Journal of Molecular Medicine

, Volume 85, Issue 6, pp 613–621 | Cite as

Candidate biomarkers for discrimination between infection and disease caused by Mycobacterium tuberculosis

  • Marc Jacobsen
  • Dirk Repsilber
  • Andrea Gutschmidt
  • Albert Neher
  • Knut Feldmann
  • Hans J. Mollenkopf
  • Andreas Ziegler
  • Stefan H. E. Kaufmann
Original Article


Infection with Mycobacterium tuberculosis is controlled by an efficacious immune response in about 90% of infected individuals who do not develop disease. Although essential mediators of protection, e.g., interferon-γ, have been identified, these factors are insufficient to predict the outcome of M. tuberculosis infection. As a first step to determine additional biomarkers, we compared gene expression profiles of peripheral blood mononuclear cells from tuberculosis patients and M. tuberculosis-infected healthy donors by microarray analysis. Differentially expressed candidate genes were predominantly derived from monocytes and comprised molecules involved in the antimicrobial defense, inflammation, chemotaxis, and intracellular trafficking. We verified differential expression for alpha-defensin 1, alpha-defensin 4, lactoferrin, Fcγ receptor 1A (cluster of differentiation 64 [CD64]), bactericidal permeability-increasing protein, and formyl peptide receptor 1 by quantitative polymerase chain reaction analysis. Moreover, we identified increased protein expression of CD64 on monocytes from tuberculosis patients. Candidate biomarkers were then assessed for optimal study group discrimination. Using a linear discriminant analysis, a minimal group of genes comprising lactoferrin, CD64, and the Ras-associated GTPase 33A was sufficient for classification of (1) tuberculosis patients, (2) M. tuberculosis-infected healthy donors, and (3) noninfected healthy donors.


Mycobacterium tuberculosis Tuberculosis Biomarkers 


In the beginning of the twenty-first century, tuberculosis (TB) remains a major cause of morbidity and mortality in humans worldwide ( Yet, only 10% of about two billion individuals infected with Mycobacterium tuberculosis develops active disease [14]. It is generally accepted that a competent immune system is crucial for protection against this pathogen, but the exact underlying mechanisms remain elusive [8]. Therefore, identification of biomarkers of protective immunity against M. tuberculosis is critical especially for the clinical evaluation of efficacious vaccines.

Interferon gamma (IFN-γ) production and a type 1-dominated immune response are widely regarded as biomarkers of protective immunity. Nevertheless, IFN-γ represents an insufficient correlate of protection [13], and therefore, additional indicators are needed to define susceptibility against TB. Candidate indicators are T cell-derived molecules like the mycobacteriocidal mediator granulysin, which correlates with protection and clinical improvement in mycobacterial disease [16] and, possibly, molecules from other immune cell populations, e.g., macrophages and granulocytes. Regarding the role of macrophages (and their precursor monocytes in the blood), several molecules are involved in the interaction between host and pathogen in TB infection. These candidates comprise molecules from distinct functional groups including pathogen receptors, e.g., toll-like receptors (reviewed in [22]), regulators of intracellular vesicle trafficking [27], molecules involved in iron metabolism [18], and antimicrobial effector molecules, e.g., reactive nitrogen and oxygen intermediates (reviewed in [20]), and defensins [12]. Although these effector mechanisms may not be specific to TB, it is tempting to speculate that the combined analysis of a pattern of differentially expressed candidates (a “biosignature”) will allow discrimination between long-term protection and disease activation in TB.

In this study/paper, we analyzed the gene expression profiles of peripheral blood mononuclear cells (PBMC) from TB patients and M. tuberculosis-infected healthy donors. Recent studies demonstrated the feasibility of using PBMC for gene expression analyses to discover characteristic patterns of cancer [5] and autoimmune diseases [3, 4]. As ethical considerations and accessibility restrict the usage of affected tissue from TB patients, PBMC are the first choice as a surrogate tissue in this chronic infection.

In a first step to narrow down the choice for relevant candidates, we performed microarray analyses comparing a randomly chosen subgroup of TB patients and healthy M. tuberculosis-infected individuals. Then categorization of preselected genes was performed to reduce the number of false positive genes. Preselected candidate genes involved in antimicrobial processes were then analyzed by quantitative polymerase chain reaction (qPCR). Two candidates, the formyl peptide receptor 1 (FPR1) and cluster of differentiation 64 (CD64), were determined for differential protein expression of monocytes from TB patients and M. tuberculosis-infected healthy donors. In a second step, we assessed candidate genes for optimal study group discrimination in a linear discriminant analysis (LDA) approach. Classification properties for these candidate biomarkers were validated in independently measured test data sets.

