark, Fermoy, P61C996, County Cork, Ireland.
| | - John F Cryan
- APC Microbiome Ireland, University College Cork, Cork, Ireland; Department of Anatomy and Neuroscience, University College Cork, Cork, Ireland.
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2
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Temel HY, Kaymak Ö, Kaplan S, Bahcivanci B, Gkoutos GV, Acharjee A. Role of microbiota and microbiota-derived short-chain fatty acids in PDAC. Cancer Med 2023; 12:5661-5675. [PMID: 36205023 PMCID: PMC10028056 DOI: 10.1002/cam4.5323] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 09/08/2022] [Accepted: 09/23/2022] [Indexed: 02/05/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive lethal diseases among other cancer types. Gut microbiome and its metabolic regulation play a crucial role in PDAC. Metabolic regulation in the gut is a complex process that involves microbiome and microbiome-derived short-chain fatty acids (SCFAs). SCFAs regulate inflammation, as well as lipid and glucose metabolism, through different pathways. This review aims to summarize recent developments in PDAC in the context of gut and oral microbiota and their associations with short-chain fatty acid (SCFA). In addition to this, we discuss possible therapeutic applications using microbiota in PDAC.
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Affiliation(s)
- Hülya Yılmaz Temel
- Department of Bioengineering, Faculty of EngineeringEge UniversityIzmirTurkey
| | - Öznur Kaymak
- Department of Bioengineering, Faculty of EngineeringEge UniversityIzmirTurkey
| | - Seren Kaplan
- Department of Bioengineering, Faculty of EngineeringEge UniversityIzmirTurkey
| | - Basak Bahcivanci
- Institute of Cancer and Genomic Sciences, University of BirminghamBirminghamUK
| | - Georgios V. Gkoutos
- Institute of Cancer and Genomic Sciences, University of BirminghamBirminghamUK
- National Institute for Health Research Surgical Reconstruction, Queen Elizabeth Hospital BirminghamBirminghamUK
- MRC Health Data Research UK (HDR UK)BirminghamUK
| | - Animesh Acharjee
- Institute of Cancer and Genomic Sciences, University of BirminghamBirminghamUK
- National Institute for Health Research Surgical Reconstruction, Queen Elizabeth Hospital BirminghamBirminghamUK
- MRC Health Data Research UK (HDR UK)BirminghamUK
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3
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Iannone LF, Gómez-Eguílaz M, De Caro C. Gut microbiota manipulation as an epilepsy treatment. Neurobiol Dis 2022; 174:105897. [DOI: 10.1016/j.nbd.2022.105897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 10/10/2022] [Accepted: 10/12/2022] [Indexed: 11/09/2022] Open
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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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Losol P, Park HS, Song WJ, Hwang YK, Kim SH, Holloway JW, Chang YS. Association of upper airway bacterial microbiota and asthma: systematic review. Asia Pac Allergy 2022; 12:e32. [PMID: 35966153 PMCID: PMC9353206 DOI: 10.5415/apallergy.2022.12.e32] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 07/28/2022] [Indexed: 12/31/2022] Open
Abstract
Individual studies have suggested that upper airway dysbiosis may be associated with asthma or its severity. We aimed to systematically review studies that evaluated upper airway bacterial microbiota in relation to asthma, compared to nonasthmatic controls. Searches used MEDLINE, Embase, and Web of Science Core Collection. Eligible studies included association between asthma and upper airway dysbiosis; assessment of composition and diversity of upper airway microbiota using 16S rRNA or metagenomic sequencing; upper airway samples from nose, nasopharynx, oropharynx or hypopharynx. Study quality was assessed and rated using the Newcastle-Ottawa scale. A total of 249 publications were identified; 17 in the final analysis (13 childhood asthma and 4 adult asthma). Microbiome richness was measured in 6 studies, species diversity in 12, and bacterial composition in 17. The quality of evidence was good and fair. The alpha-diversity was found to be higher in younger children with wheezing and asthma, while it was lower when asthmatic children had rhinitis or mite sensitization. In children, Proteobacteria and Firmicutes were higher in asthmatics compared to controls (7 studies), and Moraxella, Streptococcus, and Haemophilus were predominant in the bacterial community. In pooled analysis, nasal Streptococcus colonization was associated with the presence of wheezing at age 5 (p = 0.04). In adult patients with asthma, the abundance of Proteobacteria was elevated in the upper respiratory tract (3 studies). Nasal colonization of Corynebacterium was lower in asthmatics (2 studies). This study demonstrates the potential relationships between asthma and specific bacterial colonization in the upper airway in adult and children with asthma.
