AI algorithm combined with RNA editing-based blood biomarkers to discriminate bipolar from major depressive disorders in an external validation multicentric cohort.

Bipolar disorder (BD) is a leading cause of disability worldwide, as it can lead to cognitive and functional impairment and premature mortality. The first episode of BD is usually a depressive episode and is often misdiagnosed as major depressive disorder (MDD). Growing evidence indicates that peripheral immune activation and inflammation are involved in the pathophysiology of BD and MDD. Recently, by developing a panel of RNA editing-based blood biomarkers able to discriminate MDD from depressive BD, we have provided clinicians a new tool to reduce the misdiagnosis delay observed in patients suffering from BD. The present study aimed at validating the diagnostic value of this panel in an external independent multicentric Switzerland-based cohort of 143 patients suffering from moderate to major depression. The RNA-editing based blood biomarker (BMK) algorithm developped allowed to accurately discriminate MDD from depressive BD in an external cohort, with high accuracy, sensitivity and specificity values (82.5 %, 86.4 % and 80.8 %, respectively). These findings further confirm the important role of RNA editing in the physiopathology of mental disorders and emphasize the possible clinical usefulness of the biomarker panel for optimization treatment delay in patients suffering from BD.

Euthymic and depressed bipolar patients are characterized by different RNA editing patterns in blood.

Bipolar disorder (BD) is a worldwide leading cause of disability. Inflammation roles in this disease is well established. ADAR1-mediated RNA editing is one of the key mechanisms regulating the inflammatory response. We have identified a panel of RNA editing-based blood biomarkers which allowed to discriminate unipolar from BD depression with high accuracy. We confirmed here the diagnostic value of this panel in a new cohort of BD patients recruited in Brazil. We also identified new combinations which allow a clear discrimination of BD from healthy controls and among BD subgroups, confirming that RNA editing is a key mechanism in BD.

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.

Phosphodiesterase 8A to discriminate in blood samples depressed patients and suicide attempters from healthy controls based on A-to-I RNA editing modifications.

Mental health issues, including major depressive disorder, which can lead to suicidal behavior, are considered by the World Health Organization as a major threat to global health. Alterations in neurotransmitter signaling, e.g., serotonin and glutamate, or inflammatory response have been linked to both MDD and suicide. Phosphodiesterase 8A (PDE8A) gene expression is significantly decreased in the temporal cortex of major depressive disorder (MDD) patients. PDE8A specifically hydrolyzes adenosine 3',5'-cyclic monophosphate (cAMP), which is a key second messenger involved in inflammation, cognition, and chronic antidepressant treatment. Moreover, alterations of RNA editing in PDE8A mRNA has been described in the brain of depressed suicide decedents. Here, we investigated PDE8A A-to-I RNA editing-related modifications in whole blood of depressed patients and suicide attempters compared to age-matched and sex-matched healthy controls. We report significant alterations of RNA editing of PDE8A in the blood of depressed patients and suicide attempters with major depression, for which the suicide attempt took place during the last month before sample collection. The reported RNA editing modifications in whole blood were similar to the changes observed in the brain of suicide decedents. Furthermore, analysis and combinations of different edited isoforms allowed us to discriminate between suicide attempters and control groups. Altogether, our results identify PDE8A as an immune response-related marker whose RNA editing modifications translate from brain to blood, suggesting that monitoring RNA editing in PDE8A in blood samples could help to evaluate depressive state and suicide risk.

Discontinuous epitopes on the C2 domain of coagulation Factor VIII mapped by computer-designed synthetic peptides.

The occurrence of alloantibodies against Factor VIII (FVIII) is the main iatrogenic complication in haemophilia A (HA). Anti-FVIII autoantibodies may also spontaneously appear in non-HA patients, leading to acquired haemophilia A. In both contexts, the antibody response against FVIII is complex and difficult to analyse due to the lack of suitable tools. Our purpose was to comprehensively map, at the amino acid level, discontinuous epitopes of the C2 domain of FVIII targeted by patients' antibodies. We synthesized 33 synthetic peptides, which were predicted by the bioinformatic algorithm PEPOP to mimic C2 domain discontinuous epitopes. Using an inhibition assay based on the x-MAP technology, we evaluated their ability to block the binding to the C2 domain of anti-C2 domain antibodies from pooled plasma samples. Nine peptides were thus selected and tested again in individual plasma samples. Our results support the view that C2 domain epitopes are organized as an epitopic mosaic distributed around the molecule, showed that each patient displayed a specific anti-C2 epitopic profile, and confirmed the complexity and variability of the immune response against the C2 domain of FVIII. This ability to finely map epitopes could be further used to follow the antibody specificity modifications over time.