Introduction

A key goal of antibiotic therapy is the complete eradication of the pathogen. While many common infections respond rapidly to antibiotics, and high cure rates are achieved with 1-2 weeks of therapy^1,2, there are also infections where bacterial clearance is either very slow, or frequently incomplete. This challenge is exemplified by mycobacterial infections, where treatment courses extend from months to years to prevent relapse.

While the ability of mycobacteria to escape antibiotic-mediated killing is multifactorial, the phenomenon of antibiotic “persistence” is likely an important contributor^3–6. Studies on penicillin dating from the 1940s noted that when a population of susceptible bacteria were exposed to a bactericidal antibiotic, the majority of the population died within a few hours, but that a small sub-population of ‘persisters’ remained viable for over a week⁷. Importantly, these persister cells had not acquired a mutation conferring heritable antibiotic resistance, and do not grow in the presence of the antibiotic. Rather, they have entered into a readily-reversable epigenetic state^8–10 where, despite antibiotic-mediated inhibition of critical processes, they are able to survive. Virtually all bacterial species display the ability to form persister cells, and their development is strongly induced in response to stresses such as nutrient deprivation or acidic pH^{6–7,11–12}. Notably, these same stresses are encountered in the lysosome of an activated immune cell¹³, and studies of pathogens isolated from activated macrophages indeed show a strong immune-mediated increase in persister cells^5,14. Thus, paradoxically, the immune system may actually impede bacterial eradication by antibiotics.

Persister formation has been studied extensively in model systems such as Escherichia coli, which has provided important insights, but also highlighted uncertainties of current models. Several different pathways have been implicated in E. coli persister formation, including the HipBA toxin-antitoxin system¹⁵, guanosine pentaphosphate ((p)ppGpp) synthesis by RelA/SpoT enzymes^16,17, and Lon protease¹⁶. In each of these models, the postulated mechanism is to halt cell division and render the process targeted by antibiotics non-essential. However, important questions remain unanswered. It is unclear how persister cells remain viable when critical processes such as transcription or translation are arrested by antibiotics, as well as how the process is regulated and induced by stress.

Even the mechanism of cell death following antibiotic exposure itself remains uncertain, and somewhat controversial. Historically, antibiotics have been presumed to kill bacteria as a direct result of inhibition of their target molecule, such as β-lactam antibiotics disrupting cell wall integrity, directly leading to mechanical cell lysis¹⁸. However, a number of studies, largely from E. coli, have suggested that reactive oxygen species (ROS) accumulation triggered by antibiotic stress might also contribute to cell death^10,19–20. Conversely, other studies have found no such association^21,22, leaving the role of ROS in antibiotic-mediated cell death unresolved.

Studying persister cell physiology in mycobacterial pathogens offers several advantages. Mycobacterial persister cells are particularly resilient, as Mycobacterium smegmatis (Msmeg) and Mycobacterium tuberculosis (Mtb) persisters can endure many weeks of antibiotic exposure^10,23–24. Clinically, mycobacterial infections are difficult to eradicate. Fully-susceptible Mtb requires multiple antibiotics for four months or longer^25–26, whereas non-tuberculous mycobacteria typically require treatment for 12-18 months and have a relapse rates of roughly 50%²⁷. Mycobacterium abscessus (Mabs) is among the most difficult of all bacterial pathogens to treat, because in addition to the possibility of forming persister cells, it is intrinsically resistant to many classes of antibiotics, leaving few treatment options²⁸. This leads to the use of antibiotics with greater toxicity to patients, and a need to use these agents for prolonged periods to prevent relapse. Thus, identifying the genes that Mabs persister cells rely on for survival could be beneficial by highlighting pathways that might be targeted therapeutically to eliminate persister cells.

Previous genetic screens have studied antibiotic responses in mycobacteria, with some evaluating heritable resistance, and others investigating persister cell formation. Several studies of genetic resistance have successfully used either transposon mutagenesis with high-throughput sequencing of insertion sites (Tn-Seq) or CRISPR-based transcriptional repression with high-throughput sequencing of guide RNAs (CRISPRi) to identify genes promoting growth in sub-inhibitory concentrations of antibiotic. These studies have provided insights such as the importance of cell membrane permeability controlling antibiotic penetration into the cytoplasm^29–31. Persister cell formation has proven challenging to study, likely because their low frequency leads to population bottlenecks that confound genetic analysis. Although screens in Mtb have been conducted in macrophages and mice, and genes such as glpK and cinA identified, overall the number of mutants isolated in these screens has been low^32–34. There has been one effective in vitro Tn-Seq study of rifampin survival in Mtb that isolated over 100 mutants¹⁹, demonstrating the feasibility of genetic screening in this context. However, whether these phenotypes seen with rifampin in Mtb extend to other organisms and other antibiotics remains to be determined.

