Since Crp/cAMP-mediated metabolic changes can be induced by nutrient depletion during the stationary phase, we wanted to assess the effects of deleting the crp and cyaA genes (Δcrp and ΔcyaA) on both persister cell formation and metabolism during this phase. As anticipated, the deletion of the cyaA gene resulted in a notable reduction in intracellular cAMP concentration (Figure 1—figure supplement 1). However, the Δcrp strain exhibited an increase in cAMP concentration, potentially due to the negative feedback regulatory mechanism of the Crp/cAMP complex for the cyaA gene promoter (Keseler et al., 2005; Majerfeld et al., 1981; Figure 1—figure supplement 2). When comparing the growth curves of E. coli wild-type (WT), Δcrp, and ΔcyaA main cultures under identical conditions studied here (see Materials and methods), all three strains started to enter the stationary phase around 5 hr, with the mutant strains exhibiting slightly lower optical density levels than the WT at this time point (Figure 1—figure supplement 3). Also, our data provide evidence of an increase in cAMP levels in WT cells during their transition into the stationary phase (Figure 1—figure supplement 4), aligning with existing literature (Bettenbrock et al., 2007; Keseler et al., 2005; Park et al., 2006). For type I persister quantification, we diluted cells in the fresh medium, consistent with previous studies (Balaban et al., 2004; Orman and Brynildsen, 2015), at early (t=5 hr) and late (t=24 hr) stationary phases and subsequently exposed them to an extended period (20 hr) of ampicillin or ofloxacin treatment (200 μg/mL ampicillin and 5 μg/mL ofloxacin). These treatments were carried out at concentrations surpassing the minimum inhibitory concentrations (MIC), necessary for the selection of antibiotic-tolerant persister cells (Supplementary file 1; Keren et al., 2004a). Also, type I persisters, formed during the stationary phase, exhibit a slow transition from a non-growing state to an active state when transferred to a fresh medium in the lag phase (Balaban et al., 2004; Jõers and Tenson, 2016; Orman and Brynildsen, 2015; Vulin et al., 2018); therefore, this transition requires longer antibiotic treatment durations. Moreover, we transferred an equal number of cells from each strain to the fresh medium to ensure consistency in cell numbers. To assess persistence, we collected samples during antibiotic treatment, washed them to remove antibiotics, and plated them on agar media to quantify colony-forming units (CFU) of surviving cells (see Materials and methods). The resulting biphasic kill curves—plots of CFU levels over treatment time—are characteristic of persistence phenotypes (Figure 1). Notably, the WT strain showed a marked increase in both ampicillin- and ofloxacin-persister cells during the late stationary phase, in contrast to the mutant strains, where no such increase was observed (Figure 1A and B). However, this trend was not observed in the early stationary phase (Figure 1A and B). To confirm that the observed decrease in persister levels in the mutant strains in the late stationary phase is solely attributed to the perturbation of the Crp/cAMP regulatory network, we reintroduced crp expression to the Δcrp strain using a low-copy plasmid carrying the crp gene and its promoter. As a control, we utilized an empty vector of the same plasmid. The results demonstrated that the expression of crp restored the persister level in the mutant strain, while the plasmid itself had no impact on persister levels (Figure 1—figure supplement 5A, B). Altogether, these findings highlight the significant role of Crp/cAMP in ampicillin and ofloxacin persister formation in the late stationary phase.

Ampicillin and ofloxacin, both broad-spectrum antibiotics with a strong dependence on cell metabolism (Zheng et al., 2020), target cell wall synthesis and DNA gyrase activity, respectively. In addition to these two antibiotics, we also quantified AG-persister levels in both WT and mutant strains. This was achieved by exposing diluted cells from both early and late stationary phases to 50 μg/mL gentamicin, a concentration exceeding the MIC levels (Supplementary file 1). After prolonged gentamicin exposure (20 h), the tolerant cell colonies were found to be below the limit of detection for all strains and conditions (Figure 1C, Figure 1—figure supplement 5C). Although bacterial tolerance can vary significantly depending on the specific antibiotics and growth phase used (Hofsteenge et al., 2013), this outcome contrasts starkly with the observed levels of ampicillin and ofloxacin persisters in WT (Figure 1). The mechanism by which AGs eliminate persister cells in the WT strain may be linked to their metabolism, given that AG uptake is an energy-requiring process (Allison et al., 2011; Taber et al., 1987), and their metabolism will be explored further in the subsequent section.

We would like to highlight that antibiotic concentrations, washing procedures (to remove antibiotics), and agar plate incubation times for CFU enumeration may affect experimental outcomes. However, variations in these parameters, including lower antibiotic concentrations normalized to the MIC of each strain (5×or 10×MIC; see Figure 1—figure supplement 6), additional washing steps to ensure complete removal of antibiotics (see Figure 1—figure supplement 6), and extended agar plate incubation times of up to 48 hours (see Figure 1—figure supplement 7), did not affect the persistence phenotype. The Δcrp and ΔcyaA strains consistently showed reduced ampicillin and ofloxacin persistence compared to WT, whereas all three strains showed no detectable persisters following gentamicin treatment (Figure 1—figure supplements 6 and 7). Moreover, we examined the impact of Crp/cAMP disruption in the HipA7 strain to assess whether the role of Crp/cAMP in persistence extends to a genetically sensitized, persister-enriched background. Deletion of either crp or cyaA in the HipA7 background significantly reduced persistence to both ampicillin and ofloxacin during the late stationary phase (Figure 1—figure supplement 8), mirroring the effects observed in the WT strain. Altogether, these results show that disruption of the Crp/cAMP regulatory network impairs persister formation during the late stationary phase, underscoring its critical role in ampicillin and ofloxacin tolerance.

