Quantitative Proteomics Reveals Novel Insights into Isoniazid

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Quantitative proteomics reveals novel insights into isoniazid susceptibility in mycobacteria mediated by a universal stress protein Xinling Hu, Xiaojing Li, Lige Huang, John Chan, Yuling Chen, Haiteng Deng, and Kaixia Mi J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/pr5011058 • Publication Date (Web): 09 Feb 2015 Downloaded from http://pubs.acs.org on February 17, 2015

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Journal of Proteome Research

Quantitative proteomics reveals novel insights into isoniazid susceptibility in mycobacteria mediated by a universal stress protein Xinling Hu1#, Xiaojing Li1#, Lige Huang1, John Chan2, Yuling Chen3, Haiteng Deng3, Kaixia Mi1, 4* 1

CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of

Microbiology, CAS, Beijing 100101, China 2

Departments of Medicine and Microbiology & Immunology, Albert Einstein College of

Medicine, NY 10461, USA 3

School of Life Sciences, Tsinghua University, Beijing 100084, China

4

Beijing Key Laboratory of Microbial Drug Resistance and Resistome, Beijing 100101,

China # These authors contributed equally to this study *

To whom correspondence should be addressed: Institute of Microbiology, No.1 Beichen

West Road, Chaoyang District, Beijing 100101, China. Tel: 86-010-64806082; Email: [email protected]; Fax: 8610-64807468

ACS Paragon Plus Environment

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ABSTRACT Tuberculosis (TB) is caused by the ancient pathogen Mycobacterium tuberculosis and is one of the most serious infectious diseases in the world. Isoniazid (INH) is an important first-line drug for the treatment of active and latent TB. INH resistance is an increasing problem in the treatment of TB. Phenotypic resistance to INH, however, is poorly understood. In this study, we constructed a strain of Mycobacterium bovis BCG that overexpresses the latency-related universal stress protein (USP), BCG_2013, and designated this strain BCG-2013. BCG_2013 overexpression increased susceptibility to INH compared with the wild-type strain, BCG-pMV261. Quantitative proteomic analysis revealed that BCG_2013 overexpression resulted in the up-regulation of 50 proteins and the down-regulation of 26 proteins among the 1500 proteins identified. Upregulation of catalase-peroxidase KatG expression in BCG-2013 was observed and confirmed by qPCR, while expression of other INH resistance-related proteins did not change. In addition, differential expression of the mycobacterial persistence regulator MprA and its regulatory proteins was observed. BCG_2013 and katG mRNA levels increased in a Wayne dormancy model while MprA mRNA levels decreased. Taken together, our results suggest that the increase in KatG levels induced by increased BCG_2013 levels underlies the phenotypic susceptibility of mycobacteria to INH.

Keywords: Universal stress protein BCG_2013, peroxidase-catalase KatG, isoniazid, mycobacteria, quantitative proteomics


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Introduction Mycobacterium tuberculosis is the ancient pathogen that causes tuberculosis (TB), a disease which results in millions of deaths every year. The prevalence of multi-drug resistant (MDR) and extensively drug-resistant (XDR) TB has increased the threat to public health.1 Isoniazid (isonicotinic acid hydrazide, INH) is an important first-line antimycobacterial antibiotic that has been widely used in the treatment of active and latent TB.2 INH is a pro-drug with a simple structure comprising a pyridine group and a hydrazine group. The mechanism of action of INH was discovered years after its introduction in the 1950s.2 INH enters mycobacterial cells by passive diffusion and is activated by the bacterial catalase-peroxidase KatG, which is encoded by rv1908c.3 KatG-mediated INH activation produces a range of highly reactive oxygen species,4 including superoxide and hydroxyl radicals, and oxidative stress decreases isoniazid resistance in M. tuberculosis.5 Mutations in katG are the main cause of clinical INH resistance in M. tuberculosis. Approximately 50% of INH-resistant M. tuberculosis strains harbor mutations in katG.6 Globally, S315T is the most prevalent katG mutation. Mutations in 8 other genes (furA, inhA, kasA, rv0340, iniB, iniA, iniC, and efpA) and two regulatory DNA regions (the oxyR-ahpC intergenic region and the promoter of mabAinhA) have also been identified in INH-resistant M. tuberculosis isolates.7 Although INHrelated drug resistance has been studied extensively, the mechanisms of INH-resistance in clinical M. tuberculosis strains remain elusive. One clinical investigation indicated that a latency gene rv1996, which encodes a universal stress protein (USP) in M. tuberculosis, is located in a deletion hot spot,8 suggesting that a latency gene rv1996 may be related to drug resistance.


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USPs were first identified in Escherichia coli and are up-regulated under several stress conditions to perform undefined functions.9, 10 In the M. tuberculosis genome, 10 genes are predicted to encode USPs; of these, 6, including Rv1996, belong to the DosR dormancy regulon, suggesting that USPs play important roles in M. tuberculosis persistence.11 Although the involvement of M. tuberculosis USP Rv2623 in mycobacterial persistence has been demonstrated,12 few studies have evaluated the biological functions of mycobacterial USPs.10 The biological functions of a specific gene can be characterized by examining the proteomes of strains lacking or overexpressing that gene. This approach is particularly fruitful if an obvious phenotype is displayed by the null or overexpressing mutant. As the loss of individual USPs is compensated for by the

redundant functions of the remaining M. tuberculosis USPs,10 overexpression of a M. tuberculosis USP might increase its effect compared to other M. tuberculosis USPs, providing insights into the function of the specific USP.12 In this study, we investigate the relationship between a latency-related gene, BCG_2013, and INH resistance. Since direct study of M. tuberculosis requires a biosafety level

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laboratory13

and

the

structural

organization

of

the

BCG_2012/BCG_2013/BCG_2014 region in Mycobacterium bovis BCG is identical to the rv1995/rv1996/rv1997 region of M. tuberculosis H37Rv, we chose the low pathogenicity M. bovis BCG as a model for studying the function of rv1996. Quantitative proteomic analysis revealed that an increase in protein levels of mycobacterial USP causes an increase in KatG protein levels, in turn increasing phenotypic susceptibility to INH.

