In humans the ability to digest milk lactose is conferred by a β-galactosidase enzyme called lactase-phlorizin hydrolase (LPH). While in some humans (approximately two-thirds of humankind) the levels of this enzyme decline drastically after the weaning phase (a trait known as lactase non-persistence (LNP)), some other individuals are capable of maintaining high levels of LPH lifelong (lactase persistence (LP)), thus being able to digest milk during adulthood. Both lactase phenotypes in humans present a complex genetic basis and have been widely investigated during the last decades. The distribution of lactase phenotypes and their associated single nucleotide polymorphisms (SNPs) across human populations has also been extensively studied, though not recently reviewed. All available information has always been presented in the form of static world maps or large dimension tables, so that it would benefit from the newly available visualization tools, such as interactive world maps. Taking all this into consideration, the aims of the present review were: (1) to gather and summarize all available information on LNP and LP genetic mechanisms and evolutionary adaptation theories, and (2) to create online interactive world maps, including all LP phenotype and genotype frequency data reported to date. As a result, we have created two online interactive resources, which constitute an upgrade over previously published static world maps, and allow users a personalized data exploration, while at the same time accessing complete reports by population or ethnicity.

Vitamin D (vitD) deficiency is associated with a wide range of chronic diseases and conditions, including obesity, and with an increasing severity of metabolic dysregulation, such as insulin resistance, hyperlipidemia, liver disease, and hypertension, both in children and adults. However, the nature of the association between low vitD status and obesity remains unclear. This fact has motivated the scientific community to conduct genetic association analyses between 25-hydroxyvitamin D (25[OH]D)-related genes and obesity traits. In this line, the variation in the vitD receptor (VDR) gene represents the bulk of the findings. Specifically, polymorphisms in the VDR gene have been associated with obesity traits in some but not all, studies. Thus, results regarding this matter remain inconclusive. Other genes aside from VDR have also been investigated in relation to obesity-related traits. However, again, findings have been inconsistent. In general, results point to the fact that the DBP/GC gene could be an important protein-linking obesity and vitD status. On the other hand, several studies have attempted to determine the molecular mechanism of the relationship between 25(OH)-D levels and obesity. Some of these studies suggest that vitD, due to its fat-soluble characteristic, is retained by the adipose tissue and has the capacity to metabolize 25(OH)-D locally, and this can be altered during obesity. Additionally, vitD is capable of regulating the gene expression related to adipogenesis process, inflammation, oxidative stress, and metabolism in mature adipocytes. Therefore, the aim of the present review was to evaluate the association between obesity and vitD deficiency describing the main molecular mechanism of the relationship and the link with genetic factors. Key Messages: Low serum 25(OH)-D is positively associated with obesity or BMI in adults and children. Circulating vitD concentrations are, at least, partially determined by genetic factors. VitD plays an important role in the adipogenesis process and inflammation status in adipocytes and adipose tissue.

Until date, several machine learning approaches have been proposed for the dynamic modeling of temporal omics data. Although they have yielded impressive results in terms of model accuracy and predictive ability, most of these applications are based on “Black-box” algorithms and more interpretable models have been claimed by the research community. The recent eXplainable Artificial Intelligence (XAI) revolution offers a solution for this issue, were rule-based approaches are highly suitable for explanatory purposes. The further integration of the data mining process along with functional-annotation and pathway analyses is an additional way towards more explanatory and biologically soundness models. In this paper, we present a novel rule-based XAI strategy (including pre-processing, knowledge-extraction and functional validation) for finding biologically relevant sequential patterns from longitudinal human gene expression data (GED). To illustrate the performance of our pipeline, we work on in vivo temporal GED collected within the course of a long-term dietary intervention in 57 subjects with obesity (GSE77962). As validation populations, we employ three independent datasets following the same experimental design. As a result, we validate primarily extracted gene patterns and prove the goodness of our strategy for the mining of biologically relevant gene-gene temporal relations. Our whole pipeline has been gathered under open-source software and could be easily extended to other human temporal GED applications. Biological processes in humans are not single-gene based mechanisms, but complex systems controlled by regulatory interactions between thousands of genes. Within these gene regulatory networks, time-delay is a common phenomenon and genes interact each other within a four-dimension space. Hence, to fully understand or to control biological processes we need to unravel the principles of gene-gene temporal interactions. Until date, several approaches based on Artificial Intelligence methods have tried to address this issue. Nevertheless, the research community has claimed for more interpretable and biologically meaningful models. Particularly, scientists claim for methods able to infer gene-gene temporal interactions that could be later validated with real-life experiments at the lab. The recent revolution known as “eXplainable Artificial Intelligence” offers a solution for this issue, where a range of highly interpretable and explicable models has become available. Many of these methods could be applied to temporal gene expression data in order to decipher mentioned temporal gene-gene relationships in humans. Here, we propose and validate a new pipeline analysis including an eXplainable artificial intelligence method for the identification of comprehensible gene-gene temporal relationships from human intervention studies. Our method has been validated in six datasets from obesity research (consisting of low calorie diets interventions), where it was able to extract meaningful gene-gene temporal interactions with relevance the etiology of the disease. The application of our pipeline to other type of human temporal gene profiles would greatly expand our knowledge for complex biological processes, with a special interest for drug clinical trials, in which identified gene-gene regulatory interactions could reveal new therapeutic targets.

