metricas
covid
Buscar en
European Journal of Psychiatry
Toda la web
Inicio European Journal of Psychiatry Differential exosomal microRNA profile in the serum of a patient with depression
Información de la revista
Vol. 32. Núm. 3.
Páginas 105-112 (julio - septiembre 2018)
Compartir
Compartir
Descargar PDF
Más opciones de artículo
Visitas
3791
Vol. 32. Núm. 3.
Páginas 105-112 (julio - septiembre 2018)
Original article
Acceso a texto completo
Differential exosomal microRNA profile in the serum of a patient with depression
Visitas
3791
Y. Zhang
Autor para correspondencia
13836148558@163.com

Corresponding author.
, Y. Zhao, C. Tian, J. Wang, W. Li, C. Zhong
Department of Psychiatry, the First Hospital of Harbin City, Harbin City 150010, China
Este artículo ha recibido
Información del artículo
Resumen
Texto completo
Bibliografía
Descargar PDF
Estadísticas
Tablas (5)
Table 1. Up-regulated candidate miRNAs in secreted exosomes in a patient with depression as compared to a healthy control subject.
Table 2. Down-regulated candidate miRNAs in secreted exosomes in a patient with depression as compared to a healthy control subject.
Table 3. GO analysis of the up-regulated candidate miRNAs in secreted exosomes in a patient with depression as compared to a healthy control subject.
Table 4. GO analysis of the down-regulated candidate miRNAs in secreted exosomes in a patient with depression as compared to a healthy control subject.
Table 5. Putative target genes of the differentially expressed miRNAs in secreted exosomes in a patient with depression as compared to a healthy control subject.
Mostrar másMostrar menos
Abstract
Background and objectives

Accumulating evidence suggests that exosomal miRNAs play important roles in disease pathogenesis; however, their role in the pathogenesis of depression remains largely unknown. We explored the miRNA profile of circulating exosomes in a patient with depression and compared it with a healthy volunteer.

Methods

Serum exosomes were isolated from a patient and volunteer, and exosomal miRNAs were analyzed by miRNA-sequencing. MiRTarbase and gene ontology (GO) analysis were used to predict the downstream signaling pathways of the depression-specific miRNAs.

Results

miRNA-sequencing analysis identified 12 up-regulated miRNAs and 20 down-regulated miRNAs in the depressed patient. MiRTarbase and GO analysis revealed that the up-regulated miRNAs regulated the expression of genes involved in histone/chromatin modification and the neurotrophin signaling pathway. The down-regulated miRNAs regulated genes were associated with apoptosis, hypoxia, cell growth, and immune response.

Conclusions

Depression-specific miRNAs may modulate neurotrophin signaling, chromatin/histone modification, immune system function, inflammation, depression, apoptosis, hypoxia, and cell growth. Identifying the networks that may be regulated by these depression-specific miRNAs and their target genes may provide a novel, high-throughput screening platform for the early detection of depression as well as determining its severity and response to treatment.

Keywords:
Apoptosis
Depressive disorder
Exosomal microRNA
Polyamine
Texto completo
Introduction

Cells of the central nervous system (CNS) release exosomes that could have an active role in CNS function, development, and disease initiation or progression, such as Alzheimer's disease, Parkinson's disease, prion diseases, multiple sclerosis, brain tumors, and schizophrenia. In all of these diseases, exosomes mediate the spread of mutated or misfolded proteins, which serve as templates for the formation of disease-producing oligomers.1 However, the role of exosomes in the pathogenesis or physiological function of patients with depression is largely unknown.

In addition to proteins, lipids, microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) can also be delivered via circulating exosomes as intercellular messengers.2,3 Moreover, exosomal miRNAs play important roles in disease pathogenesis, especially in cancers.4,5 In addition, Banigan et al.6 reported the differential exosomal microRNA profiles in the prefrontal cortices of patients with schizophrenia and bipolar disorder compared to healthy controls.6 These studies suggest that exosomal miRNAs have a role in the pathogenesis of psychiatric disorders.

The structure of exosomes permits their incorporation into the cell membrane and allows them to easily cross the blood-brain barrier (BBB). Recently, Yang et al.7 demonstrated that exosomes isolated from astrocytoma, glioblastoma, endothelial, and neuroectodermal brain tumor cells can across the BBB for drug delivery in zebrafish. Exosomes with lower miR-193b levels have also been observed in the cerebral spinal fluid (CSF) of dementia of Alzheimer-type (DAT) patients.8 Raymond et al.9 also demonstrated that exosomes expressing HIV negative factor can break down the BBB and regulate neuroimmune pathogenesis.

The neuroimmune system represents a dense network of biochemical signals, including neurotransmitters, neuropeptides, neurotrophin, neurohormones, cytokines, chemokines, and growth factors synthesized by neurons, glial cells, and immune cells to maintain systemic homeostasis. Endogenous and/or exogenous, noxious stimuli in any tissue are detected by sensor cells to inform the brain; likewise, signals originating in the CNS are transmitted to peripheral immune effectors, which react to central stimuli. This multidirectional information flow makes it possible for the CNS to respond to peripheral damage and for alterations in brain function to be reflected in peripheral immune changes.10,11 Studies have demonstrated the association between depression and altered immune response, such as inflammation.12–15 In this study, we explored the microRNA profile of circulating exosomes in a patient with depression and compared it with a healthy control patient. Given the interplay between the neuroimmune system, inflammation, miRNAs, and exosomes,16 we hypothesized that a differential exosomal microRNA profile may play a role in modifying the pathogenesis of depression. Identifying the networks that may be regulated by these candidate miRNAs and their target genes may provide a novel, high-throughput screening platform for the early detection of depression as well as determining its severity and response to treatment.

