Modularity Part I: Metabolic Pathways

This is a difficult subject and I've been putting it off, even starting a new blog, put it's time to try to explain the subject of modularity in biological networks. Wiki has little on the subject, but offers the following extract:

Bolker, however, attempts to construct a definitional list of characteristics that is more abstract, and thus more suited to multiple levels of study in biology. She argues that:

1. A module is a biological entity (a structure, a process, or a pathway) characterized by more internal than external integration
2. Modules are biological individuals [refs] that can be delineated from their surroundings or context, and whose behavior or function reflects the integration of their parts, not simply the arithmetical sum. That is, as a whole, the module can perform tasks that its constituent parts could not perform if dissociated.
3. In addition to their internal integration, modules have external connectivity, yet they can also be delineated from the other entities with which they interact in some way.²¹

This isn't really very explanatory, although it pretty much sums up what modularity is.

Since my professional background is in computer programming and systems architecture, I'm going to approach this from an info systems perspective. ... Let's consider a computer program that has a number of variables, and a number of procedural subroutines that work on those variables. We can define a module as a collection of subroutines and variables that are isolated: a few variables are used as inputs and outputs to the subroutines involved in the module, while most of them are internal to the module and can only be seen by the subroutines that are part of it.

This concept has been improved in object-oriented programming, in which an object is defined both internally and externally: from the outside it is a black box with a few visible variables and "methods" (subroutines) accessible to outside modules, while internally it has many more variables and likely a number of internal methods all of which are used only by other elements of the object. Such an object is a well isolated module.

Now, we could take these definitions, and draw a rough analogy with the metabolic system of the cell: we'll let variables correspond to substrates and products: chemicals within the cell that participate in reactions catalyzed by enzymes. We'll let the subroutines correspond to the reaction, or perhaps the enzyme that catalyzes it.

Now, with either system, we can create a network graph, a system of nodes and edges.

In most analyses, the variables/substrates are made into nodes, and the subroutines/reactions are made into edges connecting them. However, in a paper¹³ still in pre-publication, the opposite has been done, allowing enzymes to be nodes, which are connected by the substrates/products they process: The conservation and evolutionary modularity of metabolism

The purpose of this paper is to analyze the conservation of enzymes throughout evolution, correlating this conservation with identified modules in the cellular metabolism.

Peregrin-Alvarez, J., Sanford, C., & Parkinson, J. (2009). The conservation and evolutionary modularity of metabolism Genome Biology, 10 (6) DOI: 10.1186/gb-2009-10-6-r63

What Is Modularity in a Network?

The hard part is pinning down modularity in a biological system. We know that, analogous to the programming example above, we should expect a module to consist of a combination of enzymes and substrates/products that are more closely connected to one another than to outside entities.


Figure 1: A large complex network with several modules identified. (From Ref 20.)

In Figure 1, you can see that several modules are pretty clear, in a high-level sense. There are groups of nodes and edges that clearly make a dense cluster of internal associations, while having few connections outside. But a precise definition is harder to pin down. Basically, modules are generally identified by complex mathematical processes that can determine clusters, in much the same way we can by eye, except they can "see" clustering our eyes can't.

The important thing to recognize about modules in metabolic networks is that what they share outside the module is less than what's shared within it. In programming, it makes it easier to change things, because when you need to change the internals of a module, you only need to verify that it works the same with the same values in the externally visible variables. You can change how internal variables are used without having to worry about anything beyond the internal subroutines. Similar advantages exist for natural selection working with biological networks, including metabolic networks. In fact, it's been verified that there's a good correlation between the variability of the environment of bacteria and the level of modularity in their metabolic networks.¹⁹

In considering metabolic modularity, we need to distinguish between the two types of network mapping. Using substrates/products as nodes (and enzymes as edges) is better in some respects, but the various network analyzes aren't really relevant to evolution, because much of the analysis depends on what happens when a particular node is removed, which is unlikely in an evolving system. Rather, what might be removed, or changed, is an enzyme.

It might happen that a mutation creates a new version enzyme that no longer creates the particular chemical (substrate/product), but it's far more likely that the enzyme will simply be removed. And, even so, if the enzyme is so changed, an analysis can simply treat it as the removal of an enzyme and its replacement with another that performs different reactions.

How This Research Uses Modularity

Thus, this is the first paper I've seen that performs a network analysis that is evolutionarily relevant. One of the things they found is that :

enzymes involved in multiple superclasses were most highly conserved and those involved in glycan metabolism were least highly conserved [ref].¹³

If we think of "superclasses" as modules, then the "enzymes involved in multiple superclasses" would be (very roughly) analogous to the variables used by multiple subroutines in our programming analogy: they are sort of the "inputs and outputs" of modules, the externally visible interfaces of the "black box" of the module. Enzymes that are only involved in one such module can be modified, removed, or added, without affecting anything outside that black box. But a change to the external interface will affect all the modules that use it. No wonder they are more conserved: any change has to be coordinated with more compensatory changes elsewhere.

As mentioned above, the whole subject of modularity is a difficult one. This paper represent an apparent departure (unless I've missed some in my searches) in how it applies network theory to metabolic networks, which makes it important:

The use of a global metabolic network map (Fig. 4) allowed the identification of a highly interconnected core of conserved enzymes many of which are involved in multiple pathways. Such enzymes support the notion that “enzyme recruitment” plays a large role in metabolic evolution where novel pathways can emerge through the recruitment of enzymes (and hence their metabolites) from existing pathways [refs. ...] Pathways involving carbohydrate, amino acid and energy metabolism form a distinct core network with many shared enzyme activities.

Thus, this "highly interconnected core of conserved enzymes many of which are involved in multiple pathways" is probably mostly made up of "module interfaces", allowing us a much better understanding of how modularity interacts with evolution, as well as a good analogy with existing analysis of modularity from the field of programming.

This is hardly everything about modularity, I've barely touched on the subject. In future posts I'll try to expand on it, as well as providing links to other discussions as I find them.

Links: (Not all of these are called out in the text. Use the back key if you came via clicking a link.)

1. Statistical mechanics of complex networks

2. The structure and function of complex networks

3. Collective dynamics of ‘small-world’ networks

4. Hierarchical Organization of Modularity in Metabolic Networks Requires free registration

5. Systems Biology: A Brief Overview Requires free registration

6. Reverse Engineering of Biological Complexity Requires free registration Requires subscription, otherwise go here and click the [Abstract/Free Full Text] link

7. Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms

8. Network Motifs: Simple Building Blocks of Complex Networks Requires free registration

9. Network motifs in the transcriptional regulation network of Escherichia coli

10. The metabolic world of Escherichia coli is not small

11. MANET: tracing evolution of protein architecture in metabolic networks

12. Functional cartography of complex metabolic networks

13. The conservation and evolutionary modularity of metabolism

14. Extraction of phylogenetic network modules from the metabolic network

15. The Phylogenetic Extent of Metabolic Enzymes and Pathways

16. Modular organization of cellular networks

17. Modular co-evolution of metabolic networks

18. The evolution of modularity in bacterial metabolic networks

19. Environmental variability and modularity of bacterial metabolic networks

20. Iterative Vector Diffusion for the Detection of Modularity in Large Networks

(I've included only the link(s) referenced here.)

