The association of multiple interacting genes with specific phenotypes in rice using gene coexpression networks

Deciphering the role of lncRNA-mediated ceRNA network in disuse osteoporosis: insights from bone marrow mesenchymal stem cells under simulated microgravity

Background

Disuse osteoporosis (DOP) poses a significant health risk during extended space missions. Although the importance of long non-coding RNA (lncRNA) in bone marrow mesenchymal stem cells (BMSCs) and orthopedic diseases is recognized, the precise mechanism by which lncRNAs contribute to DOP remains elusive. This research aims to elucidate the potential regulatory role of lncRNAs in DOP.

Methods

Sequencing data were obtained from Gene Expression Omnibus (GEO) datasets, including coding and non-coding RNAs. Positive co-expression pairs of lncRNA-mRNA were identified using weighted gene co-expression network analysis, while miRNA-mRNA expression pairs were derived from the prediction database. A mRNA-miRNA-lncRNA network was established according to the shared mRNA. Functional enrichment analysis was conducted for the shared mRNAs using genome ontology and KEGG pathways. Hub genes were identified through protein-protein interaction analysis, and connectivity map analysis was employed to identify potential therapeutic agents for DOP.

Results

Integration of 74 lncRNAs, 19 miRNAs, and 200 mRNAs yielded a comprehensive mRNA-miRNA-lncRNA network. Enrichment analysis highlighted endoplasmic reticulum stress and extracellular matrix (ECM) pathways as significant in the ceRNA network. PPI analysis revealed three hub genes (COL4A1, LAMC1, and LAMA4) and identified five lncRNA-miRNA-hub gene regulatory axes. Furthermore, three potential therapeutic compounds (SB-216763, oxymetholone, and flubendazole) for DOP were identified.

Conclusion

This study sheds light on the involvement of lncRNAs in the pathogenesis and treatment of DOP through the construction of a ceRNA network, linking protein-coding mRNA functions with non-coding RNAs.

A systems genomics and genetics approach to identify the genetic regulatory network for lignin content in Brassica napus seeds

Seed quality traits of oilseed rape, Brassica napus (B. napus), exhibit quantitative inheritance determined by its genetic makeup and the environment via the mediation of a complex genetic architecture of hundreds to thousands of genes. Thus, instead of single gene analysis, network-based systems genomics and genetics approaches that combine genotype, phenotype, and molecular phenotypes offer a promising alternative to uncover this complex genetic architecture. In the current study, systems genetics approaches were used to explore the genetic regulation of lignin traits in B. napus seeds. Four QTL (qLignin_A09_1, qLignin_A09_2, qLignin_A09_3, and qLignin_C08) distributed on two chromosomes were identified for lignin content. The qLignin_A09_2 and qLignin_C08 loci were homologous QTL from the A and C subgenomes, respectively. Genome-wide gene regulatory network analysis identified eighty-three subnetworks (or modules); and three modules with 910 genes in total, were associated with lignin content, which was confirmed by network QTL analysis. eQTL (expression quantitative trait loci) analysis revealed four cis-eQTL genes including lignin and flavonoid pathway genes, cinnamoyl-CoA-reductase (CCR1), and TRANSPARENT TESTA genes TT4, TT6, TT8, as causal genes. The findings validated the power of systems genetics to identify causal regulatory networks and genes underlying complex traits. Moreover, this information may enable the research community to explore new breeding strategies, such as network selection or gene engineering, to rewire networks to develop climate resilience crops with better seed quality.

Harnessing the predicted maize pan-interactome for putative gene function prediction and prioritization of candidate genes for important traits

The recent assembly and annotation of the 26 maize nested association mapping population founder inbreds have enabled large-scale pan-genomic comparative studies. These studies have expanded our understanding of agronomically important traits by integrating pan-transcriptomic data with trait-specific gene candidates from previous association mapping results. In contrast to the availability of pan-transcriptomic data, obtaining reliable protein-protein interaction (PPI) data has remained a challenge due to its high cost and complexity. We generated predicted PPI networks for each of the 26 genomes using the established STRING database. The individual genome-interactomes were then integrated to generate core- and pan-interactomes. We deployed the PPI clustering algorithm ClusterONE to identify numerous PPI clusters that were functionally annotated using gene ontology (GO) functional enrichment, demonstrating a diverse range of enriched GO terms across different clusters. Additional cluster annotations were generated by integrating gene coexpression data and gene description annotations, providing additional useful information. We show that the functionally annotated PPI clusters establish a useful framework for protein function prediction and prioritization of candidate genes of interest. Our study not only provides a comprehensive resource of predicted PPI networks for 26 maize genomes but also offers annotated interactome clusters for predicting protein functions and prioritizing gene candidates. The source code for the Python implementation of the analysis workflow and a standalone web application for accessing the analysis results are available at https://github.com/eporetsky/PanPPI.

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.

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.

Soybean gene co-expression network analysis identifies two co-regulated gene modules associated with nodule formation and development

Gene co-expression network analysis is an efficient systems biology approach for the discovery of novel gene functions and trait-associated gene modules. To identify clusters of functionally related genes involved in soybean nodule formation and development, we performed a weighted gene co-expression network analysis. Two nodule-specific modules (NSM-1 and NSM-2, containing 304 and 203 genes, respectively) were identified. The NSM-1 gene promoters were significantly enriched in cis-binding elements for ERF, MYB, and C2H2-type zinc transcription factors, whereas NSM-2 gene promoters were enriched in cis-binding elements for TCP, bZIP, and bHLH transcription factors, suggesting a role of these regulatory factors in the transcriptional activation of nodule co-expressed genes. The co-expressed gene modules included genes with potential novel roles in nodulation, including those involved in xylem development, transmembrane transport, the ethylene signalling pathway, cytoskeleton organization, cytokinesis and regulation of the cell cycle, regulation of meristem initiation and growth, transcriptional regulation, DNA methylation, and histone modifications. Functional analysis of two co-expressed genes using TILLING mutants provided novel insight into the involvement of unsaturated fatty acid biosynthesis and folate metabolism in nodule formation and development. The identified gene co-expression modules provide valuable resources for further functional genomics studies to dissect the genetic basis of nodule formation and development in soybean.

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.

Gene Co-expression Network Analysis and Linking Modules to Phenotyping Response in Plants

Environmental factors, including different stresses, can have an impact on the expression of genes and subsequently the phenotype and development of plants. Since a large number of genes are involved in response to the perturbation of the environment, identifying groups of co-expressed genes is meaningful. The gene co-expression network models can be used for the exploration, interpretation, and identification of genes responding to environmental changes. Once a gene co-expression network is constructed, one can determine gene modules and the association of gene modules to the phenotypic response. To link modules to phenotype, one approach is to find the correlated eigengenes of given modules or to integrate all eigengenes in regularized linear model. This manuscript describes the method from construction of co-expression network, module discovery, association between modules and phenotypic data, and finally to annotation/visualization.

