TomatoNet: A Genome-wide Co-functional Network for Unveiling Complex Traits of Tomato, a Model Crop for Fleshy Fruits

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.

Mapping of the Cladosporium fulvum resistance gene Cf-16, a major gene involved in leaf mold disease in tomato

Tomato (Solanum lycopersicum) is widely cultivated and consumed worldwide. Tomato leaf mold, caused by Cladosporium fulvum, is one of the most devastating diseases in tomato production. At present, some tomato leaf mold resistance (Cf series) genes used in production gradually lose resistance due to the continuous and rapid differentiation of C. fulvum physiological races. The Cf-16 gene derived from the "Ontario7816" tomato cultivar has shown effective resistance in field trials for many years, but few studies have reported on the mapping of the Cf-16 gene, which has not been cloned, limiting its utilization in tomato breeding. Here, we mapped Cf-16 using a novel comprehensive strategy including bulk segregation analysis (BSA), genome resequencing and SSR molecular markers. A genetic analysis revealed that Cf-16 resistance in "Ontario7816" is controlled by one major dominant locus. The Cf-16 gene was mapped in a region of 2.6 cM at chromosome 6 between two markers, namely, TGS447 and TES312, by using an F2 population from a cross between the resistant cultivar "Ontario7816" and susceptible line "Moneymaker." Two nucleotide-binding-site-leucine-rich repeat (NBS-LRR) resistance genes, namely, XM_004240667.3 and XM_010323727.1, were identified in this interval. They are strong candidates for the Cf-16 gene. The mapping of Cf-16 may speed up its utilization for breeding resistant tomato varieties and represents an important step forward in our understanding of the mechanism underlying resistance to tomato leaf mold.

A wheat integrative regulatory network from large-scale complementary functional datasets enables trait-associated gene discovery for crop improvement

Gene regulation is central to all aspects of organism growth, and understanding it using large-scale functional datasets can provide a whole view of biological processes controlling complex phenotypic traits in crops. However, the connection between massive functional datasets and trait-associated gene discovery for crop improvement is still lacking. In this study, we constructed a wheat integrative gene regulatory network (wGRN) by combining an updated genome annotation and diverse complementary functional datasets, including gene expression, sequence motif, transcription factor (TF) binding, chromatin accessibility, and evolutionarily conserved regulation. wGRN contains 7.2 million genome-wide interactions covering 5947 TFs and 127 439 target genes, which were further verified using known regulatory relationships, condition-specific expression, gene functional information, and experiments. We used wGRN to assign genome-wide genes to 3891 specific biological pathways and accurately prioritize candidate genes associated with complex phenotypic traits in genome-wide association studies. In addition, wGRN was used to enhance the interpretation of a spike temporal transcriptome dataset to construct high-resolution networks. We further unveiled novel regulators that enhance the power of spike phenotypic trait prediction using machine learning and contribute to the spike phenotypic differences among modern wheat accessions. Finally, we developed an interactive webserver, wGRN (http://wheat.cau.edu.cn/wGRN), for the community to explore gene regulation and discover trait-associated genes. Collectively, this community resource establishes the foundation for using large-scale functional datasets to guide trait-associated gene discovery for crop improvement.

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.

β-Cyclocitral, a Master Regulator of Multiple Stress-Responsive Genes in Solanum lycopersicum L. Plants

β-cyclocitral (βCC), a major apocarotenoid of β-carotene, enhances plants' defense against environmental stresses. However, the knowledge of βCC's involvement in the complex stress-signaling network is limited. Here we demonstrate how βCC reprograms the transcriptional responses that enable Solanum lycopersicum L. (tomato) plants to endure a plethora of environmental stresses. Comparative transcriptome analysis of control and βCC-treated tomato plants was done by generating RNA sequences in the BGISEQ-500 platform. The trimmed sequences were mapped on the tomato reference genome that identifies 211 protein-coding differentially expressed genes. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis and their enrichment uncovered that only upregulated genes are attributed to the stress response. Moreover, 80% of the upregulated genes are functionally related to abiotic and biotic stresses. Co-functional analysis of stress-responsive genes revealed a network of 18 genes that code for heat shock proteins, transcription factors (TFs), and calcium-binding proteins. The upregulation of jasmonic acid (JA)-dependent TFs (MYC2, MYB44, ERFs) but not the JA biosynthetic genes is surprising. However, the upregulation of DREB3, an abscisic acid (ABA)-independent TF, validates the unaltered expression of ABA biosynthetic genes. We conclude that βCC treatment upregulates multiple stress-responsive genes without eliciting JA and ABA biosynthesis.

BiomeNet: a database for construction and analysis of functional interaction networks for any species with a sequenced genome

Motivation

Owing to advanced DNA sequencing and genome assembly technology, the number of species with sequenced genomes is rapidly increasing. The aim of the recently launched Earth BioGenome Project is to sequence genomes of all eukaryotic species on Earth over the next 10 years, making it feasible to obtain genomic blueprints of the majority of animal and plant species by this time. Genetic models of the sequenced species will later be subject to functional annotation, and a comprehensive molecular network should facilitate functional analysis of individual genes and pathways. However, network databases are lagging behind genome sequencing projects as even the largest network database provides gene networks for less than 10% of sequenced eukaryotic genomes, and the knowledge gap between genomes and interactomes continues to widen.

Results

We present BiomeNet, a database of 95 scored networks comprising over 8 million co-functional links, which can build and analyze gene networks for any species with the sequenced genome. BiomeNet transfers functional interactions between orthologous proteins from source networks to the target species within minutes and automatically constructs gene networks with the quality comparable to that of existing networks. BiomeNet enables assembly of the first-in-species gene networks not available through other databases, which are highly predictive of diverse biological processes and can also provide network analysis by extracting subnetworks for individual biological processes and network-based gene prioritizations. These data indicate that BiomeNet could enhance the benefits of decoding the genomes of various species, thus improving our understanding of the Earth’ biodiversity.

