and Immunity (2011) 12, 15–22 & 2011 Macmillan Publishers Limited All rights reserved 1466-4879/11 www.nature.com/gene

ORIGINAL ARTICLE Human expression profiles of susceptibility and resistance in tuberculosis

J Maertzdorf1, D Repsilber2, SK Parida1, K Stanley3, T Roberts3, G Black3, G Walzl3 and SHE Kaufmann1 1Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany; 2Leibniz Institute for Farm Animal Biology, Genetics and Biometry, Dummerstorf, Germany and 3Division of Molecular Biology and Human Genetics, MRC Centre for Molecular and Cellular Biology, DST and NRF Centre of Excellence for Biomedical TB Research, Faculty of Health Sciences, Stellenbosch University, Cape Town, South Africa

Tuberculosis (TB) still poses a profound burden on global health, owing to significant morbidity and mortality worldwide. Although a fully functional is essential for the control of Mycobacterium tuberculosis infection, the underlying mechanisms and reasons for failure in part of the infected population remain enigmatic. Here, whole-blood microarray analyses were performed in TB patients and in latently as well as uninfected healthy controls to define biomarkers predictive of susceptibility and resistance. Fc gamma 1B (FCGRIB)was identified as the most differentially expressed gene, and, in combination with four other markers, produced a high degree of accuracy in discriminating TB patients and latently infected donors. We determined differentially expressed genes unique for active disease and identified profiles that correlated with susceptibility and resistance to TB. Elevated expression of innate immune-related genes in active TB and higher expression of particular gene clusters involved in apoptosis and activity in latently infected donors are likely to be the major distinctive factors determining failure or success in controlling M. tuberculosis infection. The gene expression profiles defined in this study provide valuable clues for better understanding of progression from latent infection to active disease and pave the way for defining predictive correlates of protection in TB. Genes and Immunity (2011) 12, 15–22; doi:10.1038/gene.2010.51; published online 23 September 2010

