Phenotypic analysis of CD25^High CD4⁺ regulatory T cells in the peripheral blood from chagasic patients.

<p>Phenotypic features of CD25^High CD4⁺ cells from patients with distinct clinical forms of Chagas' disease (IND, light gray box; CARD, dark gray box) and non-infected individuals (NI, white box) following short-term in vitro stimulation of whole blood samples with <i>T. cr</i>u<i>zi</i> antigens. Baseline levels for a range of phenotypic features of CD25^High CD4⁺ cells were obtained from control cultures (CC) maintained under the same conditions (22 h incubation at 37°C, CO₂ humidified incubator). The results are expressed in box plot format as the percentage of positive cells within CD25^High CD4⁺ cells including those expressing adhesion molecules CD62L (D) and CD54 (B), co-stimulatory receptors CD40L (A) and CTLA-4 (E), activation marker CD69 (C) and regulatory receptor IL-10R (F). The box stretches from the lower hinge (defined as the 25^th percentile) to the upper hinge (the 75^th percentile) and, therefore, contains the middle half of the score in the distribution. The median is shown as a line across the box. Therefore, 1:4 of the distribution is between this line and the bottom or the top of the box. Significant differences are identified by connecting lines for comparisons between CC and Ag, and highlighted that <i>T. cruzi</i> antigens triggered an overall change in the phenotypic features of CD25^High CD4⁺ cells towards lower frequency of CD62L⁺ and IL-10R⁺ cells besides increased levels of CD54⁺, CD40L⁺, and CD69⁺ cells in both IND and CARD groups. Although <i>T. cruzi</i> antigens were able to induce higher levels of CTLA-4 in both groups of chagasic patients (IND and CARD), the impact of <i>T. cruzi</i> antigens was more pronounced in CARD, leading to higher frequency of CTLA-4⁺ cells in comparison to NI. Adapted from <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0000992#pntd.0000992-Araujo1" target="_blank">[20]</a>.</p

High frequencies of DN αβ T-cells are associated with TB severity.

<p>Representative contour plots showing the gate strategy used for the analysis of CD4 (middle left), CD8 (middle center), DN (middle right) αβ-T cells and the expression of CD69 (upper panels) and HLA-DR (lower panels) on DN αβ-T cells (A). Percentages of CD4⁺ (left panels), CD8⁺ (middle panels) and DN (right panels) αβ T-cells in healthy donors (HD, open symbols), TB (total TB, black symbols), nsTB (non-severe TB, light gray symbols) and sTB patients (severe TB, dark gray) were measured before treatment (B). The percentage of CD69 (C) and HLA-DR (D) expression within CD4⁺ (left panels), CD8⁺ (middle panels) and DN (right panels) αβ T-cells in HD, TB, nsTB and sTB patients were analyzed ex vivo. The boxes represent the means.</p

Higher frequencies of IFN-γ producing DN αβ T-cells are found in nsTB patients.

<p>Representative contour plots showing the proportions of IFN-γ producing CD4 (left panel), CD8 (middle panel) and DN (right panel) αβ-T cells (A). The percentages of IFN-γ (B), TNF-α (C) and IL-10 (D) expression within CD4⁺ (left panels), CD8⁺ (middle panels) and DN (right panels) αβ T-cells in healthy donors (HD, open symbols), TB (total TB, black symbols), nsTB (non-severe TB, light gray symbols) and sTB patients (severe TB, dark gray) were measured before treatment. PBMCs were stimulated with (MTB-Ag) for 48 hours. The boxes represent the means.</p

Advanced TB patients display decreased proportions of DN γδ T-cells.

<p>Representative contour plots showing the gate strategy used for the analysis of CD4 (middle left), CD8 (middle center), DN (middle right) γδ-T cells and the expression of CD69 (upper panels) and HLA-DR (lower panels) on DN γδ -T cells (A). Percentages of CD4⁺ (left panels), CD8⁺ (middle panels) and DN (right panels) γδ T-cells in healthy donors (HD, open symbols), TB (total TB, black symbols), nsTB (non-severe TB, light gray symbols) and sTB patients (severe TB, dark gray) were measured before treatment (B). The percentage of CD69 (C) and HLA-DR (D) expression within CD4⁺ (left panels), CD8⁺ (middle panels) and DN (right panels) γδ T-cells in HD, TB, nsTB and sTB patients were analyzed ex vivo. The boxes represent the means.</p

Analysis of Foxp3⁺ CD25^High CD4⁺ regulatory T cells in the peripheral blood from chagasic patients.

<p>Representative dot plots illustrate that the increased levels of regulatory T cells observed in <i>T. cruzi</i> antigens-stimulated cultures from IND tend to be higher than that observed in CARD, and also reflect an increased level of Foxp3⁺ CD25⁺ cells in the IND group (bottom graphs). Quadrant statistics were used for data analysis, and the results are expressed as the percentage of positive cells within the CD25⁺ CD4⁺ selected lymphocytes. Reproduced and adapted from <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0000992#pntd.0000992-Araujo1" target="_blank">[20]</a>.</p

Proposed hypothesis for CD25^High CD4⁺ Treg cells function on immunoregulation in chronic Chagas' disease.

