- Research article
- Open Access
Meta-analysis of human gene expression in response to Mycobacterium tuberculosis infection reveals potential therapeutic targets
BMC Systems Biology volume 12, Article number: 3 (2018)
With the global emergence of multi-drug resistant strains of Mycobacterium tuberculosis, new strategies to treat tuberculosis are urgently needed such as therapeutics targeting potential human host factors.
Here we performed a statistical meta-analysis of human gene expression in response to both latent and active pulmonary tuberculosis infections from nine published datasets. We found 1655 genes that were significantly differentially expressed during active tuberculosis infection. In contrast, no gene was significant for latent tuberculosis. Pathway enrichment analysis identified 90 significant canonical human pathways, including several pathways more commonly related to non-infectious diseases such as the LRRK2 pathway in Parkinson’s disease, and PD-1/PD-L1 signaling pathway important for new immuno-oncology therapies. The analysis of human genome-wide association studies datasets revealed tuberculosis-associated genetic variants proximal to several genes in major histocompatibility complex for antigen presentation. We propose several new targets and drug-repurposing opportunities including intravenous immunoglobulin, ion-channel blockers and cancer immuno-therapeutics for development as combination therapeutics with anti-mycobacterial agents.
Our meta-analysis provides novel insights into host genes and pathways important for tuberculosis and brings forth potential drug repurposing opportunities for host-directed therapies.
The causative agent of tuberculosis (TB), the bacterium Mycobacterium tuberculosis (Mtb), infects about one third of the world’s population. In 2012, an estimated 8.6 million individuals progressed to active disease and 1.3 million died . The prolonged duration of Mtb infection as well as the alarming emergence of multi-drug resistant strains makes the development of new and effective anti-tubercular therapeutics a global health priority [2, 3].
The severity of TB is largely dependent upon the modulation of human cellular and immunity pathways by the Mtb pathogen. The majority of infected patients develop latent TB (LTB) infection, in which they are asymptomatic with no clinical evidence of disease for years or decades . During latent infection, Mtb is phagocytosed by macrophages, which trigger host immune response involving the recruitment of additional macrophages and monocytes that ultimately form an organized structure called granuloma . Mtb is dormant and non-replicative within granuloma which suppresses the immediate threat of active infection while evading further immune response . Approximately 5–10% of LTB patients will go on to develop active pulmonary TB (PTB), in which Mtb returns to the replication mode and provokes an active host immune response. When the granuloma breaks down, sequestered Mtb can be released into the airway and becomes transmissible to other hosts.
Currently, the standard TB therapy involves a regimen of four antibiotics taken during an initiation phase of two months and a continuation phase of 4–7 months [7, 8]. Despite a high efficacy at the onset of administration, the confounded intervention and prolonged treatment period present challenges for patient compliance and potentially foster the emergence of multidrug-resistance of Mtb. Thus, it is imperative to develop more effective therapeutic approaches such as host-directed therapies. Targeting the host has its advantages in potentially being less susceptible to the drug resistance problem, with greater opportunities for repositioning known drugs to new indications.
Recent genome-wide studies of host-pathogen interactions provide new insights into human genes and pathways modulated by pathogen infection and proliferation. Multiple studies have generated extensive human microarray gene expression datasets for subjects infected by Mtb with the overall objective of developing diagnostic biomarkers [9,10,11]. For example, by comparing 14 public human gene expression datasets for LTB, PTB and other respiratory diseases, Sweeney et al. identified a three-gene set that was robustly diagnostic for PTB . However, to our knowledge, no systematic search for potential host therapeutic targets against TB has been published.
Previously, we applied integrated analysis to human gene expression data in response to respiratory bacterial and viral infections in order to identify novel host targets and potential drug repurposing opportunities [13, 14]. In this study, we performed a statistical meta-analysis on human gene expression data available for Mtb infection to identify common human transcriptomic signatures characteristic of TB. The advantage of meta-analysis lies in its capability to alleviate potential study-specific biases and enhance statistical power to identify robust expression signature through increased sample size. We leveraged other evidence such as human genetic disease associations and drug-repurposing analyses to prioritize individual targets and compounds. We identified multiple host genes and pathways that were significantly altered in TB and propose several potential drug candidates for repurposing.
Data sources, filtering and selection
Human microarray gene expression datasets in response to Mtb infection were retrieved from the National Center for Biotechnology Information’s (NCBI) Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/), by searching ‘Tuberculosis’, ‘Homo sapiens’ and ‘expression profiling by array’. GEO datasets were then filtered based on the following criteria: 1) the gene expression profile was exclusively derived from human cells of tuberculosis patients and probed using a human-based genome array platform; 2) there was at least one control group and patient group in the dataset, with the control group consisting of only healthy subjects, and the patient group consisting of patients only infected by Mtb without other diseases such as HIV; 3) each patient and control group had at least three samples.
Raw gene expression data, study design table and annotation table of each dataset were obtained from the GEO database and processed using ArrayStudio v8.0 (OmicSoft, USA). All datasets retrieved are microarray datasets except for GSE41055, which is an exon array and was excluded from further analysis. Several datasets (GSE42834, GSE56153, GSE31348 and GSE36238) were further excluded due to both a noisy kernel density plot and low within group pairwise correlation (correlation cutoff 0.9 as suggested in , Additional file 1), but were included as independent datasets for validation purposes. After quality filtering, nine microarray datasets (GSE19435, GSE19439, GSE19444, GSE28623, GSE29536, GSE34608, GSE54992, GSE62525 and GSE65517) from either whole blood or peripheral blood mononuclear cells (PBMCs) were retained for further analysis.