Keywords: tuberculosis; microarray; biomarkers

Introduction control of latent M. tuberculosis infection also requires robust innate immunity. The precise mechanisms by Tuberculosis (TB) is an ancient disease caused by the which these innate processes contribute to the control of bacterial pathogen Mycobacterium tuberculosis, which has latent infection remain elusive.10,11 In part of the infected co-evolved with the human population for millennia.1 population, latent infection progresses to active disease. Infection with M. tuberculosis is controlled in most Once immune control of infection wanes, granulomas infected individuals, while in a minority of cases liquefy and containment breaks down. As a result, containment of infection fails over time, and active TB actively replicating M. tuberculosis are released into the disease develops.2 Although only a minority of exposed airways, rendering the disease contagious.12 Sensing of individuals develop active disease, morbidity and M. tuberculosis by macrophages and dendritic cells via mortality in humans worldwide remain high, with pattern recognition receptors such as the Toll-like nearly 2 million deaths annually.3,4 In the face of this receptor family trigger signalling cascades, which initiate major impact on global health, remarkably little progress secretion, suggesting a role in protection against has been made in the past towards the development of M. tuberculosis.13 However, the exact role of Toll-like new tools and elucidation of key features involved in receptor and other pattern recognition receptors in protection and pathogenesis.5–7 immunity against M. tuberculosis remains controversial.11,14 It is generally accepted that a competent immune Bacterial killing is a key function of macrophages, which response is key to control of M. tuberculosis infection.8 are activated by , notably tumour necrosis factor Although the adaptive immune response induces devel- alpha and gamma, which are mainly produced opment of solid granulomas, which contain the bacteria,9 by T cells and natural killer (NK) cells. NK cells are well- known key players in innate immunity, which actively induce death of M. tuberculosis-infected monocytes.15 Correspondence: Dr J Maertzdorf and Professor SHE Kaufmann, It has been proposed to reduce TB incidence to o1 per Department of Immunology, Max Planck Institute for Infection 106 inhabitants by 2050.4 To reach this ambitious goal, Biology, Chariteplatz 1, Berlin 10117, Germany. better intervention measures are needed.7 It has been E-mail: [email protected] or [email protected] calculated that a combination of tools for rapid diag- Received 18 May 2010; revised and accepted 12 August 2010; nosis, efficacious vaccines and novel drugs will be published online 23 September 2010 needed to reduce current TB incidence by 490%.16 Host gene expression profiles in tuberculosis J Maertzdorf et al 16 Biomarkers will be of critical importance for this Validation of differential gene expression by reverse approach as they can facilitate the identification of transcriptase (RT)-PCR targets for new drugs and vaccines, allow better monitoring of clinical trials and provide guidelines for From the genes with highest degree of differential rapid diagnosis. In the present study, we evaluated expression between TB and LTBI, 10 were selected to transcriptional signatures in TB patients, healthy donors validate differential expression levels by quantitative with latent M. tuberculosis infection (LTBI) and healthy RT-PCR. Beta-2-microglobulin was chosen as reference non-infected donors (NIDs) in an attempt to define gene as this gene showed the least variation in expres- biomarkers predictive of susceptibility for, or resistance sion within and between each study group (data not against, active TB. shown). A single sample from the TB group was not included because of insufficient amounts of RNA. Although most of the high-ranking genes showed Results distinct levels of expression between study groups, not all microarray results could be confirmed by RT-PCR Gene expression profiling in TB patients and household (Supplementary Figure S2). contacts (LTBI and NID) The most strongly differentially expressed gene that Genome-wide transcription profiles in whole blood from was identified in TB versus control groups in this study 33 TB patients, 34 LTBI and from 9 NIDs from a TB- was Fc gamma receptor 1B (FCGR1B). Expression endemic area were generated using microarray chips validation by RT-PCR confirmed significant differences comprising B45 000 unique features. Our strategy is in expression of this gene between study groups aimed at analysing three separate comparisons to (Figure 2a). An earlier study by our group had identified identify differentially expressed genes between study a minimal set of three genes (CD64, RAB33A and LTF groups, that is, TB versus LTBI, TB versus NID and LTBI (lactoferrin)), whose combined expression patterns versus NIDs (Figure 1). Our ultimate goal is the showed a discriminating power between TB and LTBI identification of unique profiles that are associated with in Caucasians.17 These candidate biomarker genes were active TB. For our first goal, that is, identifying disease- also included in our RT-PCR validation to investigate related gene profiles, we focussed on comparisons of TB whether this subset of genes could discriminate between with LTBI as well as TB with NID. We proposed that TB and LTBI individuals from the South African cohort these comparisons define patterns that distinguish studied herein as well. Similar to those previous between active TB disease and health. For our second findings, CD64 and LTF showed a significant difference goal, that is, identifying profiles that correlate with in gene expression between the study groups, whereas resistance and susceptibility, we focussed on genes that RAB33A expression levels were similar between TB and are differentially expressed in TB versus LTBI, but not in LTBI (Figure 2b). These findings underline differences TB versus NID. This approach filtered out general and communalities in candidate TB biomarkers between disease-related patterns. study populations of different ethnicities. Universal A total of 2048 differentially expressed transcripts biomarkers shared by a broad variety of ethnicities (representing 1935 genes) were identified between TB would be preferable. patients and LTBI. These included 988 transcripts (927 Random forest analysis was applied to calculate the genes) that were also differentially expressed between TB discriminating power of these three genes (CD64, LTF patients and NIDs. In this analysis, a fold-change cutoff of and RAB33A) to distinguish between TB donors and 40.2 or oÀ0.2 (log 2 scale) with qo0.01 was used LTBI. With CD64 being the most powerful discriminating (q-value equals the raw P-value corrected for multiple gene, the combination of the three markers achieved a testing). A clustering analysis was performed to sensitivity and specificity of 88 and 91%, respectively, in interrogate whether donors within this study could be identifying TB patients (Table 1a). Running a similar divided into distinct groups based on their gene expres- analysis on all 13 genes that had been subjected to sion profiles. The resulting two-way clustered heat map, RT-PCR expression evaluation, a combination of five showing intensity levels in all donors, is depicted in most prominently differentiating genes turned out to Supplementary Figure 1. This analysis revealed clustering give the highest accuracy in discriminating between TB of most of the TB donors, whereas no obvious clusters of and LTBI, that is, FCGR1B, CD64, LTF, guanylate binding LTBI and NIDs were identified. Thus, there was a 5 and Granzyme A. This intriguing subset of discriminative pattern between TB versus healthy indivi- genes formed a biosignature capable of increasing duals with or without M. tuberculosis infection, but no sensitivity and specificity to 94% (30/32) and 97% (33/ apparent difference between LTBI and NID. 34), respectively (see Table 1b).

