|Gene Expression Profiling of Tuberculous Meningitis
|Ghantasala S. Sameer Kumar1,2, Abhilash K. Venugopal1,2,3,4, Lakshmi Dhevi N. Selvan1,5, Arivusudar Marimuthu1,6, Shivakumar
Keerthikumar1, Swapnali Pathare7, Jyoti Bajpai Dikshit7, Pramila Tata7, Ramesh Hariharan7, Thottethodi Subrahmanya Keshava Prasad1, H.
C. Harsha1, Y.L Ramachandra2, Anita Mahadevan8, Raghothama Chaerkady1,2,3,4, S. K. Shankar8* and Akhilesh Pandey3,4,9,10*
|1Institute of Bioinformatics, International Technology Park, Bangalore 560066, Karnataka, India
|2Department of Biotechnology, Kuvempu University, Shimoga 577 451, India
|3McKusick-Nathans Institute of Genetic Medicine
|4Departments of Biological Chemistry
|5School of Biotechnology, Amrita university, Kollam 690525, India
|6Manipal University, Madhav Nagar, Manipal, Karnataka 576104 India
|7Strand Life Sciences, Bangalore 560024, Karnataka, India
|8Department of Neuropathology, National Institute of Mental Health and Neurosciences, Bangalore 560029, India
|9Department of Pathology
|10Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
||Dr. Akhilesh Pandey, M.D., Ph.D.,
Institute of Genetic Medicine
733 N. Broadway
BRB 569, Johns Hopkins University
Baltimore, MD 21205
||Dr. S. K. Shankar, MD, FAMS, FNASc, FIC Path
Department of Neuropathology
National Institute of Mental Health and Neurosciences
Bangalore 560029, India
|Received December 17, 2010; Accepted May 17, 2011; Published May 20, 2011
|Citation: Kumar GSS, Venugopal AK, Selvan LDN, MarimuthuA, Keerthikumar S,
et al. (2011) Gene Expression Profiling of Tuberculous Meningitis. J Proteomics
Bioinform 4: 098-105. doi:10.4172/jpb.1000174
|Copyright: © 2011 Kumar GSS, et al. This is an open-access article distributed
under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the
original author and source are credited.
|Tuberculous meningitis (TBM) is a form of extra pulmonary tuberculosis that is associated with severe
neurological deficits and a high mortality. Early diagnosis of TBM is a major challenge despite the availability of several
diagnostic methods. Existing diagnostic methods and markers are inadequate for early diagnosis of TBM owing to
poor specificity and sensitivity. DNA microarray technology permits high-throughput identification of differentially
expressed genes. In order to identify molecules as candidate biomarkers for early diagnosis or as therapeutic targets
in TBM, we carried out transcriptomic analysis of brain tissue using whole human genome oligonucleotide arrays.
From this gene expression analysis, we identified 2,434 genes that were differentially expressed at least two-fold in
TBM cases as compared to controls. The large majority of the differentially expressed genes encoded proteins that
are involved in metabolism, cell growth, transport, immune response, cell communication and signal transduction.
We confirmed the upregulation of two molecules, serpin peptidase inhibitor, clade A member 3 (SERPINA3) and
glial fibrillary acidic protein (GFAP), at the protein level by immunohistochemical analysis. The findings from our
study should help us understand the molecular mechanisms underlying TBM and to develop better diagnostic and
therapeutic strategies against this deadly disease.
|DNA microarrays; Biomarkers; Early diagnosis;
|Although the causative organism of tuberculosis was discovered
over a hundred years ago, this disease still remains a major public health
problem worldwide. Tuberculosis primarily affects the lungs but can
spread hematogenously to extra pulmonary sites such as lymph nodes,
bones, meninges and genito-urinary-tract . One of the most frequent
sites of extra pulmonary disease is tuberculous meningitis (TBM),
which is a common form of central nervous system tuberculosis with
high morbidity and mortality [2-4]. The incidence of TBM is on the
rise with the increase in immunodeficient states such as HIV/AIDS .
