Immune Response to Mycobacterium tuberculosis and
Identification of Molecular Markers of Disease
Mercedes Gonzalez-Juarrero1, Luke C. Kingry1,2, Diane J. Ordway1, Marcela Henao-Tamayo1, Marisa Harton1,
Randall J. Basaraba1, William H. Hanneman3, Ian M. Orme1, and Richard A. Slayden1,2
2
Rocky Mountain Regional Center of Excellence, 1Department of Microbiology, Immunology and Pathology, and 3Department of Environmental
and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado
The complex molecular events that occur within the host during the
establishment of a Mycobacterium tuberculosis infection are poorly
defined, thus preventing identification of predictive markers of
disease progression and state. To identify such molecular markers
during M. tuberculosis infection, global changes in transcriptional
response in the host were assessed using mouse whole genome
arrays. Bacterial load in the lungs, the lesions associated with
infection, and gene expression profiling was performed by comparing normal lung tissue to lungs from mice collected at 20, 40, and 100
days after aerosol infection with the H37Rv strain of M. tuberculosis.
Quantitative, whole lung gene expression identified signature
profiles defining different signaling pathways and immunological
responses characteristic of disease progression. This includes genes
representing members of the interferon-associated gene families,
chemokines and cytokines, MHC, and NOS2, as well as an array of cell
surface markers associated with the activation of T cells, macrophages, and dendritic cells that participate in immunity to M.
tuberculosis infection. More importantly, several gene transcripts
encoding proteins that were not previously associated with the host
response to M. tuberculosis infection, and unique molecular markers
associated with disease progression and state, were identified.
Keywords: tuberculosis; transcriptional response; immunity
Tuberculosis is a world health problem, with reports estimating
that as much as one third of world’s population is infected with the
tubercle bacilli, and 2 million people die every year as a result.
The prevalence and incidence of tuberculosis worldwide remain
high despite the intense efforts by the World Heath Organization–
sponsored directly observed therapy campaign and the availability of routine diagnostic methods, a vaccine, and effective
chemotherapy. Disease management has been hindered by the
inability to objectively assess disease state, thus preventing
a rational guide for patient management aimed at reducing the
rate of relapse and spread. The characterization of the complexities of the immune response at different stages of infection, and
identification of informative molecular markers, is one of the
most difficult aspects of understanding pathogenesis and disease
progression and in developing new strategies and tools to diagnose and treat disease.
(Received in original form July 8, 2008 and in final form August 22, 2008)
This work was supported by NIH AI-055298 (to R.A.S.) and AI-44072 (to I.M.O.).
This work was supported by resources and services provided by the Genomics
Proteomics Core of the Rocky Mountain Regional Center of Excellence U54
AI065357.
Correspondence and requests for reprints should be addressed to Richard A.
Slayden, Ph.D., Rocky Mountain Regional Center of Excellence and Department
of Microbiology, Immunology and Pathology, Colorado State University, Fort
Collins, CO 80523-1682. E-mail: richard.slayden@colostate.edu
CLINICAL RELEVANCE
This work characterizes the global host response at different stages of disease and can be used as a foundation for
further development of molecular markers that best correlate with disease state or responses to vaccines and
chemotherapy.
Pulmonary exposure to Mycobacterium tuberculosis elicits
both host innate and adaptive immune responses, yet the bacteria
are still capable of establishing chronic infections. Much of the
information about the immune response to infection and host
susceptibility has been compiled from various techniques, including passive cell transfer (1–3), the use of mice with targeted
gene disruptions (4, 5), as well as PCR, enzyme-linked immunosorbent assay, and flow cytometric methods (6, 7). Several studies
have reported the transcriptional responses to M. tuberculosis
infection, but none have analyzed global transcriptional changes
in the host genes at different stages of chronic pulmonary
infection with M. tuberculosis (8–14). This has resulted in limited
knowledge of the dynamic transcriptional changes that occur
during infection and disease progression. These data are needed
to better understand the differences in host response at various
stages of disease and to correlate these transcriptional changes
with lesion morphology and disease progression. Thus, more
comprehensive and global studies focusing on the host response
to infection with M. tuberculosis have the potential to identify
previously unrecognized immune mechanisms that better correlate with disease progression and signature profiles that are predictive of protection.
In the present work, transcriptional profiling of uninfected
mouse lungs and lungs harvested during development of disease
(Day 20 through Day 100 of the infection) allowed for the
correlation of the host immune response with the bacterial load
and resulting pathology. The results of this study provide a global
view of the dynamic changes in the host response throughout the
progression of disease and identified gene transcripts expressing
molecules that were poorly associated with the host response to
M. tuberculosis infection. In addition to defining the trends in the
immune response during pulmonary infection, molecular markers of disease progression were identified. Together, this work
characterizes the host response at different stages of disease and
can be used as a foundation for further characterization of
molecular mechanisms controlling disease progression as well
as further development of molecular markers that best correlate
with disease state or responses to vaccines and chemotherapy.
MATERIALS AND METHODS
This article has an online supplement, which is accessible from this issue’s table of
contents at www.atsjournals.org
Low Dose Aerosol Infection
Am J Respir Cell Mol Biol Vol 40. pp 398–409, 2009
Originally Published in Press as DOI: 10.1165/rcmb.2008-0248OC on September 11, 2008
Internet address: www.atsjournals.org
Six- to eight-week-old specific pathogen–free female C57BL/6 mice
(Jackson Laboratories, Bar Harbor, ME) were infected with M. tuberculosis H37Rv by low-dose aerosol exposure using a Glass-Col (Terre
Gonzalez-Juarrero, Kingry, Ordway, et al.: Host Transcriptional Responses to M. tuberculosis
Haute, IN) aerosol generator calibrated to deliver 50 to 100 viable
bacteria into the lungs. Bacterial load in the lungs of representative mice
at each time point were determined by plating serial dilutions of organ
homogenates on Middlebrook 7H11 medium and enumeration of
colony-forming units after incubation at 378C for 3 weeks.
Histologic Analysis
Lungs from mice (n 5 5) in the same groups were harvested for histologic
analysis on Days 0, 20, 40, and 100 of the infection. The accessory lung
lobe from each mouse was fixed with 10% formalin in phosphatebuffered saline (PBS). Sections from these tissues were stained using
hematoxylin and eosin. All sections were scored by a board certified
veterinary pathologist, blinded to treatment groups. Lesion scores were
based on percent lung involvement as well as specific morphologic
features like lesion necrosis and proportion of various cell types that
make up the granulomatous inflammatory responses. All pictures were
taken with a DP70 Olympus camera (Olympus, Center Valley, PA).
Transcriptional Analysis
Global expression analysis was performed using Affymetrix mouse
genome 430 2.0 array. For analysis, uninfected mice and mice at 20, 40,
and 100 days of the infection (n 5 15 per group) were killed, and the lungs
were excised and subjected to homogenization in Trizol. Nucleic acids
were partitioned from other cellular products by addition of chloroform
(1:2, vol/vol) and centrifugation at 13,000 3 g for 20 minutes at 48C. The
resulting aqueous layer was removed and total RNA was precipitated
with isopropanol (1:1,vol/vol). DNase treatment was used to remove
DNA contamination, and total RNA was purified using an RNeasy
miniprep kit (Qiagen, Valencia, CA). RNA from five mice per biological
group was pooled for labeling, resulting in replicates representing
uninfected mice and mice at 20, 40, and 100 days of infection. Global
expression analysis was performed using Affymetrix mouse genome
4302.0 gene chips (Affymetrix, Santa Clara, CA). RNA labeling and
hybridization was per standard protocols provided by Affymetrix.