Figure 1 (a) Characteristics of study groups and group wise comparisons of gene expression profiles. LTBI, healthy donors latently infected with M. tuberculosis; NID, healthy non-infected donors; TB, tuberculosis patients. (b) Pairwise comparisons of gene expression between study groups.

Genes and Immunity Host gene expression profiles in tuberculosis J Maertzdorf et al 17 Table 1 Random forest analysis of gene subsets discriminating between TB and LTBI

a Predicted Gene subset TB LTBI

CD64 TB 28 (88%) 4 (12%) LTF

RAB33A Actual LTBI 3 (9%) 31 (91%)

b Predicted Gene subset TB LTBI FCGR1B TB 30 (94%) 2 (6%) CD64 LTF Actual LTBI 1 (3%) 33 (97%) GBP5

compared with healthy individuals with (LTBI) or without (NID) M. tuberculosis infection.

Unique expression profiles in TB disease and LTBI In an attempt to identify patterns of gene expression indicative of progression to active TB disease from LTBI, we next focussed our analyses on genes differentially expressed in TB versus LTBI only. For this, we filtered out all genes from the TB versus LTBI comparison that Figure 2 Differences in gene expression levels between TB, LTBI were differentially expressed in TB versus NID, as well. and NID in the South African cohort studied in this report. (a) This filtering revealed 1060 transcripts (representing 1008 Scatter dot plot for FCGR1B expression for each individual sample, genes) between TB and LTBI, but not between TB and as PCR cycle differences in the internal reference gene beta-2- NID. A comparative analysis led to the finding that a microglobulin , indicating the mean and 95% confidence interval. gene cluster is involved in apoptosis regulation with (b) Box plots indicating expression variation of indicated genes for each study group. P value o0.01. Shown are the median with the reduced expression in TB compared with LTBI (Table 2). boundaries representing 25th and 75th percentiles, and whiskers Because genes present in both comparisons had been (error bars) above and below indicating the 90th and 10th filtered out, this particular gene cluster showed similar percentiles. levels of expression in TB and NID. Similarly, a group of genes involved in host defence responses and mainly active in granulocyte and macrophage effector function Functional categorization of differentially expressed genes and differentiation was expressed at lower levels in LTBI To categorize differentially regulated genes, we used the compared with TB and NID (Table 2). These subsets of web-based tool Database for Annotation, Visualization genes indicating decreased apoptotic activity and con- and Integrated Discovery.18,19 Functional annotation comitantly increased innate host defence are apparently clustering of genes common in comparisons of TB versus indicative for progression of TB disease from LTBI. LTBI and TB versus NID revealed a high proportion of genes involved in protein binding, cell communication Functional and cell-type-associated expression profiles and signal transduction (Figure 3). Also, genes involved Functional categorization of differentially expressed in distinct immune responses, apoptosis and stress genes unique for TB versus LTBI revealed profound responses were enriched in this analysis upregulation of Toll-like receptor-associated genes in- (enrichment of gene ontology terms depicted in Figure 3, cluding MyD88 and IRF7. Simultaneously, the data Po0.05). Genes differentially expressed in both compar- reveal an increased expression of several interferon- isons behaved similarly, 792/988 transcripts (744/927 inducible genes, including guanylate binding protein 1 genes) showed increased or decreased expression in both and 2, interferon alpha-inducible protein 6 and 27, ISG15 TB versus LTBI and TB versus NID analyses. These ubiquitin-like modifier and 20,50-oligoadenylate synthe- genes, which showed highly significant enrichment in tase 1 (see Table 3), which might illustrate the induction gene ontology terms, were mainly involved in immune of interferon responses upon activation of pattern response regulation and in apoptosis. Our results recognition receptors pathways.20,21 Intriguingly, a dis- verify that systemic changes in immune responses are tinct gene expression profile between TB and LTBI was a useful indicator for active TB disease. The similarities observed when analysing expression of well-defined in gene expression in both comparisons are likely to subsets of macrophage- and NK cell-associated genes. reflect a systemic inflammatory response in TB patients Macrophage-associated genes were upregulated in TB

