The role of a class III gibberellin 2-oxidase in tomato internode elongation

[EN] A network of environmental inputs and internal signaling controls plant growth, development and organ elongation. In particular, the growth-promoting hormone gibberellin (GA) has been shown to play a significant role in organ elongation. The use of tomato as a model organism to study elongation presents an opportunity to study the genetic control of internode-specific elongation in a eudicot species with a sympodial growth habit and substantial internodes that can and do respond to external stimuli. To investigate internode elongation, a mutant with an elongated hypocotyl and internodes but wild-type petioles was identified through a forward genetic screen. In addition to stem-specific elongation, this mutant, named tomato internode elongated -1 (tie-1) is more sensitive to the GA biosynthetic inhibitor paclobutrazol and has altered levels of intermediate and bioactive GAs compared with wild-type plants. The mutation responsible for the internode elongation phenotype was mapped to GA2oxidase 7, a class III GA 2-oxidase in the GA biosynthetic pathway, through a bulked segregant analysis and bioinformatic pipeline, and confirmed by transgenic complementation. Furthermore, bacterially expressed recombinant TIE protein was shown to have bona fide GA 2-oxidase activity. These results define a critical role for this gene in internode elongation and are significant because they further the understanding of the role of GA biosynthetic genes in organ-specific elongation.This work used the Vincent J. Coates Genomics Sequencing Laboratory at UC Berkeley, supported by NIH S10 Instrumentation Grants S10RR029668 and S10RR027303. We thank the Tomato Genetics Resource Center for providing seed of the M82 and Heinz cultivars. The material was developed by and/or obtained from the UC Davis/C M Rick Tomato Genetics Resource Center and maintained by the Department of Plant Sciences, University of California, Davis, CA 95616, USA. We thank Anthony Bolger, Alisdair Fernie and Bjorn Usadel for providing us with access to pre-publication genomic reads of the S. lycopersicum cultivar M82, and Cristina Urbez and Noel Blanco-Tourinan (IBMCP, Spain) for technical help with in vitro production of TIE1. This work was supported in part by the Elsie Taylor Stocking Memorial Fellowship awarded to ASL in 2013, by NSF grant IOS-0820854, by USDA National Institute of Food and Agriculture project CA-D-PLB-2465-H, by internal UC Davis funds, and by Spanish Ministry of Economy and Competitiveness grant BFU2016-80621-P.Lavelle, A.; Gath, N.; Devisetty, U.; Carrera Bergua, E.; Lopez Diaz, I.; Blazquez Rodriguez, MA.; Maloof, J. (2018). The role of a class III gibberellin 2-oxidase in tomato internode elongation. The Plant Journal. https://doi.org/10.1111/tpj.14145SAndrés, F., Porri, A., Torti, S., Mateos, J., Romera-Branchat, M., García-Martínez, J. L., … Coupland, G. (2014). SHORT VEGETATIVE PHASE reduces gibberellin biosynthesis at theArabidopsisshoot apex to regulate the floral transition. Proceedings of the National Academy of Sciences, 111(26), E2760-E2769. doi:10.1073/pnas.1409567111Bolger, A., Scossa, F., Bolger, M. E., Lanz, C., Maumus, F., Tohge, T., … Fernie, A. R. (2014). The genome of the stress-tolerant wild tomato species Solanum pennellii. Nature Genetics, 46(9), 1034-1038. doi:10.1038/ng.3046Bowen, M. E., Henke, K., Siegfried, K. R., Warman, M. L., & Harris, M. P. (2011). Efficient Mapping and Cloning of Mutations in Zebrafish by Low-Coverage Whole-Genome Sequencing. Genetics, 190(3), 1017-1024. doi:10.1534/genetics.111.136069Burset, M. (2000). Analysis of canonical and non-canonical splice sites in mammalian genomes. Nucleic Acids Research, 28(21), 4364-4375. doi:10.1093/nar/28.21.4364Chen, W., Yao, J., Chu, L., Yuan, Z., Li, Y., & Zhang, Y. (2015). Genetic mapping of the nulliplex-branch gene (gb_nb1) in cotton using next-generation sequencing. Theoretical and Applied Genetics, 128(3), 