Gramene 2018: unifying comparative genomics and pathway resources for plant research

John P, Umen J. Identification and preliminary characterization of conserved uncharacterized proteins from Chlamydomonas reinhardtii, Arabidopsis thaliana, and Setaria viridis

The rapid accumulation of sequenced plant genomes in the past decade has outpaced the still difficult problem of genome-wide protein-coding gene annotation. A substantial fraction of protein-coding genes in all plant genomes are poorly annotated or unannotated and remain functionally uncharacterized. We identified unannotated proteins in three model organisms representing distinct branches of the green lineage (Viridiplantae): Arabidopsis thaliana (eudicot), Setaria viridis (monocot), and Chlamydomonas reinhardtii (Chlorophyte alga). Using similarity searching, we identified a subset of unannotated proteins that were conserved between these species and defined them as Deep Green proteins. Bioinformatic, genomic, and structural predictions were performed to begin classifying Deep Green genes and proteins. Compared to whole proteomes for each species, the Deep Green set was enriched for proteins with predicted chloroplast targeting signals predictive of photosynthetic or plastid functions, a result that was consistent with enrichment for daylight phase diurnal expression patterning. Structural predictions using AlphaFold and comparisons to known structures showed that a significant proportion of Deep Green proteins may possess novel folds. Though only available for three organisms, the Deep Green genes and proteins provide a starting resource of high-value targets for further investigation of potentially new protein structures and functions conserved across the green lineage.

Exploring Pan-Genomes: An Overview of Resources and Tools for Unraveling Structure, Function, and Evolution of Crop Genes and Genomes

The availability of multiple sequenced genomes from a single species made it possible to explore intra- and inter-specific genomic comparisons at higher resolution and build clade-specific pan-genomes of several crops. The pan-genomes of crops constructed from various cultivars, accessions, landraces, and wild ancestral species represent a compendium of genes and structural variations and allow researchers to search for the novel genes and alleles that were inadvertently lost in domesticated crops during the historical process of crop domestication or in the process of extensive plant breeding. Fortunately, many valuable genes and alleles associated with desirable traits like disease resistance, abiotic stress tolerance, plant architecture, and nutrition qualities exist in landraces, ancestral species, and crop wild relatives. The novel genes from the wild ancestors and landraces can be introduced back to high-yielding varieties of modern crops by implementing classical plant breeding, genomic selection, and transgenic/gene editing approaches. Thus, pan-genomic represents a great leap in plant research and offers new avenues for targeted breeding to mitigate the impact of global climate change. Here, we summarize the tools used for pan-genome assembly and annotations, web-portals hosting plant pan-genomes, etc. Furthermore, we highlight a few discoveries made in crops using the pan-genomic approach and future potential of this emerging field of study.

Using knowledge graphs to infer gene expression in plants

Introduction

Climate change is already affecting ecosystems around the world and forcing us to adapt to meet societal needs. The speed with which climate change is progressing necessitates a massive scaling up of the number of species with understood genotype-environment-phenotype (G×E×P) dynamics in order to increase ecosystem and agriculture resilience. An important part of predicting phenotype is understanding the complex gene regulatory networks present in organisms. Previous work has demonstrated that knowledge about one species can be applied to another using ontologically-supported knowledge bases that exploit homologous structures and homologous genes. These types of structures that can apply knowledge about one species to another have the potential to enable the massive scaling up that is needed through in silico experimentation.

Methods

We developed one such structure, a knowledge graph (KG) using information from Planteome and the EMBL-EBI Expression Atlas that connects gene expression, molecular interactions, functions, and pathways to homology-based gene annotations. Our preliminary analysis uses data from gene expression studies in Arabidopsis thaliana and Populus trichocarpa plants exposed to drought conditions.

Results

A graph query identified 16 pairs of homologous genes in these two taxa, some of which show opposite patterns of gene expression in response to drought. As expected, analysis of the upstream cis-regulatory region of these genes revealed that homologs with similar expression behavior had conserved cis-regulatory regions and potential interaction with similar trans-elements, unlike homologs that changed their expression in opposite ways.

Discussion

This suggests that even though the homologous pairs share common ancestry and functional roles, predicting expression and phenotype through homology inference needs careful consideration of integrating cis and trans-regulatory components in the curated and inferred knowledge graph.

WheatCENet: A Database for Comparative Co-expression Networks Analysis of Allohexaploid Wheat and Its Progenitors

Genetic and epigenetic changes after polyploidization events could result in variable gene expression and modified regulatory networks. Here, using large-scale transcriptome data, we constructed co-expression networks for diploid, tetraploid, and hexaploid wheat species, and built a platform for comparing co-expression networks of allohexaploid wheat and its progenitors, named WheatCENet. WheatCENet is a platform for searching and comparing specific functional co-expression networks, as well as identifying the related functions of the genes clustered therein. Functional annotations like pathways, gene families, protein-protein interactions, microRNAs (miRNAs), and several lines of epigenome data are integrated into this platform, and Gene Ontology (GO) annotation, gene set enrichment analysis (GSEA), motif identification, and other useful tools are also included. Using WheatCENet, we found that the network of WHEAT ABERRANT PANICLE ORGANIZATION 1 (WAPO1) has more co-expressed genes related to spike development in hexaploid wheat than its progenitors. We also found a novel motif of CCWWWWWWGG (CArG) specifically in the promoter region of WAPO-A1, suggesting that neofunctionalization of the WAPO-A1 gene affects spikelet development in hexaploid wheat. WheatCENet is useful for investigating co-expression networks and conducting other analyses, and thus facilitates comparative and functional genomic studies in wheat. WheatCENet is freely available at http://bioinformatics.cpolar.cn/WheatCENet and http://bioinformatics.cau.edu.cn/WheatCENet.

Translational research from models to crops: comparative genomics for plant breeding

The concept of translational research, which originated in the medical field in the 1980s, consists in improving the efficient transfer of research results obtained in a species (which can be considered as a model or pivot) to all the species for which these results are of interest for its improvement in Agriculture. In this context, comparative genomics is an important tool for translational research, effectively identifying genes controlling common functions between species. Editing and phenotyping tools must thus allow the functional validation of the gene conserved within the species for which the knowledge has been extrapolated, that is to say transferred, and the identification of the best alleles and associated genotypes for exploitation in current breeding programs.

Toward a data infrastructure for the Plant Cell Atlas

We review how a data infrastructure for the Plant Cell Atlas might be built using existing infrastructure and platforms. The Human Cell Atlas has developed an extensive infrastructure for human and mouse single cell data, while the European Bioinformatics Institute has developed a Single Cell Expression Atlas, that currently houses several plant data sets. We discuss issues related to appropriate ontologies for describing a plant single cell experiment. We imagine how such an infrastructure will enable biologists and data scientists to glean new insights into plant biology in the coming decades, as long as such data are made accessible to the community in an open manner.

