ARTICLE IN PRESS
Tuberculosis (2004) 84, 263–274
Tuberculosis
www.elsevierhealth.com/journals/tube
The use of microarray analysis to determine the
gene expression profiles of Mycobacterium
tuberculosis in response to anti-bacterial
compounds$, $$
Simon J. Waddella,*, Richard A. Stablera, Ken Lainga, Laurent Kremerb,
Robert C. Reynoldsc, Gurdyal S. Besrad
a
Department of Cellular and Molecular Medicine, St. George’s Hospital Medical School, Cranmer Terrace,
Tooting, London SW17 0RE, UK
b
INSERM U447, Institut Pasteur de Lille/IBL, 1 rue du Pr. Calmette, BP245-59019 Lille Cedex, France
c
Department of Organic Chemistry, Southern Research Institute, P.O. Box 55305, Birmingham,
AL 35255, USA
d
School of Biosciences, The University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
Accepted 23 December 2003
KEYWORDS
Mycobacterium tuberculosis;
Innate drug resistance;
Microarray;
Isoniazid;
Isoxyl;
Tetrahydrolipstatin
Summary The response of Mycobacterium tuberculosis to six anti-microbial agents
was determined by microarray analysis in an attempt to define mechanisms of innate
resistance in M. tuberculosis. The gene expression profiles of M. tuberculosis after
treatment at the minimal inhibitory concentration (MIC) for 4 h with isoniazid,
isoxyl, tetrahydrolipstatin, SRI#221, SR1#967 and SR1#9190 were compared to
untreated M. tuberculosis. A common response to drug exposure was defined, and
this expression profile overlapped with a number of other mycobacterial stress
responses recently identified by microarray analysis. Compound-specific responses
were also distinguished including a number of putative transcriptional regulators and
translocation-related genes. These genes may contribute to the intrinsic resistance
of M. tuberculosis to anti-microbial compounds. Further investigation into these
mechanisms may elucidate novel pathways contributing to mycobacterial drug
resistance and influence anti-mycobacterial drug development strategies.
& 2004 Elsevier Ltd. All rights reserved.
Introduction
In 1993 the World Health Organisation declared
tuberculosis (TB) a global emergency.1 Ten years
$
doi of original article 10.1016/j.tube.2004.01.002
Supplementary data associated with this article can be
found, in the online version, at doi:10.1016/j.tube.2003.12.005.
*Corresponding author.
E-mail address: swaddell@sghms.ac.uk (S.J. Waddell).
$$
on, it has been estimated that the global incidence
rate of tuberculosis is growing at approximately
0.4%/year.2 Of the world’s new tuberculosis cases
approximately 3% were attributed to multidrugresistant tuberculosis (MDR-TB) in 2000. Although
multidrug-resistant tuberculosis may not be a
problem globally, MDR-TB is at critical levels in
many hot spots across the world.3 The emergence
of MDR-TB, the deadly link between TB and HIV
infection, the problems of treatment expense and
1472-9792/$ - see front matter & 2004 Elsevier Ltd. All rights reserved.
doi:10.1016/j.tube.2003.12.005
ARTICLE IN PRESS
264
patient compliance, and the requirement to eliminate persistent infection emphasises the need
for new anti-mycobacterial compounds to be
developed.4
Mechanisms of drug resistance in Mycobacterium
tuberculosis have been identified to all five first
line anti-mycobacterial drugsFisoniazid (INH),
rifampin, pyrazinamide, ethambutol, and streptomycin. M. tuberculosis multiple resistance in these
instances is conferred by a series of chromosomal
mutations.5 However, less is known about mechanisms of intrinsic/natural drug resistance in
M. tuberculosis such as reduced cell wall permeability, efflux systems, or the expression of druginactivating enzymes. The poor action of many
antibiotics, and the relative resistance of bacilli to
drying, alkali and many chemical disinfectants has
often been attributed to the low permeability of
the unusual cell wall structure of mycobacteria.6 In
addition to the hydrophobic barrier of the mycobacterial cell wall, several genes encoding putative
drug efflux systems have been identified in mycobacteria. The probable efflux protein efpA has been
reported to be present in slow-growing pathogenic
mycobacteria.7 The efflux pump LfrA has been
demonstrated in M. smegmatis to confer low-level
resistance to fluoroquinolones8 and to contribute
to ethidium bromide resistance,9 whereas the
M. tuberculosis P55 multidrug efflux pump has
been identified to confer aminoglycoside and
tetracycline resistance.10 Indeed, the H37Rv M.
tuberculosis genome sequencing project revealed
the presence of up to 24 members of the major
facilitator superfamily of transporters, and over 80
putative members of the ABC-transporter family.11
Of the ABC-transporter family, 21 export systems
were defined in M. tuberculosis, many of which are
implicated in the export of drugs and which may
contribute to the innate resistance of mycobacteria
to broad spectrum antibiotics.12 M. tuberculosis
has also been demonstrated to express b-lactamases13 and aminoglycoside acetyltransferases,14
which may reduce the effectiveness of b-lactams
and aminoglycosides against M. tuberculosis.
