Detection of Circulating miRNA Levels in Schizophrenia
Publication: American Journal of Psychiatry
Abstract
Objective:
Diagnosis of schizophrenia is currently dependent on symptom-based criteria and lacks objective indicators. In this study, the authors investigated whether circulating miRNA can serve as a diagnostic biomarker for schizophrenia.
Methods:
Global plasma miRNAs were profiled in a test cohort of 164 schizophrenia patients and 187 control subjects, using Solexa sequencing, TaqMan Low Density Array, and quantitative reverse transcription polymerase chain reaction (qRT-PCR) assays. The captured miRNAs were then validated by qRT-PCR assays in an independent cohort of 400 schizophrenia patients, 213 control subjects, and 162 patients with nonschizophrenia psychiatric disorders; the 400 schizophrenia patients underwent a 12-month follow up study of regular treatment with an atypical antipsychotic (risperidone and aripiprazole).
Results:
The global plasma miRNA screening revealed eight miRNAs that were up-regulated in schizophrenia, as revealed by both assay platforms. The qRT-PCR analysis showed the up-regulation of miR-130b and miR-193a-3p in schizophrenia but not in nonschizophrenia disorders.
Conclusions:
The up-regulation of miR-130b and miR-193a-3p is a state-independent biomarker for schizophrenia, and these two miRNAs could be used to develop a diagnostic tool for schizophrenia.
Schizophrenia is one of the most common severe mental disorders, with a lifetime risk of 1% in the population worldwide (1). Over the years, the diagnosis of schizophrenia has remained symptom based, relying mainly on self-reports from patients, mental state examination, and clinical interviews, and lacking objective laboratory tests (2). Such a diagnostic strategy can sometimes lead to misdiagnosis and has been criticized widely (3). To remedy this embarrassing state of affairs, a set of biomarkers has been proposed based on physical and biological tests (4).
MicroRNAs (miRNAs) are a class of small noncoding RNAs of 19–23 nucleotides in length that can inactivate a target mRNA sequence through binding to its 3′-UTR region (5). Increasing evidence shows that miRNAs take part in many biological processes, including cell proliferation, differentiation, migration, and apoptosis (6). It has been suggested that 70% of known miRNAs are expressed in the CNS, some of which are brain specific or brain-region specific (7), implying that these miRNAs may play a vital role in the development of diseases in the CNS (8). Altered miRNA levels have been found in CSF and postmortem brain of schizophrenia patients (9, 10), but no ideal miRNAs have been identified in the circulation that might be used as biomarkers for clinical applications. As plasma and serum samples can be easily collected, it is important to detect circulating miRNAs of interest as diagnostic biomarkers, as opposed to potential biomarkers in other tissues. Circulating miRNAs, first identified in 2008 (11), have been found to be highly stable under extreme conditions, such as ribonuclease digestion and extreme pH and temperature (12, 13). While levels of circulating miRNAs appear to be relatively low in healthy individuals, they may be increased in pathological conditions (14). Numerous studies have reported that circulating miRNA levels are highly associated with various diseases in humans, such as diabetes, cancer, and immunological diseases (13, 15), but there has been no systematic research on circulating miRNAs in psychiatric diseases.
In this study, we designed a multistage case-control study with follow-up plan to investigate the plasma miRNA profile as noninvasive biomarkers for schizophrenia. We globally screened plasma miRNAs initially with both Solexa sequencing and TaqMan Low Density Array (TLDA) chips, followed by a stem-loop quantitative reverse transcription polymerase chain reaction (qRT-PCR) assay. (For a flowchart of the project strategy, see Figure S1 in the data supplement that accompanies the online edition of this article.)
