Lipidomics reveals similar changes in serum phospholipid signatures
of overweight and obese paediatric subjects
Sara Anjos1, Eva Feiteira1, Frederico Cerveira3, Tânia Melo1,2, Andrea Reboredo3, Simone Colombo1 Rosa Dantas4, Elisabete Costa1, Ana Moreira1, Sónia Santos5, Ana Campos1, Rita Ferreira1, Pedro Domingues1, M. Rosário M. Domingues1,2*
1Mass Spectrometry Centre, Department of Chemistry & QOPNA, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
2Department of Chemistry & CESAM&ECOMARE, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
3Clinical Pathology, Centro Hospitalar do Baixo Vouga, Aveiro, Portugal
4Endocrinology, Diabetes and Nutrition, Centro Hospitalar do Baixo Vouga, Aveiro, Portugal
5Department of Chemistry & CICECO, University of Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
Corresponding author: M. Rosário Domingues1
Address reprint requests to: M. Rosário M Domingues,Lipidomic laboratory,
Departamento de Química, Universidade de Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro (PORTUGAL)
Phone: +351 234 370698
Fax: +351 234 370084
Abstract
Obesity is a public health problem and a risk factor for pathologies such type 2 diabetes mellitus, cardiovascular diseases and non-alcoholic fatty liver disease. Given these clinical implications, there is a growing interest to understand the pathophysiological mechanism of obesity. Changes in lipid metabolism have been associated with obesity and obesity-related complications. However, changes in the lipid profile of obese children have been overlooked. In the present work, we analysed the serum phospholipidome of overweight and obese children by HILIC-MS/MS and GC-MS. Using this approach, we have identified 165 lipid species belonging to the classes PC, PE, PS, PG, PI, LPC and SM. The phospholipidome of overweight (OW) and obese (OB) children was significantly different from normal-weight children (control). Main differences were observed in the PI class that was less abundant in OW and OB children and some PS, PE, SM and PC lipid specie are upregulated in obesity and overweight. Although further studies are needed to clarify some association between phospholipid alterations and metabolic changes, our results highlight the alteration that occurs in the serum phospholipid profile in obesity in children.
Keywords: paediatrics, obesity, diagnostic methods, phospholipids, lipid metabolism, lipidomics
Introduction
Obesity is increasing globally both in adults as in children and has become a considerable public health problem over the last two decades.1,2 Obesity is a condition characterized by an excessive accumulation of fat in the adipose tissue and is associated with a state of low-grade chronic inflammation, due to the lipotoxicity and an array of deleterious effects triggered by ectopic lipid accumulation in non-adipose tissues.3 Lipotoxicity aggravates the inflammation state and causes cell dysfunction and apoptosis in several organs and tissues, which can lead to insulin resistance, cardiovascular complications and liver disease.3 Consequently, lipotoxicity underlies the onset and the development of several obesity-related metabolic diseases, including type 2 diabetes mellitus (T2DM), dyslipidaemia, cardiovascular diseases (CVD) and non-alcoholic fatty liver disease (NAFLD).4 As obesity has become a worldwide epidemic, there is a strong need to monitor this condition and improve its early diagnosis and prevention of comorbidities.
Alterations in plasma lipids, like increased triglycerides (TGs), total cholesterol, low-density lipoprotein (LDL) and oxidized-LDL, along with reduced high-density lipoprotein (HDL) concentrations, have been widely associated with obesity.5 There are evidences that changes in the lipid metabolism, plasma lipids and lipoproteins can underlie the onset of obesity-related complications.6,7 Nowdays lipodmics approaches are being used to study of lipid profile at molecular level in biological systems, including plasma,.8 and have recently been used to give new insights into the pathogenesis of obesity and related complications.9–11 Published works reported changes in the plasma lipidome of obese subjects when compared with non-obese individuals.5,12,13 Some authors reported a positive association between increased body
