Some interesting events cropped up recently. The Marco Island Advances in Genome Biology and Technology meeting was heavily tweeted and blogged about.  Pacific Biosciences unveiled their third generation sequencer. Ostensibly, it can sequence reads of 20,000 length, but the fraction of actual long reads in a run, and their quality is still a bit hazy. The most interested to me is the Ion Torrent. Being rather low on budget, this seems like the family budget car of high throughput sequencing: cheap, reliable, and does not offer more than I really need. $50,000 for a sequencer with $500 runs with 160MB/hr? Nice. Genetic Inference has a great summary of the various technologies presented.

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I recently published in PLoS Computational Biology (yeah, yeah, shameless plug) already starts to feels somewhat outdated on the sequencing technology front.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

If the title of this post makes you cringe, then you belong to a minority of people who realize why the phrase “highly evolved” is so wrong. Unfortunately, “highly evolved” (as an absolute term) and “more evolved” (as a comparative term) seem  to be used all-too frequently.  They are uttered not only by non-scientists and non-biologists but even by scientists who should know better. Even when they catch themselves after blurting out “highly evolved” in a conversation (or, more embarrassingly, in a lecture), the damage is done. Yet another Freudian (Darwinian?) slip that tells of a fundamentally bad grasp on evolution. And, yes, I know, this topic has been written about by many of my betters, who are vastly more evolved better writers than I, with much better breadth and depth of knowledge of evolution, and a reach to a much wider audience.

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:

A  small spike on my blog traffic yesterday led me to look for the source via Google Analytics. (If you are a blogger, you should really use this tool, lots of useful traffic information.) Seems like most of the traffic came from the page of a high school science teacher at Badin High School in Hamilton, OH. Apparently the students were to be quizzed today on two of my posts about endosymbiosis (and one from 80Beats; I’m in good company.) So they were very busy Sunday. It’s encouraging to know that some of my posts are accessible enough for high school science. Finally, quite a few Miami students come from Hamilton (we’re close). So I might see some of them next year.

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

The first bioinformatics meeting I went to was in 1996 at the  Nachsholim resort,  north of Tel Aviv. I received a fellowship for the duration, and shared a room with the brilliant Golan Yona, then a grad student at the Hebrew University. I was doing biochemistry at the time and knew next to nothing about bioinformatics, except that it seemed like an interesting thing to get into if you liked biology and programming. The meeting was great: Samuel Karlin, Pavel Pevzner, Dannie Durand, Temple Smith and Eugene Myers were there. Lots of down time on the beach and in the pub by the beach.  I learned an incredible amount in four days and by the time the meeting ended, I was hooked. I wrapped up my grad school work in biochemistry as a Master’s degree, and joined Hanah Margalit’s lab for a PhD in bioinformatics.

Dan Graur gave a talk at that meeting on The One True Phylogenetic Tree of Mammals. Dan’s talks are fast and funny. His tactic of building audience interest is by making them think they are missing something great if they even dare blink when he is talking;  it works. Dan was complaining that all genomic efforts were invested in inconsequential organisms such as humans, mice and Drosophila, and no one was interested in the Aardvark or Sloth genomes. He bemoaned the situation, as he needed the Aardvark, and a few thousand other mammalian species to get the “One True Tree”. Later that day, over dinner, Pavel Pevzner suggested sequencing the X chromosome from all mammals using the then-new DNA chip technology. The X chromosome being a “microgenome”, with no transposable elements from other chromosomes, making it a perfect candidate for being a proxy for a genome.

In 1996, capillary sequencing was well established, but still quite expensive,usable only by large institutions and companies.  DNA chips, however, were thought to become the next cheap sequencing technology, and there were many expectations that they would enable mass genomics. Chips turned out to be useful in many other applications, but not in mass sequencing. We had to wait almost 10 years for pyrosequencing  and other cheap mass sequencing technologies to hit the scene.

The cost of sequencing is still dropping exponentially, so fulfilling Dan’s wishes is very much in the making now. We are getting closer to getting the genomes, not only of all mammals, but of all vertebrates. The Genome 10K initiative was officially launched in April 2009. Today, the paper describing the project has been published in the Journal of Heredity. The goal is to collect and systematically sequence 10,000 vertebrate (not just mammalian) genomes. 10,000 is a nice round number, but looking at the paper, their actual aim is 16,203. Wow! That includes some recently extinct species for which genomic material may still be obtained like the Tasmanian Wolf.

Entry of the Animals Into Noah's Ark / Jan Breughel the Elder

Note that they do not plan to begin sequencing immediately. The cost of sequencing is still too high, and they are still waiting for costs to decrease to $2500 per genome, which is one-hundred times cheaper than it is today. But at the rate cost is dropping, they estimate that mass sequencing can be started in a few years. In the meantime, they are soliciting samples from the community.

A lot of effort for the True Tree… but it’s not only for that. It is the next logical step to take after completing the genome of a few select organisms. The library of life. To achieve an understanding of animal evolution on a level that in 1996 we could only  joke about. More information can be found on their site. Here is the closing paragraph from the article:

As the printing of the first book by Johannes Gutenberg altered the course of human history, so did the human genome project forever change the course of the life sciences with the publication of the first full vertebrate genome sequence. When Gutenberg’s success was followed by the publication of other books, libraries naturally emerged to hold the fruits of this new technology for the benefit of all who sought to imbibe the vast knowledge made available by the new print medium. We must now follow the human genome project with a library of vertebrate genome sequences, a genomic ark for thriving and threatened species alike, and a permanent digital record of countless molecular triumphs and stumbles across some 600 million years of evolutionary episodes that forged the “endless forms most beautiful” that make up our living world.

