Pleiotropy

My pet theory about the human nose: breastfeeding

2013-05-22T12:08:00.003-04:00

Why is the outer nose shaped as it is? Why don't humans just have two holes in the face, rather than this protuberance that we care so much to have the right shape of?

Here's the best illustration I've seen of my pet hypothesis:

Imagine that little baby had two holes rather than a nose. It would suffocate. That huge breast would block the air intake, making it impossible to breastfeed. But when the baby's nose is pushed from the front, slits are formed that enable the baby to still breathe through the nose.

In other words, humans have a proboscis so that they can breastfeed.

Some support: Cows, dogs, and cats have a slit to the side, making them able to breathe when their noses are blocked form the front.

A caveat: Snub-nosed monkeys have no external nose, but just two holes. Admittedly, they probably breastfeed. I wonder how they do it. They also sit with their faces downwards when it rains, to prevent rain from entering their nostrils. I wonder if the olfactory abilities of these monkeys are reduced?

More on the variation of nose shapes in humans and the evolutionary origin of the human nose, but nowhere can I find anyone suggesting a link between nose-shape and breastfeeding.

Carnival of Evolution #59 is up

2013-05-02T15:44:00.000-04:00

The 59th edition of Carnival of Evolution is now up at DNA Barcoding.

Letter from the Doctor.

CoE needs hosts for the next editions, and you could host the 60th one! It's a thoroughly rewarding experience, and your blog will get lots of exposure. Plus, it's really very little work. Interested?

P.S.

Knock, knock.

Who's there?

Doctor.

Doctor who?

Knock knock.

Who's there?

How the hell did you know?

Can we predict evolution?

2013-04-13T16:26:00.001-04:00

Evolution is not predictable, right? It has famously been said that if we were to rerun the tape of life, it would be very unlikely that something like humans would evolve again. I could add that, surely, on other planets suitable for life it would be highly unlikely that organisms looking identical to us would evolve. However, I personally would not be totally surprised if there were intelligent bipedal quadrupeds on other planets somewhere, but this is very little beyond pure speculation (I could talk about it at length - but that is not the same as writing about it).

One of the problems with predicting evolutionary outcomes is that the number of influencing factors is huge. Physics is a field of science where predictions can be very accurate - just think of sending rovers to Mars: the precision needed for that to succeed is daunting, and is a testament to the success of physics. However, this success stems from the simplicity of the physical systems that people have studied. Newton studied objects falling, which is a pretty simple thing with few factors influencing the outcome. The only relevant factor involved is gravitation, and it was fairly easy to achieve good predictability. The factors influencing the rover on its way to Mars are actually quite few as well. Biologists have a much harder time because the systems the first studied were so much more complex. Living things are just that much more complicated than a dead object falling (even if it is a living apple).

Evolution is driven by random changes to the genome and environmental factors. That's it. The problem is just that those are both highly unpredictable, whereas in physics things are easier. But here is the point I want to make: It's only easier in physics because physicists elect to study systems that are simple. There is a great joke where a physicist is able to predict the winner of any horse race to multiple decimal points - provided it was a perfectly elastic spherical horse moving through a vacuum (Wikipedia). Physicists don't contend with studying what is actually there in its messy reality, but instead reduce the problem to one that can be solved. Even though heating a pot of water involves many individual particles, boiling can be predicted with high accuracy, but only if it assumed that a meteor isn't going to crash the kitchen! Externalities are ignored and assumed not to happen. And that works well for physics, because that assumption is safe in many systems. But it is not safe at all in evolution.

The number of external effects that drive evolution is immense. But trying to predict evolution by starting with a model that includes everything is never going to work. First we must understand the simplest cases - in an approach identical to the one that has made physics so successful. We have to do this even if there is no system that actually evolves in this sort of vacuum in nature. Once we understand that system, we can build on it by adding more layers of complexity. Perhaps then one day mainstream biologists will be happy...

Population Size
The core entity of evolution is the population. It is the population that evolves, not the species or the individual. And the first thing to know about a population is its size, N. The size is the main determinant of what will happen to new mutations, such as the probability of fixation (i.e., the mutation existing in all individuals) and the strength of selection (governing whether a mutation will be under the influence of selection, or just genetic drift - note that there is always drift, which comes from the stochasticity (randomness) that is inherent in the evolutionary process, but sometimes it matters little compared to selection, which completely dominates in the case of an infinitely large population).

Mutation Rate
The second parameter to know is the rate at which new mutations appear, the mutation rate, µ. If there are no mutations (no more realistic in nature than an infinitely large population size), then evolution comes to a halt when genetic drift or natural selection has eliminated all variation. If µ is very large, the the population will experience a mutational meltdown and the loss fitness due to the accumulation of harmful mutations. The product of the population size and mutation rate is called the mutation-supply rate, µN. This quantity determines how many mutations occur: if it's low, then there are few mutations. In much of evolutionary theory it is assumed that the mutation-supply rate is so low that each mutation appears and goes to fixation (or most likely is lost) before the next mutation appears. This makes mathematics much easier, but it is not a realistic assumption. Rather, pretty much all natural populations have a mutation-supple rate large enough that there is always many different mutations present in the population. This is true for humans and for bacteria - and especially for viruses.

Lots of advances have been made in evolutionary theory (population genetics) given only these two parameters. But there is another that makes a huge difference, which describes what effect mutations have.

Fitness Landscape
The fitness landscape is a map where fitness is given as a function of the genotype or the phenotype. In other words, this function describes the expected reproductive success of every type of organisms in the population. If the fitness landscape is known, then the effect of every mutation is known, and it can be predicted statistically how the population will evolve. If the landscape is smooth (see Fig. 1) then there is only one outcome possible: the population will ascend the peak and then stay there. But if the landscape is rugged, then the population risks getting stuck on a local peak (at least for a very long time), or it may be fortuitous enough that it finds the global peak.

Figure 1. Smooth and rugged one-dimensional fitness landscapes. Both are simply fitness given as a function of either genotype or phenotype. Smooth landscapes have a single peak and the dynamics on it is very predictable: for non-zero mutations rates, the top of the peak will be located sooner or later. The only exception is if the mutation rate is very high, in which case all offspring have deleterious mutations that take them off the peak, aka as a mutational meltdown. The rugged landscape have multiple peaks separate by valleys of lower fitness. In the NK landscape rugged landscapes also have a greater range in fitness than smooth landscapes. This is an effect of pleiotropy, which in NK is coupled to the K-parameter, which determines the amount of genetic interaction. P.S. Not sure why I didn't indicate the axes here. Amateur! Again, it's fitness on the vertical axis, and genotype or phenotype on the horizontal axis. I could add, though, that a picture like this is more consistent with fitness as a function of a continuous phenotype (e.g., body size), while functions of genotype will most often be discrete. From Østman and Adami (2013).

