From Clinic to Cloud: Crowdsourcing Resistance Surveillance
William Hanage from the Harvard T. H. Chan School of Public Health in the U.S. will develop an approach to better monitor the evolution and spread of microbial resistance to antibiotics, which is a major public health concern. Current approaches are generally slow, not widely available, and limited to analysis of a single pathogen, while often multiple pathogens coexist in each sample. They will trial a method they have developed to identify DNA sequences (motifs) linked with resistance to pneumococcus and gonococcus from existing genomic data. A reference database will be built that stores the identified resistance motifs and Bayesian model of resistance prediction in a cloud. This database can be updated over time. They will also adapt a mobile phone-sized portable DNA sequencer – the Oxford Nanopore minION instrument – for detecting these resistance motifs from human tissue samples such as urine that contain mixtures of DNA, and evaluate its capacity for detecting resistant organisms.