Data reduction and analysis of uninfected mice compared with mice
at 20, 40, and 100 days of the infection was performed using Genesifter
software (geospiza, Seattle, WA) (15), and Benjamini and Hochberg was
used for adjusting the P value from a comparison test based on the
number of tests performed. A principal component analysis (PCA)
comparing uninfected mice and mice at 20, 40, and 100 d of the infection
was performed to determine the similarity of the gene response to
infection at each time point. PCA is a statistical method of analysis for
determining the key variables in a multidimensional data set that explain
the differences in the observations, and can be used to simplify the
analysis and visualization of multidimensional data sets (16, 17). Hierarchical clustering and self-organizing mapping (SOM) was used to
identify patterns and partitioning to separate data into discrete groups.
Quantitative real-time PCR analysis was performed in triplicate from
three biologically independent samples of total RNA from the lungs of
uninfected mice and from mice 20, 40, and 100 days after challenge. The
fold increase in signal over the 18S housekeeping gene was determined
using the DDct calculation.
RESULTS
Progress of Disease and Development of Lung Pathology
C57BL/6 mice were infected with a low dose aerosol of M.
tuberculosis H37Rv to determine the host response at different
time intervals after infection. The bacterial burden, pathology,
and host transcriptional response was determined at 20, 40, and
100 days of the infection. Consistent with previous observations,
after aerosol exposure the bacteria in the lungs grew in an
exponential manner for 20 days, after which time the number of
cultivable bacteria remained constant, giving rise to a characteristic chronic infection (Figure 1A) (20). Examination of the
histopathology revealed that lung lesions were mild at Day 20
and mostly restricted to peribronchial and perivascular parenchyma (Figure 1B). As the infection progressed, lesions developed into organized structures containing large aggregates of
lymphocytes and epithelioid macrophages, with increasing numbers of highly vacuolated cells (referred to as foamy cells) (Figure
399
1C). By Day 100 of the infection, lesions were extensive and
consisted of coalescing foci of mixed inflammation containing
predominately lymphocytes, macrophages, and numerous foamy
cells (Figure 1D). During the acute (Day 20), subacute (Day 40),
and chronic stages of infection (Days 40 and 100), histologic
findings illustrate the dynamic nature of the immune and inflammatory responses as the disease progresses. To further
confirm that the infection in this study was consistent with
previous reports, we verified that IFN-g and TNF-a expression
increased over the course of infection (Figure 2). This information allows us to make a connection between the stage of
infection, development of lesions, and activation of the host
adaptive immune response.
Global Changes in the Transcriptional Response during the
Chronic State of Infection
The global transcriptional response in the lungs of mice to M.
tuberculosis infection was assessed through whole mouse genome
DNA microarray analysis. Compared with uninfected C57BL/6
mice (Day 0), a total of 3,308 open reading frames (ORFs), displayed a 1.5-fold or greater change in expression (P value , 0.05)
in the lungs from infected C57BL/6 mice over a 100-day infection
(see Table E1 in the online supplement for a complete list of data).
This represents altered expression of approximately 9% of the
annotated transcripts in the mouse genome. To determine the
similarity of the gene response to infection at each time point, we
used the principal components analysis (PCA) to cluster the
transcriptional response of uninfected mice and of mice at Day 20,
Day 40, and Day 100 after exposure and visualized the analysis
with a scatter plot (Figure 3A). This multivariate technique
reduces the complexity of the transcriptional response data and
preserves closeness between biological data sets, so that time
points residing in close proximity in many dimensions are
configured close to each other in the scatter plot. Accordingly,
data analysis indicated that the overall host transcriptional
response in the lungs during M. tuberculosis infection was
significantly different between uninfected mice and mice after
20 days, 40 days, or 100 days of infection, with the later time points
being highly concordant. The global ontology profile of the
differentially expressed genes revealed that there is a dynamic
change in genes involved in cellular metabolism and physiology,
and genes involved in regulation and response to stimulation
being the next dominant response (Figure 3B). Ontology analysis
of the transcriptional response of immune-specific genes substantiate this global analysis because genes associated with
stimulus and physiologic processes are the most altered in
expression, followed by cellular metabolism, regulation, and
development (Figure 3C). Together, global analysis demonstrates that there is a large transcriptional response and that the
response is progressive from Day 20, to Days 40 and 100, and in
particular a massive induction of genes involved in host defense,
including both cell-mediated and humoral responses.
Trends in the Host Immune Response to Infection with
M. tuberculosis over 100 Days
Host–pathogen interaction. M. tuberculosis infection in the
lungs elicited components of the innate immune response involved in bacterial recognition. The Toll-like Receptor tlr2 and
CD14 were induced at 20 days after infection and remained
elevated throughout the infection, whereas tlr1, tlr13, tlr4, and
tlr12 were only induced at 40 and 100 days after infection (Table 1,
section I). These data are in agreement with previous reports
indicating the importance of the TLR2 (21, 22) in recognition of
M. tuberculosis. Although TLR4 have also been reported in this
process (9, 23), our study suggests that these receptors, as well as
tlr12 and tlr13, only become expressed late in infection. The
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AMERICAN JOURNAL OF RESPIRATORY CELL AND MOLECULAR BIOLOGY VOL 40 2009
Figure 1. Development
of pulmonary granulomatous lesions in mice
exposed to Mycobacterium tuberculosis. Growth
curve of M. tuberculosis
in C57BL/6 mice after
low dose aerosol exposure. (A) Bacterial load
in the lungs was monitored at Day 1, Day 20,
Day 40, and Day 100 of
infection by plating and
enumeration of colonyforming units (CFU). Sections of formalin-fixed
and paraffin-embedded
lung tissue of C57BL/6
mice exposed to M. tuberculosis on Days 20, 40,
and 100 after M. tuberculosis challenge were
visualized with hematoxylin and eosin (H&E)
staining. (B) Mild interstitial pneumonia and
moderately sized lesions
observed at Day 20 of
the infection with M.
tuberculosis. (C) Large
aggregates of lymphocytes were seen within
the epithelioid macrophage and increasing numbers of foamy highly vacuolated cells at Day 40. (D) Development of organized inflammatory multifocal granulomas
containing lymphocytes and macrophages and large numbers of foamy cells by Day 100 of infection. Pictures were taken with an IX70 Olympus
microscope with an attached ZP70 digital camera. Total magnification: A–C, 34; insets, 32.
differential up-regulation of Toll-like receptors over time supports the notion of a change in bacterial recognition pathways and
subsequent activation of immune responses between the early
and chronic stage of infection. Complement receptors (CR3 or
CD11b/CD18 and CR4 or CD11c/CD18), and various Fc receptor
transcript elements expressing for FcgRIIIA (CD16), FcgeRI,
FcgRI (CD64), FcgRIIB (CD32), and FcgRIII (CD16), were also
induced throughout the infection. Furthermore, several studies
have reported an important role of C-type I lectins such as Ly75
(DEC-205;CD205), Mrc1 (mannose receptors;CD206), and Cd209
(DC-SIGN;CD209) in the recognition of M. tuberculosis (24, 25).