Here, we study persister cell formation in Mabs, and describe the results of genome-wide Tn-seq screens seeking to identify the genes required for both spontaneous and starvation-induced antibiotic persistence. We identified diverse pathways contributing to persister cell survival, and unexpectedly, observed a prominent role for ROS detoxifying factors such as the catalase-peroxidase enzyme KatG, which contributed to both spontaneous and starvation-induced persistence. We found that endogenous ROS were generated following antibiotic exposure, and that hypoxia significantly impaired bacterial killing. Thus, the lethality of some antibiotics is amplified by toxic ROS accumulation, and persister cells require detoxification systems.

Results

Starvation-induced persister cell formation in mycobacteria

We first sought to develop conditions suitable for genetic analysis of antibiotic persistence in mycobacteria. Genetic screens examining persister cell physiology faces two inherent obstacles. First, persister cells are rare in unstressed bacterial populations, and antibiotic-mediated cell death creates population bottlenecks that obscure mutant phenotypes. Second, most mycobacterial populations contain spontaneous drug-resistant mutants that can expand if the population is exposed to a single antibiotic. To overcome these obstacles, we sought to establish large-scale, high-density culture conditions to prevent genetic bottlenecks, and used multiple antibiotics to suppress expansion of spontaneous drug-resistant mutants. We began by assessing the feasibility of this approach using wild-type Msmeg. We exposed the cells to either the combination of rifampin, isoniazid, and ethambutol (RIF/INH/EMB) used to treat Mtb, or to the combination of tigecycline and linezolid (TIG/LZD), two translation-inhibiting antibiotics frequently used to treat Mabs^27,35, and empirically determined the minimum-inhibitory concentrations (MICs) each antibiotic under the high-density culture conditions that would be needed for genetic analysis of antibiotic persister cells. Both antibiotic combinations reduced the bacterial population >1000-fold within 72h (Figure 1A). We then evaluated both spontaneous and stress-induced persister cell formation under these conditions in Msmeg. We compared logarithmically growing (mid-log) cultures in 7H9 rich media to cultures starved for 2 days in PBS prior to addition of antibiotics. Consistent with expectations, we found a marked increase in the frequency of persister cells in starved cultures, with a 100-fold increase in survival following TIG/LZD exposure and a 10,000-fold increase following RIF/INH/EMB exposure (Figure 1A).

Figure 1.

We next examined two species of pathogenic mycobacteria to similarly assess stress-induced persister formation under these conditions, as has been reported previously^36–39. We again compared cells starved in PBS to mid-log cells growing 7H9, and found that cultures of wild-type Mabs (ATCC 19977 strain) and Mtb (Erdman) also displayed dramatic increases in the frequency of persister cells in nutrient-deprived cultures (Figure 1B,C). Notably, for Mabs and Msmeg, the development of these persister cells required an adaptation period of several days under starvation conditions, as formation of persister cells was dramatically impaired if cells were shifted immediately into nutrient-deficient conditions with antibiotics, suggesting that a regulated process needed to be completed (Figure 1D-F).

Identification of pathways needed for persister formation in Mabs

We used these conditions to carry out Tn-Seq screens in Mabs to identify genes necessary for forming both spontaneous and starvation-induced persister cells. We conducted the screen using a Mabs Himar1 Tn library comprised of ∼55,000 mutations across all 4,992 non-essential Mabs genes in strain ATCC 19977²⁹. We maintained cells in log-phase growth in 7H9 rich media, or starved cells in PBS as described above, and then exposed them to TIG/LZD for 6 days (Figure 2A), a point at which spontaneous persister cells comprise the majority of the population (Figure 1C). Following antibiotic exposure cells were then washed and placed in antibiotic-free liquid media to recover, genomic DNA was isolated, and Tn insertion sites sequenced. We then used TRANSIT software⁴⁰ to quantify the abundance of each Tn mutant across different conditions and identify mutants with statistically-significant differences in distribution, in order to identify essential genes in both spontaneous and stress-induced antibiotic persister cells. We identified 277 Mabs genes required for surviving TIG/LZD exposure in rich media, 271 genes required for survival during starvation and 362 genes required to survive the combined exposure to antibiotics and starvation using criteria for significance of Log₂ fold-change > 0.5 and Benjamini–Hochberg adjusted p-value (p-adj.) ≤ 0.05 (Figure 2B-E). Of the genes required for antibiotic persistence, ∼60% were required in both nutrient-replete and starvation states, although condition-specific determinants were also seen (Figure 2F). As expected, we identified genes with already-established functions in antibiotic responses, including MAB_2752 and MAB_2753 which are both homologs of known antibiotic transporters in Mtb, and tetracycline-responsive transcription factors such as MAB_4687 and MAB_0314c (Table S1), indicating an ability of these Tn-Seq conditions to identify physiologically relevant antibiotic-response genes.

