The effects of temperature and salinity on growth rates and toxicities of a dinoflagellate species in the genus Alexandrium will be determined. Species of Alexandrium can produce a suite of dangerous neurotoxins, the most potent being saxitoxin, that cause paralytic shellfish poisoning in humans if contaminated shellfish are consumed. These potentially lethal toxins are dangerous to humans and marine mammals alike. Toxins accumulate in both wild and farmed shellfish during a harmful algal bloom (HAB), and can result in shellfish harvesting closures causing economic losses to commercial shellfish growers. Climate change scenarios project increased freshwater input to the Puget Sound region (causing decreased salinity) and warming of surface waters. These changes were simulated in the laboratory using a temperature gradient bar – a cell culturing apparatus designed to facilitate cell growth over a range of uniformly distributed temperatures (5-28°C) and salinities (10, 15, 20, 25, 30, and 35 ppt). Nutrient concentrations in the culture media is locally relevant and light is on a 12:12 h light:dark cycle. These experiments will be conducted for two Puget Sound isolates of Alexandrium collected in 2010 – one from Cypress Island in the north and one from Quartermaster Harbor in the south. Preliminary results for the northern Puget Sound isolate indicate zero to low growth at all temperatures for the 15 ppt salinity treatment, while greatest growth occurred between 15°C and 22°C in the 20 and 25 ppt salinity treatments. Toxin analyses are yet to be performed. The growth and toxicity response surfaces generated from this study will be used in a model to assess potential range and timing of future blooms under projected climate change scenarios.

Glioblastoma Multiforme (GBM) is an aggressive type of primary brain tumor, which is treated by surgery followed by chemotherapy and radiation. A majority of cases prove to be highly resistant resulting in uniform fatality. At present, research has been moving towards developing a patient-centered model of treatment that is developed by finding the unique characteristics of a patient's tumor growth kinetics and response to therapy. GBM is a very heterogeneous disease despite having a generally poor prognosis. A promising method of investigating this heterogeneity has been through finding patterns in the molecular-genetic makeup of these tumors. Large-scale mathematical analysis on data from open-source repositories such as The Cancer Genome Atlas (TCGA) has already led to the discovery and validation of four GBM subclasses which point to characteristic areas of origination in the brain as well as differential growth behaviour. A limitation in this strictly data-oriented approach is the lack of meaningful clinical perspective applied which has resulted in the findings only being directed towards gauging patient survival. The focus of our research has been the discovery of information about individual characteristics of patients tumors that may have more clinical significance for each patient. Our hypothesis is that there is a correlation between the different biological subtypes classified by cluster analysis and the different kinetics of tumor growth measured by imaging. This work will be done by developing tools to investigate the genetic changes that may be driving different kinetics of disease progression (the proliferation and migration rates of the tumor cells) measured from actual patient imaging. This approach is guided by the belief that individual patient tumor biology can be used to inform the data analysis techniques and ultimately bring us closer to the goal of identifying the most discriminating treatment to effectively treat an individual's cancer.

Gliomas are invasive, fatal brain tumors that can be detected using magnetic resonance imaging (MRI). However, MRI does not reveal the entire size of tumor. By using a mathematical model developed by Kristin R. Swanson and colleagues, glioma growth can be quantified from the diffusion rate of the tumors cells, the net proliferation rate, and the carrying capacity of the tissue. These parameters are unique to each patient and can be determined by measuring the tumor volume from different MRI modalities as they change over time. Our lab collects data from patients consented to our study and records information about what treatments a given patient receives, since this can affect how the image appears. In my role as a member of the measurement team, I examine patient images to determine the extent of tumor burden on a given date. To obtain these measurements, I use Calcmri, a MATLAB program developed by our lab. Depending on the modality, enhancement on MRI slices can indicate tumor volume as well as other fluids. Calcmri can adjust the intensity threshold at which MRI enhancement will appear, which allows me to isolate the tumor and specify a region for the program to measure. I measure a series of two-dimensional cross section images in order to calculate a combined into 3-dimensional volume. These measurements are used to model the growth of the tumor, “in silico”. Our lab uses information from these models to discover clinical applications, such as to suggest more accurate treatment dosages and to predict life expectancy, which follows an overall goal to better capture the extent and invasion of tumor burden than what appears on MRI.

