You are here
MSI researchers presented posters of their work at the 2013 MSI Research Exhibition on Thursday, April 11, 1:00-3:30 p.m., on the fourth floor of Walter Library. This was the fourth year that MSI held this event.
Poseted were judged by a panel of MSI Principal Investigators and prizes were awarded. Posters competed in one of two categories, Physical Sciences and Engineering or Biological and Medical Sciences. Entrants were from a wide variety of disciplines.
Light refreshments were served. More information, including photos of the event, can be found on the 2013 Research Exhibition webpage.
The pictures above were taken at the 2012 MSI Research Exhibition.
Associate Professor Kylie Walters received her Ph.D. in Biophysics from Harvard University. She joined the faculty of the University of Minnesota in 2002 and is in the Department of Biochemistry, Molecular Biology, and Biophysics. Professor Walters recently talked with an MSI staffer about her research and work with MSI.
MSI: How long have you been using MSI to support your research work?
Kylie Walters: I have been using MSI resources since I first came to the University of Minnesota, in 2002.
MSI: Could you talk a little about your research on ubiquitin signaling, what the goals of the research are?
KW: Ubiquitin signals are used for a large breadth of cellular events. Their most well-known use is to target proteins for degradation, including mis-folded or damaged proteins, or healthy ones that need to be removed under certain cellular conditions. Failure in ubiquitin signaling pathways is associated with cancer, neurodegenerative diseases, viral infection, and even diabetes.
MSI: How have MSI’s resources contributed to your research?
KW: My group uses structural biology tools to solve protein structures, characterize their dynamic behavior, and understand how they interact with each other. What we hope to obtain from this is a better understanding of protein signaling pathways, and ultimately to find new targets related to carcinogenesis and neurodegenerative diseases. Our most powerful technique is NMR spectroscopy, and we use MSI to process and analyze all of our NMR data. Specifically we use Itasca for our high-performance computational needs. Without Itasca some of our structural calculations would take a very long time. As we progress we try to characterize bigger and bigger complexes; consequently the calculation becomes larger as well. These calculations are greatly facilitated by parallel processing and supercomputing.
My group has also used MSI to help us procure various pieces of software. MSI also helped implement an on-site PDB (protein data bank). So now everything from the PDB is here in our lab. For example, we use a molecular modeling program called Rosetta. Rosetta is very good at taking a limited amount of information on a specific protein, then using the whole database of solved structures to come up with a model structure for that protein. This makes difficult proteins easier to analyze and you can access some structural data fairly quickly. Running Rosetta through the web can require greater than one month of waiting time, whereas, when we use MSI resources, we get results within a day.
MSI: How have your undergraduate and graduate students benefited from working with MSI?
KW: The facilities that MSI offers as well as their computer-savvy staff have been valuable resources in helping my graduate students to move their projects forward. I have also had undergraduate students participate in the MSI summer research program. The program gave the students a unique exposure to a much broader scope of research than in the lab. My students also go to the tutorials offered at MSI. The tutorials have been very useful.
MSI: Have any particular MSI staff members been critical to the success of your research efforts?
KW: Yuk Sham was very helpful in the area of molecular modeling when he was at MSI and we even co-authored a paper together [Editor’s note: Dr. Sham is currently Assistant Director of the Center for Drug Design as well as an MSI Principal Investigator]. My students have also worked with Nancy Rowe [Scientific Computing Consultant and Manager of the BSCL and BMSDL]. For example, graduate student Aaron Ehlinger worked with Nancy to get programs and software up and running. In many cases MSI will optimize source codes for software that we use in the BSCL.
MSI: How might MSI prepare to provide the cutting-edge support you’ll need going forward? For example, software applications or staff competencies we might add?
KW: I really feel like your support, software, and hardware have been fantastic. We are all around happy with our experience with MSI.
Description of Figure: Structure of proteasome ubiquitin receptor Rpn13 calculated by XPLOR-NIH version 2.24 in the MSI Basic Sciences Computing Laboratory. 347 paramagnetic relaxation enhancement distance constraints (dashed lines) were used to define inter-domain interactions between Rpn13's ubiquitin binding domain (orange) and its Uch37-binding domain (blue).
