Application of multi-omic features clustering and pathway enrichment to clarify the impact of vitamin B2 supplementation on broiler caeca microbiome.

Background: The results of omic methodologies are often reported as separate datasets. In this study we applied for the first time multi-omic features clustering and pathway enrichment to clarify the biological impact of vitamin B2 supplementation on broiler caeca microbiome. Methods: The caeca contents of broilers fed +50 and +100 mg/kg vitamin B2 were analyzed by shotgun metagenomic and metabolomic. Latent variables extracted from NMR spectra, as well as taxonomic and functional features profiled from metagenomes, were integrated to characterize the effect of vitamin B2 in modulating caeca microbiome. A pathway-based network was obtained by mapping the observed input genes and compounds, highlighting connected strands of metabolic ways through pathway-enrichment analysis. Results: At day 14, the taxonomic, functional and metabolomic features in the caeca of tested broilers showed some degree of separation between control and treated groups, becoming fully clear at 28 days and persisting up to 42 days. In the caeca of birds belonging to the control group Alistipes spp. was the signature species, while the signature species in the caeca of broilers fed +50 and +100 mg/kg vitamin B2 were Bacteroides fragilis and Lactobacillus crispatus, Lactobacillus reuteri, Ruminococcus torques, Subdoligranum spp., respectively. The pathway enrichment analysis highlighted that the specific biochemical pathways enhanced by the supplementations of vitamin B2 were N-Formyl-L-aspartate amidohydrolase, producing Aspartate and Formate; L-Alanine:2-oxoglutarate amino transferase, supporting the conversion of L-Alanine and 2-Oxoglutarate in Pyruvate and L-Glutamate; 1D-myo-inositol 1/4 phosphate phosphohydrolase, converting Inositol 1/4-phosphate and water in myo-Inositol and Orthophosphate. The results of this study demonstrated that the caeca of birds fed +50 and + 100 mg/kg were those characterized by taxonomic groups more beneficial to the host and with a higher concentration of myo-inositol, formic acid, amino acids and pyruvate involved in glycolysis and amino acid biosynthesis. Conclusion: In this study we demonstrated how to perform multi-omic features integration to describe the biochemical mechanisms enhanced by the supplementation of different concentrations of vitamin B2 in the poultry diet. The relationship between vitamin B2 supplementation and myo-inositol production was highlighted in our study for the first time.

K-Clique Multiomics Framework: A Novel Protocol to Decipher the Role of Gut Microbiota Communities in Nutritional Intervention Trials.

The availability of omics data providing information from different layers of complex biological processes that link nutrition to human health would benefit from the development of integrated approaches combining holistically individual omics data, including those associated with the microbiota that impacts the metabolisation and bioavailability of food components. Microbiota must be considered as a set of populations of interconnected consortia, with compensatory capacities to adapt to different nutritional intake. To study the consortium nature of the microbiome, we must rely on specially designed data analysis tools. The purpose of this work is to propose the construction of a general correlation network-based explorative tool, suitable for nutritional clinical trials, by integrating omics data from faecal microbial taxa, stool metabolome (1H NMR spectra) and GC-MS for stool volatilome. The presented approach exploits a descriptive paradigm necessary for a true multiomics integration of data, which is a powerful tool to investigate the complex physiological effects of nutritional interventions.

The NMR added value to the green foodomics perspective: Advances by machine learning to the holistic view on food and nutrition.

Food is a complex matter, literally. From production to functionalization, from nutritional quality engineering to predicting effects on health, the interest in finding an efficient physicochemical characterization of food has boomed in recent years. The sheer complexity of characterizing food and its interaction with the human organism has however made the use of data driven approaches in modeling a necessity. High-throughput techniques, such as nuclear magnetic resonance (NMR) spectroscopy, are well suited for omics data production and, coupled with machine learning, are paving a promising way of modeling food-human interaction. The foodomics approach sets the framework for omic data integration in food studies, in which NMR experiments play a key role. NMR data can be used to assess nutritional qualities of food, helping the design of functional and sustainable sources of nutrients; detect biomarkers of intake and study how they impact the metabolism of different individuals; study the kinetics of compounds in foods or their by-products to detect pathological conditions; and improve the efficiency of in silico models of the metabolic network.

Integrated genomic-metabolic classification of acute myeloid leukemia defines a subgroup with NPM1 and cohesin/DNA damage mutations.

