Using the Arabidopsis Information Resource (TAIR) to Find Information About Arabidopsis Genes

The DBB Family in Populus trichocarpa: Identification, Characterization, Evolution and Expression Profiles

The B-box proteins (BBXs) encode a family of zinc-finger transcription factors that regulate the plant circadian rhythm and early light morphogenesis. The double B-box (DBB) family is in the class of the B-box family, which contains two conserved B-box domains and lacks a CCT (CO, CO-like and TOC1) motif. In this study, the identity, classification, structures, conserved motifs, chromosomal location, cis elements, duplication events, and expression profiles of the PtrDBB genes were analyzed in the woody model plant Populus trichocarpa. Here, 12 PtrDBB genes (PtrDBB1-PtrDBB12) were identified and classified into four distinct groups, and all of them were homogeneously spread among eight out of seventeen poplar chromosomes. The collinearity analysis of the DBB family genes from P. trichocarpa and two other species (Z. mays and A. thaliana) indicated that segmental duplication gene pairs and high-level conservation were identified. The analysis of duplication events demonstrates an insight into the evolutionary patterns of DBB genes. The previously published transcriptome data showed that PtrDBB genes represented distinct expression patterns in various tissues at different stages. In addition, it was speculated that several PtrDBBs are involved in the responsive to drought stress, light/dark, and ABA and MeJA treatments, which implied that they might function in abiotic stress and phytohormone responses. In summary, our results contribute to the further understanding of the DBB family and provide a reference for potential functional studies of PtrDBB genes in P. trichocarpa.

A phosphatase gene is linked to nectar dihydroxyacetone accumulation in mānuka (Leptospermum scoparium)

Floral nectar composition beyond common sugars shows great diversity but contributing genetic factors are generally unknown. Mānuka (Leptospermum scoparium) is renowned for the antimicrobial compound methylglyoxal in its derived honey, which originates from the precursor, dihydroxyacetone (DHA), accumulating in the nectar. Although this nectar trait is highly variable, genetic contribution to the trait is unclear. Therefore, we investigated key gene(s) and genomic regions underpinning this trait. We used RNAseq analysis to identify nectary-associated genes differentially expressed between high and low nectar DHA genotypes. We also used a mānuka high-density linkage map and quantitative trait loci (QTL) mapping population, supported by an improved genome assembly, to reveal genetic regions associated with nectar DHA content. Expression and QTL analyses both pointed to the involvement of a phosphatase gene, LsSgpp2. The expression pattern of LsSgpp2 correlated with nectar DHA accumulation, and it co-located with a QTL on chromosome 4. The identification of three QTLs, some of the first reported for a plant nectar trait, indicates polygenic control of DHA content. We have established plant genetics as a key influence on DHA accumulation. The data suggest the hypothesis of LsSGPP2 releasing DHA from DHA-phosphate and variability in LsSgpp2 gene expression contributing to the trait variability.

Genome-wide identification of the bHLH transcription factor family in Rosa persica and response to low-temperature stress

Background

Basic helix-loop-helix (bHLH) transcription factors are involved in plant growth and development, secondary metabolism, and abiotic stress responses have been studied in a variety of plants. Despite their importance in plant biology, the roles and expression patterns of bHLH family genes in Rosa persica have not been determined.

Methods

In this study, the RbebHLH family genes were systematically analyzed using bioinformatics methods, and their expression patterns under low-temperature stress were analyzed by transcriptome and related physiological index measurements.

Results

In total, 142 RbebHLHs were identified in the genome of R. persica, distributed on seven chromosomes. Phylogenetic analysis including orthologous genes in Arabidopsis divided RbebHLHs into 21 subfamilies, with similar structures and motifs within a subfamily. A collinearity analysis revealed seven tandem duplications and 118 segmental duplications in R. persica and 127, 150, 151, 172, and 164 segmental duplications between R. persica and Arabidopsis thaliana, Prunus mume, Fragaria vesca, Rosa chinensis, and Prunus persica, respectively. A number of cis-regulatory elements associated with abiotic stress response and hormone response were identified in RbebHLHs, and 21 RbebHLHs have potential interactions with the CBF family. In addition, the expression results showed that part of bHLH may regulate the tolerance of R. persica to low-temperature stress through the jasmonic acid and pathway. Transcriptomic data showed that the expression levels of different RbebHLHs varied during overwintering, and the expression of some RbebHLHs was significantly correlated with relative conductivity and MDA content, implying that RbebHLHs play important regulatory roles in R. persica response to low-temperature stress. Overall, this study provides valuable insights into the study of RbebHLHs associated with low-temperature stress.