. (2009). Genome 10K: A Proposal to Obtain Whole-Genome Sequence for 10 000 Vertebrate Species Journal of Heredity DOI: 10.1093/jhered/esp086

Check Hayden, E. (2009). 10,000 genomes to come Nature, 462 (7269), 21-21 DOI: 10.1038/462021a

I am preparing a class on the origins of life for next week. The textbook I am using does not  go into the Replicators First vs. Metabolism First argument, but I probably will, if I have time. Below, a quick refresher for those who know of the competing theories, and an unsatisfying introduction for those who don’t. In the end, you will ask to weigh the evidence and vote. Remember: your vote is important. I had a lousy week and seeing some numbers on the sidebar would be a nice ego-boost. Yes,  that lousy.

From James W. Brown's lab, NC State University http://www.mbio.ncsu.edu/JWB/soup.html

Replicators First

Aka RNA World: RNA emerges as the first molecule that can replicate and perform enzymatic processes. It stores information and it is biochemically active. Thus it can both replicate and control a primitive meabolism. Later came the transition to DNA as an information storage, and the enzymatic role was mostly relegated to proteins.The first replicators might not even have been RNA molecules, but some pre-RNA nucleic acid such as PNA or TNA.

This theory is supported by the present-day existence of ribozymes, RNA enzymes. Especially the ribozymic activity in the ribosome, the platform of protein translation. RNA can also catalyze its own replication, up to a certain length (189 bases was the longest self-replicating RNA synthesized in a lab).  Finally, RNA can also catalyze the formation of peptide bonds between amino-acids, setting the stage for the transition to an RNA+protein world. At some point, these reactions were cellularized by liposomes or other protobionts (pre-cellular structures with a protein, fatty or water boundary).

The arguments against the RNA World / Replicators First hypothesis are that RNA is labile, especially in water. Hence, an RNA world may not have been sustainable to become complex enough to recruit protein and bootstrap itself to the next level. Also, RNA is too complex to have been any kind of first player, and there were probably many chemical selective events prior to the appearance of RNA, as argued by the Metabolism First proponents.

Metabolism First

Metabolism First holds that metabolic processes assembled prior to the existence of replicators. Günter Wächtershäuser proposed that the pioneer organism originated in high (>100C) temperatures in hydrothermal vents.  This organism resembled the catalytic converter in a car, more than a primitive cell: it had a composite structure of a mineral base with catalytic transition metal centers, such as iron-sulfide and nickel-sulfide. Dissolved volcanic gases would flow over this natural catalytic converter, yielding more complex compounds. Some of those more complex compounds would stick around, and incrementally form more complex molecules, eventually capable of catalysis. Once strong experimental evidence in favor of Metabolism First is the ability to recreate most of the Citric Acid cycle — both universal and essential in all life — without enzymes, and in high temperature and pressure conditions, such as those existing in underwater volcanic vents, favored for being the crucible of life.
Information bearing molecules like nucleic acids, came last, rather than first. Metabolism First explains the chemical evolution of catalytic versatility before the appearance of complex polymers. Also, the argument made by Metabolism First proponents is that  RNA  itself is a precondition, but a molecule too complex to have arisen by initial chemical selection. Metabolism First offers the necessary chemical scaffolding enabling replicators to appear on the stage.

Replicators (genetics) First vs. Metabolism First. Barbara Aulicino and Morgan Ryan

There is a lot more to the two hypotheses, of course. Including experimental evidence supporting both. Here are two reviews. Read them, and don’t forget to cast your vote here → →

Trefil, J., Morowitz, H., & Smith, E. (2009). The Origin of Life American Scientist, 97 (3) DOI: 10.1511/2009.78.206

In support of the RNA World (Replicators first):

Müller, U. (2006). Re-creating an RNA world Cellular and Molecular Life Sciences, 63 (11), 1278-1293 DOI: 10.1007/s00018-006-6047-1

Stephen Colbert had an interesting lineup for the past two nights: Richard Dawkins on Sep 30, and Francis Collins last night. Enjoy the vids:

First, a short glossary.

Homologous genes are descended from a common ancestral gene.

There are two types of homology:

• Orthology is homology due to a speciation event. So if there is a gene A’ in humans and A” in mice, and they are obviously similar in sequence, we infer that they homologous. We usually also infer that they are orthologous, as the common gene ancestor A existed in the common ancestor of humans and mice, some 600 million years ago. Once the ancestral lines diverged, the genes carried over into the respective progeny.
• Paralogy is homology due to a duplication event. A gene has been duplicated in a species genome, and the genome now has two copies of this gene in place of one.

Orthology, Paralogy and Function

It has been proposed that paralogous genes would generally have different functions. The rationale being that in-species duplication, two copies of the same gene are redundant. One copy maintains its function, while the other is “free to explore” other functions. The flipside of this hypothesis is that  orthologs maintain functional similarity, because the progeny species inheriting the orthologous genes need to maintain their function.

Formation of orthologs and paralogs. The evolutionary tree shows six homologous genes from three species designated A, B and C. Genes are represented by circles and each color represents a different species; genes with paralogs are circled by a thicker line (only the gene in the A lineage does not have a paralog). Boxes at nodes represent duplication events. Duplication 1 produced paralogs α and β in the ancestor of B and C, whereas duplication 2 produced paralogs β1 and β2 in the C lineage. All genes from B and C are co-orthologs to the gene from A. Genes α and β are in-paralogs relative to speciation 1, but are out-paralogs relative to speciation 2. Genes β1 and β2 are in-paralogs relative to both speciations in the tree. Genes Bα and Cα are one-to-one orthologs. From doi:10.1016/j.tig.2009.03.004

Functional innovation through duplication has been hailed as a major driving force in evolution.  After all, it is hard to accept the Darwinian tenet that random changes — even if directionally selected — can constantly produce innovative complexities.  A duplicate gene provides an already existing complexity. Imagine many such duplications, and you can see how duplicate genes provide an genomic “functional opportunity bank” for the biosphere.

