Malondialdehyde acetaldehyde adduction of surfactant protein D attenuates <scp>SARS‐CoV</scp>‐2 spike protein binding and virus neutralization
Bibliographic record
Abstract
BACKGROUND: Over 43% of the world's population regularly consumes alcohol. Although not commonly known, alcohol can have a significant impact on the respiratory environment. Living in the time of the COVID-19 pandemic, alcohol misuse can have a particularly deleterious effect on SARS-CoV-2-infected individuals and, in turn, the overall healthcare system. Patients with alcohol use disorders have higher odds of COVID-19-associated hospitalization and mortality. Even though the detrimental role of alcohol on COVID-19 outcomes has been established, the underlying mechanisms are yet to be fully understood. Alcohol misuse has been shown to induce oxidative damage in the lungs through the production of reactive aldehydes such as malondialdehyde and acetaldehyde (MAA). MAA can then form adducts with proteins, altering their structure and function. One such protein is surfactant protein D (SPD), which plays an important role in innate immunity against pathogens. METHODS AND RESULTS: In this study, we examined whether MAA adduction of SPD (SPD-MAA) attenuates the ability of SPD to bind SARS-CoV-2 spike protein, reversing SPD-mediated virus neutralization. Using ELISA, we show that SPD-MAA is unable to competitively bind spike protein and prevent ACE2 receptor binding. Similarly, SPD-MAA fails to inhibit entry of wild-type SARS-CoV-2 virus into Calu-3 cells, a lung epithelial cell line, as well as ciliated primary human bronchial epithelial cells isolated from healthy individuals. CONCLUSIONS: Overall, MAA adduction of SPD, a consequence of alcohol overconsumption, represents one mechanism of compromised lung innate defense against SARS-CoV-2, highlighting a possible mechanism underlying COVID-19 severity and related mortality in patients who misuse alcohol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".