Visualizing Profilin

Profilin as a part of the musculo-skeletal system that motors our cells around. But how can we tell?

Our cells have structural elements called the cytoskeleton. The term is a misnomer, since the cytoskeleton comprises the muscles of the cell as well as its rigid supports. There are three types of rigid element- actin filaments, intermediate filaments, and microtubules. Intermediate filaments are the stable, relatively inert part of the equation, making up structures like keratins that shape our skin, hair, and nails. Actin and microtubules, however are highly dynamic and contribute to amoeboid motion, developmental cell motions, neural extensions, and all kinds of other shape changes cells perform. Microtubules are bigger and stiffer, (25 nm diameter, hundreds of times stiffer than actin filaments), and participate in big, discrete processes like separating the chromosomes at division, and forming the core of cilia that wave from the outside of the cell. 

Actin (6 nm diameter) is more pervasive all over the cell, and is what provides the main motive force of ameboid motions and cell shape change. Indeed, our muscles are mostly composed of great quantities of actin along with interdigitated filaments of its corresponding motor protein (myosin) in orderly, almost crystalline, arrays. Both myosin and actin create motion in two ways- by their own polymerization / depolymerization, and also by way of motors that can move along their lengths.

Images of cells showing fluorescence labeling of skeletal components. Microtubules are shown in green, and DNA in blue. Panel C shows a neuronal growth cone with actin labeled in red. Note how microtubules and actin cooperate, with actin in the lead, pushing out the cell edges by force of its own polymerization. Panel A shows resting cells, with the microtubule organizing center in red. E shows a yeast cell with microtubules spanning its length. G shows a dividing cell at M phase, where microtubules organize the separation of chromosomes, after the microtubule organizing center has itself first divided into two.

A recent paper discussed new tools in the quest to visualize profilin, one of the many accessory proteins involved in managing the cytoskeleton. The most basic role of profilin is to bind to monomers of actin helping them recharge (that is, exchange their ADP for a new ATP). There is a lot of profilin in the cell, and it mostly sits around complexed with actin, preventing it from spontaneously polymerizing. But then if a signal comes in, profilin has binding sites for formin proteins, which tend to be the main instigators of cell shape change and actin polymerization, and can orchestrate the handoff of actin from profilin to growing actin filaments.

The overall actin cycle. Actin monomers are constantly coming on and off of filaments. ATP-charged actin is held in reserve in complex with profilin (dark shapes). Then formins or other accessory proteins can encourage addition to a filament, at one end, called the barbed end. While in filaments, actin gradually hydrolyzes its ATP, forming ADP. Actin with ADP is prone to dissociation, which may be encouraged or discouraged by various other accessory proteins. The resulting actin monomers are then re-bound by profilin and the cycle begins again.

But how can we see all this? Making proteins fluorescent has been now for decades the amazingly effective way to vizualize them. And one can do that either live, or dead. For the latter, the cell is chemically embalmed and permeabilized, then treated with antibodies that bind to the protein(s) of interest. Then a second set of antibodies are applied that bind to the first set, and are labeled with some fluorescent tag, and voila- images of where your protein of interest is, or was. But much more compelling is to see all this in living, working, and moving cells. To do that, the protein of interest is mutated to add an intrinsically fluorescent tag, such as green fluorescent protein. But profilin is so small, and so packed with critical binding sites, that there is little room for a fluorescent tag protein that is, in fact, almost twice as large as profilin itself. 

What to do? These researchers attached a little tail to one end of the protein, off which they then added their tag, in this case a protein called mApple, chosen for its nice red fluorescence spectrum that doesn't interfere with the other greens and blues typically used in these experiments. The paper is mostly then a laborious verification that this new form of profilin fully functions in cells as the wild type does, engages in all the same interactions, (as far as known), and thus consitutes a wonderful new tool for the field.

An atomic structure of profilin bound to actin. Profilin is a very small protein with many important interactions. That makes altering it very tricky. How to create a fluorescent form, or squeeze in some other tag? Profilin binds to actin, to microtubules, to formins and other proteins with PLP (poly-proline) domains, and to phosphoinositide 4,5-bisphosphate (PIP2), which is not even shown here.

