The Pseudogene Hall of Fame

For "budget reasons" (supposedly), the Joint Genome Institute now requires all users to be registered and approved before they can use the (taxpayer-funded) https://img.jgi.doe.gov site. Fortunately, my registration was approved and I can use the excellent online genomics tools there, one of which produced the following table of Top Ten Organisms by Pseudogene Count.

Organism	Genes	Pseudogenes
Mus musculus C57BL/6J	60745	11398
Rattus norvegicus BN/SsNHsdMCW	38115	9178
Homo sapiens	38612	7186
Candidatus Burkholderia kirkii	6325	4011
Arabidopsis thaliana Columbia	31392	3818
Nostoc azollae 0708	5379	1670
Stenotrophomonas maltophilia SKK35	4760	1589
Pseudomonas aeruginosa VRFPA01	5775	1523
Stenotrophomonas maltophilia RA8	5016	1507
Mycobacterium leprae TN	2750	1086

Again: Don't expect the above links to work if you're not a registered JGI user. (I don't know if they will work for you or not.) This list was automagically generated by the Department of Energy's Joint Genomes Institute and I thought you might get the same kick out of it that I got. It's eye-opening to see the ratio of pseudogenes to "normal" genes in these organisms. Isn't it?

Taking the top three spots are mouse, rat, and humans. (Note: These counts should be taken with a bit of caution, as some have estimated the number of pseudogenes in the human genome to be much higher than the 7,186 shown here.) All the other spots in the chart except Arabidopsis (which is a leafy plant) are bacteria. The leprosy bacterium, which I've written about before, comes in tenth place.

If you're not familiar with the concept of pseudogenes, you might want to look at this post. Basically we're talking about genes that are thought to be disabled and no longer functional in the normal sense, although they may well be functional in some as-yet-unappreciated sense. (Otherwise, evolutionary theory says they should have been eliminated from most genomes eons ago.)

Personally, I believe pseudogenes are as much a feature of DNA as regular genes; certainly in higher life forms, they occur in great numbers. The vast majority of bacterial genomes in public databases are shown as having no pseudogenes. I find that (how shall I say?) not at all credible. Some day it will be obvious that almost every genome harbors pseudogenes; we simply lack smart enough software to detect them all right now.

Virus Genes Don't Come from Host Genes

There's a school of thought that says that viruses originated as escaped constellations of host genes. Virus genes have to originate from somewhere. One theory is that they started with host genes.

Trouble is, there's precious little evidence that viral genes originated from host genes, and plenty of evidence to the contrary. It may actually be that host genes came from viruses.

To say viral genes derive from host genes is like saying hemorrhoids derive from earlobes. Any resemblance is, at best, superficial.

In a previous post, I showed data for the relatively large phylogenetic distance between thymidine kinase genes in phages (viruses that attack bacteria) and their hosts. In one case, I showed that prophage genes (genes from viruses that have succeeded in integrating into the host DNA) are more similar to host genes than lytic-lifestyle phages, but even in the case of temperate phages, I think we have to be honest and say that a prophage is still an example of foreign DNA integrating into a host. (Prophage genes can usually be easily identified by their base composition, which differs noticeably from the base composition of host genes.) Once a prophage becomes fully integrated into the host, its DNA (under the influence of the host repairosome) will tend to ameliorate, taking on the base composition and other characteristics of the host DNA, making it superficially similar to host DNA.

What "other characteristics" does ameliorated DNA take on? Consider codon usage patterns. Recall that the genetic code is set up in such a way that most amino acids correspond to more than one codon (three-letter pattern) in the DNA. Leucine, for example, can be encoded six different ways (namely by base patterns CTA, CTG, CTT, CTC, TTA, and TTG). Likewise, alanine can be encoded four ways (GCA, GCG, GCT, or GCC). But specific organisms develop specific patterns of codon use, preferring certain synonyms over others. For example, Clostridium botulinum (the food-poisoning bug) overwhelmingly prefers to use TTA for leucine (rarely using the other 5 synonyms), whereas E. coli strongly prefers to use CTG (choosing it four-to-one over the next-most-used leucine codon). These codon preference patterns are highly specific to a given species and are thought to be related to the numbers and types of available transfer RNAs (tRNA) in the cell, although frankly it's still an open question whether codon usage adapted to tRNA availability or the reverse.