To determine whether the reduced ampicillin and ofloxacin persister levels at the late stationary phase in the mutant strains (Figure 1A and B) are linked to stationary phase metabolism, we utilized untargeted mass spectrometry (MS). This approach facilitated the quantification of metabolites in Δcrp cells, allowing for a comparison with WT controls in both the early and late stationary phases (Figure 2A). Since both Δcrp and ΔcyaA strains exhibit the same persistence phenotype, and crp deletion effectively abolishes Crp/cAMP complex function, we used the Δcrp mutant for metabolomic profiling to capture key regulatory changes while maintaining experimental feasibility. The metabolomics data were subjected to unsupervised hierarchical clustering, and metabolites identified in independent biological replicates of each strain and condition were found to cluster together (Figure 2A), thus confirming the reproducibility of our data.

To elucidate the upregulated and downregulated metabolic pathways in the mutant strain as compared to the WT strain, we performed enrichment analyses utilizing MetaboAnalyst (Lu et al., 2023). For downregulated pathways, we considered a threshold ratio of 0.5 or lower, where the ratio indicates metabolite levels in the mutant strain relative to the WT (Supplementary file 2A). Conversely, for upregulated pathways, the threshold ratio was set at 2 or higher (Supplementary file 2B). The enrichment ratio for each pathway was calculated based on the number of metabolite hits compared to the expected hits derived from the chemical structure library (Lu et al., 2023). Our extensive comparison of the mutant cells to the WT cells through pathway enrichment analysis (refer to Figure 2B, Figure 2—figure supplements 1–3 for pairwise comparison of different conditions) revealed several important findings:

Altogether, our metabolic data indicate that, in the stationary phase, WT cells maintain their energy metabolism to some extent while downregulating their anabolic pathways (Figure 2A). This metabolic state appears to be regulated by the Crp/cAMP complex, as perturbing its function leads to a significant downregulation of energy metabolism and an upregulation in the abundance of anabolic metabolites (Figure 2B).

Since the Crp/cAMP complex acts as a transcriptional regulator affecting the expression of metabolic proteins whose abundance directly affects cellular metabolites, we performed untargeted proteomics, our second genomic-level study, to further validate our results. Considering the noticeable metabolic alterations observed during the late stationary phase, we utilized MS to quantify proteins in Δcrp cells and compared them to WT controls at this stage. The resulting proteomics data were subjected to unsupervised hierarchical clustering, and the proteins identified in independent biological replicates of each strain were found to cluster together, confirming the consistency of our findings (Figure 3—figure supplement 1). By analyzing protein-protein association networks and employing functional enrichment through STRING (Szklarczyk et al., 2021; Szklarczyk et al., 2023), which integrates various functional pathway classification frameworks such as Gene Ontology annotations, KEGG pathways, and UniProt keywords, we pinpointed various upregulated and downregulated pathways in the mutant strain when compared to the WT (Supplementary file 3). The upregulated pathways are associated with anabolic metabolism and encompass peptidoglycan metabolic processes, cell wall organization or biogenesis, cellular component organization or biogenesis, regulation of cell shape, cell cycle, and cell division (Figure 3A). Also, the mutant strain displayed downregulated pathways, encompassing glycerol metabolism, TCA cycle, pyruvate metabolism, glycolysis, and various pathways associated with ribosome and transcriptional factor activity (Figure 3B). Our analysis specifically pinpointed a cluster of proteins involved in energy metabolism and respiratory processes. Notably, this cluster includes GltA, SdhB, SucC, SucD, FrdB, FrdA, AcnA, AceA, and Mdh proteins, which play crucial roles in either the TCA cycle or as membrane-bound components of ETC (Figure 3B). Altogether, the alignment between metabolomics and proteomics analyses provides additional validation for the Crp/cAMP-mediated metabolic state.

The omics data suggest that the upregulation in the abundance of anabolic metabolites and proteins, particularly those related to cell wall organization or biogenesis, cell cycle, and cell division, in the stationary-phase mutant cells, would likely enhance their ability to resume growth upon transitioning to fresh medium. To investigate this, we utilized a cell proliferation assay that employed an inducible fluorescent protein (mCherry) expression cassette. This assay provided us with the ability to monitor non-growing cells at a single-cell resolution, as described previously (Orman and Brynildsen, 2013a; Roostalu et al., 2008). The mCherry expression cassette is controlled by an isopropyl ß-D-1-thiogalactopyranoside (IPTG) inducible synthetic T5 promoter that was previously inserted into the chromosome of an E. coli strain carrying a lacI^q promoter mutation (Orman and Brynildsen, 2013a). This configuration allowed for precise regulation of mCherry expression using IPTG. Here, we introduced crp and cyaA deletions into this strain. These deletions reduced the persistence of the mCherry-expressing E. coli strain in the late stationary phase (Figure 4—figure supplement 1), consistent with the findings presented in Figure 1. To perform the growth assay, we induced mCherry expression in the main cultures and then washed the cells to remove IPTG. The cells were then inoculated into a fresh medium without IPTG, and their growth was analyzed with a flow cytometer. This allowed us to track the dilution of mCherry protein within the cells, which served as an indicator of cell proliferation. As shown in Figure 4A, initially, all cells exhibited high red fluorescence. However, as cells underwent division, the red fluorescence of the overall population decreased in the absence of the inducer (Figure 4A). Notably, within the WT strain, a subpopulation from the late stationary phase cultures displayed constant fluorescence levels, indicating their inability to divide (Figure 4B). In contrast to the WT strain, we did not detect similar subpopulations in the mutant strains (Figure 4B). Additionally, this subpopulation of non-growing cells does not emerge during the early stationary phase cultures (Figure 4A). This observation provides an explanation for the observed reduction in persister levels in these mutant strains in the late stationary phase, as the enrichment of persister cells within these non-growing cell subpopulations was reported in previous studies (Jõers et al., 2010; Orman and Brynildsen, 2013b; Roostalu et al., 2008).