It turns out that profilin binds to microtubules as well as to actin. And so do formins. As shown above in the image of a neural growth cone, though the composition of actin and microtubules and their size and other characteristics are very different, they cooperate extensively, thus must have mechanisms of crosstalk. Not much is known, unfortunately, about how this works- while a good bit is known individually how each of the actin and microtubule systems work, how they work together is poorly understood. But one thing these researchers show is that profilin, along with its abundance all over the cell, is also concentrated at the microtubule organizing center. Indeed, some mutations that cause the disease ALS occur right in these regions of profilin that bind microtubules. So something important is going on, and hopefully this new tool will speed work towards greater understanding of how the cytoskeletons operate.

Profilin imaged in a live cell, with other tagged molecules. At left, profilin occurs all over the cell in its role as actin buffer and storage partner. But note a couple of dots on each side. Next is shown the same cell labeled on alpha tubulin, the major component of microtubules. Next is show DNA, which is condensed, as this cell is undergoing division. Last is shown the merged images, with DNA in blue, tubulin in green, and profilin in red/orange. The dots turn out to be the microtubule organizing centers that run the spindle which is orchestrating chromosome segregation.

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Balancing Selection

Human signatures of balancing selection, one form and source of genomic variation.

We generally think of selection as an inexorable force towards greater fitness, eliminating mutations and less fit forms in favor of those more successful. But there is a lot else going on. For one thing, much mutation is meaningless, or "neutral". For another, our lives and traits are so complicated that interactions can lead to hilly adaptive landscapes where many successful solutions exist, rather than just one best solution. One form of adaptive and genetic complexity is balancing selection, which happens when two alleles (i.e. mutants or variants) of one gene have distinct roles in the whole organism or ecological setting, each significant, and thus each is maintained over time. 

A quick example is color in moths. Dark colors work well as camouflage in dirty urban environments, while lighter colors work better in the countryside. Since both conditions exist, and moths move around between them, both color schemes are selected for, resulting in a population that is persistently mixed for this trait. Indeed, the capacity of predators to learn these colors may also lead to an automatic advantage for the less frequent color, another form of balancing selection. Heterozygotes may also have an intrinsic advantage, as is so clearly the case for the sickle cell mutation in hemoglobin, against malaria. These are all classic examples. But to bring it home, a society has only so much capacity for people like Donald Trump. Insofar as sociopathy is genetic, there will necessarily be a frequency-dependent limit, where this trait (and other antisocial traits) may be highly successful at (extremely) low frequency, but terminally destructive at high frequencies.

Schematic selective landscapes. Sometimes selection just optimizes an existing trait by intensifying it (1), or moving it along trait space to a new optimum (2). But other times, multiple forms (i.e. variants, or mutations) of a given locus each have some useful / beneficial characteristic, and may be selected either discretely for particular effects (3), or generally for their diversity (4).

One laborious method to find such sites of balancing selection in a genome is to compare it to genomes of other species. If the same variants exist in each species over long periods of divergence, that argues that such conserved sites of diversity are maintained by balancing selection. Studies of humans and chimpanzees have found some such sites, but not many. But these methods are known to be very conservative, missing out on what is likely to be most cases.

A recent paper offered a slighly more sensitive way to find signs of balancing selection in the human genome, and found quite a lot of them. (Some background here.) It is based, as many investigations of selection are, on a special property of protein-coding genes, due to the degeneracy of the genetic code, that some mutations are "synonymous" and lead to no change in the coded protein, and others are "non-synonymous" and do change the protein. The latter would be assumed to be visible to selection, and sometimes give significant signals of conservation (i.e. low rates of change between species and populations, and few variations maintained in a population). This embedded signal/control pairing of information helps to insulate against many problems in analysis, and can tell us pretty directly how severe selection is on such sites. 

It is worth adding that each basepair in the human genome has its own selective constraints. One position may code for the active site of some enzyme and be extremely well conserved, while the next may be a "synonymous" that has very few or no selective constraints, and another lies in junk DNA that doesn't code for anything or regulate anything, is effectively neutral, and can be changed with no effect. The system is in this sense massively parallel, and able to experience evolution individually at each site concurrently. On the other hand, selection on one site affects the frequencies at nearby sites, since selective "sweeps" through that area of the genome drag the nearby regions of DNA (and whatever variants they may harbor) along, whether positively if the site is increasing in frequency, or negatively if it is deleterious and causing death of its bearers. The reach of this "linkage" effect depends on the recombination frequency, which is relatively low, leading the moderate stability (and linkage) of relatively large "haplotypes" in our genomes.

