Virtually all dormant models against tuberculosis tested in animals used laboratory strain H37Rv or Erdman strain. But major outbreaks of tuberculosis (TB) occur with the strains that have widely different genotypes and phenotypes compared to H37Rv. In this study, we used a custom oligonucleotide microarray to determine the overall transcriptional response of laboratory strain (H37Rv) and most prevalent clinical strains (S7 and S10) of M. tuberculosis from South India to hypoxia.

1. Journal of Biotechnology 210 (2015) 70–80
Contents lists available at ScienceDirect
Journal of Biotechnology
journal homepage: www.elsevier.com/locate/jbiotec
The influence of reduced oxygen availability on gene expression in
laboratory (H37Rv) and clinical strains (S7 and S10) of Mycobacterium
tuberculosis
Santhi Devasundarama
, Imran Khanb
, Neeraj Kumarb
, Sulochana Dasa
, Alamelu Rajaa,∗
a
Department of Immunology, National Institute for Research in Tuberculosis (ICMR), (Formerly Tuberculosis Research Centre), No. 1,
Mayor Sathiyamoorthy Road, Chetpet, Chennai 600 031, India
b
Department of Molecular Reproduction, Development and Genetics Biological Sciences Building, Indian Institute of Science, Bangalore 560 012, India
a r t i c l e i n f o
Article history:
Received 24 February 2015
Received in revised form 16 April 2015
Accepted 23 April 2015
Available online 19 May 2015
Keywords:
Tuberculosis
Clinical strains
Dormancy
Hypoxia
Gene regulation
a b s t r a c t
Mycobacterium tuberculosis has the ability to persist within the host in a dormant stage. One important
condition believed to contribute to dormancy is reduced access to oxygen known as hypoxia. However,
the response of M. tuberculosis to such hypoxia condition is not fully characterized. Virtually all dormant
models against tuberculosis tested in animals used laboratory strain H37Rv or Erdman strain. But major
outbreaks of tuberculosis (TB) occur with the strains that have widely different genotypes and phenotypes
compared to H37Rv. In this study, we used a custom oligonucleotide microarray to determine the overall
transcriptional response of laboratory strain (H37Rv) and most prevalent clinical strains (S7 and S10) of
M. tuberculosis from South India to hypoxia. Analysis of microarray results revealed that a total of 1161
genes were differentially regulated (≥1.5 fold change) in H37Rv, among them 659 genes upregulated
and 502 genes down regulated. Microarray data of clinical isolates showed that a total of 790 genes
were differentially regulated in S7 among which 453 genes were upregulated and 337 down regulated.
Interestingly, numerous genes were also differentially regulated in S10 (total 2805 genes) of which 1463
genes upregulated and 1342 genes down regulated during reduced oxygen condition (Wayne’s model).
One hundred and thirty-four genes were found common and upregulated among all three strains (H37Rv,
S7, and S10) and can be targeted for drug/vaccine development against TB.
© 2015 Published by Elsevier B.V.
1. Introduction
The major obstacle for the control of tuberculosis is the abil-
ity of Mycobacterium tuberculosis to persist in human tissues
despite host immune containment and is considered to be a key
mechanism to the pathogenic success of M. tuberculosis (Sherman
et al., 2001). The events involved in the establishment of latent
infection with M. tuberculosis are not completely understood. A
number of studies have identified oxygen deprivation as a poten-
tial stimulus for triggering the transition of M. tuberculosis to
a non-replicating persistent state analogous to latency in vivo
(Klinkenberg and Karakousis, 2013). Wayne and Hayes (1996) have
Abbreviations: RvD, anaerobic culture of H37Rv; S7D, anaerobic culture of S7;
S10D, anaerobic culture of S10D.
∗ Corresponding address. National Institute for Research in Tuberculosis (ICMR),
(Formerly Tuberculosis Research Centre) No.1, Sathiyamoorthy Road, Chetpet,
Chennai - 600 031, India. Tel.: +91 44 2836 9682; fax: +91 44 2836 2528.
E-mail addresses: alameluraja@gmail.com, alamelur@nirt.res.in (A. Raja).
conducted pioneering studies on the dormant state of M. tuberculo-
sis that culminated in the development of the in vitro Wayne model
of persistence. In this model, M. tuberculosis cultures are subjected
to self-generated oxygen depletion in sealed containers. Growth
under such conditions leads to a physiologically well defined anaer-
obic non replicating state of the bacilli. This ability to shift-down to
non replicating state is responsible for the ability of tubercle bacilli
to lie dormant in the host for long periods of time, with the capacity
to revive and activate disease at a later time. This dormant state of
the bacilli is resistant to the anti-mycobacterial drugs and hampers
the treatment of tuberculosis.
Several lines of evidence have supported the fact that slight dif-
ferences found in the genome sequence of M. tuberculosis strains,
the physiology and host range spectrum will differ and may cause
distinctive adaptive responses to changes in environmental con-
ditions (Rehren et al., 2007). The presence of significant sequence
diversity in M. tuberculosis would provide a basis for understand-
ing pathogenesis, immune mechanisms, and bacterial evolution.
To date, numerous hypoxia models of persistence is limited by the
use of H37Rv as the single reference strain (Rustad et al., 2008).
http://dx.doi.org/10.1016/j.jbiotec.2015.04.017
0168-1656/© 2015 Published by Elsevier B.V.

2. S. Devasundaram et al. / Journal of Biotechnology 210 (2015) 70–80 71
Only limited work has been carried out with most prevalent clinical
strains under hypoxia.
Restriction fragment length polymorphism studies in BCG trial
area of Tiruvallur district, Tamil Nadu showed that 40% of M. tuber-
culosis strains from South India contain a single copy of the IS6110
insertion sequence in their genomic DNA (Das et al., 1995). Earlier
epidemiological studies showed the predominance of IS6110 single
copy strains of M. tuberculosis and their involvement in active trans-
mission of the disease (Narayanan et al., 2002). Further screening
of these predominant strains with protein profiling and humoral
immune responses revealed that two strains, namely S7 and S10
acted distinctly. Strain S7 was able to induce Th-2 response while
strain S10 induced potent T-cell proliferation and IFN-␥ secretion
(Rajavelu and Das, 2005). Further it was found that these two strains
adopted different modes of survival strategies and infection in
macrophages. Though both strains exhibited low phagocytic index,
S7 induced minimal apoptosis whereas S10 induced higher rate
of apoptosis in macrophages (Rajavelu et al., 2007). These results
indicate differential mode of infection and their adaptation to dif-
ferent survival strategies that may lead to immune suppression and
pathogenesis of the disease. These factors attracted us to select
S7 and S10, from other clinical isolates, to study gene regulation
mechanism under depleted oxygen condition by adopting Wayne’s
model (Wayne and Hayes, 1996).
