In every cell, molecular machines called ribosomes make proteins by reading the sequence of messenger RNAs (mRNAs) and synthesizing chains of amino acids destined to become proteins. Occasionally, ribosomes moving along the mRNAs slow down or stall. Stalling can happen when the cell is low on the specific amino acid that the mRNA sequence calls for at that position. Despite clear evidence for ribosome stalls and premature termination events where the ribosome completely falls away from the mRNA, it remains unclear how much these phenomena actually affect the rate of translation in cells. This is because the rate of initiation is much slower than the speed at which the ribosome moves along the mRNA producing protein, called elongation. Therefore, to study how such a transient phenomenon can change the rate of translation, scientists in the Subramaniam Lab (Basic Sciences Division) have developed methods to artificially create stall sites in a reporter gene in the bacterium Escherichia coli and then grow the bacteria in nutrient-limiting media to promote stalls. They can then measure the rate of expression of the reporter gene across a range of nutrient-limiting conditions or covering a range of different stall-site configurations.
There are several ways by which ribosome stalling could decrease translation efficiency. In one model, multiple ribosomes pile up behind the stalled ribosome, forming a "traffic jam" that eventually blocks the initiation site. Another idea is that the stalled ribosome just falls off the mRNA, called "abortive termination". In their latest publication in eLife, scientists in the Subramaniam Lab tested which of these models could best account for the changes in translation rate caused by ribosome stalls. Interestingly, their results suggest that an integrated mechanism where a stalled ribosome falls off of the mRNA following a collision with a trailing ribosome best accounts for the observed translation rates.
Measuring the rate of elongation or abortive termination at a stall site could in principle be estimated using ribosome profiling methods, which involve isolation of ribosomes and analysis of the associated mRNAs. However, sequence and technical biases introduce a large amount of uncertainty. Therefore, the scientists chose to combine computational modeling and experimental measurements of reporter protein levels in E. coli to predict whether traffic jams, abortive termination, or an integrated "collision-stimulated abortive termination" model could describe translation rates.
In one experiment they examined how altering the initiation rate affected translation rate under the three different models. They mutated the start codon or the ribosomal binding site (called the Shine-Dalgarno sequence) upstream of start codon to create 5 different sequences that had different rates of reporter gene expression in nutrient-rich media. Next they measured reporter gene expression rate in nutrient-limiting (stall-inducing) media. When the experimental measurements were plotted alongside the predicted rates generated by their computational models, the combined "collision-stimulated abortive termination" model best fit with the experimental results.
The researchers also compared experimental measurements with the computational predictions in scenarios where the number or position of stall sites in the reporter gene were varied. Again, the collision-stimulated abortive termination model fit best with the experimental measurements across the conditions tested.
These results are exciting and inspire further investigation. The Subramaniam Lab is interested in whether ribosome collisions are widespread and whether they occur in different types of organisms. Another interesting question is whether they are involved in disease. Said lead author Michael Ferrin, "Ribosome stalling has been implicated in neurodegenerative disease when it occurs in brain cells. Future research will aim to reveal the importance of ribosome collisions at ribosome stall sites in healthy as well as diseased individuals."
Ferrin MA, Subramaniam AR. 2017. "Kinetic modeling predicts a stimulatory role for ribosome collisions at elongation stall sites in bacteria." eLife.
This research was funded by the National Institutes of Health and Fred Hutchinson Cancer Research Center.