Comprehensive resistance prediction for tuberculosis: an international consortium (CRyPTIC)


  • 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.