Method
Participants
A total of 726 patients with mental disorders were recruited in this study, of whom 564 were diagnosed as having schizophrenia according to DSM-IV criteria and 162 as having nonschizophrenia disorders (Table 1), including major depression, anxiety, bipolar disorder, and other atypical psychiatric diseases (see Table S1 in the data supplement ). All patients were diagnosed by at least two consultant psychiatrists using a structured interview and a strict assessment process. A total of 400 healthy subjects, matched on age, gender, smoking history, and education level, were recruited as control subjects. All participants were of Chinese Han origin and met all of the study’s inclusion criteria (see Table S2 in the data supplement ).
| Test Cohort | Validation Cohort | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Characteristic | Control Subjects (N=187) | Schizophrenia Patients (N=164) | Control Subjects (N=213) | Schizophrenia Patients (N=400) | Patients With Nonschizophrenia Disorders (N=162) | Remitted Patients, Baseline Values (N=79) | Remitted Patients, 1-Year Values (N=79) | Unremitted Patients, Baseline Values (N=28) | Unremitted Patients, 1-Year Values (N=28) | |||||||||
| N | % | N | % | N | % | N | % | N | % | |||||||||
| Male | 88 | 47.1 | 81 | 49.4 | 105 | 49.3 | 189 | 47.3 | 73 | 45.1 | ||||||||
| Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | |
| Age (years) | 28.7 | 9.5 | 29.2 | 9.8 | 26.2 | 5.6 | 25.0 | 7.5 | 36.0 | 14.8 | ||||||||
| Education (years) | 15.0 | 3.3 | 11.0 | 2.9 | 16.1 | 4.0 | 13.3 | 3.8 | 11.8 | 3.1 | ||||||||
| Positive and Negative Syndrome Scale | ||||||||||||||||||
| Positive subscale score | 17.8 | 6.1 | 22.9 | 5.4 | 22.3 | 5.2 | 7.3 | 1.1 | 22.1 | 6.3 | 10.7 | 5.2 | ||||||
| Negative subscale score | 17.6 | 6.8 | 20.9 | 7.4 | 21.5 | 6.8 | 9.5 | 2.8 | 21.5 | 7.3 | 16.7 | 6.2 | ||||||
| General psychopathology score | 38.5 | 9.7 | 41.5 | 8.8 | 41.4 | 7.4 | 18.4 | 2.1 | 39.5 | 7.4 | 23.6 | 6.3 | ||||||
| Total score | 79.6 | 20.7 | 85.2 | 5.3 | 85.1 | 5.9 | 35.2 | 4.4 | 83.1 | 15.9 | 51.0 | 14.7 | ||||||
Participants were divided into a test cohort and a validation cohort. The test cohort comprised 164 patients with schizophrenia, who were recruited from Shanxi Province; the validation cohort comprised 400 patients with schizophrenia, who were recruited from multiple mental health centers in Beijing, Shanghai, Sichuan, and Hunan Provinces. The 162 patients with nonschizophrenia disorders were recruited from Shanxi Province. The control subjects were recruited from local communities around each institution and showed no evidence of psychotic symptoms. All participants gave written informed consent to take part in the study, as approved by local ethics committees and in conformity with the requirements of the Declaration of Helsinki.
The schizophrenia patients in the validation cohort underwent a follow-up study under regular treatment with atypical antipsychotic drugs—risperidone and aripiprazole. The dosage for risperidone started at 1 mg/day and was gradually increased to 2–6 mg/day over 2–3 weeks, and that for aripiprazole started at 5 mg/day and was increased to 14–20 mg/day. Benzodiazepines were used temporarily in small dosages, if necessary, in patients with insomnia or anxiety, but combinations of other antipsychotics, antidepressants, or mood stabilizers were not allowed. A total of 107 patients reached the study endpoint, which was 12 months of treatment (Table 1). The definition of remission used in our study was that proposed by the Remission in Schizophrenia Working Group (16), in which both symptom and duration criteria must be met: participants had to maintain scores ≤3 on eight core items on the Positive and Negative Syndrome Scale (PANSS)—delusions (P1), concept disorganization (P2), hallucinatory behavior (P3), unusual thought content (G9), mannerisms/posturing (G5), blunted affect (N1), passive/apathetic social withdrawal (N4), and lack of spontaneity and flow of conversation (N6)—for at least 6 months.
Blood Sample Collection and RNA Extraction
Whole blood was obtained between 9 a.m. and 11 a.m. through venipuncture of a forearm vein and treated with EDTA. The blood sample was separated into plasma and cellular fractions by centrifugation (800 g, 10 minutes, 4°C) within 2 hours after collection; the plasma fraction was then centrifuged again (12,000 g, 10 minutes, 4°C) to remove cell debris. The plasma samples were then aliquoted, stored at −80°C, and transported by dry ice.
