HS-SPME-GC-MS approach for the analysis of volatile salivary metabolites and
application in a pilot study for the indirect assessment of gut microbiota
Beatrice Campanella1¶, Massimo Onor1¶, Tommaso Lomonaco2, Edoardo Benedetti3¶ and Emilia Bramanti1¶*1 National Research Council of Italy, C.N.R., Institute of Chemistry of Organo Metallic Compounds-ICCOM, Pisa, Italy.
2 Department of Chemistry and Industrial Chemistry, University of Pisa, Pisa, Italy. 3 Hematology Unit, Department of Oncology, University of Pisa, Pisa, Italy.
*Corresponding author: Emilia Bramanti bramanti@pi.iccom.cnr.it Tel: 0039-050-315-2293. Fax: +39-050-315-2555
ORCID Emilia Bramanti: orcid.org/0000-0001-8478-7370
Abstract
In this work a straightforward analytical approach based on headspace solid-phase microextraction (HS-SPME) sampler coupled to gas chromatography-mass spectrometry detection (GC–MS) was developed for the analysis of salivary volatile organic compounds (VOCs) without any prior derivatization step. With a sample volume of 500 µL, optimal conditions were achieved by allowing the sample to equilibrate for 10 min at 50°C and then extracting the samples for 10 min at the same temperature, using a carboxen/polydimethylsiloxane fiber. The method allowed the simultaneous identification and quantification of 20 compounds included short-chain fatty acids and their derivatives.
The proof of applicability of the methodology was performed in a pilot target analysis of the dynamics of volatile metabolites in saliva of a single subject undergoing 5 days treatment with antibiotic. Non-stimulated saliva samples were collected over three weeks from a nominally healthy volunteer before, during and after the antibiotic treatment with rifaximin.
Thus, although derived by a single case, this approach can be considered novel from an analytical standpoint and it encourages further studies combining saliva metabolite profiles and gut microbiota dynamics.
Introduction
The analysis of human gut microbiota is commonly performed on faecal samples using culture-dependent methodologies or metagenomics, primarily next-generation sequencing (NGS), which studies the genomes (microbioma) through 16S rRNA gene amplicon analysis [1]. Other approaches include the analysis of volatile and non-volatile metabolites of faeces using liquid (LC) and gas chromatography (GC) mass spectrometric (MS) techniques [2, 3]. Although the sampling of faeces is non-invasive, faecal sample preparation is a demanding task due to the high complexity and heterogeneity of the matrix [4, 5].
In the last years saliva emerged as a non-conventional, valuable matrices in diagnostic medicine and therapeutic drug monitoring, as well as in toxicology, and occupational and environmental exposure [6–12]. Saliva has many advantages in terms of collection, storage, shipping, and voluminous sampling and handling. It can be considered functionally equivalent to blood in reflecting the physiological state of the body due to the rapid diffusion equilibrium between the dissolved substances in the blood capillaries and salivary fluid through thin membranes of salivary glands [13]. More than 700 bacterial species have been found in the oral cavity, which also contribute to saliva chemical composition through secretion of their metabolic by products [14, 15].
Few studies (about 98 papers in Web of Science: “microbiota AND saliva*” in the title) explore the microbiota of saliva [12][16, 17]. While it is quite clear that the salivary microbiota is associated with the oral health status or local inflammation [18–22], relatively few authors investigated the dependence of salivary microbiota with life style [23, 24] or systemic pathological conditions [13] [25–30]. Mishiro et al. have reported that acid suppressive agents (e.g. proton pump inhibitors) affect gut and salivary microbiota [31].
Very recently saliva microbiota composition was correlated with body size and gender in Finnish children (https://www.frontiersin.org/articles/10.3389/fmicb.2019.00767/full).
As in the case of faeces, the analysis of salivary microbiota is usually performed by culture-dependent methodologies or NGS as well as by determining the metabolic profile by chemical approaches.
