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. 2020 Jun 30;13:80. doi: 10.3389/fnmol.2020.00080

Integrated Metabolomics and Proteomics Analysis Reveals Plasma Lipid Metabolic Disturbance in Patients With Parkinson’s Disease

Ling Hu 1,2,†, Mei-Xue Dong 2,†, Yan-Ling Huang 3,†, Chang-Qi Lu 1,†, Qian Qian 1,†, Chun-Cheng Zhang 4, Xiao-Min Xu 5, Yang Liu 1, Guang-Hui Chen 2, You-Dong Wei 1,*
PMCID: PMC7344253  PMID: 32714143

Abstract

Parkinson’s disease (PD) is a common neurodegenerative disease in the elderly with a pathogenesis that remains unclear. We aimed to explore its pathogenesis through plasma integrated metabolomics and proteomics analysis. The clinical data of consecutively recruited PD patients and healthy controls were assessed. Fasting plasma samples were obtained and analyzed using metabolomics and proteomics methods. After that, differentially expressed metabolites and proteins were identified for further bioinformatics analysis. No significant difference was found in the clinical data between these two groups. Eighty-three metabolites were differentially expressed in PD patients identified by metabolomics analysis. These metabolites were predominately lipid and lipid-like molecules (63%), among which 25% were sphingolipids. The sphingolipid metabolism pathway was enriched and tended to be activated in the following KEGG pathway analysis. According to the proteomics analysis, forty proteins were identified to be differentially expressed, seven of which were apolipoproteins. Furthermore, five of the six top ranking Gene Ontology terms from cellular components and eleven of the other fourteen Gene Ontology terms from biological processes were directly associated with lipid metabolism. In KEGG pathway analysis, the five enriched pathways were also significantly related with lipid metabolism (p < 0.05). Overall, Parkinson’s disease is associated with plasma lipid metabolic disturbance, including an activated sphingolipid metabolism and decreased apolipoproteins.

Keywords: Parkinson’s disease, metabolomics, proteomics, lipid, sphingolipid metabolism, apolipoprotein

Introduction

Parkinson’s disease (PD) is a common neurodegenerative disease and is expected to affect 14.2 million people worldwide by 2040 (Dorsey and Bloem, 2018). Its typical clinical features are motor symptoms, which result from accumulated Lewy bodies and the remarkable death of dopaminergic neurons in the substantia nigra pars compacta (SNpc). In addition, Parkinson’s disease is correlated with many non-motor symptoms, some of which even precede the motor symptoms by more than 10 years, including constipation, hyposmia, anxiety, depression, and rapid eye movement sleep behavior disorder (Emamzadeh and Surguchov, 2018). These non-motor symptoms are probably due to the disturbance of various neurotransmitters and multiple nerve systems in addition to the dopaminergic neurons in the SNpc (Lv and Ae, 2015). Most idiopathic PD patients are sporadic, and various environmental exposures have been identified as being correlated with the development of PD. Results of genome-wide association studies have also identified 90 independent mutations in more than 20 genes that increase risk factors for Parkinson’s disease (Blauwendraat et al., 2019). Therein, the strongest genetic risk factor is the Asn370Ser mutation of β-glucocerebrosidase, with an odds ratio greater than 5 (Sidransky et al., 2009). The interactions between environmental and genetic risk factors are still under investigation.

Drugs that increase intracerebral dopamine levels or directly stimulate dopamine receptors remain as the most common forms of treatment for PD patients (Connolly and Lang, 2014). These drugs are mainly for symptomatic treatment without neuroprotection or a so-called disease-modifying function. Researchers have found that mitochondrial oxidative stress and inflammation reaction play important roles in the apoptosis of dopaminergic neurons during the occurrence and development of Parkinson’s disease (Wei et al., 2018). However, drugs targeting oxidative stress have exhibited little therapeutic benefit (Trist et al., 2019). Research also indicates that Parkinson’s disease is related to the dysregulation of protein homeostasis, including intracellular and membrane protein trafficking, protein aggregation, and protein degradation by the ubiquitin-proteasome or lysosome-autophagy systems (Deng et al., 2018). Thus, the potential pathogenesis should be clarified and more effective disease-modifying treatments are urgently needed for PD patients.

Metabolomics is a comprehensive assessment of the total endogenous metabolites in a biological system, and proteomics can evaluate alterations at the protein level (Mostafa et al., 2016). These omics data can help researchers make many discoveries in the alteration of molecules and metabolic pathways following gene expression in PD patients. Plasma is an easily obtainable, non-invasive, and informative biofluid from patients, making it perfect to explore the pathogenesis of many neuropsychiatric disorders (Hu et al., 2016). Herein, we employed untargeted metabolomics and proteomics analysis to identify molecular changes in PD patients. Furthermore, an integrated bioinformatic analysis was performed to explore the probable pathogenesis of PD based on the above findings (Dong et al., 2018a).

Materials and Methods

Objects

Parkinson’s disease patients were consecutively recruited from April 2016 to February 2017 in the Department of Neurology, the First Affiliated Hospital of Chongqing Medical University, according to the criteria reported in a previous research (Dong et al., 2017b). The inclusion criteria was: (i) PD patients diagnosed using the European Federation of Neurological Societies and the International Parkinson Movement Disorder Society’s European Section (Berardelli et al., 2013), and (ii) patients only taking dopamine analogs or dopamine receptor agonists. The exclusion criteria was: (i) those with secondary Parkinson’s disease or Parkinson-plus syndrome; (ii) patients suffering from tumor, heart failure, chronic obstructive pulmonary disease, nephritis, infectious diseases, or any other severe chronic disease at the time of enrollment; and (iii) any history of stroke, brain surgery, head trauma, motor neuro disease, Alzheimer’s disease, or mental diseases.

A same amount of age- and sex-matched healthy controls (HCs) were included from the Department of Physical Examination. The controls were also without any history of illness in the central nervous system or suffering from any other severe disease. This study was approved by the ethics committee of the First Affiliated Hospital of Chongqing Medical University and performed in accordance with the Declaration of Helsinki. Informed consent was obtained from all individual participants included in the study. Clinical data, metabolomics analysis, and proteomics analysis were blindly assessed or performed (Dong et al., 2017a).