The idea that viruses mutate rapidly and evolve in close harmony with the hosts on which their reproduction depends suggests that virus codon preferences should match those of the host. (This would be particularly true if virus genes come from host genes.) Remember that a virus has no ribosomal machinery and must rely on the host's protein-making equipment in order to survive. Therefore it would make sense for a virus to adapt its codon usage patterns to the patterns most favored by the host equipment.

That's not what we find. When we look at the codon usage patterns of phage T4 (a classic enterobacterial phage) versus E. coli's codon usage, we find that they differ substantially:

Codon usage frequencies for T4 phage (left) and E. coli B (right).

In this graphic, host-cell codon usage frequencies are on the right while corresponding T4 virus frequencies are on the left. Note that the T4 chromosome encodes 274 protein genes, encompassing over 50,000 codons, so the graphic is based on fairly solid numbers; variations from E. coli can't be accounted for simply by statistical noise.

T4's codon preferences are so different from the host cell's, the T4 phage brings with it genes for 8 types of tRNA. But the differences in codon usage go well beyond 8 codons, so the presence of tRNA genes in T4 DNA doesn't, by itself, explain the divergence of the data.

But what about temperate phages, like Fels-2 (a prophage in the Salmonella genome)? Since prophage genes are, in effect, a permanent part of the host genome, we would expect to see some amelioration of codon usage. And in fact, that is what we do see:

Codon usage in Fels-2 phage (left) and Salmonella typhimurium LT2 (right).

Here, we see that the codon usage patterns of Fels-2 and its host are quite similar. The differences are easily accounted for by the fact that Fels-2 has only 47 protein-coding genes, and the amino acid composition of those genes is probably different enough from "average" host genes to sway the usage stats to the degree shown here. Nevertheless, codon usage patterns aren't sufficient to tell us where Fels-2 genes came from originally. That's still an open question. Like the Martians in War of the Worlds, Fels-2 genes probably came from "somewhere else."

Robbie: "What, you mean, like Europe?"

Tom Cruise character: "No, Robbie. Not like Europe."

Problems with the Tuberculosis Genome

These days, most genes, in most sequenced genomes, are machine-annotated with a minimum of human intervention, and as a result around 30% of gene annotations are inaccurate, either as to functional assignment or as to reading frame.

It's not hard to find serious errors in published genomes. In the genome of Mycobacterium tuberculosis ATCC 35801, for example, there's a 17-kilobase-pair section of the genome that's full of errors (see below).

A view of two tuberculosis bacterial genomes showing a 17,000-base region (denoted by pink) of 100% sequence similarity. Notice that even though the genomes are 100% sequence-identical in the region, the number, strand orientation, and sizes of genes differ. The yellow gene in the top panel is identified by GeneMarkS+ as "secretion protein EspK," while the yellow gene in the lower panel is identified as DNA Ligase (ligA) and is much smaller (and occurs on the opposite strand).

In this graphic, the same region is shown for two strains of M. tuberculosis. On top is M. tuberculosis Strain EAI5 and on the bottom is M. tuberculosis ATCC 35801. To browse the genomes in your own browser, go to this link and click the pink Run GEvo Analysis! button. When the panels appear, you'll be able to click on individual genes to see what they (supposedly) are.

The yellow-colored gene in the top panel is annotated as "secretion protein EspK," while the corresponding region in the bottom genome has two genes (green and yellow) on opposing strands of DNA, with one gene (in green) given as a "hypothetical protein" and the other (in yellow) as "DNA ligase." Note that the entire region covered by the pink bands is 100% identical from organism to organism in terms of DNA sequence. And yet one annotation program found 13 genes while the other found 17.

Sadly, this is not an unusual situation.

How can you tell which annotations are correct? Unfortunately, it takes some investigation. Consider the two yellow genes. They can't both be correct as shown, because one is twice as long as the other and each is on a different strand of the DNA! And yet, if you obtain the DNA sequence of each, and use it as a query sequence in a BLAST search, you'll come up with "good" hits for each gene (because there are other incorrectly identified genes in public databases, matching each query).

It turns out the "secretion protein EspK" (the large yellow gene in the top panel) is correctly identified. To determine this, I downloaded the FASTA sequence for the large gene, plus the sequence for the same gene in the same location in the M. canetti CIPT genome (identified as "hypothetical alanine and proline rich protein"), plus the same gene in M. canetti CIPT 140070010, with the idea of identifying the differences in the (aligned) DNA sequences with respect to their codon positions.

