Wednesday, March 9, 2011

Have I uncovered an epigenetic conspiracy?

Nope! In an effort to reduce plastic water bottle waste, UM has been installing these badass water fountains around campus that automatically fill a bottle - I suppose it could fill a shoe, too - and tell you how many plastic bottles they've saved you from wasting. I have to say they're pretty convenient...
... and repressive? I noticed the contraption is named "EZH2O." Of course, Elkay meant 'easy H2O,' but I just took an epigenetics seminar where we learned about EZH2, a key enzyme in the Polycomb Repressive Complex 2 (PRC2). EZH2 is key in catalyzing and maintaining the [tri-methylation if lysine 27 in the tail of histone 3 (written "H3K27me3")] during mitosis (Hansen et al. 2008). At least I think that's how it works (if anyone knows better, please correct me if I'm wrong!).

So, real quick, your chromosomes are made up of DNA wrapped around histone proteins. This is essential because with some 3 billion base pairs of DNA, if it weren't packaged up nicely by histones it would be a HUGE MESS inside your cells. Anyway, these histones have 'tails' sticking out that can be modified in ways that basically allow genes to be turned on or off. This is significant because all of your DNA full of the Commands for Life is in every single one of your cells - yet not all genes are expressed. The addition of methyl-groups (methylation) to histone tails (as well as to DNA itself) is associated with gene silencing.

So when your DNA replicates during mitosis, EZH2 binds to H3K27, and slaps that tri-methyl mark to it, effectively telling the gene(s) in the region to 'shut up' so they don't get transcribed (Hansen et al. 2008). This way, your liver cells stay hepatic, your kidney cells renal, and your soul cells funky. It's apparently also critical in silencing HOXD and other during early development (Tsai et al. 2010). (Again, I'm not a molecular biologist so please someone correct me if I'm wrong!)

ResearchBlogging.orgHopefully these new water fountains aren't part of a bigger epigenetic experiment in which someone's subtly altering people's gene expression through malevolent manipulation of methyltransferase...

Hansen, K., Bracken, A., Pasini, D., Dietrich, N., Gehani, S., Monrad, A., Rappsilber, J., Lerdrup, M., & Helin, K. (2008). A model for transmission of the H3K27me3 epigenetic mark Nature Cell Biology, 10 (12), 1484-1484 DOI: 10.1038/ncb1208-1484

Tsai MC, Manor O, Wan Y, Mosammaparast N, Wang JK, Lan F, Shi Y, Segal E, & Chang HY (2010). Long noncoding RNA as modular scaffold of histone modification complexes. Science (New York, N.Y.), 329 (5992), 689-93 PMID: 20616235

Saturday, March 5, 2011

Canalization of breakfast cereals?

Spring break is winding down here at UM, and I've used the free time to cram for my prelims (exams to become a candidate, finally). So that means it's Saturday night and I'm watching TV...

And So I Married an Axe Murderer (1993) is on, and it's got the all you could possibly ask from the mid-1990s: San Francisco, Mike Myers, yuppies, the Spin Doctors, etc. After a classy mid-1990s one-night stand, Myers is just going to town on this bowl of Froot Loops, and it struck me - CEREAL HASN'T CHANGED IN 20 YEARS. To the right here is a box of Froot Loops from probably before the early 1990s (lacks blue-dyed froot loops; according to the Internet, this amazing cereal was born in 1963, and didn't include the classic green, blue and purple we're all used to until the 1990s.). Look at the box! Children were impressed and sufficiently amused by holograms. HOLOGRAMS! Pitiful.

But then you think about it - how many other children's cereals we all know and love are any different now from when we were kids? One of the most popular breakfast cereals (indeed, it is to cereal what Cats is to Broadway), Cheerios was introduced when the US entered World War II. Rice Krispies presaged the Great Depression.

Why has there been such stasis in the development of children's cereal? Grown-ups' cereals have burgeoned in the mean time. Anyway. Once there was a time you were a kid and you ate Froot Loops and had fun on the weekends. But now you eat steel cut oats - if you eat breakfast at all - and Saturday night is spent working and watching sweet movies. Which I guess isn't all that bad a tradeoff.