An aliquot of 100 μL of plasma was diluted with 300 μL of DEPC-treated water, and then diluted further with a sequencing mix of 200 μL of phenol-water and 200 μL of chloroform. The sample stood at room temperature for several minutes before being centrifuged at 12,000 rpm for 20 minutes, after which the upper aqueous layer was collected. A 1/10-volume of 3-M sodium acetate and a twofold volume of isopropyl alcohol were added and mixed, and the total RNA was precipitated after incubation at −20°C for 1 hour. Subsequently, the total RNA pellet was collected by centrifugation at 12,000 rpm for 20 minutes, washed with 75% ethanol by centrifugation at 7,500 rpm for 10 minutes, and dried for 15 minutes at room temperature. Finally, the total RNA was dissolved in 20 μL of ribonuclease-free water and stored at −80°C.
Solexa Sequencing and TLDA Assay
For Solexa sequencing and TLDA assay, a 5-μL aliquot of each total RNA sample from 150 schizophrenia patients and 150 control subjects in the test cohort was taken for pooling, and the pooled samples were then re-extracted with the mirVana miRNA Isolation Kit (Applied Biosystems, Foster City, Calif.). Solexa sequencing analysis was performed on the Illumina Solexa Sequencer (Illumina, San Diego) as described previously (12), and the clean readouts were compared with the miRBase database (http://microrna.sanger.ac.uk, release 12.0). The total RNAs were reverse-transcribed into cDNAs with the TaqMan miRNA Reverse Transcription Kit (Applied Biosystems) for the TLDA Chip (Applied Biosystems, version 3.0) screening. The TLDA chip was processed on the 7900HT PCR system (Applied Biosystems) to profile the miRNA expression pattern. The results were analyzed with the RQ Manager software program (Applied Biosystems) and calculated by the comparative threshold (Ct) method (2△△Ct).
Quantitative Real-Time PCR Analysis
TaqMan miRNA Assay (Applied Biosystems) was used to quantify mature miRNAs in plasma samples in accordance with the manufacturer’s instructions. Briefly, 2 μL of total RNA was reverse-transcribed to cDNA using M-MLV reverse transcriptase (Promega, Madison, Wisc.) and stem-loop RT primers (Applied Biosystems). Real-time PCR was performed using TaqMan miRNA probes (Applied Biosystems) on the CFX-96 system (Bio-Rad, Hercules, Calif.). All reactions, including the no-template controls, were run in triplicate. After real-time PCR amplification, the Ct values were determined using the fixed threshold settings. Each miRNA was reverse transcribed and amplified separately.
Quantification of qRT-PCR Results
To calibrate plasma levels of the target miRNAs identified in the test cohort, an external standard curve was generated with synthetic miRNAs for every qRT-PCR assay in the validation cohort based on the method described by Schmittgen et al. (17). For example, synthetic miR-16 oligonucleotides were used as the external standard to calibrate plasma levels of all miRNAs of interest, with concentrations ranging from 10 to 107 fmol/L (see Figure S2A in the online data supplement ). We also repeated the experiments in the test cohort using synthetic miR-130b and miR-193a-3p as the external standards (see Figures S2B and S2C). The concentrations of all miRNAs tested in individual plasma samples fell along the standard curves.
To assess the accuracy of the calibration of plasma miRNAs with the miR-16 standard curve, 10-pM synthetic miR-130b and miR-193a-3p oligonucleotides were put in phosphate-buffered saline containing 5% BSA; the recovery rates were calculated according to the resulting data from the exact detection process as described above. In brief, cDNA samples were diluted successively in a twofold series, and the levels of miR-130b and miR-193a-3p were then detected by qRT-PCR assay, calibrated by their corresponding standard curves; their recovery rates were then worked out, and the correlation in recovery rates between synthetic miR-16 and miR-130b and between synthetic miR-16 and miR-193a-3p was tested (see Tables S3 and S4 in the data supplement ).
