June 2020

Medical Mass Spectrometry Vol. 4 No. 1

Research Paper

Global metabolomics analysis of serum from patients with
Niemann‒Pick disease type C
1,†

Anna Iwahori

1,2,3,†,, Masamitsu Maekawa
1,2,3

Yoshihisa Tomioka*, Yoshitomi Kanemitsu2,††, Yotaro Matsumoto1,2,3,
4

5

6, Aya Narita , Torayuki Okuyama , Yoshikatsu Eto ,
2,7

1,2,3

Daisuke Saigusa , Nariyasu Mano

Faculty of Pharmaceutical Sciences, Tohoku University, 1‒1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980‒8574, Japan
Department of Pharmaceutical Sciences, Tohoku University Hospital, 1‒1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980‒8574, Japan
3
Graduate School of Pharmaceutical Sciences, Tohoku University, 1‒1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980‒8574, Japan
4
Division of Child Neurology, Tottori University Hospital, 86 Nishi-machi, Yonago, Tottori 683‒8503, Japan
5
Department of Clinical Laboratory Medicine, National Center for Child Health and Development,2‒10‒1 Okura, Setagaya-ku, Tokyo 157‒8535, Japan
6
Advanced Clinical Research Center, Institute for Neurological Disorders,
255 Furusawa-Miyako, Asou-ku, Kawasaki, Kanagawa 215‒0026, Japan
7
Department of Integrative Genomics, Tohoku Medical Megabank Organization, Tohoku University,
2‒1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980‒8575, Japan
1

2

Abstract Niemann‒Pick disease type C (NPC) is an autosomal recessive disorder with a wide clinical spectrum. NPC iscaused by a lack of cholesterol transport proteins. In recent years, various lipid-derived biomarkers for NPC have been identiﬁed, suggesting extensive abnormal metabolism of various lipids. Global metabolomics is a technique that enables qualitative and semi-quantitative analysis based on accurate mass spectrometry combined with liquid chromatography. It is also
useful for identifying biomarkers. In this study, a global metabolomics approach was applied to serum from patients with
NPC to clarify lipid metabolism abnormalities. Serum samples were analyzed by liquid chromatographic separation withgradient elution and high-resolution mass spectrometry. After post-processing, all datasets were subjected to multivariate
analysis. Principal component analysis showed overlapping of sample groups between healthy subjects and NPC patients.
Orthogonal partial least square-discriminant analysis detected characteristic peaks corresponding to metabolites such as
N-palmitoyl-O-phosphocholine-serine and sphingosylphosphorylcholine, which have previously been reported as biomarkers
of NPC. Novel changes in levels of metabolites such as lysophosphatidylinositol were also observed in NPC, and peaks indicating the existence of a novel metabolic pathway, involving metabolites such as N-acylserine, were also detected. These
results indicate that global metabolomics is useful for comprehensive analysis of metabolic changes in NPC pathology.
†

These authors contributed equally to this work.
Current address: Clinical and Translational Research Center, Niigata University Medical and Dental Hospital, 1‒754
Asahi-machi dori, Chuo-ku, Niigata, Niigata 951‒8122,
Japan* Corresponding author
Masamitsu Maekawa
Department of Pharmaceutical Sciences, Tohoku University
Hospital, 1‒1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980‒
8574, Japan
Tel: ＋81‒22‒717‒7541, Fax: ＋81‒22‒717‒7545
E-mail: m-maekawa@tohoku.ac.jp
Received: January 16, 2020. Accepted: April 14, 2020.
Epub May 27, 2020.
DOI: 10.24508/mms.2020.06.006††

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Medical Mass Spectrometry Vol. 4 No. 1

Key words: Niemann‒Pick disease type C, Global metabolomics, mass spectrometry, LC/MS/MS, lipids
Abbreviations: LC/MS, liquid chromatography/mass spectrometry; NPC, Niemann‒Pick disease type C; PPCS, N-palmitoyl-O-phosphocholine-serine; SPC, sphingosylphosphorylcholine; SQC, study quality control

