control group (DMSO). The statistical significance of these effects was
tested based on post hoc comparisons. Large positive or negative effect
sizes reflect a strong increase or decrease, respectively of values upon
PTC exposure as compared to DMSO. Data extraction and analysis were
performed with the R software 4.0.2 (https://www.r-project.org/).

In silico docking

Docking poses of M06 or its amino derivative, INH and MOX were
generated on SeamDock docking web server (https://bioserv.rpbs.
univ-paris-diderot.fr/services/SeamDock/)⁷². The structure PDB 4BGF
of the arylamine N-acetyltransferase from *M. tuberculosis* cross-seeded
with the crystals of *Mycobacterium marinum* protein³⁶ and the struc-
ture PDB 5BS8 of the DNA gyrase from *M. tuberculosis*³⁰ were used to
predict ligand binding. Either Vina or Autodock was used to assess
protein-ligand interactions with the following settings: grid spacing
between 0.2 and 1Å; energy range at 5 kcal/mol; and
exhaustiveness at 32.

MIC evaluation by resazurin assay

Secondary cultures were grown up to OD<sub>600nm</sub> 0.6–0.8, diluted to OD<sub>600nm</sub> 0.005 using pre-warmed Middlebrook 7H9 broth, and 100 µL were dispensed into a 96-well plate, including negative and positive controls. ATC (100 ng/mL) was added during the assay to induce silencing of *nat*. Wells containing the highest drug concentration to be tested were filled with 200 µL of cell suspension and used for sequential two-fold dilutions. Plates were incubated under static conditions for 7 days at 37 °C, after which 10 µL of resazurin (0.01%) were added to each well. Plates were further incubated for 24 h and then visually inspected, considering blue as cidity and pink as viability. MIC was regarded as the lowest concentration of drug causing cidity.

Checkerboard assay

The efficacy of dual drug-combinations was tested by checkerboard
titration methods using 96-well plates, as described for the MIC assay.
The first top-left well was used for the negative control. The rest of the
first row and the first column were used to dilute each compound
alone, whereas the highest concentrations of the two compounds to be
tested (A and B) were alternately placed in the second row and in the
second column, to test crossed dilutions. The drug concentrations
tested ranged from 30 to 0.03 mM, except for M04, which ranged
from 300 to 0.3 µM. The fractional inhibitory concentration (FIC)
index for combinations of two compounds A and B was calculated
according to the following equation: FIC index = (A/MIC<sub>A</sub>) + (B/MIC<sub>B</sub>),
where A and B are the MIC of compound A and compound B in com-
bination, and MIC<sub>A</sub> and MIC<sub>B</sub> are the MIC of compound A and com-
pound B alone. The FIC index was interpreted as follows: synergy
(≤0.5), additivity (0.5 < FIC ≤ 1), and indifference (>1).

ATP cell viability assay

The effect of different ROS scavengers on M06 potency was assessed by crossing increasing concentrations of M06 and of a given ROS scavenger and checking its effect on bacterial viability compared with M06 alone. The following ROS scavengers were tested: 2,2'-bipyridyl; N,N'-dimethylthiourea; glutathione; N-acetyl cysteine; catalase. The same method was used to assess the effect of B02 and F05 in combination with different anti-tubercular drugs. Mid-log phase *M. tuberculosis* cultures were diluted to OD<sub>600nm</sub> 0.005 and incubated in the presence or absence of different concentrations of individual compounds or their combinations for one week. Cell viability was determined by luminescence to estimate ATP production using the BacTiter-Glo Microbial Cell Viability Assay on a GloMax Discovery System endowed with the GloMax Discover software (Promega), according to the manufacturer's instructions. *M. tuberculosis* viability was expressed as relative luminescence units of ATP (RLU) in the presence of treatment versus untreated cells.

Kinetic assay of drug-mediated mortality

Secondary cultures of *M. tuberculosis* H37Rv were grown up to OD<sub>600nm</sub> 0.5 and diluted to OD<sub>600nm</sub> 0.05 (10<sup>6</sup> to 10<sup>7</sup> bacilli) in fresh 7H9 medium containing 0.01% Tyloxapol. Drugs were added as follows: INH (MIC: 50 ng/mL, 2x MIC); RIF (156 ng/mL, 2x MIC); MOX (100 ng/mL, 2x MIC); EMB (5 µg/mL, 2x MIC); M06 (1.25 µg/mL, 1x MIC); or their combinations. Drugs were added at the beginning of the assay and never replenished. Untreated and drug-treated cultures were re-incubated at 37 °C at 50 RPM and aliquots were withdrawn sequentially for plating different dilutions on 7H11 agar medium enriched with 10% OADC and 0.5% glycerol. CFU were quantified after 4 weeks of incubation at 37 °C and normalized to 10<sup>7</sup> at time zero.

Toxicity assay

Confluent Vero cells were dissociated by trypsinization, washed, and
resuspended in complete DMEM medium at a concentration of
2.5*10⁵ cells/mL, and 100 µL were dispensed into a 96-well plate and
incubated for 24 h at 37 °C in humidified 5% CO₂ atmosphere. Each
compound (except for A1, A2, A3, A5, C2, and D10) was tested at a
maximum concentration of 8 mM or 0.5 mM, depending on reagent
availability, and serially diluted. Cells were exposed to the com-
pounds for 24 and 48 h. Survival was assessed by ATP luminescent
cell viability assay (CellTiter-Glo®, Promega), using a Biohit Micro-
plate Reader and integrated software. Half maximal inhibitory con-
centration (IC50) was estimated by nonlinear regression analysis of
compound concentration versus normalized response using Graph-
Pad Prism software.

Isolation of PTC-resistant mutants

Mutants were isolated in two steps. Secondary cultures of *M. tuber-
culosis* H37Rv were grown up to OD<sub>600nm</sub> 1. About 10<sup>9</sup> cells were har-
vested and spread on 7H10 agar plates containing drug concentrations
ranging from 4- to 20-fold MIC, and plates were incubated at 37 °C for
4 weeks. The only colonies obtained at 4-fold MIC were first inoculated
into a liquid medium containing the same drug concentration, then re-
inoculated at twice the concentration, and finally seeded on plates
containing a drug concentration equal to 8-fold MIC. The resistance
phenotype was validated by assessing the MIC at least twice for iso-
lated colonies. The mutant frequency was about 5*10E-8.

Genomic DNA isolation and whole-genome sequencing

PTC-resistant mutants were reinoculated in the presence of the compound (8x MIC) and 10 mL of exponentially growing cultures were used for genomic DNA isolation, as follows. Each cell pellet was heat-inactivated at 80 °C for 1 h. Inactivated cells were washed and resuspended in 450 µL of lysis solution (25 mM Tris-HCl pH 7.9; 10 mM EDTA 50 mM glucose), also adding 50 µL of lysozyme (10 mg/mL) and incubated at 37 °C overnight. Next, 100 µL of sodium dodecyl sulfate (10 %) and 50 µL of proteinase K (10 mg/mL) were added, mixed gently by inversion, and incubated at 55 °C for 30 min. Next, 200 µL of sodium chloride (5 M) was added and mixed gently by inversion, followed by 160 µL of preheated cetrimide saline solution (4 %), mixed and incubated at 65 °C for 10 min. Genomic DNA was extracted twice, adding equal volume (1 mL) of chloroform: isoamyl alcohol (24:1), mixing by inversion, and centrifuging for 5 min at 13500 g. The aqueous layer was transferred to a clean tube (800 µL) and 560 µL (0.7 volumes) of isopropanol was added at room temperature, and mixed by inversion until DNA precipitation. DNA was collected by centrifugation for 10 min and the pellet was washed with 70 % ethanol, dried, and resuspended in 100 µL of TE buffer. Libraries for whole-genome sequencing (WGS) were built using a TruSeq DNA PCR-Free Low Throughput Library Prep Kit (Illumina, USA) following the manufacturer's protocol. Quality control was performed on Agilent Bioanalyzer. DNA sequencing was performed on the Illumina MiSeq 250 platform using paired-end 150-bp reads.
Variant calling

Variant calling analysis was performed with Sequana 0.9.8⁷³, using the variant-calling pipeline 0.10.0 built on top of Snakemake 6.1.1⁷⁴. Briefly, whole-genome sequencing reads were aligned to the reference genome of *M. tuberculosis* H37Rv v2 using bwa 0.7.17⁷⁵. Sambamba 0.8.0⁷⁶ was used to mark and filter duplicated reads. Variants were identified using Freebayes 1.3.2²⁷⁷ and annotated using SNPeff 5.0⁷⁸. High-quality single-nucleotide polymorphisms (SNP) were selected using the following criterions: freebayesscore: 20; frequency: 0.7; mindepth: 10; forwarddepth: 3, reversed depth: 3, strandratio: 0.2. Quality control statistics were summarized using multiQC 1.10.1⁷⁹.

Total RNA extraction and sequencing

Secondary *M. tuberculosis* Erdman cultures were grown in Middlebrook 7H9 broth until the exponential growth-phase (OD<sub>600nm</sub> 0.6) and were subjected to different compounds at a concentration of 10-fold the MIC for 6 h, in triplicate. DMSO was used as a negative control. For total RNA isolation, 10 mL of each culture were collected at 4200 x g for 15 min at 4 °C and frozen at -80 °C for 24 h. RNA was extracted by hot-phenol method and precipitated with 0.5 M LiCl and three volumes of cold 100% ethanol for 2 h at -20 °C. Samples were centrifuged at 13500 x g for 35 min at 4 °C and RNA was washed with 80% ethanol. RNA samples were air-dried for 30 min and resuspended in 20 µL of nuclease-free water. DNA was depleted by double DNase treatment, using the TURBO DNA-free kit (Ambion). The quality and integrity of RNA samples were checked on Agilent 2100 Bioanalyzer, and the amount was quantified on the Qubit fluorimeter (Thermo Fisher). Ribosomal RNA was depleted using the QIAseq FastSelect 5 S/16 S/23 S kit for bacteria (Qiagen), according to the manufacturer's recommendations. Libraries were prepared using the TruSeq Stranded mRNA Library Preparation kit (Illumina) according to manufacturer's protocol: (https://support.illumina.com/content/dam/illumina-support/documents/documentation/chemistry_documentation/samplepreps_truseq/truseq-stranded-mrna-workflow/truseq-stranded-mrna-workflow-checklist-1000000040600-00.pdf). The quality, integrity and amount of the libraries were checked with the Agilent 2100 Bioanalyzer and Qubit fluorimeter. Lastly, single-end sequencing was carried out on a NextSeq500 mid-output system (Illumina), 130 M reads.

RNA-seq analysis

RNA-seq datasets were produced from three independent experi-
mental replicates. Sequana 0.9.8 rnaseq pipeline 0.10.0 built on top of
Snakemake 5.8.1 was used to perform data analysis. In short, reads
were trimmed from adapters using Cutadapt 2.10⁸⁰, then mapped to M.
tuberculosis Erdman genome (ASM35020v1) using bowtie 2.2.2⁸¹. Fea-
tureCounts 2.0.0⁸² was used to produce the count matrix, assigning
reads to features with strand-specific information. Quality control
statistics were summarized using MultiQC 1.8. Statistical analysis on
the count matrix was performed to identify DEGs, comparing M.
tuberculosis treated with a given drug relative to the DMSO control
condition. The clustering of transcriptomic profiles was assessed using
a Principal Component Analysis. Differential expression testing was
conducted with the Wald test using DESeq2 library 1.24.0⁸³ with SAR-
Tools 1.7.0⁸⁴, indicating the significance (Benjamini-Hochberg adjus-
ted P-values, false positive rate <0.05) and the effect size (fold-change)
for each comparison. GO enrichment analysis was carried out using the
Gene Ontology Resource powered by PANTHER (http://geneontology.
org/)⁸⁵,⁸⁶.

