Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Host Responses to Melioidosis and Tuberculosis Are Both Dominated by Interferon-Mediated Signaling

  • Gavin C. K. W. Koh ,

    gavin.koh@gmail.com

    Affiliations Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom, Department of Medicine, University of Cambridge, Cambridge, United Kingdom, Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand, Department of Infection and Tropical Diseases, Birmingham Heartlands Hospital, Birmingham, United Kingdom

  • M. Fernanda Schreiber,

    Affiliation Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom

  • Ruben Bautista,

    Affiliation Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom

  • Rapeephan R. Maude,

    Affiliation Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand

  • Susanna Dunachie,

    Affiliation Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand

  • Direk Limmathurotsakul,

    Affiliation Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand

  • Nicholas P. J. Day,

    Affiliation Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand

  • Gordon Dougan,

    Affiliation Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom

  • Sharon J. Peacock

    Affiliations Wellcome Trust Sanger Institute, Hinxton, Cambridge, United Kingdom, Department of Medicine, University of Cambridge, Cambridge, United Kingdom, Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand, Department of Microbiology, Mahidol University, Bangkok, Thailand

Host Responses to Melioidosis and Tuberculosis Are Both Dominated by Interferon-Mediated Signaling

  • Gavin C. K. W. Koh, 
  • M. Fernanda Schreiber, 
  • Ruben Bautista, 
  • Rapeephan R. Maude, 
  • Susanna Dunachie, 
  • Direk Limmathurotsakul, 
  • Nicholas P. J. Day, 
  • Gordon Dougan, 
  • Sharon J. Peacock
PLOS
x

Abstract

Melioidosis (Burkholderia pseudomallei infection) is a common cause of community-acquired sepsis in Northeast Thailand and northern Australia. B. pseudomallei is a soil saprophyte endemic to Southeast Asia and northern Australia. The clinical presentation of melioidosis may mimic tuberculosis (both cause chronic suppurative lesions unresponsive to conventional antibiotics and both commonly affect the lungs). The two diseases have overlapping risk profiles (e.g., diabetes, corticosteroid use), and both B. pseudomallei and Mycobacterium tuberculosis are intracellular pathogens. There are however important differences: the majority of melioidosis cases are acute, not chronic, and present with severe sepsis and a mortality rate that approaches 50% despite appropriate antimicrobial therapy. By contrast, tuberculosis is characteristically a chronic illness with mortality <2% with appropriate antimicrobial chemotherapy. We examined the gene expression profiles of total peripheral leukocytes in two cohorts of patients, one with acute melioidosis (30 patients and 30 controls) and another with tuberculosis (20 patients and 24 controls). Interferon-mediated responses dominate the host response to both infections, and both type 1 and type 2 interferon responses are important. An 86-gene signature previously thought to be specific for tuberculosis is also found in melioidosis. We conclude that the host responses to melioidosis and to tuberculosis are similar: both are dominated by interferon-signalling pathways and this similarity means gene expression signatures from whole blood do not distinguish between these two diseases.

Introduction

Melioidosis is a serious infectious disease caused by the environmental Gram-negative bacillus Burkholderia pseudomallei. The organism is distributed in soil across much of southeast Asia and northern Australia [1], and infection results from bacterial inoculation, ingestion or inhalation of the organism. The majority of cases are reported during the rainy season in northeast Thailand and northern Australia, and the most common presenting features are community-acquired pneumonia and/or bacteraemia [1]. The spectrum of clinical manifestations is very diverse, and melioidosis may present as acute, chronic and latent disease. Patients with chronic melioidosis may present with signs, symptoms and chest radiograph changes that are indistinguishable from tuberculosis [1].

Comparisons have also been drawn between melioidosis and tuberculosis based on apparent parallels in epidemiology, pathophysiology and therapy. Melioidosis and tuberculosis have risk factors in common, in that corticosteroid use and diabetes predispose to both diseases [1]. At clinical presentation, chronic melioidosis, like tuberculosis, causes chronic suppurative lesions that do not respond to commonly used first line antimicrobials (viz., aminopenicillins, first and second generation cephalosporins, macrolides or aminoglycosides). Histological examination of tissue taken from patients with melioidosis may reveal granulomas with central necrosis, which mimic those of tuberculosis [2]. At a cellular level, both B. pseudomallei and Mycobacterium tuberculosis are intracellular pathogens and this ability to parasitise cells appears crucial to their virulence [3], [4]. Melioidosis, like tuberculosis, is also able to cause latent infection, the longest documented interval between exposure and clinical melioidosis being 62 years [1].