When I had a script identify mutations by codon location, the number of differences at the three possible codon locations, were:

base 1: 4 changes
base 2: 2 changes
base 3: 19 changes

This is exactly the pattern you would expect if the reading frame is correct. Base 3 (the wobble base) tends to accumulate the majority of mutations, because changes in a codon's third base usually result in no amino acid change (due to the genetic code's degeneracy). Base 1 has a small amount of degeneracy and thus has the next-highest number of mutations. Base 2 has no degeneracy; all changes to this base result in a different amino acid. (All changes are non-synonymous, in other words.) 

I repeated this check using the so-called "DNA ligase" gene of Mycobacterium tuberculosis ATCC 35801, but using sequence data from the bottom strand of DNA, with 1329 bases' worth of additional 3'-end sequence data in order to cover the same amount of DNA as the other sequences. I got difference data of 3, 1, and 8 for bases one, two, and three. This verified that the active gene is actually on the bottom strand.  ERDMAN_4254 is incorrectly annotated as a top-strand DNA ligase. Instead of two genes, ERDMAN_4253 and ERDMAN_4254 (on two opposing strands), Mycobacterium tuberculosis ATCC 35801 should be shown as having a single large gene on the minus-one strand. The correct identification (based on more than thirty E=0, 100% identity hits in other strains) is, in fact, "secretion protein EspK."

Unfortunately, at least 3 other tuberculosis strains, namely M. tuberculosis GuangZ0019, plus Strain CCDC5180 and Strain CCDC5079, also harbor an incorrect ligA annotation, and Mycobacterium isn't the only organism with a "bogus ligA" problem. Micromonospora sp. M42 also has a fake ligA gene and I'm certain there are others.

Bottom line, don't believe everything you read in genome annotations. The annotation may say "DNA ligase," but you could actually be looking at something else entirely.

Why do some bacteria have GC-rich DNA?

 A longstanding open problem in biology is why the G+C (guanine plus cytosine) content of DNA varies so much across taxonomic groups. In theory, the amounts of the four bases in DNA (adenine, guanine, cytosine, and thymine) should be roughly equal, and regression to the mean should drive all organisms to a genomic G+C content of 50%. That's not what we find. In some organisms, like Mycobacterium tuberculosis, the G+C content is 65%, whereas in others, like Clostridium botulinum (the botulism organism) the G+C content is only 28%.

We know that, in general, G+C content correlates (not perfectly, though) with large genome size, in bacteria. Very low G+C content usually means a smaller genome size, and in fact tiny intracellular parasites and symbionts like Buchnera aphidicola (the aphid endosymbiont) have some of the lowest G+C contents of all (at 23%).

It's not hard to understand the presence of low-GC organisms, since it's well known that most transition mutations are GC-to-AT transitions. The high prevalence of mutations in the direction of A+T has often been called "AT drift."

But some organisms go the other way, developing unusually high G+C content in their genomes, indicating that something must be counteracting AT drift in those organisms.

Recently, a group of Chinese scientists (see Wu et al., "On the molecular mechanism of GC content variation among eubacterial genomes," Biology Direct, 2012, 7:2) has advanced the notion that high G+C content is due, specifically, to the presence of the dnaE2 gene, which codes for a low-fidelity DNA repair polymerase. This gene, they say, drives A:T pairs to become G:C pairs during the low-fidelity DNA repair that goes on in certain bacteria in times of stress. Not all bacteria contain the dnaE2 polymerase. Wu et al. discuss their theory in some detail in a .January 2014 article in the ISME Journal.

In earlier genomic studies of my own, I curated a list of 1373 eubacterial species (in which no species occurs twice), spanning a wide range of G+C values. When I learned of the dnaE2 hypothesis of Wu et al., I decided to check it against my own curated collection of organisms.

The first thing I did was go to UniProt.org and do a search on dnaE2. Some 1882 hits came back in the search, but many hits were for proteins inferred to be DNA polymerase III alpha subunits, not necessarily of the dnaE2 variety. In order to eliminate false positives, I decided to restrict my search to just bonafide dnaE2 entries that have been reviewed. That immediately cut the number of hits down to 77.

But among the 77 hits, some species were listed more than once (due to entries for multiple strains of the organism). I unduplicated the list at the species level and ended up with 60 unique species.