Concomitant with an increase in the incidence of TBM, development
of multi-drug resistance in AIDS patients is a major obstacle associated
with its treatment .
|The diagnosis of TBM continues to be a challenge because the gold
standard for diagnosis requires that Mycobacterium tuberculosis (M.tb)
be demonstrated by culture of the cerebrospinal fluid (CSF) of suspected
patients. This is a time consuming process which takes approximately
8 weeks . Over the past several years, different molecular and
biochemical assays have been developed for rapid diagnosis of M.tb.
PCR based assays for detection of the pathogen, ELISA to detect M.tb
protein antigens or host antibodies directed against M.tb are the most
widely used assays for detection of M.tb . A combined approach of
detecting IFN-gamma levels by radioimmunoassay and use of IS6110
primer to detect M.tb by PCR shows a reasonably high sensitivity (80%)
and specificity (92.6%), in the CSF of TBM patients . Detection of
anti-mycobacterial antibodies and mycobacterial immune complexes
(IgG) have also been employed for diagnosis with variable sensitivity and specificity . Other alternatives include demonstration of high
lactate levels [11,12] and adenosine deaminase (ADA) in CSF for
quick diagnosis and management of TBM . However, the above
described methods are still limited in their sensitivity and specificity in
the clinical setting. As the mortality rate of TBM remains high [14,15],
there is a critical need for identification of appropriate biomarkers for
early diagnosis of TBM.
|Gene expression profiling studies have previously been performed
on CSF or blood of patients with TBM [16,17]. In the present study,
we used whole genome DNA microarrays for investigating changes
at the transcriptome level in infected brain tissues from TBM cases
that were confirmed by autopsy as compared to uninfected brain
tissues from controls. Further, we performed immunohistochemical
validation of some of the differentially expressed genes identified from our microarray studies. Our results confirm that proteins encoded by
SERPINA3 and GFAP genes are indeed upregulated in TBM patients.
Further studies to detect proteins encoded by these genes in the CSF
as potential biomarkers in CSF could potentially lead to improved
methods for diagnosing TBM.
|Materials and Methods
|Human brain tissue samples from five cases of TBM (confirmed
by detection of mycobacterial antibody/immune complexes in
cerebrospinal fluid, and/or demonstration of acid fast bacilli by Ziehl
Neilson’s stain in the smear from basal exudates in meninges and
histopathological features of granulomatous or chronic meningitis)
and four control brains that were archived as frozen and formalin fixed
specimens at Human Brain Tissue Repository (Human Brain Bank)
in the Department of Neuropathology at National Institute of Mental
Health and Neuro Sciences (NIMHANS), Bangalore, India, were
used for the microarray experiments. The details of samples used are
provided in the Supplementary Table 1.
|The brains were collected at autopsy with written informed consent
from close relatives to utilize them for research purposes. The study
was approved by the Institutional Ethics Committee of NIMHANS.
The dead bodies were shifted to 4°C within one hour of death. Tissues
from frontal cortex (2x2 cm) were frozen at -86°C and preserved until
analysis. The rest of the brain was fixed in 10% buffered formalin for 12–
18 weeks. Representative tissue blocks were processed for histological
evaluation. The postmortem interval (interval from the time of death
to time the tissue was transferred to -86°C) varied from 6 hrs-13½ hrs
for control cases and 1 hr 15 minutes–18 hrs for TBM cases. Frozen
brain tissue samples with overlying meninges from the five cases and
four controls were excised as small pieces, transferred to RNA Later
(Ambion Inc Austin Tx) and incubated at 4°C for 12–16 hours to
facilitate proper penetration into the tissues and stored at -86°C until
|Approximately 100 mg of tissue from normal and infected brains
was used for RNA isolation. The tissues were pulverized in QIAzol
lysis reagent (Qiagen, Valencia, CA) using a homogenizer. Total RNA
extraction and purification was carried out using RNeasy lipid tissue
mini kit (QIAGEN, Valencia, CA) as per the manufacturer’s protocol.
