SARS-CoV-2 uses CD4 to infect T helper lymphocytes

  1. Natalia S Brunetti
  2. Gustavo G Davanzo
  3. Diogo de Moraes
  4. Allan JR Ferrari
  5. Gabriela F Souza
  6. Stéfanie Primon Muraro
  7. Thiago L Knittel
  8. Vinicius O Boldrini
  9. Lauar B Monteiro
  10. João Victor Virgílio-da-Silva
  11. Gerson S Profeta
  12. Natália S Wassano
  13. Luana Nunes Santos
  14. Victor C Carregari
  15. Artur HS Dias
  16. Flavio P Veras
  17. Lucas A Tavares
  18. Julia Forato
  19. Icaro MS Castro
  20. Lícia C Silva-Costa
  21. André C Palma
  22. Eli Mansour
  23. Raisa G Ulaf
  24. Ana F Bernardes
  25. Thyago A Nunes
  26. Luciana C Ribeiro
  27. Marcus V Agrela
  28. Maria Luiza Moretti
  29. Lucas I Buscaratti
  30. Fernanda Crunfli
  31. Raissa G Ludwig
  32. Jaqueline A Gerhardt
  33. Natália Munhoz-Alves
  34. Ana Maria Marques
  35. Renata Sesti-Costa
  36. Mariene R Amorim
  37. Daniel A Toledo-Teixeira
  38. Pierina Lorencini Parise
  39. Matheus Cavalheiro Martini
  40. Karina Bispos-dos-Santos
  41. Camila L Simeoni
  42. Fabiana Granja
  43. Virgínia C Silvestrini
  44. Eduardo B de Oliveira
  45. Vitor M Faca
  46. Murilo Carvalho
  47. Bianca G Castelucci
  48. Alexandre B Pereira
  49. Laís D Coimbra
  50. Marieli MG Dias
  51. Patricia B Rodrigues
  52. Arilson Bernardo SP Gomes
  53. Fabricio B Pereira
  54. Leonilda MB Santos
  55. Louis-Marie Bloyet
  56. Spencer Stumpf
  57. Marjorie C Pontelli
  58. Sean Whelan
  59. Andrei C Sposito
  60. Robson F Carvalho
  61. André S Vieira
  62. Marco AR Vinolo
  63. André Damasio
  64. Licio Velloso
  65. Ana Carolina M Figueira
  66. Luis LP da Silva
  67. Thiago Mattar Cunha
  68. Helder I Nakaya
  69. Henrique Marques-Souza
  70. Rafael E Marques
  71. Daniel Martins-de-Souza
  72. Munir S Skaf
  73. Jose Luiz Proenca-Modena
  74. Pedro MM Moraes-Vieira
  75. Marcelo A Mori  Is a corresponding author
  76. Alessandro S Farias  Is a corresponding author
  1. Autoimmune Research Laboratory, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Brazil
  2. Laboratory of Immunometabolism, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Brazil
  3. Laboratory of Aging Biology, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Brazil
  4. Department of Structural and Functional Biology, Institute of Biosciences, Sao Paulo State University (UNESP), Brazil
  5. Institute of Chemistry and Center for Computing in Engineering and Sciences, University of Campinas, Campinas (UNICAMP), Brazil
  6. Laboratory of Emerging Viruses, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Brazil
  7. Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Brazil
  8. Brazilian Laboratory on Silencing Technologies (BLaST), Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Brazil
  9. Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Brazil
  10. Center of Research in Inflammatory Diseases, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
  11. Department of BioMolecular Sciences, School of Pharmaceutical Sciences, University of São Paulo, Ribeirão Preto, Brazil
  12. Department of Pharmacology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto,, Brazil
  13. Department of Cell and Molecular Biology, Center for Virology Research, Ribeirão Preto Medical School, University of São Paulo, Brazil
  14. Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, Brazil
  15. Department of Internal Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Brazil
  16. Hematology and Hemotherapy Center, University of Campinas (UNICAMP), Brazil
  17. Department of Biochemistry and Immunology, Ribeirão Preto Medical School, University of São Paulo, Brazil
  18. National Biosciences Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Brazil
  19. Brazilian Synchrotron Light Laboratory (LNLS), Brazilian Center for Research in Energy and Materials (CNPEM), Brazil
  20. Laboratory of Immunoinflammation, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil;, Brazil
  21. Neuroimmunology Unit, Department of Genetics, Microbiology and Immunology, University of Campinas (UNICAMP), Brazil
  22. National Institute of Science and Technology on Neuroimmunomodulation (INCT-NIM) – Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Brazil
  23. Washington University in St Louis, Department of Molecular Microbiology, United States
  24. Laboratory of Vascular Biology and Arteriosclerosis, School of Medical Sciences, University of Campinas (UNICAMP), Brazil
  25. Laboratory of Electrophysiology, Neurobiology and Behavior, University of Campinas (UNICAMP), Brazil
  26. Experimental Medicine Research Cluster (EMRC), University of Campinas (UNICAMP), Brazil
  27. Obesity and Comorbidities Research Center (OCRC), University of Campinas (UNICAMP), Brazil
  28. D'Or Institute for Research and Education (IDOR), Brazil
  29. National Institute of Science and Technology in Biomarkers for Neuropsychiatry (INCTINBION), Brazil

Abstract

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the agent of a major global outbreak of respiratory tract disease known as Coronavirus Disease 2019 (COVID-19). SARS-CoV-2 infects mainly lungs and may cause several immune-related complications, such as lymphocytopenia and cytokine storm, which are associated with the severity of the disease and predict mortality. The mechanism by which SARS-CoV-2 infection may result in immune system dysfunction is still not fully understood. Here, we show that SARS-CoV-2 infects human CD4+ T helper cells, but not CD8+ T cells, and is present in blood and bronchoalveolar lavage T helper cells of severe COVID-19 patients. We demonstrated that SARS-CoV-2 spike glycoprotein (S) directly binds to the CD4 molecule, which in turn mediates the entry of SARS- CoV-2 in T helper cells. This leads to impaired CD4 T cell function and may cause cell death. SARS-CoV-2-infected T helper cells express higher levels of IL-10, which is associated with viral persistence and disease severity. Thus, CD4-mediated SARS-CoV-2 infection of T helper cells may contribute to a poor immune response in COVID-19 patients.

Editor's evaluation

This study represents an important contribution where SARS-CoV-2 infection of T-helper cells is implicated and found to be mediated by CD4. It identifies the interaction between spike RBD domain and N Terminal domain of CD4 molecule as the specific viral attachment strategy. This solid paper also provides a potential usefulness for future work in understanding how viruses mediate infection of T cells.

https://doi.org/10.7554/eLife.84790.sa0

Introduction

Coronavirus Disease 2019 (COVID-19) has rapidly spread across the globe, being declared a pandemic by the World Health Organization on March 11, 2020. COVID-19 has caused millions of deaths around the world. Most of the deaths are associated with acute pneumonia, cardiovascular complications, and organ failure due to hypoxia, exacerbated inflammatory responses, and widespread cell death (Candido et al., 2020; Laing et al., 2020; Li et al., 2020; Moore and June, 2020; Zhang et al., 2020). Individuals that progress to the severe stages of COVID-19 manifest marked alterations in the immune response, characterized by reduced overall protein synthesis, cytokine storm, lymphocytopenia, and T cell exhaustion (Arunachalam et al., 2020; De Biasi et al., 2020; Moore and June, 2020; Thoms et al., 2020). In addition to these acute effects on the immune system, a considerable proportion of infected individuals present low titers of neutralizing antibodies (Robbiani et al., 2020; Souza et al., 2021). Moreover, the levels of antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) decay rapidly after recovery in part of the infected individuals (Long et al., 2020), suggesting that SARS-CoV-2 infection may exert profound and long-lasting complications to adaptive immunity. Recently, it has been shown that SARS-CoV-2 is able to infect lymphocytes (Pontelli et al., 2022; Shen et al., 2022). In this context, it is urgent to characterize the replicative capacity and the effects of SARS-CoV-2 replication in different immune cells, especially those involved with the formation of immunological memory and effective adaptive response, such as CD4+ T lymphocytes.

In what has been proposed to be the canonical mechanism of SARS-CoV-2 infection, the spike glycoprotein of SARS-CoV-2 (sCoV-2) binds to the host angiotensin-converting enzyme 2 (ACE2), after which it is cleaved by TMPRSS2 (Hoffmann et al., 2020). While TMPRSS2 is ubiquitously expressed in human tissues (Figure 1—figure supplement 1), ACE2 is mainly expressed in epithelial and endothelial cells, as well as in the kidney, testis, and small intestine (Figure 1—figure supplement 1). Still, a wide variety of cell types are potentially infected by SARS-CoV-2 (Codo et al., 2020; Crunfli et al., 2022; Lamers et al., 2020; de Oliveira et al., 2022; Pascoal et al., 2021; Saccon et al., 2022; Yang et al., 2020), even though some of these cells express very low levels of ACE2. We showed that this is the case for lymphocytes (Figure 1—figure supplement 2). These findings suggest that either SARS-CoV-2 uses alternative mechanisms to enter these cells or that auxiliary molecules at the plasma membrane may promote infection by stabilizing the virus until it interacts with ACE2. In agreement with the latter, binding of sCoV-2 to certain cell surface proteins facilitates viral entry (Cantuti-Castelvetri et al., 2020; Cecon et al., 2022).

Since the structures of the spike of SARS-CoV-1 (sCoV-1) and the sCoV-2 proteins are similar (Wrapp et al., 2020; Yuan et al., 2020), we used the P-HIPSTer algorithm to uncover human proteins that putatively interact with spike (Lasso et al., 2019). Seventy-one human proteins were predicted to interact with sCoV-1 (Figure 1—figure supplement 3). We then cross-referenced the proteins with five databases of plasma membrane proteins to identify the ones located on the cell surface (see ‘Methods’ for details). CD4 was the only protein predicted to interact with sCoV-1 that appeared in all five databases (Figure 1—figure supplement 3). CD4 is expressed mainly in T helper lymphocytes and has been shown to be the co-receptor to HIV (Shaik et al., 2019). Since CD4+ T lymphocytes orchestrate innate and adaptive immune response (Gasteiger and Rudensky, 2014; O’Shea and Paul, 2010), infection of CD4+ T cells by SARS-CoV-2 could explain lymphocytopenia and dysregulated inflammatory response in severe COVID-19 patients. Moreover, from an evolutionary perspective, infection of CD4+ T cells represents an effective mechanism for viruses to escape the immune response (Xu et al., 2001).

To test whether human primary T cells are infected by SARS-CoV-2, we purified CD3+CD4+ and CD3+CD8+ T cells from the peripheral blood of noninfected healthy controls/donors (HC), incubated these cells with SARS-CoV-2 for 1 hr, and washed them the three times with PBS. The viral load was measured 24 hr post infection. We were able to detect SARS-CoV-2 RNA in CD4+ T cells, but not CD8+ T cells (Figure 1A and Figure 1—figure supplement 4A). To confirm the presence of SARS-CoV-2 in the cells, we performed in situ hybridization using probes against the viral RNA-dependent RNA polymerase (RdRp) gene, immunofluorescence for sCoV-2 using antibodies against spike protein and transmission electron microscopy (Figure 1B and C). In parallel, we infected primary CD4+ T cells with the VSV-mCherry-SARS-CoV-2 pseudotype virus (Figure 1—figure supplement 4B). All approaches confirmed that SARS-CoV-2 infects CD4+ T cells. Notably, the amount of SARS-CoV-2 RNA remains roughly stable until 48 hr post infection (Figure 1D). Of note, although most of the data presented here was generated using the ancient SARS-CoV-2 B lineage, CD4+ T cells were also infected by the P.1 (gamma) variant (Figure 1—figure supplement 4C). Furthermore, we identified the presence of the negative strand (antisense) of SARS-CoV-2 in the infected cells (Figure 1—figure supplement 4D), demonstrating that the virus replicates in T helper cells. We also found by plaque assay analysis that SARS-CoV-2-infected CD4+ T cells release infectious viral particles, although much less efficiently than Vero cells (positive control) (Figure 1E and Figure 1—figure supplement 4E).

Figure 1 with 5 supplements see all
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects CD4+ T cells in vitro and in vivo.

Peripheral blood CD3+CD4+ and CD3+CD8+ were exposed to mock control or SARS-CoV-2 (CoV-2) (Multiplicity of Infection (MOI) = 1) for 1 hr under continuous agitation. (A) Viral load was assessed by RT-qPCR 24 hr after infection. (B) Cells were washed, cultured for 24 hr, fixed with 4% paraformaldehyde (PFA), and stained with the RdRp probe for in situ hybridization or anti-sCoV-2 for immunofluorescence. Cells were analyzed by confocal microscopy. (C, C1–C5) Representative transmission electron microscopy (EM) micrographs showing viral particles (asterisks) inside lymphocytes 2 hr after infection. (D) Viral load was determined by qPCR 2 hr, 6 hr, 12 hr, 24 hr, and 48 hr after infection. (E) Vero cells were incubated with the supernatant of mock control or CoV-2-infected CD4+T cells under continuous agitation for 1 hr. The viral load in Vero cells was measured after 72 hr using plaque assay. PFU, plaque-forming unit. (F) Viral load was measured by RT-qPCR in peripheral blood CD4+ and CD8+ T cells from healthy controls (HC) and COVID-19 patients. (G) CoV-2 RNA detection in CD4+ T cells from bronchoalveolar lavage fluid (BALF) single-cell RNA sequencing data revealing the presence of CoV-2 RNA. Data represents mean ± SEM of at least two independent experiments performed in triplicate or duplicate (f). ***p<0.001; ND, not detected.

