Jessica Williams Poster 2024

jessica williams

Miss Jessica Williams

UK Health Security Agency (UKHSA), United Kingdom (UK)

Identifying immune signatures associated with inherent resistance to Mycobacterium tuberculosis


Poster Abstract

Due to the coronavirus (COVID-19) pandemic, TB incidence declines have slowed and reduced access to diagnostics and treatment services resulted in an increase in TB associated deaths in 2020. It is estimated that 70-80% of exposed individuals do not become infected with TB upon exposure, suggesting some people are more resistant to infection than others. Similar to humans, a differential ability to control disease progression is seen in different macaque species. Using this model, we can profile the immune system prior to infection to look for a link between intrinsic immune system status and an ability to control M.tb infection. An improved understanding of the immunological characteristics that provide a better base for fighting infection will aid in the development of new potential vaccines.
Identify immune correlates associated with the differential ability to control disease progression seen in rhesus and cynomolgus macaques of Asian origin following experimental infection with Erdman strain M.tb.
A comprehensive database of immunological data such as cell phenotype and function determined by flow cytometry, and M.tb antigen specific IFNɣ producing cell profiles measured by ELISPOT; combined with disease burden measures such as clinical parameters, and pathology data derived from previous studies conducted at UKHSA Porton will be generated. Multivariant analysis will then be carried out using the open-source Sequential Iterative Modelling ‘Over-night’ (SIMON) platform. Rhesus and Mauritian cynomolgus macaques will be grouped as ‘disease progressors’ and Indonesian and Chinese cynomolgus macaques as ‘disease controllers’ based on results from previous studies. The automated machine learning process in SIMON will compare 128 algorithms to select the most appropriate algorithm for the dataset based on specificity and selectivity scores. The chosen algorithm will then provide importance scores to identify immune signatures associated with control of TB disease.