However, our data indicated that while C-type I lectins displayed
only modest induction, the very poorly studied C-type II lectins
transcripts expressing for Mincle, DECTIN-2, MDL-1, and
DECTIN-1 were highly up-regulated. (Table1, section I). This
observation is consistent with a recent report that described
Dectin-1 as promoter of mycobacterial-induced IL-12p40 production by dendritic cells (26). Furthermore, we believe this
information could be used as a foundation for further characterization of molecular mechanisms involved in bacterial recognition.
T cell response. At 20 days after infection, the T cell response
was already polarized toward a TH1 response, which is thought to
be predominantly targeted toward elimination of the bacteria.
Figure 2. Real-time PCR of
IFN-g and TNF-a at different
times of infection. (A) IFN-g expression at Day 0 (D0), Day 20
(D20), Day 40 (D40), and Day
100 (D100). (B) TNF-a expression at Day 0 (D0), Day 20 (D20),
Day 40 (D40), and Day 100
(D100). The primer and probe
sequences for murine IFN-g and
TNF-a were previously published
(18, 19). Data are presented using the mean values (n 5 5) using
replicated samples and duplicate
or triplicate assays. A parametric
method, the Student t test, was
used to assess statistical significance between groups of data.
Gonzalez-Juarrero, Kingry, Ordway, et al.: Host Transcriptional Responses to M. tuberculosis
401
Figure 3. Analysis of gene expression ontology of global response and physiology of immune responses. (A) Principal component analysis and
scatter plot of the transcriptional response of uninfected mice (D0) and of mice at Day 20 (D20), Day 40 (D40), and Day 100 (D100) of infection
with M. tuberculosis. (B) Global ontology profile and (C) ontology of the immune associated genes. Data displayed are (A) based on 1,310 genes and
(B) based on 183 immune discriminant genes (1.5-fold or greater alteration; P values , 0.01).
The main cytokine of this pathway, IFN-g, was induced throughout the infection along with 19 known IFN-regulated genes. In
addition, the induction of IFN-g–associated GTPases (Ifi47, Ifit1,
Ifi35, Ifi44, Ifit2, Ifit3, Ifit4, igtp) and two members of the IFNsignaling pathway (Stat1 and Irf7) was observed. Importantly,
ifi27, ifi44, ifit1, ifit3, ift3, and irf7 were induced at 20 days after
infection, but were down-regulated as the infection progressed
(Table 1, section II). Altogether, these data indicated that despite
an increased expression of the IFN-g, there was not a corresponding increase in the activation of the IFN-g pathway throughout
the 100 days of infection. This information suggests that the IFN-g
pathway reaches a (maximal) saturation level of activation during
later chronic infection which is not enhanced by continued
stimulation.
The soluble mediator TNF-a with strong inflammatory and
apoptotic capacity synergizes with IFN-g during the TH1 response (27). While TNF-a transcriptional activity as determined
by microarray analysis was modest, other TNF-a–associated
genes were induced during infection. Specifically, TNF family–
like genes Tnfaip2, Tnfaip3, Tnfrsf, Tnfrsf1b, Tnfrsf9, Tnfsf12,
Tnip1, Traf1, Traf3, Traf3ip3, and Trafd1 were induced, substantiating the contribution of TNF-a in the inflammatory process
in response to M. tuberculosis infection (Table 1, section II).
Antimycobacterial activity and arrest of bacterial growth.
The cytokines IFN-g stimulated the production of effector
molecules such as inducible nitric oxide synthase (iNOS) and
the phagocyte oxidase (phox) which are the major source of
antimicrobial reactive nitrogen and oxygen intermediaries, respectively, known to kill intracellular M. tuberculosis (28–31).
Specifically, nos2 (iNOS) was induced throughout infection while
ncf1 (p47 Phox), ncf2 (p60 Phox), ncf4 (p40 Phox) induction being
limited to day 20 and day 40 of infection (Table 1-III). Similarly,
there were substantial changes of several transcripts encoding
chelators of proteins also known to influence bacterial growth.
Thus, the transcriptional response of type II arginase, (arg),
lactotransferrin and indoleaminepyrrole 2,3 dioxygenase (IDO)
which are known to deplete the environment of arginine, iron and
tryptophan respectively were also upregulated (32–34). The
hypoxia-responsive factor, HIF1a was upregulated. While HIF1a is induced under hypoxic conditions, there are oxygen-independent mechanisms that can also induce HIF-1a expression.
This is consistent with the fact that M. tuberculosis lesions in mice
fail to develop hypoxia as do other species (35). However, along
with it, the induction of Lip1 (lysosomal acid lipase 1), Laptm5
(lysosomal-associated protein transmembrane), the Cd68 (macrosial lysosoamyl glycoprotein), the Cd53 (membrane late endosomes) and the Rab proteins whose expression are known to
favor a niche for bacteria survival were also observed (36, 37)
(Table 1- III). Together, these data indicate that as the infection
progresses, the host-bacterial interaction is a dynamic process
resulting in a limitation of available nutrients and development of
an adequate niche capable of promoting bacterial survival.
Cellular activation mechanisms and differentiation of immune cell populations. Activation markers associated with
antigen-presenting cells and with T cells were also induced
through the course of the infection. Leukocyte specific antigens
CD2, CD45 and CD52, and T cell–specific markers CD3g, CD3d,
CD4, CD8b, CD8a, and CD44, IL7r, or CD5 associated with
activation of memory T cells were induced by Day 20 and
continued to be transcriptionally active throughout the infection.
Importantly, other genes encoding proteins with either unknown
or poorly described roles in tuberculosis immunity displayed
altered expression as well. Specifically, the signaling lymphocyte
activating molecule (Slam)-related receptors (SRR) Slamf6,
Slamf7, and Slamf8 (CD150) and CD244 (2B4) molecules and
its ligand Cd48 molecules were induced during infection. Similar
trends were observed for Cd274 (also known as B7-H1 and PDL1), a co signaling molecule involved in regulating T cell immunity in vivo (Table 1, section IV).
The main killing mechanism of CD8 T cells is through
secretion of cytotoxic granules (38). An interesting observation
was that among the nine granzymes included in this study, the
gene encoding Granzyme K (Gzmk) and the gene encoding the
perforin gene Prf1 were highly up-regulated in response to
infection (Table 1, section IV). While previous work in the
murine model of tuberculosis reported a nonessential role of
perforin and granzyme cytotoxic granules during the course of the
infection (38), GzmK was not included in these studies. Interestingly, recent reports described that circulating levels of
GzmK are significantly elevated in virus-infected patients and
that it triggers rapid cell death independently of caspase activation similar to GzmA (39, 40).
