Abstract
Objective:
This preliminary study aimed to identify microRNA (miRNA) signatures associated with bipolar disorder (BD) by profiling blood-derived extracellular vesicles (EVs) of both putative neuronal origin and from all sources.
Method:
In two parallel studies of individuals with BD and controls (CON), we characterized miRNA expression profiles of blood EVs selected for L1CAM, a putative marker of neuronal origin (n=20 BD/20 CON), as well as bulk EVs (n=21 BD/20 CON). For each study, analyses identified miRNAs differentially expressed between groups, followed by functional interrogation and testing for associations with clinical features.
Results:
Results of Study 1 showed 34 miRNAs differentially expressed between groups and implicated PTEN, a gene whose protein levels were previously found to be altered in post-mortem brain studies of BD. Results of Study 2 showed 10 miRNAs differentially expressed between groups. Limited overlap was identified between studies, with only hsa-miR-1-3p identified with the same direction of change across both types of EVs. Differentially expressed miRNAs were significantly associated with clinical features of BD only in Study 1.
Conclusions:
Our results, albeit preliminary, reiterate a crucial role for miRNAs in the pathophysiology of BD and suggest that miRNA signatures of putative neuronal origin may more closely correspond to clinical features.
Keywords: bipolar disorder, mania, depression, extracellular vesicles, microRNA, epigenetics, biomarker
1. Introduction
Bipolar disorder (BD) is a potentially severe and highly prevalent psychiatric disorder characterized by pronounced mood and energy disturbances of (hypo)manic and depressive episodes [1]. Beyond the distress associated with its symptoms, BD is also associated with other significant risks, such as high rates of psychiatric comorbidity, increased likelihood of other medical conditions and premature death, cognitive and functional impairments, and an elevated risk for suicide compared to the general population [1].
BD is presumed to have a strong biological basis. Early genetic studies demonstrated a strong heritability for BD when considering familial aggregations and patterns in mono- and dizygotic twins. More recently, several gene expression changes in the brain have been documented, providing a possible molecular basis for the pathology of BD [2]. At the same time, genome-wide association studies of common variants have only partially been able to account for the original heritability from genetic epidemiology studies [3], possibly suggesting the contribution of other factors.
Epigenetic modulation of gene expression involves both genetic and experiential influences. Epigenetic changes, which include those that operate at the transcriptional, post-transcriptional, translational, and post-translational levels of gene and protein expression, regulate gene expression and can persist across cell division cycles without changing the DNA sequence. There is a growing interest in the possibility that epigenetic alterations play a key role in the pathophysiology of BD [4], considering that they may mediate both the effects of genetic influences and environmental exposures, such as early life stress.
MicroRNAs (miRNAs) are short (~20 nucleotide) non-coding RNAs which bind the miRNA seed - typically the 3’-untranslated segment of messenger RNAs (mRNAs) - to direct downregulation of gene expression by RNA-induced silencing complex (RISC). There are estimates of thousands of distinct human miRNAs, and miRNAs may be involved in the regulation of expression of 30% of all genes. One miRNA may regulate the activity of hundreds of target mRNAs, suggesting a critical master regulatory role. Several miRNAs have been shown to be differentially expressed in BD, though convergence across studies - even within tissue type - is limited [5]. Though miRNA expression can be tested in peripheral blood, the correlation of these findings with miRNA expression changes in the brain is unclear. The present inability to directly assess brain miRNA expression in vivo restricts analyses of brain tissue to the post-mortem context, which introduces new sources of ambiguity and confounding.
Extracellular miRNAs have been detected in various biological fluids, including blood [6]. A subset of miRNAs is present in extracellular vesicles (EVs), such as exosomes, micro-vesicles, and apoptotic bodies [7]. These miRNAs are important mediators of intercellular communication, but they can also serve as biomarkers of disease. Given that EVs carry molecular markers that identify their cellular origin, they can be leveraged to better understand miRNA expression patterns in otherwise inaccessible cell populations, such as neurons. Selecting EVs for neuronal origin may provide insights into brain miRNA expression profiles in living patients, circumventing challenges associated with interpreting miRNA expression patterns in post-mortem human brain tissues.
EVs selected in blood for L1 cell adhesion molecule (L1CAM) have shown a strong signal suggestive of neuronal origin, despite some controversy [8]. Supporting evidence demonstrates the correlation of L1CAM EV contents with brain pathology [9,10], with studies showing that L1CAM EVs also show brain-specific neural markers and contain miRNAs enriched for brain functions [11]. To our knowledge, miRNA expression profiles associated with BD have not been compared between L1CAM EVs and bulk EVs, limiting our understanding of the brain-specific miRNA expression profiles of living individuals with BD.
We carried out two independent studies with non-overlapping participants to better understand miRNA expression patterns in BD. In Study 1, we assessed miRNA expression differences between individuals with BD and non-psychiatric controls (CON) in L1CAM EVs from peripheral blood. In Study 2, we assessed miRNA expression differences between individuals with BD and CON in bulk (non-specific) EVs from peripheral blood. We hypothesized that in both studies, there would be a number of significantly differentially expressed miRNAs between BD and CON, reflecting the strong evidence base suggesting epigenetic mechanisms underlying BD pathophysiology. Regarding specific biological pathways, we hypothesized that - as L1CAM EVs purportedly could reflect a more brain-specific miRNA signature - the results of Study 1 may be more strongly enriched for neurobiological pathways. As an exploratory analysis within each study, we considered whether any significantly differentially expressed miRNAs were associated with clinical characteristics among the participants with BD only.
2. Methods
2.1. Subjects:
For both independent studies, participants were recruited at the Center of Excellence in Mood Disorders, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX. The Structured Clinical Interview for DSM-IV (SCID-IV) was used for the diagnosis of BD, and individuals with BD were not required to be euthymic at recruitment. Also according to the SCID-IV, participants were admitted to each CON group if they had no lifetime or present history of any major psychiatric disorder or such a history in any first-degree relatives. For the analysis of L1CAM peripheral blood EVs (Study 1), n = 20 individuals with BD and n = 20 CON were recruited. For the analysis of bulk peripheral blood EVs (Study 2), n = 21 individuals with BD and n = 20 CON were recruited.
