Circulating-microRNAs-as-biomarkers-for-depression-- 2018 Journal-of-Affecti

Journal of Affective Disorders 233 (2018) 68–78
Contents lists available at ScienceDirect
Journal of Affective Disorders
journal homepage: www.elsevier.com/locate/jad
Review article
Circulating microRNAs as biomarkers for depression: Many candidates, few
finalists
Heidi Yuana,c, David Mischoulonb, Maurizio Favab, Michael W. Ottoa,
a
b
c
T
⁎
Department of Psychological and Brain Sciences, Boston University, United States
Depression Clinical and Research Program, Massachusetts General Hospital and Harvard Medical School, United States
Pomona College, Claremont, CA, United States
A R T I C L E I N F O
A B S T R A C T
Keywords:
MicroRNA
Depression
Biomarker
Plasma
Serum
Circulating
Background: Recent research has highlighted the potential of microRNAs to serve as physiological indicators of
disease process among clinically depressed patients.
Methods: In a comprehensive literature search through PubMed, we identified 23 articles comparing circulating
(blood, plasma, or serum) microRNA expression levels in depressed versus healthy human subjects. Six studies
examining circulatory microRNA expression through animal models of depression were also identified through
the search and details of each study were outlined. A meta-analytic evaluation of these studies was not considered feasible, given the absence of concordance in the literature to date.
Results: A total of 178 specific microRNA candidates were identified in the human studies as significantly expressed among depressed samples. Ninety-seven of these microRNAs were upregulated, 75 were downregulated,
and 6 showed mixed expression in depressed samples. Few microRNAs were consistently expressed across studies; the most consistent evidence was for microRNA-132, with replication in 4 different studies. Among animal
studies, 2 studies investigated microRNA-16 through distinct stress-induced depression models.
Limitations: Structural variations in microRNA sequences, methodological inconsistencies in technology used
among studies to measure microRNA expression levels, differences in the clinical severity and forms of depression among subjects, and the overall paucity of studies make it difficult to ascertain any robust, preliminary
targets deserving of biomarker potential.
Conclusions: Ongoing research needs to address this high rate of non-replication as well as the methodological
and reporting challenges of microRNA experimentation in order to determine valid effect sizes for the more
proliferative candidates associated with depression.
1. Introduction
There is a clear need to understand more about the underlying
biology of depressive disorders and how antidepressants exert their
biological effects that translate into improvement in mood. Although
the treatment of depression has progressed a great deal since the
emergence of the first antidepressants in the 1950s, there are still many
limitations to current therapies. As it stands, only about half of individuals with depressive disorders respond to FDA-approved antidepressants (Undurraga and Baldessarini, 2012). Among those who
respond, many will eventually relapse (Forte et al., 2015). More aggressive therapies such as ECT or ketamine often work faster and better,
but their effects tend to be short-lived. Recurrence rates following a
course of ECT, even with continuation therapy, are high (almost 40%)
within the first 6 months post-treatment, particularly without
⁎
prophylactic antidepressant administration (Jelovac et al., 2013). The
beneficial effects of single ketamine administrations can also wear off
quickly, sometimes within hours or days, and thus require frequent
intravenous or intranasal infusions to sustain the effect (Schwartz et al.,
2016), which may be inconvenient for some patients. In addition, these
therapies carry particular risks that may make them undesirable for
many depressed patients (Andrade et al., 2016; Li and Vlisides, 2016).
The treatment of depression is largely a trial-and-error process, with no
consistent evidence to suggest that any one treatment is better than
another for a specific patient presenting with depression (Kato, 2012).
The search for feasible physiological indicators to characterize depression has been a longstanding endeavor. One result of this need has
been the search for biomarkers of depression. Stable biomarkers such as
genetic variations or polymorphisms may provide information about
risk or type of depression. Other biomarkers that emerge or change over
Correspondence to: 648 Beacon St., Floor 6., Boston, MA 02215, United States.
E-mail address: [email protected] (M.W. Otto).
http://dx.doi.org/10.1016/j.jad.2017.06.058
Received 26 December 2016; Received in revised form 14 March 2017; Accepted 26 June 2017
Available online 27 June 2017
0165-0327/ © 2017 Elsevier B.V. All rights reserved.
Journal of Affective Disorders 233 (2018) 68–78
H. Yuan et al.
such as heart disease, sepsis, and stroke (Ai et al., 2010; Wang et al.,
2010; Tan et al., 2009).
The goal of using microRNAs to distinguish patients with psychiatric disorders from healthy populations is also becoming more prevalent. A number of animal, human, and bioinformatic studies have
demonstrated the potential of microRNA levels to aid in the diagnosis of
various psychiatric diseases (Issler and Chen, 2015). Growing evidence
continues to highlight the regulatory role of microRNAs in schizophrenia (Schizophrenia Psychiatric Genome-Wide Association Study
(GWAS) Consortium, 2011), bipolar disorder, autism, and major depressive disorder (Geaghan and Cairns, 2015). Regulation by microRNAs of CREB/BDNF pathways that affect neural and synaptic plasticity has been suggested as one mechanism by which microRNAs
contribute to depressive and suicidal behavior or affect (Serafini et al.,
2014). Expression of specific microRNA candidates and their target
proteins or signaling pathways has been examined in various brain
regions of stress-induced animal models and in post-mortem brain
tissue of depressed suicide patients. Indirect mechanisms by which
microRNAs modulate depression and suicidal behavior include CREB
phosphorylation, BDNF expression, 5-HT cascades, tropomyosin receptor kinase B (TrkB-T1) expression, repressor element-1 silencing
transcription factor 4 (REST4) activity, transcription factors such as
NOVA1 involved in synaptic plasticity and neurotransmitter release,
and glucocorticoid receptor (GR) expression (Serafini et al., 2014). It is
evident that microRNAs are fundamentally involved in the intracellular
signaling and developmental timing of neurons and other cells associated with depression. In spite of this, little progress has been made to
uncover the mechanisms and systems of circulating microRNAs.
