Life cycle of M. xanthus. Credit: Carla canales / citizendium.org

This post deals with another aspect of bacterial cooperation: how does it evolve? Why cooperate in the first place at all? Every time an individual cooperates, short term gains are sacrificed for long-term ones, but those long-term ones are contingent upon all or most cooperating individuals doing their bit. Think about standing in line to the bus. If everyone cooperates, we get on the bus faster, but some of us may be forced to stand. On the other hand, shoving your way to the beginning of the line will assure you a good seat, albeit at the expense of glares from your fellow-passengers, and maybe a few altercations along the way.  In evolutionary terms, selfishness seems like a sounder strategy than cooperating.  After all, if you manage to gain a better position for yourself in life’s pecking order, you pass those genes that enable that to your progeny, and further down the line. Why cooperate or act selflessly in the first place? Why let someone else share the gene pool with you when you can have it all to yourself?

Unless that “someone else” shared genes with you: that is, they were related in some way. Suddenly, cooperation seems to have evolutionary benefits: you are preserving and passing on some of the same genes.  Protecting kin is the most often-used explanation for how cooperation evolved in the first place: kin selection, meaning, favoring cooperation those individuals with which you share a larger number of genes over those who do not. Evolutionary biologists use the Hamilton’s law as a guideline:  the higher the benefit of the cooperation, the lower the cost, and the closer the relatedness of the individuals cooperating, the more likely it is that there will be cooperation. Putting it into an equation, cooperation will evolve if the following condition is met:

rb - c > 0

Where r is the relatedness (on a scale of 0 to 1 where 0: no genetic relation, 1:self), b is the benefit of cooperation and c is the cost. This rule, formulated by William Donald Hamilton is a centerpiece of evolutionary biology. Imagine going on a day’s hunt  where the quarry is a large animal that can feed one hunter for 35 days, but requires at least five hunters to take it down.  Now there are also smaller animals around, that can be hunted by one individual, and they supply enough food for one hunter for two days. Is it beneficial to hunt  alone or together? Let’s figure the benefits and costs. For hunting the large animal, the one that requires at least five hunters, the benefit is a week of food each (b= 35/5 = 7) while hunting for one day (c=1). If the individuals are cousins sharing an average of 20% of the genetic material  then:

0.2×7 – 1 = 0.4 is the benefit score

If they are siblings, sharing 50% of the genetic material, then:

0.5×7 – 1 = 2.5 the benefit score is even higher

But what about individual hunting? The benefit of the smaller quarry is is 2 days worth of food, and one day of hunting, and you do it alone. So r=1 (yourself), b=2 and c=1 giving us:

1×2-1 = 1

In this hypothetical model, a group of siblings will cooperate to hunt big game, while cousins would probably hunt smaller game alone. If you want to dig deeper into how Hamilton’s rule was derived, and further implications of the rule, I recommend this post.

Any mechanism in evolution is examined through the lens of fitness. Fitness is the relative ability to produce and support viable progeny. So if cooperation increases fitness, we can use the following simple graph to explain the difference between a cooperating and a non-cooperating  individuals in a cooperating population using Hamilton’s rule:

Figure 1: Hamilton's rule prediction: the fitness of cooperators (blue) and non-cooperators (red) increases as the number of cooperators among social neighbors (x-axis) increases. The slope of both lines is the benefit (b).

The benefit, b, is the slope of these two lines. The difference is c. Note that for any given frequency of cooperation in the population, the non-cooperating individuals (red line) have a higher fitness than the cooperating ones (blue line). It seems that it “pays off” to be a self-server no matter the social environment you are in, even though you still benefit from being in a cooperating community. Yeah, we all know the type.

But what happens when the difference between cooperating and not cooperating depends on the percentage of cooperators in the population? Not too hard to imagine: if most of the population is playing nicely together and benefiting from it, then this might change the attitude of the selfish individuals more readily then if only a small fraction of the population is cooperating. But as it stands, Hamilton’s rule does not provide for this type of model. However, the following modification of Hamilton’s rule does:

r ⋅ b – c + m ⋅ d > 0

Relatedness, r, is now not a scalar (a single number), but a vector (an ordered set of values)  r = {r₁, r₂, …} describing relatedness under different conditions. Ditto the benefit vector, b. b has the coefficients of the equation describing the fitness of non-cooperators as a function of how many neighboring cooperators there are in the population (red lines). In a linear setting (Figure 1), r = {r₁} b={b₁} and m⋅d = 0, collapsing the expanded equation into the classical Hamilton’s rule.  We won’t get into m and d in this post, they are important though, and you should read the paper to understand how they play a role

