Robin Wright

Transmission electron microscopy of yeast

The challenges of sample preparation can limit a researchers selection of transmission electron microcopy (TEM) for analysis of yeast. However, with the exception of thin sectioning, preparation of well‐fixed and infiltrated samples of yeast cells is achievable by any reasonably equipped laboratory. This review presents a general overview of TEM sample preparation methods and detailed protocols for chemical fixation of yeast for ultrastructural analysis and immunolabeling. For ultrastructural analysis, the most commonly used chemical fixation involves treatment with glutaraldehyde followed by either potassium permanganate or osmium. Prior to osmium postfixation, the cell wall must be enzymatically digested to allow optimal fixation and embedding. Freeze substitution methods continue to provide the highest quality of fixation, but equipment needed for these protocols is not generally available to many labs. The low viscosity of Spurrs resin makes it the resin of choice for ultrastructure studies. Immunoelectron microscopy has enjoyed great success in analysis of yeast molecular organization. For immunoelectron microscopy, glutaraldehyde/formaldehyde‐fixed cells are embedded in LR White resin. The thin sections are then treated in much the same way as an immunoblot: following blocking, they are incubated in primary antiserum, washed, and then incubated in gold‐labeled secondary antiserum. Microsc. Res. Tech. 51:496–510, 2000.

Isolation and characterization of PEP3, a gene required for vacuolar biogenesis in Saccharomyces cerevisiae.

The Saccharomyces cerevisiae PEP3 gene was cloned from a wild-type genomic library by complementation of the carboxypeptidase Y deficiency in a pep3-12 strain. Subclone complementation results localized the PEP3 gene to a 3.8-kb DNA fragment. The DNA sequence of the fragment was determined; a 2,754-bp open reading frame predicts that the PEP3 gene product is a hydrophilic, 107-kDa protein that has no significant similarity to any known protein. The PEP3 predicted protein has a zinc finger (CX2CX13CX2C) near its C terminus that has spacing and slight sequence similarity to the adenovirus E1a zinc finger. A radiolabeled PEP3 DNA probe hybridized to an RNA transcript of 3.1 kb in extracts of log-phase and diauxic lag-phase cells. Cells bearing pep3 deletion/disruption alleles were viable, had decreased levels of protease A, protease B, and carboxypeptidase Y antigens, had decreased repressible alkaline phosphatase activity, and contained very few normal vacuolelike organelles by fluorescence microscopy and electron microscopy but had an abundance of extremely small vesicles that stained with carboxyfluorescein diacetate, were severely inhibited for growth at 37 degrees C, and were incapable of sporulating (as homozygotes). Fractionation of cells expressing a bifunctional PEP3::SUC2 fusion protein indicated that the PEP3 gene product is present at low abundance in both log-phase and stationary cells and is a vacuolar peripheral membrane protein. Sequence identity established that PEP3 and VPS18 (J. S. Robinson, T. R. Graham, and S. D. Emr, Mol. Cell. Biol. 11:5813-5824, 1991) are the same gene.

Transmission electron microscopy and immunocytochemical studies of yeast: analysis of HMG-CoA reductase overproduction by electron microscopy.

The results and anecdotes presented here are intended only as a general guide to other would-be immunocytochemists, because other proteins will undoubtedly respond at least somewhat differently than does HMG-CoA reductase. Nevertheless, based on these experiences, we offer the following suggestions: 1. Antiserum of high specificity should be raised and affinity-purified. Using this antiserum, immunofluorescence microscopy should be attempted before resorting to electron microscopic localization. In the absence of immunolocalization at the light-microscope level, it may be a waste of time to pursue the problem to higher levels of resolution. 2. Cells should be prefixed in 1% formaldehyde-1% glutaraldehyde. Direct fixation of the growing culture and use of phosphate buffer are recommended. The prefixed sample can then be divided into two or three aliquots. One aliquot should receive no postfixation (for optimal immunoreactivity), while the others can be postfixed in osmium-potassium ferricyanide (for possible immunolocalization) or permanganate (for ultrastructural analysis). Because of its ease of use, Spurrs resin should be tried initially. If immunocytochemistry is successful, no further preparations are necessary. If unsuccessful, LR White resin is recommended, but the sample must be treated to remove the cell wall. Electron microscopy and immunocytochemistry offer views into the molecular arrangement of individual cells, a view not easily obtained by other means. It is satisfying and often enlightening to be able to see the extremes as well as the average. In studies of the organization of karmellae, for example, ultrastructural analysis easily revealed the asymmetric segregation pattern, while immunoblots and cell fractionation could not even demonstrate the existence of this membrane organization. The richness of the information available to those who can avert reductionist tendencies, even for a short time, is remarkable.