Only, maybe not. Romain A. Studer and Marc Robinson-Rechavi challenge common wisdom by publishing a study that says: “it ain’t necessarily so”. They look at three alternative models of molecular function evolution: (i) subfunctionalization after duplication; (ii) neofunctionalization after duplication; and (iii) the ‘alternative model’ of equal change after duplication or speciation. Subfunctionalization holds that after duplication, each of the two copies of the gene performs only a subset of the functions of the ancestral single copy. Neofunctionalization holds that one of the two genes possesses a new, selectively beneficial function that was absent in the population before the duplication. The ‘alternative model’ states that the gain of new function is not preferential to paralogs and that orthologs may gain new functions at the same rate that paralogs do.

Studer and Robinson-Rechavi claim that few studies have been made to study the scope of any of these proposed models. They then lay out study designs for doing so, challenging other evolutionary biologists (and themselves?) to conduct these studies and examine whether the common wisdom that orthologs maintain function while paralogs gain function. What I like about this paper is that it not only makes a strong case for challenging conventional wisdom, it also lays out a series of possible routes of study to be taken up by others.

Update: MK pointed out an obvious lacuna in this post:

I ranted in a previous post about the use of homology as a quantitative term, rather than a qualitative term. Ben Blackburne commented on that post introducing me to “micro homology”, a term I did not know existed. I ignored its existence, until I heard it spoken yesterday at a talk, which sort of rubbed me the wrong way. Going back to my office to chill, I discovered there are 152 papers indexed in PubMed that use that term in their abstract or title. Not a good way to chill… here we go again: misusing “homology” by overselling it. Apparently microhomology is used to indicate an identity of a short nucleotide sequences in two non-complementary DNA strands. This identity may facilitate strand annealing constructions of chromosomal breakpoints such as the proposed Microhomology-Mediated Break-Induced Replication or microhomology-mediated end joining for DNA repair. There should  be a term for this phenomenon, but why use “microhomology“? The use of “homology” implies that the short identical sequences originated from a common ancestor. “Micro” would mean short region from otherwise homologous sequences. This is possibly derived from “homologous recombination“, where, indeed, homologous sequences are involved.  But in the microhomology case, it may not be so. Also, even if the identity is between short subsequences of otherwise homologous sequences, “microhomology” is somewhat of a confusing term, as it implies a quantitative relationship.  Why not simply use “microidentity” as a drop-in replacement? (Heh: non-homologous replacement).

Of course nothing will change, since I am too late in the game, no one listens to me anyway and I do not see the six readers of this blog rallying to eradicate microhomology.

No I am not bitter. Mild and bitter perhaps, but only after 5 o’clock.

Quorum sensing

Social behavior is not exactly the first term that comes to mind with relation to microbes. After all, we assume a certain amount of intelligence and an ability to implement a behavioral pattern in response to peer actions. Humans, yes. Apes, yes. Birds of a feather flock together… so birds, yes. Ants and bees and other social insects, sure.  But bacteria?

Yes, bacteria are social creatures: they can cooperate as a community. For example, many bacteria live in a biofilm,  a tangled matrix of polymeric substances that includes proteins, DNA and polysaccharides. Biofilms constitute tough physical barriers that are immune to attacks by many antibiotics and other bacteriocidal agents. Indeed, many of the harder to treat infectious diseases are a result of the formation of biofilms in our bodies.  A biofilm is analogous to a bunch of humans banding together, and deciding that instead of living  in dispersed separate dwellings, they will all live together in  a walled city that is easier to defend from attacks.

To achieve this cooperation, each bacterial cell starts by sending a signal.  A molecule that says: “Yoohoo, I am here and I can help build a biofilm. Let me know if others are interested”.  As more Bacteria send this molecular signal, its concentration in the environment grows. The sending bacterium also senses the environmental concentration of this signal.    At some point, the concentration of the “yoohoo” signal reaches a certain threshold, and now each bacterium is convinced that rolling up its tiny sleeves and helping build a biofilm is actually a good use of its time and metabolic resources. The bacterial cell now begins to release biofilm building components, under the assumption that everybody around it is doing the same: after all, there is  a lot of yoohoo signaling going on. This method of signalling is called quorum sensing (QS). Quorum sensing is used not only for biofilm construction, but for other group activities by bacteria. Secretion of virulence factors that damage the host, or molecules for scavenging nutrients. All these activities that are also community based.

Biofilm. Credit: AJC1 on Flickr

Freeloaders

But wherever there is community work to be done, there is the danger of  freeloaders: those who benefit from the labor of the community, but provide little or no input themselves. Are bacterial communities an exception? This question has been asked by several research groups, experimental and theoretical.

In 2007, Stephen Diggle and his colleagues have created two types of QS-related Pseudomonas aureginosa mutants. First, those who do not send the signal, hence they make no effort in propagating the information that a biofilm is being constructed (signal-negative).    The second type produce the signal, but not the necessary products for constructing the biofilm (signal-blind).  They then examined how well these mutants did alongside regular bacteria, in a stressful environment that facilitates the creation of biofilms. They started a culture with a small percentage os signal-negative and signal lind mutants (1-3% of the total population).  Both types of cheating bacteria proliferated rather well, rising up to 45% and 66% of the populations respectively. But once cheats grew more common, their ability to proliferate of their fitness declines. Diggle and his colleagues attributed that to the decline in the number of cooperators that cannot support the cheats.

Why would cheating increase fitness, even of transiently? The answer is that producing both the quorum sensing signal, and the actual biofilm building components is metabolically costly. QS is therefore very sensitive to parasites: those strains that don’t have to produce signals nor the actual components will therefore benefit more than their hard working neighbors. Up to a point, that is.

