Potential Arabidopsis thaliana glucosinolate genes identified from the co-expression modules using graph clustering approach

Gene Co-Expression Network Analysis Reveals Key Regulatory Genes in Metisa plana Hormone Pathways

Metisa plana Walker (Lepidoptera: Psychidae) is a major oil palm pest species distributed across Southeast Asia. M. plana outbreaks are regarded as serious ongoing threats to the oil palm industry due to their ability to significantly reduce fruit yield and subsequent productivity. Currently, conventional pesticide overuses may harm non-target organisms and severely pollute the environment. This study aims to identify key regulatory genes involved in hormone pathways during the third instar larvae stage of M. plana gene co-expression network analysis. A weighted gene co-expression network analysis (WGCNA) was conducted on the M. plana transcriptomes to construct a gene co-expression network. The transcriptome datasets were obtained from different development stages of M. plana, i.e., egg, third instar larvae, pupa, and adult. The network was clustered using the DPClusO algorithm and validated using Fisher's exact test and receiver operating characteristic (ROC) analysis. The clustering analysis was performed on the network and 20 potential regulatory genes (such as MTA1-like, Nub, Grn, and Usp) were identified from ten top-most significant clusters. Pathway enrichment analysis was performed to identify hormone signalling pathways and these pathways were identified, i.e., hormone-mediated signalling, steroid hormone-mediated signalling, and intracellular steroid hormone receptor signalling as well as six regulatory genes Hnf4, Hr4, MED14, Usp, Tai, and Trr. These key regulatory genes have a potential as important targets in future upstream applications and validation studies in the development of biorational pesticides against M. plana and the RNA interference (RNAi) gene silencing method.

Single-Cell RNA Sequencing of Arabidopsis Leaf Tissues Identifies Multiple Specialized Cell Types: Idioblast Myrosin Cells and Potential Glucosinolate-Producing Cells

The glucosinolate-myrosinase defense system (GMDS), characteristic of Brassicales, is involved in plant defense. Previous single-cell transcriptomic analyses have reported the expression profiles of multiple GMDS-related cell types (i.e. myrosinase-rich myrosin idioblasts and multiple types of potential glucosinolate synthetic cells as well as a candidate S-cell for glucosinolate accumulation). However, differences in plant stages and cell-type annotation methods have hindered comparisons among studies. Here, we used the single-cell transcriptome profiles of extended Arabidopsis leaves and verified the distribution of previously used markers to refine the expression profiles of GMDS-associated cell types. Moreover, we performed beta-glucuronidase promoter assays to confirm the histological expression patterns of newly obtained markers for GMDS-associated candidates. As a result, we found a set of new specific reporters for myrosin cells and potential glucosinolate-producing cells.

Developing multifunctional crops by engineering Brassicaceae glucosinolate pathways

Glucosinolates (GSLs), found mainly in species of the Brassicaceae family, are one of the most well-studied classes of secondary metabolites. Produced by the action of myrosinase on GSLs, GSL-derived hydrolysis products (GHPs) primarily defend against biotic stress in planta. They also significantly affect the quality of crop products, with a subset of GHPs contributing unique food flavors and multiple therapeutic benefits or causing disagreeable food odors and health risks. Here, we explore the potential of these bioactive functions, which could be exploited for future sustainable agriculture. We first summarize our accumulated understanding of GSL diversity and distribution across representative Brassicaceae species. We then systematically discuss and evaluate the potential of exploited and unutilized genes involved in GSL biosynthesis, transport, and hydrolysis as candidate GSL engineering targets. Benefiting from available information on GSL and GHP functions, we explore options for multifunctional Brassicaceae crop ideotypes to meet future demand for food diversification and sustainable crop production. An integrated roadmap is subsequently proposed to guide ideotype development, in which maximization of beneficial effects and minimization of detrimental effects of GHPs could be combined and associated with various end uses. Based on several use-case examples, we discuss advantages and limitations of available biotechnological approaches that may contribute to effective deployment and could provide novel insights for optimization of future GSL engineering.

Gene Co-Expression Network Tools and Databases for Crop Improvement

Transcriptomics has significantly grown as a functional genomics tool for understanding the expression of biological systems. The generated transcriptomics data can be utilised to produce a gene co-expression network that is one of the essential downstream omics data analyses. To date, several gene co-expression network databases that store correlation values, expression profiles, gene names and gene descriptions have been developed. Although these resources remain scattered across the Internet, such databases complement each other and support efficient growth in the functional genomics area. This review presents the features and the most recent gene co-expression network databases in crops and summarises the present status of the tools that are widely used for constructing the gene co-expression network. The highlights of gene co-expression network databases and the tools presented here will pave the way for a robust interpretation of biologically relevant information. With this effort, the researcher would be able to explore and utilise gene co-expression network databases for crops improvement.

Protein–Protein Interaction (PPI) Network of Zebrafish Oestrogen Receptors: A Bioinformatics Workflow

Protein–protein interaction (PPI) is involved in every biological process that occurs within an organism. The understanding of PPI is essential for deciphering the cellular behaviours in a particular organism. The experimental data from PPI methods have been used in constructing the PPI network. PPI network has been widely applied in biomedical research to understand the pathobiology of human diseases. It has also been used to understand the plant physiology that relates to crop improvement. However, the application of the PPI network in aquaculture is limited as compared to humans and plants. This review aims to demonstrate the workflow and step-by-step instructions for constructing a PPI network using bioinformatics tools and PPI databases that can help to predict potential interaction between proteins. We used zebrafish proteins, the oestrogen receptors (ERs) to build and analyse the PPI network. Thus, serving as a guide for future steps in exploring potential mechanisms on the organismal physiology of interest that ultimately benefit aquaculture research.

Identification of Potential Genes Encoding Protein Transporters in Arabidopsis thaliana Glucosinolate (GSL) Metabolism

Several species in Brassicaceae produce glucosinolates (GSLs) to protect themselves against pests. As demonstrated in A. thaliana, the reallocation of defence compounds, of which GSLs are a major part, is highly dependent on transport processes and serves to protect high-value tissues such as reproductive tissues. This study aimed to identify potential GSL-transporter proteins (TPs) using a network-biology approach. The known A. thaliana GSL genes were retrieved from the literature and pathway databases and searched against several co-expression databases to generate a gene network consisting of 1267 nodes and 14,308 edges. In addition, 1151 co-expressed genes were annotated, integrated, and visualised using relevant bioinformatic tools. Based on three criteria, 21 potential GSL genes encoding TPs were selected. The AST68 and ABCG40 potential GSL TPs were chosen for further investigation because their subcellular localisation is similar to that of known GSL TPs (SULTR1;1 and SULTR1;2) and ABCG36, respectively. However, AST68 was selected for a molecular-docking analysis using AutoDOCK Vina and AutoDOCK 4.2 with the generated 3D model, showing that both domains were well superimposed on the homologs. Both molecular-docking tools calculated good binding-energy values between the sulphate ion and Ser419 and Val172, with the formation of hydrogen bonds and van der Waals interactions, respectively, suggesting that AST68 was one of the sulphate transporters involved in GSL biosynthesis. This finding illustrates the ability to use computational analysis on gene co-expression data to screen and characterise plant TPs on a large scale to comprehensively elucidate GSL metabolism in A. thaliana. Most importantly, newly identified potential GSL transporters can serve as molecular tools in improving the nutritional value of crops.