It’s more than genes, it’s networks and systems

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Most of you don’t understand evolution. I mean this in the most charitable way; there’s a common conceptual model of how evolution occurs that I find everywhere, and that I particularly find common among bright young students who are just getting enthusiastic about biology. Let me give you the Standard Story, the one that I get all the time from supporters of biology.

Evolution proceeds by mutation and selection. A novel mutation occurs in a gene that gives the individual inheriting it an advantage, and that person passes it on to their children who also gets the advantage and do better than their peers, and leave more offspring. Given time, the advantageous mutation spreads through the population so the entire species has it.

One example is the human brain. An ape man millions of years ago acquired a mutation that made his or her brain slightly larger, and since those individuals were slightly smarter than other ape men, it spread through the population. Then later, other mutations occured and were selected for and so human brains gradually got larger and larger.

You either know what’s wrong here or you’re feeling a little uneasy—I gave you enough hints that you know I’m going to complain about that story, but if your knowledge is at the Evolutionary Biology 101 level, you may not be sure what it is.

Just to make you even more queasy, the misunderstanding here is one that creationists have, too. If you’ve ever encountered the cryptic phrase “RM+NS” (“random mutation + natural selection”) used as a pejorative on a creationist site, you’ve found someone with this affliction. They’ve got it completely wrong.

Here’s the problem, and also a brief introduction to Evolutionary Biology 201.

First, it’s not exactly wrong — it’s more like taking one good explanation of certain kinds of evolution and making it a sweeping claim that that is how all evolution works. By reducing it to this one scheme, though, it makes evolution far too plodding and linear, and reduces it all to a sort of personal narrative. It isn’t any of those things. What’s left out in the 101 story, and in creationist tales, is that: evolution is about populations, so many changes go on in parallel; selectable traits are usually the product of networks of genes, so there are rarely single alleles that can be categorized as the effector of change; and genes and gene networks are plastic or responsive to the environment. All of these complications make the actual story more complicated and interesting, and also, perhaps to your surprise, make evolutionary change faster and more powerful.

Think populations

Mutations are the root of biological variation, of course, but we often have a naive view of their consequences. Most mutations are neutral. Even advantageous mutations are subject to laws of chance in their propagation, and a positive selection coefficient does not mean there will be an inexorable march to fixation, where every individual has the allele. This is also true of deleterious mutations: chance often dominates, and unless it is a strongly negative allele, like an embryonic lethal mutation, there’s also a chance it can spread through the population.

Stop thinking of mutations as unitary events that either get swiftly culled, because they’re deleterious, or get swiftly hauled into prominence by the uplifting crane of natural selection. Mutations are usually negligible changes that get tossed into the stewpot of the gene pool, where they simmer mostly unnoticed and invisible to selection. Look at human faces, for instance: they’re all different, and unless you’re looking at the extremes of beauty or ugliness, the variations simply don’t make much difference. Yet all those different faces really are the result of subtly different combinations of mutant forms of genes.

“Combinations” is the magic word. A single mutation rarely has a significant effect on a feature, but the combination of multiple mutations may have a detectable or even novel effect that can be seen by natural selection. And that’s what’s going on all the time: the population is a huge reservoir of genetic variation, and what we do when we reproduce is sort and mix and generate new combinations that are then tested in the environment.

Compare it to a game of poker. A two of hearts in itself seems to be a pathetic little card, but if it’s part of a flush or a straight or three of a kind, it can produce a winning hand. In the game, it’s not the card itself that has power, it’s its utility in a pattern or combination of other cards. A large population like ours is a great shuffler that is producing millions of new hands every day.

We know that this recombination is essential to the rapid acquisition of new phenotypes. Here are some results from a classic experiment by Waddington. Waddington noted that fruit flies expressed the odd trait of developing four wings (the bithorax phenotype) instead of two if they were exposed to ether early in development. This is not a mutation! This is called a phenocopy, where an environmental factor induces an effect similar to a genetic mutation.

What Waddington did next was to select for individuals that expressed the bithorax phenotype most robustly, or that were better at resisting the ether, and found that he could get a progressive strengthening of the response.

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The progress of selection for or against a bithorax-like response to ether treatment in two wild-type populations. Experiments 1 and 2 initially showed about 25 and 48% of the bithorax (He) phenotype.

This occurred over 10s of generations — far, far too fast for this to be a consequence of the generation of new mutations. What Waddington was doing was selecting for more potent combinations of alleles already extant in the gene pool.

This was confirmed in a cool way with a simple experiment: the results in the graph above were obtained from wild-caught populations. Using highly inbred laboratory strains that have greatly reduced genetic variation abolishes the outcome.

Jonathan Bard sees this as a powerful potential factor in evolution.