The game of life. For life.

A recent theoretical study in PLoS-ONE examines the evolutionary fitness of hypothetical QS mutants that freeload. Note that this is theoretical: no Pseudomonas were harmed in this study.

Czaran and Hoekstra looked at the problem from an opposite point of view than that of Diggle. They asked whether QS individuals invade and proliferate in a non-QS population.  To answer this question, they used a cellular automaton simulation. A cellular automaton is a grid in which the composing cells have different states (i.e. “full” or “empty”), and whose state depends on the neighboring cells’ state. Each time the grid is scanned, for each cell the neighboring cells determine that cell’s state in the next generation. Here is a simple cellular automaton called The Game of Life.  In the Game of Life, each cell can be either “alive” or “dead”, depending on the number of neighboring live cells. A cellular automaton is therefore a good basis for simulating bacterial communities. In Czaran and Hoekstra’s simulation, each cell is a bacterium, that can be fully QS capable or QS incapable, or partially QS capable in different manners.

Ignoramuses, Liars and Voyeurs

Creating strains in a computer is much easier than in real life, Czaran and Hoekstra used 3 loci for their simulated bacterial genomes.  C for cooperation: production of a public good molecule, such as a polysaccharide for the biofilm. The other two for quorum sensing: locus S for producing the signal molecule (“yoohoo, I’m here”) and locus R for signal response, which includes the signal receptor and the signal transduction machinery that triggers the cooperative behaviour when the threshold signal concentration has been reached. The created 2³ = 8 different strains based on the presence or absence of each active gene: Ignorant, Voyeur, Liar, Lame, Blunt, Shy, Vain, Honest. The Ignorant (csr)  lives in complete solitude, and cannot participate in QS. The Honest (CSR) is a good QS citizen. The various others are freeloaders to some extent. For example, the   Liar (cSr) produces the signal molecule, but not the actual response. Lame produces the quorum sensor and the response signal, but cannot produce the actual public-good (C) molecule.

Table 1. The 8 possible genotypes of the cooperation-quorum sensing system and the corresponding total metabolic costs m(e) of gene expression.

They then ran the simulation using cellular automata. They started with mixtures of initial different populations, and ran the automata, with each cell’s response being a function of how it can respond (a liar cannot build a biofilm even though it asks everyone else to,  while an Honest cannot help but sensing the signals and contributing). Since, as Diggle and colleagues have shown, being a good citizen is metabolically costly, Czaran and Hoekstra figured the metabolic cost in their simulations.

In a nutshell, Czaran and Hoekstra have shown that “both cooperation and the associated communication system can evolve, spread and persist in the population“. So being a good citizen pays off, and cooperation actually increases the fitness of the cooperative strains as opposed to the non-cooperative ones. This is a very elegant and informative simulation work, and I recommend reading it, since there is quite a bit more there than I have written about. My only complaint is that they did not provide some online resource to play around with seeding initial populations and seeing what happens to them after a multi-generational run. So just to psych you out a bit, here is a cellular automaton from YouTube:

And a biofilm, er, film:

Czárán T, & Hoekstra RF (2009). Microbial communication, cooperation and cheating: quorum sensing drives the evolution of cooperation in bacteria. PloS one, 4 (8) PMID: 19684853

UPDATE: I submitted this post to the National Evolutionary Synthesis Center’s sponsored contest for a travel award to ScienceOnline2010. Let’s see how it goes… #scio10

In a previous post about Hatena we saw what might very well be the beginning of a (beautiful?) endosymbiotic relationship: a unicellular predator swallows a microalga, resulting in physiological changes to both, and the resulting endosymbiont is now a phototroph, rather than a predator. “endo” – inside “symbiosis” – life together. Endosymbionts live out their symbiosis inside the host’s cells.

In this post I would like to fast-forward to another part of the endosymbiotic movie. We will see how endosymbiosis contributes to evolution much more than we thought. But first, some background information.

Primary and secondary endosymbiosis

Primary endosymbiosis happens when one free living organism engulfs another, resulting in a mutualistic  relationship. Secondary endosymbiosis is the process of engulfing  another free-living organism that already went through primary endosymbiosis.  Such is the case of Hatena: the algal endosymbiont provides the photosynthetic capability and light sensitivity (acquired by primary endosymbiosis), while the host provides motility and a cozy stable home: its cell. Plastids are organelles that  harvest light, manufacture pigments, store food and perform various other functions in plants and algae. Plastids are thought to be photosynthetic microbes that were acquired by primary and then secondary endosymbiosis: they have chromosomes encoding their own DNA transcription and translation machinery, as well as some other genes.  One strong evidence for secondary rather than primary endosymbiosis  is the number of membranes surrounding plastids:  3 membrane layers in algae, 2 in plants, strongly suggesting successive endosymbiotic events. Another evidence is molecular:  most of the proteins needed for plastids to function are encoded in the host’s nucleus.  How and why did the genes travel from the endosymbionts to the host?

Nobody is really sure yet, but here is a working hypothesis: once endosymbiosis occurs, the genome of the endosymbiont becomes mostly redundant. After all, the host takes care of most of the endosymbiont’s nutritional and metabolic needs, and maintains a stable environment in the cell. About 30% of a typical microbial genome is dedicated to genes that stabilize its internal environment in response to events in the external one. Most or all of these genes become redundant once the microbe in question becomes an endosymbiont, and enjoys the hospitality of its host, trusting it to maintain a controlled environment. They either disappear or migrate to the nucleus of the host.

Diatoms: hosting more types of algae for longer that you think

Diatoms are microscopic  algae, so named because they are often shaped from two symmetric lobes — hence “diatoms”. They are photosynthetic, and are thought to compose most of the phytoplankton biomass.

