Category Archives: evolution

On Spermatozoic-Evolution

I recently listened to an episode of RadioLab concerned with the subject of sperm. It was highly enlightening, as most of their programming is, in my opinion, and it turned me on to one concept in particular that I found of particular interest. In general amongst our close animal relatives, promiscuity is the rule; approximately 3 percent of mammalian species are considered monogamous. One predicted result of this behavioral ubiquity is the specific evolution of sperm, for if male genes are to be carried on, an individual’s sperm must compete with the sperm of others inside the female for the right to fertilize her egg(s). In fact, it has been known for some time that evolutionary selection will operate on sperm whenever access to a female’s eggs is contested by sperm from more than one male1. Furthermore, those who speculate about the subject speculate that such competition should yield larger sperm, based on the paired assumptions that larger sperm are faster, and faster sperm are more likely to fertilize an egg.


A study published in the Proceedings of the National Academy of Sciences has shown that female promiscuity does in fact, lead to the evolution of faster sperm in 29 closely related species of cichlid fishes of Lake Tanganyika, Africa. These fish in this lake are of particular interest to evolutionary researchers and theorists because the lake is large enough to constitute several environments – thus it harbors several closely related but distinct species of cichlids – and because of certain “explosive speciation events2,” the relationships amongst these species is very well documented.


These researchers scored each species, assigning them a number according to their “sperm competition rank” (see table above). Which strongly predicted the speed of those species sperm (see table, below).

This research is quite intriguing because it represents an example of behavior feeding back on evolution. The effects of behavior on evolution are fascinating because such phenomena must have played a significant role in our own evolution, and continue to be perhaps the most important determinant of our biological fate.

References:
1. Parker GA. Sperm competition and its evolutionary consequences in the insects. Biol Rev 45: 525–567, 1970.
2. Fitzpatrick JL, Montgomerie R, Desjardins JK, Stiver KA, Kolm N, Balshine S. Female promiscuity promotes the evolution of faster sperm in cichlid fishes. Proc Natl Acad Sci U S A 106: 1128-32, 2009.

On Sexual Selection

Calypte anna

This gentlebird has developed a very neat ability. He can produce a high pitched chirp through the vibrations of its tail-feathers in a high speed dive. Christopher James Clark and Teresa J. Feo at Berkeley did some careful analysis to show that this was in fact not a vocalization and they speculate that this act of sonic production is most likely useful for attracting females1.

Generally, I think of evolution as being driven by fitness, but this case reminded me of how critically important sexual selection is in determining special stability and the retention of genetic traits. Peacocks present a more well-known example, their elaborate tail feathers being of a similarly minimal utility in any other (non-reproductive) aspect of their lives.

In sexual creatures, its actually fairly obvious that this would be the case, since maximal reproduction leads to maximal offspring. It is thus important to remember that what drives evolution is not simply survival, but survival and reproduction.

References

1. Clark CJ, Feo TJ. (2008) The Anna’s hummingbird chirps with its tail: a new mechanism of sonation in birds. Proc Biol Sci. 22;275(1637):955-62.

On Making Faces

How do babies learn to make faces? With arm or leg movements, it seems plausible that, as William James suggested, one might gain insight from simply associating observed appendage position with concurrent muscle activation patterns1. However, in contrast to an attempted stirring of limbs, the infant cannot see what results from the activation of his facial muscles. Thus, the learning mechanism cannot rely on sensory confirmation that the indented action was successfull. That new humans very rapidly learn to express their emotional state through a smile, frown or furrowing of brow hints that there is some implicit path to the acquisition of this skill. It has been suggested by some that the Mirror Neuron (MN) system constitutes this road, or at least a map of it2.

