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MS method useful in diagnosing intestinal dysbiosis
Journal of Chromatography B 1130–1131 (2019) 121822
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Journal of Chromatography B journal homepage: www.elsevier.com/locate/jchromb
Development and validation of GC–MS/MS method useful in diagnosing intestinal dysbiosis
T
Łukasz Paprotnya, Agnieszka Celejewskaa, Małgorzata Frajberga, Dorota Wianowskab,
⁎
a b
Research and Development Centre, ALAB Laboratories, ul. Ceramiczna 1, 20-150 Lublin, Poland Department of Chromatographic Methods, Faculty of Chemistry, Maria Curie-Skłodowska University, Pl. Maria Curie-Skłodowska 3, 20-031 Lublin, Poland
ARTICLE INFO
ABSTRACT
Keywords: Dysbiosis markers Urine analysis Non-communicative diseases Autism Chemical diagnostic tool GC–MS/MS
Dysbiosis is a disorder of the bacterial flora of the human digestive tract. It is usually diagnosed clinically by direct detection of an abnormal pattern of the intestinal microbiota. The intermediate diagnosis based on determining the content of microflora metabolites, considered as chemical markers of this disorder, is still rarely used. This is, among others, due to the variety of properties of compounds recognised as dysbiosis markers and as a consequence, the use of different methods for their analysis. To the best of our knowledge, there is still no analytical procedure that would allow unambiguous determination of all compounds in one procedure. In the present study, we have established a detailed method for the quantitative analysis of hydrocinnamic, citramalic, p-hydroxybenzeneacetic, tartaric, hippuric, 4-hydroxybenzoic, indoxylsulfuric, tricarballylic, 3,4dihydroxyhydrocinnamic and benzoic acids along with DL-arabitol that employs the direct derivatization of compounds in a small volume of urine sample followed by gas chromatography – tandem mass spectrometry (GC–MS/MS). To show that the optimised method is a useful tool for chemical diagnosis of dysbiosis, it was applied for determination of the dysbiosis markers in the authentic urine samples.
1. Introduction The term “dysbiosis” means a disorder of the bacterial flora of the human digestive tract. There can be many causes of this disorder. Some of them are of environmental nature e.g. chronic stress, poor diet, unjustified use of antibiotics and stimulants (alcohol) [1]. The others have a genetic background and/or are associated with a human age [2]. This can manifest itself, for example in abdominal pain, bloating, loss of appetite, diarrhea, constipation, food allergy, unexplained fatigue, arthritis or malnutrition. The result of dysbiosis can be also more serious chronic diseases, included in the so-called non-communicative diseases (NCDs) such as, for example, celiac disease, irritable bowel syndrome, neuropsychiatric symptoms (e.g. autism) or breast and colon cancer [3–5]. It is estimated that in highly developed countries as many as 7 deaths per 10 are caused by non-communicative diseases [6]. With statistics such as these, it is not surprising that international bodies such as the World Health Organization & World Bank Human Development Network have identified the prevention and control of NCDs as an important discussion item on the global health agenda. Therefore, there is a great demand for developing non-invasive, fast, sensitive and more accurate analytical methods providing a better chemical diagnostic tool of the disorder. ⁎
The diagnosis of dysbiosis is usually made clinically by direct detection of an abnormal pattern of the gut microbiota. Yet this requires invasive testing that is increasingly and more openly criticized. Therefore, other non-invasive methods of diagnosis are developed. In one of such methods the abnormal concentration levels of metabolic products of these microorganisms are determined [7–9]. There are many compounds that can be considered biomarkers. It is known that some organic acids are specific products of bacterial metabolic action on dietary polyphenols or not absorbed amino acids or carbohydrates [10–15]. Their abnormally elevated levels may irritate the intestinal mucosa leading to gastrointestinal symptoms. Distributed and absorbed neurotoxic products may also cause neurological symptoms. In these cases, test profiles may include benzoate, hypuran, phenylacetate, phenylpropionate, cresol, hydroxybenzoate, hydroxyphenylacetate, hydroxyphenylpropionate and 3,4-dihydroxyphenylpropionate, indican, tricarballilate, D-lactate and D-arabinitol [16]. The intermediate diagnosis is usually performed on a urine or faeces sample [7,8,17]. This manner of diagnosis is more patient-friendly but requires a more sophisticated analytical approach. Not only urine and faeces are characterized by an extremely rich and various composition but also metabolites, that are considered as dysbiosis markers, differ clearly in physicochemical properties [16]. Similarly, their reference
Corresponding author. E-mail address: [email protected] (D. Wianowska).
https://doi.org/10.1016/j.jchromb.2019.121822 Received 25 March 2019; Received in revised form 28 September 2019; Accepted 30 September 2019 Available online 21 October 2019 1570-0232/ © 2019 Elsevier B.V. All rights reserved.
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ranges, considered to be levels characteristic of healthy people, are different [18]. For some of them the reference ranges have very low concentration levels. For others, even for the same compound various ranges can be found in the literature [10–13]. Finally, individual markers are determined by various procedures. According to our knowledge there is no procedure that would be able to determine all markers in a single analytical run. Development of a such procedure, however, is a difficult task. This is not only required that it should be a very selective but also very sensitive method. Considering that under some conditions, the level of markers in the urine is always abnormal, in others the level of characteristic substances is only sporadically high. Therefore, it is desirable to develop a standardized procedure that would allow the analysis of multiple samples to improve the unequivocal diagnosing as well as to perform extensive screening studies to unify the reference ranges for individual markers. A variety of analytical methods are used to analyse organic compounds in various biological matrices. These are frequently liquid chromatography methods, including ion chromatography, gas chromatography or capillary electrophoresis [14,15,19,20]. Of all, however, gas chromatography methods particularly those coupled with a mass spectrometric detector are most frequently applied. They combine the separation power of gas chromatography and the specific identity of mass spectrometry so they are regarded as the gold analytical standard suitable for profile analysis of organic compounds in complex biological matrices. Yet the GC analysis frequently requires prior sample preparation. The most commonly applied processes are extraction and derivatization of target compounds. The efficiency of both processes depends on the properties of the compounds being tested and the conditions for carrying them out. Hence, the key to the success of the method useful in the chemical diagnosis of dysbiosis will be the development of a method resistant to different properties of the determined compounds and guaranteeing high accuracy and precision of the results. In addition, in the context of extensive screening studies measuring the markers with a rapid high throughput method in combination with low needs of urine sample volume is crucial. In the present study we have established a detailed method for the quantitative analysis of a representative group of compounds recognised as dysbiosis markers, including hydrocinnamic, citramalic, 4hydroxybenzoic, p-hydroxybenzeneacetic, tartaric, indoxylsulfuric, tricarballylic, hippuric, 3,4-dihydroxyhyrocinnamic and benzoic acids along with DL-arabitol that employs the direct derivatization procedure in a small volume of urine sample followed by gas chromatography – tandem mass spectrometry (GC–MS/MS). To show that the optimised method is a useful tool for chemical diagnosis of dysbiosis, it was applied for determination of the dysbiosis markers in the authentic urine samples.
acid (IS, 99.6%) were purchased from Sigma-Aldrich (St. Louis, MO, USA). The standard of benzoic acid (BA, 99.0%) and DL-arabitol (DLA, 99.5%) were purchased from Merck (Darmstadt, Germany) and LGC (Augsburg, Germany). Individual stock standard solutions and working solutions of all standards, together with the internal standard solution, obtained by successive dilution of the stock solutions, were prepared in methanol. They were all kept under stable conditions at −20 °C ( ± 2 °C). 2.2. Collection and storage of urine samples The urine samples used in the study were collected in our laboratory within 6 months from two groups: from children aged 6–14 diagnosed in the direction of intestinal dysbiosis (n = 59, boys 56%, girls 44%) and from healthy children (n = 42). All samples were obtained in accordance with local and national regulations on ethics. Each individual and their family (guardian) gave informed consent (in the written form) for the study to take place. The following criteria for patients diagnosed with dysbiosis were assumed: nausea, weight loss, vomiting, lack of appetite, stunted growth, diarrhea or constipation, abdominal pain, flatulence, change in stool consistency, excessive blinking, recurrent infections, food intolerances and aggression. All analysed samples were first morning urine samples (after overnight fasting to minimize dietary influence) collected into sterile containers. Each urine sample was thoroughly mixed in order to maintain its homogeneity and aliquoted into 1.5 mL Eppendorf tubes. The samples were immediately frozen and stored at −20 °C ( ± 2 °C) until needed. 2.3. Sample preparation Directly before the analysis the urine samples were thawed at room temperature, centrifuged (at 3000g) for 10 min and an aliquot was used for creatinine analysis. Urinary creatinine concentrations were determined by the Jaffé method (ADVIA System, Siemens, Poland). Two sample preparation methods were tested, with or without preliminary extraction. In the first case 200 μL of the urine sample, after being acidified to pH 2 with 6 mol HCl, was subjected to liquid-liquid extraction using different amounts (100 or 300 μL) of different extractants in one or three extraction cycles. Ethyl acetate alone and its mixture with propanol-2 (85/15, v/v) were used as the extractant. The obtained extract was evaporated to dryness in a nitrogen stream (XcelVap Evaporation/Concentration System, Horizon Technology) at room temperature. It was then reconstituted in 100 μL of methylene chloride to remove moisture, re-evaporated to dryness in a nitrogen stream at room temperature and finally subjected to the derivatisation procedure followed by the GC–MS/MS analysis. In the latter approach, the appropriate volume of the urine sample (from about 5 to 25 μL, depending on the creatinine content) was directly evaporated in a nitrogen stream at room temperature. The dry residue was reconstituted in 100 μL methylene chloride, re-evaporated and then derivatized and analysed. In order to prevent any detection problems, the urine volume used for the GC–MS/MS analysis was adjusted to obtain 2 µmole of creatinine in the samples. In both cases for quantification tropic acid was used as an internal standard that was added to the urine sample before the first evaporation
2. Materials and methods 2.1. Materials and chemicals Methanol and acetonitrile (both of LC/MS grade), methylene chloride and n-hexane (both with GC grade), anhydrous sodium sulphate, magnesium sulphate, urea, calcium chloride, ammonia and sodium chloride (all with analytical purity grade) were purchased from Merck (Darmstadt, Germany). Hydrochloric acid (38%) was bought from POCh (Gliwice, Poland). Pyridine, trimethylchlorosilane (TMCS) and a mixture of N,O-bis(trimethylsilyl)-trifluoroacetamide (BSTFA) with 1% TMCS used as the derivatization mixture were obtained from Sigma-Aldrich (Poland). Deionized water was purified using a Milli-Q system (Millipore Sigma, Bedford, MA, USA). The standards of hydrocinnamic acid (HCA, 99.5% purity), citramalic acid (CMA, 99.9%), 4hydroxybenzoic acid (HBA, 99.96%), p-hydroxybenzeneacetic acid (HBAA, 99.8% purity), tartaric acid (TA, 99.97%), indoxylsulfuric acid (INDA, 99.9%) tricarballylic acid (TriCA, 99.8%), hippuric acid (HA, 99.6%) 3,4-dihydroxyhydrocinnamic acid (DHHCA, 99.9%) and tropic
2.4. Derivatization procedure and its optimization To obtain trimethylsilyl (TMS) derivatives of analysed compounds the dry residue was reconstituted in 100 μL of pyridine followed by the addition of 25 μL of a derivatization mixture composed of BSTFA with the 1% TMCS catalyst. The tightly closed vial was mixed thoroughly using a vortex shaker (10 s) and then heated at 70 °C for 60 min. Then the sample was left at room temperature for approximately 15 h. After 2
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this time after adding 625 μL of n-hexane the content of the vial was thoroughly mixed by means of a vortex shaker (30 s), transferred to an Eppendorf tube and centrifuged (11,000g, 5 min). Finally, the supernatant was transferred to a glass autosampler vial and subjected to chromatographic analysis. To determine the optimal derivatization conditions for the quantification of all examined compounds in urine, the effects of the following parameters were investigated: the volume of the derivatizing mixture (25 μL or 100 μL of BSTFA with 1% TMCS), the addition of a large amount of catalyst (5% TMCS), the addition of acetonitrile and/or pyridine, the volume ratio of reagents in the derivatizing mixture (1:1:1 or 1:0.4:0.4 or 1:0.4:0.1 or 0:0.4:1 or 0:0.4:0.4 v:v:v of acetonitrile, BSTFA and pyridine, respectively), the derivatization temperature (50 °C or 70 °C or 90 °C), the derivatization time at a given temperature (30 or 60 or 90 min), the time necessary to complete the silylation (0.5 h or 5 h or 15 h), and the type of organic solvent used to dissolve the resulting TMCS derivatives (acetonitrile or n-hexane). The effect of evaporation of unreacted BSTFA and its hydrolysis with MeOH was also tested. In order to evaluate whether there was a significant difference between the signals of the same analyte under different derivatization conditions, a two-way analysis of the variance (ANOVA) test was performed. In order to assess the optimal derivatization conditions for all compounds, the multi-criteria decision analysis was performed.