At any rate, as the methods for detecting selection improve, more selection is detected, which is the lesson of this paper. These authors claim that while their method still significantly under-estimates balancing selection, they find evidnce for the existence of hundreds of sites in humans, when comparing genomes between different geographic regions of the world. A couple hundred of these sites are in the MHC regions- the immunological areas of the genome that code for antibodies and related proteins. These are well-known to be hotspots both for diversity and for the ongoing selective arms race vs pathogens (as we have recently experienced vs Covid). Seeing a lot of balancing selection there makes complete sense, naturally. 

The authors note that their focus on coding regions of the genome, and other technical limitations such as the need to find these sites through population comparisons, argues strongly that their estimate is a severe undercount. Thus one can assume that there will be at least several thousand sites of balanced selection in humans. This is quite apart from the many more sites of ongoing unidirectional selection, mostly purifying against problem mutations, but also towards positive characteristics. An accounting that is only starting to get going, over the vast amounts of variation we harbor. So we live in a dynamic world, inside and out.

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God Save the Queen

Or is it the other way around? Deities and Royalties in the archetypes.

It has been entertaining, and a little moving, to see the recent celebration put on by Britain for its queen. A love fest for a "ruler" who is nearing the end of her service- a job that has been clearly difficult, often thankless, and a bit murky. A job that has evolved interestingly over the last millenium. What used to be a truly powerful rule is now a Disney-fied sop to tradition and the enduring archetypes of social hierarchy.

For we still need social hierarchy, don't we? Communists, socialists, and anarchists have fought for centuries against it, but social hierarchy is difficult to get away from. For one thing, at least half the population has a conservative temperament that demands it. For another, hierarchies are instinctive and pervasive throughout nature as ways to organize societies, keep everyone on their toes, and to bias reproduction to the fittest members. The enlightenment brought us a new vision of human society, one based on some level of equality, with a negotiated and franchise-based meritocracy, rather than one based on nature, tooth, and claw. But we have always been skittish about true democracy. Maximalist democracies like the Occupy movement never get anywhere, because too many people have veto power, and leadership is lacking. Leadership is premised naturally on hierarchy.

Hierarchy is also highly archetypal and instinctive. Maybe these are archetypes we want to fight against, but we have them anyhow. The communists were classic cases of replacing one (presumably corrupt and antiquated) social hierarchy with another which turned out to be even more anxiously vain and vicious, for all its doublespeak about serving the masses. Just looking at higher-ranking individuals is always a pleasant and rewarding experience. That is why movies are made about the high ranking and the glamorous, more than the downtrodden. And why following the royals remains fascinating.

But that is not all! The Queen is also head of the Anglican Church, another institution that has fallen from its glory days of power. It has also suffered defections and loss of faith, amid centuries-long assaults from the enlightenment. The deity itself has gone through a long transition, from classic patriarchial king in the old testament (who killed all humanity once over for its sins), to mystic cypher in the New Testament (who demanded the death of itself in order to save the shockingly persistent sinners of humanity from its own retribution), to deistic non-entity at the height of the enlightenment, to what appears to be the current state of utter oblivion. One of the deity's major functions was to explain the nature of the world in all its wonder and weirdness, which is now quite unnecessary. We must blame ourselves for climate change, not a higher power. 

While social hierarchy remains at the core of humanity, the need for deities is less clear. As a super-king, god has always functioned as the and ultimate pinnacle of the social and political system, sponsoring all the priests, cardinals, kings, pastors, and the like down the line. But if it remains stubbornly hidden from view, has lost its most significant rationales, and only peeps out from tall tales of scripture, that does not make for a functional regent at all. While the British monarchy pursues its somewhat comical, awkward performance of unmerited superintendence of state, church, and social affairs, the artist formerly known as God has vanished into nothing at all.

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Cracking the Kinome Code

Attempts to figure out what causes phosphorylation events in our cells.

