Loss of RNase J leads to multi-drug tolerance and accumulation of highly structured mRNA fragments in Mycobacterium tuberculosis.

Despite the existence of well-characterized, canonical mutations that confer high-level drug resistance to Mycobacterium tuberculosis (Mtb), there is evidence that drug resistance mechanisms are more complex than simple acquisition of such mutations. Recent studies have shown that Mtb can acquire non-canonical resistance-associated mutations that confer survival advantages in the presence of certain drugs, likely acting as stepping-stones for acquisition of high-level resistance. Rv2752c/rnj, encoding RNase J, is disproportionately mutated in drug-resistant clinical Mtb isolates. Here we show that deletion of rnj confers increased tolerance to lethal concentrations of several drugs. RNAseq revealed that RNase J affects expression of a subset of genes enriched for PE/PPE genes and stable RNAs and is key for proper 23S rRNA maturation. Gene expression differences implicated two sRNAs and ppe50-ppe51 as important contributors to the drug tolerance phenotype. In addition, we found that in the absence of RNase J, many short RNA fragments accumulate because they are degraded at slower rates. We show that the accumulated transcript fragments are targets of RNase J and are characterized by strong secondary structure and high G+C content, indicating that RNase J has a rate-limiting role in degradation of highly structured RNAs. Taken together, our results demonstrate that RNase J indirectly affects drug tolerance, as well as reveal the endogenous roles of RNase J in mycobacterial RNA metabolism.

Introduction

More than a century after the discovery that M. tuberculosis (Mtb) is the causative agent of tuberculosis (TB), this disease remains one of the major health challenges worldwide. In 2020, about 10 million people developed TB and 1.3 million died of the disease, positioning TB as a major cause of death worldwide [1]. Due to drug penetration issues and the presence of drug-tolerant Mtb populations in human lesions, anti-TB drug regimens must be administered over long durations [1-3]. Drug treatment itself may induce further drug tolerance [4], and the long treatment period provides opportunities for acquisition of mutations leading to antibiotic resistance. The emergence and spread of multidrug-resistant (MDR) TB are major concerns as it frequently leads to treatment failure and death. In 2020, 7.5% of the new TB cases were either rifampicin-resistant, MDR, or extensively drug resistant (XDR) [1] prompting the need to improve TB therapies and prevent the emergence of resistance.

Although the genes encoding the drug targets and activators in mycobacteria are well known, the mechanisms driving the development of high-level drug resistance are less well understood. In addition to high-level resistance, Mtb exhibits other forms of altered drug susceptibility that allow populations of bacteria to survive for extended periods of time in the presence of antibiotics and apparently serve as reservoirs for the eventual acquisition of high-level drug resistance-conferring mutations [5,6]. Initial studies on mycobacterial drug tolerance focused on rare persister cells that are drug-tolerant due to stochastic growth cessation ([7] and reviewed in [8,9]). Further work has demonstrated that altered drug susceptibility in Mtb can arise through multiple mechanisms, which differ in terms of frequency, duration, and magnitude of effect. For example, during infection Mtb responds to environmental and metabolic conditions by shifting to slow- or non-growing states in which it is less sensitive to many drugs [10-13]. Recently, several groups have taken a population genomics approach to identify clinically relevant stepping-stone mutations that facilitate the acquisition of high-level drug resistances. Mechanistic dissection of these mutations has revealed the importance of these different forms of altered drug susceptibility. For example, recent studies of Mtb have revealed unexpected forms of genetically-encoded low-level drug resistance such as mutations in dnaA and Rv0565c that cause low level isoniazid and ethionamide resistances, respectively [14,15]. Mutations in prpR, in contrast, were found to increase tolerance to multiple drugs without affecting resistance [16].

We and others have reported genome-wide association studies (GWAS) in cohorts of Mtb clinical isolates [16-19] and other bacterial pathogens [20-22] from around the globe. Many of the Mtb studies have identified drug resistance associated mutations in Rv2752c, here called rnj, encoding the ribonuclease RNase J. This enzyme has both endonuclease and 5’ to 3’ exonuclease activity and is involved in the 5’ end processing of ribosomal RNAs in M. smegmatis [23]. RNase J activity is essential for growth in several gram-positive bacteria that do not encode RNase E, in contrast to most gram-negative bacteria which encode RNase E and lack RNase J [24-26]. Unusually, mycobacteria encode both RNase E, which is essential and seems to play a rate-limiting step in degradation of most mRNAs [23,27-29], and RNase J, which is non-essential [23,27,29,30]. RNase J has been shown to participate in 23S rRNA processing [23] but we do not understand its impact on bacterial cell physiology and lack a model to mechanistically understand its relationship to drug responses. Here we investigate the role of RNase J in Mtb RNA metabolism and drug sensitivity. We show that rnj variants impact drug tolerance and mechanistically link the changes in drug susceptibility to altered gene expression and transcript degradation.

Materials and methods

Bacterial strains and growth conditions

M. tuberculosis H37Rv and its derivatives were grown in Middlebrook 7H9 broth supplemented with 10% OADC (0.5 g/L oleic acid, 50 g/L bovine serum albumin fraction V, 20 g/L dextrose, 8.5 g/L sodium chloride, and 40 mg/L catalase), 0.2% glycerol and 0.05% Tween 80. Liquid cultures were grown in 50 mL conical polypropylene tubes at 37°C with a shaker speed of 200 rpm except when indicated otherwise. For growth on solid media, Middlebrook 7H10 supplemented with 0.5% glycerol and OADC was used. For M. tuberculosis auxotrophic strain mc26230 (ΔpanCD, ΔRD1, [31]) pantothenate was added to 7H9 or 7H10 to a final concentration of 24 μg/mL. When required for resistant bacteria selection or plasmid maintenance, the following concentrations of antibiotics were used: 25 μg/mL kanamycin (KAN), 50 μg/mL hygromycin (HYG) or 25 μg/mL zeocin (ZEO). Knock-out strains were constructed using the recombineering system described by Murphy and collaborators [32]. For genetic complementation, an L5-site integrating plasmid or Giles-site integrating plasmid was used, and for gene overexpression the episomal plasmid pMV762 was used [33]. A description of all strains used in this study is provided in S1 Table.

For growth of Mtb mc26230 in minimal media, log phase cultures grown in minimal media were sub-cultured to an OD600nm = 0.01 in the same media. The minimal media had the following composition: 0.5 g/L asparagine, 1 g/L KH2PO4, 2.5 g/L Na2HPO4, 50 mg/L ferric ammonium citrate, 0.5 g/L MgSO4·7H2O, 0.5 mg/L CaCl2, and 0.1 mg/L ZnSO4. Minimal media was supplemented with 0.05% Tween 80, 24 μg/mL pantothenate, and 0.2% glycerol.

Antibiotic susceptibility testing

To determine the minimum inhibitory concentration (MIC) for Mtb mc26230 strains the agar proportion method was used [34]. Briefly, antibiotics were added to 7H10 plates to obtain the following concentrations: 1, 0.5, 0.25, 0.125, 0.06125, 0.0306, 0.01531 and 0.0077 μg/mL of rifampicin (RIF) or isoniazid (INH). Direct 5 μL aliquots and serial dilutions of 7H9 mid-log cultures of each strain were plated on antibiotic-containing and antibiotic-free plates. The MIC for each strain was determined as the drug concentration that reduced CFU by 90% compared to the control.