Description of figure: An example of a hexadehydro-Diels-Alder cascade (1 to 3). Simple triyne substrates like 1 cycloisomerize to reactive benzyne intermediates like 2, which are then captured by various trapping agents (in this example, an internal silylether) to produce structurally complex benzenoid products like 3.
The research group of Professor Thomas Hoye (Chemistry) uses MSI for their investigations of the thermodynamics of reactions involving the highly reactive organic species benzyne. Benzynes (cf. 2 in graphic above) are widely studied because their reactions with other chemical substances are very useful. The resulting compounds (cf. 3, “benzenoid products”) are useful in the development and synthesis of pharmaceutical drugs, agrochemicals, dyes, and polymers.
Related to this work, the Hoye group recently published some interesting and unexpected results in the prestigious journal Nature. This paper describes the generality of a fundamentally new reaction: the hexadehydro-Diels-Alder reaction (“The Hexadehydro-Diels-Alder Reaction,” Nature, DOI: 10.1038/nature11518, 2012). This chemistry generates ortho-benzyne by a simple metal- and reagent-free thermal cycloisomerization of a triyne precursor (cf 1). This is important because metals and other reagents can affect the subsequent chemical trapping reactions of the benzyne (e.g. 2 to 3). Many new types of reactions have been discovered with this new strategy for benzyne synthesis. These are both synthetically useful and mechanistically interesting.
The group is now using MSI to understand these new reactivities from the computational perspective. They are using density functional theory (DFT) calculations to study both the thermodynamics and the transition structures of various stages of the overall process. They don’t yet completely understand the mechanism of the hexadehydro-Diels-Alder reaction itself, and the DFT calculations are being used to complement the group’s experiments. One main goal is to determine whether the reaction is stepwise (cf. 4b), proceeding through diradical intermediates (as has been suggested in other systems) or concerted (cf. 4a, which is suggested by experiments). If the reaction is, in fact, concerted, it will be compatible with radical-sensitive substrates, which increases the overall synthetic utility. Similar questions about the concerted (cf. 5a) vs. stepwise (cf. 5b) nature of the benzyne trapping steps (cf. 2 to 3) are also being explored through computation.
Understanding the Biophysics of Parkinson’s Disease: Modeling Interactions Between α-Synuclein and Cellular Membranes
The research group of Assistant Professor Jonathan Sachs (Biomedical Engineering) uses MSI for their study of the behavior of proteins and membranes. Mutations in one particularly interesting and important protein, α‐Synuclein (αS), are associated with Parkinson’s disease (PD), a common neurodegenerative disorder that affects 1% of Americans over the age of 65. The pathology of PD involves the selective loss of dopaminergic neurons and the presence of dense, spherical cytoplasmic inclusions, known as Lewy bodies, in the substantia nigra. αS is a 140 amino acid, natively unfolded protein that is the primary component of the fibrillar aggregates that make up Lewy bodies. The precise role of αS in the pathology of PD remains unclear. However, findings in early-onset PD patients, namely the identification of three rare autosomal dominant mutations, along with incidences of multiplication of the αS gene, have established that αS is critical to the progression of the disease.
In its normal state, αS is abundantly expressed in neurons, where some of it is localized to the pres-synaptic nerve endings and binds tightly to synaptic vesicles. The native function of αS is poorly understood, although some evidence suggests that it may play a role both in maintaining neuronal plasticity and in the regulation of synaptic vesicle recycling. It has also been proposed that interactions of specific aggregated states of αS with cellular membranes may be a mechanism of cytotoxicity in PD. Because both its normal function as a synaptic vesicle-associated protein, as well as its proposed pathological function in PD, likely involve interactions with cellular membranes, a complete characterization of the nature of αS/membrane interactions is of great interest. Understanding these interactions is a principle goal of the Sachs lab, and a combined computational and experimental strategy that brings together resources from MSI and the University of Minnesota’s Characterization Facility is being pioneered by Sachs’s PhD student, Anthony Braun.