Waddington’s results have excited considerable controversy over the years, for example as to whether they reflect threshold effects or hidden variation. In my view, these arguments are irrelevant to the key point: within a population of organisms, there is enough intrinsic variability that, given strong selection pressures, minor but existing variants in a trait that are not normally noticeable can rapidly become the majority phenotype without new mutations. The implications for evolution are obvious: normally silent mutations in a population can lead to adaptation if selection pressures are high enough. This view provides a sensible explanation of the relatively rapid origins of the different beak morphologies of Darwin’s various finches and of species flocks.

Think networks

One question you might have at this point is that the model above suggests that mutations are constantly being thrown into the population’s gene pool and are steadily accumulating — it means that there must be a remarkable amount of genetic variation between individuals (and there is! It’s been measured), yet we generally don’t see most people as weird and obvious mutants. That variation is largely invisible, or represents mere minor variations that we don’t regard as at all remarkable. How can that be?

One important reason is that most traits are not the product of single genes, but of combinations of genes working together in complex ways. The unit producing the phenotype is most often a network of genes and gene products, such at this lovely example of the network supporting expression and regulation of the epidermal growth factor (EGF) pathway.

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The EGF pathway (from www.sabiosciences.com/pathwaycentral.php)

That is awesomely complex, and yes, if you’re a creationist you’re probably wrongly thinking there is no way that can evolve. The curious thing is, though, that the more elaborate the network, the more pieces tangled into the pathway, the smaller the effect of any individual component (in general, of course). What we find over and over again is that many mutations to any one component may have a completely indetectable effect on the output. The system is buffered to produce a reliable yield.

This is the way networks often work. Consider the internet, for example: a complex network with many components and many different routes to get a single from Point A to Point B. What happens if you take out a single node, or even a set of nodes? The system routes automatically around any damage, without any intelligent agency required to consciously reroute messages.

But further, consider the nature of most mutations in a biological network. Simple knockouts of a whole component are possible, but often what will happen are smaller effects. These gene products are typically enzymes; what happens is a shift in kinetics that will more subtly modify expression. The challenge is to measure and compute these effects.

Graph analysis is showing how networks can be partitioned and analysed, while work on the kinetics of networks has shown first that it is possible to simplify the mathematics of the differential equation models and, second, that the detailed output of a network is relatively insensitive to changes in most of the reaction parameters. What this latter work means is that most gene mutations will have relatively minor effects on the networks in which their proteins are involved, and some will have none, perhaps because they are part of secondary pathways and so redundant under normal circumstances. Indirect evidence for this comes from the surprising observation that many gene knockouts in mice result in an apparently normal phenotype. Within an evolutionary context, it would thus be expected that, across a population of organisms, most
mutations in a network would effectively be silent, in that they would give no selective advantage under normal conditions. It is one of the tasks of systems biologists to understand how and where mutations can lead to sufficient variation in networks properties for selection to have something on which to act.

Combine this with population effects. The population can accumulate many of these sneaky variants that have no significant effect on most individuals, but under conditions of strong selection, combinations of these variants, that together can have detectable effects, can be exposed to selection.

Think flexible genes

Another factor in this process (one that Bard does not touch on) is that the individual genes themselves are not invariant units. Mutations can affect how genes contribute to the network, but in addition, the same allele can have different consequences in different genetic backgrounds — it is affected by the other genes in the network — and also has different consquences in different external environments.

Everything is fluid. Biology isn’t about fixed and rigidly invariant processes — it’s about squishy, dynamic, and interactive stuff making do.

Now do you see what’s wrong with the simplistic caricature of evolution at the top of this article? It’s superficial; it ignores the richness of real biology; it limits and constrains the potential of evolution unrealistically. The concept of evolution as a change in allele frequencies over time is one small part of the whole of evolutionary processes. You’ve got to include network theory and gene and environmental interactions to really understand the phenomena. And the cool thing is that all of these perspectives make evolution an even more powerful force.

Bard J (2010) A systems biology view of evolutionary genetics. Bioessays 32: 559-563.

No metazoan is an island

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I’m one of those dreadful animal-centric zoologically inclined biologists. Plants? What are those? Fungi? They’re related to metazoans somehow. Lichens? Not even on the radar. The first step in fixing a problem, though, is recognizing that you have one. So I confess to you, O Readers, that my name is PZ, and I am a metazoaphile. But I can get better.

My path to opening up to wider horizons is to focus on what I find most interesting about animals, and that is that they are networks of cells driven by networks of genes that generate patterned responses of expression by cell signaling, or communication. See? I’m already a little weird. Show me a baby bunny, and I don’t just see a cute little furry pal with an adorable twitchy nose, I see an organized and coherent array of differentiated tissues that arose by a temporal sequence of cell-cell interactions, and I just wanna open him up and play with his widdle epithelial sheets and dismantle his pwetty ducts and struts and fibers and fluids, oochy coo. And ultimately, I want to take apart each cell and ask why it has its particular assortment of genes switched off and on, and how its state affects its neighbors and the whole of the organism.