The yield and quality of isolated total RNA was checked using the
NanoDrop spectrophotometer (Nanodrop Technologies, Wilmington,
DE). Integrity of the isolated RNA was assessed by RNA gel and/or 2100
Bioanalyzer (Agilent Technologies, Palo Alto, CA). The samples were
further processed based on RNA integrity.
|cDNA synthesis, labeling and hybridization
|Total RNA (600 ng) from each sample was reverse transcribed and
linear amplified using Quick Amp Kit, One-color (Agilent Technologies,
Palo Alto, CA) that employ OligodT-T7 promoter primers. The cDNA
generated was used as template for in vitro transcription reaction
with Cy3-CTP and RNA polymerase; thus cRNA was simultaneously
synthesized and labeled. The labeled cRNA was purified using RNeasy
spin columns (Qiagen, Valencia, CA). The samples with specific
activity >9 pmol Cy3 per µg and yield >1600µg were selected for
hybridization. Cy-3 labeled cRNA was fragmented and hybridized
onto oligonucleotide-based whole human genome DNA microarrays
(G4112F, 4x44 K, Agilent Technologies, Palo Alto, CA) for 16 hours at 65°C. The arrays were subsequently washed with gene expression wash
buffers according to the manufacturer’s hybridization protocol (Agilent
Oligo Microarray Kit, Agilent Technologies).
|Scanning and data analysis
|The slides were scanned with Agilent microarray scanner (G2505B)
using one color scan setting for 4x44 K array slides (scan resolution
5µm, dye channel was set to green, green PMT was set to 100%)
and the images processed with Agilent’s feature extraction software
(126.96.36.199) to obtain the raw data files for further analysis. The raw data
from microarray experiments were submitted to the Gene Expression
Omnibus (http://www.ncbi.nlm.nih.gov/geo accession#GSE23074).
GeneSpring GX v11.0.2 (Agilent Technologies, Santa Clara) software
was used to analyze the gene expression profiles. Raw data was
imported into the GeneSpring GX software. The recommended quantile
normalization without baseline transformation and t-test were applied.
To determine the differentially expressed genes that were statistically
significant, a p-value of <0.05 and a fold-change cut-off threshold of ≥2
were used. GO analysis was carried out for the differentially expressed
genes using GeneSpring Gx software. A p-value threshold of 0.1 was
used to filter out the significantly overrepresented GO categories.
|Biological network analysis
|Pathway analysis was carried out using Genespring GX v.11.0.2.
Differentially expressed genes obtained after filtering based on foldchange
cut off (≥4.0) was taken as input and biological networks were
generated by comparing the input list to a reference list containing >1.4
million reactions generated by natural language processing algorithm
and from different interaction databases. To obtain high confidence
networks, analysis was carried out using filters that included binding,
expression, metabolism, transport, promoter binding and regulation
of the molecules. The number of molecules per network was restricted
to 50. The entities which do not have connections were removed. The
constructed network was overlaid with final input list to visualize
differentially expressed genes.
|For validation of the upregulated molecules in cases of TBM,
immunohistochemical labeling was carried out using commercially
available monoclonal antibodies directed against GFAP (Biogenex,
Houston, Texas, USA) at 1:200 dilution and against SERPINA3 (Sigma
Aldrich, St. Louis, MO) at 1:100 dilution. HRP tagged secondary
antibody provided with Envision kit (DAKO-K4011 and K4007, DAKO,
Carpinteria, CA) was used and the immune reaction visualized with
DAB/H2O2 as the chromogen. The immunohistochemical validation
was carried out on an independent subset of 15 formalin fixed
human brains from confirmed cases of TBM (Age range 1–55 years,
postmortem interval 1 hour–18 hours) including the five cases used for
gene expression profile and four controls. These cases were processed
for paraffin embedding and histological evaluation to establish presence
|Briefly, 4µm thick serial sections from controls and TBM cases
were collected on silane coated slides. The paraffin sections were
deparaffinized and dehydrated. After stabilizing in PBS at room
temperature, the endogenous peroxidase activity was quenched by
blocking solution for 20 min at room temperature. Antigen retrieval
was carried out in citrate buffer (pH 6.0) by microwaving the sections
for 30 min. After blocking the non-specific binding sites in 3% non-fat
dry milk powder for 15 min at room temperature, the serial sections
were incubated with primary antibodies against GFAP and SERPINA3 overnight at 4°C. The sections were washed thrice in PBS and incubated
with the appropriate secondary antibodies conjugated with HRP for
30 min at room temperature. The signal was developed with DAKO
substrate buffer and chromogen. The sections were counterstained
with hematoxylin and mounted. The immunolabeled sections were
examined independently by two experienced neuropathologists (AM
and SKS). The staining pattern, intensity and subcellular localization
were visually scored.
