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.

Diabetes and frailty are highly prevalent conditions that impact the health status of older adults. Perturbations in protein/amino acid metabolism are associated with both functional impairment and type 2 diabetes mellitus (T2DM). In the present study, we compared the concentrations of a panel of circulating 37 amino acids and derivatives between frail/pre-frail older adults with T2DM and robust non-diabetic controls. Sixty-six functionally impaired older persons aged 70+ with T2DM and 30 age and sex-matched controls were included in the analysis. We applied a partial least squares-discriminant analysis (PLS-DA)-based analytical strategy to characterize the metabotype of study participants. The optimal complexity of the PLS-DA model was found to be two latent variables. The proportion of correct classification was 94.1 ± 1.9% for frail/pre-frail persons with T2DM and 100% for control participants. Functionally impaired older persons with T2DM showed higher levels of 3-methyl histidine, alanine, arginine, glutamic acid, ethanolamine sarcosine, and tryptophan. Control participants had higher levels of ornithine and taurine. These findings indicate that a specific profile of amino acids and derivatives characterizes pre-frail/frail older persons with T2DM. The dissection of these pathways may provide novel insights into the metabolic perturbations involved in the disabling cascade in older persons with T2DM.

Red beetroot (RB) is a well-known health-promoting food consumed worldwide. RB is commonly used in food processing and manufacturing thanks to the high content of components that can also be employed as natural coloring agents. These bioactive molecules vary their concentration depending on beetroot seasonality, harvest time and climate conditions. The first objective of this study was to evaluate the variation of the RB phytochemical profile related to the root development during three different harvest times, using an ¹H-NMR-based metabolomic approach. Changes of carbohydrates and secondary metabolite concentrations were observed from July to September. Secondly, we compared the metabolic profiles of the final processed beet juices in three different production years to observe the effect of climate conditions on the RB’s final product metabotype. A PCA analysis performed on juice extracts showed that production years 2016 and 2017 were characterized by a high content of choline and betaine, while 2018 by a high content of amino acids and dopamine and a low content of inorganic nitrates. This study suggests that the harvest time and roots growth conditions could be used to modulate the RB phytochemical profile, according to the final requirements of use, food or coloring agent source.

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 ¹H-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.