MYB transcription factors drive evolutionary innovations in Arabidopsis fruit trichome patterning.

Both inter- and intra-specific diversity has been described for trichome patterning in fruits, which is presumably involved in plant adaptation. However, the mechanisms underlying this developmental trait have been hardly addressed. Here we examined natural populations of Arabidopsis (Arabidopsis thaliana) that develop trichomes in fruits and pedicels, phenotypes previously not reported in the Arabidopsis genus. Genetic analyses identified five loci, MALAMBRUNO 1-5 (MAU1-5), with MAU2, MAU3, and MAU5 showing strong epistatic interactions that are necessary and sufficient to display these traits. Functional characterization of these three loci revealed cis-regulatory mutations in TRICHOMELESS1 and TRIPTYCHON, as well as a structural mutation in GLABRA1. Therefore, the multiple mechanisms controlled by three MYB transcription factors of the core regulatory network for trichome patterning have jointly been modulated to trigger trichome development in fruits. Furthermore, analyses of worldwide accessions showed that these traits and mutations only occur in a highly differentiated relict lineage from the Iberian Peninsula. In addition, these traits and alleles were associated with low spring precipitation, which suggests that trichome development in fruits and pedicels might be involved in climatic adaptation. Thus, we show that the combination of synergistic mutations in a gene regulatory circuit has driven evolutionary innovations in fruit trichome patterning in Arabidopsis.

A hierarchical Bayesian Beta regression approach to study the effects of geographical genetic structure and spatial autocorrelation on species distribution range shifts.

Global climate change (GCC) may be causing distribution range shifts in many organisms worldwide. Multiple efforts are currently focused on the development of models to better predict distribution range shifts due to GCC. We addressed this issue by including intraspecific genetic structure and spatial autocorrelation (SAC) of data in distribution range models. Both factors reflect the joint effect of ecoevolutionary processes on the geographical heterogeneity of populations. We used a collection of 301 georeferenced accessions of the annual plant Arabidopsis thaliana in its Iberian Peninsula range, where the species shows strong geographical genetic structure. We developed spatial and nonspatial hierarchical Bayesian models (HBMs) to depict current and future distribution ranges for the four genetic clusters detected. We also compared the performance of HBMs with Maxent (a presence-only model). Maxent and nonspatial HBMs presented some shortcomings, such as the loss of accessions with high genetic admixture in the case of Maxent and the presence of residual SAC for both. As spatial HBMs removed residual SAC, these models showed higher accuracy than nonspatial HBMs and handled the spatial effect on model outcomes. The ease of modelling and the consistency among model outputs for each genetic cluster was conditioned by the sparseness of the populations across the distribution range. Our HBMs enrich the toolbox of software available to evaluate GCC-induced distribution range shifts by considering both genetic heterogeneity and SAC, two inherent properties of any organism that should not be overlooked.