Biology subjects

Powers, A.

Publications and source records attributed to Powers, A..

2 recordsLinked to original sources

bioRxiv2020

Coordinated interactions between endothelial cells and macrophages in the islet microenvironment promote beta cell regeneration

Endogenous {beta} cell regeneration could alleviate diabetes, but proliferative stimuli within the islet microenvironment are incompletely understood. We previously found that {beta} cell recovery following hypervascularization-induced {beta} cell loss involves interactions with endothelial cells (ECs) and macrophages (M{Phi}s). Here we show that proliferative ECs modulate M{Phi} infiltration and phenotype during {beta} cell loss, and recruited M{Phi}s are essential for {beta} cell recovery. Furthermore, VEGFR2 inactivation in quiescent ECs accelerates islet vascular regression during {beta} cell recovery and leads to increased {beta} cell proliferation without changes in M{Phi} phenotype or number. Transcriptome analysis of {beta} cells, ECs, and M{Phi}s reveals that {beta} cell proliferation coincides with elevated expression of extracellular matrix remodeling molecules and growth factors likely driving activation of proliferative signaling pathways in {beta} cells. Collectively, these findings suggest a new {beta} cell regeneration paradigm whereby coordinated interactions between intra-islet M{Phi}s, ECs, and extracellular matrix mediate {beta} cell self-renewal.

cell biology↗

bioRxiv2020

Regression dynamic causal modeling for resting-state fMRI

AO_SCPLOWBSTRACTC_SCPLOW"Resting-state" functional magnetic resonance imaging (rs-fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks. Here, we show that a method recently developed for task-fMRI - regression dynamic causal modeling (rDCM) - extends to rs-fMRI and offers both directional estimates and scalability to whole-brain networks. First, simulations demonstrate that rDCM faithfully recovers parameter values over a wide range of signal-to-noise ratios and repetition times. Second, we test construct validity of rDCM in relation to an established model of effective connectivity, spectral DCM. Using rs-fMRI data from nearly 200 healthy participants, rDCM produces biologically plausible results consistent with estimates by spectral DCM. Importantly, rDCM is computationally highly efficient, reconstructing whole-brain networks (>200 areas) within minutes on standard hardware. This opens promising new avenues for connectomics.

neuroscience↗