…which led to the (in)famous Scopes Trial. On May 5, 1925 John Scopes was charged and subsequently tried, found guilty, and fined $100 for teaching Evolution, a violation of Tennessee’s Butler Act. The trial became a battleground for science vs. religion, evolution vs. creationism, and the interpretation of the Establishment Clause and Freedom of Speech in the US constitution.

I published a blog post two years ago, on the 85th anniversary of the trial, July 2010. Today  marks the 87th anniversary of the arrest, so it seems like a good occasion to repost. Especially since there is still some work needed in the area of teaching evolution:

Source Wikimedia Commons. Credit: John D. Croft. Based on: New Scientist Magazine 2006 191:2565 p11

To follow is the original post: “The Scope(s) of Substance”,  from July 29, 2010. Still relevant, I believe:

Bora Zivkovic, the BUCA (Best Universal Common Ancestor) of science bloggers has tagged this blog with with a Blog of Substance award. As a grateful recipient of this award I am obligated to do two things:
1. Sum up my blogging motivation, philosophy and experience in exactly 10 words.
2. Pass this award on to 10 other blogs.

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.

Here is a study that looked for a type of genes that the authors felt was neglected by classic genomic annotation. The research shows how to employed concepts in molecular evolution to validate the existence of these genes.

Some background: the first question we ask after assembling a genome is: “where are the genes”? Not an easy question to answer, since a gene is classically defined as a unit of heredity. It may code for RNA, protein, or sometimes, nothing at all. The actual implementation of the “unit of heredity” can take several physical forms, each one of them different. Therefore, the algorithms for finding genes would depend on which type gene one is looking for, exactly.

A somewhat more tractable question is: “where are the open reading frames”? Open reading frames or ORFs are those stretches of DNA that code for proteins.  Indeed, most gene calling software actually identifies ORFs. There are many attributes that go into an ORF calling algorithm: the frequency of the bases  (or k-mers of bases) in the suspected coding regions, the signals for the beginning and ends of introns, the existence of non-coding regions that aid transcription such as promoters and enhancers, the location on the chromosome with relation to other ORFs, and the length of the of the final product. The latter criterion is actually quite important, as many ORF-calling algorithms will discount anything coding for a protein that is shorter than 100 amino acids as being “too short”. The reason for employing this length cutoff, is that the number of false positives increases dramatically when ORFs coding for proteins shorter than 100aa (or 300 nucleotides) are called. Therefore, most gene-callers would just tend to discard any short peptides.

But throwing away the baby with the bathwater is not a good solution, since short peptides are known to be responsible for many of life’s activities: mating pheromones, small compound transporters, hormones, neurotransmitters and regulation of other proteins’ activities, to name a few. Many of these short peptides are the result of the cleavage of larger proteins, which means that the ORFs encoding for them are originally longer than 300bp.  But some may actually have their own ORFs, coding only for them. How can we find those small ORFs or smORFs out? How many of them are there? Is the number of smORFs large enough to make it worth re-annotating genomes?

Click to enlarge. Gene Structure. Source: Wikimedia commons. Credit: Forluvoft

Emmanuel Ladoukakis from the University of Crete and colleagues from the university of Essex, UK have set up a bioinformatic pipeline to look for smORFs in the Drosophila melanogaster genome. Bear with me, there are a few steps in this pipeline. But there’s a lot to learn about genomics just from looking at what they did, and why they took those steps.

Here’s what they did: 1) Find smORF candidates: they looked for all potential smORFs (starting with a start codon and ending with an in-frame stop codon, 30-300bp long) in those parts of D. melanogaster’s genome that were annotated as non-coding. To keep things simple, they looked only for intron-less smORFs: smORFs that are encoded consecutively in the DNA.  They found 593,586 potential sequences. 2) Remove transposons: they then removed all those that had a similarity to transposons. Transposons are DNA elements that multiply in the chromosome: something like an internal virus, only usually benign. They may carry bits of other genes they “grab” on the way, but they are not functional. They were left with 556,554 sequences 3) Big step: look for homologs in another fly species: they then looked for smORFs with similar  translated amino-acid sequences in D. pseudoobscura, which diverged from the melanogaster  25 to 55 million years ago. The reason they looked for similar amino-acid sequences was that if there is a selection to conserve a smORF, it would be on the protein, and not at the DNA level. This step reduced the number of smORF candidates by 93%: from 556,554 down to 43,210.  Looking only for 4) global alignments, (another big step)  they found 4,561 smORF candidates by looking at alignments of whole smORF sequences, not only of partial local similarities. this reduced the number of candidates by 72% from the  step (3). We are now down to 0.8% of the original 593,586 smORF candidates.

Quite a filtering process. Note the huge elimination: 99.2% of all initial smORFs candidates are gone. I believe that they decided to sacrifice sensitivity in favor of specificity

So they had 4,561 smORF candidates conserved between two flies. Still, how many ORFs got in by chance? Hard to know, but they continued to rely on evolutionary conservation as a guideline. There may be smORFs that appeared independently in melanogaster and pseudoobscura after they separated 55 million years ago,  but the main evidence for true smORFs would be their evolutionary conservation between the two fly species.

To get even more specific, they now 5) looked for shared synteny:  conservation not only of sequence, but also of the genomic context: the sequences surrounding it. That brought the number down to 3,314.

OK, so they looked for conservation based on homology and based on synteny. Anything more? Well, yes. The next step would be to 6) look for evolutionarily selected smORFs. The two evolutionary criteria they used until now were homology and synteny. Now comes a third:  selection. If  smORF candidates are actually coding, they will be subject to  purifying selection, that is, to selection that eliminates deleterious mutations. This is evident in a low rate of non-synonymous vs. synonymous substitutions, or a Ka/Ks ratio of << 1. (Read about Ka/Ks ratios also here.) 7) Looking at what actually gets transcribed in Drosophila (from looking at the transcriptome) this number was whittled down to a final 401.

So the chosen 401 smORFs are evolutionarily conserved, both in sequence and in synteny, subject to purifyng selection (by Ka/Ks ratio) and produce a transcript. The authors obviously went for specificity over sensitivity: they looked for “good bet” smORFs rather than a large number of candidates. What I like about this study is the way that the authors used a large number of evolutionary traits that can be used as attributes for identifying smORFs. They also were careful to rule out, as much as possible, that these smORFs that may be a result of a larger transcript. This is a really nice molecular evolution work. There is no experimental evidence yet of the functionality of these smORFs: those are left to future proteomic and fly geneticists. But the idea of a small(er) world of genes, hiding in plain site among the more familiar large ones, does have its appeal, and may yield some surprises about how are genomes are structured.