Clinical Data

The clinical data of all included participants were collected. Fasting plasma samples were obtained with an EDTA-K2 tube in the morning, and then stored at -80°C until experimental analysis. All the clinical scales were assessed by experienced neurologists. Statistical analyses were performed using Statistic Package for the Social Sciences 22.0 (IBM, Armonk, NY, United States). Categorical data are exhibited as absolute numbers while continuous data are exhibited as mean ± standard error. All the clinical data were compared between PD and HC groups using Pearson Chi-squared tests or Fisher exact tests for categorical data and Mann–Whitney U-tests with Bonferroni post hoc tests for continuous data, as appropriate (Dong et al., 2016).

Metabolomics Analysis

We adopted a Waters UPLC I-class system equipped with a binary solvent delivery manager (Waters Corporation, Milford, United States) to perform the untargeted liquid chromatography-mass spectrometry-based metabolomics (thirty-six objects per group); the detailed procedure was described in a previous research (Dong et al., 2018a). After that, data sets including m/z, peak retention time, and peak intensity of each ion were obtained. The m/z-peak retention time pairs were used to identify each ion based on Metlin1 and Human Metabolome Database (HMDB)2 while the peak intensity was deemed as the level of a metabolite. The data sets were further reduced by removing any peaks with a missing value in more than 60% of the total samples.

The positive and negative peak data were merged and multivariate statistical analyses were performed by the SIMCA-P 13.0 software package (Umetrics, Umeå, Sweden). The partial least squares-discriminant analysis (PLS-DA) model was constructed to show statistical differences and identify metabolites differentially expressed between PD patients and healthy controls, and this model was further validated by a permutation test with 200 iterations. Metabolites with variable influence on projection values (obtained from the PLS-DA model) of greater than 1.0, fold change values of greater than ±2, and p-values of less than 0.05 (Dong et al., 2018b) were identified to be differentially expressed. These differentially expressed metabolites were then classified by chemical taxonomy based on the HMDB database, and metabolic pathway analysis was further performed by MetaboAnalyst 4.03 (Chong et al., 2018).

Proteomics Analysis

The plasma of twenty-seven randomized healthy controls and thirty randomized PD patients were merged into three pooled samples for proteomics analysis. The details of the proteomics analysis have also been depicted previously (Dong et al., 2018a). Afterward, tandem mass spectrometry spectra were obtained and searched using the MASCOT engine 2.2 (Matrix Science, London, United Kingdom). The differentially expressed proteins were recognized by fold change values of greater than ±1.2 and p-values of less than 0.05. Gene ontology (GO) enrichment from cellular component, molecular function, and biological process, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by Fisher’s exact test, considering the whole quantified protein annotations as the background data set. The obtained p-values from enrichment analysis was further converted into q-value using Benjamini-Hochberg correction and a q-value less than 0.05 was considered significant (Hochberg and Benjamini, 1990).

Results

Results of the Metabolomics Analysis

Thirty-six PD patients and healthy controls were separately included in the metabolomics analysis. The clinical data of these participants are shown in Table 1. No significant differences were observed between these two groups, including the levels of hemoglobin A1C (HbA1C) and blood lipid, or the incidences of diabetes mellitus and hypercholesterolemia. The mean UPDRS score and Hoehn–Yahr score of these PD patients were 41.11 ± 3.72 and 2.26 ± 0.17, respectively.

TABLE 1.

Clinical data of PD patients and healthy controls included in the untargeted liquid chromatography-mass spectrometry-based metabolomics analysis.

Variable (SEM/%) HC (36) PD (36) p-value Variable (SEM/%) HC (36) PD (36) p-value
Age (year) 62.16 ± 1.73 64.03 ± 0.95 0.348 HbA1c (%) 6.17 ± 0.20 6.08 ± 0.14 0.689
Gender, Male (%) 18 (50%) 18 (50%) 1 TC (mmol/L) 4.42 ± 0.12 4.53 ± 0.13 0.516
Smoking history (%) 10 (27.8%) 9 (25.0%) 0.789 TG (mmol/L) 1.53 ± 0.09 1.44 ± 0.13 0.6
Alcohol consumption (%) 2 (5.6%) 2 (5.6%) 1 HDL-C (mmol/L) 1.30 ± 0.06 1.33 ± 0.06 0.794
Hypertension (%) 15 (46.9%) 17 (53.1%) 0.617 LDL-C (mmol/L) 2.87 ± 0.12 2.91 ± 0.11 0.816
Diabetes mellitus (%) 4 (12.5%) 8 (25.0%) 0.2 Apo-A1 (g/L) 1.37 ± 0.04 1.34 ± 0.04 0.658
Hypercholesterolemia (%) 12 (37.5%) 9 (28.1%) 0.424 Apo-B (g/L) 0.91 ± 0.04 0.91 ± 0.04 0.992
BMI (kg/m2) 24.21 ± 0.57 23.00 ± 0.64 0.169 Lpa (mg/L) 190.76 ± 42.32 279.53 ± 57.03 0.216
UPDRS score / 41.11 ± 3.72 / Hoehn-Yahr score / 2.26 ± 0.17 /
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PD, Parkinson’s disease; SEM, standard error of the mean; HC, healthy control; HbA1c, hemoglobin A1c; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; Apo-A1, apolipoprotein A1; Apo-B, apolipoprotein B; BMI, body mass index; Lpa, lipoprotein a; UPDRS, Unified Parkinson’s Disease Rating Scale.

After excluding internal standards, 6040 positive and 4363 negative peaks were detected in almost 98.81% of samples on average (Supplementary Table S1). Based on the above peaks, the PLS-DA score plot showed a significant difference between the PD and HC groups (R2X = 0.132, R2Y = 0.374, Q2 = 0.2) (Figure 1A). A permutation test with 200 iterations was performed and confirmed that the PLS-DA model was valid and not over-fitted because the original right R2 and Q2 values were observably higher than the corresponding permutated left values [R2 = (0.0, 0.746), Q2 = (0.0, -0.198)] (Figure 1B). According to the above results, eighty-three metabolites were differentially expressed between these two groups (Table 2). Of these, 63% were lipid and lipid-like molecules, 13% were organic acids and derivatives, 6% were phenylpropanoids and polyketides, 5% were organic oxygen compounds, 5% were organoheterocyclic compounds, 4% were benzenoids, 2% were nucleosides, nucleotides, and analogs, 1% was homogeneous non-metal compounds, and 1% was organosulfur compounds (Figure 1C). The lipid and lipid-like molecules were further categorized into sphingolipids (25%), glycerophospholipids (16%), glycerolipids (17%), fatty acyls (15%), prenol lipids (15%), and steroids and steroid derivatives (12%) (Figure 1D). These sphingolipids were ganglioside GD3 (d18:0/18:0), PS[15:0/20:4(8Z,11Z,14Z,17Z)], camellianin D, ceramide (d18:1/16:0), adrenorphin, ganglioside GD1a (d18:0/22:0), SM(d18:0/24:1(15Z)(OH)), thiamine(1+) diphosphate(1-), N-docosahexaenoyl phenylalanine, ganglioside GA2 (d18:1/12:0), SM(d18:0/22:0), glucosylceramide (d18:1/18:0), and AS 1–5.