mass index (BMI) and total plasma levels of TGs, diglycerides (DGs) and free fatty acids (FFAs).14–17 Total levels of sterol lipids, such as cholesterol and cholesteryl esters (CEs), were also found to be increased in obesity, probably associated with an increase of LDL concentrations.14,15 Several molecular species of phosphatidylcholine (PC) bearing polyunsaturated fatty acids, especially PC 38:3, were increased in obesity.15,18 However, Pietiläinen et al.13 reported that docosahexaenoic acid (DHA)-containing PC were decreased in obese subjects, which may be related with the anti-inflammatory effects of this n-3 polyunsaturated fatty acid (PUFA).19 Moreover, many lysophosphatidylcholine (LPC) species, along with the total LPCs levels, were reported to be decreased in obesity.20–22 Whereas saturated LPCs are usually associated with a pro-inflammatory activity, polyunsaturated LPCs may prevent the inflammatory response.23 However Bas et al.22 reported lower concentrations of saturated LPC species in obese subjects, suggesting that LPC metabolism might not be related to pro-inflammatory signs in obesity. The alterations of ether-linked phospholipids in obesity were also addressed, with some studies reporting an increase,15,24 while others a decrease13,14,25 in the levels of some species of this lipid class. The ether-linked phospholipids were associated with antioxidant properties,26 however, whether this lipid class were elevated as a response to oxidative stress in obesity remains unknown. Lastly, SMs concentrations seem to be increased in obese subjects, in particular, the species SM 32:2 and SM 34:2, which can again reflect an increase in the levels of circulating LDL.18
Studying the alterations of lipids from plasma is crucial for the identification of lipid molecular species that, after accurate validation, may contribute to the diagnosis and monitoring of obesity and associated diseases. However, although the evidence that obesity influences plasma concentrations of several lipid species, most of the data has
been strictly reported for adult subjects. Unexpectedly, there is much less information available for what concerns lipidomic studies in paediatric subjects. Regarding paediatric obesity and lipidomics, studies were mainly focused on the alteration of the plasma total FA profile and reported an increase of monounsaturated species as FA 16:1
n-7, which can reflect an increase of endogenous lipogenesis, along with a decrease of
polyunsaturated species as FA 22:5 n-6, which has been associated with metabolic syndrome.27–29 Only one metabolomic study has focused on the variations in plasma phospholipids in paediatric obesity. This study targeted 163 metabolites, including 107 lipid species of PC, LPC and SM classes, reported decreased levels of alkyl acyl PC (PC-O) and LPC species in the obese subjects.30 However, no studies have so far addressed a comprehensive phospholipidomic profiling in obese or pre-obese (overweight) children. Because lipidomics of childhood obesity has been overlooked and the prevalence of this metabolic disease has been increasing for children,2 in the present study hydrophilic interaction liquid chromatography coupled to mass spectrometry (HILIC-MS/MS) and multivariate statistics was used to analyse the serum phospholipidome of normal-weight (control, CT) overweight (OW) and obese (OB) paediatric subjects. This study aimed to test whether the phospholipid profile showed correlations with pre-obesity and obesity in children. Our study also compared the phospholipid profile of female and male subjects with pre-obesity and obesity. To the best of our knowledge, this is the first lipidomic study that characterizes the serum phospholipid and sphingomyelin profile to classify specific phospholipid signatures in OW and OB children.
Methods
Reagents/Chemicals
Phospholipid standards 1,2-dimyristoyl-sn-glycero-3-phosphocholine (dMPC), glycero-3-phospho-(1’-rac-glycerol) (dMPG), 1,2-dimyristoyl-sn-glycero-3-phosphoethanolamine (dMPE),
1-nonadecanoyl-2-hydroxy-sn-glycero-3-phosphocholine (LPC 19:0) and
N-heptadecanoyl-D-erythro-sphingosylphosphorylcholine (SM (d18:1/17:0)) were purchased from Avanti® Polar Lipids, Inc. (Alabaster, AL), nonadecanoic acid (C19:0) was obtained from Sigma-Aldrich Chemical Co. (St. Louis, MO, USA). All the standards were used without further purification. Formic acid and ammonium hydroxide were obtained from Sigma-Aldrich Chemical Co. (St. Louis, MO, USA), perchloric acid was obtained from Chem-Lab NV (Zedelgem, Germany), and potassium hydroxide was purchased from LABChem (Zelienople, PA, USA) and ammonium molybdate from Panreac (Barcelona, Spain). Ascorbic acid and sodium chloride (NaCl) were purchased from VWR Chemicals (Leuven, Belgium), sodium dihydrogen phosphate dihydrate was obtained from Riedell-de Haën (Seelze, Germany). Chloroform (CHCl3), acetonitrile (ACN), methanol (MeOH) and hexane were purchased by Fisher Scientific (Leicestershire, UK) with a degree of purity suitable for HPLC and were used without further purification. Milli-Q water was used for all experiments, filtered through a 0.22mm filter and obtained using a Milli-Q Millipore system (Synergy®, Millipore Corporation, Billerica, MA, USA).
