Biology subjects

Torok, M. E.

Publications and source records attributed to Torok, M. E..

3 recordsLinked to original sources

bioRxiv2026

Accounting for allelic diversity and multicopy gene detection improves the accuracy of antibiotic resistance genotypic determination

BackgroundGenomic prediction of antimicrobial resistance (AMR) relies on the accurate detection of resistance genes or allelic variants of core genes from raw or assembled genomes sequences. For several bacterial species and antibiotics, AMR genotype-phenotype discrepancies are common, indicating that important sources of error remain unresolved. For Enterococcus faecium, we focused on identifying the sources of discrepancies for tetracycline resistance, for which genotypic detection had shown particularly low accuracy. We investigated the effect of structural variation in antibiotic resistance genes (ARGs)--including gene duplications, truncations, interruptions, and mixed configurations of complete and partial gene copies-- as a source of genotype-phenotype discrepancies from short{square}read data. We conduct further extended investigations to other antibiotic families and into another bacterial species: Escherichia coli. MethodsWe analyzed collections of E. faecium and E. coli genomes, integrating high{square}quality complete assemblies, simulated Illumina short reads, and matched AMR phenotypic data. The integrity, copy number, and allelic diversity of ARGs were examined for multiple antibiotic classes, and their impact on ARG detection and accuracy of AMR determination was assessed using several commonly used bioinformatic tools (SRST2, ARIBA and AMRFinderPlus). ResultsFor E. faecium, after ruling out the effect of specific tet allelic variants on tetracycline susceptibility, we found that the integrity and copy number of tet(M) had a major effect on detection accuracy. Duplicated and incomplete ARGs are also common in E. faecium genomes, particularly for macrolides (erm(B)) and aminoglycosides (ant(6)-Ia and aph(3)-IIIa). In E. coli, similar patterns were observed for tet(A), erm(B) and aminoglycoside{square}associated genes (aph(3{square})-IIIa and ant(6)-Ia). Across ARGs in both species, short-read mapping methods wrongly reported interrupted genes as complete in some instances, while assembly{square}based methods often failed to resolve complete copies of duplicated genes. Detection accuracy improved when tools were adapted to account for gene integrity and when extended AMR databases incorporating species{square}specific alleles were included. ConclusionsOur findings reveal that bioinformatic limitations in dealing with ARG copy number and completeness, and in accounting for allelic variation, underly a substantial source of genotype-phenotype errors, highlighting the need for improved AMR databases and bioinformatic tools that consider these factors to achieve reliable genomic prediction of AMR.

bioinformatics↗

bioRxiv2024

Variable rates of SARS-CoV-2 evolution in chronic infections

An important feature of the evolution of the SARS-CoV-2 virus has been the emergence of highly mutated novel variants, which are characterised by the gain of multiple mutations relative to viruses circulating in the general global population. Cases of chronic viral infection have been suggested as an explanation for this phenomenon, whereby an extended period of infection, with an increased rate of evolution, creates viruses with substantial genetic novelty. However, measuring a rate of evolution during chronic infection is made more difficult by the potential existence of compartmentalisation in the viral population, whereby the viruses in a host form distinct subpopulations. We here describe and apply a novel statistical method to study within-host virus evolution, identifying the minimum number of subpopulations required to explain sequence data observed from cases of chronic infection, and inferring rates for within-host viral evolution. Across nine cases of chronic SARS-CoV-2 infection in hospitalised patients we find that non-trivial population structure is relatively common, with four cases showing evidence of more than one viral population evolving independently within the host. We find cases of within-host evolution proceeding significantly faster, and significantly slower, than that of the global SARS-CoV-2 population, and of cases in which viral subpopulations in the same host have statistically distinguishable rates of evolution. Non-trivial population structure was associated with high rates of within-host evolution that were systematically underestimated by a more standard inference method.

evolutionary biology↗

bioRxiv2020

Patterns of within-host genetic diversity in SARS-CoV-2

Monitoring the spread of SARS-CoV-2 and reconstructing transmission chains has become a major public health focus for many governments around the world. The modest mutation rate and rapid transmission of SARS-CoV-2 prevents the reconstruction of transmission chains from consensus genome sequences, but within-host genetic diversity could theoretically help identify close contacts. Here we describe the patterns of within-host diversity in 1,181 SARS-CoV-2 samples sequenced to high depth in duplicate. 95% of samples show within-host mutations at detectable allele frequencies. Analyses of the mutational spectra revealed strong strand asymmetries suggestive of damage or RNA editing of the plus strand, rather than replication errors, dominating the accumulation of mutations during the SARS-CoV-2 pandemic. Within and between host diversity show strong purifying selection, particularly against nonsense mutations. Recurrent within-host mutations, many of which coincide with known phylogenetic homoplasies, display a spectrum and patterns of purifying selection more suggestive of mutational hotspots than recombination or convergent evolution. While allele frequencies suggest that most samples result from infection by a single lineage, we identify multiple putative examples of co-infection. Integrating these results into an epidemiological inference framework, we find that while sharing of within-host variants between samples could help the reconstruction of transmission chains, mutational hotspots and rare cases of superinfection can confound these analyses.

genomics↗