At this point, I wrote a little JavaScript code to check each of the 1373 organisms in my curated list against the 60 known-dnaE2-containing organisms obtained from UniProt. There were 47 matches. The matches are plotted in red in the graph below.

Click image to enlarge. In this plot, genome A+T content (a taxonomic metric) is on the x-axis and coding-region purine content is on the y-axis. (N=1373) The points in red represent organisms that possess a dnaE2 error-prone polyerase. See text for discussion.

This graph plots A+T content (which of course is just one minus the G+C content) on the horizontal axis, against coding-region purine content (A+G) on the vertical axis. (For more information on the significance of coding-region purine content, see my previous posts here and here. It's not important, though, for the present discussion.) Notice that the red points tend to occur on the left side of the graph, in the area of high G+C (low A+T) content. The red dot furthest to the right represents the genome of Saccharophagus degradans. Only 6 out of 47 dnaE2-positive organisms have G+C content below 50% (A+T above 50%). The rest have genomes rich in G+C.

This is, of course, just a quick, informal test (a "sanity check," if you will) of the Wu hypothesis regarding dnaE2 (which is a repair polymerase not needed for normal DNA replication, nor possessed by all bacteria). Various types of sampling errors could invalidate these results. Also, the Wu hypothesis itself is open to criticism on the grounds that correlation does not prove causation. Nevertheless, it's an interesting hypothesis and a random check of 47 dnaE2-positive species in my collection of 1373 organisms tends to provide at least anecdotal verification of the Wu theory that dnaE2 causes drift toward high G+C content.

Of course, Wu's theory does not explain the wide range of G+C contents observed in organisms other than bacteria. (There is no dnaE2 in eukaryotes, for example.) The general notion, however, that genomic G+C content tends to be a reflection of the components of a cell's "repairosome" (the enzyme systems used in repairing DNA) has substantial merit, I think. On that score, be sure to see my earlier analysis of how the presence or absence of an Ogg1 gene influences coding-region purine content.

Here, by the way, are the 47 dnaE2-containing organisms that show up as red dots in the graph above:

Agrobacterium tumefaciens
Agrobacterium vitis
Alkalilimnicola ehrlichii
Anaeromyxobacter dehalogenans
Anaeromyxobacter sp.
Aromatoleum aromaticum
Azoarcus sp.
Bdellovibrio bacteriovorus
Bordetella bronchiseptica
Bordetella parapertussis
Bradyrhizobium sp.
Brucella abortus
Burkholderia mallei
Burkholderia pseudomallei
Caulobacter crescentus
Corynebacterium diphtheriae
Corynebacterium efficiens
Corynebacterium glutamicum
Corynebacterium jeikeium
Dechloromonas aromatica
Gluconobacter oxydans
Hahella chejuensis
Idiomarina loihiensis
Methylococcus capsulatus
Mycobacterium bovis
Mycobacterium tuberculosis
Nocardia farcinica
Propionibacterium acnes
Pseudomonas fluorescens
Pseudomonas mendocina
Pseudomonas putida
Pseudomonas syringae
Ralstonia pickettii
Ralstonia solanacearum
Rhizobium sp.
Rhodopseudomonas palustris
Ruegeria pomeroyi
Saccharophagus degradans
Sinorhizobium medicae
Symbiobacterium thermophilum
Synechocystis sp.
Teredinibacter turnerae
Vibrio parahaemolyticus
Vibrio vulnificus
Xanthomonas axonopodis
Xanthomonas campestris
Xanthomonas oryzae

A Simple Method for Estimating the Rate of Transition vs. Transversion Mutations

Point mutations in DNA fall into two types: transition mutations, and transversion mutations. (See graphic below.)

In a transition mutation, a purine is swapped for a different purine (for example, adenine is swapped with guanine, or vice versa), or a pyrimidine is swapped with another pyrimidine (C for T or T for C); and usually, if a purine is swapped on one strand, the corresponding pyrimidine gets swapped on the other. Thus, a GC pair gets changed out for an AT pair, or vice versa.

A transversion, on the other hand, occurs when a purine is swapped for a pyrimidine. In a pairwise sense, this means a GC pair becomes a TA pair (for example) or an AT pair gets changed out for CG, or possibly AT for TA, or GC for CG.

