Construction of a polycystic ovarian syndrome (PCOS) pathway based on the interactions of PCOS-related proteins retrieved from bibliomic data

Abstract Polycystic ovary syndrome (PCOS) is a complex but frequently occurring endocrine abnormality. PCOS has become one of the leading causes of oligo-ovulatory infertility among premenopausal women. The definition of PCOS remains unclear because of the heterogeneity of this abnormality, but it is associated with insulin resistance, hyperandrogenism, obesity and dyslipidaemia. The main purpose of this study was to identify possible candidate genes involved in PCOS. Several genomic approaches, including linkage analysis and microarray analysis, have been used to look for candidate PCOS genes. To obtain a clearer view of the mechanism of PCOS, we have compiled data from microarray analyses. An extensive literature search identified seven published microarray analyses that utilized PCOS samples. These were published between the year of 2003 and 2007 and included analyses of ovary tissues as well as whole ovaries and theca cells. Although somewhat different methods were used, all the studies employed cDNA microarrays to compare the gene expression patterns of PCOS patients with those of healthy controls. These analyses identified more than a thousand genes whose expression was altered in PCOS patients. Most of the genes were found to be involved in gene and protein expression, cell signaling and metabolism. We have classified all of the 1081 identified genes as coding for either known or unknown proteins. Cytoscape 2.6.1 was used to build a network of protein and then to analyze it. This protein network consists of 504 protein nodes and 1408 interactions among those proteins. One hypothetical protein in the PCOS network was postulated to be involved in the cell cycle. BiNGO was used to identify the three main ontologies in the protein network: molecular functions, biological processes and cellular components. This gene ontology analysis identified a number of ontologies and genes likely to be involved in the complex mechanism of PCOS. These include the insulin receptor signaling pathway, steroid biosynthesis, and the regulation of gonadotropin secretion among others.</p

Construction and analysis of protein-protein interaction network to identify the molecular mechanism in laryngeal cancer

Laryngeal cancer is the most common head and neck cancer in the world and its incidence is on the rise. However, the molecular mechanism underlying laryngeal cancer pathogenesis is poorly understood. The goal of this study was to develop a protein-protein interaction (PPI) network for laryngeal cancer to predict the biological pathways that underlie the molecular complexes in the network. Genes involved in laryngeal cancer were extracted from the OMIM database and their interaction partners were identified via text and data mining using Agilent Literature Search, STRING and GeneMANIA. PPI network was then integrated and visualised using Cytoscape ver3.6.0. Molecular complexes in the network were predicted by MCODE plugin and functional enrichment analyses of the molecular complexes were performed using BiNGO. 28 laryngeal cancer-related genes were present in the OMIM database. The PPI network associated with laryngeal cancer contained 161 nodes, 661 edges and five molecular complexes. Some of the complexes were related to the biological behaviour of cancer, providing the foundation for further understanding of the mechanism of laryngeal cancer development and progression

Transcriptome profiling of stevia rebaudiana ms007 revealed genes involved in flowedevelopment

Stevia rebaudiana is a medicinal plant recommended to diabetic or obese patients as an alternative sweetener owing to its low-calorie property. Previous studies have found that the stevioside level is highest at the time of flower bud formation and lowest at the time of preceding and following flower bud formation. Hence, this study aims to identify the genes involved in the flowering of local S. rebaudiana accession MS007 by investigating the transcriptomic data of two stages of growth, before flowering (BF) and after flowering (AF) that were deposited under accession number SRX6362785 and SRX6362784 at the NCBI SRA database. The transcriptomic study managed to annotate 108299 unigenes of S. rebaudiana with 8871 and 9832 genes that were differentially expressed in BF and AF samples, respectively. These genes involved in various metabolic pathways related to flower development, response to stimulus as well as photosynthesis. Pheophorbide A oxygenase (PAO), eukaryotic translation initiation factor 3 subunit E (TIF3E1), and jasmonate ZIM domain-containing protein 1 (JAZ1) were found to be involved in the flower development. The outcome of this study will help further research in the manipulation of the flowering process, especially in the breeding programme to develop photo-insensitive Stevia plant

A Review of Omics Technologies and Bioinformatics to Accelerate Improvement of Papaya Traits

Papaya (Carica papaya) is an economically important fruit crop that is mostly planted in tropical and subtropical regions. Major diseases of papaya, such as the papaya dieback disease (PDD), papaya ringspot virus (PRSV) disease, and papaya sticky disease (PSD), have caused large yield and economic losses in papaya-producing countries worldwide. Postharvest losses have also contributed to the decline in papaya production. Hence, there is an urgent need to secure the production of papaya for a growing world population. Integration of omics resources in crop breeding is anticipated in order to facilitate better-designed crops in the breeding programme. In papaya research, the application of omics and bioinformatics approaches are gradually increased and are underway. Hence, this review focuses on addressing omics technologies and bioinformatics that are used in papaya research. To date, four traits of the papaya have been studied using omics and bioinformatics approaches, which include its ripening process, abiotic stress, disease resistance, and fruit quality (i.e., sweetness, fruit shape, and fruit size). This review also highlights the potential of genetics and genomics data, as well as the systems biology approach that can be applied in a papaya-breeding programme in the near future

Reconstruction of the transcriptional regulatory network in Arabidopsis thaliana aliphatic glucosinolate biosynthetic pathway

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

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

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

Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom

In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies

Gene Co-Expression Network Analysis Reveals Key Regulatory Genes in <i>Metisa plana</i> 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

A review with updated perspectives on the antiviral potentials of traditional medicinal plants and their prospects in antiviral therapy

: Exploration of the traditional medicinal plants is essential for drug discovery and development for various pharmacological targets. Various phytochemicals derived from medicinal plants were extensively studied for antiviral activity. This review aims to highlight the role of medicinal plants against viral infections that remains to be the leading cause of human death globally. Antiviral properties of phytoconstituents isolated from 45 plants were discussed for five different types of viral infections. The ability of the plants’ active compounds with antiviral effects was highlighted as well as their mechanism of action, pharmacological studies, and toxicological data on a variety of cell lines. The experimental values, such as IC50, EC50, CC50, ED50, TD50, MIC100, and SI of the active compounds, were compiled and discussed to determine their potential. Among the plants mentioned, 11 plants showed the most promising medicinal plants against viral infections. Sambucus nigra and Clinacanthus nutans manifested antiviral activity against three different types of viral infections. Echinacea purpurea, Echinacea augustofolia, Echinacea pallida, Plantago major, Glycyrrhiza uralensis, Phyllanthus emblica, Camellia sinensis, and Cistus incanus exhibited antiviral activity against two different types of viral infections. Interestingly, Nicotiana benthamiana showed antiviral effects against mosquito-borne infections. The importance of phenolic acids, alkamides, alkylamides, glycyrrhizin, epicatechin gallate (ECG), epigallocatechin gallate (EGCG), epigallocatechin (EGC), protein-based plant-produced ZIKV Envelope (PzE), and anti-CHIKV monoclonal antibody was also reviewed. An exploratory approach to the published literature was conducted using a variety of books and online databases, including Scopus, Google Scholar, ScienceDirect, Web of Science, and PubMed Central, with the goal of obtaining, compiling, and reconstructing information on a variety of fundamental aspects, especially regarding medicinal plants