Introduction

N - palmitoyl - O - phosphocholine - serine (PPCS)

12,13)

havebeen reported as biomarkers for NPC so far. In general, dis-Niemann‒Pick type C (NPC) is an autosomal recessive
disorder with an incidence of approximately 1/100,000‒

ease biomarkers in the serum/plasma are generated by

120,000 . A wide clinical spectrum is one of the hall3)
marks of this disease , with visceral, central nervous sys-abnormal intracellular metabolism

tem, and psychiatric symptoms observed in clinicallack of these cholesterol transport proteins causes cellular. In addition, the scarcity of specialists for this. Oxysterols and cholenoic
acid conjugates are thought to be produced from accumu-

1,2)

1‒3)

practice

14,15)

the causative genes are NPC1

16). In the case of NPC,
17)

and NPC2 . A functional

1,18)

accumulation of cholesterol

disease presents a problem. Accordingly, the diagnosis of

19,20).
Although sphingomyelin has been reported to bind choles-

this disease is diﬃcult. Conventional laboratory tests for
4,5)
6)
NPC use ﬁlipin staining and DNA sequencing . There-

lated cholesterol by oxidative stress and metabolism

fore, in recent years, many biomarkers for NPC have beenterol , its metabolic relationship with cholesterol ischosen because they facilitate quick and simple analysis.
7)8‒10), lysophospholipOxysterols , cholenic acid conjugates

unclear.

21)

11)

ids such as sphingosylphosphorylcholine (SPC) , andFig. 1.

The
12,13)

recently

structure

of

PPCS

was

determined, but its biosynthetic pathway has not yet been

elucidated. These reports suggest that many metabolic

Principal component analysis in negative ion mode.
Circle, control; cross, NPC.

77

June 2020

Medical Mass Spectrometry Vol. 4 No. 1

abnormalities, not only those involving cholesterol, occur

diaminetetraacetic acid was purchased from Dojindo

in NPC pathology. To clarify this matter, we analyzed(Kumamoto, Japan). Ultrapure water was obtained fromserum samples, which are considered to be representative

PURELAB Ultra Genetic (Organo, Tokyo).

22)

is a
comprehensive analytical method for molecular identiﬁcaof intracellular metabolism. Global metabolomics

Serum samples

tion and semi-quantitative analysis based on liquid chromatography (LC)/high-resolution mass spectrometry (MS),Serum samples were obtained from patients with NPC(N＝45) and healthy subjects (N＝37) who provided
informed consent. All experiments were performed inwhich is eﬀective for analyzing metabolic abnormalities
23,24). In thisaccordance with the Declaration of Helsinki principles andstudy, we applied a global metabolomics method to analyze
the serum of NPC patients and aimed to elucidate any com-followed a protocol approved by the Ethics Committee ofand searching biomarkers in various diseases

prehensive metabolic alterations.

the Graduate School of Medicine in Tohoku University(approval number: 2013-1-293).

Materials and Methods

Sample preparation

Chemicals
Ethanol was purchased from Maruki Co. Ltd. (Sendai,

Twenty microliters of a mixture of ethanol and water(3 : 1, v/v), 20μL of an internal standard (IS) mixture (con-Japan). Acetonitrile was purchased from Kanto Kagaku

2taining 1μM PPCS- H3 and lysosphingomyelin (d17:1)),

and 50μL acetonitrile were added to 10 μL of serum and(Tokyo, Japan). Ammonium acetate was purchased from
FUJIFILM Wako pure chemicals (Osaka, Japan). Ethylene-

Fig. 2.

mixed adequately. After centrifugation at 14,000×g at 4°
C

Orthogonal partial least square-discriminant analysis in negative ion mode.
Circle, control; cross, NPC.

78

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Medical Mass Spectrometry Vol. 4 No. 1

for 10 min, 3μL of supernatant was injected into the LC/MS

Analytical order in batch analysis
For the initial batch analysis, the SQC was analyzed 10

apparatus for analysis.

times. Eight of the prepared serum samples were analyzed.