Materials and Methods


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Bacterial strains and culture conditions Liquid cultures of M. bovis BCG str. Pasteur 1173P2 were grown in 7H9 media comprising Middlebrook 7H9 medium (Becton, Dickinson and Co., Sparks, MD) supplemented with ADS enrichment (Albumin-Dextrose Saline containing 5% (w/v) bovine serum albumin fraction V, 2% (w/v) D-dextrose and 8.1% (w/v) NaCl),14 0.5% (v/v) glycerol, and 0.05% (v/v) Tween-80. To maintain the plasmids in the recombinant strains, 25 mg/L kanamycin was added to the medium. Generation of the BCG_2013 overexpression strain BCG-2013 The full-length BCG_2013 gene was amplified from M. bovis BCG genomic DNA using the primer pair USPF/USPR (Table S1) and cloned into pMV261 to yield pMV261BCG_2013. The plasmid was then transformed into M. bovis BCG. Transformants were identified by PCR using the primer pair pMV261F/USPR, and the correct colonies were named BCG-2013. The empty vector, pMV261, was also transformed into BCG as a negative control, denoted BCG-pMV261. The primers used are listed in Table S1. Antibiotic susceptibility testing The susceptibility of mycobacteria to INH/ethionamide (ETH) was assessed using the broth microdilution method.15 Briefly, INH/ETH was diluted 2-fold in 7H9 medium. 40 µl aliquots were then mixed with 40 µl of mycobacterial suspension (107 cells/ml) and added to the wells of 96-well microtiter plates. The plates were incubated at 37°C for 5 days. Triplicate wells were inoculated for each concentration of INH/ETH. OD600 values of the cultures were measured using a microplate reader (FLUOstar OPTIMA, BMG Labtech). The minimum inhibitory concentration (MIC) was defined as the lowest concentration of compound that inhibited visible bacterial growth after a 5-day


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incubation (OD600 < 0.05). MIC measurements were repeated independently at least 10 times. RNA isolation, RT-PCR and quantitative PCR Log-phase cultures (OD600: 0.8 - 1.0) of the tested strains were diluted 1:50 in 7H9 media. Bacterial cells were harvested by centrifugation when the OD600 of the diluted cultures reached 0.3. Bacterial pellets were resuspended in TRIzol (Invitrogen, USA), and RNA was purified according to the manufacturer’s instructions. cDNA was synthesized using a SuperScript™ III First-Strand Synthesis System (Invitrogen, USA). Quantitative real-time PCR (qRT-PCR) was performed in a Bio-Rad iCycler using 2x SYBR real-time PCR pre-mix (Takara Biotechnology Inc., Japan). The cycling program was as follows: 95°C for 90 s, followed by 40 cycles of 95°C for 10 s, 60°C for 10 s, and 72°C for 15 s. DNA-directed RNA polymerase α subunit rpoD was selected to normalize gene expression. The 2-∆∆CT method was used to evaluate relative gene expression in different strains and/or different treatments.16 The primers used are listed in Table S1. In vitro catalase activity assay Catalase activity was assayed as follows. Exponentially growing cultures were washed and resuspended in 0.05 M KH2PO4 buffer, pH7.0, containing 1 mM PMSF. Crude extracts were then prepared using a Mini-BeadBeater. Approximately 30 µg of each extract was loaded on a non-denaturing 5% polyacrylamide gel, and electrophoresis was performed at 80 V for 4 h at 4°C. Gels were washed with several changes of water for 1 h and soaked in 5 mM H2O2, followed by a brief wash in water and incubation in a 1:1 mixture of 2% FeCl3 and 2% potassium ferricyanide to detect catalase activity. Protein identification by LC-MS/MS


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Gel bands of proteins with catalase activity were excised for in-gel digestion and proteins were identified by mass spectrometry as previously described.17 Briefly, proteins were disulfide reduced with 25 mM dithiothreitol (DTT) and alkylated with 55 mM iodoacetamide. In-gel digestion was performed using sequencing grade-modified trypsin in 50 mM ammonium bicarbonate at 37°C overnight. The peptides were extracted twice with 1% trifluoroacetic acid in 50% acetonitrile aqueous solution for 30 min. The peptide extracts were then centrifuged in a Speedvac to reduce the volume. For LC-MS/MS analysis, digestion products were separated with a 60 min gradient elution at a flow rate 0.250 µl/min in an EASY-nLCII™ integrated nano-HPLC system (Proxeon, Denmark) which was directly interfaced with a Thermo LTQ-Orbitrap mass spectrometer. The analytical column was a homemade fused silica capillary (75 µm ID, 150 mm length; Upchurch, Oak Harbor, WA) packed with C-18 resin (300 Å, 5 µm, Varian, Lexington, MA). Mobile phase A consisted of 0.1% formic acid and mobile phase B consisted of 100% acetonitrile and 0.1% formic acid. The LTQ-Orbitrap mass spectrometer was operated in data-dependent acquisition mode using Xcalibur 2.0.7 software. We performed a single full-scan mass spectrum using the Orbitrap (400-1500 m/z, 30,000 resolution) followed by 20 data-dependent MS/MS scans in the ion trap at 35% normalized collision energy. Raw data files were processed to generate peak list files using Proteome Discover software (version 1.4). The protein database used for MS/MS searches was downloaded from UniProt (Mycobacterium bovis strain BCG/Pasetur 1173P2 on May 27 2014 and contained 3891 entries.). Experiments were repeated independently three times. INH-dependent nitroblue tetrazolium (NBT) reduction assay


