A shared alarmone-GTP switch underlies triggered and spontaneous persistence

A shared alarmone-GTP switch underlies triggered and spontaneous persistence | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A shared alarmone-GTP switch underlies triggered and spontaneous persistence Jue Wang, Danny Fung, Jessica Barra, Jeremy Schroeder, Jin Yang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5679108/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 May, 2025 Read the published version in Nature Microbiology → Version 1 posted You are reading this latest preprint version Abstract Persisters describe phenotypically switched cells refractory to antibiotic killing in a genetically susceptible population, while preserving the ability to resume growth when antibiotics are discontinued1,2. Since its proposal 70 years ago, great strides were made to build the framework regarding persistence, including defining triggered, spontaneous and antibiotic-induced persisters. However, challenges remain in characterizing the molecular determinants underlying the phenotypic switch into persistence3. Here we document triggered, spontaneous and antibiotic-induced persistence in a Gram-positive bacterium, all through a common switch involving the alarmone (p)ppGpp and each stemming from a different alarmone synthesis pathway. Starvation-triggered persistence is mediated by Rel synthetase, and spontaneous persistence is through self-amplification via allosteric enzyme activation of alarmone synthetases Rel and SasB, whereas lethal and sublethal concentrations of cell wall antibiotics induce alarmones through an antibiotic-induced alarmone synthetase SasA, consequently enabling adaptive persistence that promotes survival. (p)ppGpp accumulation promotes persistence by depleting intracellular GTP and antagonizing its action. We developed a fluorescent GTP reporter to visualize rare events of persister formation in wild type bacteria, revealing a rapid switch from growth to dormancy in single cells as their GTP levels drop beneath a threshold. While a modest drop of GTP in bulk population slows down growth and promotes antibiotic tolerance, (p)ppGpp drives persistence by allowing the switch-like dynamics to drop GTP beneath the persister threshold in single cells. Persistence through alarmone-GTP antagonism is likely a widespread mechanism to survive antibiotics in Gram positive bacteria and possibly beyond. Biological sciences/Microbiology/Microbial genetics/Bacterial genetics Biological sciences/Microbiology/Bacteriology Figures Figure 1 Figure 2 Figure 3 Figure 4 Main Text Antibiotic persisters are individual bacterial cells that survive prolonged bactericidal antibiotic treatment despite being genetically sensitive to antibiotics 1-3 . Joseph Bigger originally proposed that persisters can be generated before or after antibiotics treatment 1 . Later, persisters were categorized by their pathways of generation- starvation-triggered, spontaneously generated and antibiotic-induced 3 - yet their molecular mechanisms remain poorly understood. Although persistence can be triggered by nutrient deprivations 4 through intracellular accumulation of the alarmone (p)ppGpp 5-9 , it is also argued that that slow growth or a reduction in ATP levels, rather than (p)ppGpp, is responsible for triggering persistence 10,11 . In addition to starvation, persisters can form spontaneously 2 . However, spontaneous persisters exist in natural populations at low frequency, making their identification challenging and understanding the mechanisms of their formation even more difficult. While toxin-antitoxin (TA) modules are strongly implicated in persistence 2,5,8,12,13 , their roles in spontaneous antibiotic persistence of wild type bacteria remain unclear 14 . Finally, persisters were observed both before and after treatment with antibiotics 15 , and bacteriostatic antibiotics are known to induce persisters 16 . However, it is difficult to differentiate antibiotic-induced persistence from antibiotic tolerance 3 , spontaneous persistence, transcriptional response to specific drugs, or byproduct of cellular damage from drug treatment 3 . The mechanisms of antibiotic-induced persistence remain poorly understood. Here we directly visualize the formation of starvation-triggered, spontaneous and antibiotic-induced persisters in the Gram-positive model bacterium Bacillus subtilis and reveal that multiple cellular mechanisms of persistence can converge on a common pathway of alarmone-GTP antagonism. To quantify persistence in B. subtilis , we began by treating cells with the cell wall-targeting antibiotic vancomycin at concentrations 20 times higher than its minimal inhibitory concentration (Fig 1a and Supplemental Data Fig 1­). The results revealed biphasic killing kinetics, with a rapid elimination of the majority of cells followed by the prolonged survival of a small subpopulation. This surviving fraction (~0.1%) remained consistent across varying drug concentrations and, upon regrowth, became re-sensitized to antibiotics (Fig 1a), confirming that these are phenotypically switched persisters rather than genetically altered resisters 1,3,17 . We also tested the effect of prophages on persistence, given that persistence against DNA-damaging antibiotic ciprofloxacin has been shown to be complicated by prophage activation in E. coli 14 . We confirmed that, similar to E. coli , persistence against ciprofloxacin is weakened by prophages (Supplemental Data Fig 2), but persistence against vancomycin remains unaffected regardless of the presence or absence of resident prophages SPβ and PBSX within the genome. Furthermore, the levels of persisters in prophage-cured cells were consistent across different classes of antibiotics, including vancomycin, ciprofloxacin, and kanamycin, which targets translation (Fig 1b). This suggests that the persisters originate from a common subpopulation that is refractory to multiple antibiotics 3 . (p)ppGpp is a starvation-induced alarmone strongly implicated in antibiotic survival 9 . We therefore examined the effect of (p)ppGpp on antibiotic resistance, tolerance and persistence using a (p)ppGpp 0 mutant, which lacks all three (p)ppGpp synthetases 18 . Compared to the wild type, the (p)ppGpp 0 strain exhibited similar resistance to a variety of antibiotics (MIC assay, Supplemental Data Fig 1), a modestly reduced antibiotic tolerance (~5% reduction of MDK 99 , Supplemental Data Fig 3a-b), and strongly reduced antibiotic persistence (~10 fold down, Fig 1a, Supplemental Data Fig 3c). These results indicate that (p)ppGpp predominantly promotes antibiotic persistence. Antibiotic persistence can be triggered by starvation or occur spontaneously 3 . To investigate the role of (p)ppGpp in starvation-triggered persistence, we starved cells using several methods: allowing growth into stationary phase, inducing amino acid starvation, or depleting ATP with arsenate or CCCP. We then perform kill curves with vancomycin under these conditions or after starvation is removed by back-diluting in rich media. The biphasic kill curves (Fig 1c, Supplemental Data Fig 4a) showed a high level of triggered persisters (~500-fold increase from 0.1% to ~50%) upon all starvation treatments and even after starvation-removal, consistent with the established definition of triggered persistence 3 . Interestingly, all starvation conditions also induced (p)ppGpp accumulation. We observed (p)ppGpp buildup not only upon amino acid starvation but also during ATP depletion (Fig 1d, Supplemental Data Fig 5). Furthermore, starvation-triggered persiste­­nce was abolished in the (p)ppGpp 0 mutant, indicating that (p)ppGpp induction is necessary for triggered persistence (Supplemental Data Fig 4b-c). Finally, we found that even in the absence of starvation, gratuitous induction of (p)ppGpp by overexpressing its synthetase 19 was sufficient to produce high levels of persisters (Fig 1c, p-sasA ). These findings demonstrate that (p)ppGpp accumulation is both necessary and sufficient to trigger persistence. We then investigated the occurrence of spontaneous persistence. To differentiate spontaneous persisters from those triggered by starvation, we employed a classical serial passage experiment 2,3 (Fig 1e). In this method, cells are repeatedly diluted into fresh medium during exponential growth, which removes starvation-triggered persisters from the lag phase culture while leaving a consistent, basal fraction of spontaneously generated persisters. This approach enables us to identify a rare population of spontaneous persisters (~0.05%) after the starvation-trigerred persisters have been eliminated (Fig 1f). Notably, the spontaneous persisters were absent in the (p)ppGpp 0 mutant, indicating that (p)ppGpp plays a key role in both spontaneous and starvation-triggered persistence. In B. subtilis , (p)ppGpp is synthesized by three enzymes: Rel, SasA, and SasB (Fig 1g) . To assess their roles in persister formation, we analyzed single, double, and triple mutants of these synthetases (Fig 1h-i, Supplemental Data Fig 6, 7). Our findings show that only the starvation-responsive synthetase, Rel, is essential for starvation-triggered persistence (Fig 1h). In contrast, spontaneous persistence is controlled by both Rel and SasB, as mutations in either rel or sasB significantly reduce spontaneous persister levels, and in combination, they completely abolish this phenotype (Fig 1i). Rel can be activated by pppGpp (Supplemental Data Fig 8), aligning with recent findings 20 . SasB, a tetrameric enzyme, enables allosteric activation of (p)ppGpp synthesis by pppGpp, with a Hill coefficient of ~3.0 21 . A mutation that disrupts the allosteric pppGpp-binding site in SasB ( sasB F42A ) leads to a similar reduction in spontaneous persistence as observed in the Δ sasB mutant (Fig 1i). These results suggest that self-amplification of (p)ppGpp synthesis is crucial for spontaneous persister formation. Next, we search for the downstream effector of (p)ppGpp that mediates antibiotic persistence. (p)ppGpp is a bacterial alarmone with pleiotropic functions including upregulating competence and sporulation 22 . We found that disruption of competence, sporulation, or three known TA systems in B. subtilis has little impact on persistence (Supplemental Data Fig 9). (p)ppGpp also serves to antagonize the essential nucleotide GTP in B. subtilis and to downregulate macromolecular biosynthesis 18 . (p)ppGpp production consumes GTP, and (p)ppGpp directly inhibits multiple enzymes along the pathway of GTP production (Fig 2a) 23-27 . In addition, (p)ppGpp binds to the purine transcription regulator PurR to inhibit transcription of the pur operon, encoding de novo purine biosynthesis genes upstream of GTP synthesis 28 . Furthermore, (p)ppGpp competes with GTP to regulate macromolecule synthesis enzymes including the DNA replication enzyme primase 29,30 and GTPases for ribosome biogenesis, assembly and protein translation 31-34 . In starvation conditions that trigger persisters, GTP levels were significantly depleted, whereas ATP levels vary (Fig 1d), suggesting that GTP depletion may be a common effector leading to persistence. To test whether GTP depletion mediates antibiotic persistence even in the absence of (p)ppGpp induction, we used our previously published inducible strain that downregulated the expression of the guanine nucleotide synthesis gene guaB 35 , which lowered GTP without inducing (p)ppGpp (Supplemental Data Fig 10). We observed an increased persistence by ~10-fold ( guaB ↓, Fig 2b), indicating that GTP depletion is sufficient to generate persisters. In addition, we performed a non-biased Tn-Seq analysis in B. subtilis to identify genes whose disruption increased cell survival after prolonged antibiotic treatment (Supplemental Data Fig 11). The strongest hits mapped to 12 de novo purine biosynthesis genes in the pur operon and guaA encoding GMP synthetase (Supplemental Data Fig 11b, c, Supplemental Data Table 5-6). We verified that disruption of the pur operon or guaA reduces purine levels including GTP (Supplemental Data Fig 11d), with corresponding kill curves confirming increased antibiotic persistence (Fig 2b-c, Supplemental Data Fig 11e). Next, we wanted to know whether reducing GTP in complete absence of (p)ppGpp can still promote persistence. Through genetic screen, we identified a mutation in gmk encoding guanylate kinase (see Methods) that reduces GTP levels of the (p)ppGpp 0 strain back to the wild type level (Supplemental Data Fig 12). This mutant [(p)ppGpp 0 gmk Q110R ] restored persistence back to wild type (Fig 2b-c, Supplemental Data Fig 12), confirming that GTP depletion is sufficient for antibiotic persistence. In addition to increased persistence, we also noted a modest rise in antibiotic tolerance in the GTP synthesis mutants (Supplemental Data Fig 7), which aligns with our previous findings that GTP depletion leads to slower growth 35 . Slower growth has been shown to contribute to antibiotic tolerance 11,36 . This raises the question: Is increased antibiotic persistence simply a consequence of slow growth, and thus always correlated with tolerance? Genetic screens in Bacillus subtilis have identified slow-growing mutants, some of which deplete GTP while others do not. We tested a couple of slow-growing mutants that do not rely on GTP depletion. Intriguingly, while these mutants exhibited increased tolerance, they do not show an increase in persistence; in fact, some showed decreased persistence (Fig 2c). This suggests that, although tolerance and persistence are correlated in certain mutants or conditions 11 , they can be genetically uncoupled. To further distinguish between tolerance and persistence, we grew wild-type cells in different carbon sources, measuring their growth rates and antibiotic killing dynamics (Supplemental Data Fig 21a-b). Our results revealed a strong correlation between slower growth rates and increased tolerance (Fig. 2e), but no significant correlation between growth rates and persistence (Fig. 2h). This demonstrates that, while tolerance and persistence can share overlapping characteristics, they represent distinct biological phenomena that can be independently regulated. Our findings, while unexpected, are explainable: although both slow growth and antibiotic tolerance are population-level phenomena, persistence reflects the behavior of a phenotypically heterogeneous subpopulation of cells functioning as outliers. To differentiate population-based tolerance from the phenotypically heterogeneous subpopulation of persisters, it is critical to measure GTP levels in single cells. To achieve this, we constructed a fluorescent reporter for GTP levels in single cells. Building on our prior result that the ilvB gene is induced by ~20-fold upon GTP depletion via the CodY repressor- which uses GTP as a co-repressor 37,38 -we cloned the CodY operator from ilvB to control fluorescent protein expression ectopically (Supplemental Data Fig 13a). This reporter, termed P lowGTP (Figure 2d), is fully repressed in (p)ppGpp 0 cells, with fluorescence indistinguishable from background autofluorescence (Supplemental Data Fig 13b). In (p)ppGpp+ cells, the reporter displays a dose-dependent increase in fluorescence across a physiological range of (p)ppGpp induction and GTP depletion (Supplemental Data Fig 13c, 13d). This response to amino acid or ATP starvation is abolished in (p)ppGpp 0 mutants (Supplemental Data Fig 14). Additionally, neither the P lowGTP reporter nor the CodY regulator interferes with antibiotic survival (Supplemental Data Fig 15). The P lowGTP reporter activity is inversely related to the ribosomal P1 promoter activity (Supplemental Data Fig 16), which is activated by GTP 35 . Although increased fluorescence could theoretically result from a failure to dilute the fluorescent protein during slow growth, our data show that P lowGTP fluorescence respond to changes in GTP levels rather than slow growth (Supplemental Data Fig 17). After calibrating the P lowGTP reporter in bulk population, we examined whether it reflects GTP levels in individual cells. FACS sorting of cells with high P lowGTP fluorescence, followed by LC-MS analysis, confirmed that the bright cells had significantly lower GTP levels (Supplemental Data Fig 18). This demonstrates that the reporter accurately reflects GTP levels at both the population and single-cell levels. Together, these results confirm that our reporter reliably tracks GTP depletion rather than slow growth, enabling us to distinguish between slow-growing, tolerant cells and GTP-depleted persister subpopulations. Using this reporter, we examined GTP distributions in single cells with flow cytometry (Fig 2d, Supplemental Data Fig 19). Most cells are dark during growth in rich medium, consistent with bulk measurements (Supplemental Data Fig 13b). However, we identified a heavy-tailed subpopulation of rare, bright cells that depended on the presence of the reporter (Fig. 2d, Supplemental Data Fig 19), indicating significant GTP depletion. This subpopulation was absent in (p)ppGpp 0 cells but reappeared in (p)ppGpp 0 gmk cells with rescued persistence (Supplemental Data Fig 19b). The fractions of bright, low-GTP cells varied when cells were grown in media with different carbon sources (Supplemental Data Fig 21c, d). Strikingly, the fraction of low-GTP cells strongly correlated with the fraction of persisters measured via kill curves (Fig 2i, Supplemental Data Fig 20b, 5-hour survivors), but not correlated with growth rate (Fig 2g) or antibiotics tolerance (Fig 2f, Supplemental Data Fig 21d). These results distinguish persistence from tolerance, demonstrating that persistence under various growth conditions is driven by the fraction of low GTP outliers, not by the population’s average growth rate. Despite the strong correlation between persisters and low-GTP cells, it is essential to determine if these two subpopulations were indeed the same. To explore this, we used two methods. First, we applied FACS to sort exponentially growing wild-type cells containing the P lowGTP reporter (Fig 3a). FACS-isolated cells with high P lowGTP fluorescence (top 0.1%) were immediately treated with vancomycin. After 5 hours of treatment and subsequent antibiotic removal, cells were plated. ~80% of the sorted low-GTP cells formed colonies compared to only ~0.1% survival in the low-fluorescence population (Fig 3b). This demonstrates that nearly all low-GTP cells are antibiotic-refractory persisters capable of regrowth after antibiotic removal. We confirmed that this persistence was specifically dependent on P lowGTP , using a differentially labeled P veg control that measured the effect of protein dilution (Fig 3b, Supplemental Data Fig 22), or through fluorescent protein swapping (Supplemental Data Fig 22). In addition to FACS sorting, we employed time-lapse microscopy to monitor cell fate during antibiotic treatment. We found that only bright cells were non-growing before antibiotic treatment and remained viable during the tre­­atment, while the rest of the