FIGURE 1.

FIGURE 1

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Multivariate statistical analysis of metabolomics and the classification of differentially expressed metabolites between PD patients and healthy controls. (A) PLS-DA score plot derived from liquid chromatography-mass spectrometry-based metabolomics analysis of PD patients and healthy controls. (B) Statistical validation of the PLS-DA model by permutation testing with 200 iterations. (C) Pie chart of the superclass of chemical taxonomy based on the annotations from Human Metabolome Database. (D) Pie chart of the further classification of lipids and lipid-like molecules. PD, Parkinson’s disease; HC, healthy control; PLS-DA, partial least squares-discriminant analysis.

TABLE 2.

Key differentially expressed metabolites identified by untargeted liquid chromatography-mass spectrometry-based metabolomics analysis between PD patients and healthy control.

Compound Name Compound IDa m/z value RT (min) Ion mode FC valuebc VIP valuec -log(p-value)c
Coenzyme A HMDB0001423 750.0117 7.6840 Positive 0.4323 2.4685 5.292
Ganglioside GD3 (d18:0/18:0) HMDB0011859 737.8448 2.9037 Positive 2.4947 2.1712 6.520
PS(15:0/20:4(8Z,11Z,14Z,17Z)) HMDB0012312 752.4899 7.6840 Positive 0.4153 2.5890 5.192
4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanol HMDB0030032 419.2401 3.8761 Positive 0.4434 2.0036 2.761
Lupulone HMDB0030041 829.5719 2.8968 Positive 2.1749 2.4401 8.216
S-(Hydroxymethyl)glutathione HMDB0004662 113.3748 8.8773 Positive 2.5248 1.5340 3.835
Glycinoprenol 11 HMDB0002003 737.7329 2.8968 Positive 2.4538 2.2782 7.317
TG(24:0/15:0/o-18:0) HMDB47052 919.9253 2.9037 Positive 2.1137 1.8444 5.140
Camellianin D HMDB0011920 620.4129 7.7665 Positive 0.4853 2.5231 5.238
Dipiperamide C HMDB0034617 539.2666 1.9177 Positive 3.8676 1.1403 1.496
DG(24:0/24:0/0:0) HMDB0007818 758.0465 9.2399 Positive 2.0050 1.0010 1.418
Iodide HMDB0012238 65.4523 8.6504 Positive 2.4751 1.4282 1.689
TG[24:1(15Z)/20:5(5Z,8Z,11Z,14Z,17Z)/ 18:4(6Z,9Z,12Z,15Z)] HMDB52343 947.7935 2.9037 Positive 2.2586 2.3058 7.459
PC[DiMe(11,3)/DiMe(13,5)] HMDB0013456 922.6604 10.4327 Positive 2.2255 1.1533 1.568
Lucidenic acid M HMDB0035973 925.5913 9.7743 Positive 3.0668 1.6801 3.860
Pangamic acid HMDB0029949 900.5774 9.5886 Positive 2.1275 1.4546 3.466
Ceramide (d18:1/16:0) HMDB0004949 520.5085 8.7673 Positive 2.6757 1.8323 4.813
Tyramine HMDB0000306 69.5751 8.6504 Positive 2.1723 1.4084 1.849
Adrenorphin HMDB0012081 948.5108 2.9037 Positive 2.5786 2.4955 8.273
Phenylacetyl-CoA HMDB0006503 886.1336 9.9806 Positive 2.4328 1.2223 2.087
TG[22:1(13Z)/22:2(13Z,16Z)/o-18:0] HMDB51764 947.9373 2.8968 Positive 2.1808 2.4274 8.070
Ganglioside GD1a (d18:0/22:0) HMDB0011784 948.0813 2.9037 Positive 2.2465 2.4118 7.750
Kyotorphin HMDB0005768 675.3525 8.1291 Positive 2.2765 1.4563 3.057
Kerdlan HMDB0240277 853.6624 10.4396 Positive 2.0973 1.1777 1.769
Threoninyl-Glutamine HMDB0029059 495.2411 1.8902 Positive 4.7886 1.1971 1.612
TG[18:4(6Z,9Z,12Z,15Z)/22:6(4Z,7Z,10Z,13Z,16Z,19Z)/ 18:4(6Z,9Z,12Z,15Z)] HMDB0035640 883.6234 9.9875 Positive 2.5544 1.2463 2.114
SM[d18:0/24:1(15Z)(OH)] HMDB0013469 829.6815 9.7674 Positive 2.5075 1.5106 3.333
3-Methylglutaconyl-CoA HMDB0001057 858.0995 9.5886 Positive 2.4345 1.1968 2.040
Dynorphin A (6-8) HMDB0012932 887.6266 9.9737 Positive 2.1618 1.3347 2.575
Thiamine(1+) Diphosphate(1−) HMDB0011854 875.9981 6.9452 Positive 2.0855 1.1272 2.154
LysoPC[16:1(9Z)/0:0] HMDB0010383 987.6122 9.7674 Positive 2.8140 1.4239 2.992
Dihydroceramide HMDB0006752 686.6043 10.0268 Positive 2.6827 1.3579 2.580
D-Urobilinogen HMDB0004158 591.3161 3.4380 Positive 2.4318 1.1614 1.396
N-Docosahexaenoyl phenylalanine HMDB0006482 951.6668 10.4396 Positive 2.2063 1.3591 2.418
Alanyl-Asparagine HMDB0028682 407.1886 1.8211 Positive 4.3023 1.2642 1.676
MG[0:0/22:4(7Z,10Z,13Z,16Z)/0:0] HMDB0011554 840.6408 9.9875 Positive 2.7493 1.3265 2.430
Iopromide HMDB0001493 814.1201 9.5886 Positive 2.3465 1.3392 2.809
Taraxacoside HMDB0030055 797.2583 10.1989 Positive 2.3652 1.5763 3.144
Elaidic carnitine HMDB0006464 851.7010 10.5890 Positive 2.0474 1.0989 1.502
10-Hydroxy-2,8-decadiene-4,6-diynoic acid HMDB0031054 177.0544 1.6216 Positive 0.0001 2.2388 4.311
PE-NMe[18:3(6Z,9Z,12Z)/22:6(4Z,7Z,10Z,13Z,16Z,19Z)] HMDB0009281 400.7789 9.4806 Positive 2.1062 1.7956 4.829
Mandelic acid HMDB0000703 153.0546 1.6216 Positive 0.0078 2.3686 4.793
