Exploiting Circulating miRNAs as Biomarkers in Psychiatric Disorders

Abstract

Non-invasive peripheral biomarkers play a significant role in both disease diagnosis and progression. In the past few years, microRNA (miRNA) expression changes in circulating peripheral tissues have been found to be correlative with changes in neuronal tissues from patients with neuropsychiatric disorders. This is a notable quality of a biomolecule to be considered as a biomarker for both prognosis and diagnosis of disease. miRNAs, members of the small noncoding RNA family, have recently gained significant attention due to their ability to epigenetically influence almost every aspect of brain functioning. Empirical evidence suggests that miRNA-associated changes in the brain are often translated into behavioral changes. Current clinical understanding further implicates their role in the management of major psychiatric conditions including major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ). This review aims to critically evaluate the potential advantages and disadvantages of miRNAs as diagnostic/prognostic biomarkers in psychiatric disorders as well as in treatment response.

1. Introduction

Over the past several years, non-invasive peripheral biomarker analysis has reliably been used in the diagnosis of many diseases and their progression [1, 2]. Although several breakthroughs have been made in other areas of biomedical sciences, there have been very few in the area of mental illnesses [3, 4]. Biologically, homeostatic instability in the brain could be the result of maladaptive processing that can be associated with numerous extrinsic and intrinsic factors [5]. Many of them closely adhere to changes at the gene expression level. MicroRNAs (miRNAs), as members of the small non-coding RNA family and post-transcriptional regulators of gene expression, have recently gained attention due to their ability to epigenetically influence almost every aspect of brain functioning [6, 7]. Through reversible changes in gene expression, miRNAs can cause improper functioning of the central nervous system (CNS) when challenged with aversive environmental stimuli. Evidence supports the pathogenic role of miRNAs in re-patterning neural information processing under many neuropsychiatric conditions, including major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ) [8]. Converging evidence suggests the role of miRNA as a dynamic interface to converse with the internal information processing system, which is reflected by their expression changes in the brain. Besides, miRNA expression changes in the peripheral circulation (i.e., plasma, serum, cerebrospinal fluid, and non-neuronal tissue or cells such as lymphocytes) are found to be highly correlated with changes in neuronal tissue from patients with various neuropsychiatric disorders [9–12]. This is a notable feature of a biomolecule considered to be a disease biomarker both for prognosis and diagnosis [13].

The present review is intended to account for the miRNA-related changes in peripheral circulation in three major neuropsychiatric disorders: MDD, BD, and SZ. This review also aims to provide the clinical significance of circulatory miRNAs as potential diagnostic/prognostic biomarkers as well as treatment response in these psychiatric disorders. Additionally, this review critically addresses the potential advantages and disadvantages of miRNAs as biomarkers in the clinical outset of common psychiatric conditions. Moreover, the current and future methods of miRNA analysis in circulating tissues have also been discussed. The main objectives of the review are to critically assess: 1) the role of miRNAs in neurodevelopment and maturation; 2) neuromolecular functions of miRNAs and their significance as potential diagnostic and prognostic biomarkers in psychiatric illnesses; 3) clinical evidence behind altered miRNA expression in MDD, BP, and SZ; 4) miRNAs as tools in the treatment of psychiatric illnesses; 5) the potential advantages and disadvantages of miRNAs as biomarkers in psychiatric illnesses; and, 6) various methods of miRNA analysis in clinical settings.

2. miRNAs: Tiny Molecules with Significant Roles in Gene Regulation

Any reversible epigenetic process that can alter gene activity without changing the DNA sequence involves a wide range of molecular interplay both at intracellular and extracellular levels [14]. Several cellular and molecular modifiers are found to be engaged at the core of this interplay. miRNAs have recently emerged as promising modifiers in the epigenetic landscape of the brain [15]. As the name implies, miRNAs are short transcripts (19–22 nucleotides long) with no ability to code for proteins. Inherently, miRNAs can modulate gene function by depleting the cellular availability of protein-coding transcripts from being recruited to translational machinery. miRNAs can also regulate several genes at the same time, thus constructing a highly coordinated gene regulatory network which is at the core of many complex biological processes [16, 17]. Recent findings have added another layer of complexity to miRNA functions. MiRNAs act as a sponge to regulate the effect of another class of non-coding RNAs: long non-coding RNA (lncRNA). Mechanistically, lncRNAs can act as a molecular decoy to prevent the binding of miRNAs with their targets, thus creating an alternate axis of gene regulation. This happens through complementary binding at the 3’end of miRNAs named miRNA Response Elements (MRE) and can significantly reduce miRNA-mediated inhibition on target genes [18]. Therefore, the overall antagonizing role of miRNAs in orchestrating the gene regulatory function, either directly or indirectly, can be seen as an outcome of an integrated multicomponent network [19, 20]. Thus, in complex neuropsychiatric conditions that often stem from alterations in multicellular pathways, miRNAs provide a high degree of epigenetic interference by regulating gene expression changes in a highly synchronized fashion [21–24].

3. Canonical miRNA Biogenesis Pathways

MiRNA is a non-coding RNA that has a role in modulating the coding potential of a transcribed mRNA based on characteristic sequence complementarity. Since its first report as an epigenetic modifier, miRNA has emerged from being studied as the modulator of a single protein-coding gene to acting as a potential regulatory hub in controlling a complex gene network either through direct association with protein coding gene or by indirect intermediaries [16]. Despite being small molecules (~22 nucleotides, nt) and having limited potential to go through exon splicing to generate more structural variety, miRNAs exhibit functional diversity by targeting a diverse range of RNA molecules spanning from protein-coding (mRNA) to non-coding RNAs [25, 26]. In humans, so far, ~ 2500 mature miRNAs have been annotated and approximately 30% of them have been found in the intronic regions of protein-coding genes, which sometimes promote the facultative use of their host gene transcription machinery [27, 28]. However, often the miRNA loci are mapped alongside their distinct transcriptional units which mostly use RNA polymerase II for transcription [29].

Mammalian miRNA biogenesis follows a programmed pathway to produce the mature effector molecule (Fig. 1), which starts right after its transcription into a primary transcript (pri-miRNA) by RNA Polymerase II or III in nuclei. Primary transcripts (pri-miRNA) are the immediate products of miRNA transcription with a size of ~1 kb. The pri-miRNA structure typically consists of a stem, a terminal loop, and single-stranded overhang on both 5’ and 3’ sides. Together, with the help of nuclear RNase III enzyme Drosha and DGCR8 (DiGeorge syndrome chromosomal region 8), the pri-miRNA forms a microprocessor complex to crop the extended single-stranded part from both sides and releases a smaller hairpin form with 65 nucleotides in length, called precursor miRNA (pre-miRNA). After being actively exported to the cytosol via Exportin 5 (Expo5), pre-miRNAs are enzymatically processed by RNase III Dicer to generate mature miRNAs. Finally, mature miRNAs are incorporated into the RNA-induced silencing complex (RISC), which helps them to regulate the fate of target genes by pairing primarily to the 3’untranslated region (3’UTR) of protein-coding mRNAs. This canonical binding may either lead to the repression of target mRNA translation or induce target degradation by exonuclease activity [30, 31] (Fig. 1). Because of this feature, miRNAs can regulate entire gene circuitry and therefore play a role in maintaining biological homeostasis. Considering the fundamental role of miRNAs in mediating biological events, any perturbations in their expression may result in the imbalance of homeostasis, which is often reflected as an imbalance in the regulatory network that can distinguish normal vs. disease state [32].

