SUMO E2 ligase is necessary for RAS/RAF oncogenesis

Just when you think a story can't get any more complex, biology up and surprises you!

This paper isn't brand new, but I don't check PNAS as often as some of the other purely -omics oriented journals. The paper in question is from Bing Yu et al., and is available here and is open access.

Its a cool story, too.  There are really no targeted chemotherapies out there that will work on KRAS specific cancers. The function of the various GTPases and their pathways are complicated and convoluted. When they are working right they are supposed to function in signaling by GTP to GDP conversions.  This communication system is so critical to normal cell functioning that disregulating of these proteins has the nasty outcomes of the cell dying or becoming a cancer cell. As in any biological system, its certainly more complex than this, because years of work with these things comes up with a whole lot more info and no clear simple answers.

This is where we come in. Turns out that this group did a big shRNA screen (this is where you transfect cells with a great big mixture of Single Hairpin RNA that knocks out RNA production (and therefore protein production) on a huge scale. The readout is typically a phenotype. In this case, I'm assuming what they did (I'm sure its in the paper. not my area of expertise.) was see what cells did or did not become cancerous and then go back and figure out what gene they knocked out.

Of the many observations they came with 2 ligases that are the only ones known to be involved in the SUMO E1 and E2 pathways (controlling the SUMO PTM, not sure if I have the nomenclature 100% correct here). Anyway...SUMOs are small proteins that are ligated to big proteins and modulate their function as post-translational modifications (PTMs). There are bunch of them and a bunch of pathways, but here you have two major regulators of SUMOylation (more info on this PTM here) that are somehow implicated in KRAS oncogenesis? Tell me more!

So, they go in and construct an RNA interference to directly deplete these SUMOylation ligases (the things that attach the SUMO PTMs) in some cells that are crazy KRAS cancer cells. Turns out that if you can't produce these proteins even a KRAS cancer cell gets subdued. Then they study it by labeling some of the cells with SILAC and repeating the knockdowns to try to figure out the mechanism by which all this is happening and come up with a group of proteins they call KASPs which is short for KRAS Associated SUMOylated Proteins that are involved in this mechanism.

To sum up: We start with a common cancer mutation we don't have drugs for and we figure out a protein, not just that, a whole series of proteins that may be potential targets for treatment when someone has this type of cancer. Inhibiting these proteins and maybe you have a new chemotherapy. And along the way, we learn an entirely new biological modulation pathway?

I highly recommend picking this one up. Its nice to see what we do fitting seamlessly into a biological study alongside the cutting edge tools the molecular biologists are using these days!

Alzheimer's proteomics by high resolution differential analysis!

Alzheimer's disease is some terrifying stuff. Fortunately, however, it is a disease that appears to be amenable to study with the advanced tools we have these days!  Pull a Google Scholar search for "Alzheimer's disease proteomics" and you'll find a load of great studies that show that protein mass spectrometry may be exactly the way that we need to approach this for early disease diagnosis, stage monitoring, and hopefully!!!! for finding the upstream stuff so we can fix it.

Point in case: This open access study from groups at several institutions (hey! my good friend Katie is an author!) who used a combination of differential proteomic analysis and targeted MS/MS to profile disease progression. For the initial analysis they use simply MS1 high resolution monitoring and compare the peak profiles between the CSF samples of different stages of the disease. These differential lists provide them with ions to go after in their targeted assays!  This is a pretty awesome application of a way a lot of us have thought of doing proteomics over the years...by just going after the stuff that is different!

How'd they find the stuff that was different? With Elucidator (and I'm not sure it exists for today's instruments...) but we have lots of tools we could use for things like this, like SIEVE and OpenMS.

Big highlights of this assay? Seeing biomarkers without any level of antibody-based enrichment or pulldown. Just finding them by high resolution differentials!

Build pathways out of your Phosphoproteomics data with PhosphoPath!

I just downloaded this and I'm digging for something cool to feed it. This is free, easy to use, software for analysis of your phosphoproteomics data!  And it runs through Java, so it should be accessible to just about everybody!