|Results and Discussion
|Gene expression profiling using DNA microarrays was carried
out to identify differential gene expression in TBM samples. The
workflow adopted in this study is illustrated in Figure 1. We identified
2,434 genes that were differentially expressed with the p-value <0.05
and ≥2 fold change. A complete list of these genes is provided in
Supplementary Table 2. A partial list of differentially expressed genes is
shown in Table 1 along with protein associated information including
protein domains, subcellular localization, biological processes and
molecular function. For hierarchical clustering, we applied euclidean
distance metric and centroid linkage as parameters. The heat map for
differentially expressed genes is shown in Figure 2. Gene Ontology
based classification of differentially expressed genes were carried out by
using GeneSpring Gx gene expression analysis software. Significantly
overrepresented GO categories are provided in Supplementary Table
3. Genes corresponding to immune response, integral to membrane,
immune system process, intrinsic to membrane, antigen processing and
presentation of peptide or polysaccharide antigen via MHC class II were significantly overrepresented in TBM as compared to normal. Since,
TBM is an inflammatory disease, the genes related to host defense are
likely to be altered. Thus the overrepresentation of immune response
genes in TBM by GO analysis was found as anticipated.
||Table 1: A partial list of differentially regulated genes expressed in TBM.
||Figure 1: Work flow for transcriptomic studies in TBM. RNA isolation was
carried out from brain tissue (frontal cortex) samples from confirmed TBM
cases and from control cases. Based on the RNA integrity, selected samples
were further processed for microarray experiments and analyzed. Selected
upregulated molecules were validated by IHC.
||Figure 2: Heat map of differentially expressed genes in TBM. Unsupervised
hierarchical clustering of differentially expressed genes with a p value <0.05
and fold-change ≥4 is shown in the figure. Cluster A represents higher
expression levels in TBM cases while cluster B represents a lower expression
levels in TBM cases as compared to controls.
|Differentially expressed genes that have previously been
reported in TBM studies
|Earlier studies on gene expressional profiling of TBM have
identified several genes [16,17]. We found a number of genes in this
study which showed a similar differential expression as described
previously. A subset of genes or gene products that have been reported
to be differentially expressed either in TBM or in the context of
tuberculosis-associated studies are discussed below:
|Metallothionein 1F (MT1F) was shown to be upregulated in mice
infected with laboratory strains of M.tb such as H37Rv and H37Ra .
We observed 2.5-fold upregulation of MT1F in the present study. MT1F
belongs to cysteine-rich metal binding proteins family that is known to
play a key role in regeneration of tissue after damage. Metallothioneins
also have a functional role as antiapoptotic antioxidants in neurological
abnormalities . Changes in the expression levels of these
metallothioneins mediates immune response under inflammatory
conditions . Polymorphisms of major histocompatibility complex
class II, DR beta 1 (HLA-DRB1) gene have been found to be associated
with susceptibility to TB . HLA-DRB1 belongs to an important
class of molecules which play a crucial role in the process of antigen
presentation . HLA-DRB1 was shown to be upregulated 3-fold in
the current study. IL12RB1 encoded protein is a type-1 transmembrane
protein, which belongs to the hemopoietin receptor superfamily .
Upregulation of IL12RB1 has been reported earlier in M.tb infected mice . Further, nucleotide polymorphisms in IL12RB1 have
also been reported to be associated with susceptibility to TB .
In this study, we found 3-fold upregulation of IL12RB1 transcript in
TBM. The protein encoded by solute carrier family 11, member 1
(SLC11A1) belongs to the solute carrier family and is also known as
natural resistance-associated macrophage protein 1. Polymorphisms
in SLC11A1 have been associated with susceptibility to TB . We
observed a 4-fold upregulation of SLC11A1 transcript in TBM cases.
