1. Introduction
Over the past few decades, the epidemiology of fungal infections has substantially changed as the proportion of the population at risk has increased [
1,
2,
3,
4]. In fact, the prevalence and incidence of nosocomial and healthcare-associated fungal infections, mainly due to
Candida spp. and
Aspergillus spp., have witnessed an outstanding rise [
1,
2,
3].
Invasive
Candida spp. infections affect over 250,000 immunocompromised patients per year worldwide and are responsible for over 50,000 deaths [
5,
6]. Among healthcare-associated bloodstream infections, candidemia is the fourth most common cause [
2,
5,
7]. The impact of such detrimental opportunistic infections bears dramatic consequences for both patients and the healthcare economy. Indeed, the mortality rate due to
Candida infections ranged from 35% to 64% [
5,
6,
8], and hospitalization cost was estimated to be around USD 1.4 billion in 2017 in the USA [
9].
By looking at species distribution,
Candida albicans remains the most frequently isolated fungal pathogen from blood cultures [
10]. Non-
albicans Candida species distribution differs greatly from one country to another [
11,
12]. According to recent reports,
Candida glabrata was the second most frequently isolated
Candida species from blood cultures in Canada and North America [
13,
14]. In particular, the prevalence of
C. glabrata causing candidemia in the USA increased from 12% in 1992–1993 to 27% in the late 2000s [
15]. Clinical surveys from several countries in Southern Europe reported
Candida parapsilosis as the second most frequently isolated
Candida species from blood cultures and in certain scenarios even the first [
8,
16,
17,
18,
19]. Correlated to the increase in the prevalence and incidence of non-
albicans Candida species among bloodstream infections, researchers have highlighted that drug resistance rates are also increasing over time as they represent the focus of current clinical research efforts [
20,
21,
22,
23].
First-line laboratory approach to differentiate
Candida species could involve the use of chromogenic media. However, considering that the phenotypic characteristics of different
Candida species might be similar, this phenotypic test can only help microbiologists in presumptive species identification, and it is therefore mandatory to confirm the first diagnostic hypothesis with a second molecular approach [
24]. To this point, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been extensively used in routine diagnosis [
25]. MALDI-TOF MS can also be performed directly on blood cultures that tested positive for yeast after Gram staining [
26], yielding results in a few minutes [
27].
A new approach in microbial identification is Fourier transform infrared (FTIR) spectroscopy [
28]. This is a spectrum-based technique, first introduced in the 1950s, that, within its wide spectrum of applications, can quantify the absorption of infrared light by molecules present in the microbial cell. The generated IR spectrum provides a specific fingerprint that reflects the cell composition of nucleic acids, proteins, lipids, and carbohydrates [
28,
29]. Each microorganism has a highly specific infrared absorption chemical signature correlated with genetic information, thus allowing for the identification of the microbial species [
30]. The attenuated total reflection (ATR) technique is the most frequently used to investigate the chemical composition of intact microorganisms from a monomicrobial culture grown on solid media [
29]. In the ATR method, the IR beam is directed toward an ATR crystal at a defined angle, whereby the total internal reflection occurs within the crystal. Under these conditions, an evanescent wave extends beyond the surface of the ATR crystal only a few microns (0.5–5 mm). The interaction of the evanescent wave with the intact microbial cells previously deposited on the ATR crystal results in the partial attenuation of the total internally reflected IR beam at the wavelengths at which the sample absorbs the IR energy [
31]. The attenuated evanescent wave passes back to the IR beam, which exits the opposite end of the crystal to reach the detector in the IR spectrometer. The generated infrared spectrum enables microbial identification. Promising results, using ATR-FTIR spectroscopy, were reported for both bacterial and yeast identification and showed the potential for the discrimination of antibiotic- and antifungal-resistant strains [
32,
33,
34,
35].
The aim of the present study was to evaluate the performance of the I-dOne software (Alifax, Polverara (PD)—Italy) for the classification of ATR-FTIR spectra leading to the identification of clinical yeast isolates in comparison with MALDI-TOF MS, the method currently used in our laboratory.
Furthermore, a prototype version of the I-dOne software (Research Use Only, RUO) was used to evaluate the method’s capability to correctly classify the members of the Candida parapsilosis complex at the species level.
4. Discussion
The present study settled and then evaluated the diagnostic performance of the I-dOne software (Alifax, Polverara (PD)—Italy) with the ATR-FTIR spectroscopic technique regarding the identification of the most frequent yeast isolates encountered in clinical microbiology, in comparison to MALDI-TOF MS (Bruker Corporation, Billerica, MA, USA), which is the currently used method in our laboratory.
All the analyzed yeast isolates were concordantly identified by MALDI-TOF at the genus level, and 92.2% were identified at the species level. The time required for the analysis of each isolate was 90 s. Twelve isolates were not concordantly identified at the species level. Implementations done in the software upgrade and in the referral spectral library allowed for further species identification and discrimination within the C. parapsilosis species complex. This is a drawback of this molecular diagnostic technique hindering its clinical application up to this point. However, with these promising results, it is reasonable to believe that past issues with species identification within the C. parapsilosis species complex could be overcome as the I-dOne RUO software (Alifax) with the ATR-FTIR spectroscopic technique referral spectral database was able to concordantly identify all C. parapsilosis complex isolates: 100% C. parapsilosis, 100% C. metapsilosis, and 100% C. orthopsilosis.
Our results should, however, be interpreted with caution. The monocentric nature of the study and the research focus limited on the most commonly isolated yeast species in clinical practice available are all study limitations. In addition, it would be of the utmost importance to expand the analysis to Candida auris; therefore, the aim of future studies will be to pursue this goal. Conversely, the prospective nature of the study and the amplitude of the number of isolates analyzed, as well as the software implementation and upgrade delivering new insights on species identification within the C. parapsilosis species complex, are all points of strength.
Surprisingly, despite the relatively low rate of non-concordant species identification (3.9%), when we compared our results with major studies such as those reported by Lam et al. [
36], our overall species non-concordant identification rate was higher (3.9% vs. 0%). In previous studies reported by the same authors, the non-concordant identification rate was still lower (0.9%) than the one reported in our study [
31]. However, both aforementioned studies used an optimized algorithm, specifically for Sabouraud agar.
Based on the results presented in this study, it can be stated that the I-dOne software (Alifax) with the ATR-FTIR spectroscopic technique referral spectral database could be a reliable alternative to MALDI-TOF MS analysis for the molecular identification of Candida spp. in a clinical laboratory routine workflow. Nevertheless, these results should be further investigated with other studies comparing ATR-FTIR with current clinical reference standards on yeast identification and speciation from other working groups.