Genes encoding the markers CD40, CD83, CD86, and class II
MHC antigens associated with activation of lung-resident antigen
cell presentation were also up-regulated. Although previous
reports describe decreased production of MHC class II antigens
during an M. tuberculosis infection, this disagreement is
explained by the fact that down-regulation of MHC antigen
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AMERICAN JOURNAL OF RESPIRATORY CELL AND MOLECULAR BIOLOGY VOL 40 2009
TABLE 1. DIFFERENTIALLY EXPRESSED HOST GENES DISCUSSED IN TEXT
Days of the Infection
Annotation
Name
Unique ID
20
40
100
Tlr1
Tlr2
Tlr4
Tlr12
Tlr13
Itgal
Itgam
Itgax
Cd14
Itgb2
Fcgr3a
Fcer1g
Fcgr1
Fcgr2b
Fcgr3
Ly75
Cd209a
Mrc1
Mpa2l
Clec4a3
Clec4e
Clec4n
Clec5a
Clec7a
AF316985
NM_011905
AF185285
BB745017
BI655907
NM_008401
NM_021334
NM_009841
NM_008404
NM_007642
BC027310
NM_010185
AF143181
M14216
NM_010188
NM_013825
AF374470
BB528408
BG092512
AK014135
NM_019948
NM_010819
NM_021364
NM_020008
1.66
3.6
21.9
21.01
1.25
2.84
2.04
1.58
2.26
2.66
8.87
3.42
3.55
2.58
3.17
1.19
21.85
21.64
11.85
2.47
15.54
5.15
2.36
2.69
2.78
3.47
1.93
1.89
2.31
3.86
2.94
3.18
2.45
4.06
7.91
3.55
3.42
5.3
4.29
1.15
21.39
21.09
5.39
3.61
38.68
11.04
2.94
3.42
3.3
5.14
2.88
3.1
2.22
5.36
4.22
4.86
3.8
5.51
17.17
6.81
6.14
5.9
7.93
1.96
21.97
21.14
11.49
4.95
52.78
15.58
2.73
5.11
Ifng
Ifi205
Ifi27
Ifi30
Ifi35
Ifi44
Ifi47
Ifit1
Ifit2
Ifit3
Ifitm3
Igtp
Iigp1
Irf1
Irf5
Irf7
Irf8
Stat1
Stat2
Tnfaip2
Tnfrsf1b
Fasl
Ltb
Traf1
Traf3
K00083
AI481797
NM_019440
AY090098
NM_023065
AW986054
BB329808
NM_008330
NM_008331
NM_008332
NM_010501
BC010291
NM_018738
BM239828
NM_008390
NM_012057
NM_016850
BG069095
AW214029
AF088862
NM_009396
M60469
NM_010177
NM_008518
BG064103
U21050
6.13
4.01
4.82
3.35
2.26
2.28
7.47
5.14
5.03
3.97
5.33
2.42
6.69
12.51
2.64
2.47
6.43
2.45
6.77
4.03
2.24
2.75
1.71
2.47
2.4
1.68
6.62
3.15
4.58
1.04
2.18
1.6
2.18
3.95
2.58
2.16
2.37
1.48
4.75
7.7
2.4
2.38
3.18
3.48
6.25
2.81
5.53
2.83
3.06
3.77
1.63
2.35
13.98
4.55
6.05
1.57
3.35
2.39
4.5
6.13
3.68
3.02
3.65
2.26
7.98
14.34
3.17
3.17
3.85
4.69
10.63
3.4
7.28
4.16
2.9
6.57
2.73
3.1
III. Host Antimycobacterial Activity and Bacterial Cell Growth Arrest
Nitric oxide synthase 2, inducible, macrophage
Nos2
Neutrophil cytosolic factor 1/p47phox
Ncf1
Neutrophil cytosolic factor 2/p67 phox
Ncf2
Neutrophil cytosolic factor 4/p40phox
Ncf4
Arginase 1, liver
Arg1
Arginase type II
Arg2
Lactotransferrin
Ltf
Indoleamine-pyrrole 2,3 dioxygenase/IDO
Indo
Lysosomal acid lipase 1
Lip1
Lysosomal-associated protein transmembrane 5
Laptm5
CD68/macrosiali lysosomal glycoprotein
Cd68
CD53 antigen/membrane late endosomas
Cd53
RAB GTPase activating protein 1-like
Rab10
RAB guanine nucleotide exchange factor 1
Rab8a
RAB geranylgeranyl transferase, b subunit
Rab20
AF065921
AI844633
NM_010877
NM_008677
NM_007482
NM_009705
NM_008522
NM_008324
AI596237
AF364050
AK014135
BM239715
BF465974
BC019990
BG066967
1.9
4.39
1.73
3.3
1.5
2.15
2.67
7.26
2.71
3.37
3.62
2.13
1.45
21.03
1.81
3.81
6.12
2.83
4.85
21.11
3.13
1.02
12.44
4.11
4.87
6.26
1.98
1.49
1.95
1.82
5.67
2.36
1.44
2.08
21.01
3.08
1.31
11.26
1.13
3.25
11.48
3.11
2.09
2.35
3.06
I. Host–Pathogen Interaction
Toll-like receptor 1
Toll-like receptor 2
Toll-like receptor 4
Toll-like receptor 12
Toll-like receptor 13
CD11a/Integrin alpha L
CD11b/ Integrin alpha M
CD11c/ Integrin alpha X
CD14
CD18/Integrin b2
FcgRIIIA (CD16)
FceRIg
FcgRI (CD64)
FcgRIIB (CD32)
FcgRIII (CD16)
C-type lectin/ DEC-205
C-type lectin /DC-SIGN/CD209
CD206 Mannose receptor, C type 1
Macrophage activation 2 like
C-type lectin domain family 4, member
C-type lectin domain family 4, member
C-type lectin domain family 4, member
C-type lectin domain family 5, member
C-type lectin domain family 7, member
a3
e/MINCLE
n/DECTIN-2
a/MDL-1
a/DECTIN-1
II. Immune Response
Interferon gamma
Interferon activated gene 205
Interferon, alpha-inducible protein 27
Interferon gamma inducible protein 30
Interferon-induced protein 35
Interferon-induced protein 44
Interferon gamma inducible protein 47
Interferon-induced protein with tetratricopeptide repeats 1
Interferon-induced protein with tetratricopeptide repeats 2
Interferon-induced protein with tetratricopeptide repeats 3
Interferon induced transmembrane protein 3
Interferon-induced protein 44
Interferon gamma induced GTPase
Interferon inducible GTPase 1
Interferon regulatory factor 1
Interferon regulatory factor 5
Interferon regulatory factor 7
Interferon regulatory factor 8
Signal transducer and activator of transcription 1
Signal transducer and activator of transcription 2
TNF-a–induced protein 2
TNFreceptor superfamily, member 1b
Fas ligand (TNF superfamily, member 6)
Lymphotoxin B
Tnf receptor-associated factor 1
Tnf receptor-associated factor 3
(Continued)
Gonzalez-Juarrero, Kingry, Ordway, et al.: Host Transcriptional Responses to M. tuberculosis
403
TABLE 1. (CONTINUED)
Days of the Infection
Annotation
Name
Unique ID
20
40
100
RAB guanine nucleotide exchange factor 1
Rab geranylgeranyl transferase, a subunit
Rab24
Rab32
NM_009000
NM_026405
1.17
1.46
1.48
2.7
2.3
2.76
IV. Immune Cell Populations and Cellular Activation
