Sex bias determines MERS-CoV infection outcomes in a mouse model of differential pathogenicity
Bibliographic record
Abstract
Middle East respiratory syndrome coronavirus (MERS-CoV) causes a spectrum of disease outcomes in infected humans, ranging from asymptomatic or tolerant to lethal. While the virus itself contributes to pathogenesis, disease severity is primarily influenced by the host's response to infection. One factor observed to impact the host response is sex, as epidemiological data indicates that male persons have a higher case fatality rate than females infected with MERS-CoV. However, the mechanism underlying this sex bias is unknown and disease course remains difficult to predict. This study investigates how male and female transgenic mice expressing humanized dipeptidyl peptidase-4 (hDPP4) respond to MERS-CoV infection following exposure to either a tolerance-inducing low dose or lethal high dose. We observed that female hDPP4 mice display dose-dependent tolerance to infection and males experienced uniformly lethal disease in both dosing groups. Longitudinal transcriptomic analysis revealed that males suppress innate and inflammatory responses early after infection, causing delayed induction of the host antiviral response. In contrast, high dose females mount an immediate and sustained interferon and inflammatory response, activating antiviral effectors and interferon-stimulated genes. Tolerant females displayed the greatest transcriptional control, showing no pathway enrichment and minimal changes in weight throughout infection. Our results suggest that the magnitude of the response is driven by dose while the nature of the response in shaped by sex. Females mount a more robust response to MERS-CoV infection, allowing females to tolerate low-dose infection but causing uncontrolled inflammation after high dose infection. In contrast, males experienced lethal outcomes regardless of dose. By examining the dynamics of sex-biased host transcriptional responses in determining disease severity, this study highlights the importance of sex as a biological variable in coronavirus pathogenesis research.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".