Fig. 1:

MicroRNA biogenesis following canonical pathway and post transcriptional regulation of the target gene. Successful transcription of primary miRNA (pri-miR) depends on the RNA polymerase II machinery in the nuclear environment. Following a canonical path, the pri-miRNA is processed by nuclear RNase III enzyme Drosha to produce precursor miRNA (pre-miRNA). Pre-miRNA is transported to the cytosolic environment with the help of Ran-GTP and Exportin5 transporter complex. In the cytosol, the pre-miRNA is processed by the RNase III enzyme Dicer to generate mature miRNAs. Mature miRNAs are incorporated into the RNA induced silencing (RISC) complex, which regulate gene expression by pairing primarily to the 3′UTR region of protein coding mRNAs to repress target mRNA expression. The arrest in target expression can be caused by any of the three mechanisms: (i) Translational blockage by inhibiting the access of elongation factor/ribosome complex; (ii) Transcript degradation by decapping and exonuclease activity; (iii) Transcript degradation by deadenylation.

4. Role of miRNAs in Plasticity

Growing evidence suggests the involvement of miRNAs in regulating neurogenesis, synapse development, axon guidance, and neuronal plasticity in both developing and adult brains [7, 33, 34]. Thus, disruption in the expression of miRNAs or their downstream functions may lead to pathological conditions associated with neurocognitive functions [35]. Synaptic plasticity is the most adversely affected phenomenon in MDD, BD, and SZ [36]. The following section briefly highlights significant miRNA findings associated with synaptic plasticity.

Dysregulation in synaptic plasticity represents the disability of CNS to properly integrate various neuronal inputs that make adaptive changes in the neurons to promote appropriate response to external stimuli. In the forebrain neurons of Dicer knockout mice, there is a failure to reconcile synaptic plasticity related changes due to the deficiency in pre-miRNA processing [37]. At the morphological level, Dicer deficient mice showed reduced dendritic branch elaboration and a significant increase in dendritic spine length with no concomitant change in spine density. DGCR8 is an essential co-factor in the microprocessing complex responsible for synthesizing precursor miRNA from primary hairpin-like transcripts. Morphological deformities in the dendritic tree and spine development have been reported in a haploinsufficient genetic mouse model of the DGCR8 gene [38]. This study also showed a link between defective miRNA biogenesis and behavioral impairment associated with cognitive function. Additional studies have also suggested the role of miRNAs (miR-132, miR-134, miR-138, and miR-124) in regulating several plasticity related genes, including ARC, CaMKIIα, LimK1, FMRP, CREB, and BDNF [39]. The antagonizing effect of miR-134 on LimK1 was found to cause morphological changes in dendritic spines as well as physiological impairment in postsynaptic sites of excitatory synaptic transmission [40]. The involvement of miRNAs in neurotrophin related roles in synaptic plasticity has been shown in the hippocampal CA1 region of the mouse brain. A deficit in the Sirt1 gene in hippocampal neurons induced miR-134 in the synaptosomal compartment with a concomitant decline in BDNF and CREB protein expression [41]. The role of miR-134 in mediating dendritic spine modification has been found in the context of the DHX36 gene, which actively plays a role in biological processes such as genomic integrity and gene expression regulation [42]. Several other miRNAs have also been identified for their involvement in regulating plasticity-related functions in the brain. These include miR-9, miR-125a/b, and miR-188 [43, 44]. Individually, these miRNAs can epigenetically influence a battery of genes (e.g., REST, FXR1P, CAMKK2-AMPK, PSD-95, BCL-W, SYN-2, NRP-2, 2-AG, and BACE1) that are directly or indirectly associated with synaptic plasticity [45].

5. Current Understanding of miRNAs and their Neuropathogenic Roles in Psychiatric Disorders

The use of postmortem brain samples in studying mental disorders is very well appreciated. Postmortem brain tissues can provide insight about disease associated changes at the cellular and molecular levels. Underlying molecular changes have been shown to be critical to understanding the complex integration of neuronal and non-neuronal signals in the brain. The greatest benefit of molecular studies of the brain is their potential implication in predicting diagnostic biomarkers and effectively designing therapeutic strategies against the disorders. The regulation of gene expression by miRNAs has recently been considered a significant contributor to the pathophysiology of psychiatric illnesses. Over the past decade, central findings from postmortem brain have defined the discernible role of miRNAs as a critical molecular switch in known neuropsychiatric disorders such as MDD, BD, and SZ. Several studies have examined MDD in the context of suicidal behavior. It is important to mention that emotional turmoil in survivors of patients who died by suicide may last for a long time, and in some cases, may end with their own suicide. Thus, it is fundamental to understand the bereavement process after the suicide of a significant other in order to provide proper care, reduce stigma, and improve the outcomes. Although the role of miRNAs is not well understood in the bereavement process or in their link to MDD leading to suicidal behavior [46], postmortem brain studies provide critical information regarding completed suicide, which may help explain the molecular changes happening in the brain under such conditions. In the following section, a brief overview of key miRNA findings and their brain associated pathological changes in neuropsychiatric conditions has been provided: MDD, BD, and SZ. miRNA expression changes in these psychiatric disorders are summarized in Table 1. We have also provided another table (Table 2) to show shared miRNAs across disorders and their direction of expression changes. To demonstrate the reproducibility of the miRNA results, we have also sorted out the miRNA reports which were detected by more than one experimental method (Table 3).

Table 1.

The miRNA Expression Changes in Psychiatric Disorders Based on Postmortem Brain Studies