Genes and Immunity Host gene expression profiles in tuberculosis J Maertzdorf et al 18

Figure 3 Enriched biological properties and molecular functions of differentially expressed genes based on gene ontology (GO) classifications for genes shared by comparisons of TB versus LTBI and TB versus NID. Individual genes can be found under multiple GO annotations. Percentages indicate the number of genes sharing a certain GO term relative to the complete list of differentially expressed genes in both comparisons.

patients, whereas NK cell-associated genes were down- whole-genome microarray expression analysis on whole- regulated in TB patients as compared with LTBI patients blood samples from TB patients and their controls (LTBI (Table 3, list of cell-type-associated genes in part derived and NID) from a high TB-endemic area to identify such from Gaucher et al.22). Similarly, expression of comple- biomic profiles. Previous studies using microarray ment- and inflammasome-associated genes were ele- analysis in TB patients have identified gene patterns, vated in TB patients, whereas B cell-associated genes which identify subjects at risk for recurrent disease were, for the most part, decreased. It is to be noted that among patients with cured TB25 and a distinct subset of for all these functional and cell type-associated genes, genes to classify TB patients and LTBI in a Caucasian gene expression levels did not differ between TB and population.17 TB patients and healthy household control NID. However, we did not perform a comparative subjects (both latently infected and uninfected) included phenotypic analysis of cellular composition of macro- in this study were recruited from a cohort with high phages and NK cells in our samples. Thus, we cannot incidence of infection and disease. Accordingly, the formally exclude that the observed differences in gene recruitment of NID proved difficult, as almost all expression reflect divergent total cell counts of these cell individuals in this cohort were found to give a positive types in LTBI patients compared with both TB patients result in a tuberculin skin test. and NIDs. FCGR1B (Fc gamma receptor 1B) was identified as the gene with the highest degree of differential expression between the three groups of donors in this study. An earlier study by our group had identified CD64 (alias Discussion FCGR1A; Fc gamma receptor 1A) as one of the highest Although several key components involved in M. ranking differentially expressed genes in TB-diseased tuberculosis infection and active TB disease have been versus latently infected Caucasian subjects.17 Despite of described, the mechanisms that determine successful their different annotations, these genes have remarkably resistance or susceptibility to TB disease remain ill- similar sequences. The oligonucleotide probes on the defined. Definition of correlates of protection in TB will microarray chips used in both studies, were 100% most likely require a multitude of biomarkers, termed complementary to the sequences of the probes used in biosignature.23,24 The complexity of the infection process either study. The microarray chips in both studies thus that comprises LTBI and active TB disease necessitates a targeted transcripts from both genes. In contrast, in our broader strategy aimed at identifying unique gene RT-PCR evaluation, we designed primers to specifically expression profiles. In the present study, we applied target the CD64 encoding mRNA. Primers for FCGR1B

Genes and Immunity Host gene expression profiles in tuberculosis J Maertzdorf et al 19 Table 2 Unique genes related to apoptosis and defence response Table 3 Functional and cell-type-associated gene expression in TB differentially expressed between TB and LTBI versus LTBI

