Distinct Phases of Blood Gene Expression Pattern ... - Oxford Journals

MAJOR ARTICLE

Distinct Phases of Blood Gene Expression Pattern Through Tuberculosis Treatment Reflect Modulation of the Humoral Immune Response Jacqueline M. Cliff,1 Ji-Sook Lee,1 Nicholas Constantinou,1 Jang-Eun Cho,1,a Taane G. Clark,2 Katharina Ronacher,3 Elizabeth C. King,1 Pauline T. Lukey,4 Ken Duncan,5 Paul D. Van Helden,3 Gerhard Walzl,3 and Hazel M. Dockrell1 1

Immunology and Infection Department, and 2Pathogen and Molecular Biology & Non-Communicable Disease Epidemiology Departments, London School of Hygiene and Tropical Medicine, London, United Kingdom; 3DST/NRF Centre of Excellence for Biomedical TB Research/MRC Centre for Molecular and Cellular Biology, Division of Molecular Biology and Human Genetics, Faculty of Health Sciences, Stellenbosch University, South Africa; 4 GlaxoSmithKline R&D, Stevenage, United Kingdom; and 5Bill & Melinda Gates Foundation, Seattle, Washington

Background. Accurate assessment of treatment efficacy would facilitate clinical trials of new antituberculosis drugs. We hypothesized that early alterations in peripheral immunity could be measured by gene expression profiling in tuberculosis patients undergoing successful conventional combination treatment. Methods. Ex vivo blood samples from 27 pulmonary tuberculosis patients were assayed at diagnosis and during treatment. RNA was processed and hybridized to Affymetrix GeneChips, to determine expression of over 47 000 transcripts. Results. There were significant ≥2-fold changes in expression of >4000 genes during treatment. Rapid, largescale changes were detected, with down-regulated expression of 1261 genes within the first week, including inflammatory markers such as complement components C1q and C2. This was followed by slower changes in expression of different networks of genes, including a later increase in expression of B-cell markers, transcription factors, and signaling molecules. Conclusions. The fast initial down-regulation of expression of inflammatory mediators coincided with rapid killing of actively dividing bacilli, whereas slower delayed changes occurred as drugs acted on dormant bacilli and coincided with lung pathology resolution. Measurement of biosignatures during clinical trials of new drugs could be useful predictors of rapid bactericidal or sterilizing drug activity, and would expedite the licensing of new treatment regimens. Keywords. Tuberculosis; Transcriptomics; biomarker; Drug treatment; complement; B-cell; clinical trial. After decades of neglect, there is now a concerted effort to develop new tuberculosis drugs and treatment regimens, in order to shorten treatment duration and provide alternative drugs where resistance exists [1]. Ten compounds are in clinical trials, with some

Received 21 December 2011; accepted 20 June 2012; electronically published 7 August 2012. Some of the data were presented at “Tuberculosis: Immunology, Cell Biology, and Novel Vaccination Strategies” Keystone Symposium meeting in Vancouver, Canada, 15–20 January 2011 (abstract 128). a Present affiliation: Department of Biomedical Laboratory Science, Daegu Health College, Daegu, Korea. Correspondence: Jackie Cliff, PhD, Immunology and Infection Department, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel St, London, WC1E 7HT, UK ( [email protected]). The Journal of Infectious Diseases 2013;207:18–29 © The Author 2012. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: [email protected]. DOI: 10.1093/infdis/jis499

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encouraging results [2], and many compounds are undergoing preclinical development. The current directly observed treatment short-course recommended by the World Health Organization (WHO) is highly effective for first-episode tuberculosis cases infected with drug-sensitive Mycobacterium tuberculosis bacilli, with approximately 95% of patients successfully cured and remaining disease-free for 2 years. Thus, noninferiority phase III clinical trials will be expensive, as thousands of patients will be needed in each study arm. Improved methods to determine potential drug efficacy at earlier stages of development would accelerate the introduction of new licensed therapies [3]. Currently, new drugs can be given as monotherapy for 7–14 days, with sputum bacterial load measured by determination of colony-forming units [4] or time-to-detection in liquid culture [5]: this early bactericidal assay