Of the two types of mutation, transitions are more common. We also know that, in particular, GC-to-AT transitions are much more common than AT-to-GC transitions, for reasons that are well understood but that I won't discuss here. If you're curious to know what the experimental evidence is for the greater rate of GC-to-AT transitions, see Hall's 1991 Genetica paper (paywall protected, unfortunately) or the non-paywall-protected Y2K J. Bact. paper by Zhao. The latter paper is interesting because it shows that GC-to-AT transitions are more common in stationary-phase cells than exponentially-growing cells, and also, transitions in stationary E. coli are repaired by MutS and MutL gene products. (Overexpression of those two genes results in fewer transitions. Mutation of those two genes results in more transitions.)

An open question in molecular genetics is: What are the relative rates of transitions versus transversions, in natural populations? We know transitions are more common, but by what factor? Questions like this are tricky to answer, for a variety of reasons, and the answers obtained tend to vary quite a bit depending on the organism and methodology used. Van Bers et al. found a transition/transversion ratio (usually symbolized as κ) of 1.7 in Parus major (a bird species). Zhang and Gerstein looked at human DNA pseudogenes and found transitions outnumber transversions "by roughly a factor of two." Setti et al. looked at a variety of bacteria and found that the transition/transversion rate ratio for mutations affecting purines was 2.1 whereas the rate ratio for pyrimidines was 6.6. Tamura and Nei looked at nucleotide substitutions in the control region of mitochondrial DNA in chimps and humans (a region known to evolve rapidly) and found κ to be approximately 15. Yang and Yoder looked at mitochondrial cytochrome b in 28 primate species and found an average κ of 6.4. (In general, κ values tend to be considerably higher for mitochondrial DNA than other types of DNA.)

It's important to note that in all likelihood, no single value of κ will be universally applicable to all genes in all lineages, because evolutionary pressures vary from gene to gene and the rates of transition and transversion are different for different nucleotides (and so codon usage biases come into play). For an introduction to the various considerations involved in trying to estimate κ, I recommend Yang and Nielsen's 2000 paper as well as their 1998 and 1999 papers.

The reason I bring all this up is that I want to offer yet another possible way of estimating the transition/transversion rate ratio κ, using DNA composition statistics. Earlier, I presented data showing that the purine (A+G) content of coding regions of DNA correlates directly with genome A+T content. Analyzing the genomes of representatives of 260 bacterial genera, I came up with the following graph of purine mole-percent versus A+T mole-percent:

The correlation between genome A+T content and mRNA purine content is strong and positive (r=0.852) . Szybalski's Rule says that message regions tend to be purine-rich, but that's not exactly accurate. When genome A+T content is below approximately 35%, coding regions are richer in pyrimidines than purines. Above 35%, purines predominate. The concentration of purines in the mRNA-synonymous strand of DNA rises steadily with genome A+T content. It rises with a slope of 0.13013.

If you try to envision evolution taking an organism from one location on this graph to another, you can imagine that GC-to-AT transitions will move an organism to the right, whereas AT-to-GC transitions will move it to the left. To a first approximation (only!) we can say that horizontal movement on this graph essentially represents the net effect of transitions.

Vertical movement on this graph clearly involves transversions, because a net change in relative A+G content implies nothing less. To a very good first approximation, vertical movement in the graph corresponds to transversions.

Therefore, a good approximation of the relative rate of transitions versus transversions is given by the inverse of the slope. The value comes to 1.0/0.13013, or κ = 7.6846.

In an earlier post, I presented a graph like the one above applicable to mitochondrial DNA (N=203 mitochondrial genomes), which had a slope of 0.06702. Taking the inverse of that slope, we get a value of κ =14.92, which is in excellent agreement with Tamura and Nei's estimate of 15 for mitochondrial κ.

When I made a purine plot using plant and animal virus genomes (N=536), the rise rate (slope) was 0.23707, suggesting a κ value of 4.218. This agrees well with the transition/transversion rate for hepatitus C virus (as measured by Machida et al.) of 1.5 to 7.0 depending on the gene.

In short, we get very reasonable estimates of κ from calculations involving the slope of the A+G vs. A+T graph, across multiple domains.

