Detectable changes in the blood transcriptome are present after two weeks of antituberculosis therapy

Rationale: Globally there are approximately 9 million new active tuberculosis cases and 1.4 million deaths annually . Effective antituberculosis treatment monitoring is difficult as there are no existing biomarkers of poor adherence or inadequate treatment earlier than 2 months after treatment initiation. Inadequate treatment leads to worsening disease, disease transmission and drug resistance. Objectives To determine if blood transcriptional signatures change in response to antituberculosis treatment and could act as early biomarkers of a successful response. METHODS: Blood transcriptional profiles of untreated active tuberculosis patients in South Africa were analysed before, during (2 weeks and 2 months), at the end of (6 months) and after (12 months) antituberculosis treatment, and compared to individuals with latent tuberculosis. An active-tuberculosis transcriptional signature and a specific treatment-response transcriptional signature were derived. The specific treatment response transcriptional signature was tested in two independent cohorts. Two quantitative scoring algorithms were applied to measure the changes in the transcriptional response. The most significantly represented pathways were determined using Ingenuity Pathway Analysis. RESULTS: An active tuberculosis 664-transcript signature and a treatment specific 320-transcript signature significantly diminished after 2 weeks of treatment in all cohorts, and continued to diminish until 6 months. The transcriptional response to treatment could be individually measured in each patient. CONCLUSIONS: Significant changes in the transcriptional signatures measured by blood tests were readily detectable just 2 weeks after treatment initiation. These findings suggest that blood transcriptional signatures could be used as early surrogate biomarkers of successful treatment response

Transcriptional Blood Signatures Distinguish Pulmonary Tuberculosis, Pulmonary Sarcoidosis, Pneumonias and Lung Cancers

Rationale: New approaches to define factors underlying the immunopathogenesis of pulmonary diseases including sarcoidosis and tuberculosis are needed to develop new treatments and biomarkers. Comparing the blood transcriptional response of tuberculosis to other similar pulmonary diseases will advance knowledge of disease pathways and help distinguish diseases with similar clinical presentations. Objectives: To determine the factors underlying the immunopathogenesis of the granulomatous diseases, sarcoidosis and tuberculosis, by comparing the blood transcriptional responses in these and other pulmonary diseases. Methods: We compared whole blood genome-wide transcriptional profiles in pulmonary sarcoidosis, pulmonary tuberculosis, to community acquired pneumonia and primary lung cancer and healthy controls, before and after treatment, and in purified leucocyte populations. Measurements and Main Results: An Interferon-inducible neutrophil-driven blood transcriptional signature was present in both sarcoidosis and tuberculosis, with a higher abundance and expression in tuberculosis. Heterogeneity of the sarcoidosis signature correlated significantly with disease activity. Transcriptional profiles in pneumonia and lung cancer revealed an over-abundance of inflammatory transcripts. After successful treatment the transcriptional activity in tuberculosis and pneumonia patients was significantly reduced. However the glucocorticoid-responsive sarcoidosis patients showed a significant increase in transcriptional activity. 144-blood transcripts were able to distinguish tuberculosis from other lung diseases and controls. Conclusions: Tuberculosis and sarcoidosis revealed similar blood transcriptional profiles, dominated by interferon-inducible transcripts, while pneumonia and lung cancer showed distinct signatures, dominated by inflammatory genes. There were also significant differences between tuberculosis and sarcoidosis in the degree of their transcriptional activity, the heterogeneity of their profiles and their transcriptional response to treatment

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

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

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

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