While chronic melioidosis is clinically similar to active tuberculosis, and latent of forms of both melioidosis and tuberculosis undoubtedly occur, acute melioidosis has no clinical counterpart in tuberculosis. Only ∼10% of melioidosis cases are chronic (symptoms >2 months) [1], and the majority of melioidosis cases present acutely, with sepsis frequently complicated by hypotension and organ dysfunction, which rarely occurs in tuberculosis. Acute melioidosis is therefore a clinical entity quite distinct from tuberculosis. In northeast Thailand, mortality is 40% despite appropriate treatment [5], whereas tuberculosis mortality is <2% with appropriate treatment. HIV infection is also an important risk factor for tuberculosis, but there is no established association between HIV and melioidosis [6]. The taxonomic relationship between B. pseudomallei and M. tuberculosis is distant (they are in different phyla: Proteobacteria and Actinobacteria, respectively). Their cell surfaces also present different pathogen-associated molecular patterns (PAMP) to the host immune system, and it seems reasonable to expect the host to respond differently to challenge by different PAMPs.

In this study, we sought differences in host response between acute melioidosis and tuberculosis using whole genome arrays to compare gene expression in circulating peripheral blood leukocytes collected from two cohorts of patients, one with melioidosis and one with tuberculosis. We also sought to define whether whole blood gene expression profiling distinguishes between melioidosis and tuberculosis.

Materials and Methods

The melioidosis data were taken from a previously published cohort of 30 patients and 30 healthy controls, frequency-matched for diabetes and glibenclamide use (an oral hypoglycaemic drug used to treat diabetes mellitus) [5]. Each group contained 10 non-diabetics and 20 diabetics. Diabetics were divided into 10 taking glibenclamide ( = glyburide) and 10 not taking any sulphonylurea (but who may have been on insulin, metformin or diet-control alone). We adjusted for diabetes and glibenclamide because two-thirds of all melioidosis patients have diabetes, diabetes is itself a pro-inflammatory condition, and because glibenclamide is anti-inflammatory [5]. The tuberculosis cohort has been published previously and consists of 20 patients with pulmonary tuberculosis and 24 healthy controls [7]. That study did not control for the effect of confounders such as diabetes. Inclusion and exclusion criteria for both studies have been published previously [5], [7]. Eligible cases for both studies were persons aged between 18 and 75 years. In the melioidosis cohort, diabetes was defined as an abnormal Hb A1c at enrolment or a previous diagnosis of diabetes. The tuberculosis cohort excluded patients with diabetes. Both studies excluded patients who were pregnant or immunosuppressed.

Melioidosis Microarrays

The methods used in the melioidosis cohort have been reported previously [5] and the data is deposited at ArrayExpress, EMBL-EBI (accession number E-TABM-852-n). In brief, a 3 ml blood sample was collected from each study subject in a PaxGene™ Blood RNA tube (PreAnalytiX, GmbH) and stored at –70°C. RNA was extracted using the PaxGene™ Blood RNA Purification Kit (PreAnalytix) according to the manufacturer’s instructions. The RNA was amplified using the Illumina® TotalPrep RNA Amplification Kit (Applied Biosystems) and assayed using the Illumina® HumanWG-6 v3.0 Expression BeadChip (Illumina®), which probes 48,803 transcripts from across the human genome. Quantitative PCR verification of these microarrays has been reported previously [5].

Tuberculosis Microarrays

The methods used in the tuberculosis cohort have been published elsewhere previously [7]. In brief, a 3 ml blood sample was collected into Tempus tubes (Applied Biosystems, California) and stored at –20 to –80°C. RNA was extracted using the PerfectPure RNA Blood Kit (5 PRIME) according to the manufacturer’s instructions. The RNA was then amplified using the Illumina CustomPrep RNA amplification kit (Applied Biosystems) and assayed using the Illumina Human HT-12 v3 BeadChip array (Illumina), which uses the same probe set as the HumanWG-6 v3.0. Raw data was downloaded from a publicly available repository (NCBI GEO accession number GSE19491) and consists of tuberculosis patients with controls recruited in London. The study also included data from a cohort of South African tuberculosis patients, but that cohort was excluded from this analysis because it does not contain uninfected controls, which made it impossible to normalize across the cohorts. The original study was analysed using GeneSpring, but we reanalysed the raw data using Bioconductor for the sake of comparability.

