Plasma exosomal derived CCDC18AS1/miR-6835-5p/CCND2 axis sever as biomarkers for diagnosis and predicting therapeutic effect of Adolescent with MDD

doi:10.21203/rs.3.rs-2348047/v1

Background

Major depressive disorder (MDD) in adolescence seriously endangers their mental and physical health and is associated with poor social and scholastic function. However, the diagnosis and therapeutic biomarkers for adolescent with MDD remain unclear. Circulating exosomes could package nucleic acids from host cells and deliver them to recipient cells to play a vital role in intercellular communications, which are widely considered to be crucial for biomarker discovery for clinical diagnostics and therapy.

Results

In discovery set, we conducted microarray analysis to detect differential expression lncRNAs and mRNAs of plasma exosome and performed bioinformation analysis to construct lncRNAs-miRNAs-mRNAs networks from 10 adolescent MDD patients and 10 healthy controls, identifying 3752 differential expression lncRNAs and 1789 differential expression mRNAs and selecting AC156455.1/miR-126-5p/AAK1 and CCDC18AS1/miR-6835-5p/CCND2 axes from networks as candidate genes. In the validation set, candidate lncRNAs, miRNAs and mRNAs were verified in 64 adolescent MDD patients (MDD group) and 30 healthy controls (HC group) using qrt-PCR. We found that six candidate genes were differential expression between MDD group and HC group, or before and after antidepressant treatment of MDD group. The expression levels of AAK1, CCDC18AS1 and miR6835 were differences in therapeutic effects. We also found that the expression of CCDC18AS1/miR-6835-5p/CCND2 axis at baseline could predicted sertraline therapeutic effects, which may be mediated through improving suicidal ideation and cognitive function.

Conclusion

Our study identified and validated the plasma exosme derived lncRNAs, miRNAs and mRNAs altered in adolescent with MDD, and provided potential diagnosis and therapeutic biomarkers for adolescent with MDD.

Exosome

Adolescent

Major depressive Disorder

lncRNA

miRNA

The prevalence of major depressive disorder (MDD) in adolescence has increased significantly in recent years, with approximately 14% of adolescents experiencing the disorder(1, 2). According to the World Health Organization, around 23 million children and adolescents are living with MDD(3). Adolescent-onset MDD is likely to persist into adulthood and is associated with a greater burden compared to MDD that develops later in life(4). This includes poor social and academic functioning, unplanned pregnancies, and increased risk of physical illness and substance abuse(5). Adolescents with MDD are also at a high risk of suicide, which is one of the leading causes of death among 15–24 year olds(6, 7).

Adolescence is a crucial period of brain development(8), and there are significant differences in brain morphometry, connectivity, and neurotransmitter metabolism between adolescent-onset and adult-onset MDD. These differences persist even after mood symptoms have resolved, indicating that adolescence may be a critical developmental phase for MDD(9, 10). Adolescents and adults with MDD differ in many ways, including their genetic architecture, neurobiology, cognitive function, and response to treatment(11, 12). Adolescents with MDD are more likely to describe their mood as irritable or bored rather than depressed, and to show observable apathy rather than anhedonia. They also tend to display more frequent vegetative symptoms than adults with MDD(12). Despite these differences, the diagnosis of MDD in adolescents and adults is still based on the same criteria, including the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) and the International Classification of Diseases (ICD-11). These diagnostic criteria rely on the subjective judgment of the psychiatrist, which can lead to misdiagnosis or lack of diagnosis. Therefore, it is necessary to identify specific diagnostic markers for adolescents with MDD. In addition, approximately 40% of adolescents with MDD do not achieve remission after treatment with selective serotonin reuptake inhibitors (SSRIs) such as sertraline and fluoxetine(13). Identifying predicting biomarkers of therapeutic effect could help to improve the prognosis of these patients by allowing for timely adjustments to treatment strategies.

The occurrence of MDD is the result of a combination of hereditary susceptibility and childhood adversity, such as abuse, neglect, and bullying(14, 15). Gene-environment interactions, known as epigenetics, can either increase or decrease a person's vulnerability to MDD(16). Adolescents are thought to be more sensitive to the epigenetic and behavioral changes caused by stress exposure than adults(17). Differential expression of non-coding RNAs, particularly long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), is an important part of epigenetic regulation. LncRNAs and mRNAs can act as miRNA sponges and compete with each other for binding to shared miRNAs, a phenomenon known as competitive endogenous RNAs (ceRNAs) (18). CeRNA networks formed by lncRNAs, miRNAs, and mRNAs play a vital role in brain development, stress responses, and neural plasticity(19), and may serve as potential diagnostic or therapeutic markers for psychiatric disorders such as MDD and schizophrenia(19–21).

