Editorial: Editorial for the virtual issue on microbiome

Editorial: Editorial for the virtual issue on microbiome Dealing with variability and complexity: challenges in disentangling microbial communities New generation sequencing and advanced bioinformatics have provided invaluable tools to analyze complex microbial populations, especially in ecosystems such as skin and mucosal surfaces of humans and animals, plants, soil and oceans. Microbiota consequently became a new frontier in microbiology. We knew that ecosystems such as the human intestine host an astronomic number of microorganisms. Classical microbiology, thanks to progress in anaerobic culture techniques could ‘save’ strictly anaerobic, extremely oxygen-sensitive species, providing evidence for the richness and diversity of these ecosystems. It soon became obvious, however, that even the best culture methods showed limits, revealing the existence of very fastidious or strictly unculturable microbes, the famous ‘great plate count anomaly’, in other words the gap existing between the bacteria actually present in a given sample and those effectively cultivated (Staley and Konopka 1985). This gap was generally filled by developing molecular diagnostic approaches based on the 16S ribosomal RNA genes comparison method pioneered by Woese and Fox (1977), an opportunity to acknowledge the decisive role plaid by environmental microbiologists in a breakthrough that today allows global analysis of complex microbial communities in any ecosystem. These methods were soon harnessed by clinical and veterinary microbiologists. The first exhaustive analysis of the human gut (Suau et al.1999) and oral (Kroes, Lepp and Relman 1999) microbiota using 16S-based metataxonomics was published in 1999, and this method became the reference while increasing efforts were dedicated to successfully cultivate unculturable species that could be identified only by molecular techniques. The ‘art of culturomics’ became another new angle of modern microbiology, combining genome analysis, nanotechnologies and mass spectrometry to restore optimal growth conditions indispensable to functionally study these microbes (Lagier et al.2015). It then became clear for some microbiologists that the human genome that had just been sequenced was yet incomplete, due to its lack of consideration of the microbial genomes associated to human beings (Relman and Falkow. 2001). It is this context that makes the microbiota ‘a genuine human organ’. Metasequencing propelled the field in another dimension and demanded unprecedented capacities in new generation sequencing and genomic data analysis. The EU-funded Metahit project (Qin et al.2010) and NIH-funded Human Microbiome Project’ (HMP Consortium 2012) were born. The former addressed metagenomic analysis of the gut microbiota of hundreds of individuals, and the latter addressed metagenomic analysis of the various ecosystems that together constitute the global microbiota of several individuals. For microbiologists used to focus on a single microbe interacting with a given environment, such as a bacterial pathogen engaging host cells and tissues, it has been a formidable change in scale and a two-step challenge, from one species to a community in a single environment or host (ca.1000 species in the human gut) to a collection of genomes generating astronomic numbers of genes to comprehend (ca. 10 000 000 bacterial genes so far in the hundreds of metagenomes available). This huge diversity needs to be apprehended and moved from the descriptive stage characterizing microbiota in their richness, in their alpha-diversity at the scale of their individual host and in their beta-diversity at the scale of the population of hosts (Li et al.2012). It is tempting at this stage to define a core of species (difficult in 16S-based metataxonomics that identifies a collection of OTUs and not bona fide species), rather than genes (i.e. probably ca. 500 000 common genes) that are at the heart of the symbiosis, raising questions about the function of the millions of other genes provided by alpha- and beta-diversity (Zhu, Wang and Li 2010). This platform is essential as a reference knowledge to move towards the steps of correlative metagenomics in an attempt to establish correlations between healthy and diseased ecosystems, bearing in mind that differences in gene composition may reflect alternative ecological states, diet variations, but not necessarily diseased ecosystems. This is particularly obvious when trying to correlate OTU or gene composition of gut microbiota with health or various diseases such as obesity, type 2 diabetes or inflammatory bowel diseases (Cotillard et al.2013). Regarding human health, the issue is to associate alterations in species/gene composition—generally called dysbiosis—with a diseased state and define a signature that is common to a similar disease state in different individuals. This signature may actually be confirmed by identifying a metabolic profile reflecting the interaction occurring between the dysbiotic microbiota and its affected host. Dysbioses are often marked by a reduction in species/genes richness and diversity and a reduction of certain species playing a homeostatic role (i.e. anti-inflammatory), and the increase of pathobionts, which may enhance the pathogenic effect of the dysbiotic microbiota (Tamboli et al.2004). A third step will consist, beyond establishing a correlative link, in demonstrating a true causative link between the observed dysbiotic signature and the diseased state. As experimental approaches are complex and ethically sensitive in humans, experimental models in animals are generally considered. Confirmation of the causative link requires a re-evaluation of Koch's postulate at the scale of a complex microbial assembly (Neville, Forster and Lawley 2017): a true challenge and an exciting scientific reflection on the global interface between man, animals, plants and microbes. In a series of eight reviews over the past 2 years (web link), FEMS Microbiology Reviews has covered several of the above burning issues, attempting to clarify and rationalize the emerging discipline of metanalysis of complex microbial populations. These reviews, in my view, stressed two major themes that are intrinsic to this new discipline: data reproducibility and gestion of complexity. The former applies to variability in data analysis and the latter in variability in outcome of animal experiments. Regarding data analysis, possibly for the first time scientists see their data through the screen of methodologies with which they are not necessarily fully familiar. It is particularly the case for metataxonomics and metagenomics that call for complex computational programs, pipelines and algorithms whose access is in no way as straightforward as, for instance, direct microscopic observation or analysis of bands migrated on a gel. Balint et al. evaluated the more widely used techniques by analysing composition and diversity, inferring species and their interactions. They stressed the permanent search for observational and statistical biases, providing a critical analysis of current and emerging statistical methods (Balint et al.2016). On similar lines, animal models addressing the causality link between some pathologies and dysbioses are submitted to strong intra- and inter-individual heterogeneity in microbiota that causes strong experimental limitations. Improvements of technical and computational tools offer the capacity to mitigate part of this variability. It remains however essential to better identify and analyze the bases of this variability. It is vital to discriminate true effects from confounding cues. Guidelines are essential to better standardize the basic microbiota in specific pathogen-free animals across breeding places and sites of experimentation. Improving reproducibility is a priority in animal experiments (Laukens et al.2016). The second key word is complexity. Following a largely descriptive phase of bacterial community composition, two major themes emerged: how microbiota interact with and influence their host or environment, and how members of these microbiota interact among themselves to form networks that achieve robustness and resilience of the community. Decoding interspecies molecular interactions requires creative new tools and concepts encompassing single-cell sequencing and transcriptomics, imaging reconstructing the 3D structure of the bacterial communities and, indeed, high-resolution metabolomic analysis. This opens a largely new facet of ecology, providing strong bases for modeling (Abreu and Taga 2016). A way to deal with complexity is synthetic microbial ecology that emerges as an approach designing, assembling and analyzing the dynamics of ecological circuits encompassing subsets of interacting microbial genotypes. Synthetic microbial ecology emerges as a potent approach to breakdown the global network of interactions in a complex microbiota into functional units in competition. This complexity has inspired a new discipline of synthetic microbial ecology (Dolinsek, Goldschmidt and Johnson 2016). The gut microbiota has been a remarkable ecosystem to establish and validate the basic methods of large-scale metagenomic analysis of complex microbial assemblies. It now bears on