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The level of INH activation by KatG was monitored using cell lysates derived from BCG-pMV261 and BCG-2013 as described previously.18 Briefly, cell lysates were prepared from BCG-pMV261 and BCG-2013 cultures (OD600 = 0.3) after treatment with INH. Lysate samples containing 40 µg of protein were analyzed on native gels. Each gel was immersed in 50 ml of 50 mM sodium phosphate (pH 7.0) containing 68 mg of INH, 12.5 mg of NBT and 15 µl of 30% H2O2 for 30 min. Gels were then soaked in distilled water containing 7% acetic acid and 1% glycerol. AtpD protein was used as a loading control for each strain. In addition, 40 µg of protein from lysates of bacteria harboring pMV261 or pMV261-BCG_2013 were separated on SDS-PAGE gels and analyzed by Western blotting using rabbit AtpD antibodies. Levels of KatG activity were evaluated using Quantity One software. Experiments were repeated three times. Quantitative proteomics analysis To compare protein levels between the BCG-pMV261 and BCG-2013 strains, TMTlabeled proteomic analysis was performed as previously described.19 Briefly, three independent 50-ml cultures (OD600 = 0.3) of the BCG-pMV261 and BCG-2013 strains were collected. Protein pellets were washed and resuspended in 0.05 M KH2PO4 buffer, pH7.0, containing 1 mM PMSF. Crude extracts were then prepared using a Mini-Bead Beater and protein concentrations were measured using the bicinchoninic acid protein assay reagent and a bovine serum albumin standard. About 100 µg total protein from the BCG-pMV261 and BCG-2013 strains were respectively separated briefly by 1D SDSPAGE and the resulting gel bands in each lane were cut into 2 equal-sized slices. The bands in each lane were also of the same size. Each gel slice was washed three times with ultrapure water. Gel bands were reduced with 25 mM DTT and alkylated with 55 mM


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iodoacetamide. In-gel digestion was then performed using sequencing-grade modified trypsin (Promega, Fitchburg, WI) in 50 mM ammonium bicarbonate at 37°C overnight. The peptides were extracted twice with 0.1% trifluoroacetic acid in 50% acetonitrile aqueous solution for 30 min. Extracts were then centrifuged in a Speedvac to reduce the volume. Tryptic peptides were dissolved in 50 µl 200 mM Tetraethylammonium Bromide (TEAB), and 10 µl TMT 6-plex labeling reagent (Thermo, Pierce Biotechnology, Rockford, IL) was added to each sample according to the manufacturer’s instructions. The reaction was incubated for 1 h at room temperature and then quenched with 0.5 µl of 5% hydroxylamine (pH 9 - 10) for 15 min. Equal amounts of proteins from the BCGpMV261 and BCG-2013 strains were combined and analyzed by LC-MS/MS. Peptides from BCG-pMV261 were labeled with TMT6-126, -127, and -128, and peptides from BCG-2013 were labeled with TMT6-129, -130, and -131. For LC-MS/MS analysis, the TMT-labeled peptides were separated by a 120-min gradient elution at a flow rate of 0.25 µL/min using an UltiMate 3000 RSLCnano System (Thermo Scientific, USA) directly interfaced with a Thermo Q Exactive benchtop mass spectrometer. The analytical column was a homemade fused silica capillary (75 µm ID, 150 mm length; Upchurch, Oak Harbor, WA) packed with C-18 resin (300 Å, 5 µm, Varian, Lexington, MA). Mobile phase A consisted of 0.1% formic acid and mobile phase B consisted of 100% acetonitrile and 0.1% formic acid. The Q Exactive Orbitrap mass spectrometer was operated in data-dependent acquisition mode using Xcalibur 2.1.2 software. A single full-scan mass spectrum, followed by 10 data-dependent MS/MS scans were acquired at 30% normalized collision energy (HCD) and with the Dymic exclusion parameter set at 20 s in the Orbitrap (300-1800 m/z, 70,000 resolution).


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The MS/MS spectra from each LC-MS/MS run were searched against the Mycobacterium bovis (strain BCG/Pasetur 1173P2) database from UniProt (downloaded on May 27, 2014; 3891 entries) using in-house Proteome Discoverer (PD) software, v. 1.4 (Thermo Fisher Scientific, USA). The 247 common laboratory contaminants were obtained from the Max Planck Institute of Biochemistry and were added to the M. bovis search database for the MS/MS ion search. Search algorithms SequestHT (in the Proteome Discoverer software package version 1.4) were used to analyze the data. The search parameters were as follows: (a) one missed cleavage was allowed; (b) carbamidomethylation of cysteine residues and TMT sixplex (K and N-terminal) were fixed posttranslational modifications; (c) oxidation of methionine was set as the variable modification; (d) precursor ion mass tolerance was set at 10 ppm for all mass spectra acquired in an orbitrap mass analyzer; and the fragment ion mass tolerance was set at 20 mmu for the MS/MS spectra acquired. The peptide false discovery rate (FDR) was calculated using Percolator in Proteome Discoverer. Peptide spectrum match (PSM) was considered to be correct if the q value was smaller than 1%. The false discovery rate was determined based on the peptide spectrum match when searched against the reverse, decoy database. The false discovery rate was set to 0.01 for proteins considered to be unique. Peptides that only mapped to a given protein group were considered as unique. Relative protein quantification was carried out using Proteome Discoverer (Version 1.4) according to the manufacturer’s instructions on the six reporter ion intensities per peptide. Quantitation was performed for proteins with at least two unique identified peptides. Quantitative precision was expressed as protein ratio variability. When the


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protein ratio variability was smaller than 30%, the ratios were accepted as the quantitative ratios of proteins. Differentially expressed proteins were further confirmed by qPCR. The Wayne model of hypoxia-induced mycobacterial dormancy M. bovis BCG strains were cultured under relatively hypoxic conditions as described by Wayne and Hayes.20, 21 Briefly, cultures of BCG strains were grown in 7H9 media at 37°C to an OD600 of 1.0. Cultures were then inoculated at a concentration of 1 x 106 CFU/ml (initial OD600 of 0.02) in anaerobic bottles, which were then sealed tightly. The headspace ratio of the cultures was 0.5 as defined by the Wayne model, and methylene blue (1.5 mg/L) was used as an indicator of reduced oxygen tension. All cultures were prepared in triplicate. Cells were collected when methylene blue turned from blue to colorless. As the OD600 reached 0.2 when methylene blue became colorless, we used cultures grown aerobically to an OD600 of 0.2 as controls. Statistical analyses All statistical analyses were performed using GraphPad Prism 5.0c software. Significant differences in the data were determined by t-tests.