Which means, lately, that I’ve acquired a growing interest in bacteria. If I were 30 years younger, I could probably be seduced into a career in microbiology.

There are a couple of reasons why an animal-centric biologist would be interested in bacteria. One is the principle of it; the mechanisms that animal cells use to build complex arrangements of tissues were all first pioneered in single-celled organisms. We have elaborated and added details to gene- and cell-level phenomena, but it’s a collection of significant quantitative differences, with nothing known that is essentially new in metazoan cells. All the cool stuff was worked out by evolution in the 3-4billion years before the Cambrian, a potential that simply blossomed in the past half-billion years into big conglomerations of cells. Understanding how the building blocks of multicellularity work individually ought to be a prerequisite to understanding how the assemblages work.

But there’s another reason, too, a difference in perspective. It is our conceit to regard ourselves as individuals of Homo sapiens, a body of cells clonally derived from a single human cell. It’s not true. It turns out that each one of us is actually a whole population of species, linked by our evolutionary history and lumbering through the world as a team. Genus Homo is also genera Escherichi and Bacteroidetes and Firmicutes and many others.

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Physiology

Let’s begin with the most widely known factor: we’re mostly bacterial in cell numbers, with about ten times as many bacterial cells as human cells. Most of these are nestled deep in our guts, where they are indispensible. In mammals, they help break down complex polysaccharides which we can then absorb through the wall of the digestive tract — these are compounds that would be simply lost without bacterial assistance. Even more dramatically, termite guts contain colonies of bacteria that produce enzymes to break down cellulose. Another insect, aphids, live in plant saps which have negligible protein components, and they rely on gut bacteria that can synthesize nine essential amino acids. One cool feature is that the bacteria can’t complete the synthesis of leucine; the last step is carried out by aphid enzymes. The synthetic pathway is split acros two different species!

Another weird twist is that gut bacteria can affect morphology (or vice versa; physiology influences which gut bacteria thrive). Mice with a genetic predisposition to obesity were found to have a different distribution of gut bacteria; fat mice are full of Firmicutes, while lean mice are loaded with Bacteroidetes. Something in the genetics of the obese mice seems to favor the proliferation of that one species. Cause and effect is not so easily separated, though, since doing a fecal transplant and inoculating the guts of germ free mice with the bacteria from obese mice vs. lean mice has a surprising effect: the mice given obese mouse fecal enemas subsequently increased their body fat by 60%. The bacteria promoted more fat storage in the host animal.

So what, you may be thinking, it’s mice. However, it turns out that obese humans tend to have reduced amounts of Bacteroidetes species in their guts than lean people, and weight loss is accompanied by an increase in Bacteroidetes. Fecal transplants are not recommended as a weight loss technique…at least not yet.

They have worked for some other problems. Crohn’s disease and ulcerative colitis are diseases that involve intestinal inflammation, and they’re also associated with imbalances in the species distribution of gut bacteria. Some promising treatments have involved collecting feces from healthy individuals, and using a nasogastric tube to inoculate the guts of Crohn’s patients with the stuff. Ick, I know, but it seems to have worked surprisingly well in a small number of patients.

Development

Bacteria are present in the gut from a very early age, and populate the digestive epithelia. There must be interactions going on, and it appears that the bacteria are actually regulating the growth of the gut lining.

Germ-free zebrafish lines have no gut bacteria, and they also have problems. The intestinal lining arrests its development and fails to fully differentiate; the lining also grows much more slowly. They also have difficulty absorbing some nutrients. Add bacteria, though, and growth and differentiation resume. This is a case where the developmental program and the bacterial influences are interdependent, and it makes sense — they’ve co-evolved.

It’s not just fish, either — these are conserved interactions across the vertebrates. Mice exhibit the same dependence on gut flora for development of the intestinal lining.

The very best example of a developmental dependence on bacteria, though, is in squid. The bobtail squid has a light-emitting organ that relies on colonization by a luminescent bacterium, Vibrio fischeri. The animal gleans the bacteria from the water with a special ciliated epithelium and secreted mucus that seems to be just the right flavor for Vibrio, and the bacteria migrate deep into the light-emitting organ. Once colonized, the squid dismantles the harvesting cilia and downregulates the secretion of mucus. If no bacteria of the right species are present, it maintains the cilia. If the bacteria in the organ die, resumes mucus production.

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Bacterial symbionts induce light-organ morphogenesis in squid. A Adult squid (E scolopes). SEM images of epithelial fields before B and after C regression of ciliated appendage. Scale bar, 50 mm. Ciliated appendages are marked by an orange dashed line.