[View with PicLens]

It has been known for a while that diatoms acquired their plastids by a process of secondary endosymbiosis with red algae. The commonly accepted  sequence of events for the diatom / red algae endosymbiotic time-line is shown here:

(A) historical diatom (yellow) and red-algae: red ellipse is a generic plastid; (B) algal endosymbiont in diatom; (C) gene migration from alga to the diatom’s nucleal DNA; (D-E) algal cell mostly gone, only the plastid remains.

This is what Ahmed Moustafa and his colleagues also thought about the acquisition of chloroplasts by diatoms.  They therefore set out to look for genes of red algae  in the nuclear DNA of two diatom species whose genomes have been sequenced. To their surprise they discovered that 70% of the algal origin genes in the diatom were from green algae lineages, not red algae. However, there are no green algae-originating plastids in  those diatoms.  In particular, there were some genes that exist in the chloroplasts of red algae, but not in the secondary endosymbiotic chloroplasts in diatoms.  So what happened? Why is the host’s genome “mostly green” instead of  “all red”?

The answer that Moustafa and colleagues proposed was that these diatoms used to have plastids of green algae lineage.  The genes that migrated to the diatom nuclear DNA are therefore green in origin. Over evolutionary time, for reasons unknown, red algae endosymbionts displaced the green ones.  Many of the red genes that would have otherwise migrated to the nucleus already had their places take by green genes, and were simply lost.

A-D: first sequence of events: endosymbiosis of green algae, including gene migration to diatom nucleus;  (E) displacement of green algae by red, through some unknown mechanism; (F-I): endosymbiosis of red algae, including gene migration to nucleus. Nucleus now has a mixture of green lineage and red lineage genes.

Many questions remain open: why did this replacement take place? How prevalent is it? The researchers only looked at two diatom species, whose genomes have been sequenced. One way to answer this question would be a metagenomic analysis of a diatom population. This would mean analyzing samples of DNA sequences taken from many different diatom species, to get a picture of the frequency of red versus green endosymbiont lineage genes in many more diatom genomes. Also, why would one set of endosymbionts be displaced by another? What is the evolutionary time-line in which the endosymbiosis / displacement process occurs? What, if anything, triggers this replacement?

This finding sheds light upon a larger question in evolutionary biology: how big is the role of endosymbiosis in evolution? How many of an organism’s genes are acquired from other organisms? It seems that with this study, the importance of endosymbiosis as a  contributor of  genes, just went up a notch: we see yet a few more cross-growths between the not-so-separate branches of  the tree of life.

Finally, A flurry of Red and Green by The Dreamer and the Sleeper covered by Karys Rhea. The webcam self-shoot is grainy, and the sound is not much better than a laptop microphone. But Karys Rhea’s singing shines through.

The first metagenomics, metadata and metaanalysis meeting held in Stockholm June 27 2009 was a raging success. People were standing all the way back to the hall jostling for elbow room, while all the other concurrent meetings were pitifully empty after word has made it about how awesome we were.

OK, I may be exaggerating  slightly, since I was the meeting’s co-organizer, co-chair, program committee co-chair, and bartender. (If you were there and you don’t remember me tending bar then I must have done a good job). Well, maybe I wasn’t a bartender. Fine.

So what was the meta(genomics, data, analysis) meeting about?

I’ve talked about metagenomics in several earlier posts. Just in case you are a new here: metagenomics is the study of genetic material that comes directly from the environment. It is a technique used to study genetic material from organisms (usually microbes) that cannot be cultured in a lab, and to get a picture of organisms in their natural environment, which often differ from lab clones.

While in genomics we strive to obtain a full picture of an organism’s DNA, in metagenomics we sample the environment for whatever DNA we can get. We are actually merging population biology with genomics. While in population biology our basic unit of study is an organism, in metagenomics it is a DNA sequence. This presents many challenges: properly sampling the microbial habitat and extracting the DNA, understanding which organisms the DNA in the samples came from, gauging sample depth, assembling the sequences, identifying genes, assigning a biological function to those genes, to name a few.  There are many different experimental and computational procedures for doing so, and they should be meticulously documented, as Nikos Kyrpides from the Joint Genome Institute writes in this month’s Nature Biotechnology:

Like molecular biology, genomics has been fueled by the innovative energy
of many interdisciplinary activities. Unlike molecular biology, which has
thrived on the principle of standardized methods and protocols, genomics
has progressed without regard for the critical importance of shared
standards. Now, 14 years since the first complete genome was published
and with more than 900 genome sequences finished, it is astonishing to
observe the lack of standards for so many critical procedures in the
field, ranging from simple data exchange to gene finding, function
prediction and metabolic pathway description.

Now for the kick in the head:

As an example, we compared the genomes of two closely related organisms,
Burkholderia mallei ATCC 23344 (ref. 19) and Burkholderia pseudomallei
K96243
(each sequenced by a different sequencing center)
[...]
we identified 548 genes in B. mallei that are absent from B. pseudomallei
and are potentially related to their different lifestyles. Manual curation
of those 548 genes revealed that, in fact, 497 of them are also in
the B. pseudomallei genome, but there they had not been identified as
'real' genes. The reason for this discrepancy?

The two sequencing centers used different gene finding methods.
The consequence was an almost 90% error rate in the results of our
comparison.

Ouch. Ouch, ouch ouch. And that is not an anecdotal example. Furtehrmore,  it also applies to metagenomics: even more so, since many of the standard operating procedures (SOPs) in metagenomics are still in the process of inventing themselves.

Metadata is the “data about the data”: all the habitat data, SOPs and abiotic data that is in dire need of the standardization Kyrpides writes about.

Last, metaanalysis would be the analysis of genomes and metagenomes. Since the M3 meeting was held under the auspices of the International Society for Computational Biology, it attracted mainly computational biologists — the type to analyze, rather than sample and sequence (but the differences are rapidly blurring, as we saw in many talks).

