PMC:7402624 / 18450-26371 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7402624","sourcedb":"PMC","sourceid":"7402624","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7402624","text":"SARS-CoV2 infection is associated with heterogeneous CD4 T cell responses and activation of CD4 T cell subsets\nWe next examined six well-defined CD4 T cell subsets as above for the CD8 T cells, including naïve, effector memory (EM1,2,3), central memory (CM), and EMRA (Fig. 3A). Given the potential role of antibodies in the response to SARS-CoV2 (27, 29), we also analyzed circulating Tfh (CD45RA−PD1+CXCR5+ [cTfh] (36)) and activated circulating Tfh (CD38+ICOS+ [activated cTfh]), the latter of which may be more reflective of recent antigen encounter and emigration from the germinal center (37, 38) (Fig. 3A). These analyses revealed a relative loss of naïve CD4 T cells compared to controls, but increased EM2 and EMRA (Fig. 3B). The frequency of activated but not total cTfh was statistically increased in COVID-19 patients compared to HD, though this effect appeared to be driven by a subgroup of patients (Fig. 3B). It is worth noting that activated cTfh frequencies were also higher in RD compared to HD (Fig. 3B), perhaps reflecting residual COVID-19 responses in that group. Frequencies of KI67+ or CD38+HLA-DR+ non-naïve CD4 T cells were increased in COVID-19 patients (Fig. 3, C and E); however, this change was not equivalent across all CD4 T cell subsets. The most substantial increases in both KI67+ and CD38+HLA-DR+ cells were found in the effector memory populations (EM, EM2, EM3) and in cTfh (fig. S3, A and B). Although some subjects had increased activation of EMRA, this was less pronounced. In contrast, PD1 expression was increased in all other non-naïve populations compared to HD or RD (fig. S3C). Co-expression of CD38 and HLA-DR by non-naïve CD4 T cells correlated with the frequency of KI67+ non-naïve CD4 T cells (fig. S3D). Moreover, the frequency of total non-naïve CD4 T cells that were CD38+HLA-DR+ correlated with the frequency of activated cTfh (fig. S3E). In general, the activation of CD4 T cells was correlated with the activation of CD8 T cells (Fig. 3, D and F). However, whereas ~2/3 of patients had KI67+ non-naïve CD4 or CD8 T cell frequencies above controls, ~1/3 of the COVID-19 patients had no increase in frequency of KI67+ CD4 or CD8 T cells above that observed in HD (Fig. 3, D and F). Moreover, although most patients had similar proportions of activated CD4 T cells compared to CD8 T cells, there was a subgroup of patients that had disproportionate activation of CD4 T cells compared to CD8 T cells (Fig. 3, D and F). KI67+ and CD38+HLA-DR+ non-naïve CD4 T cell frequencies correlated with ferritin and with APACHE III score (fig. S3F), suggesting a relationship between CD4 T cell activation and disease severity. Immunosuppression did not impact CD4 T cell activation; however, early steroid administration was weakly associated with CD4 T cell KI67 (fig. S3F). Together, these data highlight T cell activation in COVID-19 patients similar to what has been observed in other acute infections or vaccinations (37, 39, 40), but also identify patients with high, low, or essentially no T cell response based on KI67+ or CD38+HLA-DR+ compared to control subjects.\nFig. 3 CD4 T cell activation in a subset of COVID-19 patients associates with distinct CD4 T cell subsets.\n(A) Representative flow cytometry plots of the gating strategy for CD4 T cell subsets. (B) Frequencies of CD4 T cell subsets as indicated. (C) Frequencies of KI67+ cells; green line at upper decile of healthy donors (HD). Representative flow cytometry plots. (D) KI67+ cells from non-naïve CD4 T cells versus non-naïve CD8 T cells, Spearman correlation of COVID-19 patients. (E) Frequencies of HLA-DR+CD38+ cells; green line at upper decile of healthy donors (HD). Representative flow cytometry plots. (F) HLA-DR+CD38+ cells from non-naïve CD4 versus non-naïve CD8 T cells, Spearman correlation of COVID-19 patients. (G) (Top) Global viSNE projection of non-naïve CD4 