Award Abstract # 1547796
RESEARCH: Predicting Genotypic Variation in Growth and Yield under Abiotic Stress through Biophysical Process Modeling
NSF Org: |
IOS
Division Of Integrative Organismal Systems
|
Recipient: |
UNIVERSITY OF WYOMING
|
Initial Amendment Date:
|
August 18, 2016 |
Latest Amendment Date:
|
September 3, 2024 |
Award Number: |
1547796 |
Award Instrument: |
Continuing Grant |
Program Manager: |
Diane Jofuku Okamuro
dokamuro@nsf.gov
(703)292-4508
IOS
Division Of Integrative Organismal Systems
BIO
Directorate for Biological Sciences
|
Start Date: |
September 1, 2016 |
End Date: |
February 28, 2025 (Estimated) |
Total Intended Award
Amount: |
$3,457,977.00 |
Total Awarded Amount to
Date: |
$3,457,977.00 |
Funds Obligated to Date:
|
FY 2016 = $2,798,997.00
FY 2020 = $658,980.00
|
History of Investigator:
|
-
Brent
Ewers
(Principal Investigator)
beewers@uwyo.edu
-
C. Robertson
McClung
(Co-Principal Investigator)
-
Cynthia
Weinig
(Co-Principal Investigator)
-
David
Mackay
(Co-Principal Investigator)
-
Daniel
Kliebenstein
(Co-Principal Investigator)
|
Recipient Sponsored Research
Office: |
University of Wyoming
1000 E UNIVERSITY AVE
LARAMIE
WY
US
82071-2000
(307)766-5320
|
Sponsor Congressional
District: |
00
|
Primary Place of
Performance: |
University of Wyoming
1000 E University Ave
Laramie
WY
US
82071-2000
|
Primary Place of
Performance Congressional District: |
00
|
Unique Entity Identifier
(UEI): |
FDR5YF2K32X5
|
Parent UEI: |
FDR5YF2K32X5
|
NSF Program(s): |
Plant Genome Research Project
|
Primary Program Source:
|
01001617DB NSF RESEARCH & RELATED ACTIVIT
01002021DB NSF RESEARCH & RELATED ACTIVIT
|
Program Reference
Code(s): |
7218,
7577,
9109,
9150,
9178,
9179,
BIOT
|
Program Element Code(s):
|
132900
|
Award Agency Code: |
4900
|
Fund Agency Code: |
4900
|
Assistance Listing
Number(s): |
47.074
|
ABSTRACT

Rising demand for high quality food crops due to increasing world populations along with more likely temperature and drought stress requires further crop improvements from breeding programs. A major limitation to these programs is an understanding of how the genetic information affects the characteristics of plants that improve the amount of edible portions. Moreover, the predictive understanding is even less if the plants are placed in new, stressful environments like low rainfall or high temperature. A likely avenue of inquiry to improve breeding is to better connect how information stored in genes becomes traits of plants that combine their biology, such as photosynthetic rate or the amount of resources allocated to an edible root, with the physical world, such as the amount of water available or an excessive heat wave. These connections are currently often made in a way that requires new data collection for every new crop plant or environment such as a new soil, new temperature range or even a new improved line that shows variation in the amount of resources the plant allocates to an edible root. This continuous need for new information ultimately slows down the breeding program and the ability of plant scientists to quickly respond to the needs of society. This project will test a new approach that uses large amounts of data to calculate the probability that a particular plant characteristic will be displayed by a given plant line under various environmental conditions. Specifically, the project will measure plant performance continuously by sending electrical pulses through plants, integrating the data generated with large data sets that show which genes are active as well as the level of biologically relevant molecules that contribute to major metabolic pathways within the plants at any given time. This new approach requires high performance computing to test many times how the probability of phenotypic improvement in the crop may occur. These high performance computing approaches will become a core part of a modern, competitive workforce. In this regard, the project will provide workshops for high school teachers in the use of high performance yet open source computing tools in their classrooms. In addition, the project will develop experimental and computational modules in biological and quantitative learning for students in grades 6-12 using the highly successful Wisconsin FastPlants system (http://www.fastplants.org/).