Transcript ID Gene symbol M (log 2) Gene symbol M (log 2) q

GOTERM: apoptosis IFN-induced and TLR-associated genes NM_000024 ADRB2 À0.51 AIM2 1.41 1.8EÀ08 NM_004208 AIFM1 À0.26 GBP1 1.56 1.1EÀ07 NM_000633 BCL2 À0.31 NM_014739 BCLAF1 À0.30 GBP2 0.79 2.2EÀ08 NM_001165 BIRC3 À0.63 IFI27 0.63 3.5EÀ02 NM_004052 BNIP3 À0.22 IFI6 0.24 2.5EÀ02 NM_001731 BTG1 À0.50 IFIT3 0.92 6.9EÀ04 NM_012115 CASP8AP2 À0.31 IRF7 0.51 9.1EÀ06 NM_006139 CD28 À0.58 ISG15 0.56 1.1EÀ02 NM_019604 CRTAM À0.57 MYD88 0.35 2.5EÀ04 NM_001316 CSE1L À0.27 OAS1 0.51 3.3EÀ03 NM_001008895 CUL4A À0.38 NM_001008540 CXCR4 À0.38 OASL 0.39 8.3EÀ03 NM_005494 DNAJB6 À0.25 SPP1 À0.23 1.0EÀ02 NM_004944 DNASE1L3 À0.22 TLR2 0.43 8.5EÀ04 NM_004836 EIF2AK3 À0.27 TLR4 0.30 1.8EÀ02 NM_000123 ERCC5 À0.36 TLR5 1.02 2.3EÀ09 NM_001992 F2R À0.27 TLR6 0.32 7.2EÀ05 NM_001033030 FAIM À0.32 TLR8 0.39 2.6EÀ03 NM_000639 FASLG À0.32 TRIM21 0.47 3.0EÀ09 NM_006144 GZMA À0.77 NM_004134 HSPA9 À0.32 TRIM22 0.68 5.6EÀ04 NM_014061 MAGEH1 À0.41 WARS 0.83 1.3EÀ06 NM_006785 MALT1 À0.20 NM_002520 NPM1 À0.43 Complement-associated genes NM_153719 NUP62 À0.31 C1QA 1.02990 1.0EÀ06 NM_024065 PDCL3 À0.23 C1QB 1.35988 7.2EÀ08 AK097958 PERP À0.28 C3AR1 0.37221 1.2EÀ03 NM_024900 PHF17 À0.29 SERPING1 1.13444 1.5EÀ09 NM_004703 RABEP1 À0.29 NM_006265 RAD21 À0.63 NM_016542 RP6-213H19.1 À0.81 Inflammasome-associated genes NM_003031 SIAH1 À0.21 CASP5 0.55520 9.5EÀ06 NM_012238 SIRT1 À0.32 PYCARD 0.27709 8.5EÀ03 NM_032195 SON À0.23 IL1B 0.56420 5.0EÀ04 NM_004619 TRAF5 À0.60 NM_000553 WRN À0.24 Macrophage-associated genes CD36 0.34 1.7EÀ02 GOTERM: defence response NM_001637 AOAH 0.33 CD68 0.23 1.9EÀ02 NM_145641 APOL3 0.22 IFI16 0.26 4.9EÀ02 NM_021160 BAT5 0.32 MARCO 0.85 6.3EÀ05 NM_004331 BNIP3 L 0.40 NOD2 0.79 8.1EÀ04 NM_001725 BPI 0.30 STAB1 0.25 3.7EÀ05 NM_006077 CBARA1 0.31 NM_006678 CD300C 0.39 NK cell-associated genes NM_001250 CD40 0.27 CD160 À0.26 4.4EÀ06 NM_005194 CEBPB 0.21 NM_001805 CEBPE 0.44 GNLY À0.45 4.0EÀ02 NM_203339 CLU 0.78 GZMB À0.43 1.5EÀ02 NM_172313 CSF3R 0.51 GZMK À0.89 9.5EÀ08 NM_021175 HAMP 0.38 KIR2DL2 À0.25 4.3EÀ02 NM_000634 IL8RA 0.35 KLRsa À0.72 to À0.25 o2.0EÀ02 NM_006863 LILRA1 0.38 NCR3 À0.57 2.4EÀ05 NM_006864 LILRB3 0.44 PRF1 À0.45 7.8EÀ04 NM_006840 LILRB5 0.22 NM_181657 LTB4R 0.26 NM_022468 MMP25 0.36 B cell-associated genes NM_000265 NCF1 0.52 POU2AF1 À0.57388 7.7EÀ03 NM_032206 NLRC5 0.26 BANK1 À0.82985 1.8EÀ07 NM_003005 SELP 0.38 CD19 À0.45887 3.2EÀ04 NM_001002236 SERPINA1 0.51 PIK3AP1 0.57109 4.9EÀ06 NM_015136 STAB1 0.25 NM_003152 STAT5A 0.27 NM_003190 TAPBP 0.20 Abbreviations: IFN, interferon; NK, natural killer; TLR, Toll-like NM_006019 TCIRG1 0.46 receptor. NM_005423 TFF2 0.21 M ¼ fold change (log2 scale). Positive values (in bold) indicate higher expression, negative values (in italics) indicate lower Abbreviations: LTBI, healthy donors latently infected with M. expression in TB compared with LTBI. q ¼ significance level tuberculosis; TB, tuberculosis. (P-value corrected for multiple testing). Cutoff values used: M40.2 and o–0.2; qo0.01 (M ¼ fold change aKLRs includes genes KLRB1, KLRC1, KLRC2, KLRC3, KLRD1, (log 2 scale); q equals the P-value corrected for multiple testing). KLRF1, KLRK1. Positive values (in bold) indicate higher expression; negative values (in italics) indicate lower expression in TB compared with LTBI.

