MaizeGDB: The Maize Genetics and Genomics Database

Genome-wide terpene gene clusters analysis in Euphorbiaceae

Euphorbiaceae species are renowned not only for horticultural significance but for their production of numerous bicyclic diterpenes with antitumor and antiviral activities. However, the gene clusters responsible for the biosynthesis of these terpenes remain largely unidentified. We here initiated the construction of a comprehensive procedure for terpene gene clusters in Euphorbiaceae species. A total of 1824 candidate gene clusters with the range of 30-800 kb were identified across seven representative species including Ricinus communis, Hevea brasiliensis, Euphorbia peplus, Jatropha curcas, Manihot esculenta, Vernicia montana, and Vernicia fordii in Euphorbiaceae. The 16 high-confidence terpene gene clusters were ultimately pinpointed in Euphorbiaceae after satisfied the three stringent screening criteria: TPS/CYP pairwise relationship, copathway and coexpression patterns. Notably, the well-known casbene and casbene-derived diterpenoid gene cluster, involved in the biosynthesis of casbene, neocembrene, ingenanes, and jatrophanes, were identified. It was observed that casbene gene clusters were universally presented in Euphorbiaceae species, except M. esculenta. Among the casbene gene cluster, the alcohol dehydrogenase (ADH) was initially appeared, and neocembrene synthase is exclusively present in R. communis while absent in all the other species. These findings represent a significant step toward understanding the genetic basis of terpene biosynthesis in Euphorbiaceae species. Moreover, this knowledge on gene clusters responsible for the biosynthesis of pharmacologically relevant terpenes can serve as a theoretical foundation for future applications.

PlantPan: A comprehensive multi-species plant pan-genome database

The pan-genome represents the complete genomic diversity of specific species, serving as a valuable resource for studying species evolution, crop domestication, and guiding crop breeding and improvement. While there are several single-species-specific plant pan-genome databases, the availability of multi-species pan-genome databases is limited. Additionally, variations in methods and data types used for plant pan-genome analysis across different databases hinder the comparison and integration of pan-genome information from various projects at multi-species or single-species levels. To tackle this challenge, we introduce PlantPan, a comprehensive database housing the results of pan-genome analysis for 195 genomes from 11 plant species. PlantPan aims to provide extensive information, including gene-centric and sequence-centric pan-genome information, graph-based pan-genome, pan-genome openness profiles, gene functions and its variation characteristics, homologous genes, and gene clusters across different species. Statistically, PlantPan incorporates 9 163 011 genes, 694 191 gene clusters, 526 973 370 genome variations, and 1 616 089 non-redundant genome variation groups at the species level, 33 455,098 genome synteny, and 177 827 non-redundant genome synteny groups at the species level. Regarding functional genes, PlantPan contains 5 222 720 genes related to transcription factors, 395 247 literature-reported resistance genes, 455 748 predicted microbial/disease resistance genes, and 1 612 112 genes related to molecular pathways. In summary, PlantPan is a vital platform for advancing the application of pan-genomes in molecular breeding for crops and evolutionary research for plants.

Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions

Classical genomic prediction approaches rely on statistical associations between traits and markers rather than their biological significance. Biologically informed selection of genomic regions can help prioritize polymorphisms by considering underlying biological processes, making prediction models robust and accurate. Gene ontology (GO) terms can be used for this purpose, and the information can be integrated into genomic prediction models through marker categorization. It allows likely causal markers to account for a certain portion of genetic variance independently from the remaining markers. We systematically tested a list of 5110 GO terms for their predictive performance for physiological (platform traits) and productivity traits (field grain yield) in a maize (Zea mays L.) panel using genomic features best linear unbiased prediction (GFBLUP) model. Predictive abilities were compared to the classical genomic best linear unbiased prediction (GBLUP). Predictive gains with categorizing markers based on a given GO term strongly depend on the trait and on the growth conditions, as a term can be useful for a given trait in a given condition or somewhat similar conditions but not useful for the same trait in a different condition. Overall, results of all GFBLUP models compared to GBLUP show that the former might be less efficient than the latter. Even though we could not identify a prior criterion to determine which GO terms can offer benefit to a given trait, we could a posteriori find biological interpretations of the results, meaning that GFBLUP could be helpful if more about the gene functions and their relationships with the growth conditions was known.