thousands of individual gut microbiota that have undergone metasequencing analysis. Several issues, some of which are still matter for debate, have emerged such as the existence of a functional ‘core’, the presence of community types (i.e. enterotypes), the reality of alternative stable states and their homeostatic or pathogenic function. Altogether, the expected observation of solid functional redundancy is likely to provide the ecosystem with its stability and resilience. Response to perturbations such as diet changes and antibiotic use are ways to functionally address the dynamics of microbial ecology in the gut and to provide orientations for interventions aimed at reconstituting a healthy microbiota following the occurrence of dysbiotic alterations (Shetty et al.2017). Essential to the success of the above approaches is indeed the quality of the samples analysed. The review by Vandeputte et al. addressed the absolute need for standardization of preparation methods: collection of samples, preservation methods, quality control. With the experience of large-scale studies, the authors can propose solid guidelines with the essential aim to preserve genes and species signatures in order to secure reproducibility in time and space (Vandeputte et al.2017). Rules, concepts, methods and techniques turn out to be universal and apply to other phyla, such as fungi. The mycobiome remains the poor parent of microbiota studies in spite of the importance of fungi in homeostasis and diseases of mucosal surfaces and the strong interaction that exists between bacteria in fungal species. It is time for a change and the review by Huseyin et al. (2017) shows the way. The rules also apply to other ecosystems such as forests, where a truly global and integrative scheme of microbial populations, their composition, circulation and functional capacities is warranted (Baldrian 2017). CONCLUSION Biological sciences, particularly over the last few decades, has made outstanding progress and achieved major breakthroughs thanks to the scientists’ capacity to generate novel concepts and phase them with mastering of ever evolving technologies. This has put increasing demand on them to update their scientific knowledge and technological know-how. It was in general successfully managed, although the recent irruption of meta-omics that allows to decipher the complexity of microbial assemblies and their interaction with their host and environment has dramatically increased the challenge, and thus the demand on mastering complex metabolic pathways analysis, metasequencing strategies and bioinformatic analysis of big data. As reliable results will bear on the reproducibility and resolution of techniques, the stakes are getting higher than ever and reviews like those collected in this virtual issue of FEMS Microbial Reviews are a ‘must read’ for those tempted to enter the field. Conflict of interest. None declared. REFERENCES Abreu NA, Taga ME. Decoding molecular interactions in microbial communities. FEMS Microbiol Rev  2016; 40: 648– 63. Google Scholar CrossRef Search ADS PubMed  Baldrian P. Forest microbiome: diversity, complexity and dynamics. FEMS Microbiol Rev  2017; 41: 109– 30. Google Scholar PubMed  Balint M, Bahram MA, Murat E et al.   Millions of reads, thousands of taxa: microbial community structure and associations analyzed via marker genes. FEMS Microbiol Rev  2016; 40: 686– 700. Google Scholar CrossRef Search ADS PubMed  Cotillard A, Kennedy SP, Kong LC et al.   Dietary intervention impact on gut microbiota gene richness. Nature  2013; 500: 585– 8. Google Scholar CrossRef Search ADS PubMed  Dolinsek J, Goldschmidt F, Johnson DR. Synthetic microbial ecology and the dynamic interplay between microbial genotypes. FEMS Microbiol Rev  2016; 40: 961– 79. Google Scholar CrossRef Search ADS PubMed  Human Microbiome Project Consortium. A framework for human microbiome research. Nature  2012; 486: 215– 21. CrossRef Search ADS PubMed  Huseyin CE, O’Toole PW, Cotter PD et al.   Forgotten fungi—the gut mycobiome in human health and disease. FEMS Microbiol Rev  2017; 41: 479– 511. Google Scholar CrossRef Search ADS PubMed  Kroes I, Lepp PW, Relman DA. Bacterial diversity within the human subgingival crevice. P Natl Acad Sci USA  1999; 96: 14547– 52. Google Scholar CrossRef Search ADS   Lagier JC, Hugon P, Khelaifia S et al.   The rebirth of culture in microbiology through the example of culturomics to study human gut microbiota. Clin Microbiol Rev  2015; 28: 237– 64. Google Scholar CrossRef Search ADS PubMed  Laukens D, Brinkman BM, Raes J et al.   