Jeffrey L. Blanchard
Associate Professor, Department of Biology
A.S., Electrical Engineering, Vermont Technical College, 1983
B.S., Biology, Worcester Polytechnic Institute, 1987
Ph.D., Botany, University of Georgia, 1995
We use systems-level approaches to understand bacterial stress response networks related to host-pathogen interactions and the influence of global climate change on populations of marine cyanobacteria and communities of bacteria. Our research program is characterized by a synergistic integration of computation, experiment and theory and we welcome new people from different academic disciplines. There are three fundamental aspects of our research; (1) Effectively utilize genomic data from different sources to discover functions for genes and proteins. (2) Uncover rules governing and constraining biological systems. (3) Enable predictive models of biological systems that incorporate biochemical and genetic data. Here are some of our project areas for systems research. The effects of global climate change on populations of marine cyanobacteria Global climate change is an international problem that already is impacting the evolutionary trajectory of our planet's biota. In spite of the widely appreciated magnitude of this problem, we still have a limited ability to estimate current and long-term biological effects. As the most numerically dominant species in the ocean, Prochlorococcus has become a central object of study for understanding carbon fixation by photosynthetic organisms. Prochlorococcus is very unusual for a free living organism in that it show signs of reductive genome evolution that are typically found in organelles, endosymbionts and pathogens (see Blanchard et al. 2000) and now has a minimal transcriptional regulator network for sensing environmental change. The long-term goal of our research is to integrate large-scale “genomic” data sets into probabilistic models that allow inferences and decisions regarding the trajectory of photosynthetic organisms in a changing global climate. Towards this goal we are working on; • Integrating genome sequence and expression microarray data into models that allow us to identify process and subnetworks subject to different selective pressures related to changes in ocean chemistry. • Developing and applying statistical methods to identify regulatory motifs and their relative binding strength in sequence data using a combination of microarray and sequence data (In collaboration with Erin Conlon). Stress response networks in enteric bacteria (In collaboration with Pablo Pomposiello) Signals initiated by diverse factors involved in host pathogen interactions, including antibiotics and nitric oxide, are funneled by three dissimilar sensor transcriptional regulators (SoxR, MarR and RamR) to three homologous transcriptional activators (SoxS, MarA and RamA) to regulate a similar but not completely overlapping set of genes coding for key metabolic control points and defense mechanisms. This unusual regulatory circuit design was assembled fairly recently in the ancestor of Escherichia, Salmonella and Klebsiella and soxS, marA and ramA are derived from duplications events that occurred around this time. Because, these transcriptional activators historically bound the same set of genes we are using comparative experimental and computational approaches for determining the regulatory network structure and creating models that describe the response of the bacteria to host and other environmental factors. Microbial environmental and community genomics Most microbes are currently impossible to grow in the laboratory. New approaches are being developed to sequence genomes of microbes straight from environmental samples obviating the need to first culture microbes. One spectacular application of this evolving technology was to a microbial community in the Sargasso Sea (Venter et al. 2004). The published data set included at least 1800 bacterial species many which have not been previously described. Fortunately for us there is also a lot of sequence data on marine cyanobacteria, particularly Prochlorococcus marinus. We have developed a new method for separating genomes from cocultures of bacteria (in collaboration with Derek Lovely) and are applying the methods to the Sargasso Sea and other environmental sequencing data sets. We are also interested in developing methods that utilize variation in DNA sequences to understand microbial population dynamics and methods for building a conceptual model of the interactions among various members of these communities.