21. Modularity in Development and Why It Matters to Evo-Devo


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Scientia Pro Publica 6 is Up

But I'm going to try to get a big on in today anyway, since I've been spending time on my new blog and skimping here. This one's going to be my first try with modularity, based on this paper. It'll probably be late, though, perhaps even tomorrow, so enjoy the other papers at Mauka to Makai. Read more!

No big Post Today.

I've started a new blog, and part of my time will be taken up with it. See it here. Read more!

Paraphyly

I was surfing Research Blogging a while ago, and discovered post from Systematics and Biogeography: Paraphyly Watch 3: Transitional Fossils, Microbes & Patrocladistics, tearing into three papers which are candidates for the 2009 Pewter Leprechaun award for "the most outrageous misuse and/or abuse of Paraphyly for 2009."

I'm not going to argue about any of these papers, since two I don't really care about and the third is behind a paywall. However, it brings up an important subject I've discussed before, and probably will again:

The obvious problem with [using cladistics based on parsimony to build a hierarchical classification of species based on phylogeny or evolutionary ancestry] is that our understanding of past evolution is historical in nature, that is we are attempting to determine which of a semi-infinite number of possible evolutionary paths was actually followed in any evolutionary progress. Our estimation of relative probabilities is retrospective, and says little about which path was actually followed.

Consider the following scenario: Species "A" (disregarding problems with the definition of "species") is related to species "B", and "C", but we don't know which it's more closely related to. A cladistic analysis can list a whole bunch of features, some observable in the phenotype, others involving DNA differences such as the deletion or addition of a chunk of DNA, or a point mutation of one base into another, building a table of which species posses which values for which feature. By using several (presumably) less related species as "outgroups" to help determine the ancestral state of each feature, it's possible to build a series of "trees" based on different scenarios for changes to each feature during the evolution leading from the presumed common ancestor to the three modern species.

Parsimony enters in here as the tree with the least number of changes is considered "best", as in most likely. But is it? Suppose the lowest number of changes needed to evolve our three modern species ("A", "B", and "C") is 15. However, there is only one tree involving 15 changes but there are three involving 16, five involving 17, and twelve involving 18 changes. What is the most likely number of changes?

To answer that question, we need to know the relative probability of a change occurring. If such changes are very unlikely, then the tree with 15 changes may be most likely. If changes are fairly likely, then while the tree with 15 changes may be more probable than any single tree with 16, 17, or 18, the probability that the actual (historical) evolution had some other number of changes (probably 18, considering the number of possible paths) becomes higher than that for 15 changes.

This is not a trivial question, when we go beyond static cladistics to ask how each species came to evolve. In addition to mutations that cause the changes, we also need to consider the selective pressure that went into fixing those changes into the lineage that evolved into one of our modern species. It may be that the likelihood that the most parsimonious path was followed is low compared to one in which an ancestor of two modern species followed some sort of zig-zag path, adding changes that were later reversed, adding the same change more than once (especially for morphological features that were directly driven by selection, etc.)

This is important when it comes to paraphyly. According to Wiki, "In phylogenetics, a group of organisms is said to be paraphyletic if the group contains its most recent common ancestor but does not contain all the descendants of that ancestor." The most commonly used example is the "reptiles", a group that traditionally contained all the amniotes (tetrapods that lay air-tolerant eggs or are descended from them) except for birds and mammals.

Now, what Williams and Ebach (the writers of Systematics and Biogeography) are unhappy about it the way traditional taxonomists are attempting to defend older groups that are paraphyletic: ...

Brummitt's Evolution in taxonomic perspective consists of several abuses of paraphyly:

"... emphasis has been increasingly placed on the need for a classification which recognises evolution" (Brummitt, 2008:1050).

This is what monophyly is all about. Once you discover monophyly you have discovered evolution within your group. Unfortunately, this is contrary to Brummitt (and his followers) who believe that their taxonomies alone (without any need for testing, it seems) are evolutionary. This runs counter to empiricism in science, which makes hypotheses of relationships (e.g., taxonomies) and uses cladistics to test them. It seems that Brummitt's taxonomy is the yardstick that cladistics has to abide. If it doesn't (i.e., the taxon is paraphyletic) cladistics is wrong, not Brummitt. Therefore paraphyly is evidence and empiricism politely excused

This may be true of Brummitt, I haven't read the paper. But based on the quote here, I can see a perfectly reasonable meaning: the word "evolution" here actually is a short-hand for "evolutionary process", which in turn is concerned with much more than simple ancestry: the how and why of the evolution.

Reptiles: an Example

Let's go back to the reptiles: "cold-blooded", sprawled stanced, relatively small-brained (also simple brained). Both the birds and the mammals depart from this structure by being "warm-blooded", upright stanced, and possessing much more sophisticated brains. Although this simple picture becomes much more complex when we actually study the lineages leading to the mammals and birds (the latter including most of the more impressive dinosaurs), from the point of view of actually trying to recreate the evolutionary track followed, the "mammal-like reptiles" and the archosaurs followed a divergent path, evolving away from the basic characteristics of the presumed ancestral lineage.

Thus, it makes sense from the point of view of looking at the evolutionary process to create a "class" reptilia which performs a series of adaptive radiations based around the original evolutionary inovations of the first amniote, while allowing lineages such as the archosaurs and therapsids to "leave" that class and found a new one based on a new wave of innovations.

It's important to recognize the limitations of such paraphyletic classifications. In order to be useful in modeling the evolutionary process, they must retain the primary advantageous innovations of the original ancestor, without adding any new innovations that set of a new round of adaptive radiation. Creating a group called "reptiles" that includes the early synapsids but excludes the dinosaurs and birds would be of little use, since at least some of the innovations that set the mammals apart are also present in many dinosaurs and the birds. A "class reptilia", to be useful in modeling evolution, would have to remain predominantly cold-blooded, sprawled stanced, egg-laying, and simple-brained.

OTOH, the occasional viviparous (or oviviparous) snake or lizard can be retained unless it stands at the base of a major adaptive radiation. The turtles would be a borderline case (IMO), if they had had a much larger adaptive radiation they would reasonably count as another departure.

An Adventure in Morphospace

I'm going to get a bit more technical here, and consider what adaptive radiation does, and how I think we should distinguish between a lineage that may be removed from its parent lineage to leave a useful paraphyletic classification. It begins with morphospace.

Wiki doesn't provide a definition of morphospace, so, with the aid of a page I found through Google, let me expound.