Consistent with the goal of identifying novel biomarkers of depression, initial research used animal models to identify changes in
microRNA levels associated with behavioral paradigms of depression
(Smalheiser et al., 2011) or efficacy of antidepressant treatments
(Baudry et al., 2010; Smalheiser et al., 2014; O'Connor et al., 2013). An
additional body of work has been devoted to elucidating human microRNA expression levels in depressed subjects relative to healthy
controls. With the intention to conduct a meta-analysis of the findings
in this area, we performed a literature search on studies evaluating
microRNA expression levels found exclusively in human blood, serum,
or plasma samples, which would render them relatively accessible
biomarkers of depression. Examination of the published literature,
however, revealed a notable absence of concordance among microRNA
candidates. Accordingly, rather than conducting a meta-analysis of
promising candidate(s), this report provides a qualitative summary of
the current state of knowledge in this new area of study, taking into
account both human studies as well as the animal models used to examine depression-related microRNA alterations.
time with treatment (e.g. immune markers) may help determine the
effectiveness of selected treatment, allowing clinicians to make earlier
decisions about whether to continue or alter the treatment plan
(Leuchter et al., 2010; Papakostas et al., 2013). Markers that emerge
early in treatment are the most desired ones for this reason (Leuchter
et al., 2010). Because biomarkers can potentially identify the underlying mechanism of a particular depressive disorder, they can also guide
the selection of a specific treatment to better match the biology of the
patient's condition (Bartova et al., 2010). For example, if it were determined that a particular depressive disorder results from increased
inflammatory activity, an anti-inflammatory drug could potentially be
selected as a first line of treatment, in hopes of obtaining the desired
response and bypassing unnecessary and ineffective trials of other
medications.
A number of biomarker candidates have emerged as promising diagnostic tools for major depressive disorder. These candidates include
cytokines and inflammatory markers, HPA-axis hyperactivity (via cortisol levels), neurotrophins, oxidative stress markers, thyroid function,
and other genetic or genomic markers (Young et al., 2016). However,
given the absence of consistent results and the paucity of replicated
studies, it has been difficult to distinguish particularly noteworthy
candidates as comprehensive indicators of the disease (Young et al.,
2016). Evidence to suggest a genetic basis of depression is growing, and
with the advent of proteomic and genomic profiling technologies comes
the preference to discover genetic biomarkers involved in the pathology
of depression.
MicroRNAs are small, endogenously-expressed, non-coding RNA
transcripts, approximately 22 nucleotides in length, with unique sequences targeting mRNAs for translational repression or destabilization. By binding to the 3’ untranslated region (UTR) of their target
mRNA, microRNAs reduce the overall output of associated proteins,
and are thereby crucial to a number of cell signaling, differentiation,
and proliferation processes (Bartel, 2004). Circulating microRNAs are
of particular interest due to their ease of access and their stability in
samples obtained from body fluids (Chen et al., 2008; Mráz et al.,
2009). Stability of these molecules is afforded in large part by the
packaging through which they exert their functionality. MicroRNAs in
circulation exist in high-density lipoproteins (Vickers et al., 2011) and
exosomes (Hu et al., 2012) that protect and prevent them from degradation before they are released to target cells (Montecalvo et al.,
2012). The machinery of these packaging molecules is important in
understanding the functionality of microRNAs - some models have been
proposed and investigated to explain the mechanism for microRNA
secretion. Among these is a ceramide-dependent exosomal release
pathway (Kosaka et al., 2010), plasma microvesicle transfer (Valadi
et al., 2007; Hunter et al., 2008), and for immune cells, antigen-driven
unidirectional exosomal transfer (Mittelbrunn et al., 2011). Interestingly, the majority of microRNAs found among humans are not exclusively expressed in a single tissue. In fact, a range of highly-abundant
microRNAs were found to be ubiquitously expressed in multiple human
tissues, demonstrating the low cell-type specificity of these regulatory
molecules (Landgraf et al., 2007). This further highlights the power of
microRNAs in circulating biofluids as influential moderators of various
physiological systems and processes.
Circulatory microRNAs have served as useful physiological indicators of disease states (Reid et al., 2011). The first study published
linking cancer with circulating microRNAs examined the microRNA
expression profile in the serum of patients with large B cell lymphoma,
and found a set of microRNAs to be significantly upregulated in cancerous patients (Lawrie et al., 2008). Following this discovery, a
number of additional studies emerged that characterized the microRNA
expression profile of patients with varying cancers, including prostate,
lung, colorectal, ovarian, pancreatic and breast cancer (Mitchell et al.,
2008; Hu et al., 2010; Ng et al., 2009; Resnick et al., 2009; Wang et al.,
2009; Heneghan et al., 2010). In addition, alterations in microRNA
expression profiles were shown to be implicated in other conditions
2. Method
For our literature search of the PubMed database through December
17, 2016, we used the Boolean term (((microRNA or miR or miRNA))
AND depress*) AND (blood OR serum OR plasma), to include any articles with the search terms “microRNA,” “miRNA,” or “miR”; “depress,” “depressed,” or “depression”; and “blood,” “serum,” or
“plasma.” The term “depress*” was used instead of specifying “major
