Consequently, elevated levels of sCD163 observed in the plasma of Kisumu children, likely due to high pathogen burden (mainly malaria and schistosomiasis), might affect several branches of the immune system during infection, resulting in the generation of atypical cell subsets such as CD8dim T cells

Consequently, elevated levels of sCD163 observed in the plasma of Kisumu children, likely due to high pathogen burden (mainly malaria and schistosomiasis), might affect several branches of the immune system during infection, resulting in the generation of atypical cell subsets such as CD8dim T cells. CD8dim T cells represent a transcriptionally and functionally distinct state of CD8+ T cells. Due to limitations in blood volumes obtainable from children, we decided to first examine transcriptional profiles comparing CD8bright with CD8dim T cell subsets in PBMCs collected from adults with different histories of malaria exposure. display a more conventional CD8bright profile (IFN+, TNF+, CCL4+). Furthermore, these unconventional T cells had stunted proliferation, distinct transcriptional programs, and impaired T cell receptor signaling and were enriched in hallmark TNF, NF-B, and IL-6 gene signaling pathways, reminiscent of NK cells and type-1 innate lymphoid cells. Our findings suggest that these unconventional CD8dim T cells arise in a very particular immunological context and may provide a deeper understanding of the heterogeneity in human immune responses. 0.001, **** 0.0001 (two-tailed unpaired test with Welchs correction). (C) Representative bivariate plot displaying flow cytometry gating of CD3+ CD8+ T cell functional subsets. (D) Pie charts showing the proportion of CD8+ and CD4+ T cell subsets comparing the same children over time from Nandi and Kisumu. Data accumulated from 9 impartial experiments, = 14 (Nandi), = 15 (Kisumu). The proportion of T cell subsets are different between CD8bright and CD8dim but not over time (Welchs test). Elevated parasite-specific antibody titers are associated with increased proportions of CD8dim T cells. Although our cohorts were initially defined based on malaria transmission intensity, these children also had varied history of exposure to other common infections in this region (19C21, 25). In order to study the history of past infections within our study participants, we assessed cumulative pathogen burden by measuring antibodies (IgG) directed against select liver- and blood-stage malaria antigens, EBV, and Sm, along with antibodies to vaccine antigens (tetanus toxoid and edmonston measles vaccine computer virus) (Supplemental Physique 1). Unsupervised clustering of serological data revealed coclustering of school-age children consistent with their geographic origin, suggesting that antibody titers reflect expected cumulative pathogen exposure. In contrast, toddlers displayed greater heterogeneity within study groups that was poorly associated with place of residence and prevalence of infectious diseases characteristic T863 of the region (Physique 3A). This suggests that putative exposures attributed to residing in Kisumu or Nandi, defined as an ecological variable, may not be useful to characterize cumulative exposures for children at such a young age, and it suggests that interspersed longitudinal sample collection may miss detection of transient or subpatent T863 infections. Not surprisingly, clusters in school-age children were driven by Pf and EBV antibody titers and were in accordance with previous studies (19, 25). Interestingly, we found that, in school-age children, antibody titers for Pf and Sm were positively correlated with the percentage of CD8dim T cells (Physique 3B), while antibody titers to EBV antigens, measles vaccine computer virus, or tetanus toxoid were not. Open in a separate window Physique 3 Children living in areas of elevated pathogen burden develop unique serological and plasma cytokine profiles.Serum antibody titers and plasma analytes were measured at a 4-12 months interval (toddlers to school-age) coinciding with T cell subset assays. Immunity PLD1 to vaccine antigens, measles, and tetanus were measured as controls (Nandi, = T863 33; Kisumu, = 31). Antibody titers (IgG) specific for Pf (HPR-II, MSP1-FVO, CSP), measles computer virus (edmonston vaccine strain), Clostridium tetani (tetanus), Schistosoma mansoni (SWAP), and EBV (EAD, ZEBRA, VCA, EBNA1) were measured using multiplex conjugated-bead suspension assay. (A) Heatmap of scaled antibody titers (score). Pathogen burden is usually represented with orange (low, Nandi) and purple (high, Kisumu). Data generated from 1 experiment measuring plasma antibody titers from patients. (B) Dotplots (and 95% CI) representing the association between proportion of CD3+ CD8dim T cells and pathogen-specific antibody titers in school-age children. Solid lines represent best-fit regression line and coefficient of determination (r2), and values are displayed (* 0.05, *** 0.001, **** 0.0001). (C) Steady-state plasma sCD163 levels from toddlers and school-age children. Boxplot (median and 95% IQR) displays the relative amount of sCD163 (pg/ml) (Nandi, = 14; Kisumu, = 15). T863 Black dots are values from individual children. Two-way ANOVA with Sidak multiple comparison post test was used to analyze statistical significance for the 51 analytes measured in the two groups of divergent pathogen exposure. Data generated from one experiment measuring plasma analytes titers from 29 patients. Children living in areas of high pathogen burden have significantly elevated levels of plasma soluble CD163. Next, we examined the cytokine profiles in these children to determine if cytokine milieu was associated with the increased proportion of CD8dim T cells. We used multiplex panels to measure 51 plasma analytes (IL-1, IL-1ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12(p70), IL-13, IL-15, IL-17, Eotaxin, FGF basic, G-CSF, GM-CSF, IFN-, IP-10, MCP-1, MIP-1, PDGF-BB, MIP-1, RANTES, TNF-, VEGF, APRIL, BAFF, sCD30, sCD163, Chitinase 3-like 1, gp130, IFN, IL-11, IL-19, IL-20, IL-26, IL-27, IL-28, IL-29, IL-32, IL-34, IL-35, LIGHT, Osteocalcin, Pentraxin-3, sTNF-R1, sTNF-R2, TSLP, TWEAK). After adjusting for.