Disease	Brain areas	Sample size	Methods	miRNA expression changes		Main findings and features	Reference
				Up-regulation	Down-regulation
SZ	Amygdala	22 SZ and 24 Controls	miRNA-seq	miR-196a-2, miR-1975, miR-34c, miR-451, miR-34a, miR-375, miR-144	miR-663, miR-639, miR-132, miR-124–2, miR-212, miR-483, miR-886, miR-585, miR-424, miR-520d, miR-1307		Liu et al., 2018 [20]
	BA9	13 SZ, 2 Schizoaffective, and 21 Controls	Microarray and qPCR validation	miR-106b	miR-26b, miR-30b, miR-29b, miR-195, miR-92, miR-30a-5p, miR-30d, miR-20b, miR-29c, miR-29a, miR-212, miR-7, miR-24, miR-30e, miR-9–3p	5 miRNAs (miR-26b, miR-30b, miR-92, miR-24, and miR-30e) were successfully validated by qPCR. miR-199a, miR-128a, and miR-128b are elevated in haloperidol treated rats compared to untreated rats.	Perkins et al., 2007 [55]
	BA9	35 SZ, 33 BD, and 33 Controls	qPCR			19 % of 234 miRNAs and 18 small nucleolar RNAs were changed due to schizophrenia or bipolar disorder.	Moreau et al., 2011 [57]
	BA9	8 SZ, 9 BD, and 13 Controls	Microarray and qPCR validation	miR-497		exosome sample	Banigan et al., 2013 [52]
	BA9	15 SZ and 15 Controls	Microarray and qPCR validation	let-7d, miR-101, miR-105, miR-126*, miR-128a, miR-153, miR-16, miR-181a, miR-181d, miR-184, miR-199a, miR-20a, miR-219, miR-223, miR-27a, miR-29c, miR-302a*, miR-302b*, miR-31, miR-33, miR-338, miR-409–3p, miR-512–3p, miR-519b, miR-7		10 miRNAs (let-7d, miR-128a, miR-16, miR-181b, miR-181a, miR-20a, miR-219, miR-27a, miR-29c, miR-7) were successfully validated by qPCR.	Beveridge et al., 2010 [58]
	BA10	15 SZ, 15 MDD, 15 BD, and 15 Controls	TLDA and qPCR validation	miR-17–5p, miR-331–5p, miR-16–5p, miR-106b-5p, miR-454–3p, miR-185–5p, miR-429–3p, miR-18a-5p, miR-590–5p, miR-106a-5p, miR-642a-5p, miR-625–5p, miR-219–2-3p	miR-187–3p, miR-485–5p, miR-129–2-3p, miR-511, miR-145–5p, miR-508–3p	synaptosome sample 3 miRNAs (miR-17–5p, miR-145–5p, and miR-219–2-3p) were successfully validated by qPCR	Smalheiser et al., 2014 [48]
	BA46	35 SZ, 35 BD, and 35 Controls	TLDA	miR-34a, miR-132, miR-132*, miR-212, miR-544, miR-7, miR-154*			Kim et al., 2010 [59]
	BA46	37 SCZ/Schizoaffectives and 37 Controls	Microarray and qPCR validation	miR-519c, miR-489–3p, miR-652, miR-382, miR-532, miR-199a*, miR-17–5p, miR-542–3p, miR-199b, miR-592, miR-495, miR487a, miR-425–5p, miR-152, miR-148b, miR-134, miR-150, miR-105, miR-187, miR-154, miR-767–5p, miR-548b, miR-590, miR-502, miR-452*, miR-25, miR-328, miR-92b, miR-433, miR-222	miR-512–3p, miR-423, miR-193a	6 miRNAs (miR-17, miR-107, miR-134, miR-328, miR-382, miR-652) were successfully validated by qPCR.	Santarelli et al., 2011 [61]
	BA46	35 SZ, 32 BD, and 34 Controls	qPCR		miR-346		Zhu et al., 2009 [60]
	BA46	34 SZ and 109 Controls	miRNA-seq	miR-3162, miR-936		correlation-based hierarchical clustering analysis made nine miRNA groups	Hu et al., 2019 [23]
	DLPFC	35 SZ, 31 BD, and 34 Controls	Microarray		miR-132, miR-132*		Miller et al., 2012 [53]
	DLPFC and several regions	1st set (7 SZ, 9 BD, and 10 Controls) 2nd set (35 SZ, 34 BD, and 35 Controls)	qPCR			miR-137 was not significantly changed	Guella et al., 2013 [62]
	STG	15 SZ and 15 Controls	Microarray and qPCR validation	59 of 274 (21%) miRNAs		10 miRNAs (miR-107, miR-15a, miR-15b, miR-16, miR-195, miR-181b, let-7e, miR-20a, miR-26b, miR-19a) were successfully validated by qPCR.	Beveridge et al., 2010 [58]
	STG	21 SZ and 21 Controls	Microarray and qPCR validation	miR-181b, let-7g		miR-181b was successfully validated by qPCR.	Beveridge et al., 2008 [56]
MDD	ACC	15 MDD, 8 BD, 14 Controls	qPCR		miR-34a, miR-184		Azevedo et al., 2016 [50]
	BA9	18 MDD, 17 Controls	TLDA and qPCR validation		miR-142–5p, miR-137, miR-489, miR-148b, miR-101, miR-324–5p, miR-301a, miR-146a, miR-335, miR-494, miR-20b, miR-376a*, miR-190, miR-155, miR-660, miR-130a, miR-27a, miR-497, miR-10a, miR-20a, miR-142–3p	all MDD subjects died by suicide	Smalheiser et al., 2012 [47]
	BA10	15 SZ, 15 MDD, 15 BD, and 15 Controls	TLDA		miR-508–3p, miR-152–3p	synaptosome samples	Smalheiser et al., 2014 [48]
	BA44	14 MDD, 11 Controls	qPCR		miR-1202	miR-1202 regulates the expression of GRM4 and predicts antidepressant response	Lopez et al., 2014 [49]
	BA46	15 MDD, 15 Controls	qPCR	miR-124–3p			Roy et al., 2017 [10]
	LC	9 MDD, 11 Controls	TLDA	miR-17–5p, miR-20b-5p, miR-106a-5p, miR-330–3p, miR-541–3p, miR-582–5p, miR-890, miR-99–3p, miR-550–5p, miR-1179	miR-409–5p, let-7g-3p, miR-1197	all MDD subjects died by suicide	Roy et al., 2017 [21]
BD	ACC	15 MDD, 8 BD, 14 Controls	qPCR		miR-34a, miR-132, miR-133a, miR-212		Azevedo et al., 2016 [50]
	ACC	5BD, 6 Controls	qPCR	miR-149		exosome samples	Choi et al., 2017 [51]
	BA9	35 SZ, 33 BD, and 33 Controls	qPCR			19 % of 435 miRNAs were changed due to schizophrenia or bipolar disorder	Moreau et al., 2011 [57]
	BA9	8 SZ, 9 BD, and 13 Controls	Microarray and qPCR validation	miR-29c		exosome samples	Banigan et al., 2013 [52]
	BA10	15 SZ, 15 MDD, 15 BD, and 15 Controls	TLDA	miR-17–5p, miR-579, miR-106b-5p, miR-29c-3p	miR-145–5p, miR-485–5p, miR-370, miR-500a-5p, miR-34a-5p	synaptosome samples	Smalheiser et al., 2014 [48]
	BA46	35 SZ, 35 BD, and 35 Controls	TLDA	miR-22, miR-133b, miR-145, miR-145*, miR-154*, miR-504, miR-889	miR-29a, miR-32, miR-140–3p, miR-454*, miR-520c-3p, miR-573, miR-767–5p, miR-874		Kim et al., 2010 [59]
	BA46	35 SZ, 32 BD, and 34 Controls	qPCR			miR-346 not significantly changed	Zhu et al., 2009 [60]
	BA46	1st set (7 SZ, 9 BD, and 10 Controls) 2nd set (35 SZ, 34 BD, and 35 Controls)	qPCR			miR-137 not significantly changed	Guella et al., 2013 [62]
	DLPFC	35 SZ, 31 BD, and 34 Controls	Microarray	miR-32, miR-188–5p, miR-187, miR-196b, miR-297, miR-383, miR-490–5p, miR-449b, miR-513–5p, miR-876–3p			Miller et al., 2012 [53]
	Cerebellum	29 BD, and 34 Controls	qPCR	miR-34a		upregulation was confirmed in induced neurons from human fibroblast	Bavamian et al., 2015 [54]

ACC: Anterior Cingulate Cortex, BA: Brodmann Area, BD: Bipolar Disorder, DLPFC: Dorsolateral Prefrontal Cortex, LC: Locus Coeruleus, miRNA: microRNA, MDD: Major Depressive Disorder, PBMC: Peripheral Blood Mononuclear Cells, SZ: Schizophrenia, STG: Superior Temporal Gyrus, TLDA: TaqMan Low Density Arrays

Table 2.