Mining genes regulating root system architecture in maize based on data integration analysis

KEY MESSAGE

A new strategy that integrated multiple public data resources was established to construct root gene co-expression network and mine genes regulating root system architecture in maize. A root gene co-expression network, containing 13,874 genes, was constructed. A total of 53 root hub genes and 16 priority root candidate genes were identified. One priority root candidate was further functionally verified using overexpression transgenic maize lines. Root system architecture (RSA) is crucial for crops productivity and stress tolerance. In maize, few RSA genes are functionally cloned, and effective discovery of RSA genes remains a great of challenge. In this work, we established a strategy to mine maize RSA genes by integrating functionally characterized root genes, root transcriptome, weighted gene co-expression network analysis (WGCNA) and genome-wide association analysis (GWAS) of RSA traits based on public data resources. A total of 589 maize root genes were collected by searching well-characterized root genes in maize or homologous genes of other species. We performed WGCNA to construct a maize root gene co-expression network containing 13874 genes based on public available root transcriptome data, and further discovered the 53 hub genes related to root traits. In addition, by the prediction function of obtained root gene co-expression network, a total of 1082 new root candidate genes were explored. By further overlapping the obtained new root candidate gene with the root-related GWAS of RSA candidate genes, 16 priority root candidate genes were identified. Finally, a priority root candidate gene, Zm00001d023379 (encodes pyruvate kinase 2), was validated to modulate root open angle and shoot-borne roots number using its overexpression transgenic lines. Our results develop an integration analysis method for effectively exploring regulatory genes of RSA in maize and open a new avenue to mine the candidate genes underlying complex traits.

Deciphering Macromolecular Interactions Involved in Abiotic Stress Signaling: A Review of Bioinformatics Analysis

Plant functioning and responses to abiotic stresses largely involve regulations at the transcriptomic level via complex interactions of signal molecules, signaling cascades, and regulators. Nevertheless, all the signaling networks involved in responses to abiotic stresses have not yet been fully established. The in-depth analysis of transcriptomes in stressed plants has become a relevant state-of-the-art methodology to study these regulations and signaling pathways that allow plants to cope with or attempt to survive abiotic stresses. The plant science and molecular biology community has developed databases about genes, proteins, protein-protein interactions, protein-DNA interactions and ontologies, which are valuable sources of knowledge for deciphering such regulatory and signaling networks. The use of these data and the development of bioinformatics tools help to make sense of transcriptomic data in specific contexts, such as that of abiotic stress signaling, using functional biological approaches. The aim of this chapter is to present and assess some of the essential online tools and resources that will allow novices in bioinformatics to decipher transcriptomic data in order to characterize the cellular processes and functions involved in abiotic stress responses and signaling. The analysis of case studies further describes how these tools can be used to conceive signaling networks on the basis of transcriptomic data. In these case studies, particular attention was paid to the characterization of abiotic stress responses and signaling related to chemical and xenobiotic stressors.

Bioinformatics in Plant Breeding and Research on Disease Resistance

In the context of plant breeding, bioinformatics can empower genetic and genomic selection to determine the optimal combination of genotypes that will produce a desired phenotype and help expedite the isolation of these new varieties. Bioinformatics is also instrumental in collecting and processing plant phenotypes, which facilitates plant breeding. Robots that use automated and digital technologies to collect and analyze different types of information to monitor the environment in which plants grow, analyze the environmental stresses they face, and promptly optimize suboptimal and adverse growth conditions accordingly, have helped plant research and saved human resources. In this paper, we describe the use of various bioinformatics databases and algorithms and explore their potential applications in plant breeding and for research on plant disease resistance.

Genome-wide identification, phylogenetic, and expression analysis under abiotic stress conditions of Whirly (WHY) gene family in Medicago sativa L

The WHY family is a group of plant-specific transcription factors, that can bind to single-stranded DNA molecules and play a variety of functions in plant nuclei and organelles, participating in the regulation of plant leaf senescence. It has been identified and analyzed in many species, however, the systematic identification and analysis of the WHY genes family have not yet been reported in alfalfa (Medicago sativa L.). Therefore, to explore the function of alfalfa the WHY genes, and 10 MsWHY genes were identified and further characterized their evolutionary relationship and expression patterns by analyzing the recently published genome of alfalfa. Comprehensive analysis of the chromosome location, physicochemical properties of the protein, evolutionary relationship, conserved motifs, and responses to abiotic stresses of the WHY gene family in alfalfa using bioinformatics methods. The results showed that 10 MsWHY genes were distributed on 10 chromosomes, and collinearity analysis showed that many MsWHYs might be derived from segmental duplications, and these genes are under purifying selection. Based on phylogenetic analyses, the WHY gene family of alfalfa can be divided into four subfamilies: I-IV subfamily, and approximately all the WHY genes within the same subfamily share similar gene structures. The 10 MsWHY gene family members contained 10 motifs, of which motif 2 and motif 4 are the conserved motifs shared by these genes. Furthermore, the analysis of cis-regulatory elements indicated that regulatory elements related to transcription, cell cycle, development, hormone, and stress response are abundant in the promoter sequence of the MsWHY genes. Real-time quantitative PCR demonstrated that MsWHYs gene expression is induced by drought, salt, and methyl jasmonate. The present study serves as a basic foundation for future functional studies on the alfalfa WHY family.

