June 2020

Medical Mass Spectrometry Vol. 4 No. 1

Research Paper

Longitudinal plasma amino acid proﬁling with maternal
genomic background throughout human pregnancy
1,2,3†

Matsuyuki Shirota

1,2†, Daisuke Saigusa

1,2

1,2

1,2

1,2

, Riu Yamashita , Yasutake Kato , Mitsuyo Matsumoto ,

1,2

1,2

1,2

1,2

1,2

Junya Yamagishi , Noriko Ishida , Kazuki Kumada , Yuji Oe , Hisaaki Kudo , Junji Yokozawa ,
1,2

1,2,3

Yoko Kuroki , Ikuko Motoike
2

1,2

1,2

1,2

, Fumiki Katsuoka , Masao Nagasaki , Seizo Koshiba ,
1,2

1,2

1,2

1,2,3

Keiko Nakayama , Osamu Tanabe , Jun Yasuda , Shigeo Kure , Kengo Kinoshita
1,2,4

Hirohito Metoki
1

1,2

1,2

,

1,2

1,2
, Shinichi Kuriyama , Nobuo Yaegashi , Masayuki Yamamoto , Junichi Sugawara *Tohoku University Tohoku Medical Megabank Organization, 2‒1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980‒8573, Japan
2
Graduate School of Medicine, Tohoku University, 2‒1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980‒8575, Japan
3
Graduate School of Information Sciences, Tohoku University,
6‒3‒09 Aza-aoba, Aramaki, Aoba-ku, Sendai, Miyagi 980‒8579, Japan
4
Division of Public Health, Hygiene and Epidemiology, Tohoku Medical and Pharmaceutical University,4‒4‒1 Komatsushima, Aoba-ku, Sendai, Miyagi 980‒8558, Japan

Abstract Amino acid metabolism in pregnancy is an important regulatory mechanism for fetal growth and maturation.
We aimed to clarify pregnancy-induced changes in amino acid concentrations in maternal plasma by frequent samplingthroughout pregnancy and to test the eﬀects of genomic backgrounds on amino acid concentrations. We recruited 19 pregnant women and collected plasma samples monthly during pregnancy and one month after delivery. Amino acid concentrations during pregnancy were analyzed with linear models that considered both individual diﬀerences and gestational age.
Maternal genotyping was used to evaluate the eﬀects of single-nucleotide variants on the levels of speciﬁc amino acids.
Signiﬁcant increases in the amount of threonine, histidine, glutamic acid, aspartic acid and proline and decreases in tryptophan, arginine, valine, and leucine in plasma concentrations of pregnant women were observed as gestation proceeded.
Total amino acid concentrations during pregnancy were decreased relative to post-partum concentrations, whereas theratio of essential to non-essential amino acids was increased across the sampling period. The eﬀects of alternative alleles
for three missense SNVs on the concentrations of glycine, asparagine and proline were conﬁrmed in pregnant women andwere consistent with a previous report for a non-pregnant population. Principal component analysis of the relative aminoacid proﬁles indicated that principal component (PC) 1 reﬂected advancing gestation, whereas PC2 was dominated by thediﬀerence between pregnant and non-pregnant states. The change in relative compositions of circulating amino acids was a
distinctive marker of pregnancy and genomic background dominantly aﬀected maternal metabolic changes in gestation.
Key words: pregnancy, amino acid, mass spectrometry, longitudinal analysis, single nucleotide variant†

M. Shirota and D. Saigusa contributed equally to this work.
* Corresponding author

Introduction

Pregnancy-related maternal adaptations are widely recognized as critical processes for inheritance across genera-

Junichi Sugawara
Tohoku Medical Megabank Organization, Tohoku University,
2‒1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980‒8573, Japan
Tel: ＋81‒22‒273‒6283, Fax: ＋81‒22‒273‒6410
E-mail: jsugawara@med.tohoku.ac.jp
Received November 24, 2019. Accepted February 20, 2020.
Epub April 16, 2020.
DOI: 10.24508/mms.2020.06.001

tions . Failure of these regulatory systems can result in var1)

ious maternal and fetal complications, such as hypertensive
2)

3)

disorders of pregnancy , premature labor , and fetal
4)

growth restriction .
Amino acid metabolism in pregnancy is an important36June 2020

Medical Mass Spectrometry Vol. 4 No. 1

5)cohort by integrating whole genome sequencing and NMR
plasma metabolomics to identify ﬁve missense single nucle-

regulatory mechanism for fetal growth and maturation .
During pregnancy, amino acids in maternal circulation are
mainly regulated by food intake and the feto-placental unit.

otide variants (SNVs) that aﬀect phenylalanine, glycine,

Fetal demands for rapid cellular proliferation and diﬀerenti-

asparagine, proline, and formate levels . Although many

ation require marked energy consumption from maternal
circulation through the placenta. Placental transport sys-

variations in metabolite amounts can be explained by thesetems play important roles in supplying essential factors,

plasma amino acid concentrations during pregnancy

19)

nonsynonymous variants, how these mutations aﬀect

6)

including amino acids . Accumulating evidence suggests

remains unclear.