In the adult, MNs are cells that respond when an animal performs some act – picking up a piece of fruit, say – and when it observes another individual doing the same thing, even if the observed actor is a member of another species or stranger, a robot. Beyond this, their activation potentiates the pathways that would be involved in the execution of such a movement by the observer: producing measurable sub-threshold responses in the muscles involved. The suggestion that this system is involved in motor learning and imitation from birth amounts to the assumption that MNs are prenatally wired to function as they do in the mature brain. This is a very attractive idea as it removes the need for some external form of reinforcement – like a visual confirmation of the completed movement – to inform the motor-learning process. Instead, the responsibility for being both carrot and stick is shifted internally, to the MNs. The proposition is that the genetically defined circuitry imbues the MNs with a “knowledge” of the pattern of muscle activity associated with both an observed or executed behavior.

This, unsurprisingly, presents a further question: if the MN system is present from birth and possesses such information as described above, why do infants need to learn how to move at all? This is where we must tread a bit into the realm of informed speculation. First, the MN system cannot know how to execute every movement possible: for instance it certainly cannot know at birth the set of motor commands associated with performing some complex gymnastic move, say a double backflip. If the MN is an artifact of evolution, then it is likely that there is a continuum of innate interpretability, from simple acts like smiles that are well known to the MN system to more rare or contemporary behaviors like figure skating or fixing a bicycle. Thus, using the MN system as a template, of sorts, can only be effective for behaviors on the oft-encountered end of the spectrum. Second, since babies sadly do not leap from the the womb as masters of muscular control, the wiring of the MN system must itself develop at a pace commensurate with the time-course of an individual’s behavioral procurement.

How is it then, that this internal electro-cultivation proceeds in lock-step with the infant’s newfound agility? It has been well documented that humans lose half the total number of neurons in their central nervous system by the time they’ve reached six months of age. This pruning is a synaptic refinement process, also termed neural darwinism4. It is quite beneficial to the newborn animal, since his neural connectivity is far too manifest, too spatially noisy, and must be cleaned up. This happens in all areas of cerebral cortex. For example, in the visual system, molecular cues guide the axons of nearby retinal ganglion cells to adjacent targets in the thalamus while the animal is still in the womb in a gross way, but it is the activity arising from visual stimulation which pares down the connections to the state we see them in the adult. the exquisite spatial precision of connections between the retina and thalamus cells in the retina are connected to cells in the thalamus with exquisite spatial precision because of visual experience. It was Hebb who pointed out that cells that “fire together wire together.5” This means that if two retinal cells fire at the same time, they will tend to be connected to the same post-synaptic target. That is not to say that any two cells that fire at the same time anywhere in the brain will inevitably be connected to each-other, but rather that in deciding which of the molecularly defined crude connections to keep, a post synaptic cell will retain those which tend to fire at the same time in response to stimulation. The stimuli that cause the retinal cells to fire are not simply sets of independently fluctuating pixels; rather they are full of spatial correlations. If a single vertical line passed across your field of vision, a single line of cells in the retina would be stimulated at once as the line moved by. It is almost never the case that two abutting photoreceptors see completely uncorrelated (in time) patterns of illumination. In this way, the spatial relationships in the image translate into spatial relationships in the connections in the brain.

The goings-on I’ve outlined above might generally be termed activity dependent synaptic refinement (ADSR). What I’m hypothesizing is that, as with vision, some form of ADSR is at work in those cortical-areas involved in learning how to move: that the very act of generating motor output leads to more stereotyped action through application of the Hebbian “fire together wire together” motif. The commonality between vision and movement, and indeed the unifying principle behind ADSR, is that the systems are exploiting the presence of underlying statistical content. While the MN system is biasing motor output towards certain configurations, the motor cortex is learning about the possible relationships between muscle tensions, lengths and contractile velocities. It is thought that the brain might use such a mechanism universally as an attempt at maximizing efficiency. For example: if you always listen to symphonic classical music, you might set your stereo to boost bass & treble, but if you’re more the piano concerto type, you’d want a touch more mid-range; then again, if your tastes are more varied, it might make sense to emphasize all three frequency bands equally so as not to aurally marginalize any particular genre. Another for instance more relevant to motor output: when you drive in the city, you rarely ever get out of 2nd gear, but on the highway, you’re almost always in the upper gears. In the same sense, the brain attempts to use the Hebbian rule to optimize its (sensory) inputs and (motor) outputs to the sensory stimuli impinging on it and the motor programs it generates.