It has recently become clear that amphipathic α‐helices, like αS, act by wedging between lipid headgroups and causing the membrane to curve, a morphological change necessary for a lipid vesicle to either bud or fuse. Many such proteins have been studied for their ability to cause gross‐deformations in vesicles (see the figure, above), namely the formation of lipid tubes (tubulation) or smaller vesicles (vesiculation). Theories have suggested that a helix must be sufficiently long and bind at a specific depth within the phospholipid bilayer in order to affect these changes.
The Sachs group is developing very large molecular simulations of amphipathic α‐helices bound to membranes in order to establish the relative importance of each of these attributes, as well as the contribution of helical flexibility, which this group has recently shown to contribute to membrane curvature in a unique way. The group is testing the hypothesis that the essential properties of a curvature‐inducing α‐helix, like αS, are not uniquely encoded in the primary sequence, but instead result from the sum of the protein’s physical attributes (length, hydrophobicity and flexibility). This research involves molecular dynamics computations on the supercomputers and results have been published recently in the Journal of the American Chemical Society (“alpha-Synuclein Induces Both Positive Mean Curvature and Negative Gaussian Curvature in Membranes,” A.R. Braun, E. Sevcsik, P. Chin, E. Rhoades, S. Tristram-Nagle, and J.N. Sachs, J Am Chem Soc 134, 2613-2620 (2012), DOI:10.1021/ja208316h) and the Journal of Biological Chemistry (“Curvature Dynamics of Alpha-Synuclein Familial Parkinson Disease Mutants: Molecular Simulations of the Micelle- and Bilayer-Bound Forms,” J.D. Perlmutter, A.R. Braun, and J.N. Sachs, J Biol Chem 284, 7177-7189 (2009), DOI:10.1074/jbc.M808895200).
Other work from the Sachs lab on the Tumor Necrosis Factor receptors has also utilized the MSI resources, and focuses on understanding conformation changes in these proteins to establish new therapeutic strategies for cancer and auto-inflammatory disease (“TNFR1 Signaling Is Associated With Backbone Conformational Changes of Receptor Dimers Consistent With Overactivation in the R92Q TRAPS Mutant,” A.K. Lewis, C.C. Valley, and J.N. Sachs, Biochemistry 51, 6545-6555 (2012), DOI:10.1021/bi3006626) and “Tumor Necrosis Factor-related Apoptosis-inducing Ligand (TRAIL) Induces Death Receptor 5 Networks That Are Highly Organized,” C.C. Valley, A.K. Lewis, D.J. Mudaliar, J.D. Perlmutter, A.R. Braun, C.B. Karim, D.D. Thomas, J.R. Brody, and J.N. Sachs, J Biol Chem 287, 21265-21278 (2012), DOI:10.1074/jbc.M111.306480).
Figure Description: Building on their recent study that characterized the curvature fields induced in membranes by α-Synuclein (J Am Chem Soc 134, 2613-2620 (2012), DOI:10.1021/ja208316h), and relying upon the immense computational power of MSI, the Sachs group is running massive coarse-grained molecular dynamics simulations in order to study remodeling of the membrane by the protein. Shown is a snapshot with α-Synuclein (yellow beads) sitting atop a curved membrane (blue and pink beads). Water has been excluded from the image. The simulation was started with a flat membrane, but after 1 microsecond of calculated dynamics the curvature has become clear. The membrane is comprised of more than 50,000 fully hydrated lipids, requiring more than 4x106 total beads. Periodic images are included to illustrate the global topography (green beads). For these massive molecular simulations, the group takes advantage of MSI’s Itasca cluster, operating on 1024 cores (128 nodes, 8 processors per node), and achieving approximately 250 nanoseconds per 24-hour window.
Editor’s note: As we were preparing this article for publication, we learned of the death of Dr. John Ohlfest, who was a collaborator on the research discussed here. See the paragraph at the end of the article for more information about Dr. Ohlfest and his research.