We also searched for available datasets of individual human immune cells under in vitro Mtb challenge. We identified three datasets of human dendritic cells (GSE34151, GSE360 and GSE53143) and four datasets of THP-1 human leukemia monocytic cells (GSE17477, GSE29628, GSE51029 and GSE57028), and processed them separately. By comparing gene expression profiles derived from PBMCs or whole blood to those of immune cells, we could evaluate how well blood gene expression patterns recapitulate those of immune cells during Mtb infection.
Quality Control (QC) analysis was performed for each of the nine datasets as described previously . Briefly, samples that were outliers in at least two of the four assessments: 1) kernel density; 2) Principal Component Analysis (PCA); 3) Median Absolute Deviation (MAD) score and; 4) within group pairwise correlation, were excluded from downstream analyses. For example, samples GSM484458 and GSM484465 in GSE19439, and GSM851876 and GSM851889 in GSE34608 were excluded because they were outliers in both MAD score and pairwise correlation (Additional file 2). Samples GSM484369 from GSE19435, and GSM484609 and GSM484629 from GSE19444, were outliers in both pairwise correlation and kernel density. Samples irrelevant to our study design (such as samples from sarcoidosis patients in GSE34608) were also excluded from each dataset. In total, 106 control, 76 LTB and 131 PTB samples from the nine datasets passed the QC criteria for statistical meta-analysis (Table 1).
Data processing and statistical meta-analysis
Data processing and statistical meta-analysis were performed using the web-based tool NetworkAnalyst . Briefly, probe identifiers (IDs) from different microarray platform were converted to Entrez gene IDs. If more than one probe mapped to a gene, the average expression value of these probes was used for that gene. The gene expression level in each comparison group was log2 transformed and auto-scaled. Differential expression analysis for individual datasets was performed using the limma approach , with a false discovery rate (FDR)-adjusted P-value cutoff of 0.05.
The study-specific batch effects were adjusted for the pre-processed datasets using the ComBat procedures as described in [18, 19]. Batch effects corrected individual datasets were then combined and a statistical meta-analysis was performed using INMEX within NetworkAnalyst . We chose to use the combined effect size method for the meta-analysis which generates more conservative and biologically consistent results than the alternative P-value combination method [18, 20]. The random effect model that incorporates cross-study heterogeneity was selected for the meta-analysis because of significant heterogeneity detected using Cochrans’ Q test . Differentially expressed genes (DEGs) were generated using an FDR-adjusted P-value cutoff 0.01 in the meta-analysis. A subset of DEGs with absolute combined effect size greater than 1.5 was selected for genetic variant and DrugBank analyses.
Validation of meta-analysis
To evaluate the robustness of the meta-analysis results, we validated the DEGs in four independent datasets (GSE42834, GSE56153, GSE31348 and GSE36238) using PCA and Partial Least Square Discriminant Analysis (PLS-DA). PLS-DA is a multivariate analysis that establishes a linear regression model between the observed categorical variable and multiple predicting variables. Expression data were scaled to unit variance. Collinearity was addressed using pairwise Pearson’s correlation test. One gene was selected as representative for each group of genes whose expression values were strongly mutually correlated (R2 > 0.5). Significant model components were selected by 7-fold cross validation. The model performance was evaluated in terms of R2, Q2 and the area under the Receiver Operating Characteristic (ROC) curve (AUC). R2 measures the percent of variation of the categorical variable explained by the model. Q2 measures the percent of variation of the categorical variable predicted by the model through 7-fold cross-validation. The ROC curve plots the true positive rate (sensitivity) against the false positive rate (1-specificity) when varied threshold is applied. The AUC values were calculated and compared using pROC package in R .
To explore possible prognostic value of the DEGs, a Kaplan-Meier survival analysis was performed using the top 50 DEGs in the meta-analysis on the largest lung cancer cohort (Lung Meta-base: 1053 samples, 6 cohorts, 22 K genes) in SurvExpress , as significant comorbidities have been observed between lung cancer and TB .
Pathway enrichment analysis
Pathway enrichment analysis was performed for all DEGs from the meta-analysis using MetaCore/MetaBase (GeneGo) v5.0 (Thomson Reuters, https://portal.genego.com/). The P-value for each of the 1480 human canonical pathways in MetaCore was generated using a hypergeometric test with an FDR P-value cutoff of 0.01 . Protein-protein interaction (PPI) network analysis was performed based on the STRING interactome using NetworkAnalyst [16, 26]. Experimental evidence was required with a default confidence score 0.9 for any interaction between two genes. The minimum network mode was chosen for display purposes.
Genetic variant analysis
We searched for any TB-associated single nucleotide polymorphisms (SNPs) proximal to the DEGs. Genetic variants significantly associated with TB (genome-wide significant P-value < 5e-8) were obtained from the Genome-Wide Repository of Associations Between SNPs and Phenotypes (GRASP, http://grasp.nhlbi.nih.gov/) . As usually few non-synonymous coding variants were associated with the genes, we also searched for regulatory SNPs for the DEGs by obtaining their enhancer regions from FANTOM5, a comprehensive resource on active transcripts, transcription factors, enhancers and promoters in mammalian primary cell types and cancer cell lines . The genomic region in linkage disequilibrium (LD) with each SNP was obtained from SNAP (https://www.broadinstitute.org/mpg/snap/) . Any overlap between the genomic locations of a DEG or its FANTOM5 enhancers and the LD region of a SNP would indicate a potential association between the corresponding gene and the SNP.
DrugBank and connectivity map analysis
Drug-target links between public compounds and the DEGs were obtained from DrugBank Version 4.5 (http://www.drugbank.ca/) . Only launched drugs with experimental or clinical evidence of direct interaction with the encoded gene product were included. Drug-target links were excluded if the drugs have unknown pharmacological actions or pharmacological actions of the same direction to the differential expression of the targets in TB signature.