Tumor necrosis factor receptor superfamily, member 4 (TNFRSF4) is
known to be involved in the pathogenesis of various immunological
abnormalities including infectious, autoimmune, inflammatory related
diseases. It has been shown to be upregulated in mice infected with M.tb strains H37Rv and H37Ra [18,24]. In this study, TNFRSF4 transcript
was 2-fold upregulated in TBM as compared to uninfected controls.
Chemokine (C-X-C motif) ligand 9 (CXCL9) is a T cell trafficking
chemokine , which is known to be upregulated in mice infected
with laboratory strains of M.tb and high concentrations of CXCL9 have
been reported in the CSF of TBM patients [18,26]. We observed a 36-
fold upregulation of CXCL9 transcript in our study. Met proto-oncogene
(MET), also known as hepatocyte growth factor receptor, encodes a
receptor tyrosine kinase, which has shown to be overexpressed in M.tb
stimulated monocyte-derived macrophages (MDMs) . We found
a 9-fold upregulation of this transcript in our transcriptomic study.
CHI3L1 (Chitinase 3-like 1) is involved in the process of inflammation
and tissue remodeling. CHI3L1 was also shown to be upregulated
in peripheral blood mononuclear cells (PBMCs) of recovered extra
pulmonary tuberculosis patients upon incubating their PBMCs with
whole lysates of M.tb . CHI3L1 transcript was 16-fold upregulated
in the present study.
|SLC15A2 (Solute carrier family 15, member 2 ) is known to
be involved in the induction of proton-dependent transport for
transporting small peptides . In an ex vivo experiment, M.tbstimulated
MDMs showed downregulation of SLC15A2 . In
this study, we found 5-fold downregulation of SLC15A2 transcript
in TBM. ITGB1 (Integrin beta 1) belongs to the membrane receptor
family, which is involved in cell adhesion and recognition. ITGB1 is
also involved in several cellular processes including immune response
. ITGB1 transcript was observed to be 3-fold downregulated in
this study. It has been reported that ITGB1 was downregulated in fetal
lung cell line in the presence of M.tb recombinant CFP-10/ESAT-6
protein (rCFES) . Cathepsin L1 (CTSL1) encoded protein is a
lysosomal cysteine proteinase, which is involved in intracellular protein
catabolism. CTSL1 transcript was shown to be 2-fold downregulated in
this study and the encoded protein is localized to endosomes. CTSL1
activity and maturation was affected by Mycobacterium avium and M.tb
infected macrophages .
|Differentially expressed genes that were not reported earlier
|A large number of differentially expressed genes identified in
our study have not been previously reported in the literature to be
associated with TBM. These genes are involved in various biological
functions including cell-cell communication, enzymatic activity and
|Novel upregulated genes
|Over the past several years, various studies have proven the
overexpression of GFAP (Glial fibrillary acidic protein) in CNS
tuberculosis, toxocariasis and pneumococcal meningitis [31-33]. It
has been shown to be overexpressed in astrocytes when there is an astrogliosis during CNS inflammation. In the present study, GFAP
transcript showed 5-fold upregulation. Interleukin 4 induced 1
(IL4I1) expression has been shown to be induced by IL-4 in B-cells
and this secreted protein is known to be involved in the regulation
of T lymphocytes . IL4I1 transcript was found to be 15-fold
upregulation in our study. Interleukin 21 receptor (IL21R) belongs to
the type 1 cytokine receptor family and is involved in the maturation
of natural killer cell and regulation of T lymphocytes . IL21R
binds to its ligand and activates the downstream signaling molecules
like JAK 1, JAK3, STAT1 and STAT3 . IL21R transcript showed
13-fold upregulation in the current study. Serpin peptidase inhibitor,
clade A (alpha-1 antiproteinase, antitrypsin), member 3 (SERPINA3)
is a plasma protease inhibitor and is a member of the serine protease
inhibitor class. SERPINA3 transcript was found to be 14-fold
upregulated in the current study. The overexpression of this protein
has been reported in several neurological disorders like schizophrenia
[37,38]. GFAP and SERPINA3 were chosen for further validation by
immunohistochemical analysis which is explained in the later section.
