Molecular Mechanism Biomarkers Predict Diagnosis in Schizophrenia and Schizoaffective Psychosis, with Implications for Treatment

Gene Variant Related Neurological and Molecular Biomarkers Predict Psychosis Progression, with Potential for Monitoring and Prevention

The (MTHFR) C677T gene polymorphism is associated with neurological disorders and schizophrenia. Patients diagnosed with schizophrenia and schizoaffective disorder and controls (n 134) had data collected for risk factors, molecular and neuro-sensory variables, symptoms, and functional outcomes. Promising gene variant-related predictive biomarkers were identified for diagnosis by Receiver Operating Characteristics and for illness duration by linear regression. These were then analyzed using Spearman's correlation in relation to the duration of illness. Significant correlations were ranked by strength and plotted on graphs for each MTHFR C677T variant. Homozygous MTHFR 677 TT carriers displayed a mid-illness switch to depression, with suicidality and a late-phase shift from lower to higher methylation, with activated psychosis symptoms. MTHFR 677 CC variant carriers displayed significant premorbid correlates for family history, developmental disorder, learning disorder, and head injury. These findings align with those of low methylation, oxidative stress, multiple neuro-sensory processing deficits, and disability outcomes. Heterozygous MTHFR 677 CT carriers displayed multiple shifts in mood and methylation with multiple adverse outcomes. The graphically presented ranked biomarker correlates for illness duration allow a perspective of psychosis development across gene variants, with the potential for phase of illness monitoring and new therapeutic insights to prevent or delay psychosis and its adverse outcomes.

RNA Editing Signatures Powered by Artificial Intelligence: A New Frontier in Differentiating Schizophrenia, Bipolar, and Schizoaffective Disorders

Mental health disorders are devastating illnesses, often misdiagnosed due to overlapping clinical symptoms. Among these conditions, bipolar disorder, schizophrenia, and schizoaffective disorder are particularly difficult to distinguish, as they share alternating positive and negative mood symptoms. Accurate and timely diagnosis of these diseases is crucial to ensure effective treatment and to tailor therapeutic management to each individual patient. In this context, it is essential to move beyond standard clinical assessment and employ innovative approaches to identify new biomarkers that can be reliably quantified. We previously identified a panel of RNA editing biomarkers capable of differentiating healthy controls from depressed patients and, among depressed patients, those with major depressive disorder and those with bipolar disorder. In this study, we integrated Adenosine-to-Inosine RNA editing blood biomarkers with clinical data through machine learning algorithms to establish specific signatures for bipolar disorder and schizophrenia spectrum disorders. This groundbreaking study paves the way for the application of RNA editing in other psychiatric disorders, such as schizophrenia and schizoaffective disorder. It represents a first proof-of-concept and provides compelling evidence for the establishment of an RNA editing signature for the diagnosis of these psychiatric conditions.

HERV-W upregulation expression in bipolar disorder and schizophrenia: unraveling potential links to systemic immune/inflammation status

BACKGROUND

Bipolar disorder (BD) and schizophrenia (SZ) are the two main mental disorders with unknown etiology that significantly impact individuals' quality of life. The potential pro-inflammatory role in their pathogenesis is postulated and Human Endogenous Retrovirus W (HERV-W) is an emerging candidate to modulate this pathogenic finding. HERVs, ancient retroviruses in the human genome, may play roles in inflammation and disease pathogenesis. Despite HERVs' involvement in autoimmune diseases, their influence on mental disorders remains underexplored. Therefore, the aim of this study was to assess the level of HERV-W-env expression and the systemic inflammatory profile through the concentration of IL-2, IL-4, IL-6, IL-10, TNF-α and INF-γ cytokines in BD and SZ patients.

RESULTS

All participants showed HERV-W-env expression, but its expression was higher in mental disorder patients (p < 0.01) than in control. When separated, SZ individuals exhibited higher HERV-W expression than the control group (p < 0.01). Higher serum levels of TNF-α and IL-10 were found in BD (p = 0.0001 and p = 0.001, respectively) and SZ (p = 0.01) and p = 0.01, respectively) than in the control group, while SZ showed decreased levels IFN-γ and IL-2 as compared to controls (p = 0.05) and BD patients (p = 0.05), respectively. Higher TNF-α/IL-4 and TNF-α/IL-10 ratios, and lower IFN-γ/IL-10 were observed in BD and SZ patients than controls. Significant negative correlation between HERV-W-env expression and IL-10 (r=-0.47 p < 0.05), as well as positive correlations between HERV-W-env expression and TNF-α/IL-10 or IFN-γ/IL-10 ratios (r = 0.48 p < 0.05 and r = 0.46 p < 0.05, respectively) were found in BD patients.

CONCLUSION

These findings suggest not only a potential link between HERV-W-env expression both in BD and SZ, but also a possible involvement of systemic inflammatory status in BD patients.