Characterization of MicroRNA Expression Profiles and Identification of Potential Biomarkers in Leprosy

Abstract

Leprosy is an important cause of disability in the developing world. Early diagnosis is essential to allow for cure and to interrupt transmission of this infection. MicroRNAs (miRNAs) are important factors for host-pathogen interaction and they have been identified as biomarkers for various infectious diseases. The expression profile of 377 microRNAs were analyzed by TaqMan low-density array (TLDA) in skin lesions of tuberculoid and lepromatous leprosy patients as well as skin specimens from healthy controls. In a second step, 16 microRNAs were selected for validation experiments with reverse transcription-quantitative PCR (qRT-PCR) in skin samples from new individuals. Principal-component analysis followed by logistic regression model and receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic potential of selected miRNAs. Four patterns of differential expression were identified in the TLDA experiment, suggesting a diagnostic potential of miRNAs in leprosy. After validation experiments, a combination of four miRNAs (miR-101, miR-196b, miR-27b, and miR-29c) was revealed as able to discriminate between healthy control and leprosy patients with 80% sensitivity and 91% specificity. This set of miRNAs was also able to discriminate between lepromatous and tuberculoid patients with a sensitivity of 83% and 80% specificity. In this work, it was possible to identify a set of miRNAs with good diagnostic potential for leprosy.

Keywords: biomarker; leprosy; miRNA.

FIG 1

miRNA expression profiles in leprosy patients and healthy controls. Hierarchical clustering analysis of miRNA expression data. Sample labels are on the top. C, healthy control; LEP, lepromatous; TUB, tuberculoid. On the right side, each of the four clusters are highlighted.

FIG 2

miRNA expression median differences in TLDA and qRT-PCR experiments. Distribution of −log(P value) (Kruskal-Wallis) of comparison between miRNA expression median differences in the three groups (LL and TT patients and healthy controls). Filled circles represent TLDA experiments and unfilled circles represent qRT-PCR experiments. The dashed line indicates threshold of statistical significance (P value of 0.05). LL, lepromatous leprosy; TT, tuberculoid leprosy; TLDA, TaqMan low-density array.

FIG 3

Diagnostic power evaluation of miRNAs for discrimination between leprosy patients and healthy controls using a subset of four miRNAs. (A) Principal component scores of TLDA expression data of miR-101, miR-196b, miR-27b, and miR-29c indicate separation between the leprosy and healthy control groups. (B) Normalized eigenvectors were used to transform qRT-PCR data revealing a separation between the groups and high correlation of the data. (C) The first qRT-PCR-transformed score was evaluated in a ROC analysis after logistic regression revealing 100% sensitivity and 80% specificity with the cutoff of 0.64 and an AUC of 87.3% (95% CI, 66.8% to 100.0%) for discrimination of leprosy patients and healthy controls. TLDA, TaqMan low-density array.

FIG 4

Diagnostic power evaluation of miRNAs for discrimination between LL and TT patients using a subset of four miRNAs. (A) Principal component scores of TLDA expression data of miR-101, miR-196b, miR-27b, and miR-29c indicate separation between LL and TT groups. (B) Normalized eigenvectors were used to transform qRT-PCR data revealing a separation between the groups. (C) The first qRT-PCR-transformed score was evaluated in a ROC analysis after logistic regression revealing 83.3% sensitivity and 80% specificity with the cutoff of 0.58 and an AUC of 83.3% (95% CI, 55.7% to 100.0%) for discrimination of LL and TT patients. TLDA, TaqMan low-density array.