Chronic Academic Stress Increases a Group of microRNAs
in Peripheral Blood
Manami Honda, Yuki Kuwano*, Sakurako Katsuura-Kamano, Yoshiko Kamezaki, Kinuyo Fujita,
Yoko Akaike, Shizuka Kano, Kensei Nishida, Kiyoshi Masuda, Kazuhito Rokutan
Departments of Stress Science, Institute of Health Biosciences, The University of Tokushima Graduate School, Tokushima, Japan
Abstract
MicroRNAs (miRNAs) play key roles in regulation of cellular processes in response to changes in environment. In this study,
we examined alterations in miRNA profiles in peripheral blood from 25 male medical students two months and two days
before the National Examination for Medical Practitioners. Blood obtained one month after the examination were used as
baseline controls. Levels of seven miRNAs (miR-16, -20b, -26b, -29a, -126, -144 and -144*) were significantly elevated during
the pre-examination period in association with significant down-regulation of their target mRNAs (WNT4, CCM2, MAK, and
FGFR1 mRNAs) two days before the examination. State anxiety assessed two months before the examination was positively
and negatively correlated with miR-16 and its target WNT4 mRNA levels, respectively. Fold changes in miR-16 levels from
two days before to one month after the examination were inversely correlated with those in WNT4 mRNA levels over the
same time points. We also confirmed the interaction between miR-16 and WNT4 39UTR in HEK293T cells overexpressing
FLAG-tagged WNT4 39UTR and miR-16. Thus, a distinct group of miRNAs in periheral blood may participate in the integrated
response to chronic academic stress in healthy young men.
Citation: Honda M, Kuwano Y, Katsuura-Kamano S, Kamezaki Y, Fujita K, et al. (2013) Chronic Academic Stress Increases a Group of microRNAs in Peripheral
Blood. PLoS ONE 8(10): e75960. doi:10.1371/journal.pone.0075960
Editor: Huiping Zhang, Yale University, United States of America
Received May 21, 2013; Accepted August 19, 2013; Published October 9, 2013
Copyright: ß 2013 Honda et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported in part by grants from from Grants-in-Aid for Scientific Research B (22390146) and Grant-in-Aid for Challenging Exploratory
Research (22659142) from the Japan Society for the Promotion of Science (to KR). The funders had no role in study design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: kuwanoy@tokushima-u.ac.jp
Introduction
The non-protein-coding genome is functionally important for
normal development, physiology and for disease [1]. MicroRNAs
(miRNAs) are a class of small non-coding RNAs approximately 22
nucleotides in length. They bind to partially complementary sites
mostly within the 39 untranslated region (UTR) of target mRNAs
and suppress translation of target mRNAs or facilitate their
degradation [2]. Approximately 1,000 miRNAs are expressed in
human cells, and sufficiently expressed miRNAs typically target
hundreds of different mRNAs. It is estimated that up to 30% of
mammalian mRNA transcripts are subject to regulation by
miRNAs [3]. Thus, miRNAs are one of the key regulators of
eukaryotic gene expression and involved in regulation of
fundamental cellular processes including proliferation, differentiation,
development, and cell death [4].
Various types of stressors change the biogenesis of miRNA,
activities of miRNA-protein complexes, and the expression of
mRNA targets (for a review see [5]). It has also been suggested that
miRNAs play an essential role in mediating stress responses [5].
Experimental animals with mutant miRNAs appear to normally
develop and are viable under standard conditions, while they
cannot cope with stressful conditions. This has been demonstrated
in miR-14 mutant flies [6], miR-7 knockout flies [7], miR-8inactivated
zebra fishes [8], and mice deficient in miR-208 [9],
suggesting that miRNAs are likely to help restore homeostasis
upon sudden environmental changes.
Using a miRNA microarray, we investigated psychological
stress-responsive miRNAs in whole blood from medical students
taking a nationally-administered examination for academic
promotion, and reported that miR-144/144* and miR-16 may
participate in the regulation of systemic responses when exposed to
brief naturalistic stressors in healthy young adults [10]. To confirm
the reproducibility of the miRNA response in an independent
stress-inducing situation, we examined changes in peripheral blood
miRNA levels in medical students preparing for the National
Examination for Medical Practitioners. This examination is the
most stressful event for medical students, and we used this model
for examining chronic stress-related changes in gene expression
[11] and alternative splicing of the glucocorticoid receptor gene [12].
