Trehalose catalytic shift inherently enhances phenotypic heterogeneity and multidrug resistance in Mycobacterium tuberculosis

Abstract

Drug-resistance (DR) in bacteria often develops through the repetitive formation of drug-tolerant persisters, which survive antibiotics without genetic changes. It is unclear whether Mycobacterium tuberculosis (Mtb), the bacterium that causes tuberculosis (TB), undergoes a similar transitioning process. Recent studies highlight changes in trehalose metabolism as crucial for persister formation and drug resistance. Here, we observe that mutants lacking trehalose catalytic shift activity exhibited fewer DR mutants due to decreased persisters. This shift enhances Mtb survival during antibiotic treatment by increasing metabolic heterogeneity and drug tolerance, facilitating drug resistance. Rifampicin (RIF)-resistant bacilli display cross-resistance to other antibiotics linked to higher trehalose catalytic shift, explaining how multidrug resistance (MDR) can follow RIF-resistance. In particular, the HN878 W-Beijing strain exhibits higher trehalose catalytic shift, increasing MDR risk. Both genetic and pharmacological inactivation of this shift reduces persister formation and MDR development, suggesting trehalose catalytic shift as a potential therapeutic target to combat TB resistance.

Introduction

The World Health Organization (WHO) estimated that between 2000 and 2020, over 200 million people contracted tuberculosis (TB), and more than 35 million died from the disease¹. As of 2023, approximately one-quarter of the global population remained infected with Mycobacterium tuberculosis (Mtb), the bacterium that causes TB. Conventional TB treatment typically involves administering multiple antibiotics over an extended period, sometimes up to two years. This lengthy duration is largely due to the broad antibiotic tolerance exhibited by Mtb, which allows some bacteria to survive antibiotic exposure without acquiring genetic resistance^2,3. This phenotypic heterogeneity primarily arises from the stochastic formation of a slowly replicating, metabolically distinct subpopulation known as persisters. These persisters are often triggered by antibiotic treatment^4,5,6,7, and can survive lethal antibiotic doses, typically only being eliminated through extended, multi-drug regimens. The lengthy treatment duration significantly contributes to patient noncompliance, which in turn fosters the emergence of multidrug-resistant (MDR) TB. The unchecked spread of MDR-TB poses a major challenge to global efforts to control the disease. Therefore, it is urgent to understand the intrinsic factors within Mtb that promote MDR development and to discover new treatments aimed at preventing resistance from emerging during chemotherapy.

Persisters are phenotypic variants formed through metabolic remodeling rather than genetic changes⁸. Their formation and ability to withstand antibiotic effects are well-known intrinsic factors that Mtb employs to survive antibiotic pressure. Intriguingly, the level of antibiotic susceptibility remains unaltered when persisters regrow under antibiotic-free conditions. Evidence indicates that persisters surviving lethal doses of TB antibiotics are responsible for chronic and recurrent infections^9,10,11. Furthermore, persisters are a source of genetic mutation-mediated MDR, as they often exhibit resilience to DNA damage caused by reactive oxygen species (ROS) generated within the bacilli during bactericidal antibiotic treatment^{8,12,13,14,15}.

Extensive research has focused on elucidating the molecular mechanisms underlying persister biology, aiming to advance TB chemotherapeutics. Environmental cues such as nutrient starvation, redox stress, low pH, hypoxia, and uncured DNA damage have been identified as triggers for persister formation^11,16,17,18. Bacterial intrinsic factors that help adapt to these stresses, including toxin-antitoxin (TA) modules in Escherichia coli^19,20, also regulate persistence. Recently, metabolic remodeling has emerged as a pivotal strategy used by bacterial pathogens, including Mtb, to generate genetic mutation-free persisters and escalate antibiotic tolerance^{7,8,13,21,22,23,24}. Metabolomics studies revealed that Mtb phenotypic heterogeneity is attributed to the formation of subpopulations with altered central carbon metabolism (CCM). This includes the capacity to co-catalyze multiple carbon sources, such as glycolytic and gluconeogenic substrates, to support full replication. It also involves the catabolic remodeling of cell wall glycolipids to enhance Mtb persister biology, bypassing the oxidative branch of the TCA cycle to downregulate NADH production. Additionally, Mtb can reroute carbon flux through the glyoxylate shunt or methylcitrate cycle, enhancing succinate biosynthesis and secretion to optimize membrane bioenergetics under diverse stresses^8,13,22,25. Thus, metabolic remodeling is a crucial intrinsic factor that enables Mtb persisters to survive antibiotic effects. Mtb persisters rely on a distinct metabolic network that provides a phenotypic advantage, allowing them to withstand antibiotic stresses and associated DNA damage^{21,22,24,26,27,28,29,30,31}.

Phenotypic heterogeneity has increasingly garnered attention as an additional intrinsic factor that promotes adaptive evolution in Mtb³². Expanding bacterial subpopulations with diverse metabolic activities can lead to the emergence of genotypes that differ from those of the original population. These heterogenous subpopulations serve as reservoirs for bacilli capable of withstanding bactericidal stresses. Recent mathematical simulations have demonstrated that even slight increases in mutation rates considerately accelerate the development of drug resistance, primarily driven by induced phenotypic heterogeneity^33,34. A proof-of-concept study in yeast supported these findings, showing that initial drug-resistance mutations in a small fraction resulted in higher minimal inhibitory concentration (MIC) and faster progression of MDR. Moreover, elevated expression of efflux pumps, mediated through mechanisms involving mutS, further heightened mutation rates and phenotypic heterogeneity^35,36. An example of how metabolic network remodeling fosters phenotypic heterogeneity is the random emergence of persisters, often described as a “gambler” subpopulation³³. When exposed to antibiotics, only a subset of bacilli activate their DNA repair systems and stress responses³⁷, leading to increased ROS levels. Since both the SOS response and general stress responses are essential for mutagenic DNA repair, persisters exhibit a significantly higher mutation rate. Furthermore, ample evidence indicates that adaptive metabolic remodeling enhances phenotypic heterogeneity, providing an evolutionary advantage through increased DNA mutagenesis.

We recently identified trehalose synthase (TreS) as a key mediator of metabolic remodeling and phenotypic heterogeneity that promote the formation of Mtb persisters^13,22. Trehalose, a non-reducing glucose disaccharide abundant in Mtb, functions as both a carbohydrate store and a core component of cell wall glycolipids such as trehalose monomycolate (TMM) and trehalose dimycolate (TDM)³⁸. Metabolomics analyses of Mtb bacilli collected from in vitro biofilm cultures revealed that a TreS-centered catalytic shift redirects free trehalose toward the biosynthesis of CCM intermediates. This provides an alternate source of energy and antioxidants, while diverting trehalose away from TMM and TDM production. As expected, a treS-deficient mutant (ΔtreS), lacking this catalytic shift, exhibited increased sensitivity to TB antibiotics, underscoring the importance of TreS-mediated metabolic remodeling in Mtb persister formation and antibiotic tolerance. We have also developed TreS-specific inhibitors and confirmed their potential as adjunctive therapeutic candidates³⁹. Notably, the trehalose catalytic shift activity appears to be higher in DR-TB clinical isolates compared to drug-sensitive (DS)-TB clinical isolates, suggesting that this pathway may facilitate the emergence of drug resistance.

In this study, we demonstrate that inactivating the trehalose catalytic shift via CRISPRi-dCas9 technology not only limits persister formation and phenotypic heterogeneity but also reduces the rates of resistance development against key first-line TB antibiotics, rifampicin (RIF) and isoniazid (INH). We employed mathematical modeling to show that mycobacterial bacilli enhance their chances of developing resistance by increasing trehalose catalytic shift activity and phenotypic heterogeneity. The models further indicate that the transition rate from persisters to drug-resistant mutants is similar in both wildtype and ΔtreS, implying that the metabolic remodeling involved in persister formation plays a significant role in the emergence of drug resistance. Additionally, we identified a subpopulation of Mtb, termed pre-resistant bacilli, which appear prior to the development of drug resistance. Unlike persisters, these bacilli can grow even under antibiotic stress, and their formation is largely driven by enhanced trehalose catalytic shift activity. This phenotypic heterogeneity, stemming from persisters or pre-resistant bacilli, is a critical intrinsic factor in Mtb’s ability to acquire drug resistance. Hence, the trehalose catalytic shift represents a potential target for adjunctive therapy, not only to deepen our understanding of Mtb phenotypic heterogeneity but also to prevent the emergence of MDR-TB.

Results

Recent reports indicate that trehalose acts as a growth-permissive carbon source for DR-TB clinical isolates¹³. However, the trehalose-mediated growth can be reversed when co-treated with a TreS-specific inhibitor, validamycin A (ValA)¹³. Metabolomics profiling further supports the central role of the TreS-centered trehalose catalytic shift in the metabolic networks of DR-TB clinical isolates. The catalytic conversion of trehalose into intermediates of glycolysis and the pentose phosphate pathway (PPP) suggests that DR-TB clinical isolates preferentially utilize trehalose as a substrate for biosynthesis of CCM intermediates, rather than for producing cell wall glycolipids like TDM. These findings led us to hypothesize that the trehalose catalytic shift not only contributes to transient antibiotic tolerance but also plays a critical role in the emergence of multidrug-resistant mutants.