Unleashing the power within short-read RNA-seq for plant research: Beyond differential expression analysis and toward regulomics

RNA-seq has become a state-of-the-art technique for transcriptomic studies. Advances in both RNA-seq techniques and the corresponding analysis tools and pipelines have unprecedently shaped our understanding in almost every aspects of plant sciences. Notably, the integration of huge amount of RNA-seq with other omic data sets in the model plants and major crop species have facilitated plant regulomics, while the RNA-seq analysis has still been primarily used for differential expression analysis in many less-studied plant species. To unleash the analytical power of RNA-seq in plant species, especially less-studied species and biomass crops, we summarize recent achievements of RNA-seq analysis in the major plant species and representative tools in the four types of application: (1) transcriptome assembly, (2) construction of expression atlas, (3) network analysis, and (4) structural alteration. We emphasize the importance of expression atlas, coexpression networks and predictions of gene regulatory relationships in moving plant transcriptomes toward regulomics, an omic view of genome-wide transcription regulation. We highlight what can be achieved in plant research with RNA-seq by introducing a list of representative RNA-seq analysis tools and resources that are developed for certain minor species or suitable for the analysis without species limitation. In summary, we provide an updated digest on RNA-seq tools, resources and the diverse applications for plant research, and our perspective on the power and challenges of short-read RNA-seq analysis from a regulomic point view. A full utilization of these fruitful RNA-seq resources will promote plant omic research to a higher level, especially in those less studied species.

Structural and Functional Characterization at the Molecular Level of the MATE Gene Family in Wheat in Silico

A series of multidrug extransporters known as the multidrug and potentially toxic extrusion (MATE) genes are found in all living things and are crucial for the removal of heavy metal ions, metalloids, exogenous xenobiotics, endogenous secondary metabolites, and other toxic substances from the cells. However, there has only been a small amount of them in silico analysis of the MATE family of genes in plant species. In the current study, the MATE gene family was characterized in silico where two families and seven subfamilies based on their evolutionary relationships were proposed. Plant breeders may use TraesCS1D02G030400, TraesCS4B02G244400, and TraesCS1A02G029900 genes for marker-assisted or transgenic breeding to develop novel cultivars since these genes have been hypothesized from protein-protein interaction study to play a critical role in the transport of toxic chemicals across cells. The exon number varies from 01 to 14. One exon has TraesCS1A02G188100, TraesCS5B02G562500, TraesCS6A02G256400, and TraesCS6D02G384300 genes, while 14 exons have only two genes that are TraesCS6A02G418800 and TraesCS6D02G407900. Biological stress (infestations of disease) affects the expression of most of the MATE genes, with the gene TraesCS5D02G355500 having the highest expression level in the wheat expression browser tool. Using the Grain interpretation search engine tool, it is found that the vast bulk of MATE genes are voiced throughout biotic environmental stresses caused by disease pests, with the genotype TraesCS5B02G326600.1 from family 1 exhibiting the greatest level of expression throughout Fusarium head blight infection by Fusarium graminearum after 4 days of infection. The researchers constructed 39 ternary plots, each with a distinct degree of expression under biotic and abiotic stress settings, and observed that 44% of the triplets have imbalanced outputs (extreme values) due to their higher tissue specificity and increased intensity.

24-nt phasiRNAs move from tapetal to meiotic cells in maize anthers

In maize, 24-nt phased, secondary small interfering RNAs (phasiRNAs) are abundant in meiotic stage anthers, but their distribution and functions are not precisely known. Using laser capture microdissection, we analyzed tapetal cells, meiocytes and other somatic cells at several stages of anther development to establish the timing of 24-PHAS precursor transcripts and the 24-nt phasiRNA products. By integrating RNA and small RNA profiling plus single-molecule and small RNA FISH (smFISH or sRNA-FISH) spatial detection, we demonstrate that the tapetum is the primary site of 24-PHAS precursor and Dcl5 transcripts and the resulting 24-nt phasiRNAs. Interestingly, 24-nt phasiRNAs accumulate in all cell types, with the highest levels in meiocytes, followed by tapetum. Our data support the conclusion that 24-nt phasiRNAs are mobile from tapetum to meiocytes and to other somatic cells. We discuss possible roles for 24-nt phasiRNAs in anther cell types.

Predicting Tissue-Specific mRNA and Protein Abundance in Maize: A Machine Learning Approach

Machine learning and modeling approaches have been used to classify protein sequences for a broad set of tasks including predicting protein function, structure, expression, and localization. Some recent studies have successfully predicted whether a given gene is expressed as mRNA or even translated to proteins potentially, but given that not all genes are expressed in every condition and tissue, the challenge remains to predict condition-specific expression. To address this gap, we developed a machine learning approach to predict tissue-specific gene expression across 23 different tissues in maize, solely based on DNA promoter and protein sequences. For class labels, we defined high and low expression levels for mRNA and protein abundance and optimized classifiers by systematically exploring various methods and combinations of k-mer sequences in a two-phase approach. In the first phase, we developed Markov model classifiers for each tissue and built a feature vector based on the predictions. In the second phase, the feature vector was used as an input to a Bayesian network for final classification. Our results show that these methods can achieve high classification accuracy of up to 95% for predicting gene expression for individual tissues. By relying on sequence alone, our method works in settings where costly experimental data are unavailable and reveals useful insights into the functional, evolutionary, and regulatory characteristics of genes.

Evolutionary and functional characterisation of glutathione peroxidases showed splicing mediated stress responses in Maize

Maize (Zea mays L) is an important cereal with extensive adaptability and multifaceted usages. However, various abiotic and biotic stresses limit the productivity of maize across the globe. Exposure of plant to stresses disturb the balance between reactive oxygen species (ROS) production and scavenging, which subsequently increases cellular damage and death of plants. Tolerant genotypes have evolved higher output of scavenging antioxidative defence compounds (ADCs) during stresses as one of the protective mechanisms. The glutathione peroxidases (GPXs) are the broad class of ADCs family. The plant GPXs catalyse the reduction of hydrogen peroxide (H₂O₂), lipid hydroperoxides and organic hydroperoxides to the corresponding alcohol, and facilitate the regulation of stress tolerance mechanisms. The present investigation was framed to study the maize GPXs using evolutionary and functional analyses. Seven GPX genes with thirteen splice-variants and sixty-three types of cis-acting elements were identified through whole-genome scanning in maize. Evolutionary analysis of GPXs in monocots and dicots revealed mixed and lineage-specific grouping patterns in phylogeny. The expression of ZmGPX splice variants was studied in drought and waterlogging tolerant (L1621701) and sensitive (PML10) genotypes in root and shoot tissues. Further, the differential expression of splice variants of ZmGPX1, ZmGPX3, ZmGPX6 and ZmGPX7 and regulatory network analysis suggested the splicing and regulatory elements mediated stress responses. The present investigation suggests targeting the splicing machinery of GPXs as an approach to enhance the stress tolerance in maize.

Standardized genome-wide function prediction enables comparative functional genomics: a new application area for Gene Ontologies in plants

Background

Genome-wide gene function annotations are useful for hypothesis generation and for prioritizing candidate genes potentially responsible for phenotypes of interest. We functionally annotated the genes of 18 crop plant genomes across 14 species using the GOMAP pipeline.

Results

By comparison to existing GO annotation datasets, GOMAP-generated datasets cover more genes, contain more GO terms, and are similar in quality (based on precision and recall metrics using existing gold standards as the basis for comparison). From there, we sought to determine whether the datasets across multiple species could be used together to carry out comparative functional genomics analyses in plants. To test the idea and as a proof of concept, we created dendrograms of functional relatedness based on terms assigned for all 18 genomes. These dendrograms were compared to well-established species-level evolutionary phylogenies to determine whether trees derived were in agreement with known evolutionary relationships, which they largely are. Where discrepancies were observed, we determined branch support based on jackknifing then removed individual annotation sets by genome to identify the annotation sets causing unexpected relationships.

Conclusions

GOMAP-derived functional annotations used together across multiple species generally retain sufficient biological signal to recover known phylogenetic relationships based on genome-wide functional similarities, indicating that comparative functional genomics across species based on GO data holds promise for generating novel hypotheses about comparative gene function and traits.

