The vast majority of the genetic variants (mainly SNPs) associated with various human traits and diseases map to a noncoding part of the genome and are enriched in its regulatory compartment, suggesting that many causal variants may affect gene expression. The leading mechanism of action of these SNPs consists in the alterations in the transcription factor binding via creation or disruption of transcription factor binding sites (TFBSs) or some change in the affinity of these regulatory proteins to their cognate sites. In this review, we first focus on the history of the discovery of regulatory SNPs (rSNPs) and systematized description of the existing methodical approaches to their study. Then, we brief the recent comprehensive examples of rSNPs studied from the discovery of the changes in the TFBS sequence as a result of a nucleotide substitution to identification of its effect on the target gene expression and, eventually, to phenotype. We also describe state-of-the-art genome-wide approaches to identification of regulatory variants, including both making molecular sense of genome-wide association studies (GWAS) and the alternative approaches the primary goal of which is to determine the functionality of genetic variants. Among these approaches, special attention is paid to expression quantitative trait loci (eQTLs) analysis and the search for allele-specific events in RNA-seq (ASE events) as well as in ChIP-seq, DNase-seq, and ATAC-seq (ASB events) data.

A growing body of evidence indicates that nonglycemic effects of sodium–glucose cotransporter 2 (SGLT2) inhibitors play an important role in the protective effects of these drugs in diabetes, chronic kidney disease, and heart failure. In recent years, the anti-inflammatory potential of SGLT2 inhibitors has been actively studied. This review summarizes results of clinical and experimental studies on the anti-inflammatory activity of SGLT2 inhibitors, with a special focus on their effects on macrophages, key drivers of metabolic inflammation. In patients with type 2 diabetes, therapy with SGLT2 inhibitors reduces levels of inflammatory mediators. In diabetic and non-diabetic animal models, SGLT2 inhibitors control low-grade inflammation by suppressing inflammatory activation of tissue macrophages, recruitment of monocytes from the bloodstream, and macrophage polarization towards the M1 phenotype. The molecular mechanisms of the effects of SGLT2 inhibitors on macrophages include an attenuation of inflammasome activity and inhibition of the TLR4/NF-κB pathway, as well as modulation of other signaling pathways (AMPK, PI3K/Akt, ERK 1/2-MAPK, and JAKs/STAT). The review discusses the state-of-the-art concepts and prospects of further investigations that are needed to obtain a deeper insight into the mechanisms underlying the effects of SGLT2 inhibitors on the molecular, cellular, and physiological levels.

The complementary interaction of microRNAs (miRNAs) with their binding sites in the 3′untranslated regions (3′UTRs) of target gene mRNAs represses translation, playing a leading role in gene expression control. MiRNA recognition elements (MREs) in the 3′UTRs of genes often contain single nucleotide polymorphisms (SNPs), which can change the binding affinity for target miRNAs leading to dysregulated gene expression. Accumulated data suggest that these SNPs can be associated with various human pathologies (cancer, diabetes, neuropsychiatric disorders, and cardiovascular diseases) by disturbing the interaction of miRNAs with their MREs located in mRNA 3′UTRs. Numerous data show the role of SNPs in 3′UTR MREs in individual drug susceptibility and drug resistance mechanisms. In this review, we brief the data on such SNPs focusing on the most rigorously proven cases. Some SNPs belong to conventional genes from the drug-metabolizing system (in particular, the genes coding for cytochromes P450 (CYP 450), phase II enzymes (SULT1A1 and UGT1A), and ABCB3 transporter and their expression regulators (PXR and GATA4)). Other examples of SNPs are related to the genes involved in DNA repair, RNA editing, and specific drug metabolisms. We discuss the gene-by-gene studies and genome-wide approaches utilized or potentially utilizable to detect the MRE SNPs associated with individual response to drugs.

