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ABSTRACT Antidepressants (ADs) are the most common treatment for major depressive disorder (MDD). However, only ∼30% of patients experience adequate response after a single AD trial, and

this variability remains poorly understood. Here, we investigated microRNAs (miRNAs) as biomarkers of AD response using small RNA-sequencing in paired samples from MDD patients enrolled in a

large, randomized placebo-controlled trial of duloxetine collected before and 8 weeks after treatment. Our results revealed differential expression of miR-146a-5p, miR-146b-5p, miR-425-3p

and miR-24-3p according to treatment response. These results were replicated in two independent clinical trials of MDD, a well-characterized animal model of depression, and post-mortem human

brains. Furthermore, using a combination of bioinformatics, mRNA studies and functional _in vitro_ experiments, we showed significant dysregulation of genes involved in _MAPK/Wnt_

signalling pathways. Together, our results indicate that these miRNAs are consistent markers of treatment response and regulators of the _MAPK/Wnt_ systems. SIMILAR CONTENT BEING VIEWED BY

OTHERS MIR-323A REGULATES ERBB4 AND IS INVOLVED IN DEPRESSION Article 20 November 2020 BASELINE LEVELS OF MIR-223-3P CORRELATE WITH THE EFFECTIVENESS OF ELECTROCONVULSIVE THERAPY IN PATIENTS

WITH MAJOR DEPRESSION Article Open access 13 September 2023 MIRNAS AS POTENTIAL DIAGNOSTIC BIOMARKERS AND PHARMACOGENOMIC INDICATORS IN PSYCHIATRIC DISORDERS Article 20 June 2022

INTRODUCTION Major depressive disorder (MDD) is a prevalent illness that associates with significantly increased mortality, disability and secondary morbidity, and is one of the world’s

leading causes of disease burden1. Treatment of MDD includes a variety of biopsychosocial approaches, but in medical practice, antidepressant (AD) drugs are the most common treatment for

depressive episodes and, not surprisingly, they are among the most prescribed medications in the world2. While they are clearly effective, particularly for moderate-to-severe depressive

episodes, there is substantial variability in how individuals respond to AD treatment. In spite of the many ADs available, almost 70% of patients do not respond to a single trial and 30–40%

of patients do not present with a full response following several trials3. The failure to respond has important individual, economic and social consequences for both patients and their

families. Thus, there is a great need to better understand factors associated with response to AD treatment. Genes can be regulated through the activity of several noncoding RNA transcripts

that act as fine-tuners and on–off switches of gene expression patterns4. Among the noncoding RNAs, microRNAs (miRNAs) have been the most extensively studied5. Since their discovery in the

early 1990s, miRNAs have revolutionized our understanding of gene regulation and show great potential for assisting in the elucidation of mechanisms underlying disease pathology and

treatment response6. MiRNAs are small noncoding, single-stranded RNA transcripts that regulate the expression of mRNAs through RNA degradation or translational repression. There is

increasing evidence suggesting a key role for miRNAs in the regulation of essential processes of brain functioning, as well as in the development of psychiatric disorders and their

treatments7. For instance, Baudry _et al_.8,9 found that miR-16 mediates AD action of fluoxetine in the raphe and locus coeruleus of mice, and follow-up work suggested that this same miRNA

displays antidepressant-like effects and mediates adult neurogenesis in the hippocampus. Another recent and promising finding uncovered a key role for miR-135a in the molecular mechanisms

underlying the therapeutic actions of ADs in both depressed humans and animal models of depression10. Dias _et al_.11 showed that the miRNA processing protein, Dicer, acts in brain reward

regions to promote antidepressant-like responses in animal models. Finally, we have recently reported that miR-1202, a primate-specific and brain-enriched miRNA, is downregulated in the

prefrontal cortex of depressed individuals and regulates the expression of the _metabotropic glutamate receptor_ (_GRM4_)12. These encouraging new findings underline the potential of

studying miRNAs to better understand molecular processes associated with MDD. Here, we investigate miRNAs in relation to AD response. Using complementary strategies, we demonstrate that

levels of miR-146a-5p, miR-146b-5p, miR-24-3p and miR-425-3p are modified as a function of AD response and regulate genes involved in mitogen-activated protein kinase (MAPK) and Wnt

signalling pathways. RESULTS MICRORNA DYSREGULATION AFTER ANTIDEPRESSANT TREATMENT We performed small RNA-sequencing in blood samples from a randomized placebo-controlled trial of

duloxetine, a serotonin–norepinephrine reuptake inhibitor. Blood samples were collected before and 8 weeks after treatment (discovery cohort (DRCT); _n_=516 samples from 258 patients) (Fig.

1a). We compared the expression of miRNAs before and after treatment (that is, 8-week response to baseline). Statistical analysis, using Benjamini–Hochberg correction for multiple testing

(5% false discovery rate (FDR)) and corrected _P_ values of <0.05, revealed differential expression of 22 miRNAs after 8 weeks of duloxetine treatment (Table 1) and 6 miRNAs after 8 weeks

of placebo treatment (Table 2). Except for one miRNA, these findings were specific to patients who responded to treatment with no differences found in the nonresponder groups (Fig. 1a and

Supplementary Tables 1 and 2). We defined response as >50% decrease in Montgomery–Åsberg Depression Rating Scale (MADRS) scores from baseline to trial end point13,14,15. Remarkably, the

six miRNAs that were significantly changed according to placebo response were among the 22 whose expression changed according to duloxetine response, and were altered in the same direction