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Affiliation(s)
- Purevsuren Losol
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Institute of Allergy and Clinical Immunology, Medical Research Center, Seoul National University, Seoul, Korea
| | - Hee-Sun Park
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Woo-Jung Song
- Department of Allergy and Clinical Immunology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Yu-Kyoung Hwang
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Sae-Hoon Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Institute of Allergy and Clinical Immunology, Medical Research Center, Seoul National University, Seoul, Korea
| | - John W Holloway
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Yoon-Seok Chang
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Institute of Allergy and Clinical Immunology, Medical Research Center, Seoul National University, Seoul, Korea
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Bisht V, Acharjee A, Gkoutos GV. NFnetFu: A novel workflow for microbiome data fusion. Comput Biol Med 2021; 135:104556. [PMID: 34216888 PMCID: PMC8404037 DOI: 10.1016/j.compbiomed.2021.104556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 06/04/2021] [Accepted: 06/04/2021] [Indexed: 12/18/2022]
Abstract
Microbiome data analysis and its interpretation into meaningful biological insights remain very challenging for numerous reasons, perhaps most prominently, due to the need to account for multiple factors, including collinearity, sparsity (excessive zeros) and effect size, that the complex experimental workflow and subsequent downstream data analysis require. Moreover, a meaningful microbiome data analysis necessitates the development of interpretable models that incorporate inferences across available data as well as background biomedical knowledge. We developed a multimodal framework that considers sparsity (excessive zeros), lower effect size, intrinsically microbial correlations, i.e., collinearity, as well as background biomedical knowledge in the form of a cluster-infused enriched network architecture. Finally, our framework also provides a candidate taxa/Operational Taxonomic Unit (OTU) that can be targeted for future validation experiments. We have developed a tool, the term NFnetFU (Neuro Fuzzy network Fusion), that encompasses our framework and have made it freely available at https://github.com/VartikaBisht6197/NFnetFu.
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Affiliation(s)
- Vartika Bisht
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, B15 2TT, UK
| | - Animesh Acharjee
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, B15 2TT, UK; Institute of Translational Medicine, University Hospitals Birmingham NHS, Foundation Trust, B15 2TT, UK; NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham, B15 2WB, UK; MRC Health Data Research UK HDR, UK.
| | - Georgios V Gkoutos
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, B15 2TT, UK; Institute of Translational Medicine, University Hospitals Birmingham NHS, Foundation Trust, B15 2TT, UK; NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham, B15 2WB, UK; MRC Health Data Research UK HDR, UK; NIHR Experimental Cancer Medicine Centre, B15 2TT, Birmingham, UK; NIHR Biomedical Research Centre, University Hospital Birmingham, Birmingham, B15 2TT, UK
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7
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Weiser R, Rye PD, Mahenthiralingam E. Implementation of microbiota analysis in clinical trials for cystic fibrosis lung infection: Experience from the OligoG phase 2b clinical trials. J Microbiol Methods 2021; 181:106133. [PMID: 33421446 DOI: 10.1016/j.mimet.2021.106133] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 01/02/2021] [Accepted: 01/04/2021] [Indexed: 11/28/2022]
Abstract
Culture-independent microbiota analysis is widely used in research and being increasingly used in translational studies. However, methods for standardisation and application of these analyses in clinical trials are limited. Here we report the microbiota analysis that accompanied two phase 2b clinical trials of the novel, non-antibiotic therapy OligoG CF-5/20 for cystic fibrosis (CF) lung infection. Standardised protocols (DNA extraction, PCR, qPCR and 16S rRNA gene sequencing analysis) were developed for application to the Pseudomonas aeruginosa (NCT02157922) and Burkholderia cepacia complex (NCT02453789) clinical trials involving 45 and 13 adult trial participants, respectively. Microbiota analysis identified that paired sputum samples from an individual participant, taken within 2 h of each other, had reproducible bacterial diversity profiles. Although culture microbiology had identified patients as either colonised by P. aeruginosa or B. cepacia complex species at recruitment, microbiota analysis revealed patient lung infection communities were not always dominated by these key CF pathogens. Microbiota profiles were patient-specific and remained stable over the course of both clinical trials (6 sampling points over the course of 140 days). Within the Burkholderia trial, participants were infected with B. cenocepacia (n = 4), B. multivorans (n = 6), or an undetermined species (n = 3). Colonisation with either B. cenocepacia or B. multivorans influenced the overall bacterial community structure in sputum. Overall, we have shown that sputum microbiota in adults with CF is stable over a 2 h time-frame, suggesting collection of a single sample on a collection day is sufficient to capture the microbiota diversity. Despite the uniform pathogen culture-positivity status at recruitment, trial participants were highly heterogeneous in their lung microbiota. Understanding the microbiota profiles of individuals with CF ahead of future clinical trials would be beneficial in the context of patient stratification and trial design.