To confirm the significance of Crp/cAMP in the formation of non-growing cells, we introduced the expression plasmid carrying the crp gene into the Δcrp strain. As anticipated, the introduction of the crp expression plasmid resulted in the emergence of a non-growing population within the culture, contrasting with the mutant strain containing the empty plasmid vector used as a control (Figure 4—figure supplement 2A, B). The reduced capacity of stationary-phase WT cells to initiate proliferation upon transfer to a fresh medium suggests the possible presence of an extended lag phase in these cells. To investigate this, we employed flow cytometry to precisely quantify cell numbers and generate growth curves for both WT and mutant strains. As anticipated, the growth curve of the WT strain displayed a slower initial growth rate and a prolonged lag phase duration compared to the mutant strains (Figure 4C). Conversely, the mutant strains displayed a shorter lag phase, yet they demonstrated an increased doubling time in the exponential phase compared to WT (Figure 4C), which was also anticipated, considering their decreased reliance on oxidative phosphorylation due to TCA cycle inhibition. Altogether, these results provide additional support and validation for the findings from our metabolomics and proteomics data, as our data reveals a correlation between the abundance of molecules associated with cell division and the ability of the stationary phase cells to resume growth.

The Crp/cAMP-mediated metabolic state, characterized by increased respiration in WT compared to mutant strains, was further validated using redox sensor green (RSG) dye and a reporter plasmid measuring the promoter activity of succinate:quinone oxidoreductase (SQR) genes. The SQR reporter system (Zaslaver et al., 2006) employs green fluorescent protein (GFP) expression, regulated by the promoter of the SQR operon, which includes the sdhA, sdhB, sdhC, and sdhD subunit genes. The SQR complex plays a vital role in cellular metabolism by catalyzing the oxidation of succinate to fumarate concurrently with the reduction of ubiquinone to ubiquinol, thus directly linking the TCA cycle with the respiratory ETC (Keseler et al., 2005). Our results indicate an upregulation of the SQR promoter activity in WT cells compared to the mutant strains in the stationary phase, validating the findings from our metabolomics and proteomics data (Figure 5A). The RSG dye, on the other hand, serves as a well-established metabolic indicator, measuring bacterial oxidation-reduction activity, a crucial function involving the ETC driven by the TCA cycle. Once reduced by bacterial reductases, the RSG dye emits a stable green fluorescent signal (Figure 5—figure supplement 1). Our data demonstrate that the redox activities of WT cells are much higher and more heterogeneous compared to those of the mutant strains in the late stationary phase, further corroborating the results from our preceding analyses (Figure 5B). Furthermore, we utilized a methodology that integrates the mCherry expression system, flow cytometry, and ampicillin-mediated cell lysis to determine whether persister cells in WT still maintain their respiration. In this assay, both the WT and mutant strains carrying the mCherry expression system were exposed to ampicillin after transferring them to a fresh medium. The inducer was added to both growth and treatment cultures to sustain the cells' red signals. Unlike other antibiotics, ampicillin disrupts cell wall synthesis, leading to the lysis of cells upon their resumption of growth. As seen in Figure 5—figure supplement 2, the cells that were lysed lost their mCherry signals. On the other hand, the resilient, tolerant cells that evaded ampicillin-induced lysis maintained their mCherry levels throughout the treatment. In the mutant strains, ampicillin was effective in lysing almost all cells as anticipated, leaving no or a small number of intact cells (Figure 5—figure supplement 2). However, in the WT strain, we detected a subpopulation of intact cells throughout the entire treatment period (Figure 5C, Figure 5—figure supplement 2). While the population-level redox activities of these tolerant intact cells in WT are lower than those of exponential phase cells, they still displayed a significant increase in RSG levels compared to cell populations before antibiotic treatments or untreated control cells subjected to identical conditions (Figure 5D), suggesting that they maintain steady-state energy metabolism. We want to highlight that not all intact cells in the WT strain reported here are persisters. A significant portion comprises 'viable but non-culturable' (VBNC) cells, and WT cells exhibit markedly higher VBNC levels than Δcrp and ΔcyaA strains (Figure 5—figure supplement 3). VBNC cells can be quantified from intact cells following beta-lactam treatments (Orman and Brynildsen, 2013b; Roostalu et al., 2008). These cells may exhibit metabolic activities but are unable to readily colonize upon transfer to fresh medium (Ayrapetyan et al., 2018; Orman and Brynildsen, 2013b). Collectively, our metabolic measurement data (Figure 5A–C) aligns with the findings from our omics analyses, and the number of intact cells observed in WT after beta-lactam treatment is consistent with the count of non-growing cells in WT (Figure 4B vs Figure 5—figure supplement 2). These non-growing cells are anticipated to be less susceptible to lysis by beta-lactams (Orman and Brynildsen, 2013b; Roostalu et al., 2008).

