Rapid identification of causal mutations in tomato EMS populations via mapping-by-sequencing

Journal name:
Nature Protocols
Volume:
11,
Pages:
2401–2418
Year published:
DOI:
doi:10.1038/nprot.2016.143
Published online

Abstract

The tomato is the model species of choice for fleshy fruit development and for the Solanaceae family. Ethyl methanesulfonate (EMS) mutants of tomato have already proven their utility for analysis of gene function in plants, leading to improved breeding stocks and superior tomato varieties. However, until recently, the identification of causal mutations that underlie particular phenotypes has been a very lengthy task that many laboratories could not afford because of spatial and technical limitations. Here, we describe a simple protocol for identifying causal mutations in tomato using a mapping-by-sequencing strategy. Plants displaying phenotypes of interest are first isolated by screening an EMS mutant collection generated in the miniature cultivar Micro-Tom. A recombinant F2 population is then produced by crossing the mutant with a wild-type (WT; non-mutagenized) genotype, and F2 segregants displaying the same phenotype are subsequently pooled. Finally, whole-genome sequencing and analysis of allele distributions in the pools allow for the identification of the causal mutation. The whole process, from the isolation of the tomato mutant to the identification of the causal mutation, takes 6–12 months. This strategy overcomes many previous limitations, is simple to use and can be applied in most laboratories with limited facilities for plant culture and genotyping.

At a glance

Figures

  1. An overview of the experimental design of forward genetics screening and detection of causal mutation by mapping-by-sequencing in tomato.
    Figure 1: An overview of the experimental design of forward genetics screening and detection of causal mutation by mapping-by-sequencing in tomato.

    Most such experiments start with the generation of a highly mutagenized EMS mutant collection. Alternatively, publicly available mutant collections can be screened in silico for mutants displaying the phenotype of interest. EMS mutagenesis and phenotyping: Untreated seeds (M0) are treated with EMS to yield M1 (EMS-treated) seeds. M1 plants are grown, producing M2 seeds, which are stored, treated again with EMS to increase mutation frequencies (second round of mutagenesis) or sown to give M2 plants. At this step, DNA can be collected for TILLING. M2 plants are further screened using various phenotypic descriptors, and the phenome data are stored in a database to allow in silico mining of the mutant collection for the phenotype of interest. At each generation, seeds are collected and stored. Detection of the causal mutation by mapping-by-sequencing: The experimental design is shown for a recessive mutation that is the most commonly found mutation in EMS mutants. Once a homozygous mutant carrying a recessive mutation responsible for the phenotype of interest (e.g., yellow for yellow-colored fruit) has been selected, the mutant is back-crossed (BC1) with the WT genotype used for generating the EMS mutant collection. The BC1F1 plant displays a WT-like phenotype (red fruit) because the yellow mutation is recessive. A BC1F2 population (usually 500 plants) obtained by selfing the BC1F1 plants is screened for mutant-like phenotype and the WT-like phenotype. Two bulks of pooled plants are set up: one displaying the mutant-like phenotype (yellow fruit) and one displaying the WT-like phenotype (red fruit). Each bulk is sequenced to a depth of 20–40× coverage of the tomato genome, trimmed sequences are mapped onto the tomato reference genome and EMS mutation variants are filtered. Analysis of the allelic variant frequencies in the two bulks (usually ~60 plants each) leads to the identification of the causal mutation, which displays very high frequency in the mutant-like bulk (ideally 100% having the variant allele) and lower-than-average frequency in the WT-like bulk (ideally 33% having the variant allele).

  2. Two-step bioinformatic pipeline for analysis of whole-genome sequencing data.
    Figure 2: Two-step bioinformatic pipeline for analysis of whole-genome sequencing data.

    First, raw reads are mapped to the SL2.50 Heinz 1706 tomato genome sequence using the BWA v0.7.12 aligner. Variant calling is performed using SAMtools v1.2, and output .vcf files of total variants are generated for the mutant and WT-like bulks, as well as for the Micro-Tom line. As the reference genome from Heinz 1706 used to map the reads is different from that of the Micro-Tom line, the variants identified include natural Heinz 1706/Micro-Tom polymorphisms and EMS mutations. At this step, an additional Micro-Tom sequencing is required as a control to further remove natural polymorphisms. An in-house Python script for the analysis of variants includes the (i) comparison of variants obtained in each bulk to generate SNP files, (ii) computation of variant allelic frequencies for each bulk, (iii) indications regarding the presence of the variants in the Micro-Tom line and (iv) indications regarding variants located in tomato genes according to the Heinz 1706 genome annotation. Natural polymorphisms between Micro-Tom and Heinz can be excluded by analyzing the variants present in the Micro-Tom line (control). Only EMS mutations are further considered when using the following two filtering parameters to specifically identify the causal mutation: the read depth (10<DP<100), to exclude false-positive variants, and the allelic frequency expected for a recessive mutation (allelic frequency tends toward 1 for mutant-like bulks and tends toward 0.33 for WT-like bulks).