To reveal a role of miRNAs in stress responses, we also examined
changes in expression of their putative target genes in leukocytes.
Materials and Methods
Subjects and Samples
We recruited 25 healthy male medical students (25.560.4 years
old, mean 6 SD) challenging the National Medical license
examination. All experiments were conducted in accordance with
the Declaration of Helsinki. The protocol and informed consent of
this study were approved by the Institutional Review Board of
Tokushima University Hospital, Tokushima, Japan. The experimental
procedures were fully explained to each subject and written
informed consent was obtained.
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All subjects were in good physical health, taking no medication
for at least three months prior to enrollment and during the
experimental period, and had no history of psychiatric or somatic
diseases. All subjects were non-smokers. Their body mass index
(BMI; kilograms per meter squared) was 21.862.3 (mean 6 SD).
Two months before, two days before, and one month after the
examination, saliva was collected between 16:00 and 17:00 to
avoid diurnal fluctuations, using Salivette sampling devices
(Sarstadt Inc., Rommelsdorf, Germany) prior to the collection of
blood as previously described (Kurokawa et al., 2011). Salivary
cortisol levels were measured using a commercial enzyme
immunoassay kit (Ciron, Tokyo, Japan) [12]. Blood was collected
after saliva sampling (between 16:00 and 17:00) and immediately
poured into PAXgeneTM
blood RNA tubes (Becton Dickinson,
Franklin Lakes, NJ, USA). After sufficient mixing, tubes were
stored for 2 h at room temperature, followed by storage at 280uC.
At the same time as sample collection, all subjects answered the
state version of Spielberger’s state-trait anxiety inventory (STAI)
[13] for each time point. The reliability of this Japanese STAI
version has been established [14].
Measurement of miRNAs in Whole Blood
From the stored samples, total RNA containing small RNAs
using the PAXgene Blood miRNA kit (Qiagen, Hilden, Germany)
was extracted according to the manufacturer’s protocol. RNA
concentration and purity were determined by NanoDrop ND-
1000 spectrophotometer (NanoDrop Technologies, Wilmington,
DE, USA). Purified RNA quality was assessed by Agilent 2100
Bioanalyzer using an RNA 6000 Nano Labchip kit (Agilent
Technologies, Santa Clara, CA, USA), and RNA samples with
.8.5 RNA integrity number (RIN) were used for further study.
RNA samples were immediately frozen and stored at 280uC until
analyzed. RNA samples were used to measure miRNA expression
profiles using a human miRNA microarray (G4470C; Agilent),
containing 984 miRNA probes, as previously described [10].
These data were analyzed using GeneSpring 11.5.1 (Agilent) and
GraphPad Prism version 5.02 (GraphPad Software Inc., CA,
USA). We selected miRNAs with fluorescence intensities .20 in at
least half of the RNA samples, resulting in the detection of 160
miRNAs in the whole-blood samples. The RNA samples were also
subjected to gene expression analysis using a whole human
genome microarray (4644 k, Agilent Technologies) as previously
described [15]. Microarray data were analyzed by GeneSpring
11.5.1 (Agilent Technologies). The functional pathways related to
the set of differentially expressed genes were assessed by Ingenuity
Pathway Analysis (IPA) 9.0 (http://www.ingenuity.com) [15]. The
probability of a relationship between each biological function and
the identified genes was calculated by Fisher’s exact test. The level
of significance was set at a P-value of 0.05.
Quantitative Real-time RT-PCR (qPCR) Measurement of
Target miRNAs and mRNAs
Significant changes in miRNA expression identified by microarrays
were confirmed by real-time quantitative reverse transcription
PCR (qPCR) using TaqManH MicroRNA assays (Applied
Biosystems, Foster City, CA, USA). The TaqMan specific miRNA
retrotranscription kit (Applied Biosystems) was used to generate a
specific first-strand complementary DNA from extracted total
RNA (10 ng). qPCR was performed by Applied Biosystems 7500
Real-time System (Applied Biosystems). All miRNA data were
normalized using RNU48 as an endogenous quantity control.
qPCR was also employed to validate the target mRNAs identified
by the oligoDNA microarray. Total RNA (400 ng) from each
sample was reverse-transcribed using a PrimeScript RT reagent
Kit (Takara, Otsu, Japan). WNT4, CCM2, MAK, FGFR1 and
SUMO2 mRNA levels were measured using SYBR Green Master
Mix (Applied Biosystems). GAPDH mRNA was utilized as an
internal control for normalization.
