Identifying potential cytokines involved in autocrine gene regulation by upstream analysis within IPA.

<p>(<b>A</b>) Predicted activated cytokines from IPA upstream analysis; stimulations analysed independently using Pam3CSK4 1202 and LPS 4777 transcripts lists, at each time point only genes whose expression were 1.8 fold different from the media control at that time point were taken into consideration. Predicted upstream cytokines which met the criteria (<i>p</i> value <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>) Cytokine identified from either predicted upstream analysis or canonical pathway analysis (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0097702#pone-0097702-g003" target="_blank">Fig. 3</a>) mean mRNA expression 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 cytokines whose mRNA expression is >1.8 fold upregulated relative to media control at one or more one time points are shown.</p

LPS or Pam3CSK4 stimulations results in a differential response in gene expression over time.

<p>(<b>A</b>) 1 ml of human whole blood from healthy volunteers (N = 4) was stimulated with either Pam3CSK4 (200 ng/ml), LPS (1 ng/ml) or media control for different lengths of time (0, 1, 3, 6, 12 and 24 hours). Stimulations were analysed independently: media control compared to Pam3CSK4 and media control compared to LPS revealed 1202 and 4777 significantly expressed transcripts respectively. Transcripts were identified by normalising expression values to the median of the 0 hour samples, filtering by detection from background, statistical filtering (2 way ANOVA with Benjamini Hochberg multiple testing correction <i>p</i><0.01) and retaining transcripts whose expression was greater than 1.8 FC different between the media control and stimulation samples at one or more time point. (<b>B</b>) A Venn diagram of both significant transcript lists. Within the Venn for each subset the number of transcripts is given, with unique genes within IPA in brackets. For transcript lists the top 5 canonical pathways (IPA) are shown as well as a heat map of the normalised expression values of these transcripts for both stimulations over time.</p

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

Transcript lists analysed at each time point.

<p>(<b>A</b>) A graph showing the number of genes from the respective significant transcript lists (4777 LPS and 1202 Pam3CSK4 lists) at each time point which are more than 1.8 FC different compared to the media control at that time point. (<b>B</b>) The significantly expressed transcript lists (1202 Pam3CSK4 transcript list and 4777 LPS transcript list) were analysed in IPA. For each time point only genes whose expression were 1.8 FC different from the media control at that time point were taken into consideration. Shown is a heatmap of pathway significance of the top 25 IPA canonical pathways for each time point where significance criteria met (Fishers Exact test <i>p</i><0.01). The IPA canonical pathways were chosen by identifying from the LPS stimulation analyses the top 25 most significant pathways across the time points (mean –log <i>p</i> value) and then compared to Pam3CSK4. (<b>C</b>) Venn diagrams of cytokine/chemokines and transcriptional regulators identified using IPA gene functional classification from LPS 4777 and Pam3CSK4 1202 transcript lists with mean expression greater than 1.8 FC different to media control at 1 hour. Listed adjacent to the Venn diagrams are the genes from each subset.</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