CD2
CD45
CD52 antigen
CD3g antigen,
CD3d antigen
CD4
CD8b1
Cd8a
CD5 antigen
CD44
CD127/Interleukin 7 receptor)
CD150 (SLAM family member 8)
CD319 (SLAM family member 7)
CD150 (SLAM family member 6)
CD177
CD244 (2B4-SRR)
CD247
CD274 antigen PD-L1 (B7-H1 or CD274),
CD300A antigen
CD300 antigen like family member F
Granzyme A
Granzyme B
Granzyme K
Lymphocyte antigen 6 complex, locus I
Immunoresponsive gene 1
Cd2
Ptprc
Cd52
Cd3g
Cd3d
Cd4
Cd8b1
Cd8a
cd5
Cd44
Il7r
Slamf8
Slamf7
Slamf6
Cd177
Cd244
Cd247
Cd274
Cd300a
Cd300lf
Gzma
Gzmb
Gzmk
Ly6i
Irg1
NM_013486
BM239436
NM_013706
BB398671
M58149
NM_013487
NM_013488
BB154331
NM_007650
U12434
AI573431
U34882
X67128
X66083
BC024587
AK016183
AF248636
BC027283
BE634960
AK017904
NM_010370
NM_013542
AB032200
AF232024
L38281
2.13
2.26
3.68
4.02
3.88
2.12
2.19
1.66
3.23
1.88
2.01
7.72
2.71
2.65
4.18
2.56
2.32
7.77
2.26
2.51
3.24
3.13
6.98
15.86
20.78
2.06
2.25
3.86
5.63
3.56
3.21
2.36
2.73
4.06
1.93
3.01
16.54
2.64
2.77
3.57
1.83
1.55
7.12
2.86
1.97
1.74
2.64
8.91
16.43
12.68
2.58
4.39
8.1
7.13
5.31
4.16
3.14
2.28
6.6
4.01
5.1
28.39
4.95
4.14
4.66
2.5
2.55
10.63
3.7
4.62
2.05
2.8
13.5
28.53
25.21
V. Inflammatory Response
IL-1b
IL-2
IL-10
IL-12b
IL-15
IL-16
IL-21
IL18bp
IL-4i
Interleukin 10 receptor, alpha/CD210
Interleukin 12 receptor, beta 1/CD212
Interleukin 12 receptor, beta 2/CD212
Interleukin 13 receptor, alpha 1/CD213A
Interleukin 17 receptor A/CD217
Interleukin 18 receptor accessory protein
Interleukin 1 receptor antagonist
Interleukin 2 receptor, alpha chain/CD25
Interleukin 2 receptor, beta chain/CD122
Interleukin 2 receptor, gamma chain/CD132
Interleukin 3 receptor, alpha chain/CD213
Interleukin 7 receptor/CD127
Chemokine (C-C motif) ligand 2
Chemokine (C-C motif) ligand 5
Chemokine (C-C motif) ligand 7
Chemokine (C-C motif) ligand 8
Chemokine (C-C motif) ligand 12
Chemokine (C-C motif) ligand 19
Chemokine (C-C motif) receptor 2
Chemokine (C-C motif) receptor 5
Chemokine (C-C motif) receptor 7
Chemokine (C-X-C motif) ligand 5
Chemokine (C-X-C motif) ligand 9
Chemokine (C-X-C motif) ligand 10
Chemokine (C-X-C motif) ligand 13
Chemokine (C-X-C motif) ligand 16
Chemokine (C-X-C motif) receptor 3
Chemokine (C-X-C motif) receptor 6
Serum amyloid A 3
Caspase 1
Il1b
Il2
Il10
Il12b
Il15
Il16
Il21
Il18bp
Il4i1
Il10ra
Il12rb1
Il12rb2
Il13ra1
Il17ra
Il18rap
Il1rn
Il2ra
Il2rb
Il2rg
Il3ra
Il7r
Ccl2
Ccl5
Ccl7
Ccl8
Ccl12
Ccr1
Ccr2
Ccr5
Cxcl1
Cxcl5
Cxcl9
Cxcl10
Cxcl13
Cxcl16
Cxcr3
Cxcr6
Saa3
Casp1
BC011437
AF065914
NM_010548
AF128214
NM_008357
BC026894
NM_021782
AF110803
NM_010215
NM_008348
NM_008353
NM_008354
S80963
AK010040
AV247387
M57525
AF054581
M28052
L20048
NM_008369
AI573431
AF065933
NM_013653
AF128193
NM_021443
U50712
AV231648
BB148128
D83648
NM_008176
NM_009141
NM_008599
NM_021274
AF030636
BC019961
NM_009910
AF301018
NM_011315
BC008152
3.27
21.14
1.52
1.24
1.08
2.14
2.07
4.44
1.6
21.02
1.92
2.16
1.71
2.44
2.24
3.82
21.01
4.44
2.04
1.43
2.01
2.07
7.21
2.94
16.46
2.59
4.25
2.47
4.08
2.54
3.97
54.11
17.33
2.9
2.93
4.56
5.38
57.97
2.47
2.18
21.04
1.12
2.73
1.6
2.35
1.36
7.7
2.56
2.85
2.09
1.21
2.38
2.06
2.42
4.16
1.08
2.78
2.77
1.31
3.01
1.21
16.02
1.53
15.72
2
3.15
2.39
5.22
3.6
1.91
72.14
14.98
3.75
4.14
5.08
7.75
47.09
2.96
3.3
21.09
1.23
2.14
2.13
2.98
1.94
12.3
3.81
3.29
3.19
21.04
2.15
3.02
2.31
8.19
1
4.56
4.05
2.03
5.1
1.64
21.76
2.61
32.95
2.73
4.01
3.47
7.91
6.59
4.58
122.61
18.95
5.97
7.83
7
10.67
76.47
4.25
(Continued)
404
AMERICAN JOURNAL OF RESPIRATORY CELL AND MOLECULAR BIOLOGY VOL 40 2009
TABLE 1. (CONTINUED)
Days of the Infection
Annotation
Name
Unique ID
Caspase 4,
Caspase 7
Casp4
Casp7
NM_007609
NM_007611
VI. Immunosupression
TGF-a
TGF-b1
TGF-b–induced
CD274 antigen PD-L1
CD72 antigen/ antibodyy switching
Indoleamine-pyrrole 2,3 dioxygenase/IDO
BCL2-antagonist/killer 1
Bcl2-associated X protein
B-cell leukemia/lymphoma 10
B-cell leukemia/lymphoma 2
B-cell leukemia/lymphoma 2 related protein A1a
B-cell leukemia/lymphoma 3
Tgfa
Tgfb1
Tgfbi
Cd274
Cd72
Indo
Bak1
Bax
Bcl10
Bcl2
Bcl2a1a
Bcl3
M92420
NM_011577
NM_009369
BC027283
BC003824
NM_008324
NM_007523
BC018228
AF100339
BM119782
L16462
NM_033601
20
40
100
3.54
1.89
2.89
2.09
4.53
2.41
3.56
2.84
4.03
7.77
3.59
7.26
3.31
1.42
2.17
1.62
3.51
2.47
1.54
4.54
3.12
7.12
3.97
12.44
2.98
1.66
2.02
21.04
3.97
2.69
21.51
5.39
3.84
10.63
6.5
11.26
3.89
2.21
2.56
2.05
6.41
3.69
All open reading frames were analyzed statistically using Genesifter software. All open reading frames listed have P values , 0.05.
production during M. tuberculosis is a post-translational event
(41–43).
Members of the Ly-6 superfamily (Ly-6SF), specifically Ly-6i,
were highly up-regulated (Table 1, section IV). Although the role
of Ly6i is unknown, it has been proposed as a maturation marker
for T and B lymphocytes as well as for subsets of monocytes and
granulocytes (44). The Immunoresponsive gene1 (Irg1) was
highly up-regulated as well. Although its function is also unknown, it has been proposed to act as an adhesion molecule by
binding cell surface ligands. Several studies have identified
a peculiar regulation of the Irg1 gene in M. tuberculosis–infected
macrophages (8) (Table 1, section IV).
