Research Location: University of British Columbia - Vancouver Campus

Pathological gambling is an addictive disorder characterized by repeated problematic gambling despite severe negative consequences, often linked to poor decision making and impulsivity. Research into gambling disorders has been facilitated by using rodent tasks that are directly translated from tasks used in humans. Traditional analyses for these tasks result in simplistic measures linked to decision making deficits and impulsive behavior. More recently in clinical research, computational modeling has been applied to data from human tasks to allow more sophisticated, in-depth analysis of the psychological and cognitive processes underlying these behaviors. Despite many parallels in task measures and structure, computational approaches are much less frequently used in the analysis of rodent behavioral data. Using multiple large datasets from rodent gambling and decision making tasks, this project proposes to use computational modeling to investigate the underlying cognitive processes involved in performance on these tasks. This will improve translatability of findings from preclinical research, and enable the generation of testable hypotheses for novel therapeutic/behavioral interventions to address problematic gambling behavior.

Huntington’s disease (HD) is a fatal inherited neurodegenerative disorder caused by a single gene mutation. Most patients present with disordered cognition and movement in middle age due to striatal and cortical neuron degeneration. However underlying HD processes begin earlier and are incompletely understood. Although striatal projection neurons are most vulnerable, cortical pathology likely primarily drives cognitive dysfunction in HD and may impact striatum health via extensive cortical-striatal projections. Using voltage-sensitive dye imaging, we monitored the activity of cortical neurons in HD-model mice, where we found sensory-evoked signals spread more extensively. This suggests an excitation/inhibition imbalance, the details of which we are clarifying with additional experiments. Ultimately, the above could affect motor and cognitive performance in HD and contribute to neuronal toxicity mediated by excessive excitatory neurotransmission. This research furthers our understanding of early brain circuit changes in HD and how they contribute to neurodegeneration; hopefully these findings will inform future early HD treatment interventions. Results will also be published in scientific journals and communicated to the media.

Some of the lung bacteria protect the lung against bad germs. Tests have shown that people with sick lungs often do not have these ‘good’ bacteria in their lungs. Our team has shown that breathing air pollution causes the lungs to be irritated, leading to inflammation. Inflammation of the lungs can make it hard to breathe, especially for people who have lung problems. We think that polluted air could change the lungs’ bacteria. To understand this, we asked healthy volunteers and people with lung sickness called chronic obstructive pulmonary disease (COPD) to participate in a study. They sat in a room for 2 hours and breathed either clean air or air with diesel exhaust that is a model of air pollution like that found in big cities around the world (i.e. Mexico City, or New Delhi). A doctor checked them and took small samples of fluid from within their lungs. We will use these samples to examine the lung bacteria and evaluate the effect of these bacteria on lungs’ health. This study will help uncover how air pollution affects our lungs and bacteria that are inside the lung. This study will help doctors and governments make decisions about traffic-related air pollution and respiratory health.

Vascular cognitive impairment (VCI) is caused by cerebral small vessel disease and is associated with cognitive and physical impairments in aging. Cerebral small vessel disease can lead to different types of brain damage, including myelin loss. Myelin is critical for proper brain function because it allows different brain regions to communicate with each other and intact myelin is vital for optimal human function. It is crucial for us to measure myelin loss and understand how myelin loss may be associated with cognitive and physical outcomes in older adults with VCI. 

The goals of this study are to: 1) determine whether myelin loss is associated with cognitive and physical function in VCI and; 2) determine whether myelin imaging is better than other imaging techniques at assessing cognitive and physical outcomes in older adults with VCI.

The results of this study will: 1) help us understand how myelin loss affects cognitive and physical function and; 2) help us determine whether myelin imaging can be used as an additional tool to monitor disease progression and assess the efficacy of clinical intervention studies.

Gastrointestinal (GI) infection caused by the bacterium Clostridioides difficile is a common complication of inflammatory bowel disease (IBD), a condition caused by an aberrant host immune response to the GI microbiota. C. difficile infection (CDI) typically occurs after disruption of the GI microbiota, making the immune response to microbiota during IBD a major risk factor. This is particularly important in pediatric populations where 47% may suffer from an IBD-CDI co-occurrence. Fecal microbiota transplant (FMT) is a successful experimental treatment for IBD and CDI. It is thought to work by restoring a healthy-functioning microbiome to the IBD-CDI patient, however the specific host and bacterial factors that define FMT’s success remains unknown. We will recruit pediatric IBD-CDI patients to define host immune responses to FMT treatment. Using a novel IBD-CDI mouse model, we will then attribute host responses to FMT therapy and microbiota composition. This translational research will advance our mechanistic knowledge of FMT efficacy and ultimately impact FMT safety and governance. We will present our findings at international conferences including Canadian Digestive Diseases Week and aim to publish them in high-impact journals.

Cystic fibrosis (CF) is a rare hereditary condition where patients experience frequent chest illnesses (exacerbations), resulting in a decline in lung health and premature death. Typically, exacerbation symptoms include an increase in cough and phlegm, with an accompanying decline in lung function. Up to half of all persons with CF (PWCF) require at least one course of intravenous antibiotics to manage their exacerbations each year, but at least 25% will not recover to their original lung function after treatment. While it is clear that not all chest illnesses in PWCF occur due to bacterial infections, we are unable to identify other causes when diagnosing a patient. Consequently, PWCF receive antibiotics in almost all instances of chest illness, even when bacteria may not be the cause, exposing these patients to unnecessary harm. This study will involve analysis of blood and phlegm samples from >100 PWCF to identify clinical and molecular markers that can indicate the cause of an exacerbation. Through the use of sophisticated statistical techniques, we will then develop a tool that can be used to predict exacerbation cause and allow physicians to select treatments that are more specific, appropriate and beneficial for patients.

In the modern pharmaceutical industrial, a large number of new drug candidates come from molecules isolated from microbes. These “natural products” include some of our most powerful antibiotics, chemotherapeutics, and other medicines. Unfortunately, investment into natural-product drug discovery has declined since the turn of the century, owing to the high chance of re-discovery of known molecules. However, advances in bioinformatics suggest that the number of potential new drugs available from microbes is enormous. To advance the field of natural product drug discovery and exploit these advances in bioinformatics, we are developing a creative method to discover natural products with specific chemical sub-structures, which have a high probability to become drug candidates. This method is a biosynthetically-inspired, genome-mining technique that employs 15N-NMR as a key technique. Our initial work has resulted in a structurally unprecedented molecule, together with a yet-unknown molecule with exceptional antibiotic activity against gram-negative pathogens. Here we propose to extend our approach for the targeted isolation of additional molecules, followed by assessment of their pharmaceutical properties for preclinical testing.

Mosquitoes are the world’s deadliest animals due to the pathogens they spread and are often highly invasive. Arboviruses (arthropod-borne viruses) are spread by arthropods such as mosquitoes, and frequently infect both human and animal hosts. Multiple mosquito-vectored arboviruses have spread to new regions recently, including West Nile virus and Zika virus. While the mode of introduction is often unknown, the movement of infected mosquitoes or infected animal hosts are believed to be two of the methods responsible.

East Asia is an area from which arboviruses and mosquito vectors are at increasing risk of introduction to BC. I will survey the distribution of mosquitoes that vector established arboviruses in BC, or that could serve as vectors for arboviruses at risk of emerging in BC, particularly in northern and rural regions. I will use machine learning to model the distribution of these species now and in future climate change. What makes a mosquito species likely to be invasive will be studied using genomics, ecology, and cutting-edge behavioural techniques. Once these traits are known, I will model potential habitat in BC of candidate species from east Asia that could  become invasive in BC.