The main methodological proviso that applies here has to do with the fact that technically, some horizontal movement on the graph can be accomplished with transversions (AT-to-CG, for example). We made a simplifying assumption that all horizontal movement was due to transitions. That assumption is not strictly true (although it is approximately true, since transitions do outnumber transversions; and some transversions, such as AT<-->TA and GC<-->CG, have no effect on genome A+T content). Bottom line, my method of estimating κ probably overestimates κ somewhat, by including a small proportion of AT<-->CG transversions in the numerator. Even so, the estimates agree well with other estimates, tending to validate the general approach.

I invite comments from knowledgeable specialists.

The Trouble with Darwin

As a biologist, I find Darwin's theory hugely disappointing. It's better than the alternative (which is to believe in magic, basically), but not by much, sadly.

Charles Darwin died before Mendel
proved the existence of genes.

As scientific theories go, the theory of evolution is easily the weakest of all major scientific theories. It's a commendable piece of work in its ability to stir discussion, but terrible in most other ways.

To be useful, a scientific theory has to do a minimum of two things: explain what can be observed, and provide testable predictions. Darwin's theory is weak on the first count and useless on the second.

Evolutionary theory explains practically nothing, because every explanation of the theory is rooted in "survival of the fittest," which is a circular notion, utterly content-free. "Fittest" means most able to survive. Survival of the fittest means survival of those who survive.

Ironically, Darwin's landmark work was called On the Origin of Species. Yet it doesn't actually explain speciation, except in the most vacuous and speculative of terms. Of course, we can't set too high an expectation for Darwin, since he didn't live to see the publication of Mendel's work (the word "genetics" wouldn't exist until more than 20 years after Darwin's death), but still. Speciation is portrayed by Darwin as the outcome of the accumulation of small, gradual changes. That's all the explanation he offers.

But the explanation is wrong. Or at least it doesn't accord well with the facts. It doesn't explain the Cambrian Explosion, for example, or the sudden appearance of intelligence in hominids, or the rapid recovery (and net expansion!) of the biosphere in the wake of at least five super-massive extinction events in the most recent 15% of Earth's existence.

One of the most frustrating aspects of evolutionary theory (this is no fault of the theory's, though) is that it is so hard to test in the laboratory. The fact is, no one has ever seen speciation happen in the laboratory, under repeatable conditions, and until that happens we're at a distinct disadvantage for understanding speciation. (Incidentally, I don't count plant hybridization or breeding anomalies in fruit flies whose sexuality is under the control of microbial endosymbionts as examples of speciation.)

When I was in school, we were taught that mutations in DNA were the driving force behind evolution, an idea that is now thoroughly discredited. The overwhelming majority of non-neutral mutations are deleterious (they reduce, not increase, survival). Most mutations lead to loss of function (this is easily demonstrated in the lab), not gain of function. Evolutionary theory is great at explaining things like the loss of eyesight by cave-dwelling creatures (e.g., bats). It's terrible at explaining gain of function.

Even if mutations were capable of driving evolution, they simply don't happen fast enough to account for observed rates of speciation. In bacteria, the measured rate of 16S rRNA divergence due to point mutations is only 1% per 50 million years. And yet, there were no flowering plants on earth as recently as 150 million years ago! Does it take a biologist to see the disconnect?

I bring all this up because I've spent some time recently doing genomics research aimed at exploring mechanisms for new-protein creation/differentiation (mechanisms not relying wholly nor even mainly on point mutations), and I wanted to set the stage for discussing that research here. Over the next week or so, I'll be presenting some new ideas and findings. Hopefully, we can put some much-needed flesh on Darwin by exploring testable notions of how new protein motifs can arise quickly (without reliance on magic).

Parsing the DNA Crazy Quilt

A measure of how little we know about the real-world workings of evolution is that science still can't explain why some organisms have huge imbalances in the chemical composition of their DNA. If you look at the genome of Clostridium botulinum (the botulism germ), 72% of the bases in its DNA are either 'A' or 'T': adenine or thymine. (The four possibilities are, of course, adenine, thymine, guanine, and cytosine.) Conversely, you can find many examples of organisms in which the DNA is mostly 'G' or 'C.' The question is why A, T, G, and C don't occur in roughly equal proportions (which is what you'd expect after millions of years of genetic averaging; you'd expect some sort of regression to the mean).