A cascade of bHLH-regulated pathways programs maize anther development

The spatiotemporal development of somatic tissues of the anther lobe is necessary for successful fertile pollen production. This process is mediated by many transcription factors acting through complex, multi-layered networks. Here, our analysis of functional knockout mutants of interacting basic helix-loop-helix genes Ms23, Ms32, basic helix-loop-helix 122 (bHLH122), and bHLH51 in maize (Zea mays) established that male fertility requires all four genes, expressed sequentially in the tapetum (TP). Not only do they regulate each other, but also they encode proteins that form heterodimers that act collaboratively to guide many cellular processes at specific developmental stages. MS23 is confirmed to be the master factor, as the ms23 mutant showed the earliest developmental defect, cytologically visible in the TP, with the most drastic alterations in premeiotic gene expression observed in ms23 anthers. Notably, the male-sterile ms23, ms32, and bhlh122-1 mutants lack 24-nt phased secondary small interfering RNAs (phasiRNAs) and the precursor transcripts from the corresponding 24-PHAS loci, while the bhlh51-1 mutant has wild-type levels of both precursors and small RNA products. Multiple lines of evidence suggest that 24-nt phasiRNA biogenesis primarily occurs downstream of MS23 and MS32, both of which directly activate Dcl5 and are required for most 24-PHAS transcription, with bHLH122 playing a distinct role in 24-PHAS transcription.

Subcellular Proteomics as a Unified Approach of Experimental Localizations and Computed Prediction Data for Arabidopsis and Crop Plants

In eukaryotic organisms, subcellular protein location is critical in defining protein function and understanding sub-functionalization of gene families. Some proteins have defined locations, whereas others have low specificity targeting and complex accumulation patterns. There is no single approach that can be considered entirely adequate for defining the in vivo location of all proteins. By combining evidence from different approaches, the strengths and weaknesses of different technologies can be estimated, and a location consensus can be built. The Subcellular Location of Proteins in Arabidopsis database ( http://suba.live/ ) combines experimental data sets that have been reported in the literature and is analyzing these data to provide useful tools for biologists to interpret their own data. Foremost among these tools is a consensus classifier (SUBAcon) that computes a proposed location for all proteins based on balancing the experimental evidence and predictions. Further tools analyze sets of proteins to define the abundance of cellular structures. Extending these types of resources to plant crop species has been complex due to polyploidy, gene family expansion and contraction, and the movement of pathways and processes within cells across the plant kingdom. The Crop Proteins of Annotated Location database ( http://crop-pal.org/ ) has developed a range of subcellular location resources including a species-specific voting consensus for 12 plant crop species that offers collated evidence and filters for current crop proteomes akin to SUBA. Comprehensive cross-species comparison of these data shows that the sub-cellular proteomes (subcellulomes) depend only to some degree on phylogenetic relationship and are more conserved in major biosynthesis than in metabolic pathways. Together SUBA and cropPAL created reference subcellulomes for plants as well as species-specific subcellulomes for cross-species data mining. These data collections are increasingly used by the research community to provide a subcellular protein location layer, inform models of compartmented cell function and protein-protein interaction network, guide future molecular crop breeding strategies, or simply answer a specific question-where is my protein of interest inside the cell?

PlantGF: an analysis and annotation platform for plant gene families

Gene families contain genes that come from the same ancestor and have similar sequences and structures. They perform certain specific functions within and among different species. Currently, there is no complete process or platform for the rapid analysis of plant gene families. In this study, a comprehensive query and analysis platform of plant gene families, the Plant Gene Family Platform (PlantGF), was constructed. The platform is composed of four main parts: Search, Tools, Statistics and Auxiliary. A total of 2 909 580 gene family members were identified from 138 plant species in PlantGF. The data can be queried in the Search section through a user-friendly interface. A general process for gene family analysis, having nine steps, is provided. The platform also includes four online tools (HMM-Search, BLAST, MAFFT and HMMER) in the Tools section for useful additional analyses. The statistical analysis of the relevant gene families is shown on the Statistics page. Auxiliary pages are provided for data downloading. The datasets for all 138 plant species' protein sequences and their gene families can be acquired on the Download page. A user's manual and some useful links are displayed on the Manual and Links pages, respectively. To the best of our knowledge, PlantGF is the first comprehensive platform for studying plant gene families, and it will make important contributions to plant gene family-related research. Database URL: http://biodb.sdau.edu.cn/PGF/index.html.

Evaluation of gene flow and genetic diversity in rice accessions across Kurdistan region-iraq using SSR markers

BACKGROUND

In recent years, farmers have complained that the only way to obtain seeds is to select plants that show good performance under local climate conditions in the region. This study aimed to investigate the diversity of rice accessions grown in the region to build a breeding program. METHODS AND RESULTS: A total of 62 accessions of rice from farmers and research stations were collected from the Kurdistan region, including short-grain and long-grain types, for molecular genetics and diversity analysis. In this study, 37 polymorphic simple sequence repeat (SSR) markers were selected with several molecular genetics software programs. The results show that these SSR markers are very effective for this investigation, generating a total of 152 observed alleles (Na), 75.166 Effective number of alleles (Ne) and an average of 4.1 and 2.03 alleles per locus, respectively. The average polymorphic information content (PIC) per locus was recorded as 0.404. The research presented here confirms two subpopulations, japonica (C1 and C2) and indica (C3), based on molecular genetics data analysis. Analysis of molecular variance revealed that the 72% variance was due to the variation among populations and 28% within the population.

CONCLUSIONS

Altogether, these results indicate that there is very low gene flow. These results show the importance of the study of genetic diversity and relationships for starting breeding and improvement programs for rice in the Kurdistan region.

Progress and challenges in sorghum biotechnology, a multipurpose feedstock for the bioeconomy

Sorghum [Sorghum bicolor (L.) Moench] is the fifth most important cereal crop globally by harvested area and production. Its drought and heat tolerance allow high yields with minimal input. It is a promising biomass crop for the production of biofuels and bioproducts. In addition, as an annual diploid with a relatively small genome compared with other C4 grasses, and excellent germplasm diversity, sorghum is an excellent research species for other C4 crops such as maize. As a result, an increasing number of researchers are looking to test the transferability of findings from other organisms such as Arabidopsis thaliana and Brachypodium distachyon to sorghum, as well as to engineer new biomass sorghum varieties. Here, we provide an overview of sorghum as a multipurpose feedstock crop which can support the growing bioeconomy, and as a monocot research model system. We review what makes sorghum such a successful crop and identify some key traits for future improvement. We assess recent progress in sorghum transformation and highlight how transformation limitations still restrict its widespread adoption. Finally, we summarize available sorghum genetic, genomic, and bioinformatics resources. This review is intended for researchers new to sorghum research, as well as those wishing to include non-food and forage applications in their research.