Expanding them from scalars to vectors enables a richer and more flexible description of Hamilton’s rule, allowing to describe non-linear relationships like this:

Figure 2: Note two things. First, the relationship between fitness and the fraction of cooperators in the population is not linear. Second, the difference in fitness between cooperators and non-cooperators decreases as the fraction of cooperators in the population goes up. These two phenomena cannot be described by the classic Hamilton's rule equation. They can be described using the modified rule.

This modification of Hamilton’s rule was developed by Jeff Smith and colleagues, at the department of Biology at Indiana University. Armed with the new equation, Smith and his colleagues decided to see how well it can be applied. They decided to look at Myxococcus xanthus. M. xanthus bacteria behave normally as long as food is abundant: they swim around and proliferate by cell-division as bacteria do. But when starved, they aggregate, and some cells form resistant spores, while the others die. Some cheating strains sporulate  well when in cooperating populations, but do poorly on their own. The scientists mixed a cooperator strain with a cheating strain at different frequencies, starved them, and measured the fraction of each strain in the population of surviving spores. They found the following: first, the fitness effect was non-linear; in fact, it was almost exponential. Second, cooperators were more fit than cheaters at low cooperator frequencies, but cheaters fared better at high cooperator frequencies. So it pays to freeload when most people around you behave nicely. In the case of M. xanthus, the added value to the population is quite high. In fact, the scientists found that cooperation in M. xanthus is very robust and resistant  to cheating:  cheaters were viable (i.e. had a positive fitness)  only with groups that had more than 70% cooperators. So it is only when cheaters have a large cooperating population to buffer their nasty habits that a they can thrive.

Figure 3: Relative fitness of cooperators (blue) and cheaters (red) in a populations with different relative frequencies of cooperators. Note that the fitness scale is logarithmic: the fitness increase is very much non-linear, as in Figure 2.

Moral of this story: if you got to cheat, make sure there are a lot of nice people around. Otherwise it won’t work out very well.  In evolutionary terms, the  trait for cooperation and kin selection has evolved to become strongly entrenched, so much that cheaters can only survive if cushioned by a high frequency of cooperators. Favoring your own and acting selflessly towards them seems to be the way to go, in the case of M. xanthus.

Some headlines just write themselves…

It has been known for some time that an approaching large herbivore causes aphids to abandon ship …err plant. Makes sense since, after all, there’s not much of a point in staying on the particular bit of shrubbery that will be consumed, lock, stalk and barrel by a ravenous forager. However, it was not exactly clear what in the herbivore causes the aphids to drop. Well, it is not the shaking of the twigs, as rustling the plant did not cause a substantial number of the aphids to drop. Rather, it’s the breath. The researchers had a human, a sheep and goat all breath on an aphid-infested plant, with equal results: the aphids dropped from the plant en-masse. But what in the breath causes aphids to do that? Well, it is not the CO₂ nor the air movement itself. Rather, the heat and the humidity of the breathing, as tested by Moshe Gish and his colleagues at the University of Haifa.

This is a great example of adaptation: after all, bush movement may be due to many different factors, including uninterested rodents and carnivores. Also, air movement can be simply caused by wind, including hot or humid air.  But someone breathing directly on you, hot and damp can only mean one thing to an aphid: abandon plant or be goat dinner!

Of course, I never do anything without researching it first, because I am such an awesome scientist, or detail-oriented !@#*^, depending on whether you ask me or my students. So I looked up “substance” in the Merriam-Webster dictionary. Here is what I found:

Main Entry: sub·stance
Pronunciation: \ˈsəb-stən(t)s\
Function: noun
Etymology: Middle English, from Anglo-French, from Latin substantia, from substant-, substans, present participle of substare to stand under, from sub- + stare to stand — more at stand
Date: 14th century

1 a : essential nature : essence b : a fundamental or characteristic part or quality c Christian Science : god 1b
2 a : ultimate reality that underlies all outward manifestations and change b : practical importance : meaning, usefulness
3 a : physical material from which something is made or which has discrete existence b : matter of particular or definite chemical constitution c : something (as drugs or alcoholic beverages) deemed harmful and usually subject to legal restriction