Finally, for the evolutionary biologists: read the paper; there is quite a lot more to it that what I wrote. I just gave the highlights.

Ladoukakis, E., Pereira, V., Magny, E., Eyre-Walker, A., & Couso, J. (2011). Hundreds of putatively functional small open reading frames in Drosophila Genome Biology, 12 (11) DOI: 10.1186/gb-2011-12-11-r118

http://genomebiology.com/2011/12/11/R118/abstract

The Friedberg Lab is recruiting graduate students, for both Master’s and Ph.D.

WE ARE:  A dynamic young lab  interested in gene, gene cluster and genome evolution, understanding microbial communities and microbe-host interactions by metagenomic analyses, developing algorithms for understanding gene cluster evolution, and prediction of protein function from protein sequence and structure.

YOU ARE: an independent, hard-working problem-solving, energetic and motivated scientist-to-be. You have graduated or are about to graduate in computer science and/or biology or related fields. The Friedberg Lab is a “dry” lab, so some programming skills are required (Python preferred).

Existing and planned projects include:

1. Computational protein function prediction and assessment of function prediction algorithms. The Friedberg Lab is among the leaders of the Critical Assessment of Function Annotations (CAFA), an international effort of dozens of research groups to asess and improve function prediction algorithms. We are looking for students that are excited about prediction of protein function from sequence and structure. Also, how well can we assess how well our algorithms are doing? The next CAFA meeting will take place in Berlin, July 2013 and the Friedberg Lab will play a central role in  answering these questions.

2. Metagenomics:  we are studying the interaction between the microbiome and the host using metagenomic and metatranscriptomic data. In collaboration We are looking at how the human microbiome affects gene expression in the host. Together with Robb Chapkin’s lab at Texas A&M we are analyzing microbial genomes and their effect on transcription in the human gut. We are also developing algorithms for context-based function prediction in metagenomic data. Simply put: how well can we prediction the function of a gene from its neighbors? Since many of the genes in metagenomic data have no known homologs, we are developing creative ways to computationally discover their function.

3. Microbial Evolution: we are researching the evolution of Mycoplasma, a bacteria genus which serves us as model clade for understanding genome evolution. Mycoplasma have the smallest genomes of any organism, and being parasitic evolve quickly. Together with the Balish Lab we expect to sequence several new species and strains in the next year, and we are developing computational methods and a central community database  for analyzing the Mycoplasma tree of life. Besides the biological aspect, this project is also a great opportunity to get into web programming, database design, and learn how top design and code community-based scientific software. 

4. Biopython: Biopython is a set of freely available tools for biological computation written in Python by an international team of developers. It is a distributed collaborative effort to develop Python libraries and applications which address the needs of current and future work in bioinformatics. If you would like to become a Biopython developer, part of an international community of open-source scientific software developers, the Friedberg Lab is the place for you. This option is especially attractive for Master’s students seeking to enter bioinformatics in Industry.

5. Insert your brilliant idea here! I love new projects!

The lab is equipped with its own 10-node cluster computer, several workstations, and has access to Miami University’s Supercomputing Center, and the Ohio Supercomputer Center at Ohio State University.  Students have an excellent research environment, and many opportunities to collaborate with labs on and off campus.

Students can apply to the Friedberg Lab via the following graduate programs at Miami University:

1. Microbiology (Master’s and PhD).

2. Cell, Molecular and Strcutural Biology (PhD only).

3. Computer Science (Master’s only).

You are welcome and encouraged  to inquire further. I love talking with prospective students. If you would like to set up a phone/Skype chat please send your CV to:

friedberg.lab.jobs ‘at gmail ‘dot’ com

Looking forward to hearing from you.

Iddo Friedberg, PhD

Assistant Professor, Microbiology and Computer Science (affiliate)

Miami University

Oxford, OH, USA

Hats off to Jonathan Eisen for hosting this activity on his blog. (I’ll keep mine on, thank you. It’s raining cats and dogs here right now).

A couple of weeks ago I posted a discussion about two papers that challenged the ortholog conjecture. Briefly, both papers stated that orthologs may not be such great predictors for molecular function. One study from  Indiana University by  has shown that paralogs may be better predictors than orthologs for molecular function. Or, at the very least, paralogs should not be excluded as predictors. This paper has generated quite a bit of interest and controversy. Consequently, Eisen has invited Matthew Hahn, the lead author to write about “the story behind the story” in Eisen’s well-read blog. The post is a great read, and has generated an animated discussion in the comments area. You do need to clear quite a bit of time to go through both Hahn’s guest post and the comment thread: the topic is a rather complex one, and as explained in the comments thread, one problem is that the ‘ortholog conjecture’ itself seems to be not well-defined.

I kept checking in to Eisen’s blog to read the elongating comment thread. It seems that now a special session on the topic may be in the works for the 2012 annual meeting of the Society for Molecular Biology and Evolution following this discussion. So great to see such an involved community getting together.

Sometimes, gene duplication occurs within a species: part of the chromosome may be duplicated, causing one, a few, or many genes to have more copies of themselves within the species. The descendants of the duplicates and the original are homologous are they are descended from a common ancestor. This type of homology is called paralogy: a homology due to a duplication event (para == in parallel).

In another case, the genes can be homologous due to speciation: a new species (A1) diverges from the original (A0), carrying highly similar genetic loads. The gene for, say, brown eyes in A1 and the gene for brown eyes in A0 are also homologous: derived from the gene of hemoglobin in A0. This time, the homology is called orthology: it is not due to in-species duplication, but due to speciation itself (ortho == exact).  The definitions of orthologs and paralogs were given by Walter Fitch in a seminal paper published in 1970.