Quality Control and Data Analysis
To assess the reproducibility of qRT-PCR assay, plasma samples from 10 healthy subjects and schizophrenia patients were pooled and then aliquoted into five portions (>100 μL each). Total RNA was extracted and the levels of miR-130b and miR-193a-3p were measured every other day by qRT-PCR analysis. The coefficients of variation (CV), representing the interassay deviation (i.e., the reproducibility of the qRT-PCR assay), were calculated.
Because the range of plasma miRNA measurements was quite wide among individual samples, the Mann-Whitney U test and percentile ranking were used to compare the differences in plasma miRNA levels between the patient group and the control group. The paired t test was also used to compare the plasma miRNA levels between baseline and individual tests during 1 year of treatment in the follow-up group. A p value <0.01 (two-tailed) was considered statistically significant.
Results
Global Profiling of Plasma miRNAs in Schizophrenia
Solexa sequencing and TLDA assay identified 255 and 247 miRNAs, respectively, in pooled plasma RNA samples from control subjects, and 215 and 235 miRNAs, respectively, in pooled plasma RNA samples from patients. The Solexa data revealed that miRNAs were a major fraction of small RNAs (<30 bp) in plasma (see Figure S3 in the data supplement ) and that patients with schizophrenia had 126 plasma miRNAs with increased levels of >twofold changes; TLDA chips confirmed 41 miRNAs with altered levels of ∼10-fold changes.
We selected miRNAs of interest for qRT-PCR validation by the following criteria: 1) having at least 50 copies with >twofold changes in plasma levels as shown by Solexa sequencing, 2) Ct value below 30 with >10-fold changes in plasma levels as shown by TLDA, and 3) having the same direction of altered expression shown by both Solexa sequencing and TLDA assay. A total of eight miRNAs (miR-122, miR-130a, miR-130b, miR-193a-3p, miR-193b, miR-502-3p, miR-652, and miR-886-5p) were identified by both assay platforms and then validated by qRT-PCR analysis (see Figure S4 in the data supplement ).
Evaluation of miRNA Expression by Quantitative Real-Time PCR Analysis
The qRT-PCR validation showed that of these eight miRNAs identified, only miR-130b and miR-193a-3p showed significant differences (Table 2). Percentile rank analysis showed that patients with schizophrenia had higher levels of plasma miR-130b than control subjects, including the median (χ2=112.60, p<0.001; odds ratio=∞), the 75th percentile (χ2=209.15, p<0.001; odds ratio=∞), and the 95th percentile (χ2=208.19, p<0.001; odds ratio=81.58, 95% CI=44.87–148.34). As shown in Table 3, the level of plasma miR-193a-3p was also higher in the patient group than the control group, including the median (χ2=110.96, p<0.001; odds ratio=∞), the 75th percentile (χ2=206.96, p<0.001; odds ratio=∞), and the 95th percentile (χ2=289.67, p<0.001; odds ratio=436.31, 95% CI=60.73–878.61). These two miRNAs were further evaluated in the validation cohort, and the pattern of altered miRNAs was consistent with that shown in the test cohort (Tables 2 and 3). Further analysis of plasma miR-130b and miR-193a-3p levels was performed in 162 patients with nonschizophrenia disorders but failed to show significant differences. Based on the calculation of odds ratios, these two miRNAs have a lesser effect on nonschizophrenia disorders than on schizophrenia (Tables 2 and 3), suggesting that the altered levels of plasma miR-130b and miR-193a-3p may be specific for schizophrenia.
