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Stem rust, caused by a number of varieties of the fungus Puccinia graminis, is a serious disease of wheat and barley. One variety of wheat stem rust, called Ug99, is particularly virulent, causing up to 100% crop losses. This pathogen affects most varieties of wheat that are grown today. Ug99 has spread throughout eastern Africa, and has also been detected in Yemen and Iran. While there are wheat lines that have been identified as resistant, these lines are not generally the high-yielding varieties that are useful for large-scale farming. The further spread of Ug99 could cause a disaster for food production.
Research Molecular Geneticist Nirmala Jayaveeramuthu and Adjunct Assistant Professor Matthew Rouse (Plant Pathology; USDA-ARS Cereal Disease Laboratory) study wheat-rust resistance genes in wheat and barley. Their lab uses MSI resources to map these genes. Should it be possible to identify molecular markers that are linked to resistance genes, plant breeders might be able to use them to develop strains of wheat resistant to Ug99.
In a project funded by the Bill and Melinda Gates Foundation and the US Department of Agriculture, the Rouse lab has been studying the wheat cultivar Gabo 56, which is resistant to Ug99. They have crossed this cultivar with Chinese spring wheat, a susceptible variety. Both Ugg99-susceptible and resistant progeny have resulted from this crossing. The group is now working with the RISS group to implement a novel method to analyze RNA-Seq data and identify cDNA sequences linked to the resistance gene. Using a de novo transcriptome assembly, transcripts with segregated expression are identified and SNPs are identified and used to create markers to finely map the resistance gene.
Image description: Left: Sample of genetic results of crossing susceptible and resistant plant. Right: Wheat “family tree” with genetic information.
posted on February 5, 2014
Among different types of cancers, prostate cancer has the highest incidence rate among men, and the second-highest death rate. In early stages of prostate cancer, radiation and surgical resection represent the standard of care, and often lead to good outcomes. In advanced cases, various forms of androgen depletion therapies can achieve effective control of the disease. This is because prostate cancer is hormone-dependent, wherein androgens (primarily testosterone and its derivates) fuel the growth of prostate cancer cells. Unfortunately, this only buys the patient some time, because androgen depletion eventually stops working and the cancer becomes resistant to treatment.
The research lab of Assistant Professor Scott Dehm (Masonic Cancer Center) has discovered a new molecular cause of therapeutic resistance - it arises due to the rearrangement of the Androgen Receptor in prostate cancer cells, resulting in the loss of the part of AR that binds androgens. The AR is a steroid hormone receptor transcription factor important for normal prostate function, as well as for the growth of prostate cancer.
Researchers in the Dehm lab are studying the AR using molecular, biochemical, cell biology, and genetic approaches. They are working with members of MSI’s RISS group to explore and characterize the rearrangements that occur in the 180kb Androgen Receptor (AR) locus in models of prostate cancer progression and in clinical tissues from prostate cancer patients. MSI has provided extensive CPU cycles and storage to enable the mapping and identification of structural changes in the genome.
This research has resulted in a recent paper that appeared in the Proceedings of the National Academy of Sciences of the USA. The paper, entitled “TALEN-engineered AR gene rearrangements reveal endocrine uncoupling of androgen receptor in prostate cancer” (M.D. Nyquist, Y. Li, T.H. Hwang, L.S. Manlove, R.L. Vessella, K.A.T. Silverstein, D.F. Voytas, S.M. Dehm, PNAS, 11(43):17492, DOI:10.1073/pnas.1308587110 (2013)), describes the discovery of AR gene rearrangements in prostate cancer tissues. It also discusses how the group uses genome engineering to classify the gene rearrangements as drivers of resistance. The researchers hope that this knowledge will help with the development of more effective prostate cancer treatment. (Note: M.D. Nyquist, Y. Li, and T.H. Hwang are MSI users in the Dehm group. D.F. Voytas is an MSI Principal Investigator from the Department of Genetics, Cell Biology, and Development. K.A.T. Silverstein is the MSI RISS Operations Manager and Scientific Lead.)
