AraNet v2: an improved database of co-functional gene networks for the study of Arabidopsis thaliana and 27 other nonmodel plant species

Genome-Wide Identification, Expression, and Protein Analysis of CKX and IPT Gene Families in Radish (Raphanus sativus L.) Reveal Their Involvement in Clubroot Resistance

Cytokinins (CKs) are a group of phytohormones that are involved in plant growth, development, and disease resistance. The isopentenyl transferase (IPT) and cytokinin oxidase/dehydrogenase (CKX) families comprise key enzymes controlling CK biosynthesis and degradation. However, an integrated analysis of these two gene families in radish has not yet been explored. In this study, 13 RsIPT and 12 RsCKX genes were identified and characterized, most of which had four copies in Brassica napus and two copies in radish and other diploid Brassica species. Promoter analysis indicated that the genes contained at least one phytohormone or defense and stress responsiveness cis-acting element. RsIPTs and RsCKXs were expanded through segmental duplication. Moreover, strong purifying selection drove the evolution of the two gene families. The expression of the RsIPT and RsCKX genes distinctly showed diversity in different tissues and developmental stages of the root. Expression profiling showed that RsCKX1-1/1-2/1-3 was significantly upregulated in club-resistant materials during primary infection, suggesting their vital function in clubroot resistance. The interaction network of CKX proteins with similar 3D structures also reflected the important role of RsCKX genes in disease resistance. This study provides a foundation for further functional study on the IPT and CKX genes for clubroot resistance improvement in Raphanus.

Genome-Wide Identification and Evolution-Profiling Analysis of TPS Gene Family in Triticum Plants

Terpenoids play a crucial role in plant growth and development, as well as in regulating resistance mechanisms. Terpene synthase (TPS) serves as the final step in the synthesis process of terpenoids. However, a comprehensive bioinformatics analysis of the TPS gene family in Triticum plants had not previously been systematically undertaken. In this study, a total of 531 TPS members were identified in Triticum plants. The evolutionary tree divided the TPS proteins into five subfamilies: Group1, Group2, Group3, Group4, and Group5. The results of the duplication events analysis showed that TD and WGD were major driving forces during the evolution of the TPS family. The cis-element analysis showed that the TPS genes were related to plant growth and development and environmental stress. Moreover, the GO annotation displayed that the biological function of TPS was relatively conserved in wheat plants. The RNA-seq data showed that the rice and wheat TPS genes responded to low-temperature stress and exhibited significantly different expression patterns. This research shed light on the functions of TPSs in responding to abiotic stress and demonstrated their modulatory potential during root development. These findings provide a foundation for further and deeper investigation of the TPSs' functions in Triticum plants.

Application of orthology and network biology to infer gene functions in non-model plants

Approximately 60% of the genes and gene products in the model species Arabidopsis thaliana have been functionally characterized. In non-model plant species, the functional annotation of the gene space is largely based on homology, with the assumption that genes with shared common ancestry have conserved functions. However, the wide variety in possible morphological, physiological, and ecological differences between plant species gives rise to many species- and clade-specific genes, for which this transfer of knowledge is not possible. Other complications, such as difficulties with genetic transformation, the absence of large-scale mutagenesis methods, and long generation times, further lead to the slow characterization of genes in non-model species. Here, we discuss different resources that integrate plant gene function information. Different approaches that support the functional annotation of gene products, based on orthology or network biology, are described. While sequence-based tools to characterize the functional landscape in non-model species are maturing and becoming more readily available, easy-to-use network-based methods inferring plant gene functions are not as prevalent and have limited functionality.

Recent advances in exploring transcriptional regulatory landscape of crops

Crop breeding entails developing and selecting plant varieties with improved agronomic traits. Modern molecular techniques, such as genome editing, enable more efficient manipulation of plant phenotype by altering the expression of particular regulatory or functional genes. Hence, it is essential to thoroughly comprehend the transcriptional regulatory mechanisms that underpin these traits. In the multi-omics era, a large amount of omics data has been generated for diverse crop species, including genomics, epigenomics, transcriptomics, proteomics, and single-cell omics. The abundant data resources and the emergence of advanced computational tools offer unprecedented opportunities for obtaining a holistic view and profound understanding of the regulatory processes linked to desirable traits. This review focuses on integrated network approaches that utilize multi-omics data to investigate gene expression regulation. Various types of regulatory networks and their inference methods are discussed, focusing on recent advancements in crop plants. The integration of multi-omics data has been proven to be crucial for the construction of high-confidence regulatory networks. With the refinement of these methodologies, they will significantly enhance crop breeding efforts and contribute to global food security.

Organ-delimited gene regulatory networks provide high accuracy in candidate transcription factor selection across diverse processes

Organ-specific gene expression datasets that include hundreds to thousands of experiments allow the reconstruction of organ-level gene regulatory networks (GRNs). However, creating such datasets is greatly hampered by the requirements of extensive and tedious manual curation. Here, we trained a supervised classification model that can accurately classify the organ-of-origin for a plant transcriptome. This K-Nearest Neighbor-based multiclass classifier was used to create organ-specific gene expression datasets for the leaf, root, shoot, flower, and seed in Arabidopsis thaliana. A GRN inference approach was used to determine the: i. influential transcription factors (TFs) in each organ and, ii. most influential TFs for specific biological processes in that organ. These genome-wide, organ-delimited GRNs (OD-GRNs), recalled many known regulators of organ development and processes operating in those organs. Importantly, many previously unknown TF regulators were uncovered as potential regulators of these processes. As a proof-of-concept, we focused on experimentally validating the predicted TF regulators of lipid biosynthesis in seeds, an important food and biofuel trait. Of the top 20 predicted TFs, eight are known regulators of seed oil content, e.g., WRI1, LEC1, FUS3. Importantly, we validated our prediction of MybS2, TGA4, SPL12, AGL18, and DiV2 as regulators of seed lipid biosynthesis. We elucidated the molecular mechanism of MybS2 and show that it induces purple acid phosphatase family genes and lipid synthesis genes to enhance seed lipid content. This general approach has the potential to be extended to any species with sufficiently large gene expression datasets to find unique regulators of any trait-of-interest.

Comprehensive Identification of the β-Amylase (BAM) Gene Family in Response to Cold Stress in White Clover

White clover (Trifolium repens L.) is an allopolyploid plant and an excellent perennial legume forage. However, white clover is subjected to various stresses during its growth, with cold stress being one of the major limiting factors affecting its growth and development. Beta-amylase (BAM) is an important starch-hydrolyzing enzyme that plays a significant role in starch degradation and responses to environmental stress. In this study, 21 members of the BAM gene family were identified in the white clover genome. A phylogenetic analysis using BAMs from Arabidopsis divided TrBAMs into four groups based on sequence similarity. Through analysis of conserved motifs, gene duplication, synteny analysis, and cis-acting elements, a deeper understanding of the structure and evolution of TrBAMs in white clover was gained. Additionally, a gene regulatory network (GRN) containing TrBAMs was constructed; gene ontology (GO) annotation analysis revealed close interactions between TrBAMs and AMY (α-amylase) and DPE (4-alpha-glucanotransferase). To determine the function of TrBAMs under various tissues and stresses, RNA-seq datasets were analyzed, showing that most TrBAMs were significantly upregulated in response to biotic and abiotic stresses and the highest expression in leaves. These results were validated through qRT-PCR experiments, indicating their involvement in multiple gene regulatory pathways responding to cold stress. This study provides new insights into the structure, evolution, and function of the white clover BAM gene family, laying the foundation for further exploration of the functional mechanisms through which TrBAMs respond to cold stress.