Heterogeneity of the gut microbiome in mice: guidelines for optimizing experimental design. FEMS Microbiol Rev  2016; 40: 117– 32. Google Scholar CrossRef Search ADS PubMed  Li K, Bihan M, Yooseph S et al.   Analysis of the microbial diversity across the human microbiome. PLoS One  2012; 7: e32118. Google Scholar CrossRef Search ADS PubMed  Neville BA, Forster SC, Lawley TD. Commensal Koch's postulates: establishing causation in human microbiota research. Curr Opin Microbiol  2017; 42: 47– 52. Google Scholar CrossRef Search ADS PubMed  Qin J, Li R, Raes J et al.   A human gut microbial gene catalogue established by metagenomic sequencing. Nature  2010; 464: 59– 65. Google Scholar CrossRef Search ADS PubMed  Relman DA, Falkow S. The meaning and impact of the human genome sequence for microbiology. Trends Microbiol  2001; 9: 206– 8. Google Scholar CrossRef Search ADS PubMed  Shetty SA, Hugenholtz F, Lahti L et al.   Intestinal microbiome landscaping: insight in community assemblage and implications for microbial modulation stratégies. FEMS Microbiol Rev  2017; 41: 182– 99. Google Scholar CrossRef Search ADS PubMed  Staley JT, Konopka A. Measurement of in situ activities of non-photosynthetic microorganismsin aquatic and terrestrial habitats. Ann Rev Microbiol  1985; 39: 321– 46. Google Scholar CrossRef Search ADS   Suau A, Bonnet R, Sutren M et al.   Direct analysis of genes encoding 16S rRNA from complex communities reveals many novel molecular species within the human gut. Appl Environ Microb  1999; 65: 4799– 807. Tamboli CP, Neuf C, Desreumaux P et al.   Dysbiosis in inflammatory bowel disease. Gut  2004; 53: 1– 4. Google Scholar CrossRef Search ADS PubMed  Vandeputte D, Tito RY, Vanleeuwen R et al.   Practical considerations for large-scale gut microbiome studies. FEMS Microbiol Rev  2017; 41: S154– 67. Google Scholar PubMed  Web link: https://academic.oup.com/femsre/pages/microbiome_virtual_special_issue. Woese CR, Fox GE. Phylogenic structure of the prokaryotic domain: the primary kingdoms. P Nat Acad Sci USA  1977; 74: 5088– 90. Google Scholar CrossRef Search ADS   Zhu B, Wang X, Li L. Human gut microbiome: the second genome of human body. Protein Cell  2010; 1: 718– 25. Google Scholar CrossRef Search ADS PubMed  © FEMS 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png FEMS Microbiology Reviews Oxford University Press

Editorial: Editorial for the virtual issue on microbiome

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Abstract

Dealing with variability and complexity: challenges in disentangling microbial communities New generation sequencing and advanced bioinformatics have provided invaluable tools to analyze complex microbial populations, especially in ecosystems such as skin and mucosal surfaces of humans and animals, plants, soil and oceans. Microbiota consequently became a new frontier in microbiology. We knew that ecosystems such as the human intestine host an astronomic number of microorganisms. Classical microbiology, thanks to progress in anaerobic culture techniques could ‘save’ strictly anaerobic, extremely oxygen-sensitive species, providing evidence for the richness and diversity of these ecosystems. It soon became obvious, however, that even the best culture methods showed limits, revealing the existence of very fastidious or strictly unculturable microbes, the famous ‘great plate count anomaly’, in other words the gap existing between the bacteria actually present in a given sample and those effectively cultivated (Staley and Konopka 1985). This gap was generally filled by developing molecular diagnostic approaches based on the 16S ribosomal RNA genes comparison method pioneered by Woese and Fox (1977), an opportunity to acknowledge the decisive role plaid by environmental microbiologists in a breakthrough that today allows global analysis of complex microbial communities in any ecosystem. These methods were soon harnessed by clinical and veterinary microbiologists. The first exhaustive analysis of the human gut (Suau et al.1999) and oral (Kroes, Lepp and Relman 1999) microbiota using 16S-based metataxonomics was published in 1999, and this method became the reference while increasing efforts were dedicated to successfully cultivate unculturable species that could be identified only by molecular techniques. The ‘art of culturomics’ became another new angle of modern microbiology, combining genome analysis, nanotechnologies and mass spectrometry to restore optimal growth conditions indispensable to functionally study these microbes (Lagier et al.2015). It then became clear for some microbiologists that the human genome that had just been sequenced was yet incomplete, due to its lack of consideration of the microbial genomes associated to human beings (Relman and Falkow. 