Drug killing experiments

For Mtb mc26230 and H37Rv strains, log phase cultures grown in 7H9 were diluted to an initial OD600nm = 0.1 in triplicate in absence of antibiotics. After 24 hours the following antibiotics were added to the indicated final concentrations: 0.6 μg/mL of RIF, 2.4 μg/mL of INH, 2.5 μg/mL of clarithromycin (CLA), 1 μg/mL of ofloxacin (OFX), 2 μg/mL ethambutol (EMB), or 500 μg/mL erythromycin (ERY). Triplicate cultures of each strain were incubated in absence of drug as a control. Aliquots were periodically taken, and serial dilutions plated on 7H10 agar plates without drug. CFUs were counted after 20–35 days.

Determination of the fraction of survival in INH at different growth phases

Log phase cultures of Mtb mc26230 WT or Δrnj were sub-cultured to an OD600nm = 0.02. A total of 24 tubes were used per strain (6 replicates per timepoint). INH was added after 24, 48, 72, or 96 hours to a final concentration of 2.4 μg/mL. For each timepoint, CFUs were measured before adding INH (time 0 for each timepoint) and after 2 days of incubation with INH. The fraction of survival in INH was determined as the ratio between CFUs at day 2 over CFUs at time 0.

RNA purification and quantitative PCR

For RNA purification from Mtb mc26230, frozen cultures stored at -80°C were thawed on ice and centrifuged at 4,000 rpm for 5 min at 4°C. For Mtb H37Rv, cultures were pelleted and processed immediately. The pellets were resuspended in 1 mL Trizol (Life Technologies) and placed in tubes containing Lysing Matrix B (MP Bio). Cells were lysed by bead-beating (2 cycles of 9 m/sec for 40 s, with 2 min on ice in between) in a FastPrep 5G instrument (MP Bio). 300 μL chloroform was added and samples were centrifuged for 15 min at 4,000 rpm at 4°C. The aqueous phase was collected, and RNA was purified using Direct-Zol RNA miniprep kit (Zymo) according to the manufacturer’s instructions. For Mtb mc26230 samples, the optional on-column DNase treatment step was used. For H37Rv samples, in-tube DNase treatment was done using DNase Turbo (Ambion) followed by purification with a Zymo Clean & Concentrator kit.

For cDNA synthesis, 600 ng of RNA were mixed with 0.83 μL 100 mM Tris, pH 7.5, and 0.17 μL of random primers (3 mg/mL NEB) in a total volume of 5.25 μL. The mix was denatured at 70°C for 10 min and placed on ice for 5 min. For reverse transcription, the following reagents were added to achieve the specified amounts or concentrations in 10 μL reactions: 100 U ProtoScript II reverse transcriptase (NEB), 10 U RNase inhibitor (murine; NEB), 0.5 mM each deoxynucleoside triphosphate (dNTP), and 5 mM dithiothreitol (DTT). Samples were incubated for 10 min at 25°C and reverse transcription was performed overnight at 42°C. RNA was degraded by addition of 10 μL containing 250 mM EDTA and 0.5 N NaOH and heating at 65°C for 15 min, followed by addition of 12.5 μL of 1 M Tris-HCl, pH 7.5. cDNA was purified using the MinElute PCR purification kit (Qiagen) according to the manufacturer’s instructions.

RNA abundance was determined by quantitative PCR (qPCR) using iTaq SYBR green (Bio-Rad) with 200 pg of cDNA and 0.25 μM each primer in 10 μL reaction mixtures, with 40 cycles of 15 s at 95°C and 1 min at 61°C (Applied Biosystems 7500). Primers used in this study are listed in S2 Table.

Measurement of RNA half-life

For Mtb mc26230, 5 mL of biological triplicates of log phase cultures were treated with RIF (final concentration of 50 μg/mL) to inhibit transcription, as reported by [35]. After RIF addition, tubes were placed into liquid nitrogen after 0, 2, 5, 10, 20, 40, and 80 min. Cultures were frozen at -80°C until RNA purification. RNA was purified using Direct-zol kit (Zymo) as described above. Half-lives were calculated as previously described [36,37]. Timepoints 40 and 80 min were excluded from half-life calculations in cases where there were missing values due to low abundance or cases where they did not follow the initial exponential decay trend (S8 Fig).

Construction and analysis of RNA expression and 5’ end-directed libraries

For RNAseq libraries, 5 mL of log phase cultures of Mtb H37Rv WT, Δrnj, and Δrnj::rnjOE strains grown in 7H9 were placed in liquid nitrogen and stored at -80°C. RNA purification and construction of both RNA expression and 5’ end-directed (non-5’ pyrophosphohydrolase-converted) libraries were performed as previously reported [38,39]. For both types of libraries, Illumina HiSeq 2000 paired-end sequencing producing 50 nt reads was used. Sequencing was performed at the UMass Medical School Deep Sequencing Core Facility. Raw and processed data are available in GEO, accession number GSE196357.

Bioinformatic tools and analyses

Reads from Mtb datasets were aligned to the NC_000962 reference genome using Burrows-Wheeler Aligner [40]. The FeatureCounts tool was used to assign mapped reads to genomic features, and DESeq2 was used to assess changes in gene expression in RNA expression libraries [41,42]. To differentiate fully upregulated genes from those having accumulation of reads in specific parts of the transcripts, we designed a pipeline (S1 Fig). Each gene was divided into non-overlapping 10-nucleotide (nt) windows and the mean read depth (coverage) in each window was calculated for Δrnj and WT replicates, using the Bedtools Genomecov function with the -pc option to computationally fill in coverage between reads [43], and a custom Python script to compute the coverage for each 10 nt window. The log2 ratio of coverage in Δrnj/WT was computed for each window in each gene. Then, within each gene, the window with the median log2 coverage was identified. DESeq2 also reports log2 ratios for each gene, and the genome-wide mean of these log2 ratios was close to zero as expected given the assumption that the majority of genes do not differ between strains. However, the genome-wide mean of the median 10 nt window log2 ratios was -0.07. We therefore normalized the log2 ratios of the median 10 nt windows for all genes by adding 0.07. Next, we determined the absolute difference between the DESeq2 log2 fold change and the normalized median 10 nt window log2 ratio for each gene. The standard deviation (SD) of these differences was then calculated. Among the genes reported as differentially expressed by DESeq2, we set a cutoff of two SDs, such that genes with differences of ≥2 SDs were classified as partially up or down-regulated, and genes with differences of <2 SD were classified as fully up or down-regulated. For genes that were partially up-regulated, the minimum free energy (MFE) and thermodynamic ensemble predictions of the overexpressed regions and adjacent upstream and downstream regions of the same length were determined using the RNAfold web server [44].

To specifically evaluate the characteristics of the 5’ regions of RNA fragments that accumulated in the Δrnj strains, the normalized read depths for 5’ ends in the 5’ end-directed non-pyrophosphohydrolase-treated libraries were compared. 5’ ends were considered if they had a minimum read depth of five reads and the highest read depth in a 5-nt window (S6 Table). Those 5’ ends with read depth ratios ≥10 in Δrnj/WT were classified as enriched in the Δrnj strain. A total of 381 5’ ends were classified as enriched. For a comparison, we selected 1,000 5’ ends that were equally represented in the WT and Δrnj strains. The first 50 nt downstream of each 5’ end were analyzed. The minimum free energy (MFE) of each 50-nt sequence was determined using the RNAfold web server [44], and the %GC was determined.

