Profiling the Proteome of Mycobacterium tuberculosis during Dormancy and Reactivation
2015; Elsevier BV; Volume: 14; Issue: 8 Linguagem: Inglês
10.1074/mcp.m115.051151
ISSN1535-9484
AutoresVipin Gopinath, Sajith Raghunandanan, Roshna Lawrence Gomez, Leny Jose, Arun Surendran, Ranjit Ramachandran, Akhil Raj Pushparajan, Sathish Mundayoor, Abdul Jaleel, Ramakrishnan Ajay Kumar,
Tópico(s)Gut microbiota and health
ResumoTuberculosis, caused by Mycobacterium tuberculosis, still remains a major global health problem. The main obstacle in eradicating this disease is the ability of this pathogen to remain dormant in macrophages, and then reactivate later under immuno-compromised conditions. The physiology of hypoxic nonreplicating M. tuberculosis is well-studied using many in vitro dormancy models. However, the physiological changes that take place during the shift from dormancy to aerobic growth (reactivation) have rarely been subjected to a detailed investigation. In this study, we developed an in vitro reactivation system by re-aerating the virulent laboratory strain of M. tuberculosis that was made dormant employing Wayne's dormancy model, and compared the proteome profiles of dormant and reactivated bacteria using label-free one-dimensional LC/MS/MS analysis. The proteome of dormant bacteria was analyzed at nonreplicating persistent stage 1 (NRP1) and stage 2 (NRP2), whereas that of reactivated bacteria was analyzed at 6 and 24 h post re-aeration. Proteome of normoxially grown bacteria served as the reference. In total, 1871 proteins comprising 47% of the M. tuberculosis proteome were identified, and many of them were observed to be expressed differentially or uniquely during dormancy and reactivation. The number of proteins detected at different stages of dormancy (764 at NRP1, 691 at NRP2) and reactivation (768 at R6 and 983 at R24) was very low compared with that of the control (1663). The number of unique proteins identified during normoxia, NRP1, NRP2, R6, and R24 were 597, 66, 56, 73, and 94, respectively. We analyzed various biological functions during these conditions. Fluctuation in the relative quantities of proteins involved in energy metabolism during dormancy and reactivation was the most significant observation we made in this study. Proteins that are up-regulated or uniquely expressed during reactivation from dormancy offer to be attractive targets for therapeutic intervention to prevent reactivation of latent tuberculosis. Tuberculosis, caused by Mycobacterium tuberculosis, still remains a major global health problem. The main obstacle in eradicating this disease is the ability of this pathogen to remain dormant in macrophages, and then reactivate later under immuno-compromised conditions. The physiology of hypoxic nonreplicating M. tuberculosis is well-studied using many in vitro dormancy models. However, the physiological changes that take place during the shift from dormancy to aerobic growth (reactivation) have rarely been subjected to a detailed investigation. In this study, we developed an in vitro reactivation system by re-aerating the virulent laboratory strain of M. tuberculosis that was made dormant employing Wayne's dormancy model, and compared the proteome profiles of dormant and reactivated bacteria using label-free one-dimensional LC/MS/MS analysis. The proteome of dormant bacteria was analyzed at nonreplicating persistent stage 1 (NRP1) and stage 2 (NRP2), whereas that of reactivated bacteria was analyzed at 6 and 24 h post re-aeration. Proteome of normoxially grown bacteria served as the reference. In total, 1871 proteins comprising 47% of the M. tuberculosis proteome were identified, and many of them were observed to be expressed differentially or uniquely during dormancy and reactivation. The number of proteins detected at different stages of dormancy (764 at NRP1, 691 at NRP2) and reactivation (768 at R6 and 983 at R24) was very low compared with that of the control (1663). The number of unique proteins identified during normoxia, NRP1, NRP2, R6, and R24 were 