Real-time quantitative PCR

To validate transcriptional silencing of *nat* by CRISPRi, secondary
cultures of GMT36 and related control were grown up to OD<sub>600nm</sub>
0.6-0.8, diluted to OD600 0.05, and induced with 100 ng/mL of ATC
for 4 days in triplicate. To quantify the expression of genes implicated

in stress response, *M. tuberculosis* H37Rv and NAT mutants (S9, S10, and S11) were cultured in Middlebrook 7H9 broth until OD<sub>600</sub> 0.6, in triplicate. Next, cultures were exposed to M06 (10x MIC) for 6 h. Total RNA was extracted by hot-phenol method and DNA was depleted, as described above. cDNA was generated from 150-200 ng of total RNA using SuperScrip IV (Invitrogen) and random hexamers (Thermo Fisher), according to the manufacturer's instructions. qRT-PCR analysis was carried out using the SYBR Green PCR Master Mix (Applied Biosystems), with 0.3 µM primers, and 1 µL cDNA diluted 1:4. Absolute quantification was run on the LightCycler®480 Instrument (Roche Life Science): activation at 50 °C for 2 min (ramp rate °C/s 4.8) followed by 95 °C for 10 min; 40 amplification steps at 95 °C for 15 s (ramp rate °C/s 4.8), followed by 60 °C for 30 s (ramp rate °C/s 2.5) and 72 °C for 30 s (ramp rate °C/s 4.8); melting curve at 60 °C for 15 s (ramp rate °C/s 2.5), followed by 95 °C for 15 s (ramp rate °C/s 0.29). The genomic DNA of *M. tuberculosis* was serially diluted to generate standard curves and calculate transcript copy numbers.

Snapshot microscopy and single-cell image analysis

Secondary cultures of *M. tuberculosis* H37Rv, NAT mutant, and NAT-
overexpressing strains were cultured in Middlebrook 7H9 broth until
OD<sub>600</sub> 0.5, in duplicate. Secondary cultures of *M. tuberculosis* GMT36
and control strain were diluted to OD<sub>600</sub> 0.05 and induced with ATC
(100 ng/mL) for 3 days prior to stress exposure. Mid-log phase sec-
ondary cultures were treated for 24 h with DMSO (CT), H<sub>2</sub>O<sub>2</sub> (30 mM),
or with different drugs at 10x-MIC concentration: M06 (12.5 µg/mL);
MOX (500 ng/mL); INH (250 ng/mL); RIF (800 ng/mL). Next, bacilli
were stained with either CellROX (5 µM) or FM464 (5 µg/mL) dyes for
30 min and imaged by phase contrast and fluorescence using an
inverted DeltaVision Elite Microscope (Leica) equipped with an
UPLFLN100X02/PH3/1.30 objective (Olympus). Samples for imaging
were prepared by dispensing 0.6 µL of bacterial suspensions between
two #1.5 coverslips, sealed with glue. Exposure conditions: phase con-
trast 50% T, 100 ms; Cy5 (Ex 632/22, Em 679/34) 50% T, 100 ms for
CellROX detection; TRITC (Ex 542/27, Em 597/45) 50% T, 100 ms for
FM464 detection. Individual bacilli were automatically segmented in
phase-contrast, using Omnipose⁴⁹ for cell segmentation in combina-
tion with a custom-trained model. The latter was obtained from
training on a dataset consisting of the ground truth dataset provided
as part of Omnipose and additional manually labeled images of *M.
tuberculosis*. A Python notebook was written to extract single-cell
fluorescence and cell area from snapshot images using the Omnipose-
derived segmentation masks. Inverted masks were used to extract
background fluorescence, which was subtracted from the fluorescence
of each cell.

Time-lapse microscopy and single-cell image analysis

Log-phase *M. tuberculosis* RecA-mCherry_GFP_cyt reporter (OD<sub>600nm</sub> 0.5) was diluted ten times in fresh 7H9 medium in the absence of detergents and seeded into our microfluidic hexa-device<sup>22</sup>. Three independent cultures were loaded on different areas of the device. The microfluidic assembly was mounted on a DeltaVision Microscope at 37 °C. Growth medium in the absence or presence of M06 (1.25 µg/mL, 1x MIC) and/or INH (0.2 µg/mL, 8x MIC) was injected at 10-µL/min rate from a syringe pump, according to the different experimental phases. Images were acquired every 3 h using a UPLFLN100X02/PH3/1.30 objective (Olympus) and a high-speed sCMOS camera, 2560 ×2160 pixels, pixel size 6.5 × 6.5 µm, 15-bit, the spectral range of 370-1100 nm. Exposure conditions were phase contrast 100% T, 200 ms, FITC (Ex 492/28, Em 523/23) 50% T, 50 ms; TRITC (Ex 556/25, Em 611/47) 100% T, 250 ms. An endpoint permeabilization assay was carried out by staining bacilli with DRAQ7 (1 µM) for 6 h, and imaging on CY-5 (Ex 632/22, Em 679/34) 100% T, 300 ms. Individual cells were manually segmented at specific time points using ROI Manager Macro of ImageJ 2.0.0.-rc-59/1.51n or counted over time using the Cell
Variant calling

Variant calling analysis was performed with Sequana 0.9.8⁷³, using the variant-calling pipeline 0.10.0 built on top of Snakemake 6.1.1⁷⁴. Briefly, whole-genome sequencing reads were aligned to the reference genome of *M. tuberculosis* H37Rv v2 using bwa 0.7.17⁷⁵. Sambamba 0.8.0⁷⁶ was used to mark and filter duplicated reads. Variants were identified using Freebayes 1.3.2²⁷⁷ and annotated using SNPeff 5.0⁷⁸. High-quality single-nucleotide polymorphisms (SNP) were selected using the following criterions: freebayesscore: 20; frequency: 0.7; mindepth: 10; forwarddepth: 3, reversed depth: 3, strandratio: 0.2. Quality control statistics were summarized using multiQC 1.10.1⁷⁹.

Total RNA extraction and sequencing

Secondary *M. tuberculosis* Erdman cultures were grown in Middlebrook 7H9 broth until the exponential growth-phase (OD~600nm~ 0.6) and were subjected to different compounds at a concentration of 10-fold the MIC for 6 h, in triplicate. DMSO was used as a negative control. For total RNA isolation, 10 mL of each culture were collected at 4200 x g for 15 min at 4 °C and frozen at -80 °C for 24 h. RNA was extracted by hot-phenol method and precipitated with 0.5 M LiCl and three volumes of cold 100% ethanol for 2 h at -20 °C. Samples were centrifuged at 13500 x g for 35 min at 4 °C and RNA was washed with 80% ethanol. RNA samples were air-dried for 30 min and resuspended in 20 µL of nuclease-free water. DNA was depleted by double DNase treatment, using the TURBO DNA-free kit (Ambion). The quality and integrity of RNA samples were checked on Agilent 2100 Bioanalyzer, and the amount was quantified on the Qubit fluorimeter (Thermo Fisher). Ribosomal RNA was depleted using the QIAseq FastSelect 5 S/16 S/23 S kit for bacteria (Qiagen), according to the manufacturer's recommendations. Libraries were prepared using the TruSeq Stranded mRNA Library Preparation kit (Illumina) according to manufacturer's protocol: (https://support.illumina.com/content/dam/illumina-support/documents/documentation/chemistry_documentation/samplepreps_truseq/truseq-stranded-mrna-workflow/truseq-stranded-mrna-workflow-checklist-1000000040600-00.pdf). The quality, integrity and amount of the libraries were checked with the Agilent 2100 Bioanalyzer and Qubit fluorimeter. Lastly, single-end sequencing was carried out on a NextSeq500 mid-output system (Illumina), 130 M reads.

RNA-seq analysis

RNA-seq datasets were produced from three independent experimental replicates. Sequana 0.9.8 rnaseq pipeline 0.10.0 built on top of Snakemake 5.8.1 was used to perform data analysis. In short, reads were trimmed from adapters using Cutadapt 2.10⁸⁰, then mapped to *M. tuberculosis* Erdman genome (ASM35020v1) using bowtie 2.2.2⁸¹. FeatureCounts 2.0.0⁸² was used to produce the count matrix, assigning reads to features with strand-specific information. Quality control statistics were summarized using MultiQC 1.8. Statistical analysis on the count matrix was performed to identify DEGs, comparing *M. tuberculosis* treated with a given drug relative to the DMSO control condition. The clustering of transcriptomic profiles was assessed using a Principal Component Analysis. Differential expression testing was conducted with the Wald test using DESeq2 library 1.24.0⁸³ with SAR-Tools 1.7.0⁸⁴, indicating the significance (Benjamini-Hochberg adjusted *P*-values, false positive rate <0.05) and the effect size (fold-change) for each comparison. GO enrichment analysis was carried out using the Gene Ontology Resource powered by PANTHER (http://geneontology.org/)⁸⁵,⁸⁶.

Real-time quantitative PCR

To validate transcriptional silencing of *nat* by CRISPRi, secondary cultures of GMT36 and related control were grown up to OD~600nm~ 0.6–0.8, diluted to OD600 0.05, and induced with 100 ng/mL of ATC for 4 days in triplicate. To quantify the expression of genes implicated

in stress response, *M. tuberculosis* H37Rv and NAT mutants (S9, S10, and S11) were cultured in Middlebrook 7H9 broth until OD~600~ 0.6, in triplicate. Next, cultures were exposed to M06 (10x MIC) for 6 h. Total RNA was extracted by hot-phenol method and DNA was depleted, as described above. cDNA was generated from 150–200 ng of total RNA using SuperScrip IV (Invitrogen) and random hexamers (Thermo Fisher), according to the manufacturer's instructions. qRT-PCR analysis was carried out using the SYBR Green PCR Master Mix (Applied Biosystems), with 0.3 µM primers, and 1 µL cDNA diluted 1:4. Absolute quantification was run on the LightCycler® 480 Instrument (Roche Life Science): activation at 50 °C for 2 min (ramp rate °C/s 4.8) followed by 95 °C for 10 min; 40 amplification steps at 95 °C for 15 s (ramp rate °C/s 4.8), followed by 60 °C for 30 s (ramp rate °C/s 2.5) and 72 °C for 30 s (ramp rate °C/s 4.8); melting curve at 60 °C for 15 s (ramp rate °C/s 2.5), followed by 95 °C for 15 s (ramp rate °C/s 0.29). The genomic DNA of *M. tuberculosis* was serially diluted to generate standard curves and calculate transcript copy numbers.

Snapshot microscopy and single-cell image analysis

Secondary cultures of *M. tuberculosis* H37Rv, NAT mutant, and NAT-overexpressing strains were cultured in Middlebrook 7H9 broth until OD~600~ 0.5, in duplicate. Secondary cultures of *M. tuberculosis* GMT36 and control strain were diluted to OD~600~ 0.05 and induced with ATC (100 ng/mL) for 3 days prior to stress exposure. Mid-log phase secondary cultures were treated for 24 h with DMSO (CT), H~2~O~2~ (30 mM), or with different drugs at 10x-MIC concentration: M06 (12.5 µg/mL); MOX (500 ng/mL); INH (250 ng/mL); RIF (800 ng/mL). Next, bacilli were stained with either CellROX (5 µM) or FM464 (5 µg/mL) dyes for 30 min and imaged by phase contrast and fluorescence using an inverted DeltaVision Elite Microscope (Leica) equipped with an UPLFLN100X02/PH3/1.30 objective (Olympus). Samples for imaging were prepared by dispensing 0.6 µL of bacterial suspensions between two #1.5 coverslips, sealed with glue. Exposure conditions: phase contrast 50% T, 100 ms; Cy5 (Ex 632/22, Em 679/34) 50% T, 100 ms for CellROX detection; TRITC (Ex 542/27, Em 597/45) 50% T, 100 ms for FM464 detection. Individual bacilli were automatically segmented in phase-contrast, using Omnipose⁴⁹ for cell segmentation in combination with a custom-trained model. The latter was obtained from training on a dataset consisting of the ground truth dataset provided as part of Omnipose and additional manually labeled images of *M. tuberculosis*. A Python notebook was written to extract single-cell fluorescence and cell area from snapshot images using the Omnipose-derived segmentation masks. Inverted masks were used to extract background fluorescence, which was subtracted from the fluorescence of each cell.