Sesaminol 2-O-triglucoside HMDB0029556 831.2494 10.1989 Positive 2.2506 1.6307 3.334
Polyporusterone C HMDB0000138 953.6222 10.1989 Positive 2.0235 1.5423 3.039
Ganglioside GA2 (d18:1/12:0) HMDB0004888 991.6073 9.7537 Positive 2.0988 1.3269 2.273
Glucoconringiin HMDB0001197 810.1524 9.9806 Positive 2.1748 1.4005 2.739
Mannosyl-(N-acetylglucosaminyl)2-diphosphodolichol HMDB0012255 919.6318 9.7605 Positive 2.7027 1.6124 3.791
3-phenylprop-2-enoic acid HMDB0000567 149.0597 1.6216 Positive 0.0047 2.3643 4.806
SM(d18:0/22:0) HMDB0012091 789.6817 10.1989 Positive 2.3198 1.2248 2.279
Alpha-linolenyl carnitine HMDB0006319 843.6455 9.9806 Positive 2.2240 1.4152 2.813
Perulactone B HMDB0030119 977.6050 10.1989 Positive 2.8190 1.5222 2.913
PE[DiMe(11,3)/DiMe(13,5)] HMDB0008682 880.5994 9.8293 Positive 2.0844 1.7057 4.208
Ebastine HMDB0035995 939.6535 10.4327 Positive 2.2330 1.2181 1.702
TG[18:3(6Z,9Z,12Z)/18:4(6Z,9Z,12Z,15Z)/18:3(6Z,9Z,12Z)] HMDB0010470 871.6774 10.4396 Positive 2.1182 1.2193 1.698
7-chloro-2-(3,4-dimethoxyphenyl)-3,5,6-trihydroxy-8-methoxy-4H-chromen-4-one HMDB0001484 816.1138 9.5955 Positive 2.0290 1.2475 2.113
(3beta,5alpha,9alpha,22E,24R)-5,9-Epidioxy-3-hydroxyergosta-7,22-dien-6-one HMDB0032666 885.6326 10.1989 Positive 2.1876 1.5669 3.119
Cholesterol sulfate HMDB0000653 931.6238 10.2009 Negative 2.3774 1.5823 3.259
Methylimidazole acetaldehyde HMDB0004181 371.1829 3.3773 Negative 0.4504 2.1379 2.501
Vanillactic acid HMDB0000913 211.0608 1.8145 Negative 0.0027 2.4279 4.091
PC[14:1(9Z)/22:2(13Z,16Z)] HMDB0007921 764.5535 10.0221 Negative 2.0590 1.3654 2.573
2-Octenedioic acid HMDB0000341 343.1414 1.7113 Negative 0.0230 1.4765 2.370
1,2,3,4-Tetrahydro-beta-carboline HMDB0012488 515.2876 3.2529 Negative 2.0170 1.1922 1.332
Methyldopa HMDB0011754 210.0768 1.6219 Negative 0.0000 2.5799 4.767
PE[20:2(11Z,14Z)/24:1(15Z)] HMDB0009311 834.6232 9.8316 Negative 2.1900 1.8322 5.080
Glucosylceramide (d18:1/18:0) HMDB0004972 708.6159 10.0221 Negative 2.9864 1.3608 2.665
cis-Hydroxy Perhexiline HMDB60644 585.5305 9.2354 Negative 2.7983 1.3734 2.780
TG[18:4(6Z,9Z,12Z,15Z)/20:5(5Z,8Z,11Z,14Z,17Z)/ 18:4(6Z,9Z,12Z,15Z)] HMDB55513 873.6569 9.7697 Negative 2.8400 1.6794 4.108
Cinncassiol D1 glucoside HMDB0034677 513.2730 3.3567 Negative 2.4747 1.6398 2.277
AS 1-5 HMDB0032843 714.5535 8.7898 Negative 2.1915 1.4575 2.933
Glycochenodeoxycholic acid 3-glucuronide HMDB0002579 606.3294 3.1567 Negative 2.5622 1.6289 2.650
TG[14:1(9Z)/14:0/14:1(9Z)] HMDB47724 717.5925 9.4073 Negative 2.1038 1.3365 2.913
Asparaginyl-Serine HMDB0028740 437.1628 3.6093 Negative 0.4650 1.8095 1.691
1-non-adecanoyl-glycero-3-phosphate HMDB62322 903.5960 9.7766 Negative 2.6135 1.6610 3.764
Hexahydro-6,7-dihydroxy-5-(hydroxymethyl)-3-(2-hydroxyphenyl)-2H-pyrano[2,3-d]oxazol-2-one HMDB0029234 278.0648 1.6219 Negative 0.0001 2.5755 4.609
PE[DiMe(13,5)/MonoMe(13,5)] HMDB61496 902.6103 9.8316 Negative 3.5042 1.6608 4.153
(2-hydroxy-2-{9-[(3-methylbut-2-enoyl)oxy]-2-oxo-2H,8H,9H-furo[2,3-h]chromen-8-yl}propoxy)sulfonic acid HMDB0001511 421.0596 2.7668 Negative 0.4444 1.6591 1.564
(2S)-2-amino-3-[4-hydroxy-3-(sulfooxy)phenyl]-2-methylpropanoic acid HMDB0142153 290.0342 1.6082 Negative 0.1225 2.1124 2.902
Asiaticoside HMDB36656 979.5918 9.9829 Negative 2.2756 1.7261 4.518
Ponasteroside A HMDB0034091 625.3600 4.2329 Negative 2.2531 1.0898 1.442
MG(12:0/0:0/0:0) HMDB72863 821.6323 9.9829 negative 2.2034 1.6330 4.102
7-Chloro-6-demethylcepharadione B HMDB0031833 340.0355 1.6219 Negative 0.0001 2.3257 3.726
Norpropoxyphene HMDB0011627 974.5991 8.7760 Negative 2.3974 1.1448 1.437
Hordatine B glucoside HMDB0030460 370.1782 4.0679 Negative 0.3956 1.7169 1.856
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aCompound ID was mainly exhibited based on the Human Metabolome Database (www.hmdb.ca). bFC value was calculated as the ratio of the average mass response (area) between the two groups (FC value = PD/HC). Thus, FC values >1 indicate significantly higher levels in the PD group relative to the HC group while FC values <1 indicate significantly lower levels in the PD group. cOnly metabolites with FC values greater than ±2.0, VIP values greater than 1.0 and p-values less than 0.05 were deemed statistically significant. PD, Parkinson’s disease; RT, retention time; FC, fold change; VIP, variable influence on projection; HC, healthy control.