Mutations in nuclear genes encoding mitochondrial ribosome proteins restore pollen fertility in S male-sterile maize

The interaction of plant mitochondrial and nuclear genetic systems is exemplified by mitochondria-encoded cytoplasmic male sterility (CMS) under the control of nuclear restorer-of-fertility genes. The S type of CMS in maize is characterized by a pollen collapse phenotype and a unique paradigm for fertility restoration in which numerous nuclear restorer-of-fertility lethal mutations rescue pollen function but condition homozygous-lethal seed phenotypes. Two nonallelic restorer mutations recovered from Mutator transposon-active lines were investigated to determine the mechanisms of pollen fertility restoration and seed lethality. Mu Illumina sequencing of transposon-flanking regions identified insertion alleles of nuclear genes encoding mitochondrial ribosomal proteins RPL6 and RPL14 as candidate restorer-of-fertility lethal mutations. Both candidates were associated with lowered abundance of mitochondria-encoded proteins in developing maize pollen, and the rpl14 mutant candidate was confirmed by independent insertion alleles. While the restored pollen functioned despite reduced accumulation of mitochondrial respiratory proteins, normal-cytoplasm plants heterozygous for the mutant alleles showed a significant pollen transmission bias in favor of the nonmutant Rpl6 and Rpl14 alleles. CMS-S fertility restoration affords a unique forward genetic approach to investigate the mitochondrial requirements for, and contributions to, pollen and seed development.

Cortical parenchyma wall width regulates root metabolic cost and maize performance under suboptimal water availability

We describe how increased root cortical parenchyma wall width (CPW) can improve tolerance to drought stress in maize by reducing the metabolic costs of soil exploration. Significant variation (1.0-5.0 µm) for CPW was observed in maize germplasm. The functional-structural model RootSlice predicts that increasing CPW from 2 µm to 4 µm is associated with a ~15% reduction in root cortical cytoplasmic volume, respiration rate, and nitrogen content. Analysis of genotypes with contrasting CPW grown with and without water stress in the field confirms that increased CPW is correlated with an ~32-42% decrease in root respiration. Under water stress in the field, increased CPW is correlated with 125% increased stomatal conductance, 325% increased leaf CO2 assimilation rate, 73-78% increased shoot biomass, and 92-108% increased yield. CPW was correlated with leaf mesophyll midrib parenchyma wall width, indicating pleiotropy. Genome-wide association study analysis identified candidate genes underlying CPW. OpenSimRoot modeling predicts that a reduction in root respiration due to increased CPW would also benefit maize growth under suboptimal nitrogen, which requires empirical testing. We propose CPW as a new phene that has utility under edaphic stress meriting further investigation.

Cortical cell size regulates root metabolic cost

It has been hypothesized that vacuolar occupancy in mature root cortical parenchyma cells regulates root metabolic cost and thereby plant fitness under conditions of drought, suboptimal nutrient availability, and increased soil mechanical impedance. However, the mechanistic role of vacuoles in reducing root metabolic cost was unproven. Here we provide evidence to support this hypothesis. We first show that root cortical cell size is determined by both cortical cell diameter and cell length. Significant genotypic variation for both cortical cell diameter (~1.1- to 1.5-fold) and cortical cell length (~ 1.3- to 7-fold) was observed in maize and wheat. GWAS and QTL analyses indicate cortical cell diameter and length are heritable and under independent genetic control. We identify candidate genes for both phenes. Empirical results from isophenic lines contrasting for cortical cell diameter and length show that increased cell size, due to either diameter or length, is associated with reduced root respiration, nitrogen content, and phosphorus content. RootSlice, a functional-structural model of root anatomy, predicts that an increased vacuolar: cytoplasmic ratio per unit cortical volume causes reduced root respiration and nutrient content. Ultrastructural imaging of cortical parenchyma cells with varying cortical diameter and cortical cell length confirms the in silico predictions and shows that an increase in cell size is correlated with increased vacuolar volume and reduced cytoplasmic volume. Vacuolar occupancy and its relationship with cell size merits further investigation as a phene for improving crop adaptation to edaphic stress.