that more than 20 amino acid transporters are expressed in

In the present study, we undertook frequent plasma. These active transport systems areamino acid proﬁling of pregnant women to elucidate thecontrolled by a variety of transporter proteins encoded by

eﬀect of gestational age and individual diﬀerences on

7‒9)

the human placenta

10,11). Amino acid concentrations inamino acid concentrations. We performed a longitudinalmaternal plasma decrease during pregnancy compared with
non-pregnant conditions and this decrease begins early inanalysis of plasma amino acid proﬁles for 19 pregnant

speciﬁc regulatory genes

pregnancy and persists until delivery , although changes

women by sampling plasma monthly during pregnancy andone month after delivery. The variations in amino acid con-in amino acid composition as pregnancy proceeds are notcentrations during pregnancy were divided into contribu-

well characterized in part because changes in amino acidtions from gestational age and individual diﬀerences usinglevels during pregnancy are relatively small compared tolinear models. Our analysis identiﬁed those amino acids forvariations in amino acid levels among individuals.
High-throughput technologies, such as liquid chromatog-

which levels signiﬁcantly changed with gestational age andraphy followed by mass-spectrometry (LC-MS) and nucleic

and individual diﬀerences among the study participants. We

magnetic resonance (NMR), are now used extensively toinvestigate metabolic eﬀects of pregnancy on plasma con-also demonstrated that overall amino acid concentrations
were decreased in pregnant women relative to non-pregnant

centrations of amino acids in studies having a cross-sec13,14)
15‒17)
or longitudinal design. Cross-sectional studtional

states, whereas the amounts of several amino acids were

ies often cannot detect small changes in amino acidconcentrations because pregnancy-related eﬀects can be

genotyping to investigate the eﬀect of missense SNVs that

confounded by variations between individuals. Meanwhile,results bring new insights into individual regulation of

longitudinal studies involving large cohorts can identify

amino acid proﬁles in normal pregnancy.

changes in amino acids levels that are linked to gestationalage, although the number of samples obtained per partici-

Material and Methods

pant is typically three, or once per trimester, and this low

Subjects

12)

also evaluated the relative contributions of gestational age

retained during pregnancy. Furthermore, we used maternal
can inﬂuence speciﬁc amino acid concentrations. These

Twenty-two pregnant women were recruited fromTohoku University Hospital, Japan, after providing satisfac-

number of sampling points can limit the evaluation of how
gestational age, rather than diﬀerences among individuals,
contributes to variations in amino acid levels during preg-

tory written informed consent. At study entry, all women
had singleton pregnancies, took no medications, and had nonancy.
Amino acid levels can be perturbed by critical mutations

history of cardiovascular or endocrine disease or tobacco

in genes encoding key enzymes involved in amino acid syn-use. Each participant received monthly routine obstetricthesis or uptake that are associated with genetic diseases

care that included a maternal weight check, analysis of18)

that signiﬁcantly aﬀect overall healthor alter plasma

urine and plasma, measurement of blood glucose levels,
self-reported record of time since the last meal and mea-amino acid concentrations in healthy individuals. Recent
integrations of genome analysis and metabolome measure-surement of fetal development by ultrasound echography.

ments in large-scale cohorts allowed the identiﬁcation ofThree women were excluded due to hypertensive disorders

gene variants that are associated with speciﬁc amino acidof pregnancy, premature labor or gestational diabetes. A
total of 19 women were further analyzed in this study. Clin-

19,20). Notably, Koshiba et al. performed aconcentrations
genome-wide association study (GWAS) of a Japanese

ical information was collected from obstetric and neonatal
37

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medical records. Ethical approval was obtained from thegestational age in days, xday is the gestational age on the day

Ethical Committee of the Tohoku University Graduate
School of Medicine (2012-1-443 and 2014-10).

the sample was collected, and ε is the residual error of the

Sample collection and measurement of plasma metabolites

tested) were considered to be signiﬁcant increases andmodel. Positive and negative β day values for amino acids
with a P-value＜0.05/23 (i.e., the number of amino acids
decreases with gestational age.

Plasma samples were collected from each participant at

Contribution of individual diﬀerences and gestational
age to amino acid concentrationeach hospital visit during pregnancy. Plasma samples werealso collected one month after delivery (hereafter referred
blood glucose and time since the last meal, we excluded

To calculate the contribution of each of the two factors in
Eq. 1 to the concentration of each amino acid, two addi-

those plasma samples that were collected at times when thetional linear models using individual diﬀerences (ID) alone

blood glucose levels exceeded 120 mg/dL or those foror gestational day (day) alone were used. The contributionof ID and day was calculated as the diﬀerence in the vari-to as ADM), if possible. To eliminate the eﬀect of elevatedwhich the time since the last meal was less than 3 hours.
Samples were collected between April 2013 and April
2014. Plasma samples were sent to a qualiﬁed clinical labo-

ance explained (R-squared) by the linear model in Eq. (1)ratory and amino acid concentrations were measured using
LC-MS. The protocol of amino acids analysis was followed

day or ID, respectively.

and that by the linear model using the other single variable,

21)

by the previous report . After the extraction of analytes
from plasma by means of the deproteinization of acetoni-Comparison of amino acid concentrations during andafter pregnancy

trile precipitation, the analytes were derivatized by theregent of 3-aminopyridyl-N-hydroxysuccinimidyl carba-

participants and compared amino acid concentrations

We obtained plasma samples at ADM from seven study®

mate (APDSTAG Wako Eluent, FUJIFILM Wako Pureduring pregnancy relative to those at ADM, which served
as a reference for non-pregnant conditions. We averaged the22)

Chemical Corporation, Osaka, Japan) . The LCMS was
performed by LC-MS2020 (Shimadzu, Kyoto, Japan), andplasma concentrations of a given amino acid from a single

the APDS-derivatized analytes were detected by multiple
reaction monitoring mode with the separation using a col-participant over the course of pregnancy and divided theumn (Inertsil ODS-3, 2 μm, 100 mm×2.1 mm, I.D., GL Sci-

from the same individual at ADM. Relative concentrations

ence, Tokyo, Japan). The concentrations of 20 standard

above and below unity indicated an increase and decrease,amino acids and three additional amino acids that are

respectively, of amino acids in pregnancy compared tonearly always present in plasma (taurine, ornithine, andADM.

averaged values by the concentration of the amino acidcitrulline) were analyzed in this study.

