Glucosinolates (GSLs) are plant secondary metabolites comprising sulfur and nitrogen mainly found in plants from the order of Brassicales, such as broccoli, cabbage, and Arabidopsis thaliana. The activated forms of GSL play important roles in fighting against pathogens and have health benefits to humans. The increasing amount of data on A. thaliana generated from various omics technologies can be investigated more deeply in search of new genes or compounds involved in GSL biosynthesis and metabolism. This review describes a comprehensive inventory of A. thaliana GSLs identified from published literature and databases such as KNApSAcK, KEGG, and AraCyc. A total of 113 GSL genes encoding for 23 transcription components, 85 enzymes, and five protein transporters were experimentally characterized in the past two decades. Continuous efforts are still on going to identify all molecules related to the production of GSLs. A manually curated database known as SuCCombase (http://plant-scc.org) was developed to serve as a comprehensive GSL inventory. Realizing lack of information on the regulation of GSL biosynthesis and degradation mechanisms, this review also includes relevant information and their connections with crosstalk among various factors, such as light, sulfur metabolism, and nitrogen metabolism, not only in A. thaliana but also in other crucifers.

Transcription factors (TFs) form the major class of regulatory genes and play key roles in multiple plant stress responses. In most eukaryotic plants, transcription factor (TF) families (WRKY, MADS-box and MYB) activate unique cellular-level abiotic and biotic stress-responsive strategies, which are considered as key determinants for defense and developmental processes. Arabidopsis and rice are two important representative model systems for dicot and monocot plants, respectively. A comprehensive comparative study on 101 OsWRKY, 34 OsMADS box and 122 OsMYB genes (rice genome) and, 71 AtWRKY, 66 AtMADS box and 144 AtMYB genes (Arabidopsis genome) showed various relationships among TFs across species. The phylogenetic analysis clustered WRKY, MADS-box and MYB TF family members into 10, 7 and 14 clades, respectively. All clades in WRKY and MYB TF families and almost half of the total number of clades in the MADS-box TF family are shared between both species. Chromosomal and gene structure analysis showed that the Arabidopsis-rice orthologous TF gene pairs were unevenly localized within their chromosomes whilst the distribution of exon–intron gene structure and motif conservation indicated plausible functional similarity in both species. The abiotic and biotic stress-responsive cis-regulatory element type and distribution patterns in the promoter regions of Arabidopsis and rice WRKY, MADS-box and MYB orthologous gene pairs provide better knowledge on their role as conserved regulators in both species. Co-expression network analysis showed the correlation between WRKY, MADs-box and MYB genes in each independent rice and Arabidopsis network indicating their role in stress responsiveness and developmental processes.

Ecdysone receptor (EcR) is the primary regulator of the ecdysteroid signalling pathway, a critical pathway that directly links to moulting. In addition, EcR also regulates growth, development, reproduction and regeneration in crustaceans. However, there remains a huge gap of knowledge between the detailed structure and functional role(s) of crustacean EcR compared to that of insects. Motif and phylogenetic analyses of publicly available crustacean EcR proteins revealed the evolutionary relationship of EcR in this subphylum and highlighted its conserved characteristics among crustaceans. The role of EcR in the regulation of essential physiological processes in crustaceans including moulting, chitin synthesis, general growth and development, gonadal and maturation, and limb regeneration was discussed based on the available literature. This essential moult-related nuclear receptor could serve as a useful molecular indicator of moulting and gonadal maturation, as a potential target for pesticide production against parasitic crustacean species in the aquaculture industry, as well as valuable bioindicators of environmental stressors.

Plants produce a wide range of secondary metabolites that play important roles in plant defense and immunity, their interaction with the environment and symbiotic associations. Sulfur-containing compounds (SCCs) are a group of important secondary metabolites produced in members of the Brassicales order. SCCs constitute various groups of phytochemicals, but not much is known about them. Findings from previous studies on SCCs were scattered in published literatures, hence SuCComBase was developed to store all molecular information related to the biosynthesis of SCCs. Information that includes genes, proteins and compounds that are involved in the SCC biosynthetic pathway was manually identified from databases and published scientific literatures. Sets of co-expression data was analyzed to search for other possible (previously unknown) genes that might be involved in the biosynthesis of SCC. These genes were named as potential SCC-related encoding genes. A total of 147 known and 92 putative Arabidopsis thaliana SCC-related genes from literatures were used to identify other potential SCC-related encoding genes. We identified 778 potential SCC-related encoding genes, 4026 homologs to the SCC-related encoding genes and 116 SCCs as shown on SuCComBase homepage. Data entries are searchable from the Main page, Search, Browse and Datasets tabs. Users can easily download all data stored in SuCComBase. All publications related to SCCs are also indexed in SuCComBase, which is currently the first and only database dedicated to plant SCCs. SuCComBase aims to become a manually curated and au fait knowledge-based repository for plant SCCs.