JustRNA: a database of plant long noncoding RNA expression profiles and functional network

Long noncoding RNAs (lncRNAs) are regulatory RNAs involved in numerous biological processes. Many plant lncRNAs have been identified, but their regulatory mechanisms remain largely unknown. A resource that enables the investigation of lncRNA activity under various conditions is required because the co-expression between lncRNAs and protein-coding genes may reveal the effects of lncRNAs. This study developed JustRNA, an expression profiling resource for plant lncRNAs. The platform currently contains 1 088 565 lncRNA annotations for 80 plant species. In addition, it includes 3692 RNA-seq samples derived from 825 conditions in six model plants. Functional network reconstruction provides insight into the regulatory roles of lncRNAs. Genomic association analysis and microRNA target prediction can be employed to depict potential interactions with nearby genes and microRNAs, respectively. Subsequent co-expression analysis can be employed to strengthen confidence in the interactions among genes. Chromatin immunoprecipitation sequencing data of transcription factors and histone modifications were integrated into the JustRNA platform to identify the transcriptional regulation of lncRNAs in several plant species. The JustRNA platform provides researchers with valuable insight into the regulatory mechanisms of plant lncRNAs. JustRNA is a free platform that can be accessed at http://JustRNA.itps.ncku.edu.tw.

The RHW1-ZCN4 regulatory pathway confers natural variation of husk leaf width in maize

Maize husk leaf - the outer leafy layers covering the ear - modulates kernel yield and quality. Despite its importance, however, the genetic controls underlying husk leaf development remain elusive. Our previous genome-wide association study identified a single nucleotide polymorphism located in the gene RHW1 (Regulator of Husk leaf Width) that is significantly associated with husk leaf-width diversity in maize. Here, we further demonstrate that a polymorphic 18-bp InDel (insertion/deletion) variant in the 3' untranslated region of RHW1 alters its protein abundance and accounts for husk leaf width variation. RHW1 encodes a putative MYB-like transcriptional repressor. Disruption of RHW1 altered cell proliferation and resulted in a narrower husk leaf, whereas RHW1 overexpression yielded a wider husk leaf. RHW1 positively regulated the expression of ZCN4, a well-known TFL1-like protein involved in maize ear development. Dysfunction of ZCN4 reduced husk leaf width even in the context of RHW1 overexpression. The InDel variant in RHW1 is subject to selection and is associated with maize husk leaf adaption from tropical to temperate regions. Overall, our results identify that RHW1-ZCN4 regulates a pathway conferring husk leaf width variation at a very early stage of husk leaf development in maize.

Characterization of phi112, a Molecular Marker Tightly Linked to the o2 Gene of Maize, and Its Utilization in Multiplex PCR for Differentiating Normal Maize from QPM

Quality Protein Maize (QPM) contains higher amounts of essential amino acids lysine and tryptophan. The QPM phenotype is based on regulating zein protein synthesis by opaque2 transcription factor. Many gene modifiers act to optimize the amino acid content and agronomic performance. An SSR marker, phi112, is present upstream of the opaque2 DNA gene. Its analysis has shown the presence of transcription factor activity. The functional associations of opaque2 have been determined. The putative transcription factor binding at phi112 marked DNA was identified through computational analysis. The present study is a step towards understanding the intricate network of molecular interactions that fine-tune the QPM genotype to influence maize protein quality. In addition, a multiplex PCR assay for differentiation of QPM from normal maize is shown, which can be used for Quality Control at various stages of the QPM value chain.