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Affiliation(s)
- Rebecca Weiser
- Microbiomes, Microbes and Informatics Group, Organisms and Environment Division, School of Biosciences, Cardiff University, The Sir Martin Evans Building, Museum Avenue, Cardiff, Wales, CF10 3AX, UK.
| | - Philip D Rye
- AlgiPharma AS, Industriveien 33, N-1337, Sandvika, Norway.
| | - Eshwar Mahenthiralingam
- Microbiomes, Microbes and Informatics Group, Organisms and Environment Division, School of Biosciences, Cardiff University, The Sir Martin Evans Building, Museum Avenue, Cardiff, Wales, CF10 3AX, UK.
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8
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Scott EM, Lewin AC, Leis ML. Current ocular microbiome investigations limit reproducibility and reliability: Critical review and opportunities. Vet Ophthalmol 2020; 24:4-11. [PMID: 33382917 DOI: 10.1111/vop.12854] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 11/13/2020] [Accepted: 12/11/2020] [Indexed: 12/14/2022]
Abstract
Enthusiasm for research describing microbial communities using next-generation sequencing (NGS) has outpaced efforts to standardize methodology. Without consistency in the way research is carried out in this field, the comparison of data between studies is near impossible and the utility of results remains limited. This holds true for bacterial microbiome research of the ocular surface, and other sites, in both humans and animals. In addition, the ocular surface remains under-explored when compared to other mucosal sites. Low bacterial biomass samples from the ocular surface lead to further technical challenges. Taken together, two major problems were identified: (1) Normalization of the workflow in studies utilizing NGS to investigate the ocular surface bacteriome is necessary in order to propel the field forward and improve research impact through cross-study comparisons. (2) Current microbiome profiling technology was developed for high bacterial biomass samples (such as feces or soil), posing a challenge for analyses of samples with low bacterial load such as the ocular surface. This article reviews the challenges and limitations currently facing ocular microbiome research and provides recommendations for minimum reporting standards for veterinary ophthalmologists and clinician scientists to limit inter-study variation, improve reproducibility, and ultimately render results from these studies more impactful. The move toward normalization of methodology will expedite and maximize the potential for microbiome research to translate into meaningful discovery and tangible clinical applications.
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Affiliation(s)
- Erin M Scott
- Department of Small Animal Clinical Sciences, College of Veterinary Medicine, Texas A&M University, College Station, TX, USA
| | - Andrew C Lewin
- Department of Veterinary Clinical Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA, USA
| | - Marina L Leis
- Department of Small Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK, Canada
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Haque MM, Mande SS. Decoding the microbiome for the development of translational applications: Overview, challenges and pitfalls. J Biosci 2019; 44:118. [PMID: 31719227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Recent studies have highlighted the potential of 'translational' microbiome research in addressing real-world challenges pertaining to human health, nutrition and disease. Additionally, outcomes of microbiome research have also positively impacted various aspects pertaining to agricultural productivity, fuel or energy requirements, and stability/preservation of various ecological habitats. Microbiome data is multi-dimensional with various types of data comprising nucleic and protein sequences, metabolites as well as various metadata related to host and or environment. This poses a major challenge for computational analysis and interpretation of data to reach meaningful, reproducible (and replicable) biological conclusions. In this review, we first describe various aspects of microbiomes that make them an attractive tool/target for developing various translational applications. The challenge of deciphering signatures from an information-rich resource like the microbiome is also discussed. Subsequently, we present three case-studies that exemplify the potential of microbiome- based solutions in solving real-world problems. The final part of the review attempts to familiarize readers with the importance of a robust study design and the diligence required during every stage of analysis for achieving solutions with potential translational value.