Ethics

Approval for the melioidosis study was obtained from the Oxford Tropical Research Ethics Committee (OXTREC 018-07) and the Ethics Committee of the Faculty of Tropical Medicine, Mahidol University (MUTM 2008-001-01) [5]. Approval for the tuberculosis study was obtained from the Research Ethics Committee at St Mary’s Hospital, London, UK (REC 06/Q0403/128) [7]. Written informed consent was obtained from all subjects.

Statistical Methods

Differential expression analyses were performed using Bioconductor [8] version 2.12.1 running on R 2.13.0 [9]. Pre-processing was performed using the beadarray 2.2.0 package [10], [11], and background correction was performed using normexp [12], [13]. Fluorescence intensities were quantile-normalized between arrays within each cohort and non-expressed probes were removed (detection p-value >0.05). Differential expression was performed using limma 3.8.1 [14]. For the melioidosis cohort, the linear model fit was log e = β0 + β1x1 + β2x2 + β3x3 + β12x1x2, where e is expression, x1 is melioidosis, x2 is diabetes and x3 is glibenclamide treatment. The expression values for melioidosis, β1, are therefore adjusted for diabetes and glibenclamide treatment. For the tuberculosis data, the model was log e = β0 + β1x1, where x1 is tuberculosis. The p-value cut-off of 0.01 was set following visual inspection of the histogram of unadjusted p-values for β1, as calculated from the moderated t-statistic (B-statistic) using Bayesian methods [14]. Illumina probe IDs were mapped to HUGO gene symbols [15] by illuminaHumanv3.db [16]. Networks were clustered by pathway by the Reactome [17] functional interaction network [18] plug-in for Cytoscape 2.8.1 [19], restricting the analysis to modules larger than 10 proteins. The p-values reported are for the hypergeometric test. The top 1000 probes were used to construct networks for presentation in figures. We searched specifically for interferon-regulated gene signatures on Interferome also [20]. Heat maps were drawn with gplots 2.8.0 [21] using colour blind-safe colour ramps generated by RColorBrewer 1.0–2 [22], [23]. We divided controls and patients by unsupervised k-means [24] and verified stability of the clusters under 5 random starts.

Results

The melioidosis cohort consisted of 30 patients and 30 controls. Baseline characteristics are in Table 1. In the melioidosis cohort, 6,755 probes were differentially expressed (that is, either up or downregulated) representing 4632 unique genes. Annotation was available for 1,658 of these genes, of which 651 were upregulated and 1,007 were downregulated. The tuberculosis cohort consisted of 20 patients and 24 controls. In the tuberculosis cohort, 6911 probes differentially expressed (5045 unique genes). Annotation was available for 1985 of these genes, of which 847 were upregulated and 1138 were downregulated. In both the melioidosis and the tuberculosis cohorts, the signature seen was dominated by neutrophils, which formed the bulk of the circulating leukocytes. Multiple lymphocyte-related pathways were downregulated, but this reflects the fact that lymphocyte counts were low in both melioidosis and tuberculosis. Pathways associated with transcription and translation were also prominent, in keeping with the large number of genes differentially regulated in both melioidosis and tuberculosis.

Pathway Analysis

Interferon-mediated responses were the dominant pathway seen in both melioidosis and in tuberculosis (p<0.0001 for both, Tables 2 & 3). Class 1 and class 2 interferons were prominent in both (Table 4). Of the immune-related pathways, TRAIL (TNF superfamily member 10), tumour necrosis factor α (TNFα), transforming growth factor β (TGF-β), interleukin (IL)-1, IL-2, IL-12, chemokine and Toll-like receptor (TLR) pathways were all differentially regulated (Tables 2 & 3). There was no gene signature that distinguished melioidosis from tuberculosis, and for each of the pathways differentially expressed in melioidosis, we were able to find a counterpart in tuberculosis (Tables 2 & 3). Berry et al. reported an 86-gene signature that was specific for tuberculosis [7]. This 86-gene signature was also present in melioidosis (Figure 1).

thumbnail
Figure 1. The 86-gene signature of tuberculosis is also seen in melioidosis.