In this “proof of concept” study we investigated for the first time by solid phase micro-extraction gas chromatography coupled to mass spectrometry (SPME-GC-MS) the salivary VOCs profile to verify whether the dynamics of salivary metabolites might be a useful indicator for the study of the gut microbiota status.
Fourteen saliva samples were collected from a nominally healthy volunteer over three weeks, before, during and after the treatment with rifaximin, an antibiotic that is known to exerts
anti-The results herein presented are novel and they are discussed relatively to the possible employment of saliva metabolites as biomarkers of gut microbiota “health status”.
Materials and methods
Chemicals and materialsAcetaldehyde, propanal, ethanol, acetone, methyl acetate, ethyl acetate, butanone, methanol, 2-propanol, 2-pentanone, 2-butandione, 2-butanol, 1-2-propanol, 4-heptanon, 1-butanol, heptanal, 3-OH-2-butanone, acetic acid, propionic acid, butanoic acid, D-labeled ethanol and 13C-labeled butanoic acid were purchased from Sigma-Aldrich (Italy). All compounds had a purity higher than 99% and thus were used without any further purification. Working solutions of all these analytes were prepared by diluting their stock solution with milli-Q water and then stored at 4 °C up to 1 month in an amber vial until their use.
Helium 5.6 IP was purchased from Sol Group Spa (Italy) and was further purified with a super clean filter purchased from Agilent Technologies (USA) to remove water, oxygen and hydrocarbon contaminants. Solid Phase Micro-Extraction Fiber based on 85 µm carboxen/polydimethylsiloxane (CAR/PDMS) were employed for the preconcentration of volatile compounds in the HS.
The antibiotic rifaximin (200 mg tablets, batch no. 07/071c) was supplied by GRUNENTHAL ITALIA Srl. Aloe Vera was supplied by Specchiasol (Italy). Probiotics Lactobacillus reuteri (5 billions/capsule) were supplied by Italchimici (Italy).
All the liquid solutions and saliva samples were stored in sterile polypropylene containers purchased from Eppendorf (Italy). Salivette® roll-shaped polyester swabs (Sarstaedt, Germany) were used for saliva collection. An Eppendorf Centrifuge 5810 R equipped with an A-4-44 swinging bucket rotor (Italy) was used for sample centrifugation.
Preparation/dilution of samples and solutions was performed gravimetrically using ultrapure water (MilliQ; 18.2 M cm-1 at 25 °C, Millipore, Bedford, MA, USA).
Standard solutions
Compounds were classified into three groups (aldehydes and ketones, carboxylic acids, alcohols) and three stock solutions were prepared at a concentration between 30 and 180 mg/mL by mixing the selected analytes. The stock solutions (stored at 4 °C) were subsequently diluted in ultrapure water and used for method validation.
A nominally healthy volunteer (48 y.o. female) was enrolled in this pilot study. A total of 14 unstimulated saliva samples were collected at the same time of day (6:00 am to 7:00 am) to avoid fluctuation in the results due to the circadian saliva cycle, without eating or drinking or brushing her teeth and entire mouth at least 8 h before saliva collection. Saliva samples were collected every day three days before the treatment, during rifaximin treatment (400 mg/day in the first and fifth day, 800 mg/day in the second, third and fourth day) and every 1-2 days during 14 days after the treatment.
During 21 days of the experiment the diet was kept approximately constant (1800 200 kcal/day; approximately 250 20 gr carbohydrates; 60 10 g proteins; 62 5 gr fat). The subject involved in the experiment did not show any of the symptoms reported in Birmingham IBS Symptom Questionnaire [36], but a moderate dysbiosis condition characterized by abdominal pain and swelling. At the end of the experiments the symptoms had disappeared.
Polyester swabs were used for saliva collection by keeping in the mouth for 5 min and immediately stored at −20 °C. Prior to analysis, saliva samples were centrifuged at 4500 g for 10 min at 4 °C (Eppendorf™ 5804R Centrifuge), and 500 L of centrifuged saliva were placed in vials for GC-MS.