In both studies, the severity of current depressive and manic symptoms in individuals with BD was assessed using the Montgomery-Åsberg Depression Rating Scale (MADRS) and the Young Mania Rating Scale (YMRS), respectively. Functional status was assessed in all participants with the Global Assessment of Functioning (GAF) and the Functioning Assessment Short Test (FAST). Impulsivity was assessed with the Barratt Impulsivity Scale (BIS), and the Hollingshead Four-Factor Index of Socioeconomic Status (SES-Adult) was used to assess the socioeconomic status of all participants. All procedures were performed in compliance with relevant laws, and the Institutional Review Board (IRB) approved the overall project at the University of Texas Health Science Center at Houston (IRB number HSC-MS-19-0157, 3/27/2019). All participants provided a signed informed consent after receiving a full explanation of the study procedures, and their privacy rights have been observed.
2.2. EV isolation, immunoprecipitation, and characterization:
Fasting peripheral blood was collected from all participants into heparin-containing vacutainers, followed by separation and storage of plasma in −80°C freezers. EVs were then isolated from all plasma samples with the ExoQuick® ULTRA EV Isolation Kit (Systems Biosciences). In brief, 1mL plasma samples from all subjects were initially centrifuged at 3,000 × g for 15 minutes at 4°C to remove cellular debris, followed by treatment of the supernatant with 5U thrombin. After a 5-minute incubation at room temperature, the fibrin was cleared with a second spin at 10,000 x g for 5 minutes and the resulting supernatant was transferred to a fresh tube for EV precipitation by the ExoQuick® ULTRA EV Isolation Kit, according to the manufacturer’s instructions.
For L1CAM-specific analysis (Study 1), the samples were further subjected to immunoprecipitation for the L1CAM neural adhesion protein, a marker for EVs of putative neuronal origin [12-14], using the mouse anti-human CD171 (L1 cell adhesion molecule [L1CAM]) biotinylated antibody (CD171, clone 5G3, eBioscience). Specifically, the EV samples were incubated with this antibody at 4°C for 2 hours on a rotating mixer and then precipitated using the Pierce streptavidin-agarose Ultralink resin (Thermo Scientific, Rockford, IL, USA) for 4 hours at 4°C. After centrifugation at 200 x g at 4°C for 10 minutes, the pellet containing EVs of putative neuronal origin was washed twice with PBS and stored at −80°C. Finally, the isolated L1CAM EVs were characterized by Western blot for the presence of common EVs and neural-specific markers, specifically: NCAM-L1 (C-2, sc-514360, Santa Cruz Biotechnology), microtubule-associated protein 2 (MAP2 (C-2), sc-390543, Santa Cruz Biotechnology, considered a neuron-specific marker), N-enolase (clone 5E2, MAB324, EMD Millipore, another neuron-specific marker), neuron-specific class III beta-tubulin (TUJ1, ab18207, Abcam), CD9 (H-110, sc-9148, Santa Cruz Biotechnology), and tumor suppressor TSG1 (TSG1, ab133586, Abcam).
2.3. RNA-sequencing:
Total RNA was isolated from the bulk EVs and L1CAM EVs with the exoRNeasy Midi Kit (Qiagen) and Seramir RNA Isolation Kit (Systems Biosciences), respectively, according to the manufacturer’s instructions. RNA quality was checked on Bioanalyzer (Agilent) using the Agilent RNA 6000 Pico Kit and assessed for the presence of miRNAs. For the study of L1CAM EVs (Study 1), total isolated RNA was entered into a small RNA library preparation using Seqmatic’s TailorMix for paired-end sequencing (SeqMatic). For the analysis of bulk EVs (Study 2), the total RNA population, starting with as little as 1 ng of total RNA, was entered into small RNA library prep using Qiagen’s miRNA library kit for single-end sequencing (Qiagen). In both studies, libraries were amplified by qPCR and assessed for viability by the concentration (> 1nM) to be moved on to sequencing. Successful libraries were purified by gel extraction to ensure the correct size was isolated, and libraries were sequenced on the Illumina NextSeq using a small RNA-Seq (Illumina Platform), 1x51bp, 10M (avg) raw reads per sample (single-end). To obtain read counts per miRNA per sample, sequencing reads were submitted to the ERCC exceRpt small RNA-seq pipeline (v.5.0.0) with default settings (adapter trimming, reads filtering, alignment to the human genome GRCh38 according to miRNA annotation in the miRBase v22). After filtering steps, 165 miRNAs were detected in L1CAM EVs and 188 miRNAs were detected in bulk EVs for differential expression (DE) analysis with DESeq2 (v.1.30.1). Raw RNA-sequencing data and accompanying phenotype information are deposited at the NIMH Data Archive (NDA, collection ID #3300).
2.4. Differential expression analyses:
First, for each study, demographic characteristics of the BD and CON groups were compared using chi-square for categorical variables and Mann-Whitney U tests for continuous variables. In each analysis, models comparing miRNA expression between individuals with BD and CON were adjusted for age (median split) and sex. Benjamini-Hochberg false discovery rate (FDR) correction was applied with a significance threshold of PFDR < 0.10 within each study.
2.5. Pathway over-representation analyses:
FDR-significant differentially expressed miRNAs were inputted to the miEAA 2.1 web tool [15] to identify enrichment for gene ontology (GO) processes (miRTarBase, miRWalk), diseases (MNDR), pathways (KEGG, miRWalk), and target genes (miRTarBase).
2.6. Classification analyses:
For the ten top-ranked differentially expressed miRNAs in each analysis, the classification of BD vs. CON from the normalized expression (variance stabilizing transformation from DESeq2) was evaluated with logistic regressions. miRNAs were tested independently, and the area under the ROC curve (AUC) values were calculated with the caret (v6.0.93), AUC (v0.3.2), and cvAUC (v1.1.4) R packages. ROC curves were interpreted according to general guidelines [16].
2.6. Association of miRNA expression with clinical features:
Among individuals with BD, the association of expression level of each of the significant (PFDR < 0.10) miRNAs from the main analysis with the following clinical variables was tested using Spearman or Pearson correlations: age at diagnosis of any mood disorder; age at diagnosis of BD; age at first mood stabilizer treatment; number of manic, hypomanic, mixed, depressive, and total episodes; number of prior hospitalizations; MADRS scores; YMRS scores; and FAST scores. Significant correlations were identified if PFDR < 0.05.
3. Results
3.1. Samples:
In both studies, the demographic make-up was female-predominant and the median age was late 20s to 30s (Table 1). Participants were from a variety of racial/ethnic backgrounds with most identified as White, Hispanic or Latino, Black or African-American, or more than one race. In both studies, the median body mass index (BMI) for BD and CON was overweight or obese. In both studies, MADRS and YMRS scores for participants with BD placed them in the mild depression and minimal mania ranges, and greater than ninety percent of participants with BD were taking psychotropic medications. For both studies, there were no significant differences between individuals with BD and controls on sex, age, race/ethnicity, or BMI. However, in both studies, individuals with BD had lower socio-economic status, lower functioning, and higher impulsivity. We found no significant differences among individuals with BD between the two studies or controls between the two studies on any of the demographic or clinical measures, including medications used.