LettuceGDB: The community database for lettuce genetics and omics

As a globally popular leafy vegetable and a representative plant of the Asteraceae family, lettuce has great economic and academic significance. In the last decade, high-throughput sequencing, phenotyping, and other multi-omics data in lettuce have accumulated on a large scale, thus increasing the demand for an integrative lettuce database. Here, we report the establishment of a comprehensive lettuce database, LettuceGDB (https://www.lettucegdb.com/). As an omics data hub, the current LettuceGDB includes two reference genomes with detailed annotations; re-sequencing data from over 1000 lettuce varieties; a collection of more than 1300 worldwide germplasms and millions of accompanying phenotypic records obtained with manual and cutting-edge phenomics technologies; re-analyses of 256 RNA sequencing datasets; a complete miRNAome; extensive metabolite information for representative varieties and wild relatives; epigenetic data on the genome-wide chromatin accessibility landscape; and various lettuce research papers published in the last decade. Five hierarchically accessible functions (Genome, Genotype, Germplasm, Phenotype, and O-Omics) have been developed with a user-friendly interface to enable convenient data access. Eight built-in tools (Assembly Converter, Search Gene, BLAST, JBrowse, Primer Design, Gene Annotation, Tissue Expression, Literature, and Data) are available for data downloading and browsing, functional gene exploration, and experimental practice. A community forum is also available for information sharing, and a summary of current research progress on different aspects of lettuce is included. We believe that LettuceGDB can be a comprehensive functional database amenable to data mining and database-driven exploration, useful for both scientific research and lettuce breeding.

Genome-wide association studies of grain yield and quality traits under optimum and low-nitrogen stress in tropical maize (Zea mays L.)

KEY MESSAGE

Genome-wide association study (GWAS) demonstrated that multiple genomic regions influence grain quality traits under nitrogen-starved soils. Using genomic prediction, genetic gains can be improved through selection for grain quality traits. Soils in sub-Saharan Africa are nitrogen deficient due to low fertilizer use and inadequate soil fertility management practices. This has resulted in a significant yield gap for the major staple crop maize, which is undermining nutritional security and livelihood sustainability across the region. Dissecting the genetic basis of grain protein, starch and oil content under nitrogen-starved soils can increase our understanding of the governing genetic systems and improve the efficacy of future breeding schemes. An association mapping panel of 410 inbred lines and four bi-parental populations were evaluated in field trials in Kenya and South Africa under optimum and low nitrogen conditions and genotyped with 259,798 SNP markers. Genetic correlations demonstrated that these populations may be utilized to select higher performing lines under low nitrogen stress. Furthermore, genotypic, environmental and GxE variations in nitrogen-starved soils were found to be significant for oil content. Broad sense heritabilities ranged from moderate (0.18) to high (0.86). Under low nitrogen stress, GWAS identified 42 SNPs linked to grain quality traits. These significant SNPs were associated with 51 putative candidate genes. Linkage mapping identified multiple QTLs for the grain quality traits. Under low nitrogen conditions, average prediction accuracies across the studied genotypes were higher for oil content (0.78) and lower for grain yield (0.08). Our findings indicate that grain quality traits are polygenic and that using genomic selection in maize breeding can improve genetic gain. Furthermore, the identified genomic regions and SNP markers can be utilized for selection to improve maize grain quality traits.

