Accepted Manuscript Title: The human gut microbiome, a taxonomic conundrum Author: Senthil Alias Sankar Jean-Christophe Lagier Pierre Pontarotti Didier Raoult Pierre-Edouard Fournier PII: DOI: Reference:
S0723-2020(15)00045-4 http://dx.doi.org/doi:10.1016/j.syapm.2015.03.004 SYAPM 25685
To appear in: Received date: Revised date: Accepted date:
9-12-2014 17-3-2015 18-3-2015
Please cite this article as: S.A. Sankar, J.-C. Lagier, P. Pontarotti, D. Raoult, P.-E. Fournier, The human gut microbiome, a taxonomic conundrum, Systematic and Applied Microbiology (2015), http://dx.doi.org/10.1016/j.syapm.2015.03.004 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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The human gut microbiome, a taxonomic conundrum
2 3 Senthil Alias Sankar1, Jean-Christophe Lagier1, Pierre Pontarotti2,
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Didier Raoult1, Pierre-Edouard Fournier1*
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URMITE, UM63, CNRS7278, IRD198, insermU1095, Institut Hospitalo-Universitaire Méditerranée Infection, Aix-Marseille University, Marseille, France
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* Corresponding author
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URMITE, Faculté de Médecine,
27 Bd Jean Moulin, 13385 Marseille cedex 5, France
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Tel + 33 491 385 517 Fax +33 491 387 772
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Evolutionary Biology and Modeling Group, LATP - UMR 7353, Aix-Marseille University, Marseille, France
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Abstract
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From culture to metagenomics, within only 130 years, our knowledge of the human microbiome
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has considerably improved. With > 1,000 microbial species identified to date, the gastro-
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intestinal microbiota is the most complex of human biotas. It is composed of a majority of
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Bacteroidetes and Firmicutes and, although exhibiting great inter-individual variations according
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to age, geographic origin, disease or antibiotic uptake, it is stable over time. Metagenomic
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studies have suggested associations between specific gut microbiota compositions and a variety
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of diseases, including irritable bowel syndrome, Crohn’s disease, colon cancer, type 2 diabetes
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and obesity. However, these data remain method-dependent, as no consensus strategy has been
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defined to decipher the complexity of the gut microbiota. High throughput culture-independent
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techniques have highlighted the limitations of culture by showing the importance of uncultured
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species, whereas modern culture methods have demonstrated that metagenomics underestimates
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the microbial diversity by ignoring minor populations. In this review, we highlight the progress
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and challenges that pave the way to a complete understanding of the human gastrointestinal
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microbiota and its influence on human health.
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Keywords: human microbiome, gut microbiota, diversity, culture, metagenomics
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Introduction The human microbiome is a complex and dynamic mixture of microorganisms that
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exceeds the total number of human cells by a factor 10, and by a factor 150 when considering the
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number or bacterial genes versus the human genome [49, 71, 110]. It is made of different
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microbial communities present in different parts of the human body such as the oro-naso-
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pharyngeal sphere, skin, vagina and gastrointestinal tract (GI) (Figure 1), each of which interacts
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with its host and has an impact on human health and disease. The human GI microbiota is mostly
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concentrated in the colon and is made of a majority of bacteria, completed by few archae,
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eukaryotes and viruses [113]. However, it has been estimated that only 30% of the human GI
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microbiota was characterized [60], despite the growing interest of the scientific community for
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this topic, as evidenced by the increasing number of dedicated articles in the scientific litterature.