One of the first protein structures to be solved was that of hemoglobin, the oxygen carrying protein complex in our blood. Scientists noticed that hemoglobin in jawed mammals has three different protein chains: alpha, beta and gamma. Their amino acid sequences were very similar, suggesting that the genes encoding for hemoglobin are highly similar, suggesting homology. Since all jawed mammals have hemoglobin, and they all had alpha, beta and gamma chains, the conclusion was that the duplication of the original genes happened in the common ancestor of  jawed mammals, before they split up into different species. Hence, the alpha, beta and gamma chains in hemoglobin are paralogous: homologous due to duplication preceding speciation. However, gamma-hemoglobin was shown to have a different function than beta or alpha (more on that in a bit).  The conclusion from this observation was the Ortholog Conjecture and it can be stated as follows: paralogs (reminder: homologs due to duplication) diverge in function more than orthologs (homologs due to speciation). A model was proposed for this observation: when genes duplicate within a species’ genome, there is less selective pressure on one copy to perform the same function. Thus, it can accumulate mutations and eventually adopt a different function. The ortholog conjecture states that paralogs mostly differ in function, whereas orthologs mostly do not. The ortholog conjecture is a very powerful statement because, if we have two proteins known to be orthologs, we can infer that they have the same function, whereas paralogs may not (if they had enough time to diverge). The ortholog conjecture is therefore a fundamental tenet in molecular phylogenetics, and is also a tool used to predict the function of proteins. If two homologous proteins are found out to be orthologs, then it is assumed they have the same (or highly similar) functionality.

A crack in the ortholog conjecture was formed in study published late 2009 in a paper published by Romain A. Studer and Marc Robinson-Rechavi. I blogged then about their study:

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.

Now two studies have widened this crack to a rather large crevasse. The first is a study by scientists in Indiana University. In a way, this new publication is a response to Studer & Robinson-Rechavi’s call to arms on points (i) and (ii). The IU scientists (the Radivojac lab and the Hahn lab at the School of Informatics at Indiana University, Bloomington, IN) examined hundreds of pairs of orthologous and paralogous genes from the mouse and human genomes. They then examined whether paralogs had a higher functional similarity, or rather orthologs.  What they found certainly defied the ortholog conjecture:

The relationship between functional similarity and sequence identity for human-mouse orthologs (red) and all paralogs (blue). (A) Biological pathway (B) molecular function. From PLoS Comput Biol 7(6): e1002073 under CC licence.

But before we explain the results, a word about function. The function of a protein has several aspects which are context-dependent; two important ones are the molecular function of the protein, and the biological process in which it participates. For example, the molecular function of all hemoglobins  is noted as  oxygen binding and oxygen transport. However, they are different in the processes, or pathways, in which they participate: gamma-hemoglobin participates in the transport of oxygen in the fetus. The complex which contains gamma-hemoglobin has a higher affinity to oxygen, and thus able to extract oxygen in the placenta from the maternal oxygenated hemoglobin and transport it to the fetus.

Now we can explain the figure above. Graph (A) above shows the functional similarity for the biological pathway aspect and how it is affected by the sequence identities of the hundreds of orthologs (red) and paralogs (blue) examined between human and mouse. Graph (B) shows the functional similarity of the molecular function aspect.

The X-axis is the sequence identity percentage between any pair of sequences: the higher the percent identity, the less divergent are the sequences, the more inclined we should be to think that the pair of proteins performs the same function since they diverged less. The Y-axis shows the fraction of functional similarity. Looking at graph (B) above, we see that paralogs which are 100% identical, have (almost always) the same function . But sequences of orthlogous proteins between human and mouse have only about 65% functional similarity, on average. What does that mean? In the database they looked at, each gene has a set of words associated with it, describing what it does. The IU scientists found that only about 65% of the keywords in orthologous sequence pairs overlapped, on average. Whereas for paralogs 100% overlapped. And those are for sequences which are identical! This means that even if we find identical protein sequences in human and in mouse, it does not mean that they have the same molecular function. On the other hand, paralogs, will generally have more similar functions. So the ortholog conjecture has been stood on its head here: paralogs are the ones that would generally have the same function, whereas orthologs diverge more in function. This holds true for up to about 50% sequence identity, when the picture seems to reverse itself. Graph (A) depicts the differences in the biological pathway aspect. Here, the differences are even more striking. The paralogs which are 90-100% identical between human and mouse participate in almost exactly the same pathways in both organisms. But orthologous proteins which are 90-100% identical the functional similarity is much lower:  only about 65%.

So what does this all mean?

First, it means that, at least between human and mouse, paralogs are better predictors of function than orthologs. And why would that be? To answer this question, let’s look closer at the graphs above. Note that while for paralogs the functional similarity decreases rapidly with sequence similarity, for orthologs the functional similarity remains roughly the same no matter how similar or different the orthologs are to each other, and even when they are 100% identical their functions vary to some extent! The reason: the experimental study of function in two human and in mouse  takes place in different contexts. The species-specific context is what causes the differences in annotation, and in the overall function. Also, all the orthologs in the study are of the same age, dating back to the human-mouse lineage split 75 million years ago. The paralogs predate that split, and may be of different ages: the split may predate the human / mouse split by 10 million years, 100 million years, or 1 billion years. Thus orthologs, regardless of their actual sequence similarity, have the same age, and paralogs do not. But why should proteins of the same age share the same level of (not so high) functional similarity? The authors of the study reply:

While there is no direct role for “time” in evolution that is not tied to mutation, we suggest that what time represents here is the evolution of the cellular context: the sum of the evolutionary changes over all of the directly and indirectly interacting molecules. If this context evolves at a steady rate (i.e. the average amount of functional change among all of the interacting molecules remains relatively constant), then protein function will appear to evolve at a steady rate, a rate largely disconnected from the level of an individual protein’s sequence divergence. — PLoS Comput Biol, Vol. 7, No. 6.

The strongest evidence they find for this hypothesis, is that even proteins with 100% are annotated differently. To wit:

For example, Liao and Zhang [50] found that >20% of genes that are essential for viability in humans are not essential in mouse. It is unlikely that changes to the proteins themselves have made them essential or not, but rather that their context in cellular and organismal networks has evolved. –ibid.

The proteins may not have changed substantially, but their environment changed, giving them a different role. Think about changing jobs after moving to a new place where there is no employer providing your exact old job you were used to. You may have been an embedded systems programmer, but now you are a website programmer. So context goes a long way to explain changes in ortholog function.