To investigate whether SARS-CoV-2 infects CD4+ T cells in vivo, we purified CD4+ and CD8+ T cells from peripheral blood cells of COVID-19 patients (Supplementary file 1). Similar to our ex vivo experiments, SARS-CoV-2 RNA was detected in CD4+ T cells from COVID-19 patients (Figure 1F). Using publicly available single-cell RNA sequencing (scRNAseq) data (Liao et al., 2020), we detected the presence of SARS-CoV-2 RNA in 2.1% of CD4+ T cells of the bronchoalveolar lavage (BAL) of patients with severe COVID-19 (Figure 1F and Figure 1—figure supplement 5). Thus, our data demonstrate that SARS-CoV-2 infects CD4+ T cells.

Next, we sought to explore the role of the CD4 molecule in SARS-CoV-2 infection. Based on the putative interaction found using P-HIPSTer, we performed molecular docking analyses and predicted that sCoV-2 receptor binding domain (RBD) directly interacts with the N-terminal domain (NTD) of CD4 Ig-like V type (Figure 2A and Figure 2—figure supplement 1). Molecular dynamics (MD) simulations with stepwise temperature increase were applied to challenge the kinetic stability of the docking model representatives (Figure 2B). Two models remained stable after the third step of simulation at 353 K and represent likely candidates for the interaction between the CD4 NTD and sCoV-2 RBD (Figure 2B). Additionally, we evaluated the dynamic behavior of closely related binding mode models present in the same cluster as these two models. We observed a strong structural convergence toward the putative model in one case, which indicates plausible and rather stable interaction between CD4 NTD and sCoV-2 RBD (Figure 2—figure supplement 1). The interaction region of RBD with CD4 is predicted to overlap with that of human ACE2 (Figure 2C and D). The interaction between CD4 and sCoV-2 was confirmed by co-immunoprecipitation (Figure 3A). Binding affinity isotherms, obtained by fluorescence anisotropy assay, confirmed the physical high-affinity interaction between RBD and CD4 (Kd = 22 nM) and spike full length and CD4 (Kd = 27 nM). Considering the similar affinities, these results suggest that the interaction interface between sCoV-2 and CD4 occurs at the RBD (Figure 3B and C). Consistent with the hypothesis that CD4-sCoV-2 interaction is required for infection, we observed that pre-incubation with soluble CD4 (sCD4) completely blunted viral load in CD4+ T cells exposed to SARS-CoV-2 (Figure 3D). Together, these data demonstrate that sCoV-2 binds to the CD4 molecule.

Figure 2 with 1 supplement see all
Molecular dynamics simulations of the sCoV-2 receptor binding domain (RBD) and CD4 N-terminal domain (NTD) interaction.

(A) Interaction energy (dG separated) versus root-mean-square deviation (RMSD) plot shows a high diversity of binding modes with similar energies. Among the 500 best models, 50 cluster best-ranked representatives (shown as blue crosses) were selected for further evaluation with molecular dynamics simulations. (B) RMSD to the initial docked complex as a function of molecular dynamics simulation time over 200 ns and four steps of temperature. At each temperature step, well-behaved models are depicted as colored curves, while divergent candidates are shown as gray curves. Kinetically stable models making reasonable interactions remain close to their initial docked conformation. Only two models remain stable after the third step of 50 ns at 353 K. For both models, resilient contacts throughout simulation are shown in Fig. S5. (C) Interaction models of ACE2 and sCoV-2 RBD. The two best candidates according to molecular dynamics simulation, model 95 and model 148, present distinct binding modes. For the first case, interaction occurs mainly in the N-terminal portion of CD4 NTD, while the latter have important contributions to the central part of this domain. (D) Full-length model of sCoV-2 and CD4 NTD interaction obtained by alignment of sCoV-2 RBD from model 148 to the sCoV-2 RBD open state from PDB 6vyb EM structure.

Figure 3 with 1 supplement see all
Infection of CD4+ T cells by SARS-CoV-2 is dependent on CD4 and ACE2 molecules.

(A) Recombinant sCoV-2 (with twin-strep-tag) and CD4 were co-incubated and immunoprecipitated with anti-CD4. Complex formation was determined by Affinity blotting using streptavidin-HRP. (B, C) Fluorescence anisotropy curves of Spike full length and Spike receptor binding domain (RBD) binding to CD4 labelled protein, presenting dissociation constants (Kd) of 27 ± 3 nM and 22 ± 3 nM, respectively. The CD4 was labeled with FITC by incubation with fluorescein isothiocyanate probe, at molar ratio 3FITC:1protein, 4°C for 3 hr. The probe excess was removed by a desalting column (HiTrap 5 ml, GE) in a buffer containing 137 mM NaCl, 10 mM Na phosphate, 2.7 mM KCl, pH of 7.4. To evaluate binding affinities, serial dilutions of Spike (RBD or full length) were performed, from 4.5 μM to 100 nM, over 20 nM of labeled CD4. The measurements were taken using ClarioStar plate reader (BMG, using polarization filters of 520 nm for emission and of 495 nm for excitation) and data analysis were performed using OriginPro 8.6 software. The Kds were obtained from data fitted to binding curves through the Hill model. (D) Cells were exposed to mock control or SARS-CoV-2 (CoV-2) in the presence of vehicle or soluble CD4 (sCD4) in different concentrations (200, 100, or 50 μg/ml) for 1 hr under continuous agitation. Viral load was assessed by RT-qPCR 24 hr after infection. (E) Primary human CD4+ T cells were incubated with IgG control, or monoclonal anti-CD4 (RPA- T4) antibody 18 hr prior to exposure with mock control or CoV-2. Viral load was determined by RT-qPCR 24 hr after infection. (F) A2.01 and A3.01 lineages were cultivated with pseudotype virus (VSV-mCherry-CoV-2). Percentage of infected cells and flow cytometry analysis. (G) CD4 abundance by western blotting (upper panel) and flow cytometry (lower panel). (H) Viral load of CoV-2 in A2.01 and A2.04. (I) Peripheral blood CD4+ T cells were incubated with IgG control or anti-ACE2 (αACE2) polyclonal antibody 18 hr prior to exposure with mock control or CoV-2 for 1 hr. Viral load was determined 24 hr after infection. (J) CD4+ T cells were incubated with vehicle or camostat mesylate for 18 hr before the exposure with mock control or CoV-2 for 1 hr. Viral load was analyzed by RT-qPCR. Data represents mean ± SEM of at least two independent experiments performed in triplicate. *p<0.05, **p<0.01, ***p<0.0001.

Figure 3—source data 1

Original co-immunoprecipitation blot.

Recombinant sCoV-2 and CD4 were co-incubated and immunoprecipitated with anti-CD4.

https://cdn.elifesciences.org/articles/84790/elife-84790-fig3-data1-v1.zip
Figure 3—source data 2

Original ACE2 abundance by western blotting in T cell lines.

https://cdn.elifesciences.org/articles/84790/elife-84790-fig3-data2-v1.zip
Figure 3—source data 3

Original CD4 abundance by western blotting in T cell lines.

https://cdn.elifesciences.org/articles/84790/elife-84790-fig3-data3-v1.zip
Figure 3—source data 4

Original GAPDH abundance by western blotting in T cell lines.

https://cdn.elifesciences.org/articles/84790/elife-84790-fig3-data4-v1.zip

To gain further insight into the importance of CD4-sCoV-2 binding to SARS-CoV-2 infection, we purified CD4+ T cells and preincubated them with a CD4 monoclonal antibody (RPA-T4) (O’Shea and Paul, 2010). We observed that CD4 inhibition reduced SARS-CoV-2 load (Figure 3E). Moreover, we used human T cell lines that express CD4 (A3.01) or not (A2.01) (Folks et al., 1985; Figure 3—figure supplement 1). Despite expressing higher levels of ACE2 and TMPRSS2 than primary CD4+ T cells (Figure 1—figure supplement 2 and Figure 3—figure supplement 1), the presence of CD4 was sufficient to increase viral load in A3.01 cells compared to A2.01 (Figure 3—figure supplement 1). These results were confirmed by using the VSV-mCherry-SARS-CoV-2 pseudotype model (Figure 3F). Importantly, the introduction of CD4 in A2.01 increased viral load (Figure 3H and H).

Our immunoprecipitation experiments indicated no physical interaction between CD4 and ACE2 (data not shown). Since CD4+ T cells have very low ACE2 expression, we tested whether CD4 alone was sufficient to allow SARS-CoV-2 entry. Inhibition of ACE2 using polyclonal antibody (Figure 3I) diminished SARS-CoV-2 entry in CD4+ T cells. Moreover, the inhibition of TMPRSS2 with camostat mesylate reduced SARS-CoV-2 load (Figure 3H). Altogether, these data demonstrate that ACE2, TMPRSS2, and CD4 are all required to allow the infection of CD4+ T cells by SARS-CoV-2.

To assess the consequences of SARS-CoV-2 infecting CD4+ T cells, we performed mass spectrometry-based shotgun proteomics in CD4+ T cells exposed to SARS-CoV-2 ex vivo. We found that SARS-CoV-2 infection alters multiple housekeeping pathways associated with the immune system, infectious diseases, cell cycle, and cellular metabolism (Figure 4A and B and Figure 4—figure supplements 1 and 2). SARS-CoV-2 exposure elicits alterations associated with ‘cellular responses to stress,’ which include changes in proteins involved in translation, mitochondrial metabolism, cytoskeleton remodeling, cellular senescence, and apoptosis (Figure 4B and Supplementary file 2). In agreement, ex vivo exposure of CD4+ T cells with SARS-CoV-2 resulted in 10% reduction of cell viability 24 hr after infection with a low MOI (0.1) (Figure 4—figure supplement 3). SARS-CoV-2 may cause cell death by different mechanisms. SARS-CoV-2 can induce apoptosis in different cell lines through stimulation of autophagy via inhibition of mTOR signaling (Li et al., 2021). It can also induce inflammasome activation and pyroptosis in primary monocytes and peripheral blood mononuclear cells (PBMCs) from COVID-19 patients (Alvim et al., 2020). In epithelial respiratory and kidney cell lines, SARS-CoV drives multimodal necrosis (Yue et al., 2018). In lymphocytes, however, it has been demonstrated that apoptosis is the mechanism underlying SARS-CoV-2-mediated cell death (Crunfli et al., 2022), which is in line with our proteomic data (Figure 4—figure supplements 1 and 2).

Figure 4 with 3 supplements see all
Infection of CD4+ T cells by SARS-CoV-2 alters cell function and triggers IL-10 production.

(A) Heatmap of differentially expressed proteins and their associated biological processes. (B) Network of differentially expressed proteins and their associated biochemical pathways. (C) Relative expression of IFNγ, IL-17A, TGFβ, and IL-10 genes in peripheral blood CD3+ CD4+ cells from COVID-19 patients (moderate or severe) and healthy control (HC). Data represents mean ± SEM. Each dot representes a patient sample. (D) Relative expression of IL-10 gene in primary CD4+ T cells infected with SARS-CoV-2. (E) Representative immunoblotting of phosphorylated CREB-1Ser133 (pCREB) and total or phosphorylated STAT3 in peripheral blood CD3+ CD4+ exposed to mock control or SARS-CoV-2. Phosphorylated ATF-1 is detected by the same antibody used to detect pCREB. ***p<0.001, **p<0.01, *p<0.05.

Figure 4—source data 1

Original immunoblotting of phosphorylated CREB-1Ser133 (pCREB) and phoshorylated ATF-1.

https://cdn.elifesciences.org/articles/84790/elife-84790-fig4-data1-v1.zip
Figure 4—source data 2

Original immunoblotting of phosphorylated STAT3.

https://cdn.elifesciences.org/articles/84790/elife-84790-fig4-data2-v1.zip
Figure 4—source data 3

Original immunoblotting of total STAT3.

https://cdn.elifesciences.org/articles/84790/elife-84790-fig4-data3-v1.zip
Figure 4—source data 4

Original immunoblotting of Vinculin.

https://cdn.elifesciences.org/articles/84790/elife-84790-fig4-data4-v1.zip

The expression and release of IL-10 have been widely associated with chronic viral infections and determines viral persistence (Brooks et al., 2006). Noteworthy, increased serum levels of IL-10 are associated with COVID-19 severity (Han et al., 2020; Zhao et al., 2020). We found that IL-10 expression by CD4+ T cells was higher in BAL (Figure 4—figure supplement 3) and blood (Figure 4C) of severe COVID-19 patients. These changes were at least in part cell autonomous since purified CD4+ T cells exposed to SARS-CoV-2 also expressed higher levels of IL-10 (Figure 4D). Due to the immunomodulatory role of IL-10, we measured the expression of key pro- and anti-inflammatory cytokines involved in the immune response elicited by CD4+ T cells. CD4+ T cells from severe COVID-19 patients had decreased expression of IFNγ and IL-17A in relation to cells from patients with the moderate form of the disease or healthy donors (Figure 4C). These results show that SARS-CoV-2 induces IL-10 expression in CD4+ T cells, which is associated with suppression of genes that encode key pro-inflammatory cytokines, such as IFNγ and IL-17A, and correlates with disease severity.

The activation of the transcription factor CREB-1 via Ser133 phosphorylation induces IL-10 expression (Gee et al., 2006). Consistent with IL-10 upregulation, CREB-1 phosphorylation at Ser133 was increased in SARS-CoV-2-infected CD4+ T cells (Figure 4E). Thus, SARS-CoV-2 infection triggers a signaling cascade that culminates in the upregulation of IL-10 in CD4+ T cells. Altogether, our data demonstrate that SARS-CoV-2 infects CD4+ T cells, impairs cell function, leads to increased IL-10 expression, and compromises cell viability, which in turn dampens immunity against the virus and contributes to disease severity.