Genes and Immunity Host gene expression profiles in tuberculosis J Maertzdorf et al 20 were cross-reactive with CD64 owing to the high infections often induce an increase in granulocyte sequence similarity, thus allowing expression analysis numbers in the blood, this subset of genes showed no of both genes. Although flow cytometry analyses differences in expression levels between TB and NID. revealed a higher percentage of monocytes—the main Thus, these patterns most likely indicate a downregula- cell type expressing CD64—among peripheral blood tion in expression of these genes in LTBI. The most mononuclear cells from TB donors compared with LTBI, straightforward conclusion from our findings would the B15-fold difference in mRNA expression (estimate rate increased expression of apoptosis-related genes and from RT-PCR analysis) cannot solely be explained by this simultaneous downregulated expression of genes in- slightly higher proportion of monocytes. Increased volved in granulocyte activation in LTBI beneficial for expression of the CD64 gene in monocytes from TB preventing outbreak of active disease. Alternatively, patients was also confirmed at the protein level in our higher gene expression levels and activation of granulo- previous study.17 A major function of the family of Fc cytes and monocytes in TB patients could reflect receptors for IgG (FcgRs) is binding of antibodies by their mobilization of professional phagocytes during active constant domain. The role of antibodies has long been TB in an attempt to strengthen defence in affected lung considered to be of minor importance in TB,26 although tissue despite the risk of heightened tissue damage. more recent studies indicate that antibodies may have a Control of M. tuberculosis infection depends on a role in control against M. tuberculosis infections.27 In delicate balance of immune control mainly by macro- addition to their antibody binding capacity, the members phages and T lymphocytes. Disturbance of this balance within the FcgR family comprise a group of molecules results in progression of latent infection to active TB that can simultaneously trigger activating and inhibitory disease. This imbalance in immune control apparently signalling pathways to set thresholds for cell activation correlates with increased innate immunity, as indicated and thus generate a well-balanced immune response.28 by elevated defence response gene expression, Toll-like The strong upregulation of the activating receptor receptor signalling and macrophage effector functions, as FCGR1 in TB patients as identified here could reflect a observed in TB patients in this study. Simultaneous crucial role of this molecule in the sustenance of chronic reduced expression of particular subsets of genes in- immune activation in these individuals, which might volved in apoptosis and NK cell activity could affect have a detrimental effect on disease outcome. Marked eradication of replicating M. tuberculosis and thus have a upregulation of FCGR1 in TB patients of different keyroleinfailureoftheimmunedefencetosuccessfully ethnicity warrants further elucidation of its precise role control latent infection. Reciprocally, fine-tuning of apop- in TB, notably whether FCGR1 upregulation indicates an tosis and the innate immune defence responses appears immune modulatory function, or an ‘attempt in vain’ of a critical for sustained control of M. tuberculosis in LTBI. protective response. The gene expression profiles identified in this study The profiles defined in this study comprise a sig- will need further validation by wide-scale gene expres- nificant number of differentially expressed genes be- sion analyses in different geographical and ethnical tween individuals in all three study groups. These cohorts. Such studies are currently ongoing in cohorts differences were most pronounced between diseased from other African countries to validate current biomar- donors (TB) versus both groups of healthy controls (LTBI ker gene profiles and to identify TB-specific-gene and NID), independent of M. tuberculosis infection. expression signatures, as well as expression patterns Functional annotation clustering of these genes indicated common to TB and other chronic infectious diseases. a high prevalence of factors involved in immune More detailed analyses of expression profiles that may be modulation, signal transduction, gene expression activ- crucial for protection and susceptibility in M. tuberculosis- ity, as well as protein and metal ion binding factors. infected individuals will thus be required to ultimately Additionally, distinct clusters of genes involved in define a unique biosignature for TB. apoptosis regulation and stress responses were differen- tially expressed between TB patients and both control groups, independent of infection with M. tuberculosis. Materials and methods Although these clusters can be considered to be Subject enrollment and sample collection disease susceptibility-related, these profiles may not be The study presented here was approved by ethical specific for TB. In general, we assume that many committees in both Stellenbosch (South Africa) and alterations in gene expression patterns reflect an overall Berlin (Germany) and written informed consent has activation and differentiation of a sustained inflamma- been obtained from all study participants. From a cohort tory response. To define TB-specific susceptibility- and of TB patients and household contacts recruited at resistance-related genes within these clusters, we filtered Stellenbosch University (South Africa), 33 TB patients, out all genes that were differentially expressed in both 34 healthy donors with LTBI and 9 healthy NIDs were TB versus LTBI and TB versus NID comparisons to included in this study (Figure 1a depicts the gender and exclude general pathology-related differences in genes age distribution of donors within groups). Household expression. Differentially expressed gene profiles unique contacts are defined herein as immediate family mem- for TB versus LTBI comprised an intriguing subset of bers of a TB case (acid-fast bacilli, sputum-smear genes involved in apoptosis regulation, which were positive) sharing at least 3 h contact per day within the found to be less active in TB compared with LTBI. In previous 3 months. TB patients and controls were addition, a group of gene clusters involved in defence characterized based on chest radiography, M. tuberculosis responses of granulocytes and in macrophage effector sputum culture results and tuberculin skin testing. All functions and differentiation, was expressed at lower subjects were HIV– and samples from TB patients were levels in LTBI. These expression levels were likely to be taken before chemotherapy. From every donor, 2.5 ml of modulated by M. tuberculosis infection. Although bacterial peripheral whole blood was collected in PAXgene tubes