(EBA) has excellent power to identify drugs that act against actively replicating bacilli, such as isoniazid or the nitroimidazole PA-824 [6], but is less useful for drugs with sterilizing activity against dormant bacilli. Sputum smear and culture conversion rates after 2 months of therapy also test drug bactericidal and sterilizing activity, although 2-month sputum conversion and treatment outcome are not directly linked in individuals. In a trial testing shortening treatment from 6 to 4 months, a substantially increased relapse rate was observed, even though all patients were culture negative at 2 months [7]. Changes in the host immune response could be sensitive indicators of mycobacterial killing and might give insight into the immune dysregulation in tuberculosis patients at diagnosis compared to healthy controls [8–10], in part reflecting leukocyte subpopulation frequency [11]. Plasma concentrations of markers such as interleukin (IL)-6 and interferon-inducible protein-10 are elevated at diagnosis and decline during treatment [12]. Peripheral T-cell recall responses to mycobacterial antigens in vitro, such as interferon γ (IFN-γ) production, are suppressed at diagnosis and restored by treatment [13], with changes detectable after only 1 week. Increased levels of naturally occurring regulatory T cells are a feature of tuberculosis at diagnosis [14]. We used a systems-based approach to characterize changes in peripheral blood, which correlate with bacterial clearance by conventional therapy in successfully cured tuberculosis patients. Microarrays have been successfully used to characterize differences in blood messenger RNA (mRNA) levels in active tuberculosis compared to latent infection [15–17] and for ascertaining differences in individuals who succumbed to recurrent tuberculosis [18]. Analysis of gene expression change during treatment has been restricted to samples taken at 2 months [15]. Further characterization of human immune responses to M. tuberculosis have been achieved by analysis of bronchoalveolar lavage cells from tuberculosis patients [19, 20] and of individual cell types such as macrophages [21] or CD4+ and CD8+ T cells [22]. The aim of this study was to measure tuberculosis treatment response in peripheral blood using global transcriptomics methodology. We focused on changes shortly after treatment initiation, as such changes could be invaluable for decision making in tuberculosis drug development [3]. MATERIALS AND METHODS Study Design

Pulmonary tuberculosis patients were recruited from 5 primary care clinics in Cape Town, South Africa, as part of a larger prospective cohort study [23]. They were all first-episode, smear-positive patients, 18–65 years of age, infected with isoniazid- and rifampicin-sensitive bacilli, characterized as moderate or severe disease by chest X ray. All patients were treatmentadherent, negative for human immunodeficiency virus (HIV),

not pregnant, and without comorbidities such as diabetes mellitus, cancer, chronic bronchitis, or sarcoidosis. They were treated with South African National Tuberculosis Program conventional therapy (2HRZE/4HR). All patients were cured after 6 months of therapy and remained disease-free for 2 years. Patients gave written informed consent, with ethical approval granted from Ethics Committees of Stellenbosch University (Faculty of Health Sciences), London School of Hygiene and Tropical Medicine, and Director of Health, City of Cape Town. Five milliliters of venous blood was taken at diagnosis and at intervals during treatment, mixed with 50 mL of RNA/DNA stabilization reagent for blood/bone marrow (Roche Applied Science), and stored at −80°C. The main analysis was conducted with samples from 27 patients. The independent set comprised samples from 9 patients, processed, hybridized, and analyzed independently and separately from the main study. Sample Processing