Drug repurposing analysis was performed using the Connectivity Map (CMAP, https://www.broadinstitute.org/cmap/) . The 250 top-ranked and 250 bottom-ranked DEGs from the meta-analysis were used for generating gene expression signature as recommended by Iorio et al. , which was then compared against the gene expression signatures of all Broad CMAP compounds. Significant CMAP compounds against TB were identified based on the following criteria: 1) The enrichment score of the compound was less than 0 for inversely matched drug and disease signatures; 2) FDR-adjusted P-value < 0.05; 3) The compound specificity was less than 0.1. The target, mechanism of action, therapy area and indication for each compound were obtained from PubChem (http://pubchem.ncbi.nlm.nih.gov/) and DrugBank . Antibacterial compounds and compounds with no clinical use were excluded from the drug annotation.
Statistical meta-analysis for differentially expressed genes during PTB
We employed an iterative process of database querying, filtering and computational analyses for all datasets (Fig. 1). At the time of this study (October 2016), there were 85 GEO microarray datasets available for TB-related host responses (Additional file 3). After filtering based on dataset inclusion criteria (see Methods), a final set of nine in vivo patient datasets including nine PTB and five LTB comparisons was selected for downstream analyses (Table 1). We also identified three datasets of human dendritic cells and four datasets of human THP-1 cells under in vitro Mtb challenge, and compared their expression signatures to the TB patient signatures. The complete list of the 85 GEO datasets and their annotations are shown in Additional file 3.
Statistical meta-analysis of the nine datasets was performed using NetworkAnalyst . For PTB comparisons, a total number of 16,703 genes common to all nine datasets were identified and a combined master dataset was generated. Batch effect was corrected using the ComBat approach . PCA indicates that all samples were tightly clustered according to the specific study prior to the batch effect correction. After the correction, samples from different studies were intermixed and now clustered primarily based on PTB and control groups (Additional file 4). The samples do not cluster based on sample type in either PCA plot before or after batch effect correction (Additional file 4). A total of 1655 DEGs were identified in the meta-analysis under an FDR-adjusted P-value cutoff 0.01 (Additional file 5). Of these, a subset of 407 DEGs had an absolute combined effect size as a reference for the log2 fold change greater than 1.5 (Fig. 2). For LTB comparisons, no gene was retained using the same criterion in the meta-analysis of the five datasets.
Identification of significantly enriched pathways
The 1655 significant DEGs were then analyzed for enriched functional pathways using MetaCore v5.0, a high quality, manually curated protein-interaction database supported by ‘small experiment’ evidence. In total, 90 human canonical pathways were significantly enriched from the DEGs (FDR-adjusted P < 0.01) (Table 2), many of which were involved in host immune response. The most significant pathway was ‘IFN type I signaling pathway’ (FDR-adjusted P = 3e-9, Additional file 6) which is a known host response to TB , with 17 DEGs in 37 pathway components. Notably, there was significant enrichment for several pathways more commonly associated with non-infectious diseases. These include the leucine-rich repeat kinase 2 (LRRK2) pathway in Parkinson’s disease (FDR-adjusted P = 9.4e-5, Fig. 3) and PD-1/PD-L1 (Programmed Death 1/PD-Ligand 1) signaling pathway (FDR-adjusted P = 6.5e-5, Fig. 4). A few pathways were associated with other diseases such as asthma, chronic obstructive pulmonary disease and dermatitis, albeit all with sparsely connected genes.
To explore the interactive relationships between the 1655 DEGs, we performed a protein-protein interaction network analysis based on the STRING interactome database using NetworkAnalyst [16, 26]. We obtained a subnetwork containing 1727 edges and 740 nodes, 364 of which were DEGs (Fig. 5a). Among the DEGs, 42 genes directly interacted with each other, with LCK and STAT3 having the highest degree of connectivity (Fig. 5b). Among the functional partners of the DEGs, the Ubiquitin C (UBC) gene was most highly connected in the network by interacting with 112 other genes, 110 of which were DEGs (Fig. 5a).
Validation of the meta-analysis
To evaluate the reproducibility of our results, we validated the 407 DEGs with log2 fold changes greater than 1.5 in four additional microarray datasets (GSE42834, GSE56153, GSE31348 and GSE36238). The 407 genes yielded a clear separation of PTB and control for all four datasets in PCA (Additional file 7a). Using PLS-DA, the 407 genes showed good sensitivity (above 83%) and specificity (above 82%) in all four independent datasets, and had a significantly greater AUC than a random set of 407 genes in all datasets except GSE36238, which had a small sample size (Additional file 7b). Accordingly, R2 and Q2 measures of model quality and predictability, respectively, were markedly greater for the 407 gene set than the random gene set in all four datasets (Additional file 7c).
We also performed a separate meta-analysis on the in vitro datasets available for Mtb infection. In total, 2535 and 2911 DEGs were identified for dendritic and THP-1 cells datasets respectively, from which 129 and 192 pathways were significantly enriched (FDR-adjusted P < 0.01, Additional file 8). Of them, 335 genes (13.2%) in dendritic datasets and 414 (14.2%) genes in THP-1 datasets overlap with the 1655 DEGs in patient blood expression signature (Additional files 8, 9). Some 28 pathways (15.6%) in dendritic datasets and 44 pathways (22.9%) in THP-1 datasets were shared with patient blood datasets, including key pathways of IFN type I, PKR and PD-1 signaling (Table 2, Additional file 9). We noted that DEGs shared with patient blood profiles had a significantly higher magnitude of fold change both in dendritic and THP-1 cells than the rest of DEGs (dendritic: average absolute fold change 1.903 versus 1.639, t-test P = 8.2e-5, THP-1: 0.802 versus 0.735, t-test P = 9.2e-5). MetaCore pathway enrichment of the 335 genes shared between blood and dendritic cells identified several dendritic cell related pathways such as ‘Antigen presentation by MHC class I’ and ‘Role of HMGB1 in dendritic cell maturation and migration’ among the most significant pathways (Additional file 8).