539-547. doi:10.1007/s00122-014-2452-2Cingolani, P., Platts, A., Wang, L. L., Coon, M., Nguyen, T., Wang, L., … Ruden, D. M. (2012). A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly, 6(2), 80-92. doi:10.4161/fly.19695Cuperus, J. T., Montgomery, T. A., Fahlgren, N., Burke, R. T., Townsend, T., Sullivan, C. M., & Carrington, J. C. (2009). Identification of MIR390a precursor processing-defective mutants in Arabidopsis by direct genome sequencing. Proceedings of the National Academy of Sciences, 107(1), 466-471. doi:10.1073/pnas.0913203107Curtis, M. D., & Grossniklaus, U. (2003). A Gateway Cloning Vector Set for High-Throughput Functional Analysis of Genes in Planta. Plant Physiology, 133(2), 462-469. doi:10.1104/pp.103.027979Devisetty, U. K., Covington, M. F., Tat, A. V., Lekkala, S., & Maloof, J. N. (2014). Polymorphism Identification and Improved Genome Annotation ofBrassica rapaThrough Deep RNA Sequencing. G3: Genes|Genomes|Genetics, 4(11), 2065-2078. doi:10.1534/g3.114.012526Eckardt, N. A. (2007). GA Perception and Signal Transduction: Molecular Interactions of the GA Receptor GID1 with GA and the DELLA Protein SLR1 in Rice. The Plant Cell, 19(7), 2095-2097. doi:10.1105/tpc.107.054916Ernst, H. A., Lo Leggio, L., Willemoës, M., Leonard, G., Blum, P., & Larsen, S. (2006). Structure of the Sulfolobus solfataricus α-Glucosidase: Implications for Domain Conservation and Substrate Recognition in GH31. Journal of Molecular Biology, 358(4), 1106-1124. doi:10.1016/j.jmb.2006.02.056Fillatti, J. J., Kiser, J., Rose, R., & Comai, L. (1987). Efficient Transfer of a Glyphosate Tolerance Gene into Tomato Using a Binary Agrobacterium Tumefaciens Vector. Nature Biotechnology, 5(7), 726-730. doi:10.1038/nbt0787-726Garrison , E. Marth , G. 2012 Haplotype-based variant detection from short-read sequencingHedden, P., & Graebe, J. E. (1985). Inhibition of gibberellin biosynthesis by paclobutrazol in cell-free homogenates ofCucurbita maxima endosperm andMalus pumila embryos. Journal of Plant Growth Regulation, 4(1-4), 111-122. doi:10.1007/bf02266949Kimura, S., & Sinha, N. (2008). Tomato (Solanum lycopersicum): A Model Fruit-Bearing Crop. Cold Spring Harbor Protocols, 2008(12), pdb.emo105-pdb.emo105. doi:10.1101/pdb.emo105Koenig, D., Jimenez-Gomez, J. M., Kimura, S., Fulop, D., Chitwood, D. H., Headland, L. R., … Maloof, J. N. (2013). Comparative transcriptomics reveals patterns of selection in domesticated and wild tomato. Proceedings of the National Academy of Sciences, 110(28), E2655-E2662. doi:10.1073/pnas.1309606110Li, H., & Durbin, R. (2009). Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 25(14), 1754-1760. doi:10.1093/bioinformatics/btp324Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., … Homer, N. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25(16), 2078-2079. doi:10.1093/bioinformatics/btp352Li, J., Sima, W., Ouyang, B., Wang, T., Ziaf, K., Luo, Z., … Ye, Z. (2012). Tomato SlDREB gene restricts leaf expansion and internode elongation by downregulating key genes for gibberellin biosynthesis. Journal of Experimental Botany, 63(18), 6407-6420. doi:10.1093/jxb/ers295Lorrain, S., & Fankhauser, C. (2012). Plant Development: Should I Stop or Should I Grow? Current Biology, 22(16), R645-R647. doi:10.1016/j.cub.2012.06.054Menda, N., Semel, Y., Peled, D., Eshed, Y., & Zamir, D. (2004). In silicoscreening of a saturated mutation library of tomato. The Plant Journal, 38(5), 861-872. doi:10.1111/j.1365-313x.2004.02088.xMichelmore, R. W., Paran, I., & Kesseli, R. V. (1991). Identification of markers linked to disease-resistance genes by bulked segregant analysis: a rapid method to detect markers in specific genomic regions by using segregating populations. Proceedings of the National Academy of Sciences, 88(21), 9828-9832. doi:10.1073/pnas.88.21.9828Pimenta Lange, M. J., Liebrandt, A., Arnold, L., Chmielewska, S.