Computational protocol: Mycobacterial Antigen Driven Activation of CD14++CD16− Monocytes Is a Predictor of Tuberculosis-Associated Immune Reconstitution Inflammatory Syndrome

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Protocol publication

[…] The immunophenotyping of monocytes and dendritic cells (DC) was performed in whole blood collected in heparinized vacuum tubes. For ex vivo phenotyping, aliquots of 250 µL blood were stained with five panels of antibodies prepared in PBS 1% BSA for 1 h at room temperature (RT) to characterize the myeloid populations. The panels with antibody clones and fluorochromes as well as the gating strategies are listed in . Antibodies were from eBioscience (San Diego, CA), Biolegend (San Diego, CA), BD Biosciences (San Jose, CA) and Life Technologies (Carlsbad, CA). Data from the Indian cohort were acquired on a BD FACS Canto II flow cytometer (BD Biosciences) and from the North American cohort were acquired on a BD LSR II flow cytometer (BD Biosciences). Three major monocyte subsets are considered for analysis based on CD14 and CD16 surface expression: classical/inflammatory (CD14++CD16−), intermediate (CD14+CD16+) and patrolling (CD14dimCD16+) monocytes. Data on monocyte subsets are displayed as percent of circulating mononuclear myeloid cells, defined in this study as HLA-DR+ Dump− (CD2−CD3−CD19−CD20−CD56−) cells in the blood. All compensation and gating analyses were performed using FlowJo 9.5.3 (TreeStar, Ashland, OR). [...] The inferential networks were generated from Spearman correlation matrices containing values of each biomarker measured in the plasma samples and blood cell counts (neutrophils and monocytes). The values were inputted in JMP 10.0 software (SAS, Cary, NC, USA). Each mediator is selected as a target, and the software performs a search within the other mediators for those that are correlated, with the target calculating a correlation matrix using Spearman rank tests. As a result, the features related to the selected target are linked. The links shown in the networks represent statistically significant Spearman rank correlations (P<0.05). In order to analyze the structure of the biomarker networks, the network density was calculated, which is, in the context of this study, the ratio of the number of edges inferred in the network over the total number of possible edges between all pairs of nodes . The density measure is defined as follows: density = L/(N (N-1)/2), in which L is the number of observed edges (i.e., Spearman correlations with P<0.05) and N is the total number of the nodes in the network. The density is normalized, ranging between 0 (no edges in the network) and 1 (all possible edges presents). The number of statistically significant correlations was compared between the IRIS and non-IRIS groups at the different time points using the permutation test. The same test was used to compare the changes in the number of significant correlations in the networks from week 0 to week 6/IRIS event within the two clinical groups. Graphics for the network analysis were customized using the Ingenuity Systems Pathway Analysis software (Ingenuity Systems, Redwood City, CA, USA) and Adobe Illustrator (Adobe Systems Inc.). […]

Pipeline specifications

Software tools FlowJo, Adobe Illustrator
Applications Miscellaneous, Flow cytometry
Organisms Homo sapiens
Diseases Tuberculosis, Meningeal, HIV Infections, Immune Reconstitution Inflammatory Syndrome