Sample preparation for quality control study

The analytical order of serum samples was randomized,

Serum (10μL) was mixed and used as a study quality

regardless of whether the samples were from NPC patients

control (SQC). The SQC was combined with a mixture of
formic acid and water (0.1 : 100, v/v) to produce two-foldor healthy controls. After every eight sample analyses, the(×2 SQC), four-fold (×4 SQC), eight-fold (×8 SQC), and16-fold (×16 SQC) dilutions, respectively.

SQC samples were sequentially analyzed from ×16 SQC to

Fig. 3.

SQC was analyzed. At the end of the batch analysis, diluted
SQC.

S-plots in negative ion mode.
Red colored plots show the peaks increasing in NPC patients with p(corr)＞0.75.
The peaks corresponding to No. 1 and 2 in Table 1 were marked.
Blue colored plots show the peaks decreasing in NPC patients with p(corr)＜−0.60.
The peaks corresponding to No. 1‒10 in Table 2 were marked.

Table 1.

The peak list with p(corr)＞0.75 on negative ion mode

No.

Retention time
(min)

m/z

Formula1

16.52

507.3208

C24H49N2O7P

0.6702

N-Palmitoyl-O-phosphocholine-serine

2

16.53

565.2793

C26H49O12P

1.6697

Lysophosphatidylinositol (17 : 1)

79

MS diﬀerence(ppm)

CandidateJune 2020

Medical Mass Spectrometry Vol. 4 No. 1

LC/MS analysis

attributed to the same molecules were deconvoluted and

LC/MS analysis was performed as described in a previ-

identiﬁed based on the human metabolome database and22)

ous report with minor revisions . The Orbitrap Fusion(Thermo Fisher Scientiﬁc, Waltham, MA, USA) and Van-lipidmaps. Peaks for which the coeﬃcient of variance ofquish systems were used for high-resolution tandem MS
and ultra-high-performance LC. Positive and negative sprayfrom the data processing, as were peaks whose intensity did

the peak intensity was greater than 30% were excluded
not change with the dilution factor.

voltages were set to 3,500 V and −2,000 V, respectively.

Statistical analysis

Sheath gas (arb), auxiliary gas (arb), sweep gas (arb), ion

The peak intensities were normalized by in-house quant-transfer tube temperature (°C), vaporizer temperature (°C),
m/z range, resolution, and higher collision-induced dissocia-

22)

tion energy (%) were set to 50, 10, 0, 275, 350, 200‒1,500,

bolome software using the SQC and IS peak intensities .
Multivariate analysis was performed for ﬁtting, using prin-

120,000, and 50%, respectively. An InertSustain Bio C18
column (2μm, 150 mm×2.1 mm inner diameter, GL sci-

cipal component analysis (PCA) and orthogonal partial
least square-discriminant analysis (OPLS-DA) with SIMCA

ences, Tokyo) was used as the analytical column. Mobile

version 13 (Umetrics, Umea, Sweden). p-values were calcu-

phases A and B comprised 5 mM ammonium acetate in

lated using the Wilcoxon rank sum test, and analysis ofmethanol/acetonitrile/water (1 : 1 : 3, v/v/v), and 5 mMvariance values were calculated using Progenesis QI.

ammonium acetate and 10 nM ethylenediaminetetraacetic
acid in acetone/isopropanol (1 : 1, v/v), respectively. The

Results and Discussion

program timings were set as follows: B (%) 0 to 100 over

PCA analysis of the negative ion mode

40 min. The ﬂow rate and column oven temperature wereFirst, PCA analysis was performed after collection of
LC/MS measurement data. Before the analyses, it was con-

set to 0.3 mL/min and 40°
C, respectively.

