Rapid gene isolation in barley and wheat by mutant chromosome sequencing
- Javier Sánchez-Martín†1,
- Burkhard Steuernagel†2,
- Sreya Ghosh2,
- Gerhard Herren1,
- Severine Hurni1,
- Nikolai Adamski2,
- Jan Vrána3,
- Marie Kubaláková3,
- Simon G. Krattinger1,
- Thomas Wicker1,
- Jaroslav Doležel3,
- Beat Keller1Email author and
- Brande B. H. Wulff2Email author
© The Author(s). 2016
Received: 16 May 2016
Accepted: 10 October 2016
Published: 31 October 2016
Identification of causal mutations in barley and wheat is hampered by their large genomes and suppressed recombination. To overcome these obstacles, we have developed MutChromSeq, a complexity reduction approach based on flow sorting and sequencing of mutant chromosomes, to identify induced mutations by comparison to parental chromosomes. We apply MutChromSeq to six mutants each of the barley Eceriferum-q gene and the wheat Pm2 genes. This approach unambiguously identified single candidate genes that were verified by Sanger sequencing of additional mutants. MutChromSeq enables reference-free forward genetics in barley and wheat, thus opening up their pan-genomes to functional genomics.
The extraordinary success of wheat and barley as major worldwide crops is underpinned by their adaptability to diverse environments, high yield and nutritional content. Identification and manipulation of the genes controlling these traits will help to sustainably increase yields and ensure global food security. However, the large genomes of barley (5.5 Gb) and wheat (17 Gb) coupled with extensive regions of suppressed recombination [1, 2] make traditional map-based gene isolation in these crops both time-consuming and costly. In plants with small genomes, such as Arabidopsis and rice, whole genome sequencing on mapping populations has proven a powerful approach to aid gene isolation [3, 4]. Furthermore, whole genome sequencing and comparison of multiple independently derived mutants belonging to the same complementation group permits direct gene identification with little or no requirement for recombination [5, 6]. Because of the genome size of barley and wheat, sequencing whole genomes in multiple mutants is not practical due to the cost and the difficulty of comparison and interpretation of very large datasets. In allopolyploid wheat, this analysis is further complicated by the presence of homoeologues and multiple gene duplications [2, 7].
Complexity reduction approaches to cloning-by-sequencing in barley and wheat
Chromosome flow sorting
Complex but optimised for >25 plant species, including barley and wheat
Large data set
Available in several labs
Dependent on reference gene annotation
Small data set
Simple (depending on tissue sample)
Medium (expression level dependent)
Target gene has to be expressed in sample; mutations must be in transcript
Large data set; de novo assembly problematic in polyploid wheat
Very large data set
Mutation overlap in contigs from flow sorted chromosomes of barley 2H and wheat 5D
Barley 2H (Eceriferum-q)
Wheat 5D (Pm2)
Number of contigs mutated in 0 lines
Number of contigs mutated in 1 line
Number of contigs mutated in 2 lines
Number of contigs mutated in 3 lines
Number of contigs mutated in 4 lines
Number of contigs mutated in 5 lines
Number of contigs mutated in 6 lines
Sequence homology (BLAST) search of the 12-kb candidate contig against publicly available barley resources  and analysis of whole transcriptome reads obtained from wild-type leaf sheath tissue identified a single transcriptional unit supported by one full-length barley cDNA and RNAseq reads (Additional file 1: Figure S2b) with 100 % identity to the recently cloned Eceriferum-q gene . The intronless gene spans 1229 bp from the start to the stop codons and contains 5′ and 3′ UTRs of 94 and 331 bp, respectively (Fig. 2c). We confirmed the five mutations identified by MutChromSeq by PCR amplification and Sanger sequencing and sequenced an additional six EMS-derived Eceriferum-q mutants and identified another six mutations. All the EMS-derived mutations are G/C-to-A/T transitions that cause nonsense (1) or missense (10) mutations (Additional file 1: Table S5). Based on the EMS canonical mutation frequency and the GC distribution observed in the five EMS-treated and sequenced Foma 2H chromosome contigs, we calculated the probability for a mutation to occur in the same contig in 11 EMS-treated individuals by chance alone to be 1 in 4 × 1014 (see “Methods”). In conclusion, these data provide compelling evidence for successful gene identification by MutChromSeq in barley.
