We Have Rocks in Our Heads ... And Everywhere Else, Too

On the evolution and role of iron-sulfur complexes.

Some of the more persuasive ideas about the origin of life have it beginning in the rocks of hydrothermal vents. Here was a place with plenty of energy, interesting chemistry, and proto-cellular structures available to host it. Some kind of metabolism would by this theory have come first, followed by other critical elements like membranes and RNA coding/catalysis. This early earth lacked oxygen, so iron was easily available, not prone to oxidation as now. Thus life at this early time used many minerals in its metabolic processes, as they were available for free. Now, on today's earth, they are not so free, and we have complex processes to acquire and manage them. One of the major minerals we use is the iron-sulfur complex, (similar to pyrite), which comes in a variety of forms and is used by innumerable enzymes in our cells. 

The more common iron-sulfur complexes, with sulfur in yellow, iron in orange.

The principle virtue of the iron-sulfur complex is its redox flexibility. With the relatively electronically "soft" sulfur, iron forms semi-covalent-style bonds, while being able to absorb or give up an electron safely, without destroying nearby chemicals as iron alone typically does. Depending on the structure and liganding, the voltage potential of such complexes can be tuned all over the (reduction potential) map, from -600 to +400 mV. Many other cofactors and metals are used in redox reactions, but iron-sulfur is the most common by far.

Reduction potentials (ability to take up an electron, given an electrical push) of various iron-sulfur complexes.

Researchers had assumed that, given the abundance of these elements, iron-sulfur complexes were essentially freely acquired until the great oxidation event, about two to three billion years ago, when free oxygen started rising and free iron (and sulfur) disappeared, salted away into vast geological deposits. Life faced a dilemma- how to reliably construct minerals that were now getting scarce. The most common solution was a three enzyme system in mitochondria that 1) strips a sulfur from the amino acid cysteine, a convenient source inside cells, 2) scaffolds the construction of the iron-sulfur complex, with iron coming from carrier proteins such as frataxin, and 3) employs several carrier proteins to transfer the resulting complexes to enzymes that need them. 

But a recent paper described work that alters this story, finding archaeal microbes that live anaerobically and make do with only the second of these enzymes. A deep phylogenetic analysis shows that the (#2) assembly/scaffold enzymes are the core of this process, and have existed since the last common ancestor of all life. So they are incredibly ancient, and it turns out to that iron-sulfur complexes can not just be gobbled up from the environment, at least not by any reasonably advanced life form. Rather, these complexes need to be built and managed under the care of an enzyme.

The presented structures of the dimer of SmsB (orange) and SmsC (blue) that dimerize again to make up a full iron-sulfur scaffolding and production enzyme in the archaean Methanocaldococcus jannaschii. Note the reaction scheme where ATP comes in and evicts the iron-sulfur cluster. On right is shown how ATP fits into the structure, and how it nudges the iron-sulfur binding area (blue vs green tracing).

A recent paper from this group extended their analysis to the structure of the assembly/scaffold enzyme. They find that, though it is a symmetrical dimer of a complex of two proteins, it only deals with one iron-sulfur complex at at time. It also binds and cleaves ATP. But ATP seems to have more of an inhibitory role than one that stimulates assembly directly. The authors suggest that high levels of ATP signal that less iron-sulfur complex is needed to sustain the core electron transport chains of metabolism, making this ATP inhibition an allosteric feedback control mechanism in these archaeal cells. I might add, however, that ATP binding may well also have a role in extricating the assembled iron-sulfur cluster from the enzyme, as that complex is quite well coordinated, and could use a push to pop out into the waiting arms of target enzymes.

"These ancestral systems were kept in archaea whereas they went through stepwise complexification in bacteria to incorporate additional functions for higher Fe-S cluster synthesis efficiency leading to SUF, ISC and NIF." - That is, the three-component systems present in eukaryotes, which come in three types.

In the author's structure, the iron-sulfur complex, liganded by three cysteines within the SmsC protein. But note how, facing the viewer, the complex is quite exposed, ready to be taken up by some other enzyme that has a nice empty spot for it.

Additionally, these archaea, with this simple one-step iron cluster formation pathway, get their sulfur not from cysteine, but from ambient elemental sulfur. Which is possible, as they live only in anaerobic environments, such as deep sea hydrothermal vents. So they represent a primitive condition for the whole system as may have occurred in the last common ancestor of all life. This ancestor is located at the split between bacteria and archaea, so was a fully fledged and advanced cell, far beyond the earlier glimmers of abiogenesis, the iron sulfur world, and the RNA world.

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How do Anesthetics Work?

Ask a simple question, and get an answer that gets weirder the deeper you dig.

Anesthesia is wonderful. Quite simply, it makes bearable, even unnoticeable, what would be impossible or excruciating. It is also one of the most mysterious phenomena short of consciousness itself. All animals can be anesthetized, from bacteria on up. All sorts of chemicals can be used, from xenon to simple ethers, to complex fluoride-substituted forever chemicals, all with similar effects. Yet there are also complex sub-branches of anesthesia, like pain relief, muscular immobilization, shutdown of consciousness, and amnesia against remembering what happened, that chemicals affect differentially. It resembles sleep, and shares some circuitry with that process, but is of course is induced quite differently.

The first red herring was the Meyer–Overton rule, established back in 1899, that showed that anesthetic potency correlates closely with the lipophilicity of the chemical, from nitrogen (not very good) to xenon (pretty good) to chloroform (very good). All the forever (heavily fluorinated) chemicals used as modern anesthetics, like isoflurane and sevoflurane, have extremely high lipophilicity. This suggested that the mechanism of action was simply mixing into membranes somehow, altering their structure, and thus neuronal action.. something along that line. 

Structures of several general anesthetics. 8 is isoflurane.

But when researchers looked more closely there were some chemical differences that did not track with this hypothesis. Chiral enantiomers behaved differently in some cases, indicating that these chemicals do bind to something (that is, a protein) specifically. Also, variant genes started cropping up that conferred resistance to anesthesia or were found to bind particular anesthetics at working concentrations. Also, more complex, injectable anesthetics like fentanyl and propofol have slightly more defined targets and modes of action. So while anesthetics clearly partition to membranes, and the binding sites are often at protein-membrane interfaces, the modern theory of how they work is that they bind to ion channels and neurotransmitter receptors and affect their functions. Proteins generally have hydrophobic interiors, so the lipophilicity of these chemicals may track with binding / disrupting protein interiors as much as membrane interiors. And other proteins such as microtubules have been drawn into the discussion as well (leading indirectly to some very unfortunate theories about consciousness). 

