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Quantitative proteomics for identifying biomarkers for tuberculous meningitis



Tuberculous meningitis is a frequent extrapulmonary disease caused by Mycobacterium tuberculosis and is associated with high mortality rates and severe neurological sequelae. In an earlier study employing DNA microarrays, we had identified genes that were differentially expressed at the transcript level in human brain tissue from cases of tuberculous meningitis. In the current study, we used a quantitative proteomics approach to discover protein biomarkers for tuberculous meningitis.


To compare brain tissues from confirmed cased of tuberculous meningitis with uninfected brain tissue, we carried out quantitative protein expression profiling using iTRAQ labeling and LC-MS/MS analysis of SCX fractionated peptides on Agilent’s accurate mass QTOF mass spectrometer.

Results and conclusions

Through this approach, we identified both known and novel differentially regulated molecules. Those described previously included signal-regulatory protein alpha (SIRPA) and protein disulfide isomerase family A, member 6 (PDIA6), which have been shown to be overexpressed at the mRNA level in tuberculous meningitis. The novel overexpressed proteins identified in our study included amphiphysin (AMPH) and neurofascin (NFASC) while ferritin light chain (FTL) was found to be downregulated in TBM. We validated amphiphysin, neurofascin and ferritin light chain using immunohistochemistry which confirmed their differential expression in tuberculous meningitis. Overall, our data provides insights into the host response in tuberculous meningitis at the molecular level in addition to providing candidate diagnostic biomarkers for tuberculous meningitis.


Tuberculosis (TB) is a common and sometimes fatal transmissible disease, especially in developing countries. Approximately thirty percent of the global population is exposed to the acid-fast bacilli causing TB. Of those who are infected with tuberculosis, ~10% percent develop a clinical manifestation of the disease during their lifetime. From a global perspective, approximately twenty percent of TB infected population live in India. The World Health Organization (WHO) has estimated that one million children develop TB annually worldwide which accounts for about 11% of all TB cases [1]. Tuberculous bacilli most commonly infect lungs. Mycobacterium tuberculosis (MTB) may also spread to extrapulmonary sites including the meninges, lymph nodes, genitourinary tract, skeletal system and skin [2]. Tuberculous meningitis (TBM) is the infection of meninges caused by MTB, with a mortality rate of ~30%. Further, those who survive TBM are usually left with severe neurological defects [35]. There is an increased risk of TBM in HIV-infected patients as compared to non-HIV infected cases although the clinical manifestations of the disease do not differ between the two groups [6, 7].

Culturing mycobacteria and subsequent microbiological examination is considered a gold standard for the diagnosis of TBM. However, this method is time consuming and insensitive, with a positive outcome achieved only in 25–70% of clinically diagnosed cases [8]. Although PCR assays can be an alternative rapid approach for diagnosis, they cannot differentiate between latent or active forms of the disease. Although nucleic acid amplification test (NAAT) has a high specificity when tested in body fluids, it lacks adequate sensitivity in cases of meningitis and pleuritis [9].

Amongst the existing molecular markers, Adenosine deaminase isoenzyme-2 (ADA2) has a sensitivity of 100% and a specificity of 86.4% for the detection of TBM in cerebrospinal fluid (CSF) [10]. Adenosine deaminase (ADA) activity in the CSF of TBM patients has been suggested to be useful for early differential diagnosis of TBM [11]. The ADA activity of CSF and plasma have been evaluated as a diagnostic aid in TBM [12] and ADA activity in CSF was considered to be a simple, useful and rapid diagnostic test for early recognition of TBM in children [13]. However, overexpression of ADA was also often overexpressed in other forms of meningitis including infections with pyogenic bacteria [14]. In the CSF of TBM patients, the presence of 65 kDa heat shock protein antigen might be a marker for early diagnosis of the disease [15]. High levels of CSF lactate and lactate dehydrogenase levels have also been suggested for diagnosing TBM [16].

Early diagnosis of TBM is considered a key to effective treatment and prognosis. Approximately 90% of the patients are diagnosed in stage II or III [17]. Overall, the diagnosis of TBM still remains a major challenge due to inadequate current diagnostic methods and poor sensitivity and/or specificity of existing markers. Although corticosteroids are used extensively to reduce mortality and neurological disability, it may not be the only solution to reduce the mortality and morbidity [18]. In TBM, a number of pathological changes including meningeal adhesion, infarction, tuberculoma and hydrocephalus may occur leading to neurological sequelae [4]. These sequelae are known to correlate with the stage of meningitis at admission. Patients treated at an early stage have a five times higher chance of recovery than those with advanced disease stages [19]. Therefore, patient’s clinical condition at admission and delay in starting the treatment are important factors for determining their survival [20]. These findings emphasize the need to focus on identifying candidate molecular markers which can be developed as diagnostic tools in the management of TBM.

Mass spectrometry-based quantitative proteomics has emerged as a powerful approach for identifying and studying disease biomarkers and has become one of the essential tools in biomarker discovery [21, 22]. Advances in quantitative mass spectrometry have led to identification and quantitation of biomarkers which serve as indicators of disease progression, prognosis, drug safety and help to elucidate the mechanism of drug treatment [23]. There are various labeling approaches that one can employ to carry out quantitative proteomic measurements. In vitro labeling methods include Isobaric Tags for Relative and Absolute Quantitation (iTRAQ), Isotope-Coded Affinity Tags (ICAT), 18O labeling and in vivo methods include Stable Isotope Labeling by Amino acids in Cell culture (SILAC) and 15Nlabeling [24, 25]. iTRAQ labeling is an effective method for studying differential protein expression levels in tissue samples. It has been extensively used for biomarker discovery in various disease contexts [2634].