Genotyping participants for genome-wide signiﬁcant
SNVs

Linear model to evaluate the eﬀect of gestational age
and individual diﬀerences

The genotypes of missense SNVs that are known to be

We used a linear model to evaluate the contribution ofgestational age and individual diﬀerences to the plasma

associated with speciﬁc amino acid concentrations wereobtained by whole exome sequencing of participant sam-concentration of each amino acid:

ples. Brieﬂy, blood samples for exome sequencing were

y＝βID xID＋βday xday＋εcollected at the ﬁrst health checkup for each participant.

(1)

Genomic DNA was isolated using SureSelect Human All

where y is the amino acid concentration in the plasma sample of a participant, βID is a 19-dimensional vector, wherein

Exon V4 Kits (Agilent Technologies, Santa Clara, CA).

each dimension represents the weight for each of the 19
participants and xID is another 19-dimensional vector, in(Illumina, San Diego, CA) with 101 bp paired end reads.
Variant identiﬁcation was performed as described previ-

which only one element corresponding to the participant isously .

Sequencing was performed on a HiSeq2500 instrument

23)

unity and the other elements are zero, βday is the weight for38

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Medical Mass Spectrometry Vol. 4 No. 1

Genetic factors and amino acid concentrations

Table 1.

Although the participants have number of SNVs bymeans of the exome sequencing, our sample size was too
small to statistically support novel associations between
SNVs and amino acid concentrations. Therefore, we
selected the previously identiﬁed SNVs. We examined theSummary of characteristics of the 19 participants
Basic data

MedianRange

Age (year)2
Pre-pregnancy BMI (kg/m )

37

26‒44

20.4

17.1‒35.8

Delivery data

combinations of three SNVs and amino acid levels in

Gestational weeks at delivery

39 w 5 dplasmaBirth weight (g)3,069

37 w 5 d‒41 w 5 d
2,298‒3,978Fetal sex

Male

Female10

9Nullipara

Para

10

9

of

pregnant

women:

i)rs1047891,

chr2.

g211540507.C＞A, CPS1 Thr1406Asn and glycine; ii)
rs8012505, chr14.g104571054.C＞G, ASPG Ser344Arg

Parityand asparagine; and iii) rs5747933, chr22.g18910355.G＞T,
PRODH Thr275Asn and proline (genomic coordinates are
19)

shown in the GRCh37/hg19 reference genome) . Thesemissense mutations are signiﬁcantly associated with and19)

are likely to have causal eﬀects on the corresponding

the Tohoku Medical Megabank Organization (ToMMo) .

plasma amino acid levels, based on careful consideration of
the amino acid substitutions using three-dimensional struc-

The proﬁles were grouped by sex and age and the averageof the proﬁles was calculated for the subgroups. The aver-tures of the enzymes, which are involved in the metabolic

age proﬁles for each sex and age subgroup were also19)

pathways of the three amino acids . For a linear model ofa particular amino acid (Eq. (1)), we considered the ele-

mapped in the principal component plots.

Results

ments of β ID that represent the plasma concentration in thecorresponding participants, who were classiﬁed by geno-

Participant and sample summary
The basic and obstetric background of participants is

types for each SNV as either homozygous to the reference
allele (ref) or heterozygous with the reference and alternative alleles (het). Diﬀerences in the plasma concentration ofsummarized in Table 1. The median age of participants atthe time of study entry was 37 (26‒44) years old. The

amino acids, or elements of β ID in Eq. (1), between the

median BMI was 20.5 (17.0‒35.8), with three underweight

ref and het genotype participants for the respective(BMI＜18.5), 14 in the normal range (BMI 18.5 to 25), one

SNV were tested using a Wilcoxon ranked sum test.

overweight (BMI 25 to 30) and one obese (BMI over 30).
All participants gave birth to a single child, totaling ten

Principal component analysis of amino acid proﬁlesmales and nine females.

To evaluate changes in amino acid proﬁles during preg-nancy and avoid eﬀects associated with the decrease in

Plasma samples were taken at weeks 9 and 40 of gestation. The number of samples collected from each partici-

total amino acid concentration throughout pregnancy, wepant ranged from six to nine, and among the 168 plasma

focused on the relative rather than the absolute amounts ofamino acids. The concentrations (in μM) of nineteen stan-samples collected during pregnancy, 120 met the criteria of

dard amino acids except aspartic acid, which had a concentration that was too low to allow consistent measurement,

meal. The concentrations of nineteen standard amino acids
and those of three metabolites, taurine, ornithine, and citrul-

were normalized to relative values by dividing by the sum

line, were successfully measured for all 120 pregnant sam-of the concentrations of nineteen amino acids in the same
sample. These nineteen-dimensional, relative amino acid

ples. Meanwhile, aspartic acid levels were much lower thanblood glucose level＜120 mg/dL and＞3 h since the lastthose for the other amino acids and were measurable in
only 80/120 samples during gestation. ADM plasma sam-

proﬁles of pregnant and ADM samples were subjected toprincipal component analysis, after centering and scaling
for each amino acid level. As a reference for the non-preg-

ples were also obtained for 16/19 participants, and of these,nant conditions in the general population, we obtainedmeal.

