Chronic stress and antidepressant treatment alter purine metabolism and beta oxidation within mouse brain and serum.

Major depressive disorder (MDD) is a complex condition with unclear pathophysiology. Molecular disruptions within limbic brain regions and the periphery contribute to depression symptomatology and a more complete understanding the diversity of molecular changes that occur in these tissues may guide the development of more efficacious antidepressant treatments. Here, we utilized a mouse chronic social stress model for the study of MDD and performed metabolomic, lipidomic, and proteomic profiling on serum plus several brain regions (ventral hippocampus, nucleus accumbens, and medial prefrontal cortex) of susceptible, resilient, and unstressed control mice. To identify how commonly used tricyclic antidepressants impact the molecular composition in these tissues, we treated stress-exposed mice with imipramine and repeated our multi-OMIC analyses. Proteomic analysis identified three serum proteins reduced in susceptible animals; lipidomic analysis detected differences in lipid species between resilient and susceptible animals in serum and brain; and metabolomic analysis revealed dysfunction of purine metabolism, beta oxidation, and antioxidants, which were differentially associated with stress susceptibility vs resilience by brain region. Antidepressant treatment ameliorated stress-induced behavioral abnormalities and affected key metabolites within outlined networks, most dramatically in the ventral hippocampus. This work presents a resource for chronic social stress-induced, tissue-specific changes in proteins, lipids, and metabolites and illuminates how molecular dysfunctions contribute to individual differences in stress sensitivity.

Multi-omic serum biomarkers for prognosis of disease progression in prostate cancer.

BACKGROUND: Predicting the clinical course of prostate cancer is challenging due to the wide biological spectrum of the disease. The objective of our study was to identify prostate cancer prognostic markers in patients 'sera using a multi-omics discovery platform. METHODS: Pre-surgical serum samples collected from a longitudinal, racially diverse, prostate cancer patient cohort (N = 382) were examined. Linear Regression and Bayesian computational approaches integrated with multi-omics, were used to select markers to predict biochemical recurrence (BCR). BCR-free survival was modeled using unadjusted Kaplan-Meier estimation curves and multivariable Cox proportional hazards analysis, adjusted for key pathologic variables. Receiver operating characteristic (ROC) curve statistics were used to examine the predictive value of markers in discriminating BCR events from non-events. The findings were further validated by creating a training set (N = 267) and testing set (N = 115) from the cohort. RESULTS: Among 382 patients, 72 (19%) experienced a BCR event in a median follow-up time of 6.9 years. Two proteins-Tenascin C (TNC) and Apolipoprotein A1V (Apo-AIV), one metabolite-1-Methyladenosine (1-MA) and one phospholipid molecular species phosphatidic acid (PA) 18:0-22:0 showed a cumulative predictive performance of AUC = 0.78 [OR (95% CI) = 6.56 (2.98-14.40), P < 0.05], in differentiating patients with and without BCR event. In the validation set all four metabolites consistently reproduced an equivalent performance with high negative predictive value (NPV; > 80%) for BCR. The combination of pTstage and Gleason score with the analytes, further increased the sensitivity [AUC = 0.89, 95% (CI) = 4.45-32.05, P < 0.05], with an increased NPV (0.96) and OR (12.4) for BCR. The panel of markers combined with the pathological parameters demonstrated a more accurate prediction of BCR than the pathological parameters alone in prostate cancer. CONCLUSIONS: In this study, a panel of serum analytes were identified that complemented pathologic patient features in predicting prostate cancer progression. This panel offers a new opportunity to complement current prognostic markers and to monitor the potential impact of primary treatment versus surveillance on patient oncological outcome.