Analysis of the sequence and structural contexts of mutations

Published crystal structures were used to visualize the structural contexts of RNase J point mutations using Mol* Viewer [45] on RCSB PDB. The structures used were Deinococcus radiodurans 4XWW and 4XWT [46], Thermus thermophilus 3T3O [47] and 3BK1 and 3BK2 [48], and Streptomyces coelicolor 5A0T [49]. To assess the sequence context similarity between Mtb RNase J and the orthologs used for structural analysis, RNase J orthologs were aligned by Clustal Omega 1.2.4 [50].

Results

Mutations in RNase J are associated with drug resistance in clinical Mtb strains

We recently identified mutations in non-canonical drug resistance genes that are strongly associated with drug resistance in clinical Mtb strains isolated from China [16]. As mutations in these genes do not confer high-level drug resistance, we hypothesized that they may arise as intermediate steps in the acquisition of high-level drug resistance. One of the genes associated with drug resistance was Rv2752c, hereafter referred to as rnj, which encodes the bifunctional exo/endoribonuclease RNase J [23]. Genome-wide association studies (GWAS) revealed that mutations in rnj were associated with INH resistance in Mtb [16]. Our results were consistent with two previous GWAS studies that also identified rnj mutations in association with multidrug resistance in Mtb [17,18].

In our data set, rnj was highly polymorphic in INH-resistant strains compared to drug sensitive strains and mutations were distributed throughout the CDS (Fig 1A, Table 1 and S3). In INH-resistant strains nonsynonymous mutations were the most prevalent (∼80% of the total mutations), while frameshift and nonsense mutations were also found throughout the gene, suggesting selection for loss of function variants. We used crystal structures and mutational analysis data from three other bacterial RNase J orthologs to analyze the structural contexts of clinical Mtb rnj mutations (Figs 1B and S2) [46-49]. Several mutations caused non-conservative changes to residues involved in Zn2+ coordination at the catalytic site (S3 Table) [46-49]. Several others introduced prolines predicted to disrupt α-helices or β-sheets in the C-terminal domain, which is required for dimerization and efficient cleavage in Bacillus subtilis [48]. Some mutations affected residues involved in RNA binding [46,47,49].

Fig 1

RNase J is mutated in many clinical Mtb strains.

A. Vertical lines indicate mutations identified in clinical isolates in Hicks et al., 2018. Frameshift (red), nonsynonymous (blue), synonymous (orange), and nonsense (green) mutations are highlighted. Positions in the Mtb H37Rv genome are indicated in parentheses. Numbers in grey indicate mutations that evolved twice independently. B. Point mutations in INH-resistant clinical Mtb strains modeled on a Thermus thermophilus crystal structure (PDB 3T3O, Dorleans et al., 2011). Gold and gray indicate the two monomers, with Mtb mutated residues indicated in red on the gray monomer. A 4-mer RNA is indicated in blue. A catalytic zinc is indicated by “Zn.” The second catalytic zinc is not present in the structure due to an active-site mutation needed to capture a stable RNA co-complex (Dorleans et al., 2011). Structures were annotated on RCSB PDB by Mol* Viewer (Sehnal et al., 2021). The view on the left is oriented to best visualize mutations in the C-terminal domain while the view on the right is oriented to best visualize the mutations in the catalytic region.

Table 1

Summary of rnj mutations identified in clinical strains of M. tuberculosis and their predicted consequences.

Mutation type/region	Number of independent acquisitions	Predicted consequence	References
INH resistant strains (32 independent mutations)
Frameshift	2	Loss of function	N/A
Nonsense	4	Loss of function	N/A
Catalytic site (non-conservative mutation)	4	Loss or reduction of function	[46–49]
RNA contacting region (non-conservative mutation)	2	Loss or reduction of function	[46–49]
Introduction of prolines to alpha helices or beta sheets	5	Disruption of alpha helix or beta sheet	[46–48]
Charge swap	1	Unknown	N/A
Change of hydrophobic residue to polar/charged, or change of polar/charged residue to hydrophobic.	12	Unknown	N/A
Synonymous	2	None	N/A
INH sensitive strains (9 independent mutations)
Change of hydrophobic residue to polar/charged, or change of polar/charged residue to hydrophobic.	3	Unknown	N/A
Change of charged residue to polar or vice versa.	4	Unknown	N/A
Change of hydrophobic residue to hydrophobic residue.	1	Unknown	N/A
Synonymous	1	None	N/A
Strains with mixed INH resistance and sensitivity (4 independent mutations)
Catalytic site (non-conservative mutation)	1	Loss or reduction of function	[46–49]
RNA contacting region (conservative mutation)	1	Unknown	[46–49]
Change of hydrophobic residue to hydrophobic residue.	1	Unknown	N/A
Synonymous	1	None	N/A

Bellerose and collaborators [51,52] recently found, using a TnSeq screening approach, that disruption of rnj increased survival of Mtb in mice treated with the clinical first line drug regimen. Taking this finding together with the observation of potentially deleterious mutations described above, we hypothesized that loss of RNase J function might confer a survival advantage to the bacterium in the presence of drug treatment, and that mutations in clinical strains arise as a part of an evolutionary pathway to the acquisition of high-level drug resistance.

Loss of RNase J increases tolerance to several drugs

To investigate the link between RNase J and drug sensitivity in Mtb, we first constructed RNase J deletion (Δrnj6230) and complemented strains (Δrnj6230::rnj) in the Mtb mc26230 background (ΔpanCD, ΔRD1) [31]. To determine the effects of RNase J on drug sensitivity, we assessed both MICs and bactericidal activities of clinically relevant drugs. The MICs for RIF and INH were the same for the Δrnj6230 strain and its WT parent (0.125 μg/mL for both drugs), indicating that loss of RNase J does not confer resistance to either drug. However, the Δrnj6230 strain displayed increased survival in the presence of lethal concentrations of RIF and INH compared to the WT and the complemented strains (Fig 2A), suggesting that the absence of RNase J leads to increased drug tolerance. We observed a similar behavior when exposing the strains to lethal concentrations of EMB, OFX, CLA, and ERY (Fig 2A).

Fig 2

Loss of RNase J increases tolerance to several drugs.

A. Time-kill curves in presence of the indicated drugs. The concentrations used were: 0.6 μg/mL RIF, 2.4 μg/mL INH, 2.5 μg/mL CLA, 1 μg/mL OFX, 2 μg/mL EMB, or 500 μg/mL ERY. B. Time-kill curves in presence of lethal concentrations of both INH (2.4 μg/mL) and OFX (2 μg/mL). Both experiments were performed using Mtb mc26230 strains. *p<0.05, **p<0.01, ***p<0.001 two-way ANOVA for comparisons of Δrnj to WT. FDR 0.05 (Benjamini and Hochberg). Curves are representative of at least two independent experiments.