4 : material possessions : property

Hmmm… 2a and 2b seem to be relevant. Perhaps 3c should be too, as my blogging could be construed harmful to other more productive activities, which I am obviously not engaged with at this moment. Actually you, gentle reader,  are not engaged in more productive activities either right now. Be that as it may, the word substance does seem to have an air of permanence about it, which is contrary to the perceived ephemeral nature of blogging. Bora is actually one of the people who are doing something about making blogs less ephemeral by publishing The Open Laboratory collection (full disclosure: I’m published in the 2009 book) and by supporting science bloggers, blogging and activities wherever they may be. This makes me so happy to be among Bora’s chosen 10 (OK, 11, he cheated a bit) among the hundreds of blogs he must be reading. Thanks Bora!

I do wonder though, eighty-five years from now, how many of us science bloggers would be remembered for our blogging? Well, maybe not as individuals, but what kind of impact are we having now, and how much will it remain 85 years from now? Hopefully as a collective, science bloggers are impacting the understanding of science, which is one of the reasons I am blogging. Hopefully, we do have substance, as a group if not as individuals.

Why eighty-five years? Well, the answer to that brings me to the main topic  (substance?) part of this post, which is the  anniversary of the Scopes trial. This month, 85 years ago, a schoolteacher in Tennessee was convicted of a high misdemeanor for violating the State of Tennessee’s Butler Act which prohibited the teaching of evolution in any of the state’s public schools and universities. He was fined $100.

PUBLIC ACTS

OF THE

STATE OF TENNESSEE

PASSED BY THE

SIXTY – FOURTH GENERAL ASSEMBLY

1925

________

CHAPTER NO. 27

House Bill No. 185

(By Mr. Butler)

AN ACT prohibiting the teaching of the Evolution Theory in all the Universities, Normals and all other public schools of Tennessee, which are supported in whole or in part by the public school funds of the State, and to provide penalties for the violations thereof.

Section 1. Be it enacted by the General Assembly of the State of Tennessee, That it shall be unlawful for any teacher in any of the Universities, Normals and all other public schools of the State which are supported in whole or in part by the public school funds of the State, to teach any theory that denies the story of the Divine Creation of man as taught in the Bible, and to teach instead that man has descended from a lower order of animals.

Section 2. Be it further enacted, That any teacher found guilty of the violation of this Act, Shall be guilty of a misdemeanor and upon conviction, shall be fined not less than One Hundred $ (100.00) Dollars nor more than Five Hundred ($ 500.00) Dollars for each offense.

Section 3. Be it further enacted, That this Act take effect from and after its passage, the public welfare requiring it.

Passed March 13, 1925

W. F. Barry,

Speaker of the House of Representatives

L. D. Hill,

Speaker of the Senate

Approved March 21, 1925.

Austin Peay,

Governor.

Seems incredible at this day an age… or maybe not so incredible given recent events in Louisiana.

William Jennings Bryan, counsel for the prosecution, attacking evolution

The city of Dayton as the organ grinder profiting from the Scopes trial

The trial, which originated as something of a publicity affair for the town of Dayton, Tennessee, quickly became a battleground for evolution vs. creation. In the short term, the trial actually increased the number of anti-evolution bills proposed in different state legislatures in the US. In the long term, however, Tennessee vs. Scopes is seen as a watershed moment in the teaching and public acceptance of evolution, and has had long terms ramifications in the US and internationally. Scopes himself  spoke only once at the trial, was not called to testify, and only had this to say when granted a statement after sentence was passed:

Your honor, I feel that I have been convicted of violating an unjust statute. I will continue in the future, as I have in the past, to oppose this law in any way I can. Any other action would be in violation of my ideal of academic freedom — that is, to teach the truth as guaranteed in our constitution, of personal and religious freedom. I think the fine is unjust.

Now that is substance.

Back to the award;  I still have some conditions to fulfill:

1. Sum up your blogging motivation, philosophy and experience in exactly 10 words.

¹Blogging ²motivation, ³philosophy ⁴and ⁵experience ⁶cannot ⁷be ⁸summed ⁹in ¹⁰ten ¹¹words.