Table 1.
Sample demographics.
| Within-Study Comparisons | Between-Study Comparisons |
|||||||
|---|---|---|---|---|---|---|---|---|
| Study 1: L1CAM EVs | Study 2: Bulk EVs | Controls, Study 1 vs. Study 2 |
BD, Study 1 vs. Study 2 |
|||||
| Controls (n = 20) |
BD (n= 20) |
p-value | Controls (n = 20) |
BD (n = 21) |
p-value | p-value | p-value | |
| Female sex, n (%) | 13 (60%) | 14 (70%) | 1.000a | 12 (60%) | 16 (76%) | 0.265a | 1.000a | 0.925a |
| Age, median (IQR) | 34.5 (20.0) | 34.0 (13) | 0.871b | 28.0 (15.0) | 37.0 (19.0) | 0.340b | 0.490b | 0.783b |
| Self-reported race/ethnicity (n) | -- | -- | 0.397a | -- | -- | 0.080a | 0.421a | 0.566a |
| Non-hispanic white or caucasian | 5 (25%) | 6 (30%) | -- | 2 (10%) | 4 (19%) | -- | -- | -- |
| Hispanic or latino | 6 (30%) | 2 (10%) | -- | 9 (45%) | 3 (14%) | -- | -- | -- |
| Black or African American | 8 (40%) | 7 (35%) | -- | 7 (35%) | 9 (43%) | -- | -- | -- |
| Hawaiian or pacific islander | 0 (0%) | 1 (5%) | -- | 0 (0%) | 0 (0%) | -- | -- | -- |
| Asian | 0 (0%) | 0 (0%) | -- | 1 (5%) | 0 (0%) | -- | -- | -- |
| More than one race | 1 (5%) | 3 (15%) | -- | 0 (0%) | 3 (14%) | -- | -- | -- |
| Unknown or not reported | 0 (0%) | 1 (5%) | -- | 1 (5%) | 0 (0%) | -- | -- | -- |
| Years of education, median (IQR) | 15.0 (3.0) | 15.0 (3.0) | 0.129b | 16.0 (6.0) | 13.0 (2.0) | <0.001 b | 0.080b | 0.463b |
| BMI, mean ± SD | 29.3 ± 6.3 | 31.6 ± 7.8 | 0.249c | 27.4 ± 6.9 | 32.4 ± 10.1 | 0.126c | 0.381c | 0.947c |
| SES educational, median (IQR) | 5.5 (1.0) | 5.5 (1.0) | 0.119b | 6.0 (2.0) | 5.0 (0.0) | 0.004 b | 0.277b | 0.555b |
| SES occupational, median (IQR) | 4.5 (4.0) | 0.0 (3.0) | 0.001 b | 6.0 (7.0) | 0.0 (1.0) | 0.001 b | 0.588b | 0.769b |
| SES total, median (IQR) | 39.0 (23.0) | 18.0 (13.0) | < 0.001 b | 48.0 (38.0) | 15.0 (6.0) | < 0.001 b | 0.472b | 0.425b |
| GAF, median (IQR) | 90.0 (6.0) | 51.0 (19.2) | < 0.001 b | 90.0 (6.0) | 52.0 (24.0) | < 0.001 b | 0.841b | 0.885b |
| FAST, median (IQR) | 4.0 (5.0) | 36.0 (25.0) | < 0.001 b | 5.0 (13.0) | 31.0 (36.0) | 0.012 b | 0.932b | 0.229b |
| BIS, mean ± SD | 59.9 ± 9.4 | 86.3 ± 20.8 | < 0.001 c | 60.4 ± 10.6 | 85.5 ± 16.5 | < 0.001 c | 0.848c | 0.926c |
| YMRS, median (IQR) | 0 (0) | 3.0 (10) | < 0.001 b | 0 (1) | 4 (5) | < 0.001 b | 0.399b | 0.655b |
| MADRS, median (IQR) | 0 (0) | 18.5 (23) | < 0.001 b | 0 (0) | 11 (23) | < 0.001 b | 0.072b | 0.487b |
| Medication status (yes), n | 0 (0%) | 19 (95%) | -- | 0 (0%) | 19 (90%) | -- | -- | 1.000a |
| Lithium | 0 (0%) | 6 (30%) | -- | 0 (0%) | 3 (14%) | -- | -- | 0.402a |
| Anticonvulsants | 0 (0%) | 8 (40%) | -- | 0 (0%) | 8 (38%) | -- | -- | 1.000a |
| Antidepressants | 0 (0%) | 8 (40%) | -- | 0 (0%) | 8 (38%) | -- | -- | 1.000a |
| Atypical antipsychotics | 0 (0%) | 14 (70%) | -- | 0 (0%) | 9 (43%) | -- | -- | 0.151a |
| Typical antipsychotics | 0 (0%) | 3 (15%) | -- | 0 (0%) | 1 (5%) | -- | -- | 0.563a |
| Benzodiazepines | 0 (0%) | 7 (35%) | -- | 0 (0%) | 3 (14%) | -- | -- | 0.238a |
| Stimulants | 0 (0%) | 1 (5%) | -- | 0 (0%) | 1 (5%) | -- | -- | 1.000a |
Chi-square
Mann-Whitney test
Independent t-test. BD - bipolar disorder; BIS - Barratt Impulsiveness Scale; BMI - body mass index; EV - extracellular vesicle; FAST - Functioning Assessment Short Test; GAF - Global Assessment of Functioning; IQR - interquartile range; SES - Hollingshead index of socioeconomic status.
3.2. Study 1: L1CAM EVs
3.2.1. Immunoprecipitation for L1CAM:
Western blot suggested a successful enrichment of EVs for neuronal origin using the L1CAM marker (Supplementary Figure 1), as suggested by a qualitative increase in the levels of the neural-specific markers MAP2, N-enolase, and TUJ1 after immunoprecipitation compared to total EVs.