Time-lapse microscopy and single-cell image analysis

Log-phase *M. tuberculosis* RecA-mCherry_GFP_cyt reporter (OD~600nm~ 0.5) was diluted ten times in fresh 7H9 medium in the absence of detergents and seeded into our microfluidic hexa-device²². Three independent cultures were loaded on different areas of the device. The microfluidic assembly was mounted on a DeltaVision Microscope at 37 °C. Growth medium in the absence or presence of M06 (1.25 µg/mL, 1x MIC) and/or INH (0.2 µg/mL, 8x MIC) was injected at 10-µL/min rate from a syringe pump, according to the different experimental phases. Images were acquired every 3 h using a UPLFLN100X02/PH3/1.30 objective (Olympus) and a high-speed sCMOS camera, 2560 ×2160 pixels, pixel size 6.5 × 6.5 µm, 15-bit, the spectral range of 370–1100 nm. Exposure conditions were phase contrast 100% T, 200 ms, FITC (Ex 492/28, Em 523/23) 50% T, 50 ms; TRITC (Ex 556/25, Em 611/47) 100% T, 250 ms. An endpoint permeabilization assay was carried out by staining bacilli with DRAQ7 (1 µM) for 6 h, and imaging on CY-5 (Ex 632/22, Em 679/34) 100% T, 300 ms. Individual cells were manually segmented at specific time points using ROI Manager Macro of ImageJ 2.0.0-rc-59/1.51n or counted over time using the Cell
Counter plugin. Total fluorescence was normalized to cell area and background fluorescence was also subtracted.

## Intracellular RecA induction

Differentiated THP-1 macrophages were infected with *M. tuberculosis* RecA-mCherry_GFP_cyt reporter (GMT37) diluted in RPMI without phenol red to OD~600~ 0.005 (MOI 1:5). After 3 h of incubation at 37 °C in humidified 5% CO~2~ atmosphere, cells were washed five times with complete RPMI without phenol red. Infected cells were either left untreated or were treated with M06 (1.25 µg/mL, 1x MIC). Imaging was carried out at time zero and after 2 days of infection. Eight images were acquired for each field of view through a z-stack of 16 µm, starting the scan from the middle of the sample, using a 100x oil immersion objective, 1.4NA, WD 0.12 mm (Olympus). Exposure conditions on the Delta Vision microscope: bright field 10% T, 50 ms; FITC (Ex 475/28, Em 525/48) 50% T, 25 ms; TRITC (Ex 542/27, Em 597/45) 100% T, 100 ms. Image stacks were projected by maximum-intensity in SoftWorx 7.0 Cytiva, and processed using a customized ImageJ macro, which automatically segments intracellular bacterial foci based on their green fluorescence. Derived masks were used to measure the size and both fluorescence channels to assess RecA induction and control GFP_cyt fluorescence of intracellular bacilli. Inverted masks were used to subtract background fluorescence from each ROI.

## DNA supercoiling and cleavage assays

To determine the inhibition of negative supercoiling on 0.5 µg of relaxed pBR322 (Inspiralis), we used 1.2 U of DNA gyrase from *M. tuberculosis* (Inspiralis). The reactions were carried out in a final volume of 30 µL, using the following reaction buffer: 50 mM HEPES KOH pH 7.9; 6 mM magnesium acetate; 4 mM DTT; 1 mM ATP; 100 mM potassium glutamate; 2 mM spermidine; 0.05 mg/mL albumin, in the presence of DMSO or of different concentrations of MOX and M06. After addition of DNA gyrase, the reaction was incubated for 75 min at 37 °C and stopped with equal volumes of STEB (100 mM Tris-HCl pH 8.0; 10 mM EDTA; 0.5 mg/mL Bromophenol Blue; 40% sucrose) and of chloroform:isoamyl alcohol (24:1). After vortexing and centrifuging at maximum speed for 2 min, 22 µL of the upper aqueous phase were loaded on a 1% agarose gel in 1x TAE. Samples were allowed to settle for 20 min before starting migration at 50 V for about 3 h. The gel was stained for 30 min with 1 mg/mL EtBr in 1x TAE and destained for 15 min before UV image capture via ChemiDoc XRS Quantity One Analysis Software. To determine the stabilization of the gyrase-DNA cleavage complex by MOX and M06, we used the same reaction buffer, but the reaction was incubated for 1 h at 25 °C. To denature DNA gyrase, remove DNA-bound enzyme, and reveal latent DNA breaks, 0.2% SDS and 0.1 mg/mL of proteinase K were added to the reaction, and incubated for 30 min at 37 °C. Reactions were stopped and analyzed as indicated above.

## Liquid chromatography and mass spectrometry (LC-MS) analysis of M06 metabolites

Secondary cultures of *M. tuberculosis* H37Rv were grown up to OD~600nm~ 0.8 in duplicate and treated either with 18.75 µg/mL of M06 (15-fold MIC) or with a corresponding volume of DMSO as negative control. At each time point (0, 24, and 48 h), 20 mL of culture were collected at 4200 g for 10 min at 4 °C. Cell pellets were resuspended in 1 mL of cold extraction solvent (40% acetonitrile; 40% methanol in ultrapure ddH~2~O) and frozen at -80 °C. Bacteria were lysed by bead beating 6 times for 45 s at 6000 RPM using a Precellys system, and incubating on ice between each cycle. Samples were centrifuged at 16600 x g for 10 min at 4 °C, and the resulting supernatants were decontaminated by filtration using 0.22-µm tube filters (PALL Life Sciences) and stored at -80 °C until LC-MS analysis. Cell supernatants were directly filtered through 0.22-µm filtration units and stored at 4 °C until LC-MS analysis. M06 and its metabolites were extracted from

20 mL of culture supernatant three times, by adding the same volume of water and the same volume of ethyl acetate, which was then evaporated under vacuum and the residue was dissolved in 100 µL of acetonitrile. LC-MS analysis was performed using a Q Exactive mass spectrometer (Thermo Scientific). 24 samples were analyzed, n = 2 biological replicates. The instrument was equipped with an electro-spray ionization source (H-ESI II Probe) coupled to an Ultimate 3000 RS HPLC, endowed with the Chromeleon Chromatography Data System software (Thermo Scientific). A Thermo Scientific Hypersil GOLD aQ chromatography column (100 mm x 2.1 mm; 1.9 µm particle size) was pre-warmed at 30 °C and injected with the sample. The flow rate was set to 0.3 mL/min and the column was run for 3 min in isocratic eluent consisting of 5% acetonitrile in water containing 0.1% formic acid; 5 to 100% from 3 to 8 min. Full MS in positive mode was used with resolution set at 70,000, max IT = 240 ms, and AGC Target = 1.10⁶.

## Thin-layer chromatography of lipids, fatty, and mycolic acids

Secondary cultures of *M. tuberculosis* H37Rv, S10 NAT-mutant, and GMT35 NAT-overexpressing strain were cultured in Middlebrook 7H9 broth supplemented with 10% albumin-dextrose-catalase and 0.05% Tween 80, shaking at 120 RPM, until OD~600~ 0.15–0.2. Cultures were left untreated or treated with M06 (MIC: 1.25 µg/mL), MOX (MIC: 50 ng/mL), or INH (MIC: 100 ng/mL), at different concentrations relative to the MIC: 1x; 5x; 10x; 25x; or 50x the MIC. At the same time, cultures were labeled with either 0.5 or 1 µCi/mL of ¹⁴C-acetate (ARC; specific activity 106 mCi/mmol) and incubated at 37 °C under static conditions for 24 h. Lipids and mycolic acids were also analyzed in wild-type, NAT-mutant, and NAT-overexpressing strains, metabolically labeled with 1 µCi/mL of ¹⁴C-acetate as above, during early (OD~600~ 0.15) and mid (OD~600~ 0.5) exponential phase.

Lipids were extracted from 100 µL culture aliquots with 1.5 mL chloroform: methanol (2: 1) at 65 °C for 3 h. After extraction, 150 µL of ddWater was added, the samples were mixed, and centrifuged at 3000 x g for 3 min, and the lower organic phase was removed and dried under nitrogen. Extracted lipids were washed with chloroform: methanol: ddWater (4: 2: 1), dried with nitrogen, resuspended in 50 µL chloroform: methanol (2: 1), and 5 µL were loaded on silica gel 60 F254 plates (Merck). To separate trehalose esters of mycolates (TDM; TMM) and phospholipids (PE; CL), a mixture of chloroform: methanol: ddWater (20: 4: 0.5) was used as the eluent. To separate TDM, TMM, and phosphatidylinositol mannosides (PIMs), a mixture of chloroform: methanol: NH~4~OH: ddWater (65: 25: 0.5: 4) was used as the eluent. ¹⁴C-labeled lipid profiles were visualized using an AmershamTM TyphoonTM Biomolecular Imager and control software.

For mycolic acids extraction, 100 µL cultures were treated with 1 mL of tetrabutylammonium hydroxide (15%) and incubated at 100 °C overnight. Samples were methylated by adding 1.5 mL of dichloromethane, 1 mL of ddWater, and 150 µL of iodomethane, and incubated at room temperature under rotation for 4 h. Methylated samples were washed twice with ddWater, and organic phases were dried with nitrogen. Fatty and mycolic acid methyl esters (FAME and MAME) were extracted with 2 mL of diethyl ether, dried, and resuspended in 50 µL chloroform: methanol (2: 1) and 5 µL were loaded on a TLC plate, which was developed in n-hexane: ethyl acetate (95: 5: 3 runs). Unsaturated forms of MAME were analyzed on Ag-impregnated silica plates. ¹⁴C labeled FAME and MAME were visualized as described above. Cell envelope components were quantified from TLC autoradiographs using ImageJ⁷¹ and normalized to untreated samples.

## Intracellular efficacy imaging assay

Confluent RAW macrophages were diluted to a concentration of 5·10⁴ cells per mL in complete DMEM without phenol red and without antibiotics, and 70 µL were seeded into each well of an 18-well µ-Slide (ibidi), and incubated at 37 °C in humidified 5% CO~2~ atmosphere for 24 h. Macrophages were infected with our *M. tuberculosis* ribosomal
Counter plugin. Total fluorescence was normalized to cell area and background fluorescence was also subtracted.

## Intracellular RecA induction

Differentiated THP-1 macrophages were infected with *M. tuberculosis* RecA-mCherry_GFP_cyt reporter (GMT37) diluted in RPMI without phenol red to OD<sub>600</sub> 0.005 (MOI 1:5). After 3 h of incubation at 37 °C in humidified 5% CO<sub>2</sub> atmosphere, cells were washed five times with complete RPMI without phenol red. Infected cells were either left untreated or were treated with M06 (1.25 µg/mL, 1x MIC). Imaging was carried out at time zero and after 2 days of infection. Eight images were acquired for each field of view through a z-stack of 16 µm, starting the scan from the middle of the sample, using a 100x oil immersion objective, 1.4NA, WD 0.12 mm (Olympus). Exposure conditions on the Delta Vision microscope: bright field 10% T, 50 ms; FITC (Ex 475/28, Em 525/48) 50% T, 25 ms; TRITC (Ex 542/27, Em 597/45) 100% T, 100 ms. Image stacks were projected by maximum-intensity in SoftWorx 7.0 Cytiva, and processed using a customized ImageJ macro, which automatically segments intracellular bacterial foci based on their green fluorescence. Derived masks were used to measure the size and both fluorescence channels to assess RecA induction and control GFP_cyt fluorescence of intracellular bacilli. Inverted masks were used to subtract background fluorescence from each ROI.

