Endophytic colonization of barley (Hordeum vulgare) roots by the nematophagous fungus Pochonia chlamydosporia reveals plant growth promotion and a general defense and stress transcriptomic response

Plant crop yields are negatively conditioned by a large set of biotic and abiotic factors. An alternative to mitigate these adverse effects is the use of fungal biological control agents and endophytes. The egg-parasitic fungus Pochonia chlamydosporia has been traditionally studied because of its potential as a biological control agent of plant-parasitic nematodes. This fungus can also act as an endophyte in monocot and dicot plants, and has been shown to promote plant growth in different agronomic crops. An Affymetrix 22K Barley GeneChip was used in this work to analyze the barley root transcriptomic response to P. chlamydosporia root colonization. Functional gene ontology (GO) and gene set enrichment analyses showed that genes involved in stress response were enriched in the barley transcriptome under endophytism. An 87.5 % of the probesets identified within the abiotic stress response group encoded heat shock proteins. Additionally, we found in our transcriptomic analysis an up-regulation of genes implicated in the biosynthesis of plant hormones, such as auxin, ethylene and jasmonic acid. Along with these, we detected induction of brassinosteroid insensitive 1-associated receptor kinase 1 (BR1) and other genes related to effector-triggered immunity (ETI) and pattern-triggered immunity (PTI). Our study supports at the molecular level the growth-promoting effect observed in plants endophytically colonized by P. chlamydosporia, which opens the door to further studies addressing the capacity of this fungus to mitigate the negative effects of biotic and abiotic factors on plant crops.

A semantic framework for textual data enrichment

In this work we present a semantic framework suitable of being used as support tool for recommender systems. Our purpose is to use the semantic information provided by a set of integrated resources to enrich texts by conducting different NLP tasks: WSD, domain classification, semantic similarities and sentiment analysis. After obtaining the textual semantic enrichment we would be able to recommend similar content or even to rate texts according to different dimensions. First of all, we describe the main characteristics of the semantic integrated resources with an exhaustive evaluation. Next, we demonstrate the usefulness of our resource in different NLP tasks and campaigns. Moreover, we present a combination of different NLP approaches that provide enough knowledge for being used as support tool for recommender systems. Finally, we illustrate a case of study with information related to movies and TV series to demonstrate that our framework works properly.