In order to identify genes determining antibiotic susceptibility, we expanded transposon libraries in the presence or absence of partially inhibitory antibiotic
concentrations close to the MIC, for a period of ~6.5 generations. The rationale for this particular screen design is as follows:
1. By using a partially-inhibitory antibiotic concentration, library selection and outgrowth occurs in the same phase of the screen, eliminating the need for a secondary outgrowth step. While we could alternatively have performed the screen under bacteriocidal conditions and identified mutants with altered killing kinetics,
this would have necessitated a secondary outgrowth phase to identify viable mutants at the end of the selection period. This could potentially introduce
selection-independent variation in mutant frequencies in the output libraries, if the extent of the outgrowth is not well standardized between the experimental and control groups. Standardization of the outgrowth phase is particularly complicated post-antibiotic selection due to delayed recovery of antibiotic-treated cultures and potential carryover of antibiotics from the selection culture to the outgrowth plate. A secondary outgrowth phase would also result in a loss of library diversity due to selection against slow-growing mutants in the outgrowth phase.
2. Partially inhibitory selection was carried out at an antibiotic concentration close to the MIC – this would maximize the selection pressure on the mutant libraries, allowing for the identification of a large number of both sensitive and resistant mutants. Increasing antibiotic selection pressure would also reduce the extent of outgrowth required to obtain a significant change in mutant frequencies.
3. By reducing the outgrowth duration to 6.5 generations, library diversity is improved due to reduced loss of mutants that are growth-defective in culture, even in the absence of antibiotic selection. This improves screen coverage and reduces the number of “blind-spot” genes that cannot be reliably tested statistically. Secondarily, reducing the outgrowth duration ensures that the screens can be completed in 1-2 weeks, improving experimental efficiency.
This screen design prioritizes greater genomic coverage, reduced experimental noise and a faster execution time; however, the main tradeoff of this design is that it identifies multiple trivial hits with minimal impact on antibiotic susceptibility, as defined by standard measures of sensitivity such as the MIC or the minimum bacteriocidal concentration (MBC). The possible reasons for this non-specificity will be discussed in this section.
While our TnSeq screen assesses mutant fitness at a single partially inhibitory antibiotic concentration, we chose to assess screen performance based on its ability to predict mutants with reduced MICs. The distribution of antibiotics in tuberculosis lesions is highly variable (Dartois, 2014; Prideaux et al., 2015), and the MIC is thus more reflective of a mutant’s ability to survive and replicate in the diversity of niches in a host undergoing TB chemotherapy. Despite the fact that single-concentration mutant fitness and mutant MIC are related, they are far from perfectly correlated – multiple hits in this screen and previous similar studies (Dorr et al., 2016; Girgis et al., 2009) exhibit no reduction in MICs despite showing reduced mutant fitness under specific antibiotic concentrations. It is also possible that the significant changes in mutant frequency and the implied changes in antibiotic sensitivity may be particular to the culture conditions of the TnSeq screen (e.g. inter-mutant competition/cooperation in mixed culture, insufficient recovery time from thawing) and not necessarily reproducible in the context of the single-strain MIC assay.
Nevertheless, our screen demonstrates reasonable predictive ability in the identification and ranking of mutants with MIC reductions for vancomycin, rifampicin and meropenem. Screen performance was significantly poorer for ethambutol and isoniazid, with none of the mutants within our 21 mutant validation panel exhibiting an MIC reduction of greater than 1.5-fold for isoniazid. The comparatively poor performance of the isoniazid screen could possibly be attributed to the steep Hill slope of the isoniazid Mtb-inhibition curve (i.e. the difference between the lowest concentration required to fully inhibit Mtb growth and the highest concentration that does not inhibit growth at all is very small), which results in major fitness changes over a narrow window of isoniazid concentrations. The growth curves of selected mutants under partially inhibitory antibiotic conditions of ethambutol and isoniazid (Fig. 3.) illustrate that large changes in growth rates and
consequently mutant frequencies can occur in spite of miniscule/non-apparent MIC shifts. To improve precision in the identification of “non-trivial” hits with significant reductions in MICs,
more stringent TnSeq-Fc cutoffs could be imposed, or alternatively, much lower antibiotic concentrations could be used to ensure the specific selection of mutants with significant MIC reductions. The use of lower antibiotic concentrations would however necessitate a longer outgrowth duration, which could reduce library diversity and genomic coverage of the screen.
The problem of trivial hits raises the question as to whether the antibiotic susceptibility screen design should be aligned to more clinically relevant measures of antibiotic susceptibility. A main aim of this screen was to identify targets that could potentiate the activity of existing antibiotics and reduce treatment duration. Relevant quantifiers of antibiotic synergy would thus include a reduction in MIC, faster killing kinetics and reduced persister populations; particularly under environmental conditions similar to those in vivo. Screening of libraries under bacteriocidal antibiotic concentrations would directly address the question of which mutations lead to faster killing and possibly reduced persister formation. However, bacteriocidal screens would be more difficult to implement due to the above-mentioned problems of reduced library diversity and increased
experimental noise; in particular, a screen for mutants with reduced persister formation would require selection on a much greater mutant population than in this screen to avoid bottlenecking effects, due to the low frequency of persisters in the population. At this point, it is unknown which screen design performs better in the identification of relevant hits, and extensive screen testing and mutant validation would be required to determine optimal screen conditions.
CHAPTER 4: CONCLUDING REMARKS