Fast and Efficient Screening for Wheat Loss-of-Gene Mutants Using Multiplexed Melt Curve Analyses

• Published in: PloS one Vol. 11; no. 7; p. e0159955
• Main Authors: Mieog, Jos C, Ral, Jean-Philippe F
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 26.07.2016; Public Library of Science (PLoS)
• Subjects: Amylases; Amylases - genetics; Analysis; Binding sites; Biology and Life Sciences; Bread; Carotenoids; Deoxyribonucleic acid; DNA; Gene Deletion; Gene mutation; Genes; Genetic aspects; Genetic testing; Genetics; Genomes; Genomics; Heavy ion bombardment; Intramolecular Lyases - genetics; Medical screening; Multiplex Polymerase Chain Reaction - methods; Multiplexing; Mutants; Mutation; Physical Sciences; Plant Breeding - methods; Plant Proteins - genetics; Polyploidy; Population; Population genetics; Research and Analysis Methods; Studies; Triticum - genetics; Triticum aestivum; Wheat; α-Amylase; United States; Australia; Canberra Australian Capital Territory Australia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0159955

This study describes a new approach in the screening for loss-of-gene mutants in Heavy Ion Bombardment (HIB) mutant populations of genetically complex organisms such as hexaploid bread wheat using multiplexed single-color (SYBR Green) melt curve analyses. The assay was set up for three target genes to test its validity and applicability. For each gene, three genome-specific primer pairs (one for each genome) with distinct melt curves were developed and multiplexed. This allowed screening for "single null mutants" (plants with the target gene deleted in one of the three genomes) for all three genomes in a single reaction. The first two genes (α-Amylase 3 and Epsilon Cyclase) were used to test the approach as HIB null lines for all three genomes were already available for these. The third assay was successfully applied to identify new single null lines of the target gene α-Amylase 2 in an in-house HIB wheat collection. The use of SYBR Green greatly reduced the time and/or cost investment compared to other techniques and the approach proved highly suitable for high-throughput applications.