MethodsStudy participants

The inclusion criteria for the patient with depression were as follows17: (1) 20–64 years of age, (2) a recent diagnosis of depression, (3) no history of taking any drugs, and (4) a total score of 13–34 on the Montgomery–Asberg Depression Scale (MADRS-S) indicative of mild-to-moderate depression. In addition, the patient had to meet one of the following four items to be included in the study. (1) The patient had >8 weeks of symptoms described in the MADRS-S at the time of hospital admission. (2) Under special conditions, the patient had a MRDS-S score of 13–34 at the time of admission and again 8 weeks after admission without treatment over the 8-week period. (3) The patient had <8 weeks of symptoms of depression described in the MADRS-S at the time of admission, which were treated. (4) The patient had <8 weeks of symptoms of depression described in the MADRS-S at the time of admission with aggravating symptoms during the follow-up, which were treated. Before the treatment, the patient was again evaluated using the MADRS-S. The patient received treatment only if the results of first evaluation were maintained. If the score was >34, the disease was considered severe, and treatment was not applied. Participants (both the control participant and the patient with depression) were excluded from the study if they had severe personality disorder, risk of suicide, pregnancy, thyroid disease, use of current psychotherapy of any kind, and participation in any other psychiatric intervention study.17

The patient completed MADRS-S questionnaires at baseline and after 8 weeks of follow-up (0–6, normal/without symptoms; 7–19, mild depression; 20–34, moderate depression; and >34, severe depression). The patient received antidepressants and tranquilizers, if necessary. Blood samples were collected at the same time as the assessment of self-rated symptoms before treatment. The healthy control participant was age-matched with the patient with depression; they had (1) no history of depression, (2) a MADRS-S score of 0–4 for >8 weeks, and (3) none of the conditions described in the exclusion criteria.

This study was approved by the Institutional Review Board of the First Hospital of Harbin City. Both participants provided informed consent prior to enrollment.

Sample collection

Serum samples were collected in the patient with depression prior to administration of any treatment, if needed as well as in the healthy control participant. At least 2mL of venous blood was collected from the participants in collection tubes without anticoagulant. After centrifugation at 3000rpm for 10min at room temperature, the serum samples (4–6mL/sample ideally) were stored in RNase antifreeze tubes at −80°C.

Exosome isolation

Exosomes were isolated using the Total Exosome Isolation kit (cat# 4478360; Invitrogen, Carlsbad, CA, USA) following the manufacturer's instructions. In brief, the serum samples were centrifuged at 2000×g for 30min. After the supernatants containing the clarified serum were transferred to a new tube, 0.2 volume of the Total Exosome Isolation reagent was added. The solution was vigorously mixed and incubated at 2–8°C for 30min. After centrifugation at 10,000×g for 10min at room temperature, the pellets containing the exosomes were isolated, resuspended in PBS, and stored at −20°C until further use.

Exosome RNA extraction

Exosome RNA, including miRNA, was isolated using an ExoRNeasy Serum/Plasma Midi Kit (cat# 77044, Qiagen, Germantown, MD, USA) following the manufacturer's instructions. Briefly, cold, sterile RNase-free PBS was used to dilute the serum samples to final volume 1200μL. After centrifugation for 10min at 3000×g at 4°C, the supernatant was collected and filtered using a 0.8μm Millipore Millex-AA filter syringe (cat. no. SLAA033SB). miRNA was isolated from 1mL of sample using an exoRNeasy Serum/Plasma Midi Kit. The yield and quality of the isolated RNA were analyzed using an Agilent 2100 Bioanalyzer (Santa Clara, CA, USA).

Next-generation sequencing analysis

RNA fragments of 17–30 nucleotides or 15–35 nucleotides in size were isolated by gel purification. Adapters were ligated to both ends of the isolated RNA segments, which were then reverse transcribed into cDNA as previously described.18 Expansion was done by PCR to establish a sequence library, which was subjected to Illumina HiSeq high-throughput sequencing using the SE50 strategy.18 Genomic notes in the miRBase database (Release 21) or the miRNA sequence in the database was compared to those in the genome to obtain information about the location of known mature and precursor miRNAs in the genome. Clean reads in the genome were matched to the location of miRNA19 to identify known miRNAs, and their expression, sequence, and structure were further analyzed. miRNA target genes were predicted from the sequence information using miRanda (3.3a) analysis as previously described.20 The target genes of differentially expressed miRNA were subjected to gene ontology (GO) analyses as previously described.21,22 Gender-specific and sex-hormone regulating miRNAs were excluded from the analysis.

Statistical analysis

For continuous variables, data were expressed as mean±s.d. Differences between groups were estimated using Student's t-tests. All statistical analyses were performed using JMP 5 software (JMP, Cary, NC, USA), and the findings were considered significant when the P-value was <0.05.17

ResultsIdentification of candidate miRNAs in a patient with depression using next-generation sequencing analysis

This study included one patient with depression and one healthy control. Using next-generation sequencing, miRNAs analyzed were considered candidate miRNAs if they were up- or down-regulated by ≥2-fold and the adjusted P-value was <0.05. As shown in Table 1, there were 12 up-regulated miRNAs: hsa-miR-1255a, hsa-miR-3161, hsa-miR-99a-3p, hsa-miR-205-5p, hsa-miR-26a-1-3p, hsa-miR-139-5p, hsa-miR-7849-3p, hsa-miR-195-5p, hsa-miR-125b-2-3p, hsa-miR-664a-3p, hsa-let-7c-5p, and hsa-miR-197-3p. There were also 20 down-regulated miRNAs identified by next-generation sequencing analysis (Table 2).