With increasing world populations, genetic advances to improve crop growth, yield and resistance to abiotic stress are a pressing need. Limiting the speed of crop improvement is a crucial knowledge gap regarding biophysical processes that modulate the relationship between the genome and phenome, hindering the ability to predict the phenotype of novel genotypes in novel environments. As a first step towards bridging this gap, a combination of high-throughput phenotyping and biophysical process modeling will incorporate allelic variation at key genes affecting plant carbon metabolism, hydraulics, and resource allocation, all of which are known to impact drought- and heat-stress resistance in plants. Variable selective pressures during crop diversification have caused extensive phenotypic variation among B. rapa crops, making it an excellent study system to both connect organ-level measures both down to the level of transcriptomic and metabolomic phenotypes and up to yield and to test predictive process models. Process models will be developed and refined using the mechanistic links that connect cell processes and ultimately whole plant physiology to regulatory intermediates such as metabolites and gene transcripts. If successful, the models developed will enable prediction of whole-plant stress-response phenotypes in heterogeneous genotypes and environments. The goals of the project are to: 1) deploy a novel high-throughput and real-time phenotyping method to measure diel physiological dynamics in eight B. rapa parental Nested Association Mapping (NAM) lines under drought- and heat-stress conditions; 2) predict yield in a Recombinant Inbred Line (RIL) population of B. rapa using a biophysical process model of carbon metabolism, hydraulics and resource allocation to test systems-level links between circadian, transcriptomic, metabolomic, and physiological QTL; and 3) test the predictive ability of the biophysical process model under heat- and drought-stress environments using the RIL population used in Aim 2. All data and resources generated in this project will be made accessible to the public through long-term open access repositories such as Project Github and the NCBI Short Read Archive.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 38)
(Showing: 1 - 38 of 38)
Carmela R. Guadagno, Brent E. Ewers, Cynthia Weinig
"Circadian Rhythms and Redox State in Plants: Till Stress Do Us Part"
Frontiers in Plant Science
, 2018
https://doi.org/10.3389/fpls.2018.00247
Christoph Bachofen, Shersingh Joseph Tumber-Dávila, D. Scott Mackay, Nate G. McDowell, Andrea Carminati, Tamir Klein, Benjamin D. Stocker, Maurizio Mencuccini, Charlotte Grossiord
"Tree water uptake patterns across the globe"
New Phytologist
, 2024
10.1111/nph.19762
C.J. Hubbard, B. Li, R. McMinn, M.T. Brock, L. Maignien, B.E. Ewers, D. Kliebenstein, C. Weinig
"Rhizosphere microbes and host plant genotype influence the plant metabolome and reduce insect herbivory"
Molecular Ecology
, v.28
, 2019
https://doi.org/10.1111/mec.14989
CR Guadagno, D Millar, R Lai, DS Mackay, JR Pleban, CR McClung, C Weinig, DR Wang, BE Ewer
"Use of transcriptomic data to inform biophysical models via Bayesian networks"
Ecological Modeling
, v.429
, 2020
, p.109086
CR GUADAGNO, DP BEVERLY, BE EWERS
"The lovehate relationship between chlorophyll a and water in PSII affects fluorescence products"
Photosynthetica
, 2021
10.32615/ps.2021.023
C. Robertson McClung
"A fiberoptic pipeline lets the root circadian clock see the light"
Plant Cell Environ.
