Biology subjects

Murphy, K. M.

Publications and source records attributed to Murphy, K. M..

3 recordsLinked to original sources

bioRxiv2019

Toxoplasma gondii infection drives conversion of NK Cells into ILC1s

Innate lymphoid cells (ILCs) were originally classified based on their cytokine profiles, placing natural killer (NK) cells and ILC1s together, but recent studies support their separation into different lineages at steady-state. However, tumors may induce NK cell conversion into ILC1-like cells that are limited to the tumor microenvironment and whether this conversion occurs beyond this environment remains unknown. Here we describe Toxoplasma gondii infection converts NK cells into cells resembling steady-state ILC1s that are heterogeneous and distinct from both steady-state NK cells and ILC1s in uninfected mice. Most toxoplasma-induced ILC1s were Eomes-dependent, indicating that NK cells can give rise to Eomes- Tbet-dependent ILC1-like cells that circulate widely and persist independent of ongoing infection. Moreover, these changes appear permanent, as supported by epigenetic analyses. Thus, these studies markedly expand current concepts of NK cells, ILCs, and their potential conversion.

immunology↗

bioRxiv2019

A primer on high-dimensional data analysis workflows for studying visual cortex development and plasticity

New techniques for quantifying large numbers of proteins or genes are transforming the study of plasticity mechanisms in visual cortex (V1) into the era of big data. With those changes comes the challenge of applying new analytical methods designed for high-dimensional data. Studies of V1, however, can take advantage of the known functions that many proteins have in regulating experience-dependent plasticity to facilitate linking big data analyses with neurobiological functions. Here we discuss two workflows and provide example R code for analyzing high-dimensional changes in a group of proteins (or genes) using two data sets. The first data set includes 7 neural proteins, 9 visual conditions, and 3 regions in V1 from an animal model for amblyopia. The second data set includes 23 neural proteins and 31 ages (20d-80yrs) from human post-mortem samples of V1. Each data set presents different challenges and we describe using PCA, tSNE, and various clustering algorithms including sparse high-dimensional clustering. Also, we describe a new approach for identifying high-dimensional features and using them to construct a plasticity phenotype that identifies neurobiological differences among clusters. We include an R package "v1hdexplorer" that aggregates the various coding packages and custom visualization scripts written in R Studio.

neuroscience↗

bioRxiv2019

Classification of visual cortex plasticity phenotypes following treatment for amblyopia

Monocular deprivation (MD) during the critical period (CP) has enduring effects on visual acuity and the functioning of the visual cortex (V1). This experience-dependent plasticity has become a model for studying the mechanisms, especially glutamatergic and GABAergic receptors, that regulate amblyopia. Less is known, however, about treatment-induced changes to those receptors and if those changes differentiate treatments that support the recovery of acuity versus persistent acuity deficits. Here we use an animal model to explore the effects of 3 visual treatments started during the CP (n=24, 10 male and 14 female); binocular vision (BV) that promotes good acuity versus reverse occlusion (RO) and binocular deprivation (BD) that causes persistent acuity deficits. We measured recovery of a collection of glutamatergic and GABAergic receptor subunits in V1 and modeled recovery of kinetics for NMDAR and GABAAR. There was a complex pattern of protein changes that prompted us to develop an unbiased data-driven approach for these high-dimensional data analyses to identify plasticity features and construct plasticity phenotypes. Cluster analysis of the plasticity phenotypes suggests that BV supports adaptive plasticity while RO and BD promote a maladaptive pattern. The RO plasticity phenotype appeared more similar to adults with high expression of GluA2 and the BD phenotypes were dominated by GABAA1, highlighting that multiple plasticity phenotypes can underlie persistent poor acuity. After 2-4 days of BV the plasticity phenotypes resembled normals, but only one feature, the GluN2A:GluA2 balance, returned to normal levels. Perhaps, balancing Hebbian (GluN2A) and homeostatic (GluA2) mechanisms is necessary for the recovery of vision.

neuroscience↗