We sought to verify these results in virulent H37Rv, constructing RNase J deletion (ΔrnjH37Rv) and complemented strains (ΔrnjH37Rv::rnj). We tested killing by RIF, INH, and OFX, and found that in each case, the ΔrnjH37Rv strain had a survival advantage compared to the WT and complemented strains (S3 Fig). Furthermore, complementation of ΔrnjH37Rv with rnjH86A, which is predicted to be catalytically dead [48], did not restore the WT sensitivity phenotypes for RIF, INH, or OFX. While we cannot exclude the possibility that Mtb RNase J with the catalytic site mutation is unstable, these data are consistent with the idea that the greater survival of the ΔrnjH37Rv strain is due to loss of RNase J catalytic activity (S3 Fig).

To further characterize the survival advantage conferred by loss of rnj, we exposed cultures to a combination of drugs (INH and OFX) at lethal concentrations to prevent outgrowth of resistant mutants. We observed that both strains reached a plateau at a similar number of CFUs (Fig 2B), suggesting that loss of RNase J does not affect persistence but rather increases drug tolerance by reducing the killing rate. However, we cannot exclude the possibility that RNase J affects persister levels, since detection and quantification of persister cells are affected by the choice of assay and culture conditions (many works including [7,53-56]).

We noted that Δrnj6230 colonies were smaller than those of the WT and complemented strains. We therefore measured the growth characteristics of the three strains (Fig 3A). The growth rate of Δrnj6230 in mid-log phase was statistically indistinguishable from the growth rates of the WT and complemented strains. However, Δrnj6230 had a slightly longer lag phase than WT cells. The lag phase delay was condition-dependent, as we did not observe it when growth curves were started at a higher OD (S4A Fig), or when the strains were grown in minimal media (S4B Fig). To test the possibility that the better survival of Δrnj was due to the subtle growth defect observed at low ODs, we sub-cultured Δrnj6230 and WT cultures to a low OD, added INH at a lethal concentration after one, two, three, or four days, and compared the fraction of survival after two days of incubation with drug (Fig 3B). We observed that the higher fraction of survival for Δrnj6230 was consistent regardless of the growth phase at which drug was added (Fig 3C; growth phases spanned lag phase through late log phase). In addition, we performed RIF and INH time-killing curves in minimal media, where Δrnj6230 does not show a longer lag phase, and found that Δrnj6230 had better survival than WT in this condition as well (S4C Fig). While we cannot exclude the possibility that a longer lag phase contributes to the tolerance of Δrnj6230 to some drugs, these data suggest that unlike what was previously reported in E. coli in response to intermittent treatment with ampicillin [57], lag phase differences do not explain the increased tolerance of Δrnj6230 to continuous exposure to lethal concentrations of INH or RIF.

Fig 3

Drug tolerance in Δrnj Mtb is not due to its lag phase growth defect.

A. Growth kinetics of Mtb mc26230 WT, Δrnj, and Δrnj::rnj in 7H9 media. Slopes were statistically equivalent for all three strains in mid-log phase (days 3–6, linear regression). ODs were significantly different for Δrnj vs WT for all time-points from day 3 onward (t-tests with FDR 1% correction) B. Schematic of experiment to determine the effect of growth phase on drug survival. Created with Bio.Render.com. C. Data from the experiment shown in B. Bars indicate the proportion of cells that survived after two days of incubation with INH (2.4 μg/mL) starting at the indicated days. This proportion is the value in the lower yellow bar in B divided by the value in the upper yellow bar in B. *p<0.05, **p<0.01, ***p<0.001, t-tests with Benjamini and Hochberg FDR 0.05.

RNase J affects rRNA processing and expression of a subset of genes and mRNA fragments

To investigate how RNase J mediates drug tolerance in Mtb, we assessed its role in RNA metabolism by performing RNAseq expression profiling as well as RNA 5’ end mapping. These studies were done with WTH37Rv and ΔrnjH37Rv strains transformed with the empty vector pJEB402 as well as the ΔrnjH37Rv strain complemented with rnj under a strong constitutive promoter, ΔrnjH37Rv::rnjOE [58]. As a quality control, we evaluated the 23S rRNA as previous work has shown that RNase J is necessary for 23S rRNA maturation in M. smegmatis [23]. Consistent with these data, we found that the 23S rRNA transcript was 15 nt longer in ΔrnjH37Rv compared to WTH37Rv (S5 Fig), indicating that RNase J also plays a role in 23S rRNA processing in Mtb.

Standard analysis of the RNAseq expression data using DESeq2 indicated that 57 and 16 genes had increased or decreased transcript abundance, respectively, in ΔrnjH37Rv compared to WTH37Rv (fold change ≥1.5, adj p value ≤0.01) (Fig 4A, S4 Table). Comparison of changes in transcript abundance in the ΔrnjH37Rv and ΔrnjH37Rv::rnjOE strains showed a significant negative correlation (S6 Fig), reflecting the opposite effects of rnj overexpression and deletion. However, visual inspection of RNAseq expression library coverage revealed that several differentially abundant transcripts did not have increased abundance across the entire gene in ΔrnjH37Rv, but rather displayed increased read coverage for only short segments of the genes in question (Fig 4B). We hypothesized that these short RNA fragments might have arisen from incomplete degradation of mRNAs in the absence of RNase J. Thus, standard DESeq2 analysis is not sufficient to discriminate between genes for which the abundance of the whole transcript changes and genes marked by the accumulation of RNA fragments. To address this issue, we developed a bioinformatics pipeline (see Materials and Methods and S1 Fig) to identify genes for which the abundance of the whole transcript was altered. Of the 57 genes that were reported as significantly overexpressed in ΔrnjH37Rv by DESeq2, 31 reflected increases in the whole transcript (Fig 4A, red dots and S4 Table) while the remaining genes had increased abundance of only subsections of their transcripts (Fig 4A, light pink dots, and S4 Table). All the genes with reduced abundance in ΔrnjH37Rv except for one (Fig 4A, light blue dot) showed changes in the abundance of the entire transcript (Fig 4A, blue dots, and S4 Table).

Fig 4

RNase J affects expression of genes and causes highly structured mRNA fragments to accumulate in Mtb.

A. Volcano plot showing the genes affected by RNase J in Mtb H37Rv strains. Partially and fully over/under expressed genes are distinguished with different colors. B. Schematics of the read depth of two genes presenting full overexpression (upper panel) or partial overexpression (lower panel) in Δrnj. C. Read depth of expression libraries in Mtb H37Rv strains for four genes that displayed partial overexpression in Δrnj. Grey lines below the arrows denote the sequences targeted by qPCR for regions of the genes displaying accumulation of short fragments in Δrnj (R2) and regions with similar read coverage in all strains (R1). For all H37Rv experiments, the WT and Δrnj strains contained the empty vector pJEB402. D. Determination of half-life for the gene regions shown in C using Mtb mc26230 strains. *p<0.05, **p<0.01, ***p<0.001. E. The minimum free energy of folding was predicted for overexpressed regions in A and for regions of equal lengths immediately upstream and downstream of each overexpressed region. Upstream and downstream MFEs were only calculated when the region fell within the coding sequence of the gene. Blue indicates overexpressed regions for which only the upstream adjacent region was available, purple indicates regions for which only the downstream adjacent region was available, and red indicates regions for which both upstream and downstream regions were available. **p<0.01, Wilcoxon matched-pairs signed rank test. F and G. 5’ end-mapping libraries were used to identify transcripts overexpressed in Δrnj. The 50 nt downstream of both overexpressed and unchanged 5’ ends were analyzed to predict the minimum free energy of secondary structure formation and determine G+C content. ****p<0.0001, Mann Whitney test.