Genes and Immunity Host gene expression profiles in tuberculosis J Maertzdorf et al 21 Table 4 qRT-PCR target genes and primer sequences

Gene name Accession no. Primer Sequence (50–30) Amplicon size

B2M NM_004048 Forward GCTCGCGCTACTCTCTCTTT 82 Reverse CTCTGCTGGATGACGTGAGT CASP1 NM_033292 Forward TACAGAGCTGGAGGCATTTG 88 Reverse TTCCCGAATACCATGAGACA CD64 NM_000566 Forward AGGCCTGGTTTGCAGCTTT 59 Reverse CTGCCTCGCAGGGTCTTG CEACAM1 NM_001024912 Forward TGTGCCAGGAAACAAGAGAG 139 Reverse GACAGTGGACAAAGGTGTGG GBP1 NM_002053 Forward TGCAGGAAATGCAAAGAAAG 109 Reverse AACTGGACCCTGTCGTTCTC GBP5 NM_052942 Forward TGGCAAATCCTACCTGATGA 97 Reverse CCATATCCAAATTCCCTTGG DEFA3 NM_005217 Forward CCAGAAGTGGTTGTTTCCCT 134 Reverse TAGATGCAGGTTCCATAGCG FCGR1B NM_001017986 Forward GGAAGGGGTGCACCGGAAGG 98 Reverse CACGGGGAGCAAGTGGGCAG GZMA NM_006144 Forward GAAGAGACTCGTGCAATGGA 80 Reverse AAGGCCAAAGGAAGTGACC HP NM_005143 Forward GGTGACTTCCATCCAGGACT 148 Reverse CCACATCTTATCGCATCCAC IFI27 NM_005532 Forward ACTGGGAGCAACTGGACTCT 87 Reverse TAGAACCTCGCAATGACAGC LTF NM_002343 Forward AGAGAGACTCCCCCATCCAGT 70 Reverse CCATCAAGGGTCACAGCATC Rab33A NM_004794 Forward AGATCCAGGTGCCCTCCAA 56 Reverse GAGCATGTTGTGGGCATCAG S100A12 NM_005621 Forward GGGTTAACATTAGGCTGGGA 85 Reverse CCGAACTGAGTATTGGTGGA