Results Overexpression of BCG_2013, an M. tuberculosis Rv1996 homolog, increases susceptibility to INH in M. bovis BCG Rv1996 belongs to the USP family, the functions of which are unknown. In a previous study on the functions of USPs in M. tuberculosis, no differences in growth were observed in an rv1996-knockout mutant of M. tuberculosis under various stress conditions tested, and the effects of the knockout strain on THP1 human monocyte-


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derived cells were comparable to those of the wild-type strain. Other M. tuberculosis USPs may have masked the effects of Rv1996 in these experiments.10 Conversely, overexpressing Rv1996 might increase its effects above those of the other USPs and provide insights into the functions of Rv1996. Microarray studies have indicated that Rv1996 is induced in response to hypoxia, nitric oxide and carbon monoxide.22-24 Overexpression of Rv1996 may mimic this physiological status. Here, we used M. bovis BCG to explore the functions of M. tuberculosis Rv1996 as the rv1995/rv1996/rv1997 cluster in M. tuberculosis is identical to the BCG_2012/BCG_2013/BCG_2014 cluster in M. bovis BCG. We overexpressed BCG_2013 in BCG and compared its growth rate with the wild-type strain under different stressors to define its possible biological functions. BCG_2013 from M. bovis BCG was cloned into pMV261, a mycobacterial overexpression vector, and the pMV261-BCG_2013 construct was transformed into M. bovis BCG to generate a BCG_2013-overexpression strain (denoted BCG-2013). M. bovis BCG carrying the empty vector pMV261 (BCG-pMV261) was employed as a control. The MICs of INH differed in BCG-pMV261 and BCG-2013 (Figure 1A); the MIC of INH was 0.025 mg/L against BCG-2013 and 0.05 mg/L against the control strain BCG-pMV261. To clarify the effect of BCG_2013 on INH resistance, we performed drug exposure experiments to compare the growth rates of BCG-pMV261 and BCG-2013. INH killing curves (Figure 1B) indicated that the killing effect of INH for BCG-pMV261 was maximal 2 day after INH treatment, and then the bacteria started to grow again, while the killing effect of INH for BCG-2013 was apparent by 2 day and continued for 6 days after INH treatment. Overexpression of BCG_2013 resulted in an approximately 1.4-log increase in killing by INH compared with BCG-pMV261. By contrast, no


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difference in the growth of BCG-2013 and BCG-pMV261 was observed in 7H9 medium in the absence of INH (Figure 1B). These results indicate that susceptibility to INH is affected by BCG_2013 overexpression. BCG_2013 overexpression does not affect the susceptibility of M. bovis BCG to ETH ETH is an anti-TB drug that inhibits InhA activity by a mechanism similar to that of INH but does not require activation by KatG.25 We determined the MIC of ETH in both BCG2013 and BCG-pMV261 to investigate the potential involvement of BCG_2013 in an inhA-dependent pathway. The MIC of ETH was 1.25 mg/L in both strains, suggesting that BCG_2013 is likely not involved in an inhA-dependent pathway, and that increased susceptibility to INH in BCG-2013 is likely KatG dependent. Overexpression of BCG_2013 increases KatG activity in vivo The above results suggest that BCG_2013 overexpression induces INH susceptibility via KatG in an InhA-independent manner. As KatG is a catalase-peroxidase enzyme and KatG is the only catalase in slow-growing mycobacteria,26 we measured the catalase activity of both BCG-2013 and BCG-pMV261. The catalase activity of the BCG-2013 lysate was 908.97 ± 24.4 units/mg, while that of BCG-pMV261 was 377.08 ± 8.6 units/mg (Figure 2A). Comparison of the catalase activities of KatG in BCG-2013 and BCG-pMV261 using in-gel assays confirmed that the catalase activity of KatG was 1.8fold higher in BCG-2013 compared with BCG-pMV261 during normal growth (Figure 2B). We used AtpD as a loading control to confirm that the protein concentrations were equal for the two strains. The gel bands corresponding to the catalase activity were identified as KatG using an LTQ-Orbitrap mass spectrometer (Figure 2C). All fragment ions matched the expected b and y ions of TDASQEQTDVESFAVLEPK from KatG


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(Figure 2C upper panel), and 77% of the KatG sequence was covered by MS/MS search (Figure 2C lower panel). The level of activation of INH by KatG in each strain estimated in the corresponding cell lysates using the INH-dependent nitroblue tetrazolium (NBT) reduction assay was higher in BCG-2013 than BCG-pMV261, indicating that the overexpression of BCG_2013 increased the level of activation of INH by increasing KatG activity (Figure S1). Taken together, these results indicate that BCG_2013 overexpression increases the activity of KatG and causes an increase in bacterial susceptibility to INH. Determination of BCG-2013-induced changes in protein expression To explore the effect of BCG_2013 on protein expression, we compared protein levels in BCG-pMV261 and BCG-2013 in the early log phase (OD600 = 0.3). Using TMT labeling and LC-MS/MS, we identified approximately 1500 proteins in each sample in three repeated experiments. We identified 50 up-regulated proteins and 26 down-regulated proteins with two or more unique peptides based on their TMT ratios (>1.5 or < 0.7) (Tables S2 & S3). The false-positive rate was estimated to be less than 1%. To determine the biological relevance of these proteins, Functional Annotation Clustering was used to cluster the differentially expressed proteins using the DAVID Bioinformatics Resource 6.7 Analysis Wizard.27 Against the whole genome, the 76 differentially expressed proteins were classified into 8: translation, stress response, protein transport, cellular carbohydrate catabolic process, metal ion binding, cell membrane, carboxylic acid biosynthetic

process,

GTP-binding,

ATPase

and

NAD-binding

domain.