| Group and miRNA | Mean Rank | p | |
|---|---|---|---|
| Test cohort | Control | Schizophrenia | |
| miR-122 | 70.14 | 64.34 | 0.389 |
| miR-130a | 63.20 | 56.47 | 0.298 |
| miR-130b | 97.62 | 265.38 | <0.001 |
| miR-193a-3p | 95.02 | 268.33 | <0.001 |
| miR-193b | 92.17 | 93.84 | 0.830 |
| miR-502-3p | 75.07 | 73.92 | 0.871 |
| miR-652 | 74.44 | 67.28 | 0.300 |
| miR-886-5p | 54.03 | 55.92 | 0.755 |
| Validation cohort | Control | Schizophrenia | |
| miR-130b | 107.00 | 413.50 | <0.001 |
| miR-193a-3p | 107.17 | 413.41 | <0.001 |
| Nonschizophrenia disorders | Control | Nonschizophrenia disorders | |
| miR-130b | 278.84 | 288.07 | 0.542 |
| miR-193a-3p | 274.84 | 297.95 | 0.127 |
| miRNA, Groups, and Percentile | Cutoff (fmol)a | Control Groupb | Case Groupb | χ2 | Odds Ratio | 95% CI | p |
|---|---|---|---|---|---|---|---|
| miR-130b | |||||||
| Test cohort, control versus schizophrenia group | |||||||
| Median | 43.07 | 93/187 | 164/164 | 112.60 | ∞ | <0.001 | |
| 75th percentile | 115.05 | 45/187 | 164/164 | 209.15 | ∞ | <0.001 | |
| 95th percentile | 524.51 | 9/187 | 132/164 | 208.19 | 81.58 | 44.87–148.34 | <0.001 |
| Validation cohort, control versus schizophrenia group | |||||||
| Median | 43.07 | 157/213 | 400/400 | 115.74 | ∞ | <0.001 | |
| 75th percentile | 115.05 | 125/213 | 400/400 | 192.96 | ∞ | <0.001 | |
| 95th percentile | 524.51 | 25/213 | 400/400 | 509.23 | ∞ | <0.001 | |
| Control versus nonschizophrenia disorders group | |||||||
| Median | 43.07 | 250/400 | 105/162 | 0.27 | 1.11 | 0.75–1.65 | 0.630 |
| 75th percentile | 115.05 | 170/400 | 64/162 | 0.43 | 0.88 | 0.60–1.29 | 0.571 |
| 95th percentile | 524.51 | 34/400 | 28/162 | 9.06 | 2.25 | 1.33–3.82 | 0.004 |
| miR-193a-3p | |||||||
| Test cohort, control versus schizophrenia group | |||||||
| Median | 152.76 | 93/187 | 164/164 | 110.96 | ∞ | <0.001 | |
| 75th percentile | 202.98 | 46/187 | 164/164 | 206.69 | ∞ | <0.001 | |
| 95th percentile | 273.87 | 8/187 | 156/164 | 289.67 | 436.31 | 60.73–878.61 | <0.001 |
| Validation cohort, control versus schizophrenia group | |||||||
| Median | 152.76 | 106/213 | 400/400 | 243.43 | ∞ | <0.001 | |
| 75th percentile | 202.98 | 62/213 | 400/400 | 376.25 | ∞ | <0.001 | |
| 95th percentile | 273.87 | 33/213 | 380/400 | 399.70 | 103.64 | 65.76–163.35 | <0.001 |
| Control versus nonschizophrenia disorders group | |||||||
| Median | 152.76 | 199/400 | 84/162 | 0.20 | 1.09 | 0.75–1.59 | 0.710 |
| 75th percentile | 202.98 | 108/400 | 51/162 | 1.14 | 1.24 | 0.84–1.84 | 0.302 |
| 95th percentile | 273.87 | 41/400 | 33/162 | 10.33 | 2.24 | 1.37–3.66 | 0.002 |
a
The cutoff points were set based on the miRNA expression levels in control subjects in the test cohort.
b
In the sample counts, the first number lists subjects with higher miRNA expression than cutoffs and the second lists subjects.
To clarify whether altered levels of these two plasma miRNAs were state-dependent, a 1-year follow-up study was carried out on patients treated with antipsychotics. Among 107 schizophrenia patients who completed the 1-year follow-up, 79 achieved the remission criteria. The levels of plasma miR-130b and miR-193a-3p declined significantly in patients who remitted but not in those without remission (Table 4). We also compared the baseline levels of these two miRNAs between patients who remitted and those who did not, and found that the magnitude of miRNA level change was greater in those who remitted (miR-130b, mean percentage of baseline for remitted patients, 0.35 [SD=0.33], mean percentage for unremitted patients, 1 [SD=0.64], p<0.001; miR-193a-3p, mean percentage of baseline for remitted patients, 0.29 [SD=0.25], mean percentage for unremitted patients, 0.82 [SD=0.93], p<0.001). These results demonstrate that the altered levels of plasma miR-130b and miR-193a-3p were state dependent in schizophrenia.
