Why is there no correlation between transcript and protein abundance?

 

While it's common in proteomics for us to casually throw around the fact that protein and transcript abundances don't correlate, this hasn't exactly replaced the central dogma in all the textbooks yet. 

The figure above is from this study that maybe convinced some people during my postdoc that I wasn't just a moron.  I was, at the very least, a moron who could demonstrate that other people couldn't get their RNA microarrays and SILAC proteomics on the same cell lines to make a nice correlation line. 

A and B are different ways of looking at nucleotide abundance (oligo sequence vs the complementary sequence) vs protein abundance. D) is just random. So...protein abundance is like you took random and shifted it to the side a little. (It's not quite that bad, but at this point in time imagine that I was reviewed each year to see if I got funding my fellowship renewed -- and news that I was a moron had got around. I heard things like "We spent $1M on this giant noisy Orbitrap and he's he can't even replicate the data from this $800 microarray with it".  I needed someone to sign off on my $35,000 and, while the Burger King across the street was advertising a higher salary I still had these "ideals" and wasn't ready to sell my soul to a corporation just yet. I needed to show anyone this who would still make eye contact with me.) 

Okay, so we know it doesn't correlate. Maybe biologists aren't learning it yet, but why? 

Worth noting, this team took a whack at it, and although part of the error was clearly spectral counting used in a lot of studies (not my SILAC ones) but it still looked bad even with better methods for LFQ

Okay, but again, I was going for "why" and got distracted. This is my favorite!  

This was 2016 and I don't know for sure if everyone in proteomics had gotten to the complete point of agreement on this RNA-Protein mismatched thing. So this study still had to tiptoe in and really start with technical variability problems in RNA and in proteomics data (where we're still worse than RNASeq was then, but we're getting better, I think!) but then it went right into what matches and what doesn't! 

What matches mostly? 

Okay. That sort of makes sense. Cells sometimes just hang out not really doing anything but metabolizing and their normal jobs. 

What mismatches and why? 

Wait. Translation isn't always instantaneous and it is regulated by complex feedback loops that don't always make sense to simply have running at full tilt? Weird. 

Protein doesn't just magically disappear after it is made. Controlled and amazingly regulated mechanisms degrade them. Sometimes is makes more sense to just degrade all the protein that you have around faster than you normally would. And the amount of messenger RNA for that protein will give you about zero direct info on how fast that protein is getting degraded. 

Not all proteins "cost" the same amount to make. This review introduced me (maybe a lot of people) to thinking of intracellular environments in sort of an economic model? In my head this is the easiest example. TITIN is like 30,000 amino acids. Transcription and translation, even at the rocket pace they move at have be just a little bit different in their costs (and relative speeds of each) than making a 120 amino acid histone protein (which is probably the worst example I could come up with, but in terms fo size still makes sense) 

Okay, so this was a lot of words and I've got to run, so if it only gets you to my favorite reference about protein/mRNA correlation what and why, I've done this ridiculous unpaid job of typing lots of words into this orange box that I've been doing for over 25% of my life!