Inhibitory effect of food-functioned phytochemicals on dysregulated inflammatory pathways triggered by SARS-CoV-2: a mechanistic review.

Inflammatory cascades of the dysregulated inflammatory pathways in COVID-19 can cause excessive production of pro-inflammatory cytokines and chemokines leading to cytokine storm syndrome (CSS). The molecular cascades involved in the pathways may be targeted for discovery of new anti-inflammatory agents. Many plant extracts have been used clinically in the management of COVID-19, however, their immunosuppressive activities were mainly investigated based on in silico activity. Dietary flavonoids of the extracts such as quercetin, luteolin, kaempferol, naringenin, isorhamnetin, baicalein, wogonin, and rutin were commonly identified as responsible for their inhibitory effects. The present review critically analyzes the anti-inflammatory effects and mechanisms of phytochemicals, including dietary compounds against cytokine storm (CS) and hyperinflammation via inhibition of the altered inflammatory pathways triggered by SARS-CoV-2, published since the emergence of COVID-19 in December 2019. Only a few phytochemicals, mainly dietary compounds such as nanocurcumin, melatonin, quercetin, 6-shagoal, kaempferol, resveratrol, andrographolide, and colchicine have been investigated either in in silico or preliminary clinical studies to evaluate their anti-inflammatory effects against COVID-19. Sufficient pre-clinical studies on safety and efficacy of anti-inflammatory effects of the phytochemicals must be performed prior to proper clinical studies to develop them into therapeutic adjuvants in the prevention and treatmemt of COVID-19 symptoms.

Molecular docking and dynamics simulation studies uncover the host-pathogen protein-protein interactions in Penaeus vannamei and Vibrio parahaemolyticus.

Shrimp aquaculture contributes significantly to global economic growth, and the whiteleg shrimp, Penaeus vannamei, is a leading species in this industry. However, Vibrio parahaemolyticus infection poses a major challenge in ensuring the success of P. vannamei aquaculture. Despite its significance in this industry, the biological knowledge of its pathogenesis remains unclear. Hence, this study was conducted to identify the interaction sites and binding affinity between several immune-related proteins of P. vannamei with V. parahaemolyticus proteins associated with virulence factors. Potential interaction sites and the binding affinity between host and pathogen proteins were identified using molecular docking and dynamics (MD) simulation. The P. vannamei-V. parahaemolyticus protein-protein interaction of Complex 1 (Ferritin-HrpE/YscL family type III secretion apparatus protein), Complex 2 (Protein kinase domain-containing protein-Chemotaxis CheY protein), and Complex 3 (GPCR-Chemotaxis CheY protein) was found to interact with -4319.76, -5271.39, and -4725.57 of the docked score and the formation of intermolecular bonds at several interacting residues. The docked scores of Complex 1, Complex 2, and Complex 3 were validated using MD simulation analysis, which revealed these complexes greatly contribute to the interactions between P. vannamei and V. parahaemolyticus proteins, with binding free energies of -22.50 kJ/mol, -30.20 kJ/mol, and -26.27 kJ/mol, respectively. This finding illustrates the capability of computational approaches to search for molecular binding sites between host and pathogen, which could increase the knowledge of Vibrio spp. infection on shrimps, which then can be used to assist in the development of effective treatment.

A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools.

RNA biology has risen to prominence after a remarkable discovery of diverse functions of noncoding RNA (ncRNA). Most untranslated transcripts often exert their regulatory functions into RNA-RNA complexes via base pairing with complementary sequences in other RNAs. An interplay between RNAs is essential, as it possesses various functional roles in human cells, including genetic translation, RNA splicing, editing, ribosomal RNA maturation, RNA degradation and the regulation of metabolic pathways/riboswitches. Moreover, the pervasive transcription of the human genome allows for the discovery of novel genomic functions via RNA interactome investigation. The advancement of experimental procedures has resulted in an explosion of documented data, necessitating the development of efficient and precise computational tools and algorithms. This review provides an extensive update on RNA-RNA interaction (RRI) analysis via thermodynamic- and comparative-based RNA secondary structure prediction (RSP) and RNA-RNA interaction prediction (RIP) tools and their general functions. We also highlighted the current knowledge of RRIs and the limitations of RNA interactome mapping via experimental data. Then, the gap between RSP and RIP, the importance of RNA homologues, the relationship between pseudoknots, and RNA folding thermodynamics are discussed. It is hoped that these emerging prediction tools will deepen the understanding of RNA-associated interactions in human diseases and hasten treatment processes.