To explore possible prognostic value of the DEGs, we performed a Kaplan-Meier survival analysis using the top 50 significant DEGs in the meta-analysis on the largest lung cancer cohort in SurvExpress , because significant comorbidities have been observed between lung cancer and TB . The top 50 genes showed a significant prognostic feature on lung cancer survival with a Risk Group Hazard Ratio 2.01 (P = 4.16e-13, Additional file 10).
Targets with human genetic evidence
Early selection of drug targets with human genetic support for involvement with disease pathology could significantly increase the success rate of late phase clinical development . We sought genetic evidence for the 407 DEGs in public genome-wide association study (GWAS) datasets by looking at their individual chromosomal proximity to TB-associated SNPs. A total of 547 TB-related SNPs (genome wide significant P < 5e-8) were identified in GRASP, a deeply extracted and annotated GWAS database . For each SNP, we overlapped its LD region with the genomic locations of all 407 DEGs. We also searched for regulatory variants for the genes using their enhancer regions from FANTOM5 . Any overlap between the region in LD with a SNP and the locations of a DEG or its FANTOM5 enhancers would suggest a potential association between the SNP and that gene. Of the 407 DEGs, 48 genes were proximal to at least one TB-related SNP (Table 3). The most significant variant was rs3948464 (P = 5.9e-37), located within the gene SP110 on chromosome 2, which is a known host genetic susceptibility for TB . This was followed by rs3129750 (P = 2.5e-22) proximal to HLA-DPA1, PSMB8, PSMB9, TAP1 and TAP2 genes located on chromosome 6.
Drug repurposing opportunities
We queried each of the 407 DEGs against the DrugBank database  in order to identify any launched drugs targeting these genes that might be potentially repurposed against TB. A total of 19 drug-target links were identified between 14 public drugs and 16 DEGs (Table 4). Each compound was associated with one gene target except for Carfilzomib, which is an inhibitor for multiple proteosome components (PSMB2, PSMB8, PSMB9 and PSMB10), and Intravenous Immunoglobulin (IVIg), which targets three DEGs (C5, FCGR2A and FCGR3A) as either a receptor binder or antagonist.
CMAP is another computational tool for drug repurposing which utilizes the anti-correlation relationships between gene expression signatures in diseases and drug perturbations . We used the PTB gene expression signature to recover the anti-correlation signatures of ~5000 small-molecule compounds and ~3000 genetic reagents from the Broad Institute CMAP library. Thirteen compounds were found to have a significantly anti-correlated signature to the PTB signature (FDR-adjusted P < 0.05, Specificity < 0.1, Table 5). Six of these drugs were cardiovascular related therapeutics, including two sodium channel blockers (Disopyramide, Mephenytoin), two voltage-gated calcium channel blockers (Flunarizine, Adenosine Phosphate) and one ATP-sensitive potassium channel blocker (Acetohexamide) (Table 5).
We performed a meta-analysis on public human microarray datasets to identify host genes and pathways important for TB that might be amenable to therapeutic modulation. In our study, a plethora of DEGs and pathways were identified in PTB infection. In comparison, no known host process was identified in LTB. This is consistent with a paucity of host immune response to Mtb during the latent stage  and highlights the challenges of LTB detection and treatment.
Blood gene expression represents a mixture of immune cell subpopulations, which renders the question whether expression signatures derived from whole blood or PBMCs could recapitulate those of individual cell types. Overall, the similarity between gene expression signatures of Mtb challenged patient blood and the immune cell subsets was modest, which is in agreement with previous observations of large variation in expression profiles among different immune cell types [36, 37]. Nevertheless, dendritic or THP-1 cell-types DEGs shared with those found in blood have significantly higher fold changes compared to non-shared DEGs, which implicates that genes bearing a stronger expression signal in individual cell type could be more readily detected in the broader blood expression profiles. Moreover, the finding that common genes between blood and dendritic cells were enriched in cell type specific pathways indicates that blood gene expression signatures at least partially recapitulate the major signals from these immune cell types when subjected to Mtb exposure.
Our meta-analysis supports multiple known host responses to Mtb infection and suggests several novel host targets and drug repurposing opportunities. We identified numerous IFN-inducible genes with the IFN-signaling pathway most significantly enriched, which is consistent with previous findings on the predominant response of this pathway to TB infection [9, 10]. Among the IFN-inducible genes, HLA-DPA1, PSMB8, PSMB9, TAP1 and TAP2 were proximal to rs3129750, a highly significant genetic variant associated with TB (P = 2.51e-22), providing genetic support for these genes as potential host targets. PSMB8 and PSMB9 are the targets of Carfilzomib, a proteasome inhibitor for multiple myeloma.
We found evidence for TB activation of several pathways more commonly associated with other non-infectious diseases, which could be informative for drug repurposing opportunities. For example, the LRRK2 pathway linked to Parkinson’s disease and PD-1 signaling pathway in immuno-oncology were both significantly enriched during PTB infection. LRRK2 was a highly significant DEG (fold change = 1.73, FDR-adjusted P = 1.7e-4) in the meta-analysis and directly interacts with seven other DEGs in the pathway, including two components in the NRON complex through which LRRK2 inhibits the immune response transcription factor NFAT1 . The LRRK2 gene is associated with both Parkinson’s disease and susceptibility to Mycobacterium leprae infection [39, 40]. A recent patient cohort analysis revealed a 1.38-fold risk of Parkinson’s disease in TB patients independent of other clinical factors, suggesting the co-morbidity between the two diseases . In addition, 58 genetic variants proximal to the 407 DEGs were found to be associated with Parkinson’s disease in public GWAS datasets (P < 1e-4, Additional file 11), thus providing some preliminary genetic evidence for potential common molecular mechanisms shared by both diseases.