cells, which elongated and divided, was rapidly killed (see Fig 3c, Supplementary Video 1 for an example). To further investigate persisters in biofilms, where they are commonly found 39 , we developed a microfluidic biofilm system with confocal microscopy (Supplemental Data Fig 23). Using dual labeled cells (P rrnBP1 and P lowGTP ), we identified a mixture­ of persisters and growing cells in microfluidic biofilms (Fig 3d). Upon prolonged (6 hour) antibiotic treatment, the low-GTP cells survived within the biofilm (Fig 3d), confirming that the majority of persisters in biofilms are low-GTP cells. After confirming that P lowGTP -bright cells are persisters, we investigated how these persisters form before antibiotic treatment. Our goal was to observe how rare, spontaneous persisters emerge from actively growing wild-type cells, and to examine how this process relates to GTP levels. We seeded highly diluted, growing cells on solid growth media and monitored ~30,000 growth and division events (Supplemental Data Fig 24a-b). From this, we identified ten cells that entered a persister state before reaching high cell density (Fig 3e, Supplemental Data Fig 24c-k, Supplementary Video 2). We retrospectively analyzed the dynamics of their transition into persistence by quantifying their growth, division, and GTP reporter activity. This revealed distinct, switch-like behaviors at the single-cell level (Fig 3f, Supplemental Data Fig 24c-k). Initially, each pre-persister cell maintained the same elongation rate as actively growing cells, while its P lowGTP fluorescence gradually increased, often over more than one cell division (Supplemental Data Fig 24). This was followed by a sudden stop in elongation, marking entry into dormancy when the P lowGTP fluorescence reached a threshold (Fig 3f, Supplemental Data Fig 25). In all cases, the reporter fluorescence rose before growth inhibition occurred (Fig 3f, Supplemental Data Fig 25), indicating that GTP depletion triggers growth inhibition rather than resulting from it. The fluorescence thresholds were remarkably consistent across different persister entry events (Fig 3g), suggesting that the switch to persistence is activated when intracellular GTP levels fall to a specific threshold. This threshold aligns with values seen in our microscopy and flow cytometry experiments, corresponding to an estimated GTP concentration of ~0.1-0.2 mM 40 . This range is comparable to what is observed in wild-type cells experiencing strong (p)ppGpp-accumulation (Supplemental Data Fig 13c-d), where most cells became starvation-triggered persisters. While GTP depletion leads to persistence, what is the role of (p)ppGpp in persister dynamics? Interestingly, the (p)ppGpp 0 gmk Q110R mutant, which has GTP levels similar to wild-type cells but lacks (p)ppGpp (Fig 2c), can still form spontaneous persisters (Fig 3f, Supplemental Data Fig 25j-l, Supplemental Video 3). However, these mutants do not exhibit the switch-like dynamics characteristic of persister entry in wild-type cells. Instead, their entry into dormancy is gradual, with elongation slowing as reporter activity increases (Fig 3f). Moreover, in these mutants, GTP levels and growth rates show a linear relationship (Fig. 3g), contrasting with the switch-like relationship seen in wild-type cells. Thus, (p)ppGpp promotes GTP depletion and enabling the rapid, switch-like entry into persistence seen in wild-type cells. Finally, we used our newly established system to investigate the poorly understood phenomenon of antibiotic-induced persistence 3 . We found that P lowGTP fluorescence increased in cells exposed to lethal concentrations of cell wall antibiotics, including carbenicillin, vancomycin and bacitracin (Fig 4a, Supplemental Data Fig 27a). While most cells were killed by irreversible damage, a few P lowGTP -bright cells survived and resumed growth after the antibiotics were removed (Supplemental Data Fig 27b-c, Supplementary Video 4). LC-MS analysis revealed that exposure to both lethal and sublethal doses of bacitracin led to the accumulation of alarmones, such as ppGpp and ppApp, and a decrease in GTP levels (Fig 4b-d). These results suggest that cell wall antibiotic treatment induces alarmone production and depletes GTP. Using mutants of (p)ppGpp synthetases, we found that a single alarmone synthetase, SasA 19 , is responsible for (p)ppGpp accumulation and GTP depletion during cell wall antibiotic treatment (Fig 4e, Supplemental Data Fig 28). To assess whether this alarmone response provides a protective effect, we measured persistence due to treatment with sublethal concentrations (below MIC) of bacitracin (Fig 4f, 4g Supplemental Data Fig 29). While sublethal bacitracin did not impact cell viability, it did induce SasA expression (Supplemental Data Fig 30) 41,42 . Remarkably, when followed by a lethal dose of bacitracin (3x MIC), cells pretreated with sublethal antibiotics showed approximately five times more persisters than non-pretreated cells, with minimal differences in tolerance (Fig 4g). This survival mechanism, dependent on SasA but not other alarmone synthetases (Fig 4f­­), allows pre-exposed cells to better resist lethal antibiotic exposure. Therefore, antibiotic-induced persistence may serve as an adaptive strategy, significantly enhancing cell survival against a later lethal antibiotic challenge. Our data highlight the pivotal role of (p)ppGpp in persistence. We demonstrate that conditions triggering persistence 10 also induce (p)ppGpp accumulation, that persistence depends on (p)ppGpp accumulation, and that three (p)ppGpp synthetases are differentially responsible for starvation-triggered, spontaneous and antibiotic-induced persistence (Fig 4h). While (p)ppGpp senses environmental signals to initiate persistence, GTP antagonism serves as the critical effector, likely via coordinated suspension of multiple GTP-dependent processes such as protein translation and DNA synthesis (Supplemental Data Fig 31). Our work also distinguishes persisters from tolerators, revealing that persisters arise through specific genetic pathways that generate individual cells with low GTP, independent of population-wide growth rates or bulk GTP levels. We propose a new model for spontaneous persistence. Phenotypic switch into persistence can be mediated by competition between toxins and antitoxins in TA modules to provide a threshold-like mechanism 2,12,43 . Our findings suggest that cells can also utilize an alternative mechanism for the spontaneous entrance: through allosteric enzyme cooperativity and self-amplification of (p)ppGpp synthesis (Supplemental Data Fig 32). This enables rapid amplification of a small signal, such as from noise in (p)ppGpp synthesis, to allow spontaneous persister formation in rare individual cells. Our study also provides strong evidence for bactericidal antibiotic-induced persistence. While previous research suggested that antibiotics induce persistence through cellular damage 15 and stress responses such as the SOS pathway 44 , our results show that antibiotics, particularly cell wall-targeting ones, induce persistence via the accumulation of alarmones like (p)ppGpp, driven by SasA expression in response to cell envelope damage 41,45,46 . In the soil bacterium B. subtilis , the evolution of this regulation may be shaped by a fitness advantage from its habitat to enhance survival to diffusible antimicrobials from neighboring microbes. For example, bacitracin, which we show to induce persistence, is a diffusible natural antibiotic produced by other soil Bacillus species 47 . Clinically, this model explains why (p)ppGpp mutants in pathogens such as S. aureus show reduced survival against cell wall-targeting drugs 48 and diminished clearance under combination antibiotic therapies 49 . Thus, the alarmone-dependent persistence mechanism is likely conserved across various bacterial species. Finally, persistence may play a key role in the emergence of genetic resistance by expanding the reservoir of surviving bacteria during antibiotic treatment. Given that antibiotic overuse increases bacterial exposure to these drugs, driving the evolution of resistance 50 , targeting persister formation could improve treatment outcomes and help curb the rise of antibiotic-resistant infections. Methods Bacterial strains and strain construction All bacterial strains, plasmids and oligonucleotides used in this study are listed in Supplemental Data Table 1 to 4. LB and LB agar were used for cloning and maintenance of strains. For selection in B. subtilis , media were supplemented with the following antibiotics when required: spectinomycin (80 µg/mL), chloramphenicol (5 µg/mL), kanamycin (10 µg/mL), and tetracycline (10 µg/mL). The combination of lincomycin (12.5 µg/mL) and erythromycin (0.5 µg/mL) was used to select for MLS resistance. Carbenicillin (100 µg/mL) was used for selection in E. coli . B. subtilis (p)ppGpp biosynthesis mutants were constructed by transformations of integration plasmids containing an I-sceI endonuclease cut site and regions of homology upstream and downstream of specific synthetase genes (pJW300 for ∆ sasB , pJW370 for sasB F42A , pJW306 for ∆ sasA , and pJW371 for rel D264G ) followed by transformation of pSS4332 for marker-less recombination 51 . Successful recombination was verified by PCR. For the construction of (p)ppGpp 0 mutant, ∆ sasA ∆ sasB was transformed with ∆ rel :: mls PCR product from genomic DNA using oligos oJW902/oJW903 followed by MLS resistance selection 25 . Construction of integration plasmid for sasB F42A was done by site-directed mutagenesis of pJW370 by PCR using oligos oJW2309/oJW2310. The (p)ppGpp 0 gmk Q110R mutant was obtained from isolating suppressor mutants from (p)ppGpp 0 cells by plating on S7 minimal medium plates containing 1% glucose. The surviving colonies were plated on S7 minimal medium plates containing 0.5% casamino acids and 0.5 mM 8-azaguanine, or S7 minimal medium plates containing 0.5% casamino acids and 0.1 mM guanosine 25 to differentiate between mutants containing mutation in HprT or Gmk. Colonies which can grow on guanosine but not 8-azaguanine were sequenced to identify the mutant gmk allele. Whole genome sequencing was performed to confirm that gmk Q110R is the only mutation in the strain. The guaB down ( guaB ↓) mutant in (p)ppGpp+ background was constructed by transformation of pJW305 25 , which replaces the chromosomal copy of guaB with a IPTG-inducible copy of guaB (P spac -guaB) . This allows controllable guaB expression using IPTG during strain construction and growth to avoid generation of suppressors. B. subtilis deletion mutants were constructed by serial transformation of PCR products from the B. subtilis knockout collection (BGSC, Gross lab) 52 . Where required, the lox-site flanked erm R or kan R cassette was removed using pDR244- cre followed by selection for the loss of MLS or Kan resistance. Construction of P lowGTP fluorescence reporters was done by fusion of PCR products containing the P lowGTP promoter (primers oJW1935/oJW1936) with coding regions of fluorescence proteins (primers oJW1995/oJW1996 for GFP or oJW2805/oJW2806 for mCherry) using ligase cycling reaction (LCR) 53 . The promoter-fluorescence protein gene fusions were cloned into pDR110 backbone flanked by amyE without the P spank promoter for subsequent transformation . For the construction of P rrnBP1 -GFPns (unstable GFP sequence from Griffith et al. 54 ) reporter, DNA fragments of P rrnBP1 (primers oJW2083/oJW2084), GFPns (primers oJW1995/oJW2020), flanking regions of lacA (primers oJW1990/oJW2414 and oJW2413/oJW2082), and lox-site flanked erm R cassette (primers oJW2133/oJW2134) were amplified by PCR using synthetic oligonucleotides or genome DNA. The resulting PCR products were fused by LCR followed by amplification using PCR to generate linear recombination fragment of lacA ::P rrnBP1 -GFPns-lox- ermR -lox for transformation. For the construction of P sasA -mCh reporter, DNA fragments of P sasA (primers oJW3099/oJW3079), mCh (primers oJW2805/oJW2806), flanking regions of lacA (primers oJW1990/oJW2414 and oJW2413/oJW2082), and lox-site flanked erm R cassette (primers oJW2133/oJW2134) were amplified by PCR using synthetic oligonucleotides or genome DNA. The resulting PCR products were fused by LCR followed by amplification using PCR to generate linear recombination fragment of lacA ::P sasA -mCh-lox- ermR -lox for transformation. For the construction of P veg -GFP reporter, DNA fragments of P veg (primers oJW3928/oJW2806), GFP (primers oJW1995/oJW1996), flanking regions of lacA (primers oJW1990/oJW2414 and oJW2413/oJW2082), and lox-site flanked erm R cassette (primers oJW2133/oJW2134) were amplified by PCR using synthetic oligonucleotides or genome DNA. The resulting PCR products were fused by LCR followed by amplification using PCR to generate linear recombination fragment of lacA :: P veg -GFP-lox- ermR -lox for transformation. Removal of the lox-site flanked erm R cassette was done by transformation with pDR244- cre and selected for the loss of MLS resistance. All mutants and constructs were verified by DNA sequencing. Growth conditions Bacillus subtilis strains were grown in S7 defined medium 55 ; MOPS was used at 50 mM rather than 100 mM, supplemented with 0.1% glutamate, 1% glucose, and 0.5% casamino acids. Growth of YB886 strain background was supplemented with 20 µg/mL tryptophan and 50 µg/mL methionine. For growth in minimal media, both glutamate and casamino acids were replaced with 200 µg/mL L-isoleucine, 200 µg/mL L-leucine, and 200 µg/mL L-valine, and 1% carbon sources were used as indicated. Routinely, cells from young colonies on overnight LB-agar plates at 37 °C (< 12 h) were inoculated into growth media and then grown at 37 °C with 250 rpm shaking. Cultures in logarithmic phase (OD 600 ≈ 0.1-0.3) were treated with antibiotics or inducers, including arginine hydroxamate (RHX, 0.5 mg/mL), carbonyl cyanide m-chlorophenyl hydrazone (CCCP, 5 µM), sodium azide (NaN 3 , 4 mM), or arsenate (2.5 mM). Isopropyl β-D-1-thiogalactopyranoside (IPTG) was added to a final concentration of 0.5 mM to induce guaB expression from an IPTG-inducible promoter (P spac ), while depletion of guaB expression was done by omitting IPTG in the growth media. The following inducers and concentrations were used unless otherwise specified­: RHX: 0.5 mg/mL; CCCP: 5 μM; NaN 3 : 4 mM; Arsenate: 2.5 mM; CARB: 0.5 μg/mL (0.5x MIC) or 100 μg/mL (200x MIC); BAC: 64 μg/mL (0.5x MIC) or 384 μg/mL (3x MIC); CIP: 0.1 μg/mL (0.5x MIC) or 4 μg/mL (20x MIC); KAN: 0.625 μg/mL (0.3x MIC) or 8 μg/mL (8x MIC); VAN: 0.1 μg/mL (0.5x MIC) or 4 μg/mL (20x MIC)) along with non-induction controls. Minimum inhibitory concentration (MIC) determination MICs for chloramphenicol (CAM), tetracycline (TET), kanamycin (KAN), ciprofloxacin (CIP), norfloxacin (NOR), rifampicin (RIF), bacitracin (BAC) and vancomycin (VAN) were determined using the microdilution method 56 . Logarithmic phase cells were back-diluted to a final titer of ≈ 5x10 5 CFU/mL into 96-well plates containing two-fold serial dilutions of respective antibiotics in S7 50 medium with 0.1% glutamate, 1% glucose, and 0.5% casamino acids. After 16-20 hours of incubation at 37 °C with 250 rpm shaking, the MIC was determined as the lowest drug concentration that prevented visible growth. Bacterial growth measurement For growth measurement, fresh colonies of Bacillus subtilis strains on LB agar were resuspended into different growth media as specified and diluted to OD600 ≈ 0.005 in 96-well plates. Growth was monitored by OD 600 at 37°C under shaking in the Synergy2 microplate reader (BioTek). Doubling times were estimated by fitting the growth data to the exponential growth curve using a custom python script. Persister assay To prepare exponentially growing B. subtilis populations, cells from young colonies on overnight LB-agar plates at 37 °C (< 12 h) were inoculated into S7 50 medium with 0.1% glutamate, 1% glucose and 0.5% casamino acids and grown to OD 600 ≈ 0.1-0.3 at 37°C, 250 rpm. For growth in minimal medium, both glutamate and casamino acids were replaced with 200 µg/mL L-isoleucine, 200 µg/mL L-leucine, and 200 µg/mL L-valine, and 1% carbon sources were used as indicated. Treatments with bactericidal antibiotics were done at the following concentrations: CIP, 4 μg/mL (20x MIC); VAN, 4 μg/mL (20x MIC), KAN, 8 μg/mL (8x MIC), BAC, 384 μg/mL (3x MIC). To determine cell viability, culture aliquots were taken at T=0 and at designated times after treatment, serially diluted, and plated onto Luria-Broth (LB) agar. Plates were incubated at 37°C overnight. Viability at different time points was determined as CFU/mL and relative survival (vs T 0 ) was calculated. For experiments involving pre-induction of cells with (p)ppGpp-inducing agents, cells were grown to OD 600 ≈ 0.1 and divided into two cultures: one containing the inducing agent (RHX, 0.5 mg/mL; BAC, 64 μg/mL; CCCP, 5 μM; NaN 3 , 4 mM; arsenate, 12.5 mM) and other as non-induction control. The cultures were grown for an additional 30 min under the same conditions (T=0.5 hr) and subjected to the persister assay, as described above. In the case of measuring spontaneous persistence, we followed the definition of spontaneous persisters that they are generated during growth in a non-stressed condition 2,3 . To achieve this, early exponentially growing cultures were 1:100 back-diluted into fresh media and regrew to exponential phase (OD 600 ≈ 0.1-0.3) for one or two rounds followed by antibiotic treatment 3 . Estimation of antibiotic tolerance For the estimation of population tolerance, we adopted the definition of minimum duration of killing for 99% of the population (MDK 99 ) 3 . To exclude the contribution of persisters to population tolerance, we determined the level of persisters in the biphasic killing curve and subtracted this subpopulation from the bulk population. MDK 99 of the population was estimated from the logarithmic killing phase in the killing curve. Measurement of intracellular nucleotides by thin-layer chromatography To measure intracellular nucleotides, cells were first harvested from overnight plates, back-diluted to OD 600 = 0.005, and grown in low-phosphate (0.1X phosphate, 0.5 mM) S7 50 medium with 0.1% glutamate, 1% glucose, and 0.5% casamino acids. Once cultures reached OD 600 ≈ 0.05, 1 mL cells were labeled with 50 µCi of 32 P orthophosphate (900 mCi/mmol; Perkin Elmer) for 2-3 generations before treatment or sampling. At OD 600 ≈ 0.15, RHX, CCCP, or arsenate were added to the cultures and samples were collected at regular time points for nucleotide extraction. Nucleotides were extracted by incubating 100 µL cells with 20 µL of 2 M formic acid on ice for at least 20 minutes. Samples were spotted on PEI cellulose thin-layer chromatography (TLC) plates (Selecto) and resolved in 1.5 M or 0.85 M potassium phosphate monobasic (KH 2 PO 4 , pH 3.4) buffer to separate (p)ppGpp or GTP, respectively. TLC plates were exposed on storage phosphor screens (GE Healthcare) and scanned on a Typhoon imager (GE Healthcare). Measurement of intracellular nucleotides by LC-MS LC-MS quantification of nucleotides was performed as described previously 19 . Cells were grown in S7 50 medium supplemented with 20 amino acids 19 to OD 600 ≈ 0.3 at 37°C, 250 rpm before harvesting. 