2008 — 2011 |
Robinson, Gene [⬀] Hudson, Matthew |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Comparative Genomics, Molecular Evolution, and the Evolution of Bee Societies @ University of Illinois At Urbana-Champaign
A major challenge in biology is to understand how animal societies have evolved. Bees are especially interesting because different species display the full range of sociality that exists in nature, from solitary to communal to highly social, in which individuals ("workers") care for siblings rather than reproduce themselves. Molecular tools are just becoming available that permit a new approach to questions about sociality. This proposal builds on the availability of one gigabyte of genome sequence for 10 solitary and social bees, which the PI is obtaining at no cost to NSF in conjunction with a special award from Roche Inc. Genes will be identified that have changed during the evolution of insect societies; these might be prime movers in social evolution. Genes also will be used to test a prominent theory hypothesizing that worker behavior evolved from maternal care.
This project charts a new avenue of study on the evolution of societies. It will contribute to the development of genomic resources for important species, an NSF strategic goal that will have benefits across all of biology. It will also provide training to graduate students and undergraduates in an approach that integrates evolutionary biology, behavior, molecular biology, genomics, and bioinformatics.
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1 |
2009 — 2014 |
Moose, Stephen (co-PI) [⬀] Hudson, Matthew |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Exploring the Role of Noncoding Rnas in Heterosis @ University of Illinois At Urbana-Champaign
PI: Matthew Hudson, University of Illinois Co-PI: Stephen Moose, University of Illinois
Heterosis or hybrid vigor is a scientific term to describe the more vigorous progeny from a genetic cross between parents that are not closely related. Hybrid vigor is one of the main foundations for greatly increased crop yields during the 20th century, especially in corn (maize). However, the reasons why the offspring of a cross between distantly related individuals tend to grow larger than their parents are still poorly understood. One popular and promising hypothesis is that hybrid vigor results from a combination of genes from both parents that control growth rate and stress tolerance. Noncoding, small RNAs are recently discovered regulatory molecules encoded by genes that may fit this hypothesis. Some small RNAs are known to control shoot development and others stress-responsive genes. Preliminary experiments have shown that several small RNAs are altered in their expression in hybrid corn plants when they are compared to the inbred parents. In addition, the machinery that produces these small RNAs may also be altered in its function in hybrids when compared to inbred corn. These results also hold true in the model plant Arabidopsis, indicating that small RNA may be involved in a common mechanism of hybrid vigor in all plants. This project will investigate further the effect of small RNA on hybrid vigor in both corn and Arabidopsis, focusing on the genes that control small RNA production and on whether small RNA levels can predict the performance of different hybrid combinations of corn plants. These populations represent current and historical genetic diversity in corn, and include inbred lines that are major contributors to commercially important maize varieties. A more complete understanding of the mechanisms of heterosis, and how these mechanisms relate to the increased crop yields that result from it, is anticipated from this research.
The greater vigor observed following hybridization is one of the most easily visualized genetic effects and is commonly observed in everyday life. The use of hybrids in plant and livestock breeding has greatly increased agricultural productivity with significant benefits to global society. However, the benefits of hybrids are currently unevenly spread, with corn benefiting more than any other crop. The research will provide insight into the mechanisms of heterosis, with potential improvements in crop yields, especially in crops where hybrids do not currently provide a major increase in yield. Increased yields of major crops have the potential to reduce pressure on the environment and decrease the need to convert more wild land into agricultural land. The maize germplasm used in this project is intentionally chosen to be free from intellectual property protections and the seed are freely available from the Germplasm Resource Information Network (GRIN, http://www.ars-grin.gov/). Experimental lines of maize and Arabidopsis produced as part of this project will be supplied to public repositories, the maize genetics co-op (http://maizecoop.cropsci.uiuc.edu/) and the Arabidopsis Biological Resource Center (http://www.arabidopsis.org/abrc/). Sequence data generated will be submitted to GenBank (http://www.ncbi.nlm.nih.gov/Genbank/index.html) and made available on the laboratory website (http://stan.cropsci.uiuc.edu). This project will also offer a number of educational opportunities. By using commercially relevant maize germplasm in this project, graduate-level education will be more relevant to the type of research conducted in commercial seed companies, enhancing