Figure 2.

To identify other cellular processes necessary for persister survival we performed pathway enrichment analysis on the set of genes identified by Tn-Seq. We used the DAVID⁴¹ analysis tool to perform systematic queries of the KEGG, GO, and Uniprot databases and identify over-represented processes and pathways. Interestingly, although cells were exposed to translation-inhibiting antibiotics, and no exogenous oxidative or nitrosative stress was applied to the cells, we identified a number of factors needed to combat these stresses. This includes bfrB (bactoferritin), ahpe (peroxiredoxin) and katG (catalase/peroxidase) as well as 5 components of the bacterial proteasome pathway, known to mediate resistance to nitrosative stress in Mtb⁴². We also identified multiple members of DNA-damage response pathways including recF, recG, uvrA, uvrB and uvrC (Figure 2G, Table S2). Examining starvation-induced persisters, a number of the same pathways were again seen, and the mutant with the greatest persister defect in this context was mntH, a redox-regulated Mn/Zn transporter implicated in peroxide resistance in other organisms^43–44. Taken together, these findings suggest an unexpected scenario whereby translation inhibition triggers accumulation of reactive oxygen or nitrogen species, with damage to macromolecules such as DNA occurring.

We next sought to independently confirm a role in persister survival for genes identified by Tn-Seq. We selected a set of genes with strong defects in persister formation, representing several of the functional pathways identified, and used oligonucleotide-mediated recombineering (ORBIT)⁴⁵ to disrupt their open reading frames. The initial genes selected were pafA (proteasome pathway), katG (catalase-peroxidase), recR (DNA repair), blaR (β-lactam sensing), and MAB_1456c (cobalamin synthesis). To control for non-specific effects of antibiotic selection during the recombineering process, we created a control strain using ORBIT to target a non-coding intergenic region downstream of a redundant tRNA gene (MAB_t5030c). We then individually screened each of these mutants to determine if they displayed deficits in persister cell formation by exposing cells to TIG/LZD, either in rich 7H9 media or under starvation conditions, as had been done in the pooled Tn-Seq screen. For ΔkatG we detected clear defects in persister cell survival following 6 days of antibiotic exposure in either rich media or under starvation-induced conditions, corroborating the results of our Tn-Seq analysis (Figure 3A). We observed smaller defects in the ΔpafA and ΔMAB_1456c mutants (Figure 3B,C), and in blaR found no loss of overall viability, but instead observed a delayed resumption of growth after removal of antibiotics (Figure 3D, data not shown).

Figure 3.

To further confirm the role of katG and pafA, and exclude off-target effects of recombineering, we performed genetic complementation analysis by restoring a wild-type copy of each gene into the respective ΔpafA and ΔkatG mutants. In each case, we integrated a single copy of the wild-type gene, under the control of its endogenous promoter, into the genome at the L5 attB site (hereafter pafA+, katG+ strains), and constructed isogenic control strains with empty vector integrated at the same site (hereafter pafA-, katG- strains). We confirmed expression of the re-introduced copy of each gene by RT-qPCR in the pafA+, and katG+ strains, and found expression within roughly 2-fold of endogenous wild-type levels (Figure 4A,D). We then challenged these strains with TIG/LZD as before. In rich media, where the ΔkatG mutants have a moderate persistence defect, the katG+ strain had roughly a 50-fold increase in viable cells relative to the katG- strain. We then exposed cells to antibiotics under starvation conditions, where the ΔkatG mutant phenotype is more severe. Under these conditions the katG- cells succumbed rapidly between 3d and 10d after antibiotic exposure, with a 1,000-fold decrease in viable cells relative to control cells, whereas the katG+ strain showed a near-complete restoration of persistence (Figure 4B). We analogously examined complementation of ΔpafA mutants, and although the phenotype of the ΔpafA mutant is less severe overall than a ΔkatG mutant, we saw a similar restoration of survival in pafA+ cells relative to pafA- cells (Figure 4E). We next evaluated whether the pafA-, and katG- strains were overall more sensitive to the growth inhibitory effects of TIG/LZD, or whether they had specific defects in survival above the mean bactericidal concentration. We performed MIC determination for TIG and LZD individually for each strain, comparing the katG+/katG- and pafA+/pafA- strains. We found that that the MICs for each of these strains were unchanged, demonstrating that these mutants were not more readily inhibited by these antibiotics. Instead, they have more rapid kinetics of cell death at bactericidal concentrations, consistent with a specific defect in antibiotic persistence (Figure 4C,F).

