Background Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, there has been increasing urgency to identify pathophysiological characteristics leading to severe clinical course in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Human leukocyte antigen alleles (HLA) have been suggested as potential genetic host factors that affect individual immune response to SARS-CoV-2. We sought to evaluate this hypothesis by conducting a multicenter study using HLA sequencing. Methods We analyzed the association between COVID-19 severity and HLAs in 435 individuals from Germany ( n = 135), Spain ( n = 133), Switzerland ( n = 20) and the United States ( n = 147), who had been enrolled from March 2020 to August 2020. This study included patients older than 18 years, diagnosed with COVID-19 and representing the full spectrum of the disease. Finally, we tested our results by meta-analysing data from prior genome-wide association studies (GWAS). Findings We describe a potential association of HLA-C*04:01 with severe clinical course of COVID-19. Carriers of HLA-C*04:01 had twice the risk of intubation when infected with SARS-CoV-2 (risk ratio 1.5 [95% CI 1.1–2.1], odds ratio 3.5 [95% CI 1.9–6.6], adjusted p -value = 0.0074). These findings are based on data from four countries and corroborated by independent results from GWAS. Our findings are biologically plausible, as HLA-C*04:01 has fewer predicted bindings sites for relevant SARS-CoV-2 peptides compared to other HLA alleles. Interpretation HLA-C*04:01 carrier state is associated with severe clinical course in SARS-CoV-2. Our findings suggest that HLA class I alleles have a relevant role in immune defense against SARS-CoV-2. Funding Funded by Roche Sequencing Solutions, Inc.

Background: Torque teno virus (TTV) is a circular, single-stranded DNA virus that chronically infects healthy individuals of all ages worldwide. There is a lot of data on the prevalence and genetic heterogeneity of TTV in healthy populations and in patients with various diseases now available. However, little is known about TTV load among healthy human population. In this study we analyzed TTV load in the group of 512 Russian elite athletes, who are supposed to be, by some standards, the healthiest part of the human population.

Copy-number variants (CNVs) are an important part of human genetic variation. They can be benign or can play a role in human disease by creating dosage imbalances and disrupting genes and regulatory elements. Accurate identification and clinical annotation of CNVs is essential, however, manual evaluation of individual CNVs by clinicians is challenging on a large scale. Here, we present ClassifyCNV, an easy-to-use tool that implements the 2019 ACMG classification guidelines to assess CNV pathogenicity. ClassifyCNV uses genomic coordinates and CNV type as input and reports a clinical classification for each variant, a classification score breakdown, and a list of genes of potential importance for variant interpretation. We validate ClassifyCNV’s performance using a set of known clinical CNVs and a set of manually evaluated variants. ClassifyCNV matches the pathogenicity category for 81% of manually evaluated variants with the significance of the remaining pathogenic and benign variants automatically determined as uncertain, requiring a further evaluation by a clinician. ClassifyCNV facilitates the implementation of the latest ACMG guidelines in high-throughput CNV analysis, is suitable for integration into NGS analysis pipelines, and can decrease time to diagnosis. The tool is available at https://github.com/Genotek/ClassifyCNV.

BackgroundTumor development in the human colon is commonly accompanied by epigenetic changes, such as DNA methylation and chromatin modifications. These alterations result in significant, inheritable changes in gene expression that contribute to the selection of tumor cells with enhanced survival potential.ResultsA recent high-throughput gene expression analysis conducted by our group identified numerous genes whose transcription was markedly diminished in colorectal tumors. One of these, the protein-tyrosine phosphatase receptor type R (PTPRR) gene, was dramatically downregulated from the earliest stages of cellular transformation. Here, we show that levels of both major PTPRR transcript variants are markedly decreased (compared with normal mucosal levels) in precancerous and cancerous colorectal tumors, as well in colorectal cancer cell lines. The expression of the PTPRR-1 isoform was inactivated in colorectal cancer cells as a result of de novo CpG island methylation and enrichment of transcription-repressive histone-tail marks, mainly H3K27me3. De novo methylation of the PTPRR-1 transcription start site was demonstrated in 29/36 (80%) colorectal adenomas, 42/44 (95%) colorectal adenocarcinomas, and 8/8 (100%) liver metastases associated with the latter tumors.ConclusionsEpigenetic downregulation of PTPRR seems to be an early alteration in colorectal cell transformation, which is maintained during the clonal selection associated with tumor progression. It may represent a preliminary step in the constitutive activation of the RAS/RAF/MAPK/ERK signalling, an effect that will later be consolidated by mutations in genes encoding key components of this pathway.

Recent advances in DNA sequencing open prospects to make whole-genome analysis rapid and reliable, which is promising for various applications including personalized medicine. However, existing techniques for de novo genome assembly, which is used for the analysis of genomic rearrangements, chromosome phasing, and reconstructing genomes without a reference, require solving tasks of high computational complexity. Here we demonstrate a method for solving genome assembly tasks with the use of quantum and quantum-inspired optimization techniques. Within this method, we present experimental results on genome assembly using quantum annealers both for simulated data and the $$\phi $$ ϕ X 174 bacteriophage. Our results pave a way for a significant increase in the efficiency of solving bioinformatics problems with the use of quantum computing technologies and, in particular, quantum annealing might be an effective method. We expect that the new generation of quantum annealing devices would outperform existing techniques for de novo genome assembly. To the best of our knowledge, this is the first experimental study of de novo genome assembly problems both for real and synthetic data on quantum annealing devices and quantum-inspired techniques.