Gene coexpression network analysis as a source of functional annotation for rice genes

Exploring large-scale gene coexpression networks in peach (Prunus persica L.): a new tool for predicting gene function

Peach is a model for Prunus genetics and genomics, however, identifying and validating genes associated to peach breeding traits is a complex task. A gene coexpression network (GCN) capable of capturing stable gene-gene relationships would help researchers overcome the intrinsic limitations of peach genetics and genomics approaches and outline future research opportunities. In this study, we created four GCNs from 604 Illumina RNA-Seq libraries. We evaluated the performance of every GCN in predicting functional annotations using an algorithm based on the 'guilty-by-association' principle. The GCN with the best performance was COO300, encompassing 21 956 genes. To validate its performance predicting gene function, we performed two case studies. In case study 1, we used two genes involved in fruit flesh softening: the endopolygalacturonases PpPG21 and PpPG22. Genes coexpressing with both genes were extracted and referred to as melting flesh (MF) network. Finally, we performed an enrichment analysis of MF network and compared the results with the current knowledge regarding peach fruit softening. The MF network mostly included genes involved in cell wall expansion and remodeling, and with expressions triggered by ripening-related phytohormones, such as ethylene, auxin, and methyl jasmonate. In case study 2, we explored potential targets of the anthocyanin regulator PpMYB10.1 by comparing its gene-centered coexpression network with that of its grapevine orthologues, identifying a common regulatory network. These results validated COO300 as a powerful tool for peach and Prunus research. This network, renamed as PeachGCN v1.0, and the scripts required to perform a function prediction analysis are available at https://github.com/felipecobos/PeachGCN.

Identification of hub genes that variate the qCSS12-mediated cold tolerance between indica and japonica rice using WGCNA

KEY MESSAGE

Cold-tolerant QTL qCSS12-regulated 14 hub genes are involved in the chloroplastic biological processes and in the protein synthesis and degradation processes in japonica rice. Low temperature is a main constraint factor for rice growth and production. To better understand the regulatory mechanisms underlying the cold tolerance phenotype in rice, here, we selected a cold-sensitive nearly isogenic line (NIL) NIL(qcss12) as materials to identify hub genes that are mediated by the cold-tolerant locus qCSS12 through weighted gene co-expression network analysis (WGCNA). Fourteen cold-responsive genes were identified, of which, 6 are involved in regulating biological processes in chloroplasts, including the reported EF-Tu, Prk, and ChlD, and 8 are involved in the protein synthesis and degradation processes. Differential expression of these genes between NIL(qcss12) and its controls under cold stress may be responsible for qCSS12-mediated cold tolerance in japonica rice. Moreover, natural variations in 12 of these hub genes are highly correlated with the cold tolerance divergence in two rice subspecies. The results provide deep insights into a better understanding of the molecular basis of cold adaptation in rice and provide a theoretical basis for molecular breeding.

A gene network-driven approach to infer novel pathogenicity-associated genes: application to Pseudomonas aeruginosa PAO1

IMPORTANCE

We present here a new systems-level approach to decipher genetic factors and biological pathways associated with virulence and/or antibiotic treatment of bacterial pathogens. The power of this approach was demonstrated by application to a well-studied pathogen Pseudomonas aeruginosa PAO1. Our gene co-expression network-based approach unraveled known and unknown genes and their networks associated with pathogenicity in P. aeruginosa PAO1. The systems-level investigation of P. aeruginosa PAO1 helped identify putative pathogenicity and resistance-associated genetic factors that could not otherwise be detected by conventional approaches of differential gene expression analysis. The network-based analysis uncovered modules that harbor genes not previously reported by several original studies on P. aeruginosa virulence and resistance. These could potentially act as molecular determinants of P. aeruginosa PAO1 pathogenicity and responses to antibiotics.

Network Analysis of Publicly Available RNA-seq Provides Insights into the Molecular Mechanisms of Plant Defense against Multiple Fungal Pathogens in Arabidopsis thaliana

Fungal pathogens can have devastating effects on global crop production, leading to annual economic losses ranging from 10% to 23%. In light of climate change-related challenges, researchers anticipate an increase in fungal infections as a result of shifting environmental conditions. However, plants have developed intricate molecular mechanisms for effective defense against fungal attacks. Understanding these mechanisms is essential to the development of new strategies for protecting crops from multiple fungi threats. Public omics databases provide valuable resources for research on plant-pathogen interactions; however, integrating data from different studies can be challenging due to experimental variation. In this study, we aimed to identify the core genes that defend against the pathogenic fungi Colletotrichum higginsianum and Botrytis cinerea in Arabidopsis thaliana. Using a custom framework to control batch effects and construct Gene Co-expression Networks in publicly available RNA-seq dataset from infected A. thaliana plants, we successfully identified a gene module that was responsive to both pathogens. We also performed gene annotation to reveal the roles of previously unknown protein-coding genes in plant defenses against fungal infections. This research demonstrates the potential of publicly available RNA-seq data for identifying the core genes involved in defending against multiple fungal pathogens.

Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits in Urochloa ruziziensis

Tropical forage grasses, particularly those belonging to the Urochloa genus, play a crucial role in cattle production and serve as the main food source for animals in tropical and subtropical regions. The majority of these species are apomictic and tetraploid, highlighting the significance of U. ruziziensis, a sexual diploid species that can be tetraploidized for use in interspecific crosses with apomictic species. As a means to support breeding programs, our study investigates the feasibility of genome-wide family prediction in U. ruziziensis families to predict agronomic traits. Fifty half-sibling families were assessed for green matter yield, dry matter yield, regrowth capacity, leaf dry matter, and stem dry matter across different clippings established in contrasting seasons with varying available water capacity. Genotyping was performed using a genotyping-by-sequencing approach based on DNA samples from family pools. In addition to conventional genomic prediction methods, machine learning and feature selection algorithms were employed to reduce the necessary number of markers for prediction and enhance predictive accuracy across phenotypes. To explore the regulation of agronomic traits, our study evaluated the significance of selected markers for prediction using a tree-based approach, potentially linking these regions to quantitative trait loci (QTLs). In a multiomic approach, genes from the species transcriptome were mapped and correlated to those markers. A gene coexpression network was modeled with gene expression estimates from a diverse set of U. ruziziensis genotypes, enabling a comprehensive investigation of molecular mechanisms associated with these regions. The heritabilities of the evaluated traits ranged from 0.44 to 0.92. A total of 28,106 filtered SNPs were used to predict phenotypic measurements, achieving a mean predictive ability of 0.762. By employing feature selection techniques, we could reduce the dimensionality of SNP datasets, revealing potential genotype-phenotype associations. The functional annotation of genes near these markers revealed associations with auxin transport and biosynthesis of lignin, flavonol, and folic acid. Further exploration with the gene coexpression network uncovered associations with DNA metabolism, stress response, and circadian rhythm. These genes and regions represent important targets for expanding our understanding of the metabolic regulation of agronomic traits and offer valuable insights applicable to species breeding. Our work represents an innovative contribution to molecular breeding techniques for tropical forages, presenting a viable marker-assisted breeding approach and identifying target regions for future molecular studies on these agronomic traits.

Linking genetic and environmental factors through marker effect networks to understand trait plasticity

Understanding how plants adapt to specific environmental changes and identifying genetic markers associated with phenotypic plasticity can help breeders develop plant varieties adapted to a rapidly changing climate. Here, we propose the use of marker effect networks as a novel method to identify markers associated with environmental adaptability. These marker effect networks are built by adapting commonly used software for building gene coexpression networks with marker effects across growth environments as the input data into the networks. To demonstrate the utility of these networks, we built networks from the marker effects of ∼2,000 nonredundant markers from 400 maize hybrids across 9 environments. We demonstrate that networks can be generated using this approach, and that the markers that are covarying are rarely in linkage disequilibrium, thus representing higher biological relevance. Multiple covarying marker modules associated with different weather factors throughout the growing season were identified within the marker effect networks. Finally, a factorial test of analysis parameters demonstrated that marker effect networks are relatively robust to these options, with high overlap in modules associated with the same weather factors across analysis parameters. This novel application of network analysis provides unique insights into phenotypic plasticity and specific environmental factors that modulate the genome.