Trehalose metabolism differs between DR-TB and DS-TB clinical isolates

To investigate trehalose metabolism networks in DR-TB and DS-TB clinical isolates, we collected a total of 75 TB isolates from the TB clinical isolate library at the International Tuberculosis Research Center (ITRC). This collection included 15 DS-TB, 15 rifampicin single-resistant (RSR)-TB, 15 MDR-TB, 15 extensively drug-resistant (XDR)-TB, and 15 totally drug-resistant (TDR)-TB clinical isolates (Supplementary Data file 1). All strains were cultured in Middlebrook 7H9 liquid medium (m7H9) supplemented with sodium butyrate (SB), a known permissive carbon source for TB isolates^13,23. The growth of both DS-TB and DR-TB clinical isolates was enhanced by the addition of 20 mM trehalose; however, co-treatment with ValA reduced growth in DR-TB, but not in DS-TB, clinical isolates (Fig. S1A). Although the heterogeneity in growth kinetics of DR-TB clinical isolates complicated precise statistical analysis, the observed impact of ValA indicated that DR-TB clinical isolates rely more heavily on TreS activity to utilize exogenous trehalose compared to DS-TB clinical isolates. To explore their metabolic networks, we extracted the total metabolome after culturing the isolates in m7H9 containing trehalose. We determined the trehalose-induced metabolic networks of TB clinical isolates by profiling ~200 TB metabolites in DR-TB and DS-TB clinical isolates. Bioinformatics analysis using MetaboAnalyst (v.6.0) identified metabolic networks uniquely altered in DR-TB clinical isolates, particularly those involved in trehalose consumption. Hierarchical clustering and heatmaps revealed distinct metabolomics patterns between DR-TB and DS-TB clinical isolates (Fig. S1B). Principal Component Analysis (PCA) further confirmed the divergence in metabolomics patterns (Fig. 1A, S1B). Pathway analysis highlighted significant alterations in trehalose metabolism, the D-alanine pathway, and the PPP. Targeted metabolomics indicated that trehalose abundance was significantly higher across all DR-TB clinical isolates (Fig. 1B, left). Furthermore, the biosynthesis of glycolytic and PPP intermediates, such as glucose 6-phosphate (Glc6P), pentose 5-phosphate (Pen5P), and sedoheptulose 7-phosphate (S7P) was comparable to or elevated in DR-TB clinical isolates relative to DS-TB clinical isolates (Fig. 1B, middle). This suggests that a substantial portion of exogenous trehalose in DR-TB clinical isolates is directed toward the biosynthesis of glycolytic and PPP intermediates¹³. Consistent with previous findings²³, levels of phosphoenolpyruvate (PEP), the most downstream glycolytic intermediate, remained unchanged across clinical isolates (Fig. 1B, right). Conversely, TCA cycle metabolites were unaltered or downregulated in DR-TB clinical isolates (Fig. S1C). To validate these observations, we performed isotope tracing experiment using fully ¹³C labeled trehalose ([U-¹³C] trehalose). We randomly selected three DS-TB and six DR-TB clinical isolates, grew them to mid-log phase, then transferred cultures to m7H10 containing 20% [U-¹³C] trehalose mixed with 80% unlabeled trehalose. After 24 h, cultures were harvested, revealing significantly higher labeling in Glc6P and S7P, but not in malate (a TCA cycle intermediate). This indicates that exogenous trehalose is preferentially catabolized through glycolysis and PPP in DR-TB clinical isolates compared to DS-TB clinical isolates (Fig. S1D). The metabolic flux appears to have minimal impact on the biosynthesis of TCA cycle intermediates. Notably, treS mRNA expression remained unaltered across all DR-TB clinical isolates, with a slight induction in TDR-TB isolates, likely reflecting associated genetic mutations (Fig. 1C). Collectively, these findings demonstrate that the metabolic networks involved in trehalose consumption differ between DS-TB and DR-TB clinical isolates, with regulation occurring independently of transcriptional changes.

Fig. 1: Metabolic networks of DS-TB and DR-TB clinical isolates altered by trehalose supplementation.

A 2D (left panel) and 3D (right panel) principal component analysis (PCA) of metabolome profiles from DS- (red), RSR- (green), MDR- (blue), XDR- (magenta), and TDR-TB (light blue) clinical isolates cultured in m7H9 containing 20 mM trehalose. B Targeted metabolomics analysis focusing on intermediates in trehalose metabolism, glycolysis, and the pentose phosphate pathway. PEP, phosphoenolpyruvate. Data points represent mean values ± s.e.m. of 15 biological replicates and p-values were determined by one-way ANOVA with Bonferroni post-test correction (ns, not significant). IC ion counts. C qRT-PCR analysis of treS expression in TB clinical isolates. Data points represent mean values ± SD of 15 biological replicates and p-value was determined by Student’s unpaired t-test with Welch’s correction. Source data are provided as a Source Data File.

TreS-deficient M. smegmatis phenocopies Mtb mutants that lack trehalose catalytic shift

To study the role of the trehalose catalytic shift in the development of drug resistance in mycobacteria, we employed the CRISPRi-dCas9 technique to inducibly deplete treS gene expression in M. smegmatis (Fig. S2A)^40,41. The CRISPRi treS mutant of M. smegmatis (termed ItreS^SM) was cultured to mid-log phase, and treS knockdown was induced using varying concentrations of anhydrotetracycline (ATc). The efficacy of treS mRNA suppression was assessed by qRT-PCR, with 200 ng/mL ATc achieving ~90% knockdown (Fig. S2B left). We also generated IotsA^SM, targeting otsA, which encodes trehalose 6P synthase involved in Mtb trehalose metabolism but not directly implicated in the trehalose catalytic shift. Similar to treS-deficient Mtb (ΔtreS)¹³, ItreS^SM produced persister-like bacilli within in vitro biofilm cultures (referred to as biofilm-persisters) at significantly lower levels than wildtype following ATc treatment. In contrast, both IotsA^SM and wildtype formed mature biofilm-persisters, without any apparent growth defects in Sauton media (Fig. S2C, D). ItreS^SM without ATc served as a control and exhibited biofilm-persister formation comparable to wildtype (Fig. S2D). Targeted metabolomics revealed that the inability of ItreS^SM to form intact biofilm-persisters was primarily due to impaired trehalose catalytic shift, which disrupted trehalose-mediated carbon flux through glycolysis and the PPP (e.g., Glc6P, glyceraldehyde 3P, and S7P) (Fig. S2E). Notably, depletion of PEP abundance and the PEP/pyruvate ratio has been identified as a metabolic strategy used by Mtb to induce persister formation, slow replication, and increase antibiotic tolerance²³. Interestingly, ItreS^SM accumulated PEP compared to wildtype (Fig. S2E). As a result, ItreS^SM displayed increased susceptibility to antibiotics, such as RIF, INH, and BDQ relative to wildtype or IotsA^SM (Fig. S2F). These phenotypes closely mimic those observed in ΔtreS Mtb¹³. These findings collectively indicate that ItreS^SM phenocopies ΔtreS Mtb.

Fig. 2: Trehalose catalytic shift as an adaptive strategy for the emergence of drug-resistant mycobacterial mutants.

A The rates at which the indicated mycobacterial strains acquired resistance to RIF (left panel) or INH (right panel) per generation were measured using the classical fluctuation assay. Values represent mean values ± s.e.m. of 25 biological replicates and p-values were determined by Student’s unpaired t-test with Welch’s correction. Data depict median (center bar), 25th and 75th percentile (lower and upper box bounds), and minimum and maximum values (lower and upper whiskers). B Colony formation assessed by spot assay. Ten colonies from RIF-resistant bacilli obtained in (A, left panel) were spotted onto m7H10 containing either no RIF or 25 µg/mL RIF. WT, naïve drug-sensitive M. smegmatis bacilli. C Co-culture of wildtype M. smegmatis expressing green fluorescence protein (GFP) and ItreS^SM expressing red fluorescence protein (RFP) was subjected to intermittent exposure to RIF over 5 cycles, designated G0–G5 subcultures. The relative enrichment of wildtype and ItreS^SM in each subculture was quantified by flow cytometry and expressed as percentage. Data points represent mean values ± s.e.m. of biological triplicates. Gray bars represent ItreS^SM; black bars represent wildtype. Source data are provided as a Source Data File.

The trehalose catalytic shift is an adaptive strategy that promotes the emergence of drug-resistant mycobacterial mutants

If Mtb persisters survive antibiotic-induced oxidative stresses, such as ROS, which are a known DNA mutagen, their prolonged survival increases the likelihood of developing drug-resistant mutations. The metabolic strategies employed by Mtb persisters during this stage are directly or indirectly involved in the emergence of drug resistance^42,43. To investigate whether the trehalose catalytic shift is functionally linked to the emergence of drug-resistant mutants, we performed a classical Luria-Delbrück fluctuation assay to measure the rates of spontaneous drug resistance in both wildtype and ItreS^SM following ATc treatment^44,45. We found that the mutation rate for RIF resistance in wildtype ranged from 5.1 × 10^-7 to 1 × 10^-6 mutations per generation (Fig. 2A, left). The RIF-resistance rates in ItreS^SM without ATc were comparable to those of wildtype. RIF-resistant colonies were confirmed by spotting them on m7H10 containing high concentrations of RIF, up to 100 µg/mL (Fig. 2B and S3A). The fluctuation and spot assays indicated that the mean RIF-resistance rate in wildtype was ~6.6-fold higher than in ItreS^SM. Similarly, we assessed INH-resistance, finding rates in wildtype ranging from 1.1 × 10^-5 to 5.5 × 10^-6 mutations per generation, whereas ItreS^SM exhibited rates between 1.8 × 10^-6 and 1.0 × 10^-6 mutations per generation. The wildtype developed INH resistance at a rate ~5.4-fold higher than ItreS^SM (Fig. 2A, right). These results suggest a functional link between the trehalose catalytic shift and the frequency of drug resistance development across first-line TB antibiotics, regardless of the modes-of-action.

