Integrated genomic and metabolomic methods in pancreatic cancer
It has been understood for decades that small molecules are useful for the diagnosis of disorders of inborn metabolism or cardiovascular disease, but metabolites can also be useful for the diagnosis and characterization of diseases not traditionally thought of as metabolic disease. We and others have begun looking for changes in small molecules that correlate with disease. We are particularly interested in metabolic changes that occur in cancer and how those changes contribute to disease progression and patient response to treatment. Our current work in this area is focused on 1) characterization of metabolic profiles associated with pancreatic cancer and how the peripheral fluids from pancreatic cancer represent metabolic changes in primary tumors and 2) integrating metabolic and transcriptomic measurements to identify transcriptional changes that are important in tumor initiation or progression.
The genomics of drug response in ovarian cancer
In collaboration with clinicians and scientists at UAB we have begun to explore whether genomic profiling can help predict patient drug response. We are using gene expression profiling to identify new drugs that may be used to treat ovarian cancer and also to determine whether gene expression patterns in tumors and peripheral fluids of patients might be useful for predicting response to current standard treatments.
Exploration of bacterial and fungal breakdown of polyaromatic hydrocarbons (PAHs)
We are surrounded. Bacteria and fungi are all around us and even live inside of us. There has been a vast body of work over the last decade to characterize these bugs and understand how they survive in “our world” as well as how their presence affects human health and the environment. Current work in the lab is focused on characterizing bacterial populations and transcripts that help bacteria survive in the presence of common pollutants such as PAHs. The long-term goal is to identify breakdowns that might be relevant for bioremediation.
Developing Tools for Integrated Omic Analysis
We have been fortunate to generate and have access to large genomic and metabolomic data sets. We are beginning to identify methods to integrate these data in order to accomplish two main tasks: 1) Visualize these data together in order to understand how gene expression, protein levels and metabolite levels change together and 2) To validate one data type by using another, e.g. we can identify genes whose expression patterns change and then show that metabolite levels fluctuate as predicted. This allows us to both improve confidence in smaller data sets and also shows that the changes detected at the level of transcription have functional consequences.
Characterization of metabolite levels in neuropsychiatric disorders
In collaboration with Dr. Richard Myers’ group, we have begun characterizing metabolic changes in brain tissue of individuals affected with various neuropsychiatric disorders. We have developed methods to extract metabolites from the tissue and analyzed the metabolites using two-dimensional GC with mass spectrometry. Currently we are analyzing these data in parallel with RNA-Seq data collected from the same samples. We hope that this integrated analysis will lead to an improved understanding of devastating disorders like bipolar and schizophrenia.
Characterization of natural metabolic variation in wild yeast strains
Like humans, yeast from around the world have different genetic backgrounds. We are exploring the metabolic variation among strains. In collaboration with several labs in the Yeast Resource Center we have collected samples from yeast strains from around the world. We are interested in identifying the metabolic differences that exist among these strains. Our collaborators have also measured RNA and protein levels in these strains allowing us the unique opportunity to characterize the natural variation at a variety of different levels.(Skelley et al. 2013)
Yeast as model for statin efficacy
Statins are a commonly prescribed drug to lower cholesterol. We were interested in using a combination of genomics and metabolomics to screen for compounds that affect sterol synthesis in yeast in the presence of the drug, lovastatin. Using this approach, we identified zinc and copper to significantly alter the effect of statins on sterol production. Combining expression data and metabolomics data, we showed that zinc and copper increase the production of ergosterol by increasing the flux through the entire sterol biosynthetic pathway. Because zinc and copper are consumed in the human diet, there may be an effect of these metals on statin effectiveness in a clinical setting. (Fowler et al. 2011)
Amino acid profiling in the yeast deletion collection
My initial work in the field of metabolomics and metabolite profiling focused on screening a relatively small number of metabolites in a large number of strains. We developed a high-throughput method to quantify primary amine-containing metabolites in the yeast Saccharomyces cerevisiae by the use of capillary electrophoresis in combination with fluorescent derivatization of cell extracts. We measured amino acid levels in the yeast deletion collection, a set of approximately 5000 strains each lacking a single gene, and developed a computational pipeline for data analysis. Global analysis of the deletion collection was carried out using clustering methods. We grouped strains based on their metabolite profiles, revealing groups of mutants enriched for genes encoding mitochondrial proteins, urea cycle enzymes, and vacuolar ATPase functions. Cooper et al. 2010