SoyFN: a knowledge database of soybean functional networks

GGDB: A Grameneae genome alignment database of homologous genes hierarchically related to evolutionary events

The genomes of Gramineae plants have been preferentially sequenced owing to their economic value. These genomes are often quite complex, for example harboring many duplicated genes, and are the main source of genetic innovation and often the result of recurrent polyploidization. Deciphering these complex genome structures and linking duplicated genes to specific polyploidization events are important for understanding the biology and evolution of plants. However, efforts have been hampered by the complexity of analyzing these genomes. Here, we analyzed 29 well-assembled and up-to-date Gramineae genome sequences by hierarchically relating duplicated genes in collinear regions to specific polyploidization or speciation events. We separated duplicated genes produced by each event, established lists of paralogous and orthologous genes, and ultimately constructed an online database, GGDB (http://www.grassgenome.com/). Homologous gene lists from each plant and between plants can be displayed, searched, and downloaded from the database. Interactive comparison tools are deployed to demonstrate homology among user-selected plants and to draw genome-scale or local alignment figures and gene-based phylogenetic trees corrected by exploiting gene collinearity. Using these tools and figures, users can easily detect structural changes in genomes and explore the effects of paleo-polyploidy on crop genome structure and function. The GGDB will provide a useful platform for improving our understanding of genome changes and functional innovation in Gramineae plants.

Genetic and Genomic Resources for Soybean Breeding Research

Soybean (Glycine max) is a legume species of significant economic and nutritional value. The yield of soybean continues to increase with the breeding of improved varieties, and this is likely to continue with the application of advanced genetic and genomic approaches for breeding. Genome technologies continue to advance rapidly, with an increasing number of high-quality genome assemblies becoming available. With accumulating data from marker arrays and whole-genome resequencing, studying variations between individuals and populations is becoming increasingly accessible. Furthermore, the recent development of soybean pangenomes has highlighted the significant structural variation between individuals, together with knowledge of what has been selected for or lost during domestication and breeding, information that can be applied for the breeding of improved cultivars. Because of this, resources such as genome assemblies, SNP datasets, pangenomes and associated databases are becoming increasingly important for research underlying soybean crop improvement.

A Multi-Level Iterative Bi-Clustering Method for Discovering miRNA Co-regulation Network of Abiotic Stress Tolerance in Soybeans

Although growing evidence shows that microRNA (miRNA) regulates plant growth and development, miRNA regulatory networks in plants are not well understood. Current experimental studies cannot characterize miRNA regulatory networks on a large scale. This information gap provides an excellent opportunity to employ computational methods for global analysis and generate valuable models and hypotheses. To address this opportunity, we collected miRNA-target interactions (MTIs) and used MTIs from Arabidopsis thaliana and Medicago truncatula to predict homologous MTIs in soybeans, resulting in 80,235 soybean MTIs in total. A multi-level iterative bi-clustering method was developed to identify 483 soybean miRNA-target regulatory modules (MTRMs). Furthermore, we collected soybean miRNA expression data and corresponding gene expression data in response to abiotic stresses. By clustering these data, 37 MTRMs related to abiotic stresses were identified, including stress-specific MTRMs and shared MTRMs. These MTRMs have gene ontology (GO) enrichment in resistance response, iron transport, positive growth regulation, etc. Our study predicts soybean MTRMs and miRNA-GO networks under different stresses, and provides miRNA targeting hypotheses for experimental analyses. The method can be applied to other biological processes and other plants to elucidate miRNA co-regulation mechanisms.

Overexpression of a carrot BCH gene, DcBCH1, improves tolerance to drought in Arabidopsis thaliana

BACKGROUND

Carrot (Daucus carota L.), an important root vegetable, is very popular among consumers as its taproot is rich in various nutrients. Abiotic stresses, such as drought, salt, and low temperature, are the main factors that restrict the growth and development of carrots. Non-heme carotene hydroxylase (BCH) is a key regulatory enzyme in the β-branch of the carotenoid biosynthesis pathway, upstream of the abscisic acid (ABA) synthesis pathway.

RESULTS

In this study, we characterized a carrot BCH encoding gene, DcBCH1. The expression of DcBCH1 was induced by drought treatment. The overexpression of DcBCH1 in Arabidopsis thaliana resulted in enhanced tolerance to drought, as demonstrated by higher antioxidant capacity and lower malondialdehyde content after drought treatment. Under drought stress, the endogenous ABA level in transgenic A. thaliana was higher than that in wild-type (WT) plants. Additionally, the contents of lutein and β-carotene in transgenic A. thaliana were lower than those in WT, whereas the expression levels of most endogenous carotenogenic genes were significantly increased after drought treatment.

