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.
Subject terms: Bacteriology, Tuberculosis
__________________________________________________________________
Drug resistance in bacteria often involves drug-tolerant persisters.
Mycobacterium tuberculosis uses trehalose catalytic shift to promote
persister formation, thereby developing multidrug resistant-TB
(MDR-TB). Authors show that inhibiting this shift reduces MDR-TB,
highlighting a potential therapeutic target for new treatments.
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 disease^[55]1. 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^[56]2,[57]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^[58]4–[59]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 changes^[60]8. 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^[61]9–[62]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^[63]8,[64]12–[65]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^[66]11,[67]16–[68]18. Bacterial intrinsic factors
that help adapt to these stresses, including toxin-antitoxin (TA)
modules in Escherichia coli^[69]19,[70]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^[71]7,[72]8,[73]13,[74]21–[75]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^[76]8,[77]13,[78]22,[79]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^[80]21,[81]22,[82]24,[83]26–[84]31.
Phenotypic heterogeneity has increasingly garnered attention as an
additional intrinsic factor that promotes adaptive evolution in
Mtb^[85]32. 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^[86]33,[87]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^[88]35,[89]36. An example of how metabolic network
remodeling fosters phenotypic heterogeneity is the random emergence of
persisters, often described as a “gambler” subpopulation^[90]33. When
exposed to antibiotics, only a subset of bacilli activate their DNA
repair systems and stress responses^[91]37, 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^[92]13,[93]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)^[94]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 candidates^[95]39.
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 isolates^[96]13. However, the
trehalose-mediated growth can be reversed when co-treated with a
TreS-specific inhibitor, validamycin A (ValA)^[97]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 [98]1). All strains were cultured in
Middlebrook 7H9 liquid medium (m7H9) supplemented with sodium butyrate
(SB), a known permissive carbon source for TB isolates^[99]13,[100]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. [101]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. [102]S1B). Principal Component Analysis (PCA) further confirmed
the divergence in metabolomics patterns (Fig. [103]1A, [104]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. [105]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. [106]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 intermediates^[107]13. Consistent with previous
findings^[108]23, levels of phosphoenolpyruvate (PEP), the most
downstream glycolytic intermediate, remained unchanged across clinical
isolates (Fig. [109]1B, right). Conversely, TCA cycle metabolites were
unaltered or downregulated in DR-TB clinical isolates (Fig. [110]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. [111]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. [112]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.
[113]Fig. 1
[114]Open in a new tab
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 [115]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. [116]S2A)^[117]40,[118]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.
[119]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)^[120]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. [121]S2C, D). ItreS^SM without ATc served as a
control and exhibited biofilm-persister formation comparable to
wildtype (Fig. [122]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. [123]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 tolerance^[124]23. Interestingly,
ItreS^SM accumulated PEP compared to wildtype (Fig. [125]S2E). As a
result, ItreS^SM displayed increased susceptibility to antibiotics,
such as RIF, INH, and BDQ relative to wildtype or IotsA^SM
(Fig. [126]S2F). These phenotypes closely mimic those observed in ΔtreS
Mtb^[127]13. These findings collectively indicate that ItreS^SM
phenocopies ΔtreS Mtb.
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^[128]42,[129]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^[130]44,[131]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. [132]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. [133]2B and [134]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. [135]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.
Fig. 2. Trehalose catalytic shift as an adaptive strategy for the emergence
of drug-resistant mycobacterial mutants.
[136]Fig. 2
[137]Open in a new tab
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 [138]Source Data File.
