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Weber, A. M.

Publications and source records attributed to Weber, A. M..

2 recordsLinked to original sources

Signatures of selection at drug resistance loci in Mycobacterium tuberculosis

Tuberculosis (TB) is the leading cause of death by an infectious disease, and global TB control efforts are increasingly threatened by drug resistance in Mycobacterium tuberculosis (M. tb). Unlike most bacteria, where lateral gene transfer is an important mechanism of resistance acquisition, resistant M. tb arises solely by de novo chromosomal mutation. Using whole genome sequencing data from two natural populations of M. tb, we characterized the population genetics of known drug resistance loci using measures of diversity, population differentiation, and convergent evolution. We found resistant sub-populations to be less diverse than susceptible sub-populations, consistent with ongoing transmission of resistant M. tb. A subset of resistance genes (\"sloppy targets\") were characterized by high diversity and multiple rare variants; we posit that a large genetic target for resistance and relaxation of purifying selection contribute to high diversity at these loci. For \"tight targets\" of selection, the path to resistance appeared narrower, evidenced by single favored mutations that arose numerous times on the phylogeny and segregated at markedly different frequencies in resistant and susceptible sub-populations. These results suggest that diverse genetic architectures underlie drug resistance in M. tb, and combined approaches are needed to identify causal mutations. Extrapolating from patterns observed in well-characterized genes, we identified novel candidate variants involved in resistance. The approach outlined here can be extended to identify resistance variants for new drugs, to investigate the genetic architecture of resistance, and, when phenotypic data are available, to find candidate genetic loci underlying other positively selected traits in clonal bacteria.\n\nImportanceMycobacterium tuberculosis (M. tb), the causative agent of tuberculosis (TB), is a significant burden on global health. Antibiotic treatment imposes strong selective pressure on M. tb populations. Identifying the mutations that cause drug resistance in M. tb is important for guiding TB treatment and halting the spread of drug resistance. Whole genome sequencing (WGS) of M. tb isolates can be used to identify novel mutations mediating drug resistance and to predict resistance patterns faster than traditional methods of drug susceptibility testing. We have used WGS from natural populations of drug resistant M. tb to characterize effects of selection for advantageous mutations on patterns of diversity at genes involved in drug resistance. The methods developed here can be used to identify novel advantageous mutations, including new resistance loci, in M. tb and other clonal pathogens.

microbiology

VaPoR: a high-speed validation approach for structural variation using long-read sequencing technology.

AbstractSummaryAlthough there are numerous algorithms that have been developed to identify structural variation (SVs) in genomic sequences, there is a dearth of approaches that can be used to evaluate their results. The emergence of new sequencing technologies that generate longer sequence reads can, in theory, provide direct evidence for all types of SVs regardless of the length of region through which it spans. However, current efforts to use these data in this manner require the use of large computational resources to assemble these sequences as well as manual inspection of each region. Here, we present VaPoR, a highly efficient algorithm that autonomously validates large SV sets using long read sequencing data. We assess of the performance of VaPoR on both simulated and real SVs with regards to various features including accuracy and sensitivity of breakpoint evaluation and report a high fidelity rate.\n\nAvailabilityhttps://github.com/mills-lab/VaPoR\n\nContactremills@umich.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics