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.