The related metabolic pathway is shown in Figure 2A, where only the sphingolipid metabolism pathway is significantly enriched (pathway impact = 0.473, q-value = 0.004). All six metabolites involved in sphingolipid metabolism had significantly increased, indicating that the pathway is activated in PD patients (Figure 2B).

FIGURE 2.

FIGURE 2

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Metabolic pathway analysis based on the differentially expressed metabolites from the metabolomics data using MetaboAnalyst4.0. (A) Pathway analysis indicates sphingolipid metabolism is the only statistically enriched pathway. (B) In the sphingolipid metabolism pathway there are five differentially expressed metabolites involved, and all of them have increased in patients with Parkinson’s disease.

Results of the Proteomics Analysis

The clinical data of all the participants included in the proteomics analysis are exhibited in Table 3. No statistical differences can be observed in these data, indicating that the following comparison of the two groups was reasonable. A total of 912 proteins were identified in plasma and protein ratio distributions between the two groups, which are shown in Figure 3A. Forty differentially expressed proteins were recognized, with fifteen proteins increased and twenty-five decreased (Figure 3B). The details of these proteins were shown in Table 4 and the seven differentially expressed apolipoproteins (apolipoprotein C-I, apolipoprotein C-III, protein APOC4-APOC2, apolipoprotein C-IV, apolipoprotein B variant, apolipoprotein B, and apolipoprotein M) were all significantly decreased.

TABLE 3.

Clinical data of PD patients and healthy controls included in the tandem mass tag-based proteomics analysis.

Variable (SEM/%) HC (27) PD (30) p-value Variable (SEM/%) HC (27) PD (30) p-value
Age (year) 66.59 ± 1.14 69.17 ± 1.50 0.184 HbA1c (%) 5.99 ± 0.18 6.17 ± 0.19 0.515
Gender, Male (%) 14 (51.9%) 19 (63.3%) 0.381 TC (mmol/L) 4.33 ± 0.17 4.32 ± 0.16 0.952
Smoking history (%) 9 (33.3%) 7 (23.3%) 0.402 TG (mmol/L) 1.49 ± 0.15 1.23 ± 0.14 0.206
Alcohol consumption (%) 3 (11.1%) 1 (3.3%) 0.53 HDL-C (mmol/L) 1.21 ± 0.08 1.24 ± 0.09 0.858
Hypertension (%) 15 (55.6%) 13 (43.3%) 0.357 LDL-C (mmol/L) 2.81 ± 0.15 2.73 ± 0.13 0.679
Diabetes mellitus (%) 2 (7.4%) 5 (16.7%) 0.51 Apo-A1 (g/L) 1.31 ± 0.05 1.30 ± 0.05 0.93
Hypercholesterolemia (%) 4 (14.8%) 6 (20.0%) 0.869 Apo-B (g/L) 0.89 ± 0.04 0.85 ± 0.04 0.403
BMI (kg/m2) 23.95 ± 0.69 22.79 ± 0.58 0.197 Lpa (mg/L) 201.19 ± 48.58 270.04 ± 66.49 0.407
UPDRS score / 38.49 ± 3.98 / Hoehn-Yahr score / 2.40 ± 0.16 /
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PD, Parkinson’s disease; SEM, standard error of the mean; HC, healthy control; HbA1c, hemoglobin A1c; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; Apo-A1, apolipoprotein A1; Apo-B, apolipoprotein B; BMI, body mass index; Lpa, lipoprotein a; UPDRS, Unified Parkinson’s Disease Rating Scale.

FIGURE 3.

FIGURE 3

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The exhibition of whole proteins detected in plasma and the differentially expressed proteins between PD patients and healthy controls. (A) Protein ratio distribution, (B) volcano plot showed FC values and p-values of all the identified proteins. Red dots represent increased differentially expressed proteins while green dots represent decreased proteins. FC value was calculated as the ratio of the average mass response area between the two groups (FC value = PD/HC). p-Value was calculated using Mann-Whitney U-test. PD, Parkinson’s disease; HC, healthy control; FC, fold change. Blue dots represent unchanged proteins. The horizonal red dashed lines indicate a p-value < 0.05 while the vertical red dashed lines indicate FC = 0.

TABLE 4.

Key differentially expressed proteins identified by tandem mass tag-based proteomics analysis between PD patients and healthy controls.

