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

Simulation of a Petri net-based Model of the Terpenoid Biosynthesis Pathway

<p>Abstract</p> <p>Background</p> <p>The development and simulation of dynamic models of terpenoid biosynthesis has yielded a systems perspective that provides new insights into how the structure of this biochemical pathway affects compound synthesis. These insights may eventually help identify reactions that could be experimentally manipulated to amplify terpenoid production. In this study, a dynamic model of the terpenoid biosynthesis pathway was constructed based on the Hybrid Functional Petri Net (HFPN) technique. This technique is a fusion of three other extended Petri net techniques, namely Hybrid Petri Net (HPN), Dynamic Petri Net (HDN) and Functional Petri Net (FPN).</p> <p>Results</p> <p>The biological data needed to construct the terpenoid metabolic model were gathered from the literature and from biological databases. These data were used as building blocks to create an HFPNe model and to generate parameters that govern the global behaviour of the model. The dynamic model was simulated and validated against known experimental data obtained from extensive literature searches. The model successfully simulated metabolite concentration changes over time (pt) and the observations correlated with known data. Interactions between the intermediates that affect the production of terpenes could be observed through the introduction of inhibitors that established feedback loops within and crosstalk between the pathways.</p> <p>Conclusions</p> <p>Although this metabolic model is only preliminary, it will provide a platform for analysing various high-throughput data, and it should lead to a more holistic understanding of terpenoid biosynthesis.</p

Computational Systems Analysis on Polycystic Ovarian Syndrome (PCOS)

Complex diseases are caused by a combination of genetic and environmental factors. Unraveling the molecular pathways from the genetic factors that affect a phenotype is always difficult, but in the case of complex diseases, this is further complicated since genetic factors in affected individuals might be different. Polycystic ovarian syndrome (PCOS) is an example of a complex disease with limited molecular information. Recently, PCOS molecular omics data have increasingly appeared in many publications. We conduct extensive bioinformatics analyses on the data and perform strong integration of experimental and computational biology to understand its complex biological systems in examining multiple interacting genes and their products. PCOS involves networks of genes, and to understand them, those networks must be mapped. This approach has emerged as powerful tools for studying complex diseases and been coined as network biology. Network biology encompasses wide range of network types including those based on physical interactions between and among cellular components and those baised on similarity among patients or diseases. Each of these offers distinct biological clues that may help scientists transform their cellular parts list into insights about complex diseases. This chapter will discuss some computational analysis aspects on the omics studies that have been conducted in PCOS

Computational Analysis of Rice Transcriptomic and Genomic Datasets in Search for SNPs Involved in Flavonoid Biosynthesis

This chapter describes the computational approach used in analyzing rice transcriptomics and genomics data to identify and annotate potential single nucleotide polymorphism (SNPs) as potential biomarker in the production of flavonoid. SNPs play a role in the accumulation of nutritional components (e.g. antioxidants), and flavonoid is one of them. However, the number of identified SNPs associated with flavonoid nutritional trait is still limited. We develop a knowledge-based bioinformatic workflow to search for specific SNPs and integration analysis on the SNPs and their co-expressed genes to investigate their influence on the gain/loss of functional genes that are involved in the production of flavonoids. Raw files obtained from the functional genomics studies can be analyzed in details to obtain a useful biological insight. Different tools, algorithms and databases are available to analyze the ontology, metabolic and pathway at the molecular level in order to observe the effects of gene and protein expression. The usage of different tools, algorithms and databases allows the integration, interpretation and the inference of analysis to provide better understanding of the biological meaning of the resutls. This chapter illustrates how to select and bring together several software to develop a specific bioinformatic workflow that processes and analyses omics data. The implementation of this bioinformatic workflow revealed the identification of potential flavonoid biosynthetic genes that can be used as guided-gene to screen the single nucleotide polymorphisms (SNPs) in the flavonoid biosynthetic genes from genome and transcriptomics data

Identification of a Novel Cysteine-stack Arrangement in Parallel β-helix Proteins using Computational and Knowledge-based Approach