Study design overview
Serum samples were obtained from children between the ages of 8 and 17 years, Centro Hospitalar do Baixo Vouga (Aveiro, Portugal).The samples were fasting samples. The study was approved by the local ethics Committee. The identity of the children was anonymous and informed consent was obtained from parents as appropriate. For each child, the weight and BMI were evaluated, and the children were divided into 3 groups: normal-weight (control, CT), overweight (OW) and obese (OB) children. CT children BMI were between 18.5 and 24.9 kg/m2; OW children presented a BMI between 25 and 29.9 kg/m2; OB children BMI were higher than 30 kg/m2.CT children did not present any hepatic pathology, thyroid changes or T2DM, which could interfere with lipid metabolism. For each child, the serum was collected and analysed by the Clinical Pathology Service of the Centro Hospitalar do Baixo Vouga Levels of glucose, insulin, AST and ALT aminotransferases, haemoglobin A1c, total cholesterol, HDL, LDL, TG and TSH were evaluated for each subject. The baseline characteristics of the study children were presented in Table 1. After collection and analysis, the serum samples of the children were stored at -80 ° C until further study of the lipid profile. Samples were extracted within one week.
Table 1. Baseline characteristics of the study children.
Normal weight Overweight Obese
Gender M/F 4/4 9/5 5/5
Age (years) 13.9 ± 2.5 11.9 ± 2.6 14.8 ± 2.1†
BMI (kg/m2) 21.7 ± 1.2 27.2 ± 0.9* 33.5 ± 2.1*†
Fasting glucose (mg/dl) 86.2 ± 8.5 87.1 ± 10.8 93.2 ± 7.3 Fasting Insulin (mUI/l) 15.0 ± 5.3 15.6 ± 6.5 25.7 ± 10.8*†
HbA1c (%) 4.9 ± 0.4 5.3 ± 0.3* 5.3 ± 0.3 AST (U/l) 23.6 ± 7.3 25.0 ± 6.5 24.9 ± 7.3 ALT (U/l) 18.1 ± 7.6 21.4 ± 7.2 24.2 ± 9.3 HDL cholesterol (mg/dl) 53.0 ± 11.4 49.1 ± 8.2 44.4 ± 7.7 LDL cholesterol (mg/dl) 85.8 ± 19.2 105.1 ± 24.1 114.5 ± 25.2 Total cholesterol (mg/dl) 150.9 ± 22.3 165.6 ± 27.8 173.3 ± 23.6 Triglycerides (mg/dl) 85.6 ± 54.5 73.8 ± 21.3 82.3 ± 22.9 TSH (mU/l) 2.4 ± 1.3 2.3 ± 0.7 2.4 ± 0.7
The data are expressed as the mean ± standard deviation. BMI are body mass index; HbA1c glycosylated haemoglobin; AST, aspartate aminotransferase; ALT, alanine aminotransferase; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TSH, thyroid-stimulating hormone.
*P<0.05 versus the control group (normal weight) †P<0.05 versus the overweight group
Extraction of serum phospholipids
The serum phospholipids from each child were obtained by solid phase extraction (SPE) using a Visiprep SPE Vacuum Manifold (Supelco, Sigma-Aldrich, Bellefonte, PA). Prior to extraction, 5 μg of PC internal standard (dMPC, PC 14:0/14:0) were added to each 100 μL serum sample. Extraction of phospholipids was performed as described by Pinto et al.31 with modifications. Briefly, a volume of 900 µL of
acetonitrile:formic acid (99:1, v/v) was added to each 100 μL serum sample, vortexed for 30 seconds and centrifuged at 2000 rpm for 5 minutes for protein precipitation. Then the supernatant was transferred to the Hybrid SPE-PL columns (HybridSPE® -Phospholipid 30 mg, ref. 55261-U SUPELCO, Sigma-Aldrich, Bellefonte, PA), previously conditioned with 1 mL acetonitrile. Then a volume of 1 mL acetonitrile:formic acid (99:1, v/v) and 1 mL acetonitrile were used to wash the column. The phospholipids were extracted using 2 mL of acetonitrile with 5% of aqueous ammonia. This fraction was collected together, dried under a nitrogen stream, and stored at -20 °C until further analysis.
Phospholipid quantification by phosphorous measurement
The quantification of the total amount of phospholipids recovered after extraction was performed according to Bartlett and Lewis.32 The detailed experimental procedures were previously described by Sousa et al..33 Phospholipid extracts were dissolved in 300 µL of dichloromethane, and a volume of 10 µL was transferred, in duplicate, to a glass tube, previously washed with nitric acid 5%. The solvent was dried under a nitrogen stream and a volume of 125 µL of perchloric acid 70 % was added to each tube. Samples were incubated in a heating block (Stuart, U.K.) for 1h at 180 °C. After cooling to room temperature, a volume of 825 µL of Milli-Q water, 125 µL of ammonium molybdate (2.5 g/ 100 mL of Milli-Q water), and 125 µL of ascorbic acid (0.1 g/1 mL of Milli-Q water) were added to each sample, with vortex mixing between each addition. Samples were then incubated in a water bath at 100 °C for 10 min. Afterwards samples were immediately cooled down in a cold water bath. Phosphate standards from 0.1 to 2 µg of phosphorus (P) were prepared from a sodium dihydrogen phosphate dihydrate (NaH2PO4•2H2O, 100 μg/mL of P). Standards underwent the same
experimental procedure as samples without the heating block step. The absorbance was measured at 797 nm in a Multiskan GO 1.00.38 Microplate Spectrophotometer (Thermo Scientific, Hudson, NH, USA) controlled by SkanIT software version 3.2 (Thermo Scientific™). The amount of phosphorus present in each sample was calculated by linear regression. For each lipid extract, the amount of total phospholipid was calculated by multiplying the amount of determined phosphorus by 25.
