<%@LANGUAGE="JAVASCRIPT" CODEPAGE="65001"%> Mark Johnston Lab - Washington University Department of Genetics

Projects

Functional Genomics

Projects that offer opportunities to couple training in computational biology with experimental work spring from our comparative DNA sequence analysis of the yeast genome.

1) Genome-wide identification of DNA-protein interactions in yeast: We have identified thousands of potential regulatory sequences in the yeast genome by their evolutionary conservation. The challenge is to connect each of those sequences to the proteins that bind to them. There are opportunities for developing in vitro and in vitro methods to accomplish this goal. Armed with the catalogue of regulatory proteins and their binding sites, we then need to use that information to contribute to an understanding of the gene regulatory network of this reference eukaryotic cell.

2) High-throughput dissection of regulatory sequences: The thousands of potential regulatory sequences predicted by our comparative DNA sequence analysis need to be verified experimentally. We have some ideas for high-throughput methods for analyzing gene promoters that need to be developed, tested, and hopefully implemented. If successful, this could lead to the ability to analyze promoters of many genes acting in large regulatory networks.

3) Evolution of gene regulatory networks: The premise of comparative DNA sequence analysis is that related species whose genome sequences are being compared have similar regulatory networks. Yeasts offer an excellent opportunity to test that hypothesis by several approaches. A first place to begin is to survey the gene expression in several yeast species grown under a variety of different conditions. This would begin to address the hypothesis that much of the differences between species can be accounted for by differences in gene regulation rather than protein sequence differences.

Glucose sensing and signaling

We have long known the workings of a glucose sensing pathway that acts through the Snf1 protein kinase and the Mig1 transcriptional repressor to effect glucose repression of gene expression; a different glucose signal transduction pathway that works through glucose sensors and the Rgt1 transcriptional repressor to effect glucose induction of gene expression has more recently come into focus. We now know enough about these signal transduction pathways to be able to devise incisive experiments that should give us a deep understanding of how they operate.

1) Elucidation of a novel signal transduction pathway: We now have a good working model of the glucose signaling pathway that begins with the glucose sensors at the cell surface and ends with the Rgt1 repressor in the nucleus, but several important questions remain. For example, how do the glucose sensors activate casein kinase I, the protein to which they are coupled? How does Yck1-catalyzed phosphorylation of Mth1 and Std1, the next components of the pathway affect their function? How do Mth1 and Std1 regulate Rgt1 function?

2) Understanding the rationale of the glucose sensing network: We have recently come to realize that the two different glucose signaling pathways responsible for glucose repression and glucose induction of gene expression are highly interconnected. We need to learn what each branch of this regulatory network does for the cell and for its ability to compete for ever changing amounts of glucose.

3) Yeasts as a model for cancer cell metabolism: S. cerevisiae shares an unusual metabolism with many kinds of tumor cells: it ferments most of the glucose it uses, even when oxygen is present. This unique metabolism is actually the basis for modern tumor imaging techniques. The glucose signaling pathways we study contribute to determining this lifestyle of yeast cells. S. kluyveri, one of the yeasts whose genome sequence we determined, has the same components of these glucose signaling pathways, but it does not have the unusual fermentative glucose metabolism: it metabolizes glucose like normal, non-transformed cells. Understanding the differences between the glucose signaling networks of these two yeasts may illuminate an important aspect of tumor cell growth.

4) Glucose sensing and signaling in C . albicans: The C. albicans genome sequence includes genes predicted to encode proteins similar to glucose sensors and glucose transporters in S. cerevisiae. Since the glucose sensors of S. cerevisiae are required for its growth on glucose, inhibiting their counterparts in Candida might reduce its ability to colonize the mammalian host, or may affect pathogenicity during infections. The glucose sensors are potentially excellent candidates for anti-fungal drug targets, because their cell surface location makes them accessible to drugs. Furthermore, this class of glucose sensors seem to be present only in fungi, offering the possibility of developing drugs that do not affect the infected host. We have discovered a bona fide glucose sensor in C. albicans, and found it plays a critical role in sugar sensing, filamention, and virulence in a mouse model of disseminated Candidiasis. This is leading to a fuller analysis of glucose sensing and signaling in this pathogenic yeast.