But which key protein do they bind? Here again, mysteries abound, as they do not bind just one, but many. And not just that, they turn some of their targets on, others off. One target is the GABA receptor, which characterizes the major inhibitory neurons of the central nervous system. These are turned on. At high concentrations, anesthetics can even turn these receptors on without any GABA neurotransmitter present. Another is the NMDA receptor, which is the target of opioids, and of ketamine. These receptors are turned off. So, for some reason, still somewhat obscure, the net result of many specific bindings to an array of channels by an array of chemicals results in ... anesthesia.

A recent paper raised my interest in this area, as its authors demonstrated yet another target for inhaled anesthetics like isoflurane, and dove with exquisite detail into its mechanism. They were working on the ryanodine receptor, which isn't even a cell surface protein, but sits in the endoplasmic reticulum (or sarcoplasmic reticulum in muscles) and conducts calcium out of these organelles. This receptor is huge- the largest known- coding over five thousand amino acids (RYR1 of humans), due to numerous built-in regulatory structures. For example, it is sensitive to caffeine, but in a different location than where it is sensitive to isoflurane. Calcium is a very important signal within cells, key to muscle activity, and also to neuronal activation. The endoplasmic reticulum serves as a storehouse of calcium, from which signals can be sparked as needed by outside signals, including a spike in calcium itself (thus creating a positive feedback loop). These receptors (a family of three in human) are named for an obscure chemical (indeed a poison) that activates these channels, and all three are expressed in the brain. 

The authors were led to this receptor because mutations were known to cause malignant hyperthermia, a side effect of a few of the common anesthesia drugs where body temperature rises uncontrollably, driven from muscle tissue, where ryanodine receptors in the sarcoplasmic reticulum are particularly common and heavily used to regulate muscle activity and metabolism. That suggested that anesthetics such as isoflurane might bind to this receptor directly, turning it on. That was indeed the case. They started with cultured cells expressing each receptor family member in turn, and tested each receptor's response to isoflurane. Internal (cytoplasmic) calcium rose especially with the family member RYR1. That led to various control experiments and a hunt (by mutating and doctoring the RYR1 protein) for the particular region being bound by the anesthetic. After a lengthy search, they found residue 4000 was a critical one, as a mutation from methionine to phenylalanine reduced the isoflurane response about ten-fold. This is part of a binding pocket as shown below.

Structure of isoflurane, (B), bound to the RYR1 protein pocket. This is a pocket that happens to also bind another activator of this channel, 4-CMC. A layout of the whole active binding pocket is given on the right. At bottom are calcium channel responses of the wild-type and point mutant forms of RYR1, showing the dramatic effect these single site mutations have on isoflurane response.

Fine, but what about anesthesia? The next step was to test this mutation in whole mice, where, lo and behold, isoflurane anesthesia of otherwise normal mice was made slightly more difficult by this mutant form of RYR1. Additionally, these mice had no other observable problems- not in behavior, not in sleep. That is remarkable as a finding about anesthesia, but the effect was quite small- about 10% or so shift in the needed concentration of isoflurane. They go on to mention that this is similar in scale to knockouts or mutations in other known targets of anesthetic drugs:

• 10% shift in the curve from the M4000F mutation of RYR1
• 14% shift in the isoflurane curve from a mutation in GABA receptor, GABAAR.
• 5% shift in the isoflurane curve (though a 20% or more shift for halothane) for mutations in KCNK9, a potassium channel.

What this is telling us is that there are many targets for anesthetic drugs. They are spread over many neurotransmitter and physiological systems. They each contribute modestly (and variably, depending on the drug) to the net effect of any one drug. The various affected channels and membrane receptors curiously combine to achieve anesthesia across all animals and even microorganisms, which naturally also rely on channels and transmembrane receptors for their various sensing and motion activation needs. We are left with a blended hypothesis where yes, there are specific protein targets for each anesthetic that mediate their action. On the other hand, these targets are far from unique, spread across many proteins, yet are also highly conserved, looking almost like they are implicit in the nature of transmembrane proteins in general. 

One gets the distinct impression that there should be endogenous equivalents, as there are for opioids and cannabinoids- some internal molecule that provides sedation when needed, such as for deep illness or end-of-life crisis. That molecule has not yet been found, but the natural world abounds in sedatives, (alcohol is certainly one), so the logic of anesthesia becomes one of biological and evolutionary logic, as much as one of chemical mechanism.

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The Fire Inside: Eukaryotic Locomotion

The GTP-based Rho/Rac system of actin regulation runs in unseen waves of activation.

One of the amazing capabilities of eukaryotic cells, inherited in part from their archaeal parents, is free movement and phagocytosis. These cells have an internal cytoskeleton, plus methods to anchor to a substrate, (via focal adhesions), which allows them to manipulate their membrane, their shape, and their locomotion. The cytoskeleton is composed of two main types of fibers, actin and microtubules. Microtubules are much larger than actin and organize major trackways of organelle movement around the cell (including the movement of chromosomes in mitosis), and also form the core of cilia and flagella. But it is actin that does most of the work of moving cells around, with dynamic networks that generate the forces behind spiky to ruffly protrusions, that power things like the adventuresome pathfinding of neurons as they extend their axons into distant locations.

Schematic of the actin cytoskeleton of a typical eukaryotic cell.

Actin is an ATPase all by itself. ATP promotes its stability, and also its polymerization into filaments. So, cell edges can grow just by adding actin to filament ends. Actin cross-linking proteins also exist, that create the meshwork that supports extended filopodia. But obviously, actin all by itself is not a regulated solution to cell movement. There is an ornately complex system of control, not nearly understood, that revolves around GTPase and binding proteins. These proteins (mainly RhoA, Rac1, and Cdc42, though there are twenty related family members in humans) have knife-edge regulation, being on when binding GTP, and off after they cleave off the phosphate and are left binding GDP (the typical, default, state). Yet other proteins regulate these regulators- GTPase exchange factors (GEFs) encourage release of GDP and binding of GTP, while GTPase activating proteins (GAPs) encourage the cleavage of GTP to GDP. The GTP binding proteins interact (depending on their GTP status) with a variety of effector proteins. One example is a family of formins, which chaperone the polymerization of actin. At the head of the pathway, signals coming from external or internal conditions regulate the GTPases, creating (in extremely simplified terms) a pathway that gets the cell to respond by moving toward things it wants, and away from things it does not want. 