Molecular, functional and evolutionary characterization of the gene encoding HMG-CoA reductase in the fission yeast, Schizosaccharomyces pombe

The synthesis of mevalonate, a molecule required for both sterol and isoprene biosynthesis in eukaryotes, is catalysed by 3‐hydroxy‐3‐methylglutaryl coenzyme A (HMG‐CoA) reductase. Using a gene dosage approach, we have isolated the gene encoding HMG‐CoA reductase, hmg1+, from the fission yeast Schizosaccharomyces pombe (Accession Number L76979). Specifically, hmg1+ was isolated on the basis of its ability to confer resistance to lovastatin, a competitive inhibitor of HMG‐CoA reductase. Gene disruption analysis showed that hmg1+ was an essential gene. This result provided evidence that, unlike Saccharomyces cerevisiae, S. pombe contained only a single functional HMG‐CoA reductase gene. The presence of a single HMG‐CoA reductase gene was confirmed by genomic hybridization analysis. As observed for the S. cerevisiae HMG1p, the hmg1+ protein induced membrane proliferations known as karmellae. A previously undescribed ‘feed‐forward’ regulation was observed in which elevated levels of HMG‐CoA synthase, the enzyme catalysing the synthesis of the HMG‐CoA reductase substrate, induced elevated levels of hmg1+ protein in the cell and conferred partial resistance to lovastatin.

Parallel analysis of tagged deletion mutants efficiently identifies genes involved in endoplasmic reticulum biogenesis

Increased levels of HMG‐CoA reductase induce cell type‐ and isozyme‐specific proliferation of the endoplasmic reticulum. In yeast, the ER proliferations induced by Hmg1p consist of nuclear‐associated stacks of smooth ER membranes known as karmellae. To identify genes required for karmellae assembly, we compared the composition of populations of homozygous diploid S. cerevisiae deletion mutants following 20 generations of growth with and without karmellae. Using an initial population of 1557 deletion mutants, 120 potential mutants were identified as a result of three independent experiments. Each experiment produced a largely non‐overlapping set of potential mutants, suggesting that differences in specific growth conditions could be used to maximize the comprehensiveness of similar parallel analysis screens. Only two genes, UBC7 and YAL011W, were identified in all three experiments. Subsequent analysis of individual mutant strains confirmed that each experiment was identifying valid mutations, based on the mutants sensitivity to elevated HMG‐CoA reductase and inability to assemble normal karmellae. The largest class of HMG‐CoA reductase‐sensitive mutations was a subset of genes that are involved in chromatin structure and transcriptional regulation, suggesting that karmellae assembly requires changes in transcription or that the presence of karmellae may interfere with normal transcriptional regulation. Copyright

Endoplasmic Reticulum-Associated Degradation Is Required for Cold Adaptation and Regulation of Sterol Biosynthesis in the Yeast Saccharomyces cerevisiae

ABSTRACT Endoplasmic reticulum-associated degradation (ERAD) mediates the turnover of short-lived and misfolded proteins in the ER membrane or lumen. In spite of its important role, only subtle growth phenotypes have been associated with defects in ERAD. We have discovered that the ERAD proteins Ubc7 (Qri8), Cue1, and Doa10 (Ssm4) are required for growth of yeast that express high levels of the sterol biosynthetic enzyme, 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR). Interestingly, the observed growth defect was exacerbated at low temperatures, producing an HMGR-dependent cold sensitivity. Yeast strains lacking UBC7, CUE1, or DOA10 also assembled aberrant karmellae (ordered arrays of membranes surrounding the nucleus that assemble when HMGR is expressed at high levels). However, rather than reflecting the accumulation of abnormal karmellae, the cold sensitivity of these ERAD mutants was due to increased HMGR catalytic activity. Mutations that compromise proteasomal function also resulted in cold-sensitive growth of yeast with elevated HMGR, suggesting that improper degradation of ERAD targets might be responsible for the observed cold-sensitive phenotype. However, the essential ERAD targets were not the yeast HMGR enzymes themselves. The sterol metabolite profile of ubc7Δ cells was altered relative to that of wild-type cells. Since sterol levels are known to regulate membrane fluidity, the viability of ERAD mutants expressing normal levels of HMGR was examined at low temperatures. Cells lacking UBC7, CUE1, or DOA10 were cold sensitive, suggesting that these ERAD proteins have a role in cold adaptation, perhaps through effects on sterol biosynthesis.