Table 1.

Up-regulated candidate miRNAs in secreted exosomes in a patient with depression as compared to a healthy control subject.

miRNA  Fold-change  Adjusted P-value 
hsa-miR-1255a  31.5672  0.000555 
hsa-miR-3161  17.2185  0.016613 
hsa-miR-99a-3p  14.3487  0.031591 
hsa-miR-205-5p  5.02206  0.004325 
hsa-miR-26a-1-3p  5.02206  0.046379 
hsa-miR-139-5p  4.2472  8.23E−11 
hsa-miR-7849-3p  4.01764  0.009700 
hsa-miR-195-5p  3.18861  0.006295 
hsa-miR-125b-2-3p  3.15672  0.046047 
hsa-miR-664a-3p  2.63060  0.012340 
hsa-let-7c-5p  2.09323  4.30E−07 
hsa-miR-197-3p  2.06263  0.000316 

Adjusted P-values were determined using Student's t-test.

Table 2.

Down-regulated candidate miRNAs in secreted exosomes in a patient with depression as compared to a healthy control subject.

miRNA  Fold-change  Adjusted P-value 
hsa-miR-499a-5p  0.03878  5.51E−08 
hsa-miR-4732-3p  0.05519  0.002701 
hsa-miR-222-5p  0.06522  0.007088 
hsa-miR-1291  0.06522  0.007088 
hsa-miR-668-3p  0.07972  0.018124 
hsa-miR-425-3p  0.13665  0.000904 
hsa-miR-6511a-3p  0.14349  0.026290 
hsa-miR-145-3p  0.20498  0.019027 
hsa-miR-200a-3p  0.26904  0.026290 
hsa-miR-143-3p  0.27107  3.09E−166 
hsa-miR-196b-5p  0.28185  2.69E−05 
hsa-miR-99a-5p  0.34668  6.01E−10 
hsa-miR-144-3p  0.37808  9.26E−18 
hsa-miR-584-5p  0.39186  1.90E−25 
hsa-miR-210-3p  0.40043  0.004850 
hsa-miR-183-5p  0.40854  1.08E−07 
hsa-miR-107  0.45366  6.70E−28 
hsa-miR-130b-5p  0.46058  0.000592 
hsa-miR-589-5p  0.46677  0.000598 
hsa-miR-1910-5p  0.47829  0.000605 

Adjusted P-values were determined using Student's t-test.

GO analysis of the up- and down-regulated exosomal miRNAs isolated from a patient with depression

We first used the miRNA database, miRTarbase, and GO Enrichment analysis to predict the possible downstream targets of the candidate miRNAs. As shown in Table 3, GO analysis of the up-regulated candidate miRNAs revealed that some have roles in chromatin/histone modification and posttranscriptional regulation of gene expression as well as neurotrophin signaling pathway. Overlap analysis revealed that the number of predicted downstream target genes of the candidate miRNAs identified versus the total number of genes in the GO gene expression network was as high as 30% (for the TGF-β signaling pathway).

Table 3.

GO analysis of the up-regulated candidate miRNAs in secreted exosomes in a patient with depression as compared to a healthy control subject.

Term  Overlap  P-value  Adjusted P-value 
Gene expression (GO:0010467)a  145/672  8.15E−14  3.82E−10 
Posttranscriptional regulation of gene expression (GO:0010608)a  104/438  2.80E−12  6.55E−09 
Regulation of translation (GO:0006417)a  62/264  1.07E−07  0.000144 
Neurotrophin signaling pathway (GO:0038179)b  62/278  4.98E−07  0.000212 
Chromatin modification (GO:0016568)a  92/475  2.85E−07  0.000212 
Fc receptor signaling pathway (GO:0038093)  66/352  3.30E−05  0.004182 
Neurotrophin TRK receptor signaling pathway (GO:0048011)b  61/274  6.47E−07  0.000233 
Histone modification (GO:0016570)a  64/293  6.10E−07  0.000233 
Fibroblast growth factor receptor signaling pathway (GO:0008543)  44/163  3.21E−07  0.000212 
Transforming growth factor beta receptor signaling pathway (GO:0007179)  40/134  1.23E−07  0.000144 

The overlap value indicates the number of predicted downstream target genes of the candidate miRNAs identified versus the total number of genes in the GO gene expression network.

P-values were determined using Student's t-test; adjusted P-values were determined using Student's t-test.

a

Chromatin/histone modification and posttranscriptional regulation of gene expression.

b

Neurotrophin signaling pathway.

As shown in Table 4, GO analysis of the down-regulated genes showed that some of the miRNAs have roles in apoptotic signaling, oxidation, hypoxia, cell cycle, and cellular growth. Of note, there was a 34% overlap in the cellular response to oxygen levels and a 28% overlap in the intrinsic apoptotic signaling pathway.

Table 4.

GO analysis of the down-regulated candidate miRNAs in secreted exosomes in a patient with depression as compared to a healthy control subject.

