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

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

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

Genes present in the top Ingenuity Pathway Analysis pathways in the active TB 664-transcript signature.

<p>FC  =  fold change. TREM1 =  triggering receptor expressed on myeloid cells.</p

A blood transcriptional response is detectable after only 2 weeks of treatment.

<p><b>(A)</b> Profile plot of all detectable transcripts (15837) obtained without any filtering, in the treated active TB patients in the South Africa 2011 cohort. It can be seen that gene expression changes after just 2 weeks of treatment. <b>(B)</b> 664 differentially expressed transcripts between untreated active and latent TB patients in the untreated South Africa 2011 cohort, were obtained by twofold change from the median and stringent statistical filtering (Mann Whitney, Bonferroni p<0.01). The heatmap shows the dynamic change of gene expression in response to treatment in the treated South Africa 2011 cohort normalised to the median of all transcripts. <b>(C)</b> Ingenuity Pathway Analysis (IPA) of the 664 transcripts shows the top significant pathways. <b>(D)</b> Interferon signaling pathway from the 664 list in IPA. <b>(E)</b> Weighted molecular distance to health (MDTH) of the treated South Africa 2011 cohort shows the signature significantly diminishes over time (linear mixed models, bars represent median & IQR, *** = p<0.001, ** = p<0.01, * = p<0.05). <b>(F)</b> Temporal molecular response further shows significant and early changes in response to anti-TB treatment (linear mixed models, bars represent mean & 95% confidence intervals).</p

Numbers enrolled and assigned to cohorts.

<p><b>(A)</b> South Africa: A total of 67 active and latent TB patients were enrolled into the untreated South Africa 2011 Cohort. A total of 29 active TB patients were included in the treated South Africa 2011 Cohort. 15 were randomised into the Active TB Training Set and 14 into the Active TB Test Set. <b>(B)</b> UK: A total of 8 active TB patients were enrolled into the treated UK 2011 Cohort.</p

Three dominant clusters of the 1446 differentially expressed transcripts are associated with distinct biological pathways.

<p>Each of the three dominant clusters of transcripts is associated with different study groups in the Training Set. The top transcript cluster is over-abundant in the pneumonia and cancer patients and significantly associated with IPA pathways relating to inflammation (Fisher’s exact with Benjamini Hochberg FDR = 0.05). The middle transcript cluster is over-abundant in the TB and sarcoidosis patients and significantly associated with IFN signalling and other immune response IPA pathways (Fisher’s exact with Benjamini Hochberg FDR = 0.05). The bottom transcript cluster is under-abundant in all the patients and significantly associated with T and B cell IPA pathways (Fisher’s exact Benjamini Hochberg FDR = 0.05).</p

Class prediction of 50 genes from the Koth et al study.

<p>Class prediction was performed using support vector machines (SVM). The 50 genes from the Koth <i>et al</i> study were used to build the last SVM model in the Training Set and run in the Test and Validation Sets.</p

Class prediction of 100 Agilent transcripts from the Maertzdorf et al study.

<p>Class prediction was performed using support vector machines (SVM). The 100 Agilent transcripts from the Maertzdorf <i>et al</i> study translated to 76 recognised genes using the DAVID gene converter. The SVM model was built in the Training Set and run in the Test and Validation Sets.</p

Class prediction of 144 transcripts from the Training Set.

<p>Class prediction was performed using support vector machines (SVM). The 144 transcripts derived from the Training Set were used to build the SVM model, the model was then run in the other two cohorts and the external cohort (Maertzdorf <i>et al</i>).</p