25 mL of cultures were sampled and filtered through PTFE membrane (Sartorius). For experiments involving bacitracin induction, cells were harvested before and after 0.25x MIC (sublethal) or 1.25x MIC (lethal) bacitracin treatment for 30 min. Membranes with cell pellet were submerged in 3 mL extraction solvent mix (on ice 50:50 (v/v) chloroform/water) to quench metabolism, lyse the cells and extract metabolites. Mixtures of cell extracts were centrifuged at 5000x g for 10 min to remove organic phase, and then centrifuged at 20000x g for 10 min to remove cell debris. Samples were analyzed using HPLC-MS system consisting of a Vanquish UHPLC system linked to electrospray ionization (ESI, negative mode) to a Q Exactive Orbitrap mass spectrometer (Thermo Scientific) operated in full-scan mode to detect targeted metabolites based on their accurate masses. LC was performed on an Acquity UPLC BEH C18 column (1.7 μm, 2.1 X 100 mm; Waters). Total run time was 30 min with a flow rate of 0.2 mL/min, using Solvent A as denoted above and acetonitrile as Solvent B. The gradient was as follows: 0 min, 5% Solvent B; 2.5 min, 5% Solvent B; 19 min, 100% Solvent B; 23.5 min 100% Solvent B; 24 min, 5% Solvent B; 30 min, 5% Solvent B. Quantification of metabolites was performed by using MAVEN software 57 and normalized to OD 600 at the time of cell harvest. For LC-MS analysis of FACS-sorted cells, membranes with filtered cells (about 8 x 10 6 cells) were submerged in 1.5 mL extraction solvent mix (methanol:acetonitrile:H2O=40:40:20) to quench metabolism, lyse the cells and extract metabolites. The cell extract was centrifuged at 21,000 x g for 10 min at 4°C, and 1 mL of supernatant was transferred to a new microcentrifuge tube and dried completely with SpeedVac. Dried metabolites were then resuspended in 100 μL Solvent A (97:3 (v/v) water/methanol, 10 mM tributylamine pH~8.2–8.5 adjusted with ~9 mM acetic acid). Samples were analyzed using an HPLC-tandem MS (HPLC-MS/MS) system consisting of a Vanquish UHPLC system linked to heated electrospray ionization (ESI, negative mode) to a hybrid quadrupole high resolution mass spectrometer (Q-Exactive orbitrap, Thermo Scientific) operated in full-scan selected ion monitoring (MS-SIM) mode to detect targeted metabolites based on their accurate masses. MS parameters were set to a resolution of 140,000, an automatic gain control (AGC) of 3e6, a maximum injection time of 100 ms, and a scan range of 400–1000 mz. Only ions between 10-15 minutes retention time were scanned by MS. LC was performed on an Acquity UPLC BEH C18 column (1.7 μm, 2.1 × 100 mm; Waters). Total run time was 30 min with a flow rate of 0.2 mL/min, using Solvent A and 100% acetonitrile as Solvent B. The gradient was as follows: 0 min, 5% B; 2.5 min, 5% B; 19 min, 100% B; 23.5 min 100% B; 24 min, 5% B; and 30 min, 5% B. Raw output data from the MS was converted to mzXML format using in-house-developed software. Quantification of metabolites was performed by using MAVEN software 57 and were normalized to an internal standard of six most represented nucleotides detected in the sample. Fluorescence microscopy To monitor (p)ppGpp induction using fluorescence reporters, cells were grown to OD 600 ≈ 0.1-0.3 followed by 30-min induction with the following inducers at concentrations listed below unless otherwise specified­: RHX: 0.5 mg/mL; CCCP: 5 μM; NaN 3 : 4 mM; Arsenate: 2.5 mM; CARB: 0.5 μg/mL (0.5x MIC) or 100 μg/mL (200x MIC); BAC: 64 μg/mL (0.5x MIC) or 384 μg/mL (3x MIC); CIP: 0.1 μg/mL (0.5x MIC) or 4 μg/mL (20x MIC); KAN: 0.625 μg/mL (0.3x MIC) or 8 μg/mL (4x MIC); VAN: 0.1 μg/mL (0.5x MIC) or 4 μg/mL (20x MIC)), along with non-induction controls. All imaging samples were spotted on 1.5% agarose pads made with the same growth medium, and immediately imaged with Olympus IX-83 inverted microscope (Olympus) using 60X phase contrast objective with fluorescence filters (excitation: 470/20 nm, dichroic mirror: 485 nm, emission: 515/50 nm for GFP; excitation: 575/20 nm, dichroic mirror: 595 nm, emission: 645/90 nm for mCherry or propidium iodide; and excitation: 427/10 nm, dichroic mirror: 595 nm, emission: 472/30 nm for Sytox blue). Single-cell time-lapse imaging was performed at 15-min intervals for each field at 37°C using temperature-controlled imaging chamber (Tokai Hit) coupled with automatic stage and microscope control as described previously 58 . The measurement was generally over the course from the birth of the cell until the time lapse stopped due to crowding of the microcolony or in rare cases severe drifting of focus. When comparing phenotypes between strains, both strains were imaged in parallel on the same imaging dish using the same microscope with same settings. For imaging persister survival in time-lapse experiments, final concentrations of 5 μg/mL CARB (10x MIC) and 200 nM Sytox blue or propidium iodide (Molecular Probes) were applied to the agarose pads at designated times. To remove CARB after treatment, 5 units/mL final concentration of penicillinase (Sigma) was applied. Strains without the fluorescence reporters were used for autofluorescence measurement. Biofilm growth and imaging B. subtilis biofilms were grown on a custom microfluidic device fabricated with polydimethylsiloxane (PDMS). The device contains a central chamber connected to inlet and outlet media channels which allows constant media flow through the central chamber. A semi-permeable dialysis membrane was fixed on top of the central chamber to provide a platform for biofilm growth. This setup allows diffusion of nutrients or small molecules from the media flow underneath to support biofilm growth on the membrane and allow subsequent treatment with antibiotics. To grow B. subtilis biofilms, 1 µL of early exponential phase culture (OD 600 ≈ 0.05) was applied on the membrane and grown at room temperature (25 o C) for 24 h under constant flow of S7 50 medium supplemented with 0.5% glutamate and 0.5% glycerol. Imaging of biofilms was performed using IXplore SpinSR confocal imaging microscope (Olympus) using 20X phase contrast objective with fluorescence filters (488 nm laser with 510-550 nm emission filter for GFP; 561 nm laser with 575-625 nm emission filter for mCherry). Biofilms at 24h post-inoculation were imaged before and after switching to the same growth medium containing 4 µg/mL Vancomycin. 51 stacks at 1 µm intervals were taken for each time point. Images were projected along z-axis from the top of the biofilm using maximum intensity projection using the cellSense software (Olympus). For quantitation of reporter signals within the biofilm, we sampled ~4 random regions within the center of the biofilm and measured their GFP and mCherry intensities. Background fluorescence was measured from regions without biofilm and used for background subtraction. Flow cytometry and cell sorting Flow cytometry was performed at UWCCC flow cytometry core. To prepare samples for flow cytometry, cells from young colonies on overnight LB-agar plates at 37 °C (< 12 h) were inoculated into and grown in S7 50 medium with 0.1% glutamate, 1% glucose and 0.5% casamino acids to OD 600 ≈ 0.1-0.3 at 37°C, 250 rpm. For growth in minimal medium, both glutamate and casamino acids were omitted and 1% carbon sources were used as indicated. Cells were immediately fixed with 0.4% paraformaldehyde for 15 min at room temperature, washed 3 times with 1x phosphate buffered saline (PBS), and kept at 4°C until analysis. Fixation was verified by viability plating and microscopy. Flow cytometry analysis was performed using BD LSRFortessa flow cytometer (BD Biosciences) with a 70-µm nozzle. Cell populations were detected using both forward and side scatter (FSC and SSC). Single-cell fluorescence was measured using 488 nm laser and detection filters for GFP (530/30 nm, 505LP dichroic filter). Autofluorescence was measured by analyzing parental strains without the fluorescence reporter and subtracted from the raw reporter fluorescence. Approximately 1.5 million events were measured for each sample. For the determination of antibiotic induced persistence, the frequency of low GTP cells after antibiotic induction was subtracted from the frequencies before induction. FACS was performed at UWCCC flow cytometry core. To prepare samples for cell sorting, cells were harvested from young colonies on overnight LB-agar plates at 37 °C (< 12 h) and grew in S7 50 medium with 0.1% glutamate, 1% glucose and 0.5% casamino acids to OD 600 ≈ 0.3 at 37°C, 250 rpm. FACS analysis was performed using BD FACSAria cell sorter (BD Biosciences) with a 70-µm nozzle at room temperature using 488 nm laser and 530/30 nm detection filters for GFP, and 561 nm laser and 610/20 nm detection filters for mCherry. Auto-fluorescent cells were eliminated by gating with isogenic strain without the fluorescent reporters. At least 1,000 cells were obtained from the rarest gate for each sample. Cell recovery rate was estimated to be > 90% based on viability counting on LB plates. For antibiotic treatment, cells were directly sorted in to tubes containing 4x MIC of VAN followed by shaking at 37 °C. Aliquots were taken at different times for serial dilution and plating to measure survival by colony counting. Number of cells before treatment (T 0 ) were measured by the cell sorter. For FACS-sorting of cells for LC-MS analysis, cells containing the P lowGTP reporter were grown in S7 50 medium with 0.1% glutamate, 1% glucose and 0.5% casamino acids to either exponential (OD 600 ≈ 0.2) or stationary phase (OD 600 ≈ 4.0). Both populations were mixed in 10:1 ratio, and immediately FACS-sorted into low or high fluorescence fractions. Auto-fluorescent cells were eliminated by gating with isogenic strain without the fluorescent reporters. Approximately 8 x 10 6 cells were sorted and filtered on PTFE membrane (Sartorius) to remove the sheath fluid. Membranes with filtered cells were then subjected to metabolite extraction and LC-MS analysis. CFU counts were performed from a small aliquot of the sorted fractions to account for potential variations in the number of cells sorted between fractions. Transposon sequencing (Tn-Seq) B. subtilis 168 transposon mutant library was kindly provided by the Grossman Lab 59 . Construction of the library is briefly described as follows: In vitro transposition of B. subtilis 168 genomic DNA (gDNA) with magellen6x transposon was performed by mixing 1.3 µg pCJ41 (containing magellen6x transposon), 34 ng purified MarC9 transposase, 5 µg B. subtilis gDNA, 10 µL 2x buffer A (41 mM HEPES pH 7.9, 19% glycerol, 187 mM NaCl, 19 mM MgCl2, 476 µg/mL BSA and 3.8 mM DTT) into 20 µL reaction in vitro and incubated overnight at 30°C. The transposed DNA was precipitated and resuspended in 2 µL 10x buffer B (500 mM Tris-Cl pH 7.8, 100 mM MgCl 2 , 10 mM DTT), 2 µL 1 mg/mL BSA and 11 µL H 2 O followed by 4h incubation at 37°C. After incubation, 4 µL 2.5 mM dNTPs and 1 µL of 3U/ µL T4 DNA Polymerase were added to the DNA and further incubated for 20 min at 12°C, followed by heat inactivation at 75°C for 15 min. Next, 0.2 µL 2.6 mM NAD and 1 µL of 10 U/ µL E. coli DNA ligase were added and the reaction was incubated at 16°C overnight. The resulting in vitro transposed and repaired gDNA was transformed into B. subtilis 168 and plated on LB agar containing spectinomycin and incubated overnight. Colonies containing the transposon were washed off and pooled into a single library. The library was estimated to contain ~50,000 unique transposon inserts across the genome. For the selection experiment with the transposon library, an aliquot of the library was inoculated and grown in S7 50 medium with 0.1% glutamate, 1% glucose and 0.5% casamino acids supplemented with tryptophan (20 µg/mL) at 37°C, 250 rpm. At OD 600 ≈ 0.1-0.3, cultures were treated with 20x MIC vancomycin or ciprofloxacin. Cultures before and after antibiotic treatment were plated onto LB plates and recovered after ~14 h incubation at 37°C. ~650,000 colonies from each sample were pooled and snap-frozen for genomic DNA extraction and sequencing library preparation. Preparation of the sequencing library was performed as previously described 60 . Frozen cell pellets were resuspended in 500 µL lysis buffer with lysozyme and RNase A (20 mM Tris-HCl pH 7.5, 50 mM EDTA, 100 mM NaCl, 2 mg/mL lysozyme, 120 µg/mL RNase A) and incubated at 37ºC for 20-30 min. Next, the incubated cell lysate was mixed with 60 µL 10% N-lauroylsarkosine and further incubated at 37ºC for 15 min. Genomic DNA (gDNA) was purified using 600 µL phenol, then 600 µL phenol:chloroform:isoamyl alcohol (25:24:1), and finally 600 µL pure chloroform. DNA in the aqueous phase was precipitated using 1/10 volumes of 3M NaOAc and 2 volumes of 100% ethanol. DNA pellet was washed with 70% ethanol, air-dried on bench, and resuspended in 10 mM Tris-HCl, pH 8.5 and stored at 4ºC. For each sample, 6 µg of DNA was used for MmeI digestion in 200 µL (6 µg gDNA, 6 µL MmeI (2000 U/mL, NEB), 0.5 µL 32 mM S-adenosylmethionine, 20 µL NEB CutSmart Buffer, and ddH 2 O up to 200 µL). DNA was digested for 2.5 h at 37ºC, after which 2 µL calf intestinal phosphatase (10,000 U/mL, NEB) was added to the digest and incubated for 1h at 37ºC. Digested gDNA was extracted with 200 µL phenol:chloroform:isoamyl alcohol (25:24:1) followed by 200 µL of pure chloroform. DNA in the aqueous phase was first mixed with 1/10 volume of 3M NaOAc and 67 ng/mL glycogen, and then 2.5 volumes of 100% ethanol. The tubes were then placed at -80ºC for 20 min and then centrifuged at max speed for 15 min at 4ºC. Precipitated DNA was washed with 150 µL 70% ethanol twice at room temperature, air-dried, and resuspended in 15 µL ddH 2 O. For annealing of the DNA adaptor, 20 µL of 100 µM synthesized oligos (IDT) were mixed with 1 µL of 41 mM Tris-HCl pH 8.0 (final concentration of adaptor: 50 µM in 1mM Tris-HCl pH 8.0). Oligos were annealed by heat denaturation (95 ºC for 5 min) and stepwise cool-down (94ºC for 45 s then repeat with -0.3ºC per cycle for 250 cycles, then hold at 15ºC) using PCR machine. Annealed adaptors were diluted to 3.3 µM in ddH2O and stored at -20ºC. For adaptor ligation, 5 µL of digested DNA were mixed with 1 µL of 3.3 µM DNA adaptor, 1 µL of 10x T4 DNA ligase buffer (NEB), 1 µL of T4 DNA ligase (400,000U/mL, NEB) and 2 µL ddH 2 O. The ligation mix was incubated overnight at 16ºC in a PCR machine. Amplification of adaptor-ligated DNA library was performed using barcoded primers and Phusion high-fidelity DNA polymerase (NEB) for 18 cycles according to provided instructions. The PCR products were mixed in equal amounts, purified by size-exclusion, and submitted for sequencing using Illumina sequencing primer (5’-ACACTCTTTCCCTACACGACGCTCTTCCGATCT-3’). Deep sequencing was done using Illumina HiSeq 2500 (Illumina) by the University of Michigan DNA Sequencing Core. Analysis of sequencing data was done using a custom Python script and mapped to the B. subtilis 168 reference genome (NC_000964.3). Visual inspection of transposon insertion profiles was done using GenomeBrowse (Golden Helix). Expression and purification of B. subtilis Rel The plasmid for Rel purification was constructed as follows. The B. subtilis rel coding sequence was PCR amplified from NCIB3610 genomic DNA using primers oJW3196/oJW3197. The pE-SUMO expression vector was amplified using primers oJW3194/oJW3195. The PCR products were assembled to generate pJW753 by Golden Gate assembly (New England BioLabs). Plasmids were verified by DNA sequencing. To express His 6 -SUMO-Rel, fresh transformants of E. coli BL21 carrying pE-SUMO- rel were grown in LB at 37 o C to OD 600 ≈ 0.5, followed by 1:50 dilution into Terrific Broth and grown at 30 o C until OD 600 ≈ 1.5. His 6 -SUMO-Rel expression was induced with 1 mM IPTG for 4 h at 30 o C. Cells were pelleted and stored at -80 ºC until use. Frozen cell pellets were thawed on ice, resuspended in ice-cold lysis buffer (50 mM Tris-HCl, pH 8, 1M NaCl, 10 mM imidazole, DNase and cOmplete protease inhibitor (Roche)), and lysed with French press at 4 o C. The cell lysate was centrifuged at 4 o C, 13,000 rpm for 30 min to obtain the supernatant. Filtered supernatant was injected into an ӒKTA FPLC system (GE Healthcare) and passed through a HisTrap FF column (GE Healthcare). His 6 -SUMO-Rel was eluted with a gradient of Buffer A (50 mM Tris-HCl, pH 8, 1M NaCl, 5% glycerol, 10 mM imidazole) and Buffer C (50 mM Tris-HCl, pH 8, 1M NaCl, 5% glycerol, 500 mM imidazole). Fractions containing the protein were pooled with 300 µL SUMO protease into Spectra/Por dialysis tubing (Spectrum), and dialyzed into 50 mM Tris-HCl, 1M NaCl, 1mM β-mercaptoethanol and 5% glycerol overnight. Rel without the His 6 -SUMO tag was passed through the HisTrap FF column and then size-exclusion purified using a Superose 12 10/300 GL column (GE Healthcare) with ӒKTA FPLC system. Fractions containing the Rel protein were pooled and measured for its concentration by the Bradford Assay (Bio-rad). Aliquots were snap-frozen using liquid nitrogen and stored at -80 ºC. In vitro pppGpp synthesis assay In vitro pppGpp synthesis by Rel was monitored by measuring synthesis of radiolabeled pppGpp over time. The reaction contained 236 nM B. subtilis Rel, 0.05 µM [α 32 P] GTP, 1 mM ATP, 50 mM NaCl and 10 mM ATP in 20 mM Tris-HCl pH 7.5, with or without 10 µM non-radioactive pppGpp. The reaction was devoid of manganese to avoid potential effect of pppGpp hydrolysis. The reaction was initiated by addition of ATP and incubated at 37 ºC. At indicated times, 10 µL of the reaction were mixed with 2 µL of 2M formic acid and chilled on ice for 20 min to quench the reaction. 