PD-1 is the key immune checkpoint receptor that mediates T-cell immuno-suppression in cancer. The PD-1/PD-Ls pathway is known to inhibit T-cell effector function during PTB infection [42, 43]. Both PD-L1 and PD-L2 genes were significantly up-regulated, and nine DEGs in T cells were down-regulated, indicating a PD-Ls mediated immune suppression is likely active in tuberculosis. Among the existing drugs, both Muromonab and Atezolizumab target DEGs (CD247 and CD274/PD-L1) in the pathway (Table 4). New drugs that block PD-1/PD-Ls interaction in order to enhance the T-cell adaptive immune response against tumors are revolutionizing oncology medicine . Several anti-CTLA-4/PD-1/TIM3/LAG3 compounds are under investigation as repurposing candidates against TB  (Table 6). Our results support the hypothesis that overcoming T-cell exhaustion could be a common therapeutic strategy for both cancer immuno-therapy and TB .
To gain insight into the functional relationships between the TB-related genes, we depict a protein-protein interaction network for the 1655 DEGs. The network was predominated by a few hub nodes that were highly connected to multiple other genes. Of them, the UBC gene has the highest degree of connectivity by associating with 112 genes, suggesting that UBC, although its expression was not significantly altered, could play a central role in TB through regulating the expression of numerous TB-related host factors. UBC is a stress inducible gene and is one of the four genes encoding for human ubiquitin . Mtb is known to suppress host innate immunity through the ubiquitin system . Modulating host ubiquitin pathways could be another important strategy to reactivate host innate response against Mtb.
Our searches of DrugBank using the 407 DEGs revealed 19 highly supported drug-target links encompassing 14 drugs and 16 targets. Among these, the drug intravenous immunoglobulin or IVIg was found targeting two Fc receptors and one complement component as either an antagonist or a receptor binder, thus supporting the phagocytic pathway of Mtb as a potential druggable target. IVIg is used to treat patients with primary immunodeficiency and has been applied to a wide range of autoimmune and inflammatory conditions . An in vivo mouse experiment showed the efficacy of high-dose IVIg in substantially reducing the bacterial load during Mtb infection . Our results suggest that this could be accomplished through enhancing the complement system and inhibiting Fc receptors which subsequently initiate phagocytosis to clear the bacillus .
From our CMAP analysis, we identified several public compounds that could be potentially repurposed for TB. Of particular interest are two sodium channel blockers and two voltage-gated calcium channel blockers. It has been suggested that voltage-gated calcium channels regulate the host immune response to Mtb by enhancing the pro-inflammatory gene expression and activating T-cells . The sodium channel NaV1.5 encoded by SCN5A, the drug target of Disopyramide that was top ranked in our analysis, regulates the spatial and temporal calcium signaling during mycobacterial phagocytosis . Indeed, studies screening host-targeted inhibitors demonstrated the efficacy of several calcium and sodium channel blockers in restricting mycobacterial growth both in vitro and in macrophages . Sodium or calcium channel blockers such as Carbamazepine and Verapamil are under investigation for TB treatment  (Table 6). Further experimental validation of both IVIg and ion channel blockers is merited to explore their application in TB host-directed therapies.
Previously, we performed meta-analysis of human gene expression datasets to identify host targets and drug repositioning opportunities against respiratory bacterial and viral infections [13, 14]. In this study, we applied an overall similar strategy to identify same opportunities for the treatment of TB. Despite an overall common data analysis methodology, there are multiple novelties in the current study compared to our early work in respiratory bacterial and viral infections. First, instead of using a vote-counting method with arbitrary cutoffs on the number of studies in which a gene was declared significant, we employed the combining effect size method of the statistical meta-analysis which essentially yields one single biologically interpretable measure - the pooled effect size (and P-value) of differential expression. The combining effect size method has been suggested as the most comprehensive approach for meta-analysis of two-class gene expression microarrays . Second, we assessed the robustness of the meta-analysis results by cross-validation in other independent patient blood datasets, and comparison to in vitro cell type specific datasets. Third, additional genetic evidence from public GWAS datasets was used in this study to further prioritize individual DEGs as potential therapeutic targets for TB. Fourth, a PPI network was employed to obtain protein-protein interaction modules important in TB and generate novel targets (i.e. UBC) that may play a role in regulating the expression of multiple TB-related host factors. Fifth, with respect to drug repurposing analysis, CMAP analysis was performed to generate additional hypotheses based on anti-correlation relationships between gene expression signatures in diseases and drug perturbations (Table 5).
In summary, our meta-analysis provides new insights into host genes and pathways important for TB infection and brings forth potential drug repurposing opportunities for host-directed therapies (perhaps as potential combination therapies with anti-mycobacterium drugs). The potential host targets and compounds proposed in the study were listed in Table 6. Several testable hypotheses from our study are: 1) LRRK2 inhibition reduces Mtb load in macrophages; 2) blocking PD-L1 reactivates T-cell exhaustion against Mtb infected dendritic cells and; 3) ion-channel blockers as repurposed drugs to enhance T-cell activation and suppress Mtb growth in macrophages. Experimental validation of these hypotheses would be the next step taking forward the proposed targets and compounds into the drug development pipeline.