The Divergent Roles of the Rice bcl-2 Associated Athanogene (BAG) Genes in Plant Development and Environmental Responses

Bcl-2-associated athanogene (BAG), a group of proteins evolutionarily conserved and functioned as co-chaperones in plants and animals, is involved in various cell activities and diverse physiological processes. However, the biological functions of this gene family in rice are largely unknown. In this study, we identified a total of six BAG members in rice. These genes were classified into two groups, OsBAG1, -2, -3, and -4 are in group I with a conserved ubiquitin-like structure and OsBAG5 and -6 are in group Ⅱ with a calmodulin-binding domain, in addition to a common BAG domain. The BAG genes exhibited diverse expression patterns, with OsBAG4 showing the highest expression level, followed by OsBAG1 and OsBAG3, and OsBAG6 preferentially expressed in the panicle, endosperm, and calli. The co-expression analysis and the hierarchical cluster analysis indicated that the OsBAG1 and OsBAG3 were co-expressed with primary cell wall-biosynthesizing genes, OsBAG4 was co-expressed with phytohormone and transcriptional factors, and OsBAG6 was co-expressed with disease and shock-associated genes. β-glucuronidase (GUS) staining further indicated that OsBAG3 is mainly involved in primary young tissues under both primary and secondary growth. In addition, the expression of the BAG genes under brown planthopper (BPH) feeding, N, P, and K deficiency, heat, drought and plant hormones treatments was investigated. Our results clearly showed that OsBAGs are multifunctional molecules as inferred by their protein structures, subcellular localizations, and expression profiles. BAGs in group I are mainly involved in plant development, whereas BAGs in group II are reactive in gene regulations and stress responses. Our results provide a solid basis for the further elucidation of the biological functions of plant BAG genes.

Interspecific analysis of diurnal gene regulation in panicoid grasses identifies known and novel regulatory motifs

Background

The circadian clock drives endogenous 24-h rhythms that allow organisms to adapt and prepare for predictable and repeated changes in their environment throughout the day-night (diurnal) cycle. Many components of the circadian clock in Arabidopsis thaliana have been functionally characterized, but comparatively little is known about circadian clocks in grass species including major crops like maize and sorghum.

Results

Comparative research based on protein homology and diurnal gene expression patterns suggests the function of some predicted clock components in grasses is conserved with their Arabidopsis counterparts, while others have diverged in function. Our analysis of diurnal gene expression in three panicoid grasses sorghum, maize, and foxtail millet revealed conserved and divergent evolution of expression for core circadian clock genes and for the overall transcriptome. We find that several classes of core circadian clock genes in these grasses differ in copy number compared to Arabidopsis, but mostly exhibit conservation of both protein sequence and diurnal expression pattern with the notable exception of maize paralogous genes. We predict conserved cis-regulatory motifs shared between maize, sorghum, and foxtail millet through identification of diurnal co-expression clusters for a subset of 27,196 orthologous syntenic genes. In this analysis, a Cochran–Mantel–Haenszel based method to control for background variation identified significant enrichment for both expected and novel 6–8 nucleotide motifs in the promoter regions of genes with shared diurnal regulation predicted to function in common physiological activities.

Conclusions

This study illustrates the divergence and conservation of circadian clocks and diurnal regulatory networks across syntenic orthologous genes in panacoid grass species. Further, conserved local regulatory sequences contribute to the architecture of these diurnal regulatory networks that produce conserved patterns of diurnal gene expression.

Construction, comparison and evolution of networks in life sciences and other disciplines

Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.

A Short History and Perspectives on Plant Genetic Transformation

Plant genetic transformation is an important technological advancement in modern science, which has not only facilitated gaining fundamental insights into plant biology but also started a new era in crop improvement and commercial farming. However, for many crop plants, efficient transformation and regeneration still remain a challenge even after more than 30 years of technical developments in this field. Recently, FokI endonuclease-based genome editing applications in plants offered an exciting avenue for augmenting crop productivity but it is mainly dependent on efficient genetic transformation and regeneration, which is a major roadblock for implementing genome editing technology in plants. In this chapter, we have outlined the major historical developments in plant genetic transformation for developing biotech crops. Overall, this field needs innovations in plant tissue culture methods for simplification of operational steps for enhancing the transformation efficiency. Similarly, discovering genes controlling developmental reprogramming and homologous recombination need considerable attention, followed by understanding their role in enhancing genetic transformation efficiency in plants. Further, there is an urgent need for exploring new and low-cost universal delivery systems for DNA/RNA and protein into plants. The advancements in synthetic biology, novel vector systems for precision genome editing and gene integration could potentially bring revolution in crop-genetic potential enhancement for a sustainable future. Therefore, efficient plant transformation system standardization across species holds the key for translating advances in plant molecular biology to crop improvement.

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.

TGCnA: temporal gene coexpression network analysis using a low-rank plus sparse framework

Various gene network models with distinct physical nature have been widely used in biological studies. For temporal transcriptomic studies, the current dynamic models either ignore the temporal variation in the network structure or fail to scale up to a large number of genes due to severe computational bottlenecks and sample size limitation. Although the correlation-based gene networks are computationally affordable, they have limitations after being applied to gene expression time-course data. We proposed Temporal Gene Coexpression Network Analysis (TGCnA) framework for the transcriptomic time-course data. The mathematical nature of TGCnA is the joint modeling of multiple covariance matrices across time points using a 'low-rank plus sparse' framework, in which the network similarity across time points is explicitly modeled in the low-rank component. We demonstrated the advantage of TGCnA in covariance matrix estimation and gene module discovery using both simulation data and real transcriptomic data. The code is available at https://github.com/QiZhangStat/TGCnA.

Network-based feature selection reveals substructures of gene modules responding to salt stress in rice

Rice, an important food resource, is highly sensitive to salt stress, which is directly related to food security. Although many studies have identified physiological mechanisms that confer tolerance to the osmotic effects of salinity, the link between rice genotype and salt tolerance is not very clear yet. Association of gene co-expression network and rice phenotypic data under stress has penitential to identify stress-responsive genes, but there is no standard method to associate stress phenotype with gene co-expression network. A novel method for integration of gene co-expression network and stress phenotype data was developed to conduct a system analysis to link genotype to phenotype. We applied a LASSO-based method to the gene co-expression network of rice with salt stress to discover key genes and their interactions for salt tolerance-related phenotypes. Submodules in gene modules identified from the co-expression network were selected by the LASSO regression, which establishes a linear relationship between gene expression profiles and physiological responses, that is, sodium/potassium condenses under salt stress. Genes in these submodules have functions related to ion transport, osmotic adjustment, and oxidative tolerance. We argued that these genes in submodules are biologically meaningful and useful for studies on rice salt tolerance. This method can be applied to other studies to efficiently and reliably integrate co-expression network and phenotypic data.