Genome-Wide Analysis of WUSCHEL-Related Homeobox Gene Family in Sacred Lotus (Nelumbo nucifera)

WUSCHEL-related homeobox (WOX) is a plant-specific transcription factor (TF), which plays an essential role in the regulation of plant growth, development, and abiotic stress responses. However, little information is available on the specific roles of WOX TFs in sacred lotus (Nelumbo nucifera), which is a perennial aquatic plant with important edible, ornamental, and medicinal values. We identified 15 WOX TFs distributing on six chromosomes in the genome of N. nucifera. A total of 72 WOX genes from five species were divided into three clades and nine subclades based on the phylogenetic tree. NnWOXs in the same subclades had similar gene structures and conserved motifs. Cis-acting element analysis of the promoter regions of NnWOXs found many elements enriched in hormone induction, stress responses, and light responses, indicating their roles in growth and development. The Ka/Ks analysis showed that the WOX gene family had been intensely purified and selected in N. nucifera. The expression pattern analysis suggested that NnWOXs were involved in organ development and differentiation of N. nucifera. Furthermore, the protein-protein interaction analysis showed that NnWOXs might participate in the growth, development, and metabolic regulation of N. nucifera. Taken together, these findings laid a foundation for further analysis of NnWOX functions.

Phylogeny of PmCCD Gene Family and Expression Analysis of Flower Coloration and Stress Response in Prunus mume

The CCD gene family plays a crucial role in the cleavage of carotenoids, converting them into apocarotenoids. This process not only impacts the physiology and development of plants but also enhances their tolerance toward different stresses. However, the character of the PmCCD gene family and its role in ornamental woody Prunus mume remain unclear. Here, ten non-redundant PmCCD genes were identified from the P. mume genome, and their physicochemical characteristics were predicted. According to the phylogenetic tree, PmCCD proteins were classified into six subfamilies: CCD1, CCD4, CCD7, CCD8, NCED and CCD-like. The same subfamily possessed similar gene structural patterns and numbers of conserved motifs. Ten PmCCD genes were concentrated on three chromosomes. PmCCD genes exhibited interspecific collinearity with P. armeniaca and P. persica. Additionally, PmCCD genes had obvious specificity in different tissues and varieties. Compared with white-flowered 'ZLE', PmCCD1 and PmCCD4 genes were low-expressed in 'HJH' with yellow petals, which suggested PmCCD1 and PmCCD4 might be related to the formation of yellow flowers in P. mume. Nine PmCCD genes could respond to NaCl or PEG treatments. These genes might play a crucial role in salt and drought resistance in P. mume. Moreover, PmVAR3 and PmSAT3/5 interacted with PmCCD4 protein in yeast and tobacco leaf cells. This study laid a foundation for exploring the role of the PmCCD gene family in flower coloration and stress response in P. mume.

Genome-wide analysis of the WRKY genes and their important roles during cold stress in white clover

Background

White clover (Trifolium repens L) is a high-quality forage grass with a high protein content, but it is vulnerable to cold stress, which can negatively affect its growth and development. WRKY transcription factor is a family of plant transcription factors found mainly in higher plants and plays an important role in plant growth, development, and stress response. Although WRKY transcription factors have been studied extensively in other plants, it has been less studied in white clover.

Methods and Results

In the present research, we have performed a genome-wide analysis of the WRKY gene family of white clover, in total, there were 145 members of WRKY transcription factors identified in white clover. The characterization of the TrWRKY genes was detailed, including conserved motif analysis, phylogenetic analysis, and gene duplication analysis, which have provided a better understanding of the structure and evolution of the TrWRKY genes in white clover. Meanwhile, the genetic regulation network (GRN) containing TrWRKY genes was reconstructed, and Gene Ontology (GO) annotation analysis of these function genes showed they contributed to regulation of transcription process, response to wounding, and phosphorylay signal transduction system, all of which were important processes in response to abiotic stress. To determine the TrWRKY genes function under cold stress, the RNA-seq dataset was analyzed; most of TrWRKY genes were highly upregulated in response to cold stress, particularly in the early stages of cold stress. These results were validated by qRT-PCR experiment, implying they are involved in various gene regulation pathways in response to cold stress.

Conclusion

The results of this study provide insights that will be useful for further functional analyses of TrWRKY genes in response to biotic or abiotic stresses in white clover. These findings are likely to be useful for further research on the functions of TrWRKY genes and their role in response to cold stress, which is important to understand the molecular mechanism of cold tolerance in white clover and improve its cold tolerance.

Genome-wide identification and characterization of lipoxygenase genes related to the English grain aphid infestation response in wheat

44 wheat LOX genes were identified by silico genome-wide search method. TaLOX5, 7, 10, 24, 29, 33 were specifically expressed post aphid infestation, indicating their participation in wheat-aphid interaction. In plants, LOX genes play important roles in various biological progresses including seed germination, tuber development, plant vegetative growth and most crucially in plant signal transduction, stress response and plant defense against plant diseases and insects. Although LOX genes have been characterized in many species, the importance of the LOX family in wheat has still not been well understood, hampering further improvement of wheat under stress conditions. Here, we identified 44 LOX genes (TaLOXs) in the whole wheat genome and classified into three subfamilies (9-LOXs, Type I 13-LOXs and Type II 13-LOXs) according to phylogenetic relationships. The TaLOXs belonging to the same subgroup shared similar gene structures and motif organizations. Synteny analysis demonstrated that segmental duplication events mainly contributed to the expansion of the LOX gene family in wheat. The results of protein-protein interaction network (PPI) and miRNA-TaLOXs predictions revealed that three TaLOXs (TaLOX20, 22 and 37) interacted mostly with proteins related to methyl jasmonate (MeJA) signaling pathway. The expression patterns of TaLOXs in different tissues (root, stem, leaf, spike and grain) under diverse abiotic stresses (heat, cold, drought, drought and heat combined treatment, and salt) as well as under diverse biotic stresses (powdery mildew pathogen, Fusarium graminearum and stripe rust pathogen) were systematically analyzed using RNA-seq data. We obtained aphid-responsive candidate genes by RNA-seq data of wheat after the English grain aphid infestation. Aphid-responsive candidate genes, including TaLOX5, 7, 10, 24, 29 and 33, were up-regulated in the wheat aphid-resistant genotype (Lunxuan144), while they were little expressed in the susceptible genotype (Jimai22) during late response (48 h and 72 h) to the English grain aphid infestation. Meanwhile, qRT-PCR analysis was used to validate these aphid-responsive candidate genes. The genetic divergence and diversity of all the TaLOXs in bread wheat and its relative species were investigated by available resequencing data. Finally, the 3D structure of the TaLOX proteins was predicted based on the homology modeling method. This study not only systematically investigated the characteristics and evolutionary relationships of TaLOXs, but also provided potential candidate genes in response to the English grain aphid infestation and laid the foundation to further study the regulatory roles in the English grain aphid infestation of LOX family in wheat and beyond.

Charting plant gene functions in the multi-omics and single-cell era

Despite the increased access to high-quality plant genome sequences, the set of genes with a known function remains far from complete. With the advent of novel bulk and single-cell omics profiling methods, we are entering a new era where advanced and highly integrative functional annotation strategies are being developed to elucidate the functions of all plant genes. Here, we review different multi-omics approaches to improve functional and regulatory gene characterization and highlight the power of machine learning and network biology to fully exploit the complementary information embedded in different omics layers. Finally, we discuss the potential of emerging single-cell methods and algorithms to further increase the resolution, allowing generation of functional insights about plant biology.