RNase J has a specialized role in degrading mRNA fragments with strong secondary structure and high G+C content

We hypothesized that the transcript fragments that accumulated in the ΔrnjH37Rv strain could be RNase J targets that were inefficiently degraded in the mutant strain. To test this, we chose four genes with partial-transcript abundance increases in the ΔrnjH37Rv strain and measured the half-lives of both the overrepresented regions of each transcript and regions for which read coverage was similar in both strains (Fig 4C). The overrepresented regions had longer half-lives in the absence of rnj, while half-lives of equally abundant regions did not differ between the strains (Fig 4D).

To understand why RNase J has an apparently rate-limiting role in degradation of certain mRNA fragments, we calculated the predicted minimum free energy of folding (ΔG) for each overrepresented region (S5 Table) and for adjacent upstream and downstream regions of equal size, when those regions were present within the same coding sequence. The ΔGs of the overrepresented regions were in most cases lower than the ΔGs of the upstream adjacent regions (Fig 4E, p = 0.0021), indicating that formation of secondary structure was more energetically favorable for the overrepresented regions. There was an apparent trend toward the ΔGs of the overrepresented regions also being lower than those of the downstream adjacent regions, but this was not statistically significant. We repeated the analysis using the free energies of thermodynamic ensemble predictions [44] and the results were nearly identical (p = 0.0017 for accumulated regions vs upstream regions). Inspection of the minimum free energy structures did not reveal any trends regarding the types and locations of predicted secondary structure elements. The library construction method did not permit identification of the exact 5’ and 3’ ends of each overrepresented region, which precluded analysis of the basepairing probabilities of those ends.

Since the overrepresented regions appeared to be more structured than adjacent upstream regions and RNase J is known to have 5’ to 3’ exonuclease activity, we hypothesized that RNase J may contribute to degradation of transcripts that have high levels of secondary structure near their 5’ ends. We therefore used a separate 5’-end-directed RNAseq dataset to analyze the properties of 5’-end-adjacent sequences in WTH37Rv and ΔrnjH37Rv transcriptomes. We assessed the G+C content and predicted secondary structure of the 50 nt sequences adjacent to RNA 5’ ends that had increased abundance in the ΔrnjH37Rv strain or were equally abundant in the ΔrnjH37Rv and WTH37Rv strains (S6 Table). We found that the 5’-end-adjacent sequences with increased abundance in ΔrnjH37Rv had significantly more negative predicted minimum free energies of folding (ΔG) than those that were equally represented in ΔrnjH37Rv and WTH37Rv (Fig 4F). In addition, the median G+C content of the sequences overrepresented in ΔrnjH37Rv was significantly higher than that of the equally represented sequences (Fig 4G). These results are consistent with the idea that RNase J targets RNAs with relatively strong secondary structure, particularly near their 5’ ends.

Having found that loss of RNase J increased stability of short RNA fragments, we wondered if the increased abundance of the fully overexpressed genes in ΔrnjH37Rv was a direct consequence of slower degradation rates or increased transcription. We measured the half-lives of six fully overexpressed genes and found that transcript stability was not altered (S7A and S8 Figs). However, these genes did not have high G+C contents, and technical difficulties prevented us from obtaining high-confidence half-lives for those fully overexpressed genes that were GC-rich (S7B Fig). There may therefore be genes that were fully overexpressed due to reduced degradation rates, and this should be addressed in future work. We also considered the possibility that the absence of RNase J could lead to accumulation of antisense transcripts that could affect mRNA degradation or translation. However, there were no changes in median antisense coverage for genes differentially expressed in ΔrnjH37Rv or for all expressed genes in this strain. Together, these data suggest that RNase J affects the transcript abundance of some genes through altered transcription rather than by altering their mRNA stability.

Genes differentially expressed in the absence of RNase J are enriched for sRNAs, PE/PPE family genes, SigM targets, and genes with roles in hypoxia response and carbon source switching

Examination of the genes that were fully overexpressed or underexpressed revealed several themes. First, the differentially expressed genes were enriched for sRNAs and genes of the PE/PPE family, including nine overexpressed PE_PGRS genes (S9 Fig). Second, six of the underexpressed genes (ppe50, ppe51, fadD26, ppsA, Rv0885, and Rv3137) were reported to be negatively regulated by the stress-responsive alternative sigma factor SigM, while one of the overexpressed genes (Rv3093c) was reported to be positively regulated by SigM [59]. This suggests that there may be increased SigM activity in ΔrnjH37Rv, although the sigM gene itself was not increased at the transcript level. Finally, the differentially expressed genes included several associated with hypoxia responses (the sRNAs MTS2823 and F6, and the protein-coding genes fdxA, ppe31, clgR, and Rv3740c) and several associated with utilization of various carbon sources (ppe50, ppe51, mcm1C, prpC, Rv1066) [60-68], suggesting that the metabolic status of the ΔrnjH37Rv may differ from that of WTH37Rv.

Overexpression of the sRNAs Mts2823 and Mcr11 is necessary but not sufficient for INH tolerance in Δrnj Mtb

The sRNAs Mts2823 and Mcr11 were two of the most overexpressed genes in the ΔrnjH37Rv strain (S4 Table). Since sRNAs have been implicated in adaptation to different stresses, we sought to investigate if their increased expression in Δrnj contributed to drug tolerance. We therefore deleted each of the two sRNAs in both the WT6230 and Δrnj6230 strains and performed time-killing curves in presence of RIF or INH. We found that deletion of either of these sRNAs in the Δrnj6230 background decreased INH tolerance to levels near the WT6230 (Fig 5A, 5B, 5D and 5E). In contrast, deletion of these sRNAs had no effect on INH tolerance in the WT6230 background (Fig 5C and 5F). Thus, Mts2823 and Mcr11 are necessary for the INH tolerance conferred by loss of RNase J in the Δrnj6230 strain. To determine whether either sRNA was sufficient for INH tolerance, we then constructed strains overexpressing either Mts2823 (Mts2823OE) or Mcr11 (Mcr11OE) in the WT6230 background. However, we found that both showed drug sensitivity levels comparable to that of WT6230 (Fig 5C and 5F), suggesting that overexpression of either Mts2823 or Mcr11 alone is not sufficient to increase drug tolerance. No consistent effects were observed for strains with deletions or overexpression of these sRNAs in RIF, suggesting that effectors acting downstream of RNase J deletion may act in part via drug specific mechanisms (S10 Fig).

Fig 5

Overexpression of the sRNAs Mts2823 and Mcr11 is necessary but not sufficient for INH tolerance in Δrnj Mtb.

Time-killing curves in presence of INH (2.4 μg/mL) for strains with deletion or overexpression of Mts2823 (A-C) or Mcr11 (D-F) strains are shown. *p<0.05, **p<0.001 two-way ANOVA. Pink stars: comparison of Δrnj ΔMts2823 to Δrnj. Red stars: comparison of WT to Δrnj. Tan stars: comparison of Δrnj ΔMcr11 to Δrnj.

