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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.

Alterations of gut microbiota have been identified before clinical manifestation of type 1 diabetes (T1D). To identify the associations amongst gut microbiome profile, metabolism and disease markers, the 16S rRNA-based microbiota profiling and 1 H-NMR metabolomic analysis were performed on stool samples of 52 T1D patients at onset, 17 T1D siblings and 57 healthy subjects (CTRL). Univariate, multivariate analyses and classification models were applied to clinical and -omic integrated datasets. In T1D patients and their siblings, Clostridiales and Dorea were increased and Dialister and Akkermansia were decreased compared to CTRL, while in T1D, Lachnospiraceae were higher and Collinsella was lower, compared to siblings and CTRL. Higher levels of isobutyrate, malonate, Clostridium , Enterobacteriaceae, Clostridiales, Bacteroidales, were associated to T1D compared to CTRL. Patients with higher anti-GAD levels showed low abundances of Roseburia , Faecalibacterium and Alistipes and those with normal blood pH and low serum HbA 1c levels showed high levels of purine and pyrimidine intermediates. We detected specific gut microbiota profiles linked to both T1D at the onset and to diabetes familiarity. The presence of specific microbial and metabolic profiles in gut linked to anti-GAD levels and to blood acidosis can be considered as predictive biomarker associated progression and severity of T1D.

Food allergy (FA) and, in particular, IgE-mediated cow's milk allergy is associated with compositional and functional changes of gut microbiota. In this study, we compared the gut microbiota of cow's milk allergic (CMA) infants with that of cow's milk sensitized (CMS) infants and Healthy controls. The effect of the intake of a mixture of Bifidobacterium longum subsp. longum BB536, Bifidobacterium breve M-16V and Bifidobacterium longum subsp. infantis M-63 on gut microbiota modulation of CMA infants and probiotic persistence was also investigated. Gut microbiota of CMA infants resulted to be characterized by a dysbiotic status with a prevalence of some bacteria as Haemophilus , Klebsiella , Prevotella , Actinobacillus and Streptococcus . Among the three strains administered, B. longum subsp. infantis colonized the gastrointestinal tract and persisted in the gut microbiota of infants with CMA for 60 days. This colonization was associated with perturbations of the gut microbiota, specifically with the increase of Akkermansia and Ruminococcus . Multi-strain probiotic formulations can be studied for their persistence in the intestine by monitoring specific bacterial probes persistence and exploiting microbiota profiling modulation before the evaluation of their therapeutic effects.

Several studies in recent times have linked gut microbiome (GM) diversity to the pathogenesis of cancer and its role in disease progression through immune response, inflammation and metabolism modulation. This study focused on the use of network analysis and weighted gene co-expression network analysis (WGCNA) to identify the biological interaction between the gut ecosystem and its metabolites that could impact the immunotherapy response in non-small cell lung cancer (NSCLC) patients undergoing second-line treatment with anti-PD1. Metabolomic data were merged with operational taxonomic units (OTUs) from 16S RNA-targeted metagenomics and classified by chemometric models. The traits considered for the analyses were: (i) condition: disease or control (CTRLs), and (ii) treatment: responder (R) or non-responder (NR). Network analysis indicated that indole and its derivatives, aldehydes and alcohols could play a signaling role in GM functionality. WGCNA generated, instead, strong correlations between short-chain fatty acids (SCFAs) and a healthy GM. Furthermore, commensal bacteria such as Akkermansia muciniphila , Rikenellaceae, Bacteroides , Peptostreptococcaceae, Mogibacteriaceae and Clostridiaceae were found to be more abundant in CTRLs than in NSCLC patients. Our preliminary study demonstrates that the discovery of microbiota-linked biomarkers could provide an indication on the road towards personalized management of NSCLC patients.

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.

The human gut contains trillions of microbes that play a central role in host biology, including the provision of key nutrients from the diet. Food is a major source of precursors for metabolite production; in fact, diet modulates the gut microbiota (GM) as the nutrients, derived from dietary intake, reach the GM, affecting both the ecosystem and microbial metabolic profile. GM metabolic ability has an impact on human nutritional status from childhood. However, there is a wide variability of dietary patterns that exist among individuals. The study of interactions with the host via GM metabolic pathways is an interesting field of research in medicine, as microbiota members produce myriads of molecules with many bioactive properties. Indeed, much evidence has demonstrated the importance of metabolites produced by the bacterial metabolism from foods at the gut level that dynamically participate in various biochemical mechanisms of a cell as a reaction to environmental stimuli. Hence, the GM modulate homeostasis at the gut level, and the alteration in their composition can concur in disease onset or progression, including immunological, inflammatory, and metabolic disorders, as well as cancer. Understanding the gut microbe-nutrient interactions will increase our knowledge of how diet affects host health and disease, thus enabling personalized therapeutics and nutrition.