Current trends of host-pathogen relationship in shrimp infectious disease via computational protein-protein interaction: A bibliometric analysis.

Protein-protein interactions (PPIs) are essential for understanding cell physiology in normal and pathological conditions, as they might involve in all cellular processes. PPIs have been widely used to elucidate the pathobiology of human and plant diseases. Therefore, they can also be used to unveil the pathobiology of infectious diseases in shrimp, which is one of the high-risk factors influencing the success or failure of shrimp production. PPI network analysis, specifically host-pathogen PPI (HP-PPI), provides insights into the molecular interactions between the shrimp and pathogens. This review quantitatively analyzed the research trends within this field through bibliometric analysis using specific keywords, countries, authors, organizations, journals, and documents. This analysis has screened 206 records from the Scopus database for determining eligibility, resulting in 179 papers that were retrieved for bibliometric analysis. The analysis revealed that China and Thailand were the driving forces behind this specific field of research and frequently collaborated with the United States. Aquaculture and Diseases of Aquatic Organisms were the prominent sources for publications in this field. The main keywords identified included "white spot syndrome virus," "WSSV," and "shrimp." We discovered that studies on HP-PPI are currently quite scarce. As a result, we further discussed the significance of HP-PPI by highlighting various approaches that have been previously adopted. These findings not only emphasize the importance of HP-PPI but also pave the way for future researchers to explore the pathogenesis of infectious diseases in shrimp. By doing so, preventative measures and enhanced treatment strategies can be identified.

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.

Antiviral effects of phytochemicals against severe acute respiratory syndrome coronavirus 2 and their mechanisms of action: A review.

The worldwide spreading of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed a serious threat to health, economic, environmental, and social aspects of human lives. Currently, there are no approved treatments that can effectively block the virus although several existing antimalarial and antiviral agents have been repurposed and allowed use during the pandemic under the emergency use authorization (EUA) status. This review gives an updated overview of the antiviral effects of phytochemicals including alkaloids, flavonoids, and terpenoids against the COVID-19 virus and their mechanisms of action. Search for natural lead molecules against SARS-CoV-2 has been focusing on virtual screening and in vitro studies on phytochemicals that have shown great promise against other coronaviruses such as SARS-CoV. Until now, there is limited data on in vivo investigations to examine the antiviral activity of plants in SARS-CoV-2-infected animal models and the studies were performed using crude extracts. Further experimental and preclinical investigations on the in vivo effects of phytochemicals have to be performed to provide sufficient efficacy and safety data before clinical studies can be performed to develop them into COVID-19 drugs. Phytochemicals are potential sources of new chemical leads for the development of safe and potent anti-SARS-CoV-2 agents.

Pea eggplant (Solanum torvum Swartz) is a source of plant food polyphenols with SARS-CoV inhibiting potential.