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Affiliation(s)
- Mohammed Monzoorul Haque
- Bio-Sciences R and D Division, TCS Research, Tata Consultancy Services Limited, Pune 411 013, India
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Haque MM, Mande SS. Decoding the microbiome for the development of translational applications: Overview, challenges and pitfalls. J Biosci 2019. [DOI: 10.1007/s12038-019-9932-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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García-Jiménez B, Wilkinson MD. Robust and automatic definition of microbiome states. PeerJ 2019; 7:e6657. [PMID: 30941274 PMCID: PMC6440462 DOI: 10.7717/peerj.6657] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Accepted: 02/22/2019] [Indexed: 12/17/2022] Open
Abstract
Analysis of microbiome dynamics would allow elucidation of patterns within microbial community evolution under a variety of biologically or economically important circumstances; however, this is currently hampered in part by the lack of rigorous, formal, yet generally-applicable approaches to discerning distinct configurations of complex microbial populations. Clustering approaches to define microbiome "community state-types" at a population-scale are widely used, though not yet standardized. Similarly, distinct variations within a state-type are well documented, but there is no rigorous approach to discriminating these more subtle variations in community structure. Finally, intra-individual variations with even fewer differences will likely be found in, for example, longitudinal data, and will correlate with important features such as sickness versus health. We propose an automated, generic, objective, domain-independent, and internally-validating procedure to define statistically distinct microbiome states within datasets containing any degree of phylotypic diversity. Robustness of state identification is objectively established by a combination of diverse techniques for stable cluster verification. To demonstrate the efficacy of our approach in detecting discreet states even in datasets containing highly similar bacterial communities, and to demonstrate the broad applicability of our method, we reuse eight distinct longitudinal microbiome datasets from a variety of ecological niches and species. We also demonstrate our algorithm's flexibility by providing it distinct taxa subsets as clustering input, demonstrating that it operates on filtered or unfiltered data, and at a range of different taxonomic levels. The final output is a set of robustly defined states which can then be used as general biomarkers for a wide variety of downstream purposes such as association with disease, monitoring response to intervention, or identifying optimally performant populations.
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Affiliation(s)
- Beatriz García-Jiménez
- Centro de Biotecnología y Genómica de Plantas UPM-INIA, Universidad Politécnica de Madrid, Madrid, Spain
| | - Mark D. Wilkinson
- Centro de Biotecnología y Genómica de Plantas UPM-INIA, Universidad Politécnica de Madrid, Madrid, Spain
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Cheung SG, Goldenthal AR, Uhlemann AC, Mann JJ, Miller JM, Sublette ME. Systematic Review of Gut Microbiota and Major Depression. Front Psychiatry 2019; 10:34. [PMID: 30804820 PMCID: PMC6378305 DOI: 10.3389/fpsyt.2019.00034] [Citation(s) in RCA: 316] [Impact Index Per Article: 63.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 01/21/2019] [Indexed: 11/17/2022] Open
Abstract
Background: Recently discovered relationships between the gastrointestinal microbiome and the brain have implications for psychiatric disorders, including major depressive disorder (MDD). Bacterial transplantation from MDD patients to rodents produces depression-like behaviors. In humans, case-control studies have examined the gut microbiome in healthy and affected individuals. We systematically reviewed existing studies comparing gut microbial composition in MDD and healthy volunteers. Methods: A PubMed literature search combined the terms "depression," "depressive disorder," "stool," "fecal," "gut," and "microbiome" to identify human case-control studies that investigated relationships between MDD and microbiota quantified from stool. We evaluated the resulting studies, focusing on bacterial taxa that were different between MDD and healthy controls. Results: Six eligible studies were found in which 50 taxa exhibited differences (p < 0.05) between patients with MDD and controls. Patient characteristics and methodologies varied widely between studies. Five phyla-Bacteroidetes, Firmicutes, Actinobacteria, Fusobacteria, and Protobacteria-were represented; however, divergent results occurred across studies for all phyla. The largest number of differentiating taxa were within phylum Firmicutes, in which nine families and 12 genera differentiated the diagnostic groups. The majority of these families and genera were found to be statistically different between the two groups in two identified studies. Family Lachnospiraceae differentiated the diagnostic groups in four studies (with an even split in directionality). Across all five phyla, nine genera were higher in MDD (Anaerostipes, Blautia, Clostridium, Klebsiella, Lachnospiraceae incertae sedis, Parabacteroides, Parasutterella, Phascolarctobacterium, and Streptococcus), six were lower (Bifidobacterium, Dialister, Escherichia/Shigella, Faecalibacterium, and Ruminococcus), and six were divergent (Alistipes, Bacteroides, Megamonas, Oscillibacter, Prevotella, and Roseburia). We highlight mechanisms and products of bacterial metabolism as they may relate to the etiology of depression. Conclusions: No consensus has emerged from existing human studies of depression and gut microbiome concerning which bacterial taxa are most relevant to depression. This may in part be due to differences in study design. Given that bacterial functions are conserved across taxonomic groups, we propose that studying microbial functioning may be more productive than a purely taxonomic approach to understanding the gut microbiome in depression.