Epistasis Activation Contributes Substantially to Heterosis in Temperate by Tropical Maize Hybrids

Epistasis strongly affects the performance of superior maize hybrids. In this study, a multiple-hybrid population, consisting of three hybrid maize sets with varied interparental divergence, was generated by crossing 28 temperate and 23 tropical inbred lines with diverse genetic backgrounds. We obtained 1,154 tested hybrids. Among these tested hybrids, heterosis increased steadily as the heterotic genetic distance increased. Mid-parent heterosis was significantly higher in the temperate by tropical hybrids than in the temperate by temperate hybrids. Genome-wide prediction and association mapping was performed for grain weight per plant (GWPP) and days to silking (DTS) using 20K high-quality SNPs, showing that epistatic effects played a more prominent role than dominance effects in temperate by tropical maize hybrids. A total of 33 and 420 epistatic QTL were identified for GWPP and DTS, respectively, in the temperate by tropical hybrids. Protein-protein interaction network and gene-set enrichment analyses showed that epistatic genes were involved in protein interactions, which play an important role in photosynthesis, biological transcription pathways, and protein synthesis. We showed that the interaction of many minor-effect genes in the hybrids could activate the transcription activators of epistatic genes, resulting in a cascade of amplified yield heterosis. The multiple-hybrid population design enhanced our understanding of heterosis in maize, providing an insight into the acceleration of hybrid maize breeding by activating epistatic effects.

A pan-genomic approach to genome databases using maize as a model system

Research in the past decade has demonstrated that a single reference genome is not representative of a species' diversity. MaizeGDB introduces a pan-genomic approach to hosting genomic data, leveraging the large number of diverse maize genomes and their associated datasets to quickly and efficiently connect genomes, gene models, expression, epigenome, sequence variation, structural variation, transposable elements, and diversity data across genomes so that researchers can easily track the structural and functional differences of a locus and its orthologs across maize. We believe our framework is unique and provides a template for any genomic database poised to host large-scale pan-genomic data.

EXPath 2.0: An Updated Database for Integrating High-Throughput Gene Expression Data with Biological Pathways

Co-expressed genes tend to have regulatory relationships and participate in similar biological processes. Construction of gene correlation networks from microarray or RNA-seq expression data has been widely applied to study transcriptional regulatory mechanisms and metabolic pathways under specific conditions. Furthermore, since transcription factors (TFs) are critical regulators of gene expression, it is worth investigating TFs on the promoters of co-expressed genes. Although co-expressed genes and their related metabolic pathways can be easily identified from previous resources, such as EXPath and EXPath Tool, this information is not simultaneously available to identify their regulatory TFs. EXPath 2.0 is an updated database for the investigation of regulatory mechanisms in various plant metabolic pathways with 1,881 microarray and 978 RNA-seq samples. There are six significant improvements in EXPath 2.0: (i) the number of species has been extended from three to six to include Arabidopsis, rice, maize, Medicago, soybean and tomato; (ii) gene expression at various developmental stages have been added; (iii) construction of correlation networks according to a group of genes is available; (iv) hierarchical figures of the enriched Gene Ontology (GO) terms are accessible; (v) promoter analysis of genes in a metabolic pathway or correlation network is provided; and (vi) user's gene expression data can be uploaded and analyzed. Thus, EXPath 2.0 is an updated platform for investigating gene expression profiles and metabolic pathways under specific conditions. It facilitates users to access the regulatory mechanisms of plant biological processes. The new version is available at http://EXPath.itps.ncku.edu.tw.

MaizeGDB 2018: the maize multi-genome genetics and genomics database

Since its 2015 update, MaizeGDB, the Maize Genetics and Genomics database, has expanded to support the sequenced genomes of many maize inbred lines in addition to the B73 reference genome assembly. Curation and development efforts have targeted high quality datasets and tools to support maize trait analysis, germplasm analysis, genetic studies, and breeding. MaizeGDB hosts a wide range of data including recent support of new data types including genome metadata, RNA-seq, proteomics, synteny, and large-scale diversity. To improve access and visualization of data types several new tools have been implemented to: access large-scale maize diversity data (SNPversity), download and compare gene expression data (qTeller), visualize pedigree data (Pedigree Viewer), link genes with phenotype images (MaizeDIG), and enable flexible user-specified queries to the MaizeGDB database (MaizeMine). MaizeGDB also continues to be the community hub for maize research, coordinating activities and providing technical support to the maize research community. Here we report the changes MaizeGDB has made within the last three years to keep pace with recent software and research advances, as well as the pan-genomic landscape that cheaper and better sequencing technologies have made possible. MaizeGDB is accessible online at https://www.maizegdb.org.