Further understanding of the mechanisms of
intrinsic resistance to antibiotic compounds in
M. tuberculosis may help to improve existing drug
treatments and define new drug development
strategies.
The advent of microarray technology has allowed
the transcriptional profiles of bacteria to be
examined in response to various stresses. The use
of M. tuberculosis microarrays was first reported to
describe the induction of M. tuberculosis genes in
response to INH treatment.15 Genes were identified
encoding proteins related to the mode of action of
S.J. Waddell et al.
INH, such as acpM (coding for an acyl carrier
protein), kasA and kasB (encoding b-ketoacyl
synthases). Other genes most likely involved in
the mycobacterial response to the toxicity of the
drug were also highlightedFefpA (coding for a
putative efflux protein), and aphC (alkyl hydroperoxide reductase, involved in the oxidative stress
response). Microarray analysis of gene expression
has also recently been used to predict the common
functional category of unknown anti-mycobacterial
drugs as part of a pipeline of drug discovery.16
We describe here the use of a gene-specific
M. tuberculosis microarray to compare the transcriptional response of M. tuberculosis H37Rv to
six compounds with anti-mycobacterial activity:
(i) INH, a front line anti-tuberculosis drug targeting
mycolic acid synthesis;17 (ii) Isoxyl, a drug used
in the past to treat tuberculosis, targeting a
delta-9 oleic acid desaturase and mycolic acid
synthesis;18,19 (iii) Tetrahydrolipstatin (THL),
a lipase inhibitor used in the treatment of
obesity;20 and three compounds from The Southern
Research Institute (Birmingham, Alabama, USA),
exhibiting potent anti-mycobacterial properties
(iv) SRI#221; (v) SRI#967; and (vi) SRI#9190. The
comparison of these six distinct transcriptional
profiles defines a common M. tuberculosis response
to these anti-mycobacterial compounds, and
describes drug-specific changes which may
reflect the mode of action of each drug. This
investigation also distinguishes genes of unknown
function that may contribute to the intrinsic
resistance of M. tuberculosis to anti-microbial
agents.
Materials and Methods
Growth conditions and RNA extraction
M. tuberculosis strain H37Rv was grown at 371C in
Dubos liquid medium, supplemented with bacto
Dubos medium albumin (Becton Dickinson). Mid-log
phase mycobacterial cultures were concentrated to
1/20th volume of liquid medium and incubated
overnight to recover. Anti-microbial compounds
were added at approximately 1 MIC (determined
using the Microplate Alamar Blue Assay, MABA,21 at
The Southern Research Institute); the structures
and MICs of the compounds used are detailed in
Fig. 1. The drug-treated, together with untreated
control mycobacterial cultures, were incubated at
371C for 4 h. M. tuberculosis RNA was extracted
using the GTC/TRIzols method developed by
Mangan et al.22 The RNA samples were DNAsel
ARTICLE IN PRESS
The use of microarray analysis
265
Figure 1 The structures of the anti-microbial compounds used in this investigation. The source and the approximate
MIC of the compounds against M. tuberculosis are also detailed. MICs were determined using the Microplate Alamar Blue
Assay (MABA)21 at The Southern Research Institute (Birmingham, Alabama, USA).
treated and cleaned up on RNeasys Mini-Columns
(Qiagen).
Microarray hybridisation and normalisation
strategies
cDNA derived from three separate RNA extractions
for each of the compounds tested and from
untreated control samples were hybridised to a
gene-specific PCR product H37Rv M. tuberculosis
microarray, the design and generation of which is
described in Stewart et al.23 Details of the
M. tuberculosis microarray used in this investigation can be found at http://bugs.sghms.ac.uk/.
Two colour competitive hybridisations were performed as previously described Stewart et al.23
hybridising the mycobacterial RNA-derived cDNA
against M. tuberculosis genomic DNA. The hybridised slides were scanned sequentially at 532 and
635 nm corresponding to Cy3 and Cy5 excitation
maxima using the 4.28t Array Scanner (Affymetrix). Comparative spot intensities from the images
were calculated using Imagene 4.0 (BioDiscovery),
and imported into GeneSpring 4.2 (Silicon Genetics)
for further analysis. The array data was normalised
to the 50th percentile, and values of less than zero
were adjusted to zero. Repeat hybridisations using
the same cDNA samples (between 3 and 7 replicates
for each condition) were replicated together. The
ARTICLE IN PRESS
266
experiments were then normalised to the untreated control sample using a per gene normalisation strategy.