Overlapping miRNAs across MDD, Bipolar Disorder, and Schizophrenia

miRNAs	Disease	Up-regulation	Down-regulation
miR-34a	SZ	Amygdala, Liu et al., 2018 [20]; BA46, Kim et al., 2010 [59]
	MDD		ACC, Azevedo et al., 2016 [50]
	BD	Cerebellum, Bavamian et al., 2015 [54]; Plasma, Sun et al., 2015 [84]	ACC, Azevedo et al., 2016 [50]; BA10, Smalheiser et al., 2014 [48]
miR-132	SZ	BA46, Kim et al., 2010 [59]; DLPFC, Miller et al., 2012 [53]	Amygdala, Liu et al., 2018 [20]
	BD		ACC, Azevedo et al., 2016 [50]
miR-212	SZ	BA46, Kim et al., 2010 [59]	Amygdala, Liu et al., 2018 [20]; BA9, Perkins et al., 2007 [55]
	BD		ACC, Azevedo et al., 2016 [50]
miR-106b	SZ	BA9, Perkins et al., 2007 [55]; BA10, Smalheiser et al., 2014 [48]
	BD	BA10, Smalheiser et al., 2014 [48]
miR-26b	SZ	STG, Beveridge et al., 2010 [58]	BA9, Perkins et al., 2007 [55]
miR-195	SZ	STG, Beveridge et al., 2010 [58]	BA9, Perkins et al., 2007 [55]
miR-92	SZ	BA46, Santarelli et al., 2011 [61]	BA9, Perkins et al., 2007 [55]
miR-30a-5p	SZ		BA9, Perkins et al., 2007 [55]; PBMC, Liu et al., 2017 [85]
miR-30d	SZ	Whole blood, Maffioletti et al., 2016 [11]	BA9, Perkins et al., 2007 [55]
miR-20b	SZ		BA9, Perkins et al., 2007 [55]
	MDD	LC, Roy et al., 2017 [21]	BA9, Smalheiser et al., 2012 [47]
miR-29c	SZ	BA9, Beveridge et al., 2010 [58]	BA9, Perkins et al., 2007 [55]
	MDD	Whole blood, Maffioletti et al., 2016 [11]
	BD	BA9, Banigan et al., 2013 [52]; BA10, Smalheiser et al., 2014 [48]; Whole blood, Maffioletti et al., 2016 [11]
miR-29a	SZ		BA9, Perkins et al., 2007 [55]
	BD	BA46, Kim et al., 2010 [59]
miR-7	SZ	BA9, Beveridge et al., 2010 [58]; BA46, Kim et al., 2010 [59]	BA9, Perkins et al., 2007 [55]
miR-30e	SZ	Plasma, Sun et al., 2015 [84]; PBMC, Liu et al., 2017 [85]	BA9, Perkins et al., 2007 [55]
miR-497	SZ	BA9, Banigan et al., 2013 [52]
	MDD		BA9, Smalheiser et al., 2012 [47]
Let-7d	SZ	BA9, Beveridge et al., 2010 [58]	Whole blood, Maffioletti et al., 2016 [11]
miR-101	SZ	BA9, Beveridge et al., 2010 [58]
	MDD		BA9, Smalheiser et al., 2012 [47]
miR-105	SZ	BA9, Beveridge et al., 2010 [56]; BA46, Santarelli et al., 2011 [61]
miR-16	SZ	BA9, Beveridge et al., 2010 [58], BA10, Smalheiser et al., 2014 [48]; STG, Beveridge et al., 2010 [58]
miR-184	SZ	BA9, Beveridge et al., 2010 [58]
	MDD		ACC, Azevedo et al., 2016 [50]
	BD		ACC, Azevedo et al., 2016 [50]
miR-199a	SZ	BA9, Beveridge et al., 2010 [58]; BA46, Santarelli et al., 2011 [61]
	MDD	Whole blood, Maffioletti et al., 2016 [11]
miR-20a	SZ	BA9 and STG, Beveridge et al., 2010 [58]
	MDD		BA9, Smalheiser et al., 2012 [47]
miR-27a	SZ	BA9, Beveridge et al., 2010 [58]
	MDD		BA9, Smalheiser et al., 2012 [47]
miR-512–3p	SZ	BA9, Beveridge et al., 2010 [58]	BA46, Santarelli et al., 2011 [61]
miR-17–5p	SZ	BA10, Smalheiser et al., 2014 [48]; BA46, Santarelli et al., 2011 [61]
	MDD	LC, Roy et al., 2017 [21]
	BD	BA10, Smalheiser et al., 2014 [48]
miR-590	SZ	BA10, Smalheiser et al., 2014 [48]; BA46, Santarelli et al., 2011 [61]
miR-106a-5p	SZ	BA10, Smalheiser et al., 2014 [48], LC, Roy et al., 2017 [21]
miR-154*	SZ	BA46, Kim et al., 2010 [59]
	BD	BA46, Kim et al., 2010 [59]
miR-489	SZ	BA46, Santarelli et al., 2011 [61]
	MDD	BA9, Smalheiser et al., 2012 [47]
miR-152	SZ	BA46, Santarelli et al., 2011 [61]	BA10, Smalheiser et al., 2014 [48]
miR-148b	SZ	BA46, Santarelli et al., 2011 [61]
	MDD		BA9, Smalheiser et al., 2012 [47]
miR-187	SZ	BA46, Santarelli et al., 2011 [61]	BA10, Smalheiser et al., 2014 [48]
	BD	DLPFC, Miller et al., 2012 [53]
miR-767–5p	SZ	BA46, Santarelli et al., 2011 [61]	BD, Kim et al., 2010 [59]
miR-346	SZ	Plasma, Sun et al., 2015 [84]	BA46, Zhu et al., 2009 [60]
miR-181b	SZ	STG, Beveridge et al., 2008 and Beveridge et al., 2010 [56, 58]	BA46, Zhu et al., 2009 [60]
Let-7g	SZ	STG, Beveridge et al., 2008 [56]
	MDD		LC, Roy et al., 2017 [21]
miR-508–3p	SZ		BA10, Smalheiser et al., 2014 [48]
	MDD		BA10, Smalheiser et al., 2014 [48]
miR-330–3p	MDD	LC, Roy et al., 2017 [21]; Whole blood, Maffioletti et al., 2016 [11]
	BD	Whole blood, Maffioletti et al., 2016 [11]
miR-145	SZ		BA10, Smalheiser et al., 2014 [48]
	BD	BA46, Kim et al., 2010 [58]	BA10, Smalheiser et al., 2014 [48]
miR-485–5p	SZ		BA10, Smalheiser et al., 2014 [48]
	BD		BA10, Smalheiser et al., 2014 [48]
miR-32	BD	DLPFC, Miller et al., 2012 [53]	BA46, Kim et al., 2010 [59]
miR-140–3p	BD	Whole blood, Maffioletti et al., 2016 [11]	BA46, Kim et al., 2010 [59]

ACC: Anterior Cingulate Cortex, BA: Brodmann area, BD: Bipolar Disorder, DLPFC: Dorsolateral Prefrontal Cortex, LC: Locus Coeruleus, miRNA: microRNA, MDD: Major Depressive Disorder, PBMC: Peripheral Blood Mononuclear Cells, SZ: Schizophrenia, STG: Superior Temporal Gyrus

Table 3.

Reproducible miRNAs Tested Through Different Detection Methods

Disease	Brain areas	Sample size	Methods	miRNA expression changes		Reference
				Up-regulation	Down-regulation
SZ	BA9	13 SZ, 2 Schizoaffective, and 21 Controls	Microarray and qPCR validation		miR-26b, miR-30b, miR-92, miR-24, and miR-30e	Perkins et al., 2007 [55]
	BA9	15 SZ and 15 Controls	Microarray and qPCR validation	let-7d, miR-128a, miR-16, miR-181b, miR-181a, miR-20a, miR-219, miR-27a, miR-29c, miR-7		Beveridge et al., 2010 [58]
	BA46	37 SCZ/schizoaffectives and 37 Controls	Microarray and qPCR validation	miR-17, miR-107, miR-134, miR-328, miR-382, miR-652		Santarelli et al., 2011 [61]
	STG	15 SZ and 15 Controls	Microarray and qPCR validation	miR-107, miR-15a, miR-15b, miR-16, miR-195, miR-181b, let-7e, miR-20a, miR-26b, miR-19a		Beveridge et al., 2010 [58]
	STG	21 SZ and 21 Controls	Microarray and qPCR validation	miR-181b		Beveridge et al., 2008 [56]
BD	BA9	8 SZ, 9 BD, and 13 Controls	Microarray and qPCR validation	miR-29c		Banigan et al., 2013 [52]

BA: Brodmann Area, BD: Bipolar Disorder, miRNA: microRNA, SZ: Schizophrenia, STG: Superior Temporal Gyrus

5.1. miRNAs and Major Depressive Disorder

In MDD, over 25 key reports have shown miRNA-associated expression changes in several vital brain areas (anterior cingulate cortex [ACC], Brodmann’s Area [BA] 9, BA10, BA44, BA46, and locus coeruleus [LC]). Our group [47] was the first to explore miRNA expressions in subjects with MDD who had died by suicide. A total of 21 miRNAs were found to be significantly downregulated compared to healthy control subjects. Surprisingly, an overall change in miRNA expression had a decreasing trend in MDD-suicide subjects. In the same report, several overlapping target genes were found among 21 dysregulated miRNAs; most of them were related to synaptic plasticity. Later, we studied synaptosomal miRNAs in BA10 of MDD subjects [48]. The expression of miR-508–3p and miR-152–3p was significantly downregulated in the MDD brain compared to control subjects; however, miR-508–3p expression was significantly lower in suicide subjects than non-suicide subjects. This was especially consistent with our previous study which showed a global decrease in miRNA expression in the MDD-suicide brain [47]. Recently, Lopez et al. revealed a decreased expression of miR-1202 in BA44 [49]. From microarray expression data, they identified putative target genes that were inversely correlated with miR-1202 expression. Finally, they found that miR-1202 regulates the expression of GRM4 which can predict antidepressant response.