Interestingly, about a month after the IU paper was published, another paper from the Robinson-Rechavi lab was published, which also talks about homologs between human and mouse. In this study Gharib and Robinson-Rechavi reviewed previous literature listing several types of functional divergence of orthologs between human and mouse. They had some additional findings. For example, about 11% of the orthologous genes were alternatively spliced, meaning that the end products, proteins, were different between human and mouse.  They also listed specific phenotypic effects: genes which are linked to diseases in humans, but mutations in their mouse orthologs have no effects on mice. They cite studies that found that over 20% of genes which are essential in human are non-essential in mice (an essential gene is just that: if the organism does not have it, or it is mutated, the effects are fatal, and the organism does not develop past very early stages).  Their literature review concluded that 10-20% of ortholog pairs between human and mouse cannot be used for functional transfer. The IU study implies a higher percentage. Both studies conclude that a common practice in molecular evolution studies, the use of orthologs to infer function, should be seriously looked at.

(Full disclosure: Dr. Radivojac & I are collaborators, although our collaboration is unrelated to this study).

Nehrt, N., Clark, W., Radivojac, P., & Hahn, M. (2011). Testing the Ortholog Conjecture with Comparative Functional Genomic Data from Mammals PLoS Computational Biology, 7 (6) DOI: 10.1371/journal.pcbi.1002073

Gharib, W., & Robinson-Rechavi, M. (2011). When orthologs diverge between human and mouse Briefings in Bioinformatics DOI: 10.1093/bib/bbr031

Fitch, W. (1970). Distinguishing Homologous from Analogous Proteins Systematic Zoology, 19 (2) DOI: 10.2307/2412448

I am fascinated with zombies. Always have been, but even more so since I took an interest in microbiology. The zombie apocalypse is the best known and best chronicled viral infection which hasn’t happened. But it could happen any day, so stock up on non-perishable food, medical supplies, water purification tablets, chainsaws, machetes, baseball bats, crossbows, semi-automatic firearms, and as many disposable acquaintances as you can get hold of. Signs of the zombie apocalypse may include your significant other turning a putrid-greenish shade of rotten and trying to chew your neck off. Beware of the differential diagnosis that they might just be using a bit too much makeup, are not wearing deodorant and feeling flirtatious. Remember to eliminate that possibility before cracking your beloved’s head open with a crowbar.

Here are three interesting studies all having to do with zombies.

The Zombie Roach

One problem that has to do with zombification is the loss free will. Do zombies have free will? More to the point, do humans have free will? 28 Days Later and both versions of Dawn of the Dead have survivors finding refuge from the zombie apocalypse at shopping centers. While at the shopping center, the survivors copiously consume the goods in the stores, making you think who really is the mindlessly-obsessed drone lacking free will. In two papers entitled A Wasp Manipulates Neuronal Activity in the Sub-Esophageal Ganglion to Decrease the Drive for Walking in Its Cockroach Prey and  On predatory wasps and zombie cockroaches — Investigations of “free will” and spontaneous behavior in insects, Ram Gal and Frederic Libersat from Ben Gurion University explore free will in cockroaches. Do cockroaches have free will, or are they just sophisticated automatons? And where do we draw the line between the two? Gal and Libersat  use the following definition for free will: the expression of patterns of “endogenously-generated spontaneous behavior”. That is, a  behavior which has a pattern (i.e. not just random fluctuations) and must come from within (i.e. not entirely in response to external stimuli). They cite studies where such behavior — which they define as a “precursor of free will in insects” — is observed. They then show how this behavior is removed from cockroaches when the roaches are attacked by a wasp. Their description of the process is so colorful, I shall simply reproduce it here. This is some of the most delightful and engaging prose I have ever read in a scientific paper.

Unlike most other parasitoids, this tropical Ampulicine wasp does not simply paralyze its prey to immobilize it. Instead, it stings a cockroach in the head (Fig. 1A) and injects a neurotoxic venom cocktail directly inside the cerebral ganglia (Fig. 1B). This turns the cockroach, metaphorically, into a submissive ‘zombie’: it gradually enters a long-lasting hypokinetic state, during which it becomes unresponsive to aversive stimuli and fails to self-initiate walking or escape behaviors. Although the stung cockroach is not paralyzed, it allows the wasp to cut both its antennae and drink hemolymph from the cut ends. The wasp then grabs one of the antennal stumps and pulls backwards, leading its prey into a pre-selected nest. The intoxicated cockroach, rather than fighting or fleeing its predator, actually follows the wasp submissively. In doing so it demonstrates a completely normal walking pattern, as if it was a dog led by his Master’s leash. The wasp then lays one egg on the cockroach’s leg, seals the nest and leaves the lethargic prey inside, still alive but powerless to escape under the influence of the venom. As the wasp larva hatches from the egg, it penetrates through the cockroach’s cuticle and feeds on its internal organs for several more days. Only then, roughly five days after the sting, does the cockroach finally die and the larva pupates inside its abdomen, safe from predators outside the nest.

A. The parasitoid Jewel Wasp A. compressa stings its cockroach prey inside the head. B. Schematic drawing of the cerebral nervous system (yellow) inside the cockroach's head capsule. the wasp's stinger (st., scanning electron micrograph drawn to scale) reaches to inject venom into both cerebral ganglia, namely the supra-esophageal ganglion (SupEG) and sub-esophageal ganglion (SEG). Scale bar: 0.5 mm. Source: Commun Integr Biol. 2010 Sep-Oct; 3(5): 458–461.

What they also found was that the venom injection does not affect the roach’s muscle, motor neurons or sensory neurons. Those are all intact. Stung cockroaches will walk slower and submissively follow their wasp mistress, but if placed in water they will start paddling frantically like unzombified cockroaches trying to save themselves from drowning. Indeed, the zombies paddle as frantically as non-zombies, but for a much shorter time, as if they despair quicker.

But ah, you say —  these cockroaches are not “real” zombies are they? Or rather, not the type of zombies from which the zombie apocalypse would be created. After all, a zombified roach is not infectious, and thus the disease would not spread from roach-to-roach in exponentially rising numbers by having mad roaches bite each other. In fact, the roach becomes catatonic and subservient to its mistress, more like the original Voodoo zombie, a reanimated corpse under the control of a shaman.

So how about molecular zombies?