Term  Overlap  P-value  Adjusted P-value 
Apoptotic signaling pathway (GO:0097190)a  82/327  8.50E−10  4.06E−06 
Positive regulation of cell cycle (GO:0045787)c  65/278  4.19E−07  0.000200 
Response to oxygen levels (GO:0070482)b  62/259  3.89E−07  0.000200 
Response to hypoxia (GO:0001666)b  59/241  3.94E−07  0.000200 
Response to decreased oxygen levels (GO:0036293)b  59/245  6.27E−07  0.000262 
Cell cycle phase transition (GO:0044770)c  66/280  2.66E−07  0.000200 
Cellular response to oxygen levels (GO:0071453)b  37/109  9.69E−08  0.000200 
Mitotic cell cycle phase transition (GO:0044772)c  65/277  3.76E−07  0.000200 
Intrinsic apoptotic signaling pathway (GO:0097193)a  48/170  1.51E−07  0.000200 
Growth (GO:0040007)c  72/329  8.67E−07  0.000296 

The overlap value indicates the number of predicted downstream target genes of the candidate miRNAs identified versus the total number of genes in the GO gene expression network.

P-values were determined using Student's t-test; adjusted P-values were determined using Student's t-test.

a

Apoptotic signaling.

b

Oxidation and hypoxia.

c

Cell cycle and cellular growth.

Putative target genes of the differentially expressed exosomal miRNAs in a patient with depression

Combining the data obtained by the next-generation sequencing analysis, miRTarbase target prediction, and GO Enrichment Analysis, we identified the possible genes targeted by the differentially expressed candidate miRNAs in a patient with depression. As shown in Table 5, there were eight up-regulated target genes with roles in depression, neurotrophin signaling, chromatin/histone modification, immune system function, and inflammation. There were also 12 down-regulated target genes involved in depression, apoptosis, hypoxia, immune system function, and cell growth (Table 5).

Table 5.

Putative target genes of the differentially expressed miRNAs in secreted exosomes in a patient with depression as compared to a healthy control subject.

Genes  Number of miRNAs targeted  miRNA expression  Reference(s)  Function(s) 
LCOR  4 (hsa-miR-1255a, 139-5p, 7849-3p, 205-5p)  Up  38  Chromatin/Histone modification 
IGF1R  3 (hsa-miR-139-5p, 125b-2-3p, let-7c-5p)  Up  15  Depression, neurotrophin 
VEGFA  2 (hsa-miR-195-5p, 205-5p)  Up  15  Neurotrophin 
FASN  2 (hsa-miR-195-5p, 197-3p)  Up  15,25  Antidepressant effects of immune and inflammation related processes 
APP  1 (hsa-miR-195-5p)  Up  15,25  Innate immune response, immune system process 
GSK3A  1 (hsa-let-7c-5p)  Up  11,26  Depression 
GSK3B  1 (hsa-miR-195-5p)  Up  11,26  Depression 
ICAM1  1 (hsa-miR-1255a)  Up  57  Depression, inflammation 
GATA6  5 (hsa-miR-425-3p, 6511a-3p, 200a-3p, 196b-5p, 183-5p)  Down  28  Innate immune response 
MAPK1  4 (hsa-miR-668-3p, 143-3p, 196b-5p, 584-5p)  Down  14,15  Immune response, innate immune response, immune system process 
CELF1  4 (hsa-miR-499a-5p, 6511a-3p, 200a-3p, 183-5p)  Down  57  Cell growth 
SGPL1  4 (hsa-miR-222-5p, 143-3p, 144-3p, 4732-3p)  Down  47  Apoptosis 
TGFBR3  4 (hsa-miR-196b-5p, 144-3p, 107, 130b-5p)  Down  27  Depression 
NOTCH2  3 (hsa-miR-183-5p, 107, 130b-5)  Down  15,51  Hypoxia, immune system process, immune system development 
VAV3  3 (hsa-miR-425-3p, 6511a-3p, 130b-5p)  Down  15,49  Apoptosis, hypoxia, innate immune response 
BCL2  2 (hsa-miR-143-3p, 196b-5p)  Down  15  Apoptosis, innate immune response, immune system process and development 
PMAIP1  2 (hsa-miR-499a-5p, 589-5p)  Down  15,48  Apoptosis, immune system process 
XIAP  1 (hsa-miR-143-3p)  Down  43  Apoptosis, depression 
PCNA  1 (hsa-miR-222-5p)  Down  43  Apoptosis, depression 
BNIP3  1 (hsa-miR-210-3p)  Down  50  Apoptosis, depression 
Discussion

Because the role of exosomal miRNA in the pathogenesis or physiological function of patients with depression is largely unknown, the present study compared exosomal miRNA levels from a patient with depression with that obtained from a healthy control participant. We found 12 candidate up-regulated miRNAs and 20 candidate down-regulated miRNAs. GO analysis revealed that some of the up-regulated candidate miRNAs regulated the expression of genes involved in histone/chromatin modification and neurotrophin signaling pathway. Those down-regulated miRNAs regulated genes involved in apoptosis, hypoxia, and cell growth. Analysis of potential gene targets of the dysregulated exosomal miRNAs in the depressed patient identified genes involved in neurotrophin signaling, chromatin/histone modification, immune system function, inflammation, depression, apoptosis, hypoxia, and cell growth.