(Tables 1 and 2; Fig. 1b). Thus, 16 miRNAs changed specifically according to duloxetine treatment. One miRNA (miR-503-5p) was significantly changed in responders and nonresponders in both

placebo- and duloxetine-treated patients, suggesting that the alterations in the expression of this miRNA may associate with metabolic processes that are independent of clinical outcome. We

validated our significant findings using a high-sensitivity multiplex cellular miRNA assay on a standard flow cytometer (Firefly BioWorks). Using a custom panel, we measured miRNA expression

before and after treatment in the same 516 samples used for small RNA-sequencing. We validated 87% of the miRNAs tested (Supplementary Table 3) and found a significant correlation of fold

changes across the two technologies (_r_=0.83, _P_<0.05; Fig. 1c). Since we were interested in the underlying molecular mechanisms involved in response to treatment, and based on recent

evidence suggesting that some of the mechanisms involved in placebo response are also engaged during AD treatment and could reinforce response to common AD16,17, we next focused on the

miRNAs that were differentially expressed according to response in both the duloxetine and placebo groups. These were miR-146a-5p, miR-146b-5p, miR-24-3p, miR-425-3p and miR-3074-5p that

were all downregulated after treatment, and their individual expressions were strongly correlated (Fig. 1d,e and Supplementary Table 4) suggesting a shared mechanism of action. INDEPENDENT

REPLICATION WITH COHORT 1 To test the external validity of our findings, we measured the expression of these five miRNAs before and after 8 weeks of AD treatment in a fully independent

sample composed of 61 depressed patients (122 samples) who were recruited as part of the Genome-based Therapeutic Drugs for Depression (GENDEP) study18 (replication cohort 1). Patients were

treated with escitalopram, a selective serotonin reuptake inhibitor, or nortriptyline, a tricyclic antidepressant (Fig. 2a). In line with our findings in the DRCT, we again found a

significant downregulation in the expression of miR-146a-5p, miR-146b-5p, miR-24-3p and miR-425-3p in the blood of depressed patients after treatment. Importantly, these changes were

consistent with those observed in the discovery cohort (Fig. 2b and Supplementary Table 5). Although we also saw a decrease in the expression of miR-3074-5p, this effect was not

statistically significant, and thus we did not include this miRNA in further analyses. Consistent with our initial findings, the downregulation of the validated miRNAs was only observed

among responders to treatment, as we found no changes in the expression of these miRNAs in the nonresponder group. These findings support a relationship between treatment response and

miR-146a-5p, miR-146b-5p, miR-24-3p and miR-425-3p. INDEPENDENT REPLICATION WITH COHORT 2 We measured the expression of miR-146a-5p, miR-146b-5p, miR-24-3p and miR-425-3p before and after 8

weeks of AD treatment in a second independent sample composed of 316 additional blood samples from 158 patients treated with the antidepressant escitalopram (selective serotonin reuptake

inhibitor) for 8 weeks. All patients from cohort 2 (RPCT2) were recruited as part of the Canadian Biomarker Integration Network in Depression (CAN-BIND) study19. (Fig. 2c). In line with all

our previous findings, we found consistent results, both in significance and direction, for three out of the four miRNAs tested (Fig. 2d and Supplementary Table 6). There was a

downregulation of miR-146a-5p, miR-146b-5p and miR-24-3p after antidepressant treatment. More importantly, these effects were only observed among responders to escitalopram treatment, as we

found no changes in the expression of these miRNAs in the nonresponder group. We found no significant differences in the levels of miR-425-3p in our second replication cohort (Fig. 2d and

Supplementary Table 6). These findings support a relationship between these miRNAs and treatment response in humans. RULING OUT EFFECTS OF DIFFERENTIAL BLOOD CELLULAR COMPOSITION To control

for possible biases arising from differential blood sample cellular composition between groups, we used haematology data collected in the discovery sample during the randomized

placebo-controlled trial. We found no significant differences between groups or categories (before and after treatment, between responders and nonresponders, and between duloxetine- and

placebo-treated individuals) in the levels or proportions of any of the cell types investigated in the blood samples from either duloxetine-treated patients or placebo controls

(Supplementary Tables 7 and 8). More importantly, we measured the levels of the validated and replicated miRNAs in cell-free plasma samples obtained from these subjects, and observed results

that were similar to those observed with the whole blood samples, both in significance and direction (Supplementary Fig. 1). DYSREGULATION OF MIRNAS AFTER ANTIDEPRESSANT TREATMENT IN MICE

We next investigated the relevance of our results in a well-established animal model of depression20. We tested mice exposed to the chronic social defeat stress (CSDS) paradigm followed by

the social interaction and sucrose preference tests to distinguish between susceptible animals, which show deficits in these behaviours, and resilient animals, which do not20. Susceptible

mice were then treated with the antidepressant imipramine (tricyclic antidepressant) or saline control for 14 days followed by a repeat of the social interaction and sucrose preference

tests. Mice that showed improvement in depression-like symptoms in these behavioural assays were classified as responders, while those that showed no improvements were deemed as

nonresponders (Fig. 3a). We measured the expression of miR-146a-5p, miR-146b-5p, miR-24-3p and miR-425-3p in peripheral blood samples of mice before and after treatment with imipramine or

saline control, using a NanoString, a probe-based miRNA expression assay. We found a significant downregulation of miR-146b-5p, miR-24-3p and miR-425. In addition, we found a decrease in the

expression of miR-146a-5p, but these findings did not reach statistical significance (_P_=0.09; Fig. 3b). Consistent with the data in humans, these changes were selective for the responder

group, as no differences were detected in the nonresponders or saline control groups (Table 3). These results reinforce the relationship between these miRNAs and treatment response.

DYSREGULATION OF MIRNAS IN THE VPFC OF DEPRESSED INDIVIDUALS To explore the relationship between the peripheral changes of miR-146a-5p, miR-146b-5p, miR-24-3p and miR-425-3p, and their

expression in the brain, we used quantitative real-time PCR to assess the levels of these miRNAs in the ventrolateral prefrontal cortex (vPFC) of depressed humans who died by suicide

(_n_=32) and psychiatrically healthy controls (_n_=20). We found a significant difference in the expression of each of these miRNAs compared with controls (Fig. 3c and Table 4). Whereas the

peripheral samples from AD treatment responders showed decreased expression of the candidate miRNAs, these miRNAs were all upregulated in depressed brain tissue relative to healthy controls

(Fig. 3c and Table 4). Additionally, their expression was strongly correlated in brain tissue, again suggesting a common mechanism of action (Supplementary Fig. 2 and Supplementary Table 9).