Other recent publications by the Dehm group that used MSI resources include:
- “Androgen Receptor Splice Variants Mediate Enzalutamide Resistance in Castration-Resistant Prostate Cancer Cell Lines,” Y.M. Li, S.C. Chan, L.J. Brand, T.H. Hwang, K.A.T. Silverstein, S.M. Dehm, Cancer Research, 73:483, DOI: 10.1158/0008-5472.CAN-12-3630 (2013)
- “AR Intragenic Deletions Linked to Androgen Receptor Splice Variant Expression and Activity in Models of Prostate Cancer Progression,” Y. Li, T.H. Hwang, L. Oseth, A. Hauge, R.L. Vessella, S.C. Schmechel, B. Hirsch, K.B. Beckman, K.A. Silverstein, S.M. Dehm, Oncogene, 31:4759-67 (2012)
- “Intragenic Rearrangement and Altered RNA Splicing of the Androgen Receptor in a Cell-Based Model of Prostate Cancer Progression,” Y. Li, M. Alsagabi, D. Fan, G.S. Bova, A.H. Tewfik, S.M. Dehm, Cancer Research, 71:2108-17 (2011)
Image Description: (Top): Structure of testosterone (left) and its derivate dihydrotestosterone. (Bottom): Dihydrotestosterone ligand interactions with the androgen receptor ligand-binding domain binding-pocket. Wild-type complex (gold), shown as a top view of the steroidal plane. The ligand and receptor interact mainly through H-bonds (dotted lines) and hydrophobic interactions. [bottom image adapted from Nat Clin Pract Endocrinol Metab, 2006 March; 2(3): 146-159, DOI: 10.1038/ncpendmet0120]
Posted on January 22, 2014.
Above: Random events may tip the fate of living organisms. Master equations can be used to understand these random events.
Biology is full of randomness – as random molecular events take place in organisms, populations evolve. Master probability equations can provide a complete model of this probabilistic behavior in biomolecular networks. These equations purport to govern all possible outcomes, hence the title “master.” Although these equations have enormous potential in biological research, their complexity has meant that solutions had only been found for the simplest molecular interaction networks.
MSI Principal Investigator and Fellow Yiannis Kaznessis and his PhD student Patrick Smadbeck recently published a paper that describes a numerical closure scheme for the master probability equation that governs random molecular events in chemical or biochemical reactions. The solution involves ordinary differential equations that describe the time evolution of probability distribution moments. The solution by Professor Kaznessis and Mr. Smadbeck will allow researchers to mathematically conceptualize a wide range of experimental observations. Their paper was published in the Proceedings of the National Academy of Sciences of the USA (“A Closure Scheme for Chemical Master Equations,” P Smadbeck, Y Kaznessis, PNAS, 110(35):14261, DOI: 10.1073/pnas.1306481110 (2013)).
Professor Kaznessis and his research group use MSI resources in their work to create new technologies that will be able to fight antibiotic-resistant bacteria. Microorganisms called enterococci have evolved over the years to resist almost all antibiotics, with the result that patients can develop serious infections that medical practitioners are unable to treat. The Kaznessis group uses molecular simulations to study biological interactions and functions.
Posted on January 8, 2014.
Wen Wang is a graduate student who is a member of the MSI research group of Professor Vipin Kumar (MSI Fellow and Head, Department of Computer Science and Engineering). Assistant Professor Chad Myers (Computer Science and Engineering) also advises her work. Professor Kumar specializes in the field of data mining, and Professor Myers is a computational biologist. Ms. Wang entered the graduate program in computer science at the University of Minnesota in 2009 and began using MSI for her research at about the same time. She was a finalist in the poster competition at the 2013 MSI Research Exhibition with her poster, “Leveraging Network Structure to Discover Genetic Interactions in Genome-Wide Association Studies.” Ms. Wang sat down with MSI recently to discuss her research and this poster.
