Molecular signature underlying (R)-ketamine rapid antidepressant response on anhedonic-like behavior induced by sustained exposure to stress.

Anhedonia induced by sustained stress exposure is a hallmark symptom of major depressive disorder (MDD) and in rodents, it can be accessed through the sucrose preference test (SPT). (R)-ketamine is a fast-acting antidepressant with less detrimental side effects and abuse liability compared to racemic ketamine. The present study combined high-throughput proteomics and network analysis to identify molecular mechanisms involved in chronic variable stress (CVS)-induced anhedonia and promising targets underlying (R)-ketamine rapid antidepressant response. Male Wistar rats were subjected to CVS for five weeks. Based on the SPT, animals were clustered into resilient or anhedonic-like (ANH) groups. ANH rats received a single dose of saline or (R)-ketamine (20 mg/kg, i.p.), which was proceeded by treatment response evaluation. After prefrontal cortex collection, proteomic analysis was performed to uncover the differentially expressed proteins (DEPs) related to both anhedonic-like behavior and pharmacological response. The behavioral assessment showed that the ANH animals had a significant decrease in SPT, and that (R)-ketamine responders showed a reversal of anhedonic-like behavior. On a molecular level, anhedonia-like behavior was associated with the downregulation of Neuronal Pentraxin Receptor (Nptxr) and Galectin-1 (Gal-1). These data reinforce a disruption in the inflammatory response, neurotransmitter receptor activity, and glutamatergic synapses in chronic stress-induced anhedonia. (R)-ketamine response-associated DEPs included novel potential targets involved in the modulation of oxidative stress, energetic metabolism, synaptogenesis, dendritic arborization, neuroinflammation, gene expression, and telomere length, converging to biological themes extensively documented in MDD physiopathology. Our data provide valuable insights into the molecular mechanisms underlying the response to (R)-ketamine and highlight these pathways as potential therapeutic targets for anhedonia. By addressing proteins involved in oxidative stress, energy metabolism, synaptogenesis, dendritic arborization, neuroinflammation, gene expression, and telomere length, we can target multiple key factors involved in the pathophysiology of MDD. Modulating these proteins could open avenues for novel therapeutic strategies and deepen our understanding of anhedonia, offering hope for improved outcomes in individuals facing this challenging condition. However, additional studies will be essential to validate these findings and further explore their therapeutic implications.

Proteomic Insights into Biology of Bipolar Disorder: Implications for Health Complexity and Mortality.

OBJECTIVE: The present study has the following objectives: 1) identify differentially expressed proteins and pathways in blood samples of BD compared to healthy controls by employing high-throughput proteomics and bioinformatics and 2) characterize disease-related molecular signatures through in-depth analysis of the differentially expressed proteins and pathways. METHODS: Blood samples from BD patients (n=10) classified into high (BD+) or poor functioning (BD-), based on functional and cognitive status, and healthy controls (n=5) were analyzed using mass spectrometry-based proteomic analysis. Bioinformatics was performed to detect biological processes, pathways, and diseases related to BD. RESULTS: Eight proteins exclusively characterized the molecular profile of patients with BD+ compared to HC, while 26 altered proteins were observed in the BD- group. These altered proteins were mainly enriched in biological processes related to lipid metabolism, complement system and coagulation cascade, and cardiovascular diseases; all these changes were more prominent in the BD- group. CONCLUSION: These findings may represent systemic alterations that occur with the progression of the illness and a possible link between BD and medical comorbidities. Such comprehensive understanding provides valuable insights for targeted interventions, addressing mental and physical health aspects in subjects with BD. Despite these promising findings, further research is warranted, encompassing larger sample cohorts and incorporating biological validation through molecular biology methods.

Drug Repurposing and Personalized Treatment Strategies for Bipolar Disorder Using Transcriptomic.