HS-SPME-GC-MS analysis
An Agilent 6850 gas chromatograph, equipped with a split/splitless injector, was used in combination with an Agilent 5975c mass spectrometer. A CTC CombiPAL autosampler was employed for HS sampling. The vials were incubated at 50°C for 10 min. The SPME fiber was then exposed for 10 minutes in the HS and injected in splitless mode into the GC. Just before use, SPME fiber was conditioned for 15 minutes at 280 °C inside the injector using a helium split flow of 300 mL/min (no carry-over problems were observed for all the investigated analytes). The inlet liner (1 mm internal diameter) was held at 280 °C and the injection was performed in splitless mode (15 s splitless time). The chromatographic separation was carried out on a DB-FFAP high polarity column (60 m × 0.25 mm, 0.5 m film thickness) supplied by Agilent Technologies (USA). Helium 5.6 IP was used as a carrier gas at constant flow of 1 mL/min. The oven temperature program was: 30 °C for 10 min, 4 °C/min up to 240 °C (15 min hold time). The total runtime was 77.5 min. The temperature of the transfer line and ion source was set at 240 and 250 °C, respectively. After GC separation, analytes were ionized in positive EI. The MS acquisition was performed in total ion chromatography (TIC) (range set from 20 m/z to 150 m/z) and in selected ion monitoring (SIM) modes. TIC allowed us the identification of the analytes, whereas SIM was implemented for
quantitative purposes by monitoring m/z showed in Table 1 (100 ms dwell time). For the peak identification signal-to-noise ratio cut-off of 50 was used.
Table 1 List of VOCs identified in saliva by HS-GCMS on the basis of their m/z ratio and their retention time. Quant . m/z Qual . m/z Rt Acetaldehyde 29 44 6.66 Propanal 29 58 9.48 Acetone 43 58 10.73 MethylAcetate 43 74 11.54 EthylAcetate 43 61 15.38 2-Butanone 43 72 16.16 Methanol 31 32 16.4 2-Propanol 45 59 19.11 Ethanol 45 46 19.52 2-Pentanone 58 43 22.35 2,3-Butandione 43 86 22.54 2-Butanol 45 59 26.37 1-Propanol 31 59 27.21 4-Eptanone 43 71 32.04 1-Butanol 56 43 33.18 Heptanal 70 55 35.09 3-OH-2-Butanone 45 43 39.78 Acetic Acid 60 43 44.59 Propionic Acid 74 45 46.44 Butyric Acid 60 45 48.04 Method validation
Chromatographic data were acquired by means of MSD Chem Stationr software (v. E.02.02 Agilent Technologies, USA). Reference libraries from NIST MS Search program (v. 2.0) were used to identify the VOCs. Reference compounds were used as a standard for confirming the presence of particular volatile compounds.
Calibration curves were obtained by diluting different amounts of VOCs standard solution, performing all analyses in triplicate. The method linearity was determined by evaluating the regression curves and expressed by the squared determination coefficient (R2). SIM m/z response
areas were employed after normalization with respect to the area of isotopic labelled ethanol or butyric acid as internal standards. For recovery, a pool of saliva samples added with sodium azide to block any enzymatic activity were purged with nitrogen at 40°C overnight. After the analysis of this “blank” sample, saliva was spiked with standard solution of VOCs to obtain in the samples 6-40 µM, added to a Salivette and treated for the analysis (N=3 replicates).
Data analysis
Compound identification of VOCs was achieved using ChemStation Data Analysis software and PBM search Algoritm on standard mass spectra library comparison and confirmed by injecting standard compounds.
In any given saliva HS sample, more than 100 chromatographic peaks can be observed, and it was therefore found necessary to select a subset of them to render statistical analysis tractable. Analytes were selected on the basis of their abundance in the gas chromatogram and on the basis of published works. The final list of 20 compounds included short-chain fatty acids (SCFAs), aldehydes, ketones and alcohols. A data matrix was prepared, having each sample in rows and HS-GCMS data of each metabolite in columns. To minimize possible differences in concentration between samples, the normalization and scaling of the data to unit variance were performed so that all of the variables were given equal weight regardless of their absolute value.