<p>Several leukocyte subsets have been shown to play a role in immunoregulation during chronic infections. In this model, Chagas' disease patients with the indeterminate clinical form show Treg cells able to modulate the effectors' function of CD8⁺ T cells, in a microenvironment supported by cytotoxic NK-cells, Monocytes and CD4⁺ T cells producing regulatory cytokines (IL-10 and IL-10, IL-4, respectively). This immunological milieu contributes to controlling the parasitemia and regulating the immunopathology. On the other hand, Chagas' disease patients with cardiac and digestive clinical forms display insufficient modulation by Treg cells with activated CD8⁺ T cells besides monocytes and CD4+ T cells producing inflammatory cytokines (TNF-α and IFN-γ, respectively). This microenvironment triggers immunopathological events and leads to tissue damage in the absence of regulatory mechanisms and cytotoxic NK-cell functions.</p

Analysis of IL-10⁺ CD25^High CD4⁺ regulatory T cells in the peripheral blood from chagasic patients.

<p>Frequency of regulatory T cells and intracytoplasmic IL-10 (cIL-10) levels in CD25^High CD4⁺ cells from patients with distinct clinical forms of Chagas' disease (IND, light gray box; CARD, dark gray box) and non-infected individuals (NI, white box) following short-term in vitro stimulation of whole blood samples with <i>T. cr</i>u<i>zi</i> antigens. Baseline levels of CD25^High CD4⁺ and cIL-10⁺ T cells were obtained from control cultures (CC) maintained under the same conditions (22 h incubation at 37°C, CO₂ humidified incubator). The results are expressed in box plot format for CD25^High CD4⁺ (left panels) and cIL-10⁺ T cells (right panels). The box stretches from the lower hinge (defined as the 25^th percentile) to the upper hinge (the 75^th percentile) and, therefore, contains the middle half of the score in the distribution. The median is shown as a line across the box. Therefore, 1:4 of the distribution is between this line and the bottom or the top of the box. Significant differences are identified by connecting lines for comparisons between CC and Ag, and highlighted the ability of <i>T. cruzi</i> antigens to trigger enhanced levels of CD25^High CD4⁺ and cIL-10⁺ T cells in both IND and CARD groups. Significant differences between clinical groups are identified by asterisks as compared to NI.</p

Systems biology strategy for analyzing adaptive immunity flow-cytometry data by heatmap and decision-tree analysis.

<p>(A) Bioinformatics tool applied for single-cell data mining using heatmap computational method to preprocess flow cytometry data and to identify the adaptive immunity cell attributes. (B) Decision tree analysis identifies “root” (CD3⁺HLA-DR⁺) and “secondary” (CD8⁺HLA-DR⁺ and CD8⁺ Granzyme A⁺) cell attributes with higher accuracy to distinguish between non-human primates naturally infected with <i>T</i>. <i>cruzi</i> and non-infected controls. (C) Scatter distribution plots show the potential of selected biomarkers to discriminate infected from non-infected individuals. White rectangles indicate true positive (Chagas disease) and true negative (non-infected subjects) classifications. Gray rectangles indicate subjects that require the analysis of additional characteristics for accurate classification by the algorithm sequence proposed by the decision tree. (C) ROC curve analysis illustrating the cut-off points, the global accuracy (area under the curve–AUC) and performance indexes (sensitivity–Se, specificity–Sp and likelihood ratio–LR) for each selected biomarker.</p

Adaptive immunity features from cynomolgus macaques naturally infected with <i>T</i>. <i>cruzi</i> (CH) and non-infected controls (NI).

<p>(A) The frequencies of CD3⁺ lymphocytes and T-cell subsets (CD4⁺ and CD8⁺), the expression of adhesion molecule (CD54) and activation status (CD69 and HLADR) were performed by multicolor flow cytometry. (B) The expression of cytotoxicity markers (Granzyme A, Granzyme B and Perforin) of CD8⁺ T-cells was investigated by intracellular staining flow cytometry. (C) Analysis of B-cells, the activation status (CD69), and the expression of the regulatory FcγR (CD32) were evaluated by three-color flow cytometry. The results are expressed as mean percentage with standard error. Significant differences at <i>p<</i>0.05 are identified by (*).</p

Systems biology strategy for analyzing innate immunity flow-cytometry data by heatmap and decision-tree analysis.

<p>(A) Bioinformatics tool applied for single-cell data mining using heatmap computational method to preprocess flow cytometry data and to identify the innate immunity cell attributes. (B) Decision tree analysis identifies “root” (CD14⁺CD56⁺) and “secondary” (NK Granzyme A⁺ and NK CD16⁺CD56^-) cell attributes with higher accuracy to distinguish between non-human primates naturally infected with <i>T</i>. <i>cruzi</i> and non-infected controls. (C) Scatter distribution plots show the potential of selected biomarkers to discriminate infected from non-infected individuals. White rectangles indicate true positive (Chagas disease) and true negative (non-infected subjects) classifications. Gray rectangles indicate subjects that require the analysis of additional characteristics for accurate classification by the algorithm sequence proposed by the decision tree. (C) ROC curve analysis illustrating the cut-off points, the global accuracy (area under the curve–AUC) and performance indexes (sensitivity–Se, specificity–Sp and likelihood ratio–LR) for each selected biomarker.</p