The dysregulated exosomal miRNA expression observed in the present study is consistent with Belzeaux et al.23 who found changes in several miRNAs (has-miR-107, miR-133a, miR-148a, miR-200c, miR-381, miR-425-3p, miR-494, miR-517b, miR-579, miR-589, miR-636, miR-652, miR-941, and miR-1243) in peripheral blood mononuclear cells of patients with major depressive disorder (MDD) compared with healthy controls. Of them, two miRNAs showed stable overexpression in MDD patients after an 8-week follow-up (miR-941 and miR-589).23

Studies have shown that neuroinflammation has a crucial role in the pathogenesis of depression, and exosomes may regulate some critical pathways associated with depression.16,24 For example, interferon gamma (IFNγ) may prevent spreading depression, which was mediated by induction of exosome release by microglia.24 Thus, we hypothesized that a differential exosomal miRNA profile, particularly in those miRNAs that regulate neuroimmune system function and inflammation, may play a role in modifying the pathogenesis of depression. In the present study, miRNAs targeting genes involved in depression control as well as inflammation and immune system function, including FASN, APP, GSK3A/B, ICAM1, MAPK1, and TGFBR3, were both up- and down-regulated. Given the strong link between depression and depression control with inflammation and immune system function,11,15 this finding was not surprising. Specifically, two miRNAs that potentially target GSK3A/B were up-regulated in the present study. Immune responses mediated by GSK signaling may play a role in depression control.25,26 Furthermore, four miRNAs that potentially target TGFBR3 were down-regulated. Genetic variants in TGFBR3, a member of the human activin system, also predict antidepressant treatment response in depression.27 In addition, five miRNAs that potentially target GATA6 and four that potentially target MAPK1 were down-regulated in the present study. Both GATA628 and MAPK114 have been implicated in mediating immune response in depression control.28 Thus, studies have described the potential clinical benefit of determining miRNAs, including hsa-miR-26a, hsa-miR-107, hsa-miR-130b, hsa-miR-183, hsa-miR-200c, hsa-miR-425-3p, hsa-miR-589, and hsa-miR-664, in blood and peripheral tissues to monitor response to treatment.29

Roles for synaptic plasticity and neurotrophin factors, such as IGF1, BDNF, p75NTR, VEGFR, neurotrophin-3, and the Trk pathway, in the depression mechanism have been reported. For example, genetic association analysis of polymorphisms in 10 genes contributing to IGF-I signaling revealed associations with susceptibility for depression and treatment response.30 The present study identified miRNAs, including miR-125, miR-195, miR-222, miR-139, and miR-183, that regulate synaptic plasticity and neurotrophin signaling were also dysregulated in the patient with depression. Similarly, bioinformatic analysis indicated that the predicted targets of miR-195 and miR-329 had roles in neuronal apoptosis and synaptic plasticity.31 MiR-125b, which targets NR2A and negatively regulates synaptic plasticity,32 was significantly elevated in synaptosome supernatants after depolarization, suggesting that that secreted miRNAs might be functionally active after being taken up by the synaptic fraction via the endocytic pathway.33 Furthermore, treatment with a selective serotonin reuptake inhibitor decreased the levels of miR-222, which targets ITGB3 that is necessary for serotonin transporter (SERT) activity.34 Finally, miR-183 is known to contribute brain-derived neurotrophic factor action in the brain,35–37 and miR-183 may play a role in the pathogenesis of psychiatric disorders and in the mechanism of action of antipsychotic drugs and mood stabilizers.35

The present study identified four up-regulated miRNAs (miR-1255a, miR-139-5p, miR-7849-3p, and miR-205-5p) that may target LCOR. LCOR interacts with histone deacetylase 3 (HDAC3) and HDAC6, both of which are key modulators of chromatin remodeling.38 In addition to chromatin remodeling, the polyamine system has been reported to play a role in predisposition to suicidal behavior.39,40 Our analysis identified that miR-139-5p was up-regulated in the patient with depression, which is consistent with previous studies showing that miR-139-5p was inversely correlated with the polyamine gene, SAT1.40 Thus, miRNAs may regulate polyamine gene expression in the suicidal brain via post-transcriptional activity.41,42

Studies have suggested that neurons are more vulnerable to degenerative signals in patients with MDD.43 In addition, chronic stress, hypoxia, and cell death have been linked in depression,44–46 and some mood stabilizers may prevent apoptosis in the CNS.46 Some of the down-regulated miRNAs identified in the present study may also target genes involved in apoptosis, including XIAP, PCNA, BNIP3, BCL-2, VAV3, SGPL1, and PMAIP1,15,43,47–50 as well as hypoxia, such as NOTCH2 and VAV3.15,49,51 Further studies will confirm whether these genes have a role in the pathology of depression or may be useful as markers of disease severity or treatment effects.

Changes in the exosomal miRNA profile may have therapeutic implications as a potential delivery strategy given that naturally occurring exosomes are capable of incorporation into cell membranes and crossing the BBB,7,52 and miRNAs, including miR-132, function as intercellular signals, mediating neurovascular communication and regulating vascular integrity of the brain.53 In addition to small molecules, intravenously injected exosomes have the potential to deliver miRNAs specifically to neurons, microglia, and oligodendrocytes in the brain, resulting in knockdown of specific genes.52 Indeed, exosomes isolated from patients’ cells may be engineered to express particular cell surface receptors recognized by specific cell types and loaded with therapeutic miRNAs, which may be used for personalized autologous therapies.16 In addition, mixing miRNAs and exosomes as nanoparticles has potential for delivery of therapeutic siRNAs across the BBB.54 Thus, further studies will evaluate the therapeutic potential of delivering the five down-regulated miRNAs that potentially target GATA6 and four that potentially target MAPK1 identified in the present study in exosomes for modulation of the neuroimmune response and depression control. We also identified that miR-139-5p was up-regulated, which may target LCOR and the polyamine gene, SAT1. Thus, we will also examine the impact of exosomal delivery of a miR-139-5p inhibitor in a model of depression.