2.7. Method validation and statistical analysis The guidelines for bioanalytical methods validation do not contain any recommendations for determination of endogenous compounds in biological matrices [22]. Therefore the method validation was performed according to the general validation criteria in terms of specificity, linearity, the limit of detection (LOD), the intraday and interday precision and accuracy as well as stability of measurements. The specificity of the method was evaluated by checking the stability of retention times and the ratio of fragmentation ion signals obtained for all analytes as well as the internal standard in all calibration solutions compared to those obtained for five different aliquots of the authentic urine samples. For each of the studied endogenous compounds calibration curves were constructed using three different methods [23]. In the first one, the internal calibration method with tropic acid as the internal standard and the surrogate matrix was applied. In this case, the surrogate matrix prepared according to [24] was spiked with sequentially increased amounts of the authentic standards of examined compounds and the same amount of the internal standard (0.25 µg/mL). The concentration ranges of the obtained calibration solutions were 0.01–0.1 µg/mL for BA (1); 0.0003–0.0025 µg/mL for HCA (2); 0.01–0.352 µg/mL for CMA (3); 0.002–0.1013 µg/mL HBA (4); 0.05–1.5067 µg/mL for HBAA (5); 0.005–3.0 µg/mL for TA (6); 0.2–0.5.52 µg/mL for INDA; 0.001–0.03 µg/mL for TriCA; 1.52–30.0 µg/mL for HA, 0.0031–0.5067 µg/mL for DHHCA, and 0.5–4.0 µg/mL for DLA. In the second method three authentic urine samples were separately enriched with sequentially increased amounts of authentic standards of the target analytes in the same concentration ranges as those described above and with the same amount of the internal standard i.e. 0.25 µg/ mL. To construct calibration curves the background subtraction during the data processing was used. In the third method the standard addition was applied. In this case the urine samples used in the second method were enriched separately by constantly increasing amounts of the analytes in the given concentration range. Then the analyte concentration was determined by extrapolating the calibration line to the negative part of the concentration axis. To evaluate the method linearity eight calibration levels were examined including the blank sample, the zero sample (the blank sample with the added internal standard) and six non-zero samples. Three replicated analytical procedures were performed independently for each examined concentration level. The peak areas were used for the quantification of the calibration plots for all compounds. To assess the linearity, the correlation coefficient (R2) and the statistical approach of lack of correlation were used. The quality of calibration was evaluated by back-calculation of the standard concentrations in the calibration solutions. In addition, the calibration quality was checked analysing the control samples containing low, medium and high concentrations of all analysed compounds prepared in the surrogate matrix and in the authentic urine samples at the following concentrations: 0.019, 0.055 and 0.091 µg/mL for BA; 0.0006, 0.0014 and 0.0023 µg/mL for HCA; 0.044, 0.18 and 0.316 µg/mL for CMA; 0.0117, 0.0512 and 0.0907 µg/mL for HBA; 0.1933, 0.7733 and 1.36 µg/mL for HBAA; 0.3067, 0.15067 and 2.7333 µg/mL for TA; 0.736, 0.284 and 4.96 µg/mL for INDA; 0.0039, 0.0155 and 0.0272 µg/mL for TriCA; 4.40, 15.733 and 27.20 µg/mL for HA; 0.0533, 0.2533 and 0.4533 µg/mL for DHHCA, and 0.86, 2.28 and 3.68 µg/mL for DLA. The limits of the detection and quantification values, LOD and LOQ, respectively, were determined from the analysis of the sample chromatogram obtained for the surrogate matrix enriched with the analytes at the lower limit of quantification level (LLOQ). The LOD and LOQ were considered to be the signal to noise ratios equal to 3 and 10, respectively. The intra- and inter-day precision (imprecision) and accuracy (inaccuracy) were evaluated by the statistical analysis of the quantitative results obtained on the same day and five different days for five
2.5. GC–MS/MS analysis and its optimization The Shimadzu GC–MS system (Kyoto, Japan) was used. It is composed of an AOC-6000 autosampler (Shimadzu) and a gas chromatograph with a tandem mass spectrometer detector (GCMS-TQ8040). The derivatized samples (1 µL, splitless) were separated on a Zebron ZB5MSi fused-silica capillary column (30 m × 0.25 mm i.d., 0.25 µm film thickness; Phenomenex). Helium (grade 5.0) was used as the carrier gas, and argon (grade 5.0) was used as the collision gas. The column flow was 1.08 mL/min, and 1 µL of the sample was injected using an AOC-6000 autosampler. The injector was set to the high-pressure mode (200.0 kPa for 1.1 min; the column flow at an initial temperature of 280 °C was 3.50 mL/min). The initial column temperature of 80 °C was held for 5 min, then raised to 290 °C at a rate of 7.5 °C/min. The final temperature was maintained for 5 min. The ion source and interface temperatures were 200 °C and 280 °C, respectively. The mass spectrometer was operated in the multiple reaction monitoring (MRM) mode using the electron ionization (EI) at 70 eV. To determine the optimal GC–MS conditions, the effects of the injector temperature (200 or 250 or 280 °C) and the temperature increase per time unit (7.5 °C per 1 min or 15 °C per 1 min) were investigated. To establish the MS/MS operating conditions, the standard solutions of each analyte and the internal standard were determined separately. For each compound, mass transitions of the most sensitive or selective precursor ions were optimised regarding their product ions and corresponding collision energy. 2.6. MCDA method The technique for the order of reference by similarity to the ideal solution was applied as the system of the multi-criteria decision analysis (MCDA) method [21]. The main goal of the method was to find the best conditions for the GC–MS/MS analysis of all dysbiosis markers. As criteria and alternatives of the method, which are parameter groups allowing to describe each available option (alternative) to enable concurrently their evaluation and arrangement, quality of derivatization as well as chromatographic results were established. Derivatisation effectiveness under different conditions expressed as the response ratio of analyte to the internal standard, its repeatability, similarity of derivatization efficiency for all analytes, retention time of the last eluted compound, peak shape, background signal, similarity of extraction efficiency for all compounds, and overall complexity of the sample preparation procedure were determined parameters (data not shown). 3
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Fig. 1. Representative chromatograms of the calibration standard mixture (upper) and the authentic urine sample (lower). Peak assignments correspond to compound numbers reported in Table 1.
independent samples at three concentration levels prepared in the surrogate matrix (control samples), using Student’s t-test, and they were expressed as the critical value (CV, %). The intra- and interday precision was also evaluated for the authentic urine samples. The measurements accuracy was determined by comparing the mean value of the obtained results to the nominal concentration level of the analyte in the control sample and it was expressed as BIAS (in %). In order to determine whether there was a significant difference between the results at individual analyte concentration levels, a one-way analysis of variance (ANOVA) test was performed. The stability of all compounds, in the authentic and surrogate matrices, was assessed for the quality control samples at low and high concentration levels. The stability was evaluated for the unprepared and prepared samples stored under different conditions and duration time: three freeze-thaw cycles, ambient temperature and 4 °C for 48 h. Three replicates were performed under each condition.
increase the sensitivity of the analysis, these compounds require derivatization before the analysis. 3.1. Method development The derivatization process is time-consuming, however, critical in providing access to rich and numerous thematic libraries of mass spectra of organic compounds. In the case of compounds with the functional groups containing active hydrogen atoms, this process is often performed using various silyl agents, the most common of which is BSTFA [25]. Due to its fairly good reactivity with various compounds under moderate conditions and stability of the obtained TMS derivatives, BSTFA was chosen as the derivatizing reagent in this study. To find the best reaction conditions of BSTFA with all target compounds as well as internal standard, different experiments were conducted in combination with or without the preliminary liquid – liquid extraction of analytes from the surrogate matrix. As the excess of unreacted BSTFA necessary to guarantee the quantitative course of the reaction can potentially deteriorate the performance of chromatographic system, to choose the best option of sample preparation the overall chromatographic analysis quality was considered according to the MCDA method. The ranking of the results made in accordance with different criteria (data not presented) revealed that among all derivatization mixtures tested, the highest signal ratio of analytes to the internal standard with
3. Results and discussion The aim of this study was to develop and validate a simple and accurate GC–MS/MS method for simultaneous determination of 11 compounds assumed to be chemical markers of dysbiosis. All these compounds are water-soluble with log P values according to the PubChem database, ranging from approximately −2.5 to 2.5. Due to their polar properties to reduce their tailing on the GC column and 4
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the smallest background signal was obtained for the mixture composed of fourfold excess of pyridine relative to BSTFA with 5% TMCS. Increasing the volume of BSTFA as well as the addition of acetonitrile resulted in degradation of the signals and shape of the peaks. Considering the various conditions of derivatization under investigation, the best derivatization efficiency was obtained after 1 h of heating the reaction mixture at 70 °C. To complete the reaction the mixture should be allowed to stay at room temperature for at least 5 h. Nevertheless, the best reproducibility of signals, especially those obtained for the analytes with the reference ranges at low concentration levels, was observed after allowing the mixture to stay at room temperature for 15 h. In the case of analytes able to form a few TMS derivatives, after this time the signals obtained for the other derivatives were totally or almost totally suppressed. Hence this time was considered optimal and applied for further experiments. In addition it allowed to provide appropriate throughput of the laboratory. As for the other performed experiments, contrary to the results presented in [26], there was no positive effect of removal of the unreacted BSTFA by its evaporation or hydrolysis. A similar conclusion was drawn after analysing the efficiency of liquid-liquid extraction. Only in the case of the three tested compounds, a 100% extraction yield was obtained. For the remaining compounds, the yield was in the range of 15–60%. Yet the LLE stage not only reduced the sensitivity of the analysis but also its precision. Therefore finally, the derivatization was carried out on a small volume of the urine sample adjusted to a creatinine content that had been previously evaporated and cleaned from the residual moisture with methylene chloride.