Although some metabolic genes, including those encoding the TCA cycle (e.g. sdhA, sucB, mdh, icd), have been studied in E. coli (Luidalepp et al., 2011; Ma et al., 2010; Manuse et al., 2021; Orman and Brynildsen, 2015; Yu et al., 2019), a comprehensive genomic-level screening strategy is necessary to validate which metabolic pathways are truly associated with antibiotic tolerance. To further underscore the importance of energy metabolism, we conducted a high-throughput screening of 149 different E. coli K-12 BW25113 mutant strains from the Keio knockout library (Baba et al., 2006). The selected strains are related to central carbon metabolism, encompassing glycolysis, pentose phosphate pathways, TCA cycle, ETC, ATP synthase, and fermentation pathways (Figure 5E). While the deletion of genes related to glycolysis and pentose phosphate pathways did not affect antibiotic tolerance in the cells, some mutant strains associated with cytochrome bo and quinone oxidoreductase complexes (e.g. cyo genes and nuoL) exhibited enhanced tolerance (Supplementary file 4). However, the mutant strains exhibited reduced tolerance to both antibiotics compared to the control E. coli K-12 BW25113 WT strain, which was found to be largely associated with the TCA pathway (sucA, sucB, lpd, sucC, sucD, sdhA, sdhB, sdhC, sdhD, gltA, acnB, aceE, fumA, mdh and fumC), ETC (nuoB, nuoC, nuoI, nuoK, nuoM, and narV), ATP synthesis (atpA, atpB, atpC, atpD, atpE, and atpH), and mixed acid fermentation pathways (ldhA, fdhF, pta, adhE, and frdC) (Figure 5E, Supplementary file 4). We acknowledge that the Keio strains were generated in a high-throughput manner, and there might be unknown errors in their genomic DNA. To ensure the reproducibility of our findings, we generated knockout strains for key genes associated with the TCA, ETC, ATP synthase, glycolysis, and pentose phosphate pathway (Figure 5F, see Supplementary file 5 for detailed description of genes). We then tested their antibiotic tolerance, and our results were consistent with the omics data and screening outcomes (Figure 5F), confirming the critical role of energy metabolism, specifically the TCA cycle, ETC, and ATP synthase, in bacterial persistence. We note that equal numbers of cells from each strain were transferred into antibiotic treatment media to ensure consistency in cell numbers, and no significant growth deficiencies or differences in cell density were observed in the late stationary phase of the knockout strains (Figure 5—figure supplement 4).

All experiments were conducted using E. coli K-12 MG1655 wild-type (WT) and its derivative strains. E. coli K-12 MG1655, MO strains (carrying the mCherry expression system), hipA7 (high persister strain), and pUA66 plasmids were obtained from Mark P. Brynildsen at Princeton University. E. coli MO strain was used to monitor cell proliferation at single cell level due to its chromosomally integrated isopropyl β-D-1-thiogalactopyranoside (IPTG)-inducible mCherry expression cassette (Orman and Brynildsen, 2013b; Orman and Brynildsen, 2013a; Orman and Brynildsen, 2015). E. coli K-12 BW25113 WT and single deletions were obtained from Dharmacon Keio Collection (Dharmacon, Catalog# OEC4988, Lafayette, CO, USA). The mutant strains in this study were generated using the Datsenko-Wanner method (Datsenko and Wanner, 2000). The pUA66-EV was generated by the removal of the gfp gene from the plasmid. The crp gene with its promoter was cloned into the modified pUA66 plasmid to obtain the pUA66-crp expression system. The sdhABCD reporter (pMSs201-P_sdhABCD-gfp) and the cyaA reporter (pMSs201-P_cyaA-gfp) were obtained from a previous study (Zaslaver et al., 2006). The cloning method was followed according to a standard method from NEB (Tirabassi and Bio, 2014). Genetic modifications were verified by PCR and gene sequencing (Genewiz, South Plainfield, NJ, USA). A complete list of strains, plasmids, and oligonucleotides used in this study is presented in Appendix 1—key resources table.

All chemicals used in this study were purchased from Fisher Scientific (Atlanta, GA, USA), VWR International (Pittsburgh, PA, USA), or Sigma Aldrich (St. Louis, MO, USA). Luria-Bertani (LB) medium was prepared by combining 5 g of yeast extract, 10 g of tryptone, and 10 g of sodium chloride in 1 L of autoclaved deionized (DI) water. LB agar media was prepared by mixing 40 g of pre-mixed LB agar with 1 L of autoclaved DI water; LB agar media were used to enumerate CFUs (Amato et al., 2013; Keren et al., 2004a; Orman and Brynildsen, 2015). For washing cells and removing chemicals and antibiotics before plating on agar media, 1 X Phosphate-Buffered Saline (PBS) was employed. In the persister assay, concentrations of 5 μg/mL of ofloxacin (OFX), 200 μg/mL of ampicillin (AMP), and 50 μg/mL of gentamicin (GEN) were used (Allison et al., 2011; De Groote et al., 2009; Keren et al., 2004a; Keren et al., 2004b). The retention of plasmids necessitated 50 μg/mL of kanamycin (KAN) in the culture media (Orman and Brynildsen, 2015). Fluorescent protein expression was induced using 1 mM IPTG (Orman and Brynildsen, 2015). Overnight pre-cultures were prepared in 14 mL Falcon test tubes containing 2 mL of LB medium, inoculated from a 25% glycerol cell stock stored at –80 °C, and incubated for 24 hr at 37 °C with shaking at 250 revolutions per minute (rpm). Main cultures were established by diluting the overnight pre-cultures at a ratio of 1:1000 into 2 mL fresh LB medium in 14 mL Falcon test tubes. Experimental cell cultures were prepared by further dilution of the main cultures into either 25 mL fresh LB medium in 250 mL baffled flasks or 2 mL fresh LB medium in 14 mL Falcon test tubes. Cultures at t=5 hr and t=24 hr were defined as early and late stationary phase cultures, respectively. Detailed experimental procedures are outlined below.

