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Background: C4 photosynthesis is a key domain of plant research with outcomes ranging from crop quality improvement, biofuel production and efficient use of water and nutrients. A metabolic network model of C4 "lab organism" Setaria viridis with extensive gene-reaction associations can accelerate target identification for desired metabolic manipulations and thereafter in vivo validation. Moreover, metabolic reconstructions have also been shown to be a significant tool to investigate fundamental metabolic traits. Results: A mass and charge balance genome-scale metabolic model of Setaria viridis was constructed, which was tested to be able to produce all major biomass components in phototrophic and heterotrophic conditions. Our model predicted an important role of the utilization of NH 4 + and NO 3 − ratio in balancing charges in plants. A multi-tissue extension of the model representing C4 photosynthesis was able to utilize NADP-ME subtype of C4 carbon fixation for the production of lignocellulosic biomass in stem, providing a tool for identifying gene associations for cellulose, hemi-cellulose and lignin biosynthesis that could be potential target for improved lignocellulosic biomass production. Besides metabolic engineering, our modeling results uncovered a previously unrecognized role of the 3-PGA/triosephosphate shuttle in proton balancing. Conclusions: A mass and charge balance model of Setaria viridis, a model C4 plant, provides the possibility of system-level investigation to identify metabolic characteristics based on stoichiometric constraints. This study demonstrated the use of metabolic modeling in identifying genes associated with the synthesis of particular biomass components, and elucidating new role of previously known metabolic processes. [ABSTRACT FROM AUTHOR]

Background: Gene Ontology (GO) is a useful resource of controlled vocabulary that provides information about annotated genes. Based on such resource, finding the biological function is useful for biologists to come up with different hypotheses and help further investigations of an experiment. The biological function for desired genes and gene associations is picked up from a randomly chosen list or through the analysis of differential gene expression. Many tools have been developed to utilize GO knowledge and cluster genes according to relevant biological functions. The retrieved GO terms include both specific and non-specific terms, which is not user-friendly in terms of data analysis. Thus one approach is still missing, which allows navigating through different levels of GO hierarchy manually. Result: We developed a tool, GOTrapper, which allows moving up or down to the very bottom of the GO hierarchy. This is performed manually by the user, based on an assigned threshold. This tool grabs the shared terms by the desired set of input genes of Homo sapiens. Here, two inputs are possible. "Within" is to find associated terms within one gene list, and "Between" is to find associated terms between two lists. The tool also provides the option to return the terms with the pre-selected evidence codes. Conclusion: GOTrapper is a user-friendly Java tool that helps the user move up and down the ontology tree, which leads to new hypotheses and devising new association of the input genes. It also allows returning terms of associated genes based on selected evidence codes. This tool can be accessed and is freely available at https://github.com/BioGeneTools/GOTrapper. [ABSTRACT FROM AUTHOR]

Background: In-stent restenosis occurs in 10-30% of patients following bare metal stent (BMS) implantation and has various risk factors. Mannose-binding lectin (MBL) is known to have effect on the progression of atherosclerosis. Single nucleotide polymorphisms (SNP) of the MBL2 gene intron 1 (codon 52, 54, 57) are known to modulate the bioavailability of the MBL protein. Our aim was to identify the association of these polymorphisms of the MBL gene in the occurrence of in-stent restenosis after coronary artery bare metal stent implantation.Methods: In a non-randomized prospective study venous blood samples were collected after recoronarography from 225 patients with prior BMS implantation. Patients were assigned to diffuse restenosis group and control group based on the result of the coronarography. MBL genotypes were determined using quantitative real-time PCR. Proportion of different genotypes was compared and adjusted with traditional risk factors using multivariate logistic regression.Results: Average follow-up time was 1.0 (+ - 1.4) year in the diffuse restenosis group (N = 117) and 2.7 (+ - 2.5) years in the control group (N = 108). The age, gender distribution and risk status was not different between study groups. Proportion of the MBL variant genotype was 26.8% (29 vs. 79 normal homozygous) in the control group and 39.3% (46 vs. 71 normal homozygous) in the restenosis group (p = 0.04). In multivariate analysis the mutant allele was an independent risk factor (OR = 1.96, p = 0.03) of in-stent restenosis.Conclusions: MBL polymorphisms are associated with higher incidence of development of coronary in-stent restenosis. The attenuated protein function in the mutant allelic genotype may represent the underlying mechanism. [ABSTRACT FROM AUTHOR]

Background: The use of ontologies to standardize biological data and facilitate comparisons among datasets has steadily grown as the complexity and amount of available data have increased. Despite the numerous ontologies available, one area currently lacking a robust ontology is the description of vertebrate traits. A trait is defined as any measurable or observable characteristic pertaining to an organism or any of its substructures. While there are several ontologies to describe entities and processes in phenotypes, diseases, and clinical measurements, one has not been developed for vertebrate traits; the Vertebrate Trait Ontology (VT) was created to fill this void. Description: Significant inconsistencies in trait nomenclature exist in the literature, and additional difficulties arise when trait data are compared across species. The VT is a unified trait vocabulary created to aid in the transfer of data within and between species and to facilitate investigation of the genetic basis of traits. Trait information provides a valuable link between the measurements that are used to assess the trait, the phenotypes related to the traits, and the diseases associated with one or more phenotypes. Because multiple clinical and morphological measurements are often used to assess a single trait, and a single measurement can be used to assess multiple physiological processes, providing investigators with standardized annotations for trait data will allow them to investigate connections among these data types. Conclusions: The annotation of genomic data with ontology terms provides unique opportunities for data mining and analysis. Links between data in disparate databases can be identified and explored, a strategy that is particularly useful for cross-species comparisons or in situations involving inconsistent terminology. The VT provides a common basis for the description of traits in multiple vertebrate species. It is being used in the Rat Genome Database and Animal QTL Database for annotation of QTL data for rat, cattle, chicken, swine, sheep, and rainbow trout, and in the Mouse Phenome Database to annotate strain characterization data. In these databases, data are also cross-referenced to applicable terms from other ontologies, providing additional avenues for data mining and analysis.The ontology is available at http://bioportal.bioontology.org/ontologies/50138. [ABSTRACT FROM AUTHOR]