Although targeting of cell metabolism is a promising therapeutic strategy in acute myeloid leukemia (AML), metabolic dependencies are largely unexplored. We aimed to classify AML patients based on their metabolic landscape and map connections between metabolic and genomic profiles. Combined serum and urine metabolomics improved AML characterization compared with individual biofluid analysis. At intracellular level, AML displayed dysregulated amino acid, nucleotide, lipid, and bioenergetic metabolism. The integration of intracellular and biofluid metabolomics provided a map of alterations in the metabolism of polyamine, purine, keton bodies and polyunsaturated fatty acids and tricarboxylic acid cycle. The intracellular metabolome distinguished three AML clusters, correlating with distinct genomic profiles: NPM1-mutated(mut), chromatin/spliceosome-mut and TP53-mut/aneuploid AML that were confirmed by biofluid analysis. Interestingly, integrated genomic-metabolic profiles defined two subgroups of NPM1-mut AML. One was enriched for mutations in cohesin/DNA damage-related genes (NPM1/cohesin-mut AML) and showed increased serum choline + trimethylamine-N-oxide and leucine, higher mutation load, transcriptomic signatures of reduced inflammatory status and better ex-vivo response to EGFR and MET inhibition. The transcriptional differences of enzyme-encoding genes between NPM1/cohesin-mut and NPM1-mut allowed in silico modeling of intracellular metabolic perturbations. This approach predicted alterations in NAD and purine metabolism in NPM1/cohesin-mut AML that suggest potential vulnerabilities, worthy of being therapeutically explored.

Unveiling the Correlation between Inadequate Energy/Macronutrient Intake and Clinical Alterations in Volunteers at Risk of Metabolic Syndrome by a Predictive Model.

Although lifestyle-based interventions are the most effective to prevent metabolic syndrome (MetS), there is no definitive agreement on which nutritional approach is the best. The aim of the present retrospective analysis was to identify a multivariate model linking energy and macronutrient intake to the clinical features of MetS. Volunteers at risk of MetS (F = 77, M = 80) were recruited in four European centres and finally eligible for analysis. For each subject, the daily energy and nutrient intake was estimated using the EPIC questionnaire and a 24-h dietary recall, and it was compared with the dietary reference values. Then we built a predictive model for a set of clinical outcomes computing shifts from recommended intake thresholds. The use of the ridge regression, which optimises prediction performances while retaining information about the role of all the nutritional variables, allowed us to assess if a clinical outcome was manly dependent on a single nutritional variable, or if its prediction was characterised by more complex interactions between the variables. The model appeared suitable for shedding light on the complexity of nutritional variables, which effects could be not evident with univariate analysis and must be considered in the framework of the reciprocal influence of the other variables.

Effects of Vitamin B2 Supplementation in Broilers Microbiota and Metabolome.

The study of the microbiome in broiler chickens holds great promise for the development of strategies for health maintenance and performance improvement. Nutritional strategies aimed at modulating the microbiota-host relationship can improve chickens' immunological status and metabolic fitness. Here, we present the results of a pilot trial aimed at analyzing the effects of a nutritional strategy involving vitamin B2 supplementation on the ileum, caeca and litter microbiota of Ross 308 broilers, as well as on the metabolic profile of the caecal content. Three groups of chickens were administered control diets and diets supplemented with two different dosages of vitamin B2. Ileum, caeca, and litter samples were obtained from subgroups of birds at three time points along the productive cycle. Sequencing of the 16S rRNA V3-V4 region and NMR metabolomics were used to explore microbiota composition and the concentration of metabolites of interest, including short-chain fatty acids. Vitamin B2 supplementation significantly modulated caeca microbiota, with the highest dosage being more effective in increasing the abundance of health-promoting bacterial groups, including Bifidobacterium, resulting in boosted production of butyrate, a well-known health-promoting metabolite, in the caeca environment.

WISDoM: Characterizing Neurological Time Series With the Wishart Distribution.

WISDoM (Wishart Distributed Matrices) is a framework for the quantification of deviation of symmetric positive-definite matrices associated with experimental samples, such as covariance or correlation matrices, from expected ones governed by the Wishart distribution. WISDoM can be applied to tasks of supervised learning, like classification, in particular when such matrices are generated by data of different dimensionality (e.g., time series with same number of variables but different time sampling). We show the application of the method in two different scenarios. The first is the ranking of features associated with electro encephalogram (EEG) data with a time series design, providing a theoretically sound approach for this type of studies. The second is the classification of autistic subjects of the Autism Brain Imaging Data Exchange study using brain connectivity measurements.