Canalization of Tomato Fruit Metabolism[OPEN]

Metabolomics data from two independent Solanum penellii populations cast light on the genetic robustness of primary and secondary metabolite contents in tomato. To explore the genetic robustness (canalization) of metabolism, we examined the levels of fruit metabolites in multiple harvests of a tomato introgression line (IL) population. The IL partitions the whole genome of the wild species Solanum pennellii in the background of the cultivated tomato (Solanum lycopersicum). We identified several metabolite quantitative trait loci that reduce variability for both primary and secondary metabolites, which we named canalization metabolite quantitative trait loci (cmQTL). We validated nine cmQTL using an independent population of backcross inbred lines, derived from the same parents, which allows increased resolution in mapping the QTL previously identified in the ILs. These cmQTL showed little overlap with QTL for the metabolite levels themselves. Moreover, the intervals they mapped to harbored few metabolism-associated genes, suggesting that the canalization of metabolism is largely controlled by regulatory genes.

[1]  Jianxin Ma,et al.  Plasticity and innovation of regulatory mechanisms underlying seed oil content mediated by duplicated genes in the palaeopolyploid soybean , 2017, The Plant journal : for cell and molecular biology.

[2]  Fernando J. Yuste-Lisbona,et al.  QTL mapping of fruit mineral contents provides new chances for molecular breeding of tomato nutritional traits , 2017, Theoretical and Applied Genetics.

[3]  Sanwen Huang,et al.  A chemical genetic roadmap to improved tomato flavor , 2017, Science.

[4]  G. Rodrigo,et al.  Identification, introgression, and validation of fruit volatile QTLs from a red-fruited wild tomato species , 2016, Journal of experimental botany.

[5]  Mahendra Dia,et al.  Stability of fruit quality traits in diverse watermelon cultivars tested in multiple environments , 2016, Horticulture Research.

[6]  Anthony L. Schilmiller,et al.  Introgression of acylsugar chemistry QTL modifies the composition and structure of acylsugars produced by high-accumulating tomato lines , 2016, Molecular Breeding.

[7]  K. Homma,et al.  Finlay-Wilkinson's regression coefficient as a pre-screening criterion for yield responsiveness to elevated atmospheric CO2 concentration in crops. , 2016, Physiologia plantarum.

[8]  J. Cuartero,et al.  Multi-environment QTL mapping reveals genetic architecture of fruit cracking in a tomato RIL Solanum lycopersicum × S. pimpinellifolium population , 2016, Theoretical and Applied Genetics.

[9]  A. Aharoni,et al.  Solanum pennellii backcross inbred lines (BILs) link small genomic bins with tomato traits. , 2016, The Plant journal : for cell and molecular biology.

[10]  Jianzhi Zhang,et al.  Adaptive Genetic Robustness of Escherichia coli Metabolic Fluxes. , 2016, Molecular biology and evolution.

[11]  A. Fernie,et al.  Multi-level engineering facilitates the production of phenylpropanoid compounds in tomato , 2015, Nature Communications.

[12]  B. Mueller‐Roeber,et al.  Identification and Mode of Inheritance of Quantitative Trait Loci for Secondary Metabolite Abundance in Tomato[OPEN] , 2015, Plant Cell.

[13]  Jason A. Corwin,et al.  Genetic Variation in the Nuclear and Organellar Genomes Modulates Stochastic Variation in the Metabolome, Growth, and Defense , 2015, PLoS genetics.

[14]  T. Mitchell-Olds,et al.  Unifying Genetic Canalization, Genetic Constraint, and Genotype-by-Environment Interaction: QTL by Genomic Background by Environment Interaction of Flowering Time in Boechera stricta , 2014, PLoS genetics.

[15]  Takayuki Tohge,et al.  The genome of the stress-tolerant wild tomato species Solanum pennellii , 2014, Nature Genetics.

[16]  P. G. Dominguez,et al.  Natural occurring epialleles determine vitamin E accumulation in tomato fruits , 2014, Nature Communications.

[17]  Meng Chen,et al.  Light-regulated gene repositioning in Arabidopsis , 2014, Nature Communications.

[18]  L. Farina,et al.  The plasticity of the grapevine berry transcriptome , 2013, Genome Biology.

[19]  A. Fernie,et al.  Plastic, fantastic! Phenotypic variance in the transcriptional landscape of the grape berry , 2013, Genome Biology.

[20]  Marie-Anne Félix,et al.  Robustness and flexibility in nematode vulva development. , 2012, Trends in genetics : TIG.

[21]  Adriano Nunes Nesi,et al.  Combined transcription factor profiling, microarray analysis and metabolite profiling reveals the transcriptional control of metabolic shifts occurring during tomato fruit development. , 2011, The Plant journal : for cell and molecular biology.

[22]  Daniel J. Kliebenstein,et al.  Genomic Analysis of QTLs and Genes Altering Natural Variation in Stochastic Noise , 2011, PLoS genetics.

[23]  R. McQuinn,et al.  Identification of Genes in the Phenylalanine Metabolic Pathway by Ectopic Expression of a MYB Transcription Factor in Tomato Fruit[W] , 2011, Plant Cell.

