8 research outputs found

    Transcriptomic analysis of tomato carpel development reveals alterations in ethylene and gibberellin synthesis during pat3/pat4 parthenocarpic fruit set

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    <p>Abstract</p> <p>Background</p> <p>Tomato fruit set is a key process that has a great economic impact on crop production. We employed the Affymetrix GeneChip Tomato Genome Array to compare the transcriptome of a non-parthenocarpic line, UC82, with that of the parthenocarpic line RP75/59 (<it>pat3/pat4 </it>mutant). We analyzed the transcriptome under normal conditions as well as with forced parthenocarpic development in RP75/59, emasculating the flowers 2 days before anthesis. This analysis helps to understand the fruit set in tomato.</p> <p>Results</p> <p>Differentially expressed genes were extracted with maSigPro, which is designed for the analysis of single and multiseries time course microarray experiments. 2842 genes showed changes throughout normal carpel development and fruit set. Most of them showed a change of expression at or after anthesis. The main differences between lines were concentrated at the anthesis stage. We found 758 genes differentially expressed in parthenocarpic fruit set. Among these genes we detected cell cycle-related genes that were still activated at anthesis in the parthenocarpic line, which shows the lack of arrest in the parthenocarpic line at anthesis. Key genes for the synthesis of gibberellins and ethylene, which were up-regulated in the parthenocarpic line were also detected.</p> <p>Conclusion</p> <p>Comparisons between array experiments determined that anthesis was the most different stage and the key point at which most of the genes were modulated. In the parthenocarpic line, anthesis seemed to be a short transitional stage to fruit set. In this line, the high GAs contends leads to the development of a parthenocarpic fruit, and ethylene may mimic pollination signals, inducing auxin synthesis in the ovary and the development of a jelly fruit.</p

    Quantitative genetic analysis of floral traits shows current limits but potential evolution in the wild

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    The vast variation in floral traits across angiosperms is often interpreted as the result of adaptation to pollinators. However, studies in wild populations often find no evidence of pollinator-mediated selection on flowers. Evolutionary theory predicts this could be the outcome of periods of stasis under stable conditions, followed by shorter periods of pollinator change that provide selection for innovative phenotypes. We asked if periods of stasis are caused by stabilizing selection, absence of other forms of selection or by low trait ability to respond even if selection is present. We studied a plant predominantly pollinated by one bee species across its range. We measured heritability and evolvability of traits, using genome-wide relatedness in a large wild population, and combined this with estimates of selection on the same individuals. We found evidence for both stabilizing selection and low trait heritability as potential explanations for stasis in flowers. The area of the standard petal is under stabilizing selection, but the variability is not heritable. A separate trait, floral weight, presents high heritability, but is not currently under selection. We show how a simple pollination environment coincides with the absence of current prerequisites for adaptive evolutionary change, while heritable variation remains to respond to future selection pressures

    ngs_backbone: a pipeline for read cleaning, mapping and SNP calling using Next Generation Sequence