Analysis of fatty acids by gas chromatography coupled to mass spectrometry (GC-MS)
Analysis of the FAs esterified phospholipid in the extracted serum pool was performed by GC-MS. Fatty acid methyl esters (FAMEs) were obtained using a methanolic KOH solution (2 M), according to the method described by Aued-Pimentel
et al. 34. Briefly, an amount equivalent at 15 μg of phospholipids were dissolved in 1 mL
of a solution of n-hexane containing the internal standard C19:0 (1 μg/mL). Then, 200 μL of a methanolic KOH solution (2 M) were added, followed by a vigorous vortex-mixing for 2 min. A volume of 2 mL of saturated NaCl solution was added and the samples were subjected to centrifugation for 5 min at 2000 rpm, the organic phase was collected and dried. FAMEs were dissolved in 60 μL of n-hexane, and 1 μL of this hexane solution was used for GC-MS analysis (GCMS-QP2010 Ultra, Shimadzu, Kyoto, Japan). The GC-MS system was equipped with an auto sampler and a 30 m long DB-1 column, with an internal diameter of 0.32 mm and a film thickness of 0.25 μm (Agilent J&W, Agilent Technologies, Santa Clara, USA). The GC-MS analysis was performed as previously described by Rocha-Rodrigues et al..35. FAMEs identification was performed by comparing their retention times and mass spectra against commercial
FAMEs standards (Supelco 37 Component FAME Mix, Supelco, Darmstadt, Germany) and confirmed by comparison with the spectral library “The Lipid Web”.36
Phospholipid analysis by hydrophilic interaction liquid chromatography coupled to high resolution tandem mass spectrometry (HILIC-MS/MS)
Phospholipid extracts were separated using a high-performance liquid chromatography (HPLC) system (Ultimate 3000 Dionex, Thermo Fisher Scientific, Bremen, Germany) with an auto sampler coupled online to the Q-Exactive hybrid quadrupole Orbitrap® mass spectrometer (Thermo Fisher Scientific, Bremen, Germany), according to Colombo et al.37 with modifications. Briefly, the solvent system consisted of two mobile phases: mobile phase A [ACN:MeOH:water 50:25:25 (v/v/v) with 1 mM ammonium acetate] and mobile phase B [ACN:MeOH 60:40 (v/v) with 1 mM ammonium acetate]. Initially, 40% of mobile phase A was held isocratically for 8 min, followed by a linear increase to 60% of A within 7 min and a maintenance period of 5 min, returning to the initial conditions in 5 min, followed by a re-equilibration period of 10 min prior next injection. An amount of 5 µg of each phospholipid extract corresponding to a volume of 5 µL was mixed with 79 µL of solvent system (60% of eluent B and 40% of eluent A). A volume of 5 µL of each previous diluted sample was introduced into the Ascentis®Si column (15 cm × 1 mm, 3 µm, Sigma-Aldrich, Darmstadt, Germany) with a flow rate of 40 µL min−1. The temperature of the column oven was maintained at 30 ºC. The mass spectrometer with Orbitrap® technology operated using a positive/negative switching toggles between positive (electrospray voltage 3.0 kV) and negative (electrospray voltage -2.7 kV) ion modes with a capillary temperature of 250 ºC and a sheath gas flow of 15 U. A top-10 data-dependent method was used. Cycles consisted in one full scan mass spectrum (resolution of 70,000 and
AGC target of 1e6) and ten data-dependent MS/MS scans (resolution of 17,500 and AGC target of 1e5), acquired in each polarity, and each cycle time was 2 s. Cycles were repeated continuously throughout the experiments with the dynamic exclusion of 60 seconds and intensity threshold of 1e4. A stepped normalized collisional energy of 25, 30 and 35 eV was used. Species of PE, PC, LPC and SM were analysed in the LC-MS spectra in the positive ion mode, as [M+H]+ ions, while PS, PI and PG species were analysed in negative ion mode, as [M-H]- ions. Data acquisition was carried out using the Xcalibur data system (V3.3, Thermo Fisher Scientific, USA). The mass spectra were processed and integrated through the MZmine software (v2.30)38. This software allows for filtering and smoothing, peak detection, peak alignment and integration, and assignment against an in-house database, which contains information on the exact mass and retention time for each phospholipid molecular species. During the processing of the data by MZmine, only the peaks with raw intensity higher than 1e4 and within 5 ppm deviation from the lipid exact mass were considered. The identification of each phospholipid species was validated by analysis of the MS/MS spectra, as described hereinafter37. All the MS/MS fragmentation patterns of each phospholipid classes analysed in the present study, are available as supplementary information. Raw data files were stored at Metabolights database.
