Genome-wide identification of potato long intergenic noncoding RNAs responsive to <i>Pectobacterium carotovorum</i> subspecies <i>brasiliense</i> infection

BACKGROUND: Long noncoding RNAs (lncRNAs) represent a class of RNA molecules that are implicated in regulation of gene expression in both mammals and plants. While much progress has been made in determining the biological functions of lncRNAs in mammals, the functional roles of lncRNAs in plants are still poorly understood. Specifically, the roles of long intergenic nocoding RNAs (lincRNAs) in plant defence responses are yet to be fully explored. RESULTS: In this study, we used strand-specific RNA sequencing to identify 1113 lincRNAs in potato (Solanum tuberosum) from stem tissues. The lincRNAs are expressed from all 12 potato chromosomes and generally smaller in size compared to protein-coding genes. Like in other plants, most potato lincRNAs possess single exons. A time-course RNA-seq analysis between a tolerant and a susceptible potato cultivar showed that 559 lincRNAs are responsive to Pectobacterium carotovorum subsp. brasiliense challenge compared to mock-inoculated controls. Moreover, coexpression analysis revealed that 17 of these lincRNAs are highly associated with 12 potato defence-related genes. CONCLUSIONS: Together, these results suggest that lincRNAs have potential functional roles in potato defence responses. Furthermore, this work provides the first library of potato lincRNAs and a set of novel lincRNAs implicated in potato defences against P. carotovorum subsp. brasiliense, a member of the soft rot Enterobacteriaceae phytopathogens. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-016-2967-9) contains supplementary material, which is available to authorized users

Uncertainty assessment of a process-based integrated catchment model of phosphorus

Despite the many models developed for phosphorus concentration prediction at differing spatial and temporal scales, there has been little effort to quantify uncertainty in their predictions. Model prediction uncertainty quantification is desirable, for informed decision-making in river-systems management. An uncertainty analysis of the process-based model, integrated catchment model of phosphorus (INCA-P), within the generalised likelihood uncertainty estimation (GLUE) framework is presented. The framework is applied to the Lugg catchment (1,077 km2), a River Wye tributary, on the England–Wales border. Daily discharge and monthly phosphorus (total reactive and total), for a limited number of reaches, are used to initially assess uncertainty and sensitivity of 44 model parameters, identified as being most important for discharge and phosphorus predictions. This study demonstrates that parameter homogeneity assumptions (spatial heterogeneity is treated as land use type fractional areas) can achieve higher model fits, than a previous expertly calibrated parameter set. The model is capable of reproducing the hydrology, but a threshold Nash-Sutcliffe co-efficient of determination (E or R 2) of 0.3 is not achieved when simulating observed total phosphorus (TP) data in the upland reaches or total reactive phosphorus (TRP) in any reach. Despite this, the model reproduces the general dynamics of TP and TRP, in point source dominated lower reaches. This paper discusses why this application of INCA-P fails to find any parameter sets, which simultaneously describe all observed data acceptably. The discussion focuses on uncertainty of readily available input data, and whether such process-based models should be used when there isn’t sufficient data to support the many parameters