Plasma sphingolipid abnormalities in neurodegenerative diseases

Hideki Oizumi; Yoko Sugimura; Tomoko Totsune; Iori Kawasaki; Saki Ohshiro; Toru Baba; Teiko Kimpara; Hiroaki Sakuma; Takafumi Hasegawa; Ichiro Kawahata; Kohji Fukunaga; Atsushi Takeda

doi:10.1371/journal.pone.0279315

. 2022 Dec 16;17(12):e0279315. doi: 10.1371/journal.pone.0279315

Plasma sphingolipid abnormalities in neurodegenerative diseases

Hideki Oizumi ¹, Yoko Sugimura ¹, Tomoko Totsune ¹, Iori Kawasaki ¹, Saki Ohshiro ¹, Toru Baba ¹, Teiko Kimpara ¹, Hiroaki Sakuma ¹, Takafumi Hasegawa ², Ichiro Kawahata ³, Kohji Fukunaga ³, Atsushi Takeda ^1,^4,^*

Editor: Stephan N Witt⁵

PMCID: PMC9757566 PMID: 36525454

Abstract

Background

In recent years, there has been increasing evidence that several lipid metabolism abnormalities play an important role in the pathogenesis of neurodegenerative diseases. However, it is still unclear which lipid metabolism abnormalities play the most important role in neurodegenerative diseases. Plasma lipid metabolomics (lipidomics) has been shown to be an unbiased method that can be used to explore lipid metabolism abnormalities in neurodegenerative diseases. Plasma lipidomics in neurodegenerative diseases has been performed only in idiopathic Parkinson’s disease (IPD) and Alzheimer’s disease (AD), and comprehensive studies are needed to clarify the pathogenesis.

Methods

In this study, we investigated plasma lipids using lipidomics in individuals with neurodegenerative diseases and healthy controls (CNs). Plasma lipidomics was evaluated by liquid chromatography-tandem mass spectrometry (LC–MS/MS) in those with IPD, dementia with Lewy bodies (DLB), multiple system atrophy (MSA), AD, and progressive supranuclear palsy (PSP) and CNs.

Results

The results showed that (1) plasma sphingosine-1-phosphate (S1P) was significantly lower in all neurodegenerative disease groups (IPD, DLB, MSA, AD, and PSP) than in the CN group. (2) Plasma monohexylceramide (MonCer) and lactosylceramide (LacCer) were significantly higher in all neurodegenerative disease groups (IPD, DLB, MSA, AD, and PSP) than in the CN group. (3) Plasma MonCer levels were significantly positively correlated with plasma LacCer levels in all enrolled groups.

Conclusion

S1P, Glucosylceramide (GlcCer), the main component of MonCer, and LacCer are sphingolipids that are biosynthesized from ceramide. Recent studies have suggested that elevated GlcCer and decreased S1P levels in neurons are related to neuronal cell death and that elevated LacCer levels induce neurodegeneration by neuroinflammation. In the present study, we found decreased plasma S1P levels and elevated plasma MonCer and LacCer levels in those with neurodegenerative diseases, which is a new finding indicating the importance of abnormal sphingolipid metabolism in neurodegeneration.

Introduction

The incidence of idiopathic Parkinson’s disease (IPD) is reported to be 8–18 per 1000 person-years [1] and that of dementia with Lewy bodies (DLB) is 0.5–1.6 per 1000 person-years [2], which makes both of them common neurodegenerative diseases. Lewy body diseases, such as DLB and IPD, are characterized by the presence of cytoplasmic protein aggregates known as Lewy bodies (LBs) [3]. The main component of LBs is α-synuclein, which is abundant in neurons, including synaptic vesicles in presynaptic terminals, and is a protein aggregate that has been converted to a β-sheet fibril structure [4]. Multiple system atrophy (MSA) is an adult-onset neurodegenerative disease that is clinically characterized by poor levodopa-responsive parkinsonism, cerebellar dysfunction, and autonomic failure [5]. The histopathology of MSA is characterized by the presence of protein aggregates known as glial cytoplasmic inclusions (GCIs). Similar to LBs, GCIs are largely composed of aggregates of α-synuclein [6, 7]. Therefore, LB diseases and MSA are classified as neurodegenerative diseases named synucleinopathies, which are characterized by prominent intracellular α-synuclein aggregation [8].

Alzheimer’s disease (AD) is the most common neurodegenerative disease, currently affecting approximately 40 million people worldwide [9]. In contrast to synucleinopathies, the pathological features of AD require the presence of extracellular β amyloid-positive senile plaques and phosphorylated tau-positive neurofibrillary tangles in neurons [10]. Progressive supranuclear palsy (PSP) is a neurodegenerative disease characterized by vertical supranuclear gaze palsy, postural instability and falls in the early stages of the disease [11]. The pathology of PSP is characterized by tau-positive aggregates with a characteristic 4-repeat tau in the microtubule-binding domain in neurons [12]. Therefore, AD and PSP have been classified as neurodegenerative diseases named tauopathies, which are characterized by prominent tau aggregation in neurons [13–15]. AD have been also classified as neurodegenerative diseases named amyloidopathies, which are characterized by prominent extracellular β amyloid aggregation [16].

Lipids are biomolecules that are soluble in nonpolar organic solvents, usually insoluble in water, and are known primarily for their metabolic role in energy storage [17]. Lipids are also major components of cell membranes and play an important role in cellular metabolism as components of lipid rafts, protein anchors, and signaling and transport molecules. There are eight distinct classes of lipids classified as fatty acyl, glycerolipids, glycerophospholipids, sphingolipids, sterols, prenols, saccharolipids, and polyketides [17]. Recently, abnormalities in cerebrospinal fluid (CSF) lipid metabolism have been reported in IPD and AD [18, 19]. CSF examination in neurodegenerative diseases is less costly than neuroimaging and more directly reflects the metabolic state and pathophysiology of the central nervous system than other body fluids, making it an important test for understanding pathophysiology. However, CSF testing is a rather invasive approach, and there is a need to develop more noninvasive methods of fluid collection (e.g., blood sampling) to evaluate the pathogenesis of neurodegenerative diseases.

In recent years, plasma metabolomics has attracted much attention as a method to search for metabolic abnormalities in an unbiased manner and one that reflects the pathophysiology in vivo [20]. Lipid metabolites have various characteristics, such as molecular weight, polarity, and ionization state. For accurate analysis, it has been necessary to develop new tools that can detect a large number of lipid metabolites with high resolution. LC–MS/MS can detect a large number of lipid metabolites with high resolution and has attracted attention as a tool for lipid metabolomics (lipidomics) research. Plasma lipidomics in neurodegenerative diseases has been evaluated only in IPD and AD [21, 22], and comprehensive analysis is needed to clarify the pathogenesis. In the present study, we used plasma lipidomics to examine whether abnormalities in plasma lipid metabolism were observed in IPD, DLB, MSA, AD, and PSP.

Materials and methods

Clinical information of the participants in this study

All participants were recruited at National Hospital Organization (NHO) Sendai Nishitaga Hospital and examined by board-certified neurologists. Cohort A, cohort B, and cohort C were recruited from October 2017 to September 2021. Patients with IPD, probable DLB, probable AD, probable MSA, and probable PSP according to the established clinical diagnostic criteria for each disease were included [3, 5, 23–25]. All enrolled patients had late onset (>45 years of age), and no patients had a family history. All IPD patients were treated with L-dopa or other antiparkinsonian drugs, and motor symptoms were under good control. In cohort A, we enrolled 30 patients with IPD and 28 controls (CNs) (Table 1). The 30 IPD patients included 21 females and 9 males; the age of the IPD patients ranged from 58 to 75 years, with a mean of 67.2 years. The 28 CNs included 14 females and 14 males; the age of the CNs ranged from 57 to 73 years, with a mean of 65 years. In cohort B, 28 DLB patients, 13 AD patients, and 15 CNs were enrolled (Table 1). The 28 DLB patients included 17 females and 11 males; the age of the DLB patients ranged from 72 to 95 years, with a mean of 83.3 years. The 13 AD patients included 11 females and 2 males; the age of the AD patients ranged from 73 to 88 years, with a mean of 83.6 years. The 15 CNs included 4 females and 11 males; the age of the CNs ranged from 55 to 73 years, with a mean of 66.8 years. In cohort C, 28 PD patients, 13 MSA patients, 16 PSP patients, and 6 CNs were enrolled (Table 1). The 28 IPD patients included 19 females and 9 males; the age of the IPD patients ranged from 60 to 85 years, with a mean of 75.2 years. The 13 MSA patients included 7 females and 6 males; the age of the MSA patients ranged from 50 to 92 years, with a mean of 69.6 years. The 16 PSP patients included 7 females and 9 males; the age of the PSP patients ranged from 60 to 84 years, with an average of 74.8 years; 6 CNs included 2 females and 4 males; the age of the CNs ranged from 70 to 73 years, with an average of 71.5 years.

Table 1. Demographics and clinical characteristics of the analyzed plasma samples in Cohorts A, B and C.

	cohort A
	CN	PD	p value (CN vs. PD)
number	28	30
male, %/female, %	14 (50)/14 (50)	9 (30)/21 (70)	0.1197
age, y, mean±SD	65.0±5.3	67.2±5.1	0.1095
MMSE, mean±SD	26.9±2.0	26.8±3.6	0.3532
disease duration, y, mean±SD		9.2±6.1
	cohort B
	CN	DLB	p value (CN vs. DLB)	AD	p value (CN vs. AD)
number	15	28		13
male, %/female, %	11 (73)/4 (27)	11 (39)/17 (61)	0.0333	2 (15)/11 (85)	0.0022
age, y, mean±SD	66.8±5.2	83.3±6.2	<0.0001	83.6±4.5	<0.0001
MMSE, mean±SD	26.7±2.1	24.2±6.7	0.0217	19.6±4.9	<0.0001
disease duration, y, mean±SD		3.4±3.4		1.5±2.1
	cohort C
	CN	PD	p value (CN vs. PD)	PSP	p value (CN vs. PSP)	MSA	p value (CN vs. MSA)
number	6	28		16		13
male, %/female, %	4 (67)/2 (33)	9 (32)/19 (68)	0.1143	9 (56)/7 (44)	0.6581	6 (46)/7 (54)	0.4052
age, y, mean±SD	71.5±1.2	75.2±5.9	0.1077	74.8±6.8	0.086	69.6±11.9	0.9299
MMSE, mean±SD	28.8±1	24.0±3.8	0.0014	22.5±4.5	0.0079	26.2±1.3	0.0572
disease duration, y, mean±SD		6.4±6.6		3.6±2.2		2.8±1.7

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Abbreviations: CNs, controls; PD, Parkinson’s disease; DLB, dementia with Lewy bodies; Alzheimer’s disease (AD); SD, standard deviation; MMSE: Mini-Mental State Examination; PSP, progressive supranuclear palsy; MSA, multiple system atrophy

In this study, duration of illness refers to the time since the onset of motor symptoms in the IPD, MSA, and PSP patients and the onset of cognitive impairment in the DLB and AD patients. The Mini-Mental State Examination (MMSE) was used as a global cognitive function test. All CNs, all DLB patients, all AD patients, 24 out of 30 IPD patients in cohort A, 23 out of 28 IPD patients in cohort C, 10 out of 13 MSA patients, and 16 out of 18 PSP patients completed the MMSE.

This study was approved by the ethics committee of our institution and followed the Helsinki Declaration on International Clinical Research Involving Human Beings. Written informed consent for this study was obtained from all subjects.

Sample collection

Sample collection was performed from October 2017 to September 2021. Plasma was extracted as previously described [26]. Each 500 μl plasma aliquot was stored in a -80°C freezer until use. Briefly, fasting blood was collected in Na-EDTA and centrifuged at room temperature for 10 minutes to extract plasma. The extracted plasma was collected in screw-cap microtubes (Sarstedt AG, Nümbrecht, Germany) between 10 am and 12 am and stored at -80°C until the time of metabolomic analysis.

Metabolite extraction

Metabolite extraction and metabolomic analysis were conducted at Human Metabolome Technologies (HMT) (HMT, Tsuruoka, Yamagata, Japan). Briefly, 100 μL of plasma was mixed with 300 μL of 0.1% formic acid in methanol containing internal standards and centrifuged at 9,100 ×g and 4°C for 10 minutes. Then, 250 μL of the supernatant was mixed with 550 μL of 0.1% formic acid and loaded onto an SPE column (MonoSpinC18, 5010–2170, GL Sciences Inc., Tokyo, Japan). The analytes on the SPE column were purified with 0.1% formic acid and 0.1% formic acid in 25% methanol and eluted with 200 μL of 0.1% formic acid in methanol. The elution was then used for LC–MS/MS analysis at HMT. The average recovery of sphingolipids extracted with the SPE column is 88% (range 68% to 99.9%).

Metabolomic analysis

Metabolomic analysis was conducted by the Mediator Scan package of HMT by using LC–MS/MS. Based on metabolomic analysis, 324 metabolites, including fatty acids, acylcarnitines, oxylipins, lysophospholipids, platelet-activating factors, glycosphingolipids, sphinganines, sphingosines, and steroids, were evaluated in all enrolled neurodegenerative disease groups and the CN group. Briefly, LC–MS/MS analysis was carried out by using an Agilent 1260 Infinity II and Agilent 1290 Infinity II High Speed Pump equipped with AB Sciex QTRAP 5500 (AB Sciex Pte. Ltd., Framingham, MA, USA). The multiple reaction monitoring (MRM) mode of the mass spectrometer was used to detect signals of each metabolite according to the HMT metabolite database. MRM ion chromatograms were extracted by using Multi Quant automatic integration software (AB Sciex) to obtain peak area information. Target metabolites are divided into categories (fatty acids, acylcarnitines, oxylipins, lysophospholipids, platelet-activating factors, glycosphingolipids, sphinganines, sphingosines, and steroids) according to their physical properties, and the recovery rate is corrected using the corresponding IS (Internal standards). Based on these reports, these IS were selected [27–29]. The recovery rate of analytes during extraction ranged from 68% to 129%, with a mean of 96%. IS coefficient of variation ranged from 4.4 to 9.7%, with a mean of 6.7%. The peak area of each metabolite was then normalized based on IS level and sample volume for relative quantification. The normalized each metabolite was represented as relative area and used as the quantitative value based on previous reports [30, 31].

Simoa^™ assay

Plasma samples stored at -80°C were thawed and centrifuged at 10,000 x g for 5 minutes. Samples were diluted in advance with the Sample Diluent provided with Assay Kit and applied to the plate. The assay was performed one sample at a time. Simoa^™ p-Tau181 Advantage Kit (Quanterix, #103377, Billerica, MA, USA) were used to measure plasma p-Tau181. Measurements were performed according to the instructions for kit.

Statistical evaluation

All plasma metabolites are expressed as the median (interquartile range). Differences between the groups were examined for statistical significance using one-tailed Welch’s t test in the lipidomic analysis data. Differences between the groups were examined for statistical significance using Wilcoxon tests and chi-square tests for the demographic data. Data were analyzed using the computer software system JMP13 (SAS Institute, Tokyo, Japan).

Results

Plasma sphingosine-1-phosphate (S1P) levels in neurodegenerative diseases

Plasma S1P levels were compared between the CN group and the IPD, DLB, MSA, AD and PSP groups. Statistical significance was examined using one-tailed Welch’s t tests. Plasma S1P d16.1 levels were significantly (p < 0.0001) lower in the IPD group of cohort A (N = 30) versus the control group (N = 28) (Fig 1A). Plasma S1P d16.1 levels were significantly (p < 0.0001) lower in the DLB group (N = 28) versus the control group (N = 15) (Fig 1B) and significantly (p < 0.0001) lower in the AD group (N = 13) versus the control group (N = 15) (Fig 1B). Plasma S1P d16.1 levels were significantly (p < 0.01) lower in the IPD group of cohort C (N = 28) versus the control group (N = 6) (Fig 1C), significantly (p < 0.01) lower in the MSA group (N = 13) versus the control group (N = 6) (Fig 1C), and significantly (p < 0.001) lower in the PSP group (N = 16) versus the control group (N = 6) (Fig 1C). Plasma S1P d18.1 levels were significantly (p < 0.05) lower in the IPD group of cohort A (N = 30) versus the control group (N = 28) (Fig 1D). Plasma S1P d18.1 levels were significantly (p < 0.01) lower in the DLB group (N = 28) versus the control group (N = 15) (Fig 1E) and significantly (p < 0.05) lower in the AD group (N = 13) versus the control group (N = 15) (Fig 1E). Plasma S1P d18.1 levels were significantly (p < 0.05) lower in the IPD group of cohort C (N = 28) versus the control group (N = 6) (Fig 1F), significantly (p < 0.05) lower in the MSA group (N = 13) versus the control group (N = 6) (Fig 1F), and significantly (p < 0.05) lower in the PSP group (N = 16) versus the control group (N = 6) (Fig 1F). These results indicated that plasma S1P levels were significantly lower in all neurodegenerative disease groups (IPD, DLB, MSA, AD, and PSP) than in the CN group.

Fig 1 — (A) Plasma S1P d16.1 levels were significantly lower in the IPD group of cohort A (p < 0.0001) than in the CN group. (B) Plasma S1P d16.1 levels were significantly lower in the DLB group (p < 0.0001) and AD group (p < 0.0001) than in the CN group. (C) Plasma S1P d16.1 levels were significantly lower in the IPD group of cohort C (p < 0.01), MSA group (p < 0.01) and PSP group (p < 0.001) than in the CN group. (D) Plasma S1P d18.1 levels were significantly lower in the IPD group of cohort A (p < 0.05) than in the CN group. (E) Plasma S1P d18.1 levels were significantly lower in the DLB group (p < 0.01) and AD group (p < 0.05) than in the CN group. (F) Plasma S1P d18.1 levels were significantly lower in the IPD group of cohort C (p < 0.05), MSA group (p < 0.05) and PSP group (p < 0.05) than in the CN group. Statistical significance was examined using one-tailed Welch’s t tests (P < 0.05). Circles indicate the data points between the lower and upper whiskers, and x indicates the average marker in a box/whisker diagram.

Plasma monohexylceramide (MonCer) levels in neurodegenerative diseases

Total plasma MonCer d18:1 levels were compared between the CN group and the IPD, DLB, MSA, AD, and PSP groups. Total plasma MonCer d18:1 levels were measured by summing levels of 13 types of MonCer d18:1: MonCer (d18:1/14:0), MonCer (d18:1/16:0), MonCer (d18:1/16:1), MonCer (d18:1/18:0), MonCer (d18:1/18:1), MonCer (d18:1/20:0), MonCer (d18:1/20:1), MonCer (d18:1/22:0), MonCer (d18:1/22:1), MonCer (d18:1/22:2), MonCer (d18:1/24:0), MonCer (d18:1/24:1) and MonCer (d18:1/24:2). Statistical significance was examined using one-tailed Welch’s t tests. Total plasma MonCer d18:1 levels were significantly (p < 0.01) higher in the IPD group of cohort A (N = 30) versus the control group (N = 28) (Fig 2A). Total plasma MonCer d18:1 levels were significantly (p < 0.01) higher in the DLB group (N = 28) versus the control group (N = 15) (Fig 2B) and significantly (p < 0.001) higher in the AD group (N = 13) versus the control group (N = 15) (Fig 2B). Total plasma MonCer d18:1 levels were significantly (p < 0.01) higher in the IPD group of cohort C (N = 28) versus the control group (N = 6) (Fig 2C), significantly (p < 0.05) higher in the MSA group (N = 13) versus the control group (N = 6) (Fig 2C), and significantly (p < 0.01) higher in the PSP group (N = 16) versus the control group (N = 6) (Fig 2C). These results indicated that plasma MonCer levels were significantly higher in all neurodegenerative disease groups (IPD, DLB, MSA, AD, and PSP) than in the CN group.