Uniprot ID Protein name (GeneName) MW [kDa] FC valuea p-valueb
O75339 Cartilage intermediate layer protein 1 (CILP) 132.48 1.247 0.003
A9UFC0 Caspase 14 (CASP14) 27.65 2.077 0.010
Q5T619 Zinc finger protein 648 (ZNF648) 62.30 1.276 0.010
Q04756 Hepatocyte growth factor activator (HGFAC) 70.64 1.605 0.013
Q9UQ05 Potassium voltage-gated channel subfamily H member 4 (KCNH4) 111.62 1.213 0.014
E5RIF9 Carbonic anhydrase 1 (CA1) 16.31 1.230 0.021
P01023 Alpha-2-macroglobulin (A2M) 163.19 1.251 0.025
B7Z544 cDNA FLJ51742, highly similar to Inter-alpha-trypsin inhibitor heavy chain H4 98.29 1.206 0.026
Q6UWP8 Suprabasin (SBSN) 60.50 1.230 0.028
A0A087WYF1 Laminin subunit alpha-2 (LAMA2) 343.20 1.254 0.030
P04275 von Willebrand factor (VWF) 309.06 1.210 0.031
D3DQX7 Serum amyloid A protein (SAA1) 13.55 1.264 0.032
A0A125U0U7 MS-C1 heavy chain variable region 13.09 1.210 0.039
Q96K23 cDNA FLJ14838 fis, clone OVARC1001726, weakly similar to APICAL-LIKE PROTEIN 27.99 1.380 0.041
D6RF20 Vitamin D-binding protein(GC) 16.07 1.255 0.046
P02656 Apolipoprotein C-III (APOC3) 10.85 0.718 0.001
Q1W658 Follicle-stimulating hormone beta subunit (FSHB) 8.45 0.331 0.002
K7ER74 Protein APOC4-APOC2 (APOC4-APOC2) 20.04 0.661 0.002
Q04695 Keratin, type I cytoskeletal 17 (KRT17) 48.08 0.465 0.003
Q5NV68 V4-1 protein (V4-1) 11.23 0.342 0.005
Q59HB3 Apolipoprotein B variant 183.46 0.832 0.007
P04259 Keratin, type II cytoskeletal 6B (KRT6B) 60.03 0.611 0.008
K7ERI9 Apolipoprotein C-I (APOC1) 8.64 0.710 0.009
G5E968 Chromogranin A (CHGA) 34.25 0.786 0.009
Q99592 Zinc finger and BTB domain-containing protein 18 (ZBTB18) 58.32 0.830 0.009
P10720 Platelet factor 4 variant (PF4V1) 11.55 0.778 0.010
O95445 Apolipoprotein M (APOM) 21.24 0.831 0.011
A0A0G2JPR0 Complement C4-A (C4A) 192.75 0.620 0.012
Q99857 Tenascin-C 9.76 0.818 0.013
Q07507 Dermatopontin (DPT) 23.99 0.724 0.015
O95576 Pepsinogen 8.71 0.820 0.015
C0KRQ8 Glycoprotein hormones alpha chain (CGA) 10.20 0.542 0.016
P55056 Apolipoprotein C-IV (APOC4) 14.54 0.817 0.020
P35908 Keratin, type II cytoskeletal 2 epidermal (KRT2) 65.39 0.827 0.033
E1B4S7 Apolipoprotein B (APOB) 25.22 0.831 0.034
P80108 Phosphatidylinositol-glycan-specific phospholipase D (GPLD1) 92.28 0.785 0.034
H7C0N0 Inter-alpha-trypsin inhibitor heavy chain H1 (ITIH1) 51.83 0.811 0.039
H7BZ76 Latent-transforming growth factor beta-binding protein 1 (LTBP1) 28.80 0.806 0.040
Q7RTS7 Keratin, type II cytoskeletal 74 (KRT74) 57.83 0.807 0.040
B2RCB8 cDNA, FLJ95971, highly similar to Homo sapiens protocadherin 12 (PCDH12) 128.91 0.819 0.044
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aFC value was calculated as the ratio of the average mass response (area) between the two groups (FC value = PD/HC). Thus, FC values >1 indicate significantly higher levels in the PD group relative to the HC group while FC values <1 indicate significantly lower levels in the PD group. bOnly proteins with FC values greater than ± 1.2 and p-values less than 0.05 were deemed statistically significant. PD, Parkinson’s disease; MW, molecular weight; FC, fold change; HC, healthy control.

There were 162, 25, and 22 GO terms significantly associated with biological processes, cellular components, and molecular function, respectively. The top 20 GO terms from GO enrichment analysis are shown in Figure 4 and Table 5. Five of the six top ranked GO terms from cellular components (very-low-density lipoprotein particle, triglyceride-rich lipoprotein particle, lipoprotein particle, plasma lipoprotein particle, and protein-lipid complex) were directly associated with lipids. The remaining top ranked GO term from cellular components was keratin filament. Eleven of the fourteen top ranked GO terms from biological processes were directly associated with lipid metabolism. These were regulation of very-low-density lipoprotein particle clearance, negative regulation of very-low-density lipoprotein particle clearance, regulation of the fatty acid biosynthetic process, regulation of lipoprotein particle clearance, regulation of the fatty acid metabolic process, negative regulation of lipoprotein particle clearance, the lipid metabolic process, protein–lipid complex remodeling, plasma lipoprotein particle remodeling, protein–lipid complex subunit organization, and plasma lipoprotein particle organization. The remaining three GO terms were negative regulation of receptor-mediated endocytosis, organic hydroxyl compound transport, and macromolecular complex remodeling. They were also indirectly associated with lipid metabolism because all of the involved proteins were lipoproteins.

FIGURE 4.

FIGURE 4

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The top 20 most enriched GO terms based on proteomics analysis between PD patients and healthy controls. Five of the six top ranked GO terms from the cellular component and eleven of the 14 top ranked GO terms from the biological process were directly associated with lipid metabolism. BP, biological process; CC, cellular component; GO, gene ontology; PD, Parkinson’s disease.

TABLE 5.

The top 20 most enriched GO terms and involved proteins based on proteomics analysis between PD patients and healthy controls.