597, 66, 56, 73, and 94, respectively. We analyzed various biological functions during these conditions. Fluctuation in the relative quantities of proteins involved in energy metabolism during dormancy and reactivation was the most significant observation we made in this study. Proteins that are up-regulated or uniquely expressed during reactivation from dormancy offer to be attractive targets for therapeutic intervention to prevent reactivation of latent tuberculosis. Tuberculosis (TB)1 remains a major global health problem despite Bacillus Calmette–Guérin (BCG) vaccination and effective drug therapy for more than half a century. Worldwide 8.6 million individuals are infected with the etiologic agent Mycobacterium tuberculosis (MTB) (1.WHO T. B. f. S World Health Organization (WHO). 2012; Google Scholar). Among the infected individuals, only about 10% develop active TB at some point of their lifetime (2.Gengenbacher M. Kaufmann S.H.E. Mycobacterium tuberculosis: success through dormancy.FEMS Microbiol. Rev. 2012; 36: 514-532Crossref PubMed Scopus (468) Google Scholar). Majority of MTB infections results in latent TB, where the bacteria remain in a dormant state in granulomas (3.Gorna A.E. Bowater R.P. Dziadek J. DNA repair systems and the pathogenesis of Mycobacterium tuberculosis: varying activities at different stages of infection.Clin. Sci. 2010; 119: 187-202Crossref PubMed Scopus (50) Google Scholar). Hypoxia in the fibrotic granulomatus lesions in the lung is one of the factors that triggers dormancy (4.Boshoff H. Barry 3rd, C. Tuberculosis – metabolism and respiration in the absence of growth.Nat. Rev. Microbiol. 2005; 3: 70-80Crossref PubMed Scopus (376) Google Scholar, 5.Wayne L.G. Dormancy of Mycobacterium tuberculosis and latency of disease.Eur. J. Clin. Microbiol. Infect. 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The physiology of hypoxic nonreplicating MTB has been studied extensively in vitro, by employing the Wayne's model of dormancy in which MTB is subjected to a self-generated oxygen-depletion in sealed glass tubes (8.Dick T. Dormant tubercle bacilli: the key to more effective TB chemotherapy?.J. Antimicrob. Chemother. 2001; 47: 117-118Crossref PubMed Google Scholar). Two nonreplicating stages are identified in this model—a microaerophilic stage termed nonreplicating persistence stage 1 (NRP1) that exists between the 8th and 14th days (192–336 h), and an anaerobic stage designated as nonreplicating persistence stage 2 (NRP2) from the 14th day onwards (9.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: 2062-2069Crossref PubMed Google Scholar). Under these conditions MTB is found to undergo drastic changes in its energy and metabolic status (10.Rao S.P.S. Alonso S. Rand L. Dick T. Pethe K. The protonmotive force is required for maintaining ATP homeostasis and viability of hypoxic, nonreplicating Mycobacterium tuberculosis.Proc. Natl. Acad. Sci. U.S.A. 2008; 105: 11945-11950Crossref PubMed Scopus (383) Google Scholar, 11.Shi L. Sohaskey C. Kana B. Dawes S. North R. Changes in energy metabolism of Mycobacterium tuberculosis in mouse lung and under in vitro conditions affecting aerobic respiration.Proc. Natl. Acad. Sci. U.S.A. 2005; 102: 15629-15634Crossref PubMed Scopus (246) Google Scholar). In addition to Wayne's dormancy model, various in vitro hypoxic models are used to study dormancy in MTB (12.Starck J. Kallenius G. Marklund B.I. Andersson D.I. Akerlund T. Comparative proteome analysis of Mycobacterium tuberculosis grown under aerobic and anaerobic conditions.Microbiology. 2004; 150: 3821-3829Crossref PubMed Scopus (128) Google Scholar, 13.Taneja N.K. Dhingra S. Mittal A. Naresh M. Tyagi J.S. Mycobacterium tuberculosis transcriptional adaptation, growth arrest, and dormancy phenotype development is triggered by vitamin C.PLoS One. 2010; 5: e10860Crossref PubMed Scopus (102) Google Scholar, 14.Wolfe L.M. Veeraraghavan U. Idicula-Thomas S. Schurer S. Wennerberg K. Reynolds R. Besra G.S. Dobos K.M. A chemical proteomics approach to profiling the ATP-binding proteome of Mycobacterium tuberculosis.Mol. Cell. Proteomics. 