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Predicting susceptibility to tuberculosis based on gene expression profiling in dendritic cells

John Blischak 1 Ludovic Tailleux 2, * Marsha Myrthil 1 Cécile Charlois 3 Emmanuel Bergot 4 Aurélien Dinh 5 Gloria Morizot 6 Olivia Chény 7 Cassandre Platen 7 Jean-Louis Herrmann 8, 9 Roland Brosch 2 Luis Barreiro 10, 11, * Yoav Gilad 1, *
* Corresponding author
1 University of Chicago
2 Pathogénomique mycobactérienne intégrée - Integrated Mycobacterial Pathogenomics
3 DASES - Direction de l'Action Sociale de l'Enfance et de la Santé
4 Hôpital Côte de Nacre [CHU Caen]
5 Service des Maladies Infectieuses et Tropicales [CHU Raymond Poincaré]
6 ICAReB - Investigation Clinique et d’Accès aux Ressources Biologiques (Plate-forme) - Clinical Investigation and Access to BioResources
7 CRT - Centre de Recherche Translationnelle - Center for Translational Science
8 2I - Infection et inflammation
9 HUPO - Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest
10 CHU Sainte Justine [Montréal]
11 UdeM - Université de Montréal
Abstract : Tuberculosis (TB) is a deadly infectious disease, which kills millions of people every year. The causative pathogen, Mycobacterium 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 reasonable 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.
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PASTEUR | CNRS | UVSQ | 2IC | UNIV-PARIS-SACLAY | COMUE-NORMANDIE | GS-HEALTH-DRUG-SCIENCES

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John Blischak, Ludovic Tailleux, Marsha Myrthil, Cécile Charlois, Emmanuel Bergot, et al.. Predicting susceptibility to tuberculosis based on gene expression profiling in dendritic cells. Scientific Reports, Nature Publishing Group, 2017, 7 (1), pp.5702. ⟨10.1038/s41598-017-05878-w⟩. ⟨pasteur-02046440⟩

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