Clocked: local SNPs in global pops

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R4RNA arc diagrams (top) for predicted secondary structure comparison of the (upper) G/G/U/G/C and (lower) A/U/G/C/A haplotypes, with (bottom) SNPs aligned along the LHY 5’UTR region (exons; boxes and introns; horizontal lines). [from Fig 1 (b and c) of James, Sullivan and Nimmo, PCE, 2018]
Our study on the correlation between ‘natural variation’ in a clock gene sequence with bioclimatic parameters is out now as OpenAccess in the journal Plant, Cell & Environment.

The paper is called ‘Global spatial analysis of Arabidopsis natural variants implicates 5′UTR splicing of LATE ELONGATED HYPOCOTYL in responses to temperature

The starting point for this work was the idea that the 5’UTR of the core clock gene LATE ELONGATED HYPOCOTYL, also known as LHY, could function as a thermosensor given that we previously saw temperature sensitive alternative splicing of LHY.

We tested our theory using the 1001 genomes resource, a whole-genome sequence database for at least 1001 strains of the reference plant, Arabidopsis thaliana. Arabidopsis is native to Europe, but can now be found in the United States, North Africa and temperate Asia. We examined subtle differences, or polymorphisms, in the DNA sequences of >1001 accessions. These are often referred to as single nucleotide polymorphisms (SNPs). We found that different strains tended to ‘shake out’ as particular ordered assemblies of the SNPs, called haplotypes [for example, in the picture above the G/G/U/G/C haplotype is compared to the A/U/G/C/A haplotype] .

We were interested to see if the distinct haplotypes aligned with particular features of where these plants were growing – maybe the haplotypes grouped according to latitude, longitude, or altitude? Or would they group according to climate, such as temperature? seasonality? or even rainfall? For this we made use of the WorlClim database – a free public resource offering global climate data for ecological modelling.

The key findings were that:

  1. One of the haplotypes has hallmarks of being a signature of ‘relict’ accessions (survivors of the last ice-age and the subsequent expansion of new populations). This version is the most distinct in the respect that, worldwide, the accessions bearing this haplotype are found in regions of low rainfall. They are also associated with the highest elevations with low mean annual temperatures and a wider range of maximum–minimum temperatures
  2. Two of the remaining three haplotypes seem to associate with milder annual mean temperatures and lower altitude and wetter habitats
  3. The fourth haplotype, seems to be a low temperature specialist. This haplotype is commonly found in the mountainous Pyrenees region of northern Spain and is prominent at the limit of Arabidopsis growth in northern Sweden
  4. By measuring the extent of LHY spliced upon cooling in representative strains from two haplotypes we established that haplotype does indeed affect the splicing of LHY transcripts in response to cooling
  5. We propose that the LHY haplotypes possess distinct 5′UTR pre‐mRNA folding thermodynamics and/or specific biological stabilities based around the binding of trans‐acting RNA splicing factors

There is much interest in identifying plant thermometers and how they have evolved to cope with new temperature environments. Our new work shows that subtle differences in the DNA sequence of global populations of Arabidopsis plants influences the scalable splicing sensitivity of the mRNA for this central clock component, thereby finely tuning the clock to specific temperature environments.

We anticipate that these findings will be of interest and relevant to crop breeding programs that aim to produce stable food crops in the face of changing climate. 

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RNAtips

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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:

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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.

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Are ye’ a cold kinase? Or are ye’ no a cold kinase?

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I’ve been wrestling with a couple of ‘kinase’ papers of recent. They were both published in December 2017 in the journal Developmental Cell.

Firstly there is Zhao et al. with ‘MAP Kinase Cascades Regulate the Cold Response by Modulating ICE1 Protein Stability

…and the other paper is Li et al. with ‘MPK3- and MPK6-Mediated ICE1 Phosphorylation Negatively Regulates ICE1 Stability and Freezing Tolerance in Arabidopsis

Both papers examine the early response of plants to temperature and the involvement of protein kinases – principally the mitogen-activated protein kinases (MAPKs) e.g. the MAP kinase kinase kinases (MAP3Ks; also called MAPKKKs or MEKKs), MAP kinase kinases (MAP2Ks; also called MKKs or MEKs), and MAPKs…..you can already see how complicated this can get.