Background: Pea eggplant (Solanum torvum Swartz) commonly known as turkey berry or 'terung pipit' in Malay is a vegetable plant widely consumed by the local community in Malaysia. The shrub bears pea-like turkey berry fruits (TBFs), rich in phytochemicals of medicinal interest. The TBF phytochemicals hold a wide spectrum of pharmacological properties. In this study, the TBF phytochemicals' potential inhibitory properties were evaluated against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) of the Coronavirus disease 2019 (COVID-19). The TBF polyphenols were screened against SARS-CoV receptors via molecular docking and the best receptor-ligand complex was validated further by molecular dynamics (MD) simulation. Method: The SARS-CoV receptor structure files (viral structural components) were retrieved from the Protein Data Bank (PDB) database: membrane protein (PDB ID: 3I6G), main protease (PDB ID: 5RE4), and spike glycoproteins (PDB ID: 6VXX and 6VYB). The receptor binding pocket regions were identified by Discovery Studio (BIOVIA) for targeted docking with TBF polyphenols (genistin, kaempferol, mellein, rhoifolin and scutellarein). The ligand and SARS-CoV family receptor structure files were pre-processed using the AutoDock tools. Molecular docking was performed with the Lamarckian genetic algorithm using AutoDock Vina 4.2 software. The best pose (ligand-receptor complex) from the molecular docking analysis was selected based on the minimum binding energy (MBE) and extent of structural interactions, as indicated by BIOVIA visualization tool. The selected complex was validated by a 100 ns MD simulation run using the GROMACS software. The dynamic behaviour and stability of the receptor-ligand complex were evaluated by the root mean square displacement (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent accessible surface area (SASA), solvent accessible surface volume (SASV) and number of hydrogen bonds. Results: At RMSD = 0, the TBF polyphenols showed fairly strong physical interactions with SARS-CoV receptors under all possible combinations. The MBE of TBF polyphenol-bound SARS CoV complexes ranged from -4.6 to -8.3 kcal/mol. Analysis of the structural interactions showed the presence of hydrogen bonds, electrostatic and hydrophobic interactions between the receptor residues (RR) and ligands atoms. Based on the MBE values, the 3I6G-rhoifolin (MBE = -8.3 kcal/mol) and 5RE4-genistin (MBE = -7.6 kcal/mol) complexes were ranked with the least value. However, the latter showed a greater extent of interactions between the RRs and the ligand atoms and thus was further validated by MD simulation. The MD simulation parameters of the 5RE4-genistin complex over a 100 ns run indicated good structural stability with minimal flexibility within genistin binding pocket region. The findings suggest that S. torvum polyphenols hold good therapeutics potential in COVID-19 management.

Chemoinformatics-driven classification of Angiosperms using sulfur-containing compounds and machine learning algorithm.

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.

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.

Genome-wide analysis of sulfur-encoding biosynthetic genes in rice (Oryza sativa L.) with Arabidopsis as the sulfur-dependent model plant.

Sulfur is an essential element required for plant growth and development, physiological processes and stress responses. Sulfur-encoding biosynthetic genes are involved in the primary sulfur assimilation pathway, regulating various mechanisms at the gene, cellular and system levels, and in the biosynthesis of sulfur-containing compounds (SCCs). In this study, the SCC-encoding biosynthetic genes in rice were identified using a sulfur-dependent model plant, the Arabidopsis. A total of 139 AtSCC from Arabidopsis were used as reference sequences in search of putative rice SCCs. At similarity index > 30%, the similarity search against Arabidopsis SCC query sequences identified 665 putative OsSCC genes in rice. The gene synteny analysis showed a total of 477 syntenic gene pairs comprised of 89 AtSCC and 265 OsSCC biosynthetic genes in Arabidopsis and rice, respectively. Phylogenetic tree of the collated (AtSCCs and OsSCCs) SCC-encoding biosynthetic genes were divided into 11 different clades of various sizes comprised of branches of subclades. In clade 1, nearing equal representation of OsSCC and AtSCC biosynthetic genes imply the most ancestral lineage. A total of 25 candidate Arabidopsis SCC homologs were identified in rice. The gene ontology enrichment analysis showed that the rice-Arabidopsis SCC homologs were significantly enriched in the following terms at false discovery rate (FDR) < 0.05: (i) biological process; sulfur compound metabolic process and organic acid metabolic processes, (ii) molecular function; oxidoreductase activity, acting on paired donors with incorporation or reduction of molecular oxygen and (iii) KEGG pathway; metabolic pathways and biosynthesis of secondary metabolites. At less than five duplicated blocks of separation, no tandem duplications were observed among the SCC biosynthetic genes distributed in rice chromosomes. The comprehensive rice SCC gene description entailing syntenic events with Arabidopsis, motif distribution and chromosomal mapping of the present findings offer a foundation for rice SCC gene functional studies and advanced strategic rice breeding.