3.2.2. Differential miRNA expression and pathway overrepresentation analyses in L1CAM EVs:
Thirty-four miRNAs were significantly differentially expressed between individuals with BD and CON at the significance threshold of PFDR < 0.10 in Study 1 of L1CAM EVs, twenty-seven of which were also significant at PFDR < 0.05 (Figure 1A and Table 2). The top-ranked differentially expressed miRNAs include hsa-miR-486-5p, hsa-miR-486-3p, hsa-miR-128-3p, and hsa-miR-301a-3p. Follow-up analyses implicated PTEN, TMED5, and EZH2 as the top-ranked target genes (Supplementary Figure 2A), with significant enrichment for multiple signaling pathways (Supplementary Figure 3). Finally, as exploratory analyses, we also tested the ten top-ranked miRNAs for discriminatory accuracy, with classification of BD vs. CON ranging from 91.2% for hsa-miR-486-5p to 55% for hsa-miR-150-5p (Supplementary Figure 4). The top two miRNAs, hsa-miR-486-5p and hsa-miR-486-3p, had excellent diagnostic performance at AUC > 90%, with four miRNAs showing fair diagnostic performance at 80% > AUC > 70%, two miRNAs showing poor diagnostic performances of 70% < AUC < 60%, and one miRNA showing failing diagnostic performance of 60% < AUC < 50%.
Figure 1.
Volcano plots showing differentially expressed microRNAs in individuals with bipolar disorder compared to controls. Blue dots indicate those with a false discovery rate-corrected p < 0.1. A) L1CAM extracellular vesicles; B) Bulk extracellular vesicles.
Table 2.
Differentially expressed microRNAs in L1CAM EVs (FDR < 0.1).
| miRNA | Fold Change (log-2) | SE | Statistic | P | P FDR |
|---|---|---|---|---|---|
| hsa-miR-486-5p | −1.4669665 | 0.29570203 | 23.6881651 | 1.13 x 10-6 | 9.73 x 10-5 |
| hsa-miR-486-3p | −1.4651403 | 0.29614689 | 23.5636594 | 1.21 x 10-6 | 9.73 x 10-5 |
| hsa-miR-128-3p | 2.65680457 | 0.52314647 | 22.5392913 | 2.06 x 10-6 | 0.00011049 |
| hsa-miR-301a-3p | 3.819899 | 0.67559144 | 16.3617044 | 5.23 x 10-5 | 0.00210636 |
| hsa-miR-92b-3p | −1.7274803 | 0.4164706 | 15.9222826 | 6.60 x 10-5 | 0.00212511 |
| hsa-miR-101-3p | 2.16332003 | 0.51207485 | 15.4368256 | 8.53 x 10-5 | 0.00228914 |
| hsa-miR-181a-3p | 2.58137408 | 0.61765982 | 15.0690982 | 0.00010365 | 0.00238386 |
| hsa-miR-654-5p | 2.85613228 | 0.5932544 | 13.9416686 | 0.00018857 | 0.00337334 |
| hsa-miR-335-3p | 3.12853461 | 0.61043971 | 14.0898111 | 0.00017429 | 0.00337334 |
| hsa-miR-150-5p | −2.2672009 | 0.56028808 | 13.5675432 | 0.00023013 | 0.0037051 |
| hsa-miR-140-3p | −2.1562699 | 0.56086746 | 12.2527995 | 0.00046456 | 0.00679948 |
| hsa-miR-425-5p | −1.874957 | 0.52805481 | 11.1912783 | 0.00082183 | 0.01102618 |
| hsa-miR-342-5p | −3.0930051 | 0.76830545 | 11.0325751 | 0.00089525 | 0.01108729 |
| hsa-miR-10b-5p | −1.7847615 | 0.50877097 | 10.8240953 | 0.00100188 | 0.01152158 |
| hsa-miR-191-5p | 1.40817338 | 0.42232255 | 10.2827236 | 0.00134282 | 0.01441289 |
| hsa-miR-30a-5p | 1.31521626 | 0.41278204 | 9.75031122 | 0.00179292 | 0.0167611 |
| hsa-mir-486-1 | −1.8130077 | 0.54676675 | 9.57487965 | 0.00197258 | 0.0167611 |
| hsa-miR-654-3p | 1.90531444 | 0.58282119 | 9.56982373 | 0.00197802 | 0.0167611 |
| hsa-miR-873-3p | −0.3966612 | 2.38227865 | 9.70499728 | 0.00183768 | 0.0167611 |
| hsa-miR-411-5p | 2.16616972 | 0.58611893 | 9.14337755 | 0.0024962 | 0.0200944 |
| hsa-let-7c-5p | 1.37637636 | 0.44650939 | 8.99167374 | 0.00271212 | 0.02079296 |
| hsa-miR-16-2-3p | −2.3034697 | 0.6497919 | 8.42017167 | 0.00371081 | 0.02715636 |
| hsa-miR-142-5p | 1.6905999 | 0.55103145 | 8.24177495 | 0.00409371 | 0.02828299 |
| hsa-miR-30e-3p | 1.33823837 | 0.44574262 | 8.14178118 | 0.00432568 | 0.02828299 |
| hsa-miR-185-5p | 1.77606561 | 0.55822698 | 8.11428963 | 0.00439177 | 0.02828299 |
| hsa-miR-20a-5p | −1.3842146 | 0.48272539 | 7.27507358 | 0.0069918 | 0.04329539 |
| hsa-miR-671-3p | 2.43044774 | 0.69851033 | 7.10620185 | 0.00768177 | 0.04580611 |
| hsa-miR-183-5p | −1.4094638 | 0.50094609 | 6.42295147 | 0.01126548 | 0.0647765 |
| hsa-miR-425-3p | 1.56170518 | 0.60595642 | 6.12487984 | 0.01332923 | 0.07400023 |
| hsa-mir-486-2 | −1.1677503 | 0.47265926 | 5.60128815 | 0.01794728 | 0.09321005 |
| hsa-miR-323a-3p | −1.1827989 | 0.47100062 | 5.62386967 | 0.01771749 | 0.09321005 |
| hsa-miR-1-3p | 2.32711194 | 0.6865979 | 5.33504407 | 0.02090081 | 0.09897149 |
| hsa-miR-320c | −1.4606309 | 0.60332152 | 5.42303744 | 0.01987276 | 0.09897149 |
| hsa-miR-1180-3p | −1.0621835 | 0.4563638 | 5.3539599 | 0.02067526 | 0.09897149 |
FDR - false discovery rate; SE – standard error.
3.2.3. Association of significant L1CAM-EV miRNAs with clinical features in individuals with BD:
Two of the differentially expressed miRNAs in L1CAM EVs were significantly associated with clinical features among the individuals with BD only (PFDR < 0.10) (Supplementary Figure 5A). Expression level of hsa-miR-92b-3p (downregulated in BD) was negatively associated with the number of previous manic episodes (Spearman’s rho = −0.744, PFDR = 0.007). Expression level of hsa-miR-101-3p (upregulated in BD) was negatively associated with age at first mood stabilizer treatment (rho = −0.585, PFDR = 0.098) and positively associated with total MADRS score (rho = 0.688, PFDR = 0.035).
