Embryonic transcription factor expression in mice predicts medial amygdala neuronal identity and sex-specific responses to innate behavioral cues
- The George Washington University, United States
- Children's National Medical Center, United States
- University of Colorado School of Medicine, Aurora, United States
Abstract
The medial subnucleus of the amygdala (MeA) plays a central role in processing sensory cues required for innate behaviors. However, whether there is a link between developmental programs and the emergence of inborn behaviors remains unknown. Our previous studies revealed that the telencephalic preoptic area (POA) embryonic niche is a novel source of MeA destined progenitors. Here, we show that the POA is comprised of distinct progenitor pools complementarily marked by the transcription factors Dbx1 and Foxp2. As determined by molecular and electrophysiological criteria this embryonic parcellation predicts postnatal MeA inhibitory neuronal subtype identity. We further find that Dbx1-derived and Foxp2+ cells in the MeA are differentially activated in response to innate behavioral cues in a sex-specific manner. Thus, developmental transcription factor expression is predictive of MeA neuronal identity and sex-specific neuronal responses, providing a potential developmental logic for how innate behaviors could be processed by different MeA neuronal subtypes.
Article and author information
Author details
Julieta E Lischinsky
Funding
National Institute on Drug Abuse (R01 NIDA020140)
- Joshua G Corbin
Intellectual and Developmental Disabilities Research Center (IDDRC P30HD040677)
- Joshua G Corbin
National Institute on Deafness and Other Communication Disorders (R01 DC012050)
- Joshua G Corbin
National Institute on Drug Abuse (F32 DA035754)
- Katie Sokolowski
Goldwin Foundation Grant for Pediatric Epilepsy
- Judy Liu
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Carol A Mason, Columbia University, United States
Ethics
Animal experimentation: All animal procedures were approved by the Children's National Medical Center's Institutional Animal Care (Animal Welfare Assurance Number: A3338-01) and Use Committee (IACUC) protocols (#00030435) and conformed to NIH Guidelines for animal use. All surgery was performed under ketamine/xylazine cocktail anesthesia, and every effort was made to minimize suffering.
Version history
- Received: August 26, 2016
- Accepted: February 26, 2017
- Accepted Manuscript published: February 28, 2017 (version 1)
- Accepted Manuscript updated: March 13, 2017 (version 2)
- Version of Record published: April 7, 2017 (version 3)
Copyright
© 2017, Lischinsky et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 3,082
- views
-
- 577
- downloads
-
- 33
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Embryonic transcription factor expression in mice predicts medial amygdala neuronal identity and sex-specific responses to innate behavioral cues
eLife 6:e21012.
https://doi.org/10.7554/eLife.21012
Further reading
-
- Neuroscience
Artificial neural networks (ANNs) are a powerful class of computational models for unravelling neural mechanisms of brain function. However, for neural control of movement, they currently must be integrated with software simulating biomechanical effectors, leading to limiting impracticalities: (1) researchers must rely on two different platforms and (2) biomechanical effectors are not generally differentiable, constraining researchers to reinforcement learning algorithms despite the existence and potential biological relevance of faster training methods. To address these limitations, we developed MotorNet, an open-source Python toolbox for creating arbitrarily complex, differentiable, and biomechanically realistic effectors that can be trained on user-defined motor tasks using ANNs. MotorNet is designed to meet several goals: ease of installation, ease of use, a high-level user-friendly application programming interface, and a modular architecture to allow for flexibility in model building. MotorNet requires no dependencies outside Python, making it easy to get started with. For instance, it allows training ANNs on typically used motor control models such as a two joint, six muscle, planar arm within minutes on a typical desktop computer. MotorNet is built on PyTorch and therefore can implement any network architecture that is possible using the PyTorch framework. Consequently, it will immediately benefit from advances in artificial intelligence through PyTorch updates. Finally, it is open source, enabling users to create and share their own improvements, such as new effector and network architectures or custom task designs. MotorNet’s focus on higher-order model and task design will alleviate overhead cost to initiate computational projects for new researchers by providing a standalone, ready-to-go framework, and speed up efforts of established computational teams by enabling a focus on concepts and ideas over implementation.
-
- Neuroscience
The cerebellum contributes to a diverse array of motor conditions, including ataxia, dystonia, and tremor. The neural substrates that encode this diversity are unclear. Here, we tested whether the neural spike activity of cerebellar output neurons is distinct between movement disorders with different impairments, generalizable across movement disorders with similar impairments, and capable of causing distinct movement impairments. Using in vivo awake recordings as input data, we trained a supervised classifier model to differentiate the spike parameters between mouse models for ataxia, dystonia, and tremor. The classifier model correctly assigned mouse phenotypes based on single-neuron signatures. Spike signatures were shared across etiologically distinct but phenotypically similar disease models. Mimicking these pathophysiological spike signatures with optogenetics induced the predicted motor impairments in otherwise healthy mice. These data show that distinct spike signatures promote the behavioral presentation of cerebellar diseases.