Fig 2 — (A) Plasma MonCer d18:1 levels were significantly higher in the IPD group of cohort A (p < 0.01) than in the CN group. (B) Plasma MonCer d18:1 levels were significantly higher in the DLB group (p < 0.01) and AD group (p < 0.001) than in the CN group. (C) Plasma MonCer d18:1 levels were significantly higher in the IPD group of cohort C (p < 0.01), MSA group (p < 0.05) and PSP group (p < 0.01) than in the CN group. Statistical significance was examined using one-tailed Welch’s t tests (P < 0.05). Circles indicate the data points between the lower and upper whiskers, and x indicates the average marker in a box/whisker diagram.

We compared MonCer (d18:1/14:0), MonCer (d18:1/16:0), MonCer (d18:1/16:1), MonCer (d18:1/18:0), MonCer (d18:1/18:1), MonCer (d18:1/20:0), MonCer (d18:1/20:1), MonCer (d18:1/22:0), MonCer (d18:1/22:1), MonCer (d18:1/22:2), MonCer (d18:1/24:0), MonCer (d18:1/24:1), and MonCer (d18:1/24:2) between the CN group and the IPD, DLB, MSA, AD, or PSP groups (Table 2). The chi-square test was used to examine the association between lipid abnormalities and chain length in MonCer d18:1. No statistically significant difference was found between lipid abnormalities and chain length (P = 0.5522) in all enrolled groups.

Table 2. Plasma all MonCer levels in neurodegenerative diseases.

cohort A			cohort B			cohort B
PD vs CN			DLB vs CN			AD vs CN
	ratio	p value		ratio	p value		ratio	p value
MonCer (d18:1/14:0)	1.1	0.0975	MonCer (d18:1/14:0)	1.3	0.0012	MonCer (d18:1/14:0)	1.2	0.0443
MonCer (d18:1/16:0)	1.2	0.0151	MonCer (d18:1/16:0)	1.2	0.0013	MonCer (d18:1/16:0)	1.3	0.0023
MonCer (d18:1/16:1)	1	0.5408	MonCer (d18:1/16:1)	1.1	0.2052	MonCer (d18:1/16:1)	1	0.4006
MonCer (d18:1/18:0)	1.3	0.0105	MonCer (d18:1/18:0)	1.2	0.0181	MonCer (d18:1/18:0)	1.3	0.0008
MonCer (d18:1/18:1)	1.4	0.0018	MonCer (d18:1/18:1)	1.3	0.0042	MonCer (d18:1/18:1)	1.3	0.0106
MonCer (d18:1/20:0)	1.2	0.0053	MonCer (d18:1/20:0)	1.1	0.0890	MonCer (d18:1/20:0)	1.2	0.0298
MonCer (d18:1/20:1)	1.5	0.0010	MonCer (d18:1/20:1)	1.4	0.0066	MonCer (d18:1/20:1)	1.4	0.0047
MonCer (d18:1/22:0)	1.1	0.2312	MonCer (d18:1/22:0)	1	0.5454	MonCer (d18:1/22:0)	1.1	0.2288
MonCer (d18:1/22:1)	1.4	0.0092	MonCer (d18:1/22:1)	1.5	0.0025	MonCer (d18:1/22:1)	1.5	0.0121
MonCer (d18:1/22:2)	1.3	0.0097	MonCer (d18:1/22:2)	1.4	0.0034	MonCer (d18:1/22:2)	1.5	0.0039
MonCer (d18:1/24:0)	1.1	0.2478	MonCer (d18:1/24:0)	1	0.5483	MonCer (d18:1/24:0)	0.9	0.6499
MonCer (d18:1/24:1)	1.3	0.0016	MonCer (d18:1/24:1)	1.3	0.0143	MonCer (d18:1/24:1)	1.3	0.0244
MonCer (d18:1/24:2)	1.2	0.0176	MonCer (d18:1/24:2)	1.2	0.0170	MonCer (d18:1/24:2)	1.4	0.0313
cohort C			cohort C			cohort C
PD vs CN			PSP vs CN			MSA vs CN
	ratio	p value		ratio	p value		ratio	p value
MonCer (d18:1/14:0)	1.3	0.0826	MonCer (d18:1/14:0)	1.3	0.0709	MonCer (d18:1/14:0)	1.2	0.1345
MonCer (d18:1/16:0)	1.4	0.0029	MonCer (d18:1/16:0)	1.4	0.0035	MonCer (d18:1/16:0)	1.4	0.0099
MonCer (d18:1/16:1)	1.2	0.3156	MonCer (d18:1/16:1)	1.5	0.1082	MonCer (d18:1/16:1)	1.3	0.2442
MonCer (d18:1/18:0)	1.5	0.0018	MonCer (d18:1/18:0)	1.5	0.0030	MonCer (d18:1/18:0)	1.4	0.0074
MonCer (d18:1/18:1)	1.3	0.0018	MonCer (d18:1/18:1)	1.5	0.0006	MonCer (d18:1/18:1)	1.2	0.0910
MonCer (d18:1/20:0)	1.3	0.0267	MonCer (d18:1/20:0)	1.4	0.0100	MonCer (d18:1/20:0)	1.3	0.0430
MonCer (d18:1/20:1)	1.3	0.0182	MonCer (d18:1/20:1)	1.5	0.0020	MonCer (d18:1/20:1)	1.3	0.0172
MonCer (d18:1/22:0)	0.8	0.7890	MonCer (d18:1/22:0)	1.1	0.1771	MonCer (d18:1/22:0)	0.9	0.7486
MonCer (d18:1/22:1)	1	0.5938	MonCer (d18:1/22:1)	1.4	0.0729	MonCer (d18:1/22:1)	1.2	0.2142
MonCer (d18:1/22:2)	1.5	0.0009	MonCer (d18:1/22:2)	1.6	0.0013	MonCer (d18:1/22:2)	1.6	0.0189
MonCer (d18:1/24:0)	0.4	<0.0001	MonCer (d18:1/24:0)	1.1	0.2573	MonCer (d18:1/24:0)	0.7	0.0158
MonCer (d18:1/24:1)	0.9	0.2863	MonCer (d18:1/24:1)	1.4	0.0136	MonCer (d18:1/24:1)	1.1	0.3389
MonCer (d18:1/24:2)	1.2	0.0725	MonCer (d18:1/24:2)	1.5	0.0145	MonCer (d18:1/24:2)	1.3	0.0697

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Statistical methods: The metabolite level ratio of IPD, DLB, MSA, AD, or PSP to CNs. Statistical significance was examined using one-tailed Welch’s t tests (P < 0.05).

Plasma lactosylceramide (LacCer) levels in neurodegenerative diseases

Total plasma LacCer d18:1 levels were compared between the CN group and the IPD, DLB, MSA, AD, and PSP groups. Total plasma LacCer d18:1 levels were measured by summing the levels of 13 types of LacCer d18:1: LacCer (d18:1/14:0), LacCer (d18:1/16:0), LacCer (d18:1/16:1), LacCer (d18:1/18:0), LacCer (d18:1/18:1), LacCer (d18:1/20:0), LacCer (d18:1/20:1), LacCer (d18:1/22:0), LacCer (d18:1/22:1), LacCer (d18:1/22:2), LacCer (d18:1/24:0), LacCer (d18:1/24:1) and LacCer (d18:1/24:2). Statistical significance was examined using one-tailed Welch’s t tests. Total plasma LacCer d18:1 levels were significantly (p < 0.01) higher in the IPD group of cohort A (N = 30) versus the control group (N = 28) (Fig 3A). Total plasma LacCer d18:1 levels were significantly (p < 0.001) higher in the DLB group (N = 28) versus the control group (N = 15) (Fig 3B) and significantly (p < 0.05) higher in the AD group (N = 13) versus the control group (N = 15) (Fig 3B). Total plasma LacCer d18:1 levels were significantly (p < 0.01) higher in the IPD group of cohort C (N = 28) versus the control group (N = 6) (Fig 3C), significantly (p < 0.01) higher in the MSA group (N = 13) versus the control group (N = 6) (Fig 3C), and significantly (p < 0.01) higher in the PSP group (N = 16) versus the control group (N = 6) (Fig 3C). These results indicated that plasma LacCer levels were significantly higher in all neurodegenerative disease groups (IPD, DLB, MSA, AD, and PSP) than in the CN group.

Fig 3 — (A) Plasma LacCer d18:1 levels were significantly higher in the IPD group of cohort A (p < 0.01) than in the CN group. (B) Plasma LacCer d18:1 levels were significantly higher in the DLB group (p < 0.001) and AD group (p < 0.05) than in the CN group. (C) Plasma LacCer d18:1 levels were significantly higher in the IPD group of cohort C (p < 0.01), MSA group (p < 0.01) and PSP group (p < 0.01) than in the CN group. Statistical significance was examined using one-tailed Welch’s t tests (P < 0.05). Circles indicate the data points between the lower and upper whiskers, and x indicates the average marker in a box/whisker diagram.

We compared LacCer (d18:1/14:0), LacCer (d18:1/16:0), LacCer (d18:1/16:1), LacCer (d18:1/18:0), LacCer (d18:1/18:1), LacCer (d18:1/20:0), LacCer (d18:1/20:1), LacCer (d18:1/22:0), LacCer (d18:1/22:1), LacCer (d18:1/22:2), LacCer (d18:1/24:0), LacCer (d18:1/24:1), and LacCer (d18:1/24:2) between the CN group and the IPD, DLB, MSA, AD, or PSP groups (Table 3). The chi-square test was used to examine the association between lipid abnormalities and chain length in LacCers d18:1. No statistically significant difference was found between lipid abnormalities and chain length (P = 0.5522) in all enrolled groups.

Table 3. Plasma All LacCer levels in neurodegenerative diseases.

cohort A			cohort B			cohort B
PD vs CN			DLB vs CN			AD vs CN
	ratio	p value		ratio	p value		ratio	p value
LacCer (d18:1/14:0)	1.2	0.0035	LacCer (d18:1/14:0)	1.5	<0.0001	LacCer (d18:1/14:0)	1.2	0.0297
LacCer (d18:1/16:0)	1.2	0.0026	LacCer (d18:1/16:0)	1.2	0.0006	LacCer (d18:1/16:0)	1.2	0.0284
LacCer (d18:1/16:1)	1.2	0.0161	LacCer (d18:1/16:1)	1.3	0.0002	LacCer (d18:1/16:1)	1.2	0.0655
LacCer (d18:1/18:0)	1.1	0.1188	LacCer (d18:1/18:0)	1.2	0.0110	LacCer (d18:1/18:0)	1.2	0.0340
LacCer (d18:1/18:1)	1.3	0.0024	LacCer (d18:1/18:1)	1.2	0.0278	LacCer (d18:1/18:1)	1.2	0.0850
LacCer (d18:1/20:0)	1	0.3364	LacCer (d18:1/20:0)	1.1	0.1147	LacCer (d18:1/20:0)	1.2	0.1116
LacCer (d18:1/20:1)	1.4	0.0032	LacCer (d18:1/20:1)	1.4	0.0009	LacCer (d18:1/20:1)	1.3	0.0179
LacCer (d18:1/22:0)	1	0.5292	LacCer (d18:1/22:0)	1.1	0.2660	LacCer (d18:1/22:0)	1.1	0.2379
LacCer (d18:1/22:1)	1.3	0.0095	LacCer (d18:1/22:1)	1.5	<0.0001	LacCer (d18:1/22:1)	1.4	0.0167
LacCer (d18:1/22:2)	1.3	0.0018	LacCer (d18:1/22:2)	1.3	0.0062	LacCer (d18:1/22:2)	1.3	0.0268
LacCer (d18:1/24:0)	1	0.4800	LacCer (d18:1/24:0)	1.1	0.3120	LacCer (d18:1/24:0)	1	0.6157
LacCer (d18:1/24:1)	1.4	0.0003	LacCer (d18:1/24:1)	1.3	0.0082	LacCer (d18:1/24:1)	1.2	0.1190
LacCer (d18:1/24:2)	1.3	0.0021	LacCer (d18:1/24:2)	1.3	0.0098	LacCer (d18:1/24:2)	1.1	0.1727
cohort C			cohort C			cohort C
PD vs CN			PSP vs CN			MSA vs CN
	ratio	p value		ratio	p value		ratio	p value
LacCer (d18:1/14:0)	1.4	0.0252	LacCer (d18:1/14:0)	1.3	0.0320	LacCer (d18:1/14:0)	1.4	0.0194
LacCer (d18:1/16:0)	1.3	0.0063	LacCer (d18:1/16:0)	1.4	0.0019	LacCer (d18:1/16:0)	1.3	0.0059
LacCer (d18:1/16:1)	1.2	0.0157	LacCer (d18:1/16:1)	1.5	0.0178	LacCer (d18:1/16:1)	1.2	0.0454
LacCer (d18:1/18:0)	1.2	0.0953	LacCer (d18:1/18:0)	1.5	0.2933	LacCer (d18:1/18:0)	1.3	0.0392
LacCer (d18:1/18:1)	1.2	0.1763	LacCer (d18:1/18:1)	1.5	0.1542	LacCer (d18:1/18:1)	1	0.4351
LacCer (d18:1/20:0)	1.1	0.2281	LacCer (d18:1/20:0)	1.4	0.3886	LacCer (d18:1/20:0)	1.2	0.1381
LacCer (d18:1/20:1)	1.2	0.0902	LacCer (d18:1/20:1)	1.5	0.2285	LacCer (d18:1/20:1)	1.2	0.1321
LacCer (d18:1/22:0)	0.8	0.1630	LacCer (d18:1/22:0)	1.1	0.6517	LacCer (d18:1/22:0)	0.9	0.2980
LacCer (d18:1/22:1)	1.1	0.3525	LacCer (d18:1/22:1)	1.4	0.2833	LacCer (d18:1/22:1)	1.1	0.2590
LacCer (d18:1/22:2)	1.3	0.0925	LacCer (d18:1/22:2)	1.6	0.1774	LacCer (d18:1/22:2)	1.2	0.1781
LacCer (d18:1/24:0)	0.4	0.0031	LacCer (d18:1/24:0)	1.1	0.7710	LacCer (d18:1/24:0)	0.7	0.0248
LacCer (d18:1/24:1)	0.9	0.6587	LacCer (d18:1/24:1)	1.4	0.3145	LacCer (d18:1/24:1)	1	0.4450
LacCer (d18:1/24:2)	1.2	0.2138	LacCer (d18:1/24:2)	1.5	0.2520	LacCer (d18:1/24:2)	1.2	0.2394

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Statistical methods: The metabolite level ratio of IPD, DLB, MSA, AD, or PSP to CNs. Statistical significance was examined using one-tailed Welch’s t tests (P < 0.05).

Correlation between total plasma MonCer levels and total plasma LacCer levels

Pearson Correlation Coefficient was used to correlate total plasma MonCer d18:1 levels and total plasma LacCer d18:1 levels in all enrolled groups. Total plasma MonCer d18:1 levels were significantly positively correlated with total plasma LacCer d18:1 levels (r = 0.5802, p < 0.0001) (Fig 4) in all enrolled groups. These results suggest that an increase in plasma MonCer may be directly related to an increase in LacCer in all enrolled groups.

Fig 4 — (A) Total plasma MonCer d18:1 levels were significantly positively correlated with total plasma LacCer d18:1 levels (r = 0.5802, p < 0.0001) in all enrolled groups.

Correlation between plasma p-tau levels and plasma S1P levels, total plasma MonCer levels or total plasma LacCer levels

To investigate the association between AD-associated protein and sphingolipids, Pearson Correlation Coefficient was used to correlate plasma p-tau levels and plasma S1P d16.1 levels, plasma S1P d18.1 levels, total plasma MonCer d18:1 levels or total plasma LacCer d18:1 levels in all enrolled groups. Correlation between plasma p-tau levels and plasma S1P d16.1 levels (p = 0.509), plasma S1P d18.1 levels (p = 0.468), plasma MonCer d18:1 levels (p = 0.767), or plasma LacCer d18:1 levels (p = 0.999) showed no correlation.

Plasma other lipid metabolite levels in neurodegenerative diseases

Plasma other lipid metabolite (other sphingolipids, sphinganines, gangliosides, free fatty acids, acylcarnitnes, lysophospholipids, platelet-activating factor, acylethanolamine, thyroid hormone, cholic acids, and steroids) levels were compared between the CN group and the IPD, DLB, MSA, AD and PSP groups. Oxylipins were not statistically analyzed because it is considered unsuitable for statistical analysis due to the large number of undetectable samples. Statistical significance was examined using one-tailed Welch’s t tests. Plasma ceramide-1-phosphate (C1P) levels were significantly higher in the PD, DLB, and AD groups versus the control group (S1 Table). Plasma GM3 ganglioside and GD3 ganglioside levels were significantly higher in all neurodegenerative disease groups (IPD, DLB, MSA, AD, and PSP) versus the control group (S1 Table). Plasma lysophosphatidic acid, lysophosphatidylcholine, lysophosphatidylethanolamine, lysophosphatidylglycerol, lysophosphatidylserine levels were lower in DLB group versus the control group (S1 Table). Plasma cortisone levels were significantly higher in the PD, MSA and PSP groups versus the control group (S2 Table).

Discussion

Plasma sphingolipid abnormalities in neurodegenerative diseases

Recessive mutations in the GBA1 (glucocerebrosidase) gene cause Gaucher disease. Heterozygous GBA1 mutation carriers exhibit much greater incidence of PD than the general population [32, 33]. Likewise, mutations in the NPC1 (NPC intracellular cholesterol transporter 1) and SMPD1 (sphingomyelin phosphodiesterase 1) genes, which cause Niemann-Pick disease, have been shown to be risk genes for IPD [34, 35]. One of the phospholipase A2 members, PLA2G6 or iPLA2-VIA/iPLA2β, has been isolated as the gene responsible for an autosomal recessive form of PD linked to the PARK14 locus [36]. Compared to the most common e3 isoform, the e4 isoform of ApoE (ApoE4) is the strongest genetic risk factor for late-onset AD [37]. β amyloid accumulation in NPC1 (NPC intracellular cholesterol transporter 1) gene, which cause Niemann-Pick type C, mutant cells and NPC mouse brain suggests the association between cholesterol metabolism and AD [38]. As described, several lipid-related genes have been reported as risk genes or causative genes in PD and AD. In addition, various lipid abnormalities have been reported in IPD and AD, such as fatty acids, glycerolipids, glycerophospholipids, sphingolipids, sterols, and lipoproteins [17, 39]. However, it is still unclear which lipid metabolism abnormalities play the most important role in neurodegenerative diseases. Plasma lipidomics is an unbiased method and can find important lipids in neurodegenerative diseases. For this reason, plasma lipidomics was performed in neurodegenerative diseases in this study. In this study, we found that plasma S1P levels were significantly lower and plasma MonCer and LacCer levels were significantly higher in all neurodegenerative disease groups (IPD, DLB, MSA, AD, and PSP) than in the CN group by plasma lipidomics.

