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Conley, A.

Publications and source records attributed to Conley, A..

2 recordsLinked to original sources

Mueller glia-vasculature interactions in the developing retina

Coordinated signaling among neurons, glia, and the vasculature is essential for the formation of a functional nervous system, yet how these relationships emerge during development remains unclear. Here, we investigated the developmental interplay between neural activity, Muller glia, and the retina vasculature in mice. Using quantitative confocal imaging from postnatal day 5 to eye-opening, we mapped the emergence of the superficial, intermediate, and deep vasculature layers and found that they emerged normally in mice lacking the {beta}2-containing nicotinic acetylcholine receptors, despite a dramatic reduction in cholinergic signaling. Tip cell density and overall vessel growth were unchanged, indicating cholinergic wave activity is not required for the emergence of retinal vasculature. We next defined the developmental timeline of Muller glia-vascular interactions. Sparse labeling and immunohistochemistry revealed that Muller glial lateral processes closely associate with endothelial tip cells during intermediate- and deep-layer angiogenesis and establish Aquaporin-4-enriched endfeet at vascular contact sites from the earliest stages of growth, even when vessel trajectories are perturbed. Finally, two-photon calcium imaging combined with simultaneous electrophysiology demonstrated that Muller glial endfeet exhibit robust, compartmentalized calcium transients during development. Although a subset of events was temporally correlated with retinal waves, enhancing neurotransmitter spillover selectively increased wave-associated activity in glial stalks but not endfeet. These findings indicate that calcium signaling at the glial-vascular interface is largely independent of spontaneous neuronal activity. Together, our results support a model in which Muller glia engage growing vessels through an activity-independent, parallel developmental program that may provide instructive cues for retinal angiogenesis.

neuroscience↗

Prediction of metabolic dynamics through deep learning and high-throughput multiomics data

Synthetic biologys remarkable potential to tackle important societal problems is held back by our inability to predictably engineer biological systems. Here, we collected one of the largest public multiomics synthetic biology datasets generated to date, and used it to train a novel deep learning algorithm able to predict product and metabolic dynamics with great accuracy, starting to approach the predictive capabilities found in physics and chemistry. We were able to predict production time series with 90-99% accuracy, and final production with 96% accuracy. Further, we were able to produce good predictions for a majority of extracellular metabolites, and twenty different intracellular metabolites. These predictions were provided for a target of industrial relevance: a non-model yeast (Pichia kudriavzevii) engineered to produce large amounts of malonic acid, a desirable biomanufacturing target. This approach is generally applicable to any host, pathway, and product because all required knowledge is inferred from experimental data.

bioengineering↗