Genome-Wide Identification and Expression Analyses of the Cotton AGO Genes and Their Potential Roles in Fiber Development and Stress Response

Argonaute proteins (AGOs) are indispensable components of RNA silencing. However, systematic characterization of the AGO genes have not been completed in cotton until now. In this study, cotton AGO genes were identified and analyzed with respect to their evolution and expression profile during biotic and abiotic stresses. We identified 14 GaAGO, 14 GrAGO, and 28 GhAGO genes in the genomes of Gossypium arboreum, Gossypium raimondii, and Gossypium hirsutum. Cotton AGO proteins were classified into four subgroups. Structural and functional conservation were observed in the same subgroups based on the analysis of the gene structure and conserved domains. Twenty-four duplicated gene pairs were identified in GhAGO genes, and all of them exhibited strong purifying selection during evolution. Moreover, RNA-seq analysis showed that most of the GhAGO genes exhibit high expression levels in the fiber initiation and elongation processes. Furthermore, the expression profiles of GhAGO genes tested by quantitative real-time polymerase chain reaction (qPCR) demonstrated that they were sensitive to Verticillium wilt infection and salt and drought stresses. Overall, our results will pave the way for further functional investigation of the cotton AGO gene family, which may be involved in fiber development and stress response.

GrainGenes: a data-rich repository for small grains genetics and genomics

As one of the US Department of Agriculture-Agricultural Research Service flagship databases, GrainGenes (https://wheat.pw.usda.gov) serves the data and community needs of globally distributed small grains researchers for the genetic improvement of the Triticeae family and Avena species that include wheat, barley, rye and oat. GrainGenes accomplishes its mission by continually enriching its cross-linked data content following the findable, accessible, interoperable and reusable principles, enhancing and maintaining an intuitive web interface, creating tools to enable easy data access and establishing data connections within and between GrainGenes and other biological databases to facilitate knowledge discovery. GrainGenes operates within the biological database community, collaborates with curators and genome sequencing groups and contributes to the AgBioData Consortium and the International Wheat Initiative through the Wheat Information System (WheatIS). Interactive and linked content is paramount for successful biological databases and GrainGenes now has 2917 manually curated gene records, including 289 genes and 254 alleles from the Wheat Gene Catalogue (WGC). There are >4.8 million gene models in 51 genome browser assemblies, 6273 quantitative trait loci and >1.4 million genetic loci on 4756 genetic and physical maps contained within 443 mapping sets, complete with standardized metadata. Most notably, 50 new genome browsers that include outputs from the Wheat and Barley PanGenome projects have been created. We provide an example of an expression quantitative trait loci track on the International Wheat Genome Sequencing Consortium Chinese Spring wheat browser to demonstrate how genome browser tracks can be adapted for different data types. To help users benefit more from its data, GrainGenes created four tutorials available on YouTube. GrainGenes is executing its vision of service by continuously responding to the needs of the global small grains community by creating a centralized, long-term, interconnected data repository. Database URL:https://wheat.pw.usda.gov.

GURFAP: A Platform for Gene Function Analysis in Glycyrrhiza Uralensis

Glycyrrhiza uralensis (Licorice), which belongs to Leguminosae, is famous for the function of pharmacologic action and natural sweetener with its dried roots and rhizomes. In recent years, the whole-genome sequence of G. uralensis has been completed, which will help to lay the foundation for the study of gene function. Here, we integrated the available genomic and transcriptomic data of G. uralensis and constructed the G. uralensis gene co-expression network. We then annotated gene functions of G. uralensis via aligning with public databases. Furthermore, gene families of G. uralensis were predicted by tools including iTAK (Plant Transcription factor and Protein kinase Identifier and Classifier), HMMER (hidden Markov models), InParanoid, and PfamScan. Finally, we constructed a platform for gene function analysis in G. uralensis (GURFAP, www.gzybioinfoormatics.cn/GURFAP). For analyzed and predicted gene function, we introduced various tools including BLAST (Basic local alignment search tool), GSEA (Gene set enrichment analysis), Motif, Heatmap, and JBrowse. Our analysis based on this platform indicated that the biosynthesis of glycyrrhizin might be regulated by MYB and bHLH. We also took CYP88D6, CYP72A154, and bAS gene in the synthesis pathway of glycyrrhizin as examples to demonstrate the reliability and availability of our platform. Our platform GURFAP will provide convenience for researchers to mine the gene function of G. uralensis and thus discover more key genes involved in the biosynthetic pathway of active ingredients.

Plant Reactome and PubChem: The Plant Pathway and (Bio)Chemical Entity Knowledgebases

Plant Reactome (https://plantreactome.gramene.org) and PubChem ( https://pubchem.ncbi.nlm.nih.gov ) are two reference data portals and resources for curated plant pathways, small molecules, metabolites, gene products, and macromolecular interactions. Plant Reactome knowledgebase, a conceptual plant pathway network, is built by biocuration and integrating (bio)chemical entities, gene products, and macromolecular interactions. It provides manually curated pathways for the reference species Oryza sativa (rice) and gene orthology-based projections that extend pathway knowledge to 106 plant species. Currently, it hosts 320 reference pathways for plant metabolism, hormone signaling, transport, genetic regulation, plant organ development and differentiation, and biotic and abiotic stress responses. In addition to the pathway browsing and search functions, the Plant Reactome provides the analysis tools for pathway comparison between reference and projected species, pathway enrichment in gene expression data, and overlay of gene-gene interaction data on pathways. PubChem, a popular reference database of (bio)chemical entities, provides information on small molecules and other types of chemical entities, such as siRNAs, miRNAs, lipids, carbohydrates, and chemically modified nucleotides. The data in PubChem is collected from hundreds of data sources, including Plant Reactome. This chapter provides a brief overview of the Plant Reactome and the PubChem knowledgebases, their association to other public resources providing accessory information, and how users can readily access the contents.