But things are actually looking better for standards. In 2005 the Genomics Standards Consortium was formed to address this problem. Renzo Kottman from the Max-Planck Institute for Marine Microbiology in Bremen, Germany  talked about software development within the GSC, and specifically about his own project: the Genomic Contextual Data Markup Language, or GCDML. GCDML is an XML-based standard for describing everything associated with a genomic or a metagenomic sample: where it was taken from , under what conditions, which protocols were used to extract, sequence, assemble, finish and analyze the metagenome. Again, my own personal bias here: I am a heavy user of GCDML, as I am writing my own data-insertion software, and have headed such an effort for a while at the University of California San Diego. Here are Kottmann’s slides, and you can also read more about GCDML.

Daniel Richter talked about the functional annotation  of metagenomes, using Gene Ontology, a technique he developed with Daniel Huson, at the university of Tuebingen, Germany. The Gene Ontology, or GO, is a major bioinformatics initiative to unify the representation of gene and gene product attributes across all species. It is composed of a vocabulary of some 27,000 terms, with hierarchical relationships defined between them, from the general (“catalytic activity”) to the specific (“phosphatase activity”) to the more specific (“Tyrosine phosphatase activity”). (Graph theory prudes: GO is a DAG, not really a hierarchy, I know, I know). Richter assigns functions to sequences hypothesized to be genes using the Last Common Ancestor approach. LCA works as follows: once a high enough similarity is found between a sequence from a metagenome and a sequence from a reference database, LCA looks for similarities to other, related sequences, where the similarity score is above a certain threshold. It then assign a general function using GO that may fit all.

Jack Gilbert from Plymouth Marine Laboratory, Plymouth UK talked about a year of sampling marine microbiome in the Western English Channel. He went through many different sampling and normalization problems.

Tom Matthews from the National Microbiology Laboratory in Canada talked about a profiling pipeline for pathogens. A fast typification of pathogens in case of an outbreak.

There were more presentations, but I think I’ll give it a rest and get back to them in part 2. I am also waiting for some people to upload their slides…. you know who you are!

Kottmann, R., Gray, T., Murphy, S., Kagan, L., Kravitz, S., Lombardot, T., Field, D., Glöckner, F., & , . (2008). A Standard MIGS/MIMS Compliant XML Schema: Toward the Development of the Genomic Contextual Data Markup Language (GCDML) OMICS: A Journal of Integrative Biology, 12 (2), 115-121 DOI: 10.1089/omi.2008.0A10

(Thanks to F.B.  for the inspiration).

Sigh… people don’t seem to learn. It’s been almost 22 years (yikes!) since a distinguished group of scientists published a letter in Cell calling for a responsible use of the word “homology”. If you were born when that letter was published, then in the US you can already drink legally. And you may very well want to, by the time you finish reading this post.

As of today there are one hundred and sixty seven articles listed  in PubMed with the phrases “distant homology” or “remote homology” in either the title or the abstract.

Please: make it stop.

Homology is a qualitative term.  It means having a common evolutionary origin. Two genes / proteins / organs are either homologous, or they are not. They cannot be “somewhat homologous” or “partially homologous” or (a favorite among molecular and structural biologists) “distantly / remotely homologous”.

Homology is inferred from similarity.  Similarity is quantitative. If organs are sufficiently similar, like mammalian forelimbs, then they are considered to be homologous. They maybe more similar (like the hands of humans and chimpanzees), or less similar (like human hand and a bat wing). Nevertheless, once they pass a certain similarity threshold, homology is inferred. The same applies to sequences of proteins and nucleic acids.  Similarity can be measured. Different degrees of similarities can be compared and scaled.

If two protein sequences are aligned, and 40% of the amino acids in the alignment are identical, then the two sequences have a 40% identity. The do not have a 40% homology. They are  homologous, and the homology is inferred from the similarity.  We observe that the two sequences are similar, and then we conclude that they are homologous. We use the sequence similarity, as measured by percent identity, to trace a line of common descent for those proteins we deem homologous.

(As an aside I should say that the percentage of sequence identity, or %ID is not a very good measure for inferring homology, nor is it for measuring similarity. It is an easy one to use: but it is very coarse and prone to errors. There are many better measures out there, including statistical ones like e-values, p-values or information theoretic ones like bit scores. But I digress, and this is a matter for another post.)

But once we confuse observations with conclusions, things quickly become an impossible muddle.

Am I not not just picking nits here? I mean, surely when the term “distant homology” comes up in a paper or in conversation, we all know the meaning. Distant homology means having a common evolutionary origin,  but with a common ancestor that was around a long time ago. “Distant homology” is intuitive, brief yet understandable. it is less cumbersome than: “homologous, with a distant common ancestor, as concluded form a low yet statistically significant similarity” which is what we really should say if we properly separate observations from conclusions, as captain nitpick would have us do.

Allow me to answer with two examples.  First, I have read several papers discussing “structural homology”  in the context of protein structure. Those papers that discuss structural homology were actually using a verbal shortcut for  a homology inferred from structural similarity. That is, they inferred common descent from protein structural similarity. This kind of inference is highly contentious, and while not necessarily wrong, must be done with great care and proper caveats. However, once the researchers rolled up observations with conclusions by using the “structural homology” verbal shortcut, they absolved themselves from convincing the reader that structural similarity is indeed a good measure of homology, and jumped directly to the conclusion that there is indeed an homology here. The framework for inferring homology from sequence similarity is well worked out, but not so for structure, yet.   Therefore, even if we do use the verbal shortcut “distant homology”, we can only use it by virtue of having a certain measure of similarity well-established already, as in sequence based similarity. If it is not well established, and in using structural similarities, we fail to go through the proper scientific channels that consist of providing convincing observations prior to providing conclusions.