Our previous research has shown that MDD patients have abnormal levels of lncRNA expression(22). Candidate lncRNAs (GSK3βAS1, AS2, and AS3) were found to be downregulated in MDD patients but upregulated after treatment. GSK3βAS3 was associated with the severity of MDD and the intensity of life stress, suggesting that it may be a potential therapeutic target(23). Other studies have shown that lncRNAs in peripheral blood mononuclear cells (PBMCs) can act as biomarkers for the diagnosis and treatment of MDD(24). Some lncRNAs in PBMCs have even been shown to have the potential to distinguish between schizophrenia and MDD(25). However, most research on lncRNAs has focused on adults with MDD, with few studies examining the relationship between miRNAs and adolescents with MDD. It is known that miRNAs play a critical role in the maladaptive processes associated with early life stress, which are important in the development of MDD in both adolescents and adults(26). A previous study found that altered levels of miRNAs in the prefrontal cortex are associated with adolescent-onset MDD(27). Jingjing Xu's research showed that chronic mild unpredictable stress (CUMS) administered to rats during adolescence can induce permanent depressive-like behaviors, memory impairment, and abnormal expression of miR-124a(28, 29).

Recent studies on epigenetic-related biomarkers of MDD have increasingly focused on exosome-derived non-coding RNAs. Exosomes are small (typically 40–100 nm in diameter), lipid-membrane-bound vesicles that are released by a variety of cell types into the extracellular space(30) (31). Circulating exosomes can package lncRNAs, miRNAs, and mRNAs and deliver them to recipient cells, where they play a vital role in intercellular communication(32). This makes exosomes great potential biomarkers. For example, serum exosome miR-450a-2-3p has been shown to partially mediate the association between childhood trauma, emotional abuse, and physical neglect and adolescent MDD(33). Zexu Wei's research found that injecting blood exosomes isolated from MDD patients into normal mice via the tail vein caused them to exhibit depressive-like behaviors(34). Additionally, exosomal let-7e, miR-145, and miR-146a were found to be differentially expressed in response to different antidepressants(35), and miR-335-5p and miR-1292-3p were aberrant in treatment-resistant depression patients(36). While there has been no published research examining the relationship between exosomal lncRNA expression and adolescents with MDD, differential expression of plasma exosome-derived lncRNAs has been detected in autistic children and schizophrenic patients (37, 38).

Based on the available evidence, we hypothesize that exosome-derived lncRNAs, miRNAs, and mRNAs could serve as diagnostic and therapeutic biomarkers for adolescents with MDD. In this study, we will identify and select aberrant lncRNAs, miRNAs, and mRNAs in a discovery set of adolescents with MDD. The validation set will be used to explore whether these candidate lncRNAs, miRNAs, and mRNAs are differentially expressed in a larger sample and whether they can predict therapeutic effects. Our study will provide potential diagnostic and therapeutic biomarkers for adolescents with MDD.

Demographic and Clinical characteristics of adolescents with MDD & HCs

The demographics of the study population are summarized in Table 1. There were no significant differences in age or sex between the MDD and HC groups in the discovery set or the validation set. The proportion of participants from single-child families, public/private schools, and with a family history of psychiatric disorders were also not significantly different between the two groups in the validation set. Of the adolescents with MDD in the validation set, 28 responded to sertraline treatment after 8 weeks, while the remaining 26 did not respond.

The clinical characteristics of the study population were evaluated using various scales. The scale scores were compared between the MDD and HC groups, and between the MDD group before and after treatment in the validation set. The details can be found in Supplementary Table 2. In brief, the scale scores were significantly different between the MDD and HC groups, except for the visual breadth dimension of the RBANS, and were also significantly different within the MDD group before and after treatment.

Differential Expression profiles of plasma exosomal lncRNAs and mRNAs

Differential expression was found in 3752 lncRNAs between adolescents with MDD and healthy controls (HCs) in the discovery set, with 1111 lncRNAs upregulated and 2641 downregulated (Fig. 1A). A total of 1789 mRNAs were also differentially expressed, with 671 upregulated and 1118 downregulated (Fig. 1B). The top 10 up- and downregulated lncRNAs and mRNAs are shown in Supplementary Table 3. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted for the differentially expressed mRNAs. The upregulated mRNAs were mainly enriched in molecular functions such as oxidoreductase activity, sequence-specific mRNA binding, and cytokine receptor binding (Supplementary Fig. 2A), and pathways related to adherens junction, endocytosis, and cGMP-PKG signaling (Supplementary Fig. 2B). The downregulated mRNAs were enriched in translation repressor activity, SMAD binding, and phosphoprotein phosphatase activity (Supplementary Fig. 2C), and pathways related to autophagy, primary bile acid biosynthesis, and cellular senescence (Supplementary Fig. 2D).

ceRNA Network Constructed

According to the screening method described above, four upregulated lncRNAs predicted 56 matched miRNAs, which in turn predicted 996 target mRNAs (up-network) (Supplementary Fig. 3A), while three downregulated lncRNAs predicted 77 matched miRNAs and further predicted 783 target mRNAs (down-network) (Supplementary Fig. 3B). Based on this, two ceRNA networks were constructed (Fig. 2A and 2C). We further constructed hub networks using the cytoHubba plugin in Cytoscape (Fig. 2B and 2D). In the up-hub network, the ENST00000423999 (GeneSymbol: AC156455.1)-miR-126-5p-AAK1 axis was chosen as a candidate gene. Similarly, one axis were identified in the down-hub network, and the ENST00000449305 (GeneSymbol: CCDC-AS1)-miR-6835-3p-CCND2 axis was chosen as a candidate axis based on the annotations and enriched KEGG pathways of the mRNAs (Supplementary Table 4).