PS the current song playing in So I Married an Axe Murder is "Brother" by Toad the Wet Sprocket. Classic.

Tuesday, March 1, 2011

Stimulating the drunk on the platform

Gotcha! I mean "simulating" in the title, not "stimulating." This one's about programming.

I'm interested, for various reasons, in how evolution might bring about change over time. Recall from my Evolution Overview that evolutionary changes could occur by drift or natural selection. Drift means random change, because a given polymorphism has no effect on fitness. Natural selection, on the other hand, is what my advisor likes to call the 900-lb gorilla: it does whatever the eff it wants. Selection can take existing variation in a population and mold it into all kinds of oddities. Within Primates, natural selection has fashioned inquisitive apes that walk on two legs and go to the moon, and sexual selection has festooned male mandrills in variegated visage (right). Selection can be gentle and allow gradual change (what I like to call sensual selection), or it could be strong and cause rapid change.

Selection can seem random for various reasons (e.g. why is it acting as intensely as it does when it does?), so it is hard to tell whether a given evolutionary scenario can be explained by selection for a given behavior, or if it reflects totally random change (drift).

Monte Carlo statistical methods allow one to simulate a given scenario, to test competing hypotheses. A simple null hypothesis that can be simulated is drift - change is completely random in direction, and if I reject this hypothesis I could argue that an alternative explanation (selection) is appropriate

The random walk is the oft-used analogy for this null hypothesis of random change. Now, if I'd ever been so imperfect as to have succumbed to the siren-song of spirits, maybe I'd corroborate the analogy. But using the extent of your limited but unadulterated imagination, pretend there is a drunk kid who happened to go to Loyola Chicago (like myself), and who walks onto the L platform (like I often did; left, looking north from the Lawrence Red Line stop). As booze takes the reins, he stumbles randomly between the edges of the train platform. This random walk down the train platform could result in the drunkard making it safely to the end, or he could fall off either side to a gruesome doom awaiting on the tracks below.

Thinking about limb proportions, and how to program this hypothesis/scenario, I stumbled upon the useful cumsum() function for the R statistical program. This function allows me to indicate how much change to occur for how many steps (i.e. generations), thus effectively simulating the random walk. For example (right), say I want to ask if the tibia (shin bone) gets long relative to the femur (thigh bone) in human evolution, because of drift vs. natural selection. I start with a given proportion of [tibia/femur] and simulate change in a random direction in tibia and femur length, over a quarter million years (or 18,000 15-year generations).

The figure is a bird's eye view of a random walk: at each generation the drunken tibia/femur ratio steps forward (to the right in the picture) and randomly right or left (toward the top or bottom of the picture). This took literally 2 seconds to program and graph. The dashed black line represents the relative tibia/femur relationship at the beginning of the evolutionary sequence, and the red line is the ratio 250,000 years (18,000 generations) later. Note that in this particular random scenario, not only is the final tibia/femur ratio exactly that observed, but that over the time span this ratio was reached about 20 different times. Do this randomization 500 or more times to see how often random change will result in the observed difference between time periods. Assuming the simulations realistically model reality, the observed change in limb proportions could easily be explained by drift (i.e. climate or efficiency adaptations did not have so strong a selective advantage as to be detected by this test). That is, the change in proportions could have been effected by random change or by weak directional selection.

I'm currently looking for any ways to make the model more realistic, for example:
  1. how much evolution (e.g. change) could occur per generation. Currently, each generation changes by plus or minus a given maximum. I would like to be able to simulate any amount of change between zero and an a priori maximum.
  2. it's easier to let the tibia and femur change randomly with respect to one another. However, this is unrealistic because the thigh and leg are serially homologous, their variation is not independent of one another. I would like to model each element's change per generation to reflect this covariance.
Anyway, I've only begun looking into the topic of how to analyze evolutionary change, but it looks like testing evolutionary hypotheses might not be impossible?