To further put MutChromSeq to the test we decided to target a plant resistance (R) gene in hexaploid wheat. R genes typically encode nucleotide binding and leucine-rich repeat containing proteins (NLRs), belong to large multigene families, reside in complex gene clusters with multiple sequence-related paralogues and display extreme sequence and copy number variation between accessions . This often obscures orthogonal relationships and makes functional dissection of R gene clusters by recombination alone impractical. We obtained 12 EMS-derived susceptible mutants of the dominant powdery mildew resistance gene 2 (Pm2; Additional file 1: Table S5), which has been mapped to chromosome 5D and originally derives from the Ulka donor variety . We chose six of the Pm2 mutants and the wild-type parent for MutChromSeq (Fig. 2b) and obtained highly pure chromosome samples, which were sequenced on the Illumina platform (Additional file 1: Tables S1 and S2).
Applying the same analysis pipeline we used before, we identified two candidate contigs >1 kb. One could be discarded after manual inspection due to the very large number of SNVs observed between this contig and the mapped wild-type reads indicative of an assembly artefact (Additional file 1: Figure S3), while the other contig contained a full-length NLR type gene encoding domains with homology to a coiled coil, a nucleotide binding site and leucine-rich repeats (Fig. 2d). We confirmed the nature of this short read-assembled 10-kb contig by long-range PCR amplification and sequencing of the complete coding region (Additional file 1: Figure S2d). We converted the contig into a PCR molecular marker and showed that it genetically co-localises with Pm2-mediated resistance to a 1.3-cM interval on chromosome 5D (Additional file 1: Figure S2e). Further pivotal confirmation that this gene is Pm2 was obtained by Sanger sequencing of the remaining six mutants (Additional file 1: Table S5). All mutants were found to contain G/C-to-A/T transitions typical of EMS, which gave rise to predicted nonsense (5) and missense (7) mutations. No other mutations were found in the Pm2 10-kb short read-assembled contig in the six chromosome sequenced mutants. Based on the EMS mutation frequency and GC distribution in the six sequenced Pm2 chromosome contigs, we calculated that the probability of getting 12 mutations in the same gene in 12 pm2 mutants by chance alone to be 1 in 3 × 1011 (see “Methods”).
We have used MutChromSeq to successfully reclone the barley Eceriferum-q gene and clone de novo the wheat Pm2 gene. Prior to chromosome flow sorting the target gene must be assigned to a chromosome. This usually requires the phenotypic and genotypic analysis of progeny derived from a sexual cross to allow for the independent assortment of chromosomes. However, recombination per se is not required. This makes the method particularly attractive for cloning genes in recombination-sparse regions. Indeed, the method does not require fine mapping or the construction of a physical reference sequence across a map interval. The approach is fast and robust and in the Eceriferum-q example allowed the identification of the correct gene despite the inclusion of a false positive. Our ensuing mapping data and the sequencing of multiple additional independent mutants support the validity and accuracy of MutChromSeq as a powerful approach for gene isolation.
Chromosome flow sorting is a technically demanding procedure requiring expertise in cytogenetics, flow cytometry and molecular biology and the availability of a flow sorter [15, 16]. A group aiming to clone a small number of genes is unlikely to justify the effort required to establish a chromosome sorting pipeline. However, the successful projects on draft genome sequencing in barley [25, 26], rye  and wheat  demonstrate the viability of collaborative projects.
In the MutChromSeq approach, amplified chromosomal DNA is sequenced. Only tens of thousands of copies of sorted chromosomes are required. These quantities can be purified in less than one day. Thus, we believe that only a small number of specialised laboratories can provide sufficient capacity to satisfy the community demand for chromosome sorting from agronomically important species with large genomes for which protocols for chromosome sorting are available.
Unlike methylation filtration , high-C0 t fractionation  or duplex-specific nuclease digestion , complexity reduction by chromosome sorting is lossless and all sequences from a particular chromosome are sequenced. Apart from reducing the complexity of the DNA sample to be sequenced, an important advantage of the targeted approach is that it simplifies DNA sequence analysis by avoiding homoeologues in polyploids (wheat) and paralogues and pseudogenes present on other chromosomes (all species).