Based on prior published knowledge, Azevedo et al. selected 29 miRNAs and tested their expression with qPCR assays [50]. Out of 29 miRNAs, miR-34a and miR-184 were significantly decreased in MDD subjects. As the putative target of miR-34a, three genes (NCOA1, NCOA2, and PDE4B) were predicted and later chosen for in-vitro experiments. NCOA1 and NCOR2 modulate the transcriptional activity of the glucocorticoid receptor (GR). PDE4B regulates cAMP signaling and is enriched at the synapse. In miR-34a overexpressed HEK293 cells, the expressions of PDE4B and NCOA1 were suppressed. It was suggested that miR-34a may affect MDD pathogenesis through the regulation of PDE4B and NCOA1 expression.

In another study, we showed the downregulation of miR-124–3p expression in BA46 of MDD subjects [21]. For the same miRNA, parallel expression changes were found in the serum of antidepressant-free MDD patients. Moreover, the expression of miR-124–3p was suppressed by fluoxetine treatment. Recently, we also explored the miRNA changes in LC of MDD-suicide subjects on a semi-high throughput expression platform. A total of 10 upregulated and 3 downregulated miRNAs were detected in MDD subjects. Based on target gene prediction using the upregulated miRNAs, we narrowed our study to those genes that are implicated in neuropsychiatric disorders (RELN, GSK-3β, MAOA, CHRM1, PLCB1, and GRIK1). Of those, reduced expression levels were found for RELN, GSK-3β, and MAOA.

5.2. miRNAs in Bipolar Disorder (BD)

Thus far, most of the miRNA studies in BD have been focused on the prefrontal cortex such as BA9 (2), BA10 (1), and BA46 (3). Additionally, two reports were found in ACC and one in the cerebellum. As discussed above, a study by Azevedo et al. selected 29 miRNAs for their expression [50]. Among them, 4 miRNAs (miR-34a, miR-132, miR-133a, and miR-212) were significantly downregulated in ACC of BD subjects. Another study found increased miR-149 expression in exosomes from ACC of BD subjects [51]. A relative contribution of glia over neurons, in inducing the expression of miR-149, was found in the BD brain. Banigan et al. reported an upregulation of miR-29c [52]. miR-29c is induced by canonical Wnt signaling via GSK-3 activation. GSK-3 is involved in BD and the mechanism of action of lithium. To examine the role of miRNAs in synaptic plasticity, synaptosomal miRNAs were studied in BA10 of BD subjects; 4 were upregulated: miR-17–5p, miR-579, miR-106b-5p, miR-29c-3p, and 5 were downregulated: miR-145–5p, miR-485–5p, miR-370, miR-500a-5p, miR-34a-5p [48]. The expression changes of upregulated miR-579 and downregulated miR-34a were confirmed in BD subjects. The same changes were found when suicide subjects and non-suicide subjects were compared across three psychiatric disorders (MDD, BD, and SZ). It was speculated that BD associated suicidality may be associated with these miRNA changes. Despite the significant clinical similarities between BD and SZ patients, Miller et al. did not find any overlap of dysregulated miRNAs using high throughput microarray [53]. More recently, Bavamian et al. showed the upregulation of miR-34a in the cerebellum of BD subjects compared to controls [54]. The upregulation of miR-34a was also found during the differentiation of human-induced pluripotent stem cells (iPSC) derived from BD fibroblast samples. Using in-vitro luciferase assay, they confirmed that miR-34a could regulate BD risk genes SYT1, ANK3, DDN, and CACNB3.

5.3. miRNAs in Schizophrenia (SZ)

In the past several years, various reports have elaborated on the role of miRNAs in Schizophrenia. Most of the SZ related miRNA studies focused on the prefrontal cortex such as BA9 (4), BA10 (1), BA46 (3), and DLPFC (3). Other than that, one report on the amygdala and two reports on superior temporal gyrus (STG corresponding to BA 22) have been published in the last ten years. The first miRNA study was from Perkin et al. who found significant upregulation of one miRNA and significant downregulation of 15 miRNAs in BA9 of SZ subjects [55]. Pathway analysis based on predicted targets of 15 downregulated miRNAs identified actin cytoskeleton related changes as one of the most significantly affected pathways. In addition, they found elevated miR-199a, miR-128a, and miR-128b in haloperidol treated rats compared to untreated rats, thus confirming the antipsychotic effects on miRNAs. In a separate instance, Beveridge et al. revealed the upregulation of miR-181b and let-7g in STG of SZ subjects [56]. Another report showed upregulation of 59 of 274 (21%) miRNAs in STG of the SZ brain. In the same study, upregulated miR-181b was found to target key neuronal genes VSNL1 and GRIA2. Later, Moreau et al (2011) detected 19% of 234 miRNAs and 18 small nucleolar RNA expression changes in BA9 based on diagnostic classification among SZ, BD, and control subjects [57]. All downregulated miRNAs in SZ subjects were also downregulated in BD subjects, suggesting possible overlapping miRNA changes in these two disorders.

Banigan et al. used exosomes from BA9 and tested miRNA expression by microarray analysis [52]. They found the upregulation of miR-497 by validation test using qPCR. In addition, they successfully divided SZ from BD and control groups as a result of clustering analysis with 21 top-ranked miRNAs. Separately, Beveridge et al. found that 26 of 274 (9.5%) miRNAs were elevated in BA9 of SZ subjects [58]. In addition, pre-miR-181b and miRNA biogenesis enzymes DGCR8, DROSHA, and DICER1 were also upregulated. Their findings suggested that miRNA biogenesis may be significantly affected in SZ. In another study [48], we isolated the synaptosomes from BA10 and analyzed miRNA expression. A total of 19 miRNAs (13 upregulated and 6 downregulated) were significantly altered in SZ subjects compared to control subjects. We also found that 5 of the 6 downregulated miRNAs were highly enriched in synaptosomes (ratio >1.5), while only 2 of the 3 upregulated miRNAs showed synaptic enrichment. This suggests that downregulated synaptosomal miRNAs may be more meaningful in SZ. Kim et al. found 7 significantly upregulated miRNAs in BA46 [59]. However, they revealed a negative correlation with their targets for only three miRNAs: miR-34a with TH and PGD; miR-132 with TH and GRM3; and miR-212 with TH and GRM3. Research by Zhu et al. confirmed the decreased expression of miR-346 in BA 46 of SZ subjects [60]. In the same study, they also focused on GRID1 because miR-346 lies in intron 2 of the GRID1 gene; however, GRID1 expression was not significantly changed. Santarelli et al. found 25 upregulated and 3 downregulated miRNAs. They also found an upregulation of DICER1 expression and concluded that these changes have important implications for structural and functional plasticity of the synapse [61]. Miller et al. reported significantly decreased expression of miR-132 and miR-132* in dlPFC of SZ subjects which was confirmed in two separate SZ populations [53]. Upregulation in the expression of GATA2, FKBP2, ANKRD11, and PDE7B, which were predicted targets of miR-132, was also found. Guella et al. explored miR-137 expression in dlPFC because rs1625579 present in miR-137 has a strong association with SCZ [62]. However, no significant expression change was found in SZ and BD subjects compared to control subjects.