Genome-wide identification, evolution and expression analysis of bone morphogenetic protein (BMP) gene family in chinese soft-shell turtle (Pelodiscus sinensis)

Introduction: Bone morphogenetic proteins (BMPs) play a crucial role in bone formation and differentiation. Recent RNA-Seq results suggest that BMPs may be involved in the sex differentiation of P. sinensis, yet more relevant studies about BMPs in P. sinensis are lacking. Methods: Herein, we identified BMP gene family members, analyzed the phylogeny, collinear relationship, scaffold localization, gene structures, protein structures, transcription factors and dimorphic expression by using bioinformatic methods based on genomic and transcriptomic data of P. sinensis. Meanwhile, qRT-PCR was used to verify the RNA-Seq results and initially explore the function of the BMPs in the sex differentiation of P. sinensis. Results: A total of 11 BMP genes were identified, 10 of which were localized to their respective genomic scaffolds. Phylogenetic analysis revealed that BMP genes were divided into eight subfamilies and shared similar motifs ("WII", "FPL", "TNHA", "CCVP", and "CGC") and domain (TGF-β superfamily). The results of the sexually dimorphic expression profile and qRT-PCR showed that Bmp2, Bmp3, Bmp15l, Bmp5, Bmp6 and Bmp8a were significantly upregulated in ovaries, while Bmp2lb, Bmp7, Bmp2bl and Bmp10 were remarkable upregulated in testes, suggesting that these genes may play a role in sex differentiation of P. sinensis. Discussion: Collectively, our comprehensive results enrich the basic date for studying the evolution and functions of BMP genes in P. sinensis.

Genome-wide identification of KNOX transcription factors in cotton and the role of GhKNOX4-A and GhKNOX22-D in response to salt and drought stress

Cotton is one of the most important economic and fiber crops in the world. KNOX is one class of universal transcription factors, which plays important roles in plant growth and development as well as response to different stresses. Although there are many researches on KNOXs in other plant species, there are few reports on cotton. In this study, we systematically and comprehensively identified all KNOX genes in upland cotton and its two ancestral species; we also studied their functions by employing RNA-seq analysis and virus-induced gene silence (VIGS). A total of 89 KNOX genes were identified from three cotton species. Among them, 44 were from upland cotton, 22 and 23 were found in its ancestral species G. raimondii and G. arboreum, respectively. Plant polyploidization and domestication play a selective force driving KNOX gene evolution. Phylogenetic analysis displayed that KNOX genes were evolved into three Classes. The intron length and exon number differed in each Class. Transcriptome data showed that KNOX genes of Class II were widely expressed in multiple tissues, including fiber. The majority of KNOX genes were induced by different abiotic stresses. Additionally, we found multiple cis-elements related to stress in the promoter region of KNOX genes. VIGS silence of GhKNOX4-A and GhKNOX22-D genes showed significant growth and development effect in cotton seedlings under salt and drought treatments. Both GhKNOX4-A and GhKNOX22-D regulated plant tolerance; silencing both genes induced oxidative stresses, evidenced by reduced SOD activity and induced leave cell death, and also enhanced stomatal open and water loss. Thus, GhKNOX4-A and GhKNOX22-D may contribute to drought response by regulating stomata opening and oxidative stresses.

Identification of growth regulators using cross-species network analysis in plants

With the need to increase plant productivity, one of the challenges plant scientists are facing is to identify genes that play a role in beneficial plant traits. Moreover, even when such genes are found, it is generally not trivial to transfer this knowledge about gene function across species to identify functional orthologs. Here, we focused on the leaf to study plant growth. First, we built leaf growth transcriptional networks in Arabidopsis (Arabidopsis thaliana), maize (Zea mays), and aspen (Populus tremula). Next, known growth regulators, here defined as genes that when mutated or ectopically expressed alter plant growth, together with cross-species conserved networks, were used as guides to predict novel Arabidopsis growth regulators. Using an in-depth literature screening, 34 out of 100 top predicted growth regulators were confirmed to affect leaf phenotype when mutated or overexpressed and thus represent novel potential growth regulators. Globally, these growth regulators were involved in cell cycle, plant defense responses, gibberellin, auxin, and brassinosteroid signaling. Phenotypic characterization of loss-of-function lines confirmed two predicted growth regulators to be involved in leaf growth (NPF6.4 and LATE MERISTEM IDENTITY2). In conclusion, the presented network approach offers an integrative cross-species strategy to identify genes involved in plant growth and development.

Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom

In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.

Feature importance network reveals novel functional relationships between biological features in Arabidopsis thaliana

Understanding how the different cellular components are working together to form a living cell requires multidisciplinary approaches combining molecular and computational biology. Machine learning shows great potential in life sciences, as it can find novel relationships between biological features. Here, we constructed a dataset of 11,801 gene features for 31,522 Arabidopsis thaliana genes and developed a machine learning workflow to identify linked features. The detected linked features are visualised as a Feature Important Network (FIN), which can be mined to reveal a variety of novel biological insights pertaining to gene function. We demonstrate how FIN can be used to generate novel insights into gene function. To make this network easily accessible to the scientific community, we present the FINder database, available at finder.plant.tools.

easyMF: A Web Platform for Matrix Factorization-Based Gene Discovery from Large-scale Transcriptome Data

With the development of high-throughput experimental technologies, large-scale RNA sequencing (RNA-Seq) data have been and continue to be produced, but have led to challenges in extracting relevant biological knowledge hidden in the produced high-dimensional gene expression matrices. Here, we develop easyMF ( https://github.com/cma2015/easyMF ), a web platform that can facilitate functional gene discovery from large-scale transcriptome data using matrix factorization (MF) algorithms. Compared with existing MF-based software packages, easyMF exhibits several promising features, such as greater functionality, flexibility and ease of use. The easyMF platform is equipped using the Big-Data-supported Galaxy system with user-friendly graphic user interfaces, allowing users with little programming experience to streamline transcriptome analysis from raw reads to gene expression, carry out multiple-scenario MF analysis, and perform multiple-way MF-based gene discovery. easyMF is also powered with the advanced packing technology to enhance ease of use under different operating systems and computational environments. We illustrated the application of easyMF for seed gene discovery from temporal, spatial, and integrated RNA-Seq datasets of maize (Zea mays L.), resulting in the identification of 3,167 seed stage-specific, 1,849 seed compartment-specific, and 774 seed-specific genes, respectively. The present results also indicated that easyMF can prioritize seed-related genes with superior prediction performance over the state-of-art network-based gene prioritization system MaizeNet. As a modular, containerized and open-source platform, easyMF can be further customized to satisfy users' specific demands of functional gene discovery and deployed as a web service for broad applications.

NetREx: Network-based Rice Expression Analysis Server for abiotic stress conditions

Recent focus on transcriptomic studies in food crops like rice, wheat and maize provide new opportunities to address issues related to agriculture and climate change. Re-analysis of such data available in public domain supplemented with annotations across molecular hierarchy can be of immense help to the plant research community, particularly co-expression networks representing transcriptionally coordinated genes that are often part of the same biological process. With this objective, we have developed NetREx, a Network-based Rice Expression Analysis Server, that hosts ranked co-expression networks of Oryza sativa using publicly available messenger RNA sequencing data across uniform experimental conditions. It provides a range of interactable data viewers and modules for analysing user-queried genes across different stress conditions (drought, flood, cold and osmosis) and hormonal treatments (abscisic and jasmonic acid) and tissues (root and shoot). Subnetworks of user-defined genes can be queried in pre-constructed tissue-specific networks, allowing users to view the fold change, module memberships, gene annotations and analysis of their neighbourhood genes and associated pathways. The web server also allows querying of orthologous genes from Arabidopsis, wheat, maize, barley and sorghum. Here, we demonstrate that NetREx can be used to identify novel candidate genes and tissue-specific interactions under stress conditions and can aid in the analysis and understanding of complex phenotypes linked to stress response in rice. Database URL: https://bioinf.iiit.ac.in/netrex/index.html.

ATTED-II v11: A Plant Gene Coexpression Database Using a Sample Balancing Technique by Subagging of Principal Components

ATTED-II (https://atted.jp) is a gene coexpression database for nine plant species based on publicly available RNAseq and microarray data. One of the challenges in constructing condition-independent coexpression data based on publicly available gene expression data is managing the inherent sampling bias. Here, we report ATTED-II version 11, wherein we adopted a coexpression calculation methodology to balance the samples using principal component analysis and ensemble calculation. This approach has two advantages. First, omitting principal components with low contribution rates reduces the main contributors of noise. Second, balancing large differences in contribution rates enables considering various sample conditions entirely. In addition, based on RNAseq- and microarray-based coexpression data, we provide species-representative, integrated coexpression information to enhance the efficiency of interspecies comparison of the coexpression data. These coexpression data are provided as a standardized z-score to facilitate integrated analysis with different data sources. We believe that with these improvements, ATTED-II is more valuable and powerful for supporting interspecies comparative studies and integrated analyses using heterogeneous data.