Dr. David Largaespada received his Ph.D. in Cellular and Molecular Biology at the University of Wisconsin-Madison in 1992. He joined the University of Minnesota's Department of Laboratory Medicine and Pathology in 1996 and the Department of Genetics, Cell Biology, and Development in 1999, where he is currently a professor. He holds the Margaret Harvey Schering Chair in Cancer Genetics. In 2012, he was awarded the American Cancer Society’s Research Professor Award.
Dr. Largaespada and his research assistant Sue Rathe (a graduate student in the Microbiology, Immunology, and Cancer Biology program) sat down with an MSI staffer to talk about their research and close work with MSI.
MSI: How long have you been using MSI resources?
DL: I have been using MSI resources since I first started at the U of M as a researcher. So, about 16 years.
MSI: Could you describe the research you are doing on personal tumor vaccines?
DL: The work we are doing is a collaboration with John Olfest’s lab in the Department of Pediatrics. This project is funded by a grant from the Children’s Cancer Research Fund (CCRF).
The goal of this research is to examine childhood tumors for mutations that result in altered proteins, which we can use as a basis for making a vaccine. Each vaccine would be specifically designed for a particular patient’s tumor.
Research and literature that has already been published shows that cancer cells accumulate genetic abnormalities. Some of these gene mutations actually drive cancer development (drivers) and some are the results of random changes to the DNA that occur when cells divide many times (called passengers). In fact many tumors are genetically unstable. What this means, though, is that the altered proteins, whether they are driver mutations or passenger mutations, can be recognized by the immune system as foreign proteins. If we could present those foreign proteins to the immune system in the right way, at high levels with drugs called adjuvants that boost the immune response to an antigen or foreign protein, then we might be able to get someone’s immune system to attack the tumors they have.
For this to work, because everyone’s tumor has a different suite of mutations that are present, in each and every patient we would have to sequence their DNA and find all the alterations that are present. We would then make a vaccine that is specifically for that person’s tumor. So this is the challenge: Can we develop an automated pipeline that starts with sequence information and results in a list of candidate alterations that can be used for making a multivalent peptide vaccine cocktail for that patient and produce it in enough time for it to be useful for that patient?
We are currently working with patient material that we have obtained through the tissue repository here at the University and also mouse model tumors, because we want to test this whole concept in mouse models first before we try it on people. We realized if we want to make this possible we are going to need to use very powerful deep sequencing technology.
We also realized that there is no reason to sequence the whole DNA genome of the tumor cell because most of the genome is DNA sequence in between genes and not expressed and made into proteins. So we decided to sequence the RNA, or so-called transcritptome, of the tumor cell. So what we have been doing is isolating RNA from mouse or human tumors, then that RNA is converted to DNA using reverse transcriptase enzyme so you can make a so-called cDNA library, a DNA version of all the transcripts that are expressed in the tumor cell. After this, we sequence that cDNA library from these tumors using the Illumina HiSeq machine and MSI resources to analyze that enormous amount of sequence data.
We have to take all of that sequence data and basically look for variations from normal behavior that would result in the production of altered protein sequences that could be used as a basis for making a vaccine. This part turned out to be way more complicated then I thought it would be initially, because the processing of that RNA-seq data is very complex and distilling it down to the meaningful, real alterations is not simple. As part of this project we went back and used other technologies to verify the mutations we thought were there based on our RNA-seq.
Sue, do you want to talk about how much Seq data you ended up getting and some of the challenges you faced?
MSI: Sue, was this the project you worked on with Kevin Silverstein from our RISS group and Jim Johnson from our Applications Development group, using the pipeline called Missense Mutation and Frameshift Finder (MMuFF)?
DL: Yes, Kevin and Jim are part of the team.