Recently, Liu et al. explored miRNA expression in the amygdala by miRNA-seq and RNA-seq [20]. In their report, 7 upregulated and 11 downregulated miRNAs were detected. The results from the amygdala were further tested in the dlPFC of an SZ replication cohort. Out of 18 dysregulated miRNAs, miR-1307 has shown a similar directional change in expression when the results from dlPFC were compared with the amygdala. In a separate sequencing-based study, using the dlPFC region of 39 SZ brains, upregulated expression of two other miRNAs: miR-3162 and miR-936 were found [23].

5.4. miRNAs in Early-Life Stress

Recently, miRNAs have also gained recognition for their potential role in susceptibility to psychiatric disorders after adverse early-life experiences. Two-thirds of individuals report at least one adverse childhood experience [63] and there is strong evidence for a link between early-life stress and later psychiatric disorders [64]. Furthermore, it has been reported that adult mental health after early-life stress is dependent on sex as well as stressor timing [65]. However, to date, very few miRNA studies of psychiatric disorders have considered early adverse experiences. A consensus of reports indicates that miR-124, miR-125, miR-29, miR-16, and miR-200 may be particularly important markers of early-life stress-induced MDD or SZ [66]. A promising study by Cattane et al. [67] identified miR-125b-1–3p in a sample of healthy individuals with a history of early-life stress as well as in patients with SZ, using whole blood. This finding was validated in a mouse model of prenatal stress and in-vitro. MiR-125b has an indirect effect on neuronal signal transduction by targeting specific subunits of NMDA receptors [68]. Thus, changes to miR-125b, among others, during early-life, may cause persistent alterations to brain plasticity, thereby leading to greater susceptibility for psychiatric illness.

6. miRNAs as Clinical Biomarkers in Neuropsychiatric Illnesses

It has been a little over a decade since the first study of miRNAs in psychiatric disorders was published [55]; however, we are still in the early phases to best utilize miRNAs as biomarkers for diagnosis or treatment response. Several biomarkers have been tested for their prognostic value in predicting behavioral outcomes (e.g., suicide attempts); however, only a handful of them have proved to be reliable in clinical practices. For instance, the levels of prolactin and thyroid hormone have been found to be associated with suicide attempts in psychiatric patients [69]. Earlier, we had reported that 5HT2A receptor upregulation can be used as a reliable biomarker for suicidal behavior among psychiatrically ill patients [70]. More recently, the SKA2 gene has been found to be associated with suicidal behavior [71]. Considering the number of reviews published on the use of miRNAs as biomarkers, specifically in psychiatric disorders [72–80], very few empirical studies have been conducted in the last 5 years. Extant studies have evaluated the differential expression of miRNAs across groups based on traditional DSM diagnoses and utilized regression or Receiver Operating Characteristic (ROC) curves—a test of the sensitivity and specificity of a predictive model to assess the predictive ability of individual miRNAs. Here, we describe the key findings over the past few years, which have been summarized in Table 4.

Table 4.

The miRNA Expression Changes in Psychiatric Disorders Based on Peripheral Tissue Studies

Disease	Source	miRNAs	Reference
SZ	Prefrontal cortex	miR-26b, −30b, −29b, −195, −90, −30a-5p, −30d, −20b, −29c, −29a, −212, −106b, −7, −24, −30e, −9–3p	Perkins et al., 2007 [55]
SZ	Whole blood	miR-122, −130a, −130b, −193a-3p, −193b, 502–3p, −652, −886–5p	Wei et al., 2015 [87]
SZ	Blood plasma	Diagnostic value: miR-30e, −181-b, −34a, −346, −7 Treatment response: miR-132, −181-b, −432, −30e	Sun et al., 2015 [84]
SZ	PBMC	miR-30a-5p, 30e-5p	Liu et al., 2017 [85]
SZ	Whole blood	miR-137	Wu et al., 2016 [86]
MDD	Whole blood	Let-7d-5p, −7f-5p, −7a-5p, miR-1915–3p, −29c-5p, −330–3p, −425–3p, −24–3p, −199a-5p, −345–5p	Maffioletti et al., 2016 [11]
BD	Whole blood	miR-720–5p, −3158–3p, −4521–5p, −345–5p, −1972–5p, −4440–5p, −1973–5p, −4793–3p, −140–3p, −30d-3p, −330–3p, −330–5p, −1915–5p, −378a-5p, −21–3p, −29c-5p,	Maffioletti et al., 2016 [11]
MDD	Whole blood from patients treated with Duloxetine or Escitalopram or ventral PFC from suicide subjects	miR-146a-5p, −146b-5p, −24–3p, −425–3p	Lopez et al., 2017 [81]

MDD: Major Depressive Disorder, PBMC: Peripheral Blood Mononuclear Cells, SZ: Schizophrenia

Maffioletti et. al. [11] tested whole blood expression of 1,733 miRNAs using microarray in patients with MDD (n=20) as compared to those with BD (n=20) and healthy controls (n=20). Ten miRNAs were altered in MDD and 16 were altered in BD; 3 miRNAs were altered in both patient groups compared to controls. In particular, miR-24–3p, miR-425–3p, and several members of the let-7 family were altered in MDD patients and miR-30e, miR-21–3p, and miR-140–3p were altered in BD patients. They also used KEGG (Kyoto Encyclopedia of Genes and Genomes) based pathway analysis to determine signaling pathways targeted by these miRNAs and found that Wnt and mTOR signaling were the prime candidates in both MDD and BD. Lopez et al. used next-generation sequencing (NGS) in MDD patients treated with duloxetine and found downregulation of several miRNAs that targeted Wnt and MAPK signaling [81]. They reported that six miRNAs were altered both by treatment and placebo; thus, they hypothesized that these miRNAs might be responsible for a common response to antidepressant treatment. After further replication in patients treated with escitalopram, as well as in postmortem brain, their study strongly supported that miR-146b-5p, miR-24– 3p, and miR-425–3p are downregulated by antidepressant treatment and upregulated in the brains of patients who had died by suicide. Both MAPK and WNT signaling pathways have been implicated in MDD and suicidality [82]. Moreover, it has been reported that activation of MAPK and WNT pathways are associated with decreased MDD symptoms [82]. This evidence supports that downregulation of miR-146b-5p, miR-24–3p, and miR-425–3p may be related to improved MDD symptoms via increased MAPK and WNT signaling. Thus, these miRNAs show strong promise as clinical biomarkers for diagnosis and treatment response to MDD. Our group recently published that WNT signaling genes, which are targets of miR-128–3p, are downregulated in the brain of rats showing learned helplessness behavior [83]. The use of sequencing to test several miRNAs made this study well suited to discover novel biomarkers for MDD.

A few studies have been conducted in the context of SZ. Sun et al. used qPCR to test the expression of 9 miRNAs before and six weeks after antipsychotic treatment. They found increased levels of 4 miRNAs: miR-132, miR-181b, miR-30e, and miR-432, in plasma of SZ patients with significant combined diagnostic value (AUC:0.713; sensitivity:35.5%; specificity:90.2%). [84]. All 4 miRNAs were significantly decreased in patients having six weeks of either of the medications (olanzapine, quetiapine, ziprasidone, or risperidone). Olanzapine had the strongest effect on miRNA levels. MiR-132, miR-181-b, miR-212, and miR-30e expression also significantly correlated with clinical score changes after treatment, and miR-181-b was the best independent predictor of treatment response. In another study of 30 patients with schizophrenia, miR-30a-5p and −30e-5p were significantly decreased in peripheral blood mononuclear cells (PBMCs) compared to healthy individuals [85]. It was also found that NEUROD1, a gene target of miR-30a-5p, was significantly upregulated in patients, and the early growth response protein 1(EGR1), a transcription factor for miR-30a-5p, was downregulated. EGR1-miR-30a-5p-NEUROD1 axis was found to be a strong predictor of SZ diagnosis than miR-30a-5p alone.