2013; 12: 1644-1660Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar, 15.Yuan Y. Crane D.D. Simpson R.M. Zhu Y.Q. Hickey M.J. Sherman D.R. Barry 3rd, C.E. The 16-kDa alpha-crystallin (Acr) protein of Mycobacterium tuberculosis is required for growth in macrophages.Proc. Natl. Acad. Sci. U.S.A. 1998; 95: 9578-9583Crossref PubMed Scopus (264) Google Scholar). Environmental stresses such as nutrient deprivation, iron restriction, mild acidity, and reactive nitrogen and oxygen species also induce dormancy (7.Barry 3rd, C.E. Boshoff H.I. Dartois V. Dick T. Ehrt S. Flynn J. Schnappinger D. Wilkinson R.J. Young D. The spectrum of latent tuberculosis: rethinking the biology and intervention strategies.Nat. Rev. Microbiol. 2009; 7: 845-855Crossref PubMed Scopus (990) Google Scholar, 16.Deb C. Lee C.M. Dubey V.S. Daniel J. Abomoelak B. Sirakova T.D. Pawar S. Rogers L. Kolattukudy P.E. A novel in vitro multiple-stress dormancy model for Mycobacterium tuberculosis generates a lipid-loaded, drug-tolerant, dormant pathogen.PLoS One. 2009; 4: e6077Crossref PubMed Scopus (312) Google Scholar). However, Wayne's dormancy model has proven to be a very effective and simple method to understand the molecular mechanisms in dormant bacteria, and to discover novel therapeutic agents (8.Dick T. Dormant tubercle bacilli: the key to more effective TB chemotherapy?.J. Antimicrob. Chemother. 2001; 47: 117-118Crossref PubMed Google Scholar). In addition, Wayne's model is proven to be clinically correlated to human anaerobic latent lesions containing dormant bacilli (17.Parrish N.M. Dick J.D. Bishai W.R. Mechanisms of latency in Mycobacterium tuberculosis.Trends Microbiol. 1998; 6: 107-112Abstract Full Text Full Text PDF PubMed Scopus (371) Google Scholar). Changes in the physiology of MTB during its transition from log phase to dormancy, as well as from dormancy to reactivation, have been studied using genomic, transcriptomic, proteomic, and metabolomic approaches (18.Cole S. Brosch R. Parkhill J. Garnier T. Churcher C. Harris D. Gordon S.V. Eiglmeier K. Gas S. Barry 3rd, C.E. Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence.Nature. 1998; 393: 537-544Crossref PubMed Scopus (6502) Google Scholar, 19.Eoh H. Rhee K.Y. Multifunctional essentiality of succinate metabolism in adaptation to hypoxia in Mycobacterium tuberculosis.Proc. Natl. Acad. Sci. 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Proteogenomic analysis of Mycobacterium tuberculosis by high resolution mass spectrometry.Mol. Cell. Proteomics. 2011; 10M111.011627Abstract Full Text Full Text PDF PubMed Google Scholar, 23.Mawuenyega K.G. Forst C.V. Dobos K.M. Belisle J.T. Chen J. Bradbury E.M. Bradbury A.R. Chen X. Mycobacterium tuberculosis functional network analysis by global subcellular protein profiling.Mol. Biol. Cell. 2005; 16: 396-404Crossref PubMed Scopus (175) Google Scholar), or during transition from normal replicating stage to dormancy (24.Cho S.H. Goodlett D. Franzblau S. ICAT-based comparative proteomic analysis of nonreplicating persistent Mycobacterium tuberculosis.Tuberculosis. 2006; 86: 445-460Crossref PubMed Scopus (48) Google Scholar). Starck et al. used 2-D electrophoresis to compare the proteomes of MTB grown under aerated and anerobic conditions, and found 50 proteins differentially expressed under the latter (12.Starck J. Kallenius G. Marklund B.I. Andersson D.I. Akerlund T. Comparative proteome analysis of Mycobacterium tuberculosis grown under aerobic and anaerobic conditions.Microbiology. 2004; 150: 3821-3829Crossref PubMed Scopus (128) Google Scholar). Wolfe et al. used a probe-based chemo-proteomic approach to selectively profile the ATP-binding proteome in normally growing and hypoxic MTB. They identified 122 ATP-binding proteins of which roughly 60% were reported to be essential for the in vitro survival (14.Wolfe L.M. Veeraraghavan U. Idicula-Thomas S. Schurer S. Wennerberg K. Reynolds R. Besra G.S. Dobos K.M. A chemical proteomics approach to profiling the ATP-binding proteome of Mycobacterium tuberculosis.Mol. Cell. Proteomics. 