2. Pass this award on to 10 other blogs

Given the 10ⁿ growth rate of tagged blogs, chain-letter fashion, I wonder about how this Blogging with Substance award has originated. Search engines was no help, as so many blogs are now tagged with the Blogging with Substance. If someone has an answer, let me know. Anyhow, here are my 10 tags, based on what I am reading nowadays, ephemerality of blogging substance, and all that jazz. Tough choices though, so many good blogs out there:

1. Blue Collar Bioinformatics

2. Sandwalk

3. Thoughtomics

4. The Loom

5. Mike the Mad Biologist

6. Genomics, Evolution and Pseudoscience

7. Circle of Complexity

8. Buried Treasure

9. The Tree of Life

10. Mystery Rays form Outer Space

Final word: if this post seems a bit confused, and you are not sure that you are “getting it”, well, that’s this post’s substance.

I recently received an email from a graduate student in Philosophy regarding protein function. Not sure if that person wants his name advertised, so I will keep it to myself.

“I am a fan of your blog, and interested in the philosophy of biology. One particularly interesting question is what makes something have a function; when it comes to artifacts, we just check with whoever designed the thing. It gets more complicated when functions change, and things are used for purposes other than what they were originally designed for, but it’s still pretty straightforward. However, biological functions can’t go that route (unless maybe one is a fan of intelligent design). I’m curious what you think about this, after seeing you mention your interest in predicting the function of genes and proteins. Is the function of something just the causal role that it plays in some larger mechanism? Do you have to include evolutionary considerations? If you ever have the time, I’d love to hear your thoughts about this.”

Thanks very much

My rather rambling answer follows:
“Ouff, you’ve opened a pretty big can of worms, which many of us are having a problem with.

Function in biology is context dependent. An enzyme catalyzes a biochemical reaction, say, removing a phosphate molecule from a protein, However, by removing that phosphate from the protein, the enzyme changes something in the function of the cell, as phosphate molecules are the ‘signaling currency’ of the cell. So the enzyme fulfills a cellular function as well. Finally, suppose this cell is in a developing embryo, and the phosphate removal in this type of protein in many catalyzes the creation of a limb, or a particular organ or tissue: now we have a whole organismal functional context. Which one of those: the biochemical, cellular or organismal is the ‘real’ function of the cell? Well, obviously all three are ‘real’.

To add a twist, suppose that a this enzyme is also active in removing phosphates from proteins in the adult animal. Now the animal has reached maturity, and because of a mutation in one of the cells that enzyme does not work anymore. The intra-cellular signaling becomes defective and the protein accumulates in its ‘phosphorylated’ form. This signals a division of the cell, and suddenly you have a pre-cancerous situation. So from a health point of view, this mutant plays a role in the survival and proliferation of cancer cells. Interestingly, a protein that causes our spittle to froth (don’t try doing this around other people, gross), was first discovered as a nasopharenygeal cancer associated protein, and it is named as such. Many genes and proteins are named after they are found to do one thing, even though we generally associate them with something else, simply because of the context in which they were discovered.

Also, there are moonlighting proteins, which may simply perform different functions. A protein called APIS is part of the proteasome: a cellular protein shredder which is itself a rather large protein complex. APIS also plays a role in transcribing DNA to RNA: thus, it is part of a protein creation complex, and of a destruction complex. See this short paper on Moonlighting proteins.

Yes, evolutionary considerations always come in to play, it is the lens through which we examine all biological phenomena. Evolution does cause certain proteins to be ‘multi-purposed’, also, some types of protein structures are more amenable to a certain set of functions than others. Furthermore, certain proteins are ‘promiscuous‘: certain enzymes may work on more than a single substrate (“Promiscuous” is different from “moonlighting”, where enzymes do completely different jobs; being “promiscuous” means a single enzyme does the same thing, but with different partners: i.e. catalyze the destruction of a sugar, but with different types of sugar molecules). Promiscuous enzymes can clearly show a ‘trajectory of evolution’ i.e. going from being very specific for one substrate, to non-specific for several substrates (or vice-versa). Promiscuity is a good example of molecular adaptation and tradeoff: a promiscuous enzyme means you have a jack-of-all-trades in your genomic complement, and you have to spend less energy on controlling the production of several different enzymes for several different tasks. However, the flipside of having a jack of all trades is that he is the master of none: the catalysis reactions are generally less efficient, which may cause problems for the cell/organism.

Phew, I hope I managed to convey some of the complexities of this issue, and how we try to deal with them in a systematic fashion.
[... edited out]

Cheers,

me”

The difference between moonlighting...

