The availability of genomic sequences and the technology that is rapidly developing to exploit them have opened up new opportunities and challenges for molecular genetics. For example, it has become routine experimental practice to study expression of all the genes of an organism at once, facilitating a level of biological inference at the "system level", well beyond what is possible from studying individual genes, gene assemblies or pathways.
The rapid advance of technology for studying global responses of cellular metabolism as well as gene expression, both in populations and individual cells to perturbations in growth conditions has provided sensitive new tools for studying how cells respond and adapt to their environment.
The goal of our research is to understand fully how the cellular growth rate is controlled by circumstances. Ultimately, we would like to be able to produce a theory from which the responses of cells to all kinds of environmental perturbation can be predicted.
To this end, we are finding ways to perturb cells, while at the same time rigorously controlling the background environment. The idea is to introduce a defined perturbation, such as a pulse of additional nutrient, and observe (and ultimately predict) the global cellular responses, including gene expression patterns, metabolite concentrations and fluxes, entry and exit from the cell division cycle. One challenge in these studies is to devise experimental systems (such as steady-state growth in chemostats or culture on filters that can easily be transferred from one medium to the next) that allow the desired perturbation while minimizing unintended changes in conditions caused by sampling methods or other manipulations. Another challenge is the very large data volumes generated by the DNA microarray, DNA sequence, fluorescence (both in the microscope in the cell sorter) and mass spectrometry technologies that we use. Not only do the data have to be collected, stored and analyzed—they also have to be cast in a form that allows experimenters to understand them.
Research:Genome-Scale Studies of Metabolic Homeostasis in Yeast
We are studying the ability of yeast to maintain metabolic homeostasis under a variety of growth environments. We have found that many features of transcriptional regulation that correlate with growth rate are shared among steady-state cultures regardless of the nature of the nutrient limitation. In contrast, the levels of intracellular metabolites generally depend more on the nature of the limitation than on the growth rate. We are trying to understand the regulatory systems that control the response of yeast cells when nutrients become limiting or exhausted. We seek to understand in detail how the cells assess their nutritional situation and how they regulate their growth rate based on this assessment. We have found cells will exit the cell division cycle, limit glucose fermentation, and survive for weeks when they exhaust some nutrients [e.g. phosphate, nitrogen, sulfur, or methionine (in methionine auxotrophs)] whereas they fail to exit the cell cycle, waste glucose through fermentation, and die in a day or two when they exhaust other nutrients (e.g. uracil, leucine or histidine (in auxotrophs)]. We believe these phenomena are related to the massive and unrestricted fermentation, by virtually all cancer cells and tissues, of glucose to lactate first noted in 1931 by Otto Warburg (the “Warburg Effect”). We are pursuing a variety of ways to identify the genes most directly involved in regulation at the nexus of growth rate, cell cycle and metabolism.
Recent publications in this area:
Brauer MF, Huttenhower C, Airoldi EM, Rosenstein R, Matese JC, Gresham D, Boer VM, Troyanskaya OG, Botstein D. (2008) Coordination of growth rate, cell cycle, stress response, and metabolic activity in yeast. Mol Biol Cell. Jan;19(1):352-67.
Boer VM, Amini S, Botstein D. (2008) Influence of genotype and nutrition on survival and metabolism of starving yeast. Proc Natl Acad Sci U S A. 2008 May 13;105(19):6930-5.
Perturbations of Steady-State Growth as Probes of Cellular Regulatory Networks
We are developing ways in which we can take a steady-state culture and briefly change its external or internal environment, so that we can follow kinetically the changes in gene expression and metabolism. Two successful examples of this approach are (1) deliver a pulse of preferred carbon source (glucose) to a culture growing at steady state in a chemostat under limitation by another carbon source (e.g. galactose); and (2) subject cells growing at steady state at 25o C a short heat pulse at 36o C. We are now exploring ways of producing internal perturbations, for example, by attaching regulatory genes to promoters controlled by a drug that has no other effect on yeast and then delivering a pulse of the drug to cultures growing at steady state.
It is becoming increasingly clear that studies of populations of cells, even at a comprehensive genomic scale, give only a partial picture of regulatory activity. This is especially a problem in kinetic studies. Therefore we are beginning to explore methods that allow us to look at gene expression such as fluorescent in situ hybridization (FISH) to follow mRNA levels or fusions to fluorescent proteins to monitor individual promoter activities.
Recent publications in this area:
Ronen M, Botstein D. (2006) Transcriptional response of steady-state yeast cultures to transient perturbations in carbon source. Proc Natl Acad Sci U S A. Jan 10;103(2):389-94.
Lu C, Brauer MJ, Botstein D. (2009). Slow Growth Induces Heat Shock Resistance in Normal and Respiratory-deficient Yeast. Molecular Biology of the Cell. Feb;20(3):891-903. [PMCID: PMC2633392]
Genome-Wide Gene Expression During Experimental Evolution in Yeast
When cultures of Saccharomyces cerevisiae are exposed to persistent strong selection in a constant environment, such as a limiting nutrient in continuous culture, fitter variant strains arise that “sweep” the culture. Based on the repeated observation of similar changes in patterns of genome-wide gene expression and underlying genomic rearrangements found in strains that have evolved independently under these conditions, it appears that yeast can adapt to glucose, nitrogen or sulfate or phosphate limitation in chemostats in only a small number of ways, in part by characteristic rearrangements of their genomes. We infer from these results that there must be constraints in the relevant regulatory networks that limit the ways in which gene expression can be altered in a way that improves fitness.
Like our homeostasis and perturbation studies, our evolutionary studies aim to define the functional regulatory networks in yeast. Ultimately we hope to amass a body of data sufficient to support realistic mathematical and computational models of these networks. The methods we are developing should also provide the means for experimental tests of such models.
Recent publications in this area:
Gresham D, Ruderfer DM, Pratt SC, Schacherer J, Dunham M, Botstein D, Kruglyak L. (2006) Genome-wide detection of polymorphisms at nucleotide resolution with a single DNA microarray. Science. Mar 31;311(5769):1932-6.
Gresham D, Desai MM, Tucker CM, Jenq HT, Pai DA, Ward A, DeSevo CG, Botstein D, Dunham MJ. (2008) The repertoire and dynamics of evolutionary adaptations to controlled nutrient-limited environments in yeast. PLoS Genet. Dec;4(12):e1000303.