RT Journal Article
SR Electronic
T1 Predicting susceptibility to tuberculosis based on gene expression profiling in dendritic cells
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 104729
DO 10.1101/104729
A1 John D. Blischak
A1 Ludovic Tailleux
A1 Marsha Myrthil
A1 Cécile Charlois
A1 Emmanuel Bergot
A1 Aurélien Dinh
A1 Gloria Morizot
A1 Olivia Chény
A1 Cassandre Von Platen
A1 Jean-Louis Herrmann
A1 Roland Brosch
A1 Luis B. Barreiro
A1 Yoav Gilad
YR 2017
UL http://biorxiv.org/content/early/2017/02/03/104729.abstract
AB Tuberculosis (TB) is a deadly infectious disease, which kills millions of people every year. The causative pathogen, Mycobac-terium tuberculosis (MTB), is estimated to have infected up to a third of the world’s population; however, only approximately 10% of infected healthy individuals progress to active TB. Despite evidence for heritability, it is not currently possible to predict who may develop TB. To explore approaches to classify susceptibility to TB, we infected with MTB dendritic cells (DCs) from putatively resistant individuals diagnosed with latent TB, and from susceptible individuals that had recovered from active TB. We measured gene expression levels in infected and non-infected cells and found hundreds of differentially expressed genes between susceptible and resistant individuals in the non-infected cells. We further found that genetic polymorphisms nearby the differentially expressed genes between susceptible and resistant individuals are more likely to be associated with TB susceptibility in published GWAS data. Lastly, we trained a classifier based on the gene expression levels in the non-infected cells, and demonstrated decent performance on our data and an independent data set. Overall, our promising results from this small study suggest that training a classifier on a larger cohort may enable us to accurately predict TB susceptibility.