Out of Eurasia, a Great Primate Evolutionary Bottleneck?
Author(s) -
Joseph Caspermeyer
Publication year - 2013
Publication title -
molecular biology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.637
H-Index - 218
eISSN - 1537-1719
pISSN - 0737-4038
DOI - 10.1093/molbev/mst194
Subject(s) - biology , bottleneck , evolutionary biology , primate , paleontology , computer science , embedded system
With the abundance of sequencing data, scientists can use ever more powerful evolutionary biology tools to pinpoint the transmission and death rates for epidemics such as HIV, which has remained elusive to a cure. Reconstructed evolutionary trees, called phylogenies, can trace a family of viral mutations over time. When combined with epidemiology, tree construction can allow for a greater insight into the dynamics of disease transmission and how a pathogen eludes its host to spread infection. Leventhal et al. (2014) from the ETH Zurich in Switzerland report on a new method that successfully combines evolutionary tree studies and epidemiology, using viral sequence data from ten transmission clusters of the Swiss HIV Cohort Study. For some clusters, the HIV epidemic appears saturated, with very few new cases appearing, while in others, new infections were still common. Overall, HIV transmission was characterized by initial rapid spread within subpopulations that slows down to only a small number of infections. “Using a novel methodology, we were able to estimate the number of individuals that are at risk of becoming infected within transmission clusters of the Swiss HIV epidemic and found that many of these clusters are characterized by initial rapid infection of most at risk individuals within a cluster, followed by a slowdown of new infections within each cluster,” said Leventhal. This allowed the team, for the first time, to estimate not only HIV transmission and death rates but also the total susceptible population size within certain transmission groups from viral sequence data. Their model can successfully predict how the number of infected and susceptible individuals will vary over time, giving new insight and predictions into how an ongoing epidemic will continue to develop and help guide future public health intervention strategies.
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