Innovation, conservation, and repurposing of gene function in root cell type development Graphical abstract Highlights d Tomato cell type-resolution translatome atlas reveals cell type function d Conservation and repurposing in gene regulation between Arabidopsis and tomato d The tomato exodermis is lignified, suberized, and enriched for nitrogen regulation d The root meristem is molecularly homologous across plant species

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Determining the complete Arabidopsis (Arabidopsis thaliana) protein-protein interaction network is essential for understanding the functional organization of the proteome. Numerous small-scale studies and a couple of large-scale ones have elucidated a fraction of the estimated 300,000 binary protein-protein interactions in Arabidopsis. In this study, we provide evidence that a docking algorithm has the ability to identify real interactions using both experimentally determined and predicted protein structures. We ranked 0.91 million interactions generated by all possible pairwise combinations of 1,346 predicted structure models from an Arabidopsis predicted "structure-ome" and found a significant enrichment of real interactions for the topranking predicted interactions, as shown by cosubcellular enrichment analysis and yeast two-hybrid validation. Our success rate for computationally predicted, structure-based interactions was 63% of the success rate for published interactions naively tested using the yeast two-hybrid system and 2.7 times better than for randomly picked pairs of proteins. This study provides another perspective in interactome exploration and biological network reconstruction using protein structural information. We have made these interactions freely accessible through an improved Arabidopsis Interactions Viewer and have created community tools for accessing these and ;2.8 million other protein-protein and protein-DNA interactions for hypothesis generation by researchers worldwide. The Arabidopsis Interactions Viewer is freely available at http://bar.utoronto.ca/interactions2/. Proteins rarely work alone, and most of the time they function in concert with other proteins or macromolecules. In Arabidopsis (Arabidopsis thaliana), the total number of binary interactions is estimated to be around 300,000 (Arabidopsis Interactome Mapping Consortium, 2011), but so far, only a small fraction of those interactions have been studied. Currently, there are 36,329 experimentally confirmed and 70,944 interolog-predicted protein-protein interactions (PPIs) in the Bio-Analytic Resource (BAR) interactions database (Geisler-Lee et al., 2007) that can be queried through the Arabidopsis Interactions Viewer (AIV). This huge gap indicates there is still a long way to go in elucidating the Arabidopsis interactome, both experimentally and computationally. With the arguable exception of the yeast two-hybrid method (Arabidopsis Interactome Mapping Consortium, 2011) or split ubiquitin method (Chen et al., 2012), traditional experimental methods for determining PPIs, such as mass spectrometry (Van Leene et al., 2007), protein microarrays (Popescu et al., 2007), and others (Zhang et al., 2010; Fukao, 2012), cannot readily be extended to determine the whole Arabidopsis interactome. Interolog-based computational PPI prediction methods (Geisler-Lee et al., 2007) can have a large-scale predictive ability but cannot

Plant species have evolved myriads of solutions to adapt to dynamic environments, 35 including complex cell type development and regulation. To understand this diversity, we profiled tomato root cell type translatomes and chromatin accessibility. Using xylem differentiation in tomato, relative to Arabidopsis, examples of functional innovation, repurposing and conservation of transcription factors are described. Repurposing and innovation of genes are further observed within an exodermis regulatory network and illustrate its function. Translatome 40 analyses of rice, tomato and Arabidopsis tissues suggest that root meristems are more conserved, and that the functions of constitutively expressed genes are more conserved than those of cell 45 Arabidopsis inflorescence stem vascular bundles and is not expressed in primary root xylem 4 (15), and two HD-ZIPIII TFs, SlPHB/PHV (Solyc02g069830) and SlCORONA (Solyc03g120910), whose Arabidopsis orthologs regulate root protoxylem vessel differentiation via positional signals derived from a miR165/166 gradient (2,11,16). Contrary to their function in Arabidopsis, over-expression of SlbZIP11 or SlKNAT1 was sufficient to specify additional protoxylem cell files ( Fig. 2C-D), although these files were often non-contiguous for the 5 SlbZIP11 lines (Fig. 2C) (statistical analyses in Fig. S5, Data S3). The bHLH and MYB overexpression lines had no vascular phenotype. Relative to Arabidopsis, In the case of SlKNAT1, this demonstrates "repurposed" regulation, while in the case of SlbZIP11 it represents innovation in function. miRNA-resistant versions of SlCORONA and SlPHB/PHV were sufficient to regulate protoxylem vessel identity and patterning within the vascular cylinder 10 similar to their Arabidopsis function and are thus conserved regulators (Fig. 2D, E).Cell type/tissue translatomes are likely dynamic over developmental time and in response to the environment. In Arabidopsis, cell type-enriched genes that maintain expression despite stress are also critical regulators of cell fate (3, 17). However, the majority of plant cell type profiles are 15