## DNA supercoiling and cleavage assays

To determine the inhibition of negative supercoiling on 0.5 µg of relaxed pBR322 (Inspiralis), we used 1.2 U of DNA gyrase from *M. tuberculosis* (Inspiralis). The reactions were carried out in a final volume of 30 µL, using the following reaction buffer: 50 mM HEPES KOH pH 7.9; 6 mM magnesium acetate; 4 mM DTT; 1 mM ATP; 100 mM potassium glutamate; 2 mM spermidine; 0.05 mg/mL albumin, in the presence of DMSO or of different concentrations of MOX and M06. After addition of DNA gyrase, the reaction was incubated for 75 min at 37 °C and stopped with equal volumes of STEB (100 mM Tris-HCl pH 8.0; 10 mM EDTA; 0.5 mg/mL Bromophenol Blue; 40% sucrose) and of chloroform:isoamyl alcohol (24:1). After vortexing and centrifuging at maximum speed for 2 min, 22 µL of the upper aqueous phase were loaded on a 1% agarose gel in 1x TAE. Samples were allowed to settle for 20 min before starting migration at 50 V for about 3 h. The gel was stained for 30 min with 1 mg/mL EtBr in 1x TAE and destained for 15 min before UV image capture via ChemiDoc XRS Quantity One Analysis Software. To determine the stabilization of the gyrase-DNA cleavage complex by MOX and M06, we used the same reaction buffer, but the reaction was incubated for 1 h at 25 °C. To denature DNA gyrase, remove DNA-bound enzyme, and reveal latent DNA breaks, 0.2% SDS and 0.1 mg/mL of proteinase K were added to the reaction, and incubated for 30 min at 37 °C. Reactions were stopped and analyzed as indicated above.

## Liquid chromatography and mass spectrometry (LC-MS) analysis of M06 metabolites

Secondary cultures of *M. tuberculosis* H37Rv were grown up to OD<sub>600nm</sub> 0.8 in duplicate and treated either with 18.75 µg/mL of M06 (15-fold MIC) or with a corresponding volume of DMSO as negative control. At each time point (0, 24, and 48 h), 20 mL of culture were collected at 4200 g for 10 min at 4 °C. Cell pellets were resuspended in 1 mL of cold extraction solvent (40% acetonitrile; 40% methanol in ultrapure ddH<sub>2</sub>O) and frozen at -80 °C. Bacteria were lysed by bead beating 6 times for 45 s at 6000 RPM using a Precellys system, and incubating on ice between each cycle. Samples were centrifuged at 16600 x g for 10 min at 4 °C, and the resulting supernatants were decontaminated by filtration using 0.22-µm tube filters (PALL Life Sciences) and stored at -80 °C until LC-MS analysis. Cell supernatants were directly filtered through 0.22-µm filtration units and stored at 4 °C until LC-MS analysis. M06 and its metabolites were extracted from

20 mL of culture supernatant three times, by adding the same volume of water and the same volume of ethyl acetate, which was then evaporated under vacuum and the residue was dissolved in 100 µL of acetonitrile. LC-MS analysis was performed using a Q Exactive mass spectrometer (Thermo Scientific). 24 samples were analyzed, n = 2 biological replicates. The instrument was equipped with an electro-spray ionization source (H-ESI II Probe) coupled to an Ultimate 3000 RS HPLC, endowed with the Chromeleon Chromatography Data System software (Thermo Scientific). A Thermo Scientific Hypersil GOLD aQ chromatography column (100 mm x 2.1 mm; 1.9 µm particle size) was pre-warmed at 30 °C and injected with the sample. The flow rate was set to 0.3 mL/min and the column was run for 3 min in isocratic eluent consisting of 5% acetonitrile in water containing 0.1% formic acid; 5 to 100% from 3 to 8 min. Full MS in positive mode was used with resolution set at 70,000, max IT = 240 ms, and AGC Target = 1.10⁶.

## Thin-layer chromatography of lipids, fatty, and mycolic acids

Secondary cultures of *M. tuberculosis* H37Rv, S10 NAT-mutant, and GMT35 NAT-overexpressing strain were cultured in Middlebrook 7H9 broth supplemented with 10% albumin-dextrose-catalase and 0.05% Tween 80, shaking at 120 RPM, until OD<sub>600</sub> 0.15–0.2. Cultures were left untreated or treated with M06 (MIC: 1.25 µg/mL), MOX (MIC: 50 ng/mL), or INH (MIC: 100 ng/mL), at different concentrations relative to the MIC: 1x; 5x; 10x; 25x; or 50x the MIC. At the same time, cultures were labeled with either 0.5 or 1 µCi/mL of ¹⁴C-acetate (ARC; specific activity 106 mCi/mmol) and incubated at 37 °C under static conditions for 24 h. Lipids and mycolic acids were also analyzed in wild-type, NAT-mutant, and NAT-overexpressing strains, metabolically labeled with 1 µCi/mL of ¹⁴C-acetate as above, during early (OD<sub>600</sub> 0.15) and mid (OD<sub>600</sub> 0.5) exponential phase.

Lipids were extracted from 100 µL culture aliquots with 1.5 mL chloroform: methanol (2: 1) at 65 °C for 3 h. After extraction, 150 µL of ddWater was added, the samples were mixed, and centrifuged at 3000 x g for 3 min, and the lower organic phase was removed and dried under nitrogen. Extracted lipids were washed with chloroform: methanol: ddWater (4: 2: 1), dried with nitrogen, resuspended in 50 µL chloroform: methanol (2: 1), and 5 µL were loaded on silica gel 60 F254 plates (Merck). To separate trehalose esters of mycolates (TDM; TMM) and phospholipids (PE; CL), a mixture of chloroform: methanol: ddWater (20: 4: 0.5) was used as the eluent. To separate TDM, TMM, and phosphatidylinositol mannosides (PIMs), a mixture of chloroform: methanol: NH<sub>4</sub>OH: ddWater (65: 25: 0.5: 4) was used as the eluent. ¹⁴C-labeled lipid profiles were visualized using an AmershamTM TyphoonTM Biomolecular Imager and control software.

For mycolic acids extraction, 100 µL cultures were treated with 1 mL of tetrabutylammonium hydroxide (15%) and incubated at 100 °C overnight. Samples were methylated by adding 1.5 mL of dichloromethane, 1 mL of ddWater, and 150 µL of iodomethane, and incubated at room temperature under rotation for 4 h. Methylated samples were washed twice with ddWater, and organic phases were dried with nitrogen. Fatty and mycolic acid methyl esters (FAME and MAME) were extracted with 2 mL of diethyl ether, dried, and resuspended in 50 µL chloroform: methanol (2: 1) and 5 µL were loaded on a TLC plate, which was developed in n-hexane: ethyl acetate (95: 5: 3 runs). Unsaturated forms of MAME were analyzed on Ag-impregnated silica plates. ¹⁴C labeled FAME and MAME were visualized as described above. Cell envelope components were quantified from TLC autoradiographs using ImageJ<sup>71</sup> and normalized to untreated samples.

## Intracellular efficacy imaging assay

Confluent RAW macrophages were diluted to a concentration of 5·10<sup>4</sup> cells per mL in complete DMEM without phenol red and without antibiotics, and 70 µL were seeded into each well of an 18-well µ-Slide (ibidi), and incubated at 37 °C in humidified 5% CO<sub>2</sub> atmosphere for 24 h. Macrophages were infected with our *M. tuberculosis* ribosomal
reporter, also constitutively expressing a red marker (rRNA-
GFP_DsRed2_cyt: GMT17)⁶. Secondary *M. tuberculosis* cultures were
grown up to OD₆₀₀ 0.4–0.6, diluted in complete DMEM without phe-
nol red to OD₆₀₀ 0.0025 (MOI 1:10), and medium was replaced with the
bacterial cell suspension, except for uninfected control wells. After 4 h
of incubation at 37 °C in a humidified 5% CO₂ atmosphere, infected
cells were washed five times with complete DMEM without phenol red,
and drugs were added as follows: INH (50 ng/mL, 2x MIC); RIF (156 ng/
mL, 2x MIC); M06 (1.25 µg/mL, 1x MIC); or their combinations. DMSO
was used in untreated control wells. Cells were re-incubated for 6 days
and stained with DRAQ7 mortality dye (3 µM) before imaging. Six
images were acquired for each field of view through a z-stack of 6 µm,
starting the scan from the middle of the sample, using a 100x oil
immersion objective, 1.4NA, WD 0.12 mm (Olympus). Exposure con-
ditions on the Delta Vision microscope: bright field 10% T, 100 ms; FITC
(Ex 475/28, Em 525/48) 32% T, 50 ms; TRITC (Ex 542/27, Em 597/45) 32%
T, 50 ms; Cy5 (Ex 632/22, Em 679/34) 32% T, 50 ms. Image stacks were
projected by maximum-intensity method in SoftWorx 7.0 Cytiva, and
processed using a customized ImageJ macro, which automatically
segments intracellular bacterial foci based on their red fluorescence.
Derived masks were used to measure size and rRNA-GFP fluorescence
normalized to size (as a proxy for metabolic activity) of intracellular
bacilli. Inverted masks were used to subtract the background green
fluorescence from each ROI.

Intracellular efficacy by CFU quantification

Confluent RAW macrophages were seeded into a 48-well plate, about
5*10⁴ cells/well (400 µL), and incubated for 24 h prior to infection. Log-
phase *M. tuberculosis* cultures were used for infection after diluting
them in DMEM either to OD₆₀₀nm 0.005 (MOI 1:1) or to OD₆₀₀nm 0.05
(MOI 1:10). Both conditions were run in duplicate. Macrophages and
bacteria were incubated for 4 h at 37 °C in a humidified 5% CO₂
atmosphere. Infected cells were washed three times with complete
DMEM, and drugs were added as follows: INH (0.2 µg/mL, 8x MIC); RIF
(0.624 µg/mL, 8x MIC); M06 (1.25 µg/mL, 1x MIC); or their combina-
tions. DMSO was used in untreated control wells. Cells were re-
incubated for 7 days, and washed after 2 and 4 days of incubation, also
replenishing drugs. At each time point, the medium was removed, and
cells were washed once with 1x PBS. For cell lysis, 400 µL of lysis buffer
(0.1% Tween 80 in ddH₂O) were added and cells were incubated at
37 °C for 10 min. Cellular lysis was checked under an inverted micro-
scope. Cells were scraped with a P1000 tip and pipetting 5 times before
serial dilution into fresh 7H9 medium, and plating onto 7H11 plates.
Plates were incubated for 6 weeks prior to CFU counting. CFU were
expressed as a percentage of survival relative to time zero.

Statistics

Unless otherwise specified, plots and statistical analyses were done
with GraphPad Prism 9.5.0. Plots merge datasets deriving from at least
two biologically independent replicates, and statistics were derived
from *n* ≥ 3. *P*-values, sample sizes, and statistical tests are reported in
each figure or figure legend.

Reporting summary

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

Data availability

We have no restrictions on data availability. Source data are provided
in this paper. Time-lapse image stacks and snapshot images of myco-
bacterial cells alone or during infection under drug treatment gener-
ated in this study have been deposited in the DRYAD database under
the unique digital object identifier [https://doi.org/10.5061/dryad.
r4xgxd2j8]. Raw data from whole transcriptome and whole-genome
sequencing have been deposited in the EMBL's European

Bioinformatics Institute, under the EBI accession codes E-MTAB-12306 (RNA-seq) and E-MTAB-12307 (WGS). The processed data derived from single cells, microcolonies, CFU, MIC, TLC, LC-MS, and RNA/DNA-Seq generated in this study are provided in the Supplementary Information/Source Data file. The *M. tuberculosis* PANTHER genome information used in this study is available in the GO Ontology database under the accession code MYCTU. The structure of the arylamine N-acetyltransferase³⁶ used in this study is available at the RCSB Protein Data Bank (RCSB PDB) under the accession code 4BGF. The structure of the DNA gyrase from *M. tuberculosis*³⁰ used in this study is available at the RCSB PDB under the accession code 5BS8. The interaction network of *M. tuberculosis* NAT used in this study is available at the STRING Core Data Resource [https://string-db.org/cgi/network?taskId=bvahymk8E36h&sessionId=bAEKEPVHSkc2]. Source data are provided in this paper.

