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Parker T, Bolt T, Williams T, Penmetsa RV, Mulube M, Celebioglu B, Palkovic A, Jochua CN, Del Mar Rubio Wilhelmi M, Lo S, Bornhorst G, Tian L, Kamfwa K, Farmer A, Diepenbrock C, Gepts P. Seed color patterns in domesticated common bean are regulated by MYB-bHLH-WD40 transcription factors and temperature. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024. [PMID: 39152711 DOI: 10.1111/tpj.16947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 07/03/2024] [Accepted: 07/09/2024] [Indexed: 08/19/2024]
Abstract
Seed colors and color patterns are critical for the survival of wild plants and the consumer appeal of crops. In common bean, a major global staple, these patterns are also essential in determining market classes, yet the genetic and environmental control of many pigmentation patterns remains unresolved. In this study, we genetically mapped variation for several important seed pattern loci, including T, Bip, phbw, and Z, which co-segregated with candidate genes PvTTG1, PvMYC1, PvTT8, and PvTT2, respectively. Proteins encoded by these genes are predicted to work together in MYB-bHLH-WD40 (MBW) complexes, propagating flavonoid biosynthesis across the seed coat as observed in Arabidopsis. Whole-genome sequencing of 37 accessions identified mutations, including seven unique parallel mutations in T (PvTTG1) and non-synonymous SNPs in highly conserved residues in bipana (PvMYC1) and z (PvTT2). A 612 bp intron deletion in phbw (PvTT8) eliminated motifs conserved since the Papilionoideae origin and corresponded to a 20-fold reduction in transcript abundance. In multi-location field trials of seven varieties with partial seed coat pigmentation patterning, the pigmented seed coat area correlated positively with ambient temperature, with up to 11-fold increases in the pigmented area from the coolest to the warmest environments. In controlled growth chamber conditions, an increase of 4°C was sufficient to cause pigmentation on an average additional 21% of the seed coat area. Our results shed light on key steps of flavonoid biosynthesis in common bean. They will inform breeding efforts for seed coat color/patterning to improve consumer appeal in this nutritious staple crop.
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Affiliation(s)
- Travis Parker
- Department of Plant Sciences, University of California, Davis, California, USA
| | - Tayah Bolt
- Department of Plant Sciences, University of California, Davis, California, USA
| | - Troy Williams
- Department of Plant Sciences, University of California, Davis, California, USA
| | - R Varma Penmetsa
- Department of Plant Sciences, University of California, Davis, California, USA
| | - Mwiinga Mulube
- Department of Plant Sciences, School of Agricultural Sciences, University of Zambia, Lusaka, Zambia
| | - Burcu Celebioglu
- Department of Plant Sciences, University of California, Davis, California, USA
| | - Antonia Palkovic
- Department of Plant Sciences, University of California, Davis, California, USA
| | | | | | - Sassoum Lo
- Department of Plant Sciences, University of California, Davis, California, USA
| | - Gail Bornhorst
- Department of Biological and Agricultural Engineering, University of California, Davis, California, USA
| | - Li Tian
- Department of Plant Sciences, University of California, Davis, California, USA
| | - Kelvin Kamfwa
- Department of Plant Sciences, School of Agricultural Sciences, University of Zambia, Lusaka, Zambia
| | - Andrew Farmer
- National Center for Genome Resources, Santa Fe, New Mexico, USA
| | | | - Paul Gepts
- Department of Plant Sciences, University of California, Davis, California, USA
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Adak A, DeSalvio AJ, Arik MA, Murray SC. Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize. G3 (BETHESDA, MD.) 2024; 14:jkae092. [PMID: 38776257 PMCID: PMC11228873 DOI: 10.1093/g3journal/jkae092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 04/24/2024] [Indexed: 05/24/2024]
Abstract
Field-based phenomic prediction employs novel features, like vegetation indices (VIs) from drone images, to predict key agronomic traits in maize, despite challenges in matching biomarker measurement time points across years or environments. This study utilized functional principal component analysis (FPCA) to summarize the variation of temporal VIs, uniquely allowing the integration of this data into phenomic prediction models tested across multiple years (2018-2021) and environments. The models, which included 1 genomic, 2 phenomic, 2 multikernel, and 1 multitrait type, were evaluated in 4 prediction scenarios (CV2, CV1, CV0, and CV00), relevant for plant breeding programs, assessing both tested and untested genotypes in observed and unobserved environments. Two hybrid populations (415 and 220 hybrids) demonstrated the visible atmospherically resistant index's strong temporal correlation with grain yield (up to 0.59) and plant height. The first 2 FPCAs explained 59.3 ± 13.9% and 74.2 ± 9.0% of the temporal variation of temporal data of VIs, respectively, facilitating predictions where flight times varied. Phenomic data, particularly when combined with genomic data, often were comparable to or numerically exceeded the base genomic model in prediction accuracy, particularly for grain yield in untested hybrids, although no significant differences in these models' performance were consistently observed. Overall, this approach underscores the effectiveness of FPCA and combined models in enhancing the prediction of grain yield and plant height across environments and diverse agricultural settings.
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Affiliation(s)
- Alper Adak
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA
| | - Aaron J DeSalvio
- Interdisciplinary Graduate Program in Genetics and Genomics (Department of Biochemistry and Biophysics), Texas A&M University, College Station, TX 77843-2128, USA
| | - Mustafa A Arik
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA
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Wong CYS, Gilbert ME, Pierce MA, Parker TA, Palkovic A, Gepts P, Magney TS, Buckley TN. Hyperspectral Remote Sensing for Phenotyping the Physiological Drought Response of Common and Tepary Bean. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0021. [PMID: 37040284 PMCID: PMC10076057 DOI: 10.34133/plantphenomics.0021] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 12/12/2022] [Indexed: 06/19/2023]
Abstract
Proximal remote sensing offers a powerful tool for high-throughput phenotyping of plants for assessing stress response. Bean plants, an important legume for human consumption, are often grown in regions with limited rainfall and irrigation and are therefore bred to further enhance drought tolerance. We assessed physiological (stomatal conductance and predawn and midday leaf water potential) and ground- and tower-based hyperspectral remote sensing (400 to 2,400 nm and 400 to 900 nm, respectively) measurements to evaluate drought response in 12 common bean and 4 tepary bean genotypes across 3 field campaigns (1 predrought and 2 post-drought). Hyperspectral data in partial least squares regression models predicted these physiological traits (R 2 = 0.20 to 0.55; root mean square percent error 16% to 31%). Furthermore, ground-based partial least squares regression models successfully ranked genotypic drought responses similar to the physiologically based ranks. This study demonstrates applications of high-resolution hyperspectral remote sensing for predicting plant traits and phenotyping drought response across genotypes for vegetation monitoring and breeding population screening.
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