Adaptation of M. tuberculosis to environmental changes in the
course of infection is likely mediated by differential gene expres-
sion. Whole-genome microarray technology is a robust tool used to
determine expression of many genes simultaneously in M. tubercu-
losis in response to changing environmental conditions (Wei et al.,
2013). In this work, we used microarray hybridization to com-
pare the total in vitro transcriptomes of H37Rv, S7, and S10 under
oxygen depleted culture conditions with aerated cultures of same.
Our aim is to provide an overview of gene expression variability
among these strains under oxygen deficient conditions, as a means
to identify dormancy associated genes. Genes, whose expression is
altered when grown under different growth conditions, reveal their
specific role under the condition tested with which drug targets
and vaccines can be designed with its encoded antigens. We used
60-mer oligonucleotide genome set (Agilent Technologies, USA),
representing all open reading frames (ORFs) of re-annotated H37Rv
genome sequencing project (Camus et al., 2002). Microarray was
performed with an objective to identify genes that are found to be
differentially regulated during hypoxia and this could shed light on
genes that are important for bacterial persistence mechanisms.
2. Methods
2.1. Culture conditions
The M. tuberculosis laboratory strain H37Rv (ATCC 27294),
obtained from Colorado State university, USA and clinical strains
S7 and S10 were obtained during the Model Dots study conducted
at the BGC trial area of Tiruvallur District (Das et al., 1995) and main-
tained as glycerol stocks. Aerated cultures of H37Rv, clinical isolates
S7 and S10 were grown in Middlebrook 7H9 media supplemented
with 2% (v/v) glycerol, 10% albumin–dextrose–catalase (ADC) and
0.05% (v/v) Tween 80 at 37 ◦C, 200 rpm for 25–30 days. Mycobacte-
rial culture method, oxygen depletion, termination, and pelleting of
the cultures were followed as described (Wayne and Hayes, 1996).
Briefly, laboratory strain H37Rv and clinical isolates S7 and S10
were inoculated in screw capped test tubes (20 mm × 125 mm, with
a total fluid capacity of 25.5 ml) containing supplemented MB7H9.
Stirring was achieved with 8-mm Teflon-coated magnetic stirring
bars in the tubes (120 rpm) and incubated at 37 ◦C. This gentle
stirring keeps the cultures in uniform dispersion and controls the
rate of O2 depletion. To assess the O2 depletion, sterile solution of
methylene blue was added in the medium to yield a dye final con-
centration of 1.5 ␮g ml−1. Reduction and decolorization of this dye
served as a visual indication of oxygen depletion. The cells were
pelleted from triplicate cultures, by centrifugation at 2000 × g for
5 min and frozen on dry ice.
2.2. RNA isolation from aerobic and anaerobic cultures of H37Rv,
S7, and S10
Cell pellets (107 bacterial cells) were suspended in 1 ml Tri-
zol reagent (Sigma–Aldrich, USA) and transferred to 2-ml screw
cap tubes containing 0.5 ml of 0.1 mm diameter zirconia/silica
beads (BioSpec Products, USA). Three 30-s pulses in a bead beater
disrupted the cells. Cell debris was separated by centrifugation
for 1 min at 16,000 × g. The supernatant was transferred to 2-ml
micro centrifuge tube containing 300 ␮l chloroform:isoamyl alco-
hol (24:1), inverted rapidly for 15 s, and incubated 2 min at room
temperature. Samples were centrifuged for 5 min and the aqueous
phase was precipitated using 2.5 volume of isopropanol and 1/10th
volume of 3 M sodium acetate. Samples were incubated 10 min at
room temperature and centrifuged for 15 min at 4 ◦C. The RNA pel-
lets were washed with 1 ml 75% ethanol, centrifuged 5 min, air dried
and resuspended with RNase free water. Final purification of RNA
was by RNeasy columns (Qiagen, USA). RNA quality was assessed
by measuring the ratio of absorbance of total RNA at 260/280 and
260/230 nm. RNA preparations that showed ratio of ≥2 at A260/280
were only included for cDNA preparation. Further, integrity of RNA
was also determined on a MOPS-formaldehyde denaturing agarose
gel.
2.3. cDNA synthesis, cRNA labelling, and microarray
hybridization
For cDNA synthesis Low Input Quick Amp Labeling WT
kit (Agilent Technologies, USA) was used. This kit uses cDNA
master mix containing Affinity Script reverse transcript
ase, a genetically engineered, highly thermostable version of
Moloney Murine Leukemia Virus Reverse Transcriptase (MMLV-
RT), for reverse transcription reaction. Briefly, 2 ␮g of RNA from
each sample was incubated with WT primers according to manu-
facturer instruction (Low Input Quick Amp Labeling WT kit, Agilent
Technologies, USA) for 10 min at 65 ◦C, cooled on ice, combined
with 5× standard buffer, 0.1 M DTT, 10 mM dNTP, and RNase block
mix to the final volume of 4.7 ␮l. This mixture was incubated for
2 h at 40 ◦C.
Synthesized cDNAs were converted to cRNA by T7 polymerase
transcription master mix (Low Input Quick Amp Labeling WT
kit, Agilent Technologies, USA) containing 5× transcription buffer,
0.1 M DTT, NTP, T7 polymerase and labelled with Cy3-CTP (aer-
obic cultures of H37Rv, S7, S10) or Cy5-CTP (anaerobic cultures
of H37Rv, S7, S10) and incubated for 2 h at 40 ◦C. Purification of
cRNAs was carried by Qiagen’s RNeasy mini kit. Labelling was
detected and quantified using Nanodrop ND-1000 UV–Vis Spec-
trophotomer as given in the Low Input Quick Amp Labeling WT
kit, Agilent Technologies, USA. Labelled cRNA was also checked
on 1% Agarose gel and scanned using the Typhoon 9210 scanner
(GE Life Sciences). Samples with higher labelling efficiency (Spe-
cific activity ≥ 15) were selected for competitive hybridization as
per the Agilent protocol.
A 60mer oligonucleotide based custom array chip was used
from Agilent Technologies in 8 × 15 K format. 300 ng of Cy5 labelled
cRNA from anaerobic cultures of H37Rv, S7, and S10 was hybridized
against 300 ng Cy3 labelled cRNA from aerobic cultures of H37Rv,
S7, and S10. Hybridization was done for 17 h, 10 rpm at 65 ◦C.