ﬁrmed that there were no diﬀerences between the age andData processing
Xcalibur software (Thermo Fisher Scientiﬁc) was used togender of healthy subjects and patients with NPC (data notshown). PCA analysis is useful for characterizing all sam-perform LC/MS analysis and data collection. The collected

ples. If the plots forms the cluster, it means that the samples

data were loaded into Progenesis QI (Nonlinear Dynamics,

included in the same cluster have similar characteristics. As

Newcastle, UK). A dataset was provided for peak picking,shown in Fig. 1, many overlaps were observed between thealignment, and normalization of retention time and m/z.

healthy control group (circle) and the NPC patient group

Protonated molecules, sodium adduct, potassium adduct,(cross), suggesting that there were many common serum

and ammonium adduct ions were selected on positive ion

metabolites among the healthy controls and patients withmode. Deprotonated molecules and acetic acid adduct ions

NPC.

were selected in the negative ion mode. All adduct ions

Table 2.
No.

Retention
time (min)m/z

The peak list with p(corr)＜−0.60 on negative ion mode

FormulaMS diﬀerence(ppm)

Candidates

1

4.75

369.1742

C19H30O5S

0.0927

Androsterone sulfate

2

6.52

369.1741

C19H30O5S

0.0927

Androsterone sulfate

3

7.47

369.1741

C19H30O5S

0.0927

Androsterone sulfate

4

9.08

369.1741

C19H30O5S

0.09275

19.04

465.3588

C28H50O5

0.5699Androsterone sulfate
6 α -Hydroxycastasterone

6

19.14

485.3851

C31H54NO4

7

20.26

489.3578

C30H52O6

8

20.63

467.3146

C30H46O5

9

21.25

491.3747

C30H52O5

10

23.49

615.4614

C38H64O6−4.6694−1.54−4.2467
1.1204−2.6018

Arachidonyl carnitine
6 β -Acetoxy-24-methylcholestan-3 β ,5 α ,22R,24-tetrol(3 β ,15 α ,22S,24E)-3,15,22-Trihydroxylanosta-7,9(11),24-trien-26-oic acid
25-Acetoxy-ergosta-3 β ,5 α ,6 β -triol
Triglyceride (35 : 4)80

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Medical Mass Spectrometry Vol. 4 No. 1

Fig. 4.

Principal component analysis in positive ion mode.
Circle, control; cross, NPC.

OPLS-DA analysis of the negative ion modeat upper right region in Fig. 3). Brieﬂy, these peaks

By contrast, when OPLS-DA analysis was performed, thetwo groups could be distinguished clearly (Fig. 2). In addi-

increased in NPC patients. The Peaks for which no suitabletion, an s-plot

represents a novel lipid that was recently identiﬁed as

25)

candidates were found were not included in Tables. Peak 1

was used to identify peaks that diﬀered

12,13)and had the largest p(corr) value. This peak was
identiﬁed as lysosphingomyelin-509 by Giese et al. in

between the two groups (Fig. 3). The peaks located in the

PPCSupper right of the plot indicate the metabolites that were26)

lower left correspond to metabolites whose levels were2015 . The biosynthetic pathway of this metabolite has not
27,28)
andyet been elucidated. N-acylphosphatidylserine

higher in healthy subjects. The value on the vertical axis

N-acylserine(p(corr)) indicates the reliability of the peak, similar to at-test result. The peaks with smaller p(corr) values tended tometabolites did not increase in the negative ion mode (Fig. 2

increased in NPC patients, whereas the peaks located in the

28)

were speculated to be precursors, but theirprovide signiﬁcant diﬀerence. The value (p) on the horizon-

and Table 1). Peak 2 had an accurate mass corresponding tothat of lysophosphatidylinositol (17 : 1). Phosphatidylinosi-

tal axis represents the intensity of a peak. Peaks with larger

tols are speculated to be biomarkers of various cancers .

p values appeared on the left and right of the plot. In thisHowever, this metabolite contained an odd number of fatty

study, we hypothesized that the metabolites that provided

acid chains; thus, the reliability of the peak identiﬁcation

signiﬁcant alteration regardless of changes in intensity could

was uncertain. The metabolites whose p(corr) values werelower than -0.6 are listed in Table 2. These peaks decreased28)

be useful for analyzing pathological changes in NPC. The
peaks with p(corr) values greater than 0.75 were identiﬁed

in NPC patients, and they were shown as blue spots locatedin the lower left of Fig. 3. Peaks 1‒4 had retention times ofusing the database and are listed in Table 1 (red plot located81June 2020

Medical Mass Spectrometry Vol. 4 No. 1

Fig. 5.