MIRNA TARGET PREDICTION AND PATHWAY ANALYSIS Gene targets of miR-146a-5p, miR-146b-5p, miR-24-3p and miR-425-3p were identified using seven miRNA target prediction databases. We opted to

consider only targets that were commonly predicted by at least 5 out of the 7 prediction databases, and obtained a list of 5,806 putative target genes (Fig. 4a). As the changes of these 4

miRNAs were positively correlated, we hypothesized that they had common pathways of regulation, and thus narrowed our list of targets by considering only genes that were commonly predicted

targets for at least 3 of the miRNAs, producing a list of 895 genes. We then used the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.7 (ref. 21) to identify

biological pathways that were potentially regulated by these miRNAs. Functional Annotation Clustering analysis revealed an enrichment of five signalling pathways. The _MAPK_, _Wnt_, calcium,

endocytosis and adherens junction signalling pathways were significantly enriched after Benjamini–Hochberg correction for multiple testing (_P_<0.05; Fig. 4a,b). To validate our miRNA

target prediction and pathway analysis, we performed a second, independent _in silico_ analysis using DIANA: miRPath22. Consistent with our first _in silico_ analysis, we found a significant

enrichment of the _MAPK_, _Wnt_, calcium, endocytosis and adherens junction signalling pathways after FDR correction for multiple testing (_P_<0.05). Interestingly, the _MAPK_ and _Wnt_

signalling pathways are modulated by stress and have been previously implicated in MDD and treatment response in humans and animal models11,23,24,25. MIRNA REGULATION OF MAPK/WNT SIGNALLING

AFTER TREATMENT To explore the relationship between the downregulated miRNAs and predicted signalling pathways, we cross-referenced the expression of miR-146a-5p, miR-146b-5p, miR-24-3p and

miR-425-3p with the genes identified through our computational analysis. Given the previous evidence suggesting changes in the _MAPK_ and _Wnt_ signalling pathways in depression, we focused

only on these two pathways. First, we used mRNA microarray expression data obtained from blood of the DRTC samples (_n_=460 samples from 230 patients). Interestingly, we found that most of

the predicted target genes involved in _MAPK_ and _Wnt_ signalling pathways were significantly correlated with the expression of one or more of the miRNAs tested (89%) (Supplementary Table

10). However, this effect was significantly stronger in patients who responded to treatment (Fig. 4c and Supplementary Table 11). In addition, 69% of these genes were significantly

dysregulated following duloxetine treatment (Supplementary Fig. 3). We then performed a similar analysis using complementary miRNA and gene expression data obtained from our replication

cohort (_n_=102 samples from 51 patients). We consistently found a significant correlation between predicted genes involved in _MAPK_ and _Wnt_ signalling pathways and the expression of the

downregulated miRNAs in depressed patients who responded to treatment (71%) (Supplementary Table 12). Once again, the effect was stronger in the responder group as compared with the

nonresponder group (Fig. 4d). To verify that the correlation between these miRNAs and the _MAPK/Wnt_ genes identified through our _in silico_ analysis was not a random finding, we performed

a permutation analysis to test correlations between miR-146a-5p, miR-146b-5p, miR-24-3p, miR-425-3p and 50 random genes. We found that the average percentage of significant correlations with

50 random genes after 100 permutations was 12% for all miRNAs tested, suggesting that the results found in our discovery (89%) and replication (71%) cohorts were significantly above chance

levels. Together, these results support a relationship between these miRNAs and the _MAPK/Wnt_ signalling pathways after antidepressant treatment. MICRORNA DYSREGULATION OF _MAPK/WNT_

SIGNALLING _IN VITRO_ To confirm that antidepressants have an effect on the expression of miR-146a-5p, miR-146b-5p, miR-24-3p and miR-425-3p, we treated human neural progenitor cells (NPCs)

with duloxetine, the same antidepressant that was used in the DRTC, as well as a no-drug control. NPCs display a serotonergic phenotype and express endogenous levels of the _serotonin

transporter_ (_SERT_) and several other serotonin receptors12. Following chronic duloxetine treatment for 2 weeks, we found a significant downregulation of miR-146a-5p, miR-146b-5p,

miR-24-3p and miR-425-3p (Fig. 5a), with no differences in the expression of the endogenous control _RNU6B_ after treatment or changes in these miRNAs after 2 weeks in untreated cells. In

addition, we tested the expression of the predicted target genes involved in _MAPK_ and _Wnt_ signalling pathways that were correlated in our discovery and replication cohorts. We found a

dysregulation in the expression of most (92%) _MAPK/Wnt_ genes tested after chronic duloxetine treatment, but found no significant differences in the expression of the endogenous controls

_β-Actin_ and _GAPDH_. Most importantly, and consistent with our miRNA findings, we found an upregulation of these genes after treatment as compared with controls (Fig. 5b). These results

support an interaction between these four miRNAs, the _MAPK/Wnt_ signalling pathways and antidepressant treatment. Finally, to experimentally confirm the interaction between miR-146a-5p,

miR-146b-5p, miR-24-3p, miR-425-3p and genes of the _MAPK/Wnt_ signalling pathways, we performed functional experiments using miRNA mimics on human embryonic kidney cells (HEK293). The

HEK293 cells showed relatively high levels of all genes tested, but low levels of the miRNAs tested and thus provided a good model for our functional assays. We examined whether

overexpression of any of these miRNAs affects the expression of _MAPK/Wnt_ genes _in vitro_. Cells were transfected for 24 h with a miRNA mimic for each of the individual miRNAs