Gramene: A Resource for Comparative Analysis of Plants Genomes and Pathways

Gramene is an integrated bioinformatics resource for accessing, visualizing, and comparing plant genomes and biological pathways. Originally targeting grasses, Gramene has grown to host annotations for over 90 plant genomes including agronomically important cereals (e.g., maize, sorghum, wheat, teff), fruits and vegetables (e.g., apple, watermelon, clementine, tomato, cassava), specialty crops (e.g., coffee, olive tree, pistachio, almond), and plants of special or emerging interest (e.g., cotton, tobacco, cannabis, or hemp). For some species, the resource includes multiple varieties of the same species, which has paved the road for the creation of species-specific pan-genome browsers. The resource also features plant research models, including Arabidopsis and C4 warm-season grasses and brassicas, as well as other species that fill phylogenetic gaps for plant evolution studies. Its strength derives from the application of a phylogenetic framework for genome comparison and the use of ontologies to integrate structural and functional annotation data. This chapter outlines system requirements for end-users and database hosting, data types and basic navigation within Gramene, and provides examples of how to (1) explore Gramene's search results, (2) explore gene-centric comparative genomics data visualizations in Gramene, and (3) explore genetic variation associated with a gene locus. This is the first publication describing in detail Gramene's integrated search interface-intended to provide a simplified entry portal for the resource's main data categories (genomic location, phylogeny, gene expression, pathways, and external references) to the most complete and up-to-date set of plant genome and pathway annotations.

Genome-wide identification studies - A primer to explore new genes in plant species

Genome data have accumulated rapidly in recent years, doubling roughly after every 6 months due to the influx of next-generation sequencing technologies. A plethora of plant genomes are available in comprehensive public databases. This easy access to data provides an opportunity to explore genome datasets and recruit new genes in various plant species not possible a decade ago. In the past few years, many gene families have been published using these public datasets. These genome-wide studies identify and characterize gene members, gene structures, evolutionary relationships, expression patterns, protein interactions and gene ontologies, and predict putative gene functions using various computational tools. Such studies provide meaningful information and an initial framework for further functional elucidation. This review provides a concise layout of approaches used in these gene family studies and demonstrates an outline for employing various plant genome datasets in future studies.

Scripting Analyses of Genomes in Ensembl Plants

Ensembl Plants ( http://plants.ensembl.org ) offers genome-scale information for plants, with four releases per year. As of release 47 (April 2020) it features 79 species and includes genome sequence, gene models, and functional annotation. Comparative analyses help reconstruct the evolutionary history of gene families, genomes, and components of polyploid genomes. Some species have gene expression baseline reports or variation across genotypes. While the data can be accessed through the Ensembl genome browser, here we review specifically how our plant genomes can be interrogated programmatically and the data downloaded in bulk. These access routes are generally consistent across Ensembl for other non-plant species, including plant pathogens, pests, and pollinators.

CerealsDB: A Whistle-Stop Tour of an Open Access SNP Resource

The CerealsDB website, created by members of the Functional Genomics Group at the University of Bristol, provides access to a database containing SNP and genotyping data for hexaploid wheat and, to a lesser extent, its progenitors and several of its relatives. The site is principally aimed at plant breeders and research scientists who wish to obtain information regarding SNP markers; for example, obtain primers used for their identification or the sequences upon which they are based. The database underpinning the website contains circa one million putative varietal SNPs of which several hundreds of thousands have been experimentally validated on a range of common genotyping platforms. For each SNP marker, the site also hosts the allelic scores for thousands of elite wheat varieties, landrace cultivars, and wheat relatives. Tools are available to help negotiate and visualize the datasets. The website has been designed to be simple and straightforward to use and is completely open access.

AgroLD: A Knowledge Graph Database for Plant Functional Genomics

Recent advances in high-throughput technologies have resulted in tremendous increase in the amount of data in the agronomic domain. There is an urgent need to effectively integrate complementary information to understand the biological system in its entirety. We have developed AgroLD, a knowledge graph that exploits the Semantic Web technology and some of the relevant standard domain ontologies, to integrate information on plant species and in this way facilitating the formulation of new scientific hypotheses. This chapter outlines some integration results of the project, which initially focused on genomics, proteomics and phenomics.

QTL mapping for bioenergy traits in sweet sorghum recombinant inbred lines

During the past decade, sweet sorghum (Sorghum bicolor Moench L.) has shown great potential for bioenergy production, especially biofuels. In this study, 223 recombinant inbred lines (RILs) derived from a cross between two sweet sorghum lines (Brandes × Wray) were evaluated in three trials. Single-nucleotide polymorphisms (SNPs) derived from genotyping by sequencing of 272 RILs were used to build a high-density genetic map comprising 3,767 SNPs spanning 1,368.83 cM. Multitrait multiple interval mapping (MT-MIM) was carried out to map quantitative trait loci (QTL) for eight bioenergy traits. A total of 33 QTLs were identified for flowering time, plant height, total soluble solids and sucrose (five QTLs each), fibers (four QTLs), and fresh biomass yield, juice extraction yield, and reducing sugars (three QTLs each). QTL hotspots were found on chromosomes 1, 3, 6, 9, and 10, in addition to other QTLs detected on chromosomes 4 and 8. We observed that 14 out of the 33 mapped QTLs were found in all three trials. Upon further development and validation in other crosses, the results provided by the present study have a great potential to be used in marker-assisted selection in sorghum breeding programs for biofuel production.

LIRBase: a comprehensive database of long inverted repeats in eukaryotic genomes

Small RNAs (sRNAs) constitute a large portion of functional elements in eukaryotic genomes. Long inverted repeats (LIRs) can be transcribed into long hairpin RNAs (hpRNAs), which can further be processed into small interfering RNAs (siRNAs) with vital biological roles. In this study, we systematically identified a total of 6 619 473 LIRs in 424 eukaryotic genomes and developed LIRBase (https://venyao.xyz/lirbase/), a specialized database of LIRs across different eukaryotic genomes aiming to facilitate the annotation and identification of LIRs encoding long hpRNAs and siRNAs. LIRBase houses a comprehensive collection of LIRs identified in a wide range of eukaryotic genomes. In addition, LIRBase not only allows users to browse and search the identified LIRs in any eukaryotic genome(s) of interest available in GenBank, but also provides friendly web functionalities to facilitate users to identify LIRs in user-uploaded sequences, align sRNA sequencing data to LIRs, perform differential expression analysis of LIRs, predict mRNA targets for LIR-derived siRNAs, and visualize the secondary structure of candidate long hpRNAs encoded by LIRs. As demonstrated by two case studies, collectively, LIRBase bears the great utility for systematic investigation and characterization of LIRs and functional exploration of potential roles of LIRs and their derived siRNAs in diverse species.

Fine Mapping of a New Major QTL-qGLS8 for Gray Leaf Spot Resistance in Maize

Gray leaf spot (GLS), caused by different species of Cercospora, is a fungal, non-soil-borne disease that causes serious reductions in maize yield worldwide. The identification of major quantitative trait loci (QTLs) for GLS resistance in maize is essential for developing marker-assisted selection strategies in maize breeding. Previous research found a significant difference (P < 0.01) in GLS resistance between T32 (highly resistant) and J51 (highly susceptible) genotypes of maize. Initial QTL analysis was conducted in an F_{2 : 3} population of 189 individuals utilizing genetic maps that were constructed using 181 simple sequence repeat (SSR) markers. One QTL (qGLS8) was detected, defined by the markers umc1130 and umc2354 in three environments. The qGLS8 QTL detected in the initial analysis was located in a 51.96-Mb genomic region of chromosome 8 and explained 7.89-14.71% of the phenotypic variation in GLS resistance in different environments. We also developed a near isogenic line (NIL) BC₃F₂ population with 1,468 individuals and a BC₃F₂-Micro population with 180 individuals for fine mapping. High-resolution genetic and physical maps were constructed using six newly developed SSRs. The QTL-qGLS8 was narrowed down to a 124-kb region flanked by the markers ym20 and ym51 and explained up to 17.46% of the phenotypic variation in GLS resistance. The QTL-qGLS8 contained seven candidate genes, such as an MYB-related transcription factor 24 and a C ₃ H transcription factor 347), and long intergenic non-coding RNAs (lincRNAs). The present study aimed to provide a foundation for the identification of candidate genes for GLS resistance in maize.