Orthogonal partial least square-discriminant analysis in positive ion mode.
Circle, control; cross, NPC.

4.75‒9.08 min and masses corresponding to androsterone
sulfate and sulfate-conjugated steroid hormones. The metab-

PCA analysis of the positive ion mode

olites of peaks 1‒4 had equal mass and so were speculated

ion mode, a relatively large number of samples were plot-

to be structural isomers. Steroidal compounds are present in

ted close to each other (Fig. 4).

In the positive ion mode, as was the case in the negative

NPC patients; oxysterols and cholenoic acid conjugates
7‒10,20)

have been reported

OPLS-DA analysis of positive ion mode

. It is possible that metabolism ofsteroid hormone sulfates might be aﬀected in NPC patients.
Peaks 5 and 7‒9 were speculated to be sterol metabolites;

Compared to the results for the negative ion mode, thetwo groups could be separated more distinctly in the posi-

however, their compound names were like sterol deriva-

tive ion mode (Fig. 5). The results of the s-plot are showntives. As mentioned above, abnormalities in oxysterols andin Fig. 6. The peaks with p(corr)＞0.60 and their identities

cholenoic acids have been reported in NPC. Peak 6 had aare listed in Table 3. These peaks increased in NPC patients

mass value corresponding to a metabolite classiﬁed as an

and are indicated by red plots which located at upper rightacylcarnitine. Acylcarnitines are aﬀected by mitochondrialin Fig. 6. Peak 1 was speculated to be lysophosphatidic acid
(30 : 7). The metabolite of peak 2 was also a ﬂavonoid com-functions. Peak 10 was speculated to be triglyceride (35 : 4),
but its fatty acid chain length was relatively short (carbon
number 35). Accordingly, it may be a diglyceride. No alter-

pound; it was not evident that this was an NPC-speciﬁc

ations in acylcarnitines and triglycerides have been reported

has been reported to be increased in NPC patientsin patients with NPC.

However, arachidonyl carnitine, corresponding to peak 4(Table 2), was increased in NPC patients, cervonyl carni-

metabolite. SPC (peak 3) is a sphingolipid metabolite that
11,12)

82.

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Medical Mass Spectrometry Vol. 4 No. 1

Fig. 6.

S-plots in positive ion mode.
Red colored plots show the peaks increasing in NPC patients with p(corr)＞0.60.
The peaks corresponding to No. 1‒11 in Table 3 were marked.
Blue colored plots show the peaks decreasing in NPC patients with p(corr)＜−0.55.
The peaks corresponding to No. 1‒6 in Table 4 were marked.

Table 3.

The peak list with p(corr)＞0.60 on positive ion mode

Retention
time
(min)

m/z

Formula1

8.14

593.3558

C33H53O7P−7.4436

Lysophosphatidic acid (30 : 7)

2

10.49

495.126

C26H24O11−5.0927

3,4,5-trihydroxy-6-(2-{5-hydroxy-8,8-dimethyl-2-oxo-2H,8H-pyrano[2,3-f]chromen-4-yl}phenoxy)oxane-2-carboxylic acid

−6.7193

Sphingosylphosphorylcholine−0.4976

Cervonyl carnitine(6R)-6,19-Epidioxy-1 α ,25-dihydroxy-6,19-dihydrovitamin D3

No.