Inflammatory response: soluble factors and cellular infiltration. While interleukins were induced, interleukin receptors
were altered to a larger degree in general. Specifically, interleukins 1b, IL-12b, IL-15, IL-16, and IL-21 were induced during the
course of infection (Table1, section V). Interestingly, IL-18bp
and the Il4I1 involved with the regulation of interleukin expression and functions were highly up-regulated at all time points.
IL-1 is a major mediator of inflammation and, in general, initiates
and/or amplifies a wide variety of effects associated with innate
immunity and host responses to microbial invasion and tissue
injury. In addition, TNF and IL-6 and the interleukin receptors
Il12rb2, Il17ra, Il18rap, Il1rapl2, Il1rn, Il2rb, Il2rg, and Il7r were
induced early in infection, while Il10ra, Il13ra1, and Il3ra induction was limited to later stages of infection (Table 1, section
V).
The extent of the inflammatory process is support by induction
of chemokines. Among the four chemokine families studied (the
C-, XCL, C-x-C, and the C-C), some members of the C-x-C and
C-C families were highly up-regulated (Table 1, section V). These
included the chemokines Cxcl9, Cxcl4, Cxcl10, Cxcl13, and
Cxcl16, and receptors for this family, the CxCr3 and CxCr6
(Table 1, section V). In particular, the chemokine CXCL9, which
is known to be induced by IFN-g, and which recruits activated
TH1 CD4 cells as well as monocytes, was significantly induced
during infection (45–49). This is consistent with the observed
increased serum levels of this chemokine in patients with
pulmonary tuberculosis (50). A secondary role of chemokines is
the promotion of angiogenesis. Other molecules, including
CXCL10, CXCL13, CXCR3, CCL5, CCR1, and CCR5, have all
been identified as acting as T cell recruitment molecules (51–56).
A further molecule identified here, CXCL16, is induced by
TNF-a and plays a pleiotropic role both by acting as a recruiting
molecule and by influencing (via CXCR6) local blood vessel
integrity (57, 58). This probably represents a mechanism whereby
the host attempts to maintain the local vasculature despite the
consolidating effects of the developing granuloma. Of the C-C
motif (CCL) family of chemokines, Ccl8 (MCP-2) had the highest
expression, followed by Ccl5 (RANTES). Other chemokines
from the same group, Ccl12, Ccl19 (MIP-3), and Ccl4, also had
increased expression. Interestingly, among the family of receptors used by these chemokine families, only the CCr5 was greatly
up-regulated (and, to a lesser extent, the Ccr2 and Ccr7 receptors).
Saa3, which belongs to the SAA family of proteins and
encodes the serum amyloid protein A3 (SAA3), an acute-phase
protein, displayed increased expression. The role of serum
amyloid is to facilitate phagocytosis of dying cells, thus ensuring
their swift disposal. This acute phase protein is primarily regulated by IL-1 and TNF, and serves an important tissue-specific
function in the lung during both bacterial infection and tissue
remodeling (59). Other genes involved in inflammation (as well as
in apoptosis) are the caspases family; however, among the 14
caspases analyzed in this study, only caspases 1 and 4 had
increased expression, whereas caspases 6, 9, and 14 displayed
reduced expression (Table 1, section V).
Immunosupression. One of the most significantly induced
genes was serpina 3 g, a member of the mouse serpins family
(Table1, section VI). Serpins are serine proteinase inhibitors that
are irreversible suicide inhibitors of protease enzymes regulating
processes of coagulation, fibrinolysis, complement activation,
angiogenesis, apoptosis, inflammation, and neoplasia (60). An
important cytokine family to be included under this title is the
transforming growth factor family. Within this family, only Tgfb1
and Tgfbi (but not Tgfb 2 Tgfb 3) were progressively induced
during the infection, whereas TGF-a (Tgfa), a molecule with
potent cell proliferative capacity, was up-regulated at 20 days and
reduced thereafter. Another gene transcript encoding IDO was
highly up-regulated. IDO has recently been described in the
mechanism of deactivation and conversion of dendritic cells into
regulatory and immunosuppressive dendritic type of cells (33).
The immunoglobulin-like receptors CD72 and FcgRIIB that
counter-balances chemokine signaling (61, 62); that negatively
regulate B cell receptor signaling (50, 63, 64); and CD274, the
ligand for CD273, a member of the B7 family and regarded as an
‘‘exhaustion molecule,’’ were also up-regulated during infection.
CD273 was originally described in viral infections (65, 66), but we
have recently shown CD273 expression on CD8 cells that
accumulate in the lungs during chronic tuberculosis infection
Gonzalez-Juarrero, Kingry, Ordway, et al.: Host Transcriptional Responses to M. tuberculosis
405
TABLE 2. QUANTITATIVE REAL-TIME PCR ANALYSIS OF SELECT IMMUNOLOGICALLY SIGNIFICANT GENES AT DAY 20, DAY 40,
AND DAY 100 OF INFECTION WITH Mycobacterium tuberculosis
Day 20
Day 40
Day 100
Biological Name
Gene
MA
qPCR
MA
qPCR
MA
qPCR
Interleukin 1b
Interleukin 2
Interleukin 4
Interleukin 10
Interleukin 13
Interleukin 15
Monocyte Chemotactic Protein 1
Rantes
Interferon activated gene 10
Interferon gamma
Nitric oxide synthase 2, inducible, macrophage
Transforming growth factor, beta 1
Tumor necrosis factor
IL-1b
IL-2
IL-4
IL-10
IL-13
IL-15
Ccl2
Ccl5
Cxcl10
Ifng
Nos2
Tgfb1
Tnf
3.27
21.14
21.17
1.52
1.13
1.08
2.07
7.21
7.92
6.13
1.9
2.84
21.18
1.84 6 0.24
20.79 6 0.59
20.80 6 1.31
20.93 6 0.63
20.95 6 0.82
21.60 6 0.49
0.84 6 0.25
0.22 6 0.70
4.97 6 0.13
21.08 6 0.41
5.74 6 0.11
0.26 6 0.37
3.43 6 0.63
2.18
21.04
1.12
1.12
1.09
1.6
1.21
16.02
7.71
6.62
3.81
4.54
1.14
2.98 6 0.38
20.73 6 1.21
21.66 6 1.51
20.91 6 0.33
21.19 6 0.52
21.10 6 0.59
20.30 6 0.64
2.11 6 0.25
6.58 6 0.28
20.45 6 0.52
9.94 6 0.17
1.42 6 0.51
5.98 6 0.42
3.3
21.09
1.1
1.23
21.4
2.13
1.64
21.76
8.05
13.98
5.67
5.39
21.05
4.04 6 0.12
21.30 6 0.29
20.21 6 0.73
20.54 6 0.51
21.26 6 0.64
0.66 6 0.40
20.48 6 0.50
2.39 6 0.26
6.39 6 0.36
0.90 6 0.17
9.17 6 0.29
2.47 6 0.16
5.72 6 0.41
Definition of abbreviations: MA, microarray; qPCR, quantitative real-time PCR.
Quantitative real-time PCR analysis was performed in triplicate from three biologically independent samples of total RNA from the lungs of uninfected mice and from
mice at 20, 40, and 100 d after challenge. Values represent fold changes from uninfected controls corrected to 18s rRNA.
