Predicted transcriptional regulator identification and NFκB, IRF and STAT gene expression following LPS and Pam3CSK4 stimulation.

<p>(<b>A</b>) Predicted upstream transcriptional regulators from IPA; stimulations analysed independently using Pam3CSK4 1202 and LPS 4777 transcripts lists, at each time point only genes whose expression were 1.8 FC different from the media control at that time point were taken into consideration. Predicted upstream transcription regulators which met the criteria (<i>p</i><0.01 (Fishers Exact Test) and <i>z</i> activation score >2.5) shown plotted by <i>z</i>-activation score only at the time points where significance criteria met. (<b>B</b>) Mean mRNA expression of predicted transcription regulators plotted as log2 fold change (y axis) across time (x axis; 0, 1, 3, 6, 12 and 24 hours), fold change is relative to the media control at each time point, only those predicted transcription regulators whose mRNA expression is >1.8 FC relative to media control at one or more one time points are shown. (<b>C</b>) <b>NFkB genes.</b> Mean mRNA expression of the NFkB family genes, All 5 genes were significantly expressed and present in both Pam3CSK4 1202 and LPS 4777 transcript lists. (<b>D</b>) <b>Interferon Regulatory Factors.</b> Mean mRNA expression of the IRF and STAT genes. IRF1, IRF4, IRF7, IRF8, IRF9, STAT1, STAT2, STAT3, STAT4 and STAT5A were present in the LPS 4777 significantly expressed transcript list, none of the IRF or STAT genes (except STAT5B) were present in the 1202 Pam3CSK4 significant transcript list. (<b>E</b>) <b>Temporal Kinetics of NFkB and induced IRF and STAT genes.</b> Plotted for both LPS and Pam3CSK4 are mean fold change relative to media controls of the NFkB genes (NFKB1, NFKB2, REL, RELA, RELB) and mean fold change relative to media controls of selected IRF genes (IRF1, IRF2, IRF4, IRF7, IRF8, IRF9, STAT1, STAT2, STAT3, STAT4 and STAT5A).</p

<i>k</i>-means clustering of the significant transcript lists reveals similar clusters.

<p>Mean normalised expression profile of individual clusters (y axis, ±SD, n = 4) at each time point (x axis; 0, 1, 3, 6, 12 and 24 hours) for each cluster. N: number of transcripts within cluster; P: most significant canonical pathway (IPA). Clusters are grouped by similarity in kinetic profile (Pearsons correlation, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097702#pone.0097702.s007" target="_blank">Table S2</a>) and top canonical pathway.</p

NFκB and Interferon signalling pathways.

<p>Shown at the peak of their significance, 3 and 6 hours respectively. Significantly expressed genes within the pathway (from Pam3CSK4 1202 and LPS 4777 lists) shaded red if upregulated or blue if down regulated.</p

Molecular distance to health is linked to symptoms status and site of disease.

<p>(A) Molecular distance to health (MDTH) calculated for each individual (from 3409 transcripts which represent the transcripts of the 38 annotated modules shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0162220#pone.0162220.g002" target="_blank">Fig 2A</a>, healthy controls used as the control group). Individuals grouped by disease status, (median value, error bars SD) statistical testing Kruskal Wallis with Dunn’s multiple testing correction. <b>(B)</b> Patients grouped by number of reported symptoms; night sweats, fever, weight loss, chest pain or cough (median value, error bars SD) statistical testing Kruskal Wallis with Dunn’s multiple testing correction. <b>(C)</b> Patients grouped by site of disease and number of reported symptoms (as previous). <b>(D)</b> Mean MDTH (error bars, Standard error of the mean) plotted for each site of disease against % of the cohort suffering from one or more symptom (from list of symptoms described previously). Blue line represents Pearson’s correlation (R² 0.95, <i>p</i> = 0.0090).</p

Tuberculosis and sarcoidosis have similar differentially regulated genes.