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Initial culture methods to decipher the complexity of the human microbiome have
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progressively been replaced by molecular methods, notably metagenomic studies of the human
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GI that revealed, at least partially, that the microbial diversity associated with humans was far
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from fully known (Figure 2) [17]. However, these studies have also emphasized that the
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distribution of specific microbial communities and catalogues of genes among individuals could
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be influenced by several factors, including the geographical origin, age and diet of studied
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individuals as well as antibiotic or probiotic uptake [31,39,45,100,104,110,140,146,149]. In
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addition, associations between specific gut phyla and intestinal disorders, obesity or diabetes
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were also studied [62]. Recently, a renewed interest in culture methods for the study of the
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human microbiome was motivated by the drawbacks of molecular studies of the human
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microbiome, notably that minor populations (present in concentrations < 106/mL) were ignored
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[11] and that the characterization of the detected microorganisms was not reliable at the lowest
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taxonomic levels. Based on diversified culture conditions and coupled to mass spectrometry,
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methods such as the “culturomics” strategy demonstrated to be complementary to molecular
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tools and enabled the recovery of many previously unknown species and genera. This review focuses on the successive methods used to characterize gut bacterial
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communities. The influence and limitations of these methods for the taxonomic classification of
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members of the gut microbiota are discussed.
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1) The human gut microbiota
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Among the trillions of human-associated micro-organisms, bacteria are predominant and are distributed throughout the gastrointestinal tract (GI) [22]. Bacterial communities exhibit
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quantitative and qualitative variations along the length of the GI due to host factors (pH, transit
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time, bile acids, digestive enzymes, mucus, immune system), non-host factors (nutrients,
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medication, and environmental factors), and bacterial factors (adhesion capacity, enzymes,
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metabolic capacity, as well as microbial co-evolution, interactions and competition) [21]. From
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stomach to colon, the bacterial biomass ranges from 102-3 cells/ml to 1011-1012 cells/ml [152],
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approximately 95% being anaerobic bacteria and at least 1,000 different species being listed to
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date [60, 110, 113]. Of the >30 bacterial phyla constituting the gut microbiota [115], seven
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account for the vast majority of detected species, including the Firmicutes, Bacteroidetes,
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Actinobacteria, Cyanobacteria, Fusobacteria, Proteobacteria, and Verrucomicrobia, with the
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members of the former two being the most abundant [108]. In addition, the high concentration of
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microorganisms within the human gut plays a role in prokaryotic evolution and diversification,
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facilitating lateral gene transfer (LGT), chromosomal rearrangements, gene duplications, and
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adaptation to the changing environment [48, 76, 136].
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Several evolutionary and ecological processes shape the association between the host and
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microorganisms [71]. The gut colonization by microorganisms is influenced, early in childhood,
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by the mode of birth (vaginal or cesarean delivery) and the diet (breast feeding or not), and
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stabilizes by the age of 3 [102,150]. Later, the fecal biota may also be influenced by various
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factors, the most common being the geographical environment (although globalization of food
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products tends to reduce the impact of this factor, as suggested by the reduced GI microbiota
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diversity observed in populations from Europe and USA when compared to rural Africa and
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South America [24, 118, 162]), diet, age [163], and prebiotic, probiotic or antibiotic uptake [39,
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45, 100, 140, 149], although some microbial species, in particular those acquired early in life,
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remain stable, as demonstrated during a 5-year study [33]. In addition, the GI microbial
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composition has been proven to have a direct impact on human health, resulting in beneficial or
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adverse effects. Therefore, the human gut is considered as a metabolic organ playing a crucial
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role in the digestion process, involving complex chemical fluids with the active participation of
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microorganisms [41]. As a matter of fact, the gut microbiota performs many useful functions,
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such as producing enzymes absent in humans for digestion and fermenting unused energy
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substrates, vitamins such as biotin and vitamin K, and hormons to direct the host to store fats
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[152]; preventing the colonization by harmful, pathogenic bacteria [38]; and regulating the
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development of the epithelial barrier functions and innate and host adaptive immune functions
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[15, 52, 69].
