Abstract
The rapid and accurate identification of pathogen yeast species is crucial for clinical diagnosis due to the high level of mortality and morbidity induced, even after antifungal therapy. For this purpose, new rapid, high-throughput and reliable identification methods are required. In this work we described a combined approach based on two high-throughput techniques in order to improve the identification of pathogenic yeast strains. Next Generation Sequencing (NGS) of ITS and D1/D2 LSU marker regions together with FTIR spectroscopy were applied to identify 256 strains belonging to Candida genus isolated in nosocomial environments. Multivariate data analysis (MVA) was carried out on NGS and FT-IR data-sets, separately. Strains of Candida albicans, C. parapsilosis, C. glabrata and C. tropicalis, were identified with high-throughput NGS sequencing of ITS and LSU markers and then with FTIR. Inter- and intra-species variability was investigated by consensus principal component analysis (CPCA) which combines high-dimensional data of the two complementary analytical approaches in concatenated PCA blocks normalized to the same weight. The total percentage of correct identification reached around 97.4% for C. albicans and 74% for C. parapsilosis while the other two species showed lower identification rates. Results suggested that the identification success increases with the increasing number of strains actually used in the PLS analysis. The absence of reliable FT-IR libraries in the current scenario is the major limitation in FTIR-based identification of strains, although this metabolomics fingerprint represents a valid and affordable aid to rapid and high-throughput to clinical diagnosis. According to our data, FT-IR libraries should include some tens of certified strains per species, possibly over 50, deriving from diverse sources and collected over an extensive time period. This implies a multidisciplinary effort of specialists working in strain isolation and maintenance, molecular taxonomy, FT-IR technique and chemo-metrics, data management and data basing.
Highlights
The correct identification and classification of fungi is essential for basic biological research such as the assessment of biodiversity, conservation, taxonomy and evolutionary biology and for those applications in which humanity and biodiversity intersect [1, 2].To understand the biodiversity, the ecological roles and the geographical distribution of pathogenic fungi, DNA barcoding was proven to be a powerful tool with enormous potential [3]
Twelve species were isolated from both hospitals, with C. albicans, C. glabrata, C. parapsilosis and C. tropicalis, representing the vast majority of the isolates. 256 strains of these four species and the respective type strains were employed in this study (S1 Table)
The FT-IR data was split into four blocks: The region from 3050–2800 cm-1 was defined as block one, the region from 1800–1500 cm-1 as block two, the region from 1500–1200 cm-1 as block three and the region from 1200–700 cm-1 as block four
Summary
The ecological roles and the geographical distribution of pathogenic fungi, DNA barcoding was proven to be a powerful tool with enormous potential [3]. DNA barcoding is a global initiative designed to provide rapid, accurate, and automatable species identifications by using short, standardized gene regions as internal species markers [4]. The critical issue underlying barcoding is accuracy, defined in taxonomic terms as the capability of unbiased and unequivocal identification at the species level. Accuracy depends especially on the extent of, and the separation between, intra-specific variation and inter-specific divergence in the selected marker creating a significant barcoding ‘‘gap” between intraand inter-specific variation [5]. The sequences that are unique for a single species make identification easier, but their lack of universality hamper their amplification and the whole procedure [3, 6]
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