Glucosylceramide (GlcCer) and galactosylceramide (GalCer) are isomers, and MonCer is the sum of both compounds. Although it is difficult to completely separate plasma GalCer and plasma GlcCer from plasma MonCer in present method, it has been shown that the majority of plasma MonCer is composed of plasma GlcCer [40]. S1P, GlcCer, and LacCer mentioned above are sphingolipids biosynthesized from ceramide (Fig 5). GCS is GlcCer synthase, BGTase6 is LacCer synthase, and SPHK is S1P synthase. These indicate that increased GlcCer and LacCer are caused by increased function of GCS and BGTase6, respectively, and decreased S1P is caused by a relative loss of function of SPHK.

Ceramide is hydrolyzed to sphingosine, which is further phosphorylated by sphingosine kinase to S1P (Fig 5). S1P is a sphingolipid that regulates stress tolerance, proliferation and differentiation of neuronal cells and is a neuroprotective factor involved in the suppression of neuronal cell death [41, 42]. It has been reported that S1P concentrations in CSF are significantly decreased in AD [43], and S1P concentrations in plasma are significantly decreased in vascular dementia and AD [44]. However, there has been no comprehensive analysis of plasma S1P levels in those with neurodegenerative diseases such as synucleinopathies, amyloidopathies and tauopathies. Therefore, we analyzed plasma S1P levels in individuals with neurodegenerative diseases using lipidomics in this study. We found that plasma S1P levels in all neurodegenerative disease groups (IPD, DLB, MSA, AD, and PSP) were significantly lower than those in the CN group. The finding of lower plasma S1P levels in those with all neurodegenerative disease groups analyzed (IPD, DLB, MSA, AD, and PSP) is a novel finding revealed in this study, suggesting that abnormalities in plasma S1P metabolism are common in synucleinopathies, amyloidopathies and tauopathies.

In animal models of the synucleinopathies PD and MSA administration of FTY720, an S1P agonist, has been shown to ameliorate neurodegeneration and behavioral dysfunction associated with mitochondrial dysfunction via S1P receptors [45, 46]. α-synuclein binds to lipid rafts, where it negatively regulates S1P receptor signaling [47]. S1P levels were decreased with increasing Braak stage in AD, and this was most pronounced in brain regions most affected by AD pathology [48]. In an animal model of AD in which Aβ42 peptide was injected locally into the bilateral hippocampus, administration of the S1P agonist FTY720 reduced hippocampal neuronal damage and learning and memory impairment [49]. Furthermore, in an animal model of AD using rat hippocampal slices, administration of SEW2871, an S1P agonist, was shown to suppress the expression of phosphorylated tau protein [50]. These findings suggest that S1P may act as a neuroprotective factor against aggregate formation and neuronal cell death not only in PD but also in AD. In other words, the decrease in plasma S1P levels in synucleinopathies and amyloidopathies may reflect a decrease in neuroprotection.

GlcCer is generated by glucosylceramide synthase (GCS), which transfers glucose from UDP-glucose to ceramide (Fig 5). GlcCer is a glycosphingolipid that regulates lysosomal function in general. Plasma GlcCer (a MonCer) levels have been shown to be significantly elevated in PD, autopsy-confirmed DLB, and autopsy-confirmed AD groups [51, 52]. However, there has been no comprehensive analysis of plasma GlcCer (a MonCer) levels in neurodegenerative diseases such as synucleinopathies and tauopathies. Therefore, we analyzed plasma GlcCer (a MonCer) in those with neurodegenerative diseases using lipidomics in this study. We found that the plasma GlcCer (a MonCer) levels were significantly higher in all neurodegenerative disease groups (IPD, DLB, MSA, AD, and PSP) than in the CN group. The elevated plasma GlcCer (a MonCer) levels in individuals with IPD, probable DLB, and probable AD in this study were in good accordance with the results of previous studies [51, 52]. There have been no reports of abnormal plasma GlcCer (a MonCer) levels in MSA and PSP. In this study, we found elevated plasma GlcCer (a MonCer) levels in individuals not only with LB diseases or AD but also with MSA or PSP, suggesting that abnormalities in plasma GlcCer (a MonCer) metabolism are also commonly observed in synucleinopathies, amyloidopathies and tauopathies.

GBA1 is a major causative gene for Gaucher disease. Recently, GBA1 mutations have been reported to be an important risk factor for LB diseases such as IPD and DLB [53, 54]. The GBA1 mutation reduces the activity of the lysosomal lipid metabolizing enzyme glucocerebrosidase (GCase), which catalyzes the hydrolysis of the glycosphingolipid GlcCer into ceramide and glucose, resulting in increased intracellular GlcCer levels [55]. Interestingly, elevated plasma GlcCer levels have recently been reported in both non-GBA1 mutation carriers and GBA1 mutation carriers with IPD [52, 56]. In GBA1 mutation carriers with IPD, decreased GCase activity promoted elevated intracellular GlcCer levels and increased α-synuclein aggregation [57], and this aggregation resulted in a loss of lysosomal activity and neuronal death [58–60]. In the pathological brain tissue of IPD patients without GBA1 mutations, GCase activity was also reported to be decreased [61]. This suggested that increased plasma GlcCer levels are observed in IPD with or without the GBA1 mutation and that increased intraneuronal GlcCer levels may be involved in aggregation formation and neuronal cell death. Presenilin mutation, one of the familial AD genes, is strongly involved in Aβ42 aggregation, the main component of senile plaques, and a previous report showed that presenilin deficiencies resulted in increased GlcCer synthase levels [62]. Furthermore, it has been shown that GlcCer levels were increased in the brain tissue of those with idiopathic AD [63]. This suggested that elevated GlcCer levels in the brain are also present in AD and are related to disease pathology.

LacCer is generated by LacCer synthase (β-1,4 galactosyltransferase), which transfers galactose from UDP-galactose to GlcCer (Fig 5). Plasma LacCer levels were significantly elevated in the non-GBA1 mutation carrier IPD group compared to the CN group [52]. We found that the plasma LacCer levels were significantly higher in all neurodegenerative disease groups (IPD, DLB, MSA, AD, and PSP) than in the CN group. In this study, elevated plasma LacCer levels in those with IPD were in good accordance with the results of a previous study [52]. In this study, we found elevated plasma LacCer levels not only in those with IPD but also those with DLB, MSA, AD or PSP, suggesting that abnormalities in plasma LacCer metabolism are also commonly observed in synucleinopathies, amyloidopathies and tauopathies.

LacCer is a glycosphingolipid, which is an important component of “lipid rafts,” serving as a conduit to transduce external stimuli [64]. As biologically active sphingolipids, LacCer plays diverse roles in inflammation, cell proliferation, migration/infiltration, adhesion, angiogenesis apoptosis, autophagy, and mitochondrial dysfunction [64]. LacCer generally induces neurodegeneration in the central nervous system by activating astrocytes that regulate neuroinflammation [65]. Thus, elevated plasma LacCer levels may reflect neuroinflammation in the central nervous system.

In this study, we found that plasma GM3 and GD3 ganglioside levels were significantly higher in the neurodegenerative disease groups than in the CN group. Gangliosides are lipids classified as sphingolipids. GM3 ganglioside is the starting material for gangliosides, which are biosynthesized by the binding of sialic acid to LacCer [66, 67]. Previously, plasma GM3 ganglioside levels have been shown to be elevated in PD [68]. The elevated plasma GM3 ganglioside levels in individuals with IPD in this study were in good accordance with the results of previous study. GD3 ganglioside is the gangliosides, which are biosynthesized by the binding of sialic acid to GM3 ganglioside [66, 67]. GM3 and GD3 gangliosides are components of lipid rafts and are implicated in cell death [69, 70]. Abnormalities in lipid rafts are also considered to be one of the major causes of neurodegenerative diseases [71]. Homozygous knockout mice for B4galnt1, a ganglioside synthase, have been shown to exhibit PD-like motor deficits and cause dopaminergic neuron degeneration [72]. Taken together, these results suggest that elevated plasma GM3 and GD3 gangliosides may reflect abnormal lipid rafts in neurodegenerative diseases. In this study, we found that plasma C1P levels were significantly higher in the IPD, DLB, and AD groups than in the CN group. C1P is classified as a sphingolipid, a lipid mainly involved in cell survival and inflammation [73, 74]. Neuroinflammation is also considered to be a one of the major causes in PD, DLB and AD [75–77]. Therefore, elevated C1P may reflect neuroinflammation in these diseases.

Limitations of this study

There are several limitations in this study. First, Analysis the major causative genes or risk genes of PD during lipidomics were not evaluated. GBA1 mutations were not evaluated in all enrolled IPD patients. Based on the GBA1 genotype and clinical analysis, it has been reported that GBA1 mutation is the most common genetic risk factor for IPD patients, accounting for as many as 7% of all IPD patients in multicenter analyses [32, 33]. On the other hand, only approximately 3% of Asian IPD patients with no apparent family history of parkinsonism are GBA1 mutation carriers [78]. IPD in GBA1 mutation carriers generally has an early onset [53]. However, there was no apparent family history of parkinsonism or dementia in all enrolled IPD patients, with a later mean age of onset in the enrolled IPD patients that was 67.2 years in cohort A and 65.2 years in cohort C. Elevated plasma GlcCer levels have recently been reported in GBA1 mutation carriers of IPD. Elevated plasma GlcCer levels have also been reported in non-GBA1 mutation carriers of IPD. These indicate that elevated plasma GlcCer is found in IPD with or without GBA mutation. Taken together, it is not plausible that a GBA1 mutation did not significantly affect elevated plasma GlcCer (a MonCer) levels in the IPD patients in this study. In addition, in this study LRRK2 and SNCA mutations, the major causative genes of PD, were not evaluated in all enrolled IPD patients. Analysis the major causative genes or risk genes of PD during lipidomics need to be performed in future studies. Second, this study is a small cases and cross-sectional study that could not account for multiple comparisons for several analytes detected in plasma. Future additional cases and longitudinal studies need to be performed. Third, a major limitation of this study is that the patients were not pathologically diagnosed. Fourth, we were not able to include other dementia diseases, such as frontotemporal dementia. Fifth, cohort B was not an age-matched study. In DLB and AD, correlation analysis between age and plasma S1P d16:1 levels, plasma S1P d18:1 levels, plasma MonCer d18:1 levels, or plasma LacCer d18:1 levels showed no correlation (S3 Table). Thus, changes in plasma S1P d16:1 levels, plasma S1P d18:1 levels, plasma MonCer d18:1 levels or plasma LacCer d18:1 levels were inferred to be disease-induced changes in AD or DLB. Sixth, in this study the protein levels of the enzymes involved in sphingolipid pathways were not evaluated in all enrolled patients. The protein levels of the enzymes involved in sphingolipid pathways need to be performed in future studies. Seventh, relative area was used in this study as the quantitative value for each metabolite based on previous reports [30, 31]. Lipidomics has the variability of metabolite values in each study. For this reason, each metabolite should be normalized based on the IS level and sample volume. The normalized each metabolite was represented as relative area and used as the quantitative value. Eighth, the increase and decrease in CSF sphingolipids and blood sphingolipids have coincided [30, 31, 57, 58] in previous reports. On the other hand, one report even identified different findings in serum versus CSF [53, 79]. These reports are indirect and sphingolipids need to be confirmed in CSF or brain for further validation.

Summary of the results

Using plasma lipidomics analysis, we identified decreased plasma S1P levels and increased plasma GlcCer (a MonCer) and LacCer levels in individuals with neurodegenerative diseases. These abnormalities in plasma sphingolipids might be closely related to aggregate formation, neuronal cell death and neuroinflammation. Our results provide new insights into the involvement of sphingolipids in neurodegenerative diseases.

Supporting information

S1 Table. Plasma other sphingolipids, sphinganines, gangliosides, free fatty acids, acylcarnitnes, lysophospholipids levels in neurodegenerative diseases.

Statistical methods: The metabolite level ratio of IPD, DLB, MSA, AD, or PSP to CNs. Statistical significance was examined using one-tailed Welch’s t tests (P < 0.05). Abbreviations: ceramide-1-phosphate (C1P), sphinganine-1-phosphate (SG1P), lysophosphatidic acid (LPA), lysophosphatidylcholine (LPC), lysophosphatidylethanolamine (LPE), lysophosphatidylglycerol (LPG), lysophosphatidylinositol (LPI), lysophosphatidylserine (LPS).

(DOCX)

Click here for additional data file.^{(21.2KB, docx)}

S2 Table. Plasma platelet-activating factor, acylethanolamine, thyroid hormone, cholic acids, steroids levels in neurodegenerative diseases.

(DOCX)

Click here for additional data file.^{(22.9KB, docx)}

S3 Table. Correlation analysis between age and plasma S1P d16:1 levels, plasma S1P d18:1 levels, plasma MonCer d18:1 levels, or plasma LacCer d18:1 levels in DLB and AD.

Pearson Correlation Coefficient was used to correlate between age and plasma S1P d16:1 levels, plasma S1P d18:1 levels, plasma MonCer d18:1 levels, or plasma LacCer d18:1 levels (P < 0.05).

(DOCX)

Click here for additional data file.^{(16.4KB, docx)}

Acknowledgments

We gratefully acknowledge Shiryu Takemura for technical support.

Abbreviations

AD: Alzheimer’s disease
C1P: ceramide-1-phosphate
CNs: controls
CSF: cerebrospinal fluid
DLB: dementia with Lewy bodies
GalCer: galactosylceramide
GCIs: glial cytoplasmic inclusions
GlcCer: glucosylceramide
IPD: Idiopathic Parkinson’s disease
IS: Internal standards
LacCer: lactosylceramide
LBs: Lewy bodies
LC–MS/MS: liquid chromatography-tandem mass spectrometry
lipidomics: lipid metabolomics
LPA: lysophosphatidic acid
LPC: lysophosphatidylcholine
LPE: lysophosphatidylethanolamine
LPG: lysophosphatidylglycerol
LPI: lysophosphatidylinositol
LPS: lysophosphatidylserine
MMSE: Mini-Mental State Examination
MonCer: monohexylceramide
MSA: multiple system atrophy
NHO: National Hospital Organization
PAF: platelet-activating factor
PSP: progressive supranuclear palsy
S1P: sphingosine-1-phosphate
SG1P: sphinganine-1-phosphate

Data Availability

All relevant data are within the paper and its Supporting information files.

Funding Statement

The funding for this study was provided by grants-in-aid for Scientific Research from the Project of Translational and Clinical Research Core Centers from the Japan Agency for Medical Research and Development (AMED) (JP17dm0107071 and JP18dm0107071 to KF and AT). This work was supported by grants-in-aid from the Research Committee of CNS Degenerative Diseases, Research on Policy Planning and Evaluation for Rare and Intractable Diseases, Health, Labor and Welfare Sciences Research Grants, the Ministry of Health, Labor and Welfare, Japan.