...and promiscuous

Jeffery, C. (2003). Moonlighting proteins: old proteins learning new tricks Trends in Genetics, 19 (8), 415-417 DOI: 10.1016/S0168-9525(03)00167-7

We refer to DNA as “the book of life”; some geeks refer to it as the “operating system of life”. Just like in a computer’s operating system, DNA contains all the instructions on how to “execute” life and to keep things humming.  Many genes make proteins or RNA than act as switches to activate the synthesis of other proteins, sometimes in a two- three- or higher level hierarchy.  These switches are conditional, based on environmental conditions, or whether it’s time to replicate the DNA and divide into two daughter cells, and so on. Some genes activate the transcription of other genes, but are not regulated themselves by other genes, those can be dubbed  “master regulators”. Some genes are both activated by other genes, and activate other genes themselves: “middle management”. Finally, there are genes that are activated, but do not regulate other genes: the “workhorses”. This information, known as the transcriptional regulatory network exists for 1,378 genes of the E. coli bacterium.

Paralleling this in Linux, there are programs that call other programs; again, in a hierarchical fashion.  According to the calling structure, they also can be dubbed Master Regulators (calling other programs but not being called themselves), Middle Management (calling other programs and being called), and  Workhorse (only being called).

Koon-Kiu Yan and his colleagues from Yale mapped the program call graph in Linux by setting each program as a node and drawing lines to the programs that call it, and to the programs it calls. They did the same thing for E. coli‘s transcriptional regulatory network. Here are the graphs they got:

So it seems like Linux is middle-management heavy, whereas E. coli is workhorse heavy. 30% of Linux programs are top management, as opposed to only 5% in E. coli.

Looking at the actual functions for the genes/programs, it seems that Linux programs also have much more of a  functional redundancy than in E. coli: 3.5% of E. coli‘s genes have “reusable” functions, as opposed to 8.4% of Linux programs. But if we look at entire working subgraphs of these two graphs, the subgraph overlap in Linux is 87%, whereas in E.coli the overlap is only 4.3%. This means that the division of labor in E. coli is much more distinct than in Linux. There are many ways of activating the same hierarchy in Linux, but in E. coli there is rarely more than one way to do it. Note that Linux is top-heavy, whereas E. coli has a pyramid-like structure. It is pretty obvious that the Workhorse modules in Linux go through heavy reuse while those in E. coli do not.

The scientists then decided to look into how these two networks developed.  The oldest genes in E. coli are the Workhorses, whereas the regulatory genes in middle and top management arrived more recently. In contrast, the newest programs — the most heavily rewritten ones– in Linux are the Workhorses, whereas the ones in the management echelons are  less changed than their predecessors. The oldest programs are those that are in Middle Management. they are also the most abundant type in Linux’s call graph.

Who are the Workhorses in E. coli? Those are mostly enzymes, the proteins that catalyze specific biochemical functions.  As a rule, enzymes are very specific: an enzyme would catalyze only one type of reaction, and only with a very specific chemical (substrate). Examples are enzymes that break up sugars: there is a specific enzyme for every type of sugar molecule. Who are the Workhorses in Linux? Those are the functions that get used all the time in thousands of different programs: strlen (measuring a character string’s length) or malloc (allocating memory for a data structure).  The Workhorses in Linux are non-specific while the Workhorses in E. coli are very specific.

So how to account for these differences? Nothing in biology makes sense except in light of evolution, and we have to look to the evolutionary history of both the bacterial and the computational systems for answers.  The major constraint in E. coli‘s evolution is fitness. If something breaks down in E. coli‘s Workhorse it wont get passed on to the next generation: the cell with the lethal mutation would never reproduce and will get thrown into Darwin’s rubbish bin. This leads to single-function workhorses because a multi-functional Workhorse would be too prone to messing too many systems up when it  mutates, and would never make it to the next generation, which is why the Workhorses in  E. coli‘s call graph have a lower connectivity that those in Linux’s call graph.

The authors conclude that the E. coli‘s call graph evolved bottom-up, with system robustness being the main selective trait. In contrast, Linux evolved top-bottom, with reusability of the Workhorses being the main selective trait. Reusability and robustness are tradeoffs. In the case of a man-made system like Linux, bugs in reusable modules are is not a problem, since Workhorse bugs are easily fixed in the next release. It is much less costly, in coding time, to tweak existing Workhorses than to build new ones.  Mutations in reusable workhorses in E. coli would weed out those kinds of proteins from the gene pool, and therefore E. coli‘s Workhorses are not reusable.

