Targeted surveillance strategies for efficient detection of novel antibiotic resistance variants.

Genotype-based diagnostics for antibiotic resistance represent a promising alternative to empiric therapy, reducing inappropriate antibiotic use. However, because such assays infer resistance based on known genetic markers, their utility will wane with the emergence of novel resistance. Maintenance of these diagnostics will therefore require surveillance to ensure early detection of novel resistance variants, but efficient strategies to do so remain undefined. We evaluate the efficiency of targeted sampling approaches informed by patient and pathogen characteristics in detecting antibiotic resistance and diagnostic escape variants in Neisseria gonorrhoeae, a pathogen associated with a high burden of disease and antibiotic resistance and the development of genotype-based diagnostics. We show that patient characteristic-informed sampling is not a reliable strategy for efficient variant detection. In contrast, sampling informed by pathogen characteristics, such as genomic diversity and genomic background, is significantly more efficient than random sampling in identifying genetic variants associated with resistance and diagnostic escape

Using rapid point-of-care tests to inform antibiotic choice to mitigate drug resistance in gonorrhoea

Background: The first cases of extensively drug resistant gonorrhoea were recorded in the United Kingdom in 2018. There is a public health need for strategies on how to deploy existing and novel antibiotics to minimise the risk of resistance development. As rapid point-of-care tests (POCTs) to predict susceptibility are coming to clinical use, coupling the introduction of an antibiotic with diagnostics that can slow resistance emergence may offer a novel paradigm for maximising antibiotic benefits. Gepotidacin is a novel antibiotic with known resistance and resistance-predisposing mutations. In particular, a mutation that confers resistance to ciprofloxacin acts as the ‘stepping-stone’ mutation to gepotidacin resistance. Aim: To investigate how POCTs detecting Neisseria gonorrhoeae resistance mutations for ciprofloxacin and gepotidacin can be used to minimise the risk of resistance development to gepotidacin. Methods: We use individual-based stochastic simulations to formally investigate the aim. Results: The level of testing needed to reduce the risk of resistance development depends on the mutation rate under treatment and the prevalence of stepping-stone mutations. A POCT is most effective if the mutation rate under antibiotic treatment is no more than two orders of magnitude above the mutation rate without treatment and the prevalence of stepping-stone mutations is 1–13%. Conclusion: Mutation frequencies and rates should be considered when estimating the POCT usage required to reduce the risk of resistance development in a given population. Molecular POCTs for resistance mutations and stepping-stone mutations to resistance are likely to become important tools in antibiotic stewardship

Diversity of <i>Mycobacterium tuberculosis</i> across Evolutionary Scales

<div><p>Tuberculosis (TB) is a global public health emergency. Increasingly drug resistant strains of <i>Mycobacterium tuberculosis</i> (<i>M</i>.<i>tb</i>) continue to emerge and spread, highlighting adaptability of this pathogen. Most studies of <i>M</i>.<i>tb</i> evolution have relied on ‘between-host’ samples, in which each person with TB is represented by a single <i>M</i>.<i>tb</i> isolate. However, individuals with TB commonly harbor populations of <i>M</i>.<i>tb</i> numbering in the billions. Here, we use analyses of <i>M</i>.<i>tb</i> genomic data from within and between hosts to gain insight into influences shaping genetic diversity of this pathogen. We find that the amount of <i>M</i>.<i>tb</i> genetic diversity harbored by individuals with TB can vary dramatically, likely as a function of disease severity. Surprisingly, we did not find an appreciable impact of TB treatment on <i>M</i>.<i>tb</i> diversity. In examining genomic data from <i>M</i>.<i>tb</i> samples within and between hosts with TB, we find that genes involved in the regulation, synthesis, and transportation of immunomodulatory cell envelope lipids appear repeatedly in the extremes of various statistical measures of diversity. Many of these genes have been identified as possible targets of selection in other studies employing different methods and data sets. Taken together, these observations suggest that <i>M</i>.<i>tb</i> cell envelope lipids are targets of selection within hosts. Many of these lipids are specific to pathogenic mycobacteria and, in some cases, human-pathogenic mycobacteria. We speculate that rapid adaptation of cell envelope lipids is facilitated by functional redundancy, flexibility in their metabolism, and their roles mediating interactions with the host.</p></div

Genes with <i>π</i>_<i>N</i><i>π</i>_<i>S</i> > 1 across 3 within-host <i>Mycobacterium tuberculosis</i> populations.

<p>Genes identified in at least one sample from 3 independent patients’ <i>M</i>.<i>tb</i> population with a <i>π</i>_<i>N</i><i>π</i>_<i>S</i> > 1 are displayed. Two genes meeting this criterion were excluded due to potential copy number variants identified by manual inspection of the alignments.</p><p>*Indicates the gene is in the superpathway of mycolate biosynthesis.</p><p>Genes with <i>π</i>_<i>N</i><i>π</i>_<i>S</i> > 1 across 3 within-host <i>Mycobacterium tuberculosis</i> populations.</p

Population differentiation between samples.

<p>A.) Pairwise F_ST of polymorphic sites. Each patient sample was treated as a population and F_ST was calculated individually for each polymorphic site in a population. Calculations were performed on all polymorphic sites covered by at least 10 reads per sample for which the minor allele was supported by a minimum of 6 reads across all samples from the population (<i>see <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1005257#sec013" target="_blank">Methods</a></i>). Dots show the highest observed F_ST value for each single nucleotide polymorphism (SNP) across the H37Rv genome. Red color coding indicates allele frequencies changed significantly across the sampling interval (based on a Fisher’s exact test; <i>q</i>-value < 0.01). Genes implicated in drug resistance are outlined in black. Outliers of the F_ST distribution are likely to be under positive selection, or linked to a mutation under positive selection. B.) Allele frequencies over the course of treatment for SNPs with significant changes in allele frequency (red dots in A).</p

Gene-wise estimates of Tajima’s D and <i>π</i>_<i>N</i><i>/πS</i> at the between-host scale.

<p>Each circle represents a gene in the H37Rv genome. <i>π</i>_<i>N</i><i>/π</i>_<i>S</i> values are plotted on a logarithmic (base 2) scale.</p

Within-host samples of <i>Mycobacterium tuberculosis</i>.

<p>Sample dates and resistance profiles are from the respective publications; sample timing is in reference to treatment initiation. Mean coverage of each sample reflects data passing all filters, with a minimum base quality score of 20. Treatment at the time of sampling is listed; only the first letters of drug abbreviations are capitalized. Resistance profile abbreviations: R = resistant; S = susceptible; blank = not reported; *indicates more details presented in the original paper. Drug abbreviations: INH (H), isoniazid; STR (S), streptomycin; RIF (R), rifampicin; EMB (E), ethambutol; PZA (Z), pyrazinamide; ETH, ethionamide; OFX, ofloxacin; CIP, ciprofloxacin; KAN, kanamycin; AMK, amikacin; PAS, para-aminosalicylic acid; CPR, capreomycin; D, Dipasic (isoniazid aminosalicylate); CS, Cycloserine; CFZ, clofazimine.</p><p>Within-host samples of <i>Mycobacterium tuberculosis</i>.</p