Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data

GNET2: an R package for constructing gene regulatory networks from transcriptomic data

MOTIVATION

The Gene Network Estimation Tool (GNET) is designed to build gene regulatory networks (GRNs) from transcriptomic gene expression data with a probabilistic graphical model. The data preprocessing, model construction and visualization modules of the original GNET software were developed on different programming platforms, which were inconvenient for users to deploy and use.

RESULTS

Here, we present GNET2, an improved implementation of GNET as an integrated R package. GNET2 provides more flexibility for parameter initialization and regulatory module construction based on the core iterative modeling process of the original algorithm. The data exchange interface of GNET2 is handled within an R session automatically. Given the growing demand for regulatory network reconstruction from transcriptomic data, GNET2 offers a convenient option for GRN inference on large datasets.

AVAILABILITY AND IMPLEMENTATION

The source code of GNET2 is available at https://github.com/jianlin-cheng/GNET2.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The renoprotective effects of soy protein in the aging rat kidney

Aging is a risk factor for chronic kidney disease (CKD) and is itself associated with alterations in renal structure and function. There are no specific interventions to attenuate age-dependent renal dysfunction and the mechanism(s) responsible for these deficits have not been fully elucidated. In this study, male Fischer 344 rats, which develop age-dependent nephropathy, were feed a casein- or soy protein diet beginning at 16 mon (late life intervention) and renal structure and function was assessed at 20 mon. The soy diet did not significantly affect body weight, but was renoprotective as assessed by decreased proteinuria, increased glomerular filtration rate (GFR) and decreased urinary kidney injury molecule-1 (Kim-1). Renal fibrosis, as assessed by hydroxyproline content, was decreased by the soy diet, as were several indicators of inflammation. RNA sequencing identified several candidates for the renoprotective effects of soy, including decreased expression of Twist2, a basic helix-loop-helix transcription factor that network analysis suggest may regulate the expression of several genes associated with renal dysfunction. Twist2 expression is upregulated in the aging kidney and the unilateral ureteral obstruction of fibrosis; the expression is limited to distal tubules of mice. Taken together, these data demonstrate the renoprotective potential of soy protein, putatively by reducing inflammation and fibrosis, and identify Twist2 as a novel mediator of renal dysfunction that is targeted by soy.

Rhizobial tRNA-derived small RNAs are signal molecules regulating plant nodulation

Rhizobial infection and root nodule formation in legumes require recognition of signal molecules produced by the bacteria and their hosts. Here, we show that rhizobial transfer RNA (tRNA)-derived small RNA fragments (tRFs) are signal molecules that modulate host nodulation. Three families of rhizobial tRFs were confirmed to regulate host genes associated with nodule initiation and development through hijacking the host RNA-interference machinery that involves ARGONAUTE 1. Silencing individual tRFs with the use of short tandem target mimics or by overexpressing their targets represses root hair curling and nodule formation, whereas repressing these targets with artificial microRNAs identical to the respective tRFs or mutating these targets with CRISPR-Cas9 promotes nodulation. Our findings thus uncover a bacterial small RNA-mediated mechanism for prokaryote-eukaryote interaction and may pave the way for enhancing nodulation efficiency in legumes.

Transcriptome analysis of soybean (Glycine max) root genes differentially expressed in rhizobial, arbuscular mycorrhizal, and dual symbiosis