To further explore this relationship, we performed a co-culture competition assay using wildtype expressing green fluorescent protein (GFP) and ItreS^SM expressing red fluorescent protein (RFP) (Fig. S3B). In this setup, we measured relative viability following cyclic exposure to bactericidal concentrations of RIF or D-cycloserine (DCS), with intermittent washes in antibiotic-free PBS, and established subcultures labeled G1 through G5 (Fig. S3B). Flow cytometry tracked the relative abundance of GFP- and RFP-expressing bacilli within these subcultures (Fig. 2C). The iterative cycle of antibiotic treatment and regrowth in antibiotic-free m7H9 led to a gradual accumulation of wildtype bacilli within the populations. In G4 and G5 subcultures, GFP intensity saturated but did not reach 100%, suggesting the presence of drug-resistant bacilli from both strains (Fig. 2C). Spot assays confirmed that G3 was the earliest generation to exhibit a drug-resistant phenotype, and the lag phase period during regrowth in G4 and G5 was nearly identical to that of naïve bacilli (Fig. S3, D, E). These findings indicate that the trehalose catalytic shift represents an intrinsic strategy of Mtb, conferring a fitness advantage during intermittent antibiotic stress and driving natural selection. To support these findings, we conducted a fluctuation assay using M. smegmatis overexpressing treS (pTreS), which showed approximately a 2.0-fold increase in RIF resistance compared to wildtype (Fig. S3F).

DR mutants are metabolically heterogenous by forming bacilli exhibiting greater trehalose catalytic shift activity

Using the fluctuation assay and RIF spot assay, we isolated 10 RIF-resistant M. smegmatis colonies, designated as Flux^RIF #1-#10 (Figs. 2A, B, and S3A). Consistent with previously reported DR-TB clinical isolates¹³, all Flux^RIF and naïve bacilli displayed similar growth patterns in antibiotic-free m7H9 (Fig. S4A). However, while naïve bacilli failed to form colonies on m7H10 containing 25 µg/mL or higher RIF, all Flux^RIF bacilli successfully grew on these plates (Figs. 2B and S3A). Whole genome sequencing (WGS) of Flux^RIF #1-#10 was displayed in a genome-wide coverage and variant distribution with a Circos plot. ~2.18 Gbp of high-quality reads (phred score >2.0) was obtained per sample, with an average coverage of 99.97% of the genome at a minimum read depth of 10X (Fig. S3C, upper). Across all colonies, 125 variants including single nucleotide polymorphism, insertions, deletions, and complex mutations were identified relative to the M. smegmatis reference genome (NC_008596.1); 54.4% these were frameshift variants, and 36% were located in intergenic regions (Fig. S3C, lower). The consensus sequence at all examined positions matched the reference genome, indicating no significant mutations in coding regions, except for rpoB gene in Flux^RIF #1 and #2. This suggests that all 10 Flux^RIF bacilli belong to the same strain with no detectable mutations in known RIF resistance genes (Supplementary data file 2). Sequencing confirmed that Flux^RIF #1 and #2 bacilli carried an L₄₅₂P mutation within the RIF-resistance determining region (RRDR)⁴⁶, a well-known RIF-resistant mutation in many DR-TB clinical isolates^47,48. In contrast, Flux^RIF #3–#10 may develop RIF resistance without the target-gene mutations. Thus, the presence of sequence variants in Flux^RIF #3–#10 does not fully explain observed RIF resistance. To explore the role of the trehalose catalytic shift in these drug-resistant phenotypes, we monitored growth after supplementing cultures with 20 mM trehalose. The addition of trehalose enhanced growth rates in both groups. Since ValA has minimal impact on M. smegmatis TreS activity, we employed CRISPRi-dCas9 to knock down treS expression in Flux^RIF bacilli. The suppression partially impeded trehalose-induced growth in Flux^RIF bacilli but had little effect on naïve bacilli (Fig. S4A), suggesting that Flux^RIF bacilli exhibit a more pronounced TreS-centered trehalose catalytic shift activity. Metabolomics profiling supported this, revealing that levels of Glc6P, fructose 1,6-bisphosphate (FBP), and S7P were significantly higher in Flux^RIF bacilli than in naïve bacilli, despite similar trehalose levels (Fig. 3A). Conversely, TCA cycle intermediates showed no notable differences (Fig. S4B). These results indicate that Flux^RIF bacilli have elevated catalytic activity for converting exogenous trehalose into glycolytic and PPP intermediates, aligning with observations in DR-TB clinical isolates (Fig. 1B)¹³. In addition, Flux^RIF bacilli maintained high levels of PEP, likely reflecting continued replication even in the presence of RIF (Figs. 3A and S3D)²³. Consistent with the metabolomics profile and drug-resistant phenotype, Flux^RIF bacilli also exhibited higher treS mRNA expression compared to naïve bacilli, especially in RRDR mutation-free Flux^RIF #3-#10 bacilli (Figs. 3B and S4C). Moreover, Flux^RIF bacilli contained a larger subpopulation with lower membrane potential (ΔΨm), reduced ATP levels and an induced NADH/NAD+ ratio, resembling the bioenergetic state of Mtb persisters (Figs. 3C, D and S4D, E)^13,21,23. As a result, RIF penetration into Flux^RIF bacilli was significantly decreased compared to naïve bacilli, as supported by Ethidium bromide (EtBr) permeability assays (Fig. 3E). Taken together, these observations demonstrate that Flux^RIF bacilli exhibit increased metabolic heterogeneity, characterized by expanded subpopulations with heightened trehalose catalytic shift and diminished bioenergetic function. This metabolic heterogeneity likely contributes to persister formation, antibiotic tolerance, and the development of drug resistance.

Fig. 3: Biochemical and metabolic properties of RIF-resistant M. smegmatis (Flux^RIF) bacilli.

A Targeted metabolomics profiles focusing on intermediates in trehalose metabolism, glycolysis, and the pentose phosphate pathway in naïve M. smegmatis bacilli and ten Flux^RIF bacilli. Data points represent mean values ± s.e.m. of 10 biological replicates. B Fold change of treS mRNA expression in naïve M. smegmatis and Flux^RIF bacilli under RIF treatment conditions relative to untreated conditions. Data points represent mean values ± s.e.m. of 10 biological replicates. C Membrane potential (ΔΨm) of naïve M. smegmatis and Flux^RIF bacilli measured after treatment with 30 µg/mL RIF by flow cytometry. The percentage of bacilli exhibiting high and low ΔΨm for each strain is shown relative to the untreated condition. Data points represent mean values ± s.e.m. of biological triplicates of naïve bacilli and 10 biological replicates of Flux^RIF bacilli. D Intrabacterial ATP concentrations in Flux^RIF bacilli, expressed as a fold change relative to naïve bacilli. Data points represent mean values ± s.e.m. of biological triplicates of naïve bacilli and 10 biological replicates of Flux^RIF bacilli. E Relative RIF permeability and EtBr permeability kinetics of naïve M. smegmatis or Flux^RIF bacilli. Data points represent mean values ± s.e.m. of biological triplicates of naïve bacilli and 10 biological replicates of Flux^RIF bacilli. p values were determined by Student’s unpaired t test with Welch’s correction, ns, not significant. Source data are provided as a Source Data file.

The trehalose catalytic shift confers mycobacterial cells with greater metabolic heterogeneity

Increasing metabolic heterogeneity within an isogenic population is a well-known strategy that promotes the emergence of persisters and drug-resistant mutants^49,50,51. Recent studies have shown that DR-TB clinical isolates exhibit lower levels of TDM in their cell wall due to increased trehalose catalytic shift activity^13,23,52. To explore the functional connection between the trehalose catalytic shift in Flux^RIF bacilli and their ability to augment metabolic heterogeneity, we employed the fluorogenic dye Red Molecular Rotor-trehalose (RMR-tre). This dye specifically labels mycobacterial cell wall glycolipids containing trehalose, such as TDM⁵³. We observed that RMR-tre labeling intensity in naïve bacilli during mid-log phase gradually decreased with increasing concentrations of free trehalose⁵³, suggesting that RMR-tre acts as a substrate for Ag85, an enzyme involved in TDM biosynthesis, at levels comparable to free trehalose^54,55,56. Using FACS, we quantified RMR-tre labeling before and after treatment with sublethal doses of RIF. In naïve bacilli, RMR-tre labeling was relatively homogenous prior to antibiotic treatment. However, after RIF treatment, a subfraction of RMR-tre^high bacilli emerged, indicating increased heterogeneity. This likely results from bacilli with induced trehalose catalytic shift activity preferentially consuming preexisting trehalose for generating CCM intermediates, leading to higher RMR-tre incorporation compared to endogenous trehalose. Notably, RMR-tre^high bacilli were absent in ΔtreS Mtb (Fig. S5A). To further investigate the role of the trehalose catalytic shift in forming the RMR-tre^high subfraction and associated metabolic heterogeneity, we repeated the assay using pTreS^SM, M. smegmatis overexpressing treS, and ItreS^SM. We found that the RMR-tre^high subfraction substantially overlapped with that of pTreS^SM, whereas it was absent in ItreS^SM, mirroring the phenotype in ΔtreS Mtb (Figs. 4A-C and S5A). This underscores the critical role of the trehalose catalytic shift in promoting metabolic heterogeneity in response to bactericidal antibiotics. Interestingly, the RMR-tre^high fraction was already significantly larger in Flux^RIF bacilli compared to naïve DS-bacilli, even prior to antibiotic treatment, corroborating TLC-based quantification (Figs. S4F and S5B, C). To determine whether the RMR-tre^high subfraction in Flux^RIF bacilli primarily consists of a viable population following treatment with bactericidal antibiotics, we tracked the dynamics of RMR-tre^high and RMR-tre^low subfractions after exposure to bactericidal doses of RIF. We observed a profound decrease in the RMR-tre^low subfraction, with the RMR-tre^high population becoming dominant (Fig. S5D). This suggests that the metabolic heterogeneity driven by expansion of the RMR-tre^high subfraction is largely due to enhanced trehalose catalytic shift activity, which correlates with antibiotic tolerance and the accumulation of drug-resistant mutations. Furthermore, this phenomenon was more pronounced in Flux^RIF #3-#10 bacilli than in Flux^RIF #1 and #2. The latter, which harbor the L₄₅₂P mutation in the RRDR, maintained the RMR-tre^low subfraction as a dominant population even after antibiotic treatment, with only a slight reduction (Fig. S5D). Whole-genome sequencing confirmed the absence of significant mutations in Flux^RIF #3-#10 bacilli, indicating that their resistance is mainly driven by elevated trehalose catalytic shift activity rather than genetic mutations. In contrast, RIF resistance in Flux^RIF #1 and #2 bacilli is likely mutation-mediated within the RIF target gene. RIF treatment rendered all Flux^RIF #3-#10 bacilli more homogenous, either by inducing the trehalose catalytic shift in the RMR-tre^low subfraction or by selectively killing less drug-tolerant RMR-tre^low subfraction (Fig. S5D). This indicates that RMR-tre^high bacilli may represent a significant source of viable bacilli following treatment with bactericidal antibiotics. Overall, the trehalose catalytic shift is an intrinsic factor of Mtb that elevates metabolic heterogeneity by expanding the RMR-tre^high subfraction. This expansion facilitates persister formation and the development of pre-resistant bacilli – key factors in Mtb’s ability to survive prolonged antibiotic pressure.