Genome-Wide Identification and Characterization of the WRKY Gene Family in Scutellaria baicalensis Georgi under Diverse Abiotic Stress

The WRKY gene family is an important inducible regulatory factor in plants, which has been extensively studied in many model plants. It has progressively become the focus of investigation for the secondary metabolites of medicinal plants. Currently, there is no systematic analysis of the WRKY gene family in Scutellaria baicalensis Georgi. For this study, a systematic and comprehensive bioinformatics analysis of the WRKY gene family was conducted based on the genomic data of S. baicalensis. A total of 77 WRKY members were identified and 75 were mapped onto nine chromosomes, respectively. Their encoded WRKY proteins could be classified into three subfamilies: Group I, Group II (II-a, II-b, II-c, II-d, II-e), and Group III, based on the characteristics of the amino acid sequences of the WRKY domain and genetic structure. Syntenic analysis revealed that there were 35 pairs of repetitive fragments. Furthermore, the transcriptome data of roots, stems, leaves, and flowers showed that the spatial expression profiles of WRKYs were different. qRT-PCR analysis revealed that 11 stress-related WRKYs exhibited specific expression patterns under diverse treatments. In addition, sub cellular localization analysis indicated that SbWRKY26 and SbWRKY41 were localized in nucleus. This study is the first to report the identification and characterization of the WRKY gene family in S. baicalensis, which is valuable for the further exploration of the biological function of SbWRKYs. It also provides valuable bioinformatics data for S. baicalensis and provides a reference for assessing the medicinal properties of the genus.

Computational prediction of plant metabolic pathways

Uncovering genes encoding enzymes responsible for the biosynthesis of diverse plant metabolites is essential for metabolic engineering and production of plant metabolite-derived medicine. With the availability of multi-omics data for an ever-increasing number of plant species and the development of computational approaches, the metabolic pathways of many important plant compounds can be predicted, complementing a more traditional genetic and/or biochemical approach. Here, we summarize recent progress in predicting plant metabolic pathways using genome, transcriptome, proteome, interactome, and/or metabolome data, and the utility of integrating these data with machine learning to further improve metabolic pathway predictions.

Genome-Wide Identification of Cotton (Gossypium spp.) Glycerol-3-Phosphate Dehydrogenase (GPDH) Family Members and the Role of GhGPDH5 in Response to Drought Stress

Glycerol-3-phosphate dehydrogenase (GPDH) is a key enzyme in plant glycerol synthesis and metabolism, and plays an important role in plant resistance to abiotic stress. Here, we identified 6, 7, 14 and 14 GPDH genes derived from Gossypium arboreum, Gossypium raimondii, Gossypium barbadense and Gossypium hirsutum, respectively. Phylogenetic analysis assigned these genes into three classes, and most of the genes within the family were expanded by whole-genome duplication (WGD) and segmental duplications. Moreover, determination of the nonsynonymous substitution rate/synonymous substitution rate (Ka/Ks) ratio showed that the GPDH had an evolutionary preference for purifying selection. Transcriptome data revealed that GPDH genes were more active in the early stages of fiber development. Additionally, numerous stress-related cis-elements were identified in the potential promoter region. Then, a protein-protein-interaction (PPI) network of GPDH5 in G. hirsutum was constructed. In addition, we predicted 30 underlying miRNAs in G. hirsutum. Functional validation results indicated that silencing GhGPDH5 diminished drought tolerance in the upland cotton TM-1 line. In summary, this study provides a fundamental understanding of the GPDH gene family in cotton, GhGPDH5 exerts a positive effect during drought stress and is potentially involved in stomatal closure movements.

Identification of Potential Genes Encoding Protein Transporters in Arabidopsis thaliana Glucosinolate (GSL) Metabolism

Several species in Brassicaceae produce glucosinolates (GSLs) to protect themselves against pests. As demonstrated in A. thaliana, the reallocation of defence compounds, of which GSLs are a major part, is highly dependent on transport processes and serves to protect high-value tissues such as reproductive tissues. This study aimed to identify potential GSL-transporter proteins (TPs) using a network-biology approach. The known A. thaliana GSL genes were retrieved from the literature and pathway databases and searched against several co-expression databases to generate a gene network consisting of 1267 nodes and 14,308 edges. In addition, 1151 co-expressed genes were annotated, integrated, and visualised using relevant bioinformatic tools. Based on three criteria, 21 potential GSL genes encoding TPs were selected. The AST68 and ABCG40 potential GSL TPs were chosen for further investigation because their subcellular localisation is similar to that of known GSL TPs (SULTR1;1 and SULTR1;2) and ABCG36, respectively. However, AST68 was selected for a molecular-docking analysis using AutoDOCK Vina and AutoDOCK 4.2 with the generated 3D model, showing that both domains were well superimposed on the homologs. Both molecular-docking tools calculated good binding-energy values between the sulphate ion and Ser419 and Val172, with the formation of hydrogen bonds and van der Waals interactions, respectively, suggesting that AST68 was one of the sulphate transporters involved in GSL biosynthesis. This finding illustrates the ability to use computational analysis on gene co-expression data to screen and characterise plant TPs on a large scale to comprehensively elucidate GSL metabolism in A. thaliana. Most importantly, newly identified potential GSL transporters can serve as molecular tools in improving the nutritional value of crops.

Genome-wide identification and expression analysis of the GSK gene family in wheat (Triticum aestivum L.)

BACKGROUND

Plant glycogen synthase kinase 3/shaggy kinase (GSK3) proteins contain the conserved kinase domain and play a pivotal role in the regulation of plant growth and abiotic stress responses. Nonetheless, genome-wide analysis of the GSK gene family in wheat (Triticum aestivum L.) has not been reported.

METHODS AND RESULTS

Using high-quality wheat genome sequences, a comprehensive genome-wide characterization of the GSK gene family in wheat was conducted. Their phylogenetics, chromosome location, gene structure, conserved domains, promoter cis-elements, gene duplications, and network interactions were systematically analyzed. In this study, we identified 22 GSK genes in wheat genome that were unevenly distributed on nine wheat chromosomes. Based on phylogenetic analysis, the GSK genes from Arabidopsis, rice, barley, and wheat were clustered into four subfamilies. Gene structure and conserved protein motif analysis revealed that GSK proteins in the same subfamily share similar motif structures and exon/intron organization. Results from gene duplication analysis indicate that four segmental duplications events contribute to the expansion of the wheat GSK gene family. Promoter analysis indicated the participation of TaSK genes in response to the hormone, light and abiotic stress, and plant growth and development. Furthermore, gene network analysis found that five TaSKs were involved in the regulatory network and 130 gene pairs of network interactions were identified. The heat map generated from the available transcriptomic data revealed that the TaSKs exhibited preferential expression in specific tissues and different expression patterns under abiotic stress conditions. Moreover, results from qRT-PCR analysis revealed that the randomly selected TaSK genes were abundantly expressed in spikes and grains at one specific developmental stage, as well as in responding to drought and salt stress.

CONCLUSIONS

These findings clearly depicted the evolutionary processes and the characteristics, and expression profiles of the GSK gene family in wheat, revealed their role in wheat development and response to abiotic stress responses.

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.