This is a very brief post, just touching on one experiment done on this system. Exploring its full complexity is way beyond my current expertise, though we may return to aspects of this fascinating biological pathway periodically in the future. An important paper in the field hooked up fluorescent dyes to one of the effector protein domains that binds only GTP/active RhoA. They tethered this to the (inside) membrane of their cultured cells, and took movies of what the cell looked like, using a microscopy method that looks at very thin sections- only the membrane, essentially not the rest of the cell. RhoA, though graced with a small lipid tail, is typically cytoplasmic when inactive, and travels to the membrane when activated. They were shocked to find that in resting cells, without much locomotion going on, there were recurring waves of activation of RhoA that swept hither and yon across the cell membranes. 

Four examples of RhoA getting bound in its active state in a wave-like way, over 7 1/2 minutes in a resting cell. GEF-H1 is ARHGEF2, one of the regulators that can turn RhoA on. The first three panels have ARHGEF2 versions that are operational, but the fourth (bottom right) is of a cell with an anti-RNA to ARHGEF2, turning its expression level down. In this cell, the waves of RhoA activation and recruitment to the membrane are substantially dampened.

These pulses were made even more intense if the cells were treated with nocodazole, which disrupts microtubules, destabilizes the cytoskeleton, and makes the actin regulatory / structure system work harder. They found that myosin (the motor protein that moves cargoes over actin filaments) was also rapidly relocalized, mirroring some of what happened with RhoA. They also found that ARHGEF2 contained two RhoA binding domains, (one binding active RhoA, one binding inactive RhoA), enabling it to feedback-amplify the positive activation of RhoA, thereby explaining some of the extremely dynamic activity seen here. 

And they also found that the arrival of negative regulators such as ARHGAP35 was delayed by a couple of seconds vs the activation of RhoA, providing the time window needed to see wave formation out of a mechanism of positive feedback followed by squelching by a negative regulator. Lastly, they found that these dynamics were significantly different if the cells were grown on stiffer vs softer substrates. Stiffer substrates allowed the formation of stronger surface attachments, concentrating RhoA and myosin at these adhesion locations. 

These researchers are clearly only scratching the surface of this system, as there are endless complexities left to investigate. The upshot of this one set of observations is that neurons are not the only excitable cells. With a bit of molecular / experimental magic, heretofore unseen intracellular dynamics can be visualized to show that eukaryotic cells have an exquisitely regulated internal excitation system that is part of what drives their shape-shifting capabilities, including processes like phagocytosis and neuronal growth / path-finding. 

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Tiny Tunings in a Buzz of Neural Activity

Lots of what neurons do adds up to zero: how the cerebellum controls muscle movement.

Our brains are always active. Meditating, relaxing, sleeping ... whatever we are up to, the brain doesn't take a holiday, except in the deepest levels of sleep, when slow waves help the brain to reset and heal itself. Otherwise, it is always going, with only slight variations based on computational needs and transient coalitions, which are extremely difficult to pick out of the background (fMRI signals are typically under 3% of the noise). That leads to the general principle that action in a productive sense is merely a tuned overlay on top of a baseline of constant activity, through most of the brain.

A recent paper discussed how this property manifests in the cerebellum, the "little" brain attached to our big brain, which is a fine-tuning engine especially for motion control, but also many other functions. The cerebellum has relatively simple and massively parallelized circuitry, a bit like a GPU to the neocortex's CPU. It gets inputs from the places like the the spinal cord and sensory areas of the brain, and relays a tuned signal out to, in the case of motor control, the premotor cortex. The current authors focused on the control of eye movement ("saccades") which is a well characterized system and experimentally tractable, in marmoset monkeys. 

After poking a massive array of electrodes into their poor monkey's brains, they recorded from hundreds of cells, including all the relevant neuron types (of which there are only about six). They found that inside the cerebellum, and inside the region they already knew is devoted to eye movement, neurons form small-world groups that interact closely with each other, revealing a new level of organization for this organ.

More significantly, they were able to figure out the central tendency or vector for Purkinje (P) cells they ran across. These are the core cells of the cerebellar circuit, so their firing should correlate in principle with the eye movements that they were simultaneously tracking in these monkeys. So one cell might be an upward directing cell, while another one might be leftward, and so forth. They also found that P cells come in two types- those (bursters) that fire positively around the preferred direction, and others (pausers) that fire all the other times, but fire less when eyes are heading in the "preferred" direction. These properties are already telling us that the neural system does not much care to save energy. Firing most of the time, but then pausing when some preferred direction is hit is perfectly OK. 

Two representative cells are recorded, around the cardinal/radial directions. The first of each pair is recorded from 90 degrees vs its preferred direction, and adding up the two perpendicular vectors (bottom) gives zero net activity. The second of each pair is recorded from the preferred direction (or potent axis), vs 180 degrees away, and when these are subtracted, a net signal is visible (difference). Theta is the direction the eyes are heading.

Their main finding, though, was that there is a lot of vector arithmetic going on, in which cells fire all over their preferred fields, and only when you carefully net their activity over the radial directions can you discern a preferred direction. The figure above shows a couple of cells, firing away no matter which direction the eyes are going. But if you subtract the forward direction from the backward direction, a small net signal is left over, which these authors claim is the real signal from the cerebellum, which signals a change in direction. When this behavior is summed across a large population, (below), the bursters and pausers cancel out, as do the signals going in stray directions. All you are left with is a relatively clean signal centered on the direction being instructed to the eye muscles. Wasteful? Yes. Effective? Apparently. 

The same analysis as above, but on a population basis. Now, in net terms, after all the canceled activity is eliminated, and adding up the pauser and burster activity, strong signals arise from the cerebellum as a whole, in this anatomical region, directing eye saccades

Why does the system work this way? One idea is that, like for the rest of the brain, default activity is the norm, and learning is, perforce, a matter of tuning ongoing activity, not of turning on cells that are otherwise off. The learning (or error) signal is part of the standard cerebellum circuitry- a signal so strong that it temporarily shuts off the normal chatter of the P cell output and retunes it slightly, rendering the subsequent changes seen in the net vector calculation.

A second issue raised by these authors is the nature of inputs to these calculations. In order to know whether and how to change the direction of eye movement, these cells must be getting information about the visual scene and also about the current state of the eyes and direction of movement. 