These heat maps demonstrate the gene expression profiles for two cohorts: (A) melioidosis and (B) tuberculosis. The 86 genes displayed are those identified by Berry et al. [7] as being specific for tuberculosis, after excluding genes differentially regulated in other infections (Staphylococcus aureus and Group A Streptococcus) and inflammatory conditions (adult onset Still’s disease and systemic lupus erythematosus). Each column in the heat map is the gene expression profile of an individual, with control subjects on the left and patients on the right. Each cell within the heat map is the expression of a single gene: orange genes are upregulated and purple genes are downregulated, with expression normalized across the rows. We used this 86-gene signature to cluster study participants into two groups (marked black and red in the coloured banner at the top of each heat map). In the tuberculosis cohort, three controls clustered with the patients, and two patients clustered with the controls. In the melioidosis cohort, the same 86-gene signature also allowed us to distinguish controls and patients, with the exception of four patients who clustered with the controls. Despite the same microarray platform being used, the two cohorts were assayed as separate batches, so the absolute fluorescence intensities are different, making a direct comparison of melioidosis and tuberculosis impossible. All patients were therefore compared to their own controls.

https://doi.org/10.1371/journal.pone.0054961.g001

thumbnail
Table 2. Genes upregulated in melioidosis and tuberculosis, arranged by pathway.

https://doi.org/10.1371/journal.pone.0054961.t002

thumbnail
Table 3. Genes downregulated in melioidosis and tuberculosis, arranged by pathway.

https://doi.org/10.1371/journal.pone.0054961.t003

Modular Analysis

In a modular analysis of the upregulated genes (Figure 2A), interferon and cytokine signalling clustered together in the centre of the network, causing the complement (cluster 1), NOD-like receptor (cluster 2) and TLR (cluster 3) pathways to gain prominence. In the downregulated genes (Figure 2B), the most prominent clusters were the ribosomal proteins (cluster 1) and zinc finger proteins (cluster 2).

thumbnail
Figure 2. Network representation of genes differentially expressed in melioidosis.

‘Canonical’ pathways (such as those presented in a standard biochemistry textbook) are manually curated collections of protein interactions arranged in a manner that aids human understanding, and as artificial constructs the boundaries between pathways are subjective. Pathways that are conceptually distinct often have proteins in common and overlap, so in modular analysis, multiple pathways may collapse into a single module, causing other pathways and relationships to gain prominence. These two networks (A and B) represent those genes that are differentially expressed in melioidosis. For simplicity of presentation, we have used only a subset of genes in these networks. The top 221 upregulated genes (as ranked by p-value) are presented in A, and the top 155 downregulated genes are in B. The same clusters were found in an analysis of the whole gene set and those results are presented in Tables 2 & 3. Network A. IFN-γ, TNF-α, IL-12 signalling pathways cluster together with the glypican network in the centre of the graph, but the complement/chemokine receptor (cluster 1), inflammasome (cluster 2) and Toll-like receptor pathways come to prominence in this analysis (cluster 3). Network B. IFN-γ, TGF-β and TNF signalling again cluster in the middle of the network. The two most prominent clusters are ribosomal proteins (cluster 1) and zinc finger proteins (cluster 2).

https://doi.org/10.1371/journal.pone.0054961.g002

PAMP-specific Responses

B. pseudomallei expresses lipopolysaccharide on its outer membrane, while M. tuberculosis does not and has a lipid-rich cell wall. Lipopolysaccharide is recognized by TLR4 and CD14, and both are upregulated in melioidosis (P = 0.0016 and 1.5 × 10–6, respectively); however, TLR4 and CD14 are also upregulated in tuberculosis (P = 1.5 × 10–6 and 9.4 × 10–4). B. pseudomallei is a flagellated, motile bacterium, while M. tuberculosis is immotile with no flagellum. Flagellin is a ligand for TLR5 [25] and NLRC4 [26]. Both TLR5 and NLRC4 were upregulated in melioidosis (P = 5.4 × 10–13 and 4.2 × 10–10, respectively), but both were upregulated in tuberculosis also (P = 8.1 × 10–10 and 2.4 × 10–11).