Leveraging class hierarchy for detecting missing annotations on hierarchical multi-label classification

With the development of new sequencing technologies, availability of genomic data has grown exponentially. Over the past decade, numerous studies have used genomic data to identify associations between genes and biological functions. While these studies have shown success in annotating genes with functions, they often assume that genes are completely annotated and fail to take into account that datasets are sparse and noisy. This work proposes a method to detect missing annotations in the context of hierarchical multi-label classification. More precisely, our method exploits the relations of functions, represented as a hierarchy, by computing probabilities based on the paths of functions in the hierarchy. By performing several experiments on a variety of rice (Oriza sativa Japonica), we showcase that the proposed method accurately detects missing annotations and yields superior results when compared to state-of-art methods from the literature.

dynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using time-course single-cell gene expression data

Time-course single-cell RNA sequencing (scRNA-seq) data have been widely used to explore dynamic changes in gene expression of transcription factors (TFs) and their target genes. This information is useful to reconstruct cell-type-specific gene regulatory networks (GRNs). However, the existing tools are commonly designed to analyze either time-course bulk gene expression data or static scRNA-seq data via pseudo-time cell ordering. A few methods successfully utilize the information from multiple time points while also considering the characteristics of scRNA-seq data. We proposed dynDeepDRIM, a novel deep learning model to reconstruct GRNs using time-course scRNA-seq data. It represents the joint expression of a gene pair as an image and utilizes the image of the target TF-gene pair and the ones of the potential neighbors to reconstruct GRNs from time-course scRNA-seq data. dynDeepDRIM can effectively remove the transitive TF-gene interactions by considering neighborhood context and model the gene expression dynamics using high-dimensional tensors. We compared dynDeepDRIM with six GRN reconstruction methods on both simulation and four real time-course scRNA-seq data. dynDeepDRIM achieved substantially better performance than the other methods in inferring TF-gene interactions and eliminated the false positives effectively. We also applied dynDeepDRIM to annotate gene functions and found it achieved evidently better performance than the other tools due to considering the neighbor genes.

Global transcriptome and coexpression network analyses reveal cultivar-specific molecular signatures associated with different rooting depth responses to drought stress in potato

Potato is one of the most important vegetable crops worldwide. Its growth, development and ultimately yield is hindered by drought stress condition. Breeding and selection of deep-rooted and drought-tolerant potato varieties has become a prime approach for improving the yield and quality of potato (Solanum tuberosum L.) in arid and semiarid areas. A comprehensive understanding of root development-related genes has enabled scientists to formulate strategies to incorporate them into breeding to improve complex agronomic traits and provide opportunities for the development of stress tolerant germplasm. Root response to drought stress is an intricate process regulated through complex transcriptional regulatory network. To understand the rooting depth and molecular mechanism, regulating root response to drought stress in potato, transcriptome dynamics of roots at different stages of drought stress were analyzed in deep (C119) and shallow-rooted (C16) cultivars. Stage-specific expression was observed for a significant proportion of genes in each cultivar and it was inferred that as compared to C16 (shallow-rooted), approximately half of the genes were differentially expressed in deep-rooted cultivar (C119). In C16 and C119, 11 and 14 coexpressed gene modules, respectively, were significantly associated with physiological traits under drought stress. In a comparative analysis, some modules were different between the two cultivars and were associated with differential response to specific drought stress stage. Transcriptional regulatory networks were constructed, and key components determining rooting depth were identified. Through the results, we found that rooting depth (shallow vs deep) was largely determined by plant-type, cell wall organization or biogenesis, hemicellulose metabolic process, and polysaccharide metabolic process. In addition, candidate genes responding to drought stress were identified in deep (C119) and shallow (C16) rooted potato varieties. The results of this study will be a valuable source for further investigations on the role of candidate gene(s) that affect rooting depth and drought tolerance mechanisms in potato.

Integrative System Biology Analysis of Transcriptomic Responses to Drought Stress in Soybean (Glycine max L.)

Drought is a major abiotic stressor that causes yield losses and limits the growing area for most crops. Soybeans are an important legume crop that is sensitive to water-deficit conditions and suffers heavy yield losses from drought stress. To improve drought-tolerant soybean cultivars through breeding, it is necessary to understand the mechanisms of drought tolerance in soybeans. In this study, we applied several transcriptome datasets obtained from soybean plants under drought stress in comparison to those grown under normal conditions to identify novel drought-responsive genes and their underlying molecular mechanisms. We found 2168 significant up/downregulated differentially expressed genes (DEGs) and 8 core modules using gene co-expression analysis to predict their biological roles in drought tolerance. Gene Ontology and KEGG analyses revealed key biological processes and metabolic pathways involved in drought tolerance, such as photosynthesis, glyceraldehyde-3-phosphate dehydrogenase and cytokinin dehydrogenase activity, and regulation of systemic acquired resistance. Genome-wide analysis of plants' cis-acting regulatory elements (CREs) and transcription factors (TFs) was performed for all of the identified DEG promoters in soybeans. Furthermore, the PPI network analysis revealed significant hub genes and the main transcription factors regulating the expression of drought-responsive genes in each module. Among the four modules associated with responses to drought stress, the results indicated that GLYMA_04G209700, GLYMA_02G204700, GLYMA_06G030500, GLYMA_01G215400, and GLYMA_09G225400 have high degrees of interconnection and, thus, could be considered as potential candidates for improving drought tolerance in soybeans. Taken together, these findings could lead to a better understanding of the mechanisms underlying drought responses in soybeans, which may useful for engineering drought tolerance in plants.

Phytohormones and Transcriptome Analyses Revealed the Dynamics Involved in Spikelet Abortion and Inflorescence Development in Rice

Panicle degeneration, sometimes known as abortion, causes heavy losses in grain yield. However, the mechanism of naturally occurring panicle abortion is still elusive. In a previous study, we characterized a mutant, apical panicle abortion1331 (apa1331), exhibiting abortion in apical spikelets starting from the 6 cm stage of panicle development. In this study, we have quantified the five phytohormones, gibberellins (GA), auxins (IAA), abscisic acid (ABA), cytokinins (CTK), and brassinosteroids (BR), in the lower, middle, and upper parts of apa1331 and compared these with those exhibited in its wild type (WT). In apa331, the lower and middle parts of the panicle showed contrasting concentrations of all studied phytohormones, but highly significant changes in IAA and ABA, compared to the upper part of the panicle. A comparative transcriptome of apa1331 and WT apical spikelets was performed to explore genes causing the physiological basis of spikelet abortion. The differential expression analysis revealed a significant downregulation and upregulation of 1587 and 978 genes, respectively. Hierarchical clustering of differentially expressed genes (DEGs) revealed the correlation of gene ontology (GO) terms associated with antioxidant activity, peroxidase activity, and oxidoreductase activity. KEGG pathway analysis using parametric gene set enrichment analysis (PGSEA) revealed the downregulation of the biological processes, including cell wall polysaccharides and fatty acids derivatives, in apa1331 compared to its WT. Based on fold change (FC) value and high variation in expression during late inflorescence, early inflorescence, and antherdevelopment, we predicted a list of novel genes, which presumably can be the potential targets of inflorescence development. Our study not only provides novel insights into the role of the physiological dynamics involved in panicle abortion, but also highlights the potential targets involved in reproductive development.

Identification of major candidate genes for multiple abiotic stress tolerance at seedling stage by network analysis and their validation by expression profiling in rice (Oryza sativa L.)

A wealth of microarray and RNA-seq data for studying abiotic stress tolerance in rice exists but only limited studies have been carried out on multiple stress-tolerance responses and mechanisms. In this study, we identified 6657 abiotic stress-responsive genes pertaining to drought, salinity and heat stresses from the seedling stage microarray data of 83 samples and used them to perform unweighted network analysis and to identify key hub genes or master regulators for multiple abiotic stress tolerance. Of the total 55 modules identified from the analysis, the top 10 modules with 8-61 nodes comprised 239 genes. From these 10 modules, 10 genes common to all the three stresses were selected. Further, based on the centrality properties and highly dense interactions, we identified 7 intra-modular hub genes leading to a total of 17 potential candidate genes. Out of these 17 genes, 15 were validated by expression analysis using a panel of 4 test genotypes and a pair of standard check genotypes for each abiotic stress response. Interestingly, all the 15 genes showed upregulation under all stresses and in all the genotypes, suggesting that they could be representing some of the core abiotic stress-responsive genes. More pertinently, eight of the genes were found to be co-localized with the stress-tolerance QTL regions. Thus, in conclusion, our study not only provided an effective approach for studying abiotic stress tolerance in rice, but also identified major candidate genes which could be further validated by functional genomics for abiotic stress tolerance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03182-7.

