Comprehensive resistance prediction for tuberculosis: an international consortium (CRyPTIC)
Year of award: 2015
Grantholders
Prof Derrick Crook
University of Oxford
Prof Timothy Peto
University of Oxford
Dr Zamin Iqbal
University of Oxford
Prof David Moore
London School of Hygiene and Tropical Medicine
Prof Guy Thwaites
University of Oxford
Dr Daniel Wilson
University of Oxford
Prof Ajit Lalvani
Imperial College London
Prof Jim Davies
University of Oxford
Dr David Clifton
University of Oxford
Dr Daniela Maria Cirillo
San Raffaele Scientific Institute
Prof Guangxue He
Chinese Center for Disease Control and Prevention
Dr Camilla Rodrigues
P D Hinduja Hospital & Medical Research Centre
Dr Nazir Ismail
National Institute of Communicable Diseases
Prof Eleanor Grace Smith
Heart of England NHS Foundation Trust
Dr James Posey
Centers for Disease Control and Prevention
Dr Nerges Mistry
The Foundation for Medical Research
Prof Ann Sarah Walker
University of Oxford
Project summary
In 2013, 9 million people developed tuberculosis (TB) and 1.5 million died from it. An estimated 480,000 new TB cases were resistant to the main antibiotics in 2013, known as multi-drug resistant TB (MDR-TB). But under half of drug-resistant cases were detected, reducing the chance of curing infections and complicating how we control the spread of disease. To address this problem, we need to be able to quickly test which antibiotics kill TB so that the best combination of drugs can be given. We currently rely on slow, cumbersome, labour-intensive and expensive techniques to do this.
This research will use whole-genome sequencing, a method of reading the more than 4 million letters of each TB germ’s genetic code. We will study more than 90,000 TB germs from around the world, many of which will be drug-resistant. We need to study such large numbers to find nearly all the changes in the genetic code that could cause drug-resistance, including very rare ones. We will develop new computer methods to analyse this large amount of genetic data to accurately predict drug-resistance in new TB germs.
Our findings will allow future TB cases to be treated with the best drugs more quickly, thus contributing to worldwide TB elimination.