Figure 4.

Reactive oxygen contributes to antibiotic lethality in Mabs

We next investigated the role of KatG in persister cell cells more broadly. We began by assessing whether katG conferred protection from other antibiotics with diverse mechanisms of action, selecting antibiotics that are used clinically for mycobacterial infections. Because katG- mutants showed the greatest defects in starvation-induced persistence, we analyzed survival of katG+ and katG- strains in starvation-adapted cultures exposed to a panel of different antibiotics. Because both TIG and LZD both act by inhibiting translation, we began by exposing cells to either TIG or LZD alone. As expected, the degree of bacterial killing was significantly less with either agent alone than when they are added in combination. Upon exposure to either of these antibiotics the katG- cells died more rapidly than katG+ cells, though we note that the final proportion of persisters in the population was unchanged in katG- cells (Figure 5A). When we exposed cells to rifabutin (RFB), an RNA polymerase inhibitor, we saw a similar effect, with a 100-fold loss of viability in katG- cells relative to the katG+ cells (Figure 5B). In contrast, when we exposed cultures to either levofloxacin (gyrase inhibitor) or cefoxitin (β-lactam inhibitor of peptidoglycan cross-linking), katG had little to no effect on cell viability (Figure 5C-D). Thus, the role of KatG is context-dependent, suggesting that some antibiotics generate oxidative stress that can be ameliorated by KatG catalase/peroxidase activity while others do not.

Figure 5.

The identification of katG as essential for persister cells to survive exposure to TIG/LZD suggests that ROS are present and causing damage. Although TIG/LIN are translation inhibitors that do not directly generate ROS we evaluated whether they might nonetheless be triggering ROS accumulation as a secondary effect. We examined ROS levels in katG+ Mabs using the ROS indicator dye cellROX, that is retained in cells when it becomes oxidized⁴⁶. At baseline, during log-phase growth in rich media < 3% of cells had ROS accumulation (Figure 5E). We saw a moderate increase in ROS accumulation in starved cultures, with roughly 10% of the population cellROX+. However, when starved cultures were exposed to the TIG/LZD we saw a dramatic increase in ROS accumulation with over 30% cellROX+ cells. Taken together, these data indicate that translation inhibition does indeed have unanticipated downstream effects on cellular redox balance, with ROS accumulation that could be contributing to cell death.

We next tested whether ROS were contributing to cell death by reducing ROS production and assessing the impact on cell viability. A well-established system for studying hypoxia in mycobacteria is the Wayne Model of gradual-onset hypoxia, whereby low-density cultures are inoculated in sealed vessels with minimal headspace. As the culture slowly grows, the soluble O₂ is consumed, resulting in the onset of hypoxia over several days, a process that can be monitored by the decolorization of Methylene Blue dye in the media⁴⁷. Under aerobic conditions in rich media, we observed the expected rapid killing of Mabs over the first 5 days with the combination of TIG/LZD, with more rapid loss of viability in KatG-cells. However, under hypoxic conditions, where ROS production is suppressed, we saw much slower bacterial killing. Importantly, under hypoxic conditions katG- cells no longer had a survival defect relative to katG+ cells, supporting the hypothesis that translation-inhibiting antibiotics also cause secondary accumulation of lethal ROS in antibiotic-treated cells that need to be detoxified by KatG (Figure 5F).

Incomplete penetrance of katG phenotype among Mabs strains

Finally, we tested the role of KatG in Mabs clinical strains to determine how widely the role of katG is conserved among different Mabs strains. We obtained 2 clinical isolates of Mabs, used ORBIT to disrupt the katG locus, and evaluated the ability of these strains to form both spontaneous and starvation-induced persister cells upon exposure to TIG/LZD. For clinical strain-1, the ΔkatG mutant showed no defects in either condition (Figure 6B). In contrast, for clinical strain-2, katG contributed to starvation-induced persistence, as the ΔkatG mutant rapidly lost viability in a manner similar to the ATCC 19977 reference strain. However, unlike ATCC 19977, this phenotype only manifested in starvation-induced persister cells, and was not seen in the absence of stress (Figure 6C). Thus, the katG phenotype displays incomplete penetrance, and the degree of protection it confers falls along a continuum among Mabs strains.

