Graphical abstract

Human milk is known to carry its own microbiota, of which the precise origin remains obscure. Breastfeeding allows mother-to-baby transmission of microorganisms as well as the transfer of many other milk components, such as human milk oligosaccharides (HMOs), which act as metabolizable substrates for particular bacteria, such as bifidobacteria, residing in infant intestinal tract. In the current study, we report the HMO composition of 249 human milk samples, in 163 of which we quantified the abundance of members of the Bifidobacterium genus using a combination of metagenomic and flow cytometric approaches. Metagenomic data allowed us to identify four clusters dominated by Bifidobacterium adolescentis and Bifidobacterium pseudolongum, Bifidobacterium crudilactis or Bifidobacterium dentium, as well as a cluster represented by a heterogeneous mix of bifidobacterial species such as Bifidobacterium breve and Bifidobacterium longum. Furthermore, in vitro growth assays on HMOs coupled with in silico glycobiome analyses allowed us to elucidate that members of the Bifidobacterium bifidum and B. breve species exhibit the greatest ability to degrade and grow on HMOs. Altogether, these findings indicate that the bifidobacterial component of the human milk microbiota is not strictly correlated with their ability to metabolize HMOs.

Necrotizing enterocolitis (NEC) is a severe gastrointestinal disease occurring predominantly in premature infants whose etiology is still not fully understood. In this study, the analysis of infant fecal samples through shotgun metagenomics approaches revealed a marked reduction of the intestinal (bio)diversity and an overgrowth of (opportunistic) pathogens associated with the NEC development.

The human vaginal environment harbours a community of bacteria that plays an important role in maintaining vaginal health and in protecting this environment from various urogenital infections. This bacterial population, also known as vaginal microbiota, has been demonstrated to be dominated by members of the Lactobacillus genus. Several studies employing 16S rRNA gene-based amplicon sequencing have classified the vaginal microbiota into five distinct community state types (CSTs) or vaginotypes. To deepen our understanding of the vaginal microbiota we performed an in-depth metaanalysis of 1312 publicly available datasets concerning healthy vaginal microbiome information obtained by metagenomics sequencing. The analysis confirmed the predominance of taxa belonging to the Lactobacillus genus, followed by members of the genera Gardnerella, Vibrio and Atopobium. Moreover, the statistical robustness offered by this metaanalysis allowed us to disentangle the species-level composition of dominant and accessory taxa constituting each vaginotype and to revisit and refine the previously proposed CST classification. In addition, a functional characterization of the metagenomic datasets revealed particular genetic features associated with each assigned vaginotype.

The genomic era has resulted in the generation of a massive amount of genetic data concerning the genomic diversity of bacterial taxa. As a result, the microbiological community is increasingly looking for ways to define reference bacterial strains to perform experiments that are representative of the entire bacterial species. Despite this, there is currently no established approach allowing a reliable identification of reference strains based on a comprehensive genomic, ecological, and functional context. In the current study, we developed a comprehensive multi-omics approach that will allow the identification of the optimal reference strains using the Bifidobacterium genus as test case. Strain tracking analysis based on 1664 shotgun metagenomics datasets of healthy infant faecal samples were employed to identify bifidobacterial strains suitable for in silico and in vitro analyses. Subsequently, an ad hoc bioinformatic tool was developed to screen local strain collections for the most suitable species-representative strain alternative. The here presented approach was validated using in vitro trials followed by metagenomics and metatranscriptomics analyses. Altogether, these results demonstrated the validity of the proposed model for reference strain selection, thus allowing improved in silico and in vitro investigations both in terms of cross-laboratory reproducibility and relevance of research findings.