New direct PDS relations extracted from the biological literature.

<p>The new direct links result from the Bio3graph processing of 9,586 articles. Bio3graph extracted 14 new direct relations between the components which were not identified in the manually built PDS model topology. Note that two of these triplets are trivial (<i>SAG_metabolite, activates, SA_metabolite)</i> and <i>(NIMIN1_protein, inhibits, NPR1_protein</i>).</p

Summary of all component types of the manually constructed SA, JA and ET sub-models represented at level 2 of the PDS taxonomy of Figure 1A.

<p>Summary of all component types of the manually constructed SA, JA and ET sub-models represented at level 2 of the PDS taxonomy of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051822#pone-0051822-g001" target="_blank">Figure 1A</a>.</p

Recall and precision analysis for 50 full-length papers.

<p>Recall and precision analysis for 50 full-length papers.</p

Signalling Network Construction for Modelling Plant Defence Response

<div><p>Plant defence signalling response against various pathogens, including viruses, is a complex phenomenon. In resistant interaction a plant cell perceives the pathogen signal, transduces it within the cell and performs a reprogramming of the cell metabolism leading to the pathogen replication arrest. This work focuses on signalling pathways crucial for the plant defence response, i.e., the salicylic acid, jasmonic acid and ethylene signal transduction pathways, in the <i>Arabidopsis thaliana</i> model plant. The initial signalling network topology was constructed manually by defining the representation formalism, encoding the information from public databases and literature, and composing a pathway diagram. The manually constructed network structure consists of 175 components and 387 reactions. In order to complement the network topology with possibly missing relations, a new approach to automated information extraction from biological literature was developed. This approach, named Bio3graph, allows for automated extraction of biological relations from the literature, resulting in a set of <i>(component1, reaction, component2)</i> triplets and composing a graph structure which can be visualised, compared to the manually constructed topology and examined by the experts. Using a plant defence response vocabulary of components and reaction types, Bio3graph was applied to a set of 9,586 relevant full text articles, resulting in 137 newly detected reactions between the components. Finally, the manually constructed topology and the new reactions were merged to form a network structure consisting of 175 components and 524 reactions. The resulting pathway diagram of plant defence signalling represents a valuable source for further computational modelling and interpretation of omics data. The developed Bio3graph approach, implemented as an executable language processing and graph visualisation workflow, is publically available at <a href="http://ropot.ijs.si/bio3graph/and" target="_blank">http://ropot.ijs.si/bio3graph/and</a> can be utilised for modelling other biological systems, given that an adequate vocabulary is provided.</p></div

Principle of decomposing families of components by decoupling of reactions.

<p>The example shown in this figure presents two conversion types illustrating the transformation from the biological reaction representation into the edge-labelled graph representation. First, the linolenic acid node is connected to the reaction product 13-HPT directly with an arc labelled as A. Second, the decomposing of LOX node is done from the protein family level (level 2) to the single protein level (level 3). The final result of the conversion is a graph with 8 nodes and 7 edges.</p

Manually constructed PDS model topology visualised as an edge-labelled graph.

<p>This graph, consisting of 175 nodes and 387 edges, is provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051822#pone.0051822.s002" target="_blank">Supporting Information S2</a> as an interactive graph visualised with the Biomine graph visualisation engine, enabling its closer inspection by zooming into its subparts and rearranging the node and the arc positions in the 2D space. The graph is organised into SA, JA and ET pathways with their crosstalk connections. The node borders of the main pathway components SA, JA and ET are coloured with red.</p

PDS reaction types.

<p>There are three groups of reactions. A) <i>Activation (A)</i> denotes all the reactions directly involving two components <i>X</i> and <i>Y</i> in the production of Z, where the concentration Z depends on the concentration of both substrates. B) <i>Binding (B)</i> results in the formation of a protein-protein complex or in the binding of a protein to a DNA promoter region to regulate its gene expression. C) <i>Inhibition (I)</i> is a process in which one component blocks the performance of another component.</p

Summary of all reaction types of the manually constructed SA, JA and ET sub-models, including the crosstalk connections, represented at level 1 of the PDS taxonomy of Figure 1B.

<p>Summary of all reaction types of the manually constructed SA, JA and ET sub-models, including the crosstalk connections, represented at level 1 of the PDS taxonomy of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051822#pone-0051822-g001" target="_blank">Figure 1B</a>.</p

Illustration of the triplet extraction.

<p>We show a part of the flow from input of POS tagging box from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051822#pone-0051822-g005" target="_blank">Figure 5</a> until output of triplet extraction box of the same figure. The input to the Genia POS tagger is previously pre-processed sentence. After the shallow parsing with Genia POS tagger, the algorithm performs the step 2. The final output from the triplet extraction part of Bio3graph approach is a triplet in the form <i>(subject, predicate, object)</i> which will be then transformed and visualised as an edge-labelled graph with the Biomine visualiser.</p

Analysis of Glioblastoma Patients' Plasma Revealed the Presence of MicroRNAs with a Prognostic Impact on Survival and Those of Viral Origin

<div><p>Background</p><p>Glioblastoma multiforme (GBM) is among the most aggressive cancers with a poor prognosis in spite of a plethora of established diagnostic and prognostic biomarkers and treatment modalities. Therefore, the current goal is the detection of novel biomarkers, possibly detectable in the blood of GBM patients that may enable an early diagnosis and are potential therapeutic targets, leading to more efficient interventions.</p><p>Experimental Procedures</p><p>MicroRNA profiling of 734 human and human-associated viral miRNAs was performed on blood plasma samples from 16 healthy individuals and 16 patients with GBM, using the nCounter miRNA Expression Assay Kits.</p><p>Results</p><p>We identified 19 miRNAs with significantly different plasma levels in GBM patients, compared to the healthy individuals group with the difference limited by a factor of 2. Additionally, 11 viral miRNAs were found differentially expressed in plasma of GBM patients and 24 miRNA levels significantly correlated with the patients’ survival. Moreover, the overlap between the group of candidate miRNAs for diagnostic biomarkers and the group of miRNAs associated with survival, consisted of ten miRNAs, showing both diagnostic and prognostic potential. Among them, hsa miR 592 and hsa miR 514a 3p have not been previously described in GBM and represent novel candidates for selective biomarkers. The possible signalling, induced by the revealed miRNAs is discussed, including those of viral origin, and in particular those related to the impaired immune response in the progression of GBM.</p><p>Conclusion</p><p>The GBM burden is reflected in the alteration of the plasma miRNAs pattern, including viral miRNAs, representing the potential for future clinical application. Therefore proposed biomarker candidate miRNAs should be validated in a larger study of an independent cohort of patients.</p></div