Differential Virulence and Host-Specific Fitness of Regionally Distinct Human-Derived Powassan Virus Lineage 2 Strains
Bibliographic record
Abstract
Powassan virus (POWV; family Flaviviridae) is a tick-borne encephalitic virus endemic to Canada, the United States, and Russia. In the United States, POWV is transmitted by ixodid ticks, and transmission foci reflect the geographic range of these vectors, primarily Ixodes scapularis. Thus, northeastern and midwestern regions of the United States contain the highest human case burdens and prevalence of infected ticks. Notably, New York (NY) and Minnesota (MN) have a long history of POWV transmission to humans. Over time, genetic divergence has occurred in these regions, giving rise to distinct midwestern and northeastern clades. Despite the established circulation of POWV, increases in reported human cases, and documented genetic distinction, an understanding of strain-specific POWV virulence is limited because of the lack of human isolates. In 2020 and 2021, two POWV strains were isolated from fatal human cases from MN (deer tick virus [DTV] MN-PV320) and NY (DTV NY21-027). Here, we provide the first characterization of geographically distinct, contemporary, human POWV isolates. Comprehensive genetic characterization was completed and phenotypic variability was determined in vitro and in vivo . Although strain fitness was similar in I. scapularis, higher mortality rates were measured in a susceptible POWV mouse model after infection with DTV NY21-027 compared with DTV MN-PV320. Genetic analysis revealed several variable amino acid substitutions, including I2173L in DTV NY21-027, which was selected for in all strains after neurological infection. These data suggest that genetic divergence of POWV strains from regionally distinct transmission foci could contribute to strain-dependent pathogenic potential in humans.
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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) | 0.000 | 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 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".