Global quantification of mammalian gene expression control

I’ve chosen as my first topic this paper:

“Global quantification of mammalian gene expression control”  Nature 473, 337-342, 2011

I’ve chosen this paper for several reasons. One, it’s cool.  Two, it was the last thing I read and, three, it tackles a question I was concerned with during my PhD studies albeit on a much larger scale and in mammals rather than bacteria.  In prokaryotes the fundamental biological processes of transcription and translation are coupled.  There is no nuclear membrane to divide the two and so as soon as an mRNA transcript is produced (even as it’s being produced!) ribosomes bind and initiate translation.  Therefore there is a strong correlation between the amount of mRNA and the quantity of it’s resulting protein in prokaryotes.  Obviously the rate of decay for both the transcript and the protein leads to cases where this is not true, but as a general rule it seems to hold up ok.

In eukaryotes its a whole different ball game.  For one, the nucleus separates the physical process of transcription from translation.  Higher organisms also have much more complex processes to prepare mRNA for translation – the removal of introns for a start – which are not present in prokaryotes.  Because of this it has always been hard to categorically state that a gene’s transcript levels are truly reflective of it’s protein level.  This causes a problem.  For a variety of reasons, most of which are technological and financial, modern molecular biology is largely based on the measurement of mRNA levels from which the state of a cell is inferred. However, proteins are the real functional unit of a cell and if we can’t be sure that the mRNA levels actually reflect their concentration then we can’t be sure of the cellular state as a whole.

We’ve been waiting for a systematic comparison of mRNA and protein levels on a global scale to tease apart this relationship.  The technological limitations that held us back are now being overcome and this paper is the first (to my knowledge) to provide such a comprehensive comparison.  The authors have not only quantified the levels of both mRNA (with high throughput sequencing) and protein (liquid chromatography coupled with tandem mass spec) but have also been able to generate half lives (the rate of decay/turnover) for both as well.  To do this they grew their cells (murine fibroblasts and then later a human breast cancer cell line) in media that contained labeled amino acids and a nucleoside analogue that allowed the team to differentiate newly synthesised mRNA and protein from the pre-existing.  A ratio of the new and pre-existing concentrations compared to total RNA/protein allowed the group to calculate half lives.  In total they have data for 5,028 mRNA-protein pairs.  It is worth noting that they were able to collect mRNA and protein data in the same cells (literally the same cells, not just the same cell type) which means the data is entirely compatible. Further, the method does not use any destructive chemicals to inhibit transcription or translation in order to calculate half lives meaning the cell remains intact and functioning normally throughout the experiment.

I guess the headline result is that they show that the correlation between mRNA and protein levels is approx. 0.4.  Although this is not massive, it’s greater than anyone had predicted in the past and is good news for all of us mRNA observers out there.  Next the group were able to construct a mathematical model that allowed them to explore the contribution of the four main processes involved (the synthesis and degradation of mRNA and protein).  They discover that the rate of translational initiation by the ribosome is the most fundamental check on protein abundance and not the rate of mRNA transcription, another reminder not to focus solely on the rate of transcription.

They classify proteins based on their mRNA and protein stabilities and find that those which are stable as both mRNA and protein are enriched for some fundamental cellular processes such as translation and metabolism.  Those which are unstable both as mRNA and protein are involved in signalling and regulatory systems (including epigenetic mechanisms).  Those with unstable proteins but stable mRNAs are concerned with functions such as cellular defence where the protein needs to be produced rapidly hence the pre-existing pool of mRNA.  This is largely as expected and indicates that the regulation of protein production evolved in a resource contrained environment and has adapted to fit the needs of the cell in an energy efficient optima.  The authors explore numerous other aspects which I won’t go into here. 

It is important to note that these experiments were conducted on a large, non-synchronised population of cells and as such the results reflect the average over the cell cycle.  It will be the case that at the level of the individual cell a particular protein may have quite different synthesis/degradation characteristics.  Nevertheless such a resource will now be invaluable to scientists looking to create systems biology models of cellular pathways where quantities and synthesis/turnover rates are required for accurate computation.  It will be the case that the data shown here derived from mouse fibroblasts will not be applicable to many models but at least they will allow us to move on from our current uninformed guestimates.


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