1 µL samples were spotted on PEI cellulose thin-layer chromatography (TLC) plates (Millipore) and resolved in 1.5 M potassium phosphate monobasic (KH 2 PO 4 , pH 3.4) buffer to separate pppGpp. TLC plates were dried and exposed on storage phosphor screens (GE Healthcare) and scanned on a Typhoon imager (GE Healthcare). Quantification and Statistical Analysis For TLC experiments, intensities of nucleotide spots were quantified using ImageQuant software (Molecular Dynamics). The raw intensities were corrected to the number of phosphates in the corresponding nucleotide and normalized to OD 600 or ATP level before treatment (ATP T=0 ) for comparison between samples. For in vitro pppGpp synthesis assays, changes in pppGpp levels were normalized to T=0. Microscopy image analysis and cell parameters (cell area and fluorescence intensity) measurements were done using Metamorph software (Molecular Devices). Background and autofluorescence were subtracted by comparing images obtained from identical strains without the fluorescence reporter. Single cell specific growth rate (µ) at each frame was calculated using the equation: , where l is the cell length, Δ l is the change in cell length and Δ t is the change in time (in min). Flow-cytometry data were analyzed using FlowJo X software (FlowJo, LLC). Cells within a narrow range of cell sizes were gated, sub-gated to filter cell aggregates, and then measured for their fluorescence distribution. Autofluorescence was measured using isogenic strains without the fluorescent reporters and subtracted from raw reporter signals. Gating for P lowGTP -high cells was set at 5-fold or higher above mean population fluorescence. This cut-off agrees with our FACS sorting experiment where cells with reporter fluorescence above this threshold are predominantly persisters. For the determination of antibiotic induced persistence, the frequency of low GTP cells after antibiotic induction was subtracted from the frequencies before induction. Statistical information of individual experiments is included in the figure legends. n represents the number of biological replicates or number of cells for experiments involving single-cell measurements as indicated in the legends. Significance was tested using Student’s t test. Prism 7 (GraphPad) was used for statistical analysis. Declarations Acknowledgements: We thank Alan Grossman, Kevin Griffith, and Dan Kearns for generously sharing strains and reagents. We thank Bruce Levin, Petra Levin, Michael Cox and members of the Wang lab for comments on the manuscript. This study is supported by HHMI Faculty Scholar Grant, NIH R35GM127088 and Hatch (to J.D.W). 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L. & Grossman, A. D. Inducible protein degradation in Bacillus subtilis using heterologous peptide tags and adaptor proteins to target substrates to the protease ClpXP. Mol Microbiol 70 , 1012-1025, doi:10.1111/j.1365-2958.2008.06467.x (2008). Harwood, C. R. & Cutting, S. M. Molecular biological methods for Bacillus . (Wiley, 1990). Adimpong, D. B. et al. Antimicrobial susceptibility of Bacillus strains isolated from primary starters for African traditional bread production and characterization of the bacitracin operon and bacitracin biosynthesis. Appl Environ Microbiol 78 , 7903-7914, doi:10.1128/AEM.00730-12 (2012). Clasquin, M. F., Melamud, E. & Rabinowitz, J. D. LC-MS data processing with MAVEN: a metabolomic analysis and visualization engine. Curr Protoc Bioinformatics Chapter 14 , Unit14 11, doi:10.1002/0471250953.bi1411s37 (2012). Young, J. W. et al. Measuring single-cell gene expression dynamics in bacteria using fluorescence time-lapse microscopy. Nat Protoc 7 , 80-88, doi:10.1038/nprot.2011.432 (2011). Johnson, C. M. & Grossman, A. D. Identification of host genes that affect acquisition of an integrative and conjugative element in Bacillus subtilis. Mol Microbiol 93 , 1284-1301, doi:10.1111/mmi.12736 (2014). van Opijnen, T., Lazinski, D. W. & Camilli, A. Genome-Wide Fitness and Genetic Interactions Determined by Tn-seq, a High-Throughput Massively Parallel Sequencing Method for Microorganisms. Curr Protoc Mol Biol 106 , 7 16 11-24, doi:10.1002/0471142727.mb0716s106 (2014). Additional Declarations There is NO Competing Interest. Supplementary Files Suppmovie1.avi Supplemental Video 1. Antibiotic survival of low-GTP cell Suppmovie2.avi Supplemental Video 2. Spontaneous persistence entrance in wild type B. subtilis Suppmovie3.avi Supplemental Video 3. Spontaneous persistence entrance in (p)ppGpp0 gmk Q110R mutant Suppmovie4.avi Supplemental Video 4. Antibiotic-induced persistence in single cells SuppFiganTabs.pdf Cite Share Download PDF Status: Published Journal Publication published 15 May, 2025 Read the published version in Nature Microbiology → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5679108","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":396848891,"identity":"9a92b6bb-2115-4524-a2e2-88e59328237e","order_by":0,"name":"Jue Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYLCCjw0Q+gASiR8wzgRq4SFJCzMvVAuyXbiBwfGzh1/b7rCTt2fgMTz4o4ZBju9GAgEtZ/LSrHPPJBv2MPAYHOY5xmAsSUiL2YEcM+PcNuYEHgbeDYcZGxgSNxDUcv6NmbFlWz1Yy8GfDQz1hLXcyDF+zNh2GKzlADAcEgwIabG/8caMsffMccOew/wfgH6RMJx55gF+LZL9OcYffu6olmdvb0v++KPGRp7vOAFbgIBNAkwxg0kJgsrBaj8QpWwUjIJRMApGLgAAjoBGjXOmy+0AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-1503-170X","institution":"University of Wisconsin Madison","correspondingAuthor":true,"prefix":"","firstName":"Jue","middleName":"","lastName":"Wang","suffix":""},{"id":396848892,"identity":"aef79592-fefb-4b24-b79b-b226aa12e15a","order_by":1,"name":"Danny Fung","email":"","orcid":"","institution":"University of Wisconsin Madison","correspondingAuthor":false,"prefix":"","firstName":"Danny","middleName":"","lastName":"Fung","suffix":""},{"id":396848893,"identity":"cac2563c-fa4c-470b-b6a7-d28cd44b43a1","order_by":2,"name":"Jessica Barra","email":"","orcid":"","institution":"University of Wisconsin Madison","correspondingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"","lastName":"Barra","suffix":""},{"id":396848894,"identity":"c7a50e35-af3e-4810-842f-08578e556c39","order_by":3,"name":"Jeremy Schroeder","email":"","orcid":"","institution":"University of Michigan","correspondingAuthor":false,"prefix":"","firstName":"Jeremy","middleName":"","lastName":"Schroeder","suffix":""},{"id":396848895,"identity":"b9526908-1530-45c7-b3c5-bdd8709b3dd7","order_by":4,"name":"Jin Yang","email":"","orcid":"https://orcid.org/0000-0003-3442-5608","institution":"University of Wisconsin-Madison","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Yang","suffix":""},{"id":396848896,"identity":"fc6e1360-4626-45cc-adf6-3ab77236f0fd","order_by":5,"name":"Fukang She","email":"","orcid":"","institution":"University of Wisconsin Madison","correspondingAuthor":false,"prefix":"","firstName":"Fukang","middleName":"","lastName":"She","suffix":""},{"id":396848897,"identity":"5de8da6b-53b9-4d6f-87fc-184faee38db0","order_by":6,"name":"Megan Young","email":"","orcid":"","institution":"University of Wisconsin Madison","correspondingAuthor":false,"prefix":"","firstName":"Megan","middleName":"","lastName":"Young","suffix":""},{"id":396848898,"identity":"7637464d-ab08-4e91-86eb-7681b8dd7fcb","order_by":7,"name":"Daniel Amador-Noguez","email":"","orcid":"https://orcid.org/0000-0002-3568-3070","institution":"University of Wisconsin-Madison","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Amador-Noguez","suffix":""},{"id":396848899,"identity":"71c9d841-45a1-4a27-91fc-0b9f863e849d","order_by":8,"name":"David Ying","email":"","orcid":"","institution":"University of Wisconsin Madison","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Ying","suffix":""}],"badges":[],"createdAt":"2024-12-19 19:10:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5679108/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5679108/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41564-025-02015-6","type":"published","date":"2025-05-15T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":72877279,"identity":"95cefd09-efa6-4b8a-9e03-92d874127477","added_by":"auto","created_at":"2025-01-03 08:31:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":74496,"visible":true,"origin":"","legend":"\u003cp\u003e(p)ppGpp mediates starvation triggered and spontaneous persistence. (a) Survival curves of Bacillus subtilis wild type (WT) and (p)ppGpp-null mutant ((p)ppGpp0) to lethal concentrations of vancomycin (VAN). MIC: minimal inhibitory concentration. Rekill: cells which survived 20x MIC vancomycin treatments were regrown and treated with vancomycin again. (b) Survival curves of prophage-cured wild type to 20x MIC vancomycin, 20x MIC ciprofloxacin or 8x MIC kanamycin. (c) Starvation-trigggered persistence of WT depends on (p)ppGpp. Biphasic kill curves of vancomycin treatment were performed after several starvation or (p)ppGpp inducing conditions: stationary: stationary phase, RHX: pre-treated with amino acid starvation inducer arginine hydroxamate, +CCCP: pre-treated with ATP synthesis inhibitor CCCP, p-sasA or p-sasASyn: (p)ppGpp0 strains overexpressing the (p)ppGpp synthetase SasA or its synthetase-dead variant SasASyn (SasAD87G). See supplemental data fig 4 for plots in log scale. (d) Nucleotide levels in cells pre-treated with RHX, or ATP synthesis inhibitors CCCP or arsenate (As) for 30 min. Nucleotide levels were measured by thin layer chromatography (TLC) and normalized to their levels before induction. Bottom panel represents persistence to vancomycin treatment of the pre-treated cells. (e-f) Survival of serial-passaged populations to vancomycin for 5 h. Persisters which remained through the passage were defined as spontaneous persisters3. (g) In B. subtilis and many gram-positive bacteria, (p)ppGpp is synthesized by three (p)ppGpp synthetases. Rel is the bifunctional (p)ppGpp synthetase and hydrolase, while SasB (SAS1/RelQ/YjbM) and SasA (SAS2/RelP/YwaC) are monofunctional synthetases. (h) Persister levels in starvation-induced (black) WT and (p)ppGpp mutants with RHX. (i) Levels of spontaneous persisters (green, 2nd passage) in WT and (p)ppGpp mutants. relSyn (RelD264G) is a synthetase inactive variant of Rel which retained its hydrolase activity essential for viability in the presence of sasB and sasA. sasBF42A encodes an allosteric pppGpp binding site mutant of SasB. Values represent mean (n \u0026gt;= 3) and error bars indicate s.d. *: p \u0026lt; 0.05, **: p \u0026lt; 0.01, ns: not significant (Student’s t test).\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5679108/v1/41f9ad607dea14108634ff5b.png"},{"id":72878529,"identity":"6df547f1-eab4-46ec-8444-a8d32fb76eae","added_by":"auto","created_at":"2025-01-03 08:39:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77701,"visible":true,"origin":"","legend":"\u003cp\u003e(p)ppGpp mediates persistence by reducing GTP and is independent of tolerance. (a) (p)ppGpp robustly inhibits de novo GTP biosynthesis by inhibiting the GDP synthetase Gmk and the IMP dehydrogenase GuaB, as well as inhibiting expression of the pur operon through the PurR repressor. (p)ppGpp also inhibits GTP synthesis by purine salvage which is not illustrated here. (b-c) Vancomycin survival curves of wild type (WT), purine biosynthesis mutants ΔpurB, ΔpurL, ΔpurF, GTP synthesis mutants ΔguaA and guaB repressed mutant (guaB↓), slow growth mutants Δpyk (pyruvate kinase) and ΔctaA (heme synthase), as well as (p)ppGpp⁰ and (p)ppGpp⁰ gmkQ110R. (d) Measurement of low GTP cells in bacterial population. The PlowGTP reporter is activated when cellular GTP level is low. Flow cytometry analysis of wild-type population containing the reporter (~1.5x106 cells). Representative image of cells with low or high PlowGTP fluorescence was shown. (e-j) Correlation between growth rate, tolerance, pesistence, population-averaged PlowGTP signals, and frequency of low GTP cells (high PlowGTP fluorescence cells) in wild-type under different growth conditions. Wild type containing the PlowGTP reporter were grown in minimal media with different carbon sources. Populations were measured for their growth rate, PlowGTP fluorescence, and levels of high PlowGTP fluorescence cells using flow cytometry (~1.5x106 cells), as well as tolerance and persistence from vancomycin survival curves as shown in Supplemental Data Fig 21. Each value represents a single replicate. Three replicates for each condition were shown. r, Pearson correlation coefficient, p, p-value.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5679108/v1/c619c0ccd3a55dfbb8fb9ec5.png"},{"id":72877285,"identity":"06e26062-c8ec-43d9-ad74-4b5d50e46215","added_by":"auto","created_at":"2025-01-03 08:31:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":511185,"visible":true,"origin":"","legend":"\u003cp\u003eDirect observation of persistence switch in cells through (p)ppGpp-GTP antagonism. (a) Schematic of FACS sorting and antibiotic survival assay using the PlowGTP reporter. Bacterial population containing the PlowGTP reporter were sorted based on PlowGTP fluorescence and immediately treated with antibiotic. Survival was measured by serial dilution and plating followed by colony counting. (b) Wild type containing PlowGTP-GFP reporter was FACS-sorted into low or high PlowGTP fluorescence. Approximately 1 x 106 cells were sorted per sample per replicate. Fractions were treated with vancomycin and measured for survival over time. Values represent mean ± s.d., n = 3. See also supplemental data figure 22. (c) Time lapse micrograph of wild type cells containing the PlowGTP reporter in response to antibiotic treatment. Exponentially growing wild type containing the PlowGTP reporter were patched on agarose pads made with growth media. High PlowGTP fluorescence cells were identified (arrow) and monitored for antibiotic survival using carbenicillin and Sytox-blue staining (cyan) for viability. Values in yellow indicate time in min. (d) Survival of low GTP cells in biofilm. Wild type containing both PlowGTP (for low GTP, green) and PrrnBP1 (for high GTP, red) reporters were grown into colony biofilm followed by vancomycin treatment for 6 h. Bar graphs indicate the changes in high GTP and low GTP populations before and after treatment. Four random regions in the biofilm were measured. Values represent mean (n \u0026gt;= 3) and error bars indicate s.d. *: p \u0026lt; 0.05, **: p \u0026lt; 0.01, ns: not significant (Student’s t test). (e) Representative single cell dynamic trace of persister formation. Cells were patched onto agarose pad made with growth media at roughly one cell per field of view, and the monitored for growth till before saturation (see also supplemental data figure 24a-b). A total of roughly 30,000 cell growth and division events were recorded (~150 events per microcolony) to reveal ~10 persistence entrance events. Numbers indicate time in min. Arrows indicate a cell with increased PlowGTP fluorescence followed by growth attenuation. (f) Quantitated changes in cell length vs PlowGTP fluorescence (left) vs PlowGTP fluorescence (right) from the single trace in (e). Dotted lines indicate persistence entrance. (g) Summary data of specific growth rate and reporter fluorescence from ~100 independent single cell traces similar to e (ten entrance traces). Solid lines indicate non-linear regression (variable slope) model fitted to the data.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5679108/v1/e1788f6d7c8f532b2ba71715.png"},{"id":72877287,"identity":"75d24377-2f7e-4c65-8900-3f6a4cbe5eae","added_by":"auto","created_at":"2025-01-03 08:31:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":52177,"visible":true,"origin":"","legend":"\u003cp\u003eAntibiotic-induced persistence through alarmone accumulation contributes to adaptive survival. (a) Cell wall antibiotics induce PlowGTP reporter fluorescence. WT containing the PlowGTP reporter were treated with 20x MIC ciprofloxacin (CIP), 4x MIC kanamycin (KAN), 200x MIC carbenicillin (CARB), 20x MIC vancomycin (VAN) or 3x MIC bacitracin (BAC) for 2 h. Fluorescence was measured using microscopy (\u0026gt; 200 cells each, n = 3) UT: before treatment. (b to d) Levels of ppGpp, ppApp and GTP in wild type (WT) treated with sublethal or lethal concentrations of bacitracin for 0.5 h. UT: before treatment, SL: 0.5x MIC bacitracin, L: 3x MIC bacitracin. (e-f) Levels of cell wall antibiotic-induced persisters in WT and (p)ppGpp mutants. (e) Strains containing the PlowGTP reporter before and after 0.5x MIC bacitracin treatment for 0.5 h were measured for generation of low GTP cells using flow cytometry (~1x106 cells per sample per replicate, n = 3, see also Supplemental data figure 28). Levels of induced low GTP cells were determined by subtracting the levels in uninduced populations. (f) Survival of WT or (p)ppGpp mutants pretreated with 0.5x MIC for 0.5 h, followed by lethal (3 x MIC) bacitracin treatment for up to 5 h. Levels of induced persistence were determined by subtracting survival of uninduced populations at 5 h. See also Supplemental data figure 29. Values represent mean and error bars represent s.d. Statistical tests were done between WT and mutant pairs. *: p \u0026lt; 0.05, **: p \u0026lt; 0.01, ns: not significant (Student’s t test). (g) Survival curve of WT or ΔsasA pretreated with sublethal (SL, 0.5x MIC) bacitracin for 0.5 h, followed by lethal (L, 3 x MIC) bacitracin treatment for up to 5 h. Statistical tests were done between WT and mutant pairs. *: p \u0026lt; 0.05, **: p \u0026lt; 0.01, ns: not significant (Student’s t test). (h) Model for persistence pathways mediated by (p)ppGpp-GTP antagonism. Starvation-triggered persistence (purple) is mediated primarily through the (p)ppGpp synthetase Rel. Spontaneous persistence is mediated through activities of two (p)ppGpp synthetases Rel and SasB (green). Persisters can also be induced by cell wall antibiotics through SasA (orange). Thus, (p)ppGpp enables integration of different signals to trigger persistence through GTP depletion.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-5679108/v1/2485da5a3352fe53022e4a96.png"},{"id":82861792,"identity":"dc8a6f5e-c3d5-4f9b-aa74-6889650fde51","added_by":"auto","created_at":"2025-05-16 07:05:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1509453,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5679108/v1/e8970247-b5b7-4316-acb6-b04235f42c27.pdf"},{"id":72877283,"identity":"f4c5ba89-0b5e-497c-a41d-ae45e3bc5131","added_by":"auto","created_at":"2025-01-03 08:31:51","extension":"avi","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7679488,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Video 1. Antibiotic survival of low-GTP cell\u003c/p\u003e","description":"","filename":"Suppmovie1.avi","url":"https://assets-eu.researchsquare.com/files/rs-5679108/v1/b2f504694cce1fbd61b8efb8.avi"},{"id":72878530,"identity":"0a20037d-dff6-49f9-ae15-0b5adfeb94a9","added_by":"auto","created_at":"2025-01-03 08:39:51","extension":"avi","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":8137216,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Video 2. Spontaneous persistence entrance in wild type \u003cem\u003eB. subtilis\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Suppmovie2.avi","url":"https://assets-eu.researchsquare.com/files/rs-5679108/v1/cb73513dff8345938a966400.avi"},{"id":72877290,"identity":"7faeedd2-9941-47a5-87aa-ce42507edfc3","added_by":"auto","created_at":"2025-01-03 08:31:51","extension":"avi","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":12483072,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Video 3. Spontaneous persistence entrance in (p)ppGpp0 \u003cem\u003egmk\u003c/em\u003eQ110R mutant\u003c/p\u003e","description":"","filename":"Suppmovie3.avi","url":"https://assets-eu.researchsquare.com/files/rs-5679108/v1/2e9cdbfad0bf0dd085e5dec3.avi"},{"id":72877291,"identity":"ea590d9e-e940-45c6-aa82-6903cc9515b3","added_by":"auto","created_at":"2025-01-03 08:31:51","extension":"avi","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":7595008,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Video 4. Antibiotic-induced persistence in single cells\u003c/p\u003e","description":"","filename":"Suppmovie4.avi","url":"https://assets-eu.researchsquare.com/files/rs-5679108/v1/b6908ca13274c4f6fe8dde16.avi"},{"id":72877288,"identity":"a419df85-d76b-4c70-9b56-4bcaa0b318fa","added_by":"auto","created_at":"2025-01-03 08:31:51","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":11390010,"visible":true,"origin":"","legend":"","description":"","filename":"SuppFiganTabs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5679108/v1/aa2a91ebb4b62fa3b9b2abd2.