The type VI secretion system (T6SS) is a large protein complex utilized by bacteria to transport proteins into other cells. Initially implicated in virulence, the T6SS also participates in interbacterial interactions. A T6SS found in the opportunistic pathogen Pseudomonas aeruginosa, the hemolysin coregulated protein secretion island I T6SS (H1-T6SS), has been found to deliver a set of toxins to other bacteria. This provides P. aeruginosa a fitness advantage over competing bacteria. The H1-T6SS can target neighboring P. aeruginosa, which protects itself with cognate immunity proteins. Deleting these immunity proteins leaves P. aeruginosa susceptible to self-attack. However, the H1-T6SS as well as other T6SSs can target other bacteria, specifically they only target certain species. One of the goals of this project is determine the nature of such specificity. These findings would implicate new functions for the H1-T6SS. To further this, I am seeking to extend the scope in which the T6SS is viewed, both mechanistically and genetically. In doing so I am searching for new genetic loci that participate in T6SS-dependent interactions. Taken together, this work provides insights into a new aspect of type VI secretion and may change the genetic composition of T6SSs.

Gliomas are a uniformly fatal type of primary brain cancer characterized by their diffuse and invasive nature. Dr. Kristin Swanson and her team have developed a mathematical model of glioma growth that uses an individual’s unique tumor characteristics to derive metrics that enable the prediction of patient specific growth patterns and survival times. To date, relatively few studies have looked at predicting clinical progression of disease, which may be a function of disease localization. Clinical progression of disease incorporates variables such as mental cognition and impairment and is typically quantified using the widely used Karnofsky Performance Score (KPS). KPS is an accepted prognostic marker of a patient’s likelihood of tolerating chemotherapy and radiotherapy. In one study accessing functional status after gross total resection (GTR) of a patient’s enhancing tumor region, only 28% of patients had higher KPS post-operatively. 49% had stable scores, while 23% had declines in KPS of 10 to 30 points (Barker 1998). The variability in post GTR KPS scores suggests that there are patient-specific drivers of KPS. We will do a multivariate analysis to investigate which functional brain regions influence the mental and physical impairments that determine KPS scoring. We will identify brain regions, “functional drivers of KPS”, where tumor presence, as calculated by Dr. Swanson’s mathematical model, is well associated with a particular range of KPS scores. For each functional driver, we will use the region’s tumor cell concentration at time of tumor diagnosis, to quantify the future KPS. We will compare the predicted KPS to the actual KPS. If the initial and next time points are located pre and post- radiotherapy, chemotherapy, or resection, this will allow us another way of assessing the efficacy of therapies in palliating symptoms.

Inhibitor of differentiation (Id) is a transcription factor that negatively regulates basic helix-loop-helix (bHLH) transcription factors. Of four Ids identified (Id1-4), Id1 and Id3 have been shown to regulate glial development in the central nervous system. In the retina, there is a special type of glia called the Müller glia. Currently, it is not known whether the Ids are important for Müller glial development. Knockout studies in mice demonstrated that Id1/Id3 double knockout (KO) are embryonic lethal, while either Id1 or Id3 KO mice develop grossly normal. We investigated if Id1/Id3 are necessary for Müller glial development by performing Id1 shRNA knockdown (KD) in Id3 KO retinas. Five Id1 shRNAs were tested for their efficiency in NIH 3T3 cells (mouse fibroblast cell line), and the Western blot results showed over 90% decrease in Id1 expression using three shRNAs. We will electroporate Id3 KO retinal explants with these Id1 shRNAs and determine the effects of Id1/Id3 loss on Muller glial development. We expect to see a reduction in the number of Müller glia if Id1/Id3 play a role in their development.