The goal of our study was to identify and assess the functionally significant SNPs with potentially important roles in the development of type 2 diabetes mellitus (T2DM) and/or their effect on individual response to antihyperglycemic medication with metformin. We applied a bioinformatics approach to identify the regulatory SNPs (rSNPs) associated with allele-asymmetric binding and expression events in our paired ChIP-seq and RNA-seq data for peripheral blood mononuclear cells (PBMCs) of nine healthy individuals. The rSNP outcomes were analyzed using public data from the GWAS (Genome-Wide Association Studies) and Genotype-Tissue Expression (GTEx). The differentially expressed genes (DEGs) between healthy and T2DM individuals (GSE221521), including metformin responders and non-responders (GSE153315), were searched for in GEO RNA-seq data. The DEGs harboring rSNPs were analyzed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We identified 14,796 rSNPs in the promoters of 5132 genes of human PBMCs. We found 4280 rSNPs to associate with both phenotypic traits (GWAS) and expression quantitative trait loci (eQTLs) from GTEx. Between T2DM patients and controls, 3810 rSNPs were detected in the promoters of 1284 DEGs. Based on the protein-protein interaction (PPI) network, we identified 31 upregulated hub genes, including the genes involved in inflammation, obesity, and insulin resistance. The top-ranked 10 enriched KEGG pathways for these hubs included insulin, AMPK, and FoxO signaling pathways. Between metformin responders and non-responders, 367 rSNPs were found in the promoters of 131 DEGs. Genes encoding transcription factors and transcription regulators were the most widely represented group and many were shown to be involved in the T2DM pathogenesis. We have formed a list of human rSNPs that add functional interpretation to the T2DM-association signals identified in GWAS. The results suggest candidate causal regulatory variants for T2DM, with strong enrichment in the pathways related to glucose metabolism, inflammation, and the effects of metformin.

Alzheimer’s disease (AD) is a prevalent type of dementia in elderly populations with a significant genetic component. The accumulating evidence suggests that AD involves a reconfiguration of the epigenetic landscape, including DNA methylation, post-translational modification of histone proteins, and chromatin remodeling. Along with environmental factors, individual specific genetic features play a considerable role in the formation of epigenetic architecture. In this study, we attempt to identify the non-coding regulatory SNPs (rSNPs) able to affect the epigenetic mechanisms in AD. To this end, the multi-omics approach is used. The GEO (Gene Expression Omnibus) available data (GSE153875) for AD patients and controls are integrated to reveal the rSNPs that display allele-specific features in both ChIP-seq profiles of four histone modifications and RNA-seq. Furthermore, we analyze the presence of rSNPs in the promoters of genes reported to be differentially expressed between AD and the normal brain (AD-related genes) and involved in epigenetic regulation according to the EpiFactors database. We also searched for the rSNPs in the promoters of the genes coding for transcription regulators of the identified AD-related genes. These regulators were selected based on the corresponding ChIP-seq peaks (ENCODE) in the promoter regions of these genes. Finally, we formed a panel of rSNPs localized to the promoters of genes that contribute to the epigenetic landscape in AD and, thus, to the genetic predisposition for this disease.

(1) Background: Transcription factors (TFs) are main regulators of eukaryotic gene expression. The cooperative binding to genomic DNA of at least two TFs is the widespread mechanism of transcription regulation. Cooperating TFs can be revealed through the analysis of co-occurrence of their motifs. (2) Methods: We applied the motifs co-occurrence tool (MCOT) that predicted pairs of spaced or overlapped motifs (composite elements, CEs) for a single ChIP-seq dataset. We improved MCOT capability for the prediction of asymmetric CEs with one of the participating motifs possessing higher conservation than another does. (3) Results: Analysis of 119 ChIP-seq datasets for 45 human TFs revealed that almost for all families of TFs the co-occurrence with an overlap between motifs of target TFs and more conserved partner motifs was significantly higher than that for less conserved partner motifs. The asymmetry toward partner TFs was the most clear for partner motifs of TFs from the ETS (E26 Transformation Specific) family. (4) Conclusion: Co-occurrence with an overlap of less conserved motif of a target TF and more conserved motifs of partner TFs explained a substantial portion of ChIP-seq data lacking conserved motifs of target TFs. Among other TF families, conservative motifs of TFs from ETS family were the most prone to mediate interaction of target TFs with its weak motifs in ChIP-seq.