3. 72 S. Devasundaram et al. / Journal of Biotechnology 210 (2015) 70–80
Following image analysis, feature extraction was performed using
Feature extraction tool version 9.5.3.1 (Agilent Technologies, USA).
2.4. Microarray data analysis
Microarray data analysis was performed by R-Bioconductor
LIMMA package. The background-corrected raw intensity values
were used for analysis. LOWESS algorithm was used to normal-
ize the data and fold change (Fc) was calculated based on the
ratio of Cy5/Cy3 (anaerobic/aerobic) intensities. For statistical anal-
ysis, Student’s t-test against zero was performed using Benjamini
Hochberg multiple testing correction. Hierarchical cluster was done
by Mev4.1 using Pearson correlation method. The data was clus-
tered by averaged linkage. Adjusted p-value cut-off of 0.05 and
fold change of ≥1.5 was used for identifying differentially regu-
lated genes. Gene expression data are deposited into GEO database
(GEO accession no: GPL18248).
2.5. Quantitative real-time reverse transcription RT-PCR
A 2 ␮g of the total RNA extracted from both aerobic and anaero-
bic cultures of H37Rv, S7, and S10 was reverse transcribed using
a High Capacity cDNA synthesis kit (Applied Biosystems, USA)
and 20 ng of cDNA was used per 20 ␮l PCR reactions. Quantitative
real-time RT-PCRs were performed in triplicate using DynamoTM
SYBRgreen 2× mix kit (Finnzymes, Finland). Real time PCR quan-
titations were performed in ABI Prism 7000 sequence detection
system and analysed with SDS 2.1 software (Applied Biosystems,
USA). Relative expression levels were calculated using the 16s rRNA
transcript as normalizing internal control.
3. Results
3.1. Growth and gene expression pattern in H37Rv, S7, and S10
under oxygen depletion
Anaerobic cultures of H37Rv is denoted as “RvD” in the text and
for the clinical isolates S7 and S10 it is denoted as “S7D” and “S10D”,
respectively. Aerobic cultures are depicted as “Rv” for H37Rv, “S7”
and “S10” for clinical isolates in the text. Rapid growth was seen in
aerobic cultures where bacilli entered into log phase on day 12 and
growth was stabilized on day 21. But in anaerobic cultures growth
were stabilized from day 14 in all three strains (data not shown) and
there were no significant differences found in growth pattern as
well methylene blue decolorization among these strains. At inter-
vals, tubes from anaerobic cultures were checked for methylene
blue indicator decolorization. Gradual decolorization of methy-
lene blue was observed, with all cultures, during the incubation at
120 rpm, 37 ◦C and completed decolorization was obtained by day
25. No decolorization of methylene blue dye in the blank tube was
observed, as no inoculum was introduced and it remained in the
same color till 25–30 days (Fig. 1). These findings were in agreement
with the earlier observations (Wayne and Hayes, 1996).
Our main focus was to sort common regulated genes between
H37Rv, S7, and S10 clinical strain (H37Rv vs S7 vs S10), followed by
genes that are shared by H37Rv with either of the clinical isolates
(H37Rv vs S7 or H37Rv vs S10) were predicted.
Variable gene expression to single environmental factor was
observed between the strains of same organisms. Thus identifying
the set of genes whose expression levels are less variable between
the strains is vital to develop the drugs or vaccines. To identify the
upregulated genes, we set up a threshold value of 1.5 fold change,
rather than the levels of changes (individual expression values),
for each gene. Gene expressions above the threshold value were
considered as highly expressed. Total numbers of genes that were
differentially regulated in H37Rv, S7, and S10 during hypoxia is
given in Fig. 2. Out of 3951 genes tested, in custom array chip, 15.6%
of genes in H37Rv were overexpressed under oxygen depletion,
whereas in S7 11.5% genes were upregulated; surprisingly in S10
the percentage of genes that responded to hypoxia was higher than
other two strains (37%). Approximately 13% (12.7%) genes in H37Rv
were under expressed while in S7 and S10 8.2% and 29.8% of genes
were down regulated under hypoxia, respectively. Among all three
strains compared, S10 was having more responding gene counter-
part, under Wayne’s dormancy model, than other two strains.
3.2. Expressed genes and their functional classes under oxygen
depletion in H37Rv, S7, and S10
Out of all the differentially expressed genes, based on the mean
expression of triplicate arrays 134 genes (p ≤ 0.05 and ≥1.5 fold)
were found to be common and upregulated among all three strains
during hypoxia as compared to aerobic cultures (Table 1, Fig. 3). Out
of 134 genes, that showed significant response to oxygen depletion,
most were classified as genes involved in transport and binding
proteins (11.2%), fatty acid and phospholipid metabolism (5.2%),
purines, pyrimidines, nucleosides, and nucleotides conversions
(3%), cell envelope (10.4%), energy metabolism (11%), unknown
function (6%), mobile and extrachromosomal element functions
(2.3%), unclassified (25.3%), cellular process (5.2%), biosynthe-
sis of cofactors, prosthetic groups, and carriers (2.9%), conserved
hypothetical proteins (13.4%), intermediary metabolism and tran-
scription genes (0.74%), hypothetical proteins and regulatory
proteins (3%), protein fate (3%), DNA metabolism (2.2%) (Fig. 4).
In addition to 134 common genes, 12 genes were found to
be shared only between S7 and H37Rv but not with S10. These
12 genes were categorized under amino acid synthesis (Rv2499c)
and protein synthesis (Rv0041), conserved hypothetical protein
(Rv1775) and regulatory functions (Rv3058c). Four genes (Rv1079,
Rv1305, Rv1625c, and Rv3154) were categorized under “unclas-
sified genes” and two genes with unknown functions (Rv0637,
Rv1301). Interestingly Rv1920 is categorized under both cell enve-
lope and fatty acid phospholipid metabolism and Rv3828c plays a
role in DNA metabolism and Mobile and extrachromosomal ele-
ment functions (Table 2).
S10 shared 367 genes exclusively with H37Rv, in addition to 134
genes, that are upregulated under the oxygen depletion. Among
367 genes, higher percentage (14.3%) of genes are found to be cell
envelope genes followed by transport and binding genes (13.3%),
amino acid biosynthesis (11.7%), central intermediary metabolism
(11.3%) (Data not shown).
