PubMed · 15124643
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P J Quinn. 2001. Dennis Chapman.. https://doi.org/10.1098/rsbm.2001.0004
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The stria vascularis (SV) is an essential component of the inner ear that regulates the ionic environment required for hearing. SV degeneration disrupts cochlear homeostasis, leading to irreversible hearing loss, yet a comprehensive understanding of the SV, and consequently therapeutic availability for SV degeneration, is lacking. We developed a whole-tissue explant model from neonatal and mature mice to create a platform for advancing SV research. We validated our model by demonstrating that the proliferative behavior of the SV in vitro mimics SV in vivo. We also provided evidence for pharmacological experimentation by investigating the role of Wnt/β-catenin signaling in SV proliferation. Finally, we performed single-cell RNA sequencing from in vivo neonatal and mature mouse SV and surrounding tissue and revealed key genes and pathways that may play a role in SV proliferation and maintenance. Together, our results contribute new insights into investigating biological solutions for SV-associated hearing loss.
Due to the variety and importance of roles performed by signalling networks, understanding their function and evolution is of great interest. Signalling networks allow organisms to process and react to changes in their internal and external environment. Current estimates suggest that two to three percent of all genomes code for proteins involved in signalling networks. The study of signalling networks is hindered by the complexities of the networks and difficulties in ascribing function to form. For example, a very complex dense network might comprise eighty or more densely connected proteins. In the majority of cases there is very little understanding of how these networks process signals. Unlike in electronics, where there is a broad practical and theoretical understanding of how to construct devices that can process almost any kind of signal, in biological signalling networks there is no equivalent theory. Part of the problem stems from the fact that in most cases it is unknown what particular signal processing circuits would look like in a biological form. This paper describes the evolutionary methods used to generate networks with particular signal- and computational-processing capabilities. The techniques involved are described, and the approach is illustrated by evolving computational circuits such as multiplication, radicals and logarithmic functions. The experiments also illustrate the evolution of modularity within biochemical reaction networks.