Consider plants. Not zombie plants, but zombie genes in plants. Actually, transposable elements or TEs. TEs are DNA elements that self-replicate, in their own genomes or may be transferred into other genomes. Most eukaryotes have them. Indeed, 17% of the human genome is composed of Long Interspersed Integrated Elements or LINES. LINES simply replicate through the genome, and their contribution, if any, to the fitness of the organism is not known. some TEs are like internal viruses: replicating, adding their sequence, but little else, to their “host” genome. Only by increasing their numbers, they may at some point change their host genome. They may write themselves into some vital piece of DNA, and cause genetic damage that, if it does not kill the carrier while still an embryo, may cause long-term debilitating damage to the species by fixing itself in the population and moving down the generations. The genetic load of TEs in plants is huge: in Maize they make up the majority of the genome, with 85% of the genome coding for TE genes.

But the self-selection of TEs to spread in the genome is also their undoing. Small pieces of RNA derived from TEs are used by the host to target full TEs and degrade them through a group of processes known collectively as RNA silencing. Some TEs do generate an RNA or a protein end product. Those smaller, derived aberrant TEs were nicknamed ‘zombies’ in a paper by Damon Lish from University of California, Berkeley and Jeffrey Bennetzen from the University of Georgia, Athens. There is an ongoing battle between the TEs and the zombie TEs, with the latter silencing the former, yet metaphorically feeding off the original TEs existence. If the ‘live’ TEs did not exist, the ‘zombie’ version of them would not either. The more copies of TEs a genome has, the more zombie TEs it will have. TEs inserting themselves near active genes may subject these genes to silencing by zombie TEs. This ability of plants to silence their own genes by shuffling TEs around generates new patterns of gene expression. Also, the same genes that may be inhibited due to their proximity to TEs may, at some point, re-express themselves if the TE is removed from that position in the chromosomse. So zombie TEs, which are a form of defense against viral TEs that cause chromosome damage, are also a mechanism for generating new genes and expression patterns: an evolutionary tool.

Transposable elements ("jumping genes") are responsible for the different colors in Indian corn. TEs are controlled by their "zombie offspring": short interfereing RNAs whose sequences comlement those of the TEs, silencing TE expression.

Finally, there are the zombie ants

I have written about zombie ants before:

Here is a parasitic fungus that infects ants. The infected ant wanders away from its nest; the ant then reaches a leaf or another plant part. The fungus makes the ant to bite the leaf so powerfully, it hangs from the leaf until it eventually dies; and then (excited-by-gross-stuff six-year old emerging): the fungus grows an upside down stalk out of the dead ant’s head, releasing spores that fall to the ground. The spores are then picked up by ants that walk over them, causing them to wander away from the nest, bite other leaves…  Ad nauseam. Wow.

Briefly, once infected, the ant’s behavior is hijacked to  act as a delivery system for the fungus, which is finding a  good location to die and infect more ants.

 

Ophiocordyceps unilateralis is the fungus that wreaks this havoc on the ants. Indeed, an O. unilateralis infection in ants is the closest parallel we can find to the human zombie apocalypse. The infection changes the ant’s behavior to infect more ants, and can decimate whole colonies.

But is there only one species of fungus? Harry Evans, Simon Elliot and David Hughes were inrigued by the original description of Torrubia unilateralis, as it was called at the time. In 1865, Louis René Tulasne a French mycologist, described a leaf-cutting and as the host for the fungus. His brother, Charles Tulasne, worked with him and illustrated their findings. Charles’s drawing of an infected ant does not depict the leaf-cutter: rather, it appears to be a carpenter ant, with its characteristic spines. The fungus has only ever been found infecting carpenter ants. Could Louis have made a mistake? Maybe there other species of ant-zombifying fungi out there?

a. Original plate from the 1865 Selecta Fungorum Carpologia of the Tulasne brothers, illustrating the holotype of Ophiocordyceps (Torrubia) unilateralis and said to be on the leaf-cutting ant, Atta cephalotes; b. Detail from plate showing the distinctive pronotal plate of Camponotus sericeiventris, as well as a side view of the host which is clearly a carpenter ant and not a leaf-cutter; compare with c. Live worker of C. sericeiventris showing the spines on the pronotal plate (arrow). Source: doi:10.1371/journal.pone.0017024.g001

Indeed there are, and the authors of this study found four new species in the Brazilian rain forest, all distinct by shape and development. All infecting carpenter ants, (no leaf cutter ants found to be infected yet!)  but four different species of carpenter ants: Camponotus rufipes, C. balzani, C. melanoticus and C. novogranadensis – are each attacked by a distinct species of Ophiocordyceps. Plenty of great pictures in the paper describing the differences between the ant-zombifying fungi.

Happy zombie-bashing! (Warning: Pop-up link to an external flash game).

Gal, R., & Libersat, F. (2010). A Wasp Manipulates Neuronal Activity in the Sub-Esophageal Ganglion to Decrease the Drive for Walking in Its Cockroach Prey PLoS ONE, 5 (4) DOI: 10.1371/journal.pone.0010019

Gal, R., & Libersat, F. (2010). On predatory wasps and zombie cockroaches: Investigations of free will and spontaneous behavior in insects Communicative & Integrative Biology, 3 (5), 458-461 DOI: 10.4161/cib.3.5.12472

Lisch, D., & Bennetzen, J. (2011). Transposable element origins of epigenetic gene regulation Current Opinion in Plant Biology, 14 (2), 156-161 DOI: 10.1016/j.pbi.2011.01.003

Evans, H., Elliot, S., & Hughes, D. (2011). Hidden Diversity Behind the Zombie-Ant Fungus Ophiocordyceps unilateralis: Four New Species Described from Carpenter Ants in Minas Gerais, Brazil PLoS ONE, 6 (3) DOI: 10.1371/journal.pone.0017024

OK, I think the tree of life is obsolete. I have been spending a lot of time looking at horizontal gene transfer, reading about it, looking at it in genomes until my eyes water and my brain dessicates, occasionally blogging about it and soon to be publishing about it. Life is not a tree. To what extent it is not a tree it is debatable, but horizontal gene transfer is pervasive, if not rampant, in all kingdoms.