Impaired innate and adaptive immunity is a hallmark of COVID-19, particularly in patients that progress to the critical stages of the disease (Hadjadj et al., 2020; Mathew et al., 2020). Here, we propose that the alterations in immune responses associated with severe COVID-19 are at least partially triggered by infection of CD4+ T helper cells by SARS-CoV-2 and consequent dysregulation of immune function. T helper cells are infected by SARS-CoV-2 using a mechanism that involves binding of sCoV-2 to CD4 and entry mediated by the canonical ACE2/TMPRSS2 pathway. We propose a model where CD4 stabilizes SARS-CoV-2 on the cell membrane until the virus encounters ACE2 to enter the cell. Moreover, Cecon and colleagues showed that CD4 co- expression with ACE2 in HEK293 cells decreases the affinity and the maximal binding between the sCoV-2 RBD and ACE2 (Cecon et al., 2022), which is in line with our MD results and the binding assays (Figure 3), suggesting an intricate mechanism of modulation of SARS- CoV-2 infection in CD4+ T cells (Figure 2C and D and Figure 2—figure supplement 1). Taken together, these data indicate that in CD4-expressing cells direct binding of sCoV-2 to the CD4 molecule represents a potential mechanism to favor viral entry.

Once in CD4+ T cells, SARS-CoV-2 leads to protein expression changes consistent with alterations in pathways related to stress response, apoptosis, and cell cycle regulation, which, if sustained, culminate in cell dysfunction and may lead to cell death. SARS-CoV-2 also results in phosphorylation of CREB-1 transcription factor and induction of its target gene IL-10 in a cell-autonomous manner. IL-10 is a powerful anti-inflammatory cytokine and has been previously associated with viral persistence (Brooks et al., 2006). Serum levels of IL-10 increase during the early stages of the disease – when viral load reaches its peak – and may predict COVID-19 outcome (Han et al., 2020; Zhao et al., 2020). This increase occurs only in patients with the severe form of COVID-19 (Zhao et al., 2020). In contrast, we found IFNγ and IL-17A to be upregulated in CD4+ T cells of patients with moderate illness, indicating a protective role for these cytokines. However, in patients with severe illness, the expression of IFNγ and IL-17A in CD4+ T cells is dampened. IL-10 is a known suppressor of Th1 and Th17 responses and it is likely to contribute to the changes in IFNγ and IL-17A. These features will ultimately reflect in the quality of the immune response, which in combination with T cell death and consequent lymphopenia, may result in transient/acute immunodeficiency and impair adaptive immunity in severe COVID-19 patients (Arunachalam et al., 2020; De Biasi et al., 2020; Thoms et al., 2020). Lymphocytopenia is a characteristic feature of severe COVID-19 (Zheng et al., 2020). Here, we demonstrated that infected CD4+ T cells undergo cell death, which may contribute to the development of lymphocytopenia. In addition, cytokines such as IL-1β released by infected monocytes can promote T cell dysfunction (Codo et al., 2020). Therefore, the decrease in lymphocyte count may be attributed to both infected T cell death and dysfunctional T cells resulting from inflammatory cytokine storm.

How long these alterations in T cell function persist in vivo and whether they have long-lasting impacts on adaptive immunity remains to be determined. Hence, avoiding T cell infection by blocking sCoV-2-CD4 interaction and boosting T cell resistance against SARS-CoV-2 might represent complementary therapeutic approaches to preserve immune response integrity and prevent patients from progressing to the severe stages of COVID-19.

Methods

Subjects

The samples from patients diagnosed with COVID-19 were obtained at the Clinical Hospital of the University of Campinas (SP-Brazil). Both COVID-19 patients and healthy subjects included in this work signed a term of consent approved by the University of Campinas Committee for Ethical Research (CAAE: 32078620.4.0000.5404; CAEE: 30227920.9.0000.5404). Human blood samples from severe COVID-19 patients analyzed in this study were obtained from individuals admitted at the Clinics Hospital, University of Campinas, and included in a clinical trial (UTN: U1111-1250-1843). In vitro experiments were performed with buffy coats from healthy blood donors provided by The Hematology and Hemotherapy Center of the University of Campinas (CAAE: 31622420.0.0000.5404).

Diagnosis

Nasopharyngeal swabs or bronchoalveolar lavage fluids (BALFs) were tested for SARS-CoV- 2 by real-time qPCR. The tests were performed in the Laboratory of High-Performance Molecular Diagnostic or Laboratory of Clinical Pathology, all located at the University of Campinas. The samples were aliquoted and extracted manually using the MagMAX Viral/Pathogen Nucleic Acid Isolation Kit (Applied Biosystems). Some RNAs were automatically isolated by MagMAX Express-96 Magnetic Particle Processor (Applied Biosystems) following the manufacturer’s protocol. Real-time qPCRs were performed in duplicates using TaqMan Fast Virus 1-Step Master Mix (Applied Biosystems) for the detection of SarbecoV E-gene with specific primers (10 µM) and probe (5 µM). Primer sequences were: forward-ACAGGTACGTTAATAGTTAATAGCGT; reverse-ATATTGCAGCAGTACGCACACA, Probe:6FAM-ACACTAGCCATCCTTACTGCGCTTCG- QSY. Patients were considered positive for COVID-19 when Ct < 38. qPCRs were performed using the Applied Biosystems 7500 Fast Real-time PCR system.

Blood sample collection and lymphocyte separation

Each COVID-19 patient had heparin and plain blood tubes collected. Whole blood, serum, and plasma samples were separated. PBMCs from COVID-19 patients and buffy coats were obtained through the Histopaque-1077 density gradient (Sigma-Aldrich). Samples were diluted in Hanks (1:1) and gently poured into 15 or 50 ml conical tubes containing 3 or 10 ml of Histopaque, respectively. Then, the samples were centrifuged at 1400 rpm for 30 min at 4°C without brake. The PBMC layer was collected into a new tube, lymphocytes were sorted (see below), and incubated overnight at 37°C with 5% CO2 atmosphere with RPMI 1640 (Gibco) containing 10% fetal bovine serum (FBS) and 1% Penicillin-Streptomycin (P/S).

A2.01, A2.04, and A3.01 cell lines

The A3.01 (ARP-166) and A2.01 (ARP-2059) cells were acquired from the NIH AIDS Reagent Program (MD) and maintained in RPMI 1640 (Gibco) containing 10% FBS and 1% P/S at 37°C with 5% CO2 atmosphere. Mycoplasma contamination was not detected. A3.01 is a lymphoblastic leukemia cell line that expresses high levels of CD4, while A2.01 does not show detectable levels of CD4. The A2.04 was produced for this article. In brief, the coding sequence for CD4 was amplified from pCMV.CD4 and cloned into the BglII/SalI cloning site of pMSCV-IRES-GFP. The retrovirus carrying pMSCV-CD4-IRES-GFP vector was produced in Peak cells by using pVSV-G and pCL-Eco packing vectors as previously described (Tavares et al., 2016; Tavares et al., 2020). The A2.01 cells (Folks et al., 1986) were transduced with this retrovirus and sorted for GFP positive in the BD FACSCanto II flow cytometer in the Ribeirão Preto Center for Cell-Based Therapy/Hemotherapy, and the confirmation for CD4 expression was done by western blot and flow cytometry.

Virus and infection

The ancient SARS-CoV-2 viral lineage [CoV-2(B)] was isolated from the second confirmed case of COVID-19 in Brazil and kindly donated by Prof. Dr. Edson Luiz Durigon (University of São Paulo, Brazil; SARS.CoV-2/SP02.2020, GenBank accession number MT126808). The SARS-CoV-2 gamma variant [CoV-2(P.1)] was isolated from residual nasopharyngeal lavage specimens of a patient from Manaus City, Brazil, who tested positive for SARS-CoV-2 (GISAID accession ID EPI_ISL_1708318). SARS-CoV-2 virus stocks were propagated in Vero cell line CCL-81 (STR profiling authentication method, mycoplasma contamination not detected, origin: ATCC) and supernatant was harvested at 2 dpi. The viral titers were determined by plaque assays on Vero cells. Vero cells were propagated in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% FBS, 1% P/S, and maintained at 37°C in a 5% CO2 atmosphere. Cells were incubated with the CoV-2(B) or CoV-2(P.1) lineage (MOI = 1 unless indicated otherwise) for 1 hr at room temperature. After viral adsorption, cells were washed three times with PBS and incubated with a culture medium under standard culture conditions (37°C and 5% CO2 atmosphere). Most of the in vitro experiments were done with 1 million sorted CD3+CD4+ live cells plated over a well of a 48-well plate. For proteomic and immunoblotting experiments, 4 million live CD3+CD4+ cells were used. All experiments with SARS-CoV-2 were done in a biosafety level 3 laboratory (BSL-3) located in the Institute of Biology from the University Campinas.

Flow cytometry

CD4+ T cells were sorted with FACSJazz or FACS Melody (BD Biosciences) as CD4+CD3+ and, in some experiments, CD8-CD3+. CD8+ T cells were sorted as CD8+CD3+ or CD4+CD3+ (see the list of antibodies in Supplementary file 3). We performed flow cytometry with a lineage marker, Lin CD3, CD14, CD16, CD19, CD20 and CD56 to avoid contaminations. Sorted cells were incubated overnight in RPMI 1640 media containing 10% FBS and 1% P/S at 37°C with 5% CO2 atmosphere for infection. All flow cytometry analyses were performed in FACS Symphony A5 (BD Biosciences). All antibodies used in the present study were sodium azide free.

Biotinylated DNA probe synthesis

Probe synthesis is based on two PCR steps both using the same pair of primers (primer sense: AACACGCAAATTAATGCCTGTCTG; primer antisense: GTAACAGCATCAGGTGAAGAAACA) for the RdRp gene. The first amplifying PCR mix contains 1× buffer, 1.5 mM MgCl2, 0.2 mM dNTP, 0.1 µM of each primer, 1 µl of cDNA (1:10), and 1.5 U Taq polymerase (GoTaq DNA Polymerase, Promega) in a final volume of 50 µl. The reaction was subjected to 35 cycles of PCR including denaturation at 95°C for 30 s, annealing at 55°C for 30 s, and extension at 72°C for 30 s, following incubation at 72°C for 5 min. PCR samples were analyzed by electrophoresis on a 1% agarose gel in order to verify a final amplicon of 300 pb. The second PCR contained slight modifications in relation to the first, that is, we used 0.2 mM of d (C, G, A) TP and 0.2 µM of 16-dUTP instead of the regular dNTP mix, and used 1 µl of the previous PCR as a template (Day et al., 1990).

Plaque assays

Vero cells were grown in 24-well plates up to 80% confluency. Supernatant samples from infected lymphocytes were added and incubated at 37°C with 5% CO2 for an hour for virus adsorption. Samples were replaced with a semi-solid overlay medium (1% w/v carboxy methyl cellulose in complete DMEM) and incubated at 37°C with 5% of CO2 for 3–4 d. Plates were fixed in 10% w/v paraformaldehyde (PFA), stained in 1% w/v methylene blue, and results were expressed as viral plaque-forming units per milliliter of sample (PFU/ml).

Co-immunoprecipitation

Co-immunoprecipitation was performed using Pierce protein A/G magnetics beads (Cat# 88803, Thermo Fisher Scientific) immobilized with anti-CD4 antibody (Cat# IM0566, Rhea Biotech). Briefly, 100 µg of magnetic bead was washed in PBS containing 0.05% Tween-20 and then incubated with 10 µg anti-CD4 antibody for 2 hr at 4°C with mixing. For complex formation, we co-incubated 60 pmoles of recombinant CD4 (Cat# 10400-H08H, Sino Biological) and 60 pmoles of Spike S1 Twin-Strep-Tag recombinant protein expressed and generously provided by Dr. Leda Castilho from the Federal University of Rio de Janeiro (Alvim et al., 2020). Incubation was performed in 300 μl of PBS and kept overnight at 4°C with gentle agitation. After incubation, the recombinant protein solution was transferred to a 1.5 ml tube with the washed immobilized beads and incubated for 1 hr at room temperature. The complex was precipitated and proteins that did not bind to the beads were subsequently removed during the washes. The protein was eluted from the bead using Laemmli buffer and heat (95° for 5 min). The sample was analyzed by affinity blot using a streptavidin-HRP detection (Cat# 405210, BioLegend). Full-range Rainbow (Cat# RPN800E) was used as the molecular weight marker.

Spike target prediction

The P-HIPSTER database (http://phipster.org; last accessed: June 1, 2020) was used to find potential SARS-CoV-2 spike-human interactions. P-HIPSTER discoveries are predicted using protein similarities of known interactions (Lasso et al., 2019). Only interactions with a final likelihood ratio (LR) score ≥100 were used in the analysis. According to the authors, this threshold has a validation rate of 76%. To evaluate whether these proteins were found on the cellular membrane, four databases were used. The Human Protein Atlas (https://www.proteinatlas.org; last accessed: June 1, 2020) contains tissue and anatomical protein annotations; 1917 proteins classified as ‘Cell Membrane’ were retrieved. Panther GO is a gene set enrichment analysis tool (Mi H, PANTHER version 14: More genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools); 1504 genes annotated as ‘integral components of plasma membrane’ (GO:0005887) were retrieved. EnrichR (https://amp.pharm.mssm.edu/Enrichr/ ;last accessed: June 1, 2020) is a web-based app that integrates various enrichment analysis tools (Kuleshov et al., 2016), which library was used to retrieve JENSEN compartment annotations (https://compartments.jensenlab.org/; last accessed: June 1, 2020) for ‘external side of plasma membrane’ (212), ‘plasma membrane’ (1148), and ‘cell surface’ (715). To find common elements between the datasets and generate the Venn diagram, jvenn (http://jvenn.toulouse.inra.fr/app/; last accessed: June 1, 2020) was used (Bardou et al., 2014).

Gene set enrichment analysis

EnrichR was used for enrichment analysis for SARS-CoV-2 spike protein predicted interactions. GO Molecular Function data was downloaded, and enrichments with an adjusted p-value<0.05 were retrieved.