You may have noticed in all this that I’ve skipped over one important point: how (genetic wiring notwithstanding), do mirror neurons extract the information about a movement being performed by another agent? It is not at all understood how this occurs in the adult, so how it could be happening in babies is even more mysterious, especially in light of the messiness of infant brains that I’ve spoken of. It simply must be the case that visual stimuli are translated into data concerning the movement of bodies. It is possible that specialized structures for recognizing arms and hands, faces and feet, become sophisticated very early on, but nothing like this has been observed to-date. The needed course of research is clear, but developing experiments to elucidate what might be at work is not. We can only wait and watch from the wings while the scientific players act, and perhaps deliver soto voce direction from time to time.

References

1. James, W. The Principles of Psychology Vol. 1. Henry Holt & Company (1890)
2. Lepage JF, Théoret H. (2007) The mirror neuron system: grasping others’ actions from birth? Dev Sci. 10(5):513-23.
3. Rizzolatti, G., & Craighero, L., (2004) The Mirror Neuron System. Annu. Rev. Neurosci. 27, 169-192.
4. Hebb, D.O. The Organization of Behavior. John H. Wiley & Sons (1967)
5. Edelman, G.M. Neural Darwinism. Oxford Paperbacks (1990)

On Quorum Sensing and Antibiotics

In your body, cells belonging to other organisms are more numerous than your own1. Most of these are not parasitic, we benefit significantly from some of our inhabitants. This is one of the reasons that traditional antibiotics are potentially harmful. Their action is indiscriminate, targeting both harmful and helpful bacteria. The wholesale killing off of our microbial boarders makes many vacancies, providing an opportunity for more virulent creatures to invade. As if this weren’t bad enough, left behind after a course of antibiotics are any bacteria that might have developed immunity to the drugs that put down their brethren. Thus, prescribing such medications also amounts to a selective pressure, an evolutionary nudge towards ever stronger infectors.

Once in your body, harmful bacteria must wait until their colony reaches a certain size for their attacks to be effective. This means that they must posses the ability to detect how many individuals of their species are present. Indeed, this behavior has been the subject of extensive research, and is referred to as Quorum Sensing (QS). The way this works is actually rather simple, each bacterium secretes a small molecule called an autoinducer (AI) at an approximately constant rate (in time and across individuals). Once the concentration of AI is high enough, the colony knows their population has risen to a level where the release virulence factors stands a good chance of successfully inducing pathology.


from reference 2

This example of cell-to-cell communication, in addition to providing a unique system to study such information transfer systems, presents an opportunity to attack unwanted microorganisms in a more species selective way. Thus avoiding both of the issues with antibiotics mentioned above.

Just such a feat was accomplished several years in the laboratory of Hiroaki Suga at SUNY Buffalo. This team of researchers was able to successfully reduce the virulence of Pseudomonas aeruginosa which is the main infectious killer of those with weakened immune systems, such as cancer, AIDS, and cistic fibrosis patients3. This was a great triumph, but another entry in this category has come along which further bolsters the case for attacking the bacterial-telegraph-system.

A group led by Kim Janda at Scripps was able to have a similar impact on Staphylococcus aureus. This bacteria is the main cause of infections in hospitals, and thus represents one of the strains most likely to evolve immunity to antibiotics4. Beyond this and in contrast to the earlier work, these authors were able to use antibodies to target the AIs, making the work potentially generalizable and inexpensive.

It is impossible to understate the beneficial effects that penicillin and it’s derivatives have had in western medicine. As we move forward, however, we must find ways to keep pace with our miniscule counterparts. These two examples of top notch research are exactly the kind of thinking that we need.