Figure 6.

In summary, the results of these studies point to an important effect of ROS in amplifying the lethality of some antibiotics in Mabs. Through genetic analysis we identified a number of ROS detoxification factors, including KatG, as necessary for persisters to remain viable. This suggested that antibiotics might induce an oxidative state in cells, and direct measurement of ROS following antibiotic exposure indicated that this was indeed the case. Further supporting the deleterious effects of ROS in this context, we found that removal of oxygen both slowed bacterial killing and rendered KatG dispensable. Taken together these results suggest that antibiotic lethality is accelerated by toxic ROS accumulation, and persister survival requires active detoxification systems.

Discussion

Pathways necessary for persister formation in Mabs

The phenomenon of bacterial persistence has been recognized for decades, and has been observed in a broad array of bacterial species. Despite this, a unifying mechanism of persister cell formation has not emerged, suggesting that different pathways may play a role in different contexts. A large body of work comes from E. coli where toxin-antitoxin systems such as HipBA¹⁵, Lon protease^16,48 and the (p)ppGpp synthase RelA all contribute to the formation of persister cells^49–52. However, even within this species other mechanisms seem to function, as RelA contributes to persister cell formation following exposure to β-lactams, but not quinolones⁵³. In addition, how exactly these pathways maintain cell viability remains unclear. For example, while (p)ppGpp produced by RelA seems to act through Lon protease¹⁶, the critical substrates in this process are note unknown. It is also unclear how mechanisms identified in one bacterial species may relate to the mechanisms in another. RelA plays a role in multiple species of bacteria, including Pseudomonas aeruginosa⁵⁴, Staphylococcus aureus⁵⁵, and Mtb⁵⁶. However, the role of this pathway does not seem to be universal, as deletion of neither relA nor lon had an effect on persister formation in Msmeg⁵⁷. Interestingly, in our Tn-Seq analysis, we did not identify relA. However, a prior study of the Mabs relA mutant demonstrates that this strain still synthesizes (p)ppGpp⁵⁸, suggesting genetic redundancy and a need to disrupt additional genes in a relA mutant in order to study the role of (p)ppGpp in Mabs.

Mechanisms of antibiotic lethality

The mechanisms of bacterial cell death following antibiotic exposure remains somewhat controversial. The most straightforward explanation is that antibiotics kill by directly disrupting the function of their targets. However, more recently it was suggested that antibiotics kill through a final common pathway of lethal ROS generation, leading to oxidative damage of DNA and other macromolecules⁵⁹. Since that time, there have been studies, both supporting^10,19–20 and refuting the role of ROS^21–22 in antibiotic-mediated cell death.

Our analysis of Mabs persister cells provides new insights. The identification of multiple genes involved in ROS detoxification through an unbiased genome-wide screen suggests that ROS also promotes antibiotic lethality in Mabs. This idea is further supported by the detection of elevated levels of ROS after exposure to translation inhibitors, and that removal of oxygen slowed antibiotic killing. Taken together, these data strongly support the idea that ROS are a significant contributor to antibiotic lethality in some contexts. These findings are supported by other published studies in mycobacteria. In Mabs and Msmeg, other groups have also observed reduced antibiotic-mediated killing in hypoxic conditions^10,36. In Mtb, exposure to rifampin also generates ROS^19,60, and katG contributes to survival in rifampin-treated cells¹⁹.

The source of ROS under these conditions is uncertain. In principle, any of several derangements could lead to ROS accumulation. One of the major sources of cellular ROS is oxidative phosphorylation, as hydrogen peroxide, superoxide, and hydroxyl radicals are natural byproducts. Thus, increased ROS generation by oxidative phosphorylation is an attractive hypothesis. Alternatively, particularly under starvation conditions, it is possible that antioxidants and ROS scavengers may become depleted, creating a more oxidizing environment. Our Tn-Seq analysis provides additional insight on this. We noted a small class of Tn mutants that were paradoxically protected from antibiotic lethality (Figure 2B). Prominent among this class of mutants were several independent components of the NADH dehydrogenase complex (Table S1). Also known as Complex I of the electron transport chain, it is one of the key entry points for electrons into the oxidative phosphorylation pathway. The observation that mutants in this complex are protected suggests that decreasing flux through oxidative phosphorylation, with a concomitant decrease in ROS generation, may enhance survival during antibiotic exposure.