Fig. 4: Enhanced metabolic heterogeneity of mycobacterial bacilli due to trehalose catalytic shift activity.

RMR-tre fluorescence labeling patterns are shown for A wildtype M. smegmatis, B ItreS^SM, and C pTreS^SM (M. smegmatis overexpressing TreS) following treatment with a sublethal dose of RIF. The bacilli within the area labeled R1 are defined as the RMR-tre^high subfraction. Additionally, the RMR-tre fluorescence labeling patterns for D a representative Flux^RIF bacillus, and E its CRISPRi treS knockdown strain after ATc treatment, are depicted.

The trehalose catalytic shift is necessary to increase drug-resistance frequency by expanding the persister subpopulation

Pathogenic bacteria can transiently acquire a drug-tolerant phenotype through a non-genetic mechanism by forming persisters. They can regrow once the effects diminish, creating a cycle that repeats until drug-resistant mutants emerge. The phenotypic reversibility between drug-sensitive bacilli and drug-tolerant persisters occurs when antibiotic priming is intermittent. However, continuous antibiotic pressure leads to the accumulation of genetic mutations that establish stable, resistant mutants (Fig. 5A)^57,58.

Fig. 5: Mathematical modeling of the role of trehalose catalytic shift in the emergence of drug tolerance and drug resistance in Mtb.

A The schematic diagram illustrates a phenotypic transition model in which drug-sensitive population (gray) evolve into drug-resistant population (black) via the formation of drug-tolerant population (red). In this model, bacilli can reversibly switch between drug-sensitive and drug-tolerant states during the intermittent antibiotic treatment. Once a population becomes drug-tolerant, it remains in that state for multiple generations before reverting to a drug-sensitive state. After prolonged antibiotic exposure, each drug-tolerant bacillus irreversibly transitions to drug-resistant state and replicate. To assess the impact of the trehalose-catalytic shift on the frequency of each transition step, wildtype M. smegmatis and ItreS^SM were utilized in a fluctuation assay as depicted in Fig. S6A. The reversibility and transition capacities were calculated as described in the Mathematical Modeling section in the “Methods”. Schematic diagram was created in BioRender. Lee, J. (2025) https://BioRender.com/otwhxsl. B The rates of formation of drug-resistant mutants in wildtype and ItreS^SM against RIF were determined using the classical Luria-Delbrück fluctuation assay and the Lea-Coulson method (m/Nt, where m is the number of resistant colonies and Nt is the total input). Data points represent mean values ± s.e.m. of biological triplicates for the short-term exposure and 60 biological replicates for the long-term exposure. p-values were determined by Student’s unpaired t test with Welch’s correction. Source data are provided as a Source Data File.

To understand how the trehalose catalytic shift influences this process, we employed mathematical modeling and developed analytical formulas to predict its impact on the kinetics of phenotypic reversibility and clone-to-clone fluctuations within surviving populations. These populations serve as reservoirs for eventual drug-resistant bacilli (Fig. 5A)^59,60. Our model captures the emergence of drug-tolerant persisters during population growth by allowing individual bacilli to reversibly switch between drug-sensitive and drug-tolerant states⁶¹. Once a bacillus becomes tolerant, it remains in that state for several generations before reverting to a drug-sensitive state (Figs. 5A and S6A, B). Using this framework, we previously modeled reversible switching in fluctuation assays, enabling analytical predictions of the statistical variation in tolerant bacilli across colonies derived from a single progenitor⁶². Analysis of the fluctuation assay data with this reversible switching model shows that ItreS^SM generates roughly six-fold fewer drug-resistant colonies compared to wildtype (Fig. 5B right). Assuming equal mutation probabilities, this reduction in ItreS^SM likely results from the production of less stable persisters that revert to a drug-sensitive state more rapidly. Furthermore, our findings indicate that the decreased number of resistant colonies in ItreS^SM compared to wildtype is primarily due to a six-fold reduction in persister formation rate (Fig. 5B, left), as detailed in the Mathematical Modeling section of the Methods. Since the emergence of drug resistance is facilitated by a higher number of persisters^{8,50,63,64,65,66,67}, we conclude that mycobacterial bacilli evolve into drug-resistant mutants through repeated cycles of persister and pre-resistant bacilli formation. The trehalose catalytic shift acts as a strategic mechanism to expand the subpopulation of persisters and pre-resistant bacilli, especially under conditions of high ROS-induced damage, thereby facilitating the emergence of drug-resistant mutants.

RIF-resistant mycobacterial cells are also resistant to INH and BDQ

Clinical data from Taiwan Medical Center indicate that 94.6% of RIF-resistant Mtb strains are also resistant to INH, while only 0.5% are mono-resistant to RIF⁶⁸. Similar patterns have been observed in retrospective TB case studies conducted in New York City between 2010 and 2021⁶⁹. These findings suggest that RIF resistance can serve as a predictive biomarker for MDR-TB. Based on this, we hypothesize that RIF-resistant strains could possess a metabolic advantage that confers increased tolerance to second antibiotics like INH, even without prior exposure. RMR-tre labeling patterns indicate that Flux^RIF bacilli contain a high abundance of RMR-tre^high subfraction (Figs. 4D and S5B, C). To test this hypothesis, we performed minimum inhibitory concentration (MIC) shift assays using selected Flux^RIF bacilli and their CRISPRi treS mutant, referred to as ItreS^Flux, comparing their antibiotic sensitivities to naïve bacilli. Flux^RIF bacilli exhibited significantly higher tolerance to INH, with MIC values ~3.82 µg/mL, compared to around 1.84 µg/mL in naïve bacilli. Notably, this elevated INH tolerance was reduced in ItreS^Flux after treatment with ATc, dropping to around 1.49 µg/mL (Fig. 6A, left). No such reduction was observed in ItreS^Flux without ATc, indicating that the elevated tolerance to second antibiotics in Flux^RIF bacilli depends on active trehalose catalytic shift (Fig. 4D and S5D). Spot assays on m7H10 containing bactericidal doses of INH corroborated the MIC shift assay results (Figs. 6B and S7A). Additionally, Flux^RIF bacilli demonstrated increased tolerance to BDQ as well, highlighting the role of the trehalose catalytic shift in cross-resistance to multiple TB antibiotics (Fig. 6A, right). Interestingly, among 1500 ITRC TB clinical isolates, only 15 (<1%) were RSR-TB clinical isolates, and an inverse relationship, where INH resistance confers RIF cross-resistance, was not clearly observed. To further explore this, we examined INH-resistant bacilli (referred to as Flux^INH) obtained from fluctuation assays (Fig. 2A, right). Testing two randomly selected Flux^INH bacilli revealed that they were significantly more sensitive to RIF than naïve bacilli (Fig. 6C, D), likely due to increased RIF uptake (Fig. S7B). This altered permeability was tested by the EtBr permeability assay and the SDS sensitivity assay (Fig. S7C). Higher EtBr uptake in Flux^INH bacilli, combined with similar SDS uptake compared to naïve bacilli, suggests that reduced efflux pump activity in Flux^INH bacilli contributed to increased RIF uptake. INH requires metabolic activation through NAD⁺ adduct formation to exert its antimicrobial effect⁷⁰. As shown in Fig. 3, Flux^RIF bacilli demonstrated distinct metabolic networks, characterized by higher trehalose catalytic shift and reduced bioenergetic markers such as NAD⁺, ΔΨm, and ATP (Figs. 3C, D and S4D). This metabolic state likely influences the formation of INH-NAD adducts. The observed cross-resistance in Flux^RIF bacilli was significantly reduced when treS was inhibited via CRISPRi-dCas9 (Fig. 6A), supporting the hypothesis that the trehalose catalytic shift contributes to the emergence of MDR-TB cases.

Fig. 6: The role of trehalose catalytic shift in the emergence of antibiotic cross-resistance.

A IC₅₀ values for INH (left panel) and BDQ (right panel) were measured in naïve M. smegmatis, Flux^RIF, and ItreS^Flux bacilli, both with and without ATc treatment. Data points represent mean values ± s.e.m. of biological triplicates. B Spot assay on m7H10 containing 10X MIC of INH (isoniazid) or BDQ (bedaquiline), using naïve M. smegmatis, Flux^RIF, and ItreS^Flux bacilli. C IC₅₀ values for BDQ were determined for naïve M. smegmatis and two selected Flux^INH bacilli. Data points represent mean values ± s.e.m. of biological triplicates. D Spot assay on m7H10 containing 10X MIC of BDQ (left panel) with naïve M. smegmatis and Flux^INH bacilli. Data points represent mean values ± s.e.m. of 6 biological replicates of naïve bacilli and 10 biological replicates of Flux^INH bacilli. The right panel displayed the average colony diameters and standard deviations of colonies grown on m7H10 containing 10X MIC of BDQ. Source data are provided as a Source Data File.