The prevalence, evolution and chromatin signatures of plant regulatory elements

Chromatin accessibility and modification is a hallmark of regulatory DNA, the study of which led to the discovery of cis-regulatory elements (CREs). Here, we characterize chromatin accessibility, histone modifications and sequence conservation in 13 plant species. We identified thousands of putative CREs and revealed that distal CREs are prevalent in plants, especially in species with large and complex genomes. The majority of distal CREs have been moved away from their target genes by transposable-element (TE) proliferation, but a substantial number of distal CREs also seem to be created by TEs. Finally, plant distal CREs are associated with three major types of chromatin signatures that are distinct from metazoans. Taken together, these results suggest that CREs are prevalent in plants, highly dynamic during evolution and function through distinct chromatin pathways to regulate gene expression.

Widespread long-range cis-regulatory elements in the maize genome

Genetic mapping studies on crops suggest that agronomic traits can be controlled by gene-distal intergenic loci. Despite the biological importance and the potential agronomic utility of these loci, they remain virtually uncharacterized in all crop species to date. Here, we provide genetic, epigenomic and functional molecular evidence to support the widespread existence of gene-distal (hereafter, distal) loci that act as long-range transcriptional cis-regulatory elements (CREs) in the maize genome. Such loci are enriched for euchromatic features that suggest their regulatory functions. Chromatin loops link together putative CREs with genes and recapitulate genetic interactions. Putative CREs also display elevated transcriptional enhancer activities, as measured by self-transcribing active regulatory region sequencing. These results provide functional support for the widespread existence of CREs that act over large genomic distances to control gene expression.

MaizeDIG: Maize Database of Images and Genomes

Background: An organism can be described by its observable features (phenotypes) and the genes and genomic information (genotypes) that cause these phenotypes. For many decades, researchers have tried to find relationships between genotypes and phenotypes, and great strides have been made. However, improved methods and tools for discovering and visualizing these phenotypic relationships are still needed. The maize genetics and genomics database (MaizeGDB, www.maizegdb.org) provides an array of useful resources for diverse data types including thousands of images related to mutant phenotypes in Zea mays ssp. mays (maize). To integrate mutant phenotype images with genomics information, we implemented and enhanced the web-based software package BioDIG (Biological Database of Images and Genomes). Findings: We developed a genotype-phenotype database for maize called MaizeDIG. MaizeDIG has several enhancements over the original BioDIG package. MaizeDIG, which supports multiple reference genome assemblies, is seamlessly integrated with genome browsers to accommodate custom tracks showing tagged mutant phenotypes images in their genomic context and allows for custom tagging of images to highlight the phenotype. This is accomplished through an updated interface allowing users to create image-to-gene links and is accessible via the image search tool. Conclusions: We have created a user-friendly and extensible web-based resource called MaizeDIG. MaizeDIG is preloaded with 2,396 images that are available on genome browsers for 10 different maize reference genomes. Approximately 90 images of classically defined maize genes have been manually annotated. MaizeDIG is available at http://maizedig.maizegdb.org/. The code is free and open source and can be found at https://github.com/Maize-Genetics-and-Genomics-Database/maizedig.

