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Showing posts with label molecular genetics. Show all posts
Showing posts with label molecular genetics. Show all posts

Why Mitochondrial DNA is Different

Most genomes that are high in A+T content (or low in G+C content) show a surprising DNA strand asymmetry: The message strand of genes tends to be rich in purines. This rule applies across all domains I've looked at except mitochondria, where message strands tend to be pyrimidine-rich rather than purine-rich. The following two graphs makes this clearer.


This is a graph of message-strand (or RNA-synonymous-strand) purine content plotted vertically, against A+T plotted horizontally, for 1,373 bacterial species. Each dot represents a genome. High-GC/low-AT organisms like Streptomyces and Bordetella are on left and low-GC/high-AT organisms like Clostridium botulinum are toward the right. The few dots on the far right are intracellular endosymbionts that have lost a good bit of DNA over the millennia. They tend to be extremely high in A+T.

Compare the above graph with the graph below, which is the same thing (message-strand A+G vs. A+T) for mitochondrial DNA (N=2543 genomes). There is still an upward slope to the data (and in fact it is steeper than it looks, because the range of y-values is different in the graph below than in the graph above). The slope of the regression line is very nearly the same (0.148 vs. 0.149) for both graphs. But you can see that in the graph below, nearly all the points are below y = 0.50. That means message-strands are high in pyrimidines rather than purines.



I speculated in a previous post that the reason mitochondrial DNA is pyrimidine-heavy on the message strand is that mtDNA encodes a very small number of proteins (13, in all), and they tend to be membrane-associated proteins, which use mostly non-polar amino acids. It turns out that codons for the non-polar amino acids are pyrimidine-rich.

To see if that's really what's going on, I obtained the DNA sequences for cytochrome-c oxidase and NADH dehydrogenase (the two must fundamental enzyme systems of mitochondria) from several hundred bacterial species. Actually, I was able to obtain DNA sequences for a total of 942 bacterial NADH dehydrogenase (subunit L) proteins. I also succeeded in obtaining DNA sequences for 647 bacterial cytochrome-c oxidase subunit 1 proteins. In mitochondria, these genes are known as ND5 and Cox1. In bacteria they're better known as nuoL and cyoB.

The graph below shows A+G for the two enzymes versus whole-chromosome A+T, for the relevant organisms.

Message strand purine content was derived from the DNA sequences of cyoB (pink) genes from 942 bacteria, and from nuoL (blue) genes from 647 bacterial species. The A+G values were plotted against host-organism whole-genome A+T content. All cyoB and nuoL sequences tended to be pyrimidine rich. But pyrimidine content was less for organisms with high A+T content. (Note the slightly positive slope of the regression line.)

The pink points are for cytochrome-c oxidase subunit 1 (cyoB) while the blue points are for NADH dehydrogenase subunit 5 (nuoL). Two things are worth noting. One is that the regression line is upward-sloping, meaning that as an organism's DNA gets richer in A+T content, the purine content on the message strand rises. This effect seems to be universal. The second thing to note is that almost all of the points in the graph lie below y = 0.5, as is the case for mitochondria. These two signature "mitochondrial" enzyme systems, critical to oxidative phosphorylation (in bacteria as well as higher organisms), do tend to use pyrimidine-rich codons—rendering the relevant genes pyrminidine-rich on the RNA-synonymous (message) strand of DNA. The hypothesis is upheld.

For you bio students, a bit of homework: You might want to think about why it is that membrane-associated proteins are rich in non-polar amino acids. (In human mitochondria, leucine and isoleucine are the most-used amino acids. Together they account for an amazing 30% of all amino acids used in mtDNA-encoded gene products.) Hint: Most membranes have a lipid bilayer, and lipids don't like water.
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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.

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