Figure 1: Morphospace schematic. Click on image to see original caption. (From the website of Mark Ridley's Evolution)

Basically, you have to imagine an n-dimensional space with each of the n axes representing the extent of one characteristic of the morphology. The location in that space represents the specific morphology. An adaptive radiation spreads out in this space, along one or several dimensions.

A key innovation, OTOH, involves a movement along dimensions not really involved in previous adaptive radiations. Thus, early "reptiles" spread out in various ways based on their "lizard-like" shape, their "cold blood", and minor modifications of their body plans and egg styles, while the lineages leading to birds and mammals innovated with upright stance, "warm blood", milk, feathers capable of flight, and so on. These key innovations opened up a new area of morphospace for adaptive radiation, meaning that a qualitatively different evolutionary process was followed.

This, in my view, represents a good justification for allowing paraphyletic classes such as "reptiles": they help in modeling the evolutionary process, because the divergence involved in creating the lineages that "leave" the class are qualitatively different from those involved in the adaptive radiations within it.

Obviously, there are some semantic boundary problems here. In some ways, the differences between the types of evolution involved in simple adaptive radiation and the development of the archosaurs and mammals can be seen as differences in degree rather than kind. However, we can judge by the results, and also by the likelihood of the development. If mutations leading to a particular development were happening all the time, there would have been many instances of it. OTOH, if we're talking about the kind of mutation whose probability of occurrence is (on average) once in every 100 million years for a large population, then we can probably allow such mutations to count as "key" innovations when they lead to a major adaptive radiation.

Cladistic Reconciliation

How can this be reconciled with the more extreme cladistic demands? Well, we need to start by realizing that these "grade-based" paraphyletic classifications are only useful tools for modeling evolutionary processes, they aren't for resolving issues of "genetic relatedness". We need to carefully distinguish between genetic relatedness and the morpho-ecological "relationships" that tie together creatures with similar morphology and ecological niches. When we are discussing ancestry and genetic relatedness we need to concentrate on cladistic analyses, allowing some modification based on specific theoretical evolutionary scenarios.

Only when we're trying to build those scenarios, especially when the cladistic issues cannot be resolved (as is the case, for instance, with the earliest animals), should we allow discussion of paraphyletic classifications such as "reptiles" or "sponges".

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Development Genes in the Earliest Animals

I recently discussed the earliest animals, and what the original animal might have been like. (Or rather, the originals of several purported animal clades.) A just-released paper⁷ has added support for my model, although it also supports several others.

Lapébie, P., Gazave, E., Ereskovsky, A., Derelle, R., Bézac, C., Renard, E., Houliston, E., & Borchiellini, C. (2009). WNT/β-Catenin Signalling and Epithelial Patterning in the Homoscleromorph Sponge Oscarella PLoS ONE, 4 (6) DOI: 10.1371/journal.pone.0005823

What this paper demonstrates is that one particular branch of the sponges, the homoscleromorphs, use one of the oldest mechanisms of controlling animal development in ways very much like much more complex animals such as insects and mammals.

The specific mechanism is the Wnt/TGF-β system, which I've also mentioned elsewhere, making the point that it's probably a more powerful calculator than is usually assumed. The Wnt system is one of those whose origins seem to go back to the beginning of animals, although it doesn't seem to be present in the choanoflagellates.¹⁰

The expression of Wnt and TGF-β in the Homoscleromorph Sponge Oscarella:

[...] showed complementary expression patterns in relation to the evenly spaced ostia (canal openings) of the exopinacoderm (ectoderm), highly reminiscent of Wnt expression during skin appendage formation in vertebrates. Furthermore, experimental activation of the Wnt/β-catenin pathway using GSK3β inhibitors provoked formation of ectopic ostia, as has been shown for epithelial appendages in Eumetazoa. We thus suggest that deployment of Wnt signalling is a common and perhaps ancient feature of metazoan epithelial patterning and morphogenesis.⁷

Figure 1: Complementary expression patterns of Wnt ligands in Oscarella lobularis exopinacoderm. Click on image to see full figure and original caption. (From Ref 7 figure 2.) ...

The homoscleromorphs have been thought to be part of a more distantly related group of sponges, the demosponges (so says Wiki as of this writing), but recent evidence is that they form a separate clade, more closely related to us than the calcareous sponges, previously thought to be the closest relatives of the eumetazoans (including us).^{6, 7, 8}

This is particularly important to deciphering the earliest history of the animals. In Poriferan paraphyly and its implications for Precambrian palaeobiology by Sperling, Pisant, and Peterson, there is a good discussion of how we can use the paraphyly of the sponges to find the sequence of development of some of the features of the more advanced animals, the eumetazoans.

As is happens, many genes and features long thought to be absent in sponges are turning out to be present, especially in the larvae. One important feature is the ability to create epithelia with basal membranes and cell junctions capable of a water-tight seal. As it happens, all sponges can do this, but most only do it intermittently while creating spicules.²⁰


Figure 2: Schematic of a representative epithelium. Click on image to see original caption. (From Ref 20, figure 1.)

The epithelium is extremely important in development, because it is a topological surface capable of supporting a coordinate system in which each cell has a unique location, which, in turn, can be fed into its gene expression system.

(One mechanism by which this can be, and often is, done is to set up a pair of diffusion gradients at right angles, so that each cell gets a different mix of signaling molecules. This mix can be translated, within each cell, into different ratios of transcription factors (TF's), which in turn can react differently on gene expression.³⁶)

Another thing that can be done with an epithelium is to create regularly spaced "spots" through a mechanism involving diffusion. I'm not going to go into details here, but the results can be seen in Figure 1, and is discussed in the paper.⁷

As mentioned above, all the major lineages of sponge have larvae that can produce epithelia, of sorts. However:

Homoscleromorpha exhibit a true epithelial morphogenesis-morphogenetic movements of cells united with their neighbours in a layer.[ref] Epithelial folding is one of the basic morphogenetic processes reiterated throughout embryonic development in Eumetazoa. Interactions between epithelial cells and the extracellular matrix play a fundamental role in this morphogenesis.[ref] In Homoscleromorpha, this process is observed during the egg's follicle formation,[ref] during the metamorphosis in a rhagon (earliest developmental stage with a functional aquiferous system),[ref] during the sponge growth starting as formation of projections of the exopinacoderm,[ref] during asexual reproduction by budding,[ref] during ostia formation and reparative regeneration ([ref]).⁸

I should mention the paper the above quote comes from, My favourite animal The Homoscleromorph sponge Oscarella lobularis, a promising sponge model in evolutionary and developmental biology by Alexander V. Ereskovsky, Carole Borchiellini, Eve Gazave, Julijana Ivanisevic, Pascal Lapébie, Thierry Perez, Emmanuelle Renard, Jean Vacelet. This paper, like Lapébie et al.⁷ (see above) and Sperling et al.,⁶ (see above) offers a good discussion of the place of the Homoscleromorpha in early animal evolution.

