Metabisotopomics of triacylglycerols from animal origin: A simultaneous metabolomic and isotopic profiling using 13C INEPT

Journal Pre-proofs Metabisotopomics of triacylglycerols from animal origin: A simultaneous metabolomic and isotopic profiling using 13C INEPT Ghina Hajjar, Toufic Rizk, Joseph Bejjani, Serge Akoka PII: DOI: Reference:

S0308-8146(20)30176-X https://doi.org/10.1016/j.foodchem.2020.126325 FOCH 126325

To appear in:

Food Chemistry

Received Date: Revised Date: Accepted Date:

24 July 2019 7 January 2020 27 January 2020

Please cite this article as: Hajjar, G., Rizk, T., Bejjani, J., Akoka, S., Metabisotopomics of triacylglycerols from animal origin: A simultaneous metabolomic and isotopic profiling using 13C INEPT, Food Chemistry (2020), doi: https://doi.org/10.1016/j.foodchem.2020.126325

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2020 Elsevier Ltd. All rights reserved.

Metabisotopomics of triacylglycerols from animal origin: A simultaneous metabolomic and isotopic profiling using 13C INEPT

Ghina HAJJAR a, b, Toufic RIZK b, Joseph BEJJANI b, *, Serge AKOKA a

a EBSI

team, Interdisciplinary Chemistry: Synthesis, Analysis, Modelling (CEISAM), University of

Nantes - CNRS UMR 6230, 2 rue de la Houssinière, BP 92208, F-44322 Nantes Cedex 3, France b Laboratory

of Metrology and Isotopic Fractionation, Research Unit: Technologies et Valorisation

Agroalimentaire (TVA), Faculty of Science, Saint Joseph University of Beirut, P.O. Box 17-5208 Mar Mikhael, Beirut 1104 2020, Lebanon * Corresponding

author. Tel.: +961 1 421683; fax: +961 4 532657. E-mail: [email protected]

1

Abstract In previous works, we developed a 13C NMR method for analyzing triacylglycerols in olive oil using an adiabatic refocused INEPT sequence. This allowed spectral acquisition to be done in only 8 min with sufficient precision for isotopic measurements. In the present study, we made use of the same methodology to investigate the potential of triacylglycerols as source of biomarkers in animal origin matrices. To this end, egg yolk was taken as a model matrix. We called our profiling approach metabisotopomics since it was simultaneously metabolomic and isotopic profiling. Beside its ability to quantitate several fatty acids, metabisotopomics of triacylglycerols in egg yolk allowed the multivariate classification of samples according to the hen breed, to the farming system and origin. Achieved results confirmed our presumption that

13C

metabisotopomics of triacylglycerols from animal sources is a

powerful tool for metabolic studies as well as for food authentication processes.

Keywords: 13C INEPT; Isotopic profiling; Metabolomics; Egg lipids; Triacylglycerols; Fatty acids; Chemometrics

2

1. Introduction Triacylglycerols (TAG) present in most of animal tissues and biological fluids are “information-rich” molecules that act as source of biomarkers either in metabolic disorder and specific disease studies or in characterization and classification of food from animal origin. Just recently, it was demonstrated that very long saturated and mono-unsaturated fatty acids (C26:0 and C26:1) are present in human milk (Koulman, Furse, Baumert, Goldberg, & Bluck, 2019) and that omega-3 (ω3) and omega-6 (ω6) fatty acids in lipids modulate processes of breast carcinogenesis (Lira et al., 2019). Moreover, it was also demonstrated that the isotopic fingerprint and the metabolomic profile of TAG are closely related to surrounding factors such as geographical origin, botanical origin, and agricultural practices (Merchak et al., 2015, 2018; Merchak, El Bacha, et al., 2017; Standal, Axelson, & Aursand, 2009), which makes them attractive as predictors in food authentication issues. It is thus of great interest to develop robust analytical methods allowing to quantitate such metabolomic and isotopic biomarkers in matrices from animal origin. In this respect, 13C NMR is the technique of choice for several reasons. First, it permits simultaneous quantitation of metabolomic and isotopic variables within TAG (Merchak et al., 2015, 2018). Second, due to its wide spectral width, 13C NMR permits to address most of signal overlapping issues encountered in 1H NMR spectra, and thus allows more detailed metabolomic profilings (Merchak, Silvestre, et al., 2017). Third, 13C NMR has a higher sensitivity and much larger spectral width than 2H NMR making it more attractive in Position Specific Isotope Analysis (PSIA). Nevertheless, isotope ratio monitoring by 13C NMR (irm-13C NMR) has a considerable drawback since it should be highly precise (few per mil) due to the restricted intramolecular range of 13C content (δ13C). As an example, the difference in 13C amount between glycerol carbon atoms ranges from 0.16 to 4.1% (Caytan et al., 2006). For this reason, signal-to-noise ratio (SNR) should be greater than 500 since the maximum relative error on a given peak area (A*100/A) due to spectrum noise is equal to 100/2SNR (Caytan et al., 2007). Besides, repetition time must be 10 times the longest longitudinal relaxation time of the molecule to avoid partial saturation and to eliminate the impact of nuclear Overhauser effect 3

(nOe) (Saito et al., 2004; Vögeli et al., 2014). Considering these two factors, experiment duration can become very long for routine analysis due to the low natural abundance of 13C, its small gyromagnetic ratio and long longitudinal relaxation. However, the use of a polarization transfer sequence in the acquisition of 13C NMR spectra permits to reduce the experiment time. In this aim, an optimized Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) sequence was developed by our team at CEISAM laboratory (Supplemental Fig. S-1) (Bussy et al., 2011; Tenailleau & Akoka, 2007; Thibaudeau, Remaud, Silvestre, & Akoka, 2010). This sequence, i.e., adiabatic refocused INEPT, was tested in the authentication of olive oils. An experiment time reduction by a factor of 7 with respect to the single-pulse sequence was observed and a potent discriminating power was demonstrated (Merchak et al., 2015). In this work, we aimed to evaluate the same triacylglycerol metabisotopomic approach (simultaneous metabolomic profiling and position-specific isotopic fingerprinting of complex mixtures) in the characterization and classification of animal origin matrices. In this respect, hen egg yolk was considered as a model matrix and egg samples from different producers, hen breeds, and farming systems were collected. Corresponding metabisotopomic INEPT data were investigated in the training of various multivariate classification and prediction models.