7 met the criteria for blood glucose and time since the lastplasma amino acid proﬁles for 5,093 Japanese individuals
determined by NMR that were collected and analyzed by39

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Table 2. Trends for amino acid concentrations with gestational age calculated using linear models(Eq. (1)). The estimated slopes and SDs are given as μM/day.
Amino acid

Estimate

Std. Errort value

Pr(＞|t|)Threonine0.40

Tryptophan−0.0450.036

100.0068−6.6

7.0E-19
1.6E-09

Arginine−0.0390.0071−5.5

Histidine

0.049

0.010

4.8

3.0E-07
6.2E-06

Glutamic acid

0.053

0.013

4.0

0.00014

Aspartic acid

0.00610.00154.0

0.00016

Proline

0.084

0.024

3.5

0.00072

Valine

−0.11

0.032

−3.5

0.00080

Leucine−0.056

0.017

−3.4

0.0011

Asparagine

0.020

0.00663.0

0.0036

Methionine

0.00820.00332.5

0.014

Isoleucine−0.026

0.011

−2.4

0.019

Alanine

0.12

0.052

2.4

0.020

Total

0.42

0.21

2.0

0.047

−0.073

0.047

−1.5

0.12

Serine

0.015

0.013

1.2

0.23

Cystine−0.00540.0048−1.1

0.27

Taurine0.0096

0.012

0.83

0.41

Lysine

0.016

0.023

0.70

0.48

Tyrosine−0.0055

0.0079

−0.70

0.49

Glycine−0.012

0.020

−0.59

0.56

Phenylalanine

0.00270.00620.43

0.67

Citrulline

0.00035

0.0051

0.069

0.94

Ornithine−0.00018

0.0049−0.037

0.97

Glutamine

Correlations of amino acid concentration and gestational age

Contribution of gestational age and individual diﬀerences to variances in amino acid concentrations

We ﬁrst determined whether plasma amino acid concentrations changed with advancing gestational age using lin-

The fractions of variances in amino acid concentration
explained by individual diﬀerences and gestational age are

ear models of plasma samples from 19 participants (Eq.

shown in Table 2. Variations in the levels of most amino(1)). The number of plasma samples used for regression

acids could be explained by individual diﬀerences that
ranged from 0.21 to 0.80, whereas the fractions of vari-

was 120 for the standard amino acids (except aspartic acid)and for citrulline, ornithine, and taurine. A total of 80 sam-ances that could be explained by gestational age were lower

ples were considered for aspartic acid. After separating theeﬀect of individual diﬀerences, ﬁve positive and four nega-

for most of the amino acids. The nine amino acids thattive correlations with gestational age were found (Table 2,larger fraction that could be explained by gestational age

Fig. 1, Fig. S1). Amino acids having a positive correlation
with gestational age were threonine, histidine, glutamic

compared to the other amino acids. Variations in the levelsof threonine and tryptophan, which had the largest signiﬁ-acid, aspartic acid and proline, whereas those with negative

cant increase and decrease in concentration, respectively,

correlations were tryptophan, arginine, valine, and leucine

with gestational age had contributions from gestational age(Fig. 1). Total amino acid concentrations were unchangedof 0.39 and 0.16, respectively, whereas for most of thethroughout pregnancy (Fig. S2).

other amino acids variations due to gestational age were

exhibited signiﬁcant changes with gestational age had aless than 0.1.

40June 2020

Fig. 1.

Medical Mass Spectrometry Vol. 4 No. 1

The relationship between gestational age and plasma concentrations of amino acids that showed signiﬁcant changes
with gestational age using linear models of Eq. (1).
(a) Threonine (red), histidine (green) and tryptophan (blue). (b) Proline (red), glutamic acid (green) and aspartic acid (blue). (c) Valine (red), leucine (green) and arginine (blue).
The time series of the 19 subjects are represented by separate thin lines and data points. Values represented by thick lines were
calculated using a locally weighted scatterplot smoothing (LOESS) method that included all sample points, which were treated as
independent from each other; grey regions indicate conﬁdence intervals.

Changes in amino acid concentrations during and after pregnancy

trations of ornithine, glycine, arginine, citrulline and tyrosine were decreased compared with that of total amino

To evaluate changes in amino acid concentrations during

acids.

and after pregnancy, the average concentration of amino

decreased during pregnancy by 27% compared to ADM.

Relationship between amino acid concentration trends
during pregnancy and changes in pregnancy relative
to ADM

Levels of most individual amino acids, except threonine,

We examined the relationship between the trends foracids throughout pregnancy relative to those at ADM werecompared (Fig. 2). The total amino acid concentration was

also decreased during pregnancy relative to ADM, whichamino acid levels during pregnancy (quantiﬁed by βday insuggests a blood dilution eﬀect during gestation. Notably,

Eq. (1) normalized by standard deviation) and their relative

levels of essential amino acids remained relatively stablelevel in pregnancy to that for ADM. There was a modest
positive correlation between these two values, with Pear-

compared to that for the total amino acid concentration.
The concentrations of some non-essential amino acids, suchson s r＝0.50 (P＝0.01) and Spearman s ρ＝0.46 (P＝0.03) .

as aspartic acid, asparagine, glutamic acid, alanine, taurine

The amino acids that perverted this trend were tryptophan,and glutamine, were also increased compared to the total

valine, leucine, cysteine (decreased during gestation andamino acid concentrations. Taurine is also an essential

retained pregnant/ADM ratio) and proline (increased during

24)

amino acid for the fetus

and had a higher median value

gestation and suppressed pregnant/ADM ratio).

than that for total amino acids. On the other hand, concen41

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Medical Mass Spectrometry Vol. 4 No. 1

Table 3.

