SARS-CoV-2 uses CD4 to infect T helper lymphocytes
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eLife assessment
The manuscript by Brunetti et al. represents an important contribution where SARS-CoV-2 infection of T-helper cells is implicated and found to be mediated by CD4. The work progressed through a computationally driven hypothesis, by analyzing the interaction partners of SARS-CoV-2 spike glycoprotein (as initially modelled through similar SARS-CoV-1), followed by experimental validations, and further computational and experimental insights on the mechanism of binding. The study identifies the interaction between spike RBD domain and N Terminal domain of CD4 molecule as the specific viral attachment strategy. The evidence supporting the claims of the authors is solid, the results look significant and the data is clear and enough for understanding the manuscript. It also provides a potential usefulness of their approach in future work in understanding how viruses mediate infection of T cells. The work will be of interest to medical biologists working on SARS-CoV-2.
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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.
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eLife assessment
The manuscript by Brunetti et al. represents an important contribution where SARS-CoV-2 infection of T-helper cells is implicated and found to be mediated by CD4. The work progressed through a computationally driven hypothesis, by analyzing the interaction partners of SARS-CoV-2 spike glycoprotein (as initially modelled through similar SARS-CoV-1), followed by experimental validations, and further computational and experimental insights on the mechanism of binding. The study identifies the interaction between spike RBD domain and N Terminal domain of CD4 molecule as the specific viral attachment strategy. The evidence supporting the claims of the authors is solid, the results look significant and the data is clear and enough for understanding the manuscript. It also provides a potential usefulness of their approach in …
eLife assessment
The manuscript by Brunetti et al. represents an important contribution where SARS-CoV-2 infection of T-helper cells is implicated and found to be mediated by CD4. The work progressed through a computationally driven hypothesis, by analyzing the interaction partners of SARS-CoV-2 spike glycoprotein (as initially modelled through similar SARS-CoV-1), followed by experimental validations, and further computational and experimental insights on the mechanism of binding. The study identifies the interaction between spike RBD domain and N Terminal domain of CD4 molecule as the specific viral attachment strategy. The evidence supporting the claims of the authors is solid, the results look significant and the data is clear and enough for understanding the manuscript. It also provides a potential usefulness of their approach in future work in understanding how viruses mediate infection of T cells. The work will be of interest to medical biologists working on SARS-CoV-2.
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Reviewer #1 (Public Review):
Brunetti et al. investigated the mechanism used by the SARS CoV-2 virus to infect CD4 T cells and the potential impact on the immune system by viral infection. They find that SARS CoV-2 infects CD4 helper T cells and not CD8 T cells present in the blood and bronchoalveolar fluid of infected patients. The ACE-2 receptor expression on T lymphocytes is less compared to epithelial and endothelial cells, but still, the virus is able to establish a productive infection of T lymphocytes by some alternative mechanism. The group also demonstrated that interaction between CD4 and SARS virus spike protein further enhances viral entry and infectivity as CD4 is acting as an auxiliary molecule for viral entry.
By performing a technically impressive analysis of the infectivity of CD4 T cells isolated from healthy …
Reviewer #1 (Public Review):
Brunetti et al. investigated the mechanism used by the SARS CoV-2 virus to infect CD4 T cells and the potential impact on the immune system by viral infection. They find that SARS CoV-2 infects CD4 helper T cells and not CD8 T cells present in the blood and bronchoalveolar fluid of infected patients. The ACE-2 receptor expression on T lymphocytes is less compared to epithelial and endothelial cells, but still, the virus is able to establish a productive infection of T lymphocytes by some alternative mechanism. The group also demonstrated that interaction between CD4 and SARS virus spike protein further enhances viral entry and infectivity as CD4 is acting as an auxiliary molecule for viral entry.