In this study, we used an iTRAQ-based quantitative proteomic approach to identify differentially expressed proteins from brain tissues of tuberculous meningitis cases as compared to controls. We identified several proteins which are differentially expressed in TBM. These proteins include both novel and previously reported candidate protein markers. We validated some of these candidate biomarkers using immunohistochemical labeling.

Materials and methods

Sample collection

The study was approved by scientific ethics committee of National Institute of Mental Health and Neuro Sciences (NIMHANS), Bangalore, India. Samples from frontal cortex with overlying meninges from cases of tuberculous meningitis (n=6) and similar, but uninfected, control brain tissues (n=6) from victims of road traffic accidents were collected at the time of autopsy. The autopsy was conducted within 8–16 h postmortem with the body kept at 4°C after death. The samples were obtained from the Human Brain Tissue Repository, Department of Neuropathology, NIMHANS, Bangalore. Sample details including the selection criteria are provided in Additional file 1: Table S1.

Sample preparation and iTRAQ labeling

Brain tissue samples were lysed in 0.5% SDS, sonicated, homogenized and centrifuged at 13,000 rpm for 10 min at 4°C. Supernatant was collected and protein quantitation was carried out by Lowry’s assay (Bio-Rad Hercules, CA; USA). For each condition, 160 μg of protein sample was utilized for the experiment. Each sample was treated with 4 μL of reducing agent (tris (2-carboxyethyl) phosphine (TCEP)) at 60°C for 1 h and alkylated with 2 μL of cysteine blocking reagent, methyl methanethiosulfonate (MMTS) for 10 min at room temperature. After alkylation, the samples were subjected to trypsin digestion (Sequencing Grade Modified Trypsin, Promega Cat#:V511A) using 1:20 (w/w) at 37°C for 16 h. Peptide samples from each condition was split in equal halves (80 μg each) and labeled with iTRAQ 4-plex reagents (catalog # 4352135, Applied Biosystems, Foster City, CA, USA) as per manufacturer’s protocol. We used technical replicates for control and TBM samples. Peptides from control samples were labeled with iTRAQ reagents yielding reporter ions 114 and 115 while peptides from TBM were labeled with 116 and 117. The samples were pooled following iTRAQ labeling.

Strong cation exchange chromatography (SCX)

Pooled iTRAQ labeled peptides were fractionated by strong cation exchange chromatography on PolySULFOETHYL A column (200 x 2.1 mm; 5 μm; 200Å PolyLC, Columbia, MD) using Agilent’s 1200 series HPLC system. The peptides were reconstituted in SCX solvent A (10 mM potassium phosphate, 25% acetonitrile, pH 2.8) and loaded on SCX column isocratically using 100% solvent A for 20 min at a flow rate of 200 ul per minute. Peptides were eluted using a 30 min gradient from 8% to 35% solvent B (350 mM KCl in solvent A). Fractions were collected every minute using a fraction collector. The fractions were vacuum dried and stored at −80°C until LC-MS/MS analysis.

LC-MS/MS analysis

The samples were analyzed on HPLC chip-cube interfaced with Accurate Mass 6520 quadrupole time of flight mass (QTOF) spectrometer (Agilent Technologies, Santa Clara, CA). The HPLC-Chip contains a 40 nl enrichment column and 43 mm x 75 um analytical column. These columns were made up of a reversed-phase material Zorbax 300SB-C18, with a particle size of 5 μm. The samples were loaded onto the enrichment column using Agilent’s 1200 series capillary liquid chromatography pump at a flow rate of 3 μl/min using 97% solvent A and 3% solvent B. An injection flush volume of 4 μl was applied during enrichment step. The peptides were eluted at a flow rate of 400 nl/min using a gradient of solvent A (0.1% formic acid) and solvent B (0.1% formic acid in 90% acetonitrile). The gradient was started from 3% to18% of solvent B over 8 min, subsequently changed to 22% B in the next 7 min and finally changed to 45% of solvent B for 25 min. MassHunter workstation data acquisition software (Version B.01.03) was used for data dependent acquisition. MS spectra were acquired for 1 second from m/z 350–1800 followed by three MS/MS spectra in next second comprising the duty cycle of 2.1 second including an interscan delay of 0.1 second. Precursor ions were preferred based on charge state in the order of 2+, 3+ and >3+. The capillary and fragmentor voltage of 1950V and 175 V respectively was applied with a medium isolation width of 4 m/z and a collision energy slope of 3 V plus at offset of 2 V.