2001). It is this context that makes the microbiota ‘a genuine human organ’. Metasequencing propelled the field in another dimension and demanded unprecedented capacities in new generation sequencing and genomic data analysis. The EU-funded Metahit project (Qin et al.2010) and NIH-funded Human Microbiome Project’ (HMP Consortium 2012) were born. The former addressed metagenomic analysis of the gut microbiota of hundreds of individuals, and the latter addressed metagenomic analysis of the various ecosystems that together constitute the global microbiota of several individuals. For microbiologists used to focus on a single microbe interacting with a given environment, such as a bacterial pathogen engaging host cells and tissues, it has been a formidable change in scale and a two-step challenge, from one species to a community in a single environment or host (ca.1000 species in the human gut) to a collection of genomes generating astronomic numbers of genes to comprehend (ca. 10 000 000 bacterial genes so far in the hundreds of metagenomes available). This huge diversity needs to be apprehended and moved from the descriptive stage characterizing microbiota in their richness, in their alpha-diversity at the scale of their individual host and in their beta-diversity at the scale of the population of hosts (Li et al.2012). It is tempting at this stage to define a core of species (difficult in 16S-based metataxonomics that identifies a collection of OTUs and not bona fide species), rather than genes (i.e. probably ca. 500 000 common genes) that are at the heart of the symbiosis, raising questions about the function of the millions of other genes provided by alpha- and beta-diversity (Zhu, Wang and Li 2010). This platform is essential as a reference knowledge to move towards the steps of correlative metagenomics in an attempt to establish correlations between healthy and diseased ecosystems, bearing in mind that differences in gene composition may reflect alternative ecological states, diet variations, but not necessarily diseased ecosystems. This is particularly obvious when trying to correlate OTU or gene composition of gut microbiota with health or various diseases such as obesity, type 2 diabetes or inflammatory bowel diseases (Cotillard et al.2013). Regarding human health, the issue is to associate alterations in species/gene composition—generally called dysbiosis—with a diseased state and define a signature that is common to a similar disease state in different individuals. This signature may actually be confirmed by identifying a metabolic profile reflecting the interaction occurring between the dysbiotic microbiota and its affected host. Dysbioses are often marked by a reduction in species/genes richness and diversity and a reduction of certain species playing a homeostatic role (i.e. anti-inflammatory), and the increase of pathobionts, which may enhance the pathogenic effect of the dysbiotic microbiota (Tamboli et al.2004). A third step will consist, beyond establishing a correlative link, in demonstrating a true causative link between the observed dysbiotic signature and the diseased state. As experimental approaches are complex and ethically sensitive in humans, experimental models in animals are generally considered. Confirmation of the causative link requires a re-evaluation of Koch's postulate at the scale of a complex microbial assembly (Neville, Forster and Lawley 2017): a true challenge and an exciting scientific reflection on the global interface between man, animals, plants and microbes. In a series of eight reviews over the past 2 years (web link), FEMS Microbiology Reviews has covered several of the above burning issues, attempting to clarify and rationalize the emerging discipline of metanalysis of complex microbial populations. These reviews, in my view, stressed two major themes that are intrinsic to this new discipline: data reproducibility and gestion of complexity. The former applies to variability in data analysis and the latter in variability in outcome of animal experiments. Regarding data analysis, possibly for the first time scientists see their data through the screen of methodologies with which they are not necessarily fully familiar. It is particularly the case for metataxonomics and metagenomics that call for complex computational programs, pipelines and algorithms whose access is in no way as straightforward as, for instance, direct microscopic observation or analysis of bands migrated on a gel. Balint et al. evaluated the more widely used techniques by analysing composition and diversity, inferring species and their