Materials and methods

Patients and M. tuberculosis-infected healthy donors

TB patients were recruited at the Asklepios Center for Respiratory Medicine and Thoracic Surgery München-Gauting, Germany. Diagnosis was based on chest radiography and laboratory confirmation by mycobacterial culture. All TB patients were HIV negative and received standard chemotherapeutic treatment except for two donors with acute TB who had been included before treatment.

Healthy donors without a history of clinical TB were recruited from laboratory staff at the Max Planck Institute for Infection Biology, in Berlin, Germany, and hospital staff at the Asklepios Center in Gauting. Latently, M. tuberculosis-infected healthy donors had repeated close contact to TB patients (as a nurse, physician, or family member) and were tuberculin skin test positive (TST > 10 mm). Healthy M. tuberculosis-noninfected donors were tuberculin skin test negative (TST < 5 mm). In general, blood take was performed before the test to avoid any influences of the test on the analyses. Features of patients and healthy donors are summarized in Table 1. Due to limitation in the sample material, not all donors could be used in each experiment. The numbers of samples per study group used in each experiment are indicated in the respective methods sections. Nine M. tuberculosis-infected healthy donors were randomly selected for comparative PBMC microarray analyses with nine pulmonary TB patients who received chemotherapeutic treatment for less than 6 weeks. The patient study group comprised six men and three women, and the M. tuberculosis-infected healthy donor group comprised five men and four women. Both groups had a similar age distribution. All donors gave informed consent. This study was approved by the local ethics committees (ek.205-18.1).
Table 1

Characteristics of tuberculosis patients and healthy controls


Tuberculosis patients

Healthy infected donors

Healthy noninfected donors


After recovery

Total no.

























Age, mean years





Disease characteristics

Pulmonary TB





Lymph node TB




Urogenital TB




na Not applicable

Preparation of RNA from PBMC

Forty milliliter of heparinized peripheral venous blood from each patient and control donor were drawn, and PBMC were isolated on Ficoll gradients. PBMC were immediately mixed with TRIzol® reagent (Invitrogen, Carlsbad, CA) and frozen at −80°C until RNA was extracted according to manufacturer’s instructions. RNA content, purity, and integrity were determined using Agilent 2100 Bioanalyzer (Agilent Technologies, Forster City, CA).

Microarray procedures, experimental design, and analysis

Total RNA was labeled with the Fluorescent Linear Amplification Kit (Agilent Technologies) following manufacturer’s instructions. After photometric quantification of cRNA and determination of labeling efficiency, 2 μg of the samples were fragmented and hybridized for 20 h on custom designed oligonucleotide microarrays (AMADID 011412; Agilent Technologies) containing 8,033 human genes. The entire list of reporters can be accessed in the Gene Expression Omnibus (GEO) public database ( Each sample was cohybridized with a PBMC RNA pool derived from healthy donors in a common reference design. As each glass slide contained two identical arrays, we were able to apply a dye swap within the same hybridization procedure. Microarrays were processed according to manufacturer’s instructions and scanned at 5 μm resolution using an Agilent scanner. Image analysis was performed with feature extraction software (Feature Extractor Version 6.1.1, Agilent Technologies) using default settings and global background normalization. Candidates for differentially expressed genes between TB patients and healthy controls were identified by a modified t-test with a variance inflation factor according to:
$$t_{{{\text{VI}}}} = \frac{{\overline{{M_{{\text{P}}} }} - \overline{{M_{{\text{C}}} }} }}{{{\text{sd}}_{{{\text{Welsh}}}} + \alpha }}$$
In this study, \(\overline{{M_{{\text{P}}} }} \) denotes the means of log ratios for the TB patients and the common reference, and \(\overline{{M_{{\text{C}}} }} \) the corresponding means for log ratios for the controls. sdWelsh is short for the denominator in the standard Welsh t-statistics, in detail:
$${\text{sd}}_{{{\text{Welsh}}}} = {\sqrt {\raise0.7ex\hbox{${{\text{sd}}^{2}_{{\text{P}}} }$} \!\mathord{\left/ {\vphantom {{{\text{sd}}^{2}_{{\text{P}}} } {n_{{\text{P}}} }}}\right.\kern-\nulldelimiterspace}\!\lower0.7ex\hbox{${n_{{\text{P}}} }$} + \raise0.7ex\hbox{${{\text{sd}}^{{\text{2}}}_{{\text{C}}} }$} \!\mathord{\left/ {\vphantom {{{\text{sd}}^{{\text{2}}}_{{\text{C}}} } {n_{{\text{C}}} }}}\right.\kern-\nulldelimiterspace}\!\lower0.7ex\hbox{${n_{{\text{C}}} }$}} }$$
with empirical standard deviations sdP and sdC and the sample sizes nP = nC = 9. The influence of small variances on tVI is controlled by α, representing the 90% quantile of all deviations sdP and sdC. By this approach, we avoided high tVI statistics resulting from accidentally very small standard deviations but, otherwise, only minor differences of mean values. Candidate genes were ranked according to these tVI statistics, and a cutoff was taken at the first gene below a 1.5-fold change in the absolute difference of gene expression (Fig. 1). Regarding experimental design and sample size, we were able to detect an absolute change in log ratios (M values) between groups of 0.35 (i.e., 1.27-fold change) with an approximate power of 80% at significance level of 5% for each single gene (data not shown)—we considered this appropriate for our objective of a candidate screening for differential expression.
Fig. 1