| miRNA Concentration (fmol/L) | ||||||
|---|---|---|---|---|---|---|
| Baseline | 1 Year | |||||
| Stage and miRNA | Mean | SD | Mean | SD | Proportion of 1 Year to Baseline | p |
| Remitted (N=79) | ||||||
| miR-130b | 10,570 | 12,300 | 4,636 | 5,380 | 0.44 | <0.0001 |
| miR-193a-3p | 3,395 | 4,641 | 1,041 | 2,281 | 0.31 | <0.0001 |
| Unremitted (N=28) | ||||||
| miR-130b | 15,800 | 7,097 | 12,840 | 4,559 | 0.81 | 0.051 |
| miR-193a-3p | 543.7 | 319.3 | 371.3 | 399.2 | 0.68 | 0.072 |
Accuracy Assessment of the RNA Extraction and qRT-PCR
As shown in Table 5, the miR-130b assay had a CV of 2.44% and the miR-193a-3p assay had a CV of 3.05%. The correlation (r) in recovery rates between synthetic miR-16 and miR-130b was 0.752 (p=0.032) and that between synthetic miR-16 and miR-193a-3p was 0.953 (p=0.0003). These results suggest that the RNA extraction and qRT-PCR assay were reproducible and reliable.
| Time Point (Day) | ||||||||
|---|---|---|---|---|---|---|---|---|
| miRNA | 1 | 2 | 3 | 4 | 5 | Mean | SD | CV (%) |
| miR-130b (Ct) | 25.41 | 24.64 | 24.89 | 26.10 | 24.37 | 25.08 | 0.61 | 2 |
| miR-193a-3p (Ct) | 29.05 | 28.99 | 29.39 | 30.32 | 31.08 | 29.77 | 0.91 | 3 |
a
Ct=comparative threshold; CV=coefficient of variation.
Discussion
Abnormal miRNA expression has widely been reported in numerous human diseases. Postmortem examinations have revealed an alteration of several miRNAs in brain tissue from patients with schizophrenia (18–20). However, such examinations cannot be applied to living patients, and the specificity and accuracy of abnormal miRNAs detected in the postmortem brain have yet to be fully investigated. In the peripheral circulatory system, Gardiner et al. (21) found that several miRNAs were down-regulated in peripheral blood mononuclear cells of schizophrenia patients, indicating a significant relationship between miRNA expression and schizophrenia (21). Recently, abnormal miRNA levels in the circulation were found in some human diseases, suggesting the potential for them to serve as a novel biomarker in clinical diagnosis and prognosis of these diseases (22, 23). Abnormalities of circulating miRNAs have not been confirmed in psychiatric diseases, although Shi et al. (24) tested the levels of 25 plasma miRNAs in 115 patients with schizophrenia and 40 control subjects and found that nine plasma miRNAs showed abnormal levels in schizophrenia; no firm conclusions could be drawn, however, because of the study’s small sample size (24).
To profile circulating miRNA levels, we used both Solexa sequencing and TLDA to detect miRNAs in plasma. The Solexa sequencing is a next-generation sequencing technology with the ability to read short fragments in a high-throughput pattern (25). Because next-generation sequencing is not fully matured and can be influenced by sequencing errors, we used TLDA to filter out the false signal. To obtain a large amount of total RNA for genome-wide miRNA profiling (20 μg for Solexa sequencing and 2 μg for TLDA chips), we pooled the samples of low total RNA extraction yields (about 500 ng per 100 μL plasma), although this strategy’s analysis accuracy has limitations. The pooled samples could mask variance and obscure heterogeneity, and some miRNAs of small effect may be missed. In addition, the pooled samples may generate a miRNA cumulative effect, leading to a false positive signal in the genome-wide screening stage. This may be the reason why the other six candidate miRNAs failed to show a significant change in the test cohort of schizophrenia. To avoid generating a false positive outcome, however, we performed qRT-PCR validation individually in several independent sample groups to confirm an initial finding. Based on the assessment of the interassay deviations (Table 5), the reproducibility of the miRNA assay used was satisfactory, suggesting that our findings should be reliable. Moreover, there is no endogenous reference miRNA available for normalization of qRT-PCR data; this is the reason why we applied the miR-16 external standard curve to calibrate circulating levels of eight candidate miRNAs, as proposed in a previous study (26). The recovery rate of miR-130b and miR-193a-3p, which was calculated through the miR-16 calibration, was highly correlated with that through the miR-130b and miR-193a-3p calibrations (see Tables S3 and S4 in the data supplement ), suggesting that the results from the miR-16 external standard curve are reliable. However, the lower recovery rate of miR-16 calculation may affect the sensitivity of miRNA assay, which means that some useful signals may be missed. This is a limitation of this study.