SR: Yes, that’s one part of it. We are looking for any type of mutation that will modify the proteins. The types of mutations that we are looking for are missense mutations, frame shift mutations which result from small insertions or deletions, fusions, between two genes that aren’t normally together, and any kind of abnormal splicing in the gene that has never been seen before. We have been tackling this in three separate areas: the missense frameshift mutation is one, fusion is the second, and then the alternative splicing is the third part.
We have made a lot of progress on the MMuFF software. Jim has done an amazing job on this project. He has distilled all of the potential candidates down by looking for data anomalies, so he has added a lot of coding to eliminate those from the data. He is also checking SNP databases. SNPs are single nucleotide polymorphisms that naturally occur either in mouse or human genomes. They’re known mutations that are considered normal so we don’t need to look at those. We are only looking for the abnormal ones, and Jim has code that eliminates normal mutations from consideration. So, in addition to distilling the information into the real candidates, he also formats the information in a meaningful way to us. He is giving us information that we can then immediately use to go and create a protein that matches the novel protein. It’s a wonderful tool set.
MSI: What other resources do you utilize at MSI, and how have these resources enhanced your research capabilities?
DL: The Galaxy tool set is great.
SR: Yes, the Galaxy tool set is wonderful. MSI is providing tools that biologists can use to analyze their own data. This is helpful in the sense that you don’t have to wait on IT resources. For example the MMuFF tool Jim is working on will be hugely valuable to everyone once we have perfected it and get it added to the Galaxy suite of tools.
Right now there is an incredible amount of data being generated and to try to process this amount of data without a supercomputer would be impossible. So just the physical resources that are provided in terms of disk space and processor power is beyond measure.
MSI: One of our missions at MSI is to contribute to the education of the U’s graduate and undergraduate students. Have your students been able to attend any of our tutorials or software seminars and then apply what they have learned and has MSI facilitated your students’ development in any other way?
DL: Sue is a graduate student and I know she has gone to a number of tutorials. Sue, what do you think?
SR: Yes, I have gone to many of them and I know a number of other graduate students have as well. In regards to the tutorials I know that, initially, MSI was playing catch up because the BMGC brought in a HiSeq machine and started to produce huge amounts of data that MSI wasn’t ready for. There were no tutorials set up for using Galaxy at that point. When we first started using Galaxy it was a painful process, but I have been very impressed with how well MSI has developed training material. I have gone to all of MSI’s new training sessions so I could experience them and give feedback on how well they are doing and it’s quite impressive. They have caught up now and are providing some very useful training for the biologists, in that the biologists can do their own analysis. I will say it is still very daunting to the non-tech-savvy researcher and I believe there is more that can be done to make using these tools more user friendly to the biologists, but MSI is definitely doing a great deal to progressing to that end.
Description of image above: The normal (reference) transcript appears at the bottom of the first diagram and near the top of the second diagram. The thickest bars on the reference transcripts show the translated portions of the gene, the thinnest bars show the introns, and the medium size bars show the untranslated regions (UTRs). The arrows overlaying the introns show the direction of transcription. The top graph depicts the number of reads mapping to each area of the genome. It shows abnormal transcription occurring within the sixth intron of Dck and beyond the end of the 3’-UTR in the B117H sample, indicating an abnormally spliced gene. This same pattern appeared when the transcripts were assembled de novo (without the benefit of a transcript reference file), and then mapped back to the reference genome (bottom diagram). Sanger sequencing of the RNA verified the unusual Dck transcript found in B117H, and Sanger sequencing of the DNA identified an 878 bp deletion starting within the last intron and extending into the last exon.
MSI is sorry to note that Professor Largaespada’s collaborator, Dr. John Ohlfest, passed away on January 21, 2013. Dr. Ohlfest studied molecular biology Iowa State University and received his B.S. in 2001. He received his Ph.D. at the University of Minnesota in 2004 working on gene therapy approaches to treating malignant gliomas. He joined the faculty of the Department of Neurosurgery in 2005 and was the Director of the Neurosurgery Gene Therapy Program and a researcher in the Masonic Cancer Center. MSI staff extend their deepest condolences to Dr. Ohlfest’s family, friends, and colleagues.