Chemical Genetic Interrogation of Neural Stem Cells: Phenotype and Function of Neurotransmitter Pathways in Normal and Brain Tumor Initiating Neural Precursor Cells

The identification of self-renewing and multipotent neural stem cells (NSCs) in the mammalian brain brings promise for the treatment of neurological diseases and has yielded new insight into brain cancer. The complete repertoire of signaling pathways that governs these cells however remains largely uncharacterized. This thesis describes how chemical genetic approaches can be used to probe and better define the operational circuitry of the NSC. I describe the development of a small molecule chemical genetic screen of NSCs that uncovered an unappreciated precursor role of a number of neurotransmitter pathways commonly thought to operate primarily in the mature central nervous system (CNS). Given the similarities between stem cells and cancer, I then translated this knowledge to demonstrate that these neurotransmitter regulatory effects are also conserved within cultures of cancer stem cells. I then provide experimental and epidemiologically support for this hypothesis and suggest that neurotransmitter signals may also regulate the expansion of precursor cells that drive tumor growth in the brain. Specifically, I first evaluate the effects of neurochemicals in mouse models of brain tumors. I then outline a retrospective meta-analysis of brain tumor incidence rates in psychiatric patients presumed to be chronically taking neuromodulators similar to those identified in the initial screen. Lastly, by further exploring the phenotype and function of neurotransmitter pathways in purified populations of human NSCs, I determined that neurotransmitter pathway gene expression exists in a functionally heterogeneous phase-varying state that restricts the responsiveness of these populations to various stimuli. Taken together, this research provides novel insights into the phenotypic and functional landscape of neurotransmitter pathways in both normal and cancer-derived NSCs. In additional to a better fundamental understanding of NSC biology, these results suggest how clinically approved neuromodulators can be used to remodel the mature CNS and find application in the treatment of brain cancer.Ph

Generalizable electroencephalographic classification of Parkinson's disease using deep learning

Growing interest surrounds the use of electroencephalography (EEG) and deep learning for diagnosing neurological conditions like Parkinson's Disease (PD). Despite the existing proof-of-concept literature demonstrating the potential of deep learning in classifying PD from EEG data, neurologists have been slow to adopt these tools due to insufficient evidence of their real-world diagnostic generalizable performance. Our study aimed to evaluate the potential of deep learning for inter-subject PD classification using a conservative training approach and testing on an external independent dataset. Specifically, we utilized publicly available resting-state EEG data from PD patients at two separate centers, the University of New Mexico (n = 54) and the University of Iowa (n = 28), as our training and testing sets, respectively. Each of these recordings had a minimum of 2 minutes of data. We implemented a channel-wise convolutional neural network, tuning it with a leave-one-subject-out cross-validation approach. Our approach achieved a patient-level accuracy of 80.4% (epoch-level accuracy = 72.7%), which remained consistent when tested on the external dataset (patient-level accuracy = 82.8%, epoch-level accuracy = 75.7%). Our model performs equal-or-better than other standard classification models and our approach compares favourably to similar works. Our publicly available code serves as a foundation for future research exploring different deep learning architectures, investigating other pathologies, and involving larger datasets with the hope of accelerating the adoption of objective computational approaches for the diagnosis and monitoring of neurological disorders

Deciphering of Adult Glioma Vulnerabilities through Expression Pattern Analysis of GABA, Glutamate and Calcium Neurotransmitter Genes

Adult infiltrating gliomas are highly aggressive tumors of the central nervous system with a dismal prognosis despite intensive multimodal therapy (chemotherapy and/or radiotherapy). In this study, we studied the expression, methylation and interacting miRNA profiles of GABA-, glutamate- and calcium-related genes in 661 adult infiltrating gliomas available through the TCGA database. Neurotransmitter-based unsupervised clustering identified three established glioma molecular subgroups that parallel major World Health Organization glioma subclasses (IDH-wildtype astrocytomas, IDH-mutant astrocytomas, IDH-mutant oligodendroglioma). In addition, this analysis also defined a novel, neurotransmitter-related glioma subgroup (NT-1), mostly comprised of IDH-mutated gliomas and characterized by the overexpression of neurotransmitter-related genes. Lower expression of neurotransmission-related genes was correlated with increased aggressivity in hypomethylated IDH-wildtype tumors. There were also significant differences in the composition of the tumor inflammatory microenvironment between neurotransmission-based tumor categories, with lower estimated pools of M2-phenotype macrophages in NT-1 gliomas. This multi-omics analysis of the neurotransmission expression landscape of TCGA gliomas—which highlights the existence of neurotransmission-based glioma categories with different expression, epigenetic and inflammatory profiles—supports the existence of operational neurotransmitter signaling pathways in adult gliomas. These findings could shed new light on potential vulnerabilities to exploit in future glioma-targeting drug therapies

Deciphering of Adult Glioma Vulnerabilities through Expression Pattern Analysis of GABA, Glutamate and Calcium Neurotransmitter Genes

Adult infiltrating gliomas are highly aggressive tumors of the central nervous system with a dismal prognosis despite intensive multimodal therapy (chemotherapy and/or radiotherapy). In this study, we studied the expression, methylation and interacting miRNA profiles of GABA-, glutamate- and calcium-related genes in 661 adult infiltrating gliomas available through the TCGA database. Neurotransmitter-based unsupervised clustering identified three established glioma molecular subgroups that parallel major World Health Organization glioma subclasses (IDH-wildtype astrocytomas, IDH-mutant astrocytomas, IDH-mutant oligodendroglioma). In addition, this analysis also defined a novel, neurotransmitter-related glioma subgroup (NT-1), mostly comprised of IDH-mutated gliomas and characterized by the overexpression of neurotransmitter-related genes. Lower expression of neurotransmission-related genes was correlated with increased aggressivity in hypomethylated IDH-wildtype tumors. There were also significant differences in the composition of the tumor inflammatory microenvironment between neurotransmission-based tumor categories, with lower estimated pools of M2-phenotype macrophages in NT-1 gliomas. This multi-omics analysis of the neurotransmission expression landscape of TCGA gliomas—which highlights the existence of neurotransmission-based glioma categories with different expression, epigenetic and inflammatory profiles—supports the existence of operational neurotransmitter signaling pathways in adult gliomas. These findings could shed new light on potential vulnerabilities to exploit in future glioma-targeting drug therapies