Childhood tuberculosis is associated with decreased abundance of T cell gene transcripts and impaired T cell function

The WHO estimates around a million children contract tuberculosis (TB) annually with over 80 000 deaths from dissemination of infection outside of the lungs. The insidious onset and association with skin test anergy suggests failure of the immune system to both recognise and respond to infection. To understand the immune mechanisms, we studied genome-wide whole blood RNA expression in children with TB meningitis (TBM). Findings were validated in a second cohort of children with TBM and pulmonary TB (PTB), and functional T-cell responses studied in a third cohort of children with TBM, other extrapulmonary TB (EPTB) and PTB. The predominant RNA transcriptional response in children with TBM was decreased abundance of multiple genes, with 140/204 (68%) of all differentially regulated genes showing reduced abundance compared to healthy controls. Findings were validated in a second cohort with concordance of the direction of differential expression in both TBM (r2 = 0.78 p = 2x10-16) and PTB patients (r2 = 0.71 p = 2x10-16) when compared to a second group of healthy controls. Although the direction of expression of these significant genes was similar in the PTB patients, the magnitude of differential transcript abundance was less in PTB than in TBM. The majority of genes were involved in activation of leucocytes (p = 2.67E-11) and T-cell receptor signalling (p = 6.56E-07). Less abundant gene expression in immune cells was associated with a functional defect in T-cell proliferation that recovered after full TB treatment (p<0.0003). Multiple genes involved in T-cell activation show decreased abundance in children with acute TB, who also have impaired functional T-cell responses. Our data suggest that childhood TB is associated with an acquired immune defect, potentially resulting in failure to contain the pathogen. Elucidation of the mechanism causing the immune paresis may identify new treatment and prevention strategies

Mycobacterium tuberculosis exploits a molecular off switch of the immune system for intracellular survival

Mycobacterium tuberculosis (M. tuberculosis) survives and multiplies inside human macrophages by subversion of immune mechanisms. Although these immune evasion strategies are well characterised functionally, the underlying molecular mechanisms are poorly understood. Here we show that during infection of human whole blood with M. tuberculosis, host gene transcriptional suppression, rather than activation, is the predominant response. Spatial, temporal and functional characterisation of repressed genes revealed their involvement in pathogen sensing and phagocytosis, degradation within the phagolysosome and antigen processing and presentation. To identify mechanisms underlying suppression of multiple immune genes we undertook epigenetic analyses. We identified significantly differentially expressed microRNAs with known targets in suppressed genes. In addition, after searching regions upstream of the start of transcription of suppressed genes for common sequence motifs, we discovered novel enriched composite sequence patterns, which corresponded to Alu repeat elements, transposable elements known to have wide ranging influences on gene expression. Our findings suggest that to survive within infected cells, mycobacteria exploit a complex immune "molecular off switch" controlled by both microRNAs and Alu regulatory elements

Rapid Chagas Disease Drug Target Discovery Using Directed Evolution in Drug-Sensitive Yeast

Recent advances in cell-based, high-throughput phenotypic screening have identified new chemical compounds that are active against eukaryotic pathogens. A challenge to their future development lies in identifying these compounds' molecular targets and binding modes. In particular, subsequent structure-based chemical optimization and target-based screening require a detailed understanding of the binding event. Here, we use directed evolution and whole-genome sequencing of a drug-sensitive S. cerevisiae strain to identify the yeast ortholog of TcCyp51, lanosterol-14-alpha-demethylase (TcCyp51), as the target of MMV001239, a benzamide compound with activity against Trypanosoma cruzi, the etiological agent of Chagas disease. We show that parasites treated with MMV0001239 phenocopy parasites treated with another TcCyp51 inhibitor, posaconazole, accumulating both lanosterol and eburicol. Direct drug-protein binding of MMV0001239 was confirmed through spectrophotometric binding assays and X-ray crystallography, revealing a binding site shared with other antitrypanosomal compounds that target Cyp51. These studies provide a new probe chemotype for TcCyp51 inhibition

Rapid Chagas Disease Drug Target Discovery Using Directed Evolution in Drug-Sensitive Yeast

Recent advances in cell-based, high-throughput phenotypic screening have identified new chemical compounds that are active against eukaryotic pathogens. A challenge to their future development lies in identifying these compounds’ molecular targets and binding modes. In particular, subsequent structure-based chemical optimization and target-based screening require a detailed understanding of the binding event. Here, we use directed evolution and whole-genome sequencing of a drug-sensitive <i>S. cerevisiae</i> strain to identify the yeast ortholog of <i>Tc</i>Cyp51, lanosterol-14-alpha-demethylase (<i>Tc</i>Cyp51), as the target of MMV001239, a benzamide compound with activity against <i>Trypanosoma cruzi</i>, the etiological agent of Chagas disease. We show that parasites treated with MMV0001239 phenocopy parasites treated with another <i>Tc</i>Cyp51 inhibitor, posaconazole, accumulating both lanosterol and eburicol. Direct drug–protein binding of MMV0001239 was confirmed through spectrophotometric binding assays and X-ray crystallography, revealing a binding site shared with other antitrypanosomal compounds that target Cyp51. These studies provide a new probe chemotype for <i>Tc</i>Cyp51 inhibition

Modelled temporal changes in gene expression.