(PreAnalytix, Europe BD, Erembodegem, Belgium) and lowess normalized to obtain as unbiased red/green stored at À801C before processing. ratios as possible. For global comparability, the data of all arrays were quantile normalized.32 Further, features with RNA extraction and microarray procedure mean log intensities o7 were also flagged out, as for RNA was extracted from 2.5 ml of peripheral whole these low-intensity spots random 1 noise in the data blood collected in PAXgene tubes, using the PAXgene would lead to spurious signals. To statistically verify Blood RNA Kit following the manufacturer’s protocol differential expression in the three pairwise comparisons (both tubes and kits from PreAnalytix). RNA concentra- of LTBI, NID and TB cases, we applied a combination of tion and integrity were determined using an Agilent 2100 t-test with subsequent false discovery rate correction Bioanalyzer (Agilent Technologies, Foster City, CA, based on the approach of Efron and Tibshirani.33 USA). RNA labelling was done with the Fluorescent Data were log transformed and differentially ex- Linear Amplification Kit (Agilent Technologies) accord- pressed genes were identified based on log-fold changes ing to the manufacturer’s instructions. Quantity and (M-values) in average gene expression with a q value labelling efficiency were verified before hybridization o0.01 (q equals the P-value corrected for multiple of the samples to whole-genome oligonucleotide testing) by two-sample t-test. Functional annotation microarray (Agilent Technologies). In this study, we analysis and clustering was performed with the help of used two-colour microarray slides on which Cy3- and the Database for Annotation, Visualization and Inte- Cy5- labelled RNA from each study group (TB, LTBI or grated Discovery Bioinformatics Resources 2008 (http:// NID) was co-hybridized with RNA from a gender- and david.abcc.ncifcrf.gov).18,19 Random forest analysis was age-matched individual from the other groups. Dye done according to the method of Liaw and Wiener.34 colours were balanced within group comparisons to avoid any dye-specific bias. Because of the lower number Quantitative RT-PCR of NID available in this study, each RNA sample from Differential expression of several genes was validated by this group was co-hybridized with a pool of RNA from quantitative RT-PCR (qRT-PCR). cDNA was generated by three matched individuals from the other two groups. reverse transcription using oligo-dT primers and Super- script II (Invitrogen GmbH, Darmstadt, Germany). Data analysis Transcripts were quantified by qRT-PCR based on SYBR Microarray chips were processed and scanned at 5 mm Green incorporation (Applied Biosystems GmbH, Darm- using an Agilent scanner. Image analysis was performed stadt, Germany) on an ABI PRISM 7900 thermocycler. with Feature Extraction software (version 6.1.1, Agilent Target genes and primer sequences are listed in Table 4. Technologies). Raw microarray data were processed using the function read.maimages of the Bioconductor29 30 R package limma and intensities of the spots not Conflict of interest flagged out were background corrected using the method norm-exp.31 Background-corrected values were The authors declare no conflict of interest.

Genes and Immunity Host gene expression profiles in tuberculosis J Maertzdorf et al 22 Acknowledgements 16 World Health Organization. The Stop TB Strategy, Building on and Enhancing DOTS to Meet the TB-Related Millennium We thank ML Grossman for thoroughly revising the Development Goals. WHO: Geneva, 2006 (WHO/HTM/TB/ paper, J Weiner for biostatistical support and the 2006.368). Microarray Core Facility unit at the Max Planck Institute 17 Jacobsen M, Repsilber D, Gutschmidt A, Neher A, Feldmann for Infection Biology in Berlin for sample processing. K, Mollenkopf HJ et al. Candidate biomarkers for discrimina- This study was funded by Grant number 37772 from the tion between infection and disease caused by Mycobacterium 85 Bill & Melinda Gates Foundation through the Grand tuberculosis. J Mol Med 2007; : 613–621. 18 Dennis Jr G, Sherman BT, Hosack DA, Yang J, Gao W, Lane Challenges in Global Health Initiative, to all authors HC et al. 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