Differentially expressed gene
False discovery rate
Gene expression omnibus
Genome-wide repository of associations between SNPs and phenotypes
Genome-wide association studies
- LRRK2 :
Leucine-rich repeat kinase 2
Median absolute deviation
Major histocompatibility complex
- Mtb :
Peripheral blood mononuclear cell
Principal component analysis
- PKR :
Protein kinase R
Active pulmonary tuberculosis
World Health Organization. Global tuberculosis report 2016. Ref type: report. Geneva: World Health Organization; 2016. p. 1–306. http://apps.who.int/medicinedocs/documents/s23098en/s23098en.pdf.
Karlas A, Machuy N, Shin Y, Pleissner KP, Artarini A, Heuer D, et al. Genome-wide RNAi screen identifies human host factors crucial for influenza virus replication. Nature. 2010;463(7282):818–22. doi: https://doi.org/10.1038/nature08760.
Kumar D, Nath L, Kamal MA, Varshney A, Jain A, Singh S, et al. Genome-wide analysis of the host intracellular network that regulates survival of mycobacterium tuberculosis. Cell. 2010;140(5):731–43. doi: https://doi.org/10.1016/j.cell.2010.02.012.
Lin PL, Flynn JL. Understanding latent tuberculosis: a moving target. J Immunol. 2010;185(1):15–22. doi: https://doi.org/10.4049/jimmunol.0903856.
Wayne LG, Sohaskey CD. Nonreplicating persistence of mycobacterium tuberculosis. Ann Rev Microbiol. 2001;55:139–63. doi: https://doi.org/10.1146/annurev.micro.55.1.139.
Lillebaek T, Dirksen A, Vynnycky E, Baess I, Thomsen VO, Andersen AB. Stability of DNA patterns and evidence of mycobacterium tuberculosis reactivation occurring decades after the initial infection. J Infect Dis. 2003;188(7):1032–9. doi: https://doi.org/10.1086/378240.
Mitchison DA. Antimicrobial therapy of tuberculosis: justification for currently recommended treatment regimens. Semin Respiry Crit Care Med. 2004;25(3):307–15. doi: https://doi.org/10.1055/s-2004-829503.
Zhang Y. The magic bullets and tuberculosis drug targets. Ann Rev Pharmacol Toxicol. 2005;45:529–64. doi: https://doi.org/10.1146/annurev.pharmtox.45.120403.100120.
Berry MP, Graham CM, McNab FW, Xu Z, Bloch SA, Oni T, et al. An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature. 2010;466(7309):973–7. doi: https://doi.org/10.1038/nature09247.
Maertzdorf J, Weiner J 3rd, Mollenkopf HJ, Network TB, Bauer T, Prasse A, et al. Common patterns and disease-related signatures in tuberculosis and sarcoidosis. Proc the Natl Acad Sci U S A. 2012;109(20):7853–8. doi: https://doi.org/10.1073/pnas.1121072109.
Lee SW, Wu LS, Huang GM, Huang KY, Lee TY, Weng JT. Gene expression profiling identifies candidate biomarkers for active and latent tuberculosis. BMC bioinformatics. 2016;17(Suppl 1):3. doi: https://doi.org/10.1186/s12859-015-0848-x.
Sweeney TE, Braviak L, Tato CM, Khatri P. Genome-wide expression for diagnosis of pulmonary tuberculosis: a multicohort analysis. Lancet Respir Med. 2016;4(3):213–24. doi: https://doi.org/10.1016/S2213-2600(16)00048-5.
Smith SB, Dampier W, Tozeren A, Brown JR, Magid-Slav M. Identification of common biological pathways and drug targets across multiple respiratory viruses based on human host gene expression analysis. PLoS One. 2012;7(3):e33174. doi: https://doi.org/10.1371/journal.pone.0033174.
Smith SB, Magid-Slav M, Brown JR. Host response to respiratory bacterial pathogens as identified by integrated analysis of human gene expression data. PLoS One. 2013;8(9):e75607. doi: https://doi.org/10.1371/journal.pone.0075607.
Chen K. Microarray data analysis using Arraystudio. 2006.
Xia J, Gill EE, Hancock RE. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat Protoc. 2015;10(6):823–44. doi: https://doi.org/10.1038/nprot.2015.052.
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. doi: https://doi.org/10.1093/nar/gkv007.
Zhou G, Stevenson MM, Geary TG, Xia J. Comprehensive Transcriptome meta-analysis to characterize host immune responses in Helminth infections. PLoS Negl Trop Dis. 2016;10(4):e0004624. doi: https://doi.org/10.1371/journal.pntd.0004624.
Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8(1):118–27. doi: https://doi.org/10.1093/biostatistics/kxj037.
Marot G, Foulley JL, Mayer CD, Jaffrezic F. Moderated effect size and P-value combinations for microarray meta-analyses. Bioinformatics. 2009;25(20):2692–9. doi: https://doi.org/10.1093/bioinformatics/btp444.
Cochran W. The combination of estimates from different experiments. Biometrics. 1954;10:101–29.
Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77. doi: https://doi.org/10.1186/1471-2105-12-77.
Aguirre-Gamboa R, Gomez-Rueda H, Martinez-Ledesma E, Martinez-Torteya A, Chacolla-Huaringa R, Rodriguez-Barrientos A, et al. SurvExpress: an online biomarker validation tool and database for cancer gene expression data using survival analysis. PLoS One. 2013;8(9):e74250. doi: https://doi.org/10.1371/journal.pone.0074250.
Varol Y, Varol U, Unlu M, Kayaalp I, Ayranci A, Dereli MS, et al. Primary lung cancer coexisting with active pulmonary tuberculosis. Int J Tuberc Lung Dis. 2014;18(9):1121–5. doi: https://doi.org/10.5588/ijtld.14.0152.