We recently used an NLR exome capture to clone two resistance genes in wheat by sequence comparison of multiple independently derived EMS mutants . However, not all R genes are NLRs. For example, pleiotropic adult plant resistance (APR) genes in wheat encode proteins belonging to disparate structural classes, including an ABC transporter (Lr34), a START kinase (Yr36) and a hexose transporter (Lr67) [14, 31, 32]. Furthermore, genes conferring resistance to necrotrophs, hemibiotrophs and pathogens with an apoplastic lifestyle are less likely to be encoded by NLRs. MutChromSeq avoids the potential problems in gene cloning due to the bias introduced by exome-based capture approaches, including the absence of some genes in reference genome assemblies. Furthermore, compared to ExomeSeq and RNAseq, MutChromSeq is more likely to directly identify mutations in regulatory regions and assemble into the same contig exons separated by large introns. The bridging of exons separated by large introns and identification of native regulatory elements may also be achieved by combining exome capture with single molecule long read sequencing technology . However, long-read technology would at present be expensive to apply to multiple, complete wheat exomes.
We propose MutChromSeq, a gene cloning method which does not rely on recombination or fine-mapping. Our approach combines mutagenesis, genome complexity reduction by chromosome flow sorting (which is established for a number of plants, including members of the Triticeae) and high-throughput sequencing. Thus, the requirements for our method to work are that the plant species is amenable to mutagenesis, that the target gene can be associated with a clear phenotype and knowledge of which chromosome the gene is on. For genes fulfilling these prerequisites, we offer a fast and inexpensive method that opens up the possibility to clone a large number of previously intractable genes.
The aim of this study was to develop a gene cloning method in wheat and barley that would overcome the challenges imposed by their large genomes and extensive regions of suppressed recombination. To this end we combined mutagenesis, which is independent of recombination, with chromosome flow sorting and sequencing. By only sequencing the chromosome a gene has been rough-mapped to, we obtained 21-fold and sevenfold reductions in complexity in wheat and barley, respectively. Comparing the chromosome sequence of multiple mutants allowed the rapid identification of a single candidate gene.
Chromosome flow sorting and preparation of amplified chromosomal DNA
Liquid suspensions of mitotic metaphase chromosomes were prepared from synchronised root tips of barley  and wheat seedlings . GAA microsatellites on chromosomes in suspension were labelled with a fluorescein isothiocyanate (FITC) conjugate  and chromosomal DNA was stained by 4′,6-diamidino-2-phenylindole (DAPI) at 2 μg/ml. The chromosome samples were analysed at a rate of 2000 chromosomes/s by a FACSAria SORP (BD Biosciences, San Jose, CA, USA) and sort windows were set up on FITC versus DAPI dot plots to sort chromosome 2H in barley and 5D in wheat. The chromosomes were sorted at rates of 25/s into 0.5-ml PCR tubes containing 40 μl deionised water. Three independent samples of 50,000 chromosomes were sorted from each line and their DNA amplified by multiple displacement amplification  using the Illustra GenomiPhi V2 DNA amplification kit (GE Healthcare Life Sciences, Pittsburgh, PA, USA). Amplified DNA samples derived from each line were pooled to achieve higher sequence representation. In order to determine the purity in flow-sorted fractions, during each sort run, 2000 chromosomes were sorted into a 5-μl drop of P5 buffer on a microscope slide and sorted chromosomes were identified by FISH using probes for GAA microsatellites in barley and GAA microsatellites and Afa family repeats in wheat .
lllumina library construction and sequencing
The barley Illumina libraries were constructed following the Broad Institute’s DISCOVAR protocol (http://www.broadinstitute.org/software/discovar/blog/?page_id=375) for PCR free libraries, except New England Biolab reagents were used instead of Kapa. Each mutant library was sequenced on one lane of a HiSeq 2500 with 125 bp paired-end (PE) reads (for each of the mutants) and 250 bp PE reads for wild-type Foma. The wheat libraries were constructed using the Illumina TruSeq protocol with a 250-bp insert size and subjected to 125-bp PE sequencing on an Illumina HiSeq2000 platform. Libraries corresponding to Federation*4/Ulka, Federation and Chancellor were sequenced using independent lanes. In the case of CI12362/8*Cc and the mutants, three libraries were pooled per lane. The average on target chromosome coverage, after correcting for PCR duplicates, ranged from 27 (for the Eceriferum-q.295 line) to 35 (for the Pm2 wild-type line). The details of raw sequence data generated are shown in Additional file 1: Table S2.
