The mechanisms by which genetic variants, such as single nucleotide polymorphisms (SNPs), identified in genome-wide association studies act to influence body mass remain unknown for most of these SNPs, which continue to puzzle the scientific community. Recent evidence points to the epigenetic and chromatin states of the genome as having important roles. We genotyped 355 healthy young individuals for 52 known obesity-associated SNPs and obtained DNA methylation levels in their blood using the Illumina 450 K BeadChip. Associations between alleles and methylation at proximal cytosine residues were tested using a linear model adjusted for age, sex, weight category, and a proxy for blood cell type counts. For replication in other tissues, we used two open-access datasets (skin fibroblasts, n = 62; four brain regions, n = 121–133) and an additional dataset in subcutaneous and visceral fat (n = 149). We found that alleles at 28 of these obesity-associated SNPs associate with methylation levels at 107 proximal CpG sites. Out of 107 CpG sites, 38 are located in gene promoters, including genes strongly implicated in obesity (MIR148A, BDNF, PTPMT1, NR1H3, MGAT1, SCGB3A1, HOXC12, PMAIP1, PSIP1, RPS10-NUDT3, RPS10, SKOR1, MAP2K5, SIX5, AGRN, IMMP1L, ELP4, ITIH4, SEMA3G, POMC, ADCY3, SSPN, LGR4, TUFM, MIR4721, SULT1A1, SULT1A2, APOBR, CLN3, SPNS1, SH2B1, ATXN2L, and IL27). Interestingly, the associated SNPs are in known eQTLs for some of these genes. We also found that the 107 CpGs are enriched in enhancers in peripheral blood mononuclear cells. Finally, our results indicate that some of these associations are not blood-specific as we successfully replicated four associations in skin fibroblasts. Our results strongly suggest that many obesity-associated SNPs are associated with proximal gene regulation, which was reflected by association of obesity risk allele genotypes with differential DNA methylation. This study highlights the importance of DNA methylation and other chromatin marks as a way to understand the molecular basis of genetic variants associated with human diseases and traits.

Metformin is a widely used first-line drug for treatment of type 2 diabetes. Despite its advantages, metformin has variable therapeutic effects, contraindications, and side effects. Here, for the very first time, we investigate the short-term effect of metformin on the composition of healthy human gut microbiota. We used an exploratory longitudinal study design in which the first sample from an individual was the control for further samples. Eighteen healthy individuals were treated with metformin (2 × 850 mg) for 7 days. Stool samples were collected at three time points: prior to administration, 24 hours and 7 days after metformin administration. Taxonomic composition of the gut microbiome was analyzed by massive parallel sequencing of 16S rRNA gene (V3 region). There was a significant reduction of inner diversity of gut microbiota observed already 24 hours after metformin administration. We observed an association between the severity of gastrointestinal side effects and the increase in relative abundance of common gut opportunistic pathogen Escherichia-Shigella spp. One week long treatment with metformin was associated with a significant decrease in the families Peptostreptococcaceae and Clostridiaceae_1 and four genera within these families. Our results are in line with previous findings on the capability of metformin to influence gut microbiota. However, for the first time we provide evidence that metformin has an immediate effect on the gut microbiome in humans. It is likely that this effect results from the increase in abundance of opportunistic pathogens and further triggers the occurrence of side effects associated with the observed dysbiosis. An additional randomized controlled trial would be required in order to reach definitive conclusions, as this is an exploratory study without a placebo control arm. Our findings may be further used to create approaches that improve the tolerability of metformin.

Background: The Genome Database of the Latvian Population (LGDB) is a national biobank that collects, maintains, and processes health information, data, and biospecimens collected from representatives of the Latvian population. These specimens serve as a foundation for epidemiological research and prophylactic and therapeutic purposes.Methods: Participant recruitment and biomaterial and data processing were performed according to specifically designed standard protocols, taking into consideration international quality requirements. Legal and ethical aspects, including broad informed consent and personal data protection, were applied according to legal norms of the Republic of Latvia.Results: Since its start in 2006, the LGDB is comprised of biosamples and associated phenotypic and clinical information from over 31,504 participants, constituting approximately 1.5% of the Latvian population. The LGDB represents a mixed-design biobank and includes participants from the general population as well as disease-based cohorts. The standard set of biosamples stored in the LGDB consists of DNA, plasma, serum, and white blood cells; in some cohorts, these samples are complemented by cancer biopsies and microbiome and urine samples. The LGDB acts as a core structure for the Latvian Biomedical Research and Study Centre (BMC), representing the national node of Latvia in Biobanking and BioMolecular resources Research Infrastructure – European Research Infrastructure Consortium (BBMRI-ERIC).Conclusions: The development of the LGDB has enabled resources for biomedical research and promoted genetic testing in Latvia. Further challenges of the LGDB are the enrichment and harmonization of collected biosamples and data, the follow-up of selected participant groups, and continued networking and participation in collaboration projects.