The Expression Levels of Plasma Exosamal Candidate lncRNAs, miRNAs and mRNAs in validation set

The expression levels of plasma exosomal AC156455.1 in adolescents with MDD at baseline were significantly decreased compared to those after treatment (z=-2.580, P = 0.010) and the healthy control group (z=-3.720, P < 0.001) (Fig. 3A). The levels of miR-126-5p expression before treatment were significantly higher than after treatment in the MDD group (z=-2.943, P = 0.003), but there were no significant differences between the MDD group at baseline and the healthy control group (z=-1.771, P = 0.077) (Fig. 3B). The expression levels of AAK1 were also significantly increased in the MDD group compared to the healthy control group (z=-4.083, P < 0.001), and decreased after treatment (z=-6.334, P < 0.001) (Fig. 3C). The expression levels of CCDC18-AS1 and miR-6835-5p in the MDD group before treatment were significantly reduced compared to the healthy control group (z=-3.262, P = 0.001; z=-7.157, P < 0.001), and then significantly elevated after 8 weeks of treatment (z=-6.154, P < 0.001; z=-4.818, P < 0.001) (Fig. 3D and 3E). The CCND2 expression levels in the MDD group at baseline were significantly increased compared to the healthy control group (z=-2.268, P = 0.023), and significantly decreased after 8 weeks of antidepressant treatment (z=-2.887, P = 0.004) (Fig. 3F).

To explore the predictability of plasma exosomal lncRNAs, miRNAs, and mRNAs in distinguishing MDD patients from healthy controls, we conducted a ROC analysis to estimate the sensitivity and specificity of these genes. Due to the lack of significant differences in the expression levels of miR-126-5p between the MDD group and the healthy control group, it was not included in the analysis. The ROC curves and AUC values of AC156455.1, AAK1, CCDC18-AS1, miR-6835-5P, and CCND2 are shown in Fig. 4A and 4B, and the values of the 95% confidence interval (CI), sensitivity, and specificity are shown in Supplementary Table 4. The results showed that these genes had poor discrimination between the MDD group and the healthy control group, except for miR-6835-5p. Therefore, we combined these genes as a combined indicator to perform ROC analysis again. The results showed that the combination of AC156455.1 and AAK1 had acceptable discrimination between the MDD group and the healthy control group (AUC = 0.836, 95%CI = 0.7500.921, sensitivity = 0.958, specificity = 0.635). The combination of CCDC18-AS1, miR-6835-5P, and CCND2 also showed greater discrimination than when used separately (AUC = 0.890, 95%CI = 0.8110.969, sensitivity = 0.720, specificity = 0.942).

Analysis between candidate lncRNAs, miRNAs and mRNAs and therapeutic effects

The response group and nonresponse group were divided based on the reductive ratio of HAMD24 total scores as mentioned above. Compared to the nonresponse group, the expression levels of AAK1 were significantly reduced in the response group (z=-2.041, P = 0.041) (Fig. 5C). The expression levels of CCDC18-AS1 (z=-2.128, P = 0.033) and miR-6835-5p (z=-2.532, P = 0.011) in the response group were significantly elevated compared to the nonresponse group (Fig. 5D and 5E). There were no significant differences in the expression levels of AC156455.1 (z=-0.925, P = 0.355), miR-126-5p (z=-0.860, P = 0.390), and CCND2 (z=-1.226, P = 0.220) between the two groups (Fig. 5A, 5B, and 5F).

The predictive Analysis of the candidate lncRNAs, miRNAs and mRNAs for therapeutic effects

We researched whether the expression levels of candidate lncRNAs, miRNAs and mRNAs at baseline could predict sertraline therapeutic effects using binary logistic regression. The results showed that the levels of CCDC18AS1 (OR = 7.024, 95%CI: 2.190 ~ 22.530, P = 0.001), miR6835-5p (OR = 3.734, 95%CI: 1.002 ~ 13.912, P = 0.050), CCND2 (OR = 0.371, 95%CI: 0.172 ~ 0.802, P = 0.012) expression before treatment could predict the sertraline therapeutic effects after 8-week treatment. However, there are no significant predictive effects were found in the expression levels of AC156455.1, miR126-5p and AAK1 (Table 2).