Genome-wide identification and genetic characterization of the CaMYB family and its response to five types of heavy metal stress in hot pepper (Capsicum annuum cv. CM334)

MYB proteins play a crucial role in plant growth and development and stress responses. In this study, 160 members of the MYB gene family from the pepper genome database were used to analyze gene structures, chromosome localization, collinearity, genetic affinity and expression in response to heavy metals. The results identified R2R3-MYB members and further phylogenetically classified them into 35 subgroups based on highly conserved gene structures and motifs. Collinearity analysis showed that segmental duplication events played a crucial role in the functional expansion of the CaMYB gene family by intraspecific collinearity, and at least 12 pairs of CaMYB genes existed between species prior to the differentiation between monocots and dicots. Moreover, the upstream CaMYB genes were mainly localized to the phytohormone elements ABRE and transcription factor elements MYB and MYC. Further analysis revealed that MYB transcription factors were closely associated with a variety of abiotic stress-related proteins (e.g., MAC-complex and SKIP). Under the stress of five metal ions, Cd²⁺, Cu²⁺, Pb²⁺, Zn²⁺, and Fe³⁺, the expression levels of some CaMYB family genes were upregulated. Of these genes, pairing homologous 1 (PH-1), PH-13, and PH-15 in the roots of Capsicum annuum were upregulated to the greatest extent, indicating that these three MYB family members are particularly sensitive to these five metals. This study provides a theoretical reference for the analysis of the molecular regulatory mechanism of MYB family genes in mediating the response to heavy metals in plants. This study reveals the mode of interaction between MYB and a variety of abiotic stress proteins and clarifies the biological functions of CaMYB family members in the regulation of heavy metal stress.

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.

A novel probabilistic generator for large-scale gene association networks

MOTIVATION

Gene expression data provide an opportunity for reverse-engineering gene-gene associations using network inference methods. However, it is difficult to assess the performance of these methods because the true underlying network is unknown in real data. Current benchmarks address this problem by subsampling a known regulatory network to conduct simulations. But the topology of regulatory networks can vary greatly across organisms or tissues, and reference-based generators-such as GeneNetWeaver-are not designed to capture this heterogeneity. This means, for example, benchmark results from the E. coli regulatory network will not carry over to other organisms or tissues. In contrast, probabilistic generators do not require a reference network, and they have the potential to capture a rich distribution of topologies. This makes probabilistic generators an ideal approach for obtaining a robust benchmarking of network inference methods.

RESULTS

We propose a novel probabilistic network generator that (1) provides an alternative to address the inherent limitation of reference-based generators and (2) is able to create realistic gene association networks, and (3) captures the heterogeneity found across gold-standard networks better than existing generators used in practice. Eight organism-specific and 12 human tissue-specific gold-standard association networks are considered. Several measures of global topology are used to determine the similarity of generated networks to the gold-standards. Along with demonstrating the variability of network structure across organisms and tissues, we show that the commonly used "scale-free" model is insufficient for replicating these structures.

AVAILABILITY

This generator is implemented in the R package "SeqNet" and is available on CRAN (https://cran.r-project.org/web/packages/SeqNet/index.html).

Multi-omics network-based functional annotation of unknown Arabidopsis genes

Unraveling gene function is pivotal to understanding the signaling cascades that control plant development and stress responses. As experimental profiling is costly and labor intensive, there is a clear need for high-confidence computational annotation. In contrast to detailed gene-specific functional information, transcriptomics data are widely available for both model and crop species. Here, we describe a novel automated function prediction method, which leverages complementary information from multiple expression datasets by analyzing study-specific gene co-expression networks. First, we benchmarked the prediction performance on recently characterized Arabidopsis thaliana genes, and showed that our method outperforms state-of-the-art expression-based approaches. Next, we predicted biological process annotations for known (n = 15 790) and unknown (n = 11 865) genes in A. thaliana and validated our predictions using experimental protein-DNA and protein-protein interaction data (covering >220 000 interactions in total), obtaining a set of high-confidence functional annotations. Our method assigned at least one validated annotation to 5054 (42.6%) unknown genes, and at least one novel validated function to 3408 (53.0%) genes with computational annotations only. These omics-supported functional annotations shed light on a variety of developmental processes and molecular responses, such as flower and root development, defense responses to fungi and bacteria, and phytohormone signaling, and help fill the information gap on biological process annotations in Arabidopsis. An in-depth analysis of two context-specific networks, modeling seed development and response to water deprivation, shows how previously uncharacterized genes function within the respective networks. Moreover, our automated function prediction approach can be applied in future studies to facilitate gene discovery for crop improvement.

Genome-Wide Identification and Low-Temperature Expression Analysis of bHLH Genes in Prunus mume

Prunus mume is an illustrious ornamental woody plant with colorful flowers, delicate fragrances, and graceful tree forms. Low temperature limits its geographical distribution. The basic helix-loop-helix (bHLH) proteins exist in most eukaryotes as a transcription factor superfamily, which play a crucial role in metabolism, physiology, development, and response to various stresses of higher organisms. However, the characteristics of the bHLH gene family and low-temperature response remain unknown in P. mume. In the present study, we distinguished 95 PmbHLH genes in the P. mume whole-genome and analyzed their features. PmbHLHs were divided into 23 subfamilies and one orphan by phylogenetic analysis. Similar gene structures and conserved motifs appeared in the same subfamily. These genes were situated in eight chromosomes and scaffolds. Gene duplication events performed a close relationship to P. mume, P. persica, and P. avium. Tandem duplications probably promoted the expansion of PmbHLHs. According to predicted binding activities, the PmbHLHs were defined as the Non-DNA-binding proteins and DNA-binding proteins. Furthermore, PmbHLHs exhibited tissue-specific and low-temperature induced expression patterns. By analyzing transcriptome data, 10 PmbHLHs which are responsive to low-temperature stress were selected. The qRT-PCR results showed that the ten PmbHLH genes could respond to low-temperature stress at different degrees. There were differences in multiple variations among different varieties. This study provides a basis to research the evolution and low-temperature tolerance of PmbHLHs, and might enhance breeding programs of P. mume by improving low-temperature tolerance.

Regulation of Oryza sativa molybdate transporter1;3 degradation via RING finger E3 ligase OsAIR3

High concentrations of As in contaminated environments pose a serious threat to plant, human, and animal health. In this study, we characterized an As-responsive Really Interesting New Gene (RING) E3 ubiquitin ligase gene under arsenate (AsV) stress, named as Oryza sativa As-Induced RING E3 ligase 3 (OsAIR3). AsV treatment highly induced the expression of OsAIR3. OsAIR3-EYFP was localized to the nucleus in rice protoplasts and exhibited E3 ligase activity. Yeast two-hybrid screening and bimolecular fluorescence complementation and pull-down assays revealed the interaction of OsAIR3 with an O. sativa molybdate transporter (OsMOT1;3) in the plasma membrane and cytoplasm. In addition, an in vitro cell-free degradation assay was performed to demonstrate the degradation of OsMOT1;3 by OsAIR3 via the 26S proteasome system. Heterogeneous overexpression of OsAIR3 in Arabidopsis yielded AsV-tolerant phenotypes, as indicated by the comparison of cotyledon expansion, root elongation, shoot fresh weight, and As accumulation between the OsAIR3-overexpressing and control plants. Collectively, these findings suggest that OsAIR3 positively regulates plant response to AsV stress.

Potential Arabidopsis thaliana glucosinolate genes identified from the co-expression modules using graph clustering approach

Background

Glucosinolates (GSLs) are plant secondary metabolites that contain nitrogen-containing compounds. They are important in the plant defense system and known to provide protection against cancer in humans. Currently, increasing the amount of data generated from various omics technologies serves as a hotspot for new gene discovery. However, sometimes sequence similarity searching approach is not sufficiently effective to find these genes; hence, we adapted a network clustering approach to search for potential GSLs genes from the Arabidopsis thaliana co-expression dataset.

