Urine samples, collected over the course of two years, 2013-2014, at Policlinico Universitario di Monserrato-Cagliari (Italy) from a total of 133 subjects were analyzed: 51 patients with E. coli-associated UTI (E. coli-pos), 21 patients with UTI caused by other pathogens (Enterococcus spp. = 6; Staphylococcus spp. = 4; Proteus spp. = 3; Candida spp. = 8), and 61 healthy controls (CTRLs). All samples had previously been collected for routine mandatory diagnostic analysis from patients with acute uncomplicated cystitis and manifesting symptoms of dysuria, urinary frequency, or urinary urgency, and in a few cases, hematuria. Urine samples with negative or low colony counts and without evidence of inflammatory disease were used as the control group (Table 1). The following parameters were registered for each patient: the collection date, age, sex, identification of the bacteria strain associated with UTI, and the results of the antimicrobial susceptibility test. Mid-stream urine samples were obtained before starting antimicrobial therapy and analyzed using standard microbiological methods. Samples were collected and immediately aliquoted for the analysis after the addition of sodium azide 0.1% (w/v) to stop bacteria growth.
Samples for urine culture were analysed within one hour of sampling; otherwise, they were stored at 4 °C and processed until 24 h after collection. All samples were inoculated in CLED agar as well as MacConkey agar and Sabouraud dextrose agar (all from bioMérieux, Marcy-l’Étoile, France), and were incubated under aerobic conditions for 18-48 h at 35 °C. A positive culture was defined as having a number of yielded colonies ≥103 CFU/ml when associated with clinically significant signs in symptomatic patients and in light of the patient’s immunological status. Non-UTI controls were samples with no microbial growth from subjects with a negative history of UTI. The identification and antimicrobial susceptibility of the isolated strains were performed using automatized Vitek2 (bioMérieux, Marcy-l’Étoile, France). The ATCC 25922 E. coli was used for quality control and susceptibility defined in accordance with EUCAST recommendations (http://www.eucast.org/clinical_breakpoints/).
After sample collection, fractions of urine samples were added with a solution of 0.1% sodium azide, then centrifuged for 10 min at 4 °C at 12000 rpm to remove whole cell debris and to avoid contaminants. The supernatant was used for subsequent NMR analysis.
NMR spectroscopy analysis
For each sample 630 μl-aliquots were collected from the supernatant and processed as previously described . Samples were then transferred into a 5 mm NMR tube for analysis. The 1H-NMR spectra were acquired using a Varian Unity Inova 500 MHz spectrometer (Agilent Technologies, Santa Clara, CA, USA). The acquisition conditions of the NMR spectra were the following: standard temperature of 27 °C, 1D NOESY sequence with a 90° pulse of 10.4 μs, acquisition time of 1.5 s and spectral width of 6000 Hz. Free induction decay (FID) was acquired 128 times to increase signal-to- noise ratio. FIDs were weighted by an exponential function with a 0.5-Hz line broadening factor prior to Fourier Transformation. Finally, acquired spectra were phase and baseline corrected (Version 7.1.2, Mestrelab Research S.L.).
The 1H-NMR spectra were reduced to regions of 0.04 ppm in the region 0.5–9.5 ppm, excluding urea and residual water region. Total area for each spectum bin was normalized to a constant sum of 100 to minimize the effects of variable concentration among different samples .
The resulting dataset was then analyzed by using SIMCA-P+ (Version 13.0, Umetrics, Umeå, Sweden). Multivariate statistical models were validate as follows: one-third of the samples were removed from the total set and used as a validation set, and the rest of the samples were used for constructing the training set. The class of samples in the validation dataset was then predicted based on the model built from the training set.
PCA was performed to get an overview of similarities/differences between sample profiles and to detect possible outliers.
Additionally, PLS-DA was applied to maximize the separation between samples and to identify subsets (linear combinations) of metabolic features associated with a specific sample class. OPLS-DA was applied to achieve a better interpretation of PLS-DA models, as it removes systematic variations from the data by placing them in orthogonal components and maximizing class separation in the OPLS-DA component . A permutation test was performed using 200 permutations to check overfitting of the PLS-DA model. Once the supervised model has been estimated from the training set, the model can be used to predict new observations. All observation of the prediction-set can be predicted using the supervised model calculating the predicted value of Y variable. The Y predicted value could be used to build a classifier.
Six metabolites were identified as discriminant for the separation between the E.coli-pos and CTRLs samples from the S-Plot line and, then, quantified using ChenomX NMR Suite 7.1 (Chenomx Inc., Canada) . Univariate statistical summaries and tests were performed based on the creatinine-normalized concentrations. The univariate statistics were calculated using the Mann Withney U test to estimate the significance of group differences. P-values of less than 0.05 were considered statistically significant.
The discriminant metabolites were also quantified in a group of 21 urine samples with infections with other pathogens in order to test E. coli specificity. Each metabolite was used to build a classifier to discriminate between E. coli-pos versus CTRLs, E. coli-pos versus non-E. coli and E. coli-pos versus “ALL” (CTRLs + non-E. coli).
The performance of the classifiers was assessed using a ROC curve performed using GraphPad Prism version 7.00 (GraphPad Software, La Jolla California USA, http://www.graphpad.com). ROC curves are summarized in a single value, the area under the curve (AUC), that ranges from 0 to 1.0. .