Award Abstract # 1557417
RII Track-1: Center for Root and Rhizobiome Innovation (CRRI)

NSF Org: OIA
OIA-Office of Integrative Activities
Recipient: BOARD OF REGENTS OF UNIVERSITY OF NEBRASKA
Initial Amendment Date: June 11, 2016
Latest Amendment Date: April 8, 2021
Award Number: 1557417
Award Instrument: Cooperative Agreement
Program Manager: Jose Colom
jcolom@nsf.gov
 (703)292-7088
OIA
 OIA-Office of Integrative Activities
O/D
 Office Of The Director
Start Date: June 15, 2016
End Date: May 31, 2022 (Estimated)
Total Intended Award Amount: $20,000,000.00
Total Awarded Amount to Date: $20,000,000.00
Funds Obligated to Date: FY 2016 = $4,000,000.00
FY 2017 = $5,279,032.00

FY 2018 = $2,720,968.00

FY 2019 = $4,000,000.00

FY 2020 = $4,000,000.00
History of Investigator:
  • Matthew Andrews (Principal Investigator)
    matt.andrews@nebraska.edu
  • Edgar Cahoon (Co-Principal Investigator)
  • F Choobineh (Former Principal Investigator)
  • James Alfano (Former Co-Principal Investigator)
Recipient Sponsored Research Office: University of Nebraska
3835 HOLDREGE ST
LINCOLN
NE  US  68503-1435
(402)472-2111
Sponsor Congressional District: 01
Primary Place of Performance: University of Nebraska
3835 Holdrege Street
Lincoln
NE  US  68503-1435
Primary Place of Performance
Congressional District:
01
Unique Entity Identifier (UEI): LF77GRM3FWG5
Parent UEI:
NSF Program(s): EPSCoR Research Infrastructure
Primary Program Source: 01001617DB NSF RESEARCH & RELATED ACTIVIT
01001718DB NSF RESEARCH & RELATED ACTIVIT

01001819DB NSF RESEARCH & RELATED ACTIVIT

01001920DB NSF RESEARCH & RELATED ACTIVIT

01002021DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7217, 7569, 7715, 9150, BIOT, SMET
Program Element Code(s): 721700
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.083

ABSTRACT

Non-technical Description
The Center for Root and Rhizobiome Innovation (CRRI) promises to break new ground in understanding of how plants interact with their environment via their root systems, with the goal of applying that knowledge to provide society with new technologies for more secure and resilient food systems. The project is built upon an excellent statewide foundation of research facilities and expertise, while also making critical improvements to this research infrastructure. Much of this knowledge will be made publicly available online. The Center will be strongly integrated across many disciplines, involving scientists and engineers with expertise in plant systems, chemical ecology, and microbiology, among others. In parallel with its research agenda, the project will continue several successful programs that provide outreach and workforce development opportunities aimed at improving the accessibility of Science, Technology, Engineering, and Mathematics (STEM) education throughout the state.

Technical Description
The CRRI will employ integrated systems and synthetic biology approaches to investigate plant root metabolism and its interdependence on the near-root soil microbial community (i.e., the rhizobiome). The genetic variability of maize lines will be studied in the context of their influence on root exudate production and in turn on the rhizobiome. These results will be used to develop improved models to describe genetic influences on plant systems behavior and to drive the development of new engineered maize lines with improved responses to abiotic stresses. The project will lead to improved understanding of the genetic diversity of plant roots and the rhizobiome. It will also lead to technological advances toward the engineering of agriculturally vital crops.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 196)
Abbas Atefi, Yufeng Ge, Santosh Pitla and James Schnable "Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives" Frontiers in Plant Science , 2021
Abbas Atefi, Yufeng Ge, Santosh Pitla, James Schnable "Robotic detection and grasp of maize and sorghum: Stem measurement with contact. Robotics 9(3): 58." Robotics , 2020 https://doi.org/10.3390/robotics9030058
Alexander J. Rogge & Jae Sung Park "On the Underlying Drag-Reduction Mechanisms of Flow-Control Strategies in a Transitional Channel Flow: Temporal Approach" Flow, Turbulence and Combustion , 2022
Alsiyabi A, Solis AG, Cahoon EB, Saha R "Dissecting the regulatory roles of ORM proteins in the sphingolipid pathway of plants" PLoS Computational Biology 17, e1008284 , 2021 https://doi.org/10.1371/journal.pcbi.1008284
Atefi, Abbas and Ge, Yufeng and Pitla, Santosh and Schnable, James "In vivo human-like robotic phenotyping of leaf traits in maize and sorghum in greenhouse" Computers and Electronics in Agriculture , v.163 , 2019 10.1016/j.compag.2019.104854 Citation Details
Atefi, Abbas and Ge, Yufeng and Pitla, Santosh and Schnable, James "Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives" Frontiers in Plant Science , v.12 , 2021 https://doi.org/10.3389/fpls.2021.611940 Citation Details
Atefi, A., Ge, Y.*, Pitla, S., Schnable, J., "In vivo human-like robotic phenotyping of leaf traits in maize and sorghum in greenhouse" Computers and Electronics in Agriculture , 2019
Avuthu, Nagavardhini and Guda, Chittibabu "Meta-Analysis of Altered Gut Microbiota Reveals Microbial and Metabolic Biomarkers for Colorectal Cancer" Microbiology Spectrum , 2022 https://doi.org/10.1128/spectrum.00013-22 Citation Details
Behera S, Deogun JS, Moriyama EN "Identifying conserved non-coding elements using min-wise hashing. In:Advances in Computer Vision and Computational Biology. Arabnia HR, Deligiannidis L, Shouno H, Tinetti FG, Tran Q, editors.Cham.: Sprin" Advances in Computer Vision and Computational Biology , 2021
Behera, S., J. S. Deogun, and E. N. Moriyama. 2020. MinIsoClust: Isoform clustering using minhash and locality sensitive hashing. In Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. Asso "MinIsoClust: Isoform clustering using minhash and locality sensitive hashing" Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics , 2020 https://doi.org/10.1145/3388440.3412424
Behera S, Voshall, A, Moriyama EN "Plant transcriptome assembly: review and benchmarking" Bioinformatics, Exon Publications. , 2021
(Showing: 1 - 10 of 196)