Second: even worse is the use of the term “functional homology”. This is a clear case of the word homology used as a drop-in synonym for similarity. The misnomer “functional homology”  is typically used in studies where proteins that are clearly not homologous perform similar functions. Why infer evolutionary descent when clearly that was not intended in the first place? Well, once you start confusing similarity with homology, observations with conclusions, and make them synonymous, this is what happens.

So don’t even start this confusion.  Separate observations from conclusions, and make the former support the latter. Homology is qualitative, similarity is quantitative.  Genes cannot be distantly homologous any more than a woman can be a little pregnant.

Now you can have that drink. Unless you are a little pregnant.

Gerald R. Reeck, Christoph de Haëna, David C. Teller, Russell F. Doolittle, Walter M. Fitch, Richard E. Dickerson, Pierre Chambon, Andrew D. McLachlan, Emanuel Margoliash, Thomas H. Jukes and Emile Zuckerkandl (1987). “Homology” in proteins and nucleic acids: A terminology muddle and a way out of it Cell, 50 (5) DOI: 10.1016/0092-8674(87)90322-9

In the Hatena story about symbiosis, I posted the following picture drawn by Ernst Haeckel:

Lichen, from "Art Forms of Nature" / E. Haeckl

Beautiful!  In this day and age of imaging, high resolution photography, and molecular graphics, we forget that scientific drawing was a skill as necessary to life scientists  as microscopic imaging, or molecular graphics is today.  Indeed, biology was very much a descriptive discipline in the 19th century, and being able to articulate your findings –in words as well as in art — was as valuable a skill as the ability to posit a hypothesis and then design an experiment to test it.  Possibly even more valuable in some circles. Scientists won medals and were awarded promotions based on their drawing skills. A naturalist’s drawings could not be inaccurate, nor could the images be occluded or embellished, they had to be very precise. But that does not mean that there was no room for artistic input.  Those could be found in the hues, the lighting, the composition, point of view angles and arrangement of the subjects drawn. Haeckel definitely had his distinctive style: part Romantic, with a Gothic undercurrent. In Art Forms of Nature he would take a series of subjects in the same genus, and arrange them in the contemporary style tessellated form, like this (all pictures are from Wikimedia Commons and are in the public domain):

Darwin himself was so impressed by Haeckel’s drawings that he wrote they were “..the most magnificent works which I have ever seen, & I am proud to possess a copy from the author“. Haeckel himself was a staunch supporter of evolution by natural selection, and held Darwin in great esteem. It very brave stand taken by a young assistant professor in Jena in Germany  at a time when evolution itself was already quite accepted in many scientific circles, but Darwin’s theory of natural selection was still hotly debated. Haeckel served to popularize evolution, and his popular lecture series in Jena attracted hundreds of listeners. His beautiful drawings serve as a frontispiece to his scientific writings, serving not only to illustrate, but to attract his readers.

His was a troubled soul. The love of his life, Anna Sethe Haeckel died of a sudden illness at a young age. He remarried, but never recovered from his broken heart, calling Anna the “only true love” of his life. While being a popular advocate of natural selection, he also attracted a lot of ire form his peers and the public. He was strongly opposed to any form of organized religion, even more so following his personal tragedy. His was the religion of Spinoza and Goethe — that of monism. Unlike Darwin, Haeckel zealously recruited Evolution to a cultural fight, and has caused a massive backlash from established religious circles that is lasting to this very day. He was  Romantic, in the sense that he was influenced by such as Goethe and Humboldt, seeking underlying unities in nature and in life, a quest which may have lead him to his greatest  — yet  false — achievement.

The central medusae are Desmonema annasethe. Haeckel named them after his late wife Anna Sethe. The medusae tentacles reminded him of her "long flowing hair".

Haeckel is best known for formulating the now-rejected Biogenetic Law which states that the embryonic development of an individual organism (its ontogeny) followed the same path as the evolutionary history of its species (its phylogeny). The Biogenetic Law, or Law of Recapitulation (ontogeny recapitulates phylogeny) was in the books until the late 20th century. Although it is pretty clear that the phases an embryo goes through do not match the species history, it is still an idea that is circulating in popular culture and pseudo-scientific circles. Haeckel was accused at his time and later of forging the famous embryo sketches, an accusation that, if it weren’t for the support of Darwin and other prominent scientists of his time, would probably have caused him to lose his job. The forgery vs. overzealous interpretation debate continues to this very day, and unfortunately serves  in very a warped interpretation as an argument against evolution. The creationist reasoning goes something like: “Haeckel lied –> the law of recapitulation is founded on a fraud –> all evolution is a fraud”.  Not a very smart argument, since recapitulation was never a pillar of natural selection.

In 1997 Michael Richardson and colleagues published an article titled:  “There is no highly conserved embryonic stage in the vertebrates: implications for current theories of evolution and development”.  In the article, they compared photographs of embryos to Haeckel’s illustrations, and found gross discrepancies, which they interpreted as probable fraud.The other side of the story is that Richardson et. al left the yolk sacs in, (Haeckel removed them) and compared their photos to a derivative work rather that Haeckel’s drawings where the embryos are actually quite different from each other.  In 2002 they published another paper, where they explored Haeckel’s ideas as well as his drawings, and concluded that although deeply flawed, it is hard to show fraud especially since Haeckel himself was not the strict recapitulationist that his later followers were.

Haeckel, E. 1874. Anthropogenie: Keimes- und Stammes-Geschichte des Menschen; From left to right: fish, salamander, turtle, chicken, pig, cow, rabbit, human. The uppermost row of illustrations represents a conserved stage across Vertebrata

I’m not getting into this discussion, really, which sometime seems like a 150 year old flame war on fark.com (Let’s see if this link gets my blog farked, hehe). My take is that an amazing artist and naturalist such as Haeckel was seeing from his own heart’s desire, but so were a lot of other embryologists, way down to  my biology teacher in high school. (I dropped the developmental biology course in college, something I regret now, so I don’t’ know what went on there). The seduction of an all-encompassing elegant theory explaining embryonic development has caused many to go, in some form or another, for the Biogenetic Law.