"The burst-pause pattern in the P cells implied a computation associated with predicting when to stop the saccade. For this to be possible, this region of the cerebellum should receive two kinds of information—a copy of the motor commands in muscle coordinates and a copy of the goal location in visual coordinates."

These inputs come from two types of mossy fiber neurons, which are the single inputs to the typical cerebellar circuit. One "state" type encodes the state of the motor system and current position of the eye. The other "goal" type encodes, only in case of a reward, where the eye "wants" to move, based on other cortical computations, such as the attention system. The two inputs go through separate individual cerebellar circuits, and then get added up on a population basis. When movement is unmotivated, the goal inputs are silent, and resulting cerebellar-directed saccades are more sporadic, scanning the scene haphazardly.

The end result is that we are delving ever more deeply into the details of mental computation. This paper trumpets itself as having revealed "vector calculus" in the cerebellum. However, to me it looks much more like arithmetic than calculus, and nor is the overall finding of small signals among a welter of noise and default activity novel either. All the same, more detail, and deeper technical means, and greater understanding of exactly how all these billions of dumb cells somehow add up to smart activity continues to be a great, and fascinating, quest.

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Lipid Pumps and Fatty Shields- Asymmetry in the Plasma Membrane

The two faces constituting eukaryotic cell membranes are asymmetric.

Membranes are one of those incredibly elegant things in biology. Simple chemicals forces are harnessed to create a stable envelope for the cell, with no need to encode the structure in complicated ways. Rather, it self-assembles, using the oil-vs-water forces of surface tension to form a huge structure with virtually no instruction. Eukaryotes decided to take membranes to the max, growing huge cells with an army of internal membrane-bound organelles, individually managed- each with its own life cycle and purposes.

Yet, there are complexities. How do proteins get into this membrane? How do they orient themselves? Does it need to be buttressed against rough physical insult, with some kind of outer wall? How do nutrients get across, while the internal chemistry is maintained as different from the outside? How does it choose which other cells to interact with, preventing fusion with some, but pursuing fusion with others? For all the simplicity of the basic structure, the early history of life had to come up with a lot of solutions to tough problems, before membrane management became such a snap that eukaryotes became possible.

The authors present their model (in atomic simulation) of a plasma membrane. Constituents are cholesterol, sphingomyelin (SM), phosphatidyl choline (PC) phosphatidyl serine (PS), phosphatidyl ethanolamine (PE), and phosphotidyl ethanolamine plasmalogen. Note how in this portrayal, there is far more cholesterol in the outer leaflet (top), facing the outside world, than there is in the inner leaflet (bottom).

The major constituents of the lipid bilayer are cholesterol, phospholipids, and sphingomyelin. The latter two have charged head groups and long lipid (fatty) tails. The head groups keep that side of the molecule (and the bilayer) facing water. The tails hate water and like to arrange themselves in the facing sheets that make up the inner part of the bilayer. Cholesterol, on the other hand, has only a mildly polar hydroxyl group at one end, and a very hydrophobic, stiff, and flat multi-ring body, which keeps strictly with the lipid tails. The lack of a charged head group means that cholesterol can easily flip between the bilayer leaflets- something that the other molecules with charged headgroups find very difficult. It has long been known that our genomes code for flippases and floppases: ATP-driven enzymes that can flip the charged phospholipids and sphingomyelin from one leaflet to the other. Why these enzymes exist, however, has been a conundrum.

Pumps that drive phospholipids against their natural equilibrium distribution, into one or the other leaflet.

It is not immediately apparent why it would be helpful to give up the natural symmetry and fluidity of the natural bilayer, and invest a lot of energy in keeping the compositions of each leaflet different. But that is the way it is. The outer leaflet of the plasma membrane tends to have more sphingomyelin and cholesterol, and the inner leaflet has more phospholipids. Additionally, those phospholipids tend to have unsaturated tails- that is, they have double bonds that break up the straight fatty tails that are typical in sphingomyelin. Membrane asymmetry has a variety of biological effects, especially when it is missing. Cells that lose their asymmetry are marked for cell suicide, intervention of the immune system, and also trigger coagulation in the blood. It is a signal that they have broken open or died. But these are doubtless later (maybe convenient) organismal consequences of universal membrane asymmetry. They do not explain its origin. 

A recent paper delved into the question of how and why this asymmetry happens, particularly in regard to cholesterol. Whether cholesterol even is asymmetric is controversial in the field, since measuring their location is very difficult. Yet these authors carefully show that, by direct measurement, and also by computer simulation, cholesterol, which makes up roughly forty percent of the membrane (its most significant single constituent, actually), is highly asymmetric in human erythrocyte membranes- about three fold more abundant in the outer leaflet than in the cytoplasmic leaflet. 

Cholesterol migrates to the more saturated leaflet. B shows a simulation where a poly-unstaturated (DAPC) phospholipid with 4 double bonds (blue) is contrasted with a saturated phospholipid (DPPC) with staight lipid tails (orange). In this simulation, cholesterol naturally migrates to the DPPC side as more DAPC is loaded, relieving the tension (and extra space) on the inner leaflet. Panel D shows that in real cells, scrambling the leaflet composition leads to greater cholesterol migration to the inner leaflet. This is a complex experiment, where the fluorescent signal (on the right-side graph) comes from a dye in an introduced cholesterol analog, which is FRET-quenched by a second dye that the experimenters introduced which is confined to the outer membrane. In the natural case (purple), signal is more quenched, since more cholesterol is in the outer leaflet, while after phospholipid scrambling, less quenching of the cholesterol signal is seen. Scrambling is verified (left side) by fluorescently marking the erythrocytes for Annexin 5, which binds to phosphatidylcholine, which is generally restricted to the inner leaflet. 

But no cholesterol flippase is known. Indeed, such a thing would be futile, since cholesterol equilibrates between the leaflets so rapidly. (The rate is estimated at milliseconds, in very rough terms.) So what is going on? These authors argue via experiment and chemical simulation that it is the pumped phospholipids that drive the other asymmetries. It is the straight lipid tails of sphingomyelin that attract the cholesterol, as a much more congenial environment than the broken/bent tails of the other phospholipids that are concentrated in the cytoplasmic leaflet. In turn, the cholesterol also facilitates the extreme phospholipid asymmetry. The authors show that without the extra cholesterol in the outer leaflet, bilayers of that extreme phospholipid composition break down into lipid globs.

When treated (time course) with a chemical that scrambles the plasma membrane leaflet lipid compositions, a test protein (top series) that normally (0 minutes) attaches to the inner leaflet floats off and distributes all over the cell. The bottom series shows binding of a protein (from outside these cells) that only binds phosphatidylcholine, showing that scrambling is taking place.