-M., Felsberger, A., Freier, E., … Lange, T. (2013). Functional characterization of gibberellin oxidases from cucumber, Cucumis sativus L. Phytochemistry, 90, 62-69. doi:10.1016/j.phytochem.2013.02.006Raskin, I., & Kende, H. (1984). Role of Gibberellin in the Growth Response of Submerged Deep Water Rice. Plant Physiology, 76(4), 947-950. doi:10.1104/pp.76.4.947Reinecke, D. M., Wickramarathna, A. D., Ozga, J. A., Kurepin, L. V., Jin, A. L., Good, A. G., & Pharis, R. P. (2013). Gibberellin 3-oxidase Gene Expression Patterns Influence Gibberellin Biosynthesis, Growth, and Development in Pea. PLANT PHYSIOLOGY, 163(2), 929-945. doi:10.1104/pp.113.225987Robinson, M. D., & Oshlack, A. (2010). A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biology, 11(3), R25. doi:10.1186/gb-2010-11-3-r25Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2009). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139-140. doi:10.1093/bioinformatics/btp616Robinson, J. T., Thorvaldsdóttir, H., Winckler, W., Guttman, M., Lander, E. S., Getz, G., & Mesirov, J. P. (2011). Integrative genomics viewer. Nature Biotechnology, 29(1), 24-26. doi:10.1038/nbt.1754Schneeberger, K., Ossowski, S., Lanz, C., Juul, T., Petersen, A. H., Nielsen, K. L., … Andersen, S. U. (2009). SHOREmap: simultaneous mapping and mutation identification by deep sequencing. Nature Methods, 6(8), 550-551. doi:10.1038/nmeth0809-550Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012). NIH Image to ImageJ: 25 years of image analysis. Nature Methods, 9(7), 671-675. doi:10.1038/nmeth.2089Schomburg, F. M., Bizzell, C. M., Lee, D. J., Zeevaart, J. A. D., & Amasino, R. M. (2002). Overexpression of a Novel Class of Gibberellin 2-Oxidases Decreases Gibberellin Levels and Creates Dwarf Plants. The Plant Cell, 15(1), 151-163. doi:10.1105/tpc.005975Seo, M., Jikumaru, Y., & Kamiya, Y. (2011). Profiling of Hormones and Related Metabolites in Seed Dormancy and Germination Studies. Methods in Molecular Biology, 99-111. doi:10.1007/978-1-61779-231-1_7Sun, T. (2011). The Molecular Mechanism and Evolution of the GA–GID1–DELLA Signaling Module in Plants. Current Biology, 21(9), R338-R345. doi:10.1016/j.cub.2011.02.036Sun, T., & Gubler, F. (2004). MOLECULAR MECHANISM OF GIBBERELLIN SIGNALING IN PLANTS. Annual Review of Plant Biology, 55(1), 197-223. doi:10.1146/annurev.arplant.55.031903.141753Thorvaldsdottir, H., Robinson, J. T., & Mesirov, J. P. (2012). Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Briefings in Bioinformatics, 14(2), 178-192. doi:10.1093/bib/bbs017(2012). The tomato genome sequence provides insights into fleshy fruit evolution. Nature, 485(7400), 635-641. doi:10.1038/nature11119Trapnell, C., Pachter, L., & Salzberg, S. L. (2009). TopHat: discovering splice junctions with RNA-Seq. Bioinformatics, 25(9), 1105-1111. doi:10.1093/bioinformatics/btp120Trapnell, C., Roberts, A., Goff, L., Pertea, G., Kim, D., Kelley, D. R., … Pachter, L. (2012). Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nature Protocols, 7(3), 562-578. doi:10.1038/nprot.2012.016Tsai, H., Howell, T., Nitcher, R., Missirian, V., Watson, B., Ngo, K. J., … Comai, L. (2011). Discovery of Rare Mutations in Populations: TILLING by Sequencing. Plant Physiology, 156(3), 1257-1268. doi:10.1104/pp.110.169748Ueguchi-Tanaka, M., Nakajima, M., Katoh, E., Ohmiya, H., Asano, K., Saji, S., … Matsuoka, M. (2007). Molecular Interactions of a Soluble Gibberellin Receptor, GID1, with a Rice DELLA Protein, SLR1, and Gibberellin. The Plant Cell, 19(7), 2140-2155. doi:10.1105/tpc.106.043729Wickham, H. (2016). ggplot2. Use R! doi:10.1007/978-3-319-24277-4Winter, D., Vinegar, B., Nahal, H., Ammar, R., Wilson, G. V., & Provart, N. J. (2007). An «Electronic Fluorescent Pictograph» Browser for Exploring and Analyzing Large-Scale Biological Data Sets. PLoS ONE, 2(8), e718. doi:10.1371/journal.pone.0000718Xu, H., Liu, Q., Yao, T., & Fu, X. (2014). Shedding light on integrative GA signaling. Current Opinion in Plant Biology, 21, 89-95. doi:10.1016/j.pbi.2014.06.010Yamaguchi, S. (2008). Gibberellin Metabolism and its Regulation. Annual Review of Plant Biology, 59(1), 225-251. doi:10.1146/annurev.arplant.59.032607.09280

