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Reproductive success hinges on precisely coordinated meiosis, yet our understanding of how structural rearrangements of chromatin and phase transitions during meiosis are transcriptionally regulated is limited. In crop plants, detailed analysis of the meiotic transcriptome could identify regulatory genes and epigenetic regulators that can be targeted to increase recombination rates and broaden genetic variation, as well as provide a resource for comparison among eukaryotes of different taxa to answer outstanding questions about meiosis. We conducted a meiotic stage-specific analysis of messenger RNA (mRNA), small non-coding RNA (sncRNA), and long intervening/intergenic non-coding RNA (lincRNA) in wheat (Triticum aestivum L.) and revealed novel mechanisms of meiotic transcriptional regulation and meiosis-specific transcripts. Amidst general repression of mRNA expression, significant enrichment of ncRNAs was identified during prophase I relative to vegetative cells. The core meiotic transcriptome was comprised of 9309 meiosis-specific transcripts, 48 134 previously unannotated meiotic transcripts, and many known and novel ncRNAs differentially expressed at specific stages. The abundant meiotic sncRNAs controlled the reprogramming of central metabolic pathways by targeting genes involved in photosynthesis, glycolysis, hormone biosynthesis, and cellular homeostasis, and lincRNAs enhanced the expression of nearby genes. Alternative splicing was not evident in this polyploid species, but isoforms were switched at phase transitions. The novel, stage-specific regulatory controls uncovered here challenge the conventional understanding of this crucial biological process and provide a new resource of requisite knowledge for those aiming to directly modulate meiosis to improve crop plants. The wheat meiosis transcriptome dataset can be queried for genes of interest using an eFP browser located at https://bar.utoronto.ca/efp_wheat/cgi-bin/efpWeb.cgi?dataSource=Wheat_Meiosis.

Summary Mass spectrometry is the predominant analytical tool used in the field of plant lipidomics. However, there are many challenges associated with the mass spectrometric detection and identification of lipids because of the highly complex nature of plant lipids. Studies into lipid biosynthetic pathways, gene functions in lipid metabolism, lipid changes during plant growth and development, and the holistic examination of the role of plant lipids in environmental stress responses are often hindered. Here, we leveraged a robust pipeline that we previously established to extract and analyze lipid profiles of different tissues and developmental stages from the model plant Arabidopsis thaliana. We analyzed seven tissues at several different developmental stages and identified more than 200 lipids from each tissue analyzed. The data were used to create a web‐accessible in silico lipid map that has been integrated into an electronic Fluorescent Pictograph (eFP) browser. This in silico library of Arabidopsis lipids allows the visualization and exploration of the distribution and changes of lipid levels across selected developmental stages. Furthermore, it provides information on the characteristic fragments of lipids and adducts observed in the mass spectrometer and their retention times, which can be used for lipid identification. The Arabidopsis tissue lipid map can be accessed at http://bar.utoronto.ca/efp_arabidopsis_lipid/cgi‐bin/efpWeb.cgi., Significance Statement With the complexity of plant lipids, a web database collecting information on plant lipids is of great value. Here the most comprehensive and detailed tissue‐specific lipid profile of Arabidopsis thaliana across developmental stages is provided in the form of an open‐source, electronic Fluorescent Pictograph (eFP) browser, along with insights into lipid dynamics across plant growth and development. This resource will significantly advance future research into the Arabidopsis lipidome and facilitate research into other plants.

While root diseases are among the most devastating stresses in global crop production, our understanding of root immunity is still limited relative to our knowledge of immune responses in leaves. Considering that root performance is based on the concerted functions of its different cell types, we undertook a cell type-specific transcriptome analysis to identify gene networks activated in epidermis, cortex, and pericycle cells of Arabidopsis (Arabidopsis thaliana) roots challenged with two immunity elicitors, the bacterial flagellin-derived flg22 and the endogenous Pep1 peptide. Our analyses revealed distinct immunity gene networks in each cell type. To further substantiate our understanding of regulatory patterns underlying these cell type-specific immunity networks, we developed a tool to analyze paired transcription factor binding motifs in the promoters of cell type-specific genes. Our study points toward a connection between cell identity and cell type-specific immunity networks that might guide cell types in launching immune response according to the functional capabilities of each cell type.