2. Materials and Methods 2.1. Egg samples Fresh eggs were either purchased from Lebanese local grocery stores or collected from non-commercial farms (two in Lebanon and one in France, see Table 1). In the former case, white and brown eggs, from two commonly raised hen breeds, were obtained. Considering the hens’ nutrition and welfare, samples were divided into 4 groups or farming systems: conventional cages, where birds were raised for largescale egg commercialization and were automatically supplied with a controlled mixture of grains; freerange, where hens were raised in barns and fed with grains, yet they had access to outdoors for a part of the day; backyard cages, where hens roamed freely outdoors during the day and were supplied with a 4

mixture of grains and a high amount of human food leftovers; and organic farming, where hens were fed organic non-GMO diet consisting of mainly grains and a small amount of organic agriculture leftovers, and were given access to outdoors for a part of the day. In total, 41 egg samples were collected from 7 different farms (A, B, C, D, E, F, and G) as described in Table 1. However, since available information about group G were not sufficient, samples of this group were only used in quantitation of individual fatty acids.

2.2. Chemicals Extra pure ethanol, extra pure acetone, and petroleum ether (boiling range 35-60 °C, ACS basic) were purchased from Scharlab; diethylether (GPR rectapur) was purchased from VWR chemicals; and deuterated chloroform was purchased from Eurisotop. Whatman Purasil silica gel (60A, 230-400 Mesh ASTM) was used for column chromatography.

2.3. Triacylglycerols extraction Eggshell was carefully broken and yolk was manually separated from egg white using a yolk extractor. Yolk was homogenized after removal of vitellus membrane and chalaza. Absolute ethanol (36 mL) was added to 10 g of yolk and mixed for 10 min (Palacios & Wang, 2005). The mixture was then filtered and the residue was washed with 10 mL of ethanol:petroleum ether (1:1 v/v) until complete extraction of lipids, assessed by Thin Layer Chromatography (TLC). Solvents were evaporated under vacuum at 45 °C, extracted lipids were mixed with 18 mL of petroleum ether. Acetone (36 mL) was added and the mixture was kept 40 min at -20 °C for phospholipids precipitation (Gładkowski, Chojnacka, Kiełbowicz, Trziszka, & Wawrzeńczyk, 2012). The liquid phase was decanted off and phospholipids were washed with cold acetone (-20 °C). Acetone-soluble fractions were pooled and solvents were evaporated under vacuum at 40 °C. The residue, consisting of egg yolk neutral lipids (mainly TAG and cholesterol), was dissolved in 10 mL of 10% diethylether in petroleum ether and passed through a chromatographic column prepared with 6 g of silica gel. 70 mL of the aforementioned mixture of 5

solvents were used to elute TAG. Solvents were evaporated under vacuum at 35 °C and isolated TAG samples were stored at -20 °C without any further purification steps.

2.4. Gas chromatographic analysis Fatty acid methyl esters were prepared by shaking 200 mg of TAG in 3 mL of hexane with 0.4 mL of 2 N methanolic potassium hydroxide (European Comission, 1977; International Olive Council, 2015). A 1:10 dilution in hexane was performed. An Agilent Technologies chromatograph equipped with a flame ionization detector and a fused-silica capillary column (SGE-054616, 30m×0.32mm i.d., BPX70 0.25 μm) was used in this study with helium as carrier gas at flow of 1.0 mL/min. Oven temperature was programmed as follows: at 160 °C for 15 min, to 200 °C at a rate of 10 °C/min, at 200 °C for 10 min. A volume of 1 μL was injected with a split ratio of 50:1 and an injector temperature of 200 °C. The detector temperature was set at 270 °C. Two injections were performed for each sample with a total elution time of 58 min. Mass percentages of each fatty acid within egg yolk TAG were determined by dividing its peak area by the total peak area of detected fatty acids in each sample. Standard fatty acid methyl esters, purchased from sigma Aldrich, were used to determine their retention time.

2.5. Isotope ratio monitoring by Mass Spectrometry (irm-MS) For each sample, the global isotopic composition of TAG was determined by irm-MS using an Integra2 spectrometer (Sercon Instruments, Crewe, UK) linked to a Sercon elemental analyser fitted with an autosampler (Sercon Instruments, Crewe, UK). Approximately 0.5 mg of the triacylglycerol fraction was sealed in a tin capsule (2 × 5 mm, Thermo Fisher scientific) and 13C composition was determined relative to glutamic acid, a laboratory reference, calibrated against international reference material NBS22, SUCROSE-C6, and IAEA-CH-7 PEF-1 (IAEA, Vienna, Austria) (International Atomic Energy Agency, 1995). For each sample, measurements were carried out in triplicate and corresponding standard deviations never exceeded 0.2‰. The global isotopic compositions were expressed as isotopic

6

deviation (δ13Cg) relative to the international reference Pee Dee Belemnite (PDB) (Craig, 1957) according to the following equation: 𝛿13𝐶𝑔(‰) =

(

𝑅𝑔 𝑅𝑃𝐷𝐵

)

― 1 × 1000

(1)

where Rg is the global isotopic ratio of the sample and RPDB (0.0112372) is the global isotopic ratio of PDB.

