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ERC

BEEHIVE Report Summary

Project ID: 339251
Funded under: FP7-IDEAS-ERC
Country: United Kingdom

Mid-Term Report Summary - BEEHIVE (Bridging the Evolution and Epidemiology of HIV in Europe)

The BEEHIVE project (BEEHIVE = Bridging the Evolution and Epidemiology of HIV in Europe) aims to uncover the viral genetic determinants of virulence in HIV. The project aims to collect a blood samples from a large number very well characterised HIV infected patients from across Europe, and read and interpret the genetic code of the virus. So far, the project team have characterised viruses from 2,909 patients from 8 countries, with date of infection ranging from 1983 to 2014.

A special challenge faced by the team was that not all viruses in one infected individual share the same genetic code, but rather each patient is infected with a cloud of related viruses. Sequencing machines used to read genetic codes read codes from only small fragments of virus RNA at a time (in the manner of a shotgun), and so software are needed to piece together again the original genetic code (in the manner of solving a jigsaw puzzle). This complex process is called genome assembly. In this case, the problem was particularly complex, since for each patient, the team were faced with millions of pieces of thousands of similar and yet subtly different jigsaw puzzles. The team developed new software (called SHIVER) to solve this problem, which is now publicly available, and so will help other researchers facing the same problem.

Following this, the team developed a companion software (called PHYLOSCANNER) to characterise how the viruses in an infected individual are related to each other. This software is already finding many unexpected applications. The team have discovered, for example, that nearly 5% of infected individuals are dually infected (infected more than once).

The team have also discovered that the diversity of viruses in one patient encodes the ancestry of the viral cloud, and so can be used to reconstruct chains of transmission between patients. (The team are working on carefully anonymised data.) This allows the team to study the patterns of transmission across the study area (at least, within this sample), and so to inform prevention strategies that could break these chains of transmission.

Having solved these technical challenges with the data generated by the project, the team returned to the original question of how the genetic code of the virus influences the severity of infection. Using what is now one of the more definitive data collections to study this question, the team developed methods to reconstruct the evolution of virulence across the ancestry of the whole viral population, and so quantify the extent to which variation in the severity of infection could be attributed to this underlying genetic effect. The final estimate is that approximately one third of population variation in the virulence of infection is attributable to the viral strain.

The next stage of the project is direct identification of genetic traits which cause viruses to induce more or less severe infection. This will help better understand the biology of infection.

Reported by

THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
United Kingdom
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