SARS-CoV-2 infection results in a unique lung proteome long after virus resolution in the hamster
Bibliographic record
Abstract
Abstract Long COVID or post-acute sequelae of COVID-19 (PASC) remains an ongoing public health issue that causes impairment for those afflicted and diminishes their ability to contribute to society. To address the host response underpinning respiratory PASC, we used the Golden Syrian hamster model infected with ancestral SARS-CoV-2 and examined its lung proteome in a longitudinal experiment. We infected young 6-week old male and female hamsters with 105 TCID50 of virus via the intranasal route and sampled the lung at 1, 3, 5, and 31 days post infection (dpi). We compared the infected lung proteome to that of uninfected sex-matched controls. We found almost no differences in protein levels at 1 dpi, with hundreds at 3 dpi, and thousands at 5 dpi. Many overlapping differential protein levels and pathways were seen in both sexes at 3 and 5 dpi including the Coagulation and Complement cascades. Notably, we found differences between the sexes at 31 dpi which included many targets with decreased levels of protein in the males. We also noted an increase in 7 proteins in both sexes at 31 dpi including proteins responsible for airway mucosal layer integrity such as Mucin 5B and Calcium-activated chloride channel regulator 1. Longitudinally, 38 proteins were changed in levels across more than one timepoint in the males but only three proteins were in the females, Secretoglobin family 1 A member 1, Poly [ADP-ribose] polymerase, and Apolipoprotein D. Overall, we show that there are changes to the lung proteome at 31 dpi, a time when no SARS-CoV-2 remains, and that there are sex differences in that proteome after infection with the ancestral strain. We conclude that biological sex should be examined as a variable when testing medical countermeasures for PASC in the Golden Syrian hamster due to host differences between the sexes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".