References

1.de Lau LML, Breteler MMB. Epidemiology of Parkinson’s disease. Lancet Neurol. 2006;5: 525–535. doi: 10.1016/S1474-4422(06)70471-9 [DOI] [PubMed] [Google Scholar]
2.Hogan DB, Fiest KM, Roberts JI, Maxwell CJ, Dykeman J, Pringsheim T, et al. The prevalence and incidence of dementia with lewy bodies: a systematic review. Can J Neurol Sci. 2016;43 Suppl 1: S83–S95. [DOI] [PubMed] [Google Scholar]
3.Gibb WR, Lees AJ. The relevance of the Lewy body to the pathogenesis of idiopathic Parkinson’s disease. J Neurol Neurosurg Psychiatry. 1988;51: 745–752. doi: 10.1136/jnnp.51.6.745 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Spillantini MG, Crowther RA, Jakes R, Hasegawa M, Goedert M. alpha-Synuclein in filamentous inclusions of Lewy bodies from Parkinson’s disease and dementia with lewy bodies. Proc Natl Acad Sci U S A. 1998;95: 6469–6473. doi: 10.1073/pnas.95.11.6469 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Gilman S, Wenning GK, Low PA, Brooks DJ, Mathias CJ, Trojanowski JQ, et al. Second consensus statement on the diagnosis of multiple system atrophy. Neurology. 2008;71: 670–676. doi: 10.1212/01.wnl.0000324625.00404.15 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Wakabayashi K, Yoshimoto M, Tsuji S, Takahashi H. Alpha-synuclein immunoreactivity in glial cytoplasmic inclusions in multiple system atrophy. Neurosci Lett. 1998;249: 180–182. doi: 10.1016/s0304-3940(98)00407-8 [DOI] [PubMed] [Google Scholar]
7.Wakabayashi K, Takahashi H. Cellular pathology in multiple system atrophy. Neuropathology. 2006;26: 338–345. doi: 10.1111/j.1440-1789.2006.00713.x [DOI] [PubMed] [Google Scholar]
8.McCann H, Stevens CH, Cartwright H, Halliday GM. α-Synucleinopathy phenotypes. Park Relat Disord. 2014;20: S62–S67. [DOI] [PubMed] [Google Scholar]
9.Prince M, Bryce R, Albanese E, Wimo A, Ribeiro W, Ferri CP. The global prevalence of dementia: a systematic review and metaanalysis. Alzheimers Dement. 2013;9: 63–75.e2. doi: 10.1016/j.jalz.2012.11.007 [DOI] [PubMed] [Google Scholar]
10.Selkoe DJ. Cell biology of protein misfolding: the examples of Alzheimer’s and Parkinson’s diseases. Nat Cell Biol. 2004;6: 1054–1061. doi: 10.1038/ncb1104-1054 [DOI] [PubMed] [Google Scholar]
11.Litvan I, Agid Y, Calne D, Campbell G, Dubois B, Duvoisin RC, et al. Clinical research criteria for the diagnosis of progressive supranuclear palsy (Steele-Richardson-Olszewski syndrome): report of the NINDS-SPSP international workshop. Neurology. 1996;47: 1–9. doi: 10.1212/wnl.47.1.1 [DOI] [PubMed] [Google Scholar]
12.Boxer AL, Yu J-T, Golbe LI, Litvan I, Lang AE, Höglinger GU. Advances in progressive supranuclear palsy: new diagnostic criteria, biomarkers, and therapeutic approaches. Lancet Neurol. 2017;16: 552–563. doi: 10.1016/S1474-4422(17)30157-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Rosler TW, Tayaranian Marvian A, Brendel M, Nykanen NP, Hollerhage M, Schwarz SC, et al. Four-repeat tauopathies. Prog Neurobiol. 2019;180: 101644. doi: 10.1016/j.pneurobio.2019.101644 [DOI] [PubMed] [Google Scholar]
14.Bourdenx M, Koulakiotis NS, Sanoudou D, Bezard E, Dehay B, Tsarbopoulos A. Protein aggregation and neurodegeneration in prototypical neurodegenerative diseases: Examples of amyloidopathies, tauopathies and synucleinopathies. Prog Neurobiol. 2017;155: 171–193. doi: 10.1016/j.pneurobio.2015.07.003 [DOI] [PubMed] [Google Scholar]
15.Guo T, Noble W, Hanger DP. Roles of tau protein in health and disease. Acta Neuropathol. 2017;133: 665–704. doi: 10.1007/s00401-017-1707-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Niedowicz DM, Nelson PT, Murphy MP. Alzheimer’s disease: pathological mechanisms and recent insights. Curr Neuropharmacol. 2011;9: 674–684. doi: 10.2174/157015911798376181 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Xicoy H, Wieringa B, Martens GJM. The Role of Lipids in Parkinson’s Disease. Cells. 2019;8: 27. doi: 10.3390/cells8010027 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.LeWitt PA, Li J, Lu M, Guo L, Auinger P. Metabolomic biomarkers as strong correlates of Parkinson disease progression. Neurology. 2017;88: 862–869. doi: 10.1212/WNL.0000000000003663 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Koal T, Klavins K, Seppi D, Kemmler G, Humpel C. Sphingomyelin SM(d18:1/18:0) is significantly enhanced in cerebrospinal fluid samples dichotomized by pathological amyloid-beta42, tau, and phospho-tau-181 levels. J Alzheimers Dis. 2015;44: 1193–1201. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Quinones MP, Kaddurah-Daouk R. Metabolomics tools for identifying biomarkers for neuropsychiatric diseases. Neurobiol Dis. 2009;35: 165–176. doi: 10.1016/j.nbd.2009.02.019 [DOI] [PubMed] [Google Scholar]
21.Zhang J, Zhang X, Wang L, Yang C. High Performance Liquid Chromatography-Mass Spectrometry (LC-MS) Based Quantitative Lipidomics Study of Ganglioside-NANA-3 Plasma to Establish Its Association with Parkinson’s Disease Patients. Med Sci Monit. 2017;23: 5345–5353. doi: 10.12659/msm.904399 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Liu Y, Thalamuthu A, Mather KA, Crawford J, Ulanova M, Wong MWK, et al. Plasma lipidome is dysregulated in Alzheimer’s disease and is associated with disease risk genes. Transl Psychiatry. 2021;11: 344. doi: 10.1038/s41398-021-01362-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR, Kawas CH, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7: 263–269. doi: 10.1016/j.jalz.2011.03.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Hoglinger GU, Respondek G, Stamelou M, Kurz C, Josephs KA, Lang AE, et al. Clinical diagnosis of progressive supranuclear palsy: The movement disorder society criteria. Mov Disord. 2017;32: 853–864. doi: 10.1002/mds.26987 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.McKeith IG, Boeve BF, Dickson DW, Halliday G, Taylor JP, Weintraub D, et al. Diagnosis and management of dementia with Lewy bodies: Fourth consensus report of the DLB Consortium. Neurology. 2017;89: 88–100. doi: 10.1212/WNL.0000000000004058 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Saiki S, Hatano T, Fujimaki M, Ishikawa KI, Mori A, Oji Y, et al. Decreased long-chain acylcarnitines from insufficient beta-oxidation as potential early diagnostic markers for Parkinson’s disease. Sci Rep. 2017;7: 7328. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Hayasaka R, Tabata S, Hasebe M, Ikeda S, Ohnuma S, Mori M, et al. Metabolomic Analysis of Small Extracellular Vesicles Derived from Pancreatic Cancer Cells Cultured under Normoxia and Hypoxia. Metabolites. 2021;11. doi: 10.3390/metabo11040215 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Suzuki Y, Hayasaka R, Hasebe M, Ikeda S, Soga T, Tomita M, et al. Comparative Metabolomics of Small Molecules Specifically Expressed in the Dorsal or Ventral Marginal Zones in Vertebrate Gastrula. Metabolites. 2022;12. doi: 10.3390/metabo12060566 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Ikeda K. Mass Spectrometric Analysis of Phospholipids by Target Discovery Approach. 2015. pp. 349–356. [Google Scholar]
30.Mori A, Ishikawa KI, Saiki S, Hatano T, Oji Y, Okuzumi A, et al. Plasma metabolite biomarkers for multiple system atrophy and progressive supranuclear palsy. PLoS One. 2019;14: e0223113. doi: 10.1371/journal.pone.0223113 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Saiki S, Sasazawa Y, Fujimaki M, Kamagata K, Kaga N, Taka H, et al. A metabolic profile of polyamines in parkinson disease: A promising biomarker. Ann Neurol. 2019;86: 251–263. doi: 10.1002/ana.25516 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Sidransky E, Nalls MA, Aasly JO, Aharon-Peretz J, Annesi G, Barbosa ER, et al. Multicenter analysis of glucocerebrosidase mutations in Parkinson’s disease. N Engl J Med. 2009;361: 1651–1661. doi: 10.1056/NEJMoa0901281 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Lesage S, Anheim M, Condroyer C, Pollak P, Durif F, Dupuits C, et al. Large-scale screening of the Gaucher’s disease-related glucocerebrosidase gene in Europeans with Parkinson’s disease. Hum Mol Genet. 2011;20: 202–210. doi: 10.1093/hmg/ddq454 [DOI] [PubMed] [Google Scholar]
34.Foo JN, Liany H, Bei JX, Yu XQ, Liu J, Au WL, et al. Rare lysosomal enzyme gene SMPD1 variant (p.R591C) associates with Parkinson’s disease. Neurobiol Aging. 2013;34: 2890 e2813–2895. doi: 10.1016/j.neurobiolaging.2013.06.010 [DOI] [PubMed] [Google Scholar]
35.Kluenemann HH, Nutt JG, Davis MY, Bird TD. Parkinsonism syndrome in heterozygotes for Niemann-Pick C1. J Neurol Sci. 2013;335: 219–220. doi: 10.1016/j.jns.2013.08.033 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Gregory A, Westaway SK, Holm IE, Kotzbauer PT, Hogarth P, Sonek S, et al. Neurodegeneration associated with genetic defects in phospholipase A(2). Neurology. 2008;71: 1402–1409. doi: 10.1212/01.wnl.0000327094.67726.28 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Chartier-Harlin MC, Parfitt M, Legrain S, Pérez-Tur J, Brousseau T, Evans A, et al. Apolipoprotein E, epsilon 4 allele as a major risk factor for sporadic early and late-onset forms of Alzheimer’s disease: analysis of the 19q13.2 chromosomal region. Hum Mol Genet. 1994;3: 569–574. doi: 10.1093/hmg/3.4.569 [DOI] [PubMed] [Google Scholar]
38.Yamazaki T, Chang TY, Haass C, Ihara Y. Accumulation and aggregation of amyloid beta-protein in late endosomes of Niemann-pick type C cells. J Biol Chem. 2001;276: 4454–4460. doi: 10.1074/jbc.M009598200 [DOI] [PubMed] [Google Scholar]
39.Yin F. Lipid metabolism and Alzheimer’s disease: clinical evidence, mechanistic link and therapeutic promise. FEBS J. 2022. doi: 10.1111/febs.16344 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Xu H, Boucher FR, Nguyen TT, Taylor GP, Tomlinson JJ, Ortega RA, et al. DMS as an orthogonal separation to LC/ESI/MS/MS for quantifying isomeric cerebrosides in plasma and cerebrospinal fluid. J Lipid Res. 2019;60: 200–211. doi: 10.1194/jlr.D089797 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Maceyka M, Spiegel S. Sphingolipid metabolites in inflammatory disease. Nature. 2014;510: 58–67. doi: 10.1038/nature13475 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Jesko H, Stepien A, Lukiw WJ, Strosznajder RP. The Cross-Talk Between Sphingolipids and Insulin-Like Growth Factor Signaling: Significance for Aging and Neurodegeneration. Mol Neurobiol. 2019;56: 3501–3521. doi: 10.1007/s12035-018-1286-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Torretta E, Arosio B, Barbacini P, Casati M, Capitanio D, Mancuso R, et al. Particular CSF sphingolipid patterns identify iNPH and AD patients. Sci Rep. 2018;8: 13639. doi: 10.1038/s41598-018-31756-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Chua XY, Chai YL, Chew WS, Chong JR, Ang HL, Xiang P, et al. Immunomodulatory sphingosine-1-phosphates as plasma biomarkers of Alzheimer’s disease and vascular cognitive impairment. Alzheimers Res Ther. 2020;12: 122. doi: 10.1186/s13195-020-00694-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Motyl J, Strosznajder JB. Sphingosine kinase 1/sphingosine-1-phosphate receptors dependent signalling in neurodegenerative diseases. The promising target for neuroprotection in Parkinson’s disease. Pharmacol Rep. 2018;70: 1010–1014. doi: 10.1016/j.pharep.2018.05.002 [DOI] [PubMed] [Google Scholar]
46.Vidal-Martinez G, Segura-Ulate I, Yang B, Diaz-Pacheco V, Barragan JA, De-Leon Esquivel J, et al. FTY720-Mitoxy reduces synucleinopathy and neuroinflammation, restores behavior and mitochondria function, and increases GDNF expression in Multiple System Atrophy mouse models. Exp Neurol. 2020;325: 113120. doi: 10.1016/j.expneurol.2019.113120 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Badawy SMM, Okada T, Kajimoto T, Hirase M, Matovelo SA, Nakamura S, et al. Extracellular alpha-synuclein drives sphingosine 1-phosphate receptor subtype 1 out of lipid rafts, leading to impaired inhibitory G-protein signaling. J Biol Chem. 2018;293: 8208–8216. [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Couttas TA, Kain N, Daniels B, Lim XY, Shepherd C, Kril J, et al. Loss of the neuroprotective factor Sphingosine 1-phosphate early in Alzheimer’s disease pathogenesis. Acta Neuropathol Commun. 2014;2: 9. doi: 10.1186/2051-5960-2-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Asle-Rousta M, Kolahdooz Z, Oryan S, Ahmadiani A, Dargahi L. FTY720 (fingolimod) attenuates beta-amyloid peptide (Abeta42)-induced impairment of spatial learning and memory in rats. J Mol Neurosci. 2013;50: 524–532. [DOI] [PubMed] [Google Scholar]
50.St-Cyr Giguere F, Attiori Essis S, Chagniel L, Germain M, Cyr M, Massicotte G. The sphingosine-1-phosphate receptor 1 agonist SEW2871 reduces Tau-Ser262 phosphorylation in rat hippocampal slices. Brain Res. 2017;1658: 51–59. doi: 10.1016/j.brainres.2017.01.014 [DOI] [PubMed] [Google Scholar]
51.Savica R, Murray ME, Persson XM, Kantarci K, Parisi JE, Dickson DW, et al. Plasma sphingolipid changes with autopsy-confirmed Lewy Body or Alzheimer’s pathology. Alzheimers Dement (Amst). 2016;3: 43–50. doi: 10.1016/j.dadm.2016.02.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Mielke MM, Maetzler W, Haughey NJ, Bandaru VV, Savica R, Deuschle C, et al. Plasma ceramide and glucosylceramide metabolism is altered in sporadic Parkinson’s disease and associated with cognitive impairment: a pilot study. PLoS One. 2013;8: e73094. doi: 10.1371/journal.pone.0073094 [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Sato C, Morgan A, Lang AE, Salehi-Rad S, Kawarai T, Meng Y, et al. Analysis of the glucocerebrosidase gene in Parkinson’s disease. Mov Disord. 2005;20: 367–370. doi: 10.1002/mds.20319 [DOI] [PubMed] [Google Scholar]
54.Nalls MA, Duran R, Lopez G, Kurzawa-Akanbi M, McKeith IG, Chinnery PF, et al. A multicenter study of glucocerebrosidase mutations in dementia with Lewy bodies. JAMA Neurol. 2013;70: 727–735. doi: 10.1001/jamaneurol.2013.1925 [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Stirnemann J, Belmatoug N, Camou F, Serratrice C, Froissart R, Caillaud C, et al. A review of gaucher disease pathophysiology, clinical presentation and treatments. Int J Mol Sci. 2017;18: 441. doi: 10.3390/ijms18020441 [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Guedes LC, Chan RB, Gomes MA, Conceicao VA, Machado RB, Soares T, et al. Serum lipid alterations in GBA-associated Parkinson’s disease. Parkinsonism Relat Disord. 2017;44: 58–65. doi: 10.1016/j.parkreldis.2017.08.026 [DOI] [PubMed] [Google Scholar]
57.Mazzulli JR, Xu YH, Sun Y, Knight AL, McLean PJ, Caldwell GA, et al. Gaucher disease glucocerebrosidase and alpha-synuclein form a bidirectional pathogenic loop in synucleinopathies. Cell. 2011;146: 37–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Cullen V, Sardi SP, Ng J, Xu YH, Sun Y, Tomlinson JJ, et al. Acid beta-glucosidase mutants linked to Gaucher disease, Parkinson disease, and Lewy body dementia alter alpha-synuclein processing. Ann Neurol. 2011;69: 940–953. [DOI] [PubMed] [Google Scholar]
59.Yap TL, Gruschus JM, Velayati A, Westbroek W, Goldin E, Moaven N, et al. Alpha-synuclein interacts with Glucocerebrosidase providing a molecular link between Parkinson and Gaucher diseases. J Biol Chem. 2011;286: 28080–28088. doi: 10.1074/jbc.M111.237859 [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Magalhaes J, Gegg ME, Migdalska-Richards A, Doherty MK, Whitfield PD, Schapira AH. Autophagic lysosome reformation dysfunction in glucocerebrosidase deficient cells: relevance to Parkinson disease. Hum Mol Genet. 2016;25: 3432–3445. doi: 10.1093/hmg/ddw185 [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Murphy KE, Gysbers AM, Abbott SK, Tayebi N, Kim WS, Sidransky E, et al. Reduced glucocerebrosidase is associated with increased alpha-synuclein in sporadic Parkinson’s disease. Brain. 2014;137: 834–848. [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Grimm MO, Hundsdorfer B, Grosgen S, Mett J, Zimmer VC, Stahlmann CP, et al. PS dependent APP cleavage regulates glucosylceramide synthase and is affected in Alzheimer’s disease. Cell Physiol Biochem. 2014;34: 92–110. doi: 10.1159/000362987 [DOI] [PubMed] [Google Scholar]
63.Chan RB, Oliveira TG, Cortes EP, Honig LS, Duff KE, Small SA, et al. Comparative lipidomic analysis of mouse and human brain with Alzheimer disease. J Biol Chem. 2012;287: 2678–2688. doi: 10.1074/jbc.M111.274142 [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Chatterjee S, Balram A, Li W. Convergence: lactosylceramide-centric signaling pathways induce inflammation, oxidative stress, and other phenotypic outcomes. Int J Mol Sci. 2021;22: 1816. doi: 10.3390/ijms22041816 [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Yu W, Ying J, Wang X, Liu X, Zhao T, Yoon S, et al. The Involvement of Lactosylceramide in Central Nervous System Inflammation Related to Neurodegenerative Disease. Front Aging Neurosci. 2021;13: 691230. doi: 10.3389/fnagi.2021.691230 [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Merrill AH. Sphingolipid and glycosphingolipid metabolic pathways in the era of sphingolipidomics. Chem Rev. 2011;111: 6387–6422. doi: 10.1021/cr2002917 [DOI] [PMC free article] [PubMed] [Google Scholar]
67.Yamaji T, Hanada K. Sphingolipid metabolism and interorganellar transport: localization of sphingolipid enzymes and lipid transfer proteins. Traffic. 2015;16: 101–122. doi: 10.1111/tra.12239 [DOI] [PubMed] [Google Scholar]
68.Chan RB, Perotte AJ, Zhou B, Liong C, Shorr EJ, Marder KS, et al. Elevated GM3 plasma concentration in idiopathic Parkinson’s disease: A lipidomic analysis. PLoS One. 2017;12: e0172348. doi: 10.1371/journal.pone.0172348 [DOI] [PMC free article] [PubMed] [Google Scholar]
69.Sohn H, Kim YS, Kim HT, Kim CH, Cho EW, Kang HY, et al. Ganglioside GM3 is involved in neuronal cell death. FASEB J. 2006;20: 1248–1250. doi: 10.1096/fj.05-4911fje [DOI] [PubMed] [Google Scholar]
70.De Maria R, Lenti L, Malisan F, d’Agostino F, Tomassini B, Zeuner A, et al. Requirement for GD3 ganglioside in CD95- and ceramide-induced apoptosis. Science. 1997;277: 1652–1655. doi: 10.1126/science.277.5332.1652 [DOI] [PubMed] [Google Scholar]
71.Schengrund CL. Lipid rafts: keys to neurodegeneration. Brain Res Bull. 2010;82: 7–17. doi: 10.1016/j.brainresbull.2010.02.013 [DOI] [PubMed] [Google Scholar]
72.Wu G, Lu ZH, Kulkarni N, Amin R, Ledeen RW. Mice lacking major brain gangliosides develop parkinsonism. Neurochem Res. 2011;36: 1706–1714. doi: 10.1007/s11064-011-0437-y [DOI] [PMC free article] [PubMed] [Google Scholar]
73.Chalfant CE, Spiegel S. Sphingosine 1-phosphate and ceramide 1-phosphate: expanding roles in cell signaling. J Cell Sci. 2005;118: 4605–4612. doi: 10.1242/jcs.02637 [DOI] [PubMed] [Google Scholar]
74.Arana L, Gangoiti P, Ouro A, Trueba M, Gomez-Munoz A. Ceramide and ceramide 1-phosphate in health and disease. Lipids Health Dis. 2010;9: 15. doi: 10.1186/1476-511X-9-15 [DOI] [PMC free article] [PubMed] [Google Scholar]
75.Leng F, Edison P. Neuroinflammation and microglial activation in Alzheimer disease: where do we go from here? Nat Rev Neurol. 2021;17: 157–172. doi: 10.1038/s41582-020-00435-y [DOI] [PubMed] [Google Scholar]
76.Gelders G, Baekelandt V, Van der Perren A. Linking Neuroinflammation and Neurodegeneration in Parkinson’s Disease. J Immunol Res. 2018;2018: 4784268. doi: 10.1155/2018/4784268 [DOI] [PMC free article] [PubMed] [Google Scholar]
77.Surendranathan A, Rowe JB, O’Brien JT. Neuroinflammation in Lewy body dementia. Parkinsonism Relat Disord. 2015;21: 1398–1406. doi: 10.1016/j.parkreldis.2015.10.009 [DOI] [PubMed] [Google Scholar]
78.Wu YR, Chen CM, Chao CY, Ro LS, Lyu RK, Chang KH, et al. Glucocerebrosidase gene mutation is a risk factor for early onset of Parkinson disease among Taiwanese. J Neurol Neurosurg Psychiatry. 2007;78: 977–979. doi: 10.1136/jnnp.2006.105940 [DOI] [PMC free article] [PubMed] [Google Scholar]
79.Huh YE, Park H, Chiang MSR, Tuncali I, Liu G, Locascio JJ, et al. Glucosylceramide in cerebrospinal fluid of patients with GBA-associated and idiopathic Parkinson’s disease enrolled in PPMI. NPJ Parkinsons Dis. 2021;7: 102. doi: 10.1038/s41531-021-00241-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0279315.r001

Decision Letter 0

Stephan N Witt

27 Jun 2022

PONE-D-22-15953Plasma sphingolipid abnormalities in neurodegenerative diseasesPLOS ONE

Dear Dr. Takeda,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Comments of reviewer #1 Please address these two issues:1. Information on the separation of GalCer from GlcCer is necessary to understand to which compound are referred the results.