Study participant genotypes for the three SNVs
that aﬀect speciﬁc amino acid levelsaGenotypes

Ref

Het

Glycine13

6

Asparagine

14

5

Proline16

3aGenotypes were determined for CPS1 Thr1406Asn (rs1047891)
for glycine, ASPG Ser344Arg (rs8012505) for asparagine, andPRODH Thr275Asn (rs5747933) for proline. Het: heterozygous;
Ref: reference allele.

CPS1 genotypes were signiﬁcant, whereas diﬀerences inasparagine and proline concentrations between the ASPG
and PRODH genotypes, respectively, were not. These outcomes could be associated with the relatively small size of
Fig. 2.

this cohort. Although the median concentrations of glycine

Variations in plasma amino acid concentration
changes during pregnancy compared to ADM.

(55%), asparagine (11%), and proline (47%) decreased
during pregnancy compared with those at ADM (Fig. 2),
we observed increasing trends for the plasma concentra-

The relative concentration of an amino acid in pregnancy
was calculated as the ratio of the average concentration
over the entire pregnancy and that at ADM for each individual. The values for the seven individuals are plottedin box and jitter plots. Red and blue colors indicate essential and non-essential amino acids, respectively. Solidand dotted horizontal lines indicate the median value fortotal amino acid (0.73) and unity, respectively. Asterisks
indicate signiﬁcant change (P＜0.05/23, paired t-test)in the distribution of the relative concentrations of totalamino acids and those for each amino acid.

tions of these three amino acids that were associated withthe presence of an alternative allele for the corresponding
SNVs, which is consistent with the increasing trend inamino acid concentrations seen in a non-pregnant Japanese
population when alternative alleles were present . Interest19)

ingly, the amounts of these three amino acids showed alarge variance that could be explained by individual diﬀerences, and represented 0.80, 0.71, and 0.58 of the variances

Genetic factors and amino acid concentrations

explained for glycine, asparagine, proline, respectively

Six, ﬁve and three participants were heterozygous forgenome-wide signiﬁcant SNVs (Het) in CPS1, ASPG and(Table 4).

PRODH, respectively, whereas the other participants were

Dynamic shift in amino acid proﬁles in pregnancy

homozygous for the reference allele (Ref) for these SNVs

The changes in relative amino acid proﬁles throughout(Table 3). Thus, we examined how the CPS1, ASPG, andPRODH genotypes aﬀected changes in glycine, asparagine,

pregnancy were determined by PCA of 120 pregnant andseven ADM samples (Fig. 4a). Clustering of pregnant (sub-and proline concentrations in this pregnancy cohort.

classiﬁed as T1, T2 and T3) and ADM samples from theAmong the eﬀects of SNVs on amino acid concentra-

same individuals along PC1 indicated that it partly reﬂects

tions, participants heterozygous for CPS1 had a 44% higher
median plasma glycine concentration than those homozy-diﬀerences between individuals (Fig. 4b). On the othergous to the reference CPS1 (increase of 46 nmol/L; Fig. 3a,b). Participants heterozygous for ASPG had 30% higher(Fig. 4c) and located ADM samples in a large negative PC2
region. Non-pregnant controls measured by NMR in a Japa-

median asparagine concentrations than those homozygous

nese population

to the reference ASPG (increase of 10 nmol/L; Fig. 3c, d).

values, which was similar to that for ADM samples andParticipants heterozygous for PRODH had slightly (21%)
higher median proline concentrations than those homozy-supports the ability of PC2 to discriminate pregnant from
non-pregnant values. Furthermore, PC1 separated male andgous to the reference PRODH (increase of 16 nmol/L; Fig.

female samples in a Japanese population, whereas PC2 cor-3e, f). Diﬀerences in glycine concentration between therelated with decreasing age for both sexes. The relative

hand, PC2 clearly separated pregnant from ADM samples

19)

42

clustered with the large negative PC2

June 2020

Fig. 3.

Medical Mass Spectrometry Vol. 4 No. 1

Eﬀects of SNVs on amino acid concentrations.
(a) and (b) CPS1 Thr1406Asn (rs1047891) for glycine, (c) and (d) ASPG Ser344Arg (rs8012505) for asparagine, and (e) and (f)PRODH Thr275Asn (rs5747933) for proline. (a), (c) and (e) The time series for the 19 subjects is represented by separate lines.
Red and blue colors indicate heterozygous (Het) and reference (Ref) genotypes, respectively. (b), (d) and (e) Individual diﬀerences
for the participants ( βID in Eq. (1)) are shown in boxplots for Het and Ref genotypes.

amino acid proﬁles of pregnant women usually moved into that of PC1 for the relative amino acid proﬁles (Fig. 4d,

the direction of large negative PC1 and small negative PC2

Fig. S2e) that had negative values for branched chain(Fig. 4a grey arrow), indicating that PC1 partly describedamino acids. Although PC1 of the absolute proﬁleschanges with advancing gestation. The PC1 loadings
showed large positive values for the four amino acids thatreﬂected blood dilution that occurs during pregnancy, PC2of the relative proﬁles was better able to discriminate preg-showed decreasing values (valine, leucine, arginine andnant and non-pregnant samples than the PC1 of the absolute

tryptophan, Fig. 1) and large negative values for threonine,

proﬁles (Fig. 4e and Fig. S2b), indicating that the change inwhich showed the largest increase (Fig. 1), indicating thatthe relative amino acid proﬁle can be another marker of

PC1 is correlated with advancing gestation. Meanwhile,

pregnancy in addition to the absolute dilution of blood.