3.3. Operons that are expressed
Operons are the most basic units of organization in bacterial
genomes, and they provide the basis for understanding transcrip-
tional regulation and the entire regulatory network of an organism.
Based on intergenic distance between two genes and
expression levels, 7 putative operons, common among
H37Rv, S7, and S10, were predicted from our study. They
are Rv0212c–Rv02123c, Rv0347–Rv0349, Rv0501–Rv0502,
Rv1304–Rv1308, Rv2266–Rv2268c, Rv2949c–Rv2951c, and
Rv3821–Rv3825c and found to be upregulated during hypoxia in
all three strains used in this study. The proximity and orientation
of these ORFs to each other, as well as the high level of expression
with respect to the rest of the genome, strongly suggest that these
genes form an operon. Intergenic distance between two genes was
calculated by subtracting the ending nucleotide location on the
genome of the first gene from the beginning nucleotide location
on the genome of the second gene.
The well characterized MosR (regulator of mycobacterial oper-
ons of survival) operon includes Rv0347–Rv0348–Rv0349. Genes

4. S. Devasundaram et al. / Journal of Biotechnology 210 (2015) 70–80 73
Table 1
Genes that are commonly upregulated during hypoxia in H37Rv (lab strain) and clinical strains (S7 and S10).
Rv No. Gene symbol Fold change in H37Rv
dormancy (RvD)
Fold change in S7
dormancy (S7D)
Fold change in S10
dormancy (S10D)
Predicted function
Rv0011c 2.47 2.15 2.51 Probable conserved transmembrane protein
Rv0067c 2.18 1.51 1.84 Possible transcriptional regulatory protein (possibly
TetR-family)
Rv0113 GmhA 2.73 1.63 2.66 Probable sedoheptulose-7-phosphate isomerase
Rv0116c 1.81 2.37 2.59 Probable l,d-transpeptidase LdtA
Rv0144 1.62 2.13 2.55 Probable transcriptional regulatory protein (possibly
TetR-family)
Rv0157A 1.58 2.08 2.40 Hypothetical protein
Rv0168 YrbE1B 2.65 1.75 2.16 Conserved integral membrane protein
Rv0170 Mce1B 2.50 1.82 1.83 Mce-family protein
Rv0212c NadR 2.65 2.00 2.58 Possible transcriptional regulatory protein
Rv0213c 2.93 1.97 1.72 Possible methyltransferase
Rv0214 fadD4 4.06 3.03 2.36 Probable fatty-acid-CoA ligase FadD4
Rv0246 2.87 1.78 1.99 Probable conserved integral membrane protein
Rv0248c 4.38 2.66 2.73 Probable succinate dehydrogenase (iron–sulfur subunit)
Rv0331 2.48 1.54 3.18 Possible dehydrogenase/reductase
Rv0347 3.48 1.74 4.08 Probable conserved membrane protein
Rv0348 3.89 2.57 2.43 Possible transcriptional regulatory protein
Rv0349 3.78 1.87 3.05 Hypothetical protein
Rv0402c mmpL1 3.73 1.73 3.66 Probable conserved transmembrane transport protein
Rv0423c ThiC 2.15 2.55 1.70 Probable thiamine biosynthesis protein ThiC
Rv0425c CtpH 1.74 1.95 2.55 Possible metal cation transporting P-type ATPase
Rv0501 GalE2 1.79 2.10 1.93 Possible UDP-glucose 4-epimerase
Rv0502 1.50 1.80 1.56 Hypothetical protein
Rv0503c cmaA2 1.78 2.25 1.78 Cyclopropane-fatty-acyl-phospholipid synthase 2 CmaA2
Rv0633c 1.77 1.54 1.53 Possible exported protein
Rv0634c GLXII 1.92 1.84 2.52 Possible glyoxalase II
Rv0719 rplF 3.27 2.17 3.03 50S ribosomal protein L6 RplF
Rv0840c Pip 2.46 1.65 2.00 Probable proline iminopeptidase
Rv0911 1.94 2.37 3.73 Hypothetical protein
Rv0933 PstB 2.89 2.14 2.26 Phosphate-transport ATP-binding protein ABC transporter
Rv0936 pstA2 6.06 1.99 3.23 Phosphate-transport integral membrane ABC transporter
PstA2
Rv0985c MscL 2.51 1.95 1.83 Possible large-conductance ion mechanosensitive channel
Rv0989c GrcC2 2.37 2.01 2.94 Probable polyprenyl-diphosphate synthase
Rv1030 KdpB 3.20 1.91 2.35 Probable potassium-transporting P-type ATPase B chain
Rv1096 1.58 1.70 1.58 Possible glycosyl hydrolase
Rv1101c 2.22 1.74 1.62 Hypothetical protein
Rv1183 MmpL10 3.41 2.77 2.28 Probable conserved transmembrane transport protein
Rv1185c FadD21 2.81 2.72 1.69 Probable fatty-acid-AMP ligase
Rv1237 sugB 7.46 2.39 4.38 Probable sugar-transport integral membrane protein ABC
transporter SugB
Rv1239c CorA 2.80 2.13 4.14 Possible magnesium and cobalt transport transmembrane
protein
Rv1282c oppC 3.84 1.55 3.48 Probable oligopeptide-transport integral membrane protein
Rv1294 ThrA 2.70 1.58 2.58 Probable homoserine dehydrogenase
Rv1304 AtpB 2.58 2.58 2.46 Probable ATP synthase A chain
Rv1306 AtpF 2.51 2.12 1.77 Probable ATP synthase B chain
Rv1307 AtpH 2.35 4.84 2.17 Probable ATP synthase delta chain
Rv1308 AtpA 2.22 2.75 2.40 Probable ATP synthase alpha chain
Rv1347c MbtK 2.09 1.55 4.38 Lysine N-acetyltransferase
Rv1356c 4.92 2.64 5.28 Hypothetical protein
Rv1357c 4.47 3.63 6.23 Hypothetical protein
Rv1373 2.00 1.95 1.74 Glycolipid sulfotransferase
Rv1502 4.69 1.67 1.66 CHP
Rv1505c 3.36 2.25 1.82 Hypothetical protein
Rv1521 FadD25 2.89 1.95 3.76 Probable fatty-acid-AMP ligase
Rv1522c mmpL12 6.96 2.23 3.76 Probable conserved transmembrane transport protein
Rv1724c 1.64 1.69 1.69 Hypothetical protein
Rv1753c PPE24 3.43 2.23 2.87 PPE family protein
Rv1876 BfrA 4.14 2.08 1.72 Probable bacterioferritin
Rv1881c LppE 2.94 1.99 1.91 Possible conserved lipoprotein