Arabidopsis bioinformatics: tools and strategies

The sequencing of the Arabidopsis thaliana genome 21 years ago ushered in the genomics era for plant research. Since then, an incredible variety of bioinformatic tools permit easy access to large repositories of genomic, transcriptomic, proteomic, epigenomic and other '-omic' data. In this review, we cover some more recent tools (and highlight the 'classics') for exploring such data in order to help formulate quality, testable hypotheses, often without having to generate new experimental data. We cover tools for examining gene expression and co-expression patterns, undertaking promoter analyses and gene set enrichment analyses, and exploring protein-protein and protein-DNA interactions. We will touch on tools that integrate different data sets at the end of the article.

LjaFGD: Lonicera japonica functional genomics database

Lonicera japonica Thunb., a traditional Chinese herb, has been used for treating human diseases for thousands of years. Recently, the genome of L. japonica has been decoded, providing valuable information for research into gene function. However, no comprehensive database for gene functional analysis and mining is available for L. japonica. We therefore constructed LjaFGD (www.gzybioinformatics.cn/LjaFGD and bioinformatics.cau.edu.cn/LjaFGD), a database for analyzing and comparing gene function in L. japonica. We constructed a gene co-expression network based on 77 RNA-seq samples, and then annotated genes of L. japonica by alignment against protein sequences from public databases. We also introduced several tools for gene functional analysis, including Blast, motif analysis, gene set enrichment analysis, heatmap analysis, and JBrowse. Our co-expression network revealed that MYB and WRKY transcription factor family genes were co-expressed with genes encoding key enzymes in the biosynthesis of chlorogenic acid and luteolin in L. japonica. We used flavonol synthase 1 (LjFLS1) as an example to show the reliability and applicability of our database. LjaFGD and its various associated tools will provide researchers with an accessible platform for retrieving functional information on L. japonica genes to further biological discovery.

Comprehensive identification and expression analysis of B-Box genes in cotton

BACKGROUND

B-BOX (BBX) proteins are zinc-finger transcription factors with one or two BBX domains and sometimes a CCT domain. These proteins play an essential role in regulating plant growth and development, as well as in resisting abiotic stress. So far, the BBX gene family has been widely studied in other crops. However, no one has systematically studied the BBX gene in cotton.

RESULTS

In the present study, 17, 18, 37 and 33 BBX genes were detected in Gossypium arboreum, G. raimondii, G. hirsutum and G. barbadense, respectively, via genome-wide identification. Phylogenetic analysis showed that all BBX genes were divided into 5 main categories. The protein motifs and exon/intron structures showed that each group of BBX genes was highly conserved. Collinearity analysis revealed that the amplification of BBX gene family in Gossypium spp. was mainly through segmental replication. Nonsynonymous (Ka)/ synonymous (Ks) substitution ratios indicated that the BBX gene family had undergone purification selection throughout the long-term natural selection process. Moreover, transcriptomic data showed that some GhBBX genes were highly expressed in floral organs. The qRT-PCR results showed that there were significant differences in GhBBX genes in leaves and shoot apexes between early-maturing materials and late-maturing materials at most periods. Yeast two-hybrid results showed that GhBBX5/GhBBX23 and GhBBX8/GhBBX26 might interact with GhFT. Transcriptome data analysis and qRT-PCR verification showed that different GhBBX genes had different biological functions in abiotic stress and phytohormone response.

CONCLUSIONS

Our comprehensive analysis of BBX in G. hirsutum provided a basis for further study on the molecular role of GhBBXs in regulating flowering and cotton resistance to abiotic stress.

Molecular Traits and Functional Analysis of the CLAVATA3/Endosperm Surrounding Region-Related Small Signaling Peptides in Three Species of Gossypium Genus

The CLAVATA3/endosperm surrounding region-related (CLE) small peptides are a group of C-terminally encoded and post-translationally modified signal molecules involved in regulating the growth and development of various plants. However, the function and evolution of these peptides have so far remained elusive in cotton. In this study, 55, 56, and 86 CLE genes were identified in the Gossypium raimondii, Gossypium arboreum, and Gossypium hirsutum genomes, respectively, and all members were divided into seven groups. These groups were distinctly different in their protein characteristics, gene structures, conserved motifs, and multiple sequence alignment. Whole genome or segmental duplications played a significant role in the expansion of the CLE family in cotton, and experienced purifying selection during the long evolutionary process in cotton. Cis-acting regulatory elements and transcript profiling revealed that the CLE genes of cotton exist in different tissues, developmental stages, and respond to abiotic stresses. Protein properties, structure prediction, protein interaction network prediction of GhCLE2, GhCLE33.2, and GhCLE28.1 peptides were, respectively, analyzed. In addition, the overexpression of GhCLE2, GhCLE33.2, or GhCLE28.1 in Arabidopsis, respectively, resulted in a distinctive shrub-like dwarf plant, slightly purple leaves, large rosettes with large malformed leaves, and lack of reproductive growth. This study provides important insights into the evolution of cotton CLEs and delineates the functional conservatism and divergence of CLE genes in the growth and development of cotton.

