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Background: The human intestinal microbiome plays a central role in overall health status, especially in early life stages. 16S rRNA amplicon sequencing is used to profile its taxonomic composition; however, multiomic approaches have been proposed as the most accurate methods for study of the complexity of the gut microbiota. In this study, we propose an optimized method for bacterial diversity analysis that we validated and complemented with metabolomics by analyzing fecal samples., Methods: Forty-eight different analytical combinations regarding (1) 16S rRNA variable region sequencing, (2) a feature selection approach, and (3) taxonomy assignment methods were tested. A total of 18 infant fecal samples grouped depending on the type of feeding were analyzed by the proposed 16S rRNA workflow and by metabolomic analysis., Results: The results showed that the sole use of V4 region sequencing with ASV identification and VSEARCH for taxonomy assignment produced the most accurate results. The application of this workflow showed clear differences between fecal samples according to the type of feeding, which correlated with changes in the fecal metabolic profile., Conclusion: A multiomic approach using real fecal samples from 18 infants with different types of feeding demonstrated the effectiveness of the proposed 16S rRNA-amplicon sequencing workflow.

Hepatic biopsy is the gold standard for staging nonalcoholic fatty liver disease (NAFLD). Unfortunately, accessing the liver is invasive, requires a multidisciplinary team and is too expensive to be conducted on large segments of the population. NAFLD starts quietly and can progress until liver damage is irreversible. Given this complex situation, the search for noninvasive alternatives is clinically important. A hallmark of NAFLD progression is the dysregulation in lipid metabolism. In this context, recent advances in the area of machine learning have increased the interest in evaluating whether multi-omics data analysis performed on peripheral blood can enhance human interpretation. In the present review, we show how the use of machine learning can identify sets of lipids as predictive biomarkers of NAFLD progression. This approach could potentially help clinicians to improve the diagnosis accuracy and predict the future risk of the disease. While NAFLD has no effective treatment yet, the key to slowing the progression of the disease may lie in predictive robust biomarkers. Hence, to detect this disease as soon as possible, the use of computational science can help us to make a more accurate and reliable diagnosis. We aimed to provide a general overview for all readers interested in implementing these methods.

Polyamines are involved in the regulation of many cellular functions and are promising biomarkers of numerous physiological conditions. Since the concentrations of these compounds in biological fluids are low, sample extraction is one of the most critical steps of their analysis. Here, we developed a comprehensive, sensitive, robust, and high-throughput LC-MS/MS stable-isotope dilution method for the simultaneous determination of 19 metabolites related to polyamine metabolism, including polyamines, acetylated and diacetylated polyamines, precursors, and catabolites from liquid biopsies. The sample extraction was optimized to remove interfering compounds and to reduce matrix effects, thus being useful for large clinical studies. The method consists of two-step liquid-liquid extraction with a Folch extraction and ethyl acetate partitioning combined with dansyl chloride derivatization. The developed method was applied to a small gender-related trial concerning human serum and urine samples from 40 obese subjects. Sex differences were found for cadaverine, putrescine, 1,3-diaminopropane, γ-aminobutyric acid, N8-acetylspermidine, and N-acetylcadaverine in urine; N1-acetylspermine in serum; and spermine in both serum and urine. The results demonstrate that the developed method can be used to analyze biological samples for the study of polyamine metabolism and its association with human diseases.