Whole-genome sequencing reveals a recent transmission network within a large isoniazid-resistant tuberculosis outbreak in London
PHE ePoster Library. Macdonald N. Apr 10, 2019; 257513; 15423
Neil Macdonald
Neil Macdonald
Login now to access Regular content available to all registered users.

You may also access this content "anytime, anywhere" with the Free MULTILEARNING App for iOS and Android
Abstract
Rate & Comment (0)
Abstract Introduction
Since January 2018, whole genome sequencing (WGS) has been performed routinely on all people with culture confirmed TB in London. A PHE prototype TB relatedness application (Forest) assigns sequences within 12 single nucleotide polymorphisms (SNPs) to clusters. Of the 113 clusters identified in London by November 2018, the most frequent was AA208-4 with 11 new cases. This was a large isoniazid mono-resistant outbreak first identified in 2000 with over 500 cases previously linked using MIRU-VNTR typing methods. We examined the phylogenetic relationships of these new cases to identify recent transmission networks.
Methods:
Data and a phylogenetic tree for AA208-4 were extracted from Forest that included demographic and clinical data from local and national TB surveillance systems. Cluster questionnaires were completed by TB case managers.
Results:
The tree featured 30 clades with maximum root to tip distance of 12 SNPs. The 11 new cases were distributed across eight clades, of which seven featured only one new patient in 2018. However one clade contained three recent cases notified between April and June, two within 2 SNPs of each other and a third at 3 SNPs, positioned to suggest a common ancestor. These cases lived in different areas of North London and were diagnosed and treated at different hospitals. However two had a history of drug use and another two were born in the same non-UK country. Detailed contact investigations, including for workplace contacts, were recommended by the Health Protection Team and conducted by TB nurses.
Conclusions:
In this instance WGS allowed the differentiation of patients involved in a transmission network, focussing epidemiological investigations and control measures. In contrast the previous typing method would have grouped all 11 patients together. This ability of WGS to detect transmission networks may be particularly useful in large established clusters to prioritise cluster investigation.
    This eLearning portal is powered by:
    This eLearning portal is powered by MULTIEPORTAL
Anonymous User Privacy Preferences

Strictly Necessary Cookies (Always Active)

MULTILEARNING platforms and tools hereinafter referred as “MLG SOFTWARE” are provided to you as pure educational platforms/services requiring cookies to operate. In the case of the MLG SOFTWARE, cookies are essential for the Platform to function properly for the provision of education. If these cookies are disabled, a large subset of the functionality provided by the Platform will either be unavailable or cease to work as expected. The MLG SOFTWARE do not capture non-essential activities such as menu items and listings you click on or pages viewed.


Performance Cookies

Performance cookies are used to analyse how visitors use a website in order to provide a better user experience.


Save Settings