Anne G. Rosenwald

The Genomics Education Partnership: Successful Integration of Research into Laboratory Classes at a Diverse Group of Undergraduate Institutions

Genomics is not only essential for students to understand biology but also provides unprecedented opportunities for undergraduate research. The goal of the Genomics Education Partnership (GEP), a collaboration between a growing number of colleges and universities around the country and the Department of Biology and Genome Center of Washington University in St. Louis, is to provide such research opportunities. Using a versatile curriculum that has been adapted to many different class settings, GEP undergraduates undertake projects to bring draft-quality genomic sequence up to high quality and/or participate in the annotation of these sequences. GEP undergraduates have improved more than 2 million bases of draft genomic sequence from several species of Drosophila and have produced hundreds of gene models using evidence-based manual annotation. Students appreciate their ability to make a contribution to ongoing research, and report increased independence and a more active learning approach after participation in GEP projects. They show knowledge gains on pre- and postcourse quizzes about genes and genomes and in bioinformatic analysis. Participating faculty also report professional gains, increased access to genomics-related technology, and an overall positive experience. We have found that using a genomics research project as the core of a laboratory course is rewarding for both faculty and students.

Genomics Education Partnership

The Genomics Education Partnership offers an inclusive model for undergraduate research experiences, with students pooling their work to contribute to international databases.

ARL1 and membrane traffic in Saccharomyces cerevisiae.

To examine the functions of the Arf‐like protein, Arl1p, in Saccharomyces cerevisiae, a null allele, arl1Δ::HIS3, was constructed in two strains. In one background only, loss of ARL1 resulted in temperature‐sensitive (ts) growth (suppressed on high‐osmolarity media). Allelic variation at the SSD1 locus accounted for differences between strains. Strains lacking ARL1 exhibited several defects in membrane traffic. First, arl1Δ strains secreted less protein as measured by TCA‐precipitable radioactivity found in the media of [35S]‐labelled cells. A portion of newly synthesized carboxypeptidase Y (CPY) was secreted rather than correctly targeted to the vacuole. Uptake of the fluid‐phase marker, lucifer yellow, was reduced. All these phenotypes were exacerbated in an ssd1 background. The ts phenotype of the arl1Δssd1 strain was suppressed by YPT1, the yeast Rab1a homologue, suggesting that ARL1 and YPT1 have partially overlapping functions. These findings demonstrate that ARL1 encodes a regulator of membrane traffic. Copyright

A Course-Based Research Experience: How Benefits Change with Increased Investment in Instructional Time

While course-based research in genomics can generate both knowledge gains and a greater appreciation for how science is done, a significant investment of course time is required to enable students to show gains commensurate to a summer research experience. Nonetheless, this is a very cost-effective way to reach larger numbers of students.

Yeast ARL1 encodes a regulator of K+ influx

A molecular genetic approach was undertaken in Saccharomyces cerevisiae to examine the functions of ARL1, encoding a G protein of the Ras superfamily. We show here that ARL1 is an important component of the control of intracellular K+. The arl1 mutant was sensitive to toxic cations, including hygromycin B and other aminoglycoside antibiotics, tetramethylammonium ions, methylammonium ions and protons. The hygromycin-B-sensitive phenotype was suppressed by the inclusion of K+ and complemented by wild-type ARL1 and an allele of ARL1 predicted to be unbound to nucleotide in vivo. The arl1 mutant strain internalized ∼25% more [14C]-methylammonium ion than did the wild type, consistent with hyperpolarization of the plasma membrane. The arl1 strain took up 30-40% less 86Rb+ than did the wild type, showing an inability to regulate K+ import properly, contributing to membrane hyperpolarity. By contrast, K+ and H+ efflux were undisturbed. The loss of ARL1 had no effect on the steady-state level or the localization of a tagged version of Trk1p. High copy suppressors of the hygromycin-B phenotype included SAP155, encoding a protein that interacts with the cell cycle regulator Sit4p, and HAL4 and HAL5, encoding Ser/Thr kinases that regulate the K+-influx mediators Trk1p and Trk2p. These results are consistent with a model in which ARL1, via regulation of HAL4/HAL5, governs K+ homeostasis in cells.

A Central Support System Can Facilitate Implementation and Sustainability of a Classroom-Based Undergraduate Research Experience (CURE) in Genomics

There have been numerous calls to engage students in science as science is done. A survey of 90-plus faculty members explores barriers and incentives when developing a research-based genomics course. The results indicate that a central core supporting a national experiment can help overcome local obstacles.

