Severe acute respiratory syndrome coronavirus 2–mediated dysregulation of drug processing genes is dependent on pathogenic variants, target site of infection, age, and sex
Bibliographic record
Abstract
It is important to identify diagnostic biomarkers and demographic factors that characterize patients with COVID-19-drug interactions to mitigate safety and efficacy issues. This study investigated the impact of 6 different severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants on the mRNA expression of 36 genes associated with inflammation, anti-inflammation, transcriptional regulation, drug metabolism, and membrane transport in the lung and nasal turbinate tissues of 21 male and 21 female Syrian golden hamsters. Furthermore, the study investigated how individual and combined treatments with angiotensin II and D614G variant influence the lung expression of these genes in 7- and 4-month-old 23 male and 23 female Syrian golden hamsters. This study showed for the first time that SARS-CoV-2 variants cause greater heterogeneous dysregulation of drug processing genes in hamster lung tissue compared with nasal turbinate, because of an imbalance between inflammatory and anti-inflammatory responses, with P.1, D614G, and Delta variants playing a major role in this dysregulation. The study discovered a sex-dependent dysregulation of lung NAT2 expression by the D614G variant, and a more severe age-dependent dysregulation of genes associated with inflammation, anti-inflammation, transcriptional regulation, drug metabolism, and membrane transport in D614G variant-infected Syrian golden hamster lung tissues. On the contrary, angiotensin II administration did not contribute to the dysregulation of any of these genes in hamster lung tissues. Finally, potential biomarkers were identified for diagnosing dysregulation of drug processing genes based on SARS-CoV-2 variants, infection site, age, and sex. SARS-CoV-2 variants, infection site, age, and sex should be considered when treating patients with COVID-19-drug interactions. SIGNIFICANCE STATEMENT: COVID-19-drug interactions have been observed in several hospitalized patients; however, the clinicopathologic and demographic factors that define at risk patient population are poorly understood. This study shows for the first time that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, infection site, age, and sex impact SARS-CoV-2-mediated dysregulation of drug processing genes in Syrian golden hamsters. Potential biomarkers for SARS-CoV-2-drug processing gene interactions based on these factors were discovered and may be useful for the diagnosis and management of patients at risk of COVID-19-drug interactions.
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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) | 0.002 | 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".