However, antibiotic-induced ROS accumulation is not a universal phenomenon. With some antibiotics we examined in Mabs, loss of katG had no effect. In addition, ROS detoxification does not always play a protective role, as loss of catalase and peroxidase activities in E. coli also had no effect on persister survival²². Taken together, this suggests a model in which antibiotics cause direct toxicity by acting on their target, but in addition, some antibiotics, notably transcription inhibitors and translation inhibitors, also appear to have a secondary toxic effect of causing ROS accumulation. How exactly transcription or translation blockade leads to increased ROS levels is currently unclear and will require further investigation, although our data suggest that the electron transport chain might play an important role.

Therapeutic implications

Mabs infections are particularly challenging to eradicate, with relapse rates over 50%²⁸. Our results highlight several bacterial processes such as the bacterial proteasome or ROS detoxification that might be targeted therapeutically to reduce the development or survival of persister cells. Agents targeting these process might not have any intrinsic antimicrobial activity alone, but might act to target the unique physiology of persister cells that develop upon antibiotic exposure. This would represent a new therapeutic class of “persistence inhibitors” that might act synergistically with traditional antibiotics to eliminate the subpopulation of persister cells that would otherwise remain viable despite prolonged antibiotic exposure in Mabs and other chronic infections.

Limitations

Tn-Seq has inherent drawbacks, including an inability to identify mutants in essential genes, or in cases of genetic redundancy. Thus, there are likely genes needed for antibiotic persistence in Mabs that were not identified in this study. In addition, we studied the response to a single class of antibiotic, focusing on the translation inhibitors often used to treat Mabs infections, and we studied only spontaneous and starvation-induced persister cells. It is likely that studying other antibiotics, with different mechanisms of action, or different stresses that induce persister cells, would identify additional genes contributing to persister formation and would allow better identification of core survival pathways that might be shared among different forms of stress.

Materials and methods

Bacterial strains and culture conditions

Mabs ATCC 19977, clinical Mabs strains and Msmeg (MC2 155) were grown in BD Middlebrook 7H9 media (liquid) or 7H10 media (solid) supplemented with 0.5% glycerol (Sigma) and 0.2% Tween-80 (Fisher) but without any OADC supplementation except for transformations. Sacramento clinical isolates were obtained from the Sacramento County Department of Public Health Mycobacteriology Laboratory. Confirmation of clinical isolates as Mabs was performed by amplifying the 16s rRNA locus and Sanger sequencing. Mtb (Erdman) was grown in 7H9 (liquid) or 7H10 (solid) supplemented with 0.5% glycerol, 0.1% Tween-80, and 10% OADC (BD). All cultures were grown at 37°C with gentle shaking. Except for specific hypoxia conditions, all liquid cultures were grown with 90% container headspace or using a gas permeable cap to ensure culture oxygenation. PBS starvation was achieved by washing OD0.5-1.0 Mabs 1X in DPBS (− Ca/Mg, Gibco) and resuspending in DPBS at OD=1, supplemented with 0.1% tyloxapol (Sigma).

Mabs antibiotic experiments

For PBS starvation experiments, stocks of Mabs were grown for 48 hours in 7H9 passaging continuously in log phase, then PBS starved or passaged in log phase for an additional 48h. Log phase or PBS starved Mabs were then resuspended in antibiotic containing media at OD1.0. For experiments with hypoxia, Mabs in mid-log aerobic growth was adjusted to OD0.001 in media with 1.5ug/ml methylene blue and added to a rubber septum sealed glass vial with 50% headspace. Methylene blue discoloration was observed at 3d and antibiotics were added at 5d. We empirically determined the half-life of each antibiotic in 7H9 media at 37-deg and for those with half-lives shorter than the experiment, supplemented cultures to with additional antibiotic to maintain the concentration of active antibiotic. Antibiotics were used at the following concentrations: tigecycline (Chem-Impex) at 10ug/ml (8 fold above MIC, re-administered every 3 days), linezolid (Chem-Impex) at 100ug/ml (20 fold above MIC), levofloxacin (Sigma) at 40ug/ml (8 fold above MIC), cefoxitin (Chem Impex) at 80ug/ml (8 fold above MIC, re-administered every 3 days), and rifabutin (Cayman) at 40ug/ml (4 fold above MIC). After antibiotic administration, colony forming units over time were measured.