Coincidentally, the release of this Research Topic in Frontiers in Endocrinology takes place 25 years after the discovery of the adrenocorticotropic hormone receptor (ACTHR) by Mountjoy and colleagues. In subsequent years, following the discovery of other types of mammalian melanocortin receptors (MCRs), ACTHR also became known as melanocortin type 2 receptor (MC2R). At present, five types of MCRs have been reported, all of which share significant sequence similarity at the amino acid level, and all of which specifically bind melanocortins (MCs)—a group of biologically active peptides generated by proteolysis of the proopiomelanocortin precursor. All MCs share an identical –H–F–R–W– pharmacophore sequence. α-Melanocyte-stimulating hormone (α-MSH) and adrenocorticotropic hormone (ACTH) are the most extensively studied MCs and are derived from the same region. Essentially, α-MSH is formed from the first 13 amino acid residues of ACTH. ACTHR is unique among MCRs because it binds one sole ligand—ACTH, which makes it a very attractive research object for molecular pharmacologists. However, much research has failed, and functional studies of this receptor are lagging behind other MCRs. The reason for these difficulties has already been outlined by Mountjoy and colleagues in their publication on ACTHR coding sequence discovery where the Cloudman S91 melanoma cell line was used for receptor expression because it was a “more sensitive assay system.” Subsequent work showed that ACTHR could be successfully expressed only in endogenous MCR-expressing cell lines, since in other cell lines it is retained within the endoplasmic reticulum. The resolution of this methodological problem came in 2005 with the discovery of melanocortin receptor accessory protein, which is required for the formation of functionally active ACTHR. The decade that followed this discovery was filled with exciting research that provided insight into the molecular mechanisms underlying the action of ACTHR. The purpose of this review is to summarize the advances in this fascinating research field.

Although the host defense mechanisms against SARS-CoV-2 infection are still poorly described, they are of central importance in shaping the course of the disease and the possible outcome. Metabolomic profiling may complement the lacking knowledge of the molecular mechanisms underlying clinical manifestations and pathogenesis of COVID-19.

Background: The study was conducted to investigate the effects of metformin treatment on the human gut microbiome’s taxonomic and functional profile in the Latvian population, and to evaluate the correlation of these changes with therapeutic efficacy and tolerance. Methods: In this longitudinal observational study, stool samples for shotgun metagenomic sequencing-based analysis were collected in two cohorts. The first cohort included 35 healthy nondiabetic individuals (metformin dose 2x850mg/day) at three time-points during metformin administration. The second cohort was composed of 50 newly-diagnosed type 2 diabetes patients (metformin dose–determined by an endocrinologist) at two concordant times. Patients were defined as Responders if their HbA1c levels during three months of metformin therapy had decreased by ≥12.6 mmol/mol (1%), while in Non-responders HbA1c were decreased by <12.6 mmol/mol (1%). Results: Metformin reduced the alpha diversity of microbiota in healthy controls (p = 0.02) but not in T2D patients. At the species level, reduction in the abundance of Clostridium bartlettii and Barnesiella intestinihominis, as well as an increase in the abundance of Parabacteroides distasonis and Oscillibacter unclassified overlapped between both study groups. A large number of group-specific changes in taxonomic and functional profiles was observed. We identified an increased abundance of Prevotella copri (FDR = 0.01) in the Non-Responders subgroup, and enrichment of Enterococcus faecium, Lactococcus lactis, Odoribacter, and Dialister at baseline in the Responders group. Various taxonomic units were associated with the observed incidence of side effects in both cohorts. Conclusions: Metformin effects are different in T2D patients and healthy individuals. Therapy induced changes in the composition of gut microbiome revealed possible mediators of observed short-term therapeutic effects. The baseline composition of the gut microbiome may influence metformin therapy efficacy and tolerance in T2D patients and could be used as a powerful prediction tool.