Autophagy in Saccharomyces cerevisiae requires the monomeric GTP-binding proteins, Arl1 and Ypt6

ABSTRACT Macroautophagy/autophagy is a cellular degradation process that sequesters organelles or proteins into a double-membrane structure called the phagophore; this transient compartment matures into an autophagosome, which then fuses with the lysosome or vacuole to allow hydrolysis of the cargo. Factors that control membrane traffic are also essential for each step of autophagy. Here we demonstrate that 2 monomeric GTP-binding proteins in Saccharomyces cerevisiae, Arl1 and Ypt6, which belong to the Arf/Arl/Sar protein family and the Rab family, respectively, and control endosome-trans-Golgi traffic, are also necessary for starvation-induced autophagy under high temperature stress. Using established autophagy-specific assays we found that cells lacking either ARL1 or YPT6, which exhibit synthetic lethality with one another, were unable to undergo autophagy at an elevated temperature, although autophagy proceeds normally at normal growth temperature; specifically, strains lacking one or the other of these genes are unable to construct the autophagosome because these 2 proteins are required for proper traffic of Atg9 to the phagophore assembly site (PAS) at the restrictive temperature. Using degron technology to construct an inducible arl1Δ ypt6Δ double mutant, we demonstrated that cells lacking both genes show defects in starvation-inducted autophagy at the permissive temperature. We also found Arl1 and Ypt6 participate in autophagy by targeting the Golgi-associated retrograde protein (GARP) complex to the PAS to regulate the anterograde trafficking of Atg9. Our data show that these 2 membrane traffic regulators have novel roles in autophagy.

Ability of Sit4p To Promote K Efflux via Nha1p Is Modulated by Sap155p and Sap185p

ABSTRACT We demonstrate here that SAP155 encodes a negative modulator of K+ efflux in the yeast Saccharomyces cerevisiae. Overexpression of SAP155 decreases efflux, whereas deletion increases efflux. In contrast, a homolog of SAP155, called SAP185, encodes a positive modulator of K+ efflux: overexpression of SAP185 increases efflux, whereas deletion decreases efflux. Two other homologs, SAP4 and SAP190, are without effect on K+ homeostasis. Both SAP155 and SAP185 require the presence of SIT4 for function, which encodes a PP2A-like phosphatase important for the G1-S transition through the cell cycle. Overexpression of either the outwardly rectifying K+ channel, Tok1p, or the putative plasma membrane K+/H+ antiporter, Kha1p, increases efflux in both wild-type and sit4Δ strains. However, overexpression of the Na+-K+/H+ antiporter, Nha1p, is without effect in a sit4Δ strain, suggesting that Sit4p signals to Nha1p. In summary, the combined activities of Sap155p and Sap185p appear to control the function of Nha1p in K+ homeostasis via Sit4p.

Identification of Yeast Genes Involved in K + Homeostasis: Loss of Membrane Traffic Genes Affects K + Uptake

Using the homozygous diploid Saccharomyces deletion collection, we searched for strains with defects in K+ homeostasis. We identified 156 (of 4653 total) strains unable to grow in the presence of hygromycin B, a phenotype previously shown to be indicative of ion defects. The most abundant group was that with deletions of genes known to encode membrane traffic regulators. Nearly 80% of these membrane traffic defective strains showed defects in uptake of the K+ homolog, 86Rb+. Since Trk1, a plasma membrane protein localized to lipid microdomains, is the major K+ influx transporter, we examined the subcellular localization and Triton-X 100 insolubility of Trk1 in 29 of the traffic mutants. However, few of these showed defects in the steady state levels of Trk1, the localization of Trk1 to the plasma membrane, or the localization of Trk1 to lipid microdomains, and most defects were mild compared to wild-type. Three inositol kinase mutants were also identified, and in contrast, loss of these genes negatively affected Trk1 protein levels. In summary, this work reveals a nexus between K+ homeostasis and membrane traffic, which does not involve traffic of the major influx transporter, Trk1.

