Proteomic profiling of multidrug-Susceptible and resistant strains of mycobacterium tuberculosis - Truong Quoc Phong

TAP CHI SINH HOC 2015, 37(1se): 18-26 DOI: 10.15625/0866-7160/v37n1se. PROTEOMIC PROFILING OF MULTIDRUG-SUSCEPTIBLE AND RESISTANT STRAINS OF Mycobacterium tuberculosis Truong Quoc Phong1*, Do Thi Thu Ha1, Uwe Volker2, Elke Hammer2 1Hanoi University of Science and Technology, Vietnam, *phong.truongquoc@hust.edu.vn 2Ernst Moritz Arndt University Greifswald, Germany ABSTRACT: In recent years, the increasing emergence of resistance to drugs being used in tuberculosis treatment has been reported. Drug resistance significantly affects the choice for effective treatment, especially in multidrug-resistant cases. The emergence of multidrug-resistant tuberculosis has attracted a great deal of interest in understanding the mechanism of drug resistance in Mycobacterium tuberculosis and the development of new therapeutics, diagnostics and vaccines. In this study, a label-free proteomics approach was used to analyze proteome of multidrug resistant and susceptible clinical isolates of M. tuberculosis. With this approach, we identified a total of 1583 proteins. The majority of identified proteins have predicted roles in lipid metabolism, intermediary metabolism, cell wall and cell processes. Of these proteins, 335 proteins were predicted as membrane proteins with at least one transmembrane domain (TMD). The distributions of the molecular weight, pI, hydrophobicity (GRAVY) and predicted subcellular location of the identified proteins were analyzed and interpreted. Keywords: Mycobacterium tuberculosis, drug resistance, liquid chromatography – mass spectrometry (LC-MS), proteome, proteomic profiling. INTRODUCTION Infection of Mycobacterium tuberculosis leads to 8.6 million people felling ill every year due to tuberculosis [32]. Multidrug-resistant TB (MDR-TB) is caused by a mycobacterium that is resistant to at least isoniazid and rifampicin, the two most powerful first-line TB drugs to treat patients with TB disease. Multidrug-resistant TB is increasing at a rate of 3.6% of new TB patients in the world have multidrug-resistant strains. WHO reported that MDR-TB was present in virtually all countries surveyed and there were 450,000 new MDR-TB cases in the world in 2012 [31]. Multidrug resistance tuberculosis leads to a serious threat in global tuberculosis control and treatment of patients seems to be impossible using currently available drugs. The emergence of multidrug-resistant tuberculosis has increased interest in understanding the mechanism of drug resistance in M. tuberculosis and the development of new therapeutics, diagnostics and vaccines. Early detection of the drug resistance is one of the priorities of tuberculosis control and plays an important role in effective treatment. Besides early diagnosis, the development of effective vaccines is critical in the control and prevention of tuberculosis. The prevention of tuberculosis could be performed since 1921 by vaccination with Bacillus Calmette Guerin (BCG) vaccine. However, its efficacy continues to be debated. Recently, several subunit vaccine candidates have been developing and however they are in the pipeline of discovery [6, 10, 30]. Thus, discovery of multidrug resistance-associated proteins of M. tuberculosis could provide new biomarkers for detection and development of effective vaccines. Proteomics is a powerful tool for studying the protein composition of complex biological samples. The global study of the protein profile of resistant and susceptible strains by proteomic approach could help in further revealing of resistance mechanisms and determining multidrug resistance-associated biomarkers. The obtained findings support to develop newer drugs, vaccine and rapid diagnosis tool for multidrug resistance tuberculosis. Investigations of protein expression profiles of M. tuberculosis by proteomics approach under various growth conditions, genetic backgrounds, geographic distribution, subcellular fraction have been performed [1-3, 7, 8, 12, 17-22, 26-28, 33, 34]. In the present study, a combinantion of orbitrap mass spectrometry and nano-liquid chromatography was performed to identify the multdrug-resistant and susceptible M. tuberculosis proteomes. MATERIALS AND METHODS Mycobacterial growth Three resistant and sensitive M. tuberculosis clinical isolates were obtained from National Institute of Hygiene and Epidemiology (NIHE). Bacteria were grown in Middlebrook 7H9 broth (Difco) supplemented with 0.2% glycerol, 0.05% Tween 80, and 1X OADC (0.5% bovine serum albumin, 0.2% Dextrose, 0.85% NaCl, 0.0004% catalase, 0.005% oleic acid) at 37oC for four weeks (107-108 cfu/ml) This experiment was carried out at Tuberculosis laboratory - NIHE. Protein preparation of M. tuberculosis Mycobacterial cell extract was prepared according to modified protocol of Sharma et al. (2010) [25]. Briefly, cells were washed three times with phosphate saline buffer (1X PBS buffer, pH 7.4) and then suspended in UT buffer containing 8 M urea, 2 M thiourea. The cell suspension was broken by intermittent sonication (15 seconds ON, 15 seconds OFF) for 4 min on ice at 80% energy using sonicator (Sonics & Materials Inc, USA). Subsequently, the lysate was clarified by centrifugation at 16,000 x g for 1 hour at 4oC. The supernatant was collected in a new eppendorf tube and protein concentration of the supernatant was determined using a Bradford assay kit (Sigma-Aldrich, USA). Sample aliquots were stored at -80oC for later use. Peptide preparation for Mass Spectrometry analysis Protein samples were proteolytically digested in solution using the following produce: four µg protein of each sample, (three technical and two biological replicates per sample), were reduced by incubation with a finial concentration of 25 mM DTT and alkylated with 100 mM iodoacetamide. Proteolysis was carried out with trypsin (Promega) in the ratio of 1:25 overnight (16 h) at 37oC. The digestion was quenched by the addition of acetic acid to a final concentration of 1%. The digested peptides were purified on µC18-ZipTip columns (Merck Millipore). Identification of proteins by ESI-LC tandem mass spectrometry The purified peptides were separated on an Acclaim PepMap 100 reverse phase column with an EASY-nLC (Thermo Electron) using a 86 min non-linear gradient ranging from 2-100% ACN in 0.1% acetic acid at a flow rate of 0.3 µL/min before full MS data and data-dependent fragment ion spectra were recorded with a LTQ-Orbitrap-Velos mass spectrometer (Thermo Electron). Raw data were analyzed with the Refiner software (GeneData) employing alignment across all 12 MS runs with subsequent peak identification and annotation. Identification of peptides and assignment to proteins was performed via automated Mascot search (rel. 2.3, Matrix Science) against the Uniprot/Trembl reference proteome database for Mycobacterium tuberculosis with a peptide mass tolerance of 10 ppm and 0.6 Da for fragment ions. This work was carried out at Laboratory for Functional Genomics, Ernst-Moritz-Arndt-University Greifswald, Germany. Analysis of identified proteins The physicochemical properties of all identified proteins were analyzed by using the theoretical molecular weight, pI value and GRAVY scores were obtained from calculation in Protein transmembrane domains (TMDs) were predicted with the SOSUI (Batch) engine ver. 1.10 ( sosuiG/ sosuigsubmit.html), TMHMM server v. 2.0 ( and Phobius ( Possible signal peptide and lipoprotein signal peptide sequence prediction were predicted using the SignalP and LipoP programs, respectively ( Prediction of subcellular localization of the identified proteins was carried out using the PSORT v3.0 program that available at Lipoproteins were identified by comparing the identified proteins with the list of M. tuberculosis predicted lipoproteins described by Sutcliffe et al. (2004) [29]. Functional classifications of all proteins were determined according to the TubercuList database [15]. RESULTS AND DISCUSSION Identification of the M. tuberculosis proteins by free-gel approach Traditional approaches for the proteomic analysis of a biological sample based on the resolution of proteins using 2-DE and the identification of resolved protein by MS have been efficient and successful. However, 2-DE approach presents limitations in the analysis of very small (100 kDa) proteins, basic proteins, low abundance and hydrophobic proteins (GRAVY score > 2) [9, 16]. To improve protein coverage and extend the analysis to membrane proteins or e.g proteins with pI outside the pH range 4-7 employed we have performed an alternative approach for the global analysis of M. tuberculosis proteome (susceptible and multidrug-resistant M. tuberculosis) by mass spectrometry LC-MS/MS analysis to resolve the thousands of proteins. The resulting MS/MS spectra were searched using automated Mascot search against the Uniprot/Trembl reference proteome database for M. tuberculosis. Applying the filtering criteria described in method section, a total of 6110 peptide sequences was obtained. LC-ESI-LTQ ORBITRAP-MS analysis resulted in identification of a total of 1583 mycobacterial proteins in both susceptible and multidrug resistant group. Of those, 448 proteins were identified with one peptide only and 1135 proteins were identified with two and more peptides. Peptide