OBJECTIVE: The present study combined transcriptomic data and computational techniques based on gene expression signatures to identify novel bioactive compounds or FDA-approved drugs for the management of Bipolar Disorder (BD). METHODS: Five transcriptomic datasets, comprising a total of 165 blood samples from BD case-control, were selected from the Gene Expression Omnibus repository (GEO). The number of subjects varied from 6 to 60, with a mean age ranging from 35 to 48, with a gender variation between them. Most of the patients were on pharmacological treatment. Master Regulator Analysis (MRA) and Gene Set Enrichment Analysis (GSEA) were performed to identify statistically significant genes between BD and HC and their association with the mood states of BD. Additionally, existing molecules with the potential to reverse the transcriptomic profiles of disease-altered regulons in BD were identified using the LINCS and cMap databases. RESULTS: MRA identified 59 potential MRs candidates modulating the regulatory units enriched with genes altered in BD, while the GSEA identified 134 enriched genes, and a total of 982 regulons had their activation state determined. Both analyses showed genes exclusively associated with mania, depression, or euthymia, and some genes were common between the three mood states. We identified bioactive compounds and licensed drug candidates, including antihypertensives and antineoplastics, as promising candidates for treating BD. Nevertheless, experimental validation is essential to authenticate these findings in subsequent studies. CONCLUSION: Although preliminary, our data provides some insights regarding the biological patterns of BD into distinct mood states and potential therapeutic targets. The combined transcriptomic and bioinformatics strategy offers a route to advance drug discovery and personalized medicine by tapping into gene expression information.

Drug repurposing and personalized treatment strategies for bipolar disorder using transcriptomics: an exploratory study

Objective: The present study combined transcriptomic data and computational techniques based on gene expression signatures to identify new bioactive compounds or Food and Drug Administration-approved drugs for the treatment of bipolar disorder (BD). Methods: Five transcriptomic datasets containing 165 blood samples from individuals with BD were selected from the Gene Expression Omnibus (GEO). The number of participants varied from six to 60, with a mean age between 35 and 48 years and a gender difference between them. Most of these patients were receiving pharmacological treatment. Master regulator analysis (MRA) and gene set enrichment analysis (GSEA) were performed to identify genes that were significantly different between patients with BD and healthy controls and their associations with mood states in patients with BD. In addition, molecules that could reverse the transcriptomic profiles of BD-altered regulons were identified from the Library of Network-Based Cellular Signatures Consortium (LINCS) and the Broad Institute Connectivity Map Drug Repurposing Database (cMap) databases. Results: MRA identified 59 candidate master regulators (MRs) that modulate regulatory units enriched with BD-altered genes. In contrast, GSEA identified 134 enriched genes and 982 regulons whose activation state was determined. Both analyses revealed genes exclusively associated with mania, depression, or euthymia, and some genes were shared among these three mood states. We identified bioactive compounds and licensed drug candidates, including antihypertensives and antineoplastic agents, as promising candidates for the treatment of BD. However, experimental validation is essential to confirm these findings in further studies. Conclusion: Although our data are still preliminary, they provide some insights into the biological patterns of different mood states in patients with BD and their potential therapeutic targets. The strategy of transcriptomics plus bioinformatics offers a way to advance drug discovery and personalized medicine by using gene expression information.

Potential Candidates for Biomarkers in Bipolar Disorder: A Proteomic Approach through Systems Biology.

Bipolar disorder (BD) is one of the most disabling diseases characterized by severe humor fluctuation. It is accompanied by cognitive and functional impairment in addiction to high suicide rates. BD is often underdiagnosed and treated incorrectly because many of the reported symptoms are not exclusive to the disorder. Once the diagnosis is exclusively clinical, it is not possible to state precisely. From that, proteomic approaches were used to identify, in a large scale, all proteins involved in cellular or tissue processes. This review aggregate data from blood proteomes, by using protein association network, of subjects with BD and healthy controls to suggest dysfunctional molecular pathways involved in disease. Original articles containing proteomic analysis were searched in PubMed. Seven studies were selected and data were extracted for posterior analysis. A protein-protein interaction network was created by STRING database. A final set of proteins in this network were employed as input in ClueGO and, the main biological process was visualized using R package pathview. The analysis revealed proteins associated with many biological processes, including growth and endocrine regulation, iron transportation, protease inhibition, protection against pathogens and cholesterol transport. Moreover, pathway analysis indicated the association of uncovered proteins with two main metabolic pathways: complement system and coagulation cascade. Thus, a better understanding on the pathophysiology of psychiatric disorders and the identification of potential biomarker candidates are essential to improve diagnostic, prognostic and design pharmacological strategies.