Ereskovsky, A., Borchiellini, C., Gazave, E., Ivanisevic, J., Lapébie, P., Perez, T., Renard, E., & Vacelet, J. (2009). The Homoscleromorph sponge Oscarella lobularis, a promising sponge model in evolutionary and developmental biology BioEssays, 31 (1), 89-97 DOI: 10.1002/bies.080058

Now, let me go way back to my thoughts about the earliest animal, discussed in Ur... Ur... Ur...:

The original animal was somewhat like a larval sponge: a small ball of cells, but with differentiation and differential expression of a bunch of genes associated with developmental control in almost all animals.^{30, 15, 16, 31}

Like sponges and choanoflagellates, it used a collared flagellum for its food, and like sponge larvae it could swim and possessed phototaxis and chemosensing abilities, able to guide its swimming based on external sensory information.16

I made the argument in Searching for the Urbilaterian that no matter what symmetry an animal has, or not, it has to know its left from its right, and its up from its down, in order to guide its activity. The same is true for phototaxis. Thus, even the larvae of sponges must have some genes differentially expressed to let cells know where they are in the body. The same almost certainly holds true for placozoans, which also have differentially expressed genes, although none have yet been proven to provide lateral directionality.^{37, 38, 39} [Footnotes and links have been updated.]

Thus, we have a small ball of cells, with a bunch of collared flagella, probably in front, ciliated cells along the equator, and some sort of sensory abilities.

Based on the information in these new papers, and some of the references (that I hadn't found previously), I'll add to that.

First, one of the biggest problems with the sponges as ancestors for the rest of the metazoa is the water-canal system. Most observers seem to think that it is unique enough that it must have evolved once, so it would have had to have been lost by the eumetazoans when they diverged from their common ancestor with the Homoscleromorpha. That, in turn, has prompted the idea that they evolved from a sponge larva.¹

However, some larval sponges have non-functional choanocyte chambers:

The larva of many species in the class Demospongiae, the parenchymella, has also been reinterpreted recently as a post-gastrulation stage in which no inversion of layers occurs. The late stereoblastula differentiates intermingled micromeres and macromeres, followed by a selective ‘‘centrifugal migration’’ [ref] of micromeres towards the surface (Fig. 2). This long-known process has now been interpreted as gastrulation by ‘‘mixed delamination’’ [ref]. Then, cell differentiation begins to produce a solid parenchymella made of macromere-derived unciliated cells and micromere-derived ciliated cells, up to a total of 11 cell types (unpubl. obs.), occasionally including choanocytes organized into non-functional chambers. [emphasis mine]⁴

It's completely plausible, IMO, that a small animal like a floating sponge might have had a system of internal water-canals, while still having a regular shape and the ability to swim. Thus, each lineage would have branched off from the main line (with swimming adults), then developed a sessile lifestyle.

It may be that the swimming done by some groups of sponge larvae doesn't actually require the knowledge of left/right and up/down, at least beyond the cellular level:

The ring-cells around the posterior pole (relative to direction of motion) of the parenchyma larva of the demosponge Amphimedon has been shown to be photosensitive and to respond to blue light ([refs]). These cells effectively steer the sponge, using long cilia providing for a phototactic response.¹³

It may well be that there was a steady progression of advance in developmental mechanisms, with one lineage of sponges branching off after each advance. Thus, the last branch, the Homoscleromorpha, would have had almost the full suite of mechanisms, probably including biradial symmetry.

Another problem with sponges involves the spicules. These have often been thought to have evolved once, but Sperling et al. disagree:

Therefore, it is clear from the emerging sponge phylogeny that spicules arose at least three times within ‘Porifera’: at least once within Silicispongia, once within Calcispongia and once at either at the base or within Homoscleromorpha. Given the clear homoplasy of massive calcareous skeletons within demo- and calcisponges ([ref]), convergence of spicule structure as well should not be too surprising.⁶

Finally, let me mention communication. It seem unlikely that this animal had nerve cells (although it's been suggested²³), however much of the cellular machinery needed for chemical communication is present in at least some lineages of sponges.²³

For a small animal, with few fast-moving predators, chemical communication may well have been sufficient. Sponges are capable of coordinated actions across even large bodies,²¹ and the hexactinellid sponge Rhabdocalyptus dawsoni uses an "action potential" rather similar to that of nerve cells:

Impulses propagated at 0.27±0.1cms-1 with an absolute refractory period of 29 s and a relative refractory period of approximately 150 s.²²

If this model is true, our most distant ancestor might have had a fairly sophisticated system of communication long before the invention of nerve cells. It would be directly ancestral to our modern system of chemical emotions, and may have been even more sophisticated, since there were no nerve cells to handle more "digital" types of calculation. Indeed, the chemical intelligence displayed by modern vertebrates in controlling their development may well have been used to control active behavior in this ancient ancestor.

Links: (Some of these are duplicates of links from earlier posts. Not all have been called out in the text. Many are derived from the main article. Use the back key if you came via clicking a footnote.) (I've included only the links referenced in this leader.)

1. Six major steps in animal evolution: are we derived sponge larvae?

2. A Phylogenomic Investigation into the Origin of Metazoa

3. The Protistan Origins of Animals and Fungi

4. Choanoflagellates, choanocytes, and animal multicellularity

5. Cell-Cell Adhesion in the Cnidaria: Insights Into the Evolution of Tissue Morphogenesis

6. Poriferan paraphyly and its implications for Precambrian palaeobiology

7. WNT/β-Catenin Signalling and Epithelial Patterning in the Homoscleromorph Sponge Oscarella

8. My favourite animal The Homoscleromorph sponge Oscarella lobularis, a promising sponge model in evolutionary and developmental biology

9. The Premetazoan Ancestry of Cadherins Requires free registration

10. The genome of the choanoflagellate Monosiga brevicollis and the origin of metazoans

11. Early evolution of animal cell signaling and adhesion genes

12. Alternative Wnt Signaling Is Initiated by Distinct Receptors requires free registration (use Science login)

13. Evolution of sensory structures in basal metazoa

14. The Trichoplax Genome and the Nature of Placozoans

15. Wnt and TGF-β Expression in the Sponge Amphimedon queenslandica and the Origin of Metazoan Embryonic Patterning

16. Developmental expression of transcription factor genes in a demosponge: insights into the origin of metazoan multicellularity