YFP Reporter Plasmids
The 39UTR of human WNT4 was cloned into pd2EYFP-N1
(BD Biosciences, San Jose, CA). In brief, the PCR was performed
using human cDNA as template. The region of the WNT4 39UTR
(3481–3905) containing a predicted binding site only for miR-16
(3767–3791) was amplified using the following primers (a NotI
restriction site is underlined):
59-AAAAAGCGGCCGCTCACCGTGGCC
TGGAAATTGGCCAG-39 (forward) and 59-
AAAAAGCGGCCGCGATAAAGAAATAATTGTTTTATCGTGC-39
(reverse). The amplified product was subcloned
into the pd2EYFP-N1 vector using a Not1 restriction site
(pd2EYFP-WNT4 39UTR).
Table 1. Examination stress-responsive miRNAs identified by miRNA array.
miRNA
2 months
before exam (a)
2 days
before exam (b)
1 month
after exam (c) p-value1)
Time points exhibiting significant
difference2)
miR-16 1904465201 1748563902 1070462341 0.016 (a) vs. (c), (b) vs. (c)
miR-20b 4206117 394691 251679 0.043 (b) vs. (c)
miR-26a 493677 420678 289646 0.011 (a) vs. (c), (b) vs. (c)
miR-26b 6026150 6156124 3826127 0.031 (a) vs. (c), (b) vs. (c)
miR-29a 98617 89617 57612 0.005 (a) vs. (c), (b) vs. (c)
miR-126 724673 284660 164650 0.019 (a) vs. (c), (b) vs. (c)
miR-223 1020862116 993461934 711061289 0.017 (a) vs. (c), (b) vs. (c)
miR-199a-5p 3566 2764 2163 0.004 (a) vs. (c), (b) vs. (c)
miR-1914-3p 3568 2669 1864 0.024 (a) vs. (c)
Values indicate fluorescence intensities (mean 6 SEM, n = 4).
1)
P-values were calculated by repeated measures ANOVA.
2)
Significantly different by Tukey’s post hoc test (p,0.05).
doi:10.1371/journal.pone.0075960.t001
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Western Blotting
Human embryonic kidney cells (HEK293T) were cultured in
Dulbecco’s modified essential medium (Invitrogen, Carlsbad, CA)
supplemented with 10% fetal bovine serum. After 10 nM of
control small interfering RNA (Ctrl siRNA; Qiagen, Chatsworth,
CA) or precursor (pre)-miR-16 (Applied Biosystems) was transfected
with Lipofectamine RNAi MAX (Invitrogen) for 24 hours,
these cells were co-transfected with a pd2EYFP-N1 plasmid
containing WNT4 39UTR. Whole-cell lysates were prepared in a
RIPA buffer (10 mM Tris-HCl, pH 7.4; 1% Nonidet P-40; 1 mM
EDTA; 0.1% SDS; 150 mM NaCl) containing a protease and
phosphatase inhibitor cocktail (Roche Diagnostics, Basel, Switzerland).
The extracted proteins were separated by SDS-PAGE and
transferred to a polyvinylidene difluoride membrane (Millipore,
Billerica, MA). After blocking with 4% non-fat dry milk,
membranes were incubated overnight at 4uC with anti-GFP
Figure 1. qPCR validation of time-dependent changes in seven miRNA levels. Time-dependent changes in miR-16 (A), miR-20b (B), miR-26
(C), miR-126 (D), miR-144 (E), miR-144* (F), and miR-29a (G) levels were measured by qPCR using RNU48 as endogenous quantity control. Values are
mean 6 SEM (n = 25). *In the graphs, significantly different by repeated measured ANOVA and Bonferroni post hoc test (p,0.05).
doi:10.1371/journal.pone.0075960.g001
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(1:1000; Abcam, Cambridge, UK) or anti-GAPDH (Santa Cruz
Biotech., Santa Cruz, CA) antibody.
