Psychotic disorders (which include schizophrenia, schizoaffective and bipolar disorders) are common mental illnesses, affecting about 3 per cent of the population. Women face a number of challenges when dealing with these disorders, especially when it comes to pregnancy, childbirth and parenting. Women with a history of a psychotic disorder have substantial risks for a postpartum episode of mental illness like depression or psychosis. Postpartum mental illness carries risks for suicide and infanticide, as well as other less dramatic but still significant problems like difficulties with parenting skills and problems with mother-child bonding and attachment. Research has shown that, in general, psychotic disorders stem from interactions between genetic and environmental influences. The specific genetic variations that increase risk for postpartum episodes of mental illness are largely unknown. Dr. Jehannine Austin will use a new approach to investigate whether a variation to one particular gene contributes to risk for postpartum episodes of mental illness in women with a history of mental illness. This gene is known to encode a protein whose function is dependant on the B vitamin, folate. Dr. Austin will not only look at genetic variations, but will also measure folate levels in pregnant women at high risk of postpartum mental illness. If her work shows that the genetic variation plays a role in risk for postpartum mental illness, it may be possible to decrease risk for postpartum episodes of mental illness by providing folate supplements for these women.
Program: Scholar
Predicting the outcomes of cancer care services
With an aging population, rising costs and an increasing number of cancer cases, predicting the outcome of cancer care services is important for health care planning. Predictions can be based on computer models that take information from simple processes into larger systems. A model’s accuracy can be determined by comparing its predictions with real-world data and activity. As an MSFHR scholar, Dr. Chris Bajdik created a model to predict demand for hereditary cancer services in BC. He is now working to further develop prediction models for cancer care services. These new models will predict outcomes associated with cancer screening, treatment, supportive and palliative care. The predictions described through modeling will be compared with observed outcomes from provincial, national and international cancer care services. Dr. Bajdik’s approach provides a cost-effective way to predict outcomes – using the experience reflected in previously-collected data. Most importantly, these models will provide healthcare planners with a tool to predict the outcomes associated with new cancer care services and health policies. If the predictions are considered accurate, health care agencies can better plan and evaluate their services to care for those with cancer. The methods can be generalized to develop models for other forms of health care and other diseases.
Health Innovation Design and Evaluation (HeIDE)
In the last decade, the Canadian government has invested billions of dollars in development of a Canadian health information infrastructure. Health information technology goals are varied but they usually include faster, more efficient delivery of care based on shared information through electronic health records. However, despite the investment to develop an information technology infrastructure, the potential gains for the health system have been slow to materialize. Dr. Ellen Balka’s research focuses on the challenges associated with realizing Canada’s vision of an information technology-rich health care sector. She is working with stakeholders in actual health care settings, including technology developers, health system decision makers and health care providers, to assess design shortcomings, usability, implementation challenges, and issues related to governance of information technology within organizations. Dr. Balka’s studies will contribute to a more comprehensive understanding of how complex it is to introduce new information-based technologies into the health sector, and will lead to development of strategies that improve the rate of success for these initiatives within the health system. This will ensure that the potential benefits of these systems and technologies (administrative efficiencies, improved patient care and development of health data for research purposes) can be achieved.