Go terms Category Involved proteins q-value Rich factor
Very-low-density lipoprotein particle CC O95445, K7ER74, P55056, Q59HB3, P02656, K7ERI9, E1B4S7 0.021 0.292
Triglyceride-rich lipoprotein particle CC O95445, K7ER74, P55056, Q59HB3, P02656, K7ERI9, E1B4S7 0.021 0.292
Keratin filament CC P04259, P35908, Q04695, Q7RTS7 0.030 0.500
Lipoprotein particle CC O95445, K7ER74, P55056, P02656, K7ERI9, Q59HB3, E1B4S7 0.030 0.206
Plasma lipoprotein particle CC O95445, K7ER74, P55056, P02656, K7ERI9, Q59HB3, E1B4S7 0.030 0.206
Protein-lipid complex CC O95445, K7ER74, P55056, P02656, K7ERI9, Q59HB3, E1B4S7 0.030 0.206
Negative regulation of receptor-mediated endocytosis BP K7ER74, P02656, K7ERI9 0.021 1.000
Regulation of very-low-density lipoprotein particle clearance BP K7ER74, P02656, K7ERI9 0.021 1.000
Negative regulation of very-low-density lipoprotein particle clearance BP K7ER74, P02656, K7ERI9 0.021 1.000
Regulation of fatty acid biosynthetic process BP K7ER74, P02656, K7ERI9 0.030 0.750
Regulation of lipoprotein particle clearance BP K7ER74, P02656, K7ERI9 0.030 0.750
Regulation of fatty acid metabolic process BP K7ER74, P02656, K7ERI9 0.030 0.750
Negative regulation of lipoprotein particle clearance BP K7ER74, P02656, K7ERI9 0.030 0.750
Organic hydroxy compound transport BP O95445, K7ER74, G5E968, Q59HB3, P02656, K7ERI9, E1B4S7 0.030 0.212
Lipid metabolic process BP O95445, Q1W658, O75339, K7ER74, P55056, P02656, K7ERI9, Q59HB3, D6RF20, E1B4S7, P80108 0.030 0.133
Protein-lipid complex remodeling BP O95445, K7ER74, P02656, K7ERI9, Q59HB3, E1B4S7 0.030 0.240
Plasma lipoprotein particle remodeling BP O95445, K7ER74, P02656, K7ERI9, Q59HB3, E1B4S7 0.030 0.240
Protein-lipid complex subunit organization BP O95445, K7ER74, P02656, K7ERI9, Q59HB3, E1B4S7 0.030 0.240
Plasma lipoprotein particle organization BP O95445, K7ER74, P02656, K7ERI9, Q59HB3, E1B4S7 0.030 0.240
Macromolecular complex remodeling BP O95445, K7ER74, P02656, K7ERI9, Q59HB3, E1B4S7 0.030 0.240
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GO, gene ontology; PD, Parkinson’s disease; CC, cellular component; BP, biological process.

These differentially expressed proteins were also mapped to KEGG pathways and enriched in five pathways with p-values less than 0.05 (Table 6), including ovarian steroidogenesis, the GnRH signaling pathway, neuroactive ligand–receptor interaction, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and regulation of lipolysis in adipocytes. However, no significant differences can be observed after being adjusted by Benjamini-Hochberg correction (q-value > 0.05), indicating that these results were unstable.

TABLE 6.

Enriched KEGG pathways and involved proteins based on proteomics analysis between PD patients and healthy control.

Enriched KEGG pathway Involved protein p-value q-value Rich factor
Ovarian steroidogenesis Q1W658 C0KRQ8 0.005 0.134 0.667
GnRH signaling pathway Q1W658 C0KRQ8 0.025 0.217 0.333
Neuroactive ligand-receptor interaction Q1W658 C0KRQ8 0.034 0.217 0.286
Glycosylphosphatidylinositol (GPI)-anchor biosynthesis P80108 0.043 0.217 1
Regulation of lipolysis in adipocytes C0KRQ8 0.043 0.217 1
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KEGG, Kyoto Encyclopedia of Genes and Genomes; PD, Parkinson’s disease.

Discussion

Systemic biology is the most useful way to overview the metabolic alterations of an organism in a pathological condition. Here, we have adopted an integrated metabolomics and proteomics analysis of plasma in PD patients to explore its potential pathogenesis for the first time. No significant differences were found in any of the clinical data between these two groups, suggesting the following omics analyses were reasonable.

Researchers from the United States performed the first metabolomics profiling of plasma in PD patients (Bogdanov et al., 2008). They identified altered levels of 8-hydroxy-2-deoxyguanosine, uric acid, and glutathione as potential blood biomarkers for PD patients without further pathway analysis. Other researchers also performed metabolomics analysis of cerebrospinal fluid (Lewitt et al., 2013) and urine (Luan et al., 2015) from PD patients, suggesting various perturbed metabolic pathways related to lipids, energy, fatty acids, bile acids, polyamine, and amino acids (Shao and Le, 2019). In this untargeted metabolomics analysis, significant metabolic differences were found between PD patients and healthy controls from the PLS-DA plot. Eighty-three differentially expressed metabolites were identified between the two groups, most of which were lipid and lipid-like molecules. The lipid and lipid-like molecules were further classified, and sphingolipids accounted for 25%. Furthermore, pathway analysis indicated that sphingolipid metabolism was the only significantly enriched metabolic pathway, and the pathway tended to be activated. Sphingolipids are a class of lipids containing a backbone of sphingoid bases and a set of aliphatic amino alcohols (e.g., ceramides, sphingomyelins, gangliosides, and cerebrosides). The involvement of sphingolipid metabolism was first reported in a yeast model of Parkinson’s disease (Lee et al., 2011). The Saccharomyces cerevisiae lipid elongase null mutants exhibited severe growth defects, accumulation of reactive oxygen species, aberrant protein trafficking, and a dramatic decrease in the survival of aged cells. α-Synuclein (α-syn) is a small soluble synaptic protein that is the major proteinaceous component of Lewy bodies, the pathological hallmark of Parkinson’s disease (Jo et al., 2004). It was reported that the toxicity of α-syn could be enhanced as a result of the disruption of ceramide-sphingolipid homeostasis in the endoplasmic reticulum (Lee et al., 2011). Whereas Gaucher disease-related sphingolipids (glucosylceramide, glucosylsphingosine, sphingosine, sphingosine-1-phosphate) are reported to induce α-syn aggregation, glucosylsphingosine can trigger the formation of oligomeric α-syn in human neurons (Taguchi et al., 2017). Autophagy can be impaired by the increased levels of glucosylceramide and sphingomyelin while also enhanced by the increased levels of ceramide (Indellicato and Trinchera, 2019). In total, the disruption of the sphingolipid metabolism leads to α-syn accumulation, influences the traffic of vesicles toward lysosomes, and affects autophagy. Mutations of glucocerebrosidase 1, an enzyme hydrolyzing glucocerebroside (a subclass of sphingolipids), are the most common genetic risk factor for PD, although the underlying biological processes are poorly understood (Gan-Or et al., 2008). Metabolomics analysis mainly focuses on small molecules (<1 KD), and in this research a proteomics analysis was further performed.