Just to give you an idea of what GC/AT imbalance really looks like, here's the gene for the enzyme adenine deaminase from Clostridium botulinum, with all the A and T values in red:

ATGTATAAAAATATACAAAGAGAAATCTATAAAAATACAAAAGGAGACGGGGATATGTTTAATAAATTTGATACAAAGCCTCTTTGGGAGGTAAGTAAA ACTTTATCAAGTGTAGCACAGGGGCTTGAACCGGCTGATATGGTTATTATAAATTCAAGGCTTATAAATGTCTGTACAAGAGAAGTCATAGAAAACACA GATGTAGCAATTAGCTGTGGAAGAATTGCTTTAGTAGGTGATGCAAAACATTGCATAGGGGAAAACACAGAGGTAATTGATGCAAAAGGACAATATATT GCACCAGGTTTTTTAGATGGTCATATTCATGTTGAATCATCAATGTTAAGTGTAAGCGAATATGCTCGTTCAGTAGTTCCACATGGTACTGTCGGAATA TATATGGATCCACATGAAATTTGTAATGTACTCGGATTAAATGGTGTACGTTATATGATTGAAGATGGCAAGGGTACTCCACTTAAAAATATGGTGACC ACACCATCCTGTGTACCAGCAGTTCCAGGTTTTGAAGATACAGGAGCGGCTGTAGGACCAGAAGATGTTAGAGAAACAATGAAGTGGGATGAAATAGTT GGATTAGGAGAAATGATGAACTTCCCAGGTATACTTTATTCTACAGATCATGCTCATGGAGTAGTAGGAGAAACTTTAAAAGCTAGTAAAACAGTAACA GGACATTATTCTTTACCTGAAACAGGAAAAGGATTAAATGGATATATTGCATCAGGTGTAAGATGTTGTCATGAATCCACAAGAGCGGAAGATGCTCTT GCTAAAATGCGCCTTGGAATGTATGCAATGTTTAGAGAAGGATCTGCATGGCATGACTTAAAGGAAGTAAGTAAAGCCATTACAGAAAATAAGGTAGAT AGTAGATTTGCTGTTTTAATATCTGATGATACTCACCCACACACATTGCTTAAGGATGGACATTTAGATCATATTATAAAACGTGCTATAGAAGAAGGG ATAGAGCCATTAACTGCAATTCAAATGGTAACAATAAATTGTGCACAATGTTTCCAAATGGATCATGAATTAGGTTCTATAACTCCAGGAAAATGTGCA GATATTGTATTTATAGAAGATTTAAAAGATGTAAAAATAACAAAGGTTATTATAGATGGAAATTTAGTTGCAAAGGGTGGACTATTAACTACTTCAATA GCTAAATATGATTATCCTGAAGATGCTATGAATTCAATGCATATTAAGAATAAAATAACACCAGATTCCTTTAATATTATGGCTCCTAATAAAGAAAAA ATAACTGCAAGGGTTATTGAAATTATACCTGAAAGAGTTGGTACATATGAGAGACATGTTGAACTTAATGTTAAAGATGATAAAGTTCAATGTGATCCA AGTAAAGATGTTTTAAAAGCAGTTGTATTTGAAAGACACCATGAAACAGGAACAGCAGGATATGGTTTTGTTAAAGGTTTTGGTATTAAGAGAGGAGCT ATGGCTGCAACAGTTGCCCATGATGCTCACAACTTATTAGTTATAGGAACAAATGATGAAGATATGGCATTAGCTGCTAATACATTAATAGAATGTGGT GGAGGAATGGTAGCCGTACAAGATGGTAAAGTATTAGGCTTAGTTCCATTACCAATAGCAGGACTTATGAGTAATAAGCCTTTAGAAGAAATGGCAGAA ATGGTAGAAAAACTAGATAGTGCATGGAAAGAAATAGGATGTGATATAGTTTCACCATTTATGACAATGGCACTTATTCCACTTGCCTGCCTACCAGAA TTAAGACTAACTAATAGAGGGTTAGTTGATTGTAATAAGTTTGAATTTGTATCATTATTTGTAGAAGAATAA

View gene at FastaView.