Horizontal Gene Transfer. Credit: Wikimedia Commons

So we need another term to describe the interconnection of species and genes. In a tree structure, each node can have only one parent, whereas each parent can have many offspring. Sibling nodes always have the same parent. As far as genomes are concerned…. hoo boy. Too many HGT events to count. For any given gene or chromosome/plasmid segment, we cannot reliably assume that it was vertically transferred. So if a node in our tree represents a genome, it may have multiple parents. Hence, not a tree anymore. Rather, a Directed Acyclic Graph or DAG.

A Directed Acyclic Graph, (DAG). Credit: wikimedia commons

However, it may very well be that a genome which contributed genetic material may also be contributing genetic material to the genome that gave it material in the first place. That happens in endosymbiotic events, but not only. Can happen, for example, with many species of bacteria or archaea living in proximity. Contribution of genetic material may be reciprocal. So a more precise definition for life would be a directed reciprocal acyclic graph or a DRAG.

But then, you already know that, don’t you?

(Yeah, and I know that reciprocity technically voids the acyclic condition. Shaddup.)

If you are unfamiliar with the Weasel program, read the Wikipedia entry before reading on.

In honor of The Bard’s birthday, I have penned a short Python program which performs Weasel. You may need to tweak it for Windows (remove the first #! line which is a Linux thing). When running the program, the user selects 3 parameters: the target net result (in the form of a string), the number of offspring per generation, and the mutation rate.

Here is a demo run, with 300 offspring per generation, and a mutation rate of 0.05 per position:

 

   ./weasel.py "methinks it is like a weasel" 300 0.05
   1 `,<o?[}= g!yq[}?*v=.+ :y<<:g
   2 `,<o?[}= gjyq[}?*v=.m :y<<eg
   3 `,<o?[}= gjyq[ ?*v=.m :y<<eg
   4 `,<o?[}= ijyq[ ?*v=.m :y<<eg
   5 `,<o?[k= ijyq[ ?*v=.m :y<<eg
   6 `g<o?[k= ijyq[ l*v=.m :y<<eg
   7 `g<o?[k= ijyq[ l*ve.m :y<<eg
   8 `g<o?[k= ij q[ l*ve.m :y<<eg
   9 `v<o?[k= ij qs l*ve.m :p<<eg
  10 ev<o?[k= ij qs l*ve.v :paieg
  11 ev<o?[k( ij qs l*ke.v :paieg
  12 ev<o?[k( it qs l*ke.v :paieg
  13 ev<o?[k( it qs l_ke.v wpaieg
  14 ev<oi[k( it qs l_ke.v wpaieg
  15 ev<oi[k( it qs l_ke.a wpaieg
  16 ev<oi[k( it [s l_ke.a wpaiel
  17 ev<oi[k( it is l_ke.a wpaiel
  18 ev<oi[k( it is l_ke.a wea*el
  19 ev<oi[k( it is l_ke.a wea*el
  20 mv<oi[k( it is l_ke.a wea*el
  21 mv<oi[k( it is llke\a weasel
  22 mv<,i[k( it is llke\a weasel
  23 mv<hi[k( it is llke\a weasel
  24 mvzhi[k( it is like\a weasel
  25 mvzhi[k( it is like\a weasel
  26 mv(hi[k( it is like\a weasel
  27 mv(hi[ks it is like\a weasel
  28 mv(hi[ks it is like\a weasel
  29 mv(hi[ks it is like\a weasel
  30 mvthi[ks it is like&a weasel
  31 mvthi[ks it is like&a weasel
  32 mvthimks it is like&a weasel
  33 mvthimks it is like&a weasel
  34 mvthimks it is like&a weasel
  35 mvthimks it is like&a weasel
  36 mvthimks it is like&a weasel
  37 mxthimks it is like&a weasel
  38 mxthimks it is like&a weasel
  39 mxthimks it is like&a weasel
  40 mxthimks it is like&a weasel
  41 mxthimks it is like&a weasel
  42 mxthimks it is like&a weasel
  43 mxthimks it is like a weasel
  44 mxthimks it is like a weasel
  45 mxthilks it is like a weasel
  46 mxthilks it is like a weasel
  47 mxthilks it is like a weasel
  48 mxthinks it is like a weasel
  49 mxthinks it is like a weasel
  50 mxthinks it is like a weasel
  51 mxthinks it is like a weasel
  52 mxthinks it is like a weasel
  53 mxthinks it is like a weasel
  54 mxthinks it is like a weasel
  55 mxthinks it is like a weasel
  56 mxthinks it is like a weasel
  57 mxthinks it is like a weasel
  58 methinks it is like a weasel

And here is the Python code:

#!/usr/bin/python
import string
import random
import sys
import copy
# Copyright(C) 2011 Iddo Friedberg
# Released under Biopython license. http://www.biopython.org/DIST/LICENSE
# Do not remove this comment

ALLCHARS = string.lowercase+' '+string.punctuation
def loopweasel(target_string,n_offspring,mut_rate):
    i = 1
    target_string = target_string.lower()
    current_string = list(''.join(random.choice(ALLCHARS)
                        for i in range(len(target_string))))
    print "    %s" % target_string
    while target_string != ''.join(current_string):
        print "%4d %s" % (i,''.join(current_string))
        i += 1
        offsprings = create_offspring(current_string,
                                    n_offspring,mut_rate)
        current_string = evolve_string(offsprings, target_string)
    print "%4d %s" % (i,''.join(current_string))

def create_offspring(current_string,n_offspring,mut_rate):
    offspring_list = []
    for i in (range(n_offspring)):
        offspring = []
        for c in current_string:
            if random.random() < mut_rate:
                offspring.append(random.choice(ALLCHARS))
            else:
                offspring.append(c)
        offspring_list.append(offspring)
    return offspring_list

def diffseq(a,b):
    diffcount = 0
    for i,j in zip(a,b):
        if i != j:
            diffcount += 1
    return diffcount

def evolve_string(offspring_list, target_string):
    best_match = (2000,'')
    for offspring in offspring_list:
        diffscore = diffseq(offspring, target_string)
        if diffscore < best_match[0]:
            best_match = (diffscore,offspring)
    return best_match[1]

if __name__ == '__main__':
    if len(sys.argv) < 4:
        print "Usage: weasel target_string n_offspring mutation_rate"
        print
        print "target_string: the string you would eventually evolve into"
        print "n_offspring: number of offspring per generation"
        print "mutation_rate rate of mutation per position, 0=< m <1"
        sys.exit(1)
    target_string = sys.argv[1]
    n_offspring = int(sys.argv[2])
    mut_rate = float(sys.argv[3])
    for i in target_string:
        if i not in ALLCHARS:
            print "Error, string can only contain %s" % ALLCHARS
            sys.exit(1)
    if mut_rate >= 1.0 or mut_rate < 0:
        print "Error: 0 =< mutation rate < 1"
        sys.exit(1)
    loopweasel(target_string,n_offspring, mut_rate)