A lot of proteomics research has been conducted in PD patients, mainly from brain tissues (substantia nigra, thalamus, locus coeruleus, olfactory bulb, and cerebral cortex), biofluids (cerebrospinal fluid, tears, and blood) and subcellular structures (mitochondria) (Dixit et al., 2019). Most of this research referred to pathways involved in “mitochondrial dysfunction, oxidative stress, protein aggregation or degradation, autophagy, and inflammation”(Ren et al., 2015; Monti et al., 2018). Here, forty proteins were differentially expressed in PD patients from the plasma proteomics analysis and the levels of all the seven apolipoproteins (apolipoprotein C-I, apolipoprotein C-III, protein APOC4-APOC2, apolipoprotein C-IV, apolipoprotein B variant, apolipoprotein B, and apolipoprotein M) were significantly decreased. Another former study reported decreases of apolipoprotein AII and apolipoprotein E in Parkinson’s disease (Zhang et al., 2008). The decrease of apolipoprotein B-100 (Lehnert et al., 2012) and apolipoprotein A-I (Zhang et al., 2012) was also reported in some studies. There are a large number of lipids in the central nervous system, and about one-fourth of the total cholesterol has a function or is stored in the brain. These apolipoproteins can influence the deposition process of many proteins in neurodegenerative diseases, such as α-syn in PD (Zhang et al., 2012). According to the enriched GO analysis, five of the six top ranking GO terms from cellular components were directly associated with lipids, and eleven of the fourteen top ranking GO terms from biological processes were directly associated with lipid metabolism. Keratin filament from the cellular component is a part of the intermediate filament and constitutes the cytoskeleton while Lewy bodies are the accumulation of neurofilaments (intermediate filament) in Parkinson’s disease. The underlying pathogenesis needs further research to confirm. The remaining three GO terms from biological processes were also related with lipid metabolism, as all the involved proteins were lipoproteins. KEGG pathway analysis was performed with those differentially expressed proteins and it seemed to be enriched in five pathways. The GnRH signaling pathway and ovarian steroidogenesis directly regulate steroid hormones and participate in the production of steroids. These processes belong to neuroactive ligand–receptor interaction. Glycosylphosphatidylinositol (GPI)–anchor biosynthesis and regulation of lipolysis in adipocytes are direct detailed processes of lipid metabolism. However, the results were unstable after Benjamini-Hochberg correction, as no significant differences could be observed (q-value > 0.05).

Both sphingolipids and apolipoproteins participate in plasma lipid metabolism, and are mainly related with atherosclerotic diseases. A number of lipidomics studies have reported specific lipid alterations in the brain or plasma from PD patients, such as the composition alteration of lipid rafts in the frontal cortex (Alecu and Bennett, 2019). The interaction of α-syn and the synaptic membrane is thought to be critical in Parkinson’s disease and has been explored for many years (Kubo et al., 2005). It is reported that lipids and the ratio of lipids to proteins can regulate the aggregation propensity of α-syn (Galvagnion, 2017). α-Syn can interact with the lipid membrane, and that interaction further affects the oligomerization and aggregation of α-syn (Suzuki et al., 2018). The alteration of lipid chemical properties can also induce α-syn aggregation and affect the balance between functional and aberrant behavior of α-syn (Galvagnion et al., 2016). In addition, α-syn can bind to specific lipid molecules, and these lipid–protein conjugates can further help the transport of α-syn in the blood or across the blood-brain barrier (Emamzadeh and Allsop, 2017), enhance its interaction with synaptic membranes, and interfere with the catalytic activity of cytoplasmic and lysosomal lipases, thereby disrupting lipid metabolism (Alecu and Bennett, 2019). In the meantime, lipid dysregulation can also promote PD pathogenesis through oxidative stress and inflammation reaction. Apolipoprotein M can increase the level of circulating sphingosine 1-phosphate, activate the following signaling pathway, and induce inflammation reaction (Kurano et al., 2013). Platelet activating factors, a lipid proinflammatory mediator, play an important role in modulating progressive neurodegeneration in PD patients by intracellular trafficking of α-syn. Furthermore, there are a large number of genetic risk factors of PD involved in lipid metabolism, including PLA2G6 and SCARB2. These two genes are directly or indirectly involved in glycerophospholipid and sphingolipid metabolism (Alecu and Bennett, 2019). Mutations in the glucocerebrosidase 1 gene, which encodes a degrading enzyme for the glycolipid glucosylceramide, are regarded as strong risk factors for PD and dementia with Lewy bodies, and glucosylceramide has been confirmed to promote toxic conversion of α-syn. Moreover, pathological research has demonstrated the existence of α-syn in the brains of patients with lysosomal storage disorders, in which glycosphingolipids is found to be accumulated (Suzuki et al., 2018). In addition, α-syn can antagonize neurotrophic signaling of TrkB by repressing TrkB lipid raft distribution, decreasing its internalization, and reducing its axonal trafficking (Kang et al., 2017). The possible pathogenesis is that α-syn sequesters the early peroxidation products of fatty acids, thereby reducing the level of highly reactive lipid species (De Franceschi et al., 2017). All in all, dysregulated lipid metabolism is implicated in Parkinson’s disease, which might open a new therapeutic method for modifying Parkinson’s disease.

Conclusion

Integrated proteomics and metabolomics analysis reveals that Parkinson’s disease is associated with plasma lipid metabolic disturbance. The activated sphingolipid metabolism and decreased apolipoproteins are the probable potential pathogenesis for PD.

Data Availability Statement

The datasets presented in this study can be found in the supplementary materials (Supplementary Table S1) and online repository PeptideAtlas, with accession number PASS01568 (http://www.peptideatlas.org/PASS/PASS01568).

Ethics Statement

The studies involving human participants were reviewed and approved by the Ethics committee of the First Affiliated Hospital of Chongqing Medical University. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

LH and Y-DW designed this study. LH, C-QL, QQ, C-CZ, and M-XD assessed the clinical data. LH, X-MX, and YL performed the experiments. M-XD, Y-LH, and G-HC wrote and revised the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding. This study was funded by the Chongqing Health and Family Planning commission (No. 2017MSXM023) and the Fundamental Research Funds for the Central Universities (2042020kf0056).

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnmol.2020.00080/full#supplementary-material

TABLE S1

The matrix of metabolomics data included in the paper.

Click here for additional data file. (12.9MB, xlsx)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

TABLE S1

The matrix of metabolomics data included in the paper.

Click here for additional data file. (12.9MB, xlsx)

Data Availability Statement

The datasets presented in this study can be found in the supplementary materials (Supplementary Table S1) and online repository PeptideAtlas, with accession number PASS01568 (http://www.peptideatlas.org/PASS/PASS01568).

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