Recent experiments have revealed surprising behavior in the yeast galactose (GAL) pathway, one of the preeminent systems for studying gene regulation. Under certain circumstances, yeast cells display memory of their prior nutrient environments. We distinguish two kinds of cellular memory discovered by quantitative investigations of the GAL network and present a conceptual framework for interpreting new experiments and current ideas on GAL memory. Reinduction memory occurs when cells respond transcriptionally to one environment, shut down the response during several generations in a second environment, then respond faster and with less cell-to-cell variation when returned to the first environment. Persistent memory describes a long-term, arguably stable response in which cells adopt a bimodal or unimodal distribution of induction levels depending on their preceding environment. Deep knowledge of how the yeast GAL pathway responds to different sugar environments has enabled rapid progress in uncovering the mechanisms behind GAL memory, which include cytoplasmic inheritance of inducer proteins and positive feedback loops among regulatory genes. This network of genes, long used to study gene regulation, is now emerging as a model system for cellular memory.

Organisms must carefully control their metabolism in order to survive. On the other hand, enzymes must adapt in response to evolutionary pressures on the pathways in which they are imbedded. Taking advantage of the newly available whole-genome sequences of 12 Drosophila species, we examined how protein function and metabolic network architecture influence rates of enzyme evolution. We found that despite high overall constraint, there were significant differences in rates of amino acid substitution among functional classes of enzymes. This heterogeneity arises because proteins involved in the metabolism of foreign compounds evolve relatively rapidly, whereas enzymes that act in "core" metabolism exhibit much slower rates of amino acid replacement, suggesting strong selective constraint. Network architecture also influences enzymes' rates of amino acid replacement. In particular, enzymes that share metabolites with many other enzymes are relatively constrained, although apparently not because they are more likely to be essential. Our analyses suggest that this pattern is driven by strong constraint of enzymes acting at branch points in metabolic pathways. We conclude that metabolic network architecture and enzyme function separately affect enzyme evolution rates.

In many of our courses, particularly laboratory courses, students are expected to engage in scientific writing. Despite various efforts by other courses and library resources, as instructors we are often faced with the frustration of student plagiarism and related writing problems. Here, we describe a simple Writing in Your Own Voice intervention designed to help students become more aware of different types of plagiarism and writing problems, avoid those problems, and practice writing in their own voice. In this article, we will introduce the types of plagiarism and writing problems commonly encountered in our molecular biology laboratory course, the intervention, and the results of our study. From the evaluation of 365 student reports, we found the intervention resulted in nearly 50% fewer instances of plagiarism and common writing problems. We also observed significantly fewer instances of severe plagiarism (e.g. several sentences copied from an external source). In addition, we find that the effects last for several weeks after the students complete the intervention assignment. This assignment is particularly easy to implement and can be a very useful tool for teaching students how to write in their own voices.

When a cell encounters a new environment, its transcriptional response can be constrained by its history. For example, yeast cells in galactose induce GAL genes with a speed and unanimity that depends on previous nutrient conditions. Cellular memory of longterm glucose exposure delays GAL induction and makes it highly variable with in a cell population, while other nutrient histories lead to rapid, uniform responses. To investigate how cell-level gene expression dynamics produce population-level phenotypes, we built living vector fields from thousands of single-cell time courses of the proteins Gal3p and Gal1p as cells switched to galactose from various nutrient histories. We show that, after sustained glucose exposure, the lack of these GAL transducers leads to induction delays that are long but also variable; that cellular resources constrain induction; and that bimodally distributed expression levels arise from lineage selection-a subpopulation of cells induces more quickly and outcompetes the rest. Our results illuminate cellular memory in this important model system and illustrate how resources and randomness interact to shape the response of a population to a new environment.

When a cell encounters a new environment, its transcriptional response can be constrained by its history. For example, yeast cells in galactose induce GAL genes with a speed and unanimity that depends on previous nutrient conditions. Cellular memory of long‐term glucose exposure delays GAL induction and makes it highly variable with in a cell population, while other nutrient histories lead to rapid, uniform responses. To investigate how cell‐level gene expression dynamics produce population‐level phenotypes, we built living vector fields from thousands of single‐cell time courses of the proteins Gal3p and Gal1p as cells switched to galactose from various nutrient histories. We show that, after sustained glucose exposure, the lack of these GAL transducers leads to induction delays that are long but also variable; that cellular resources constrain induction; and that bimodally distributed expression levels arise from lineage selection—a subpopulation of cells induces more quickly and outcompetes the rest. Our results illuminate cellular memory in this important model system and illustrate how resources and randomness interact to shape the response of a population to a new environment.

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The Wild Yeast Biodiversity Project is an inquiry-based module developed for Molecular Methods in Evolution and Ecology, an upper-division biology lab class at the University of California, San Diego. It serves as one of three major projects that student complete over the course of the 10-week class. In the project, students work to isolate strains of wild yeast from the chaparral at a local nature reserve. They take field samples, culture the samples to isolate individual strains of yeast, and identify the strains using DNA barcoding. The project has 4 goals: 1) Guide students through an authentic process of scientific discovery in which they collect novel data and contribute to our understanding of microbial diversity and ecology.2) Teach students scientific skills: field collecting, microbiology culturing, molecular biology techniques, basic bioinformatics skills, microbiology safety, and aseptic technique.3) Build a living archive of the fungal diversity at the Scripps Coastal Reserve. 4) Discover and document new species of wild yeasts, in collaboration with the Hittinger Lab at the University of Wisconsin-Madison.On the initial collecting trip, students are introduced to the coastal sage scrub habitat, an important and threatened ecosystem. They learn to identify the most common native plants in our region and find out about the plants' adaptations to San Diego's Mediterranean-type ecosystem. Then they set off in pairs to explore the habitat and choose their own samples. Most students sample plant tissue -leaves, fresh and dried flowers, decaying prickly pear pads, etc. -but some sample the arthropods they find, such as bees or caterpillars. Each individual student aseptically collects seven samples. By this point, some students are already articulating scientific questions: Will the fresh and dried flowers of the same plant yield different yeasts? Will leaves yield fewer yeasts than flowers do?Back at the lab, students incubate their samples in liquid media that they make and sterilize themselves. The culturing conditions favor the growth of fungi and discourage bacteria: limited oxygen (capped tubes, no agitation), high osmotic stress (8% glucose), and antibiotics. The cultures go through two rounds of liquid media and then multiple rounds of agar plate culture, as students try to isolate individual yeast strains from the diverse community of microbes that are originally present on each sample. During this process, students learn microbiology culturing techniques, including proper streaking technique, and gain experience in identifying yeast morphology. Each student starts with 7 samples and ends up with an average of 2-3 isolated strains.Throughout the culturing phase of the project, we strongly emphasize microbiology safety and implement Biosafety Level 2 protocols. Yeast are rarely pathogenic compared to bacteria and mold, but we take extra precautions because the strains are unidentified at this point. Former students have reported that these skills proved useful in their subsequent positions in resea...