New & Noteworthy
February 13, 2013
We all know that some people march to the beat of a different drummer. But now we’re finding out that mRNAs also have their own particular rhythms as they move along the ribosome.
It’s long been known that some codons just work better than others. They are translated faster and more accurately mostly because they interact more strongly with their tRNAs and because there are more of their specific tRNAs around. So why hasn’t evolution gotten rid of all the “slow” codons? With only optimal codons, translation could move at a marching beat all the time.
One idea has been that a few pauses every now and then are a good thing. For example, maybe slowing down translation at the end of a stretch coding for a discrete protein domain gives that domain time to fold properly. This would make it less likely for the polypeptide chain to end up tangled, or misfolded. Great thought, but even when researchers looked in multiple organisms, they couldn’t find a consistent correlation between codons used and protein structure. Until now, that is.
In a recent study published in Nature Structural and Molecular Biology, Pechmann and Frydman took a novel approach to this question. They derived a new formula to measure codon optimality. Using it they found that codon usage was highly conserved between even distantly related species, and that this conservation reflected the domain structure of the particular protein a ribosome was translating.
First, the authors came up with a more accurate way of classifying codons as optimal or non-optimal. They took advantage of the huge amount of data available for S. cerevisiae and included a lot more of it in the calculation, such as the abundance of hundreds of mRNAs and their level of ribosome association. They also took into account competition between tRNAs based on supply and demand, something that the previous studies had not done.
Once they developed this new translational efficiency scale, they applied it to ten other yeast species – from closely related budding yeasts all the way out to the evolutionarily distant Schizosaccharomyces pombe. The authors found that positions of optimal and non-optimal codons were indeed highly conserved across the yeasts. And codon optimality was highly correlated with protein structure.
One of the better examples of this is alpha helices. These protein domains form while still inside the ribosomal tunnel. The authors found that the mRNA regions coding alpha helices use a characteristic pattern of optimal and non-optimal codons to encode the first turn of the helix. They theorize that this sets the rhythm for folding the rest of the helix. Other structural elements are coded by distinct codon signatures too.
This isn’t just interesting basic research. It has some far-reaching practical implications too.
When using yeast to make some sort of industrial product, the thought has been to use as many optimal codons as possible. This has not always worked out, and now we may know why. A gene that tailors the codon usage to the rhythm of the protein structure is probably the best way to make a lot of correctly folded protein.
And the factory isn’t the only place where this kind of information will come in handy. Protein misfolding is the known or suspected culprit in a whole slew of human neurodegenerative diseases such as Alzheimer’s, ALS, Huntington’s chorea, and Parkinson’s disease. A better understanding of its causes might give us insights into managing those diseases.
Who knew in 1971 that translation actually is a rhythmic dance?
August 2, 2012
Translating a gene is easy, right? Hop on the end of an mRNA and start translating at the first AUG.
Of course nothing in biology is that simple! Not all AUGs in the beginning of mRNAs serve as the starts of translation and occasionally translation will start at a codon other than AUG. There is obviously more to a translation start than an AUG.
In a recent study, Kochetov and coworkers set out to better define what makes a ribosome sit down and start translating. They used a dataset compiled from S. cerevisiae in 2009 that included a wide range of translation starts ranging from the traditional to the barely recognizable.
The researchers focused on three classes of translation starts:
1) Traditional yeast gene start sites
2) AUG-containing uORFs
3) uORFs that lack an AUG
The last two sets are translation starts that happen upstream of traditional genes (hence the name upstream open reading frame or uORF). These tend to be weaker than traditional translation starts, have very short associated ORFs, and are thought to play a regulatory role in the translation of the “real” gene.
When Kochetov and coworkers analyzed the data, they confirmed some previous studies that showed that strong translation starts have an AUG, upstream RNA that is predicted to be unfolded and to be A-rich between nucleotides -6 and -1, and downstream RNA that is predicted to form a hairpin. Most of the traditional yeast genes possessed most of these attributes. The uORF translation starts were a different matter though.
The uORFs that had an AUG lacked the other features of a strong translation start. They tended to have fewer A’s in the upstream region and their RNA was structured in all the wrong ways. The uORFs that lacked an AUG apparently made up for it by having all of the other features of a strong translation start. They were A-rich between -6 and -1, had an unstructured RNA upstream and a hairpin downstream of the translation start. The thought is that translation starts that lack an AUG make up for it with all of the rest of the translation context being exceptionally strong.
These kinds of studies will make the tough job of identifying genes a bit easier. Which can only be a good thing as more and more genomes come on line.
How translation worked at Stanford in the 70′s
January 20, 2012
As scientists peer ever more deeply into a cell, the picture of how things work becomes more and more complicated. This was true when scientists took a hard look at transcription and gene regulation and found lots of little RNAs scurrying around the cell, regulating genes. And it now appears to be true for what is being translated and how translation is regulated.
In a new study, Brar and coworkers used ribosome profiling to explore what happens in yeast cells during meiosis at the level of translation. What they found was that a whole lot more was being translated (or at the very least gumming up the translation machinery) than anyone expected. They also found that translation is as finely regulated as is transcription.
And this doesn’t just happen in yeast. The same group has also generated similar findings in mice embryos as well. Results with human cells should be right around the corner…
In ribosome profiling, scientists determine what RNAs are contained in a ribosome at a given time point. The basic idea is that they isolate ribosomes, treat them with nucleases and then harvest the associated 30-35 nucleotide long mRNAs. They then sequence all of the isolated RNAs and identify where they came from.
Like lots of biology these days, this technique has only become possible with the advent of cheap, robust sequencing. In fact, the size of these sequences is ideal for modern sequencing techniques.
Researchers in the Weissman lab are finding all sorts of interesting things using this new tool. For example, in meiosis they were better able to determine which proteins are involved at various stages of meiosis, to see how involved “untranslated” mRNA leaders are in translation, and to identify smaller, previously ignored transcripts associated with ribosomes. In this post we’ll just focus on the last point but encourage the reader to learn about the study’s other findings here.
Of Shorter ORFs
Ribosome profiling has revealed that a lot more is being translated in yeast than the standard set of genes identified in the Saccharomyces Genome Database (SGD). For example, Brar and coworkers found that the mRNA of many open reading frames (ORFs) shorter than the usual 250 or so base pairs were associated with the ribosomes. Shorter ORFs like these aren’t routinely thought of as genes and so have not been extensively studied.
However, given how many of these ORFs were associated with ribosomes, scientists probably should start paying more attention now. Even before meiosis, 5% of the ribosomes tested in yeast contained RNAs from these shorter ORFs. Once meiosis kicked in, the number went up to an astonishing 30%.
Since scientists have only just started to focus on them, it isn’t surprising they don’t know how many of these smaller ORFs are translated into smaller peptides. Or what any of these peptides that do get translated might be doing in a cell.
In a recent study, Kondo and coworkers have shown that one of these ORFs is translated into a peptide and proposed it affects how the transcription regulator Shavenbaby works in Drosophila. Work similar to this will need to get underway before we have a good handle on what exactly is going on with these shorter ORFs.
Whatever they turn out to do, these small ORFs will probably change what we consider to be a gene. Again. The cell just keeps getting more and more complicated!
Lengthy but informative lecture on ribosome profiling.