By performing a technically impressive analysis of the infectivity of CD4 T cells isolated from healthy donors/controls invitro with the SARS CoV-2 virus and also by testing the infected population of CD4 cells invivo from COVID patients, the authors find that SARS CoV-2 infects CD4 T cells using Insitu Hybridization using probes against viral polymerase (RdRP), immunofluorescence and electron microscopy. Further, the authors also identified the region of SARS CoV-2 spike protein that interacts with CD4 by performing molecular docking analysis and found CD4 NTD interacts directly with the RBD region of the SARS Virus. The specific interaction between CD4 and SARS virus is further demonstrated by the use of anti-CD4 blocking antibodies and cell lines that over-express CD4 molecules. Interestingly by antibody inhibition of ACE-2 and camostat inhibition of TMPRSS2, the authors demonstrated that SARS CoV-2 infection of CD4 T cells requires ACE-2, TMPRSS2, and CD4. The data also show that viral infection of CD4 T cells leads to the expression of cytokines like 1L-10 that may impact cell viability and dampens immune response.
The experiments in the paper are well- performed and the conclusions of this paper are mostly well supported by data, but some aspects of the impact of viral infection of CD4 cells leading to lymphopenia need to be clarified and extended.
A major weakness of the paper is the reference citations. Inconsistency in maintaining the citation style and numbering in the manuscript draft drastically impacts the readability. For example, the use of superscript format references in the introduction and results section and paraphrasing format in materials and methods could not make the readers identify the correct citations.1. In this paper, the authors describe infection of CD4 T cells may lead to T cell death. Although the extended Fig. 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? The exact mechanism of CD4 cell death needs to be discovered and adding control experiments to assess the exact mechanism of cell death would increase confidence in the presented results of the functional interaction of proteins (Ext Fig 9).
2. 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 could be addressed by performing an animal experiment, but it would be interesting to see what are the author's opinion about other possibilities of lymphocytopenia.
3. 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. Better data would have helped to substantiate the authors' conclusions.
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Reviewer #2 (Public Review):
The manuscript by Brunetti et al. represents an important contribution where SARS-CoV-2 infection of T-helper cells is implicated and found to be mediated by CD4. Interestingly and appealingly, the work progressed through a computationally driven hypothesis, by analysing the interaction partners of SARS-CoV-2 spike glycoprotein (as initially modelled through similar SARS-CoV-1), followed by experimental validations, and further computational and experimental insights on the mechanism of binding. I find most of the computational outcomes well validated, and the results and claims well supported by the performed experiments. There are a few points where the manuscript will benefit from dedicated discussion and additional simulation/exploratory plots to establish and validate the adopted methodology for …
Reviewer #2 (Public Review):
The manuscript by Brunetti et al. represents an important contribution where SARS-CoV-2 infection of T-helper cells is implicated and found to be mediated by CD4. Interestingly and appealingly, the work progressed through a computationally driven hypothesis, by analysing the interaction partners of SARS-CoV-2 spike glycoprotein (as initially modelled through similar SARS-CoV-1), followed by experimental validations, and further computational and experimental insights on the mechanism of binding. I find most of the computational outcomes well validated, and the results and claims well supported by the performed experiments. There are a few points where the manuscript will benefit from dedicated discussion and additional simulation/exploratory plots to establish and validate the adopted methodology for analogous future usage in protein binding characterisations by others.
Major comments:
The bioinformatics selection method to arrive at CD4 as the main interaction partner is interesting, and the zoomed-in finding is well justified by the whole body of the experimentation as brought in the manuscript. However, it is interesting from a computational biology perspective that were we to remove GO database (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 Fig. 3), then we would be left with 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. I wonder what would be the computational docking/MD results were you to attempt modelling an interaction between the spike glycoprotein and CD8? Should you not arrive at stable complexes with your MD workflow and 4 Angstrom cutoff for temperature-induced stability scrutinization, that would be extra validation and weight on the adopted computational scheme for the discovery.
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?
The 4 Angstrom 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 Angstrom is in the whole distribution, additionally attributing a p-value on the selected cutoff.
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SciScore for 10.1101/2020.09.25.20200329: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar …
SciScore for 10.1101/2020.09.25.20200329: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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SciScore for 10.1101/2020.09.25.20200329: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
About SciScore
SciScore …
SciScore for 10.1101/2020.09.25.20200329: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.
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