Viral phylodynamics
With the rate of evolution measured in real units of time, it is possible to infer the date of the most recent common ancestor (MRCA) for a set of viral sequences.
For example, an application to HIV sequences within infected hosts showed that viral substitution rates dropped to effectively zero following the initiation of antiretroviral drug therapy.
[18] Antiviral treatment also creates selective pressure for the evolution of drug resistance in virus populations, and can thereby affect patterns of genetic diversity.
In an effort to bridge the gap between traditional evolutionary approaches and epidemiological models, several analytical methods have been developed to specifically address problems related to phylodynamics.
These methods are based on coalescent theory, birth-death models,[24] and simulation, and are used to more directly relate epidemiological parameters to observed viral sequences.
and nonoverlapping generations (the Wright Fisher model), the expected time for a sample of two gene copies to coalesce (i.e., find a common ancestor) is
In the absence of selection and population structure, the tree topology may be simulated by picking two lineages uniformly at random after each coalescent interval
The expected waiting time to find the MRCA of the sample is the sum of the expected values of the internode intervals, Two corollaries are : Consequently, the TMRCA estimated from a relatively small sample of viral genetic sequences is an asymptotically unbiased estimate for the time that the viral population was founded in the host population.
[8] Infectious disease epidemics are often characterized by highly nonlinear and rapid changes in the number of infected individuals and the effective population size of the virus.
Consequently, estimates of effective population size based on the Kingman coalescent will be proportional to prevalence of infection during the early period of exponential growth of the epidemic.
For example, in an application to rabies virus, Streicker and colleagues estimated rates of cross-species transmission by considering host species as the attribute.
[5][35] Generally, compartmental models offer significant advantages in terms of speed and memory usage, but may be difficult to implement for complex evolutionary or epidemiological scenarios.
Because computing likelihoods for genealogical data under complex simulation models has proven difficult, an alternative statistical approach called Approximate Bayesian Computation (ABC) is becoming popular in fitting these simulation models to patterns of genetic variation, following successful application of this approach to bacterial diseases.
Here, subtypes are denoted according to their hemagglutinin (H or HA) and neuraminidase (N or NA) genes, which as surface proteins, act as the primary targets for the humoral immune response.
Phylogenetic analysis of H3N2 influenza has shown that putative epitope sites of the HA protein evolve approximately 3.5 times faster on the trunk of the phylogeny than on side branches.
However, estimates of migration rates that are jointly based on epidemiological and evolutionary simulations appear robust to a large degree of undersampling or oversampling of a particular region.
Forward simulation-based approaches for addressing how immune selection can shape the phylogeny of influenza A/H3N2's hemagglutinin protein have been actively developed by disease modelers since the early 2000s.
Under a parameterization of long host lifespan and short infectious period, they found that strains would form self-organized sets that would emerge and replace one another.
They showed that under strain-specific immunity alone (with partial cross-immunity between strains based on their amino acid similarity), the phylogeny of influenza A/H3N2's HA was expected to exhibit 'explosive genetic diversity', a pattern that is inconsistent with empirical data.
The phylodynamic model designed by Koelle and coauthors argued that this pattern reflected a many-to-one genotype-to-phenotype mapping, with the possibility of strains from antigenically distinct clusters of influenza sharing a high degree of genetic similarity.
These antigenic dynamics would be consistent with a ladder-like phylogeny of influenza exhibiting low genetic diversity and continual strain turnover.
The rapid early growth of HIV-1 in Central Africa is reflected in the star-like phylogenies of the virus, with most coalescent events occurring in the distant past.
[61] The rate of exponential growth of HIV in Central Africa in the early 20th century preceding the establishment of modern subtypes has been estimated using coalescent approaches.
Several estimates based on parametric exponential growth models are shown in table 1, for different time periods, risk groups and subtypes.
[2] At the opposite extreme, HIV-1 group O, a relatively rare group that is geographically confined to Cameroon and that is mainly spread by heterosexual sex, has grown at a lower rate than either subtype B or C. HIV-1 sequences sampled over a span of five decades have been used with relaxed molecular clock phylogenetic methods to estimate the time of cross-species viral spillover into humans around the early 20th century.
Very dense sampling of viral sequences within cities over short periods of time has given a detailed picture of HIV transmission patterns in modern epidemics.
[70] In a separate analysis, Volz et al.[71] found that simple epidemiological dynamics explain phylogenetic clustering of viruses collected from patients with PHI.
These results therefore provided further support for Lewis et al.'s findings that HIV transmission occurs frequently from individuals early in their infection.
There is some evidence from comparative phylogenetic analysis and epidemic simulations that HIV adapts at the level of the population to maximize transmission potential between hosts.
This article was adapted from the following source under a CC BY 4.0 license (2013) (reviewer reports): Erik M Volz; Katia Koelle; Trevor Bedford (21 March 2013).