Main cultures were prepared by diluting the overnight pre-cultures at a ratio of 1:1000 into 2 mL of fresh LB medium in 14 mL Falcon test tubes. These cultures were then incubated at 37 °C with shaking at 250 rpm. Cell growth was monitored by measuring the optical density at 600 nm wavelength (OD₆₀₀) using a Varioskan LUX Multimode Microplate Reader (Thermo Fisher, Waltham, MA, USA). The plate reader data was collected using Skanlt Software V 5.0. Cell cultures at both early and late stationary phases were collected from the test tubes and transferred to the baffled flasks to achieve ~5 × 10⁷ cells/mL. This concentration represents an approximately 100-fold dilution of the WT main culture into a fresh medium within the flask. It is important to highlight that we consistently employed flow cytometry to quantify the initial cell count (refer to the section ‘Monitoring cell division’ for comprehensive details). As needed, adjustments in cell number-to-volume were executed to ensure the same cell number among both WT and mutant strains. These cultures were then treated with antibiotics at the indicated concentrations and were cultured with shaking at 37 °C for 20 hr. After treatment, a 1  mL sample from each flask was transferred to a microcentrifuge tube for a rigorous washing protocol to minimize antibiotic carryover. The samples were centrifuged at 13,300 RPM (17,000 × g) for 3 min, and >950  µL of supernatant was carefully removed without disturbing the pellet. The pellet was then resuspended in 950  µL of PBS, achieving a >20-fold dilution of residual antibiotics. This wash step was repeated once more, resulting in a cumulative >400-fold dilution. After the final wash, cells were resuspended in 100  µL of PBS. A 10  µL aliquot of this suspension was serially diluted in a 96-well round-bottom plate, and 10  µL from each dilution was spotted onto an agar plate. The remaining 90  µL of the sample was also plated to quantify CFU levels around the limit of detection, (~1  CFU/mL). Plates were incubated at 37  °C for 16 hr (or up to 48 hr) to allow CFUs to develop. When necessary, this washing procedure was repeated up to six times to assess its impact on CFU recovery.

Overnight cultures of E. coli K-12 MG1655 or BW25113 WT, and mutant strains were diluted 1:100 in 2 mL of LB medium in 14 mL Falcon tubes and incubated at 37 °C with shaking at 250 rpm. At mid-exponential phase (OD₆₀₀~0.25), cells were challenged with the indicated antibiotics and concentrations. At designated time points, cells (1 mL) were collected, washed with 1 X PBS, and plated on agar to determine colony-forming units (CFU). To quantify the initial cell count, 10 µL of the culture before treatments was serially diluted and plated on LB agar. CFU levels were assessed after incubating the plates for at least 16 hr at 37 °C.

An overnight pre-culture of E. coli K-12 MG1655 WT was prepared in test tubes with 2 mL of LB medium. The culture was incubated at 37 °C with shaking at 250 rpm for 24 hr. For the experimental cultures, a 1:1000 dilution was made in fresh medium. At time points 0, 2, 4, 6, 8, and 24 hr, 100 μL of the experimental culture was washed with cold 1 X PBS. After centrifuging at 13,300 rpm (17,000 x g), the supernatant was removed. The cells were then resuspended in 100 μL of cell lysis buffer from the Cyclic AMP XP Assay Kit (Catalog# 4339 S, Cell Signaling Technology, Danvers, MA, USA) on ice for 10 min. Next, 50 µL of lysed cells was mixed with the kit’s horseradish peroxidase (HRP)-linked cAMP solution in the cAMP assay plate. This mixture was incubated at room temperature for 3 hr on a plate shaker at 250 rpm. Following the 3 hr incubation, the plate content was discarded, and the plate was washed four times with the kit’s Wash Buffer. Then, 100 µL of tetramethylbenzidine (TMB) substrate was added to allow color development, and after 30 min, 100 µL of the stop solution provided by the kit was added. The absorbance was measured at 450 nm using a plate reader. The standard curve was prepared using the same conditions and the standard cAMP solutions provided by the kit to determine cAMP concentrations.

Metabolites from both Δcrp and WT cells were analyzed at the Metabolon, Inc facility (Morrisville, NC, USA). Cells were cultured until the early stationary phase and late stationary phase at 37 °C with shaking at 250 rpm. Afterward, cells were collected through centrifugation at 4700 rpm at 37 °C for 15 min, yielding a pellet of approximately 100 μL containing around 10¹⁰ cells. Subsequently, the cells were washed once with 1 X PBS and then centrifuged (13,000 rpm, 3 min at 4 °C). Following this, they were frozen in an ethanol/dry ice bath for 10 min. The extracts from both mutant and WT cells were subjected to analysis using ultra-high-performance liquid chromatography-tandem accurate mass spectrometry (MS), a process aimed at identifying a wide range of metabolites. Sample extraction, preparation, instrument settings, and conditions for the MS platform adhered to Metabolon’s protocols (as detailed in our previous study) (Mohiuddin et al., 2022). To identify the sample’s metabolites among potential false positives from instrument noise, process artifacts, and redundant ion features, the results were cross-referenced with Metabolon’s extensive metabolite library (standards). Data normalization was carried out based on protein concentration, determined using the Bradford assay. The significant difference between mutant and WT was identified using Welch’s two-sample t-test. Further analysis involved pathway enrichment assessment using MetaboAnalyst (Lu et al., 2023). This involved inputting the upregulated and downregulated metabolites based on chosen thresholds. A comprehensive overview of metabolite measurements, pathway enrichment, statistical analyses, and data representations can be found in our previously published study (Mohiuddin et al., 2022).

