Proteomics and Metabolomics
We have a wide scope of projects ranging from developing novel algorithms for studying RNA processing in diseases, development and personalized medicine, and for analyzing single-cell RNA-seq data.
cAMP-dependent protein kinase (PKA) is ubiquitous in every mammalian cell with the PKA signaling network regulating processes as diverse as memory, differentiation, development, the cell cycle, and circadian rhythms. One of our goals, in addition to elucidating structures of the PKA subunits, is to map the PKA proteome as it relates to PKA signaling. The PKA interaction network consists not only of the PKA regulatory and catalytic subunits as well as the GPCRs, G-Proteins, cyclases, and phosphodiesterases, as well as PKA substrates but also the scaffold proteins (A Kinase Anchoring Proteins: AKAPs) that target PKA to specific sites in the cell. We are interested, in particular to map PKA that is targeted to organelles such as the mitochondria. A second goal is to map the activity of PKA in live cells using FRET PKA activity reporters that are targeted to specific sites such as the plasma membrane, the mitochondria, or the nucleus.
The focus of my research is on the fields of insulin resistance and obesity in humans, rodents and cell culture models. A new aspect of this research is the study of how insulin resistance promotes breast cancer. We employ systems biology approaches for complex data analysis. Research efforts include the generation of large transcriptomic, metabolomic, proteomic and lipidomic data sets and the analysis of these data sets independently and as network overlays using bioinformatic pathway tools. Our goals are to identify and characterize individual genes and biochemical pathways that regulate insulin resistance and pharmacological insulin sensitization.
Our work aims to develop new mass spectrometry based methods to understand the chemistry of microbes, our microbiome and their ecological niche. In short, we develop tools that translate the chemical language between cells. This research requires the understanding of (microbial) genomics, proteomics, imaging mass spectrometry, genome mining, enzymology, small molecules structure elucidation, bioactivity screening, antibiotic resistance and an understanding of small molecule structure elucidation methods. The collaborative mass spectrometry innovation center that he directs is well equipped and now has twelve mass spectrometers, that are used in the studies to investigate capture cellular chatter (e.g. metabolic exchange), metabolomics, metabolism and to develop methods to characterize natural products. These tools are used to defining the spatial distribution of natural products in 2D, 3D and in some cases real-time. Areas of recent research directions are capturing mass spectrometry knowledge to understand the microbiome, non invasive drug metabolism monitoring, informatics of metabolomics, microbe-microbe, microbe-immune cells, microbe-host, stem cell-cancer cell interactions and diseased vs. non-disease model organisms and the development of strategies for mass spectrometry based genome mining and to detect and structurally characterize metabolites through crowd source annotation of molecular information on the Global Natural Products Social Molecular Networking site through the NIH supported center for computational mass spectrometry that is co-developed with Nuno Bandeira. A more detailed biography can be found in this Nature article.
Our lab is focused on design and implementation of algorithms for biological data interpretation. Within this broad framework, we have a number of open projects relating to problems in proteomics (interpretation of mass spectrometry data), genetics, and genomics. The projects listed below are a small sampling of available projects. Interested students should be have taken a class in algorithms design, and have some facility with machine learning approaches.
The Bandeira lab develops novel computational mass spectrometry approaches for the discovery and characterization of biomarker metabolites, proteins, post-translational modifications and protein-protein interactions, with the ultimate goal of substantially improving the capabilities of proteomics discovery pipelines towards the development of novel drug therapeutics.