CD4+ T Cell Regulatory Network Underlies the Decrease in Th1 and the Increase in Anergic and Th17 Subsets in Severe COVID-19

Round 1

Reviewer 1 Report

COVID-19, caused by the highly contagious respiratory virus SARS-CoV-2, has initiated an ongoing global pandemic. COVID-19 exhibits a wide range of clinical manifestations, from asymptomatic infection to severe illness. T cells play a central role in defense against intracellular pathogens, including SARS-CoV-2. The authors used a Boolean regulatory network model to test the differentiation of CD4+ T cells in different cytokine microenvironments and explain the behavior of CD4+ T cells during COVID-19, as well as proposing possible interventions aiming to modulate these cells. Patients with COVID-19 were divided into three groups: patients with mild disease without signs of pneumonia or hypoxia, patients with moderate COVID-19 who have clinical signs of pneumonia not requiring mechanical ventilation, and patients with severe COVID-19 who required invasive mechanical ventilation and admission to the intensive care unit. The differentiation of CD4+ T cells into effector Th1, Th2, Th17 cells, regulatory Treg, Foxp3-Tr, Th1-like regulatory (Th1R), Th2-like regulatory (Th2R) cells was analyzed, as well as their transition into anergic, exhausted T- cells (Tex). The authors' most important conclusion is that most of the cytokine signatures prevalent among hospitalized patients with COVID-19 interfere with or do not promote the establishment of the Th1 responses required to clear the infection. The model also showed that contradictory signals in microenvironments associated with severe COVID-19 favor Tex, Th1R, and Th17 subpopulations by increasing basin size, stability, and transitions to these cell types. With mild to moderate COVID-19, conditions are created for Th1 differentiation, but Th1R cells are also stimulated. In addition, the mild course of the disease is the only microenvironment that favors Treg cells. This balanced profile can be beneficial to the host enabling to combat the infection with minimal tissue damage costs. Conversely, in severe cases, the most common subpopulation is Tex. The results of the analysis also show that the low number of Th1 cells and the high number of exhausted, abnormally activated, and apoptotic T cells in COVID-19 resulted due not only to abnormal TCR activation, but to differentiation problems caused by the abnormal immune response in severe COVID-19 as well. The model suggests at least four possible interventions to stimulate a Th1 protective response: activation of the IFN-γ pathway, blocking of the IL-10 or TGF-β pathways, and inhibition of SOCS1. Although the model has certain limitations (does not analyze TCR signaling pathway modulation by key cytokines such as IL-2 and TNF-α, cannot clearly differentiate Th0 and Tex attractors and so on), it is certainly interesting and emphasizes the need to obtain additional information about the immune profile of candidate patients before the introduction of immunotherapeutic agents, since the effects of each proposed intervention may vary in different microenvironments. There are a number of typos in the text: Line 79: cell cell differentiation; Line 80 – citokine; Line 87 – favoured; Fig.2 “SOCS proteins (hexagons)” - on Fig.2 – ellipses; Figure 3 - “attractors are separated by white spaces and cell types by black bars” - black bars are absent. Line 265 - Table ?? Nonetheless, the manuscript can be published after minor corrections.

Author Response

Point 1. There are a number of typos in the text: Line 79: cell cell differentiation; Line 80 – citokine; Line 87 – favoured; Fig.2 “SOCS proteins (hexagons)” - on Fig.2 – ellipses; Figure 3 - “attractors are separated by white spaces and cell types by black bars” - black bars are absent. Line 265 - Table ?? Nonetheless, the manuscript can be published after minor corrections.

Response 1. We thank the reviewer for their comments. We have corrected the noted typos and double-checked the manuscript for other mistakes.

Reviewer 2 Report

The authors presented a model to explain the behavior of T CD4+ cells during COVID-19 and to propose possible interventions to modulate these cells. They found that most cytokine signatures prevalent among hospitalized COVID-19 patients interfere or do not favor the establishment of Th1 responses required to clear the infection. On the other hand, the model  showed the contradictory signals in the micro-environments associated with severe COVID-19 favor Tex, Th1R, and Th17. They recommend the inhibition of SOCS1 to obtain enough Th1 responses to overcome the disease.

1- Their model fails to capture the dynamics of the TCR and TNF-a as a consequence of using the Boolean approach and the network construction. Their model also fails to differentiate the Th0 and Tex attractors. Even the authors also stated these limitations in the text, I believe that this is a pioneering study in efforts to find a cure for COVID-19.

2- There are some minor typographical mistakes such as Introduction, line 71 “including CD4+ T cells [55–57] including CD4+ T cells [58–62].” There is repetition

3-Introduction, line 72 “Some of these models haven been able” please revise I suggest havent

Author Response

Point 1- Their model fails to capture the dynamics of the TCR and TNF-a as a consequence of using the Boolean approach and the network construction. Their model also fails to differentiate the Th0 and Tex attractors. Even the authors also stated these limitations in the text, I believe that this is a pioneering study in efforts to find a cure for COVID-19.
Response 1: We thank the reviewer for his or her comments.  Indeed those are serious limitations of our study, as we have indicated in the discussion. We hope to address some of those limitations in our future work.

Point 2- There are some minor typographical mistakes such as Introduction, line 71 “including CD4+ T cells [55–57] including CD4+ T cells [58–62].” There is repetition
Point 3-Introduction, line 72 “Some of these models haven been able” please revise I suggest havent
Response 2: We have corrected the noted typos and double-checked the manuscript for other mistakes.