Elsevier

Tuberculosis

Volume 95, Issue 4, July 2015, Pages 421-425
Tuberculosis

Diagnostics
A real-time PCR signature to discriminate between tuberculosis and other pulmonary diseases

https://doi.org/10.1016/j.tube.2015.04.008Get rights and content

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.

Introduction

Despite increasing treatment success rates, tuberculosis (TB) continues to spread worldwide. The World Health Organization (WHO) estimates that 8.6 million people developed TB and 1.3 million died from the disease in 2012 [15]. Lack of accurate and rapid test to diagnose TB remains an important obstacle to TB control. The most common clinical dilemma encountered in TB diagnosis is the differentiation of pulmonary TB from other common lung diseases. When a patient is found to have an abnormal chest X-ray, TB is not necessarily on the top of the list of differential diagnosis in most settings.

Unbiased microarray gene expression profiling of whole blood cells has provided candidate biosignatures to discriminate TB patients from healthy donors [1], [4], [9], [10], [12]. However, these studies that compare TB to healthy controls mostly reveal a general state of persistent inflammation and not TB per se [14].

Recently, several groups have sought TB-specific biomarkers by comparing them in patients with TB versus other inflammatory pulmonary diseases [3], [6], [7], [11]. Despite overlapping gene up-regulation in proinflammatory pathways and general immunopathological mechanisms, they could identify transcript signatures to distinguish TB from other diseases, including in HIV infected and uninfected subjects [6].

Here, we selected a restricted set of genes to assess their ability to differentiate TB from other pulmonary diseases (OPD) patients. These genes were previously identified by Maertzdorf et al. [10] as a biosignature to discriminate TB and healthy latently infected (LTBI) subjects: granzyme A (GZMA), guanylate binding protein 5 (GBP5), Fc gamma receptor 1A (CD64), Fc gamma receptor 1B (FCGR1B) and lactotransferrin (LTF). Maertzdorf et al. [10] did not include OPD patients in their study, but since then, CD64, FCGR1B and LTF were shown to be differentially expressed in TB versus other lung diseases [3], [5], [11].

Section snippets

Study participants

Subjects were recruited between March 1, 2011 and March 30, 2013. Written informed consent was obtained from all participants. Our cohort included 27 patients with active tuberculosis (TB), 27 healthy donors with latent Mycobacterium tuberculosis infection (LTBI), 25 healthy non-infected donors (NIDs), and 22 patients suffering from other pulmonary diseases (OPD)--14 patients with asthma and eight patients with streptococcal pneumonia (PN). All subjects were older than 18, and responded to a

Results

We assessed the gene expression level of CD64, FCGR1B, GZMA, GBP5 and LTF by real-time PCR in whole blood samples from 25 NIDs, 27 LTBI, 27 TB, 14 asthma and eight PN donors (Table 1). B2M was chosen as reference gene based on Maertzdorf et al.'s study [10]. There was a difference in age distribution between the study groups, but we found no evidence to suggest gene expression levels correlated with the donor's age. Although we did find evidence that suggests FCGR1B and LTF expression is

Discussion

Clinically relevant TB biomarkers need to be able to distinguish pulmonary TB and non-TB pulmonary diseases. Otherwise, such tests would be no more specific than a chest x-ray. Here, we wished to be able to distinguish TB from other common pulmonary diseases. Asthma represents a chronic non-infectious inflammatory airways disease. Non-TB pneumonias represent the most common infectious pulmonary disease that healthcare providers need to rapidly distinguish from TB, when patients seek health care

Funding

This work was supported by the Fogarty International Center at the National Institutes of Health [grant number U2RTW006885] and the UBS Optimus Foundation.

Competing interests

None of the authors that contributed to this work have any conflict of interest to declare.

Ethical approval

The study was accepted by the ethical committees of the Prefeitura de Porto Alegre (IRB approval 630), the Hospital de Clínicas in Porto Alegre (IRB approval 110201) and the Fundação Estadual de Produção e Pesquisa em Saúde (IRB approval 03/2011).

References (15)

  • M.P. Berry et al.

    An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis

    Nature

    (2010)
  • S. Blankley et al.

    The application of transcriptional blood signatures to enhance our understanding of the host response to infection: the example of tuberculosis

    Philos Trans R Soc Lond B Biol Sci

    (2014)
  • C.I. Bloom et al.

    Transcriptional blood signatures distinguish pulmonary tuberculosis, pulmonary sarcoidosis, pneumonias and lung cancers

    PLoS One

    (2013)
  • M. Jacobsen et al.

    Candidate biomarkers for discrimination between infection and disease caused by Mycobacterium tuberculosis

    J Mol Med Berl

    (2007)
  • S.A. Joosten et al.

    A helicopter perspective on TB biomarkers: pathway and process based analysis of gene expression data provides new insight into TB pathogenesis

    PLoS One

    (2013)
  • M. Kaforou et al.

    Detection of tuberculosis in HIV-infected and -uninfected African adults using whole blood RNA expression signatures: a case-control study

    PLoS Med

    (2013)
  • L.L. Koth et al.

    Sarcoidosis blood transcriptome reflects lung inflammation and overlaps with tuberculosis

    Am J Respir Crit Care Med

    (2011)
There are more references available in the full text version of this article.

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These authors contributed equally to this work.

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