Global expression analysis of ACE2, TMPRSS2, and CD4

To evaluate which tissues and cells express ACE2, TMPRSS2, and CD4, expression data (both protein and consensus mRNA expression, NX) from The Human Protein Atlas (https://www.proteinatlas.org; last accessed: July 7, 2020) (Thul and Lindskog, 2018) and microarray expression from BioGPS (http://biogps.org/#goto=welcome; last accessed: Jul 7, 2020) (Wu et al., 2016) were downloaded and plotted. In The Human Protein Atlas data, only normalized expression level ≥1 and protein detection ≥ ‘low’ of at least one of the three genes was plotted. In the BioGPS data, this threshold was ≥10 a.u. (arbitrary units).

sCoV-2-ACE2-interacting peptide (βACE2) design and synthesis

A peptide representing the interacting region of SARS-CoV-2 Spike protein with ACE2 and present at the receptor binding domain (RBD receptor binding doming) was designed based on the tridimensional structure described elsewhere (Shang et al., 2020). Based on the structure, we selected an unstructured and continuous segment of 16 amino acid residues (THR487-GLY502), which contained most of the interaction points with ACE2. We added to the segment a substitution of CYS488 to SER, which intended to avoid random disulfide bond formation and structural alterations, keeping the same hydrophilicity in the interacting region. An amidated CYS residue was added at the C-terminal of the peptide to allow simple and specific conjugation with accessory detection molecules. The complete 17 amino acid residues (487NCYFPLQSYGFQPTNG502C) were synthesized using standard FMOC solid-phase peptide synthesis chemistry as previously described (Fields and Noble, 1990) at a 100 µM scale using RINK-amide resin 0.7 mmol/g (Advanced ChemTech, Cat# SA5130). All FMOC-aas (Advanced ChemTech) were used with 2.5 excess. FMOC-aa coupling reaction assisted by six cycles of 2 min in a home-made microwave device. At the end of coupling reactions, the peptide was cleaved from the resin using a solution of 88% trifluoroacetic acid solution, 4% water, 4% triisopropylsilane, 2% anisole + 30 mg dithiothreitol for 2 hr. Cleaved peptide was precipitated with ethylbutylether and then purified on C18 seppack solid phase extraction cartridges (10 mg, Sigma-Aldrich) using a water:acetonitrile solvent system.

Proteomic analysis

Infected and mock cells were resuspended in RIPA buffer (150 mM NaCl, 1 mM EDTA, 100 mM Tris-HCl, 1% Triton X-100) with freshly added protease and phosphatase inhibitors (Protease Inhibitor Cocktail, Sigma). After three cycles of 30 s of ultra-sonication for cell lysis, the protein amount was quantified by the Pierce BCA protein assay kit (Thermo Scientific).

Aiming to obtain a higher yield, we performed the FASP protocol for subsequent analyses (Distler et al., 2016). The FASP protocol is a method that allows to concentrate proteins and clean up the samples through washing steps in a microcolumn tip with a 10 kDa MW cut off and perform the tryptic digestion in this column. Also, 10 μg of protein were used to carry out the FASP protocol, where the samples were reduced, alkylated, and later digested using trypsin. An amount of 100 fmol/μl of digestion products of Enolase from Saccharomyces cerevisiae was added to each sample as internal standard, then separation of tryptic peptides was performed on an ACQUITY MClass System (Waters Corporation). Then, 1 µg of each digested samples was loaded onto a Symmetry C18 5 μm, 180 μm × 20 mm precolumn (Waters Corporation) and subsequently separated by a 120 min reversed phase gradient at 300 nl/min (linear gradient, 2–85% CH3CN over 90 min) using a HSS T3 C18 1.8 μm, 75 μm × 150 mm nanoscale LC column (Waters Corporation) maintained at 40°C. After peptide separation, the ionized peptides were acquired by a Synapt G2-Si (Waters Corporation). Differential protein expression was evaluated with data-independent acquisition (DIA) of shotgun proteomics analysis by Expression configuration mode using the Ion Mobility cell (HDMSe). All spectra have been acquired in Ion Mobility Mode by applying a wave velocity for the ion separation of 800 m/s and a transfer wave velocity of 175 m/s. The mass spectrometer operated in ‘Expression Mode,’ switching between low (4 eV) and high (25–60 eV) collision energies on the gas cell, using a scan time of 0.5 s per function over 50–2000 m/z. The processing through low and elevated energy, added to the data of the reference lock mass ([Glu1]-Fibrinopeptide B Standard, Waters Corporation) provides a time-aligned inventory of accurate mass-retention time components for both low and elevated-energy (exact mass retention time [EMRT]).

Each sample was analyzed in four technical replicates. Continuum LC-MS data from three replicate experiments for each sample was processed for qualitative and quantitative analysis using the software Progenesis QC for Proteomics (PLGS, Waters Corporation). The qualitative identification of proteins was obtained by searching in the Homo sapiens database (UniProtKB/Swiss-Prot Protein reviewed 2020). The expression analysis was performed considering technical replicates available for each experimental condition following the hypothesis that each group is an independent variable. Protein identifications were based on the detection of more than two fragment ions per peptide, and more than two peptides measured per protein. The list of normalized proteins was screened according to the following criteria: protein identified in at least 70% of the runs from the same sample and only modulated proteins with a p<0.05 were considered significant. Raw data are available in ProteomeXchange database under accession number PXD020967.

In situ hybridization

Sorted CD4+ T cells were incubated for 24 hr with the SARS-CoV-2 virus (MOI of 1) or mock. After 24 hr, the cells were washed three times with PBS and fixed with 4% PFA for 15 min, at room temperature, followed by two washes with PBS (pH 7.4) prepared with diethylpyrocarbonate (DEPC)-treated water. Further, the cells were seeded on silanized glass slides and kept on a prewarmed surface until liquid is fully evaporated. The slides were washed with PBS and pretreated with 2% H2O2 in methanol for 30 min to bleach auto- and avoid nonspecific fluorescence. They were then washed twice with PBS 0.05% Triton X-100 (PBST), treated with Proteinase K (10 µg/ml) for 2 min, followed by PBST-Glycin (2 mg/ml) for 10 min, and a second fixation step with PFA 4% for 10 min, followed by three more washes with PBST at room temperature. Each sample was pre-hybridized with hybridization solution (Hyb) (50% formamide, 10% dextran sulfate, 2× saline-sodium citrate buffer [SSC] pH 7.0, and 100 µg salmon DNA), without the probe, for at least 2 hr at 37°C in a humid box. Samples were then incubated with Hyb containing the biotinylated probe (100 µl of the probe in 500 µl of Hyb), pre-denaturated (85°C for 10 min), in a humid box overnight at 37°C. The slides were then subjected to sequential washes with 50% Hyb in 2× SSC and 25% Hyb in 2× SSC, for 20 min at 37°C each, and two washes for 10 min each with 2× SSC, 0.2× SSC, and PBS. All samples were incubated with fluorescent streptavidin (Streptavidin, DyLight 594 Conjugated, Thermo Scientific, #21842, 1:300) for 2 hr at room temperature in the humid box protected from light. The slides were washed with PBST, incubated with DAPI (Santa Cruz Biotechnology, SC-3598), diluted 1:1000 in PBST for 5 min at room temperature, protected from light, and mounted in an aqueous mounting solution for confocal imaging.

Detection of SARS-CoV-2 antisense strand

cDNA was generated using SuperScript III First-Strand Synthesis SuperMix (Thermo Fisher) according to the manufacturer’s instructions. Then, 60 ng of RNA were incubated at 65°C for 5 min with 1× annealing buffer and 250 nM of a primer complementary to the anti-sense CoV-2 strand containing a nonviral tag at the 5′ end. The sample was chilled at 4°C for 1 min and incubated at 55°C for 50 min with SuperScript III/RNaseOUT and 1× master mix for reverse transcription (RT). The sample was heated to 85°C for enzyme inactivation. The antisense strand was amplified in a PCR using 100 nM of primers complementary to the nonviral tag and to the antisense CoV-2 strand. As a control for false-priming (Bessaud et al., 2008), we performed the RT without the tagged reverse primer and a PCR using both the RT and the PCR primers at 100 nM. The sense strand was also amplified as a control with the same method using nontagged primers for the RT and PCR. The samples were loaded in a 4% agarose gel in Tris/borate/EDTA buffer containing 1× SybrSafe (Thermo Fisher).

Immunoblotting

Cells were infected for 3 hr with the SARS-CoV-2 virus (MOI of 1) or mock, washed with PBS, and the protein was extracted using lysis buffer (Tris-HCl 100 mM pH 7.5; EDTA 1 mM; NaCl 150 mM; 1% Triton X-100; 1× protease and phosphatase inhibitors). Protein quantification was performed using Pierce BCA protein assay kit (Thermo Fisher Scientific). Then, 5 µg protein of each sample was diluted with Laemmli buffer and heated at 95°C during 5 min. Samples were subjected to 10% SDS-PAGE and transferred to PVDF membrane (Cat# 1620177, Bio-Rad). Membranes were blocked with StartingBlock (37539, Thermo Fisher Scientific) for 30 min, and incubated overnight with anti-phospho-CREB (Cat# 9198, Cell Signaling, dilution 1:1000), anti-phospho-STAT3 (Cat# 8059, Santa Cruz, dilution 1:250), anti-STAT3 (Cat# 4904, Cell Signaling, dilution 1:1000), anti-ACE-2 (Cat# ab15348, Abcam, dilution 1:2000), or anti-CD4 (Cat# sc-7219, Santa Cruz, dilution 1:2000). Membranes were washed and incubated during 1 hr with rabbit IgG horseradish peroxidase-conjugated secondary antibodies (Cat# RPN4301, GE Healthcare, diluted 1:10,000 in block buffer). Membranes were then washed and incubated with Immobilon Western Chemiluminescent HRP Substrate (Cat# WBKLS0500, Millipore), and the images were acquired in ChemiDoc Gel Imaging System (Bio-Rad). Anti-Vinculin (Cat#, ab91459, Abcam, dilution 1:1000), anti-actin-beta (Cat#, sc-47778, Santa Cruz, dilution 1:4000), and anti-GAPDH (Cat# G9545, Sigma-Aldrich, dilution 1:5000) antibodies were used as loading controls. Quantification was performed using ImageJ.

Immunofluorescence

Cells were prepared onto silanized glass slides as previously described for in situ hybridization (Alamri et al., 2017), followed by a washing step using PBST 0.1 M pH 7.4. To avoid autofluorescence, the cells were treated with 2% H2O2 in methanol for 30 min, washed with PBST, and treated with 0.1 M glycine in PBST for 10 min at room temperature. The samples were then washed and treated with 1% bovine serum albumin (BSA) solution in PBST for 30 min (according to the manufacturer’s suggestion), to block nonspecific epitopes. Cells were incubated with SARS-CoV-2 Spike S1 Antibody (HC2001) (GenScript, A02038) diluted 1:100 in 1% BSA solution in PBST, and incubated overnight at 4°C in a humid box. The slides were then washed and incubated with anti-human IgG Alexa 488 (Thermo Fisher, A11013) diluted 1:500 in 1% BSA solution in PBST for 2 hr at room temperature in a humid box, protected from light. The samples were washed again, incubated with DAPI (Santa Cruz Biotechnology, SC-3598) diluted 1:1000 in 1% BSA solution in PBST for 5 min at room temperature protected from light, and mounted in an aqueous mounting solution for confocal imaging. Microscopy images were acquired with a Zeiss LSM880 with Airyscan on an Axio Observer 7 inverted microscope (Carl Zeiss AG, Germany) with a C Plan-Apochromat ×63/1.4 Oil DIC objective, 4× optical zoom. Prior to image analysis, raw .czi files were automatically processed into deconvoluted Airyscan images using the Zen Black 2.3 software. For DAPI, conventional confocal images were acquired using a 405 nm laser line for excitation and a pinhole set to 1 AU.

Transmission electron microscopy

T lymphocyte cell cultures were pelleted by centrifugation at 500 × g for 10 min. Cell culture supernatant was removed and cells were resuspended in 100 µl of the fixative solution, which consisted of 1 M sodium cacodylate aqueous solution supplemented with 2.5 % v/v glutaraldehyde and 3 mM calcium chloride at pH 7.2, and kept overnight (16 hr) at 4°C. Cells were pelleted (1500 rpm for 2 min) and washed in cacodylate buffer with calcium chloride for a total of five times. Post-fixation was performed with 1% reduced osmium tetroxide plus 0.8% potassium ferrocyanide in a cacodylate buffer with calcium chloride for 2 hr at 4°C covered from light. Pellet was recovered by centrifugation at 1500 rpm at 4°C and washed three times in ddH2O for 2 min. Cells were resuspended in 4% low melting agarose (approximately 50°C) and centrifuged at 1500 rpm for 10 min at 30°C and kept 20 min on ice for solidification. Agarose pellets were trimmed in small blocks and stained for contrast enhancement with 2% aqueous uranyl acetate overnight at 4°C. Later, samples were washed in ddH2O and dehydrated in increasing concentrations of ethanol (20, 50, 70, 80, 90, 100% twice), 1:1 ethanol:acetone, and acetone (twice), 20 min each. Samples were embedded in Embed-812 (Electron Microscopy Science, USA, #14120) following manufacturer’s recommendations. Ultrathin sections (70 nm) were mounted in 200 mesh copper grids and stained with 2% aqueous uranyl acetate and Reynold’s lead citrate. Finished grids were imaged in a JEOL JEM-1400 Transmission Electron Microscope (120 kV accelerating voltage) and/or Helios Nanolab Dualbeam 660 Scanning Transmission Electron Microscope (30 kV accelerating voltage) at the Electron Microscopy Laboratory, Brazilian Nanotechnology National Laboratory, LNNano, CNPEM.

