Monthly Archives: February 2015

miRNA annotation in Capsella rubella (Camelineae) indicates rapid divergence

Rapid divergence and high diversity of miRNAs and miRNA targets in the Camelineae

Lisa M. Smith, Hernan A. Burbano, Xi Wang, Joffrey Fitz, George Wang, Yonca Ural-Blimke and Detlef Weigel

Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Sheffield S10 2TN, UK

Department of Molecular Biology, Max Planck Institute for Developmental Biology, Spemannstrasse 35, 72076 Tubingen

doi:   10.1111/tpj.12754

PMID:  25557441

This paper is from the most recent issue of The Plant Journal, and I thought it made some rather interesting points.  The paper focused on small RNA seq of several tissues from Capsella rubella, a member of the Camelineae tribe and frequent outgroup to the Arabidopsis genus.  With sRNA annotations from A. thaliana and A. lyrata, the authors look at the evolution of miRNAs within closely related species.

First of all, I was interested in the bioinformatics suite that this group chose to perform their annotation.  After aligning unique reads with bowtie, they used several clustering softwares (miR-deep 1.3, DSAP, UEA sRNA toolkit), resulting in some wildly different loci and annotations.  Within figure 1c, it appears that only half of known miRNA loci annotated by DSAP or miR-deep are found in common with each other, though this is higher when looking at miRNA families.  Is this because of failings within these softwares (the authors mention a high false negative rate in miR-deep)?

The article goes on to look at the variation in miRNAs in relation to their target genes between the 3 species.  They found that unique miRNA-target pairs were highly species divergent, with most pairings being unique to the different species.  Of the non-divergent pairs, almost all are more ancient pairings that are present outside of brassicaceae, leading the authors to hypothesize that there are two differentially evolving subsets of miRNAs: “young, evolutionarily dynamic miRNAs, and older miRNAs with a conserved subset of mRNA targets”.

The authors go on to look at the levels of polymorphism in miRNAs and their targets throughout A. thaliana.  This analysis ultimately lead to higher mutation rate in miRNA sequences themselves, forcing the authors to conclude that the target sites are undergoing stronger selection.  I thought this was a bit confusing, as you might expect higher conservation in target sites which could be in the CDS of genes, a point mentioned by the authors but not elaborated upon.

– Nate

Rcount: dealing with multi-mapping reads in RNAseq data

Rcount: simple and flexible RNA-Seq read counting

Marc W. Schmid* and Ueli Grossniklaus

Institute of Plant Biology and Zu€rich-Basel Plant Science Center, University of Zurich, 8008 Zu€rich, Switzerland

Bioinformatics. doi:10.1093/bioinformatics/btu680, PMID: 25322836

Nate showed me this paper today which is of some interest to us given my obsession with finding a better way to deal with the issue of multi-mapping reads in small RNA-seq data (e.g., with the butter program). This paper describes a tool called Rcount, which is a counter for ‘normal’ mRNA-seq data. As described in the paper, Rcount takes in a BAM file, and deals with multireads. According to figure 1 (copied below), the way they do this is to use the density of local uniquely mapped reads and make a probability assessment… the more uniquely mapped reads in an area, the more likely it is that the multi-read also came from that location. They then place it, noting their calculated probability in the SAM line with a custom tag. Rcount then performs another task (dealing with counting reads that overlap more than one gene annotation) and counts up reads in annotated genes for the user.

Rcount is clearly geared toward counting reads in annotated genes with reference to mRNA-seq data. For that reason, I doubt the program itself will be that useful for small RNA-seq data, where we are not generally interested in counting reads in pre-defined intervals (like gene annotations). But it is striking that Rcount is using pretty much exactly the method that my butter program uses for assigning reads … using the density of the unique mappers to create a probability set used to guide decisions on multi-mappers. I think Nate is going to try and use Rcount for small RNA-seq data.

I don’t think this precludes continued development of butter or it’s successor, because Rcount is pretty clearly geared toward mRNA-seq data. But it is worth testing, if possible, against butter and other methods for small RNA-seq to try and determine for our own lab purposes an optimal method for aligning multi-mapped small RNA-seq reads that is both precise and reproducible.

– Mike Axtell

Exosome is not related to small RNA or RdDM based silencing in plants

DOI: 10.1371/journal.pgen.1003411   PMID:  23555312

I looked at the paper The Role of the Arabidopsis Exosome in siRNA– Independent Silencing of Heterochromatic Loci.  This was an interesting topic for me, as it connected some aspects of RNA metabolism and use I hadn’t originally thought as interacting.  The paper is examining the premise that the exosome might be indirectly involved in the regulation of small RNA production and RdDM in plants, as has been reported in yeast.  This effect was previously examined in Bühler M et al. 2008, in the yeast model organism S. pombe, where exosome deficient mutants were found to have vastly altered levels of siRNAs.  This is thought to be caused by a buildup of aberrant non-degraded ncRNAs interacting with small RNAs and siRNA machinery.

The authors used small RNA sequencing to determine the global make-up of annotated small RNAs in Arabidopsis, looking for a difference in exosome deficient plants.  The authors couldn’t find an effect in small RNA quantity or distribution, considering both the type of small RNA (miRNA, genic RNA, ncRNA…) as well as the possible target location (TEs, inverted/tandem/double repeats..)

Despite the apparent lack of exosome mutation on small RNAs, the authors did find a downstream effect, where mutant plants had an increase in the quantities of RdDM-regulated heterochromatic loci.  They examined this in the context of POL IV and V mutants, in which exosome deficiency lead to a dramatic loss of regulation in these (2) loci.  Following data shows that this is not from a decrease in methylation of these sites.  Histone association is seen to be lower in these loci in exosome deficient plants, as well as an association between exosome and flanking scaffold regions, leading the group to speculate that there is a cooperative effect between these structures, acting independently of RdDM.

Looking at the data in this paper, there are still several enigmatic results that are poorly explained by their model.  Their data indicate that there is a combinatorial effect between the exosome and RNA pol V in silencing heterochromatin loci, but later data contradicts this, showing that mutant plants containing mutations in both have higher enrichment when pulled down by histone (figure 6a).  I struggled to find reasoning for this observation in the paper, but perhaps I missed it in my readings.  Despite some of these confusing points, I thought the results presented provided an interesting context for alternative forms of heterochromatin regulation, rather than RdDM. Overall a broad and interesting read.  My take-away points are 1) that the exosome in plants (as opposed to yeasts) must have some layer of insulation between RNA degradation and RdDM machinery and 2)  there are alternative forms of RNA directed heterochromatin regulation.

Hope this isn’t too far off topic, just thought it was interesting.