Selection criteria for candidate genes from global transcriptome comparisons. A graphic illustration of preselected genes based on a modified t-test ranking list (x-axis) and fold change differences (y-axis). Fold changes between TB patients and healthy contacts, calculated from the difference in means of the log ratios for both groups (log ratios to the common reference). Top 150 candidate genes of the ranking list resulting from variable selection with the modified t-test are shown. Triangles indicate genes upregulated in PBMC from TB patients. Squares indicate genes upregulated in PBMC from M. tuberculosis-infected healthy donors. Black filled circles represent genes selected for verification by qPCR analyses. The dotted arrow indicates the threshold for genes categorized in further detail. The TB patient study group comprised six men and three women, and the M. tuberculosis-infected healthy donor group comprised five men and four women

Real-time qPCR analysis

RNA was reverse transcribed to cDNA as described earlier [10]. SYBR® Green (Applied Biosystems, Foster City, CA) uptake in double stranded DNA was measured using the ABI PRISM™ 7000 thermocycler (Applied Biosystems) according to manufacturer’s instructions. We designed primer pairs with the ABI PRISM™ primer express Version 2.0.0 software (Applied Biosystems) for real-time qPCR analysis. Altogether, RNA of PBMC from 18 TB patients (13 men and 5 women) and 17 M. tuberculosis-infected healthy donors (9 men and 8 women) was analyzed for expression of alpha-defensins 1 and 4 (DEFA1 and DEFA4), lactoferrin (LTF), CD64, bactericidal permeability increasing protein (BPI), and FPR1. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as an internal control.

Protein quantification

We performed flow cytometry to quantify protein expression of selected candidate genes. Median fluorescence intensities of the FPR1 and the CD64 were determined in parallel staining attempts with phycoerythrin-labeled monoclonal antibodies (anti-FPR1, clone 5F1, BD Pharmingen; anti-CD64, clone 10.1, eBioscience). A single antibody staining procedure was used to avoid possible interference between different colors [10]. Monocytes were gated according to size (forward scatter) and granularity (side scatter). In total, randomly chosen samples from 24 TB patients and 15 healthy controls were analyzed in two independent experiments.

Donor classification

The discriminatory power for classifying patients and healthy controls was investigated using LDA [6] based on qPCR data of nine selected candidate genes. These candidates were DEFA1, DEFA4, LTF, CD64, BPI, FPR1, Rab13, Rab24, and Rab33A. We optimized the combination of genes with the best discriminatory power using a leave-three-out cross-validation for all possible combinations of genes. We assessed the proportion of correctly classified patients (hit rate) in the left-out group. From these analyses, we chose the combination of genes with maximal hit rate. In a validation step, we used a novel data set for temporal validation [1]. Performance of LDA for these validation data was then assessed using the parameters from the training step. According to this procedure, we performed two approaches for donor classification. Altogether, 37 TB patients (23 donors in the training step, 10 donors in the validation step, 2 donors before treatment, and 2 former TB patients 6 months after termination of treatment) were included. These were compared on the one hand with M. tuberculosis-infected healthy donors (17 donors in the training step and 5 donors in the validation step) and, on the other hand, with a combined study group of healthy M. tuberculosis-infected and noninfected (tuberculin skin test negative) donors. The noninfected study group comprised 15 donors (10 donors in the training step and 5 donors in the validation step).


Differential expression of monocyte-derived genes in PBMC from TB patients and M. tuberculosis-infected healthy donors

We performed microarray analyses to identify differentially expressed genes in PBMC from TB patients and M. tuberculosis-infected healthy donors. The entire data set is accessible in the GEO public database (GSE6112). A modified t-test (for details, see “Materials and methods”) was applied to determine a ranking list of genes differentially expressed between these study groups. A cutoff was set according to the first gene below the 1.5 fold-change threshold (Fig. 1).