PC2 was characterized by positive essential amino acids
and negative non-essential amino acids, similar to the trend

Discussion

seen for the change during pregnancy relative to ADM,

In the present study we performed frequent (monthly)with proline, tyrosine and arginine values having the largest

proﬁling of plasma amino acids in a longitudinal cohort ofpregnant women to clarify changes in amino acid concen-

diﬀerence (Fig. 4e).
pregnant and ADM samples indicated that half of the vari-

trations during pregnancy and with gestational age, as wellas the eﬀects of genetics as determined by exome sequenc-

ance could be explained by PC1, which represents theing. Compared to previous reports of maternal plasma pro-decrease in the concentrations of all amino acids that likely

ﬁling reports

can be attributed to blood dilution during pregnancy (Fig.

plasma per individual during gestation, which enabled us toS3). The PC2 of the absolute amino acid proﬁles is similar

quantify relative contributions of individual diﬀerence andNotably, the PCA of absolute amino acid proﬁles in our

13‒17)

43, we performed more frequent sampling ofJune 2020

Table 4.

Medical Mass Spectrometry Vol. 4 No. 1

Variances explained by individual diﬀerences and gestational age. Asterisks indicate amino acids that exhibited signiﬁcant increases or decreases with gestational age
Signiﬁcant change withbgestational age

Individual diﬀerence

Gestational age

Number of signiﬁcantlyadiﬀerent individuals

Glycine0.80

0.00067

8.83 (±3.90)

Serine0.74

0.0035

6.60 (±3.31)

Asparagine

0.70

0.023

5.08 (±3.28)

Ornithine

0.67

0.0000046

4.38 (±3.25)

Arginine

0.66

0.063

7.13 (±3.43)*

Glutamic acid

0.62

0.052

4.29 (±4.06)*

Tyrosine

0.59

0.00192.88 (±2.95)

Proline0.58

0.0456

4.13 (±3.40)

Glutamine

0.58

0.010

2.46 (±2.87)

Citrulline

0.57

0.000020

2.58 (±2.34)

Alanine

0.53

0.025

1.83 (±2.33)

Total

0.52

0.019

1.75 (±2.38)

Lysine0.52

0.0024

1.46 (±2.70)

Methionine

0.47

0.030

1.25 (±1.94)

Histidine

0.45

0.10

1.25 (±1.94)

Phenylalanine

0.42

0.00110.88 (±1.15)

Tryptophan

0.38

0.16

1.42 (±2.47)*

Valine

0.38

0.066

0.92 (±1.14)*

Isoleucine

0.34

0.035

0.46 (±0.72)

Leucine

0.25

0.074

0.29 (±0.46)

Taurine0.25

0.00510 (±0)

Aspartic acid

0.23

0.16

0 (±0)

Cystine0.22

0.00950 (±0)

Threonine

0.21

0.39

0.71 (±1.08)

Amino acid

3

3

3

*

*

*
*
*

a

Each of the 19 participants was selected as a reference individual, and the average and standard deviation of the number of individuals who
showed signiﬁcantly (P＜0.05/(23*19)) diﬀerent amino acid concentration from the reference individual by the linear model (Eq. (1)) wasb
3
calculated; signiﬁcant change with gestational day; amino acids with genome-wide signiﬁcant SNV.

gestational time on plasma amino acid concentrations

linking perturbations in amino acid concentrations with the(Table 4). The variances in amino acid concentrations

formation and progress of gestation is diﬃcult. However,explained by gestational time were＜0.1 for most amino

several implications for amino acid metabolism during

acids (except threonine and tryptophan), suggesting that the

pregnancy were apparent from our ﬁndings.

amino acid concentrations are strongly regulated comparedFirst, we observed a decrease in total amino acid concen-with e.g. lipid levels, which signiﬁcantly increase in latepregnancy. We also classiﬁed the participants by their geno-

tration of about 27% during pregnancy. Considering thatthe net increase in circulating blood volume during preg-

types for the GWAS-signiﬁcant SNVs on amino acid concentrations and demonstrated their eﬀect on amino acidnancy is estimated to be 30‒50% over that of the non-preg25)
nant state , the absolute amount of amino acids in mater-concentrations in maternal plasma (Fig. 3). In addition, theabsolute quantiﬁcation methods for amino acid concentra-

nal circulation would also increase relative to thenon-pregnant (ADM) condition and this increase would betions with LC-MS enabled us to calculate the abundance of

beneﬁcial for meeting the needs of the fetus.

each amino acid relative to total, and this relative aminoacid proﬁle was useful to discriminate plasma from preg-

We also compared the relative amount of amino acids in
pregnant women to the non-pregnant state and with advanc-nant participants (Fig. 4). As amino acid metabolism is car-ing gestation and found that the plasma amino acid compo-

ried out by complex networks involving multiple enzymes,

sition was altered in pregnant women. During pregnancy,
44

June 2020

Fig. 4.

