A real-time PCR signature to discriminate between tuberculosis and other pulmonary diseases - 16/06/15

Doi : 10.1016/j.tube.2015.04.008

Lucas Laux da Costa ^a,^b,^{^{1
These authors contributed equally to this work.}} , Melaine Delcroix ^c,^⁎,^{^{1
These authors contributed equally to this work.}} , Elis R. Dalla Costa ^a , Isaías V. Prestes ^d , Mariana Milano ^a,^b , Steve S. Francis ^e , Gisela Unis ^f , Denise R. Silva ^b,^g , Lee W. Riley ^c , Maria L.R. Rossetti ^a,^h
^a Fundação Estadual de Produção e Pesquisa em Saúde, Avenida Ipiranga, 5400, Porto Alegre, RS 90610-000, Brazil
^b Universidade Federal do Rio Grande do Sul, Avenida Bento Gonçalves, 9500, Porto Alegre, RS 91501-970, Brazil
^c Division of Infectious Disease and Vaccinology, School of Public Health, University of California, 530 Li Ka Shing, Berkeley, CA 94720, USA
^d Programa de Pós Graduação em Epidemiologia da Universidade Federal do Rio Grande do Sul, Rua Ramiro Barcelos, 2400, Porto Alegre, RS 90035-003, Brazil
^e Division of Epidemiology, School of Public Health, University of California, 50 University Hall, Berkeley, CA 94720, USA
^f Hospital Sanatório Partenon, Avenida Bento Gonçalves, 3722, Porto Alegre, RS 90650-001, Brazil
^g Hospital de Clínicas de Porto Alegre, Rua Ramiro Barcelos, 2350, Porto Alegre, RS 90035-903, Brazil
^h Universidade Luterana do Brazil, Avenida Farroupilha, 8001, Bairro São José, Canoas, RS 92425-900, Brazil

^∗Corresponding author. 530 Li Ka Shing, Berkeley, CA 94720, USA. Tel.: +1 510 643 2949; fax: +1 510 643 4966.

Summary

The goal of this study was to identify a host gene signature that can distinguish tuberculosis (TB) from other pulmonary diseases (OPD). We conducted real-time PCR on whole blood samples from patients in Brazil. TB and OPD patients (asthma and non-TB pneumonia) differentially expressed granzyme A (GZMA), guanylate binding protein 5 (GBP5) and Fc gamma receptor 1A (CD64). Receiver operating characteristic, tree classification and random forest analyses were applied to evaluate the discriminatory power of the three genes and find the gene panel most predictive of patients' disease classification. Tree classification produced a model based on GBP5 and CD64 expression. In random forest analysis, the combination of the three genes provided a robust biosignature to distinguish TB from OPD with 95% specificity and 93% sensitivity. Our results suggest that GBP5 and CD64 in tandem may be the most predictive combination. However, GZMA contribution to the prediction model requires further investigation. Regardless, these three genes show promise as a rapid diagnostic marker separating TB from OPD.

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Keywords : Tuberculosis, Diagnostics, GBP5, CD64, GZMA, Biomarkers

Abbreviations : CD64, FCGR1B, GBP5, GZMA, LTBI, LTF, NID, OPD, PN, TB

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RNA extraction and real-time PCR quantification

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Vol 95 - N° 4

P. 421-425 - juillet 2015 Retour au numéro

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A real-time PCR signature to discriminate between tuberculosis and other pulmonary diseases - 16/06/15

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