Transcriptome changes in the developing sugarcane culm associated with high yield and early-season high sugar content

Sugarcane, with its exceptional carbon dioxide assimilation, biomass and sugar yield, has a high potential for the production of bio-energy, bio-plastics and high-value products in the food and pharmaceutical industries. A crucial challenge for long-term economic viability and environmental sustainability is also to optimize the production of biomass composition and carbon sequestration. Sugarcane varieties such as KQ228 and Q253 are highly utilized in the industry. These varieties are characterized by a high early-season sugar content associated with high yield. In order to investigate these correlations, 1,440 internodes were collected and combined to generate a set of 120 samples in triplicate across 24 sugarcane cultivars at five different development stages. Weighted gene co-expression network analysis (WGCNA) was used and revealed for the first time two sets of co-expressed genes with a distinct and opposite correlation between fibre and sugar content. Gene identification and metabolism pathways analysis was used to define these two sets of genes. Correlation analysis identified a large number of interconnected metabolic pathways linked to sugar content and fibre content. Unsupervised hierarchical clustering of gene expression revealed a stronger level of segregation associated with the genotypes than the stage of development, suggesting a dominant genetic influence on biomass composition and facilitating breeding selection. Characterization of these two groups of co-expressed key genes can help to improve breeding program for high fibre, high sugar species or plant synthetic biology.

Exploration of anti-chromium mechanism of marine Penicillium janthinellum P1 through combinatorial transcriptomic analysis and WGCNA

Fungi have a promising application prospect in the remediation of heavy-metal wastewater pollution which is a sticky global problem. New marine-derived strain Penicillium janthinellum P1 is of high chromium resistance. However, a comprehensive study of the transcriptomics in Penicillium janthinellum P1 strains is lacking. Firstly, the changing trends of a series of physiological and biochemical indices of P1 strain at 0 M and 1 M Cr concentration were investigated to track the ROS variation. Secondly, transcriptome sequencing of P1 was performed by RNA-Seq sequencing technology. The transcriptome data indicated that 12,352 coding protein regions were predicted, and 6655 differentially expressed genes were identified by DESeq2, of which 4234 genes were up-regulated, and 2421 genes down-regulated. Through further co-expression network of WGCNA analysis, the filtered unigenes were clustered into 19 modules. Combined with the physiological and biochemical findings, the three modules with the highest correlation with the six traits were selected to construct the network, and 52 hub genes were obtained. Furthermore, 10 speculative hub genes related to chromium resistance were selected and verified by real-time PCR. The results were in line with the expected experimental assumption. These results improve our understanding of the transcriptomic dimensions of the high chromium resistance of Penicillium janthinellum P1 strains.

Identification of Flowering Regulatory Networks and Hub Genes Expressed in the Leaves of Elymus sibiricus L. Using Comparative Transcriptome Analysis

Flowering is a significant stage from vegetative growth to reproductive growth in higher plants, which impacts the biomass and seed yield. To reveal the flowering time variations and identify the flowering regulatory networks and hub genes in Elymus sibiricus, we measured the booting, heading, and flowering times of 66 E. sibiricus accessions. The booting, heading, and flowering times varied from 136 to 188, 142 to 194, and 148 to 201 days, respectively. The difference in flowering time between the earliest- and the last-flowering accessions was 53 days. Furthermore, transcriptome analyses were performed at the three developmental stages of six accessions with contrasting flowering times. A total of 3,526 differentially expressed genes (DEGs) were predicted and 72 candidate genes were identified, including transcription factors, known flowering genes, and plant hormone-related genes. Among them, four candidate genes (LATE, GA2OX6, FAR3, and MFT1) were significantly upregulated in late-flowering accessions. LIMYB, PEX19, GWD3, BOR7, PMEI28, LRR, and AIRP2 were identified as hub genes in the turquoise and blue modules which were related to the development time of flowering by weighted gene co-expression network analysis (WGCNA). A single-nucleotide polymorphism (SNP) of LIMYB found by multiple sequence alignment may cause late flowering. The expression pattern of flowering candidate genes was verified in eight flowering promoters (CRY, COL, FPF1, Hd3, GID1, FLK, VIN3, and FPA) and four flowering suppressors (CCA1, ELF3, Ghd7, and COL4) under drought and salt stress by qRT-PCR. The results suggested that drought and salt stress activated the flowering regulation pathways to some extent. The findings of the present study lay a foundation for the functional verification of flowering genes and breeding of new varieties of early- and late-flowering E. sibiricus.

Joint GWAS and WGCNA uncover the genetic control of calcium accumulation under salt treatment in maize seedlings

Soil salinization is an important factor threatening the yield and quality of maize. Ca²⁺ plays a considerable role in regulating plant growth under salt stress. Herein, we examined the shoot Ca²⁺ concentrations, root Ca²⁺ concentrations, and transport coefficients of seedlings in an association panel composed of 305 maize inbred lines under normal and salt conditions. A genome-wide association study was conducted by using the investigated phenotypes and 46,408 single-nucleotide polymorphisms of the panel. As a result, 53 significant SNPs were specifically detected under salt treatment, and 544 genes were identified in the linkage disequilibrium regions of these SNPs. According to the expression data of the 544 genes, we carried out a weighted coexpression network analysis. Combining the enrichment analyses and functional annotations, four hub genes (GRMZM2G051032, GRMZM2G004314, GRMZM2G421669, and GRMZM2G123314) were finally determined, which were then used to evaluate the genetic variation effects by gene-based association analysis. Only GRMZM2G123314, which encodes a pentatricopeptide repeat protein, was significantly associated with Ca²⁺ transport and the haplotype G-CT was identified as the superior haplotype. Our study brings novel insights into the genetic and molecular mechanisms of salt stress response and contributes to the development of salt-tolerant varieties in maize.

Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches

Hevea brasiliensis (rubber tree) is a large tree species of the Euphorbiaceae family with inestimable economic importance. Rubber tree breeding programs currently aim to improve growth and production, and the use of early genotype selection technologies can accelerate such processes, mainly with the incorporation of genomic tools, such as marker-assisted selection (MAS). However, few quantitative trait loci (QTLs) have been used successfully in MAS for complex characteristics. Recent research shows the efficiency of genome-wide association studies (GWAS) for locating QTL regions in different populations. In this way, the integration of GWAS, RNA-sequencing (RNA-Seq) methodologies, coexpression networks and enzyme networks can provide a better understanding of the molecular relationships involved in the definition of the phenotypes of interest, supplying research support for the development of appropriate genomic based strategies for breeding. In this context, this work presents the potential of using combined multiomics to decipher the mechanisms of genotype and phenotype associations involved in the growth of rubber trees. Using GWAS from a genotyping-by-sequencing (GBS) Hevea population, we were able to identify molecular markers in QTL regions with a main effect on rubber tree plant growth under constant water stress. The underlying genes were evaluated and incorporated into a gene coexpression network modelled with an assembled RNA-Seq-based transcriptome of the species, where novel gene relationships were estimated and evaluated through in silico methodologies, including an estimated enzymatic network. From all these analyses, we were able to estimate not only the main genes involved in defining the phenotype but also the interactions between a core of genes related to rubber tree growth at the transcriptional and translational levels. This work was the first to integrate multiomics analysis into the in-depth investigation of rubber tree plant growth, producing useful data for future genetic studies in the species and enhancing the efficiency of the species improvement programs.

Genome-Wide Identification and Analyses of Drought/Salt-Responsive Cytochrome P450 Genes in Medicago truncatula

Cytochrome P450 monooxygenases (P450s) catalyze a great number of biochemical reactions and play vital roles in plant growth, development and secondary metabolism. As yet, the genome-scale investigation on P450s is still lacking in the model legume Medicago truncatula. In particular, whether and how many MtP450s are involved in drought and salt stresses for Medicago growth, development and yield remain unclear. In this study, a total of 346 MtP450 genes were identified and classified into 10 clans containing 48 families. Among them, sixty-one MtP450 genes pairs are tandem duplication events and 10 MtP450 genes are segmental duplication events. MtP450 genes within one family exhibit high conservation and specificity in intron–exon structure. Meanwhile, many Mt450 genes displayed tissue-specific expression pattern in various tissues. Specifically, the expression pattern of 204 Mt450 genes under drought/NaCl treatments were analyzed by using the weighted correlation network analysis (WGCNA). Among them, eight genes (CYP72A59v1, CYP74B4, CYP71AU56, CYP81E9, CYP71A31, CYP704G6, CYP76Y14, and CYP78A126), and six genes (CYP83D3, CYP76F70, CYP72A66, CYP76E1, CYP74C12, and CYP94A52) were found to be hub genes under drought/NaCl treatments, respectively. The expression levels of these selected hub genes could be induced, respectively, by drought/NaCl treatments, as validated by qPCR analyses, and most of these genes are involved in the secondary metabolism and fatty acid pathways. The genome-wide identification and co-expression analyses of M. truncatulaP450 superfamily genes established a gene atlas for a deep and systematic investigation of P450 genes in M. truncatula, and the selected drought-/salt-responsive genes could be utilized for further functional characterization and molecular breeding for resistance in legume crops.