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. [139]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. [140]S3B). Flow
cytometry tracked the relative abundance of GFP- and RFP-expressing
bacilli within these subcultures (Fig. [141]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. [142]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. [143]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. [144]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. [145]2A, B, and [146]S3A). Consistent with previously reported
DR-TB clinical isolates^[147]13, all Flux^RIF and naïve bacilli
displayed similar growth patterns in antibiotic-free m7H9
(Fig. [148]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. [149]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. [150]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
([151]NC_008596.1); 54.4% these were frameshift variants, and 36% were
located in intergenic regions (Fig. [152]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 [153]2). Sequencing confirmed
that Flux^RIF #1 and #2 bacilli carried an L[452]P mutation within the
RIF-resistance determining region (RRDR)^[154]46, a well-known
RIF-resistant mutation in many DR-TB clinical isolates^[155]47,[156]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. [157]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. [158]3A). Conversely, TCA cycle intermediates showed no
notable differences (Fig. [159]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. [160]1B)^[161]13. In
addition, Flux^RIF bacilli maintained high levels of PEP, likely
reflecting continued replication even in the presence of RIF
(Figs. [162]3A and [163]S3D)^[164]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. [165]3B and
[166]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. [167]3C, D and [168]S4D, E)^[169]13,[170]21,[171]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. [172]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.
[173]Fig. 3
[174]Open in a new tab
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 [175]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^[176]49–[177]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^[178]13,[179]23,[180]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 TDM^[181]53. We observed that RMR-tre labeling intensity in naïve
bacilli during mid-log phase gradually decreased with increasing
concentrations of free trehalose^[182]53, suggesting that RMR-tre acts
as a substrate for Ag85, an enzyme involved in TDM biosynthesis, at
levels comparable to free trehalose^[183]54–[184]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. [185]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. [186]4A-C and [187]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. [188]S4F and [189]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. [190]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 L[452]P mutation in the RRDR,
maintained the RMR-tre^low subfraction as a dominant population even
after antibiotic treatment, with only a slight reduction
(Fig. [191]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. [192]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.
[193]Fig. 4
[194]Open in a new tab
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. [195]5A)^[196]57,[197]58.
Fig. 5. Mathematical modeling of the role of trehalose catalytic shift in the
emergence of drug tolerance and drug resistance in Mtb.
Fig. 5
[198]Open in a new tab
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. [199]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) [200]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 [201]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. [202]5A)^[203]59,[204]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 states^[205]61. Once a bacillus
becomes tolerant, it remains in that state for several generations
before reverting to a drug-sensitive state (Figs. [206]5A and [207]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
progenitor^[208]62. 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. [209]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. [210]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^[211]8,[212]50,[213]63–[214]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 RIF^[215]68. Similar patterns have been observed
in retrospective TB case studies conducted in New York City between
2010 and 2021^[216]69. 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. [217]4D and [218]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. [219]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. [220]4D and
[221]S5D). Spot assays on m7H10 containing bactericidal doses of INH
corroborated the MIC shift assay results (Figs. [222]6B and [223]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. [224]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. [225]2A, right).
Testing two randomly selected Flux^INH bacilli revealed that they were
significantly more sensitive to RIF than naïve bacilli (Fig. [226]6C,
D), likely due to increased RIF uptake (Fig. [227]S7B). This altered
permeability was tested by the EtBr permeability assay and the SDS
sensitivity assay (Fig. [228]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
effect^[229]70. As shown in Fig. [230]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. [231]3C, D and [232]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. [233]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.
[234]Fig. 6
[235]Open in a new tab
A IC[50] 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 IC[50] 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 [236]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^[237]45,[238]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. [239]4, [240]5, and [241]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. [242]7A). Additionally, HN878 exhibited faster growth in m7H9
containing trehalose as the sole carbon source (Fig. [243]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. [244]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. [245]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.
