Due to the widespread of the COVID-19 pandemic, the SARS-CoV-2 genome is evolving in diverse human populations. Several studies already reported different strains and an increase in the mutation rate. Particularly, mutations in SARS-CoV-2 spike-glycoprotein are of great interest as it mediates infection in human and recently approved mRNA vaccines are designed to induce immune responses against it. We analyzed 1,036,030 SARS-CoV-2 genome assemblies and 30,806 NGS datasets from GISAID and European Nucleotide Archive (ENA) focusing on non-synonymous mutations in the spike protein. Only around 2.5% of the samples contained the wild-type spike protein with no variation from the reference. Among the spike protein mutants, we confirmed a low mutation rate exhibiting less than 10 non-synonymous mutations in 99.6% of the analyzed sequences, but the mean and median number of spike protein mutations per sample increased over time. 5,472 distinct variants were found in total. The majority of the observed variants were recurrent, but only 21 and 14 recurrent variants were found in at least 1% of the mutant genome assemblies and NGS samples, respectively. Further, we found high-confidence subclonal variants in about 2.6% of the NGS data sets with mutant spike protein, which might indicate co-infection with various SARS-CoV-2 strains and/or intra-host evolution. Lastly, some variants might have an effect on antibody binding or T-cell recognition. These findings demonstrate the continuous importance of monitoring SARS-CoV-2 sequences for an early detection of variants that require adaptations in preventive and therapeutic strategies.

Summary The detection and prediction of true neoantigens is of great importance for the field of cancer immunotherapy. We searched the literature for proposed neoantigen features and integrated them into a toolbox called NeoFox (NEOantigen Feature toolbOX). NeoFox is an easy-to-use Python package that enables the annotation of neoantigen candidates with 16 neoantigen features. Availability NeoFox is freely available as an open source Python package released under the GNU General Public License (GPL) v3 license at https://github.com/TRON-Bioinformatics/neofox. Supplementary information Supplementary data are available at Bioinformatics online.

Background: The outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) resulted in the global COVID-19 pandemic. The urgency for an effective SARS-CoV-2 vaccine has led to the development of the first series of vaccines at unprecedented speed. The discovery of SARS-CoV-2 spike-glycoprotein mutants, however, and consequentially the potential to escape vaccine-induced protection and increased infectivity, demonstrates the persisting importance of monitoring SARS-CoV-2 mutations to enable early detection and tracking of genomic variants of concern. Results: We developed the CoVigator tool with three components: (1) a knowledge base that collects new SARS-CoV-2 genomic data, processes it and stores its results; (2) a comprehensive variant calling pipeline; (3) an interactive dashboard highlighting the most relevant findings. The knowledge base routinely downloads and processes virus genome assemblies or raw sequencing data from the COVID-19 Data Portal (C19DP) and the European Nucleotide Archive (ENA), respectively. The results of variant calling are visualized through the dashboard in the form of tables and customizable graphs, making it a versatile tool for tracking SARS-CoV-2 variants. We put a special emphasis on the identification of intrahost mutations and make available to the community what is, to the best of our knowledge, the largest dataset on SARS-CoV-2 intrahost mutations. In the spirit of open data, all CoVigator results are available for download. The CoVigator dashboard is accessible via covigator.tron-mainz.de. Conclusions: With increasing demand worldwide in genome surveillance for tracking the spread of SARS-CoV-2, CoVigator will be a valuable resource of an up-to-date list of mutations, which can be incorporated into global efforts.

Leptospira borgpetersenii serovar Hardjo colonizes cattle kidneys and may occasionally infect humans and other mammals. Strains belonging to two clonal subtypes (types A and B) with marked differences in their pathogenicity in the hamster experimental model have been described for this serovar. Such differences have been attributed to point mutations in individual genes, although those genes have not yet been characterized. In order to better understand genetic variability among L. borgpetersenii serovar Hardjo isolates, we sequenced and compared the genomes of two laboratory-adapted strains and three abattoir-derived field isolates of L. borgpetersenii serovar Hardjo. Relatively low genetic variability was observed within isolates of the same subtype, with most of the mutations of moderate or high impact found in the laboratory-adapted isolates. In contrast, several differences regarding gene content and genetic variants were observed between the two subtypes. Putative type-specific genes appear to encode proteins associated with functions that are critical for infection. Some of these genes seem to be involved in transcriptional regulation, possibly leading to a distinct regulatory pattern in each type. These results show that changes in regulatory mechanisms, previously suggested to be critical during Leptospira speciation, may occur in L. borgpetersenii. In addition, the bioinformatics methodology used in this study for variant calling can be useful to other groups working with nonmodel prokaryotic organisms such as Leptospira species.

Introduction: The B.1.1.529 (Omicron) SARS-CoV-2 variant has raised global concerns due to its high number of mutations and its rapid spread. It is of major importance to understand the impact of this variant on the acquired and induced immunity. Several preliminary studies have re-ported the impact of antibody binding and to this date, there are few studies on Omicron CD8+ T-cell immune escape. Methods: We first assessed the impact of Omicron and B.1.617.2 (Delta) variant mutations on the SARS-CoV-2 spike epitopes submitted to the Immune Epitope Database (IEDB) with positive outcome on MHC ligand or T-cell assays (n=411). From those epitopes modified by a mutation, we found the corresponding homologous epitopes in Omicron and Delta. We then ran the netMHCpan computational MHC binding prediction on the pairs of IEDB epitopes and matching homologous epitopes over top 5 MHC I alleles on some selected populations. Lastly, we applied a Fisher test to find mutations enriched for homologous epitopes with decreased predicted binding affinity. Results: We found 31 and 78 IEDB epitopes modified by Delta and Omicron mutations, respectively. The IEDB spike protein epitopes redundantly cover the protein sequence. The WT pMHC with a strong predicted binding tend to have homologous mutated pMHC with decreased binding. A similar trend is observed in Delta over all HLA genes, while in Omicron only for HLA-B and HLA-C. Finally, we obtained one and seven mutations enriched for homologous mutated pMHC with decreased MHC binding affinity in Delta and Omicron, respectively. Three of the Omicron mutations, VYY143-145del, K417N and Y505H, are replacing an aromatic or large amino acid, which are reported to be enriched in immunogenic epitopes. K417N is common with Beta variants, while Y505H and VYY143-145del are novel Omicron mutations. Conclusion: In summary, pMHC with Delta and Omicron mutations show decreased MHC binding affinity, which results in a trend specific to SARS-CoV-2 variants. Such epitopes may decrease overall presentation on different HLA alleles suggesting evasion from CD8+ T-cell responses in specific HLA alleles. However, our results show B.1.1.529 (Omicron) will not totally evade the immune system through a CD8+ immune escape mechanism. Yet, we identified mutations in B.1.1.529 (Omicron) introducing amino acids associated with increased immunogenicity.