Analysis of Powassan Virus Genome Sequences from Human Cases Reveals Substantial Genetic Diversity with Implications for Molecular Assay Development
Bibliographic record
Abstract
Powassan virus (POWV) is an emerging tick-borne virus that causes severe meningoencephalitis in the United States, Canada, and Russia. Serology is generally the preferred diagnostic modality, but PCR on cerebrospinal fluid, blood, or urine has an important role, particularly in immunocompromised patients who are unable to mount a serologic response. Although the perceived poor sensitivity of PCR in the general population may be due to the biology of infection and health-seeking behavior (with short viremic periods that end before hospital presentation), limitations in assay design may also contribute. Genome sequences from clinical POWV cases are extremely scarce; PCR assay design has been informed by those available, but the numbers are limited. Larger numbers of genome sequences from tick-derived POWV are available, but it is not known if POWV genomes from human infections broadly mirror genomes from tick hosts, or if human infections are caused by a subset of more virulent strains. We obtained viral genomic data from 10 previously unpublished POWV human infections and showed that they broadly mirror the diversity of genome sequences seen in ticks, including all three major clades (lineage I, lineage II Northeast, and lineage II Midwest). These newly published clinical POWV genome sequences include the first confirmed lineage I infection in the United States, highlighting the relevance of all clades in human disease. An in silico analysis of published POWV PCR assays shows that many assays were optimized against a single clade and have mismatches that may affect their sensitivity when applied across clades. This analysis serves as a launching point for improved PCR design for clinical diagnostics and environmental surveillance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".