Draghici S, Khatri P, Martins RP, Ostermeier GC, Krawetz SA. Global functional profiling of gene expression. Genomics. 2003;81(2):98–104.
Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43(Database issue):D447–52. doi: https://doi.org/10.1093/nar/gku1003.
Leslie R, O'Donnell CJ, Johnson AD. GRASP: analysis of genotype-phenotype results from 1390 genome-wide association studies and corresponding open access database. Bioinformatics. 2014;30(12):i185–94. doi: https://doi.org/10.1093/bioinformatics/btu273.
Kawaji H, Severin J, Lizio M, Waterhouse A, Katayama S, Irvine KM, et al. The FANTOM web resource: from mammalian transcriptional landscape to its dynamic regulation. Genome Biol. 2009;10(4):R40. doi: https://doi.org/10.1186/gb-2009-10-4-r40.
Johnson AD, Handsaker RE, Pulit SL, Nizzari MM, O'Donnell CJ, de Bakker PI. SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics. 2008;24(24):2938–9. doi: https://doi.org/10.1093/bioinformatics/btn564.
Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006;34(Database issue):D668–72. doi: https://doi.org/10.1093/nar/gkj067.
Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313(5795):1929–35. https://doi.org/10.1126/science.1132939.
Iorio F, Bosotti R, Scacheri E, Belcastro V, Mithbaokar P, Ferriero R, et al. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc Natl Acad Sci U S A. 2010;107(33):14621–6. https://doi.org/10.1073/pnas.1000138107.
Novikov A, Cardone M, Thompson R, Shenderov K, Kirschman KD, Mayer-Barber KD, et al. Mycobacterium tuberculosis triggers host type I IFN signaling to regulate IL-1beta production in human macrophages. J Immunol. 2011;187(5):2540–7. https://doi.org/10.4049/jimmunol.1100926.
Nelson MR, Tipney H, Painter JL, Shen J, Nicoletti P, Shen Y, et al. The support of human genetic evidence for approved drug indications. Nat Genet. 2015;47(8):856–60. https://doi.org/10.1038/ng.3314.
Lei X, Zhu H, Zha L, Wang Y. SP110 gene polymorphisms and tuberculosis susceptibility: a systematic review and meta-analysis based on 10 624 subjects. Infect Genet Evol. 2012;12(7):1473–80. https://doi.org/10.1016/j.meegid.2012.05.011.
Conkle JL, Lattao C, White JR, Cook RL. Competitive sorption and desorption behavior for three fluoroquinolone antibiotics in a wastewater treatment wetland soil. Chemosphere. 2010;80(11):1353–9. https://doi.org/10.1016/j.chemosphere.2010.06.012.
McLaren PJ, Mayne M, Rosser S, Moffatt T, Becker KG, Plummer FA, et al. Antigen-specific gene expression profiles of peripheral blood mononuclear cells do not reflect those of T-lymphocyte subsets. Clin Diagn Lab Immunol. 2004;11(5):977–82. https://doi.org/10.1128/CDLI.11.5.977-982.2004.
Liu Z, Lee J, Krummey S, Lu W, Cai H, Lenardo MJ. The kinase LRRK2 is a regulator of the transcription factor NFAT that modulates the severity of inflammatory bowel disease. Nat Immunol. 2011;12(11):1063–70. https://doi.org/10.1038/ni.2113.
Zhang FR, Huang W, Chen SM, Sun LD, Liu H, Li Y, et al. Genomewide association study of leprosy. N Engl J Med. 2009;361(27):2609–18. https://doi.org/10.1056/NEJMoa0903753.
Oosterveld LP, Allen JC Jr, Ng EY, Seah SH, Tay KY, Au WL, et al. Greater motor progression in patients with Parkinson disease who carry LRRK2 risk variants. Neurology. 2015;85(12):1039–42. https://doi.org/10.1212/WNL.0000000000001953.
Shen CH, Chou CH, Liu FC, Lin TY, Huang WY, Wang YC, et al. Association between tuberculosis and Parkinson disease: a Nationwide, population-based cohort study. Medicine. 2016;95(8):e2883. https://doi.org/10.1097/MD.0000000000002883.
Yin W, Tong ZH, Cui A, Zhang JC, Ye ZJ, Yuan ML, et al. PD-1/PD-Ls pathways between CD4(+) T cells and pleural mesothelial cells in human tuberculous pleurisy. Tuberculosis. 2014;94(2):131–9. https://doi.org/10.1016/j.tube.2013.10.007.
Shen L, Gao Y, Liu Y, Zhang B, Liu Q, Wu J, et al. PD-1/PD-L pathway inhibits M.Tb-specific CD4+ T-cell functions and phagocytosis of macrophages in active tuberculosis. Sci Rep. 2016;6:38362. https://doi.org/10.1038/srep38362.
Dolan DE, Gupta S. PD-1 pathway inhibitors: changing the landscape of cancer immunotherapy. Cancer Control. 2014;21(3):231–7. https://doi.org/10.1177/107327481402100308.
Zumla A, Maeurer M, Host-Directed Therapies N, Chakaya J, Hoelscher M, Ntoumi F, et al. Towards host-directed therapies for tuberculosis. Nat Rev Drug Discov. 2015;14(8):511–2. https://doi.org/10.1038/nrd4696.
Pauken KE, Wherry EJ. Overcoming T cell exhaustion in infection and cancer. Trends Immunol. 2015;36(4):265–76. doi: https://doi.org/10.1016/j.it.2015.02.008.
