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Publication

Stress Increases Peripheral Axon Growth and Regeneration through Glucocorticoid Receptor-Dependent Transcriptional Programs.

eNeuro | 2017

Stress and glucocorticoid (GC) release are common behavioral and hormonal responses to injury or disease. In the brain, stress/GCs can alter neuron structure and function leading to cognitive impairment. Stress and GCs also exacerbate pain, but whether a corresponding change occurs in structural plasticity of sensory neurons is unknown. Here, we show that in female mice (Mus musculus) basal GC receptor (Nr3c1, also known as GR) expression in dorsal root ganglion (DRG) sensory neurons is 15-fold higher than in neurons in canonical stress-responsive brain regions (M. musculus). In response to stress or GCs, adult DRG neurite growth increases through mechanisms involving GR-dependent gene transcription. In vivo, prior exposure to an acute systemic stress increases peripheral nerve regeneration. These data have broad clinical implications and highlight the importance of stress and GCs as novel behavioral and circulating modifiers of neuronal plasticity.

Pubmed ID: 28828403 RIS Download

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Associated grants

Agency: NINDS NIH HHS, United States
Id: R01 NS083942
Agency: NICHD NIH HHS, United States
Id: R01 HD057632

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This is a list of tools and resources that we have found mentioned in this publication.

JASPAR (tool)

RRID:SCR_003030

Open source database of curated, non-redundant set of profiles derived from published collections of experimentally defined transcription factor binding sites for multicellular eukaryotes. Consists of open data access, non-redundancy and quality. JASPAR CORE is smaller set that is non-redundant and curated. Collection of transcription factor DNA-binding preferences, modeled as matrices. These can be converted into Position Weight Matrices (PWMs or PSSMs), used for scanning genomic sequences. Web interface for browsing, searching and subset selection, online sequence analysis utility and suite of programming tools for genome-wide and comparative genomic analysis of regulatory regions. New functions include clustering of matrix models by similarity, generation of random matrices by sampling from selected sets of existing models and a language-independent Web Service applications programming interface for matrix retrieval.

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NEURON (tool)

RRID:SCR_005393

NEURON is a simulation environment for modeling individual neurons and networks of neurons. It provides tools for conveniently building, managing, and using models in a way that is numerically sound and computationally efficient. It is particularly well-suited to problems that are closely linked to experimental data, especially those that involve cells with complex anatomical and biophysical properties. NEURON has benefited from judicious revision and selective enhancement, guided by feedback from the growing number of neuroscientists who have used it to incorporate empirically-based modeling into their research strategies. NEURON's computational engine employs special algorithms that achieve high efficiency by exploiting the structure of the equations that describe neuronal properties. It has functions that are tailored for conveniently controlling simulations, and presenting the results of real neurophysiological problems graphically in ways that are quickly and intuitively grasped. Instead of forcing users to reformulate their conceptual models to fit the requirements of a general purpose simulator, NEURON is designed to let them deal directly with familiar neuroscience concepts. Consequently, users can think in terms of the biophysical properties of membrane and cytoplasm, the branched architecture of neurons, and the effects of synaptic communication between cells. * helps users focus on important biological issues rather than purely computational concerns * has a convenient user interface * has a user-extendable library of biophysical mechanisms * has many enhancements for efficient network modeling * offers customizable initialization and simulation flow control * is widely used in neuroscience research by experimentalists and theoreticians * is well-documented and actively supported * is free, open source, and runs on (almost) everything

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