Aliphatic glucosinolate is an important secondary metabolite responsible in plant defense mechanism and carcinogenic activity. It plays a crucial role in plant adaptation towards changes in the environment such as salinity and drought. However, in many plant genomes, there are thousands of genes encoding proteins still with putative functions and incomplete annotations. Therefore, the genome of Arabidopsis thaliana was selected to be investigated further to identify any putative genes that are potentially involved in the aliphatic glucosinolate biosynthesis pathway, most of its gene are with incomplete annotation. Known genes for aliphatic glucosinolates were retrieved from KEGG and AraCyc databases. Three co-expression databases i.e., ATTED-II, GeneMANIA and STRING were used to perform the co-expression network analysis. The integrated co-expression network was then being clustered, annotated and visualized using Cytoscape plugin, MCODE and ClueGO. Then, the regulatory network of A. thaliana from AtRegNet was mapped onto the co-expression network to build the transcriptional regulatory network. This study showed that a total of 506 genes were co-expressed with the 61 aliphatic glucosinolate biosynthesis genes. Five transcription factors have been predicted to be involved in the biosynthetic pathway of aliphatic glucosinolate, namely SEPALLATA 3 (SEP3), PHYTOCHROME INTERACTING FACTOR 3-like 5 (AtbHLH15/PIL5), ELONGATED HYPOCOTYL 5 (HY5), AGAMOUS-like 15 (AGL15) and GLABRA 3 (GL3). Meanwhile, three other genes with high potential to be involved in the aliphatic glucosinolates biosynthetic pathway were identified, i.e., methylthioalkylmalate-like synthase 4 (MAML-4) and aspartate aminotransferase (ASP1 and ASP4). These findings can be used to complete the aliphatic glucosinolate biosynthetic pathway in A. thaliana and to update the information on the glucosinolate-related pathways in public metabolic databases.

The inhibition of dipeptidyl peptidase-IV (DPPIV) is a popular route for the treatment of type-2 diabetes. Commercially available gliptin-based drugs such as sitagliptin, anagliptin, linagliptin, saxagliptin, and alogliptin were specifically developed as DPPIV inhibitors for diabetic patients. The use of Gynura bicolor in treating diabetes had been reported in various in vitro experiments. However, an understanding of the inhibitory actions of G. bicolor bioactive compounds on DPPIV is still lacking and this may provide crucial information for the development of more potent and natural sources of DPPIV inhibitors. Evaluation of G. bicolor bioactive compounds for potent DPPIV inhibitors was computationally conducted using Lead IT and iGEMDOCK software, and the best free-binding energy scores for G. bicolor bioactive compounds were evaluated in comparison with the commercial DPPIV inhibitors, sitagliptin, anagliptin, linagliptin, saxagliptin, and alogliptin. Drug-likeness and absorption, distribution, metabolism, and excretion (ADME) analysis were also performed. Based on molecular docking analysis, four of the identified bioactive compounds in G. bicolor, 3-caffeoylquinic acid, 5-O-caffeoylquinic acid, 3,4-dicaffeoylquinic acid, and trans-5-p-coumaroylquinic acid, resulted in lower free-binding energy scores when compared with two of the commercially available gliptin inhibitors. The results revealed that bioactive compounds in G. bicolor are potential natural inhibitors of DPPIV.

Background Phytochemicals or secondary metabolites are low molecular weight organic compounds with little function in plant growth and development. Nevertheless, the metabolite diversity govern not only the phenetics of an organism but may also inform the evolutionary pattern and adaptation of green plants to the changing environment. Plant chemoinformatics analyzes the chemical system of natural products using computational tools and robust mathematical algorithms. It has been a powerful approach for species-level differentiation and is widely employed for species classifications and reinforcement of previous classifications. Results This study attempts to classify Angiosperms using plant sulfur-containing compound (SCC) or sulphated compound information. The SCC dataset of 692 plant species were collected from the comprehensive species-metabolite relationship family (KNApSAck) database. The structural similarity score of metabolite pairs under all possible combinations (plant species-metabolite) were determined and metabolite pairs with a Tanimoto coefficient value > 0.85 were selected for clustering using machine learning algorithm. Metabolite clustering showed association between the similar structural metabolite clusters and metabolite content among the plant species. Phylogenetic tree construction of Angiosperms displayed three major clades, of which, clade 1 and clade 2 represented the eudicots only, and clade 3, a mixture of both eudicots and monocots. The SCC-based construction of Angiosperm phylogeny is a subset of the existing monocot-dicot classification. The majority of eudicots present in clade 1 and 2 were represented by glucosinolate compounds. These clades with SCC may have been a mixture of ancestral species whilst the combinatorial presence of monocot-dicot in clade 3 suggests sulphated-chemical structure diversification in the event of adaptation during evolutionary change. Conclusions Sulphated chemoinformatics informs classification of Angiosperms via machine learning technique.

Soil salinity is one of the most serious environmental challenges, posing a growing threat to agriculture across the world. Soil salinity has a significant impact on rice growth, development, and production. Hence, improving rice varieties’ resistance to salt stress is a viable solution for meeting global food demand. Adaptation to salt stress is a multifaceted process that involves interacting physiological traits, biochemical or metabolic pathways, and molecular mechanisms. The integration of multi-omics approaches contributes to a better understanding of molecular mechanisms as well as the improvement of salt-resistant and tolerant rice varieties. Firstly, we present a thorough review of current knowledge about salt stress effects on rice and mechanisms behind rice salt tolerance and salt stress signalling. This review focuses on the use of multi-omics approaches to improve next-generation rice breeding for salinity resistance and tolerance, including genomics, transcriptomics, proteomics, metabolomics and phenomics. Integrating multi-omics data effectively is critical to gaining a more comprehensive and in-depth understanding of the molecular pathways, enzyme activity and interacting networks of genes controlling salinity tolerance in rice. The key data mining strategies within the artificial intelligence to analyse big and complex data sets that will allow more accurate prediction of outcomes and modernise traditional breeding programmes and also expedite precision rice breeding such as genetic engineering and genome editing.