Different immune responses lead to different disease outcomes after SARS-CoV-2 infection

Understanding the nature of the immune response that leads to recovery over severe disease is key to developing effective treatments for COVID-19. Coronaviruses typically induce strong inflammatory responses and associated lymphopenia [1, 2], and it has been reported that during COVID-19 there is an increase in inflammatory monocytes and neutrophils, a sharp decrease in lymphocytes and an inflammatory milieu containing interleukins IL-1β, IL-6 and tumor necrosis factor (TNF) during severe disease [3-5]. 

Immune responses against pathogens are roughly divided into three types: 

- Type I immunity: It is generated against intracellular pathogens such as viruses, and its clearance is mediated through effector cells like group 1 innate lymphocytes (ILC1), natural killer (NK) cells, cytotoxic T lymphocytes and T helper 1 (Th1) cells. 

- Type II immunity: Mediates defense against helminths through effector molecules such as IL-4, IL-5, IL-13 and IgE, which work to expel these pathogens through the concerted action of epithelial cells, mast cells, eosinophils and basophils. 

- Type III immunity: It is orchestrated by the IL-17and IL-22 secreted by ILC3 and Th17 cells, which fight against fungi and extracellular bacteria to elicit neutrophil dependent clearance. 

The three major types of innate and adaptive cell-mediated immunity. CLp: Common lymphoid progenitor. CILp: Common innate lymphoid progenitor. LN: Lymph node. LTi: Lymphoid tissue inducer. PP: Peyer patch. Tp: T-cell progenitor. Annunziato F, et al. The 3 major types of innate and adaptive cell-mediated effector immunity. J Allergy Clin Immunol. 2015 Mar;135(3):626-35. doi: 10.1016/j.jaci.2014.11.001. 

In a recent paper published in Nature [6], Carolina Lucas et al. have focused on the longitudinal analysis of the three types of immune responses in patients with COVID-19, identifying correlations between distinct immune phenotypes and disease course, as well as a maladapted immune response profile associated with severe COVID-19 and poor clinical outcome. 

Initial presenting symptoms demonstrated a preponderance of headache, fever, cough and dyspnea with no difference in symptom presentation between patients who developed a moderate or severe disease. As previously reported, all patients presented marked reduction in the number and frequency of both CD4+ (helper) and CD8+ (cytotoxic) T cells, although they were more activated than T cells of healthy controls. Patients with severe disease had an increment in monocytes, low density neutrophils and eosinophils. Researchers defined a ‘core COVID-19 signature’ shared by both moderate and severe disease patients defined by a set of inflammatory cytokines: IL1-α, IL-1β, IL-17A, IL-12p70 and IFNα. Patients with severe disease presented an additional inflammatory cluster defined by thrombopoietin (TPO), IL-22, IL-16, IL 21, IL-23, IFNλ, eotaxin and eotaxin3, as well as an increment in most of the cytokines linked to cytokine release syndrome (CRS) such as IL1-α, IL-1β, IL-10, IL-18 and TNF.

Major differences in immune phenotypes between moderate and severe disease were apparent 10 days from symptom onset (DfSO), as in the first 10 DfSO patients with severe or moderate disease displayed similar correlation intensity and markers, including the overall core COVID 19 signature. After day 10, these markers declined steadily in patients with moderate disease, whereas patients with severe disease maintained elevated levels of these core signature markers. They also found sharp differences in the expression of inflammatory markers along disease progression between patients who exhibit moderate versus severe symptoms. 

Patients with severe disease showed increased levels of IFNα and inflammasome induced cytokines, an increased number of monocytes and an increment of IL-12, a key inducer of type I immunity [7, 8]. Again, these markers declined over the time in patients with moderate disease. Patients with severe disease also showed an increment of type II and type III immune responses, which were not present in patients with moderate disease. 

Overview cytokine and chemokine profiles of COVID-19 patients. Each dor represents a separate time point per subject. HCW: Healthcare workers used as a control. Lucas, C., et al., Longitudinal analyses reveal immunological misfiring in severe COVID-19. Nature, 2020. 584(7821): p. 463-469.

Although there were no differences in viral RNA load between patients with moderate and severe disease at any specific time point analyzed, patients with moderate disease showed a steady decline in viral load over the course of disease, whereas those with severe disease did not. 

Authors proposed that specific early cytokine responses could be associated with severe COVID-19. Clustering analysis using measurements collected before 12 DfSO identified three main clusters which correlate to distinct disease outcomes and that were characterized by the following four distinct immune signatures: signature A, that contained stromal growth factors which are mediators of wound healing and tissue repair, as well as IL-7, a lymphocyte growth factor; signature B, which represent type II and III immune effectors; signature C, comprising a mixture of all immunotypes, including type I, II and III cytokines; and signature D, containing a number of chemokines involved in leukocyte trafficking.

Cluster 1 comprised patients with moderate disease who experienced low occurrences of coagulopathy, shorter lengths of hospitality stay and no mortality, low levels of inflammatory markers and increased levels in signature A. Clusters 2 and 3 were characterized by a rise in inflammatory markers and had higher incidences of coagulopathy and mortality, which was more pronounced in cluster 3. Cluster 2 patients presented higher levels of markers in signatures C and D than patients in cluster 1, but lower expression of markers in signature B, C and D than those in cluster 3. Patients in cluster 3 showed higher expression of markers in signature B, C and D than those in other clusters, with a particular enrichment in signature B markers. 

In sum, these findings suggest that the three distinct profiles influenced the evolution and severity of COVID-19. Cluster 1 is characterized by low expression of proinflammatory cytokines and enrichment in tissue repair genes, following a trajectory that remained moderate and led to eventual recovery. Clusters 2 and 3 were characterized by highly elevated expression of proinflammatory cytokines, worse disease and, eventually, death. 

This study raises the possibility that early immunological interventions that target inflammatory markers that are predictive of worse disease outcome would be more beneficial than those that block late-appearing cytokines, therefore preventing the development of CRS or the predominance of cluster3-like patients during the course of disease. 

References

1. Jose, R.J. and A. Manuel, COVID-19 cytokine storm: the interplay between inflammation and coagulation. Lancet Respir Med, 2020. 8(6): p. e46-e47.

2. Chen, J. and K. Subbarao, The Immunobiology of SARS*. Annu Rev Immunol, 2007. 25: p. 443-72.

3. Chen, N., et al., Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet, 2020. 395(10223): p. 507-513.

4. Mathew, D., et al., Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications. Science, 2020. 369(6508).

5. Giamarellos-Bourboulis, E.J., et al., Complex Immune Dysregulation in COVID-19 Patients with Severe Respiratory Failure. Cell Host Microbe, 2020. 27(6): p. 992-1000 e3.

6. Lucas, C., et al., Longitudinal analyses reveal immunological misfiring in severe COVID-19. Nature, 2020. 584(7821): p. 463-469.

7. Iwasaki, A. and R. Medzhitov, Control of adaptive immunity by the innate immune system. Nat Immunol, 2015. 16(4): p. 343-53.

8. Annunziato, F., C. Romagnani, and S. Romagnani, The 3 major types of innate and adaptive cell-mediated effector immunity. J Allergy Clin Immunol, 2015. 135(3): p. 626-35.

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