Soybean (Glycine max) roots establish associations with nodule-inducing rhizobia and arbuscular mycorrhizal (AM) fungi. Both rhizobia and AM fungi have been shown to affect the activity of and colonization by the other, and their interactions can be detected within host plants. Here, we report the transcription profiles of genes differentially expressed in soybean roots in the presence of rhizobial, AM, or rhizobial-AM dual symbiosis, compared with those in control (uninoculated) roots. Following inoculation, soybean plants were grown in a glasshouse for 6 weeks; thereafter their root transcriptomes were analyzed using an oligo DNA microarray. Among the four treatments, the root nodule number and host plant growth were highest in plants with dual symbiosis. We observed that the expression of 187, 441, and 548 host genes was up-regulated and 119, 1,439, and 1,298 host genes were down-regulated during rhizobial, AM, and dual symbiosis, respectively. The expression of 34 host genes was up-regulated in each of the three symbioses. These 34 genes encoded several membrane transporters, type 1 metallothionein, and transcription factors in the MYB and bHLH families. We identified 56 host genes that were specifically up-regulated during dual symbiosis. These genes encoded several nodulin proteins, phenylpropanoid metabolism-related proteins, and carbonic anhydrase. The nodulin genes up-regulated by the AM fungal colonization probably led to the observed increases in root nodule number and host plant growth. Some other nodulin genes were down-regulated specifically during AM symbiosis. Based on the results above, we suggest that the contribution of AM fungal colonization is crucial to biological N2-fixation and host growth in soybean with rhizobial-AM dual symbiosis.

Genome-Wide Analysis of Grain Yield Stability and Environmental Interactions in a Multiparental Soybean Population

Genetic improvement toward optimized and stable agronomic performance of soybean genotypes is desirable for food security. Understanding how genotypes perform in different environmental conditions helps breeders develop sustainable cultivars adapted to target regions. Complex traits of importance are known to be controlled by a large number of genomic regions with small effects whose magnitude and direction are modulated by environmental factors. Knowledge of the constraints and undesirable effects resulting from genotype by environmental interactions is a key objective in improving selection procedures in soybean breeding programs. In this study, the genetic basis of soybean grain yield responsiveness to environmental factors was examined in a large soybean nested association population. For this, a genome-wide association to performance stability estimates generated from a Finlay-Wilkinson analysis and the inclusion of the interaction between marker genotypes and environmental factors was implemented. Genomic footprints were investigated by analysis and meta-analysis using a recently published multiparent model. Results indicated that specific soybean genomic regions were associated with stability, and that multiplicative interactions were present between environments and genetic background. Seven genomic regions in six chromosomes were identified as being associated with genotype-by-environment interactions. This study provides insight into genomic assisted breeding aimed at achieving a more stable agronomic performance of soybean, and documented opportunities to exploit genomic regions that were specifically associated with interactions involving environments and subpopulations.

Inference of Transcription Regulatory Network in Low Phytic Acid Soybean Seeds

A dominant loss of function mutation in myo-inositol phosphate synthase (MIPS) gene and recessive loss of function mutations in two multidrug resistant protein type-ABC transporter genes not only reduce the seed phytic acid levels in soybean, but also affect the pathways associated with seed development, ultimately resulting in low emergence. To understand the regulatory mechanisms and identify key genes that intervene in the seed development process in low phytic acid crops, we performed computational inference of gene regulatory networks in low and normal phytic acid soybeans using a time course transcriptomic data and multiple network inference algorithms. We identified a set of putative candidate transcription factors and their regulatory interactions with genes that have functions in myo-inositol biosynthesis, auxin-ABA signaling, and seed dormancy. We evaluated the performance of our unsupervised network inference method by comparing the predicted regulatory network with published regulatory interactions in Arabidopsis. Some contrasting regulatory interactions were observed in low phytic acid mutants compared to non-mutant lines. These findings provide important hypotheses on expression regulation of myo-inositol metabolism and phytohormone signaling in developing low phytic acid soybeans. The computational pipeline used for unsupervised network learning in this study is provided as open source software and is freely available at https://lilabatvt.github.io/LPANetwork/.

