Candice N Hirsch

The highly active family of Mutator (Mu) DNA transposons has been widely used for forward and reverse genetics in maize. There are examples of Mu-suppressible alleles that result in conditional phenotypic effects based on the activity of Mu. Phenotypes from these Mu-suppressible mutations are observed in Mu-active genetic backgrounds, but absent when Mu activity is lost. For some Mu-suppressible alleles, phenotypic suppression likely results from an outward-reading promoter within Mu that is only active when the autonomous Mu element is silenced or lost. We isolated 35 Mu alleles from the UniformMu population that represent insertions in 24 different genes. Most of these mutant alleles are due to insertions within gene coding sequences, but several 5′ UTR and intron insertions were included. RNA-seq and de novo transcript assembly were utilized to document the transcripts produced from 33 of these Mu insertion alleles. For 20 of the 33 alleles, there was evidence of transcripts initiating within the Mu sequence reading through the gene. This outward-reading promoter activity was detected in multiple types of Mu elements and does not depend on the orientation of Mu. Expression analyses of Mu-initiated transcripts revealed the Mu promoter often provides gene expression levels and patterns that are similar to the wild-type gene. These results suggest the Mu promoter may represent a minimal promoter that can respond to gene cis-regulatory elements. Findings from this study have implications for maize researchers using the UniformMu population, and more broadly highlight a strategy for transposons to co-exist with their host.

Understanding how plants adapt to specific environmental changes and identifying genetic markers associated with phenotypic plasticity can help breeders develop plant varieties adapted to a rapidly changing climate. Here, we propose the use of marker effect networks as a novel method to identify markers associated with environmental adaptability. These marker effect networks are built by adapting commonly used software for building gene coexpression networks with marker effects across growth environments as the input data into the networks. To demonstrate the utility of these networks, we built networks from the marker effects of ∼2,000 nonredundant markers from 400 maize hybrids across 9 environments. We demonstrate that networks can be generated using this approach, and that the markers that are covarying are rarely in linkage disequilibrium, thus representing higher biological relevance. Multiple covarying marker modules associated with different weather factors throughout the growing season were identified within the marker effect networks. Finally, a factorial test of analysis parameters demonstrated that marker effect networks are relatively robust to these options, with high overlap in modules associated with the same weather factors across analysis parameters. This novel application of network analysis provides unique insights into phenotypic plasticity and specific environmental factors that modulate the genome.