| Literature DB >> 18584026 |
A Marm Kilpatrick1, Mark A Meola, Robin M Moudy, Laura D Kramer.
Abstract
The distribution and intensity of transmission of vector-borne pathogens can be strongly influenced by the competence of vectors. Vector competence, in turn, can be influenced by temperature and viral genetics. West Nile virus (WNV) was introduced into the United States of America in 1999 and subsequently spread throughout much of the Americas. Previously, we have shown that a novel genotype of WNV, WN02, first detected in 2001, spread across the US and was more efficient than the introduced genotype, NY99, at infecting, disseminating, and being transmitted by Culex mosquitoes. In the current study, we determined the relationship between temperature and time since feeding on the probability of transmitting each genotype of WNV. We found that the advantage of the WN02 genotype increases with the product of time and temperature. Thus, warmer temperatures would have facilitated the invasion of the WN02 genotype. In addition, we found that transmission of WNV accelerated sharply with increasing temperature, T, (best fit by a function of T(4)) showing that traditional degree-day models underestimate the impact of temperature on WNV transmission. This laboratory study suggests that both viral evolution and temperature help shape the distribution and intensity of transmission of WNV, and provides a model for predicting the impact of temperature and global warming on WNV transmission.Entities:
Mesh:
Year: 2008 PMID: 18584026 PMCID: PMC2430533 DOI: 10.1371/journal.ppat.1000092
Source DB: PubMed Journal: PLoS Pathog ISSN: 1553-7366 Impact factor: 6.823
Regression analysis (no intercept) of midgut infection, disseminated infection, and transmission after arc-sin square root transformation with Degree Days (DD) and a genotype (GT) by Degree Day interaction as predictors, as in Table 1, except DD term was tT (t = days since feeding on WNV-infected blood; T = temperature).
| Term | Transmission ( | p-value | Disseminated Infection ( | p-value | Infection ( | p-value |
| DD*108 | 8.00±0.46 | <0.0005 | 14.7±0.80 | <0.0005 | 22.2±2.3 | <0.0005 |
| GT-DD*108 | −2.68±0.65 | <0.0005 | −2.2±1.1 | 0.05 | 0.43±3.2 | 0.89 |
| Residual Error | 0.99 | 3.0 | 24.2 | |||
| Total Error | 6.38 | 24.7 | 82.9 |
Coefficient±1SD is given.
Figure 1The relationship between genotype (NY99 and WN02), temperature, and days since feeding and the fraction of Culex pipiens mosquitoes infected (A), with disseminated infections (B), or transmitting WNV (C), after 0.5–40 days as the proportion of mosquitoes tested.
Figure 2Fitted relationships between the fraction of mosquitoes transmitting virus for two genotypes of WNV and time and temperature, based on the statistical model in Table 2(WN02: Tr = (sin(8.00tT4/108))2; NY99: Tr = (sin(5.32tT4/108))2.
Each curve shows the fraction of mosquitoes transmitting at a fixed time period after feeding on WNV-infected blood (4, 7 or 14 days) with points showing increasing temperatures (12°C to 32°C, symbol every 2°C).
Regression analysis (no intercept) of midgut infection, disseminated infection, and transmission after arc-sin square root transformation with Degree Days (DD) and a genotype (GT) by Degree Day interaction as predictors.
| Term | Transmission | p-value | Disseminated Infection | p-value | Infection | p-value |
| DD*103 | 0.62±0.084 | <0.0005 | 1.20±0.16 | <0.0005 | 2.0±0.28 | <0.0005 |
| GT-DD*103 | −0.27±0.12 | 0.029 | −0.32±0.23 | 0.17 | −0.11±0.39 | 0.77 |
| Residual Error | 3.41 | 12.9 | 37.5 | |||
| Total Error | 6.38 | 24.7 | 82.9 |
Coefficient±1SD is given.