The

corresponding information of the p value and count was obtained from by DAVID Bioinformatics Resource. We found that differentially proteins identified were enriched


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for the function of translation (p = 2.0 x 10-5), stress response (p = 2.9 x 10-9) (Figure 3, supplemental Table S4). Protein levels of BCG_2013 increased 3.0 ± 0.6 fold in BCG2013 compared to BCG-pMV261 (Table 1 and Figure S2). With respect to KatG, we identified 25 unique KatG peptides covering 49% of the KatG sequence. The ratios of these peptides in BCG-pMV261 and BCG-2013 ranged from 1.5 to 2.4 with a mean of 1.9 and a standard error of 0.2, and the MS/MS spectrum from the proteomic analysis matched the sequence AAGHNITVPFTPGR in KatG (Figure 4A). qPCR analysis indicated that the mRNA level of katG increased by a factor of 2.36 ± 0.16 in BCG-2013 compared to BCG-pMV261 (Figure 4B). However, no differences were observed in the levels of other proteins reportedly involved in INH resistance, including InhA, AhpC, Fur, KasA, Rv0340, IniB, IniA, IniC, EfpA, MabA, and Lsr2 (data not shown). Among the differentially expressed proteins, Acr2, a protein reported to contribute to persistent infection,28 was induced most highly in BCG-2013. Interestingly, MprA, a mycobacterial persistence regulator, was down-regulated (Table 1). qPCR was performed to confirm the results of the quantitative proteomic analysis (Figure 5). The mRNA expression levels of BCG_2013, katG, acr2, mihF were up-regulated in BCG-2013, consistent with the changes in the expression of the corresponding proteins (Figure 5 A-D). Similarly, the mRNA levels of Mpr2, nusG, oxi, pstS1, and ald were down-regulated in BCG-2013, consistent with the changes in the expression of the corresponding proteins (Figure 5 EI). Confirmation of the differential expression of proteins in BCG-2013 in the Wayne dormancy model As BCG_2013 belongs to the DevR regulon, a dormancy regulon, we used a Wayne


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dormancy model and qPCR to examine the mRNA levels of proteins identified as differentially expressed in BCG-2013.11, 20, 29 The mRNA level of BCG_2013 increased by 31.68 ± 9.28-fold, compatible with the increase in the mRNA level of BCG_2013 observed in BCG-2013 (Figure 6A). The relative mRNA expression of katG increased by 2.73 ± 0.23-fold in the Wayne model, whereas the ratio of mRNA expression in BCG2013 compared to BCG-pMV261 was approximately 2.36 ± 0.23 (Figure 6B). MprA expression decreased by 2.50 ± 0.11-fold in the Wayne model, similar to the decrease in the mRNA level observed in BCG-2013 (Figure 6D). A large increase in acr2 of 65.37 ± 6.88-fold was also observed (Figure 6C).

Discussion In this study, we have described a latency protein, the USP BCG_2013, which has been associated with INH resistance in mycobacteria. Our results demonstrate that overexpression of the homolog of M. tuberculosis-rv1996 in M. bovis, BCG_2013, increases KatG expression, which in turn decreases mycobacterial INH resistance. INH is an important anti-tuberculosis drug that kills active and latent bacilli. INH is a pro-drug that is activated by the mycobacterial enzyme KatG.3 The mechanism of INH resistance is complex and remains to be fully elucidated. A recent study demonstrated that INH resistance is growth phase dependent. In stationary phase, the protein level of the DNA-binding protein MDP1 increases, resulting in down-regulation of KatG and INH tolerance.18 Results from the above study suggest that phenotypic INH drug tolerance is a potential mechanism of INH resistance. As a successful pathogen, M. tuberculosis can sense a stringent environment, switch its regulatory system and enter


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latency. Rv1996 belongs to the USP family and is regulated by the dormancy regulator DosR.11 It is present in a deletion hot spot of the genome,8 further suggesting that rv1996 may be related to drug resistance. In this study, we explored the role of the M. bovis Rv1996 homolog, BCG_2013, in INH resistance in BCG. We observed differences in both the MIC of INH and bacterial growth in the presence of INH in BCG-pMV261 and BCG-2013 (Figure 1), but not in the MIC of ETH, indicating that BCG_2013 is involved in KatG-dependent INH resistance. Furthermore, our enzyme assays support the conclusion that overexpression of BCG_2013 induces an increase in KatG activity and, consequently, INH susceptibility in vivo (Figure 2 and Figure S1). Quantitative proteomic analysis revealed that the KatG protein level increased in BCG-2013 compared with BCG-pMV261 (Figure 4A). Moreover, expression of MprA (Mycobacterium persistence regulator) was down-regulated in BCG2013 compared with BCG-pMV261. The mprA/mprB locus is highly conserved in all Mycobacterium strains, including M. tuberculosis, M. bovis, M. marinum, and M. smegmatis,30, 31 and MprA is required for entrance into and maintenance of persistent infection.32 MprA positively or negatively regulates approximately 200 genes in M. tuberculosis.33, 34 A previous study revealed that MprA directly regulates Acr2, the most highly induced protein in BCG-2013.28 Other differentially expressed proteins were also identified, including Mce1B (A1KEZ0), AtpC (A1KI99), AcpM (A1KKT7) and Ffh (A1KMR0). In the Wayne dormancy model, in which bacteria were cultured under gradually decreasing oxygen tensions to adjust to decreased oxygen and enter a latent state,20, 21 we observed an increase in the mRNA levels of katG and BCG_2013 and a decrease in the


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mRNA level of MprA in hypoxic cultures compared with aerobic cultures of M. bovis BCG (Figure 6). This study is the first to demonstrate a relationship between a latency gene and drug resistance. The results of our proteomic analysis suggest that increased BCG_2013 expression leads to a decrease in MprA expression, followed by up-regulation of KatG and, consequently, increased INH susceptibility.