Evolutionary dynamics in rugged landscapes are much harder to predict. That goes for the endpoint of evolution (the genotype or phenotype that the population ends at), which is easy to predict in smooth landscapes, and it is also true for the path that is taken. In one dimension the path is not hard to predict (if the population start to the left in the smooth landscape in Fig. 1, then it will move to the right until it hits the peak), but if there are many paths, then the situation is not so simple. If we look at the fitness landscape in Fig. 2A, which is reconstructed from Khan et al (2011), we see that there are many paths that can lead from genotype 00000 to 11111. Note that in this figure fitness is given for each of the 32 genotypes, but are plotted as a function of how many mutations away they are from the (arbitrarily chosen) 00000 genotype.

A population that starts at 00000 will end up on 11111, but it can take a number of routes there. Because the fitness gains are highest going through 00010 first, and then through 00110, 10110, 11110 to 11111, that route is most likely, but because evolution is a stochastic process (mutations are random and high fitness makes it more likely that you get to reproduce, but not certain), other routes are possible. In this example, Khan et al. actually found that the path taken was via 01000.

Figure 2. Two fitness landscapes where fitness are given by the genotype. However, the genotype consists of five loci - each of which can take the value of 0 or 1 - and the horizontal axis therefore cannot contain that information. Instead the axis shows the number of mutations each genotype is away from an arbitrarily chosen "wild-type". It edge indicates a one-mutant connection between genotypes. (A) Taken from Khan et al. (2011), evolution in this landscape is easy to predict. With a population starting at 00000, genotype 11111 will eventually be located. There are no other peaks than 11111 that the population can get stuck on. Which path will actually be taken is a different matter: even though it is most likely the population will go to 00010 first, and then 00110, 10110, and 11110 to 11111, the actual path taken was different, going through 00001. (B) This instance of an NK fitness landscape is rugged, with three distinct peaks at 11110, 11101, and 00000. Starting a population at 11111 it cannot be said with certainty where the population will end up. Most like the population will go though one or both of the local peaks at 11110 and 11101. If the mutation-supply rate is low there won't be enough mutations available for the population to cross the valley going through genotypes 10101 or 01001. However, for higher mutation-supply rates (which can be achieved both by increasing the population size and the mutation rate) the higher number of mutations will ensure that the valley is eventually crossed (without having to wait for eons), and the global peak is reached.

We can of course discuss the relevance of different paths. Does it really matter which path is taken? Where the population ends up seems more relevant, but suppose the population is a virus that we are fighting with anti-virals. In that case, we'd like to kill all the viruses, but if they evolve resistance, we could perhaps combat them with targeted drugs if we know which path they were going to take.

Figure 2B shows a rugged landscape with several peaks (at 11110, 11101, and 00000). If the population starts at 11111, it is most likely to go through 11110 and/or 11101. However, if the supply of mutations is low (WM, weak-mutaiton regime), the population risks getting permanently stuck there, and thus not able to locate the global optimum at 00000. By "permanently" I mean that the population will sit there for a very long time. Eventually it will move up, but it can take longer than other events that can change the landscape, and, if the fitness of those suboptimal genotypes are too low, the population can go extinct before they are able to escape to higher fitness. Fitness also affects population size, so that a population sitting on 11110 would in this case be about 10% lower than if it were sitting on 00000. In the simulation shown in Vid. 1 the population size is fixed at N=1,000, so this effect does therefore not occur. The mutation rate is very high, so the population is able to escape 11101 and 11110 by crossing the valley to reach 00000.

Video 1. Evolution in a rugged landscape. A population of 1,000 individuals starts at genotype 11111. Mutation rate is 0.1 per generation (= chance to have one mutation; no double-mutations allowed here). The simulation is a Moran process (one individual is replaced by the offspring of another every update). The area of the circles are proportional to the number of individuals at that genotype.

One way to quantify how predictable evolution is is using information theory. The Shannon entropy was used by Szendro et al. (2013) to quantify how predictable evolution is in simulations of evolution in the Aspergillus niger fitness landscape (Fig. 3). One simply counts the frequency, p_i, of each endpoint in a large number of replicate simulations (here, 100), and calculates the entropy as

The lower the entropy S is, the higher predictability is. When all states are the same, p_i=1, and S=0.

Figure 3. Predictability of endpoints of evolutionary paths measured using entropy. When the entropy is low, predictability is high. Entropy is basically a measure for how uniform the results are: if all endpoints are the same, then entropy is zero. The different colors are for different mutation rates (black highest). Predictability is given as a function of population size (A), mutation-supply rate (B), and rate of double-mutants (C). The finding is that predictability is not monotonic, that is, at very high population sizes predictability goes down again (entropy increases). d=4 indicates the Hamming distance from the global optimum, which is easier to find the more mutations there are, which is why predictability is higher for larger N. Szendro et al. (2013) explains this decrease in predictability as "a consequence of increased appearance of double mutants", by which they might mean that the number of mutations is so high that the mutational load (decrease in average fitness due to deleterious mutations) makes finding the global optimum harder.

Evolution in smooth landscapes is very predictable (in terms of endpoints), while rugged landscapes can decrease predictability because the population can get stuck on different fitness peaks (which doesn't mean that a rugged landscape can cause speciation - for that, other factors are needed, like reproductive incompatibilities between different genotypes or negative frequency-dependent selection). Just as is seen in Fig. 3, increasing the mutation rate (up to a point) in rugged NK landscapes increases the chance that the population will find the global peak (Østman et al,, 2012).

Yes, we can predict evolution, but to achieve high predictability, certain conditions have to be met. A large mutation-supply rate helps, as does low ruggedness of the fitness landscape. So too does static landscapes; if the fitness landscape changes on time-scales comparable to the population dynamics, then predictability will suffer. There is very little research done on such dynamic fitness landscapes and how they affect evolutionary dynamics. One obvious question to tackle before answering this question is just how the landscape changes over time. Not much is known about this in natural populations, but it could be studied computational for different classes of changes, like peaks suddenly disappearing vs. more subtle changes in fitness values.

In summary, evolution is predictable if we know population size, mutation rate, and the fitness landscape.