interactions. They stressed the permanent search for observational and statistical biases, providing a critical analysis of current and emerging statistical methods (Balint et al.2016). On similar lines, animal models addressing the causality link between some pathologies and dysbioses are submitted to strong intra- and inter-individual heterogeneity in microbiota that causes strong experimental limitations. Improvements of technical and computational tools offer the capacity to mitigate part of this variability. It remains however essential to better identify and analyze the bases of this variability. It is vital to discriminate true effects from confounding cues. Guidelines are essential to better standardize the basic microbiota in specific pathogen-free animals across breeding places and sites of experimentation. Improving reproducibility is a priority in animal experiments (Laukens et al.2016). The second key word is complexity. Following a largely descriptive phase of bacterial community composition, two major themes emerged: how microbiota interact with and influence their host or environment, and how members of these microbiota interact among themselves to form networks that achieve robustness and resilience of the community. Decoding interspecies molecular interactions requires creative new tools and concepts encompassing single-cell sequencing and transcriptomics, imaging reconstructing the 3D structure of the bacterial communities and, indeed, high-resolution metabolomic analysis. This opens a largely new facet of ecology, providing strong bases for modeling (Abreu and Taga 2016). A way to deal with complexity is synthetic microbial ecology that emerges as an approach designing, assembling and analyzing the dynamics of ecological circuits encompassing subsets of interacting microbial genotypes. Synthetic microbial ecology emerges as a potent approach to breakdown the global network of interactions in a complex microbiota into functional units in competition. This complexity has inspired a new discipline of synthetic microbial ecology (Dolinsek, Goldschmidt and Johnson 2016). The gut microbiota has been a remarkable ecosystem to establish and validate the basic methods of large-scale metagenomic analysis of complex microbial assemblies. It now bears on thousands of individual gut microbiota that have undergone metasequencing analysis. Several issues, some of which are still matter for debate, have emerged such as the existence of a functional ‘core’, the presence of community types (i.e. enterotypes), the reality of alternative stable states and their homeostatic or pathogenic function. Altogether, the expected observation of solid functional redundancy is likely to provide the ecosystem with its stability and resilience. Response to perturbations such as diet changes and antibiotic use are ways to functionally address the dynamics of microbial ecology in the gut and to provide orientations for interventions aimed at reconstituting a healthy microbiota following the occurrence of dysbiotic alterations (Shetty et al.2017). Essential to the success of the above approaches is indeed the quality of the samples analysed. The review by Vandeputte et al. addressed the absolute need for standardization of preparation methods: collection of samples, preservation methods, quality control. With the experience of large-scale studies, the authors can propose solid guidelines with the essential aim to preserve genes and species signatures in order to secure reproducibility in time and space (Vandeputte et al.2017). Rules, concepts, methods and techniques turn out to be universal and apply to other phyla, such as fungi. The mycobiome remains the poor parent of microbiota studies in spite of the importance of fungi in homeostasis and diseases of mucosal surfaces and the strong interaction that exists between bacteria in fungal species. It is time for a change and the review by Huseyin et al. (2017) shows the way. The rules also apply to other ecosystems such as forests, where a truly global and integrative scheme of microbial populations, their composition, circulation and functional capacities is warranted (Baldrian 2017). CONCLUSION Biological sciences, particularly over the last few decades, has made outstanding progress and achieved major breakthroughs thanks to the scientists’ capacity to generate novel concepts and phase them with mastering of ever evolving technologies. This has put increasing demand on them to update their scientific knowledge and technological know-how. It was in general successfully managed, although the recent irruption of meta-omics that allows to decipher the complexity