Messenger RNA was isolated using mRNA isolation kit for blood/bone marrow (Roche) and turbo DNA-free-treated (Applied Biosystems, Cheshire, UK). The mRNA concentration was determined using Quant IT RiboGreen (Invitrogen, Paisley, UK), and quality-verified by quantitative reverse transcription polymerase chain reaction (RT-PCR), using primers directed at 5′ and 3′ ends of 3 housekeeping genes. In sum, 3 ng mRNA was processed using Ovation whole blood solution (NuGEN Technologies Inc, San Carlos, California), which enhances the sensitivity of GeneChip experiments [24]. The resultant fragmented/biotinylated complementary DNA (cDNA; 4.4 μg) samples were hybridized to human genome U133 plus 2.0 array GeneChips (Affymetrix, Santa Clara, California) for 18 hours/45°C/60 rpm, washed and stained using Affymetrix standard reagents and an Affymetrix GeneChip fluidics station 450, and scanned using an Affymetrix GeneChip scanner 3000. Microarray Analysis

All microarray data are deposited in the NCBI-GEO database, accession numbers GSE31348 and GSE36238. Data were analyzed using GeneSpring GX11.5 (Agilent Technologies). Affymetrix CEL files were imported, summarized using the GC-RMA algorithm, and baseline-normalized to the median of all arrays. Gene entities that were not expressed were removed from analysis by including only 43 865/54 675 entities in the 20th–100th percentile in ≥50% of samples from any one time point. Analysis of variance (ANOVA) was performed, to determine which gene entities were significantly differentially expressed over the treatment time-course, and Benjamini-Hochberg false discovery rate (FDR) multiple testing correction was applied. Modular analysis was performed on all gene entities that were significantly differentially expressed in pair-wise comparisons between 2 time points and the proportion of genes

Transcriptome Change During Tuberculosis Treatment

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within previously described modules [25] within these comparisons were determined. For biological pathway and network analysis using Ingenuity IPA 8.8 software, only genes that were significantly differentially expressed (P < .05 paired Mann–Whitney U test, Benjamini-Hochberg FDR correction) and with a mean fold-difference ≥2 were included. To determine the expression patterns of gene networks, the normalized hybridization intensity for all of the network genes at each time point were aggregated for each patient, with genes up-regulated at the earlier time point analyzed separately from those that were down-regulated. A nonparametric Wilcoxon signed rank test was applied to test for differences between time points (GraphPad Prism 5.0). Prediction models were built using GeneSpring GX11.5, using data from 18 of 27 patients as a training set, and tested using the remaining 9 of 27 patients’ data. Further details about the models and the ingenuity pathways analysis are available in the supplementary material in the electronic edition of Journal of Infectious Diseases.

RESULTS A Rapid Change in Blood Gene Expression Profile Occurs When Tuberculosis Patients Commence Therapy

Ex vivo blood samples were collected from 27 first-episode pulmonary tuberculosis patients prior to starting standard therapy and after 1, 2, 4, and 26 weeks of successful treatment. Genome-wide gene expression profiles were obtained from ex vivo blood samples. In ANOVA, 4151 gene entities were at least 2-fold differentially expressed between time points (Figure 1A). The largest change in gene expression occurred very rapidly, with expression of 1261 genes changing during the first week of treatment, of which the majority were downregulated (Figure 1B; Supplementary Table 1A). The expression of 780 genes remained significantly different after 2 and 4 weeks treatment compared to diagnosis, whereas the expression of the remaining 481 genes exhibited temporary changes, being no longer significantly different at 4 weeks compared to diagnosis. These changes likely reflect rapid killing of actively replicating M. tuberculosis bacilli. Further Changes in Gene Expression Occur Later in Therapy

Substantial changes in blood transcriptomic profile also occurred between week 4 and treatment end at week 26 (Figure 1C; Supplementary Table 1B), with 549 gene entities at least 2-fold significantly differentially expressed between the 2 time points. The majority (342) of these gene entities were not 2-fold significantly differentially expressed during the initial week of treatment. These expression changes likely reflect the continued killing and clearance of the bacilli, and disease resolution. 20

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Modified Modular Analysis of Changes During Treatment Time Course

