An updated gene atlas for maize reveals organ-specific and stress-induced genes.

Hoopes GM, Hamilton JP, Wood JC, Esteban E, Pasha A, Vaillancourt B, Provart NJ, Buell CR

Plant J. 2019 Mar; 97(6):1154-1167

PMID: 30537259

Abstract

Maize (Zea mays L.), a model species for genetic studies, is one of the two most important crop species worldwide. The genome sequence of the reference genotype, B73, representative of the stiff stalk heterotic group was recently updated (AGPv4) using long-read sequencing and optical mapping technology. To facilitate the use of AGPv4 and to enable functional genomic studies and association of genotype with phenotype, we determined expression abundances for replicated mRNA-sequencing datasets from 79 tissues and five abiotic/biotic stress treatments revealing 36 207 expressed genes. Characterization of the B73 transcriptome across six organs revealed 4154 organ-specific and 7704 differentially expressed (DE) genes following stress treatment. Gene co-expression network analyses revealed 12 modules associated with distinct biological processes containing 13 590 genes providing a resource for further association of gene function based on co-expression patterns. Presence-absence variants (PAVs) previously identified using whole genome resequencing data from 61 additional inbred lines were enriched in organ-specific and stress-induced DE genes suggesting that PAVs may function in phenological variation and adaptation to environment. Relative to core genes conserved across the 62 profiled inbreds, PAVs have lower expression abundances which are correlated with their frequency of dispersion across inbreds and on average have significantly fewer co-expression network connections suggesting that a subset of PAVs may be on an evolutionary path to pseudogenization. To facilitate use by the community, we developed the Maize Genomics Resource website (maize.plantbiology.msu.edu) for viewing and data-mining these resources and deployed two new views on the maize electronic Fluorescent Pictograph Browser (bar.utoronto.ca/efp_maize).

A gene expression map of shoot domains reveals regulatory mechanisms.

Tian C, Wang Y, Yu H, He J, Wang J, Shi B, Du Q, Provart NJ, Meyerowitz EM, Jiao Y

Nat Commun 2019 01 11; 10(1):141

PMID: 30635575

Abstract

Gene regulatory networks control development via domain-specific gene expression. In seed plants, self-renewing stem cells located in the shoot apical meristem (SAM) produce leaves from the SAM peripheral zone. After initiation, leaves develop polarity patterns to form a planar shape. Here we compare translating RNAs among SAM and leaf domains. Using translating ribosome affinity purification and RNA sequencing to quantify gene expression in target domains, we generate a domain-specific translatome map covering representative vegetative stage SAM and leaf domains. We discuss the predicted cellular functions of these domains and provide evidence that dome seemingly unrelated domains, utilize common regulatory modules. Experimental follow up shows that the RABBIT EARS and HANABA TARANU transcription factors have roles in axillary meristem initiation. This dataset provides a community resource for further study of shoot development and response to internal and environmental signals.

Proteome-wide, Structure-Based Prediction of Protein-Protein Interactions/New Molecular Interactions Viewer.

Dong S, Lau V, Song R, Ierullo M, Esteban E, Wu Y, Sivieng T, Nahal H, Gaudinier A, Pasha A, Oughtred R, Dolinski K, Tyers M, Brady SM, Grene R, Usadel B, Provart NJ

Plant Physiol. 2019 04; 179(4):1893-1907

PMID: 30679268

Abstract

Determining the complete Arabidopsis () 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 top-ranking 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/.