17. A maternally localised Wnt ligand required for axial patterning in the cnidarian Clytia hemisphaerica

18. Review: How was metazoan threshold crossed? The hypothetical Urmetazoa

19. The last common bilaterian ancestor

20. Epithelium—The Primary Building Block for Metazoan Complexity

21. Coordinated contractions effectively expel water from the aquiferous system of a freshwater sponge

22. Impulse conduction in a sponge

23. A Post-Synaptic Scaffold at the Origin of the Animal Kingdom

24. Cytological Basis of Photoresponsive Behavior in a Sponge Larva

25. Purinergic transmission in the central nervous system

26. Size independent selective filtration of ultraplankton by hexactinellid glass sponges

27. Concatenated Analysis Sheds Light on Early Metazoan Evolution and Fuels a Modern “Urmetazoon” Hypothesis

28. Lower Cambrian Vendobionts from China and Early Diploblast Evolution

29. Molecular phylogeny of choanoflagellates, the sister group to Metazoa

30. Hox, Wnt, and the evolution of the primary body axis: insights from the early-divergent phyla

31. Emergence, development and diversification of the TGF-β signalling pathway within the animal kingdom


32. Neuroactive substances specifically modulate rhythmic body contractions in the nerveless metazoon Tethya wilhelma (Demospongiae, Porifera)


33. The left-right axis in the mouse: from origin to morphology

34. A transcriptional regulatory cascade that controls left/right asymmetry in chemosensory neurons of C. elegans

35. Key innovations, convergence, and success: macroevolutionary lessons from plant phylogeny

36. Evolution and Morphogenesis of Differentiated Multicellular Organisms: Autonomously Generated Diffusion Gradients for Positional Information

37. The Trox-2 Hox/ParaHox gene of Trichoplax (Placozoa) marks an epithelial boundary

38. The Early ANTP Gene Repertoire: Insights from the Placozoan Genome

39. The Trichoplax PaxB Gene: A Putative Proto-PaxA/B/C Gene Predating the Origin of Nerve and Sensory Cells

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Memory, Emotions, and State-Dependent Learning

What is State Dependent Memory?

Experiments early last century first discovered that memories laid down under the influence of some drugs, especially opiates, were much easier to retrieve under the influence of those same drugs. However, for some while the focus of memory studies was on "memory molecules", in a mistaken analogy to the molecular basis of genetics (DNA).²⁹ By the '70's, however, some work was being done on this subject,³ and by the '90's it was an area of research, if not heavily subscribed.²

Basically, there appear to be a number of drugs that produce this behavior: an animal (and, presumably a person) learning certain things (behaviors and perhaps facts) under the influence of a specific drug will find it harder to recall those memories unless under the same influence. There's some overlap, that is some drugs can substitute for others, while there are combinations of drugs that increase the effect.

There continues to be on-going research in this area, ... although much is behind paywalls,^{7, 8, 9, 10, 11, 14, 15, 16, 17} and quite a bit seems to be only "ruling out" state-dependent learning as part of the cause of whatever effect it's studying.^{17, 18, 19, 20, 21, 22} There are papers that (IMO) ought to mention the subject but don't,^{23, 29} including three whole "special issues" of one periodical.^{24, 25, 26}

I was able to find only two very recent papers on the subject that were open access, one of which is in Iran,¹³ and the other⁶ is Arcaine and MK-801 make recall state-dependent in rats by Ana Paula Chiapinotto Ceretta, Keli Camera, Carlos Fernando Mello, and Maribel Antonello Rubin.

Ceretta, A., Camera, K., Mello, C., & Rubin, M. (2008). Arcaine and MK-801 make recall state-dependent in rats Psychopharmacology, 201 (3), 405-411 DOI: 10.1007/s00213-008-1304-7

Researching the NMDA Receptor

In the latter paper a new antagonist for one of the binding sites on the NMDA receptor was tested to see if it induced state-dependent learning effects, and whether it was interchangeable with a better-known antagonist with the same site. The NMDA receptor is one of the "smartest" molecules in the nerve cell membrane, with at least six different binding sites, which can be influenced by a number of endogenous substances, as well as a massive number of drugs and poisons.


Figure 1: the NMDA receptor present the nervous system. Click on image to see full figure and legend. (From Wiki)

This receptor has a single known effect, opening a channel for all types of cations, especially including sodium, potassium, and calcium. However, its effect can be modulated by all of the known binding sites, as well as any number of as-yet unknown sites. It consists of a tetramer of two types (two each) each of which comes in a wide variety of sub-types. It performs a very complex "calculation" based on the activity on all its binding sites, resulting in the actual extent the channel is open: its output. Thus, like the nerve cell itself, this molecule integrates a large input into a single scalar output (as far as we know).

One type of binding site responds to the polyamines putrescine, spermidine, and spermine (10 in Figure 1), a set of chemicals known for a while to have some influence on brain activity.²⁸

In the experiments here,⁶ rats were "trained" by placing them in a cage with an electrified floor, on a small platform. When they stepped off, they got a shock. They were then dosed with one of the drugs (which interfere with the binding site mentioned above) or a "placebo", and put back in their cages. After a day, they were put back into the cage on the the small platform, and the "latency" before they stepped off onto the (previously electrified) floor was measured.

The results were that either drug administered right after the training interfered with their memory later, in that they stepped off the platform pretty quickly the next day. However, if they were administered a new dose of either drug, they remembered as well as the ones who got "placebos" all around. This is a pretty clear indication of state-dependent learning, and that the two drugs create pretty much the same state.

Memory and Emotional State

While the NMDA receptor is probably the best-known receptor involved in state-dependent learning, there are other receptors involved in memory, and there may be (probably are) many undiscovered binding sites for endogenous molecules that can modulate their activity. This, then, shows that all the necessary mechanisms are present in the body for memory, and everything depending on it, to be somewhat specific to emotional states.

Redefining Emotional State

Back in '97, Candace Pert published Molecules of emotion, the first part of which detailed her adventures with the scientific establishment (of which she was a part) while participating in the discoveries of opiate receptors and the body's natural opiates: endorphins, enkephalins, dynorphins, etc. (Since then she appears to have moved towards the woo-woo area, perhaps because of the money available, or perhaps due to disagreements with part of the more hidebound medical establishment. However, a search of Google Scholar yields 19 scholarly papers published since 1998 (out of 28 total references the extras being books and references in other papers).)

In this book she outlines an interesting hypothesis: that since emotions are, at the least, accompanied by hormones that act on the same receptors that these drugs do, perhaps we can put a scientific basis under the idea that its easier to remember things when you're in the same emotional state as when they happened.

I recently discussed the idea of redefining "emotional state" as the total hormonal state of the body: an n-dimensional vector where n is the total number of hormones available and the relative concentration of each hormone represents its contribution to the vector.

Let's see how these two ideas interact: If the emotional state consists of all the hormones in the body, it must involve all the receptors those hormones interact with. Such interaction is probably not one-to-one, although that assumption seems to be built into much current research (IMO).

Of course, this doesn't mean that every possible emotional state has its own memories. In the first place, there are probably only a limited number of binding sites on a limited number of receptors that are involved in this process. However, that doesn't mean that any hormone without such a binding site can't affect memory. The body contains a number of cells that both secrete hormones and express receptors (for the same or other hormones) on their surfaces. As yet, we know very little about how the body computes its emotional state, although we know that there's a good deal of hard (number crunching) calculation by the more traditional central nervous system (CNS), especially the limbic system, and the autonomic nervous system, as well as secretion by cells of many types. Nerve cells do a great deal of secretion, and express a great number of receptors, any or all of which may influence both what/when they secrete hormones and how they perform the calculations more traditionally associated with the nerve cell.