Molecular Imaging of Cancer with Positron Emission Tomography
Recent developments in imaging devices provide researchers with powerful tools to detect cancers and explore the impact of therapy on tumour cells. This research program plans to leverage the strengths of positron emission tomography combined to computed tomography (PET/CT) to characterize and rapidly assess response to therapy in 3 common cancers (breast, prostate, and lymphoma) and combine this information with other predictors of aggressiveness and treatment failure. PET/CT imaging is a powerful technique that combines the strenghts of a PET scanner (which can measure tumor receptors and metabolic activity) with those of a CT scanner (which provides detailed images of a patient’s anatomy). The combination of both approaches could rapidly identify patients that are likely to fail conventional therapy and offer them alternatives that are better suited to the nature of their cancer. The research program is designed around 3 core themes. The first research them focuses on the development of methods to predict the outcome of patients with breast cancer who are treated with chemotherapy or hormone therapy. We will pursue ongoing work to develop animal models of breast cancer and imaging methods to monitor response of these tumors to chemotherapy and hormone therapy. We will also conduct clincial studies to correlate the results of imaging studies performed with PET/CT with outcome and response to therapy. The second theme focuses on the development of new probes that target specific proteins that are overexpressed at the surface of breast and prostate tumors. These probes might eventually be translated into clinical studies as breast and prostate cancer diagnostic agents for use with PET/CT, or even for therapy by tagging them with radioisotopes that can destroy tumor cells by proximity. The last theme proposes practical research studies of immediate clinical interest. We will assess the accuracy of PET/CT imaging in staging prostate cancer (with 2 radiopharmaceuticals designed to assess tumor lipid synthesis and bone turnover). We will also extend to the Vancouver site an ongoing study that assesses PET/CT imaging to predict the early response to chemotherapy in large cell lymphoma.
Carbohydrate recognition and metabolism in streptococcus pneumoniae: Structural and functional dissection of unique virulence factors
Pneumonia is an acute respiratory disease, the major cause of which is the bacterium Streptococcus pneumoniae. This bacterium is the leading cause of death from infectious disease in North America and a leading cause of death worldwide, particularly in children and the elderly. This bacterium can also cause meningitis, septicemia, and otitis media (middle ear infection). Reports indicate that 40 per cent of pneumonia cases caused by S. pneumoniae are resistant to penicillin and new multidrug resistant strains are beginning to emerge. To reduce increasing rates of antibiotic resistance and augment judicious use of the pneumococcal vaccine, alternative methods for treating S. pneumoniae infections must be found. Several proteins have been found in S. pneumoniae that are believed to contribute to its virulence. It is suspected some of these proteins destroy sugars such as glycogen in specific lung cells that normally serve to protect the lungs against infection. These damaging proteins are potential targets for preventing or slowing the infection. Dr. Alisdair Boraston will focus on two aspects of these S. pneumoniae proteins: if and how these proteins are destroying sugars and how to inhibit this activity. Biochemical studies will provide understanding about how these enzymes degrade sugars and whether any inhibitor molecules can interfere with this. Structural studies using X-ray crystallography will show structural features of the proteins that contribute to their activity and aid in the design of new inhibitors. Taken together, this information will lead to new approaches and agents to target pneumonia caused by S. pneumoniae.
Defining the structural basis of surface antigen glycoprotein mediated virulence in Toxoplasma gondii
Toxoplasmosis is a serious human pathogen carried by about one-third of the population. People develop toxoplasmosis either after ingesting undercooked meat that contains T. gondii cysts, or by coming into contact with cat feces from an infected animal. Once infected, healthy adults initially show a range of temporary flu-like symptoms; however, while these symptoms pass, the parasite Toxoplasma gondii remains in the body for life, with limited drug treatment available. Infection during pregnancy can cause miscarriage, neonatal death and a variety of fetal abnormalities, including developmental delays. It is also harmful to those whose immune systems are compromised, such as those with HIV/AIDS, cancer or who have had an organ transplant. Very little is known about how T. gondii causes disease. Dr. Martin Boulanger is studying the structure of host-pathogen interactions to determine the activities that allow T. gondii to attach to and invade human cells. With this information, treatments can be developed to prevent or manage Toxoplasmosis. This work will also apply to better understanding of other parasite-caused disease such as malaria and cryptosporidiosis.
Development and application of data standards for flow cytometry
Flow cytometry is a method of identifying and sorting cells and their components by staining with a fluorescent dye and detecting the resulting fluorescence (usually by laser beam illumination). Flow cytometry is widely used in health research (e.g. for stem cell identification and vaccine development), and in the diagnosis, monitoring and treatment of a variety of diseases, including cancers and HIV/AIDS.