References
Khan A, Dinh D, Schneider D, Lenski R, and Cooper T (2011). Negative Epistasis Between Beneficial Mutations in an Evolving Bacterial Population Science, 332 (6034), 1193-1196 DOI: 10.1126/science.1203801

Szendro IG, Franke J, de Visser JA, and Krug J (2013). Predictability of evolution depends nonmonotonically on population size. Proceedings of the National Academy of Sciences of the United States of America, 110 (2), 571-6 PMID: 23267075

Østman B and Adami C (2013). Predicting evolution and visualizing high-dimensional fitness landscapes in "Recent Advances in the Theory and Application of Fitness Landscapes" (A. Engelbrecht and H. Richter, eds.). Springer Series in Emergence, Complexity, and Computation (to appear). arXiv: 1302.2906v2

Østman B, Hintze A, and Adami C (2012). Impact of epistasis and pleiotropy on evolutionary adaptation. Proceedings. Biological sciences / The Royal Society, 279 (1727), 247-56 PMID: 21697174

The war on science™

2013-03-13T07:03:00.003-04:00

According to Science Left Behind (book review), there's a war on science, and it isn't raged by conservatives alone. Progressives are also ideologues who refuse to listen to the science - it's just other areas that they dislike.

Conservatives:

Climate change (science: it is happening, and is caused by humans)
Evolution (science: it is true)

Progressives:

Homeopathy (science: it doesn't work)
Vaccines (science: they work and are do not cause autism)
GMOs (science: they are not harmful)
Organic foods (science: they are not always better)
In vitro meat (science: it is fine, and better in so many ways)
Biological gender differences (science: there are some)

Anything else?

My guide to better talks

2013-03-03T18:29:00.002-05:00

I like public speaking, and would like to be better at it. Two rules I have been living by are:

Love the words that you speak
Always have something to say

Number 2 is a general rule, as it means that you should have done something, thought about something, and have formed an opinion that you can speak about.

All very well, until the day I saw myself on video giving a talk that I had not practiced, thinking I'd be fine and convey the points I wanted to. I did convey those points, but was also horrified at some things that I did. I made some observations, and here is the list to remedy those deficiencies:

Stand up straight (I have bad posture)
Speak clearly (no mumbling)
Finish sentences (no trailing sentences)
Speak precisely (say only what you need to say)
Shave and get a hair-cut (don't look like a bum)
Be modest (don't be smug)
Arms down (or think about your gesturing, at least)

3 to 9 apply to me, and are not general rules. I do think different styles of speaking can allow for breaking each of these, but in general they are good to follow. Either way, I really need to work on them.

Here's Hans Rosling, my favorite speaker.

Professorship in France

2013-02-24T13:05:00.000-05:00

On EvolDir there is this job-posting:

*The lab of "Biometry and Evolutionary Biology" UMR CNRS 5558, (***University of ******Lyon**, France)* offers a permanent position for 2013 : Assistant professor (Maître de conférences) in Modelling approach in Genetics-Ecology *

*Teaching :Mathematics**and statistics applied to biology, animal biology*

The applicant will join the teaching staff of the "Agronomy section" of the Biotechnology Departmentof the Technology Institute at Lyon 1 University.He or She will teach mathematical functions and data analysis (1^st year students). The applicant may also be called upon to teach zoology (anatomy and histology of mammals and insects) and genetics (1^st year students). Finally, He or She will participate to the supervision of the numerous tutoring works made by the students of the 2^nd year of the Agronomy section (breeding of animal laboratory models, experimental methodology, reports...) and to the monitoring of the students during their work placement.Beyond teaching, He or She will engage in collective responsibilities of the department and be willing to develop new vocational training.

*Research: Evolution in fluctuating environments: modelling approach in Genetics-Ecology*

Understanding fast evolution of traits (morphological, behaviour, life histories) and population evolvability in fluctuating environments needs on taking into account genetic architecture of these traits in complement to phenotypic approach. The aim is to build models (genetic-ecology) to study how the genetic architecture influences trait evolution. Temporal and spatial components of the environment will be considered. The candidate will be theoretician and modeller with good experiment at the interface between theory, modelling and biological data. He/she will use the data basis (insects and vertebrates) of the evolutionary ecology department in order to build realistic models. He/she will interact greatly with the field ecologists. The candidate must be very familiar with the concepts and modelling/programming tools in evolutionary ecology and, quantitative and population genetic and be able to connect teaching (including animal and vegetal biology) and research activities.

Seriously, what is up with that? The work sounds really interesting, and it sounds like they have really good data. However, this reads like a postdoc position in some professors's lab - not like an assistant professor position! It's a permanent job, and yet they want to dictate this level of detail for someone who is supposed to be an independent researcher. I want to scream that this is not acceptable, but I suppose I should just be glad that things are much more liberal in academia in the United States.

Titles in evolution

2013-01-16T12:05:00.000-05:00

Can you spot the odd one out in today's list of titles in evolutionary biology? The category is open, though. For example, the journal Evolution insists on its titles having all-caps, which annoys me like an oyster (but since there are two of those...).

Conflictual speciation: species formation via genomic conflict
Predictability of evolution depends nonmonotonically on population size
Ecological strategies shape the insurance potential of biodiversity
Ecological speciation along an elevational gradient in a tropical passerine bird?
Mutation rate dynamics in a bacterial population reflect tension between adaptation and genetic load
Can a collapse of global civilization be avoided?
Long-term culture at elevated atmospheric CO2fails to evoke specific adaptation in seven freshwater phytoplankton species
MIGRATION ENHANCES ADAPTATION IN BACTERIOPHAGE POPULATIONS EVOLVING IN ECOLOGICAL SINKS
RUNAWAY SEXUAL SELECTION LEADS TO GOOD GENES
Evolution of sperm structure and energetics in passerine birds
Wormholes record species history in space and time
Who Speaks with a Forked Tongue?
Evolutionary mode routinely varies among morphological traits within fossil species lineages
The effect of spontaneous mutations on competitive ability
Adaptive Genetic Variation on the Landscape: Methods and Cases

A general template explaining how different factors influence adaptive genetic variation in the landscape over evolutionary time (Schoville et al., 2013, Adaptive Genetic Variation on the Landscape: Methods and Cases).

Weird comments

2012-12-19T10:10:00.000-05:00

I get quite a few anonymous comments to random posts. They are random in that there is no apparent correlation between the content of my posts and their comments.

Examples:

Anonymous has left a new comment on your post "The trouble over inclusive fitness theory and euso...":

I think this is among the most vital info for me. And i am satisfied reading your article. However should remark on some general issues, The website style is perfect, the articles is in reality excellent : D. Good activity, cheers Here is my webpage :: the 2012

Anonymous has left a new comment on your post "Carnival of Evolution statistics":

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Clearly the point is to promote some site (yes, I have changed the links). But why these robotish comment with no relevance to my blog posts?

None of these comments ever get through, because I moderate submitted comments to posts over a week old. And when they do get through to newer posts, they always delete them themselves right away. Why?

I really wish I knew.