T cells for all subjects pooled, non-naïve CD4 T cells from HD, RD, and COVID-19 patients concatenated and overlaid. (Bottom) viSNE projections of indicated protein expression. (H) viSNE projection of non-naïve CD4 T cell clusters identified by FlowSOM clustering. (I) Mean fluorescence intensity (MFI) as indicated, column-scaled z-score. (J) Percentage of non-naïve CD4 T cells from each cohort in each FlowSOM cluster. Boxes represent IQR. (B, C, E, J) Each dot represents an individual healthy donor (HD; green), recovered donor (RD; blue), or COVID-19 patient (red). Significance determined by unpaired Wilcoxon test with BH correction: *p \u003c 0.05, **p \u003c 0.01, ***p \u003c 0.001, and ****p \u003c 0.0001. Projecting the global CD4 T cell differentiation patterns into the high-dimensional tSNE space again identified major alterations in the CD4 T cell response during COVID-19 infection compared to HDs and RDs (Fig. 3G). In COVID-19 infection, there was a notable increase in density in tSNE regions that mapped to expression of CD38, HLA-DR, PD1, CD39, KI67, and CD95 (Fig. 3G), similar to CD8 T cells. To gain more insight into these CD4 T cell changes, we again used a FlowSOM clustering approach (Fig. 3, H and I). This analysis identified an increase in Clusters 13 and 14 in COVID-19 patients compared to HD and RD that represent populations expressing HLA-DR, CD38, PD1, KI67 and CD95, as well as Cluster 15 that contained Tbet+CX3CR1+ “effector-like” CD4 T cells (Fig. 3, I and J, and fig. S3G). In contrast, this clustering approach identified reduction in CXCR5+ cTfh-like cells (Clusters 2, 3) in COVID-19 subjects compared to HD (Fig. 3, I and H). Taken together, this multidimensional analysis revealed distinct populations of activated/proliferating CD4 T cells that were enriched in COVID-19 patients.\nA key feature of COVID-19 disease is thought to be an inflammatory response that, at least in some patients, is linked to clinical disease manifestation (2, 4) and high levels of chemokines/cytokines, including IL-1RA, IL-6, IL-8, IL-10, and CXCL10 (11, 41). To investigate the potential connection of inflammatory pathways to T cell responses, we performed 31-plex Luminex analysis on paired plasma and culture supernatants of anti-CD3/anti-CD28 stimulated PBMC from a subset of COVID-19 patients and HD controls. Due to biosafety restrictions, only eight COVID-19 patient blood samples that were confirmed SARS-CoV2 RNA negative in the blood by PCR could be studied (fig. S4A). Half of these COVID-19 patients had plasma CXCL10 concentrations that were ~15 fold higher than HD controls, whereas the remainder showed only a limited increase (fig. S4B). CXCL9, CCL2, and IL-1RA were also significantly increased. In contrast, chemokines involved in the recruitment of eosinophils (eotaxin) or activated T cells (CCL5) were decreased. IL-6 was not elevated in this group of patients, in contrast to the subset of individuals tested clinically (fig. S1B), potentially because IL-6 was measured in the hospital setting often when systemic inflammation was suspected. Following stimulation in vitro, PBMC from COVID-19 patients produced more CCL2, CXCL10, eotaxin, and IL-1RA than HD (fig. S4, C and D) and concentrations of CXCL10 and CCL2 correlated between the matched supernatant from stimulated PBMC and plasma samples (fig. S4E). Finally, we investigated whether CD8 T cells from COVID-19 subjects were capable of producing IFNɣ following polyclonal stimulation. Following ɑCD3+ɑCD28 stimulation, similar proportions of CD8 T cells from COVID-19 patients and HD controls produced IFNɣ, suggesting that PBMC from COVID-19 patients were responsive to TCR crosslinking (fig. S4, F to H). The ability of T cells to produce IFNɣ following stimulation occurred in patients with increases in KI67 as well as patients with low KI67 (fig. S4, F to H). Taken together, these data support the notion that a subgroup of COVID-19 patients has elevated systemic cytokines and chemokines, including myeloid recruiting 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