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    Background: The possibilities offered by next generation sequencing (NGS) platforms are revolutionizing biotechnological laboratories. Moreover, the combination of NGS sequencing and affordable high-throughput genotyping technologies is facilitating the rapid discovery and use of SNPs in non-model species. However, this abundance of sequences and polymorphisms creates new software needs. To fulfill these needs, we have developed a powerful, yet easy-to-use application. Results: The ngs_backbone software is a parallel pipeline capable of analyzing Sanger, 454, Illumina and SOLiD (Sequencing by Oligonucleotide Ligation and Detection) sequence reads. Its main supported analyses are: read cleaning, transcriptome assembly and annotation, read mapping and single nucleotide polymorphism (SNP) calling and selection. In order to build a truly useful tool, the software development was paired with a laboratory experiment. All public tomato Sanger EST reads plus 14.2 million Illumina reads were employed to test the tool and predict polymorphism in tomato. The cleaned reads were mapped to the SGN tomato transcriptome obtaining a coverage of 4.2 for Sanger and 8.5 for Illumina. 23,360 single nucleotide variations (SNVs) were predicted. A total of 76 SNVs were experimentally validated, and 85% were found to be real. Conclusions: ngs_backbone is a new software package capable of analyzing sequences produced by NGS technologies and predicting SNVs with great accuracy. In our tomato example, we created a highly polymorphic collection of SNVs that will be a useful resource for tomato researchers and breeders. The software developed along with its documentation is freely available under the AGPL license and can be downloaded from http://bioinf. comav.upv.es/ngs_backbone/ or http://github.com/JoseBlanca/franklin.Blanca Postigo, JM.; Pascual Bañuls, L.; Ziarsolo Areitioaurtena, P.; Nuez Viñals, F.; Cañizares Sales, J. (2011). Ngs_backbone: a pipeline for read cleaning, mapping and SNP calling using Next Generation Sequence. BMC Genomics. 12:1-8. doi:10.1186/1471-2164-12-285S1812Metzker ML: Sequencing technologies - the next generation. Nature Reviews Genetics. 2010, 11 (1): 31-46. 10.1038/nrg2626.454 sequencing. [ http://www.454.com/ ]Illumina Inc. [ http://www.illumina.com/ ]Flicek P, Birney E: Sense from sequence reads: methods for alignment and assembly (vol 6, pg S6, 2009). Nature Methods. 2010, 7 (6): 479-479.Chevreux B, Pfisterer T, Drescher B, Driesel AJ, Muller WEG, Wetter T, Suhai S: Using the miraEST assembler for reliable and automated mRNA transcript assembly and SNP detection in sequenced ESTs. Genome Research. 2004, 14 (6): 1147-1159. 10.1101/gr.1917404.Li H, Durbin R: Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009, 25 (14): 1754-1760. 10.1093/bioinformatics/btp324.Langmead B, Trapnell C, Pop M, Salzberg SL: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology. 2009, 10 (3):Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, Genome Project Data P: The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009, 25 (16): 2078-2079. 10.1093/bioinformatics/btp352.1000 Genomes. A deep Catalog of Human Genetic Variation. [ http://1000genomes.org/wiki/doku.php?id=1000_genomes:analysis:vcf4.0 ]The seqanswers internet forum. [ http://seqanswers.com/ ]Blankenberg D, Taylor J, Schenck I, He JB, Zhang Y, Ghent M, Veeraraghavan N, Albert I, Miller W, Makova KD, Ross CH, Nekrutenko A: A framework for collaborative analysis of ENCODE data: Making large-scale analyses biologist-friendly. Genome Research. 2007, 17 (6): 960-964. 10.1101/gr.5578007.CloVR Automated Sequence Analysis from Your Desktop. [ http://clovr.org/ ]Papanicolaou A, Stierli R, Ffrench-Constant RH, Heckel DG: Next generation transcriptomes for next generation genomes using est2assembly. Bmc Bioinformatics. 2009, 10:Applied Biosystems by life technologies. [ http://www.appliedbiosystems.com/absite/us/en/home/applications-technologies/solid-next-generation-sequencing.html ]Wall PK, Leebens-Mack J, Chanderbali AS, Barakat A, Wolcott E, Liang HY, Landherr L, Tomsho LP, Hu Y, Carlson JE, Ma H, Schuster SC, Soltis DE, Soltis PS, Altman N, dePamphilis CW: Comparison of next generation sequencing technologies for transcriptome characterization. Bmc Genomics. 2009, 10:Murchison EP, Tovar C, Hsu A, Bender HS, Kheradpour P, Rebbeck CA, Obendorf D, Conlan C, Bahlo M, Blizzard CA, Pyecroft S, Kreiss A, Kellis M, Stark A, Harkins TT, Marshall Graves JA, Woods GM, Hanon GJ, Papenfuss AT: The Tasmanian Devil Transcriptome Reveals Schwann Cell Origins of a Clonally Transmissible Cancer. Science. 