Involvement of heat shock protein 40 in the wing dimorphism of the house cricket Acheta domesticus

Wing dimorphism is a common phenomenon in a wide range of insect taxa. In most insects, the two morphs are macropterous and micropterous, in extreme cases of the latter, wing shedding can occur. Wing dimorphism contributes significantly to the ecological success of many insect species. However, the molecular basis of wing dimorphism is not fully understood, especially for wing-shed. Here, differentially expressed genes over eight developmental stages of the house cricket Acheta domesticus, which undergoes wing-shed dimorphism, were studied. The results revealed a wing-shed peak during adult development in which many DEGs were highly upregulated and it's influenced by cricket population density. A weighted correlation network analysis (WGCNA) grouped 21,922 DEGs among 141,456 unigenes into 18 modules of different expression patterns. The module in which the gene expression pattern correlated with the wing-shed phenotypic data was selected for further analyses with STEM and Cytoscape, and three candidate genes (AdomHSP40: Heat shock protein 40, AdomCFDP: Craniofacial development protein, AdomDIS3L: DIS3 Like 3'-5' Exoribonuclease) were identified by gene network analysis as the DEGs most relevant to wing-shed occurrence. The RNA interference of these genes together with an insulin receptor and Nylanderia fulva virus showed that the silencing of AdomHSP40 significantly decreased wing-shed occurrence, whereas the silencing of other candidate genes did not, suggesting that AdomHSP40 plays a crucial role in the wing-shed of Acheta domesticus. These findings provide insights into the molecular mechanisms underlying wing dimorphism in the house crickets, which differ from those found in other insects such as the planthopper.

MPIGeneNet: Parallel Calculation of Gene Co-Expression Networks on Multicore Clusters

In this work, we present MPIGeneNet, a parallel tool that applies Pearson's correlation and Random Matrix Theory to construct gene co-expression networks. It is based on the state-of-the-art sequential tool RMTGeneNet, which provides networks with high robustness and sensitivity at the expenses of relatively long runtimes for large scale input datasets. MPIGeneNet returns the same results as RMTGeneNet but improves the memory management, reduces the I/O cost, and accelerates the two most computationally demanding steps of co-expression network construction by exploiting the compute capabilities of common multicore CPU clusters. Our performance evaluation on two different systems using three typical input datasets shows that MPIGeneNet is significantly faster than RMTGeneNet. As an example, our tool is up to 175.41 times faster on a cluster with eight nodes, each one containing two 12-core Intel Haswell processors. The source code of MPIGeneNet, as well as a reference manual, are available at https://sourceforge.net/projects/mpigenenet/.

Coexpression network revealing the plasticity and robustness of population transcriptome during the initial stage of domesticating energy crop Miscanthus lutarioriparius

Coexpression network revealing genes with Co-variation Expression pattern (CE) and those with Top rank of Expression fold change (TE) played different roles in responding to new environment of Miscanthus lutarioriparius. Variation in gene expression level, the product of genetic and/or environmental perturbation, determines the robustness-to-plasticity spectrum of a phenotype in plants. Understanding how expression variation of plant population response to a new field is crucial to domesticate energy crops. Weighted Gene Coexpression Network Analysis (WGCNA) was used to explore the patterns of expression variation based on 72 Miscanthus lutarioriparius transcriptomes from two contrasting environments, one near the native habitat and the other in one harsh domesticating region. The 932 genes with Co-variation Expression pattern (CE) and other 932 genes with Top rank of Expression fold change (TE) were identified and the former were strongly associated with the water use efficiency (r ≥ 0.55, P ≤ 10^-7). Functional enrichment of CE genes were related to three organelles, which well matched the annotation of twelve motifs identified from their conserved noncoding sequence; while TE genes were mostly related to biotic and/or abiotic stress. The expression robustness of CE genes with high genetic diversity kept relatively stable between environments while the harsh environment reduced the expression robustness of TE genes with low genetic diversity. The expression plasticity of CE genes was increased less than that of TE genes. These results suggested that expression variation of CE genes and TE genes could account for the robustness and plasticity of acclimation ability of Miscanthus, respectively. The patterns of expression variation revealed by transcriptomic network would shed new light on breeding and domestication of energy crops.

Identification of key gene modules for human osteosarcoma by co-expression analysis

BACKGROUND

Osteosarcoma is a type of bone cancer casting huge threat to the human health worldwide. Previously, gene expression analyses were performed to identify biomarkers for cancer; however, systemic co-expression analysis for osteosarcoma is still in need. The aim of this study was to construct a gene co-expression network that predicts clusters of candidate genes associated with the pathogenesis of osteosarcoma.

METHODS

Here, we extracted the large scale of datasets from the GEO database. With systematical approaches, we identified the co-expression modules by using weighted gene co-expression network analysis (WGCNA) and investigated the functional enrichments of important modules at GO and KEGG terms.

RESULTS

First, seven co-expression modules, which contain different genes, were conducted for 2228 genes in the 22 human osteosarcoma samples. Then, correlation study showed that the hub genes between pairwise modules displayed great differences. Lastly, functional enrichments of the co-expression modules showed that the module 5 enriched in immune response, antigen processing, and presentation, which is in consistence with GO result. Therefore, we speculated that the module 5 may play a key role in the pathogenesis of osteosarcoma.

CONCLUSIONS

Here, we speculated that genes of the module 5 were the essential genes that were associated to human osteosarcoma. Together, our findings not only provided outline of co-expression gene modules for human osteosarcoma, but also promoted the understanding of these modules at functional aspects.

Gene Coexpression Networks Drive and Predict Reproductive Effects in Daphnia in Response to Environmental Disturbances

Increasing effects of anthropogenic stressors and those of natural origin on aquatic ecosystems have intensified the need for predictive and functional models of their effects. Here, we use gene expression patterns in combination with weighted gene coexpression networks and generalized additive models to predict effects on reproduction in the aquatic microcrustacean Daphnia. We developed models to predict effects on reproduction upon exposure to different cyanobacteria, different insecticides and binary mixtures of cyanobacteria and insecticides. Models developed specifically for groups of stressors (e.g., either cyanobacteria or insecticides) performed better than general models developed on all data. Furthermore, models developed using in silico generated mixture gene expression profiles from single stressor data were able to better predict effects on reproduction compared to models derived from the mixture exposures themselves. Our results highlight the potential of gene expression data to quantify effects of complex exposures at higher level organismal effects without prior mechanistic knowledge or complex exposure data.