Data analysis

The mass spectrometry raw data was processed to peak list format by using MassHunter Qualitative Analysis software (Agilent Technologies, Version B.03.3). These processed peak list files were then searched against Human RefSeq Protein Database (Release 40) containing 31,811 protein sequences through Proteome Discoverer platform (Thermo Scientific, Version 1.2). The workflow includes spectrum files, spectrum selector, Mascot and Sequest search nodes followed by peptide validator for false discovery analysis whereas a reporter ion quantifier was used for quantitation. Search parameters included trypsin as the enzyme with one missed cleavage allowed, oxidation of methionine, deamidation at aspargine and glutamine were set as a variable modification whereas methyl-thio at cysteine and iTRAQ label at N-terminus of the peptide and lysine were set as a fixed modification. The reporter ion window tolerance was set at 100 ppm. The identified peptides were filtered using 1% false discovery rate derived using decoy database search strategy and top ranked hit based on peptide score, XCorr and IonScore for Sequest and Mascot respectively. The search results from both Masoct and Sequest were merged and unique peptide(s) identified for each protein were used to calculate relative protein quantitation in Proteome Discoverer workflow. The average ratio was used for relative protein quantification for proteins with multiple peptide matches. Bioinformatic analysis was carried out to categorize proteins based on biological processes, cellular component and molecular function classification using annotations in Human Protein Reference Database (HPRD, [35], which is in compliance with gene ontology (GO) standards.

Immunohistochemical labeling

Formalin fixed and paraffin embedded autopsy tissues were collected and cut into 4 μm thick sections on glass slides. These slides were subjected for deparaffinization and rehydration. Endogenous peroxidase activity was quenched by 3% H2O2 for 20 min at room temperature. For antigen retrieval, the tissue sections were microwaved in citrate buffer (pH 6.0) for 30 min. The tissue sections were incubated with 3% skimmed milk in PBS, pH 7.4 at room temperature. The tissue sections were incubated with primary antibodies at following dilutions - anti-amphiphysin (dilution 1:100, catalog # ab52646), anti-neurofascin (dilution1:250, catalog # ab31457), anti-ferritin light chain (dilution 1:500, catalog # ab69090) which were purchased from Abcam (Cambridge UK). In parallel with test slides, negative and positive controls were also subjected for overnight incubation at room temperature, followed by incubation with prediluted secondary antibody conjugated with poly HRP (catalog # K4011) from Dako. The reaction was visualized with chromogen substrate DAB/H2O2 (Dako catalog # K4007) as per manufacturer’s instructions. The sections were counterstained with hematoxylin.

Results and discussion

We used six TBM and six uninfected samples for our quantitative proteomic analysis. The overall workflow for the study is depicted in Figure 1. The tandem mass spectra were searched against human NCBI RefSeq release 40 (31,811 protein sequences) using Mascot and Sequest search engines through the Proteome Discoverer software. A total of 81,494 spectra were acquired which resulted in 5,988 peptide-spectrum matches. We identified a total of 434 proteins with 919 unique peptides. The protein and peptide data is provided in Additional file 2: Table S2 and Additional file 3: Table S3, respectively. In all, we identified 110 upregulated and 24 downregulated proteins at 1% FDR. The distribution of cellular localization and biological processes of the differentially expressed proteins is shown in Figure 2A and B. The subcellular localization and biological processes of the differentially expressed proteins were classified based on annotations in Human Protein Reference Database (HPRD, [35]. Partial list of differentially expressed proteins are provided in Tables 1 and 2.

Figure 1

Outline of the quantitative proteomic strategy. TBM-infected and uninfected frontal cortex tissues were used to extract proteins and subjected to digestion with trypsin. Peptides from the two groups were labeled with iTRAQ reagents followed by LC-MS/MS on a QTOF mass spectrometer. A subset of the differentially expressed molecules were validated by immunohistochemistry.

Figure 2

Subcellular localization and biological process of differentially expressed proteins. Subcellular localization (A) and biological process (B) of differential expressed proteins are shown.

Table 1 A partial list of proteins overexpressed in TBM
Table 2 A partial list of downregulated proteins in TBM

Proteins previously implicated in TBM and tuberculosis associated studies

Proteins that have previously been implicated in TBM and tuberculosis associated studies included Vimentin (VIM), Peroxiredoxin 5 (PRDX5), Glial fibrillary acidic protein (GFAP), Signal-regulatory protein alpha (SIRPA) and Protein disulfide isomerase family A, member 6 (PDIA6). VIM is an intermediate filament family protein and is also the potential ligand for natural cytotoxicity triggering receptor 1 (NKp46). VIM was shown to be 2.8-fold upregulated in the present study. VIM is involved in the lysis of the MTB-infected cells through NKp46 [36, 37]. PRDX5 is a member of the peroxiredoxin family of antioxidant enzymes. PRDX5 was found to be overexpressed in MTB (H37Rv) infected mice study [38]. In our study, it was found to be 2.2-fold overexpressed in TBM as compared to controls. PRDX5 plays an antioxidant protective role in tissues. GFAP showed 3.2-fold upregulation in TBM as compared to controls. Our finding correlated with a recent study that described a 4.7-fold overexpression of GFAP at mRNA level in TBM cases [39]. Representative MS/MS and reporter ion spectra of differentially regulated molecules are provided in Figure 3. MS/MS spectrum of GFAP is depicted in Figure 3A. SIRPA belongs to the signal regulatory family. The regulation of SIRPA expression can change the effectiveness of signaling processes. Growth factor receptors and growth hormone receptor signaling is suppressed by the upregulation of SIRPA[4043]. SIRPA regulates the NFk B activity that renders the cells resistant to TNF mediated apoptosis [44]. A recent study reported SIRPA to be 2-fold upregulated at the mRNA level in TBM [39]. In our study, also SIRPA protein was found to be 2-fold overexpressed in TBM. Protein disulfide isomerase family A, member 6 (PDIA6) is a protein that belongs to the endoplasmic reticulum (ER) and it is involved in the folding of disulphide bonded proteins [45]. This protein was shown to be downregulated (2.6-fold) in TBM at the mRNA level [39] and confirmed to be downregulated (2.5-fold) at the protein level in our study.