HITAC-seq enables high-throughput cost-effective sequencing of plasmids and DNA fragments with identity

DNA sequencing is vital for many aspects of biological research and diagnostics. Despite the development of second and third generation sequencing technologies, Sanger sequencing has long been the only choice when required to precisely track each sequenced plasmids or DNA fragments. Here, we report a complete set of novel barcoding and assembling system, Highly-parallel Indexed Tagmentation-reads Assembled Consensus sequencing (HITAC-seq), that could massively sequence and track the identities of each individual sequencing sample. With the cost of much less than that of single read of Sanger sequencing, HITAC-seq can generate high-quality contiguous sequences of up to 10 kilobases or longer. The capability of HITAC-seq was confirmed through large-scale sequencing of thousands of plasmid clones and hundreds of amplicon fragments using approximately 100 pg of input DNAs. Due to its long synthetic length, HITAC-seq was effective in detecting relatively large structural variations, as demonstrated by the identification of a ∼1.3 kb Copia retrotransposon insertion in the upstream of a likely maize domestication gene. Besides being a practical alternative to traditional Sanger sequencing, HITAC-seq is suitable for many high-throughput sequencing and genotyping applications.

Megabase-scale presence-absence variation with Tripsacum origin was under selection during maize domestication and adaptation

BACKGROUND

Structural variants (SVs) significantly drive genome diversity and environmental adaptation for diverse species. Unlike the prevalent small SVs (< kilobase-scale) in higher eukaryotes, large-size SVs rarely exist in the genome, but they function as one of the key evolutionary forces for speciation and adaptation.

RESULTS

In this study, we discover and characterize several megabase-scale presence-absence variations (PAVs) in the maize genome. Surprisingly, we identify a 3.2 Mb PAV fragment that shows high integrity and is present as complete presence or absence in the natural diversity panel. This PAV is embedded within the nucleolus organizer region (NOR), where the suppressed recombination is found to maintain the PAV against the evolutionary variation. Interestingly, by analyzing the sequence of this PAV, we not only reveal the domestication trace from teosinte to modern maize, but also the footprints of its origin from Tripsacum, shedding light on a previously unknown contribution from Tripsacum to the speciation of Zea species. The functional consequence of the Tripsacum segment migration is also investigated, and environmental fitness conferred by the PAV may explain the whole segment as a selection target during maize domestication and improvement.

CONCLUSIONS

These findings provide a novel perspective that Tripsacum contributes to Zea speciation, and also instantiate a strategy for evolutionary and functional analysis of the "fossil" structure variations during genome evolution and speciation.

Using precision phenotyping to inform de novo domestication

An update on the use of precision phenotyping to assess the potential of lesser cultivated species as candidates for de novo domestication or similar development for future agriculture.

PyRice: a Python package for querying Oryza sativa databases

SUMMARY

Currently, gene information available for Oryza sativa species is located in various online heterogeneous data sources. Moreover, methods of access are also diverse, mostly web-based and sometimes query APIs, which might not always be straightforward for domain experts. The challenge is to collect information quickly from these applications and combine it logically, to facilitate scientific research. We developed a Python package named PyRice, a unified programing API to access all supported databases at the same time with consistent output. PyRice design is modular and implements a smart query system, which fits the computing resources to optimize the query speed. As a result, PyRice is easy to use and produces intuitive results.

AVAILABILITY AND IMPLEMENTATION

https://github.com/SouthGreenPlatform/PyRice.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Gene Ontology Meta Annotator for Plants (GOMAP)

Annotating gene structures and functions to genome assemblies is necessary to make assembly resources useful for biological inference. Gene Ontology (GO) term assignment is the most used functional annotation system, and new methods for GO assignment have improved the quality of GO-based function predictions. The Gene Ontology Meta Annotator for Plants (GOMAP) is an optimized, high-throughput, and reproducible pipeline for genome-scale GO annotation of plants. We containerized GOMAP to increase portability and reproducibility and also optimized its performance for HPC environments. Here we report on the pipeline's availability and performance for annotating large, repetitive plant genomes and describe how GOMAP was used to annotate multiple maize genomes as a test case. Assessment shows that GOMAP expands and improves the number of genes annotated and annotations assigned per gene as well as the quality (based on [Formula: see text]) of GO assignments in maize. GOMAP has been deployed to annotate other species including wheat, rice, barley, cotton, and soy. Instructions and access to the GOMAP Singularity container are freely available online at https://bioinformapping.com/gomap/ . A list of annotated genomes and links to data is maintained at https://dill-picl.org/projects/gomap/ .

Metabolic networks of the Nicotiana genus in the spotlight: content, progress and outlook

Manually curated metabolic databases residing at the Sol Genomics Network comprise two taxon-specific databases for the Solanaceae family, i.e. SolanaCyc and the genus Nicotiana, i.e. NicotianaCyc as well as six species-specific databases for Nicotiana tabacum TN90, N. tabacum K326, Nicotiana benthamiana, N. sylvestris, N. tomentosiformis and N. attenuata. New pathways were created through the extraction, examination and verification of related data from the literature and the aid of external database guided by an expert-led curation process. Here we describe the curation progress that has been achieved in these databases since the first release version 1.0 in 2016, the curation flow and the curation process using the example metabolic pathway for cholesterol in plants. The current content of our databases comprises 266 pathways and 36 superpathways in SolanaCyc and 143 pathways plus 21 superpathways in NicotianaCyc, manually curated and validated specifically for the Solanaceae family and Nicotiana genus, respectively. The curated data have been propagated to the respective Nicotiana-specific databases, which resulted in the enrichment and more accurate presentation of their metabolic networks. The quality and coverage in those databases have been compared with related external databases and discussed in terms of literature support and metabolic content.

Underpinning the molecular programming attributing heat stress associated thermotolerance in tea (Camellia sinensis (L.) O. Kuntze)

The most daunting issue of global climate change is the deleterious impact of extreme temperatures on tea productivity and quality, which has resulted in a quest among researchers and growers. The current study aims to unravel molecular programming underpinning thermotolerance by characterizing heat tolerance and sensitivity response in 20 tea cultivars. The significantly higher negative influence of heat stress was recorded in a sensitive cultivar with reduced water retention (47%), chlorophyll content (33.79%), oxidation potential (32.48%), and increase in membrane damage (76.4%). Transcriptional profiling of most tolerant and sensitive cultivars identified 78 differentially expressed unigenes with chaperon domains, including low and high molecular weight heat shock protein (HSP) and heat shock transcription factors (HSFs) involved in heat shock response (HSR). Further, predicted transcriptional interactome network revealed their key role in thermotolerance via well-co-ordinated transcriptional regulation of aquaporins, starch metabolism, chlorophyll biosynthesis, calcium, and ethylene mediated plant signaling system. The study identified the key role of HSPs (CsHSP90) in regulating HSR in tea, wherein, structure-based molecular docking revealed the inhibitory role of geldanamycin (GDA) on CsHSP90 by blocking ATP binding site at N-terminal domain of predicted structure. Subsequently, GDA mediated leaf disc inhibitor assay further affirmed enhanced HSR with higher expression of CsHSP17.6, CsHSP70, HSP101, and CsHSFA2 genes in tea. Through the current study, efforts were made to extrapolate a deeper understanding of chaperons mediated regulation of HSR attributing thermotolerance in tea.