ChromSeq mutant hunter
All raw data were quality trimmed using sickle v.1.2 (https://github.com/najoshi/sickle). We created draft de novo assemblies of chromosome 2H of the wild-type barley cultivar Foma and chromosome 5D of the wild-type wheat line CI12632/8*Cc (Additional file 1: Table S3) using CLC Assembly Cell (https://www.qiagenbioinformatics.com/; version 4.3.0) and standard parameters. The assemblies were masked for repeats using RepeatMasker (http://repeatmasker.org) and the Triticeae Repeat Database (TREP; http://wheat.pw.usda.gov/ITMI/Repeats/) as external library. We then mapped the raw data of the chromosomes from the EMS mutants to the repeat-masked assemblies using BWA  v0.7.12. Mappings were filtered for reads not mapping as a pair (samtools view –f2) and PCR duplicates (samtools rmdup) using SAMtools  v0.1.19. Mappings were subsequently converted to the mpileup format (samtools mpileup –BQ0). The individual data sets in mpileup format were then integrated to count the number of mutants that had a SNV per contig. Potential SNVs were discarded if the allele frequency was below 99.99 %, the coverage was below 15 and the position was not mutated in more than two mutants. Note, the values above are input parameters to a custom script (http://github.com/steuernb/MutChromSeq). The stringency is dependent on the quality and depth of input data.
Calculating mutation probabilities
Taking into account that (i) the majority of EMS mutations are G/C-to-A/T transitions , (ii) that the majority of our repeat-masked barley and wheat contigs had a similar GC content (Additional file 1: Figure S4) and (iii) that EMS mutations tend to be randomly distributed [40–42], we calculated the probability of a false positive as a function of the length of the contig (Additional file 1: “Supplementary materials and methods” and Table S8). Thus, in barley, the largest probability of a 12-kb contig being mutated in a single mutant was calculated to be 0.05 based on the canonical mutation density observed in Eceriferum.q-327 (Additional file 1: Tables S4 and S9). Therefore, the probability of the contig being mutated across all 11 mutants by chance alone is (0.05)11 or 1 in 4 × 1014. Similarly, in wheat, we calculated the largest probability of a 10-kb contig being mutated as 0.11 based on the canonical mutation density observed in mutant pm2_78.56 (Additional file 1: Tables S4 and S9). Therefore, the probability of the contig being mutated across all 12 Pm2 mutants is (0.11)12 or 1 in 3 × 1011.
Further details of materials and methods are shown in Additional file 1: “Supplementary materials and methods”.
We thank Jarmila Číhalíková, Romana Šperková and Zdenka Dubská (IEB) for help with chromosome sorting and preparation of amplified chromosomal DNA, and Darren Heavens (Earlham Institute, Norwich) for sequencing advice.
This research was supported with funding from the JIC Innovation Fund IF2015BW22, the Biotechnology and Biological Sciences Research Council, the Swiss National Science Foundation grants 310030B_144081, 310030_163260 and PZ00P3_154694, the Czech Science Foundation (award number P501/12/G090), the Czech Republic National Program of Sustainability (award number LO1204), and the Plant Fellows Program, co-funded by the COFUND FP7 EU (grant agreement number 267423) to JSM.
Availability of data and material
DNA and RNA raw sequencing data are available from the European Bioinformatics Institute (EBI) Short Read Archive (SRA) under accession numbers PRJEB12101 (barley cultivar Foma RNA sequence), PRJEB12151 (MutChromSeq on Eceriferum-q), PRJEB12098 (MutChromSeq on Pm2) and PRJEB12158 (flow-sorted chromosomes 5D of Chancellor, Federation and Federation*4/Ulka).
JSM, BS, SG, GH, SH, NA, TW, SGK, JV and MK performed experiments. JSM, BS, JD, BK and BBHW wrote the manuscript. JSM, BS, JD, BK and BBHW contributed to the design of the study. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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