Thymosin alpha 1 restores the immune homeostasis in lymphocytes during Post-Acute sequelae of SARS-CoV-2 infection

Abstract

The complex alterations of the immune system and the immune-mediated multiorgan injury plays a key role in host response to SARS-CoV-2 infection and in the pathogenesis of COVID-19, being also associated with adverse outcomes. Thymosin alpha 1 (Tα1) is one of the molecules used in the treatment of COVID-19, as it is known to restore the homeostasis of the immune system during infections and cancer. The use of Tα1 in COVID-19 patients had been widely used in China and in COVID-19 patients, it has been shown to decrease hospitalization rate, especially in those with greater disease severity, and reduce mortality by restoring lymphocytopenia and more specifically, depleted T cells. Persistent dysregulation with depletion of naive B and T cell subpopulations and expansion of memory T cells suggest a chronic stimulation of the immune response in individuals with post-acute sequelae of SARS-CoV-2 infection (PASC). Our data obtained from an ex vivo study, showed that in PASC individuals with a chronically altered immune response, Tα1 improve the restoration of an appropriate response, most evident in those with more severe illness and who need respiratory support during acute phase, and in those with specific systemic and psychiatric symptoms of PASC, confirming Tα1 treatment being more effective in compromised patients. The results obtained, along with promising reports on recent trials on Tα1 administration in patients with COVID-19, offer new insights into intervention also for those patients with long-lasting inflammation with post-infectious symptoms, some of which have a delayed onset.

Keywords: Anti-inflammatory response; Immune regulation; Post-acute SARS-CoV-2 symptoms; Thymosin alpha 1.

Graphical abstract

Fig. 1

Flow cytometry analysis of differentiation and exhaustion markers in individuals with a-COV, PASC, and HD. The A) box plot represented the percentage of lymphocytes in HD (green), a-COV (red), and PASC (orange) individuals. Statistically significant values were obtained by Kruskal-Wallis test and considered when p < 0.001 (***). B) Representative dot plots of differentiation markers (CD45RA and CCR7) and C) Exhaustion PD-1 marker in the subsets of CD3⁺ CD4⁺ and CD3⁺ CD8⁺ lymphocytes. All data are reported in Table S1. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Flow cytometry analysis of differentiation and exhaustion markers in PASC individuals treated with Tα1. A) Representative dot plots of differentiation markers (CD45RA and PD-1 CCR7) and exhaustion marker in CD3⁺CD4⁺ (A) and CD3⁺CD8⁺ (C) lymphocytes in presence or not of Tα1. B) Data are represented as Histogram (Delta of Tα1 vs untreated) of differentiation and exhaustion markers in CD3⁺CD4⁺ (B) and CD3⁺CD8⁺ (D) T cells. The nonparametric Kruskal-Wallis test for independent samples and the Friedman test for dependent samples were used. Statistically significant values were considered when p < 0.001 (***).

Fig. 3

Flow cytometry analysis of intracellular cytokine expression in PASC individuals treated with Tα1. A) representative histogram overlay (N = 3 individuals) of IL-6, IFN and IL-10 in lymphocytes in presence or absence of Tα1 (red line untreated, blue line treated cells). B) representative histogram overlay (N = 1 individuals) of IL-6, IFN and IL-10 in CD3⁺CD4⁺ and CD3⁺CD8⁺ T cells in presence or absence of Tα1. C) Box plot of changes in Tα1 treatment (delta of Tα1 vs. untreated) of cytokines protein changes in both the CD3⁺CD4 ⁺ and CD3⁺CD8⁺ subsets. Statistically significant values were considered when p < 0.01 (**). The nonparametric Kruskal-Wallis test was used for independent samples and the Friedman test for dependent samples. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

Flow cytometry analysis of intracellular IL-6 expression in activated CD8 + and CD4 + expressing CD38 in PASC individuals treated with Tα1. A) Histogram of the percentage of cells expressing IL-6 in the CD8⁺CD38⁺ and CD4⁺CD38⁺ cell subsets. B) representative histogram overlay (N = 3 individuals) of IL-6 in CD8⁺CD38⁺T cells in presence or not of Tα1 (red line untreated, blue line treated cells). Statistically significant values were considered when p < 0.01 (**). The Friedman test was used. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

Tα1 immune modification in association with acute and PASC symptoms. Effects of Tα1 treatment (Delta = Tα1-untreated, red line divided down with respect to up modulated proteins) in PASC individuals. A) Box plot of the percentage of CD4⁺ T cells expressing PD-1 and IL-10 and of CD8⁺ cells expressing IFN-γ in group divided respect the stage of severity of the acute phase (MILD vs SEV). B) Box plot of the percentage of CD4 ⁺ naive and in CD8⁺ TEM cells respecting the respiratory support (AA vs Resp Sup). C) Box plot of the percentage of CD4⁺ cells expressing PD-1 respecting the Systemic PASC symptoms, left panel, and CD8⁺CM cells expressing respect the psychiatric symptoms, right panel. Statistically significant values were considered when p < 0.01, the Friedman test was used. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 6

Effects on immune regulation by Tα1 treatment in blood cells from Acute COVID-19 and PASC individuals. Transcriptional levels in human blood samples from individuals with acute COVID-19 (a-COV, N = 15), PASC (N = 10) and healthy donors (HD, N = 5). Data are represented as a box plot, showing mild (grey) and extreme (point) outliers. Expression levels were analyzed by real-time PCR and represented in logarithmic scale. A) Relative expression in the presence or absence of Tα1 and (B) Tα1 treatment changes (delta of Tα1 vs untreated) of IL-6, TNF-α, IL-1β, IL-17 and IL-10. Statistically significant values were considered when p < 0.010 (**). The nonparametric Kruskal-Wallis tests in the case of independent samples and the Friedman test for dependent samples were used.