Structural epitope profiling identifies antibodies associated with critical COVID-19 and long COVID

Reviewer #1 (Public Review):

Summary of the Study:

The manuscript delves into the COVID-19 virus membrane protein M1-subtype and its IgM responses in COVID-19 cohorts. The authors conducted an extensive epitope screening and prediction through Differential Antibody Screening Assay (DASA) and validated their findings across multiple cohorts in Europe. The study aims to provide novel insights into the immune responses to COVID-19 and explore potential clinical implications for long COVID prognostics.

Strengths:

(1) Innovative Approach:
The use of DASA for epitope screening is innovative and allows for detailed mapping of immune responses.

(2) Validation Across Cohorts:
The study's validation of findings across multiple European cohorts adds robustness and generalizability to the results.

(3) Comprehensive Analysis:
The manuscript presents a thorough analysis of IgM responses, contributing valuable data to the understanding of immune responses in COVID-19.

Weaknesses:

(1) Lack of Clarity on T-Independent B Cell Reactions:
The rationale and results regarding T-independent B cell reactions are not well-explained, requiring additional bridging sentences or data for better comprehension.

(2) Limited Sample Size for B Cell Stimulation:
The in vitro B cell stimulation experiments involve a very small number of individuals (2 reacted vs 1 unreacted), which weakens the strength of the conclusions drawn from these experiments.

(3) Insufficient Exploration of Comorbidities:
The manuscript could benefit from exploring correlations with other clinical data on comorbidities or sub-grouping the long COVID cohort by specific outcomes.

Appraisal of the Study's Aims and Conclusions :

The authors have partially achieved their aims by providing novel insights into COVID-19 immune responses and highlighting the potential for using IgM responses in long COVID prognostics. However, the conclusions would be more convincing with additional data and clarity on certain aspects, such as the T-independent B cell reactions and the impact of comorbidities.

Impact on the Field and Utility to the Community:

This study has the potential to significantly impact the field of COVID-19 research by advancing the understanding of immune responses to the virus. The novel insights into IgM responses and epitope screening could inform future diagnostic and prognostic tools for COVID-19, particularly in the context of long COVID. Additionally, the methods and data presented could be valuable to researchers exploring similar viral immune responses.

Additional Context:

For readers and researchers, it is essential to note that while the study offers intriguing results, the manuscript would benefit from more comprehensive data and clearer explanations in certain areas. The inclusion of the DASA equation in the manuscript or a figure would improve readability and contextual comprehension. Further exploration of clinical comorbidities and additional external validation data would enhance the study's robustness and applicability.

Reviewer #2 (Public Review):

Summary:

This paper identifies a novel SARS-CoV-2 epitope that measures host-virus interactions that have clinical correlations and can act as a signature of infection. In doing so, the authors present a novel structure-driven epitope profiling pipeline that allows them to rapidly iterate through multiple possible peptide epitope candidates for directly measuring host-virus binding. With this approach, the authors identify an IgM antibody response driven by the N-terminus of the Membrane protein of SARS-CoV-2, and demonstrate that epitope is directly correlative with cell-level measurements of infection, and can even act as a clinical signature of infection. The findings are significant to those interested in epitope identification and present a unique step forward for incorporating structural data in an iterative screening approach. The study itself presents some unique connections between the models presented, the IgM being generated, and clinical outcomes, but the claim that these IgM levels are indicative of anything more than past infection will require further detailed analysis.

Strengths:

(1) The methodological approach presented in this study is incredibly powerful and shows major promise to identify other peptide epitopes of proteins for antibody profiling. The simplicity of the methodological approach to string together established protocols and measurements offers a unique elegant promise that this is a generalizable method to many other systems and disease contexts.

(2) The clever use of a SASA metric to study and identify each of the major components demonstrates how structural information is a powerful way to approach identifying and nominating candidate peptides.