Statistical Analysis
Data were analyzed using SPSS statistical software package
version 15.0 (Chicago, IL, USA). To estimate time differences in
the measures tested, repeated-measures ANOVA with Bonferroni
multiple testing correction and then Tukey’s post hoc test were
employed. For repeated-measures ANOVA, Greenhouse-Geisser
correction was performed when violations of sphericity were
detected by the Mauchly test. All significant findings remained
significant when thus corrected. Therefore, we reported degrees of
freedom from uncorrected analyses. Univariate correlations
between identified miRNA levels and state anxiety scores, salivary
cortisol levels, or mRNA levels were analyzed by Pearson or
Figure 2. Correlation between miR-16 levels and STAI-state scores. (A) Correlation between miR-16 and STAI-state scores two months
before the examination in 25 students was analyzed by Pearson correlation coefficients. miR-16 levels normalized by RNU48 were significantly and
positively correlated with STAI-state scores. (B) Correlation between miR-16 levels and the state anxiety scores measured in all three time points in 25
students was analyzed by Pearson correlation coefficients. The closed circle, square, and triangle indicate two months before, two days before, and
one month after the examination, respectively.
doi:10.1371/journal.pone.0075960.g002
Table 2. Time-dependent changes in selected target mRNA levels.
Gene
symbol
2 months
before exam (a)
2 days
before exam (b)
1 month
after exam (c)
Time points exhibiting significant
difference1)
Putative regulator
miRNA(s)
WNT4 1.00860.021 0.96160.015 1.05060.032 (b) vs. (c) miR-16
CCM2 0.93560.036 0.88660.038 0.95260.034 (b) vs. (c) miR-20b
MAK 0.78160.037 0.76360.037 0.87360.041 (b) vs. (c) miR-16, miR-144
FGFR1 0.92260.032 0.93960.027 1.05960.056 (a) vs. (c) miR-16
SUMO2 0.56060.050 0.54260.053 0.55160.048 n.s. miR-144
Values are mRNA levels (mean 6 SEM, n = 25) standardized by GAPDH mRNA levels.
1)
Statistical significance was determined by repeated measures ANOVA and Bonferroni post hoc test (p,0.05).
doi:10.1371/journal.pone.0075960.t002
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Spearman correlation coefficients. Statistical significance was set at
a = 0.05 for all analyses.
Results
Changes in Anxiety and Salivary Cortisol Levels
All medical students have to pass the national medical license
examination to become clinical physicians. This examination
consists of a three day test and is the most stressful, important
event for medical students. Samples were taken at three time
points: two months and two days before the examination (preexamination
period), and one month after the examination (postexamination
period). All subjects in this study were newly recruited
and different from those examined in our previous studies [11,12].
Repeated measures ANOVA showed that STAI-state scores were
significantly changed during the pre- and post-examination
periods (F(2, 48) = 22.42, p,0.001, gp
2
= 0.82). STAI-state scores
measured two months (58.862.3; mean 6 SEM, n = 25) and two
days (51.362.3, n = 25) before the examination were fairly high,
when considering the threshold value of 40 in the Japanese version
of STAI [14]. Scores returned to baseline levels one month after
the examination (34.662.3, n = 25). Tukey’s post hoc test
demonstrated significantly higher state anxiety scores two months
before and two days before the examination, compared with those
measured one month after the examination (p,0.001). Repeated
measures ANOVA revealed that salivary cortisol levels (F(2,
28) = 3.78, p,0.05, gp
2
= 0.997) also significantly responded to the
examination, and the levels measured two months before
(0.1860.03 mmol/dl; mean 6 SEM, n = 25), but not two days
before the examination (0.1660.03), were significantly higher than
those measured one month after the examination (0.1060.01)
(p,0.05 by Tukey’s post hoc test). Thus, these students were likely
to be under great pressure for more than two months and then
were relaxed within one month after the examination. Thus, the
present study seemed to be an appropriate model for the analysis
of responses to chronically stressful situations.