MSI: What resources do you use at MSI?
Wen Wang: Some of my works is done on Professor Kumar’s proprietary machine, but most computational works for this project were done on Elmo. The software I use is MATLAB.
MSI: Let’s get into what your poster describes. This is related to genome-wide association studies, where you can look a couple of different genes and find differences in them?
WW: The purpose of our research project is to study the genetic causes of complex human diseases. The traditional methods used to analyze genome-wide associations (GWAS) data only test single genetic variation between patients and healthy subjects. GWAS data contains hundreds of thousands or even millions of genetic variables - single nucleotide polymorphism (SNP), and so this univariate analysis approach involves testing hundreds of thousands hypotheses. As a result, the statistical score (p-value) obtained needs to be corrected based on the number of hypotheses tested. Thus, to discover a single genetic variation with significant statistic power is a challenging task. In the past 10 years there have been about 1350 published GWAS studies and altogether these GWAS studies have successfully discovered more than 2000 loci which are significantly associated with one or more complex traits. However, these discovered genetic factors only can explain a very small amount of the heritability.
So maybe it’s not the single genetic variant that causes most of a disease. Instead, it could be the interaction of two genetic variants that brings more risk for a disease. However, to study pairwise genetic interactions is difficult since the test space is tremendous and at least half million samples are needed to achieve the statistical significance. This is not practicable and so it seems that this is a hopeless cause.
However, in the yeast research community, genetic interactions have been well studied. It has been proven that genetic interactions are more likely to happen between two pathways with redundant or complementary functions. So we were motivated to test the genetic interaction in the context of pathway-pathway interaction since many well-defined human pathways exist.
We developed a method that explicitly searches for such larger structures, guided by established sets of genes belonging to characterized pathways or gene modules. We applied this approach to a Parkinson's disease GWAS data and discovered tens of pathway-pathway interactions which are statistically significant. We also found biological evidence for many of these interactions. A significant fraction of them also can be validated in two independent cohorts.
MSI: How many subjects will you run the calculations for?
WW: The more the better. The data we tested has a number of subjects ranging from around 500 to 4,000.
MSI: So, something will get your attention if you see a lot more interactions than you expect?
WW: Yes. And we also did permutation tests to make sure it is significant.
MSI: You wrote your code in MATLAB and ran it on Elmo?
WW: Yes. We have different scenarios and different parameters to test. We like to run our experiments in parallel. Also, as you can tell, we’re dealing with big data and our approach needs lots of memory support. Elmo provided all that we need and allowed conducting the experiments in a much more efficient way compared to regular computers.
MSI: Yes, we sometimes have users who say they have programs that would take days on a desktop computer.
WW: Sometime even worse than that. It could be weeks or months. I try to make good use of MSI resources to get results as soon as possible.
MSI: Is this research basic science, or is there an immediate application?
WW: It’s kind of both. We study genetic interaction to help us understand how our biological system works - more specifically understand the underlying cause of disease. However, the ultimate goal of this research is to develop disease model which can be used for disease risk screening, and also to support the development of individualized medicine.
MSI: This research seems to be very collaborative among different disciplines, with data mining and computational biology.
WW: Absolutely! We have [Assistant Professor] Nathan Pankratz in Lab Medicine and Pathology, and [Professor] Brian Van Ness, in Genetics, Cell Biology, and Development involved in this project. We’re from computer science and we like to have experts from biology side to help us understand and interpret our discoveries.
Posted on December 11, 2013.
Understanding the genetic makeup of food crops is critical if we want to develop sustainable ways of protecting those crops against disease. To this end, researchers are using genomics to study plants and their characteristics. Distinguished McKnight Professor Nevin Young (Plant Pathology), together with Associate Professor Peter Tiffin (Plant Biology), Professor Mike Sadowsky (Soil, Water and Climate), and Assistant Professor Bob Stupar (Agronomy and Plant Genetics) have been using MSI for many years as part of their investigations into legumes, the family of plants that includes soybeans, peas, and alfalfa. Legumes are especially interesting in that they form symbiotic relationships with rhizobial bacteria and arbuscular mycorrhizal fungi. These symbiotic relationships allow legumes to extract compounds such as nitrogen and phosphorus out of the soil and provide natural forms of fertilizer for agriculture.
