Microbiome data obtained after ribosomal RNA or shotgun sequencing represent a challenge for their ecological and statistical interpretation. Microbiome data is compositional data, with a very different sequencing depth between sequenced samples from the same experiment and harboring many zeros. To overcome this scenario, several normalizations and transformation methods have been developed to correct the microbiome data’s technical biases, statistically analyze these data more optimally, and obtain more confident biological conclusions. Most existing studies have compared the performance of different normalization methods mainly linked to microbial differential abundance analysis methods but without addressing the initial statistical task in microbiome data analysis: alpha and beta-diversities. Furthermore, most of the studies used simulated microbiome data. The present study attempted to fill this gap. A public whole shotgun metagenomic sequencing dataset from a USA cohort related to gastrointestinal diseases has been used. Moreover, the performance comparison of eleven normalization methods and the transformation method based on the centered log ratio (CLR) has been addressed. Two strategies were followed to attempt to evaluate the aptitude of the normalization methods between them: the centered residuals obtained for each normalization method and their coefficient of variation. Concerning alpha diversity, the Shannon-Weaver index has been used to compare its output to the normalization methods. Regarding beta-diversity (multivariate analysis), it has been explored three types of analysis: principal coordinate analysis (PCoA) as an exploratory method; distance-based redundancy analysis (db-RDA) as interpretative analysis; and sparse Partial Least Squares Discriminant Analysis (sPLS-DA) as machine learning discriminatory multivariate method. Moreover, other microbiome statistical approaches were compared along the normalization and transformation methods: permutational multivariate analysis of variance (PERMANOVA), analysis of similarities (ANOSIM), beta-dispersion and multi-level pattern analysis in order to associate specific species to each type of diagnosis group in the dataset used. The GMPR (geometric mean of pairwise ratios) normalization method presented the best results regarding the dispersion of the new matrix obtained after being scaled. For the case of α diversity, no differences were detected among the normalization methods compared. In terms of β diversity, the db-RDA and the sPLS-DA analysis have allowed us to detect the most meaningful differences between the normalization methods. The CLR transformation method was the most informative in biological terms, allowing us to make more predictions. Nonetheless, it is important to emphasize that the CLR method and the UQ normalization method have been the only ones that have allowed us to make predictions from the sPLS-DA analysis, so their use could be more encouraged.

NAFLD has emerged as a significant public health concern, with its prevalence increasing globally. Emphasizing the complex relationship between dietary patterns and epigenetic modifications such as DNA methylation or miRNA expression can exert a positive impact on preventing and managing metabolic disorders, including NAFLD, within the 2030 Sustainable Development Goals. This review aims to evaluate the influence of dietary patterns on hepatic epigenetic gene modulation and provide dietary recommendations for the prevention and management of NAFLD in the general population. Methods: Comprehensive screening and eligibility criteria identified eleven articles focusing on epigenetic changes in NAFLD patients through dietary modifications or nutrient supplementation. Results and Discussion: Data were organized based on study types, categorizing them into evaluations of epigenetic changes in NAFLD patients through dietary pattern modifications or specific nutrient intake. Conclusions: The study highlights the importance of dietary interventions in managing and preventing NAFLD, emphasizing the potential of dietary patterns to influence hepatic epigenetic gene modulation. This study provides valuable insights and recommendations to mitigate the risk of developing NAFLD: (i) eat a primarily plant-based diet; (ii) increase consumption of high-fiber foods; (iii) consume more polyunsaturated and monounsaturated fatty acids; (iv) limit processed foods, soft drinks, added sugars, and salt; and (v) avoid alcohol.

A preliminary study to evaluate the effect of dietary supplementation with olive phenols (oleuropein, hydroxytyrosol and secoiridoids), thyme phenols and a combination of these (5 mg per kg rat weight per day) on the α-tocopherol concentrations in the muscle and liver of healthy adult Wistar rats over 21 days was conducted. In addition, the excretion of α-tocopherol through the faeces was examined. The results demonstrated that the diet supplemented with some phenolic compounds of olive and thyme increased α-tocopherol (P < 0.05) in the liver of female rats, although the α-tocopherol content in the diet of all groups was identical. In addition, a synergic effect between the olive phenols and thyme was observed. Therefore, our study indicates a protective effect of olive and thyme phenols supplemented in the diet on α-tocopherol, resulting in a higher concentration of endogenous α-tocopherol in the rat liver.

When collecting oral and fecal samples for large epidemiological microbiome studies, optimal storage conditions such as immediate freezing, are not always feasible. It is fundamental to study the impact of temporary room temperature (RT) storage and shipping on the microbiome diversity obtained in different types of samples. We performed a pilot study aimed at validating the sampling protocol based on the viability of the 16S rRNA gene sequencing in microbiome samples. Fecal and oral samples from five participants were collected and preserved in different conditions: a) 70% ethanol; b) in a FIT tube for stool samples; and c) in a chlorhexidine solution for oral wash samples. Four aliquots were prepared per sample, which were stored at RT, and frozen at days 0, 5, 10 and 15, respectively. In terms of alpha diversity, the maximum average decrease in 5 days was 0.3%, 1.6% and 1.7% for oral, stool in ethanol and stool in FIT, respectively. Furthermore, the relative abundances of the most important phyla and orders remained stable over the two weeks. The stability of fecal and oral samples for microbiome studies preserved at RT with 70% ethanol, chlorhexidine and in FIT tubes was verified for a 15-day window, with no substantial changes in terms of alpha diversity and relative abundances.