Research

Linking viral phylogenetics with epidemiology to study transmission of HIV

We use the large scale population database of HIV sequences maintained by the UK Collaborative Group on HIV Drug Resistance to estimate the patterns of HIV transmission among different communities in the UK. The structure of the sexual contact network is is a key issue in the epidemiology of sexually transmitted infections. As HIV is only transmitted with low efficiency compared to many STIs, the transmission network structure is more readily reconstructed from the viral genotypes than from interview data. Using the approach of molecular phylodynamics to analyse anonymized HIV genotypes from MSM patients (men who have sex with men) in London, we found that 25% of patients with a link to any other were linked to 6 or more individuals. In these clusters, almost 25% of transmissions occurred within 6 months of first infection (Lewis et al. 2008)*.

Moving to HIV-1 subtypes A and C, which in the UK are predominantly associated with heterosexual transmission, the picture was quite different. Large clusters were far less frequent and there was very little evidence of transmission in acute infection (Hughes et al. 2009). Those that were found in these subtypes have recently been found to be associated with "crossover" of these strains into MSM. The striking difference is likely to be associated with the low incidence of within-UK infection of non-B subtypes.

Most recently we have extended this work by using the phylodynamic approach to estimate the parameters of the network structure within which HIV is spreading among MSM, exploring the well-known "power law" effect in greater detail (Leigh Brown et al. 2011). We have found that the distribution of cluster size ("degree distribution") is such that a randomly distributed intervention would never stop the epidemic. This level of detailed knowledge can provide important insights into delivery of interventions such as pre-exposure prophylaxis.

*You can listen to an interview about this work broadcast on March 17th 2008 on the BBC Radio 4 programme "The World Tonight" here.

Influenza Evolution

a. Evolutionary, antigenic and epidemiological dynamics of human influenza virus

Influenza viruses are amongst the fastest evolving organisms known resulting in an ability to continually adapt to existing host immunity. Thus influenza has been one of the most consistent global burdens over the last century. Influenza viruses have genes which are arranged on 8 RNA molecules (“segments”). However, the exact characteristics of each type of segment varies according to the mutations it carries, and different combinations (reassortments) of segment types yield influenza strains with different pathogenicities, transmissibilities and drug resistances.

The aim of this project is to study the genomic and evolutionary basis of traits of human influenza A virus, exploiting the large body of full-genome influenza sequences now available using statistical phylogenetic tools on high performance computational platforms.

Together with Andrew Rambaut we are using whole genome data to improve the quantitative understanding of influenza virus evolution in humans, in order to inform predictions about the characteristics of future strains. In particular we are interested in: quantifying the rates and patterns of reassortment events; detecting and characterising intra and inter segment interactions. This work is funded by the Wellcome Trust.

b. Pandemic Influenza

The 2009 H1N1 pandemic was first detected in Scotland in late April 2009 and persisted throughout the course of the pandemic. It is unknown whether initial cases in the summer arose from multiple imports of the virus from other parts of the UK or elsewhere, and if cases in the winter represented persistent Scottish lineages or new imports of the virus into Scotland. Under the gravity model for disease transmission, the largest centres of population determine the pattern of spread, consequently we would expect that viruses from London will dominate the rest of the United Kingdom. However, air travel from other cities, particularly from Scotland, is increasingly routed independently of London.

In this project we obtained viral genome sequences of scottish isolates and analysed them with additional UK and worldwide sequences to infer the pattern of transmission between countries and within the UK. Using Bayesian Phylogeography we are able to detect different epidemic patterns in the summer and winter phase. This work is supported by Scotland’s Chief Scientist Office, the University of Edinburgh Interdisciplinary Centre for Human and Avian Influenza Research and the BBSRC.

c. Swine Influenza in Swine

Swine are often considered as a mixing vessel for different influenza strains, since they may become infected with viruses with avian-like or human-like sialic acid binding receptors. The co-circulation in pigs of a North American H3N2 strain (itself a reassortant between avian, human and swine viruses) with an Eurasian avian-like swine H1N1 strain, led to the production of a novel reassortant virus (presumably in swine) which caused the 2009 human pandemic (Smith et al 2009).