Changes in Peripheral Blood miRNA Levels
Among 25 male subjects, we selected 4 subjects who showed a
typical stress response for microarray analysis: their STAI-state
scores and salivary cortisol levels were significantly elevated during
the pre-examination period. The STAI-state scores measured two
months before (60.064.9; mean 6 SEM) and two days before
(50.566.2) were significantly higher than those measured one
month after the examination (32.065.4), and salivary cortisol
levels measured two months before (0.1760.05 mmol/dl) and two
days before (0.1460.03) were higher than those measured one
month after (0.1060.03). Agilent human miRNA microarrays
(G4470C), which have 984 miRNA probes in total, detected 160
miRNAs with fluorescence intensities .20 for at least half of the
peripheral blood RNA samples. Repeated measures ANOVA
without multiple testing correction revealed that levels of nine
miRNAs (miR-126, miR-16, miR-1914*, miR-199a-5q, miR-20b,
miR-223, miR-26a, miR-26b, and miR-29a) were significantly
and time-dependently changed (Table 1). Tukey’s post hoc test
revealed that all these miRNA levels measured two months and
two days before the examination were significantly higher than
those measured one month after the examination (p,0.05)
(Table 1). The raw and normalized values for all samples by
microarray analysis were deposited in the Gene Expression
Omnibus (GEO) database (accession number: GSE49677).
qPCR Measurement of Identified miRNAs
Among the nine identified miRNAs, two miRNAs were not
further examined, miR-1914* and miR-199a-5q, since these two
miRNAs had insufficient expression levels based on their
fluorescence intensities (,50) (Table 1). miR-144 and miR-144*,
which had been identified as miRNAs responsive to an examination
for academic promotion [10], were selected for inclusion in
the further assessed miRNAs. Consequently, time-dependent
changes in nine miRNA levels (miR-16, miR-20b, miR-26a,
miR-26b, miR-29a, miR-126, miR-223, miR-144 and miR-144*)
in 25 subjects were measured by qPCR using RNU48 as an
endogenous quantity control. Repeated measures ANOVA with
multiple testing corrections revealed that miR-16 (F(2,48) = 7.155,
p = 0.0019, gp
2
= 0.86), miR-144 (F(2,48) = 9.138, p = 0.0004,
gp
2
= 0.91), miR-144* (F(2,48) = 11.80, p,0.0001, gp
2
= 0.97),
miR-126 (F(2,48) = 6.800, p = 0.0025, gp
2
= 0.98), miR-20b
(F(2,48) = 8.256, p = 0.0008, gp
2
= 0.90), miR-26b
(F(2,48) = 11.61, p = 0.0001, gp
2
= 0.86) and miR-29a
(F(2,38) = 3.880, p = 0.0293, gp
2
= 0.95) significantly responded to
the examination stress, while miR-26a and miR-223 did not. As
shown in Fig. 1, Bonferroni post hoc test showed that the levels of
miR-16, -20b, -26b, -126, -144, and -144* measured two months
and two days before the examination were significantly higher,
Figure 3. Correlation of WNT4 mRNA and miR-16 expression
levels. (A) Correlation between WNT4 mRNA levels and STAI-state
scores two months before the examination in 25 students was analyzed
by Pearson correlation coefficients. WNT4 mRNA levels normalized by
GAPDH mRNA levels were significantly and negatively correlated with
STAI-state scores. (B) Correlation between fold changes in miR-16 levels
from two days before to one month after the examination and fold
changes in WNT4 mRNA levels over the same time points was analyzed
by Pearson correlation coefficients.
doi:10.1371/journal.pone.0075960.g003
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and miR-29a levels were significantly elevated only two days
before the examination, when compared with those measured one
month after the examination (Fig. 1). In addition, we measured U6
snRNA levels as another internal quantity control. The results using
U6 snRNA as an internal control were similar to those using
RNU48 (Figure S1). These results suggested that a prolonged
stressful situation might persistently increase these specific
miRNAs in peripheral blood.
Correlation between miRNA Levels and STAI-state Scores
or Salivary Cortisol Levels
To assess that the miRNA response constituted a part of the
integrated stress response, correlations were analyzed between
seven miRNA levels and STAI-state score for the individual
students, and found that miR-16 levels were significantly and
positively correlated with STAI-state scores two months before the
examination (Fig. 2A), at which time the anxiety score showed the
highest values. Moreover, as shown in Fig. 2B, miR-16 levels and
the state anxiety scores measured for the individual students at all
three time points were also significantly and positively correlated,
suggesting that miR-16 might be a new biomarker for anxiety. In
contrast, no significant correlation was observed between the other
six miRNAs and the state anxiety scores at any time points. In
contrast to the correlation between miRNA levels and STAI-state
scores, no significant relationship was detected between the seven
miRNAs and salivary cortisol levels.