References （1）Zumla, A.; Raviglione, M.; Hafner, R.; von Reyn, C. F., Tuberculosis. N. Engl. J. Med. 2013, 368 (8), 745-55. （ 2 ） Vilcheze, C.; Jacobs, W. R., Jr., The mechanism of isoniazid killing: clarity through the scope of genetics. Annu. Rev. Microbiol. 2007, 61, 35-50. （3）Bardou, F.; Raynaud, C.; Ramos, C.; Laneelle, M. A.; Laneelle, G., Mechanism of isoniazid uptake in Mycobacterium tuberculosis. Microbiology 1998, 144 ( Pt 9), 253944. （4）Shoeb, H. A.; Bowman, B. U., Jr.; Ottolenghi, A. C.; Merola, A. J., Peroxidasemediated oxidation of isoniazid. Antimicrob. Agents Chemother. 1985, 27 (3), 399-403. （5）Bulatovic, V. M.; Wengenack, N. L.; Uhl, J. R.; Hall, L.; Roberts, G. D.; Cockerill, F. R., 3rd; Rusnak, F., Oxidative stress increases susceptibility of Mycobacterium tuberculosis to isoniazid. Antimicrob. Agents Chemother. 2002, 46 (9), 2765-71. （ 6 ） Zhang, Y.; Yew, W. W., Mechanisms of drug resistance in Mycobacterium tuberculosis. Int. J. Tuberc. Lung Dis. 2009, 13 (11), 1320-30. （7）Laurenzo, D.; Mousa, S. A., Mechanisms of drug resistance in Mycobacterium tuberculosis and current status of rapid molecular diagnostic testing. Acta Trop. 2011, 119 (1), 5-10. （8）Tsolaki, A. G.; Hirsh, A. E.; DeRiemer, K.; Enciso, J. A.; Wong, M. Z.; Hannan, M.; Goguet de la Salmoniere, Y. O.; Aman, K.; Kato-Maeda, M.; Small, P. M., Functional and evolutionary genomics of Mycobacterium tuberculosis: insights from genomic deletions in 100 strains. Proc. Natl. Acad. Sci. U. S. A. 2004, 101 (14), 4865-70. （9）Nachin, L.; Nannmark, U.; Nystrom, T., Differential roles of the universal stress proteins of Escherichia coli in oxidative stress resistance, adhesion, and motility. J. Bacteriol. 2005, 187 (18), 6265-72. （10）Hingley-Wilson, S. M.; Lougheed, K. E.; Ferguson, K.; Leiva, S.; Williams, H. D., Individual Mycobacterium tuberculosis universal stress protein homologues are dispensable in vitro. Tuberculosis (Edinb) 2010, 90 (4), 236-44. （11）Park, H. D.; Guinn, K. M.; Harrell, M. I.; Liao, R.; Voskuil, M. I.; Tompa, M.; Schoolnik, G. K.; Sherman, D. R., Rv3133c/dosR is a transcription factor that mediates


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the hypoxic response of Mycobacterium tuberculosis. Mol. Microbiol. 2003, 48 (3), 83343. （12）Drumm, J. E.; Mi, K.; Bilder, P.; Sun, M.; Lim, J.; Bielefeldt-Ohmann, H.; Basaraba, R.; So, M.; Zhu, G.; Tufariello, J. M.; Izzo, A. A.; Orme, I. M.; Almo, S. C.; Leyh, T. S.; Chan, J., Mycobacterium tuberculosis universal stress protein Rv2623 regulates bacillary growth by ATP-Binding: requirement for establishing chronic persistent infection. PLoS Pathog. 2009, 5 (5), e1000460. （13）Singh, S., Bio-safety precautions in tuberculosis laboratory. Indian J. Tuberc. 2013, 60 (3), 135-7. （14）Jacobs, W. R., Jr.; Kalpana, G. V.; Cirillo, J. D.; Pascopella, L.; Snapper, S. B.; Udani, R. A.; Jones, W.; Barletta, R. G.; Bloom, B. R., Genetic systems for mycobacteria. Methods Enzymol. 1991, 204, 537-55. （15）Wallace, R. J., Jr.; Nash, D. R.; Steele, L. C.; Steingrube, V., Susceptibility testing of slowly growing mycobacteria by a microdilution MIC method with 7H9 broth. J. Clin. Microbiol. 1986, 24 (6), 976-81. （16）Livak, K. J.; Schmittgen, T. D., Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 2001, 25 (4), 402-8. （17）Li, X.; Tao, J.; Han, J.; Hu, X.; Chen, Y.; Deng, H.; Zhang, G.; Hu, X.; Mi, K., The gain of hydrogen peroxide resistance benefits growth fitness in mycobacteria under stress. Protein Cell 2014, 5 (3), 182-5. （18）Niki, M.; Niki, M.; Tateishi, Y.; Ozeki, Y.; Kirikae, T.; Lewin, A.; Inoue, Y.; Matsumoto, M.; Dahl, J. L.; Ogura, H.; Kobayashi, K.; Matsumoto, S., A novel mechanism of growth phase-dependent tolerance to isoniazid in mycobacteria. J. Biol. Chem. 2012, 287 (33), 27743-52. （19）Jin, L.; Huo, Y.; Zheng, Z.; Jiang, X.; Deng, H.; Chen, Y.; Lian, Q.; Ge, R.; Deng, H., Down-regulation of Ras-related protein Rab 5C-dependent endocytosis and glycolysis in cisplatin-resistant ovarian cancer cell lines. Mol. Cell. Proteomics 2014, 13 (11), 313851. （20）Wayne, L. G.; Hayes, L. G., An in vitro model for sequential study of shiftdown of Mycobacterium tuberculosis through two stages of nonreplicating persistence. Infect. Immun. 1996, 64 (6), 2062-9. （21）Wayne, L. G., In Vitro Model of Hypoxically Induced Nonreplicating Persistence of Mycobacterium tuberculosis. Methods Mol. Med. 2001, 54, 247-69. （22）Voskuil, M. I.; Schnappinger, D.; Visconti, K. C.; Harrell, M. I.; Dolganov, G. M.; Sherman, D. R.; Schoolnik, G. K., Inhibition of respiration by nitric oxide induces a Mycobacterium tuberculosis dormancy program. J. Exp. Med. 2003, 198 (5), 705-13. （23）Shiloh, M. U.; Manzanillo, P.; Cox, J. S., Mycobacterium tuberculosis senses host-derived carbon monoxide during macrophage infection. Cell Host Microbe 2008, 3 (5), 323-30. （24）Kumar, A.; Deshane, J. S.; Crossman, D. K.; Bolisetty, S.; Yan, B. S.; Kramnik, I.; Agarwal, A.; Steyn, A. J., Heme oxygenase-1-derived carbon monoxide induces the Mycobacterium tuberculosis dormancy regulon. J. Biol. Chem. 2008, 283 (26), 18032-9. （25）Banerjee, A.; Dubnau, E.; Quemard, A.; Balasubramanian, V.; Um, K. S.; Wilson, T.; Collins, D.; de Lisle, G.; Jacobs, W. R., Jr., inhA, a gene encoding a target for


