Search bioRxiv⌕ Search

Biology subjects

Wolfson, D.

Publications and source records attributed to Wolfson, D..

2 recordsLinked to original sources

Inhibition of Tgfβ signaling enables durable ventricular pacing by TBX18 gene transfer

Implantable cardiac pacemaker devices are generally effective for patients with symptomatic bradyarrhythmia. However, device-dependent cardiac pacing is far from ideal and often inadequate, particularly for pediatric patients who need to go through invasive revision of the indwelling hardware. Biological pacemakers have been proposed as device-free alternatives to the current treatment, but sustained, unwavering biological pacing beyond days after the biologic delivery has not been demonstrated. We have previously demonstrated that re-expression of an embryonic transcription factor, TBX18, could reprogram ventricular cardiomyocytes into induced pacemaker myocytes (iPMs). Here, we report that exogenous expression of TBX18 per se leads to severe fibrosis in situ, impairing the iPMs ability to pace together. Acute fibrosis is accompanied with proliferation and activation of cardiac fibroblasts via Tgf{beta}-Smad2/3 pathway. Small molecule inhibition of Tgf{beta} signaling mitigated the interstitial remodeling, independent from TBX18-induced iPM reprogramming at the single-cell level. Direct and focal gene transfer of TBX18 into the left ventricular myocardium created ventricular pacing in a rat model of chronic atrioventricular block, but such activity began to wane in a week. In contrast, a combination therapy consisting of TBX18 gene transfer and Tgf{beta} inhibition enabled sustained biological pacing beyond the four-week study period. Our data demonstrate that inhibition of Tgf{beta} signaling suffices to achieve durable cardiac pacing by TBX18-induced biological pacemakers.

cell biology↗

Using Piecewise Regression to Identify Biological Phenomena in Biotelemetry Datasets

Technological advances in the field of animal tracking have greatly expanded the potential to remotely monitor animals, opening the door to exploring how animals shift their behavior over time or respond to external stimuli. A wide variety of animal-borne sensors can provide information on an animals location, movement characteristics, external environmental conditions, and internal physiological status. Here, we demonstrate how piecewise regression can be used to identify the presence and timing of potential shifts in a variety of biological responses using GPS telemetry and other biologging data streams. Different biological latent states can be inferred by partitioning a time-series into multiple segments based on changes in modeled responses (e.g., their mean, variance, trend, degree of autocorrelation) and specifying a unique model structure for each interval. We provide five example applications highlighting a variety of taxonomic species, data streams, timescales, and biological phenomena. These examples include a short-term behavioral response (flee and return) by a trumpeter swan (Cygnus buccinator) immediately following a GPS collar deployment; remote identification of parturition based on movements by a pregnant moose (Alces alces); a physiological response (spike in heart-rate) in a black bear (Ursus americanus) to a stressful stimulus (presence of a drone); a mortality event of a trumpeter swan signaled by changes in collar temperature and Overall Dynamic Body Acceleration; and an unsupervised method for identifying the onset, return, duration, and staging use of sandhill crane (Antigone canadensis) migration. We implement analyses using the mcp package in R, which provides functionality for specifying and fitting a wide variety of user-defined model structures in a Bayesian framework and methods for assessing and comparing models using information criterion and cross-validation measures. This approach uses simple modeling approaches that are accessible to a wide audience and is a straightforward means of assessing a variety of biologically relevant changes in animal behavior.

ecology↗