CONCLUSIONS

DcBCH1 can increase the antioxidant capacity and promote endogenous ABA levels of plants by regulating the synthesis rate of carotenoids, thereby regulating the drought resistance of plants. These results will help to provide potential candidate genes for plant drought tolerance breeding.

Coriander Genomics Database: a genomic, transcriptomic, and metabolic database for coriander

Coriander (Coriandrum sativum L.), also known as cilantro, is a globally important vegetable and spice crop. Its genome and that of carrot are models for studying the evolution of the Apiaceae family. Here, we developed the Coriander Genomics Database (CGDB, http://cgdb.bio2db.com/) to collect, store, and integrate the genomic, transcriptomic, metabolic, functional annotation, and repeat sequence data of coriander and carrot to serve as a central online platform for Apiaceae and other related plants. Using these data sets in the CGDB, we intriguingly found that seven transcription factor (TF) families showed significantly greater numbers of members in the coriander genome than in the carrot genome. The highest ratio of the numbers of MADS TFs between coriander and carrot reached 3.15, followed by those for tubby protein (TUB) and heat shock factors. As a demonstration of CGDB applications, we identified 17 TUB family genes and conducted systematic comparative and evolutionary analyses. RNA-seq data deposited in the CGDB also suggest dose compensation effects of gene expression in coriander. CGDB allows bulk downloading, significance searches, genome browser analyses, and BLAST searches for comparisons between coriander and other plants regarding genomics, gene families, gene collinearity, gene expression, and the metabolome. A detailed user manual and contact information are also available to provide support to the scientific research community and address scientific questions. CGDB will be continuously updated, and new data will be integrated for comparative and functional genomic analysis in Apiaceae and other related plants.

Identification of Anti-cancer Peptides Based on Multi-classifier System

AIMS AND OBJECTIVE

Cancer is one of the deadliest diseases, taking the lives of millions every year. Traditional methods of treating cancer are expensive and toxic to normal cells. Fortunately, anti-cancer peptides (ACPs) can eliminate this side effect. However, the identification and development of new anti-cancer peptides through experiments take a lot of time and money, therefore, it is necessary to develop a fast and accurate calculation model to identify the anti-cancer peptide. Machine learning algorithms are a good choice.

MATERIALS AND METHODS

In our study, a multi-classifier system was used, combined with multiple machine learning models, to predict anti-cancer peptides. These individual learners are composed of different feature information and algorithms, and form a multi-classifier system by voting.

RESULTS AND CONCLUSION

The experiments show that the overall prediction rate of each individual learner is above 80% and the overall accuracy of multi-classifier system for anti-cancer peptides prediction can reach 95.93%, which is better than the existing prediction model.

MinE-RFE: determine the optimal subset from RFE by minimizing the subset-accuracy–defined energy

Recursive feature elimination (RFE), as one of the most popular feature selection algorithms, has been extensively applied to bioinformatics. During the training, a group of candidate subsets are generated by iteratively eliminating the least important features from the original features. However, how to determine the optimal subset from them still remains ambiguous. Among most current studies, either overall accuracy or subset size (SS) is used to select the most predictive features. Using which one or both and how they affect the prediction performance are still open questions. In this study, we proposed MinE-RFE, a novel RFE-based feature selection approach by sufficiently considering the effect of both factors. Subset decision problem was reflected into subset-accuracy space and became an energy-minimization problem. We also provided a mathematical description of the relationship between the overall accuracy and SS using Gaussian Mixture Models together with spline fitting. Besides, we comprehensively reviewed a variety of state-of-the-art applications in bioinformatics using RFE. We compared their approaches of deciding the final subset from all the candidate subsets with MinE-RFE on diverse bioinformatics data sets. Additionally, we also compared MinE-RFE with some well-used feature selection algorithms. The comparative results demonstrate that the proposed approach exhibits the best performance among all the approaches. To facilitate the use of MinE-RFE, we further established a user-friendly web server with the implementation of the proposed approach, which is accessible at http://qgking.wicp.net/MinE/. We expect this web server will be a useful tool for research community.

