Abstract
Leishmaniasis is a neglected disease with a broad clinical spectrum which includes asymptomatic infection. A thorough diagnosis, able to distinguish and quantify Leishmania parasites in a clinical sample, constitutes a key step in choosing an appropriate therapy, making an accurate prognosis and performing epidemiological studies. Several molecular techniques have been shown to be effective in the diagnosis of leishmaniasis. In particular, a number of PCR methods have been developed on various target DNA sequences including kinetoplast minicircle constant regions. The first aim of this study was to develop a SYBR green-based qPCR assay for Leishmania (Leishmania) infantum detection and quantification, using kinetoplast minicircle constant region as target. To this end, two assays were compared: the first used previously published primer pairs (qPCR1), whereas the second used a nested primer pairs generating a shorter PCR product (qPCR2). The second aim of this study was to evaluate the possibility to discriminate among subgenera Leishmania (Leishmania) and Leishmania (Viannia) using the qPCR2 assay followed by melting or High Resolution Melt (HRM) analysis. Both assays used in this study showed good sensitivity and specificity, and a good correlation with standard IFAT methods in 62 canine clinical samples. However, the qPCR2 assay allowed to discriminate between Leishmania (Leishmania) and Leishmania (Viannia) subgenera through melting or HRM analysis. In addition to developing assays, we investigated the number and genetic variability of kinetoplast minicircles in the Leishmania (L.) infantum WHO international reference strain (MHOM/TN/80/IPT1), highlighting the presence of minicircle subclasses and sequence heterogeneity. Specifically, the kinetoplast minicircle number per cell was estimated to be 26,566±1,192, while the subclass of minicircles amplifiable by qPCR2 was estimated to be 1,263±115. This heterogeneity, also observed in canine clinical samples, must be taken into account in quantitative PCR-based applications; however, it might also be used to differentiate between Leishmania subgenera.