Discussion

There were 4632 genes differentially expressed in melioidosis and 5045 genes in tuberculosis, thus approximately 20% of the human genome is differentially regulated in each disease. The most prominent pathway in melioidosis was interferon (IFN)-γ and the same was true of tuberculosis. There were no pathways differentially regulated in melioidosis that were not also differentially regulated in tuberculosis, and there was no signature which reliably distinguished melioidosis and tuberculosis.

Berry et al. identified an 86-gene signature as being specific for tuberculosis after eliminating differentially regulated genes common to Streptococcus pyogenes and Staphylococcus aureus infections, and to two auto-inflammatory diseases (systemic lupus erythematosus and Still’s disease). This signature was also present in melioidosis, which is surprising given that all melioidosis patients recruited had acute rather than chronic melioidosis, which is clinically distinct from tuberculosis.

Interferon-mediated Responses

The IFN-γ pathway was reported as the most prominent pathway identified in gene expression studies of a mouse model of melioidosis [27], and blocking IFN-γ dramatically increases host susceptibility to melioidosis [3]. In human studies, plasma IFN-γ concentrations were high in melioidosis [3], and IFN-γ-mediated responses were also the most prominent feature in a gene expression study of melioidosis in another human cohort [28]. The finding here that this feature is shared with tuberculosis is unsurprising, because IFN-γ responses are crucial for the host response against intracellular pathogens such as B. pseudomallei and M. tuberculosis. IFN-γ treatment has a role in the management of multidrug-resistant tuberculosis, and adjunctive therapy with IFN-γ is beneficial in a mouse model of melioidosis [29], although its role in clinical melioidosis remains undefined [30].

In their original report on this tuberculosis cohort, Berry et al. noted that type 2 IFN-γ responses were prominent, but noted that type 1 IFN-αβ responses were present also [7]. We found that type 1 interferon-αβ responses were just as prominent in melioidosis, but the clinical relevance of this remains to be defined.

Type 1 interferons can be produced by almost any cell type (leukocytes, fibroblasts and endothelial cells) and are induced by a range of bacterial pathogens, whereupon they proceed to modulate the host response in a manner that is as yet incompletely understood [31]. The signalling pathways initiated by type 1 interferons are best described in terms of their activation of signal transducer and activator of transcription (STAT) family members (STAT1 to STAT6) [32], the best studied of which are STAT1 and STAT3. STAT1 activation is dependent on both type 1 and type 2 interferons and results in a pro-inflammatory response, with recruitment of inflammatory cells and the enhancement of antigen presentation [31]. On the other hand, STAT3 activation is a key mediator of IL-10 signalling, and results in inhibition of inflammatory responses and directly inhibits STAT1 activation [31]. The role of STAT4 is less well described, but STAT4 activation may play a role in T helper 1 lymphocyte differentiation, which is an essential part of the host response to intracellular pathogens. Type 1 interferons are also necessary for the production of inducible nitric oxide synthase [33], which is in turn necessary for the clearance of intracellular bacteria. Interestingly, type 1 interferons are able to inhibit IL-1β production and inflammasome assembly by two separate mechanisms: the first is via inhibition of NLRP1 and NLRP3 inflammasomes in a STAT1-dependent manner; the second, is a reduction in pro-IL-1 levels via a STAT3-dependent pathway [34]. It has previously be shown that host response to B. pseudomallei is inflammasome-dependent [35].

The role of type 1 interferons in tuberculosis is unclear, since mice deficient in the production of type 1 interferons are better able to control M. tuberculosis infections [36], but type 1 interferons also play a non-redundant protective role in the absence of type 2 interferon signaling [37]. The role of type 1 interferons in the pathogenesis of melioidosis remains to be studied.

PAMP-specific Responses

TLR4 and CD14 are upregulated in both melioidosis and tuberculosis. The classical ligand for TLR4 [38] and for CD14 [39] is lipopolysaccharide (LPS), which would explain this finding for B. pseudomallei. TLR4 will recognize heparin-binding haemagglutinin [40], and CD14 will bind lipoarabinomannan [41], both of which are expressed by M. tuberculosis.