All statistical analyses were performed by means of R version 2.15.2 software.
Results and discussion
Method development and validation
Few authors propose the analysis of VOCs [37, 38], a variegated group of carbon-based chemicals classified on the basis of their retention time and boiling point (ranging from 50 °C to 260 °C) [39], and that may be collected from exhaled breath or sampled in the HS of biological fluids (saliva, urine, faeces, and sweat) as products of metabolic processes [40, 41]. VOCs can be exogenous (inhaled from the external environment) or endogenous, i.e. produced in human metabolic pathways, or symbiotic bacterial strains, or compounds released by pathogenic microbes. Thus, the human volatilome can be correlated with a variety of pathologies, including cancer, asthma, cystic fibrosis, diabetes, tuberculosis, chronic obstructive pulmonary disease, heart allograft rejection, and irritable bowel syndrome [42, 43]. Bacteria may be distinguished by means of their individual VOCs profiles [44–46]. Thus, different patterns of VOCs production may indicate variations in the
human microbiota, eventually related to a specific disease processes. Al Kateb et al. reported the most comprehensive characterization of VOCs in saliva, identifying 317 compounds over the 166 already present in the literature [37].
Effect of saliva volume. The average volume recovery of saliva for a healthy subject after Salivette
centrifugation is almost 1 mL. To reduce the eventual matrix effect at first we diluted the sample with deionized water before the HS analysis testing several ratio of water to saliva. This procedure was unnecessary considering that the results were not statistically different if compared with the analysis of whole, undiluted saliva. The number and abundances of VOCs detected increased, instead, by increase the saliva mass (data not shown for brevity), reaching a limit of VOCs identified for 500 µL of sample, as a result of fibre overload.
Effects of extraction and incubation time. As saliva contains several VOCs in a wide range of
concentrations, the effects of extraction and incubation time on the analytical performances of the method were evaluated. We found that both the number of VOCs and their abundances increased with the extraction temperature up to 50°C. Over this limit, a significant decrease in the features of the chromatograms occurred. Six incubation times were tested (1, 2, 5, 7, 10 and 15 min), finding an increase in the signal of most of the analytes up to 10 min. After 10 min, the signal of all the studied compounds reached a constant plateau except for ethyl acetate, 2-pentanone and 4-heptanon, for which the signal started to decrease. Thus 10 min incubation time was chosen.
Figures of merit. In the optimized conditions 20 volatile compounds were extracted and identified
in saliva using CAR/PDMS fiber (Table 1). The linear range, determination coefficients, limits of quantification (LOQs) and limits of detection (LODs) for each compound obtained using the developed method are presented in Table 2.
Table 2 Main analytical figures of merit.
Compound Calibration curve Linear range (µM) LOD (µM) LOQ (µM)
Acetaldehyde y = 0.0101x + 0.0082 3-64 1.05 3.5 R² = 0.994 Propanal y = 0.1201x - 0.0295 3-60 0.03 0.1 R² = 0.996 Ethanol y = 0.0024x + 0.0039 8-185 0.7 2.2 R² = 0.999 Acetone y = 0.1631x - 0.0664 8-35 0.03 0.12 R² = 0.996 Methyl acetate y = 7.9772x + 0.1076 0.3-6 0.008 0.025 R² = 0.999 Ethyl acetate y = 1.6596x - 0.8851 3-60 0.018 0.06 R² = 0.999 2-Butanone y = 0.8645x + 0.227 3-65 0.02 0.07 R² = 0.999 Methanol y = 0.0002x + 0.003 6-130 7 25 R² = 0.998 2-Propanol y = 0.0246x + 0.0645 3-15 0.4 1.2 R² = 0.994 2-Pentanone y = 0.2018x - 0.0758 3-15 0.02 0.07 R² = 0.993 2-3-Butandione y = 0.3358x - 0.1355 3-65 0.01 0.025 R² = 0.995 2-Butanol y = 0.1756x + 0.0344 3-15 0.0088 0.029 R² = 0.999 1-Propanol y = 0.045x + 0.013 3-15 0.085 0.28 R² = 0.999 4-Heptanon y = 2.648x - 0.033 1.7-7 0.025 0.083 R² = 0.992 1-Butanol y = 0.1207x - 0.0018 3-15 0.052 0.17 R² = 0.999 Heptanal y = 0.2323x - 0.1521 1.7-7 0.008 0.025 R² = 0.966 3-OH-2-Butanone y = 0.0036x + 0.0016 1.7-7 0.18 0.6 R² = 0.974 Acetic acid y = 0.0003x + 0.0045 5-25 14 46 R² = 0.997 Propionic acid y = 0.0003x + 0.001 3-15 1.3 4.3 R² = 0.998 Butanoic acid y = 0.0019x + 0.0097 3-15 0.3 0.9 R² = 0.999
All of the compounds showed good linearity after normalization with the internal standard (13C Butanoic acid for SCFAs, ethanol-d3 for the other) with determination coefficients between 0.992 and 0.999 (except in the worst cases of heptanal, R2 0.966, and 3-OH-2-Butanone, R2 0.974).
