Sex- and Age-Specific Development of ssRNA Virus Receptor Expression in the Human Brain
Bibliographic record
Abstract
Neurotropic single-stranded RNA (ssRNA) viruses can disrupt brain function, yet little is known about how host virus receptor expression develops across the human lifespan or whether these trajectories differ between females and males. We focused on postnatal development using postmortem transcriptomic data from 33 human donors (4 months-82 years; both sexes), comprising 52 cerebral hemispheres and 15 brain areas, to characterize developmental patterns of 67 host receptor genes that mediate entry of major ssRNA viral families. Using sex-specific LOESS trajectory modelling, hierarchical clustering, and sliding-window differential expression analyses, we identified multiple non-linear developmental programs governing virus receptor expression. About half of female-male gene-area trajectory pairs exhibited divergent developmental patterns. Sex differences were most pronounced in the cortex, where high-dimensional receptor expression profiles were sufficient to predict the sex of individual cases. Sex differential expression was most frequent during early childhood, coinciding with sensitive periods of cortical circuit refinement. A targeted prenatal analysis revealed that virus receptor expression was predominantly male-biased before birth, contrasting with the predominantly female-biased expression observed during the early postnatal period. Although receptors from most viral families were broadly distributed across developmental programs, a subset of sex-by-age expression clusters showed some enrichment for specific viral lineages and glial-associated cell-type signatures. Together, these findings reveal that virus receptor expression in the human brain is organized into structured, sex-biased trajectories that dynamically reorganize over development, providing the first developmental atlas of viral entry receptors in the human brain.
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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 | 0.001 | 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) | 0.000 | 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".