Urodynamic Evaluation in Multiple System Atrophy: A Retrospective Cohort Study.

Background: Urological dysfunction in patients with multiple system atrophy (MSA) is one of the main manifestations of autonomic failure. Urodynamic examination is clinically relevant since underlying pathophysiology of lower urinary tract (LUT) dysfunction can be variable. Objective: Evaluation of the pathophysiology of urological symptoms and exploration of differences in urodynamic patterns of LUT dysfunction between MSA-P and MSA-C. Methods: Retrospective study of patients with possible and probable MSA who were referred for urodynamic studies between 2004 and 2019. Demographic data, medical history, physical examination and urodynamic studies assessing storage and voiding dysfunction were obtained. Results: Seventy-four patients were included in this study (MSA-P 64.9% n = 48; median age 62.5 (IQR 56.8-70) years). Detrusor overactivity during filling phase was noted in 58.1% (n = 43) of the patients. In the voiding phase, detrusor sphincter dyssynergia and detrusor underactivity were observed in 24.6% (n = 17) and in 62.1% (n = 41) of the patients, respectively. A postmicturition residual volume of over 100 ml was present in 71.4% (n = 50) of the patients. Comparison of MSA subtypes showed weaker detrusor contractility in MSA-P compared to MSA-C [pdetQmax 26.2 vs. 34.4 cmH20, P = 0.04]. In 56.2% (n = 41) of patients pathophysiology of LUT dysfunction was deemed to be neurogenic and consistent with the diagnosis of MSA. In 35.6% (n = 26) urodynamic pattern suggested other urological co-morbidities. Conclusion: Urodynamic evaluation is an important tool to analyze the pattern of LUT dysfunction in MSA. Impaired detrusor contractility was seen more in MSA-P which needs to be investigated in further studies

Linking Proteomic and Transcriptional Data through the Interactome and Epigenome Reveals a Map of Oncogene-induced Signaling

Cellular signal transduction generally involves cascades of post-translational protein modifications that rapidly catalyze changes in protein-DNA interactions and gene expression. High-throughput measurements are improving our ability to study each of these stages individually, but do not capture the connections between them. Here we present an approach for building a network of physical links among these data that can be used to prioritize targets for pharmacological intervention. Our method recovers the critical missing links between proteomic and transcriptional data by relating changes in chromatin accessibility to changes in expression and then uses these links to connect proteomic and transcriptome data. We applied our approach to integrate epigenomic, phosphoproteomic and transcriptome changes induced by the variant III mutation of the epidermal growth factor receptor (EGFRvIII) in a cell line model of glioblastoma multiforme (GBM). To test the relevance of the network, we used small molecules to target highly connected nodes implicated by the network model that were not detected by the experimental data in isolation and we found that a large fraction of these agents alter cell viability. Among these are two compounds, ICG-001, targeting CREB binding protein (CREBBP), and PKF118–310, targeting β-catenin (CTNNB1), which have not been tested previously for effectiveness against GBM. At the level of transcriptional regulation, we used chromatin immunoprecipitation sequencing (ChIP-Seq) to experimentally determine the genome-wide binding locations of p300, a transcriptional co-regulator highly connected in the network. Analysis of p300 target genes suggested its role in tumorigenesis. We propose that this general method, in which experimental measurements are used as constraints for building regulatory networks from the interactome while taking into account noise and missing data, should be applicable to a wide range of high-throughput datasets.National Science Foundation (U.S.) (DB1-0821391)National Institutes of Health (U.S.) (Grant U54-CA112967)National Institutes of Health (U.S.) (Grant R01-GM089903)National Institutes of Health (U.S.) (P30-ES002109

Towards Bridging Translational Gap in Cardiotoxicity Prediction: an Application of Progressive Cardiac Risk Assessment Strategy in TdP Risk Assessment of Moxifloxacin

Drug-induced cardiac arrhythmia, especially occurrence of torsade de pointes (TdP), has been a leading cause of attrition and post-approval re-labeling and withdrawal of many drugs. TdP is a multifactorial event, reflecting more than just drug-induced cardiac ion channel inhibition and QT interval prolongation. This presents a translational gap in extrapolating pre-clinical and clinical cardiac safety assessment to estimate TdP risk reliably, especially when the drug of interest is used in combination with other QT-prolonging drugs for treatment of diseases such as tuberculosis. A multi-scale mechanistic modeling framework consisting of physiologically based pharmacokinetics (PBPK) simulations of clinically relevant drug exposures combined with Quantitative Systems Toxicology (QST) models of cardiac electro-physiology could bridge this gap. We illustrate this PBPK-QST approach in cardiac risk assessment as exemplified by moxifloxacin, an anti-tuberculosis drug with abundant clinical cardiac safety data. PBPK simulations of moxifloxacin concentrations (systemic circulation and estimated in heart tissue) were linked with in vitro measurements of cardiac ion channel inhibition to predict the magnitude of QT prolongation in healthy individuals. Predictions closely reproduced the clinically observed QT interval prolongation, but no arrhythmia was observed, even at ×10 exposure. However, the same exposure levels in presence of physiological risk factors, e.g., hypokalemia and tachycardia, led to arrhythmic event in simulations, consistent with reported moxifloxacin-related TdP events. Application of a progressive PBPK-QST cardiac risk assessment paradigm starting in early development could guide drug development decisions and later define a clinical “safe space” for post-approval risk management to identify high-risk clinical scenarios