Code availability

Detailed functions and scripts used for the analysis of the PTC
screening were packaged and are available at Gitlab [https://gitlab.
pasteur.fr/svolant/ptc-screening/]. The custom Omnipose model and
Python notebook used for single-cell snapshot analysis are available at
Gitlab [https://gitlab.pasteur.fr/iah-public/automated_segmentation-
mycobacterium_tuberculosis_snapshots]. The ImageJ macro (Macro-
Analysis_THP1 MTUB.ijm) generated in this study to analyze infected
macrophages is available at DRYAD.

**References**

1. Darby, E. M. et al. Molecular mechanisms of antibiotic resistance revisited. *Nat. Rev. Microbiol.* https://doi.org/10.1038/s41579-022-00820-y (2022).

2. Dewachter, L., Fauvart, M. & Michiels, J. Bacterial heterogeneity and antibiotic survival: Understanding and combatting persistence and heteroresistance. *Mol. Cell* **76**, 255–267 (2019).

3. Verstraete, L., Van den Bergh, B., Verstraeten, N. & Michiels, J. Ecology and evolution of antibiotic persistence. *Trends Microbiol.* **30**, 466–479 (2022).

4. Balaban, N. Q. et al. Definitions and guidelines for research on antibiotic persistence. *Nat. Rev. Microbiol.* **17**, 441–448 (2019).

5. Bamford, R. A. et al. Investigating the physiology of viable but non-culturable bacteria by microfluidics and time-lapse microscopy. *BMC Biol.* **15**, 121 (2017).

6. Manina, G., Dhar, N. & McKinney, J. D. Stress and host immunity amplify *Mycobacterium tuberculosis* phenotypic heterogeneity and induce nongrowing metabolically active forms. *Cell Host Microbe* **17**, 32–46 (2015).

7. Levin-Reisman, I. et al. Antibiotic tolerance facilitates the evolution of resistance. *Science* **355**, 826–830 (2017).

8. Windels, E. M., Michiels, J. E., Van den Bergh, B., Fauvart, M. & Michiels, J. Antibiotics: Combatting tolerance to stop resistance. *mBio* **10**, e02095–19 (2019).

9. Davis, K. M. For the greater (bacterial) good: Heterogeneous expression of energetically costly virulence factors. *Infect. Immun.* **88**, e00911–e00919 (2020).

10. Urbaniec, J., Xu, Y., Hu, Y., Hingley-Wilson, S. & McFadden, J. Phenotypic heterogeneity in persisters: a novel ‘hunker’ theory of persistence. *FEMS Microbiol. Rev.* **46**, fuab042 (2022).

11. Theuretzbacher, U., Outterson, K., Engel, A. & Karlén, A. The global preclinical antibacterial pipeline. *Nat. Rev. Microbiol* **18**, 275–285 (2020).

12. Defraine, V., Fauvart, M. & Michiels, J. Fighting bacterial persistence: current and emerging anti-persist strategies and therapeutics. *Drug Resist Update* **38**, 12–26 (2018).

13. Ronneau, S., Michaux, C. & Helaine, S. Decline in nitrosative stress drives antibiotic persister regrowth during infection. *Cell Host Microbe* **31**, 993–1006.e6 (2023).
reporter, also constitutively expressing a red marker (rRNA-
GFP_DsRed2_cyt: GMT17)⁶. Secondary *M. tuberculosis* cultures were
grown up to OD₆₀₀ 0.4–0.6, diluted in complete DMEM without phe-
nol red to OD₆₀₀ 0.0025 (MOI 1:10), and medium was replaced with the
bacterial cell suspension, except for uninfected control wells. After 4 h
of incubation at 37 °C in a humidified 5% CO₂ atmosphere, infected
cells were washed five times with complete DMEM without phenol red,
and drugs were added as follows: INH (50 ng/mL, 2x MIC); RIF (156 ng/
mL, 2x MIC); M06 (1.25 µg/mL, 1x MIC); or their combinations. DMSO
was used in untreated control wells. Cells were re-incubated for 6 days
and stained with DRAQ7 mortality dye (3 µM) before imaging. Six
images were acquired for each field of view through a z-stack of 6 µm,
starting the scan from the middle of the sample, using a 100x oil
immersion objective, 1.4NA, WD 0.12 mm (Olympus). Exposure con-
ditions on the Delta Vision microscope: bright field 10% T, 100 ms; FITC
(Ex 475/28, Em 525/48) 32% T, 50 ms; TRITC (Ex 542/27, Em 597/45) 32%
T, 50 ms; Cy5 (Ex 632/22, Em 679/34) 32% T, 50 ms. Image stacks were
projected by maximum-intensity method in SoftWorx 7.0 Cytiva, and
processed using a customized ImageJ macro, which automatically
segments intracellular bacterial foci based on their red fluorescence.
Derived masks were used to measure size and rRNA-GFP fluorescence
normalized to size (as a proxy for metabolic activity) of intracellular
bacilli. Inverted masks were used to subtract the background green
fluorescence from each ROI.

Intracellular efficacy by CFU quantification

Confluent RAW macrophages were seeded into a 48-well plate, about
5*10⁴ cells/well (400 µL), and incubated for 24 h prior to infection. Log-
phase *M. tuberculosis* cultures were used for infection after diluting
them in DMEM either to OD₆₀₀nm 0.005 (MOI 1:1) or to OD₆₀₀nm 0.05
(MOI 1:10). Both conditions were run in duplicate. Macrophages and
bacteria were incubated for 4 h at 37 °C in a humidified 5% CO₂
atmosphere. Infected cells were washed three times with complete
DMEM, and drugs were added as follows: INH (0.2 µg/mL, 8x MIC); RIF
(0.624 µg/mL, 8x MIC); M06 (1.25 µg/mL, 1x MIC); or their combina-
tions. DMSO was used in untreated control wells. Cells were re-
incubated for 7 days, and washed after 2 and 4 days of incubation, also
replenishing drugs. At each time point, the medium was removed, and
cells were washed once with 1x PBS. For cell lysis, 400 µL of lysis buffer
(0.1% Tween 80 in ddH₂O) were added and cells were incubated at
37 °C for 10 min. Cellular lysis was checked under an inverted micro-
scope. Cells were scraped with a P1000 tip and pipetting 5 times before
serial dilution into fresh 7H9 medium, and plating onto 7H11 plates.
Plates were incubated for 6 weeks prior to CFU counting. CFU were
expressed as a percentage of survival relative to time zero.

Statistics

Unless otherwise specified, plots and statistical analyses were done
with GraphPad Prism 9.5.0. Plots merge datasets deriving from at least
two biologically independent replicates, and statistics were derived
from *n* ≥ 3. *P*-values, sample sizes, and statistical tests are reported in
each figure or figure legend.

Reporting summary

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

Data availability

We have no restrictions on data availability. Source data are provided
in this paper. Time-lapse image stacks and snapshot images of myco-
bacterial cells alone or during infection under drug treatment gener-
ated in this study have been deposited in the DRYAD database under
the unique digital object identifier [https://doi.org/10.5061/dryad.
r4xgxd2j8]. Raw data from whole transcriptome and whole-genome
sequencing have been deposited in the EMBL's European

Bioinformatics Institute, under the EBI accession codes E-MTAB-12306 (RNA-seq) and E-MTAB-12307 (WGS). The processed data derived from single cells, microcolonies, CFU, MIC, TLC, LC-MS, and RNA/DNA-Seq generated in this study are provided in the Supplementary Information/Source Data file. The *M. tuberculosis* PANTHER genome information used in this study is available in the GO Ontology database under the accession code MYCTU. The structure of the arylamine N-acetyltransferase³⁶ used in this study is available at the RCSB Protein Data Bank (RCSB PDB) under the accession code 4BGF. The structure of the DNA gyrase from *M. tuberculosis*³⁰ used in this study is available at the RCSB PDB under the accession code 5BS8. The interaction network of *M. tuberculosis* NAT used in this study is available at the STRING Core Data Resource [https://string-db.org/cgi/network?taskId=bvahymk8E36h&sessionId=bAEKEPVHSkc2]. Source data are provided in this paper.

Code availability

Detailed functions and scripts used for the analysis of the PTC
screening were packaged and are available at Gitlab [https://gitlab.pasteur.fr/svolant/ptc-screening/]. The custom Omnipose model and
Python notebook used for single-cell snapshot analysis are available at
Gitlab [https://gitlab.pasteur.fr/iah-public/automated_segmentation_mycobacterium_tuberculosis_snapshots]. The ImageJ macro (Macro-Analysis_THP1 MTUB.ijm) generated in this study to analyze infected
macrophages is available at DRYAD.

**References**

1. Darby, E. M. et al. Molecular mechanisms of antibiotic resistance revisited. *Nat. Rev. Microbiol.* https://doi.org/10.1038/s41579-022-00820-y (2022).

2. Dewachter, L., Fauvart, M. & Michiels, J. Bacterial heterogeneity and antibiotic survival: Understanding and combatting persistence and heteroresistance. *Mol. Cell* **76**, 255–267 (2019).

3. Verstraete, L., Van den Bergh, B., Verstraeten, N. & Michiels, J. Ecology and evolution of antibiotic persistence. *Trends Microbiol.* **30**, 466–479 (2022).

4. Balaban, N. Q. et al. Definitions and guidelines for research on antibiotic persistence. *Nat. Rev. Microbiol.* **17**, 441–448 (2019).

5. Bamford, R. A. et al. Investigating the physiology of viable but non-culturable bacteria by microfluidics and time-lapse microscopy. *BMC Biol.* **15**, 121 (2017).

6. Manina, G., Dhar, N. & McKinney, J. D. Stress and host immunity amplify *Mycobacterium tuberculosis* phenotypic heterogeneity and induce nongrowing metabolically active forms. *Cell Host Microbe* **17**, 32–46 (2015).

7. Levin-Reisman, I. et al. Antibiotic tolerance facilitates the evolution of resistance. *Science* **355**, 826–830 (2017).

8. Windels, E. M., Michiels, J. E., Van den Bergh, B., Fauvart, M. & Michiels, J. Antibiotics: Combatting tolerance to stop resistance. *mBio* **10**, e02095–19 (2019).

9. Davis, K. M. For the greater (bacterial) good: Heterogeneous expression of energetically costly virulence factors. *Infect. Immun.* **88**, e00911–e00919 (2020).

10. Urbaniec, J., Xu, Y., Hu, Y., Hingley-Wilson, S. & McFadden, J. Phenotypic heterogeneity in persisters: a novel ‘hunker’ theory of persistence. *FEMS Microbiol. Rev.* **46**, fuab042 (2022).

11. Theuretzbacher, U., Outterson, K., Engel, A. & Karlén, A. The global preclinical antibacterial pipeline. *Nat. Rev. Microbiol* **18**, 275–285 (2020).

12. Defraine, V., Fauvart, M. & Michiels, J. Fighting bacterial persistence: current and emerging anti-persist strategies and therapeutics. *Drug Resist Update* **38**, 12–26 (2018).

13. Ronneau, S., Michaux, C. & Helaine, S. Decline in nitrosative stress drives antibiotic persister regrowth during infection. *Cell Host Microbe* **31**, 993–1006.e6 (2023).
14. Dartois, V. A. & Rubin, E. J. Anti-tuberculosis treatment strategies and drug development: challenges and priorities. *Nat. Rev. Microbiol.* **20**, 685–701 (2022).

