Cellular responses to chemical gradients are likely essential to the life cycles of all species, and are thus of fundamental interest to cell, organismal, and developmental biologists. Cellular movement toward a chemical source is called chemotaxis; the orientation of cellular growth toward a chemical source is called chemotropism. These processes are required for a wide range of biological phenomena. For example, chemotaxis plays a vital role in development, inflammation, immunity, wound healing, and the spread of cancer cells; chemotropism is integral to the growth of blood vessels, the connection of neurons, and fungal pathogenicity. Although they ultimately exhibit quite different behaviors, chemotactic and chemotropic cells face similar challenges: the responding cell must determine the position of the gradient source by sensing small differences in chemical concentration across its surface and then polarizing its cytoskeleton in that direction.  

Despite their importance to both basic and applied biology, the mechanisms that guide directional cell movement and growth within chemical gradients are not well understood. In this project, the mating response of the budding yeast Saccharomyces cerevisiae was used as a model chemotropic system. Study of this prototypical eukaryotic cell over the last 100 years has contributed greatly to our understanding of basic cell systems. Yeast cells exist as two mating types, each of which secretes a pheromone (which is like a hormone) that binds to surface receptors on cells of the opposite type. Activation of the pheromone receptors triggers a series of conserved signaling events that cause the cells to polarize their growth (i.e., elongate) in the direction of the closest potential mating partner. By tracking the pheromone gradient produced by their partner, mating cells orient towards one another and ultimately fuse at their growing tips. Yeast cells exhibit a remarkable ability to interpret chemical gradients. It has been estimated that a 1% difference in the number of activated receptors across the diameter of a yeast cell in a pheromone gradient is sufficient to elicit orientation toward the pheromone source. Numerous models have been proposed to explain yeast gradient sensing, but none address how mating cells reliably track such weak spatial signals.

Based on observations made during this investigation, we have proposed a model that explains both how yeast orient toward the source of shallow pheromone gradients, and how they reorient in response to a change in gradient direction. We discovered that receptor activity is preferentially maintained on the surface of the cell nearest to the pheromone source and that the mechanisms that generate this state are essential for gradient tracking. We also demonstrated that the pheromone receptor and associated signaling proteins concentrate at a predetermined assembly site, building what we call the Gradient Tracking Machine (GTM) at the beginning of the mating process. Once assembled, the GTM moves along the plasma membrane to the point of maximal pheromone concentration, where it stabilizes and triggers chemotropic growth. A computational model accurately simulated the gradient tracking behavior of real yeast cells, and correctly predicted that GTM assembly at a predetermined site contributes to mating fidelity. Because the key components of the GTM are highly conserved from yeast to man, our model will aid the study of gradient tracking in other cell types.

A primary broader impact of this project was STEM education at multiple levels. In total, four undergraduate students, nine graduate students, and two postdoctoral fellows contributed to the work. Three of the four undergraduate students completed Honors Projects and presented their results at an Undergraduate Research Colloquium in their senior years. The collaborative nature of the investigation gave the PI?s students and postdocs the chance to work with experts in diverse areas ? proteomics, advanced imaging, next generation probes and reporters, and systems biology. A second broader impact of this project was its integration of empirical and computational approaches. Because the use of computational tools in cell biology is in its infancy, the interactions between the experimentalists and theoreticians was especially valuable. In two of the papers funded by this award, computational modeling was used to strengthen the conclusions. These papers will serve as examples of how computational approaches can inform cell biology research. The PI's interdisciplinary experiences in this project contributed to the creation of NSF-sponsored workshops designed to help traditional cell biologists use computational modeling in their research, and to catalyze collaborations between computational modelers and cell biologists. A more ambitious series of meetings for an expanded community of biologists and modelers from all disciplines is planned.

Last Modified: 11/01/2019
Modified by: David E Stone