PIAS genes as disease markers in bipolar disorder

Endo-Lysosomal and Autophagy Pathway and Ubiquitin-Proteasome System in Mood Disorders: A Review Article

Mood disorders are disabling conditions that cause significant functional impairment. Due to the clinical heterogeneity and complex nature of these disorders, diagnostic and treatment strategies face challenges. The etiology of mood disorders is multifactorial, involving genetic and environmental aspects that are associated with specific biological pathways including inflammation, oxidative stress, and neuroprotection. Alterations in these pathways may reduce the cell's ability to recover from stress conditions occurring during mood episodes. The endo-lysosomal and autophagy pathway (ELAP) and the ubiquitin-proteasome system (UPS) play critical roles in protein homeostasis, impacting neuroplasticity and neurodevelopment. Thus, emerging evidence has suggested a role for these pathways in mental disorders. In the case of neurodegenerative diseases (NDDs), a deeper understanding in the role of ELAP and UPS has been critical to discover new treatment targets. Since it is suggested that NDDs and mood disorders share clinical symptomatology and risk factors, it has been hypothesized that there might be common underlying molecular pathways. Here, we review the importance of the ELAP and UPS for the central nervous system and for mood disorders. Finally, we discuss potential translational strategies for the diagnosis and treatment of major depressive disorder and bipolar disorder associated with these pathways.

A Complex Heterogeneous Network Model of Disease Regulated by Noncoding RNAs: A Case Study of Unstable Angina Pectoris

MicroRNAs (miRNAs) are important types of noncoding RNAs, and there is a lack of holistic and systematic understanding of the functions they play in disease. We proposed a research strategy, including two parts network analysis and network modelling, to analyze, model, and predict the regulatory network of miRNAs from a network perspective, using unstable angina pectoris as an example. In the network analysis section, we proposed the WGCNA & SimCluster method using both correlation and similarity to find hub miRNAs, and validation on two datasets showed better results than the methods using correlation or similarity alone. In the network modelling section, we used six knowledge graph or graph neural network models for link prediction of three types of edges and multilabel classification of two types of nodes. Comparative experiments showed that the RotatE model was a good model for link prediction, while the RGCN model was the best model for multilabel classification. Potential target genes were predicted for hub miRNAs and validation of hub miRNA-target gene interactions, target genes as biomarkers and target gene functions were performed using a three-step validation approach. In conclusion, our study provides a new strategy to analyze and model miRNA regulatory networks.

Tricyclic antidepressants target FKBP51 SUMOylation to restore glucocorticoid receptor activity

FKBP51 is an important inhibitor of the glucocorticoid receptor (GR) signaling. High FKBP51 levels are associated to stress-related disorders, which are linked to GR resistance. SUMO conjugation to FKBP51 is necessary for FKBP51's inhibitory action on GR. The GR/FKBP51 pathway is target of antidepressant action. Thus we investigated if these drugs could inhibit FKBP51 SUMOylation and therefore restore GR activity. Screening cells using Ni²⁺ affinity and in vitro SUMOylation assays revealed that tricyclic antidepressants- particularly clomipramine- inhibited FKBP51 SUMOylation. Our data show that clomipramine binds to FKBP51 inhibiting its interaction with PIAS4 and therefore hindering its SUMOylation. The inhibition of FKBP51 SUMOylation decreased its binding to Hsp90 and GR facilitating FKBP52 recruitment, and enhancing GR activity. Reduction of PIAS4 expression in rat primary astrocytes impaired FKBP51 interaction with GR, while clomipramine could no longer exert its inhibitory action. This mechanism was verified in vivo in mice treated with clomipramine. These results describe the action of antidepressants as repressors of FKBP51 SUMOylation as a molecular switch for restoring GR sensitivity, thereby providing new potential routes of antidepressant intervention.

Application of machine learning in diagnostic value of mRNAs for bipolar disorder

PURPOSE

Bipolar disorder (BD) is a type of severe mental illness with symptoms of mania or depression, it is necessary to find out effective diagnostic biomarkers for BD due to diagnosing BD is based on clinical interviews without objective indicators.