Empty talk about the evolution of complexity

2012-12-15T09:57:00.000-05:00

PZ Myers has a post on the evolutionary origins of complexity: αEP: Complexity is not usually the product of selection I find it frustrating that people talk about terms that they don't clearly define, and assume everyone agrees on. Especially about complexity, which is hard to define, and I know not everyone has the same idea of. I'll just quote my comment on Pharyngula:

But, you have not quantified complexity, let alone say when there was an increase in it in the hypothetical example you give. If you don’t do this, you can’t talk about the evolution of complexity; it becomes a guessing game what we are talking about, and there is no chance that everyone will think of complexity as the same thing.

On top of that, there seems to be no distinction made anywhere between ‘complexity’ and ‘complex traits’. They need not be the same thing. Without defining complexity here(!), I’ll say that complexity can indeed easily arise by neutral processes, whereas complex traits cannot (but does have neutral and random processes involved) – it requires selection. And with that you’re going to ask me for a definition of a trait, so here is one: A single measurable component of the phenotype that has a function.

The key word here is function, without which I don’t know of any that can evolve without selection. Not selection every step of the way, as random processes are required (at least that’s how it occurs in nature), but selection at some point. The moment the trait acquires function, it becomes selected for.

On the other hand, ‘genomic complexity’ may not describe the state of a trait, but rather is the idea that the genome has many components that are intricately connected – which can arise by neutral processes.

Title in evolution quiz

2012-12-09T14:19:00.000-05:00

For those unawares (prolly all), I post these titles in evolution i) as a reminder to myself when I skim the numerous eTOCs that I get in my inbox every week, ii) to point out that evolutionary biology is a very active area of research (while creationism is not), and iii) to share the awesomeness of evolution.

Today it also comes with a quiz.

Wormholes record species history in space and time
Quasispecies Dynamics of RNA Viruses
Genetic background affects epistatic interactions between two beneficial mutations
Epistasis between mutations is host-dependent for an RNA virus
Evolution of clonal populations approaching a fitness peak
Competition and the origins of novelty: experimental evolution of niche-width expansion in a virus
Stochastic effects are important in intrahost HIV evolution even when viral loads are high
Variable evolutionary routes to host establishment across repeated rabies virus host shifts among bats
eplaying the Tape of Life: Quantification of the Predictability of Evolution
Phenotypic landscapes: phenological patterns in wild and cultivated barley
EVOLUTION OF TRANSCRIPTION NETWORKS IN RESPONSE TO TEMPORAL FLUCTUATIONS
Are elder siblings helpers or competitors? Antagonistic fitness effects of sibling interactions in humans
Ecological selection as the cause and sexual differentiation as the consequence of species divergence?
Adaptation to a new environment allows cooperators to purge cheaters stochastically
Fixation of mutators in asexual populations: the role of genetic drift and epistasis Public good dynamics drive evolution of iron acquisition strategies in natural bacterioplankton populations

Guess which of the papers above this figure is from:

54th Carnival of Evolution is up

2012-12-01T18:08:00.003-05:00

54th edition is up at ideonexus.com: Carnival of Evolution #54: A Walkabout Mount Improbable.

And it's a super-fancy one, so don't miss it, and let everyone else know, too.

Titles in Evolution overload

2012-11-17T16:58:00.000-05:00

There are simply too many interesting papers published in evolutionary biology to keep up with. Not even just reading the abstracts is feasible. Here's a sample of what I find the most interesting from the last couple of weeks:

Analyses of pig genomes provide insight into porcine demography and evolution
Non-random gene flow: an underappreciated force in evolution and ecology
Strengths and weaknesses of experimental evolution
Gene duplication as a mechanism of genomic adaptation to a changing environment
Revisiting an Old Riddle: What Determines Genetic Diversity Levels within Species?
Selection of Penicillin-sensitive Mutants of Escherichia coli following Ultraviolet Irradiation
Understanding specialism when the jack of all trades can be the master of all
How does adaptation sweep through the genome? Insights from long-term selection experiments
Evolutionary layering and the limits to cellular perfection
The effects of competition on the strength and softness of selection
Crossing the threshold: gene flow, dominance and the critical level of standing genetic variation required for adaptation to novel environments
From nature to the laboratory: the impact of founder effects on adaptation
Spatially explicit models of divergence and genome hitchhiking
Why Transcription Factor Binding Sites Are Ten Nucleotides Long
Epistasis as the primary factor in molecular evolution
The spatial architecture of protein function and adaptation
The effects of stochastic and episodic movement of the optimum on the evolution of the G-matrix and the response of the trait mean to selection
TOWARDS A GENERAL THEORY OF GROUP SELECTION
PLEIOTROPY IN THE WILD: THE DORMANCY GENE DOG1 EXERTS CASCADING CONTROL ON LIFE-CYCLES

Titles in creationism:

Mammalian Ark Kinds

Check it out here: Answers Research Journal. They estimate that there are 137 extant kinds, which means that they must admit to substantial diversification and evolution since the Flood...?

Knowing what I know now...

2012-11-14T13:55:00.000-05:00

I'm an evolutionary and computational biologist doing my second postdoc at Michigan State University. I have learned all sorts of thing in this short career, and Jeremy Yoder has asked for advice for a new blog canival.

If I knew then what I know now, I would have...

Focused more on my writing skills very early on. At least as early as my graduate degrees at KGI and UCSB, but perhaps I should really have gotten more into writing during my undergrad in Copenhagen. No one ever told me that being a scientists really amounts to being a writer. I have done nearly nothing but reading and writing for at least six months now, save for giving some talks at meetings and writing 32 lines of code. Write even if you have no data and no conclusions. Write your thoughts down on what you read, what you do in lab, and then it will be easier to write the thesis and papers when it really counts.

Read more. As a scientist, reading is treading water. If you stop, you drown. It's a never ending game, and it is the only way to keep abreast with what is going on. Going to talks is fine, but simply not enough. You must read constantly, or you will be left behind. Often it just means reading abstracts, sometimes also looking over figures (and reading captions) - not that I count, but I count reading abstract and figures as having read a paper. Sign up for eToCs from the major journals in your field. I recommend: Nature, Science, PNAS, Proc. R. Soc. B, Genetics, Evolution, Journal of Evolutionary Biology, The American Naturalist, Journal of Theoretical Biology, Frontiers in Evolutionary and Population Genetics, PLoS Biology, PLoS Comp Bio. After my first year in grad school, I read the advice from a senior scientist that one should spend the entire first year of grad school mostly reading. I did read a lot, but wish I had read more.

Stopped taking myself so seriously. Actually, I haven't done that in years, but I do think this is invaluable advice. It's just science, after all. If I am wrong about the prevalence of epistasis in adaptation, nobody is going to care. No bridge will collapse and no one is going to die of a misdiagnosis. Keep that in mind, and enjoy yourself. Unless you're an engineer or an M.D, in which case you should stop reading this blog and get back to fukcing work already, or I'll sue your ass off!