LODs have been calculated as 3.9sy,b/b where sy,b is the standard deviation of the blank signals and b the slope of the calibration curve. LOQs have been obtained by multiplying the LODs for 3.3. For the vast majority of the analysed compounds, the LODs varied between 8 nM and 14 µM.
The coefficient of variation (CV%) of measurements performed on the same and different vial was always below 5%, highlighting that our procedure allows a reliable determination of VOCs in saliva samples.
Figure 1 shows representative HS-GC-MS chromatograms of human saliva obtained in the optimized conditions. The chromatograms reported correspond to the saliva sampled in the first and in the last day of the experiment, i.e. before and after rifaximin treatment (see experimental part). 5 1 0 1 0 1 0 1 5 1 0 2 0 1 0 2 5 1 0 3 0 1 0 0 2 0 0 0 0 0 4 0 0 0 0 0 6 0 0 0 0 0 8 0 0 0 0 0 1 0 0 0 0 0 0 1 -B u ta n o l 1 -P ro p a n o l 2 ,3 -B u ta n d io n e 2 -O H -3 -B u a ta n o n e A ce tic A ci d 2 -B u ta n o n e E th yl A ce ta te B u ta n a l M e th yl A ce ta te P ro p a n a l E th an o l A ce ta ld e hy d e C o u n ts S c a n N u m b e r F I R S T D A Y L A S T D A Y A ce to n e
Fig. 1 Representative HS-GCMS chromatograms of human saliva obtained in the optimized conditions. The chromatograms reported correspond to the saliva sampled in the first and in the last day of the experiment, i.e. before and after rifaximin treatment (see experimental part).
Recovery DA RIFARE.
Application to VOCs monitoring during antibiotic treatment
The employment of saliva to probe the microbiota status is a novel, challenging task from the analytical point of view, and it would turn saliva into a rapid, non-invasive diagnosis instrument. Saliva can be sampled easily, and its handling is faster and less complex than faeces sample treatment. With respect to the analysis of VOCs in exhaled breath samples, saliva sampling and storage is easier, while breath is characterized by the absence of standard sampling and analysis protocols. Saliva is also relatively free from confounding factors generally involved in metabolic profiling, such as haemoglobin level, blood pressure, gender and BMI thus making it an ideal bio-matrix for attaining reliable diagnostic results [46].
The good correlation recently found between the volatile metabolites in breath with gut microbioma in Crohn’s disease [47] would suggest that saliva, as well, may have biomarkers related to microbiota composition.
Metabolites of saliva may be, indeed, important indicators of general health status and disease [48]. The salivary metabolites were studied in type I diabetic children studied by NMR [49] and in Alzheimer's disease (AD) patients [50]. NMR and GC mass spectrometry techniques allowed to find a significant discrimination between individuals following different diet [51, 52] and between smokers and non-smokers [53]. Saliva in AD patients was also studied by faster ultra-performance LC-MS [54, 55].