In order to estimate the analytical utility of the developed method, its validation procedure was performed. To choose the best method of constructing calibration curves in the preliminary experiments there were applied three different approaches: (1) internal calibration with the solutions prepared using the surrogate matrix; (2) internal calibration with the calibration solutions prepared using three different authentic urine samples, and (3) the method of standard addition applied for the urine samples. The quality of the obtained curves was roughly estimated based on the analysis of their course (their linearity) and comparison of the coefficient “a” values in the obtained curves equations in the general form as y = ax + b (three independent calibration curves were obtained for each approach). High degree of results compatibility as well as R2 > 0.98 were obtained in the surrogate matrix approach. As for the authentic urine approaches, only in the case of one urine aliquot the obtained curves were linear and the coefficients “a” were comparable with the previous values (CV < 10%). However, in the case of the other two portions of urine, irrespective of the tested calibration method, the signal for hydrocinnamic and 3,4-dihydroxyhydrocinnamic acids in the blank sample exceeded that obtained after enriching the sample with the standard of the given compound, making both methods impractical for quantitative purposes. A more detailed analysis of the quality of the calibration curves obtained by the surrogate matrix approach, with regard to the accuracy and precision of the results, was performed using back-calculation of the standard concentrations in the calibration solutions, the statistical method, and the analysis of the control samples prepared at three concentration levels of all compounds. In assessing the accuracy of the concentration calculations for the calibration solutions, the model of linear regression (with or without weighing) as well as the second-order polynomial were taken into consideration. In the case of citramalic, 4hydroxybenzoic, 4-hydroxyphenylacetic, indoxylsulfate, tricarballylic, and hippuric acids a linear regression model without weighing resulted in the best accuracy and precision over the entire calibration range and was considered as the most appropriate model for these compounds. However, in the case of benzoic, 3,4-dihydroxyhydrocinnamic, benzenepropanoic and tartaric acids as well as DL-arabitol, it turned out that a linear regression model does not give a good fit of the data. In the case
3.2. Method validation The chemical structures of TMS derivatives of analysed compounds are shown in Fig. 1. The retention times and the optimised MRM parameters obtained for every single analyte and the internal standard are summarised in Table 1. The exemplary MRM chromatograms of a surrogate matrix spiked with a mixture of compounds as well as the authentic urine sample are presented in Fig. 2. The obtained data prove that the applied GC–MS/MS conditions are acceptable for both the qualitative and quantitative analyses.
Table 1 Retention times and ions used in the MRM mode for the analysis of TMS derivatives. Comp. No
TMS derivative
Shortcut
Retention time [min]
MRM transitions (Collision energy in eV) Target m/z
Reference m/z 194.00 194.00 208.00 248.00 247.00 280.00 281.00 267.00 223.00 252.00 296.00 292.00 219.00 277.00 277.00 378.00 377.00 217.00 319.00 206.00 206.00 398.00 267.00
1
Benzoic Acid, TMS
BA
11.42
194.00 > 179.10 (6)
2 3
Benzenepropanoic acid, TMS D-(-)-Citramalic acid, 3TMS
HCA CMA
14.84 16.04
222.00 > 104.10 (6) 247.00 > 203.20 (6)
–
Tropic acid, 2TMS derivative
IS
17.92
280.00 > 118.10 (9)
4
4-Hydroxybenzoic acid, 2TMS
HBA
18.43
267.00 > 193.10 (18)
5
4-Hydroxyphenylacetic acid, 2TMS
HBAA
18.62
252.00 > 164.10 (6)
6
Tartaric acid (R*,S*)-, 4TMS
TA
18.88
292.00 > 102.10 (12)
7
Indoxylsulfuric acid, 2TMS
INDA
19.75
278.00 > 73.10 (21)
8
Tricarballylic acid, 3TMS
TriCA
20.10
377.00 > 147.10 (18)
9
D(L)-Arabitol, 5TMS
DLA
20.18
217.00 > 129.10 (9)
10
Hippuric acid, 2TMS
HA
21.54
236.00 > 105.10 (9)
11
3,4-Dihydroxyhydrocinnamic acid, 3TMS
DHHCA
23.06
398.00 > 179.10 (27)
5
> > > > > > > > > > > > > > > > > > > > > > >
105.10 (18) 135.10 (18) 75.10 (9) 73.10 (18) 73.10 (18) 90.10 (27) 118.10 (9) 73.10 (27) 73.10 (27) 73.10 (18) 73.10 (18) 73.10 (21) 73.10 (18) 73.10 (21) 189.10 (6) 147.10 (18) 73.20 (30) 73.10 (18) 73.10 (27) 73.10 (18) 190.10 (9) 267.20 (12) 179.10 (9)
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Fig. 2. Chemical structures of TMS derivatives of analysed compounds. Peak assignments correspond to compound numbers reported in Table 1.
Table 2 Calibration ranges along with analytical figures of merit the calibration curves. Compound BA HCA CMA HBA HBAA TA INDA TriCA DLA HA DHHCA
Calibration range µg/mL 0.01–0.1 0.0008–0.0025 0.01–0.352 0.002–0.1013 0.05–1.5067 0.005–3.0 0.2–0.5.52 0.001–0.03 0.5–4.0 1.52–30.0 0.0031–0.5067
R2 0,998 0,999 0,998 0,999 0,998 0,998 0,996 0,998 0,998 0,992 0,991
Calibration curve equation
Bias,%
CV,%
Fcal
LOD µg/mL
2
90–115 86–114 96–113 89–102 96–103 88–104 90–120 92–104 86–107 80–106 87–107
97–116 91–130 97–111 99–112 99–104 97–108 98–113 99–125 99–120 100–120 99–107
0.87 1.17 0.20 0.04 1.12 2.95 0.46 0.20 0.01 0.20 2.02
0.0012 0.0004 0.0012 0.0003 0.0015 0.0014 0.0369 0.0006 0.0540 0.0032 0.0015
y = 0,1914 x + 0,1827x − 0,0002 y = 34,355 x2 + 0,8261x + 2 * 10−6 y = 0,2742x − 0,0011 y = 1,5239 × + 0.0061 y = 0,2984x − 0,0063 y = 0,0128 x2 + 0,2953x − 0,0501 y = 0,2277x − 0,1168 y = 0,5215x − 0,0009 y = 0,0008 x2 + 0,2539x + 0,0055 y = 0,2725x − 0,9392 y = 0,0734 x2 + 0,6966x − 0,0216
Ftab = 5,29.
6
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Table 3 Intra- and interday imprecision and inaccuracy for the analysis of compounds in the surrogate matrix (n = 15, 5 replicates per day for 5 days). Comp.
Nominal concentration (ng/ml)
Intraday (n = 5)
Interday (n = 5)
Measured concentration (mean ± SD), (ng/ml)
Imprecision (% CV)
Inaccuracy (% BIAS)
Measured concentration (mean ± SD), ng/ml
Imprecision (% CV)
Inaccuracy (% BIAS)
BA
0.04 0.16 0.27
0.04 ± 0.004 0.15 ± 0.021 0.23 ± 0.015
8.6 13.8 6.4
7.7 −4.4 −14.7
0.04 ± 0.002 0.16 ± 0.006 0.24 ± 0.009
3.7 3.7 3.6
12.7 −0.2 −10.1
HCA
0.001 0.003 0.005
0.001 ± 0.00 0.002 ± 0.00 0.004 ± 0.01
9.8 9.5 12.2
12.5 −7.4 −4.4
0.001 ± 0.00 0.003 ± 0.00 0.004 ± 0.00
12.4 4.7 5.1
−3.1 −0.9 −10.6
CMA
0.09 0.41 0.72
0.09 ± 0.003 0.40 ± 0.013 0.76 ± 0.032
3.8 3.2 4.2
0.6 −3.1 4.6
0.09 ± 0.00 0.42 ± 0.05 0.78 ± 0.02
5.6 10.9 3.0
2.2 3.1 8.2
HBA
0.042 0.200 0.363
0.042 ± 0.003 0.195 ± 0.004 0.367 ± 0.008
7.7 2.3 2.0
−1.0 −2.3 1.0
0.044 ± 0.003 0.158 ± 0.003 0.377 ± 0.010
7.4 1.7 2.5
−1.7 −2.6 3.9
HBAA
0.35 1.53 2.73
0.30 ± 0.02 1.46 ± 0.07 2.76 ± 0.07
7.1 4.5 2.4
−13.4 −4.5 0.9
0.31 ± 0.005 1.46 ± 0.032 2.82 ± 0.061
1.5 2.2 2.1
−11.8 −4.9 3.4
TA
0.41 2.00 3.60
0.41 ± 0.071 1.91 ± 0.276 3.55 ± 0.317
17.3 14.4 8.9
0.5 −4.3 −1.3
0.39 ± 0.014 2.05 ± 0.148 3.61 ± 0.153
3.6 7.2 4.2
−3.9 2.3 0.2
INDA
1.20 5.12 9.04
1.191 ± 0.074 5.013 ± 0.293 8.856 ± 0.676
6.2 5.9 7.6
−0.8 −2.1 −2.0
1.18 ± 0.027 5.04 ± 0.113 9.21 ± 0.279
2.3 5.9 3.0
−1.3 −2.1 1.9
TriCA
0.01 0.05 0.09
0.01 ± 0.001 0.05 ± 0.001 0.08 ± 0.004
10.7 2.4 5.6
−13.8 3.2 −13.9
0.01 ± 0.001 0.05 ± 0.001 0.08 ± 0.002
2.9 1.9 1.8
−10.3 1.5 −11.9
DLA
1.96 7.80 13.60
2.16 ± 0.162 7.63 ± 0.313 13.30 ± 1.74
7.5 4.1 13.1
10.4 −2.2 −2.2
2.20 ± 0.03 7.38 ± 0.34 12.52 ± 1.74
1.2 4.6 13.9
12.1 −5.3 −7.9
HA
4.67 17.33 29.87
4.42 ± 0.70 14.56 ± 1.077 27.46 ± 2.68
15.9 7.4 9.6
−5.2 −15.9 −6.3
4.43 ± 0.01 14.99 ± 0.38 29.78 ± 1.56
0.2 2.5 5.3
−5.1 −13.5 −0.2
DHHA
0.06 0.31 0.54
0.06 ± 0.00 0.32 ± 0.02 0.54 ± 0.03
7.4 6.4 6.5
−5.3 5.4 −0.6
0.06 ± 0.002 0.32 ± 0.006 0.55 ± 0.010
2.5 1.7 1.7
−2.7 4.8 0.6
of these compounds, it was estimated that the better results are obtained applying the polynomial functions. The applied regression models resulted in small and evenly distributed residual errors and a high coefficient of regression. The statistical method of lack of fit confirmed the validity of these assumptions (Fcal < Ftab, Ftab = 5.29). The accuracy of all compounds ranged from 86–110% and 96–102% at the lower limit of quantification (LLOQ) and the upper limit of quantification (ULOQ), respectively. The equations of the calibration curves along with their basic characteristics are summarized in Table 2. The table also collects the accuracy (Bias) and precision (CV) ranges together with the Fcal values (determined assuming a two-way distribution and the values of numerator and denominator equal 7 and 5, respectively) obtained for the individual substances in the calibration solutions over the calibration ranges. The results of the intra- and interday imprecision and inaccuracy obtained for all analytes prepared in the surrogate matrix at three concentrations levels are gathered in Table 3. Intraday imprecision and inaccuracy ranged from 2.0% to 17.3% and from −15.9% to 12.5%, respectively. Interday imprecision and inaccuracy ranged from 0.2% to 13.9% and from −13.5% to 12.7%, respectively. These data indicate adequate accuracy and precision of measurements. This conclusion is independently confirmed during the assays on six authentic urine samples (intra- and interday imprecision ranged from 3.5% to 16.7% and from 2.3% to 17.5%, respectively).Table 4.