depressive disorder” or any of its derivatives to include all articles examining microRNAs in relation to any form of depression. This search
yielded 126 articles. The bibliographies of 7 reviews produced from this
search (Dwivedi, 2013, 2014; Dwivedi, 2015; Rucker and McGuffin,
2014; Kichukova et al., 2016; Vijayan and Reddy, 2016; Eyre et al.,
2015) were consulted and 26 additional studies perceived as relevant
by title or citation context were added to the list of articles. Human
studies were included if they met the following inclusion criteria: examined microRNA expression levels, microRNAs obtained from circulating biofluid such as plasma or serum, and included depressed human
subjects and control group. Animal studies were included if they met
69
70
PSD
3
18
32
8
63
20
32
40
18
36
30
6
169
34
5
40
3
11
16
32
50
10
16
Depressed
patients sample
size
3
17
32
9
73
20
30
20
18
30
30
6
52
20
5
40
3
11
14
18
41
10
13
Healthy
patients
sample size
N
N
N
N
N
N
Y
Y
Y
N
N
N
Y
N
Y
N
N
Y
N
Y
N
Y
Y
Treatment
Response
Examined
Y
Y
Y
Y
Y
N
–
–
–
–
Y
N
SNRI, SSRI, TeCA, ECT,
benzodiazepines
–
–
KET, ECT
Y
Y
Y
N
N
N
N
Y
N
N
N
N
Y
–
–
SSRI
–
SSRI
–
SSRI
–
–
–
SSRI
–
SSRI
N
N
Candidates identified via
previous research,
bioinformatics or database
scan
Unspecified
(naturalistic)
SSRI
Antidepressant Class
Y
N
N
N
N
Y
N
Y
N
N
N
Y
Y
Y
Y
N
Y
Y
Y
Y
N
Y
Y
Candidates identified via
comprehensive screen
(microarray)
HDRS−17
MADRS-S
HDRS−17
BDI
HDRS−17
HDRS−17
HDRS−24
HDRS
HDRS−17
HDRS−24
unspecified
HDRS−24
MADRS-S
BDI
HDRS−17
SDS
HDRS
HDRS−24
HDRS−17
HDRS−21
HDRS, MADRS
HDRS
HDRS
Clinical assessment
used to assess
depression
MDE, major depressive episode; MDD, major depressive disorder; PBMC, peripheral blood mononuclear cells; HDRS, Hamilton Depression Rating Scale; BDI, Beck Depression Inventory; MADRS-S, Montgomery–Åsberg Depression Rating Scale (selfrated); SDS, Self-rating Depression Score; Y, yes; N, no, SSRI, selective serotonin reuptake inhibitor; KET, ketamine; TeCA, tetracyclic antidepressant; ECT, electroconvulsive therapy; SNRI, selective norepinephrine reuptake inhibitors; AAD, atypical
antidepressant.
Zhang et al. (2016)
MDD
MDD
MDD
PBMCs
Serum
Peripheral blood
leukocytes
Plasma
MDD
Blood
MDD
MDD
MDD
MDD
MDD
MDD
PTSD & MDD
MDD
Blood
Blood
Blood
Serum
Plasma
Blood
Plasma
Plasma
MDD
MDD
MDD
MDD
MDD
MDD
Serum
PBMCs
Blood
Plasma
Blood
Plasma
MDD
MDD
MDD
Blood
Blood
Blood
MDE
PBMCs
Belzeaux et al.
(2012)
Bocchio-Chiavetto
et al. (2013)
Li et al. (2013)
Fan et al. (2014)
Issler et al. (2014)
Liu et al. (2014)
Lopez et al. (2014)
Camkurt et al.
(2015)
Li et al. (2015)
Song et al. (2015)
Su et al. (2015)
Wan et al. (2015)
Wang et al. (2015)
Wingo et al. (2015)
Enatescu et al.
(2016)
Gururajan et al.
(2016)
He et al. (2016)
Liu et al. (2016)
Maffioletti et al.
(2014)
Marques et al.
(2016)
Roy et al. (2017)
Sun et al. (2016)
Clinical
condition
miRNA source
Study
Table 1
Details of studies comparing circulating microRNA expression data between depressed subjects and healthy controls.
H. Yuan et al.
Journal of Affective Disorders 233 (2018) 68–78
Journal of Affective Disorders 233 (2018) 68–78
H. Yuan et al.
Fig. 1. Venn diagram showing 178 microRNAs that were differentially expressed (significant, with p < 0.05) across the 23 relevant human studies. Ninety-seven miRNAs are upregulated
and 75 miRNAs are downregulated in MDD/depression patients compared to controls. Six show mixed results (both upregulation and downregulation) across the studies. All microRNAs
in Venn diagram are specific microRNAs. MicroRNAs that appear more than once across studies are indicated. Note on nomenclature: miR-548ai refers to specific microRNA sequence
with accession number MI0016813 in miRBase. All microRNAs are specific microRNAs pertaining to human samples; thus, the hsa- prefix is implied.
2015; Gururajan et al., 2016) and miRNA-16 was also repeatedly analyzed in 3 studies (Song et al., 2015; Issler et al., 2014; Gururajan et al.,
2016), although no significant relation with depression was observed
(Table 2).
the following criteria: examined expression of one or multiple microRNAs in circulating biofluid (i.e. blood, serum, plasma, or CSF) and
induced depression among animal subjects via an established model.
Human studies were excluded if they were in review format (13), did
not clinically measure depression (51), used bioinformatics but did not
provide microRNA expression levels in biofluid samples (5), had no
microRNA expression data (33), or did not measure microRNA expression in blood or its derivatives (4). All animal studies identified
from title and abstract review were included. This resulted in 22 human
studies, 5 animal studies, and 1 human and animal study, all of which
met criteria for potential quantitative analysis. Attempts were made to
obtain more microRNA expression data from some authors of the selected studies; no information was received.
3.2. MicroRNA expression in animal models of depression
The diverse nature of the studies conducted using animal models of
depression rendered it difficult to abstract data pertaining to particular
microRNAs of interest. Table 3 shows the details of the 6 animal studies
that involved the expression of one or multiple circulatory microRNAs
in an animal model of stress-induced depression. Three studies extracted microRNA expression from rat blood samples (Song et al., 2015;
Ryan et al., 2013; Zhao et al., 2013) and 3 other studies did so from rat
serum (Zurawek et al., 2016; Aschrafi et al., 2016; Duan et al., 2016)
(Table 3). Among the 6 studies, only examination of microRNA-16
expression was repeated: one study investigated the effect of altering
miR-16 levels on antidepressant response whereas the other measured
miR-16 levels in stress-induced subjects. Four studies used a stress-inducing method among animal subjects using chronic (unpredictable)
mild stress. The source of microRNAs examined varied and included
blood (Ryan et al., 2013; Zhao et al., 2013; Song et al., 2015), cerebrospinal fluid (Song et al., 2015), and serum (Zurawek et al., 2016;
Aschrafi et al., 2016; Duan et al., 2016).
