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Nitric oxide (NO) in brain has been implicated in neuronal regulatory processes and in neuropathologies. Previously we showed that NO modified quinpirole-induced yawning, a behavioral measure of dopamine (DA) Da receptor activation in rats. The aim of this study was to characterize the effect of nitro-L-arginine methyl ester HC1 (NAME) and L-arginine HC1 on reactivity of rats to the DA DI receptor agonist SKF 38393 and DA DI antagonist SCH 23390 in intact and neonatal 6-hydroxydopamine (o-OHDA)-lesioned rats (134 ng of base ICV at 3rd day after birth). L-arginine HC1 (300 mg/kg IP) increased the oral activity response in 6-OHDA-lesioned rats, like SKF 38393, and induced catalepsy in intact control rats, like SCH 23390. In contrast, NAME had no effect on oral activity or catalepsy, but fully attenuated SKF 38393-induced oral activity. These findings indicate that L-arginine HC1 has no apparent effect at the DA DI receptor, but that NAME is effective in attenuating a DA DI agonist induced effect. Consequently NO may be an intracellular second messenger for supersensitized receptors associated with DA DI agonist - induced oral activity.

The management of missing data is a major concern in classification model generation in all fields but poses a particular challenge in situations where there is only a small quantity of sparse data available. In the field of medicine, this is not an uncommon problem. While widely subscribed methodologies like logistic regression can, with minor modifications and potentially much labor, provide reasonable insights from the larger and less sparse datasets that are anticipated when analyzing diagnosis of common conditions, there are a multitude of rare conditions of interest. Primary angiitis of the central nervous system (PACNS) is a rare but devastating entity that given its range of presenting symptoms can be suspected in a variety of circumstances. It unfortunately continues to be a diagnosis that is hard to make. Aside from some general frameworks, there isn’t a rigorously defined diagnostic approach as is the case in other more common neuroinflammatory conditions like multiple sclerosis. Instead, clinicians currently rely on experience and clinical judgement to guide the reasonable exclusion of potential inciting entities and mimickers. In effect this results in a smaller quantity of heterogenous that may not optimally suited for more traditional classification methodology (e.g., logistic regression) without substantial contemplation and justification of appropriate data cleaning / preprocessing. It is therefore challenging to make and analyze systematic approaches that could direct clinicians in a way that standardizes patient care. In this thesis, a machine learning approach was presented to derive quantitatively justified insights into the factors that are most important to consider during the diagnostic process to identify conditions like PACNS. Modern categorization techniques (i.e., random forest and support vector machines) were used to generate diagnostic models identifying cases of PACNS from which key elements of diagnostic importance could be identified. A novel variant of a random forest (RF) approach was also demonstrated as a means of managing missing data in a small sample, a significant problem encountered when exploring data on rare conditions without clear diagnostic frameworks. A reduced need to hypothesize the reasons for missingness when generating and applying the novel variant was discussed. The application of such tools to diagnostic model generation of PACNS and other rare and / or emerging diseases and provide objective feedback was explored. This primarily centered around a structured assessment on how to prioritize testing to rapidly rule out conditions that require alternative management and could be used to support future guidelines to optimize the care of these patients. The material presented herein had three components. The first centered around the example of PACNS. It described, in detail, an example of a relevant medical condition and explores why the data is both rare and sparse. Furthermore, the reasons for the sparsity are heterogeneous or non-monotonic (i.e., not conducive to modelling with a singular model). This component concludes with a search for candidate variables to diagnose the condition by means of scoping review for subsequent comparative demonstration of the novel variant of random forest construction that was proposed. The second component discussed machine learning model development and simulates data with varying degrees and patterns of missingness to demonstrate how the models could be applied to data with properties like what would be expected of PACNS related data. Finally, described techniques were applied to separate a subset of patients with suspected PACNS from those with diagnosed PACNS using institutional data and proposes future study to expand upon and ultimately verify these insights. Further development of the novel random forest approach is also discussed.

Central nervous system (CNS) tissues are highly metabolically active which makes them particularly susceptible to vascular injury. Disruption to the supply of oxygen and nutrients by damaged vasculature can result in neurodegeneration in both the eye and brain. The retina is an accessible part of the CNS that can be taken advantage of to study neurovascular diseases through live, non-invasive visualization of vascular and neuronal conditions upon injury. Retinal vein occlusion (RVO) is a common neurovascular disease of the eye and is the second leading cause of blindness in working age adults. While pathophysiology is well described and can be determined by retinal edema, breakdown of the blood-retina-barrier (BRB), inflammation, and neurodegeneration, the underlying signaling pathways behind the pathology is not well understood. To understand the mechanism of disease in RVO, the Troy lab has employed a mouse model to investigate pathways. Previous studies in the lab determined that as early as 1 hour post RVO, there was a large induction of caspase-9, a known cell death protease, in endothelial cells. When further investigated, it was confirmed that these cells were not dying despite the high expression of caspase-9, implying a non-apoptotic role. Deletion of endothelial caspase-9 was sufficient to protect against the development of retinal edema, capillary ischemia, and neuronal death, indicating caspase-9 is a key player in the mechanism of disease. This thesis work aims to investigate which signaling events drive non-apoptotic endothelial caspase-9 signaling by investigating upstream and downstream mechanisms of endothelial caspase-9. To interrogate this question, the mouse model of RVO was optimized, limiting the variability previously observed to ensure accurate and reproducible results. Then, we used a tamoxifen inducible endothelial cell Apaf-1 (apoptosis protease activating factor-1) knock out (Apaf-1 iECKO) mouse line in order to investigate the contribution of upstream activation of non-apoptotic endothelial caspase-9 signaling. Apaf-1 iECKO mice and WT littermates were subjected to RVO. Then, expression of caspase-9 and -7, retinal edema, capillary ischemia, neuronal death, vision dysfunction, and BRB integrity were measured. The deletion of endothelial Apaf-1 resulted in reduced expression of cl-caspase-9 and caspase-7, indicating endothelial caspase-9 was activated by Apaf-1. Apaf-1 deletion also resulted in protection against some of the pathologies seen after RVO including retinal edema, capillary ischemia, and neurodegeneration. Lastly, in order to elucidate the signaling pathway further, experiments using endothelial cell-specific AAVs (adeno-associated virus) packaged with a downstream caspase-7 inhibitor were proposed and described. In sum, this thesis work reveals that endothelial caspase-9 is canonically activated by Apaf-1, but still leads to non-apoptotic signaling, indicating downstream caspase-9 substrates could be the source for non-apoptotic function within endothelial cells.