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However, in certain conditions, some species and/or biota disequilibria (dysbiosis) are
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thought to be capable of causing infections. Moreover, alterations in the microbial composition
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have been associated to various GI diseases including irritable bowel syndrome [55], polyposis
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and colo-rectal cancer [127], necrotizing enterocolitis [134], Crohn’s disease [80], functional
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dyspepsia [126], as well as metabolic diseases, notably obesity [5, 72, 146], type 2 diabetes [65,
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126] and nonalcoholic steato-hepatitis [112]. Our understanding of the gut microbiota evolved considerably over time, markedly influenced by methodological progresses. Prior to the 1990s, our knowledge was restricted to a
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small number of culturable bacterial species whereas the development of culture-independent
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molecular approaches has revolutionized our knowledge of this complex biota. 2) Methods to decipher the human gut microbiota 3-1) Culture-based microbiota assessment
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3-1-1) Conventional culture
Since the pioneering description of Bacterium coli commune by Escherich in 1885,
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culture has been the cornerstone of clinical microbiology, notably for the study of the human gut
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microbiota. However, limitations of culture were numerous. In particular, early discrepancies
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were noted in the gut microbiota composition, both in terms of numbers and diversity, between
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Gram staining (majority of Gram-negative bacteria) and culture (majority of Gram-positive
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bacteria) [42, 93]. A major breakthrough was the application of anaerobic conditions to the
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culture of gut microorganisms in the 1970s, demonstrating the predominance of anaerobes
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among culturable bacteria [35] and enabling the recovery of up to 113 distinct bacterial species
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in a single study [84]. These species were classified within the genera Clostridium, Eubacterium,
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Lactobacillus, Peptostreptococcus and Ruminococcus for Gram-positive bacteria, and
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Bacteroides, Bifidobacterium and Fusobacterium for Gram-negative bacteria [92]. On the basis
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of these landmark studies, it was estimated that the number of distinct bacterial species within
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the human gut microbiota was approximately 400 [35, 92] but only 25 to 40 dominant species
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could be cultivated per individual [9].
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The reasons underlying the inadequacy of conventional culture to study the human gut
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microbiota are multiple [8]. These reasons include the time and money needed, notably to
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identify the different colonies grown in agar; the lack of sensitivity, the most common culture
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conditions favoring fast-growing and non-fastidious species and ignoring those in low
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concentration or requiring unusual culture conditions [34] and the development of culture-
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independent methods, notably 16S rRNA amplification and sequencing. Therefore, although still
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widely used in most microbiology laboratories worldwide for the routine diagnosis of bacterial
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infections, culture was progressively replaced by molecular methods for the study of complex
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microbiotas, despite the requirement that a strain should be established in culture and its
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phenotype characterized using a polyphasic approach in order to be proposed as a potential new
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species and eventually validated by the international committee for the systematics of
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prokaryotes [36].
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3-1-2) Culturomics
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In 2012, a new strategy named “culturomics” was proposed for the study of the human
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gut microbiota [60]. This strategy is based on the use of combinations of diversified culture
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characteristics (atmosphere, incubation temperature and time, culture medium composition [pH,
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nutrients, minerals, antibiotics]), with the objective of mimicking as much as possible the natural
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conditions within the gut. In this study, Lagier et al.[60] using 212 distinct culture condition
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combinations, isolated 32,500 bacterial colonies that they first identified using MALDI-TOF
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mass spectrometry (MALDI-TOF-MS) and, when no correct identification was obtained at the
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species level (i.e., the MALDI-TOF MS score was lower than 2.0), by 16S rRNA amplification
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and sequencing. A total of 340 species from 7 phyla were identified, including 174 species that
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had not previously been described in the human gut, 31 of which were new species [62].
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Interestingly, these authors also performed a 16S rRNA-based metagenomic study of the same
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specimens and identified 698 phylogenetic types, or phylotypes, including 282 known species,
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but only 51 of these species were detected by both culturomics and metagenomics [60]. The
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main reason proposed to explain this discrepancy was that 65% of cultivated species were
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present at low concentrations, ranging from 103 to 106 CFU/mL, which is below the detection
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threshold of metagenomics (a drawback referred to as depth bias) [62].