Minimal Impact of Organic Chemistry Prerequisite on Student Performance in Introductory Biochemistry

Curriculum design assumes that successful completion of prerequisite courses will have a positive impact on student performance in courses that require the prerequisite. We recently had the opportunity to test this assumption concerning the relationship between completion of the organic chemistry prerequisite and performance in introductory biochemistry. We found no statistically significant differences between average biochemistry grades or grade distribution among students with or without the organic chemistry prerequisite. However, students who had not completed the organic chemistry prerequisite before biochemistry were more likely to withdraw from the course than those who had completed the prerequisite. In contrast to the lack of correlation between performance in biochemistry and completion of organic chemistry, we observed a strong, highly significant positive relationship between cumulative GPA and the biochemistry grade. Our data suggest that excluding students without organic chemistry would have less positive impact on student success in biochemistry than would providing additional support for all students who enroll in biochemistry with a cumulative GPA below 2.5.

Mutational analysis of the karmellae‐inducing signal in Hmg1p, a yeast HMG–CoA reductase isozyme

In response to elevated levels of HMG–CoA reductase, an integral endoplasmic reticulum (ER) membrane protein, cells assemble novel ER arrays. These membranes provide useful models for exploration of ER structure and function, as well as general features of membrane biogenesis and turnover. Yeast express two functional HMG–CoA reductase isozymes, Hmg1p and Hmg2p, each of which induces morphologically different ER arrays. Hmg1p induces stacks of paired nuclear‐associated membranes called karmellae. In contrast, Hmg2p induces peripheral ER membrane arrays and short nuclear‐associated membrane stacks. In spite of their ability to induce different cellular responses, both Hmg1p and Hmg2p have similar structures, including a polytopic membrane domain containing eight predicted transmembrane helices. By examining a series of recombinant HMG–CoA reductase proteins, our laboratory previously demonstrated that the last ER‐lumenal loop (Loop G) of the Hmg1p membrane domain contains a signal needed for proper karmellae assembly. Our goal was to examine the primary sequence requirements within Loop G that were critical for proper function of this signal. To this end, we randomly mutagenized the Loop G sequence, expressed the mutagenized Hmg1p in yeast, and screened for inability to generate karmellae at wild‐type levels. Out of approximately 4000 strains with Loop G mutations, we isolated 57 that were unable to induce wild‐type levels of karmellae assembly. Twenty‐nine of these mutants contained one or more point mutations in the Loop G sequence, including nine single point mutants, four of which had severe defects in karmellae assembly. Comparison of these mutations to single point mutations that did not affect karmellae assembly did not reveal obvious patterns of sequence requirements. For example, both conservative and non‐conservative changes were present in both groups and changes that altered the total charge of the Loop G region were observed in both groups. Our hypothesis is that Loop G serves as a karmellae‐inducing signal by mediating protein–protein or protein–lipid interactions and that amino acids revealed by this analysis may be important for maintaining the proper secondary structure needed for these interactions. Copyright

Proliferation of the endoplasmic reticulum occurs normally in cells that lack a functional unfolded protein response

Increased expression of certain ER membrane proteins leads to biogenesis of novel ER membrane arrays. These structures provide models in which to explore the mechanisms by which cells control the size and organization of organelles in response to changing physiological demands. In yeast, elevated levels of HMG‐CoA reductase induce ER arrays known as karmellae. Cox and co‐workers (1997) discovered that karmellae assembly is toxic to ire1 mutants. These mutants are unable to initiate the unfolded protein response, which enables cells to adjust levels of ER chaperones in response to stresses. We sought to determine whether the karmellae‐dependent death of ire1 mutants was due to karmellae assembly or to increased levels of HMG‐CoA reductase activity. Unexpectedly, we found that ire1 cells could assemble normal levels of karmellae that were structurally identical to those of wild‐type cells. In addition, karmellae assembly did not itself induce the unfolded protein response. Certain ire1 strains produced significant numbers of transformants that were unable to utilize galactose as sole carbon source. These results suggest that the karmellae‐dependent death of certain ire1 strains may simply reflect their inability to grow on galactose. Copyright

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.