3. Results
3.1. MicroRNA expression in depressed human subjects
Across the 23 studies providing human microRNA data (Table 1), a
total of 178 distinct microRNAs were identified as significantly related
to depression (Fig. 1). Ninety-seven of these microRNAs were significantly upregulated while 75 of these were downregulated in patients meeting criteria for depression in at least one of the 23 studies.
The remaining 6 microRNAs (let-7b, miR-133a, miR-331-5p, miR-335,
miR-345, miR-433) showed mixed results, with some studies indicating
significant upregulation among depressed patients compared to healthy
controls, and others showing significant downregulation (Fig. 1). In
addition, we tried to obtain expression data from all experimental microarray screens as well as real-time PCR verification analyses, including those that were not (significantly) differentially expressed between depression patients versus controls. From the readily available
data, we found that 1 specific microRNA (miR-132) had consistent
expression data in 4 independent studies (Li et al., 2013; Su et al., 2015;
Bocchio-Chiavetto et al., 2013; Liu et al., 2016). Comparative expression levels for miRNA-182 was available in 3 studies (Li et al., 2013,
4. Discussion
Given this limited concordance among human studies to date, we
deemed a meaningful meta-analysis unfeasible. This absence of concordance was evident for both associations to diagnostic group (depressed vs. healthy, n = 20) and treatment response (degree of response, n = 7). For example, the most prevalent microRNA investigated
in human studies to date, miR-132, was examined in both studies
comparing its expression levels between depressed patients and healthy
controls (Li et al., 2013; Su et al., 2015; Liu et al., 2016) and studies
71
Journal of Affective Disorders 233 (2018) 68–78
H. Yuan et al.
Table 2
Circulating microRNAs whose expression in blood, plasma, or serum of patients with depression is measured in at least 2 of the 23 human studies.
Specific microRNA
Relative expression in depressed patients (vs. controls)
Author of study in which specific microRNA appears
Number of independent studies
let-7b
Downregulated
Upregulated
Upregulated
Upregulated
NS
NS
NS
Upregulated
Upregulated
Upregulated
Upregulated
NS
Upregulated
NS
Downregulated
Upregulated
Downregulated
NS
NS
NS
Upregulated
NS
upregulated
NS
Upregulated
Upregulated
Upregulated
Upregulated
Upregulated
Downregulated
Upregulated
Upregulated
Upregulated
Upregulated
Upregulated
NS
NS
Upregulated
downregulated
Upregulated
downregulated
Upregulated
downregulated
Upregulated
NS
Downregulated
NS
Downregulated
Upregulated
Upregulated
Downregulated
Upregulated
Upregulated
Downregulated
Upregulated
Upregulated
Upregulated
Upregulated
Downregulated
Downregulated
Upregulated
Upregulated
Gururajan et al. (2016)
Belzeaux et al. (2012)
Bocchio-Chiavetto et al.
Enatescu et al. (2016)
Song et al. (2015)
Issler et al. (2014)
Gururajan et al. (2016)
Bocchio-Chiavetto et al.
Enatescu et al. (2016)
Fan et al. (2014)
Bocchio-Chiavetto et al.
Wang et al. (2015)
Bocchio-Chiavetto et al.
Wan et al. (2015)
Wang et al. (2015)
Sun et al. (2016)
Bocchio-Chiavetto et al.
Camkurt et al. (2015)
Liu et al. (2014)
Wan et al. (2015)
Bocchio-Chiavetto et al.
Sun et al. (2016)
Belzeaux et al. (2012)
Wan et al. (2015)
Enatescu et al. (2016)
Li et al. (2013)
Su et al. (2015)
Bocchio-Chiavetto et al.
Liu et al. (2016)
Zhang et al. (2016)
Belzeaux et al. (2012)
Fan et al. (2014)
Enatescu et al. (2016)
Li et al. (2013)
Li et al. (2015)
Gururajan et al. (2016)
Gururajan et al. (2016)
Enatescu et al. (2016)
Belzeaux et al. (2012)
Belzeaux et al. (2012)
Li et al. (2015)
Bocchio-Chiavetto et al.
Fan et al. (2014)
Belzeaux et al. (2012)
Wan et al. (2015)
Enatescu et al. (2016)
Sun et al. (2016)
Belzeaux et al. (2012)
Maffioletti et al. (2014)
Belzeaux et al. (2012)
Enatescu et al. (2016)
Belzeaux et al. (2012)
Camkurt et al. (2015)
Wan et al. (2015)
Bocchio-Chiavetto et al.
Belzeaux et al. (2012)
Bocchio-Chiavetto et al.
Belzeaux et al. (2012)
Zhang et al. (2016)
Belzeaux et al. (2012)
Enatescu et al. (2016)
Belzeaux et al. (2012)
2
let-7d
miR-16
miR-26a
miR-26b
miR-29b-2–5p
miR-30a-5p
miR-34c-5p
miR-93–5p
miR-106b-5p
miR-107
miR-125a-5p
miR-132
miR-133a
miR-146b-5p
miR-182
miR-223
miR-331–5p
miR-335
miR-345
miR-375
miR-381
miR-425–3p
miR-433
miR-451a
miR-494
miR-589
miR-636
miR-652
(2013)
2
3
(2013)
2
2
(2013)
2
(2013)
2
2
(2013)
2
2
(2013)
2
2
4
(2013)
2
2
3
2
2
2
(2013)
2
2
2
2
2
2
(2013)
2
(2013)
2
2
2
NS, not significant.
for two of its mature products (miR-29a-5p and miR-29b-2-5p), as did
Belzeaux et al. (2012) for miR-331 (miR-331-3p and miR-331-5p).
These distinctions are clinically important, because establishing one
specific microRNA as a biomarker would imply the need to clearly
distinguish between individual mature sequences when designing detection probes that bind specifically to the sequence of interest.