When plants adapt to cold there are large changes in the expression of thousands of genes, and its now well established that these changes are mediated by what are known as the CBF genes and they do this by regulating another subset of genes known as the COR genes. Its a bit like a domino effect. Once cold is triggered the dominoes start falling leading to COR gene expression and the plants physiological response to temperature.

Whereas quite a lot is now known about the later stages of the domino line at the molecular level, less is known about the ‘who’ and ‘what’ starts to triggering the domino line. Moving slightly up from the CBFs are the ICEs (ICE1 and ICE2).  ICE1 is a transcription factor that binds to CBF promoters and activates their expression. ICE is therefore seen as a very interesting ‘domino’ and how it is knocked over (..or activated) in this cascade is of great interest.  Several suspects were on the wanted list, including the MAPKs: MPK3, MPK4 and MPK6 and MEKK1 and MKK2, and these are the focus of both these papers.

I won’t go into much more detail. However, here are the highlights from both papers:

Li et al.:

1. Cold activates mitogen-activated protein kinases MPK3 and MPK6

2. MPK3/MPK6 phosphorylate and destabilises ICE1 protein, and

3. MPK3/MPK6 activation attenuates plant freezing tolerance

Zhao et al. :

1. The MKK4/5-MPK3/6 cascade negatively regulates freezing tolerance

2. The MEKK1-MKK2-MPK4 cascade positively regulates freezing tolerance

3. MPK3/6-mediated phosphorylation of ICE1 promotes ICE1 degradation

I think the other important thing is that Zhao et al. show that MPK4 positively regulates the cold response by constitutively suppressing MPK3 and MPK6 activity i.e. MPK4 blocks the ‘destruction’ of ICE1 by MPK3 and MPK6.

Anyway there is also a commentary article in the same issue in Developmental Cell on the background and features of these two papers called ‘MAP kinase Signaling Turns to ICE’

Must check it out. I think it will articulate these results much better than I can…

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1001 Genomes

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A resource that we are increasingly using in our research is what’s known as the ‘1001 Genomes‘.

This is where the genome sequences for over 1000 different Arabidopsis plants are available for anyone to play with.

Just like you and me, we are all slightly different. Different hair colour, different height, different shoe size. The same is true for plants. The sequences for the 1001 genomes  helps us work out how Arabidopsis has evolved, and might therefore help us understand how, for example, climate change will affect plants (and ultimately our food crops). For our research, we’re interested in how plants perceive and respond to temperature. How do plants survive and adapt to very different temperature environments?  We can use the 1001 Genomes resource to help us address this question.

The flagship paper that describes this invaluable resource is a paper published by the 1001 Genomes Consortium in the journal Cell in 2016.

The paper shows that Arabidopsis ‘relict’ plants – something akin to the plant’s Founding Fathers – were prominent in the Iberian (Spain and Portugal) Peninsula – and seemed to hang about on the periphery of the last ice age (around 12,000 years ago), whereupon there was an expansion, or ‘sweep’ into more Northerly latitudes. What changes to the genome helped Arabidopsis survive in different habitats in their sweep North?

As pointed out in their Conclusion the authors state that “temperature and precipitation vary greatly across the species’ range and between groups and one would expect differences in physiological and developmental responses of Spanish and Swedish accessions”.

Why is all this important? Well, its been demonstrated that rice grain yield declines by 10% for each 1°C increase in growing-season minimum (i.e. night-time) temperature. One approach might be to therefore grow crops at higher latitudes, but by doing this our crops will need to adapt to different day-lengths. A higher latitude results in greater seasonality i.e. larger differences in day-length and temperature at higher latitudes compared to regions nearer the equator.

Why don’t we work directly with rice, wheat, barley, potato etc? Why do we work with Arabidopsis, which is a weed after all. Well the genome size of our staple crop plants are much bigger and more complex and so obtaining 1001 potato genomes would be a truly mammoth (and expensive) task. We also have a wealth of genetic resources available for Arabidopsis,  And for me – never known for my horticultural skills – Arabidopsis is easy to grow (its a weed after all), and it lives and dies quickly (around 5 weeks) meaning that there can be a quick turn around of experiments.

There are some good web-resources that allows us to play around with the 1001+ sequences from all of these different natural variants. One that I find particularly useful is the SALK 1001 genomes browser, where you can plot all the single base pair changes across a gene region for as many of the 1001 genomes you can fit on your web-browser – see an example below.