identification by MS was considered as evidence for the existence of the gene products predicted by genome annotation [4, 5]. The physiochemical analysis of the identified proteins Using the predicted protein sequences, a series of parameters of physiochemical characteristics (molecular mass, pI, hydrophobicity (GRAVY), and predicted subcellular location) were calculated. These data are showed below. Molecular weight and pI distribution of the identified proteins The distribution of the molecular weights of the identified proteins is presented in fig. 1a. The theoretical Mr distribution for the identified proteins ranged from 2.67 kDa (a putative secreted protein) to 413.6 kDa (polyketide synthase, Pks12). The majority of identified proteins were 10-60 kDa in molecular weight, representing approximately 83.4% of all predicted in this range. This result highly consists with the observation by Zheng et al. (2012) [35] in M. bovis. It is notably that 104 identified proteins showing molecular weight outside the range of 10-100 kDa will poorly resolved by 2-DE method. Similarly the theoretical pI values for the identified proteins ranged from 3.55 (Rv1466) to 12.69 (a 50S ribosomal protein L32) and whole pI distribution is depicted in Fig. 1b. Out of 1583 identified proteins, 485 (30%) proteins displayed a pI outside the range 4.0-7.0 and hence were not covered by the 2-DE approach. Hydrophobic analyses of the identified proteins A GRAVY score is a single value indication of the overall hydrophobicity of a protein sequence based on Kyte and Doolittle algorithms with the more positive the score, the more hydrophobic the overall sequence [13]. In the present study, there were 186 proteins with GRAVY scores ³ 0.2 (fig. 1c), which were so hydrophobic and hard to be analyzed by 2-DE. Among 1583 totally identified proteins, 335 proteins (21%) were predicted to have at least one transmembrane helix (TMH) for integration into the cytoplasmic membrane by three programs of TMHMM ver. 2.0, SOSUI ver. 1.10 and Phobius. Among them, 261, 290 and 247 proteins were predicted transmembrane proteins by TMHMM, SOSUI and Phobius, respectively. By comparison, a total of 209 transmembrane domain proteins were overlapping in all three resultant searches (fig. 2a). c d a b a b Figure 1. Percentages of total identified proteins by molecular mass range (a), pI range (b), GRAVY range (c) and subcellular location (d). These physiochemical properties of identified proteins were determined based on databases as mentioned above. Figure 2. The Venn diagram (a) displays the degree of overlap of transmembrane helices protein prediction by three different programs; numbers in parenthesis indicate the total number of TMH proteins predicted by each program. The distribution of the TMHs of identified proteins (b) in M. tuberculosis. All the annotated proteins in the TubercuList database [15] were subjected to the TMHMM v2.0 program to predict the transmembrane helices in proteins. The results indicated that 807 proteins were predicted to transmembrane proteins and therefore 32% of TMH proteins have been identified in this study. The predicted TMH numbers of these proteins ranged from 1 to 15 (fig. 2b). Some important proteins with multiple TMHs were identified. Remarkably one of three transmembrane proteins with 15 TMHs was identified as probable peptidoglycan biosynthesis protein (MviN). In addition, 47 proteins were predicted by SignalP program to have a cleavable signal peptide for export across the cytoplasmic membrane. Lipoproteins are characterized by lipidated N-terminus and anchored in the membrane via a posttranslational modification [23]. Mycobacterial lipoproteins have been illustrated in forming a functionally diverse class of membrane-anchored or associated proteins [14, 24, 29]. Interestingly, we identified 41 out of the 99 predicted lipoproteins of M. tuberuclosis H37Rv [24], in which 16 proteins were detected by using LipoP program containing lipoprotein signal peptide that predicted that these proteins could be lipidated during the process of export [11]. Subcellular location of the identified proteins All identified proteins were loaded to the PSORTb v3.0.2 program to predict their subcellular localizations. The subcellular localization information for the identified proteins is displayed in fig. 1d and showing that the majority of identified proteins were determined as cytoplasmic proteins. Approximately 17% proteins were predicted as cytoplasmic membrane and periplasmic proteins. There were also a large proportion of identified proteins (27%) with unknown localization. a b Figure 3. Functional assignment of proteins of the M. tuberculosis