Evolution

If something affects development and physiology, it affects evolution, so evolutionary importance is simply rather unavoidable. However, there’s also one somewhat surprising observation (to me, at least — microbiologists probably expect it): different species of related organisms can have different microbial populations, even when raised in identical conditions. Different Hydra species in the lab under controlled conditions have recognizably different populations of bacteria living on their epithelia, and Hydra of the same species collected in the wild have similar distributions of species. The properties of each Hydra species uniquely favor different distributions of bacteria, and the bacteria are also preferentially colonizing particular species of Hydra.

Hydra are wonderful experimental animals in that one can ablate stem cells for a particular tissue type, and still get an animal that develops and lives; do the same thing to a vertebrate, for instance knocking out the mesodermal lineage in the embryo, and you get an aborted blob. In Hydra, you get a tissue that survives and is colonized by bacteria…but the kinds of bacteria populating it is different from the populations in the intact animal. The animal and the bacteria are swapping molecular signals that specify favored relationships. Again, these are coevolved populations that recognize molecular properties of the host and symbiont.

This is all getting very complicated. I’m used to thinking in terms of networks of genes: there are regulatory interactions between genes in a single cell that establish cell-type specific patterns of gene activity; all express a common core of genes, but different cell types, such as a neuron vs. a cell of the digestive epithelia, will also have their own unique special-purpose genes switched on. I’m also comfortable thinking of networks of cells: cells are in constant negotiations with their neighbors, mainting a common pattern of expression within a tissue, and defining interacting edges with other tissues. Cells are continually sending out messages about their state into the system and responding to local and global signals. All this is part of the normal process of thinking developmentally.

Now, though, there’s another layer: we have to think in terms of networks of species that cooperate in the development and physiology of individual multi-cellular organisms. Purity is compromised. My precious animalia — they’re inconceivable without bringing bacteria into the picture.

Fraune S, Bosch TCG (2010) Why bacteria matter in animal development and evolution. Bioessays 32:571-580.

An unpaleontological lament for lost molecules and shattered cells and the cruelty of time

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Sometimes, I really hate fossils. I hate them with the passion of a spurned lover, one who is consumed with desire but knows that he will never, ever be satisfied. They drive me mad.

Right now we’re at a point in our technology where we can take a small sample from a living organism and break it down into amazing detail — we can extract every gene, throw them into a computer, and compare them with every other gene that has been similarly sampled. We can look for the scars of evolution, we can analyze and figure out where on the tree of life this cell resides, we can even figure out what local populatons it lived in, who its ancestors bred with, and to a certain extent, what various alleles contributed to its form and physiology. We don’t know everything, but every time someone works out some new detail in a related species, it goes into the databases and presto, the information cascades through every other relative. I’d call it magic, but that would insult the science with cheap understatement.

We can’t do that with most fossils (with some recent exceptions). The cells are gone. Their contents are obliterated — DNA fragmented, dissolved, corrupted, lost. And the farther back in time we go, the less information we have, but the more interesting the problems become.

All organisms are built of cells — they’re like the Lego building blocks of biology, with specific features that snap them together. With Legos, of course, you can build all kinds of different forms: stick them together and build a Lego Triceratops or a Lego T. rex. Different on the outside, different in arrangement, different in pattern, but all fundamentally built of the same kinds of blocks. I can get into the coolness of digging up a Triceratops or a T. rex, but these are all variations on a theme of phylum Chordata, superclass Tetrapoda, and they’re all using the same building blocks, and all the really interesting stuff, the details in the genome that make one morphology different than another, have all been bled out on the sands of time and gnawed by all-devouring bacteria and reduced to at best a non-specific smear of carbon. That makes me frustrated.

Even worse, most familiar fossils are big bony animals — they’re all pretty much the same, deep down. If they’re built of Legos, there are whole other clades of multicellular organisms that are the equivalent of meccano, lincoln logs, Capsela, and tinkertoys. How were they put together? And how did they evolve these different patterns of connections? To know that, we have to go way back into deep time, and look at the unicellular organisms, the cells that first pioneered patterns of interactions and laid down the possible rules of development that enabled big clumsy multicellular to accumulate the bulk that made them more likely to be fossilized. Those pioneers are practically nonexistent in the fossil record.

What prompts my lament for lost cells is this recent amazing discovery: a collection of fossilized multicellular organisms unearthed in Gabon that are 2.1 billion years old. Keep in mind that in comparison, the Cambrian explosion, the event that was the root of familiar animal diversity, was a mere half billion years ago, so these are genuinely ancient. They’re also beautiful.