(miR-146a-5p, miR-146b-5p, miR-24-3p and miR-425-3p), a miRNA-mimic scramble control or a mock vehicle, and results were compared with untreated cells. A one-way analysis of variance showed

that all of the _MAPK/Wnt_ genes tested were downregulated by at least one of the miRNA mimics transfected (Fig. 5c,d). Seven of the tested genes were downregulated by each of the four miRNA

mimics. Thirteen genes were downregulated by three of the four miRNA mimics. Two genes were downregulated by two of the four miRNA mimics and one gene was only downregulated by one of the

four miRNA mimics (Fig. 5c,d and Supplementary Table 13). We found no effects on any of these genes after treatment with the scramble or vehicle controls. In addition, we did not find any

changes in the expression of _GAPDH_ or _β-Actin_ after transfection of the mimics, scramble or vehicle controls. These results confirm an interaction between miR-146a-5p, miR-146b-5p,

miR-24-3p, miR-425-3p and the _MAPK/Wnt_ signalling pathways. DISCUSSION These miRNAs are transcribed from different genomic loci: chr5 (miR-146a-5p), chr10 (miR-146b-5p), chr9/chr19

(miR-24-3p) and chr3 (miR-425-3p). They are ubiquitously expressed, highly correlated in blood and brain tissue, and also expressed in other human biological fluids (Supplementary Figs 4 and

5). Based on our results, we propose a potential mechanism of action for these miRNAs through regulation of the _MAPK_ and _Wnt_ signalling pathways. Interestingly, inhibition of these

pathways has been shown to lead to a depressive phenotype, while activation has been strongly associated with a decrease in depressive symptoms and improved antidepressant response in both

central and peripheral systems26,27,28,29,30. Here, we showed that miR-146a-5p, miR-146b-5p, miR-24-3p and miR-425-3p regulate the expression of >30 genes involved in _MAPK_ and _Wnt_

signalling. Remarkably, most of these genes have been previously associated with MDD or antidepressant activity (Supplementary Table 14). In addition, we found a dysregulation of key genes

involved in neurotrophic factor signalling that is directly coupled to _MAPK_ activity. _The tropomyosin receptor kinase B_ (_NTRK2_) is the preferred receptor of the _brain-derived

neurotrophic factor_ (_BDNF_) and a key player in the neurotropic hypothesis of depression31,32, while the _fibroblast growth factor_ (_FGF2_) is a vital growth factor recently shown to

promote neuronal plasticity, proliferation, survival, differentiation and synaptic function33. Both of these genes have been shown to regulate neurogenic mechanisms and antidepressant

response23,26. Other genes involved in calcium signalling, endocytosis and adherens junctions are also potentially regulated by these miRNAs. In summary, we report a consistent dysregulation

of miRNAs in peripheral blood samples from depressed patients after antidepressant treatment. Our findings indicate that miR-146a-5p, miR-146b-5p, miR-24-3p and miR-425-3p are consistent

blood markers of antidepressant response and regulators of psychiatrically relevant signalling pathways. More importantly, we showed consistent changes in the expression of these miRNAs in

patients who respond to treatment with different classes of ADs, as well as in stressed mice that respond to AD, suggesting their involvement in a common mechanism of action. These results

have important implications for the elucidation of biological mechanisms involved in clinically relevant antidepressant effects and suggest that these miRNAs could potentially be used as

correlates or state biomarkers of treatment response. Ultimately, our results underline the potential of studying miRNAs to better understand molecular processes associated with MDD and

provide valuable information for the development of diagnostic tools, precautionary approaches and successful pharmacological interventions for MDD. METHODS HUMAN SAMPLES A total of 1,006

human samples were included in the present study, and include (1) discovery cohort (DRCT; _n_=516 samples from 258 patients) obtained in collaboration with the CAN-BIND and Lundbeck A/S

sponsored clinical trials; (2) replication cohort 1 (RPCT1, _n_=122 samples from 61 patients) obtained in collaboration with the GENDEP consortium; (3) replication cohort 2 (RPCT2, _n_=316

samples from 158 patients) obtained in collaboration with CAN-BIND; and (4) post-mortem, prefrontal cortex brain tissue (_n_=52) that was obtained in collaboration with the Quebec Coroner’s

Office and the Douglas-Bell Canada Brain Bank (Douglas Mental Health University Institute, Montreal, Canada). Ethics approval for this study was obtained from the institutional review board

of the Douglas Mental Health University Institute, and written informed consent was obtained from patients or family members, as appropriate. Our DRCT consisted of 258 well-characterized

individuals (females _n_=178; males=80) diagnosed with MDD in a current major depressive episode (MDE) who were enrolled in a clinical trial and received treatment with either duloxetine or

placebo. Duloxetine is a serotonin–norepinephrine reuptake inhibitor. For each patient, peripheral blood samples were collected at baseline and after treatment. Participants, aged 19–74

years, were recruited based on a primary diagnosis of MDD and MDE lasting at least 3 months, with a severity score on the MADRS at baseline of ≥22. Participants resistant to at least two

previous AD treatments or who had received electroconvulsive therapy in the 6 weeks before study beginning were excluded. Other exclusion criteria were: MDE in bipolar disorder, presence of

psychotic features and recent substance use disorder. Eligible participants were equally randomized to double-blind treatment with either duloxetine (60 mg) or placebo. All participants were

assessed prospectively for 8 weeks, including measures of depressive severity and potential related adverse events. To quantify treatment response, we calculated percentage change of MADRS

scores (from baseline to after treatment). We used percentage change to correct for the potential effects of differential baseline scores. Additionally, we classified participants as

responder/nonresponder based on >50% decrease in MADRS scores from baseline. This project was approved by ethics boards of participating centres, and all participants provided written

informed consent. Clinical trials are registered at www.ClinicalTrials.gov (11984A NCT00635219; 11918A NCT00599911; 13267A NCT01140906) Our RPCT1 consisted of 61 well-characterized

individuals (females _n_=40; males=21) diagnosed with MDD and enrolled in the GENDEP project. Adult patients with major depression of at least moderate severity according to ICD-10

(International Statistical Classification of Diseases and Related Health Problems, 10th revision) or DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th Edition) criteria were

recruited at five European centres. Patients were aged 21–70 years and of Caucasian European parentage. Exclusion criteria were personal and family history of schizophrenia or bipolar

disorder, or current substance dependence. Eligible participants were allocated to treatment with either escitalopram or nortriptyline that represent different antidepressant classes and

mechanisms of action. Patients with contraindications for one drug were offered the other. Severity of depression was assessed at baseline and after treatment by three established rating

scales. The GENDEP project was approved by ethics boards of participating centres, and all participants provided written informed consent. The GENDEP trial is registered at EudraCT (no.