RNA-Seq analysis of nodule development at five different developmental stages of soybean (Glycine max) inoculated with Bradyrhizobium japonicum strain 113-2

Nodule development directly affects nitrogen fixation efficiency during soybean growth. Although abundant genome-based information related to nodule development has been released and some studies have reported the molecular mechanisms that regulate nodule development, information on the way nodule genes operate in nodule development at different developmental stages of soybean is limited. In this report, notably different nodulation phenotypes in soybean roots inoculated with Bradyrhizobium japonicum strain 113-2 at five developmental stages (branching stage, flowering stage, fruiting stage, pod stage and harvest stage) were shown, and the expression of nodule genes at these five stages was assessed quantitatively using RNA-Seq. Ten comparisons were made between these developmental periods, and their differentially expressed genes were analysed. Some important genes were identified, primarily encoding symbiotic nitrogen fixation-related proteins, cysteine proteases, cystatins and cysteine-rich proteins, as well as proteins involving plant-pathogen interactions. There were no significant shifts in the distribution of most GO functional annotation terms and KEGG pathway enrichment terms between these five development stages. A cystatin Glyma18g12240 was firstly identified from our RNA-seq, and was likely to promote nodulation and delay nodule senescence. This study provides molecular material for further investigations into the mechanisms of nitrogen fixation at different soybean developmental stages.

Drought Stress Responses in Soybean Roots and Nodules

Drought is considered to be a major threat to soybean production worldwide and yet our current understanding of the effects of drought on soybean productively is largely based on studies on above-ground traits. Although the roots and root nodules are important sensors of drought, the responses of these crucial organs and their drought tolerance features remain poorly characterized. The symbiotic interaction between soybean and rhizobia facilitates atmospheric nitrogen fixation, a process that provides essential nitrogen to support plant growth and development. Symbiotic nitrogen fixation is important for sustainable agriculture, as it sustains plant growth on nitrogen-poor soils and limits fertilizer use for crop nitrogen nutrition. Recent developments have been made in our understanding of the drought impact on soybean root architecture and nodule traits, as well as underpinning transcriptome, proteome and also emerging metabolome information, with a view to improve the selection of more drought-tolerant soybean cultivars and rhizobia in the future. We conclude that the direct screening of root and nodule traits in the field as well as identification of genes, proteins and also metabolites involved in such traits will be essential in order to gain a better understanding of the regulation of root architecture, bacteroid development and lifespan in relation to drought tolerance in soybean.

RNA-Seq Analysis of Differential Gene Expression Responding to Different Rhizobium Strains in Soybean (Glycine max) Roots

The root nodule symbiosis (RNS) between legume plants and rhizobia is the most efficient and productive source of nitrogen fixation, and has critical importance in agriculture and mesology. Soybean (Glycine max), one of the most important legume crops in the world, establishes a nitrogen-fixing symbiosis with different types of rhizobia, and the efficiency of symbiotic nitrogen fixation in soybean greatly depends on the symbiotic host-specificity. Although, it has been reported that rhizobia use surface polysaccharides, secretion proteins of the type-three secretion systems and nod factors to modulate host range, the host control of nodulation specificity remains poorly understood. In this report, the soybean roots of two symbiotic systems (Bradyrhizobium japonicum strain 113-2-soybean and Sinorhizobium fredii USDA205-soybean)with notable different nodulation phenotypes and the control were studied at five different post-inoculation time points (0.5, 7-24 h, 5, 16, and 21 day) by RNA-seq (Quantification). The results of qPCR analysis of 11 randomly-selected genes agreed with transcriptional profile data for 136 out of 165 (82.42%) data points and quality assessment showed that the sequencing library is of quality and reliable. Three comparisons (control vs. 113-2, control vs. USDA205 and USDA205 vs. 113-2) were made and the differentially expressed genes (DEGs) between them were analyzed. The number of DEGs at 16 days post-inoculation (dpi) was the highest in the three comparisons, and most of the DEGs in USDA205 vs. 113-2 were found at 16 dpi and 21 dpi. 44 go function terms in USDA205 vs. 113-2 were analyzed to evaluate the potential functions of the DEGs, and 10 important KEGG pathway enrichment terms were analyzed in the three comparisons. Some important genes induced in response to different strains (113-2 and USDA205) were identified and analyzed, and these genes primarily encoded soybean resistance proteins, NF-related proteins, nodulins and immunity defense proteins, as well as proteins involving flavonoids/flavone/flavonol biosynthesis and plant-pathogen interaction. Besides, 189 candidate genes are largely expressed in roots and\or nodules. The DEGs uncovered in this study provides molecular candidates for better understanding the mechanisms of symbiotic host-specificity and explaining the different symbiotic effects between soybean roots inoculated with different strains (113-2 and USDA205).

