Validation of Novel Mycobacterium tuberculosis Isoniazid Resistance Mutations Not Detectable by Common Molecular Tests.

Resistance to the first-line antituberculosis (TB) drug isoniazid (INH) is widespread, and the mechanism of resistance is unknown in approximately 15% of INH-resistant (INH-R) strains. To improve molecular detection of INH-R TB, we used whole-genome sequencing (WGS) to analyze 52 phenotypically INH-R Mycobacterium tuberculosis complex (MTBC) clinical isolates that lacked the common katG S315T or inhA promoter mutations. Approximately 94% (49/52) of strains had mutations at known INH-associated loci that were likely to confer INH resistance. All such mutations would be detectable by sequencing more DNA adjacent to existing target regions. Use of WGS minimized the chances of missing infrequent INH resistance mutations outside commonly targeted hotspots. We used recombineering to generate 12 observed clinical katG mutations in the pansusceptible H37Rv reference strain and determined their impact on INH resistance. Our functional genetic experiments have confirmed the role of seven suspected INH resistance mutations and discovered five novel INH resistance mutations. All recombineered katG mutations conferred resistance to INH at a MIC of ≥0.25 µg/ml and should be added to the list of INH resistance determinants targeted by molecular diagnostic assays. We conclude that WGS is a useful tool for detecting uncommon INH resistance mutations that would otherwise be missed by current targeted molecular testing methods and suggest that its use (or use of expanded conventional or next-generation-based targeted sequencing) may provide earlier diagnosis of INH-R TB.

Fine-scale differentiation between Bacillus anthracis and Bacillus cereus group signatures in metagenome shotgun data.

BACKGROUND: It is possible to detect bacterial species in shotgun metagenome datasets through the presence of only a few sequence reads. However, false positive results can arise, as was the case in the initial findings of a recent New York City subway metagenome project. False positives are especially likely when two closely related are present in the same sample. Bacillus anthracis, the etiologic agent of anthrax, is a high-consequence pathogen that shares >99% average nucleotide identity with Bacillus cereus group (BCerG) genomes. Our goal was to create an analysis tool that used k-mers to detect B. anthracis, incorporating information about the coverage of BCerG in the metagenome sample. METHODS: Using public complete genome sequence datasets, we identified a set of 31-mer signatures that differentiated B. anthracis from other members of the B. cereus group (BCerG), and another set which differentiated BCerG genomes (including B. anthracis) from other Bacillus strains. We also created a set of 31-mers for detecting the lethal factor gene, the key genetic diagnostic of the presence of anthrax-causing bacteria. We created synthetic sequence datasets based on existing genomes to test the accuracy of a k-mer based detection model. RESULTS: We found 239,503 B. anthracis-specific 31-mers (the Ba31 set), 10,183 BCerG 31-mers (the BCerG31 set), and 2,617 lethal factor k-mers (the lef31 set). We showed that false positive B. anthracis k-mers-which arise from random sequencing errors-are observable at high genome coverages of B. cereus. We also showed that there is a "gray zone" below 0.184× coverage of the B. anthracis genome sequence, in which we cannot expect with high probability to identify lethal factor k-mers. We created a linear regression model to differentiate the presence of B. anthracis-like chromosomes from sequencing errors given the BCerG background coverage. We showed that while shotgun datasets from the New York City subway metagenome project had no matches to lef31 k-mers and hence were negative for B. anthracis, some samples showed evidence of strains very closely related to the pathogen. DISCUSSION: This work shows how extensive libraries of complete genomes can be used to create organism-specific signatures to help interpret metagenomes. We contrast "specialist" approaches to metagenome analysis such as this work to "generalist" software that seeks to classify all organisms present in the sample and note the more general utility of a k-mer filter approach when taxonomic boundaries lack clarity or high levels of precision are required.

Population structure of Neisseria gonorrhoeae based on whole genome data and its relationship with antibiotic resistance.

Neisseria gonorrhoeae is the causative agent of gonorrhea, a sexually transmitted infection (STI) of major importance. As a result of antibiotic resistance, there are now limited options for treating patients. We collected draft genome sequence data and associated metadata data on 76 N. gonorrhoeae strains from around the globe and searched for known determinants of antibiotics resistance within the strains. The population structure and evolutionary forces within the pathogen population were analyzed. Our results indicated a cosmopolitan gonoccocal population mainly made up of five subgroups. The estimated ratio of recombination to mutation (r/m = 2.2) from our data set indicates an appreciable level of recombination occurring in the population. Strains with resistance phenotypes to more recent antibiotics (azithromycin and cefixime) were mostly found in two of the five population subgroups.