2004-001723-38) and ISRCTN (no. 03693000). RPCT2 consisted of 158 well-characterized individuals (females _n_=98; males=60) diagnosed with MDD and enrolled in the CAN-BIND project19. Adult

patients with major depression of at least moderate severity according to DSM-IV-Text Revision (TR) criteria were recruited at six Canadian clinical centres. Patients were aged 18–60 years.

Exclusion criteria were personal and family history of schizophrenia or bipolar disorder, or current substance dependence. Eligible participants were allocated to treatment with escitalopram

(10–20 mg per day) for 8 weeks. Severity of depression was assessed at baseline and after treatment by the MADRS. The CAN-BIND project was approved by ethics boards of participating

centres, and all participants provided written informed consent. The CAN-BIND trial is registered at ClinicalTrials.gov identifier NCT01655706. Registered 27 July 2012. Human post-mortem

prefrontal cortex brain tissue was obtained in collaboration with the Quebec Coroner’s Office and the Douglas-Bell Canada Brain Bank (Douglas Mental Health University Institute, Montreal,

Quebec, Canada). A total of 52 brain samples were included in the present study. Thirty two depressed suicide completers and 20 non-suicide controls were randomly selected for miRNA

analysis. All individuals were of French–Canadian origin, a homogeneous population with a well-documented founder effect, and were matched for refrigeration delay, age and pH. Refrigeration

delay refers to the difference between the estimated time of death (determined by the pathologist through external body examination details) and the time at which the brain was refrigerated.

Psychological autopsies were performed post mortem on both cases and controls by a panel of psychiatrists, and diagnoses were assigned based on DSM-IV criteria. The control group had no

history of suicidal behaviour or major mood or psychotic disorders. Peripheral blood samples were collected in PAXgene blood RNA tubes (PreAnalytix). PAXgene tubes were frozen using a

sequential freezing process: tubes were stored at room temperature for 3 h, transferred to 4 °C overnight, followed by 6–8 h at −20 °C and then final storage at −80 °C. Total RNA (including

the miRNA fraction) was isolated from whole blood using the PAXgene Blood miRNA Kit (Qiagen, Canada) according to the manufacturer’s instructions. Brain tissues were processed and dissected

at 4 °C, then snap-frozen in liquid nitrogen before storage at −80 °C. Total RNA was isolated from 40 mg of frozen PFC tissue using the miRNeasy Mini Kit protocol in combination with the

RNeasy MinElute Cleanup Kit (Qiagen, Canada), with no modifications. All samples were treated with DNase digestion during RNA purification using the RNase-Free DNase kit (Qiagen). RNA and

miRNA yield and quality were determined using the Nanodrop 1000 (Thermo Scientific, USA) and Agilent 2100 Bioanalyzer (Agilent Technologies, USA). The average RNA Integrity Number (RIN)

value for all our samples by group were as follows: discovery cohort RIN: 8.6, replication cohort 1 RIN: 8.3, replication cohort 2 RIN: 8.2, and post-mortem brain samples RIN: 6.7. All

libraries were prepared using the Illumina TruSeq Small RNA protocol following the manufacturer’s instructions with 12 cycles of PCR amplification after ligation of the 3′ and 5′ adapters,

as described elsewhere5. Libraries were purified using biotinylated magnetic AMPure beads that allow for selection of specified complementary DNA (cDNA) products bound to streptavidin. A

total of 50 μl of amplified cDNA were mixed and purified twice with AMPure XP beads in a 1.8:1 ratio (beads/sample). This ratio allows for optimal selection of all products higher than 100

nucleotides. Library qualities were assessed using an Agilent 2100 Bioanalyzer High Sensitivity DNA chip, as well as quantitative real-time PCR with the KAPA library quantification kit (Kapa

Biosystems, USA). Samples were sequenced at the McGill University and Genome Quebec Innovation Centre (Montreal, Canada) using the HiSeq2500 Illumina sequencer with 50-nucleotide single-end

reads. We obtained an average number of 12.1 million raw reads, with an average quality value of 37 in all libraries produced. All sequencing data were processed using CASAVA 1.8+(ref. 34)

and extracted from FASTQ files. The Fastx_toolkit was used to trim the Illumina adapter sequences. Additional filtering based on defined cutoffs was applied to obtain high-quality data.

These filters included: (1) Phred quality (Q) mean scores higher than 30, (2) reads between 15 and 40 nucleotides in length, (3) adapter detection based on perfect 10-nucleotide match and

(4) removal of reads without detected adapter. Additionally, we used Bowtie35 to align reads to the human genome (GRCh37)36 and ncPRO-seq37 in combination with miRBase (V20) to match them to

known miRNA sequences38,39. We used a detection threshold of 10 counts per miRNA (present at least in 80% of libraries tested). A total of 281 miRNAs survived our criteria and were included

in the analysis. All small RNA-sequencing data were normalized with the Bioconductor – DESeq2 package using the variance-stabilizing transformation method40. To identify significant

microRNAs based on differential expression changes across time, we used significance analysis of microarrays method, adapted for small RNA-sequencing data (SAMSeq)41. SAMSeq was implemented

using a two-class paired model that accounts for changes not only between groups, but also within subjects as there is a one-to-one pairing between subjects across time. We used the