2. Quantitative data are necessary to understand the yield of extraction and the correct determination and comparison within samples. Comments of reviewer #2 Comments 1: Do you have information on fluid biomarkers such as plasma Ab42/Ab40 ratio and ptau? Comment 2: Can you address the question as to how changes in plasma sphingolipids relate to brain and CSF changes? Comment 3: you must address the issue relating to the different ages (are changes age-related or AD-related?) Comment 4: you must address this concern. Comment 5: please answer these two questions comment 6: address the issue regarding adding statements in the limitations section Note: Given that age is a significant risk factor in Alzheimer’s disease, the age differences should be factored into the statistical analyses.

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Additional Editor Comments:

Major issue:

lines 190-213 (Results section on S1P): The authors repeat in words what is depicted in the figures. This is far too repetitive. What is the purpose of the figures, if all of the findings are given in words?

Example: " Plasma

S1P d16.1 levels were significantly lower in the IPD group of cohort A (plasma S1P d16.1 levels:

30 IPD vs. 28 CNs; 0.0091 (0.0103-0.00713) vs. 0.0115 (0.013-0.0103); t = 4.34, p 0.0001) (Fig 1A),

Suggested way to rephrase the above sentence:

Plasma S1P d16.1 levels were significantly (p,0.0001) lower in the IPD group of cohort A (N=

30) versus the control group (N=28) (Fig 1A)..."

Bottom line: the entire RESULTS section of the manuscript should be simplified by eliminating this redundancy of saying in words what is given in the figures.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This manuscript is interesting, but very preliminary. Only a few sphingolipids on the total species present in plasma were analyzed. Of course the analysis of plasma lipids from GBA1 associated neurodegenerative disease patients would be necessary, but I believe that more important would be the knowledge on the others, not considered sphingolipids.

Information on the separation of GalCer from GlcCer is necessary to understand to which compound are referred the results.

Quantitative data are necessary to understand the yield of extraction and the correct determination and comparison within samples.

Reviewer #2: General Comment to authors

The authors examine plasma sphingolipid in several neurodegenerative diseases and show striking changes in sphingosine-1-phosphate and GluCer and LacCer species. However, the small sample sizes and the significant age differences in cohort B are concerns, given that age is a significant risk factor in Alzheimer’s disease.

Specific Comments to Authors

1) Interestingly, sphingolipids change in all neurodegenerative diseases examined. How do the changes in S1P, GluCer, and LacCer relate to known changes in fluid biomarkers such as plasma A�42 and A�40 ratio, and plasma ptau levels?. It would be necessary also to measure these fluid biomarkers and not merely rely on MMSE, given the small sample sizes.

2) How do the plasma changes in sphingolipids relate to brain and CSF changes?

3) The age of the CN population (cohort B) is significantly lower than that of the DLB and AD populations. Since age is a significant risk factor for AD, it is unclear whether the changes are AD-related or age-related. Additional analyses with age as a variable may help tease out whether these data are age-related or pathological.

4) What is the recovery of sphingolipids extracted with the SPE column (page 11)? What internal standards were used, and how quantitative were these standards?

5) Is there a correlation/link between the decrease in S1P and the increase in GlcCer and LacCer in plasma? What enzyme pathways may cause the decrease in S1P and the increase in GluCer and LacCer?

6) The limitations (page 22) should include that this is a small discovery/cross sectional study that can not account for multiple comparisons for several analytes detected in plasma.

**********

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Reviewer #1: No

Reviewer #2: No

**********

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PLoS One. 2022 Dec 16;17(12):e0279315. doi: 10.1371/journal.pone.0279315.r002

Author response to Decision Letter 0

22 Sep 2022

Additional Editor Comments:

Major issue:

Example: " Plasma

S1P d16.1 levels were significantly lower in the IPD group of cohort A (plasma S1P d16.1 levels:

30 IPD vs. 28 CNs; 0.0091 (0.0103-0.00713) vs. 0.0115 (0.013-0.0103); t = 4.34, p 0.0001) (Fig 1A),

Suggested way to rephrase the above sentence:

Plasma S1P d16.1 levels were significantly (p,0.0001) lower in the IPD group of cohort A (N=30) versus the control group (N=28) (Fig 1A)..."

Thank you for your suggestion. We agree with your opinion. We added sentences in Results section as you have indicated.

Plasma S1P d16.1 levels were significantly (p 0.0001) lower in the IPD group of cohort A (N=30) versus the control group (N=28) (Fig 1A). Plasma S1P d16.1 levels were significantly (p 0.0001) lower in the DLB group (N=28) versus the control group (N=15) (Fig 1B) and significantly (p 0.0001) lower in the AD group (N=13) versus the control group (N=15) (Fig 1B). Plasma S1P d16.1 levels were significantly (p 0.01) lower in the IPD group of cohort C (N=28) versus the control group (N=6) (Fig 1C), significantly (p 0.01) lower in the MSA group (N=13) versus the control group (N=6) (Fig 1C), and significantly (p 0.001) lower in the PSP group (N=16) versus the control group (N=6) (Fig 1C). Plasma S1P d18.1 levels were significantly (p 0.05) lower in the IPD group of cohort A (N=30) versus the control group (N=28) (Fig 1D). Plasma S1P d18.1 levels were significantly (p 0.01) lower in the DLB group (N=28) versus the control group (N=15) (Fig 1E) and significantly (p 0.05) lower in the AD group (N=13) versus the control group (N=15) (Fig 1E). Plasma S1P d18.1 levels were significantly (p 0.05) lower in the IPD group of cohort C (N=28) versus the control group (N=6) (Fig 1F), significantly (p 0.05) lower in the MSA group (N=13) versus the control group (N=6) (Fig 1F), and significantly (p 0.05) lower in the PSP group (N=16) versus the control group (N=6) (Fig 1F). (p13-14, lines 204-219)

Total plasma MonCer d18:1 levels were significantly (p 0.01) higher in the IPD group of cohort A (N=30) versus the control group (N=28) (Fig 2A). Total plasma MonCer d18:1 levels were significantly (p 0.01) higher in the DLB group (N=28) versus the control group (N=15) (Fig 2B) and significantly (p 0.001) higher in the AD group (N=13) versus the control group (N=15) (Fig 2B). Total plasma MonCer d18:1 levels were significantly (p 0.01) higher in the IPD group of cohort C (N=28) versus the control group (N=6) (Fig 2C), significantly (p 0.05) higher in the MSA group (N=13) versus the control group (N=6) (Fig 2C), and significantly (p 0.01) higher in the PSP group (N=16) versus the control group (N=6) (Fig 2C). (p15, lines 243-251)

Total plasma LacCer d18:1 levels were significantly (p 0.01) higher in the IPD group of cohort A (N=30) versus the control group (N=28) (Fig 3A). Total plasma LacCer d18:1 levels were significantly (p 0.001) higher in the DLB group (N=28) versus the control group (N=15) (Fig 3B) and significantly (p 0.05) higher in the AD group (N=13) versus the control group (N=15) (Fig 3B). Total plasma LacCer d18:1 levels were significantly (p 0.01) higher in the IPD group of cohort C (N=28) versus the control group (N=6) (Fig 3C), significantly (p 0.01) higher in the MSA group (N=13) versus the control group (N=6) (Fig 3C), and significantly (p 0.01) higher in the PSP group (N=16) versus the control group (N=6) (Fig 3C). (p16-17, lines 270-278)

Reviewers' comments:

Reviewer's Responses to Questions

Information on the separation of GalCer from GlcCer is necessary to understand to which compound are referred the results.

Thank you for your important remarks.

Unfortunately, it has been reported that the majority of plasma Monohexylceramides (MonCer) is composed of plasma GlcCer [1], but it is difficult to completely separate plasma GalCer from plasma GlcCer with present methods. Thank you for your advice. As you have indicated, plasma MonCer is the sum of plasma GalCer and plasma GlcCer, so we described plasma GlcCer as plasma MonCer. In addition, Mielke et al. also described plasma GlcCer as GlcCer (a MonCer) in their PROS ONE manuscript [2]. Thus, we also described plasma GlcCer as GlcCer (a MonCer) in reference to their PROS ONE manuscript.

In response to your suggestion, we have added text to the Abstract, Results and Discussion sections, and have referred to plasma GlcCer as plasma MonCer or GlcCer (a MonCer) in this manuscript.

(2) Plasma monohexylceramide (MonCer) and lactosylceramide (LacCer) were significantly higher in all neurodegenerative disease groups (IPD, DLB, MSA, AD, and PSP) than in the CN group. (3) Plasma MonCer levels were significantly positively correlated with plasma LacCer levels in all enrolled groups. Conclusion: S1P, Glucosylceramide (GlcCer), the main component of MonCer, and LacCer are sphingolipids that are biosynthesized from ceramide. (p3, lines 48-52)

Plasma Monohexylceramide (MonCer) levels in neurodegenerative diseases

Total plasma MonCer d18:1 levels were compared between the CN group and the IPD, DLB, MSA, AD, and PSP groups. Total plasma MonCer d18:1 levels were measured by summing levels of 13 types of MonCer d18:1: MonCer (d18:1/14:0), MonCer (d18:1/16:0), MonCer (d18:1/16:1), MonCer (d18:1/18:0), MonCer (d18:1/18:1), MonCer (d18:1/20:0), MonCer (d18:1/20:1), MonCer (d18:1/22:0), MonCer (d18:1/22:1), MonCer (d18:1/22:2), MonCer (d18:1/24:0), MonCer (d18:1/24:1) and MonCer (d18:1/24:2). Statistical significance was examined using one-tailed Welch's t tests. Total plasma MonCer d18:1 levels were significantly (p 0.01) higher in the IPD group of cohort A (N=30) versus the control group (N=28) (Fig 2A). Total plasma MonCer d18:1 levels were significantly (p 0.01) higher in the DLB group (N=28) versus the control group (N=15) (Fig 2B) and significantly (p 0.001) higher in the AD group (N=13) versus the control group (N=15) (Fig 2B). Total plasma MonCer d18:1 levels were significantly (p 0.01) higher in the IPD group of cohort C (N=28) versus the control group (N=6) (Fig 2C), significantly (p 0.05) higher in the MSA group (N=13) versus the control group (N=6) (Fig 2C), and significantly (p 0.01) higher in the PSP group (N=16) versus the control group (N=6) (Fig 2C). These results indicated that plasma MonCer levels were significantly higher in all neurodegenerative disease groups (IPD, DLB, MSA, AD, and PSP) than in the CN group.

Fig 2. Plasma MonCer Levels in Neurodegenerative Diseases. (A) Plasma MonCer d18:1 levels were significantly higher in the IPD group of cohort A (p 0.01) than in the CN group. (B) Plasma MonCer d18:1 levels were significantly higher in the DLB group (p 0.01) and AD group (p 0.001) than in the CN group. (C) Plasma MonCer d18:1 levels were significantly higher in the IPD group of cohort C (p 0.01), MSA group (p 0.05) and PSP group (p 0.01) than in the CN group. (p15-16, lines 235-260)

Correlation between total plasma MonCer levels and total plasma LacCer levels.

Pearson Correlation Coefficient was used to correlate total plasma MonCer d18:1 levels and total plasma LacCer d18:1 levels in all enrolled groups. Total plasma MonCer d18:1 levels were significantly positively correlated with total plasma LacCer d18:1 levels (r = 0.5802, p 0.0001) (Fig 4) in all enrolled groups. These results suggest that an increase in plasma MonCer may be directly related to an increase in LacCer in all enrolled groups.

Fig 4. Correlation between total plasma MonCer levels and total plasma LacCer levels. (A) Total plasma MonCer d18:1 levels were significantly positively correlated with total plasma LacCer d18:1 levels (r = 0.5802, p 0.0001) in all enrolled groups.

(p17-18, lines 289-298)

In this study, we found that plasma S1P levels were significantly lower and plasma MonCer and LacCer levels were significantly higher in the neurodegenerative disease groups than in the CN group in a plasma lipidomics study. Glucosylceramide (GlcCer) and galactosylceramide (GalCer) are isomers, and MonCer is the sum of both compounds. Although it is difficult to completely separate plasma GalCer and plasma GlcCer from plasma MonCer in present method, it has been shown that the majority of plasma MonCer is composed of plasma GlcCer [1]. (p19, lines 312-317)

Plasma GlcCer (a MonCer) levels have been shown to be significantly elevated in PD, autopsy-confirmed DLB, and autopsy-confirmed AD groups [2,3]. However, there has been no comprehensive analysis of plasma GlcCer (a MonCer) levels in neurodegenerative diseases such as synucleinopathies and tauopathies. Therefore, we analyzed plasma GlcCer (a MonCer) in those with neurodegenerative diseases using lipidomics in this study. We found that the plasma GlcCer (a MonCer) levels were significantly higher in all neurodegenerative disease groups (IPD, DLB, MSA, AD, and PSP) than in the CN group. The elevated plasma GlcCer (a MonCer) levels in individuals with IPD, probable DLB, and probable AD in this study were in good accordance with the results of previous studies [2,3]. There have been no reports of abnormal plasma GlcCer (a MonCer) levels in MSA and PSP. In this study, we found elevated plasma GlcCer (a MonCer) levels in individuals not only with LB diseases or AD but also with MSA or PSP, suggesting that abnormalities in plasma GlcCer (a MonCer) metabolism are also commonly observed in synucleinopathies, amyloidopathies and tauopathies. (p21, lines 359-371)

Quantitative data are necessary to understand the yield of extraction and the correct determination and comparison within samples.

We agree with your opinion. Thank you for your important remarks.

In present lipidomic analysis, the coefficient of variation (CV) ranged from 4.4 to 9.7%, with a mean of 6.7%. Because of the variability of metabolites values, present method should be compared with healthy subjects for each cohort. Therefore, the relative areas were used as metabolites values in this study.

Based on what you have pointed out, we added sentences in Discussion section.

Seventh, Quantitative data were not used as metabolites values in this study. In present lipidomic analysis, the coefficient of variation (CV) ranged from 4.4 to 9.7%, with a mean of 6.7%. Because of the variability of metabolites values, present method should be compared with healthy subjects for each cohort [4,5]. Therefore, the relative areas were used as metabolites values in this study. (p24, lines 427-431)

Reviewer #2: General Comment to authors

Specific Comments to Authors

Interestingly, sphingolipids change in all neurodegenerative diseases examined. How do the changes in S1P, GluCer, and LacCer relate to known changes in fluid biomarkers such as plasma amyloid beta 42 and amyloid beta 40 ratio, and plasma ptau levels?. It would be necessary also to measure these fluid biomarkers and not merely rely on MMSE, given the small sample sizes.

Thank you for your suggestion and your important remarks. Since our laboratory could analyze plasma p-tau levels, we analyzed plasma p-tau levels. Pearson Correlation Coefficient was used to correlate plasma p-tau levels and plasma S1P d16.1 levels, plasma S1P d18.1 levels, total plasma MonCer d18:1 levels or total plasma LacCer d18:1 levels in all enrolled groups. Correlation between plasma p-tau levels and plasma S1P d16.1 levels (p = 0.509), plasma S1P d18.1 levels (p = 0.468), plasma MonCer d18:1 levels (p = 0.767), or plasma LacCer d18:1 levels (p = 0.999) showed no correlation.

Based on your suggestion, we added sentences in Results section, Materials and Methods section.

Correlation between plasma p-tau levels and plasma S1P levels, total plasma MonCer levels or total plasma LacCer levels.

Simoa™ Assay

Plasma samples stored at -80°C were thawed and centrifuged at 10,000 x g for 5 minutes. Samples were diluted in advance with the Sample Diluent provided with Assay Kit and applied to the plate. The assay was performed one sample at a time. Simoa™ p-Tau181 Advantage Kit (Quanterix, #103377, Billerica, MA, USA) were used to measure plasma p-Tau181. Measurements were performed according to the instructions for kit. (p12, lines 185-190)

*It was proposed by reviewer 1 to show information on the separation of GalCer from GlcCer. Glucosylceramide (GlcCer) and galactosylceramides (GalCer) are isomers, and MonCer is the sum of both compounds. Although it is difficult to completely separate plasma GalCer and plasma GlcCer from plasma MonCer in present method, it has been shown that the majority of plasma MonCer is composed of plasma GlcCer. In addition, Mielke et al. also described plasma GlcCer as GlcCer (a MonCer) in their PROS ONE manuscript [2]. Thus, we also described plasma GlcCer as GlcCer (a MonCer) in reference to their PROS ONE manuscript.

How do the plasma changes in sphingolipids relate to brain and CSF changes?

Thank you for your suggestion. We added sentences in Discussion section as you have indicated.

In previous reports, the increase and decrease in CSF sphingolipids and blood sphingolipids have coincided, suggesting that blood sphingolipids may reflect the dynamics of the central nervous system in neurodegenerative diseases. (p19, lines 322-324)

The age of the CN population (cohort B) is significantly lower than that of the DLB and AD populations. Since age is a significant risk factor for AD, it is unclear whether the changes are AD-related or age-related. Additional analyses with age as a variable may help tease out whether these data are age-related or pathological.

Thank you for your important remarks. Based on your suggestion, we have performed additional statistical analysis.

Pearson Correlation Coefficient was used to correlate age and plasma S1P d16:1 levels, plasma S1P d18:1 levels, plasma MonCer d18:1 levels, or plasma LacCer d18:1 levels in DLB or AD. In DLB, correlation between age and plasma S1P d16:1 levels (p = 0.544), plasma S1P d18:1 levels (p = 0.644), plasma MonCer d18:1 levels (p = 0.074), or plasma LacCer d18:1 levels (p = 0.315) showed no correlation. In AD, correlation between age and plasma S1P d16:1 levels (p = 0.506), plasma S1P d18:1 levels (p = 0.762), plasma MonCer d18:1 levels (p = 0.434), or plasma LacCer d18:1 levels (p = 0.742) showed no correlation. Therefore, it was inferred that changes in plasma S1P d16:1 levels, plasma S1P d18:1 levels, plasma MonCer d18:1 levels or plasma LacCer d18:1 levels were inferred to be disease-induced changes in AD or DLB.

We added sentences in Discussion section as you have indicated.

Fifth, cohort B was not an age-matched study. In DLB and AD, correlation analysis between age and plasma S1P d16:1 levels, plasma S1P d18:1 levels, plasma MonCer d18:1 levels, or plasma LacCer d18:1 levels showed no correlation (data not shown). Thus, changes in plasma S1P d16:1 levels, plasma S1P d18:1 levels, plasma MonCer d18:1 levels or plasma LacCer d18:1 levels were inferred to be disease-induced changes in AD or DLB. (p24, lines 421-425)

What is the recovery of sphingolipids extracted with the SPE column (page 11)? What internal standards were used, and how quantitative were these standards?

Thank you for your suggestion. Based on your suggestion, we added sentences in Materials and Methods section.

The average recovery of sphingolipids extracted with the SPE column is 88% (range 68% to 99.9%). (p11, lines 165-166)

Internal standards are used at a concentration of 50 uM. Cer/Sph Mixture II (Avanti Polar Lipids, LM6005, Birmingham, AL, USA) was used as internal standards of sphingolipid. (p12, lines 181-183)

Is there a correlation/link between the decrease in S1P and the increase in GlcCer and LacCer in plasma?

Thank you for your suggestion. We agree with your opinion. Based on your suggestion, we have performed additional statistical analysis.