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(Click for larger image)
Samples show a disparity of forms based on: external size and shape characteristics; peripheral radial microfabric (missing in view d); patterns of topographic thickness distribution; general inner structural organization, including occurrence of folds (seen in views b and c) and of a nodular pyrite concretion in the central part of the fossil (absent in views a and b). a, Original specimen. b, Volume rendering in semi-transparency. c, Transverse (axial) two-dimensional section. d, Longitudinal section running close to the estimated central part of the specimen. Scale bars, 5 mm. Specimens from top to bottom: G-FB2-f-mst1.1, G-FB2-f-mst2.1, G-FB2-f-mst3.1, G-FB2-f-mst4.1.

These small, flat, furrowed sheets lived at a kind of temporal boundary, a few hundred million years after a rise in atmospheric oxygen called the Great Oxygenation Event — a crisis in the history of life on earth which occured when the production of oxygen by photosynthetic organisms could no longer be buffered by reacting chemically with minerals, and began to build up in the atmosphere. This was catastrophic for most of the organisms living at that time, which were anaerobic and found oxygen to be a caustic poison. It was an advantage to a subset that adapted to use oxygen as a fuel in chemical reactions, though, so there was also the beginnings of new forms which exploited this newly oxygenated atmosphere. That’s where these mysterious blobs come in; they were found in formations that had a chemical signature indicating the presence of free oxygen.

These were almost certainly colonial organisms that took advantage of the higher concentration of oxygen to build denser mats on top of the sea floor. They probably weren’t true multi-cellular organisms; they were a step up from a colony of bacteria that you might see growing on a petri dish, but with additional molecular features that permitted greater coordination and the development of more elaborate spatial patterning.

We also know that these had to have been very different from organisms that exist now. Those are not animals, they are not plants, they are not fungi — they are something primeval and radically different, organisms that most likely do not have any living descendants. Those are real aliens in the photo above. There is no category in your experience which you can put them into.

It’s what we don’t know that inflames my curiousity. One of the other things that was going on during the Great Oxygenation Event was the steady loss of dissolved iron in the seas — it was all being oxidized, rusted out, and precipitating out, forming geological structures like the banded iron formations. It was also facilitating the preservation of these organisms by pyritizing them — all their soft gooey bits, the whole of creature, were being replaced by fool’s gold, iron pyrite. There are no cells left here. We don’t even know for sure that these are eukaryotic cells; they probably are, indicated by the presence of a sterane chemical signature in the rocks that is characteristic of eukaryotes, but there isn’t even enough fine detail to tell whether there was a nucleus in these cells. It just breaks my heart.

It’s a beautiful tease. We can see that life was exploring the edges of multicellularity over 2 billion years ago, but…the molecular sinews that stitched them together are all gone. The signals and receptors that enabled communication between them are all gone. The genes that drove their growth are all gone. There is nothing left but a blurry crystal-ruptured outline of what once was.

I have to shake an angry fist at you, fossils. I won’t go all Mel Gibson in incoherent rage at you because I like you too much, but still…you taunt me. I want your cells. Nothing less will do.

El Albani A, Bengtson S, Canfield DE, Bekker A, Macchiarelli R, Mazurier A, Hammarlund EU, Boulvais P, Dupuy JJ, Fontaine C, Fürsich FT, Gauthier-Lafaye F, Janvier P, Javaux E, Ossa FO, Pierson-Wickmann AC, Riboulleau A, Sardini P, Vachard D, Whitehouse M, Meunier A. (2010) Large colonial organisms with coordinated growth in oxygenated environments 2.1 Gyr ago. Nature 466(7302):100-4.

Chris Nedin, who should know, does not think these fossils represent multicellular organisms at all — they are fossilized, folded microbial mats. Which is fine by me — 2 billion year old microbial mats are also exceedingly cool, and I still want their cells.

You do know that if you want to know more about anything pre-Cambrian, you should be reading Ediacaran, right?

How insects and crustaceans molt

I was mildly surprised at the reaction to this cool timelapse video of a molting crab — some people didn’t understand how arthropods work. The only thing to do, of course, is to explain the molting process of insects and crustaceans, called ecdysis.

Let’s go back to the basics first. In the beginning was the epithelium, a continuous sheet of linked cells that envelops multicellular organisms. These are living, dividing, dynamic cells that are flexible, can repair damage to themselves, and represent the boundary between the carefully maintained internal environment of the organism, and the more variable and often hostile external environment. And that’s where the problem lies: living cells are relatively fragile and sensitive, and in particular don’t cope well with drying out. Cells like it wet, yet if you look at insects and people, we live under horrible conditions for living cells, surrounded by dryness and heat and cold.

Our external epithelia have evolved different solutions to this problem of the basic inhospitability of terrestrial life. In us, our bounding epithelia divide frequently, pushing new cells outward. As these cells move, they commit suicide, producing a fibrous protein called keratin which forms dense, matted tangles inside the cells; these cells also build tight protein connections between their neighbors. It is these dead, protein-packed cells that face the outside world, protecting the delicate interior. These cells are steadily worn away and cast off — dandruff flakes, for instance, are sheets of these dead epithelial cells — and new protective cells produced by cell division and pushed up from the inside out to replace them. It’s a good solution that allows for constant growth and flexibility.

