Martha L. Bulyk

Division of Genetics, Department of Medicine; Dept. of Pathology
Harvard-MIT Division of Health Sciences & Technology (HST) Associate Member of The Broad Institute of MIT and Harvard
Harvard Medical School
New Research Building, Room 466d
77 Ave. Louis Pasteur
Boston, MA 02115
Tel: (617) 525-4725
Fax: (617) 525-4705
Email:mlbulyk@receptor.med.harvard.edu
Web Page: The Bulyk Lab Page
6 postdoctoral fellows, 3 graduate students, 2 lab technicians

Although numerous eukaryotic genomes have been sequenced, much still remains to be understood about how the genes in those genomes are regulated. In metazoans, even general themes regarding the locations of DNA regulatory elements, such as where transcription factor (TF) binding sites are generally found, are still unknown. The interactions between TFs and their DNA binding sites are an integral part of the regulatory networks within cells.

To permit a genome-wide scan of all possible TF binding sites in a given genome, we recently developed protein binding microarrays (PBMs), a DNA microarray-based technology that allows rapid, high-throughput characterization of the DNA binding site sequence specificities of proteins. We are now examining essentially all known and predicted S. cerevisiae double-stranded DNA binding proteins in order to identify novel TFs, and to infer their condition-specific regulatory roles. We are also using PBMs to study human and >1,000 mouse TFs, so that we may predict sets of co-regulatory TFs acting together at candidate transcriptional cis regulatory modules, such as transcriptional enhancers. We are also examining the effects of protein-protein interactions in DNA binding.

Regulatory complexity in higher eukaryotes is achieved in part through combinatorial interactions between TFs. Therefore, we have developed strategies that employ comparative genomics methods for computational analyses and predictions of cis regulatory modules in metazoan genomes. By identifying over-represented clusterings of TF binding sites that are conserved across related genomes, we have predicted novel tissue-specific transcriptional enhancers in fly, and separately in human. Many of these predicted enhancers recently have been validated in vivo. More recently, in a systematic examination of a large set of Boolean combinations of TF binding site motifs, we have been able to infer each TF’s relative importance for a given cell-type-specific gene expression pattern. Analyses of genome-scale in vivo binding site data for these TFs may help to identify what sequence context features contribute to the usage of particular DNA binding sites under particular environmental conditions. The results of these experiments and analyses may allow a better understanding of the locations and organization of regulatory DNA elements, and will permit the development of more accurate algorithms for the prediction of such elements in eukaryotic genomes. We are also examining the structural basis of protein-DNA binding specificity.

Future projects may examine other types of regulation, such as chromatin structure, RNA binding proteins, and the role of small molecule cofactors and post-translational modifications, or may involve combining DNA-protein interaction data with data from other genomic and proteomic approaches for deciphering the regulatory networks within cells.

References:

• McCord RP, Berger MF, Philippakis AA, Bulyk ML. Inferring condition-specific transcription factor function from DNA binding and gene expression. Molecular Systems Biology, 2007; 3:100. Epub 2007 Apr 17.
• *Berger MF, *Philippakis AA, Qureshi A, He FS, Estep PW 3rd, Bulyk ML. Compact, universal DNA microarrays to comprehensively determine transcription-factor binding site specificities. Nature Biotechnology 2006 Nov;24(11):1429-1435. Epub 2006 Sept 24. (* these authors contributed equally to this work)
• *Philippakis AA, *Busser B, Gisselbrecht SS, He FS, Estrada B, Michelson AM, Bulyk ML. Expression-guided in silico evaluation of candidate cis regulatory codes for Drosophila muscle founder cells. PLoS Computational Biology 2006 May;2(5):e53. Epub 2006 May 26. (* these authors contributed equally to this work)