Radici L, Bianchi M, Crinelli R, Magnani M. Ubiquitin C gene: structure, function, and transcriptional regulation. Adv Biosci Biotechnol. 2013;4:1057–62.
Wang J, Li BX, Ge PP, Li J, Wang Q, Gao GF, et al. Mycobacterium tuberculosis suppresses innate immunity by coopting the host ubiquitin system. Nat Immunol. 2015;16(3):237–45. https://doi.org/10.1038/ni.3096.
Hartung HP, Mouthon L, Ahmed R, Jordan S, Laupland KB, Jolles S. Clinical applications of intravenous immunoglobulins (IVIg)--beyond immunodeficiencies and neurology. Clin Exp Immunol. 2009;158(Suppl 1):23–33. https://doi.org/10.1111/j.1365-2249.2009.04024.x.
Roy E, Stavropoulos E, Brennan J, Coade S, Grigorieva E, Walker B, et al. Therapeutic efficacy of high-dose intravenous immunoglobulin in mycobacterium tuberculosis infection in mice. Infect Immun. 2005;73(9):6101–9. https://doi.org/10.1128/IAI.73.9.6101-6109.2005.
Jacob S, Rajabally YA. Current proposed mechanisms of action of intravenous immunoglobulins in inflammatory neuropathies. Curr Neuropharmacol. 2009;7(4):337–42. https://doi.org/10.2174/157015909790031166.
Gupta S, Salam N, Srivastava V, Singla R, Behera D, Khayyam KU, et al. Voltage gated calcium channels negatively regulate protective immunity to mycobacterium tuberculosis. PLoS One. 2009;4(4):e5305. https://doi.org/10.1371/journal.pone.0005305.
Carrithers LM, Hulseberg P, Sandor M, Carrithers MD. The human macrophage sodium channel NaV1.5 regulates mycobacteria processing through organelle polarization and localized calcium oscillations. FEMS Immunol Med Microbiol. 2011;63(3):319–27. https://doi.org/10.1111/j.1574-695X.2011.00853.x.
Stanley SA, Barczak AK, Silvis MR, Luo SS, Sogi K, Vokes M, et al. Identification of host-targeted small molecules that restrict intracellular mycobacterium tuberculosis growth. PLoS Pathog. 2014;10(2):e1003946. https://doi.org/10.1371/journal.ppat.1003946.
Ramasamy A, Mondry A, Holmes CC, Altman DG. Key issues in conducting a meta-analysis of gene expression microarray datasets. PLoS Med. 2008;5(9):e184. https://doi.org/10.1371/journal.pmed.0050184.
Ward JH Jr. Hierarchical grouping to optimize an objective function. J Am Stat Assoc. 1963;58:236–44.
We thank David Mayhew of Computational Biology, GSK for bioinformatics advice and Reinhard C. Laubenbacher of Jackson Laboratory for insightful discussions.
ZW was supported by the GSK Early Talent Post-Doctoral Fellow.
Availability of data and materials
All data generated or analysed during this study are included in this published article and its supplementary information files.
Ethics approval and consent to participate
Consent for publication
ZW, SA, JRB and MMS were employees in GlaxoSmithKline at the time of this study.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The quality control analysis for (A) GSE56153 and (B) GSE19435. The kernel density plot and heatmap for within-group pairwise correlation are shown for both datasets. Both a noisy kernel density plot and extremely low within-group pairwise correlation were observed for GSE56153. (PDF 827 kb)
An example of quality control analysis for (A) GSE19439 and (B) GSE34608. Within group pairwise correlation and MAD score plots are shown for both datasets. Outlier samples are highlighted in red and indicated by arrows. (PDF 455 kb)
List of the 85 GEO datasets available for TB-related host responses at the time of the study, their inclusion (color shaded) or exclusion in the meta-analysis and the reasons for their exclusion. (XLSX 23 kb)
PCA plots before (A) and after (B) batch effect correction. (PDF 422 kb)
List of 1655 DEGs and their fold changes and FDR P-values in the meta-analysis. (XLSX 73 kb)
Pathway map for “IFN type I signaling pathway”. Significant up-regulation of genes was denoted as up-pointing bars colored in red, and significant down-regulation of genes was denoted as down-pointing bars colored in blue. The length of the colored bar was proportional to the fold change of the gene in the meta-analysis. (PDF 1079 kb)
Validation of the 407 DEGs in four independent datasets. (A). PCA using the 407 DEGs showed a clear separation of PTB and control samples in all four datasets. PLS-DA using the 407 DEGs showed significantly better model performance in classifying PTB and control samples than a random set of 407 genes in terms of (B) Area under ROC and (C) R2 and Q2. (PDF 614 kb)
List of DEGs and pathways in the meta-analysis of in vitro dendritic and THP-1 datasets, the DEGs shared with patient blood datasets, and the enriched MetaCore pathways from the shared DEGs. (XLSX 292 kb)
Venn diagram for the overlap in DEGs and pathways between patient blood and in vitro dendritic and THP-1 datasets. (PDF 370 kb)
Kaplan-Meier survival analysis plot showing a significant prognostic feature of the top 50 DEGs in the meta-analysis on the survival of the largest lung cancer cohort in SurvExpress . (PDF 163 kb)
List of 58 Parkinson’s disease-associated genetic variants proximal to the 407 DEGs. (XLSX 12 kb)
About this article
Cite this article
Wang, Z., Arat, S., Magid-Slav, M. et al. Meta-analysis of human gene expression in response to Mycobacterium tuberculosis infection reveals potential therapeutic targets. BMC Syst Biol 12, 3 (2018). https://doi.org/10.1186/s12918-017-0524-z
- Host-direct therapies
- Gene expression signature
- Parkinson’s disease
- Drug repurposing