Overnight cultures for both E. coli K-12 MG1655 WT and Δcrp strains were prepared using 2 mL of LB medium. Incubation was carried out at 37 °C and 250 rpm for 24 hr. The following day, the main cultures were established under the same conditions, using a 1000-fold dilution of the overnight culture in 2 mL fresh LB medium. After 24 hours, the OD₆₀₀ of both WT and mutant strains was measured and adjusted to an OD₆₀₀ of 2.5. For further processing, 2 mL of the main culture was washed twice with cold 1 X PBS, maintaining the cold environment throughout. Centrifugation conditions were set at 4 °C, 13,000 rpm for 3 min. Before the final centrifugation, a cell count was conducted using flow cytometry. This involved using 10 µL of washed culture and 990 µL of 1 X PBS. Subsequent to centrifugation, the pellets were collected. Cell lysis was carried out using 300 µL of NEBExpress E. coli Lysis Reagent (Catalog# P8116S, Ipswich, MA, USA) at room temperature for 30 mins. Following this, the lysed samples were centrifuged at 16,600 x g for 10 min, and 250 µL of supernatants were collected from each sample for the assay. The total protein concentration of the supernatants was determined using the bicinchoninic acid (BCA) assay (Catalog# 23225, Thermo Fisher Scientific, Waltham, MA, USA). In a 96-well plate, 25 µL of each cell lysate sample, diluted 5 and 10 times with ultra-pure DI water, were loaded into each well. Subsequently, 200 µL of the BCA working reagent (50:1, Reagent A:B) was added to each well and mixed on a plate shaker for 30 s. The plate was then incubated at 37 °C for 30 min, followed by cooling for 5 min at room temperature, shielded from light. The absorbance was finally measured at 562 nm using a plate reader, and the total protein concentration in each sample was calculated using a standard curve prepared from standard protein solutions. Protein analysis for both WT and mutant was conducted by the proteomics service at UT Health’s Clinical and Translational Proteomics Service Center (Houston, TX). The samples underwent acetone precipitation, during which proteins were precipitated by exposing them to –20 °C for 3 hours. Following this, a centrifugation step at 12,000 x g for 5 min separated the precipitated pellets. These pellets were subsequently subjected to denaturation and reduction using a mixture containing 30 μL of 6 M urea, 20 mM DTT in 150 mM Tris HCl (pH 8.0) at 37 °C for 40 min. Afterward, alkylation was carried out with 40 mM iodoacetamide in the absence of light for 30 min. To prepare for digestion, the reaction mixture was diluted 10-fold using 50 mM Tris-HCl (pH 8.0) and then incubated overnight at 37 °C with trypsin at a 1:30 enzyme-to-substrate ratio. The digestion process was terminated by adding an equal volume of 2% formic acid, followed by desalting using Waters Oasis HLB 1 mL reverse phase cartridges, following the vendor’s recommended procedure. Finally, the elutes were dried using vacuum centrifugation. Approximately 1 µg of the tryptic digest, prepared in a solution containing 2% acetonitrile and 0.1% formic acid in water, underwent analysis using LC/MS/MS. The instrument used was the Orbitrap Fusion Tribrid mass spectrometer by Thermo Fisher Scientific, connected to a Dionex UltiMate 3000 Binary RSLCnano System. The separation of peptides occurred on an analytical C18 column with dimensions of 100 μm ID x 25 cm, featuring 5 μm particles and an 18 Å pore size. Peptides were eluted at a flow rate of 350 nL/min. The gradient conditions applied were as follows: a gradient starting from 3% B and increasing to 22% B over a duration of 90 min, followed by a step to 22–35% B for 10 min, then another step to 35–90% B for 10 min, and finally, maintaining 90% B for an additional 10 min (Solvent A was composed of 0.1% formic acid in water, while solvent B contained 0.1% formic acid in acetonitrile). The peptides were analyzed using a data-dependent acquisition method. The Orbitrap Fusion MS operated by measuring FTMS1 spectra with a resolution of 120,000 FWHM, scanning in the m/z range of 350–1500, using an AGC target set to 2E5, and with a maximum injection time of 50ms. Within a maximum cycle time of 3 s, ITMS2 spectra were collected in rapid scan mode. High Collision Dissociation (HCD) was employed with a normalized collision energy (NCE) of 34, an isolation window of 1.6 m/z, an AGC target set to 1E4, and a maximum injection time of 35ms. Dynamic exclusion was implemented for a duration of 35 s to prevent repeated analysis of the same ions. For the experimental analysis, the Thermo Fisher Scientific Proteome Discoverer software version 1.4 was utilized to process the raw data files. The spectra were subjected to analysis against the E. coli proteome database (Swiss-Prot 29,161) through the Sequest HT search engine. Additionally, the spectra were compared against a decoy database, employing a target false discovery rate (FDR) of 1% for stringent criteria and 5% for more relaxed criteria. The enzymatic cleavage allowance for trypsin included up to two potential missed cleavages. The MS tolerance was defined as 10 ppm, while the MS/MS tolerance was set at 0.6 Da. Fixed modification involved carbamidomethylation on cysteine residues, and variable modifications encompassed methionine oxidation and asparagine deamidation. For proteomics data processing and fold change calculations, the approaches were essentially followed by the method paper from Aguilan et al., 2020. Then, the STRING tool V12.0 (Szklarczyk et al., 2021; Szklarczyk et al., 2023) was employed to find the significant networks among input proteins. To generate the protein network and pathway enrichment analysis, we input the protein identifiers (Accession numbers) for upregulated or downregulated proteins with at least a twofold increase or reduction, respectively. E. coli K-12 was selected as the organism of interest. We opted for evidence as the criterion for network edges, prioritizing the type of interaction evidence, helping us conduct an automated pathway-enrichment analysis, centering on the entered proteins and identifying pathways that occurred more frequently than expected. This analysis was grounded in the statistical background of the entire genome and encompasses various functional pathway classification frameworks, such as Gene Ontology annotations, KEGG pathways, and Uniprot keywords, as detailed elsewhere (Szklarczyk et al., 2021; Szklarczyk et al., 2023). In pathway enrichment analysis, the ‘strength score’, calculated as Log₁₀(observed/expected), serves to assess the degree or significance of enrichment within a specific biological pathway. This metric reflects the magnitude of the enrichment effect, with a higher score indicating stronger enrichment. The score is derived from the ratio of (i) annotated proteins in the network for a given term to (ii) the expected number of proteins annotated with the same term in a random network of equivalent size (Szklarczyk et al., 2021; Szklarczyk et al., 2023). To gauge the significance of enrichment, False Discovery Rate (FDR) is employed. FDR scores represent p-values corrected for multiple testing within each category using the Benjamini–Hochberg procedure (Szklarczyk et al., 2021; Szklarczyk et al., 2023).