Msmeg antibiotic experiments

Individual colonies were picked and grown for 48 hours in log phase before being PBS starved or passaged in log phase for 48h. Log phase or PBS starved Msmeg were then resuspended in antibiotic containing media at OD1.0. Antibiotics were used at the following concentrations: tigecycline (Chem-Impex) at 1.25ug/ml (8 fold above MIC, re-administered every 3 days), linezolid (Chem-Impex) at 2.5ug/ml (8 fold above MIC), rifampin (Sigma) at 32ug/ml (8 fold above MIC, re-administered every 6 days), isoniazid (Sigma) at 32ug/ml (8 fold above MIC, re-administered every 6 days), and ethambutol (Thermo) at 4ug/ml (8 fold above MIC, re-administered every 3 days). After antibiotic administration, colony forming units over time were measured.

Mtb antibiotic experiments

Freezer stocks of Mtb were thawed and grown for 5-7 days in log phase before being starved for 14d or longer. Non-starved control Mtb were thawed such that they were also grown for 5-7 days in log phase before experimental use. Log phase or PBS starved Mtb was then resuspended in antibiotic containing media and adjusted to OD1.0. Antibiotics were used at the following concentrations: rifampin (Sigma) at 0.1ug/ml (4 fold above MIC, re-administered every 6 days), isoniazid (Sigma) at 0.1ug/ml (4 fold above MIC, re-administered every 6 days), and ethambutol (Thermo) at 8ug/ml (4 fold above MIC, re-administered every 6 days). After antibiotic administration, colony forming units over time were measured.

Transposon insertion sequencing

The construction of this Himar1 transposon Tn library has been described previously ²⁹. Screening was performed by growing a freezer stock of the library for 2.5 days in log phase before 48-hour PBS starvation or further continuous log-phase growth. Samples were then resuspended in media containing either tigecycline/linezolid or an equal volume of DMSO solvent and incubated for 6 days, with a re-administration of tigecycline or matching DMSO on day 3. The samples were then washed 2X in antibiotic free liquid media, resuspended in antibiotic free liquid media (10X the original culture volume), and grown until OD0.5-1.0. Subsequently, the samples underwent three more rounds of 100-fold passaging in liquid media to amplify surviving bacteria before the samples were collected in Trizol (Invitrogen). A sample taken at the time of the commencement of PBS starvation was collected in Trizol and used as the input control. Three independent trials of this experiment were submitted to the UC Davis DNA Technologies Core, where Tn insertion site flanking sequences were amplified as described previously ²⁹ and sequenced on sequenced on an Illumina AVITI. Sequence reads were mapped to the ATCC 19977 genome and analyzed using TRANSIT software with the following parameters: 0% of N/C termini ignored, 10,000 samples, TTR normalization, LOESS correction, include sites with all zeros, site restricted resampling. Genes with significant changes were defined as those with adjusted p-value (p-adj.) <0.05 and log₂ fold change >0.5. P-adj. was calculated using the Benjamini-Hochberg correction.

Pathway enrichment analysis

To improve gene annotation, Mabs orthologs to Mtb genes were identified. Mabs genes were first converted into protein sequences using Mycobrowser, and protein sequences were then used to perform reciprocal BLASTp searches. Mabs genes and Mtb genes that mapped to each other using independent one-way BLASTp searches with a maximum e-value cutoff of 0.1 were considered orthologs. For pathway analysis, gene lists (Mtb orthologs) were then imported into the DAVID knowledgebase⁴¹ and pathway enrichment analysis performed for Gene Ontology biological process, Uniprot keyword and KEGG databases with statistical analysis Fisher’s exact test and nominal p-value reported.

Gene deletion and complementation

Knockout strains were generated using ORBIT⁴⁵. Briefly, Mabs was transformed with the kanamycin-resistant ORBIT recombineering plasmid pkm444. 20ml Mabs at OD0.5-1.0 was washed 2X in 10% glycerol and resuspended in 200ul 10% glycerol. 500ng plasmid was added and electroporated at 2.5kV in 0.2cm cuvettes. The bacteria were allowed to recover overnight before plating on 150ug/ml kanamycin plates. Clones were selected and regrown in liquid media supplemented with 150ug/ml kanamycin and 10% OADC (BD) to OD0.5-1.0. For recombineering, the pkm444-Mabs was grown to mid-log, then diluted to OD0.1 and 200mM glycine (Fisher) was added to the media. 16 hours later, 500mM sucrose (Sigma) and 500ng/ml anhydrotetracycline (Cayman) were added and incubated for an additional 4 hours. Subsequently, the Mabs was washed 2X in ice cold 10% glycerol + 500mM sucrose. 200ul of 10X concentrated Mabs was then electroporated with 600ng of the zeocin-R ORBIT payload pkm496 plasmid and 2ug of targeting oligonucleotide (Table S3) at 2.5kV in 0.2cm cuvettes. The Mabs was then allowed to recover overnight in liquid media with 10% OADC and 500ng/ml anhydrotetracycline before being plated on 150ug/ml zeocin plates. Mutants were then selected and screened for gene deletion by PCR amplification and Sanger sequencing. For genetic complementation, the endogenous loci including promoter and terminator sequences were amplified by PCR and cloned into the EcoRV site of pmv306 with kanamycin resistance⁶¹. In the case of katG, the upstream gene furA was also included in the complementation construct to achieve optimal katG expression.

