Transcriptome profiling reveals the spatial-temporal dynamics of gene expression essential for soybean seed development

A genomic toolkit for winged bean Psophocarpus tetragonolobus

A sustainable supply of plant protein is critical for future generations and needs to be achieved while reducing green house gas emissions from agriculture and increasing agricultural resilience in the face of climate volatility. Agricultural diversification with more nutrient-rich and stress tolerant crops could provide the solution. However, this is often hampered by the limited availability of genomic resources and the lack of understanding of the genetic structure of breeding germplasm and the inheritance of important traits. One such crop with potential is winged bean (Psophocarpus tetragonolobus), a high seed protein tropical legume which has been termed 'the soybean for the tropics'. Here, we present a chromosome level winged bean genome assembly, an investigation of the genetic diversity of 130 worldwide accessions, together with two linked genetic maps and a trait QTL analysis (and expression studies) for regions of the genome with desirable ideotype traits for breeding, namely architecture, protein content and phytonutrients.

Full-Length Transcriptome Sequencing and RNA-Seq Analysis Offer Insights into Terpenoid Biosynthesis in Blumea balsamifera (L.) DC

Blumea balsamifera (L.) DC., an important economic and medicinal herb, has a long history of being used as a traditional Chinese medicine. Its leaves have always been used as a raw material for the extraction of essential oils, comprising large amounts of terpenoids, which have good therapeutic effects on many diseases, such as eczema, bacterial infection, and hypertension. However, the genetic basis of terpenoid biosynthesis in this plant is virtually unknown on account of the lack of genomic data. Here, a combination of next-generation sequencing (NGS) and full-length transcriptome sequencing was applied to identify genes involved in terpenoid biosynthesis at five developmental stages. Then, the main components of essential oils in B. balsamifera were identified using GC-MS. Overall, 16 monoterpenoids and 20 sesquiterpenoids were identified and 333,860 CCS reads were generated, yielding 65,045 non-redundant transcripts. Among these highly accurate transcripts, 59,958 (92.18%) transcripts were successfully annotated using NR, eggNOG, Swissprot, KEGG, KOG, COG, Pfam, and GO databases. Finally, a total of 56 differently expressed genes (DEGs) involved in terpenoid biosynthesis were identified, including 38 terpenoid backbone genes and 18 TPSs, which provide a significant amount of genetic information for B. balsamifera. These results build a basis for resource protection, molecular breeding, and the metabolic engineering of this plant.

Spatio-temporal transcriptome and storage compound profiles of developing faba bean (Vicia faba) seed tissues

Faba bean (Vicia faba) is a legume grown in diverse climate zones with a high potential for increased cultivation and use in food due to its nutritional seeds. In this study, we characterized seed tissue development in faba bean to identify key developmental processes; from embryo expansion at the expense of the endosperm to the maturing storage stages of the bean seed. A spatio-temporal transcriptome profiling analysis, combined with chemical nutrient analysis of protein, starch, and lipid, of endosperm and embryo tissues at different developmental stages, revealed gene expression patterns, transcriptional networks, and biochemical pathways in faba bean. We identified key players in the LAFL (LEC1, ABI3, FUS3, and LEC2) transcription factor network as well as their major repressors VAL1 and ASIL1. Our results showed that proteins accumulated not only in the embryo but also in the endosperm. Starch accumulated throughout seed development and oil content increased during seed development but at very low levels. The patterns of differentially expressed transcripts encoding proteins with functions in the corresponding metabolic pathways for the synthesis of these storage compounds, to a high extent, aligned with these findings. However, the early expression of transcripts encoding WRI1 combined with the late expression of oil body proteins indicated a not manifested high potential for lipid biosynthesis and oil storage. Altogether, this study contributes to increased knowledge regarding seed developmental processes applicable to future breeding methods and seed quality improvement for faba bean.

Seed Development and Protein Accumulation Patterns in Faba Bean (Vicia faba, L.)

A major objective in faba bean breeding is to improve its protein quality by selecting cultivars with enhanced desirable physicochemical properties. However, the protein composition of the mature seed is determined by a series of biological processes occurring during seed growth. Thus, any attempt to explain the final seed composition must consider the dynamics of the seed proteome during seed development. Here, we investigated the proteomic profile of developing faba bean seeds across 12 growth stages from 20 days after pollination (DAP) to full maturity. We analyzed trypsin-digested total protein extracts from the seeds at different growth stages by liquid chromatography-tandem mass spectrometry (LC-MS/MS), identifying 1217 proteins. The functional clusters of these proteins showed that, in early growth stages, proteins related to cell growth, division, and metabolism were most abundant compared to seed storage proteins that began to accumulate from 45 DAP. Moreover, label-free quantification of the relative abundance of seed proteins, including important globulin proteins, revealed several distinct temporal accumulation trends among the protein classes. These results suggest that these proteins are regulated differently and require further understanding of the impact of the different environmental stresses occurring at different grain filling stages on the expression and accumulation of these seed storage proteins.

Gene selection for tumor classification using neighborhood rough sets and entropy measures

With the development of bioinformatics, tumor classification from gene expression data becomes an important useful technology for cancer diagnosis. Since a gene expression data often contains thousands of genes and a small number of samples, gene selection from gene expression data becomes a key step for tumor classification. Attribute reduction of rough sets has been successfully applied to gene selection field, as it has the characters of data driving and requiring no additional information. However, traditional rough set method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, we propose a novel gene selection method based on the neighborhood rough set model, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. Moreover, this paper addresses an entropy measure under the frame of neighborhood rough sets for tackling the uncertainty and noisy of gene expression data. The utilization of this measure can bring about a discovery of compact gene subsets. Finally, a gene selection algorithm is designed based on neighborhood granules and the entropy measure. Some experiments on two gene expression data show that the proposed gene selection is an effective method for improving the accuracy of tumor classification.