3.3. Study 2: Bulk EVs
3.3.1. Differential miRNA expression and pathway overrepresentation analyses in bulk EVs:
Ten miRNAs were significantly differentially expressed between individuals with BD and CON at the significance threshold of PFDR < 0.10 in the bulk EVs, three of which were also significant at PFDR < 0.05 (Figure 1B and Table 3). The top-ranked differentially expressed miRNAs include hsa-miR-423-5p, hsa-miR-486-5p, and the hsa-miR-140-3p. Follow-up analyses implicated ACER2, UST, and ATP6V1E1 as the top-ranked target genes (Supplementary Figure 2B), with significant enrichment for multiple signaling pathways (Supplementary Figure 6). As exploratory analyses, we also tested the ten top-ranked miRNAs for discriminatory accuracy, with classification of BD vs. CON ranging from 88.7% for hsa-miR-423-5p to 63% for hsa-miR-1-3p (Supplementary Figure 7). Two miRNAs, hsa-miR-423-5p and hsa-miR-221-3p, had considerable diagnostic performance at 90% > AUC > 80%, with five miRNAs showing fair diagnostic performance at 80% > AUC > 70%, and three miRNAs showing poor diagnostic performances of 70% < AUC < 60%.
Table 3.
Differentially expressed microRNAs in bulk EVs (FDR < 0.1).
| miRNA | Fold Change (log-2) | SE | Statistic | P | P FDR |
|---|---|---|---|---|---|
| hsa-miR-423-5p | 0.80352464 | 0.15439345 | 26.705456 | 2.37 x 10-7 | 4.43 x 10-5 |
| hsa-miR-486-5p | 1.00710602 | 0.27418755 | 13.5853845 | 0.00022795 | 0.02131364 |
| hsa-miR-140-3p | 0.74282238 | 0.21685028 | 11.4685759 | 0.00070783 | 0.04412134 |
| hsa-miR-221-3p | −0.5920304 | 0.18812329 | 9.84134791 | 0.00170632 | 0.06381652 |
| hsa-miR-1-3p | 2.03756803 | 0.50103404 | 10.0028933 | 0.00156294 | 0.06381652 |
| hsa-miR-99b-5p | 0.66248958 | 0.21805435 | 8.96678646 | 0.00274932 | 0.08507664 |
| hsa-miR-98-5p | 0.74285555 | 0.25125364 | 8.69851886 | 0.00318469 | 0.08507664 |
| hsa-let-7f-5p | 0.35174182 | 0.12596727 | 7.77959259 | 0.00528397 | 0.09881026 |
| hsa-miR-96-5p | 2.0721563 | 0.69002176 | 7.83458483 | 0.00512559 | 0.09881026 |
| hsa-miR-486-3p | 1.75176683 | 0.60948852 | 7.8437722 | 0.00509961 | 0.09881026 |
FDR - false discovery rate; SE – standard error.
3.3.2. Association of significant bulk EVs miRNAs with clinical features in individuals with BD:
None of the differentially expressed miRNAs in bulk EVs were significantly associated with clinical features among the individuals with BD only (PFDR < 0.10) (Supplementary Figure 5B).
3.4. Comparison of Study 1 (L1CAM) and Study 2 (Bulk EV) results:
Four miRNAs were significantly (PFDR < 0.10) differentially expressed in both Study 1 (L1CAM) and Study 2 (Bulk) EVs: hsa-miR-486-5p, hsa-miR-140-3p, hsa-miR-1-3p, and hsa-miR-486-3p. Only hsa-miR-1-3p had the same direction of change across both types of EVs (upregulated in BD), with the remaining three miRNAs showing opposing directions of change (Supplementary Table 1).
4. Discussion
4.1. Overview:
In this set of two parallel, independent studies, we characterized peripheral blood for miRNA expression in EVs of putative neuronal origin and EVs from all sources in individuals with BD and control participants. To our knowledge, this is the first study to characterise miRNA expression signatures associated with BD in peripherally sourced EVs that are of presumed neuronal origin. In both sub-studies, we found evidence for differential miRNA expression signatures associated with BD.
4.2. Study 1:
In Study 1, we first validated that our L1CAM EVs showed protein markers consistent with neuronal origin (e.g., MAP2, N-Enolase, TUJ1). This finding supported the evidence that the L1CAM EVs may originate from neurons in the human brain, providing a possible ‘liquid biopsy’ of brain miRNA expression. As we will expand upon, though, this evidence is supportive but not definitive.
Results of Study 1 returned thirty-four miRNAs significantly differentially expressed in individuals with BD when compared to CON. Some of these miRNAs have been previously associated with BD. hsa-miR-140-3p, which was significantly downregulated in BD in our study, was previously found to be differentially expressed in the peripheral blood of individuals with BD, but in that previous study, it was upregulated [17]. hsa-miR-185-5p, which was significantly upregulated in BD in our study, was also previously found to be upregulated in circulating plasma exosomes of individuals with BD [18].
The other significantly differentially expressed miRNAs from Study 1 have not been previously associated with BD, to our knowledge. The top-ranked miRNA, hsa-miR-486-5p, which was downregulated in Study 1, has not been previously associated with BD, per se, but in a study of suicide attempter psychiatric patients, 44% of which had BD, increased serum expression of hsa-miR-486-5p showed good diagnostic performance in differentiating patients from controls (AUC = 75%) and significant positive association with aggression, depression, and anxiety [19]. hsa-miR-486-5p has also been found to regulate neurogenesis [20].
Thirty (out of 34) of the significantly differentially expressed miRNAs from Study 1 are detected in the human brain, according to the miEAA online tool [21]. In a previous study, expression of the top implicated target gene, PTEN, was found to be decreased in prefrontal cortex of individuals with BD [22]. Our finding that PTEN was the strongest target gene of the Study 1 differentially expressed miRNAs is thus convergent with the prior study indicating PTEN alterations in BD and may further support that the L1CAM EVs are reflecting a brain pathology. We did not have information on mRNA expression in this study, but future work could investigate miRNA-mRNA alterations in parallel to better understand the impact of our observed miRNA differential expression patterns on mRNA expression.
In the pathway analysis results of Study 1, several interesting processes emerged that may also reflect brain biology, including synaptic vesicle uncoating and Alzheimer’s disease. However, at the same time, there were many other processes that were general or specific to other tissues. Of particular interest were several processes related to general metabolism (with the leading KEGG pathway being insulin signaling) or genomic regulation, as well as those related to immune system functioning. Diagnostic performance of the top ten miRNAs from Study 1 was good, with 7/10 showing AUC > 70%. Notably, the top two miRNAs showed excellent diagnostic performance, with AUC > 90%.