Building a successful international research community through data sharing: The case of the Wheat Information System (WheatIS)

The International Wheat Information System (WheatIS) Expert Working Group (EWG) was initiated in 2012 under the Wheat Initiative with a broad range of contributing organizations. The mission of the WheatIS EWG was to create an informational infrastructure, establish data standards, and build a single portal that allows search, retrieval, and display of globally distributed wheat data sets that are indexed in standard data formats at servers around the world. The web portal at WheatIS.org was released publicly in 2015, and by 2020, it expanded to 8 geographically-distributed nodes and around 20 organizations under its umbrella.  

In this paper, we present our experience, the challenges we faced, and the answer we brought for establishing an international research community to build an informational infrastructure. Our hope is that our experience with building wheatis.org will guide current and future research communities to facilitate institutional and international challenges to create global tools and resources to help their respective scientific communities.

The prohibitins (PHB) gene family in tomato: Bioinformatic identification and expression analysis under abiotic and phytohormone stresses

The prohibitins (PHB) are SPFH domain-containing proteins found in the prokaryotes to eukaryotes. The plant PHBs are associated with a wide range of biological processes, including senescence, development, and responses to biotic and abiotic stresses. The PHB proteins are identified and characterized in the number of plant species, such as Arabidopsis, rice, maize, and soybean. However, no systematic identification of PHB proteins was performed in Solanum lycopersicum. In this study, we identified 16 PHB proteins in the tomato genome. The analysis of conserved motifs and gene structure validated the phylogenetic classification of tomato PHB proteins. It was observed that various members of tomato PHB proteins undergo purifying selection based on the Ka/Ks ratio and are targeted by four families of miRNAs. Moreover, SlPHB proteins displayed a very unique expression pattern in different plant parts including fruits at various development stages. It was found that SlPHBs processed various development-related and phytohormone responsive cis-regulatory elements in their promoter regions. Furthermore, the exogenous phytohormones treatments (Abscisic acid, indole-3-acetic acid, gibberellic acid, methyl jasmonate) salt and drought stresses induce the expression of SlPHB. Moreover, the subcellular localization assay revealed that SlPHB5 and SlPHB10 were located in the mitochondria. This study systematically summarized the general characterization of SlPHBs in the tomato genome and provides a foundation for the functional characterization of PHB genes in tomato and other plant species.

The DUF221 domain-containing (DDP) genes identification and expression analysis in tomato under abiotic and phytohormone stress

The domain of unknown function (DUF221 domain-containing) proteins regulates various aspects of plant growth, development, responses to abiotic stresses, and hormone transduction pathways. To understand the role of DDP proteins in tomato, a comprehensive genome-wide analysis was performed in the tomato genome. A total of 12 DDP genes were identified and distributed in 8 chromosomes in the tomato genome. Phylogenetically all SlDDPs were clustered into four clades, subsequently supported by their gene structure and conserved motifs distribution. The SlDDPs contained various cis-acting elements involved in plant responses to abiotic and various phytohormones stresses. The tissue-specific expression profile analysis revealed the constitutive expression of SlDDPs in roots, leaves, and developmental phases of fruit. It was found that SlDDP1, SlDDP3, SlDDP4, SlDDP9, SlDDP10, and SlDDP12 exhibited high expression levels in fruits at different development stages. Of these genes, SlDDP12 contained ethylene (ERE) responsive elements in their promoter regions, suggesting its role in ethylene-dependent fruit ripening. It was found that a single SlDDP induced by two or more abiotic and phytohormone stresses. These include, SlDDP1, SlDDP2, SlDDP3, SlDDP4, SlDDP7, SlDDP8, and SlDDP10 was induced under salt, drought, ABA, and IAA stresses. Moreover, tomato SlDDPs were targeted by multiple miRNA gene families as well. In conclusion, this study predicted that the putative DDP genes might help improve abiotic and phytohormone tolerance in plants, particularly tomato, rice, and other economically important crop plant species.

Bioinformatic Tools in Arabidopsis Research

Bioinformatic tools are now an everyday part of a plant researcher's collection of protocols. They allow almost instantaneous access to large data sets encompassing genomes, transcriptomes, proteomes, epigenomes, and other "-omes," which are now being generated with increasing speed and decreasing cost. With the appropriate queries, such tools can generate quality hypotheses, sometimes without the need for new experimental data. In this chapter, we will investigate some of the tools used for examining gene expression and coexpression patterns, performing promoter analyses and functional classification enrichment for sets of genes, and exploring protein-protein and protein-DNA interactions in Arabidopsis. We will also cover additional tools that allow integration of data from several sources for improved hypothesis generation.

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.

