The objectives of this study were to explore bacterial community assembly from cow teat skin to raw milk cheeses and to evaluate the role of farming systems on this assembly using 16S rRNA gene high-throughput sequencing. The two grazing systems studied (extensive vs. semi-extensive) had a greater effect on the microbiota of cow teat skin than on that of raw milks and cheeses. On teat skin, the relative abundance of several taxa at different taxonomic levels (Coriobacteriia, Bifidobacteriales, Corynebacteriales, Lachnospiraceae, Atopobium, and Clostridium) varied depending on the grazing system and the period (early or late summer). In cheese, the abundance of sub-dominant lactic acid bacteria (LAB) varied depending on the grazing system. Overall, 85% of OTUs detected in raw milks and 27% of OTUs detected in ripened cheeses were also found on cow teat skin. Several shared OTUs were assigned to taxa known to be involved in the development of cheese sensory characteristics, such as Micrococcales, Staphylococcaceae, and LAB. Our results highlight the key role of cow teat skin as a reservoir of microbial diversity for raw milk, and for the first time, that cow teat skin serves as a potential source of microorganisms found in raw-milk cheeses.

The metabolism of lactate impacts infant gut health and may lead to acute accumulation of lactate and/or H2 associated with pain and crying of colicky infants. Because gut microbiota studies are limited due to ethical and safety concerns, in vitro fermentation models were developed as powerful tools to assess effects of environmental conditions on the gut microbiota. In this study, we established a continuous colonic fermentation model (PolyFermS), inoculated with immobilized fecal microbiota and mimicking the proximal colon of 2-month-old infants. We investigated the effects of pH and retention time (RT) on lactate metabolism and of lactate-utilizing bacteria (LUB) exhibiting little or no H2 production. We observed that a drop in pH from 6.0 to 5.0 increased the number of lactate-producing bacteria (LPB) and decreased LUB concomitantly with lactate accumulation. Increasing RT from 5 to 10 h at pH 5.0 resulted in complete lactate consumption associated with increased LUB. Supplementation with dl-lactate (60 mM) to mimic lactate accumulation promoted propionate and butyrate production with no effect on acetate production. We further demonstrated that lactate-utilizing Propionibacterium avidum was able to colonize the reactors 4 days after spiking, suggesting its ability to compete with other lactate-utilizing bacteria producing H2. In conclusion, we showed that PolyFermS is a suitable model for mimicking young infant colonic microbiota. We report for the first time pH and RT as strong drivers for composition and metabolic activity of infant gut microbiota, especially for the metabolism of lactate, which is a key intermediate product for ecology and infant health. IMPORTANCE The metabolism of lactate is important for infant gut health and may lead to acute lactate and/or H2 accumulation, pain, and crying as observed in colicky infants. Functional human studies often faced ethical challenges due to invasive medical procedures; thus, in this study, we implemented PolyFermS fermentation models to mimic the infant proximal colon, which were inoculated with immobilized fecal microbiota of two 2-month-old infants. We investigated the impact of pH, retention time, and accumulation of dl-lactate on microbiota composition and metabolic activity. We found that a drop in pH from 6.0 to 5.0 led to increased LPB and decreased LUB concomitantly with lactate accumulation. Increasing the RT resulted in complete lactate consumption associated with increased LUB. Our data highlight for the first time the impact of key abiotic factors on the metabolism of lactate, which is an important intermediate product for ecology and infant health.

Background Reads assignment to taxonomic units is a key step in microbiome analysis pipelines. To date, accurate taxonomy annotation of 16S reads, particularly at species rank, is still challenging due to the short size of read sequences and differently curated classification databases. The close phylogenetic relationship between species encountered in dairy products, however, makes it crucial to annotate species accurately to achieve sufficient phylogenetic resolution for further downstream ecological studies or for food diagnostics. Curated databases dedicated to the environment of interest are expected to improve the accuracy and resolution of taxonomy annotation. Results We provide a manually curated database composed of 10’290 full-length 16S rRNA gene sequences from prokaryotes tailored for dairy products analysis ( https://github.com/marcomeola/DAIRYdb ). The performance of the DAIRYdb was compared with the universal databases Silva, LTP, RDP and Greengenes. The DAIRYdb significantly outperformed all other databases independently of the classification algorithm by enabling higher accurate taxonomy annotation down to the species rank. The DAIRYdb accurately annotates over 90% of the sequences of either single or paired hypervariable regions automatically. The manually curated DAIRYdb strongly improves taxonomic annotation accuracy for microbiome studies in dairy environments. The DAIRYdb is a practical solution that enables automatization of this key step, thus facilitating the routine application of NGS microbiome analyses for microbial ecology studies and diagnostics in dairy products. Electronic supplementary material The online version of this article (10.1186/s12864-019-5914-8) contains supplementary material, which is available to authorized users.

Bioinformatic tools for marker gene sequencing data analysis are continuously and rapidly evolving, thus integrating most recent techniques and tools is challenging. We present an R package for data analysis of 16S and ITS amplicons based sequencing. This workflow is based on several R functions and performs automatic treatments from fastq sequence files to diversity and differential analysis with statistical validation. The main purpose of this package is to automate bioinformatic analysis, ensure reproducibility between projects, and to be flexible enough to quickly integrate new bioinformatic tools or statistical methods. rANOMALY is an easy to install and customizable R package, that uses amplicon sequence variants (ASV) level for microbial community characterization. It integrates all assets of the latest bioinformatics methods, such as better sequence tracking, decontamination from control samples, use of multiple reference databases for taxonomic annotation, all main ecological analysis for which we propose advanced statistical tests, and a cross-validated differential analysis by four different methods. Our package produces ready to publish figures, and all of its outputs are made to be integrated in Rmarkdown code to produce automated reports.