Pearson Correlation Coefficient was used to correlate plasma MonCer levels and plasma LacCer levels and plasma S1P levels in all enrolled groups. Total plasma MonCer d18:1 levels were significantly positively correlated with total plasma LacCer d18:1 levels (r = 0.5802, p 0.0001). There was no difference between total plasma LacCer d18:1 levels and plasma S1P d16:1 levels (r = -0.0092, p = 0.9091) or plasma S1P d18:1 levels (r = 0.0989, p = 0.2195). There was no difference between total plasma MonCer d18:1 levels and plasma S1P d16:1 levels (r = 0.0719, p = 0.3721), but total plasma MonCer d18:1 levels were significantly slight positively correlated with plasma S1P d18:1 levels (r = 0.2480, p = 0.0028). These results suggest that an increase in plasma MonCer d18:1 levels may be directly related to an increase in LacCer d18:1 levels in all enrolled groups.

We found interesting results, as you pointed out. Thank you very much.

We added sentences in Discussion section, based on the above results.

Correlation between total plasma MonCer levels and total plasma LacCer levels.

Pearson Correlation Coefficient was used to correlate total plasma MonCer d18:1 levels and total plasma LacCer d18:1 levels in all enrolled groups. Total plasma MonCer d18:1 levels were significantly positively correlated with total plasma LacCer d18:1 levels (r = 0.5802, p 0.0001) (Fig 4) in all enrolled groups. These results suggest that an increase in plasma MonCer may be directly related to an increase in LacCer in all enrolled groups.

What enzyme pathways may cause the decrease in S1P and the increase in GluCer and LacCer?

Thank you for your suggestion. Based on your suggestion, we added sentences in Discussion section.

GCS is GlcCer synthase, BGTase6 is LacCer synthase, and SPHK is S1P synthase. These indicate that increased GlcCer and LacCer are caused by increased function of GCS and BGTase6, respectively, and decreased S1P is caused by a relative loss of function of SPHK. (p19, lines 319-321)

The limitations (page 22) should include that this is a small discovery/cross sectional study that can not account for multiple comparisons for several analytes detected in plasma.

Thank you for your suggestion. We added sentences in Discussion section as you have indicated.

Sixth, this study is a small discovery/cross sectional study that could not account for multiple comparisons for several analytes detected in plasma. (p24, lines 425-427)

1. Xu H, Boucher FR, Nguyen TT, Taylor GP, Tomlinson JJ, Ortega RA, et al. DMS as an orthogonal separation to LC/ESI/MS/MS for quantifying isomeric cerebrosides in plasma and cerebrospinal fluid. J Lipid Res. 2019;60: 200-211.

2. Mielke MM, Maetzler W, Haughey NJ, Bandaru VV, Savica R, Deuschle C, et al. Plasma ceramide and glucosylceramide metabolism is altered in sporadic Parkinson's disease and associated with cognitive impairment: a pilot study. PLoS One. 2013;8: e73094.

3. Savica R, Murray ME, Persson XM, Kantarci K, Parisi JE, Dickson DW, et al. Plasma sphingolipid changes with autopsy-confirmed Lewy Body or Alzheimer's pathology. Alzheimers Dement (Amst). 2016;3: 43-50.

4. Mori A, Ishikawa KI, Saiki S, Hatano T, Oji Y, Okuzumi A, et al. Plasma metabolite biomarkers for multiple system atrophy and progressive supranuclear palsy. PLoS One. 2019;14: e0223113.

5. Saiki S, Sasazawa Y, Fujimaki M, Kamagata K, Kaga N, Taka H, et al. A metabolic profile of polyamines in parkinson disease: A promising biomarker. Ann Neurol. 2019;86: 251-263.

Attachment

Submitted filename: Response to Reviewers20220918.docx

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PLoS One. doi: 10.1371/journal.pone.0279315.r003

Decision Letter 1

Stephan N Witt

17 Oct 2022

PONE-D-22-15953R1Plasma sphingolipid abnormalities in neurodegenerative diseasesPLOS ONE

Dear Dr. Takeda,

1) Is the manuscript technically sound, and do the data support the conclusions?In response to this question, reviewer #3 says "partly" while reviewer #1 had no response. 2) Have the authors made all data underlying the findings in their manuscript fully available?Reviewer #3 says "no." 3) Reviewer #1 says that his comments pertaining to the original manuscript were not addressed:

(i) I believe that more important would be the knowledge on the others, not considered sphingolipids.

Editor: Point 3(i) has not been addressed, and this was also brought up by reviewer #3.

Information on the separation of GalCer from GlcCer is necessary to understand to which compound are referred the results. Editor: I accept your explanation on the difficulties in separating GalCer from GlcCer.

(iii) Quantitative data are necessary to understand the yield of extraction and the correct determination and comparison within samples.

Editor: The reviewer was not satisfied as to your response to 3(iii).

4) Reviewer#3 comments:(i)The authors do not provide an explanation of which enzyme pathways can be affected in the observed alterations. It would provide important insights if the authors can perform additional experiments in testing the protein levels of the enzymes involved in the pathways. Editor: Please respond to 4(i). Testing protein levels of the enzymes could be helpful. Can this be done?

(ii) What is the outcome of other lipids that were analyzed in this lipidomics analyses? For completeness, they should also include those, even if no differences were observed. If no other lipids were analyzed, the authors should include a reasoning why they only tested these lipids. Editor: You should include the results for other lipids tested.

(iii) The collection of probands remains on very low, which lowers the impact of the manuscript.

Editor: Please respond to this comment.

(iv) The authors should be careful in claiming that the SL are similar in plasma compared to CSF or brain as was questioned by a reviewer; the papers the authors refer to are indirect and one even identified different findings in serum versus CSF. Perhaps it is better to include this in the limitations that these findings need to be confirmed in CSF or brain for further validation.Editor: please address this comment.

(v) In addition, a few other questions are still open. Did the authors check for different lipid species as different chain length is important to define their function and hence a more in-depth analyses provides a better understanding of the findings.Editor: please address this question.

(vi) Can the authors test if the IPD samples contain mutations in LRRK2 and SNCA as these are regularly identified in IPD and may provide a link to genetic forms of PDEditor: Can you test for these mutations; doing so would definitely increase the impact of your paper. 5) Editor: lines 427-431, revised: "Seventh, Quantitative data were not used as metabolites values in

this study. In present lipidomic analysis, the coefficient of variation (CV) ranged from 4.4 to

9.7%, with a mean of 6.7%. Because of the variability of metabolites values, present method

should be compared with healthy subjects for each cohort. Therefore, the relative areas

were used as metabolites values in this study."

Editor: The above explanation is very hard to understand.

Please submit your revised manuscript by Dec 01 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Stephan N. Witt, Ph.D.

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #3: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: (No Response)

Reviewer #3: Partly

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: (No Response)

Reviewer #3: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: (No Response)

Reviewer #3: No

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: (No Response)

Reviewer #3: Yes

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6. Review Comments to the Author

Reviewer #1: I requested additional results, but the Authors did not add any technical new result. According to this I maintain my previous comments.

Reviewer #3: Oizumi et al., performed a lipidomics analyses on plasma of patients suffering from different neurodegenerative disorders, including idiopathic Parkinson’s disease and dementia with Lewy bodies. The lipidomics focused on sphingolipidomics and revealed S1P, MonCer, and LacCer to be altered in the plasma from patients suffering from neurodegenerative diseases compared to control groups.

In this revised manuscript the authors do not fully address the questions raised by the reviewers for instance:

The authors do not provide an explanation of which enzyme pathways can be affected in the observed alterations. It would provide important insights if the authors can perform additional experiments in testing the protein levels of the enzymes involved in the pathways

What is the outcome of other lipids that were analyzed in this lipidomics analyses? For completeness, they should also include those, even if no differences were observed. If no other lipids were analyzed, the authors should include a reasoning why they only tested these lipids.

The collection of probands remains on very low, which lowers the impact of the manuscript.

The authors should be careful in claiming that the SL are similar in plasma compared to CSF or brain as was questioned by a reviewer; the papers the authors refer to are indirect and one even identified different findings in serum versus CSF. Perhaps it is better to include this in the limitations that these findings need to be confirmed in CSF or brain for further validation.

In addition, a few other questions are still open. Did the authors check for different lipid species as different chain length is important to define their function and hence a more in-depth analyses provides a better understanding of the findings.

Can the authors test if the IPD samples contain mutations in LRRK2 and SNCA as these are regularly identified in IPD and may provide a link to genetic forms of PD

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #3: No

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PLoS One. 2022 Dec 16;17(12):e0279315. doi: 10.1371/journal.pone.0279315.r004

Author response to Decision Letter 1

14 Nov 2022

1) Is the manuscript technically sound, and do the data support the conclusions?

In response to this question, reviewer #3 says "partly" while reviewer #1 had no response.

Thanks for the suggestion. We have made the new revised manuscript to improve on original manuscript.

2) Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #3 says "no."

Thank you for your suggestion. Supporting information (S1 Table, S2 Table, and S3 Table) and additional Table (Table 2 and Table 3) has been added to the new revised manuscript to improve on original manuscript.

3) Reviewer #1 says that his comments pertaining to the original manuscript were not addressed:

(i) I believe that more important would be the knowledge on the others, not considered sphingolipids.

Editor: Point 3(i) has not been addressed, and this was also brought up by reviewer #3.

Thank you for your suggestion. We have described a comprehensive knowledge of lipid abnormalities reported in neurodegenerative diseases.

We added sentences in Discussion section. Changes in the revised manuscript are highlighted in yellow.

Recessive mutations in the GBA1 (glucocerebrosidase) gene cause Gaucher disease. Heterozygous GBA1 mutation carriers exhibit much greater incidence of PD than the general population [1,2]. Likewise, mutations in the NPC1 (NPC intracellular cholesterol transporter 1) and SMPD1 (sphingomyelin phosphodiesterase 1) genes, which cause Niemann-Pick disease, have been shown to be risk genes for IPD [3,4]. One of the phospholipase A2 members, PLA2G6 or iPLA2-VIA/iPLA2β, has been isolated as the gene responsible for an autosomal recessive form of PD linked to the PARK14 locus [5]. Compared to the most common e3 isoform, the e4 isoform of ApoE (ApoE4) is the strongest genetic risk factor for late-onset AD [6]. β amyloid accumulation in NPC1 (NPC intracellular cholesterol transporter 1) gene, which cause Niemann-Pick type C, mutant cells and NPC mouse brain suggests the association between cholesterol metabolism and AD [7]. As described, several lipid-related genes have been reported as risk genes or causative genes in AD and IPD. In addition, various lipid abnormalities have been reported in IPD and AD, such as fatty acids, glycerolipids, glycerophospholipids, sphingolipids, sterols, and lipoproteins [8,9]. However, it is still unclear which lipid metabolism abnormalities play the most important role in neurodegenerative diseases. Plasma lipidomics is an unbiased method and can find important lipids in neurodegenerative diseases. For this reason, plasma lipidomics was performed in neurodegenerative diseases in this study. (p25, lines 363-379)

(ii) Information on the separation of GalCer from GlcCer is necessary to understand to which compound are referred the results.

Editor: I accept your explanation on the difficulties in separating GalCer from GlcCer.

Thank you for your acceptance.

(iii) Quantitative data are necessary to understand the yield of extraction and the correct determination and comparison within samples.

Editor: The reviewer was not satisfied as to your response to 3(iii).

Thank you for your suggestion. We measured relative areas as quantitative value based on PROS ONE manuscript [10]. Lipidomics has the variability of metabolite values in each study. For this reason, each metabolite should be normalized based on the internal standard (IS) level and sample volume. The normalized each metabolite was represented as relative area and used as the quantitative value. Additionally, information on IS was described in Materials and Methods section.

We changed sentence in Materials and Methods, and Discussion section. Changes in the revised manuscript are highlighted in yellow.

Target metabolites are divided into categories (fatty acids, acylcarnitines, oxylipins, lysophospholipids, platelet-activating factors, glycosphingolipids, sphinganines, sphingosines, and steroids) according to their physical properties, and the recovery rate is corrected using the corresponding IS (Internal standards). Based on these reports, these IS were selected [11-13]. The recovery rate of analytes during extraction ranged from 68% to 129%, with a mean of 96%. IS coefficient of variation ranged from 4.4 to 9.7%, with a mean of 6.7%. The peak area of each metabolite was then normalized based on IS level and sample volume for relative quantification. The normalized each metabolite was represented as relative area and used as the quantitative value based on previous reports [10,14]. (p12, lines 182-191)

Seventh, relative area was used in this study as the quantitative value for each metabolite based on previous reports [10,14]. Lipidomics has the variability of metabolite values in each study. For this reason, each metabolite should be normalized based on the IS level and sample volume. The normalized each metabolite was represented as relative area and used as the quantitative value. (p32-33, lines 518-522)

4) Reviewer#3 comments:

(i)The authors do not provide an explanation of which enzyme pathways can be affected in the observed alterations. It would provide important insights if the authors can perform additional experiments in testing the protein levels of the enzymes involved in the pathways.

Editor: Please respond to 4(i). Testing protein levels of the enzymes could be helpful. Can this be done?

Thank you for your suggestion. However, in this study we cannot perform additional experiments in testing the protein levels of the enzymes involved in sphingolipid pathways.

We added sentences in Discussion section. Changes in the revised manuscript are highlighted in yellow.

Sixth, in this study the protein levels of the enzymes involved in sphingolipid pathways were not evaluated in all enrolled patients. The protein levels of the enzymes involved in sphingolipid pathways need to be performed in future studies. (p32, lines 516-518)

Editor: You should include the results for other lipids tested.

Thank you for your suggestion. We included the results for other lipids tested.

We added sentences in Results, Discussion, Supporting information section. We added S1 Table and S2 Table. Changes in the revised manuscript are highlighted in yellow.

Plasma other lipid metabolite levels in neurodegenerative diseases.

Plasma other lipid metabolite (other sphingolipids, sphinganines, gangliosides, free fatty acids, acylcarnitnes, lysophospholipids, platelet-activating factor, acylethanolamine, thyroid hormone, cholic acids, and steroids) levels were compared between the CN group and the IPD, DLB, MSA, AD and PSP groups. Oxylipins were not statistically analyzed because it is considered unsuitable for statistical analysis due to the large number of undetectable samples. Statistical significance was examined using one-tailed Welch’s t tests. Plasma ceramide-1-phosphate (C1P) levels were significantly higher in the PD, DLB, and AD groups versus the control group (S1 table). Plasma GM3 ganglioside and GD3 ganglioside levels were significantly higher in all neurodegenerative disease groups (IPD, DLB, MSA, AD, and PSP) versus the control group (S1 table). Plasma lysophosphatidic acid, lysophosphatidylcholine, lysophosphatidylethanolamine, lysophosphatidylglycerol, lysophosphatidylserine levels were lower in DLB group versus the control group (S1 table). Plasma cortisone levels were significantly higher in the PD, MSA and PSP groups versus the control group (S2 Table). (p19-20, lines 332-345)

In this study, we found that plasma GM3 and GD3 ganglioside levels were significantly higher in the neurodegenerative disease groups than in the CN group. Gangliosides are lipids classified as sphingolipids. GM3 ganglioside is the starting material for gangliosides, which are biosynthesized by the binding of sialic acid to LacCer [15,16]. Previously, plasma GM3 ganglioside levels have been shown to be elevated in PD [17]. The elevated plasma GM3 ganglioside levels in individuals with IPD in this study were in good accordance with the results of previous study. GD3 ganglioside is the gangliosides, which are biosynthesized by the binding of sialic acid to GM3 ganglioside [15,16]. GM3 and GD3 gangliosides are components of lipid rafts and are implicated in cell death [18,19]. Abnormalities in lipid rafts are also considered to be one of the major causes of neurodegenerative diseases [20]. Homozygous knockout mice for B4galnt1, a ganglioside synthase, have been shown to exhibit PD-like motor deficits and cause dopaminergic neuron degeneration [21]. Taken together, these results suggest that elevated plasma GM3 and GD3 gangliosides may reflect abnormal lipid rafts in neurodegenerative diseases. In this study, we found that plasma C1P levels were significantly higher in the IPD, DLB, and AD groups than in the CN group. C1P is classified as a sphingolipid, a lipid mainly involved in cell survival and inflammation [22,23]. Neuroinflammation is also considered to be a one of the major causes in PD, DLB and AD [24-26]. Therefore, elevated C1P may reflect neuroinflammation in these diseases. (p30-31, lines 472-489)

S1 Table. Plasma other sphinogolipids, sphinganines, gangliosides, free fatty acids, acylcarnitnes, lysophospholipids levels in neurodegenerative diseases.

Statistical methods: The metabolite level ratio of IPD, DLB, MSA, AD, or PSP to CNs. Statistical significance was examined using one-tailed Welch's t tests (P 0.05).

Abbreviations: ceramide-1-phosphate (C1P), sphinganine-1-phosphate (SG1P), lysophosphatidic acid (LPA), lysophosphatidylcholine (LPC), lysophosphatidylethanolamine (LPE), lysophosphatidylglycerol (LPG), lysophosphatidylinositol (LPI), lysophosphatidylserine (LPS) (p46, lines 758-764)

S2 Table. Plasma platelet-activating factor, acylethanolamine, thyroid hormone, cholic acids, steroids levels in neurodegenerative diseases.

Statistical methods: The metabolite level ratio of IPD, DLB, MSA, AD, or PSP to CNs. Statistical significance was examined using one-tailed Welch's t tests (P 0.05).

Abbreviations: platelet-activating factor (PAF) (p46, lines 765-769)

(iii) The collection of probands remains on very low, which lowers the impact of the manuscript.

Editor: Please respond to this comment.

Thank you for your suggestion. We considered the collection of probands to be genetic analysis.

We could not test GBA, LRRK2, and SNCA mutations in this study, so we changed sentences in Discussion section. Changes in the revised manuscript are highlighted in yellow.

First, Analysis the major causative genes or risk genes of PD during lipidomics were not evaluated. GBA1 mutations were not evaluated in all enrolled IPD patients. Based on the GBA1 genotype and clinical analysis, it has been reported that GBA1 mutation is the most common genetic risk factor for IPD patients, accounting for as many as 7% of all IPD patients in multicenter analyses [1,2]. On the other hand, only approximately 3% of Asian IPD patients with no apparent family history of parkinsonism are GBA1 mutation carriers [27]. IPD in GBA1 mutation carriers generally has an early onset [28]. However, there was no apparent family history of parkinsonism or dementia in all enrolled IPD patients, with a later mean age of onset in the enrolled IPD patients that was 67.2 years in cohort A and 65.2 years in cohort C. Elevated plasma GlcCer levels have recently been reported in GBA1 mutation carriers of IPD. Elevated plasma GlcCer levels have also been reported in non-GBA1 mutation carriers of IPD. These indicate that elevated plasma GlcCer is found in IPD with or without GBA mutation. Taken together, it is not plausible that a GBA1 mutation did not significantly affect elevated plasma GlcCer (a MonCer) levels in the IPD patients in this study. In addition, in this study LRRK2 and SNCA mutations, the major causative genes of PD, were not evaluated in all enrolled IPD patients. Analysis the major causative genes or risk genes of PD during lipidomics need to be performed in future studies. (p31-32, lines 491-507)

In addition, we considered the collection of probands to be the number of cases, so we changed sentences in Discussion section.