[246]Fig. 7
[247]Open in a new tab
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 IC[50] 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 [248]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. [249]S2A and [250]S8A). Consistent with
previous reports^[251]45, HN878 displayed roughly a 5.0-fold higher
frequency of developing RIF resistance compared to lineage 4 strains
(Fig. [252]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. [253]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. [254]7E and [255]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^[256]72,[257]73. We found that the
frequency of persister formation in HN878 was the highest among all
clinical strains tested (Fig. [258]S8D). The reduction in persister
formation following co-treatment with ValA (Fig. [259]S8D, left) or
using ItreS^HN (Fig. [260]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. [261]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^[262]8,[263]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^[264]33,[265]75,[266]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^[267]8,[268]11,[269]77–[270]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^[271]52,[272]64,[273]65,[274]76,[275]81–[276]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^[277]30,[278]84,[279]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^[280]12,[281]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^[282]13,[283]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 hosts^[284]87. Therefore, the trehalose catalytic shift plays
multiple roles, including carbon storage, essential components for
persister biology, antibiotic tolerance, immune evasion, and
transmission^[285]38,[286]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 report^[287]53, 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. [288]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^[289]13,[290]22,[291]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. [292]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. [293]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 isolates^[294]90, Mtb persisters may revert to a DS-state once
antibiotic effects diminish (Fig. [295]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. [296]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 RIF^[297]39. 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 [298]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 growth^[299]21. 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:H[2]O (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
(ddH[2]O 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 log[10].
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 ^13C[12] trehalose
To investigate trehalose metabolism in DS- and DR- TB clinical
isolates, we performed isotope tracing experiments using fully ^13C
labeled trehalose (^13C[12] 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% ^13C[12] 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/H[2]O (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 [300]2. Gene expression levels were quantified by calculating
the ΔΔCt values, normalized to the housekeeping gene sigA. Fold changes
were expressed as log[2] values relative to control samples.
Lipid extraction and TLC analysis
TDM and TMM from mycobacterial bacilli were prepared as previously
described^[301]13. 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: H[2]O (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 [302]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 study^[303]45. To
generate single-cell suspensions of M. smegmatis wildtype and ItreS^SM,
cultures were diluted to an OD[595] of 0.00005 in 200 µL within 96-well
plates. The cultures were then incubated and allowed to grow until
reaching an OD[595] 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 cells^[304]91.
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 OD[595] 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 OD[595] of 0.05, was allowed to grow until reaching an OD[595] 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
OD[595] 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)^[305]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;
[306]https://github.com/tseemann/snippy). The M. smegmatis reference
genome (accession: [307]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)^[308]93,
III. Filtering variants with BCFtools (v1.18)^[309]94, and IV.
Annotating variant effects with SnpEff (v5.0e)^[310]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. smegmatis^[311]96. 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 OD[595] of ~1.0 in fresh m7H9.
Cultures were stained with 15 µM DiOC[2] 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) spectroscopy^[312]53. 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. [313]S6A). To generate single-lineage bacilli of M.
smegmatis wildtype and ItreS^SM, cultures were diluted to an OD[595] of
0.00005 in 200 µL and dispensed into 96-well plates. Cultures were
incubated until reaching an OD[595] 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. [314]5A), we utilized
recent mathematical models that relate fluctuation assay data to these
rates^[315]60. 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^[316]61,[317]97,[318]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. [319]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 reported^[320]60. Assuming colonies expand over
[MATH: T :MATH]
generations, the variability in the number of tolerant bacilli,
measured by the squared coefficient of variation (standard deviation
divided by the mean),
[MATH:
CVN<
/mrow>T2 :MATH]
, is given by:
[MATH:
CVN<
/mrow>T2×f=2<
mi>ToneT−2TTon<
/mrow>−2−Ton<
/mrow>2eT<
/mrow>−1Ton<
/mrow>−2
:MATH]
1
where
[MATH: T :MATH]
is the normalized expansion time (in bacterial doubling times),
[MATH:
Ton
:MATH]
is the average duration (also normalized to bacterial doubling times)
that a single bacillus spends in the drug-tolerant state, and
[MATH: f :MATH]
is the fraction of persisters^[321]59,[322]60. Here,
[MATH:
CVN<
/mrow>T2 :MATH]
is the model-predicted squared coefficient of variation of the
drug-tolerant population across clones. For a fixed
[MATH: f :MATH]
, smaller
[MATH:
Ton
:MATH]
(faster switching between cell states) yields reduced inter-clonal
fluctuations, while larger
[MATH:
Ton
:MATH]
enhances these fluctuations^[323]59,[324]60.