The CourseSource Bioinformatics Learning Framework

To The Editor: According to the Oxford English Dictionary (OED), bioinformatics is defined as “the branch of science concerned with information and information flow in biological systems, esp. the use of computational methods in genetics and genomics” (OED, 2015 ). Because the use of bioinformatics tools and approaches is becoming increasingly important for life scientists of all disciplines at all levels, it would be particularly advantageous for life sciences undergraduates to have some training in this field. As of yet, there is little agreement on a set of bioinformatics learning goals appropriate for undergraduate biology students. In an effort to move toward consensus in this area, we have developed a learning framework for a bioinformatics course that is part of the CourseSource initiative (Supplemental Material Table 1). CourseSource builds on the goals of Vision and Change in Undergraduate Education: A Call to Action (American Association for the Advancement of Science, 2011 ) by serving as a repository for tested teaching resources in a variety of different biological disciplines (Wright et al., 2013 ). CourseSource organizes teaching materials into courses that are part of the standard biology curriculum (http://coursesource.org). Each course is informed by a framework that has been vetted by an appropriate disciplinary society (e.g., the CourseSource framework for a genetics course was developed by representatives from the Education Committee of the Genetics Society of America). Core competencies for bioinformatics have been defined by the Curriculum Task Force of the Education Committee of the International Society for Computational Biology (Welch et al., 2014 ). The task force related the competencies to three different types of individuals requiring bioinformatics training: 1) bioinformatics engineers, who create novel computational methods needed by bioinformatics users and scientists; 2) bioinformatics scientists, who employ computational methods to advance the scientific understanding of living systems; and 3) bioinformatics users, who access data resources and bioinformatics tools to perform duties in specific application domains (e.g., medicine, law, agriculture, food science, education, etc.). As the starting place for the framework we used the bioinformatics user and bioinformatics scientist personas in particular (see table 2 in Welch et al., 2014 ) as well as our collective experience of integrating bioinformatics into our teaching. Three of us (M.A.P., A.G.R., and L.W.) worked collaboratively on the framework over several months, with input from S.C.R.E. and R.W. We then asked for feedback on the framework from groups with an interest in bioinformatics education, including members of the Genomics Education Partnership (http://gep.wustl.edu), the Network for Integrating Bioinformatics into Life Science Education (NIBLSE; http://niblse.unomaha.edu), and the Genome Consortium for Active Teaching NextGen Sequencing (http://lycofs01.lycoming.edu/∼gcat-seek), and participants in the Howard Hughes Medical Institute–sponsored Bioinformatics Workshop for Student/Scientist Partnerships that took place in June 2012 (http://gep.wustl.edu/hhmi_bioinformatics_workshop/index.html). The feedback we received was used to revise the framework. We are currently working with the International Society for Computational Biology to vet the framework. In addition, we expect that NIBLSE will also play a role in its ongoing development. As with most of the frameworks for other courses, the bioinformatics framework is organized around major topics with associated learning goals (framed as questions). A set of sample learning objectives, not meant to be exhaustive, is associated with each learning goal. In devising the framework (Supplemental Material Table 1), we organized the information around biological topics and computational ideas needed to address them. The first topic involves the role of computation in the life sciences. Subsequent topics involve concepts associated with the central dogma, beginning with DNA as the repository of genetic information, then considering RNA and proteins as means to express the genetic information. We next considered metabolomics and systems biology, exploring cellular homeostasis, and then examined topics in ecology and evolution, including metagenomics, thus moving from the level of individual cells to environmental samples. The final topic describes computational skills. CourseSource learning frameworks, including this one for bioinformatics, are not meant to be proscriptive. That is, there is no implication that a course should necessarily contain all of the elements in the associated framework. Instead, a course based on the learning framework will make use of an agreed-upon set of learning goals, and can take advantage of the associated expertise and materials posted in that particular field on CourseSource. For example, several of us teach bioinformatics courses that do not include substantial time spent on computer science skills, yet adhere to the overall learning goals and learning objectives within the framework. Overall, we feel that the existing framework will be generally applicable and useful to those attempting to launch a bioinformatics course at their institution for the first time. We therefore encourage all faculty members who are currently teaching bioinformatics to help populate the CourseSource bioinformatics framework with useful teaching materials to maximize utility of the site. Bioinformatics is an excellent way to introduce students to authentic research and is thus an effective means to achieve the goals of Vision and Change. We envision that the bioinformatics learning framework will continue to evolve as the field of bioinformatics grows. We welcome feedback from the life sciences community and encourage members to consider submitting their lessons, whether in bioinformatics or in other disciplines, to CourseSource.