Rv1884c RpfC 3.03 2.30 3.94 Probable resuscitation-promoting factor
Rv1888c 1.83 1.63 1.59 Possible transmembrane protein
Rv1903 5.17 1.89 2.46 CHP
Rv1926c 2.17 1.92 2.55 Immunogenic protein Mpt63
Rv1943c MazE5 3.25 2.36 1.56 Possible antitoxin
Rv1948c 4.53 1.96 2.01 Hypothetical protein
Rv1957 2.64 1.92 1.56 Hypothetical protein
Rv1979c 2.63 2.11 2.05 Possible conserved permease
Rv1990A 1.83 2.31 1.54 Possible dehydrogenase
Rv2000 3.94 2.11 3.43 Hypothetical protein
Rv2001 2.13 1.81 4.79 Hypothetical protein
Rv2008c 3.86 2.53 5.78 CHP
Rv2009 1.81 2.54 1.64 Antitoxin VapB15

5. 74 S. Devasundaram et al. / Journal of Biotechnology 210 (2015) 70–80
Table 1 (Continued)
Rv No. Gene symbol Fold change in H37Rv
dormancy (RvD)
Fold change in S7
dormancy (S7D)
Fold change in S10
dormancy (S10D)
Predicted function
Rv2017 2.34 1.56 2.10 Transcriptional regulatory protein
Rv2040c 3.05 2.71 2.46 Probable sugar-transport integral membrane protein ABC
transporter
Rv2041c 1.60 2.08 2.47 Probable sugar-binding lipoprotein
Rv2107 PE22 2.17 3.30 2.54 PE family protein PE22
Rv2108 PPE36 2.93 3.48 1.67 PPE family protein
Rv2253 3.89 2.58 2.64 Possible secreted unknown protein
Rv2266 Cyp126 2.62 2.15 4.03 Possible cytochrome P450 126
Rv2267c 2.50 2.10 1.72 CHP
Rv2268c Cyp128 2.14 1.93 3.12 Probable cytochrome P450 128
Rv2276 Cyp121 3.03 2.07 3.27 Cytochrome P450 121
Rv2277c 3.23 1.77 1.11 Possible glycerolphosphodiesterase
Rv2289 Cdh 3.46 2.00 1.12 Probable CDP-diacylglycerol pyrophosphatase
Rv2293c 9.85 3.16 5.46 CHP
Rv2300c 1.94 1.86 7.17 Hypothetical protein
Rv2316 uspA 2.07 2.22 1.88 Probable sugar-transport integral membrane protein ABC
transporter
Rv2339 mmpL9 5.66 2.30 1.96 Probable conserved transmembrane transport protein
Rv2477c 2.42 1.98 1.62 Probable macrolide-transport ATP-binding protein ABC
transporter
Rv2515c 3.27 2.17 1.61 Hypothetical protein
Rv2716 3.03 2.73 2.32 Hypothetical protein
Rv2790c Ltp1 2.05 2.48 2.05 Probable lipid-transfer protein
Rv2820c 3.18 1.75 1.95 Hypothetical protein
Rv2823c 3.48 2.41 3.84 Hypothetical protein
Rv2855 Mtr 2.40 1.86 1.79 NADPH-dependent mycothiol reductase
Rv2856 NicT 2.92 2.56 2.05 Possible nickel-transport integral membrane protein
Rv2873 Mpt83 1.99 1.92 2.39 Cell surface lipoprotein Mpt83
Rv2874 DipZ 2.93 1.64 2.40 Possible integral membrane C-type cytochrome biogenesis
protein
Rv2928 TesA 3.92 1.95 3.39 Probable thioesterase
Rv2935 PpsE 1.97 2.27 2.05 Phenolpthiocerol synthesis type-I polyketide synthase
Rv2937 DrrB 1.91 2.31 2.26 Daunorubicin-dim-transport integral membrane protein ABC
transporter
Rv2949c 3.73 3.34 1.57 Chorismate pyruvate lyase
Rv2950c fadD29 3.14 3.36 1.93 Fatty-acid-AMP ligase FadD29
Rv2951c 4.17 2.57 2.30 Possible oxidoreductase
Rv2952 3.06 2.28 3.36 Possible methyltransferase
Rv2964 PurU 3.14 2.41 1.72 Probable formyltetrahydrofolate deformylase
Rv3007c 3.43 2.57 2.99 Possible oxidoreductase
Rv3008 1.83 2.28 1.75 Hypothetical protein
Rv3047c 3.06 2.28 1.76 Hypothetical protein
Rv3050c 1.83 2.29 2.48 Probable transcriptional regulatory protein (probably
AsnC-family)
Rv3054c 2.54 2.31 2.38 CHP
Rv3056 dinP 5.28 1.66 3.25 Possible DNA-damage-inducible protein P DinP
Rv3059 Cyp136 2.85 1.71 1.73 Probable cytochrome P450 136
Rv3138 PflA 2.10 1.57 1.53 Probable pyruvate formate lyase activating protein
Rv3190c 2.19 1.53 1.81 Hypothetical protein
Rv3320c VapC44 2.75 2.05 3.80 Possible toxin VapC44 Contains PIN domain
Rv3377c 4.11 2.35 4.11 Halimadienyl diphosphate synthase
Rv3378c 6.96 2.99 3.25 Diterpene synthase
Rv3382c LytB1 2.11 1.66 2.41 Probable LYTB-related protein LytB1
Rv3425 PPE57 2.36 2.96 1.58 PPE family protein PPE57
Rv3476c kgtP 4.82 2.08 1.84 Probable dicarboxylic acid transport integral membrane
protein
Rv3479 3.07 1.59 3.03 Possible transmembrane protein
Rv3531c 2.21 1.67 4.12 Hypothetical protein
Rv3631 1.78 1.71 1.96 Possible transferase (possibly glycosyltransferase)
Rv3719 2.23 1.62 1.83 CHP
Rv3727 2.14 1.84 1.94 Possible oxidoreductase
Rv3766 3.92 1.72 1.99 Hypothetical protein
Rv3767c 3.29 2.48 1.68 Possible S-adenosylmethionine-dependent methyltransferase
Rv3783 RfbD 2.67 1.62 2.08 Probable O-antigen/lipopolysaccharide transport integral
membrane protein ABC transporter
Rv3821 1.76 3.08 1.79 Probable conserved integral membrane protein
Rv3822 2.93 1.62 2.49 Hypothetical protein
Rv3823c mmpL8 4.92 2.11 1.91 Conserved integral membrane transport protein
Rv3824c papA1 4.59 2.45 2.13 Conserved polyketide synthase associated protein
Rv3825c pks2 5.10 1.74 2.30 Polyketide synthase
Rv3868 EccA1 1.96 1.98 1.66 ESX conserved component EccA1 ESX-1 type VII secretion
system protein
Rv3922c 3.14 1.60 2.36 Possible haemolysin
Fold change differences are averaged ratios from 3 biological replicates. Each ratio was calculated between the numbers of cDNA copies for each gene in three strains,
normalized to 16srRNA. Gene numbers given in bold letters indicate putative operons with qPCR validation reports from other publications except for Rv0212c–Rv0213c
operon (reported in present study).