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In a second step, bacterial isolates considered as representatives from potentially new
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species were further characterized using “taxono-genomics”, a polyphasic strategy including
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both phenotypic and genetic characteristics [32, 89, 116,117]. The phenotypic characteristics
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used included the culture conditions, cell aspect in electron microscopy and Gram staining, main
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chemical properties and MALDI-TOF MS mass spectrum, whereas the genetic properties
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comprised the 16S rRNA sequence similarity and complete genome sequence analysis (size,
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DNA G+C content, percentage of coding sequences, gene content, gene distribution in COG
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categories, presence of mobile genetic elements, numbers of RNA genes, signal peptides and
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transmembrane helices, and determination of the average genomic identity of orthologous gene
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sequences [AGIOS] by comparison with phylogenetically-close species). The combination of
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culturomics and taxono-genomics enabled the description of 68 potential novel species from the
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human gut [32, 61, 89, 116], including 9 whose names have officially been validated [100].
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Other culturomics studies further demonstrated the usefulness of this strategy for the study of the
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human gut microbiota. Dubourg et al. observed a reduced bacterial diversity (but not
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concentration) in the feces specimens from patients treated with broad-spectrum antibiotics but
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identified 16 known species and 8 new species for the first time [29,30]. Pfleiderer et al.
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identified 11 new bacterial species in the stool from a patient with anorexia nervosa [105].
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3-2) Culture-independent techniques
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The introduction of molecular methods in microbiology constituted a revolution and
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paved the way for culture-independent methods to characterize the gut microbiome.
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3-2-1) 16S rRNA-based methods One of the earliest and the most widely used molecular approach for diagnostic,
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phylogenetic and taxonomic applications has been to target the 16S rRNA gene. This gene
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exhibits several advantages including its distribution in all bacterial species, its stability over
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time and its size (~1,500 bp) that makes it suitable for bioinformatic analyses [53, 63, 159, 160].
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Composed of conserved and variable regions among species, this gene has the advantage of
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being easily amplified using broad range primers flanking variable fragments. Based on 16s
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rRNA sequence identity with the phylogenetically-closest species with standing in nomenclature,
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a bacterial strain can potentially be classified asbelonging to a novel species (90%
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[110]. Recently, Li et al. [73] established the gene catalog of the human gut microbiome,
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comprising 9,879,896 genes. Comparative shotgun metagenomic analyses of the human gut
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microbiota among individuals of various ages showed remarkable inter-individual variations in
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species and gene content. [59, 163]. In particular, Bifidobacterium and Enterobacteriaceae were
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predominant in infants whereas Bacteroidetes and Firmicutes were dominant in adults and
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weaned children [59, 163]. However, despite the variations observed according to age, gender,
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nationality or body mass index, Arumugam et al. could group the human GI microbiota into
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three main enterotypes [6]. Shotgun metagenomics was also used to study GI microbiota
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variations between healthy individuals and patients with inflammatory bowel disease (IBD),
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obesity and diabetes (Table 2) [43, 67, 80, 110, 111,146].
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Overall, shotgun metagenomics was able to overcome some of the defaults of 16S rRNA-
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based metagenomics, notably the poor amplification of some bacterial populations, as was the
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case for bifidobacteria [59]. However, shotgun metagenomics suffers from several pitfalls: a) the
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proportion of the reads that cannot be assigned a taxonomic identification (for poorly abundant
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species or in presence of many closely related species) or a function due to a lack of close match
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in reference databases is often elevated [1]; b) the selection of an appropriate nucleic acid
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extraction method is essential to provide a sufficient amount of DNA , adapted to the nature of
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the sample and that avoids cross-contamination; c) sequence read length, coverage, error rate and
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chimeric contigs are major issues during downstream processing of the microbial population [56,
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143]; d) different bioinformatic tools are used for assembly according to the sample, sequencer
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and user satisfactory requirements [110]; e) the gene prediction depends on the read length,
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computational tools and reference database; f) the lack of a reference database often prevents
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accurate assessment of sequences or designates them as ORFans; g) only the major bacterial
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populations are revealed; and h) detection of HGT events in metagenomic data is a difficult task
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which would lead to misidentification.