GelFAP: Gene Functional Analysis Platform for Gastrodia elata

Gastrodia elata, also named Tianma, is a valuable traditional Chinese herbal medicine. It has numerous important pharmacological roles such as in sedation and lowering blood pressure and as anticonvulsant and anti-aging, and it also has effects on the immune and cardiovascular systems. The whole genome sequencing of G. elata has been completed in recent years, which provides a strong support for the construction of the G. elata gene functional analysis platform. Therefore, in our research, we collected and processed 39 transcriptome data of G. elata and constructed the G. elata gene co-expression networks, then we identified functional modules by the weighted correlation network analysis (WGCNA) package. Furthermore, gene families of G. elata were identified by tools including HMMER, iTAK, PfamScan, and InParanoid. Finally, we constructed a gene functional analysis platform for G. elata . In our platform, we introduced functional analysis tools such as BLAST, gene set enrichment analysis (GSEA), and cis-elements (motif) enrichment analysis tool. In addition, we analyzed the co-expression relationship of genes which might participate in the biosynthesis of gastrodin and predicted 19 mannose-binding lectin antifungal proteins of G. elata. We also introduced the usage of the G. elata gene function analysis platform (GelFAP) by analyzing CYP51G1 and GFAP4 genes. Our platform GelFAP may help researchers to explore the gene function of G. elata and make novel discoveries about key genes involved in the biological processes of gastrodin.

Methodology: an optimized, high-yield tomato leaf chloroplast isolation and stroma extraction protocol for proteomics analyses and identification of chloroplast co-localizing proteins

BACKGROUND

Chloroplasts are critical organelles that perceive and convey metabolic and stress signals to different cellular components, while remaining the seat of photosynthesis and a metabolic factory. The proteomes of intact leaves, chloroplasts, and suborganellar fractions of plastids have been evaluated in the model plant Arabidopsis, however fewer studies have characterized the proteomes of plastids in crops. Tomato (Solanum lycopersicum) is an important world-wide crop and a model system for the study of wounding, herbivory and fruit ripening. While significant advances have been made in understanding proteome and metabolome changes in fruit ripening, far less is known about the tomato chloroplast proteome or its subcompartments.

RESULTS

With the long-term goal of understanding chloroplast proteome dynamics in response to stress, we describe a high-yielding method to isolate intact tomato chloroplasts and stromal proteins for proteomic studies. The parameters that limit tomato chloroplast yields were identified and revised to increase yields. Compared to published data, our optimized method increased chloroplast yields by 6.7- and 4.3-fold relative to published spinach and Arabidopsis leaf protocols, respectively; furthermore, tomato stromal protein yields were up to 79-fold higher than Arabidopsis stromal proteins yields. We provide immunoblot evidence for the purity of the stromal proteome isolated using our enhanced methods. In addition, we leverage our nanoliquid chromatography tandem mass spectrometry (nanoLC-MS/MS) data to assess the quality of our stromal proteome. Using strict criteria, proteins detected by 1 peptide spectral match, by one peptide, or were sporadically detected were designated as low-level contaminating proteins. A set of 254 proteins that reproducibly co-isolated with the tomato chloroplast stroma were identified. The subcellular localization, frequency of detection, normalized spectral abundance, and functions of the co-isolating proteins are discussed.

CONCLUSIONS

Our optimized method for chloroplast isolation increased the yields of tomato chloroplasts eightfold enabling the proteomics analysis of the chloroplast stromal proteome. The set of 254 proteins that co-isolate with the chloroplast stroma provides opportunities for developing a better understanding of the extensive and dynamic interactions of chloroplasts with other organelles. These co-isolating proteins also have the potential for expanding our knowledge of proteins that are co-localized in multiple subcellular organelles.

A Genome-Wide Survey of MATE Transporters in Brassicaceae and Unveiling Their Expression Profiles under Abiotic Stress in Rapeseed

The multidrug and toxic compound extrusion (MATE) protein family is important in the export of toxins and other substrates, but detailed information on this family in the Brassicaceae has not yet been reported compared to Arabidopsis thaliana. In this study, we identified 57, 124, 81, 85, 130, and 79 MATE genes in A. thaliana, Brassica napus, Brassica oleracea, Brassica rapa, Brassica juncea, and Brassica nigra, respectively, which were unevenly distributed on chromosomes owing to both tandem and segmental duplication events. Phylogenetic analysis showed that these genes could be classified into four subgroups, shared high similarity and conservation within each group, and have evolved mainly through purifying selection. Furthermore, numerous B. napusMATE genes showed differential expression between tissues and developmental stages and between plants treated with heavy metals or hormones and untreated control plants. This differential expression was especially pronounced for the Group 2 and 3 BnaMATE genes, indicating that they may play important roles in stress tolerance and hormone induction. Our results provide a valuable foundation for the functional dissection of the different BnaMATE homologs in B. napus and its parental lines, as well as for the breeding of more stress-tolerant B. napus genotypes.

De novo assembly and annotation of transcriptomes from two cultivars of Cannabis sativa with different cannabinoid profiles

Cannabis has been cultivated for millennia for medicinal, industrial and recreational uses. Our long-term goal is to compare the transcriptomes of cultivars with different cannabinoid profiles for therapeutic purposes. Here we describe the de novo assembly, annotation and initial analysis of two cultivars of Cannabis, a high THC variety and a CBD plus THC variety. Cultivars were grown under different lighting conditions; flower buds were sampled over 71 days. Cannabinoid profiles were determined by ESI-LC/MS. RNA samples were sequenced using the HiSeq4000 platform. Transcriptomes were assembled using the DRAP pipeline and annotated using the BLAST2GO pipeline and other tools. Each transcriptome contained over twenty thousand protein encoding transcripts with ORFs and flanking sequence. Identification of transcripts for cannabinoid pathway and related enzymes showed full-length ORFs that align with the draft genomes of the Purple Kush and Finola cultivars. Two transcripts were found for olivetolic acid cyclase (OAC) that mapped to distinct locations on the Purple Kush genome suggesting multiple genes for OAC are expressed in some cultivars. The ability to make high quality annotated reference transcriptomes in Cannabis or other plants can promote rapid comparative analysis between cultivars and growth conditions in Cannabis and other organisms without annotated genome assemblies.