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Affiliation(s)
- Stephanie G. Cheung
- Division of Consultation-Liaison Psychiatry, Columbia University, New York, NY, United States
- Department of Psychiatry, Columbia University, New York, NY, United States
| | - Ariel R. Goldenthal
- Department of Psychiatry, Columbia University, New York, NY, United States
- Molecular Imaging & Neuropathology Area, New York State Psychiatric Institute, New York, NY, United States
| | - Anne-Catrin Uhlemann
- Division of Infectious Diseases, Department of Medicine, Columbia University, New York, NY, United States
- Microbiome & Pathogen Genomics Core, Columbia University, New York, NY, United States
| | - J. John Mann
- Department of Psychiatry, Columbia University, New York, NY, United States
- Molecular Imaging & Neuropathology Area, New York State Psychiatric Institute, New York, NY, United States
- Department of Radiology, Columbia University, New York, NY, United States
| | - Jeffrey M. Miller
- Department of Psychiatry, Columbia University, New York, NY, United States
- Molecular Imaging & Neuropathology Area, New York State Psychiatric Institute, New York, NY, United States
| | - M. Elizabeth Sublette
- Department of Psychiatry, Columbia University, New York, NY, United States
- Molecular Imaging & Neuropathology Area, New York State Psychiatric Institute, New York, NY, United States
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Silverman JD, Durand HK, Bloom RJ, Mukherjee S, David LA. Dynamic linear models guide design and analysis of microbiota studies within artificial human guts. MICROBIOME 2018; 6:202. [PMID: 30419949 PMCID: PMC6233358 DOI: 10.1186/s40168-018-0584-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 10/23/2018] [Indexed: 05/19/2023]
Abstract
BACKGROUND Artificial gut models provide unique opportunities to study human-associated microbiota. Outstanding questions for these models' fundamental biology include the timescales on which microbiota vary and the factors that drive such change. Answering these questions though requires overcoming analytical obstacles like estimating the effects of technical variation on observed microbiota dynamics, as well as the lack of appropriate benchmark datasets. RESULTS To address these obstacles, we created a modeling framework based on multinomial logistic-normal dynamic linear models (MALLARDs) and performed dense longitudinal sampling of four replicate artificial human guts over the course of 1 month. The resulting analyses revealed how the ratio of biological variation to technical variation from sample processing depends on sampling frequency. In particular, we find that at hourly sampling frequencies, 76% of observed variation could be ascribed to technical sources, which could also skew the observed covariation between taxa. We also found that the artificial guts demonstrated replicable trajectories even after a recovery from a transient feed disruption. Additionally, we observed irregular sub-daily oscillatory dynamics associated with the bacterial family Enterobacteriaceae within all four replicate vessels. CONCLUSIONS Our analyses suggest that, beyond variation due to sequence counting, technical variation from sample processing can obscure temporal variation from biological sources in artificial gut studies. Our analyses also supported hypotheses that human gut microbiota fluctuates on sub-daily timescales in the absence of a host and that microbiota can follow replicable trajectories in the presence of environmental driving forces. Finally, multiple aspects of our approach are generalizable and could ultimately be used to facilitate the design and analysis of longitudinal microbiota studies in vivo.