As part of the Combating Swine Influenza Initiative consortium (under the lead of Ian H. Brown, Veterinary Laboratories Agency-Weybridge, UK, and James Wood, University of Cambridge, UK), we and our collaborators obtained whole genome sequences from a retrospective swine influenza surveillance study. The aim of this work is to characterise the reassortants present in European swine and estimate the rate of reassortment of subtypes in swine using time resolved bayesian phylogenetics. See this article for general information.

Previous work

1. Modelling the genetic basis of antiretroviral resistance

The development of combination antiretroviral therapy was an immense breakthrough in HIV treatment, turning a previously lethal infection into a chronic treatable condition. In the absence of a vaccine, combination therapy is the only way HIV infection can be controlled, but there were two major problems, side effects due to toxicity, which have been greatly reduced in newer drugs, and the development of resistance. We used statistical modelling to analyse the contribution of genetic variants to antiretroviral failure in treated patients (Precious et al. 2000). We extended this approach to include the use of machine learning methods which are particularly useful for analysing data with more than two states (amino acids) at each field (amino acid position) using both in vitro drug susceptibility (Leigh Brown et al 2004) and in vivo response to treatment (Quigg et al 2002). More recently, working with Dr Michael Miller at Gilead Sciences, we developed new models for predicting resistance to tenofovir, which is now the most widely used thymidine analogue. These models, which introduced the random forests approach, revealed that amino acid 215 was more significant in resistance to the drug than many clinicians had previously thought (Murray et al. 2009).

This work was a collaboration with Dr Deenan Pillay and others in the UK Collaborative Group on HIV Drug Resistance, and was funded by the Wellcome Trust and the BBSRC.

2. Evolution and adaptation of HIV

HIV evolves very rapidly, both within patients and in the infected population. We have been working on the evolutionary genetics of HIV for almost 20 years (see eg. Balfe et al (1990), Simmonds et al (1991), Holmes et al (1993). We have been particularly interested in factors affecting the ability of the virus to adapt to selection, including effective population size Leigh Brown (1997), or population structuring, Frost et al. (2001), and we have considered both adaptation to antiretroviral drugs Leigh Brown & Richman (1997), and to the host immune response Yang et al 2005.

Working with Dr Sergei Kosakovsky Pond and Dr Simon Frost at UCSD we have concentrated on the use of powerful statistical tools to analyse adaptive evolution at the codon level. We have applied these methods to investigate whether adaptation occurs differentially in genetically different human populations. This has revealed that host adaptation is a continuing phenomenon both between (Kosakovsky Pond et al. 2006) and within populations (Poon et al. 2007). We are currently extending this work to the study of different pathogens, including influenza.

This work has been supported by the National Institute of Allergy and Infectious Diseases.

3. Modelling the determinants of virulence and host range in H5N1 influenza genome sequences

H5N1 influenza has generated global concern because of the virulence associated with this strain in poultry outbreaks, and the high mortality (60%) associated with human infections reported by the World Health Organization. The current outbreak is also unusual in the high mortality which has been observed in wildfowl, the natural hosts for influenza viruses. Some reports indicate its virulence has increased over the last 10 years.

There are over 8,000 sequences deposited in Genbank for H5N1 isolates, providing a rich resource for analysis. We are using full length genome sequences in multivariate analyses of amino acid variants associated with virulence and host range using graphical models. The use of graphical models in genetics goes back to Sewall Wright (Wright, S., 1921), but in combination with Bayesian inference and high performance computers they provide powerful tools, particularly when intereactions between sites are important. We have used BGMs to infer combinations of mutations that influence the virulence of H5N1 strains in mammals, and found that in addition to hemagglutinin sites, mutations in the polymerases and non-structural 1 protein were also important (Lycett et al 2009). This work has been funded by the BBSRC