Arthropods, on the other hand, start with a similar sheet of living epithelial cells, but do something completely different. Instead of pushing out a continuous column of dying cells, they secrete dense layers of complex chemical compounds that harden into a tough cuticle. The exoskeleton of an insect or crustacean is acellular — the living cells have protected themselves by secreting an initially fluid set of chemicals that harden like epoxy to form a tough protective armor around themselves. We protect ourselves with sheets of leather; arthropods make plates like fiberglass on their outsides.

And there’s the rub. The cuticles of insects do not gradually slough away, replaced steadily by the addition of new material from the inside. They’re mostly fixed and rigid and static. This does have the advantage of providing a solid protective armor and a rigid framework for muscles, but isn’t so great for accommodating growth. Fiberglass isn’t stretchy and flexible!

Here’s a closer look at the structure of the arthropod cuticle.

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In the diagram on the left, the living epithelium is at the bottom, labeled “epidermis”. Above it are multiple acellular layers called the cuticle made up of substances like chitin and waxes (notice that it is also perforated by pores containing ducts of the glands that secrete the chemical substances, and also places where hairs called setae can dangle into the exterior.

In order to grow, the animal must discard the old cuticle and build a new one from the inside out. In (b), this process begins by peeling away the living epidermal cells from the dead cuticle, creating a gap called the exuvial space, which is filled with a fluid called molting fluid. The cells then begin secreting a new cuticle from underneath, which is initially flexible.

What is poorly shown in these diagrams is that the new cuticle can be larger than the old. What that means is that epithelium inside the old cuticle is wrinkled and convoluted to have a larger surface area. Again, it is soft, not hard, so it can wrinkle up freely to fit. Also, to make room, the molting fluid in (c) is busily digesting the old cuticle from underneath, and the protein components are absorbed and reused to build the new cuticle.

In (d), the new cuticle is nearly fully formed, the old cuticle has been reduced to a thinner rind, and the two are separated by a thin fluid-filled space. Ecdysis, the actual molt, then occurs, and the old cuticle is discarded. Free of its confining shell, the animal inflates itself to extend the wrinkled new cuticle into larger smoothness, and the process of sclerotization, or hardening of the cuticle, begins from the outside in. Tanning agents, like polyphenols are secreted through ducts onto the surface, where they are oxidized into quinones, which trigger chemical reactions that cross-link the various substances of the cuticle into a rigid structure.

If you’ve ever eaten soft-shell crabs, you’ve caught the poor creature just after a molt and before its cuticle has hardened — in large arthropods, it can take several days for the post-molt cuticle to be fully cured. The hardening is also regional. Next time you’re eating a crab leg, notice that the shaft of the limb is rigid and strong and a bit brittle, but it grades into softer, less thickly sclerotized material at the joints called arthrodial membranes, which retains the flexibility of the pre-molt cuticle.

Now go watch the video again, and it should make more sense. What you’re seeing near the end is the crab pulling soft and rubbery limbs out of the shell of its old legs, and then resting as the new cuticle slowly hardens.

Radial tree of life

I use a very pretty radial tree of life diagram fairly often — the last time was in my talk on Friday — and every time I do, people ask where I got it. Here it is: it’s from the David Hillis lab, with this description:

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This file can be printed as a wall poster. Printing at least 54″ wide is recommended.
(If you would prefer a simplified version with common names, please see below.)
Blueprint shops and other places with large format printers can print this file for you.
You are welcome to use it for non-commercial educational purposes.
Please cite the source as David M. Hillis, Derrick Zwickl, and Robin Gutell, University of Texas.
About this Tree: This tree is from an analysis of small subunit rRNA sequences sampled
from about 3,000 species from throughout the Tree of Life. The species were chosen based
on their availability, but we attempted to include most of the major groups, sampled
very roughly in proportion to the number of known species in each group (although many
groups remain over- or under-represented). The number of species
represented is approximately the square-root of the number of species thought to exist on Earth
(i.e., three thousand out of an estimated nine million species), or about 0.18% of the 1.7 million
species that have been formally described and named. This tree has been used
in many museum displays and other educational exhibits, and its use for educational purposes
is welcomed.

There’s also a simplified version:

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Both of those are available as scalable pdfs, so you can zoom in and out to get just the right view, which is very handy.

Autism and the search for simple, direct answers

I’ve gotten some email asking for a simplified executive summary of this paper, so here it is.

A large study of almost a thousand autistic individuals for genetic variations that make them different from control individuals has found that Autism Spectrum Disorder has many different genetic causes: there isn’t one single gene responsible for ASD, but a constellation of hundreds, each with the potential to affect the development of the brain and cause the symptoms of autism. They don’t know exactly how each of these genes contributes to the disorder, but they have found that many of them are involved in growth and cell communication and the formation of synapses in the brain.