This sets up the major compositional asymmetry between the leaflets that creates marked differences in their properties. For example, the outer leaflet, due to the straight sphingomyelin tails and the cholesterol, is much stiffer, and packed much tighter, than the cytoplasmic leaflet. It forms a kind of shield against the outside world, which goes some way to explain the whole phenomenon. It is also almost twice as impermeable to water. Conversely, the cytoplasmic leaflet is more loosely packed, and indeed frequently suffers gaps (or defects) in its lipid integrity. This has significant consequences because many cellular proteins, especially those involved in signaling from the surface into the rest of the cytoplasm, have small lipid tails or similar anchors that direct them (temporarily) to the plasma membrane. The authors show that such proteins localize to the inner leaflet precisely because that leaflet has this loose, accepting structure, and are bound less well if the leaflets are scrambled / homogenized.

When the fluid mosaic model of biological membranes was first conceived, it didn't enter into anyone's head that the two leaflets could be so different, or that cells would have an interest in making them so. Sure, proteins in those membranes are rigorously oriented, so that they point in the right direction. But the lipids themselves? What for? Well, they do and now there are some glimmerings of reasons why. Whether this has implications for human disease and health is unknown, but just as a matter of understanding biology, it is deeply interesting.

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Man is Wolf to Man

The current administration's predatory and corrupt version of capitalism.

What is corruption? Isn't capitalism all about getting as much money as you can? Then doesn't it follow that there can be no such thing as corruption, which is defined as going against the rules? What rules?

We as a country go on a trip with every new president, learning about their nature and values as we accompany them through their brief span of history. Few presidents wear very well after their honeymoon, since the process of getting elected requires some shading of the truth, truth that inevitably comes out later on. The current administration is an odd example, since in his first term, Trump was not allowed (for very good reasons!) to be himself. The second time round has been a different story, and we are getting a deep look at his character. 

The US has always had a double relationship with capitalism, tilting between rampant competition / exploitation and reverence for rules and legal systems. Slavery, obviously, is the foremost example, with slaveholders enshrining in a document dedicated to human freedom their own legal rights to property in comprehensively oppressed people. The founders, on making their constitution, feverishly set to work creating institutions for the common good, such as the treasury, mail system, judicial system, patents, and military. But, at the same time, we have long had an ideology of free enterprise- of land, resource, and human exploitation almost without limit. 

Charles Ponzi was not working in Italy, after all, but in the US, as was Bernie Madoff. Now crypto is the popular mechanism of picking people's pockets, facilitating mundane crime such as money laundering and ransomware attacks at the same time that it provides flourishing vistas of direct fraud, in rug pulls, hacks, and market manipulation. A recent article reviewed the pathetic world of multilevel marketing, another model of predation where ambitious entrepreneurs are sucked into schemes that are engineered both to fail, and to induce the victims to blame themselves.

The administration has clearly made it its mission to celebrate these forms of business- the predators, the grifters, the destructive businessmen among us who think that taxes are for little people, and rules for someone else. Consumer protection agencies have been shuttered, the IRS eviscerated, investigations cancelled. Pardons have been going out, not only to the January 6th conspirators and their militias, but to the money launderers, the corrupt politicians, and crypto bros. It is a sustained campaign of norm and rule-breaking by a grossly tasteless, shockingly greedy and small-minded president, (and sexual predator), who cannot conceive of rational policy, uncorrupted institutions, or fairness, much less civility, as a principle. A person with deep psychological problems. And thus, is incapable of long-term policy that is the bedrock of durable, functional institutions, either commercial or governmental.

Following gold, like a cat following a laser pointer.

We are all worried about fascism, as that seems to be the aesthetic and the model of power the administration is tending towards. But what they have done so far doesn't even come up to the level of fascism, really. The president is not smart enough to have a coherent policy or ideological platform. The weave does not leave room for a program that would be attractive beyond the nihilistic base. There are inclinations, and moods, and tantrums, love for Putin, and a lot of nostalgia for policies of decades, if not centuries, ago. There is hate. But without a program that binds all these ingredients into even a marginally coherent approach to the future, it will inevitably fall apart. True believers don't do policies or reason- conspiracy theories are enough. Thinking, apparently, is for libtards. 

The fact of the matter is that capitalism is not equivalent to the law of the jungle. A legal system, and rules, are required to prevent capitalists from making military forays into each other's empires, and to prevent the workers from taking up their pitchforks, among much else. It is founded on the limited liability company, itself a legal construct, not to mention all the financial, educational, and physical infrastructure that forms its essential background. There is no going Galt here. Indeed, the whole point of captialism is not to screw everyone and make a few people very rich. Rather, it is to diffuse labor, useful products and productive technologies across society in a way that utilizes everyone's talents and supplies everyone's needs.  The point is general prosperity, not inequality. 

Institutions are built on rules, and they can die two ways- either people disregard and lose faith in the rules, (maybe because they are made by corrupt processes), or the rules become so elaborate and sclerotic that the point of the institution is lost. These ways map roughly onto our political divide, which, when it compromises to the middle produces something akin to a functional mean. But the current administration, and its ideology of thorough-going corruption on personal, business, and governmental levels, is, with the connivance of an equally unhinged supreme court, creating a legacy of cruelty and destruction that is surely a sad way to mark next year's anniversary of our institutional founding.

• How the wingnut evangelicals, rightist Catholics, and their funders have bought into the burn-it-all-down program of predation, with a little help from the Russians.
• Being populist means lying, unfortunately.
• We can and should give real help to Ukraine. How about blockading Russian rather than Venezuelan tankers?
• A hiring hellscape, with AI battling on both sides.
• Destroying science, at the NIH.

Mutations That Make Us Human

The ongoing quest to make biologic sense of genomic regions that differentiate us from other apes.

Some people are still, at this late date, taken aback by the fact that we are animals, biologically hardly more than cousins to fellow apes like the chimpanzee, and descendants through billions of years of other life forms far more humble. It has taken a lot of suffering and drama to get to where we are today. But what are those specific genetic endowments that make us different from the other apes? That, like much of genetics and genetic variation, is a tough question to answer.

At the DNA level, we are roughly one percent different from chimpanzees. A recent sequencing of great apes provided a gross overview of these differences. There are inversions, and larger changes in junk DNA that can look like bigger differences, but these have little biological importance, and are not counted in the sequence difference. A difference of one percent is really quite large. For a three gigabyte genome, that works out to 30 million differences. That is plenty of room for big things to happen.