Changes in Expression of mRNAs Including miRNA
Targets
To examine whether changes in the identified miRNAs were
associated with altered expression of their target mRNAs in
leukocytes, gene expression in peripheral blood was measured at
each time point using a whole human genome microarray.
Microarray analysis showed that 18,861 probes had fluorescence
intensities higher than a cut-off value of 20 among all samples.
Repeated measures ANOVA without multiple testing correction
revealed that the expression levels of 1,356 probes were
significantly and time-dependently changed (p,0.05). Tukey’s
post hoc test showed that 1,023 mRNA levels measured two
months and two days before the examination were commonly and
significantly altered compared with those measured one month
after the examination. Ingenuity Pathway Analysis ranked 1) Cell
Figure 4. WNT4 is an in vivo target for miR-16. (A) miR-16 sequence and a predicted binding site for miR-16 in WNT4 39UTR are shown. After
HEK293 cells were transfected with 10 nM of control (ctrl) or precursor miR-16 for 48 h, mature miR-16 levels (B) and WNT4 mRNA levels (C) were
measured by qPCR using RNU48 and GAPDH mRNA as endogenous quantity controls. Values are mean 6 SD from three independent experiments.
*
Significantly different by ANOVA and Bonferroni test (P,0.05) compared with those in control siRNA-treated cells. (D) Schematic representation of
the parent reporter plasmid pd2EYFP-N1 and the pd2EYFP-WNT4 39UTR reporter plasmid containing the WNT4 39UTR (3481-3905). (E) HEK293 cells
were transfected with each reporter plasmid, together with either control (ctrl) or precursor miR-16. Forty-eight hours after transfection, YFP mRNA
levels were measured by qPCR using GAPDH mRNA as an endogenous quantity control. Values are mean 6 SD from three independent experiments.
*
Significantly different by ANOVA and Bonferroni test (p,0.05) compared with those in control siRNA-treated cells. (F) YFP and GAPDH protein levels
were measured by Western blot analysis.
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Death and Survival (p = 2.16E-07), 2) Gene Expression (p = 5.74E-
07), and 3) Infectious Disease (p = 2.46E-06) as the top-three
biofunctions related to the set of affected genes.
Using TargetScan (http://www.targetscan.org/), we searched
for maximal local complementarity alignment between the seven
miRNAs and 39UTR sequences of putative mRNA targets, and
found 1,112 predicted mRNA targets after filtering with context
percentile .90.0 and their conservation. Of the 1,023 differentially
expressed genes between pre- and post-examination, 58 (6%)
were included in the 1,112 putative targets for the seven miRNAs.
Among them, the levels of 28 mRNAs were inversely correlated to
changes in the miRNA levels. Among the 28 putative targets,
changes in mRNA levels of miR-16 targets (WNT4 and FGFR1), a
miR-20b target (CCM2), a miR-16 or miR-144 target (MAK), and
miR-144 target (SUMO2) were validated. Repeated measures
ANOVA and Bonferroni post hoc test demonstrated that
preparation for the examination caused small, but significant
decreases in WNT4 (F(2,46) = 4.805, p = 0.0127, gp
2
= 0.76), CCM2
(F(2,48) = 4.546, p = 0.0156, gp
2
= 0.94), MAK (F(2,48) = 3.801,
p = 0.0294, gp
2
= 0.82), and FGFR1 (F(2,42) = 4.638, p = 0.0151,
gp
2
= 0.76) mRNA levels two days before the examination,
compared with those measured one month after the examination
(Table 2).
Correlation between miR-16 Levels and Target mRNA
Levels
Among the seven miRNAs, miR-16 has been suggested to serve
an important role in the cellular stress response [16]. Moreover,
miR-16 was relatively abundant in peripheral blood and had been
identified as one of the acute psychological stress-responsive
miRNAs [10]. Among the miR-16 target mRNAs measured
(WNT4, FGFR1, and MAK), WNT4 mRNA levels were significantly
and negatively correlated with STAI-state scores (Fig. 3A).
Fold changes in miR-16 levels from two days before to one month
after the examination were significantly (r = 20.517, p = 0.010)
correlated with fold changes in WNT4 mRNA levels over the same
time points (Fig. 3B).