2.6. NMR experiments 2.6.1. Acquisition TAG of egg yolk (403 mg) were dissolved in 420 µL of CDCl3 and the mixture was transferred to a 5 mm NMR tube. 13C NMR spectra were recorded at 20 °C using an optimized refocused adiabatic INEPT pulse sequence (Supplemental Fig. S-1) on a 500 MHz Bruker Avance-III spectrometer equipped with a 5 mm dual 13C/1H cryoprobe (tuned at the 13C recording frequency of 125.76 MHz) (Bussy et al., 2011; Merchak et al., 2015). The 90° 1H and 13C pulse widths were calibrated to 10 µs and 11 µs, respectively, with an acquisition time of 1 s, recovery time of 24 s (the longest 1H T1 (2.5 s) was measured for the methyl group of the linolenic acid using the inversion-recovery pulse sequence), 4 dummy scans, and 16 scans leading to a signal-to-noise ratio higher than 590 on the sn2 carbon of glycerol (Supplemental Fig. S-2). 13C and 1H offsets were set at the middle of the frequency range and were 73 ppm and 4.5 ppm, respectively. Evolution and refocusing periods were adjusted to 2.7 and 1.4 ms, respectively. Adiabatic full passage pulses were generated using Mathcad (MathSoft, Inc.). They were designed with a cosine amplitude modulation of the radiofrequency (RF) field (ω1max = 157.1 or 93.89 kHz for 13C or 1H, respectively) and an offset-independent adiabaticity with an optimized frequency sweep ΔF (ΔF = 39 or 17 kHz for 13C or 1H, respectively) (Tenailleau & Akoka, 2007). For inversion pulses, adiabatic full passage pulses were used. For refocusing pulses, adiabatic composite pulses were used (Thibaudeau et al., 2010). 1H decoupling was performed using an optimized phase cycle and adiabatic full passage RF pulses with a cosine square amplitude modulation (ω2max = 110.6 7

kHz) and an offset-independent adiabaticity with an optimized frequency sweep (ΔF = 14 kHz) (Tenailleau & Akoka, 2007). For each sample, six spectra were recorded in 50 min 10 s.

2.6.2. Processing of 13C INEPT spectra Prior to Fourier transform, free induction decays were zero-filled to 128 K and submitted to an exponential apodization inducing a line broadening of 1.5 Hz. Spectra were manually phased and an automatic polynomial (n = 5) baseline correction was applied. The curve fitting was carried out in accordance with a Lorentz-Gauss mathematical model using PERCH Software (PERCH NMR Software, University of Kuopio, Finland). For each sample, peak areas were computed and normalized relative to the signal of methylene at position 2 (C2), sum of peak areas related to this signal was set to 1000. This spectral processing has led up simultaneously to metabolomic and position-specific isotopic variables. A metabolomic variable is relative to the profile of fatty acids within TAG or to their positional distribution on the glycerol backbone. A position-specific isotopic variable represents a ratio of two carbon sites within the same molecule or in two different molecules present in exactly known stoichiometry (e.g., the ratio of 1 mole of carbon atoms at position 2 of the glycerol backbone (sn2) to 3 moles of carbon atoms at position 2 of fatty acid chains in TAG; the ratio “L18/L17” of 1 mole of carbon atoms at position 18 to 1 mole of carbon atoms at position 17 in linoleic acid). Deconvoluted peaks of spectral regions related to C(n), C(n-1), C(n-2), aliphatic (Cx), allylic (Cy), diallylic (Cz), and vinylic (Cv) carbon atoms of fatty acid chains (see Supplemental Fig. S-2 for peak labeling) were individually used as input variables for fatty acids quantitation when they were obtained with a precision lower than 10%. Otherwise, peaks were combined with their appropriate neighbors in order to reach this precision. Besides, isotopic variables as well as variables indicating the distribution of fatty acids on the glycerol chain were also obtained from regions corresponding to the glycerol moiety, C2, C3, and Cv carbon atoms (Table1, see Supplemental Fig. S-2 for peak labeling). Peak areas of specific signals were used to calculate additional isotopic variables listed in Table 2. As a result, 83 8

variables (23 isotopic and 60 metabolomic variables) were obtained and used in multivariate analysis (Supplemental Table S-1).

2.7. Data analysis Chemometric analyses were performed by means of TANAGRA data mining software (Rakotomalala, 2005). Peak areas from 13C INEPT spectra were used as inputs in Canonical Discriminant Analysis (CDA) and Linear Discriminant Analysis (LDA) for the classification of egg samples. The backward elimination approach was used to select variables for classification models based on their Wilks’ λ values. Partial Least Squares Discriminant Analysis (PLS-DA) was also used as classifier when a relatively high number of variables were necessary for group discrimination. Detection of the number of relevant PLS components to retain was based on the redundancy (proportion of explained variance) for the dummy variables associated to the classes (redundancy cut value was set to 0.025) and on LeaveOne-Out (LOO) and k-fold Cross-Validation (CV, k =10 with 100repetitions) tests. Variable Importance in Projection (VIP) coefficients and relative performance at LOO and CV tests were used as indicators to discard irrelevant variables in the selected PLS components. In each case, performance of the model was assessed using the parameter LDA-Error rate (LDA-Er) or PLS-DA-Error rate (PLS-DA-Er). Robustness of the classification result was assessed using LeaveOne-Out Error rate (LOO-Er) and, when relevant, k-fold Cross-Validation Error rate (CV-Er) as indicators. One-way univariate and multivariate analysis of variance (ANOVA and MANOVA, respectively) were used to determine whether there were any statistically significant differences between the means of independent groups. Peak areas from 13C INEPT spectra and mass percentages of fatty acids obtained by Gas Chromatography (GC) have served as independent and dependent variables, respectively, in Partial Least Square Regression (PLSR) for the construction of fatty acid quantitation models. PLSR models were built by eliminating non-relevant variables according to their standardized regression coefficient 9

while adjusting the number of components (h) based on the cross-validation test (by randomly leaving out 10% of the training samples). The maximum cumulative Q2 (Q2cum) and the Predicted Residual Error Sum of Squares (PRESS) were used as robustness indicators (Gauchi & Chagnon, 2001; Hawkins, Basak, & Mills, 2003). Q2cum and PRESS parameters are related by the following equation: PRESS𝑗