Furthermore, we also investigated the relationship between changes in certain clinical characteristics (depression, anxiety, anhedonia, suicidal ideation, suicidal risk, cognitive function and pain) and changes in the expression levels of three different genes (CCDC18-AS1, miR6835-5p, and CCND2). It appears that the change in CCDC18-AS1 expression levels was negatively correlated with changes in suicidal ideation (r=-0.416, P = 0.006) and suicidal risk (r=-0.471, P = 0.008), the change in miR6835-5p expression levels was negatively correlated with changes in suicidal ideation (r=-0.386, P = 0.015), and the change in CCND2 expression levels was negatively correlated with changes in visual breadth (r=-0.800, P < 0.001), attentional function (r=-0.510, P = 0.018), and RBANS total scores (r=-0.501, P = 0.021), and positively correlated with changes in suicidal ideation(r=-0.484, P = 0.001) (Table 4). These results suggest that changes in the expression levels of these genes may play a role in mediating the relationship between changes in suicidal ideation and cognitive function. It is need to note that correlation does not necessarily imply causation, so further research is needed to confirm these findings.

It appears that this study focused on identifying differentially expressed lncRNAs and mRNAs in the plasma exosomes of adolescents with MDD and HC. We used bioinformatic analysis to identify miRNAs that were matched with the differentially expressed lncRNAs, and then constructed lncRNA-miRNA-mRNA regulatory networks to identify candidate genes. These genes were then validated in a separate validation set. The results showed that the AC156455.1-miR126-AAK1 and CCDC18AS1-miR6835-CCND2 regulatory axes were differentially expressed between the MDD and HC groups, or before and after treatment in the MDD group. These genes were also able to distinguish the MDD group from the HC group, though with poorer ability than a combined indicator. The levels of AAK1, CCDC18AS1, and miR6835-5p expression were also different between the response and nonresponse groups. Furthermore, the CCDC18-AS1, miR-6835-5p, and CCND2 expression levels at baseline were able to predict the therapeutic effect of sertraline, possibly mediated through changes in suicidal ideation and cognitive function. Overall, these findings suggest that these genes may play a role in the development and treatment of MDD.

Adolescence is a special period of whole life with the transformation of brain development, sleep patterns, mental health, nutrition, education and schooling, parenting programmes for parents(39). Some researcher considered adolescence is a necessary and typical time of emotional disequilibrium and unpredictable behavior(40). The neurocognitive maturation continuing past 20 years of age, thus the common definition of adolescence of 10 ~ 18 years is overly restricted, 10 ~ 24 years are supposed to better fit with the development of adolescents(41). Therefore, we set the inclusion criteria of 10 ~ 23 years in this study may provide a more comprehensive understanding of the issues being studied.

Exosomes are small vesicles that are released by cells under both normal and pathological conditions, they carry nucleic acids and proteins that can provide information about the health of the cells they come from(42). Exosomal lncRNAs and miRNAs can be stably detected in circulating plasma and serum, and are not affected by RNase-dependent degradation(43, 44). Exosomes can also be easily isolated from various biofluids, such as plasma, serum, and urine(45). These makes them great candidates for biomarker discovery in clinical diagnostics. Previous research has shown that serum exosome-derived miR-139-5p could be a potential diagnostic biomarker for major depressive disorder (MDD), and that serum exosomal let-7e, miR-145, and miR-146a can predict antidepressant response. Overall, exosomes have great potential for the diagnosis and treatment of MDD(46, 47).

Starbase is a database that is commonly used to decode miRNA-lncRNA, miRNA-mRNA, miRNA-ceRNA, miRNA-circRNA, miRNA-pseudogene, and protein-RNA interaction networks from CLIP-Seq data(48). This database focus on tumor-related non-coding RNAs, there is a lack of databases related to neuropsychiatric disorders. But starbase v.20 includes approximately 10,000 miRNA-lncRNA interactions, which can provide comprehensive matched-miRNAs for differentially expressed lncRNAs(48). The target mRNAs of these matched-miRNAs were predicted using the datasets from miRDB and TargetScan, and only those that were also differentially expressed in the microarray analysis were selected. miRDB uses machine learning methods to predict miRNA targets by analyzing thousands of miRNA-target interactions from high-throughput sequencing experiments(49). TargetScan, on the other hand, predicts miRNA targets by searching for the presence of conserved 8mer, 7mer, and 6mer sites that match the seed region of each miRNA(50). By using both databases with different prediction mechanisms, the researchers were able to screen for target mRNAs in a more reliable and comprehensive way.

AC156455.1 is an m6A and immune-related lncRNA and can be diagnostic biomarker of colorectal cancer, even predicting prognosis(51). There have no published studies associated with neurological or psychiatric disorders. The function of miR-126 are mainly associated with angiogenesis, erythropoiesis, endothelial/leukocyte interaction, inflammation and immune response(52). Recently study indicated that miR-126 was downregulated in treatment-resistant depressed patients after 8-week psychotherapy (53). Another study also displayed that plasma exosomal miR-126 was downregulated in bipolar disorder patients compared to HCs(54). Adaptor-associated kinase 1 gene was abbreviated to AAK1. A twin study of genome-wide profiling of DNA methylome and transcriptome in peripheral blood monocytes for MDD showed that AAK1 was one of the significant differentially methylated regions associated with lifetime history of MDD(55). The AAK1 gene is contributed to neurotransmitter release and recycling of synaptic vesicle proteins by involving in intracellular vesicle trafficking(56). The differential expressed AAK1 protein were detected in hippocampus of CUMS mice(57). Nowadays, AAK1 inhibitors were widely researched for the treatment of diverse neurological and psychiatric disorders, such as schizophrenia, bipolar disorder, Parkinson's disease and neuropathic pain(58). In our study, it is the first time to detected AC156455.1, miR-126-5p and AAK1 in plasma exosome of adolescents with MDD, and the expression trend of them was consistent with previous studies.