The majority of these preselected genes (76/95) were upregulated in TB patients (Supplementary Table 2). A categorization of candidate genes according to originating cell types revealed that a large number of upregulated genes were expressed by monocytes. Gene annotation revealed prevalence of distinct functional groups (Fig. 2). Antimicrobial effector molecules, inflammatory markers, and chemokines constituted the most common clusters (24/76) of upregulated genes in TB patients. These comprised surface receptors involved in sensing microbial invaders by professional phagocytes like the FPR1. The FPR1 is a multifunctional G protein-coupled receptor expressed by professional phagocytes, which binds prototypic N-formyl-methionyl-leucyl-phenylalanine peptides (reviewed in [15]). Signaling through FPR1 induces chemotaxis, inflammation, as well as phagocytosis, and the conserved peptide structure is recognized by mycobacteria reactive T cells [7]. Other pathogen-specific surface receptors identified were the macrophage receptor with collagenous structure and two Fcγ receptors, one with high affinity (CD64) and one with low affinity (CD32). Effector molecules with antimicrobial activities that were upregulated in patients included DEFA1, 3, and 4 and BPI. DEFA belongs to a family of cysteine-rich antibiotic peptides released by leukocytes, which are upregulated in various kinds of infections and exhibit anti-TB activity [19]. DEFA1 and 3 are almost identical molecules indistinguishable in our assays. Another key molecule in the host response against infection, which was strongly overexpressed in TB patients, was LTF, a transporter molecule with high affinity for iron [28]. Furthermore, molecules involved in the regulation of intracellular vesicle trafficking were differentially expressed between both study groups. Very recently, we identified three members of the Ras-associated small GTPase (Rab) family, namely, Rab33A, Rab24, and Rab13, as probable correlates of susceptibility in active TB [9]. Rab13 gene expression was increased in TB patients’ PBMC (Fig. 2), whereas Rab33A- and Rab24-specific oligonucleotides were not present on this microarray. Therefore, these three candidates were included in the last experiment, which aimed to determine an optimally suited cluster for discrimination between both study groups. After stringent adjustment for multiple testing, only FPR1 showed a significant expression difference likely because of high variances and insufficient study group sizes (Fig. 2). Taking this into account, we increased the cohort sizes for qPCR and selected a subgroup of six candidate genes derived from the cluster of antimicrobial molecules, namely, DEFA1, DEFA4, LTF, CD64, BPI, and FPR1. We chose this procedure to minimize the likelihood of selecting false positive genes.
Fig. 2

Predominant upregulation of innate immunity genes in PBMC from TB patients. Increased gene expression is marked in red, decreased gene expression in green. Red identifiers indicate TB patients (right), blue identifiersM. tuberculosis-infected healthy donors (left). Functional classification of genes revealed four major groups. Ranking positions, test statistics (modified t-test), and p values adjusted for multiple testing are given [29]. Test statistics indicate the direction of effect, i.e., relative up- (positive sign of test statistics) or downregulation (negative sign of test statistics) of transcription in TB patients relative to controls

DEFA1, DEFA4, LTF, CD64, BPI, and FPR1 are differentially expressed between TB patients and M. tuberculosis-infected healthy donors

Differences measured by microarray analyses did not reach significant levels likely because of high variances within study groups. Therefore, we increased the cohort sizes and analyzed candidate genes in PBMC by qPCR. GAPDH was chosen as a housekeeping gene because microarray analyses indicated no disease-specific influence on GAPDH expression (data not shown). Analyses revealed differential expression for DEFA1 (p < 0.01), DEFA4 (p = 0.02), LTF (p = 0.03), CD64 (p < 0.01), BPI (p = 0.01), and FPR1 (p < 0.01) between TB patients and M. tuberculosis-infected healthy donors (Fig. 3). Differences in gender distribution between study groups did not affect the results. In addition, we excluded a possible bias of chemotherapy length on gene expression differences (data not shown). We conclude that our microarray comparisons revealed verifiable results, which may lead to additional candidate biomarkers in further studies.
Fig. 3

Differentially expressed genes between TB patients and M. tuberculosis-infected healthy donors. a Six molecules from the antimicrobial gene cluster were analyzed for their expression in PBMC. Mean cycle differences compared to GAPDH are shown as box plots and error bars (5, 25, 50, 75, and 95 percentiles) for TB patients (dark gray) and M. tuberculosis-infected healthy donors (light gray). RNA of PBMC from 18 TB patients (13 men and 5 women) and 17 M. tuberculosis-infected healthy donors (9 men and 8 women) was analyzed. Asterisks mark significant differences between study groups as determined by the exact Mann–Whitney U-test

CD64 protein expression differs significantly between TB patients and M. tuberculosis-infected healthy donors

In our subsequent analysis, we determined whether differences in RNA expression are reflected on the protein level. We exemplify our results for two of the candidates, namely, CD64 and FPR1. Both of them are exclusively expressed on monocytes within the PBMC population and can be measured by flow cytometry. Mean fluorescence analyses revealed that CD64 cell surface expression was increased on monocytes from TB patients (p < 0.01; Fig. 4, left graph). For FPR1, we identified a tendency of upregulation, but these differences did not reach significant levels (p = 0.18; Fig. 4, right graph). These results strengthen the necessity of protein analyses for the interpretation of transcriptome analysis and exclude the possibility that differences in the cellular distribution—here, an enrichment of monocytes in TB patients blood—biased microarray results for CD64. We conclude that CD64 protein expression changes on monocytes are prevalent in PBMC from TB patients compared to M. tuberculosis-infected healthy donors.
Fig. 4