The pattern recognition receptors TLR5 [25] and NLRC4 [26] both recognize flagellin. No alternative ligand has yet been described for TLR5, so it is more difficult to explain why tuberculosis should apparently induce a flagellin-response. One explanation is that upregulation of pattern recognition receptors is not driven by their ligands. TLR5 expression is induced as part of the type 1 interferon response [42], while NLRC4 is upregulated as part of the TNF-α response [43]. Both pathways are prominent in the host response to melioidosis. In support of this hypothesis, the TLRs are upregulated as a group in both melioidosis (TLR1, TLR2, TLR4, TLR5, TLR6, TLR8 and TLR10) and tuberculosis (TLR2, TLR4, TLR5, TLR6, TLR7, TLR8).

Limitations and Future Research

Tuberculosis is strongly associated with HIV infection, but melioidosis is not. HIV targets primarily CD4-positive T-lymphocytes and lymphocyte depletion is a feature of all sepsis. Lymphocytes were depleted in both the melioidosis and the TB cohorts, so lymphocyte-related pathways and modules are missing from the whole blood gene expression data of both cohorts, making it difficult to make any comment about the relative role of CD4-positive cells in melioidosis compared to tuberculosis. The whole blood signature was dominated by neutrophils which may also have obscured any lymphocyte signature. Future studies that use purified lymphocytes harvested from melioidosis patients may shed light on this issue.

Microarrays generate large amounts of data that are useful for the development of hypotheses. Our analysis has identified a number of other pathways that are differentially regulated in melioidosis, but which are unstudied to date. Notably, the TRAIL pathway is differentially regulated in melioidosis, but its role remains undefined at present. The glypicans (cell surface proteoglycans) contribute to cell proliferation and growth, both essential processes in the host response to infection. To date, investigations into the role of glypicans have been confined primarily to cancer biology, although glypican-deficient mice are more susceptibility to respiratory infections [44]. In tuberculosis, the glypican network appears to have greater prominence than even the interferon-mediated responses.

Conclusions

Host responses to melioidosis and TB are dominated by interferon-signalling events, despite the fact that the organisms are unrelated and present completely different cell-surface PAMPs to the host. This is likely because they both stimulate host responses common to intracellular pathogens, and because the expression of pattern recognition receptors is not driven by their ligands, but by cytokine responses (primarily IFN-γ and TNF-α). The 86-gene signature identified by Berry et al. clusters melioidosis patients just as effectively as it clusters tuberculosis. It therefore seems likely that whole blood gene signatures will not be able to diagnose tuberculosis in areas where melioidosis and TB are co-endemic, but may find utility when interpreted in combination with clinical features. Further studies using direct comparisons will be required to confirm this finding.

Acknowledgments

We thank the staff of Sappasithiprasong Hospital, Ubon Ratchathani, who cared for the melioidosis patients in this study, and the staff of the Wellcome Trust Mahidol-Oxford Tropical Medicine Research Unit, who recruited the patients and collected the samples. We also thank Roslin Russell, Keith James, Mike Smith, Robert Stojnic, Mark Dunning, Wei Shi for help with scripting for R and Bioconductor; Joost Wiersinga, Tassili Weehuizen, Ana Luisa Toribio for helpful discussions; Damien Chaussabel, Christine Graham and Anne O’Garra with help using the raw data from their TB cohort.

Author Contributions

Conceived and designed the experiments: GCKWK MFS GD SJP. Performed the experiments: GCKWK MFS RRM DL. Analyzed the data: GCKWK MFS RB SD. Contributed reagents/materials/analysis tools: MFS RB NPJD GD SJP. Wrote the paper: GCKWK SD SJP.

