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Type 1 diabetes (T1D) is a chronic autoimmune metabolic disorder with onset in pediatric/adolescent age, characterized by insufficient insulin production, due to a progressive destruction of pancreatic β-cells. Evidence on the correlation between the human gut microbiota (GM) composition and T1D insurgence has been recently reported. In particular, 16S rRNA-based metagenomics has been intensively employed in the last decade in a number of investigations focused on GM representation in relation to a pre-disease state or to a response to clinical treatments. On the other hand, few works have been published using alternative functional omics, which is more suitable to provide a different interpretation of such a relationship. In this work, we pursued a comprehensive metaproteomic investigation on T1D children compared with a group of siblings (SIBL) and a reference control group (CTRL) composed of aged matched healthy subjects, with the aim of finding features in the T1D patients' GM to be related with the onset of the disease. Modulated metaproteins were found either by comparing T1D with CTRL and SIBL or by stratifying T1D by insulin need (IN), as a proxy of β-cells damage, showing some functional and taxonomic traits of the GM, possibly related to the disease onset at different stages of severity. [ABSTRACT FROM AUTHOR]

During the last decade, the evidences on the relationship between neurodevelopmental disorders and the microbial communities of the intestinal tract have considerably grown. Particularly, the role of gut microbiota (GM) ecology and predicted functions in Autism Spectrum Disorders (ASD) has been especially investigated by 16S rRNA targeted and shotgun metagenomics, trying to assess disease signature and their correlation with cognitive impairment or gastrointestinal (GI) manifestations of the disease. Herein we present a metaproteomic approach to point out the microbial gene expression profiles, their functional annotations, and the taxonomic distribution of gut microbial communities in ASD children. We pursued a LC-MS/MS based investigation, to compare the GM profiles of patients with those of their respective relatives and aged-matched controls, providing a quantitative evaluation of bacterial metaproteins by SWATH analysis. All data were managed by a multiple step bioinformatic pipeline, including network analysis. In particular, comparing ASD subjects with CTRLs, up-regulation was found for some metaproteins associated with Clostridia and with carbohydrate metabolism (glyceraldehyde-3-phosphate and glutamate dehydrogenases), while down-regulation was observed for others associated with Bacteroidia (SusC and SusD family together with the TonB dependent receptor). Moreover, network analysis highlighted specific microbial correlations among ASD subgroups characterized by different functioning levels and GI symptoms. To the best of our knowledge, this study represents the first metaproteomic investigation on the gut microbiota of ASD children compared with relatives and age-matched CTRLs. Remarkably, the applied SWATH methodology allowed the attribution of differentially regulated functions to specific microbial taxa, offering a novel and complementary point of view with respect to previous studies. [Display omitted] • SWATH analysis supported the metaproteomic outcome when comparing ASD and controls. • The gut microbiota of controls differs from both that of ASD and their relatives. • The enhanced functions in the gut microbiota of ASD are ascribed to Clostridia. [ABSTRACT FROM AUTHOR]

Breastfeeding is nowadays known to be one of the most critical factors contributing to the development of an efficient immune system. In the last decade, a consistent number of pieces of evidence demonstrated the relationship between a healthy organism and its gut microbiota. However, this link is still not fully understood and requires further investigation. We recently adopted a murine model to describe the impact of either maternal milk or parental genetic background, on the composition of the gut microbial population in the first weeks of life. A metaproteomic approach to such complex environments is a big challenge that requires a strong effort in both data production and analysis, including the set-up of dedicated multitasking bioinformatics pipelines. Herein we present an LC-MS/MS based investigation to monitor mouse gut microbiota in the early life, aiming at characterizing its functions and metabolic activities together with a taxonomic description in terms of operational taxonomic units. We provided a quantitative evaluation of bacterial metaproteins, taking into account differential expression results in relation to the functional and taxonomic classification, particularly with proteins from orthologues groups. This allowed the reduction of the bias arising from the presence of a high number of shared peptides, and proteins, among different bacterial species. We also focused on host mucosal proteome and its modulation, according to different microbiota composition. This paper would represent a reference work for investigations on gut microbiota in early life, from both a microbiological and a functional proteomic point of view. We focused on the shaping of the mouse gut microbiota in dependence on the feeding modality, defining a reliable taxonomic description, highlighting some functional characteristics of the microbial community, and performing a first quantitative evaluation by data independent analysis in metaproteomics. Unlabelled Image • Lack of IgA in breast milk favors Escherichia predominance. • Gut microbiota shaping is not driven by breastfeeding only. • The two cross-feeding conditions led to more similar gut microbial populations. • An altered microbiota composition could reflect in different host mucosal proteomes. [ABSTRACT FROM AUTHOR]