|Novel downregulated genes
|RELN mRNA was observed to be 19-fold downregulated in the
present study. Reelin (RELN) encodes a secreted extracellular matrix
protein. RELN is involved in the molecular mechanism of cognitive
functions. Defects in RELN expression have been associated with
the abnormality of neuronal position and dendritic development in
mouse models . The protein encoded by Vacuolar protein sorting
26 (VPS26) is involved in retrograde transport of proteins. VPS26
was found to be 2-fold downregulated in TBM cases. Sorting nexin 3
(SNX3), sorting nexin 12 (SNX12) and sorting nexin 18 (SNX18) belong
to the sorting nexin family and are involved in intracellular trafficking. Inhibited expression of the SNX3 has been shown to affect membrane
trafficking from early endosomes to recycling endosomes . In this
study, SNX3 transcript was found to be 3-fold downregulated and
protein encoded by this gene is localized to the endosomes. SNX12 and
SNX18 were found to be downregulated 3-fold and 4-fold, respectively.
Defensin alpha 3, neutrophil-specific (DEFA3) protein belongs to the
family of microbicidal peptides which is involved in antimicrobial
activity. DEFA3 mRNA and protein expression levels have been
reported to be decreased in transmigrated monocytes . It showed
15-fold downregulation in our study.
|Biological network data analysis
|Several genes are involved in carrying out a specific biological
process and they often interact with each other. We carried out biological
network analysis using GeneSpring Gx software to identify such
networks in relation to TBM. Natural language processing is used by
GeneSpring to generate a database of interacting molecules. Genes that
were differentially expressed in TBM were used as input which resulted
in the generation of a complex network depending on the connectivity
between the genes. It was comprised of several nodes forming distinct
subnetworks. Expression values were overlaid onto the network. We
identified a subnetwork with SERPINA3 being the key molecule which
is shown in Figure 3. SERPINA3 is known to be associated with several
neurological disorders including Parkinson’s disease , Alzheimer’s disease , schizophrenia and cerebrovascular disease . Notably,
we observed GFAP and CHI3L1 which were upregulated in TBM to be
part of this subnetwork. Earlier reports have shown that CHI3L1 and
GFAP were co localized in the brain infarction and in other neurological
diseases . Other molecules that were part of the subnetwork include
P704P, FKSG30, ACTBL2, ACTG2, ACTO7 and FHL5. Although it
is intriguing that we see the interaction of these molecules in the
represented network, the exact functional role and significance with
respect to TBM has to be investigated further.
||Figure 3: Biological network analysis of differentially expressed
genes in TBM. Illustration of subnetwork identified by biological network
analysis. CHI3L1 and GFAP which were upregulated in TBM formed a highly
interconnected network through SERPINA3.
|Validation of candidate upregulated biomarkers by
|In the present study, we chose to carry out IHC-based validation
for two of the novel upregulated molecules identified in this study
based on their biological significance and their fold expression. This
was performed in 15 TBM cases, including the cases used for gene
|GFAP: The staining pattern of GFAP in TBM cases as compared
to control cases is shown in histological microphotographs in Figure
4. As shown in the figure, we observed thick subpial carpet staining of
GFAP in the whole mount preparation of the frontal cortex of the TBM
cases (Figure 4B) as compared to the control groups (Figure 4A). In
the frontal cortex, astrocytes in the superficial cortex along with thick
processes (Figure 4C), the white matter in the gyri and subcortical band
were densely stained with GFAP in TBM cases. In controls (Figure 4D),
GFAP labeling is conspicuous around the blood vessels with the foot
processes (arrow) impinging on the vessel walls and forming a fine
lacy background. In TBM cases, we found strong labeling of GFAP in
the hypertrophic astrocytes (Figure 4E) with dark cell body and thick
||Figure 4: Immunohistochemical labeling of GFAP. A: Whole mount
preparation of frontal cortex from a control shows thin subpial band of gliosis
(arrow) and very light labeling of protoplasmic astrocytes. The white matter
(w) shows diffuse, low intensity staining. (GFAP, Obj x5). B: Whole mount
preparation of frontal cortex from a case of TBM showing thick subpial carpet
(arrow) extending into the cortical grey matter. Note the reduced gradient of
labeling of grey matter and diffuse dark staining of white matter (w). (GFAP,
Obj x 5). C: Low power view of the frontal cortex from a case of TBM shows
thick gliotic pial band (arrow) and hypertrophic glial cells in the superficial
cortex. Above the pial band in the subarachnoid space, inflammatory exudates
are observed. (GFAP, Obj x 10). D: The white matter at higher magnification
from the control shows astrocytes with small body and thin long processes,
some of them impinging on the vessel wall to form a fine lacy background.