Man, but his drawings are amazing. I can’t wait for my mail order of  Art Forms of Nature to come in. and I wonder how Haeckel, if he had lived today, would have injected from his artistic talent into macromolecular drawings such as this:

Illustration of aquaporin, a membrane molecule that controls the flow of water to the cell. The central mesh in A and B shows the water flow. ischer G, Kosinska-Eriksson U, Aponte-Santamaría C, Palmgren M, Geijer C, et al. 2009 Crystal Structure of a Yeast Aquaporin at 1.15 Å Reveals a Novel Gating Mechanism. PLoS Biol 7(6): e1000130. doi:10.1371/journal.pbio.1000130

Update: Art forms of Nature in PDF and HTML is available here.

Richardson, M., Hanken, J., Gooneratne, M., Pieau, C., Raynaud, A., Selwood, L., & Wright, G. (1997). There is no highly conserved embryonic stage in the vertebrates: implications for current theories of evolution and development Anatomy and Embryology, 196 (2), 91-106 DOI: 10.1007/s004290050082

RICHARDSON, M., & KEUCK, G. (2002). Haeckel’s ABC of evolution and development Biological Reviews of the Cambridge Philosophical Society, 77 (4), 495-528 DOI: 10.1017/S1464793102005948

Robert J. Richards (2008). The Tragic Sense of Life

Ernst Haeckl Kunstformen der Natur (in HTML and PDF format, German).

The story of a predator that, upon eating a certain food, suddenly becomes a peaceful plant. Sort of.

Free-living versus symbiotic

A working definition for symbiosis is two or more species that live and interact. Mutualism means that each derives a certain benefit from the other, or at most causing no harm to each other. Their relationship is that of “give and take”. For example cleaning fish serve other fish by cleaning off parasites and getting protection, food and rides in return. Sometimes the mutualistic symbionts have practically fused into a single functional organism. The Portuguese Man o’ War is a colony of four different organisms which form a composite jellyfish;  None of the individuals which can exist in a free-living form. Lichen is a colony of two: a photosynthetic partner providing sugars, and a fungus providing other nutrients as well as preventing the dehydration of the photosynthetic partner.

Lichen, from "Art Forms of Nature" / E. Haeckel

The Endosymbiotic Hypothesis

The endosymbiotic hypothesis maintains that eukaryotes evolved from  symbiotic interactions between bacteria. There is plenty of evidence for that in  chloroplasts and mitochondria: they have their own DNA; their membranes, their DNA,  their ribosomes all resemble those of bacteria. The relationship between a eukaryotic cell and its mitochondria is heavily mutualistic: the cell gets ATP, the mitochondria / chloroplasts (M/C) get.. well, to live and reproduce, which they cannot do outside a living cell. Over time, M/C have have lost most of their genomic material to the host: many of the proteins needed to construct an M/C are not encoded in the M/C but in the host’s nucleus, and transported to the M/C.  This is probably as intimately connected as two organisms can get, before you cannot tell that they were two separate organisms before they fused into an organism and an organelle. Indeed, the threshold set for distinguishing between an endosymbiont and an organelle lies in protein import. According to this working definition, once an endosymbiont starts importing proteins, it is no longer considered an endosymbiont and becomes an organelle.  As with any working definition, if you scratch the surface a bit you will find cases where this rule does not apply well. Viruses are a case in point, acquiring host proteins and actually acting as a vector transferring them between hosts.

Vegging out

Two researchers have shown a striking example of   endosymbiosis forming  now:  in 2005 Noriko Okamoto an  Isao Inouye reported on a unicellular organism called Hatena. Hatena (“enigma” in Japanese) leads a curious life cycle. Hatena is a single-cell organism, swimming around in the water, using a little feeding apparatus to eat cells and organic material smaller than itself.  At some point, it would feed on another unicellular algae, the Nephroselmis. Once Hatena swallows Nephroselmis, it does not digest it. Rather, Nephrosolmis makes itself comfortable home inside Hatena. The alga starts growing inside Hatena: it grows to about 10 times its original size, filling up most of Hatena. The alga also seems to lose most of its own organelles, except for the chloroplast. The chloroplast actually grows bigger.

Hatena changes too as a result. Before ingesting the alga, it has a rather complex “mouth”, or feeding apparatus. After ingesting the algae, this mouth disappears only to be replaced by an eyespot from the algae. The eyespot is a light sensing organelle, a very primitive eye, that guides algae to light sources. In this case, it also guides the host, Hatena, to light. Hatena has obviously stopped feeding, and least through its mouth. It is now swimming to the light, letting the alga photosynthesize its food for both of them.

I get the plant, you get the steakhouse coupons

Hatena reproduces by binary fission. So once it splits itself,  what happens to the symbiotic alga? Well, one daughter cell gets the alga, and the other gets to be a predator… at least until it eats another alga. So here we are, looking at a fascinating evolutionary snapshot: two creatures, they can live apart or together. One is a predator,but is ready to be a plant under the right circumstances; the other is not quite an organelle of the first yet, but definitely on its way.

Right: Hatena with chloroplast, and without. Left: the red bit on the top of the cell marks the eyespot.

This article has been Slashdotted. Exercise extreme caution.

OKAMOTO, N., & INOUYE, I. (2006). Hatena arenicola gen. et sp. nov., a Katablepharid Undergoing Probable Plastid Acquisition Protist, 157 (4), 401-419 DOI: 10.1016/j.protis.2006.05.011