3

12.47

426.3686

C23H49N2O5P

4

12.74

489.3684

C29H45NO4

5

12.77

431.3155

C27H44O5

MS diﬀerence(ppm)−0.2491

Candidates

6

14.37

597.3343

C28H55O12P−9.00187

16.30

509.3351

C24H49N2O7P

0.15718

16.37

553.2987

C29H44O10−3.61219

16.96

510.3193

C24H48NO8P

0.4581

Lysophosphatidylserine (P-18 : 0)

10

19.59

559.3485

C28H48NO7P−4.0641

Lysophosphatidylcholine (20 : 5)

11

19.60

537.3663

C26H50NO7P

0.0121

1217.30

326.2687

C19H37NO4

Lysophosphatidylcholine (18 : 2)
N-Palmitoyl serine−0.6527

Lysophosphatidylinositol (19 : 0)
N-Palmitoyl-O-phosphocholine-serine
Desglucocheirotoxol83June 2020

Medical Mass Spectrometry Vol. 4 No. 1

Table 4.

The peak list with p(corr)＜−0.55 on positive ion mode

No.

Retention time
(min)

m/z

1

21.96

586.5043

C34H64O6

0.3688Triglyceride (31 : 0)

2

23.86

640.5514

C38H70O6*

0.4859Triglyceride (35 : 1)

3

24.17

612.5199

C36H66O6*

0.2139Triglyceride (33 : 1)

4

24.72

614.5357

C36H68O6

0.3835Triglyceride (33 : 0)

5

24.96

642.5671

C38H72O6

0.5641

Triglyceride (35 : 0)

6

32.35

742.5753

C42H82NO8P

0.9857

Phosphatidylcholine (34 : 1)

FormulaMS diﬀerence(ppm)

Candidates

tine, which is classiﬁed as an acylcarnitine, decreased(Table 3). Fatty acid groups might also aﬀect to the diﬀer-

serum samples of NPC patients, in order to elucidate theence. Peak 5 was speculated to be a vitamin D metabolite.

NPC caused by mutations in cholesterol transport proteins.

No changes in vitamin D levels in NPC pathology have

Many peaks were detected using an accurate MS system.

been reported. Phosphatidylinositol (peak 6) was also

Criteria for peak reduction based on SQC and DQC andobserved in the negative ion mode (peak 2 in Table 1). Peakmultivariate analyses were used to focus on the signiﬁcant

7 was also detected in the positive ion mode and identiﬁed

peaks. As a result, many novel changes in lipid molecules
containing phosphatidylinositols and N-acylserine weremolecular mechanism of abnormal lipid metabolism in

as PPCS. Peak 8 corresponded to desglucocheirotoxol, withunknown origin. Ether-linked phosphatidylserine and N-palmitoylserine, which may be related to PPCS, were detected

observed. Although the metabolic pathway of PPCS has not
yet been elucidated, changes in N-acylserine and phosphati-(peaks 9 and 12), in contrast to the negative ion mode.

dylserine were observed. These metabolites are speculated

Peaks 10 and 11 were identiﬁed as lysophosphatidylcholine(20 : 5 and 18 : 2, respectively). In a previous report, levels

to be precursors of PPCS . These results were achieved by
the excellent molecular identiﬁcation and semi-quantitativeof lysophosphatidylcholine with saturated fatty acids wereanalysis capabilities provided by a global metabolomics

13)

13)

decreased in NPC ; however, the alteration of unsaturated

strategy. In the future, we intend to elucidate the detailed

fatty acids has been not reported previously.

molecular mechanisms of the central metabolic pathways,

There were 6 metabolites which decreased in NPC

as well as focus on biosynthesis using various cell lines.

patients (Table 4 and Fig. 6, blue plots located in lower left
region). Five of the decreased peaks (Table 4) were classi-

Acknowledgments

ﬁed as triglycerides (peaks 1‒5). However, they all had an

We are grateful to all donors who provided serum andneeded. Lysophosphatidylcholine increased (peaks 12 andplasma samples, and to the doctors who collected serum
samples. This work was supported in part by JSPS KAK-

13, Table 3), and phosphatidylcholine decreased. In thisENHI 18K15699, Japan AMED (JP19 km0105001 andstudy, lipid metabolites were detected mainly owing to theuse of reverse-phase LC/MS. Changes in cholesterol, chole-