Table 4 F-values and P-values obtained during variance analysis for the amount of individual compounds estimated for the control group (C) and the patients group (P) from Fig. 3 (Ftab = 3.95). Compound
F-value
P-value
BA HCA CMA HBA HBAA TA INDA TriCA DLA HA DHHCA
5.18 10.73 11.00 1.12 5.53 4.29 5.29 7.97 61.85 6.38 6.36
2.5 · 10−02 1.7 · 10−03 1.3 · 10−03 2.9 · 10−01 2.1 · 10−02 4.10 · 10−02 2.4 · 10−02 5.9 · 10−03 8.2 · 10−12 1.3 · 10−02 1.3 · 10−02
Various storage and handling conditions were evaluated to determine their effect on stability of analysed compounds. All compounds remained stable in the urine samples during three freeze–thaw cycles. Storing the sample in a refrigerator at 4 °C resulted in the stability of the compounds up to 24 h. After this time a marked increase in benzoic acid was observed which was followed by the benzenepropanoic acid increase after five days. The samples stored in the freezer at −20 °C were
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Fig. 3. Analytical results of BA (1), HCA (2), CMA (3), HBA (4), HBAA (5), TA (6), INDA (7), TriCA (8), DLA (9), HA (10), and DHHCA (11) concentrations, normalized to creatinine, in the urine samples of children diagnosed with dysbiosis (grey box, P) compared to the content of compounds in the control group (white box, C). The dotted and hatched boxes represent a group of girls (G) and boys (B) patients, respectively.
stable for a period of two weeks. Stability tests of the sample prepared for the analysis and stored at room temperature showed stability up to 48 h (CV < 3%, Bias < 10%). Stock solutions were stable for at least 5 months.
satisfactory intra- and interday precision and accuracy for the analysis of all compounds indicated as chemical markers of dysbiosis in the authentic urine samples. To demonstrate the practical applicability of the proposed method, it was used for estimation of the concentration of BA, HCA, CMA, HBA, HBAA, TA, INDA, TriCA, HA, DHHCA, and DLA in the urine samples of children diagnosed with dysbiosis. For the samples the creatinine content was determined to compensate for variation in urine volume. The means and standard deviation were 0.8874 ± 0.53 mg/L and 0.6065 ± 0.56 mg/L for the controls and the
3.3. Practical applicability The results of validation experiments prove that the proposed method is characterized by good linearity, low detection limits and
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patients, respectively with the values from 0.1212 mg/L to 3.1372 mg/ L. The analytes content was expressed as the ratio to creatinine. The obtained results are presented in the form of box plots in Fig. 3. Fig. 3 compares box plots showing the distribution of the content of individual compounds (from BA to DHHCA) in urine samples obtained from children from the control group (white boxes; C) and children diagnosed with dysbiosis (grey boxes; P). Dotted and hatched boxes visible in the figure represent a group of girls (G) and boys (B) patients, respectively. In each plot, the box represents the contents range from lower to upper quartile (25–75%), the line marks the mid-point of the data set (the median), the cross indicates the average value, the whiskers (the upper and lower) represents scores outside the middle 50%, and the circle marks the data far away from other data values (outliers). Because for TA the range of outliers is very wide (up to 350 mg/g, see “TA a” in Fig. 3), to enable comparison of both groups an additional plot was prepared for a narrower concentration range of this compound (up to 50 mg/g, see “TA b” in Fig. 3). To assess the significance of differences in the amount of individual compounds estimated for the control and patients group, the analysis of variance (ANOVA, P = 0.05) and F test were used. To check the significance of each Fisher coefficient the P-values were used. The lack of differences in the studied groups was considered insignificant for the F value lower than Ftab. The importance of the differences for the individual compounds is listed in Table 4. The results from Fig. 3 and Table 4 show that for most compounds the boxes ranges and their position obtained for the control group are different from the ranges and positions observed for the individual compounds in the patients group (4.29 < Fcal < 61.85, Ftab = 3.95). These features suggest that the presented method is suitable for the routine analysis of dysbiosis markers in the urine samples. Regarding the ranges of the individual compounds obtained for the patient group, they are wide and their shape is asymmetric with large share of the results higher than the median (marked as a line across the box). For the vast majority of the tested compounds, some data values are far away from the others (these outliers are shown as circles in the figure). Only in the case of HBA the control and patients boxes overlap with one another (Fcal < Ftab). These observations are consistent with the knowledge of high levels of concentration of some organic acids being metabolic products of microorganisms and leading to gastrointestinal and/or neurological symptoms. The more detailed analysis of the obtained results, presented separately for girls and boys patients, shows different distributions of the results for a given compound. This can be more clearly seen in the case of HCA for which not only the median but also the mean values, together with the box ranges and the distributions, observed for the girls group are evidently different from those observed for the boys group. The obtained results show the complexity of the problem of diagnosing dysbiosis and suggest the necessity of conducting further research aimed at univocal definition of reference ranges in healthy people and the assessment of the impact of parameters that may differentiate the obtained results such as sex and diet.
data show that the method is characterized by good linearity, very low detection limits and satisfactory inter- and intraday precisions as well as accuracy. These features suggest that the method can be routinely used as a clinical tool for the indirect diagnosis of dysbiosis in humans. Declaration of Competing Interest The authors declare that they have no conflict of interest. References [1] A.K. DeGruttola, D. Low, A. Mizoguchi, E. Mizoguchi, Current understanding of dysbiosis in disease in human and animal models, Inflamm. Bowel Dis. 22 (2016) 1137–1150, https://doi.org/10.1097/MIB.0000000000000750. [2] A. Picca, F. Fanelli, R. Calvani, G. Mulè, V. Pesce, A. Sisto, C. Pantanelli, R. Bernabei, F. Landi, E. Marzetti, Gut dysbiosis and muscle aging: searching for novel targets against Sarcopenia, Mediators Inflamm. 2018 (2018) 1–15, https:// doi.org/10.1155/2018/7026198. [3] A.M. Sheflin, A.K. Whitney, T.L. Weir, Cancer-promoting effects of microbial dysbiosis, Curr. Oncol. 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Nutr. 139 (2009) 2309–2314, https://doi.org/10.3945/jn.109. 113613. [12] P. Hušek, Z. Švagera, D. Hanzlíková, L. Řimnáčová, H. Zahradníčková, I. Opekarová, P. Šimek, Profiling of urinary amino-carboxylic metabolites by in-situ heptafluorobutyl chloroformate mediated sample preparation and gas chromatography-mass spectrometry, J. Chromatogr. A 1443 (2016) 211–232, https://doi. org/10.1016/j.chroma.2016.03.019. [13] F. Keyfi, Z. Lukacs, A. Varasteh, A description of reference ranges for organic acids in urine samples from a pediatric population in Iran, Rep. Biochem. Mol. Biol. 6 (2017) 40–50 http://www.ncbi.nlm.nih.gov/pubmed/29090228%0Ahttp://www. pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC5643446. [14] N. Sriboonvorakul, N. Leepipatpiboon, A.M. Dondorp, T. Pouplin, N.J. White, J. Tarning, N. Lindegardh, Liquid chromatographic-mass spectrometric method for simultaneous determination of small organic acids potentially contributing to acidosis in severe malaria, J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 941 (2013) 116–122, https://doi.org/10.1016/j.jchromb.2013.10.005. [15] K. Nakagawa, S. Kawana, Y. Hasegawa, S. Yamaguchi, Simplified method for the chemical diagnosis of organic aciduria using GC/MS, J. Chromatogr. B 878 (2010) 942–948 (accessed February 24, 2019), https://www.sciencedirect.com/science/ article/pii/S1570023210001054. [16] R.S. Lord, J.A. Bralley, Clinical applications of urinary organic acids. Part 2. Dysbiosis markers, Altern. Med. Rev. 13 (2008) 292–306 (accessed March 6, 2019). [17] Z. Ling, F. Liu, L. Shao, Y. Cheng, L. Li, Dysbiosis of the urinary microbiota associated with urine levels of proinflammatory chemokine interleukin-8 in female type 2 diabetic patients, Front. Immunol. 8 (2017) 1032, https://doi.org/10.3389/ fimmu.2017.01032. [18] P. Liu, E. Dong, C. Roehring, J. Poelzer, N. Psychogios, D.S. Wishart, C. Knox, J. Huynh, R. Mandal, A.C. Guo, R. Bogumil, T.C. Bjorndahl, F. Aziat, R. Krishnamurthy, Z.T. Dame, M.R. Wilson, F. Saleem, F.S. Yallou, S. Bouatra, The human urine metabolome, PLoS One 8 (2013) e73076, https://doi.org/10.1371/ journal.pone.0073076. [19] J. Fernández-Bravo, F. de Andrés, M. Zougagh, Á. Ríos, Selective screening of glutaric acid acidurias by capillary electrophoresis-mass spectrometry, J. Pharm. Biomed. Anal. 145 (2017) 40–45, https://doi.org/10.1016/j.jpba.2017.06.037. [20] W. Struck-Lewicka, M. Kordalewska, R. Bujak, A. Yumba Mpanga, M. Markuszewski, J. Jacyna, M. Matuszewski, R. Kaliszan, M.J. Markuszewski,
4. Conclusions In this paper a GC–MS/MS method was proposed for the analysis of 11 organic compounds (i.e. hydrocinnamic, citramalic, p-hydroxybenzeneacetic, tartaric, hippuric, 4-hydroxybenzoic, indoxylsulfuric, tricarballylic, 3,4-dihydroxyhydrocinnamic and benzoic acids together with DL-arabitol) recognised as chemical dysbiosis markers in a single analytical run. The procedure involves direct derivatization of compounds to their methyl esters in a small volume of urine sample and GC–MS/MS analysis. Derivatization has been optimised to yield the best results. The proposed method was validated and tested. The obtained
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Ł. Paprotny, et al. Urine metabolic fingerprinting using LC-MS and GC-MS reveals metabolite changes in prostate cancer: a pilot study, J. Pharm. Biomed. Anal. 111 (2015) 351–361, https://doi.org/10.1016/j.jpba.2014.12.026. [21] M. Bystrzanowska, R. Marcinkowska, F. Pena-Pereira, M. Tobiszewski, Selection of derivatisation agents for chlorophenols determination with multicriteria decision analysis, Microchem. J. 145 (2019) 664–671, https://doi.org/10.1016/j.microc. 2018.11.024. [22] US Food and Drug Administration, Guidance for Industry: Bioanalytical Method Validation, U.S. Dep. Heal. Hum. Serv. Food Drug Adm. Cent. Drug Eval. Res., May 2001, 1–22. doi: http://www.labcompliance.de/documents/FDA/FDA-Others/ Laboratory/f-507-bioanalytical-4252fnl.pdf. [23] R. Thakare, Y.S. Chhonker, N. Gautam, J.A. Alamoudi, Y. Alnouti, Quantitative
analysis of endogenous compounds, J. Pharm. Biomed. Anal. 128 (2016) 426–437, https://doi.org/10.1016/j.jpba.2016.06.017. [24] N.C. van de Merbel, Quantitative determination of endogenous compounds in biological samples using chromatographic techniques, TrAC, Trends Anal. Chem. 27 (2008) 924–933, https://doi.org/10.1016/J.TRAC.2008.09.002. [25] Sigma-aldrich.com/derivatization, Derivatization Reagents For Selective Response and Detection in Complex Matrices, n.d. [26] C. Basheer, H.K. Lee, Analysis of endocrine disrupting alkylphenols, chlorophenols and bisphenol-A using hollow fiber-protected liquid-phase microextraction coupled with injection port-derivatization gas chromatography-mass spectrometry, J. Chromatogr. A 1057 (2004) 163–169, https://doi.org/10.1016/j.chroma.2004.09. 083.