Recent advances in high-throughput flow cytometry allows for the analysis of thousands of samples per day, creating detailed descriptions about millions of individual cells. Managing and analyzing this volume of data is a challenge that Dr. Ryan Brinkman is addressing through the development of data standards, algorithms, and bioinformatics tools. Dr. Brinkman is also applying these methodologies to the analysis of several large clinical flow cytometry datasets in an effort to identify biomarkers for lymphoma, neonatal auto-immunity, and graft versus host disease.
Pathogen bioinformatics and the evolution of microbial virulence
Infectious diseases are responsible for roughly a third of annual deaths worldwide and contribute greatly to productivity loss. Antimicrobial resistance and newly emerging diseases are both cause for significant concern. With the advent of microbial whole-genome sequencing, there has been renewed optimism that computational analyses of microbial genomes will allow for faster identification of promising new therapeutic targets, which can then be further investigated with laboratory studies. At the moment, however, current computational practices are not accurate enough to be truly effective. Dr. Fiona Brinkman is interested in improving computational methods used to identify new potential bacterial vaccine components or drug/diagnostic targets. She is focusing in particular on improving identification methods for two regions: bacterial cell surface and secreted proteins, since they are the most accessible targets; and clusters of genes called genomic islands, which appear to disproportionately contain virulence genes and so could aid investigations of bacterial pathogenicity. Her research group is also studying the evolution of microbial virulence, both from the pathogen and host perspective, using bioinformatic approaches supported by laboratory studies. This work aims to develop methods and insights that may accelerate the identification of promising new targets from pathogen genomes. With the ability to analyze multiple infectious disease-causing microbes in parallel, this research has the potential to have a wide reaching impact on efforts to control multiple infectious diseases.
Population-Based Genetic Studies of Cancer and Healthy Aging
The number of elderly Canadians is increasing as the baby boomers age. Insight into how to promote healthy aging, coupled with advice that can be provided to our population as it ages, will influence Canada’s healthcare costs, as well as the quality of life of a large segment of our population. Cancer and aging are intimately connected. Cancer incidence rises with age, and this increase accelerates dramatically over 60 years of age. Cancer and other aging-associated diseases like cardiovascular disease are thought to result from the interaction of numerous genetic and environmental or lifestyle factors. Population-based studies that use large groups of affected and unaffected individuals are now the preferred method to study the genetics of complex diseases. This program has clinical relevance and involves close collaboration with clinical experts to study healthy aging and two specific cancers, non-Hodgkin lymphoma and cervical cancer. The overall objective is to discover genetic factors that contribute to susceptibility to cancer or confer long-term good health. The program will use state-of-the-art genetic analysis methods, and over the next 5 years will expand these projects and add additional types of cancer. This coordinated study of cancer and healthy aging is a unique and innovative approach by which we will increase our understanding of the connection between cancer and aging and benefit from new knowledge regarding the basis of common aging-associated diseases like cancer. This research will lead to development of clinically useful markers that will help individuals avoid developing diseases as they age.
Neighborhood Social Capital and Population Health: Exploring Community Resources and Access
Recently, the concept of community social capital – the extent and quality of community social ties – is receiving a great deal of interest from population health researchers and policymakers. This interest stems from efforts to understand relationships between the social and economic conditions of communities and the health and well-being of the people who live in these communities. Research on social capital to date has been focused primarily on the extent of social ties and interpersonal trust in communities. This limited focus has overlooked crucial elements that make community social ties useful for maintaining or improving population health: the various socioeconomic, political, and psychosocial resources that are possessed by members of social networks and how residents access (or are restricted from accessing) these network-based resources.
Dr. Richard Carpiano is determining how specific resources based in neighbourhood social ties, and access to these resources, matter for adult health and well-being. He will analyze one of the best available community health datasets for investigating social capital and neighborhood socioeconomic conditions: the Los Angeles Family and Neighborhood Survey (L.A.FANS). This project has two major benefits. It will extend population health planners’ understanding of community social capital by showing what aspects of neighbourhood social ties matter for health and well-being and how social conditions of local areas have health promoting and health damaging consequences. More broadly, it will help extend population health research on neighbourhoods and local communities by exploring the different ways that social context matters for adult health and well-being.