References

1. French, K. Randall, D & Burggren, W. (2001) Eckert Animal Physiology. W.H. Freeman

2. Waters, C.M. & Bassler, B.L. (2005) Quorum Sensing: Cell-to-Cell Communication in Bacteria. Annu. Rev. Cell Dev. Biol. 21:319–446

3. Smith, K.M. Yigong, B. & Suga, H. (2003) Induction and Inhibition of Pseudomonas aeruginosa Quorum Sensing by Synthetic Autoinducer Analogs. Chemistry & Biology 10:81-89

4. Park, J. Jagasia R. Kaufmann, G.F. Mathison, J.C. Ruiz, D.I. Moss, J.A. Meijler, M.M. Ulevitich, R.J. & Janda, K.M. (2007) Infection Control by Antibody Disruption of Bacterial Quorum Sensing Signaling. Chemistry & Biology 14:1119-1127

Towards What Are We Evolving?

A group of researchers has found that our choice of diet has had an effect on our genomes. Specifically, they’ve found that individuals from groups with traditionally high-starch diets have more copies of the gene for Salivary Amylase, the enzyme which breaks down starches in our mouths and stomachs, as compared to those with low-starch diets. This is fascinating news because it is the beginning of an answer to the question: towards what are we evolving? What are the selective pressures acting on our genes to produce further iterations of our species’ existence? In general, this is an extremely difficult question to answer, if not impossible.

There are some examples of human intervention into evolution; our efforts at breeding plants and animals have yielded several well known successes (including the development of a transparent frog, see below) and we also use evolution to design molecules with specific properties (ref. 1). These, however, are examples where many generations can be generated rapidly and only those individuals with desired traits are allowed produce offspring.


Transparent Frog (HO / REUTERS)

Fitness is the term that is generally used to quantify how likely an individual is to be reproductively successful, and is thus a most relevant concept in determining the direction in which evolution is nudging us. When the environmental variables and set of possbile behaviors are simple, it is possible to make predictions concerning fitness. For instance, a salt marsh is an environment in which organisms are subject to varying levels of salinity. It is a fair bet that after continued but not overwhelming exposure to increased levels of salt, an initially non-salt-tolerant plant will become salt tolerant. This is the case because the individuals with some salt tolerance are presumably more fit than others, and their offspring will retain that advantage.

When one tries to analyze what makes a human being fit, however, there are several obstacles. First, the set of circumstances we’re adapting to are quite complex, meaking it no minor task to pick out which elements might be most important. Second, we define what constitutes fitness through our influence on social structure. Third, even if we’re somehow genetically most-fit, we can choose to thwart evolution by not having any children. One might think that the rich constitute a good candidate for the title of most-fit, but they certainly do not reproduce the most. If anything, the group with the highest reproductive success is the poor.

Even if we believe that Darwinian evolution is the dominant force in defining how we will change over the coming epochs, we must admit that it plays some roll. In attempting to understand the future of our species, and how to act in our own best interests, we must acknowledge the forces at work in shaping our selves. Darwinism is clearly important for analyzing broad trends, such as in the research presented above. However, the fast-and-faster acting influence of cultural evolution which currently influences every aspect of our lives, will undoubtedly be of paramount importance to understanding humanity as well. Our challenge is now to understand how the effects of cultural evolution will play out, feeding back on our biology.

References

1. Farinas ET, Bulter T, Arnold FH. (2001) Directed enzyme evolution. Curr Opin Biotechnol. 12(6):545-51
2. Perry GH, Dominy NJ, Claw KG, Lee AS, Fiegler H, Redon R, Werner J, Villanea FA, Mountain JL, Misra R, Carter NP, Lee C, Stone AC. (2007) Diet and the evolution of human amylase gene copy number variation. Nat Genet. 39(10):1256-60.

On the Difficulty of Understanding Evolved Objects, Namely Biology

(a map of yeast protein interactions1

Imagine a machine designed to slice bread which, through some pathological design concept, posessed the trait that its blade was also somehow its power source. Removing the blade/power supply would clearly render the device inoperable, but understanding how this action had achieved its effect would be quite difficult. This is the essence one of the main problems which confronts anyone interested in teasing apart the complex web of interactions that is molecular biology.