Detecting change via competence model

In real world applications, interested concepts are more likely to change rather than remain stable, which is known as concept drift. This situation causes problems on predictions for many learning algorithms including case-base reasoning (CBR). When learning under concept drift, a critical issue is to identify and determine "when" and "how" the concept changes. In this paper, we developed a competence-based empirical distance between case chunks and then proposed a change detection method based on it. As a main contribution of our work, the change detection method provides an approach to measure the distribution change of cases of an infinite domain through finite samples and requires no prior knowledge about the case distribution, which makes it more practical in real world applications. Also, different from many other change detection methods, we not only detect the change of concepts but also quantify and describe this change. © 2010 Springer-Verlag

Growth factor restriction impedes progression of wound healing following cataract surgery: identification of VEGF as a putative therapeutic target

Secondary visual loss occurs in millions of patients due to a wound-healing response, known as posterior capsule opacification (PCO), following cataract surgery. An intraocular lens (IOL) is implanted into residual lens tissue, known as the capsular bag, following cataract removal. Standard IOLs allow the anterior and posterior capsules to become physically connected. This places pressure on the IOL and improves contact with the underlying posterior capsule. New open bag IOL designs separate the anterior capsule and posterior capsules and further reduce PCO incidence. It is hypothesised that this results from reduced cytokine availability due to greater irrigation of the bag. We therefore explored the role of growth factor restriction on PCO using human lens cell and tissue culture models. We demonstrate that cytokine dilution, by increasing medium volume, significantly reduced cell coverage in both closed and open capsular bag models. This coincided with reduced cell density and myofibroblast formation. A screen of 27 cytokines identified nine candidates whose expression profile correlated with growth. In particular, VEGF was found to regulate cell survival, growth and myofibroblast formation. VEGF provides a therapeutic target to further manage PCO development and will yield best results when used in conjunction with open bag IOL designs