Crocodilian relatives that walked upright?

2012-10-30T14:27:00.002-04:00

I seriously have trouble believing this. Can anybody shed some light?

It's from the Royal Ontario Museum in Toronto. Wikipedia on Crurotarsans (spelling?) says nothing of it.

Pleiotropy saves the day for evolving new genes

2012-10-29T19:39:00.001-04:00

What is the origin of new genes? In order to do new stuff, new genes are needed. Where do they come from, then?

Horizontal gene transfer (HGT) - direct transfer of a gene from one organism to another - is rampant within bacteria, so they may gain new function this way. However, that does not explain how the gene came to be in the first place.

Neofunctionalization: If the function is carried out by the original, the copy is free to evolve a new function by point mutations (etc.). However, such copies are much more likely to degrade by those mutations and lose the original function, thereby becoming a pseudogene.

Subfunctionalization: If the gene is pleiotropic, i.e. it has more than one function (expressed in more than one trait or cell-type or at different times), then the gene and its new copy can turn off gene expression differentially such that they share the set of functions. However this doesn't allow either much chance for evolving new function by mutation.

So what to do?

Näsvall et al. gave me this present for my 40th: Real-Time Evolution of New Genes by Innovation, Amplification, and Divergence.

They describe a new model/mechanism by which duplicated genes can retain the selection pressure to not succumb to deleterious mutations. They call it the innovation-amplification-divergene model (IAD).

IAD works like this: A gene initially has one function only (A). Then some genetic changes makes it also have a new function, b, which at first is not of too great importance. Then some environmental change favors the gene variants with the minor b-function (the innovation stage). This is then followed by duplication of the gene, such that there are now more than one copy that carries out A and b (the amplification stage). At this stage there is selection for more b, and at some point genetic changes in one of the copies results in a gene that is better at the new function, B. At this point, selection for the genes that do both A and b is relaxed, because the new gene (blue) carries out the new function. The original gene then loses the b function, and we are left with two distinct genes. Viola!

In other words, the green gene first becomes pleiotropic, is copied, followed by divergence, and then loss of pleiotropy. (How they could fail to mention pleiotropy in the article is beyond me.) The crucial feature is that at no point is the gene or any of the copes under no selection; there is always selection for them to be retained, so gene loss never occurs (pseudogenes are not created).

The researchers then look at a preexisting parental gene in Salmonella enterica that has low levels of two distinct activities that allows them to grow without the amino acids histidine and tryptophan, respectively.

Multiple evolutionary trajectories recovered through IAD. The x-axis indicates the HisA activity (assayed as growth rate in minimal glycerol medium with added tryptophan); the y axis indicates the TrpF activity (assayed as growth rate in minimal glycerol medium with added histidine). (A) Evolution of specialist enzymes (yellow) in which one activity is improved at the expense of the other. (B) Evolution of specialist enzymes (yellow) after initial evolution of a generalist enzyme (blue).

The figures here show how the generalist gene evolved to become specialists genes with increased function, doing better without both amino acids.

This is a model that explains how a gene with two functions can evolve to become two genes with distinct function under continued selection. It is this last part about selection that makes it novel, but it relies on the idea that the original gene had already evolved two distinct functions - that it was pleiotropic.

Pleiotropy comes from the Greek πλείων pleion, meaning "more", and τρέπειν trepein, meaning "to turn, to convert". It designates the occurrence of a single gene affecting multiple traits, and is a hugely important concept in evolutionary biology.

Reference:
Näsvall J, Sun L, Roth JR, and Andersson DI (2012). Real-time evolution of new genes by innovation, amplification, and divergence. Science (New York, N.Y.), 338 (6105), 384-7 PMID: 23087246

Carnival of Evolution statistics

2012-10-22T14:29:00.002-04:00

David Morrison just hosted Carnival f Evolution #52, and now he has written a post with lots of statistics of CoE: The network history of the Carnival of Evolution.

In short, we're doing quite well compared to many other carnivals who have gone extinct. This is especially true for science carnivals, of which CoE is the only active carnival listed on BlogCarnival.com.

The steady growth of CoE through time. "Fortunately, the number of posts has shown a steady upward curve, as indicated in the sixth graph, although not always at the one-blog-post-per-day rate set in the earliest days. However, over the past 20 Carnivals there has been an average of 1.06 blog posts cited per day of passing time, so we are certainly holding our own."

Titles in evolution - and in creation?

2012-10-17T14:35:00.000-04:00

Here we go again with the new articles on evolution. This is just a very small sample that I chose out of interest from the last couple of weeks - and just from a few journals that I get eToCs sent from. In the meantime there has been nothing in Answers Research Journal, and I don't know where else to check. If you do, please let me know.

Variation in personality and fitness in wild female baboons
Biodiversity tracks temperature over time
Epistasis as the primary factor in molecular evolution
Aposematism and the Handicap Principle
Turning Back the Clock: Slowing the Pace of Prehistory
Physico-Genetic Determinants in the Evolution of Development
The spatial architecture of protein function and adaptation
Complex brain and optic lobes in an early Cambrian arthropod
Crossing the threshold: gene flow, dominance and the critical level of standing genetic variation required for adaptation to novel environments
Mutational meltdown in selfing Arabidopsis lyrata
Aging: An Evolutionarily Derived Condition
What could arsenic bacteria teach us about life?
Genomic Variation in Natural Populations of Drosophila melanogaster
Clonal Interference in the Evolution of Influenza
Evolutionary Dynamics on Protein Bi-stability Landscapes can Potentially Resolve Adaptive Conflicts
Criticality Is an Emergent Property of Genetic Networks that Exhibit Evolvability

Ochman on bacterial evolution

2012-10-10T09:52:00.003-04:00

Yesterday I went to the annual Thomas S Whittam Memorial Lecture here at MSU. Howard Ochman talked about "Evolutionary Forces Affecting Bacterial Genomes", though he had changed the title to "Determinants of Genome Size and complexity.

Based on research in his lab had two conclusions about the evolution of bacterial genomes:

Genome size is drifting
GC-content is under selection

The very tight correlation between gene content and genome size (i.e., there is a linear relationship between number of genes and length of the genome) is driven by genetic drift, and not selection. People often say that small genomes are selected for, but clearly there are not. This is in part caused by a bias towards deletions (compared to insertions). I asked him why there is this bias, and he did not have an answer. (Well, he gave me an answer, but he also acknowledged that it didn't get to the point.) The common ancestor had a large genome, and many bacteria - particularly pathogens and endosymbionts - have subsequently lost DNA resulting in a reduced genome size (Kuo et al, 2009, but see Kuo and Ochman, 2010).

