I stole this figure above from this Nature Review from 2004, but I think the figure really shows how interesting this brand new paper from Peter Kubiniok et al., really is!
Chances are you've heard of RAS -- if it it messed up in a tumor -- it's real bad. And tons of really smart people have been studying RAS for years for that reason.
RAF is short for Rapidly Accelerated Fibrosarcoma -- DO NOT GOOGLE IMAGE SEARCH THE FINAL WORD. You might see a cat with it....and might lose interest in your breakfast...
Okay -- so now I know another reason for wanting to know more about RAF besides that figure at the top of this post that probably still hangs in my old boss's office. Look at all those question marks around the A/B/C RAFs!
Ready to get rid of some of them?!? Go Go Time Resolved Phosphoproteomics! (It almost works)
In this impeccable study they start by getting 2 colon cancer cell lines that respond differently to a RAF inhibitor drug and SILAC label them. They hit the 2 cell lines with the drug and pull samples every 5 minutes. Counting the zero time point it looks like 15 time points! Wait. This study gets harder.
They combine heavy and light peptides at each time point -- phospho enrich with titanosphere beads according to this protocol then offline SCX fractionate each time point with their own handmade spin columns (cool! I don't know how to do that. They briefly describe it, but I don't think I could replicate from this paper. It might be in the paper linked in this paragraph).
It looks like they pull 6 SCX fractions. We're at 90 or so phospho-enriched runs -- that go into a Q Exactive Plus. All RAW files are available at PRIDE -- file PASS00897.
MaxQuant was used for processing with normalization at each time point. Okay -- this group deserves some serious credit for their downstream analysis and how clearly they describe the methodology! It goes --> we used this tool --> it has a funny name --> here is where you read about it and get it for yourself --> here are the settings we used and why. Not to be critical, but a lot of the time you get to this point and "here is a formula that you might be able to understand if you hadn't taken your last stats class in the 90s...and forgotten what all these big Greek letters are..." This one is a joy to read!
Enough flattery! How'd they do? I lied. I'm not done with the flattery. This study is killer. Almost 38,000 phosphorylation sites....
....LET ME FINISH....but around 650 that clearly modulate in response to the drug treatment! When they extract them out after all these super cool tools --- they are left with the most comprehensive phosphorylation interaction map of RAS/RAF/ERK that I've ever personally seen! They cut right through the noise and it takes them right to where they want to be. Seriously...WOW....you have to check this out!
Disclaimer: I know a lot of work has been done on these pathways since the review photo at the top of this post and maybe moving from that one to the ridiculously improved one in this paper (you'll have to look at it yourself, it's Open Access) isn't solely this work -- but -- man, this is a really good phosphoproteomics study!
Final note: I get a little uncomfortable when we're doing phosphoproteomics without whole proteomics to show that up-regulation isn't due to whole protein level changes and down-regulation isn't caspase activation or whatever -- but if you're pulling a sample at 5/10/15/60 minutes after treating with a drug -- I'm just fine with your assumption that is a phospho change, especially if it is up-regulation!
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