Summary Improvements in next‐generation sequencing technologies have resulted in dramatically reduced sequencing costs. This has led to an explosion of ‘‐seq’‐based methods, of which RNA sequencing (RNA‐seq) for generating transcriptomic data is the most popular. By analysing global patterns of gene expression in organs/tissues/cells of interest or in response to chemical or environmental perturbations, researchers can better understand an organism's biology. Tools designed to work with large RNA‐seq data sets enable analyses and visualizations to help generate hypotheses about a gene's function. We present here a user‐friendly RNA‐seq data exploration tool, called the ‘eFP‐Seq Browser’, that shows the read map coverage of a gene of interest in each of the samples along with ‘electronic fluorescent pictographic’ (eFP) images that serve as visual representations of expression levels. The tool also summarizes the details of each RNA‐seq experiment, providing links to archival databases and publications. It automatically computes the reads per kilobase per million reads mapped expression‐level summaries and point biserial correlation scores to sort the samples based on a gene's expression level or by how dissimilar the read map profile is from a gene splice variant, to quickly identify samples with the strongest expression level or where alternative splicing might be occurring. Links to the Integrated Genome Browser desktop visualization tool allow researchers to visualize and explore the details of RNA‐seq alignments summarized in eFP‐Seq Browser as coverage graphs. We present four cases of use of the eFP‐Seq Browser for ABI3,SR34,SR45a and U2AF65B, where we examine expression levels and identify alternative splicing. The URL for the browser is https://bar.utoronto.ca/eFP-Seq_Browser/. Open research badges This article has earned an Open Data Badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. Tool is at http://sps:urlprefix::https; RNA‐seq data at http://sps:urlprefix::https and http://sps:urlprefix::https. Code is available at http://sps:urlprefix::https, Significance Statement We present a tool, the eFP‐Seq Browser, for rapidly identifying RNA sequencing samples with strong expression levels of a given gene, or where the read maps for a given gene/sample best match a particular gene model. Details can be called up with convenient links to the Integrated Genome Browser.

The early maize (Zea mays) seed undergoes several developmental stages after double fertilization to become fully differentiated within a short period of time, but the genetic control of this highly dynamic and complex developmental process remains largely unknown. Here, we report a high temporal-resolution investigation of transcriptomes using 31 samples collected at an interval of 4 or 6 h within the first six days of seed development. These time-course transcriptomes were clearly separated into four distinct groups corresponding to the stages of double fertilization, coenocyte formation, cellularization, and differentiation. A total of 22,790 expressed genes including 1415 transcription factors (TFs) were detected in early stages of maize seed development. In particular, 1093 genes including 110 TFs were specifically expressed in the seed and displayed high temporal specificity by expressing only in particular period of early seed development. There were 160, 22, 112, and 569 seed-specific genes predominantly expressed in the first 16 h after pollination, coenocyte formation, cellularization, and differentiation stage, respectively. In addition, network analysis predicted 31,256 interactions among 1317 TFs and 14,540 genes. The high temporal-resolution transcriptome atlas reported here provides an important resource for future functional study to unravel the genetic control of seed development.

Drought is an important environmental stress that limits crop production. Guard cells (GC) act to control the rate of water loss. To better understand how GCs change their gene expression during a progressive drought we generated guard cell-specific RNA-seq transcriptomes during mild, moderate, and severe drought stress. We additionally sampled re-watered plants that had experienced severe drought stress. These transcriptomes were generated using the INTACT system to capture the RNA from GC nuclei. We optimized the INTACT protocol for Arabidopsis thaliana leaf tissue, incorporating fixation to preserve RNA during nuclear isolation. To be able to identify gene expression changes unique to GCs, we additionally generated transcriptomes from all cell types, using a 35S viral promoter to capture the nuclei of all cell types in leaves. These data sets highlight shared and unique gene expression changes between GCs and the bulk leaf tissue. The timing of gene expression changes is different between GCs and other cell types: we found that only GCs had detectable gene expression changes at the earliest drought time point. The drought responsive GC and leaf RNA-seq transcriptomes are available in the Arabidopsis ePlant at the Bio-Analytic Resource for Plant Biology website.