2010, 327 (5961): 84-87. 10.1126/science.1180616.Parchman TL, Geist KS, Grahnen JA, Benkman CW, Buerkle CA: Transcriptome sequencing in an ecologically important tree species: assembly, annotation, and marker discovery. Bmc Genomics. 2010, 11:Babik W, Stuglik M, Qi W, Kuenzli M, Kuduk K, Koteja P, Radwan J: Heart transcriptome of the bank vole (Myodes glareolus): towards understanding the evolutionary variation in metabolic rate. BMC Genomics. 2010, 11: 390-10.1186/1471-2164-11-390.Miller JC, Tanksley SD: RFLP analysis of phylogenetic-relationships and genetic-variation in the genus Lycopersicon. Theoretical and Applied Genetics. 1990, 80 (4): 437-448.Williams CE, Stclair DA: Phenetic relationships and levels of variability detected by restriction-fragment-length-polymorphism and random amplified polymorphic DNA analysis of cultivated and wild accessions of Lycopersicon-esculentum. Genome. 1993, 36 (3): 619-630. 10.1139/g93-083.Rick CM: Tomato, Lycopersicon esculentum (Solanaceae). Evolution of crop plants. Edited by: Simmonds NW. 1976, London: Longman Group, 268-273.Labate JA, Baldo AM: Tomato SNP discovery by EST mining and resequencing. Molecular Breeding. 2005, 16 (4): 343-349. 10.1007/s11032-005-1911-5.Yano K, Watanabe M, Yamamoto N, Maeda F, Tsugane T, Shibata D: Expressed sequence tags (EST) database of a miniature tomato cultivar, Micro-Tom. Plant and Cell Physiology. 2005, 46: S139-S139.Jimenez-Gomez JM, Maloof JN: Sequence diversity in three tomato species: SNPs, markers, and molecular evolution. Bmc Plant Biology. 2009, 9:Yang WC, Bai XD, Kabelka E, Eaton C, Kamoun S, van der Knaap E, Francis D: Discovery of single nucleotide polymorphisms in Lycopersicon esculentum by computer aided analysis of expressed sequence tags. Molecular Breeding. 2004, 14 (1): 21-34.Van Deynze A, Stoffel K, Buell CR, Kozik A, Liu J, van der Knaap E, Francis D: Diversity in conserved genes in tomato. Bmc Genomics. 2007, 8:Sim SC, Robbins MD, Chilcott C, Zhu T, Francis DM: Oligonucleotide array discovery of polymorphisms in cultivated tomato (Solanum lycopersicum L.) reveals patterns of SNP variation associated with breeding. Bmc Genomics. 2009, 10:Bioinformatics at COMAV. [ http://bioinf.comav.upv.es/ngs_backbone/index.html ]Broad institute. [ http://www.broadinstitute.org/igv ]Bioinformatics at COMAV. [ http://bioinf.comav.upv.es/ngs_backbone/install.html ]Github social coding. [ http://github.com/JoseBlanca/franklin ]Chou HH, Holmes MH: DNA sequence quality trimming and vector removal. Bioinformatics. 2001, 17 (12): 1093-1104. 10.1093/bioinformatics/17.12.1093.Picard. [ http://picard.sourceforge.net/index.shtml ]McKenna A, Hanna M, Banks E, Sivachenko A, Citulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA: The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research. 2010, 20: 1297-1303. 10.1101/gr.107524.110.Sol Genomics Network. [ ftp://ftp.solgenomics.net/ ]NCBI Genbank. [ http://www.ncbi.nlm.nih.gov/genbank/ ]Gundry CN, Vandersteen JG, Reed GH, Pryor RJ, Chen J, Wittwer CT: Amplicon melting analysis with labeled primers: A closed-tube method for differentiating homozygotes and heterozygotes. Clinical Chemistry. 2003, 49 (3): 396-406. 10.1373/49.3.396

    Atlas of phenotypic, genotypic and geographical diversity present in the European traditional tomato

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    The Mediterranean basin countries are considered secondary centres of tomato diversification. However, information on phenotypic and allelic variation of local tomato materials is still limited. Here we report on the evaluation of the largest traditional tomato collection, which includes 1499 accessions from Southern Europe. Analyses of 70 traits revealed a broad range of phenotypic variability with different distributions among countries, with the culinary end use within each country being the main driver of tomato diversification. Furthermore, eight main tomato types (phenoclusters) were defined by integrating phenotypic data, country of origin, and end use. Genome-wide association study (GWAS) meta-analyses identified associations in 211 loci, 159 of which were novel. The multidimensional integration of phenoclusters and the GWAS meta-analysis identified the molecular signatures for each traditional tomato type and indicated that signatures originated from differential combinations of loci, which in some cases converged in the same tomato phenotype. Our results provide a roadmap for studying and exploiting this untapped tomato diversity
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