Statistical analysis
The experimental results are presented in terms of mean ± standard deviation. Physiological parameters were analysed using one-way ANOVA combined with Sidak's multiple comparisons test, whereas GC-MS datasets were studied using one-way ANOVA combined with the Bonferroni’s multiple comparisons test in order to highlight differences between CT, OW and OB children. Datasets composed of the XIC
areas obtained by the HILIC-MS and MS/MS analysis were auto-scaled and analysed statistically. Missing values were replaced by half of the minimum positive values detected in the dataset39,40. Principal component analysis (PCA) were performed with the R built in function and ellipses were drown using the R package ellipse41, assuming a multivariate normal distribution and a level of 0.95. Partial Least Squares-Discriminant Analysis (PLS-DA) was performed using the R package Metaboanalyst and ellipses were drown, as described previously. Univariate statistical analysis was performed using Kruskal-Wallis test following post hoc Dunn test. P-value <0.05 was considered as an indicator of statistical significance. Univariate statistical analysis of GC-MS datasets was performed using PRISM® GraphPad, Inc. v6 software for windows. Heatmaps were created using the R package pheatmap42 using “Euclidean” as clustering distance and “ward.D” as the clustering method. Univariate and multivariate statistical analysis were performed using R version 3.5.143 in Rstudio version 1.1.4.44 All graphics and boxplots were created using the R package ggplot2.45 Other R packages used for data management and graphics included plyr,46 dplyr47 and tidyr.48
Results
The serum phospholipid profile in paediatric obesity can reflect the course of inflammatory events underlying the development of comorbidities.49 Therefore, we employed a high resolution HILIC-LC-MS/MS platform to characterize the serum phospholipid profile of groups with different BMI, control(CT), overweight (OW) and obese(OB) children. The data sets obtained were subjected to multivariate and univariate statistical analysis, aiming to identify significant changes in the phospholipid profile between the three analysed groups.
Through the HILIC-LC-MS/MS platform, we identified 165 phospholipid species belonging to the following classes: phosphatidylcholine (PC), lysophosphatidylcholine(LPC), phosphatidylethanolamine (PE), phosphatidylserine (PS), phosphatidylglycerol (PG), phosphatidylinositol (PI) and SM (Supplementary Table S1, Supplementary Figures S1-S6). The relative quantification of all the identified species was performed, after normalization of the extracted ion chromatograms peak areas of each phospholipid species using the peak area of the internal standard (Supplementary Table S2; Supplementary Figure S7). Prior to perform the principal component analysis (PCA), data were log transformed and auto-scaled.
PCA scores plot demonstrated the clustering pattern where subjects were clearly clustered into three distinct groups, cluster CT, cluster OW and cluster OB (Figure 1). The plot shows that the model captured 61.3% of the total variance in the dataset and that the variation between the different biological group is more pronounced on the PC1 (46.6%) which counts for the highest variation in the models. In this model, PC2 (14.7%) describes the variation within the groups. CT samples were scattered on the left region of the plot, whereas OW and OB samples were scattered in the right region of the plot. However, the 95% confidence curves of the OW and OB clusters are touching, but these two groups were differentiated in a three-dimensional score plot (Supplementary Figure S8).
Figure 1. Principal component analysis (PCA) in a two-dimensional score plot of phospholipid profiles obtained from normal-weight (control, CT), overweight (OW) and obese (OB) children. Loading plot for PCA analysis is shown in Supplementary Figure S9.
Furthermore, PCA was also performed to investigate differences in phospholipid profile between male and female individuals belonging to the same BMI group. Male and female individuals within the same group could not be clustered in a two-dimensional score plot, although some group separation could be observed for overweight male and female groups (Supplementary Figure S10). Therefore, for this number of samples and statistical analysis, it seems that the phospholipid profile did not significantly differ between OW and OB boys and girls.