Gross alignment of one chromosome between the great apes. [HSA- human, PTR- chimpanzee, PPA- bonobo, GGO- gorilla, PPY- orangutan (Borneo), PAB- orangutan (Sumatra)]. Fully aligned regions (not showing smaller single nucleotide differences) are shown in blue. Large inversions of DNA order are shown in yellow. Other junk DNA gains and losses are shown in red, pink, purple. One large-scale jump of a DNA segment is show in green. One can see that there has been significant rearrangement of genomes along the way, even as most of this chromosome (and others as well) are easly alignable and traceable through the evolutionary tree.

But most of those differences are totally unimportant. Mutations happen all the time, and most have no effect, since most positions (particularly the most variable ones) in our DNA are junk, like transposons, heterochromatin, telomeres, centromeres, introns, intergenic space, etc. Even in protein-coding genes, a third of the positions are "synonymous", with no effect on the coded amino acid, and even when an amino acid is changed, that protein's function is frequently unaffected. The next biggest group of mutations have bad effects, and are selected against. These make up the tragic pool of genetic syndromes and diseases, from mild to severe. Only a tiny proportion of mutations will have been beneficial at any point in this story. But those mutations have tremendous power. They can drag along their local DNA regions as they are positively selected, and gain "fixation" in the genome, which is to say, they are sufficiently beneficial to their hosts that they outcompete all others, with the ultimate result that mutation becomes universal in the population- the new standard. This process happens in parallel, across all positions of the genome, all at the same time. So a process that seems painfully slow can actually add up to quite a bit of change over evolutionary time, as we see.

So the hunt was on to find "human accelerated regions" (HAR), which are parts of our genome that were conserved in other apes, but suddenly changed on the way to humans. There roughly three thousand such regions, but figuring out what they might be doing is quite difficult, and there is a long tail from strong to weak effects. There are two general rationales for their occurrence. First, selection was lost over a genomic region, if that function became unimportant. That would allow faster mutation and divergence from the progenitors. Or second, some novel beneficial mutation happened there, bringing it under positive selection and to fixation. Some recent work found, interestingly, that clusters of mutations in HAR segments often have countervailing effects, with one major mutation causing one change, and a few other mutations (vs the ancestral sequence) causing opposite changes, in a process hypothesized to amount to evolutionary fine tuning. 

A second property of HARs is that they are overwhelmingly not in coding regions of the genome, but in regulatory areas. They constitute fine tuning adjustments of timing and amount of gene regulation, not so much changes in the proteins produced. That is, our evolution was more about subtle changes in management of processes than of the processes themselves. A recent paper delved in detail into HAR5, one of the strongest such regions, (that is, strongest prior conservation, compared with changes in human sequence), which lies in the regulatory regions upstream of Frizzled8 (FZD8). FZD8 is a cell surface receptor, which receives signals from a class of signaling molecules called WNT (wingless and int). These molecules were originally discovered in flies, where they signal body development programs, allowing cells to know where they are and when they are in the developmental program, in relation to cells next door, and then to grow or migrate as needed. They have central roles in embryonic development, in organ development, and also in cancer, where their function is misused.

For our story, the WNT/FZD8 circuit is important in fetal brain development. Our brains undergo massive cell division and migration during fetal development, and clearly this is one of the most momentous and interesting differences between ourselves and all other animals. The current authors made mutations in mice that reproduce some of the HAR5 sequences, and investigated their effects. 

Two mouse brains at three months of age, one with the human version of the HAR5 region. Hard to see here, but the latter brain is ~7% bigger.

The authors claim that these brains, one with native mouse sequence, and the other with the human sequences from HAR5, have about a seven percent difference in mass. Thus the HAR5 region, all by itself, explains about one fourteenth of the gross difference in brain size between us and chimpanzees. 

HAR5 is a 619 base-pair region with only four sequence differences between ourselves and chimpanzees. It lies 300,000 bases upstream of FZD8, in a vast region of over a million base pairs with no genes. While this region contains many regulatory elements, (generally called enhancers or enhancer modules, only some of which are mapped), it is at the same time an example of junk DNA, where most of the individual positions in this vast sea of DNA are likely of little significance. The multifarious regulation by all these modules is of course important because this receptor participates in so many different developmental programs, and has doubtless been fine-tuned over the millennia not just for brain development, but for every location and time point where it is needed.

Location of the FZD8 gene, in the standard view of the genome at NIH. I have added an arrow that points to the tiny (in relative terms) FZD8 coding region (green), and a star at the location of HAR5, far upstream among a multitude of enhancer sequences. One can see that this upstream region is a vast area (of roughly 1.5 million bases) with no other genes in sight, providing space for extremely complicated and detailed regulation, little of which is as yet characterized.

Diving into the HAR5 functions in more detail, the authors show that it directly increases FZD8 gene expression, (about 2 fold, in very rough terms), while deleting the region from mice strongly decreases expression in mice. Of the four individual base changes in the HAR5 region, two have strong (additive) effects increasing FZD8 expression, while the other two have weaker, but still activating, effects. Thus, no compensatory regulation here.. it is full speed ahead at HAR5 for bigger brain size. Additionally, a variant in human populations that is responsible for autism spectrum disorders also resides in this region, and the authors show that this change decreases FZD8 expression about 20%. Small numbers, sure, but for a process that directs cell division over many cycles in early brain development, this kind of difference can have profound effects.

The HAR5 region causes increased transcription of FZD8, in mice, compared to the native version and a deletion.

The HAR5 region causes increased cell proliferation in embryonic day 14.5 brain areas, stained for neural markers.

"This reveals Hs-HARE5 modifies radial glial progenitor behavior, with increased self-renewal at early developmental stages followed by expanded neurogenic potential. ... Using these orthogonal strategies we show four human-specific variants in HARE5 drive increased enhancer activity which promotes progenitor proliferation. These findings illustrate how small changes in regulatory DNA can directly impact critical signaling pathways and brain development."

So there you have it. The nuts and bolts of evolution, from the molecular to the cellular, the organ, and then the organismal, levels. Humans do not just have bigger brains, but better brains, and countless other subtle differences all over the body. Each of these is directed by genetic differences, as the combined inheritance of the last six million years since our divergence versus chimpanzees. Only with the modern molecular tools can we see Darwin's vision come into concrete focus, as particular, even quantum, changes in the code, and thus biology, of humanity. There is a great deal left to decipher, but the answers are all in there, waiting.