Exploring Emotion-Space

If we consider the n-dimensional space defined by the "emotion vector", we could presumably map out the various areas where memory pretty much crosses over, and those areas where small changes to the internal state tend to create large "fades" in memory. Those wouldn't necessarily correlate with the effects of drugs, which are sort of "random scatter-shot" influences on what evolved as a fairly consistent system. But it seems likely that different natural emotional states will tend to have their own associated memories.

The observed effects of drugs, then, would modify the emotional state, that is the location in (n-dimensional) emotion space, often in unnatural ways, since they could well have effects on receptors in combinations never seen naturally.

If we assume that much of what we consider our personalities consists of memories at the level of the aversion the rats in the featured experiments were trained in, then it makes sense that much of our personality is itself dependent on our emotional state, and that we truly are, in some sense, "different people" when in a different mood or other emotional state.

Indeed, it seems reasonable that many cases of "multiple personality" are nothing but extreme cases of this effect, perhaps involving hormones that lack a strong effect on the more traditional types of "emotion".

It also brings up an interesting possibility regarding drug use: if a person has lots of "aversive" childhood memories, and the use of any drug with "state-dependent learning" effects makes those memories fade, you would expect that any new drug would automatically be pleasurable by contrast with normal life. Perhaps for many people it's not just the actual "mind-altering" effect of the drug that makes it so attractive, but the fact that they are more "distant" from unpleasant memories and associations.

This is important research, with many implications. It reaches into the fundamentals of how our brains create and maintain our personalities and consciousness.

Links (I've mentioned any where I've linked to the abstract, because there's a paywall, and in one case because the full PDF is in Iran and may not always be available everywhere. Not all links are necessarily referenced in the text. Use the back key if you came via clicking a footnote.) (I've included only the links referenced in this leader.)

1. Opiate States of Memory: Receptor Mechanisms

2. The influence of central administration of dopaminergic and cholinergic agents on morphine-induced amnesia in morphine-sensitized mice

3. Amnesic trace locked into the benzodiazepine state of memory

4. The locus coeruleus–noradrenergic system: modulation of behavioral state and state-dependent cognitive processes

5. Nicotine does not produce state-dependent effects on learning in a Pavlovian appetitive goal-tracking task with rats

6. Arcaine and MK-801 make recall state-dependent in rats

7. The dopaminergic system plays a role in the effect of lithium on inhibitory avoidance memory in mice paywall

8. Possible involvement of mu-opioid receptors in effect of lithium on inhibitory avoidance response in mice paywall

9. Repeated administration of histamine improves memory retrieval of inhibitory avoidance by lithium in mice paywall

10. Effects of repeated yohimbine on the extinction and reinstatement of cocaine seeking paywall

11. Morphine-Induced Behavioral Sensitization Increased the mRNA Expression of NMDA Receptor Subunits in the Rat Amygdala paywall

12. Modulation of ethanol state-dependent learning by dorsal hippocampal NMDA receptors in mice paywall

13. Intra-dorsal hippocampal microinjections of lithium and scopolamine induce a cross state-dependent learning in mice abstract. Full text (free) supposedly here, in Iran.

14. Implicit motivational states influence memory: Evidence for motive by state-dependent learning in personality paywall

15. Combining Exposure and Pharmacotherapy in the Treatment of Social Anxiety Disorder: A Preliminary Study of State Dependent Learning paywall

16. Ethanol state-dependent memory: involvement of dorsal hippocampal muscarinic and nicotinic receptors paywall

17. Relapse following combined treatment discontinuation in a placebo-controlled trial for panic disorder paywall

18. Memory and psychostimulants: modulation of Pavlovian fear conditioning by amphetamine in C57BL/6 mice

19. Revealing Past Memories: Proactive Interference and Ketamine-Induced Memory Deficits

20. Role of dopamine D3 receptors in the expression of conditioned fear in rats

21. Motor-skill learning in a novel running-wheel task is dependent on D1 dopamine receptors in the striatum

22. β2 Subunit Containing Acetylcholine Receptors Mediate Nicotine Withdrawal Deficits in the Acquisition of Contextual Fear Conditioning

23. Neurobiology of Memory and Anxiety: From Genes to Behavior

24. Clinical Pharmacology of Memory

25. Pathological Changes in Memory Processing

26. Plasticity and Anxiety

27. Putrescine, Spermidine, and Spermine

28. Selective Release of Sperrnine and Spermidine from the Rat Striatum by N-Methyl-D-Aspartate Receptor Activation In Vivo

29. From the Search for a Molecular Code of Memory to the Role of Neurotransmitters: A Historical Perspective

Read more!

Adding a Dimension to Intercell Signaling

I've discussed the cell's internal computing abilities, and mentioned how it ties to the signaling between cells, but the general assumption in intercell signaling has been that one signaling molecule represents a scalar message: a one-dimensional number represented by concentration. In principle, any two molecules could be combined to create a 2-dimensional signal, representing a point on a plane, but in a preprint article¹ we hear of an actual 2-dimensional receptor: one molecule embedded in the cell's membrane that has two specific reactions within the cell to ligands outside, and the amount of those reactions depends on the ligand involved.

Zidar, D., Violin, J., Whalen, E., & Lefkowitz, R. (2009). Selective engagement of G protein coupled receptor kinases (GRKs) encodes distinct functions of biased ligands Proceedings of the National Academy of Sciences DOI: 10.1073/pnas.0904361106

A Bit About G Proteins and GPCRs

Let's start with some background regarding "G proteins", short for guanine nucleotide-binding proteins. These proteins "plug into" the "G-socket system": that is they use GTP as a power source, acting as "second messengers" for signals from outside the cell. Typically, a G protein will be bound in a trimer: a combination of three molecules, usually all different. When the ligand outside the cell binds to the receptor it changes its shape, transmitting its message through the membrane where the new shape can cause the G protein trimer to split into a dimer and a singleton, each of which may now act to catalyse internal signal reactions.


Figure 1: Signal Transduction that uses G Proteins. Click image to see original caption. (From Ref 3.) ...

These receptors are called G protein-coupled receptors (GPCRs), and they form one big, highly conserved, family.¹ They are a subset (or sometimes used interchangeably) of seven-transmembrane receptors (7TMRs), one of the oldest types of protein.^{7, 8}

Once the ligand is bound, the new receptor shape is also vulnerable to phosphorylation by enzymes called G protein–coupled receptor kinases (GRKs), which represents a further change to the receptor. Next, the phosphorylated receptor will bind a type of molecule called a ß-arrestin, which pretty much neutralizes the receptor. In many cases it also pulls the receptor out of the cell membrane, internalizing it into a vesicle or endosome.