Then, we performed a partial least squares discriminant analysis (PLS-DA) of the log-transformed and auto-scaled dataset to maximize the samples classification. The PLS-DA model exhibited the performance statistics of R2X = 0.83208, R2Y = 0.9593 and a high prediction parameter Q2X= 0.79507 and Q2Y= 0.91475. The resulting two-dimensional score plot revealed that the three groups were well separated (Figure 2).
Figure 2. Partial least square discriminant analysis (PLS-DA) score plot of phospholipid profiles obtained from normal-weight (control, CT), overweight (OW) and obese (OB) children. . Loading plot for PCA analysis is shown in Supplementary Figure S11.
The PLS-DA score plot described 56.9% of the total variance, including component 1 (46.3%) and component 2 (10.6%). Along the component 1, the samples were scattered with the following order: CT, OB and OW. We also obtained a quantitative estimation of the discriminatory power of each phospholipid species, using the variable importance in projection (VIP). Figure 3 shows the VIP list of the 16 phospholipid species with higher discriminant power, the majority of which belong to the PI class. We performed a univariate analysis (Kruskal-Wallis and the post hoc Dunn’s multiple comparisons tests) was performed in order to evaluate the significance of the phospholipid molecular species in the three conditions. We ranked the estimated coefficients (loadings) of component 1 of the PLS-DA and the major 16 contributors were selected (Figure 4). Boxplots reporting the normalized intensities of these 16 phospholipid species are shown in Figure 4. Among these 16 species that showed variation it were identified 9PI, 3PE, 1 PS, 1PC, 1 ether-linked PC, 1LPC. We observed a statistically significant lower levels of all PI, PE 38:9, PE 42:9, LPC 14:1 and PC 44:11 in OW and OB children, when compared to CT individuals. On the other side, the species CT of PS 30:2 and PE 30:3 were statistically higher in OB and OW individuals, compared to CT individuals PC-O 30:1/PC-P 30:0 were not detected on CT and OB children, although these phospholipid species were detected in OW children (Figure 3 and Figure 4).
Figure 3. Variable importance in projection (VIP) values obtained from partial least square discriminant analysis (PLS-DA) of the 16 phospholipid species with higher discriminant power, along with their respective changes.
Figure 4. Boxplot of 16 phospholipid molecular species with higher discriminant power (VIP list) obtained from partial least square discriminant analysis (PLS-DA). p<0.05 was considered statistically significant: a, control vs obese; b, control vs overweight; and c, obese vs overweight.
We have used the results from the univariate analysis to create a dendrogram with a two-dimensional hierarchical clustering, using the top 40 p-values (p<0.032) from the Kruskal-Wallis test (Figure 5). The primary split in the upper hierarchical dendrogram shows that the samples clustered independently into the three groups. The clustering of individual phospholipids with respect to their similarity in changes of phospholipid expression shows that they cluster in two principal groups: The first group
composed mainly of PI species, that were more abundant in the CT cluster and the second group containing mainly PC, PC plasmalogens and SM species, along with some species belonging to the PE, PI and PS classes, that were less abundant in the CT group.
Figure 5. Two-dimensional hierarchical clustering heat map of the phospholipid data of the three studied groups. Levels of relative abundance are indicated on the colour scale, with numbers indicating the fold difference from the grand mean. The clustering of the sample groups is represented by the dendrogram in the top. The clustering of individual phospholipid species with respect to their similarity in change of relative abundance is represented by the dendrogram to the left.
Finally, for an overview of the composition of phospholipids fatty acids, we performed a GC-MS analysis. Regardless of the group of children, palmitic acid (C16:0) was the most abundant (Figure 6).
Figure 6. Phospholipid fatty acid profile of normal-weight (control, CT), overweight (OW) and obese (OB) children. Values are mean ± standard deviation of 8 biological replicates for each condition. *Significantly different between selected conditions (p<0.05); ***Significantly different between selected conditions (p<0.001).
The levels of this fatty acid were higher in OW children when compared with CT. This increase in palmitic acid was also observed in OB children, even though these changes were not statistically significant. In both OW and OB children, stearic acid (C18:0) was statistically more abundant in comparison to the levels in CT individuals. We also observed a lower abundance of linoleic acid (C18:2 n-6) in OW and OB children. We did not observe any statistically significant difference for oleic acid (C18:1), arachidonic acid (C20:4), dihomo-γ-linolenic acid (C20:3, n-6) and docosahexaenoic acid (C22:6) between groups.
Discussion
Alterations in lipid metabolism have been related to obesity and other pathologies. Knowledge of the pathophysiological mechanisms of obesity may help to prevent comorbidities, like T2DM and CVD, and improve their early diagnosis. Despite the vast amount of information concerning adults, the lipid profile of children in obesity has been overlooked. In our work, we performed a phospholipidomic profiling on serum of OW and OB children with the aim of elucidating the alterations caused by weight gain.