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Journal of Chromatography B journal homepage: www.elsevier.com/locate/jchromb
Development and validation of GC–MS/MS method useful in diagnosing intestinal dysbiosis
T
Łukasz Paprotnya, Agnieszka Celejewskaa, Małgorzata Frajberga, Dorota Wianowskab,
⁎
a b
Research and Development Centre, ALAB Laboratories, ul. Ceramiczna 1, 20-150 Lublin, Poland Department of Chromatographic Methods, Faculty of Chemistry, Maria Curie-Skłodowska University, Pl. Maria Curie-Skłodowska 3, 20-031 Lublin, Poland
ARTICLE INFO
ABSTRACT
Keywords: Dysbiosis markers Urine analysis Non-communicative diseases Autism Chemical diagnostic tool GC–MS/MS
Dysbiosis is a disorder of the bacterial flora of the human digestive tract. It is usually diagnosed clinically by direct detection of an abnormal pattern of the intestinal microbiota. The intermediate diagnosis based on determining the content of microflora metabolites, considered as chemical markers of this disorder, is still rarely used. This is, among others, due to the variety of properties of compounds recognised as dysbiosis markers and as a consequence, the use of different methods for their analysis. To the best of our knowledge, there is still no analytical procedure that would allow unambiguous determination of all compounds in one procedure. In the present study, we have established a detailed method for the quantitative analysis of hydrocinnamic, citramalic, p-hydroxybenzeneacetic, tartaric, hippuric, 4-hydroxybenzoic, indoxylsulfuric, tricarballylic, 3,4dihydroxyhydrocinnamic and benzoic acids along with DL-arabitol that employs the direct derivatization of compounds in a small volume of urine sample followed by gas chromatography – tandem mass spectrometry (GC–MS/MS). To show that the optimised method is a useful tool for chemical diagnosis of dysbiosis, it was applied for determination of the dysbiosis markers in the authentic urine samples.
1. Introduction The term “dysbiosis” means a disorder of the bacterial flora of the human digestive tract. There can be many causes of this disorder. Some of them are of environmental nature e.g. chronic stress, poor diet, unjustified use of antibiotics and stimulants (alcohol) [1]. The others have a genetic background and/or are associated with a human age [2]. This can manifest itself, for example in abdominal pain, bloating, loss of appetite, diarrhea, constipation, food allergy, unexplained fatigue, arthritis or malnutrition. The result of dysbiosis can be also more serious chronic diseases, included in the so-called non-communicative diseases (NCDs) such as, for example, celiac disease, irritable bowel syndrome, neuropsychiatric symptoms (e.g. autism) or breast and colon cancer [3–5]. It is estimated that in highly developed countries as many as 7 deaths per 10 are caused by non-communicative diseases [6]. With statistics such as these, it is not surprising that international bodies such as the World Health Organization & World Bank Human Development Network have identified the prevention and control of NCDs as an important discussion item on the global health agenda. Therefore, there is a great demand for developing non-invasive, fast, sensitive and more accurate analytical methods providing a better chemical diagnostic tool of the disorder. ⁎
The diagnosis of dysbiosis is usually made clinically by direct detection of an abnormal pattern of the gut microbiota. Yet this requires invasive testing that is increasingly and more openly criticized. Therefore, other non-invasive methods of diagnosis are developed. In one of such methods the abnormal concentration levels of metabolic products of these microorganisms are determined [7–9]. There are many compounds that can be considered biomarkers. It is known that some organic acids are specific products of bacterial metabolic action on dietary polyphenols or not absorbed amino acids or carbohydrates [10–15]. Their abnormally elevated levels may irritate the intestinal mucosa leading to gastrointestinal symptoms. Distributed and absorbed neurotoxic products may also cause neurological symptoms. In these cases, test profiles may include benzoate, hypuran, phenylacetate, phenylpropionate, cresol, hydroxybenzoate, hydroxyphenylacetate, hydroxyphenylpropionate and 3,4-dihydroxyphenylpropionate, indican, tricarballilate, D-lactate and D-arabinitol [16]. The intermediate diagnosis is usually performed on a urine or faeces sample [7,8,17]. This manner of diagnosis is more patient-friendly but requires a more sophisticated analytical approach. Not only urine and faeces are characterized by an extremely rich and various composition but also metabolites, that are considered as dysbiosis markers, differ clearly in physicochemical properties [16]. Similarly, their reference
Corresponding author. E-mail address: [email protected] (D. Wianowska).
https://doi.org/10.1016/j.jchromb.2019.121822 Received 25 March 2019; Received in revised form 28 September 2019; Accepted 30 September 2019 Available online 21 October 2019 1570-0232/ © 2019 Elsevier B.V. All rights reserved.
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ranges, considered to be levels characteristic of healthy people, are different [18]. For some of them the reference ranges have very low concentration levels. For others, even for the same compound various ranges can be found in the literature [10–13]. Finally, individual markers are determined by various procedures. According to our knowledge there is no procedure that would be able to determine all markers in a single analytical run. Development of a such procedure, however, is a difficult task. This is not only required that it should be a very selective but also very sensitive method. Considering that under some conditions, the level of markers in the urine is always abnormal, in others the level of characteristic substances is only sporadically high. Therefore, it is desirable to develop a standardized procedure that would allow the analysis of multiple samples to improve the unequivocal diagnosing as well as to perform extensive screening studies to unify the reference ranges for individual markers. A variety of analytical methods are used to analyse organic compounds in various biological matrices. These are frequently liquid chromatography methods, including ion chromatography, gas chromatography or capillary electrophoresis [14,15,19,20]. Of all, however, gas chromatography methods particularly those coupled with a mass spectrometric detector are most frequently applied. They combine the separation power of gas chromatography and the specific identity of mass spectrometry so they are regarded as the gold analytical standard suitable for profile analysis of organic compounds in complex biological matrices. Yet the GC analysis frequently requires prior sample preparation. The most commonly applied processes are extraction and derivatization of target compounds. The efficiency of both processes depends on the properties of the compounds being tested and the conditions for carrying them out. Hence, the key to the success of the method useful in the chemical diagnosis of dysbiosis will be the development of a method resistant to different properties of the determined compounds and guaranteeing high accuracy and precision of the results. In addition, in the context of extensive screening studies measuring the markers with a rapid high throughput method in combination with low needs of urine sample volume is crucial. In the present study we have established a detailed method for the quantitative analysis of a representative group of compounds recognised as dysbiosis markers, including hydrocinnamic, citramalic, 4hydroxybenzoic, p-hydroxybenzeneacetic, tartaric, indoxylsulfuric, tricarballylic, hippuric, 3,4-dihydroxyhyrocinnamic and benzoic acids along with DL-arabitol that employs the direct derivatization procedure in a small volume of urine sample followed by gas chromatography – tandem mass spectrometry (GC–MS/MS). To show that the optimised method is a useful tool for chemical diagnosis of dysbiosis, it was applied for determination of the dysbiosis markers in the authentic urine samples.