Continuing with biological codes, this week's topic is protein phosphorylation sites. Phosphate groups are negatively charged, so they have dramatic electrical field as well as steric effects when attached to a protein. Though many other forms of protein modification are known, phosphorylation is an extremely common route of biological regulation in cells- a way to supplement binding, complex formation, and allosteric interactions between proteins for regulatory purposes. The human genome is estimated to encode 518 protein kinases- that is, proteins that phosphorylate other proteins. Because each one can have hundreds of substrates or targets, this is a lot, gives rise to complex networks of reguation, and is called the "kinome", in analogy with the genome, microbiome, proteome, etc. Kinases are roughly divided between those that target tyrosines (Y) in substrate proteins, and those that target the chemically similar serine and threonine (S/T).

These are images of all proteins from rat neurons, spread out on a 2-dimensional gel, one dimension (vertical) by weight, and the other dimension (horizontal) by isoelectric point. At top, the experiment is stained for overall protein. At bottom, it is labeled for all the phosphotyrosines that exist. That is, all proteins that have been, under these conditions, phosphorylated by the minority of kinases that are tyrosine-targeting. There is clearly a lot going on.

The typical sequence of events is that some upstream signal, such as binding a hormone at the cell membrane, will turn on a kinase, which then phosphorylates a target protein. This will cause the target protein to interact with new partners, perhaps to be degraded, perhaps to be transported to the nucleus, or perhaps to phosphorylate yet other targets in an amplifying cascade of regulatory events. While not as fast as neuronal action, this regulation is typically much faster than the typical gene expression route, where a signal activates transcription of some gene, which is transcribed to mRNA, which is spliced and processed, and eventually translated to make the target proteins. Thus regulation by phosphorylation is critical for all sorts of rapid biological responses, like metabolic tuning and hormonal actions. 

One key problem in the field is mapping exactly what each kinase does, and what kinase is responsible for each phosphorylated target site. Massively parallel methods are now able to identify all the phosphorylated proteins in a cell (after killing it, naturally). But knowing what process and individual kinase was responsible for each of those events... we are much farther away from mastering that level of knowledge.

Similarly to the transcription regulator problem a couple of weeks ago, the sites that kinases act at are characterized by motifs that can be illustrated by a diagram of colored probabilities (below). In this case, the kinase (AKT, one of the most influential in the cell), is a serine/threonine targeting enzyme, so the center of its site must have one of those two amino acids. Then there are a couple of argenines (R) at the minus 3/4/5 positions, a hydrophobic amino acid at +1, and otherwise there are few restrictions. 

A probabilistic view of the AKT kinase target sequence, where this serine/threonine kinase attaches phosphate groups on other proteins that it regulates.

Like in the transcription regulator case, the targeting code is pretty loose and degenerate. That drives researchers to probabalistic methods to wring as much mapping as they can out of current data, which was the topic of a recent paper. The title is "Accurate, high-coverage assignment of in vivo protein kinases to phosphosites from in vitro phosphoproteomic specificity data". But "accurate" is a relative word. The graph below of recall and precision, which are standard terms of art in probability, using reserved portions of the data to test data accuracy, show a maximum of ~65% and 70%, respectively. That means that about 65% of true values are successfully collected from the underlying data, and 70% of the data collected is actually true. That may be best of class, but one wouldn't want to stake one's life, or even one's drug development program, on it.

Measures of accuracy of various methods of guessing what kinase is responsible for a given phosphorylated target site. Precision and recall are developed vs reserved (non-training) test data. The current author's method is IV-KAPhE in yellow.

This researcher set up an extensive pipeline to add together numerous sources of information. First is PhosphoSite, a database of kinase target sites and other interactions gathered both by hand from the literature and from private mass-scale data sources. Then he added co-expression data, which can hint that a kinase and target are present in the same cell, and thus candidates for interaction. Then came semantic data from general gene classifications, which can hint that a kinase and target work in the same process, and thus again likely to act in concert. A few additional databases, and he could, in classic Bayesian fashion, assemble a new resource that outperforms any of the individual ones in predicting what kinase is responsible for any given phosphoprotein that one has dug up in some mass-spec experiment. All that said, the method only covers kinases about which something is known, which currently runs to 349 kinases, well short of the total number mentioned above. So both in coverage and accuracy, we have a great deal to learn.

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