Dynamic alterations of circulating lymphocytes during the trajectory of Hantaan virus-induced hemorrhagic fever with renal syndrome

Abstract

Introduction: Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic disease with high mortality. Almost 90% of global cases of HFRS are induced by Hantaan virus (HTNV) infection. Although lymphocyte dysfunction is a critical factor in HFRS progression, the specific immune dynamics of HTNV remain unexplored, and current analyses predominantly depend on single-time point sampling. Therefore, comprehensive longitudinal studies are needed to characterize circulating lymphocyte dynamics during HTNV-induced HFRS progression.

Methods: In this study, we conducted a flow cytometric analysis of circulating lymphocytes in 39 patients with HTNV-induced HFRS across different clinical phases. The analysis encompassed conventional T cells, unconventional T cells, B cells, NK cells and their respective repertoires.

Results and discussion: Here, we revealed phase-specific immune patterns: CD8⁺ T, CD8⁺ Tems, and activated CD8⁺ T, MAIT and NKT cells peaked during febrile/oliguric phases before declining in polyuria/recovery, while CD4⁺ T and MAIT cells showed inverse fluctuation patterns. Higher frequencies of CD8⁺ Tem, B, and CD56^dim NK cells during the febrile phase correlated with severe disease, enabling early risk stratification. Lower CD4⁺ Tcm levels in the oliguric phase marked progression to severe HFRS, indicating potential therapeutic strategies aimed at enhancing CD4⁺ Tcm generation or inhibiting effector differentiation. Additionally, CD38 and CD161 expression predicted specific lymphocyte subset dynamics, offering novel biomarkers for immunomodulatory strategies. Our study thus provides the first comprehensive atlas of lymphocyte evolution in HTNV-induced HFRS, connecting immune dysregulation with clinical outcomes.

Keywords: HFRS; HTNV; biomarkers; dynamic alterations; lymphocyte subsets.

Figure 1

Dynamic perturbations of conventional T cells during the progression of HTNV-induced HFRS. (A) Scheme for circulating lymphocyte analysis. (B, C) The frequencies of CD8⁺ T cells (B) and CD4⁺ T cells (C) in the context of HTNV-induced HFRS. (D) Fluctuations of CD8⁺ T cell repertoires. (E) Representative FACS analysis of the phenotype of CD8⁺ T cell subsets (gated on CD3⁺CD8⁺ cells). (F, G) Subsets of CD4⁺ T cells following HTNV infection. (H, I) The activation of CD8⁺ T cells in each group (gated on CD3⁺CD8⁺ cells). Data are represented as mean ± SD. One-way ANOVA or the Kruskal–Wallis test with Dunn’s multiple comparison post-hoc test. *p< 0.05, **p< 0.01, ***p< 0.001, ****p< 0.0001.

Figure 2

Dynamic fluctuations of unconventional T cells in HTNV-induced HFRS trajectories. (A) Scheme for sample collection and unconventional T cell detection. (B) Frequencies of total NKT cells and activated NKT cells during different clinical phases of HFRS. (C) Representative flow-cytometric plots of activated NKT cell subsets (gated on NKT cells). (D) MAIT cells and their activation status in the context of HFRS. (E) Representative flow cytometric plots showing activated MAIT cells. (F) Ratios of γδ T cells and their subsets following HTNV infection. (G) Representative flow plots depicting Vδ1 and Vδ2 (gated on γδ T cells). (H, I) Fluctuations of the subsets of γδ T cells in the context of HTNV-induced HFRS (gated on γδ T cells). Data are represented as mean ± SD. One-way ANOVA or the Kruskal–Wallis test with Dunn’s multiple comparison post-hoc test, *p< 0.05, **p< 0.01, ****p< 0.001, ****p< 0.0001.

Figure 3

B and NK cell responses following HTNV infection. (A) Frequencies of B cells, PB cells and non-PB cells during different clinical phases of HTNV-induced HFRS. (B) Representative flow cytometric plots defining PB cells and non-PB cells (gated on B cells). (C) Perturbations of NK cells and their repertoires in the context of HFRS. (D) Representative flow-cytometric plots of CD56^dimNK and CD56^brightNK cells in each group. (E, F) The activation and exhaustion of NK cells following HTNV infection (gated on NK cells). Data are represented as mean ± SD. One-way ANOVA or the Kruskal–Wallis test with Dunn’s multiple comparison post-hoc test, *p< 0.05, **p< 0.01, ***p< 0.001, ****p< 0.0001.

Figure 4

Relationships between the frequencies of lymphocyte subsets during the fever stage and HFRS severity. Data are represented as mean ± SD. Mann–Whitney U test post-hoc test. *p< 0.05.

Figure 5

Relationships between the frequencies of lymphocyte subsets during the oliguria stage and HFRS severity. Data are represented as mean ± SD. Mann–Whitney U test post-hoc test. *p< 0.05.

Figure 6

CD38 and CD161 as potential biomarkers for predicting dynamic perturbations of specific lymphocyte subsets during the progression of HTNV-induced HFRS. (A. B) The expression of CD38 (A) and CD161 (B) on circulating lymphocytes. (C, D) Spearman correlations of altered lymphocyte subsets and CD38 (C) and CD161 (D) expression. Data are represented as mean ± SD. One-way ANOVA or the Kruskal–Wallis test with Dunn’s multiple comparison post-hoc test, *p< 0.05, **p< 0.01, ***p< 0.001, ****p< 0.0001.