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Affiliation(s)
- Justin D. Silverman
- Program in Computational Biology and Bioinformatics, Duke University, CIEMAS, Room 2171, 101 Science Drive, Box 3382, Durham, NC 27708 USA
- Medical Scientist Training Program, Duke University, Durham, NC 27708 USA
- Center for Genomic and Computational Biology, Duke University, Durham, NC 27708 USA
| | - Heather K. Durand
- Department of Molecular Genetics and Microbiology, Duke University, Durham, NC 27708 USA
| | - Rachael J. Bloom
- University Program in Genetics and Genomics, Duke University, Durham, NC 27708 USA
| | - Sayan Mukherjee
- Program in Computational Biology and Bioinformatics, Duke University, CIEMAS, Room 2171, 101 Science Drive, Box 3382, Durham, NC 27708 USA
- Departments of Statistical Science, Mathematics, Computer Science, Biostatistics & Bioinformatics, Duke University, Durham, NC 27708 USA
| | - Lawrence A. David
- Program in Computational Biology and Bioinformatics, Duke University, CIEMAS, Room 2171, 101 Science Drive, Box 3382, Durham, NC 27708 USA
- Center for Genomic and Computational Biology, Duke University, Durham, NC 27708 USA
- University Program in Genetics and Genomics, Duke University, Durham, NC 27708 USA
- Department of Molecular Genetics and Microbiology, Duke University, Durham, NC 27708 USA
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García-Jiménez B, de la Rosa T, Wilkinson MD. MDPbiome: microbiome engineering through prescriptive perturbations. Bioinformatics 2018; 34:i838-i847. [PMID: 30423107 PMCID: PMC6129268 DOI: 10.1093/bioinformatics/bty562] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Motivation Recent microbiome dynamics studies highlight the current inability to predict the effects of external perturbations on complex microbial populations. To do so would be particularly advantageous in fields such as medicine, bioremediation or industrial scenarios. Results MDPbiome statistically models longitudinal metagenomics samples undergoing perturbations as a Markov Decision Process (MDP). Given a starting microbial composition, our MDPbiome system suggests the sequence of external perturbation(s) that will engineer that microbiome to a goal state, for example, a healthier or more performant composition. It also estimates intermediate microbiome states along the path, thus making it possible to avoid particularly undesirable/unhealthy states. We demonstrate MDPbiome performance over three real and distinct datasets, proving its flexibility, and the reliability and universality of its output 'optimal perturbation policy'. For example, an MDP created using a vaginal microbiome time series, with a goal of recovering from bacterial vaginosis, suggested avoidance of perturbations such as lubricants or sex toys; while another MDP provided a quantitative explanation for why salmonella vaccine accelerates gut microbiome maturation in chicks. This novel analytical approach has clear applications in medicine, where it could suggest low-impact clinical interventions that will lead to achievement or maintenance of a healthy microbial population, or alternately, the sequence of interventions necessary to avoid strongly negative microbiome states. Availability and implementation Code (https://github.com/beatrizgj/MDPbiome) and result files (https://tomdelarosa.shinyapps.io/MDPbiome/) are available online. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Beatriz García-Jiménez
- Center for Plant Biotechnology and Genomics UPM - INIA, Universidad Politecnica de Madrid, Madrid, Spain
| | - Tomás de la Rosa
- Department of Computer Science, Universidad Carlos III de Madrid, Madrid, Spain
| | - Mark D Wilkinson
- Center for Plant Biotechnology and Genomics UPM - INIA, Universidad Politecnica de Madrid, Madrid, Spain
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Poussin C, Sierro N, Boué S, Battey J, Scotti E, Belcastro V, Peitsch MC, Ivanov NV, Hoeng J. Interrogating the microbiome: experimental and computational considerations in support of study reproducibility. Drug Discov Today 2018; 23:1644-1657. [PMID: 29890228 DOI: 10.1016/j.drudis.2018.06.005] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 05/03/2018] [Accepted: 06/06/2018] [Indexed: 12/12/2022]
Abstract
The microbiome is an important factor in human health and disease and is investigated to develop novel therapeutics. Metagenomics leverages advances in sequencing technologies and computational analysis to identify and quantify the microorganisms present in a sample. This field has, however, not yet reached maturity and the international metagenomics community, aware of the current limitations and of the necessity for standardization, has started investigating sources of variability in experimental and computational workflows. The first studies have already resulted in the identification of crucial steps and factors affecting metagenomics data quality, quantification and interpretation. This review summarizes experimental and computational considerations for interrogating the microbiome and establishing reproducible and robust analysis workflows.