Prostate cancer is one of the most prevalent forms of cancer in men worldwide. Traditional screening strategies such as serum PSA levels, which are not necessarily cancer-specific, or digital rectal exams, which are often inconclusive, are still the screening methods used for the disease. Some studies have focused on identifying biomarkers of the disease but none have been reported for diagnosis in routine clinical practice and few studies have provided tools to assist the pathologist in the decision-making process when analyzing prostate tissue. Therefore, a classifier is proposed to predict the occurrence of PCa that provides physicians with accurate predictions and understandable explanations. A selection of 47 genes was made based on differential expression between PCa and normal tissue, GO gene ontology as well as the literature to be used as input predictors for different machine learning methods based on eXplainable Artificial Intelligence. These methods were trained using different class-balancing strategies to build accurate classifiers using gene expression data from 550 samples from 'The Cancer Genome Atlas'. Our model was validated in four external cohorts with different ancestries, totaling 463 samples. In addition, a set of SHapley Additive exPlanations was provided to help clinicians understand the underlying reasons for each decision. An in-depth analysis showed that the Random Forest algorithm combined with majority class downsampling was the best performing approach with robust statistical significance. Our method achieved an average sensitivity and specificity of 0.90 and 0.8 with an AUC of 0.84 across all databases. The relevance of DLX1, MYL9 and FGFR genes for PCa screening was demonstrated in addition to the important role of novel genes such as CAV2 and MYLK. This model has shown good performance in 4 independent external cohorts of different ancestries and the explanations provided are consistent with each other and with the literature, opening a horizon for its application in clinical practice. In the near future, these genes, in combination with our model, could be applied to liquid biopsy to improve PCa screening.

Childhood obesity poses a significant public health challenge, yet the molecular intricacies underlying its pathobiology remain elusive. Leveraging extensive multi-omics profiling (methylome, miRNome, transcriptome, proteins and metabolites) and a rich phenotypic characterization across two parts of Europe within the population-based Human Early Life Exposome project, we unravel the molecular landscape of childhood obesity and associated metabolic dysfunction. Our integrative analysis uncovers three clusters of children defined by specific multi-omics profiles, one of which characterized not only by higher adiposity but also by a high degree of metabolic complications. This high-risk cluster exhibits a complex interplay across many biological pathways, predominantly underscored by inflammation-related cascades. Further, by incorporating comprehensive information from the environmental risk-scape of the critical pregnancy period, we identify pre-pregnancy body mass index and environmental pollutants like perfluorooctanoate and mercury as important determinants of the high-risk cluster. Overall, our work helps to identify potential risk factors for prevention and intervention strategies early in the life course aimed at mitigating obesity and its long-term health consequences.

Oxidative stress could help explain the relationship between childhood obesity and a metabolically unhealthy (MU) status. Moreover, puberty could also influence this relationship, since it entails physiological cardiometabolic changes. We aimed to evaluate plasma antioxidants and oxidative stress biomarkers in MU and metabolically healthy (MH) prepubertal and pubertal children and their associations with pro-inflammatory and endothelial damage biomarkers, taking puberty into account. A total of 1444 Spanish children aged 3–17 years (48.9% males, 66% prepubertal, 47.1% with obesity) were recruited. Blood pressure, anthropometric and biochemical parameters were measured, and children were categorized as having a MU or MH status according to risk factors. Retinol, carotenes, tocopherols, total antioxidant capacity (TAC), oxidized low-density lipoprotein and selected pro-inflammatory and endothelial damage biomarkers were analyzed. General linear models adjusted for age, sex, recruitment center and body mass index, partial correlations and stepwise linear regressions were performed. Lower carotenes and tocopherols levels were found in MU than in MH children. Plasma TAC was lower in prepubertal and higher in pubertal children with obesity compared to normal-weight children. Antioxidants and oxidative stress biomarkers showed novel associations with several pro-inflammatory and endothelial damage biomarkers, with pubertal differences, supporting the importance of considering both the antioxidant and oxidative stress status and puberty in the prevention of metabolic diseases in childhood.