Plant Reactome: a knowledgebase and resource for comparative pathway analysis

Plant Reactome (https://plantreactome.gramene.org) is an open-source, comparative plant pathway knowledgebase of the Gramene project. It uses Oryza sativa (rice) as a reference species for manual curation of pathways and extends pathway knowledge to another 82 plant species via gene-orthology projection using the Reactome data model and framework. It currently hosts 298 reference pathways, including metabolic and transport pathways, transcriptional networks, hormone signaling pathways, and plant developmental processes. In addition to browsing plant pathways, users can upload and analyze their omics data, such as the gene-expression data, and overlay curated or experimental gene-gene interaction data to extend pathway knowledge. The curation team actively engages researchers and students on gene and pathway curation by offering workshops and online tutorials. The Plant Reactome supports, implements and collaborates with the wider community to make data and tools related to genes, genomes, and pathways Findable, Accessible, Interoperable and Re-usable (FAIR).

Text mining meets community curation: a newly designed curation platform to improve author experience and participation at WormBase

Biological knowledgebases rely on expert biocuration of the research literature to maintain up-to-date collections of data organized in machine-readable form. To enter information into knowledgebases, curators need to follow three steps: (i) identify papers containing relevant data, a process called triaging; (ii) recognize named entities; and (iii) extract and curate data in accordance with the underlying data models. WormBase (WB), the authoritative repository for research data on Caenorhabditis elegans and other nematodes, uses text mining (TM) to semi-automate its curation pipeline. In addition, WB engages its community, via an Author First Pass (AFP) system, to help recognize entities and classify data types in their recently published papers. In this paper, we present a new WB AFP system that combines TM and AFP into a single application to enhance community curation. The system employs string-searching algorithms and statistical methods (e.g. support vector machines (SVMs)) to extract biological entities and classify data types, and it presents the results to authors in a web form where they validate the extracted information, rather than enter it de novo as the previous form required. With this new system, we lessen the burden for authors, while at the same time receive valuable feedback on the performance of our TM tools. The new user interface also links out to specific structured data submission forms, e.g. for phenotype or expression pattern data, giving the authors the opportunity to contribute a more detailed curation that can be incorporated into WB with minimal curator review. Our approach is generalizable and could be applied to additional knowledgebases that would like to engage their user community in assisting with the curation. In the five months succeeding the launch of the new system, the response rate has been comparable with that of the previous AFP version, but the quality and quantity of the data received has greatly improved.

Unraveling candidate genes underlying biomass digestibility in elephant grass (Cenchrus purpureus)

BACKGROUND

Elephant grass [Cenchrus purpureus (Schumach.) Morrone] is used for bioenergy and animal feed. In order to identify candidate genes that could be exploited for marker-assisted selection in elephant grass, this study aimed to investigate changes in predictive accuracy using genomic relationship information and simple sequence repeats for eight traits (height, green biomass, dry biomass, acid and neutral detergent fiber, lignin content, biomass digestibility, and dry matter concentration) linked to bioenergetics and animal feeding.

RESULTS

We used single-step, genome-based best linear unbiased prediction and genome association methods to investigate changes in predictive accuracy and find candidate genes using genomic relationship information. Genetic variability (p < 0.05) was detected for most of the traits evaluated. In general, the overall means for the traits varied widely over the cuttings, which was corroborated by a significant genotype by cutting interaction. Knowing the genomic relationships increased the predictive accuracy of the biomass quality traits. We found that one marker (M28_161) was significantly associated with high values of biomass digestibility. The marker had moderate linkage disequilibrium with another marker (M35_202) that, in general, was detected in genotypes with low values of biomass digestibility. In silico analysis revealed that both markers have orthologous regions in other C4 grasses such as Setaria viridis, Panicum hallii, and Panicum virgatum, and these regions are located close to candidate genes involved in the biosynthesis of cell wall molecules (xyloglucan and lignin), which support their association with biomass digestibility.

CONCLUSIONS

The markers and candidate genes identified here are useful for breeding programs aimed at changing biomass digestibility in elephant grass. These markers can be used in marker-assisted selection to grow elephant grass cultivars for different uses, e.g., bioenergy production, bio-based products, co-products, bioactive compounds, and animal feed.