Figure 2: Model of ß-arrestin-regulated internalization of a prototypic GPCR. Click on image to see original caption. (From Ref 5.)

The ß-arrestin-bound receptor can also act as a signaling molecule, catalyzing further changes.^{2, 3} Further, the ß-arrestin, after binding to the GPCR, can:

translocate from the cytoplasm to the nucleus and associate with transcription cofactors such as p300 and cAMP-response element-binding protein (CREB) at the promoters of target genes to promote transcription. They also interact with regulators of transcription factors, such as IκBα and MDM2, in the cytoplasm and regulate transcription indirectly. This ß-arrestin-mediated regulation of transcription appears to play important roles in cell growth, apoptosis and modulation of immune functions.²

The New Discovery

These two paths have been regarded as "tied together": when the ligand bound to the GPCR, it set off both paths of reaction. The second path, based on phosphorylation of the receptor, tends to reduce its power, allowing it to "desensitize" itself to the incoming signal: an important feature of many sensory systems. However, Zidar et al.¹ have demonstrated that a GPCR called C-C chemokine receptor type 7 (CCR7) responds differently to two natural ligands: Chemokine (C-C motif) ligand 19 (CCL19), and Chemokine (C-C motif) ligand 21 (CCL21):

CCL19 leads to robust CCR7 phosphorylation and ß-arrestin2 recruitment catalyzed by both GRK3 and GRK6 whereas CCL21 activates GRK6 alone. This differential GRK activation leads to distinct functional consequences. Although each ligand leads to ß-arrestin2 recruitment, only CCL19 leads to redistribution of ß-arrestin2-GFP into endocytic vesicles and classical receptor desensitization. In contrast, these agonists are both capable of signaling through GRK6 and ß-arrestin2 to ERK kinases. Thus, this mechanism for ‘‘ligand bias’’ whereby endogenous agonists activate different GRK isoforms leads to functionally distinct pools of ß-arrestin.¹

We don't really have to understand everything in the blockquote (from the abstract, there's more explanation in the main text), we just need to see that the amount of reaction along each of the two paths in (our) Figure 1 is different for CCL19 and CCL21. This is functionally equivalent to a 2-dimensional signal, that can be emitted by any cell(s) that can vary their relative rates of secretion of these two ligands depending on internal calculations. If there are many cells emitting such signals, they will be roughly additive (depending on distance for diffusion and mixing).

The complexity of the signaling network within the body is well known, but this (literally) adds a whole new dimension to what it's capable of.

Links: Not all of these are called out in the text. Use the back key if you came via a footnote.

1. Selective engagement of G protein coupled receptor kinases (GRKs) encodes distinct functions of biased ligands

2. ß-arrestin signaling and regulation of transcription

3. Transduction of Receptor Signals by ß-Arrestins

4. Regulation of G Protein-Coupled Receptor Kinases and Arrestins During Receptor Desensitization

5. Arrestin-Independent Internalization of G Protein-Coupled Receptors

6. GRKs and arrestins: regulators of migration and inflammation

7. Application of comparative genomics in the identification and analysis of novel families of membrane-associated receptors in bacteria

8. On the origins of arrestin and rhodopsin

9. Phylogenetic analysis of 277 human G-protein-coupled receptors as a tool for the prediction of orphan receptor ligands

Read more!

FoxP2: Evolving Human Intelligence

Like an answer floating to the window of a magic 8-ball, Foxp2 has floated back to the top of the attention heap with a just-published paper in Cell: A Humanized Version of Foxp2 Affects Cortico-Basal Ganglia Circuits in Mice by Enard et al. This gene/enzyme/Transcription Factor (TF) is an item of extreme interest in the study of human evolution because it's highly conserved across tetrapods, with just a one-amino-acid difference between chimpanzees and mice, but humans have managed to acquire two changed amino acids since our lineage split from chimpanzees and gorillas.

I'm going to refer to this change (both amino acids) as an "improvement", although I recognize it's somewhat chauvinistic of me. It will make it easier to distinguish from other mutants in the discussion.

The FoxP2 protein is an enzyme that, at least, participates in the complex dance that is gene activation. Defective mutant alleles of this gene in humans cause cause a "severe disorder, involving profound deficits in the control of complex coordinated face and mouth movements, resulting in disrupted speech ([ref's])."¹¹ The combination of this with the fact that humans have a recent improvement in this gene⁸ is what makes it so interesting.

Biochemical Effects of Foxp2 Change

What are the effects of this improvement? There are several ways to approach this question, and I'm going to start with biochemistry: I've discussed the fact that the cell is like a powerful analog computer with a large collection of enzymes that can be phosphorylated, which changes their behavior, or can phosphorylate other proteins, or, most importantly, both. ...

Each enzyme in this system may be envisioned as a transistor in a large network of transistors wired together (along with appropriate resistors according to the type of transistor and how they're connected). Each transistor also has a "knob" for programming its contribution to the network, which corresponds to the total quantity of enzyme in all phosphorylation states, a function of its gene expression level (and other mechanisms that control overall quantities of proteins: see my How Smart is the Cell? Part II: The Gene Activation network as an Analog Computer).

Note that I said all states rather than both. Many proteins, including many enzymes, can be phosphorylated in more than one place -- often by different kinases. Whether a protein can be phosphorylated at one place can depend on whether it's phosphorylated at another (or not), or more than one other place, creating a complex logic in its phorphorylation. And this logic can differ for different kinases. The same is potentially true for dephosphorylation, although research is just beginning into the specificity of phosphatases.

Now, the reason this is important is that one of the changes in human Foxp2 involves potential phosphorylation sites. One change, at position 303, replaces a threonine with an asparagine. Evidently, the original threonine was not a site for phosphorylation, although this residue can be phosphorylated under certain circumstances.

The other change, at position 325, replaces an asparagine with a serine. One study³ showed that "the human-specific change at position 325 creates a potential target site for phosphorylation by protein kinase C together with a minor change in predicted secondary structure."3 It goes on to say:

Several studies have shown that phosphorylation of forkhead transcription factors can be an important mechanism mediating transcriptional regulation. Thus, although the FOXP2 protein is extremely conserved among mammals, it acquired two amino-acid changes on the human lineage, at least one of which may have functional consequences.3

This may well be true, but even if this effect is not present, the results are extremely important.

How does this relate to our transistor network analogy? The first change probably has no effect, but the second may be compared to adding a whole new set of connections, from previously unconnected transistors to this one (the one that represents the foxp2 enzyme).

(In fact, it's more than this: our analogy is simplistic in that a transistor always (AFAIK) has a scalar output while an enzyme with multiple sites for phosphorylation, or any other type of activation, will output an n-dimensional vector where n is one less than the total number of possible states involved, including the completely inactivated one. Adding a site for phosphorylation, then, adds an entire dimension to the output as well as adding the new connections from kinases capable of phosphorylating it.)

