Dissertation
Molecular signatures of neuron activation: transcriptional responses to stimulation, experience, and learning
University of Iowa
Doctor of Philosophy (PhD), University of Iowa
Autumn 2023
DOI: 10.25820/etd.006947
Abstract
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.
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.
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.
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.
Details
- Title: Subtitle
- Molecular signatures of neuron activation: transcriptional responses to stimulation, experience, and learning
- Creators
- Ethan M. Bahl
- Contributors
- Jacob J Michaelson (Advisor)Thomas Nickl-Jockschat (Committee Member)Richard Smith (Committee Member)Nicholas Trapp (Committee Member)Joshua Weiner (Committee Member)
- Resource Type
- Dissertation
- Degree Awarded
- Doctor of Philosophy (PhD), University of Iowa
- Degree in
- Genetics (Computational Genetics)
- Date degree season
- Autumn 2023
- Publisher
- University of Iowa
- DOI
- 10.25820/etd.006947
- Number of pages
- xiv, 168 pages
- Copyright
- Copyright 2023 Ethan M. Bahl
- Comment
This thesis has been optimized for improved web viewing. If you require the original version, contact the University Archives at the University of Iowa: https://www.lib.uiowa.edu/sc/contact/.
- Language
- English
- Date submitted
- 12/02/2023
- Description illustrations
- Illustrations, tables, graphs, charts
- Description bibliographic
- Includes bibliographical references (pages 151-168).
- Public Abstract (ETD)
The brain’s ability to store experiences as long-term memories involves altering connections between neurons in the brain. These alterations are controlled by transcription, a process where cells use pieces of DNA, or genes, to produce essential proteins. Gene usage can change, such as when neurons activate during learning. When a neuron is activated, it temporarily boosts the use of certain genes, leading to changes that impact how neurons communicate. This is vital for forming long-term memories. Despite years of study, we still don’t fully grasp how specific genes are influenced by neuron activity or how the alter neuron connections.
The first section of this work examines how certain proteins influence activity-dependent transcription. We find that learning alters the transcription of genes related to circadian rhythm. We also find that learning-regulated genes, the Nr4a family of genes, ensure the structural integrity of new proteins being made, and makes sure they are being sent to the correct parts of the neuron. We found that by targeting these processes, we can improve memory in a mouse model of Alzheimer’s disease.
The second section of this thesis uses artificial intelligence to gain a comprehensive view of genes responsive to activity. Using datasets measuring gene transcription before and after stimulation, we developed a computer program that can assess the activity of individual neurons. Our tool successfully identifies brain cells activated by drugs and learning experiences. We also used our tool to create a map of the brain regions activated by learning.
In summary, this thesis sheds light on critical processes in activity-dependent transcription and introduces a new tool for future brain research, enhancing our understanding of learning and long-term memory.
- Academic Unit
- Interdisciplinary Graduate Program in Genetics
- Record Identifier
- 9984546749702771
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