Wu et al. selected miR-137 as a potential biomarker for SZ based on prior literature and found increased miR-137 expression in whole blood from 44 SZ patients using qPCR [86]. They also used ROC analysis and found that miR-137 expression had 70.5% sensitivity and 84.1% specificity to differentiate SZ patients from controls. In another study, Wei et al. showed the upregulation of miR-130b and miR-193a-3p in whole blood of 81 SZ and 88 control subjects. They confirmed that these miRNAs were the same in the second set of samples from 189 SZ and 105 control subjects [87]. The above mentioned studies show that miRNA changes could be a viable measure in diagnosing psychiatric disorders. However, it will be more beneficial to patients to predict response to a specific treatment before it is prescribed and used. This will require large-scale longitudinal studies that can track miRNA expression before, during, and after treatment.

6.1. Common miRNA Changes between Brain and Peripheral Tissues

To explore the similarities of miRNA changes between the brain and peripheral tissues, we compared and summarized all the miRNA related findings discussed in this review in Table 5. MDD subjects showed upregulation of miR-330–3p that was common in locus coeruleus and whole blood. In BD patients, the upregulation of miR-29c was detected both in BA9 and whole blood. Interestingly, the finding of miR-29c in BA9 of BD brain was based on exosome samples. As mentioned later in this review, exosomes, as extracellular vesicles, could pack miRNAs and be transferred from the brain to peripheral tissue by penetrating the blood-brain barrier (BBB). It has been shown recently that changes detected in exosomal miRNA contents from peripheral tissue may be useful in developing biomarkers because that reflects miRNA changes in the brain. Lastly, from SZ studies, relatively large numbers of overlapping miRNA changes were found to be common between brain and blood. For example, increased expression of miR-181b was the same between the brain (BA9 and STG) and plasma. A similar trend was found for miR-34a, showing upregulated expression in both brain (amygdala and BA46) and plasma. Furthermore, decreased expression of miR-30e was found to be common in BA9 and peripheral blood monocytes of SZ subjects. Our previous studies showed similar expression patterns for miR-124–3p (PFC and serum) and miR-19a-3p (dlPFC and PBMC) in MDD subjects [21, 88].

Table 5.

The Common Changes in miRNA Expression between Brain and Peripheral Tissues

Diagnosis	miRNA		Brain region	Blood source	Reference
	Up-regulation	Down-regulation			Brain	Blood
MDD	miR-330–3p		LC	Whole blood	Roy et al., 2017 [21]	Maffioletti et al., 2016 [11]
BD	miR-29c		BA9	Whole blood	Banigan et al., 2013 [51]	Maffioletti et al., 2016 [11]
SZ	miR-181b		BA9	Plasma	Beveridge et al., 2010 [57]	Sun et al., 2015 [84]
			STG		Beveridge et al., 2008 [55], Beveridge et al., 2010 [57]
	miR-34a		Amygdala	Plasma	Liu et al., 2018 [20]	Sun et al., 2015 [84]
			BA46		Kim et al., 2010 [58]
		miR-30e	BA9	PBMC	Perkins et al., 2007 [54]	Sun et al., [84]

BA: Brodmann Area, BD: Bipolar Disorder, LC: Locus Coeruleus, miRNA: microRNA, MDD: Major Depressive Disorder, PBMC: Peripheral Blood Mononuclear Cells, SZ: Schizophrenia, STG: Superior Temporal Gyrus

6.2. Promises and Caveats Associated with miRNAs as Biomarkers in Circulation

The presence of miRNAs in peripheral tissues, particularly in blood cells, provides a promising approach to use them as potential biomarkers for both diagnosis and treatment response. However, several issues need to be considered before the use of circulating miRNAs as biomarkers. For example, the source of miRNAs in blood cells cannot be determined. MiRNAs are either found to circulate peripherally in conjunction with RNA-binding proteins (RBPs), such as the Argonaute complex (AGO2), which aid in their transport [89] or become cargo within enclosed liposomal vesicles called exosomes. However, unprotected from ribonucleases, circulating miRNAs may become susceptible to enzymatic degradation [90], but this susceptibility is relatively low given their small size (~22nt).

Some miRNAs affect the leakiness of the blood brain barrier, both promoting their own circulation in peripheral blood [91]. The presence of these miRNAs could impact circulating miRNA profile, potentially masking disease-related miRNA changes. In this case, profiling extracellular vesicle (EV) exosomal miRNAs derived from the brain may prove to be very useful. As mentioned above, the actively secreted miRNAs are enclosed in exosomes, which can cross the blood–brain barrier and are well protected from degradation. Exosomal miRNAs are processed by the same synthesizing machinery as used in miRNA biogenesis, and thus have widespread consequences within the cell by inhibiting the expression of target protein-coding genes. Evidence showing that exosomal miRNAs are excreted physiologically in response to stress lend credence to the fact that exosomal miRNAs can be used as potential biomarkers for stress-related disorders [92]. The surface of these EVs contains protein marks corresponding to their origin (i.e., brain, heart, lungs, etc.). For example, brain-derived exosomes may be marked with neural cell adhesion markers [93], which can be immunoprecipitated using specific antibodies. Thus, it is possible to assess miRNAs that are released by the central nervous system. These brain-derived EVs in the blood may be one of the best sources of miRNAs relevant to psychiatric disorders. Although studied in neurodegenerative disorders such as Alzheimer’s disease [94], Parkinson’s disease [95], and mild traumatic brain disorder, exosomal miRNAs have not been studied in psychiatric illnesses except for one study which was done in a small number of subjects showing exosomal miRNA changes in BD subjects [96].

From the above discussion, it appears that free circulating or vesicle bound miRNAs can be used for diagnostic purposes in various psychiatric illnesses; however, there are some inherent limitations that need to be considered. One of them is the heterogeneity of sources contributing to peripheral circulation [97]. Second, it is challenging to select the appropriate tissue type, such as plasma, serum, or nucleated cells in the blood stream as opposed to cerebrospinal fluid (CSF) originating in CNS [98]. In addition, saliva could be another possible source of miRNAs that can be used in biomarker discovery. Being non-invasive, salivary miRNA assessment can offer advantages over invasive or less invasive procedures. The potential of salivary miRNAs has been tested in an exploratory analysis in 24 healthy participants by applying social-trier stress [99]. The results show significant stress-associated changes in the expression levels of salivary miRNAs (miR-20b, miR-21, and miR-26b).

Exosomal miRNAs can offer substantial advantages over other freely circulating miRNAs in peripheral body fluids [100]. As mentioned above, exosomes contain specific set(s) of protein markers on their surface, which represent their tissue of origin. Neuron-derived exosomal fraction can be selectively immuno-enriched with antibody and can be used in the reliable detection of candidate miRNAs [101]. However, due to the very low abundance of neuron-derived exosomes in peripheral body fluid, it is recommended to apply sensitive miRNA detection assay methods such as next generation sequencing or microarray (Gene-Chip) platforms which can help in analyzing a large number of miRNA simultaneously [100]. The costs of high-throughput experiments may pose a limitation in the number of individuals to be included in a study. Thus, it is recommended to follow a study design that includes a discovery phase and a validation phase for the detection of candidate miRNAs. Technically, in the discovery phase, parallel detection of many miRNAs is typically done following NGS or Chip-based high-throughput expression assay platforms. This process enables the identification of candidate biomarker/s using a small cohort of participants to be later tested in a larger validation cohort using high-throughput sequencing (NGS) or semi-high-throughput qPCR methods [102, 103].