Whitefly Network Analysis Reveals Gene Modules Involved in Host Plant Selection, Development and Evolution

Whiteflies are Hemipterans that typically feed on the undersides of plant leaves. They cause severe damage by direct feeding as well as transmitting plant viruses to a wide range of plants. However, it remains largely unknown which genes play a key role in development and host selection. In this study, weighted gene co-expression network analysis was applied to construct gene co-expression networks in whitefly. Nineteen gene co-expression modules were detected from 15560 expressed genes of whitefly. Combined with the transcriptome data of salivary glands and midgut, we identified three gene co-expression modules related to host plant selection. These three modules contain genes related to host-plant recognition, such as detoxification genes, chemosensory genes and some salivary gland-associated genes. Results of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses elucidated the following pathways involved in these modules: lysosome, metabolic and detoxification pathways. The modules related to the development contain two co-expression modules; moreover, the genes were annotated to the development of chitin-based cuticle. This analysis provides a basis for future functional analysis of genes involved in host-plant recognition.

The genome of Cleistogenes songorica provides a blueprint for functional dissection of dimorphic flower differentiation and drought adaptability

Cleistogenes songorica (2n = 4x = 40) is a desert grass with a unique dimorphic flowering mechanism and an ability to survive extreme drought. Little is known about the genetics underlying drought tolerance and its reproductive adaptability. Here, we sequenced and assembled a high-quality chromosome-level C. songorica genome (contig N50 = 21.28 Mb). Complete assemblies of all telomeres, and of ten chromosomes were derived. C. songorica underwent a recent tetraploidization (~19 million years ago) and four major chromosomal rearrangements. Expanded genes were significantly enriched in fatty acid elongation, phenylpropanoid biosynthesis, starch and sucrose metabolism, and circadian rhythm pathways. By comparative transcriptomic analysis we found that conserved drought tolerance related genes were expanded. Transcription of CsMYB genes was associated with differential development of chasmogamous and cleistogamous flowers, as well as drought tolerance. Furthermore, we found that regulation modules encompassing miRNA, transcription factors and target genes are involved in dimorphic flower development, validated by overexpression of CsAP2_9 and its targeted miR172 in rice. Our findings enable further understanding of the mechanisms of drought tolerance and flowering in C. songorica, and provide new insights into the adaptability of native grass species in evolution, along with potential resources for trait improvement in agronomically important species.

Ubiquitinome Profiling Reveals the Landscape of Ubiquitination Regulation in Rice Young Panicles

Ubiquitination, an essential post-transcriptional modification (PTM), plays a vital role in nearly every biological process, including development and growth. Despite its functions in plant reproductive development, its targets in rice panicles remain unclear. In this study, we used proteome-wide profiling of lysine ubiquitination in rice (O. sativa ssp. indica) young panicles. We created the largest ubiquitinome dataset in rice to date, identifying 1638 lysine ubiquitination sites on 916 unique proteins. We detected three conserved ubiquitination motifs, noting that acidic glutamic acid (E) and aspartic acid (D) were most frequently present around ubiquitinated lysine. Enrichment analysis of Gene Ontology (GO) annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of these ubiquitinated proteins revealed that ubiquitination plays an important role in fundamental cellular processes in rice young panicles. Interestingly, enrichment analysis of protein domains indicated that ubiquitination was enriched on a variety of receptor-like kinases and cytoplasmic tyrosine and serine-threonine kinases. Furthermore, we analyzed the crosstalk between ubiquitination, acetylation, and succinylation, and constructed a potential protein interaction network within our rice ubiquitinome. Moreover, we identified ubiquitinated proteins related to pollen and grain development, indicating that ubiquitination may play a critical role in the physiological functions in young panicles. Taken together, we reported the most comprehensive lysine ubiquitinome in rice so far, and used it to reveal the functional role of lysine ubiquitination in rice young panicles.

Data-independent acquisition proteomic analysis of biochemical factors in rice seedlings following treatment with chitosan oligosaccharides

Chitosan oligosaccharides (COS) can elicit plant immunity and defence responses in rice plants, but exactly how this promotes plant growth remains largely unknown. Herein, we explored the effects of 0.5 mg/L COS on plant growth promotion in rice seedlings by measuring root and stem length, investigating biochemical factors in whole plants via proteomic analysis, and confirming upregulated and downregulated genes by real-time quantitative PCR. Pathway enrichment results showed that COS promoted root and stem growth, and stimulated metabolic (biosynthetic and catabolic processes) and photosynthesis in rice plants during the seedling stage. Expression levels of genes related to chlorophyll a-b binding, RNA binding, catabolic processes and calcium ion binding were upregulated following COS treatment. Furthermore, comparative analysis indicated that numerous proteins involved in the biosynthesis, metabolic (catabolic) processes and photosynthesis pathways were upregulated. The findings indicate that COS may upregulate calcium ion binding, photosynthesis, RNA binding, and catabolism proteins associated with plant growth during the rice seedling stage.

Implementation of homology based and non-homology based computational methods for the identification and annotation of orphan enzymes: using Mycobacterium tuberculosis H37Rv as a case study

Background

Homology based methods are one of the most important and widely used approaches for functional annotation of high-throughput microbial genome data. A major limitation of these methods is the absence of well-characterized sequences for certain functions. The non-homology methods based on the context and the interactions of a protein are very useful for identifying missing metabolic activities and functional annotation in the absence of significant sequence similarity. In the current work, we employ both homology and context-based methods, incrementally, to identify local holes and chokepoints, whose presence in the Mycobacterium tuberculosis genome is indicated based on its interaction with known proteins in a metabolic network context, but have not been annotated. We have developed two computational procedures using network theory to identify orphan enzymes (‘Hole finding protocol’) coupled with the identification of candidate proteins for the predicted orphan enzyme (‘Hole filling protocol’). We propose an integrated interaction score based on scores from the STRING database to identify candidate protein sequences for the orphan enzymes from M. tuberculosis, as a case study, which are most likely to perform the missing function.

Results

The application of an automated homology-based enzyme identification protocol, ModEnzA, on M. tuberculosis genome yielded 56 novel enzyme predictions. We further predicted 74 putative local holes, 6 choke points, and 3 high confidence local holes in the genome using ‘Hole finding protocol’. The ‘Hole-filling protocol’ was validated on the E. coli genome using artificial in-silico enzyme knockouts where our method showed 25% increased accuracy, compared to other methods, in assigning the correct sequence for the knocked-out enzyme amongst the top 10 ranks. The method was further validated on 8 additional genomes.

Conclusions

We have developed methods that can be generalized to augment homology-based annotation to identify missing enzyme coding genes and to predict a candidate protein for them. For pathogens such as M. tuberculosis, this work holds significance in terms of increasing the protein repertoire and thereby, the potential for identifying novel drug targets.

Genome-Wide Identification and Coexpression Network Analysis of DNA Methylation Pathway Genes and Their Differentiated Functions in Ginkgo biloba L

DNA methylation plays a vital role in diverse biological processes. DNA methyltransferases (DNMTs) genes and RNA-directed DNA methylation (RdDM)-related genes are key genes responsible for establishing and maintaining genome DNA methylation in plants. In the present study, we systematically identified nine GbDNMTs in Ginkgo biloba, including the three common families of GbMET1a/1b, GbCMT2, and GbDRMa/b/2a/2b/2c, and a fourth family—GbDNMT3—which is absent in most angiosperms. We also identified twenty RdDM-related genes, including four GbDCLs, six GbAGOs, and ten GbRDRs. Expression analysis of the genes showed the different patterns of individual genes, and 15 of 29 genes displayed expression change under five types of abiotic stress. Gene coexpression analysis and weighted gene co-expression network analysis (WGCNA) using 126 public transcriptomic datasets revealed that these genes were clustered into two groups. In group I, genes covered members from all six families which were preferentially expressed in the ovulate strobile and fruit. A gene ontology (GO) enrichment analysis of WGCNA modules indicated that group I genes were most correlated with the biological process of cell proliferation. Group II only consisted of RdDM-related genes, including GbDRMs, GbAGOs, and GbRDRs, but no GbDCLs, and these genes were specifically expressed in the cambium, suggesting that they may function in a dicer-like (DCL)-independent RdDM pathway in specific tissues. The gene module related to group II was most enriched in signal transduction, cell communication, and the response to the stimulus. These results demonstrate that gene family members could be conserved or diverged across species, and multi-member families in the same pathway may cluster into different modules to function differentially. The study provides insight into the DNA methylation genes and their possible functions in G. biloba, laying a foundation for the further study of DNA methylation in gymnosperms.

