Neurotoxicology has historically focused on substances that directly damage nervous tissue. Behavioral assays that test sensory, cognitive, or motor function are used to identify neurotoxins. But, the outcomes of behavioral assays may also be influenced by the physiological status of non-neural organs. Therefore, toxin induced damage to non- neural organs may contribute to behavioral modifications. Heavy metals and metalloids are persistent environmental pollutants and induce neurological deficits in multiple organisms. However, in the honey bee, an important insect pollinator, little is known about the sublethal effects of heavy metal and metalloid toxicity though they are exposed to these toxins chronically in some environments. In this thesis I investigate the sublethal effects of copper, cadmium, lead, and selenium on honey bee behavior and identify potential mechanisms mediating the behavioral modifications. I explore the honey bees’ ability to detect these toxins, their sensory perception of sucrose following toxin exposure, and the effects of toxin ingestion on performance during learning and memory tasks. The effects depend on the specific metal. Honey bees detect and reject copper containing solutions, but readily consume those contaminated with cadmium and lead. And, exposure to lead may alter the sensory perception of sucrose. I also demonstrate that acute selenium exposure impairs learning and long-term memory formation or recall. Localizing selenium accumulation following chronic exposure reveals that damage to non-neural organs and peripheral sensory structures is more likely than direct neurotoxicity. Probable mechanisms include gut microbiome alterations, gut lining

damage, immune system activation, impaired protein function, or aberrant DNA methylation. In the case of DNA methylation, I demonstrate that inhibiting DNA methylation dynamics can impair long-term memory formation, while the nurse-to- forager transition is not altered. These experiments could serve as the bases for and reference groups of studies testing the effects of metal or metalloid toxicity on DNA methylation. Each potential mechanism provides an avenue for investigating how neural function is influenced by the physiological status of non-neural organs. And from an ecological perspective, my results highlight the need for environmental policy to consider sublethal effects in determining safe environmental toxin loads for honey bees and other insect pollinators.

ABSTRACT



Auditory hallucinations are a characteristic symptom of schizophrenia. Research has documented that the auditory cortex is metabolically activated when this process occurs, and that imbalances in the dopaminergic transmission in the striatum contribute to its physiopathology. Most animal models have focused the effort on pharmacological approaches like non-competitive N-methyl-D-aspartate (NMDA) receptor antagonists to produce activation of the auditory cortex, or dopamine antagonists to alleviate it. I hypothesize that these perceptual phenomena can be explained by an imbalance activation of spiny projecting neurons in the striatal pathways, whereby supersensitive postsynaptic D2-like receptor, signaling in the posterior caudatoputamen generates activation of the auditory cortex. Therefore, I characterized the neuroanatomical component involved in the activation of the auditory cortex. I evaluated the participation of dopamine D2-like receptor using selective dopamine antagonist manipulations and identified the circuits related to the auditory cortex by retrograde trans-synaptic tracing using pseudorabies virus (PRV-152). My results show that dopamine infused in the posterior caudatoputamen dose dependently increases the transcription of the immediate early gene, zif268 in the auditory cortex, predominantly in layers III and IV, but also in cortical columns, suggesting enhanced functional auditory activity. This indicates the participation of the posterior striatum in the modulation of the secondary auditory cortex. I was able to demonstrate also that a coinfusion of a selective dopamine D2-like receptor antagonist, eticlopride and dopamine, attenuate the activation of the auditory cortex. Furthermore, using PRV-152 I delineate the distinctive circuit by axial mapping of the infected neurons. Thus, I found secondary projections from the posterior caudatoputamen that synapse in the thalamus before reaching the auditory cortex. These striatal projections correspond to the same brain region affected by dopamine during auditory cortical activation. My results further characterized a mechanism to generate intrinsic perception of sound that may be responsible for auditory hallucinations. I propose this paradigm may elucidate insight on the biological basis of psychotic behavior.

Patients with schizophrenia have impaired cognitive flexibility, as evidenced by behaviors of perseveration. Cognitive impairments may be due to dysregulation of glutamate and/or loss of neuronal plasticity in the medial prefrontal cortex (mPFC). The purpose of these studies was to examine the effects of mGluR5 positive allosteric modulators (PAMs) alone and in combination with the NMDAR antagonist MK-801, a pharmacological model of schizophrenia. An operant-based cognitive set-shifting task was utilized to assess cognitive flexibility, in vivo microdialysis procedures to measure extracellular glutamate levels in the mPFC, and diolistic labeling to assess the effects on dendritic spine density and morphology in the mPFC. Results revealed that chronic administration of the mGluR5 PAM CDPPB was able to significantly reduce the effects of chronically administered MK-801 on both behavioral perseveration and glutamate neurotransmission. Results also showed that CDPPB had no evidence of an effect on dendritic spine density or morphology, but the mGluR5 negative allosteric modulator fenobam caused significant increases in spine density and the frequency of occurrence of spines with smaller head diameters. Conclusions include that CDPPB is able to reverse MK-801 induced cognitive deficits as well as alterations in mPFC glutamate neurochemistry. The culmination of these studies add further support for targeting mGluR5 with PAMs as a novel mechanism to alleviate cognitive impairments in patients with schizophrenia.

Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. To validate these approaches in a disease-specific context, we built a schizophreniaspecific network based on the inferred associations and performed a comprehensive prioritization of human genes with respect to the disease. These results are expected to be validated empirically, but computational validation using known targets are very positive.