Multivariate Computational Analysis of Gamma Delta T Cell Inhibitory Receptor Signatures Reveals the Divergence of Healthy and ART-Suppressed HIV+ Aging

Abstract

Even with effective viral control, HIV-infected individuals are at a higher risk for morbidities associated with older age than the general population, and these serious non-AIDS events (SNAEs) track with plasma inflammatory and coagulation markers. The cell subsets driving inflammation in aviremic HIV infection are not yet elucidated. Also, whether ART-suppressed HIV infection causes premature induction of the inflammatory events found in uninfected elderly or if a novel inflammatory network ensues when HIV and older age co-exist is unclear. In this study we measured combinational expression of five inhibitory receptors (IRs) on seven immune cell subsets and 16 plasma markers from peripheral blood mononuclear cells (PBMC) and plasma samples, respectively, from a HIV and Aging cohort comprised of ART-suppressed HIV-infected and uninfected controls stratified by age (≤35 or ≥50 years old). For data analysis, multiple multivariate computational algorithms [cluster identification, characterization, and regression (CITRUS), partial least squares regression (PLSR), and partial least squares-discriminant analysis (PLS-DA)] were used to determine if immune parameter disparities can distinguish the subject groups and to investigate if there is a cross-impact of aviremic HIV and age on immune signatures. IR expression on gamma delta (γδ) T cells exclusively separated HIV+ subjects from controls in CITRUS analyses and secretion of inflammatory cytokines and cytotoxic mediators from γδ T cells tracked with TIGIT expression among HIV+ subjects. Also, plasma markers predicted the percentages of TIGIT+ γδ T cells in subjects with and without HIV in PSLR models, and a PLS-DA model of γδ T cell IR signatures and plasma markers significantly stratified all four of the subject groups (uninfected younger, uninfected older, HIV+ younger, and HIV+ older). These data implicate γδ T cells as an inflammatory driver in ART-suppressed HIV infection and provide evidence of distinct “inflamm-aging” processes with and without ART-suppressed HIV infection.

Cancer immunotherapy with γδ T cells: many paths ahead of us

“…Tumor cells and suppressive cells such as MDSCs frequently express ligands for inhibitory checkpoint receptors; for instance, PD-L1 and γδ T cells can express such receptors to varying degrees. 125 Moreover, tumor cells themselves, tumor-associated macrophages, MDSCs and other cells within the microenvironment can produce a range of inhibitory molecules, including (but not limited to) TGF-β, IL-4, galectins, and indoleamine-2,3-dioxygenase (IDO), all of which may inhibit intratumoral γδ T cells from attacking the tumor. 126 – 130 Arginase-I, an enzyme that suppresses both Vδ2 T cell cytotoxicity and IFN-γ production, can be produced by both tumor cells and MDSCs.…”

γδ T‐cell responses during HIV infection and antiretroviral therapy

“…A transition of the cd compartment from 'resting' CD160 + phenotype to an 'activated/exhausted' TIGIT + PD-1+ phenotype was associated with plasma-derived proinflammatory profile. 150 While an inversion of the Vd2:Vd1 ratio was confirmed for a subset of HIV-infected study participants, no data were available to assess the separate contribution of Vd1 and Vd2 cells to the HIVassociated inflammation and ageing. Such information will be critical to understanding which cd subset primarily expressed the TIGIT + PD-1+ phenotype and/or correlates mostly strongly with plasma inflammatory biomarkers.…”

“…In a fascinating study, Belkina et al comprehensively assessed the expression of inhibitory surface receptors on a wide range of lymphocyte subsets including two NK cell populations, conventional T cells, Tregs, iNKT cells and cdT cells in ART-treated and control participants. 150 Importantly, these cohorts were stratified for age, allowing for a simultaneous assessment of immune ageing in each group. Among all lymphocyte subsets, only cd phenotype was sufficient to distinguish between the control and infected groups.…”

Automated optimized parameters for t-distributed stochastic neighbor embedding improve visualization and allow analysis of large datasets

“…Data pre-processing. Singlet events from several data recordings were digitally concatenated and a randomly subsampled file of 1,000,000 mass 15 or flow cytometry 16 events was created and used for analyses of the mass41parameter and flow18parameter datasets. All observations from 27 recordings of flow cytometry data were concatenated to generate the flow20M dataset.…”