Our team’s previous five-year NSF Plant Genome Research Program-funded project (award #1546869) investigated how the root system modifies the phenotype of the shoot system in grapevine. Results and resources from that project serve as foundations of our present studies. As in our current program, grafting a scion variety to genetically distinct rootstocks enabled us to dissect the influence of the root and the shoot system. Growing replicated vineyards of the resulting composite plants under diverse climatic and soil conditions enabled us to study the influence of the environment on scion phenotypic plasticity. Below is a brief synopsis of the major outcome of our previous efforts.
Improved understanding of the range of phenotypic plasticity in grapevine. We showed that traits (including berry metabolites, wine volatiles, water use efficiency (WUE), leaf elemental composition and leaf shape) expressed by clonal replicates of a single genotype are strongly modulated by the rootstock genotype and the environment. We demonstrated that the breadth of the scion’s WUE can be expanded beyond its current limits by grafting it on a wild grapevine-based hybrid population, suggesting that relying on the currently available repertoire of commercial rootstocks limit the extent of the scion’s phenotypic plasticity.
Novel genetic loci associated with grapevine physiology. Using two different marker platforms, we developed genetic maps for a V rupestris x V. riparia mapping progeny and examined the genetic architecture of WUE, leaf elemental content, nitrogen metabolism, vine vigor, disease resistance, and leaf shape. We replicated this population in New York, South Dakota, and Missouri both as intact vines and as rootstocks grafted with the scion cultivar ‘Marquette’, which enabled us to map QTL for traits in both the vines’ own shoot system and the shoot system of a genetically identical scion grafted on their roots in each climate.
New genomic resources for hybrid grapevines. We sequenced and annotated the genomes of hybrid grapevines ‘Chambourcin’ and ‘Marquette’, two important commercial cultivars in the eastern US. Using these resources, we demonstrated how gene expression in these vines was modulated by the rootstock daily, seasonally, and annually, and how epigenomic changes correlated with water availability to the vine.
Service to industry and the scientific community. Within the framework of Grant Opportunities for Academic Liaisons with Industry program, we placed 20 project participants in commercial vineyards where they took part in research and training activities. Overall, the project trained 9 post-doctoral scholars, 29 graduate, 49 undergraduate, and 2 high school students, published 37 peer-reviewed journal articles, reviews, commentaries and software packages and gave about 150 presentations to academic, industry and stakeholder groups and to the general public.
Publications
Bhattarai, G., Fennell, A., Londo, J. P., Coleman, C., and Kovacs, L. G. (2021). A Novel Grape Downy Mildew Resistance Locus from Vitis rupestris. Am. J. Enol. Vitic. 72, 12–20. doi: 10.5344/ajev.2020.20030
Bryson, A. E., Wilson Brown, M., Mullins, J., Dong, W., Bahmani, K., Bornowski, N., et al. (2020). Composite modeling of leaf shape along shoots discriminates Vitis species better than individual leaves. Appl. Plant Sci. 8, e11404. doi: 10.1002/aps3.11404
Chitwood, D. H. (2021). The shapes of wine and table grape leaves: An ampelometric study inspired by the methods of Pierre Galet. Plants People Planet 3, 155–170. doi: 10.1002/ppp3.10157
Chitwood, D. H., Mullins, J., Migicovsky, Z., Frank, M., VanBuren, R., and Londo, J. P. (2021). Vein-to-blade ratio is an allometric indicator of leaf size and plasticity. Am. J. Bot. 108, 571–579. doi: 10.1002/ajb2.1639