Figure 3

Representative MS and MS/MS spectra of upregulated as well as downregulated proteins. MS and MS/MS spectra of peptides from selected differentially expressed proteins identified in this study. Insets shows relative intensities of reporter ions. (A) Glial fibrilllary acid protein (GFAP); (B) Amphiphysin (AMPH); (C) Neurofascin (NFASC) (D) Ferritin, light chain (FTL).

In our previous study on TBM using gene expression microarray, we had identified 2,434 differentially regulated transcripts [39]. In current proteomic study, 33 out of 134 differentially regulated proteins were also identified to be differentially expressed in the microarray dataset. However, only three proteins (GFAP, SIRPA, ACTB) were found to be correlated at both mRNA and protein levels, which is likely due to low correlation frequently observed between transcriptomic and proteomic studies.

Differentially expressed proteins with no previous association with TBM

We identified a large number of differentially expressed proteins, which have not been reported earlier in the literature to be associated with TBM. These novel proteins include Ca++−dependent secretion activator 2 (CADPS2) which belongs to the calcium-dependent activator of secretion (CAPS) protein family, and was found 3-fold upregulated in the present study. CADPS2 protein regulates the exocytosis of synaptic and dense-core vesicles in neurons and neuroendocrine cells [46, 47]. Amphiphysin (AMPH) (Figure 3B) is an adapter molecule which plays a role in synaptic vesicle endocytosis [48] and it was found to be 3.7-fold overexpressed in TBM compared to control cases. Heat shock protein 90kDa alpha (cytosolic), class A member 1 (HSP90AA1) is a highly conserved molecular chaperone and was found to be 2.1-fold upregulated in the present study. HSP90AA1 is a molecular chaperone, which plays a role in signal transduction, protein degradation, protein folding and is expressed under stress conditions and according to a recent report it plays a role in gene expression in mammalian cells [49]. Neurofascin (NFASC) (Figure 3C) is the L1 family immunoglobulin cell adhesion molecule, found to be 2-fold upregulated in the current study. NFASC plays a role in organization of the axon initial segment (AIS) and nodes of Ranvier in central nervous system, neurite extension and neurite fasciculation [5052]. Ferritin, light chain (FTL) (Figure 3D) protein is an iron storage protein which, play a role in neurodegeneration [53] and found to be 66-fold downregulated in the present study. Downregulation of FTL causes the depigmentation in metastatic melanoma cells [54] and responds during inflammation especially where oxidative stress and reactive oxygen intermediates are generated [55, 56].

Validation of candidate biomarkers by immunohistochemical labeling

We used immunohistochemistry (IHC) to validate a subset of differentially expressed proteins identified in iTRAQ-based study. The results from IHC validation of AMPH, NFASC and FTL from fifteen TBM cases are summarized below.

Amphiphysin (AMPH) is an adapter molecule associated with cytoplasmic surface of the synaptic vesicle, which was confirmed to be overexpressed in all 15 TBM cases tested (Figure 4). AMPH protein expression was seen diffusely distributed in synaptic fashion in the cortical ribbon in control cases (Figure 4A, B). The neurons in the grey matter showed cytoplasmic labeling with AMPH (Figure 4C). In comparison, cases of tuberculous meningitis revealed intense staining of the neuropil of the cortical ribbon reflecting upregulation of protein expression (Figure 4D, E). Several neurons showed cytoplasmic accumulation of protein within the neuronal soma (Figure 4F). Astrocytes and microglia were not labeled and white matter was devoid of labeling. Inflammatory exudates in the meninges in the cases of tuberculous meningitis also did not reveal positivity.

Figure 4

Immunohistochemical labeling of AMPH protein overexpressed in TBM. Normal controls (A-C): Whole mount preparation of cerebral cortex (A) shows labeling of cortical ribbon in a synaptic pattern (B). Higher magnification highlights scattered strong labeling of pyramidal neurons (arrow, C) with synaptic pattern of neuropil labeling. TBM (D-F): Strong labeling of the cortical ribbon seen in contrast to controls (D) with intense labeling of neuropil in grey matter (E) and pyramidal neurons in the cortex (F). [A, D: whole mounts x10, B, E: Obj x5, C: Obj x40, F: Obj x 20] [g – grey matter, w – white matter].

Neurofascin (NFASC) is an adhesion molecule, which plays an important role in cell communication and signal transduction. Overexpression of this protein was validated in all 15 TBM cases. NFASC in control cases revealed positive labeling of neuropil in cortex (Figure 5A, B) with only occasional neurons showing cytoplasmic labeling in the lower cortical layers. No labeling of astrocytes or oligodendroglia was noted in control cases. In cases of tuberculous meningitis, intense labeling was seen in the neuropil of grey matter in synaptic fashion reflecting upregulated expression of protein (Figure 5C, D). Several small granule neurons expressed cytoplasmic positivity in the upper cortical layers close to the surface exudates (Figure 5G). In addition, several reactive hypertrophic subpial astrocytes beneath the surface exudates expressed NFASC protein in cytoplasm extending into its branching processes (Figure 5F), in addition to macrophages in the inflammatory exudates in the subarachnoid space (Figure 5E).