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SUMMARY Plant responses to environmental change are mediated via changes in cellular metabolomes. However, <5% of signals obtained from liquid chromatography tandem mass spectrometry (LC‐MS/MS) can be identified, limiting our understanding of how metabolomes change under biotic/abiotic stress. To address this challenge, we performed untargeted LC‐MS/MS of leaves, roots, and other organs of Brachypodium distachyon (Poaceae) under 17 organ–condition combinations, including copper deficiency, heat stress, low phosphate, and arbuscular mycorrhizal symbiosis. We found that both leaf and root metabolomes were significantly affected by the growth medium. Leaf metabolomes were more diverse than root metabolomes, but the latter were more specialized and more responsive to environmental change. We found that 1 week of copper deficiency shielded the root, but not the leaf metabolome, from perturbation due to heat stress. Machine learning (ML)‐based analysis annotated approximately 81% of the fragmented peaks versus approximately 6% using spectral matches alone. We performed one of the most extensive validations of ML‐based peak annotations in plants using thousands of authentic standards, and analyzed approximately 37% of the annotated peaks based on these assessments. Analyzing responsiveness of each predicted metabolite class to environmental change revealed significant perturbations of glycerophospholipids, sphingolipids, and flavonoids. Co‐accumulation analysis further identified condition‐specific biomarkers. To make these results accessible, we developed a visualization platform on the Bio‐Analytic Resource for Plant Biology website (https://bar.utoronto.ca/efp_brachypodium_metabolites/cgi-bin/efpWeb.cgi), where perturbed metabolite classes can be readily visualized. Overall, our study illustrates how emerging chemoinformatic methods can be applied to reveal novel insights into the dynamic plant metabolome and stress adaptation.

High throughput sequencing has opened the doors for investigators to probe genetic variation present in large populations of organisms. In plants, the 1001 Genomes Project (1001genomes.org) is one such effort that sought to characterize the extant worldwide variation in Arabidopsis thaliana for future analyses to compare and draw upon. We developed a web application that accesses the 1001 Genomes database called The Variant Viewer, for investigators to view variants in any A. thaliana gene and within gene families. These variants may be visualized in the context of alignments of queried genes, across splice isoforms of these genes and in relation to conserved domains.

Plant responses to environmental change are mediated via changes in cellular metabolomes. However, <5% of signals obtained from tandem liquid chromatography mass spectrometry (LC-MS/MS) can be identified, limiting our understanding of how different metabolite classes change under biotic/abiotic stress. To address this challenge, we performed untargeted LC-MS/MS of leaves, roots and other organs of Brachypodium distachyon, a model Poaceae species, under 17 different organ-condition combinations, including copper deficiency, heat stress, low phosphate and arbuscular mycorrhizal symbiosis (AMS). We used a combination of information theory-based metrics and machine learning-based identification of metabolite structural classes to assess metabolomic changes. Both leaf and root metabolomes were significantly affected by the growth medium. Leaf metabolomes were more diverse than root metabolomes, but the latter were more specialized and more responsive to environmental change. We also found that one week of copper deficiency shielded the root metabolome, but not the leaf metabolome, from perturbation due to heat stress. Using a recently published deep learning based method for metabolite class predictions, we analyzed the responsiveness of each metabolite class to environmental change, which revealed significant perturbations of various lipid classes and phenylpropanoids such as cinnamic acids and flavonoids. Co-accumulation analysis further identified condition-specific metabolic biomarkers. Finally, to make these results publicly accessible, we developed a novel visualization platform on the Bioanalytical Resource website, where significantly perturbed metabolic classes can be readily visualized. Overall, our study illustrates how emerging chemoinformatic methods can be applied to reveal novel insights into the dynamic plant metabolome and plant stress adaptation.