Characterization of LRL5 as a key regulator of root hair growth in maize

Root hair, a special type of tubular-shaped cell, outgrows from the root epidermal cell and plays important roles in the acquisition of nutrients and water, as well as interactions with biotic and abiotic stresses. Studies in the model plant Arabidopsis have revealed that root-hair initiation and elongation are hierarchically regulated by a group of basic helix-loop-helix (bHLH) transcription factors (TFs). However, knowledge regarding the regulatory pathways of these bHLH TFs in controlling root hair growth remains limited. In this study, RNA-seq analysis was conducted to profile the transcriptome in the elongating maize root hair and >1000 genes with preferential expression in root hair were identified. A consensus cis-element previously featured as the potential bHLH-TF binding sites was present in the regulatory regions for the majority of the root hair-preferentially expressed genes. In addition, an individual change in ZmLRL5, the highest-expressed bHLH-TF in maize root hair resulted in a dramatic reduction in the elongation of root hair, and rendered the growth of root hair hypersensitive to translational inhibition. Moreover, RNA-seq, yeast-one-hybrid and ribosome profile analysis suggested that ZmLRL5 may function as a key player in orchestrating the translational process by directly regulating the expression of translational processes/ribosomal genes during maize root hair growth.

Raising orphans from a metadata morass: A researcher's guide to re-use of public 'omics data

More than 15 petabases of raw RNAseq data is now accessible through public repositories. Acquisition of other 'omics data types is expanding, though most lack a centralized archival repository. Data-reuse provides tremendous opportunity to extract new knowledge from existing experiments, and offers a unique opportunity for robust, multi-'omics analyses by merging metadata (information about experimental design, biological samples, protocols) and data from multiple experiments. We illustrate how predictive research can be accelerated by meta-analysis with a study of orphan (species-specific) genes. Computational predictions are critical to infer orphan function because their coding sequences provide very few clues. The metadata in public databases is often confusing; a test case with Zea mays mRNA seq data reveals a high proportion of missing, misleading or incomplete metadata. This metadata morass significantly diminishes the insight that can be extracted from these data. We provide tips for data submitters and users, including specific recommendations to improve metadata quality by more use of controlled vocabulary and by metadata reviews. Finally, we advocate for a unified, straightforward metadata submission and retrieval system.

ZmCom1 Is Required for Both Mitotic and Meiotic Recombination in Maize

CtIP/Ctp1/Sae2/Com1, a highly conserved protein from yeast to higher eukaryotes, is required for DNA double-strand break repair through homologous recombination (HR). In this study, we identified and characterized the COM1 homolog in maize. The ZmCom1 gene is abundantly expressed in reproductive tissues at meiosis stages. In ZmCom1-deficient plants, meiotic chromosomes are constantly entangled as a formation of multivalents and accompanied with chromosome fragmentation at anaphase I. In addition, the formation of telomere bouquet, homologous pairing and synapsis were disturbed. The immunostaining assay showed that the localization of ASY1 and DSY2 was normal, while ZYP1 signals were severely disrupted in Zmcom1 meiocytes, indicating that ZmCom1 is critically required for the proper SC assembly. Moreover, RAD51 signals were almost completely absent in Zmcom1 meiocytes, implying that COM1 is required for RAD51 loading. Surprisingly, in contrast to the Atcom1 and Oscom1 mutants, Zmcom1 mutant plants exhibited a number of vegetative phenotypes under normal growth condition, which may be partly attributed to mitotic aberrations including chromosomal fragmentation and anaphase bridges. Taken together, our results suggest that although the roles of COM1 in HR process seem to be primarily conserved, the COM1 dysfunction can result in the marked dissimilarity in mitotic and meiotic outcomes in maize compared to Arabidopsis and rice. We suggest that this character may be related to the discrete genome context.

Species-Specific Genome Sequence Databases: A Practical Review

This chapter presents a use case illustrating the search for homologues of a known protein in species-specific genome sequence databases. The results from different species-specific resources are compared to each other and to results obtained from a more general genome sequence database (Phytozome). Various options and settings relevant when searching these databases are discussed. For example, it is shown how the choice of reference sequence set in a given database influences the results one obtains. The provided examples illustrate some problems and pitfalls related to interpreting results obtained from species-specific genome sequence databases.