Cellular Morse Code

For decades now, the biological community has been focused on the question of how cells transmit information from place to place.  It’s a central problem if you want to understand pretty much anything about cell behavior.  A signal to grow, for example, might start when a growth factor arrives on the outside of a cell, say in your tissue culture dish when you add fresh medium with growth-factor-containing serum in it.  The information that it’s time to grow might be transmitted across the membrane by a membrane-spanning receptor, triggering a series of events such as a cascade of phosphorylations that cause enzymes within the cell to change activity.  The final result might be a change of activity of a transcription factor; the presence of a signal outside the cell has thus been converted into a change in the gene expression profile inside the nucleus of the cell.  We chiefly think of these processes as linear — a pathway — with a well-defined flow of information from A to B to C. We draw diagrams that show A near the cell membrane, passing information to B (closer to the nucleus) and then to C (closer still).  But of course this is just an analogy we use to make it easier for us to think about what’s going on, and like all convenient analogies it has the potential to be seriously misleading.  Our so-called “pathways” loop and branch and pass information forward and backward and sideways, losing precision all the way; A, B and C are most often distinguished by the timing of their activation, rather than by their location in the cell; and while it’s easy to tell a general story about how an external stimulus leads to a response inside the cell, it’s still hard to know why the response is the size it is, or happens at the time it does.

One of the most puzzling aspects of signal transduction is what happens when multiple signals impinge on the same mediator — when “paths” cross, or diverge, or merge.  In the case of the important anti-oncogene p53, we draw several paths coming in to p53 and several paths going out again.  The downstream consequences of p53 activation vary dramatically, from transient cell cycle arrest to senescence and apoptosis.  How does this single protein receive and transmit several different types of information?

One idea is that the p53 network is in fact many different, distinct pathways, each using a different p53 isoform (say, p53′, p53” and so on).  All these pathways look as if they overlap because they all involve an increase in the total level of p53 protein, but p53 can be modified in many ways (phosphorylation, acetylation, ubiquitination, methylation… ) at many different sites, producing modified versions of p53 that have varying functions.  It’s well established that this happens, and that the modifications do indeed modulate p53’s behavior.  But there’s another dimension, literally, to explore here: time.  Although activating the p53 pathway always causes p53 protein levels to increase — by definition — that doesn’t mean that the timing and duration of the response is always the same.  The role of protein dynamics in the transmission and processing of information in biology is seriously under-explored.

Here’s a dramatic example: exposure to gamma radiation, which causes double-strand breaks in DNA, leads to repeated individual pulses of p53 that have a stereotyped size and shape and appear at defined intervals.  Increasing the dose of radiation doesn’t increase the average size of the pulses; instead, it increases the number of pulses.  Irradiation with ultraviolet light also causes damage to DNA, but this time the breaks are primarily single-stranded.  The response of p53 to UV is quite different from its response to gamma.  Instead of repeated pulses of unchanging average size, you get a single wave whose size varies depending on the amount of irradiation: the bigger the radiation dose, the bigger the wave.  But what do these differences mean?  The Lahav lab has been pursuing this question pretty much ever since the lab began, and now they think they have an answer (Purvis et al. (2012) p53 dynamics control cell fate. Science doi:10.1126/science.1218351). 

Going into this study, here is what we know.  We know that damage by UV produces a different cell fate from damage by gamma: UV most often leads to apoptosis, while gamma most often leads to cell cycle arrest and DNA repair.  We know that this difference in eventual cell fate is mirrored by a difference in the activation of p53-dependent transcription, with different sets of genes activated in each case.  And we know, from earlier work in the Lahav lab, that these differences can be traced back to the very earliest step in activation: UV-induced breaks activate one of two DNA-damage-sensing kinases, ATR, while gamma-induced breaks activate the other one, ATM.  These kinases are so similar that many people don’t bother to distinguish between them, writing ATM/ATR instead; but they do have different activities and different regulation.  Most notably, a key feedback on ATM (through the p53 target Wip1) that is essential for the gamma-induced pulses is absent for ATR.  This initial difference in which kinase is activated thus leads to the difference between gamma and UV in p53’s dynamic behavior.  The question, then, is this: does the difference in p53’s dynamics cause the downstream differences in gene activation and cell fate, or are dynamics and gene activation/cell fate each independently affected by the initial choice between ATM and ATR?

