Predicting susceptibility to tuberculosis based on gene expression profiling in dendritic cells.
Predicting susceptibility to tuberculosis based on gene expression profiling in dendritic cells.
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DOI：
10.1038/s41598-017-05878-w
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发表时间：
2017-07-18
影响因子：
4.6
通讯作者：
Gilad Y
中科院分区：
文献类型：
--
作者：
Blischak JD;Tailleux L;Myrthil M;Charlois C;Bergot E;Dinh A;Morizot G;Chény O;Platen CV;Herrmann JL;Brosch R;Barreiro LB;Gilad Y
结核病是一种致命的传染病，每年导致数百万人死亡。据估计，引起结核病的病原体结核分枝杆菌(MTB)感染了多达三分之一的世界人口；然而，只有大约10%的受感染的健康人进展为活动性结核病。尽管有证据表明存在遗传性，但目前还不可能预测谁可能患上结核病。为了探索结核病易感性的分类方法，我们用结核分枝杆菌树突状细胞(DC)感染了被诊断为潜伏性结核病的疑似耐药个体和从活动期结核病康复的易感个体。我们测量了感染细胞和未感染细胞中的基因表达水平，发现在未感染细胞中有数百个敏感和耐药个体之间的差异表达基因。我们进一步发现，在已发表的Gwas数据中，易感和耐药个体之间差异表达基因附近的遗传多态更有可能与结核病易感性有关。最后，我们根据未感染细胞中的基因表达水平训练了一个分类器，并在我们的数据和一个独立的数据集上展示了合理的性能。总体而言，我们从这项小型研究中获得的令人振奋的结果表明，在较大的队列中训练分类器可能使我们能够准确预测结核病的易感性。
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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影响因子：
6.4
作者：
Cobat, Aurelie;Gallant, Caroline J.;Schurr, Erwin
通讯作者：
Schurr, Erwin
影响因子：
64.8
作者：
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影响因子：
30.8
作者：
Curtis, James;Luo, Yang;Nejentsev, Sergey
通讯作者：
Nejentsev, Sergey
DOI：
10.1073/pnas.1115761109
发表时间：
2012-01-24
影响因子：
11.1
作者：
Barreiro, Luis B.;Tailleux, Ludovic;Gilad, Yoav
通讯作者：
Gilad, Yoav
影响因子：
20.3
作者：
Chaussabel, D;Semnani, RT;Nutman, TB
通讯作者：
Nutman, TB