The previous studies showed that CCDC18-AS1 was associated with immune microenvironment, and was positively correlated with activated memory CD4 T cell, activated myeloid dendritic cell, neutrophils, macrophage M1, and T follicular helper cells, while negatively correlated with T regulatory cell(59) (60). The miR-6835 could bind to Insulin-like growth factor 2 (IGF2) (61). IGF2 belongs to the insulin growth factor family, which can modulate the proliferation, survival, and differentiation of many types of neuronal systems, including cholinergic, dopaminergic, and serotonergic neurons(62). The level of IGF2 was reduced in the hippocampus of CUMS mice, and increasing the level of IGF2 can alleviate depression-like behaviors in CUMS mice(63). D-type cyclins (D1, D2, and D3), as the regulatory partners for cyclin-dependent kinases 4 and 6 (CDK4 and CDK6), are core components of the machinery that drives cell cycle progression(64). A previous study implied that CCND2 is linked to the etiology of schizophrenia(65). The CCND2 knocked-out mouse model displayed a pattern of hippocampal over-activity shared similarities with that seen in prodromal schizophrenia patients and novelty-induced hyperlocomotion (a rodent correlate of positive symptoms of schizophrenia), decreased sucrose preference and executive function, impaired working memory(66). The study of Ahmed, et al also illustrated that CCND2 knockout mice exhibited increased activity of dopaminergic neuronal in the ventral tegmental area, behavioral hyperresponsiveness to amphetamine, and impaired hippocampus-dependent cognition(67). Moreover, GSK3β could inhibit the expression of CCND2(68). Our previous studies have shown that GSK3β level was reduced in MDD patients(69). Based on this result, perhaps CCND2 may be elevated in MDD patients, consistent with our current results.

The top 5 pathway of CCND2 enriched was FoxO signaling pathway, Cell cycle, p53 signaling pathway, PI3K-Akt signaling pathway, and cellular senescence. MiR-6835 was associated with LPS-induced inflammation and resulted in the dysregulated expression of FOXO3a(70). FOXOs are core genes in the FOX signaling pathway. FOXOs may be regulated by serotonin or norepinephrine signaling, the hypothalamic-pituitary-adrenal (HPA) axis and brain-derived neurotrophic factor (BDNF), which are all associated with the occurrence and development of MDD(71). A previous study implied that activation of PI3K/Akt/FoxO3a and PKA/CREB signaling pathways are closely related to the pathogenesis of depression and may be downstream targets of fluoxetine(72).

Our study also has a few limitations. Firstly, the sample size of validation set was small, especially the HC group. Small sample sizes may be produced false negatives, for example, the P-value of the miR-126-5p expression levels between MDD group and HC group was 0.77. if validation could be carried out in a larger sample, more accurate results may be obtained. Secondly, the interaction among lncRNAs, miRNAs and mRNAs were predicted by databases, there have no experiments to validate. We will perform cell and animal experiments in the future to explore the practical regulatory relationship among them.

In this study, we identified that the plasma exosme derived lncRNAs, miRNAs and mRNAs expression levels were altered in adolescent with MDD. The six candidate genes were still altered in validation set. In addition, CCDC18AS1, miR6835 and CCND2 could predict sertraline therapeutic effects, which may be mediated through improving suicidal ideation and cognitive function. Our study would provide potential diagnosis and therapeutic biomarkers for adolescent with MDD.

Participants

Two sets were included in this study. Ten adolescents with MDD and 10 health controls (HCs) were enrolled in discovery set, while 64 adolescents with MDD and 30 health controls were participated in validation set. All the patients were recruited from the psychiatric outpatient ward of First Hospital of Shanxi Medical University, and HCs were recruited from the nearby community. The inclusion criteria for the patients were as follows: 1) age between 10–23 years; 2) meet the diagnostic criteria for MDD in Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V) and diagnosed by at least 2 psychiatrists; 3) first-episode; 4) drug-naïve. The exclusion criteria are as follows: 1) with severe or unstable nervous system, cardiovascular, liver, kidney, or endocrine diseases; 2) a previous diagnosis of bipolar disorder, schizophrenia, or other psychiatry disorders; 3) a history of neurological disease or traumatic brain injury; 4) alcohol or psychoactive substance dependence/abuse; 5) other circumstances that the researchers consider inappropriate to participate in this study. The inclusion criteria for HCs were age between 10–23 years, and past or currently have no psychiatry disorders and physical diseases. The exclude criteria of HCs were same as patients. All the subjects were interviewed by Structured Clinical Interview for DSM-5 Disorders (SCID-5) before enrolled. All the subjects agreed to join the study and signed the informed consent.