<p><b>A</b>. Heat map showing modelled changes in expression of the significant gene transcripts in TBM patients from the time of diagnosis (0) to 180 days. Green represents lower transcript abundance, red represents higher transcript abundance and black represents no difference in expression as compared to healthy children with a past history of TB sampled at least one year after diagnosis and treatment. The relative degree of transcript abundance is indicated by the colour intensity derived from the fitted mean expression levels over time (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185973#sec002" target="_blank">methods</a>). Genes showing similar temporal patterns of expression have been clustered together. The apparent linear change in colour is derived from the statistical model that interpolates the observed time points and can therefore be represented as a continuum. <b>B and C</b>. Example plots of two significantly differentially expressed gene transcripts. Expression levels for each TBM patient (red circles n = 9) are shown from diagnosis (time 0) to day 180. Blue circles are expression levels for healthy children (n = 9) with a past history of TB sampled at least one year after diagnosis and treatment. M = “minus” and denotes the log₂ ratio of the red and green channels. The line represents the fitted mean gene expression level over time, from linear mixed-effects model (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185973#sec002" target="_blank">methods</a>). 1b = <i>TARP</i>; 1c = <i>IL1R2</i>.</p

Confirmation of significantly differentially expressed genes from Cohort 1 in Cohort 2.

<p><b>A</b>. Average log fold change in the SDE transcripts identified in the time-course study (cohort 1) and their corresponding log fold change in the single time-point study (cohort 2). 140/262 transcripts identified in cohort 1 were measured in cohort 2. 129 transcripts followed the same regulation pattern (purple crosses); and 11 showed opposite regulation (represented by red crosses, annotated by gene symbol). Correlation coefficient was r2 = 0.78, 95% CI = [0.71, 0.82] p<2x10-16. The y-axis shows log fold change of SDE gene transcripts in cohort 1 relative to cohort 1 Healthy Controls (HC), and the x-axis shows their log fold change in cohort 2 relative to cohort 2 HC. <b>B</b>. Average log fold change in TBM patients relative to cohort 2 HC (x-axis) plotted against average log fold change in PTB patients relative to cohort 2 HC (y-axis) of the significant transcripts (140) that were identified in cohort 1 and common to both cohorts. Least-squares fitted line is shown in dashes. Correlation coefficient was <i>r</i>^<i>2</i> = 0.71, 95% CI = [0.62, 0.79] <i>p</i><2x10^-16. <b>C.</b> Heat map showing almost complete discrimination between TBM cases from cohort 1 and cohort 2 and healthy controls (cohort 2) using 129 transcripts significantly differentially expressed in both cohorts. Gene list is provided in Table C in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185973#pone.0185973.s002" target="_blank">S1 File</a>. Hierarchical clustering was performed by the complete linkage method to identify similar clusters. Solid red bar (top) shows cases, green bar shows controls. Intensity of colour indicates degree of reduced (green) or elevated (red) abundance of each transcript relative to healthy controls. White indicates no expression.</p

Functional T-cell responses in cohort 3.

<p><b>A.</b> Adjusted T-cell proliferative responses (cpm) to PHA in acute TB (TBM n = 19, EPTB n = 29, PTB n = 27) and healthy Mantoux positive controls n = 26. Normalised proliferative responses were determined by deducting the value for the unstimulated well from that of the PHA well. Means are shown by horizontal bars together with standard error of the mean. Asterisk denotes significant differences in corrected p values. PTB vs HC * <i>p</i> = 0.018, TBM vs HC ** <i>p</i> = 0.001, EPTB vs HC *** <i>p<</i>0.0003. <b>B.</b> IFNγ production in response to PHA in acute TB (TBM n = 36, other EPTB n = 57, PTB n = 55) and healthy Mantoux positive controls (HC) n = 75. Medians are shown by horizontal bars together with their interquartile ranges. Asterisk denotes significant difference in corrected p value between TBM and controls * <i>p</i><0.0003.</p

Gene expression of T-cell receptor signalling pathway and validation.

<p><b>A</b>. Transcripts that were SDE in TBM patients at admission compared to the 6 month time point that mapped to the T-cell receptor signalling pathway. After activation of the T-cell receptor, a cascade of signalling events is initiated leading to gene induction. Gene products highlighted green are significantly less abundant in TBM patients at admission compared to the 6 month time point. Corrected <i>p</i> value on Ingenuity Pathways Analysis = 1.47E^-11. Gene list provided in Tables D and E in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185973#pone.0185973.s002" target="_blank">S1 File</a>. <b>B</b>. Validation of T-cell signalling pathway genes by RT-PCR in TBM patients (cohort 1). Selected genes in the T-cell signalling pathway were validated by RT-PCR including seven that were significantly less abundant at admission compared to post treatment (TRA, ZAP70, CD3G, CD3D, LAT, LCK, NFATC2) and one showing no change (NFATC3). Two genes were also included that were more abundant at admission compared to post treatment (AREG, SLC7A5) that acted as the positive controls. Fold change between TBM patients at admission and post treatment (n = 8) are shown relative to Beta actin control. Boxes show 25^th and 75^th percentile. Whiskers show lowest and highest data point and horizontal lines show medians. * <i>p</i><0.05, ** <i>p</i><0.01 shows significance using paired Wilcoxon rank test.</p