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

The National Science Foundation?s EPSCoR Research Infrastructure Improvement (RII) Track-1 project, Center for Root and Rhizobiome Innovation (OIA-1557417) or ?CRRI,? spanned agricultural workspaces from labs to greenhouses to crop fields?with Nebraska scientists aiming to develop and integrate more detailed understanding of the root environment of plants, specifically crops to help better feed the world.

Initially awarded for 2016 to 2021, CRRI received an additional extension year during the COVID-19 pandemic. The project gathered more than 20 scientists and educators from four Nebraska university campuses (University of Nebraska-Lincoln, University of Nebraska at Kearney, University of Nebraska Medical Center, and Doane University) to apply diverse expertise for examining the biochemistry, plant pathology, and genetic variation of corn, soybeans, and sorghum. This research developed tools and strategies that shape both the microbes that associate with the root and also how root-microbe interactions are influenced by soil nitrogen and phosphorus levels.

CRRI advanced synthetic biology, by generating ways to engineer the production of root exudates that promote interactions with specific beneficial microbes for crops to more efficiently gain soil nutrients. CRRI researchers produced a Root-Associated Genes & Promoters database as an online, public resource (http://sysbio.unl.edu/RGPDB/). Computational biologists can use CRRI-generated information to model specific points in root metabolic networks that regulate such responses. These models, in turn, yield processes for innovating improved crop varieties.

CRRI continues to boost plant science strengths at Nebraska through new tenure-track hires which the project?s funding helped bring to the state: Katarzyna Glowacka and Marc Libault at the University of Nebraska-Lincoln, and Greg Pec at the University of Nebraska at Kearney. In addition, CRRI?s Glowacka?as well as Toshihiro Obata and Rajib Saha--earned NSF CAREER Awards during the project term. The $58 million+ in relevant further funding that CRRI researchers earned by the project?s Year 5 includes at least one NSF EPSCoR Track-2 Award built upon CRRI science and teamwork: the RII Track-2 FEC ?Functional Analysis of Nitrogen Responsive Networks in Sorghum,? involving three early-career CRRI researchers (Jinliang Yang, Yufeng Ge, and James Schnable). CRRI also generated more than 70 journal publications and products during the project.

CRRI?s Outreach and Workforce Development efforts brought a plant ?science caf? on wheels?--showcasing rhizotrons and phenotyping robots--to large events including Nebraska?s State Fair, Husker Harvest Days, and more. Via Nebraska EPSCoR?s Young Nebraska Scientists program, CRRI conducted Remote Researcher lessons with supply kits and weekly Zoom mentoring for 10 high school students across the state. In each of CRRI?s six funded years, at least three REU (Research Experiences for Undergraduates) placements per summer paired students from throughout the nation with Nebraska mentors in plant science. CRRI annually convened well-attended research conferences; topics included ?Microbiomes From Different Habitats: Soil, Water and Gut? and ?Predictive Crop Design: Genome to Phenome?; CRRI also led gatherings of science teachers where its scientists shared inquiry- and problem-based learning tools and approaches to promote skill development in data analysis, modeling, and computational thinking in their courses, to augment schools? Next-Generation Science Standards.

 


Last Modified: 08/24/2022
Modified by: Matthew T Andrews

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