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3) Future prospects
The variations in the composition of the gut microbiota and the relationships with the
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hosts are extremely complex. Continuous efforts should be made to characterize gut
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microorganisms, which will aid in diagnosing/treating certain human disorders. High throughput
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sequencing coupled to metagenomics has shed light on the hidden microbial world, enabling
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both a taxonomic and functional analysis of the human gut microbiota, which may have wide
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applications in various fields, notably medicine. It demonstrated in particular that the human gut
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is a dynamic environment, even though over time its composition appears to be stable. However,
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a critical limitation of metagenomics, despite the massive amount of information provided and
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the permanent progress in read length and output, is that it only partially unveiled the diversity
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and genetic characteristics of GI microorganisms, with a doubt on the accuracy of their
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identification at the species level, as the current taxonomic tools applied to metagenomic datasets
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are imperfect. Therefore, culture cannot, as yet, be replaced by in silico assays only to fully
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characterize a bacterial species. Efforts such as the culturomics strategy, in addition to
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uncovering >100 new gut bacterial species, have enabled to fully describe and confirm the
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authenticity of these species, even when present at low concentrations [60].
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This demonstrates that the human microbiome partially remains a terra incognita and that
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further new species will undoubtedly enrich the human microbiome panel in coming years.
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Therefore, efforts to develop standardized computational methods to interpret metagenomics
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datasets are needed to improve the quality of taxonomic assessment of complex human biotas
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and enable homogenous and comparable analysis of the complex human microbiotas. In
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addition, the use of single-cell based approaches may help overcome the current limitations of
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metagenomics in terms of sensitivity and degree of characterization of gut bacteria. Among
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those, flow cytometry was successfully applied in enumerating and identifying human faecal
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bacteria in both patients with Crohn’s disease and healthy individuals [47], in whom rare taxa
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from the orders Sphingomonadales, Pseudomonadales and Burkholderiales were identified [23].
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Similarly, the recent development of single-cell genomics, combined to flow cytometry or FISH
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and micromanipulation, enabled the genomic characterization of uncultured bacteria, either
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targeted or not [66], such as the previously uncharacterized TM7 or chloroflexi from the oral
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microbiota [13,81].
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Finally, the introduction of improved metagenomic strategies with a higher
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discriminatory power, as well as a renewed interest in culture will undoubtedly contribute to a
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better understanding of the human gut microbiota and its role in human health.
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Figure legends
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Figure 1: Distribution of bacterial phyla in human body habitats (Adapted from [4, 6, 27, 44,
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119].
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Figure 2: Evolution of methods used to study the taxonomic diversity of human gut bacterial
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species. Red: microbiology era; Blue: main strategies and tools used to estimate the gut
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microbiota diversity; white: studied characteristics.
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Figure 3: Flow chart depicting the steps involved in deciphering microbial communities based
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on 16S rRNA sequencing.