(unpublished data). The tetraspanin CD151 is a cell-surface
molecule known interfere with cell adhesion via interaction with
the laminin-binding integrin a3b1. Other transcripts within the
Bcl-2 family and close homologs were also changed during the
infection. It is known that activation of transcription factors such
as Bcl-xL promote cell survival, while other relatives such as Bax
antagonize this function (67). We identified up-regulation of both
proapoptotic (Bax, Bak) as well as antiapoptotic (Bcl-2, Bcl-XL)
transcription factors, specifically Bcl2-A1, which is known to
prevent apoptosis (Table 1, section VI).
To confirm the transcriptional response of immunologically
significant genes identified in the global analysis, the transcriptional response of the cytokines IL-1b, IL-2, IL-4, IL-10, IL-13,
IL-15, Tnf, infg, and Tgfb1 and the chemokines ccl2 (MCP-1),
ccl5 (RANTES), and cxcl10, and nos2 where accessed in uninfected and at Days 20, 40, and 100 after challenge by quantitative
real-time PCR (Table 2). Analysis revealed that the microarray
data and the real-time PCR was 82% concordant. Although the
values obtained by microarray analysis for IL-10, IL-13, and
IFN-g were different from those determined quantitative realtime PCR, the overall trends over the course of infection were
similar. This information allows us to make a connection between
the stage of infection, development of lesions, and activation of
the host adaptive immune response.
Transcriptional Differences between Day 20, Day 40, and
Day 100
Inspection of the transcriptional response of genes encoding immune function revealed some interesting trends at early compared with later states of disease. Anxa11 (Annexin 11), Hrh1
(Histamine receptor H1), Ppap2b (Phosphatidic acid phosphatase type 2B), Cd2ap (CD2-associated protein), Itgb1 (Integrin b
1), Fnrb (fibronectin receptor b), Tcrb-J (T cell receptor b, joining
region), Cyp4a10 (Cytochrome P450), and TGFfa (Transforming
growth factor a, TGF-a) were all induced at Day 20 but repressed
at later time points (Table 3). The other trends are those genes
that were repressed early in infection but induced by Day 40 and
Day 100. In this group are Gpr35 (G protein–coupled receptor
35), Tlr4 (Toll-like receptor 4) and Tlr12 (Toll-like receptor 12),
Ly6 d (Lymphocyte antigen 6 complex, locus D), Ly9 (Lymphocyte antigen 9, CD229), Il10ra (Interleukin 10 receptor, a), Hk3
(Hexokinase 3), Trem2 (Triggering receptor expressed on myeloid cells 2), and many members of the immunoglobulin family
(see below). Importantly, later stages of disease was characterized
by B cell and antibody expression. Specifically, the B cell–specific
genes cd5, Cd19, Cd22, Cd79a, CD5, CD19, CD22, CD79a,
CD79b, and CD52 were increased at 40 days after infection and
remained transcriptionally active to time of killing (Table 3). This
observation is consistent with our previous findings indicating
that the B lymphocytes in the granulomatous lesions appear in
clusters similar to those found in the germinal center and
constitute the predominant type of lymphocyte infiltration during
pulmonary chronic infection with M. tuberculosis (68). The
marker CD72 associated with regulatory B cell function, and
antibody switching was also up-regulated during the course of the
infection (67). In addition, Bcl10 and Bcl3 associated with B cell
differentiation and proliferation were also induced late in infection. Importantly, this study revealed that there was a negative
regulation or no changes in the expression of immunoglobulin
genes at 20 days after infection, but after 40 days, the immunoglobulin heavy and light chain families—namely igh-6, Igj, IghVJ558, Igk-V32, IgkV28, Igk-V1, Igl-V1 specific for heavy chain of
IgM, join and kappa chain variable protein, and heavy lambda
chain, respectively—were significantly induced. In some instances at 100 days after infection, Igh-6, Igk-V32, and Igj were
induced as much as 10 to 30 times. Altogether, when analyzing the
B cell response during this infection, we identified a phenotype of
genes expressing for IL-21, CD22, CD52, and CD5 and activation
of transcription factors from the BcL family such as Bcl 10 and Bcl
3, which are factors reported for the progression of particular
forms of B cell lymphomas (67).
Identification of Molecular Markers of Disease State
and Progression
While trends in the immune response were identified for different
times of disease, there is a need for the identification of molecular
markers of disease state and progression. Knowing molecular
markers provides a means to monitor disease progression,
particularly during treatment. Accordingly, tandem-SOM analysis was performed to identify molecular markers characteristic of
disease state and progression. These features can be used to
inform disease state and progression. When the host transcriptional response to infection was analyzed using SOM, the 1,854
genes were grouped into 20 global SOM-groups (gSOM) (Figure
4A). This analysis generally clustered genes induced at Day 40 or
Day 100 into groups 0 to 2, genes induced at Day 20 into groups 11
and 12, and genes induced at Days 40 and 100 and at Days 20, 40,
and 100 into groups 14 to 19 based on expression trends. However,
406
AMERICAN JOURNAL OF RESPIRATORY CELL AND MOLECULAR BIOLOGY VOL 40 2009
TABLE 3. TRANSCRIPTIONAL DIFFERENCES BETWEEN DAY 20, DAY 40, AND DAY 100 OF INFECTION WITH Mycobacterium tuberculosis
Days of the Infection
Annotation
Annexin A11
Cytochrome P450, family 4, subfamily a, polypeptide 10
Early B-cell factor 3
Fibroblast growth factor 7
G protein–coupled receptor 22
Heparan sulfate 6-O-sulfotransferase 2
Histamine receptor H 1
Histone cluster 1, H3a
Histone cluster 1, H4i
Integrin alpha V
Interleukin 12 receptor, beta 2
Phosphatidic acid phosphatase type 2B
Pre B-cell leukemia transcription factor 3
Procollagen, type V, alpha 3
Surfactant associated protein C
Transforming growth factor alpha
Triggering receptor expressed on myeloid cells 1
G protein–coupled receptor 35
Toll-like receptor 4
Lymphocyte antigen 6 complex, locus D
Toll-like receptor 12
Solute carrier family 7, member 6 opposite strand
Hexokinase 3
Lymphocyte antigen 9
Interleukin 10 receptor, alpha
Triggering receptor expressed on myeloid cells 2
Membrane-spanning 4-domains, subfamily A, member 1
CD86 antigen
CD5 antigen
CD19 antigen
CD22 antigen
CD5 antigen-like
CD72 antigen/antibody switching
Cd79a/B cell receptor
Cd79b/B cell receptor
CD52 antigen
B-cell leukemia/lymphoma 10
B-cell leukemia/lymphoma 3
Immunoglobulin heavy chain 6 (heavy chain of IgM)
Immunoglobulin joining chain
Immunoglobulin kappa chain variable 1-135
Immunoglobulin kappa chain variable 28 (V28)
Immunoglobulin kappa chain variable 32 (V32)
Immunoglobulin lambda chain, variable 1
Name
Unique ID
20
40
100
Anxa11
Cyp4a10
Ebf3
Fgf7
Gpr22
Hs6st2
Hrh1
Hist1h3a
—
—
Il12rb2
Ppap2b
Pbx3
Col5a3
—
Tgfa
Trem1
Gpr35
Tlr4
Ly6d
Tlr12
Slc7a6os
Hk3
Ly9
Il10ra
Trem2
Ms4a1
Cd86
cd5
Cd19
Cd22
Cdl5
Cd72
Cd79a
Cd79b
Cd52
Bcl10
Bcl3
Igh-6
Igj
Igkv1-135
Igk-V28
Igk-V32
Igl-V1
AU019881
BC013476
NM_010096
AK015893
BB232423
AW536432
AF388053
NM_013550
BC019757
AK011583