(Differential) Co-Expression Analysis of Gene Expression: A Survey of Best Practices

Analysis of gene expression data is widely used in transcriptomic studies to understand functions of molecules inside a cell and interactions among molecules. Differential co-expression analysis studies diseases and phenotypic variations by finding modules of genes whose co-expression patterns vary across conditions. We review the best practices in gene expression data analysis in terms of analysis of (differential) co-expression, co-expression network, differential networking, and differential connectivity considering both microarray and RNA-seq data along with comparisons. We highlight hurdles in RNA-seq data analysis using methods developed for microarrays. We include discussion of necessary tools for gene expression analysis throughout the paper. In addition, we shed light on scRNA-seq data analysis by including preprocessing and scRNA-seq in co-expression analysis along with useful tools specific to scRNA-seq. To get insights, biological interpretation and functional profiling is included. Finally, we provide guidelines for the analyst, along with research issues and challenges which should be addressed.

Long-term transcriptional memory in rice plants submitted to salt shock

A first salt shock event alters transcriptional and physiological responses to a second event, being possible to identify 26 genes associated with long-term memory. Soil salinity significantly affects rice cultivation, resulting in large losses in growth and productivity. Studies report that a disturbing event can prepare the plant for a subsequent event through memory acquisition, involving physiological and molecular processes. Therefore, genes that provide altered responses in subsequent events define a category known as "memory genes". In this work, the RNA-sequencing (RNA-Seq) technique was used to analyse the transcriptional profile of rice plants subjected to different salt shock events and to characterise genes associated with long-term memory. Plants subjected to recurrent salt shock showed differences in stomatal conductance, chlorophyll index, electrolyte leakage, and the number of differentially expressed genes (DEGs), and they had lower Na⁺/K⁺ ratios than plants that experienced only one stress event. Additionally, the mammalian target of rapamycin (mTOR) pathways, and carbohydrate and amino acid-associated pathways were altered under all conditions. Memory genes can be classified according to their responses during the first event (+ or -) and the second shock event (+ or -), being possible to observe a larger number of transcripts for groups [+ /-] and [-/ +], genes characterised as "revised response." This is the first long-term transcriptional memory study in rice plants under salt shock, providing new insights into the process of plant memory acquisition.

Meta Gene Regulatory Networks in Maize Highlight Functionally Relevant Regulatory Interactions

The regulation of gene expression is central to many biological processes. Gene regulatory networks (GRNs) link transcription factors (TFs) to their target genes and represent maps of potential transcriptional regulation. Here, we analyzed a large number of publically available maize (Zea mays) transcriptome data sets including >6000 RNA sequencing samples to generate 45 coexpression-based GRNs that represent potential regulatory relationships between TFs and other genes in different populations of samples (cross-tissue, cross-genotype, and tissue-and-genotype samples). While these networks are all enriched for biologically relevant interactions, different networks capture distinct TF-target associations and biological processes. By examining the power of our coexpression-based GRNs to accurately predict covarying TF-target relationships in natural variation data sets, we found that presence/absence changes rather than quantitative changes in TF gene expression are more likely associated with changes in target gene expression. Integrating information from our TF-target predictions and previous expression quantitative trait loci (eQTL) mapping results provided support for 68 TFs underlying 74 previously identified trans-eQTL hotspots spanning a variety of metabolic pathways. This study highlights the utility of developing multiple GRNs within a species to detect putative regulators of important plant pathways and provides potential targets for breeding or biotechnological applications.

Predicting Protein Functions Based on Differential Co-expression and Neighborhood Analysis

Proteins are polypeptides essential in biological processes. Protein physical interactions are complemented by other types of functional relationship data including genetic interactions, knowledge about co-expression, and evolutionary pathways. Existing algorithms integrate protein interaction and gene expression data to retrieve context-specific subnetworks composed of genes/proteins with known and unknown functions. However, most protein function prediction algorithms fail to exploit diverse intrinsic information in feature and label spaces. We develop a novel integrative method based on differential Co-expression analysis and Neighbor-voting algorithm for Protein Function Prediction, namely CNPFP. The method integrates heterogeneous data and exploits intrinsic and latent linkages via global iterative approach and genomic features. CNPFP performs three tasks: clustering, differential co-expression analysis, and predicts protein functions. Our aim is to identify yeast cell cycle-specific proteins linked to differentially expressed proteins in the protein-protein interaction network. To capture intrinsic information, CNPFP selects the most relevant feature subset based on global iterative neighbor-voting algorithm. We identify eight condition-specific modules. The most relevant subnetwork has 87 genes highly enriched with cyclin-dependent kinases, a protein kinase relevant for cell cycle regulation. We present comprehensive annotations for 3538 Saccharomyces cerevisiae proteins. Our method achieves an AUROC of 0.9862, accuracy of 0.9710, and F-score of 0.9691. From the results, we can summarize that exploiting intrinsic nature of protein relationships improves the quality of function prediction. Thus, the proposed method is useful in functional genomics studies.

Identification of Regulatory Networks and Hub Genes Controlling Nitrogen Uptake in Tea Plants [Camellia sinensis (L.) O. Kuntze]

Nitrogen (N) uptake, as the first step of N metabolism, is a key limiting factor for plant growth. To understand the gene expression networks that control N absorption and metabolism in tea plants, we analyzed transcriptomes in the young roots of two groups of tea plants with significantly different growth rates under different N treatments (0, 0.2, and 2 mM). Using pairwise comparisons and weighted gene co-expression network analyses (WGCNA), we successfully constructed 16 co-expression modules. Among them, a specific module (turquoise) that substantially responded to the low N treatment was identified. Based on KEGG analysis, the relative genes that enriched in the "N metabolism" pathways were used to construct gene co-expression networks of N metabolism. Finally, a high-affinity ammonium (NH₄⁺) transporter designated CsAMT1.2 was identified as a hub gene in the N metabolism network in tea plant roots and the gene expression could be highly induced by N resupply. The gene functional analysis revealed that CsAMT1.2 could make functional complementation of MEP1, MEP2, and MEP3 genes in 31019b yeast cells and improve NH₄⁺ uptake rate in 31019b at low NH₄⁺ level. Thus, CsAMT1.2 was a key gene controlling N uptake in tea plants and might play a vital role in promoting NH₄⁺ uptake from the environment in tea roots. This study provided a useful foundation for improving the NUE in tea plantations.

Molecular Analysis of UV-C Induced Resveratrol Accumulation in Polygonum cuspidatum Leaves

Resveratrol is one of the most studied plant secondary metabolites owing to its numerous health benefits. It is accumulated in some plants following biotic and abiotic stress pressures, including UV-C irradiation. Polygonum cuspidatum represents the major natural source of concentrated resveratrol but the underlying mechanisms as well as the effects of UV-C irradiation on resveratrol content have not yet been documented. Herein, we found that UV-C irradiation significantly increased by 2.6-fold and 1.6-fold the resveratrol content in irradiated leaf samples followed by a dark incubation for 6 h and 12 h, respectively, compared to the untreated samples. De novo transcriptome sequencing and assembly resulted into 165,013 unigenes with 98 unigenes mapped to the resveratrol biosynthetic pathway. Differential expression analysis showed that P.cuspidatum strongly induced the genes directly involved in the resveratrol synthesis, including phenylalanine ammonia-lyase, cinnamic acid 4-hydroxylase, 4-coumarate-CoA ligase and stilbene synthase (STS) genes, while strongly decreased the chalcone synthase (CHS) genes after exposure to UV-C. Since CHS and STS share the same substrate, P. cuspidatum tends to preferentially divert the substrate to the resveratrol synthesis pathway under UV-C treatment. We identified several members of the MYB, bHLH and ERF families as potential regulators of the resveratrol biosynthesis genes.

Global Proteomic Analysis Reveals Widespread Lysine Succinylation in Rice Seedlings

Lysine succinylation (Ksu) is a dynamic and reversible post-translational modification that plays an important role in many biological processes. Although recent research has analyzed Ksu plant proteomes, little is known about the scope and cellular distribution of Ksu in rice seedlings. Here, we report high-quality proteome-scale Ksu data for rice seedlings. A total of 710 Ksu sites in 346 proteins with diverse biological functions and subcellular localizations were identified in rice samples. About 54% of the sites were predicted to be localized in the chloroplast. Six putative succinylation motifs were detected. Comparative analysis with succinylation data revealed that arginine (R), located downstream of Ksu sites, is the most conserved amino acid surrounding the succinylated lysine. KEGG pathway category enrichment analysis indicated that carbon metabolism, tricarboxylic acid cycle (TCA) cycle, oxidative phosphorylation, photosynthesis, and glyoxylate and dicarboxylate metabolism pathways were significantly enriched. Additionally, we compared published Ksu data from rice embryos with our data from rice seedlings and found conserved Ksu sites between the two rice tissues. Our in-depth survey of Ksu in rice seedlings provides the foundation for further understanding the biological function of lysine-succinylated proteins in rice growth and development.