Second, this study is a small cases and cross-sectional study that could not account for multiple comparisons for several analytes detected in plasma. Future additional cases and longitudinal studies need to be performed. (p32, lines 507-509)

Editor: please address this comment.

Thank you for your suggestion. We changed sentence in Discussion section as you indicated. Changes in the revised manuscript are highlighted in yellow.

Eighth, the increase and decrease in CSF sphingolipids and blood sphingolipids have coincided [30,31,58,59] in previous reports. On the other hand, one report even identified different findings in serum versus CSF [29,30]. These reports are indirect and sphingolipids need to be confirmed in CSF or brain for further validation. (p33, lines 523-526)

Editor: please address this question.

Thank you for your suggestion. To check for different lipid species as different chain length, we examined the association between lipid abnormalities and chain length in MonCers and LacCers.

We added sentences in Results section and additional Table (Table 2 and Table 3). Changes in the revised manuscript are highlighted in yellow.

We compared MonCer (d18:1/14:0), MonCer (d18:1/16:0), MonCer (d18:1/16:1), MonCer (d18:1/18:0), MonCer (d18:1/18:1), MonCer (d18:1/20:0), MonCer (d18:1/20:1), MonCer (d18:1/22:0), MonCer (d18:1/22:1), MonCer (d18:1/22:2), MonCer (d18:1/24:0), MonCer (d18:1/24:1), and MonCer (d18:1/24:2) between the CN group and the IPD, DLB, MSA, AD, or PSP groups (Table 2). The χ-square test was used to examine the association between lipid abnormalities and chain length in MonCer d18:1. No statistically significant difference was found between lipid abnormalities and chain length (P = 0.5522) in all enrolled groups. (p16, lines 261-268)

We compared LacCer (d18:1/14:0), LacCer (d18:1/16:0), LacCer (d18:1/16:1), LacCer (d18:1/18:0), LacCer (d18:1/18:1), LacCer (d18:1/20:0), LacCer (d18:1/20:1), LacCer (d18:1/22:0), LacCer (d18:1/22:1), LacCer (d18:1/22:2), LacCer (d18:1/24:0), LacCer (d18:1/24:1), and LacCer (d18:1/24:2) between the CN group and the IPD, DLB, MSA, AD, or PSP groups (Table 3). The χ-square test was used to examine the association between lipid abnormalities and chain length in LacCers d18:1. No statistically significant difference was found between lipid abnormalities and chain length (P = 0.5522) in all enrolled groups. (p17-18, lines 296-302)

(vi) Can the authors test if the IPD samples contain mutations in LRRK2 and SNCA as these are regularly identified in IPD and may provide a link to genetic forms of PD

Editor: Can you test for these mutations; doing so would definitely increase the impact of your paper.

Thank you for your suggestion. However, we could not test LRRK2, and SNCA mutations in this study.

We added sentences in Discussion section. Changes in the revised manuscript are highlighted in yellow.

In addition, in this study LRRK2 and SNCA mutations, the major causative genes of PD, were not evaluated in all enrolled IPD patients. Analysis the major causative genes or risk genes of PD during lipidomics need to be performed in future studies. (p32, lines 504-507)

5) Editor: lines 427-431, revised: "Seventh, Quantitative data were not used as metabolites values in this study. In present lipidomic analysis, the coefficient of variation (CV) ranged from 4.4 to 9.7%, with a mean of 6.7%. Because of the variability of metabolites values, present method should be compared with healthy subjects for each cohort. Therefore, the relative areas were used as metabolites values in this study."

Editor: The above explanation is very hard to understand.

Thank you for your suggestion. We agree with your opinion. We measured relative areas as quantitative value based on PROS ONE manuscript [10]. Lipidomics has the variability of metabolite values in each study. For this reason, each metabolite should be normalized based on the internal standard (IS) level and sample volume. The normalized each metabolite was represented as relative area and used as the quantitative value.

We changed sentence in Discussion section as you indicated. Changes in the revised manuscript are highlighted in yellow.

1. Sidransky E, Nalls MA, Aasly JO, Aharon-Peretz J, Annesi G, Barbosa ER, et al. Multicenter analysis of glucocerebrosidase mutations in Parkinson's disease. N Engl J Med. 2009;361: 1651-1661.

2. Lesage S, Anheim M, Condroyer C, Pollak P, Durif F, Dupuits C, et al. Large-scale screening of the Gaucher's disease-related glucocerebrosidase gene in Europeans with Parkinson's disease. Hum Mol Genet. 2011;20: 202-210.

3. Foo JN, Liany H, Bei JX, Yu XQ, Liu J, Au WL, et al. Rare lysosomal enzyme gene SMPD1 variant (p.R591C) associates with Parkinson's disease. Neurobiol Aging. 2013;34: 2890 e2813-2895.

4. Kluenemann HH, Nutt JG, Davis MY, Bird TD. Parkinsonism syndrome in heterozygotes for Niemann-Pick C1. J Neurol Sci. 2013;335: 219-220.

5. Gregory A, Westaway SK, Holm IE, Kotzbauer PT, Hogarth P, Sonek S, et al. Neurodegeneration associated with genetic defects in phospholipase A(2). Neurology. 2008;71: 1402-1409.

6. Chartier-Harlin MC, Parfitt M, Legrain S, Pérez-Tur J, Brousseau T, Evans A, et al. Apolipoprotein E, epsilon 4 allele as a major risk factor for sporadic early and late-onset forms of Alzheimer's disease: analysis of the 19q13.2 chromosomal region. Hum Mol Genet. 1994;3: 569-574.

7. Yamazaki T, Chang TY, Haass C, Ihara Y. Accumulation and aggregation of amyloid beta-protein in late endosomes of Niemann-pick type C cells. J Biol Chem. 2001;276: 4454-4460.

8. Yin F. Lipid metabolism and Alzheimer's disease: clinical evidence, mechanistic link and therapeutic promise. FEBS J. 2022.

9. Xicoy H, Wieringa B, Martens GJM. The Role of Lipids in Parkinson's Disease. Cells. 2019;8.

10. Mori A, Ishikawa KI, Saiki S, Hatano T, Oji Y, Okuzumi A, et al. Plasma metabolite biomarkers for multiple system atrophy and progressive supranuclear palsy. PLoS One. 2019;14: e0223113.

11. Hayasaka R, Tabata S, Hasebe M, Ikeda S, Ohnuma S, Mori M, et al. Metabolomic Analysis of Small Extracellular Vesicles Derived from Pancreatic Cancer Cells Cultured under Normoxia and Hypoxia. Metabolites. 2021;11.

12. Suzuki Y, Hayasaka R, Hasebe M, Ikeda S, Soga T, Tomita M, et al. Comparative Metabolomics of Small Molecules Specifically Expressed in the Dorsal or Ventral Marginal Zones in Vertebrate Gastrula. Metabolites. 2022;12.

13. Ikeda K. Mass Spectrometric Analysis of Phospholipids by Target Discovery Approach. 2015. pp. 349-356.

14. Saiki S, Sasazawa Y, Fujimaki M, Kamagata K, Kaga N, Taka H, et al. A metabolic profile of polyamines in parkinson disease: A promising biomarker. Ann Neurol. 2019;86: 251-263.

15. Merrill AH, Jr. Sphingolipid and glycosphingolipid metabolic pathways in the era of sphingolipidomics. Chem Rev. 2011;111: 6387-6422.

16. Yamaji T, Hanada K. Sphingolipid metabolism and interorganellar transport: localization of sphingolipid enzymes and lipid transfer proteins. Traffic. 2015;16: 101-122.

17. Chan RB, Perotte AJ, Zhou B, Liong C, Shorr EJ, Marder KS, et al. Elevated GM3 plasma concentration in idiopathic Parkinson's disease: A lipidomic analysis. PLoS One. 2017;12: e0172348.

18. Sohn H, Kim YS, Kim HT, Kim CH, Cho EW, Kang HY, et al. Ganglioside GM3 is involved in neuronal cell death. FASEB J. 2006;20: 1248-1250.

19. De Maria R, Lenti L, Malisan F, d'Agostino F, Tomassini B, Zeuner A, et al. Requirement for GD3 ganglioside in CD95- and ceramide-induced apoptosis. Science. 1997;277: 1652-1655.

20. Schengrund CL. Lipid rafts: keys to neurodegeneration. Brain Res Bull. 2010;82: 7-17.

21. Wu G, Lu ZH, Kulkarni N, Amin R, Ledeen RW. Mice lacking major brain gangliosides develop parkinsonism. Neurochem Res. 2011;36: 1706-1714.

22. Chalfant CE, Spiegel S. Sphingosine 1-phosphate and ceramide 1-phosphate: expanding roles in cell signaling. J Cell Sci. 2005;118: 4605-4612.

23. Arana L, Gangoiti P, Ouro A, Trueba M, Gomez-Munoz A. Ceramide and ceramide 1-phosphate in health and disease. Lipids Health Dis. 2010;9: 15.

24. Leng F, Edison P. Neuroinflammation and microglial activation in Alzheimer disease: where do we go from here? Nat Rev Neurol. 2021;17: 157-172.

25. Gelders G, Baekelandt V, Van der Perren A. Linking Neuroinflammation and Neurodegeneration in Parkinson's Disease. J Immunol Res. 2018;2018: 4784268.

26. Surendranathan A, Rowe JB, O'Brien JT. Neuroinflammation in Lewy body dementia. Parkinsonism Relat Disord. 2015;21: 1398-1406.

27. Wu YR, Chen CM, Chao CY, Ro LS, Lyu RK, Chang KH, et al. Glucocerebrosidase gene mutation is a risk factor for early onset of Parkinson disease among Taiwanese. J Neurol Neurosurg Psychiatry. 2007;78: 977-979.

28. Sato C, Morgan A, Lang AE, Salehi-Rad S, Kawarai T, Meng Y, et al. Analysis of the glucocerebrosidase gene in Parkinson's disease. Mov Disord. 2005;20: 367-370.

29. Mielke MM, Maetzler W, Haughey NJ, Bandaru VV, Savica R, Deuschle C, et al. Plasma ceramide and glucosylceramide metabolism is altered in sporadic Parkinson's disease and associated with cognitive impairment: a pilot study. PLoS One. 2013;8: e73094.

30. Huh YE, Park H, Chiang MSR, Tuncali I, Liu G, Locascio JJ, et al. Glucosylceramide in cerebrospinal fluid of patients with GBA-associated and idiopathic Parkinson's disease enrolled in PPMI. NPJ Parkinsons Dis. 2021;7: 102.

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PLoS One. doi: 10.1371/journal.pone.0279315.r005

Decision Letter 2

Stephan N Witt

29 Nov 2022

PONE-D-22-15953R2Plasma sphingolipid abnormalities in neurodegenerative diseasesPLOS ONE

Dear Dr. Takeda,

Thank you for submitting your manuscript to PLOS ONE. Please make the minor corrections to table S1, and resubmit with no other changes. See my comments below.

Please submit your revised manuscript by Jan 13 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Stephan N. Witt, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments :

I will accept your manuscript after you make corrections to S1 Table. In S1 Table, you repeatedly misspell the word "sphingolipids." Throughout the table sphingolipids is spelled "sphinogolipids." Please fix.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: (No Response)

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #1: The human plasma sphingolipid pattern has been described using different methodological approaches. It would be important to understand which are the changes of sphingolipid pattern in neurodegenerative diseases. At least a simple TLC procedure could give the preliminary information.

GlcCer and GalCer sphingolipids are the result of completely different metabolic pathways. In my opinion it is necessary to know the behavior of these two compounds. There are several procedures that allow their separation.

Reviewer #3: The authors have sufficiently addressed my comments and added profound explanation to unresolved questions that they also included in the manuscript.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #3: No

**********

PLoS One. 2022 Dec 16;17(12):e0279315. doi: 10.1371/journal.pone.0279315.r006

Author response to Decision Letter 2

2 Dec 2022

Additional Editor Comments :

Thank you for your suggestion. We have changed the misspelled "sphinogolipids." in S1 Table and Supporting information to correct "sphingolipids." Changes in the revised manuscript are highlighted in yellow.

S1 Table. Plasma other sphingolipids, sphinganines, gangliosides, free fatty acids, acylcarnitnes, lysophospholipids levels in neurodegenerative diseases. (p47, lines 758-759)

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PLoS One. doi: 10.1371/journal.pone.0279315.r007

Decision Letter 3

Stephan N Witt

5 Dec 2022

Plasma sphingolipid abnormalities in neurodegenerative diseases

PONE-D-22-15953R3

Dear Dr. Takeda,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Stephan N. Witt, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0279315.r008

Acceptance letter

Stephan N Witt

8 Dec 2022

PONE-D-22-15953R3

Plasma sphingolipid abnormalities in neurodegenerative diseases

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Associated Data

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

Supplementary Materials

S1 Table. Plasma other sphingolipids, sphinganines, gangliosides, free fatty acids, acylcarnitnes, lysophospholipids levels in neurodegenerative diseases.

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S2 Table. Plasma platelet-activating factor, acylethanolamine, thyroid hormone, cholic acids, steroids levels in neurodegenerative diseases.

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S3 Table. Correlation analysis between age and plasma S1P d16:1 levels, plasma S1P d18:1 levels, plasma MonCer d18:1 levels, or plasma LacCer d18:1 levels in DLB and AD.

Pearson Correlation Coefficient was used to correlate between age and plasma S1P d16:1 levels, plasma S1P d18:1 levels, plasma MonCer d18:1 levels, or plasma LacCer d18:1 levels (P < 0.05).

(DOCX)

Click here for additional data file.^{(16.4KB, docx)}

Attachment

Submitted filename: Response to Reviewers20220918.docx

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Submitted filename: Response to reviewers20221113.docx

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Submitted filename: Response to reviewers20221202.docx

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Data Availability Statement

All relevant data are within the paper and its Supporting information files.

[pone.0279315.ref001] 1.de Lau LML, Breteler MMB. Epidemiology of Parkinson’s disease. Lancet Neurol. 2006;5: 525–535. doi: 10.1016/S1474-4422(06)70471-9 [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref002] 2.Hogan DB, Fiest KM, Roberts JI, Maxwell CJ, Dykeman J, Pringsheim T, et al. The prevalence and incidence of dementia with lewy bodies: a systematic review. Can J Neurol Sci. 2016;43 Suppl 1: S83–S95. [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref003] 3.Gibb WR, Lees AJ. The relevance of the Lewy body to the pathogenesis of idiopathic Parkinson’s disease. J Neurol Neurosurg Psychiatry. 1988;51: 745–752. doi: 10.1136/jnnp.51.6.745 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref004] 4.Spillantini MG, Crowther RA, Jakes R, Hasegawa M, Goedert M. alpha-Synuclein in filamentous inclusions of Lewy bodies from Parkinson’s disease and dementia with lewy bodies. Proc Natl Acad Sci U S A. 1998;95: 6469–6473. doi: 10.1073/pnas.95.11.6469 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref005] 5.Gilman S, Wenning GK, Low PA, Brooks DJ, Mathias CJ, Trojanowski JQ, et al. Second consensus statement on the diagnosis of multiple system atrophy. Neurology. 2008;71: 670–676. doi: 10.1212/01.wnl.0000324625.00404.15 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref006] 6.Wakabayashi K, Yoshimoto M, Tsuji S, Takahashi H. Alpha-synuclein immunoreactivity in glial cytoplasmic inclusions in multiple system atrophy. Neurosci Lett. 1998;249: 180–182. doi: 10.1016/s0304-3940(98)00407-8 [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref007] 7.Wakabayashi K, Takahashi H. Cellular pathology in multiple system atrophy. Neuropathology. 2006;26: 338–345. doi: 10.1111/j.1440-1789.2006.00713.x [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref008] 8.McCann H, Stevens CH, Cartwright H, Halliday GM. α-Synucleinopathy phenotypes. Park Relat Disord. 2014;20: S62–S67. [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref009] 9.Prince M, Bryce R, Albanese E, Wimo A, Ribeiro W, Ferri CP. The global prevalence of dementia: a systematic review and metaanalysis. Alzheimers Dement. 2013;9: 63–75.e2. doi: 10.1016/j.jalz.2012.11.007 [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref010] 10.Selkoe DJ. Cell biology of protein misfolding: the examples of Alzheimer’s and Parkinson’s diseases. Nat Cell Biol. 2004;6: 1054–1061. doi: 10.1038/ncb1104-1054 [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref011] 11.Litvan I, Agid Y, Calne D, Campbell G, Dubois B, Duvoisin RC, et al. Clinical research criteria for the diagnosis of progressive supranuclear palsy (Steele-Richardson-Olszewski syndrome): report of the NINDS-SPSP international workshop. Neurology. 1996;47: 1–9. doi: 10.1212/wnl.47.1.1 [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref012] 12.Boxer AL, Yu J-T, Golbe LI, Litvan I, Lang AE, Höglinger GU. Advances in progressive supranuclear palsy: new diagnostic criteria, biomarkers, and therapeutic approaches. Lancet Neurol. 2017;16: 552–563. doi: 10.1016/S1474-4422(17)30157-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref013] 13.Rosler TW, Tayaranian Marvian A, Brendel M, Nykanen NP, Hollerhage M, Schwarz SC, et al. Four-repeat tauopathies. Prog Neurobiol. 2019;180: 101644. doi: 10.1016/j.pneurobio.2019.101644 [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref014] 14.Bourdenx M, Koulakiotis NS, Sanoudou D, Bezard E, Dehay B, Tsarbopoulos A. Protein aggregation and neurodegeneration in prototypical neurodegenerative diseases: Examples of amyloidopathies, tauopathies and synucleinopathies. Prog Neurobiol. 2017;155: 171–193. doi: 10.1016/j.pneurobio.2015.07.003 [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref015] 15.Guo T, Noble W, Hanger DP. Roles of tau protein in health and disease. Acta Neuropathol. 2017;133: 665–704. doi: 10.1007/s00401-017-1707-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref016] 16.Niedowicz DM, Nelson PT, Murphy MP. Alzheimer’s disease: pathological mechanisms and recent insights. Curr Neuropharmacol. 2011;9: 674–684. doi: 10.2174/157015911798376181 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref017] 17.Xicoy H, Wieringa B, Martens GJM. The Role of Lipids in Parkinson’s Disease. Cells. 2019;8: 27. doi: 10.3390/cells8010027 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref018] 18.LeWitt PA, Li J, Lu M, Guo L, Auinger P. Metabolomic biomarkers as strong correlates of Parkinson disease progression. Neurology. 2017;88: 862–869. doi: 10.1212/WNL.0000000000003663 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref019] 19.Koal T, Klavins K, Seppi D, Kemmler G, Humpel C. Sphingomyelin SM(d18:1/18:0) is significantly enhanced in cerebrospinal fluid samples dichotomized by pathological amyloid-beta42, tau, and phospho-tau-181 levels. J Alzheimers Dis. 2015;44: 1193–1201. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref020] 20.Quinones MP, Kaddurah-Daouk R. Metabolomics tools for identifying biomarkers for neuropsychiatric diseases. Neurobiol Dis. 2009;35: 165–176. doi: 10.1016/j.nbd.2009.02.019 [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref021] 21.Zhang J, Zhang X, Wang L, Yang C. High Performance Liquid Chromatography-Mass Spectrometry (LC-MS) Based Quantitative Lipidomics Study of Ganglioside-NANA-3 Plasma to Establish Its Association with Parkinson’s Disease Patients. Med Sci Monit. 2017;23: 5345–5353. doi: 10.12659/msm.904399 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref022] 22.Liu Y, Thalamuthu A, Mather KA, Crawford J, Ulanova M, Wong MWK, et al. Plasma lipidome is dysregulated in Alzheimer’s disease and is associated with disease risk genes. Transl Psychiatry. 2021;11: 344. doi: 10.1038/s41398-021-01362-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref023] 23.McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR, Kawas CH, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7: 263–269. doi: 10.1016/j.jalz.2011.03.005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref024] 24.Hoglinger GU, Respondek G, Stamelou M, Kurz C, Josephs KA, Lang AE, et al. Clinical diagnosis of progressive supranuclear palsy: The movement disorder society criteria. Mov Disord. 2017;32: 853–864. doi: 10.1002/mds.26987 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref025] 25.McKeith IG, Boeve BF, Dickson DW, Halliday G, Taylor JP, Weintraub D, et al. Diagnosis and management of dementia with Lewy bodies: Fourth consensus report of the DLB Consortium. Neurology. 2017;89: 88–100. doi: 10.1212/WNL.0000000000004058 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref026] 26.Saiki S, Hatano T, Fujimaki M, Ishikawa KI, Mori A, Oji Y, et al. Decreased long-chain acylcarnitines from insufficient beta-oxidation as potential early diagnostic markers for Parkinson’s disease. Sci Rep. 2017;7: 7328. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref027] 27.Hayasaka R, Tabata S, Hasebe M, Ikeda S, Ohnuma S, Mori M, et al. Metabolomic Analysis of Small Extracellular Vesicles Derived from Pancreatic Cancer Cells Cultured under Normoxia and Hypoxia. Metabolites. 2021;11. doi: 10.3390/metabo11040215 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref028] 28.Suzuki Y, Hayasaka R, Hasebe M, Ikeda S, Soga T, Tomita M, et al. Comparative Metabolomics of Small Molecules Specifically Expressed in the Dorsal or Ventral Marginal Zones in Vertebrate Gastrula. Metabolites. 2022;12. doi: 10.3390/metabo12060566 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref029] 29.Ikeda K. Mass Spectrometric Analysis of Phospholipids by Target Discovery Approach. 2015. pp. 349–356. [Google Scholar]