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"A shared alarmone-GTP switch underlies triggered and spontaneous persistence","fulltext":[{"header":"Main Text","content":"\u003cp\u003eAntibiotic persisters are individual bacterial cells that survive prolonged bactericidal antibiotic treatment despite being genetically sensitive to antibiotics\u003csup\u003e1-3\u003c/sup\u003e. Joseph Bigger originally proposed that persisters can be generated before or after antibiotics treatment\u003csup\u003e1\u003c/sup\u003e. Later, persisters were categorized by their pathways of generation- starvation-triggered, spontaneously generated and antibiotic-induced\u003csup\u003e3\u003c/sup\u003e- yet their molecular mechanisms remain poorly understood. Although persistence can be triggered by nutrient deprivations\u003csup\u003e4\u003c/sup\u003e through intracellular accumulation of the alarmone (p)ppGpp\u003csup\u003e5-9\u003c/sup\u003e, it is also argued that that slow growth or a reduction in ATP levels, rather than (p)ppGpp, is responsible for triggering persistence\u003csup\u003e10,11\u003c/sup\u003e. In addition to starvation, persisters can form spontaneously\u003csup\u003e2\u003c/sup\u003e. However, spontaneous persisters exist in natural populations at low frequency, making their identification challenging and understanding the mechanisms of their formation even more difficult. While toxin-antitoxin (TA) modules are strongly implicated in persistence\u003csup\u003e2,5,8,12,13\u003c/sup\u003e, their roles in spontaneous antibiotic persistence of wild type bacteria remain unclear\u003csup\u003e14\u003c/sup\u003e. Finally, persisters were observed both before and after treatment with antibiotics\u003csup\u003e15\u003c/sup\u003e, and bacteriostatic antibiotics are known to induce persisters\u003csup\u003e16\u003c/sup\u003e.\u0026nbsp;However, it is difficult to differentiate antibiotic-induced persistence from antibiotic tolerance\u003csup\u003e3\u003c/sup\u003e, spontaneous persistence, transcriptional response to specific drugs, or byproduct of cellular damage from drug treatment\u003csup\u003e3\u003c/sup\u003e. The mechanisms of antibiotic-induced persistence remain poorly understood.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere we directly visualize the formation of starvation-triggered, spontaneous and antibiotic-induced persisters in the Gram-positive model bacterium \u003cem\u003eBacillus subtilis\u003c/em\u003e and reveal that multiple cellular mechanisms of persistence can converge on a common pathway of alarmone-GTP antagonism. To quantify persistence in \u003cem\u003eB. subtilis\u003c/em\u003e, we began by treating cells with the cell wall-targeting antibiotic vancomycin at concentrations 20 times higher than its minimal inhibitory concentration (Fig 1a and Supplemental Data Fig 1\u0026shy;). The results revealed biphasic killing kinetics, with a rapid elimination of the majority of cells followed by the prolonged survival of a small subpopulation. This surviving fraction (~0.1%)\u0026nbsp;remained consistent across varying drug concentrations and, upon regrowth, became re-sensitized to antibiotics (Fig 1a), confirming that these are phenotypically switched persisters rather than genetically altered resisters\u003csup\u003e1,3,17\u003c/sup\u003e. We also tested the effect of prophages on persistence, given that persistence against DNA-damaging antibiotic ciprofloxacin has been shown to be complicated by prophage activation in \u003cem\u003eE. coli\u003c/em\u003e \u003csup\u003e14\u003c/sup\u003e. We confirmed that, similar to \u003cem\u003eE. coli\u003c/em\u003e, persistence against ciprofloxacin is weakened by prophages (Supplemental Data Fig 2), but persistence against vancomycin remains unaffected regardless of the presence or absence of resident prophages SP\u0026beta; and PBSX within the genome. Furthermore, the levels of persisters in prophage-cured cells were consistent across different classes of antibiotics, including vancomycin, ciprofloxacin, and kanamycin, which targets translation (Fig 1b). This suggests that the persisters originate from a common subpopulation that is refractory to multiple antibiotics\u003csup\u003e3\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(p)ppGpp is a starvation-induced alarmone strongly implicated in antibiotic survival\u003csup\u003e9\u003c/sup\u003e. We therefore examined the effect of (p)ppGpp on antibiotic resistance, tolerance and persistence using a (p)ppGpp\u003csup\u003e0\u003c/sup\u003e mutant, which lacks all three (p)ppGpp synthetases\u003csup\u003e18\u003c/sup\u003e. Compared to the wild type, the (p)ppGpp\u003csup\u003e0\u003c/sup\u003e strain exhibited similar resistance to a variety of antibiotics (MIC assay, Supplemental Data Fig 1), a modestly reduced antibiotic tolerance (~5% reduction of MDK\u003csub\u003e99\u003c/sub\u003e, Supplemental Data Fig 3a-b), and strongly reduced antibiotic persistence (~10 fold down, Fig 1a, Supplemental Data Fig 3c). These results indicate that (p)ppGpp predominantly promotes antibiotic persistence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAntibiotic persistence can be triggered by starvation or occur spontaneously\u003csup\u003e3\u003c/sup\u003e. To investigate the role of (p)ppGpp in starvation-triggered persistence, we starved cells using several methods: allowing growth into stationary phase, inducing amino acid starvation, or depleting ATP with arsenate or CCCP. We then perform kill curves with vancomycin under these conditions or after starvation is removed by back-diluting in rich media. The biphasic kill curves (Fig 1c, Supplemental Data Fig 4a) showed a high level of triggered persisters (~500-fold increase from 0.1% to ~50%) upon all starvation treatments and even after starvation-removal, consistent with the established definition of triggered persistence\u003csup\u003e3\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInterestingly, all starvation conditions also induced (p)ppGpp accumulation. We observed (p)ppGpp buildup not only upon amino acid starvation but also during ATP depletion (Fig 1d, Supplemental Data Fig 5). Furthermore, starvation-triggered persiste\u0026shy;\u0026shy;nce was abolished in the (p)ppGpp\u003csup\u003e0\u003c/sup\u003e mutant, indicating that (p)ppGpp induction is necessary for triggered persistence (Supplemental Data Fig 4b-c). Finally, we found that even in the absence of starvation, gratuitous induction of (p)ppGpp by overexpressing its synthetase\u003csup\u003e19\u003c/sup\u003e was sufficient to produce high levels of persisters (Fig 1c, \u003cem\u003ep-sasA\u003c/em\u003e). These findings demonstrate that (p)ppGpp accumulation is both necessary and sufficient to trigger persistence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe then investigated the occurrence of spontaneous persistence. To differentiate spontaneous persisters from those triggered by starvation, we employed a classical serial passage experiment \u003csup\u003e2,3\u003c/sup\u003e (Fig 1e). In this method, cells are repeatedly diluted into fresh medium during exponential growth, which removes starvation-triggered persisters from the lag phase culture while leaving a consistent, basal fraction of spontaneously generated persisters. This approach enables us to identify a rare population of spontaneous persisters (~0.05%) after the starvation-trigerred persisters have been eliminated (Fig 1f). Notably, the spontaneous persisters were absent in the (p)ppGpp\u003csup\u003e0\u003c/sup\u003e mutant, indicating that (p)ppGpp plays a key role in both spontaneous and starvation-triggered persistence.\u003c/p\u003e\n\u003cp\u003eIn \u003cem\u003eB. subtilis\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003e(p)ppGpp is synthesized by three enzymes: Rel, SasA, and SasB (Fig 1g)\u003cem\u003e.\u003c/em\u003e To assess their roles in persister formation, we analyzed single, double, and triple mutants of these synthetases (Fig 1h-i, Supplemental Data Fig 6, 7). Our findings show that only the starvation-responsive synthetase, Rel, is essential for starvation-triggered persistence (Fig 1h). In contrast, spontaneous persistence is controlled by both Rel and SasB, as mutations in either \u003cem\u003erel\u003c/em\u003e or \u003cem\u003esasB\u003c/em\u003e significantly reduce spontaneous persister levels, and in combination, they completely abolish this phenotype (Fig 1i). Rel can be activated by pppGpp (Supplemental Data Fig 8), aligning with recent findings\u003csup\u003e20\u003c/sup\u003e. SasB, a tetrameric enzyme, enables allosteric activation of (p)ppGpp synthesis by pppGpp, with a Hill coefficient of ~3.0\u003csup\u003e21\u003c/sup\u003e. A mutation that disrupts the allosteric pppGpp-binding site in SasB \u0026nbsp;(\u003cem\u003esasB\u003csup\u003eF42A\u003c/sup\u003e\u003c/em\u003e) leads to a similar reduction in spontaneous persistence as observed in the \u0026Delta;\u003cem\u003esasB\u003c/em\u003e mutant (Fig 1i). These results suggest that self-amplification of (p)ppGpp synthesis is crucial for spontaneous persister formation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, we search for the downstream effector of (p)ppGpp that mediates antibiotic persistence. (p)ppGpp is a bacterial alarmone with pleiotropic functions including upregulating competence and sporulation\u003csup\u003e22\u003c/sup\u003e. We found that disruption of competence, sporulation, or three known TA systems in \u003cem\u003eB. subtilis\u003c/em\u003e has little impact on persistence (Supplemental Data Fig 9). (p)ppGpp also serves to antagonize the essential nucleotide GTP in \u003cem\u003eB. subtilis\u003c/em\u003e and to downregulate macromolecular biosynthesis\u003csup\u003e18\u003c/sup\u003e. (p)ppGpp production consumes GTP, and (p)ppGpp directly inhibits multiple enzymes along the pathway of GTP production (Fig 2a)\u003csup\u003e23-27\u003c/sup\u003e. In addition, (p)ppGpp binds to the purine transcription regulator PurR to inhibit transcription of the \u003cem\u003epur\u003c/em\u003e operon, encoding \u003cem\u003ede novo\u003c/em\u003e purine biosynthesis genes upstream of GTP synthesis\u003csup\u003e28\u003c/sup\u003e. Furthermore, (p)ppGpp competes with GTP to regulate macromolecule synthesis enzymes including the DNA replication enzyme primase\u003csup\u003e29,30\u003c/sup\u003e and GTPases for ribosome biogenesis, assembly and protein translation\u003csup\u003e31-34\u003c/sup\u003e. In starvation conditions that trigger persisters, GTP levels were significantly depleted, whereas ATP levels vary (Fig 1d), suggesting that GTP depletion may be a common effector leading to persistence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo test whether GTP depletion mediates antibiotic persistence even in the absence of (p)ppGpp induction, we used our previously published inducible strain that downregulated the expression of the guanine nucleotide synthesis gene \u003cem\u003eguaB\u003c/em\u003e\u003csup\u003e35\u003c/sup\u003e, which lowered GTP without inducing (p)ppGpp (Supplemental Data Fig 10). We observed an increased persistence by ~10-fold (\u003cem\u003eguaB\u003c/em\u003e\u0026darr;, Fig 2b), indicating that GTP depletion is sufficient to generate persisters. In addition, we performed a non-biased Tn-Seq analysis in \u003cem\u003eB. subtilis\u003c/em\u003e to identify genes whose disruption increased cell survival after prolonged antibiotic treatment (Supplemental Data Fig 11). The strongest hits mapped to 12 \u003cem\u003ede novo\u003c/em\u003e purine biosynthesis genes in the \u003cem\u003epur\u003c/em\u003e operon and \u003cem\u003eguaA\u003c/em\u003e encoding GMP synthetase (Supplemental Data Fig 11b, c, Supplemental Data Table 5-6). We verified that disruption of the \u003cem\u003epur\u003c/em\u003e operon or \u003cem\u003eguaA\u003c/em\u003e reduces purine levels including GTP (Supplemental Data Fig 11d), with corresponding kill curves confirming increased antibiotic persistence (Fig 2b-c, Supplemental Data Fig 11e). Next, we wanted to know whether reducing GTP in complete absence of (p)ppGpp can still promote persistence. Through genetic screen, we identified a mutation in \u003cem\u003egmk\u003c/em\u003e encoding guanylate kinase (see Methods) that reduces GTP levels of the (p)ppGpp\u003csup\u003e0\u003c/sup\u003e strain back to the wild type level (Supplemental Data Fig 12). This mutant [(p)ppGpp\u003csup\u003e0\u003c/sup\u003e \u003cem\u003egmk\u003c/em\u003e\u003csup\u003eQ110R\u003c/sup\u003e] restored persistence back to wild type (Fig 2b-c, Supplemental Data Fig 12), confirming that GTP depletion is sufficient for antibiotic persistence. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition to increased persistence, we also noted a modest rise in antibiotic tolerance in the GTP synthesis mutants (Supplemental Data Fig 7), which aligns with our previous findings that GTP depletion leads to slower growth\u003csup\u003e35\u003c/sup\u003e. Slower growth has been shown to contribute to antibiotic tolerance\u003csup\u003e11,36\u003c/sup\u003e. This raises the question: Is increased antibiotic persistence simply a consequence of slow growth, and thus always correlated with tolerance?\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Genetic screens in \u003cem\u003eBacillus subtilis\u003c/em\u003e have identified slow-growing mutants, some of which deplete GTP while others do not. We tested a couple of slow-growing mutants that do not rely on GTP depletion. Intriguingly, while these mutants exhibited increased tolerance, they do not show an increase in persistence; in fact, some showed decreased persistence (Fig 2c). This suggests that, although tolerance and persistence are correlated in certain mutants or conditions\u003csup\u003e11\u003c/sup\u003e, they can be genetically uncoupled.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further distinguish between tolerance and persistence, we grew wild-type cells in different carbon sources, measuring their growth rates and antibiotic killing dynamics (Supplemental Data Fig 21a-b). Our results revealed a strong correlation between slower growth rates and increased tolerance (Fig. 2e), but no significant correlation between growth rates and persistence (Fig. 2h). This demonstrates that, while tolerance and persistence can share overlapping characteristics, they represent distinct biological phenomena that can be independently regulated.\u003c/p\u003e\n\u003cp\u003eOur findings, while unexpected, are explainable: although both slow growth and antibiotic tolerance are population-level phenomena, persistence reflects the behavior of a phenotypically heterogeneous subpopulation of cells functioning as outliers. To differentiate population-based tolerance from the phenotypically heterogeneous subpopulation of persisters, it is critical to measure GTP levels in single cells.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo achieve this, we constructed a fluorescent reporter for GTP levels in single cells. Building on our prior result that the \u003cem\u003eilvB\u003c/em\u003e gene is induced by ~20-fold upon GTP depletion via the CodY repressor- which uses GTP as a co-repressor\u003csup\u003e37,38\u003c/sup\u003e-we cloned the CodY operator from \u003cem\u003eilvB\u003c/em\u003e to control fluorescent protein expression ectopically (Supplemental Data Fig 13a). This reporter, termed P\u003csub\u003elowGTP\u0026nbsp;\u003c/sub\u003e(Figure 2d), is fully repressed in (p)ppGpp\u003csup\u003e0\u003c/sup\u003e cells, with fluorescence indistinguishable from background autofluorescence (Supplemental Data Fig 13b). In (p)ppGpp+ cells, the reporter displays a dose-dependent increase in fluorescence across a physiological range of (p)ppGpp induction and GTP depletion (Supplemental Data Fig 13c, 13d). This response to amino acid or ATP starvation is abolished in (p)ppGpp\u003csup\u003e0\u003c/sup\u003e mutants (Supplemental Data Fig 14). Additionally, neither the P\u003csub\u003elowGTP\u003c/sub\u003e reporter nor the CodY regulator interferes with antibiotic survival (Supplemental Data Fig 15). The P\u003csub\u003elowGTP\u003c/sub\u003e reporter activity is inversely related to the ribosomal P1 promoter activity (Supplemental Data Fig 16), which is activated by GTP\u003csup\u003e35\u003c/sup\u003e. Although increased fluorescence could theoretically result from a failure to dilute the fluorescent protein during slow growth, our data show that P\u003csub\u003elowGTP\u003c/sub\u003e fluorescence respond to changes in GTP levels rather than slow growth (Supplemental Data Fig 17).\u003c/p\u003e\n\u003cp\u003eAfter calibrating the P\u003csub\u003elowGTP\u003c/sub\u003e reporter in bulk population, we examined whether it reflects GTP levels in individual cells. FACS sorting of cells with high P\u003csub\u003elowGTP\u003c/sub\u003e fluorescence, followed by LC-MS analysis, confirmed that the bright cells had significantly lower GTP levels (Supplemental Data Fig 18). This demonstrates that the reporter accurately reflects GTP levels at both the population and single-cell levels. Together, these results confirm that our reporter reliably tracks GTP depletion rather than slow growth, enabling us to distinguish between slow-growing, tolerant cells and GTP-depleted persister subpopulations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing this reporter, we examined GTP distributions in single cells with flow cytometry (Fig 2d, Supplemental Data Fig 19). Most cells are dark during growth in rich medium, consistent with bulk measurements (Supplemental Data Fig 13b). However, we identified a heavy-tailed subpopulation of rare, bright cells that depended on the presence of the reporter (Fig. 2d, Supplemental Data Fig 19), indicating significant GTP depletion. This subpopulation was absent in (p)ppGpp\u003csup\u003e0\u003c/sup\u003e cells but reappeared in (p)ppGpp\u003csup\u003e0\u003c/sup\u003e\u003cem\u003egmk\u003c/em\u003e cells with rescued persistence (Supplemental Data Fig 19b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe fractions of bright, low-GTP cells varied when cells were grown in media with different carbon sources (Supplemental Data Fig 21c, d). Strikingly, the fraction of low-GTP cells strongly correlated with the fraction of persisters measured via kill curves (Fig 2i, Supplemental Data Fig 20b, 5-hour survivors), but not correlated with growth rate (Fig 2g) or antibiotics tolerance (Fig 2f, Supplemental Data Fig 21d). These results distinguish persistence from tolerance, demonstrating that persistence under various growth conditions is driven by the fraction of low GTP outliers, not by the population\u0026rsquo;s average growth rate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite the strong correlation between persisters and low-GTP cells, it is essential to determine if these two subpopulations were indeed the same. To explore this, we used two methods. First, we applied FACS to sort exponentially growing wild-type cells containing the P\u003csub\u003elowGTP\u003c/sub\u003e reporter (Fig 3a). FACS-isolated cells with high P\u003csub\u003elowGTP\u003c/sub\u003e fluorescence (top 0.1%) were immediately treated with vancomycin. After 5 hours of treatment and subsequent antibiotic removal, cells were plated. ~80% of the sorted low-GTP cells formed colonies compared to only ~0.1% survival in the low-fluorescence population (Fig 3b). This demonstrates that nearly all low-GTP cells are antibiotic-refractory persisters capable of regrowth after antibiotic removal. We confirmed that this persistence was specifically dependent on P\u003csub\u003elowGTP\u003c/sub\u003e, using a differentially labeled P\u003csub\u003eveg\u003c/sub\u003e control that measured the effect of protein dilution (Fig 3b, Supplemental Data Fig 22), or through fluorescent protein swapping (Supplemental Data Fig 22).