Methods

We used known GSL genes to construct a comprehensive GSL co-expression network. This network was analyzed with the DPClusOST algorithm using a density of 0.5. 0.6. 0.7, 0.8, and 0.9. Generating clusters were evaluated using Fisher’s exact test to identify GSL gene co-expression clusters. A significance score (SScore) was calculated for each gene based on the generated p-value of Fisher’s exact test. SScore was used to perform a receiver operating characteristic (ROC) study to classify possible GSL genes using the ROCR package. ROCR was used in determining the AUC that measured the suitable density value of the cluster for further analysis. Finally, pathway enrichment analysis was conducted using ClueGO to identify significant pathways associated with the GSL clusters.

Results

The density value of 0.8 showed the highest area under the curve (AUC) leading to the selection of thirteen potential GSL genes from the top six significant clusters that include IMDH3, MVP1, T19K24.17, MRSA2, SIR, ASP4, MTO1, At1g21440, HMT3, At3g47420, PS1, SAL1, and At3g14220. A total of Four potential genes (MTO1, SIR, SAL1, and IMDH3) were identified from the pathway enrichment analysis on the significant clusters. These genes are directly related to GSL-associated pathways such as sulfur metabolism and valine, leucine, and isoleucine biosynthesis. This approach demonstrates the ability of the network clustering approach in identifying potential GSL genes which cannot be found from the standard similarity search.

Arabidopsis Coexpression Tool: a tool for gene coexpression analysis in Arabidopsis thaliana

Gene coexpression analysis refers to the discovery of sets of genes which exhibit similar expression patterns across multiple transcriptomic data sets, such as microarray experiment data of public repositories. Arabidopsis Coexpression Tool (ACT), a gene coexpression analysis web tool for Arabidopsis thaliana, identifies genes which are correlated to a driver gene. Primary microarray data from ATH1 Affymetrix platform were processed with Single-Channel Array Normalization algorithm and combined to produce a coexpression tree which contains ∼21,000 A. thaliana genes. ACT was developed to present subclades of coexpressed genes, as well as to perform gene set enrichment analysis, being unique in revealing enriched transcription factors targeting coexpressed genes. ACT offers a simple and user-friendly interface producing working hypotheses which can be experimentally verified for the discovery of gene partnership, pathway membership, and transcriptional regulation. ACT analyses have been successful in identifying not only genes with coordinated ubiquitous expressions but also genes with tissue-specific expressions.

De Novo Mutation and Rapid Protein (Co-)evolution during Meiotic Adaptation in Arabidopsis arenosa

A sudden shift in environment or cellular context necessitates rapid adaptation. A dramatic example is genome duplication, which leads to polyploidy. In such situations, the waiting time for new mutations might be prohibitive; theoretical and empirical studies suggest that rapid adaptation will largely rely on standing variation already present in source populations. Here, we investigate the evolution of meiosis proteins in Arabidopsis arenosa, some of which were previously implicated in adaptation to polyploidy, and in a diploid, habitat. A striking and unexplained feature of prior results was the large number of amino acid changes in multiple interacting proteins, especially in the relatively young tetraploid. Here, we investigate whether selection on meiosis genes is found in other lineages, how the polyploid may have accumulated so many differences, and whether derived variants were selected from standing variation. We use a range-wide sample of 145 resequenced genomes of diploid and tetraploid A. arenosa, with new genome assemblies. We confirmed signals of positive selection in the polyploid and diploid lineages they were previously reported in and find additional meiosis genes with evidence of selection. We show that the polyploid lineage stands out both qualitatively and quantitatively. Compared with diploids, meiosis proteins in the polyploid have more amino acid changes and a higher proportion affecting more strongly conserved sites. We find evidence that in tetraploids, positive selection may have commonly acted on de novo mutations. Several tests provide hints that coevolution, and in some cases, multinucleotide mutations, might contribute to rapid accumulation of changes in meiotic proteins.

Comparative transcriptomics reveals desynchronisation of gene expression during the floral transition between Arabidopsis and Brassica rapa cultivars

Comparative transcriptomics can be used to translate an understanding of gene regulatory networks from model systems to less studied species. Here, we use RNA-Seq to determine and compare gene expression dynamics through the floral transition in the model species Arabidopsis thaliana and the closely related crop Brassica rapa. We find that different curve registration functions are required for different genes, indicating that there is no single common 'developmental time' between Arabidopsis and B. rapa. A detailed comparison between Arabidopsis and B. rapa and between two B. rapa accessions reveals different modes of regulation of the key floral integrator SOC1, and that the floral transition in the B. rapa accessions is triggered by different pathways. Our study adds to the mechanistic understanding of the regulatory network of flowering time in rapid cycling B. rapa and highlights the importance of registration methods for the comparison of developmental gene expression data.

Integrative inference of transcriptional networks in Arabidopsis yields novel ROS signalling regulators

Gene regulation is a dynamic process in which transcription factors (TFs) play an important role in controlling spatiotemporal gene expression. To enhance our global understanding of regulatory interactions in Arabidopsis thaliana, different regulatory input networks capturing complementary information about DNA motifs, open chromatin, TF-binding and expression-based regulatory interactions were combined using a supervised learning approach, resulting in an integrated gene regulatory network (iGRN) covering 1,491 TFs and 31,393 target genes (1.7 million interactions). This iGRN outperforms the different input networks to predict known regulatory interactions and has a similar performance to recover functional interactions compared to state-of-the-art experimental methods. The iGRN correctly inferred known functions for 681 TFs and predicted new gene functions for hundreds of unknown TFs. For regulators predicted to be involved in reactive oxygen species (ROS) stress regulation, we confirmed in total 75% of TFs with a function in ROS and/or physiological stress responses. This includes 13 ROS regulators, previously not connected to any ROS or stress function, that were experimentally validated in our ROS-specific phenotypic assays of loss- or gain-of-function lines. In conclusion, the presented iGRN offers a high-quality starting point to enhance our understanding of gene regulation in plants by integrating different experimental data types.

Removing auto-activators from yeast-two-hybrid assays by conditional negative selection

Yeast-two-hybrid (Y2H) is widely used as a strategy to detect protein–protein interactions (PPIs). Recent advancements have made it possible to generate and analyse genome-wide PPI networks en masse by coupling Y2H with next-generation sequencing technology. However, one of the major challenges of yeast two-hybrid assay is the large amount of false-positive hits caused by auto-activators (AAs), which are proteins that activate the reporter genes without the presence of an interacting protein partner. Here, we have developed a negative selection to minimize these auto-activators by integrating the pGAL2-URA3 fragment into the yeast genome. Upon activation of the pGAL2 promoter by an AA, yeast cells expressing URA3 cannot grow in media supplemented with 5-Fluoroorotic acid (5-FOA). Hence, we selectively inhibit the growth of yeast cells expressing auto-activators and thus minimizing the amount of false-positive hits. Here, we have demonstrated that auto-activators can be successfully removed from a Marchantia polymorpha cDNA library using pGAL2-URA3 and 5-FOA treatment, in liquid and solid-grown cultures. Furthermore, since URA3 can also serve as a marker for uracil autotrophy, we propose that our approach is a valuable addition to any large-scale Y2H screen.

ADAP is a possible negative regulator of glucosinolate biosynthesis in Arabidopsis thaliana based on clustering and gene expression analyses

Glucosinolates (GSLs) are plant secondary metabolites consisting of sulfur and nitrogen, commonly found in Brassicaceae crops, such as Arabidopsis thaliana. These compounds are known for their roles in plant defense mechanisms against pests and pathogens. 'Guilt-by-association' (GBA) approach predicts genes encoding proteins with similar function tend to share gene expression pattern generated from high throughput sequencing data. Recent studies have successfully identified GSL genes using GBA approach, followed by targeted verification of gene expression and metabolite data. Therefore, a GSL co-expression network was constructed using known GSL genes obtained from our in-house database, SuCComBase. DPClusO was used to identify subnetworks of the GSL co-expression network followed by Fisher's exact test leading to the discovery of a potential gene that encodes the ARIA-interacting double AP2-domain protein (ADAP) transcription factor (TF). Further functional analysis was performed using an effective gene silencing system known as CRES-T. By applying CRES-T, ADAP TF gene was fused to a plant-specific EAR-motif repressor domain (SRDX), which suppresses the expression of ADAP target genes. In this study, ADAP was proposed as a negative regulator in aliphatic GSL biosynthesis due to the over-expression of downstream aliphatic GSL genes (UGT74C1 and IPMI1) in ADAP-SRDX line. The significant over-expression of ADAP gene in the ADAP-SRDX line also suggests the behavior of the TF that negatively affects the expression of UGT74C1 and IPMI1 via a feedback mechanism in A. thaliana.