The current animal literature is also limited and inconsistent, given
measuring its alteration levels after a particular intervention (BocchioChiavetto et al., 2013). Variations in the form of specific microRNAs
further intensify this absence of concordance. For example, Maffioletti
et al. (2014) showed significantly different expression levels between
depressed and healthy subjects for three distinct but related let-7 mature sequences: let-7a-5p, let-7d-5p, and let-7f-5p. Similarly, Wang
et al. (2015) found significant differential expression in levels of miR-29
72
73
Serum, Edinger–Westphal nucleus (EWcp)
Serum
Aschrafi et al.
(2016)
Duan et al.
(2016)
Wistar rats (m)
Albino Wistar-R
Amsterdam rats (m)
Wistar-Han rats (m)
Chronic unpredictable mild
stress (CUMS)
Chronic variable mild stress
(CVMS)
Chronic mild stress (CMS)
Injection of anti-miR-16
(antagomir)
Stroke induced by cerebral
artery occlusion, then chronic
mild stress (CMS)
None
Induced depression (stress)
model
Sex of animal subjects are indicated (m = male, f = female) as described in the study.
Serum, ventral tegmental area (VTA),
nucleus accumbens septi (NAcc),
hippocampus, medial prefrontal cortex
CSF, blood
Song et al.
(2015)
Zurawek et al.
(2016)
Sprague-Dawley
rats (m)
Peripheral blood, brain
Zhao et al.
(2014)
Sprague-Dawley
rats (f)
Sprague-Dawley
rats (m)
Blood, dentate gyrus, hippocampus,
frontal cortex, cerebellum
Ryan et al.
(2013)
Animal type
miRNA source
Study
Table 3
Details of animal studies examining circulating microRNA expression through a depression model.
miR-383–5p, miR764–5p
miR−326
miR−16
miR-16
miR-137
miR-212
Main miRNAs
manipulated or
discovered
miR-212 and BDNF levels in whole blood and in dentate
gyrus significantly increased following chronic
administration of electroconvulsive shock therapy (ECS)
Significantly lower miR-137 levels in the brain and
peripheral blood of post-stroke depression rats, miR-137
overexpression in the brain improves behavioral deficits
in post-stroke depression rats
Rats injected with anti-miR-16 antagomir after 2-week
period showed increased anhedonic behaviors, lower
miR-16 levels in CSF, and increased expression of SERT
(major target for the selective serotonin reuptake
inhibitor) protein in rat raphe
Significant increase in serum levels of miR−16
expression after 7 weeks of CMS, significant alterations in
miR−16 levels in VTA, NAcc, and hippocampus after 7
weeks of CMS
Reduced miR-326 levels in the EWcp of CVMS rats; in
fully-recovered CVMS rats, miR-326 levels in both serum
and EWcp recovered to non-stressed levels
6 significantly altered miRNAs in serum of CUMS rats
compared to controls, 16 significantly altered miRNAs in
serum of CUMS rats with electro-acupuncture (EA)
treatment; expression of miR-383–5p and miR-764–5p
were upregulated after CUMS and then downregulated
by EA intervention
Main microRNA-related finding (s)
Investigation of EA on miRNA
expression
No antidepressants
No antidepressants
Investigation of miRNA on
antidepressant response
No antidepressants; investigation
of miRNA on depression behavioral
response
Investigation of ECS therapy on
miRNA expression
Involvement of antidepressants/
treatment intervention
H. Yuan et al.
Journal of Affective Disorders 233 (2018) 68–78
Journal of Affective Disorders 233 (2018) 68–78
H. Yuan et al.
2015; He et al., 2016; Sun et al., 2016; Camkurt et al., 2015; Su et al.,
2015; Issler et al., 2014; Liu et al., 2016; Roy et al., 2017; Marques
et al., 2016; Lopez et al., 2014; Li et al., 2013). Maffioletti et al. (2016)
employed both methods to select their set of microRNAs (Table 1).
These considerations are relevant because they underpin the fundamental technical discrepancies that may emerge from experimental
analyses of microRNA expression. Of the currently available technology
for microRNA expression profiling, the most common approaches include bioinformatics and high-throughput screens via microarrays, RNA
sequencing, or real-time quantitative PCR (Issler and Chen, 2015). The
inherent biological variability of microRNA expression patterns among
participants can further complicate results obtained from a qPCR analysis. One important step that must be taken in order for microRNA
expression profiling to be valid across a number of independent studies
is to establish the natural variance of circulating microRNA levels in a
healthy population of individuals (Issler and Chen, 2015). For studies
employing PCR to quantify expression levels among patients in comparison to healthy controls, identifying adequate reference genes is
essential. In fact, the normalization of microRNA expression data requires reference genes that demonstrate considerably stable expression
and abundance levels similar to the full amount of microRNA in the
original sample (Bustin et al., 2009). Most studies have used statistical
methods or database-identified RNAs or microRNAs as normalization
controls (Mestdagh et al., 2009). Only one study thus far (Liu et al.,
2014) has attempted to systematically and experimentally identify
suitable plasma-based reference genes for microRNA expression analysis, a clear indication that this critical methodological measure has
been underutilized in current research. Repeated experiments are necessary in order to confirm the validity of the selected reference genes
and assert that they demonstrate consistently stable expression across
blood, serum, and plasma).
the diversity of depression models, type and sex of animals used, and
the source of the microRNA (Table 3). Although evidence is scarce, the
data highlight miR-16 involvement in depression models as well as its
potential function in the regulatory mechanisms of antidepressants such
as fluoxetine. Early evidence showed that the antidepressant effect of
fluoxetine involves miR-16-mediated hippocampal neurogenesis that is
propagated by raphe-secreted S100ß neurotrophic factor in the locus
corueleus (Launay et al., 2011). A more recent study (Song et al., 2015)
reveals a significant reduction in cerebrospinal fluid serotonin in antimiR-16 treated rats, suggesting that this circulating microRNA participates in the serotonergic transmission system involved in antidepressant treatment mechanisms. Zurawek et al. (2016) further identified stress-resiliency as a conditional phenotype for increased rat
serum levels of miR-16 in response to induced chronic stress. Despite
these intriguing findings from animal models, none of the four human
studies investigating miR-16 found differences between healthy and
depressed subjects (Camkurt et al., 2015; Gururajan et al., 2016; Issler
et al., 2014; Song et al., 2015), raising questions about the meaning of
these findings in animal models, at least in relation to the circulating
microRNA assays used in the human studies.