Figure 5

Immunohistochemical labeling of NFASC protein overexpressed in TBM. Normal controls (A, B): Light labeling of the cortical ribbon (A) in the frontal cortex with synaptic pattern (B). TBM(C-G): Whole mount preparation (C) highlight the darker labeling of the cortical ribbon and the inflammatory exudate in the subarachnoid space. The macrophages (E) entrapped in the inflammatory exudate are strongly labeled. Glial cells (F, arrows) and linearly arranged neuronal cytoplasm (G, arrows) are strongly labeled in the grey matter. [A, C: x10, B, D: xObj.5, E-G: xObj.20].

Ferritin light chain (FTL) is an intracellular iron storage protein and downregulation of this protein was validated by IHC in 15 TBM cases (Figure 6). Expression of this protein was observed in control cases within the grey and white matter (Figure 6A), with grey matter showing higher intensity of expression compared to white matter. In the grey matter, neurons revealed intense labelling of the cytoplasm while dendritic processes in neuropil also revealed strong labelling (Figure 6B). In the white matter, oligodendroglial cells expressed the protein in its cytoplasm in addition to neuropil showing positivity (Figure 6C). In cases of tuberculous meningitis, marked down regulation of protein expression was seen in contrast to the normal cortex (Figure 6D). Interestingly, while neuronal expression was markedly downregulated, labelling was seen in microglial cells ensheathing neurons, as also in subpial and perivascular astrocytes seen below the inflammatory exudates in the subarachnoid spaces (Figure 6E). In the white matter, neuropil and perivascular microglia revealed protein expression in the branching ramified processes of activated miciroglia (Figure 6F).

Figure 6

Immunohistochemical labeling of FTL protein downregulation in TBM. Normal controls (A-C): Intense labeling of grey and white matter (A). In the grey matter (B), the neurons (arrow) and dendritic processes are intensely labeled (B) while in the white matter (C), neuropil and oligodendroglial cells are stained intensely. TBM (D-F): In the grey and white matter of the frontal cortex, showed very light labeling in contrast to control. In the subpial zones and deep grey matter beneath the exudate and the inflammatory cells in the subarachnoid space are stained (E) while the neuropil and neurons are less intense. Microglial cells in white matter and perivascular zones show labeling (F). [A, D: whole mountsx10, B, C: xObj.20, E: xObj.x5, F: xObj.20].

Public availability of proteomic data

We have submitted the peptide data to Human Proteinpedia ( [57] and raw data files to Tranche data repository ( for easy access to the research community. The raw data is freely available in Tranche using the following hash:



The combination of mass spectrometry and quantitative proteomics is being increasingly utilized for discovering potential biomarkers. In the current study, using an iTRAQ-based quantitative proteomic approach, we were able to identify several known and novel molecules that are differentially expressed in TBM. We validated the expression levels of three novel biomarker candidates - AMPH, NFASC and FTL - by IHC on brain tissue sections from TBM cases as well as controls. These promising candidate markers warrant further evaluation in CSF from TBM patients to determine their clinical utility.



Cerebrospinal fluid


Tuberculous meningitis


Isobaric tag for relative and absolute quantitation


Quadrupole Time-of-Flight




Tris (2-carboxyethyl) phosphine




Triethylammonium bicarbonate buffer




Polymer conjugate of horseradish peroxidase




High performance liquid chromatography


Strong cation exchange


False discovery rate.


  1. 1.

    World Health Organization, Geneva, Switzerland: The International Journal of Tuberculosis and Lung Disease. 2006, 10: 1091-1097.

    Google Scholar 

  2. 2.

    Fanning A: Tuberculosis: 6. Extrapulmonary disease. CMAJ. 1999, 160: 1597-1603.

    PubMed Central  CAS  PubMed  Google Scholar 

  3. 3.

    Garg RK: Tuberculosis of the central nervous system. Postgrad Med J. 1999, 75 (881): 133-40.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  4. 4.

    Hosoglu S, Geyik MF, Balik I: Predictors of outcome in patients with tuberculous meningitis. Int J Tuberc Lung Dis. 2002, 6 (1): 64-70.

    CAS  PubMed  Google Scholar 

  5. 5.

    Thwaites GE, Nguyen DB, Nguyen HD: Dexamethasone for the treatment of tuberculous meningitis in adolescents and adults. N Engl J Med. 2004, 351 (17): 1741-51. 10.1056/NEJMoa040573

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Berenguer J, Moreno S, Laguna F: Tuberculous meningitis in patients infected with the human immunodeficiency virus. N Engl J Med. 1992, 326 (10): 668-72. 10.1056/NEJM199203053261004

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Azuaje C, Fernandez Hidalgo N, Almirante B: Tuberculous meningitis: a comparative study in relation to concurrent human immunodeficiency virus infection. Enferm Infecc Microbiol Clin. 2006, 24 (4): 245-250. 10.1016/S0213-005X(06)73770-3

    Article  PubMed  Google Scholar 

  8. 8.

    Thwaites G, Chau TT, Mai NT, Drobniewski F, McAdam K, Farrar J: Tuberculous meningitis. J Neurol Neurosurg Psychiatry. 2000, 68 (3): 289-99. 10.1136/jnnp.68.3.289

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  9. 9.

    Dinnes J, Deeks J, Kunst H: A systematic review of rapid diagnostic tests for the detection of tuberculosis infection. Health Technol Assess. 2007, 11 (3): 1-196.

