Down-regulation of PRKCB1 expression in Han Chinese patients with subsyndromal symptomatic depression

A game changer for bipolar disorder diagnosis using RNA editing-based biomarkers

In clinical practice, differentiating Bipolar Disorder (BD) from unipolar depression is a challenge due to the depressive symptoms, which are the core presentations of both disorders. This misdiagnosis during depressive episodes results in a delay in proper treatment and a poor management of their condition. In a first step, using A-to-I RNA editome analysis, we discovered 646 variants (366 genes) differentially edited between depressed patients and healthy volunteers in a discovery cohort of 57 participants. After using stringent criteria and biological pathway analysis, candidate biomarkers from 8 genes were singled out and tested in a validation cohort of 410 participants. Combining the selected biomarkers with a machine learning approach achieved to discriminate depressed patients (n = 267) versus controls (n = 143) with an AUC of 0.930 (CI 95% [0.879-0.982]), a sensitivity of 84.0% and a specificity of 87.1%. In a second step by selecting among the depressed patients those with unipolar depression (n = 160) or BD (n = 95), we identified a combination of 6 biomarkers which allowed a differential diagnosis of bipolar disorder with an AUC of 0.935 and high specificity (Sp = 84.6%) and sensitivity (Se = 90.9%). The association of RNA editing variants modifications with depression subtypes and the use of artificial intelligence allowed developing a new tool to identify, among depressed patients, those suffering from BD. This test will help to reduce the misdiagnosis delay of bipolar patients, leading to an earlier implementation of a proper treatment.

PAID study design on the role of PKC activation in immune/inflammation-related depression: a randomised placebo-controlled trial protocol

Background

Inflammation that is mediated by microglia activation plays an important role in the pathogenesis of depression. Microglia activation can lead to an increase in the levels of proinflammatory cytokines, including TNF-α, which leads to neuronal apoptosis in the specific neural circuits of some brain regions, abnormal cognition and treatment-resistant depression (TRD). Protein kinase C (PKC) is a key regulator of the microglia activation process. We assume that the abnormality in PKC might result in abnormal microglia activation, neuronal apoptosis, significant changes in emotional and cognitive neural circuits, and TRD. In the current study, we plan to target at the PKC signal pathway to improve the TRD treatment outcome.

Methods and analysis

This is a 12-week, ongoing, randomised, placebo-controlled trial. Patients with TRD (N=180) were recruited from Shanghai Mental Health Center, Shanghai Jiao Tong University. Healthy control volunteers (N=60) were recruited by advertisement. Patients with TRD were randomly assigned to 'escitalopram+golimumab (TNF-α inhibitor)', 'escitalopram+calcium tablet+vitamin D (PKC activator)' or 'escitalopram+placebo' groups. We define the primary outcome as changes in the 17-item Hamilton Depression Rating Scale (HAMD-17). The secondary outcome is defined as changes in anti-inflammatory effects, cognitive function and quality of life.

Discussion

This study might be the first randomised, placebo-controlled trial to target at the PKC signal pathway in patients with TRD. Our study might help to propose individualised treatment strategies for depression.

Trial registration number

The trial protocol is registered with ClinicalTrials.gov under protocol ID 81930033 and ClinicalTrials.gov ID NCT04156425.

Weighted gene co-expression network analysis identifies specific modules and hub genes related to subsyndromal symptomatic depression

Objectives: The identification of the potential molecule targets for subsyndromal symptomatic depression (SSD) is critical for improving the effective clinical treatment on the mental illness. In the current study, we mined the genome-wide expression profiling and investigated the novel biological pathways associated with SSD.Methods: Expression of differentially expressed genes (DEGs) were analysed with microarrays of blood tissue cohort of eight SSD patients and eight healthy subjects. The gene co-expression is calculated by WGCNA, an R package software. The function of the genes was annotated by gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.Results: We identified 11 modules from the 9,427 DEGs. Three co-expression modules (blue, cyan and red) showed striking correlation with the phenotypic trait between SSD and healthy controls. Gene ontology and KEGG pathway analysis demonstrated that the function of these three modules was enriched with the pathway of inflammatory response and type II diabetes mellitus. Finally, three hub genes, NT5DC1, SGSM2 and MYCBP, were identified from the blue module as significant genes.Conclusions: This first blood gene expression study in SSD observed distinct patterns between cases and controls which may provide novel insight into understanding the molecular mechanisms of SSD.