Biology subjects

Azgari, C.

Publications and source records attributed to Azgari, C..

3 recordsLinked to original sources

bioRxiv2026

Hairpin Architecture and Extended Sequence Context Shape APOBEC3 Mutagenesis in Monkeypox Virus at Different Genomic Scales

The 2022 global mpox outbreak was characterized by an unusual accumulation of more than fifty APOBEC-signature substitutions in MPXV genomes. A previous study suggested that these substitutions preferentially occur within and near hairpins or cruciforms - stem-loop structures formed by inverted repeats. Here, we show that this conclusion resulted from an inappropriate statistical analysis and, using two substantially larger datasets, demonstrate that APOBEC-signature mutations are specifically enriched at TC motifs located at the 3' ends of hairpin loops, but not in other regions of hairpins/cruciforms or in their vicinity. We further identified a preference for stem-loop structures with longer stems and shorter loops, consistent with patterns previously reported for the APOBEC mutagenesis in cancer genomes. Our analysis also revealed a positive association between APOBEC-signature mutation propensity and hairpin stability in single-stranded DNA. Nevertheless, stem-loop structures with long stems and short loops are relatively rare in the MPXV genome, limiting their contribution to the genome-wide distribution of APOBEC-signature mutations to a predominantly local effect. In contrast, extended nucleotide context downstream of the TC motif showed strong genome-wide predictive value, with a marked preference for guanine-rich trinucleotide immediately downstream. Analysis of the upstream nucleotide context was consistent with the mutational specificity of APOBEC3A. Together, these results identify distinct structural and sequence determinants of the APOBEC-associated mutagenesis in MPXV and show that local hairpin/cruciform architecture and extended nucleotide context contribute at different genomic scales.

microbiology↗

bioRxiv2025

Predicting Future SARS-CoV-2 Mutations using Deep Learning

SARS-CoV-2 continues to spread over the world steadily as opposed to many earlier estimations that it would disappear in less than two years. Even though SARS-CoV-2 vaccines have reduced the speed of the infection significantly, they could not fully stop it. On the contrary, the World Health Organization has recently published cautionary statements that infection counts are on the rise, and a huge wave is expected in winter. Vaccines mostly target specific regions of the virus. The high mutation rate of SARS-CoV-2 is one essential tool that the virus exploits to escape from the available vaccines. Therefore, researchers have been working on designing next-generation vaccines against the new variants of the virus. Nevertheless, SARS-CoV-2 acquires new mutations faster than we can adapt our vaccines due to long clinical trial periods. Hence, there is a need for computational tools that can predict future SARS-CoV-2 mutations before they even emerge. In this paper, we propose several deep-learning-based methods to estimate the possible future mutations in SARS-CoV-2 genome. We design and evaluate various ensemble and bagging architectures enriched with a large set of genomic, biochemical, and phylogenetic features. We evaluate our models on the GISAID data and demonstrate that the best-performing methods achieve an F1-Macro score of 0.78.

bioinformatics↗

bioRxiv2021

The mutation profile of SARS-CoV-2 is primarily shaped by the host antiviral defense

Understanding SARS-CoV-2 evolution is a fundamental effort in coping with the COVID-19 pandemic. The virus genomes have been broadly evolving due to the high number of infected hosts world-wide. Mutagenesis and selection are the two inter-dependent mechanisms of virus diversification. However, which mechanisms contribute to the mutation profiles of SARS-CoV-2 remain under-explored. Here, we delineate the contribution of mutagenesis and selection to the genome diversity of SARS-CoV-2 isolates. We generated a comprehensive phylogenetic tree with representative genomes. Instead of counting mutations relative to the reference genome, we identified each mutation event at the nodes of the phylogenetic tree. With this approach, we obtained the mutation events that are independent of each other and generated the mutation profile of SARS-CoV-2 genomes. The results suggest that the heterogeneous mutation patterns are mainly reflections of host (i) antiviral mechanisms that are achieved through APOBEC, ADAR, and ZAP proteins and (ii) probable adaptation against reactive oxygen species. ImportanceSARS-CoV-2 genomes are evolving worldwide. Revealing the evolutionary characteristics of SARS-CoV-2 is essential to understand host-virus interactions. Here, we aim to understand whether mutagenesis or selection is the primary driver of SARS-CoV-2 evolution. This study provides an unbiased computational method for profiling and analyzing independently occurring SARS-CoV-2 mutations. The results point out three host antiviral mechanisms shaping the mutational profile of SARS-CoV-2 through APOBEC, ADAR, and ZAP proteins. Besides, reactive oxygen species might have an impact on the SARS-CoV-2 mutagenesis.

evolutionary biology↗