There are trends in the GC-content (amount of guanine and cytosize in the DNA) that differs among bacterial taxa. But closely related species have similar GC-content, and it turns out that genetic drift is not responsible for this, but that it is driven by selection. "Escherichia coli strains harboring G+C-rich versions of genes display higher growth rates" (Raghavan, 2012).

Genome size and abundance of pseudogenes correlates with the size of the effective population size: a larger N_e gives larger genomes, while smaller N_e results in smaller genomes. Pseudogene abundance is less straightforward, with the largest and smallest genomes and N_e both having few pseudogenes, but those intermediate in size having many.

References
Kuo CH, & Ochman H (2010). The extinction dynamics of bacterial pseudogenes. PLoS genetics, 6 (8) PMID: 20700439
Kuo CH, Moran NA, & Ochman H (2009). The consequences of genetic drift for bacterial genome complexity. Genome research, 19 (8), 1450-4 PMID: 19502381
Raghavan R, Kelkar YD, & Ochman H (2012). A selective force favoring increased G+C content in bacterial genes. Proceedings of the National Academy of Sciences of the United States of America, 109 (36), 14504-7 PMID: 22908296

Genotype-phenotype maps and mathy biology

2012-09-28T20:45:00.004-04:00

I'm reading a book chapter by Peter Stadler from 2002 called Landscapes and Effective Fitness [1]. It has this absolutely gorgeous figure:

I love it. But just before this figure he has this equation:

I hate it. I hate it because all it says is that each type, x, is at a frequency P_x of the total population, so those P_x sum to one. But of course. I just don't think this kind of writing is conducive to discourse, because in biology there is already a huge gap between the majority who don't read (and cite) papers with equations, and those who write them. So why muddy the waters with equations like this that says next to nothing?

However, I reiterate (and is why I'm reading the chapter) that this figure of a genotype-phenotype-fitness map is super cool.There are many more different genotypes (the genetic make-up of an organism) than there are different phenotypes (the combined physical attributes of the organism). This must be so, because we now know that each trait is affected by many genes; it takes more than one gene to make a trait (there may be exceptions where only one gene encodes a trait).

The figure is a conceptual map, but real g-p mapping is sort of the holy grail in evolutionary biology at the moment. With a real map like in hand evolutionary dynamics can be predicted, and we will be able to say which genetic changes are required to change the phenotype. However, realistically we can only map a very small portion of the genotype on to the phenotype, and there even seems to be some confusion about what the proper answer is to the question of what the genotype-phenotype map looks like. Hopefully the answer won't be too mathy...

References
[1] Peter F. Stadler, & Christopher R. Stephens (2003). Landscapes and Effective Fitness Comm. Theor. Biol DOI: 10.1080/08948550302439
[20] A testable genotype-phenotype map: Modeling evolution of RNA molecules. In: Lässig, M. and Valleriani, A., editors, Biological Evolution and Statistical Physics, pp. 56–83. Springer-Verlag, Berlin, 2002.

How to be a good speaker

2012-09-19T11:36:00.004-04:00

Bjørn's two rules of being a good speaker:

Love the words that you speak
Always have something to say

An engaged speaker is more enjoyable to listen to than a bored one. If you love the words as they leave your mouth, you are more likely to engage the audience. Caveat: we all hate someone who loves to speak - too much. I am here talking about giving a presentation, where you are expected to deliver a monologue. In dialogue, be a good listener.

If you don't have something to say, don't give a talk. As a scientist, this is the same as not having done anything, in which case you are not doing your job. But I also mean this in a more general sense: live life learning, and have your lessons to share. If not, then it's a waste, in my opinion.

I'm at the 16th Evolutionary Biology Meeting in Marseille, and I trust I don't need to say that some of the presentations don't measure up to the science behind them. And that's a shame; people being bored listening to your talk when they really should be excited about the science. It's a total myth that all one needs to do is do good science, and people will be interested in your talk. Rather, unless it is the something you are supremely interested in (which is probably only a small fraction of what you hear at conferences and seminars), then people tend to lose interest, tune out, and sometimes even feel antipathy for the speaker.

There are other things a speaker can do, but those are not my rules.

I am speaking tomorrow evening on the Impact of Epistasis and Pleiotropy on Adaptation.

Epistasis in evolution

2012-09-17T18:26:00.000-04:00

[The following is a post written for BEACON.]

What is epistasis?
Epistasis is a measure of the strength of epistatic interactions. Epistatic interactions are non-additive interactions between alleles, loci, or mutations. That is, if the combined effect of a pair of mutations is not what we expect from their individual effects, we then say there is epistasis between those two mutations.

Two mutations that are both detrimental on their own can be beneficial when they occur together. An example of this is from Joe Thornton’s lab: the present function of reduced sensitivity to hormone in vertebrate glucocorticoid receptor is an example of this. Two mutations both reduced sensitivity and destabilized the newly duplicated gene shortly after its birth 450 million years ago. A third mutation – neutral without the first two mutations – buffered the destabilization, and allowed to gene to go fixation (Carroll et al., 2010).

Epistasis is mostly measured in terms of fitness, as the deviation from additivity, but in principle any trait-value can be used*. If mutation A increases fitness by 5% and B increases fitness by 10%, then we might expect that an organism with both mutations get a fitness increase of 1.05×1.10=1.155 or 15.5%. This would be the case if the two mutations do not interact, so that their effects on fitness are independent of each other. The deviation can be measured in various ways, but the proper way of doing it would be like this:

ε = log₁₀[W_AB × W₀/ (W_A × W_B)],

where W₀is the fitness of the organisms with neither mutation. This is the best definition (!), because we assumed above that the effects of the mutations are to increase fitness by a fraction of the current fitness, rather than by adding a number. If mutations did increase fitness by an absolute number, we might measure epistasis as

ε = W_AB + W₀ – (W_A + W_B).

Both of these measures are then zero when there is no epistasis, and both can be extended to deal with more than two mutations interacting. When ε>0 we call it positive epistasis, and negative epistasis when ε<0 (Fig. 1).

So, if an organism with both mutations have a fitness of 1.20, then the amount of epistasis is ε = log₁₀[1.20 / (1.05 × 1.10)] = 0.01660. If two deleterious mutations together have a beneficial effect, the sign of the joint effect is reversed, and this is called reciprocal sign epistasis (e.g., W_A= 0.95, W_B = 0.90, W_AB = 1.20, giving ε = 0.1472). A trivial case of negative epistasis is when both mutations are independently neutral, but their joint effect is deleterious (e.g., W_A= 1.0, W_B = 1.0, W_AB = 0.90, ε = -0.04576). I say this is a trivial case, because this type of interaction could be one where two genes carry out the same function, thereby exhibiting robustness by being redundant; the organisms then only suffers a fitness decrease when both genes are not working properly.