Abstract Parallel β-helices, a subclass of β-sheet proteins, represent the folding of a polypeptide chain into an elongated topologically simpler fold than globular β-sheets. However, the amino acid sequence rules that specify β-sheet structure in protein remain unelucidated. In this study, a combination of knowledge-based and computational analysis using the novel cysteine staple pattern found in pectin methylesterase A to predict the right-handed parallel β-helix structural motif in primary amino acid sequences is presented. The novel staple pattern was used to find further pectin lyase-like protein families displaying this pattern, and to produce high confidence 3D models for all detectable members of this superfamily. To achieve these goals, candidate sequences were retrieved from GenPept and SUPERFAMILY databases. Possible pectin lyase-like proteins were detected with two different fold recognition algorithms, and stringent criteria were designed to minimise false positive predictions. The filtered datasets were clustered, resulting into two main categories; Category A included all sequences with homologues in the PDB and Category B comprised all sequences with unknown homologues. Four families have been identified as new family, where prior to this study, the proteins in this new family had not been identified nor classified as pectin lyase-like proteins. Multiple sequence alignments were generated from each family in both categories, and were carefully inspected to finally produce highly accurate alignments to be used as input for large-scale automatic modeling. There were 298 high quality 3D models of pectin lyase-like proteins produced, which may be used to provide a valuable resource for biological research in this area. Models inspection has revealed numerous instances of possible cysteine stacks in the β-helix cores, where asparagine ladders are common as well as the existence of disulphide bridges

Alteration of Abiotic Stress Responsive Genes in Polygonum minus Roots by Jasmonic Acid Elicitation

Plants are continuously exposed to both biotic and abiotic stress in their natural environment. Unlike animals, plants are immobilized organisms which tend to be vulnerable to various environmental stresses. In order to survive, plants have evolved a wide range of defense mechanism to cope with these stresses. Both biotic and abiotic stresses might share some common signaling pathway in triggering the defense system in plants. Recent researches have revealed that phytohormones such as abscisic acid (ABA), jasmonic acid (JA), salicylic acid (SA) and ethylene (ET) are intermediate molecules which play key roles in the crosstalk between biotic and abiotic signaling network (Fujita et al. 2006). In this chapter, we highlight the effects of exogenous applied jasmonic acid in triggering the synthesis of some molecules and activating their respective biosynthetic genes in plants as a response towards abiotic stresses.Full Tex

Evaluation of potential molecular interaction between quorum sensing receptor, LuxP and grouper fatty acids: in-silico screening and simulation

Pathologically relevant behaviors of Vibrio, such as the expression of virulence factors, biofilm production, and swarming motility, have been shown to be controlled by quorum sensing. The autoinducer-2 quorum sensing receptor protein LuxP is one of the target proteins for drug development to suppress the virulence of Vibrio. Here, we reported the potential molecular interaction of fatty acids identified in vibriosis-resistant grouper with LuxP. Fatty acid, 4-oxodocosahexaenoic acid (4R8) showed significant binding affinity toward LuxP (−6.0 kcal/mol) based on molecular docking analysis. The dynamic behavior of the protein–ligand complex was illustrated by molecular dynamic simulations. The fluctuation of the protein backbone, the stability of ligand binding, and hydrogen bond interactions were assessed, suggesting 4R8 possesses potential interaction with LuxP, which was supported by the low binding free energy (−29.144 kJ/mol) calculated using the molecular mechanics Poisson–Boltzmann surface area

Structural and kinetic studies of a novel nerol dehydrogenase from Persicaria minor, a nerol-specific enzyme for citral biosynthesis

Geraniol degradation pathway has long been elucidated in microorganisms through bioconversion studies, yet weakly characterised in plants; enzyme with specific nerol-oxidising activity has not been reported. A novel cDNA encodes nerol dehydrogenase (PmNeDH) was isolated from Persicaria minor. The recombinant PmNeDH (rPmNeDH) is a homodimeric enzyme that belongs to MDR (medium-chain dehydrogenases/reductases) superfamily that catalyses the first oxidative step of geraniol degradation pathway in citral biosynthesis. Kinetic analysis revealed that rPmNeDH has a high specificity for allylic primary alcohols with backbone ≤10 carbons. rPmNeDH has ∼3 fold higher affinity towards nerol (cis-3,7-dimethyl-2,6-octadien-1-ol) than its trans-isomer, geraniol. To our knowledge, this is the first alcohol dehydrogenase with higher preference towards nerol, suggesting that nerol can be effective substrate for citral biosynthesis in P. minor. The rPmNeDH crystal structure (1.54 Å) showed high similarity with enzyme structures from MDR superfamily. Structure guided mutation was conducted to describe the relationships between substrate specificity and residue substitutions in the active site. Kinetics analyses of wild-type rPmNeDH and several active site mutants demonstrated that the substrate specificity of rPmNeDH can be altered by changing any selected active site residues (Asp280, Leu294 and Ala303). Interestingly, the L294F, A303F and A303G mutants were able to revamp the substrate preference towards geraniol. Furthermore, mutant that exhibited a broader substrate range was also obtained. This study demonstrates that P. minor may have evolved to contain enzyme that optimally recognise cis-configured nerol as substrate. rPmNeDH structure provides new insights into the substrate specificity and active site plasticity in MDR superfamily