    CAS  Article  PubMed  Google Scholar 

  10. 10.

    Eintracht S, Silber E, Sonnenberg P, Koornhof HJ, Saffer D: Analysis of adenosine deaminase isoenzyme-2 (ADA(2)) in cerebrospinal fluid in the diagnosis of tuberculosis meningitis. J Neurol Neurosurg Psychiatry. 2000, 69 (1): 137-8. 10.1136/jnnp.69.1.137

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  11. 11.

    Donald PR, Malan C, van der Walt A, Schoeman JF: The simultaneous determination of cerebrospinal fluid and plasma adenosine deaminase activity as a diagnostic aid in tuberculous meningitis. S Afr Med J. 1986, 69 (8): 505-7.

    CAS  PubMed  Google Scholar 

  12. 12.

    Mishra OP, Loiwal V, Ali Z, Nath G, Chandra L: Cerebrospinal fluid adenosine deaminase activity for the diagnosis of tuberculous meningitis in children. J Trop Pediatr. 1996, 42 (3): 129-32. 10.1093/tropej/42.3.129

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Kashyap RS, Kainthla RP, Mudaliar AV, Purohit HJ, Taori GM, Daginawala HF: Cerebrospinal fluid adenosine deaminase activity: a complimentary tool in the early diagnosis of tuberculous meningitis. Cerebrospinal Fluid Res. 2006, 3: 5- 10.1186/1743-8454-3-5

    PubMed Central  Article  PubMed  Google Scholar 

  14. 14.

    Nishida Y, Komachi H, Mizusawa H: A case of Listeria meningitis associated with increased adenosine deaminase in cerebrospinal fluid. Diagn Microbiol Infect Dis. 2007, 57 (4): 435-7. 10.1016/j.diagmicrobio.2006.09.005

    Article  PubMed  Google Scholar 

  15. 15.

    Mudaliar AV, Kashyap RS, Purohit HJ, Taori GM, Daginawala HF: Detection of 65 kD heat shock protein in cerebrospinal fluid of tuberculous meningitis patients. BMC Neurol. 2006, 6: 34- 10.1186/1471-2377-6-34

    PubMed Central  Article  PubMed  Google Scholar 

  16. 16.

    Donald PR, Malan C: Cerebrospinal fluid lactate and lactate dehydrogenase levels as diagnostic aids in tuberculous meningitis. S Afr Med J. 1985, 67 (1): 19-20.

    CAS  PubMed  Google Scholar 

  17. 17.

    Molavi A, LeFrock JL: Tuberculous meningitis. Med Clin North Am. 1985, 69 (2): 315-31.

    CAS  PubMed  Google Scholar 

  18. 18.

    Wilder-Smith A, Wilder-Smith E: Tuberculous meningitis and corticosteroids: a review. Neurol J Southeast Asia. 1998, 3: 57-60.

    Google Scholar 

  19. 19.

    Lorber J: The results of treatment of 549 cases of tuberculous meningitis. Am Rev Tuberc. 1954, 69 (1): 13-25.

    CAS  PubMed  Google Scholar 

  20. 20.

    Verdon R, Chevret S, Laissy JP, Wolff M: Tuberculous meningitis in adults: review of 48 cases. Clin Infect Dis. 1996, 22 (6): 982-8. 10.1093/clinids/22.6.982

    CAS  Article  PubMed  Google Scholar 

  21. 21.

    Chaerkady R, Pandey A: Applications of proteomics to lab diagnosis. Annu Rev Pathol. 2008, 3: 485-98. 10.1146/annurev.pathmechdis.3.121806.151419

    CAS  Article  PubMed  Google Scholar 

  22. 22.

    Han X, Aslanian A, Yates JR: Mass spectrometry for proteomics. Curr Opin Chem Biol. 2008, 12 (5): 483-90. 10.1016/j.cbpa.2008.07.024

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  23. 23.

    Ackermann BL, Hale JE, Duffin KL: The role of mass spectrometry in biomarker discovery and measurement. Curr Drug Metab. 2006, 7 (5): 525-39. 10.2174/138920006777697918

    CAS  Article  PubMed  Google Scholar 

  24. 24.

    Chaerkady R, Pandey A: Quantitative proteomics for identification of cancer biomarkers. Proteomics Clin Appl. 2007, 1 (9): 1080-9. 10.1002/prca.200700284

    CAS  Article  PubMed  Google Scholar 

  25. 25.

    Shiio Y, Aebersold R: Quantitative proteome analysis using isotope-coded affinity tags and mass spectrometry. Nat Protoc. 2006, 1 (1): 139-45. 10.1038/nprot.2006.22

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Chaerkady R, Harsha HC, Nalli A: A quantitative proteomic approach for identification of potential biomarkers in hepatocellular carcinoma. J Proteome Res. 2008, 7 (10): 4289-98. 10.1021/pr800197z

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  27. 27.

    Choe L, D'Ascenzo M, Relkin NR: 8-plex quantitation of changes in cerebrospinal fluid protein expression in subjects undergoing intravenous immunoglobulin treatment for Alzheimer's disease. Proteomics. 2007, 7 (20): 3651-60. 10.1002/pmic.200700316

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  28. 28.

    Martin B, Brenneman R, Becker KG, Gucek M, Cole RN, Maudsley S: iTRAQ analysis of complex proteome alterations in 3xTgAD Alzheimer's mice: understanding the interface between physiology and disease. PLoS One. 2008, 3 (7): e2750- 10.1371/journal.pone.0002750

    PubMed Central  Article  PubMed  Google Scholar 

  29. 29.