CRISPR/Cas9-Mediated Multiply Targeted Mutagenesis in Orange and Purple Carrot Plants

The clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein 9 (Cas9) system has been successfully used for precise genome editing in many plant species, including in carrot cells, very recently. However, no stable gene-editing carrot plants were obtained with CRISPR/Cas9 system to date. In the present study, four sgRNA expression cassettes, individually driven by four different promoters and assembled in a single CRISPR/Cas9 vector, were transformed into carrots using Agrobacterium-mediated genetic transformation. Four sites of DcPDS and DcMYB113-like genes were chosen as targets. Knockout of DcPDS in orange carrot 'Kurodagosun' resulted in the generation of albino carrot plantlets, with about 35.3% editing efficiency. DcMYB113-like was also successfully edited in purple carrot 'Deep purple', resulting in purple depigmented carrot plants, with about 36.4% rate of mutation. Sequencing analyses showed that insertion, deletion, and substitution occurred in the target sites, generating heterozygous, biallelic, and chimeric mutations. The highest efficiency of mutagenesis was observed in the sites targeted by AtU6-29-driven sgRNAs in both DcPDS- and DcMYB113-like-knockout T0 plants, which always induced double-strand breaks in the target sites. Our results proved that CRISPR/Cas9 system could be for generating stable gene-editing carrot plants.

The leaf transcriptome of fennel (Foeniculum vulgare Mill.) enables characterization of the t-anethole pathway and the discovery of microsatellites and single-nucleotide variants

Fennel is a plant species of both agronomic and pharmaceutical interest that is characterized by a shortage of genetic and molecular data. Taking advantage of NGS technology, we sequenced and annotated the first fennel leaf transcriptome using material from four different lines and two different bioinformatic approaches: de novo and genome-guided transcriptome assembly. A reference transcriptome for assembly was produced by combining these two approaches. Among the 79,263 transcripts obtained, 47,775 were annotated using BLASTX analysis performed against the NR protein database subset with 11,853 transcripts representing putative full-length CDS. Bioinformatic analyses revealed 1,011 transcripts encoding transcription factors, mainly from the BHLH, MYB-related, C2H2, MYB, and ERF families, and 6,411 EST-SSR regions. Single-nucleotide variants of SNPs and indels were identified among the 8 samples at a frequency of 0.5 and 0.04 variants per Kb, respectively. Finally, the assembled transcripts were screened to identify genes related to the biosynthesis of t-anethole, a compound well-known for its nutraceutical and medical properties. For each of the 11 genes encoding structural enzymes in the t-anethole biosynthetic pathway, we identified at least one transcript showing a significant match. Overall, our work represents a treasure trove of information exploitable both for marker-assisted breeding and for in-depth studies on thousands of genes, including those involved in t-anethole biosynthesis.

A comprehensive review of web-based resources of non-coding RNAs for plant science research

Non-coding RNAs (ncRNAs) are transcribed from genome but not translated into proteins. Many ncRNAs are key regulators of plants growth and development, metabolism and stress tolerance. In order to make the web-based ncRNA resources for plant science research be more easily accessible and understandable, we made a comprehensive review for 83 web-based resources of three types, including genome databases containing ncRNA data, microRNA (miRNA) databases and long non-coding RNA (lncRNA) databases. To facilitate effective usage of these resources, we also suggested some preferred resources of miRNAs and lncRNAs for performing meaningful analysis.

Down-regulation of genes coding for core RNAi components and disease resistance proteins via corresponding microRNAs might be correlated with successful Soybean mosaic virus infection in soybean

Plants protect themselves from virus infections by several different defence mechanisms. RNA interference (RNAi) is one prominent antiviral mechanism, which requires the participation of AGO (Argonaute) and Dicer/DCL (Dicer-like) proteins. Effector-triggered immunity (ETI) is an antiviral mechanism mediated by resistance (R) genes, most of which encode nucleotide-binding site-leucine-rich repeat (NBS-LRR) family proteins. MicroRNAs (miRNAs) play important regulatory roles in plants, including the regulation of host defences. Soybean mosaic virus (SMV) is the most common virus in soybean and, in this work, we identified dozens of SMV-responsive miRNAs by microarray analysis in an SMV-susceptible soybean line. Amongst the up-regulated miRNAs, miR168a, miR403a, miR162b and miR1515a predictively regulate the expression of AGO1, AGO2, DCL1 and DCL2, respectively, and miR1507a, miR1507c and miR482a putatively regulate the expression of several NBS-LRR family disease resistance genes. The regulation of target gene expression by these seven miRNAs was validated by both transient expression assays and RNA ligase-mediated rapid amplification of cDNA ends (RLM-RACE) experiments. Transcript levels for AGO1, DCL1, DCL2 and five NBS-LRR family genes were repressed at different time points after SMV infection, whereas the corresponding miRNA levels were up-regulated at these same time points. Furthermore, inhibition of miR1507a, miR1507c, miR482a, miR168a and miR1515a by short tandem target mimic (STTM) technology compromised SMV infection efficiency in soybean. Our results imply that SMV can counteract soybean defence responses by the down-regulation of several RNAi pathway genes and NBS-LRR family resistance genes via the induction of the accumulation of their corresponding miRNA levels.