The trehalose catalytic shift enables the HN878 W-Beijing strain to acquire a high frequency of multidrug resistance

Clinical Mtb strains are classified into phylogeographic lineages 1 through 7, each exhibiting varying capacities for acquiring MDR mutations^45,71. Notably, lineage 2 strains, including the HN878 W-Beijing strain (HN878), are associated with a heightened risk of MDR-TB emergence on a global scale. Our findings suggest that the trehalose catalytic shift in Mtb contributes to this increased MDR propensity by promoting persister formation and cross-resistance to multiple antibiotics (Figs. 4, 5, and 6). We hypothesize that elevated trehalose catalytic shift activity in HN878 plays a key role in its tendency to accumulate MDR mutations more frequently than other lineages. To investigate this, we examined treS expression in HN878 following exposure to sublethal doses of RIF. Results showed that treS mRNA was significantly upregulated—by ~7.3-fold—compared to untreated controls, a much higher induction than the 2 to 3-fold increase observed in lineage 4 strains such as Erdman and CDC1551 (Fig. 7A). Additionally, HN878 exhibited faster growth in m7H9 containing trehalose as the sole carbon source (Fig. 7B). When co-treated with ValA, trehalose-mediated growth was restored to levels comparable to lineage 4 strains, suggesting that trehalose may serve as a more favorable carbon source for HN878, likely due to its higher TreS activity (Fig. 7B). Metabolomics analysis revealed that the conversion of trehalose into glycolytic and PPP intermediates, such as Glc6P, Pen5P, and S7P, was significantly higher in HN878 than in lineage 4 strains (Fig. 7C). Collectively, these findings suggest that HN878 undergoes a more pronounced trehalose catalytic shift, which likely contributes to its higher frequency of MDR mutations.

Fig. 7: Characterization of HN878’s trehalose catalytic shift activity.

A treS mRNA expression levels in HN878 and lineage 4 strains (e.g., H37Rv, Erdman, and CDC1551) were measured before and after treatment with RIF. The closed black circles indicate fold changes relative to untreated controls. Data points represent mean values ± s.e.m. of biological triplicates. B Growth kinetics of HN878 and lineage 4 strains on m7H9 with trehalose as the sole carbon source. The effect of ValA on HN878 growth is also illustrated. Values represent mean values ± s.e.m. of triplicates. C RIF treatment induced changes in the levels of trehalose, glucose 6P, and pentose 5P in HN878 and lineage 4 strains, relative to the untreated controls (No RIF). FC, fold change. Values are mean values ± s.e.m. of biological triplicates. D The effects of ValA (left panel) or CRISPRi-mediated treS inactivation (right panel) on the rates of RIF-resistance acquisition per generation in HN878 and lineage 4 clinical strains, measured via the classical fluctuation assay. Values are mean values ± s.e.m. of biological triplicates. p values were determined by Student’s unpaired t-test with Welch’s correction, ns, not significant. In a box plot (right panel), data depict median (center bar), 25th and 75th percentile (lower and upper box bounds), and minimum and maximum values (lower and upper whiskers). E Impact of ValA on the IC₅₀ values of RIF against the indicated Mtb clinical strains: HN878 (~62 ng/mL), ERD (~31 ng/mL), CDC1551 (~22 ng/mL) and HN878 treated with ValA (~22 ng/mL). Source data are provided as a Source Data File.

To further validate the role of the trehalose catalytic shift in HN878 for the emergence of drug-resistant mutants, we conducted fluctuation assays with HN878 and lineage 4 strains, both with and without ValA, as well as CRISPRi treS mutants of HN878 (ItreS^HN), CDC1551 (ItreS^CDC), or Erdman (ItreS^Erd) (Figs. S2A and S8A). Consistent with previous reports⁴⁵, HN878 displayed roughly a 5.0-fold higher frequency of developing RIF resistance compared to lineage 4 strains (Fig. 7D, left). Treatment with ValA significantly reduced this mutation rate to levels comparable to lineage 4 strains. Similarly, mutation frequencies of ItreS^HN and ItreS^CDC were comparable, confirming the involvement of trehalose catalytic shift (Fig. 7D, right). Furthermore, HN878 exhibited a higher MIC for RIF (~0.06 µg/mL) than lineage 4 strains (~0.03 µg/mL), due to its enhanced trehalose catalytic shift activity. When co-treated with ValA or in ItreS^HN, the MIC value decreased to ~ 0.02 µg/mL (Figs. 7E and S8B, C). To link this metabolic activity to persister formation and drug tolerance, we utilized the most probable number (MPN) assay, which quantifies the frequency of total persisters including both traditional persisters and differentially detectable (DD) bacilli under RIF treatment and nutrient-starved conditions^72,73. We found that the frequency of persister formation in HN878 was the highest among all clinical strains tested (Fig. S8D). The reduction in persister formation following co-treatment with ValA (Fig. S8D, left) or using ItreS^HN (Fig. S8D, right) was most pronounced, indicating that the high frequency of MDR development in HN878 is largely attributed to its elevated trehalose catalytic shift activity and subsequent persister formation. Based on our mathematical modeling (Fig. 5), the frequent emergence of MDR-TB cases linked to infections with HN878 is primarily driven by increased trehalose catalytic shift activity and persister formation. Therefore, targeting this pathway offers a promising strategy for developing adjunctive therapies to prevent the emergence of MDR-TB.

Discussion

Persister formation is a widespread adaptive strategy among bacterial pathogens including Mtb, allowing survival under antibiotic effects for extended periods without developing genetic resistance^8,74. The pathogenic cycle of TB includes a latent infection stage, during which Mtb bacilli often enter a persister state. In this state, they can opportunistically recur, increasing bacterial burden and serving as reservoirs for genetic mutations that lead to drug resistance^33,75,76. Compared to heritable drug resistance, the biology of Mtb persisters remains in the early stages of investigation. Our study indicates that mycobacterial persisters are indeed an adaptive method that plays a crucial role in the pathogenic lifecycle of TB. This process is triggered by metabolic remodeling, such as trehalose catalytic shift, and is directly or indirectly linked to the evolutionary traits that promote the emergence of drug-resistant mutants^{8,11,77,78,79,80}. Beyond enabling intermittent antibiotic tolerance and opportunistic relapse, our findings support the notion that Mtb persisters act as reservoirs for the development of multidrug resistance and contribute to the global spread of MDR-TB^{52,64,65,76,81,82,83}. Notably, the accumulation of ROS resulting from antibiotic effects has been identified as a primary factor that kills invading bacilli. However, when pathogens survive this oxidative stress, ROS can also induce DNA mutagenesis^30,84,85. Prolonged survival in the persister state, followed by regrowth through metabolic remodeling, is directly associated with the emergence of populations harboring genetic mutations conferring drug resistance^12,86. These adaptive metabolic strategies underpin the accelerated development and dissemination of MDR-TB.

A handful of investigations have begun to elucidate the key metabolic remodeling strategies for persister formation. Our metabolomics studies, using Mtb persisters collected from in vitro biofilm cultures or under hypoxic stress, have validated the functional importance of preexisting Mtb cell wall glycolipids as alternative carbon sources^13,22. Bioinformatic analysis of the metabolomics data revealed that trehalose metabolism is among the most significantly altered pathways compared to replicating Mtb. These findings suggest that Mtb persisters shift the catalytic direction of trehalose metabolism to biosynthesize intermediates in glycolysis and the PPP, a process we term the trehalose catalytic shift. Indeed, ΔtreS of Mtb, which lacks this catalytic shift activity, exhibited hypersensitivity to first-line TB antibiotics such as INH and RIF. Trehalose serves as a structural component of Mtb cell wall glycolipids, including TDM, which modulates host immune responses. Additionally, trehalose is a core carbohydrate in sulfolipid-1 (SL-1), a cell wall component recently reported to be linked to the opportunistic transmission of Mtb bacilli to new hosts⁸⁷. Therefore, the trehalose catalytic shift plays multiple roles, including carbon storage, essential components for persister biology, antibiotic tolerance, immune evasion, and transmission^38,88.

This study uncovers an additional role of the trehalose catalytic shift in accelerating the development of permanent MDR in Mtb. The bacilli maintain viability by forming persisters through this shift activity, even under bactericidal levels of oxidative stress. This process induces DNA mutagenesis via activation of the trehalose catalytic shift. Notably, RIF-resistant bacilli, harboring high levels of trehalose catalytic shift, tend to exhibit increased antibiotic tolerance to a second antibiotic, even without prior exposure, highlighting how this adaptive strategy can facilitate the progression to MDR-TB. Furthermore, our findings demonstrate that the trehalose catalytic shift enhances phenotypic heterogeneity within the population. By inhibiting treS expression through CRISPRi or chemically deactivating TreS with ValA, a key enzyme in this pathway, we observed a significant reduction in the emergence of DR mycobacterial mutants against clinically relevant TB antibiotics. Fascinatingly, our mathematical modeling clarifies that the trehalose catalytic shift uniquely facilitates persister formation and confers phenotypic stability, preventing persisters from reverting to the DS-state. This suggests that the frequency of DR mutant emergence is predominantly influenced by the extent of persister formation, phenotypic heterogeneity and stability. Our findings also show that the transition rates from persisters to permanent DR mutants are nearly identical between wildtype and ΔtreS. Therefore, the metabolic remodeling strategies that promote Mtb persister formation represent promising targets for therapeutic intervention. Targeting these pathways could not only eradicate Mtb persisters but also prevent the onset of MDR-TB.