A Genome-Wide Association Study Identifies Variants Underlying the Arabidopsis thaliana Shade Avoidance Response

Shade avoidance is an ecologically and molecularly well-understood set of plant developmental responses that occur when the ratio of red to far-red light (R∶FR) is reduced as a result of foliar shade. Here, a genome-wide association study (GWAS) in Arabidopsis thaliana was used to identify variants underlying one of these responses: increased hypocotyl elongation. Four hypocotyl phenotypes were included in the study, including height in high R∶FR conditions (simulated sun), height in low R∶FR conditions (simulated shade), and two different indices of the response of height to low R∶FR. GWAS results showed that variation in these traits is controlled by many loci of small to moderate effect. A known PHYC variant contributing to hypocotyl height variation was identified and lists of significantly associated genes were enriched in a priori candidates, suggesting that this GWAS was capable of generating meaningful results. Using metadata such as expression data, GO terms, and other annotation, we were also able to identify variants in candidate de novo genes. Patterns of significance among our four phenotypes allowed us to categorize associations into three groups: those that affected hypocotyl height without influencing shade avoidance, those that affected shade avoidance in a height-dependent fashion, and those that exerted specific control over shade avoidance. This grouping allowed for the development of explicit hypotheses about the genetics underlying shade avoidance variation. Additionally, the response to shade did not exhibit any marked geographic distribution, suggesting that variation in low R∶FR–induced hypocotyl elongation may represent a response to local conditions

Morphological Plant Modeling: Unleashing Geometric and Topological Potential within the Plant Sciences

The geometries and topologies of leaves, flowers, roots, shoots, and their arrangements have fascinated plant biologists and mathematicians alike. As such, plant morphology is inherently mathematical in that it describes plant form and architecture with geometrical and topological techniques. Gaining an understanding of how to modify plant morphology, through molecular biology and breeding, aided by a mathematical perspective, is critical to improving agriculture, and the monitoring of ecosystems is vital to modeling a future with fewer natural resources. In this white paper, we begin with an overview in quantifying the form of plants and mathematical models of patterning in plants. We then explore the fundamental challenges that remain unanswered concerning plant morphology, from the barriers preventing the prediction of phenotype from genotype to modeling the movement of leaves in air streams. We end with a discussion concerning the education of plant morphology synthesizing biological and mathematical approaches and ways to facilitate research advances through outreach, cross-disciplinary training, and open science. Unleashing the potential of geometric and topological approaches in the plant sciences promises to transform our understanding of both plants and mathematics

LIN-44/Wnt Directs Dendrite Outgrowth through LIN-17/Frizzled in C. elegans Neurons

Nervous system function requires proper development of two functional and morphological domains of neurons, axons and dendrites. Although both these domains are equally important for signal transmission, our understanding of dendrite development remains relatively poor. Here, we show that in C. elegans the Wnt ligand, LIN-44, and its Frizzled receptor, LIN-17, regulate dendrite development of the PQR oxygen sensory neuron. In lin-44 and lin-17 mutants, PQR dendrites fail to form, display stunted growth, or are misrouted. Manipulation of temporal and spatial expression of LIN-44, combined with cell-ablation experiments, indicates that this molecule is patterned during embryogenesis and acts as an attractive cue to define the site from which the dendrite emerges. Genetic interaction between lin-44 and lin-17 suggests that the LIN-44 signal is transmitted through the LIN-17 receptor, which acts cell autonomously in PQR. Furthermore, we provide evidence that LIN-17 interacts with another Wnt molecule, EGL-20, and functions in parallel to MIG-1/Frizzled in this process. Taken together, our results reveal a crucial role for Wnt and Frizzled molecules in regulating dendrite development in vivo