I’m not exactly sure what insight we can get by comparing natural vs. man-made networks. But hey, sometimes science is not about insight – sometimes is just about being totally cool; and The Coolness is strong with this work.

Nature is colorful. And the family of pigments that is mostly responsible for these colors are carotenoids. Carotenoids  make the apples and tomatoes red, the lemons and grapefruit yellow, the pumpkins oranges and, yes carrots, (from which their name is derived), orange.

Carotenoid cart. Credit: malayalm, Flickr

Carotenoids also make flamingos and salmon pink, and color the puffin’s bill orange. But those animals cannot produce carotenoids: rather carotenoids are in their diet, and in the case of flamingos and puffins they have a physiological mechanism of concentrating the carotene molecules and bringing them to display their strong colors. Indeed  Some of us also use carotenoids as an ornamental physiological addition: remember that horrid orange tan Auntie Mae sported last time you saw her in the dead of winter? Beta-carotene pills. Carotene is not just ornamental in animals, they are also important for eyesight, the immune system, and in decreasing DNA damage that may lead to cancer.

Puffins. Credit: United States Fishing and Wildlife Services. Flickr.

Very pretty. Credit: MrClean_1982. Source: Flickr

Even Nemo uses carotenoids. Credit shaferlens, Flickr.

The orange ring surrounding Grand Prismatic Spring is due to carotenoid molecules, produced by huge mats of algae and bacteria. Source: wikimedia commons. Credit: Jim Peaco, US National Park Service

The guy in the blue shirt... yup, carotene. Not so pretty.

An article published today in Science shows the first case of animals synthesizing carotenoids. Nancy Moran and Tyler Jarvik form the University of Arizona looked at the recently sequenced genome of the pea aphid. The pea aphid is known for having two different colors: green and red. It was not very clear though how the aphids got their color. Aphids feed on sap, and sap does not contain carotenoids. When looking at the genomes of the aphids, Moran and Jarvik found that they contained genes for synthesizing carotenoids: this is the first time carotenoid synthesizing genes are found in animals. The question they naturally asked is “where did those genes come from”? The animal kingdom does not contain genes for making carotenoids, so how come aphids have them?  Indeed, when they looked for the most similar genes to the aphid carotenoid synthesizing genes they found that they came from fungi, which means they somehow jumped between fungi and aphids, in a process known as horizontal gene transfer.  Horizontal gene transfer is not unheard-of in animals, and is actually quite common in plants (yeah, fungi are not plants, I know that), but this is the first time someone has shown a jump from fungi to animals, and that the trait that this gene conveys — color — became embedded  and functional in the genome.

Aphid color is important: red aphids get picked easily by predators off green plants, and vice-versa. So there is an evolutionary aspect here: the carotenoid genes play a role in the predator-driven selection of aphids. So in the case of aphids, as opposed to puffins and flamingo, the selective pressure is that of predation, not of mating. (I’ll refrain from comments about Auntie Mae.)

"Long ago, an ancestor to today's pea aphid somehow internalized a large important chunk of DNA from a fungus. This DNA now allows the aphid to generate its own carotenoid molecules. All animals need carotenoids for body functions as important as eyesight. However this aphid is the only organism in the Animal Kingdom so far to have been reported capable of producing it internally. The rest of us must forage for foods such as carrots, containing carotenoids. The precise way the DNA transfer occurred is not yet understood; however patterns within the DNA conclusively show a link to a fungus. DNA transfer from fungus to animal is unprecedented." (text taken from the NSF announcement). Credit: Zina Deretsky, National Science Foundation

As an aside, many of our pseudogenes and other contents of “junk DNA” are thought to have been acquired by horizontal gene transfer. Still, this is the first time a case of gene transfer that is so clear between two different kingdoms. However, I have the sneaking suspicion that as we sequence more animal, plant, fungal and other genomes of multicellular organism, we would find more cases of  “large-leap” HGT of functional genes happening: we just don’t have enough genomes yet to appreciate the frequency of these occurrences!

Update: this post has been slashdotted. Exercise extreme caution.