The Ethical Committee for Medicine of the First Hospital of Shanxi Medical University, China approved this study (ChiCTR2000039503).

Assessment of Clinical Characteristics

Hamilton Rating Scale for Anxiety (HAMA) was used to evaluate severity of anxiety symptoms, the higher the total score, the more anxiety. 24-item Hamilton Rating Scale for Depression (HAMD₂₄) was the typical scale to assess severity of depression symptoms, and the reductive ratio of HAMD₂₄ total score was the common used to measure treatment effect, it was calculated as (HAMD₂₄ total score after treatment- HAMD₂₄ total score at baseline)/ HAMD₂₄ total score at baseline×100%. The reductive ratio of HAMD₂₄ total score was used to distinguish between response group (≥ 50%) and nonresponse group (< 50%). Using self-reported Snaith Hamilton Pleasure Scale (SHAPS) to investigate the pleasurable experiences of participants. Cognitive function was evaluated with Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) which could quickly assess various cognitive domains, including immediate attention, visual breadth, speech function, attentional function, and delayed memory(73). The Beck Scale for Suicide Ideation (BSI) is a self-report scale to evaluate participants’ suicidal ideation and risk. The first 5 items of the BSI are a screening device for suicidal ideation in the last week, the fourth and fifth items are filter questions to assess active or passive suicidal ideations, if one of them is endorsed by subjects, they need to complete the subsequent 14 items which allow for an assessment of the possible suicide risk(74). The perceived pain was measured by Short-Form McGill Pain Questionnaire (SF-MPQ) including 3 dimensions: Pain Rating Index (PRI), 10-cm visual analog scale (VAS) for average pain, Present Pain Intensity (PPI)(75). All participants were assessed on these scales at baseline and after 8 weeks of treatment.

Treatment

Sixty-four adolescents with MDD received sertraline treatment with an initial dose of 25mg, slowly increasing to a final dose of 100–150 mg over the course of 8 weeks. Fifty-four patients adhered to the end of treatment (12 males, 42 females). The remaining 10 patients were ruled out from further study due to dropout (n = 5), intolerance for side effects (n = 3), or reluctance to receive any treatment (n = 2). Benzodiazepines were unrestricted from use during the research period. In addition, a part of the patients received repetitive transcranial magnetic stimulation (rTMS) therapy (n = 12) or psychotherapy (n = 16).

Plasma Exosomes Isolation and Exosomal Total RNA Extraction

We collected 10ml blood samples from the cubital vein per subjects, and the blood samples were centrifuged at 3500rpm for 10 min immediately to obtain plasma, and then frozen in the − 80℃ refrigerator for further use. The exoRNeasy Midi and Maxi Kits (Qiagen, Cat. NO: 77144, Germany) were used to isolate the plasma exosome and extract exosomal total RNA per the manufacturer’s instruction. Briefly, the first step was to isolate the plasma exosome. 4ml pre-filtered plasma was mixed with Buffer XBP and bound to an exoEasy membrane affinity spin column. The bound exosomes are washed with 10 ml Buffer XWP. The plasma-derived exosomes were bounded in the membrane and are then ready to use in exosome total RNA extraction. The bound exosomes were lysed with QIAzol. Chloroform is added to the QIAzol eluate, and the aqueous phase is recovered and mixed with ethanol. Then the mixture was transferred to the spin column and centrifuged. Total RNA binds to the spin column, where it is washed three times. The purified total RNAs were eluted with 14 µl RNase-free water. The total RNAs quantity and quality were measured by NanoDrop™ One (Thermo Fisher, ND-ONE-W, MA, USA) and then frozen in -80℃refrigerator for further use.

Microarray Analysis

The Arraystar Human LncRNA Microarray version 5.0 technology (Agilent Technologies, Santa Clara, CA, USA) enables to detect lncRNAs and mRNAs simultaneously and can detect approximately 39,317 lncRNAs and 21,174 mRNAs. Ten adolescents MDD with NSSI behavior and 10 HCs were enrolled in microarray analysis. The microarray analysis was performed at KangChen Bio-tech (Shanghai, China). The rRNA was removed from the total RNA to obtain the purified mRNA. Then, each sample was amplified and transcribed into cDNA with Cy3 labeled. The labeled probes were hybridized with the high-density microarray. After hybridization, the fluorescence intensity of the microarray was scanned with a GenePix-4000B scanner, and the results were converted to digital for preservation. The raw data were analyzed using Agilent feature extraction software. Differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) between the two groups were identified with fold change > 2.0 and FDR < 0.05. Volcano plots were performed by R version 3.6.3.

The microarray data have been uploaded to the National Center for Biotechnology Information (NCBI) gene expression omnibus (GEO) database with the accession number GSE217811.