Benjamini–Hochberg FDR correction for multiple testing set at 5% that corrects for the proportion of miRNAs likely to have been identified by chance as being significant (adjusted _P_

values<0.05). We also tested for potential confounding effects of age, gender, race and RIN but found no significant differences across groups or time. Small RNA-sequencing results were

validated with a custom miRNA panel using circulatory miRNA assays by Firefly BioWorks. Briefly, RNA samples were hybridized to miRNA-specific probes coated on FirePlex hydrogel beads. A

universal biotinylated adapter was then ligated onto the captured miRNAs and labelled with a fluorescent reporter. The level of fluorescence for a given miRNA (particle) was detected using a

Guava easyCyte 8HT flow cytometer. Additionally, miR-19b-3p, Let-7i-5p and Let-7b-5p were included as endogenous controls, as they demonstrated the least variation across all samples in our

sequencing results. Raw data were processed using FireCode software (Firefly BioWorks), and normalized to the geometric mean of the endogenous controls. We used a two-class paired

significant analysis of microarray analysis to determine differential gene expression before and after treatment. _P_<0.05 was considered statistically significant. Frozen plasma was

thawed at room temperature, and 625 μl of each sample was used for total RNA extraction using the mirVana Isolation kit (ThermoFisher), following the manufacturer’s instructions. Libraries

were prepared using the Ion PI Template OT2 200 kit Version 3 and sequenced on an Ion Proton System (ThermoFisher). FASTQ files were downloaded from Ion Suite software and aligned using

miRBase (V20) and the hg19 genome build. Gene targets of miR-146a-5p, miR-146b-5p, miR-24–3p and miR-425-3p were identified using seven miRNA target prediction databases: miRWalk 2.0, miRDB,

miRanda, RNA22 v2, RNAHybrid, TargetScan and DIANA Tools42,43,44,45,46,47,48. We then used DAVID v6.7 to identify potential biological pathways regulated by these miRNAs. To validate our

miRNA target prediction and pathway analysis, we performed a second, independent, _in silico_ analysis for miR-146a-5p, miR-146b-5p, miR-24-3p and miR-425-3p using DIANA: miRPath. This

program allows identification of common miRNA targets provided by the DIANA-microT-CDS algorithm derived from DIANA-TarBase v6.0. In addition, miRPath can perform a pathway analysis based on

various levels of miRNA interactions. For genome-wide expression analysis in peripheral blood samples, we used the Human HT-12 v4 Expression Bead Chip (Illumina). Background adjustment

using variance-stabilizing transformation correction and quantile normalization was performed using the FlexArray package implemented in R (version 1.6.3). Probes with >70% absent points

among all samples were removed from downstream analysis. A total of ∼15,000 probes were included in the final analysis. Microarray data were analysed using a two-class paired significant

analysis of microarray, as well as Pearson’s correlations to determine differential gene expression before and after treatment using or correlations between miRNAs and target genes,

respectively. All analyses were performed using MultiExperiment Viewer 4 (MeV4, TM4 software suite). _P_<0.05 was considered statistically significant. For microRNA quantification, total

RNA samples were reverse transcribed and quantified using TaqMan RT-PCR microRNA assays (Applied Biosystems) according to the manufacturer’s instructions. Real-time PCR reactions were run in

triplicate using the ABI 7900HT Fast Real-Time PCR System and data were collected using the Sequence Detection System 2.4 (SDS) software (Applied Biosystems). To measure miRNA expression,

we used miRNA TaqMan probes, considered to be the gold standard for miRNA quantification5. Expression was calculated using the absolute quantitation standard curve method. RNU6B was used as

an endogenous control as it showed expression levels that remained relatively constant with low variance and high abundance across the samples tested. All primers used are described in

Supplementary Table 15. All numerical data are expressed as the mean±s.e.m. Statistical differences among groups were analysed by Student’s _t_-test. Statistical significance was calculated

using GraphPad Prism5 and SPSS 20. _P_<0.05 was considered statistically significant. For messenger RNA quantification, total RNA was reverse transcribed using MMLV reverse transcriptase

(Gibco) and oligo(dT)16 primers (Invitrogen). Real-time PCR reactions were run in triplicate using the ABI QuantStudio 6 Flex Real-Time PCR System and data were collected using the

QuantStudio Real-Time PCR software (Applied Biosystems). Expression levels were calculated using the relative quantification method. Both _β-actin_ and _GAPDH_ were used as endogenous

controls. All primers can be found in Supplementary Table 15. Gene expression data have been deposited in the Gene Expression Omnibus (GEO) database under the accession code GSE97154

(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE97154). ANIMAL EXPERIMENTS Male 10-week-old C57BL/6J mice, and 6-month-old CD1 retired breeders, were maintained on a 12 h light–dark

cycle (lights on at 0700, h) at 22–25 °C with _ad libitum_ access to food and water. C57 mice were housed 5 per cage except following defeat experiments at which point mice were singly

housed. All behavioural testing occurred during the animals’ light cycle. The experimenter was blinded to the experimental group and the order of testing was counterbalanced during

behavioural experiments. All experiments were conducted in accordance with the guidelines of the Institutional Animal Care and Use Committee at Icahn School of Medicine at Mount Sinai.

