HLA molecules are expressed in almost all nucleated cells of the body. These molecules are extremely variable and responsible for tissue specificity recognition. Precise HLA typing is crucial for tissue and organ transplantation. Usually, HLA‐typing NGS methods are based on the assignment of reads to a certain previously reported HLA allele presented in the IPD‐IMGT/HLA Database. But there is a limited number of tools able to identify novel alleles that have not yet been reported and thus absent in the database. Such alleles carry mismatches distinguishing them from all known alleles in the database. Therefore, manual evaluation of the identified HLA alleles in the genome browser is a compulsory step in the analysis, and one that is labor intensive and time consuming. We present the development and validation of a freely available web‐application for the identification of novel HLA alleles in the most relevant HLA class I and II genes from NGS data. The tool can also be used for automated data quality assessment. The software was validated by analyzing 330 alleles. The results are concordant with orthogonal methods.

Evolution of SARS-CoV-2 in immunocompromised hosts may result in novel variants with changed properties. While escape from humoral immunity certainly contributes to intra-host evolution, escape from cellular immunity is poorly understood. Here, we report a case of long-term COVID-19 in an immunocompromised patient with non-Hodgkin’s lymphoma who received treatment with rituximab and lacked neutralizing antibodies. Over the 318 days of the disease, the SARS-CoV-2 genome gained a total of 40 changes, 34 of which were present by the end of the study period. Among the acquired mutations, 12 reduced or prevented the binding of known immunogenic SARS-CoV-2 HLA class I antigens. By experimentally assessing the effect of a subset of the escape mutations, we show that they resulted in a loss of as much as ~1% of effector CD8 T cell response. Our results indicate that CD8 T cell escape represents a major underappreciated contributor to SARS-CoV-2 evolution in humans.

Evolution of SARS-CoV-2 in immunocompromised hosts may result in novel variants with changed properties, but the mode of selection underlying this process remains unclear. While escape from humoral immunity certainly plays a role in intra-host evolution, escape from cellular immunity is poorly understood. Here, we report a case of long-term COVID-19 in an immunocompromised patient with non-Hodgkin’s lymphoma who received treatment with rituximab and lacked neutralizing antibodies. Over the 318 days of the disease, the SARS-CoV-2 genome gained a total of 40 changes, 34 of which were present by the end of the study period. Among the acquired mutations, 12 reduced or prevented binding of known immunogenic SARS-CoV-2 HLA class I antigens, suggesting that virus immunoediting is largely driven by cytotoxic CD8 T cell clones. The two changes with the strongest effect, nsp3:T504A and nsp3:T504P, were experimentally assessed in a cytotoxic assay of the patient's CD8 T cells. Both these changes were associated with immune escape, with a stronger effect observed for nsp3:T504P, the change which ultimately got fixed. Together, these results suggest that CD8 T cell escape may be an underappreciated contributor to SARS-CoV-2 evolution in humans.

BackgroundMultiplex polymerase chain reaction (PCR) is a common enrichment technique for targeted massive parallel sequencing (MPS) protocols. MPS is widely used in biomedical research and clinical diagnostics as the fast and accurate tool for the detection of short genetic variations. However, identification of larger variations such as structure variants and copy number variations (CNV) is still being a challenge for targeted MPS. Some approaches and tools for structural variants detection were proposed, but they have limitations and often require datasets of certain type, size and expected number of amplicons affected by CNVs. In the paper, we describe novel algorithm for high-resolution germinal CNV detection in the PCR-enriched targeted sequencing data and present accompanying tool.ResultsWe have developed a machine learning algorithm for the detection of large duplications and deletions in the targeted sequencing data generated with PCR-based enrichment step. We have performed verification studies and established the algorithm’s sensitivity and specificity. We have compared developed tool with other available methods applicable for the described data and revealed its higher performance.ConclusionWe showed that our method has high specificity and sensitivity for high-resolution copy number detection in targeted sequencing data using large cohort of samples.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1272-6) contains supplementary material, which is available to authorized users.

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