During extended antibiotic exposure, each drug-tolerant bacillus
irreversibly transitions to a drug-resistant state with a small
probability
[MATH: p≪1 :MATH]
. Given the number of drug-tolerant bacilli
[MATH: NT
:MATH]
, the number of drug-resistant colonies
[MATH: NR
:MATH]
follows a binomial distribution. The clone-to-clone variation in
[MATH: NR
:MATH]
can be approximated as:
[MATH:
CVN<
/mrow>R2=CVNT2+1−pN¯R≈C
VNT2+1N¯R :MATH]
2
where
[MATH: N¯R :MATH]
is the average number of drug-resistant colonies and
[MATH:
CVNR :MATH]
denotes the coefficient of variation of
[MATH: NR
:MATH]
. Using equation (2), the coefficient of variation of
[MATH: NR
:MATH]
allows estimation of
[MATH:
CVN<
/mrow>T2 :MATH]
:
[MATH:
CVN<
/mrow>T2≈CVNR2−1N¯R :MATH]
Combining this with equation (1), we can estimate
[MATH:
Ton
:MATH]
. Applying this analysis to the fluctuation assay data for
[MATH: NR
:MATH]
across 60 single-cell lineages, we obtained:
[MATH:
CVN<
/mrow>T2≈2.41±1.5 :MATH]
for the wildtype, where the
[MATH: ± :MATH]
denoted the 95% confidence interval estimated by bootstrapping. Using
this value in (1), with
[MATH: T=30 :MATH]
(representing 30 bacterial divisions before plating on RIF-containing
plates) and an average frequency of drug-tolerant persisters of
[MATH:
f=10−3
:MATH]
, we estimate the transient heritability of the wildtype persister
state as:
[MATH:
Ton=9.6±0.75generations
:MATH]
For ItreS^SM, the number of colonies surviving antibiotic treatment was
roughly six-fold lower than in wildtype (Figs. [325]2A, [326]5B right).
Assuming the same mutation probability
[MATH: p :MATH]
for both genotypes, this reduction likely results from around six-fold
decrease in persister-formation rate in ItreS^SM following short-term
treatment (Fig. [327]5B left), while
[MATH:
Ton
:MATH]
remains unchanged. The fluctuation assay data for ItreS^SM yields:
[MATH:
CVN<
/mrow>T2≈2.52±2.4 :MATH]
and, with
[MATH:
f=10−36
:MATH]
, the estimated
[MATH:
Ton
:MATH]
from (1) is:
[MATH:
Ton=7.4±1.1generations
:MATH]
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: H[2]O (40:40:20) −40 °C. RIF
detection and quantification were performed by LC-MS, as previously
described^[328]21,[329]23,[330]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 OD[595] of 0.7. Cultures were
centrifuged at 13,000 rpm for 3 min, the supernatant discarded, and the
pellets washed with PBS. The OD[595] 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.^[331]100. Briefly, cultures in
mid-log phase were diluted with growth medium to an O.D[595] 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 reported^[332]73. 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 OD[595] 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. OD[595] 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 [333]Nature
Portfolio Reporting Summary linked to this article.
Supplementary information
[334]Supplementary Information^ (1.9MB, pdf)
[335]41467_2025_61703_MOESM2_ESM.pdf^ (17.7KB, pdf)
Description of Additional Supplementary Files
[336]Supplementary Dataset 1^ (19.3KB, xlsx)
[337]Supplementary Dataset 2^ (17.2KB, xlsx)
[338]Reporting Summary^ (81.5KB, pdf)
[339]Transparent Peer Review file^ (252.9KB, pdf)
Source data
[340]Source Data File^ (85.5KB, xlsx)
Acknowledgements