Integration of gel-based and gel-free proteomic data for functional analysis of proteins through Soybean Proteome Database

The Soybean Proteome Database (SPD) stores data on soybean proteins obtained with gel-based and gel-free proteomic techniques. The database was constructed to provide information on proteins for functional analyses. The majority of the data is focused on soybean (Glycine max 'Enrei'). The growth and yield of soybean are strongly affected by environmental stresses such as flooding. The database was originally constructed using data on soybean proteins separated by two-dimensional polyacrylamide gel electrophoresis, which is a gel-based proteomic technique. Since 2015, the database has been expanded to incorporate data obtained by label-free mass spectrometry-based quantitative proteomics, which is a gel-free proteomic technique. Here, the portions of the database consisting of gel-free proteomic data are described. The gel-free proteomic database contains 39,212 proteins identified in 63 sample sets, such as temporal and organ-specific samples of soybean plants grown under flooding stress or non-stressed conditions. In addition, data on organellar proteins identified in mitochondria, nuclei, and endoplasmic reticulum are stored. Furthermore, the database integrates multiple omics data such as genomics, transcriptomics, metabolomics, and proteomics. The SPD database is accessible at http://proteome.dc.affrc.go.jp/Soybean/.

BIOLOGICAL SIGNIFICANCE

The Soybean Proteome Database stores data obtained from both gel-based and gel-free proteomic techniques. The gel-free proteomic database comprises 39,212 proteins identified in 63 sample sets, such as different organs of soybean plants grown under flooding stress or non-stressed conditions in a time-dependent manner. In addition, organellar proteins identified in mitochondria, nuclei, and endoplasmic reticulum are stored in the gel-free proteomics database. A total of 44,704 proteins, including 5490 proteins identified using a gel-based proteomic technique, are stored in the SPD. It accounts for approximately 80% of all predicted proteins from genome sequences, though there are over lapped proteins. Based on the demonstrated application of data stored in the database for functional analyses, it is suggested that these data will be useful for analyses of biological mechanisms in soybean. Furthermore, coupled with recent advances in information and communication technology, the usefulness of this database would increase in the analyses of biological mechanisms.

A MYB transcription factor, DcMYB6, is involved in regulating anthocyanin biosynthesis in purple carrot taproots

Carrots are widely grown and enjoyed around the world. Purple carrots accumulate rich anthocyanins in the taproots, while orange, yellow, and red carrots accumulate rich carotenoids in the taproots. Our previous studies indicated that variation in the activity of regulatory genes may be responsible for variations in anthocyanin production among various carrot cultivars. In this study, an R2R3-type MYB gene, designated as DcMYB6, was isolated from a purple carrot cultivar. In a phylogenetic analysis, DcMYB6 was grouped into an anthocyanin biosynthesis-related MYB clade. Sequence analyses revealed that DcMYB6 contained the conserved bHLH-interaction motif and two atypical motifs of anthocyanin regulators. The expression pattern of DcMYB6 was correlated with anthocyanin production. DcMYB6 transcripts were detected at high levels in three purple carrot cultivars but at much lower levels in six non-purple carrot cultivars. Overexpression of DcMYB6 in Arabidopsis led to enhanced anthocyanin accumulation in both vegetative and reproductive tissues and upregulated transcript levels of all seven tested anthocyanin-related structural genes. Together, these results show that DcMYB6 is involved in regulating anthocyanin biosynthesis in purple carrots. Our results provide new insights into the regulation of anthocyanin synthesis in purple carrot cultivars.