CyMIRA: The Cytonuclear Molecular Interactions Reference for Arabidopsis

The function and evolution of eukaryotic cells depend upon direct molecular interactions between gene products encoded in nuclear and cytoplasmic genomes. Understanding how these cytonuclear interactions drive molecular evolution and generate genetic incompatibilities between isolated populations and species is of central importance to eukaryotic biology. Plants are an outstanding system to investigate such effects because of their two different genomic compartments present in the cytoplasm (mitochondria and plastids) and the extensive resources detailing subcellular targeting of nuclear-encoded proteins. However, the field lacks a consistent classification scheme for mitochondrial- and plastid-targeted proteins based on their molecular interactions with cytoplasmic genomes and gene products, which hinders efforts to standardize and compare results across studies. Here, we take advantage of detailed knowledge about the model angiosperm Arabidopsis thaliana to provide a curated database of plant cytonuclear interactions at the molecular level. CyMIRA (Cytonuclear Molecular Interactions Reference for Arabidopsis) is available at http://cymira.colostate.edu/ and https://github.com/dbsloan/cymira and will serve as a resource to aid researchers in partitioning evolutionary genomic data into functional gene classes based on organelle targeting and direct molecular interaction with cytoplasmic genomes and gene products. It includes 11 categories (and 27 subcategories) of different cytonuclear complexes and types of molecular interactions, and it reports residue-level information for cytonuclear contact sites. We hope that this framework will make it easier to standardize, interpret, and compare studies testing the functional and evolutionary consequences of cytonuclear interactions.

An Undergraduate Research Project Utilizing CRISPR-Cas9 Gene Editing Technology to Study Gene Function in Arabidopsis thaliana

The CRISPR-Cas9 system functions in microbial viral pathogen recognition pathways by identifying and targeting foreign DNA for degradation. Recently, biotechnological advances have allowed scientists to use CRISPR-Cas9-based elements as a molecular tool to selectively modify DNA in a wide variety of other living systems. Given the emerging need to bring engaging CRISPR-Cas9 laboratory experiences to an undergraduate audience, we incorporated a CRISPR-based research project into our Genetics class laboratories, emphasizing its use in plants. Our genetic manipulations were designed for Arabidopsis thaliana, which despite serving as a plant research model, has traditionally been difficult to use in a classroom setting. For this project, students transformed plasmid DNA containing the essential CRISPR-Cas9 gene editing elements into A. thaliana. Expression of these elements in the plant genome was expected to create a deletion at one of six targeted genes. The genes we chose had a known seedling and/or juvenile loss-of-function phenotype, which made genetic analysis by students with a limited background possible. It also allowed the project to reach completion in a typical undergraduate semester timeframe. Assessment efforts demonstrated several learning gains, including students' understanding of CRISPR-Cas9 content, their ability to apply CRISPR-Cas9 gene editing tools using bioinformatics and genetics, their ability to employ elements of experimental design, and improved science communication skills. They also felt a stronger connection to their scientific education and were more likely to continue on a STEM career path. Overall, this project can be used to introduce CRISPR-Cas9 technology to undergraduates using plants in a single-semester laboratory course.

Identification and characterization of candidates involved in production of OMEGAs in microalgae: a gene mining and phylogenomic approach

Optimizing the production of the high-value renewables such as OMEGAs through pathway engineering requires an in-depth understanding of the structure-function relationship of genes involved in the OMEGA biosynthetic pathways. In this preliminary study, our rationale is to identify and characterize the ∼221 putative genes involved in production of OMEGAs using bioinformatic analysis from the Streptophyte (plants), Chlorophyte (green algae), Rhodophyta (red algae), and Bacillariophyta (diatoms) lineages based on their phylogenomic profiling, conserved motif/domain organization and physico-chemical properties. The MEME suite predicted 12 distinct protein domains, which are conserved among these putative genes. The phylogenomic analysis of the putative candidate genes [such as FAD2 (delta-12 desaturase); ECR (enoyl-CoA reductase); FAD2 (delta-12 desaturase); ACOT (acyl CoA thioesterase); ECH (enoyl-CoA hydratase); and ACAT (acetyl-CoA acyltransferase)] with similar domains and motif patterns were remarkably well conserved. Furthermore, the subcellular network prediction of OMEGA biosynthetic pathway genes revealed a unique interaction between the light-dependent chlorophyll biosynthesis and glycerol-3-phosphate dehydrogenase, which predicts a major cross-talk between the key essential pathways. Such bioinformatic analysis will provide insights in finding the key regulatory genes to optimize the productivity of OMEGAs in microalgal cell factories.

AgBioData consortium recommendations for sustainable genomics and genetics databases for agriculture

The future of agricultural research depends on data. The sheer volume of agricultural biological data being produced today makes excellent data management essential. Governmental agencies, publishers and science funders require data management plans for publicly funded research. Furthermore, the value of data increases exponentially when they are properly stored, described, integrated and shared, so that they can be easily utilized in future analyses. AgBioData (https://www.agbiodata.org) is a consortium of people working at agricultural biological databases, data archives and knowledgbases who strive to identify common issues in database development, curation and management, with the goal of creating database products that are more Findable, Accessible, Interoperable and Reusable. We strive to promote authentic, detailed, accurate and explicit communication between all parties involved in scientific data. As a step toward this goal, we present the current state of biocuration, ontologies, metadata and persistence, database platforms, programmatic (machine) access to data, communication and sustainability with regard to data curation. Each section describes challenges and opportunities for these topics, along with recommendations and best practices.