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Open Access Research article

A predictive signature gene set for discriminating active from latent tuberculosis in Warao Amerindian children

Lilly M Verhagen12, Aldert Zomer13, Mailis Maes2, Julian A Villalba24, Berenice del Nogal56, Marc Eleveld1, Sacha AFT van Hijum37, Jacobus H de Waard26 and Peter WM Hermans1*

Author Affiliations

1 Laboratory of Pediatric Infectious Diseases, Radboud University Medical Centre, PO Box 9101 (internal post 224), Nijmegen, 6500 HB, The Netherlands

2 Laboratorio de Tuberculosis, Instituto de Biomedicina, Caracas, Venezuela

3 Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, Radboud University Medical Centre, Nijmegen, The Netherlands

4 Lovelace Respiratory Research Institute, Albuquerque, USA

5 Departamento de Pediatría, Hospital de Niños J.M. de los Ríos, Caracas, Venezuela

6 Facultad de Medicina, Universidad Central de Venezuela, Caracas, Venezuela

7 NIZO food research, Kluyver Centre for Genomics of Industrial Fermentation, Ede, The Netherlands

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BMC Genomics 2013, 14:74  doi:10.1186/1471-2164-14-74

Published: 1 February 2013

Abstract

Background

Tuberculosis (TB) continues to cause a high toll of disease and death among children worldwide. The diagnosis of childhood TB is challenged by the paucibacillary nature of the disease and the difficulties in obtaining specimens. Whereas scientific and clinical research efforts to develop novel diagnostic tools have focused on TB in adults, childhood TB has been relatively neglected. Blood transcriptional profiling has improved our understanding of disease pathogenesis of adult TB and may offer future leads for diagnosis and treatment. No studies applying gene expression profiling of children with TB have been published so far.

Results

We identified a 116-gene signature set that showed an average prediction error of 11% for TB vs. latent TB infection (LTBI) and for TB vs. LTBI vs. healthy controls (HC) in our dataset. A minimal gene set of only 9 genes showed the same prediction error of 11% for TB vs. LTBI in our dataset. Furthermore, this minimal set showed a significant discriminatory value for TB vs. LTBI for all previously published adult studies using whole blood gene expression, with average prediction errors between 17% and 23%. In order to identify a robust representative gene set that would perform well in populations of different genetic backgrounds, we selected ten genes that were highly discriminative between TB, LTBI and HC in all literature datasets as well as in our dataset. Functional annotation of these genes highlights a possible role for genes involved in calcium signaling and calcium metabolism as biomarkers for active TB. These ten genes were validated by quantitative real-time polymerase chain reaction in an additional cohort of 54 Warao Amerindian children with LTBI, HC and non-TB pneumonia. Decision tree analysis indicated that five of the ten genes were sufficient to classify 78% of the TB cases correctly with no LTBI subjects wrongly classified as TB (100% specificity).

Conclusions

Our data justify the further exploration of our signature set as biomarkers for potential childhood TB diagnosis. We show that, as the identification of different biomarkers in ethnically distinct cohorts is apparent, it is important to cross-validate newly identified markers in all available cohorts.

Keywords:
Biomarker; Children; Mycobacterium tuberculosis; Transcriptomics