RNA extraction cDNA synthesis

Total RNA was extracted from samples using TRI Reagent (Sigma) (200 µl per pellet of cells) according to the manufacturer’s instructions and quantified using NanoDrop 200 Spectrophotometer (Thermo Scientific). cDNA was synthesized using 500 ng of total RNA and the GoScript Reverse Transcriptase Kit (Promega) or the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems) following the manufacturer’s instructions. The cDNA final concentration was 25 ng/μl.

qPCR

Real-time PCR (qPCR) was performed using QuantiNova SYBR Green PCR Kit (QIAGEN) with specific primers (HPRT – forward: GACCAGTCAACAGGGGACAT, reverse: AACACTTCGTGGGGTCCTTTTC; IL10 – forward: GCCTAACATGCTTCGAGATC; reverse: CTCATGGCTTTGTAGATGCC; TGFB1 – forward: AAGTTGGCATGGTAGCCCTT, reverse: GCCCTGGATACCAACTATTGC; IFNG – forward: TTTAATGCAGGTCATTCAGATGTA, reverse: CACTTGGATGAGTTCATGTATTGC; and IL17A – forward: TCCCACGAAATCCAGGATGC, reverse: TGTTCAGGTTGACCATCACAGT). Expression levels of each gene were normalized to a housekeeping gene (i.e., hypoxanthine phosphoribosyltransferase 1 [HPRT1]). qPCR reaction was performed using 15 ng of cDNA, 300 nM of specific primers, and QuantiNova SYBR Green (QIAGEN). Amplification occurred upon cycling conditions: 95°C for 3 min and 45 cycles of 95°C for 15 s, 60°C for 20 s, and 72°C for 30 s. Amplification plots, melting curves, and Ct values were obtained using CFX Manager (Bio-Rad). Gene expression fold change was calculated with the ΔΔCt method (Supplementary file 4). Viral load was determined using specific SARS-CoV-2 N1 primers, as described previously (Won et al., 2020) and using qPCRBIO Probe 1-Step Go Lo-Rox (PCR Biosystem) with specific SarbecoV E-gene primers and probe. For the preparation of a standard curve, serial dilutions of SARS-CoV-2 were used. qPCR was performed using the Bio-Rad CFX384 Touch Real-Time PCR Detection System and Thermo Fisher QuantStudio 3. Relative expression of the viral envelope (E), nucleocapsid (N), and RNA-dependent RNA polymerase (RdRp) genes from in vitro CD3+CD4+ cells were detected using GeneFinder COVID-19 Plus RealAmpKit (OSANG Healthcare Co). Expression level of each gene was normalized to internal control of the kit and relative gene expression was calculated as 2- ΔCT. For this kit, the qPCR was performed using the Applied Biosystems 7500 Fast Real-time PCR system.

Single-cell mRNA sequencing (scRNAseq)

We reanalyzed the scRNAseq data from six samples of BALF-containing immune cells of severe COVID-19 patients (C145, C146, C148, C149, and C152) by downloading the respective single-cell processed data matrices from GEO under the accession number GSE145926. The number of viral transcripts mapped to SARS-CoV-2 was previously integrated by their authors as an additional feature into data matrices called ‘NcoV’ used to quantify the viral load of SARS-CoV-2. The data matrices were then imported to R version 3.6.3 and analyzed using Seurat v3.1 (Stuart et al., 2019).

A quality control filtering was applied to remove low-quality cells considering a gene number between 200 and 6000, unique molecular identifier (UMI) count > 1000, and mitochondrial gene percentage <0.1. After the filtering step, a total of 37,197 cells and 25,722 features (including SARS-CoV-2 viral load) were left for downstream analysis. The filtered gene-barcode matrix of all samples was integrated with FindIntegrationAnchors and IntegrateData functions considering the first 50 dimensions of canonical correlation analysis (CCA). Then, the filtered data matrix was normalized using the ‘LogNormalize’ method in Seurat with a scale factor of 10,000. The top 3000 variable genes were then identified using the ‘vst’ method in Seurat using the FindVariableFeatures function. Variables ‘nCount_RNA’ and ‘percent.mito’ were regressed out in the scaling step, and principal component analysis was performed using the top 3000 variable genes using 100 dimensions. Then, Uniform Manifold Approximation and Projection and t-Distributed Stochastic Neighbor Embedding were performed on the top 40 principal components for visualizing the cells. Additionally, a clustering analysis was performed on the first 40 principal components using a resolution of 0.8. Then, differential gene expression analysis was performed with the Wilcoxon rank-sum test using the FindAllMarkers function to obtain a list of representative gene markers for each cluster of cells. Representative gene markers were considered based on adjusted p-value<0.05 and average log fold change > 0.25 among cells. T cell cluster annotation was performed using an evidence-based score approach with scCATCH (Shao et al., 2020) based on differential gene markers list. Finally, CD4+ T cell subpopulations were annotated considering a high differential expression of CD4, CCR7, and IL32 gene markers (Figure 1—figure supplement 5). Based on the methodology applied above, a total of 1846 single cells were identified as CD4+ T cells. T cells were considered infected assuming a threshold with at least 10% of viral RNA expression. The applied threshold resulted in 39 single cells infected with SARS-CoV-2.

Docking

The RosettaDock4.0 protein docking protocol (Leman et al., 2020; Marze et al., 2018) was applied to model the interaction between CD4 NTD (Wu et al., 1997) (PDB ID 1wio, residues 1–178) and sCov2 RBD (Wang et al., 2020) (PDB ID 6lzg, residues 333–527) as described previously. Briefly, the first step consisted of generating an ensemble of 100 conformers for each target. The two-stage automated Rosetta docking protocol simulates the physical encounter of the proteins and maximizes interactions, which may lead to binding. In the first stage (global docking), a rigid-body translation-rotation of one of the protein samples’ possible interaction modes using a low-resolution, centroid-based representation of the side chains. RosettaDock4.0 also mimics binding-induced conformational adaptations by assessing a pre-generated list of conformers. This is an important feature for cases where backbone perturbation is inherent to the interface of interaction. Additionally, a score term named Motif Dock Score ranks low-resolution models according to their chance to generate promising high-resolution models, reducing the number of candidates to proceed to the next stage. In the second stage (local docking), the centroid mode is converted into full-atom representation, where small random rigid-body perturbations take place, followed by side-chain relaxation that aims to optimize local interactions. A first set of models consisted of generating 10,000 low-resolution candidates using global docking with 3 Å translation and 8° rotation perturbation parameters. The 2000 highest-ranked models according to Motif Dock Score proceeded to the local docking stage using default parameters (0.1 Å translation and 3° rotation perturbation parameters). Twenty-five high-resolution candidates were generated for each low-resolution input model totaling 50,000 models. A second set of models was generated by first randomizing CD4 NTD orientation previously to docking run. For this strategy, 40,000 low-resolution and 300,000 high-resolution models were generated similarly to the description above. As an alternative docking approach, monomeric chains were also docked using the HADDOCK 2.4 server (van Zundert et al., 2016), which returned 40 model candidates spread into 13 clusters. A total of 800 additional models were generated using these models as input to the high-resolution docking stage at RosettaDock4.0. Next, best-ranked 2000 models according to interface score (I_sc) were fully relaxed with Relax application (beta_genpot score function, Rosetta Package) and evaluated with InterfaceAnalyzer application (Rosetta package) to extract interface metrics. A total of 500 top-ranked models according to interface binding energy (dG_separated) were evaluated for pairwise structural similarity with LovoAlign (Martínez et al., 2007) and were clustered at 2.0 Å radius. Best-ranked models of each of the first 50 clusters were selected for MD simulation. This set of models is available at https://github.com/ajrferrari/CD4-RBD-interaction-models (copy archived at Ferrari, 2023).

Molecular dynamics simulations

MD simulations have been used to discriminate among protein complexes decoys by challenging their thermal stability (Radom et al., 2018). All-atom MD simulations were carried out with pmemd within AMBER20 software suite for 50 representative models of CD4 NTD sCov2 RBD interaction obtained as described above. Glycosylation at sCov2 RBD N343 residue (Watanabe et al., 2020; Woo et al., 2020) was built using the Glycam web server. Each system was solvated in a periodic octahedral water box such that the initial structure was more than 24 Å of its closest image. Neutralizing ions (Na+ and Cl-) were added to a final concentration of 0.15 M (Machado and Pantano, 2020). The ff14SB force field (Maier et al., 2015) was used for modeling proteins, the GLYCAM06 force field (Kirschner et al., 2008) for modeling the glycan, and water was described with the TIP3P model (Jorgensen et al., 1983). Electrostatic interactions were evaluated using the Particle Mesh Ewald algorithm (Darden et al., 1998), nonbonded interactions were truncated at 9 Å, bonds involving hydrogen atoms were constrained at their equilibrium values, and a time step of 2 fs was used for the numerical integration of the equations of motion. Before production runs, all systems were equilibrated in five sequential steps: (1) 2500 steps of steepest descent line minimization followed by 2500 of conjugate gradient minimization; (2) 1 ns simulation at 303 K in the NVT ensemble with harmonic restraints of 10 kcal mol–1 Å–2 to protein and glycan atoms; (3) 1 ns simulation at 303 K in the NPT ensemble with harmonic restraints of 10 kcal mol–1 Å–2 to protein and glycan atoms; (4) 1 ns simulation at 303 K in the NPT ensemble with harmonic restraints of 10 kcal mol–1 Å–2 to protein backbone atoms; and (5) 1 ns simulation at 303 K in the NPT ensemble without restraints. Subsequently, production runs for assessment of the kinetic stability of the proposed structural models (Radom et al., 2018) were performed in the NPT ensemble in four sequential steps: (1) 50 ns simulation at 303 K; (2) 50 ns simulation at 323 K; (3) 50 ns simulation at 353 K; and (4) 50 ns simulation at 383 K. At each production step, models were evaluated according to their root-mean-square deviation (RMSD) to the initial model such that models with RMSD > 4 Å did not proceed to the next step. The 4 Å threshold was rationalized based on the fluctuations observed in the MD simulations on the sCov2 RBD and CD4 monomers independently. These simulations revealed that both monomers exhibited fluctuations ranging between 2 Å and 4 Å when evaluated up to 323 K (Figure 2—figure supplement 1). Additionally, for the data contained in Figure 2—figure supplement 1, triplicate production runs were performed for 100 ns at 303 K in the NPT ensemble. All trajectory analyses were performed with CPPTRAJ (Roe and Cheatham, 2013). Model structural visualization and image rendering were performed either in PyMOL (The PyMOL Molecular Graphics System, Version Schrödinger, LLC, n.d.) or VMD (Humphrey et al., 1996).

Fluorescence anisotropy

Human CD4 (Sino Biological/10400-H08H), ACE2 (GenScript), and purified human thyroid receptor (TR) (negative control) proteins were labeled with FITC by incubation with fluorescein isothiocyanate probe, at molar ratio 3 FITC:1 protein, 4°C for 3 hr. The probe excess was removed by a desalting column (HiTrap 5 ml, GE) in a buffer containing 137 mM NaCl, 10 mM Na phosphate, 2.7 mM KCl, pH of 7.4. To evaluate the affinities between RBD and CD4, TR or ACE2, serial dilutions of RBD (0.01–10 μM) were performed. To evaluate the affinity between Spike full length (LECC/UFRJ LM220720) and CD4, serial dilutions of Spike (0.01–4.5 μM) were performed. The anisotropy curves were assembled in 0three replicates in black 384-well plates (Greiner). After serial dilution setup, each point was incubated with 20 nM of the labeled proteins (CD4, ACE2, or TR) for at least 2 hr at 4°C. For each fluorescence curve, the mixtures were submitted to fluorescence anisotropy measurements using ClarioStar plate reader (BMG, using polarization filters of 520 nm for emission and of 495 nm for excitation). Data analysis was performed using OriginPro 8.6 software, and dissociation constant (Kd) was obtained from data fitted to binding curves through Hill model.

Data availability

Proteomic raw data are available in ProteomeXchange database under accession number PXD020967, Molecular dynamics dataset is available at https://github.com/ajrferrari/CD4-RBD-interaction-models, copy archived at Ferrari, 2023.

The following data sets were generated
    1. Farias AS
    (2023) PRIDE
    ID PXD020967. SARS-CoV-2 Uses CD4 to Infect T Helper Lymphocytes.
The following previously published data sets were used
    1. Liao M
    2. Liu Y
    3. Yuan J
    4. Wen Y
    5. Xu G
    6. Zhao J
    7. Cheng L
    8. Li J
    9. Wang X
    10. Wang F
    11. Liu L
    12. Amit I
    13. Zhang S
    14. Zhang Z
    (2020) NCBI Gene Expression Omnibus
    ID GSE145926. Single-cell landscape of bronchoalveolar immune cells in COVID-19 patients.

References

Decision letter

  1. Frank L van de Veerdonk
    Reviewing Editor; Radboud University Nijmegen Medical Centre, Netherlands
  2. Jos W van der Meer
    Senior Editor; Radboud University Medical Centre, Netherlands

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "SARS-CoV-2 Uses CD4 to Infect T Helper Lymphocytes" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Jos van der Meer as the Senior Editor.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

Comments and suggestions:

1. A weakness of the paper is the reference citations.

Please be consistent in maintaining the citation style and numbering in the manuscript.

2. Although the extended Figure 9 suggests the expression of a multitude panel of gene expression, including apoptotic genes, the authors could not provide a piece of direct evidence to show how CD4 T cell death happens. What is the underlying mechanism of cell death? Is it by necrosis or apoptosis or pyroptosis? Please discuss.

3. Some previous studies suggested that lymphocytopenia in COVID infection could be due to impaired T cell proliferation or extravasation of T cells into tissue. The exact mechanism for lymphocytopenia should be discussed.

4. The data on CREB-1 Ser 133 in Figure 4E is not sufficiently convincing. It is difficult to understand what is the difference between every three lanes within mock and SARS CoV-2 infection. There is a pCREB band in lane 5 (2nd lane of CoV-2), but not in the other two. Please clarify.