For whatever reason, whether it be a basic feature of human intelligence or simply a sort of paradigmatic immaturity as a species, we tend not to design things in the same way the evolution does. By that I mean employing multi-purpose parts in the way the fictional device mentioned above does. One human-designed object posessing that property is the bicycle. There, it so happens, that the rotation of the wheels actually tends to keep the bike up-right. The wheels are like gyroscopes: their rotational intertia tends to keep them in their plane of rotation in the same way that the linear inertia of an object moving in a straight line tends to keep it on that trajectory. In the sense that the idea of the bicycle has retained this accidental advantage, it resembles an evolution-designed object. However, examples of human engineering that fit this category are few.

In biology, it appears, this type of overlapping, redundant functionality is the norm. For example, insulin is a molecule which is well known to many layman as being involved in the metabolism of glucose: the regulation of blood sugar. However, if one simply consults the wikipedia article on insulin, it immediately becomes clear that this is far to simple a tale. The functions of insulin listed there are:

1. Increased glycogen synthesis – insulin forces storage of glucose in liver (and muscle) cells in the form of glycogen; lowered levels of insulin cause liver cells to convert glycogen to glucose and excrete it into the blood. This is the clinical action of insulin which is directly useful in reducing high blood glucose levels as in diabetes.
2. Increased fatty acid synthesis – insulin forces fat cells to take in blood lipids which are converted to triglycerides; lack of insulin causes the reverse.
3. Increased esterification of fatty acids – forces adipose tissue to make fats (i.e., triglycerides) from fatty acid esters; lack of insulin causes the reverse.
4. Decreased proteinolysis – forces reduction of protein degradation; lack of insulin increases protein degradation.
5. Decreased lipolysis – forces reduction in conversion of fat cell lipid stores into blood fatty acids; lack of insulin causes the reverse.
6. Decreased gluconeogenesis – decreases production of glucose from non-sugar substrates, primarily in the liver (remember, the vast majority of endogenous insulin arriving at the liver never leaves the liver) ; lack of insulin causes glucose production from assorted substrates in the liver and elsewhere.
7. Increased amino acid uptake – forces cells to absorb circulating amino acids; lack of insulin inhibits absorption.
8. Increased potassium uptake – forces cells to absorb serum potassium; lack of insulin inhibits absorption.
9. Arterial muscle tone – forces arterial wall muscle to relax, increasing blood flow, especially in micro arteries; lack of insulin reduces flow by allowing these muscles to contract.

Even as I’m writing this, I have come across an article in Nature about a previously unknown action of insulin in a biochemical pathway involving a protein called TORC22.

Some would say that I am pointing to an inherent flaw in reductionist thinking. That our tendency to search for the smallest parts in order to build up a description of everything from the universe itself to the many varied forms of matter we find within it, cannot hope to penetrate these massively interconnected systems. It seems true that our current notions of what the smallest parts are will lead us to descriptions which are simply too large scale to be intuitively understood. However, this doesn’t necessarily point to a flaw in reductionism, especially since the alternative approach of holism doesn’t seem to offer any ways forward which circumnavigate such a problem. Rather, I would suggest that we need to fundamentally shift the way we think about evolved things in order to make significant progress towards understanding that which falls under the blanket term of “complex systems.”

Early in the last century, physics was spurred on by a shift in thinking: quantum theory, often thought of as one of the canonical scientific revolutions. I am hopeful that this century, or some time in mankind’s future, will do the same for biology, and complexity in general.