15. Oh, S., Trifonov, L., Yadav, V. D., Barry, C. E. & Boshoff, H. I. Tuberculosis drug discovery: a decade of hit assessment for defined targets. *Front. Cell. Infect. Microbiol.* **11**, 611304 (2021).

16. Edwards, B. D. & Field, S. K. The struggle to end a millennia-long pandemic: novel candidate and repurposed drugs for the treatment of tuberculosis. *Drugs* **82**, 1695–1715 (2022).

17. Gold, B. & Nathan, C. Targeting phenotypically tolerant Mycobacterium tuberculosis. *Microbiol. Spectr.* **5**, 5.1.27 (2017).

18. Meylan, S., Andrews, I. W. & Collins, J. J. Targeting antibiotic tolerance, pathogen by pathogen. *Cell* **172**, 1228–1238 (2018).

19. Huemer, M., Mairpady Shambat, S., Brugger, S. D. & Zinkernagel, A. S. Antibiotic resistance and persistence—Implications for human health and treatment perspectives. *EMBO Rep.* **21**, e51034 (2020).

20. Ernest, J. P. et al. Development of new tuberculosis drugs: Translation to regimen composition for drug-sensitive and multidrug-resistant tuberculosis. *Annu. Rev. Pharmacol. Toxicol.* **61**, 495–516 (2021).

21. Chung, E. S., Johnson, W. C. & Aldridge, B. B. Types and functions of heterogeneity in mycobacteria. *Nat. Rev. Microbiol.* **20**, 529–541 (2022).

22. Manina, G., Griego, A., Singh, L. K., McKinney, J. D. & Dhar, N. Pre-existing variation in DNA damage response predicts the fate of single mycobacteria under stress. *EMBO J.* **38**, e101876 (2019).

23. Ortseifen, V., Viefhues, M., Wobbe, L. & Grünberger, A. Microfluidics for biotechnology: Bridging gaps to foster microfluidic applications. *Front. Bioeng. Biotechnol.* **8**, 589074 (2020).

24. Scheler, O., Postek, W. & Garstecki, P. Recent developments of microfluidics as a tool for biotechnology and microbiology. *Curr. Opin. Biotechnol.* **55**, 60–67 (2019).

25. Mistretta, M., Gangneux, N. & Manina, G. Microfluidic dose-response platform to track the dynamics of drug response in single mycobacterial cells. *Sci. Rep.* **12**, 19578 (2022).

26. Lima-Noronha, M. A. et al. Sending out an SOS - the bacterial DNA damage response. *Genet. Mol. Biol.* **45**, e20220107 (2022).

27. Ayotte, Y. et al. Fragment-based phenotypic lead discovery to identify new drug seeds that target infectious diseases. *ACS Chem. Biol.* **16**, 2158–2163 (2021).

28. Coulibaly, S. et al. Phenanthrolinic analogs of quinolones show antibacterial activity against *M. tuberculosis*. *Eur. J. Med. Chem.* **207**, 112821 (2020).

29. Wang, J. D., Douville, N. J., Takayama, S. & ElSayed, M. Quantitative analysis of molecular absorption into PDMS microfluidic channels. *Ann. Biomed. Eng.* **40**, 1862–1873 (2012).

30. Blower, T. R., Williamson, B. H., Kerns, R. J. & Berger, J. M. Crystal structure and stability of gyrase–fluoroquinolone cleaved complexes from *Mycobacterium tuberculosis*. *Proc. Natl Acad. Sci. USA* **113**, 1706–1713 (2016).

31. Zhou, X., Ma, Z., Dong, D. & Wu, B. Arylamine N-acetyltransferases: a structural perspective: Understanding of NATs with their 3D structures. *Br. J. Pharm.* **169**, 748–760 (2013).

32. Payton, M., Auty, R., Delgoda, R., Everett, M. & Sim, E. Cloning and characterization of arylamine N-acetyltransferase genes from *Mycobacterium smegmatis* and *Mycobacterium tuberculosis*: Increased expression results in isoniazid resistance. *J. Bacteriol.* **181**, 1343–1347 (1999).

33. Bhakta, S. et al. Arylamine N-acetyltransferase is required for synthesis of mycolic acids and complex lipids in *Mycobacterium bovis* BCG and represents a novel drug target. *J. Exp. Med.* **199**, 1191–1199 (2004).

34. Sim, E. et al. Arylamine N-acetyltransferases in mycobacteria. *CDM* **9**, 510–519 (2008).

35. Palmer, A. C. & Kishony, R. Opposing effects of target over-expression reveal drug mechanisms. *Nat. Commun.* **5**, 4296 (2014).

36. Abuhammad, A. et al. Structure of arylamine N -acetyltransferase from *Mycobacterium tuberculosis* determined by cross-seeding with the homologous protein from *M. marinum*: triumph over adversity. *Acta Crystallogr. D. Biol. Crystallogr.* **69**, 1433–1446 (2013).

37. Grzegorzewicz, A. et al. Inhibition of mycolic acid transport across the *Mycobacterium tuberculosis* plasma membrane. *Nat. Chem. Biol.* **8**, 334–341 (2012).

38. Matviiuk, T. et al. Pyrrolidinone and pyrrolidine derivatives: Evaluation as inhibitors of InhA and *Mycobacterium tuberculosis*. *Eur. J. Med. Chem.* **123**, 462–475 (2016).

39. Singh, V. et al. Identification of aminopyrimidine-sulfonamides as potent modulators of Wag31-mediated cell elongation in mycobacteria. *Mol. Microbiol.* **103**, 13–25 (2017).

40. Chiarelli, L. R. et al. New insights into the mechanism of action of the thienopyrimidine antitubercular prodrug TP053. *ACS Infect. Dis.* **6**, 313–323 (2020).

41. Meneguello, J. E. et al. Insight about cell wall remodulation triggered by rifampicin in *Mycobacterium tuberculosis*. *Tuberculosis* (Edinb.) **120**, 101903 (2020).

42. Collet, J. F., Cho, S. H., Iorga, B. I. & Goemans, C. V. How the assembly and protection of the bacterial cell envelope depend on cysteine residues. *J. Biol. Chem.* **295**, 11984–11994 (2020).

43. Feng, L. et al. The pentapeptide-repeat protein, MfpA, interacts with mycobacterial DNA gyrase as a DNA T-segment mimic. *Proc. Natl Acad. Sci. USA* **118**, e2016705118 (2021).

44. Melin, J. & Quake, S. R. Microfluidic large-scale integration: The evolution of design rules for biological automation. *Annu. Rev. Biophys. Biomol. Struct.* **36**, 213–231 (2007).

45. Scott, S. & Ali, Z. Fabrication methods for microfluidic devices: an overview. *Micromachines* **12**, 319 (2021).

46. Nakagawa, S. & Cuthill, I. C. Effect size, confidence interval and statistical significance: a practical guide for biologists. *Biol. Rev.* **82**, 591–605 (2007).

47. Abbas-Aghababazadeh, F., Lu, P. & Fridley, B. L. Nonlinear mixed-effects models for modeling in vitro drug response data to determine problematic cancer cell lines. *Sci. Rep.* **9**, 14421 (2019).

48. Levin-Reisman, I. et al. Automated imaging with ScanLag reveals previously undetectable bacterial growth phenotypes. *Nat. Methods* **7**, 737–739 (2010).

49. Cutler, K. J. et al. Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation. *Nat. Methods* **19**, 1438–1448 (2022).

50. Selzer, G. J. et al. napari-imagej: ImageJ ecosystem access from napari. *Nat. Methods* **20**, 1443–1444 (2023).

51. Lu, Y. et al. Screening for gene expression fluctuations reveals latency-promoting agents of HIV. *Proc. Natl Acad. Sci. USA* **118**, e2012191118 (2021).

52. Rego, E. H., Audette, R. E. & Rubin, E. J. Deletion of a mycobacterial divisome factor collapses single-cell phenotypic heterogeneity. *Nature* **546**, 153–157 (2017).

53. Salaikumaran, M. R., Badiger, V. P. & Burra, V. L. S. P. 16S rRNA methyltransferases as novel drug targets against tuberculosis. *Protein J.* **41**, 97–130 (2022).

54. Laborde, J., Deraeve, C. & Bernardes-Génisson, V. Update of anti-tubercular prodrugs from a molecular perspective: Mechanisms of action, bioactivation pathways, and associated resistance. *Chem-MedChem* **12**, 1657–1676 (2017).

55. Kim, D.-W. et al. Identification of the enzyme responsible for N-acetylation of norfloxacin by *Microbacterium* sp. Strain 4N2-2. *Appl. Environ. Microbiol.* **79**, 314–321 (2013).

56. Abuhammad, A. et al. Piperidinols that show anti-tubercular activity as inhibitors of arylamine N-acetyltransferase: An essential enzyme
14. Dartois, V. A. & Rubin, E. J. Anti-tuberculosis treatment strategies and drug development: challenges and priorities. *Nat. Rev. Microbiol.* **20**, 685–701 (2022).

15. Oh, S., Trifonov, L., Yadav, V. D., Barry, C. E. & Boshoff, H. I. Tuberculosis drug discovery: a decade of hit assessment for defined targets. *Front. Cell. Infect. Microbiol.* **11**, 611304 (2021).

16. Edwards, B. D. & Field, S. K. The struggle to end a millennia-long pandemic: novel candidate and repurposed drugs for the treatment of tuberculosis. *Drugs* **82**, 1695–1715 (2022).

17. Gold, B. & Nathan, C. Targeting phenotypically tolerant Mycobacterium tuberculosis. *Microbiol. Spectr.* **5**, 5.1.27 (2017).

18. Meylan, S., Andrews, I. W. & Collins, J. J. Targeting antibiotic tolerance, pathogen by pathogen. *Cell* **172**, 1228–1238 (2018).

19. Huemer, M., Mairpady Shambat, S., Brugger, S. D. & Zinkernagel, A. S. Antibiotic resistance and persistence—Implications for human health and treatment perspectives. *EMBO Rep.* **21**, e51034 (2020).

20. Ernest, J. P. et al. Development of new tuberculosis drugs: Translation to regimen composition for drug-sensitive and multidrug-resistant tuberculosis. *Annu. Rev. Pharmacol. Toxicol.* **61**, 495–516 (2021).

21. Chung, E. S., Johnson, W. C. & Aldridge, B. B. Types and functions of heterogeneity in mycobacteria. *Nat. Rev. Microbiol.* **20**, 529–541 (2022).

22. Manina, G., Griego, A., Singh, L. K., McKinney, J. D. & Dhar, N. Pre-existing variation in DNA damage response predicts the fate of single mycobacteria under stress. *EMBO J.* **38**, e101876 (2019).

23. Ortseifen, V., Viefhues, M., Wobbe, L. & Grünberger, A. Microfluidics for biotechnology: Bridging gaps to foster microfluidic applications. *Front. Bioeng. Biotechnol.* **8**, 589074 (2020).

24. Scheler, O., Postek, W. & Garstecki, P. Recent developments of microfluidics as a tool for biotechnology and microbiology. *Curr. Opin. Biotechnol.* **55**, 60–67 (2019).

25. Mistretta, M., Gangneux, N. & Manina, G. Microfluidic dose-response platform to track the dynamics of drug response in single mycobacterial cells. *Sci. Rep.* **12**, 19578 (2022).

26. Lima-Noronha, M. A. et al. Sending out an SOS - the bacterial DNA damage response. *Genet. Mol. Biol.* **45**, e20220107 (2022).

27. Ayotte, Y. et al. Fragment-based phenotypic lead discovery to identify new drug seeds that target infectious diseases. *ACS Chem. Biol.* **16**, 2158–2163 (2021).