Microarray data analysis
Two measures of significance were applied to the
normalised data set to identify differentially
regulated genes (i) a minimum p-value of 0.05
incorporating the cross-gene error model (GeneSpring) was set to discriminate genes significantly
deviating from the 1:1 ratio (treated : untreated)
which were then subjected to Benjamini and
Hochberg correction to take into account multiple
experiment testing and (ii) a one-way ANOVA
(GeneSpring).
A technique of single spot replacement, SSR
(J. Bacon, personal communication) was also used
to enhance the original data set. The un-normalised
cDNA : genomic DNA ratios for each replicate under
each condition were imported into Microsoft Excel.
For each element on the microarray the individual
ratio furthest from the median of the replicates
was replaced with the mean of the remaining
ratios. In this way the effect of extreme values was
minimised from the data set. This SSR data set was
then normalised as previously described, and
subjected to two measures of significance: (i) the
statistical group comparison (ANOVA); and (ii) the
statistical package SAM (Significance Analysis of
Microarrays, version 1.1524) was used to identify
genes differently expressed in the normalised data
sets. A minimum fold change of 1.5 between
control and drug-treated data sets, and a false
discovery rate (FDR) of less than one (of the
median) was used as a measure of significance.
The hypergeometric distribution was used to
determine if particular functional categories of
genes were enriched in response to each drug
treatment. The hypergeometric p-values were
calculated as described by Boldrick et al.25 where
N ¼ 3924 the total number of genes in the population, A ¼ the number of genes within each
functional classification, x ¼ the number of genes
identified as up-regulated in response to each drug,
and n ¼ the total number of genes up-regulated
after treatment by each anti-microbial compound.
Results
The transcriptional response of M. tuberculosis to
each of the six anti-microbial agents was defined as
the subset of genes identified as significantly
differentially expressed in two or more statistical
S.J. Waddell et al.
tests (described in the Methods). Using this analysis
strategy, 155 genes were demonstrated to be upregulated by INH treatment (32 down-regulated);
Isoxyl (231 up, 21 down); Tetrahydrolipstatin (208
up, 24 down); SRI#221 (182 up, 25 down); SRI#967
(116 up, 30 down); and SRI#9190 (124 up, 22 down).
The fold changes and predicted function of these
genes are described as supplementary information
Table S1.
Dissecting the transcriptional response of
M. tuberculosis to the six drug compounds by
functional classification (as described by Cole
et al.11) revealed that the genes induced by drug
treatment broadly represent most of the range of
pathways that are present in M. tuberculosis. The
hypergeometric distribution25 was used to determine whether the enrichment of genes within a
particular functional category in response to each
drug treatment was significant (p-value o0:05).
Table 1 shows that the number of genes within the
functional categories of energy metabolism and
chaperones/heat shock were significantly enhanced
after treatment with each of 3 or more antimicrobial agents. The functional category of lipid
metabolism was significantly enriched in response
to INH and isoxyl treatment, as was the category of
polyketide synthesis after treatment with SRI#967.
Additionally, the proportion of genes involved in
the metabolism of the cell envelope was significantly increased after treatment with SRI#221 and
SRI#967 (Table 1).
Common response to anti-mycobacterial
agents
By comparing the similarities between the
M. tuberculosis drug-induced expression profiles,
a common response to anti-mycobacterial agents
could be defined. A subset of 80 genes were
identified which were significantly up-regulated
after treatment with 3 or more anti-microbial
compounds (of a maximum 6). These genes are
listed in Table 2. Many of these common genes
induced by exposure to anti-microbial compounds
were involved in the mycobacterial stress response.
Genes associated with DNA repair such as end
(coding for a probable endonuclease) and recA
(encoding recombinase A26) were up-regulated;
together with Rv3049c (a probable monoxygenase)
and aphC (alkyl hydroperoxide reductase) expressed in response to oxidative stress.27 Also
over-expressed after drug treatment (43 drugs)
were gltA1 (a probable citrate synthase) and icl
(isocitrate lyase) similar to changes in metabolism
seen under stress conditions.28 RNA polymerase
ARTICLE IN PRESS
The use of microarray analysis
267
Table 1 The M. tuberculosis response to 6 anti-microbial compounds examined by functional category, as
defined by Cole et al.11 Only genes identified as significantly over-expressed in two or more of the statistical tests
described in response to isoniazid (INH), isoxyl (ISO), tetrahydrolipstatin (THL), SRI#221/967/9190 are detailed in
these tables. These gene lists are described in Supplementary Table S1. The hypergeometric probabilities25 of the
enrichment of particular functional categories of genes in response to each drug treatment are indicated if
a
p-value o0.05, bo0.01, co0.001.