Downregulation of ppe50-ppe51 is necessary for the INH and RIF tolerance phenotypes of Δrnj Mtb, and deletion of ppe50-ppe51 confers drug tolerance to a WT strain

Two of the most strongly downregulated genes in the ΔrnjH37RV strain were ppe50 and ppe51, which are expressed in an operon and have previously been implicated in drug sensitivity [51,52,69]. To test the hypothesis that downregulation of ppe50 and ppe51 contributes to the drug tolerance of the Δrnj strains, we ectopically overexpressed them in the Δrnj6230 background and performed drug killing experiments. Overexpression of the ppe50-ppe51 operon in the Δrnj6230 strain impaired survival of Mtb in the face of both INH and RIF, producing drug sensitivity levels similar to those observed in the WT strain (Fig 6A). Thus, the dysregulated expression of ppe50-ppe51 is also necessary for the altered drug susceptibility associated with loss of rnj. To test if loss of ppe50-ppe51 is sufficient to confer drug tolerance, we deleted the ppe50-ppe51 operon from WT6230 and measured killing by INH and RIF (Fig 6B). Δppe50-ppe516230 displayed increased tolerance to both drugs compared to WT6230, despite rnj being intact. Normal drug sensitivity was restored when we complemented Δppe50-ppe516230 with ectopically expressed ppe50-ppe51. Loss of ppe50-ppe51 in a WT background therefore appears to be sufficient to confer tolerance to both RIF and INH.

Fig 6

Downregulation of ppe50-ppe51 is required for the drug tolerance phenotype of Δrnj Mtb, and deletion of ppe50-ppe51 is sufficient to induce drug tolerance in a WT background.

Time-kill curves in the presence of RIF (0.6 μg/mL) or INH (2.4 μg/mL) in Mtb mc26230 strains. A. ppe50-ppe51 was ectopically expressed from a strong constitutive promoter in the Δrnj strain. *p<0.05, **p<0.01, two-way ANOVA. Blue stars: comparison of WT::EmptypJEB402 to Δrnj::EmptypJEB402. Magenta stars: comparison of Δrnj::ppe50/ppe51 to Δrnj::EmptypJEB402. B. ppe50-ppe51 was deleted from the WT strain and then ectopically expressed from a strong constitutive promoter. *p<0.05, **p<0.01 ***p<0.001, two-way ANOVA. Blue stars: comparison of WT to WT Δppe50-ppe51. Lavender stars: comparison of WT Δppe50/ppe51::ppe50/ppe51 to WT Δppe50-ppe51. FDR 0.05 (Benjamini and Hochberg) for all comparisons.

Discussion

Previous studies have shown that mutations in the RNase J-encoding gene were more prevalent in drug resistant Mtb clinical isolates compared to drug-sensitive isolates [16-18]. Here we show that loss of RNase J increases drug tolerance, an advantageous trait to overcome drug stress during TB treatment that could promote further development of high-level drug resistance in clinical settings. We have furthermore defined a role for RNase J in mycobacterial mRNA metabolism as a specialized degradation factor.

Despite the non-essentiality of RNase J in mycobacteria, a recent study showed that RNase J is a major component of the degradosome in Mtb [28]. Previous work also implicated RNase J as having roles in rRNA processing in M. smegmatis [23]. In M. abscessus, deletion of the rnj gene (MAB_3083c) resulted in changes in colony morphology, biofilm formation, and sliding motility [70]. However, the role of this nuclease in mycobacterial mRNA metabolism remained elusive. Our finding that deletion of RNase J affects expression of a limited number of genes is consistent with the near-wildtype growth characteristics of the mutant. Taken together with the essentiality of RNase E in mycobacteria, our results point to RNase E as playing a more important role in bulk mRNA decay. This is consistent with work in two other species that encode both RNase E and RNase J, Rhodobacter sphaeroides and Synechocystis sp. PCC6803, where the respective roles of the two nucleases have been investigated [26,71]. In contrast, RNase J plays a leading role in bulk mRNA degradation in several species that naturally lack RNase E, such as B. subtills, C. diphtheriae, S. aureus and H. pylori [72-75].

The previously reported role of RNase J in maturation of the 5’ end of the 23S rRNA in M. smegmatis [23] was confirmed here in Mtb. In the absence of RNase J, the great majority of 23S rRNA had an additional 15 nt on its 5’ end compared to the WT and complemented strains. The impact of this 5’ extension is unknown. The 5’ end of the 23S rRNA extends from the external face of the 50S ribosome roughly opposite from the side where the 30S subunit binds [76]. It is adjacent to ribosomal protein L13, which is required for the early stages of 30S ribosome assembly [77]. Future work should therefore investigate the impact of the 15 nt 5’ extension on Mtb ribosome assembly.

Our analyses allowed us to identify native RNase J targets in Mtb as having higher G+C content and stronger predicted secondary structure than non-targets. It is important to note that the mRNA fragments we identified as direct RNase J targets generally had longer half-lives than other fragments of the same transcript even when RNase J was present (Fig 4D, comparison of region 1 to region 2 in WT strain), in agreement with the idea that RNase J targets RNAs that are refractory to degradation by the bulk degradation machinery. This role for mycobacterial RNase J is consistent with a recently described duplex-unwinding activity in the archaeal mpy-RNase J, which was shown to degrade highly structured RNAs in vitro [78].

We demonstrated here that deletion of rnj reduces bacterial killing when Mtb is exposed to lethal concentrations of several drugs and showed that these observations are likely due to an increase in drug tolerance rather to a higher formation of persister cells. The drug tolerance is explained at least in part by gene expression changes. The overexpression of two PE/PPE genes that were downregulated in Δrnj, ppe50-ppe51, restored the RIF and INH sensitive phenotype in the RNase J mutant to the WT levels, demonstrating that downregulation of these genes is necessary for drug tolerance in Δrnj. Deletion of ppe50-ppe51 in a WT background conferred levels of INH and RIF tolerance similar to that seen in the Δrnj strain, indicating that loss of these genes is also sufficient to increase drug tolerance. With respect to INH, this finding is consistent with previous studies reporting that deletion of ppe51 lead to increased bacterial survival in INH-treated mice [51,52] and reduced sensitivity to INH in vitro [52,69]. The relationship between ppe51 and RIF sensitivity appears to be more complex. Deletion of ppe51 was previously found to cause a small but significant increase in the MIC for RIF in vitro, but led to increased RIF sensitivity in mice [52]. The impact of ppe51 on RIF sensitivity may therefore be condition-dependent.

It is possible that the effects of ppe50-ppe51 on drug sensitivity are related to carbon metabolism. Dechow and collaborators reported that specific PPE51 variants promoted glycerol uptake and prevented growth arrest in acidic conditions when glycerol was the sole carbon source [62]. On the other hand, knockdown of ppe51 affected uptake of disaccharides and attenuated Mtb growth in minimal media with disaccharides as the sole carbon source [65]. There is an increasing body of literature implicating glycerol metabolism as a pathway that affects sensitivity of Mtb to various drugs [6,51,69]. It is therefore conceivable that reduced expression of ppe51 in the Δrnj strains leads to INH tolerance through a mechanism related to glycerol metabolism.

