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IntroductionThroughout domestication, crop plants have gone through strong genetic bottlenecks, dramatically reducing the genetic diversity in today’s available germplasm. This has also reduced the diversity in traits necessary for breeders to develop improved varieties. Many strategies have been developed to improve both genetic and trait diversity in crops, from backcrossing with wild relatives, to chemical/radiation mutagenesis, to genetic engineering. However, even with recent advances in genetic engineering we still face the rate limiting step of identifying which genes and mutations we should target to generate diversity in specific traits. MethodsHere, we apply a comparative evolutionary approach, pairing phylogenetic and expression analyses to identify potential candidate genes for diversifying soybean (Glycine max) canopy cover development via the nuclear auxin signaling gene families, while minimizing pleiotropic effects in other tissues. In soybean, rapid canopy cover development is correlated with yield and also suppresses weeds in organic cultivation. Results and discussionWe identified genes most specifically expressed during early canopy development from the TIR1/AFB auxin receptor, Aux/IAA auxin co-receptor, and ARF auxin response factor gene families in soybean, using principal component analysis. We defined Arabidopsis thaliana and model legume species orthologs for each soybean gene in these families allowing us to speculate potential soybean phenotypes based on well-characterized mutants in these model species. In future work, we aim to connect genetic and functional diversity in these candidate genes with phenotypic diversity in planta allowing for improvements in soybean rapid canopy cover, yield, and weed suppression. Further development of this and similar algorithms for defining and quantifying tissue- and phenotype-specificity in gene expression may allow expansion of diversity in valuable phenotypes in important crops. 

Gene expression in Arabidopsis is regulated by more than 1,900 transcription factors (TFs), which have been identified genome-wide by the presence of well-conserved DNA-binding domains. Activator TFs contain activation domains (ADs) that recruit coactivator complexes; however, for nearly all Arabidopsis TFs, we lack knowledge about the presence, location and transcriptional strength of their ADs1. To address this gap, here we use a yeast library approach to experimentally identify Arabidopsis ADs on a proteome-wide scale, and find that more than half of the Arabidopsis TFs contain an AD. We annotate 1,553 ADs, the vast majority of which are, to our knowledge, previously unknown. Using the dataset generated, we develop a neural network to accurately predict ADs and to identify sequence features that are necessary to recruit coactivator complexes. We uncover six distinct combinations of sequence features that result in activation activity, providing a framework to interrogate the subfunctionalization of ADs. Furthermore, we identify ADs in the ancient AUXIN RESPONSE FACTOR family of TFs, revealing that AD positioning is conserved in distinct clades. Our findings provide a deep resource for understanding transcriptional activation, a framework for examining function in intrinsically disordered regions and a predictive model of ADs.