Crowdsourcing the nodulation gene network discovery environment

Background

The Legumes (Fabaceae) are an economically and ecologically important group of plant species with the conspicuous capacity for symbiotic nitrogen fixation in root nodules, specialized plant organs containing symbiotic microbes. With the aim of understanding the underlying molecular mechanisms leading to nodulation, many efforts are underway to identify nodulation-related genes and determine how these genes interact with each other. In order to accurately and efficiently reconstruct nodulation gene network, a crowdsourcing platform, CrowdNodNet, was created.

Results

The platform implements the jQuery and vis.js JavaScript libraries, so that users are able to interactively visualize and edit the gene network, and easily access the information about the network, e.g. gene lists, gene interactions and gene functional annotations. In addition, all the gene information is written on MediaWiki pages, enabling users to edit and contribute to the network curation.

Conclusions

Utilizing the continuously updated, collaboratively written, and community-reviewed Wikipedia model, the platform could, in a short time, become a comprehensive knowledge base of nodulation-related pathways. The platform could also be used for other biological processes, and thus has great potential for integrating and advancing our understanding of the functional genomics and systems biology of any process for any species. The platform is available at http://crowd.bioops.info/, and the source code can be openly accessed at https://github.com/bioops/crowdnodnet under MIT License.

In silico Transcriptional Regulatory Networks Involved in Tomato Fruit Ripening

Tomato fruit ripening is a complex developmental programme partly mediated by transcriptional regulatory networks. Several transcription factors (TFs) which are members of gene families such as MADS-box and ERF were shown to play a significant role in ripening through interconnections into an intricate network. The accumulation of large datasets of expression profiles corresponding to different stages of tomato fruit ripening and the availability of bioinformatics tools for their analysis provide an opportunity to identify TFs which might regulate gene clusters with similar co-expression patterns. We identified two TFs, a SlWRKY22-like and a SlER24 transcriptional activator which were shown to regulate modules by using the LeMoNe algorithm for the analysis of our microarray datasets representing four stages of fruit ripening, breaker, turning, pink and red ripe. The WRKY22-like module comprised a subgroup of six various calcium sensing transcripts with similar to the TF expression patterns according to real time PCR validation. A promoter motif search identified a cis acting element, the W-box, recognized by WRKY TFs that was present in the promoter region of all six calcium sensing genes. Moreover, publicly available microarray datasets of similar ripening stages were also analyzed with LeMoNe resulting in TFs such as SlERF.E1, SlERF.C1, SlERF.B2, SLERF.A2, SlWRKY24, SLWRKY37, and MADS-box/TM29 which might also play an important role in regulation of ripening. These results suggest that the SlWRKY22-like might be involved in the coordinated regulation of expression of the six calcium sensing genes. Conclusively the LeMoNe tool might lead to the identification of putative TF targets for further physiological analysis as regulators of tomato fruit ripening.

Soybean Roots Grown under Heat Stress Show Global Changes in Their Transcriptional and Proteomic Profiles

Heat stress is likely to be a key factor in the negative impact of climate change on crop production. Heat stress significantly influences the functions of roots, which provide support, water, and nutrients to other plant organs. Likewise, roots play an important role in the establishment of symbiotic associations with different microorganisms. Despite the physiological relevance of roots, few studies have examined their response to heat stress. In this study, we performed genome-wide transcriptomic and proteomic analyses on isolated root hairs, which are a single, epidermal cell type, and compared their response to stripped roots. On average, we identified 1849 and 3091 genes differentially regulated in root hairs and stripped roots, respectively, in response to heat stress. Our gene regulatory module analysis identified 10 key modules that might control the majority of the transcriptional response to heat stress. We also conducted proteomic analysis on membrane fractions isolated from root hairs and compared these responses to stripped roots. These experiments identified a variety of proteins whose expression changed within 3 h of application of heat stress. Most of these proteins were predicted to play a significant role in thermo-tolerance, as well as in chromatin remodeling and post-transcriptional regulation. The data presented represent an in-depth analysis of the heat stress response of a single cell type in soybean.