Protein expression of selected candidate cell surface proteins. Mean protein expression of CD64 (left graph) and FPR1 (right graph) compared between monocytes from tuberculosis patients (dark gray) and M. tuberculosis-infected healthy donors (light gray) is shown in box plots and error bars (5, 25, 50, 75, and 95 percentiles). 24 TB patients and 15 healthy controls were analyzed in two independent experiments. Asterisks mark significant differences between study groups as determined by the exact Mann–Whitney U-test

Optimal gene expression markers for discrimination between TB patients and M. tuberculosis-infected healthy donors

To determine whether RNA expression of candidates determined by qPCR is sufficient for classification of TB patients and M. tuberculosis-infected healthy donors, we included three members of the Ras-associated small GTPase (Rab) family, namely, Rab13, Rab24, and Rab33A in addition to DEFA1, DEFA4, LTF, BPI, FPR1, and CD64. Recently, we demonstrated that RNA of these Rab molecules was differentially expressed between PBMC from TB patients and healthy M. tuberculosis-infected contacts [9]. LDA was applied in this approach. For training, qPCR data from TB patients and M. tuberculosis-infected healthy donors were used to optimize the set of discriminating genes with respect to the hit rate across all possible combinations by leave-three-out cross-validations. This analysis revealed Rab33A, CD64, and LTF as one of two top discriminating gene sets for study group clustering. Results showed a cross-validated prediction accuracy of 88% in the training step and 80% prediction accuracy in the independent temporal validation data set. In addition, when we included M. tuberculosis-noninfected healthy donors into training and validation data sets, Rab33A, CD64, and LTF comprised the best discriminating gene set (Fig. 5a). We achieved a prediction accuracy of 96% in the training step (Fig. 5b) and 85% prediction accuracy in the validation data set (Fig. 5c). The prediction accuracy for M. tuberculosis-noninfected healthy donors was 100% in both steps (Fig. 5b and c). Further studies will determine whether false prediction in the M. tuberculosis-infected healthy donor cohort is associated with disease risk.
Fig. 5

LDA for selected candidate genes in TB patients and healthy controls. a The three-dimensional scatter plot shows a graphical illustration of discrimination by LDA for the optimal combination of genes, RAB33A, CD64, and LTF. TB patients are shown as dark gray circles, or bright-gray-framed black circles if falsely predicted. Controls are shown as bright gray triangles or black-framed dark gray triangles if falsely predicted. 37 TB patients (23 donors in the training step, 10 donors in the validation step, 2 donors before treatment, and 2 former tuberculosis patients 6 months after termination of treatment) were included. These were compared on the one hand with M. tuberculosis-infected healthy donors (17 donors in the training step and 5 donors in the validation step) and, on the other hand, with a combined study group of healthy M. tuberculosis-infected and noninfected (tuberculin skin test negative) donors. The noninfected study group comprised 15 donors (10 donors in the training step and 5 donors in the validation step). b Results from the training step cross-validation are shown in a bar chart. Each bar represents an individual donor. TB patients are shown on the left, healthy controls on the right side. M. tuberculosis-noninfected donors are indicated by vertical stripes, extrapulmonary tuberculosis patients are indicated by black circles. Negative bar values represent disease prediction including prediction validity as the bar height. Positive values predict healthy controls. c A new data set was used for external validation of results from the LDA training step. Illustration as described in (b). d Classification of TB patients before initial treatment (left panel) and donors 6 months after successful termination of therapy (right panel) are shown. Illustration as described in (b)

To examine whether expression patterns are influenced by chemotherapy, we analyzed discrimination in two TB patients before chemotherapy. Both donors were classified correctly (Fig. 5d). Two former TB patients were examined 6 months after termination of chemotherapy to determine whether the gene expression pattern had returned to normal upon recovery. Interestingly, one patient was classified as a healthy control, whereas no prediction was possible for the other one (Fig. 5d). Further experiments have to be performed to clarify whether this divergence is indeed associated with relapse risk. Four patients with extrapulmonary, TB were included (three in the training set and one patient before chemotherapy in the test set) to determine the influence of different organs affected by M. tuberculosis. None of those were falsely predicted as controls (Fig. 5b and 5d). Therefore, a bias introduced by extrapulmonary TB cases on the gene expression analyses could be excluded. We conclude that gene expression analyses by qPCR of a minimal biomarker set comprising CD64, LTF, and Rab33A suffices for a biosignature, which allows robust discrimination between TB patients and healthy donors.