Medical Mass Spectrometry Vol. 4 No. 1

Principal Component Analysis (PCA) of relative amino acid proﬁles.
(a) PCA of relative amino acid proﬁles for pregnant and ADM samples. Pregnant and ADM samples from the same individual
are identiﬁed by separate point types and joined by lines. Pregnant samples collected during the ﬁrst (T1), second (T2) and third(T3) trimesters and ADM are colored light brown, medium brown, dark brown and black, respectively. The grey arrow joining theﬁrst and last sample points during pregnancy was averaged over the 19 participants. As reference data for amino acid proﬁles for
non-pregnant people, we used plasma amino acid proﬁles from 5,093 Japanese individuals measured by NMR that were collected
and analyzed by the Tohoku Medical Megabank Organization (ToMMo) [19]. These proﬁles were divided into 12 subgroups by sex
and age (20s, 30s, 40s, 50s, 60s and 70s) and average relative amino acid proﬁles were calculated for each subgroup. Male (jM)and Female (jF) groups are colored blue and red, respectively, and joined by separate lines with increasing age from 20s (denoted
as 20) to 70s (denoted as 70). (b), (c). Box and scatter plots of (b) PC1 and (c) PC2 scores for pregnant and ADM samples and theToMMo general cohort age and sex subgroups. Sample points are colored as in Panel A. (d), (e). (d) PC1 and (e) PC2 loadings ofthe 19 amino acids, which are arranged in ascending order. Essential and non-essential amino acids are colored in red and blue, respectively.

amino acids are transported from the maternal plasma to
the fetal circulation by amino acid transporters in the pla-

ates, e.g., arginine, ornithine and citrulline, during preg-

8,26,29). Due to this active amino

concentrations. Amino acids transferred from maternalacid transport, amino acid concentrations are higher in theplasma to the fetus through the placenta are used either toplacental intervillous space and in the umbilical vein thansynthesize proteins for the fetus or as an oxidized energy

cental syncytiotrophoblast

27)

nancy as compared with the decrease in total amino acid

28)

11)

in maternal plasma . Amino acid infusion experiments

source wherein the amide group is converted to urea

and measurement of arteriovenous diﬀerences in amino

fetal amino acid consumption can decrease amounts of

29)

acid concentration

showed that most of the essential

andmaternal urea cycle intermediates.

amino acids are actively transported from maternal blood

Using linear models applied to longitudinal plasma

through the placenta to the fetus. Enrichment of essential

amino acid proﬁling, we found that threonine, histidine,glutamic acid, aspartic acid and proline showed signiﬁ-

amino acids in the plasma of pregnant women may reﬂect
diﬀerences in transport activities among amino acids, or

cantly increased concentrations and arginine, tryptophan,valine and leucine had signiﬁcantly decreased concentra-

arise from other maternal adaptations. We also observed
signiﬁcantly pronounced decreases in urea-cycle intermedi-tions with increasing gestational age (Fig. 1). These results
45June 2020

Medical Mass Spectrometry Vol. 4 No. 1

were consistent with previous cross-sectional or longitudinal reports that involved a relatively small number of sam-these TCA cycle intermediate metabolites, including citricacid, isocitric acid, and α -ketoglutaric acid are increased inplings per individual- usually once per trimester. Thus,later trimesters , and could reﬂect the increased metabo-trends for amino acid concentrations with gestational age

lism of the fetus, and explain the observed increase inappear to be reproducible despite diﬀerences in ethnicities

aspartic acid and glutamic acid with gestational age.

13‒17)

15)

and other genetic and environmental backgrounds of the

In this study, we did not observe signiﬁcant changes incohorts.

concentrations of several amino acids that were reported in
17‒19). These diﬀerences between studies
may be due to diﬀerences in genetic and environmental fac-

Although the metabolic and biological mechanisms that

previous studies

underlie these changes in amino acid concentrations await
further characterization, several trends were apparent. Thre-

tors of the participants, such as ethnicity, age, and diet. We

onine is metabolized to acetyl-CoA and glycine by either

assumed that most changes in amino acid concentrations

threonine oxidase or serine dehydratase and increases inthis amino acid may be associated with changes in the thre-with advancing gestational age were small compared tovariations resulting from diﬀerences between individuals or

30)

onine oxidation state during pregnancy . Decreases intryptophan concentrations between T2 and T3 were previ-

within individuals (Table 4) that could complicate thedetection of trends during pregnancy.
Although the MS-based amino acids analysis, the quanti-

ously shown to be accompanied by decreases in theamounts of some metabolites in the tryptophan metabolism
pathway, such as 5-hydroxytryptophan, and could be associ-ﬁed levels of the analyte, i.e. aspartic acid were too low toated with pregnancy-induced depression . Arginine isderivatization eﬃciency. Therefore, the quantiﬁed levels of

involved in the urea cycle and decreases in arginine are

the amino acids should be concerned for comparing with theconsistent with decreases in urea production and excretion

other analytical results when the values were used for theallow consistent measurement because of the limitation of

14)

31)

35)

in late pregnancy . Although levels of two additional

prediction of disease expression as the plasma biomarkers .
We conﬁrmed the eﬀect of genotypes for genome-wide

amino acids that are important for the urea cycle, ornithineand citrulline, did not decrease signiﬁcantly with gesta-

signiﬁcant SNVs on the concentrations of three aminoacids, glycine, asparagine and proline (Fig. 3). Interest-

tional age (Fig. S1), overall arginine, ornithine, and citrulline concentrations were strongly decreased during preg-

ingly, the large variance for these three amino acids could

nancy compared to the non-pregnant state (Fig. 3),
suggesting that systemic nitrogen balance in pregnancy

be explained by individual diﬀerences, representing 0.80,
0.71, and 0.58 of the variances explained for glycine, aspar-

may inﬂuence levels of these amino acids. Arginine is also
involved in the synthesis of nitric oxide, which plays a cru-

agine, proline, respectively (Table 4). These results indicatethat the genetic background of the participants likely inﬂu-