2013; 12: 1644-1660Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar). Extracellular proteins of nutrient-starved MTB were analyzed by Albrethsen et al. They identified 1176 proteins, of which 230 were up-regulated, and 208 were down-regulated (25.Albrethsen J. Agner J. Piersma S.R. Hojrup P. Pham T.V. Weldingh K. Jimenez C.R. Andersen P. Rosenkrands I. Proteomic profiling of Mycobacterium tuberculosis identifies nutrient-starvation-responsive toxin-antitoxin systems.Mol. Cell. Proteomics. 2013; 12: 1180-1191Abstract Full Text Full Text PDF PubMed Scopus (104) Google Scholar). Galagan et al. carried out proteome profiling of dormant and re-aerated MTB using a defined hypoxia model, and identified a total of approximately one thousand proteins (26.Galagan J.E. Minch K. Peterson M. Lyubetskaya A. Azizi E. Sweet L. Gomes A. Rustad T. Dolganov G. Glotova I. Abeel T. Mahwinney C. Kennedy A.D. Allard R. Brabant W. Krueger A. Jaini S. Honda B. Yu W.H. Hickey M.J. Zucker J. Garay C. Weiner B. Sisk P. Stolte C. Winkler J.K. Van de Peer Y. Iazzetti P. Camacho D. Dreyfuss J. Liu Y. Dorhoi A. Mollenkopf H.J. Drogaris P. Lamontagne J. Zhou Y. Piquenot J. Park S.T. Raman S. Kaufmann S.H. Mohney R.P. Chelsky D. Moody D.B. Sherman D.R. Schoolnik G.K. The Mycobacterium tuberculosis regulatory network and hypoxia.Nature. 2013; 499: 178-183Crossref PubMed Scopus (306) Google Scholar). The process of reactivation of MTB from dormancy is a critical step in the development of active TB. For understanding the molecular mechanisms involved in the reactivation of MTB, it is important to identify the proteins specifically or differentially expressed during reactivation. In the present study, by re-aerating the medium after establishing the Wayne's dormancy model, we could successfully induce the bacilli to grow actively again. To identify the proteins, we employed a label-free, one-dimensional liquid chromatography coupled with tandem mass spectrometry (LC/MS/MS), to analyze the proteomes of normoxic, dormant, and reactivated MTB H37Rv, the virulent laboratory strain. Genome-based computational analyses were conducted and integrated into the proteomics data. MTB virulent laboratory strain H37Rv was subcultured on Löwenstein-Jensen slants and incubated at 37 °C for 4–6 weeks. All steps involving handling of MTB were carried out in a biosafety level three (BSL3) facility. Broth cultures were prepared by inoculation of one loopful of bacterial colony into Dubos broth base (Difco, Franklin Lake, NJ) containing 5% (v/v) glycerol, supplemented with Dubos albumin (2%, v/v, Difco). Culture grown to A600 of 0.6 (∼108 bacteria per ml) in 250 ml conical flask on a shaker incubator at 150 rpm and 37 °C was used as the inoculum. To achieve dormancy, we used 16 × 24 mm flat-bottomed glass tubes with a width of 25.5 mm and a liquid holding capacity of 31.0 ml. Dubos Tween-albumin broth containing 2 × 106 bacteria per milliliter was dispensed (20.4 ml) into required number of tubes. Cultures were grown with limited internal agitation of 130 rpm using 8 mm Teflon-coated magnetic bars (Sigma-Aldrich, St. Louis, MO) on multipoint magnetic stirrers (Variomag Poly 15, Thermo Scientific, Walthan, MA). The cap (Pressure compensation set, Duran, Germany) of the glass tube was connected to a 0.2 micron filter using a silicon tubing (3 cm length and 1.6 mm internal diameter). The tubing was closed using a pinchcock clamp (Fig. 1A). The whole setup was placed inside a custom-made 37 °C incubator (Santhom Scientific, Bangalore, India) that could accommodate four multipoint magnetic stirrers simultaneously. The dormant bacteria were re-aerated by removing the pinchcock clamp from the silicone tubing (Fig. 1B). The agitation was increased to 200 rpm to facilitate aeration. The status of self-generated hypoxia and reactivation was monitored visually using methylene blue (1.5 μg/ml). This indicator imparts a greenish blue color to the culture in the presence