To gain biological insight into the changing patterns of gene expression, we performed a modified modular analysis [25] that involved groups of genes previously found to be coordinately regulated in blood in a variety of diseases across multiple data sets. Published lists of modules were used to determine the proportion of each module that was differentially expressed, from diagnosis to week 26, followed by comparisons between pairs of time points through treatment (diagnosis/week 1, weeks 1/4, weeks 4/26) to determine when changes occurred. Week 2 data were not analyzed as they were largely similar to week 1 (Supplementary Table 1C). The modules exhibited different patterns of differential gene expression during treatment (Figure 2; Supplementary Table 2). Compared to gene expression at week 26, the most highly significantly up-regulated module at diagnosis was module 1.6, which currently has no function ascribed [25] but contains many signaling molecules and transcription factors. Enhanced understanding of these genes’ functions would provide insight into how M. tuberculosis subverts the immune response in tuberculosis. Genes within the interferon module (module 3.1) were also overexpressed at diagnosis and down-regulated during treatment, as reported elsewhere [15]; again, the most significant changes occurred during the first week of treatment. Other modules, such as neutrophils (module 2.2), platelets (module 1.2), and undetermined function modules 2.7 and 2.11 exhibited later decreases in gene expression. The most significantly down-regulated modules at diagnosis compared to treatment end were B cells, cytotoxic cells, T cells, and ribosomal proteins (modules 1.3, 2.1, 2.8, and 2.4, respectively). These modules exhibited different kinetics of change, with changes in cytotoxic cells occurring rapidly within the first week, whereas there were more extensive changes in the B-cell compartment between 4 and 26 weeks of treatment (Supplementary Table 2A). Specific Components of the Complement System are Rapidly Down-Regulated After Treatment Initiation

To establish the biological mechanisms underlying gene expression changes, we used ingenuity network analysis, limited to 1051 gene entities that were ≥2-fold differentially expressed (corrected P < .05) between any 2 time points and whose expression change remained constant (Supplementary Table 1C). Of these, 780 genes exhibited an early rapid change (ERC) in expression in the diagnosis/week 1 comparison; these were grouped into 23 overlapping networks on the basis of known interactions. Complement components, especially C1q, C2, and Serpin G1 from the classical pathway, featured strongly in the top network (ERC network 1; Figure 3A), along with signaling molecules such as MAP4K4, interleukin1 receptor-associated kinase 3 (IRAK3), zinc finger protein

Figure 1. Changes in blood gene expression occurring during tuberculosis treatment. A, Average hybridization intensities of 4151 gene entities at least 2-fold differentially expressed between at least 2 time points during antituberculosis treatment (P < .05 ANOVA with Benjamini-Hochberg false discovery rate [FDR] correction applied). B, Rapid changes in gene expression after treatment initiation. Individual patient’s hybridization intensities are shown for 1261 gene entities that were at least 2-fold differentially expressed between diagnosis and week 1 of treatment (P < .05). C, Later changes in transcriptomic profiles occurring between week 4 and treatment end (week 26). In total, 549 gene entities were 2-fold differentially expressed (P < .05). There are 207 gene entities in common between the profiles depicted in panels B and C.


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Figure 2. Modular analysis of changes in gene expression during treatment. Twenty-eight modules, containing genes commonly coregulated in blood samples, were obtained from literature [25], and are shown in the main panel grid (M1.1–M3.9), with the key at the bottom indicating the main function of genes within an individual module. The gene expression changes occurring between diagnosis and week-1 samples are shown in central circles, changes between week 1 and week 4 in the middle ring, and between week 4 and week 26 in the outer ring, for each module. The size of the outer circle is proportional to the percentage of differentially expressed genes between diagnosis and week 26 for each module. The proportion of genes differentially expressed between 2 time points in each module was determined using GeneSpring GX11.5 software ( paired Mann–Whitney U test, Benjamini-Hochberg false discovery rate [FDR] correction). The intensity of the color of the circles and rings indicates the proportion of a module that has been differentially expressed: red indicates a decrease and blue an increase as treatment progresses.