References

  1. 1. Limmathurotsakul D, Peacock S (2011) Melioidosis: a clinical overview. Br Med Bull 99: 125–139.
  2. 2. Wong KT, Puthucheary SD, Vadivelu J (1995) The histopathology of human melioidosis. Histopathology 26: 51–55.
  3. 3. Wiersinga WJ, van der Poll T, White NJ, Day NP, Peacock SJ (2006) Melioidosis: insights into the pathogenicity of Burkholderia pseudomallei. Nat Rev Microbiol 4: 272–282
  4. 4. Collins HL, Kaufmann SH (2001) The many faces of host responses to tuberculosis. Immunology 103: 1–9.
  5. 5. Koh GCKW, Maude RR, Schreiber MF, Limmathurotsakul D, Wiersinga WJ, et al. (2011) Glyburide is anti-inflammatory and associated with reduced mortality in melioidosis. Clin Infect Dis 52: 717–725
  6. 6. Chierakul W, Wuthiekanun V, Chaowagul W, Amornchai P, Cheng AC, et al. (2005) Short report: disease severity and outcome of melioidosis in HIV coinfected individuals. Am J Trop Med Hyg 73: 1165–1166.
  7. 7. Berry MPR, Graham CM, McNab FW, Xu Z, Bloch SAA, et al. (2010) An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature 466: 973–977
  8. 8. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, et al. (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5: R80
  9. 9. R Development Core Team (2010) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. p. Available: http://www.R-project.org/.
  10. 10. Dunning MJ, Smith ML, Ritchie ME, Tavaré S (2007) beadarray: R classes and methods for Illumina bead-based data. Bioinformatics 23: 2183–2184
  11. 11. Cairns JM, Dunning MJ, Ritchie ME, Russell R, Lynch AG (2008) BASH: a tool for managing BeadArray spatial artefacts. Bioinformatics 24: 2921–2922
  12. 12. Ritchie ME, Silver J, Oshlack A, Holmes M, Diyagama D, et al. (2007) A comparison of background correction methods for two-colour microarrays. Bioinformatics 23: 2700–2707
  13. 13. Silver JD, Ritchie ME, Smyth GK (2009) Microarray background correction: maximum likelihood estimation for the normal-exponential convolution. Biostatistics 10: 352–363
  14. 14. Smyth GK, Gentleman R, Carey V, Dudoit S, Irizarry R, et al. (2005) Limma: linear models for microarray data. Bioinformatics and Computational Biology Solutions using R and Bioconductor. New York: Springer. 397–420.
  15. 15. Bruford EA, Lush MJ, Wright MW, Sneddon TP, Povey S, et al. (2008) The HGNC Database in 2008: a resource for the human genome. Nucleic Acids Res 36: D445–448
  16. 16. Barbosa-Morais NL, Dunning MJ, Samarajiwa SA, Darot JFJ, Ritchie ME, et al. (2009) A re-annotation pipeline for Illumina BeadArrays: improving the interpretation of gene expression data. Nucleic Acids Res 38: e17
  17. 17. Matthews L, Gopinath G, Gillespie M, Caudy M, Croft D, et al. (2009) Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Res 37: D619–622
  18. 18. Wu G, Feng X, Stein L (2010) A human functional protein interaction network and its application to cancer data analysis. Genome Biol 11: R53
  19. 19. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, et al. (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research 13: 2498–2504.
  20. 20. Samarajiwa SA, Forster S, Auchettl K, Hertzog PJ (2009) INTERFEROME: the database of interferon regulated genes. Nucleic Acids Res 37: D852–7.
  21. 21. Warnes GR (2010) gplots: Various R programming tools for plotting data. Available: http://CRAN.R-project.org/package=gplots.
  22. 22. Neuwirth E (2007) RColorBrewer: ColorBrewer palettes. Available: http://cran.r-project.org/web/packages/RColorBrewer/index.html.
  23. 23. Brewer CA, Hatchard GW, Harrower MA (2003) ColorBrewer in print: a catalog of color schemes for maps. Cartography and geographic information science 30: 5–32.
  24. 24. Hartigan JA, Wong MA (1979) A K-means clustering algorithm. Applied Statistics 28: 100–108.