ℎ

𝑄2𝑐𝑢𝑚 = 1 ― ∏𝑗 = 1RSS𝑗 ― 1

(2) 𝑛

where h is the number of components in a PLSR model; PRESS = ∑𝑖 = 1(𝑦𝑖 ― 𝑦( ―𝑖))2; Residual Sum of 𝑛

Squares RSS = ∑𝑖 = 1(𝑦𝑖 ― 𝑦𝑖)2; n is the number of samples in the training set; 𝑦𝑖 is the fatty acid amount determined by GC; 𝑦( ―𝑖) values are the prediction of 𝑦𝑖 for omitted samples with a model constructed after this omission from the training set and using the initial descriptors; and 𝑦𝑖 is the prediction of 𝑦𝑖 while the corresponding sample is included in the training set when the model is constructed. The best compromise between the lowest PRESS and the highest Q2cum values was used to determine the number of components to retain. The coefficient of determination R2 was used to determine the proportion of the variance in the dependent variable that is predictable by the independent variables. In other words, it was used to assess the goodness-of-fit. Adjusted R2, given by equation 3, was used to compare models constructed with different number of predictors (Schinka, Velicer, & Weiner, 2003). 𝑛―1

Adjusted 𝑅2 = 1 ― 𝑛 ― 𝑚 ― 1(1 ― 𝑅2)

(3)

where n is the sample size in the training set and m is the number of predictors (the number of components in this case) taking into account the constant term. However, since internal validation parameters (Q2cum and PRESS) are not sufficient to evaluate the predictive power of models (Golbraikh & Tropsha, 2002), external validation was applied using the coefficient of determination for the test set (Pred-R2) as indicator (Roy & Roy, 2008). This parameter is given by the following equation: 𝑛

2

Pred ― 𝑅 = 1 ―

∑𝑖 = 1(𝑦𝑖 ― 𝑦𝑖)2 𝑛

∑𝑖 = 1(𝑦𝑖 ― 𝑦)2

(4) 10

where n is the sample size in the test set; 𝑦𝑖 is the fatty acid amount of the test samples determined by GC; 𝑦𝑖 is the prediction of 𝑦𝑖 by the constructed regression model; and 𝑦 is the mean of 𝑦𝑖 values for all the training set samples. The best model was retained by compromising between the goodness-of-fit (R2 and Adjusted R2) and validation (Q2cum, PRESS, and Pred-R2) parameters.

3. Results and Discussion 3.1. Precision of the whole analytical method Aiming to evaluate the precision of our measurements, the global within-lab reproducibility (SRwg) of the whole experimental procedure, starting from TAG extraction to the final spectral processing step, was determined. For that purpose, 5 egg yolks were combined and 5 aliquots of 10 g each were extracted after homogenization. Each aliquot was then submitted to the whole experimental procedure described in sections 2.3, 2.5, and 2.6. SRwg is the relative standard deviation of mean peak areas from the 5 aliquots analyzed in the same NMR session. Average TAG percentage in lipids extracted from the five aliquots was 63%, with a standard deviation of 1% (Supplemental Table S-2). For comparison purposes, we mention that the average TAG percentage in yolk lipids of the forty-one egg samples considered herein was 62% with a standard deviation of 3%. The average global 13C isotopic composition determined by irm-MS expressed as isotopic deviation (13Cg) was -17.82‰ for the 5 aliquots, with a standard deviation of 0.34‰ (Supplemental Table S-2). Precision of metabolomic and positional isotopic variables obtained through deconvolution of INEPT spectra was determined. Global within-lab reproducibilities of isotopic variables are reported in Table 2 as relative standard deviations (in per mil). Precision (SRwg) did not exceed 1.5‰ for two-thirds of these variables and ranged between 3.2 and 7‰ for the rest. The reached precision was sufficient to conduct 13C

isotopomic analyses since the relative standard deviation over the whole set of samples (σ) was at

least 7 times higher than SRwg for two-thirds of the variables. For the remaining variables, the ratio 11

σ/SRwg was at least 3.8 (Table 2), which could permit to differentiate between samples. Global within-lab reproducibilities of the other deconvoluted peak areas, which were metabolomic variables, are shown in Supplemental Table S-1. Relative standard deviations (SRwg) did not exceed 5.7%, which means that the precision was high enough for metabolomics.

3.2. Classification of egg samples Generally, hens’ diet consists of grains that can be either C3 or C4 plants based on the pathway used for the synthesis of carbohydrates. These two metabolisms affect the 13C concentration in plant products and as a result, influence the isotopic composition of egg yolk. The global isotopic composition of C3 plants ranges from -33 to -22‰ whereas that of C4 plants ranges from -20 to -10‰ (Bender, 1971). In our study, as shown in Fig. 1, δ13Cg of egg yolk TAG, determined using irm-MS, was in good accordance with birds feed composition (Table 1). In other words, samples of groups A, B, and C, derived from hens fed with high percentages of wheat (C3 plant), exhibited high depletion in δ13Cg of TAG. On the contrary, samples of groups D, E, and F, derived from hens fed with high percentages of corn (C4 plant), exhibited low depletion in δ13Cg of TAG. Based on δ13Cg values, a significant separation (F(3,33) = 215.76, p = 0, and LDA-Er = 2.7%) was reached between groups, but this was after combination of samples from D, E, and F into one group. This indicated that δ13Cg was not sufficient to discriminate samples of relatively close origins. In this respect, metabolomic and positionspecific isotopic variables obtained from 13C INEPT spectra were used for further sample discriminations. Based on the information provided about the collected samples, a sequential strategy was developed in order to construct models allowing to classify egg samples according to their origin and particularities. For that purpose, eggs were grouped according to the hens farming system. First, a significant separation (F(1,35) = 122.91; p = 0) of organic (group B) and non-organic (other groups) samples was observed using a single peak area corresponding to C15 of linolenic acid at 127.04 ppm (Fig. 2a). This 12