WNT4 is an in vivo Target for miR-16
The 39 UTR of WNT4 mRNA contains a miR-16-binding site
(Fig. 4A). Overexpression of precursor miR-16 reduced WNT4
mRNA levels (Fig. 4B and C). Finally, interaction between miR-16
and 39 UTR of WNT4 mRNA was confirmed using HEK293 cells
transfected with a plasmid encoding YFP-tagged WNT4 39UTR
(Fig. 4D). As shown in Fig. 4E and F, overexpression of miR-16
reduced both protein and mRNA levels of YFP-tagged WNT4
39UTR, suggesting that WNT4 is an in vivo target for miR-16.
Discussion
miRNAs are involved in the control and maintenance of normal
physiological functioning of the central nervous system, including
brain development [17], neuron maturation [18], neurogenesis
[19,20], and synaptic plasticity [21,22]. Both acute and chronic
stressors alter miRNA expression profiles in a brain regiondependent
fashion [23,24], and changes in miRNA expression
have been suggested to represent an integral part of the stress
response. It is possible that specifically up-regulated miRNAs may
serve as down-regulators of target mRNAs, the high expression of
which may lead to detrimental effects. The stress-mediated
miRNA response may activate a program of gene expression that
works in concert to produce an adaptive response to the stressor.
In fact, recent animal studies suggest an important role of miRNAs
in stress coping reactions [5–9].
In a previous study, we analyzed miRNA expression profiles in
peripheral blood of medical students challenged by a nationallyadministered
examination for academic promotion and we
identified miR-144/144* and miR-16 as acute psychological
stress-responsive miRNAs [10]. Presently, any psychophysiological
role of these miRNAs in peripheral blood remains unclear.
However, we suggested that miR-144* and miR-16 might function
as down-regulators of inflammatory cytokine responses when
exposed to brief naturalistic stressors [10]. Compared to the
examination for academic promotion, the national examination
for medical practitioners employed in this study is a much more
stressful event for medical students. Most students were under
pressure during the preparation period for more than six months.
Thus, preparation for the national examination for medical
practitioners is considered to cause chronic psychological stress
[11,12]. Even in this stress model, we could again detect upregulation
of miR-16 and miR-144/144*. Furthermore, four
miRNAs (miR-20b, -26b, -29a, and -126) were newly identified as
psychological stress-responsive miRNAs.
Among the identified miRNAs, the elevation of miR-16 levels is
significantly and positively correlated with anxiety levels, but not
with salivary cortisol levels. There was no relationship between
STAI-state scores and salivary cortisol. These results suggest that
the miR-16 response might be related to anxiety rather than
cortisol responses. However, further studies are needed to prove
this hypothesis. Recent observation indicated that miR-16 was
induced by psychological stressors such as maternal deprivation
and chronic unpredictable stress and reduced brain-derived
neurotoropic factor (BDNF) in rat hippocampus [25]. Serotonin
transporter (SERT) mRNA is a target of miR-16, and miR-16 is
suggested to contribute to the therapeutic action of selective
serotonin reuptake inhibitors [26]. miR-16 elevation during the
pre-examination periods was associated with down-regulation of
miR-16 target mRNA expression (WNT4, FGFR1, and MAK).
WNT4 encodes wingless-type MMTV integration site family
member 4 (Wnt4) that is the first signaling molecule shown to
influence the sex-determination cascade. Wnt4 also regulates
neuronal differentiation and angiogenesis in the central nervous
system [27]. Although any function of Wint4 in leukocytes and its
psychophysiological role have not been documented, WNT4
mRNA levels were negatively correlated with STAI-state scores
two months before the examination. Moreover, WNT4 mRNA
expression did recover during the post-examination period, and
this recovery was inversely correlated with the decrease of miR-16
to the baseline level over the same time points. Experiments with
HEK293T cells transiently overexpressing Flag-tagged WNT4
39UTR and miR-16 show that miR-16 interacted with WNT4
39UTR and reduced Flag expression. Although psychophysiological
role of miR16/Wnt4 in leukocytes still remains unclear, miR-
16 may respond to stressful situations and participate in the stress
responses during the pre-examination period.
