Figure 2. Impact of COVID-19 outbreak in different field of society.
Different kinds of tests for COVID-19, namely molecular, antigen, and serological tests, have been key factors in the diagnosis and management of the pandemic. Real-time reverse transcription-polymerase chain reaction (RT-PCR) and rapid antigen detection tests are the most effective and rapid strategies developed to diagnose patients with COVID-19 and control infections in hospitals and communities [11,12]. However, the collection of nasopharyngeal exudates is an invasive procedure that puts healthcare workers at risk of disease transmission owing to patients’ sneezing, coughing, or gag reflexes [13]. Other limitations of the RT-PCR test are related to the long turnaround time and the requirement for expensive laboratory equipment and highly trained laboratory staff [12,14]. In turn, the results obtained by the antigen detection test may be unreliable because there is no RNA amplification, and in the case of low viral loads, the virus may not be detected, leading to false-negative results [15]. Additionally, the associated costs are relatively high. Besides the enormous capabilities of RT-PCR and other diagnosis platforms, more sensitive and accurate detection assays of SARS-CoV-2 are needed for an early diagnosis. In this context the development of improved analytical tests, simultaneously highly accurate, sensitive, and ultrafast, are extremely important, to diagnose early-stage and even asymptomatic individuals and, therefore, increase and improve the prevention and treatment efficiency.
Studies on secondary volatile organic metabolites (VOMs) have shown promising results in the identification of potential biomarkers for oncological, inflammatory, and respiratory infectious diseases [16-21]. VOMs are a rich source of information regarding the health status of an individual, as changes in the levels of these metabolites may be characteristic of specific disease processes [16,22]. VOMs can be found in various biological matrices, such as blood, urine, saliva, exhaled air, faeces, and skin exudates [18,20,23-27]. Urinary VOMs are considered intermediate or final products of metabolic pathways [16,28]. Additionally, urine sampling is non-invasive and causes no discomfort to the patients. VOMs analyses require sensitive procedures to avoid contamination and sample loss. Solid-phase microextraction (SPME) is a simple, highly efficient, and easy-to-perform extraction technique that does not require a preconcentration step before analysis [29,30]. The combination of SPME in headspace mode (HS) with gas chromatography coupled with mass spectrometry (GC-MS) analysis allows reliable and reproducible results and has been widely used for the analysis of urinary VOMs [16,17,20,27].
In this study a non-invasive approach, based on the establishment of the urinary volatilomic profile of COVID-19 patients using the solid-phase microextraction technique in headspace mode, (HS-SPME), combined with gas chromatography hyphenated with mass spectrometry (GC-MS), was proposed as useful and novel strategy to identify potential biomarkers to diagnose COVID-19 infection. The chromatographic data obtained were subjected to multivariable statistical analysis to identify volatile signatures that could discriminate between the presence of COVID-19 and its progression.
2. MATERIALS AND METHODS
2.1. Chemicals and reagents
Sodium chloride (NaCl, 99.5%) was obtained from Panreac AppliChem ITW Reagents (Barcelona, Spain). Ultrapure water, produced by a Milli-Q water purification system (Millipore, Bedford, PA, USA), was used to prepare the solutions of hydrochloric acid (HCl, 37%) 5 M and 3-octanol (internal standard, 99%) 2.5 ppm, both acquired to Sigma-Aldrich (St. Louis, MO, USA). Helium of purity 99.9% (He, N60, Air Liquide, Algés, Portugal) was used as the GC mobile phase. Glass vials, SPME holder, and fused silica fibre coating partially cross-linked with 50/30 µm divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) were purchased from Supelco (Merck KGaA, Darmstadt, Germany). DVB/CAR/PDMS fibres were conditioned according to the manufacturer’s guidelines. Before the first daily analysis, the fibres were conditioned for at least 10 min at the operating temperature of the GC injector port.
