Roles of non-coding RNA in sugarcane-microbe interaction

Studies have highlighted the importance of non-coding RNA regulation in plant-microbe interaction. However, the roles of sugarcane microRNAs (miRNAs) in the regulation of disease responses have not been investigated. Firstly, we screened the sRNA transcriptome of sugarcane infected with Acidovorax avenae. Conserved and novel miRNAs were identified. Additionally, small interfering RNAs (siRNAs) were aligned to differentially expressed sequences from the sugarcane transcriptome. Interestingly, many siRNAs aligned to a transcript encoding a coppertransporter gene whose expression was induced in the presence of A. avenae, while the siRNAs were repressed in the presence of A. avenae. Moreover, a long intergenic non-coding RNA was identified as a potential target or decoy of miR408. To extend the bioinformatics analysis, we carried out independent inoculations and the expression patterns of six miRNAs were validated by quantitative reverse transcription-PCR (qRT-PCR). Among these miRNAs, miR408—a copper- microRNA—was downregulated. The cleavage of a putative miR408 target, a laccase, was confirmed by a modified 50RACE (rapid amplification of cDNA ends) assay. MiR408 was also downregulated in samples infected with other pathogens, but it was upregulated in the presence of a beneficial diazotrophic bacteria. Our results suggest that regulation by miR408 is important in sugarcane sensing whether microorganisms are either pathogenic or beneficial, triggering specific miRNA-mediated regulatory mechanisms accordingly

Bio-Strings: A Relational Database Data-Type for Dealing with Large Biosequences

DNA sequencers output a large set of very long biological data strings that we should persist in databases rather than basic text file systems. Many different data models and database management systems (DBMS) may deal with both storage and efficiency issues regarding genomic datasets. Specifically, there is a need for handling strings with variable sizes while keeping their biological meaning. Relational database management systems (RDBMS) provide several data types that could be further explored for the genomics context. Besides, they enforce integrity, consistency, and enable good abstractions for more conventional data. We propose the relational text data type to represent and manipulate biological sequences and their derivatives. We present a logical schema for representing the core biological information, which may be inferred from a given biological conceptual data schema and the corresponding function manipulations. We implement and evaluate these stored functions into an actual RDBMS for both efficacy and efficiency. We show that it is possible to enforce basic and complex requirements for the genomic domain. We claim that the well-established relational text data type in RDBMS may appropriately handle the representation and persistency of biological sequences. We base our approach on the idea of domain-specific abstract data types that can store data with semantically defined functions while hiding those details from non-technical end-users

Sugarcane transcriptome analysis in response to infection caused by <i>Acidovorax avenae</i> subsp. <i>avenae</i>

<div><p>Sugarcane is an important tropical crop mainly cultivated to produce ethanol and sugar. Crop productivity is negatively affected by <i>Acidovorax avenae</i> subsp <i>avenae</i> (<i>Aaa</i>), which causes the red stripe disease. Little is known about the molecular mechanisms triggered in response to the infection. We have investigated the molecular mechanism activated in sugarcane using a RNA-seq approach. We have produced a <i>de novo</i> transcriptome assembly (TR7) from sugarcane RNA-seq libraries submitted to drought and infection with <i>Aaa</i>. Together, these libraries present 247 million of raw reads and resulted in 168,767 reference transcripts. Mapping in TR7 of reads obtained from infected libraries, revealed 798 differentially expressed transcripts, of which 723 were annotated, corresponding to 467 genes. GO and KEGG enrichment analysis showed that several metabolic pathways, such as code for proteins response to stress, metabolism of carbohydrates, processes of transcription and translation of proteins, amino acid metabolism and biosynthesis of secondary metabolites were significantly regulated in sugarcane. Differential analysis revealed that genes in the biosynthetic pathways of ET and JA PRRs, oxidative burst genes, NBS-LRR genes, cell wall fortification genes, SAR induced genes and pathogenesis-related genes (PR) were upregulated. In addition, 20 genes were validated by RT-qPCR. Together, these data contribute to a better understanding of the molecular mechanisms triggered by the <i>Aaa</i> in sugarcane and opens the opportunity for the development of molecular markers associated with disease tolerance in breeding programs.</p></div

Validation of RNA-seq analysis by qRT-PCR using genes from different pathways.

<p>Two biological replicates were used. Gene names correspond to those listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0166473#pone.0166473.s009" target="_blank">S9</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0166473#pone.0166473.s010" target="_blank">S10</a> Tables. Relative expression by qRT-PCR. The bars represent the relative expression of three technical replicates (n = 3) and standard deviation (Green bars: replicate 1 and blue bars: replicate 2). The relative expression values above the dotted line are upregulated genes, whereas below line correspond to downregulated genes. GAPDH was used as a reference gene for normalization of gene-expression data. These 20 genes validated in replicates were grouped into four categories, <b>(A)</b> genes related to stress, <b>(B)</b> genes that coding to several pathways, <b>(C)</b> primary carbohydrate metabolism pathways genes and <b>(D)</b> genes encoding for PRRs. The values of the quantitative method ΔΔCt can be seen in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0166473#pone.0166473.s010" target="_blank">S10 Table</a></p

Histogram presentation of the GO enrichment analysis of sugarcane plantlets infected by <i>Aaa</i>.

<p>TRAPID system calculated GO enrichment based on the upregulated and downregulated dataset compared to a background (p-value 0.01). The x-axis indicates the percent of genes and the y-axis indicates the GO terms. GO analysis to <b>(A)</b> upregulated and <b>(B)</b> downregulated DEGs under biotic stress. For additional details, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0166473#pone.0166473.s004" target="_blank">S4 Table</a>.</p

Histogram presentation of the 8 KEGG metabolic pathways significantly enriched to DEGs of sugarcane plantlets infected by pathogen.

<p>The x-axis indicates the number of genes assigned to a specific pathway, the y-axis indicates the KEGG pathway. Enriched metabolic pathways to <b>(A)</b> upregulated and <b>(B)</b> downregulated DEGs. For additional details, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0166473#pone.0166473.s005" target="_blank">S5 Table</a>.</p

Workflow of analysis of the construction and analyses of the sugarcane reference TR7.

<p><b>(A)</b> The transcriptome assembly <i>De novo</i> was generated from sugarcane RNA libraries drought treated and libraries <i>Aaa</i> pathogen treated obtained from of Illumina. After was applied to quality filter to all raw reads the quality filter and next, were removed duplicate genome sequences from the dataset. The Velvet and Oases software was used for the <i>de novo</i> assembly of clean reads to generate the sugarcane reference transcriptome TR7 with 168,767 transcripts. <b>(B)</b> Differential expression analysis and annotated functional in TRAPID. About 14 millions of reads were mapping no TR7. Transcripts differentially expressed were selected by Fisher’s exact-test, p-value < 0.01 and transcripts that have the same expression on both replicas.</p