FINDER: an automated software package to annotate eukaryotic genes from RNA-Seq data and associated protein sequences

BACKGROUND

Gene annotation in eukaryotes is a non-trivial task that requires meticulous analysis of accumulated transcript data. Challenges include transcriptionally active regions of the genome that contain overlapping genes, genes that produce numerous transcripts, transposable elements and numerous diverse sequence repeats. Currently available gene annotation software applications depend on pre-constructed full-length gene sequence assemblies which are not guaranteed to be error-free. The origins of these sequences are often uncertain, making it difficult to identify and rectify errors in them. This hinders the creation of an accurate and holistic representation of the transcriptomic landscape across multiple tissue types and experimental conditions. Therefore, to gauge the extent of diversity in gene structures, a comprehensive analysis of genome-wide expression data is imperative.

RESULTS

We present FINDER, a fully automated computational tool that optimizes the entire process of annotating genes and transcript structures. Unlike current state-of-the-art pipelines, FINDER automates the RNA-Seq pre-processing step by working directly with raw sequence reads and optimizes gene prediction from BRAKER2 by supplementing these reads with associated proteins. The FINDER pipeline (1) reports transcripts and recognizes genes that are expressed under specific conditions, (2) generates all possible alternatively spliced transcripts from expressed RNA-Seq data, (3) analyzes read coverage patterns to modify existing transcript models and create new ones, and (4) scores genes as high- or low-confidence based on the available evidence across multiple datasets. We demonstrate the ability of FINDER to automatically annotate a diverse pool of genomes from eight species.

CONCLUSIONS

FINDER takes a completely automated approach to annotate genes directly from raw expression data. It is capable of processing eukaryotic genomes of all sizes and requires no manual supervision-ideal for bench researchers with limited experience in handling computational tools.

Genome-wide analysis of plant miRNA action clarifies levels of regulatory dynamics across developmental contexts

Development of complex organisms requires the delicate and dynamic spatiotemporal regulation of gene expression. Central to this are microRNAs (miRNAs). These mobile small RNAs offer specificity in conveying positional information and versatility in patterning the outcomes of gene expression. However, the parameters that shape miRNA output during development are still to be clarified. Here, we address this question on a genome-wide scale, using the maize shoot apex as a model. We show that patterns and levels of miRNA accumulation are largely determined at the transcriptional level, but are finessed post-transcriptionally in a tissue-dependent manner. The stem cell environments of the shoot apical meristem and vasculature appear particularly liable to this. Tissue-specific effects are also apparent at the level of target repression, with target cleavage products in the vasculature exceeding those of other tissues. Our results argue against a clearance mode of regulation purely at the level of transcript cleavage, leading us to propose that transcript cleavage provides a baseline level of target repression, onto which miRNA-driven translational repression can act to toggle the mode of target regulation between clearance and rheostat. Our data show how the inherent complexities of miRNA pathways allow the accumulation and activity of these small RNAs to be tailored in space and time to bring about the gene expression versatility needed during development.

Beyond gene ontology (GO): using biocuration approach to improve the gene nomenclature and functional annotation of rice S-domain kinase subfamily

The S-domain subfamily of receptor-like kinases (SDRLKs) in plants is poorly characterized. Most members of this subfamily are currently assigned gene function based on the S-locus Receptor Kinase from Brassica that acts as the female determinant of self-incompatibility (SI). However, Brassica like SI mechanisms does not exist in most plants. Thus, automated Gene Ontology (GO) pipelines are not sufficient for functional annotation of SDRLK subfamily members and lead to erroneous association with the GO biological process of SI. Here, we show that manual bio-curation can help to correct and improve the gene annotations and association with relevant biological processes. Using publicly available genomic and transcriptome datasets, we conducted a detailed analysis of the expansion of the rice (Oryza sativa) SDRLK subfamily, the structure of individual genes and proteins, and their expression.The 144-member SDRLK family in rice consists of 82 receptor-like kinases (RLKs) (67 full-length, 15 truncated),12 receptor-like proteins, 14 SD kinases, 26 kinase-like and 10 GnK2 domain-containing kinases and RLKs. Except for nine genes, all other SDRLK family members are transcribed in rice, but they vary in their tissue-specific and stress-response expression profiles. Furthermore, 98 genes show differential expression under biotic stress and 98 genes show differential expression under abiotic stress conditions, but share 81 genes in common.Our analysis led to the identification of candidate genes likely to play important roles in plant development, pathogen resistance, and abiotic stress tolerance. We propose a nomenclature for 144 SDRLK gene family members based on gene/protein conserved structural features, gene expression profiles, and literature review. Our biocuration approach, rooted in the principles of findability, accessibility, interoperability and reusability, sets forth an example of how manual annotation of large-gene families can fill in the knowledge gap that exists due to the implementation of automated GO projections, thereby helping to improve the quality and contents of public databases.

Characterization of CHARK, an unusual cytokinin receptor of rice

The signal transduction of the plant hormone cytokinin is mediated by a His-to-Asp phosphorelay. The canonical cytokinin receptor consists of an extra cytoplasmic hormone binding domain named cyclase/histidine kinase associated sensory extracellular (CHASE) and cytoplasmic histidine kinase and receiver domains. In addition to classical cytokinin receptors, a different type receptor—named CHASE domain receptor serine/threonine kinase (CHARK)—is also present in rice. It contains the same ligand binding domain as other cytokinin receptors but has a predicted Ser/Thr—instead of a His-kinase domain. Bioinformatic analysis indicates that CHARK is a retrogene and a product of trans-splicing. Here, we analyzed whether CHARK can function as a bona fide cytokinin receptor. A biochemical assay demonstrated its ability to bind cytokinin. Transient expression of CHARK in protoplasts increased their response to cytokinin. Expression of CHARK in an Arabidopsis receptor double mutant complemented its growth defects and restored the ability to activate cytokinin response genes, clearly demonstrating that CHARK functions as a cytokinin receptor. We propose that the CHARK gene presents an evolutionary novelty in the cytokinin signaling system.

Gramene 2021: harnessing the power of comparative genomics and pathways for plant research

Gramene (http://www.gramene.org), a knowledgebase founded on comparative functional analyses of genomic and pathway data for model plants and major crops, supports agricultural researchers worldwide. The resource is committed to open access and reproducible science based on the FAIR data principles. Since the last NAR update, we made nine releases; doubled the genome portal's content; expanded curated genes, pathways and expression sets; and implemented the Domain Informational Vocabulary Extraction (DIVE) algorithm for extracting gene function information from publications. The current release, #63 (October 2020), hosts 93 reference genomes-over 3.9 million genes in 122 947 families with orthologous and paralogous classifications. Plant Reactome portrays pathway networks using a combination of manual biocuration in rice (320 reference pathways) and orthology-based projections to 106 species. The Reactome platform facilitates comparison between reference and projected pathways, gene expression analyses and overlays of gene-gene interactions. Gramene integrates ontology-based protein structure-function annotation; information on genetic, epigenetic, expression, and phenotypic diversity; and gene functional annotations extracted from plant-focused journals using DIVE. We train plant researchers in biocuration of genes and pathways; host curated maize gene structures as tracks in the maize genome browser; and integrate curated rice genes and pathways in the Plant Reactome.

