Medicinal chemists are extremely familiar with G-protein coupled receptors (GPCRs), and it’s a safe bet that any pretty much any neurotransmitter (for example) that can be named by the general public is a GPCR ligand, too. Serotonin, dopamine, histamine – all the classics are there, and that’s reflected in the number of marketed drugs that target this signaling family.
I started out in the industry working on dopamine receptors, and after a year or so, I thought I knew a reasonable amount about them. But that was my peak of confidence. Since then, the more I’ve learned, the less sure I am that I know anything useful about them at all. (I had a chance a few years ago to try that line out on Duke’s Robert Lefkowitz, a Nobel winner for his GPCR studies, and he said that he can report that it doesn’t get any better the further you go!) Like everything else in molecular and cellular biology, GPCR signaling and function is wildly, inhumanly detailed and complex. Every new technique we come up with to study such things only reveals new layers of ever-more-finely-grained crosstalk, feedback, and regulation. This would be a good time to reference Robert Graves’ poem “Warning to Children“!
I bring this up because of this recent paper, which gives us a look at GPCR effects that we haven’t had before. The authors (a multicenter team from the Max Planck Institute (Martinsreid), Innsbruck, Temple, and Novo Nordisk) are using high-throughput mass spec proteomics to look at phosphorylation states in response to GPCR signaling. Phosphorylation is, of course, a ubiquitous mechanism for altering the functions of proteins, and the number of such switches and signals I would very much not like to count. In this case, the group is looking at 50,000 phosphorylation sites in brain tissue via their previously described “EasyPhos” technique, a streamlined LC/MS/MS protocol. (There are plenty of other ways to study phosphorylated proteins, but this one seems to be a particularly effective wholesale method).
That’s a lot of data. Five minutes of treatment with a classic kappa-opioid agonist (U-50488H) showed plenty of changes in phosphorylation states, in the order striatum > hippocampus > cortex > medulla oblongata > cerebellum (which is pretty much the order of opioid receptor density as well). You don’t see these effect in kappa-knockout mice. Comparing that five-minute time point with a 30-minute reading across the different brain regions shows still more differences, and these don’t correlate simply with the expression levels of the kinase enzymes and substrates involved. No, these changes (which I will not attempt to summarize!) do seem to be specific kappa-opioid-mediated ones. The proteins that show up are involved in a whole range of cellular processes, from the cell membrane down to the nucleus (and those are just the ones that have some degree of useful annotation – if you think that the proteome is truly annotated for function in every case, think again, hard).
Comparing this profile with another kappa-ligand, 6′-GNTI, showed about a 30% to 50% overlap in affected proteins, depending on brain region and time point. That makes sense – it’s an agonist, but better described as a “funny partial agonist” as compared to something like U-50488. Whether you find that overlap reassuring large or terrifying small depends on your personality, I’d say, and it’s permissible to alternate between those two views.
Focusing in further on a set of five structurally distinct kappa agonists, each known to have its own behavioral profile in mice, showed that there are clusters of phosphorylation events that can be binned and apparently assigned to behavioral phenotypes. This is quite interesting, and I hope that it’s a robust result (five compounds can be described as merely a good start on that question). That’s really the state that we find ourselves in much of the time these days: looking at huge, detailed list of cellular and molecular differences, without a clear understanding of what’s going on in almost all of them, and hoping to find correlations that can (A) give useful predictions and (B) furnish clues as to what some of those mechanisms might be. Our high-throughput tools generate far more information than we know how to interpret down at the fine levels, so we have to look for broader strokes and patterns and work from there.
If someone with a fundamentalist religious outlook reads this paper – the intersection set does not have to be null, but it will not be well populated, either – they might be reminded of Paley’s “watchmaker analogy“. That’s occurred to many other people as well, but his famous formulation was of stumbling across a pocket watch while walking in a field, which would immediately suggest the existence of a watchmaker. Perhaps that last word should be capitalized?
In this case, though, as in all of molecular biology, closer inspection not only reveals a wealth of impressive detail, but huge numbers of things which do not so much appear to have been carefully crafted by a divine hand, as much as assembled by a blind lunatic with infinite time, infinite willingness to tinker (and a correspondingly infinite willingness to accept whatever works as soon as it does), and infinite supplies of duct tape, super glue, and baling wire. What’s more, the whole mechanism keeps falling apart over time in subtle (and not so subtle) ways, which just leads to casual repurposing of the altered pieces. This series of tweets illustrates some of that on a higher systems level in the brain, but the same sort of “Hey dude, what’s your problem, it worked didn’t it?” style is in operation from top to bottom. And never forget, that’s because all of those zillions of variations that were tried and didn’t work as well as what we have now are dead. Never forget, either, that the definition of “work as well” is subject to brutal, random restatement at any moment as conditions change. Supervolcano drops the Earth’s temperature for ten years? Deal. Asteroid strike? Bummer. Your savannah ecosystem is turning into an ocean again after a million-year hiatus? It happens – if you don’t know how to swim, you’d better know how to fly. And so on.
So studies like this phosphoproteomics one are a look at what evolution has left us with after all this time. It’s a mess! But somehow it works, and since no instruction manual is provided, we have to write our own. And here’s where that stands, as of the latest publications. . .