274 (ZNF274), and TRAF-interacting protein with forkheadassociated domain (TIFA), which are involved in regulating NFκB activity. Expression of other immunological factors, such as IL-8, CCL23, and IL-5RA, were depressed at diagnosis and increased by week 1. Next, all complement pathway gene entities were examined in individual patients across all time points. All 3 components of C1q, C2, BF, and Serpin G1 were the only genes strongly up-regulated in the majority of patients at diagnosis and down-regulated at week 1. C5, CD55, and CD59 were less dramatically up-regulated at diagnosis, and other components showed inconsistent patterns or negligible change (Figure 3B). Expression levels of all genes in ERC network 1 were determined for each patient over treatment to create gene 22

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expression signatures for genes that were up-regulated or reduced at diagnosis (Figure 3C). The changes in the expression signature were highly significant between diagnosis and week 1, with less dramatic changes occurring later. Thus, measurement of this network provided a clear indication of drug action during the first week of treatment. Changes in Expression of B-Cell Genes Occur During Disease Resolution

Next we sought to characterize biological changes that occurred later in treatment, correlating with cure and disease resolution. Of the 549 gene entities that were ≥2-fold significantly differentially expressed between weeks 4 and 26, there were 373 that did not show a reversion pattern across the

Figure 3. Pathway and network analysis of early rapid changes in gene expression. A, Expression levels of all components of the complement system, as defined in Ingenuity IPA 8.8, were examined in GeneSpring GX11.5, for individual patients. Only gene entities showing deviation from the baseline are shown. B, Network 1 from network analysis of 780 gene entities differentially expressed between diagnosis and week 1. Red color indicates up-regulation at diagnosis and blue indicates down-regulation; color intensity indicates fold-change between time points. C, The hybridization intensities for the genes in network 1 were summed for each patient, with those up-regulated at diagnosis shown in the left panel, and those downregulated shown in the right panel. (***P < .0005 in a Wilcoxon matched pairs signed rank test.)


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Figure 4. Network analysis of late slow changes in gene expression. The 373 gene entities differentially expressed between week 4 and treatment end (week 26) were grouped into 17 networks using Ingenuity IPA 8.8. A, Differential gene expression in combined overlapping networks 1 and 3. Red color indicates higher expression and blue indicates lower expression at week 4 compared to week 26, with intensity reflecting the fold-change. The green rectangle indicates B-cell–related genes. B, Hybridization intensities for the genes in the combined network were summed for each patient, with those up-regulated at week 4 compared to week 26 shown in the left panel, and those down-regulated at week 4 shown in the right panel. (***P < .0005 in a Wilcoxon matched pairs signed rank test.)

whole time course; these were included in the analysis of late/ slow change (LSC) genes. Ingenuity network analysis revealed 2 overlapping networks of genes (Figure 4A). The top network contained mainly genes involved in B-cell biology, such as immunoglobulin M, immunoglobulin D, CD79, CD19, CD22, BLK, and Pax5, that were lower in expression at week 4 than week 26, implying a depletion in peripheral B-cell number or altered gene expression within B cells in tuberculosis. This Bcell network overlapped with a second network, containing 24

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many genes whose products interact with the IFN-γ pathway, such as IFIT2 and IFIT3, Septin 4, and guanylate binding protein 5; these tended to be up-regulated at week 4 and decreased to week 26. Analysis of expression levels of the combined LSC network in individual patients showed expression was not greatly changed initially but changed substantially later in treatment (Figure 4B), indicating correlation with the later sterilizing treatment phase and disease pathology resolution. Table 1 shows the most differentially expressed genes in

Table 1.