In addition to being a potential new therapeutic delivery system, serum exosomes as well as exosomal miRNAs may represent accessible prognostic and diagnostic biomarkers for depression. Moreover, the resistance of exosomal miRNAs to RNase-dependent degradation permits their detection in circulating plasma and serum, which is necessary for broad clinical diagnostic applications.55 Given the presence of cell-type specific markers of the surface of exosomes, neural-derived exosomes may be isolated from serum samples using a neural-specific surface marker for diagnosis of psychiatric conditions.52 Exosomes may also contain disease biomarkers other than miRNAs, which may be of diagnostic importance.56 Thus, further studies are necessary to fully explore the diagnostic potential of exosomes and exosomal miRNA for psychiatric disorders, including depression.

The present study is limited in that the exosomal miRNA of only one patient with depression was compared with only one healthy control. Furthermore, we only used one miRNA database, miRTarbase, for target gene prediction without validation. Thus, further studies will examine the levels of target genes in patients with depression.

In conclusion, exosomal miRNAs with the potential to regulate neurotrophin signaling, chromatin/histone modification, immune system function, inflammation, depression, apoptosis, hypoxia, and cell growth are differentially expressed in depression. Further studies will evaluate whether the dysregulation of any specific miRNA or combination of miRNAs has a role in the pathology of depression or whether they may represent potential markers of the disease or treatment response.

Funding

This work was supported by the Special Foundation for Harbin Science and Technology Innovation Talents Research [grant numbers: 2013SYYRCYJ01].

Conflict of interest

None.

Acknowledgement

None.