Functional classification (as defined by Cole et al.11)
Genes identified as up-regulated
I. Small-molecule metabolism
A. Degradation (163)
B. Energy metabolism (292)
C. Central intermediary metabolism (45)
D. Amino acid biosynthesis (95)
E. Polyamine synthesis (1)
F. Purines, pyrimidines, nucleosides and nucleotides (60)
G. Biosynthesis of cofactors, prosthetic groups and carriers (117)
H. Lipid biosynthesis (65)
I. Polyketide and non-ribosomal peptide synthesis (41)
J. Broad regulatory functions (187)
II. Macromolecule metabolism
A. Synthesis and modification of macromolecules (215)
B. Degradation of macromolecules (87)
C. Cell envelope (360)
III. Cell processes
A. Transport/binding proteins (123)
B. Chaperones/heat shock (16)
C. Cell division (19)
D. Protein and peptide secretion (14)
E. Adaptations and atypical conditions (12)
F. Detoxification (22)
IV. Other
A. Virulence (38)
B. IS Elements, repeated sequences and phage (135)
C. PE and PPE families (167)
D. Antibiotic production and resistance (14)
E. Bacteriocin-like proteins (3)
F. Cytochrome P450 enzymes (22)
G. Coenzyme F420-dependent enzymes (3)
H. Miscellaneous transferases (61)
I. Miscellaneous phosphatases, lyases and hydrolases (18)
J. Cyclases (6)
K. Chelatases (2)
INH
ISO
THL
221
967
9190
8
10
3
4
8
32c
3
7
8
18
1
1
5
18a
4
2
8
9
2
7
14a
1
2
2
5
7b
4
4
2
2
11c
3
5
4
4
2
7
5
7
5
3
9
2
3
4a
8
3
3
2
5
10
3
16
17a
7
23
17a
5
18
12
5
26b
8
1
20b
10
2
9
7
3a
7
6c
2
2
9
3a
3
1
4
1
1
1
1
3
3a
1
1
1
1
1
3
9a
10
1
6
1
11a
9
1
1
1
1
3
2
1
1
2
1
1
3
1
1
4
2
3
2
1
1
1
1
1
1
3
4
1
V. Conserved hypotheticals (915)
36
61
40
26
29
30
VI. Unknowns (606)
Total genes (3924)
12
20
37
33
11
19
155
31
231
35
208
37
182
32
116
34
124
40
Total up-regulated genes after drug treatment
Percentage of unknown function (39% of total genes unknown)
sigma factors A and B were also induced, together
with serine/threonine protein kinases B and G. sigB
has been implicated in the M. tuberculosis response
to a number of stress conditions.29 The product of
pknG is predicted to be a soluble protein (the
transcription of which may be controlled by the
Rv No
3
4
5
3
4
4
4
3
3
3
3
3
3
3
3
4
4
3
3
4
3
3
4
5
3
4
3
3
4
4
3
3
4
4
4
3
4
3
4
4
pknB
rpsF
icd2
fbpC2
pntAB
bglS
Rv0247c
fadD2
Rv0349
pknG
Rv0412c
Rv0446c
icl
mmpS2
end
rpsS
rpmC
rplR
Rv0851c
fadE10
Rv0910
sucC
esxl
Rv1109c
gltA1
narH
papA3
Rv1184c
esxK
esxL
atpG
PPE19
appC
Rv1683
Rv1733c
narK2
Rv1738
Rv1747
esxN
Rv1813c
Rv0014c
Rv0053
Rv0066c
Rv0129c
Rv0156
Rv0186
Rv0247c
Rv0270
Rv0349
Rv0410c
Rv0412c
Rv0446c
Rv0467
Rv0506
Rv0670
Rv0705
Rv0709
Rv0720
Rv0851c
Rv0873
Rv0910
Rv0951
Rv1037c
Rv1109c
Rv1131
Rv1162
Rv1182
Rv1184c
Rv1197
Rv1198
Rv1309
Rv1361c
Rv1623c
Rv1683
Rv1733c
Rv1737c
Rv1738
Rv1747
Rv1793
Rv1813c
INH
2
2
ISO
2
2
4
3
2
2
4
2
2
4
2
4
2
2
3
2
2
2
3
2
THL
2
2
2
3
2
2
2
221
967
9190
Putative function
2
2
3
3
3
3
3
3
3
3
Serine/threonine protein kinase B
Ribosomal protein S6
Isocitrate dehydrogenase
Antigen 85c, mycolyl transferase C
Probable NAD(P) transhydrogenase
Probable beta-glucosidase
Probable succinate dehydrogenase
Probable long-chain fatty acid CoA ligase
Unknown
Serine/threoine protein kinase G
Possible conserved membrane protein
Possible conserved membrane protein
Isocitrate lyase
Unknown, probable membrane protein
Probable endonuclease IV
30s ribosomal protein s19
50s ribosomal protein L29
50s ribosomal protein L18
Probable dehydrogenase/reductase
Probable acyl-CoA dehydrogenase
Unknown
Probable succinyl-CoA synthetase
Putative ESAT-6-like protein
Unknown
Probable citrate synthase
Probable respiratory nitrate reductase
Polyketide associated protein
Unknown, possible exported protein
Putative ESAT-6 like protein
Putative ESAT-6 like protein