Gaut, B. S., Miller, A. J., and Seymour, D. K. (2019). Living with two genomes: Grafting and its implications for plant genome-to-genome interactions, phenotypic variation, and evolution. Annu. Rev. Genet. 53, 195–215. doi: 10.1146/annurev-genet-112618-043545
Harris, Z. N., Awale, M., Bhakta, N., Chitwood, D. H., Fennell, A., Frawley, E., et al. (2021). Multi-dimensional leaf phenotypes reflect root system genotype in grafted grapevine over the growing season. Gigascience 10. doi: 10.1093/gigascience/giab087
Harris, Z. N., Klein, L. L., Awale, M., Swift, J. F., Migicovsky, Z., Bhakta, N., et al. (2020). Root system influence on high dimensional leaf phenotypes over the grapevine growing season. bioRxiv. doi: 10.1101/2020.11.10.376947
Harris, Z. N., Kovacs, L. G., and Londo, J. P. (2017). RNA-seq-based genome annotation and identification of long-noncoding RNAs in the grapevine cultivar “Riesling.” BMC Genomics 18, 937. doi: 10.1186/s12864-017-4346-6
Harris, Z. N., Pratt, J. E., Bhakta, N., Frawley, E., Klein, L. L., Kwasniewski, M. T., et al. (2022). Temporal and environmental factors interact with rootstock genotype to shape leaf elemental composition in grafted grapevines. Plant Direct 6, e440. doi: 10.1002/pld3.440
Harris, Z. N., Pratt, J. E., Kovacs, L. G., Klein, L. L., Kwasniewski, M. T., Londo, J. P., et al. (2023). Grapevine scion gene expression is driven by rootstock and environment interaction. BMC Plant Biol. 23, 211. doi: 10.1186/s12870-023-04223-w
Iqbal, R., Sargent, K., and Kovacs, L. (2021). Towards automatic detection and quantification of mildew on grape leaf disks., in Proceedings of the 18th International Conference on Signal Processing and Multimedia Applications, (SCITEPRESS - Science and Technology Publications). doi: 10.5220/0010583900810086
Li, M., Klein, L. L., Duncan, K. E., Jiang, N., Londo, J. P., Miller, A. J., et al. (2019). Characterizing grapevine (Vitis spp.) inflorescence architecture using X-ray imaging: implications for understanding cluster density. bioRxiv. doi: 10.1101/557819
Maimaitiyiming, M., Maimaitijiang, M., Sidike, P., Sagan, V., Migicovsky, Z., Chitwood, D. H., et al. (2020a). Modeling early indicators of grapevine physiology using hyperspectral imaging and partial least squares regression (PLSR)., in IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, (IEEE). doi: 10.1109/igarss39084.2020.9323679
Maimaitiyiming, M., Sagan, V., Sidike, P., Maimaitijiang, M., Miller, A. J., and Kwasniewski, M. (2020b). Leveraging very-high spatial resolution hyperspectral and thermal UAV imageries for characterizing diurnal indicators of grapevine physiology. Remote Sens. (Basel) 12, 3216. doi: 10.3390/rs12193216
Migicovsky, Z. (2020). Tasting improvement in fruit flavor using genomics. New Phytol. 226, 1539–1540. doi: 10.1111/nph.16591
Migicovsky, Z., Cousins, P., Jordan, L. M., Myles, S., Striegler, R. K., Verdegaal, P., et al. (2021). Grapevine rootstocks affect growth-related scion phenotypes. Plant Direct 5, e00324. doi: 10.1002/pld3.324
Migicovsky, Z., Swift, J. F., Awale, M., Helget, Z., Klein, L. L., Pinkner, L., et al. (2024). Terroir and rootstock effects on leaf shape in California Central Valley vineyards. Plants People Planet. doi: 10.1002/ppp3.10620
Migicovsky, Z., Swift, J. F., Helget, Z., Klein, L. L., Ly, A., Maimaitiyiming, M., et al. (2022). Grapevine leaf size influences vine canopy temperature. bioRxiv. doi: 10.1101/2022.07.07.499216
Monier, B., McDermaid, A., Wang, C., Zhao, J., Miller, A., Fennell, A., et al. (2019). IRIS-EDA: An integrated RNA-Seq interpretation system for gene expression data analysis. PLoS Comput. Biol. 15, e1006792. doi: 10.1371/journal.pcbi.1006792
Patel, S., Harris, Z. N., Londo, J. P., Miller, A., and Fennell, A. (2023). Genome assembly of the hybrid grapevine Vitis “Chambourcin.” GigaByte 2023, gigabyte84. doi: 10.46471/gigabyte.84
Patel, S., Robben, M., Fennell, A., Londo, J. P., Alahakoon, D., Villegas-Diaz, R., et al. (2020). Draft genome of the Native American cold hardy grapevine Vitis riparia Michx. “Manitoba 37.” Hortic. Res. 7, 92. doi: 10.1038/s41438-020-0316-2
