Associations of body mass index (BMI).

<p>(A) Box-and-whisker plot showing the relative distribution of BMI by vaccine response group (median, Interquartile range, minimum and maximum shown) (B) Spearman’s correlation of age at first vaccination and BMI.</p

Relative distribution of vaccine non-response by body mass index (BMI) category.

<p>Illustrates the proportion of non-responders within each standard BMI category. The underweight category has been excluded as only 4 participants fell into this group.</p

Baseline plasma concentrations of analytes in slow and fast responders.

<p>Baseline plasma concentrations of analytes in slow and fast responders.</p

A combination of baseline plasma immune markers can predict therapeutic response in multidrug resistant tuberculosis

<div><p>Objective</p><p>To identify plasma markers predictive of therapeutic response in patients with multidrug resistant tuberculosis (MDR-TB).</p><p>Methods</p><p>Fifty HIV-negative patients with active pulmonary MDR-TB were analysed for six soluble analytes in plasma at the time of initiating treatment (baseline) and over six months thereafter. Patients were identified as sputum culture positive or negative at baseline. Culture positive patients were further stratified by the median time to sputum culture conversion (SCC) as fast responders (< 76 days) or slow responders (≥ 76 days). Chest X-ray scores, body mass index, and sputum smear microscopy results were obtained at baseline.</p><p>Results</p><p>Unsupervised hierarchical clustering revealed that baseline plasma levels of IP-10/CXCL10, VEGF-A, SAA and CRP could distinguish sputum culture and cavitation status of patients. Among patients who were culture positive at baseline, there were significant positive correlations between plasma levels of CRP, SAA, VEGF-A, sIL-2Rα/CD40, and IP-10 and delayed SCC. Using linear discriminant analysis (LDA) and Receiver Operating Curves (ROC), we showed that a combination of MCP-1/CCL2, IP-10, sIL-2Rα, SAA, CRP and AFB smear could distinguish fast from slow responders and were predictive of delayed SCC with high sensitivity and specificity.</p><p>Conclusion</p><p>Plasma levels of specific chemokines and inflammatory markers measured before MDR-TB treatment are candidate predictive markers of delayed SCC. These findings require validation in a larger study.</p></div

Baseline clinical and microbiological characteristics of MDR-TB patients.

<p>Baseline clinical and microbiological characteristics of MDR-TB patients.</p

Expression of plasma markers in fast and slow responders.

<p>(A) Distribution of time to culture conversion (TCC) in study cohort; (B–F) Correlation between baseline levels of individual plasma markers and TCC, shown as slow (red) or fast (black) responders. (G) Principal component analysis (PCA) plot of slow (red) and fast (black) responders, analyzed as above.</p

Baseline levels of plasma markers.

<p>(A) Two-dimensional unsupervised hierarchical clustering of baseline analyte profiles in 50 patients, characterized by sputum smear (SS) and sputum culture (SC) status and cavitary vs non-cavitary disease. Normalized and log2 transformed values of analyte levels are indicated by the color scale, where yellow and blue represent expression levels above and below the median, respectively. Three-dimensional plots of principal component analysis (PCA) of (B) SS negative (orange) and SS positive (blue); (C) SC negative (yellow) and SC positive (blue); (D) cavitary (pink) and non-cavitary disease (green). Statistical comparisons using non-parametric Mann-Whitney U test were corrected for multiple comparisons through a false discovery rate (FDR) step down procedure (*: q<0.05, **: q<0.01, ***: q<0.001).</p

Plasma markers as predictors of fast vs slow response to treatment.

<p>Receiver Operating Characteristic (ROC) curve analysis and baseline LDA scores of (A and B) the optimal combination of plasma markers, and (C and D) the optimal combination of markers plus clinical data (sputum smear). Horizontal bars indicate median and interquartile range. Statistical analyses between unpaired groups were performed using non-parametric Wilcoxon paired tests. Differences between groups were assessed by Mann-Whitney U test. P<0.05 was considered significant.</p

Representative plot illustrating CD25 gating strategy.

<p>Lymphocytes were gated according to forward and side scatter. CD3 positive lymphocytes were selected. CD25 expression on the x-axis was plotted against CD4 on the y-axis. A gate was set according to CD25 expression on the CD4⁻ population. This gave a percentage of CD3⁺CD4⁺CD25⁺ lymphocytes as a percentage of CD3⁺ (figure in top-right quadrant). This percentage was then used to calculate the percentage of CD3⁺CD4⁺CD25⁺ lymphocytes as a percentage of the CD3⁺CD4⁺ population (CD4 population being top-left and right quadrants added together).</p

FOXP3 expression after T cell receptor stimulation.

<p><i>Panel A</i>: FOXP3 expression as a percentage of T lymphocytes at baseline and after 4 days of cell culture, with and without T cell receptor stimulation with anti-CD3. <i>Panel B</i>: Proliferation of total CD4⁺ T cells and FOXP3⁺ expression in proliferated CD4⁺ T cells following T cell receptor stimulation with aCD3. <i>Panel C</i>: Representative plot of FOXP3⁺ expression in proliferated T cells (left-hand plot) following anti-CD3 stimulation compared with an unstimulated sample(right-hand plot). The small plots above show ancestry – lymphocytes were gated; followed by exclusion of events with high CFSE; followed by selection of CD3⁺CD4⁺ T cells. Proliferation is demonstrated by halving of CFSE fluorescence in cells that have divided (large plots below). In the anti-CD3 stimulated sample, FOXP3 expression is noted in cells which have proliferated (top-left quadrant) as well as those that have not proliferated (top-right quadrant).</p