Our subjects also had significantly increased miR-20b, miR-
26b, and miR-126 levels both two months and two days before the
license examination. At present, however, it is difficult to exactly
explain the psychophysiological meanings of the elevation of these
miRNAs. miR-20b modulates VEGF expression by targeting HIF-
1a and STAT3 in MCF-7 breast cancer cells [28]. It has been
reported that miR-26b expression is up-regulated in the frontal
cortex of CD1 mice after acute restraint stress [24]. Recently, it
has been shown that miR-29 suppresses IFN-c production and
immune responses to intracellular pathogens by directly targeting
IFN-c mRNA [29]. The elevation of miR-26b might be involved
in the suppression of IFN-c. In addition, miR-16 and miR-144*
might play a role in the inhibition of inflammatory cytokine
Chronic Stress-Responsive microRNAs in Blood
PLOS ONE | www.plosone.org 7 October 2013 | Volume 8 | Issue 10 | e75960
response. miR-144*
is overexpressed in T cells from patients with
pulmonary tuberculosis and is suggested to suppress IFN-c and
TNF-a production and T cell proliferation [30]. Recently, it has
been shown that 39 UTRs of IL-6 and TNF-a mRNAs contain the
miR-16-binding sites, and over-expression of miR-16 could
significantly down-regulate TNF-a and IL-6 expression level in
A549 cells [31]. In a previous study, we suggested that miR-144*
and miR-16 might be one of the negative regulators for
inflammatory cytokine responses after exposure to naturalistic
stressors [10]. In the present study, we also measured circulating
IL-1b, IL-6, TNF-a, and IFN-c at the three time points, while no
significant change was detected (Table S1). Although we could not
detect any significant relationship between changes in miR-20b, -
26b, -29a, or -126 levels and changes in salivary cortisol levels,
state anxiety, or well-known stress responsive mRNA levels, the
identified miRNAs might at least in part activate a program of
gene expression that works in concert to produce an integrated
response to the stressor.
The fold-level changes in miRNAs in our healthy subjects are
smaller, when compared with those in peripheral blood of several
disorders such as Alzheimer disease [32], depression [33], and
autism [34]. It is possible that disease-associated changes are likely
to be larger than those observed in stress responses of healthy
subjects. In addition, as described in a previous manuscript [10],
individual variations of miRNA levels in peripheral blood from
healthy subjects are much smaller than those of mRNA levels. We
applied repeated measures ANOVA to extract candidate genes by
microarray, and 78% of candidate miRNAs could be validated as
significantly differentially expressed miRNAs by qPCR.
The present study is limited to a small sample size of young,
healthy males and may lack statistical power. Presently, sex
differences in miRNA expression profiles and their responses have
not been fully documented. Our findings should be further
examined using a larger number of subjects that includes both
sexes. Considering potential roles of miRNAs in the regulation of
gene expression under stressful conditions [10], the identified
miRNAs may participate in integrated stress responses when
exposed to naturalistic stressors in healthy young men. Further
studies are needed to elucidate the psychophysiological meaning of
the elevated miRNA levels.
Supporting Information
Figure S1 qPCR validation of time-dependent changes
in seven miRNA levels. Time-dependent changes in miR-16
(A), miR-20b (B), miR-26b (C), miR-126 (D), miR-144 (E), miR-
144* (F), and miR-29a (G) levels were confirmed by qPCR using
U6 snRNA as an endogenous quantity control. Values are mean 6
SEM (n = 25). *In the graphs, significantly different by repeated
measured ANOVA and Bonferroni post hoc test (p,0.05).
(PDF)
Table S1 Time-dependent changes in serum cytokines. Venous
blood was collected after the sampling of saliva (between 16:00 and
17:00) and immediately poured into serumseparator tubes
(Becton-Dickinson, Franklin Lakes, NJ, USA) for cytokine
measurement. Separated serum was stored at 280uC until
analysis. Their serum concentrations were measured using the
Bio-PlexPro Human Cytokine x-plex assay (Bio-Rad, Richmond,
CA, USA). Data were collected using the Bio-Plex suspension
array system according to the manufacturer’s instructions (BioRad)
and were expressed as pg/ml.
(DOC)
Author Contributions
Conceived and designed the experiments: MH Y. Kuwano KR. Performed
the experiments: MH S. Katsuura-Kamano KF YA. Analyzed the data: Y.
Kuwano KM KN. Contributed reagents/materials/analysis tools: Y.
Kamezaki S. Kano. Wrote the paper: MH Y. Kuwano KR.
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