was coherent with the results obtained by GC showing higher percentages of linoleic and linolenic acids within TAG of organic samples (Supplemental Table S-3). Second, non-organic samples were classified by means of CDA/LDA according to hens farming system, i.e., conventional cages, free-range, and backyard cages. A classification model was constructed with 8 variables and allowed to classify samples with LDA-Er and LOO-Er both equal to 0% and CV-Er of 2.7% (See Fig. 2b and Supplemental Table S-4 for all statistical parameters). Four of the variables used were indicators of position-specific isotopic contents within TAG, which proved the importance of the information added by irm-NMR. In addition, the most two important variables in this classification were metabolomic variables corresponding to linolenic acid, which is explained by the significant difference (F(2,27) = 20.76; p = 0) in the percentage of this acid in eggs laid by hens raised in conventional cages, backyard cages, and free-range (0.21, 0.35, and 0.14%, respectively; Supplemental Table S-3). Trying to improve the farming system classification, PLS-DA was used as a classifier. Three PLS components based on 20 variables allowed discrimination of samples with PLS-DA-Er, LOO-Er, and CV-Er of 0%, 0%, and 0.03%, respectively (See Supplemental Table S-5 for variables used along with their coefficients in the classification functions and their VIP parameters). Furthermore, eggs laid by hens raised in backyard cages and conventional cages were considered separately. Samples of each of the aforementioned groups were classified according to their origin. Both models were shown to be robust, LDA-Er and LOO-Er being equal to 0% (Fig. 2c & 2d). The discriminators L18/L11 and L18/L13 used in the case of backyard cages (Fig. 2c) were both isotopic variables related to linoleic acid. These variables reflected at the same time the isotopic deviation between the two carbon atoms of acetyl group in acetyl-coenzyme A (acetyl-CoA) units (Zhang, Buddrus, & Martin, 2000) and the isotopic fractionations occurring during the elongation of fatty acid acyl chains (L18/L11 and L18/L13), and during the dehydrogenation leading to linoleic acid (L18/L13). For samples from conventional cages, the discrimination between D and E groups (Fig. 2d) was due to the isotopic variable L18/L13 and to Ln12 related to linolenic acid percentage.

13

Classification of samples according to hens breed was also investigated. Egg samples from conventional cages D and E were considered together and divided into white and brown egg groups as coming from two hen breeds raised under the same conditions. Variables OPo10(sn1,3) and OPo9(sn1,3/sn2) –related to carbon atoms 10 and 9 in oleic and palmitoleic acids and representing their positional distribution on the sn1,3 and sn2 positions of the glycerol moiety– had the ability to classify these samples according to hens breed with both LDA-Er and LOO-Er equal to 0% (Fig. 3a). The plot of OPo9(sn1,3/sn2) versus OPo10(sn1,3) (Supplemental Fig. S-3) clearly indicated that percentage of oleic and palmitoleic acids at position sn1,3 was higher in brown samples than in white ones. However, global percentages of these fatty acids in TAG were not able to discriminate between white and brown egg samples as given by ANOVA and MANOVA tests (Supplemental Table S-6). Similarly, samples of group F –corresponding to 4 hen breeds– were perfectly discriminated using 2 positional isotopic variables, sn2/sn1,3 and sn2/C3 (Fig. 3b). Variable sn2/sn1,3 reflected the isotopic deviation between carbon atoms of precursor sugars, i.e., C2 and C5, on one hand, and C1, C3, C4, and C6, on the other hand (Feher, 2017). In variable sn2/C3, sn2 and C3 represent isotopic contents of atoms coming from the same sites in precursor sugars, i.e., C2 and C5, but the former in the glycerol moiety of TAG and the latter in their fatty acid chains. This means that variable sn2/C3 reflected the isotopic fractionation occurring during transformation of pyruvate to acetyl-CoA and during elongation of fatty acids (Hamilton, 2007; Velíšek & Cejpek, 2006). Sample classifications according to hens breed were not possible by irm-MS (Supplemental Fig. S-6), which proves the high discrimination potential of the present metabisotopomic approach conducted using 13C INEPT.

3.3. Quantitation of individual fatty acids First, linear correlation between mass percentages of fatty acids and metabolomic variables derived from corresponding peak areas of 13C INEPT spectra was used as an exploratory analysis. Due to the wide spectral range in 13C NMR and to the high signal-to-noise ratio afforded by the INEPT sequence, even peaks of minor fatty acids were deconvoluted with high precision. It was the case of palmitoleic 14

(Po, Equation 5), hypogeic (H, Equation 6), and linolenic (Ln, Equation 7) acids that highly correlated with C(n-1)-c, H8, and Ln15 variables (Supplemental Fig. S-5 and Table S-1), respectively, leading to their quantitation. Po = 0.081 × C(n­1)­c ― 0.790 (n = 40, R2 = 0.981, p = 0)

(5)

where C(n-1)-c was a peak deconvoluted from the spectral region corresponding to C(n-1) carbons of fatty acids with a global within-lab reproducibility (SRwg) of 1,3%. H = 0.122 × H8 + 0.166 (n = 40, R2 = 0.982, p = 0)

(6)

where H8 was the C8 peak area of hypogeic acid deconvoluted from the vinylic region with SRwg of 2.4%. Ln = 10.753 × Ln15 + 1.051 (n = 40, R2 = 0.953, p = 0)

(7)

where Ln15 was the C15 peak area of linolenic acid deconvoluted from the vinylic region with SRwg of 3.5%. Similarly, a high correlation between mass percentage of linoleic acid and the peak area of its C13 atom (peak labeled L13, Supplemental Fig. S-5) was observed (n = 40, R2 = 0.991, p = 0). The correlation equation was given by: L = 0.122 × L13 ― 1.709