Genes Exhibiting the Highest Fold-Change Between Diagnosis and Week 1 and Between Week 4 and Week 26 Diagnosis to Week 1 Comparison

Gene Expression Pattern Up-regulated at earlier time point and decreasing through treatment

Gene

Average Fold Change

Gene

Average Fold Change

KIAA1632

6.49

SLC17A5

5.91

JAK2 CD177

6.20 6.01

RNF213 AFF1

5.03 4.66

DMXL2

5.54

DKFZP761E198

4.65

EFCAB2 METTL7B

5.34 5.30

INPP5D WSB1

4.46 4.35

C1QB

5.08

CWF19L1

4.00

SEPT4 C1QC

4.87 4.68

CD274 SEPT4

3.78 3.59

CFLAR Down-regulated at earlier time point and increasing through treatment

Week 4 to Week 26 Comparison

4.68

PRSS33 OLIG2

−5.50 −5.00

LRRFIP2

3.55 −3.96 −3.75

EIF5B EBF1

IL5R

−3.91

SPIB

−3.16

NOV ITLN1

−3.51 −3.40

IGHD FCRLA

−2.96 −2.94

SPP1

−3.23

PARP15

−2.93

KIT RPS6KA2

−3.10 −3.07

TCL1A KCNH8

−2.93 −2.87

SIGLEC8

−3.01

SEZ6L

−2.85

FGFR2

−2.97

PTPRK

−2.85

Only gene entities that were significantly differentially expressed (P < .05 in ANOVA) are included.

the early and late phases. There was little overlap, with only septin 4 featured in both lists. Prediction of Tuberculosis Treatment Response

Class prediction tools within GeneSpring were used to determine whether a combination of the top ERC and LSC networks, totaling 62 genes (Supplementary Table 2B), could determine a patient’s stage of response. Samples from 18 patients formed a training set, used to build 4 models using commonly applied algorithms, for samples with no treatment, short-term treatment, or complete treatment (Figure 5A). Of these, the neural network model had the highest overall accuracy of 94.4% for the training set, with 51 of 54 samples correctly classified. The samples from the remaining 9 patients formed the test set: the neural network model was able to assign 25 of 27 samples to the correct treatment time point, an accuracy of 92.7% (Figure 5B). Thus a neural network model can be developed to predict tuberculosis treatment response from a small subset of genes.

measured at diagnosis and week 26 (Figure 6A). When the combined 62 gene list was used to hierarchically cluster the independent set samples, the majority of samples segregated into 2 clusters separating the diagnosis and treatmentcompletion samples. One end-of-treatment sample was misclassified with the diagnosis samples (Figure 6B). Finally, the 9 independent set patients were tested using the neural network model developed with the main study training set (Figure 6C). There was 89% accuracy for predicting no treatment, and 89% accuracy for predicting progress through treatment, with 4 patients being perfectly classified. Some posttreatment samples were misallocated to an intermediate time point, likely reflecting technical differences between this independent experiment and the training/test set analysis, such that the overall accuracy was 72%. The one patient not showing treatment-response was also misclassified using hierarchical clustering.

DISCUSSION Gene Expression Patterns in Independent Samples From a Distinct Group of Patients

In an existing microarray data set, from 9 different successfully cured South African tuberculosis patients analyzed independently, expression of the 62 genes in the combined ERC/LSC networks exhibited a similar pattern to the main cohort when

We have shown there are substantial changes in blood gene expression profiles of tuberculosis patients undergoing successful treatment with conventional treatment, with distinct sets of genes being modulated during the early and later stages of treatment, the former likely reflecting the rapid action of


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Figure 5. Predictive models for tuberculosis treatment response. Patients from the main cohort were randomly assigned to a training set (18 patients) or a test set (9 patients). A, Diagnosis, week 1 (short-term treatment) and week 26 (completion of successful treatment) samples were used to build class prediction models using 4 GeneSpring GX11.5 algorithms: support vector machines, naive Bayes, neural network, and partial least-squares discrimination, with default settings. The models were formed using a combined gene list from the early rapid change (ERC; Figure 3) and late/slow change (LSC; Figure 4) biological networks (62 genes). The models were subjected to N-fold internal validation, and the no. of samples from each time point correctly assigned using each model is shown. B, The neural network model was tested using the remaining 9 patients’ samples from the main study cohort. The no. of samples correctly assigned to each time point is shown, along with the percentage accuracy.