4.1. Methodological caveats
The state of the literature is further complicated by the variable
decision to analyze samples of whole blood (Song et al., 2015; BocchioChiavetto et al., 2013; Maffioletti et al., 2014; Wingo et al., 2015; Li
et al., 2015), serum (Wan et al., 2015), plasma (Camkurt et al., 2015;
Liu et al., 2014; Wang et al., 2015; Zhang et al., 2016), peripheral blood
leukocytes (Sun et al., 2016), or peripheral blood mononuclear cells (He
et al., 2016; Fan et al., 2014). These variations in sample collection,
coupled with the range of clinical scales used to determine depression
among patients (i.e. Hamilton Depression Rating Scale [HAMD;
Hamilton, 1960], Beck Depression Inventory [BDI; Beck et al., 1961], or
Montgomery-Asberg Depression Rating Scale [MADRS; Montgomery
and Asberg, 1979] scores) and the marked heterogeneity of this clinical
condition (Cassano and Fava, 2002), make it difficult to determine an
overall effect size across a limited number of studies. Furthermore, to be
as inclusive as possible, two of the studies selected for review assessed
patients with comorbid disorders such as PTSD (Wingo et al., 2015) or
specific forms of depression such as post-stroke depression (Zhang
et al., 2016). It is possible that data for such specific clinical populations
would lower the generalizability of any findings to all depressed patients.
It is also important to note that the therapies used in the studies
covered in this review have diverse mechanisms of action. Some are
primarily serotonergic (selective serotonin reuptake inhibitors, SSRIs),
noradrenergic (tricyclic and tetracyclic antidepressants), combined
serotonergic and noradrenergic (serotonin-norepinephrine reuptake
inhibitors-SNRIs), GABAergic (benzodiazepines), glutamatergic (ketamine), or work via seizure induction followed by yet unclear physiologic effects (ECT). This diversity of psychotropic mechanisms of action
could explain the lack of replication across studies in humans. Further
verification of the mechanisms of microRNAs has the potential to clarify
the variable results in the depression literature for these microRNAs,
helping to identify those factors and conditions that may have a tighter
relationship to microRNA expression than diagnostic status or treatment response.
Concerns surrounding the methodology in quantifying microRNA
levels in human subjects merit additional attention. Whereas some
studies conducted a microarray expression profile prior to verifying a
set of experimental microRNA candidates via quantitative RT-PCR
(Wingo et al., 2015; Fan et al., 2014; Wang et al., 2015; Liu et al., 2014;
Zhang et al., 2016; Wan et al., 2015; Gururajan et al., 2016; Belzeaux
et al., 2012; Bocchio-Chiavetto et al., 2013; Enatescu et al., 2016),
others sought to determine these candidates based on prior studies,
bioinformatics, or microRNA databases (Song et al., 2015; Li et al.,
4.2. Mechanistic explanations for the role of miRNA-132 in MDD
Little is known about the ways in which circulating microRNAs are
shuttled and delivered to their target mRNA transcripts, or the putative
mechanisms of action by which miRNAs regulate or are regulated by
depression. The paucity of these mechanistic explanations calls for
continued comprehensive screening for viable circulating miRNA candidates followed by targeted verification of prominent ones. Although
the data presented in this study cannot confer a target, it is worth acknowledging miR-132 as the most replicated candidate among studies,
with a consistent expression pattern across four independent studies
(Table 2). miR-132 is known to target the 3′ untranslated region (3’
UTR) of the methyl CpG-binding protein 2 (MeCP2) gene that influences the in vivo expression of BDNF (Klein et al., 2007). Numerous
studies have acknowledged the involvement of BDNF in modulating
antidepressant response in conjunction with its signaling receptor TrkB,
although unclear associations prevail with regards to the role of BDNF
in the pathophysiology of major depression (Castrén and Rantamäki,
2010; Lee and Kim, 2010; Martinowich et al., 2007). As Li et al. (2013)
have pointed out, the dysregulation of BDNF in other neurodegenerative and neuropsychiatric diseases (Yasutake et al., 2006; Harrisberger
et al., 2015; Ikeda et al., 2008; Cunha et al., 2006) marks it a nonspecific indicator of disease, thereby requiring additional depressionspecific biomarkers in order to serve as a clinically useful diagnostic.
Proposed mechanisms for the link between circulating miR-132 and
BDNF levels are scarce or incomplete. Evidence suggests feedback
regulation of of miRNA-132 expression by BDNF via a MAPK/ERK1/2
signaling pathway, which alters the concentration of postsynaptic glutamate receptors (Kawashima et al., 2010) and neuronal proliferation in
the hippocampus (Yi et al., 2014). A recent study also reported that
blood levels of miR-132 were associated with visual memory deficits
among depressed patients (Liu et al., 2016). This recognizes that miR132 targets proteins of signaling pathways that regulate the formation
of dendritic spines, whose density and functionality are known to be
74
Journal of Affective Disorders 233 (2018) 68–78
H. Yuan et al.
Ayerst Laboratories.
involved in visual memory (Liu et al., 2016). However, these models do
not relate the mechanism of circulating microRNA-132 to its activity in
the brain, a crucial step in determining whether it can effectively diagnose depression.
Taken together, these findings imply that it may be advantageous to
identify additional circulatory microRNA candidates that indirectly or
directly regulate BDNF among depressed patients, such as miR-182 (Li
et al., 2013). These microRNAs may be not only indicative of MDD, but
also predictive of the BDNF-dependent antidepressant mechanisms associated with treatment response. However, it must be noted that these
proposed mechanisms fall short in elucidating the mode by which circulatory miRNAs participate in the aforementioned processes, further
underscoring a critical gap in the studies to date. More effort must be
made to confirm a correlation between circulatory and brain-localized
microRNAs, in addition to clarifying the transport systems through
which microRNAs access their targets.