References
[1]
I. Tsilioni, S. Panagiotidou, T.C. Theoharides.
Exosomes in neurologic and psychiatric disorders.
[2]
A. Conigliaro, V. Costa, A. Lo Dico, L. Saieva, S. Buccheri, F. Dieli, et al.
CD90+liver cancer cells modulate endothelial cell phenotype through the release of exosomes containing H19 lncRNA.
Mol Cancer, 14 (2015), pp. 155
[3]
A. Longatti.
The dual role of exosomes in hepatitis A and C virus transmission and viral immune activation.
Viruses, 7 (2015), pp. 6707-6715
[4]
Y. Liu, Y. Gu, X. Cao.
The exosomes in tumor immunity.
Oncoimmunology, 4 (2015), pp. e1027472
[5]
P. Neviani, M. Fabbri.
Exosomic microRNAs in the tumor microenvironment.
Front Med (Lausanne), 2 (2015), pp. 47
[6]
M.G. Banigan, P.F. Kao, J.A. Kozubek, A.R. Winslow, J. Medina, J. Costa, et al.
Differential expression of exosomal microRNAs in prefrontal cortices of schizophrenia and bipolar disorder patients.
[7]
T. Yang, P. Martin, B. Fogarty, A. Brown, K. Schurman, R. Phipps, et al.
Exosome delivered anticancer drugs across the blood–brain barrier for brain cancer therapy in Danio rerio.
Pharm Res, 32 (2015), pp. 2003-2014
[8]
C.G. Liu, J. Song, Y.Q. Zhang, P.C. Wang.
MicroRNA-193b is a regulator of amyloid precursor protein in the blood and cerebrospinal fluid derived exosomal microRNA-193b is a biomarker of Alzheimer's disease.
Mol Med Rep, 10 (2014), pp. 2395-2400
[9]
A.D. Raymond, P. Diaz, S. Chevelon, M. Agudelo, A. Yndart-Arias, H. Ding, et al.
Nef+exosome impairment of the blood–brain barrier is treatable by nanomedicine-based delivery of Nef peptides.
J Neurovirol, 22 (2016), pp. 129-139
[10]
R. Cacabelos, C. Torrellas, L. Fernandez-Novoa, G. Aliev.
Neuroimmune crosstalk in CNS disorders: the histamine connection.
Curr Pharm Des, 22 (2016), pp. 819-848
[11]
F.M. Schmidt, K.C. Kirkby, N. Lichtblau.
Inflammation and immune regulation as potential drug targets in antidepressant treatment.
Curr Neuropharmacol, 14 (2016), pp. 674-687
[12]
M. Huckans, B.E. Fuller, A.L. Chalker, M. Adams, J.M. Loftis.
Plasma inflammatory factors are associated with anxiety, depression, and cognitive problems in adults with and without methamphetamine dependence: an exploratory protein array study.
Front Psychiatry, 6 (2015), pp. 178
[13]
D. Kim, L.D. Kubzansky, A. Baccarelli, D. Sparrow, A. Spiro III, L. Tarantini, et al.
Psychological factors and DNA methylation of genes related to immune/inflammatory system markers: the VA Normative Aging Study.
BMJ Open, 6 (2016), pp. e009790
[14]
A.H. Miller, C.L. Raison.
The role of inflammation in depression: from evolutionary imperative to modern treatment target.
Nat Rev Immunol, 16 (2016), pp. 22-34
[15]
A. Sharma.
Systems genomics support for immune and inflammation hypothesis of depression.
Curr Neuropharmacol, 14 (2016), pp. 749-758
[16]
D. Brites, A. Fernandes.
Neuroinflammation and depression: microglia activation, extracellular microvesicles and microRNA dysregulation.
Front Cell Neurosci, 9 (2015), pp. 476
[17]
X. Wang, K. Sundquist, A. Hedelius, K. Palmer, A.A. Memon, J. Sundquist.
Circulating microRNA-144-5p is associated with depressive disorders.
Clin Epigenetics, 7 (2015), pp. 69
[18]
M. Hafner, P. Landgraf, J. Ludwig, A. Rice, T. Ojo, C. Lin, et al.
Identification of microRNAs and other small regulatory RNAs using cDNA library sequencing.
[19]
A.R. Quinlan, I.M. Hall.
BEDTools: a flexible suite of utilities for comparing genomic features.
Bioinformatics, 26 (2010), pp. 841-842
[20]
A.J. Enright, B. John, U. Gaul, T. Tuschl, C. Sander, D.S. Marks.
MicroRNA targets in Drosophila.
Genome Biol, 5 (2003), pp. R1
[21]
J. Liang, J. Lv, Z. Liu.
Identification of stage-specific biomarkers in lung adenocarcinoma based on RNA-seq data.
Tumour Biol, 36 (2015), pp. 6391-6399
[22]
K.Y. Nahm, J.S. Heo, J.H. Lee, D.Y. Lee, K.R. Chung, H.W. Ahn, et al.
Gene profiling of bone around orthodontic mini-implants by RNA-sequencing analysis.
Biomed Res Int, 2015 (2015), pp. 538080
[23]
R. Belzeaux, A. Bergon, V. Jeanjean, B. Loriod, C. Formisano-Treziny, L. Verrier, et al.
Responder and nonresponder patients exhibit different peripheral transcriptional signatures during major depressive episode.
Transl Psychiatry, 2 (2012), pp. e185
[24]
A.D. Pusic, R.P. Kraig.
Phasic treatment with interferon gamma stimulates release of exosomes that protect against spreading depression.
J Interferon Cytokine Res, 35 (2015), pp. 795-807
[25]
J.H. Lee, E. Ko, Y.E. Kim, J.Y. Min, J. Liu, Y. Kim, et al.
Gene expression profile analysis of genes in rat hippocampus from antidepressant treated rats using DNA microarray.
BMC Neurosci, 11 (2010), pp. 152
[26]
J. Li, A. Csakai, J. Jin, F. Zhang, H. Yin.
Therapeutic developments targeting toll-like receptor-4-mediated neuroinflammation.
ChemMedChem, 11 (2016), pp. 154-165
[27]
K. Ganea, A. Menke, M.V. Schmidt, S. Lucae, G. Rammes, C. Liebl, et al.
Convergent animal and human evidence suggests the activin/inhibin pathway to be involved in antidepressant response.
Transl Psychiatry, 2 (2012), pp. e177
[28]
M. Shapira, B.J. Hamlin, J. Rong, K. Chen, M. Ronen, M.W. Tan.
A conserved role for a GATA transcription factor in regulating epithelial innate immune responses.
Proc Natl Acad Sci U S A, 103 (2006), pp. 14086-14091
[29]
Y. Dwivedi.
Emerging role of microRNAs in major depressive disorder: diagnosis and therapeutic implications.
Dialogues Clin Neurosci, 16 (2014), pp. 43-61
[30]
A. Kopczak, G.K. Stalla, M. Uhr, S. Lucae, J. Hennings, M. Ising, et al.
IGF-I in major depression and antidepressant treatment response.
Eur Neuropsychopharmacol, 25 (2015), pp. 864-872
[31]
H.Y. Du, D.N. Cao, Y. Chen, L. Wang, N. Wu, J. Li.
Alterations of prefrontal cortical microRNAs in methamphetamine self-administering rats: from controlled drug intake to escalated drug intake.
Neurosci Lett, 611 (2016), pp. 21-27