  3. Mapping-by-sequencing of Micro-Tom EMS mutants.
    Figure 3: Mapping-by-sequencing of Micro-Tom EMS mutants.

    (a) Identification of the chromosome associated with the yellow-fruit phenotype. Pattern of the mutation allelic frequencies obtained in the mutant and WT-like bulks are represented along tomato chromosomes by yellow and red lines, respectively. The plot represents allelic frequencies (y axis) against genome positions (x axis). To obtain this result, a sliding window is used to analyze the allelic frequency for a small window of 20 SNPs. At each new SNP position, the window incrementally advances across the genome. The mean frequency value is then calculated for each window using Microsoft Excel and plotted against genome positions. Chromosome 3 exhibits a marked increase in the mutation allelic frequency for the mutant-like bulk as compared with the WT-like bulk, and therefore it is likely to correspond to the chromosome carrying the causal mutation. (b) Fine mapping of the causal mutation using the BC1F2 population. Recombinant analysis of BC1F2 individuals displaying the yellow fruit phenotype allowed us to define more precisely the chromosomal region associated with the mutant phenotype. Recombination events between linked mutations are used to discriminate the causal mutation from the adjacent ones. Only mutant alleles are represented for the mutation at position 4,327,086 (yellow mutant lines), whereas WT alleles (red lines) are identified for the adjacent mutations. Recombinant scoring of 150 BC1F2 individual plants allows the accurate identification of the causal mutation at position 4,327,086 on chromosome 3.

  4. Mutation in the phytoene synthase gene PSY1 affects fruit metabolism.
    Figure 4: Mutation in the phytoene synthase gene PSY1 affects fruit metabolism.

    (a) The PSY1 gene (above) consists of six exons. The trans-isoprenyl diphosphate synthase domain is indicated in gray. The mutation in PSY1 in the coding region (below) results in a premature stop codon at position 152. (b) Scheme showing block of tomato fruit carotenoid pathway resulting from the yellow mutation. Red–pink colors indicate the degree of reduction in amounts of metabolite, and green color represents accumulation of metabolite in red ripe (Breaker+7) fruit. (c) Metabolite profiling by GC–MS further revealed differences in primary metabolite content in the WT (Micro-Tom line) and psy1 mutant lines. Heat map showing the fruit metabolic profiling of yellow mutant (psy1) and WT, harvested during fruit ripening at the Breaker+5 stage (yellow Br+5) and at the red ripe Breaker+7 stage (WT-Br+7, WT-Br+7, yellow-Br+7), respectively. Red and blue rectangles depict increases and decreases of metabolite content with respect to the average of all lines. Hierarchical clustering of samples and metabolites is shown in the dendrogram.

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Author information

  1. These authors contributed equally to this work.

    • Virginie Garcia,
    • Cécile Bres,
    • Daniel Just &
    • Lucie Fernandez

Affiliations

  1. Institut National de la Recherche Agronomique and Université de Bordeaux, Unité Mixte de Recherche 1332 Biologie du Fruit et Pathologie, Villenave d'Ornon, France.

    • Virginie Garcia,
    • Cécile Bres,
    • Daniel Just,
    • Lucie Fernandez,
    • Fabienne Wong Jun Tai,
    • Jean-Philippe Mauxion &
    • Christophe Rothan
  2. Institut National de la Recherche Agronomique US1279 Etude du Polymorphisme des Génomes Végétaux, CEA-Institut de Génomique-CNG, Evry, France.

    • Marie-Christine Le Paslier,
    • Aurélie Bérard &
    • Dominique Brunel
  3. Graduate School of Life and Environmental Sciences, Osaka Prefecture University, Osaka, Japan.

    • Koh Aoki
  4. Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam-Golm, Germany.

    • Saleh Alseekh &
    • Alisdair R Fernie
  5. School of Biological Sciences, Royal Holloway University of London, Egham, UK.

    • Paul D Fraser

Contributions

C.B., D.J., L.F., V.G. and C.R. developed the original protocol. D.B., J.-P.M. and A.B. performed the sequencing experiments. F.W.J.T. performed computational analyses. C.B., L.F., D.J., F.W.J.T., M.-C.L.P., K.A., S.A., A.R.F., P.D.F. and C.R. contributed sections to the manuscript. C.B., L.F., D.J. and C.R. collated and standardized the text. All authors read and approved the final version of the manuscript.

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The authors declare no competing financial interests.

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Supplementary information

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  1. Supplementary Software (4,378 KB)

    The ‘compare_WT_mutant_samtools_vcf_v5.py’ script.

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  1. Supplementary Table 1 (42 KB)

    Typical carotenoid content found in ripe fruit (Breaker+7) from yellow mutant as compared with the WT background (Micro-Tom line). Separations were performed by UPLC-PDA and quantitative determinations from dose response curves. FW: Fresh Weight.

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