All these studies would suggest that salivary metabolites might be a reliable “mirror” of gut metabolites, thus reflecting the gut microbiota composition and any gut dysbiosis. In none of these studies the dynamics of salivary VOCs were tentatively investigated in single subjects over time during, e.g., gut dysbiosis or intake of antibiotics, probiotics or supplements.
To test the potentiality of our method for microbiota-related investigations, we applied it to the study of a single healthy subject which went into an antibiotic and subsequently a probiotics and prebiotics treatment. The analysis of saliva over time in the same subject is fundamental, at least in the proof of applicability of the methodology, in order to investigate the saliva sample variability range, keeping controlled only one condition, i.e. the saliva sampling in the morning after 8 h
fasting, before any treatment. Figure 3 shows the representative trend of ethanol during the experiment. 0 20 40 60 80 100 120 16/1 0/18 17/1 0/18 18/1 0/18 19/1 0/18 20/1 0/18 21/1 0/18 22/1 0/18 23/1 0/18 24/1 0/18 25/1 0/18 26/1 0/18 27/1 0/18 28/1 0/18 29/1 0/18 30/1 0/18 31/1 0/18 01/1 1/18 02/1 1/18 03/1 1/18 04/1 1/18 05/1 1/18 06/1 1/18 Et ha no l, µM R 4 0 0 R 8 0 0 R 8 0 0 R 8 0 0 R 4 0 0
Fig.3 Representative trend of ethanol during the experiment. The orange bars indicate the estimated, qualitative amount in the gut based on its pharmacokinetics [56]. The rifaximin dosage taken the day before
the sampling is reported.
Table 3 shows the results of quantitative analysis for the selected VOCs in 14 saliva samples collected during the experiment.
Table 3 Minimum and maximum value, median, and mean concentration (µM) of the selected VOCs in the analyzed 14 saliva samples.
Min Max Median Mean SD
Acetaldehyde 0.90 19.8 5.9 7.5 5.3 Propanal 0.00 0.40 0.15 0.17 0.1 Ethanol 2.23 113 30.3 42.3 30.7 Acetone 1.9 7.1 3.4 3.5 1.3 Methyl acetate 0.00 0.48 0.07 0.11 0.1 Ethyl acetate 0.00 0.64 0.14 0.19 0.2 2-Butanone 0.08 0.27 0.10 0.11 0.0 Methanol 22.1 54.8 33.8 34.6 9.4 2-Propanol 0.01 4.25 0.09 1.17 1.8 2-Pentanone 0.02 0.11 0.03 0.05 0.0 2-3-Butandione 0.66 4.4 1.2 1.4 0.9 2-Butanol 0.00 0.09 0.05 0.05 0.0
1-Propanol 0.25 0.63 0.35 0.39 0.1 4-Eptanon 0.00 0.05 0.01 0.01 0.0 1-Butanol 0.08 0.20 0.12 0.12 0.0 Heptanal 0.02 0.10 0.03 0.04 0.0 3-OH-2-Butanone 8.3 44.0 12.6 15.8 8.9 Acetic acid 976 8545 2936 3192 1801 Propionic acid 107 740 332 334 212 Butanoic acid 21.5 95.2 52.2 53.2 21.9
Figure 4 shows the correlation plot of the 20 VOCs selected for saliva analysis on the basis of their abundances and relevance in microbioma investigation.
Fig.4 Correlation plot for the 20 analyzed VOCs obtained after data scaling.
The results of Figure 4 shows that the concentrations of many VOCs in saliva are significantly correlated. Despite is beyond the aim of this paper to give an interpretation of these data it is interesting to observe the trend of ethanol and acetone during the experiment. Ethanol is positively correlated with most VOCs, primarily with methyl and ethyl acetate (R2>0.95), while it is negatively correlated with acetone (R2 -0.55).
Figure 5 shows the box-plot for ethanol (a), and acetone (b) levels measured in saliva after grouping into basal (first 3 samples), rifaximin and post-rifaximin samples (the box-plot shows: the minimum, the 5th and the 25th percentiles, the median, the 75th and 95th percentiles and the maximum value for each variable investigated. The red cross inside the box shows the mean value).