JP19 km0105002) and by the Project for Promoting Public

odd number of carbon chains; thus, further investigation is

Utilization of Advanced Research Infrastructure (MEXT).

nic acid, sphingolipids, and N-acyl fatty acid metabolites
have been reported in NPC; however, changes in other lip-Conﬂicts of Interest
The authors declare no conﬂicts of interest.

ids, such as phosphatidylcholine, sterols, and vitamin D,
were also detected in this study. As many lipid molecules
exhibit biological activity, changes in these lipids may lead

References

to further systemic physiological changes in NPC. It will be1) Vanier MT: Niemann-Pick disease type C. Orphanet J

necessary to further analyze the relationships among lipid

Rare Dis 5: 16, 2010.

abnormalities, NPC pathology, and symptoms.

2) Geberhiwot T, Moro A, Dardis A, Ramaswami U, Sirrs S,
et al: Consensus clinical management guidelines for Nie-

Conclusion

mann-Pick disease type C. Orphanet J Rare Dis 13: 50,

In this study, we used global metabolomics to analyze

2018.
84

June 2020

Medical Mass Spectrometry Vol. 4 No. 1

3) Patterson MC, Hendriksz CJ, Walterfang M, Vanier MT,

newborns for inborn errors of metabolism by tandem

Wijburg F, et al: Recommendations for the diagnosis andmass spectrometry. New Engl J Med 348: 2304‒2312,

management of Niemann-Pick disease type C: An update.

2003.

Mol Genet Metab 106: 330‒344, 2012.

15) Mashima R, Maekawa M: Lipid biomarkers for the per-4) Pentchev PG, Boothe AD, Kruth HS, Weintroub H, Stiv-

oxisomal and lysosomal disorders: Their formation, me-ers J, et al: A genetic storage disorder in BALB/C micetabolism and measurement. Biomarkers Med 12: 83‒95,

with a metabolic block in esteriﬁcation of exogenous cho-

2018.

lesterol. J Biol Chem 250: 5784‒5791, 1984.

16) Carstea ED, Morris JA, Coleman KG, Loftus SK, Zhang

5) Vanier MT, Latour P: Laboratory diagnosis of Niemann‒D, et al: Niemann-Pick C1 disease gene: Homology to

Pick disease type C: The ﬁlipin staining test. Method Cell

mediators of cholesterol homeostasis. Science 277: 227‒

Biol 126: 357‒375, 2015.

231, 1997.

6) Mckay K, Gissen BP: Genetic and laboratory diagnostic17) Naureckiene S, Sleat DE, Lackland H, Fensom A, Vanierapproach in Niemann Pick disease type C. J Neurol 261:

MT, et al: Identiﬁcation of HE1 as the second gene ofS569‒S575, 2014.

Niemann‒Pick C disease. Science 290: 2298‒2301, 2000.

7) Porter FD, Scherrer DE, Lanier MH, Langmade J, Molu-

18) Kwon HJ, Abi-Mosleh L, Wang ML, Deisenhofer J, Gold-

gu V, et al: Cholesterol oxidation products are sensitivestein JL, et al: Structure of N-terminal domain of NPC1

and speciﬁc blood-based biomarkers for Niemann-Pickreveals distinct subdomains for binding and transfer of

C1 disease. Sci Transl Med 2: 56ra81, 2010.

cholesterol. Cell 137: 1213‒1223, 2009.

8) Maekawa M, Jinnoh I, Narita A, Iida T, Saigusa D, et al:19) Reddy JV, Ganley IG, Pfeﬀer SR: Clues to neuro-degener-

Investigation of diagnostic performance of ﬁve urinary

ation in Niemann‒Pick Type C disease from global genecholesterol metabolites for Niemann‒Pick disease type C.

expression proﬁling. PLoS One 1: e19, 2006.

J Lipid Res 60: 2074‒2081, 2019.

