Viral Enrichment Methods Affect the Detection but Not Sequence Variation of West Nile Virus in Equine Brain Tissue
Bibliographic record
Abstract
West Nile virus (WNV), a small, positive sense, single stranded RNA virus continues to encroach into new locales with emergence of new viral variants. Neurological disease in the equine can be moderate to severe in the face of low to undetectable virus loads. Physical methods of virus enrichment may increase sensitivity of virus detection and enhance analysis of viral diversity, especially for deep sequencing studies. However, the use of these techniques is limited mainly to non-neural tissues. We investigated the hypothesis that elimination of equine brain RNA enhances viral detection without limiting viral variation. Eight different WNV viral RNA enrichment and host RNA separation methods were evaluated to determine if elimination of host RNA enhanced detection of WNV and increase the repertoire of virus variants for sequencing. Archived brain tissue from 21 different horses was inoculated with WNV, homogenized, before enrichment and separation. The protocols utilized combinations of low-speed centrifugation, syringe filtration, and nuclease treatment. Viral and host RNA were analyzed using real-time PCR targeting the WNV Envelope (E) protein and equine G3PDH to determine relative sensitivity for WNV and host depletion, respectively. To determine the effect of these methods on viral variation, deep sequencing of the E protein was performed. Our results demonstrate that additional separation and enrichment methods resulted in loss of virus in the face of host RNA depletion. DNA sequencing showed no significant difference in total sequence variation between the RNA enrichment protocols. For equine brain infected with WNV, direct RNA extraction followed by host RNA depletion was most suitable. This study highlights the importance of evaluating viral enrichment and separation methods according to tissue type before embarking on studies where quantification of virus and viral variants is essential to the outcome of the study.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".