References

1. Durrett, Rick. Random Graph Dynamics New York: Cambridge University Press, 2006.
2. Dentin, R. Liu, Y., Koo, S.H., Hendrick, S., Vargas, T., Heredia, J., Yates, J. III, Montiminy, M. (2007) Insulin Modulated gluconeogenesis by inhibition of the coactivator TORC2 Nature 449: 366-369

Life


This is Carl Woese, he’s a biologist. Although I’ve been aware of several of the theories that he has espoused over the years, it wasn’t until recently that I attributed their authorship to this great thinker. Three of the “biggest” ideas that he’s responsible for are: the RNA world hypothesis, the current organization of the tree of life with three domains at the bottom, and the concept that there was a time, before species existed, when Darwinian evolution was not dominant because of the prevalence of horizontal gene transfer. Briefly, the RNA world hypothesis suggests that the most primitive version of life as we know it must have consisted entirely of RNA because RNA can act as both an enzyme (for which we mainly use proteins) and as an information storage molecule (for which we use DNA). The three domain system split the prokaryotes (simple cells having little to no internal membrane structure like bacteria) into two separate groups: bacteria & archaea. As to pre-Darwinian evolution and horizontal gene transfer, well the idea there is that before there were individual species, all the forms of life were so similar that there was massive intermixing of genetic information betwen living organisms such as we do with bits of electronic data today. This is incredible because it’s essentially akin to lizards appropriating wings from birds because they’re an effective way to avoid ground predators (excuse the hyperbole).

This is Gertrude (gerry) Brin and her grandson Colby, another great thinker. In reading Colby’s blog post from today, about his grandmother and life in general, I was reminded of what I think is Woese’s most powerful idea.

As an undergraduate student of Physics and Mathematics just starting to become interested in Neuroscience, and delighted by the fact that I could use my beloved equations to explain the behavior of biological systems, it none the less seemed to me that we would need an entirely new form of Mathematics, spurred by a paradigmatic shift in thinking, to really understand such complex systems as brains and indeed life in general. The best that I could do was to think of life as a temporary reduction in entropy. Perhaps you remember from some physics course that the universe is constantly tending towards an increasing state of disorder (entropy). This is true on a global (all-universe) scale, but smaller scale things such as life defy this. Life forms, temporarily, organize molecules. I’ve never been able to do much more with this idea, but I am fond of it and try to consider its ramifications once in a while.

One of the big problems we’ve had with understanding these very complex systems, is that all of our science has been reductionist for a very long time. We take something we don’t understand (a watch is one classic though not ideal example) and we open it up and look at all the pieces and how they fit and work together, and then we can understand in some way how the watch functions, but only in terms of the smaller pieces. I could say much much more about this, but I think Dr. Woese says it far better in the piece he wrote in Microbiology and Molecular Biology Reviews in 2004. I must also preface the following quote from that work by saying that I was turned on to ALL of this by Freeman Dyson’s fantastic article in the July 19th issue of the New York Review of Books (that link may expire fairly soon, I found it by googling the second paragraph of the text below), which also uses a substantial portion of the quote that follows.

“Let’s stop looking at the organism purely as a molecular machine. The machine metaphor certainly provides insights, but these come at the price of overlooking much of what biology is. Machines are not made of parts that continually turn over, renew. The organism is. Machines are stable and accurate because they are designed and built to be so. The stability of an organism lies in resilience, the homeostatic capacity to reestablish itself. While a machine is a mere collection of parts, some sort of “sense of the whole” inheres in the organism, a quality that becomes particularly apparent in phenomena such as regeneration in amphibians and certain invertebrates and in the homeorhesis exhibited by developing embryos.

If they are not machines, then what are organisms? A metaphor far more to my liking is this. Imagine a child playing in a woodland stream, poking a stick into an eddy in the flowing current, thereby disrupting it. But the eddy quickly reforms. The child disperses it again. Again it reforms, and the fascinating game goes on. There you have it! Organisms are resilient patterns in a turbulent flow—patterns in an energy flow. A simple flow metaphor, of course, fails to capture much of what the organism is. None of our representations of organism capture it in its entirety. But the flow metaphor does begin to show us the organism’s (and biology’s) essence. And it is becoming increasingly clear that to understand living systems in any deep sense, we must come to see them not materialistically, as machines, but as (stable) complex, dynamic organization.”

That last sentence just kills me, we must in some sense abandon our devotion to the material. For what is life about if not interaction.