Discovering Condition-Specific Gene Co-Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study

A gene co-expression network (GCN) describes associations between genes and points to genetic coordination of biochemical pathways. However, genetic correlations in a GCN are only detectable if they are present in the sampled conditions. With the increasing quantity of gene expression samples available in public repositories, there is greater potential for discovery of genetic correlations from a variety of biologically interesting conditions. However, even if gene correlations are present, their discovery can be masked by noise. Noise is introduced from natural variation (intrinsic and extrinsic), systematic variation (caused by sample measurement protocols and instruments), and algorithmic and statistical variation created by selection of data processing tools. A variety of published studies, approaches and methods attempt to address each of these contributions of variation to reduce noise. Here we describe an approach using Gaussian Mixture Models (GMMs) to address natural extrinsic (condition-specific) variation during network construction from mixed input conditions. To demonstrate utility, we build and analyze a condition-annotated GCN from a compendium of 2,016 mixed gene expression data sets from five tumor subtypes obtained from The Cancer Genome Atlas. Our results show that GMMs help discover tumor subtype specific gene co-expression patterns (modules) that are significantly enriched for clinical attributes.

Connectivity in gene coexpression networks negatively correlates with rates of molecular evolution in flowering plants

Gene coexpression networks are a useful tool for summarizing transcriptomic data and providing insight into patterns of gene regulation in a variety of species. Though there has been considerable interest in studying the evolution of network topology across species, less attention has been paid to the relationship between network position and patterns of molecular evolution. Here, we generated coexpression networks from publicly available expression data for seven flowering plant taxa (Arabidopsis thaliana, Glycine max, Oryza sativa, Populus spp., Solanum lycopersicum, Vitis spp., and Zea mays) to investigate the relationship between network position and rates of molecular evolution. We found a significant negative correlation between network connectivity and rates of molecular evolution, with more highly connected (i.e., “hub”) genes having significantly lower nonsynonymous substitution rates and dN/dS ratios compared to less highly connected (i.e., “peripheral”) genes across the taxa surveyed. These findings suggest that more centrally located hub genes are, on average, subject to higher levels of evolutionary constraint than are genes located on the periphery of gene coexpression networks. The consistency of this result across disparate taxa suggests that it holds for flowering plants in general, as opposed to being a species-specific phenomenon.

Suppressive effect of microRNA319 expression on rice plant height

KEY MESSAGE: miR319 was identified as a dwarf-inducing gene from Shiokari and its dwarf near isogenic line, and its transgenic rice showed a reduced plant height. This finding reveals the potential application of miR319 in future molecular breeding. It is well known that microRNAs (miRNAs) play important roles in plant physiology, especially in development and stress responses. However, little is known about the role of miRNAs in plant height. In this study, the rice cultivar Shiokari and its dwarf near isogenic line Shiokari-d6 were analysed to identify and characterize plant height-associated miRNAs. This anatomic and morphological investigation revealed that the major cause of the shorter height of Shiokari-d6 is the significantly dis-elongated internodes, particularly the second internode and those underneath it. The results of miRNA microarray profiling and real-time RT-PCR indicated that miR319 is expressed at a significantly higher level in Shiokari-d6 than in Shiokari. Transgenic rice overexpressing miR319 in Oryza sativa L. cv. Tainung 67 generated through Agrobacterium-mediated transformation had a stable dwarf phenotype regardless of whether the plants were from the T1 or T2 generation. We also found that the internodes of miR319-overexpressing rice are shortened, particularly the third internode and those underneath it. Furthermore, we identified three putative miR319 target genes that were previously uncharacterized with expression levels that were negatively correlated with the expression of miR319. In conclusion, miR319 is the first miRNA proposed to be involved in plant height regulation, and its function may influence the elongation of internodes, which leads to decreased plant height.

Identification of Key Gene Modules of Neuropathic Pain by Co-Expression Analysis

Neuropathic pain (NP) is a substantial clinical problem causing great injury to people word-widely. Although gene expression analyses had been performed previously, the mechanisms underlying the etiology and development of NP are still poorly understood. To understand the function genes involved in the etiology and development of NP, we built the co-expression modules and performed function enrichment analysis for neuropathic pain. In the present study, from a public microarray data set (GSE69901) from NCBI, gene co-expression modules were contributed with the help of WGCNA for 12 neuropathic pain samples and 13 control samples, respectively. And functional enrichment analyses were followed by DAVID database. Firstly, we established 21 co-expression modules and 19 co-expression modules out of 5,000 high-express genes in NP and control samples, respectively. Then, it showed great difference in interaction relationships of total genes and hub-genes between pairwise modules, which indicated the high confidence of gene co-expression modules. Finally, functional enrichment analysis of the top five co-expression modules in NP exhibited great differences and significant enrichment in transcription regulation of RNA polymerase II promoter and ubiquitin mediated proteolysis pathway. RNA polymerase II promoter and ubiquitin-mediated proteolysis pathway played important role in etiology and development of NP. Anyhow, our findings provided the framework of gene co-expression modules of NP and furthered the understanding of these modules from functional aspect. J. Cell. Biochem. 118: 4436-4443, 2017. © 2017 Wiley Periodicals, Inc.

Identification of Key Gene Modules in Human Osteosarcoma by Co-Expression Analysis Weighted Gene Co-Expression Network Analysis (WGCNA)

Osteosarcoma is the eighth-most common form of childhood cancer, comprising about 20% of all primary bone cancers. To date, systemic co-expression analysis for this cancer is still insufficient to explain the pathogenesis of poorly understood OC. The objective of this study was to construct a gene co-expression network to predict clusters of candidate genes involved in the pathogenesis of osteosarcoma. First, we contributed co-expression modules via weighted gene co-expression network analysis (WGCNA) and investigated the functional enrichment analysis of co-expression genes in terms of GO and KEGG. In result, seven co-expression modules were identified, containing 2,228 differentially expressed genes identified from the 22 human osteosarcoma samples. Subsequently, correlation study showed that the hub-genes between pair-wise modules displayed significant differences. Lastly, functional enrichment analysis of the co-expression modules showed that the module 5 enriched in progresses of immune response, antigen processing, and presentation. In conclusion, we identified essential genes in module 5 which were associated to human osteosarcoma. The key genes in our findings might provide the framework of co-expression gene modules of human osteosarcoma. Further, the functional analysis of these associated genes provides references to understand the mechanism of Osteosarcoma. J. Cell. Biochem. 118: 3953-3959, 2017. © 2017 Wiley Periodicals, Inc.

