Editorial: Editorial for the virtual issue on microbiome

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 somemicrobiologists 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 alphaand 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 compositionmay 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 signaturemay 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, whichmay 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