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isoniazid and ethionamide in Mycobacterium tuberculosis. Science 1994, 263 (5144), 227-30. （ 26 ） Ando, H.; Kitao, T.; Miyoshi-Akiyama, T.; Kato, S.; Mori, T.; Kirikae, T., Downregulation of katG expression is associated with isoniazid resistance in Mycobacterium tuberculosis. Mol. Microbiol. 2011, 79 (6), 1615-28. （27）Huang da, W.; Sherman, B. T.; Lempicki, R. A., Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 2009, 4 (1), 44-57. （28）Pang, X.; Howard, S. T., Regulation of the alpha-crystallin gene acr2 by the MprAB two-component system of Mycobacterium tuberculosis. J. Bacteriol. 2007, 189 (17), 6213-21. （ 29 ） Sherman, D. R.; Voskuil, M.; Schnappinger, D.; Liao, R.; Harrell, M. I.; Schoolnik, G. K., Regulation of the Mycobacterium tuberculosis hypoxic response gene encoding alpha -crystallin. Proc. Natl. Acad. Sci. U. S. A. 2001, 98 (13), 7534-9. （ 30 ） Bretl, D. J.; Bigley, T. M.; Terhune, S. S.; Zahrt, T. C., The MprB extracytoplasmic domain negatively regulates activation of the Mycobacterium tuberculosis MprAB two-component system. J. Bacteriol. 2014, 196 (2), 391-406. （31）Bretl, D. J.; Demetriadou, C.; Zahrt, T. C., Adaptation to environmental stimuli within the host: two-component signal transduction systems of Mycobacterium tuberculosis. Microbiol. Mol. Biol. Rev. 2011, 75 (4), 566-82. （32）Zahrt, T. C.; Deretic, V., Mycobacterium tuberculosis signal transduction system required for persistent infections. Proc. Natl. Acad. Sci. U. S. A. 2001, 98 (22), 12706-11. （33）He, H.; Hovey, R.; Kane, J.; Singh, V.; Zahrt, T. C., MprAB is a stress-responsive two-component system that directly regulates expression of sigma factors SigB and SigE in Mycobacterium tuberculosis. J. Bacteriol. 2006, 188 (6), 2134-43. （34）Pang, X.; Vu, P.; Byrd, T. F.; Ghanny, S.; Soteropoulos, P.; Mukamolova, G. V.; Wu, S.; Samten, B.; Howard, S. T., Evidence for complex interactions of stressassociated regulons in an mprAB deletion mutant of Mycobacterium tuberculosis. Microbiology 2007, 153 (Pt 4), 1229-42.

Supporting Information Figure S1: Level of KatG activation of INH. Figure S2: MS and MS/MS spectra for the peptide IVEHSYLVAQAHQIVEQAHK from BCG_2013. Table S1: Oligonucleotide primers used in this study. Table S2: Proteins up-regulated in BCG-2013 compared with BCG-pMV261. Table S3: Proteins down-regulated in BCG-2013 compared with BCGpMV261. Table S4: Functional Annotation Clustering of the differentially proteins expressed between BCG-Rv1996 and BCG-pMV261.This material is available free of


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charge via the Internet at http://pub.acs.org Funding This work was supported by the National Basic Research Program of China (2012CB518700 and 2014CB744400), the National Natural Science Foundation of China (31270178 and 31070118), the Key Program of the Chinese Academy of Sciences (KJZD-EW-L02); and the Chinese Academy of Sciences Visiting Professorship (2010T1S18 to John Chan). Acknowledgments We thank Deng Jiaoyu for the gift of the rabbit antibody against AtpD. Figure Legends Figure 1. Comparison of INH susceptibility in strain BCG-2013 and the control strain BCG-pMV261. A. Broth microdilution method. The minimum inhibitory concentration of isoniazid was determined by inoculating each bacterial strain in 7H9 media containing serially diluted INH. MICs determinations were repeated 10 times. B. INH mycobacterial killing curve. Mycobacterial cells were grown to mid-log phase (OD600 of 0.6-0.8) and diluted approximately 107-fold in fresh medium. After addition of 0.5 mg/L INH, aliquots were removed at the times indicated times and plated on 7H10ADS. Experiments were performed in triplicate. Triangles and dashed line: BCGpMV261 strain; circles and solid line: BCG-2013 strain; squares and dashed line: BCGpMV261 strain in the presence of 0.5 mg/L INH; and asterisks and solid line: BCG-strain in the presence of 0.5 mg/L INH. Standard deviations are indicated by error bars. T-tests were