The Relationship between Selection, Network Connectivity, and Regulatory Variation within a Population of Capsella grandiflora

Interactions between genes can have important consequences for how selection shapes sequence variation at these genes. Specifically, genes that have pleiotropic effects by affecting the expression level of many other genes may be under stronger selective constraint. We used coexpression networks to measure connectivity between genes and investigated the relationship between gene connectivity and selection in a natural population of the plant Capsella grandiflora. We observed that network connectivity was negatively correlated with genetic divergence due to stronger negative selection on highly-connected genes even when controlling for variation in gene expression level. However, the presence of local regulatory variation for a gene's expression level was also associated with reduced negative selection and lower gene connectivity. While it is difficult to disentangle the causal relationships between these factors, our results show that both connectivity and local regulatory variation are important factors for explaining variation in selection between genes.

Genomewide Expression and Functional Interactions of Genes under Drought Stress in Maize

A genomewide transcriptome assay of two subtropical genotypes of maize was used to observe the expression of genes at seedling stage of drought stress. The number of genes expressed differentially was greater in HKI1532 (a drought tolerant genotype) than in PC3 (a drought sensitive genotype), indicating primary differences at the transcriptional level in stress tolerance. The global coexpression networks of the two genotypes differed significantly with respect to the number of modules and the coexpression pattern within the modules. A total of 174 drought-responsive genes were selected from HKI1532, and their coexpression network revealed key correlations between different adaptive pathways, each cluster of the network representing a specific biological function. Transcription factors related to ABA-dependent stomatal closure, signalling, and phosphoprotein cascades work in concert to compensate for reduced photosynthesis. Under stress, water balance was maintained by coexpression of the genes involved in osmotic adjustments and transporter proteins. Metabolism was maintained by the coexpression of genes involved in cell wall modification and protein and lipid metabolism. The interaction of genes involved in crucial biological functions during stress was identified and the results will be useful in targeting important gene interactions to understand drought tolerance in greater detail.

Statistical Approaches for Gene Selection, Hub Gene Identification and Module Interaction in Gene Co-Expression Network Analysis: An Application to Aluminum Stress in Soybean (Glycine max L.)

Selection of informative genes is an important problem in gene expression studies. The small sample size and the large number of genes in gene expression data make the selection process complex. Further, the selected informative genes may act as a vital input for gene co-expression network analysis. Moreover, the identification of hub genes and module interactions in gene co-expression networks is yet to be fully explored. This paper presents a statistically sound gene selection technique based on support vector machine algorithm for selecting informative genes from high dimensional gene expression data. Also, an attempt has been made to develop a statistical approach for identification of hub genes in the gene co-expression network. Besides, a differential hub gene analysis approach has also been developed to group the identified hub genes into various groups based on their gene connectivity in a case vs. control study. Based on this proposed approach, an R package, i.e., dhga (https://cran.r-project.org/web/packages/dhga) has been developed. The comparative performance of the proposed gene selection technique as well as hub gene identification approach was evaluated on three different crop microarray datasets. The proposed gene selection technique outperformed most of the existing techniques for selecting robust set of informative genes. Based on the proposed hub gene identification approach, a few number of hub genes were identified as compared to the existing approach, which is in accordance with the principle of scale free property of real networks. In this study, some key genes along with their Arabidopsis orthologs has been reported, which can be used for Aluminum toxic stress response engineering in soybean. The functional analysis of various selected key genes revealed the underlying molecular mechanisms of Aluminum toxic stress response in soybean.

Two novel antimicrobial defensins from rice identified by gene coexpression network analyses

Defensins form an antimicrobial peptides (AMP) family, and have been widely studied in various plants because of their considerable inhibitory functions. However, their roles in rice (Oryza sativa L.) have not been characterized, even though rice is one of the most important staple crops that is susceptible to damaging infections. Additionally, a previous study identified 598 rice genes encoding cysteine-rich peptides, suggesting there are several uncharacterized AMPs in rice. We performed in silico gene expression and coexpression network analyses of all genes encoding defensin and defensin-like peptides, and determined that OsDEF7 and OsDEF8 are coexpressed with pathogen-responsive genes. Recombinant OsDEF7 and OsDEF8 could form homodimers. They inhibited the growth of the bacteria Xanthomonas oryzae pv. oryzae, X. oryzae pv. oryzicola, and Erwinia carotovora subsp. atroseptica with minimum inhibitory concentration (MIC) ranging from 0.6 to 63μg/mL. However, these OsDEFs are weakly active against the phytopathogenic fungi Helminthosporium oryzae and Fusarium oxysporum f.sp. cubense. This study describes a useful method for identifying potential plant AMPs with biological activities.

Transgenic trait deployment using designed nucleases

The demand for crops requiring increasingly complex combinations of transgenes poses unique challenges for transgenic trait deployment. Future value-adding traits such as those associated with crop performance are expected to involve multiple transgenes. Random integration of transgenes not only results in unpredictable expression and potential unwanted side effects but stacking multiple, randomly integrated, independently segregating transgenes creates breeding challenges during introgression and product development. Designed nucleases enable the creation of targeted DNA double-strand breaks at specified genomic locations whereby repair can result in targeted transgene integration leading to precise alterations in DNA sequences for plant genome editing, including the targeting of a transgene to a genomic locus that supports high-level and stable transgene expression without interfering with resident gene function. In addition, targeted DNA integration via designed nucleases allows for the addition of transgenes into previously integrated transgenic loci to create stacked products. The currently reported frequencies of independently generated transgenic events obtained with site-specific transgene integration without the aid of selection for targeting are very low. A modular, positive selection-based gene targeting strategy has been developed involving cassette exchange of selectable marker genes which allows for targeted events to be preferentially selected, over multiple cycles of sequential transformation. This, combined with the demonstration of intragenomic recombination following crossing of transgenic events that contain stably integrated donor and target DNA constructs with nuclease-expressing plants, points towards the future of trait stacking that is less dependent on high-efficiency transformation.

Identification of Crowding Stress Tolerance Co-Expression Networks Involved in Sweet Corn Yield

Tolerance to crowding stress has played a crucial role in improving agronomic productivity in field corn; however, commercial sweet corn hybrids vary greatly in crowding stress tolerance. The objectives were to 1) explore transcriptional changes among sweet corn hybrids with differential yield under crowding stress, 2) identify relationships between phenotypic responses and gene expression patterns, and 3) identify groups of genes associated with yield and crowding stress tolerance. Under conditions of crowding stress, three high-yielding and three low-yielding sweet corn hybrids were grouped for transcriptional and phenotypic analyses. Transcriptional analyses identified from 372 to 859 common differentially expressed genes (DEGs) for each hybrid. Large gene expression pattern variation among hybrids and only 26 common DEGs across all hybrid comparisons were identified, suggesting each hybrid has a unique response to crowding stress. Over-represented biological functions of DEGs also differed among hybrids. Strong correlation was observed between: 1) modules with up-regulation in high-yielding hybrids and yield traits, and 2) modules with up-regulation in low-yielding hybrids and plant/ear traits. Modules linked with yield traits may be important crowding stress response mechanisms influencing crop yield. Functional analysis of the modules and common DEGs identified candidate crowding stress tolerant processes in photosynthesis, glycolysis, cell wall, carbohydrate/nitrogen metabolic process, chromatin, and transcription regulation. Moreover, these biological functions were greatly inter-connected, indicating the importance of improving the mechanisms as a network.