Cymbidium Mosaic Virus Infecting Orchids: What, How, and What Next?

Context: Cymbidium mosaic virus (CymMV) is one of the most devastating viruses causing losses in the orchid industry, affecting economies worth millions of US dollars. CymMV significantly affects the orchid population and could be controlled through an integrated management strategy consisting of virus detection, good sanitation care of gardeners and their tools, and maintaining virus-free explants. Evidence acquisition: This review was written based on research publications relevant to the CymMV infection in orchids. The literature cited were obtained from online literature databases such as web of Science, Scopus, and Google Scholar. The searched term used was "Cymbidium mosaic virus". Related publications to the initial search were also examined. Results & Conclusions: This review describes the threat of CymMV to the orchid population by examining its history, genome organization, symptoms on individual orchids, detection, and management. Current research has been focusing on the prospect of transgenic orchids with viral resistance. This review also highlights the potential role of the symbiotic relationship between orchids and arbuscular mycorrhiza fungi that could be useful to improve the protection of orchids against virus infection. Overall, this review provides information on how CymMV infection impacts the orchid population.

TCGA-My: A Systematic Repository for Systems Biology of Malaysian Colorectal Cancer.

Colorectal cancer (CRC) ranks second among the most commonly occurring cancers in Malaysia, and unfortunately, its pathobiology remains unknown. CRC pathobiology can be understood in detail with the implementation of omics technology that is able to generate vast amounts of molecular data. The generation of omics data has introduced a new challenge for data organization. Therefore, a knowledge-based repository, namely TCGA-My, was developed to systematically store and organize CRC omics data for Malaysian patients. TCGA-My stores the genome and metabolome of Malaysian CRC patients. The genome and metabolome datasets were organized using a Python module, pandas. The variants and metabolites were first annotated with their biological information using gene ontologies (GOs) vocabulary. The TCGA-My relational database was then built using HeidiSQL PorTable 9.4.0.512, and Laravel was used to design the web interface. Currently, TCGA-My stores 1,517,841 variants, 23,695 genes, and 167,451 metabolites from the samples of 50 CRC patients. Data entries can be accessed via search and browse menus. TCGA-My aims to offer effective and systematic omics data management, allowing it to become the main resource for Malaysian CRC research, particularly in the context of biomarker identification for precision medicine.

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.

Comparative genome-wide analysis of WRKY, MADS-box and MYB transcription factor families in Arabidopsis and rice.

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.

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

BACKGROUND: Glucosinolates (GSLs) are plant secondary metabolites that contain nitrogen-containing compounds. They are important in the plant defense system and known to provide protection against cancer in humans. Currently, increasing the amount of data generated from various omics technologies serves as a hotspot for new gene discovery. However, sometimes sequence similarity searching approach is not sufficiently effective to find these genes; hence, we adapted a network clustering approach to search for potential GSLs genes from the Arabidopsis thaliana co-expression dataset. METHODS: We used known GSL genes to construct a comprehensive GSL co-expression network. This network was analyzed with the DPClusOST algorithm using a density of 0.5. 0.6. 0.7, 0.8, and 0.9. Generating clusters were evaluated using Fisher's exact test to identify GSL gene co-expression clusters. A significance score (SScore) was calculated for each gene based on the generated p-value of Fisher's exact test. SScore was used to perform a receiver operating characteristic (ROC) study to classify possible GSL genes using the ROCR package. ROCR was used in determining the AUC that measured the suitable density value of the cluster for further analysis. Finally, pathway enrichment analysis was conducted using ClueGO to identify significant pathways associated with the GSL clusters. RESULTS: The density value of 0.8 showed the highest area under the curve (AUC) leading to the selection of thirteen potential GSL genes from the top six significant clusters that include IMDH3, MVP1, T19K24.17, MRSA2, SIR, ASP4, MTO1, At1g21440, HMT3, At3g47420, PS1, SAL1, and At3g14220. A total of Four potential genes (MTO1, SIR, SAL1, and IMDH3) were identified from the pathway enrichment analysis on the significant clusters. These genes are directly related to GSL-associated pathways such as sulfur metabolism and valine, leucine, and isoleucine biosynthesis. This approach demonstrates the ability of the network clustering approach in identifying potential GSL genes which cannot be found from the standard similarity search.

