Friday, April 8, 2016

Influenza A glycoproteomics revealed by a seriously smart and thorough experimental design!

I've been thinking about viruses a lot this week thanks to contracting a super gross one over the weekend. Rather than continue to be whiny and dramatic about it, I thought I'd try wrapping my head in duct tape to hold it together and see what we know about the topic.

LOTS! Way too much for me to absorb without pre-existing information or degrees, is an approachable and super cool (and recent) triple-omics approach on the topic. The paper in question is from Kshitij Khatri et al., out of Boston University.

In this study they take some Influenza A and infect some poor miserable chicken cells with them. Not just happy with a single -omics approach to the different viruses grown in the cells, they go for 3. They do some glycomics (sugars only), glycoproteomics (sugars still stuck to peptides) and some regular shotgun proteomics.

Why'd they do all this? Well these glycoprotein things appear to be how these miserable virus things hide from our immune responses. From implications in the introduction session it sounds like the glycan chain conformations are really the most important aspects of the disease and immunity.  The triple tiered approach helps them to be able compare/contrast the viruses at the different levels.

I walked away for a bit, had some more coffee and whined about my head thought about it some more. A lot of the time when we see glycoproteomics studies we get them in one of two flavors:

1) Glycans cleaved off and the peptides analyzed for the cleavage mass shift (which, I'm mostly okay with if you are really careful. Low mass resolution/accuracy instruments confuse this +1 Da shift with the C13 isotopes, and even with high res the results can be a little dicey if you aren't prepared to deal with it.)
2) Enriched intact glycopeptide analysis - which I'm totally cool with, but there are SO many combinations that it always feels like some data is left on the table. ETD and the algorithms to work with the data have gotten so much better in the last couple of years that its kinda unbelievable, but that is still a HUGE search space. I encourage you to try it. Go get one of the really good recent glycoproteomics datasets offline from PRIDE and, when you've got some free search time, search it with everything you've got. As good as the spectra are, and as good as new high-powered algorithms like Byonic are, there are a lot of unexplained spectra there. And FDR never works great when you've got such a huge number of combinations. (Good data gets thrown out sometimes and its hard to tell the good from the bad.)

Is there a better answer?  Maybe this approach!  Is it triple the amount of work?  Kinda...But look at how much data they get and how good it is!

And they have this level of certainty because they have free glycan analysis and they've got the intact glycopeptides and they can go back to their PGNase (or whatever) modified peptides. Obviously, they go further than I can as they bust out some arrays and 3D modeling, but they've got the data from three directions.

Now, it is worth throwing out there that they did all of this without ETD (QE Plus, FTW!) so the extra data points are even more critical for verification, but Yeah, and its probably also fair to point out that this is a virus so it is relatively simple, but

I go through this paper and at every turn I think "this is seriously smart..." from the fact someone on this team is WAY better at running PEAKS than I am through the downstream analysis (to end up at immunogenic modeling!?!? what!?!?)  So, if you are sitting there thinking that you'd love to understand what effects glycoslylation has in your model, but you can't get into glycoproteomics cause you've just got a Q Exactive, I suggest you download this new paper and reconsider. Cause I think this is as good a study as I've ever seen.

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