title: Molecular signatures of neuron activation
title: transcriptional responses to stimulation, experience, and learning
titleType: Subtitle
Creators: name: Bahl, Ethan M.
Publisher: name: University of Iowa
Year: 2023
Subjects: deep learning
long-term memory
neuron activity
single cell
synaptic plasticity
Gene Expression
Molecular Biology

description: Perhaps the most impressive feature of the brain is its ability to incorporate our individual experiences into long-term memories that persist throughout our lifetime. This ability requires the brain to reconfigure connections amongst neurons activated in response to an experience. Neuronal activity-dependent transcription is the molecular process that orchestrates these changes by transiently manipulating gene expression in activated neurons, thereby initiating a cascade of cellular changes that constitute the physical trace of a long-term memory. While decades of work has focused on understanding this process, we still lack a comprehensive understanding of how activity-dependent gene expression is regulated, and how these genes dictate downstream molecular changes to the cell.
description: In the first part of this thesis, we investigate how proteins control specific modules of the activity-dependent transcriptional response. Specifically, we explored the role of CBP, a co-activator protein of activity-dependent transcription. We demonstrate that CBP plays a role in regulating expression of circadian rhythm genes and transcription factors in response to learning tasks. We also investigate how activity-regulated gene expression promotes cellular functions that support long-term memory. Specifically, we demonstrate the activity-regulated \textit{Nr4a} family genes exhibit regulatory control over protein folding and cellular trafficking functions. We further show that targeting these molecular processes can rescue memory impairments in mouse models of Alzheimer's disease and related dementias, thus providing a promising approach for potential therapeutic interventions in neurodegenerative disease.
description: In the second part of this thesis, we apply deep learning to better understand the transcriptional response to neuronal activation. We leverage new large-scale datasets of single cell gene expression to train a model capable of quantifying activity-response in individual neurons based on transcriptome-wide signals. We demonstrate the ability of our model to robustly identify molecular traces of stimulation from chemical agents, cocaine, sensory experience, and learning. We further applied our model to novel spatial transcriptomic data to produce a cross-sectional map of brain regions activated by learning.
description: This work presented here is the culmination of interdisciplinary collaborations which are founded upon the synergy between experimental molecular work and computational analyses. The work in this thesis elucidates novel regulatory modules at multiple levels of the activity-dependent transcriptional response, identifies potential therapeutic approaches for neurodegenerative disease, and introduce an innovative gene expression-based deep learning model to quantify the activity in individual neurons. In summation, this thesis advances our scientific understanding of how gene expression in the brain gives rise to cognitive functions such as learning and long-term memory, and provides an important tool to aid in future neuroscience research.