MATERIALS AND METHODS

The mRNA expression levels of genes included PIK3R1, FYN, TP53, PRKCZ, PRKCB, and YWHAB in the peripheral blood of 43 patients with bipolar disorder and 47 healthy controls were detected. Machine learning methods included Artificial Neural Networks, Extreme Gradient Boosting, Random Forest, and Support Vector Machine were adopted to fit different gene combinations to evaluate diagnostic value for bipolar disorder.

RESULTS

The combination 'PIK3R1 + FYN' in the SVM model showed the best diagnostic value, with AUC, sensitivity, and specificity values of 0.951, 0.928, and 0.937, respectively.

CONCLUSIONS

The diagnostic efficiency for bipolar disorder was significantly improved by fitting PIK3R1 and FYN through the Support Vector Machine model.

Expression Analysis of Protein Inhibitor of Activated STAT in Inflammatory Demyelinating Polyradiculoneuropathy

Protein inhibitors of activated STAT (PIAS) are involved in the regulation of the JAK/STAT signaling pathway and have interactions with NF-κB, p73 and p53. These proteins regulate immune responses; therefore dysregulation in their expression leads to several immune-mediated disorders. In the present study, we examined expression of PIAS1-4 in peripheral blood of patients with acute/chronic inflammatory demyelinating polyradiculoneuropathy (AIDP/CIDP) compared with healthy subjects. We demonstrated down-regulation of all PIAS genes in both AIDP and CIDP cases compared with controls. Similarly, comparisons in gender-based groups revealed down-regulation of these gene0s in patients of each gender compared with gender-matched controls. There was no significant difference in expression of PIAS genes between AIDP and CIDP cases. Based on the area under the receiver operating characteristic curves, PIAS1-4 genes could distinguish between inflammatory demyelinating polyradiculoneuropathy and healthy status with accuracy values of 0.87, 0.87, 0.79 and 0.80, respectively. In differentiation between AIDP cases and healthy controls, these values were 0.92, 0.92, 0.83 and 0.86, respectively. Finally, PIAS1-4 genes could discriminate CIDP from healthy status with accuracy values of 0.82, 0.83, 0.75 and 0.75, respectively. The current study underscores the role of PIAS genes in the pathogenesis of inflammatory demyelinating polyradiculoneuropathy and their potential usage as biomarkers.

Expression of PIAS Genes in Migraine Patients

Migraine is a complex disabling condition which is associated with dysregulation of several pathways particularly those being associated with immune responses. In order to assess contribution of protein inhibitor of activated STAT (PIAS) in the pathogenesis of migraine, we quantified expression levels of PIAS1-PIAS4 genes in the circulation of patients with migraine compared with controls. Expression of PIAS1 was substantially lower in total migraineurs compared with controls (ratio of mean expressions (RME) = 0.18, SE = 0.29, P value < 0.001) and in both male and female migraineurs compared with sex-matched controls. Expression of PIAS2 was lower in migraineurs without aura compared with controls (RME = 0.64, SE = 0.31, P value = 0.04) and in male subgroup of these patients compared with male controls (RME = 0.60, SE = 0.22, P value < 0.001). In migraineurs with aura, downregulation of PIAS2 was only observed among male subgroups (RME = 0.37, SE = 0.49, P value = 0.01). PIAS3 was downregulated in total male migraineurs (RME = 0.52, SE = 0.43, P value = 0.04) and in male migraineurs with aura (RME = 0.49, SE = 0.45, P value = 0.03) compared with male controls. Finally, PIAS4 was upregulated in total migraineurs (RME = 3.78, SE = 0. 34, P value < 0.001), female migraineurs (RME = 5.26, SE = 0.36, P value < 0.001), migraineurs with aura (RME = 4.24, SE = 0.42, P value < 0.001), female migraineurs with aura (RME = 6.13, SE = 0.47, P value < 0.001), migraineurs without aura (RME = 3.33, SE = 0.38, P value < 0.001), and female migraineurs without aura (RME = 4.47, SE = 0.41, P value < 0.001) compared with the corresponding controls. The present study suggests contribution of PIAS genes in the pathogenesis of migraine and warrants future studies to clarify the functional routes of their contribution.