of oxygen, and turns colorless when oxygen concentration in the medium becomes less than 1% (27.Raffia A. Fahim U. Photoredox reaction of methylene blue and lactose in alcoholic buffered solution.J. Appl. Chem. Res. 2010; 13: 72-84Google Scholar). Growth was monitored every 24 h by measuring A600 in a colorimeter (Aimil Photochem, New Delhi, India) and the viability was assessed by plating 100 μl of suitably diluted bacterial culture on 7H10 agar (Difco) in triplicate and incubating at 37 °C for 6–8 weeks. Bacteria were harvested at the 288 and 504 h (12 and 21 days) to represent NRP1 and NRP2 stages of dormancy, respectively. Cells harvested at 6 and 24 h after introduction of air represented early and late stages of reactivation (R6 and R24), respectively. The cells from normoxia served as the control. We extracted proteins by employing a modified protocol used by Cho et al. (24.Cho S.H. Goodlett D. Franzblau S. ICAT-based comparative proteomic analysis of nonreplicating persistent Mycobacterium tuberculosis.Tuberculosis. 2006; 86: 445-460Crossref PubMed Scopus (48) Google Scholar). Briefly, 80 ml of MTB cultures were pelleted by centrifuging them at 2500 × g at 4 °C for 15 min, and the pellets were washed three times in 10 ml ice-cold phosphate buffered saline (PBS, pH 7.4) and were resuspended in PBS (1 ml PBS per gram of bacteria). Equal weight of bacteria from different stages were transferred to 2 ml screw-cap microcentrifuge tubes containing glass beads (0.5 mm), protease inhibitor (1 mm PMSF, Sigma-Aldrich) and incubated on ice for 5 mins. The tubes were then placed in a Mini Bead beater (BioSpec Products Inc., Bartlesville, OK) and subjected to three, one-minute pulses at 4200 rpm with 1 min interval. The suspensions were centrifuged for 10 min at 13,000 × g at 4 °C (Eppendorf, Hauppauge, NY) and the supernatants were recovered. Protein concentration was determined by bicinchoninic acid protein assay reagent (Thermo Scientific) (28.Smith P. Krohn R. Hermanson G. Malliya A. Gartner F. Provenzano M. Fujimoto E. Goeke N. Olson B. Klenk D. Measurement of protein using bicinchoninic acid assay.Anal. Biochem. 1985; 150: 76-85Crossref PubMed Scopus (18583) Google Scholar). Fifty micrograms of protein from each sample (normoxia, NRP1, NRP2, R6, and R24) was used for in-solution trypsin digestion. The disulfide bonds were reduced by treating the proteins with 10 mm dl-dithiothreitol (Sigma-Aldrich) in 50 mm ammonium bicarbonate (Sigma-Aldrich) buffer at 60 °C for 30 min. The proteins were subsequently alkylated with 200 mm iodoacetamide (Sigma-Aldrich) in the same buffer at 27 °C for 30 min in the dark. Proteins were then digested with trypsin (sequencing-grade modified trypsin, Sigma-Aldrich; 1:25 w/w) in 50 mm ammonium bicarbonate buffer by incubating overnight at 37 °C. Trypsin digestion was terminated by adding formic acid (Sigma-Aldrich; 1% v/v) to the reaction mixture. The digested peptide solutions were centrifuged at 14,000 rpm at 4 °C for 12 min, and the supernatants were stored at −20 °C until the LC/MS/MS analysis. The peptide samples were analyzed by nano-LC/MSE (MS at elevated energy) using a nano ACQUITY UPLC® System (Waters, Hertfordshire, UK) coupled to a Quadrupole-Time of Flight (Q/TOF) mass spectrometer (SYNAPT-G2, Waters). Both the systems were operated and controlled by MassLynx4.1 SCN781 software (Waters). In the nano-LC, the peptides were separated by reverse phase column chromatography. Briefly, 3 μl of each sample, equivalent to 3 μg of protein, was injected in “partial loop” mode and was loaded into the reverse phase column with 0.1% formic acid in water as mobile phase A, and 0.1% formic acid in acetonitrile as mobile phase B, using the binary solvent manager. The sample was then trapped in the trap column (Symmetry® 180 μm × 20 mm C18 5 μm, Waters) to remove any salt by employing a high flow rate (15 μl/minute) with 99.9% mobile phase A and 0.1% mobile phase B for 1 min. The peptide separation was performed on a 75 μm × 100 mm BEH C18 column (Waters), with particle