Multiple host factors determine the outcome of M. tuberculosis infection and, thus, susceptibility and pathogenesis. We determined candidate biomarkers for classification of TB patients and healthy donors. Combining microarray analyses with qPCR in a discriminant analysis approach revealed an optimal group of genes for classification including CD64, LTF, and Rab33A. These candidates for discrimination between TB patients and healthy donors were validated in a second data set.

Further studies are ongoing to determine the role of these molecules in TB and to reveal the reasons for differential expression. As long as the TB specificity is not proven, other causes, e.g., an inflammation induced effect, cannot be excluded. These studies particularly focus on disease specificity and the prognostic or diagnostic value of these markers in comparison to IFN-γ, a marker currently introduced into diagnosis of TB. In this context, it remains to be determined whether differential classification within the M. tuberculosis-infected healthy donor group will represent a robust correlate of protection. Long-term follow-up studies in recently M. tuberculosis-infected donors are necessary to examine the value of our candidate biomarkers in this context.

Although discrimination between probable pathologic or protective influences of these molecules remains impossible, known functions of these markers revealed involvement of most genes in host/pathogen interactions. Notably, CD64 is capable of inducing phagocytosis, respiratory burst, and antibody-dependent cell-mediated cytotoxicity in monocytes, macrophages, and granulocytes (reviewed in [25]). A crucial role of CD64 in infectious diseases is supported by studies showing regulation of gene expression in macrophages and dendritic cells by cytokines such as IFN-γ [17] and interleukin-10 [23]. IFN-γ is a key mediator in anti-mycobacterial host defense [8, 13]. It is also a major target of the survival strategy of M. tuberculosis that blocks various transcriptional responses including induction of CD64 [24]. Interestingly and apparently controversial, CD64 protein expression is increased in monocytes from TB patients [26]. We found increased CD64 expression at the RNA and cell surface protein level.

Numerous efforts have been made to characterize molecules involved in the regulation of CD64. The role of N-formyl-methionyl-leucyl-phenylalanine peptides from bacteria as ligands of FPR1 in this process remains controversial. N-formyl peptides are released as a consequence of their destruction by the immune system or by autolysis (reviewed in [21]). Despite their proinflammatory function, N-formyl-methionyl-leucyl-phenylalanine peptides induce downregulation of CD64 in monocytes thus IFN-γ and interleukin-10, which induce enhanced CD64 expression [2]. Like CD64 and FPR1, LTF is essential for antimicrobial defense. LTF is a transporter molecule with high affinity for iron, which modulates host defense likely by competing with microbes for iron [28]. A direct effect of LTF on M. tuberculosis infection has been revealed in a murine model, in which correction of iron overload by LTF inverted increased susceptibility to TB [18]. Increased expression of LTF in TB patients’ PBMC could restrict mycobacterial access to iron. Because LTF is a crucial host protective factor against TB, we hypothesize that increased expression of at least some candidates in TB patients indicates fine tuned balance between key processes in host pathogen interactions.

The third molecule in the optimal discriminating group of genes was Rab33A. Rab33A is a member of the Ras-associated small GTPase family that is likely involved in the regulation of intracellular trafficking [30]. Recently, we demonstrated that Rab33A is downregulated in PBMC from TB patients and that it is preferentially expressed in CD8+ T cells [9]. The induction of Rab33A expression depends on T cell receptor activation [9]. Further studies will clarify the biological function of Rab33A and its exact role in TB.

The bactericidal BPI and DEFA1, 3, and 4 are well-known antibacterial effector molecules [11]. BPI is crucial in the immune response against Gram-negative bacteria and increased serum concentrations are prevalent in patients with active TB [11]. High titers of DEFA in bronchoalveolar lavage fluid and plasma from TB patients and specific mycobactericidal activity of DEFA have been described [19]. Thus, the molecular biosignature identified here includes several candidates of known relevance to host defense against TB.

Accessibility restricts the use of affected pulmonary tissue for research and diagnostic purposes, demanding accessible surrogate tissue samples in TB. PBMC heterogeneity markedly confounds microarray analysis because differences in immune cell populations cannot be separated from real single cell RNA expression [31]. Nevertheless, PBMC have been used as surrogate tissue with diagnostic or prognostic value in different malignant and autoimmune diseases [3, 4, 5]. Our data demonstrate that it is feasible to extend this approach to chronic infections where active disease is a possible, but not conclusive, outcome of infection.



This study was supported in part by the National Genome Research Network (Germany), the EU FP6 funded IP “TBVAC”, and Grand Challenge 6 of the Bill & Melinda Gates Foundation to S. H. E. Kaufmann and M. Jacobsen. H.-J. Mollenkopf and S. H. E. Kaufmann acknowledge additional funding by the European Fund for Regional Development/State of Berlin. The authors have no conflicting financial interests. We thank M. L. Grossman for carefully revising the manuscript.