Deletion of two non-coding sRNAs overexpressed in the mutant, Mts2823 and Mcr11, in the Δrnj background also showed partial restoration of drug sensitivity, suggesting that RNase J likely modulates drug tolerance via multiple mechanisms. Mts2823 is an sRNA orthologous to the E. coli 6S RNA, which interacts with the RNA polymerase core preventing gene expression in mycobacteria and is highly expressed in stationary phase [79,80]. Overexpression of Mts2823 was shown to have a slight effect on the growth rate in Mtb [79], consistent with our observations for the Mts2823OE strain in absence of drug. Mcr11 is highly expressed during mouse infection [81]. Interestingly, the expression of this sRNA was increased for only ∼80% of the transcript sequence, with the 3’ end region showing similar levels as in the WT strain (S11 Fig), indicating that RNase J could be involved in maturation of the 3’ end of the transcript. However, more work needs to be done to understand the mechanisms of such regulation.

Taken together, our results suggest a scenario in which RNase J activity affects expression of multiple genes that together affect drug tolerance. Some of these expression changes have relatively well-delineated impacts (e.g., loss or reduction of ppe51 expression consistently causes reduced INH sensitivity in our work and that of others), while others appear to have different effects in WT and Δrnj backgrounds. These findings, together with our observation that Δrnj strains accumulate mRNA degradation intermediates, are consistent with the idea that mutations in rnj have pleiotropic effects on the physiology of Mtb and may therefore be selected in vivo in response to a variety of pressures.

Strains used in this study.

(XLSX)

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Primers used in this study.

(XLSX)

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Mutations in Rv2752c found in Mtb clinical isolates reported in Hicks et al., 2018 [16].

(XLSX)

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DEseq2 analysis of RNAseq expression libraries.

(XLSX)

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Approximate coordinates of overexpressed regions in genes labeled as partially upregulated in S4 Table.

(XLSX)

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Coverage data from 5’-end-directed RNAseq.

(XLSX)

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Bioinformatic analysis of RNASeq data.

(PDF)

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Multiple sequence alignment of RNase J from M. tuberculosis (Mtb), M. smegmatis (Msm), Streptomyces coelicolor (Sco), Thermus thermophilus (Tth), and Deinococcus radiodurans (Dra).

(PDF)

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Loss of RNase J affects drug sensitivity in Mtb H37Rv.

(PDF)

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Growth kinetics in drug-free 7H9 and minimal media and time-kill curves in minimal media.

(PDF)

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RNase J contributes to 23S rRNA maturation in Mtb.

(PDF)

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Genes affected by loss of rnj are inversely affected by rnj overexpression.

(PDF)

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Some of the genes fully overexpressed in Δrnj do not display increased stability.

(PDF)

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Decay curves used to calculate half-lives in S7 Fig.

(PDF)

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Genes that are differentially expressed in the absence of rnj in the H37Rv background are enriched for stable RNAs and PE/PPE family genes.

(PDF)

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The sRNAs Mts2823 and Mcr11 do not affect RIF tolerance in WT or Δrnj Mtb.

(PDF)

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RNAseq coverage plots of sRNAs Mts2823 and Mcr11.

(PDF)

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17 Mar 2022

Dear Shell,

Thank you very much for submitting your manuscript "Loss of RNase J leads to multi-drug tolerance and accumulation of highly structured mRNA fragments in Mycobacterium tuberculosis" for consideration at PLOS Pathogens. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

The broad impact of the findings are somewhat limited by a lack of clear mechanism. Nevertheless, the global changes affected by RnaseJ are important and the manuscript could be strengthened by a more rigorous analysis of the RNA segments and the query about the associated half-lives. The ppe50-ppe51 needs the relevant control in the wild-type background. The authors also need to analyze the mutations observed in the clinical strains (predicted effect on the enzyme, association with known phenotype of the strain)

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The broad impact of the findings are somewhat limited my a lack of clear mechanism. Nevertheless, the global changes affected by RnaseJ are important and the manuscript could be strengthened by a more rigorous analysis of the RNA segments and the query about the associated half-lives. The ppe50-ppe51 needs the relevant control in the wild-type background. The authors also need to analyze the mutations observed in the clinical strains (predicted effect on the enzyme, association with known phenotype of the strain)

Reviewer's Responses to Questions

Part I - Summary

Please use this section to discuss strengths/weaknesses of study, novelty/significance, general execution and scholarship.

Reviewer #1: Although there is a growing body of literature on the mechanisms of antibiotic tolerance in model organisms, the specific mechanisms mediating drug tolerance in M. tuberculosis are still poorly understood. In this work, Martini, Hicks and colleagues investigate the role of RNAseJ in multi-drug tolerance in Mtb. Bacterial GWAS studies from the authors and other groups had previously identified (probable loss of function) mutations in RNAseJ as being associated with drug-resistance. Furthermore, Tnseq analyses by Sassetti and colleagues had shown that transposon insertions in RNAseJ are associated with drug tolerance in vitro and in animal infection. Here, the authors verify that lack of RNAseJ, or replacement with a catalytically null mutant is associated with tolerance of Mtb to multiple antibiotics in vitro. They confirm that RNAseJ processes 23S rRNA. Deletion results in up/downregulation of a fairly discrete set of genes including some PPE genes and sRNAs. The authors suggest RNAseJ ‘client mRNAs’ have high GC content and more stable secondary structures. Surprisingly, measuring the half-life of several candidate RNAseJ mRNAs doesn’t show alteration of half-life +/- RNAseJ. Mechanistically, the most interesting experiments are that deletion of two sRNAs, Mts2823 and Mcr11 rescued the RNAse J tolerance phenotype to INH but not RIF, but had no phenotype in WT bacilli. Similarly, since expression of ppe50-ppe51 is decreased in the KO, the showed that OE of the two genes increased drug susceptibility of the deletion mutant to wild-type levels.

Overall, this a carefully performed study that advances mechanistic study of drug tolerance in Mtb and makes a substantial advance for the field. However, a few points of interpretation may require further nuance, and/or lack a critical control.

Reviewer #2: This study verifies the predicted role of Mtb RNaseJ in 23S rRNA maturation and degradation of more stable RNA structures; and provides new evidence for its role in drug susceptibility. Although specific mechanisms are still unknown (e.g., how is SigM activated in the rnj deletion mutant, why are sRNAs specific for INH, how exactly the loss of RNase J increases drug tolerance), this well-presented and rigorously conducted work provides good foundation for future studies.

Reviewer #3: This manuscript investigates the role of RnaseJ in Mtb in drug tolerance by characterizing an RnaseJ deletion strain of H37Rv and an auxotrophic strain of Mtb. The motivation for the study is the prior finding that WGS of clinical Mtb strains has revealed mutations in the gene encoding RnaseJ in drug resistant strains. The RnaseJ ko has the following phenotypes:

1) Loss of RnaseJ attenuates the rate of killing of Mtb by INH, Rif, Oflox, and ethambutol and these phenotypes are complemented by the wild type gene. The magnitude of the effect is most dramatic with INH at early time points.

2) Small colony size of the RnaseJ KO

3) RNA sequencing of RnaseJ ko revealed transcripts that are altered, and mapping of the RNAseq reads reveals that specific regions of transcripts are overabundant, rather than entire mRNAs, and these regions have different predicted secondary structure.

4) The authors go on to interrogate several differentially abundant transcripts and sRNAs and show suppression of the antibiotic sensitivity phenotype for some, but overexpression of these targets was not sufficient to produce drug sensitivity.

Major Comments:

Experimental

Overall, the study is rigorously performed, and I have very few experimental criticisms except for some minor technical points given below. Appropriate controls and statistical tests are applied, and the scientific quality is very high. The major experimental challenge is the magnitude of some of the phenotypes observed, which in many cases are subtle. The most significant antibiotic sensitization is to INH, but in all cases the effects are mild but statistically significant.

