RESUMEN
Non-coding RNAs (ncRNAs) constitute an important set of transcripts produced in the cells of organisms. Among them, there is a large amount of a particular class of long ncRNAs that are difficult to predict, the so-called long intergenic ncRNAs (lincRNAs), which might play essential roles in gene regulation and other cellular processes. Despite the importance of these lincRNAs, there is still a lack of biological knowledge and, currently, the few computational methods considered are so specific that they cannot be successfully applied to other species different from those that they have been originally designed to. Prediction of lncRNAs have been performed with machine learning techniques. Particularly, for lincRNA prediction, supervised learning methods have been explored in recent literature. As far as we know, there are no methods nor workflows specially designed to predict lincRNAs in plants. In this context, this work proposes a workflow to predict lincRNAs on plants, considering a workflow that includes known bioinformatics tools together with machine learning techniques, here a support vector machine (SVM). We discuss two case studies that allowed to identify novel lincRNAs, in sugarcane (Saccharum spp.) and in maize (Zea mays). From the results, we also could identify differentially-expressed lincRNAs in sugarcane and maize plants submitted to pathogenic and beneficial microorganisms.
RESUMEN
Sugarcane interacts with particular types of beneficial nitrogen-fixing bacteria that provide fixed-nitrogen and plant growth hormones to host plants, promoting an increase in plant biomass. Other benefits, as enhanced tolerance to abiotic stresses have been reported to some diazotrophs. Here we aim to study the effects of the association between the diazotroph Gluconacetobacter diazotrophicus PAL5 and sugarcane cv. SP70-1143 during water depletion by characterizing differential transcriptome profiles of sugarcane. RNA-seq libraries were generated from roots and shoots of sugarcane plants free of endophytes that were inoculated with G. diazotrophicus and subjected to water depletion for 3 days. A sugarcane reference transcriptome was constructed and used for the identification of differentially expressed transcripts. The differential profile of non-inoculated SP70-1143 suggests that it responds to water deficit stress by the activation of drought-responsive markers and hormone pathways, as ABA and Ethylene. qRT-PCR revealed that root samples had higher levels of G. diazotrophicus 3 days after water deficit, compared to roots of inoculated plants watered normally. With prolonged drought only inoculated plants survived, indicating that SP70-1143 plants colonized with G. diazotrophicus become more tolerant to drought stress than non-inoculated plants. Strengthening this hypothesis, several gene expression responses to drought were inactivated or regulated in an opposite manner, especially in roots, when plants were colonized by the bacteria. The data suggests that colonized roots would not be suffering from stress in the same way as non-inoculated plants. On the other hand, shoots specifically activate ABA-dependent signaling genes, which could act as key elements in the drought resistance conferred by G. diazotrophicus to SP70-1143. This work reports for the first time the involvement of G. diazotrophicus in the promotion of drought-tolerance to sugarcane cv. SP70-1143, and it describes the initial molecular events that may trigger the increased drought tolerance in the host plant.