(8)

where L13 variable was deconvoluted from the vinylic region with SRwg of 0.2% (Supplemental Table S1). Second, PLSR was used to construct models for the quantitation of oleic, palmitic, stearic, and vaccenic acids. The number of retained components (h) as well as performance and validation parameters corresponding to each model are given in Fig. 4. Unstandardized coefficients of predictors are presented in Supplemental Table S-7. Moreover, we observed that oleic acid highly correlated with variable C(n-2)-b (n = 40, R2 = 0.966) allowing its quantitation, yet with a lower performance than its prediction by means of the multivariate model (cf. Fig. 4a & Supplemental Table S-7a). It should be noted here that the correlation of variable C(n-2)-b with oleic acid was clearly stronger than with MUFA (n = 40, R2 = 0.817). 15

For the quantitation of stearic acid, a peak from the aliphatic region (Cx-k) was assigned a positive coefficient (Supplemental Table S-7c). The correlation (R2) of the area of this peak with the percentage of stearic acid was 0.360 whereas its correlations with percentages of oleic, linoleic, and linolenic acids were 0.932, 0.558, and 0.534, respectively. This explained the use of peak Cy-a (from the allylic region) and peak L12 (corresponding to linoleic acid) –both assigned a negative coefficient in the model (Supplemental Table S-7c)– in order to subtract the contribution of oleic, linoleic, and linolenic acids from variable Cx-k. It was also noted that both vaccenic and palmitoleic acids showed correlations with areas of C(n-2)-c (n = 40, R2 = 0.774 and n = 40, R2 = 0.970, respectively) and C(n-1)-c (n = 40, R2 = 0.793 and n = 40, R2 = 0.981, respectively). As a result, we concluded that, in the considered matrix, C(n-1)-b, C(n-1)-c, C(n-2)-b, and C(n-2)-c correspond to MUFA (Supplemental Fig. S-5). More precisely, C(n-1)-b and C(n-2)-b correspond to omega-9 MUFA while C(n-1)-c and C(n-2)-c correspond to omega-7 MUFA (See Supplemental Table S1 for variable labeling). Finally, margaric acid, found in minute amounts within egg yolk TAG (0.18%), was roughly predicted (Model: n = 33; h = 2; R2 = 0.679; Adjusted R2 = 0.658; p = 0; F(2,30) = 31.765; Q2cum = 0.761 / Validation: n = 7; Pred-R2 = 0.688 ; see Supplemental Table S-7e for variables used in the model and their coefficients).

4. Conclusion Fast and precise 13C metabisotopomics of triacylglycerols from animal origin matrices was possible by NMR using an adiabatic refocused INEPT sequence. One single experiment allowed to extract variables pertaining to fatty acid composition and position in TAG as well as to 13C contents at specific sites within the same molecules. Using egg yolk as a model matrix from animal origin, we successfully proved by means of various chemometric techniques that powerful biomarkers, either metabolomic or isotopic, can be obtained allowing samples discrimination according to their origin, to the animal 16

species or breed, and to the farming system. Besides, quantitation of individual fatty acids within triacylglycerols was possible through linear multivariate models or using a specific metabolomic variable. 13C metabisotopomics herein presented can be directly applied to extracted triacylglycerols and requires neither prior chemical preparations nor calibration curves, which minimizes error sources. It is noteworthy to mention that since our analytical method affords both metabolomic and isotopic biomarkers, it becomes quasi impossible to pass falsified products from animal origin for authentic ones. Making falsified mixtures that retain authentic metabolomic and isotopic profiles at the same time is hardly possible. Since triacylglycerols are abundant in adipose tissue, our methodology can be applied in numerous health fields to identify biomarkers of dietary intake, of heart diseases such as coronary artery disease, of lipid metabolism disorders and other metabolic issues.

Acknowledgment The authors would like to acknowledge the National Council for Scientific Research of Lebanon (CNRS-L) and the Research Council of Saint Joseph University of Beirut for granting a doctoral fellowship to G.H. The CORSAIRE platform from Biogenouest is also acknowledged. The authors also thank Mathilde GRAND and Anne-Marie SCHIPHORST for their help in irm-MS measurements and Denis LOQUET for his help in gas chromatographic analysis.

References Bender, M. M. (1971). Variations in the

13C/12C

ratios of plants in relation to the pathway of

photosynthetic carbon dioxide fixation. Phytochemistry, 10(6), 1239–1244. Bussy, U., Thibaudeau, C., Thomas, F., Desmurs, J. R., Jamin, E., Remaud, G. S., … Akoka, S. (2011). Isotopic finger-printing of active pharmaceutical ingredients by 13C NMR and polarization transfer techniques

as

a

tool

to

fight

against

counterfeiting.

Talanta,

85(4),

1909–1914.

https://doi.org/10.1016/j.talanta.2011.07.022 Caytan, E., Cherghaoui, Y., Barril, C., Jouitteau, C., Rabiller, C., & Remaud, G. S. (2006). Strategy for 17

specific isotope ratio determination by quantitative NMR on symmetrical molecules: application to glycerol. Tetrahedron: Asymmetry, 17, 1622–1624. https://doi.org/10.1016/j.tetasy.2006.05.031 Caytan, E., Botosoa, E. P., Silvestre, V., Robins, R. J., Akoka, S., & Remaud, G. S. (2007). Accurate quantitative