A quick guide: “loopweasel” (line 11) is the main bit that loops through generations. In each loop iteration it first calls “create_offspring” (line 25) which creates a list with the “offspring” strings (300 in the above example). Mutations are inserted (or not) in line 30. Control returns to loopweasel, which calls evolve_string (line 44). All offspring strings are score to the target (“methinks it is like a weasel”) string, using the “diffseq” code (line 37). (Is there a built-in way in Python for doing that? I could not find any.) The best scoring match is not he chosen offspring. It is printed, and the loop is repeated. Repeat until best-fitted offspring matches the target string.

It is actually quite fun to play with different mutation rates and offspring / generation numbers. Try it. Then rent a good Shakespeare movie. As a horror fan, I’m going to watch Titus tonight.

Titus Andronicus. Source: Wikipedia

Quite a few people think that microbes are evil, disease causing minions of Hell that should be eradicated. Supermarkets are handing out sanitary wipes: wipe the handlebar if you want to live, never mind that 90% of the food in the supermarket is worse for you than anything you may catch off that cart handle.  Almost every public space looks like the secret basement level of the CDC, with alcoholic hand sanitizers and posters portraying the horrors of aerosol-borne infections. Microbes are the invisible enemy: you can’t see them, but they are deadly. You can sure kill them with copious amounts of ethanol.

Actually, only a minority of microbes are pathogens. Some eukaryotes are parasitic and disease causing.  There is Athlete’s foot (caused by a fungus) amoebal dysentery and other unpleasant experiences. But most are not. Also, most bacteria that live in us or on us are symbiotic and like us for our throwaway proteins, carbohydrates, nice 36.6C temperature, high humidity (armpit or mouth) and other goodies. Yes, some are pathogenic, and some do seem like evil little minions of the Devil.  Those have  ingenious mechanism which infect, wreak havoc, sometimes kill, and move on.  But for every plague bacillus or burger bug out there, there are millions of other kinds of bacteria that really don’t do much, good or bad.

About archaea

There is one group of microbes that have no known pathogens: Archaea.  Archaea are… different. An archaeon is as different from a bacterium as either is from a human. Superficially, bacteria and archaea look the same. Both are unicellular. Both do not have well-formed cellular organelles on the level that eukaryotes have. For those two reasons, archaea were thought, for a very long time, to be a type of bacteria. Today, virtually all microbiologists classify archaea in a domain of their own. Archaeal cell membranes are made up of their own unique type of building-blocks (lipids), the type which bacteria do not have, and neither do eukaryotes. Their cell wall is different than bacteria. Many live in extreme conditions: ocean smokers, geysers, hyper-saline lakes, the frozen Tundra, termite guts, cow stomachs and Charlie Sheen’s pants. Actually, the latter may be a bit too extreme even for archaea. Looking at phylogenetic marker genes, such as small subunit ribosomal RNA, (SSUrRNA) archaea indeed cluster as a domain unto themselves.

But  of the hundreds of disease-causing microbes or pathogens that we know of, none are archaea. Which is odd. Plenty of disease causing eukaryotes and bacteria, but no archaea? Why is that? In a new paper published in Bioessays, Erin Gill and Fiona Brinkman try to answer this question.

First, Gill and Brinkman examined the most trivial hypothesis: we may just not have discovered archaeal pathogens yet. Their statistical analysis shows that this is possible, but unlikely. Here is the way the authors explain this: about 0.36% of known bacterial species cause disease (585 out of about 151,000 known cultured and uncultured species, a very low-bound estimate). Assuming that the diversity in archaea is about the same, we should have identified a few (the authors estimate .0036 x 4,508 species of archaea = 16) archaeal species which cause disease. This somewhat back-of-the-envelope calculation is a bit rough and laden with assumptions: one, that the diversity among known archaea is the same as among known bacteria. It was recently discovered that there is a huge marine diversity of mesophilic archaea for which we only have metagenomic (fragmented DNA sequence) data.  Also, there may be many diseases we know nothing about, simply because our census of life on earth is far less than complete. Some of these archaea (and more of these bacteria) may be pathogens, only many have not been identified as such. Finally, historically, with bacteria, we were biased towards looking for pathogens. Bacteriology started as a medical discipline, and to this day many microbiology departments reside in universities’ medical schools. On the other hand, archaea were studied mostly by environmental microbiologists, who are not looking for pathogens necessarily, but are more interested in biogeochemical cycles and the diversity of life. But its claim does cause us to raise an eyebrow: not even one known archaeal pathogen? OK that’s odd. Quite worth looking into. Although the number of archaea we can examine may be too small.

So what exactly is going on?

Bacteria don’t kill people. Bacteriophages kill people?

A clue may lie in how virulence genes are arranged in the bacterial genome. Virulence genes are genes that code for proteins that let bacteria invade our body, cause disease and evading the immune system and drugs. Many of these genes are recognized as mobile: they can easily jump together from a disease causing strain to a benign strain, causing the latter to now become virulent. In many cases they can jump between different species. The vector that carries those genes is typically a bacterial virus, or bacteriophage. When a virus invades bacteria, it can uptake some of its DNA and incorporate it into its own genome. This DNA may later be deposited in another bacterium, turning a benign strain into a virulent one. The process of moving DNA between bacteria with a virus is called transduction, and viruses may also leave very specific “fingerprints” in transduced DNA.

Generalized transduction. Source: Indian River State College

One might say that pathogenic bacteria are actually a vehicle to help bacteriophages proliferate. Better yet, bacteriophages and bacteria both can be viewed as vehicles to help virulence genes proliferate.