size of 1.7 μm. A gradient elution with 1–40% mobile phase B, for 55.5 min at 300 nl/minute flow rate, was employed. After separation, the column was washed with 80% mobile phase B for 7.5 min and re-equilibrated with 1% mobile phase B for 20 min. The column temperature was maintained at 40 °C. Three biological replicates for each condition were performed and each sample was run in triplicates. Peptides eluted from the nano-LC was subjected to mass spectrometric analysis on a SYNAPT® G2 High Definition MS™ System (Waters). The following parameters were used: nano-ESI capillary voltage, 3.3 KV; sample cone, 35 V; extraction cone, 4 V; transfer CE, 4 V; trap gas flow, (2 ml/minute); IMS gas (N2) flow, (90 ml/minute). To perform the mobility separation, the IMS T-Wave™ pulse height was set to 40 V during transmission and the IMS T-Wave™ velocity was set to 800 m/s. The traveling wave height was ramped over 100% of the IMS cycle between 8 and 20 V. The time of flight analyzer (TOF) was calibrated with a solution of 500 fmole/μl of human [Glu1]-Fibrinopeptide B (Sigma-Aldrich), and the lock mass acquisition was performed every 30 s by the same peptide delivered through the reference sprayer of the nano-LockSpray source at a flow rate of 500 nl/minute. This calibration set the analyzer to detect ions in the range of 50–2000 m/z. The mass spectrometer was operated in the “resolution mode” with a resolving power of 18,000 FWHM, and the data acquisition was performed in “continuum” format. The data was acquired by rapidly alternating between two functions – Function-1 (low energy) and Function-2 (high energy). In Function-1, only low energy mass spectra (MS) were acquired and in Function-2, mass spectra at elevated collision energy (MSE) with ion mobility were acquired. In Function-1, collision energy was set to 4 V in the trap region and 2 V in the transfer region. In Function-2, collision energy was set to 4 V in the trap region and is ramped from 20 V to 45 V in the transfer region. Each spectrum was acquired for 0.9 s with an interscan delay of 0.024 s. The LC/MSE data was analyzed using ProteinLynx Global SERVER™ v2.5.3 (PLGS, Waters) for protein identification as well as for the relative protein quantification. Data processing included lock-mass correction post acquisition. Noise reduction thresholds for low energy scan ion, high-energy scan ion, and peptide intensity were fixed at 150, 50, and 500 counts, respectively. The database Mycobacterium tuberculosis H37Rv uid17053 NC_01 8143.faa (November 30/2013) downloaded from NCBI was used for database search. The parameters for protein identification were made in such a way that a peptide was required to have at least one fragment ion match, a protein was required to have at least three fragment ion matches, and at least two peptide matches for identification. Mass tolerance was set to 10 ppm for precursor ions and 20 ppm for fragment ions. Oxidation of methionine was selected as the variable modification and carbamidomethylation of cysteine was selected as the fixed modification. Trypsin was chosen as the enzyme with a specificity of one missed cleavage. The false positive rate (FPR) of the algorithm for identification was set to 4% with a randomized database, appended to the original one. Only those proteins with 50% or more probability to be present in the mixture and detected with a score above 20, as calculated by the software, were selected for proteomic analysis (29.Cockman M.E. Webb J.D. Kramer H.B. Kessler B.M. Ratcliffe P.J. Proteomics-based identification of novel factor inhibiting hypoxia-inducible factor (FIH) substrates indicates widespread asparaginyl hydroxylation of ankyrin repeat domain-containing proteins.Mol. Cell. Proteomics. 