identified by LC-MS/MS. Proteins were assigned to functional categories based on the TubercuList database. The pie chart (A) and (B) represents the distribution of all identified M. tuberculosis proteins and membrane proteins respectively according to functional categories in percentage. Numbers in parenthesis indicate number of proteins in total and membrane proteins respectively in each category. Functional classification of identified proteins All identified proteins were grouped by functional category as defined by Lew et al. (2011) [15], in which each protein was associated with a functional category. Totally 1583 identified proteins can be classified into 10 different functional categories (fig. 3a). The majority of identified proteins have predicted roles in lipid metabolism, intermediary metabolism, cell wall and cell processes. The highest number of identified proteins was classified into intermediary metabolism category (508; 32%). In addition, high numbers of the identifications were involved in cell wall and cell processes (272; 17%), lipid metabolism (153; 10%). A large list of conserved hypothetical proteins of unknown function was also identified in this study (352; 22%). This pattern of functional category is similar to previous reports in proteomic analysis of the M. tuberculosis [8, 19, 27]. Functional classification of membrane proteins showed that 175 (52%) proteins were associated with cell wall processes (group 4) and 59 (18%) proteins were related to intermediary metabolism. In addition, 10% were conserved hypotheticals. This data demonstrates that 90% of identified membrane proteins had known functions. This observation is coincidence with Wolfe et al. (2010) [33]. Comparisons with other studies of Mbt proteome by LC-MS approach At present, characterization of the proteome of M. tuberculosis have been performed by several different studies [1, 7, 8, 12, 17-19, 21, 26, 27, 33, 34], of those the major studies based on LC-MS approach are listed in the [fig 4]. Previous studies in profiling of M. tubercolusis proteome have focused on whole cell lysate and even several subcellular fractions including cytoplasmic membrane, cell wall, cytosol, and culture filtrate. By comparing the list of protein identified in this study with previous studies, we found that the majority of the proteins identified here overlapped with those identifications in previous studies. In comparison with each previous study, the result indicated that quite high proportion of identified proteins was non-overlapping. We believe that our identification will provide more data and confirming evidence for the existence of the gen products in M. tuberculosis. Figure 4. Comparison in LC-MS proteome profile of M. tuberculosis with various previous studies including 1 - [21], 2 - [19], 3 - [27], 4 - [18], 5 - [12], 6 - [1], 7 - [8]. CONCLUSION We used a label-free quantitative approach to identify proteins in multidrug-resistant and susceptible clinical isolates of M. tuberculosis. This study is being considered as the first report applying free-gel approach for analysis of multidrug resistant M. tuberculosis. Protein identification by MS was considered as evidence for the existence of the gen products predicted by genome annotation. 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NGHIÊN CỨU PHÂN TÍCH HỆ PROTEIN CỦA CHỦNG VI KHUẨN LAO Mycobacterium tuberculosis NHẠY CẢM VÀ ĐA KHÁNG THUỐC BẰNG CÔNG CỤ PROTEOMICS Trương Quốc Phong1*, Đỗ Thị Thu Hà1, Uwe Volker2, Elke Hammer2 1Trường Đại học Bách khoa Hà Nội, Việt Nam 2Trường đại học Tổng hợp Ernst-Moritz-Arndt Greifswald, CHLB Đức TÓM TẮT Hiện nay, tỷ lệ kháng các thuốc đang được dùng để điều trị lao tăng trong những năm gần đây. Sự kháng thuốc ảnh hưởng lớn đến hiệu quả điều trị, đặc biệt là những trường hợp đa kháng thuốc. Vấn đề đa kháng thuốc này đã và đang được nhiều nhà khoa học quan tâm nghiên cứu để phát triển liệu pháp điều trị, phương pháp chẩn đoán và tìm vaccine phòng lao mới. Trong nghiên cứu này, hướng tiếp cận proteomics không đánh dấu đã được sử dụng để phân tích hệ protein của các chủng Mycobacterium tuberculosis nhạy cảm và đa kháng thuốc. Với hướng tiếp cận này, 1583 proteins đã được nhận diện và phân loại theo nhóm chức năng như trao đổi lipid, trao đổi chất trung gian, cấu trúc tế bào. Từ những protein được nhận diện, 335 protein được dự đoán là các protein màng với ít nhất một vùng xuyên màng. Các thông tin liên quan đến khối lượng phân tử, điểm đẳng điện, độ kỵ nước và vị trí dưới tế bào của những protein này cũng đã được phân tích. Từ khóa: Mycobacterium tuberculosis, hệ protein, kháng thuốc, proteomics, sắc ký lỏng - khối phổ (LC-MS). Ngày nhận bài: 22-10-2014