the relevance of the research to modern agriculture. Young scientists, including a summer intern, will be offered excellent opportunities to learn and apply some of the most recent advances in plant genomics research. Because heterosis is a topic of high general interest, research results will be communicated broadly through publications, presentations, and public educational efforts that emphasize the positive impacts of plant genome research. Specific examples include distribution of an educational kit of seeds that exhibit strong seedling heterosis; living demonstrations of hybrids and their inbred parents to the public via field days; presentations at high schools and community colleges and an educational web page (hosted on http://stan.cropsci.uiuc.edu and also contributed to Wikipedia) on the importance of hybrid vigor
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1 |
2016 — 2021 |
Hudson, Matthew White, Bryan (co-PI) [⬀] Hwu, Wen-Mei (co-PI) [⬀] Robinson, Gene (co-PI) [⬀] Iyer, Ravishankar [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
I/Ucrc: Computing and Genomics-An Essential Partnership For Biology Breakthroughs @ University of Illinois At Urbana-Champaign
The application of genomics across the life sciences industries is currently challenged by an inadequate ability to generate, interpret, and apply genomic data quickly and accurately for a wide variety of applications. Major Innovations in the applicability, timeliness, efficiency, and accuracy of computational genomic methods are needed, and these innovations will develop best when an interdisciplinary team of scientists, engineers, and physicians from academia and industry, spanning computer systems, health care/pharmaceuticals, and life sciences, work together. The University of Illinois at Urbana-Champaign (UIUC) and the Mayo Clinic are building on their longstanding collaboration to form the Center for Computational Biotechnology and Genomic Medicine (CCBGM), which will bring together their excellence in computing, genomic biology, and patient-specific individualized medicine. Working closely with industry, the CCBGM's multidisciplinary teams will use the power of computational genomics to advance pressing societal issues, such as enabling patient-specific cancer treatment, understanding and modifying microbial communities in diverse environments related to human health and agriculture, and supporting humanity's rapidly expanding need for food by improving the efficiency of plant and animal agriculture. The CCBGM will leverage UIUC's long-standing prowess in large-scale parallel systems, big data analytics, and hardware and software system design, to develop new technologies that enable future genomic breakthroughs. A key element of the Center's vision is to advance breakthroughs at the interface of biology and computing to transform health-care delivery while enhancing efforts that focus on the health science needs of underrepresented minorities.
The CCBGM will bring together an interdisciplinary team to address the colossal genomic data challenge. Academia/industry partnerships will enhance research, education, and entrepreneurship while performing important technology transfer. The Center will achieve transformational computing innovations on three fronts. (1) It will innovate computing and data management to deal with issues of scaling to the ever-growing volume, velocity, and variety of genomic data. It will concentrate initially on scaling the computation of epistatic interactions (interactions between two or more genes or DNA variants) in genome-wide association study data, generating lists of genomic features that are maximally predictive of phenotypes, and information-compression algorithms for genomic data storage and transfer. (2) It will revolutionize the generation of actionable intelligence from multimodal structured and unstructured data, to generate knowledge from big data. The emphasis will be on the processing and integration of genomic and multi-omic data, and on the merging of unstructured phenotypic data with information from curated data sources (e.g., electronic medical records, annotation databases). The integration of these diverse data types will improve discovery research, predictive genomics, diagnostics, prognostics, and theranostics. Application areas include targeted cancer therapy, pharmacogenomics, crop improvement, and predictive microbiome analysis. (3) It will achieve systems innovation by designing computer systems specially suited for computational genomics, providing unprecedented speed and energy efficiency while preserving the accuracy of the analytics. The systems will be used to quantify and improve the accuracy of detecting genomic variation and, more generally, to optimize computing architectures for the execution of genome analysis workflows.
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2020 |
Hudson, Matthew Bryant |
P20Activity Code Description: To support planning for new programs, expansion or modification of existing resources, and feasibility studies to explore various approaches to the development of interdisciplinary programs that offer potential solutions to problems of special significance to the mission of the NIH. These exploratory studies may lead to specialized or comprehensive centers. |
Therapeutic Use of Extracellular Vesicles For Dystrophic Cardiomyopathy |
0.919 |