Overall, we are starting to see a divergence in sequencing technologies, as each tech concentrates on having clearly defined advantages and potential applications that differ from all others. This means that the scientists themselves can more closely tailor their choice of tech to fit their situation. Are you a small lab that needs 10 high-quality genomes on a budget? Go to Complete. Want a cheap, fast machine for library validation? Use Ion Torrent. Setting up a pipeline for sequencing thousands of genomes? Go Illumina.

The review article on metagenomics I recently published in PLoS Computational Biology (yeah, yeah, shameless plug) already starts to feels somewhat outdated on the sequencing technology front.

Carnival of Evolution #21 the superstar edition is up: check it out. It’s a nice and detailed one,. Some posts I liked included talking about how human fingers evolved, and why it is important to consult evolutionary biologists when making decision about conservation.

An interesting email I got yesterday: PubGet, a search engine for PDFs of scientific articles, is no linked to PLoS. PubGet is a very useful service that gets  you the article PDF immediately, without going through he usual clickeroo via Google,  pubmed, publisher’s gateway, journal gateway and then squinting along the sidebar to find the PDF link. Nice to see that these two are teaming up.

Finally, two reminders. First, Ada Lovelace day, a blogging day celebrating the achievements of women in science and technology is coming up, March 24. Go ahead, pledge and blog! Second, the Byte Size Biology will be hosting a Carnival of Bioinformatics. Quite a few posts have been submitted already, please submit yours, deadline: March 9.

More evolved? Source: Wikimedia commons, public domain.

So why am I writing about it? Well, this is my blog and ranting in it is my prerogative. And despite the Richard Dawkinses and Steven Jay Goulds of this world, the use of this phrase still persists. So it is up to us foot soldiers of the blogging community to do our own modest bit. If I prevent any of my six readers from being tempted to utter this phrase the next time it is (wrongly) deemed appropriate, then I have done my bit.

Why is this “highly evolved” used so much? And why is it wrong?

Consider the sponge, and then consider Albert Einstein. There are certain traits that Einstein had, that a sponge does not. We deem these traits to be of merit. Einstein developed a fundamental theory in physics. He  played the violin. He  ate with a knife and fork, had binocular color vision, opposable thumbs and he cultivated his facial hair in the form of a mustache.

A sponge… well, to be brief, does not have all those qualities we hold in such high merit. It kinda sits there at the bottom of the shallow ocean, flopping about, filter feeding, pooping and apparently not much else. Clearly, there are qualities to Einstein that make him more interesting than the sponge.

Less evolved? Source: Wikimedia Commons

Einstein seems, intuitively, to be more complex than a sponge, and that complexity can be quantified directly, in many ways. Actually, this is a pretty contentious point by itself: can we speak of organism complexity? Can we quantify the complexity of an organism and compare between different species? And what exactly would the complexity metric we choose tell us?

But let us assume, for argument’s sake, that our intuition that Einstein is more complex than a sponge is correct. For example, we can imagine a measure derived from the diversity and number of cells. Obviously there are more cell types in Einstein than in a sponge. Does that mean he is also more evolved? Are humans a more  evolved than sponges? Chimps? After all, did life not start 3.85 billion years ago as simple and over time became more complex? Progressing, as it were from simple unicellular bacteria through more complex sponges all the way to the crowning achievement of humans? Had humans not, in a sort of (alas, Pyrrhic) victory, mastered the Earth and competed with many of earth’s species to the latter’s extinction? Isn’t competition what evolution is all about? And isn’t human victory a direct result of human complexity making humans “more evolved”? So isn’t “complexity” an end product of evolution, the more complex you are the more successful you are, and the more evolved you are?

No, no, no, no, no, and no.

The reason for this series of compounding errors is the mistaken notion that evolution by natural selection is a progression resulting in a production of increasingly complex life.  Evolution is not goal oriented, and there is no teleology involved. The increasing complexity of organisms along time may seem to involve a  progressive process, but there is none. It is a “statistical illusion”.  What do I mean by that? Well, life did start out in less complex forms, that became more complex. But the less complex forms remained as well. Thus the distribution of complexity increased over time, but there is no directionality towards progress: the less complex life remained around as well. But over 3.85B years, complexity has had a chance to manifest itself in life, as natural selection favored some initial complexities, and those extended to become even more complex. Yes, we can trace a direct route from the first multicellular organisms, through sponges, invertebrates, vertebrates. But humans, chimps, sponges and bacteria living on Earth today are the result of exactly the same selective forces that have shaped life since  it crawled out of an underwater volcano, or wherever. Complexity emerged over time, and is still emerging. But complex organisms are being added to the pool of life, rather than replacing the simple organisms. The result is an increase of a distribution of complexity levels, not the moving of an entire curve of complexity rightwards.