Bioinformatics Analysis

StarBase database(76) were used to predict matched miRNAs with the top 10 ranked up and down regulated lncRNAs respectively. Four of the upregulated lncRNAs and 3 of the downregulated lncRNAs were found matched-miRNAs in this database. MiRDB(49) and TargetScan(77) databases were used to predict the target mRNAs of matched-miRNAs. Then, the predicted target mRNAs intersected with the differentially expressed mRNAs of the microarray analysis. Detailed flow was provided in supplementary Fig. 1. Cytoscape software 3.7.1 was adopted to construct lncRNA-miRNA-mRNA regulated networks, and the cytoHubba plugin was used to screen hub networks.

Gene Ontology (GO) is a gene function classification entry (http://www.geneontology.org), which covers three domains: Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). The Kyoto encyclopedia of genes and genomes (KEGG) is a database that integrates genomic, chemical, and systemic functional information to understand biological function. Statistical analysis and visualization were performed in clusterProfiler and ggplot2 package of R version 3.6.3, p-value < 0.05 was considered significantly enriched.

Quantitative Reverse-Transcription Polymerase Chain Reaction (qRT-PCR)

Reverse transcription was accomplished by 2 kits. TransScript® Uni All-in-One First-Strand cDNA Synthesis SuperMix (TransGen Biotech, AU341, Beijing, China) was used to synthesize the cDNA of long-chain RNAs like lncRNAs and mRNAs. TransScript® miRNA First-Strand cDNA Synthesis Super Mix (TransGen Biotech, AT351, Beijing, China) took miRNA as a template, poly-A tailing, and cDNA synthesis were completed in the same reaction system. The experiment was carried out according to the manufacturer’s instructions. The primer sequences were designed by Oligo7 software and compounded at General Biol (Shanghai, China) (Supplementary Table 1). The amplification conditions were: pre-denaturation at 94℃ for 10 min, followed by 45 cycles of denaturation at 94℃ for 5s, and annealing and extension at 60℃for for the 30s. Gene expression was quantified using the software provided by the manufacturer (BIO-RAD CFX connect real-time system, #1855201, CA, USA), and analyzed according to the Standard amplification curves.

Statistical Analysis

Statistical analyses were performed using SPSS v27.0 software. Data were presented as mean ± standard deviation (SD), and p-value < 0.05 was considered statistically significant. The test of normal distribution for quantitative data, including age, scale scores, expression levels of lncRNAs, miRNAs and mRNAs in subjects were analyzed by Shapiro–Wilk test. If the data conformed to the normal distribution, the parametric test was used, if not, the nonparametric test was used. Age and scale scores were conformed to normal distributions, so Student’s t-test was used to analysis different between MDD group and HC group at baseline, paired t-test was performed to analysis the difference before and after treatment in MDD group. The expression levels of candidate lncRNAs, miRNAs and mRNAs were not conformed to normal distributions, therefore, Mann-Whitney U test was used to analyze the difference between HCs and MDD group at baseline, response group and nonresponse group. Wilcoxon's signed-rank test was used to analyze the expression levels of MDD group before and after treatment. Qualitative data, including sex, non-single child, public/private school, family history, were analyzed by chi-squared tests. Binary logistic regression was used to explore whether candidate lncRNAs, miRNAs and mRNAs at baseline could predict on the post-treatment effect. Spearman correlation was applied to investigate the relationship between the change of scale scores and the expression level changes of candidate lncRNAs, miRNAs and mRNAs before and after treatment. In addition,

Receiver operating characteristics (ROC) curves and areas under the ROC curves (AUC) were used to estimate diagnostic performance of candidate lncRNAs, miRNAs and mRNAs. The closer the AUC is to 1.0, the better the prediction method. The best combination of sensitivity and specificity will be reflected by the highest Youden index.

MDD	major depressive disorder
WHO	World Health Organization
DSM-V	Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition
ICD-11	International Classification of Diseases 11
SSRIs	selective serotonin reuptake inhibitors
ceRNA	competitive endogenous RNAs
PBMCs	peripheral blood mononuclear cells
CUMS	chronic mild unpredictable stress
AAK1	Adaptor-associated kinase 1
CCND2	D-type cyclins 2
HPA axis	hypothalamic-pituitary-adrenal
BDNF	brain-derived neurotrophic factor

Ethics approval and consent to participate

All the subjects agreed to join the study and signed the informed consent.

The Ethical Committee for Medicine of the First Hospital of Shanxi Medical University, China approved this study (ChiCTR2000039503).

Consent for publication

Not applicable

Availability of data and materials

The microarray data have been uploaded to the National Center for Biotechnology Information (NCBI) gene expression omnibus (GEO) database with the accession number GSE217811. The validation set data used and analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

The work was supported by funding from the National Science Foundation of China (No. 82171534; No. 81601193); Key Research and Development Project (International Cooperation) of Shanxi Province (No. 201903D421059); Central government guided local science and technology development fund (No. YDZJSX2022A063). Shanxi Province Science Foundation for Youths (No. 2015021204); Research Project Supported by Shanxi Scholarship Council of China (No. 2015-100); and The First Hospital of Shanxi Medical University Foundation for Youths Innovation (No. YC1409).