Status and prospects of genome-wide association studies in plants

Genome-wide association studies (GWAS) have developed into a powerful and ubiquitous tool for the investigation of complex traits. In large part, this was fueled by advances in genomic technology, enabling us to examine genome-wide genetic variants across diverse genetic materials. The development of the mixed model framework for GWAS dramatically reduced the number of false positives compared with naïve methods. Building on this foundation, many methods have since been developed to increase computational speed or improve statistical power in GWAS. These methods have allowed the detection of genomic variants associated with either traditional agronomic phenotypes or biochemical and molecular phenotypes. In turn, these associations enable applications in gene cloning and in accelerated crop breeding through marker assisted selection or genetic engineering. Current topics of investigation include rare-variant analysis, synthetic associations, optimizing the choice of GWAS model, and utilizing GWAS results to advance knowledge of biological processes. Ongoing research in these areas will facilitate further advances in GWAS methods and their applications.

Genome-wide characterization and analysis of the CCT motif family genes in soybean (Glycine max)

MAIN CONCLUSION

Soybean possesses 19 CMF genes which mainly arose from duplication events. Their features and motifs are highly conserved but transcriptional data indicated functional diversity in metabolism and stress responses. CCT [for CONSTANS, CONSTANS-like (CO-like), and timing of CAB expression1 (TOC1)] domain-containing genes play important roles in regulating flowering, plant growth, and grain yield and are also involved in stress responses. The CMF (CCT motif family) genes, included in the CCT family, contain a single CCT domain as the only identifiable domain in their predicted protein sequence and are interesting targets for breeding programs. In this study, we identified 19 putative GmCMF genes, based on the latest soybean (Glycine max) genome annotation. The predicted GmCMF proteins were characterized based on conserved structural features, and a phylogenetic tree was constructed including all CMF proteins from rice and Arabidopsis as representative examples of the monocotyledonous (monocot) and dicotyledonous (dicot) plants, respectively. High similarities in the conserved motifs of the protein sequences and the gene structures were found. In addition, by analyzing the CMF gene family in soybean, we identified seven pairs of genes that originated from segmental chromosomal duplication events attributable to the most recent whole-genome duplication (WGD) event in the Glycine lineage. Expression analysis of GmCMF genes in various tissues and after specific treatments demonstrated tissue and stress-response specific differential expression. Gene expression analysis was complemented by the identification of putative cis-elements present in the promoter regions of the genes through a bioinformatics approach, using the existing soybean reference genome sequence and gene models. Co-functional networks inferred from distinct types of genomics data-including microarrays and RNA-seq samples from soybean-revealed that GmCMF genes might play crucial roles in metabolism and transport processes. The results of this study, the first systematic analysis of the soybean CCT gene family, can serve as a strong foundation for further elucidation of their physiological functions and biological roles.

Genome-wide identification of PYL gene family in wheat: Evolution, expression and 3D structure analysis

Here, 38 wheat PYL genes (TaPYLs) belonging to 13 homoeologous groups were identified using the genome-search method, with 26 and 12 PYL genes identified in Triticum dicoccoides and Aegilops tauschii, respectively. Phylogenetic relationship, conserved domain and molecular evolution analysis revealed that PYL genes showed highly conservative between wheat and theprogenitors. Interaction network and miRNA target prediction found that TaPYLs could interact with the important components of ABA signaling pathway and Tae-miR966b-3p might be a hub regulator mediating wheat ABA signal network. Furthermore, the tissue-specific and stress-responsive TaPYLs were detected through RNA-seq analysis. Expressions of 10 TaPYLs were validated by QPCR analysis and the homoeologous genes showed significantly differential expression, suggesting subfunctionalization of them has occurred. Finally, 3D structures of the TaPYL proteins were predicted by homology modeling. This study lays the foundation for further functional study of PYL genes for development and stress tolerance improvement in wheat and beyond.

PAFway: pairwise associations between functional annotations in biological networks and pathways

Motivation

Large gene networks can be dense and difficult to interpret in a biologically meaningful way.

Results

Here, we introduce PAFway, which estimates pairwise associations between functional annotations in biological networks and pathways. It answers the biological question: do genes that have a specific function tend to regulate genes that have a different specific function? The results can be visualized as a heatmap or a network of biological functions. We apply this package to reveal associations between functional annotations in an Arabidopsis thaliana gene network.

Availability and implementation

PAFway is submitted to CRAN. Currently available here: https://github.com/ezer/PAFway.

Supplementary information

Supplementary data are available at Bioinformatics online.

Fungi.guru: Comparative genomic and transcriptomic resource for the fungi kingdom

The fungi kingdom is composed of eukaryotic heterotrophs, which are responsible for balancing the ecosystem and play a major role as decomposers. They also produce a vast diversity of secondary metabolites, which have antibiotic or pharmacological properties. However, our lack of knowledge of gene function in fungi precludes us from tailoring them to our needs and tapping into their metabolic diversity. To help remedy this, we gathered genomic and gene expression data of 19 most widely-researched fungi to build an online tool, fungi.guru, which contains tools for cross-species identification of conserved pathways, functional gene modules, and gene families. We exemplify how our tool can elucidate the molecular function, biological process and cellular component of genes involved in various biological processes, by identifying a secondary metabolite pathway producing gliotoxin in Aspergillus fumigatus, the catabolic pathway of cellulose in Coprinopsis cinerea and the conserved DNA replication pathway in Fusarium graminearum and Pyricularia oryzae. The tool is available at www.fungi.guru.

Transcriptome-wide identification of MAPKKK genes in bermudagrass (Cynodon dactylon L.) and their potential roles in low temperature stress responses

As upstream components of MAPK cascades, mitogen-activated protein kinase kinase kinases (MAPKKKs) act as adaptors linking upstream signaling steps to the core MAPK cascades. MAPK cascades are universal modules of signal transduction in eukaryotic organisms and play crucial roles in plant development processes and in responses to biotic and abiotic stress and signal transduction. Members of the MAPKKK gene family have been identified in several plants,however, MAPKKKs have not been systematically studied in bermudagrass (Cynodon dactylon L.). In this study, 55 potential CdMAPKKKs were produced from bermudagrass transcriptome data, of which 13 belonged to the MEKK, 38 to the Raf, and 4 to the ZIK subfamily. Multiple alignment and conserved motif analysis of CdMAPKKKs supported the evolutionary relationships inferred from phylogenetic analyses. Moreover, the distribution pattern in Poaceae species indicated that members of the MAPKKK family were conserved among almost all diploid species, and species-specific polyploidy or higher duplication ratios resulted in an expansion of the MAPKKK family. In addition, 714 co-functional links which were significantly enriched in signal transduction, responses to temperature stimuli, and other important biological processes of 55 CdMAPKKKs were identified using co-functional gene networks analysis; 30 and 19 co-functional genes involved in response to cold or heat stress, respectively, were also identified. Results of promoter analyses, and interaction network investigation of all CdMAPKKKs based on the rice homologs suggested that CdMAPKKKs are commonly associated with regulation of numerous biological processes. Furthermore, 12 and 13 CdMAPKKKs were significantly up- and downregulated, respectively, in response to low temperature stress; among them, six CdMAPKKKs were significantly induced by low temperature stress, at least at one point in time. This is the first study to conduct identification and functional analysis of the MAPKKK gene family in bermudagrass, and our results provide a foundation for further research on the functions of CdMAPKKKs in response to low temperature stress.