acid (IS, 99.6%) were purchased from Sigma-Aldrich (St. Louis, MO, USA). The standard of benzoic acid (BA, 99.0%) and DL-arabitol (DLA, 99.5%) were purchased from Merck (Darmstadt, Germany) and LGC (Augsburg, Germany). Individual stock standard solutions and working solutions of all standards, together with the internal standard solution, obtained by successive dilution of the stock solutions, were prepared in methanol. They were all kept under stable conditions at −20 °C ( ± 2 °C). 2.2. Collection and storage of urine samples The urine samples used in the study were collected in our laboratory within 6 months from two groups: from children aged 6–14 diagnosed in the direction of intestinal dysbiosis (n = 59, boys 56%, girls 44%) and from healthy children (n = 42). All samples were obtained in accordance with local and national regulations on ethics. Each individual and their family (guardian) gave informed consent (in the written form) for the study to take place. The following criteria for patients diagnosed with dysbiosis were assumed: nausea, weight loss, vomiting, lack of appetite, stunted growth, diarrhea or constipation, abdominal pain, flatulence, change in stool consistency, excessive blinking, recurrent infections, food intolerances and aggression. All analysed samples were first morning urine samples (after overnight fasting to minimize dietary influence) collected into sterile containers. Each urine sample was thoroughly mixed in order to maintain its homogeneity and aliquoted into 1.5 mL Eppendorf tubes. The samples were immediately frozen and stored at −20 °C ( ± 2 °C) until needed. 2.3. Sample preparation Directly before the analysis the urine samples were thawed at room temperature, centrifuged (at 3000g) for 10 min and an aliquot was used for creatinine analysis. Urinary creatinine concentrations were determined by the Jaffé method (ADVIA System, Siemens, Poland). Two sample preparation methods were tested, with or without preliminary extraction. In the first case 200 μL of the urine sample, after being acidified to pH 2 with 6 mol HCl, was subjected to liquid-liquid extraction using different amounts (100 or 300 μL) of different extractants in one or three extraction cycles. Ethyl acetate alone and its mixture with propanol-2 (85/15, v/v) were used as the extractant. The obtained extract was evaporated to dryness in a nitrogen stream (XcelVap Evaporation/Concentration System, Horizon Technology) at room temperature. It was then reconstituted in 100 μL of methylene chloride to remove moisture, re-evaporated to dryness in a nitrogen stream at room temperature and finally subjected to the derivatisation procedure followed by the GC–MS/MS analysis. In the latter approach, the appropriate volume of the urine sample (from about 5 to 25 μL, depending on the creatinine content) was directly evaporated in a nitrogen stream at room temperature. The dry residue was reconstituted in 100 μL methylene chloride, re-evaporated and then derivatized and analysed. In order to prevent any detection problems, the urine volume used for the GC–MS/MS analysis was adjusted to obtain 2 µmole of creatinine in the samples. In both cases for quantification tropic acid was used as an internal standard that was added to the urine sample before the first evaporation
2. Materials and methods 2.1. Materials and chemicals Methanol and acetonitrile (both of LC/MS grade), methylene chloride and n-hexane (both with GC grade), anhydrous sodium sulphate, magnesium sulphate, urea, calcium chloride, ammonia and sodium chloride (all with analytical purity grade) were purchased from Merck (Darmstadt, Germany). Hydrochloric acid (38%) was bought from POCh (Gliwice, Poland). Pyridine, trimethylchlorosilane (TMCS) and a mixture of N,O-bis(trimethylsilyl)-trifluoroacetamide (BSTFA) with 1% TMCS used as the derivatization mixture were obtained from Sigma-Aldrich (Poland). Deionized water was purified using a Milli-Q system (Millipore Sigma, Bedford, MA, USA). The standards of hydrocinnamic acid (HCA, 99.5% purity), citramalic acid (CMA, 99.9%), 4hydroxybenzoic acid (HBA, 99.96%), p-hydroxybenzeneacetic acid (HBAA, 99.8% purity), tartaric acid (TA, 99.97%), indoxylsulfuric acid (INDA, 99.9%) tricarballylic acid (TriCA, 99.8%), hippuric acid (HA, 99.6%) 3,4-dihydroxyhydrocinnamic acid (DHHCA, 99.9%) and tropic
2.4. Derivatization procedure and its optimization To obtain trimethylsilyl (TMS) derivatives of analysed compounds the dry residue was reconstituted in 100 μL of pyridine followed by the addition of 25 μL of a derivatization mixture composed of BSTFA with the 1% TMCS catalyst. The tightly closed vial was mixed thoroughly using a vortex shaker (10 s) and then heated at 70 °C for 60 min. Then the sample was left at room temperature for approximately 15 h. After 2
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this time after adding 625 μL of n-hexane the content of the vial was thoroughly mixed by means of a vortex shaker (30 s), transferred to an Eppendorf tube and centrifuged (11,000g, 5 min). Finally, the supernatant was transferred to a glass autosampler vial and subjected to chromatographic analysis. To determine the optimal derivatization conditions for the quantification of all examined compounds in urine, the effects of the following parameters were investigated: the volume of the derivatizing mixture (25 μL or 100 μL of BSTFA with 1% TMCS), the addition of a large amount of catalyst (5% TMCS), the addition of acetonitrile and/or pyridine, the volume ratio of reagents in the derivatizing mixture (1:1:1 or 1:0.4:0.4 or 1:0.4:0.1 or 0:0.4:1 or 0:0.4:0.4 v:v:v of acetonitrile, BSTFA and pyridine, respectively), the derivatization temperature (50 °C or 70 °C or 90 °C), the derivatization time at a given temperature (30 or 60 or 90 min), the time necessary to complete the silylation (0.5 h or 5 h or 15 h), and the type of organic solvent used to dissolve the resulting TMCS derivatives (acetonitrile or n-hexane). The effect of evaporation of unreacted BSTFA and its hydrolysis with MeOH was also tested. In order to evaluate whether there was a significant difference between the signals of the same analyte under different derivatization conditions, a two-way analysis of the variance (ANOVA) test was performed. In order to assess the optimal derivatization conditions for all compounds, the multi-criteria decision analysis was performed.
2.7. Method validation and statistical analysis The guidelines for bioanalytical methods validation do not contain any recommendations for determination of endogenous compounds in biological matrices [22]. Therefore the method validation was performed according to the general validation criteria in terms of specificity, linearity, the limit of detection (LOD), the intraday and interday precision and accuracy as well as stability of measurements. The specificity of the method was evaluated by checking the stability of retention times and the ratio of fragmentation ion signals obtained for all analytes as well as the internal standard in all calibration solutions compared to those obtained for five different aliquots of the authentic urine samples. For each of the studied endogenous compounds calibration curves were constructed using three different methods [23]. In the first one, the internal calibration method with tropic acid as the internal standard and the surrogate matrix was applied. In this case, the surrogate matrix prepared according to [24] was spiked with sequentially increased amounts of the authentic standards of examined compounds and the same amount of the internal standard (0.25 µg/mL). The concentration ranges of the obtained calibration solutions were 0.01–0.1 µg/mL for BA (1); 0.0003–0.0025 µg/mL for HCA (2); 0.01–0.352 µg/mL for CMA (3); 0.002–0.1013 µg/mL HBA (4); 0.05–1.5067 µg/mL for HBAA (5); 0.005–3.0 µg/mL for TA (6); 0.2–0.5.52 µg/mL for INDA; 0.001–0.03 µg/mL for TriCA; 1.52–30.0 µg/mL for HA, 0.0031–0.5067 µg/mL for DHHCA, and 0.5–4.0 µg/mL for DLA. In the second method three authentic urine samples were separately enriched with sequentially increased amounts of authentic standards of the target analytes in the same concentration ranges as those described above and with the same amount of the internal standard i.e. 0.25 µg/ mL. To construct calibration curves the background subtraction during the data processing was used. In the third method the standard addition was applied. In this case the urine samples used in the second method were enriched separately by constantly increasing amounts of the analytes in the given concentration range. Then the analyte concentration was determined by extrapolating the calibration line to the negative part of the concentration axis. To evaluate the method linearity eight calibration levels were examined including the blank sample, the zero sample (the blank sample with the added internal standard) and six non-zero samples. Three replicated analytical procedures were performed independently for each examined concentration level. The peak areas were used for the quantification of the calibration plots for all compounds. To assess the linearity, the correlation coefficient (R2) and the statistical approach of lack of correlation were used. The quality of calibration was evaluated by back-calculation of the standard concentrations in the calibration solutions. In addition, the calibration quality was checked analysing the control samples containing low, medium and high concentrations of all analysed compounds prepared in the surrogate matrix and in the authentic urine samples at the following concentrations: 0.019, 0.055 and 0.091 µg/mL for BA; 0.0006, 0.0014 and 0.0023 µg/mL for HCA; 0.044, 0.18 and 0.316 µg/mL for CMA; 0.0117, 0.0512 and 0.0907 µg/mL for HBA; 0.1933, 0.7733 and 1.36 µg/mL for HBAA; 0.3067, 0.15067 and 2.7333 µg/mL for TA; 0.736, 0.284 and 4.96 µg/mL for INDA; 0.0039, 0.0155 and 0.0272 µg/mL for TriCA; 4.40, 15.733 and 27.20 µg/mL for HA; 0.0533, 0.2533 and 0.4533 µg/mL for DHHCA, and 0.86, 2.28 and 3.68 µg/mL for DLA. The limits of the detection and quantification values, LOD and LOQ, respectively, were determined from the analysis of the sample chromatogram obtained for the surrogate matrix enriched with the analytes at the lower limit of quantification level (LLOQ). The LOD and LOQ were considered to be the signal to noise ratios equal to 3 and 10, respectively. The intra- and inter-day precision (imprecision) and accuracy (inaccuracy) were evaluated by the statistical analysis of the quantitative results obtained on the same day and five different days for five
2.5. GC–MS/MS analysis and its optimization The Shimadzu GC–MS system (Kyoto, Japan) was used. It is composed of an AOC-6000 autosampler (Shimadzu) and a gas chromatograph with a tandem mass spectrometer detector (GCMS-TQ8040). The derivatized samples (1 µL, splitless) were separated on a Zebron ZB5MSi fused-silica capillary column (30 m × 0.25 mm i.d., 0.25 µm film thickness; Phenomenex). Helium (grade 5.0) was used as the carrier gas, and argon (grade 5.0) was used as the collision gas. The column flow was 1.08 mL/min, and 1 µL of the sample was injected using an AOC-6000 autosampler. The injector was set to the high-pressure mode (200.0 kPa for 1.1 min; the column flow at an initial temperature of 280 °C was 3.50 mL/min). The initial column temperature of 80 °C was held for 5 min, then raised to 290 °C at a rate of 7.5 °C/min. The final temperature was maintained for 5 min. The ion source and interface temperatures were 200 °C and 280 °C, respectively. The mass spectrometer was operated in the multiple reaction monitoring (MRM) mode using the electron ionization (EI) at 70 eV. To determine the optimal GC–MS conditions, the effects of the injector temperature (200 or 250 or 280 °C) and the temperature increase per time unit (7.5 °C per 1 min or 15 °C per 1 min) were investigated. To establish the MS/MS operating conditions, the standard solutions of each analyte and the internal standard were determined separately. For each compound, mass transitions of the most sensitive or selective precursor ions were optimised regarding their product ions and corresponding collision energy. 2.6. MCDA method The technique for the order of reference by similarity to the ideal solution was applied as the system of the multi-criteria decision analysis (MCDA) method [21]. The main goal of the method was to find the best conditions for the GC–MS/MS analysis of all dysbiosis markers. As criteria and alternatives of the method, which are parameter groups allowing to describe each available option (alternative) to enable concurrently their evaluation and arrangement, quality of derivatization as well as chromatographic results were established. Derivatisation effectiveness under different conditions expressed as the response ratio of analyte to the internal standard, its repeatability, similarity of derivatization efficiency for all analytes, retention time of the last eluted compound, peak shape, background signal, similarity of extraction efficiency for all compounds, and overall complexity of the sample preparation procedure were determined parameters (data not shown). 3
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Fig. 1. Representative chromatograms of the calibration standard mixture (upper) and the authentic urine sample (lower). Peak assignments correspond to compound numbers reported in Table 1.