Identify bilayer modules via pseudo-3D clustering: applications to miRNA-gene bilayer networks

Module identification is a frequently used approach for mining local structures with more significance in global networks. Recently, a wide variety of bilayer networks are emerging to characterize the more complex biological processes. In the light of special topological properties of bilayer networks and the accompanying challenges, there is yet no effective method aiming at bilayer module identification to probe the modular organizations from the more inspiring bilayer networks. To this end, we proposed the pseudo-3D clustering algorithm, which starts from extracting initial non-hierarchically organized modules and then iteratively deciphers the hierarchical organization of modules according to a bottom-up strategy. Specifically, a modularity function for bilayer modules was proposed to facilitate the algorithm reporting the optimal partition that gives the most accurate characterization of the bilayer network. Simulation studies demonstrated its robustness and outperformance against alternative competing methods. Specific applications to both the soybean and human miRNA-gene bilayer networks demonstrated that the pseudo-3D clustering algorithm successfully identified the overlapping, hierarchically organized and highly cohesive bilayer modules. The analyses on topology, functional and human disease enrichment and the bilayer subnetwork involved in soybean fat biosynthesis provided both the theoretical and biological evidence supporting the effectiveness and robustness of pseudo-3D clustering algorithm.

Identification and characterization of DcUCGalT1, a galactosyltransferase responsible for anthocyanin galactosylation in purple carrot (Daucus carota L.) taproots

Purple carrots (Daucus carota ssp. sativus var. atrorubens Alef.) accumulate large amounts of cyanidin-based anthocyanins in their taproots. Cyanidin can be glycosylated with galactose, xylose, and glucose in sequence by glycosyltransferases resulting in cyanidin 3-xylosyl (glucosyl) galactosides in purple carrots. The first step in the glycosylation of cyanidin is catalysis by UDP-galactose: cyanidin galactosyltransferase (UCGalT) transferring the galactosyl moiety from UDP-galactose to cyanidin. In the present study, a gene from 'Deep purple' carrot, DcUCGalT1, was cloned and heterologously expressed in E. coli BL21 (DE3). The recombinant DcUCGalT1 galactosylated cyanidin to produce cyanidin-3-O-galactoside and showed optimal activity for cyanidin at 30 °C and pH 8.6. It showed lower galactosylation activity for peonidin, pelargonidin, kaempferol and quercetin. It accepted only UDP-galactose as a glycosyl donor when cyanidin was used as an aglycone. The expression level of DcUCGalT1 was positively correlated with anthocyanin biosynthesis in carrots. The enzyme extractions from 'Deep purple' exhibited galactosylation activity for cyanidin, peonidin and pelargonidin, while those from 'Kuroda' (a non-purple cultivar) did not.

Gaining insight into soybean defense responses using functional genomics approaches

Soybean pathogens significantly impact yield, resulting in over $4 billion dollars in lost revenue annually in the United States. Despite the deployment of improved soybean cultivars, pathogens continue to evolve to evade plant defense responses. Thus, there is an urgent need to identify and characterize gene networks controlling defense responses to harmful pathogens. In this review, we focus on major advances that have been made in identifying the genes and gene networks regulating defense responses with an emphasis on soybean-pathogen interactions that have been amenable to gene function analyses using gene silencing technologies. Further we describe new research striving to identify genes involved in durable broad-spectrum resistance. Finally, we consider future prospects for functional genomic studies in soybean and demonstrate that understanding soybean disease and stress tolerance will be expedited at an unprecedented pace.

Expanding Omics Resources for Improvement of Soybean Seed Composition Traits

Food resources of the modern world are strained due to the increasing population. There is an urgent need for innovative methods and approaches to augment food production. Legume seeds are major resources of human food and animal feed with their unique nutrient compositions including oil, protein, carbohydrates, and other beneficial nutrients. Recent advances in next-generation sequencing (NGS) together with "omics" technologies have considerably strengthened soybean research. The availability of well annotated soybean genome sequence along with hundreds of identified quantitative trait loci (QTL) associated with different seed traits can be used for gene discovery and molecular marker development for breeding applications. Despite the remarkable progress in these technologies, the analysis and mining of existing seed genomics data are still challenging due to the complexity of genetic inheritance, metabolic partitioning, and developmental regulations. Integration of "omics tools" is an effective strategy to discover key regulators of various seed traits. In this review, recent advances in "omics" approaches and their use in soybean seed trait investigations are presented along with the available databases and technological platforms and their applicability in the improvement of soybean. This article also highlights the use of modern breeding approaches, such as genome-wide association studies (GWAS), genomic selection (GS), and marker-assisted recurrent selection (MARS) for developing superior cultivars. A catalog of available important resources for major seed composition traits, such as seed oil, protein, carbohydrates, and yield traits are provided to improve the knowledge base and future utilization of this information in the soybean crop improvement programs.