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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.
Researchers in the group of Professor Tom Jones (MSI Fellow; Astronomy) are engaged in a long-term, unique, and highly successful study of the dynamics of diffuse, conducting media in astrophysical environments and their roles in mediating the acceleration and propagation of high-energy charged particles (so-called “cosmic rays”). The group has developed codes that combine high-performance, highly scalable, multidimensional magnetohydrodyamics (MHD) algorithms with uniquely efficient treatments of diffusive transport of cosmic rays.
In the image above, the left side illustrates results from an innovative MHD simulation carried out in November by Pete Mendygral and Tom Jones on MSI's Itasca supercomputer. The right side shows radio (pink) and X-ray (blue) astronomical observations of the astrophysical system that the simulation was intended to model (image credit: NASA and the National Radio Astronomy Observatory). The simulation modeled the dynamics of two pairs of supersonic, magnetized plasma jets. The jets were generated from sources that were orbiting each other while being subjected to a strong, supersonic cross wind. This simulation, which took roughly 10,000 CPU hours to complete, is the first of its kind. It was possible to set up, test, and execute the simulation over a span of three days prior to a symposium because of excellent HPC support from MSI. The underlying science is described below.The jets represent highly supersonic outflows from massive black holes at the centers of a pair of merging galaxies that are components of a pair of merging clusters of galaxies. As the black holes swallow nearby matter some of the energy released drives fast jets along the spin axes of the black holes. These are details of ongoing construction in the universe. On the biggest scales, clusters of galaxies spanning millions of light years fall together due to mutual gravity and collide to form bigger clusters over a time span of a few billion years. Some of the individual galaxies within those clusters also fall together, collide and merge together over time spans of a few hundred million years. Astronomers have established that all galaxies have massive black holes anchoring their centers. As two galaxies merge, gravitational effects cause their individual black holes to merge as well, also over a span of about 100 million years. Although cluster mergers are long lived enough to make them easy to find, it is much rarer to witness the faster merger of two massive black holes. But there are examples, including the very interesting pair of black holes in the galaxy cluster known as Abell 400, which is about 300 million light years from Earth. In this case two galaxies and their black holes are bound into an orbit about 20,000 light years apart and just beginning their inward spiral towards a merger. Each black hole, as it turns out is making plasma jets, so both are easy to find. The orbital motions of the black holes is causing the jets to become intertwined. At this point in the cluster merger the hot atmosphere of the clusters (revealed by X-rays) is "sloshing" in the gravitational well of the combined system. In effect the two merging galaxies are sitting in a very strong wind, which is blowing away the radio jets.
Work by Dr. Mendygral that involved a large data transfer was featured in a previous Research Spotlight.
MSI honored the retirement of the University of Minnesota Vice President for Research (VPR), Tim Mulcahy, with a special event on October 29, 2012. VP Mulcahy will be retiring from the University on December 31.
MSI came under the Office of the VPR in July 2008, and VP Mulcahy has been a staunch supporter of MSI’s mission during the past four years. He was instrumental in the procurement of Itasca, the Institute’s flagship supercomputer, and he has been a force in MSI’s expansion into new areas of research support, such as the University-wide informatics effort.
Also, on a day in 2011 that MSI staff will never forget, VP Mulcahy performed a song set to “La Donna e Mobile,” while dressed in full Renaissance costume. This event was a prize from VP Mulcahy to MSI for “highest increase in participation” among OVPR units in the 2011 Community Fund Drive.
The MSI retirement event for VP Mulcahy included a video game on the LMVL’s interactive screen, where he was asked to select various humorous options for his retirement. He was also presented with a paperweight that included a processor from Itasca and a musical card with an excerpt from his 2011 musical performance for MSI.
The MSI staff are grateful for VP Mulcahy’s support and we wish him well in his retirement.
Scientists are now using computers to help discover new drugs. Researchers at the University’s Center for Drug Design are in the forefront of these efforts. Professor Yuk Sham and his research group are studying the molecular recognition process of how molecules selectively associate with one another. Understanding the specific interactions involved is important for identifying and designing new drugs that can inhibit the normal function of viral proteins and stop the proliferation of viruses.
HIV/AIDS is an incurable disease. With the continuing emergence of drug resistance, it is extremely important to continue developing new drugs for the long-term management of the HIV/AIDS patients. Dr. Sham and his group, working closely with other members of the Center for Drug Design, use MSI high-performance computing clusters to develop accurate structural models to better understand these interactions to design a better inhibitor for the discovery and development of new antiviral drugs. The image above shows the structural model of HIV integrase developed by Professor Sham's research group. It is an essential viral enzyme that incorporates the HIV genome into human DNA. Effective inhibition of this enzyme is one of the validated approaches for the effective treatment of HIV/AIDS.
Dr. Sham’s group has also created videos describing the drug-development process against HIV/AIDS. They can be seen on YouTube:
"Targeting HIV Replication” (3:02 min)
"Making of an AIDS Drug” (2:26 min)