The bottom line is that there are many different ways to cause the symptoms of autism, and it’s a mistake to try to pin it all on single, simple causes. Any hope for amelioration lies in understanding the general functional processes that are disrupted by mutations in various pathways.

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Coming up with simple, one-size-fits-all answers to serious problems is so tempting and so satisfying. Look at autism, for instance: a mysterious disease with a wide range of expression, so wide that it is more properly called Autism Spectrum Disorder (ASD), and the popular press and various celebrities all want it to be pegged to a simple cause: it’s vaccines, or it’s mercury, or it’s the dose of the vaccines, and all we have to do to fix it is not vaccinate, or reduce the number of vaccinations, or use chelation therapy to extract poisons, and presto, a cure! This is magical thinking, pure and simple, and it doesn’t work.

ASD isn’t simple, it’s not one disease, it doesn’t have one cause, and vaccines are definitely not the cause: if there’s one thing the research has done, it’s to thoroughly rule out the idea that giving kids shots at an early age causes autism. What we’re actually discovering more and more is that ASD can be traced to genetic variation.

Again, though, the causes aren’t simple. There is no single mutation to which ASD can be pinned.

For example, one hot spot for an association of genes with autism is the long arm of chromosome 22; cases of developmental delays and autistic behavior have been associated with partial deletions in chromosome 22, and the problems have even been narrowed down to one specific gene, SHANK3, which is expressed in neurons and localized to synapses. We know that if you’ve got a broken copy of this particular gene, you’re likely to have ASD.

How many ASD individuals have this specific genetic change? 0.75%. It is a cause in less than 1% of all affected individuals, but it cannot be the sole cause of ASD in all cases. We have to get out of this mindset that tries to find single causes for complex phenomena; ASD is a case where we have a complex range of disorders with multiple, complex causes.

So how do we get a handle on ASD? This is where the work gets interesting: just because something is multi-causal does not mean that science can’t get a grip on it and that we can’t learn anything interesting about it. We’ve got lots of new tools for analyzing broad properties of genomes now, and one promising line of attack are methods for measuring and identifying copy number variants in individuals and populations.

Copy number variants (CNVs) are surprisingly common. If you’ve had any biology instruction at all, you’re probably familiar with the Mendelian concept that we have two copies of each chromosome, and two copies of each gene. As it turns out, that is an oversimplification: sometimes, a piece of a chromosome is accidentally duplicated, and then you’ll carry two copies of the associated gene on one chromosome, and one copy on another chromosome, for a total of 3 copies. And in some cases, these duplications have occurred often enough that you’ll have many more than 3; the median number of copies of the amylase gene (an enzyme that breaks down starch) in European American populations is 7, with a range of 2 to 15 in different individuals. Get used to it, this kind of variation in copy number seems to happen fairly often.

Now in the case of amylase, the effect of this variation is mild — individuals with more copies of the gene produce more of the enzyme and break down starchy foods faster. It does have evolutionary effects, since cultures with diets rich in starch contain individuals who have, on average, more copies of the gene than individuals where starches are less common in the diet. But what if these chance variations in copy number affect genes involved in the function of the brain? We might see more profound effects on behavior or cognitive ability. The defect in SHANK3 mutations is an example of a reduction in copy number of that gene; what if we could screen populations of ASD individuals not for a specific gene variant, but for the more general occurrence of frequent variations in copy number of any genes…and then we could ask which genes are often affected?

It’s being done. A new paper in Nature describes a screen of control and ASD individuals to identify rare copy number variants associated with autism. It worked! In fact, it worked maybe a little too well, since we now have an embarrassment of riches, a great many genes that may be related to ASD.

The autism spectrum disorders (ASDs) are a group of conditions characterized by impairments in reciprocal social interaction and communication, and the presence of restricted and repetitive behaviours. Individuals with an ASD vary greatly in cognitive development, which can range from above average to intellectual disability. Although ASDs are known to be highly heritable (~90%), the underlying genetic determinants are still largely unknown. Here we analysed the genome-wide characteristics of rare (<1% frequency) copy number variation in ASD using dense genotyping arrays. When comparing 996 ASD individuals of European ancestry to 1,287 matched controls, cases were found to carry a higher global burden of rare, genic copy number variants (CNVs) (1.19 fold, P = 0.012), especially so for loci previously implicated in either ASD and/or intellectual disability (1.69 fold, P = 3.4 × 10-4). Among the CNVs there were numerous de novo and inherited events, sometimes in combination in a given family, implicating many novel ASD genes such as SHANK2, SYNGAP1, DLGAP2 and the X-linked DDX53-PTCHD1 locus. We also discovered an enrichment of CNVs disrupting functional gene sets involved in cellular proliferation, projection and motility, and GTPase/Ras signalling. Our results reveal many new genetic and functional targets in ASD that may lead to final connected pathways.

