Isolation, cloning and expression of a multifunctional O-methyltransferase capable of forming 2,5-dimethyl-4-methoxy-3(2H)-furanone, one of the key aroma compounds in strawberry fruits.

Strawberry fruits contain an uncommon group of key aroma compounds with a 2,5-dimethyl-3(2H)-furanone structure. Here, we report on the methylation of 2,5-dimethyl-4-hydroxy-3(2H)-furanone (DMHF) to 2,5-dimethyl-4-methoxy-3(2H)-furanone (DMMF) by a S-adenosyl-L-methionine dependent O-methyltransferase, the cloning of the corresponding cDNA and characterization of the encoded protein. Northern-hybridization indicated that the Strawberry-OMT specific transcripts accumulated during ripening in strawberry fruits and were absent in root, petiole, leaf and flower. The protein was functionally expressed in E. coli and exhibited a substrate specificity for catechol, caffeic acid, protocatechuic aldehyde, caffeoyl CoA and DMHF. A common structural feature of the accepted substrates was a o-diphenolic structure also present in DMHF in its dienolic tautomer. FaOMT is active as a homodimer and the native enzyme shows optimum activity at pH 8.5 and 37 degrees C. It does not require a cofactor for enzymatic activity. Due to the expression pattern of FaOMT and the enzymatic activity in the different stages of fruit ripening we suppose that FaOMT is involved in lignification of the achenes and the vascular bundles in the expanding fruit. In addition, it is concluded that the Strawberry-OMT plays an important role in the biosynthesis of strawberry volatiles such as vanillin and DMMF

Enhancement of COPD biological networks using a web-based collaboration interface

The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website (https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks

Original networks, NVC networks and COPD data sets used in: Enhancement of COPD biological networks using a web-based collaboration interface

<p>Original networks, NVC networks and their descriptions.<br>The file contains the names of the original networks (as they were published), agglomerated NVC networks (as presented on the Bionet website), and network descriptions. The 15 networks that were discussed during jamboree are indicated by “X” in the column Discussed in Jamboree.</p> <p>COPD data sets, their descriptions, and the comparisons used to build the COPD models during Phase 1.<br>Reverse causal reasoning was performed using COPD and emphysema data sets from lung, small airway, and alveolar macrophages of early COPD patients and healthy smokers. Data Sets, the Gene Expression Omnibus (GEO) used to build the COPD networks. SCs, state changes defined using differentially expressed genes that meet the following criteria: FDR adjusted p<0.05, fold change ≥1.3, and minimum expression of 100 (for Affy platforms). HYPs, mechanisms or hypotheses predicted from the SCs and the Selventa Knowledgebase [1] with the following cutoffs: richness p<0.1, concordance p<0.1.</p> <p>Early COPD was defined as Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages 1 and 2.<br>The three small airway data sets were merged using ComBat [2] because of the small sample size of early COPD patients within each data set.<br>Lone emphysema is defined in the GSE10006 data set as patients who have normal spirometry but decreased transfer factor and evidence of emphysema on chest computed tomography scans. The lone emphysema data were selected because they might be useful in understanding COPD onset.</p> <p>References<br>1. Catlett NL, Bargnesi AJ, Ungerer S, Seagaran T, Ladd W, Elliston KO, Pratt D: Reverse causal reasoning: applying qualitative causal knowledge to the interpretation of high-throughput data. BMC bioinformatics 2013, 14:340.<br>2. Chen C, Grennan K, Badner J, Zhang D, Gershon E, Jin L, Liu C: Removing batch effects in analysis of expression microarray data: an evaluation of six batch adjustment methods. PloS one 2011, 6:e17238.</p> <p> </p