Current Events

Thesis Seminars

Marco Sorani (Giacomini Lab)
Thursday, September 13, 2007, 3:00 PM - Mission Bay, GH Auditorium (N106)
Genetic variation in AQP4 and mannitol dosing after brain injury

Aquaporins are highly-conserved membrane proteins that transport water and small solutes. In humans, genetic variation in aquaporins is responsible for various diseases and other phenotypes. For example, cerebral edema is a common pathophysiology after brain injury. Aquaporin-4, a water channel in brain astrocytes, plays a role in edema. We performed cellular, computational, and clinical studies of genetic variation in aquaporins as well as clinical and computational studies of outcomes.

In cells, we used site-directed mutagenesis to construct AQP4 mutants that we had identified in an ethnically-diverse cohort. We then performed a permeability measurement assay. We identified four rare SNPs that reduced water permeability in cells. Computationally, we developed a map of aquaporin variation and examined uncharacterized variants by sequence and structure analysis. Factors that predict whether variants will have effects include conservation and their structural nature, but most methods analyze mutations in the context of the individual protein. Thus, we also compiled a data set of structures and mutations and used modeling to describe the location and effect of SNPs relative to oligomeric interfaces. In the clinic, we are currently collecting DNA samples from brain injury patients to test whether there is an association between genetic variation and drug response or outcome. Since many environmental and genetic factors may affect outcome, we are also conducting an epidemiological study to assess outcome by race and ethnicity. Preliminary results indicate that in our cohort Asians were older, had the lowest proportion of males, and experienced poor outcome.

Mannitol is the drug most commonly used to treat cerebral edema and elevated intracranial pressure (ICP), but there is no consensus regarding the optimal dosage. We characterized the dose-response relationship between mannitol and ICP using data collected with a high frequency data collection system. We showed that mannitol's effect on ICP is dose-dependent and that higher doses provide a more durable reduction in ICP. In a meta-analysis we aggregated data from previous studies that have described the dose-response relationship between mannitol and ICP. Meta-regression found a weak relationship between change in ICP and dose due to protocol variability. Finally, we analyzed physiological data beyond ICP and used hierarchical clustering to construct multivariate physiological "profiles" to classify patients for diagnosis and treatment.

Reception to follow in GH 1st Floor Atrium

Courtney Harper (Babbitt Lab)
Friday, June 29th, 2007, 4:00 PM - Rock Hall Auditorium (RH-102)
Sequence Analysis for an International Gene Trap Resource

Gene knockouts in a model organism such as mouse provide a valuable resource for the study of basic biology and human disease. High- throughput gene trapping, which results in untargeted interruption of genes, can be used to create an invaluable resource for scientists without requiring the time and background knowledge needed for targeted gene inactivation. BayGenomics is a large undertaking to create and analyze thousands of gene trapping events, and to provide information and cell lines to the public. BayGenomics member laboratories are currently creating knockout stem cells and mice, determining the phenotype caused by interrupted genes, and studying the expression of mouse genes during development using in situ hybridization. I will discuss work undertaken as a member of the informatics component of BayGenomics, which is charged with bioinformatic analysis and interpretation of gene trap data, as well as presenting this information to the public through an online interface.

Barbara Novak (Jain Lab)
Thursday, June 21st, 2007, 10:00 AM - Genentech Hall Room S-201
QPACA: Quantitative Pathway Analysis in Cancer

QPACA (Quantitative Pathway Analysis in Cancer) is a system for pathway visualization and analysis. It addresses three aspects of the general problem: 1) representation and visualization of pathways in the context of biological data, 2) recognition of gene sets that are part of a pathway or coordinated process, and 3) augmentation of pathways by prediction of pathway membership. The pathway representation is designed to be flexible and extensible in order to enable the widest variety of pathway structures and components possible, while the analytical methods directly address the issues inherent in analysis of human systems without making limiting assumptions about the structure of pathways or discretizing data.

QPACA has been used to analyze a number of microarray data sets, employing both yeast and human samples. Four primary results are presented, each of which derives from aspects of QPACA's application to microarray data for analysis or visualization: 1) statistical analysis of differential expression patterns in the context of pathway representations supports the generation of biological hypotheses; 2) gene expression data are sufficient to support computational recognition of hypothesized pathway gene sets across a broad variety of biological processes; 3) gene expression data can be used to produce ranked lists of gene products that are enriched for proteins that interact with members of a predefined pathway; and 4) the surprising ubiquity of detectable signals in expression data that bear on human pathway structure appears to be due to largely non- annotated transcriptional programs present within established pathways.