They analyzed both affected individuals and their parents, and found both familial transmission — that is, the child with ASD had received a copy number variant from a parent who was a carrier — and de novo events — that is, the child had a spontaneous, new mutation that was not present in either parent. There is no one single gene that can be tagged as the cause of autism: they identified 226 de novo and 219 inherited copy number variants in affected individuals. No one individual carries all of these variants, of course — the results tell us that there are many different paths to ASD.

Oh, no, you may be tempted to wail, autism is hundreds of diseases, with even more possible combinations of variants, and every individual is unique — this is no way to get a handle on what’s actually happening to autistic kids! Don’t despair, though, this is just the start. Although there are many genes involved, we can try to ask what all of them have in common functionally. There may be common consequences from all of these different genes, so maybe we can identify the common errors in the process of building a brain that lead to ASD.

Here’s a first stab at puzzling out what these genes do. The genes that have been identified as being deficient in ASD individuals are mapped out by known functions, and what jumps out at you is that the hundreds of specific genes fall into a smaller number of functional categories. Many of them cluster in a few functional roles: cell proliferation (genes that affect the number of cells in a tissues) and cell projection (particularly important in neurons, where cells will extend long processes that project into target regions), and a specific class of cell signaling molecules, RAS-GTPases, which are involved in how cells communicate with one another and are particularly important in synapses, or the linkages between neurons.

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(Click for larger image)
Enrichment results were mapped as a network of gene sets (nodes) related by mutual overlap (edges), where the colour (red, blue or yellow) indicates the class of gene set. Node size is proportional to the total number of genes in each set and edge thickness represents the number of overlapping genes between sets. a, Gene sets enriched for deletions are shown (red) with enrichment significance (FDR q-value) represented as a node colour gradient. Groups of functionally related gene sets are circled and labelled (groups, filled green circles; subgroups, dashed line). b, An expanded enrichment map shows the relationship between gene sets enriched in deletions (a) and sets of known ASD/intellectual disability genes. Node colour hue represents the class of gene set (that is, enriched in deletions, red; known disease genes (ASD and/or intellectual disability (ID) genes), blue; enriched only in disease genes, yellow). Edge colour represents the overlap between gene sets enriched in deletions (green), from disease genes to enriched sets (blue), and between sets enriched in deletions and in disease genes or between disease gene-sets only (orange). The major functional groups are highlighted by filled circles (enriched in deletions, green; enriched in ASD/intellectual disability, blue).

The second map above ties the various copy number variants to previously known disease genes involved in ASD, and what catches my eye is the dense cloud of variants associated with central nervous system development. That tells me right there that it is inappropriate to treat ASD as something that is switched on or off by simple causal factors: ASD is the product of long-developing, subtle changes in the growth of the nervous system in embryos and infants.

So the conclusion, as expected, is that ASD is a multi-factorial disorder with a strong genetic component — but definitely not single-locus inheritance, as many different genes are involved.

Our findings provide strong support for the involvement of multiple rare genic CNVs, both genome-wide and at specific loci, in ASD. These findings, similar to those recently described in schizophrenia, suggest that at least some of these ASD CNVs (and the genes that they affect) are under purifying selection. Genes previously implicated in ASD by rare variant findings have pointed to functional themes in ASD pathophysiology. Molecules such as NRXN1, NLGN3/4X and SHANK3, localized presynaptically or at the post-synaptic density (PSD), highlight maturation and function of glutamatergic synapses. Our data reveal that SHANK2, SYNGAP1 and DLGAP2 are new ASD loci that also encode proteins in the PSD. We also found intellectual disability genes to be important in ASD. Furthermore, our functional enrichment map identifies new groups such as GTPase/Ras, effectively expanding both the number and connectivity of modules that may be involved in ASD. The next step will be to relate defects or patterns of alterations in these groups to ASD endophenotypes. The combined identification of higher-penetrance rare variants and new biological pathways, including those identified in this study, may broaden the targets amenable to genetic testing and therapeutic intervention.

There aren’t any simple answers. There are some hints of hope for future treatment, though, in the recognition that there are a few functional modules that are being commonly impaired by these many different genes — it at least focuses the direction of future research in to some narrower domains.

One fact is so obvious that it’s unfortunate I have to mention it: no external agent, such as a vaccine, can generate a consistent pattern of duplication and deletions in an affected individual’s cells. These data say it’s an error to chase down transient environmental agents given relatively late in life to people.

Pinto D et al. (2010) Functional impact of global rare copy number variation in autism spectrum disorders Nature doi:10.1038/nature09146.