• Who's the poodle?
• We are going totally tin-pot.

Ground Truth for Genetic Mutations

Saturation mutagenasis shows that our estimates of the functional effect of uncharacterized mutations are not so great.

Human genomes can now be sequenced for less than $1,000. This technological revolution has enabled a large expansion of genetic testing, used for cancer tissue diagnosis and tracking, and for genetic syndrome analysis both of embryos before birth and affected people after birth. But just because a base among the 3 billion of the genome is different from the "reference" genome, that does not mean it is bad. Judging whether a variant (the modern, more neutral term for mutation) is bad takes a lot of educated guesswork.

A recent paper described a deep dive into one gene, where the authors created and characterized the functional consequence of every possible coding variant. Then they evaluated how well our current rules of thumb and prediction programs for variant analysis compare with what they found. It was a mediocre performance. The gene is CDKN2A, one of our more curious oddities. This is an important tumor suppressor gene that inhibits cell cycle progression and promotes DNA repair- it is often mutated in cancers. But it encodes not one, but two entirely different proteins, by virtue of a complex mRNA splicing pattern that uses distinct exons in some coding portions, and parts of one sequence in two different frames, to encode these two proteins, called p16 and p14. 

One gene, two proteins. CDKN2A has a splicing pattern (mRNA exons shown as boxes at top, with pink segments leading to the p14 product, and the blue segments leading the p16 product) that generates two entirely different proteins from one gene. Each product has tumor suppressing effects, though via distinct mechanisms.

Regardless of the complex splicing and protein coding characteristics, the authors generated all possible variants in every possible coded amino acid (156 amino acids in all, as both produced proteins are relatively short). Since the primary roles of these proteins are in cell cycle and proliferation control, it was possible to assay function by their effect when expressed in cultured pancreatic cells. A deleterious effect on the protein was revealed as, paradoxically, increased growth of these cells. They found that about 600 of the 3,000 different variants in their catalog had such an effect, or 20%.

This is an expected rate of effect, on the whole. Most positions in proteins are not that important, and can be substituted by several similar amino acids. For a typical enzyme, for instance, the active site may be made up of a few amino acids in a particular orientation, and the rest of the protein is there to fold into the required shape to form that active site. Similar folding can be facilitated by numerous amino acids at most positions, as has been richly documented in evolutionary studies of closely-related proteins. These p16 and p14 proteins interact with a few partners, so they need to maintain those key interfacial surfaces to be fully functional. Additionally, the assay these researchers ran, of a few generations of growth, is far less sensitive than a long-term true evolutionary setting, which can sift out very small effects on a protein, so they were setting a relatively high bar for seeing a deleterious effect. They did a selective replication of their own study, and found a reproducibility rate of about 80%, which is not great, frankly.

"Of variants identified in patients with cancer and previously reported to be functionally deleterious in published literature and/or reported in ClinVar as pathogenic or likely pathogenic (benchmark pathogenic variants), 27 of 32 (84.4%) were functionally deleterious in our assay"

"Of 156 synonymous variants and six missense variants previously reported to be functionally neutral in published literature and/or reported in ClinVar as benign or likely benign (benchmark benign variants), all were characterized as functionally neutral in our assay "

"Of 31 VUSs previously reported to be functionally deleterious, 28 (90.3%) were functionally deleterious and 3 (9.7%) were of indeterminate function in our assay."

"Similarly, of 18 VUSs previously reported to be functionally neutral, 16 (88.9%) were functionally neutral and 2 (11.1%) were of indeterminate function in our assay"

Here we get to the key issues. Variants are generally classified as benign, pathogenic/deleterious, or "variant of unknown/uncertain significance". The latter are particularly vexing to clinical geneticists. The whole point of sequencing a patient's tumor or genomic DNA is to find causal variants that can illuminate their condition, and possibly direct treatment. Seeing lots of "VUS" in the report leaves everyone in the dark. The authors pulled in all the common prediction programs that are officially sanctioned by the ACMG- Americal College of Medical Genetics, which is the foremost guide to clinical genetics, including the functional prediction of otherwise uncharacterized sequence variants. There are seven such programs, including one driven by AI, AlphaMissense that is related to the Nobel prize-winning AlphaFold. 

These programs strain to classify uncharacterized mutations as "likely pathogenic", "likely benign", or, if unable to make a conclusion, VUS/indeterminate. They rely on many kinds of data, like amino acid similarity, protein structure, evolutionary conservation, and known effects in proteins of related structure. They can be extensively validated against known mutations, and against new experimental work as it comes out, so we have a pretty good idea of how they perform. Thus they are trusted to some extent to provide clinical judgements, in the absence of better data. 

Each of seven programs (on bottom) gives estimations of variant effect over the same pool of mutations generated in this paper. This was a weird way to present simple data, but each bar contains the functional results the authors developed in their own data (numbers at the bottom, in parentheses, vertical). The bars were then colored with the rate of deleterious (black) vs benign (white) prediction from the program. The ideal case would be total black for the first bar in each set of three (deleterious) and total white in the third bar in each set (benign). The overall lineup/accuracy of all program predictions vs the author data was then overlaid by a red bar (right axis). The PrimateAI program was specially derived from comparison of homologous genes from primates only, yielding a high-quality dataset about the importance of each coded amino acid. However, it only gave estimates for 906 out of the whole set of 2964 variants. On the other hand, cruder programs like PolyPhen-2 gave less than 40% accuracy, which is quite disappointing for clinical use.

As shown above, the algorithms gave highly variable results, from under 40% accurate to over 80%. It is pretty clear that some of the lesser programs should be phased out. Of programs that fielded all the variants, the best were AlphaMissense and VEST, which each achieved about 70% accuracy. This is still not great. The issue is that, if a whole genome sequence is run for a patient with an obscure disease or syndrome, and variants vs the reference sequence are seen in several hundred genes, then a gene like CDKN2A could easily be pulled into the list of pathogenic (and possibly causal) variants, or be left out, on very shaky evidence. That is why even small increments in accuracy are critically important in this field. Genetic testing is a classic needle-in-a-haystack problem- a quest to find the one mutation (out of millions) that is driving a patient's cancer, or a child's inherited syndrome.