Especially compelling was the finding that expression levels of two of the significantly differentially expressed miRNAs in Study 1 were significantly associated with clinical features among the individuals with BD. Lower expression of hsa-miR-92b-3p was associated with a higher number of previous manic episodes, which pairs well with the finding of lower expression of this miRNA in BD compared to controls. Higher expression of hsa-miR-101-3p was associated with earlier age at first mood stabilizer treatment (suggesting earlier onset of clinical severity) and current depression, also pairing well with our study’s finding of higher expression of this miRNA in BD compared to controls. Further study of these miRNAs, considering their associations with not just disease status but also specific clinical features, may have strong implications for future prognostics and disease understanding.
4.3. Study 2:
Results of Study 2 revealed ten miRNAs that were significantly differentially expressed in the bulk peripheral EVs of individuals with BD compared to CON. Again, hsa-miR-486-5p, the miRNA which was significantly downregulated in Study 1 and has been previously associated with aggression, depression, anxiety, and neurogenesis, was found to be differentially expressed. However, in Study 2, hsa-miR-486-5p was significantly upregulated in individuals with BD. Similarly, hsa-miR-140-3p, which was significantly downregulated in Study 1, was upregulated in individuals with BD in Study 2, a direction of effect consistent with the previous study in peripheral blood [17]. hsa-let-7f-5p, which was significantly upregulated in BD in Study 2, was previously found to be downregulated in major depression in the same peripheral blood study [17]. Other significantly differentially expressed miRNAs from Study 2 have not yet been linked to BD in the extant literature.
Nine (out of 10) of the significantly differentially expressed miRNAs from Study 2 are detected in the human brain, according to the miEAA online tool [21]. This is expected since bulk EVs include vesicles released by all tissues, including the brain. In contrast to the PTEN finding from Study 1, none of the top target genes from Study 2 are convincingly linked to BD based on the extant literature. At best, PAK1 has been linked to intellectual disability with macrocephaly and seizures by trio exome sequencing, supporting a role in brain development [23].
Pathway analysis results of Study 2 also contained some brain-related processes, such as long-term depression and Alzheimer’s Disease, as well as other general processes. As in Study 1, metabolism was particularly implicated. Interestingly, type II diabetes mellitus was one of the implicated processes, which may be a similar finding to the insulin signaling process in Study 1. Though diagnostic performance of the top ten miRNAs from Study 2 was nominally similar to in Study 1 (7/10 showing AUC > 70%), none of the miRNAs in Study 2 showed better than considerable diagnostic performance, as opposed to two miRNAs showing excellent (> 90%) performance in Study 1.
As opposed to in Study 1, none of the differentially expressed miRNAs from Study 2 were significantly associated with clinical features in individuals with BD. This could be partly due to the lower number of significant miRNAs in Study 2, but it also lends further evidence to there being something more disease- or brain-specific in the results of Study 1, possibly due to the miRNA disease profile more closely corresponding to brain pathology, and therefore disease features.
4.4. Convergence and Divergence across Study 1 and Study 2: Evidence that L1CAM EV Expression Profiling Captures Brain Pathology and A Closer Reflection of BD?
Here, we performed two sub-studies in independent, non-overlapping samples of individuals with BD and controls. The samples did not significantly differ on any of the measured clinical or demographic variables. However, they did differ on how the peripheral EVs were sourced, with the EVs from Study 1 being associated with L1CAM, a putative neuronal marker, and the EVs from Study 2 being unselected, or bulk in origin from peripheral blood.
In both studies, we were able to capture a miRNA signature which differentiated individuals with BD from controls. Interestingly, there was little overlap between the differentially expressed miRNAs between studies. On the one hand, some may consider this a failure to replicate, but if the studies are capturing expression in different tissues (brain vs. bulk), then cell-type specificity in miRNA expression patterns could explain the discrepancies, which is the exact reasoning behind conducting two separate studies in the first place.
Even among the four miRNAs which were significantly differentially expressed in both studies, only one of them, hsa-miR-1-3p, had a conserved direction of effect across studies. If we assume that the L1CAM EVs are truly neuronal in origin, then this finding supports that disease-associated miRNA expression patterns are cell-type specific. Interestingly, miR-1-3p has been shown to target the brain-derived neurotrophic factor (BDNF) gene and suppress its expression [24], with evidence of a negative correlation between miR-1-3p expression and serum BDNF levels in clinical samples [25]. BDNF has been repeatedly found to be downregulated in individuals with BD, particularly during acute mood episodes [26], and our results point to a possible miR-1-3p-mediated mechanism driving this downregulation. Of note, miR-30a-5p, which was increased in BD in L1CAM EVs, can also target and reduce the expression of BDNF [27], suggesting that many miRNAs could be acting in synergy to reduce BDNF levels in BD. However, the reason why this miRNA shows a conserved direction of effect across studies but not the other three remains to be understood.
There were some commonalities worth commenting on across studies. For example, the pathway results across studies particularly implicated metabolic aberrations, including insulin signaling and type II diabetes mellitus. Interestingly, the top-ranked KEGG pathway enriched with the L1CAM EVs miRNAs was the insulin signaling pathway, which has been previously linked to BD’s pathology [28] and further suggested as a therapeutic target of the mood stabilizer lithium [29]. This finding is also supported by a previous report showing an association between insulin receptor substrate 1 (IRS-1) phosphorylation at serine site 312 (an indicator of insulin resistance) measured in neural-derived EVs and cognitive dysfunction in individuals with BD [30]. Metabolic abnormalities are highly associated with BD [31]. This does not necessarily mean that they are a cause of BD or that they are specific to the pathophysiology of BD. In fact, they may reflect components of the pathophysiology of BD but may also reflect differences in lifestyle-environmental factors among individuals with BD. Regardless of the aetiology of these differences, however, this finding continues to underscore the profound need to better understand the involvement of metabolism in BD including implications for prognostics and treatment.
Some findings may point to L1CAM EVs better capturing brain pathology and therefore more closely corresponding to BD disease risk and progression. For one, it is a prerequisite to note that almost all of the miRNAs found to be significantly differentially expressed in individuals with BD from Study 1 have been detected in human brain tissue. This is not sufficient to claim that L1CAM EV expression profiles capture brain pathology, as miRNAs can be expressed across multiple tissues (and this finding is similar in Study 2), but it would be necessary if we are to begin to make this claim. It is particularly compelling that the top-ranked target gene from Study 1, PTEN, has been established to be involved in BD from studies of post-mortem human brains. None of the top-ranked target genes from Study 2 were associated with BD, let alone in the human brain. Also, only differentially expressed miRNAs from Study 1 were associated with clinical features of BD, which would also support that the expression profile more closely captures brain-disease processes, and would be a critical finding to follow-up on as it could lend itself to future diagnostic or prognostic markers.