of microbial assemblies and their interaction with their host and environment has dramatically increased the challenge, and thus the demand on mastering complex metabolic pathways analysis, metasequencing strategies and bioinformatic analysis of big data. As reliable results will bear on the reproducibility and resolution of techniques, the stakes are getting higher than ever and reviews like those collected in this virtual issue of FEMS Microbial Reviews are a ‘must read’ for those tempted to enter the field. Conflict of interest. None declared. REFERENCES Abreu NA, Taga ME. Decoding molecular interactions in microbial communities. FEMS Microbiol Rev  2016; 40: 648– 63. Google Scholar CrossRef Search ADS PubMed  Baldrian P. Forest microbiome: diversity, complexity and dynamics. FEMS Microbiol Rev  2017; 41: 109– 30. Google Scholar PubMed  Balint M, Bahram MA, Murat E et al.   Millions of reads, thousands of taxa: microbial community structure and associations analyzed via marker genes. FEMS Microbiol Rev  2016; 40: 686– 700. Google Scholar CrossRef Search ADS PubMed  Cotillard A, Kennedy SP, Kong LC et al.   Dietary intervention impact on gut microbiota gene richness. Nature  2013; 500: 585– 8. Google Scholar CrossRef Search ADS PubMed  Dolinsek J, Goldschmidt F, Johnson DR. Synthetic microbial ecology and the dynamic interplay between microbial genotypes. FEMS Microbiol Rev  2016; 40: 961– 79. Google Scholar CrossRef Search ADS PubMed  Human Microbiome Project Consortium. A framework for human microbiome research. Nature  2012; 486: 215– 21. CrossRef Search ADS PubMed  Huseyin CE, O’Toole PW, Cotter PD et al.   Forgotten fungi—the gut mycobiome in human health and disease. FEMS Microbiol Rev  2017; 41: 479– 511. Google Scholar CrossRef Search ADS PubMed  Kroes I, Lepp PW, Relman DA. Bacterial diversity within the human subgingival crevice. P Natl Acad Sci USA  1999; 96: 14547– 52. Google Scholar CrossRef Search ADS   Lagier JC, Hugon P, Khelaifia S et al.   The rebirth of culture in microbiology through the example of culturomics to study human gut microbiota. Clin Microbiol Rev  2015; 28: 237– 64. Google Scholar CrossRef Search ADS PubMed  Laukens D, Brinkman BM, Raes J et al.   Heterogeneity of the gut microbiome in mice: guidelines for optimizing experimental design. FEMS Microbiol Rev  2016; 40: 117– 32. Google Scholar CrossRef Search ADS PubMed  Li K, Bihan M, Yooseph S et al.   Analysis of the microbial diversity across the human microbiome. PLoS One  2012; 7: e32118. Google Scholar CrossRef Search ADS PubMed  Neville BA, Forster SC, Lawley TD. Commensal Koch's postulates: establishing causation in human microbiota research. Curr Opin Microbiol  2017; 42: 47– 52. Google Scholar CrossRef Search ADS PubMed  Qin J, Li R, Raes J et al.   A human gut microbial gene catalogue established by metagenomic sequencing. Nature  2010; 464: 59– 65. Google Scholar CrossRef Search ADS PubMed  Relman DA, Falkow S. The meaning and impact of the human genome sequence for microbiology. Trends Microbiol  2001; 9: 206– 8. Google Scholar CrossRef Search ADS PubMed  Shetty SA, Hugenholtz F, Lahti L et al.   Intestinal microbiome landscaping: insight in community assemblage and implications for microbial modulation stratégies. FEMS Microbiol Rev  2017; 41: 182– 99. Google Scholar CrossRef Search ADS PubMed  Staley JT, Konopka A. Measurement of in situ activities of non-photosynthetic microorganismsin aquatic and terrestrial habitats. Ann Rev Microbiol  1985; 39: 321– 46. Google Scholar CrossRef Search ADS   Suau A, Bonnet R, Sutren M et al.   Direct analysis of genes encoding 16S rRNA from complex communities reveals many novel molecular species within the human gut. Appl Environ Microb  1999; 65: 4799– 807. Tamboli CP, Neuf C, Desreumaux P et al.   Dysbiosis in inflammatory bowel disease. Gut  2004; 53: 1– 4. Google Scholar CrossRef Search ADS PubMed  Vandeputte D, Tito RY, Vanleeuwen R et al.   Practical considerations for large-scale gut microbiome studies. FEMS Microbiol Rev  2017; 41: S154– 67. Google Scholar PubMed  Web link: https://academic.oup.com/femsre/pages/microbiome_virtual_special_issue. Woese CR, Fox GE. Phylogenic structure of the prokaryotic domain: the primary kingdoms. P Nat Acad Sci USA  1977; 74: 5088– 90. Google Scholar CrossRef Search ADS   Zhu B, Wang X, Li L. Human gut microbiome: the second genome of human body. Protein Cell  2010; 1: 718– 25. Google Scholar CrossRef Search ADS PubMed  © FEMS 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

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Published: Mar 1, 2018

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