Human SNORA31 variations impair cortical neuron-intrinsic immunity to HSV-1 and underlie herpes simplex encephalitis
Bibliographic record
Abstract
Herpes simplex virus-1 (HSV-1) encephalitis (HSE) is typically sporadic. Inborn errors of TLR3- and DBR1-mediated central nervous system cell-intrinsic immunity can account for forebrain and brainstem HSE, respectively. We report five unrelated patients with forebrain HSE, each heterozygous for one of four rare variants of SNORA31, encoding a small nucleolar RNA of the H/ACA class that are predicted to direct the isomerization of uridine residues to pseudouridine in small nuclear RNA and ribosomal RNA. We show that CRISPR/Cas9-introduced bi- and monoallelic SNORA31 deletions render human pluripotent stem cell (hPSC)-derived cortical neurons susceptible to HSV-1. Accordingly, SNORA31-mutated patient hPSC-derived cortical neurons are susceptible to HSV-1, like those from TLR3- or STAT1-deficient patients. Exogenous interferon (IFN)-β renders SNORA31- and TLR3- but not STAT1-mutated neurons resistant to HSV-1. Finally, transcriptome analysis of SNORA31-mutated neurons revealed normal responses to TLR3 and IFN-α/β stimulation but abnormal responses to HSV-1. Human SNORA31 thus controls central nervous system neuron-intrinsic immunity to HSV-1 by a distinctive mechanism.
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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".