It’s not trivial to isolate the effect of dynamical behavior in this system. Purvis et al. decided to take the approach of attempting to convert the gamma-induced pulses into the sustained waves of activation induced by UV.  If they could do this, then they would be able to compare a cell experiencing normal gamma-induced pulses with a cell that had received the same signal and activated the same kinase, but was experiencing a UV-style p53 wave. Of course this is not trivial either.  It’s easy to break the p53 pulses — just remove Wip1 — but this would almost certainly have other effects as well.  There is a drug, Nutlin-3, that very specifically blocks the interaction between p53 and its main regulator, Mdm2, which you would think would allow you to prevent p53 degradation and increase p53 levels; and it does, but there’s a problem.  Because the Mdm2 gene itself is regulated by p53, increasing p53 levels leads to increased Mdm2 levels, which overcomes the effect of the drug and brings p53 levels down again.

To make a long story short, Purvis et al. found it necessary to use a computational model to predict the effect of addition of different doses of Nutlin-3 to cells exposed to gamma irradiation.  Using this model they found a regimen of three carefully spaced doses (at 2.5 h, 3.5 h and 5.5 h) that changes the profile of most cells from gamma-style to UV-style.  And then they looked at the expression of genes involved in cell cycle arrest, DNA repair, apoptosis and senescence, and also looked at the eventual fate of cells treated in this way.

The results: most genes care about p53 dynamics.  In the majority of cases you can see a clear difference between the expression of a gene when p53 is pulsing and its expression when p53 is at sustained high levels for several hours.   Some genes are simply not induced when p53 is pulsing, but reach high levels in the sustained treatment, most prominently genes related to senescence.  Even genes that are induced in both treatments (such as the genes related to cell cycle arrest, DNA repair and p53 dynamics) show significant differences in the two regimes.  A couple of genes related to apoptosis aren’t induced in either situation.  And the general pattern seen in the gene expression is reflected in the cell fate.  Pulsing cells, typically, recover from the damage and go on to divide and grow.  Cells with sustained p53, much more often, don’t.  But — and this is interesting — they don’t undergo apoptosis (unlike cells that see UV radiation); instead, as you might expect from the gene expression results, they senesce.

What does this mean?  To me, it means that the p53 network has at least two ways of conveying information about the stress that a cell has experienced (at least, for this pair of stresses).  One information-transfer mechanism resembles Morse code in that dots and dashes convey different information.   When Purvis et al. convert the “dots” of the normal gamma response into “dashes”, there is a clear shift from the normal cell cycle arrest/DNA repair response to a senescent response.  But the Morse code works in concert with some other mechanism, most likely the differential post-translational modifications resulting from activating either ATR or ATM.  This is why “dashes” produced by UV lead to apoptosis, whereas “dashes” produced by gamma plus Nutlin lead to senescence.

This post is already too long — if you’re still with me, thank you — but for the aficionados out there I do want to mention that the authors considered the possibility that p53 dynamics might not matter as much as the total exposure of the cell to p53.  In other words, they worried that the area under the p53 curve might be the real difference between the gamma-only and the gamma-plus-Nutlin treatments.  To test this, they monitored cells carrying a fluorescently-labeled p53 in both conditions using live-cell microscopy, then fixed the cells at a carefully chosen time point: 12 hours for the +Nutlin cells, 21 hours for the no-Nutlin cells.  This allowed them to identify individual cells that had experienced the same total amount of p53 (some cells saw p53 as pulses, others saw sustained high levels) and measure the expression of genes involved either in cell cycle arrest or senescence.  Even when total p53 exposure is matched, cells in which the p53 was forced into a sustained pattern by the Nutlin additions expressed significantly higher levels of the genes involved in senescence, whereas cells in which p53 was allowed to pulse expressed higher levels of the cell cycle arrest genes instead.  So the dots and dashes really are being interpreted as information content.

How?  Well, now, that might be a story for another day.

Purvis JE, Karhohs KW, Mock C, Batchelor E, Loewer A, & Lahav G (2012). p53 dynamics control cell fate. Science, 336 (6087), 1440-4 PMID: 22700930