Authors' contributions

YF and XZ designed the study, completed the experiments, analyzed the data and was major contributors in writing the manuscript. RZ and YX participated in the experiment and collected the subjects. YG participated in the experiment and data analysis. YJ and DJ collected the subjects. ZF and NS designed and coordinated the study and revised the manuscript. All authors read and approved the final manuscript.

Acknowledgements

We would like to show our deepest gratitude to all the subjects, the study would not have happened without their contributions.

Authors' information (optional)

Not applicable

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Table 1. Demographic characteristics of subjects

		MDDgroup	HC group	Statistics	P
	N	10	10
Discovery Set	Age (years, mean±SD)	16.80±2.35	17.20±1.93	t=-0.416	0.682
Discovery Set	Sex (Male/Female)	4/6	4/6	x²=0.000	1.000
Validation Set	N	64	30
	Age(years, mean±SD)	15.70±1.79	16.00±1.89	t₁=-0.889	0. 093
	Sex (Male/Female)	14/50	9/21	x²=0.730	0.393
	Single-child Family (yes/no)	28/36	11/19	x²=0.442	0.516
	Public/Private School	49/15	23/17	x²=0.002	0.991
	Family History (with/without)	7/57	3/27	x²=0.019	0.890
	Response/Non-response	28/26	-

* represents P<0.05, ** represents P<0.001.

Table 2. The predictive Analysis of the candidate lncRNAs, miRNAs and mRNAs for therapeutic effects

	β	SEM	P-value	OR	95%CI
AC156455.1	-0.161	0.522	0.758	0.852	0.306~2.370
miR126-5p	-0.222	0.207	0.282	0.801	0.534~1.200
AAK1	-0.356	0.491	0.468	0.700	0.268~1.834
CCDC18-AS1	1.9494	0.595	0.001*	7.024	2.190~22.530
MiR6835-5p	1.317	0.617	0.050*	3.734	1.002~13.912
CCND2	-0.991	0.393	0.012*	0.371	0.172~0.802

* represents P<0.05, ** represents P<0.001.

Table 3. The correlation analysis between the changes of gene expression and the changes of clinical characteristics before and after treatment.

					RBANS						BSI		SF-MPQ
		HAMA	HAMD	SHAPS	Immediate memory	Visual breadth	Speech function	Attentional function	Delayed memory	Total score	Suicidal ideation	Suicidal risk	PRI	VAS	PPT
CCDC18-AS1	r	-0.290	-0.278	0.013	0.005	0.004	0.190	0.223	-0.293	0.004	-0.416^*	-0.471^*	-0.092	-0.245	-0.058
CCDC18-AS1	P-value	0.056	0.078	0.938	0.983	0.986	0.437	0.360	0.223	0.986	0.006	0.008	0.599	0.156	0.740
miR6835	r	-0.183	-0.073	0.124	-0.174	0.260	-0.231	0.154	-0.351	-0.150	-0.386^*	-0.016	0.010	-0.064	-0.061
miR6835	P-value	0.253	0.645	0.464	0.534	0.350	0.408	0.584	0.199	0.593	0.015	0.938	0.955	0.726	0.739
CCND2	r	0.026	-0.179	-0.136	-0.288	-0.800^**	0.133	-0.510^*	-0.120	-0.501^*	0.484^**	0.174	-0.297	-0.173	-0.295
CCND2	P-value	0.866	0.262	0.406	0.206	<0.001	0.567	0.018	0.603	0.021	0.001	0.357	0.088	0.327	0.091

* represents P<0.05, ** represents P<0.001.

No competing interests reported.

supplementarymaterials.docx

Plasma exosomal derived CCDC18AS1/miR-6835-5p/CCND2 axis sever as biomarkers for diagnosis and predicting therapeutic effect of Adolescent with MDD

Status:

Version 1

Abstract

Background

Results

Conclusion

Figures

Introduction

Results

Demographic and Clinical characteristics of adolescents with MDD & HCs

Differential Expression profiles of plasma exosomal lncRNAs and mRNAs

ceRNA Network Constructed

The Expression Levels of Plasma Exosamal Candidate lncRNAs, miRNAs and mRNAs in validation set

Analysis between candidate lncRNAs, miRNAs and mRNAs and therapeutic effects

The predictive Analysis of the candidate lncRNAs, miRNAs and mRNAs for therapeutic effects

Discussion

Conclusion

Materials And Methods

Participants

Assessment of Clinical Characteristics

Treatment

Plasma Exosomes Isolation and Exosomal Total RNA Extraction

Microarray Analysis

Bioinformatics Analysis

Quantitative Reverse-Transcription Polymerase Chain Reaction (qRT-PCR)

Statistical Analysis

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1