To our knowledge, this is the first study of L1CAM EV miRNA expression in BD to date. Our group has previously found no significantly differentially expressed bulk EV miRNAs between individuals with BD and controls (n=41) after adjustment for multiple comparisons, and we proceeded with exploring the 33 nominally-significant miRNAs instead [32]. Using a larger sample size (n=110), Ceylan et al. (2020) identified four differentially expressed miRNAs in bulk EVs between individuals with BD and controls after controlling for multiple comparisons [18]. Among the differentially expressed miRNAs identified in this study, only the miR-185-5p (L1CAM EVs) has been previously found in bulk EVs from individuals with BD [18]. Encouragingly, our study also replicated the previously identified increase in miR-185-5p levels in BD compared to controls. This miRNA, which has also been found to be differentially expressed in peripheral blood of individuals with major depressive disorder [33], has been linked to multiple pathways related to cholesterol homeostasis [34], mammalian target of rapamycin complex (mTORC) regulation [35], and beta-cell proliferation [36], all of which are mechanisms previously linked to BD [37-39]. Interestingly, miR-185 has also been linked to telomere shortening and accelerated aging [40], which is another finding consistently reported for BD [41]. All in all, the replication of its upregulation in BD and its link to many BD-related pathways suggest it as a key target in the disorder and warrant further studies focusing on this particular miRNA.
4.5. Limitations:
Many limitations of this preliminary study need to be acknowledged. The use of EVs enriched for neuronal origin as a source of biomarkers in clinical studies has been supported by many investigations in the field of neuropsychiatry [42]. Initial studies have proposed that the neural cell adhesion protein L1CAM (CD171) could be a good target for immunoprecipitation of EVs due to its specific expression in neural tissue [42], which was followed by multiple studies using this marker for the isolation of so-called neural-derived EVs (NDEVs) [13]. Nonetheless, this approach has been met with controversy over the past few years, given evidence that L1CAM is also expressed in other tissues, thus reducing the neural specificity of EVs obtained with this marker [43]. Indeed, a recent study found that EV subpopulations can be identified in human biofluids, including those of neuronal origin, but further supported that L1CAM may not serve as a reliable marker to isolate NDEVs [44]. For this reason, the neural specificity of these EVs cannot be guaranteed, although recent work revisiting this controversy provides further support for L1CAM as a reliable biomarker of NDEVs [8]. The similar detection rate of miRNAs detected in the brain (using miEAA) for both L1CAM and bulk EVs also weakens our claim of neural enrichment. In our experiments, we found an increase in the levels of the neural-specific markers MAP2, N-enolase, and TUJ1 in the Study 1 EVs, supporting a possible neuronal origin. However, we were not able to perform comprehensive EV characterization following the minimal information for studies of extracellular vesicles (MISEV2018) [45], including vesicle morphology, assessment of multiple markers for EVs, and size distribution using nanoparticle tracking analysis or immunogold transmission electron microscope, which would be important to further validate EV isolation and purity. On that note, we fully acknowledge that not meeting the MISEV2018 Guidelines for EV characterization [45] is a major limitation of this preliminary study, which prevents a definite determination that the miRNAs investigated are indeed coming from EVs. Our Western blot experiments also lacked a negative control consisting of plasma input without EV isolation, which cannot fully guarantee that the protein markers assessed are truly within the EVs and not free-floating in the plasma. It is also noteworthy that Exoquick®, which was our method of choice for the EV isolation, is a polymer-based precipitation method that is not specific and can co-isolate non-EV contaminants. Finally, L1CAM-based immunocapture is sensitive to methodological variations (e.g., antibody specificity, centrifugation protocols). With our preliminary study being the first to examine L1CAM EV miRNAs in BD, we hope that follow-up studies will be able to perform more comprehensive EV characterization to further validate our findings and place them in a more rigorous biological context. Importantly, regarding the study design, this project was composed of two independent studies with non-overlapping participants examining L1CAM and bulk EVs, respectively. As such, differences between the findings of the two studies cannot definitively be attributed to differing EV sources, as they could be confounded by other between-study differences. Reassuringly, the participants with BD and the controls did not significantly differ between the two studies on any of the measured demographic or clinical variables. However, future work using paired samples (i.e., L1CAM and bulk EVs from the same individuals) and employing the same kit for RNA isolation (to reduce possible technical batch effects affecting the miRNA profiles) would be needed to more rigorously make this type of comparison. In addition, the sample sizes for both sub-studies were small, which may have led to underpowered statistical analyses and prevented us from further exploring the results in specific subgroups of individuals. The samples were heterogeneous in terms of medication use, acute mood symptoms, and clinical presentations, all of which may have acted as confounders in our analyses. Many unmeasured external factors could also be influencing the levels of miRNAs assessed in our study, such as diet, physical activity, and sleep patterns. Finally, our results should also be acknowledged as preliminary given our lack of validation with alternative, complementary methods (such as qPCR) or with independent, replication samples.
4.6. Conclusion:
This is the first study comparing miRNA expression patterns associated with BD in peripheral blood L1CAM and bulk EVs, providing further insight into the possible pathophysiology of BD, including a potential window into brain miRNA expression differences in vivo. This preliminary study also provides important methodological lessons for a future, more robust follow-up study to advance the understanding of L1CAM EVs in BD. Despite important controversies concerning the use of L1CAM as a neuronal marker, the use of the L1CAM EVs as potential liquid biopsies of brain gene expression in BD and other psychiatric disorders is further supported by the established relevance of the identified target genes and pathways known to be relevant to BD’s pathophysiology, such as PTEN, BDNF pathway, and insulin resistance, as well as the association of significant miRNAs with clinical features in the L1CAM EVs analysis only. Further investigations will be required to better understand the functional implications of the observed miRNA alterations, as well as their concordance with studies conducted in post-mortem brain tissues. In particular, given the higher burden for certain diseases (e.g., diabetes) in individuals with BD, miRNA alterations may be one possible pathway to disease risk, although this hypothesis is preliminary. Regardless, these preliminary findings offer a promising first step to incorporating brain-specific miRNAs into monitoring as well as intervention work.