Advisory Board/ consultant
Maurizio Fava
Abbott Laboratories; Acadia; Affectis Pharmaceuticals AG;
Alkermes, Inc.; Amarin Pharma Inc.; Aspect Medical Systems;
AstraZeneca; Auspex Pharmaceuticals; Avanir Pharmaceuticals;
AXSOME Therapeutics; Bayer AG; Best Practice Project Management,
Inc.; Biogen; BioMarin Pharmaceuticals, Inc.; Biovail Corporation;
BrainCells Inc; Bristol-Myers Squibb; CeNeRx BioPharma; Cephalon,
Inc.; Cerecor; CNS Response, Inc.; Compellis Pharmaceuticals; Cypress
Pharmaceutical, Inc.; DiagnoSearch Life Sciences (P) Ltd.; Dinippon
Sumitomo Pharma Co. Inc.; Dov Pharmaceuticals, Inc.; Edgemont
Pharmaceuticals, Inc.; Eisai Inc.; Eli Lilly and Company; EnVivo
Pharmaceuticals, Inc.; ePharmaSolutions; EPIX Pharmaceuticals, Inc.;
Euthymics Bioscience, Inc.; Fabre-Kramer Pharmaceuticals, Inc.; Forest
Pharmaceuticals, Inc.; Forum Pharmaceuticals; GenOmind, LLC;
GlaxoSmithKline; Grunenthal GmbH; Indivior; i3 Innovus/Ingenis;
Intracellular; Janssen Pharmaceutica; Jazz Pharmaceuticals, Inc.;
Johnson & Johnson Pharmaceutical Research & Development, LLC;
Knoll Pharmaceuticals Corp.; Labopharm Inc.; Lorex Pharmaceuticals;
Lundbeck Inc.; MedAvante, Inc.; Merck & Co., Inc.; MSI Methylation
Sciences, Inc.; Naurex, Inc.; Nestle Health Sciences; Neuralstem,
Inc.; Neuronetics, Inc.; NextWave Pharmaceuticals; Novartis
AG;Nutrition
21;
Orexigen
Therapeutics,
Inc.;
Organon
Pharmaceuticals; Osmotica; Otsuka Pharmaceuticals; Pamlab, LLC.;
Pfizer Inc.; PharmaStar; Pharmavite® LLC.; PharmoRx Therapeutics;
Precision Human Biolaboratory; Prexa Pharmaceuticals, Inc.; PPD;
Puretech Ventures; PsychoGenics; Psylin Neurosciences, Inc.; RCT
Logic, LLC (formerly Clinical Trials Solutions, LLC); Rexahn
Pharmaceuticals, Inc.; Ridge Diagnostics, Inc.; Roche; Sanofi-Aventis US
LLC.; Sepracor Inc.; Servier Laboratories; Schering-Plough Corporation;
Shenox Pharmaceuticals; Solvay Pharmaceuticals, Inc.; Somaxon
Pharmaceuticals, Inc.; Somerset Pharmaceuticals, Inc.; Sunovion
Pharmaceuticals; Supernus Pharmaceuticals, Inc.; Synthelabo; Taisho
Pharmaceutical; Takeda Pharmaceutical Company Limited; Tal
Medical, Inc.; Tetragenex Pharmaceuticals, Inc.; TransForm
Pharmaceuticals, Inc.; Transcept Pharmaceuticals, Inc.; Vanda
Pharmaceuticals, Inc.; VistaGen.
Research support
Speaking/Publishing
Abbott Laboratories; Acadia Pharmaceuticals (pending as of 8-1116); Alkermes, Inc.; American Cyanamid;Aspect Medical Systems;
AstraZeneca; Avanir Pharmaceuticals; AXSOME Therapeutics;
BioResearch; BrainCells Inc.; Bristol-Myers Squibb; CeNeRx BioPharma;
Cephalon; Cerecor; Clintara, LLC; Covance; Covidien; Eli Lilly and
Company;EnVivo Pharmaceuticals, Inc.; Euthymics Bioscience, Inc.;
Forest Pharmaceuticals, Inc.; FORUM Pharmaceuticals; Ganeden
Biotech, Inc.; GlaxoSmithKline; Harvard Clinical Research Institute;
Hoffman-LaRoche; Icon Clinical Research; i3 Innovus/Ingenix; Janssen
R & D, LLC; Jed Foundation; Johnson & Johnson Pharmaceutical
Research & Development;
Lichtwer
Pharma
GmbH;
Lorex
Pharmaceuticals; Lundbeck Inc.; MedAvante; Methylation Sciences Inc;
National Alliance for Research on Schizophrenia & Depression
(NARSAD); National Center for Complementary and Alternative
Medicine (NCCAM);National Coordinating Center for Integrated
Medicine (NiiCM); National Institute of Drug Abuse (NIDA); National
Institute of Mental Health (NIMH); Neuralstem, Inc.; NeuroRx; Novartis
AG; Organon Pharmaceuticals; PamLab, LLC.; Pfizer Inc.; PharmaciaUpjohn; Pharmaceutical Research Associates., Inc.; Pharmavite®
LLC;PharmoRx Therapeutics; Photothera; Reckitt Benckiser; Roche
Pharmaceuticals; RCT Logic, LLC (formerly Clinical Trials Solutions,
LLC); Sanofi-Aventis US LLC; Shire; Solvay Pharmaceuticals, Inc.;
Stanley Medical Research Institute (SMRI); Synthelabo; Takeda
Pharmaceuticals;Tal Medical; VistaGen (pending as of 8-11-16); Wyeth-
Adamed, Co; Advanced Meeting Partners; American Psychiatric
Association; American Society of Clinical Psychopharmacology;
AstraZeneca; Belvoir Media Group; Boehringer Ingelheim GmbH;
Bristol-Myers Squibb; Cephalon, Inc.; CME Institute/Physicians
Postgraduate Press, Inc.; Eli Lilly and Company; Forest
Pharmaceuticals, Inc.; GlaxoSmithKline; Imedex, LLC; MGH Psychiatry
Academy/Primedia; MGH Psychiatry Academy/Reed Elsevier; Novartis
AG; Organon Pharmaceuticals; Pfizer Inc.; PharmaStar; United
BioSource,Corp.; Wyeth-Ayerst Laboratories.