isoniazid on actively dividing bacilli, and the later changes the sterilization and disease-resolution phases of treatment, respectively. A neural network model based on expression of a 62 gene list gave almost 93% accuracy of discriminating between no treatment, short-term treatment, and end of successful treatment time points in the test data set. These changes could be developed into field-friendly tools to assess efficacy of new drugs, potentially as a replacement for the EBA as a rapid molecular test, to reduce the cost of clinical trials. Among genes that were substantially down-regulated within the first week of treatment were the specific complement components indicating a rapid drop in the bacterial load, which had been driving their production. In addition to conventional roles in microbial lysis, opsonization, and chemotaxis, specific components of the complement system have complex roles in orchestration of immune responses [26]. C1q is synthesized predominantly by macrophages and dendritic cells [27] but not by monocytes; thus its presence in the blood samples may reflect 26

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expression in activated APCs trafficking between lungs and lymphoid tissue. C1q binding directly to microbes or to antibodyopsonized microbes initiates the classical complement cascade, and mycobacteria-induced complement consumption has been reported [28]. Complement receptor blockade inhibits alveolar macrophage uptake of mycobacteria [29] and bronchoalveolar lavage fluid from healthy donors promotes complement fixation by M. tuberculosis in vitro [30], together showing that complement activation could be beneficial following infection. However, C1q also has separate antiinflammatory properties; deposited C1q inhibits the T-cell stimulatory capacity of dendritic cells, leading to reduced proliferation and IFN-γ production [31] while promoting IL-10 production [32] and helping maintain Tcell tolerance [33]. Moreover, C1q opsonizes apoptotic cells facilitating their phagocytosis, reducing local inflammatory responses [34]. Overproduction of C1q in tuberculosis could contribute to local immunosuppression in lung lesions, permitting survival and replication of M. tuberculosis.

Figure 6. Gene expression patterns in an independent microarray data set. Gene expression in an independent microarray data set, created from 9 different tuberculosis patients from the same South African cohort in an independent experiment. A, Expression of the 62-gene list from the combined early rapid change (ERC) and late/slow change (LSC) networks is shown. The sum of expression for all genes up-regulated at diagnosis is shown for the 9 individual patients in the left panel and for the genes down-regulated at diagnosis in the right panel. (**P < .005 in a Wilcoxon matched pairs signed rank test). B, Diagnosis and treatment-completion samples were hierarchically clustered in GeneSpring, on the basis of expression of the 62 genes from the combined network list. C, The neural network shown in Figure 5 was used to classify the diagnosis and treatment-completed independent samples, according to the predicted duration of treatment received.

Surprisingly, modulation within the humoral arm of the immune system was most significant. The increase in B-cell markers during later stages of therapy likely indicates depletion of the peripheral B-cell pool in active tuberculosis, as observed elsewhere [35, 36]. Differences in B-cell-specific gene expression in blood can contribute to discrimination between active and latent tuberculosis infection [37]; in future studies it would be interesting to determine whether absolute B-cell numbers can predict tuberculosis treatment response. The role of B cells in tuberculosis has been largely overlooked until recently, but discovery of tertiary lymphoid B-cell follicles within human tuberculous granulomas [38], which provide the structure for APC/T-cell/B-cell interaction, has renewed interest. B-cell deficient mice have enhanced tuberculosis susceptibility, especially during acute primary infection, with increased neutrophil influx [39], reducing M. tuberculosis uptake

by macrophages and subsequent antigen presentation. B cells within the follicles may present antigens and secrete cytokines to orchestrate the local response, alongside antibody production. In turn, antibodies may act via opsonization, promoting phagocytosis, activate complement, or modulate cellular responses via binding of immune complexes to activating and inhibitory FcγRs [40]. The late restoration of B-cell gene expression in blood in tuberculosis patients might imply a continuing role during mycobacterial clearance. Changes in gene expression from diagnosis to treatment completion may provide insight into disease pathogenesis, although biomarkers could aid drug development without this understanding [11]. Our findings were substantiated by analysis of gene expression in blood samples from a different independent set of patients. In a parallel study, rapid changes of serum protein marker concentrations have also been detected in peripheral