Gene Networks in Plant Biology: Approaches in Reconstruction and Analysis

Even though vast amounts of genome-wide gene expression data have become available in plants, it remains a challenge to effectively mine this information for the discovery of genes and gene networks, for instance those that control agronomically important traits. These networks reflect potential interactions among genes and, therefore, can lead to a systematic understanding of the molecular mechanisms underlying targeted biological processes. We discuss methods to analyze gene networks using gene expression data, specifically focusing on four common statistical approaches used to reconstruct networks: correlation, feature selection in supervised learning, probabilistic graphical model, and meta-prediction. In addition, we discuss the effective use of these methods for acquiring an in-depth understanding of biological systems in plants.

From Gigabyte to Kilobyte: A Bioinformatics Protocol for Mining Large RNA-Seq Transcriptomics Data

RNA-Seq techniques generate hundreds of millions of short RNA reads using next-generation sequencing (NGS). These RNA reads can be mapped to reference genomes to investigate changes of gene expression but improved procedures for mining large RNA-Seq datasets to extract valuable biological knowledge are needed. RNAMiner--a multi-level bioinformatics protocol and pipeline--has been developed for such datasets. It includes five steps: Mapping RNA-Seq reads to a reference genome, calculating gene expression values, identifying differentially expressed genes, predicting gene functions, and constructing gene regulatory networks. To demonstrate its utility, we applied RNAMiner to datasets generated from Human, Mouse, Arabidopsis thaliana, and Drosophila melanogaster cells, and successfully identified differentially expressed genes, clustered them into cohesive functional groups, and constructed novel gene regulatory networks. The RNAMiner web service is available at http://calla.rnet.missouri.edu/rnaminer/index.html.

Transcriptomic analysis identifies gene networks regulated by estrogen receptor α (ERα) and ERβ that control distinct effects of different botanical estrogens

The estrogen receptors (ERs) ERα and ERβ mediate the actions of endogenous estrogens as well as those of botanical estrogens (BEs) present in plants. BEs are ingested in the diet and also widely consumed by postmenopausal women as dietary supplements, often as a substitute for the loss of endogenous estrogens at menopause. However, their activities and efficacies, and similarities and differences in gene expression programs with respect to endogenous estrogens such as estradiol (E2) are not fully understood. Because gene expression patterns underlie and control the broad physiological effects of estrogens, we have investigated and compared the gene networks that are regulated by different BEs and by E2. Our aim was to determine if the soy and licorice BEs control similar or different gene expression programs and to compare their gene regulations with that of E2. Gene expression was examined by RNA-Seq in human breast cancer (MCF7) cells treated with control vehicle, BE or E2. These cells contained three different complements of ERs, ERα only, ERα+ERβ, or ERβ only, reflecting the different ratios of these two receptors in different human breast cancers and in different estrogen target cells. Using principal component, hierarchical clustering, and gene ontology and interactome analyses, we found that BEs regulated many of the same genes as did E2. The genes regulated by each BE, however, were somewhat different from one another, with some genes being regulated uniquely by each compound. The overlap with E2 in regulated genes was greatest for the soy isoflavones genistein and S-equol, while the greatest difference from E2 in gene expression pattern was observed for the licorice root BE liquiritigenin. The gene expression pattern of each ligand depended greatly on the cell background of ERs present. Despite similarities in gene expression pattern with E2, the BEs were generally less stimulatory of genes promoting proliferation and were more pro-apoptotic in their gene regulations than E2. The distinctive patterns of gene regulation by the individual BEs and E2 may underlie differences in the activities of these soy and licorice-derived BEs in estrogen target cells containing different levels of the two ERs.