We have developed a technique to monitor mycobacterial phenotypic heterogeneity resulting from active trehalose catalytic shift by labeling with an RMR-tre fluorogenic dye combined with FACS analysis. In our recent report⁵³, we demonstrated that RMR-tre serves as a substrate of Ag85, an enzyme involved in the biosynthesis of TDM, at levels comparable to free trehalose. In this work, we found that RMR-tre labeling intensity correlates with TreS activity (Fig. A-C). This is likely because mycobacterial bacilli with higher TreS channel more trehalose toward the biosynthesis of CCM intermediates, limiting internal trehalose availability for TDM biosynthesis. Consequently, when exogenous RMR-tre is supplied, bacilli with greater TreS utilize more RMR-tre as a substrate for TDM production, whereas TreS-deficient bacilli rely more on endogenous trehalose. In response to antibiotic treatment, Mtb exhibited an accumulation of RMR-tre^high bacilli, a subfraction displaying labeling patterns similar to those of pTreS, but absent in ItreS^SM or ΔtreS Mtb. This suggests that activation of the trehalose catalytic shift increases metabolic and phenotypic heterogeneity, resulting in the formation of a population that exhibits tolerance to antibiotics. Notably, Flux^RIF bacilli showed a higher proportion of RMR-tre^high subfraction compared to naïve DS-bacilli, underscoring the essential role of the trehalose catalytic shift in the metabolic networks of DR-bacilli and thus providing insight into mechanisms that contribute to their cross-resistance to other antibiotics. Treatment with bactericidal concentrations of antibiotics caused a notable increase in the proportion of bacilli with high TreS activity. This effect may stem from the greater antibiotic susceptibility of the subfraction with low TreS activity or from antibiotic-induced alterations in metabolic heterogeneity, which promotes the induction of TreS activity.

The exploration of metabolic strategies beyond the trehalose catalytic shift is warranted, as a substantial subset of ItreS^SM or ΔtreS Mtb strains continue to develop drug-resistant mutations, albeit at a significantly reduced rate. Consistent with data obtained from Flux^RIF bacilli, metabolomics analyses of DR-TB clinical isolates revealed distinct metabolic activities involved in trehalose catalysis compared to DS-TB and some RSR-TB clinical isolates. These DR-TB clinical isolates exhibited biochemical features similar to Mtb persisters, characterized by dysregulated membrane bioenergetics and active glycolysis and PPP, which serve as alternate sources of energy and antioxidants. Additionally, they showed a reduced abundance of cell wall TDM, a proinflammatory ligand of Mtb, as part of an immune evasion strategy^13,22,89. The catabolic remodeling of TDM provides infected Mtb bacilli with a spatiotemporal advantage, allowing them to maintain their latent state without relying on host nutrients or provoking excessive immune responses. TB clinical isolates often utilize host fatty acids or cholesterol as primary carbon sources, which require endergonic pathways such as the TCA cycle, glyoxylate shunt, methylmalonyl CoA pathway, and methylcitrate cycle, followed by gluconeogenic reactions. Gluconeogenesis involves primarily energy-consuming reactions that biosynthesize carbohydrate intermediates. Therefore, the trehalose catalytic shift offers a catalytic advantage by enabling the exploitation of largely exergonic metabolic networks to support the energy demands and antioxidant needs critical for Mtb persister biology, antibiotic tolerance, and the eventual emergence of MDR mutants.

Labeling Flux^RIF bacilli with RMR-tre dye revealed that those carrying RRDR mutations exhibited significantly fewer RMR-tre^high bacilli compared to the RRDR mutation-free Flux^RIF bacilli (Fig. S5B, C). Additionally, treatment with bactericidal antibiotics selectively eliminated the RMR-tre^low subfraction, a phenomenon more pronounced in RRDR mutation-free Flux^RIF bacilli (Fig. S5D). This suggests that RRDR mutation-free Flux^RIF bacilli may serve as a primary reservoir for the future development of DR mutations and the emergence of permanent MDR mutants. Consequently, these pre-resistant subpopulations likely require higher levels of trehalose catalytic shift to sustain their drug-resistant phenotype. Collectively, Mtb can attain a permanent DR phenotype through the formation of Mtb persisters or pre-resistant bacilli by inducing the trehalose catalytic shift activity. Similar to carbapenem-resistant Enterobacteriaceae (CRE) clinical isolates⁹⁰, Mtb persisters may revert to a DS-state once antibiotic effects diminish (Fig. 5A). In contrast, pre-resistant bacilli were found to be phenotypically stable, consistently managing their metabolic networks with high levels of trehalose catalytic shift. The functional relevance of the trehalose catalytic shift in the formation of Mtb persisters and/or pre-resistant bacilli was confirmed by genetic inactivation of treS in Flux^RIF bacilli (Fig. 4D, E).

Consistent with previous findings, we observed that the higher frequency of MDR mutations in lineage 2 clinical strains such as HN878 is largely attributed to their increased trehalose catalytic shift activity. This elevated activity is linked to the induced formation of persisters and pre-resistant bacilli. Our mathematical modeling suggests that the greater propensity of persister formation correlates with an increased likelihood of phenotypic heterogeneity and the emergence of MDR-TB. Additionally, our cross-resistance studies revealed that Mtb bacilli with elevated trehalose catalytic shift activity are more prone to develop MDR-TB mutations. This research illuminates the metabolic basis underlying the higher incidence of MDR-TB cases in infections with HN878. Targeting the trehalose catalytic shift in HN878 presents a therapeutic strategy to improve the efficacy of existing TB antibiotics by preventing both the formation of persisters and the emergence of MDR-TB cases. Recently, we demonstrated that certain trehalose structural analogues can disrupt Mtb persister formation and antibiotic tolerance by inhibiting TreS-centered trehalose catalytic shift activity, thereby enhancing the antimicrobial effects of INH or RIF³⁹. The potential for these compounds to synergize with clinically relevant TB antibiotics against HN878 infection warrants further investigation.

Methods

Bacterial strains, culture conditions, and chemicals

Mycobacterium smegmatis, including drug-sensitive and drug-resistant strains, along with their CRISPRi strains including ItreS^SM and IotsA^SM were cultured at 37 °C in Middlebrook 7H9 broth (m7H9) (Difco) or on Middlebrook 7H10 agar (m7H10) (Difco). The media were supplemented with 0.04% Tyloxapol (for planktonic growth in m7H9 only), 0.5 g L⁻¹ BSA (Fraction V), 0.2% glycerol, 0.2% dextrose, and 0.085% NaCl. Kanamycin (50 µg/mL) was used to select CRISPRi mutants. Mycobacterium tuberculosis strains including HN878, Erdman, H37Rv, and CDC1551, along their corresponding CRISPRi strains (ItreS^HN, ItreS^Erd, ItreS^CDC, and ItreS^Rv) were cultured in a biosafety level 3 (BSL-3) facility. The TB clinical isolates, including drug-sensitive (DS), rifampicin single-resistant (RSR), MDR, extensively drug-resistant (XDR), and totally drug-resistant (TDR) TB clinical isolates, were isolated from sputum samples of patients with active pulmonary TB at the National Masan Hospital (NMH), South Korea. All procedures adhered to relevant ethical regulations, and the isolates were collected as part of a prospective observational cohort study (ClinicalTrials.gov NCT00341601) conducted between 2005 and 2008. The study was approved by the ethics review boards of both NMH and the National Institute of Allergy and Infectious Diseases (NIAID), with all participants providing written informed consent.

When appropriate, cultures were supplemented with 10 mM sodium butyrate, 20 mM trehalose, 200 µM Validamycin A (ValA), 200 ng/mL anhydrotetracycline (ATc), or varying MIC of rifampicin (RIF), isoniazid (INH), bedaquiline (BDQ), or d-cycloserine (DCS). These compounds were purchased from Sigma and Advanced ChemBlocks Inc. Experiments involving CRISPRi mutants were conducted with or without ATc; untreated cultures served as controls.

Metabolite extraction and LC-MS analysis

M. smegmatis- or Mtb-laden filters were generated and incubated at 37 °C for 5 days to reach mid-log phase of growth²¹. To prepare for filter culture-based metabolomics, cultures on agar-supported filters were treated with trehalose and/or ValA. M. smegmatis or Mtb-laden filters were metabolically quenched by immersion in a precooled mixture of acetonitrile:methanol:H₂O (40:40:20, v:v:v) at −40 °C. Metabolites were extracted via mechanical lysis using 0.1-mm zirconia beads in a Precellys tissue homogenizer for 4 min at 6000 rpm, repeated twice under continuous cooling at or below 2 °C. The lysates were clarified by centrifugation and filtered through a 0.22-µm Spin-X column. The residual protein content was measured with a BCA protein assay kit (Thermo Scientific) to normalize metabolite levels to cell biomass.

Extracted metabolites were separated using a Cogent Diamond Hydride type C column (gradient 3) with mobile phase comprising solvent A (ddH₂O with 0.2% formic acid) and solvent B (acetonitrile with 0.2% formic acid). An Agilent 6230 TOF mass spectrometer (MS) was coupled to an Agilent 1290 Liquid Chromatography (LC) system. Dynamic mass axis calibration was maintained via continuous infusion of a reference mass solution through an isocratic pump with a 100:1 splitter. This setup achieved mass errors of ~5 ppm and a mass resolution between 10,000 to 25,000 over the m/z range of 62–966, with a dynamic range of 5 log₁₀. Ions were identified based on unique accurate mass and retention time identifiers corresponding to expected isotopomer distributions. Data processing was conducted using Agilent Qualitative Analysis B.07.00 and Profinder B.07.00 software (Agilent Technologies), with a mass tolerance of <0.005 Da. Clustered heatmaps, hierarchical clustering, principal component analysis (PCA), and pathway enrichment analysis were performed using MetaboAnalyst (ver. 6.0). All metabolomics data represent the average of at least two independent triplicates.

