Peripheral blood E2F1 mRNA in depression and following electroconvulsive therapy

Establishment of a schizophrenia classifier based on peripheral blood signatures and investigation of pathogenic miRNA-mRNA regulation

To date, the diagnosis of schizophrenia (SCZ) mainly relies on patients' or guardians' self-reports and clinical observation, and the pathogenesis of SCZ remains elusive. In this study, we sought to develop a reliable classifier for diagnosing SCZ patients and provide clues to the etiology and pathogenesis of SCZ. Based on the high throughput sequencing analysis of peripheral blood miRNA expression profile and weighted gene co-expression network analysis (WGCNA) in our previous study, we selected eleven hub miRNAs for validation by qRT-PCR in 51 SCZ patients and 51 controls. miR-939-5p, miR-4732-3p let-7d-3p, and miR-142-3p were confirmed to be significantly up-regulated, and miR-30e-3p and miR-23a-3p were down-regulated in SCZ patients. miR-30e-3p with the most considerable fold change and statistically significance was selected for targeting validation. We first performed bioinformatics prediction followed by qRT-PCR and verified the up-regulation of potential target mRNAs (ABI1, NMT1, HMGB1) expression. Next, we found that the expression level of ABI1 was significantly up-regulated in SH-SY5Y cells transfected with miR-30e-3p mimics. Lastly, we conducted a luciferase assay in 293T cells confirming that miR-30e-3p could directly bind with the 3'untranslated region (3'-UTR) of ABI1, revealing that miR-30e-3p might play a role in the polymerization of neuronal actin and the reconstruction of the cytoskeleton via the downstream regulation of ABI1. In addition, we constructed a classifier by a series of bioinformatics algorithms and evaluated its diagnostic performance. It appears that the classifier consists of miRNAs and mRNAs possess a better discrimination performance than individual miRNA or mRNA in SCZ.

The interaction of P11 methylation and early-life stress impacts the antidepressant response in patients with major depressive disorder

PURPOSE

The present research investigates the influence of P11 gene DNA methylation combined with life stress on the response to antidepressants in the first two weeks.

METHODS

A total of 291 Han Chinese patients with major depressive disorder and 100 healthy controls were included. The Life Events Scale and the Childhood Trauma Questionnaire were used to assess stress. The primary endpoint was the Hamilton Depression Rating Scale-17 reduction rate after two weeks of treatment. The Illumina HiSeq Platform was used to detect the methylation of 74 CpG sites of the P11 gene in peripheral blood samples.

RESULTS

The mean methylation of all P11 CpG sites, as well as the methylation at 4 CpG sites (P11-2-169, P11-2-192, P11-2-202, P11-2-204), were significantly higher in patients with MDD than in healthy controls (FDR-corrected P < 0.05). The response to antidepressants was associated with the following interactions: the CTQ score and P11-3-185 site methylation (OR = 0.297, FDR-corrected P = 0.023), the CTQ physical neglect score and P11-2-117 site methylation (OR = 0.005, FDR-corrected P = 0.033), and the CTQ emotional abuse score and P11-3-185 site methylation (OR = 0.001, FDR-corrected P = 0.023).

CONCLUSIONS

The methylation of the P11 gene was significantly higher in patients with major depressive disorder. The interaction of P11 DNA methylation and early-life stress may influence the short-term antidepressant treatment response.

Long non-coding RNA screening and identification of potential biomarkers for type 2 diabetes

BACKGROUND

To investigate new lncRNAs as molecular markers of T2D.

METHODS

We used microarrays to identify differentially expressed lncRNAs and mRNAs from five patients with T2D and paired controls. Through bioinformatics analysis, qRT-PCR validation, ELISA, and receiver operating characteristic (ROC) curve analysis of 100 patients with T2D and 100 controls to evaluate the correlation between lncRNAs and T2D, and whether lncRNAs could be used in the diagnosis of T2D patients.

RESULTS

We identified 68 and 74 differentially expressed lncRNAs and mRNAs, respectively. The top five upregulated lncRNAs are ENST00000381108.3, ENST00000515544.1, ENST00000539543.1, ENST00000508174.1, and ENST00000564527.1, and the top five downregulated lncRNAs are TCONS_00017539, ENST00000430816.1, ENST00000533203.1, ENST00000609522.1, and ENST00000417079.1. The top five upregulated mRNAs are Q59H50, CYP27A1, DNASE1L3, GRIP2, and lnc-TMEM18-12, and the top five downregulated mRNAs are GSTM4, PODN, GLYATL2, ZNF772, and CLTC. Examination of lncRNA-mRNA interaction pairs indicated that the target gene of lncRNA XR_108954.2 is E2F2. Multiple linear regression analysis showed that XR_108954.2 (r = 0.387, p < 0.01) and E2F2 (r = 0.368, p < 0.01) expression levels were positively correlated with glucose metabolism indicators. Moreover, E2F2 was positively correlated with lipid metabolism indicators (r = 0.333, p < 0.05). The area under the ROC curve was 0.704 (95% CI: 0.578-0.830, p = 0.05) for lncRNA XR_108954.2 and 0.653 (95% CI: 0.516-0.790, p = 0.035) for E2F2.

CONCLUSIONS

This transcriptome analysis explored the aberrantly expressed lncRNAs and identified E2F2 and lncRNA XR_108954.2 as potential biomarkers for patients with T2D.

Identification of potential Mitogen-Activated Protein Kinase-related key genes and regulation networks in molecular subtypes of major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a heterogeneous and prevalent mental disorder associated with increased morbidity, disability, and mortality. However, its underlying mechanisms remain unclear.

MATERIALS AND METHODS

All analyses were conducted based on integrated samples from the GEO database. Differential expression analysis, unsupervised consensus clustering analysis, enrichment analysis, and regulation network analysis were performed.

RESULTS

Mitogen-activated protein kinase (MAPK) signaling pathway was identified as an associated pathway in the development of MDD. From transcriptional signatures, we classified the MDD patients into two subgroups using unsupervised clustering and revealed 13 differential expression genes between subgroups, which indicates the probably relative complications. We further illustrated potential molecular mechanisms of MDD, including dysregulation in the neurotrophin signaling pathway, peptidyl-serine phosphorylation, and endocrine resistance. Moreover, we identified hub genes, including MAPK8, TP53, and HRAS in the maintenance of MDD. Furthermore, we demonstrated that the axis of miRNAs-TFs-HRAS/TP53/MAPK8 may play a critical role in MDD.

CONCLUSION

Taken together, we demonstrated an overview of MAPK-related key genes in MDD, determined two molecular subtypes, and identified the key genes and core network that may contribute to the procession of MDD.