Genome-Wide Association Studies in Arabidopsis thaliana: Statistical Analysis and Network-Based Augmentation of Signals

Genome-wide association studies (GWAS) have proven effective at identifying genetic variants and genes that are associated with phenotypes in humans, animals, and plants. Since most phenotypes of plant species are complex traits regulated by many genes and their functional interactions, GWAS are increasing in popularity for genetic dissections of plant phenotypes. For the reference plant, Arabidopsis thaliana, detailed information on genetic variations became available with the completion of the 1001 Genomes Project, enabling highly resolved association mapping between chromosomal loci and complex traits. Improvements have been made in the statistical analysis methods for testing the significance of genotype-to-phenotype associations, thereby substantially reducing the confounding effects of population structures. Furthermore, there have been large efforts toward post-GWAS augmentation of signals via integration with other types of information to overcome the limited statistical power of GWAS. This chapter describes the stepwise procedure of GWAS in Arabidopsis, focusing on data analysis processes including preprocessing of genotype and phenotype data, statistical analysis to identify phenotype-associated chromosomal loci, identification of phenotype-associated genes based on the phenotype-associated loci, and finally network-based augmentation of GWAS signals to identify additional candidate genes for the phenotype.

Genome-wide identification, phylogeny and expression analysis of the SPL gene family in wheat

BACKGROUND

Members of the plant-specific SPL gene family (squamosa promoter-binding protein -like) contain the SBP conserved domain and are involved in the regulation of plant growth and development, including the development of plant flowers and plant epidermal hair, the plant stress response, and the synthesis of secondary metabolites. This family has been identified in various plants. However, there is no systematic analysis of the SPL gene family at the genome-wide level of wheat.

RESULTS

In this study, 56 putative TaSPL genes were identified using the comparative genomics method; we renamed them TaSPL001 - TaSPL056 on their chromosomal distribution. According to the un-rooted neighbor joining phylogenetic tree, gene structure and motif analyses, the 56 TaSPL genes were divided into 8 subgroups. A total of 81 TaSPL gene pairs were designated as arising from duplication events and 64 interacting protein branches were identified as involve in the protein interaction network. The expression patterns of 21 randomly selected TaSPL genes in different tissues (roots, stems, leaves and inflorescence) and under 4 treatments (abscisic acid, gibberellin, drought and salt) were detected by quantitative real-time polymerase chain reaction (qRT-PCR).

CONCLUSIONS

The wheat genome contains 56 TaSPL genes and those in same subfamily share similar gene structure and motifs. TaSPL gene expansion occurred through segmental duplication events. Combining the results of transcriptional and qRT-PCR analyses, most of these TaSPL genes were found to regulate inflorescence and spike development. Additionally, we found that 13 TaSPLs were upregulated by abscisic acid, indicating that TaSPL genes play a positive role in the abscisic acid-mediated pathway of the seedling stage. This study provides comprehensive information on the SPL gene family of wheat and lays a solid foundation for elucidating the biological functions of TaSPLs and improvement of wheat yield.

Systems biology of responses to simultaneous copper and iron deficiency in Arabidopsis

Plant responses to coincident nutrient deficiencies cannot be predicted from the responses to individual deficiencies. Although copper (Cu) and iron (Fe) are essential micronutrients for plant growth that are often and concurrently limited in soils, the combinatorial response to Cu-Fe deficiency remains elusive. In the present study, we characterised the responses of Arabidopsis thaliana plants deprived of Cu, Fe or both (-Cu-Fe) at the level of plant development, mineral composition, and reconfiguration of transcriptomes, proteomes and metabolomes. Compared to single deficiencies, simultaneous -Cu-Fe leads to a distinct pattern in leaf physiology and microelement concentration characterised by lowered protein content and enhanced manganese and zinc levels. Conditional networking analysis of molecular changes indicates that biological processes also display different co-expression patterns among single and double deficiencies. Indeed, the interaction between Cu and Fe deficiencies causes distinct expression profiles for 15% of all biomolecules, leading to specific enhancement of general stress responses and protein homeostasis mechanisms, at the same time as severely arresting photosynthesis. Accordingly, central carbon metabolites, in particular photosynthates, decrease especially under -Cu-Fe conditions, whereas the pool of free amino acids increases. Further meta-analysis of transcriptomes and proteomes corroborated that protein biosynthesis and folding capacity were readjusted during the combinatorial response and unveiled important rearrangements in the metabolism of organic acids. Consequently, our results demonstrate that the response to -Cu-Fe imposes a distinct reconfiguration of large sets of molecules, not triggered by single deficiencies, resulting into a switch from autotrophy to heterotrophy and involving organic acids such as fumaric acid as central mediators of the response.

Crosstalk between heterotrimeric G protein-coupled signaling pathways and WRKY transcription factors modulating plant responses to suboptimal micronutrient conditions

Nutrient stresses induce foliar chlorosis and growth defects. Here we propose heterotrimeric G proteins as signaling mediators of various nutrient stresses, through meta-analyses of >20 transcriptomic data sets associated with nutrient stresses or G protein mutations. Systematic comparison of transcriptomic data yielded 104 genes regulated by G protein subunits under common nutrient stresses: 69 genes under Gβ subunit (AGB1) control and 35 genes under Gα subunit (GPA1) control. Quantitative real-time PCR experiments validate that several transcription factors and metal transporters changed in expression level under suboptimal iron, zinc, and/or copper concentrations, while being misregulated in the Arabidopsis Gβ-null (agb1) mutant. The agb1 mutant had altered metal ion profiles and exhibited severe growth arrest under zinc stress, and aberrant root waving under iron and zinc stresses, while the Gα-null mutation attenuated leaf chlorosis under iron deficiency in both Arabidopsis and rice. Our transcriptional network analysis inferred computationally that WRKY-family transcription factors mediate the AGB1-dependent nutrient responses. As corroborating evidence of our inference, ectopic expression of WRKY25 or WRKY33 rescued the zinc stress phenotypes and the expression of zinc transporters in the agb1-2 background. These results, together with Gene Ontology analyses, suggest two contrasting roles for G protein-coupled signaling pathways in micronutrient stress responses: one enhancing general stress tolerance and the other modulating ion homeostasis through WRKY transcriptional regulatory networks. In addition, tolerance to iron stress in the rice Gα mutant provides an inroad to improve nutrient stress tolerance of agricultural crops by manipulating G protein signaling.

Coexpression Analysis Reveals Dynamic Modules Regulating the Growth and Development of Cirri in the Rattans (Calamus simplicifolius and Daemonorops jenkinsiana)

Rattan is regarded as one of the major non-timber forest products, second only to wood and bamboo, worldwide. Although the published genomes of Calamus simplicifolius and Daemonorops jenkinsiana have facilitated genome-wide gene functional analyses, coexpression networks (CENs) provide more comprehensive and complete annotations of gene function at the transcriptome level. Thus, we analyzed the CENs of the two rattans, C. simplicifolius and D. jenkinsiana, by integrating the genome sequences and analyzing in-house transcriptome data from different development stages of their cirri using a well-developed strategy. A total of 3,504 and 3,027 functional modules were identified in C. simplicifolius and D. jenkinsiana, respectively, based on a combination of CENs, gene family classification, and function enrichment tools. These modules covered the major developmental processes, including photosynthesis, lignin biosynthesis, flavonoid biosynthesis, and phenylpropanoid biosynthesis. Reference annotations were refined using CENs and functional modules. Moreover, we obtained novel insights into the regulation of cirrus growth and development in rattans. Furthermore, Rattan-NET (http://rattan.bamboogdb.org/), an online database with analysis tools for gene set enrichment analysis, module enrichment, network comparison analysis, and cis-element analysis, was constructed for the easy analysis of gene function and regulation modules involved in the growth and development of cirri in rattans.