Isotope tracing analysis using ¹³C₁₂ trehalose

To investigate trehalose metabolism in DS- and DR- TB clinical isolates, we performed isotope tracing experiments using fully ¹³C labeled trehalose (¹³C₁₂ trehalose), purchased from Cambridge Isotope Laboratory (CIL). Nine TB clinical isolates were randomly selected, three DS- and six DR- strains, and cultured in m7H9 until mid-log phase. The cultures were then transferred to fresh m7H9 containing 20 mM trehalose, composed of 20% ¹³C₁₂ trehalose and 80% ¹²C unlabeled trehalose. After 1 day, the cultures were harvested, washed with PBS, and rapidly quenched by immersion into a precooled mixture of acetonitrile/methanol/H₂O (40:40:20) at −40 °C. The extent of isotopic labeling in key metabolites such as glucose 6-phosphate (glycolysis), sedoheptulose 7 phosphate (PPP), and malate (TCA cycle) was quantified by dividing the summed peak height of all labeled isotopologue species by the total peak heights of both labeled and unlabeled isotopologues, expressed as a percentage. To correct for naturally occurring ¹³C isotopologues (i.e., [M + 1] and [M + 2]), label-specific ion counts were adjusted accordingly.

qRT-PCR analysis

M. smegmatis or Mtb strains were cultured in m7H9 until reaching mid-log phase. Bacilli were harvested by adding an equal volume of guanidine thiocyanate buffer. Total RNA was extracted using TRIzol reagent and the PureLink RNA Mini Kit (Invitrogen), following the manufacturer’s instructions. Genomic DNA contamination was eliminated using the Turbo DNA-free kit (Invitrogen). cDNA was synthesized from 500 ng of RNA using the iScript cDNA Synthesis Kit (Bio-Rad). Quantitative PCR was performed on a C1000 Thermal Cycler (Bio-Rad). Primers and probes were designed using the PrimerQuest^TM Tool (Integrated DNA Technologies), with sequences provided in Supplementary data file 2. Gene expression levels were quantified by calculating the ΔΔCt values, normalized to the housekeeping gene sigA. Fold changes were expressed as log₂ values relative to control samples.

Lipid extraction and TLC analysis

TDM and TMM from mycobacterial bacilli were prepared as previously described¹³. Bacilli were transferred to a 15 mL amber glass bottle and incubated overnight with 3 mL of chloroform: methanol (2:1, v:v) to sterilize the bacteria and extract total lipids. Subsequently, 10 mL acetone was added, and the mixture was incubated for 24 h at − 80 °C. After centrifugation, the lipid-containing supernatant was decanted. The lipids were then resuspended in 1 mL of chloroform: methanol (2:1, v:v), and equal amounts from each condition were loaded onto thin-layer chromatography (TLC) plates. TMM and TDM were resolved using TLC in a solvent system of chloroform: methanol: H₂O (90:10:1, v:v:v). Lipids were visualized by spraying the plates with 1% molybdophosphoric acid in ethanol, followed by charring.

CRISPRi knockdown generation

Single guide RNA (sgRNA) sequences were designed to target the 3’-end of the non-template strand within the open reading frame of the target genes. Each sgRNA consists of ~20 nucleotides located upstream of an effective protospacer adjacent motif (PAM) sequence. The PLJR962 M. smegmatis CRISPRi backbone plasmid was amplified in E. coli, selected with kanamycin (50 µg/mL), and subsequently digested with BsmBI restriction enzymes (NEB). The digested plasmid was then purified. Designed oligonucleotide primers (see Supplementary Table 1) were annealed and ligated into the BsmBI-digested plasmid. Competent M. smegmatis cells were prepared by washing mid-log phase cultures multiple times with ice-cold 15% glycerol. The recombinant plasmid was introduced into these competent cells via electroporation using a Pulse Controller II and Gene Pulser II (BioRad). Transformed cultures were grown to mid-log phase and plated on m7H10 containing 50 µg/mL kanamycin to select for successful mutants. Selected colonies were regrown in m7H9 with kanamycin and incubated with ATc (200 ng/mL) for at least 1 day to induce target gene repression. Knockdown of the target genes was confirmed by qRT-PCR.

Luria-Delbrück fluctuation assay and analysis

The classical fluctuation assay was modified for this study⁴⁵. To generate single-cell suspensions of M. smegmatis wildtype and ItreS^SM, cultures were diluted to an OD₅₉₅ of 0.00005 in 200 µL within 96-well plates. The cultures were then incubated and allowed to grow until reaching an OD₅₉₅ of 0.7–1.0. From each of 60 randomly selected cultures, 100 µL was plated on m7H10 containing either 100 µg/mL RIF or 200 µg/mL INH. Spontaneous resistant colonies were counted after incubation for up to 10 days. To determine the total viable cell input, the remaining 100 µL of each culture was serially diluted and plated on antibiotic-free m7H10. Mutation rates were calculated using the Lea-Coulson method: m/Nt, where m is number of resistant colonies, and Nt is the total number of input cells⁹¹.

Co-culture competition assay

The plasmids pTE-OX-GCT5 and pGMEH-p38-mRFP (Addgene) were transformed into wildtype M. smegmatis and ItreS^SM, respectively, to generate wildtype::GFP and ItreS^SM::RFP strains. Using these strains, we evaluated their relative viability following cyclic exposure to bactericidal concentrations of RIF or D-cycloserine (DCS). Briefly, equal volumes of mid-log phase cultures of wildtype::GFP and ItreS^SM::RFP, each at an OD₅₉₅ of ~0.7, were mixed to create the initial untreated culture (G0). This mixture was then treated with 100 µg/mL RIF or DCS for 1 day, followed by washing with PBS and resuspension in antibiotic-free m7H9. The resulting culture, adjusted to an OD₅₉₅ of 0.05, was allowed to grow until reaching an OD₅₉₅ of ~0.7, referred to as G1 subculture. This cycle of treatment, washing, and regrowth was repeated sequentially until G5. Flow cytometry was employed to determine the relative abundance of wildtype::GFP and ItreS^SM::RFP during each generation (G0 to G5), allowing to track changes in strain viability over successive cycles of antibiotic exposure.

Spot assay

Cultures of Flux^RIF and Flux^INH at mid-log phase were diluted to an OD₅₉₅ of 0.1. Five 10-fold serial dilutions were then prepared from each culture, and 2 µL of each dilution were spotted onto m7H10 containing various concentrations of antibiotics: 8–100 µg/mL RIF, 4–32 µg/mL INH, or 0.0075–0.03 µg/mL BDQ. The plates were incubated at 37 °C until colonies became visible.

Whole genome sequencing of RIF resistant M. smegmatis

Genomic DNA was extracted from Flux^RIF #1-#10 bacilli and sequenced using the Illumina HiSeq platform (Novogen Corp). To ensure high-quality reads, raw sequences were trimmed with Trimmomatic (v0.39.2)⁹² using the parameters “SLIDINGWINDOW:4:20 MINLEN:50.” Variant analysis across the ten samples was performed using the Snippy pipeline (v4.6.0, default options; https://github.com/tseemann/snippy). The M. smegmatis reference genome (accession: NC_008596.1) was downloaded in GenBank format from NCBI and used for alignment. The pipeline involved: I. Mapping high-quality reads to the reference genome with BWA-MEM (v0.7.17-r1188), II. Calling variants with FreeBayes (v1.3.6)⁹³, III. Filtering variants with BCFtools (v1.18)⁹⁴, and IV. Annotating variant effects with SnpEff (v5.0e)⁹⁵. Given the stringent criteria of the Snippy pipeline in variant calling, we manually inspected the mapped bases at each position using SAMtools (v1.18) mpileup on the BAM files generated by BWA-MEM. The SnpEff results from all samples were organized by variant position and effect, then visualized using the ComplexUpset package (v1.3.3) in R. The reference bases and variant alleles were plotted with ggplot2 (v3.5.1), based on sequencing depth.

RRDR sequencing of lab-made RIF resistant M. smegmatis

The rpoB gene sequences of the Flux^RIF mutants were compared to the reference rpoB gene sequences for wildtype M. smegmatis⁹⁶. ApE plasmid-editing software was utilized to identify mutations. Flux^RIF strains were streaked onto antibiotic-free LB agar plates and incubated overnight at 37 °C to isolate single colonies for sequencing. The RRDR (RIF Resistance Determining Region) of the rpoB gene was amplified and sequenced using Sanger Sequencing (Quintara Biosciences) using the following primers: Msmeg-rpoB-fwd (5’-gctgatccagaaccagatcc-3’) and Msmeg-rpoB-rev (5’-gatgacaccggtcttgtcg-3’).

Membrane bioenergetics—membrane potential, NAD/NADH, and energy charge

For membrane potential (ΔΨm) measurement, cultures were grown in m7H9 to mid-log phase and concentrated to an OD₅₉₅ of ~1.0 in fresh m7H9. Cultures were stained with 15 µM DiOC₂ and incubated at 37 °C for 40 min. Following incubation, cultures were washed with PBS to remove excess dye. As a positive control for membrane depolarization, one culture was treated with 5 µM of the carbonyl-cyanide 3-chlorophenylhydrazone (CCCP; Invitrogen), while PBS served as the vehicle control. The assay was performed in black, clear-bottom 96-well plates (Costar). Fluorescence was measured using a SpectraMax M4 spectrofluorimeter (Molecular Devices), recording green fluorescence (excitation 488 nm, emission 530 nm) and shifts to red fluorescence (excitation 488 nm, emission 610 nm). ΔΨm was calculated as the ratio of red to green fluorescence, with each condition measured in triplicate.

M. smegmatis-laden filters generated for the metabolomics profiling were used to measure intrabacterial ATP and NADH/NAD levels. ATP concentrations were determined using the BacTiter Glo Microbial Cell Viability Assay kit (Promega), following the manufacturer’s instructions. NAD and NADH levels were measured with the FluroNAD/NADH detection kit (Cell Technology), also according to the provided instructions. Bacterial metabolism was rapidly quenched by immersing the filters into the respective assay buffer or reagent, ensuring immediate stabilization of metabolic states. ADP and AMP levels were quantified using LC-MS metabolomics, with calibration curves generated from chemical standards spiked into homologous mycobacterial extracts to correct for matrix-associated ion suppression effects.