6. S. Devasundaram et al. / Journal of Biotechnology 210 (2015) 70–80 75
Fig. 1. Decolorization of methylene blue indicator from anaerobic cultures of H37Rv, S7, and S10 strains of M. tuberculosis. (a) Day 1 inoculation of laboratory strain H37Rv
(Rv) and clinical strains S7 and S10, B indicates Blank. Methylene blue, redox indicator, was added to all the tubes. (b) Decolorization, in all three strains, indicated oxygen
depletion in the media and bacteria were able to adapt oxygen deficient condition. In Blank, no decolorization occurred as there was no inoculum introduced, thus no oxygen
depletion occurred.
Fig. 2. Venn diagram representation of differentially regulated genes during hypoxia. (a) No. of genes that are expressed greater than 1.5 fold during hypoxia. (b) No. of
genes that are suppressed greater than 1.5 fold during hypoxia. Venn diagram representation of the genes found to be differentially regulated during oxygen depletion in
Mycobacterium tuberculosis lab strain H37Rv and south India prevalent strain S7 and S10. (a) and (b) Venn diagram are plotted for up and down regulated genes, respectively.
of this operon were identified to be upregulated during hypoxia
from all three strains of our study. MosR has the ability to bind
to its promoter (Rv0347) and control few other operons including
Rv2948c–Rv2950c (Abomoelak et al., 2009). Genes of this operon
(except Rv2948c) are also found to be upregulated from our obser-
vation.
F0 ATP synthase operon comprises of atpB, atpE, and atpF genes
and the F1ATP synthase operon contains atpH, atpA, atpG, atpD,
and atpC genes. Upregulation of atpB and atpF of F0 ATP synthase
operon and atpH, atpA of F1ATP synthase operon were observed
during hypoxia from H37Rv, S7, and S10 strains. Their expression
was equal (∼2 fold change) in all three strains of our study except
for atpH in S7 clinical isolates (4.8 fold change).
Based on the arrangement and expression levels during
oxygen depletion, Rv0212c–Rv0213c forms a transcriptional
unit (operon). Special attention was given to transcriptional
Table 2
Genes that are over expressed (1.5 fold change) and shared only by H37Rv and S7.
Rv No. Gene symbol Fold change in H37Rv
dormancy (RvD)
Fold change in S7
dormancy (S7D)
Predicted function
Rv0041 LeuS 2.47 1.68 Probable leucyl-tRNA synthetase
Rv0637 HadC 1.91 1.53 (3R)-hydroxyacyl-ACP dehydratase subunit
Rv1079 MetB 2.35 1.56 Cystathionine gamma-synthase
Rv1301 1.90 1.80 Hypothetical proteins
Rv1305 AtpE 1.87 3.58 Probable ATP synthase C chain
Rv1625c Cya 1.97 2.00 Membrane-anchored adenylyl cyclase
Rv1775 1.64 1.60 Hypothetical proteins
Rv1920 1.86 1.60 Probable membrane protein
Rv2499c 1.82 1.61 Possible oxidase regulatory-related protein
Rv3058c 2.75 1.85 Possible transcriptional regulatory protein (probably TetR-family)
Rv3154 NuoJ 3.86 1.95 Probable NADH dehydrogenase I (chain J)
Rv3828c 3.18 1.72 Possible resolvase
Fold change differences are averaged ratios from 3 biological replicates of H37Rv and S7 strains. Each ratio was calculated between the numbers of cDNA copies for each
gene in between these two strains, normalized to 16srRNA.

7. 76 S. Devasundaram et al. / Journal of Biotechnology 210 (2015) 70–80
Fig. 3. Hierarchical clustering of commonly regulated genes among different strains of Mycobecterium upon oxygen deprivation. Hierarchical clustering of commonly
regulated genes (134) found to be differentially expressed during the adaptation to oxygen-depleted NRP in M. tuberculosis stains H37Rv, S7, and S10 (p ≤ 0.05 and ≥1.5 fold).
Microarray experiments were carried in triplicates for each samples and based on the mean expression of triplicates, gene were categorized as upregulated. Red color in heat
map indicates induced genes, green indicates repressed genes, and black indicates no change in gene expression. (For interpretation of the references to colour in this figure
legend, the reader is referred to the web version of the article.)
regulators as they play a crucial role in the survival of the
mycobacteria. Quantitative real time PCR (qPCR) was used to
determine Rv0212c–Rv0213c transcript levels from the iso-
lated RNA of anaerobic cultures of H37Rv, S7, and S10 strains.
There was a good correlation between the expression levels
of microarray and qPCR with Rv0212c–Rv0213c transcript
(Fig. 5).
The next operon which was expressed in all three strains was
Rv3821–Rv3822 which encodes Chp1 and Sap proteins, that com-
pletes the sulfolipid biosynthesis (SL-1) pathway of M. tuberculosis.

8. S. Devasundaram et al. / Journal of Biotechnology 210 (2015) 70–80 77
Fig. 4. Genes that are overexpressed >1.5 fold change and their role categories. Genes which showed a significant response to hypoxia were classified based on their
biological role. Higher percentage of the genes in H37Rv is categorized into transport and binding proteins followed by fatty acid and phospholipid metabolism and cell
envelope proteins. Genes of transcription and fatty acid and phospholipid metabolism was found to be expressed higher in S7 clinical strains during hypoxia. In S10 clinical
strain, transport and binding proteins, cell envelope proteins, and energy metabolism genes forms the major category of upregulated genes under hypoxia.