(3) This paper spans an exciting range of structural data to clinical-derived measurements, demonstrating the powerful possibilities that can arise from connecting structural biophysical data to clinical measurements to build generalized pipelines or models

Weaknesses:

(1) While the authors use SASA as a great way to screen peptides based on the presumption that SASA can act as a measure of the stability of protein folding, there are many caveats that may come with this measurement that can reduce generalizability. Assessing SASA per residue is a high variance metric that requires many additional layers of further analysis to make inferences about peptide stability. Further, since proteins are inherently dynamic, alternative configurations may yield fluctuating SASA values that inherently bias and introduce noise into the results. It would be useful to compare these SASA metrics for peptides to other structural measures often associated with protein stability used in the literature, such as Radius of Gyration, Hydrodynamic Radius, Secondary Structure degree, etc.

(2) In Figure 3G, the author put forth that IgM ELISA results and whole spike IgG correlate with one another. While it is clear that IgM for M1 and IgM for spike S1' subunit both correlate similarly to whole spike IgG levels, the correlation in both cases is incredibly weak, with whole spike IgG fluctuating widely across a narrow range of IgM for M1 values. This interpretation is also contradicted by 3G's best-fit lines that would have a large residual value to the data. Lastly, the Pearson correlation values for both correlations are misleading here as Pearson correlation indicates the strength and direction of two linear variables. This means that any dataset will inherently have a Pearson r value of ~0.40 but one may not be predictive of the other. It would be better for the authors to instead use measures such as Spearman R or additional statistical analysis like histogramming to demonstrate this coupling.

(3) It is not clear from the text if the authors are the first to use LASSO models to correlate IgM levels with infection scores in patients. LASSO-based logistic regressions are powerful tools used widely in statistical approaches to measure the association between two variables. However, there is a lack of citations indicating that the authors' approach is based on previous efforts and matches the best practice in generating these models on clinical data. It would be useful to add citations to indicate that this approach is following established statistical best practices in line with the field. If the use of the LASSO approach is novel, it would be key to mention this and highlight why the authors feel a LASSO model is the appropriate approach here.

(4) The authors demonstrate in Figure 5 that their IgM levels are very clearly correlative with a history of SARS-CoV-2 infection, and provides another avenue for the detection of prior infections. However, these claims are extended to compare to direct symptoms such as fatigue, depression, and quality of life. Specifically, the authors claim that IgM persistence is correlated with lower quality of life and stress-indicative symptoms. However, Figure 5D contradicts this, highlighting that both persistent and non-persistent IgM groups have similar trends and patterns in fatigue, depression, and quality of life. The authors should reexamine this interpretation of their data, and revisit if there are alternative analyses that may indicate where persistent and non-persistent IgM groups separate.

(5) One under-discussed component of this paper is the potential for sequence variation impacting IgM generation and detection. With resistance being a consistent issue amongst infectious diseases and immune evasion, it may be useful to discuss the possible sequence variance seen in the M protein sequence of M1, as well as to see if the IgM levels induced upon M1 presentation can be separated out from their existing analyses (it may not be!). Regardless, it would be useful for the authors to consider the potential for sequence variation in the M1 peptide and its downstream effects.

Reviewer #3 (Public Review):

Summary:

Kearns et al. explored a computational approach DASAr to identify stable peptide epitopes on SARS-CoV-2 proteins. They find that the computational approach has a high success rate at identifying stable and soluble peptides that may reserve the native conformation. The approach identified multiple peptides in Spike, Nucleoprotein, Membrane, and Envelope proteins of SARS-CoV-2. Most surprisingly, a high prevalence of IgM response is to recognize a newly exposed Membrane epitope, M1. Anti-M1 IgM titer is associated with a protective anti-Spike titer, severe disease and long COVID. The data also indicate that anti-M1 IgM may arise from T cell-independent B cell activation.

Strengths:

The computational approach can be widely applied to study antibody epitopes in many pathogens. The observations from this study provide clues to further understanding the role of anti-M1 response and the mechanisms of anti-M1 IgM response to SARS-CoV-2 associated diseases.

Weaknesses:

A subset of the conclusions of this paper are well supported by data, but some statements and analyses need to be clarified, revised, and extended.