5. Conclusions
In summary, across 23 human studies, a total of 178 distinct
microRNAs were identified as significantly related to depression, with
little replication across studies. The most consistent findings were for
upregulated expression of microRNA-132 among depressed patients.
Animal studies fail to align with data from human studies with regards
to microRNA-16. Given the high rates of non-replication to date, it will
be important for future studies examining microRNA correlates of depression to address both the methodological and reporting challenges
for this area of study. Knowing which candidates fail to replicate may
be as important as positive findings during this time of rapid proliferation of targets.
Author disclosures
Role of funding source
This research did not receive any specific grant from funding
agencies in the public, commercial, or not-for-profit sectors.
Stock/Other financial options
None.
Equity holdings
Compellis; PsyBrain, Inc.
Royalty/patent, other income
Patents for Sequential Parallel Comparison Design (SPCD), licensed
by MGH to Pharmaceutical Product Development, LLC (PPD); and patent application for a combination of Ketamine plus Scopolamine in
Major Depressive Disorder (MDD), licensed by MGH to Biohaven.
75
Journal of Affective Disorders 233 (2018) 68–78
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Ment. Health 1 (1), 1003.
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Copyright
For the MGH Cognitive & Physical Functioning Questionnaire
(CPFQ), Sexual Functioning Inventory (SFI), Antidepressant Treatment
Response
Questionnaire
(ATRQ),
Discontinuation-Emergent
Signs & Symptoms (DESS), Symptoms of Depression Questionnaire
(SDQ), and SAFER; Lippincott, Williams & Wilkins; Wolkers Kluwer;
World Scientific Publishing Co. Pte. Ltd.
David Mischoulon
Research Support
Dr. Mischoulon has received research support from the
FisherWallace, Nordic Naturals, Methylation Sciences, Inc. (MSI), and
PharmoRx Therapeutics. He has received honoraria for speaking from
the Massachusetts General Hospital Psychiatry Academy. He has received royalties from Lippincott Williams & Wilkins for published book
“Natural Medications for Psychiatric Disorders: Considering the
Alternatives.”
Acknowledgements
The first author would like to thank Dr. Otto for his mentorship and
guidance in the literature search and review process, and Dr.
Mischoulon and Dr. Fava for devoting their collective time and effort to
editing and improving the draft of this paper.
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Heidi Yuan is currently pursuing her bachelor’s degree in Molecular Biology at Pomona
College in Claremont, California. She worked in the lab of Dr. Michael Otto during the
summer of 2016, where her research centered on exploring and evaluating potential and
current clinical interventions for a variety of psychiatric disorders.
David Mischoulon is the Director of the Depression Clinical and Research Program
(DCRP) at the Massachusetts General Hospital, and an Associate Professor of Psychiatry at
Harvard Medical School. His research interests have focused on the role of complementary and alternative medicine in psychiatry, as well as the role of biomarkers as
moderators and mediators of treatment response with antidepressant therapies. His research has been funded by NARSAD, by the National Institutes of Health, and by the
National Center for Complementary and Integrative Health. He has published more than
230 articles and a textbook on the use of natural medications for the treatment of psychiatric disorders.
Maurizio Fava is Director, Division of Clinical Research of the Massachusetts General
Hospital (MGH) Research Institute, Executive Vice Chair of the MGH Department of
Psychiatry, and Associate Dean in Clinical and Translation Science at Harvard Medical
School. He has had over 30 years of experience as a clinical investigator. Under his direction from 1990 until 2014, the MGH Depression Clinical and Research Program
(DCRP) became one of the most highly regarded depression programs in the world,
conducting research projects in a variety of areas including pharmacotherapy of treatment-resistant depression, alternative medicine, neuroimaging, genetics, neurophysiology, neuroendocrinology, novel pharmacotherapies, and psychotherapy. He has authored or co-authored more than 800 original articles published in medical journals with
international circulation, and which have been cited more than 50,000 times in the literature articles published in medical journals with international circulation, and which
have been cited more than 50,000 times in the literature (h factor > 115). In 2007, he
founded and is now the Executive Director of the MGH Psychiatry Clinical Trials Network
and Institute (CTNI), the first academic Contract Research Organization specialized in the
coordination of multi-center clinical trials in the CNS area. He has successfully obtained
funding, as principal or co-principal investigator, from the National Institute of Health
and industry for a total of more than $95,000,000 in the past 30 years. He has also trained
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H. Yuan et al.
promotion of the emotional and cognitive benefits of exercise. Dr. Otto has been identified as a “top producer” in the clinical empirical literature; he is author of Exercise for
Mood and Anxiety: Proven Strategies for Overcoming Depression and Enhancing Well
Being, with Oxford University Press, as well as over 300 other articles, chapters, and
books spanning his clinical and research interests. Dr. Otto is a past President of the
Association for Behavioral and Cognitive Therapies (formerly AABT), a fellow of the
American Psychological Association, and a member of the Scientific Advisory Board for
the Anxiety Disorders Association of America. Dr. Otto is a regular provider of continuing
education and continuing medical education workshops across the United States.
and mentored over 50 research fellows.
Michael Otto is Professor of Psychology at Boston University. He specializes in the
cognitive-behavioral treatment (CBT) of anxiety, mood, and substance use disorders. An
enduring theme across these disorders is the role of exposure-based emotional tolerance/
acceptance strategies in improving mental health. Current research includes investigations of potential moderators of CBT efficacy, including several translational-research
agendas such as the application of putative memory enhancers (e.g., d-cycloserine) to
facilitate exposure-based treatments. Dr. Otto also focuses on health behavior promotion
ranging from medication adherence and smoking cessation to documentation and
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