Lastly, the choice of sources for reproducible detection of miRNAs in peripheral circulation needs to be evaluated carefully. As with other disorders, such as cancer, cardiac, and pulmonary diseases [104], blood-based serum or plasma remain preferred sources in neuropsychiatric conditions. However, CSF based analysis of miRNAs appears to be encouraging because of several reasons: 1) CNS based origin of CSF, 2) no hemolysis as in blood samples that can interfere with miRNA signal detection, and 3) stability under repeated freeze thaw and long-term storage conditions [97] that can otherwise cause depletion in the miRNA level [28]. The only drawback is the invasive nature of the CSF collection procedure. Collectively, based on the above discussion, it may be concluded that the choice of biological fluids needs to be considered carefully, especially when used under clinical settings.

7. Use of miRNAs in the Treatment of Psychiatric Illnesses

There has been an increasing interest in using miRNAs or biologics that can interact with miRNAs as a treatment for psychiatric disorders. Psychiatric disorders are, by nature, complex, and heterogeneous. Often, different cellular pathways work in tandem to create the disease state by maintaining (or altering) common target genes [105]. Therefore, it is challenging to relieve symptoms by targeting a single gene with pharmacotherapy. Since individual miRNAs can target many genes across biological systems [106], they are in a better position to alter behavior; however, this also creates a major challenge for the use of miRNAs for therapeutics because of their potential off-target side effects, as has been seen in clinical trials in cancer patients [107]. So far, little work has been done to test or develop such miRNA-based therapies for psychiatric disorders. Pre-clinical studies have focused on the use of antagomirs to knockdown the expression of specific miRNAs. Antagomirs are nucleic acid strands chemically engineered to target specific miRNAs to decrease their expression [108]. In one study, using corticosterone induced mice, it was shown that a miR-124 antagomir injected into the brain reduced depressive behaviors in the sucrose preference test, increased expression of BDNF and GR, and increased dendritic spine density in the hippocampus [21]. In another study, miR-16 antagomir increased depressive behaviors in sucrose preference and forced swim tests while increasing the expression of the serotonin transporter (SERT) protein [109]. However, further technological development is needed to reduce the invasiveness of antagomirs for human use. Suryawanshi et al. have proposed an antagomir that can be administered peripherally and guided to the brain by rabies virus glycoprotein (RVG) [110]. RVGs may target receptors in the CNS by bypassing blood brain barrier [111]. Previously, exosomes were engineered with RVG embedded in their surface and directly injected into the brain to target specific miRNAs. Using this technique, miR-124 was targeted to induce neurogenesis in an induced brain injury [112]. However, peripherally administered RVG-guided antagomirs have not been validated in animal models [110]. The field of cancer biology has shown progress in developing miRNA-based therapies. Some studies have tested the expression of a panel of miRNAs in order to accurately diagnose types or malignancy of cancer [113, 114]. Most of the research reviewed here has evaluated one or a few miRNAs as predictive biomarkers [84–86]. Meiri et al. was able to identify 42 tumor types using a panel of 48 miRNAs [114]. Likewise, in Parkinson’s disease, miRNA expression has been shown to distinguish treated patients from untreated patients [115]. Moreover, Sheinerman et al. used separate train and test samples and found that expression of 37 exosomal miRNAs could accurately differentiate Alzheimer’s, Parkinson’s, fronto-temperoral dementia, and amyotrophic lateral sclerosis patients from controls [116]. A similar approach could be applied to distinguish symptoms of psychiatric disorders and to determine treatment efficacy. Going forward, it is important that the field of psychiatry adopts these methods from cancer and neurodegenerative disorder research.

8. Concluding Remarks

Biomarkers offer promise to reduce the length, cost, and uncertainty of drug development and potentially unlock targets for precision medicine. They do so by providing rapid and reliable information on certain key metrics [117]. So far, miRNA-related research in the neuropsychiatric field has advanced our understanding of the clinical significance of miRNA in disease pathogenesis. However, their potential value as biomarker to predict disease outcome is yet to be thoroughly tested under clinical settings. At this point, their role as an exploratory biomarker may fit well with a goal to devise suitable panels that may later be validated under more stringent clinical conditions [118]. Like any other disease, the fundamental criteria for diagnostic and prognostic biomarkers for a neuropsychiatric disorder are high in sensitivity and specificity. Additionally, it has been recommended that biological diversity poses a challenge for biomarker discovery using unsupervised methods and requires preclinical validation, which is essential for biomarker identification [119]. There is a significant caveat in implying the preclinical concept of biomarker testing in the psychiatric field. This is largely because of the complex nature of psychiatric conditions due to multi-dimensional psychopathological syndromes with multifactorial causation [120]. In this regard, one can follow preclinical studies and translate them into clinical settings. For example, several pre-clinical studies have focused on examining the functional relevance of miRNAs in behavior [32]. It has been reported that miR-30 family of miRNAs mediate chronic stress-induced depression-like phenotype by altering hippocampal neurogenesis [121]. MicroRNA-34a regulates depression-like behavior in mice by modulating the expression of target genes in the dorsal raphè [122]. We have reported that miR-124 can be involved in stress-induced depression when examined in the frontal cortex of rats [10]. Interestingly, the same miRNA can cause resiliency to chronic stress when examined in the hippocampus [123]. Our earlier study also shows that enoxacin, a fluoroquinolone antibiotic that binds HIV-1 TAR RNA binding protein (TRBP) and stabilizes the dicer–TRBP complex, can reverse depression associated behavioral phenotypes in rats [124]. The translational values of these preclinical studies in developing biomarkers and targeted therapy need to be tested in humans.

Over the past decade, the potential of circulating miRNA as diagnostic biomarker has been individually assessed in the blood of patients with major depressive disorder [49, 125, 126]. So far, no panel of circulating miRNAs has fully met the criterion described above, which has delayed the identification of miRNA as an ideal biomarker for any of the three neuropsychiatric conditions described. However, a recent serum based case control study has shown promising results by identifying a panel of three miRNAs (miR-16, miR-135a, and miR-1202) in 39 patients with depression and 36 healthy controls. As suggested, the identified miRNA panel can improve the diagnostic sensitivity and specificity for depression in comparison to a single miRNA [127]. Very recently, a panel of seven miRNAs (miR-7–5p, miR-23b-3p, miR-142–3p, miR-221–5p, and miR-370–3p) was found to be associated with bipolar II disorder [128] These seven miRNAs had significant diagnostic power with a receiver operating curve (ROC).

Microvesicles have emerged as promising sources of miRNAs in biomarker discovery. Microvesicles encapsulated EVs from body fluid provide reliable profiling of miRNA panels [129]. However, one has to be careful in selecting the EV population to overcome the issues associated with the specificity and purity of the samples.

Despite some shortcomings and challenges, the premise of miRNA research is strong and has significant potential in developing biomarkers in the field of neuropsychiatric disorders. The most important among them is their ability to cross the blood brain barrier due to their small size and their ability to stay stable in a biofluid, particularly in blood. Besides, once established as validated biomarkers, their monitoring through repetitive assays are much faster and less complex under clinical laboratory settings. Moreover, their detection as non-invasive markers is relatively cost effective since the collection, processing, and storage of these samples are relatively straightforward and less invasive in standard laboratory conditions [130].

In conclusion, the current review summarizes the key aspects associated with the brain and circulating miRNAs in neuropsychiatric disorders. More importantly, advantages and caveats associated with determining circulating miRNAs as diagnostic and prognostic biomarkers are discussed critically in clinical management of neuropsychiatric conditions.

Key Points.

1. Studies suggest the growing role of circulating miRNAs as potential biomarkers in disease diagnosis and progression.

2. The current clinical understanding of circulating miRNAs implicates them as predictive diagnostic biomarkers in major neuropsychiatric conditions such as major depressive disorder, bipolar disorder, and schizophrenia.

3. Newer approaches are being considered to better assess brain-specific miRNA changes in circulation.