The human serine protease serine 2 TMPRSS2 is involved in the priming of proteins of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and represents a possible target for COVID-19 therapy. The TMPRSS2 gene may be co-expressed with SARS-CoV-2 cell receptor genes angiotensin-converting enzyme 2 (ACE2) and Basigin (BSG), but only TMPRSS2 demonstrates tissue-specific expression in alveolar cells according to single-cell RNA sequencing data. Our analysis of the structural variability of the TMPRSS2 gene based on genome-wide data from 76 human populations demonstrates that a functionally significant missense mutation in exon 6/7 in the TMPRSS2 gene is found in many human populations at relatively high frequencies, with region-specific distribution patterns. The frequency of the missense mutation encoded by rs12329760, which has previously been found to be associated with prostate cancer, ranged between 10% and 63% and was significantly higher in populations of Asian origin compared with European populations. In addition to single-nucleotide polymorphisms, two copy number variants were detected in the TMPRSS2 gene. A number of microRNAs have been predicted to regulate TMPRSS2 and BSG expression levels, but none of them is enriched in lung or respiratory tract cells. Several well-studied drugs can downregulate the expression of TMPRSS2 in human cells, including acetaminophen (paracetamol) and curcumin. Thus, the interactions of TMPRSS2 with SARS-CoV-2, together with its structural variability, gene–gene interactions, expression regulation profiles, and pharmacogenomic properties, characterize this gene as a potential target for COVID-19 therapy.

As interest in genetic resequencing increases, so does the need for effective mathematical, computational, and statistical approaches. One of the difficult problems in genome annotation is determination of precise positions of transcription start sites. In this paper we present TransPrise—an efficient deep learning tool for prediction of positions of eukaryotic transcription start sites. Our pipeline consists of two parts: the binary classifier operates the first, and if a sequence is classified as TSS-containing the regression step follows, where the precise location of TSS is being identified. TransPrise offers significant improvement over existing promoter-prediction methods. To illustrate this, we compared predictions of TransPrise classification and regression models with the TSSPlant approach for the well annotated genome of Oryza sativa. Using a computer equipped with a graphics processing unit, the run time of TransPrise is 250 minutes on a genome of 374 Mb long. The Matthews correlation coefficient value for TransPrise is 0.79, more than two times larger than the 0.31 for TSSPlant classification models. This represents a high level of prediction accuracy. Additionally, the mean absolute error for the regression model is 29.19 nt, allowing for accurate prediction of TSS location. TransPrise was also tested in Homo sapiens, where mean absolute error of the regression model was 47.986 nt. We provide the full basis for the comparison and encourage users to freely access a set of our computational tools to facilitate and streamline their own analyses. The ready-to-use Docker image with all necessary packages, models, code as well as the source code of the TransPrise algorithm are available at (http://compubioverne.group/). The source code is ready to use and customizable to predict TSS in any eukaryotic organism.

Increasing evidence suggests that both coding and non-coding regions of sarcomeric protein genes can contribute to hypertrophic cardiomyopathy (HCM). Here, we introduce an experimental workflow (tested on four patients) for complete sequencing of the most common HCM genes (MYBPC3, MYH7, TPM1, TNNT2, and TNNI3) via long-range PCR, Oxford Nanopore Technology (ONT) sequencing, and bioinformatic analysis. We applied Illumina and Sanger sequencing to validate the results, FastQC, Qualimap, and MultiQC for quality evaluations, MiniMap2 to align data, Clair3 to call and phase variants, and Annovar’s tools and CADD to assess pathogenicity of variants. We could not amplify the region encompassing exons 6–12 of MYBPC3. A higher sequencing error rate was observed with ONT (6.86–6.92%) than with Illumina technology (1.14–1.35%), mostly for small indels. Pathogenic variant p.Gln1233Ter and benign polymorphism p.Arg326Gln in MYBPC3 in a heterozygous state were found in one patient. We demonstrated the ability of ONT to phase single-nucleotide variants, enabling direct haplotype determination for genes TNNT2 and TPM1. These findings highlight the importance of long-range PCR efficiency, as well as lower accuracy of variant calling by ONT than by Illumina technology; these differences should be clarified prior to clinical application of the ONT method.

Defects in the low-density lipoprotein receptor (LDLR) are associated with familial hypercholesterolemia (FH), manifested by atherosclerosis and cardiovascular disease. LDLR deficiency in hepatocytes leads to elevated blood cholesterol levels, which damage vascular cells, especially endothelial cells, through oxidative stress and inflammation. However, the distinctions between endothelial cells from individuals with normal and defective LDLR are not yet fully understood. In this study, we obtained and examined endothelial derivatives of induced pluripotent stem cells (iPSCs) generated previously from conditionally healthy donors and compound heterozygous FH patients carrying pathogenic LDLR alleles. In normal iPSC-derived endothelial cells (iPSC-ECs), we detected the LDLR protein predominantly in its mature form, whereas iPSC-ECs from FH patients have reduced levels of mature LDLR and show abolished low-density lipoprotein uptake. RNA-seq of mutant LDLR iPSC-ECs revealed a unique transcriptome profile with downregulated genes related to monocarboxylic acid transport, exocytosis, and cell adhesion, whereas upregulated signaling pathways were involved in cell secretion and leukocyte activation. Overall, these findings suggest that LDLR defects increase the susceptibility of endothelial cells to inflammation and oxidative stress. In combination with elevated extrinsic cholesterol levels, this may result in accelerated endothelial dysfunction, contributing to early progression of atherosclerosis and other cardiovascular pathologies associated with FH.

The LDLR locus has clinical significance for lipid metabolism, Mendelian familial hypercholesterolemia (FH), and common lipid metabolism-related diseases (coronary artery disease and Alzheimer’s disease), but its intronic and structural variants are underinvestigated. The aim of this study was to design and validate a method for nearly complete sequencing of the LDLR gene using long-read Oxford Nanopore sequencing technology (ONT). Five PCR amplicons from LDLR of three patients with compound heterozygous FH were analyzed. We used standard workflows of EPI2ME Labs for variant calling. All rare missense and small deletion variants detected previously by massively parallel sequencing and Sanger sequencing were identified using ONT. One patient had a 6976 bp deletion (exons 15 and 16) that was detected by ONT with precisely located breakpoints between AluY and AluSx1. Trans-heterozygous associations between mutation c.530C>T and c.1054T>C, c.2141-966_2390-330del, and c.1327T>C, and between mutations c.1246C>T and c.940+3_940+6del of LDLR, were confirmed. We demonstrated the ability of ONT to phase variants, thereby enabling haplotype assignment for LDLR with personalized resolution. The ONT-based method was able to detect exonic variants with the additional benefit of intronic analysis in one run. This method can serve as an efficient and cost-effective tool for diagnosing FH and conducting research on extended LDLR haplotype reconstruction.