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Background & Aim: Sarcopenia is frequent in cirrhosis and is associated with unfavourable outcomes. The role of the gut-liver-muscle axis in this setting has been poorly investigated. The aim of this study was to identify gut microbiota, metabolic and inflammatory signatures associated with sarcopenia in cirrhotic patients. Methods: Fifty cirrhotic patients assessed for the presence of sarcopenia by the quantification of muscle mass and strength were compared with age- and sex-matched controls. A multiomic analysis, including gut microbiota composition and metabolomics, serum myokines and systemic and intestinal inflammatory mediators, was performed. Results: The gut microbiota of sarcopenic cirrhotic patients was poor in bacteria associated with physical function (Methanobrevibacter, Prevotella and Akkermansia), and was enriched in Eggerthella, a gut microbial marker of frailty. The abundance of potentially pathogenic bacteria, such as Klebsiella, was also increased, to the detriment of autochthonous ones. Sarcopenia was associated with elevated serum levels of pro-inflammatory mediators and of fibroblast growth factor 21 (FGF21) in cirrhotic patients. Gut microbiota metabolic pathways involved in amino acid, protein and branched-chain amino acid metabolism were up-regulated, in addition to ethanol, trimethylamine and dimethylamine production. Correlation networks and clusters of variables associated with sarcopenia were identified, including one centred on Klebsiella/ethanol/FGF21/Eggerthella/Prevotella. Conclusions: Alterations in the gut-liver-muscle axis are associated with sarcopenia in patients with cirrhosis. Detrimental but also compensatory functions are involved in this complex network.

Autism spectrum disorders (ASDs) are neurodevelopmental disorders characterized by behavioral alterations and currently affect about 1% of children. Significant genetic factors and mechanisms underline the causation of ASD. Indeed, many affected individuals are diagnosed with chromosomal abnormalities, submicroscopic deletions or duplications, single-gene disorders or variants. However, a range of metabolic abnormalities has been highlighted in many patients, by identifying biofluid metabolome and proteome profiles potentially usable as ASD biomarkers. Indeed, next-generation sequencing and other omics platforms, including proteomics and metabolomics, have uncovered early age disease biomarkers which may lead to novel diagnostic tools and treatment targets that may vary from patient to patient depending on the specific genomic and other omics findings. The progressive identification of new proteins and metabolites acting as biomarker candidates, combined with patient genetic and clinical data and environmental factors, including microbiota, would bring us towards advanced clinical decision support systems (CDSSs) assisted by machine learning models for advanced ASD-personalized medicine. Herein, we will discuss novel computational solutions to evaluate new proteome and metabolome ASD biomarker candidates, in terms of their recurrence in the reviewed literature and laboratory medicine feasibility. Moreover, the way to exploit CDSS, performed by artificial intelligence, is presented as an effective tool to integrate omics data to electronic health/medical records (EHR/EMR), hopefully acting as added value in the near future for the clinical management of ASD.

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.

The colonization and development of gut microbiota immediately after birth is highly variable and depends on several factors, such as delivery mode and modality of feeding during the first months of life. A cohort of 31 mother and neonate pairs, including 25 at-term caesarean (CS) and 6 vaginally (V) delivered neonates (DNs), were included in this study and 121 meconium/faecal samples were collected at days 1 through 30 following birth. Operational taxonomic units (OTUs) were assessed in 69 stool samples by phylogenetic microarray HITChip and inter- and intra-individual distributions were established by inter-OTUs correlation matrices and OTUs co-occurrence or co-exclusion networks. 1H-NMR metabolites were determined in 70 stool samples, PCA analysis was performed on 55 CS DNs samples, and metabolome/OTUs co-correlations were assessed in 45 CS samples, providing an integrated map of the early microbiota OTUs-metabolome. A microbiota "core" of OTUs was identified that was independent of delivery mode and lactation stage, suggesting highly specialized communities that act as seminal colonizers of microbial networks. Correlations among OTUs, metabolites, and OTUs-metabolites revealed metabolic profiles associated with early microbial ecological dynamics, maturation of milk components, and host physiology