Learning from Co-expression Networks: Possibilities and Challenges

Plants are fascinating and complex organisms. A comprehensive understanding of the organization, function and evolution of plant genes is essential to disentangle important biological processes and to advance crop engineering and breeding strategies. The ultimate aim in deciphering complex biological processes is the discovery of causal genes and regulatory mechanisms controlling these processes. The recent surge of omics data has opened the door to a system-wide understanding of the flow of biological information underlying complex traits. However, dealing with the corresponding large data sets represents a challenging endeavor that calls for the development of powerful bioinformatics methods. A popular approach is the construction and analysis of gene networks. Such networks are often used for genome-wide representation of the complex functional organization of biological systems. Network based on similarity in gene expression are called (gene) co-expression networks. One of the major application of gene co-expression networks is the functional annotation of unknown genes. Constructing co-expression networks is generally straightforward. In contrast, the resulting network of connected genes can become very complex, which limits its biological interpretation. Several strategies can be employed to enhance the interpretation of the networks. A strategy in coherence with the biological question addressed needs to be established to infer reliable networks. Additional benefits can be gained from network-based strategies using prior knowledge and data integration to further enhance the elucidation of gene regulatory relationships. As a result, biological networks provide many more applications beyond the simple visualization of co-expressed genes. In this study we review the different approaches for co-expression network inference in plants. We analyse integrative genomics strategies used in recent studies that successfully identified candidate genes taking advantage of gene co-expression networks. Additionally, we discuss promising bioinformatics approaches that predict networks for specific purposes.

Identification of Photosynthesis-Associated C4 Candidate Genes through Comparative Leaf Gradient Transcriptome in Multiple Lineages of C3 and C4 Species

Leaves of C4 crops usually have higher radiation, water and nitrogen use efficiencies compared to the C3 species. Engineering C4 traits into C3 crops has been proposed as one of the most promising ways to repeal the biomass yield ceiling. To better understand the function of C4 photosynthesis, and to identify candidate genes that are associated with the C4 pathways, a comparative transcription network analysis was conducted on leaf developmental gradients of three C4 species including maize, green foxtail and sorghum and one C3 species, rice. By combining the methods of gene co-expression and differentially co-expression networks, we identified a total of 128 C4 specific genes. Besides the classic C4 shuttle genes, a new set of genes associated with light reaction, starch and sucrose metabolism, metabolites transportation, as well as transcription regulation, were identified as involved in C4 photosynthesis. These findings will provide important insights into the differential gene regulation between C3 and C4 species, and a good genetic resource for establishing C4 pathways in C3 crops.

Inference of Longevity-Related Genes from a Robust Coexpression Network of Seed Maturation Identifies Regulators Linking Seed Storability to Biotic Defense-Related Pathways

Seed longevity, the maintenance of viability during storage, is a crucial factor for preservation of genetic resources and ensuring proper seedling establishment and high crop yield. We used a systems biology approach to identify key genes regulating the acquisition of longevity during seed maturation of Medicago truncatula. Using 104 transcriptomes from seed developmental time courses obtained in five growth environments, we generated a robust, stable coexpression network (MatNet), thereby capturing the conserved backbone of maturation. Using a trait-based gene significance measure, a coexpression module related to the acquisition of longevity was inferred from MatNet. Comparative analysis of the maturation processes in M. truncatula and Arabidopsis thaliana seeds and mining Arabidopsis interaction databases revealed conserved connectivity for 87% of longevity module nodes between both species. Arabidopsis mutant screening for longevity and maturation phenotypes demonstrated high predictive power of the longevity cross-species network. Overrepresentation analysis of the network nodes indicated biological functions related to defense, light, and auxin. Characterization of defense-related wrky3 and nf-x1-like1 (nfxl1) transcription factor mutants demonstrated that these genes regulate some of the network nodes and exhibit impaired acquisition of longevity during maturation. These data suggest that seed longevity evolved by co-opting existing genetic pathways regulating the activation of defense against pathogens.

Identification of altered metabolic pathways of γ-irradiated rice mutant via network-based transcriptome analysis

In order to develop rice mutants for crop improvement, we applied γ-irradiation mutagenesis and selected a rice seed color mutant (MT) in the M14 targeting-induced local lesions in genome lines. This mutant exhibited differences in germination rate, plant height, and root length in seedlings compared to the wild-type plants. We found 1645 different expressed probes of MT by microarray hybridization. To identify the modified metabolic pathways, we conducted integrated genomic analysis such as weighted correlation network analysis with a module detection method of differentially expressed genes (DEGs) in MT on the basis of large-scale microarray transcriptional profiling. These modules are largely divided into three subnetworks and mainly exhibit overrepresented gene ontology functions such as oxidation-related function, ion-binding, and kinase activity (phosphorylation), and the expressional coherences of module genes mainly exhibited in vegetative and maturation stages. Through a metabolic pathway analysis, we detected the significant DEGs involved in the major carbohydrate metabolism (starch degradation), protein degradation (aspartate protease), and signaling in sugars and nutrients. Furthermore, the accumulation of amino acids (asparagine and glutamic acid), sucrose, and starch in MT were affected by gamma rays. Our results provide an effective approach for identification of metabolic pathways associated with useful agronomic traits in mutation breeding.

Deciphering Cis-Regulatory Element Mediated Combinatorial Regulation in Rice under Blast Infected Condition

Combinations of cis-regulatory elements (CREs) present at the promoters facilitate the binding of several transcription factors (TFs), thereby altering the consequent gene expressions. Due to the eminent complexity of the regulatory mechanism, the combinatorics of CRE-mediated transcriptional regulation has been elusive. In this work, we have developed a new methodology that quantifies the co-occurrence tendencies of CREs present in a set of promoter sequences; these co-occurrence scores are filtered in three consecutive steps to test their statistical significance; and the significantly co-occurring CRE pairs are presented as networks. These networks of co-occurring CREs are further transformed to derive higher order of regulatory combinatorics. We have further applied this methodology on the differentially up-regulated gene-sets of rice tissues under fungal (Magnaporthe) infected conditions to demonstrate how it helps to understand the CRE-mediated combinatorial gene regulation. Our analysis includes a wide spectrum of biologically important results. The CRE pairs having a strong tendency to co-occur often exhibit very similar joint distribution patterns at the promoters of rice. We couple the network approach with experimental results of plant gene regulation and defense mechanisms and find evidences of auto and cross regulation among TF families, cross-talk among multiple hormone signaling pathways, similarities and dissimilarities in regulatory combinatorics between different tissues, etc. Our analyses have pointed a highly distributed nature of the combinatorial gene regulation facilitating an efficient alteration in response to fungal attack. All together, our proposed methodology could be an important approach in understanding the combinatorial gene regulation. It can be further applied to unravel the tissue and/or condition specific combinatorial gene regulation in other eukaryotic systems with the availability of annotated genomic sequences and suitable experimental data.