However, as far as we know, bacteriophages do not invade archaea. Archaea do have their own viruses, but those are different from bacteriophages. Archaea are a separate domain of life, and whatever parasitises one domain would be ill fit to parasitise another. After all, viruses that invade eukaryotes are also quite different from bacteriophages. (As an aside, this is what makes bacteriophages such an attractive idea as an anti-bacterial treatment method: after all, if we can inundate the human body with viruses that only infect bacteria, moreover only specific disease-causing bacteria leaving those that we need unharmed, that would make for a great silver bullet. But bacteriophage treatment is a matter for another post.) The differences are in shape, biochemistry  and in genomes. There is little to no similarity in the genomic sequences of archaeal viruses and bacteriophages. No bacteriophages are known to infect archaea and vice-versa. That said, we know precious little about the diversity of bacteriophages, and close to nothing about archaeal viruses.

We do know that archaea have a very different cell-wall biochemistry than bacteria, and lack the receptor proteins which bacteriophages use to infect bacteria. So bacteriophages cannot infect archaea, cannot transmit virulence genes, and cannot transmit virulence. Gill and Brinkman present virulence from the bacteriophage’s (or rather the bacteriophage’s genes) point of view: both bacteria and their hosts are vehicles for propagating bacteriophage genes. A rather complex evolutionary mechanism.

But why haven’t archaea developed virulence of their own, independently of bacteria? Wouldn’t archaeal viruses develop a similar mechanisms? The authors claim the reason is that virulence evolution is a rare event. They argue that the evolution of virulence, at least the virus-transmitted secondary type is a multi-step process, and is therefore rare. My take on this argument: yes, it might be true for phage-transmitted virulence, but both bacteria and eukarya have evolved virulence mechanisms independent of viruses, encompassing many diverse mechanism that appear to have evolved independently. Hence, virulence itself is not so rare, even if the gene-island type may be.

All-in-all a thought provoking paper, which was very exciting to read.  The authors qualify their hypothesis heavily, knowing that with bacterial, archaeal and their viruses, there are unknown unknowns, as the following bit of poetry illustrates:

The Unknown
As we know,
There are known knowns.
There are things we know we know.
We also know
There are known unknowns.
That is to say
We know there are some things
We do not know.
But there are also unknown unknowns,
The ones we don’t know
We don’t know.

—Donald Rumsfeld, Feb. 12, 2002, Department of Defense news briefing

Gill, E., & Brinkman, F. (2011). The proportional lack of archaeal pathogens: Do viruses/phages hold the key? BioEssays, 33 (4), 248-254 DOI: 10.1002/bies.201000091

In 1909 Charles D. Walcott made one of the most incredible discoveries in paleontology, and probably in biology in general. A treasure trove of fossils was revealed in Burgess Pass in the Canadian Rockies, eastern British Columbia. Because of unique process of mineralization in the shale, animal soft tissue was also fossilized. The number and diversity of species found was enormous. The quality of the fossils found was excellent. In many cases, soft tissue was preserved, which helped us better understand the anatomy of the creatures found. Together with other fossil beds the Burgess Shale taught us about the Cambrian Explosion: a relatively brief period in the history of life when most of the ancestors of today’s animals appeared. In a few geological heaves for about 10 million years between 550 and 540 Mya, the arthropods, echinoderms (starfish, sea-urchins and trilobites),  brachiopods, molluscs and many others appeared and diversified into thousands of species. Eventually, many of those early species went extinct, but those animals that remain today on earth are their descendants.

It’s not clear what, if anything, prompted this rapid appearance and diversification of species. The ancient super-continent Gondwana was breaking up, so maybe the addition of ecological niches, such as shallow seas, accelerated diversification. Shallow seas would have been more oxygenated than the deep ocean surrounding the supercontinent,  creating more habitable niches to be filled.  A mass-extinction is also thought to have occurred just prior to the Cambrian Explosion: the end-Ediacaran extinction.  Mass-extinctions are known to wipe the evolutionary slate clean, and prompt the diversification of the species surviving the mass-extinction. Think of mammals before and after the dinosaur extinction. The niches occupied by large predators and herbivores were populated by mammals only after the K-T extinction. Or maybe it was a combination of these factors, or maybe the genotypic diversity has attained a critical mass that it triggered a consequent phenotypic and species diversity to follow.

Burgess Shale life Credit: the Smithsonian Institute

Recently, Lawrence David and Eric Alm from MIT published an article in Nature that describes the microbial and molecular equivalent of the Cambrian Explosion. They show an explosion of new genes appearing in very short evolutionary time: a mere 470 million years or so. 27% of modern gene families were born in a starting about 3.3 billion years ago. The accelerated gene birth lasted until 2.85 billion years ago, when the rate of gene birth slowed down to what it is now. New genes still occur, but at a much lower rate. Today, and since about 2.5 billion years ago, genomes gain two to four new genes every 10 million years. But during the Archaean Expansion, as David and Alm named this period of accelerated gene appearance, the gene gain rate peaked at 10 genes per genome per 10 million years. This explosion of new genes was accompanied by the appearance of many new bacterial species too. David and Alm used software they developed called AnGST: “analyzer of gene and species trees”.  AnGST looks at specific gene phylogeny as well as the broader species phylogeny from the tree of life. This enabled them to trace the gene history back to the Archean period, and to view this expansion.

Just to place things in perspective: if the history of life on earth was compressed into one year,  unicellular organisms were the only life you could find from January through most of August, which is when multicellular life emerged.  The Cambrian explosion did not happen until mid-November, and did not last much more than a day. It was quite a formative day though, because even through at least three mass-extinctions took place after that day, all animals you can find on December 31 (today) can trace their ancestry to that day in November. But the Archaean gene expansion took place much earlier: for whole 40 days starting late January through early March; much longer and quite earlier than the Cambrian explosion.

Click for larger image. Source: Wikimedia commons

It took  nearly half a billlion  years for the Archaean gene expansion, but the stage was set. Those “two months” life spent acquiring those genes resulted in permanent long term changes to earth’s biosphere. Over the next 2.5 billion years (or “eight months”) microorgnisms eventually colonized the whole planet, creating nutrient cycles,  the soil system, and constantly raising the oxygen concentration until… boom! One day in November there was enough oxygen for the animals to step in. Life has not been the same since. Actually, life has never been the same ever. Which is why life is so cool.

EDIT: This post has been submitted to the 2011 NESCENT best evolution-themed blog post.

David, L., & Alm, E. (2010). Rapid evolutionary innovation during an Archaean genetic expansion Nature, 469 (7328), 93-96 DOI: 10.1038/nature09649