The effects of RnaseJ loss of RNA stability are convincing, but in most cases shown are due to persistence of a certain segment of the RNA within the transcript. Although this is plausibly attributed to the structure of that region, I think this analysis could be more extensive. The overabundant regions are quite short in most cases. Are there secondary structures of the RNAs that can be predicted in those segments? Have unannotated small RNAs been excluded as the cause of these overabundant regions as several other annotated sRNAs are found?

Given that most of the RnaseJ mutations found in clinical strains are not predicted to be null, it would be useful to engage in some structural modeling of the mutations to attempt to predict their effect.

Interpretation of findings

The major challenge of distilling the findings into a model of how RnaseJ affects antimicrobial sensitivity is the pleiotropic nature of the effects of RnaseJ deletion. This is not really a criticism, but a reflection of attributing specific mechanisms to pathways that are global. As such, the findings do not yield a clear picture of what downstream pathways are controlling this antibiotic phenotype. It is possibly a reflection of a globally altered cell, including possible changes in permeability due to cell wall alteration, metabolic changes, etc. I am sympathetic to the challenge, but the firmest conclusion is that loss of RnaseJ globally affects antibiotic sensitivity, without a clear mechanism of how this occurs. This lack of downstream mechanism somewhat limits the broad impact of the findings.

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Part II – Major Issues: Key Experiments Required for Acceptance

Please use this section to detail the key new experiments or modifications of existing experiments that should be absolutely required to validate study conclusions.

Generally, there should be no more than 3 such required experiments or major modifications for a "Major Revision" recommendation. If more than 3 experiments are necessary to validate the study conclusions, then you are encouraged to recommend "Reject".

Reviewer #1: 1. I’m not sure I agree with the interpretation with regards to whether the deletion mutant phenotype isn’t via lag phase modulation or doesn’t influence persister levels. As per the classic Balaban paper (PMID: 25043002), which incidentally, hasn’t been cited, lag time is strongly associated with persistence. Furthermore, the growth curve in Fig. 2A, does seem to show a difference in growth rates (although subtle, it is a log10 scale). The experiments meant to exclude lag phase aren’t totally convincing: The expt in 2D goes some way to address the issue, but even at day 4, the OD was still only ~0.01 and not really log phase. Ideally, repeating a kill curve when cultures are > OD 0.1 would help resolve the issue.

2. A puzzling result for me is with regards to the upregulation of certain transcripts in the deletion mutant. The authors nicely show that RNAseJ targets high GC-rich transcripts, which nicely fits the upregulated transcripts. Rather surprisingly, however, the half-life of some of these transcripts is not altered in the deletion mutant. That doesn’t make sense to this reader. Are the data for Fig. 3E/F for all upregulated transcripts, or for just the partially upregulated (which appear to be degraded by RNAseJ)? One would expect the partially upregulated transcripts to have a different GC content to the fully upregulated transcripts (i.e. those whose half-life is not altered).

3. The ppe50-ppe51 experiment lacks a control. Much like the former experiment with the sRNAs, the drug tolerance should also be performed in OE on WT background. Otherwise the statements on lines 361/399 that the phenotype in part explains drug tolerance in absence of RNAseJ cannot be made. Indeed, the Bellerose data suggest that this phenotype doesn’t require RNAseJ deletion.

Reviewer #2: None

Reviewer #3: I think the aspects of the paper that could be improved are the analysis of the possible structures of the RNA segments and more information about the predicted effect of RNAseJ mutations in clinical strains, but these experiments are secondary to the editorial judgement about whether the findings are of broad interest given the limitations noted above.

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Part III – Minor Issues: Editorial and Data Presentation Modifications

Please use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity.

Reviewer #1: 1. The lack of full processing of 23S rRNA was intriguing. I’m wondering whether at least some of the phenotype is via effects on translational speed. Addressing this experimentally is beyond the scope of the current work, but may be worth discussing in the Discussion?

2. In the text, Fig. 4 is called out as Fig. 6

3. The reference formatting is off, including, but not limited to unusually abbreviated journal names. This should be corrected.

Reviewer #2: In the RNA half-life measurements, 160 min time point was excluded because “it did not follow the initial exponential decay trend”. How many times was this experiment repeated and was this observation consistent? Is there any mechanistic explanation for this observation? It would be good to include graphs in SI that show these data.

2021 WHO TB report should be cited, not the one from 2020, since the report from 2020 shows data from year 2019, not 2020, as the authors claim in the intro. Also, TB is not top 10 cause of death anymore. The report from 2021 states that it was 13th leading cause of death in 2019 (Fig. 7), while report from 2020 shows data for year 2016, when TB was 10th (Fig. 4.14). The text should be updated to reflect newer data and more recent WHO report. Note that WHO report from 2021 was mentioned in the first paragraph, but it is not listed as a reference.

Line 63 – Period is missing before “For example…”

Line 106 – liter should be L

Line 107 – “Tween 80” instead of “tween”

Line 342 – Fig 6 should be Fig 4

Reviewer #3: Figure 1A. given the compelling finding that RnaseJ mutations are found in clinical drug resistant strains, I think this panel could be better presented. The mutation types are difficult to see.

Figure 2: the colony size phenotype: is this conserved in the non-auxotrophic strain background and is it complemented by the wild type rnaseJ?

For the complementation experiments using the H86A mutation, it should be noted that the lack of complementation, although consistent with a necessity for RnaseJ activity, could also be due to protein instability unless an antibody is available to confirm expression.

Many of the references are misformatted and missing journal names and other information.

The delta symbol in front of italicized bacterial gene names should not be also italicized

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Reviewer #2: No

Reviewer #3: No

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24 Jun 2022

Submitted filename: Martini et al response to reviewers.docx

Click here for additional data file.

27 Jun 2022

Dear Shell,

We are pleased to inform you that your manuscript 'Loss of RNase J leads to multi-drug tolerance and accumulation of highly structured mRNA fragments in Mycobacterium tuberculosis' has been provisionally accepted for publication in PLOS Pathogens.

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Best regards,

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Associate Editor

PLOS Pathogens

JoAnne Flynn

Section Editor

PLOS Pathogens

Kasturi Haldar

Editor-in-Chief

PLOS Pathogens

​orcid.org/0000-0001-5065-158X

Michael Malim

Editor-in-Chief

PLOS Pathogens

orcid.org/0000-0002-7699-2064

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The authors have addressed the reviewers' concerns

Reviewer Comments (if any, and for reference):

8 Jul 2022

Dear Shell,

We are delighted to inform you that your manuscript, "Loss of RNase J leads to multi-drug tolerance and accumulation of highly structured mRNA fragments in Mycobacterium tuberculosis," has been formally accepted for publication in PLOS Pathogens.

We have now passed your article onto the PLOS Production Department who will complete the rest of the pre-publication process. All authors will receive a confirmation email upon publication.

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Soon after your final files are uploaded, the early version of your manuscript, if you opted to have an early version of your article, will be published online. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.

Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Pathogens.

Best regards,

Kasturi Haldar

Editor-in-Chief

PLOS Pathogens

​orcid.org/0000-0001-5065-158X

Michael Malim

Editor-in-Chief

PLOS Pathogens

orcid.org/0000-0002-7699-2064