ATP synthase gamma chain
PPE family protein
Probable cytochrome D ubiquinol oxidase
Possible long-chain acyl-CoA synthase
Probable conserved transmembrane protein
Possible nitrate/nitrite transporter
Unknown
Probable membrane transport protein
Putative ESAT-6-like protein
Unknown
3
2
3
3
2
2
2
2
2
2
2
2
3
2
2
4
3
4
2
4
2
3
4
4
2
2
4
4
3
2
4
2
3
3
3
2
2
2
2
4
2
4
2
2
2
2
2
3
2
4
4
2
2
2
3
3
3
3
2
2
2
3
3
2
3
3
3
2
2
3
3
4
2
2
3
2
4
3
4
2
3
4
2
2
3
2
2
2
3
3
2
4
2
2
2
2
2
2
3
3
3
2
A
B
C
D
E
F
G
H
I
J
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
ARTICLE IN PRESS
Gene name
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
S.J. Waddell et al.
N
268
Table 2 The common response of M. tuberculosis to 6 anti-microbial agents, detailing the genes identified to be up-regulated in response to 3 or more of the antimicrobial compounds tested (maximum 6).
Rv1987
Rv1997
Rv1998c
Rv2005c
Rv2007c
Rv2032
Rv2091c
Rv2147c
Rv2185c
Rv2202c
Rv2243
Rv2244
Rv2245
Rv2299c
Rv2346c
Rv2405
Rv2428
Rv2467
Rv2626c
Rv2627c
Rv2629
Rv2703
Rv2710
Rv2737c
Rv2744c
Rv2754c
Rv2818c
Rv2846c
Rv2959c
Rv3049c
Rv3136
Rv3250c
Rv3270
Rv3388
Rv3478
Rv3592
Rv3601c
Rv3823c
Rv3824c
Rv3874
3
4
2
3
3
2
4
2
3
2
3
3
2
2
4
4
4
3
3
2
3
4
4
2
4
3
2
4
4
2
2
2
2
2
4
4
3
2
4
3
2
4
2
2
2
3
2
2
4
3
2
2
3
2
3
3
2
2
2
3
3
2
2
2
2
2
2
3
2
4
3
3
2
2
2
3
2
2
2
2
3
4
2
3
3
4
2
2
2
3
2
2
2
4
3
2
2
2
2
4
4
3
2
3
3
2
3
2
4
3
3
2
2
2
2
2
3
2
2
2
2
3
4
3
3
3
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
4
2
2
2
Probable chitinase
Probable metal cation transporter
Unknown
Unknown
Probable ferredoxin
Unknown, possible nitroreductase
Unknown, probable membrane protein
Unknown protein
Unknown (TB16.3)
Probable carbohydrate kinase
Malonyl CoA-acyl carrier transacylase
Meromycolate extension acyl carrier protein
Beta-ketoacyl-ACP synthase
Probable heat shock protein
Putative ESAT-6 like protein
Unknown
Alkyl hydroperoxide reductase C
Probable aminopeptidase (pepN)
Unknown
Unknown
Unknown
RNA polymerase sigma factor A
RNA polymerase sigma factor B
Recombinase A protein
35-kd alanine rich antigen
Probable thymidylate synthase
Unknown
Putative efflux protein
Possible methyltransferase
Probable monoxygenase
PPE family protein
Probable rubredoxin
Probable metal cation transporter
PE PGRS family protein
PPE family protein
Unknown (TB11.2)
Aspartate1-decarboxylase
Conserved large membrane protein
Polyketide associated protein
10 KDa culture filtrate antigen (cfp10)
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
ARTICLE IN PRESS
Rv1987
ctpF
Rv1998c
Rv2005c
fdxA
acg
Rv2091c
Rv2147c
Rv2185c
cbhK
fabD
acpM
kasA
htpG
esxO
Rv2405
ahpC
pepD
Rv2626c
Rv2627c
Rv2629
sigA
sigB
recA
35kd ag
thyX
Rv2818c
efpA
Rv2959c
Rv3049c
PPE51
rubB
ctpC
PE PGRS52
PPE60
Rv3592
panD
mmpL8
papA1
esxB
The use of microarray analysis
4
3
4
3
3
6
4
3
3
5
4
5
4
3
4
3
3
4
5
3
3
3
4
3
6
4
3
3
3
5
3
3
3
3
3
3
4
3
3
5
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
d
269
Numbers in the columns (INH isoniazid, ISO isoxyl, THL tetrahydrolipstatin, SRI# 221/967/9190) indicate the number of statistical tests in which each gene was found to be significantly
induced (minimum of 2, maximum 4). The left column labelled N, details the number of drugs in which each gene was significantly differentially expressed. Dots present in the columns
A–J indicate that the gene has been previously identified to be up-regulated in response to various other stresses; A INH treatment,15 B INH and TLM treatment,16 C low oxygen,32 D
nutrient starvation,37 E nitric oxide treatment,35 F phagocytosis,36 G carbon starvation (Hampshire et al., this issue), H detergent stress,34 I heat shock,23 J acid shock.33 This table is
ordered by Rv number.
