Rapid identification of causal mutations in tomato EMS populations via mapping-by-sequencing

The tomato is the model species of choice for fleshy fruit development and for the Solanaceae family. Ethyl methanesulfonate (EMS) mutants of tomato have already proven their utility for analysis of gene function in plants, leading to improved breeding stocks and superior tomato varieties. However, until recently, the identification of causal mutations that underlie particular phenotypes has been a very lengthy task that many laboratories could not afford because of spatial and technical limitations. Here, we describe a simple protocol for identifying causal mutations in tomato using a mapping-by-sequencing strategy. Plants displaying phenotypes of interest are first isolated by screening an EMS mutant collection generated in the miniature cultivar Micro-Tom. A recombinant F2 population is then produced by crossing the mutant with a wild-type (WT; non-mutagenized) genotype, and F2 segregants displaying the same phenotype are subsequently pooled. Finally, whole-genome sequencing and analysis of allele distributions in the pools allow for the identification of the causal mutation. The whole process, from the isolation of the tomato mutant to the identification of the causal mutation, takes 6-12 months. This strategy overcomes many previous limitations, is simple to use and can be applied in most laboratories with limited facilities for plant culture and genotyping

Integrated single-base resolution maps of transcriptome, sRNAome and methylome of Tomato yellow leaf curl virus (TYLCV) in tomato

Geminiviruses are plant ssDNA viruses that replicate through dsDNA intermediates and form minichromosomes which carry the same epigenetic marks as the host chromatin. During the infection, geminiviruses are targets of the post-transcriptional and transcriptional gene silencing machinery. To obtain insights into the connection between virus-derived small RNAs (vsRNAs), viral genome methylation and gene expression, we obtained the transcriptome, sRNAome and methylome from the geminivirus Tomato yellow leaf curl virus-infected tomato plants. The results showed accumulation of transcripts just at the viral ORFs, while vsRNAs spanned the entire genome, showing a prevalent accumulation at regions where the viral ORFs overlapped. The viral genome was not homogenously methylated showing two highly methylated regions located in the C1 ORF and around the intergenic region (IR). The compilation of those results showed a partial correlation between vsRNA accumulation, gene expression and DNA methylation. We could distinguish different epigenetic scenarios along the viral genome, suggesting that in addition to its function as a plant defence mechanism, DNA methylation could have a role in viral gene regulation. To our knowledge, this is the first report that shows integrative single-nucleotide maps of DNA methylation, vsRNA accumulation and gene expression from a plant virus.This research was supported by grants from the Spanish Ministerio de Economía, Industria y Competitividad/FEDER (AGL2016-75819-C2-1-R, AGL2016-75529-R and PT17/0009/0019). A.P.A. was awarded with a Predoctoral Fellowship from the Spanish Ministerio de Educación y Cultura. M.D. was supported by a contract from the Spanish Ministerio de Economía, Industria y Competitividad (PTA2014‐09515)

The UEA small RNA workbench:A suite of computational tools for small RNA analysis

RNA silencing (RNA interference, RNAi) is a complex, highly conserved mechanism mediated by short, typically 20-24 nt in length, noncoding RNAs known as small RNAs (sRNAs). They act as guides for the sequence-specific transcriptional and posttranscriptional regulation of target mRNAs and play a key role in the fine-tuning of biological processes such as growth, response to stresses, or defense mechanism. High-throughput sequencing (HTS) technologies are employed to capture the expression levels of sRNA populations. The processing of the resulting big data sets facilitated the computational analysis of the sRNA patterns of variation within biological samples such as time point experiments, tissue series or various treatments. Rapid technological advances enable larger experiments, often with biological replicates leading to a vast amount of raw data. As a result, in this fast-evolving field, the existing methods for sequence characterization and prediction of interaction (regulatory) networks periodically require adapting or in extreme cases, a complete redesign to cope with the data deluge. In addition, the presence of numerous tools focused only on particular steps of HTS analysis hinders the systematic parsing of the results and their interpretation. The UEA small RNA Workbench (v1-4), described in this chapter, provides a user-friendly, modular, interactive analysis in the form of a suite of computational tools designed to process and mine sRNA datasets for interesting characteristics that can be linked back to the observed phenotypes. First, we show how to preprocess the raw sequencing output and prepare it for downstream analysis. Then we review some quality checks that can be used as a first indication of sources of variability between samples. Next we show how the Workbench can provide a comparison of the effects of different normalization approaches on the distributions of expression, enhanced methods for the identification of differentially expressed transcripts and a summary of their corresponding patterns. Finally we describe individual analysis tools such as PAREsnip, for the analysis of PARE (degradome) data or CoLIde for the identification of sRNA loci based on their expression patterns and the visualization of the results using the software. We illustrate the features of the UEA sRNA Workbench on Arabidopsis thaliana and Homo sapiens datasets