At this point we're ready to consider the new paper.

Enard, W., Gehre, S., Hammerschmidt, K., Hölter, S., Blass, T., Somel, M., Brückner, M., Schreiweis, C., Winter, C., & Sohr, R. (2009). A Humanized Version of Foxp2 Affects Cortico-Basal Ganglia Circuits in Mice Cell, 137 (5), 961-971 DOI: 10.1016/j.cell.2009.03.041

What these researchers did was to induce an improvement similar to the normal human type into mice, and compare the results with both the wild type and some mice with defects like those of the human defective mutants. They demonstrated that the results were totally unlike those of the defective allele, and influenced vocalization, the dopamine system, exploratory behavior, and dendrite growth and neural plasticity in the striatum, "a part of the basal ganglia affected in humans with a speech deficit due to a nonfunctional FOXP2 allele".¹ The last four are probably related, and the first may be.

What we should not assume is that this mutation makes mice "more like humans", and especially that they will be able to speak. As it happens, adult male mice actually have "songs" they use during courtship,^{5, 6} although this fact wasn't mentioned in this paper. I would be interested how the adult male mice in their study performed in experiments recording their songs.

The most important implication of this research is that, when applied to an arbitrary mammal, it produces a viable animal with a consistent set of changes. Subsequent evolution can take it from there, and probably did in humans.

Network Implications

To understand the importance of this improvement, we need to make a short excursion into network theory. This field of study maps various objects and the relationships among them into "nodes" and "edges" respectively: the nodes are points on a graph, the edges are lines connecting them. The resulting network can then be analyzed without reference to how those objects relate, considering only which objects relate (are connected by an edge) to which, how many relationships (edges) each object has, looking at averages, loops, path lengths between nodes without direct connections, and so on. Obviously, creating a bunch of new "edges", connecting previously unconnected nodes, will change the characteristics of the network. It will be more "tightly" connected, and potentially modules that were only distantly connected will now be cross-talking.

In How Smart is the Cell? Parts II and III I discussed how the gene activation system can "program" the enzyme system by "twisting knobs", that is varying the total amount of each enzyme created. Mutations to the control sequences (TF binding sites) can potentially make fine adjustments to gene expression levels, as well as major changes. Other types of mutation can add whole new logic modules, duplicating an existing one, which can then begin to diverge (evolve away), or grafting in logic from the control regions(s) of some other gene(s). With the new version of Foxp2 in place, protohuman lineages could then begin accumulating, and selecting, such mutations.

What makes Foxp2 particularly interesting is that it is a TF itself, which means mutations could arise not only to its control sequences, but to those it interacts with. Thus Foxp2 is right at the intersection of a complex phosphorylation network and a complex gene activation network. Adding a new site for phosphorylation adds considerable complexity to those networks, making it an even more important participant in the "cellular brain" that controls development, especially in the "neural brain" of amniotes.

Foxp2 Across the Amniotes

Although Foxp2 is generally conserved, it has seen some changes, and since there's considerable interest in its relationship to human vocalization, these changes have been studied and correlated with such things as learned vocalization.

Foxp2 is present in birds, although they lack the changes that constitute the human "improvement".⁴


Figure 1: Alignment of deduced amino acid sequences from the zebra finch FoxP2 cDNA with three mammalian sequences. Click on image to see original caption. (From Ref 4.)

A more complete study⁷ analyzed 18 animals spread through the amniotes, and determined that the single change present in songbirds occurred independently in the mouse lineage (see Ref 7, figure 1.) It's interesting that mice also have a "song" somewhat reminiscent of birds, although much higher pitched.^{4, 5, 6} Although it's been a popular idea that the human improvements might contribute to "song learning" no such correlation has been found,⁸ only the one between mouse "songs" and bird songs.

While the human improvement hasn't been found to correlate to any sort of "song learning", important discoveries have been made regarding the times and places of Foxp2 expression in such animals.^{6, 7, 10}

Another important discovery involves bats: although none of them seem to share the human improvement, they have seen an impressive number of changes to this gene.² Instead it has been speculated:

[... T]hat observed variation in bats might be associated with aspects of echolocation. As mentioned, amino acid variation at both exons 7 and 17 in bats corresponds well to echolocation types/phylogenetic boundaries, with almost complete conservation across groups of confamilial species but contrasting signatures between families. Such high sequence diversity at exons 7 and 17 in bats relative to other mammals, including echolocating cetaceans, indicates that FoxP2 plays a role in the sensorimotor demands that are peculiar to bat echolocation rather than echolocation in general. Indeed while all cetaceans shared three amino acid substitutions (Pro302, Ala304 and Met316) no differences were observed between echolocating and non-echolocating baleen cetaceans, also supporting an earlier comparison of one example from each suborder [ref].²

I should note, however, that the closest non-cetacean relative, the hippopotamus, does not share their changes.⁸

Another interesting fact is that the carnivores share one of the human changes, the important one that changes the network characteristics.^{8, 9}


Figure 2: Table of various animals and their status in terms of the human improvement. Click on image to see original capation. (From Ref 9.)

The Human Improvement

In view of all the above, we can make a hypothesis regarding the mutation that led to the improvement in human language ability. The original mutation, like the changes in mice,¹ led to a creature with a few changes to neural development, but a more complex network in the "cellular computer" involved in controlling development. There were changes to vocalization and learning-related mechanisms, perhaps directly freeing up vocal behavior to be learned in a way that even chimpanzees don't seem to be able to. After this, a series of more minor mutations to the control sequences (and, perhaps, to non-coding RNA sequences) worked over the new, tighter, network, adapting it and its phenotype to fit the new behavioral and ecological niche made possible by the improvement.

Human language, then, represents the end (so far) of a long process of small mutations and selection, building on the original enabling improvement. Not a "big bang", but more than a simple progression of small mutations.

Links: (Not all of these are called out in the text. Use the back key if you came via one of the footnotes.) (I've included only the links referenced in this leader.)

1. A Humanized Version of Foxp2 Affects Cortico-Basal Ganglia Circuits in Mice

2. Accelerated FoxP2 Evolution in Echolocating Bats

3. Molecular evolution of FOXP2, a gene involved in speech and language

4. Parallel FoxP1 and FoxP2 Expression in Songbird and Human Brain Predicts Functional Interaction

5. Ultrasonic Songs of Male Mice

6. Singing Mice, Songbirds, and More: Models for FOXP2 Function and Dysfunction in Human Speech and Language

7. An evolutionary perspective on FoxP2: strictly for the birds?

8. FoxP2 in Song-Learning Birds and Vocal-Learning Mammals

9. Accelerated Protein Evolution and Origins of Human-Specific Features: FOXP2 as an Example

10. FoxP2 Expression in Avian Vocal Learners and Non-Learners

11. FOXP2 expression during brain development coincides with adult sites of pathology in a severe speech and language disorder

Read more!