5. Please add more specific information on the Insitu hybridization method. What is PK in the Insitu hybridization method? '…a couple of more washes'. Please be specific on experimental details.

6. Detection of antisense strand: Any reason to use 4% agarose gel? Is it due to the difficulty to detect the smaller size of the amplicon?

7. Immunoblotting: More experimental details are required. How many cells were infected or what is the total number of cells started with to get MOI=1? What is the exact time point used in Figure 4. E?

8. Immunofluorescence: Normally immunofluorescence protocol suggests 5% BSA for blocking. Is there any specific reason to use 1% BSA for blocking?

9. Transmission electron microscopy: More details on cacodylate buffer with calcium chloride in terms of concentration/molarity.

10. RNA extraction and cDNA synthesis: Please specify the sample volumes used for extracting the RNA. Specify the quantity of total RNA or cDNA used for qRT PCR.

11. What is the housekeeping gene HPRT? Please expand it.

12. Please watch the abbreviations and provide the expanded version wherever required. For example Single-cell mRNA seq: What is CCA? What is PCA?

13. It is interesting from a computational biology perspective that if GO database is removed (too unvalidated), and "Cell surface" component of the Jensen database (considering its more dedicated "Plasma membrane" and "External side of plasma membrane" components considered in the work) out of the Venn diagram (Extended Data Figure 3), then what will be left is more interaction partners shared between the remaining 3 databases. Interestingly, these additional partners would include CD8A and CD8B. However, the authors show that the interaction was experimentally noted to happen with CD4+ T cells but not with CD8+ ones. This warrants some discussion on why this might be the case. Moreover, what would be the computational docking/MD results if the authors were to attempt modelling an interaction between the spike glycoprotein and CD8? Would they not arrive at stable complexes with the MD workflow and 4 Å cutoff for temperature-induced stability scrutinization, that would be extra validation and weight on the adopted computational scheme for the discovery.

14. Looking at the last complex in Figure 2, where the full-length sCov2 is recovered on top of the modelled fragment, one can see some additional interaction points or potential clashes with CD4 NTD. Were some of the models discarded on the ground of the orientation between CD4 NTD and sCov2 RBD being incompatible with the full length sCov2 due to possible steric clashes?

15. The 4 Å cutoff for the temperature gradient-based structural stability check sounds reasonable, but would be more justifiable if the authors would also present a histogram of all RMSDs (of final aberrations) for all the tried models and show how outlying the 4 Å is in the whole distribution, additionally attributing a p-value on the selected cutoff.

16. "Methods" section, "Target selection" subsection. Please, define/describe LR in the text for the benefit of the readers.

17. "Methods" section, the last "Molecular Dynamics Simulations" subsection. There is a sentence that has "… for the data contained in Figure Sx…", please correct which supplementary figure is meant, as likely the Sx notation is left from the earlier drafts of the manuscript.

18. Figure 2 caption, an extra "." after (B).

19. Figure 2. It would help if model 95 and model 148 were brought roughly in the same spatial orientation as the final CD4 NTD and full sCov2 RBD model, to aid the readers in matching and getting more insight.

20. Extended Data Figure 3. In Figure A (Venn diagram), at "Plasma membrane Jensen compartments" notation closing bracket is missing.

https://doi.org/10.7554/eLife.84790.sa1

Author response

Essential revisions:

Comments and suggestions:

1. A weakness of the paper is the reference citations.

Please be consistent in maintaining the citation style and numbering in the manuscript.

We have edited the references according to the eLife citation style.

2. Although the extended Figure 9 suggests the expression of a multitude panel of gene expression, including apoptotic genes, the authors could not provide a piece of direct evidence to show how CD4 T cell death happens. What is the underlying mechanism of cell death? Is it by necrosis or apoptosis or pyroptosis? Please discuss.

We would like to thank the Reviewer for the question, which gave us the opportunity to review the text and include a discussion about this topic in the paper. Please see sentences between lines 240 and 246. SARS-CoV-2 can induce cell death by different mechanisms, depending on the infected cell type and the microenvironment and inflammatory condition where these cells are inserted. For example, SARS-CoV-2 can induce apoptosis in different cell lines through stimulation of autophagy via suppression of mTOR signaling (DOI: 10.1016/j.bbadis.2021.166260). SARS-CoV-2 can also induce inflammasome activation and cell death by pyroptosis in primary monocytes and PBMCs from COVID-19 patients (DOI: 10.1084/jem.20201707). Immunomodulatory drugs that inhibit inflammasome pathways impair SARS-CoV-2 replication in monocytes and other cell types (DOI: 10.1126/sciadv.abo5400) and may have beneficial effects to the treatment of COVID-19. In addition, SARS-CoV-2 drives multimodal necrosis in epithelial respiratory and kidney cell lines by a mechanism dependent on Open Reading Frame-3a of the virus (doi:10.1038/s41419‐018‐0917‐y) and it is able to induce PANoptosis by a mechanism dependent on ISG production, such as Z-DNA-binding protein 1 (ZBP1), in a context of cytokine storm and IFN-I treatment (10.1126/sciimmunol.abo6294). Thus, cell death induced by SARS-CoV-2 appears to play a pivotal role in the pathogenesis of COVID-19. Interestingly and in line with this idea, proinflammatory cytokines produced by SARS-CoV-2-infected apoptotic cells inhibit the expression of efferocytic receptors and impairs the continual process of clearance of dying cells by macrophages (DOI: 10.7554/eLife.74443). In the present paper, we demonstrated that SARS-CoV-2 can infect and induce cell death of CD4 T cells. Although the mechanisms of cell death have not been explored in depth in this article, data in the literature suggests that apoptosis is the mechanism underlying SARS-CoV-2-infected T cell death (DOI: 10.1093/jmcb/mjac021).

3. Some previous studies suggested that lymphocytopenia in COVID infection could be due to impaired T cell proliferation or extravasation of T cells into tissue. The exact mechanism for lymphocytopenia should be discussed.

Lymphocytopenia is a common characteristic of COVID-19 (https://www.nature.com/articles/s41392-020-0148-4). The decrease of the number of circulating lymphocytes might be due to innumerous factors, such as cell death, migration into the inflamed tissue as well as by promoting systemic inflammation and direct neutralization in human spleen and lymph nodes (https://doi.org/10.3389/fimmu.2021.661052). Single-cell analysis demonstrated that lymphocytes are not the main infiltrated cell population in the infected tissue (see figure 1E). Hence, it is likely that the decrease in lymphocyte count is attributed to both infected T cell death and dysfunctional T cells resulting from the inflammatory cytokine storm. This discussion has been incorporated into the manuscript.

4. The data on CREB-1 Ser 133 in Figure 4E is not sufficiently convincing. It is difficult to understand what is the difference between every three lanes within mock and SARS CoV-2 infection. There is a pCREB band in lane 5 (2nd lane of CoV-2), but not in the other two. Please clarify.

Each lane in the western blotting experiment represents a different sample and each group were done in triplicate (see Author response image 1). Hence, a combination of experimental and biological variability, which is common when using human samples, explains the differences between the lanes, particularly in the 5th lane of pCREB.

Author response image 1

5. Please add more specific information on the Insitu hybridization method. What is PK in the Insitu hybridization method? '…a couple of more washes'. Please be specific on experimental details.

PK is an abbreviation for Proteinase K (https://www.sigmaaldrich.com/BR/pt/product/mm/124568) used in the antigen retrieval step by digesting mRNA bound proteins that could prevent the DNA probes to hybridize to their target mRNA. As for the wash steps after fixation with 4% PFA, three washes were performed to remove possible fixative residues. More specific information was added to the manuscript.

6. Detection of antisense strand: Any reason to use 4% agarose gel? Is it due to the difficulty to detect the smaller size of the amplicon?

The 4% agarose gel was employed in this protocol to attain the necessary resolution for discriminating between the sense and antisense strands based on their size difference. The difference in size between the two strands is only 21 nucleotides, which is caused by the non-viral tag present in the antisense strand. To effectively separate the small DNA fragments, a higher percentage agarose gel was required as it provides better resolution.

7. Immunoblotting: More experimental details are required. How many cells were infected or what is the total number of cells started with to get MOI=1? What is the exact time point used in Figure 4. E?

Most of the experiments were done with one million sorted CD3+CD4+ live cells plated over a well of a 48-well plate. For proteomic and immunoblotting experiments, four million live CD3+CD4+ cells were used. All experiments with SARS-CoV-2 were done in a biosafety level 3 laboratory (BSL-3) located in the Institute of Biology from the University Campinas. This information was clarified in the text.

8. Immunofluorescence: Normally immunofluorescence protocol suggests 5% BSA for blocking. Is there any specific reason to use 1% BSA for blocking?

Percentage of BSA for blocking in immunofluorescence usually varies from 1-5%. We used different concentrations and decided to follow the manufacturer's suggestion (https://www.abcam.com/protocols/immunocytochemistry-immunofluorescence-protocol).

9. Transmission electron microscopy: More details on cacodylate buffer with calcium chloride in terms of concentration/molarity.

We added the following sentence to the manuscript:

“T lymphocyte cell cultures were pelleted by centrifugation at 500 x g for 10 min. Cell culture supernatant was removed and cells were resuspended in 100 µL of the fixative solution, which consisted of 1 M sodium cacodylate aqueous solution supplemented with 2.5% v/v glutaraldehyde and 3 mM calcium chloride at pH 7.2, and kept overnight (16h) at 4°C”.

10. RNA extraction and cDNA synthesis: Please specify the sample volumes used for extracting the RNA. Specify the quantity of total RNA or cDNA used for qRT PCR.

We included more information in the text regarding the methods for RNA extraction and cDNA synthesis. Briefly, pelleted cells were resuspended in 200 µL of TRI Reagent and the RNA extraction performed following the manufacturer's instructions. 500 ng of RNA was converted into cDNA. The cDNA final concentration was 25 ng/μL and qPCR reaction was performed using 15 ng of cDNA.

11. What is the housekeeping gene HPRT? Please expand it.

The HPRT is a gene that encodes the hypoxanthine phosphoribosyltransferase 1 protein, an enzyme responsible for recycling purines (purine salvage pathway) in DNA molecules. This enzyme catalyzes conversion of hypoxanthine to inosine monophosphate (IMP) and guanine to guanosine monophosphate (GMP), when transferring phosphoribose from phosphoribosyl pyrophosphate (PRPP). This gene is present in all somatic cells and is considered a housekeeping gene because GTP is necessary for DNA synthesis and cellular energy. For lymphocytes, especially in non-activated conditions, HPRT has shown to be a reliable housekeeping gene (https://pubmed.ncbi.nlm.nih.gov/35645343/).

12. Please watch the abbreviations and provide the expanded version wherever required. For example Single-cell mRNA seq: What is CCA? What is PCA?

We thank the Reviewer for pointing this out. Abbreviations were defined as they appeared for the first time.

13. It is interesting from a computational biology perspective that if GO database is removed (too unvalidated), and "Cell surface" component of the Jensen database (considering its more dedicated "Plasma membrane" and "External side of plasma membrane" components considered in the work) out of the Venn diagram (Extended Data Figure 3), then what will be left is more interaction partners shared between the remaining 3 databases. Interestingly, these additional partners would include CD8A and CD8B. However, the authors show that the interaction was experimentally noted to happen with CD4+ T cells but not with CD8+ ones. This warrants some discussion on why this might be the case. Moreover, what would be the computational docking/MD results if the authors were to attempt modelling an interaction between the spike glycoprotein and CD8? Would they not arrive at stable complexes with the MD workflow and 4 Å cutoff for temperature-induced stability scrutinization, that would be extra validation and weight on the adopted computational scheme for the discovery.

Although the Reviewer raises an interesting observation, our experiments beyond the computational prediction do not support a role for CD8 in T lymphocyte infection. Hence, we decided not to focus on a potential interaction between CD8 and SARS-CoV spike protein, although this does not exclude that the interaction may exist. Indeed, a highly stable interaction between Spike and CD8 molecules could result in a 'stable' complex that would trap the viral particles outside the cell precluding infection. This would be consistent with T CD8 cells not being infected by SARS-CoV-2. However, we would need more experiments to prove this mechanism and this is beyond the scope of this manuscript, which focuses on the interaction between spike and CD4 and its role in CD4 T cell infection.

14. Looking at the last complex in Figure 2, where the full-length sCov2 is recovered on top of the modelled fragment, one can see some additional interaction points or potential clashes with CD4 NTD. Were some of the models discarded on the ground of the orientation between CD4 NTD and sCov2 RBD being incompatible with the full length sCov2 due to possible steric clashes?

We appreciate the Reviewer's observation regarding the potential clashes between CD4 NTD and the full-length sCov2 in the last complex presented in Figure 2. In constructing the full model, we initially performed a superposition of the RBD domain onto the full-length sCov2. The primary criterion for selecting the final models was the kinetic stability analysis, as we aimed to identify the most stable configurations. We acknowledge that the compatibility between the docking model and the full-length sCov2, considering possible steric clashes, is an essential factor to consider. For the two most stable models, namely model95 and model148, we carefully examined their structures. In the case of model148, illustrated in Figure 2D, we did not observe any apparent clashes between CD4 NTD and the full-length sCov2.

However, we would like to emphasize that in model148, there is a possibility of CD4 NTD interacting with a second sCov2 RBD in its closed state. These interactions may involve polar interactions or mediation by water molecules when solvated. Nevertheless, based on our analysis, we do not anticipate that these few interactions play a significant role in the overall stability and function of the complex. To provide further clarity, we have made available a PyMOL session containing the structures used for the analysis in Figure 2. This session can be accessed via the following GitHub page: https://github.com/ajrferrari/CD4-RBD-interaction-models under the 'pymol-session' folder. We hope this additional information addresses the concern raised by the Reviewers.