performed

using

online


GraphPad

software

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(http://www.graphpad.com/quickcalcs/ttest1.cfm); *** P < 0.001, ** P < 0.01 Figure 2.The effect of BCG_2013 overexpression on KatG enzyme expression and INH activation. A) Catalase activity in whole protein lysates from BCG-2013 and BCGpMV261. Catalase activity was assayed on mycobacterial cells grown to early log phase (OD600 of 0.3) Experiments were performed in triplicate. The standard deviation is indicated by error bars. B) Catalase activity assay. Total cell lysates from BCG-pMV261 and BCG-2013 were analyzed by separation on native gels. Catalase activity appeared as a clear area on a dark green background after staining the gels with 1% FeCl3 and 2% potassium ferricyanide. Figures shown are representative of 10 experiments. C) Identification of KatG by mass spectrometry. Samples of whole lysates, approximately 50 µg protein each, were separated in non-denaturing 7.4% polyacrylamide gels. The MS/MS spectrum was matched

to the sequence

TDASQEQTDVESFAVLEPK by searching the M. bovis protein database. The highlighted peak matches the sequence of KatG. The underlined peptides were identified by MS/MS searching of the bands covering 77% of the KatG sequence. Figure 3. Functional classification of proteins differentially expressed in BCG-pMV261 and

BCG-2013

using

the

DAVID

Analysis

Wizard

(http://david.abcc.ncifcrf.gov/tools.jsp). Figure 4. BCG_2013 affects the expression of KatG at the mRNA and protein levels. BCG-pMV261 and BCG-2013 cells were grown at 37°C to an OD600 of 0.3. A) Quantitative comparison of KatG expression using MS/MS. B) The katG mRNA level was analyzed by qRT-PCR. Experiments were performed in triplicate. The error bars


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indicate the standard deviation. Figure 5. Verification of differentially expressed proteins in BCG-pMV261 and BCG2013 by q-PCR. (A) qPCR analysis of the mRNA expression of BCG_2013. (B) qPCR analysis of the mRNA expression of katG. (C) qPCR analysis of the mRNA expression of acr2. (D) qPCR analysis of the mRNA expression of mhiF. (E) qPCR analysis of the mRNA expression of Oxi. (F) qPCR analysis of the mRNA expression of MprA. (G) qPCR analysis of the mRNA expression of nusG. (H) qPCR analysis of the mRNA expression of pstS1. (I) qPCR analysis of the mRNA expression of ald. * P < 0.05, ** P < 0.01, *** P < 0.001. Figure 6. Relative mRNA levels in bacteria in the Wayne model. (A) qPCR analysis of the mRNA expression of BCG_2013. (B) qPCR analysis of the mRNA expression of katG. (C) qPCR analysis of the mRNA expression of acr2. (D) qPCR analysis of the mRNA expression of MprA. * P < 0.05, ** P < 0.01, *** P < 0.001. Table 1. Selected proteins that were differentially expressed in BCG-Rv1996 and BCGpMV261 Figure S1 Level of KatG activation of INH. The level of activation of INH by KatG in BCG-pMV261 and BCG-2013 was examined by the NBT reduction assay. Samples were collected at an OD600 of 0.3 after treatment with INH, and 40 µg of protein from whole-cell extracts was analyzed on native gels. AtpD was used as a loading control.

Figure S2 MS and MS/MS spectra for the peptide IVEHSYLVAQAHQIVEQAHK from


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BCG_2013. TMT6-128 represents BCG-pMV261, and TMT6-129 represents BCG-2013. Table S1 Oligonucleotide primers used in this study Table S2 Proteins up-regulated in BCG-2013 compared with BCG-pMV261 Table S3 Proteins down-regulated in BCG-2013 compared with BCG-pMV261 Table S4 Functional Annotation Clustering of the differentially proteins expressed between BCG-Rv1996 and BCG-pMV261

Table 1. Selected proteins that were differentially expressed in BCG-Rv1996 and BCG-pMV261*

Accession A1KK40

Protein Description Universal stress protein BCG_2013

A1KJX3

Score 2401

Coverage 80

Peptides matched 16

Variability（%） 2.0-24.6

Ratio± SD 3.0± 0.6

Catalase-peroxidase KatG

409

49

25

0.3-21.2

1.9± 0.2

A1KF36

Putative uncharacterized protein BCG_0253

95

26

7

5.4-25.2

0.7± 0.2

A1KGB9

Transcription antitermination protein NusG

89

56

7

2.3-16.6

0.6± 0.1

A1KIH7

Putative integration host factor MihF

78

30

7

4.1-22.4

1.7± 0.1

A1KF72 A1KH68

Heat shock protein Acr2 Periplasmic phosphatebinding lipoprotein PstS1

40 28

57 12

7 2

15-28.2 0.3-25.7

3.3± 0.3 0.6± 0.1

A1KN45

Ribonucleoside-diphosphate reductase (Beta chain) NrdF2

22

13

3

3.9-11.8

0.7± 0.1

A1KHB7

Response regulator MprA

20

26

2

0.6-17.1

0.5± 0.1

A1KQB1 A1KJ77

Putative oxidoreductase Possible chalcone synthase Pks10

19 17

17 10

2 2

2.0-7.1 2.2-17.9

0.7± 0.1 0.6± 0.1

A1KPL9

4-hydroxy-2-oxovalerate aldolase

11

14

2

6.0-22.2

0.5± 0.1

* The “Peptides Matched” was assigned when the criteria were as followed: 1) quantitation was performed for proteins with at least two unique identified peptides. 2) quantitative precision was expressed as protein ratio variability. When the protein ratio variability was smaller than 30%, the ratios were accepted as the quantitative ratios of proteins.


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Figure 1 159x88mm (300 x 300 DPI)



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Figure 2 159x121mm (300 x 300 DPI)


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Figure 3 192x132mm (150 x 150 DPI)



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Figure 5 159x120mm (300 x 300 DPI)



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Abstract Graphic 62x43mm (600 x 600 DPI)


Quantitative Proteomics Reveals Novel Insights into Isoniazid

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