independent samples at three concentration levels prepared in the surrogate matrix (control samples), using Student’s t-test, and they were expressed as the critical value (CV, %). The intra- and interday precision was also evaluated for the authentic urine samples. The measurements accuracy was determined by comparing the mean value of the obtained results to the nominal concentration level of the analyte in the control sample and it was expressed as BIAS (in %). In order to determine whether there was a significant difference between the results at individual analyte concentration levels, a one-way analysis of variance (ANOVA) test was performed. The stability of all compounds, in the authentic and surrogate matrices, was assessed for the quality control samples at low and high concentration levels. The stability was evaluated for the unprepared and prepared samples stored under different conditions and duration time: three freeze-thaw cycles, ambient temperature and 4 °C for 48 h. Three replicates were performed under each condition.
increase the sensitivity of the analysis, these compounds require derivatization before the analysis. 3.1. Method development The derivatization process is time-consuming, however, critical in providing access to rich and numerous thematic libraries of mass spectra of organic compounds. In the case of compounds with the functional groups containing active hydrogen atoms, this process is often performed using various silyl agents, the most common of which is BSTFA [25]. Due to its fairly good reactivity with various compounds under moderate conditions and stability of the obtained TMS derivatives, BSTFA was chosen as the derivatizing reagent in this study. To find the best reaction conditions of BSTFA with all target compounds as well as internal standard, different experiments were conducted in combination with or without the preliminary liquid – liquid extraction of analytes from the surrogate matrix. As the excess of unreacted BSTFA necessary to guarantee the quantitative course of the reaction can potentially deteriorate the performance of chromatographic system, to choose the best option of sample preparation the overall chromatographic analysis quality was considered according to the MCDA method. The ranking of the results made in accordance with different criteria (data not presented) revealed that among all derivatization mixtures tested, the highest signal ratio of analytes to the internal standard with
3. Results and discussion The aim of this study was to develop and validate a simple and accurate GC–MS/MS method for simultaneous determination of 11 compounds assumed to be chemical markers of dysbiosis. All these compounds are water-soluble with log P values according to the PubChem database, ranging from approximately −2.5 to 2.5. Due to their polar properties to reduce their tailing on the GC column and 4
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the smallest background signal was obtained for the mixture composed of fourfold excess of pyridine relative to BSTFA with 5% TMCS. Increasing the volume of BSTFA as well as the addition of acetonitrile resulted in degradation of the signals and shape of the peaks. Considering the various conditions of derivatization under investigation, the best derivatization efficiency was obtained after 1 h of heating the reaction mixture at 70 °C. To complete the reaction the mixture should be allowed to stay at room temperature for at least 5 h. Nevertheless, the best reproducibility of signals, especially those obtained for the analytes with the reference ranges at low concentration levels, was observed after allowing the mixture to stay at room temperature for 15 h. In the case of analytes able to form a few TMS derivatives, after this time the signals obtained for the other derivatives were totally or almost totally suppressed. Hence this time was considered optimal and applied for further experiments. In addition it allowed to provide appropriate throughput of the laboratory. As for the other performed experiments, contrary to the results presented in [26], there was no positive effect of removal of the unreacted BSTFA by its evaporation or hydrolysis. A similar conclusion was drawn after analysing the efficiency of liquid-liquid extraction. Only in the case of the three tested compounds, a 100% extraction yield was obtained. For the remaining compounds, the yield was in the range of 15–60%. Yet the LLE stage not only reduced the sensitivity of the analysis but also its precision. Therefore finally, the derivatization was carried out on a small volume of the urine sample adjusted to a creatinine content that had been previously evaporated and cleaned from the residual moisture with methylene chloride.
In order to estimate the analytical utility of the developed method, its validation procedure was performed. To choose the best method of constructing calibration curves in the preliminary experiments there were applied three different approaches: (1) internal calibration with the solutions prepared using the surrogate matrix; (2) internal calibration with the calibration solutions prepared using three different authentic urine samples, and (3) the method of standard addition applied for the urine samples. The quality of the obtained curves was roughly estimated based on the analysis of their course (their linearity) and comparison of the coefficient “a” values in the obtained curves equations in the general form as y = ax + b (three independent calibration curves were obtained for each approach). High degree of results compatibility as well as R2 > 0.98 were obtained in the surrogate matrix approach. As for the authentic urine approaches, only in the case of one urine aliquot the obtained curves were linear and the coefficients “a” were comparable with the previous values (CV < 10%). However, in the case of the other two portions of urine, irrespective of the tested calibration method, the signal for hydrocinnamic and 3,4-dihydroxyhydrocinnamic acids in the blank sample exceeded that obtained after enriching the sample with the standard of the given compound, making both methods impractical for quantitative purposes. A more detailed analysis of the quality of the calibration curves obtained by the surrogate matrix approach, with regard to the accuracy and precision of the results, was performed using back-calculation of the standard concentrations in the calibration solutions, the statistical method, and the analysis of the control samples prepared at three concentration levels of all compounds. In assessing the accuracy of the concentration calculations for the calibration solutions, the model of linear regression (with or without weighing) as well as the second-order polynomial were taken into consideration. In the case of citramalic, 4hydroxybenzoic, 4-hydroxyphenylacetic, indoxylsulfate, tricarballylic, and hippuric acids a linear regression model without weighing resulted in the best accuracy and precision over the entire calibration range and was considered as the most appropriate model for these compounds. However, in the case of benzoic, 3,4-dihydroxyhydrocinnamic, benzenepropanoic and tartaric acids as well as DL-arabitol, it turned out that a linear regression model does not give a good fit of the data. In the case
3.2. Method validation The chemical structures of TMS derivatives of analysed compounds are shown in Fig. 1. The retention times and the optimised MRM parameters obtained for every single analyte and the internal standard are summarised in Table 1. The exemplary MRM chromatograms of a surrogate matrix spiked with a mixture of compounds as well as the authentic urine sample are presented in Fig. 2. The obtained data prove that the applied GC–MS/MS conditions are acceptable for both the qualitative and quantitative analyses.
Table 1 Retention times and ions used in the MRM mode for the analysis of TMS derivatives. Comp. No
TMS derivative
Shortcut
Retention time [min]
MRM transitions (Collision energy in eV) Target m/z
Reference m/z 194.00 194.00 208.00 248.00 247.00 280.00 281.00 267.00 223.00 252.00 296.00 292.00 219.00 277.00 277.00 378.00 377.00 217.00 319.00 206.00 206.00 398.00 267.00
1
Benzoic Acid, TMS
BA
11.42
194.00 > 179.10 (6)
2 3
Benzenepropanoic acid, TMS D-(-)-Citramalic acid, 3TMS
HCA CMA
14.84 16.04
222.00 > 104.10 (6) 247.00 > 203.20 (6)
–
Tropic acid, 2TMS derivative
IS
17.92
280.00 > 118.10 (9)
4
4-Hydroxybenzoic acid, 2TMS
HBA
18.43
267.00 > 193.10 (18)
5
4-Hydroxyphenylacetic acid, 2TMS
HBAA
18.62
252.00 > 164.10 (6)
6
Tartaric acid (R*,S*)-, 4TMS
TA
18.88
292.00 > 102.10 (12)
7
Indoxylsulfuric acid, 2TMS
INDA
19.75
278.00 > 73.10 (21)
8
Tricarballylic acid, 3TMS
TriCA
20.10
377.00 > 147.10 (18)
9
D(L)-Arabitol, 5TMS
DLA
20.18
217.00 > 129.10 (9)
10
Hippuric acid, 2TMS
HA
21.54
236.00 > 105.10 (9)
11
3,4-Dihydroxyhydrocinnamic acid, 3TMS
DHHCA
23.06
398.00 > 179.10 (27)
5
> > > > > > > > > > > > > > > > > > > > > > >
105.10 (18) 135.10 (18) 75.10 (9) 73.10 (18) 73.10 (18) 90.10 (27) 118.10 (9) 73.10 (27) 73.10 (27) 73.10 (18) 73.10 (18) 73.10 (21) 73.10 (18) 73.10 (21) 189.10 (6) 147.10 (18) 73.20 (30) 73.10 (18) 73.10 (27) 73.10 (18) 190.10 (9) 267.20 (12) 179.10 (9)
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Fig. 2. Chemical structures of TMS derivatives of analysed compounds. Peak assignments correspond to compound numbers reported in Table 1.
Table 2 Calibration ranges along with analytical figures of merit the calibration curves. Compound BA HCA CMA HBA HBAA TA INDA TriCA DLA HA DHHCA
Calibration range µg/mL 0.01–0.1 0.0008–0.0025 0.01–0.352 0.002–0.1013 0.05–1.5067 0.005–3.0 0.2–0.5.52 0.001–0.03 0.5–4.0 1.52–30.0 0.0031–0.5067
R2 0,998 0,999 0,998 0,999 0,998 0,998 0,996 0,998 0,998 0,992 0,991
Calibration curve equation
Bias,%
CV,%
Fcal
LOD µg/mL
2
90–115 86–114 96–113 89–102 96–103 88–104 90–120 92–104 86–107 80–106 87–107
97–116 91–130 97–111 99–112 99–104 97–108 98–113 99–125 99–120 100–120 99–107
0.87 1.17 0.20 0.04 1.12 2.95 0.46 0.20 0.01 0.20 2.02
0.0012 0.0004 0.0012 0.0003 0.0015 0.0014 0.0369 0.0006 0.0540 0.0032 0.0015
y = 0,1914 x + 0,1827x − 0,0002 y = 34,355 x2 + 0,8261x + 2 * 10−6 y = 0,2742x − 0,0011 y = 1,5239 × + 0.0061 y = 0,2984x − 0,0063 y = 0,0128 x2 + 0,2953x − 0,0501 y = 0,2277x − 0,1168 y = 0,5215x − 0,0009 y = 0,0008 x2 + 0,2539x + 0,0055 y = 0,2725x − 0,9392 y = 0,0734 x2 + 0,6966x − 0,0216
Ftab = 5,29.
6
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Table 3 Intra- and interday imprecision and inaccuracy for the analysis of compounds in the surrogate matrix (n = 15, 5 replicates per day for 5 days). Comp.