13C

NMR spectroscopy: Repeatability over time of site-specific

13C

isotope ratio

determination. Anal. Chem., 79, 8266–8269. https://doi.org/10.1021/ac070826k Craig, H. (1957). Isotopic standards for carbon and oxygen and correction factors for massspectrometric analysis of carbon dioxide. Geochimica et Cosmochimica Acta, 12(1–2), 133–149. https://doi.org/10.1016/0016-7037(57)90024-8 European Comission. (1977). Comission Regulation No 72/77 of 13 January 1977 Amending Regulation ( EEC ) No 1470 / 68 on the drawing and reduction of samples and the determination of oil content, impurities and moisture in oil seeds. Retrieved from http://eur-lex.europa.eu Feher, J. (2017). ATP Production I: Glycolysis. In Quantitative Human Physiology (pp. 171–179). Elsevier. https://doi.org/10.1016/b978-0-12-800883-6.00022-7 Gauchi, J. P., & Chagnon, P. (2001). Comparison of selection methods of explanatory variables in PLS regression with application to manufacturing process data. Chemometrics and Intelligent Laboratory Systems, 58(2), 171–193. https://doi.org/10.1016/S0169-7439(01)00158-7 Gładkowski, W., Chojnacka, A., Kiełbowicz, G., Trziszka, T., & Wawrzeńczyk, C. (2012). Isolation of pure phospholipid fraction from egg yolk. Journal of the American Oil Chemists’ Society, 89(1), 179–182. https://doi.org/10.1007/s11746-011-1893-x Golbraikh,

A.,

&

Tropsha,

A.

(2002).

Beware

of

q2!,

20,

269–276.

https://doi.org/https://doi.org/10.1016/S1093-3263(01)00123-1 Hamilton, R. J. (2007). Fatty acids: structure, occurrence, nomenclature, biosynthesis and properties. In Trans Fatty Acids (pp. 1–24). Hawkins, D. M., Basak, S. C., & Mills, D. (2003). Assessing Model Fit by Cross-Validation. Journal of Chemical Information and Computer Sciences, 43(2), 579–586. International Atomic Energy Agency. (1995). Reference and intercomparison matrerials for stable 18

isotopes of light elements. In IAEA-Techdoc-825A (Ed.), Proceedings of a consultants meeting, Vienna (p. 159). Vienna: IAEA. https://doi.org/10.1016/0020-708X(73)90108-7 International Olive Council. (2015). Determination of fatty acid methyl esters by gas chromatography. Madrid. https://doi.org/COI/T.20/Doc. No 33 Koulman, A., Furse, S., Baumert, M., Goldberg, G., & Bluck, L. (2019). Rapid profiling of triglycerides in human breast milk using LESA­FTMS reveals new VLCFAs and differences within individuals. Rapid Communications in Mass Spectrometry. https://doi.org/10.1002/rcm.8465 Lira, L. G., Justa, R. M. D. E., Carioca, A. A. F., Verde, S. M. M. L., Sampaio, G. R., Torres, E. A. F. da S., & Damasceno, N. R. T. (2019). Plasma and erythrocyte ω-3 and ω-6 fatty acids are associated with multiple inflammatory and oxidative stress biomarkers in breast cancer. Nutrition, 58, 194–200. https://doi.org/10.1016/j.nut.2018.07.115 Merchak, N., Bejjani, J., Rizk, T., Silvestre, V., Remaud, G. S., & Akoka, S. (2015). 13C isotopomics of triacylglycerols using NMR with polarization transfer techniques. Analytical Methods, 7(12), 4889–4891. https://doi.org/10.1039/c5ay01250c Merchak, N., El Bacha, E., Bou Khouzam, R., Rizk, T., Akoka, S., & Bejjani, J. (2017). Geoclimatic, morphological, and temporal effects on Lebanese olive oils composition and classification: A 1H NMR

metabolomic

study.

Food

Chemistry,

217,

379–388.

https://doi.org/10.1016/j.foodchem.2016.08.110 Merchak, N., Rizk, T., Silvestre, V., Remaud, G. S., Bejjani, J., & Akoka, S. (2018). Olive oil characterization and classification by 13C NMR with a polarization transfer technique: A comparison with gas chromatography and 1H NMR. Food Chemistry, 245(October 2017), 717– 723. https://doi.org/10.1016/j.foodchem.2017.12.005 Merchak, N., Silvestre, V., Loquet, D., Rizk, T., Akoka, S., & Bejjani, J. (2017). A strategy for simultaneous determination of fatty acid composition, fatty acid position, and position-specific isotope contents in triacylglycerol matrices by 13C-NMR. Analytical and Bioanalytical Chemistry, 409(1), 307–315. https://doi.org/10.1007/s00216-016-0005-z 19

Palacios, L. E., & Wang, T. (2005). Egg-yolk lipid fractionation and lecithin characterization. Journal of the American Oil Chemists’ Society, 82(8), 571–578. https://doi.org/10.1007/s11746-005-1111-4 Rakotomalala, R. (2005). Tanagra: a free software for research and academic purposes. Proceedings of EGC. Roy, P. P., & Roy, K. (2008). On some aspects of variable selection for partial least squares regression models.

QSAR

and

Combinatorial

Science,

27(3),

302–313.

https://doi.org/10.1002/qsar.200710043 Saito, T., Nakaie, S., Kinoshita, M., Ihara, T., Kinugasa, S., Nomura, A., & Maeda, T. (2004). Practical guide for accurate quantitative solution state NMR analysis. Metrologia, 41(3), 213–218. https://doi.org/10.1088/0026-1394/41/3/015 Schinka, J. A., Velicer, W. F., & Weiner, I. B. (2003). Handbook of Psychology. Standal, I. B., Axelson, D. E., & Aursand, M. (2009). Differentiation of Fish Oils According to Species by 13C-NMR Regiospecific Analyses of Triacyglycerols. Journal of American Oil Society, 86, 401–407. https://doi.org/10.1007/s11746-009-1370-y Tenailleau, E., & Akoka, S. (2007). Adiabatic 1H decoupling scheme for very accurate intensity measurements

in

13C

NMR.