C57BL/6J mice were subjected to CSDS for 10 consecutive days, as described previously49,50. Briefly, C57BL/6J mice were subjected to 10 daily, 5 min defeats by a novel CD1 aggressor mouse

and were then housed across a plexiglass divider to allow for sensory contact. Control mice were housed in cages separated from other control mice by a plexiglass divider and were rotated to

a different cage daily. Resilient and susceptible mice were identified by their respective preference for, or avoidance of, interaction with a novel mouse after 10 days of defeat in a

social interaction test. Only control and susceptible mice were used for further study with imipramine. Social-avoidance behaviour was assessed with a novel CD1 mouse in a two-stage

social-interaction test. In the first 2.5 min test (no target), the experimental mouse was allowed to freely explore an arena (44 × 44 cm) containing a plexiglass and wire mesh enclosure (10

× 6 cm) against one wall of the arena. In the second 2.5 min test (target), the experimental mouse was returned to the arena with a novel CD1 mouse enclosed in the plexiglass wire mesh

cage. Time spent in the ‘interaction zone’ (14 × 26 cm) surrounding the plexiglass wire mesh cage, ‘corner zones’ (10 × 10 cm) and ‘distance travelled’ within the arena was measured by video

tracking software (Ethovision 5.0, Noldus). Sucrose preference was assessed using a 2% sucrose solution or tap water in 50 ml tubes with stoppers fitted with ball-point sipper tubes. All

animals were acclimatized to two-bottle choice conditions before testing conditions. Daily, at ∼1600, h, fluid levels were noted, and tube positions were interchanged. Sucrose preference was

calculated as a percentage (100 × volume of sucrose consumed/total volume consumed (sucrose and water)) and was averaged over 2 days of testing. Susceptible mice were randomly assigned to

treatment with either imipramine (20 mg per kg, daily) or a saline control (daily) treatment for 14 days. Blood was collected from all animals via the saphenous vein before treatment. Rapid

sampling from the saphenous vein was used because it causes minimal discomfort or stress to the animal. The blood was stored in RNAlater at −70 °C until use. After treatment and the second

round of behaviour testing, animals were killed and blood was collected via truncation into RNAlater and frozen at −70 °C until use. Then, 100 ng of total RNA was used to quantify expression

levels of miRNAs from animal blood samples using a probe-based miRNA expression assay (NanoString Technologies, USA). Briefly, hybridization of specific tags to target miRNAs was annealed

to create multiplex probe libraries from all samples. Each miRNA of interest is identified by a ‘colour code’ generated by six ordered fluorescent spots present on a reporter probe and read

by the nCounter Prep Station Digital Analyzer. Control RNA was included to monitor ligation efficiency and specificity throughout each step of the reactions. In addition, this protocol does

not include any amplification steps that might introduce bias to the results. Raw data were imported into NSolver Analysis Software v2.0 (NanoString technologies) and normalized to the

average counts to all endogenous genes. Data were further normalized using internal positive spike-in controls to account for variability in the hybridization process. Normalized data were

imported into MultiExperiment Viewer (MeV) software v.4.9 for further analysis. All numerical data are expressed as the mean±s.e.m. Statistical differences among groups were analysed by

Student’s _t_-test. _P_<0.05 was considered statistically significant. CELL CULTURE For miRNA Overexpression experiments, human embryonic kidney cells (HEK293) were cultured in Dulbecco’s

modified Eagle’s medium supplemented with 10% fetal bovine serum, 100 U ml−1 penicillin and 100 μg ml−1 streptomycin (Invitrogen) in a 5% CO2 humidified incubator at 37 °C. For miRNA mimic

treatments, cells were grown in the continuous presence of 5 nM miRNA Mimic (miR-146a-5p, miR-146b-5p, miR-24-3p or miR-425-3p), 5 nM miR-Mimic scramble control (AllStars Negative Control

siRNA, Qiagen) or mock vehicle (HiPerFect Transfection Reagent, Qiagen) for 24 h after which cell pellets were collected and both mRNA and miRNA were extracted as previously explained. All

experiments were performed in triplicate. All numerical data are expressed as the mean±s.e.m. Statistical differences among groups were analysed by one-way analysis of variance with _post

hoc_ correction and Pearson’s correlation coefficients. Statistical significance was calculated using GraphPad Prism5 and SPSS 20. _P_<0.05 was considered statistically significant. Human

NPCs were treated as previously explained12. To find adequate antidepressant concentrations for treatment, cells were screened for cytotoxic effects by measuring the activity of

mitochondrial dehydrogenase using the 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay (Sigma-Aldrich Co) and antidepressants were applied at concentrations that

showed no toxicity in the MTT test. For chronic antidepressant treatment, cells were grown in the continuous presence of 10 μM duloxetine, or no-drug control for 15 days, after which cell

pellets were collected and both mRNA and miRNA were extracted. All experiments were performed in triplicate. All numerical data are expressed as the mean±s.e.m. Statistical differences among

groups were analysed by independent Student’s _t_-tests. Statistical significance was calculated using GraphPad Prism5 and SPSS 20. _P_<0.05 was considered statistically significant.

DATA AVAILABILITY Gene expression data have been deposited in the GEO database under the accession code GSE97154. All other data that support the findings of this study are available from

the corresponding author on reasonable request (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE97154). ADDITIONAL INFORMATION HOW TO CITE THIS ARTICLE: Lopez, J. P. _et al_. MicroRNAs

146a/b-5p and 425-3p and 24-3p are markers of antidepressant response and regulate MAPK/Wnt-system genes. _Nat. Commun._ 8, 15497 doi: 10.1038/ncomms15497 (2017). PUBLISHER’S NOTE: Springer

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7, 137–151 (2006). Article  CAS  Google Scholar  Download references ACKNOWLEDGEMENTS This research was conducted with the support of the Ontario Brain Institute, an independent non-profit

corporation, funded partially by the Ontario government. G.T. is supported by grants from the Canadian Institute of Health Research (CIHR) (MOP93775, MOP11260, MOP119429, and MOP119430),

from the US National Institutes of Health (NIH) (1R01DA033684) and by the Fonds de Recherche du Québec - Santé (FRQS) through a Chercheur National salary award and through the Quebec Network

on Suicide, Mood Disorders and Related Disorders. J.P.L. received a Frederick Banting and Charles Best Canada Graduate Scholarships doctoral funding award from CIHR. E.J.N. is funded in

part by the National Institute of Mental Health (NIMH). GENDEP was funded by the European Commission Framework 6 grant, EC Contract Ref.: LSHB-CT-2003-503428. K.J.A. is supported by the

