AtMYB93 is a novel negative regulator of lateral root development in Arabidopsis.
Bottom Line: Furthermore, Atmyb93 mutant lateral root development is insensitive to auxin, indicating that AtMYB93 is required for normal auxin responses during lateral root development.We propose that AtMYB93 is part of a novel auxin-induced negative feedback loop stimulated in a select few endodermal cells early during lateral root development, ensuring that lateral roots only develop when absolutely required.Putative AtMYB93 homologues are detected throughout flowering plants and represent promising targets for manipulating root systems in diverse crop species.
Affiliation: School of Biosciences, University of Birmingham, Birmingham, B15 2TT, UK.Show MeSH
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Mentions: Seedlings were grown vertically. To calculate emerged LR density in different genotypes (see the Results section, Figs 4c, 8g, S2c, S3d, S7), visible emerged LRs were counted 7–12 d after germination under a compound microscope. For clarity, static data from one time-point are shown, but the same trends were seen over the entire time-course of each experiment. Root length was measured from digital photographs using ImageJ (http://rsb.info.nih.gov/ij/). The density of emerged LRs was defined as LRs per cm of PR for each seedling; similar trends were also observed when the ‘branching density’ (i.e. LR density per cm of PR branching zone; Dubrovsky & Forde, 2012) was calculated. For statistical analysis, the hypothesis that there is no difference in mean LR density between wild-type and each mutant genotype was tested using pairwise t-tests. For LRP staging experiments, seedlings were cleared in Hoyer's medium and the number of LRPs at each developmental stage (Malamy & Benfey, 1997) was scored per root with a Leica DMRB microscope (Leica, Milton Keynes, UK); the percentage of LRPs at each developmental stage was then calculated for every root. For statistical analysis, the counts obtained are too low to apply a chi-squared test, so counts for each genotype were compared with those for the wild-type using a generalized likelihood test combined with a randomization procedure to generate P-values, in a manner analogous to methods for cDNA library comparison (Stekel et al., 2000; Herbert et al., 2008, 2011). For each strain comparison (wild-type versus mutant), the hypothesis is that the frequency of LRPs at any given stage is the same between the two strains; the alternative hypothesis is that these frequencies are different. The log likelihood ratio of the observed frequencies under the two hypotheses was constructed using multinomial distributions to generate the test statistic. To generate a P-value, 10 000 simulated data sets were constructed using a multinomial distribution and the hypothesis frequencies, and a test statistic was computed for each simulated data set. The P-value is approximated by the proportion of test statistics in the simulated data sets that are more extreme than the test statistic for the true data. Error bars were calculated using the standard error for a proportion, equal to sqrt(p(1 – p)/n), where p is the proportion and n is the population size.
Affiliation: School of Biosciences, University of Birmingham, Birmingham, B15 2TT, UK.