Impairment of T cells' antiviral and anti-inflammation immunities may be critical to death from COVID-19

Abstract

Clarifying dominant factors determining the immune heterogeneity from non-survivors to survivors is crucial for developing therapeutics and vaccines against COVID-19. The main difficulty is quantitatively analysing the multi-level clinical data, including viral dynamics, immune response and tissue damages. Here, we adopt a top-down modelling approach to quantify key functional aspects and their dynamical interplay in the battle between the virus and the immune system, yielding an accurate description of real-time clinical data involving hundreds of patients for the first time. The quantification of antiviral responses gives that, compared to antibodies, T cells play a more dominant role in virus clearance, especially for mild patients (96.5%). Moreover, the anti-inflammatory responses, namely the cytokine inhibition and tissue repair rates, also positively correlate with T cell number and are significantly suppressed in non-survivors. Simulations show that the lack of T cells can lead to more significant inflammation, proposing an explanation for the monotonic increase of COVID-19 mortality with age and higher mortality for males. We propose that T cells play a crucial role in the immunity against COVID-19, which provides a new direction-improvement of T cell number for advancing current prevention and treatment.

Keywords: COVID-19; T cell; immunology and inflammation; mathematical model.

Figure 1.

Simplified schematic diagram of COVID-19. Key components modelled in this work are highlighted in yellow, i.e. the virus (V), effective T-cell (T_e), neutralizing antibody (NAb, A), non-neutralizing antibody (Non-NAb, A_n), interleukin 6 (IL-6), the coagulation marker (D-dimer, S_d), the heart injury marker (High-sensitivity cardiac troponin I, HSCT, S_h). Red arrows represent activation, and the black arrows represent inhibition. Greek letters are the activation/inhibition rates or characteristic time associated with each interaction. Other components are macrophage (Mϕ) and regulatory T cells (Treg), which contribute to the dynamics of the key components but are not explicitly modelled.

Figure 2.

Comparison of simulation of viral dynamics and adaptive immune response to data. (a,e,i) are fits of the first patient (patient ID labelled as P1); (b,f,j) are fits of the second patient (patient ID labelled as P3); (c,g,k) are fits of the third patient (patient ID labelled as P5) [35]; (d,h,l) are fits of the fourth patient (patient ID labelled as 902). Mild patients are in blue and severe patients are in green. Red dashed lines are the limit of detection [36]. Black dotted lines are normal ranges [4]. Viral load is from the nasopharyngeal swab. The effective T cell data is the reduction of CD3+ T cells in serum (T_serum) from its initial value (T₀), which is assumed proportional to the concentration of effector T cells in organs. We assume the CD3+ T cell data of the patient with the same severity share similarities based on the distinction of CD3+ T cell data between groups of different severities [4] and approximate the CD3+ T cell dynamics of the fourth severe patient using the median data of the severe group [4].

Figure 3.

An overall statistic of the fraction of virus killed by T cells (a) and antibodies (b) for all cases. Solid markers are individual data, and hollow markers are group data. Error bars represent standard errors.

Figure 4.

Comparison of predictions to clinical data of survivors and non-survivors. The data are the median of the group with error bars [3]. For parameter estimation, see Methods and electronic supplementary material. For IgG simulation, we use the ratio of the saturation values of critical and non-critical patients [48] to approximate the ratio of the saturation values of non-survivors and survivors. The red dashed line is the limit of detection and the black dashed lines are the normal ranges of the corresponding markers (see the reference from the electronic supplementary material).

Figure 5.

Initial T cell concentration as the background immunity of individuals against SARS-CoV-2 and reduces mortality. (a) Positive correlation of T cell's antiviral contribution with initial T cell concentration for mild patients (blue), severe patients (green), survivor group (magenta) and non-survivor group (black). Data points are the means, and error bars are the standard deviations. (b) Positive correlation of inhibition rates of IL-6, D-dimer and high sensitive cardiac troponin (HSCT) with initial T cell concentration for survivor group and non-survivor group. Data points are the means, and error bars are the standard deviations. It is worth mentioning that the three rates of the non-survivor group are all zero and overlap with each other; The inhibition rates of D-dimer and HSCT of the survivor group are closed and overlap with each other. (c) D-dimer dynamics of non-survivors with an increase of initial T cell concentration (T₀) reduces organ damage at the later stage. The Red dashed line is the normal upper limit of the D-dimer [4]. For parameters of the simulation, see Methods. (d) Lymphocyte count decreases with age, and mortality (case fatality rate, CFR) increases with age. Males (dashed line) have higher mortality than females (dotted line).