28. Coulibaly, S. et al. Phenanthrolinic analogs of quinolones show antibacterial activity against *M. tuberculosis*. *Eur. J. Med. Chem.* **207**, 112821 (2020).

29. Wang, J. D., Douville, N. J., Takayama, S. & ElSayed, M. Quantitative analysis of molecular absorption into PDMS microfluidic channels. *Ann. Biomed. Eng.* **40**, 1862–1873 (2012).

30. Blower, T. R., Williamson, B. H., Kerns, R. J. & Berger, J. M. Crystal structure and stability of gyrase–fluoroquinolone cleaved complexes from *Mycobacterium tuberculosis*. *Proc. Natl Acad. Sci. USA* **113**, 1706–1713 (2016).

31. Zhou, X., Ma, Z., Dong, D. & Wu, B. Arylamine N-acetyltransferases: a structural perspective: Understanding of NATs with their 3D structures. *Br. J. Pharm.* **169**, 748–760 (2013).

32. Payton, M., Auty, R., Delgoda, R., Everett, M. & Sim, E. Cloning and characterization of arylamine N-acetyltransferase genes from *Mycobacterium smegmatis* and *Mycobacterium tuberculosis*: Increased expression results in isoniazid resistance. *J. Bacteriol.* **181**, 1343–1347 (1999).

33. Bhakta, S. et al. Arylamine N-acetyltransferase is required for synthesis of mycolic acids and complex lipids in *Mycobacterium bovis* BCG and represents a novel drug target. *J. Exp. Med.* **199**, 1191–1199 (2004).

34. Sim, E. et al. Arylamine N-acetyltransferases in mycobacteria. *CDM* **9**, 510–519 (2008).

35. Palmer, A. C. & Kishony, R. Opposing effects of target over-expression reveal drug mechanisms. *Nat. Commun.* **5**, 4296 (2014).

36. Abuhammad, A. et al. Structure of arylamine N -acetyltransferase from *Mycobacterium tuberculosis* determined by cross-seeding with the homologous protein from *M. marinum*: triumph over adversity. *Acta Crystallogr. D. Biol. Crystallogr.* **69**, 1433–1446 (2013).

37. Grzegorzewicz, A. et al. Inhibition of mycolic acid transport across the *Mycobacterium tuberculosis* plasma membrane. *Nat. Chem. Biol.* **8**, 334–341 (2012).

38. Matviiuk, T. et al. Pyrrolidinone and pyrrolidine derivatives: Evaluation as inhibitors of InhA and *Mycobacterium tuberculosis*. *Eur. J. Med. Chem.* **123**, 462–475 (2016).

39. Singh, V. et al. Identification of aminopyrimidine-sulfonamides as potent modulators of Wag31-mediated cell elongation in mycobacteria. *Mol. Microbiol.* **103**, 13–25 (2017).

40. Chiarelli, L. R. et al. New insights into the mechanism of action of the thienopyrimidine antitubercular prodrug TP053. *ACS Infect. Dis.* **6**, 313–323 (2020).

41. Meneguello, J. E. et al. Insight about cell wall remodulation triggered by rifampicin in *Mycobacterium tuberculosis*. *Tuberculosis* (Edinb.) **120**, 101903 (2020).

42. Collet, J. F., Cho, S. H., Iorga, B. I. & Goemans, C. V. How the assembly and protection of the bacterial cell envelope depend on cysteine residues. *J. Biol. Chem.* **295**, 11984–11994 (2020).

43. Feng, L. et al. The pentapeptide-repeat protein, MfpA, interacts with mycobacterial DNA gyrase as a DNA T-segment mimic. *Proc. Natl Acad. Sci. USA* **118**, e2016705118 (2021).

44. Melin, J. & Quake, S. R. Microfluidic large-scale integration: The evolution of design rules for biological automation. *Annu. Rev. Biophys. Biomol. Struct.* **36**, 213–231 (2007).

45. Scott, S. & Ali, Z. Fabrication methods for microfluidic devices: an overview. *Micromachines* **12**, 319 (2021).

46. Nakagawa, S. & Cuthill, I. C. Effect size, confidence interval and statistical significance: a practical guide for biologists. *Biol. Rev.* **82**, 591–605 (2007).

47. Abbas-Aghababazadeh, F., Lu, P. & Fridley, B. L. Nonlinear mixed-effects models for modeling in vitro drug response data to determine problematic cancer cell lines. *Sci. Rep.* **9**, 14421 (2019).

48. Levin-Reisman, I. et al. Automated imaging with ScanLag reveals previously undetectable bacterial growth phenotypes. *Nat. Methods* **7**, 737–739 (2010).

49. Cutler, K. J. et al. Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation. *Nat. Methods* **19**, 1438–1448 (2022).

50. Selzer, G. J. et al. napari-imagej: ImageJ ecosystem access from napari. *Nat. Methods* **20**, 1443–1444 (2023).

51. Lu, Y. et al. Screening for gene expression fluctuations reveals latency-promoting agents of HIV. *Proc. Natl Acad. Sci. USA* **118**, e2012191118 (2021).

52. Rego, E. H., Audette, R. E. & Rubin, E. J. Deletion of a mycobacterial divisome factor collapses single-cell phenotypic heterogeneity. *Nature* **546**, 153–157 (2017).

53. Salaikumaran, M. R., Badiger, V. P. & Burra, V. L. S. P. 16S rRNA methyltransferases as novel drug targets against tuberculosis. *Protein J.* **41**, 97–130 (2022).

54. Laborde, J., Deraeve, C. & Bernardes-Génisson, V. Update of anti-tubercular prodrugs from a molecular perspective: Mechanisms of action, bioactivation pathways, and associated resistance. *Chem-MedChem* **12**, 1657–1676 (2017).

55. Kim, D.-W. et al. Identification of the enzyme responsible for N-acetylation of norfloxacin by *Microbacterium* sp. Strain 4N2-2. *Appl. Environ. Microbiol.* **79**, 314–321 (2013).

56. Abuhammad, A. et al. Piperidinols that show anti-tubercular activity as inhibitors of arylamine N-acetyltransferase: An essential enzyme
for mycobacterial survival inside macrophages. *PLoS ONE* 7, e52790 (2012).

57. Sambandan, D. et al. Keto-mycolic acid-dependent pellicle formation confers tolerance to drug-sensitive *Mycobacterium tuberculosis*. *mBio* 4, e00222-13 (2013).

58. Madacki, J. et al. Impact of the epoxide hydrolase EphD on the metabolism of mycolic acids in mycobacteria. *J. Biol. Chem.* **293**, 5172–5184 (2018).

59. Miller, C. et al. SOS response induction by β-Lactams and bacterial defense against antibiotic lethality. *Science* **305**, 1629–1631 (2004).

60. Dwyer, D. J., Collins, J. J. & Walker, G. C. Unraveling the physiological complexities of antibiotic lethality. *Annu. Rev. Pharmacol. Toxicol.* **55**, 313–332 (2015).

61. Li, H. et al. Reactive oxygen species in pathogen clearance: The killing mechanisms, the adaption response, and the side effects. *Front. Microbiol.* **11**, 622534 (2021).

62. Aubry, A., Pan, X.-S., Fisher, L. M., Jarlier, V. & Cambau, E. *Mycobacterium tuberculosis* DNA gyrase: Interaction with quinolones and correlation with antimycobacterial drug activity. *Antimicrob. Agents Chemother.* **48**, 1281–1288 (2004).

63. Luan, G., Hong, Y., Drlica, K. & Zhao, X. Suppression of reactive oxygen species accumulation accounts for paradoxical bacterial survival at high quinolone concentration. *Antimicrob. Agents Chemother.* **62**, e01622–17 (2018).

64. Nepali, K., Lee, H. Y. & Liou, J. P. Nitro-group-containing drugs. *J. Med. Chem.* **62**, 2851–2893 (2019).

65. Atmane, N., Dairou, J., Paul, A., Dupret, J.-M. & Rodrigues-Lima, F. Redox regulation of the human xenobiotic metabolizing enzyme arylamine N-acetyltransferase 1 (NAT1). *J. Biol. Chem.* **278**, 35086–35092 (2003).

66. Baharoglu, Z. & Mazel, D. SOS, the formidable strategy of bacteria against aggressions. *FEMS Microbiol. Rev.* **38**, 1126–1145 (2014).

67. Sutormin, D. et al. Interaction between transcribing RNA polymerase and topoisomerase I prevents R-loop formation in *E. coli*. *Nat. Commun.* **13**, 4524 (2022).

68. VanDrisse, C. M. & Escalante-Semerena, J. C. Protein acetylation in bacteria. *Annu. Rev. Microbiol.* **73**, 111–132 (2019).

69. Bienvenut, W. V. et al. Dual lysine and N-terminal acetyltransferases reveal the complexity underpinning protein acetylation. *Mol. Syst. Biol.* **16**, e9464 (2020).

70. Kim, J.-E., Choi, J.-S., Kim, J.-S., Cho, Y.-H. & Roe, J.-H. Lysine acetylation of the housekeeping sigma factor enhances the activity of the RNA polymerase holoenzyme. *Nucleic Acids Res.* **48**, 2401–2411 (2020).

71. Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. *Nat. Methods* **9**, 671–675 (2012).

72. Murail, S., de Vries, S. J., Rey, J., Moroy, G. & Tufféry, P. SeamDock: an interactive and collaborative online docking resource to assist small compound molecular docking. *Front. Mol. Biosci.* **8**, 716466 (2021).

73. Cokelaer, T., Desvillechabrol, D., Legendre, R. & Cardon, M. Sequana*: a Set of Snakemake NGS pipelines. *JOSS* **2**, 352 (2017).

74. Köster, J. & Rahmann, S. Snakemake—a scalable bioinformatics workflow engine. *Bioinformatics* **28**, 2520–2522 (2012).

75. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. *Bioinformatics* **25**, 1754–1760 (2009).

76. Tarasov, A., Vilella, A. J., Cuppen, E., Nijman, I. J. & Prins, P. Sambamba: fast processing of NGS alignment formats. *Bioinformatics* **31**, 2032–2034 (2015).

77. Sandmann, S. et al. appreci8: a pipeline for precise variant calling integrating 8 tools. *Bioinformatics* **34**, 4205–4212 (2018).

78. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the

genome of Drosophila melanogaster strain w<sup>1118</sup>; iso-2; iso-3. *Fly* **6**, 80–92 (2012).

79. Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. *Bioinformatics* **32**, 3047–3048 (2016).

80. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. *EMBnet. J.* **17**, 10–12 (2011).

81. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. *Nat. Methods* **9**, 357–359 (2012).

82. Liao, Y., Smyth, G. & Shi, W. FeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features. *Bioinformatics* **30**, 923–930 (2014).

83. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. *Genome Biol.* **15**, 550 (2014).

84. Varet, H., Brillet-Guéguen, L., Coppée, J. Y. & Dillies, M. A. SAR-Tools: a DESeq2- and EdgeR-based R pipeline for comprehensive differential analysis of RNA-Seq data. *PLoS ONE* **11**, e0157022 (2016).

85. The Gene Ontology Consortium. et al. The Gene Ontology resource: enriching a GOld mine. *Nucleic Acids Res.* **49**, D325–D334 (2021).

86. Mi, H., Muruganujan, A., Ebert, D., Huang, X. & Thomas, D. PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. *Nucleic Acids Res.* **47**, D419–D426 (2019).