    Pawar H, Kashyap MK, Sahasrabuddhe NA: Quantitative tissue proteomics of esophageal squamous cell carcinoma for novel biomarker discovery. Cancer Biol Ther. 2011, 12 (6): 510-22. 10.4161/cbt.12.6.16833

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  30. 30.

    Prasad TSK, Keerthikumar S: Comparative proteomic analysis of Candida albicans and Candida glabrata. Clinical Proteomics. 2010, 6: 163-173. 10.1007/s12014-010-9057-9. 10.1007/s12014-010-9057-9

    CAS  Article  Google Scholar 

  31. 31.

    Ross PL, Huang YN, Marchese JN: Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics. 2004, 3 (12): 1154-69. 10.1074/mcp.M400129-MCP200

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Venugopal A, Chaerkady R, Pandey A: Application of mass spectrometry-based proteomics for biomarker discovery in neurological disorders. Ann Indian Acad Neurol. 2009, 12 (1): 3-11.

    PubMed Central  PubMed  Google Scholar 

  33. 33.

    Yang W, Woltjer RL, Sokal I: Quantitative proteomics identifies surfactant-resistant alpha-synuclein in cerebral cortex of Parkinsonism-dementia complex of Guam but not Alzheimer's disease or progressive supranuclear palsy. Am J Pathol. 2007, 171 (3): 993-1002. 10.2353/ajpath.2007.070015

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  34. 34.

    Zieske LR: A perspective on the use of iTRAQ reagent technology for protein complex and profiling studies. J Exp Bot. 2006, 57 (7): 1501-8. 10.1093/jxb/erj168

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Goel R, Muthusamy B, Pandey A, Prasad TS: Human protein reference database and human proteinpedia as discovery resources for molecular biotechnology. Mol Biotechnol. 2011, 48 (1): 87-95. 10.1007/s12033-010-9336-8

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Garg A, Barnes PF, Porgador A: Vimentin expressed on Mycobacterium tuberculosis-infected human monocytes is involved in binding to the NKp46 receptor. J Immunol. 2006, 177 (9): 6192-8.

    CAS  Article  PubMed  Google Scholar 

  37. 37.

    Thuong NT, Dunstan SJ, Chau TT: Identification of tuberculosis susceptibility genes with human macrophage gene expression profiles. PLoS Pathog. 2008, 4 (12): e1000229- 10.1371/journal.ppat.1000229

    PubMed Central  Article  PubMed  Google Scholar 

  38. 38.

    Beisiegel M, Mollenkopf HJ, Hahnke K: Combination of host susceptibility and Mycobacterium tuberculosis virulence define gene expression profile in the host. Eur J Immunol. 2009, 39 (12): 3369-84. 10.1002/eji.200939615

    CAS  Article  PubMed  Google Scholar 

  39. 39.

    Kumar GSS, Venugopal AK, Selvan LDN, Marimuthu A, Keerthikumar S, Pathare S, Dikshit JB, Tata P, Hariharan R, Prasad TSK, Harsha HC, Ramachandra YL, Mahadevan A, Chaerkady R, Shankar SK, Pandey A: Gene expression profiling of tuberculous meningitis. J Proteomics Bioinformatics. 2011, 4 (5): 98-105.

    CAS  Google Scholar 

  40. 40.

    Wu CJ, Chen Z, Ullrich A, Greene MI, O'Rourke DM: Inhibition of EGFR-mediated phosphoinositide-3-OH kinase (PI3-K) signaling and glioblastoma phenotype by signal-regulatory proteins (SIRPs). Oncogene. 2000, 19 (35): 3999-4010. 10.1038/sj.onc.1203748

    CAS  Article  PubMed  Google Scholar 

  41. 41.

    Stofega MR, Argetsinger LS, Wang H, Ullrich A, Carter-Su C: Negative regulation of growth hormone receptor/JAK2 signaling by signal regulatory protein alpha. J Biol Chem. 2000, 275 (36): 28222-9.

    CAS  PubMed  Google Scholar 

  42. 42.

    Kharitonenkov A, Chen Z, Sures I, Wang H, Schilling J, Ullrich A: A family of proteins that inhibit signalling through tyrosine kinase receptors. Nature. 1997, 386 (6621): 181-6. 10.1038/386181a0

    CAS  Article  PubMed  Google Scholar 

  43. 43.

    Neznanov N, Neznanova L, Kondratov RV, O'Rourke DM, Ullrich A, Gudkov AV: The ability of protein tyrosine phosphatase SHP-1 to suppress NFkappaB can be inhibited by dominant negative mutant of SIRPalpha. DNA Cell Biol. 2004, 23 (3): 175-82. 10.1089/104454904322964779

    CAS  Article  PubMed  Google Scholar 

  44. 44.

    Neznanov N, Neznanova L, Kondratov RV: Dominant negative form of signal-regulatory protein-alpha (SIRPalpha/SHPS-1) inhibits tumor necrosis factor-mediated apoptosis by activation of NF-kappa B. J Biol Chem. 2003, 278 (6): 3809-15. 10.1074/jbc.M210698200

    CAS  Article  PubMed  Google Scholar 

  45. 45.