Supplementary material

109_2007_157_MOESM1_ESM.xls (82 kb)
Supplementary Table S2Top 95 candidate genes from microarray comparisons between TB patients and latently M. tuberculosis infected healthy contacts (XLS 84 kb)


  1. 1.
    Altman DG, Royston P (2000) What do we mean by validating a prognostic model? Stat Med 19:453–473PubMedCrossRefGoogle Scholar
  2. 2.
    Beigier-Bompadre M, Barrionuevo P, Alves-Rosa F, Rubel CJ, Palermo MS, Isturiz MA (2001) N-formyl-methionyl-leucyl-phenylalanine inhibits both gamma interferon-and interleukin-10-induced expression of FcgammaRI on human monocytes. Clin Diagn Lab Immunol 8:402–408PubMedCrossRefGoogle Scholar
  3. 3.
    Bennett L, Palucka AK, Arce E, Cantrell V, Borvak J, Banchereau J, Pascual V (2003) Interferon and granulopoiesis signatures in systemic lupus erythematosus blood. J Exp Med 197:711–723PubMedCrossRefGoogle Scholar
  4. 4.
    Bomprezzi R, Ringner M, Kim S, Bittner ML, Khan J, Chen Y, Elkahloun A, Yu A, Bielekova B, Meltzer PS, Martin R, McFarland HF, Trent JM (2003) Gene expression profile in multiple sclerosis patients and healthy controls: identifying pathways relevant to disease. Hum Mol Genet 12:2191–2199PubMedCrossRefGoogle Scholar
  5. 5.
    Bullinger L, Dohner K, Bair E, Frohling S, Schlenk RF, Tibshirani R, Dohner H, Pollack JR (2004) Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia. N Engl J Med 350:1605–1616PubMedCrossRefGoogle Scholar
  6. 6.
    Chan YH (2005) Biostatistics 303. Discriminant analysis. Singap Med J 46:54–61, quiz 62Google Scholar
  7. 7.
    Chun T, Serbina NV, Nolt D, Wang B, Chiu NM, Flynn JL, Wang CR (2001) Induction of M3-restricted cytotoxic T lymphocyte responses by N-formylated peptides derived from Mycobacterium tuberculosis. J Exp Med 193:1213–1220PubMedCrossRefGoogle Scholar
  8. 8.
    Flynn JL, Chan J (2001) Immunology of tuberculosis. Annu Rev Immunol 19:93–129PubMedCrossRefGoogle Scholar
  9. 9.
    Jacobsen M, Repsilber D, Gutschmidt A, Neher A, Feldmann K, Mollenkopf HJ, Ziegler A, Kaufmann SH (2005) Ras-associated small GTPase 33A, a novel T cell factor, is downregulated in patients with tuberculosis. J Infect Dis 192:1211–1218PubMedCrossRefGoogle Scholar
  10. 10.
    Jacobsen M, Schweer D, Ziegler A, Gaber R, Schock S, Schwinzer R, Wonigeit K, Lindert RB, Kantarci O, Schaefer-Klein J, Schipper HI, Oertel WH, Heidenreich F, Weinshenker BG, Sommer N, Hemmer B (2000) A point mutation in PTPRC is associated with the development of multiple sclerosis. Nat Genet 26:495–499PubMedCrossRefGoogle Scholar
  11. 11.
    Juffermans NP, Verbon A, van Deventer SJ, Buurman WA, van Deutekom H, Speelman P, van der Poll T (1998) Serum concentrations of lipopolysaccharide activity-modulating proteins during tuberculosis. J Infect Dis 178:1839–1842PubMedCrossRefGoogle Scholar
  12. 12.
    Kalita A, Verma I, Khuller GK (2004) Role of human neutrophil peptide-1 as a possible adjunct to antituberculosis chemotherapy. J Infect Dis 190:1476–1480PubMedCrossRefGoogle Scholar
  13. 13.
    Kaufmann SH (2001) How can immunology contribute to the control of tuberculosis? Nat Rev Immunol 1:20–30PubMedCrossRefGoogle Scholar
  14. 14.
    Kaufmann SH, McMichael AJ (2005) Annulling a dangerous liaison: vaccination strategies against AIDS and tuberculosis. Nat Med 11:S33–S44PubMedCrossRefGoogle Scholar
  15. 15.
    Le Y, Murphy PM, Wang JM (2002) Formyl-peptide receptors revisited. Trends Immunol 23:541–548PubMedCrossRefGoogle Scholar
  16. 16.
    Ochoa MT, Stenger S, Sieling PA, Thoma-Uszynski S, Sabet S, Cho S, Krensky AM, Rollinghoff M, Nunes Sarno E, Burdick AE, Rea TH, Modlin RL (2001) T-cell release of granulysin contributes to host defense in leprosy. Nat Med 7:174–179PubMedCrossRefGoogle Scholar
  17. 17.