20) Mazzacuva F, Mills P, Mills K, Camuzeaux S, Gissen P, et

9) Maekawa M, Narita A, Jinnoh I, Iida T, Marquardt T, et

al: Identiﬁcation of novel bile acids as biomarkers for theal: Diagnostic performance evaluation of sulfate-conjugat-

early diagnosis of Niemann‒Pick C disease. FEBS Letted cholesterol metabolites as urinary biomarkers of Nie-

590: 1651‒1662, 2016.

mann‒Pick disease type C. Steroids 494: 58‒63, 2019.

21) Slotte JP: Biological functions of sphingomyelins. Prog

10) Maekawa M, Misawa Y, Sotoura A, Yamaguchi H, Toga-

Lipid Res 52: 424‒437, 2013.

wa M, et al: LC/ESI-MS/MS analysis of urinary 3β-sul-22) Saigusa D, Okamura Y, Motoike I, Katoh Y, Kurosawa Y,

fooxy-7β-N-acetylglucosaminyl-5-cholen-24-oic acid andet el: Establishment of protocols for global metabolomics

its amides: New biomarkers for the detection of Nie-

by LC-MS for biomarker discovery. PLoS One 11:mann‒Pick type C disease. Steroids 78: 967‒972, 2013.

e0160555, 2016.

11) Welford RW, Garzotti M, Lourenco CM, Mengel E, Mar-

23) Sato T, Kawasaki Y, Maekawa M, Takasaki S, Saigusa D,

quardt T, et al: Plasma lysosphingomyelin demonstrates

et al: Value of global metabolomics in association with di-great potential as a diagnostic biomarker for Niemann‒agnosis and clinicopathological factors of renal cell carci-

Pick disease type C in a retrospective study. PLoS One 9:

noma. Int J Cancer 145: 484‒493, 2019.
24) Kikuchi K, Saigusa D, Kanemitsu Y, Matsumoto Y, Tha-

e114669, 2014.

nai P, et al: Gut microbiome-derived phenyl sulfate con-12) Maekawa M, Jinnoh I, Matsumoto Y, Narita A, Mashima
R, et al: Structural determination of lysosphingomy-

tributes. Nat Commun 10: 1835, 2019.

elin-509 and discovery of novel class lipids from patients

25) Wiklund S, Johansson E, Sjölström L, Mellerowicz EJ,

with Niemann‒Pick disease type C. Int J Mol Sci 20:

Edlund U, et al: Visualization of GC/TOF-MS-based me-

2019, 5018.

tabolomics data for identiﬁcation of biochemically inter-13) Sidhu R, Mondjinou Y, Qian M, Song H, Kumar AB, etesting compounds using OPLS class models. Anal Chem

al: N-acyl-O-phosphocholineserines: Structures of a novel

80: 115‒122, 2008.

class of lipids that are biomarkers for Niemann‒Pick C126) Giese AK, Mascher H, Grittner U, Eichler S, Kramp G, etdisease. J Lipid Res 60: 1410‒1424, 2019.

al: A novel, highly sensitive and speciﬁc biomarker for
Niemann-Pick type C1. Orphan J Rare Dis 10: 78, 2015.

14) Wilcken B, Wiley V, Hammond J, Carpenter K: Screening
85June 2020

Medical Mass Spectrometry Vol. 4 No. 1

27) Guan Z, Li S, Smith DC, Shaw WA, Raetz CRH: Identiﬁ-

Neurotransmitter 1: e263 (2014).

cation of N-acylphosphatidylserine molecules in eukary-29) Makinoshima H, Umemura S, Suzuki A, Nakanishi H,

otic cells. Biochemistry 46: 14500‒14513, 2007.

Maruyama A, et al: Metabolic determinants of sensitivity28) Wood PL: Accumulation of N-acylphosphatidylserines

to phosphatidylinositol 3-kinase pathway inhibitor. Can-and N-acylserines in the frontal cortex in schizophrenia.

cer Res 78: 2179‒2191, 2018.

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