[24]  M. Hirai,et al.  Functional Compensation of Primary and Secondary Metabolites by Duplicate Genes in Arabidopsis thaliana , 2010, Molecular biology and evolution.

[25]  Takayuki Tohge,et al.  Combining genetic diversity, informatics and metabolomics to facilitate annotation of plant gene function , 2010, Nature Protocols.

[26]  Laura Fanti,et al.  Hsp90 prevents phenotypic variation by suppressing the mutagenic activity of transposons , 2010, Nature.

[27]  Ben Lehner,et al.  Genes Confer Similar Robustness to Environmental, Stochastic, and Genetic Perturbations in Yeast , 2010, PloS one.

[28]  L. Willmitzer,et al.  13C isotope-labeled metabolomes allowing for improved compound annotation and relative quantification in liquid chromatography-mass spectrometry-based metabolomic research. , 2009, Analytical chemistry.

[29]  Justin J. Cassidy,et al.  A MicroRNA Imparts Robustness against Environmental Fluctuation during Development , 2009, Cell.

[30]  Takayuki Tohge,et al.  Web-based resources for mass-spectrometry-based metabolomics: a user's guide. , 2009, Phytochemistry.

[31]  Z. Fei,et al.  Flavour compounds in tomato fruits: identification of loci and potential pathways affecting volatile composition , 2008, Journal of experimental botany.

[32]  S. Kanaya,et al.  Metabolite annotations based on the integration of mass spectral information , 2008, The Plant journal : for cell and molecular biology.

[33]  J. Selbig,et al.  Mode of Inheritance of Primary Metabolic Traits in Tomato[W][OA] , 2008, The Plant Cell Online.

[34]  D. Mackill,et al.  Marker-assisted selection: an approach for precision plant breeding in the twenty-first century , 2008, Philosophical Transactions of the Royal Society B: Biological Sciences.

[35]  M. Purugganan,et al.  Genetics of microenvironmental canalization in Arabidopsis thaliana , 2007, Proceedings of the National Academy of Sciences.

[36]  M. Zanor,et al.  Integrated Analysis of Metabolite and Transcript Levels Reveals the Metabolic Shifts That Underlie Tomato Fruit Development and Highlight Regulatory Aspects of Metabolic Network Behavior1[W] , 2006, Plant Physiology.

[37]  Naama Menda,et al.  Overdominant quantitative trait loci for yield and fitness in tomato , 2006, Proceedings of the National Academy of Sciences.

[38]  U. Roessner,et al.  Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement , 2006, Nature Biotechnology.

[39]  I. Dworkin.  A STUDY OF CANALIZATION AND DEVELOPMENTAL STABILITY IN THE STERNOPLEURAL BRISTLE SYSTEM OF DROSOPHILA MELANOGASTER , 2005, Evolution; international journal of organic evolution.

[40]  F. Carrari,et al.  Zooming In on a Quantitative Trait for Tomato Yield Using Interspecific Introgressions , 2004, Science.

[41]  R. Milo,et al.  Response to Comment on "Network Motifs: Simple Building Blocks of Complex Networks" and "Superfamilies of Evolved and Designed Networks" , 2004, Science.

[42]  Uri Alon,et al.  Response delays and the structure of transcription networks. , 2003, Journal of molecular biology.

[43]  S. Lindquist,et al.  Hsp90 as a capacitor of phenotypic variation , 2002, Nature.

[44]  Vincent J. Carey,et al.  Mixed-Effects Models in S and S-Plus , 2001 .

[45]  U. Alon,et al.  Robustness in bacterial chemotaxis , 1999, Nature.

[46]  S. Lindquist,et al.  Hsp90 as a capacitor for morphological evolution , 1998, Nature.

[47]  D. Zamir,et al.  An introgression line population of Lycopersicon pennellii in the cultivated tomato enables the identification and fine mapping of yield-associated QTL. , 1995, Genetics.

[48]  J. Léon,et al.  Stability Analysis in Plant Breeding , 1988 .

[49]  C. Waddington.  Canalization of Development and the Inheritance of Acquired Characters , 1942, Nature.

[50]  J. Haldane.  Organisers and Genes , 1940, Nature.

[51]  Fernando J. Yuste-Lisbona,et al.  Multi-environment QTL mapping reveals genetic architecture of fruit cracking in a tomato RIL Solanum lycopersicum × S. pimpinellifolium population , 2016, Theoretical and Applied Genetics.

[52]  Б ВологдінаГ..  THE ANALYSIS OF ADAPTATION IN A PLANT-BREEDING PROGRAMME , 2016 .

[53]  C. Queitsch,et al.  REVIEW: PART OF A SPECIAL ISSUE ON DEVELOPMENTAL ROBUSTNESS AND SPECIES DIVERSITY Molecular mechanisms governing differential robustness of development and environmental responses in plants , 2016 .

[54]  Mitchel van Loon,et al.  Mixed effects models , 2013 .

[55]  Arjen Lommen,et al.  Data (pre-)processing of nominal and accurate mass LC-MS or GC-MS data using MetAlign. , 2012, Methods in molecular biology.

[56]  I. Dworkin.  Canalization, Cryptic Variation, and Developmental Buffering: A Critical Examination and Analytical Perspective , 2005 .