    Perussia B, Dayton ET, Lazarus R, Fanning V, Trinchieri G (1983) Immune interferon induces the receptor for monomeric IgG1 on human monocytic and myeloid cells. J Exp Med 158:1092–1113PubMedCrossRefGoogle Scholar
  18. 18.
    Schaible UE, Collins HL, Priem F, Kaufmann SH (2002) Correction of the iron overload defect in beta-2-microglobulin knockout mice by lactoferrin abolishes their increased susceptibility to tuberculosis. J Exp Med 196:1507–1513PubMedCrossRefGoogle Scholar
  19. 19.
    Sharma S, Verma I, Khuller GK (2000) Antibacterial activity of human neutrophil peptide-1 against Mycobacterium tuberculosis H37Rv: in vitro and ex vivo study. Eur Respir J 16:112–117PubMedCrossRefGoogle Scholar
  20. 20.
    Shiloh MU, Nathan CF (2000) Reactive nitrogen intermediates and the pathogenesis of Salmonella and mycobacteria. Curr Opin Microbiol 3:35–42PubMedCrossRefGoogle Scholar
  21. 21.
    Snyderman R, Pike MC (1984) Chemoattractant receptors on phagocytic cells. Annu Rev Immunol 2:257–281PubMedCrossRefGoogle Scholar
  22. 22.
    Stenger S, Modlin RL (2002) Control of Mycobacterium tuberculosis through mammalian Toll-like receptors. Curr Opin Immunol 14:452–457PubMedCrossRefGoogle Scholar
  23. 23.
    te Velde AA, de Waal Malefijt R, Huijbens RJ, de Vries JE, Figdor CG (1992) IL-10 stimulates monocyte Fc gamma R surface expression and cytotoxic activity. Distinct regulation of antibody-dependent cellular cytotoxicity by IFN-gamma, IL-4, and IL-10. J Immunol 149:4048–4052Google Scholar
  24. 24.
    Ting LM, Kim AC, Cattamanchi A, Ernst JD (1999) Mycobacterium tuberculosis inhibits IFN-gamma transcriptional responses without inhibiting activation of STAT1. J Immunol 163:3898–3906PubMedGoogle Scholar
  25. 25.
    van de Winkel JG, Capel PJ (1993) Human IgG Fc receptor heterogeneity: molecular aspects and clinical implications. Immunol Today 14:215–221PubMedCrossRefGoogle Scholar
  26. 26.
    Vanham G, Edmonds K, Qing L, Hom D, Toossi Z, Jones B, Daley CL, Huebner B, Kestens L, Gigase P, Ellner JJ (1996) Generalized immune activation in pulmonary tuberculosis: co-activation with HIV infection. Clin Exp Immunol 103:30–34PubMedCrossRefGoogle Scholar
  27. 27.
    Via LE, Deretic D, Ulmer RJ, Hibler NS, Huber LA, Deretic V (1997) Arrest of mycobacterial phagosome maturation is caused by a block in vesicle fusion between stages controlled by rab5 and rab7. J Biol Chem 272:13326–13331PubMedCrossRefGoogle Scholar
  28. 28.
    Ward PP, Uribe-Luna S, Conneely OM (2002) Lactoferrin and host defense. Biochem Cell Biol 80:95–102PubMedCrossRefGoogle Scholar
  29. 29.
    Young PHWaSS (1993) Resampling-based multiple testing: examples and methods for p-value adjustment. WileyGoogle Scholar
  30. 30.
    Zerial M, McBride H (2001) Rab proteins as membrane organizers. Nat Rev Mol Cell Biol 2:107–117PubMedCrossRefGoogle Scholar
  31. 31.
    Jacobsen M, Repsilber D, Gutschmidt A, Neher A, Feldmann K, Mollenkopf H-J, Kaufmann SHE, Ziegler A (2006) Deconfounding microarray analyses: independent measurements of cell type proportions used in a regression model to resolve tissue heterogeneity bias. Methods Inf Med 45:557–563PubMedGoogle Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Marc Jacobsen
    • 1
  • Dirk Repsilber
    • 2
    • 5
  • Andrea Gutschmidt
    • 1
  • Albert Neher
    • 3
  • Knut Feldmann
    • 3
  • Hans J. Mollenkopf
    • 4
  • Andreas Ziegler
    • 2
  • Stefan H. E. Kaufmann
    • 1
  1. 1.Department of ImmunologyMax Planck Institute for Infection BiologyBerlinGermany
  2. 2.Institute for Medical Biometry and StatisticsUniversity at LübeckLübeckGermany
  3. 3.Asklepios Center for Respiratory Medicine and Thoracic SurgeryMunich-GautingGermany
  4. 4.Microarray Core FacilitiesMax Planck Institute for Infection BiologyBerlinGermany
  5. 5.Institute for Biochemistry and BiologyUniversity PotsdamPotsdam-GolmGermany

Personalised recommendations