Medicago truncatula (barrel medic) is used by many researchers as a model for legume genomics. The Young group collaborates with research groups worldwide in studying M. truncatula, using massive genomic sequencing methods to study the gene systems and genomic variations that impact these valuable plant-microbe interactions.
MSI’s RISS group is working with the Young lab on this NSF-funded research. Dr. Kevin Silverstein, Operations Manager and Scientific Lead of the RISS group, is the co-PI on the most recent NSF grant to fund this work. The researchers are using MSI’s Itasca system to map the immense collection of raw sequencing data generated by the project and they are also using the Institute’s high-performance storage capabilities. They have also developed graph analytic applications that offer the opportunity to extend MSI’s compute resources in the area of bioinformatics.
The Young group has published numerous papers about their research. A sampling of recent papers includes:
- “Phylogenetic signal variation in the genomes of the genus Medicago (Fabaceae),” JB Yoder, R Briskine, J Mudge, A Farmer, T Paape, K Steel, GD Weiblen, A Bharti, P Zhou, GD May, ND Young, P Tiffin, Systematic Biology, 62(3): 424-438, DOI:10.1093/sysbio/syt009 (2013)
- “Selection, Genome Wide Fitness Effects, and Evolutionary Rates in the Model Legume Medicago truncatula,” T Paape, T Bataillon, P Zhou, T Kono, R Briskine, ND Young, P Tiffin, Molecular Ecology, 22(13):3525-3538, DOI:10.1111/mec.12329 (2013)
- “Estimating Heritability With Whole-Genome Data,” J Stanton-Geddes, J Yoder, R Briskine, ND Young, P Tiffin, Methods in Ecology and Evolution, DOI:10.1111/2041-210X.12129 (2013)
- “Candidate Genes and Genetic Architecture of Symbiotic and Agronomic Traits Revealed by Whole-Genome, Sequence-Based Association Genetics in Medicago truncatula,” J Stanton-Geddes, T Paape, B Epstein, R Briskine, J Yoder, J Mudge, AK Bharti, AD Farmer, P Zhou, R Denny, GD May, S Erlandson, M Sugawara, MJ Sadowsky, ND Young, P Tiffin, PLoS ONE, 8(5): e65688, doi:10.1371/journal.pone.0065688 (2013)
- “Whole-Genome Nucleotide Diversity, Recombination, and Linkage-Disequilibrium in the Model Legume Medicago truncatula,” A Branca, T Paape, P Zhou, R Briskine, AD Farmer, J Mudge, AK Bharti, JE Woodward, GD May, L Gentzbittel, C Ben, R Denny, MJ Sadowsky, J Ronfort, T Bataillon, ND Young, P Tiffin, Proceedings of the National Academy of Sciences of the USA, 108: E864-870, DOI:10.1073/pnas.1104032108 (2011)
- “The Medicago Genome Provides Insight Into The Evolution of Rhizobial Symbioses,” ND Young, F Debellé, G Oldroyd, R Geurts, SB Cannon, MK Udvardi, VA Benedito, KFX Mayer, J Gouzy, H Schoof, et al., Nature, 480: 520-524, DOI:10.1038/nature10625 (2011)
Note: Authors in bold are MSI Principal Investigators.
Image Description: Left: A standard circular plot of Medicago truncatula’s eight chromosomes. Lines are drawn between regions of the genome that have evidence of ancient duplication events, which are plentiful in this genome. On the outer ring, dots represent individual members of two large gene families known to have association with plant-microbe interactions. Right: Medicago truncatula (barrel medic).
Posted on November 27, 2013.