RNA Sequencing Reveals That Both Abiotic and Biotic Stress-Responsive Genes are Induced during Expression of Steroidal Glycoalkaloid in Potato Tuber Subjected to Light Exposure

Steroidal glycoalkaloids (SGAs), which are widely produced by potato, even in other Solanaceae plants, are a class of potentially toxic compounds, but are beneficial to host resistance. However, changes of the other metabolic process along with SGA accumulation are still poorly understood and researched. Based on RNA sequencing (RNA-seq) and bioinformatics analysis, the global gene expression profiles of potato variety Helan 15 (Favorita) was investigated at four-time points during light exposure. The data was further verified by using quantitative Real-time PCR (qRT-PCR). When compared to the control group, 1288, 1592, 1737, and 1870 differentially expressed genes (DEGs) were detected at 6 h, 24 h, 48 h, and 8 d, respectively. The results of both RNAseq and qRT-PCR showed that SGA biosynthetic genes were up-regulated in the potato tuber under light exposure. Functional enrichment analysis revealed that genes related to PS light reaction and Protein degradation were significantly enriched in most time points of light exposure. Additionally, enriched Bins included Receptor kinases, Secondary metabolic process in flavonoids, Abiotic stress, and Biotic stress in the early stage of light exposure, but PS Calvin cycle, RNA regulation of transcription, and UDP glucosyl and glucoronyl transferases in the later stage. Most of the DEGs involved in PS light reaction and Abiotic stress were up-regulated at all four time points, whereas DEGs that participated in biotic stresses were mainly up-regulated at the later stage (48 h and 8 d). Cis-element prediction and co-expression assay were used to confirm the expressional correlation between genes that are responsible for SGA biosynthesis and disease resistance. In conclusion, the expressions of genes involved in PS light reaction, Abiotic stress, and Biotic stress were obviously aroused during the accumulation of SGAs induced by light exposure. Moreover, an increased defense response might contribute to the potato resistance to the infection by phytopathogenic microorganisms.

WGCNA Analysis of Salt-Responsive Core Transcriptome Identifies Novel Hub Genes in Rice

Rice, being a major staple food crop and sensitive to salinity conditions, bears heavy yield losses due to saline soil. Although some salt responsive genes have been identified in rice, their applications in developing salt tolerant cultivars have resulted in limited achievements. Herein, we used bioinformatic approaches to perform a meta-analysis of three transcriptome datasets from salinity and control conditions in order to reveal novel genes and the molecular pathways underlying rice response to salt. From a total of 28,432 expressed genes, we identify 457 core differentially expressed genes (DEGs) constitutively responding to salt, regardless of the stress duration, genotype, or the tissue. Gene co-expression analysis divided the core DEGs into three different modules, each of them contributing to salt response in a unique metabolic pathway. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses highlighted key biological processes and metabolic pathways involved in the salt response. We identified important novel hub genes encoding proteins of different families including CAM, DUF630/632, DUF581, CHL27, PP2-13, LEA4-5, and transcription factors, which could be functionally characterized using reverse genetic experiments. This novel repertoire of candidate genes related to salt response in rice will be useful for engineering salt tolerant varieties.

Depicting the Core Transcriptome Modulating Multiple Abiotic Stresses Responses in Sesame (Sesamum indicum L.)

Sesame is a source of a healthy vegetable oil, attracting a growing interest worldwide. Abiotic stresses have devastating effects on sesame yield; hence, studies have been performed to understand sesame molecular responses to abiotic stresses, but the core abiotic stress-responsive genes (CARG) that the plant reuses in response to an array of environmental stresses are unknown. We performed a meta-analysis of 72 RNA-Seq datasets from drought, waterlogging, salt and osmotic stresses and identified 543 genes constantly and differentially expressed in response to all stresses, representing the sesame CARG. Weighted gene co-expression network analysis of the CARG revealed three functional modules controlled by key transcription factors. Except for salt stress, the modules were positively correlated with the abiotic stresses. Network topology of the modules showed several hub genes predicted to play prominent functions. As proof of concept, we generated over-expressing Arabidopsis lines with hub and non-hub genes. Transgenic plants performed better under drought, waterlogging, and osmotic stresses than the wild-type plants but did not tolerate the salt treatment. As expected, the hub gene was significantly more potent than the non-hub gene. Overall, we discovered several novel candidate genes, which will fuel investigations on plant responses to multiple abiotic stresses.

Identification of putative drought-responsive genes in rice using gene co-expression analysis

Drought is one of the major abiotic stresses causing yield losses and restricted growing area for several major crops. Rice being a major staple food crop and sensitive to water-deficit conditions bears heavy yield losses due to drought stress. To breed drought tolerant rice cultivars, it is of interest to understand the mechanisms of drought tolerance. In this regard, large amount of publicly available transcriptome datasets could be utilized. In this study, we used different transcriptome datasets obtained under drought stress in comparison to normal conditions (control) to identify novel drought responsive genes and their underlying molecular mechanisms. We found 517 core drought responsive differentially expressed genes (DEGs) and different modules using gene co-expression analysis to specifically predict their biological roles in drought tolerance. Gene ontology and KEGG analyses showed key biological processes and metabolic pathways involved in drought tolerance. Further, network analysis pinpointed important hub DEGs and major transcription factors regulating the expression of drought responsive genes in each module. These identified novel DEGs and transcription factors could be functionally characterized using systems biology approaches, which can significantly enhance our knowledge about the molecular mechanisms of drought tolerance in rice.

Gene network modules associated with abiotic stress response in tolerant rice genotypes identified by transcriptome meta-analysis

Abiotic stress tolerance is a complex trait regulated by multiple genes and gene networks in plants. A range of abiotic stresses are known to limit rice productivity. Meta-transcriptomics has emerged as a powerful approach to decipher stress-associated molecular network in model crops. However, retaining specificity of gene expression in tolerant and susceptible genotypes during meta-transcriptome analysis is important for understanding genotype-dependent stress tolerance mechanisms. Addressing this aspect, we describe here "abiotic stress tolerant" (ASTR) genes and networks specifically and differentially expressing in tolerant rice genotypes in response to different abiotic stress conditions. We identified 6,956 ASTR genes, key hub regulatory genes, transcription factors, and functional modules having significant association with abiotic stress-related ontologies and cis-motifs. Out of the 6956 ASTR genes, 73 were co-located within the boundary of previously identified abiotic stress trait-related quantitative trait loci. Functional annotation of 14 uncharacterized ASTR genes is proposed using multiple computational methods. Around 65% of the top ASTR genes were found to be differentially expressed in at least one of the tolerant genotypes under different stress conditions (cold, salt, drought, or heat) from publicly available RNAseq data comparison. The candidate ASTR genes specifically associated with tolerance could be utilized for engineering rice and possibly other crops for broad-spectrum tolerance to abiotic stresses.

Development of introgression lines of AA genome Oryza species, O. glaberrima, O. rufipogon, and O. nivara, in the genetic background of O. sativa L. cv. Taichung 65

To evaluate and utilize potentially valuable quantitative trait loci or genes of wild relatives in the genetic background of domesticated crop species, chromosome segment substitution lines (CSSLs) are a valuable tool. CSSLs can be constructed through the exchange of chromosome segments of AA genome species of the genus Oryza with cultivated rice, Oryza sativa L. Here we report the development of three sets of CSSLs carrying segments of AA genome species closely related to Oryza sativa-O. glaberrima (IRGC 103777 from Mali), O. rufipogon (W1962 from China), and O. nivara (IRGC 105715 from Cambodia)-in the genetic background of ssp. japonica cultivar Taichung 65 through the use of 101 to 121 simple-sequence-repeat markers in whole-genome genotyping and marker-assisted selection. The materials are available via the National Bioresource Project (Rice) Oryzabase Web page.

Meta-analysis of drought-tolerant genotypes in Oryza sativa: A network-based approach

BACKGROUND

Drought is a severe environmental stress. It is estimated that about 50% of the world rice production is affected mainly by drought. Apart from conventional breeding strategies to develop drought-tolerant crops, innovative computational approaches may provide insights into the underlying molecular mechanisms of stress response and identify drought-responsive markers. Here we propose a network-based computational approach involving a meta-analytic study of seven drought-tolerant rice genotypes under drought stress.