2.2. Study design and samples
The sample consisted of a set of 133 adult individuals: 42 individuals with no infection and never been infected by SARS-CoV-2(Control Group, CTRL), 61 individuals infected with SARS-CoV-2 (COVID Group, COVID) admitted to the Dr Nelio Mendonça Hospital between 15 May and 20 June 2020 (59 % male and 41% female, age average=56.8±18.6Y), and 30 recovered COVID-19 patients (Recovered Group, RECOV) (58 % male and 42% female, age average=60.7±16.3Y), with a recovery period of more than 3 months. Urine samples were collected at Dr Nélio Mendonça Hospital (Funchal, Portugal). Samples from healthy subjects were obtained from blood donors, samples from the COVID-19 group were collected from patients diagnosed with COVID-19, and samples from the RECOV group were obtained from patients in their recovery period one month after COVID-19 infection. Upon collection, the samples were frozen at -20 °C until further analysis. This study was approved by the Ethics Committee of Dr Nélio Mendonça Hospital. All the work described was carried out following The Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans. Informed consent was obtained from all the subjects recruited for this study, their privacy was strictly preserved, and any data beyond the SARS-CoV-2 infection were included in the study. Therefore, dimensions such as age, diet, previous diseases, sex, and sex were not considered in this study.
2.3. Urinary volatilome analysis and data processing
HS-SPME extraction was performed according to previously optimised conditions for urine sampling (9). Briefly, 4 mL of urine sample, previously adjusted to pH 1–2 with 500 µL of HCl (5 M), 0.8 g NaCl and 5 µL 3-octanol 2.5 ppm were placed in an 8 mL glass vial. The vial was placed in a water bath set at 50.0 ± 0.1 C with stirring at 800 rpm, and the SPME fibre was exposed to the headspace for 60 min. After extraction, the SPME fibre was collected and inserted into the injector port of the GC-MS instrument for 6 min at 250 °C, where the analytes were desorbed and transferred directly to the column. Each sample was analysed in triplicate.
The GC-MS analysis was performed in a gas chromatograph Agilent Technologies 6890N Network GC System (Palo Alto, CA, USA) equipped with a BP-20 fused silica column (30 m × 0·25 mm ID × 0·25 µm (SGE, Dortmund, Germany)), and connected to an Agilent 5975 quadrupole inert mass selective detector. The separation of the VOMs was carried out with a temperature gradient of 35 °C for 2 min, followed by an increase to 220 °C (2.5 °C min-1), remaining at this temperature for 5 min, for a total GC run time of 81 min. The column flow rate was maintained at 1 mL min-1. The injector port was operated in the splitless mode and maintained at 250 °C. For the 5975MS system, the temperatures of the transfer line, quadrupole, and ionisation source were 270 °C, 150 °C, and 230 °C, respectively. Data acquisition was performed in scan mode, 30-300 m/z, and the electron multiplier was set to the auto-tune procedure, with the electron impact mass spectra at 70 eV and the ionisation current at 10 µA. The VOMs were identified by comparing the mass spectra obtained with those available in Agilent MS Chemstation software (Palo Alto, CA, USA), which was equipped with a NIST05 mass spectral library with a similarity threshold of 80%.
2.4. Statistical analyses
The data analyses were performed using Microsoft 365® and MetaboAnalyst 5.0 (12). The data matrix was normalised using the median, log transformation, and mean centring. The normalised data were processed through univariate analysis, specifically a t-test (p-values < 0·05) to identify statistically significant VOMs. Subsequently, multivariate analysis was performed using partial least squares discriminant analysis (PLS-DA). Important variables of the generated PLS-DA model were identified based on the variable importance in projection (VIP) score. The model was further evaluated using a 10-fold cross-validation (CV) and permutation tests. Finally, potential biomarkers were validated through receiver operating characteristic (ROC) curves created using the Monte Carlo CV (MCCV) methodology to evaluate the accuracy and precision of the biomarkers.