2009; 8: 535-546Abstract Full Text Full Text PDF PubMed Scopus (115) Google Scholar). Data sets were normalized using the “auto-normalization” function of PLGS, and label-free quantitative analysis was performed by comparing the normalized peak area/intensity of the peptides identified. Thus, parameters such as score, sequence coverage, and number of peptides were obtained for each protein. Furthermore, only those proteins with a fold change higher than 50% difference (ratio of either 1.5) were considered to be expressed at significantly altered levels. The proteins identified were subjected to Gene Ontology (GO) analysis using DAVID functional ontology analyzer (http://david.abcc.ncifcrf.gov/) (30.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: 44-57Crossref PubMed Scopus (25331) Google Scholar, 31.Huang da W. Sherman B.T. Lempicki R.A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists.Nucleic Acids Res. 2009; 37: 1-13Crossref PubMed Scopus (10263) Google Scholar). Proteins identified in any one of the three technical replicates were included in the analysis. The NCBI IDs of the proteins were used for defining the list of total proteins detected in the dataset. GO terms for the identified proteins were extracted, and over-represented functional categories for differentially abundant proteins were determined. GO groups were selected based on the confidence score (p value < 0.05), and for overlapping GO groups, one representative category was selected within the dataset. For the construction of interaction network BioNet Builder plugin (http://apps.org/apps/bionetbuilder) in the Cytoscape 2.8.3 platform was used (32.Avila-Campillo I. Drew K. Lin J. Reiss D.J. Bonneau R. BioNetBuilder: automatic integration of biological networks.Bioinformatics. 2007; 23: 392-393Crossref PubMed Scopus (51) Google Scholar). The quantitative data from the 1-D LC/MS/MS analysis was incorporated into the cellular network thus created, and the differences in protein levels during dormancy and reactivation in comparison with the control, and with each other were estimated. The proteins were classified based on their function, physical interaction, or location. The quantitative profiles of proteins that had consistent representation in all the three biological replicates were subjected to statistical comparison employing nonparametric Friedman test (GraphPad Prism V6). RNA was isolated following the protocol described by Larsen (33.Larsen M.H. Some common methods in mycobacterial genetics.Molecular genetics of mycobacteria. 2000; : 316Google Scholar). Fifteen ml of MTB culture (normoxia, NRP1, NRP2, R6, and R24) was centrifuged at 6000 × g for 10 min followed by lysis of cell pellet in ice-cold chloroform/methanol (3:1). The solution was vortexed and 5 ml of Trizol reagent (Sigma-Aldrich) was added, and was centrifuged at 2000 × g for 15 min. To the top layer, an equal volume of ice-cold isopropanol was added, kept at −20 °C overnight, and was centrifuged at 18,000 × g for 30 min. The pellet was resuspended in 70% ice-cold ethanol, and centrifuged at 18,000 × g for 20 min. The pellet thus obtained was dried to remove any trace amount of ethanol left, and was treated with DNase-1 (Sigma-Aldrich) and the RNA was quantified spectrophotometrically (Nanovue, GE Healthcare, Buckinghamshire, UK). Complementary DNA (cDNA) was prepared using Reverse Transcriptase Core kit (Eurogentec, Seraing, Belgium) according to the manufacturer's protocol. Quantitative real-time PCR was performed using gene-specific primers (Bio-Rad, Hercules, CA). The thermal cycling protocol was set as follows – an initial denaturation of 94 °C for 1 min; 35 cycles of denaturation at 94 °C for 30 s, primer annealing at 60 °C for 45 s, and extension at 72 °C for 40 s. Following amplification, a melt curve analysis was performed to confirm the specificity of the amplified product. Relative changes of gene expression were calculated using -ΔΔCt method, with sigA as the housekeeping gene. A major reason why TB is a global menace, despite effective drugs and BCG vaccine, is the ability of MTB to remain dormant in the human body for a long time, and to be reactivated when the host immune system becomes weak. Study of critical proteins expressed during reactivation may identify novel ta
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