Analysis of global miRNA profiling in a large sample identified eight miRNAs that showed differential profiling in plasma from patients with schizophrenia, in which plasma miR-130b and miR-193a-3p levels were up-regulated in schizophrenia in both study cohorts. This work suggests that these miRNAs are novel biomarkers that can be used to develop a laboratory-based test for diagnosis of schizophrenia. In this study, we also found that the increased levels of miR-130b and miR-193a-3p in plasma could be suppressed in remitted patients after 1 year of treatment with antipsychotic drugs (aripiprazole and risperidone), suggesting that these two miRNAs can also be used as biomarkers for prognosis of schizophrenia patients on medication.
Although the origin of circulating miRNAs remains unclear, it has been reported that they may be derived from three different pathways: passive leakage from broken cells, active secretion via microvesicles, and active secretion through an RNA-binding protein-dependent pathway (27). Numerous reports have suggested that active secretion was the major source of circulating miRNAs (28). Several studies reported that both miR-130b and miR-193a-3p were up-regulated in pathologic lymphocytes, in which miR-130b was able to be packed into microvesicles and taken up by recipient cells (29–31). Most recently, the Schizophrenia Working Group of the Psychiatric Genomics Consortium confirmed 108 schizophrenia-associated loci and found that these loci were significantly enriched in B-lymphocyte lineages (32). It is possible that up-regulated plasma miRNAs in schizophrenia comes from these abnormally activated lymphocytes. Functional studies of these miRNAs would be helpful for a better understanding of their contribution to the pathophysiology of schizophrenia. The validated downstream target genes for miR-130b include PDGFRA, RUNX3, ITGB1, PPARG, FMR1, and STAT3, and for miR-193a-3p they include ErbB4, S6K2, and MCL1 (33–39). These genes can be classified into three groups: schizophrenia susceptibility genes (PDGFRA, PPARG, ErbB4), neurodevelopment-related genes (RUNX3, ITGB1, FMR1, STAT3), and neuroprotective genes (S6K2 and MCL1). The down-regulation of these genes by miRNAs may disturb neuronal function, leading in turn to dysfunction of the neural circuits. Interestingly, both the upstream regions of miR-130b and miR-193a-3p genes contain a CpG island, which can be regulated by DNA methylation and histone modification (40, 41). These two miRNAs may contribute to the pathogenesis of schizophrenia via epigenetic pathways, although additional studies are needed to clarify the precise mechanism by which miR-130b and miR-193a-3p may play a crucial role in developing schizophrenia.
In conclusion, plasma miR-130b and miR-193a-3p may be useful biomarkers for the development of a diagnostic tool for clinical application. Abnormal levels of these two plasma miRNAs also provide a clue to the natural history of the illness.
Acknowledgments
The authors thank Wu L. and Chu C. for their help with qRT-PCR assay.
Supplementary Material
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History
Received: 3 March 2014
Revision received: 28 October 2014
Revision received: 6 January 2015
Revision received: 14 March 2015
Accepted: 3 April 2015
Published online: 17 July 2015
Published in print: November 01, 2015
Authors
Author Contributions
The first two authors contributed equally to this work.
Competing Interests
The authors report no financial relationships with commercial interests.
Funding Information
Supported by the National 973 Projects (2013CB531300, 2012CB517902, 2014CB542300, 201302018), National Key Project of Scientific and Technical Supporting Programs funded by MOST of China (2007BAI17B04), the National Natural Science Foundation of China (31222031, 31430048, 81471325), the Fundamental Research Funds for the Central Universities (2012S05), and the Peking Union Medical College Youth Fund (2012J09, NCET-12-0071).
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