32)

cial role in vasodilation during pregnancy . In addition,
degradation of arginine by fetal arginase-2 is important toences amino acid concentrations during pregnancy and thetheimportance of longitudinal analysis for identifying eﬀects
of individual diﬀerences on pregnancy-related changes in

fetus. These multiple factors can contribute to thedecrease in arginine concentration seen with advancing ges-parameters such as amino acid concentrations.
Maternal pre-pregnancy BMI and fetal sex were associ-tation. We observed consistent pregnancy-related decreases
in branched-chain amino acids (valine, leucine, and isoleu-

ated with the concentrations of several amino acids. We
tested the eﬀect of pre-pregnancy BMI, fetal sex and moth-cine). Branched-chain amino acids are essential aminoacids and consumption of ﬁsh oil during pregnancy caners parity on amino acid concentrations (βID in Eq. (1))
using Pearson s correlation coeﬃcient or Wilcoxon-rankcompensate for decreases in plasma concentrations of thesesum test. We found no signiﬁcant correlations after adjust-maintainmaternal

immunosuppression

toward33,34)

17)

amino acids in late gestation . Meanwhile, we observed

ment for multiple testing (data not shown). In a previous

signiﬁcant increases in the concentrations of glutamic acidstudy that compared maternal plasma samples from normal

and aspartic acid. These two amino acids, which are
directly converted to α -ketoglutaric acid and oxaloacetic

and obese groups, concordant decreases in most amino
36)acid, respectively, both act as intermediate metabolites inacids in the obese group were reported . Male fetus isreported to be a possible risk for gestational diabetes melli-

the tricarboxylic acid (TCA) cycle. The plasma levels oftus
46

37,38). Due to the small number of subjects enrolled forJune 2020

Medical Mass Spectrometry Vol. 4 No. 1

this study, it was diﬃcult to evaluate the eﬀect of theseclinical factors for the participants, but they could be con-sonable request.

founding factors for changes in amino acid concentrations

Competing interests
The authors declare that they have no conﬂict of interest.

during pregnancy.

Conclusions

Ethical approval

Amino acid proﬁles were changed signiﬁcantly by pregnancy and with advancing gestation. The amounts of essen-

All procedures performed in studies involving humanparticipants were in accordance with the ethical standards

tial amino acids remained relatively consistent during preg-of the institutional committee and with the 1964 Helsinki

nancy, although levels of urea cycle components weredecreased relative to post-delivery, which supported thatdeclaration and its later amendments or comparable ethicalstandards.

changes in amino acid proﬁles do occur during pregnancy.
An eﬀect of genotypes for three genome-wide signiﬁcantInformed consent

Informed consent was obtained from all individual par-SNVs on the concentrations of glycine, asparagine, andticipants included in the study.

proline in pregnant women was observed, indicating thatgenetic variations can dominate changes in amino acid con-

Authors Contributions

centrations. Together our results indicated that relative

Matsuyuki Shirota: Project development, Data analysis

amino acid proﬁles reﬂect the formation and progression ofand Manuscript writing

gestation in pregnant women.

Daisuke Saigusa: Project development, Data analysis,

Acknowledgements

Data collection and Manuscript writing

This work was supported in part by the Tohoku Medical

Riu Yamashita: Project development and Data collection

Megabank Project (Tohoku University) from the Special

Yasutake Kato: Project development and Data collection

Account for reconstruction from the Great East Japan

Mitsuyo Matsumoto: Project development and Data col-Earthquake (Grant Number, 19 km0105001), from thelectionJapan Agency for Medical Research and Development

Junya Yamagishi: Project development and Data collection(AMED) and from the Ministry of Education, Culture,

Noriko Ishida: Project development and Data collection

Sports, Science and Technology (MEXT).

Kazuki Kumada: Data analysis
Yuji Oe: Data analysis

The authors would like to thank all the study participants
and the staﬀ of the Tohoku Medical Megabank Organization at Tohoku University (A full list of members is avail-Hisaaki Kudo: Data managementable

http://www.megabank.tohoku.ac.jp/english/
a151201/) and the Department of Obstetrics and Gynecol-

Yoko Kuroki: Data collection

ogy, Tohoku University Hospital for their eﬀorts and contri-

Fumiki Katsuoka: Data analysis

butions to this study.

Masao Nagasaki: Data analysis

Junji Yokozawa: Data collection

at:Ikuko Motoike: Project development and Data analysis

Seizo Koshiba: Project development

Declarations

Keiko Nakayama: Project development

Compliance with Ethical Standards

Osamu Tanabe: Project development and Manuscript

This study was approved by the Tohoku University Ethics Committee (authorization numbers, 2012-1-443 andwriting
Jun Yasuda: Project development and Manuscript writing

2014-10). Written informed consent was obtained from all

Shigeo Kure: Project development

participants.

Kengo Kinoshita: Project development and Data analysis
Hirohito Metoki: Project development and Data collec-

Availability of data and materialtion

The datasets used and/or analyzed during the current

Shinichi Kuriyama: Project development

study are available from the corresponding author on rea-

Nobuo Yaegashi: Project development
47

June 2020

Medical Mass Spectrometry Vol. 4 No. 1

Masayuki Yamamoto: Project development and Manu-

Res 14(2): 1263‒1274, 2015.
14) Luan H, Meng N, Liu P, Feng Q, Lin S, et al: Pregnan-

script writing

cy-induced metabolic phenotype variations in maternal

Junichi Sugawara: Project development, Data collection

plasma. J Proteome Res 13(3): 1527‒1536, 2014.

and management, Data analysis and Manuscript writing

15) Lindsay KL, Hellmuth C, Uhl O, Buss C, Wadhwa PD, et

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Immunosuppressive CD71＋erythroid cells compromise49