Construction of citrus gene coexpression networks from microarray data using random matrix theory

After the sequencing of citrus genomes, gene function annotation is becoming a new challenge. Gene coexpression analysis can be employed for function annotation using publicly available microarray data sets. In this study, 230 sweet orange (Citrus sinensis) microarrays were used to construct seven coexpression networks, including one condition-independent and six condition-dependent (Citrus canker, Huanglongbing, leaves, flavedo, albedo, and flesh) networks. In total, these networks contain 37 633 edges among 6256 nodes (genes), which accounts for 52.11% measurable genes of the citrus microarray. Then, these networks were partitioned into functional modules using the Markov Cluster Algorithm. Significantly enriched Gene Ontology biological process terms and KEGG pathway terms were detected for 343 and 60 modules, respectively. Finally, independent verification of these networks was performed using another expression data of 371 genes. This study provides new targets for further functional analyses in citrus.

A combination of gene expression ranking and co-expression network analysis increases discovery rate in large-scale mutant screens for novel Arabidopsis thaliana abiotic stress genes

As challenges to food security increase, the demand for lead genes for improving crop production is growing. However, genetic screens of plant mutants typically yield very low frequencies of desired phenotypes. Here, we present a powerful computational approach for selecting candidate genes for screening insertion mutants. We combined ranking of Arabidopsis thaliana regulatory genes according to their expression in response to multiple abiotic stresses (Multiple Stress [MST] score), with stress-responsive RNA co-expression network analysis to select candidate multiple stress regulatory (MSTR) genes. Screening of 62 T-DNA insertion mutants defective in candidate MSTR genes, for abiotic stress germination phenotypes yielded a remarkable hit rate of up to 62%; this gene discovery rate is 48-fold greater than that of other large-scale insertional mutant screens. Moreover, the MST score of these genes could be used to prioritize them for screening. To evaluate the contribution of the co-expression analysis, we screened 64 additional mutant lines of MST-scored genes that did not appear in the RNA co-expression network. The screening of these MST-scored genes yielded a gene discovery rate of 36%, which is much higher than that of classic mutant screens but not as high as when picking candidate genes from the co-expression network. The MSTR co-expression network that we created, AraSTressRegNet is publicly available at http://netbio.bgu.ac.il/arnet. This systems biology-based screening approach combining gene ranking and network analysis could be generally applicable to enhancing identification of genes regulating additional processes in plants and other organisms provided that suitable transcriptome data are available.

An integrated genomic and metabolomic framework for cell wall biology in rice

Background

Plant cell walls are complex structures that full-fill many diverse functions during plant growth and development. It is therefore not surprising that thousands of gene products are involved in cell wall synthesis and maintenance. However, functional association for the majority of these gene products remains obscure. One useful approach to infer biological associations is via transcriptional coordination, or co-expression of genes. This approach has proved useful for several biological processes. Nevertheless, combining co-expression with other large-scale measurements may improve the biological inferences.

Results

In this study, we used a combined approach of co-expression and cell wall metabolomics to obtain new insight into cell wall synthesis in rice. We initially created a weighted gene co-expression network from publicly available datasets, and then established a comprehensive cell wall dataset by determining cell wall compositions from 29 tissues that almost cover the whole life cycle of rice. We subsequently combined the datasets through the conversion of co-expressed gene modules into eigen-vectors, representing expression profiles for the genes in the modules, and performed comparative analyses against the cell wall contents. Here, we made three major discoveries. First, we confirmed our approach by finding primary and secondary wall cellulose biosynthesis modules, respectively. Second, we found co-expressed modules that strongly correlated with re-organization of the secondary cell walls and with modifications and degradation of hemicellulosic structures. Third, we inferred that at least one module is likely to play a regulatory role in the production of G-rich lignification.

Conclusions

Here, we integrated transcriptomic associations and cell wall metabolism and found that certain co-expressed gene modules are positively correlated with distinct cell wall characteristics. We propose that combining multiple data-types, such as coordinated transcription and cell wall analyses, may be a useful approach to glean new insight into biological processes. The combination of multiple datasets, as illustrated here, can further improve the functional inferences that typically are generated via a single type of datasets. In addition, our data extend the typical co-expression approach to allow deeper insight into cell wall biology in rice.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-596) contains supplementary material, which is available to authorized users.

Discovering functional modules across diverse maize transcriptomes using COB, the Co-expression Browser

Tools that provide improved ability to relate genotype to phenotype have the potential to accelerate breeding for desired traits and to improve our understanding of the molecular variants that underlie phenotypes. The availability of large-scale gene expression profiles in maize provides an opportunity to advance our understanding of complex traits in this agronomically important species. We built co-expression networks based on genome-wide expression data from a variety of maize accessions as well as an atlas of different tissues and developmental stages. We demonstrate that these networks reveal clusters of genes that are enriched for known biological function and contain extensive structure which has yet to be characterized. Furthermore, we found that co-expression networks derived from developmental or tissue atlases as compared to expression variation across diverse accessions capture unique functions. To provide convenient access to these networks, we developed a public, web-based Co-expression Browser (COB), which enables interactive queries of the genome-wide networks. We illustrate the utility of this system through two specific use cases: one in which gene-centric queries are used to provide functional context for previously characterized metabolic pathways, and a second where lists of genes produced by mapping studies are further resolved and validated using co-expression networks.