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270
S.J. Waddell et al.
redox status of the cell) which may be involved in
glutamine uptake and which may be up-regulated
under nitrogen-limiting conditions.30 Additionally the
putative nitrate/nitrite transporter narK2, the nitroreductase acg and the nitrate reductase narH
were also identified to be induced. Indeed, five genes
demonstrated to be part of the ACG (acr-coregulated
gene) family were found to be up-regulated
after exposure to anti-mycobacterial compoundsF
Rv1733c, narK2, Rv1738, Rv2005c, and acg.31
Many of the ‘common’ genes induced by drug
treatment have been identified as part of the
mycobacterial response to other stresses such as
low oxygen,32 heat shock,23 acid shock,33 detergent
stress,34 nitric oxide treatment,35 phagocytosis36 or
nutrient/carbon starvation37 Hampshire et al., this
issue. Those genes, which have also been previously
identified to be up-regulated in response to these
other stresses are marked in Table 2. Of the
remaining genes induced by 3 or more drugs, five
are annotated as efflux proteins or transportersF
narK2 (a possible nitrate/nitrite transporter),
Rv1747 (a probable ABC transporter), ctpF (a
putative metal cation transporter), efpA (an efflux
protein), and ctpC (a probable metal cation
transporter). A second subset of six genes belonging
to the ESAT-6 family of proteins was also identified
as up-regulated after 4 h drug exposure (Rv1037c,
Rv1197-Rv1198, Rv1793, Rv2346c, and Rv3874).
The ESAT-6 gene clusters in M. tuberculosis have
been associated with the generation and transportation of T-cell antigens lacking detectable secretion
signals.38 These genes linked to the transportation of
unknown moieties may be directly involved in the
mycobacterial response to drug compounds which
contributes to the intrinsic resistance of mycobacteria to anti-microbial agents.
Drug-specific expression responses
The expression profiles of M. tuberculosis treated
with each of the six anti-microbial compounds were
compared, by generating a similarity matrix detailing the number of overlapping genes between two
drug treatments as a proportion of the possible
maximum (Fig. 2). The mycobacterial response to
INH and isoxyl exposure was most similar as may be
expected as both drugs target aspects of fatty acid
and mycolic acid biosynthesis.17,18,19 The expression profiles of M. tuberculosis treated with the
compounds SRI#967 and SRI#9190 were also similar.
Aspects of the mycobacterial response to individual
drugs focusing on the possible action of the
compounds is briefly presented below.
INH and isoxyl
Both INH and isoxyl inhibit fatty acid and mycolic
acid biosynthesis in M. tuberculosis.17,18,19 INH has
INH
ISO
THL
#221
#967
#9190
INH
155
67
27
19
24
30
ISO
0.568
231
44
36
39
44
THL
0.826
0.788
208
32
29
25
#221
0.877
0.802
0.824
182
36
30
#967
0.793
0.664
0.750
0.690
116
44
#9190
0.758
0.645
0.798
0.758
0.621
124
Figure 2 A similarity matrix comparing the genes up-regulated in response to the 6 anti-microbial compounds tested
(isoniazid, INH; isoxyl, ISO; tetrahydrolipstatin, THL; SRI#221/967/9190). Numbers in the top half of the figure
represent the number of common genes up-regulated in response to two compounds. The maximum number of genes
identified as significantly up-regulated (in more than 2 statistical tests) after treatment with each drug are displayed in
the shaded cells. The bottom half of the matrix describes the number of genes common to two drug responses as a
proportion of the possible maximum, calculated as 1-(common genes/maximum possible genes). The smaller this
proportion is the greater the extent of the overlap between expression responses.