(GFAP, Obj x 20). E: Higher magnification of the subpial zone from the case of
TBM showing large, hypertrophic reactive astrocytes with darkly labeled body
and thick cell processes. The neuropil between the cells also has coarse fibres
unlike the control. (GFAP, Obj x 40).
|SERPINA3: Whole mount preparations of immuno stained
sections from the frontal cortex in TBM (Figure 5B) showed marginally
enhanced staining of the cortical ribbon in contrast to control (Figure
5A). On closer examination in controls, the neuronal cytoplasm in the
superficial layers (Figure 5) and astrocytes in the grey and white matter
was labeled strongly with thick processes, but the density of astrocytes
was conspicuously lower (Figure 5D). In the case of TBM, a thick subpial
carpet was found in addition to numerous subpial astrocytes beneath
the dense chronic granulomatous exudates (Figure 5F) and the cortex
and white matter (Figure 5E). In the subarachnoid space the histiocytes
in the exudates and around the vessels were densely labeled (Figure 5F and Figure 5G). The cortical neuronal labeling is essentially similar to
controls, but marginally higher intensity of cell labeling involving all
the layers of cortical ribbon unlike control. All these cellular elements
contribute to the upregulated gene expression protein profile.
||Figure 5: Immunohistochemical labeling of SERPINA3. A: Whole mount
preparation of frontal cortex from a control showing low diffuse labeling of the
cortical ribbon and low intensity staining of white matter (w). (SERPINA3, Obj
x 5). B: Whole mount preparation of frontal cortex from a case of TBM showing
diffuse intense staining of the cortical ribbon and exudates in the subarachnoid
space (w - white matter). (SERPINA3, Obj x 5). C: The pyramidal neurons
of the superficial cortex in controls have cytoplasmic labeling. (SERPINA3,
Obj x 20). D: The astrocytes in the cortex of controls are stained well, though
the density of labeled astrocytes is low. (SERPINA3, Obj x 20). E: The white
matter shows a higher density of the SERPINA3 labeled astrocytes in TBM.
(SERPINA3, Obj x 10). F: Low power view of the frontal cortex showing dense
labeling of subpial glial membrane, reactive astrocytes underneath and the
granulomatous exudates in the subarachnoid space. (TBM case) (v: vessel)
(SERPINA3, Obj x 20). G: Higher magnification of the subarachnoid exudates
showing labeling of the round histocytes. (TBM case) (SERPINA3, Obj x 20).
|Though many clinical, morphological and biochemical studies
have been carried out earlier on tuberculous meningitis, only a limited
number of gene expression profiling studies are have been undertaken.
In tropical developing countries, TBM is one of the commonest forms
of chronic meningitis. A rise in HIV cases in these geographic regions
further worsens the situation. Biomarkers that could facilitate early
diagnosis of TBM provide a better opportunity for clinical management
of this disease. By carrying out gene expression profiling of infected
brain from TBM patients and control subjects, we have identified
several differentially expressed genes. Systematic validation of some of
the candidate molecules may provide biomarkers with potential clinical
utility. Some of the differentially expressed genes encode proteins that
are detectable in CSF and targeted studies to develop assay systems to
monitor these molecules in CSF may prove useful.
|The study was supported by a research grant “DBT Programme Support
on Neuroproteomics of Neurological Disorders” to the Institute of Bioinformatics
and National Institute of Mental Health and Neurosciences by the Department of
Biotechnology (DBT), Government of India. T. S. Keshava Prasad is supported by
a research grant on “Establishment of a National Database on Tuberculosis” and is
a recipient of a Young Investigator Award 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 Bank 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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