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition to FACS sorting, we employed time-lapse microscopy to monitor cell fate during antibiotic treatment. We found that only bright cells were non-growing before antibiotic treatment and remained viable during the tre\u0026shy;\u0026shy;atment, while the rest of the cells, which elongated and divided, was rapidly killed (see Fig 3c, Supplementary Video 1 for an example).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further investigate persisters in biofilms, where they are commonly found\u003csup\u003e39\u003c/sup\u003e, we developed a microfluidic biofilm system with confocal microscopy (Supplemental Data Fig 23). Using dual labeled cells (P\u003cem\u003e\u003csub\u003errnBP1\u003c/sub\u003e\u003c/em\u003e and P\u003csub\u003elowGTP\u003c/sub\u003e), we identified a mixture\u0026shy; of persisters and growing cells in microfluidic biofilms (Fig 3d). Upon prolonged (6 hour) antibiotic treatment, the low-GTP cells survived within the biofilm (Fig 3d), confirming that the majority of persisters in biofilms are low-GTP cells.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter confirming that P\u003csub\u003elowGTP\u003c/sub\u003e-bright cells are persisters, we investigated how these persisters form before antibiotic treatment. Our goal was to observe how rare, spontaneous persisters emerge from actively growing wild-type cells, and to examine how this process relates to GTP levels. We seeded highly diluted, growing cells on solid growth media and monitored ~30,000 growth and division events (Supplemental Data Fig 24a-b). From this, we identified ten cells that entered a persister state before reaching high cell density (Fig 3e, Supplemental Data Fig 24c-k, Supplementary Video 2). We retrospectively analyzed the dynamics of their transition into persistence by quantifying their growth, division, and GTP reporter activity. This revealed distinct, switch-like behaviors at the single-cell level (Fig 3f, Supplemental Data Fig 24c-k). Initially, each pre-persister cell maintained the same elongation rate as actively growing cells, while its P\u003csub\u003elowGTP\u003c/sub\u003e fluorescence gradually increased, often over more than one cell division (Supplemental Data Fig 24). This was followed by a sudden stop in elongation, marking entry into dormancy when the P\u003csub\u003elowGTP\u003c/sub\u003e fluorescence reached a threshold (Fig 3f, Supplemental Data Fig 25). In all cases, the reporter fluorescence rose before growth inhibition occurred (Fig 3f, Supplemental Data Fig 25), indicating that GTP depletion triggers growth inhibition rather than resulting from it.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe fluorescence thresholds were remarkably consistent across different persister entry events (Fig 3g), suggesting that the switch to persistence is activated when intracellular GTP levels fall to a specific threshold. This threshold aligns with values seen in our microscopy and flow cytometry experiments, corresponding to an estimated GTP concentration of ~0.1-0.2 mM \u003csup\u003e40\u003c/sup\u003e. This range is comparable to what is observed in wild-type cells experiencing strong (p)ppGpp-accumulation (Supplemental Data Fig 13c-d), where most cells became starvation-triggered persisters.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile GTP depletion leads to persistence, what is the role of (p)ppGpp in persister dynamics? Interestingly, the (p)ppGpp\u003csup\u003e0\u003c/sup\u003e \u003cem\u003egmk\u003c/em\u003e\u003csup\u003eQ110R\u003c/sup\u003e mutant, which has GTP levels similar to wild-type cells but lacks (p)ppGpp (Fig 2c), can still form spontaneous persisters (Fig 3f, Supplemental Data Fig 25j-l, Supplemental Video 3). However, these mutants do not exhibit the switch-like dynamics characteristic of persister entry in wild-type cells. Instead, their entry into dormancy is gradual, with elongation slowing as reporter activity increases (Fig 3f). Moreover, in these mutants, GTP levels and growth rates show a linear relationship (Fig. 3g), contrasting with the switch-like relationship seen in wild-type cells. Thus, (p)ppGpp promotes GTP depletion and enabling the rapid, switch-like entry into persistence seen in wild-type cells.\u003c/p\u003e\n\u003cp\u003eFinally, we used our newly established system to investigate the poorly understood phenomenon of antibiotic-induced persistence\u003csup\u003e3\u003c/sup\u003e. We found that P\u003csub\u003elowGTP\u003c/sub\u003e fluorescence increased in cells exposed to lethal concentrations of cell wall antibiotics, including carbenicillin, vancomycin and bacitracin (Fig 4a, Supplemental Data Fig 27a). While most cells were killed by irreversible damage, a few P\u003csub\u003elowGTP\u003c/sub\u003e-bright cells survived and resumed growth after the antibiotics were removed (Supplemental Data Fig 27b-c, Supplementary Video 4). LC-MS analysis revealed that exposure to both lethal and sublethal doses of bacitracin led to the accumulation of alarmones, such as ppGpp and ppApp, and a decrease in GTP levels (Fig 4b-d). These results suggest that cell wall antibiotic treatment induces alarmone production and depletes GTP. Using mutants of (p)ppGpp synthetases, we found that a single alarmone synthetase, SasA\u003csup\u003e19\u003c/sup\u003e, is responsible for (p)ppGpp accumulation and GTP depletion during cell wall antibiotic treatment (Fig 4e, Supplemental Data Fig 28).\u003c/p\u003e\n\u003cp\u003eTo assess whether this alarmone response provides a protective effect, we measured persistence due to treatment with sublethal concentrations (below MIC) of bacitracin (Fig 4f, 4g Supplemental Data Fig 29). While sublethal bacitracin did not impact cell viability, it did induce SasA expression (Supplemental Data Fig 30)\u003csup\u003e41,42\u003c/sup\u003e. Remarkably, when followed by a lethal dose of bacitracin (3x MIC), cells pretreated with sublethal antibiotics showed approximately five times more persisters than non-pretreated cells, with minimal differences in tolerance (Fig 4g). This survival mechanism, dependent on SasA but not other alarmone synthetases (Fig 4f\u0026shy;\u0026shy;), allows pre-exposed cells to better resist lethal antibiotic exposure. Therefore, antibiotic-induced persistence may serve as an adaptive strategy, significantly enhancing cell survival against a later lethal antibiotic challenge.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur data highlight the pivotal role of (p)ppGpp in persistence. We demonstrate that conditions triggering persistence\u003csup\u003e10\u003c/sup\u003e also induce (p)ppGpp accumulation, that persistence depends on (p)ppGpp accumulation, and that three (p)ppGpp synthetases are differentially responsible for starvation-triggered, spontaneous and antibiotic-induced persistence (Fig 4h). While (p)ppGpp senses environmental signals to initiate persistence, GTP antagonism serves as the critical effector, likely via coordinated suspension of multiple GTP-dependent processes such as protein translation and DNA synthesis (Supplemental Data Fig 31). Our work also distinguishes persisters from tolerators, revealing that persisters arise through specific genetic pathways that generate individual cells with low GTP, independent of population-wide growth rates or bulk GTP levels.\u003c/p\u003e\n\u003cp\u003eWe propose a new model for spontaneous persistence. Phenotypic switch into persistence can be mediated by competition between toxins and antitoxins in TA modules to provide a threshold-like mechanism\u003csup\u003e2,12,43\u003c/sup\u003e. Our findings suggest that cells can also utilize an alternative mechanism for the spontaneous entrance: through allosteric enzyme cooperativity and self-amplification of (p)ppGpp synthesis (Supplemental Data Fig 32). This enables rapid amplification of a small signal, such as from noise in (p)ppGpp synthesis, to allow spontaneous persister formation in rare individual cells. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study also provides strong evidence for bactericidal antibiotic-induced persistence. While previous research suggested that antibiotics induce persistence through cellular damage\u003csup\u003e15\u003c/sup\u003e and stress responses such as the SOS pathway\u003csup\u003e44\u003c/sup\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003eour results show that antibiotics, particularly cell wall-targeting ones, induce persistence via the accumulation of alarmones like (p)ppGpp, driven by SasA expression in response to cell envelope damage\u003csup\u003e41,45,46\u003c/sup\u003e. In the soil bacterium \u003cem\u003eB. subtilis\u003c/em\u003e, the evolution of this regulation may be shaped by a fitness advantage from its habitat to enhance survival to diffusible antimicrobials from neighboring microbes. For example, bacitracin, which we show to induce persistence, is a diffusible natural antibiotic produced by other soil \u003cem\u003eBacillus\u003c/em\u003e species\u003csup\u003e47\u003c/sup\u003e. Clinically, this model explains why (p)ppGpp mutants in pathogens such as \u003cem\u003eS. aureus\u003c/em\u003e show reduced survival against cell wall-targeting drugs\u003csup\u003e48\u003c/sup\u003e and diminished clearance under combination antibiotic therapies\u003csup\u003e49\u003c/sup\u003e. Thus, the alarmone-dependent persistence mechanism is likely conserved across various bacterial species.\u003c/p\u003e\n\u003cp\u003eFinally, persistence may play a key role in the emergence of genetic resistance by expanding the reservoir of surviving bacteria during antibiotic treatment. Given that antibiotic overuse increases bacterial exposure to these drugs, driving the evolution of resistance \u003csup\u003e50\u003c/sup\u003e, targeting persister formation could improve treatment outcomes and help curb the rise of antibiotic-resistant infections.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eBacterial strains and strain construction\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll bacterial strains, plasmids and oligonucleotides used in this study are listed in Supplemental Data Table 1 to 4. LB and LB agar were used for cloning and maintenance of strains. For selection in \u003cem\u003eB. subtilis\u003c/em\u003e, media were supplemented with the following antibiotics when required: spectinomycin (80 \u0026micro;g/mL), chloramphenicol (5 \u0026micro;g/mL), kanamycin (10 \u0026micro;g/mL), and tetracycline (10 \u0026micro;g/mL). The combination of lincomycin (12.5 \u0026micro;g/mL) and erythromycin (0.5 \u0026micro;g/mL) was used to select for MLS resistance. Carbenicillin (100 \u0026micro;g/mL) was used for selection in \u003cem\u003eE. coli\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eB. subtilis\u003c/em\u003e (p)ppGpp biosynthesis mutants were constructed by transformations of integration plasmids containing an I-sceI endonuclease cut site and regions of homology upstream and downstream of specific synthetase genes (pJW300 for ∆\u003cem\u003esasB\u003c/em\u003e, pJW370 for \u003cem\u003esasB\u003c/em\u003e\u003csup\u003eF42A\u003c/sup\u003e, pJW306 for ∆\u003cem\u003esasA\u003c/em\u003e, and pJW371 for \u003cem\u003erel\u003c/em\u003e\u003csup\u003eD264G\u003c/sup\u003e) followed by transformation of pSS4332 for marker-less recombination\u003csup\u003e51\u003c/sup\u003e. Successful recombination was verified by PCR. For the construction of (p)ppGpp\u003csup\u003e0\u003c/sup\u003e mutant, ∆\u003cem\u003esasA\u003c/em\u003e ∆\u003cem\u003esasB\u003c/em\u003e was transformed with ∆\u003cem\u003erel\u003c/em\u003e::\u003cem\u003emls\u003c/em\u003e PCR product from genomic DNA using oligos oJW902/oJW903 followed by MLS resistance selection\u003csup\u003e25\u003c/sup\u003e. Construction of integration plasmid for \u003cem\u003esasB\u003c/em\u003e\u003csup\u003eF42A\u003c/sup\u003e was done by site-directed mutagenesis of pJW370 by PCR using oligos oJW2309/oJW2310.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe (p)ppGpp\u003csup\u003e0\u003c/sup\u003e \u003cem\u003egmk\u003c/em\u003e\u003csup\u003eQ110R\u003c/sup\u003e mutant was obtained from isolating suppressor mutants from (p)ppGpp\u003csup\u003e0\u003c/sup\u003e cells by plating on S7 minimal medium plates containing 1% glucose. The surviving colonies were plated on S7 minimal medium plates containing 0.5% casamino acids and 0.5 mM 8-azaguanine, or S7 minimal medium plates containing 0.5% casamino acids and 0.1 mM guanosine\u003csup\u003e25\u003c/sup\u003e to differentiate between mutants containing mutation in HprT or Gmk. Colonies which can grow on guanosine but not 8-azaguanine were sequenced to identify the mutant \u003cem\u003egmk\u003c/em\u003e allele. Whole genome sequencing was performed to confirm that \u003cem\u003egmk\u003c/em\u003e\u003csup\u003eQ110R\u003c/sup\u003e is the only mutation in the strain.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eguaB\u003c/em\u003e\u003csup\u003edown\u003c/sup\u003e (\u003cem\u003eguaB\u003c/em\u003e\u0026darr;) mutant in (p)ppGpp+ background was constructed by transformation of pJW305\u003csup\u003e25\u003c/sup\u003e, which replaces the chromosomal copy of \u003cem\u003eguaB\u003c/em\u003e with a IPTG-inducible copy of \u003cem\u003eguaB\u003c/em\u003e (P\u003cem\u003e\u003csub\u003espac\u003c/sub\u003e-guaB)\u003c/em\u003e. This allows controllable \u003cem\u003eguaB\u003c/em\u003e expression using IPTG during strain construction and growth to avoid generation of suppressors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eB. subtilis\u0026nbsp;\u003c/em\u003edeletion mutants were constructed by serial transformation of PCR products from the \u003cem\u003eB. subtilis\u003c/em\u003e knockout collection (BGSC, Gross lab)\u003csup\u003e52\u003c/sup\u003e. Where required, the lox-site flanked \u003cem\u003eerm\u003c/em\u003e\u003csup\u003eR\u003c/sup\u003e or \u003cem\u003ekan\u003c/em\u003e\u003csup\u003eR\u003c/sup\u003e cassette was removed using pDR244-\u003cem\u003ecre\u003c/em\u003e followed by selection for the loss of MLS or Kan resistance.\u003c/p\u003e\n\u003cp\u003eConstruction of P\u003csub\u003elowGTP\u003c/sub\u003e fluorescence reporters was done by fusion of PCR products containing the P\u003csub\u003elowGTP\u003c/sub\u003e promoter (primers oJW1935/oJW1936) with coding regions of fluorescence proteins (primers oJW1995/oJW1996 for GFP or oJW2805/oJW2806 for mCherry) using ligase cycling reaction (LCR)\u003csup\u003e53\u003c/sup\u003e. The promoter-fluorescence protein gene fusions were cloned into pDR110 backbone flanked by \u003cem\u003eamyE\u003c/em\u003e without the P\u003cem\u003e\u003csub\u003espank\u003c/sub\u003e\u003c/em\u003e promoter for subsequent transformation\u003cem\u003e.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor the construction of P\u003cem\u003e\u003csub\u003errnBP1\u003c/sub\u003e\u003c/em\u003e-GFPns (unstable GFP sequence from Griffith \u003cem\u003eet al.\u0026nbsp;\u003c/em\u003e\u003csup\u003e54\u003c/sup\u003e) reporter, DNA fragments of P\u003cem\u003e\u003csub\u003errnBP1\u003c/sub\u003e\u003c/em\u003e (primers oJW2083/oJW2084), GFPns (primers oJW1995/oJW2020), flanking regions of \u003cem\u003elacA\u003c/em\u003e (primers oJW1990/oJW2414 and oJW2413/oJW2082), and lox-site flanked \u003cem\u003eerm\u003c/em\u003e\u003csup\u003eR\u003c/sup\u003e cassette (primers oJW2133/oJW2134) were amplified by PCR using synthetic oligonucleotides or genome DNA. The resulting PCR products were fused by LCR followed by amplification using PCR to generate linear recombination fragment of \u003cem\u003elacA\u003c/em\u003e::P\u003cem\u003e\u003csub\u003errnBP1\u003c/sub\u003e\u003c/em\u003e-GFPns-lox-\u003cem\u003eermR\u003c/em\u003e-lox for transformation.\u003c/p\u003e\n\u003cp\u003eFor the construction of P\u003cem\u003e\u003csub\u003esasA\u003c/sub\u003e\u003c/em\u003e-mCh reporter, DNA fragments of P\u003cem\u003e\u003csub\u003esasA\u003c/sub\u003e\u003c/em\u003e (primers oJW3099/oJW3079), mCh (primers oJW2805/oJW2806), flanking regions of \u003cem\u003elacA\u003c/em\u003e (primers oJW1990/oJW2414 and oJW2413/oJW2082), and lox-site flanked \u003cem\u003eerm\u003c/em\u003e\u003csup\u003eR\u003c/sup\u003e cassette (primers oJW2133/oJW2134) were amplified by PCR using synthetic oligonucleotides or genome DNA. The resulting PCR products were fused by LCR followed by amplification using PCR to generate linear recombination fragment of \u003cem\u003elacA\u003c/em\u003e::P\u003cem\u003e\u003csub\u003esasA\u003c/sub\u003e\u003c/em\u003e-mCh-lox-\u003cem\u003eermR\u003c/em\u003e-lox for transformation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the construction of P\u003csub\u003eveg\u003c/sub\u003e-GFP reporter, DNA fragments of P\u003csub\u003eveg\u003c/sub\u003e (primers oJW3928/oJW2806), GFP (primers oJW1995/oJW1996), flanking regions of \u003cem\u003elacA\u003c/em\u003e (primers oJW1990/oJW2414 and oJW2413/oJW2082), and lox-site flanked \u003cem\u003eerm\u003c/em\u003e\u003csup\u003eR\u003c/sup\u003e cassette (primers oJW2133/oJW2134) were amplified by PCR using synthetic oligonucleotides or genome DNA. The resulting PCR products were fused by LCR followed by amplification using PCR to generate linear recombination fragment of \u003cem\u003elacA\u003c/em\u003e:: P\u003csub\u003eveg\u003c/sub\u003e-GFP-lox-\u003cem\u003eermR\u003c/em\u003e-lox for transformation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRemoval of the lox-site flanked \u003cem\u003eerm\u003c/em\u003e\u003csup\u003eR\u003c/sup\u003e cassette was done by transformation with pDR244-\u003cem\u003ecre\u0026nbsp;\u003c/em\u003eand selected for the loss of MLS resistance. All mutants and constructs were verified by DNA sequencing.