Sharma, P., Thilakarathna, I., and Fennell, A. (2024a). Hyperspectral imaging and artificial intelligence enhance remote phenotyping of grapevine rootstock influence on whole vine photosynthesis. Front. Plant Sci. 15, 1409821. doi: 10.3389/fpls.2024.1409821
Sharma, P., Villegas-Diaz, R., and Fennell, A. (2024b). Predicting grapevine physiological parameters using hyperspectral remote sensing integrated with hybrid Convolutional Neural Network and ensemble stacked regression. Remote Sens. (Basel) 16, 2626. doi: 10.3390/rs16142626
Swift, J. F., Migicovsky, Z., Trello, G. E., and Miller, A. J. (2023). Grapevine bacterial communities across the Central Valley of California. bioRxiv. doi: 10.1101/2023.07.01.547327
Williams, B. R., Edwards, C. E., Kwasniewski, M. T., and Miller, A. J. (2020). Epigenomic patterns reflect irrigation and grafting in the grapevine clone ‘Chambourcin.’ bioRxiv. doi: 10.1101/2020.09.09.290072
Yang, J., Chen, X., McDermaid, A., and Ma, Q. (2017). DMINDA 2.0: integrated and systematic views of regulatory DNA motif identification and analyses. Bioinformatics 33, 2586–2588. doi: 10.1093/bioinformatics/btx223
Zhang, Y., Xie, J., Yang, J., Fennell, A., Zhang, C., and Ma, Q. (2017). QUBIC: a bioconductor package for qualitative biclustering analysis of gene co-expression data. Bioinformatics 33, 450–452. doi: 10.1093/bioinformatics/btw635
Bhattarai, G., Fennell, A., Londo, J. P., Coleman, C., and Kovacs, L. G. (2021). A Novel Grape Downy Mildew Resistance Locus from Vitis rupestris. Am. J. Enol. Vitic. 72, 12–20. doi: 10.5344/ajev.2020.20030
Bryson, A. E., Wilson Brown, M., Mullins, J., Dong, W., Bahmani, K., Bornowski, N., et al. (2020). Composite modeling of leaf shape along shoots discriminates Vitis species better than individual leaves. Appl. Plant Sci. 8, e11404. doi: 10.1002/aps3.11404
Chitwood, D. H. (2021). The shapes of wine and table grape leaves: An ampelometric study inspired by the methods of Pierre Galet. Plants People Planet 3, 155–170. doi: 10.1002/ppp3.10157
Chitwood, D. H., Mullins, J., Migicovsky, Z., Frank, M., VanBuren, R., and Londo, J. P. (2021). Vein-to-blade ratio is an allometric indicator of leaf size and plasticity. Am. J. Bot. 108, 571–579. doi: 10.1002/ajb2.1639
Gaut, B. S., Miller, A. J., and Seymour, D. K. (2019). Living with two genomes: Grafting and its implications for plant genome-to-genome interactions, phenotypic variation, and evolution. Annu. Rev. Genet. 53, 195–215. doi: 10.1146/annurev-genet-112618-043545
Harris, Z. N., Awale, M., Bhakta, N., Chitwood, D. H., Fennell, A., Frawley, E., et al. (2021). Multi-dimensional leaf phenotypes reflect root system genotype in grafted grapevine over the growing season. Gigascience 10. doi: 10.1093/gigascience/giab087
Harris, Z. N., Klein, L. L., Awale, M., Swift, J. F., Migicovsky, Z., Bhakta, N., et al. (2020). Root system influence on high dimensional leaf phenotypes over the grapevine growing season. bioRxiv. doi: 10.1101/2020.11.10.376947
Harris, Z. N., Kovacs, L. G., and Londo, J. P. (2017). RNA-seq-based genome annotation and identification of long-noncoding RNAs in the grapevine cultivar “Riesling.” BMC Genomics 18, 937. doi: 10.1186/s12864-017-4346-6
Harris, Z. N., Pratt, J. E., Bhakta, N., Frawley, E., Klein, L. L., Kwasniewski, M. T., et al. (2022). Temporal and environmental factors interact with rootstock genotype to shape leaf elemental composition in grafted grapevines. Plant Direct 6, e440. doi: 10.1002/pld3.440
Harris, Z. N., Pratt, J. E., Kovacs, L. G., Klein, L. L., Kwasniewski, M. T., Londo, J. P., et al. (2023). Grapevine scion gene expression is driven by rootstock and environment interaction. BMC Plant Biol. 23, 211. doi: 10.1186/s12870-023-04223-w