Carbapenem-resistant Acinetobacter baumannii strains cause life-threatening infections due to the lack of therapeutic options. Although the main mechanisms underlying antibiotic-resistance have been extensively studied, the general response to maintain bacterial viability under antibiotic exposure deserves to be fully investigated. Since the periplasmic space contains several proteins with crucial cellular functions, besides carbapenemases, we decided to study the periplasmic proteome of the multidrug-resistant (MDR) A. baumannii AB5075 strain, grown in the absence and presence of imipenem (IMP). Through the proteomic approach, 65 unique periplasmic proteins common in both growth conditions were identified: eight proteins involved in protein fate, response to oxidative stress, energy metabolism, antibiotic-resistance, were differentially expressed. Among them, ABUW_1746 and ABUW_2363 gene products presented the tetratricopeptide repeat motif, mediating protein-protein interactions. The expression switch of these proteins might determine specific protein interactions to better adapt to changing environmental conditions. ABUW_2868, encoding a heat shock protein likely involved in protection against oxidative stress, was upregulated in IMP-exposed bacteria. Accordingly, the addition of periplasmic proteins from A. baumannii cultured with IMP increased bacterial viability in an antioxidant activity assay. Overall, this study provides the first insights about the composition of the periplasmic proteins of a MDR A. baumannii strain, its biological response to IMP and suggests possible new targets to develop alternative antibiotic drugs.

The Mediterranean diet (MD) is considered one of the healthiest dietary models. Many of the characteristic components of the MD have functional features with positive effects on health and wellness. The MD adherence, calculated through various computational scores, can lead to a reduction of the incidence of major diseases (e.g., cancers, metabolic and cardiovascular syndromes, neurodegenerative diseases, type 2 diabetes and allergy). Furthermore, eating habits are the main significant determinants of the microbial multiplicity of the gut, and dietary components influence both microbial populations and their metabolic activities from the early stages of life. For this purpose, we present a study proposal relying on the generation of individual gut microbiota maps from MD-aware children/adolescents. The maps, based on meta-omics approaches, may be considered as new tools, acting as a systems biology-based proof of evidence to evaluate MD effects on gut microbiota homeostasis. Data integration of food metabotypes and gut microbiota "enterotypes" may allow one to interpret MD adherence and its effects on health in a new way, employable for the design of targeted diets and nutraceutical interventions in childcare and clinical management of food-related diseases, whose onset has been significantly shifted early in life.

Polyunsaturated fatty acids (PUFAs) are required to maintain the fluidity, permeability and integrity of cell membranes. Maternal dietary supplementation with ω-3 PUFAs during pregnancy has beneficial effects, including increased gestational length and reduced risk of pregnancy complications. Significant amounts of ω-3 docosahexaenoic acid (DHA) are transferred from maternal to fetal blood, hence ensuring high levels of DHA in the placenta and fetal bloodstream and tissues. Fetal DHA demand increases exponentially with gestational age, especially in the third trimester, due to fetal development. According to the World Health Organization (WHO) and the Food and Agriculture Organization of the United Nations (FAO), a daily intake of DHA is recommended during pregnancy. Omega-3 PUFAs are involved in several anti-inflammatory, pro-resolving and anti-oxidative pathways. Several placental disorders, such as intrauterine growth restriction, premature rupture of membranes (PROM) and preterm-PROM (pPROM), are associated with placental inflammation and oxidative stress. This pilot study reports on a preliminary evaluation of the significance of the daily DHA administration on PROM and pPROM events in healthy pregnant women. Further extensive clinical trials will be necessary to fully elucidate the correlation between DHA administration during pregnancy and PROM/pPROM occurrence, which is related in turn to gestational duration and overall fetal health.

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide as a result of the increasing prevalence of obesity, starting from early life stages. It is characterized by a spectrum of liver diseases ranging from simple fatty liver (NAFL) to steatohepatitis (NASH), with a possible progression to fibrosis, thus increasing liver-related morbidity and mortality. NAFLD development is driven by the co-action of several risk factors, including obesity and metabolic syndrome, which may be both genetically induced and diet-related. Recently, particular attention has been paid to the gut-liver axis, which may play a physio-pathological role in the onset and progression of the disease. The gut microbiota is intended to act as a bioreactor that can guarantee autonomous metabolic and immunological functions and that can drive functional strategies within the environment of the body in response to external stimuli. The complexity of the gut microbiota suggests that it behaves as an organ. Therefore, the concept of the gut-liver axis must be complemented with the gut-microbiota-liver network due to the high intricacy of the microbiota components and metabolic activities; these activities form the active diet-driven power plant of the host. Such complexity can only be revealed using systems biology, which can integrate clinical phenomics and gut microbiota data.