[pone.0279315.ref030] 30.Mori A, Ishikawa KI, Saiki S, Hatano T, Oji Y, Okuzumi A, et al. Plasma metabolite biomarkers for multiple system atrophy and progressive supranuclear palsy. PLoS One. 2019;14: e0223113. doi: 10.1371/journal.pone.0223113 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref031] 31.Saiki S, Sasazawa Y, Fujimaki M, Kamagata K, Kaga N, Taka H, et al. A metabolic profile of polyamines in parkinson disease: A promising biomarker. Ann Neurol. 2019;86: 251–263. doi: 10.1002/ana.25516 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref032] 32.Sidransky E, Nalls MA, Aasly JO, Aharon-Peretz J, Annesi G, Barbosa ER, et al. Multicenter analysis of glucocerebrosidase mutations in Parkinson’s disease. N Engl J Med. 2009;361: 1651–1661. doi: 10.1056/NEJMoa0901281 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref033] 33.Lesage S, Anheim M, Condroyer C, Pollak P, Durif F, Dupuits C, et al. Large-scale screening of the Gaucher’s disease-related glucocerebrosidase gene in Europeans with Parkinson’s disease. Hum Mol Genet. 2011;20: 202–210. doi: 10.1093/hmg/ddq454 [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref034] 34.Foo JN, Liany H, Bei JX, Yu XQ, Liu J, Au WL, et al. Rare lysosomal enzyme gene SMPD1 variant (p.R591C) associates with Parkinson’s disease. Neurobiol Aging. 2013;34: 2890 e2813–2895. doi: 10.1016/j.neurobiolaging.2013.06.010 [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref035] 35.Kluenemann HH, Nutt JG, Davis MY, Bird TD. Parkinsonism syndrome in heterozygotes for Niemann-Pick C1. J Neurol Sci. 2013;335: 219–220. doi: 10.1016/j.jns.2013.08.033 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref036] 36.Gregory A, Westaway SK, Holm IE, Kotzbauer PT, Hogarth P, Sonek S, et al. Neurodegeneration associated with genetic defects in phospholipase A(2). Neurology. 2008;71: 1402–1409. doi: 10.1212/01.wnl.0000327094.67726.28 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref037] 37.Chartier-Harlin MC, Parfitt M, Legrain S, Pérez-Tur J, Brousseau T, Evans A, et al. Apolipoprotein E, epsilon 4 allele as a major risk factor for sporadic early and late-onset forms of Alzheimer’s disease: analysis of the 19q13.2 chromosomal region. Hum Mol Genet. 1994;3: 569–574. doi: 10.1093/hmg/3.4.569 [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref038] 38.Yamazaki T, Chang TY, Haass C, Ihara Y. Accumulation and aggregation of amyloid beta-protein in late endosomes of Niemann-pick type C cells. J Biol Chem. 2001;276: 4454–4460. doi: 10.1074/jbc.M009598200 [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref039] 39.Yin F. Lipid metabolism and Alzheimer’s disease: clinical evidence, mechanistic link and therapeutic promise. FEBS J. 2022. doi: 10.1111/febs.16344 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref040] 40.Xu H, Boucher FR, Nguyen TT, Taylor GP, Tomlinson JJ, Ortega RA, et al. DMS as an orthogonal separation to LC/ESI/MS/MS for quantifying isomeric cerebrosides in plasma and cerebrospinal fluid. J Lipid Res. 2019;60: 200–211. doi: 10.1194/jlr.D089797 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref041] 41.Maceyka M, Spiegel S. Sphingolipid metabolites in inflammatory disease. Nature. 2014;510: 58–67. doi: 10.1038/nature13475 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref042] 42.Jesko H, Stepien A, Lukiw WJ, Strosznajder RP. The Cross-Talk Between Sphingolipids and Insulin-Like Growth Factor Signaling: Significance for Aging and Neurodegeneration. Mol Neurobiol. 2019;56: 3501–3521. doi: 10.1007/s12035-018-1286-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref043] 43.Torretta E, Arosio B, Barbacini P, Casati M, Capitanio D, Mancuso R, et al. Particular CSF sphingolipid patterns identify iNPH and AD patients. Sci Rep. 2018;8: 13639. doi: 10.1038/s41598-018-31756-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref044] 44.Chua XY, Chai YL, Chew WS, Chong JR, Ang HL, Xiang P, et al. Immunomodulatory sphingosine-1-phosphates as plasma biomarkers of Alzheimer’s disease and vascular cognitive impairment. Alzheimers Res Ther. 2020;12: 122. doi: 10.1186/s13195-020-00694-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref045] 45.Motyl J, Strosznajder JB. Sphingosine kinase 1/sphingosine-1-phosphate receptors dependent signalling in neurodegenerative diseases. The promising target for neuroprotection in Parkinson’s disease. Pharmacol Rep. 2018;70: 1010–1014. doi: 10.1016/j.pharep.2018.05.002 [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref046] 46.Vidal-Martinez G, Segura-Ulate I, Yang B, Diaz-Pacheco V, Barragan JA, De-Leon Esquivel J, et al. FTY720-Mitoxy reduces synucleinopathy and neuroinflammation, restores behavior and mitochondria function, and increases GDNF expression in Multiple System Atrophy mouse models. Exp Neurol. 2020;325: 113120. doi: 10.1016/j.expneurol.2019.113120 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref047] 47.Badawy SMM, Okada T, Kajimoto T, Hirase M, Matovelo SA, Nakamura S, et al. Extracellular alpha-synuclein drives sphingosine 1-phosphate receptor subtype 1 out of lipid rafts, leading to impaired inhibitory G-protein signaling. J Biol Chem. 2018;293: 8208–8216. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref048] 48.Couttas TA, Kain N, Daniels B, Lim XY, Shepherd C, Kril J, et al. Loss of the neuroprotective factor Sphingosine 1-phosphate early in Alzheimer’s disease pathogenesis. Acta Neuropathol Commun. 2014;2: 9. doi: 10.1186/2051-5960-2-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref049] 49.Asle-Rousta M, Kolahdooz Z, Oryan S, Ahmadiani A, Dargahi L. FTY720 (fingolimod) attenuates beta-amyloid peptide (Abeta42)-induced impairment of spatial learning and memory in rats. J Mol Neurosci. 2013;50: 524–532. [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref050] 50.St-Cyr Giguere F, Attiori Essis S, Chagniel L, Germain M, Cyr M, Massicotte G. The sphingosine-1-phosphate receptor 1 agonist SEW2871 reduces Tau-Ser262 phosphorylation in rat hippocampal slices. Brain Res. 2017;1658: 51–59. doi: 10.1016/j.brainres.2017.01.014 [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref051] 51.Savica R, Murray ME, Persson XM, Kantarci K, Parisi JE, Dickson DW, et al. Plasma sphingolipid changes with autopsy-confirmed Lewy Body or Alzheimer’s pathology. Alzheimers Dement (Amst). 2016;3: 43–50. doi: 10.1016/j.dadm.2016.02.005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref052] 52.Mielke MM, Maetzler W, Haughey NJ, Bandaru VV, Savica R, Deuschle C, et al. Plasma ceramide and glucosylceramide metabolism is altered in sporadic Parkinson’s disease and associated with cognitive impairment: a pilot study. PLoS One. 2013;8: e73094. doi: 10.1371/journal.pone.0073094 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref053] 53.Sato C, Morgan A, Lang AE, Salehi-Rad S, Kawarai T, Meng Y, et al. Analysis of the glucocerebrosidase gene in Parkinson’s disease. Mov Disord. 2005;20: 367–370. doi: 10.1002/mds.20319 [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref054] 54.Nalls MA, Duran R, Lopez G, Kurzawa-Akanbi M, McKeith IG, Chinnery PF, et al. A multicenter study of glucocerebrosidase mutations in dementia with Lewy bodies. JAMA Neurol. 2013;70: 727–735. doi: 10.1001/jamaneurol.2013.1925 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref055] 55.Stirnemann J, Belmatoug N, Camou F, Serratrice C, Froissart R, Caillaud C, et al. A review of gaucher disease pathophysiology, clinical presentation and treatments. Int J Mol Sci. 2017;18: 441. doi: 10.3390/ijms18020441 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref056] 56.Guedes LC, Chan RB, Gomes MA, Conceicao VA, Machado RB, Soares T, et al. Serum lipid alterations in GBA-associated Parkinson’s disease. Parkinsonism Relat Disord. 2017;44: 58–65. doi: 10.1016/j.parkreldis.2017.08.026 [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref057] 57.Mazzulli JR, Xu YH, Sun Y, Knight AL, McLean PJ, Caldwell GA, et al. Gaucher disease glucocerebrosidase and alpha-synuclein form a bidirectional pathogenic loop in synucleinopathies. Cell. 2011;146: 37–52. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref058] 58.Cullen V, Sardi SP, Ng J, Xu YH, Sun Y, Tomlinson JJ, et al. Acid beta-glucosidase mutants linked to Gaucher disease, Parkinson disease, and Lewy body dementia alter alpha-synuclein processing. Ann Neurol. 2011;69: 940–953. [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref059] 59.Yap TL, Gruschus JM, Velayati A, Westbroek W, Goldin E, Moaven N, et al. Alpha-synuclein interacts with Glucocerebrosidase providing a molecular link between Parkinson and Gaucher diseases. J Biol Chem. 2011;286: 28080–28088. doi: 10.1074/jbc.M111.237859 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref060] 60.Magalhaes J, Gegg ME, Migdalska-Richards A, Doherty MK, Whitfield PD, Schapira AH. Autophagic lysosome reformation dysfunction in glucocerebrosidase deficient cells: relevance to Parkinson disease. Hum Mol Genet. 2016;25: 3432–3445. doi: 10.1093/hmg/ddw185 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref061] 61.Murphy KE, Gysbers AM, Abbott SK, Tayebi N, Kim WS, Sidransky E, et al. Reduced glucocerebrosidase is associated with increased alpha-synuclein in sporadic Parkinson’s disease. Brain. 2014;137: 834–848. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref062] 62.Grimm MO, Hundsdorfer B, Grosgen S, Mett J, Zimmer VC, Stahlmann CP, et al. PS dependent APP cleavage regulates glucosylceramide synthase and is affected in Alzheimer’s disease. Cell Physiol Biochem. 2014;34: 92–110. doi: 10.1159/000362987 [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref063] 63.Chan RB, Oliveira TG, Cortes EP, Honig LS, Duff KE, Small SA, et al. Comparative lipidomic analysis of mouse and human brain with Alzheimer disease. J Biol Chem. 2012;287: 2678–2688. doi: 10.1074/jbc.M111.274142 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref064] 64.Chatterjee S, Balram A, Li W. Convergence: lactosylceramide-centric signaling pathways induce inflammation, oxidative stress, and other phenotypic outcomes. Int J Mol Sci. 2021;22: 1816. doi: 10.3390/ijms22041816 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref065] 65.Yu W, Ying J, Wang X, Liu X, Zhao T, Yoon S, et al. The Involvement of Lactosylceramide in Central Nervous System Inflammation Related to Neurodegenerative Disease. Front Aging Neurosci. 2021;13: 691230. doi: 10.3389/fnagi.2021.691230 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref066] 66.Merrill AH. Sphingolipid and glycosphingolipid metabolic pathways in the era of sphingolipidomics. Chem Rev. 2011;111: 6387–6422. doi: 10.1021/cr2002917 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref067] 67.Yamaji T, Hanada K. Sphingolipid metabolism and interorganellar transport: localization of sphingolipid enzymes and lipid transfer proteins. Traffic. 2015;16: 101–122. doi: 10.1111/tra.12239 [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref068] 68.Chan RB, Perotte AJ, Zhou B, Liong C, Shorr EJ, Marder KS, et al. Elevated GM3 plasma concentration in idiopathic Parkinson’s disease: A lipidomic analysis. PLoS One. 2017;12: e0172348. doi: 10.1371/journal.pone.0172348 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref069] 69.Sohn H, Kim YS, Kim HT, Kim CH, Cho EW, Kang HY, et al. Ganglioside GM3 is involved in neuronal cell death. FASEB J. 2006;20: 1248–1250. doi: 10.1096/fj.05-4911fje [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref070] 70.De Maria R, Lenti L, Malisan F, d’Agostino F, Tomassini B, Zeuner A, et al. Requirement for GD3 ganglioside in CD95- and ceramide-induced apoptosis. Science. 1997;277: 1652–1655. doi: 10.1126/science.277.5332.1652 [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref071] 71.Schengrund CL. Lipid rafts: keys to neurodegeneration. Brain Res Bull. 2010;82: 7–17. doi: 10.1016/j.brainresbull.2010.02.013 [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref072] 72.Wu G, Lu ZH, Kulkarni N, Amin R, Ledeen RW. Mice lacking major brain gangliosides develop parkinsonism. Neurochem Res. 2011;36: 1706–1714. doi: 10.1007/s11064-011-0437-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref073] 73.Chalfant CE, Spiegel S. Sphingosine 1-phosphate and ceramide 1-phosphate: expanding roles in cell signaling. J Cell Sci. 2005;118: 4605–4612. doi: 10.1242/jcs.02637 [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref074] 74.Arana L, Gangoiti P, Ouro A, Trueba M, Gomez-Munoz A. Ceramide and ceramide 1-phosphate in health and disease. Lipids Health Dis. 2010;9: 15. doi: 10.1186/1476-511X-9-15 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref075] 75.Leng F, Edison P. Neuroinflammation and microglial activation in Alzheimer disease: where do we go from here? Nat Rev Neurol. 2021;17: 157–172. doi: 10.1038/s41582-020-00435-y [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref076] 76.Gelders G, Baekelandt V, Van der Perren A. Linking Neuroinflammation and Neurodegeneration in Parkinson’s Disease. J Immunol Res. 2018;2018: 4784268. doi: 10.1155/2018/4784268 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref077] 77.Surendranathan A, Rowe JB, O’Brien JT. Neuroinflammation in Lewy body dementia. Parkinsonism Relat Disord. 2015;21: 1398–1406. doi: 10.1016/j.parkreldis.2015.10.009 [DOI] [PubMed] [Google Scholar]

[pone.0279315.ref078] 78.Wu YR, Chen CM, Chao CY, Ro LS, Lyu RK, Chang KH, et al. Glucocerebrosidase gene mutation is a risk factor for early onset of Parkinson disease among Taiwanese. J Neurol Neurosurg Psychiatry. 2007;78: 977–979. doi: 10.1136/jnnp.2006.105940 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0279315.ref079] 79.Huh YE, Park H, Chiang MSR, Tuncali I, Liu G, Locascio JJ, et al. Glucosylceramide in cerebrospinal fluid of patients with GBA-associated and idiopathic Parkinson’s disease enrolled in PPMI. NPJ Parkinsons Dis. 2021;7: 102. doi: 10.1038/s41531-021-00241-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Plasma sphingolipid abnormalities in neurodegenerative diseases

Hideki Oizumi

Yoko Sugimura

Tomoko Totsune

Iori Kawasaki

Saki Ohshiro

Toru Baba

Teiko Kimpara

Hiroaki Sakuma

Takafumi Hasegawa

Ichiro Kawahata

Kohji Fukunaga

Atsushi Takeda

Roles

Abstract

Background

Methods

Results

Conclusion

Introduction

Materials and methods

Clinical information of the participants in this study

Table 1. Demographics and clinical characteristics of the analyzed plasma samples in Cohorts A, B and C.

Sample collection

Metabolite extraction

Metabolomic analysis

Simoa™ assay

Statistical evaluation

Results

Plasma sphingosine-1-phosphate (S1P) levels in neurodegenerative diseases

Fig 1. Plasma S1P levels in neurodegenerative diseases.

Plasma monohexylceramide (MonCer) levels in neurodegenerative diseases

Fig 2. Plasma MonCer levels in neurodegenerative diseases.

Table 2. Plasma all MonCer levels in neurodegenerative diseases.

Plasma lactosylceramide (LacCer) levels in neurodegenerative diseases

Fig 3. Plasma LacCer levels in neurodegenerative diseases.

Table 3. Plasma All LacCer levels in neurodegenerative diseases.

Correlation between total plasma MonCer levels and total plasma LacCer levels

Fig 4. Correlation between total plasma MonCer levels and total plasma LacCer levels.

Correlation between plasma p-tau levels and plasma S1P levels, total plasma MonCer levels or total plasma LacCer levels

Plasma other lipid metabolite levels in neurodegenerative diseases

Discussion

Plasma sphingolipid abnormalities in neurodegenerative diseases

Fig 5. Ceramide, sphingosine and glycosphingolipid metabolism.

Limitations of this study

Summary of the results

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Stephan N Witt

Roles

Author response to Decision Letter 0

Decision Letter 1

Stephan N Witt

Roles

Author response to Decision Letter 1

Decision Letter 2

Stephan N Witt

Roles

Author response to Decision Letter 2

Decision Letter 3

Stephan N Witt

Roles

Acceptance letter

Stephan N Witt

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases

Simoa^™ assay