ARTICLE IN PRESS
The use of microarray analysis
been demonstrated to target the enoyl-AcpM
reductase InhA, a component of the fatty acid
synthaseFII (FAS-II).39 Isoxyl treatment inhibits
mycolic acid and shorter-chain fatty acid synthesis
leading to the hypothesis that isoxyl may act on
other components of FAS-II.18 Interestingly, it has
also recently been shown to target a delta-9
desaturase in mycobacteria.19 Genes coding for
enzymes involved in FAS-II were up-regulated after
exposure to both drugs: fabD (coding for a malonylCoA::acyl carrier protein (ACP) transferase), acpM
(an acyl carrier protein), kasA and kasB (both
b-ketoacyl ACP synthases). These have been previously identified to be induced by INH, ethionamide
and
thiolactomycin
treatment.15,16
Interestingly amongst other fatty acid biosynthetic
genes induced by isoxyl treatment alone was mabA,
a gene coding for a b-ketoacyl ACP reductase,
which also belongs to the FAS-II system. The
induction of mabA (which is transcriptionally linked
to inhA in M. tuberculosis), after exposure to
isoxyl, but not INH, may reflect differences in the
mode of action of these two compounds. Further
experiments such as the overexpression of mabA
during isoxyl exposure may help to elucidate the
primary target of isoxyl.40
Tetrahydrolipstatin
Tetrahydrolipstatin (THL) is a reversible inhibitor of
lipases used in the treatment of obesity (Xenicals,
Roche). A number of M. tuberculosis putative
lipases were up-regulated in response to THL
treatmentFRv1683, lipD and lipV (although this
was not significant by hypergeometric testing). Of
the remaining induced genes, 4 encoding putative
transporters (ctpl, sugA, Rv3253c, and Rv3781) and
6 coding for probable transcriptional regulators
were identified (Rv0043c, Rv0823c, sigE, Rv3167c,
Rv3687c and Rv3855). Also up-regulated on exposure to THL were 4 genes located in a gene cluster
Rv0676c-Rv0679c. Rv0676c (mmpL5) belongs to a
family of conserved large membrane proteins,
Rv0677c (mmpS5) is part of a related small
membrane protein family (which appears to overlap stop and start codons with mmpL5), the
function of Rv0678 is unknown, and Rv0679c
codes for a threonine-rich protein of undetermined
function. The functional significance of this
cluster of genes in the M. tuberculosis response
to THL treatment cannot be elucidated by microarray analysis alone. However, further experimentation into this cluster or other genes of interest
may define novel mechanisms of drug resistance in
M. tuberculosis.
271
SRI#221
Figure 1 shows that SRI#221 is a potent antitubercular compound with a low MIC value, however the primary mode of action of this antimicrobial compound is unknown. Treatment of
M. tuberculosis with SRI#221 induced two clusters
of genes involved in complex lipid biosynthesis. The
first cluster containing tesA (a probable thioesterase), drrB (an ABC-type transporter), papA5 (a
polyketide synthase associated protein), and
fadD28 (a fatty acid-CoA ligase), is involved in the
biosynthesis and translocation of the multi-methyl
branched mycocerosic acids in the generation of
phthiocerol dimycocerosates.41 The second gene
cluster includes Rv3822 (of unknown function),
mmpL8 (a conserved large membrane protein),
papA1 (a polyketide synthase associated protein)
and fadD23 (a probable fatty acid-CoA ligase).
These genes cluster around the polyketide synthase
pks2, responsible for the biosynthesis of the multimethyl branched phthioceranic acids present in the
sulpholipid complex lipids.42 The induction of these
gene clusters may be part of a compensatory
network to minimise the anti-mycobacterial effects
of SRI#221, may reflect the broad nature of SRI#221
action, or highlight the primary mode of SRI#221
action to be within shared basic lipid biosynthetic
pathways.