The multivariate analyses performed indicated that there are alterations in the phospholipid profile of OB children, as well as in the phospholipid profile of OW children (Figure 1 and 2). The VIP score (Figure 3) and the boxplots (Figure 4) of 16 phospholipid species, evidenced PI as the phospholipid class with the highest discriminant power, although there were significant alterations also in other classes.
Few studies have analysed PI levels in obesity. Although Heimerl et al.50 reported an increase of PI levels in obese adults, and we observed as well that several PI molecular species were lower in OW and OB children compared with CT. This decrease may be associated, to some extent, with the phosphorylation of PI species by phosphatidylinositol kinases, such as phosphatidylinositol 3-kinase (PI3K). Indeed, it has been reported that PI3K plays a role in the development of obesity-induced inflammation and that inhibition of this enzyme ameliorates insulin resistance associated with obesity.51
In our study, most of the PC molecular species were more abundant in OW and OB children (Figure 5). These results are in concordance with previous studies on OB adults that reported an increase of several PC species that were also identified in this
study, such as PC 36:4, PC 38:3, PC 38:4 and PC38:5.15,18 Additionally, we observed higher levels of some PC species, like PC 38:3 and PC 40:5 that were associated with T2DM in adults.52 One possible explanation for increased PC levels could be the altered lecithin-cholesterol acyltransferase (LCAT) activity.21 This enzyme removes a FA from PC and transfers it to cholesterol, originating LPC and CE.53 Remarkably, LCAT activity is decreased in obesity,54 so the LCAT can be responsible for higher PC levels as well as lower levels of LPC.18 Although several studies in OB adults reported decreased levels several LPC species,18,25,50 we only observed statistically significant difference for one LPC species, namely LPC 14:1, that was less abundant in OW and OB children. However, Syme et al.11 reported a positive association between LPC 14:1 levels and fasting insulin, visceral fat and TGs levels in normal weight adolescents. We also observed an up-regulation of some ether-linked PC in OW and OB children. Several studies showed that the levels of ether-linked PC increased in OB adults,24,55 while others reported a decrease of its levels in obesity.13,14,25 Ether-linked phospholipids, in particular plasmalogens, have been described as physiological antioxidants.24,56,57 The increase in ether-linked PC that we observed in the present study could reflect an antioxidant response to the oxidative stress occurring in obesity. Since ether-linked phospholipids have antioxidant properties, it may be useful to further clarify their alterations in obesity to elucidate the role of these lipids in metabolic diseases. 24,58
We also observed that six molecular species of SM were more abundant in OB children, but only one was increased in OW children. These differences in SM levels between OW and OB individuals could be correlate with the increasing severity of the condition. Previously published studies presented similar results, showing increased levels of several SM species in obese adults.13,18,59 Low-density lipoproteins (LDL) are
responsible for the transport of most of plasmatic SM, thereby increased levels of SM could be related with up-regulation of LDL, often occurring in obese individuals18,60 and that has been associated with CVD risk.61
Lastly, the global analysis of phospholipid FA profile showed higher levels of palmitic acid (C16:0) in OW children and stearic acid (C18:0) in OW and OB children compared to CT. Our results were in accordance with other studies that demonstrated an increase of saturated fatty acids (SFA), mainly stearic acid and palmitic acid, in plasma phospholipids of OW and OB children62,63 and OB adults.64 SFA have been associated with obesity-related inflammation.65 Therefore Pickens et al.64 suggested that SFA incorporation into phospholipids could be a mechanism to prevent the toxicity of these FA. Palmitic acid was associated with higher concentrations of palmitoleic acid (16:1 n-7) in adolescents with metabolic syndrome.29,63 Many studies reported an increase of this FA in plasma of OB children.28,29,66 However, Gil-Campos and co-workers29 suggested that 16:1 n-7 is not incorporated in the phospholipid pool during the early stages of obesity, which would be in accordance with the results of present study since palmitoleic acid was not identified. On the other hand, we observed that linoleic acid (C18:2 n-6) was less abundant in OW and OB children. Previous studies reported a down-regulation of C18:2 n-6 in plasma of OB children.28,66 Although Pickens et al.64 described a down-regulation of linoleic acid in phospholipids of OW and OB adults, several studies in OW and OB children and adolescents found no differences in levels of linoleic acid esterified in phospholipids after comparison with normal-weight children.29,62,63 However, the same studies demonstrated that concentrations of phospholipids esterified with C18:2 n-6 were significantly lower in OB and OW children with metabolic syndrome when compared with healthy OW and OB children.60,61 Despite these controversial results, it has been suggested that lower linoleic
acid concentrations in obese individuals are related to delta-6-desaturase (D6D) activity.62,64,66 This enzyme is necessary to produce arachidonic acid (C20:4 n-6).66 Oxidation of C20:4 n-6 may be accelerated due to increased β-oxidation in obesity.66 Therefore, the increase of D6D activity in OW and OB individuals64 could be a compensatory mechanism that explains the maintenance of arachidonic acid (C20:4 n-6) levels in OW and OB individuals as suggested by Okada et al..66 Overall our results highlight a clear correlation between the serum phospholipidome and the development of paediatric obesity pathology in children.