, v.41
, 2018
, p.1739
10.1111/pce.13343
Daniel P. Beverly1,2, *, Carmela R. Guadagno1, Brent E. Ewers1,3
"Biophysically Informed Imaging Acquisition of Plant Water Status"
Frontiers in Forests and Global Change Forest Ecophysiology
, v.3
, 2020
, p.125
D.J. Kliebenstein
"Using networks to identify and interpret natural variation"
Current Opinion in Plant Biology
, v.54
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10.1016/j.pbi.2020.04.005
D.R. Wang, C.R. Guadagno, X. Mao, D.S. Mackay, J.R. Pleban, R.L. Baker, C. Weinig, J.-L. Jannink, B. E. Ewers
"A framework for genomics-informed ecophysiologicalmodeling in plants"
Journal of Experimental Botany
, 2019
doi:10.1093/jxb/erz090
Erb, M. and D.J. Kliebenstein
"Plant secondary metabolites as defenses, regulators and primary metabolites: the blurred functional trichotomy"
Plant Physiology
, v.184
, 2020
, p.39
10.1104/pp.20.00433
Garrido, A.N., Supijono, E., Boshara, P., Douglas, S.J., Stronghill, P.E., Li, B., Ejii, N., Kliebenstein, D.J. and C.D. Riggs
"flasher , a novel mutation in a glucosinolate modifying enzyme, conditions changes in plant architecture and hormone homeostasis"
The Plant Journal
, 2020
10.1111/tpj.14878
Garrido, A.N., Supijono, E., Boshara, P., Douglass, S.J., Stronghill, P.E., Li, B., Nambara, E., Kliebenstein, D.J., and C.D. Riggs
"Flasher, a novel mutation in a glucosinolate modifying enzyme, conditions changes in plant architecture and hormone homeostasis"
Plant Journal
, v.103
, 2020
, p.1989
10.1111/tpj.14878
Gleason, S.N., D.M. Barnard, T.R. Green, D.S. Mackay, D.R. Wang, E.A. Ainsworth, J. Altenhofen, T.J. Brodribb, H. Cochard, L.H. Comas, M. Cooper, D. Creek, K.C. DeJonge, S. Delzon, F.B. Fritschi, G. Hammer, C. Hunter, D. Lombardozzi, C.D. Messina, T. Oche
"Physiological trait networks enhance understanding of crop growth and water use in contrasting environments"
Plant, Cell & Environment
, v.45
, 2022
, p.2554
10.1111/pce.14382
Greenham, K., C.R. Guadagno, M.A. Gehan, T.C. Mockler, C. Weinig, B.E. Ewers & C.R. McClung.
"Temporal network analysis identifies early physiological and transcriptomic indicators of mild drought in Brassica rapa."
eLIFE
, v.6
, 2017
, p.e29655
10.7554/eLife.29655
Greenham, K., R.C. Sartor, S. Zorich, P. Lou, T.C. Mockler, & C.R. McClung
"Expansion of the circadian transcriptome in Brassica rapa and genome wide diversification of paralog expression patterns"
eLIFE
, v.9
, 2020
, p.e58993
10.7554/eLife.58993
Jonthan R. Pleban, D. Scott Mackay, Timothy L. Aston, Brent E. Ewers, Cynthia Weinig
"Phenotypic Trait Identification Using a Multimodel Bayesian Method: A Case Study Using Photosynthesis in Brassica rapa Genotypes"
Frontiers in Plant Science
, v.9
, 2018
, p.448
10.3389/fpls.2018.00448
Katz, E., Bagchi, R., Rasmussen, A.R.M., Hopper, A., Estelle, M., and D.J. Kliebenstein
"Diverse Allyl Glucosinolate catabolites independently influence root growth and development"
Plant Physiology
, v.183
, 2020
, p.1376
10.1104/pp.20.00170
Kim, D, CR Guadagno, BE Ewers, DS Mackay
"Combining PSII photochemistry and hydraulics improves predictions of photosynthesis and water use from mild to lethal drought"
Plant, Cell and Environment
, v.47
, 2024
, p.1255
10.1111/pce.14806
Kliebenstein, D.J.
"Using networks to identify and interpret natural variation. Current Opinion in Plant Biology"
Current Opinion in Plant Biology
, v.54
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, p.122
10.1016/j.pbi.2020.04.005
Leinonen, P.H., M.J. Salmela, K. Greenham, C.R. McClung, & J.H. Willis
"Populations are differentiated in biological rhythms without explicit elevational clines in the plant Mimulus laciniatus"
J. Biol. Rhythms
, v.35
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Li, B., Tang, M., Caseys, C., Nelson, A., Zhou, M., Zhou, X., Brady, S.M., and D.J. Kliebenstein
"Epistatic Transcription Factor Networks Differentially Modulate Arabidopsis Growth and Defense"
Genetics
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BioRxiv
, 2019
10.1101/583047
Lou, P., S. Woody, K. Greenham, R. VanBuren, M. Colle, P.P. Edger, R. Sartor, Y. Zheng, N. Levendoski, J. Lim, C. So, B. Stoveken, T. Woody, J. Zhao, S.X. Shen, R.M. Amasino, & C.R. McClung
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Plant Direct
, v.4
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10.1002/pld3.285
Mackay, DS, PR Savory, C Grossiord, X Tai, JR Pleban, DR Wang, NG McDowell, HD Adams, JS Sperry
"Conifers depend on established roots during drought: results from a coupled model of carbon allocation and hydraulics"
New Phytologist
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, p.10.1111/n
Mackay, D.S., P.R. Savoy, C. Grossiord, X. Tai, J.R. Pleban, D.R. Wang, N.G. McDowell, H.D. Adams, and J.S. Sperry
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New Phytologist
, v.225
, 2020
, p.679
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Marshall-Colon, A. and D.J. Kliebenstein
"Plant networks as traits and hypothesis: Moving beyond description."