<p>  </p> <p>Pediatric hydrocephalus is a complex neurological condition associated with a pathological accumulation of cerebrospinal fluid (CSF), typically within the brain ventricular system. Pediatric hydrocephalus can be primary (due to genetic abnormalities or idiopathic causes), or secondary to injuries such as hemorrhage, trauma, or infection. The current permanent treatment paradigms for pediatric hydrocephalus are exclusively surgical and include the diversion of CSF via shunt or ventriculostomy. These surgical interventions are wrought with failures, burdening both the United States healthcare system and patients with repeat neurosurgical procedures. Thus, the development of nonsurgical interventions to treat hydrocephalus represents a clinically unmet need. To study hydrocephalus, we use a genetic rat model of primary neonatal hydrocephalus, the <em>Tmem67</em>P394L mutant. In several proof-of-concept studies, we identify antagonism of the transient receptor potential vanilloid 4 (TRPV4) channel and associated upstream regulatory kinase, serum-and-glucocorticoid-induced kinase 1 (SGK1) as therapeutics for the treatment of hydrocephalus. Using <em>in vitro</em> models of the choroid plexus epithelium, the tissue which produces CSF, we show compelling proof-of-mechanism for TRPV4 antagonism and SGK1 inhibition at preventing CSF production. Therefore, the studies in this dissertation provide substantive evidence on the role of TRPV4 in the choroid plexus in health and disease. </p>

Drosophila melanogaster is an attractive model organism for the study of numerous fundamental processes including nervous system development and function. This is due to the power of Drosophila genetics combined with the high degree of similarity between this organism and vertebrate systems, not only at the molecular level but also at the cellular and behavioural levels. The first part of my thesis focused on trophic interactions occurring in Drosophila larval central nervous system. Specifically, it describes the interaction of serotonin (5HT)-producing neurons with other three groups of neurons: the larval photoreceptors expressing Rhodopsin 5 (Rh5), the photoreceptor subset expressing Rhodopsin 6 (Rh6), and the larval circadian pacemakers (LNv). I found that both Rh5-and Rh6-expressing fibers contact a 5-HT arborization in the larval optic neuropil, where the 5-HT processes also overlaps with the dendrites of the LNv. The results of my experiments also indicate that the Rh6-expressing terminus is the neural process providing the signal required for the outgrowth of the serotonergic arborization. Furthermore, proper branching of this arborization requires normal Rae function. These findings further support the importance of extrinsic and intrinsic signalling for the assembly ofthe nervous system. The remainder of my studies attempted to investigate candidate neurons modulating Drosophila larval photobehaviour. Using the larval response to light as a behavioural paradigm and neuronal silencing experiments, my results demonstrate that 5HT neurons located in the brain regulate the larval photoresponse during development. In addition, my findings suggest that this modulation occurs at a central level and that is mediated by 5-HT1A(Dro) receptors. These observations provide new insights into the functions of serotonergic neurons in Drosophila as well as how neuromodulators shape neuronal circuit function and ultimately behaviour. / Thesis / Doctor of Philosophy (PhD)

Trace elements are involved in many biological processes and serve important functions to maintain the normal development of the central nervous system (CNS). In the CNS, iron (Fe), copper (Cu), and zinc (Zn) are some of the most important elements that play critical roles as catalysts, cofactors, and structural components for many cellular enzymes and proteins. The deficiency or excess of these metals may lead to various neurological disorders. Demyelination is a condition of loss of myelin and leads to neurological diseases like Multiple Sclerosis. Myelin consists of transition metals and hence it would be interesting to study concentrations of these elements in normal and demyelinated models. X-Ray Fluorescence (XRF) is a popular non-destructive technique applied in trace element studies. The principle involves exciting a sample and detecting characteristic X-rays, which provide information on elemental concentrations in the sample. In the present studies the feasibility of XRF for trace element studies was explored. A total of 120 samples of brain and spinal cord tissues were collected from Long Evans (control) and Long Evans Shaker (dysmyelinated)–an incomplete formation of myelin sheaths–rats at ages of 3 weeks and 16 weeks. The samples were excited using x-rays from an Energy Dispersive X-Ray Diffraction (EDXRF) set-up. The spectral data was collected using an Silicon Drift Detector (SDD) and the resultant data were analysed to see if statistically significant changes in concentrations were present in the samples. The results were discussed and suggestions for future work were made. / Thesis / Master of Science (MSc)

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