Recent significant progress in culture-independent techniques, together with the parallel development of -omics technologies and data analysis capabilities, have led to a new perception of the milk microbiota as a complex microbial community with great diversity and multifaceted biological roles, living in an environment that was until recently believed to be sterile. In this review, we summarize and discuss the latest findings on the milk microbiota in dairy cows, with a focus on the role it plays in bovine physiology and health. Following an introduction on microbial communities and the importance of their study, we present an overview of the -omics methods currently available for their characterization, and outline the potential offered by a systems biology approach encompassing metatranscriptomics, metaproteomics, and metametabolomics. Then, we review the recent discoveries on the dairy cow milk microbiome enabled by the application of -omics approaches. Learning from studies in humans and in the mouse model, and after a description of the endogenous route hypothesis, we discuss the role of the milk microbiota in the physiology and health of both the mother and the offspring, and report how it can be changed by farming practices and during infection. In conclusion, we shortly outline the impact of the milk microbiota on the quality of milk and of dairy products.

The development of powerful sequencing techniques has allowed, albeit with some biases, the identification and inventory of complex microbial communities that inhabit different body sites or body fluids, some of which were previously considered sterile. Notably, milk is now considered to host a complex microbial community with great diversity. Milk microbiota is now well documented in various hosts. Based on the growing literature on this microbial community, we address here the question of what milk microbiota is. We summarize and compare the microbial composition of milk in humans and in ruminants and address the existence of a putative core milk microbiota. We discuss the factors that contribute to shape the milk microbiota or affect its composition, including host and environmental factors as well as methodological factors, such as the sampling and sequencing techniques, which likely introduce distortion in milk microbiota analysis. The roles that milk microbiota are likely to play in the mother and offspring physiology and health are presented together with recent data on the hypothesis of an enteromammary pathway. At last, this fascinating field raises a series of questions, which are listed and commented here and which open new research avenues.

The presence of Anaplasma phagocytophilum , a tick-transmitted zoonotic pathogen, was investigated in Sardinia using a molecular approach. Phylogenetic analysis revealed that Sardinian strains are genetically distinct from the two lineages previously described in Europe and are closely related to strains isolated in different areas of the United States.

Elucidating the role of gut microbiota in physiological and pathological processes has recently emerged as a key research aim in life sciences. In this respect, metaproteomics, the study of the whole protein complement of a microbial community, can provide a unique contribution by revealing which functions are actually being expressed by specific microbial taxa. However, its wide application to gut microbiota research has been hindered by challenges in data analysis, especially related to the choice of the proper sequence databases for protein identification. Here, we present a systematic investigation of variables concerning database construction and annotation and evaluate their impact on human and mouse gut metaproteomic results. We found that both publicly available and experimental metagenomic databases lead to the identification of unique peptide assortments, suggesting parallel database searches as a mean to gain more complete information. In particular, the contribution of experimental metagenomic databases was revealed to be mandatory when dealing with mouse samples. Moreover, the use of a “merged” database, containing all metagenomic sequences from the population under study, was found to be generally preferable over the use of sample-matched databases. We also observed that taxonomic and functional results are strongly database-dependent, in particular when analyzing the mouse gut microbiota. As a striking example, the Firmicutes/Bacteroidetes ratio varied up to tenfold depending on the database used. Finally, assembling reads into longer contigs provided significant advantages in terms of functional annotation yields. This study contributes to identify host- and database-specific biases which need to be taken into account in a metaproteomic experiment, providing meaningful insights on how to design gut microbiota studies and to perform metaproteomic data analysis. In particular, the use of multiple databases and annotation tools has to be encouraged, even though this requires appropriate bioinformatic resources.

Metaproteomics enables the investigation of the protein repertoire expressed by complex microbial communities. However, to unleash its full potential, refinements in bioinformatic approaches for data analysis are still needed. In this context, sequence databases selection represents a major challenge. This work assessed the impact of different databases in metaproteomic investigations by using a mock microbial mixture including nine diverse bacterial and eukaryotic species, which was subjected to shotgun metaproteomic analysis. Then, both the microbial mixture and the single microorganisms were subjected to next generation sequencing to obtain experimental metagenomic- and genomic-derived databases, which were used along with public databases (namely, NCBI, UniProtKB/SwissProt and UniProtKB/TrEMBL, parsed at different taxonomic levels) to analyze the metaproteomic dataset. First, a quantitative comparison in terms of number and overlap of peptide identifications was carried out among all databases. As a result, only 35% of peptides were common to all database classes; moreover, genus/species-specific databases provided up to 17% more identifications compared to databases with generic taxonomy, while the metagenomic database enabled a slight increment in respect to public databases. Then, database behavior in terms of false discovery rate and peptide degeneracy was critically evaluated. Public databases with generic taxonomy exhibited a markedly different trend compared to the counterparts. Finally, the reliability of taxonomic attribution according to the lowest common ancestor approach (using MEGAN and Unipept software) was assessed. The level of misassignments varied among the different databases, and specific thresholds based on the number of taxon-specific peptides were established to minimize false positives. This study confirms that database selection has a significant impact in metaproteomics, and provides critical indications for improving depth and reliability of metaproteomic results. Specifically, the use of iterative searches and of suitable filters for taxonomic assignments is proposed with the aim of increasing coverage and trustworthiness of metaproteomic data.