RESULTS

Co-expression networks enable large-scale analysis of gene-pair associations and tightly coupled clusters that may represent coordinated biological processes. Considering differentially expressed genes in the co-expressed modules and supplementing external information such as resistance/tolerance QTLs, transcription factors, network-based topological measures, we identify and prioritize drought-adaptive co-expressed gene modules and potential candidate genes. Using the candidate genes that are well-represented across the datasets as 'seed' genes, two drought-specific protein-protein interaction networks (PPINs) are constructed with up- and down-regulated genes. Cluster analysis of the up-regulated PPIN revealed ABA signalling pathway as a central process in drought response with a probable crosstalk with energy metabolic processes. Tightly coupled gene clusters representing up-regulation of core cellular respiratory processes and enhanced degradation of branched chain amino acids and cell wall metabolism are identified. Cluster analysis of down-regulated PPIN provides a snapshot of major processes associated with photosynthesis, growth, development and protein synthesis, most of which are shut down during drought. Differential regulation of phytohormones, e.g., jasmonic acid, cell wall metabolism, signalling and posttranslational modifications associated with biotic stress are elucidated. Functional characterization of topologically important, drought-responsive uncharacterized genes that may play a role in important processes such as ABA signalling, calcium signalling, photosynthesis and cell wall metabolism is discussed. Further transgenic studies on these genes may help in elucidating their biological role under stress conditions.

CONCLUSION

Currently, a large number of resources for rice functional genomics exist which are mostly underutilized by the scientific community. In this study, a computational approach integrating information from various resources such as gene co-expression networks, protein-protein interactions and pathway-level information is proposed to provide a systems-level view of complex drought-responsive processes across the drought-tolerant genotypes.

Study on Differential Protein Expression in Natural Selenium-Enriched and Non-Selenium-Enriched Rice Based on iTRAQ Quantitative Proteomics

This work was designated to scrutinize the protein differential expression in natural selenium-enriched and non-selenium-enriched rice using the Isobaric-tags for relative and absolute quantification (iTRAQ) proteomics approach. The extracted proteins were subjected to enzyme digestion, desalting, and identified by iTRAQ coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS) technology. High pH C18 separation analysis was performed, and the data were then analyzed by Protein Pilot^TM (V4.5) search engine. Protein differential expression was searched out by comparing relatively quantified proteins. The analysis was conducted using gene ontology (GO), cluster of orthologous groups of proteins (COG) and Kyoto encyclopedia of genes and genomes (KEGG) metabolic pathways. A total of 3235 proteins were detected and 3161 proteins were quantified, of which 401 were differential proteins. 208 down-regulated and 193 up-regulated proteins were unveiled. 77 targeted significant differentially expressed proteins were screened out for further analysis, and were classified into 10 categories: oxidoreductases, transferases, isomerases, heat shock proteins, lyases, hydrolases, ligases, synthetases, tubulin, and actin. The results indicated that the anti-stress, anti-oxidation, active oxygen metabolism, carbohydrate and amino acid metabolism of natural selenium-enriched rice was higher than that of non-selenium rice. The activation of the starch synthesis pathway was found to be bounteous in non-selenium-enriched rice. Cysteine synthase (CYS) and methyltransferase (metE) might be the two key proteins that cause amino acid differences. OsAPx02, CatC, riPHGPX, HSP70 and HSP90 might be the key enzymes regulating antioxidant and anti-stress effect differences in two types of rice. This study provides basic information about deviations in protein mechanism and secondary metabolites in selenium-enriched and non-selenium-enriched rice.

Comparison of gene co-networks analysis provide a systems view of rice (Oryza sativa L.) response to Tilletia horrida infection

The biotrophic soil-borne fungus Tilletia horrida causes rice kernel smut, an important disease affecting the production of rice male sterile lines in most hybrid rice growing regions of the world. There are no successful ways of controlling this disease and there has been little study of mechanisms of resistance to T. horrida. Based on transcriptional data of different infection time points, we found 23, 782 and 23, 718 differentially expressed genes (fragments per kilobase of transcript sequence per million, FPKM >1) in Jiangcheng 3A (resistant to T. horrida) and 9311A (susceptible to T. horrida), respectively. In order to illuminate the differential responses of the two rice male sterile lines to T. horrida, we identified gene co-expression modules using the method of weighted gene co-expression network analysis (WGCNA) and compared the different biological functions of gene co-expression networks in key modules at different infection time points. The results indicated that gene co-expression networks in the two rice genotypes were different and that genes contained in some modules of the two groups may play important roles in resistance to T. horrida, such as DTH8 and OsHop/Sti1a. Furthermore, these results provide a global view of the responses of two different phenotypes to T. horrida, and assist our understanding of the regulation of expression changes after T. horrida infection.

A Novel Data-Driven Boolean Model for Genetic Regulatory Networks

A Boolean model is a simple, discrete and dynamic model without the need to consider the effects at the intermediate levels. However, little effort has been made into constructing activation, inhibition, and protein decay networks, which could indicate the direct roles of a gene (or its synthesized protein) as an activator or inhibitor of a target gene. Therefore, we propose to focus on the general Boolean functions at the subfunction level taking into account the effectiveness of protein decay, and further split the subfunctions into the activation and inhibition domains. As a consequence, we developed a novel data-driven Boolean model; namely, the Fundamental Boolean Model (FBM), to draw insights into gene activation, inhibition, and protein decay. This novel Boolean model provides an intuitive definition of activation and inhibition pathways and includes mechanisms to handle protein decay issues. To prove the concept of the novel model, we implemented a platform using R language, called FBNNet. Our experimental results show that the proposed FBM could explicitly display the internal connections of the mammalian cell cycle between genes separated into the connection types of activation, inhibition and protein decay. Moreover, the method we proposed to infer the gene regulatory networks for the novel Boolean model can be run in parallel and; hence, the computation cost is affordable. Finally, the novel Boolean model and related Fundamental Boolean Networks (FBNs) could show significant trajectories in genes to reveal how genes regulated each other over a given period. This new feature could facilitate further research on drug interventions to detect the side effects of a newly-proposed drug.

Comparison of gene co-networks reveals the molecular mechanisms of the rice (Oryza sativa L.) response to Rhizoctonia solani AG1 IA infection

Rhizoctonia solani causes rice sheath blight, an important disease affecting the growth of rice (Oryza sativa L.). Attempts to control the disease have met with little success. Based on transcriptional profiling, we previously identified more than 11,947 common differentially expressed genes (TPM > 10) between the rice genotypes TeQing and Lemont. In the current study, we extended these findings by focusing on an analysis of gene co-expression in response to R. solani AG1 IA and identified gene modules within the networks through weighted gene co-expression network analysis (WGCNA). We compared the different genes assigned to each module and the biological interpretations of gene co-expression networks at early and later modules in the two rice genotypes to reveal differential responses to AG1 IA. Our results show that different changes occurred in the two rice genotypes and that the modules in the two groups contain a number of candidate genes possibly involved in pathogenesis, such as the VQ protein. Furthermore, these gene co-expression networks provide comprehensive transcriptional information regarding gene expression in rice in response to AG1 IA. The co-expression networks derived from our data offer ideas for follow-up experimentation that will help advance our understanding of the translational regulation of rice gene expression changes in response to AG1 IA.

De novo assembly of a Chinese soybean genome

Soybean was domesticated in China and has become one of the most important oilseed crops. Due to bottlenecks in their introduction and dissemination, soybeans from different geographic areas exhibit extensive genetic diversity. Asia is the largest soybean market; therefore, a high-quality soybean reference genome from this area is critical for soybean research and breeding. Here, we report the de novo assembly and sequence analysis of a Chinese soybean genome for "Zhonghuang 13" by a combination of SMRT, Hi-C and optical mapping data. The assembled genome size is 1.025 Gb with a contig N50 of 3.46 Mb and a scaffold N50 of 51.87 Mb. Comparisons between this genome and the previously reported reference genome (cv. Williams 82) uncovered more than 250,000 structure variations. A total of 52,051 protein coding genes and 36,429 transposable elements were annotated for this genome, and a gene co-expression network including 39,967 genes was also established. This high quality Chinese soybean genome and its sequence analysis will provide valuable information for soybean improvement in the future.