Fig. 5. Genomic organization and real-time RT-PCR results of Rv0212c (nadR) to Rv0213c. (a) Schematic representation of the arrangement of ORFs Rv0212c to Rv0213c in
the M. tuberculosis H37Rv genome. The arrows indicate the lengths and transcriptional orientations of annotated genes and predicted ORFs. (b) Real-time RT-PCR (qPCR)
was performed on RNA isolated from anaerobic cultures of H37Rv, S7, and S10D and aerated cultures of the same strains. Data are presented as the mean fold change of
expression ± standard deviation for each gene.
This putative operon was reported earlier by Seeliger et al. (2012)
but its expression under hypoxia was not reported by any other
earlier studies. Thus, we consider our result is the first report on
expression of this operon under hypoxia and their expressions in
the clinical isolates make them an important target to explore the
biology of dormancy.
3.4. Genes of hypoxia
To identify previously reported hypoxic genes of mycobacteria,
we compared our gene expression data with existing reports of
oxygen depletion model. Total of 24 reported hypoxic genes were
identified upon comparing only H37Rv gene expression data of our
results. Among these 24 genes, 5 genes (Rv0096, Rv1130, Rv1518,
Rv1964, and Rv2386c) were listed in, 77 significantly induced
genes, microarray of H37Rv under low oxygen state (Bacon et al.,
2004). Rv1130, 2-methylcitrate hydratase, is associated with pro-
pionate metabolism and previously identified as induced during
macrophage infection and in SDS treatment (Schnappinger et al.,
2003). Rv1130 and Rv1518 transcript levels were also checked by
RT-PCR and were found to be upregulated 1.81 fold and 1.74 fold,
respectively (Fig. 6).
Upon comparing 134 common genes of our results with exist-
ing reports, only 5 genes were reported to be hypoxia related genes
(Rv0634c, Rv1884c, Rv2477c, Rv2873, and Rv3008) (Bacon et al.,
2004; Sherman et al., 2001). In contrast to our work, where 3 dif-
ferent isolates of M. tuberculosis are used, these earlier reports
included only H37Rv as a study organism. Hence only few overlap-
ping genes were found among the 134 common genes. Total of 30
differentially regulated genes were selected for RT-PCR validation
and they were correlated with microarray data (Data not shown).
But RT-PCR results of Rv0212c, Rv0213c, Rv1130, and Rv1518 is
given (Figs. 5 and 6).
4. Discussion
Unlike many pathogens that are overtly toxic to their hosts,
the primary virulence determinant of M. tuberculosis appears to

9. 78 S. Devasundaram et al. / Journal of Biotechnology 210 (2015) 70–80
R
v
R
vd
-0.5
0.0
0.5
1.0
Rv1130
Rv1518
log2ratio
Fig. 6. Real-time RT-PCR of Rv1130 and Rv1518. Expression levels of Rv1130 and
Rv1518 under low oxygen conditions. Total RNA was isolated from anaerobic and
aerobic cultures of H37Rv. Real-time RT-PCR was performed, and the expression
level of each Rv1130 and Rv1518 transcript was calculated using the 16srRNA tran-
script for normalization for RNA amounts and aerated cultures as a control. Data are
presented as the mean fold change of expression ± standard deviation for each gene
and are averages from three independent experiments.
be its ability to persist for years or decades within humans in a
clinically dormant state. Several lines of evidence link latent tuber-
culosis and inhibition of MTB growth/metabolism with hypoxic
conditions within the host (Sherman et al., 2001; Fang et al., 2012).
Tuberculosis infections are preferentially associated with the most
oxygen-rich sites in the body suggesting that reduced levels of O2
may limit M. tuberculosis growth in vivo.
We investigated how M. tuberculosis responded to a reduced
oxygen tension in terms of gene expression by growing cells under
both aerobic and low-oxygen conditions. To our knowledge, this
genome-wide transcriptomics approach has produced the first
insights into the response of South Indian prevalent clinical iso-
lates of M. tuberculosis when exposed to hypoxia. Laboratory strains
might not represent the virulence of naturally occurring tuber-
culosis strains in patients and hence activity of a given vaccine
or treatment cannot be guaranteed from assays using these lab-
oratory strains. This is an important consideration when testing
new vaccine candidates or drugs. Thus, we aimed at using the
most prevalent clinical strains (S7 and S10) from South India for
the hypoxia model of persistence along with the laboratory strain
H37Rv.
In our experiments, both aerobic and anaerobic cultures were
terminated during late exponential growth (25–27 days) in order
to minimize growth-related differences between strains. The pat-
tern of methylene blue decolorization was more similar in H37Rv,
S7, and S10 anaerobic cultures which indicate depletion of oxy-
gen was achieved in our culture methods. The DosR–DosS, two
component regulatory system plays a pivotal role in mediating
the adaptive response to hypoxia. Overexpression of DosR regu-
lon genes like 3128c (in RvD and S10D cultures), Rv1997, Rv2004c,
Rv2005c, Rv2007c, Rv3127c, and dosS (Rv3132c) in S7 anaerobic
cultures and Rv2025c in S10 anaerobic cultures (GEO accession
no: GPL18248) indicated that the oxygen depletion was faithfully
achieved in our culture methods. We were mainly interested in
listing upregulated genes common between all three strains dur-
ing hypoxia. Since a great number of published studies have used
only H37Rv as a model strain for their hypoxia experiment model,
very few reported hypoxic genes of H37Rv were found in our study
where three different M. tuberculosis strains were used.
Better quality RNA is indispensable for efficient microarray tech-
nique and guanidinium thiocyanate containing TRIzol method is a
standard method for the RNA extraction and followed here. This
method of RNA extraction was proven to yield good quality of total
RNA (Ojaniemi et al., 2003) and followed in recent mycobacterial
microarray work (Wei et al., 2013).
In addition to 134 common genes, 367 genes were found over-
lapping between H37Rv and S10 strains whereas only 12 genes
were shared by S7 with H37Rv that are upregulated during hypoxia.
Cytosolic protein analysis (unpublished work) of these strains also
conferred that the expressed protein profiles during hypoxia of
S10 was similar to H37Rv, but S7 differed from H37Rv. Recently, a
study was published from our department on the genomic features
of 4 M. tuberculosis clinical isolates (NIRT202, NIRT203, NIRT204,
and NIRT206) from South India. The isolate NIRT206 represents
the strain S7 (Narayanan and Deshpande, 2013) and their results
showed NIRT206 (S7) genome contains only 3414 genes when
compared to H37Rv that has 4111 genes, but no such genomic
data is available for the clinical isolate S10. Thus it is unclear
whether direct link between gene number and expression pattern
exist between these two strains. Future work on whole genome
sequencing of S10 could help to elucidate its genomic features
(gene numbers) as well as the reason for its higher hypoxic gene
counterparts with H37Rv.