MIC analysis

Two-fold serial dilutions of antibiotics were prepared in a 96 well plate in 100ul volume. 100ul of 2X bacteria were added (for Mabs: used a final OD of 0.001, Msmeg: OD0.001, Mtb: OD0.01), making a final volume of 200ul. The plates were incubated until there was visible growth in the no antibiotic control well. At this time, the bacteria were transferred to a new plate with 20ul of 40% paraformaldehyde and OD620 measurements were taken with a FilterMax F3 plate reader (Molecular Devices).

Flow cytometry

OD1 Mab was stained with cellROX green (Invitrogen) at a final concentration of 5uM for 1hr at 37C. The cells were then washed in PBS and resuspended in PBS with 4% paraformaldehyde and 5ug/ml DAPI (Sigma). The samples were run on a LSRII flow cytometer (BD). Fluorophores were excited with the 405nm (DAPI) and 488nm (cellROX) lasers. Detection was performed using the 450/50 (505LP) filter for DAPI and a 525/50 (555LP) filter for cellROX. Data were analyzed with FlowJo software (BD).

DNA/RNA Purification

Samples were resuspended in 5 volumes of Trizol and were bead beat with 0.1mm zirconia beads (Biospec) 6×2min at 4°C in a Mini-Beadbeater-16 (Biospec). Chloroform was added and RNA in the aqueous phase removed. For DNA isolation, a second RNA extraction was performed with 0.8M guanidine thiocyanate and 0.5M guanidine hydrochloride, 60mM Acetate pH 5.2, 1mM EDTA. DNA was then isolated with back-extraction buffer (4M Guanidine Thiocyanate, 50mM Sodium Citrate, 1M Tris base (without pH adjustment ∼pH 11) and DNA purified using a PureLink RNA Mini Kit (Invitrogen).

RT-qPCR

RNA was purified using PureLink RNA Mini Kit per manufacturer’s instructions. The samples were DNAseI (NEB) treated for 15min/37°C before stopping the reaction by adding 3.5mM EDTA and heating for 10min/75°C. cDNA was synthesized from 500ng total RNA using random hexamers and Maxima H minus reverse transcriptase (Thermo). No reverse-transcription controls were also included and used to confirm the lack of genomic DNA-driven amplification. qPCR reactions used Taq polymerase (NEB) and EvaGreen (Biotium) and were run on Biorad CFX Opus 96 Real-Time PCR System. Melt curves were included for each sample to confirm uniform amplicon identity between samples. Gene-specific amplification was quantified by comparison to a standard curve generated from 3-fold serial dilutions of a control sample, then normalized to 16S rRNA within each sample.

Acknowledgements

We would like to thank Nick Campbell-Kruger for his technical insights on identifying Mabs/Mtb orthologs, Jonathan Van Dyke for his assistance with flow cytometry analysis, Emily Kumimoto and Siranoosh Ashtari for their assistance with Tn-Seq library preparation and sequencing, and Jessie Li and Bradley Jenner for their assistance with Tn-seq bioinformatics. We would also like to thank Caroline Dominic at the Sacramento County Department of Public Health for providing clinical isolates of Mabs for analysis.

Additional information

Funding

Pew Biomedical Fellowship BHP

NIH RO1 1R01AI144149 BHP

NIH R01 1R01AI143722 SAS

NIH Shared Instrumentation Grant 1S10OD010786-01 DNA Technologies and Expression Analysis Core, UC Davis Genome Center

NCI Cancer Center Support Grant P30CA093373 Flow Cytometry Shared Resource, UC Davis

Conflicts of interest

BHP and SAS serve on the scientific advisory board of X-Biotics Therapeutics.

Supporting information

Supplemental Table 1

Supplemental Table 2

Supplemental Table 3