RMR-tre labeling and flow cytometry

RMR-tre was synthesized as previously described and characterized by nuclear magnetic resonance (NMR) spectroscopy⁵³. Wildtype M. smegmatis, ItreS^SM, pTreS^SM, Flux^RIF, or ItreS^Flux cultures in mid-log phase were treated with 1X MIC of RIF for 1 day. Following treatment, cultures were stained with the fluorogenic dye RMR-tre at a final concentration of 10 µM and incubated at 37 °C for 1 h. After incubation, the cultures were analyzed using a flow cytometer (Attune NxT, Thermo Fisher Scientific). The reported values represent the gated cell fractions. Data were exported from the flow cytometer and analyzed using FlowJo software (BD Biosciences). Error bars indicate the standard deviation from biological replicates.

Mathematical modeling

The classical Luria-Delbrück fluctuation assay was adapted for this study (Fig. S6A). To generate single-lineage bacilli of M. smegmatis wildtype and ItreS^SM, cultures were diluted to an OD₅₉₅ of 0.00005 in 200 µL and dispensed into 96-well plates. Cultures were incubated until reaching an OD₅₉₅ of 0.7–1.0. From 60 randomly selected wells, 100 µL was plated on m7H10 containing 100 µg/mL RIF, and the number of spontaneous RIF-resistant colonies was counted after 10 days. To estimate the total viable input cell number, the remaining 100 µL of each culture was serially diluted and plated on antibiotic-free m7H10. To infer the reversible switching rates between drug-sensitive and drug-tolerant states (Fig. 5A), we utilized recent mathematical models that relate fluctuation assay data to these rates⁶⁰. The calculation is based on the fluctuation assay results, where single-cell clones are subjected to lethal stress, and the clone-to-clone variation in surviving bacilli is used to estimate switching rates. This method has successfully been applied to decipher reversible phenotypic transitions between drug-sensitive and drug-tolerant states in both bacterial pathogens and cancer cells^61,97,98. The basic mathematical model encompasses the following ingredients:

• Single cells exist in two phenotypic states: drug-sensitive and drug-tolerant, and reversibly switch between them with certain kinetic rates (Fig. 5A).

• These transitions occur spontaneously prior to drug exposure.

• Cells proliferate at a given rate, assumed equal in both states.

• During division, both daughter cells inherit the mother’s cell state just before division.

A detailed formulation for this stochastic population dynamics model, which describes clonal expansion with state switching, has been recently reported⁶⁰. Assuming colonies expand over $T$ generations, the variability in the number of tolerant bacilli, measured by the squared coefficient of variation (standard deviation divided by the mean), $C{V}_{{N}_{T}}^{2}$, is given by:

$${{CV}}_{{N}_{T}}^{2}\times f=\frac{2{T}_{{on}}{e}^{T-\frac{2T}{{T}_{{on}}}}-2-{T}_{{on}}}{\left(2{e}^{T}-1\right)\left({T}_{{on}}-2\right)}$$

(1)

where $T$ is the normalized expansion time (in bacterial doubling times), ${T}_{{on}}$ is the average duration (also normalized to bacterial doubling times) that a single bacillus spends in the drug-tolerant state, and $f$ is the fraction of persisters^59,60. Here, $C{V}_{{N}_{T}}^{2}$ is the model-predicted squared coefficient of variation of the drug-tolerant population across clones. For a fixed $f$, smaller ${T}_{{on}}$ (faster switching between cell states) yields reduced inter-clonal fluctuations, while larger ${T}_{{on}}$ enhances these fluctuations^59,60.

During extended antibiotic exposure, each drug-tolerant bacillus irreversibly transitions to a drug-resistant state with a small probability $p\ll 1$. Given the number of drug-tolerant bacilli ${N}_{T}$, the number of drug-resistant colonies ${N}_{R}$ follows a binomial distribution. The clone-to-clone variation in ${N}_{R}$ can be approximated as:

$$C{V}_{{N}_{R}}^{2}=C{V}_{{N}_{T}}^{2}+\frac{1-p}{{\bar{N}}_{R}}\approx C{V}_{{N}_{T}}^{2}+\frac{1}{{\bar{N}}_{R}}$$

(2)

where ${\bar{N}}_{R}$ is the average number of drug-resistant colonies and $C{V}_{{N}_{R}}$ denotes the coefficient of variation of ${N}_{R}$. Using equation (2), the coefficient of variation of ${N}_{R}$ allows estimation of ${{CV}}_{{N}_{T}}^{2}$:

$${{CV}}_{{N}_{T}}^{2}\approx C{V}_{{N}_{R}}^{2}-\frac{1}{{\bar{N}}_{R}}$$

Combining this with equation (1), we can estimate ${T}_{{on}}$. Applying this analysis to the fluctuation assay data for ${N}_{R}$ across 60 single-cell lineages, we obtained:

$$C{V}_{{N}_{T}}^{2}\approx 2.41\pm 1.5$$

for the wildtype, where the $\pm$ denoted the 95% confidence interval estimated by bootstrapping. Using this value in (1), with $T=30$ (representing 30 bacterial divisions before plating on RIF-containing plates) and an average frequency of drug-tolerant persisters of $f={10}^{-3}$, we estimate the transient heritability of the wildtype persister state as:

$${T}_{{on}}=9.6\pm 0.75\,{{\rm{generations}}}$$

For ItreS^SM, the number of colonies surviving antibiotic treatment was roughly six-fold lower than in wildtype (Figs. 2A, 5B right). Assuming the same mutation probability $p$ for both genotypes, this reduction likely results from around six-fold decrease in persister-formation rate in ItreS^SM following short-term treatment (Fig. 5B left), while ${T}_{{on}}$ remains unchanged. The fluctuation assay data for ItreS^SM yields:

$${{CV}}_{{N}_{T}}^{2}\approx 2.52\pm 2.4$$

and, with $f=\frac{{10}^{-3}}{6}$, the estimated ${T}_{{on}}$ from (1) is:

$${T}_{{on}}=7.4\pm 1.1\,{{\rm{generations}}}$$

This represents approximately a 20% decrease compared to wildtype, suggesting that ItreS^SM persisters are less stable and revert to a drug-sensitive state more rapidly. Overall, the primary reason for the six-fold reduction in drug-resistant colonies appears to be a corresponding decrease in persister formation rates in ItreS^SM relative to wildtype.

Antibiotic permeability (RIF uptake)

M. smegmatis wildtype, Flux^RIF, or Flux^INH in mid-log phase were incubated with 1X MIC of RIF at 37 °C. Bacteria were harvested at 0, 2, 4, 24, and 48 h and CFUs were determined by serial dilution plating on m7H10. The cell-free supernatant was collected by filtering through a 0.22 µm filter. RIF was extracted by adding a precooled solution of LC-MS grade acetonitrile: methanol: H₂O (40:40:20) −40 °C. RIF detection and quantification were performed by LC-MS, as previously described^21,23,99. The intrabacterial RIF concentration was calculated as [RIF]_{drug only} – [RIF]_filtrate, with three biological replicates per group.

EtBr permeability assay

M. smegmatis wildtype and drug-resistant strains in mid-log phase were cultured in m7H9 until reaching an OD₅₉₅ of 0.7. Cultures were centrifuged at 13,000 rpm for 3 min, the supernatant discarded, and the pellets washed with PBS. The OD₅₉₅ was adjusted to 0.4, and glucose was added to a final concentration of 0.4%. Ethidium bromide (EtBr) was added at 8 mg/mL, and 100 µL aliquots were transferred into each well of black, clear-bottom 96-well plates (Costar). Fluorescence was measured every 60 s for 60 min using a SpectraMax M5 spectrofluorometer (excitation 530, emission 595 nm). This assay assesses EtBr uptake as an indicator of cell membrane permeability.

SDS susceptibility test

The susceptibilities of wildtype and Flux^INH were evaluated following a method adapted from Banaei et al.¹⁰⁰. Briefly, cultures in mid-log phase were diluted with growth medium to an O.D₅₉₅ of 0.01 and incubated with 0.01% SDS in triplicate. At 0 and 8 h of incubation, CFUs were monitored by plating on m7H10 to assess viability.

MPN (most probable number) assay to detect total persister bacilli

The MPN assay was performed as previously reported⁷³. Briefly, HN878, H37Rv, Erdman, and CDC1551 strains in mid-log phase were grown in m7H9, harvested by centrifugation at 5000 rpm for 8 min, and washed twice with PBS. The cultures were then centrifuged at 800 rpm for 8 min to generate a single-cell suspension. The supernatant was transferred to a separate tube and diluted with PBS to achieve an OD₅₉₅ of 0.1. CFU assay was used for the inoculum quantification. A total of 20 mL of the single-cell suspension was prepared for each condition, transferred to a vented flask, and incubated at 37 °C for 2 weeks. Starved cultures were split into two 8 mL cultures in vented flasks and treated with either 100 µg/mL RIF with or without 200 µM Validamycin A (ValA). These cultures were incubated at 37 °C for 5 days. After incubation, cultures were harvested by centrifugation at 5000 rpm for 8 min, washed with PBS, and resuspended in 1 mL PBS. For the MPN assay, 15 µL of this suspension was diluted into 135 µL of m7H9 in 96-well plates. Ten-fold serial dilutions were carried out across the wells, which were then incubated at 37 °C. OD₅₉₅ was measured after 3 and 5 weeks to determine bacterial growth. The remaining culture from the assay was also used for CFU enumeration, with dilutions ranging from 10^-5 and 10^-1. ItreS^HN and ItreS^CDC, treated with or without ATc, were included in the assay.

Statistical analysis

All data were analyzed using Prism (v10.0; GraphPad Software). Statistical significance was determined using Student’s unpaired t-test with Welch’s correction or one-way ANOVA with Bonferroni post-test correction. P values less than 0.05 were considered statistically significant.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.