To monitor cell division and quantify non-growing cells, we utilized inducible fluorescent protein (mCherry) expression. Overnight pre-cultures of E. coli MO were prepared with 2 mL of LB medium containing 1 mM of IPTG. These cultures were grown in test tubes at 37 °C with shaking at 250 rpm for 24 hr. Main cultures were established by diluting the overnight pre-cultures (at a ratio of 1:1000) into 2 mL of fresh LB medium in 14 mL Falcon test tubes. These cultures were incubated at 37 °C with shaking at 250 rpm. Cells were allowed to grow until they reached the early stationary phase and the late stationary phase. The mCherry-positive cells were then collected, washed twice with 1 X PBS to remove the IPTG from the culture, and subsequently re-suspended in fresh 2 ml LB media in test tubes to achieve ~5 × 10⁷ cells/mL. This concentration represents an approximately 100-fold dilution. When needed, adjustments in cell number-to-volume were made to ensure the same cell number among both WT and mutant strains. In the experimental culture test tubes, 2 mL of LB medium was added, and the volume of washed cells was inoculated to achieve an OD₆₀₀ of 0.0286. The culture was then incubated at 37 °C with shaking at 250 rpm. At specific time points (0, 1, 2, and 2.5 hr), cells were collected and re-suspended in 1 X PBS to measure their fluorescent protein content using flow cytometry. For flow cytometry analysis, cells were collected and diluted to a desired cell density (~10⁶–10⁷ cells/mL) in 1 mL of 1 X PBS in flow cytometry tubes (5 mL round-bottom Falcon tubes, size: 12x75 mm). The flow cytometry analysis was conducted using a NovoCyte 3000RYB instrument (ACEA Bioscience Inc, San Diego, CA, USA). During flow cytometry analysis, a slow sample flow rate of 14 μL/min was chosen, along with a sample stream diameter (core diameter) of 7.7 μm. The instrument maintained a constant sheath flow rate of 6.5 mL/min. The core diameter was calculated using the ratio of the sample flow rate to the sheath flow rate. These specific conditions were selected to achieve improved data resolution for the size of E. coli cells. Flow diagrams utilized forward and side scatter signals from viable cells, alongside a control of solvent devoid of cells, to ascertain the presence of cells. For the flow cytometry analysis, cells were excited at a 561 nm wavelength, and the red fluorescence was detected using a 615/20 nm bandpass filter.

Overnight cultures of E. coli K-12 MG1655 MO WT and mutant strains were diluted at a ratio of 1:1000 into 2 mL of fresh LB medium, placed in 14 mL Falcon test tubes, and incubated at 37 °C with shaking at 250 rpm for 24 hr. For the main cultures, a similar strategy was employed. The cultures were diluted at a ratio of 1:100 into 2 mL of fresh LB medium in 14 mL Falcon test tubes. These cultures were then incubated at 37 °C with shaking at 250 rpm. At specific time points, including t=0, 20 min, 40 min, and 1–5 hr, the cell growth was halted. This was achieved by diluting the cells in 1 X PBS containing 25 μg/mL of chloramphenicol (CAM). The CAM treatment allowed for subsequent analysis without further division. Flow cytometry was then utilized to measure the number of cells at each of these time points. This approach provided insight into cell division dynamics and allowed for the quantification of cell populations under specific conditions.

Mutant and control strains were derived from E. coli K-12 MG1655 and carried pMSs201-gfp plasmids incorporating P_sdhABCD or P_cyaA gene promoters. Overnight pre-cultures were prepared using 2 mL of LB medium supplemented with 50 μg/mL KAN. These cultures were incubated in test tubes within a shaker at 37 °C for 24 hr. Main cultures were established by diluting the overnight pre-cultures at a ratio of 1:1000 into 2 mL of fresh LB medium within 14 mL Falcon test tubes. These main cultures were maintained at 37 °C with shaking at 250 rpm. Cell cultures at the desired growth phase were collected and then diluted to attain a desired cell density of around 10⁶–10⁷ cells/mL in 1 mL of 1 X PBS within flow cytometry tubes. This allowed for subsequent flow cytometry analysis, using the same conditions as described earlier for monitoring cell division (refer to ‘Monitoring cell division’). During analysis, a laser emitting light at 488 nm was used to excite the cells, and the resulting green fluorescence was detected using a 530/30 nm bandpass filter. This setup enabled the examination of the fluorescence patterns of the cells, offering insights into their dynamics under different conditions.