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGrowth conditions\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBacillus subtilis\u003c/em\u003e strains were grown in S7 defined medium\u003csup\u003e55\u003c/sup\u003e; MOPS was used at 50 mM rather than 100 mM, supplemented with 0.1% glutamate, 1% glucose, and 0.5% casamino acids. Growth of YB886 strain background was supplemented with 20 \u0026micro;g/mL tryptophan and 50 \u0026micro;g/mL methionine. For growth in minimal media, both glutamate and casamino acids were replaced with 200 \u0026micro;g/mL L-isoleucine, 200 \u0026micro;g/mL L-leucine, and 200 \u0026micro;g/mL L-valine, and 1% carbon sources were used as indicated. Routinely, cells from young colonies on overnight LB-agar plates at 37 \u0026deg;C (\u0026lt; 12 h) were inoculated into growth media and then grown at 37 \u0026deg;C with 250 rpm shaking. Cultures in logarithmic phase (OD\u003csub\u003e600\u003c/sub\u003e \u0026asymp; 0.1-0.3) were treated with antibiotics or inducers, including arginine hydroxamate (RHX, 0.5 mg/mL), carbonyl cyanide m-chlorophenyl hydrazone (CCCP, 5 \u0026micro;M), sodium azide (NaN\u003csub\u003e3\u003c/sub\u003e,\u003csub\u003e\u0026nbsp;\u003c/sub\u003e4 mM), or arsenate (2.5 mM). Isopropyl \u0026beta;-D-1-thiogalactopyranoside (IPTG) was added to a final concentration of 0.5 mM to induce \u003cem\u003eguaB\u003c/em\u003e expression from an IPTG-inducible promoter (P\u003cem\u003e\u003csub\u003espac\u003c/sub\u003e\u003c/em\u003e), while depletion of \u003cem\u003eguaB\u003c/em\u003e expression was done by omitting IPTG in the growth media.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe following inducers and concentrations were used unless otherwise specified\u0026shy;: RHX: 0.5 mg/mL; CCCP: 5 \u0026mu;M; NaN\u003csub\u003e3\u003c/sub\u003e: 4 mM; Arsenate: 2.5 mM; CARB: 0.5 \u0026mu;g/mL (0.5x MIC) or 100 \u0026mu;g/mL (200x MIC); BAC: 64 \u0026mu;g/mL (0.5x MIC) or 384 \u0026mu;g/mL (3x MIC); CIP: 0.1 \u0026mu;g/mL (0.5x MIC) or 4 \u0026mu;g/mL (20x MIC); KAN: 0.625 \u0026mu;g/mL (0.3x MIC) or 8 \u0026mu;g/mL (8x MIC); VAN: 0.1 \u0026mu;g/mL (0.5x MIC) or 4 \u0026mu;g/mL (20x MIC)) along with non-induction controls.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMinimum inhibitory concentration (MIC) determination\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMICs for chloramphenicol (CAM), tetracycline (TET), kanamycin (KAN), ciprofloxacin (CIP), norfloxacin (NOR), rifampicin (RIF), bacitracin (BAC) and vancomycin (VAN) were determined using the microdilution method\u003csup\u003e56\u003c/sup\u003e. Logarithmic phase cells were back-diluted to a final titer of \u0026asymp; 5x10\u003csup\u003e5\u003c/sup\u003e CFU/mL into 96-well plates containing two-fold serial dilutions of respective antibiotics in S7\u003csub\u003e50\u003c/sub\u003e medium with 0.1% glutamate, 1% glucose, and 0.5% casamino acids. After 16-20 hours of incubation at 37 \u0026deg;C with 250 rpm shaking, the MIC was determined as the lowest drug concentration that prevented visible growth.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBacterial growth measurement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor growth measurement, fresh colonies of \u003cem\u003eBacillus subtilis\u003c/em\u003e strains on LB agar were resuspended into different growth media as specified and diluted to OD600 \u0026asymp; 0.005 in 96-well plates. Growth was monitored by OD\u003csub\u003e600\u003c/sub\u003e at 37\u0026deg;C under shaking in the Synergy2 microplate reader (BioTek). Doubling times were estimated by fitting the growth data to the exponential growth curve using a custom python script.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePersister assay\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo prepare exponentially growing \u003cem\u003eB. subtilis\u003c/em\u003e populations, cells from young colonies on overnight LB-agar plates at 37 \u0026deg;C (\u0026lt; 12 h) were inoculated into S7\u003csub\u003e50\u003c/sub\u003e medium with 0.1% glutamate, 1% glucose and 0.5% casamino acids and grown to OD\u003csub\u003e600\u0026nbsp;\u003c/sub\u003e\u0026asymp; 0.1-0.3 at 37\u0026deg;C, 250 rpm.\u0026nbsp;For growth in minimal medium, both glutamate and casamino acids were replaced with 200 \u0026micro;g/mL L-isoleucine, 200 \u0026micro;g/mL L-leucine, and 200 \u0026micro;g/mL L-valine, and 1% carbon sources were used as indicated. Treatments with bactericidal antibiotics were done at the following concentrations: CIP, 4 \u0026mu;g/mL (20x MIC); VAN, 4 \u0026mu;g/mL (20x MIC), KAN, 8 \u0026mu;g/mL (8x MIC), BAC, 384 \u0026mu;g/mL (3x MIC). To determine cell viability, culture aliquots were taken at T=0 and at designated times after treatment, serially diluted, and plated onto Luria-Broth (LB) agar. Plates were incubated at 37\u0026deg;C overnight. Viability at different time points was determined as CFU/mL and relative survival (vs T\u003csub\u003e0\u003c/sub\u003e) was calculated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor experiments involving pre-induction of cells with (p)ppGpp-inducing agents, cells were grown to OD\u003csub\u003e600\u0026nbsp;\u003c/sub\u003e\u0026asymp; 0.1 and divided into two cultures: one containing the inducing agent (RHX, 0.5 mg/mL; BAC, 64 \u0026mu;g/mL; CCCP, 5 \u0026mu;M; NaN\u003csub\u003e3\u003c/sub\u003e, 4 mM; arsenate, 12.5 mM) and other as non-induction control. The cultures were grown for an additional 30 min under the same conditions (T=0.5 hr) and subjected to the persister assay, as described above.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the case of measuring spontaneous persistence, we followed the definition of spontaneous persisters that they are generated during growth in a non-stressed condition\u003csup\u003e2,3\u003c/sup\u003e. To achieve this, early exponentially growing cultures were 1:100 back-diluted into fresh media and regrew to exponential phase (OD\u003csub\u003e600\u003c/sub\u003e \u0026asymp; 0.1-0.3) for one or two rounds followed by antibiotic treatment\u003csup\u003e3\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEstimation of antibiotic tolerance\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor the estimation of population tolerance, we adopted the definition of minimum duration of killing for 99% of the population (MDK\u003csub\u003e99\u003c/sub\u003e)\u003csup\u003e3\u003c/sup\u003e. To exclude the contribution of persisters to population tolerance, we determined the level of persisters in the biphasic killing curve and subtracted this subpopulation from the bulk population. MDK\u003csub\u003e99\u003c/sub\u003e of the population was estimated from the logarithmic killing phase in the killing curve.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMeasurement of intracellular nucleotides by thin-layer chromatography\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo measure intracellular nucleotides, cells were first harvested from overnight plates, back-diluted to OD\u003csub\u003e600\u003c/sub\u003e = 0.005, and grown in low-phosphate (0.1X phosphate, 0.5 mM) S7\u003csub\u003e50\u003c/sub\u003e medium with 0.1% glutamate, 1% glucose, and 0.5% casamino acids. Once cultures reached OD\u003csub\u003e600\u003c/sub\u003e \u0026asymp; 0.05, 1 mL cells were labeled with 50 \u0026micro;Ci of \u003csup\u003e32\u003c/sup\u003eP orthophosphate (900 mCi/mmol; Perkin Elmer) for 2-3 generations before treatment or sampling. At OD\u003csub\u003e600\u003c/sub\u003e \u0026asymp; 0.15, RHX, CCCP, or arsenate were added to the cultures and samples were collected at regular time points for nucleotide extraction. Nucleotides were extracted by incubating 100 \u0026micro;L cells with 20 \u0026micro;L of 2 M formic acid on ice for at least 20 minutes. Samples were spotted on PEI cellulose thin-layer chromatography (TLC) plates (Selecto) and resolved in 1.5 M or 0.85 M potassium phosphate monobasic (KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, pH 3.4) buffer to separate (p)ppGpp or GTP, respectively. TLC plates were exposed on storage phosphor screens (GE Healthcare) and scanned on a Typhoon imager (GE Healthcare).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMeasurement of intracellular nucleotides by LC-MS\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLC-MS quantification of nucleotides was performed as described previously\u003csup\u003e19\u003c/sup\u003e. Cells were grown in S7\u003csub\u003e50\u003c/sub\u003e medium supplemented with 20 amino acids\u003csup\u003e19\u003c/sup\u003e to OD\u003csub\u003e600\u003c/sub\u003e \u0026asymp; 0.3 at 37\u0026deg;C, 250 rpm before harvesting. 25 mL of cultures were sampled and filtered through PTFE membrane (Sartorius). For experiments involving bacitracin induction, cells were harvested before and after 0.25x MIC (sublethal) or 1.25x MIC (lethal) bacitracin treatment for 30 min. Membranes with cell pellet were submerged in 3 mL extraction solvent mix (on ice 50:50 (v/v) chloroform/water) to quench metabolism, lyse the cells and extract metabolites. Mixtures of cell extracts were centrifuged at 5000x g for 10 min to remove organic phase, and then centrifuged at 20000x g for 10 min to remove cell debris. Samples were analyzed using HPLC-MS system consisting of a Vanquish UHPLC system linked to electrospray ionization (ESI, negative mode) to a Q Exactive Orbitrap mass spectrometer (Thermo Scientific) operated in full-scan mode to detect targeted metabolites based on their accurate masses. LC was performed on an Acquity UPLC BEH C18 column (1.7 \u0026mu;m, 2.1 X 100 mm; Waters). Total run time was 30 min with a flow rate of 0.2 mL/min, using Solvent A as denoted above and acetonitrile as Solvent B. The gradient was as follows: 0 min, 5% Solvent B; 2.5 min, 5% Solvent B; 19 min, 100% Solvent B; 23.5 min 100% Solvent B; 24 min, 5% Solvent B; 30 min, 5% Solvent B. Quantification of metabolites was performed by using MAVEN software\u003csup\u003e57\u003c/sup\u003e and normalized to OD\u003csub\u003e600\u003c/sub\u003e at the time of cell harvest.\u003c/p\u003e\n\u003cp\u003eFor LC-MS analysis of FACS-sorted cells, membranes with filtered cells (about 8 x 10\u003csup\u003e6\u003c/sup\u003e cells) were submerged in 1.5\u0026thinsp;mL extraction solvent mix (methanol:acetonitrile:H2O=40:40:20) to quench metabolism, lyse the cells and extract metabolites. The cell extract was centrifuged at 21,000 x g for 10 min at 4\u0026deg;C, and 1 mL of supernatant was transferred to a new microcentrifuge tube and dried completely with SpeedVac. Dried metabolites were then resuspended in 100 \u0026mu;L Solvent A (97:3 (v/v) water/methanol, 10\u0026thinsp;mM tributylamine pH~8.2\u0026ndash;8.5 adjusted with ~9\u0026thinsp;mM acetic acid). Samples were analyzed using an HPLC-tandem MS (HPLC-MS/MS) system consisting of a Vanquish UHPLC system linked to heated electrospray ionization (ESI, negative mode) to a hybrid quadrupole high resolution mass spectrometer (Q-Exactive orbitrap, Thermo Scientific) operated in full-scan selected ion monitoring (MS-SIM) mode to detect targeted metabolites based on their accurate masses. MS parameters were set to a resolution of 140,000, an automatic gain control (AGC) of 3e6, a maximum injection time of 100\u0026thinsp;ms, and a scan range of 400\u0026ndash;1000\u0026thinsp;mz. Only ions between 10-15 minutes retention time were scanned by MS. LC was performed on an Acquity UPLC BEH C18 column (1.7\u0026thinsp;\u0026mu;m, 2.1\u0026thinsp;\u0026times;\u0026thinsp;100\u0026thinsp;mm; Waters). Total run time was 30\u0026thinsp;min with a flow rate of 0.2\u0026thinsp;mL/min, using Solvent A and 100% acetonitrile as Solvent B. The gradient was as follows: 0\u0026thinsp;min, 5% B; 2.5\u0026thinsp;min, 5% B; 19\u0026thinsp;min, 100% B; 23.5\u0026thinsp;min 100% B; 24\u0026thinsp;min, 5% B; and 30\u0026thinsp;min, 5% B. Raw output data from the MS was converted to mzXML format using in-house-developed software. Quantification of metabolites was performed by using MAVEN software\u003csup\u003e57\u003c/sup\u003e and were normalized to an internal standard of six most represented nucleotides detected in \u0026nbsp;the sample.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFluorescence microscopy\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo monitor (p)ppGpp induction using fluorescence reporters, cells were grown to OD\u003csub\u003e600\u0026nbsp;\u003c/sub\u003e\u0026asymp; 0.1-0.3 followed by 30-min induction with the following inducers at concentrations listed below unless otherwise specified\u0026shy;: RHX: 0.5 mg/mL; CCCP: 5 \u0026mu;M; NaN\u003csub\u003e3\u003c/sub\u003e: 4 mM; Arsenate: 2.5 mM; CARB: 0.5 \u0026mu;g/mL (0.5x MIC) or 100 \u0026mu;g/mL (200x MIC); BAC: 64 \u0026mu;g/mL (0.5x MIC) or 384 \u0026mu;g/mL (3x MIC); CIP: 0.1 \u0026mu;g/mL (0.5x MIC) or 4 \u0026mu;g/mL (20x MIC); KAN: 0.625 \u0026mu;g/mL (0.3x MIC) or 8 \u0026mu;g/mL (4x MIC); VAN: 0.1 \u0026mu;g/mL (0.5x MIC) or 4 \u0026mu;g/mL (20x MIC)), along with non-induction controls.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll imaging samples were spotted on 1.5% agarose pads made with the same growth medium, and immediately imaged with Olympus IX-83 inverted microscope (Olympus) using 60X phase contrast objective with fluorescence filters (excitation: 470/20 nm, dichroic mirror: 485 nm, emission: 515/50 nm for GFP; excitation: 575/20 nm, dichroic mirror: 595 nm, emission: 645/90 nm for mCherry or propidium iodide; and excitation: 427/10 nm, dichroic mirror: 595 nm, emission: 472/30 nm for Sytox blue). Single-cell time-lapse imaging was performed at 15-min intervals for each field at 37\u0026deg;C using temperature-controlled imaging chamber (Tokai Hit) coupled with automatic stage and microscope control as described previously\u003csup\u003e58\u003c/sup\u003e. The measurement was generally over the course from the birth of the cell until the time lapse stopped due to crowding of the microcolony or in rare cases severe drifting of focus. When comparing phenotypes between strains, both strains were imaged in parallel on the same imaging dish using the same microscope with same settings. For imaging persister survival in time-lapse experiments, final concentrations of 5 \u0026mu;g/mL CARB (10x MIC) and 200 nM Sytox blue or propidium iodide (Molecular Probes) were applied to the agarose pads at designated times. To remove CARB after treatment, 5 units/mL final concentration of penicillinase (Sigma) was applied. Strains without the fluorescence reporters were used for autofluorescence measurement.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBiofilm growth and imaging\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eB. subtilis\u003c/em\u003e biofilms were grown on a custom microfluidic device fabricated with polydimethylsiloxane (PDMS). The device contains a central chamber connected to inlet and outlet media channels which allows constant media flow through the central chamber. A semi-permeable dialysis membrane was fixed on top of the central chamber to provide a platform for biofilm growth. This setup allows diffusion of nutrients or small molecules from the media flow underneath to support biofilm growth on the membrane and allow subsequent treatment with antibiotics. To grow \u003cem\u003eB. subtilis\u003c/em\u003e biofilms, 1 \u0026micro;L of early exponential phase culture (OD\u003csub\u003e600\u0026nbsp;\u003c/sub\u003e\u0026asymp; 0.05) was applied on the membrane and grown at room temperature (25\u003csup\u003eo\u003c/sup\u003eC) for 24 h under constant flow of S7\u003csub\u003e50\u003c/sub\u003e medium supplemented with 0.5% glutamate and 0.5% glycerol.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImaging of biofilms was performed using IXplore SpinSR confocal imaging microscope (Olympus) using 20X phase contrast objective with fluorescence filters (488 nm laser with 510-550 nm emission filter for GFP; 561 nm laser with 575-625 nm emission filter for mCherry). Biofilms at 24h post-inoculation were imaged before and after switching to the same growth medium containing 4 \u0026micro;g/mL Vancomycin. 51 stacks at 1 \u0026micro;m intervals were taken for each time point. Images were projected along z-axis from the top of the biofilm using maximum intensity projection using the cellSense software (Olympus). For quantitation of reporter signals within the biofilm, we sampled ~4 random regions within the center of the biofilm and measured their GFP and mCherry intensities. Background fluorescence was measured from regions without biofilm and used for background subtraction.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFlow cytometry and cell sorting\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFlow cytometry was performed at UWCCC flow cytometry core. To prepare samples for flow cytometry, cells from young colonies on overnight LB-agar plates at 37 \u0026deg;C (\u0026lt; 12 h) were inoculated into and grown in S7\u003csub\u003e50\u003c/sub\u003e medium with 0.1% glutamate, 1% glucose and 0.5% casamino acids to OD\u003csub\u003e600\u0026nbsp;\u003c/sub\u003e\u0026asymp; 0.1-0.3 at 37\u0026deg;C, 250 rpm.\u0026nbsp;For growth in minimal medium, both glutamate and casamino acids were omitted and 1% carbon sources were used as indicated.\u0026nbsp;Cells were immediately fixed with 0.4% paraformaldehyde for 15 min at room temperature, washed 3 times with 1x phosphate buffered saline (PBS), and kept at 4\u0026deg;C\u0026nbsp;until analysis. Fixation was verified by viability plating and microscopy. Flow cytometry analysis was performed using BD LSRFortessa flow cytometer (BD Biosciences) with a 70-\u0026micro;m nozzle. Cell populations were detected using both forward and side scatter (FSC and SSC). Single-cell fluorescence was measured using 488 nm laser and detection filters for GFP (530/30 nm, 505LP dichroic filter). Autofluorescence was measured by analyzing parental strains without the fluorescence reporter and subtracted from the raw reporter fluorescence. Approximately 1.5 million events were measured for each sample. For the determination of antibiotic induced persistence, the frequency of low GTP cells after antibiotic induction was subtracted from the frequencies before induction.\u003c/p\u003e\n\u003cp\u003eFACS was performed at UWCCC flow cytometry core. To prepare samples for cell sorting, cells were harvested from young colonies on overnight LB-agar plates at 37 \u0026deg;C (\u0026lt; 12 h) and grew in S7\u003csub\u003e50\u003c/sub\u003e medium with 0.1% glutamate, 1% glucose and 0.5% casamino acids to OD\u003csub\u003e600\u0026nbsp;\u003c/sub\u003e\u0026asymp; 0.3 at 37\u0026deg;C, 250 rpm. FACS analysis was performed using BD FACSAria cell sorter (BD Biosciences) with a 70-\u0026micro;m nozzle at room temperature using 488 nm laser and 530/30 nm detection filters for GFP, and 561 nm laser and 610/20 nm detection filters for mCherry. Auto-fluorescent cells were eliminated by gating with isogenic strain without the fluorescent reporters. At least 1,000 cells were obtained from the rarest gate for each sample. Cell recovery rate was estimated to be \u0026gt; 90% based on viability counting on LB plates. For antibiotic treatment, cells were directly sorted in to tubes containing 4x MIC of VAN followed by shaking at\u0026nbsp;37 \u0026deg;C. Aliquots were taken at different times for serial dilution and plating to measure survival by colony counting. Number of cells before treatment (T\u003csub\u003e0\u003c/sub\u003e) were measured by the cell sorter.