A challenge of understanding the mechanisms underlying cognition including neurodevelopmental and neuropsychiatric disorders is mainly given by the potential severity of cognitive disorders for the quality of life and their prevalence. However, the field has been focused predominantly on protein coding variation until recently. Given the importance of tightly controlled gene expression for normal brain function, the goal of the study was to assess the functional variation including non-coding variation in human genome that is likely to play an important role in cognitive functions. To this end, we organized and utilized available genome-wide datasets from genomic, transcriptomic and association studies into a comprehensive data corpus. We focused on genomic regions that are enriched in regulatory activity—overlapping transcriptional factor binding regions and repurpose our data collection especially for identification of the regulatory SNPs (rSNPs) that showed associations both with allele-specific binding and allele-specific expression. We matched these rSNPs to the nearby and distant targeted genes and then selected the variants that could implicate the etiology of cognitive disorders according to Genome-Wide Association Studies (GWAS). Next, we use DeSeq 2.0 package to test the differences in the expression of the certain targeted genes between the controls and the patients that were diagnosed bipolar affective disorder and schizophrenia. Finally, we assess the potential biological role for identified drivers of cognition using DAVID and GeneMANIA. As a result, we selected fourteen regulatory SNPs locating within the loci, implicated from GWAS for cognitive disorders with six of the variants unreported previously. Grouping of the targeted genes according to biological functions revealed the involvement of processes such as ‘posttranscriptional regulation of gene expression’, ‘neuron differentiation’, ‘neuron projection development’, ‘regulation of cell cycle process’ and ‘protein catabolic processes’. We identified four rSNP-targeted genes that showed differential expression between patient and control groups depending on brain region: NRAS—in schizophrenia cohort, CDC25B, DDX21 and NUCKS1—in bipolar disorder cohort. Overall, our findings are likely to provide the keys for unraveling the mechanisms that underlie cognitive functions including major depressive disorder, bipolar disorder and schizophrenia etiopathogenesis.

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.

Currently, the detection of the allele asymmetry of gene expression from RNA-seq data or the transcription factor binding from ChIP-seq data is one of the approaches used to identify the functional genetic variants that can affect gene expression (regulatory SNPs or rSNPs). In this study, we searched for rSNPs using the data for human pulmonary arterial endothelial cells (PAECs) available from the Sequence Read Archive (SRA). Allele-asymmetric binding and expression events are analyzed in paired ChIP-seq data for H3K4me3 mark and RNA-seq data obtained for 19 individuals. Two statistical approaches, weighted z-scores and predicted probabilities, were used to improve the efficiency of finding rSNPs. In total, we identified 14,266 rSNPs associated with both allele-specific binding and expression. Among them, 645 rSNPs were associated with GWAS phenotypes; 4746 rSNPs were reported as eQTLs by GTEx, and 11,536 rSNPs were located in 374 candidate transcription factor binding motifs. Additionally, we searched for the rSNPs associated with gene expression using an SRA RNA-seq dataset for 281 clinically annotated human postmortem brain samples and detected eQTLs for 2505 rSNPs. Based on these results, we conducted Gene Ontology (GO), Disease Ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and constructed the protein–protein interaction networks to represent the top-ranked biological processes with a possible contribution to the phenotypic outcome.

It is well-known that the morbidity and clinical burden of type 2 diabetes mellitus (T2DM) are predominantly associated with its chronic complications, in which fibrosis is a significant contributor. Recently, sodium–glucose cotransporter 2 (SGLT2) inhibitors have made a pivotal advancement in the therapeutic landscape not only improving glycemic control, but also demonstrating high effectiveness in the prevention and treatment of T2DM complications. In this work, we aimed to assess the transcription factors (TFs) mediating the effects of SGLT2 inhibitor empagliflozin (EMPA) treatment by a comprehensive analysis of the allele-specific expression (ASE) events utilizing the RNA-seq data. Initial logistic regression analysis of the in vitro transcriptomic data for EMPA-treated peripheral blood mononuclear cells (PBMCs) of three healthy donors revealed a significant inter-individual variation in ASE for 240 genes linked to EMPA treatment beyond the glucose-lowering effects. Then, 146 TFs were predicted to regulate the expression of the corresponding targets using motifbreakR and DESeq2. Among these, multiple TFs (including ATF3, ATF4, E2F1, EGR1, FOS, JUN, JUNB, IRF8, KLF6, KLF11, SNAI1, TWIST1, and ZEB1) were involved in the TGF-β/SMAD3 canonical profibrotic signaling cascade, pertinent to diabetes-related fibrosis, playing a significant role in the development of diabetic complications. Further analysis of the in vivo data for the PBMCs from ten T2DM patients initiating EMPA therapy identified 98 TFs related to the ASE variation in both in vitro and in vivo cohorts. To conclude, our integrative allele-specific approach enables the prediction of novel EMPA-responsive regulatory interactions and suggests the important mediators of the mechanisms underlying the effects of EMPA on human PBMCs.