## Acknowledgements

We thank Guillaume Duménil (Pathogenesis of vascular infections, Institut Pasteur, Paris, France), Katarína Mikušová (Comenius University in Bratislava, Slovakia), and Pietro Slavich (LPTHE, Paris, France) for critical readings of the manuscript. We thank Samy Gobaa for providing us access to the Biomaterials & Microfluidics core facility of the Institut Pasteur. We thank the FabLab of the Institut Pasteur for manufacturing holders and reservoirs associated with the microfluidic device. We thank the Biomics Platform, C2RT, Institut Pasteur, Paris, France, supported by France Génomique (ANR-10-INBS-09-09) and IBISA for assistance in RNA-Seq and WGS. The mutant strains in Supplementary Data 6 were obtained from the Collection of the Institut Pasteur (CIP), Biological Resource Center of Institut Pasteur (CRBIP), Paris, France. This work was supported by the following grants: French Medical Research Foundation grant ING20160435202 to G.M.; French National Research Agency grant ANR-17-CE11-0007-01 PersisTB to G.M.; French National Research Agency grant ANR-10-LABX-62-IBEID to G.M.; and Institut Pasteur core funding to G.M. The work was further supported by the French National Research Agency grant ANR-21-CE15-0045 TREATABLE to G.M. and C.R.; by the Slovak Research and Development Agency grant n. APVV-19-0189 and the OPII, ACCORD, ITMS2014 + : 313021×329, co-financed by ERDF to J.K.; and by the Programme d'Investissement d'Avenir MUSTART ANR-20-PAMR-0005 to G.M.

## Author contributions

**Conceptualization:** G.M. Methodology: M.M., P.C., S.V., G.M. Software: P.C., S.V., E.K., M.A., M.M., L.P., G.M. Validation: M.M., M.C., P.C., S.V., E.K., N.G., J.K., S.V.G., G.M. Formal analysis: M.M., P.C., S.V., G.M., N.G., L.P., J.K., M.Z., S.V.G., M.C. Investigation: M.M., M.C., G.M., N.G., J.K., M.Z., S.V.G. Resources: M.M., M.C., G.M., O.H., C.R., P.D., C.L., C.T., A.L., Y.A., S.R.L., J.K., M.Z., S.V.G., F.B., P.C., S.V., E.K., M.A., J.Y.T. Data cura-tion: M.M., P.C., S.V., E.K., G.M., M.C., Y.A. Writing—original draft: G.M. Writing—review and editing: all authors. Visualization: G.M., M.M., P.C., S.V., E.K., J.K., S.V.G., Y.A. Supervision: G.M., C.R., P.D., C.L., S.R.L., J.K., J.Y.T. Project administration: G.M. Funding acquisition: G.M., C.R., J.K. These authors contributed equally: M.M., M.C.
for mycobacterial survival inside macrophages. *PLoS ONE* 7, e52790 (2012).

57. Sambandan, D. et al. Keto-mycolic acid-dependent pellicle formation confers tolerance to drug-sensitive *Mycobacterium tuberculosis*. *mBio* 4, e00222-13 (2013).

58. Madacki, J. et al. Impact of the epoxide hydrolase EphD on the metabolism of mycolic acids in mycobacteria. *J. Biol. Chem.* **293**, 5172–5184 (2018).

59. Miller, C. et al. SOS response induction by β-Lactams and bacterial defense against antibiotic lethality. *Science* **305**, 1629–1631 (2004).

60. Dwyer, D. J., Collins, J. J. & Walker, G. C. Unraveling the physiological complexities of antibiotic lethality. *Annu. Rev. Pharmacol. Toxicol.* **55**, 313–332 (2015).

61. Li, H. et al. Reactive oxygen species in pathogen clearance: The killing mechanisms, the adaption response, and the side effects. *Front. Microbiol.* **11**, 622534 (2021).

62. Aubry, A., Pan, X.-S., Fisher, L. M., Jarlier, V. & Cambau, E. *Mycobacterium tuberculosis* DNA gyrase: Interaction with quinolones and correlation with antimycobacterial drug activity. *Antimicrob. Agents Chemother.* **48**, 1281–1288 (2004).

63. Luan, G., Hong, Y., Drlica, K. & Zhao, X. Suppression of reactive oxygen species accumulation accounts for paradoxical bacterial survival at high quinolone concentration. *Antimicrob. Agents Chemother.* **62**, e01622–17 (2018).

64. Nepali, K., Lee, H. Y. & Liou, J. P. Nitro-group-containing drugs. *J. Med. Chem.* **62**, 2851–2893 (2019).

65. Atmane, N., Dairou, J., Paul, A., Dupret, J.-M. & Rodrigues-Lima, F. Redox regulation of the human xenobiotic metabolizing enzyme arylamine N-acetyltransferase 1 (NAT1). *J. Biol. Chem.* **278**, 35086–35092 (2003).

66. Baharoglu, Z. & Mazel, D. SOS, the formidable strategy of bacteria against aggressions. *FEMS Microbiol. Rev.* **38**, 1126–1145 (2014).

67. Sutormin, D. et al. Interaction between transcribing RNA polymerase and topoisomerase I prevents R-loop formation in *E. coli*. *Nat. Commun.* **13**, 4524 (2022).

68. VanDrisse, C. M. & Escalante-Semerena, J. C. Protein acetylation in bacteria. *Annu. Rev. Microbiol.* **73**, 111–132 (2019).

69. Bienvenut, W. V. et al. Dual lysine and N-terminal acetyltransferases reveal the complexity underpinning protein acetylation. *Mol. Syst. Biol.* **16**, e9464 (2020).

70. Kim, J.-E., Choi, J.-S., Kim, J.-S., Cho, Y.-H. & Roe, J.-H. Lysine acetylation of the housekeeping sigma factor enhances the activity of the RNA polymerase holoenzyme. *Nucleic Acids Res.* **48**, 2401–2411 (2020).

71. Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. *Nat. Methods* **9**, 671–675 (2012).

72. Murail, S., de Vries, S. J., Rey, J., Moroy, G. & Tufféry, P. SeamDock: an interactive and collaborative online docking resource to assist small compound molecular docking. *Front. Mol. Biosci.* **8**, 716466 (2021).

73. Cokelaer, T., Desvillechabrol, D., Legendre, R. & Cardon, M. Sequana*: a Set of Snakemake NGS pipelines. *JOSS* **2**, 352 (2017).

74. Köster, J. & Rahmann, S. Snakemake—a scalable bioinformatics workflow engine. *Bioinformatics* **28**, 2520–2522 (2012).

75. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. *Bioinformatics* **25**, 1754–1760 (2009).

76. Tarasov, A., Vilella, A. J., Cuppen, E., Nijman, I. J. & Prins, P. Sambamba: fast processing of NGS alignment formats. *Bioinformatics* **31**, 2032–2034 (2015).

77. Sandmann, S. et al. appreci8: a pipeline for precise variant calling integrating 8 tools. *Bioinformatics* **34**, 4205–4212 (2018).

78. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the

genome of Drosophila melanogaster strain w<sup>1118</sup>; iso-2; iso-3. *Fly* **6**, 80–92 (2012).

79. Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. *Bioinformatics* **32**, 3047–3048 (2016).

80. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. *EMBnet. J.* **17**, 10–12 (2011).

81. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. *Nat. Methods* **9**, 357–359 (2012).

82. Liao, Y., Smyth, G. & Shi, W. FeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features. *Bioinformatics* **30**, 923–930 (2014).

83. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. *Genome Biol.* **15**, 550 (2014).

84. Varet, H., Brillet-Guéguen, L., Coppée, J. Y. & Dillies, M. A. SAR-Tools: a DESeq2- and EdgeR-based R pipeline for comprehensive differential analysis of RNA-Seq data. *PLoS ONE* **11**, e0157022 (2016).

85. The Gene Ontology Consortium. et al. The Gene Ontology resource: enriching a GOld mine. *Nucleic Acids Res.* **49**, D325–D334 (2021).

86. Mi, H., Muruganujan, A., Ebert, D., Huang, X. & Thomas, D. PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. *Nucleic Acids Res.* **47**, D419–D426 (2019).

## Acknowledgements

We thank Guillaume Duménil (Pathogenesis of vascular infections, Institut Pasteur, Paris, France), Katarína Mikušová (Comenius University in Bratislava, Slovakia), and Pietro Slavich (LPTHE, Paris, France) for critical readings of the manuscript. We thank Samy Gobaa for providing us access to the Biomaterials & Microfluidics core facility of the Institut Pasteur. We thank the FabLab of the Institut Pasteur for manufacturing holders and reservoirs associated with the microfluidic device. We thank the Biomics Platform, C2RT, Institut Pasteur, Paris, France, supported by France Génomique (ANR-10-INBS-09-09) and IBISA for assistance in RNA-Seq and WGS. The mutant strains in Supplementary Data 6 were obtained from the Collection of the Institut Pasteur (CIP), Biological Resource Center of Institut Pasteur (CRBIP), Paris, France. This work was supported by the following grants: French Medical Research Foundation grant ING20160435202 to G.M.; French National Research Agency grant ANR-17-CE11-0007-01 PersisTB to G.M.; French National Research Agency grant ANR-10-LABX-62-IBEID to G.M.; and Institut Pasteur core funding to G.M. The work was further supported by the French National Research Agency grant ANR-21-CE15-0045 TREATABLE to G.M. and C.R.; by the Slovak Research and Development Agency grant n. APVV-19-0189 and the OPII, ACCORD, ITMS2014 + : 313021×329, co-financed by ERDF to J.K.; and by the Programme d'Investissement d'Avenir MUSTART ANR-20-PAMR-0005 to G.M.

## Author contributions

**Conceptualization:** G.M. Methodology: M.M., P.C., S.V., G.M. Software: P.C., S.V., E.K., M.A., M.M., L.P., G.M. Validation: M.M., M.C., P.C., S.V., E.K., N.G., J.K., S.V.G., G.M. Formal analysis: M.M., P.C., S.V., G.M., N.G., L.P., J.K., M.Z., S.V.G., M.C. Investigation: M.M., M.C., G.M., N.G., J.K., M.Z., S.V.G. Resources: M.M., M.C., G.M., O.H., C.R., P.D., C.L., C.T., A.L., Y.A., S.R.L., J.K., M.Z., S.V.G., F.B., P.C., S.V., E.K., M.A., J.Y.T. Data cura-tion: M.M., P.C., S.V., E.K., G.M., M.C., Y.A. Writing—original draft: G.M. Writing—review and editing: all authors. Visualization: G.M., M.M., P.C., S.V., E.K., J.K., S.V.G., Y.A. Supervision: G.M., C.R., P.D., C.L., S.R.L., J.K., J.Y.T. Project administration: G.M. Funding acquisition: G.M., C.R., J.K. These authors contributed equally: M.M., M.C.
## Competing interests

M.M. and G.M. are designated as inventors in the pending international patent application WO 2020/229629 filed by the Institut Pasteur. This patent application covers the multi-condition microfluidic device used for the screening. M.C., P.D., and C.R. are designated as inventors in the pending international patent application WO/2019/138084 filed by the Institut Pasteur, Université de Caen Normandie, and Université Felix Houphouët-Boigny. This patent application covers phenanthroline derivatives. All other authors declare no financial competing interests, and all authors declare no non-financial competing interests.

## Additional information

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41467-024-48269-2.

**Correspondence** and requests for materials should be addressed to Giulia Manina.

**Peer review information** *Nature Communications* thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.

**Reprints and permissions information** is available at http://www.nature.com/reprints

**Publisher's note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

© The Author(s) 2024
## Competing interests

M.M. and G.M. are designated as inventors in the pending international patent application WO 2020/229629 filed by the Institut Pasteur. This patent application covers the multi-condition microfluidic device used for the screening. M.C., P.D., and C.R. are designated as inventors in the pending international patent application WO/2019/138084 filed by the Institut Pasteur, Université de Caen Normandie, and Université Felix Houphouët-Boigny. This patent application covers phenanthroline derivatives. All other authors declare no financial competing interests, and all authors declare no non-financial competing interests.

## Additional information

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41467-024-48269-2.

**Correspondence** and requests for materials should be addressed to Giulia Manina.

**Peer review information** *Nature Communications* thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.

**Reprints and permissions information** is available at http://www.nature.com/reprints

**Publisher's note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

© The Author(s) 2024