Did you just jump up out of your chair and yell a happy profanity? Or was that me?

It seems too good to be true to me, but I sure have a JAR file, and an instruction manual, and a practice dataset that I downloaded here.

It is described in this new paper (sorry, paywalled, I can't read the whole original paper yet either...) in JPR from Linsey M. Raaijmakers et al.,

If you get to check it out first, I'd love to know your impressions. I had a weird Java permissions issue (probably me and my PC settings) and I had to "unblock" the .JAR file, but I almost always have to.

Proteome "phasing" revealed by transcriptomics!

Okay....want a reason for a whole lot of those unmatched spectra in your human LC-MS/MS runs?

Check out this paper in Nature Biotechnology from Hagen Tilgner et al.,!  In this study they used transcriptomics (in this case, long-read RNA-Seq) and looked at different tissues. Turns out there is splicing everywhere!

Splicing? Well, that's when DNA that should be over here making this protein -->
ends up hanging out
with DNA
that's way over here -->
(who says blogging can't be hi-tech!)

and you end up with a transcript (and therefore a protein!) that, from a purely DNA perspective, TOTALLY SHOULDN'T EXIST AT ALL! (Definitely isn't in Uniprot/Swissprot...!)

Sorry for shouting.  I'm excited. This paper focused on mouse brains and found a whole ton of these things. In regards to some of the recent discoveries in brain proteins, maybe this isn't that big of a deal, but tissue-wise, holy cow....should we be using a different FASTA file if we are profiling liver tissue than if we are doing tissue that came from brain samples?  Sure looks like it!  Heck, if nothing else, its another great argument for PROTEOGENOMICS!  (Yeah, I'm shouting again!)

Worth a read. Sorry it isn't open access. And sorry if this is jumbled. Its kind of late and I've been excited about one thing or another for the last couple of days.

Assessment of longitudinal interlab variability

This one is pretty cool .

What happens when 64 different labs submit BSA samples that they run every month for 9 months and people sit down and assess the data?  Sounds like an ABRF study to me!

As we've come to expect, intralab variability (same lab over the 9 months) was smaller than interlab variability (from one place to another...I get them mixed up). That makes sense.  My LC my mass spec, I'm going to keep it pretty consistent for 9 months, compared to the way I run it versus those wackos over at Whats-It-Called University.

Variability among all the samples really doesn't look all that bad. It his, however, a single protein digest -- so we'd kind of expect that. Sampling of 100,000 peptides from a normal mammalian line might be a more sensitive indicator, but I still think this is a promising measurement.  As a field, we're getting better all the time!

Interestingly, the real outliers seem to show up right after LC-MS preventative maintenance (PM). And this makes sense, too. If you've had your LC open and changed some thingies in it recently then peakwidths and retention times might have shifted a bit pre- and post- opening it.  Sure does emphasize the frequent use and recording of quality control standards, particularly after maintenance and things.

Oh yeah, and this paper is currently open access in early release format at MCP here.

Can you use the SMART digest kits for proteomics?

Okay, y'all know I've been trying to find a good, easy, and reproducible protein digestion method to get behind. And I've mentioned the SMART (previously Perfinity FLASH) digests kits before. The big question that is floating around is: it works for single proteins just fine, but how well does it work for proteomics?

According to this paper it works pretty darned well. Now, this is just one paper and all, but I really can't come up with a reason that a method that digests one protein wouldn't digest a whole bunch of 'em and do it well (so long as the protein to enzyme ratios aren't all wonky).

Sure, its N=1, but it sure looks like it works!

First impressions of the free LFQ node from OpenMS

Over the weekend I got to finally toy around with one of the cool free nodes from the Kohlbacher lab that we can install into Proteome Discoverer 2.0.  The LFQ is short for "Label Free Quan" and the nodes are freely available to download for anybody here.   Now, before I go forward I should probably reiterate something that is on that page. These are 2nd party nodes and these won't be supported by Thermo's Proteome Discoverer team. Questions should be directed to the node developers. Fortunately, they seem quite straight forward!