The organism Actinomyces oris (which occurs in the film that builds up on teeth) has an adenine deaminase gene that looks like this:

ATGGCCGATCAACCGTCCGCAGACCTGCTTATCAAGGACGCGCGCATCGTCCCTTTCCGGTCCCGTACCGAACTGGGTGCGCTGCGCCGAGGTGACCCT CACCCCGGCGCCTTGGCCGCGCCGCCGCCCCCGGGTGAGCCCGTGGATGTGCGTATCAAGGCGGGCCGGGTCGTCGAGGTGGGACAGGGGCTGAGTGCT CCCGGGACACGGGTCCTTGAGGCCGAGGGCTCCTTCCTCATTCCCGGCCTGTGGGACGCTCACGCCCACCTGGACATGGAGGCGGCGCGCTCGGCACGC ATCGACACGCTGGCCACCCGCAGCGCGGAGGAGGCCCTGGAGCTGGTGGCACGGGCGCTGCGGGATCATCCGGCCGGTTCGCCTCCGGCCACGATCCAG GGCTTCGGGCACCGCCTGTCCAACTGGCCCCGGGTGCCCACGGTGGCCGAGCTCGACGCCGTCACCGGGGAGGTTCCCACGCTGCTCATCTCCGGGGAC GTGCACTCCGGGTGGCTGAACTCGGCGGCGCTGCGTGTCTTCGGCCTGCCGGGGGCCAGCGCCCAGGACCCGGGAGCACCGATGAAGGAGGACCCGTGG TTCGCCCTACTCGACCGCCTCGATGAGGTCCCGGGGACACGCGAGCTGCGGGAGTCCGGCTACCGACAGGTCCTGGCCGACATGCTGTCCCGGGGCGTC ACCGGCGTGGTGGACATGAGCTGGTCGGAGGATCCCGATGACTGGCCGCGGCGCCTGCGGGCCATGGCGGACGAGGGCGTACTCCCCCAGGTGCTGCCC CGCATCCGCATCGGGGTCTACCGCGACAAGCTGGAACGGTGGATCGCCCGGGGCCTGCGCACCGGGACCGCGCTGGCAGGCTCACCCCGCCTGCCCGAC GGTTCCCCGGTGCTGGTGCAGGGGCCGCTCAAGGTGATCGCAGACGGCTCGATGGGCTCGGGCAGCGCACACATGTGCGAGCCCTATCCCGCCGAGCTG GGCCTGGAGCACGCCTGCGGCGTGGTCAACATCGACCGGGCCGAGCTCACCGACCTCATGGCCCACGCCTCCCGGCAGGGTTATGAGATGGCCATCCAC GCCATCGGGGACGCGGCGGTCGACGACGTCGCCGCGGCCTTCGCGCACTCGGGTGCCGCCGGGCG

For whatever reason (and that's the point: we have no idea why), Actinomyces has chosen an AT-poor dialect for its DNA, even though it has to make many of the same types of genes as Clostridium.

Some people don't see this as a major puzzle: One organism evolved its DNA to a super-AT-rich state, another one didn't. So what? It's all random drift.

I disagree. It's not drift. We know of two strong forces that should keep organisms like Actinomyces from developing high G+C content. First is "AT pressure." It's known that mutations naturally tend to go in the GC-->AT direction. (One study found that in Salmonella typhimurium, GC-->AT mutations outnumbered AT-->GC mutations 50 to 1.) In the absence of corrective measures, natural mutations would very quickly lead all organisms in the direction of DNA with a very low G+C content.

A second important force is that of lateral gene transfer, which we know is common in microorganisms; common enough, certainly, to "even out" GC/AT ratios over evolutionary timescales. Random uptake of foreign genes by cells should tend to make A, G, C, and T levels equal, over time. For organisms like Clostridium and Actinomyces (and many others), this clearly hasn't happened.

In an earlier post I mentioned one possible reason organisms drift away from the 50-50 GC/AT centerline. DNA replication is more efficient when the template is biased toward one extreme (GC) or the other (AT), assuming endogenous nucleotide levels can be regulated in a similarly biased fashion (which they presumably are, in these organisms).

One might speculate that GC/AT extremism also simplifies DNA maintenance and repair. Imagine that your DNA is 70% G+C. A super-simple DNA repair tactic for deaminated purines would be to just replace every defective purine with a guanine. Seven out of ten times, blind replacement of defective purines with guanine would be the correct repair, if you're Actionymyces. And one out of three times, mistakes wouldn't matter anyway, because high-GC codons tend to be fourfold degenerate. (In a fourfold degenerate codon, you can replace the third base with anything—A, G, C, or T—without changing the codon's meaning.) Blind guanine substitution would have a better than 80% success rate in a high-GC organism that needed to replace defective purines.

It turns out there are other reasons to live "away from centerline," if you're a bacterium. I'll talk about those in another post.