Journal

of

Magnetic

Resonance,

185(1),

50–58.

https://doi.org/10.1016/j.jmr.2006.11.007 Thibaudeau, C., Remaud, G. S., Silvestre, V., & Akoka, S. (2010). Performance evaluation of quantitative adiabatic 13C NMR pulse sequences for site-specific isotopic measurements. Analytical Chemistry, 82(13), 5582–5590. Velíšek, J., & Cejpek, K. (2006). Biosynthesis of Food Constituents : Lipids . and Glyceroglycolipids – a Review. Czech Journal of Food Sciences, 24(5), 193–216. Vögeli, B., Orts, J., Strotz, D., Chi, C., Minges, M., Wälti, M. A., … Riek, R. (2014). Towards a true protein movie: A perspective on the potential impact of the ensemble-based structure determination using

exact

NOEs.

Journal

of

Magnetic

Resonance,

241(1),

53–59.

https://doi.org/10.1016/j.jmr.2013.11.016 20

Zhang, B. L., Buddrus, S., & Martin, M. L. (2000). Site-specific hydrogen isotope fractionation in the biosynthesis

of

glycerol.

Bioorganic

Chemistry,

28(1),

1–15.

https://doi.org/10.1006/bioo.1999.1145

Figures

Fig. 1. Classification of egg samples based on the global isotopic composition (δ13Cg) of yolk triacylglycerols obtained by irm-MS. Samples of D, E, and F groups were combined in a single group for one-way ANOVA test. p = 0 given by one-way ANOVA means that it is less than 0.0001. The error rate given by Linear Discriminant Analysis (LDA-Er) was used as classifier performance indicator.

21

Fig. 2. Discrimination of organic and non-organic eggs (a); classification of non-organic eggs according to the hens farming system (b); and classification of eggs laid by hens raised in conventional cages (c) and in backyard cages (d) according to their origin. p = 0 given by one-way ANOVA means that it is less than 0.0001. Error rates given by Linear Discriminant Analysis (LDA-Er) and Leave-One-Out (LOO-Er) were used as classifier performance and robustness indicators, respectively. Variables used in classifications are reported in decreasing order of importance based on the value of their Wilks’ λ parameter. See Supplemental Table S-1 for variable labeling.

22

Fig. 3. Classification of combined D, E (a) and F (b) egg samples according to the hen breed. Error rates given by Linear Discriminant Analysis (LDA-Er) and Leave-One-Out (LOO-Er) were used as classifier performance and robustness indicators, respectively. Variables used in classifications are reported in decreasing order of importance based on the value of their Wilks’ λ parameter. See Supplemental Table S-1 for variable labeling.

23

24

Fig. 4. Correlations between fatty acid percentages obtained by GC (x-axis) and those predicted using 13C

INEPT spectra processing (y-axis). ◦ training samples, • test samples for external validation, p = 0

means that it is less than 0.0001.

Tables Table 1 Egg samples description Group Country of origin

Farming system

A

France

B

Lebanon

Backyard cage Organic farming

C

Lebanon

D

Lebanon

Number Eggshell of color samples 3 (2) a Off-White 6

Brown

Backyard cage

3 1

Conventional cage

2 4 4

White LightBrown Off-White White Brown

Wheat b – Human food leftovers (mainly bread b) Fava b – Corn c – Barley b – Wheat b – Cotton b – Soybean b – Agricultural leftovers d (organic fruits and vegetables) Wheat b – Corn c – All kinds of human food leftovers

Corn c – Soybean b – Other grains (Wheat b, Barley b) – Vitamins – Minerals – Amino acids – Soybean oil b Corn c – Soybean b - Vitamins

White Brown White F Lebanon Corn c (200 kg) – Barley b (50 kg) – Wheat b (20 kg) – Soybean b (15 kg) – Brown CaCO3 (7 kg) – NaCl (0.5 kg) – All Lightkinds of human food leftovers Brown 2 Off-White a G Lebanon Not specified 0 (4) Brown Not specified a Values outside brackets correspond to the number of samples used in discriminant analysis whereas E

Lebanon

Conventional cage Free-range

3 3 2 2 2

Hen feed composition

values between brackets correspond to the number of samples used in regression analysis b

C3 plants

c

C4 plants

d Mainly

C3 plants 25

Table 2 Precision of isotopic variables and their variability Variablesa SRwgb ‰ σc ‰ σ/SRwg d Cn-t* 1.5 11 7.1 C(n-1)-t* d 0.67 11 17 e C3-t 0.46 5.8 13 d C(n-2)-t* 0.68 11 16 C2(sn2) 0.61 7.4 12 C2(sn1,3)-t 0.31 3.7 12 sn2 1.3 5.6 4.3 sn1,3 0.65 4.77 7.3 L18/L17 4.7 29 6.3 L18/L16 3.2 12 3.8 L18/L11 4.7 83 18 L18/L9 7.0 106 15 L18/L10 4.9 62 13 L18/L12 4.6 57 13 L18/L13 5.3 44 8.3 sn2/sn1,3 1.1 6.6 6.0 C2(sn2)/C2(sn1,3) 0.92 11 12 sn2/C2(sn2) 1.3 7.7 5.8 sn1,3/C2(sn1,3) 0.72 6.6 9.2 sn2/C3(sn2) 0.90 5.8 6.4 sn1,3/C3(sn1,3) 0.71 5.8 8.2 [C(n-1)/ C(n-2)]-t* d 0.47 3.3 7.0 d [C(n-1)/ Cn]-t* 1.1 4.4 4.1 a See Supplemental Table S-1 for variable labeling, the last 15 variables of the table represent additional isotopic variables calculated from corresponding deconvoluted peak areas bS

Rwg

is the relative standard deviation of deconvoluted peak areas obtained from analysis of the 5

aliquots cσ

is the relative standard deviation of deconvoluted peak areas over the whole set of 41 samples

d t* et

represents the sum of peaks corresponding to non-omega-3 fatty acids

represents the sum of all peaks in the considered region

26