    Hayano T, Kikuchi M: Cloning and sequencing of the cDNA encoding human P5. Gene. 1995, 164 (2): 377-8. 10.1016/0378-1119(95)00474-K

    CAS  Article  PubMed  Google Scholar 

  46. 46.

    Berwin B, Floor E, Martin TF: CAPS (mammalian UNC-31) protein localizes to membranes involved in dense-core vesicle exocytosis. Neuron. 1998, 21 (1): 137-45. 10.1016/S0896-6273(00)80521-8

    CAS  Article  PubMed  Google Scholar 

  47. 47.

    Renden R, Berwin B, Davis W: Drosophila CAPS is an essential gene that regulates dense-core vesicle release and synaptic vesicle fusion. Neuron. 2001, 31 (3): 421-37. 10.1016/S0896-6273(01)00382-8

    CAS  Article  PubMed  Google Scholar 

  48. 48.

    Wigge P, Kohler K, Vallis Y: Amphiphysin heterodimers: potential role in clathrin-mediated endocytosis. Mol Biol Cell. 1997, 8 (10): 2003-15.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  49. 49.

    Vozzolo L, Loh B, Gane PJ: Gyrase B inhibitor impairs HIV-1 replication by targeting Hsp90 and the capsid protein. J Biol Chem. 2010, 285 (50): 39314-28. 10.1074/jbc.M110.155275

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  50. 50.

    Zonta B, Tait S, Melrose S: Glial and neuronal isoforms of Neurofascin have distinct roles in the assembly of nodes of Ranvier in the central nervous system. J Cell Biol. 2008, 181 (7): 1169-77. 10.1083/jcb.200712154

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  51. 51.

    Zonta B, Desmazieres A, Rinaldi A: A critical role for Neurofascin in regulating action potential initiation through maintenance of the axon initial segment. Neuron. 2011, 69 (5): 945-56. 10.1016/j.neuron.2011.02.021

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  52. 52.

    Koticha D, Babiarz J, Kane-Goldsmith N, Jacob J, Raju K, Grumet M: Cell adhesion and neurite outgrowth are promoted by neurofascin NF155 and inhibited by NF186. Mol Cell Neurosci. 2005, 30 (1): 137-48. 10.1016/j.mcn.2005.06.007

    CAS  Article  PubMed  Google Scholar 

  53. 53.

    Friedman A, Arosio P, Finazzi D, Koziorowski D, Galazka-Friedman J: Ferritin as an important player in neurodegeneration. Parkinsonism Relat Disord. 2011, 423-430. 6,

  54. 54.

    Maresca V, Flori E, Cardinali G: Ferritin light chain down-modulation generates depigmentation in human metastatic melanoma cells by influencing tyrosinase maturation. J Cell Physiol. 2006, 206 (3): 843-8. 10.1002/jcp.20543

    CAS  Article  PubMed  Google Scholar 

  55. 55.

    Roeser H, Jacobs A, Worwood M: Iron metabolism in biochemistry and medicine. Volume 2. Edited by: Jacobs A, Worwood M. 1980, 605-640.

    Google Scholar 

  56. 56.

    Kwak EL, Larochelle DA, Beaumont C, Torti SV, Torti FM: J Biol Chem. 1995, 270: 15285-15293. 10.1074/jbc.270.25.15285

    CAS  Article  PubMed  Google Scholar 

  57. 57.

    Kandasamy K, Keerthikumar S, Goel R: Human Proteinpedia: a unified discovery resource for proteomics research. Nucleic Acids Res. 2009, 37 (Database issue): D773-781.

    PubMed Central  CAS  Article  PubMed  Google Scholar 

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This study was supported by “DBT Programme Support on Neuroproteomics for Proteomic Investigation of Neurological Disorders.” Nandini Sahasrabuddhe and Harsh Pawar are recipients of Senior Research fellowship award from the Council for Scientific and Industrial Research (CSIR) of the Government of India. Santosh Renuse is a recipient of Senior Research fellowship award from the University Grants Commission (UGC) of the Government of India. Rakesh Sharma is a Research associate supported by DBT. T. S. Keshava Prasad is supported by a research grant on “Establishment of a National Database on Tuberculosis” and “Development of Infrastructure and a Computational Framework for Analysis of Proteomic Data” from DBT. Harsha Gowda is a Wellcome Trust-DBT India Alliance Early Career Fellow. Human brain tissues for the study were obtained from the Human Brain Tissue Repository, a national research facility in the Department of Neuropathology, National Institute of Mental Health and Neuro Sciences, Bangalore, India. Secretarial assistance of Mrs. Manjula Madan is acknowledged.

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Correspondence to K Shankar or Akhilesh Pandey.

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The authors have declared no conflict of interest.

Authors’ contributions

RC, TSKP, SKS and AP conceived the study, GSSK, YLR, PS, RC, TSKP, SKS and AP designed the experiments; GSSK, AKV, AM, SR, HCH, NAS, HP and RS, carried out the experiments; GSSK, SR, PK, RS, SRG, KW and AP analyzed the data; GSSK, SKS, and AP wrote the manuscript. All authors read and approved the final manuscript.

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Kumar, G.S.S., Venugopal, A.K., Mahadevan, A. et al. Quantitative proteomics for identifying biomarkers for tuberculous meningitis. Clin Proteom 9, 12 (2012).

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  • Relative quantitation
  • Cerebrospinal fluid
  • Histopathology
  • Early diagnosis
  • Tuberculosis