Gramene 2021: harnessing the power of comparative genomics and pathways for plant research

Gramene (http://www.gramene.org), a knowledgebase founded on comparative functional analyses of genomic and pathway data for model plants and major crops, supports agricultural researchers worldwide. The resource is committed to open access and reproducible science based on the FAIR data principles. Since the last NAR update, we made nine releases; doubled the genome portal's content; expanded curated genes, pathways and expression sets; and implemented the Domain Informational Vocabulary Extraction (DIVE) algorithm for extracting gene function information from publications. The current release, #63 (October 2020), hosts 93 reference genomes-over 3.9 million genes in 122 947 families with orthologous and paralogous classifications. Plant Reactome portrays pathway networks using a combination of manual biocuration in rice (320 reference pathways) and orthology-based projections to 106 species. The Reactome platform facilitates comparison between reference and projected pathways, gene expression analyses and overlays of gene-gene interactions. Gramene integrates ontology-based protein structure-function annotation; information on genetic, epigenetic, expression, and phenotypic diversity; and gene functional annotations extracted from plant-focused journals using DIVE. We train plant researchers in biocuration of genes and pathways; host curated maize gene structures as tracks in the maize genome browser; and integrate curated rice genes and pathways in the Plant Reactome.

Two cyclin Bs are differentially modulated by glucose and sucrose during maize germination

Cell proliferation during seed germination is determinant for an appropriate seedling establishment. The present work aimed to evaluate the participation of two maize B-type Cyclins during germination and under the stimulus of two simple sugars: sucrose and glucose. We found out that the corresponding genes, ZmCycB1;2 and ZmCycB2;1, increased their expression at 24 h of germination, but only ZmCycB1;2 responded negatively to sugar type at the highest sugar concentration tested (120 mM). Also, CycB1;2 showed differential protein levels along germination in response to sugar, or its absence. Both CycBs interacted with CDKA;1 and CDKB1;1 by pull down assays. By an immunoprecipitation approach, it was found that each CycB associated with two CDKB isoforms (34 and 36 kDa). A higher proportion of CycB1;2-CDKB-36kDa was coincident to an increased kinase activity in the presence of sugar and particularly in glucose treatment at 36 h of imbibition. CycB1;2-CDKB activity increased in parallel to germination advance and this was dependent on sugar: glucose > sucrose > No sugar treatment. At RAM, CycB1;2 was more abundant in nuclei on Glucose at late germination; DNA-CycB1;2 colocalization was parallel to CycB1;2 inside the nucleus. Overall, results point out CycB1;2 as a player on promoting proliferation during germination by binding a specific CDKB isoform partner and changing its cellular localization to nuclei, co-localizing with DNA, being glucose a triggering signal.

Single-Cell Transcriptome Analysis in Plants: Advances and Challenges

The rapid and enthusiastic adoption of single-cell RNA sequencing (scRNA-seq) has demonstrated that this technology is far more than just another way to perform transcriptome analysis. It is not an exaggeration to say that the advent of scRNA-seq is revolutionizing the details of whole-transcriptome snapshots from a tissue to a cell. With this disruptive technology, it is now possible to mine heterogeneity between tissue types and within cells like never before. This enables more rapid identification of rare and novel cell types, simultaneous characterization of multiple different cell types and states, more accurate and integrated understanding of their roles in life processes, and more. However, we are only at the beginning of unlocking the full potential of scRNA-seq applications. This is particularly true for plant sciences, where single-cell transcriptome profiling is in its early stage and has many exciting challenges to overcome. In this review, we compare and evaluate recent pioneering studies using the Arabidopsis root model, which has established new paradigms for scRNA-seq studies in plants. We also explore several new and promising single-cell analysis tools that are available to those wishing to study plant development and physiology at unprecedented resolution and scale. In addition, we propose some future directions on the use of scRNA-seq technology to tackle some of the critical challenges in plant research and breeding.

Resistance and susceptibility QTL identified in a rice MAGIC population by screening with a minor-effect virulence factor from Xanthomonas oryzae pv. oryzae

Effective and durable disease resistance for bacterial blight (BB) of rice is a continuous challenge due to the evolution and adaptation of the pathogen, Xanthomonas oryzae pv. oryzae (Xoo), on cultivated rice varieties. Fundamental to this pathogens' virulence is transcription activator-like (TAL) effectors that activate transcription of host genes and contribute differently to pathogen virulence, fitness or both. Host plant resistance is predicted to be more durable if directed at strategic virulence factors that impact both pathogen virulence and fitness. We characterized Tal7b, a minor-effect virulence factor that contributes incrementally to pathogen virulence in rice, is a fitness factor to the pathogen and is widely present in geographically diverse strains of Xoo. To identify sources of resistance to this conserved effector, we used a highly virulent strain carrying a plasmid borne copy of Tal7b to screen an indica multi-parent advanced generation inter-cross (MAGIC) population. Of 18 QTL revealed by genome-wide association studies and interval mapping analysis, six were specific to Tal7b (qBB-tal7b). Overall, 150 predicted Tal7b gene targets overlapped with qBB-tal7b QTL. Of these, 21 showed polymorphisms in the predicted effector binding element (EBE) site and 23 lost the EBE sequence altogether. Inoculation and bioinformatics studies suggest that the Tal7b target in one of the Tal7b-specific QTL, qBB-tal7b-8, is a disease susceptibility gene and that the resistance mechanism for this locus may be through loss of susceptibility. Our work demonstrates that minor-effect virulence factors significantly contribute to disease and provide a potential new approach to identify effective disease resistance.

Comparative Transcriptome Analysis of Iron and Zinc Deficiency in Maize (Zea mays L.)

Globally, one-third of the population is affected by iron (Fe) and zinc (Zn) deficiency, which is severe in developing and underdeveloped countries where cereal-based diets predominate. The genetic biofortification approach is the most sustainable and one of the cost-effective ways to address Fe and Zn malnutrition. Maize is a major source of nutrition in sub-Saharan Africa, South Asia and Latin America. Understanding systems’ biology and the identification of genes involved in Fe and Zn homeostasis facilitate the development of Fe- and Zn-enriched maize. We conducted a genome-wide transcriptome assay in maize inbred SKV616, under –Zn, –Fe and –Fe–Zn stresses. The results revealed the differential expression of several genes related to the mugineic acid pathway, metal transporters, photosynthesis, phytohormone and carbohydrate metabolism. We report here Fe and Zn deficiency-mediated changes in the transcriptome, root length, stomatal conductance, transpiration rate and reduced rate of photosynthesis. Furthermore, the presence of multiple regulatory elements and/or the co-factor nature of Fe and Zn in enzymes indicate their association with the differential expression and opposite regulation of several key gene(s). The differentially expressed candidate genes in the present investigation would help in breeding for Fe and Zn efficient and kernel Fe- and Zn-rich maize cultivars through gene editing, transgenics and molecular breeding.

Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks

Over the last decade, a giant leap forward has been made in resolving the main bottleneck in metabolomics, i.e., the structural characterization of the many unknowns. This has led to the next challenge in this research field: retrieving biochemical pathway information from the various types of networks that can be constructed from metabolome data. Searching putative biochemical pathways, referred to as biotransformation paths, is complicated because several flaws occur during the construction of metabolome networks. Multiple network analysis tools have been developed to deal with these flaws, while in silico retrosynthesis is appearing as an alternative approach. In this review, the different types of metabolome networks, their flaws, and the various tools to trace these biotransformation paths are discussed.