NM_008354
BB312387
BG066541
AB040491
AV169310
M92420
NM_021406
NM_022320
AF185285
NM_010742
BB745017
AK010254
BB334625
NM_008534
NM_008348
NM_031254
BB236617
NM_019388
NM_007650
NM_009844
AF102134
NM_009690
BC003824
NM_007655
NM_008339
NM_013706
AF100339
NM_033601
BC025447
BC006026
BF301241
BI107286
U25103
AK008145
2.21
2.28
2.07
2.81
2.26
2.17
2.03
2.54
2.01
2.32
2.16
2.07
2.22
2
2.7
3.56
2.28
21.09
21.9
22.96
21.01
21.04
21.02
21.56
21.02
21.04
21.35
21.11
3.23
21.13
1.69
1.35
3.59
1.22
1.05
3.68
2.17
2.47
2.84
21.2
1.06
1.52
2.34
21.19
21.73
21.02
21.06
21.19
21.01
1.14
21.4
21.24
1.12
21.17
1.21
21.25
21.15
1.07
24.68
1.54
21.07
2.28
1.93
1.59
1.89
1.91
2.2
2.09
2.85
2.24
1.98
3.04
4.06
1.79
2.88
2.14
3.97
2.31
1.46
3.86
2.02
2.69
16.05
3.8
2.56
4.67
5.52
4.3
23.31
21.14
21.18
21.17
21.21
21.05
21.36
21.12
21.11
21.14
21.04
21.03
21.04
21.25
21.44
21.51
21.15
2.86
2.88
3.09
3.1
3.15
3.2
3.26
3.29
3.42
4.05
4.98
6.6
2.38
4.71
3.35
6.5
4.62
2.8
8.1
2.56
3.69
36.19
10.61
4.34
13.08
10.48
13.39
All open reading frames were analyzed statistically using Genesifter software. All open reading frames listed have P values , 0.05.
to achieve more resolution, further grouping was accomplished
by subjecting genes from these groups to another round of SOM
analysis (sSOM) that, when inspected, revealed five discriminant
groups (Figure 4B). These discriminant groups correspond to
Day 20 (discriminant group 1; mean expression 5 2.3), Day 40
(discriminant group 2; mean expression 5 2.4), Day 100 (discriminant group 3; mean expression 5 2.7), Days 40 and 100
(discriminant group 4; mean expression 5 2.6 [D40], 2.6 [D100]),
and Days 20, 40, and 100 (discriminant group 5; mean expression 5 5.4 [D20], 14.1 [D40], 20.8 [D100]). This analysis resulted
in the identification of 712 genes that can serve as predictive
markers for disease state and can be used to inform disease
progression (Table E2).
CONCLUSIONS
One of the most challenging questions in M. tuberculosis research
is the dynamic interplay between the host and pathogen. Much
work has been performed to define the immune response to
infection, and while these studies have provided a wealth of
information, it is difficult to truly analyze the host response to
infection in an unbiased way. An approach often used to visualize
global trends in the response to infection is the use of whole
genome microarrays. Accordingly, we used this post-genomic
approach to identify global trends of the host response to
infection with M. tuberculosis and to identify molecular markers
of disease progression. The results of this study are consistent with
a massive mobilization of IFN-g–related genes, transcription
factors, inflammatory signals dominated by a strong chemokine
profile, and activated T cell and macrophage cell responses during
the chronic phase of the disease process, and are in keeping with
the established demonstration of an ongoing activation of protective immunity associated with strong inflammatory process
during the chronic infection (20). The trends in the responses
were progressively increased over time and were still in progress
during the late chronic stage of infection. However, the transcriptional response indicated that the host response to M.
tuberculosis infection at 20 days was different than that at 40
and 100 days after infection. Presumably, the early modulated
genes are host responses related to M. tuberculosis–induced
Gonzalez-Juarrero, Kingry, Ordway, et al.: Host Transcriptional Responses to M. tuberculosis
407
Figure 4. Identification of molecular markers of disease state and progression. Tandem self-organizing mapping (tandem-SOM) analysis was
performed to categorize genes and identify discriminant groups of disease state and progression. (A) gSOM analysis of transcriptional active genes
differentially regulated . 1.5-fold (P , 0.01). This analysis distributed genes into 20 groups (0–19). (B) Discriminant groups identified from
sSOM analysis. Discriminant groups correspond to Day 20 (discriminant group 1; mean expression 5 2.3), Day 40 (discriminant group 2; mean
expression 5 2.4), Day 100 (discriminant group 3; mean expression 5 2.7), Days 40 and 100 (discriminant group 4; mean expression 5 2.6 [D40],
2.6 [D100]), and Days 20, 40, and 100 (discriminant group 5; mean expression 5 5.4 [D20], 14.1 [D40], 20.8 [D100]).
primary changes rather than a more complex scenario formed by
concomitant M. tuberculosis–induced inflammation and antiinflammatory host responses as observed at Days 40 and 100.
Visualization of bacterial growth, pathology, and the PCA
analysis revealed that although the bacterial load reaches a plateau around 20 days after exposure, the pathology and host
response continues to progress. These data confirm that the
progressive inflammatory response in the subacute and chronic
stages of infection in mice is independent of the total number of
cultureable bacilli. The solid or nonnecrotic lesions that typify
experimental M. tuberculosis infection in mice reflect the early
tuberculosis lesions of humans. However, in the chronic stages of
infection, lesions in most susceptible and resistant strains of mice
fail to progress to necrosis and cavitation, where bacilli are often
extracellular admixed with degenerate cells and necrotic cellular
debris. While no one animal model consistently develops the
spectrum of lesions seen in the naturally occurring disease in
humans, comparative studies including those in mice reveal
important clues in the complex pathogenesis of tuberculosis and
the host response to infection.
The overall message derived from this study is that limiting
bacterial replication occurs at the cost of progressive and poorly
regulated cellular influx that compromises lung function and is
thus detrimental in the chronic stages of infection. While there are
limitations to the mouse model, the overall general trends
observed therein are likely to be similar to the response in other
hosts, including humans, thus allowing for the characterization of
immune response to infection and the identification of molecular
markers of disease progression. These markers may prove useful
for discerning disease progression and development and characterization of vaccines with increased efficacy against M. tuberculosis infection. Indeed, the availability of molecular markers
indicative of early, middle, and chronic infection may provide
a foundation for tools that can be used to follow disease and
response to chemotherapy. Overall, knowledge of the global
response to M. tuberculosis at different stages of disease provides
much-needed knowledge for antigen discovery, and vaccine
development, and can be applied to other clinically relevant
research questions, including the identification of markers that
can be used to monitor the success or failure of therapy.
Conflict of Interest Statement: None of the authors has a financial relationship
with a commercial entity that has an interest in the subject of this manuscript.
Acknowledgments: The authors thank Dr. Alan Schenkel for critical reading and
comments of the manuscript.
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