Makes you wonder, looking at all of these small changes in DNA sequence – what do they mean for the plant, and how best do we test what these changes make?

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abundance of transcript=amount of protein?

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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.

“now acceptable for publication” :-)

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Ahhh….the best 4 words in scientific research? It’s been a long, arduous trip but finally we shall be adding a few dents to our current knowledge of alternative splicing/splicing factors/temperature and the clock….will keep you posted.

Made me think about the time it takes to publish scientific research, and I came across this commentary article in Nature from 2016.

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I think many of us working at the coal-face of research will recognise a lot of what it says, e.g.

Many….feel trapped in a cycle of submission, rejection, review, re-review and re-re-review that seems to eat up months of their lives, interfere with job, grant and tenure applications and slow down the dissemination of results.”

Also talks about “resetting the clock” – not to do with circadian clocks, but related to the time stamp of submission and resubmission(s).

Is it taking longer to publish? One contributor to the article says that the average time for their group of papers took 9 months…[9 months is good, no?]

Anyway, for the time being lets focus on…”now acceptable for publication” 🙂

 

Splicing based body-temperature thermometer

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This work from the Lab of RNA Biochemistry at the Freie University Berlin shows just how sensitive splicing is to small changes in body temperature.

They looked at alternative splicing (AS) of U2af26 across a physiologically relevant temperature range (35-40oC). [U2af26 is a component of the essential splicing factor U2af (U2 auxiliary factor) where it can substitute for U2af35 in heterodimers with U2af65]

The authors show that U2af26 exon 6/7 skipping showed a very nice linear correlation with the temperature (see their figure below), suggesting that AS is able to react in a thermometer like way to read body temperature changes.

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The paper goes on to show an involvement for SR proteins in temperature-regulated U2af26 AS, primarily via modulation of the phosphorylation state of SRs. The authors speculate that there will be a physiological role for temperature-controlled AS in other phenomena, such as hypothermia and fever.

 

 

Are RNA thermosensors all around us?

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Are RNA thermosensors more common than we thought? Interesting article in the Journal of Experimental Biology speculating whether RNA thermometers (RNATs), well-studied in Prokaryotes, are prevalent in the other Kingdoms of Life.

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….

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Improved Gateway

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Trying to re-learn the language of GATEWAY cloning. ‘BP reactions’, ‘LR cloning’, ‘attB sites’, ‘entry clones’, ‘destination vectors’, ‘binary vectors’…I could go on. Thinking of trying a series of ‘Improved Gateway Binary Vectors (ImpGWBs)‘ to make plants express our genes of interest (GOIs) fused to markers. These are described in this paper:

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As for the choice of marker, there is a dizzying choice, so as well as trying to get to grips with GATEWAY, there is the question of what best to use as marker. I’ve had disappointing results fusing our GOIs to green fluorescent protein (GFP) in the past, so maybe time to try something else. As you can see, there is no shortage of choice:

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Feeling Fly

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This fly paper caught my eye. It examines how Drosophila monitors daily temperature changes via network of circadian clock regulated neurons. It seems the fly continually integrates temperature informations in order to coordinate sleep and activity patterns.

The work shows that nodes within the circadian network are sensitive to brief changes in temperature, and show that particular neurons are inhibited by heating and excited by cooling. It seems also that light and temperature are processed in distinct ways in the clock neutron network.

Interested to see the use of a fluorescent protein tool called CaMPARI that photo-converts from green to red in proportion to Ca2+ levels –  could this be used in plant work? Would require both light (photo-activation) and temperature manipulation…

The kinetics of temperature response was monitored by measuring intracellular Ca2+ concentrations using a calcium sensor called GCaMP6m and showed that particular neurons showed increases in intracellular calcium during cooling and decreases during heating.

The authors state that their findings reveal that the circadian network transduces brief and transient temperature changes and prolonged increases in temperature in distinct ways.

Thermoreceptors are found in structures in the antennae, called the aristae. Each arista contains both cold-sensitive and heat-sensitive cells. From their figure (below), they found that the responses to cooling and heating were attenuated when the aristae was removed.

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This work is interesting to use since we are trying to understand how plants respond to everyday changes in temperature – both short-term (daily fluctuations) and long term (seasonal) changes in temperature.