Supplementary Material
Supplementary Table 1. Overlapping microRNAs between the analysis of L1CAM (Study 1) and bulk (Study 2) extracellular vesicles. FC – fold change; FDR – false discovery rate.
Supplementary Figure 1. Characterization of bulk and L1CAM (neural-enriched) extracellular vesicles. CD171 – cell adhesion molecule L1 (L1CAM); EV – extracellular vesicle; MAP2 – microtubule-associated protein 2; NPC – neuroprogenitor cells; TSG1 – tumor suppressor TSG1; TUJ1 – tubulin, beta 2 class III.
Supplementary Figure 2. Target genes of the differentially expressed microRNAs in A) Study 1 of L1CAM (neural-enriched) extracellular vesicles and B) Study 2 of bulk extracellular vesicles (according to miRTarBase).
Supplementary Figure 3. Pathway analyses with differentially expressed microRNAs in Study 1 of L1CAM (neural-enriched) extracellular vesicles. A) Pathways identified with KEGG database; B) gene ontology terms identified with miRTarBase; C) gene ontology terms identified with miRWalk; d) disease terms identified by the mammal ncRNA–disease repository (MNDR).
Supplementary Figure 4. Receiving operating curves (ROC) for individual (top-ranked) microRNAs identified to be differentially expressed between individuals with bipolar disorder and controls in Study 1 of L1CAM (neural-enriched) extracellular vesicles. AUC - area under the curve; ci - confidence interval, FP - false positive; TP - true positive.
Supplementary Figure 5. Correlation matrices for individual (top-ranked) microRNAs identified to be differentially expressed between individuals with bipolar disorder and controls in A) Study 1 of L1CAM (neural-enriched) extracellular vesicles and B) Study 2 of bulk extracellular vesicles. MADRS - Montgomery-Asberg Depression Rating Scale; YMRS - Young Mania Rating Scale; FAST - Functioning Assessment Short Test.
Supplementary Figure 6. Pathway analyses with differentially expressed microRNAs in Study 2 of bulk extracellular vesicles. A) Pathways identified with KEGG database; B) gene ontology terms identified with miRTarBase; C) gene ontology terms identified with miRWalk; d) disease terms identified by the mammal ncRNA–disease repository (MNDR).
Supplementary Figure 7. Receiving operating curves (ROC) for individual (top-ranked) microRNAs identified to be differentially expressed between individuals with bipolar disorder and controls in Study 2 of bulk extracellular vesicles. AUC - area under the curve; ci - confidence interval, FP - false positive; TP - true positive.
Article Highlights.
Extracellular vesicles represent a valuable source of bipolar disorder biomarkers.
L1CAM (putative neuronal origin) vesicles led to the identification of thirty-four miRNAs and converged on a gene previously implicated in post-mortem brain studies.
Clinical associations were only found with miRNAs from L1CAM vesicles.
L1CAM vesicles for biomarker identification warrant further studies.
Acknowledgments:
We would like to thank the study participants for their willingness to participate in the study.
Funding:
This manuscript was funded by the National Institute of Mental Health [R21MH117636 and K01MH121580], the University of Texas Health Science Center at Houston, and the Louis A. Faillace, MD Endowment Funds. Center of Excellence on Mood Disorders (USA) is funded by the Pat Rutherford Jr Chair in Psychiatry, the John S. Dunn Foundation, and the Anne and Don Fizer Foundation Endowment for Depression Research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
Footnotes
Disclosure Statement: The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
Writing assistance disclosure: No writing assistance was utilized in the production of this manuscript.
Ethical conduct of research statement: All procedures were performed in compliance with relevant laws, and the Institutional Review Board (IRB) approved the overall project at the University of Texas Health Science Center at Houston (IRB number HSC-MS-19-0157, 3/27/2019). All participants provided a signed informed consent after receiving a full explanation of the study procedures, and their privacy rights have been observed.
Data Availability Statement:
Raw RNA-sequencing data and accompanying phenotype information are deposited at the NIMH Data Archive (NDA, collection ID #3300).
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Table 1. Overlapping microRNAs between the analysis of L1CAM (Study 1) and bulk (Study 2) extracellular vesicles. FC – fold change; FDR – false discovery rate.
Supplementary Figure 1. Characterization of bulk and L1CAM (neural-enriched) extracellular vesicles. CD171 – cell adhesion molecule L1 (L1CAM); EV – extracellular vesicle; MAP2 – microtubule-associated protein 2; NPC – neuroprogenitor cells; TSG1 – tumor suppressor TSG1; TUJ1 – tubulin, beta 2 class III.
Supplementary Figure 2. Target genes of the differentially expressed microRNAs in A) Study 1 of L1CAM (neural-enriched) extracellular vesicles and B) Study 2 of bulk extracellular vesicles (according to miRTarBase).
Supplementary Figure 3. Pathway analyses with differentially expressed microRNAs in Study 1 of L1CAM (neural-enriched) extracellular vesicles. A) Pathways identified with KEGG database; B) gene ontology terms identified with miRTarBase; C) gene ontology terms identified with miRWalk; d) disease terms identified by the mammal ncRNA–disease repository (MNDR).
Supplementary Figure 4. Receiving operating curves (ROC) for individual (top-ranked) microRNAs identified to be differentially expressed between individuals with bipolar disorder and controls in Study 1 of L1CAM (neural-enriched) extracellular vesicles. AUC - area under the curve; ci - confidence interval, FP - false positive; TP - true positive.
Supplementary Figure 5. Correlation matrices for individual (top-ranked) microRNAs identified to be differentially expressed between individuals with bipolar disorder and controls in A) Study 1 of L1CAM (neural-enriched) extracellular vesicles and B) Study 2 of bulk extracellular vesicles. MADRS - Montgomery-Asberg Depression Rating Scale; YMRS - Young Mania Rating Scale; FAST - Functioning Assessment Short Test.
Supplementary Figure 6. Pathway analyses with differentially expressed microRNAs in Study 2 of bulk extracellular vesicles. A) Pathways identified with KEGG database; B) gene ontology terms identified with miRTarBase; C) gene ontology terms identified with miRWalk; d) disease terms identified by the mammal ncRNA–disease repository (MNDR).
Supplementary Figure 7. Receiving operating curves (ROC) for individual (top-ranked) microRNAs identified to be differentially expressed between individuals with bipolar disorder and controls in Study 2 of bulk extracellular vesicles. AUC - area under the curve; ci - confidence interval, FP - false positive; TP - true positive.
Data Availability Statement
Raw RNA-sequencing data and accompanying phenotype information are deposited at the NIMH Data Archive (NDA, collection ID #3300).