Gene-to-Gene Network Analysis of the Mediation of Plant Innate Immunity by the Eliciting Plant Response-Like 1 (Epl1) Elicitor of Trichoderma formosa

A new clade, Trichoderma formosa, secretes eliciting plant response-like 1 (Epl1), a small peptide elicitor that stimulates plant immunity. Nicotiana benthamiana pretreated with Epl1 for 3 days developed immunity against Tomato mosaic virus (ToMV) infection. The transcriptome profiles of T. formosa and N. benthamiana were obtained by deep sequencing; the transcript of Epl1 is 736 nt in length and encodes a 12-kDa peptide. Identifying critical genes in Epl1-mediated immunity was challenging due to high similarity between the transcriptome expression profiles of Epl1-treated and ToMV-infected N. benthamiana samples. Therefore, an efficient bioinformatics data mining approach was used for high-throughput transcriptomic assays in this study. We integrated gene-to-gene network analysis into the ContigViews transcriptome database, and genes related to jasmonic acid and ethylene signaling, salicylic acid signaling, leucine-rich repeats, transcription factors, and histone variants were hubs in the gene-to-gene networks. In this study, the Epl1 of T. formosa triggers plant immunity against various pathogen infections. Moreover, we demonstrated that high-throughput data mining and gene-to-gene network analysis can be used to identify critical candidate genes for further studies on the mechanisms of plant immunity.

Identification of cis-regulatory elements associated with salinity and drought stress tolerance in rice from co-expressed gene interaction networks

Rice, a staple food crop, is often subjected to drought and salinity stresses thereby limiting its yield potential. Since there is a cross talk between these abiotic stresses, identification of common and/or overlapping regulatory elements is pivotal for generating rice cultivars that showed tolerance towards them. Analysis of the gene interaction network (GIN) facilitates identifying the role of individual genes and their interactions with others that constitute important molecular determinants in sensing and signaling cascade governing drought and/or salinity stresses. Identification of the various cis-regulatory elements of the genes constituting GIN is equally important. Here, in this study graphical Gaussian model (GGM) was used for generating GIN for an array of genes that were differentially regulated during salinity and/or drought stresses to contrasting rice cultivars (salt-tolerant [CSR11], salt-sensitive [VSR156], drought-tolerant [Vandana], drought-sensitive [IR64]). Whole genome transcriptom profiling by using microarray were employed in this study. Markov Chain completed co-expression analyses of differentially expressed genes using Dynamic Bayesian Network, Probabilistic Boolean Network and Steady State Analysis. A compact GIN was identified for commonly co-expressed genes during salinity and drought stresses with three major hubs constituted by Myb2 transcription factor (TF), phosphoglycerate kinase and heat shock protein (Hsp). The analysis suggested a pivotal role of these genes in salinity and/or drought stress responses. Further, analysis of cis-regulatory elements (CREs) of commonly differentially expressed genes during salinity and drought stresses revealed the presence of 20 different motifs.

Co-expression network analysis of the transcriptomes of rice roots exposed to various cadmium stresses reveals universal cadmium-responsive genes

BACKGROUND

The migration of cadmium (Cd) from contaminated soil to rice is a cause for concern. However, the molecular mechanism underlying the response of rice roots to various Cd stresses remains to be clarified from the viewpoint of the co-expression network at a system-wide scale.

RESULTS

We employed a comparative RNAseq-based approach to identify early Cd-responsive differentially expressed genes (DEGs) in rice 'Nipponbare' seedling roots after 1 h of high-Cd treatment. A multiplicity of the identified 1772 DEGs were implicated in hormone signaling and transcriptional regulation, particularly NACs and WRKYs were all upregulated under Cd stress. All of the 6 Cd-upregulated ABC transporters were pleiotropic drug resistance proteins (PDRs), whereas all of the 6 ZRT/IRT-like proteins (ZIPs) were consistently downregulated by Cd treatment. To further confirm our results of this early transcriptomic response to Cd exposure, we then conducted weighted gene co-expression network analysis (WGCNA) to re-analyze our RNAseq data in combination with other 11 previously published RNAseq datasets for rice roots exposed to diverse concentrations of Cd for extended treatment periods. This integrative approach identified 271 transcripts as universal Cd-regulated DEGs that are key components of the Cd treatment coupled co-expression module. A global view of the 164 transcripts with annotated functions in pathway networks revealed several Cd-upregulated key functional genes, including transporter ABCG36/OsPDR9, 12-oxo-phytodienoic acid reductases (OPRs) for JA synthesis, and ZIM domain proteins JAZs in JA signaling, as well as OsWRKY10, NAC, and ZFP transcription factors. More importantly, 104 of these, including ABCG36/OsPDR9, OsNAC3, as well as several orthologs in group metalloendoproteinase, plastocyanin-like domain containing proteins and pectin methylesterase inhibitor, may respond specifically to various Cd pressures, after subtracting the 60 general stress-responsive genes reported to be commonly upregulated following multiple stresses.

CONCLUSION

An integrative approach was implemented to identify DEGs and co-expression network modules in response to various Cd pressures, and 104 of the 164 annotatable universal Cd-responsive DEGs may specifically respond to various Cd pressures. These results provide insight into the universal molecular mechanisms beneath the Cd response in rice roots, and suggest many promising targets for improving the rice acclimation process against Cd toxicity.

A composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma

Differential co-expression network analyses have recently become an important step in the investigation of cellular differentiation and dysfunctional gene-regulation in cell and tissue disease-states. The resulting networks have been analyzed to identify and understand pathways associated with disorders, or to infer molecular interactions. However, existing methods for differential co-expression network analysis are unable to distinguish between various forms of differential co-expression. To close this gap, here we define the three different kinds (conserved, specific, and differentiated) of differential co-expression and present a systematic framework, CSD, for differential co-expression network analysis that incorporates these interactions on an equal footing. In addition, our method includes a subsampling strategy to estimate the variance of co-expressions. Our framework is applicable to a wide variety of cases, such as the study of differential co-expression networks between healthy and disease states, before and after treatments, or between species. Applying the CSD approach to a published gene-expression data set of cerebral cortex and basal ganglia samples from healthy individuals, we find that the resulting CSD network is enriched in genes associated with cognitive function, signaling pathways involving compounds with well-known roles in the central nervous system, as well as certain neurological diseases. From the CSD analysis, we identify a set of prominent hubs of differential co-expression, whose neighborhood contains a substantial number of genes associated with glioblastoma. The resulting gene-sets identified by our CSD analysis also contain many genes that so far have not been recognized as having a role in glioblastoma, but are good candidates for further studies. CSD may thus aid in hypothesis-generation for functional disease-associations.

GeNET: a web application to explore and share Gene Co-expression Network Analysis data

Gene Co-expression Network Analysis (GCNA) is a popular approach to analyze a collection of gene expression profiles. GCNA yields an assignment of genes to gene co-expression modules, a list of gene sets statistically over-represented in these modules, and a gene-to-gene network. There are several computer programs for gene-to-gene network visualization, but these programs have limitations in terms of integrating all the data generated by a GCNA and making these data available online. To facilitate sharing and study of GCNA data, we developed GeNET. For researchers interested in sharing their GCNA data, GeNET provides a convenient interface to upload their data and automatically make it accessible to the public through an online server. For researchers interested in exploring GCNA data published by others, GeNET provides an intuitive online tool to interactively explore GCNA data by genes, gene sets or modules. In addition, GeNET allows users to download all or part of the published data for further computational analysis. To demonstrate the applicability of GeNET, we imported three published GCNA datasets, the largest of which consists of roughly 17,000 genes and 200 conditions. GeNET is available at bengi.cs.mun.ca/genet.

Maize network analysis revealed gene modules involved in development, nutrients utilization, metabolism, and stress response

BACKGROUND

The advent of big data in biology offers opportunities while poses challenges to derive biological insights. For maize, a large amount of publicly available transcriptome datasets have been generated but a comprehensive analysis is lacking.

RESULTS

We constructed a maize gene co-expression network based on the graphical Gaussian model, using massive RNA-seq data. The network, containing 20,269 genes, assembles into 964 gene modules that function in a variety of plant processes, such as cell organization, the development of inflorescences, ligules and kernels, the uptake and utilization of nutrients (e.g. nitrogen and phosphate), the metabolism of benzoxazionids, oxylipins, flavonoids, and wax, and the response to stresses. Among them, the inflorescences development module is enriched with domestication genes (like ra1, ba1, gt1, tb1, tga1) that control plant architecture and kernel structure, while multiple other modules relate to diverse agronomic traits. Contained within these modules are transcription factors acting as known or potential expression regulators for the genes within the same modules, suggesting them as candidate regulators for related biological processes. A comparison with an established Arabidopsis network revealed conserved gene association patterns for specific modules involved in cell organization, nutrients uptake & utilization, and metabolism. The analysis also identified significant divergences between the two species for modules that orchestrate developmental pathways.

CONCLUSIONS

This network sheds light on how gene modules are organized between different species in the context of evolutionary divergence and highlights modules whose structure and gene content can provide important resources for maize gene functional studies with application potential.