14,311 research outputs found
Urea‑functionalized amorphous calcium phosphate nanofertilizers: optimizing the synthetic strategy towards environmental sustainability and manufacturing costs
This work has been performed thanks to the funding by Fondazione CARIPLO (Project No. 2016-0648: Romancing
the stone: size-controlled HYdroxyaPATItes for sustainable Agriculture – HYPATIA). JMDL acknowledges
Spanish Ministry of Science, Innovation and Universities of Spain (MCIU/AEI/FEDER, UE) for funding through
the projects NanoVIT (RTI-2018-095794-A-C22) and NanoSmart (RYC-2016-21042). GBRR also acknowledges
the Spanish MICIU for her postdoctoral contract within the Juan de la Cierva Program (JdC-2017). Financial
support for this work was also provided by the Marie Skłodowska-Curie Standard Fellowships (888972-PSust-
MOF, F.J.C.) within the European Union research and innovation framework programme (2014-2020). We thank
Prof. Jan Skov Pedersen (Aarhus University, DK) for technical and scientific assistance on SAXS experiments.Nanosized fertilizers are the new frontier of nanotechnology towards a sustainable agriculture. Here, an efficient N-nanofertilizer is obtained by post-synthetic modification (PSM) of nitrate-doped amorphous calcium phosphate (ACP) nanoparticles (NPs) with urea. The unwasteful PSM protocol leads to N-payloads as large as 8.1 w/w%, is well replicated by using inexpensive technical-grade reagents for cost-effective up-scaling and moderately favours urea release slowdown. Using the PSM approach, the N amount is ca. 3 times larger than that obtained in an equivalent one-pot synthesis where urea and nitrate are jointly added during the NPs preparation. In vivo tests on cucumber plants in hydroponic conditions show that N-doped ACP NPs, with half absolute N-content than in conventional urea treatment, promote the formation of an equivalent amount of root and shoot biomass, without nitrogen depletion. The high nitrogen use efficiency (up to 69%) and a cost-effective preparation method support the sustainable real usage of N-doped ACP as a nanofertilizer.Fondazione Cariplo
2016-0648Spanish Ministry of Science, Innovation and Universities of Spain (MCIU/AEI/FEDER, UE)
RTI-2018-095794-A-C22
RYC-2016-21042Marie Sklodowska-Curie Standard Fellowships within the European Union research and innovation framework programme (2014-2020)
888972-PSustMOFSpanish MICIU within the Juan de la Cierva Program (JdC-2017
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., GarcÃa-MartÃnez, 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
Association of VAV2 and VAV3 polymorphisms with cardiovascular risk factors
Hypertension, diabetes and obesity are cardiovascular risk factors closely associated to the development of renal and cardiovascular target organ damage. VAV2 and VAV3, members of the VAV family proto-oncogenes, are guanosine nucleotide exchange factors for the Rho and Rac GTPase family, which is related with cardiovascular homeostasis. We have analyzed the relationship between the presence of VAV2 rs602990 and VAV3 rs7528153 polymorphisms with cardiovascular risk factors and target organ damage (heart, vessels and kidney) in 411 subjects. Our results show that being carrier of the T allele in VAV2 rs602990 polymorphism is associated with an increased risk of obesity, reduced levels of ankle-brachial index and diastolic blood pressure and reduced retinal artery caliber. In addition, being carrier of T allele is associated with increased risk of target organ damage in males. On the other hand, being carrier of the T allele in VAV3 rs7528153 polymorphism is associated with a decreased susceptibility of developing a pathologic state composed by the presence of hypertension, diabetes, obesity or cardiovascular damage, and with an increased risk of developing altered basal glycaemia. This is the first report showing an association between VAV2 and VAV3 polymorphisms with cardiovascular risk factors and target organ damage
Formation of Ferroelectrically Defined Ag Nanoarray Patterns
In order to produce the most effective Ag nanoarrays for plasmon enhanced fluorescence and Raman scattering made using ferroelectric substrates, the optimum conditions for the creation of arrays must be identified. We study here Ag nanopattern arrays formed using ferroelectric lithography based on periodically proton exchanged (PPE) template methods. We examine different conditions in regard to deposition of Ag nanoparticles and analyze the plasmon enhanced signal from the resulting nanoarray. We apply FLIM (fluorescence lifetime imaging) to assess different Ag nanoarray preparation conditions on fluorescence emission from selected fluorphores. In addition, we apply Raman and luminescence spectroscopy with AFM (atomic force microscopy) to study the plasmon enhancement of luminescence and Raman from the Ag nanoarrays
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