Iqbal, R., Sargent, K., and Kovacs, L. (2021). Towards automatic detection and quantification of mildew on grape leaf disks., in Proceedings of the 18th International Conference on Signal Processing and Multimedia Applications, (SCITEPRESS - Science and Technology Publications). doi: 10.5220/0010583900810086
Li, M., Klein, L. L., Duncan, K. E., Jiang, N., Londo, J. P., Miller, A. J., et al. (2019). Characterizing grapevine (Vitis spp.) inflorescence architecture using X-ray imaging: implications for understanding cluster density. bioRxiv. doi: 10.1101/557819
Maimaitiyiming, M., Maimaitijiang, M., Sidike, P., Sagan, V., Migicovsky, Z., Chitwood, D. H., et al. (2020a). Modeling early indicators of grapevine physiology using hyperspectral imaging and partial least squares regression (PLSR)., in IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, (IEEE). doi: 10.1109/igarss39084.2020.9323679
Maimaitiyiming, M., Sagan, V., Sidike, P., Maimaitijiang, M., Miller, A. J., and Kwasniewski, M. (2020b). Leveraging very-high spatial resolution hyperspectral and thermal UAV imageries for characterizing diurnal indicators of grapevine physiology. Remote Sens. (Basel) 12, 3216. doi: 10.3390/rs12193216
Migicovsky, Z. (2020). Tasting improvement in fruit flavor using genomics. New Phytol. 226, 1539–1540. doi: 10.1111/nph.16591
Migicovsky, Z., Cousins, P., Jordan, L. M., Myles, S., Striegler, R. K., Verdegaal, P., et al. (2021). Grapevine rootstocks affect growth-related scion phenotypes. Plant Direct 5, e00324. doi: 10.1002/pld3.324
Migicovsky, Z., Swift, J. F., Awale, M., Helget, Z., Klein, L. L., Pinkner, L., et al. (2024). Terroir and rootstock effects on leaf shape in California Central Valley vineyards. Plants People Planet. doi: 10.1002/ppp3.10620
Migicovsky, Z., Swift, J. F., Helget, Z., Klein, L. L., Ly, A., Maimaitiyiming, M., et al. (2022). Grapevine leaf size influences vine canopy temperature. bioRxiv. doi: 10.1101/2022.07.07.499216
Monier, B., McDermaid, A., Wang, C., Zhao, J., Miller, A., Fennell, A., et al. (2019). IRIS-EDA: An integrated RNA-Seq interpretation system for gene expression data analysis. PLoS Comput. Biol. 15, e1006792. doi: 10.1371/journal.pcbi.1006792
Patel, S., Harris, Z. N., Londo, J. P., Miller, A., and Fennell, A. (2023). Genome assembly of the hybrid grapevine Vitis “Chambourcin.” GigaByte 2023, gigabyte84. doi: 10.46471/gigabyte.84
Patel, S., Robben, M., Fennell, A., Londo, J. P., Alahakoon, D., Villegas-Diaz, R., et al. (2020). Draft genome of the Native American cold hardy grapevine Vitis riparia Michx. “Manitoba 37.” Hortic. Res. 7, 92. doi: 10.1038/s41438-020-0316-2
Sharma, P., Thilakarathna, I., and Fennell, A. (2024a). Hyperspectral imaging and artificial intelligence enhance remote phenotyping of grapevine rootstock influence on whole vine photosynthesis. Front. Plant Sci. 15, 1409821. doi: 10.3389/fpls.2024.1409821
Sharma, P., Villegas-Diaz, R., and Fennell, A. (2024b). Predicting grapevine physiological parameters using hyperspectral remote sensing integrated with hybrid Convolutional Neural Network and ensemble stacked regression. Remote Sens. (Basel) 16, 2626. doi: 10.3390/rs16142626
Swift, J. F., Migicovsky, Z., Trello, G. E., and Miller, A. J. (2023). Grapevine bacterial communities across the Central Valley of California. bioRxiv. doi: 10.1101/2023.07.01.547327
Williams, B. R., Edwards, C. E., Kwasniewski, M. T., and Miller, A. J. (2020). Epigenomic patterns reflect irrigation and grafting in the grapevine clone ‘Chambourcin.’ bioRxiv. doi: 10.1101/2020.09.09.290072
Yang, J., Chen, X., McDermaid, A., and Ma, Q. (2017). DMINDA 2.0: integrated and systematic views of regulatory DNA motif identification and analyses. Bioinformatics 33, 2586–2588. doi: 10.1093/bioinformatics/btx223
Zhang, Y., Xie, J., Yang, J., Fennell, A., Zhang, C., and Ma, Q. (2017). QUBIC: a bioconductor package for qualitative biclustering analysis of gene co-expression data. Bioinformatics 33, 450–452. doi: 10.1093/bioinformatics/btw635