[32]
D. Edbauer, J.R. Neilson, K.A. Foster, C.F. Wang, D.P. Seeburg, M.N. Batterton, et al.
Regulation of synaptic structure and function by FMRP-associated microRNAs miR-125b and miR-132.
[33]
J. Xu, Q. Chen, K. Zen, C. Zhang, Q. Zhang.
Synaptosomes secrete and uptake functionally active microRNAs via exocytosis and endocytosis pathways.
J Neurochem, 124 (2013), pp. 15-25
[34]
K. Oved, A. Morag, M. Pasmanik-Chor, M. Rehavi, N. Shomron, D. Gurwitz.
Genome-wide expression profiling of human lymphoblastoid cell lines implicates integrin beta-3 in the mode of action of antidepressants.
Transl Psychiatry, 3 (2013), pp. e313
[35]
V. Caputo, L. Sinibaldi, A. Fiorentino, C. Parisi, C. Catalanotto, A. Pasini, et al.
Brain derived neurotrophic factor (BDNF) expression is regulated by microRNAs miR-26a and miR-26b allele-specific binding.
[36]
H. Kawashima, T. Numakawa, E. Kumamaru, N. Adachi, H. Mizuno, M. Ninomiya, et al.
Glucocorticoid attenuates brain-derived neurotrophic factor-dependent upregulation of glutamate receptors via the suppression of microRNA-132 expression.
Neuroscience, 165 (2010), pp. 1301-1311
[37]
G.A. Wayman, M. Davare, H. Ando, D. Fortin, O. Varlamova, H.Y. Cheng, et al.
An activity-regulated microRNA controls dendritic plasticity by down-regulating p250GAP.
Proc Natl Acad Sci U S A, 105 (2008), pp. 9093-9098
[38]
A. Palijan, I. Fernandes, Y. Bastien, L. Tang, M. Verway, M. Kourelis, et al.
Function of histone deacetylase 6 as a cofactor of nuclear receptor coregulator LCoR.
J Biol Chem, 284 (2009), pp. 30264-30274
[39]
L.M. Fiori, J.A. Gross, G. Turecki.
Effects of histone modifications on increased expression of polyamine biosynthetic genes in suicide.
Int J Neuropsychopharmacol, 15 (2012), pp. 1161-1166
[40]
J.P. Lopez, L.M. Fiori, J.A. Gross, B. Labonte, V. Yerko, N. Mechawar, et al.
Regulatory role of miRNAs in polyamine gene expression in the prefrontal cortex of depressed suicide completers.
Int J Neuropsychopharmacol, 17 (2014), pp. 23-32
[41]
M.Q. Cao, D.H. Chen, C.H. Zhang, Z.Z. Wu.
[Screening of specific microRNA in hippocampus of depression model rats and intervention effect of Chaihu Shugan San].
Zhongguo Zhong Yao Za Zhi, 38 (2013), pp. 1585-1589
[42]
Y. Dwivedi.
Pathogenetic and therapeutic applications of microRNAs in major depressive disorder.
Prog Neuropsychopharmacol Biol Psychiatry, 64 (2016), pp. 341-348
[43]
J.J. Miguel-Hidalgo, A. Whittom, A. Villarreal, M. Soni, A. Meshram, J.C. Pickett, et al.
Apoptosis-related proteins and proliferation markers in the orbitofrontal cortex in major depressive disorder.
J Affect Disord, 158 (2014), pp. 62-70
[44]
B.H. Harvey.
Is major depressive disorder a metabolic encephalopathy?.
Hum Psychopharmacol, 23 (2008), pp. 371-384
[45]
M. Hermes-Lima, D.C. Moreira, G.A. Rivera-Ingraham, M. Giraud-Billoud, T.C. Genaro-Mattos, E.G. Campos.
Preparation for oxidative stress under hypoxia and metabolic depression: revisiting the proposal two decades later.
Free Radic Biol Med, 89 (2015), pp. 1122-1143
[46]
D.P. McKernan, T.G. Dinan, J.F. Cryan.
Killing the Blues”: a role for cellular suicide (apoptosis) in depression and the antidepressant response?.
Prog Neurobiol, 88 (2009), pp. 246-263
[47]
S. Colie, P.P. Van Veldhoven, B. Kedjouar, C. Bedia, V. Albinet, S.C. Sorli, et al.
Disruption of sphingosine 1-phosphate lyase confers resistance to chemotherapy and promotes oncogenesis through Bcl-2/Bcl-xL upregulation.
Cancer Res, 69 (2009), pp. 9346-9353
[48]
J. Hagenbuchner, M.J. Ausserlechner, V. Porto, R. David, B. Meister, M. Bodner, et al.
The anti-apoptotic protein BCL2L1/Bcl-xL is neutralized by pro-apoptotic PMAIP1/Noxa in neuroblastoma, thereby determining bortezomib sensitivity independent of prosurvival MCL1 expression.
J Biol Chem, 285 (2010), pp. 6904-6912
[49]
T. Nomura, M. Yamasaki, K. Hirai, T. Inoue, R. Sato, K. Matsuura, et al.
Targeting the Vav3 oncogene enhances docetaxel-induced apoptosis through the inhibition of androgen receptor phosphorylation in LNCaP prostate cancer cells under chronic hypoxia.
Mol Cancer, 12 (2013), pp. 27
[50]
M. Tohda, S. Mingmalairak, Y. Murakami, K. Matsumoto.
Enhanced expression of BCL2/adenovirus EIB 19-kDa-interacting protein 3 mRNA, a candidate for intrinsic depression-related factor, and effects of imipramine in the frontal cortex of stressed mice.
Biol Pharm Bull, 33 (2010), pp. 53-57
[51]
S.J. Oh, S. Ahn, Y.H. Jin, C. Ishifune, J.H. Kim, K. Yasutomo, et al.
Notch 1 and Notch 2 synergistically regulate the differentiation and function of invariant NKT cells.
J Leukoc Biol, 98 (2015), pp. 781-789
[52]
A. Narahari, M. Hussain, V. Sreeram.
MicroRNAs as biomarkers for psychiatric conditions: a review of current research.
Innov Clin Neurosci, 14 (2017), pp. 53-55
[53]
B. Xu, Y. Zhang, X.F. Du, J. Li, H.X. Zi, J.W. Bu, et al.
Neurons secrete miR-132-containing exosomes to regulate brain vascular integrity.
Cell Res, 27 (2017), pp. 882-897
[54]
Z. Zhao, B.V. Zlokovic.
Remote control of BBB: a tale of exosomes and microRNA.
Cell Res, 27 (2017), pp. 849-850
[55]
J. Lin, J. Li, B. Huang, J. Liu, X. Chen, X.M. Chen, et al.
Exosomes: novel biomarkers for clinical diagnosis.
Scient World J, 2015 (2015), pp. 657086
[56]
F. Properzi, M. Logozzi, S. Fais.
Exosomes: the future of biomarkers in medicine.
Biomark Med, 7 (2013), pp. 769-778
[57]
L. Xia, C. Sun, Q. Li, F. Feng, E. Qiao, L. Jiang, et al.
CELF1 is up-regulated in glioma and promotes glioma cell proliferation by suppression of CDKN1B.
Int J Biol Sci, 11 (2015), pp. 1314-1324
Descargar PDF
Opciones de artículo
es en pt

¿Es usted profesional sanitario apto para prescribir o dispensar medicamentos?

Are you a health professional able to prescribe or dispense drugs?

Você é um profissional de saúde habilitado a prescrever ou dispensar medicamentos