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blood samples between diagnosis and week 1 (Ronacher, written communication). We successfully employed classification tools and prediction models to predict whether patient samples were collected before, during, or after successful tuberculosis drug treatment using only 62 genes. In the future, such modeling could be combined with low-cost high-throughput assays, to determine efficacy of candidate drugs and regimens. There was one independent-set patient whose results did not fit the treatment-response pattern, possibly due to an unrecognized comorbidity; further work and much larger sample sizes are required to fully evaluate the utility of such models. The changes in gene expression were consistent in a published data set [15], with significant changes in the ERC network detectable between diagnosis and 2 months of treatment, and in the LSC network between 2 and 12 months, in UK patients. In the reverse comparison, gene expression patterns previously reported [15] were identifiable within our data set, despite using different microarray technology. In modular analysis, 21 of 28 modules that varied between tuberculosis patients and healthy controls exhibited highly similar patterns in our tuberculosis treatment-response analysis, with 3 showing minor differences. Modules M1.6 (undetermined function), M1.7 (ribosomal proteins), and M2.7 (undetermined function) were highly represented in our analysis but absent in the previous report [15], whereas module M2.11 (undetermined function) exhibited a reverse pattern; these differences are likely due to differential representation of transcripts in Affymetrix versus Illumina arrays. In total, 78 of 86 genes forming a “TB-specific signature” [15] were significantly differentially expressed in the current study. The high-affinity FCγR (CD64) was highly up-regulated at tuberculosis diagnosis, in accordance with previous literature [17], although FCγR1A, FCγR1B, and FCγR1C expression cannot be distinguished using Affymetrix arrays. Expression of other genes reported to discriminate active from latent tuberculosis [17] changed significantly during treatment, with guanylate binding protein 5 elevated and Rab33A suppressed at diagnosis; the reduction in lactotransferrin expression through treatment failed to reach significance. Future studies will aim to validate our results in prospective samples from distinct patient populations and different geographical locations. Inclusion of additional intermediate time points would facilitate comparison of gene expression changes with 8-week culture conversion. Testing gene expression in patients with tuberculosis treatment failure of tuberculosis relapse, or infected with drug-resistant strains would indicate the robustness of these assays in measuring tuberculosis drug efficacy. Tools predicting successful tuberculosis treatment outcome would revolutionize drug and regiment clinical trials. Quantitative host biomarker measurements have promise, as they may be more sensitive and efficient as surrogate end points [3]. Here, we show large-scale changes in blood gene 28

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expression pattern occur during successful treatment and that this occurs in a phased response, reflecting the complex action of combination therapy on M. tuberculosis bacilli including a rapid bactericidal effect within the first week. Measurement of expression of selected host genes in combination with microbiological measurement and clinical indices could be developed as rapid tests to determine drug efficacy, indicating early bactericidal activity, sterilization, or cure, substantially reducing the cost of clinical trials and expediting licensing. Supplementary Data Supplementary materials are available at The Journal of Infectious Diseases online (http://jid.oxfordjournals.org/). Supplementary materials consist of data provided by the author that are published to benefit the reader. The posted materials are not copyedited. The contents of all supplementary data are the sole responsibility of the authors. Questions or messages regarding errors should be addressed to the author.

Notes Acknowledgments. The authors thank the staff of the Desmond Tutu TB Centre under the leadership of Prof Nulda Beyers, for providing clinical samples and for database support. We are grateful to the clinical staff and patients at Ravensmaed, Uitsig, Elsierriver, Adriaanse, and Leonsdale, and the Cape Town City Health Directorate for permitting this study, and we thank Dr Chris Clayton and Prof John Whittaker for advice on study design. Financial support. This work was funded by the Bill and Melinda Gates Foundation (grant 48941), EDCTP (IP. 09.32040.011 and 2004.1.R. d1), and GlaxoWellcome ActionTB. Potential conflicts of interest. K. D. and P. L. declare they hold shares in GlaxoSmithKline. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

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