<p><b>(A)</b> Differentially expressed genes identified from new dataset for TB and sarcoidosis groups compared to healthy controls (transcripts filtered which were not significantly detected from background in at least 10% of samples, low expressed transcripts filtered (less than 2 fold change from median in 10% of samples), followed by statistical testing (independent t-test with Benjamini Hochberg multiple testing correction (<i>q-value</i> <0.05) between groups of interest). Transcripts were matched to Entrez gene identifiers and duplicates and non-matched transcripts filtered. Venn diagram showing the overlap of DEGs between these two diseases and the most significant 15 gene list from the meta-analysis. <b>(B)</b> Heatmap of normalised expression using the Bloom et al 144 transcript list are able to broadly differentiate pulmonary TB patients from sarcoidosis patients. Clustering (Pearson’s uncentred (Cosine) with averaged linkage) on transcripts (rows) and individual patient blood samples (columns). <b>(C)</b> The same 144 transcript list (16) is unable to differentiate between mediastinal TB patients and sarcoidosis patients by clustering (as before) <b>(D)</b> Molecular scores calculated for transcripts from 380 gene meta-signature, MDTH and transcripts representing the interferon modules (healthy controls acting as control group). Z scores calculated (with healthy controls used as reference group for calculating mean and SD) and then TB patients and sarcoidosis patients ranked according to Z score of the 380 gene meta-signature. For all three outcomes the mean of pulmonary TB patients was significantly higher than both the extra-pulmonary TB and sarcoidosis patients (p<0.05). Differences among means were tested with a generalized linear model assuming a normal distribution and a Bonferroni multiple testing correction. No symptoms indicates absence of any of the five symptoms listed previously.</p

Individual patient’s transcriptional response occurred at a variable rate.

<p>320 gene list, differentially expressed genes derived from comparing the untreated expression profiles and their corresponding end of treatment (6 months) expression profiles in the South Africa 2011 Active TB Training Set. <b>(A)</b> Heatmap of South Africa 2011 cohort Active TB Training Set, normalised to the median of all transcripts, shows hierarchical clustered transcripts differentiating over time per individual. <b>(B)</b> Each patient’s temporal molecular response diminishes in the Active TB Training Set cohort.</p

Testing the meta-signature and the most consistently identified genes in a new dataset.

<p><b>(A)</b> The 380 gene meta-signature were mapped to 687 Illumina transcripts, of which 113 transcripts were excluded as they were not significantly detected from background in 10% of samples. Hierarchical clustering (Pearson uncentred (cosine) with averaged linkage) on individuals and transcripts broadly cluster healthy controls from pulmonary and extra-pulmonary TB patients. <b>(B)</b> Receiver operator curves for extra-pulmonary and pulmonary cohorts against healthy controls using MDTH derived from the most consistently identified genes (15 genes identified in at least 15 of the meta-analysis datasets [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0162220#pone.0162220.ref018" target="_blank">18</a>]–representing the most robustly identified genes in that analysis, healthy controls used as control group for MDTH) as potential diagnostic biomarker. Both prediction results were validated using k-fold cross validation with k equal to 10 with 1,000 iterations. The mean AUC for the EPTB and PTB validation results are 0.865 (95% confidence interval: 0.857–0.872) and 0.977 (95% confidence interval: 0.974–0.981) respectively.</p

Change in treatment specific signature is validated in an independent UK cohort.

<p>320 gene list derived from the differentially expressed genes between the untreated and 6 month treated samples in the treated South Africa 2011 cohort. <b>(A)</b> Heatmap of the treated UK 2011 Cohort, normalised to the median of all transcripts, shows diminution of the treatment specific transcriptional signature in the UK cohort in response to successful anti-TB treatment. <b>(B)</b> Temporal molecular response shows significant changes in response at 2 weeks in the UK cohort (linear mixed models, bars represent mean & 95% confidence intervals, *** = p<0.001, ** = p<0.01, * = p<0.05). <b>(C)</b> A diminished response can be seen in each patient by their temporal molecular response.</p

Top 10 differentially upregulated genes in TB and sarcoidosis^a.

<p>Top 10 differentially upregulated genes in TB and sarcoidosis<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0162220#t001fn001" target="_blank">^a</a>.</p

Specific treatment response signature significantly diminishes at 2 weeks onwards.

<p>A specific TB treatment response signature was derived from significantly differentially expressed genes between untreated samples in the South Africa Active TB Training Set and their corresponding 6 month samples, 320 transcripts. <b>(A)</b> Heatmap of South Africa 2011 Active TB Training Set, normalised to the median of all transcripts, shows transcripts differentiating over time in response to treatment. <b>(B)</b> Temporal molecular response further shows significant and early changes in response to TB treatment in the Active TB Training Set (linear mixed models, bars represent mean & 95% confidence intervals, *** = p<0.001, ** = p<0.01, * = p<0.05). <b>(C)</b> Heatmap of South Africa 2011 Active TB Test Set, normalised to the median of all transcripts, shows transcripts differentiating over time in response to treatment. <b>(D)</b> Temporal molecular response also shows in the Active TB Test Set significant and early changes in response to TB treatment. <b>(E)</b> IPA of the 320 transcripts showing the most significant pathways. <b>(F)</b> Venn diagram shows many overlapping genes between the active TB 664-transcript signature and the treatment specific 320-signature.</p