\u003c/p\u003e\n\u003cp\u003eFor FACS-sorting of cells for LC-MS analysis, cells containing the P\u003csub\u003elowGTP\u003c/sub\u003e reporter were grown in S7\u003csub\u003e50\u003c/sub\u003e medium with 0.1% glutamate, 1% glucose and 0.5% casamino acids to either exponential (OD\u003csub\u003e600\u0026nbsp;\u003c/sub\u003e\u0026asymp; 0.2) or\u0026nbsp;stationary phase (OD\u003csub\u003e600\u0026nbsp;\u003c/sub\u003e\u0026asymp; 4.0). Both populations were\u0026nbsp;mixed in 10:1 ratio, and immediately FACS-sorted into low or high fluorescence fractions. Auto-fluorescent cells were eliminated by gating with isogenic strain without the fluorescent reporters. Approximately 8 x 10\u003csup\u003e6\u003c/sup\u003e cells were sorted and filtered on PTFE membrane (Sartorius) to remove the sheath fluid. Membranes with filtered cells were then subjected to metabolite extraction and LC-MS analysis. CFU counts were performed from a small aliquot of the sorted fractions to account for potential variations in the number of cells sorted between fractions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTransposon sequencing (Tn-Seq)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eB. subtilis\u003c/em\u003e 168 transposon mutant library was kindly provided by the Grossman Lab\u003csup\u003e59\u003c/sup\u003e. Construction of the library is briefly described as follows: \u003cem\u003eIn vitro\u003c/em\u003e transposition of \u003cem\u003eB. subtilis\u003c/em\u003e 168 genomic DNA (gDNA) with magellen6x transposon was performed by mixing 1.3 \u0026micro;g pCJ41 (containing magellen6x transposon), 34 ng purified MarC9 transposase, 5 \u0026micro;g \u003cem\u003eB. subtilis\u003c/em\u003e gDNA, 10 \u0026micro;L 2x buffer A (41 mM HEPES pH 7.9, 19% glycerol, 187 mM NaCl, 19 mM MgCl2, 476 \u0026micro;g/mL BSA and 3.8 mM DTT) into 20 \u0026micro;L reaction \u003cem\u003ein vitro\u003c/em\u003e and incubated overnight at 30\u0026deg;C. The transposed DNA was precipitated and resuspended in 2 \u0026micro;L 10x buffer B (500 mM Tris-Cl pH 7.8, 100 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 10 mM DTT), 2 \u0026micro;L 1 mg/mL BSA and 11 \u0026micro;L H\u003csub\u003e2\u003c/sub\u003eO followed by 4h incubation at 37\u0026deg;C. After incubation, 4 \u0026micro;L 2.5 mM dNTPs and 1 \u0026micro;L of 3U/ \u0026micro;L T4 DNA Polymerase were added to the DNA and further incubated for 20 min at 12\u0026deg;C, followed by heat inactivation at 75\u0026deg;C for 15 min. Next, 0.2 \u0026micro;L 2.6 mM NAD and 1 \u0026micro;L of 10 U/ \u0026micro;L \u003cem\u003eE. coli\u003c/em\u003e DNA ligase were added and the reaction was incubated at 16\u0026deg;C overnight. The resulting in vitro transposed and repaired gDNA was transformed into \u003cem\u003eB. subtilis\u003c/em\u003e 168 and plated on LB agar containing spectinomycin and incubated overnight. Colonies containing the transposon were washed off and pooled into a single library. The library was estimated to contain ~50,000 unique transposon inserts across the genome.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the selection experiment with the transposon library, an aliquot of the library was inoculated and grown in S7\u003csub\u003e50\u003c/sub\u003e medium with 0.1% glutamate, 1% glucose and 0.5% casamino acids supplemented with tryptophan (20 \u0026micro;g/mL) at 37\u0026deg;C, 250 rpm. At OD\u003csub\u003e600\u0026nbsp;\u003c/sub\u003e\u0026asymp; 0.1-0.3, cultures were treated with 20x MIC vancomycin or ciprofloxacin. Cultures before and after antibiotic treatment were plated onto LB plates and recovered after ~14 h incubation at 37\u0026deg;C. ~650,000 colonies from each sample were pooled and snap-frozen for genomic DNA extraction and sequencing library preparation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePreparation of the sequencing library was performed as previously described\u003csup\u003e60\u003c/sup\u003e. Frozen cell pellets were resuspended in 500 \u0026micro;L lysis buffer with lysozyme and RNase A (20 mM Tris-HCl pH 7.5, 50 mM EDTA, 100 mM NaCl, 2 mg/mL lysozyme, 120 \u0026micro;g/mL RNase A) and incubated at 37\u0026ordm;C for 20-30 min. Next, the incubated cell lysate was mixed with 60 \u0026micro;L 10% N-lauroylsarkosine and further incubated at 37\u0026ordm;C for 15 min. Genomic DNA (gDNA) was purified using 600 \u0026micro;L phenol, then 600 \u0026micro;L phenol:chloroform:isoamyl alcohol (25:24:1), and finally 600 \u0026micro;L pure chloroform. DNA in the aqueous phase was precipitated using 1/10 volumes of 3M NaOAc and 2 volumes of 100% ethanol. DNA pellet was washed with 70% ethanol, air-dried on bench, and resuspended in 10 mM Tris-HCl, pH 8.5 and stored at 4\u0026ordm;C. For each sample, 6 \u0026micro;g of DNA was used for MmeI digestion in 200 \u0026micro;L (6 \u0026micro;g gDNA, 6 \u0026micro;L MmeI (2000 U/mL, NEB), 0.5 \u0026micro;L 32 mM S-adenosylmethionine, 20 \u0026micro;L NEB CutSmart Buffer, and ddH\u003csub\u003e2\u003c/sub\u003eO up to 200 \u0026micro;L). DNA was digested for 2.5 h at 37\u0026ordm;C, after which 2 \u0026micro;L calf intestinal phosphatase (10,000 U/mL, NEB) was added to the digest and incubated for 1h at 37\u0026ordm;C. Digested gDNA was extracted with 200 \u0026micro;L phenol:chloroform:isoamyl alcohol (25:24:1) followed by 200 \u0026micro;L of pure chloroform. DNA in the aqueous phase was first mixed with 1/10 volume of 3M NaOAc and 67 ng/mL glycogen, and then 2.5 volumes of 100% ethanol. The tubes were then placed at -80\u0026ordm;C for 20 min and then centrifuged at max speed for 15 min at 4\u0026ordm;C. Precipitated DNA was washed with 150 \u0026micro;L 70% ethanol twice at room temperature, air-dried, and resuspended in 15 \u0026micro;L ddH\u003csub\u003e2\u003c/sub\u003eO. \u003c/p\u003e\n\u003cp\u003eFor annealing of the DNA adaptor, 20 \u0026micro;L of 100 \u0026micro;M synthesized oligos (IDT) were mixed with 1 \u0026micro;L of 41 mM Tris-HCl pH 8.0 (final concentration of adaptor: 50 \u0026micro;M in 1mM Tris-HCl pH 8.0). Oligos were annealed by heat denaturation (95 \u0026ordm;C for 5 min) and stepwise cool-down (94\u0026ordm;C for 45 s then repeat with -0.3\u0026ordm;C per cycle for 250 cycles, then hold at 15\u0026ordm;C) using PCR machine. Annealed adaptors were diluted to 3.3 \u0026micro;M in ddH2O and stored at -20\u0026ordm;C.\u003c/p\u003e\n\u003cp\u003eFor adaptor ligation, 5 \u0026micro;L of digested DNA were mixed with 1 \u0026micro;L of 3.3 \u0026micro;M DNA adaptor, 1 \u0026micro;L of 10x T4 DNA ligase buffer (NEB), 1 \u0026micro;L of T4 DNA ligase (400,000U/mL, NEB) and 2 \u0026micro;L ddH\u003csub\u003e2\u003c/sub\u003eO. The ligation mix was incubated overnight at 16\u0026ordm;C in a PCR machine.\u003c/p\u003e\n\u003cp\u003eAmplification of adaptor-ligated DNA library was performed using barcoded primers and Phusion high-fidelity DNA polymerase (NEB) for 18 cycles according to provided instructions. The PCR products were mixed in equal amounts, purified by size-exclusion, and submitted for sequencing using Illumina sequencing primer (5\u0026rsquo;-ACACTCTTTCCCTACACGACGCTCTTCCGATCT-3\u0026rsquo;). Deep sequencing was done using Illumina HiSeq 2500 (Illumina) by the University of Michigan DNA Sequencing Core. Analysis of sequencing data was done using a custom Python script and mapped to the \u003cem\u003eB. subtilis\u003c/em\u003e 168 reference genome (NC_000964.3). Visual inspection of transposon insertion profiles was done using GenomeBrowse (Golden Helix).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExpression and purification of B. subtilis\u0026nbsp;\u003c/em\u003eRel\u003c/p\u003e\n\u003cp\u003eThe plasmid for Rel purification was constructed as follows. The \u003cem\u003eB. subtilis rel\u003c/em\u003e coding sequence was PCR amplified from NCIB3610 genomic DNA using primers oJW3196/oJW3197. The pE-SUMO expression vector was amplified using primers oJW3194/oJW3195. The PCR products were assembled to generate pJW753 by Golden Gate assembly (New England BioLabs). Plasmids were verified by DNA sequencing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo express His\u003csub\u003e6\u003c/sub\u003e-SUMO-Rel, fresh transformants of \u003cem\u003eE. coli\u003c/em\u003e BL21 carrying pE-SUMO-\u003cem\u003erel\u003c/em\u003e were grown in LB at 37\u003csup\u003eo\u003c/sup\u003eC to OD\u003csub\u003e600\u003c/sub\u003e \u0026asymp; 0.5, followed by 1:50 dilution into Terrific Broth and grown at 30\u003csup\u003eo\u003c/sup\u003eC until OD\u003csub\u003e600\u003c/sub\u003e \u0026asymp; 1.5. His\u003csub\u003e6\u003c/sub\u003e-SUMO-Rel expression was induced with 1 mM IPTG for 4 h at 30\u003csup\u003eo\u003c/sup\u003eC. Cells were pelleted and stored at -80\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u0026ordm;C until use. Frozen cell pellets were thawed on ice, resuspended in ice-cold lysis buffer (50 mM Tris-HCl, pH 8, 1M NaCl, 10 mM imidazole, DNase and cOmplete protease inhibitor (Roche)), and lysed with French press at 4\u003csup\u003eo\u003c/sup\u003eC. The cell lysate was centrifuged at 4\u003csup\u003eo\u003c/sup\u003eC, 13,000 rpm for 30 min to obtain the supernatant. Filtered supernatant was injected into an ӒKTA FPLC system (GE Healthcare) and passed through a HisTrap FF column (GE Healthcare). His\u003csub\u003e6\u003c/sub\u003e-SUMO-Rel was eluted with a gradient of Buffer A (50 mM Tris-HCl, pH 8, 1M NaCl, 5% glycerol, 10 mM imidazole) and Buffer C (50 mM Tris-HCl, pH 8, 1M NaCl, 5% glycerol, 500 mM imidazole). Fractions containing the protein were pooled with 300 \u0026micro;L SUMO protease into Spectra/Por dialysis tubing (Spectrum), and dialyzed into 50 mM Tris-HCl, 1M NaCl, 1mM \u0026beta;-mercaptoethanol and 5% glycerol overnight. Rel without the His\u003csub\u003e6\u003c/sub\u003e-SUMO tag was passed through the HisTrap FF column and then size-exclusion purified using a Superose 12 10/300 GL column (GE Healthcare) with ӒKTA FPLC system. Fractions containing the Rel protein were pooled and measured for its concentration by the Bradford Assay (Bio-rad). Aliquots were snap-frozen using liquid nitrogen and stored at -80\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u0026ordm;C. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIn vitro\u003c/em\u003e \u003cem\u003epppGpp synthesis assay\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIn vitro\u003c/em\u003e pppGpp synthesis by Rel was monitored by measuring synthesis of radiolabeled pppGpp over time. The reaction contained 236 nM \u003cem\u003eB. subtilis\u003c/em\u003e Rel, 0.05 \u0026micro;M [\u0026alpha;\u003csup\u003e32\u003c/sup\u003eP] GTP, 1 mM ATP, 50 mM NaCl and 10 mM ATP in 20 mM Tris-HCl pH 7.5, with or without 10 \u0026micro;M non-radioactive pppGpp. The reaction was devoid of manganese to avoid potential effect of pppGpp hydrolysis. The reaction was initiated by addition of ATP and incubated at 37\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u0026ordm;C. At indicated times, 10 \u0026micro;L of the reaction were mixed with 2 \u0026micro;L of 2M formic acid and chilled on ice for 20 min to quench the reaction. 1 \u0026micro;L samples were spotted on PEI cellulose thin-layer chromatography (TLC) plates (Millipore) and resolved in 1.5 M potassium phosphate monobasic (KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, pH 3.4) buffer to separate pppGpp. TLC plates were dried and exposed on storage phosphor screens (GE Healthcare) and scanned on a Typhoon imager (GE Healthcare).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQuantification and Statistical Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor TLC experiments, intensities of nucleotide spots were quantified using ImageQuant software (Molecular Dynamics). The raw intensities were corrected to the number of phosphates in the corresponding nucleotide and normalized to OD\u003csub\u003e600\u003c/sub\u003e or ATP level before treatment (ATP\u003csub\u003eT=0\u003c/sub\u003e) for comparison between samples. For \u003cem\u003ein vitro\u003c/em\u003e pppGpp synthesis assays, changes in pppGpp levels were normalized to T=0.\u003c/p\u003e\n\u003cp\u003eMicroscopy image analysis and cell parameters (cell area and fluorescence intensity) measurements were done using Metamorph software (Molecular Devices). Background and autofluorescence were subtracted by comparing images obtained from identical strains without the fluorescence reporter. Single cell specific growth rate (\u0026micro;) at each frame was calculated using the equation: \u003cimg src=\"data:image/png;base64,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\" width=\"61\" height=\"30\"\u003e , where \u003cem\u003el\u003c/em\u003e is the cell length, \u0026Delta;\u003cem\u003el\u003c/em\u003e\u0026nbsp; is the change in cell length and \u0026Delta;\u003cem\u003et\u003c/em\u003e is the change in time (in min). Flow-cytometry data were analyzed using FlowJo X software (FlowJo, LLC). Cells within a narrow range of cell sizes were gated, sub-gated to filter cell aggregates, and then measured for their fluorescence distribution. Autofluorescence was measured using isogenic strains without the fluorescent reporters and subtracted from raw reporter signals. Gating for P\u003csub\u003elowGTP\u003c/sub\u003e-high cells was set at 5-fold or higher above mean population fluorescence. This cut-off agrees with our FACS sorting experiment where cells with reporter fluorescence above this threshold are predominantly persisters. For the determination of antibiotic induced persistence, the frequency of low GTP cells after antibiotic induction was subtracted from the frequencies before induction.\u003c/p\u003e\n\u003cp\u003eStatistical information of individual experiments is included in the figure legends. n represents the number of biological replicates or number of cells for experiments involving single-cell measurements as indicated in the legends. Significance was tested using Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e test. Prism 7 (GraphPad) was used for statistical analysis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Alan Grossman, Kevin Griffith, and Dan Kearns for generously sharing strains and reagents. We thank Bruce Levin, Petra Levin, Michael Cox and members of the Wang lab for comments on the manuscript. This study is supported by HHMI Faculty Scholar Grant, NIH R35GM127088 and Hatch (to J.D.W).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.D.W. conceptualized the study and supervised the research. D.K.F. and J.L.T. designed the experiments. D.K.F., J.L.T. and D.Y. conducted experiments. J.Y. performed LC/MS measurement of cellular nucleotides. J.W.S. performed bioinformatics analysis. F.S. performed biofilm growth and imaging experiments, as well as LC/MS measurement of sorted cells. M.Y. performed Rel activity assays. D.K.F., J.L.T., J.W.S. and J.D.W. discussed the results and interpretations. D.K.F., J. L. T. and J.D.W. wrote the paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBigger, J. 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Genome-Wide Fitness and Genetic Interactions Determined by Tn-seq, a High-Throughput Massively Parallel Sequencing Method for Microorganisms. \u003cem\u003eCurr Protoc Mol Biol\u003c/em\u003e \u003cstrong\u003e106\u003c/strong\u003e, 7 16 11-24, doi:10.1002/0471142727.mb0716s106 (2014).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5679108/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5679108/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Persisters describe phenotypically switched cells refractory to antibiotic killing in a genetically susceptible population, while preserving the ability to resume growth when antibiotics are discontinued1,2. Since its proposal 70 years ago, great strides were made to build the framework regarding persistence, including defining triggered, spontaneous and antibiotic-induced persisters. However, challenges remain in characterizing the molecular determinants underlying the phenotypic switch into persistence3. Here we document triggered, spontaneous and antibiotic-induced persistence in a Gram-positive bacterium, all through a common switch involving the alarmone (p)ppGpp and each stemming from a different alarmone synthesis pathway. Starvation-triggered persistence is mediated by Rel synthetase, and spontaneous persistence is through self-amplification via allosteric enzyme activation of alarmone synthetases Rel and SasB, whereas lethal and sublethal concentrations of cell wall antibiotics induce alarmones through an antibiotic-induced alarmone synthetase SasA, consequently enabling adaptive persistence that promotes survival. (p)ppGpp accumulation promotes persistence by depleting intracellular GTP and antagonizing its action. We developed a fluorescent GTP reporter to visualize rare events of persister formation in wild type bacteria, revealing a rapid switch from growth to dormancy in single cells as their GTP levels drop beneath a threshold. While a modest drop of GTP in bulk population slows down growth and promotes antibiotic tolerance, (p)ppGpp drives persistence by allowing the switch-like dynamics to drop GTP beneath the persister threshold in single cells. Persistence through alarmone-GTP antagonism is likely a widespread mechanism to survive antibiotics in Gram positive bacteria and possibly beyond.","manuscriptTitle":"A shared alarmone-GTP switch underlies triggered and spontaneous persistence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-03 08:31:46","doi":"10.21203/rs.3.rs-5679108/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-microbiology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nmicrobiol","sideBox":"Learn more about [Nature Microbiology](http://www.nature.com/nmicrobiol/)","snPcode":"","submissionUrl":"","title":"Nature Microbiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"07b89c6e-2bfc-48b8-88a6-32945f816122","owner":[],"postedDate":"January 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":42279721,"name":"Biological sciences/Microbiology/Microbial genetics/Bacterial genetics"},{"id":42279722,"name":"Biological sciences/Microbiology/Bacteriology"}],"tags":[],"updatedAt":"2025-05-16T07:05:54+00:00","versionOfRecord":{"articleIdentity":"rs-5679108","link":"https://doi.org/10.1038/s41564-025-02015-6","journal":{"identity":"nature-microbiology","isVorOnly":false,"title":"Nature Microbiology"},"publishedOn":"2025-05-15 04:00:00","publishedOnDateReadable":"May 15th, 2025"},"versionCreatedAt":"2025-01-03 08:31:46","video":"","vorDoi":"10.1038/s41564-025-02015-6","vorDoiUrl":"https://doi.org/10.1038/s41564-025-02015-6","workflowStages":[]},"version":"v1","identity":"rs-5679108","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5679108","identity":"rs-5679108","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}