Fig.5 Box-plot for ethanol (A), and acetone (B) levels measured in saliva after grouping into basal (first 3 samples), rifaximin and post-rifaximin samples.
Rifaximin is a non-absorbable antibiotic derived from rifampin and characterized by a broad spectrum of antibacterial activity against Gram-positive and -negative, aerobic and anaerobic bacteria. Currently, Rifaximin is used to treat traveller’s diarrhea and prevent recurrent episodes of hepatic encephalopathy in advanced liver disease [57, 58]. Recently rifaximin has been employed in patients with ulcerative colitis [59], irritable bowel syndrome [60–64], non-alcoholic steatohepatitis (NASH) and non-alcoholic fatty liver disease (NAFLD) [65, 66] and in patients undergoing allogeneic stem cell transplantation for the treatment of graft vs Host Disease (GVHD)[67]. Thus, it is clear that rifaximin acts on gut microbiota [68, 69].
The treatment with rifaximin significantly affects the concentration of ethanol and acetone in the saliva samples studied. Ethanol (as well as 1-butanol, 1-propanol and acetic acid e.g.) decreases during the rifaximin treatment with respect to the basal value and even more they decrease after the treatment. Ethyl and methyl acetate are the products of the spontaneous esterification of acetic acid and their presence shows the remarkable production of ethanol.
In agreement with the literature, these results show that the treatment with rifaximin leads to a change in microbiota, promoting the growth of rifaximin-resistant Lactobacillus species [70]. Higher levels of 2-butanone have been found in NAFL patients [71] in which rifaximin is used to treat dysbiosis [72]. Acetone is a by-product of the fat metabolism process [73]. The observation of their increase in saliva is novel and in agreement with the increase of Lactobacilli after 5 days of
0 20 40 60 80 100 120 140 E th an o l, µM
basal rifaximin post-rifaximin
A 1 2 3 4 5 6 7 8 9 A ce to n e, µ M
basal rifaximin post-rifaximin
rifaximin administration, persisting also after treatment interruption, as observed by Ponziani et al. [33].
Conclusion
A direct analytical approach consisting of an automated head space solid-phase microextraction sampler coupled to gas chromatography/mass spectrometry detection has been proposed for the detection and quantitation of VOCs in saliva without any prior derivatization.
In this pilot study the novel analytical approach has been applied to investigate for the first time the dynamics of salivary volatile metabolites related to the perturbation of gut microbiota before, during and after treatment with antibiotic.
Saliva is a very feasible biological specimen as it can be easily collected and comprises metabolites absorbed in all gastrointestinal (GI) mucosae. Metabolites flow in the blood stream and are partitioned from blood trough salivary glands in saliva, thus reflecting globally the GI microbiota. This would represent an advantage with respect to the faecal analysis, considering that the composition of microbiota is different in the various parts of gut and that faeces composition reflects only the last part of gut. Furthermore, the results obtained from culture-based methods are not always in agreement with those obtained from culture-independent techniques [74]. Moreover, the processing of faecal samples requires optimised and standardised protocols for their collection, homogenisation, microbial disruption and nucleic acid extraction [5].
Although the vast majority of studies on VOC biomarkers have been conducted using exhaled breath samples, thanks to the non-invasiveness and safety of the sampling process, VOCs analysis in exhaled breath is still challenging due to the low concentrations of the VOCs, the difficult in breath storage and to the absence of standard sampling and analysis protocols [47].
The variations of some metabolites, known to be produced by the microbiota and by bacteria that are susceptible to antibiotic, suggest that the study of the dynamics of salivary metabolites can be an excellent indirect method for analyzing the gut microbiota, with the advantage of being less influenced by the sampling error typical of direct bacterial evaluations.
Aknowledgements
This work is dedicated to Dr Giovanni Battista Gervasi of Laboratori Baldacci, Pisa (Italy) that believed in the goodness of saliva analysis and in its potentialities in drug development.
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