Trends in Plant Science
, v.24
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McClung, C. Robertson
"Circadian clock components offer targets for crop domestication and improvement"
Genes
, v.12
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McDowell, N., G. Sapes, A. Pivovaroff, H. Adams, C. D. Allen, W.R.L. Anderegg, M. Arend, D.D. Breshears, T. Brodribb, B. Choat, H. Cochard, M. De Caceres, M.G. De Kauwe, C. Grossiord, W.M. Hammond, H. Hartmann, G. Hoch, A. Kahmen, T. Klein, D.S. Mackay, M
"Mechanisms of woody-plant mortality under rising drought, CO2, and vapor pressure deficit"
Nature Reviews Earth & Environment
, v.3
, 2022
10.1038/s43017-022-00272-1
M. J. Salmela and C. Weinig
"The adaptive benefits of genetic variation in the circadian clock"
Current Opinion in Plant Biology
, v.49
, 2019
https://doi.org/10.1016/j.pbi.2019.06.003
Pleban, J.R.*, D.S. Mackay, B.E. Ewers, T.L. Aston, and C. Weinig
"Phenotypic trait identification using a multimodel Bayesian method: a case study using photosynthesis in Brassica rapa genotypes"
Frontiers in Plant Science
, v.8
, 2018
, p.448
10.3389/fpls.2018.00448
Pleban, J. R.*; Guadagno, C.R.*; Mackay, D. S.; Ewers, B. E.; Weinig, C.
"Rapid Chlorophyll a Fluorescence Light Response Curves Mechanistically Inform Photosynthesis Modeling"
Plant Physiology
, v.183
, 2020
, p.602
10.1104/pp.19.00375
Salehin, M., Li, B., Tang, M., Katz, E., Song, L., Ecker, J.R., Kliebenstein, D.J., and M. Estelle
"Auxin-sensitive Aux/IAA proteins mediate drought tolerance in Arabidopsis by regulating glucosinolate levels"
Nature Communications
, v.10
, 2019
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10.1038/s41467-019-12002-1
Salehin, M., Li, B., Tang, M., Kliebenstein, D.J., and M. Estelle.
"Auxin-sensitive Aux/IAA proteins promote drought tolerance in Arabidopsis by regulating glucosinolate levels."
Nature Communications
, v.10
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10.1038/s41467-019-12002-1
Swift, Joseph Greenham, Kathleen Ecker, Joseph R. Coruzzi, Gloria M.McClung, C. Robertson
"The biology of time: dynamic responses of cell types to developmental, circadian, and environmental cues"
Plant Journal
, v.109
, 2021
, p.764
10.1111/tpj.15589
Wang, D.R., C.R. Guadagno, X. Mao, D.S. Mackay, J.R. Pleban*, R.L. Baker, C. Weinig, J.-L. Jannink, and B.E. Ewers.
"A framework for genomics-informed ecophysiological modeling in plants"
Journal of Experimental Botany
, v.70
, 2019
, p.2561
10.1093/jxb/erz090
Wang, D.R., M.D. Venturas, D.S. Mackay, D.J. Hunsaker, K.R. Thorp, M.A. Gore, and D. Pauli.
"Use of hydraulic traits for modeling genotype-specific acclimation in cotton under drought"
New Phytologist
, 2020
10.1111/nph.16751
(Showing: 1 - 10 of 38)
(Showing: 1 - 38 of 38)
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