Translational Traffic Jams

Are all genes made equal?  In other words, do the protein recipes that they encode all get turned out at the same rate?  Surprisingly, the answer is no. As he explains to Chris Smith, with the help of a mathematician, molecular biologist Ian Stansfield is trying to find out why...

Ian -   We're used to thinking of DNA as the book of life and the source of all the information that makes us what we are.  What we're interested in is how the information encoded in the DNA is translated into proteins. This is interesting because it's the proteins in the cells which really do all the catalytic jobs and the relative amounts of all the different proteins that are made from each of the genes determines how a cell functions and what its properties are.  The basic problem is that you can't simply say that if you have one mRNA, it will make 10 copies of protein. Each messenger RNA, which is a copy of the information of the gene, is capable of producing proteins with differing efficiencies.  So gene 1 may have a very efficient mRNA which produces a lot of protein, whereas genes 2 and 3 may produce messenger RNAs which are much less efficient at making protein.

Chris -   And we just don't know why?

Ian -   We don't know why.  There are some theories.  One them involves the idea that when the messenger RNA is interpreted, small particles called ribosomes move along that messenger RNA incorporating amino acids and building them up into a long chain which eventually becomes the protein. That process is called translation.  The protein is then free to go off and do its job.  One theory that we're pursuing is that it's the rate at which those ribosome particles move along the messenger RNA that governs their efficiency of making protein.  The problem is that these are like small cars, moving along a road or a track.  The cars are the ribosomes and the track is the messenger RNA. These cars can't overtake one another which means that if one of the ribosomes encounters a site on the messenger RNA where it has to pause, then the other ribosomes will queue up behind it and that will form a traffic jam, in a sense a ribosome traffic jam.  What we believe may be possible is that ribosome traffic jams may govern the relative efficiencies or inefficiencies of an mRNA and its ability to make protein quickly and fast.

Chris -   Is this a mistake or is this by intention? Some organisms use the fact that as they're making proteins there's this pause, which then causes the ribosome to slip a bit and change the message it makes.  So is this an accident or is it something which the cell does on purpose?

Ian -   That's an interesting question.  what you might assume is that for the most part, these are pauses by intention, on purpose because evolution has been working a long time to optimise the sequence of messenger RNA. So there is an idea that if pauses are happening, they have some functional consequence. One idea which has been around for a little while now is that pausing halfway through the manufacture of a long string of amino acids, which make up the protein, may enable part of that protein to fold more efficiently before the ribosome then continues progression along the mRNA to complete the synthesis of the rest of the protein.

Chris -   If you look at the ribosome when this pausing is happening, what chemically is making it slow down?

Ian -   Well the way in which a ribosome works is that it will pause at a particular triplet of bases and wait for something called a transfer RNA (tRNA) to bring in the correct amino acid.  Not all of those tRNAs are present in equal abundance.  Some are quite rare, so when the ribosome encounters a rare one, it has to pause while it fumbles around and selects the right tRNA.  It's as if I gave you a big sack of white billiard balls and black ones. There are only 4 or 5 black ones, and thousands of white ones and I ask you to pull out a black one but you can only do it by reaching into the bag and pulling out a ball at random.  It will take quite a long time, on average, to find a black ball because they're so rare.  When a ribosome pauses at a rare tRNA, the pause will be longer and that will cause a traffic jam queuing of the ribosomes.

Chris -   Ian, you've got a mathematician sitting in your office - why?

Ian -   Well, when we were initiating this project a few years ago, we realised there was a big opportunity here to bring in some interdisciplinary expertise to help us. We wanted to begin to construct mathematical models of how ribosomes move along a messenger RNA and respond to both the slow and quick codons that we've been discussing.  And so, we've brought in some colleagues from the Department of Physics here in Aberdeen who had the required expertise to develop those rather complicated mathematical models. We use these models to predict for any given messenger RNA how efficiently it would make protein.

Mamen -   My name Mamen Romano.  I work at the physics department at the University of Aberdeen.  What we are doing is developing a mathematical model in order to predict how fast the ribosomes move along the mRNA. What we do is to construct the biological process in a lattice composed of different sites. Then the ribosomes are represented in our mathematical model as particles that jump from one side of the lattice to the next.  This can be described very well by a stochastic process because when one ribosome hops from one side of the lattice to the next, it has to wait for the correct transfer RNA in order to move ahead.  Additionally we have an exclusion process because if the next site of the lattice, the next codon of the messenger RNA, is occupied then the ribosome or the particle in our model cannot move ahead.  It has to wait and this can produce traffic jams.

Chris::  And does this mean you can now predict ahead and you can say to Ian, "On the basis of you wanting to make this particle molecule, we would anticipate the following performance of the ribosome under these circumstances?"

Mamen -   Exactly, depending on the configuration of the slow codons and how abundant the corresponding tRNAs are, we can predict how fast this protein is going to be made. This can be compared to the experimental data that Ian measures in his lab.

Chris -   And how is this useful to you, Ian?  In what way are you going to apply that data that you then get back?

Ian -   Well we're interested in asking the question how efficiently does each mRNA in a cell make protein and whether it can therefore be used as a tool to predict how any cell type will interpret its genome to make a particular population of proteins.  This is just the beginning of the story though, because although the modelling we're doing in collaboration with Mamen is helping us to predict the amount of protein that is manufactured from an mRNA,  the other half of the story is that as a cell grows, proteins are being turned over, degraded, sent to the cellular dustbin at a certain rate as well. The steady state level in the cell is both a function of how quickly the protein is being made and how quickly it's being turned over.  What we're doing is we're hoping to generate models which provide one part of that jigsaw and then integrate it with research that other colleagues are doing to look at how proteins are turned over at particular rates in the cell.