Here’s an interesting tool I came across recently. It’s called RNAtips (RNA (temperature-induced perturbation of structure). It’s a web based tool that aims to tell you regions of RNA that are likely to undergo structural alterations due to temperature change. Here’s the citation:
The input for RNAtips is either one or two RNA sequences of the same length (in FASTA format). You then specify two temperatures to define the temperature range for which the RNA structural perturbation should be calculated (the default range is 32–39oC).
One of the types of output plots is at the top of this post, where the density of the most temperature-sensitive positions over the whole RNA sequence together with localization of clusters (red blocks) and localisation of nucleotide substitutions (if any) are plotted.
How does it do this? RNAtips deciphers those nucleotides within the RNA sequence, which change the most in their probability to form Watson–Crick bonds in response to a given temperature change. One of the first steps appears to be the calculation of probabilities of nucleotides being paired in a double-stranded conformation by using the RNAfold tool of the ViennaRNA package. The calculations performed are derived from those described in paper in RNA Biology in 2012. The web server output plots clusters of these temperature-sensitive positions within a sequence, therefore representing the most temperature-sensitive structural regions of that RNA.
This is a very interesting tool and it is useful to make interesting hypotheses relating to your RNA of interest. For example, how do the temperature sensitive regions of an RNA relate to possible alternative splice sites or predicted RNA binding site regions, or to particular secondary structure folds or sequence polymorphisms.
I wonder to what extent the temperature range selected when inputing the sequence data on the web-form influences things. The range 32-39oC is not really applicable to (most) plants. Does this mean the inferences made on RNA structure are ‘wrong’. It would be nice, also if the sequence plots (e.g. picture at the top of post) could be overlaid with multiple plots in order to compare multiple sequences.
You can follow ‘Its About Time’ on Twitter @Splice Time
Thought that this was an interesting paper, published recently in PNAS.
The paper shows that alternative splicing produces different transcript isoforms for the 5’UTR region of the human gene encoding α-1-antitrypsin called SERPINA1, such that splicing of 5’UTR modulates the inclusion of long upstream ORFs (uORFs). What’s new with all this I hear you say. Well, the authors go on to show that while SERPINA1 transcripts produce the same protein isoform, they do so with different translation efficiencies. Differences in uORF content and 5’UTR secondary structure combine to differentiate the translational efficiencies of SERPINA1 transcripts.
α-1-antitrypsin is of interest because deficiencies in this protein are associated with chronic obstructive pulmonary disease (COPD), liver disease, and asthma. This work points to the possibility that genetic alterations in noncoding gene regions, such as the 5’UTR region, could result in α-1-antitrypsin deficiency.
The work also reinforces the idea that the amount of protein produced from a gene is not a simple function of the abundance of the transcript.
The reference is: Proc Natl Acad Sci U S A. 2017 Nov 21;114(47):E10244-E10253. doi: 10.1073/pnas.1706539114. Epub 2017 Nov 6.
The image used is their Figure 3. SHAPE-MaP structure probing data for SERPINA1 transcripts.
Changes in the conformation of RNATs (see their Figure 1, above) typically involve melting of short regions of the mRNA, for example hairpin structures, in response to elevated temperature (a ‘zipper’ mechanism) or a shift between alternative conformations of the mRNA that involve larger regions of the molecule (a ‘switch’ mechanism).
The RNAT contains the Shine–Dalgarno (S–D) sequence (AGGAGG) that, when fully exposed, can bind to the small (30S) ribosomal subunit and allow translation to commence. The start codon (AUG) is often located eight nucleotides downstream from the S–D sequence. Thus melting of the ‘thermometer’ allows the S–D sequence and start codon to interact with the 30S subunit, promoting translation of the mRNA.
Interesting read – I wasn’t familiar with the concept of ‘marginal stability’ – the idea that for RNA secondary and tertiary structures, thermosensor regions must have the right stability – or ‘balancing act’ – to allow temperature-driven changes in shape to take place when (and only when) a signalling function is required.
I particularly liked the section on ‘Differential translation of allelic mRNAs: another way to modulate the proteome?‘ – the concept that natural variants (allozymes) with different thermal optima can provide a species with an opportunity to establish populations with adaptively different thermal optima in regions of its biogeographic range where temperatures differ. Thus a cold-optimised allozyme might be more common in populations living in colder regions of a species’ range, whereas the warm-optimised allozyme would be dominant in warmer regions, and therefore crucially that slight changes in base composition likely alter the thermally sensitive mRNA structures that govern translational ability in a way that ensures differential translation of distinct allelic messages.
The author, George Somero, make an interesting point that we might assume that “changes in temperature often are regarded as having negative influences on macromolecular stability” adding “However, there is also a ‘good’ side to this thermal perturbation: the alteration in conformation of the macromolecule that is caused by a change in temperature can function as a thermosensing mechanism and lead to downstream changes that are adaptive to the cell.”
Exciting times lie ahead for RNA structure and temperature sensing….
A tad whiffy due to using beta-mercaptoethanol – hope my lab colleagues don’t mind too much.
Follow the Qiagen protocol pretty much as is, except for the bit where you add ethanol to the QIAshredder flow through.
Instead of adding 96% ethanol, we routinely add 70% ethanol – see point 4 in the picture below. This seems to result in a better yield and quality of RNA. Our samples are for Arabidopsis plants (quite old….around 5 weeks old) and sometimes these plants have experienced constant light so they have lots of polyphenols.
The RNA Journal is twenty years old and as part of their anniversary around 130 researchers in the field of RNA biology have contributed some of their personal reflections of working in this area. Contributors include Douglas Black, Michael Rosbash and Alberto Kornblihtt.
I’ve browsed through some of the essays and one that caught my attention was ‘Thoughts on NGS, alternative splicing and what we still need to know‘ by Kristen Lynch. Here she emphasises the need to determine the functional consequences of alternative splicing for an organism, and as she pointedly says ‘To truly appreciate the full impact of alternative splicing on biologic processes, and argue against those who wonder if it might all be “noise,” we need to do better. The question is how to achieve this goal’. [Note that NGS in the title of the article refers to Next Generation Sequencing]
As a relative newcomer to the field of AS, I think it’ll be useful for me to delve into these articles – they seem to be a refreshing way to learn how quickly research into AS has ‘evolved’ as well as providing an honest outlook as to what areas seem to be a priority for future work.
The cover art in interesting too – it is entitled ‘Group in Sea, 1979, by Philip Guston‘. He was an American abstract expressionist painter.
Follow us on Twitter – you can find us @SpliceTime