International audience; A population of endosperm cells adjacent to the embryo scutellum shows transcriptomic enrichment in transport functions and is influenced by embryo development.Seeds are complex biological systems comprising three genetically distinct tissues nested one inside another (embryo, endosperm, and maternal tissues). However, the complexity of the kernel makes it difficult to understand intercompartment interactions without access to spatially accurate information. Here, we took advantage of the large size of the maize (Zea mays) kernel to characterize genome-wide expression profiles of tissues at different embryo/endosperm interfaces. Our analysis identifies specific transcriptomic signatures in two interface tissues compared with whole seed compartments: the scutellar aleurone layer and the newly named endosperm adjacent to scutellum (EAS). The EAS, which appears around 9 d after pollination and persists for around 11 d, is confined to one to three endosperm cell layers adjacent to the embryonic scutellum. Its transcriptome is enriched in genes encoding transporters. The absence of the embryo in an embryo specific mutant can alter the expression pattern of EAS marker genes. The detection of cell death in some EAS cells together with an accumulation of crushed cell walls suggests that the EAS is a dynamic zone from which cell layers in contact with the embryo are regularly eliminated and to which additional endosperm cells are recruited as the embryo grows.

Selaginella moellendorffii is a representative of the lycophyte lineage that is studied to understand the evolution of land plant traits such as the vasculature, leaves, stems, roots, and secondary metabolism. However, only a few studies have investigated the expression and transcriptional coordination of Selaginella genes, precluding us from understanding the evolution of the transcriptional programs behind these traits. We present a gene expression atlas comprising all major organs, tissue types, and the diurnal gene expression profiles for S. moellendorffii. We show that the transcriptional gene module responsible for the biosynthesis of lignocellulose evolved in the ancestor of vascular plants and pinpoint the duplication and subfunctionalization events that generated multiple gene modules involved in the biosynthesis of various cell wall types. We demonstrate how secondary metabolism is transcriptionally coordinated and integrated with other cellular pathways. Finally, we identify root-specific genes and show that the evolution of roots did not coincide with an increased appearance of gene families, suggesting that the development of new organs does not coincide with increased fixation of new gene functions. Our updated database at conekt.plant.tools represents a valuable resource for studying the evolution of genes, gene families, transcriptomes, and functional gene modules in the Archaeplastida kingdom.

Modern wheat production comes from two polyploid species, Triticum aestivum and Triticumturgidum (var durum), which putatively arose from diploid ancestors Triticumurartu, Aegilops speltoides, and Aegilopstauschii How gene expression during embryogenesis and grain development in wheats has been shaped by the differing contributions of diploid genomes through hybridization, polyploidization, and breeding selection is not well understood. This study describes the global landscape of gene activities during wheat embryogenesis and grain development. Using comprehensive transcriptomic analyses of two wheat cultivars and three diploid grasses, we investigated gene expression at seven stages of embryo development, two endosperm stages, and one pericarp stage. We identified transcriptional signatures and developmental similarities and differences among the five species, revealing the evolutionary divergence of gene expression programs and the contributions of A, B, and D subgenomes to grain development in polyploid wheats. The characterization of embryonic transcriptional programming in hexaploid wheat, tetraploid wheat, and diploid grass species provides insight into the landscape of gene expression in modern wheat and its ancestral species. This study presents a framework for understanding the evolution of domesticated wheat and the selective pressures placed on grain production, with important implications for future performance and yield improvements.plantcell;31/12/2888/FX1F1fx1.

Summary 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)., Significance Statement We developed a comprehensive transcriptome resource for the maize reference genotype B73 based on the AGPv4 long‐read assembly using replicated and publicly available RNA‐sequencing datasets. Gene expression analyses were performed on a robust B73 developmental and stress gene expression atlas, utilizing core and dispensable genes of the maize pan‐genome as a case study to demonstrate the utility of the data.

Root pathogens are a major threat in global crop production and protection strategies are required to sustainably enhance the efficiency of root immunity. Our understanding of root immunity is still limited relative to our knowledge of immune responses in leaves. In an effort to reveal the organisation of immunity in roots, we undertook a cell type-specific transcriptome analysis to identify gene networks activated in epidermis, cortex and pericycle cells ofArabidopsisroots upon treatment with two immunity elicitors, the bacterial microbe-associated molecular pattern flagellin, and the endogenous damage-associated molecular pattern Pep1. Our analyses revealed that both elicitors induced immunity gene networks in a cell type-specific manner. Interestingly, both elicitors did not alter cell identity-determining gene networks. Using sophisticated paired motif promoter analyses, we identified key transcription factor pairs involved in the regulation of cell type-specific immunity networks. In addition, our data show that cell identity networks integrate with cell immunity networks to activate cell type-specific immune response according to the functional capabilities of each cell type.Material Distribution FootnoteThe author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantcell.org) is: Patrick Schäfer (p.schafer@warwick.ac.uk).