Still outstanding is the issue of non-coding variants. Genes are not just affected by mutations in their protein coding regions (indeed many important genes do not code for proteins at all), but by regulatory regions nearby and far. This is a huge area of mutation effects that are not really algorithmically accessible yet. As a prediction problem, it is far more difficult than predicting effects on a coded protein. It will requiring modeling of the entire gene expression apparatus, much of which remains shrouded in mystery.

• Oh, no! Not the shopping cart!
• Jevons and Malthus will have their say, if we do not get our act together.
• It is time to scrap GDP, and the mania for growth.
• Blaming the victim.
• Everything crypto will be just perfect.
• Can I play piano with my mind?
• We are killers.

When the Battery Goes Dead

How do mitochondria know when to die?

Mitochondria are the energy centers within our cells, but they are so much more. They are primordial bacteria that joined with archaea to collaborate in the creation of eukaryotes. They still have their own genomes, RNA transcription and protein translation. They play central roles in the life and death of cells, they divide and coalesce, they motor around the cell as needed, kiss other organelles to share membranes, and they can get old and die. When mitochondria die, they are sent to the great garbage disposal in the sky, the autophagosome, which is a vesicle that is constructed as needed, and joins with a lysosome to digest large bits of the cell, or of food particles from the outside.

The mitochondrion spends its life (only a few months) doing a lot of dangerous reactions and keeping an electric charge elevated over its inner membrane. It is this charge, built up from metabolic breakdown of sugars and other molecules, that powers the ATP-producing rotary enzyme. And the decline of this charge is a sign that the mitochondrion is getting old and tired. A recent paper described how one key sensor protein, PINK1, detects this condition and sets off the disposal process. It turns out that the membrane charge does not only power ATP synthesis, but it powers protein import to the mitochondrion as well. Over the eons, most of the mitochondrion's genes have been taken over by the nucleus, so all but a few of the mitochondrion's proteins arrive via import- about 1500 different proteins in all. And this is a complicated process, since mitochondria have inner and outer membranes, (just as many bacteria do), and proteins can be destined to any of these four compartments- in either membrane, in the inside (matrix), or in the inter-membrane space. 

Textbook representation of mitochondrial protein import, with a signal sequence (red) at the front (N-terminus) of the incoming protein (green), helping it bind successively to the TOM and TIM translocators. 

The outer membrane carries a protein import complex called TOM, while the inner membrane carries an import complex called TIM. These can dock to each other, easing the whole transport process. The PINK1 protein is a somewhat weird product of evolution, spending its life being synthesized, transported across both mitochondrial membranes, and then partially chopped up in the mitochondrial matrix before its remains are exported again and fully degraded. That is when everything is working correctly! When the mitochondrial charge declines, PINK1 gets stuck, threaded through TOM, but unable to transit the TIM complex. PINK1 is a kinase, which phosphorylates itself as well as ubiquitin, so when it is stuck, two PINK1 kinases meet on the outside of the outer membrane, activate each other, and ultimately activate another protein, PARKIN, whose name derives from its importance in parkinson's disease, which can be caused by an excess of defective mitochondria in sensitive tissues, specifically certain regions and neurons of the brain. PARKIN is a ubiquitin ligase, which attaches the degradation signal ubiquitin to many proteins on the surface of the aged mitochondrion, thus signaling the whole mess to be gobbled up by an autophagosome.

A data-rich figure 1 from the paper shows purification of the tagged complex (top), and then the EM structure at bottom. While the purification (B, C) show the presence of TIM subunits, they did not show up in the EM structures, perhaps becuase they were not stable enough or frequent enough in proportion to the TOM subunits. But the PINK1+TOM_VDAC2 structures are stunning, helping explain how PINK1 dimerized so easily when it translocation is blocked.

The current authors found that PINK1 had convenient cysteine residues that allowed it to be experimentally crosslinked in the paired state, and thus freeze the PARKIN-activating conformation. They isolated large amounts of such arrested complexes from human cells, and used electon microscopy to determine the structure. They were amazed to see, not just PINK1 and the associated TOM complex, but also VDAC2, which is the major transporter that lets smaller molecules easily cross the outer membrane. The TOM complexes were beautifully laid out, showing the front end (N-terminus) of PINK1 threaded through each TOM complex, specifically the TOM40 ring structure.

What was missing, unfortunately, was any of the TIM complex, though some TIM subunits did co-purify with the whole complex. Nor was PARKIN or ubiquitin present, leaving out a good bit of the story. So what is VDAC2 doing there? The authors really don't know, though they note that reactive oxygen byproducts of mitochondrial metabolism would build up during loss of charge, acting as a second signal of mitochondrial age. These byproducts are known to encourage dimerization of VDAC channels, which naturally leads by the complex seen here to dimerization and activation of the PINK1 protein. Additionally, VDACs are very prevalent in the outer membrane and prominent ubiquitination targets for autophagy signaling.

To actually activate PARKIN ubiquitination, PINK1 needs to dissociate again, a process that the authors speculate may be driven by binding of ubiquitin by PINK1, which might be bulky enough to drive the VDACs apart. This part was quite speculative, and the authors promise further structural studies to figure out this process in more detail. In any case, what is known is quite significant- that the VDACs template the joining of two PINK1 kinases in mid-translocation, which, when the inner membrane charge dies away, prompts the stranded PINK1 kinases to activate and start the whole disposal cascade. 

Summary figure from the authors, indicating some speculative steps, such as where the reactive oxygen species excreted by VDAC2 sensitise PINK1, perhaps by dimerizing the VDAC channel itself. And where ubiquitin binding by PINK1 and/or VDAC prompts dissociation, allowing PARKIN to come in and get activated by PINK1 and spread the death signal around the surface of the mitochondrion.

It is worth returning briefly to the PINK1 life cycle. This is a protein whose whole purpose, as far as we know, is to signal that mitochondria are old and need to be given last rites. But it has a curiously inefficient way of doing that, being synthesized, transported, and degraded continuously in a futile and wasteful cycle. Evolution could hardly have come up with a more cumbersome, convoluted way to sense the vitality of mitochondria. Yet there we are, doubtless trapped by some early decision which was surely convenient at the time, but results today in a constant waste of energy, only made possible by the otherwise amazingly efficient and finely tuned metabolic operations of PINK1's target, the mitochondrion.

• More on mitochondria.
• The Hitler thing is becoming.. a thing.
• What Mormonism is all about.
• Injustice reigns supreme.
• Death on the roads.
• Graph of the week- sea level over the last four million years.

Note that at the glacial maxima, sea levels were almost 500 feet (150 meters) lower than today. And today, we are hitting a 3 million year peak level.