The present study reports the application of a mass spectrometry-based lipidomic approach to perform a comprehensive investigation of blood serum phospholipidome profile of normal BMI, overweight and obese children for the first time. This methodology reveals to be of high potential for deciphering the alterations that can occur in polar lipidome as a result of dysregulation in lipid metabolic pathways. After a proper and accurate validation, some of the phospholipid molecular species that contribute to the discrimination between normal BMI and obese children may have a clinical application in diagnosis or for monitoring the progression of this metabolic condition or its comorbidities. Nevertheless, since this was a first study, we acknowledge some limitations that need to be pointed out. The small sample size is a limitation, since it may reduce the power and the meaningful of the results obtained due to a higher margin of error caused by an inflated false discovery rate or due to low reproducibility. Smaller-scale studies, as ours, has also weaknesses related with lack of accurate validation of the phospholipid molecular species considered as potential candidates that may contribute to the diagnosis and monitoring of obesity, and associated complications. However they are important to give new clues for further studies considering the population in study, children, from whom it could be quite
difficult to obtain samples for research analysis. Diet and physical activity may also have some contribution to the results obtained, and could be associated with alterations in phospholipid profile, and should be considered in future studies. The lack of longitudinal analysis hinder the possibility to evaluate the overall progression of phospholipid profile alterations over time in the cohort of obese and overweight children, and its relationship with the onset of potential comorbidities. These constraints should be take into account and improved in future studies.
Conclusions
In conclusion, our findings showed that the phospholipidome of OW and OB children is significantly altered when compared to the phospholipidome of healthy children. The fact that OW children presented similar phospholipids alterations to OB children suggested that phospholipid-related metabolic changes occur in the initial stages of obesity. We clearly demonstrated that plasma phospholipids could vary upon OW and OB children, especially PI, SM and PC classes, including ether-linked PC. These findings provides new insights in the alteration of phospholipidome of obesity in children.
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Table S1. MS-based identification of the phospholipid molecular species quantified in the present study. The total chain length (C) and degree of unsaturation (N) are included.
Table S2. Peak area of each lipid molecular species identified obtained using MZmine software.
Figure S1. An example of the total ion chromatogram (TIC)
Figure S2. ESI-MS/MS spectrum of the [M+H]+ ion of PC 34:2 (m/z 758.57). Fragment ion characteristic for the PC class was highlighted in red.
Figure S3. ESI-MS/MS spectrum of the [M+H]+ ion of LPC 16:0 (m/z 496.34). Fragment ion characteristic for the LPC class was highlighted in red.
Figure S4. ESI-MS/MS spectrum of the [M+H]+ ion of SM 34:1 (m/z 703.50). Fragment ion characteristic for the SM class was highlighted in red.
Figure S5. ESI-MS/MS spectrum of the [M+H]+ ion of PE 38:6 (m/z 764.63). Fragment ion formed by the characteristic neutral loss of 141 Da for the PE class was highlighted in red.
Figure S6. ESI-MS/MS spectrum of the [M-H]- ion of PI 38:4 (m/z 885.54). Fragment ion at m/z 241 characteristic for the PI class was highlighted in red.
Figure S7. Representation of the distribution of the normalized peak area.
Figure S8. Principal component analysis in a two-dimensional score plot of phospholipid profiles obtained from male and female individuals with overweight (EP) and obesity (OB).”
Figure S9. Loading plot for PCA analysis shown on figure 1.
Figure S10. Principal component analysis in a two-dimensional score plot of phospholipid profiles obtained from male and female individuals with overweight (EP) and obesity (OB).”
Acknowledgements
Thanks are due for the financial support to QOPNA (FCT UID/QUI/00062/2019) and CESAM (UID/AMB/50017/2019), and Portuguese Mass Spectrometry Network (LISBOA-01-0145-FEDER-402-022125) to FCT/MCTES through national funds (PIDDAC), and the co-funding by the FEDER, within the PT2020 Partnership Agreement and Compete 2020.Tânia Melo (BPD/UI51/5388/2017) is grateful to FCT for her grant. Simone Colombo grant was funded by the European Commission's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement number 675132 (MSCA-ITN-ETN MASSTRPLAN).