Leveraging computational genomics to understand the molecular basis of metal homeostasis

Genome-based data is helping to reveal the diverse strategies plants and algae use to maintain metal homeostasis. In addition to acquisition, distribution and storage of metals, acclimating to feast or famine can involve a wealth of genes that we are just now starting to understand. The fast-paced acquisition of genome-based data, however, is far outpacing our ability to experimentally characterize protein function. Computational genomic approaches are needed to fill the gap between what is known and unknown. To avoid misconstruing bioinformatically derived data, which is the root cause of the inaccurate functional annotations that plague databases, functional inferences from diverse sources and contextualization of that evidence with a robust understanding of protein family evolution is needed. Phylogenomic- and comparative-genomic-based studies can aid in the interpretation of experimental data or provide a spark for the discovery of a new function. These analyses not only lead to novel insight into a target protein's function but can generate thought-provoking insights across protein families.

Amino Acid and Carbohydrate Metabolism Are Coordinated to Maintain Energetic Balance during Drought in Sugarcane

The ability to expand crop plantations without irrigation is a major goal to increase agriculture sustainability. To achieve this end, we need to understand the mechanisms that govern plant growth responses under drought conditions. In this study, we combined physiological, transcriptomic, and genomic data to provide a comprehensive picture of drought and recovery responses in the leaves and roots of sugarcane. Transcriptomic profiling using oligoarrays and RNA-seq identified 2898 (out of 21,902) and 46,062 (out of 373,869) transcripts as differentially expressed, respectively. Co-expression analysis revealed modules enriched in photosynthesis, small molecule metabolism, alpha-amino acid metabolism, trehalose biosynthesis, serine family amino acid metabolism, and carbohydrate transport. Together, our findings reveal that carbohydrate metabolism is coordinated with the degradation of amino acids to provide carbon skeletons to the tricarboxylic acid cycle. This coordination may help to maintain energetic balance during drought stress adaptation, facilitating recovery after the stress is alleviated. Our results shed light on candidate regulatory elements and pave the way to biotechnology strategies towards the development of drought-tolerant sugarcane plants.

TaDrAp1 and TaDrAp2, Partner Genes of a Transcription Repressor, Coordinate Plant Development and Drought Tolerance in Spelt and Bread Wheat

Down-regulator associated protein, DrAp1, acts as a negative cofactor (NC2α) in a transcription repressor complex together with another subunit, down-regulator Dr1 (NC2β). In binding to promotors and regulating the initiation of transcription of various genes, DrAp1 plays a key role in plant transition to flowering and ultimately in seed production. TaDrAp1 and TaDrAp2 genes were identified, and their expression and genetic polymorphism were studied using bioinformatics, qPCR analyses, a 40K Single nucleotide polymorphism (SNP) microarray, and Amplifluor-like SNP genotyping in cultivars of bread wheat (Triticum aestivum L.) and breeding lines developed from a cross between spelt (T. spelta L.) and bread wheat. TaDrAp1 was highly expressed under non-stressed conditions, and at flowering, TaDrAp1 expression was negatively correlated with yield capacity. TaDrAp2 showed a consistently low level of mRNA production. Drought caused changes in the expression of both TaDrAp1 and TaDrAp2 genes in opposite directions, effectively increasing expression in lower yielding cultivars. The microarray 40K SNP assay and Amplifluor-like SNP marker, revealed clear scores and allele discriminations for TaDrAp1 and TaDrAp2 and TaRht-B1 genes. Alleles of two particular homeologs, TaDrAp1-B4 and TaDrAp2-B1, co-segregated with grain yield in nine selected breeding lines. This indicated an important regulatory role for both TaDrAp1 and TaDrAp2 genes in plant growth, ontogenesis, and drought tolerance in bread and spelt wheat.

Transforming the study of organisms: Phenomic data models and knowledge bases

The rapidly decreasing cost of gene sequencing has resulted in a deluge of genomic data from across the tree of life; however, outside a few model organism databases, genomic data are limited in their scientific impact because they are not accompanied by computable phenomic data. The majority of phenomic data are contained in countless small, heterogeneous phenotypic data sets that are very difficult or impossible to integrate at scale because of variable formats, lack of digitization, and linguistic problems. One powerful solution is to represent phenotypic data using data models with precise, computable semantics, but adoption of semantic standards for representing phenotypic data has been slow, especially in biodiversity and ecology. Some phenotypic and trait data are available in a semantic language from knowledge bases, but these are often not interoperable. In this review, we will compare and contrast existing ontology and data models, focusing on nonhuman phenotypes and traits. We discuss barriers to integration of phenotypic data and make recommendations for developing an operationally useful, semantically interoperable phenotypic data ecosystem.

PCOSKBR2: a database of genes, diseases, pathways, and networks associated with polycystic ovary syndrome

PolyCystic Ovary Syndrome KnowledgeBase (PCOSKBR2) is a manually curated database with information on 533 genes, 145 SNPs, 29 miRNAs, 1,150 pathways, and 1,237 diseases associated with PCOS. This data has been retrieved based on evidence gleaned by critically reviewing literature and related records available for PCOS in databases such as KEGG, DisGeNET, OMIM, GO, Reactome, STRING, and dbSNP. Since PCOS is associated with multiple genes and comorbidities, data mining algorithms for comorbidity prediction and identification of enriched pathways and hub genes are integrated in PCOSKBR2, making it an ideal research platform for PCOS. PCOSKBR2 is freely accessible at http://www.pcoskb.bicnirrh.res.in/ .

VviERF6Ls: an expanded clade in Vitis responds transcriptionally to abiotic and biotic stresses and berry development

Background

VviERF6Ls are an uncharacterized gene clade in Vitis with only distant Arabidopsis orthologs. Preliminary data indicated these transcription factors may play a role in berry development and extreme abiotic stress responses. To better understand this highly duplicated, conserved clade, additional members of the clade were identified in four Vitis genotypes. A meta-data analysis was performed on publicly available microarray and RNA-Seq data (confirmed and expanded with RT-qPCR), and Vitis VviERF6L1 overexpression lines were established and characterized with phenotyping and RNA-Seq.

Results

A total of 18 PN40024 VviERF6Ls were identified; additional VviERF6Ls were identified in Cabernet Sauvignon, Chardonnay, and Carménère. The amino acid sequences of VviERF6Ls were found to be highly conserved. VviERF6L transcripts were detected in numerous plant organs and were differentially expressed in response to numerous abiotic stresses including water deficit, salinity, and cold as well as biotic stresses such as red blotch virus, N. parvum, and E. necator. VviERF6Ls were differentially expressed across stages of berry development, peaking in the pre-veraison/veraison stage and retaining conserved expression patterns across different vineyards, years, and Vitis cultivars. Co-expression network analysis identified a scarecrow-like transcription factor and a calmodulin-like gene with highly similar expression profiles to the VviERF6L clade. Overexpression of VviERF6L1 in a Seyval Blanc background did not result in detectable morphological phenotypes. Genes differentially expressed in response to VviERF6L1 overexpression were associated with abiotic and biotic stress responses.

Conclusions

VviERF6Ls represent a large and distinct clade of ERF transcription factors in grapevine. The high conservation of protein sequence between these 18 transcription factors may indicate these genes originate from a duplication event in Vitis. Despite high sequence similarity and similar expression patterns, VviERF6Ls demonstrate unique levels of expression supported by similar but heterogeneous promoter sequences. VviERF6L gene expression differed between Vitis species, cultivars and organs including roots, leaves and berries. These genes respond to berry development and abiotic and biotic stresses. VviERF6L1 overexpression in Vitis vinifera results in differential expression of genes related to phytohormone and immune system signaling. Further investigation of this interesting gene family is warranted.