Fig. 1: Schematic illustration of epistasis. Two mutations A and B can interact epistatically in different ways with varying effects on fitness. The fitness of the wild-type is represented by the black baselines, and the heights of arrows represent the fitness after one mutation (W_A or W_B) and after both mutations (W_AB). Green, positive epistasis, red, negative epistasis, black, no epistasis. In (a), two independently beneficial mutations may have their joint effect increased or diminished (W_AB larger or smaller), while in (b) the independent effect of the two mutations is deleterious and beneficial, respectively, and the combined expected effect on fitness is deleterious. In (c), each mutation by itself is deleterious, but when they interact, the result can be reciprocal sign epistasis (green arrow). These sketches illustrate an additive model, where the sum of W_A and W_B is equal to W_AB without epistasis. In our model, using the geometric mean this corresponds to taking the logarithms of the fitness. From Østman et al. (2012).

Epistasis is a feature of the genotype-phenotype map, and of genetic architecture. The genes that together are responsible for a trait (e.g., eyes, lungs, blood-clotting) are likely to interact and have non-zero epistasis. Many genes are also pleiotropic, i.e. part of gene-networks of more than one trait (Fig. 2), as they are expressed in different contexts (tissues, cell-types, in response to different environmental cues, etc.).
Fig 2: Epistatic modules. (A) Hypothetical genotype-phenotype map with three modules of groups of genes affecting three traits: eyes, lungs, and blood-clotting. The genes within each module interact epistatically, while some genes exhibit pleiotropy (black arrows). Not all pairs of genes affecting the same trait necessarily have a non-zero epistasis. (B) Human liver coexpression network and corresponding gene modules. The gene coexpression network consists of the top 12.5% most differentially expressed genes (5,012 expression traits). The colors of the nodes represent their module assignments. Each of the colors correspond to a trait, and most genes are only expressed in that trait, though some are expressed in more than one (pleiotropy), as indicated by lines signifying coexpression. From Friend (2010).

Why is epistasis important in evolution?
One reason why epistasis is so important in evolutionary biology is that it affects the fitness landscape. The structure of the fitness landscape in large part determines many important things in evolution, such as evolvability, robustness, repeatability, contingency, and speciation. If the environment dictates that on set of genes/loci has a particular combination of alleles that optimizes fitness, then without epistasis each gene can be optimized individually until the optimal combination is reached (i.e., there is one peak in the local fitness landscape, aka smooth landscape). Deterministically, the population will end up on the peak. However, if the genes/loci interact, then fitness values are modified, and the fitness landscape will no longer be smooth, but contain multiple local peaks with valleys in between. Evolution in such a rugged fitness landscape will not be predictable, and multiple outcomes are now possible. Because there are multiple peaks the population might get stuck on a local peak with lower fitness than the highest peak in the landscape. Another possibility is that more than one peak is climbed at the same time, and if such a situation can be sustained, it can lead to evolutionary branching and even speciation.

Another reason why epistasis is so important is that interactions between genes means that much more complex traits can be made. If genes did not interact, then no trait would be affected by more than one gene (is this necessarily always true?). It is of course not possible to make a complex structure with only one kind of protein. Conversely, the more genes interact within a module, the more complex the trait can be, which in turn translates into higher fitness. With only a handful of genes available, only a simple eye can develop, while many genes together can make a more complex structure, which can increase the organism’s fitness. The fact that genes interact epistatically is why complex multicellular organisms with abundant cellular differentiation are possible at all.

How prevalent is epistasis?
Very. Basically, when people measure it, pretty much all pairs of mutations are epistatic. That’s hard to believe is true, and it probably isn’t. Measuring fitness is generally difficult; you have to measure the fitness of four organisms, and just a little bit of error will give ε different from zero. Therefore it is reasonable to attribute lots of non-zero measures below some limit to no epistasis. And then still, it turns out lots of pairs of mutations have significant epistasis between them.

For example, Costanzo et al. (2010), using data from a genome-wide, quantitative analysis of genetic interactions in yeast, showed that even when including only high values of epistasis (|ε|>0.08), then a large fraction of gene pairs are epistatic (Fig. 3A). Or in Drosophila melanogaster, where 15 insertions in the genes involved in startle-induced locomotion show extensive genetic interactions (Fig. 3B)

Fig. 3: Prevalance of epistasis. (A) The distribution of genetic interaction network degree for negative (red) and positive (green) interactions involving query genes. From Costanzo et al. (2010). (B) Epistatic interactions for startle-induced locomotion among 15 P[GT1] insertion lines in double heterozygous genotypes. From Yamamoto et al. (2008).

What is the current research focus?
Two major areas of research in evolution are adaptation and speciation. This has been so for a long time, and while we do know a lot about both, there is little doubt that this will not change in the foreseeable future. Adaptation is particularly affected by epistasis and pleiotropy, and it is an outstanding question to what extent adaptation is enhanced or mitigated by epistasis. Empirical data suggest that epistasis causes diminishing returns (e.g., Kahn et al, 2010), but this probably just means that the shape of fitness peaks are shallower the closer you get to the apex, which would just mean that the biggest returns on fitness comes with the first beneficial mutations (which are more likely to go to fixation in the first place). How much does epistasis affect evolvability? Fitness landscape ruggedness can limit a population’s ability to evolve, and ruggedness depends on the amount of epistasis among and within genes. But are these epistatic interactions set in stone, or are they malleable? In other words, how easy is it to create epistatic interactions, and once formed, can they be broken and allow for new advances in adaptation?

Speciation is also a much studied area of evolutionary biology, but the impact of genetic architecture is only recently coming into focus. Epistasis can cause Dobzhansky-Muller incompatibilities, which can lead to reproductive isolation (which is cool if your gold standard of speciation is the Biological Species Concept). But more generally, the epistastic nature of the genetic architecture causing multiple fitness peaks implies that evolutionary branching can occur. It remains an open question how much this is governed by epistasis, and particularly whether epistasis is a prerequisite for speciation of microbes.

* Not that I am thereby saying that fitness is just another trait. I hold the view that fitness – reproductive success – is a function of other traits, such that a network would point from genes to traits, and traits to fitness.