Nominal concentration (ng/ml)
Intraday (n = 5)
Interday (n = 5)
Measured concentration (mean ± SD), (ng/ml)
Imprecision (% CV)
Inaccuracy (% BIAS)
Measured concentration (mean ± SD), ng/ml
Imprecision (% CV)
Inaccuracy (% BIAS)
BA
0.04 0.16 0.27
0.04 ± 0.004 0.15 ± 0.021 0.23 ± 0.015
8.6 13.8 6.4
7.7 −4.4 −14.7
0.04 ± 0.002 0.16 ± 0.006 0.24 ± 0.009
3.7 3.7 3.6
12.7 −0.2 −10.1
HCA
0.001 0.003 0.005
0.001 ± 0.00 0.002 ± 0.00 0.004 ± 0.01
9.8 9.5 12.2
12.5 −7.4 −4.4
0.001 ± 0.00 0.003 ± 0.00 0.004 ± 0.00
12.4 4.7 5.1
−3.1 −0.9 −10.6
CMA
0.09 0.41 0.72
0.09 ± 0.003 0.40 ± 0.013 0.76 ± 0.032
3.8 3.2 4.2
0.6 −3.1 4.6
0.09 ± 0.00 0.42 ± 0.05 0.78 ± 0.02
5.6 10.9 3.0
2.2 3.1 8.2
HBA
0.042 0.200 0.363
0.042 ± 0.003 0.195 ± 0.004 0.367 ± 0.008
7.7 2.3 2.0
−1.0 −2.3 1.0
0.044 ± 0.003 0.158 ± 0.003 0.377 ± 0.010
7.4 1.7 2.5
−1.7 −2.6 3.9
HBAA
0.35 1.53 2.73
0.30 ± 0.02 1.46 ± 0.07 2.76 ± 0.07
7.1 4.5 2.4
−13.4 −4.5 0.9
0.31 ± 0.005 1.46 ± 0.032 2.82 ± 0.061
1.5 2.2 2.1
−11.8 −4.9 3.4
TA
0.41 2.00 3.60
0.41 ± 0.071 1.91 ± 0.276 3.55 ± 0.317
17.3 14.4 8.9
0.5 −4.3 −1.3
0.39 ± 0.014 2.05 ± 0.148 3.61 ± 0.153
3.6 7.2 4.2
−3.9 2.3 0.2
INDA
1.20 5.12 9.04
1.191 ± 0.074 5.013 ± 0.293 8.856 ± 0.676
6.2 5.9 7.6
−0.8 −2.1 −2.0
1.18 ± 0.027 5.04 ± 0.113 9.21 ± 0.279
2.3 5.9 3.0
−1.3 −2.1 1.9
TriCA
0.01 0.05 0.09
0.01 ± 0.001 0.05 ± 0.001 0.08 ± 0.004
10.7 2.4 5.6
−13.8 3.2 −13.9
0.01 ± 0.001 0.05 ± 0.001 0.08 ± 0.002
2.9 1.9 1.8
−10.3 1.5 −11.9
DLA
1.96 7.80 13.60
2.16 ± 0.162 7.63 ± 0.313 13.30 ± 1.74
7.5 4.1 13.1
10.4 −2.2 −2.2
2.20 ± 0.03 7.38 ± 0.34 12.52 ± 1.74
1.2 4.6 13.9
12.1 −5.3 −7.9
HA
4.67 17.33 29.87
4.42 ± 0.70 14.56 ± 1.077 27.46 ± 2.68
15.9 7.4 9.6
−5.2 −15.9 −6.3
4.43 ± 0.01 14.99 ± 0.38 29.78 ± 1.56
0.2 2.5 5.3
−5.1 −13.5 −0.2
DHHA
0.06 0.31 0.54
0.06 ± 0.00 0.32 ± 0.02 0.54 ± 0.03
7.4 6.4 6.5
−5.3 5.4 −0.6
0.06 ± 0.002 0.32 ± 0.006 0.55 ± 0.010
2.5 1.7 1.7
−2.7 4.8 0.6
of these compounds, it was estimated that the better results are obtained applying the polynomial functions. The applied regression models resulted in small and evenly distributed residual errors and a high coefficient of regression. The statistical method of lack of fit confirmed the validity of these assumptions (Fcal < Ftab, Ftab = 5.29). The accuracy of all compounds ranged from 86–110% and 96–102% at the lower limit of quantification (LLOQ) and the upper limit of quantification (ULOQ), respectively. The equations of the calibration curves along with their basic characteristics are summarized in Table 2. The table also collects the accuracy (Bias) and precision (CV) ranges together with the Fcal values (determined assuming a two-way distribution and the values of numerator and denominator equal 7 and 5, respectively) obtained for the individual substances in the calibration solutions over the calibration ranges. The results of the intra- and interday imprecision and inaccuracy obtained for all analytes prepared in the surrogate matrix at three concentrations levels are gathered in Table 3. Intraday imprecision and inaccuracy ranged from 2.0% to 17.3% and from −15.9% to 12.5%, respectively. Interday imprecision and inaccuracy ranged from 0.2% to 13.9% and from −13.5% to 12.7%, respectively. These data indicate adequate accuracy and precision of measurements. This conclusion is independently confirmed during the assays on six authentic urine samples (intra- and interday imprecision ranged from 3.5% to 16.7% and from 2.3% to 17.5%, respectively).Table 4.
Table 4 F-values and P-values obtained during variance analysis for the amount of individual compounds estimated for the control group (C) and the patients group (P) from Fig. 3 (Ftab = 3.95). Compound
F-value
P-value
BA HCA CMA HBA HBAA TA INDA TriCA DLA HA DHHCA
5.18 10.73 11.00 1.12 5.53 4.29 5.29 7.97 61.85 6.38 6.36
2.5 · 10−02 1.7 · 10−03 1.3 · 10−03 2.9 · 10−01 2.1 · 10−02 4.10 · 10−02 2.4 · 10−02 5.9 · 10−03 8.2 · 10−12 1.3 · 10−02 1.3 · 10−02
Various storage and handling conditions were evaluated to determine their effect on stability of analysed compounds. All compounds remained stable in the urine samples during three freeze–thaw cycles. Storing the sample in a refrigerator at 4 °C resulted in the stability of the compounds up to 24 h. After this time a marked increase in benzoic acid was observed which was followed by the benzenepropanoic acid increase after five days. The samples stored in the freezer at −20 °C were
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Fig. 3. Analytical results of BA (1), HCA (2), CMA (3), HBA (4), HBAA (5), TA (6), INDA (7), TriCA (8), DLA (9), HA (10), and DHHCA (11) concentrations, normalized to creatinine, in the urine samples of children diagnosed with dysbiosis (grey box, P) compared to the content of compounds in the control group (white box, C). The dotted and hatched boxes represent a group of girls (G) and boys (B) patients, respectively.
stable for a period of two weeks. Stability tests of the sample prepared for the analysis and stored at room temperature showed stability up to 48 h (CV < 3%, Bias < 10%). Stock solutions were stable for at least 5 months.
satisfactory intra- and interday precision and accuracy for the analysis of all compounds indicated as chemical markers of dysbiosis in the authentic urine samples. To demonstrate the practical applicability of the proposed method, it was used for estimation of the concentration of BA, HCA, CMA, HBA, HBAA, TA, INDA, TriCA, HA, DHHCA, and DLA in the urine samples of children diagnosed with dysbiosis. For the samples the creatinine content was determined to compensate for variation in urine volume. The means and standard deviation were 0.8874 ± 0.53 mg/L and 0.6065 ± 0.56 mg/L for the controls and the
3.3. Practical applicability The results of validation experiments prove that the proposed method is characterized by good linearity, low detection limits and
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patients, respectively with the values from 0.1212 mg/L to 3.1372 mg/ L. The analytes content was expressed as the ratio to creatinine. The obtained results are presented in the form of box plots in Fig. 3. Fig. 3 compares box plots showing the distribution of the content of individual compounds (from BA to DHHCA) in urine samples obtained from children from the control group (white boxes; C) and children diagnosed with dysbiosis (grey boxes; P). Dotted and hatched boxes visible in the figure represent a group of girls (G) and boys (B) patients, respectively. In each plot, the box represents the contents range from lower to upper quartile (25–75%), the line marks the mid-point of the data set (the median), the cross indicates the average value, the whiskers (the upper and lower) represents scores outside the middle 50%, and the circle marks the data far away from other data values (outliers). Because for TA the range of outliers is very wide (up to 350 mg/g, see “TA a” in Fig. 3), to enable comparison of both groups an additional plot was prepared for a narrower concentration range of this compound (up to 50 mg/g, see “TA b” in Fig. 3). To assess the significance of differences in the amount of individual compounds estimated for the control and patients group, the analysis of variance (ANOVA, P = 0.05) and F test were used. To check the significance of each Fisher coefficient the P-values were used. The lack of differences in the studied groups was considered insignificant for the F value lower than Ftab. The importance of the differences for the individual compounds is listed in Table 4. The results from Fig. 3 and Table 4 show that for most compounds the boxes ranges and their position obtained for the control group are different from the ranges and positions observed for the individual compounds in the patients group (4.29 < Fcal < 61.85, Ftab = 3.95). These features suggest that the presented method is suitable for the routine analysis of dysbiosis markers in the urine samples. Regarding the ranges of the individual compounds obtained for the patient group, they are wide and their shape is asymmetric with large share of the results higher than the median (marked as a line across the box). For the vast majority of the tested compounds, some data values are far away from the others (these outliers are shown as circles in the figure). Only in the case of HBA the control and patients boxes overlap with one another (Fcal < Ftab). These observations are consistent with the knowledge of high levels of concentration of some organic acids being metabolic products of microorganisms and leading to gastrointestinal and/or neurological symptoms. The more detailed analysis of the obtained results, presented separately for girls and boys patients, shows different distributions of the results for a given compound. This can be more clearly seen in the case of HCA for which not only the median but also the mean values, together with the box ranges and the distributions, observed for the girls group are evidently different from those observed for the boys group. The obtained results show the complexity of the problem of diagnosing dysbiosis and suggest the necessity of conducting further research aimed at univocal definition of reference ranges in healthy people and the assessment of the impact of parameters that may differentiate the obtained results such as sex and diet.
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4. Conclusions In this paper a GC–MS/MS method was proposed for the analysis of 11 organic compounds (i.e. hydrocinnamic, citramalic, p-hydroxybenzeneacetic, tartaric, hippuric, 4-hydroxybenzoic, indoxylsulfuric, tricarballylic, 3,4-dihydroxyhydrocinnamic and benzoic acids together with DL-arabitol) recognised as chemical dysbiosis markers in a single analytical run. The procedure involves direct derivatization of compounds to their methyl esters in a small volume of urine sample and GC–MS/MS analysis. Derivatization has been optimised to yield the best results. The proposed method was validated and tested. The obtained
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