Availability and implementation

The BiomeNet is freely available at http://kobic.re.kr/biomenet/.

Supplementary information

Supplementary data are available at Bioinformatics online.

PlantEAR: Functional Analysis Platform for Plant EAR Motif-Containing Proteins

The Ethylene-responsive element binding factor-associated Amphiphilic Repression (EAR) motifs, which were initially identified in members of the Arabidopsis ethylene response factor (ERF) family, are transcriptional repression motifs in plants and are defined by the consensus sequence patterns of either LxLxL or DLNxxP. EAR motif-containing proteins can function as transcription repressors, thus interacting with co-repressors, such as TOPLESS and AtSAP18, affecting the structure of chromatin by histone modifications and thereby repressing gene transcription. EAR motif-containing proteins are highly conserved across diverse plant species and play important roles in hormone signal transduction, stress responses and development, but they have not been identified in most plants. In this study, we identified 20,542 EAR motif-containing proteins from 71 plant species based on a Hidden Markov Model and orthologous gene search, and then we constructed a functional analysis platform for plant EAR motif-containing proteins (PlantEAR, http://structuralbiology.cau.edu.cn/plantEAR) by integrating a variety of functional annotations and processed data. Several tools were provided as functional support for EAR motif-containing proteins, such as browse, search, co-expression and protein-protein interaction (PPI) network analysis as well as cis-element analysis and gene set enrichment analysis (GSEA). In addition, basing on the identified EAR motif-containing proteins, we also explored their distribution in various species and found that the numbers of EAR motif-containing proteins showed an increasing trend in evolution from algae to angiosperms.

Gene Discovery of Characteristic Metabolic Pathways in the Tea Plant (Camellia sinensis) Using 'Omics'-Based Network Approaches: A Future Perspective

Characteristic secondary metabolites, including flavonoids, theanine and caffeine, in the tea plant (Camellia sinensis) are the primary sources of the rich flavors, fresh taste, and health benefits of tea. The decoding of genes involved in these characteristic components is still significantly lagging, which lays an obstacle for applied genetic improvement and metabolic engineering. With the popularity of high-throughout transcriptomics and metabolomics, 'omics'-based network approaches, such as gene co-expression network and gene-to-metabolite network, have emerged as powerful tools for gene discovery of plant-specialized (secondary) metabolism. Thus, it is pivotal to summarize and introduce such system-based strategies in facilitating gene identification of characteristic metabolic pathways in the tea plant (or other plants). In this review, we describe recent advances in transcriptomics and metabolomics for transcript and metabolite profiling, and highlight 'omics'-based network strategies using successful examples in model and non-model plants. Further, we summarize recent progress in 'omics' analysis for gene identification of characteristic metabolites in the tea plant. Limitations of the current strategies are discussed by comparison with 'omics'-based network approaches. Finally, we demonstrate the potential of introducing such network strategies in the tea plant, with a prospects ending for a promising network discovery of characteristic metabolite genes in the tea plant.

Whole Root Transcriptomic Analysis Suggests a Role for Auxin Pathways in Resistance to Ralstonia solanacearum in Tomato

The soilborne pathogen Ralstonia solanacearum is the causal agent of bacterial wilt and causes significant crop loss in the Solanaceae family. The pathogen first infects roots, which are a critical source of resistance in tomato (Solanum lycopersicum L.). Roots of both resistant and susceptible plants are colonized by the pathogen, yet rootstocks can provide significant levels of resistance. Currently, mechanisms of this 'root-mediated resistance' remain largely unknown. To identify the molecular basis of this resistance, we analyzed the genome-wide transcriptional response of roots of resistant 'Hawaii 7996' and susceptible 'West Virginia 700' (WV) tomatoes at multiple timepoints after inoculation with R. solanacearum. We found that defense pathways in roots of the resistant Hawaii 7996 are activated earlier and more strongly than roots of susceptible WV. Further, auxin signaling and transport pathways are suppressed in roots of the resistant variety. Functional analysis of an auxin transport mutant in tomato revealed a role for auxin pathways in bacterial wilt. Together, our results suggest that roots mediate resistance to R. solanacearum through genome-wide transcriptomic changes that result in strong activation of defense genes and alteration of auxin pathways.

araGWAB: Network-based boosting of genome-wide association studies in Arabidopsis thaliana

Genome-wide association studies (GWAS) have been applied for the genetic dissection of complex phenotypes in Arabidopsis thaliana. However, the significantly associated single-nucleotide polymorphisms (SNPs) could not explain all the phenotypic variations. A major reason for missing true phenotype-associated loci is the strict P-value threshold after adjustment for multiple hypothesis tests to reduce false positives. This statistical limitation can be partly overcome by increasing the sample size, but at a much higher cost. Alternatively, weak phenotype-association signals can be boosted by integrating other types of data. Here, we present a web application for network-based Arabidopsis genome-wide association boosting-araGWAB-which augments the likelihood of association with the given phenotype by integrating GWAS summary statistics (SNP P-values) and co-functional gene network information. The integration utilized the inherent values of SNPs with subthreshold significance, thus substantially increasing the information usage of GWAS data. We found that araGWAB could more effectively retrieve genes known to be associated with various phenotypes relevant to defense against bacterial pathogens, flowering time regulation, and organ development in A. thaliana. We also found that many of the network-boosted candidate genes for the phenotypes were supported by previous publications. The araGWAB is freely available at http://www.inetbio.org/aragwab/ .