Here are some early impressions of the nodes.

1) They are easy to install. Download the file from SourceForge, make sure all Proteome Discoverer versions on the PC are closed and run the file.  When you reopen, the nodes are there!

2) The node developer's even have workflows ready for us! There is a Processing Workflow and Consensus Workflow. Which is great! Cause, honestly, I wouldn't have thought to set them up as above....

3) Interesting note.  SequestHT and Percolator are mandatory. Gotta have 'em or you won't go anywhere, it seems.

4) LFQProfiler appears to multithread.

Windows performance loggers are always kind of hard to interpret, but all 8 cores on this desktop appeared to be doing something when the LFQ Profiler kicked in.  In the consensus you can actually tell that LFQ node how many cores it is allowed to use!  On other runs, it looked like I was maybe only using 4 cores, but this really isn't a good measurement.

5) Disclaimer here: I've got like 10 versions of Proteome and Compound Discoverer on my desktop because I have been alpha/beta testing them for years. I've got some versions that are locked down for different projects so my working environment is probably sub-ideal. But... I'm gonna be honest here, and I'm likely doing something wrong, but I'm finding the node a little difficult to integrate into my workflows, in an odd way. I keep getting "Execution failed" in my Administration tabs, but the failed workflow can be opened and looks just fine. I do have to unhide my Intensity but the numbers are here and it looks like it ran real fast!

So...first impressions. The LFQ node installs easy, has convenient pre-made workflows (additional downloads required) and seems to run fast. More analysis required to see how it works, but its Sunday and this is all the PD I think I'll do today!

UPDATE: 12/2/15.  Downloaded the LFQ nodes and they are AWESOME!!!

More statistical analysis of PSM FDR from the Qu lab!

We have an awful lot of search engines these days and we have almost as many (more?) ways of working out our automatic false discovery rates.  The Qu lab seems to have stepped back and said, let's try to sort it out, meaning, which FDR is more appropriate for large datasets -- and when?

This is a heavy analysis of three different search engines available for running in or through Proteome Discoverer as well as an analysis of what false discovery rate algorithm/method or filter will leave you with the best possible results.  Interestingly, the answers appear to be very analyzer and fragmentation-type dependent.

I'll leave this here for you guys who took more maths!  The answer appears to be...there is no easy answer...these are things we're definitely need to spend more time working on as proteomics moves further and further into the BIG DATA world.

You can find the abstract for this (paywall) paper here.

Histone post-translational modifications in monocyte-derived macrophages

(Picture credit: Laxmi Iyer, original url [unrelated to this article] here.)

It turns out that most of the histone work that has been done out there has been done on mouse cell lines or immortalized human lines. While this is undoubtedly useful information, immortalized cell lines tend to be kind of messed up and we all know about the plus/minuses of studying mice.

The Ciborowski lab has a plan of long term, in-depth studying of histones and their post-translational modifications of normal human macrophages. In this first study (available here, open access) they work on establishing their normal, baseline conditions for resting macrophages. Once they get that, they can go on to further studies.

For this analysis they are primarily using an LTQ-Orbitrap XL with ETD and employing both CID and ETD fractionation. Surprisingly, the majority of the information being obtained for PTM matches is not coming from the ETD. This is likely due to the lower speed/efficiency of the earliest ETD system compared to the ones I normally get to mess around with. It does, however, contribute meaningfully to the study. This is a nice clear study but I mostly highlight it here because I'm very interested in what they are going to do next AND how this data is going to line up with other well established histone PTM datasets we have from other models.  So...this post is kind of to remind myself to check back on these guys later...sorry...

EuPA 2016 --- Turkey?

The theme of the conference is "Challenge Accepted. Standardization and Interpretation of Proteomics." Ummm.....YES!!!!  And the lineup of speakers is already AWESOME!  You can check it out here.