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Affiliation(s)
- Carine Poussin
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - Nicolas Sierro
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - Stéphanie Boué
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - James Battey
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - Elena Scotti
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - Vincenzo Belcastro
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - Manuel C Peitsch
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - Nikolai V Ivanov
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland
| | - Julia Hoeng
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchâtel, Switzerland.
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Human Microbiome Acquisition and Bioinformatic Challenges in Metagenomic Studies. Int J Mol Sci 2018; 19:ijms19020383. [PMID: 29382070 PMCID: PMC5855605 DOI: 10.3390/ijms19020383] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Revised: 01/21/2018] [Accepted: 01/24/2018] [Indexed: 12/21/2022] Open
Abstract
The study of the human microbiome has become a very popular topic. Our microbial counterpart, in fact, appears to play an important role in human physiology and health maintenance. Accordingly, microbiome alterations have been reported in an increasing number of human diseases. Despite the huge amount of data produced to date, less is known on how a microbial dysbiosis effectively contributes to a specific pathology. To fill in this gap, other approaches for microbiome study, more comprehensive than 16S rRNA gene sequencing, i.e., shotgun metagenomics and metatranscriptomics, are becoming more widely used. Methods standardization and the development of specific pipelines for data analysis are required to contribute to and increase our understanding of the human microbiome relationship with health and disease status.
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Noyes N, Cho KC, Ravel J, Forney LJ, Abdo Z. Associations between sexual habits, menstrual hygiene practices, demographics and the vaginal microbiome as revealed by Bayesian network analysis. PLoS One 2018; 13:e0191625. [PMID: 29364944 PMCID: PMC5783405 DOI: 10.1371/journal.pone.0191625] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Accepted: 01/06/2018] [Indexed: 12/13/2022] Open
Abstract
The vaginal microbiome plays an influential role in several disease states in reproductive age women, including bacterial vaginosis (BV). While demographic characteristics are associated with differences in vaginal microbiome community structure, little is known about the influence of sexual and hygiene habits. Furthermore, associations between the vaginal microbiome and risk symptoms of bacterial vaginosis have not been fully elucidated. Using Bayesian network (BN) analysis of 16S rRNA gene sequence results, demographic and extensive questionnaire data, we describe both novel and previously documented associations between habits of women and their vaginal microbiome. The BN analysis approach shows promise in uncovering complex associations between disparate data types. Our findings based on this approach support published associations between specific microbiome members (e.g., Eggerthella, Gardnerella, Dialister, Sneathia and Ruminococcaceae), the Nugent score (a BV diagnostic) and vaginal pH (a risk symptom of BV). Additionally, we found that several microbiome members were directly connected to other risk symptoms of BV (such as vaginal discharge, odor, itch, irritation, and yeast infection) including L. jensenii, Corynebacteria, and Proteobacteria. No direct connections were found between the Nugent Score and risk symptoms of BV other than pH, indicating that the Nugent Score may not be the most useful criteria for assessment of clinical BV. We also found that demographics (i.e., age, ethnicity, previous pregnancy) were associated with the presence/absence of specific vaginal microbes. The resulting BN revealed several as-yet undocumented associations between birth control usage, menstrual hygiene practices and specific microbiome members. Many of these complex relationships were not identified using common analytical methods, i.e., ordination and PERMANOVA. While these associations require confirmatory follow-up study, our findings strongly suggest that future studies of the vaginal microbiome and vaginal pathologies should include detailed surveys of participants' sanitary, sexual and birth control habits, as these can act as confounders in the relationship between the microbiome and disease. Although the BN approach is powerful in revealing complex associations within multidimensional datasets, the need in some cases to discretize the data for use in BN analysis can result in loss of information. Future research is required to alleviate such limitations in constructing BN networks. Large sample sizes are also required in order to allow for the incorporation of a large number of variables (nodes) into the BN, particularly when studying associations between metadata and the microbiome. We believe that this approach is of great value, complementing other methods, to further our understanding of complex associations characteristic of microbiome research.
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Affiliation(s)
- Noelle Noyes
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado, United States of America
| | - Kyu-Chul Cho
- Department of Statistics, University of Idaho, Moscow, Idaho, United States of America
| | - Jacques Ravel
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore Maryland, United States of America
| | - Larry J. Forney
- Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America
| | - Zaid Abdo
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado, United States of America
- * E-mail:
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