Molecular characterization and enzyme inhibition studies of NADP+- farnesol dehydrogenase from diamondback moth, Plutella xylostella (Lepidoptera: Plutellidae).

Juvenile hormone III (JH III) plays an important role in insect reproduction, development, and behavior. The second branch of JH III production includes oxidation of farnesol to farnesal by farnesol dehydrogenase. This study reported the identification and characterization of Plutella xylostella farnesol dehydrogenase (PxFoLDH). Our results showed that PxFoLDH belongs to the short-chain dehydrogenase/reductase superfamily, consisting of a single domain with a structurally conserved Rossman fold, an NAD(P) (H)-binding region and a structurally diverse C-terminal region. The purified enzyme displayed maximum activity at 55$\ $°C with pH 9.5 and was stable in the temperature below 70$\ ^\circ $C. PxFoLDH was determined to be a monomer with a relative molecular weight of 27 kDa and highly specific for trans, trans-farnesol, and NADP+. Among analog inhibitors tested, farnesyl acetate was the most effective inhibitor with the lowest Ki value of 0.02 µm. Our findings showed this purified enzyme may represent as NADP+-farnesol dehydrogenase.

Dietary polyphenols suppress chronic inflammation by modulation of multiple inflammation-associated cell signaling pathways.

The high failure rate of the reductionist approach to discover effective and safe drugs to treat chronic inflammatory diseases has led scientists to seek alternative ways. Recently, targeting cell signaling pathways has been utilized as an innovative approach to discover drug leads from natural products. Cell signaling mechanisms have been identified playing key role in diverse diseases by inducing proliferation, cell survival and apoptosis. Phytochemicals are known to be able to modulate the cellular and molecular networks which are associated to chronic diseases including cancer-associated inflammation. In this review, the roles of dietary polyphenols (apigenin, kaempferol, quercetin, curcumin, genistein, isoliquiritigenin, resveratrol and gallic acid) in modulating multiple inflammation-associated cell signaling networks are deliberated. Scientific databases on suppressive effects of the polyphenols on chronic inflammation via modulation of the pathways especially in the recent five years are gathered and critically analyzed. The polyphenols are able to modulate several inflammation-associated cell signaling pathways, namely nuclear factor-kappa ß, mitogen activated protein kinases, Wnt/ß-catenin and phosphatidylinositol 3-kinase and protein kinase B via selective actions on various components of the networks. The suppressive effects of the polyphenols on the multiple cell signaling pathways reveal their potential use in prevention and treatment of chronic inflammatory disorders. Understanding the mechanistic effects involved in modulation of the signaling pathways by the polyphenols is necessary for lead identification and development of future functional foods for prevention and treatment of chronic inflammatory diseases.

Construction of Protein Expression Network.

In this post-genomic era, protein network can be used as a complementary way to shed light on the growing amount of data generated from current high-throughput technologies. Protein network is a powerful approach to describe the molecular mechanisms of the biological events through protein-protein interactions. Here, we describe the computational methods used to construct the protein network using expression data. We provide a list of available tools and databases that can be used in constructing the network.

A Comprehensive Gene Inventory for Glucosinolate Biosynthetic Pathway in Arabidopsis thaliana.

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.