In the current study, the gradation (S10 > H37Rv > S7) observed
in total number of genes expressed under hypoxia suggests that
each strain behaves differently for similar stimuli. The clinical strain
S7 responds minimal to the given stimuli thereby masking its pres-
ence and reducing the outgoing signals to the host for its survival
strategy. In our earlier studies, we reported that S7 induced min-
imal apoptosis compared to S10 and H37Rv in THP1 cell lines
(Rajavelu and Das, 2005; Rajavelu et al., 2007). In another report,
upon in vitro infection with S7 and S10 of human monocyte-derived
dendritic cells (DCs) (MoDC), S7 reduced the expression of surface
markers (CD86, CD80, and CD83) thereby inhibiting DC maturation,
but S10 infected MoDC expressed significantly high levels of these
markers (Rajashree et al., 2008). The migration of the infected DCs
towards the lymph nodes to activate naive T lymphocytes is cru-
cial during M. tuberculosis infection. This migratory property of DC
was lowered (50%) upon S7 infection compared to S10 infected DCs.
These observations support our results, where S7 shows minimal
gene expression compared to S10, and highlights the need of study-
ing the most prevalent clinical strains of the outbreaks, like S7 and
S10, under the potential stimuli.
Among 134 induced hypoxic genes, majority of them are
transport and binding proteins and genes that are involved in
the lipid metabolism. Three interesting classes of genes involved
in host–pathogen interactions are T-cell antigens, PE/PPE fam-
ily genes, and genes involved in lipid metabolism. The PE/PPE
genes families constitute 10% of the total coding capacity of the M.
tuberculosis genome and are characterized by highly conserved N-
terminal domains. PE/PPE proteins have the ability to modulate the
antigen processing of the infected host cells which confirms their
importance in terms of their significance in overall pathogenesis
associated with the tuberculosis disease (Koh et al., 2009). Three of
such genes (Rv1753c–PPE24, Rv2107–PE22, and Rv2108–PPE36)
were upregulated under oxygen depletion which was observed
in all three strains. Strikingly 16 genes of this family were over
expressed both in H37Rv and one of the clinical strains (S10).
They are Rv0096 (PPE1), Rv0151c (PE1), Rv0159c (PE3), Rv0305c
(PPE6), Rv0354c (PPE7), Rv0355c (PPE8), Rv1548c (PPE21), Rv1808
(PPE32), Rv1809 (PPE33), Rv1917c (PPE34), Rv3347c (PPE55),
Rv3539 (PPE63), and Rv3738c (PPE66).
PE3, a recently characterized gene, was considered to be essen-
tial for the maintenance of impermeable cell wall and the virulence
(Singh et al., 2013) and proposed as a molecule for serodiagnos-
tic application and vaccine development (Li et al., 2010) against
tuberculosis. Interestingly PE 34 and PPE 55 were reported to be
upregulated in human lung granuloma (Rachman et al., 2006).
Together with other published expression data, results from our
hypoxia experiments lend support for the important role of PE/PPE
genes during dormancy establishment of M. tuberculosis.
Genes that are involved in the biosynthesis of sulfolipids (SL)
include polyketide synthase Pks2 (Rv3825c), PapA1 (Rv3824c), and
PapA2 (Rv3820c). PapA1 and PapA2 are responsible for sequential

10. S. Devasundaram et al. / Journal of Biotechnology 210 (2015) 70–80 79
acylation of trehalose sulfate. Increased expression of pks2 in
response to nutrient starvation (Betts et al., 2002) and in oxygen
depletion, from our results, strongly reveals its association with
mycobacterial persistence. Pks2, papA1, papA2, and mmpL8 are co-
located in M. tuberculosis genome with Rv3821 and Rv3822 operon.
This operon function is potentiated by the sulfolipid transporter
MmpL8–Rv3823c. Increased expression of this gene in addition to
Pks2 (Rv3825c), PapA1 (Rv3824c), and PapA2 (Rv3820c) from our
study suggest their role in dormancy induced by hypoxia.
In addition, the high pks2, papA1, and papA2 transcription
in NRP2 suggests over-expression of these genes is required for
sulpholipid biosynthesis (Sirakova et al., 2001) and is reported to
be induced upon macrophage infection (Graham and Clark-Curtiss,
1999). Thus the transcriptional response of sulfolipids biosynthe-
sis genes during hypoxia signifies the metabolic stress generated
by gradual oxygen depletion and its role in adaptation mechanism.
A well characterized MosR operon (Rv0347–Rv0348–Rv0349)
and its expression during the late stage of chronic TB has already
been reported from in vivo murine model (Mehra and Kaushal,
2009). Rv0348 gene encodes a novel transcriptional factor that
regulates several operons involved in mycobacterial survival, espe-
cially during hypoxia. Moderate expression of Rv0348 transcript
under hypoxia and its potential involvement in the expression of
␴F which orchestrates entry into the chronic stage of TB was already
reported (Muttucumaru et al., 2004; Geiman et al., 2004).
Albeit majority of the studies reported down regulation of ATP
synthase operon (Karakousis et al., 2004), dormant mycobacteria
do possess residual ATP synthase enzymatic activity, as observed in
the present work, which is indispensable for its survival and making
it a promising target to tackle dormant infections (Koul et al., 2008).
Variation in gene expression among clinical isolates has implica-
tions for pathogenicity and the identification of candidate genes for
drug targets, vaccine antigens, and diagnostic assays. The enrich-
ment of lipid metabolism genes, PE/PPE genes and T-cell antigens
genes that are variably expressed under hypoxia suggest that clin-
ical isolates may differ in their host interactions and adaptation
during stress.
5. Conclusion
Genes that are responding to external stimuli, on the expression
level, would provide versatile regulatory machinery for M. tubercu-
losis adaptation and analyzing them will be an important step to
understand mycobacterial latency, key pathways involved during
latency and develop direct antibiotic therapy to TB in future. Using
prevalent clinical strains are advantageous as major disease out-
breaks are resulted with its infection. Thus genes that are highly
common and upregulated under given stress condition would be a
better target for drug/vaccine development against TB.
Acknowledgments
We thank Indian Council of Medical Research for the Senior
Research fellowship awarded to Santhi Devasundaram. We also
acknowledge Prof. Paturu Kondaiah, MRDG Department, IISc, Ban-
galore for providing microarray and qPCR facility for this work and
for helpful discussions.
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