To gauge bacterial metabolic activity, we employed the Redox Sensor Green (RSG) dye from Thermo Fisher (Catalog# B34954, Thermo Fisher Scientific, Waltham, MA). E. coli K-12 MG1655 WT and mutant cells from the desired growth phase were diluted at a ratio of 1:100 in 1 mL of 1 X PBS. To this solution, 1 μL of the RSG dye was added to flow cytometry tubes. After a brief vortexing to ensure uniform mixing, the samples were incubated at 37 °C in darkness for 10 min. Subsequently, these samples were subjected to flow cytometry analysis. For the flow cytometry analysis, the same methodology as employed in ‘Monitoring cell division’ was followed, with one variation. Cells were excited at 488 nm during analysis, and the resulting green fluorescence was detected using a 530/30 nm bandpass filter. This setup allowed us to assess the fluorescence patterns, reflecting the metabolic activity of the bacterial cells under different conditions. As a control measure, cells were treated with 20 μM of carbonyl cyanide m-chlorophenyl hydrazone (CCCP) for 5 min before the addition of the RSG dye. This served to validate the assay’s sensitivity to changes in metabolic activity (Figure 5—figure supplement 1).

Overnight cultures of E. coli K-12 MG1655 MO WT and mutant strains were diluted at a ratio of 1:1000 into 2 mL of fresh LB medium supplemented with 1 mM IPTG. These cultures were established in 14 mL Falcon test tubes and incubated at 37 °C with shaking at 250 rpm for 24 hr. Treatment cultures were prepared by diluting the main cultures at a ratio of 1:100 into 25 mL of fresh LB medium supplemented with 1 mM IPTG. These cultures were set up in 250 mL baffled flasks and contained 200 μg/mL AMP. They were then cultured at 37 °C with shaking at 250 rpm for 20 hr. Both before and after the treatment, 1 mL samples were collected from the cultures. These samples were subjected to a washing procedure with 1 X PBS to eliminate the antibiotic present in the samples. The washed cells were then resuspended in 1 mL of 1 X PBS within flow cytometry tubes. To measure the metabolic activity of the non-lysing cells, the RSG dye was employed as described above. Intact cells (following antibiotic treatment), stained as live with RSG, comprised both persister and VBNC cells. Persister levels were quantified by plating the cells on an agar medium, as described previously (Orman and Brynildsen, 2013b). As VBNC cells cannot grow on agar medium, their enumeration involved subtracting the number of persister cells from the total number of intact cells.

Overnight cultures of individual mutant strains, along with their parental strain K-12 BW25113 WT harboring a kanamycin-resistant marker, were diluted at a ratio of 1:1000 in fresh LB medium containing 50 μg/mL of KAN. This was done in 14 mL Falcon test tubes and the cultures were then incubated at 37 °C with shaking at 250 rpm. Upon reaching the late stationary phase, cells were further diluted at a ratio of 1:100 in fresh medium supplemented with antibiotics at specified concentrations. These cultures were once again incubated at 37 °C with shaking for 20 hr. Following the 20 hr treatment period, the same methodology described in the section ‘Cell growth and persister assays’ was employed to quantify the number of persisters. This approach allowed for an assessment of the impact of antibiotics on the formation of persister cells for both mutant strains and the parental K-12 BW25113 WT strain.

Overnight cultures of mutant strains were diluted at a ratio of 1:1000 in 14 mL Falcon test tubes containing 2 mL of LB medium. These cultures were then incubated at 37 °C with shaking at 250 rpm. Upon reaching the late stationary phase, cells were diluted at a ratio of 1:100 in fresh medium supplemented with antibiotics at specified concentrations. The cultures were once again subjected to shaking at 37 °C for 20 hr. The same method described earlier, referred to as ‘Cell growth and persister assays’, was employed to quantify the number of persister cells resulting from this treatment. This approach allowed for the assessment of the impact of antibiotic exposure on persister cell formation within the mutant strains.

A nonlinear logarithmic model was employed to create biphasic kill curves (Mohiuddin et al., 2020a; Windels et al., 2019). The significance of these kill curves was determined through the utilization of F-statistics (Mohiuddin et al., 2020a; Windels et al., 2019). Metabolomics data were subjected to analysis using Welch’s two-sample t-test in order to identify metabolites that significantly differed between the control and mutant groups (Yuen, 1974). For all experiments, a minimum of three independent biological replicates were conducted, unless explicitly stated otherwise. In each figure (excluding flow diagrams), the data for each time point are represented as the mean value accompanied by the standard deviation. In terms of statistical significance analysis, the designated threshold values for p were set as follows: *p<0.05, **p<0.01, ***p<0.001, and ****p<0.0001. All figures were generated using GraphPad Prism 10.3.0. The statistical analyses were carried out using the statistical functions of GraphPad Prism 10.3.0. For the clustering of metabolomics and proteomics data, the ‘Clustergram’ function of MATLAB (V R2020b) was employed. FlowJo (V 10.8.1) was the tool used to analyze the data acquired from flow cytometry.