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Table 1: Tools available for the taxonomic assignment of metagenomic sequences. The tools are
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based on different algorithms and/or pipelines used for binning the metagenomic data sets. Taxonomic
Composition-based
Composition- and
Homology-based
homology-based
NBC classifier
SOrt-ITEMS
TACOA
mOTU
S-GSOM
QIIME
PhymmBL
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CARMA
SPHINX
MetaCluster
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CONCOCT
MG-RAST
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RDP classifier
MOTHUR
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TANGO
UniFrac DOTUR MNS MyTaxa
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* PhymmBL [12],S-GSOM [15], TANGO [18], TACOA [26], MEGAN [48], Metacluster [64],
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MyTaxa [74], Phylopythia [80],SPHINX [85], SOrt-ITEMS [86] and RDP classifier [148] are
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taxonomic assignment tools based on reference genomes or algorithms; UniFrac [73], IMG/M
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[78], MG-RAST [81] are taxonomic assignment tools based on phylogenetic composition and 19 Page 19 of 49
can perform functional analysis/comparison between communities); DOTUR [124], Mothur
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[125], mOTU [136] use clustering to OTUs and richness estimation using rRNA or marker
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genes; QIIME [13] is a combined software package; NBC classifier [117] is based on taxonomic
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assignments using N-mer frequency; CONCOCT [2] is an algorithm that combines sequence
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composition and coverage; MNS [140] uses nucleotide diversity along the sequenced 16S rRNA
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gene regions to differentiate the human gut microbial nucleotide signatures; CARMA [55] is
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based on phylogenetic classification of reads (454) containing Pfam domains and protein
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families..
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Table 2: Examples of human disease in which abnormal compositions of the human gut
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microbiota have been documented.
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Phylum/Class*
Family/Genus*
Species*
Obesity
Actinobacteria
Enterobacteriaceae
Lactobacillus reuteri
Reference [72, 87, 146]
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Disease
Firmicutes
Staphylococcus aureus
Faecalibacterium prausnitzii
M an
Bacteroidetes
Escherichia coli
Bifidobacterium spp.
Lactobacillus plantarum
ed
Methabacteriodes
ce pt
Lactobacillus paracasei
Firmicutes
Enterobacteriaceae
Escherichia coli
Role of Tenericutes
[74, 81, 121, 125]
Clostridium spp.
Ac
Crohn's disease
Lactobacillus caseri
Bacteroidetes
Clostridium leptum
Reduced diversity
Clostridium coccoides
of Firmicutes
Faecalibacterium prausnitzii Lactobacillus coleohominis Bacteroides spp. 22 Page 22 of 49
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Bacteroidetes
Enterobacteriaceae
Ruminococcus spp.
[79, 107, 133]
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Irritable bowel
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Streptococcus gallolyticus
syndrome
Lactobacilli spp.
M an
Clostridium spp.
Bifidobacterium
Firmicutes
carcinoma
Bacteroidetes
Enterobacteriaceae
Streptococcus spp.
Fusobacterium spp.
ce pt
Bacteroides spp. Prevotella spp.
Betaproteobacteria
Bacteroides spp.
Firmicutes
Prevotella spp.
Ac
Type 2 diabetes
403
[137, 154]
Enterococcus spp.
ed
Colorectal
Clostridia
Lactobacillus spp.
Bacilli
Clostridium coccoides
[65]
* Red and blue letters indicate quantitatively increased or decreased microbial populations, respectively.
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Figure 1
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Oral cavity
Stomach
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M
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Skin
Vagina
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Colon
Firmicutes Actinobacteria Bacteroidetes
Proteobacteria Fusobacteria
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Figure 2
MOLECULAR METHODS
1980
CULTURE GENOMICS
Metagenomics
Culturomics Taxonogenomics
16S rRNA amplification Cloning Sequencing
Microarray, QPCR, DNA Fingerprinting, FISH
ed
1960
Ac
Late 19th century
Chemotaxonomy Numerical taxonomy DNA-DNA hybridization
ce pt
Morphology Growth requirements Pathogenic potential
PCR
M an
Physiological, biochemical and genetic properties
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CULTURE
1990
2000
Cultivation, proteomic and genomic properties
2010
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Figure 3
M an
DNA extraction
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Gut microflora
16S rRNA amplification
ed
High throughput sequencing
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Sequence grouping into OTUs
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Comparison to NCBI, RDP, GreenGenes, Silva databases
Flora structure
Abundance of OTUs
Phylogenetic approach
Sequence variation SNP detection Page 49 of 49