Apparent progress due to a to a 'wall' restricting where random change can take things. Adapted from SJ Gould. Reproduced under CC from talkorigins.org

The point I am trying to make is that humans may be more complex than sponges, but we are not “more evolved” nor are we “highly evolved”.  There is no progressive process, and all of life on earth is the result of the same 3.85B years of selective pressures.

For a really good historical overview of teleological, or purpose-driven, thought in evolution, look to talkorigins.org.

Few know that Einstein was teaching evolution at Princeton. Physics was just a cover.

All of this does not mean that Highly Evolved by The Vines is not a kick-ass song. Listening to it is also  a good way to get the rage from hearing “highly evolved” out of your system.  Note the low complexity of the video:

Muahahaha!

After hearing Jonathan Eisen and Nikos Kyripdes talk about GEBA in various meetings, it is great to see the paper finally come out, and under a CC license too. Good move for everyone.

GEBA is the Genomic Encyclopedia of Bacteria and Archaea. The idea is simple: we have >1000 prokaryotic genomes in GenBank as of today.  But those were sequenced under a myriad of interests: clinical, functional, ease, biotechnological or pharmaceutical potential, etc.  In evolutionary terms, those 1000 genomes provide a very biased view of the tree of microbial life. That would be like sampling mammalian life in Europe and North America only: you would miss out on most big cats, Elephants, Rhinos, not to mention all the marsupials. To correct this situation, teams from the  Joint Genome Institute,  UC Davis and several others set out to perform a more uniform sampling across the tree of prokaryotic life. The first batch of 56 genomes from GEBA is published today in Nature; fifty-three bacterial and three archaeal.

Maximum-likelihood phylogenetic tree of the bacterial domain based on a concatenated alignment of 31 broadly conserved protein-coding genes. Phyla are distinguished by colour of the branch and GEBA genomes are indicated in red in the outer circle of species names. Click to open original in Nature.

It seems that they are on the right track to enrich our understanding of bacterial genes and genomes using this phylogenetically-mindful sampling strategy.  For example, they show that their sampling enables the discovery of an average of 1,060 protein families/genome. Sampling a single bacterial family would provide 121 new protein families, sampling within a bacterial phylum would give an average of 308 new protein families, and within a bacterial domain, 650. They have discovered a total of 1,798 families that seem to have no similarity to any existing family, hinting at new bacterial functionality (or maybe some new prophages?) They have  discovered a few new cellulases, genes that break down cellulose, the polymer that makes up plant cell walls. Cellulases are the holy grail of the biofuel prospecting industry: specifically,  a cellulase that can be exploited en-masse to turn plant matter into fuel economically. They also discovered a homolog of Actin, a cytoskeletal protein thought until now to only exist in eukaryotes.

One thing that is sorely missing is accessibility. Yes, the individual genome papers are all published in SIGS and in Nature under open access, which is great. But when you go to the GEBA site, you get a simple description of the candidate genomes. The annotations are somewhere behind a password-protected site, but I could not seem to get an account to view them. A proper genomic browser for the sequenced and annotated genomes, with some phylogenetic map showing who is located where on the tree would go a long way towards  helping the rest of us explore this new comprehensive picture of prokaryotic genome space.

Finally, if you want to hear more about how they did what, here’s Eisen talking about GEBA.

Wu, D., Hugenholtz, P., Mavromatis, K., Pukall, R., Dalin, E., Ivanova, N., Kunin, V., Goodwin, L., Wu, M., Tindall, B., Hooper, S., Pati, A., Lykidis, A., Spring, S., Anderson, I., D’haeseleer, P., Zemla, A., Singer, M., Lapidus, A., Nolan, M., Copeland, A., Han, C., Chen, F., Cheng, J., Lucas, S., Kerfeld, C., Lang, E., Gronow, S., Chain, P., Bruce, D., Rubin, E., Kyrpides, N., Klenk, H., & Eisen, J. (2009). A phylogeny-driven genomic encyclopaedia of Bacteria and Archaea Nature, 462 (7276), 1056-1060 DOI: 10.1038/nature08656