A modified TILLING approach to detect induced mutations in tetraploid and hexaploid wheat

Abstract

Background: Wheat (Triticum ssp.) is an important food source for humans in many regions around the world. However, the ability to understand and modify gene function for crop improvement is hindered by the lack of available genomic resources. TILLING is a powerful reverse genetics approach that combines chemical mutagenesis with a high-throughput screen for mutations. Wheat is specially well-suited for TILLING due to the high mutation densities tolerated by polyploids, which allow for very efficient screens. Despite this, few TILLING populations are currently available. In addition, current TILLING screening protocols require high-throughput genotyping platforms, limiting their use.

Results: We developed mutant populations of pasta and common wheat and organized them for TILLING. To simplify and decrease costs, we developed a non-denaturing polyacrylamide gel set-up that uses ethidium bromide to detect fragments generated by crude celery juice extract digestion of heteroduplexes. This detection method had similar sensitivity as traditional LI-COR screens, suggesting that it represents a valid alternative. We developed genome-specific primers to circumvent the presence of multiple homoeologous copies of our target genes. Each mutant library was characterized by TILLING multiple genes, revealing high mutation densities in both the hexaploid (~1/38 kb) and tetraploid (~1/51 kb) populations for 50% GC targets. These mutation frequencies predict that screening 1,536 lines for an effective target region of 1.3 kb with 50% GC content will result in ~52 hexaploid and ~39 tetraploid mutant alleles. This implies a high probability of obtaining knock-out alleles (P = 0.91 for hexaploid, P = 0.84 for tetraploid), in addition to multiple missense mutations. In total, we identified over 275 novel alleles in eleven targeted gene/genome combinations in hexaploid and tetraploid wheat and have validated the presence of a subset of them in our seed stock.

Conclusion: We have generated reverse genetics TILLING resources for pasta and bread wheat and achieved a high mutation density in both populations. We also developed a modified screening method that will lower barriers to adopt this promising technology. We hope that the use of this reverse genetics resource will enable more researchers to pursue wheat functional genomics and provide novel allelic diversity for wheat improvement.

Figure 1

TILLING using a non-denaturing polyacrylamide detection method: A) Visualization of four-fold DNA pools digested with CJE after running on a non-denaturing 3% polyacrylamide gel for 75 minutes. Putative mutations in the pools are identified by the presence of two bands (indicated by white arrows) whose sizes add up to the full length PCR product. In pool 5, more than two bands are visible, representing two mutations within this pool (yellow arrows). Size markers (M) are included throughout the gel. This is a composite of four images whose contrast has been adjusted differently to allow better visualization. B) For each positive pool (labeled 1 through 7), the four individual DNAs (labeled a through d) are organized in a 96-well plate and used for PCR amplification of the target region. After PCR, paired pools are assembled by combining 6 μl of PCR product from two individuals and organizing them into a new 96-well plate. For example, row a+b contains 6 μl from individual a and 6 μl from individual b. C) Heteroduplexes are formed through denaturing and annealing of the pooled PCR products and mismatches were digested with CJE. Cleaved fragments were visualized using the non-denaturing polyacrylamide gel electrophoresis set-up as before. Each column is run in adjacent lanes, such that the first four lanes contain the four two-fold pools (a+b, c+d, a+c and b+d) from column 1. True mutations are replicated in two separate gel lanes within each set of four, producing a unique banding pattern (represented below each set of four lanes and represented in panel D). According to this pattern, the mutation can be unequivocally assigned to one of the individual DNAs. E) The PCR product from these individuals (leftover from the PCR on panel B) is sequenced and the identity of the mutation is determined.

Figure 2

Comparison of predicted and observed mutation types in the TILLING populations. All mutation types were classified as either silent (synonymous mutations or within introns), missense (non-synonymous amino acid change) or truncation (splice junction mutations or nonsense). The predicted effects for each amplicon were calculated using CODDLE and considers all possible EMS mutations within the target region. The observed percentages describe the effects of all mutations in the hexaploid (N = 186 mutations) and tetraploid (N = 93 mutation) populations.

Figure 3

Distribution of mutations detected by the polyacrylamide/ethidium bromide platform within the target sequence. The position of each confirmed mutation (N = 141) in the seven targeted gene/genome combinations in hexaploid wheat is plotted against the target sequence scaled to 100%, with each bin representing 5% of the target sequence. No mutations were detected in the first and last two bins (0–10% and 90–100%) which represent the sequence closest to either forward or reverse primers.

Figure 4

Alignment of homoeologous SBEIIa sequences used to design genome-specific forward (A) and reverse (B) primers. Primers are surrounded by boxes and genome specific polymorphisms are indicated in bold red. Exon 4 is in grey highlight and all other sequence corresponds to intron 4 (A) or intron 9 (B). Bold underlined bases in panel A indicate positions of introduced mismatches in primers relative to the genomic sequence. In/del events are represented by dashed lines except in the A genome of intron 9 (B) which has a large in/del event relative to the B and D genomes that is represented by bold red letters.