SRI#967 and SRI#9190
The mode of action of the anti-mycobacterial
compounds SRI#967 and SRI#9190 are yet to be
determined. The M. tuberculosis response to
SRI#967 exposure includes the up-regulation of
Rv0076c and Rv0077c. Rv0076c encodes a probable
membrane protein, whereas Rv0077c codes for a
possible oxidoreductase. Similarly, Rv0135c (a
putative transcriptional regulator) and Rv0136
(belonging to the cytochrome P450 group of
monoxygenases) were induced by SRI#9190 treatment alone. The induction of these two distinct
clusters (on exposure to different anti-mycobacterial compounds) may be part of a similar oxidative
stress response. The identification that these
genes may play a role in the intrinsic resistance of
M. tuberculosis to anti-microbial agents using
microarray analysis enables more specific experimental strategies to be employed.
Of particular interest was a cluster of 4 genes
which were significantly induced on exposure to
both SRI#967 and SRI#9190. Rv3159c (encoding
PPE53, a member of the PPE family), Rv3160c
(a putative transcriptional regulator), Rv3161c
ARTICLE IN PRESS
272
(a probable dioxygenase) and Rv3162c (a possible
integral membrane protein) were up-regulated on
exposure to these compounds alone (none of these
genes were induced by the other anti-microbial
agents tested). The probable dioxygenase Rv3161c
is most similar to ring hydroxylating dioxygenases,
so it is likely that this enzyme is involved in the
degradation of benzene ring structures (which
SRI#967 and SRI#9190 both contain, Fig. 1).
Rv3160c and Rv3161c have recently been identified
to be induced by triclosan treatment.16 Triclosan
(2,4,40 -trichloro-2-hydroxydiphenyl ether) contains
two chlorinated benzene rings. This cluster of
genes may therefore be induced on exposure to
compounds containing halogenated benzene rings,
this would explain the up-regulation of the cluster
in response to SRI#967 and SRI#9190, but not to the
benzene ring structures in isoxyl and SRI#221. This
gene cluster may be induced as part of a response
to render halogenated benzene compounds benign,
so contributing to the natural resistance of
M. tuberculosis to a range of anti-microbial agents.
S.J. Waddell et al.
Supplementary Data Table S1 (on the
web)
The differentially regulated genes in response to 6
anti-microbial compounds. These tables describe
the genes identified as significantly differentially
expressed in the M. tuberculosis responses to each
of the six anti-microbial compounds tested. Each
table consists of the gene name (and unique gene
identifier), Rv number, a brief description of the
proposed function of the gene and the fold
expression ratios determined using each of the
four statistical tests described. The number of
times each gene has been identified as significantly
differentially expressed is detailed in the column
labelled N. Cells in this column coloured purple
indicate the presence of consecutive Rv numbers.
Only genes identified by two or more statistical
tests have been included in these tables. These
tables are ordered by Rv number.
Acknowledgements
Discussion
The response of M. tuberculosis to six antimicrobial compounds was determined by microarray analysis. The microarray expression data set
was analysed using several statistical methods, the
use of multiple statistical methods added extra
depth to the interpretation of the data sets.
Additionally, a single spot replacement strategy
was used alongside the original data set to allow
the maximum amount of information to be extracted from the data sets without significantly
shifting the expression patterns.
Using this microarray analysis strategy, elements
of a M. tuberculosis common stress response and
specific drug-induced changes were identified to six
anti-microbial compounds. The up-regulation of
genes specific to each compound may reflect the
mode of action of the drug or define innate
resistance mechanisms in M. tuberculosis. This
investigation has defined an initial subset of genes
which may be important in the innate resistance of
M. tuberculosis to anti-microbial agents. Microarray profiling of M. tuberculosis gene expression
after exposure to drugs enables compounds of
unknown mechanism of action to be classified into
similarity groups, but in this study has not been
helpful to elucidate the site of or mechanism of
action. This would require complementary genetic
and biochemical studies informed by microarray
profiling.
The anti-microbial compounds used in this investigation were gifted from the following sourcesF
Isoxyl from M.J. Colston and P. Draper, National
Institute of Medical Research, London, UK; THL
from P. Hadvary and Hoffmann-La Roche, Basel,
Switzerland; and SRI#221/967/9190 from The
Southern Research Institute, Birmingham, AL
35255, USA. Anti-mycobacterial MIC data were
provided by the Tuberculosis Anti-microbial Acquisition and Coordinating Facility (TAACF) through
a research and development contract with the
US National Institute of Allergy and Infectious
Diseases.
This work was supported by the NIH/NIAD (RCR),
GSB is currently a Lister Institute Jenner Research
Fellow and acknowledges support from The Medical
Research Council. SJW acknowledges financial
support for a Ph.D. studentship from GlaxoSmithKline. The authors thank Professor Philip D. Butcher
and Dr. Jason Hinds of The Wellcome Trust funded
multi-collaborative microbial pathogen microarray
group at St. George’s Hospital Medical School,
London, for access to M. tuberculosis microarrays.
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