References
Carroll SM, Ortlund EA, and Thornton JW (2011). Mechanisms for the evolution of a derived function in the ancestral glucocorticoid receptor. PLoS Genetics, 7 (6) PMID: 21698144
Costanzo M, et al. (2010). The Genetic Landscape of a Cell Science, 327 DOI: 10.1126/science.1180823
Friend SH (2010). The need for precompetitive integrative bionetwork disease model building. Clinical pharmacology and therapeutics, 87 (5), 536-9 PMID: 20407459
Khan AI, Dinh DM, Schneider D, Lenski RE, and Cooper TF (2011). Negative epistasis between beneficial mutations in an evolving bacterial population. Science (New York, N.Y.), 332 (6034), 1193-6 PMID: 21636772
Yamamoto A, Zwarts L, Callaerts P, Norga K, Mackay TF, and Anholt RR (2008). Neurogenetic networks for startle-induced locomotion in Drosophila melanogaster. Proceedings of the National Academy of Sciences of the United States of America, 105 (34), 12393-8 PMID: 18713854
Østman B, Hintze A, and Adami C (2012). Impact of epistasis and pleiotropy on evolutionary adaptation. Proceedings of The Royal Society Biological sciences, 279 (1727), 247-56 PMID: 21697174

Sexual selection in bacteria

2012-09-13T15:19:00.002-04:00

Did I fool you for a moment?

What would surprise you?

2012-09-11T12:58:00.002-04:00

How often do you go "shiiiiiiiiiiit, so that's how it is!?!" What would really shock you? "FUCK! I never thought that would be the case..."

Probably not that often. But those moments are so great, and as a scientist, I'd say we sort of live for them.

I was thinking about this in terms of working in evolution. What would be a really big moment that I could say advanced my understanding of how living things evolve? Most papers I read anymore are incremental advances. Actually, all of them are. When I first started learning about evolution, I was in near-constant shock/revelational mode. It was pure delight to discover what we know about evolution. But now that I know most of it, nothing much surprises me anymore. Which is a shame.

So it got me thinking about where I could search for such moments. Something akin to learning that the Earth is not the center of the universe, or that everything is made of atoms. Or that there were dinosaurs, and that we evolved. The rest seems to be details. Important details, but not revelational.

I do various things in evolution, but my overarching focus is the origin of evolutionary novelty (but I like speciation, too). How do new things come into existence? The first eyes, first brain, first blood. People will then say that those things are derived from previous structures. Eyes from simpler photoreceptors, brains from simple nervous systems, blood cells from other cells. And these systems derived from yet simpler cells, but along the way, something new happened at least at some points that enabled these new systems/structures to form. New proteins were added to the mix, encoded by new genes. So where did these new genes come from? Well, they were derived from other genes, by duplication and neofunctionalization: a new gene is a copy and a refashioning of an old gene. So far so good. Then where did the first gene come from? Sorry, I don't work on origin-of-life stuff.

Is that it? Not quite. There are some major transitions in evolution to be explained. Unicellularity to multicellularity, cellular differentiation, asexual to sexual reproduction, and stuff like that.

But then, I am still left with this feeling at times that there is really nothing that would really upset my world-view (of evolution) much anymore. Still nothing revelational in sight. I hope I'm wrong.

Titles in evolutionary biology

2012-09-10T17:17:00.001-04:00

These are the new papers for the last couple of weeks that I would like to read but will probably never get to. Gone are the days of the polymaths already, and now this!

Systematic underestimation of the age of selected alleles
Predatory Fish Select for Coordinated Collective Motion in Virtual Prey
Rapid evolution of Wolbachia incompatibility types
Avoidance of roads and selection for recent cutovers by threatened caribou: fitness-rewarding or maladaptive behaviour?
Weak Selection and Protein Evolution
Patterns of Neutral Diversity Under General Models of Selective Sweeps
Selective Sweeps in Multilocus Models of Quantitative Traits
Distinct evolutionary patterns of morphometric sperm traits in passerine birds
A selective force favoring increased G+C content in bacterial genes
Evolutionary Dynamics of Strategic Behavior in a Collective-Risk Dilemma
Evolution of Stress Response in the Face of Unreliable Environmental Signals
Network Context and Selection in the Evolution to Enzyme Specificity
Clade Age and Species Richness Are Decoupled Across the Eukaryotic Tree of Life
Evolutionary medicine: its scope, interest and potential*
The role of ‘soaking’ in spiteful toxin production in Pseudomonas aeruginosa
On the evolutionary origins of the egalitarian syndrome
Clade Age and Species Richness Are Decoupled Across the Eukaryotic Tree of Life

* Because I am meeting with Stephen Stearns when he visits MSU this Thursday, I will take an actual look at this review article. Paul Ewald was here last week, and we had a good talk about selection in pathogens and human disease. I also met with Randolph Nesse last semester, so evolutionary medicine has been in focus a lot lately.

ENCODE: What defines genomic function?

2012-09-05T17:31:00.000-04:00

A new wealth of articles by the ENCODE (the ENCyclopedia Of DNA Elements) consortium suggest that far more of the human genome carries out some function or other, and one might conclude that very little DNA is junk:

From an introduction to the new ENCODE papers:

Collectively, the papers describe 1,640 data sets generated across 147 different cell types. Among the many important results there is one that stands out above them all: more than 80% of the human genome's components have now been assigned at least one biochemical function.
[Emphasis added.]

80%? That is a lot (see [2] for details). It doesn't throw out the idea of junk-DNA, i.e., that there is DNA that has no function - but it puts the number much closer to zero than the 90% that I have heard before. But I seriously wonder what is meant by "function". Take a look at this image[1]:

Gene regulation is a very spatial thing, which means that if you were to move a gene (i.e., the protein-coding DNA, or exons) somewhere else, then if would probably not be transcribed at the right time. So, if you were to cut out a length of DNA that doesn't have any function, then other DNA will be shifted spatially, and this might screw up proper transcription. So, DNA without function might be important as a filler. On the other hand, ENCODE includes in the 80% everything that is transcribed (i.e., DNA is used to produce RNA), but that doesn't mean that it has a function, as defined in my book. RNA may be floating around in the cell, and may never be translated (into protein), and may not have any other (e.g. regulatory) function either. On top of that, ever if it is translated and a protein is created based on that DNA, it doesn't necessarily follow that the protein does anything (could even be detrimental to the organism), and then that surely isn't functional.

To me, this is one of those moments where my understanding of how things work is challenged. If it really is true that no more than 20% of the human genome is junk (and it apparently could be a lot less than that), then I am happy to update my understanding, but I am super-skeptical that there is that little junk in the human genome. But I am not too happy with the usage of the words junk and non-functional here.

References
[1] Joseph R. Ecker, Wendy A. Bickmore, Inês Barroso, Jonathan K. Pritchard, Yoav Gilad & Eran Segal (2012). Genomics: ENCODE explained Nature, 489 DOI: 10.1038/489052a
[2] The ENCODE Project Consortium (2012). An integrated encyclopedia of DNA elements in the human genome Nature, 489 DOI: 10.1038/nature11247