Coexpression network analysis associated with call of rice seedlings for encountering heat stress

Coexpression network analysis is useful tool for identification of functional association of coexpressed genes. We developed a coexpression network of rice from heat stress transcriptome data. Global transcriptome of rice leaf tissues was performed by microarray at three time points--post 10 and 60 min heat stress at 42 °C and 30 min recovery at 26 °C following 60 min 42 °C heat stress to investigate specifically the early events in the heat stress and recovery response. The transcriptome profile was significantly modulated within 10 min of heat stress. Strikingly, the number of up-regulated genes was higher than the number of down-regulated genes in 10 min of heat stress. The enrichment of GO terms protein kinase activity/protein serine threonine kinase activity, response to heat and reactive oxygen species in up-regulated genes after 10 min signifies the role of signal transduction events and reactive oxygen species during early heat stress. The enrichment of transcription factor (TF) binding sites for heat shock factors, bZIPs and DREBs coupled with up-regulation of TFs of different families suggests that the heat stress response in rice involves integration of various regulatory networks. The interpretation of microarray data in the context of coexpression network analysis identified several functionally correlated genes consisting of previously documented heat upregulated genes as well as new genes that can be implicated in heat stress. Based on the findings on parallel analysis of growth of seedlings, associated changes in transcripts of selected Hsps, genome-wide microarray profiling and the coexpression network analysis, this study is a step forward in understanding heat response of rice, the world's most important food crop.

The identification of candidate radio marker genes using a coexpression network analysis in gamma-irradiated rice

Plant physiological and biochemical processes are significantly affected by gamma irradiation stress. In addition, gamma-ray (GA) differentially affects gene expression across the whole genome. In this study, we identified radio marker genes (RMGs) responding only to GA stress compared with six abiotic stresses (chilling, cold, anoxia, heat, drought and salt) in rice. To analyze the expression patterns of differentially expressed genes (DEGs) in gamma-irradiated rice plants against six abiotic stresses, we conducted a hierarchical clustering analysis by using a complete linkage algorithm. The up- and downregulated DEGs were observed against six abiotic stresses in three and four clusters among a total of 31 clusters, respectively. The common gene ontology functions of upregulated DEGs in clusters 9 and 19 are associated with oxidative stress. In a Pearson's correlation coefficient analysis, GA stress showed highly negative correlation with salt stress. On the basis of specific data about the upregulated DEGs, we identified the 40 candidate RMGs that are induced by gamma irradiation. These candidate RMGs, except two genes, were more highly induced in rice roots than in other tissues. In addition, we obtained other 38 root-induced genes by using a coexpression network analysis of the specific upregulated candidate RMGs in an ARACNE algorithm. Among these genes, we selected 16 RMGs and 11 genes coexpressed with three RMGs to validate coexpression network results. RT-PCR assay confirmed that these genes were highly upregulated in GA treatment. All 76 genes (38 root-induced genes and 38 candidate RMGs) might be useful for the detection of GA sensitivity in rice roots.

Genes and co-expression modules common to drought and bacterial stress responses in Arabidopsis and rice

Plants are simultaneously exposed to multiple stresses resulting in enormous changes in the molecular landscape within the cell. Identification and characterization of the synergistic and antagonistic components of stress response mechanisms contributing to the cross talk between stresses is of high priority to explore and enhance multiple stress responses. To this end, we performed meta-analysis of drought (abiotic), bacterial (biotic) stress response in rice and Arabidopsis by analyzing a total of 386 microarray samples belonging to 20 microarray studies and identified approximately 3100 and 900 DEGs in rice and Arabidopsis, respectively. About 38.5% (1214) and 28.7% (272) DEGs were common to drought and bacterial stresses in rice and Arabidopsis, respectively. A majority of these common DEGs showed conserved expression status in both stresses. Gene ontology enrichment analysis clearly demarcated the response and regulation of various plant hormones and related biological processes. Fatty acid metabolism and biosynthesis of alkaloids were upregulated and, nitrogen metabolism and photosynthesis was downregulated in both stress conditions. WRKY transcription family genes were highly enriched in all upregulated gene sets while ‘CO-like’ TF family showed inverse relationship of expression between drought and bacterial stresses. Weighted gene co-expression network analysis divided DEG sets into multiple modules that show high co-expression and identified stress specific hub genes with high connectivity. Detection of consensus modules based on DEGs common to drought and bacterial stress revealed 9 and 4 modules in rice and Arabidopsis, respectively, with conserved and reversed co-expression patterns.

Genome-wide expression of transcriptomes and their co-expression pattern in subtropical maize (Zea mays L.) under waterlogging stress

Waterlogging causes extensive damage to maize crops in tropical and subtropical regions. The identification of tolerance genes and their interactions at the molecular level will be helpful to engineer tolerant genotypes. A whole-genome transcriptome assay revealed the specific role of genes in response to waterlogging stress in susceptible and tolerant genotypes. Genes involved in the synthesis of ethylene and auxin, cell wall metabolism, activation of G-proteins and formation of aerenchyma and adventitious roots, were upregulated in the tolerant genotype. Many transcription factors, particularly ERFs, MYB, HSPs, MAPK, and LOB-domain protein were involved in regulation of these traits. Genes responsible for scavenging of ROS generated under stress were expressed along with those involved in carbohydrate metabolism. The physical locations of 21 genes expressed in the tolerant genotype were found to correspond with the marker intervals of known QTLs responsible for development of adaptive traits. Among the candidate genes, most showed synteny with genes of sorghum and foxtail millet. Co-expression analysis of 528 microarray samples including 16 samples from the present study generated seven functional modules each in the two genotypes, with differing characteristics. In the tolerant genotype, stress genes were co-expressed along with peroxidase and fermentation pathway genes.

A systems-genetics approach and data mining tool to assist in the discovery of genes underlying complex traits in Oryza sativa

Many traits of biological and agronomic significance in plants are controlled in a complex manner where multiple genes and environmental signals affect the expression of the phenotype. In Oryza sativa (rice), thousands of quantitative genetic signals have been mapped to the rice genome. In parallel, thousands of gene expression profiles have been generated across many experimental conditions. Through the discovery of networks with real gene co-expression relationships, it is possible to identify co-localized genetic and gene expression signals that implicate complex genotype-phenotype relationships. In this work, we used a knowledge-independent, systems genetics approach, to discover a high-quality set of co-expression networks, termed Gene Interaction Layers (GILs). Twenty-two GILs were constructed from 1,306 Affymetrix microarray rice expression profiles that were pre-clustered to allow for improved capture of gene co-expression relationships. Functional genomic and genetic data, including over 8,000 QTLs and 766 phenotype-tagged SNPs (p-value < = 0.001) from genome-wide association studies, both covering over 230 different rice traits were integrated with the GILs. An online systems genetics data-mining resource, the GeneNet Engine, was constructed to enable dynamic discovery of gene sets (i.e. network modules) that overlap with genetic traits. GeneNet Engine does not provide the exact set of genes underlying a given complex trait, but through the evidence of gene-marker correspondence, co-expression, and functional enrichment, site visitors can identify genes with potential shared causality for a trait which could then be used for experimental validation. A set of 2 million SNPs was incorporated into the database and serve as a potential set of testable biomarkers for genes in modules that overlap with genetic traits. Herein, we describe two modules found using GeneNet Engine, one with significant overlap with the trait amylose content and another with significant overlap with blast disease resistance.