  25. 25. Hayashi F, Smith KD, Ozinsky A, Hawn TR, Yi EC, et al. (2001) The innate immune response to bacterial flagellin is mediated by Toll-like receptor 5. Nature 410: 1099–1103
  26. 26. Sutterwala FS, Flavell RA (2009) NLRC4/IPAF: a CARD carrying member of the NLR family. Clin Immunol 130: 2–6
  27. 27. Chin C-Y, Monack DM, Nathan S (2010) Genome wide transcriptome profiling of a murine acute melioidosis model reveals new insights into how Burkholderia pseudomallei overcomes host innate immunity. BMC Genomics 11: 672
  28. 28. Pankla R, Buddhisa S, Berry M, Blankenship D, Bancroft G, et al. (2009) Genomic transcriptional profiling identifies a candidate blood biomarker signature for the diagnosis of septicemic melioidosis. Genome Biology 10: R127
  29. 29. Propst KL, Troyer RM, Kellihan LM, Schweizer HP, Dow SW (2010) Immunotherapy markedly increases the effectiveness of antimicrobial therapy for treatment of Burkholderia pseudomallei infection. Antimicrob Agents Chemother 54: 1785–1792
  30. 30. Koh GCKW, Limmathurotsakul D (2010) Gamma interferon supplementation for melioidosis. Antimicrob Agents Chemother 54: 4520
  31. 31. González-Navajas JM, Lee J, David M, Raz E (2012) Immunomodulatory functions of type I interferons. Nat Rev Immunol 12: 125–135
  32. 32. Hebenstreit D, Wirnsberger G, Horejs-Hoeck J, Duschl A (2996) Signaling mechanisms, interaction partners, and target genes of STAT6. Cytokine Growth Factor Rev 17: 173–188.
  33. 33. Utaisincharoen P, Anuntagool N, Arjcharoen S, Limposuwan K, Chaisuriya P, et al. (2004) Induction of iNOS expression and antimicrobial activity by interferon (IFN)-beta is distinct from IFN-gamma in Burkholderia pseudomallei-infected mouse macrophages. Clin Exp Immunol 136: 277–283.
  34. 34. Guarda G, Braun M, Staehli F, Tardivel A, Mattmann C, et al. (2011) Type I interferon inhibits interleukin-1 production and inflammasome activation. Immunity 34: 213–223.
  35. 35. Breitbach K, Sun GW, Koehler J, Eske K, Wongprompitak P, et al. (2009) Caspase-1 mediates resistance in murine melioidosis. Infect Immun. Available: http://www.ncbi.nlm.nih.gov/pubmed/19179418. Accessed 1 February 2009.
  36. 36. Stanley SA, Johndrow JE, Manzanillo P, Cox JS (2007) The Type I IFN response to infection with Mycobacterium tuberculosis requires ESX-1-mediated secretion and contributes to pathogenesis. J Immunol 178: 3143–3152.
  37. 37. Desvignes L, Wolf AJ, Ernst JD (2012) Dynamic roles of type I and type II IFNs in early infection with Mycobacterium tuberculosis. J Immunol 188: 6205–6215.
  38. 38. Qureshi ST, Larivière L, Leveque G, Clermont S, Moore KJ, et al. (1999) Endotoxin-tolerant mice have mutations in Toll-like receptor 4 (Tlr4). J Exp Med 189: 615–625.
  39. 39. Kitchens RL (2000) Role of CD14 in cellular recognition of bacterial lipopolysaccharides. Chem Immunol 74: 61–82.
  40. 40. Jung ID, Jeong SK, Lee C-M, Noh KT, Heo DR, et al. (2011) Enhanced efficacy of therapeutic cancer vaccines produced by co-treatment with Mycobacterium tuberculosis heparin-binding hemagglutinin, a novel TLR4 agonist. Cancer Res 71: 2858–2870
  41. 41. Pugin J, Heumann ID, Tomasz A, Kravchenko VV, Akamatsu Y, et al. (1994) CD14 is a pattern recognition receptor. Immunity 1: 509–516.
  42. 42. Khoo JJ, Forster S, Mansell A (2011) Toll-like receptors as interferon-regulated genes and their role in disease. J Interferon Cytokine Res 31: 13–25
  43. 43. Gutierrez O, Pipaon C, Fernandez-Luna JL (2004) Ipaf is upregulated by tumor necrosis factor-alpha in human leukemia cells. FEBS Lett 568: 79–82
  44. 44. Cano-Gauci DF, Song HH, Yang H, McKerlie C, Choo B, et al. (1999) Glypican-3-deficient mice exhibit developmental overgrowth and some of the abnormalities typical of Simpson-Golabi-Behmel syndrome. J Cell Biol 146: 255–264.