Tuberculosis chemotherapy is dependent on the use of the antibiotic pyrazinamide, which is being threatened by emerging drug resistance. Resistance is mediated through mutations in the bacterial gene pncA. Methods for testing pyrazinamide susceptibility are difficult and rarely performed, and this means that the full spectrum of pncA alleles that confer clinical resistance to pyrazinamide is unknown. Here, we performed in vitro saturating mutagenesis of pncA to generate a comprehensive library of PncA polymorphisms resultant from a single-nucleotide polymorphism. We then screened it for pyrazinamide resistance both in vitro and in an infected animal model. We identify over 300 resistance-conferring substitutions. Strikingly, these mutations map throughout the PncA structure and result in either loss of enzymatic activity and/or decrease in protein abundance. Our comprehensive mutational and screening approach should stand as a paradigm for determining resistance mutations and their mechanisms of action.

Background. Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes Coronavirus disease-19 (COVID-19), a respiratory illness with influenza-like symptoms that can result in hospitalization or death. We investigated human genetic determinants of COVID-19 risk and severity in 455,838 UK Biobank participants, including 2,003 with COVID-19. Methods. We defined eight COVID-19 phenotypes (including risks of infection, hospitalization and severe disease) and tested these for association with imputed and exome sequencing variants. Results. We replicated prior COVID-19 genetic associations with common variants in the 3p21.31 (in LZTFL1) and 9q34.2 (in ABO) loci. The 3p21.31 locus (rs11385942) was associated with disease severity amongst COVID-19 cases (OR=2.2, P=3x10-5), but not risk of SARS-CoV-2 infection without hospitalization (OR=0.89, P=0.25). We identified two loci associated with risk of infection at P<5x10-8, including a missense variant that tags the epsilon 4 haplotype in APOE (rs429358; OR=1.29, P=9x10-9). The association with rs429358 was attenuated after adjusting for cardiovascular disease and Alzheimer′s disease status (OR=1.15, P=0.005). Analyses of rare coding variants identified no significant associations overall, either exome-wide or with (i) 14 genes related to interferon signaling and reported to contain rare deleterious variants in severe COVID-19 patients; (ii) 36 genes located in the 3p21.31 and 9q34.2 GWAS risk loci; and (iii) 31 additional genes of immunologic relevance and/or therapeutic potential. Conclusions. Our analyses corroborate the association with the 3p21.31 locus and highlight that there are no rare protein-coding variant associations with effect sizes detectable at current sample sizes. Our full analysis results are publicly available, providing a substrate for meta-analysis with results from other sequenced COVID-19 cases as they become available. Association results are available at https://rgc-covid19.regeneron.com

Many antibacterial drugs have multiple mechanisms of resistance, which are often represented simultaneously by a mixture of resistance mutations (some more frequent than others) in a clinical population. This presents a challenge for Genome-Wide Association Studies (GWAS) methods, making it difficult to detect less prevalent resistance mechanisms purely through (weak) statistical associations. Homoplasy, or the occurrence of multiple independent mutations at the same site, is often observed with drug resistance mutations and can be a strong indicator of positive selection. However, traditional GWAS methods, such as those based on allele counting or linear regression, are not designed to take homoplasy into account. In this article, we present a new method, called ECAT (for Evolutionary Cluster-based Association Test), that extends traditional regression-based GWAS methods with the ability to take advantage of homoplasy. This is achieved through a preprocessing step which identifies hypervariable regions in the genome exhibiting statistically significant clusters of distinct evolutionary changes, to which association testing by a linear mixed model (LMM) is applied using GEMMA (a well-established LMM-based GWAS tool). Thus, the approach can be viewed as extending GEMMA from the usual site- or gene-level analysis to focusing on clustered regions of mutations. This approach was evaluated on a large collection of more than 600 clinical isolates of multidrug-resistant (MDR) Mycobacterium tuberculosis from Lima, Peru. We show that ECAT does a better job of detecting known resistance mutations for several antitubercular drugs (including less prevalent mutations with weaker associations), compared with (site- or gene-based) GEMMA, as representative of existing GWAS methods. The power of the multiphase approach in ECAT comes from focusing association testing on the hypervariable regions of the genome, which reduces complexity in the model and increases statistical power.

BackgroundPhylogeny estimation for bacteria is likely to reflect their true evolutionary histories only if they are highly clonal. However, recombination events could occur during evolution for some species. The reconstruction of phylogenetic trees from an alignment without considering recombination could be misleading, since the relationships among strains in some parts of the genome might be different than in others. Using a single, global tree can create the appearance of homoplasy in recombined regions. Hence, the identification of recombination breakpoints is essential to better understand the evolutionary relationships of isolates among a bacterial population.ResultsPreviously, we have developed a method (called ACR) to detect potential breakpoints in an alignment by evaluating compatibility of polymorphic sites in a sliding window. To assess the statistical significance of candidate breakpoints, we propose an extension of the algorithm (ptACR) that applies a permutation test to generate a null distribution for comparing the average local compatibility. The performance of ptACR is evaluated on both simulated and empirical datasets. ptACR is shown to have similar sensitivity (true positive rate) but a lower false positive rate and higher F1 score compared to basic ACR. When used to analyze a collection of clinical isolates of Staphylococcus aureus, ptACR finds clear evidence of recombination events in this bacterial pathogen, and is able to identify statistically significant boundaries of chromosomal regions with distinct phylogenies.ConclusionsptACR is an accurate and efficient method for identifying genomic regions affected by recombination in bacterial genomes.

This manuscript describes our experience in early identifying MDR-TB cases in high-risk populations by setting up a single-referral molecular diagnosis laboratory in Taiwan. Taiwan Centers for Disease Control designated a single-referral laboratory to provide the GenoType MTBDRplus test for screening high-risk MDR-TB populations nationwide in 2012–2015. A total of 5,838 sputum specimens from 3,308 patients were tested within 3 days turnaround time. Compared with the conventional culture and drug susceptibility testing, the overall performance of the GenoType MTBDRplus test for detecting TB infection showed accuracy of 70.7%, sensitivity of 85.9%, specificity of 65.7%, positive predictive value of 45.5%, and negative predictive value of 93.3%. And the accuracy of detecting rifampin (RIF) resistance, isoniazid (INH) resistance, and MDR-TB (resistant to at least RIF and INH) were 96.5%, 95.2%, and 97.7%, respectively. MDR-TB contacts presented a higher rate of mutated codons 513–519, GenoType MTBDRplus banding pattern: rpoB WT3(−), and rpoB WT4(−) than the treatment failure group. The MDR-TB contact group also had a higher rate of inhA C15T mutation, banding pattern: inhA WT1(−), and inhA MUT1(+) than the recurrent group. Resistance profiles of MDR-TB isolates also varied geographically. The referral molecular diagnosis system contributed to rapid detection and initiation of appropriate therapy.