15. The 4 Å cutoff for the temperature gradient-based structural stability check sounds reasonable, but would be more justifiable if the authors would also present a histogram of all RMSDs (of final aberrations) for all the tried models and show how outlying the 4 Å is in the whole distribution, additionally attributing a p-value on the selected cutoff.

We appreciate the Reviewer’s feedback regarding the justification for the 4 Å cutoff used in our temperature gradient-based structural stability check. We agree that providing additional analysis to support the chosen cutoff would enhance the validity of our findings. To further rationalize the selected cutoff, we conducted MD simulations on the sCov2 RBD and CD4 monomers independently (see figure 2—figure supplement 1e). These simulations revealed that both monomers exhibited fluctuations ranging between 2 Å and 4 Å when evaluated up to 323K. These fluctuations serve as a baseline, indicating the inherent flexibility and dynamics of the individual monomers. In our analysis, models that crossed the 4 Å cutoff at any point during the MD trajectory were considered kinetically unstable, as they exhibited higher fluctuations than the individual monomers. We have now clarified this rationale in the main text to provide a clearer understanding of the criteria used for assessing stability. To address the Reviewer’s suggestion, we have generated a histogram of the maximum backbone RMSD values for all the tried models. This histogram is provided in figure 2—figure supplement 1f.

It is important to note that the p-value obtained from the distribution of maximum RMSDs is 0.15. However, it is crucial to consider the characteristics of our evaluation process. Not all models completed the four MD steps, as some diffused away early during the evaluation. As a result, the distribution of maximum RMSDs observed is biased toward lower values. Consequently, the 4 Å cutoff appears less of an outlier than it would be if all models were equally sampled.

We believe that this additional analysis, including the histogram of maximum RMSDs, provides a more comprehensive understanding of the distribution and relative position of the 4 Å cutoff. It accounts for the limitations of the sampling process and strengthens the justification for its selection. We have revised the manuscript to include these findings, addressing the Reviewer’s' concern.

16. "Methods" section, "Target selection" subsection. Please, define/describe LR in the text for the benefit of the readers.

LR (likelihood ratio) has been defined in the manuscript.

17. "Methods" section, the last "Molecular Dynamics Simulations" subsection. There is a sentence that has "… for the data contained in Figure Sx…", please correct which supplementary figure is meant, as likely the Sx notation is left from the earlier drafts of the manuscript.

We have solved this issue in the new version of the manuscript.

18. Figure 2 caption, an extra "." after (B).

We have solved this issue in the new version of the manuscript.

19. Figure 2. It would help if model 95 and model 148 were brought roughly in the same spatial orientation as the final CD4 NTD and full sCov2 RBD model, to aid the readers in matching and getting more insight.

We appreciate the Reviewer's suggestion regarding Figure 2 and the importance of presenting model 95 and model 148 in a spatial orientation similar to that of the final CD4 NTD and full sCov2 RBD model. We agree that this would facilitate the readers' ability to match and gain deeper insights from the figure. In response to this valuable feedback, we have revised Figure 2 to ensure that model 95 and model 148 are now presented in a comparable spatial orientation to the final CD4 NTD and full sCov2 RBD model. This modification allows for a more straightforward visual comparison and enhances the clarity and comprehensibility of the figure. We also provide a pymol session that will allow the readers to explore in more details the models shown in Figure 2. The pymol session can be accessed via the following GitHub page: https://github.com/ajrferrari/CD4-RBD-interaction-models under the 'pymol-session' folder.

20. Extended Data Figure 3. In Figure A (Venn diagram), at "Plasma membrane Jensen compartments" notation closing bracket is missing.

We have solved this issue in the new version of the manuscript.

https://doi.org/10.7554/eLife.84790.sa2

Article and author information

Author details

  1. Natalia S Brunetti

    Autoimmune Research Laboratory, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Validation, Investigation, Methodology, Writing – original draft, Writing – review and editing
    Contributed equally with
    Gustavo G Davanzo, Diogo de Moraes, Allan JR Ferrari, Gabriela F Souza, Stéfanie Primon Muraro, Thiago L Knittel, Vinicius O Boldrini, Lauar B Monteiro and João Victor Virgílio-da-Silva
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2803-3763
  2. Gustavo G Davanzo

    Laboratory of Immunometabolism, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing
    Contributed equally with
    Natalia S Brunetti, Diogo de Moraes, Allan JR Ferrari, Gabriela F Souza, Stéfanie Primon Muraro, Thiago L Knittel, Vinicius O Boldrini, Lauar B Monteiro and João Victor Virgílio-da-Silva
    Competing interests
    No competing interests declared
  3. Diogo de Moraes

    1. Laboratory of Aging Biology, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    2. Department of Structural and Functional Biology, Institute of Biosciences, Sao Paulo State University (UNESP), Botucatu, Brazil
    Contribution
    Data curation, Formal analysis, Investigation, Methodology, Writing – original draft
    Contributed equally with
    Natalia S Brunetti, Gustavo G Davanzo, Allan JR Ferrari, Gabriela F Souza, Stéfanie Primon Muraro, Thiago L Knittel, Vinicius O Boldrini, Lauar B Monteiro and João Victor Virgílio-da-Silva
    Competing interests
    No competing interests declared
  4. Allan JR Ferrari

    Institute of Chemistry and Center for Computing in Engineering and Sciences, University of Campinas, Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Data curation, Formal analysis, Investigation, Methodology, Writing – original draft
    Contributed equally with
    Natalia S Brunetti, Gustavo G Davanzo, Diogo de Moraes, Gabriela F Souza, Stéfanie Primon Muraro, Thiago L Knittel, Vinicius O Boldrini, Lauar B Monteiro and João Victor Virgílio-da-Silva
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9199-2257
  5. Gabriela F Souza

    Laboratory of Emerging Viruses, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Investigation, Methodology, Writing – original draft
    Contributed equally with
    Natalia S Brunetti, Gustavo G Davanzo, Diogo de Moraes, Allan JR Ferrari, Stéfanie Primon Muraro, Thiago L Knittel, Vinicius O Boldrini, Lauar B Monteiro and João Victor Virgílio-da-Silva
    Competing interests
    No competing interests declared
  6. Stéfanie Primon Muraro

    Laboratory of Emerging Viruses, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Investigation, Visualization, Methodology
    Contributed equally with
    Natalia S Brunetti, Gustavo G Davanzo, Diogo de Moraes, Allan JR Ferrari, Gabriela F Souza, Thiago L Knittel, Vinicius O Boldrini, Lauar B Monteiro and João Victor Virgílio-da-Silva
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5105-6659
  7. Thiago L Knittel

    Laboratory of Aging Biology, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Validation, Investigation, Methodology, Writing – original draft
    Contributed equally with
    Natalia S Brunetti, Gustavo G Davanzo, Diogo de Moraes, Allan JR Ferrari, Gabriela F Souza, Stéfanie Primon Muraro, Vinicius O Boldrini, Lauar B Monteiro and João Victor Virgílio-da-Silva
    Competing interests
    No competing interests declared
  8. Vinicius O Boldrini

    Autoimmune Research Laboratory, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Investigation, Methodology
    Contributed equally with
    Natalia S Brunetti, Gustavo G Davanzo, Diogo de Moraes, Allan JR Ferrari, Gabriela F Souza, Stéfanie Primon Muraro, Thiago L Knittel, Lauar B Monteiro and João Victor Virgílio-da-Silva
    Competing interests
    No competing interests declared
  9. Lauar B Monteiro

    Laboratory of Immunometabolism, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Investigation, Methodology, Writing – original draft
    Contributed equally with
    Natalia S Brunetti, Gustavo G Davanzo, Diogo de Moraes, Allan JR Ferrari, Gabriela F Souza, Stéfanie Primon Muraro, Thiago L Knittel, Vinicius O Boldrini and João Victor Virgílio-da-Silva
    Competing interests
    No competing interests declared
  10. João Victor Virgílio-da-Silva

    Laboratory of Immunometabolism, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Investigation, Methodology, Writing – original draft
    Contributed equally with
    Natalia S Brunetti, Gustavo G Davanzo, Diogo de Moraes, Allan JR Ferrari, Gabriela F Souza, Stéfanie Primon Muraro, Thiago L Knittel, Vinicius O Boldrini and Lauar B Monteiro
    Competing interests
    No competing interests declared
  11. Gerson S Profeta

    Laboratory of Aging Biology, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Investigation, Methodology
    Contributed equally with
    Natália S Wassano, Luana Nunes Santos, Victor C Carregari and Artur HS Dias
    Competing interests
    No competing interests declared
  12. Natália S Wassano

    Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Investigation, Methodology
    Contributed equally with
    Gerson S Profeta, Luana Nunes Santos, Victor C Carregari and Artur HS Dias
    Competing interests
    No competing interests declared
  13. Luana Nunes Santos

    Brazilian Laboratory on Silencing Technologies (BLaST), Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Investigation, Methodology
    Contributed equally with
    Gerson S Profeta, Natália S Wassano, Victor C Carregari and Artur HS Dias
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0092-0803
  14. Victor C Carregari

    Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Formal analysis, Investigation, Methodology
    Contributed equally with
    Gerson S Profeta, Natália S Wassano, Luana Nunes Santos and Artur HS Dias
    Competing interests
    No competing interests declared
  15. Artur HS Dias

    Institute of Chemistry and Center for Computing in Engineering and Sciences, University of Campinas, Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Data curation, Formal analysis, Methodology
    Contributed equally with
    Gerson S Profeta, Natália S Wassano, Luana Nunes Santos and Victor C Carregari
    Competing interests
    No competing interests declared
  16. Flavio P Veras

    1. Center of Research in Inflammatory Diseases, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
    2. Department of BioMolecular Sciences, School of Pharmaceutical Sciences, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
    3. Department of Pharmacology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto,, São Paulo, Brazil
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
  17. Lucas A Tavares

    Department of Cell and Molecular Biology, Center for Virology Research, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7883-1443
  18. Julia Forato

    Laboratory of Emerging Viruses, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
  19. Icaro MS Castro

    Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
    Contribution
    Data curation, Formal analysis, Methodology
    Competing interests
    No competing interests declared
  20. Lícia C Silva-Costa

    Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Formal analysis, Methodology
    Competing interests
    No competing interests declared
  21. André C Palma

    Department of Internal Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  22. Eli Mansour

    Department of Internal Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6450-6930
  23. Raisa G Ulaf

    Department of Internal Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  24. Ana F Bernardes

    Department of Internal Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  25. Thyago A Nunes

    Department of Internal Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0202-0550
  26. Luciana C Ribeiro

    Department of Internal Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  27. Marcus V Agrela

    Department of Internal Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  28. Maria Luiza Moretti

    Department of Internal Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  29. Lucas I Buscaratti

    Brazilian Laboratory on Silencing Technologies (BLaST), Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  30. Fernanda Crunfli

    Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Formal analysis, Methodology
    Competing interests
    No competing interests declared
  31. Raissa G Ludwig

    Laboratory of Aging Biology, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  32. Jaqueline A Gerhardt

    Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  33. Natália Munhoz-Alves

    Autoimmune Research Laboratory, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2756-3666
  34. Ana Maria Marques

    Autoimmune Research Laboratory, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  35. Renata Sesti-Costa

    1. Autoimmune Research Laboratory, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    2. Hematology and Hemotherapy Center, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  36. Mariene R Amorim

    Laboratory of Emerging Viruses, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
  37. Daniel A Toledo-Teixeira

    Laboratory of Emerging Viruses, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4055-058X
  38. Pierina Lorencini Parise

    Laboratory of Emerging Viruses, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  39. Matheus Cavalheiro Martini

    Laboratory of Emerging Viruses, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  40. Karina Bispos-dos-Santos

    Laboratory of Emerging Viruses, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0305-367X
  41. Camila L Simeoni

    Laboratory of Emerging Viruses, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  42. Fabiana Granja

    Laboratory of Emerging Viruses, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
  43. Virgínia C Silvestrini

    Department of Biochemistry and Immunology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
    Contribution
    Resources, Methodology
    Competing interests
    No competing interests declared
  44. Eduardo B de Oliveira

    Department of Biochemistry and Immunology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
    Contribution
    Resources, Methodology
    Competing interests
    No competing interests declared
  45. Vitor M Faca

    Department of Biochemistry and Immunology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
    Contribution
    Resources, Methodology
    Competing interests
    No competing interests declared
  46. Murilo Carvalho

    1. National Biosciences Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Brazil
    2. Brazilian Synchrotron Light Laboratory (LNLS), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  47. Bianca G Castelucci

    1. National Biosciences Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Brazil
    2. Brazilian Synchrotron Light Laboratory (LNLS), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  48. Alexandre B Pereira

    National Biosciences Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  49. Laís D Coimbra

    National Biosciences Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  50. Marieli MG Dias

    National Biosciences Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Brazil
    Contribution
    Formal analysis, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0246-8884
  51. Patricia B Rodrigues

    Laboratory of Immunoinflammation, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil;, Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  52. Arilson Bernardo SP Gomes

    Laboratory of Immunoinflammation, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil;, Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  53. Fabricio B Pereira

    Hematology and Hemotherapy Center, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  54. Leonilda MB Santos

    1. Neuroimmunology Unit, Department of Genetics, Microbiology and Immunology, University of Campinas (UNICAMP), Campinas, Brazil
    2. National Institute of Science and Technology on Neuroimmunomodulation (INCT-NIM) – Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
    Contribution
    Resources, Methodology
    Competing interests
    No competing interests declared
  55. Louis-Marie Bloyet

    Washington University in St Louis, Department of Molecular Microbiology, St. Louis, United States
    Contribution
    Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5648-3190
  56. Spencer Stumpf

    Washington University in St Louis, Department of Molecular Microbiology, St. Louis, United States
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  57. Marjorie C Pontelli

    Washington University in St Louis, Department of Molecular Microbiology, St. Louis, United States
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  58. Sean Whelan

    Washington University in St Louis, Department of Molecular Microbiology, St. Louis, United States
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
  59. Andrei C Sposito

    Laboratory of Vascular Biology and Arteriosclerosis, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Methodology
    Competing interests
    No competing interests declared
  60. Robson F Carvalho

    Department of Structural and Functional Biology, Institute of Biosciences, Sao Paulo State University (UNESP), Botucatu, Brazil
    Contribution
    Data curation, Formal analysis, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4901-7714
  61. André S Vieira

    Laboratory of Electrophysiology, Neurobiology and Behavior, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
  62. Marco AR Vinolo

    1. Laboratory of Immunoinflammation, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil;, Campinas, Brazil
    2. Experimental Medicine Research Cluster (EMRC), University of Campinas (UNICAMP), Campinas, Brazil
    3. Obesity and Comorbidities Research Center (OCRC), University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Resources, Funding acquisition, Methodology, Writing – original draft
    Competing interests
    No competing interests declared
  63. André Damasio

    1. Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    2. Experimental Medicine Research Cluster (EMRC), University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Resources, Investigation, Methodology
    Competing interests
    No competing interests declared
  64. Licio Velloso

    1. Department of Internal Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
    2. Obesity and Comorbidities Research Center (OCRC), University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Resources, Methodology
    Competing interests
    No competing interests declared
  65. Ana Carolina M Figueira

    National Biosciences Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Brazil
    Contribution
    Funding acquisition, Methodology
    Competing interests
    No competing interests declared
  66. Luis LP da Silva

    Center of Research in Inflammatory Diseases, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
    Contribution
    Resources, Investigation, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3558-0087
  67. Thiago Mattar Cunha

    1. Center of Research in Inflammatory Diseases, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
    2. Department of Pharmacology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto,, São Paulo, Brazil
    Contribution
    Resources, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1084-0065
  68. Helder I Nakaya

    Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
    Contribution
    Resources, Data curation, Formal analysis, Funding acquisition, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5297-9108
  69. Henrique Marques-Souza

    1. Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    2. Brazilian Laboratory on Silencing Technologies (BLaST), Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Resources, Formal analysis, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5008-8413
  70. Rafael E Marques

    National Biosciences Laboratory (LNBio), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Brazil
    Contribution
    Resources, Formal analysis, Investigation
    Competing interests
    No competing interests declared
  71. Daniel Martins-de-Souza

    1. Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    2. Experimental Medicine Research Cluster (EMRC), University of Campinas (UNICAMP), Campinas, Brazil
    3. D'Or Institute for Research and Education (IDOR), São Paulo, Brazil
    4. National Institute of Science and Technology in Biomarkers for Neuropsychiatry (INCTINBION), São Paulo, Brazil
    Contribution
    Resources, Formal analysis, Funding acquisition, Investigation, Methodology, Writing – original draft
    Competing interests
    No competing interests declared
  72. Munir S Skaf

    Institute of Chemistry and Center for Computing in Engineering and Sciences, University of Campinas, Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Conceptualization, Resources, Data curation, Formal analysis, Funding acquisition, Writing – original draft
    Competing interests
    No competing interests declared
  73. Jose Luiz Proenca-Modena

    1. Laboratory of Emerging Viruses, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    2. Experimental Medicine Research Cluster (EMRC), University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4996-3153
  74. Pedro MM Moraes-Vieira

    1. Laboratory of Immunometabolism, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    2. Experimental Medicine Research Cluster (EMRC), University of Campinas (UNICAMP), Campinas, Brazil
    3. Obesity and Comorbidities Research Center (OCRC), University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
  75. Marcelo A Mori

    1. Laboratory of Aging Biology, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    2. Experimental Medicine Research Cluster (EMRC), University of Campinas (UNICAMP), Campinas, Brazil
    3. Obesity and Comorbidities Research Center (OCRC), University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    morima@unicamp.br
    Competing interests
    No competing interests declared
  76. Alessandro S Farias

    1. Autoimmune Research Laboratory, Department of Genetics, Microbiology and Immunology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
    2. Washington University in St Louis, Department of Molecular Microbiology, St. Louis, United States
    3. Experimental Medicine Research Cluster (EMRC), University of Campinas (UNICAMP), Campinas, Brazil
    4. Obesity and Comorbidities Research Center (OCRC), University of Campinas (UNICAMP), Campinas, Brazil
    Contribution
    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    asfarias@unicamp.br
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6759-1819

Funding

Fundo de Apoio ao Ensino, à Pesquisa e Extensão, Universidade Estadual de Campinas (#2295/20)

  • Vinicius O Boldrini
  • Luana Nunes Santos
  • Daniel Martins-de-Souza
  • Jose Luiz Proenca-Modena
  • Alessandro S Farias

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2021/08354-2)

  • Marco AR Vinolo

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2015/15626-8)

  • Helder I Nakaya

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2019/14465-1)

  • Ana Carolina M Figueira

Instituto Nacional de Ciência e Tecnologia em Neuroimunomodulação (#465489/2014-1)

  • Alessandro S Farias

Financiadora de Estudos e Projetos (#01.20.0003.00)

  • Rafael E Marques

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

  • Daniel Martins-de-Souza

Conselho Nacional de Desenvolvimento Científico e Tecnológico (#306248/2017-4)

  • Marcelo A Mori
  • Alessandro S Farias

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2019/17007-4)

  • Natalia S Brunetti

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2019/04726-2)

  • Thiago L Knittel

Fundo de Apoio ao Ensino, à Pesquisa e Extensão, Universidade Estadual de Campinas (#2319/20)

  • Vinicius O Boldrini
  • Luana Nunes Santos
  • Daniel Martins-de-Souza
  • Jose Luiz Proenca-Modena
  • Alessandro S Farias

Fundo de Apoio ao Ensino, à Pesquisa e Extensão, Universidade Estadual de Campinas (#2274/20)

  • Vinicius O Boldrini
  • Luana Nunes Santos
  • Daniel Martins-de-Souza
  • Jose Luiz Proenca-Modena
  • Alessandro S Farias

Fundo de Apoio ao Ensino, à Pesquisa e Extensão, Universidade Estadual de Campinas (#2266/20)

  • Vinicius O Boldrini
  • Luana Nunes Santos
  • Daniel Martins-de-Souza
  • Jose Luiz Proenca-Modena
  • Alessandro S Farias

Fundo de Apoio ao Ensino, à Pesquisa e Extensão, Universidade Estadual de Campinas (#2458/20)

  • Vinicius O Boldrini
  • Luana Nunes Santos
  • Daniel Martins-de-Souza
  • Jose Luiz Proenca-Modena
  • Alessandro S Farias

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2019/16116-4)

  • Marco AR Vinolo

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2019/06372-3)

  • Marco AR Vinolo

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2020/04583-4)

  • Marco AR Vinolo

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2013/08293-7)

  • Marco AR Vinolo

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2020/04579-7)

  • Marco AR Vinolo

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2018/14933-2)

  • Helder I Nakaya

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2020/04746-0)

  • Helder I Nakaya

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2019/00098-7)

  • Helder I Nakaya

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2020/04919-2)

  • Helder I Nakaya

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2017/01184-9)

  • Helder I Nakaya

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2020/04558-0)

  • Ana Carolina M Figueira

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2016/00194-8)

  • Ana Carolina M Figueira

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2016/18031- 8)

  • Natalia S Brunetti

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2019/22398-2)

  • Natalia S Brunetti

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2019/13552-8)

  • Natalia S Brunetti

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2019/05155-9)

  • Natalia S Brunetti

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2019/06459-1)

  • Natalia S Brunetti

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2017/23920-9)

  • Thiago L Knittel

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2016/24163-4)

  • Thiago L Knittel

Fundação de Amparo à Pesquisa do Estado de São Paulo (#2016/23328-0)

  • Thiago L Knittel

Conselho Nacional de Desenvolvimento Científico e Tecnológico (#310287/2018-9)

  • Marcelo A Mori
  • Alessandro S Farias

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We acknowledge the technical support of Elzira E Saviani, Paulo A Baldasso, and Mariana Ozello Baratti. We thank Dr. Leda Castilho (UFRJ) for providing sCoV-2 recombinant protein, Dr. Edson Luiz Durigon (USP) for providing viral strains, and Dr. Hernandes F de Carvalho (UNICAMP) for providing valuable reagents for immunofluorescence. We also thank the National Institute of Science and Technology of Photonics Applied to Cell Biology (INFABIC) for aid with confocal microscopy. We also thank Antonio Borges, Fabiano Montoro, and LNNano/CNPEM for the use of the electron microscopy facility (TEM-C2-26912, TEM-C2-26935). We acknowledge the Spectroscopy and Calorimetry Laboratory of the Brazilian Biosciences National Laboratory (LNBio), CNPEM, Campinas, Brazil, for the support in fluorescence anisotropy assays. This work was supported by grants from FAEPEX-UNICAMP (#2295/20, #2458/20, #2266/20 and #2274/20), São Paulo Research Foundation (FAPESP) (#2021/08354-2, #2019/16116-4, #2019/06372-3, #2020/04583-4, #2013/08293-7, #2020/04579-7, #2015/15626-8, #2018/14933-2, #2020/04746-0, #2019/00098-7, #2020/04919-2, #2017/01184-9, #2019/14465-1, #2020/04558-0, #2016/00194-8), the National Institute of Science and Technology in Neuroimmunomodulation (INCT-NIM) (#465489/2014-1) and FINEP (#01.20.0003.00). ASF and MAM. were supported by CNPq productivity awards (#306248/2017-4 and #310287/2018-9). AJRF, GGD, NSB, LBM, FC, VCC, AB, TLK, GSP, and RGL were supported by FAPESP fellowships (#2019/17007-4, #2016/18031-8, #2019/22398-2, #2019/13552-8, #2019/05155-9, #2019/06459-1, #2019/04726-2, #2017/23920-9, #2016/24163-4, #2016/23328-0). VOB and LNS were supported by FAEPEX fellowship (#2295/20 and #2319/20). DM was supported by CAPES fellowship.

Ethics

The samples from patients diagnosed with COVID-19 were obtained at the Clinical Hospital of the University of Campinas (SP-Brazil). Both COVID-19 patients and healthy subjects included in this work signed a term of consent approved by the University of Campinas Committee for Ethical Research (CAAE: 32078620.4.0000.5404, CAEE: 30227920.9.0000.5404). Human blood samples from severe COVID-19 patients analyzed in this study were obtained from individuals admitted at the Clinics Hospital, University of Campinas and included in a clinical trial (UTN: U1111-1250-1843). Besides, in vitro experiments were performed with buffy coats from healthy blood donors provided by The hematology and hemotherapy Center of the University of Campinas (CAAE: 31622420.0.0000.5404).

Senior Editor

  1. Jos W van der Meer, Radboud University Medical Centre, Netherlands

Reviewing Editor

  1. Frank L van de Veerdonk, Radboud University Nijmegen Medical Centre, Netherlands

Version history

  1. Preprint posted: September 28, 2020 (view preprint)
  2. Received: November 30, 2022
  3. Accepted: July 13, 2023
  4. Version of Record published: July 31, 2023 (version 1)

Copyright

© 2023, Brunetti, Davanzo, de Moraes et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Natalia S Brunetti
  2. Gustavo G Davanzo
  3. Diogo de Moraes
  4. Allan JR Ferrari
  5. Gabriela F Souza
  6. Stéfanie Primon Muraro
  7. Thiago L Knittel
  8. Vinicius O Boldrini
  9. Lauar B Monteiro
  10. João Victor Virgílio-da-Silva
  11. Gerson S Profeta
  12. Natália S Wassano
  13. Luana Nunes Santos
  14. Victor C Carregari
  15. Artur HS Dias
  16. Flavio P Veras
  17. Lucas A Tavares
  18. Julia Forato
  19. Icaro MS Castro
  20. Lícia C Silva-Costa
  21. André C Palma
  22. Eli Mansour
  23. Raisa G Ulaf
  24. Ana F Bernardes
  25. Thyago A Nunes
  26. Luciana C Ribeiro
  27. Marcus V Agrela
  28. Maria Luiza Moretti
  29. Lucas I Buscaratti
  30. Fernanda Crunfli
  31. Raissa G Ludwig
  32. Jaqueline A Gerhardt
  33. Natália Munhoz-Alves
  34. Ana Maria Marques
  35. Renata Sesti-Costa
  36. Mariene R Amorim
  37. Daniel A Toledo-Teixeira
  38. Pierina Lorencini Parise
  39. Matheus Cavalheiro Martini
  40. Karina Bispos-dos-Santos
  41. Camila L Simeoni
  42. Fabiana Granja
  43. Virgínia C Silvestrini
  44. Eduardo B de Oliveira
  45. Vitor M Faca
  46. Murilo Carvalho
  47. Bianca G Castelucci
  48. Alexandre B Pereira
  49. Laís D Coimbra
  50. Marieli MG Dias
  51. Patricia B Rodrigues
  52. Arilson Bernardo SP Gomes
  53. Fabricio B Pereira
  54. Leonilda MB Santos
  55. Louis-Marie Bloyet
  56. Spencer Stumpf
  57. Marjorie C Pontelli
  58. Sean Whelan
  59. Andrei C Sposito
  60. Robson F Carvalho
  61. André S Vieira
  62. Marco AR Vinolo
  63. André Damasio
  64. Licio Velloso
  65. Ana Carolina M Figueira
  66. Luis LP da Silva
  67. Thiago Mattar Cunha
  68. Helder I Nakaya
  69. Henrique Marques-Souza
  70. Rafael E Marques
  71. Daniel Martins-de-Souza
  72. Munir S Skaf
  73. Jose Luiz Proenca-Modena
  74. Pedro MM Moraes-Vieira
  75. Marcelo A Mori
  76. Alessandro S Farias
(2023)
SARS-CoV-2 uses CD4 to infect T helper lymphocytes
eLife 12:e84790.
https://doi.org/10.7554/eLife.84790

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