Government of Alberta funded Alberta Centennial Addiction and Mental Health Research Chair. R.U. is supported by the Canada Research Chair program (http://www.chairs-chaires.gc.ca/). C.M.L.

is funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. CAN-BIND is an

Integrated Discovery Program carried out in partnership with the Ontario Brain Institute. Additional funding is provided by the Canadian Institutes of Health Research (CIHR), Brain Canada,

Lundbeck, Bristol-Myers Squibb, Pfizer, and Servier. Funding and/or in-kind support was also provided by the investigators’ universities and academic institutions. The opinions, results and

conclusions are those of the authors and no endorsement is intended or should be inferred. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Psychiatry, McGill Group for Suicide

Studies, Douglas Mental Health University Institute, McGill University, Montreal, H4H 1R3, Quebec, Canada Juan Pablo Lopez, Laura M. Fiori, Cristiana Cruceanu, Rixing Lin, Naguib Mechawar 

& Gustavo Turecki * Fishberg Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, 10029, New York, USA Benoit Labonte, Hannah M.

Cates, Elizabeth A. Heller, Vincent Vialou, Stacy M. Ku, Christophe Gerald, Ming-Hu Han & Eric J. Nestler * Department of Psychiatry, University Health Network, University of Toronto,

Toronto, M5T 2S8, Ontario, Canada Jane Foster, Daniel J. Müller, Faranak Farzan, Peter Giacobbe, Susan Rotzinger & Sidney H. Kennedy * McMaster University and St Joseph’s Healthcare

Hamilton, Hamilton, L8S 4L8, Ontario, Canada Benicio N. Frey * St Michael’s Hospital, Toronto, M5B 1M4, Ontario, Canada Claudio N. Soares * Department of Psychiatry, Queen’s University,

Kingston, K7L 3N6, Ontario, Canada Claudio N. Soares * Centre for Addiction and Mental Health, Toronto, M6J 1A8, Ontario, Canada Daniel J. Müller & Faranak Farzan * School of Mechatronic

Systems Engineering, Surrey, V3T 0A3, British Columbia, Canada Faranak Farzan * University of Guelph, Guelph, N1G 2W1, Ontario, Canada Francesco Leri * University of Calgary Hotchkiss Brain

Institute, Calgary, T2N 1N4, Alberta, Canada Glenda M. MacQueen * Department of Pathology and Molecular Medicine, Queen’s University, Kingston, K7L 3N6, Ontario, Canada Harriet Feilotter, 

Kathrin Tyryshkin & Kenneth R. Evans * Indoc Research, Toronto, M5A 1N1, Ontario, Canada Kenneth R. Evans * University of Ottawa Institute of Mental Health Research, Ottawa, K1Z 7K4,

Ontario, Canada Pierre Blier * University of British Columbia and Vancouver Coastal Health Authority, Vancouver, V6T 2A1, British Columbia, Canada Raymond W. Lam * Queen’s University,

Providence Care, Mental Health Services, Kingston, K7L 4X3, Ontario, Canada Roumen Milev * University of Michigan, Ann Arbor, 48109, Michigan, USA Sagar V. Parikh * Rotman Research Institute

at Baycrest Centre, Toronto, M6A 2E1, Ontario, Canada Steven C. Strother * MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience,

King’s College London, London, SE5 8AF, UK Cathryn M. Lewis & Rudolf Uher * Departments of Psychiatry and Medical Genetics, University of Alberta, Edmonton, T6G 2B7, Alberta, Canada

Katherine J. Aitchison * Department of Psychiatry, Dalhousie University, Halifax, B3H 2E2, Nova Scotia, Canada Katherine J. Aitchison & Rudolf Uher * Janssen Research & Development,

LLC, San Diego, 92121, California, USA Gayle M. Wittenberg Authors * Juan Pablo Lopez View author publications You can also search for this author inPubMed Google Scholar * Laura M. Fiori

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author inPubMed Google Scholar CONTRIBUTIONS J.P.L. was involved in conducting and coordination of all aspects of the research, including testing feasibility, planning the experiments,

processing of human tissues, validation of results, execution of most molecular and cellular experiments, data analysis, interpretation and preparation of the manuscript. L.M.L. conducted

the overexpression of all miRNAs on HEK293 cells, and reviewed the manuscript. J.P.L. and L.M.F. were responsible for bioinformatics and statistical analysis of the data. C.C. planned and

carried out antidepressant treatment of human NPCs, screening for cytotoxic effects and preparation of the manuscript. B.L. and E.N. were responsible for all animal experiments. V.V., C.G.,

M.-H.H. and S.M.K. performed the CSDS, behavioural analyses and collected blood samples from mice. H.M.C., E.A.H. and G.M.W. processed all the animal blood and prepared samples for miRNA

quantification. R.U., K.J.A. and C.M.L. provided the replication cohort1 and participated in the interpretation of the data and preparation of the manuscript. H.F. and K.T. produced the

plasma miRNA data. B.N.F., C.N.S., D.J.M., F.F., F.L., G.M.M., K.R.E., P.G., P.B., R.W.L., R.M., S.V.P. and S.C.S. provided the replication cohort 2, participated in the interpretation of

the data and preparation of the manuscript. N.M. participated in the post-mortem study, interpretation of the data and preparation of the manuscript. J.F. and S.R. participated in the design

of the study, interpretation of the data and reviewed the manuscript. G.T. and S.H.K. conceived, supported and designed this study. G.T. was responsible for overseeing the experiments,

including all aspects of design, interpretation of data and preparation of the manuscript and figures. All authors discussed the results presented in the manuscript. CORRESPONDING AUTHOR

Correspondence to Gustavo Turecki. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing financial interests. SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION

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are markers of antidepressant response and regulate MAPK/Wnt-system genes. _Nat Commun_ 8, 15497 (2017). https://doi.org/10.1038/ncomms15497 Download citation * Received: 07 November 2016 *

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