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Are there known chains of beneficial mutations?

Are there known chains of beneficial mutations?


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Are there known examples of chains of beneficial mutations?

What I mean by that is a mutation that leads to a series of mutations occurring after each other over a relatively short period of time and all of those being beneficial for the organism. And I mean also that there is some etiological impact of the prior mutations on the subsequent ones, that is any mutation in the chain will increase significantly the likelihood of occurrence of the next mutation in the chain. And if there are such chains then would those be considered as a mechanism for speeding up evolution?


Adaptive radiation

In evolutionary biology, adaptive radiation is a process in which organisms diversify rapidly from an ancestral species into a multitude of new forms, particularly when a change in the environment makes new resources available, creates new challenges, or opens new environmental niches.

The key-innovation that can lead a species to go through rapid adaptive evolution might well be an initial beneficial mutation. You might want to rad about these key-innovations at Salzburger et al. (2005).

As an example, much of the diversification in ciclid fishes is thought to be driven by jaw morphology driven by a few key genes (a ligand; bmp4, a receptor; bmpr1b and an antagonist; nog2; Brawand et al., 2014). While I don't know the literature well on the topic, it sounds plausible to me that a key mutation may have allowed for the diversity of jaw morphology to diversify (which lead lineages to diversify).

Note, I am aware, I focused my answer on diversification and not on single lineage adaptation per se but I do not know better examples (or a better concept) of a single mutation that could lead to a "chain of beneficial mutations".


Sickle-Cell Anemia: Example of a “Beneficial Mutation”?

Sickle-cell anemia is a genetic disease common to persons of West and Central African ancestry. It is characterized by severe anemia with symptoms of pallor, muscle cramps, weakness, and susceptibility to fatigue. Additional symptoms include heart enlargement, brain cell atrophy, and severe pain in the abdomen, back, head, and extremities (see diagram below). Many victims of sickle-cell anemia die before the age of twenty, though some survive past fifty.

Causes of symptoms seen in sickle-cell anemia victims (based on Starr and Taggart, p. 173

The symptoms of this disease arise from the tendency of the victim’s red blood cells to “sickle,” that is, to take on irregular shapes. These cells last for only half as long as normal red blood cells they tend to rupture and are destroyed by the body. In addition, sickled cells often get caught in the capillaries, the narrowest blood vessels, where they clump and produce a blockage. The resulting impaired circulation causes damage to various tissues, in many cases leading to premature death.

Normal (A) versus sickled (B) red blood cells (from McFadden and Keeton, p. 293)

What Causes Sickle-Cell Anemia?

The primary function of red blood cells is to carry oxygen from the lungs to all tissues of the body. Each red blood cell contains millions of molecules of hemoglobin, the protein that does the work of oxygen transport. The hemoglobin molecule is composed of four polypeptides (chains of amino acids): two alpha (α) chains, each with 141 amino acids, and two beta (β) chains, each with 146 amino acids. Each of these four polypeptide chains associates with one iron-containing heme group, which has the ability to reversibly bind a single oxygen molecule. Thus the hemoglobin molecule as a whole contains four heme groups and is able to transport four oxygen molecules.

Hemoglobin molecule (from Price, p. 333, based on Cerami and Peterson)

The cause of sickle-cell anemia is a “point mutation,” that is, the alteration of a single nucleotide base within the DNA of the gene coding for the beta-hemoglobin polypeptide. The sixth DNA triplet, CTC, has been changed to CAC (the nitrogenous base thymine is replaced by adenine in the mutant gene).

This mutation in the beta-hemoglobin gene results in the change of just a single amino acid within the polypeptide encoded by that gene. The sixth amino acid in beta-hemoglobin is normally glutamate, which has an acidic, very polar, “hydrophilic” (water-attracting) side chain. But in the mutant beta-hemoglobin this glutamate is replaced by valine, which has a nonpolar, “hydrophobic” (water-repelling) side chain. This valine at position six in the mutant polypeptide forms a “hydrophobic association” with the nearby valine at position one, causing the abnormal hemoglobin molecule (called “hemoglobin S”) to be less soluble in water than the normal version (called “hemoglobin A”).

As a result of this single altered amino acid in each of the two beta chains, the hemoglobin molecule, when deoxygenated, tends to polymerize and “crystallize” (sometimes irreversibly) into long fibrils, stiff rod-like structures that force the red blood cells containing them to twist into bizarre, jagged shapes. When this occurs, the hemoglobin can no longer load and unload oxygen efficiently even worse, the red blood cells become distorted and obstruct the capillaries. (Normal hemoglobin has a jelly-like consistency, allowing red blood cells to squeeze easily through the narrowest blood vessels.)

Sickle cell anemia — long crystallized fibrils (from Cerami and Peterson, p. 45)

How Can This Be An Example Of A Beneficial Mutation?

Normal individuals have two “good” genes for the beta chain of hemoglobin, one from each parent, in addition to two normal genes for the alpha chain. The genetic trait of normal hemoglobin is designated “HbA,” and the genotype of the individual is written “HbA HbA.” Such people are said to be homozygous for normal hemoglobin 100% of their hemoglobin will be normal.

Individuals with sickle-cell anemia have two mutant beta-hemoglobin genes their genotype for hemoglobin is written “HbS HbS.” These people are said to be homozygous for the mutated hemoglobin all of their hemoglobin will contain valine instead glutamate at the sixth position in each beta chain.

It is possible, however, for a child to inherit one of each version of the beta-hemoglobin gene. This heterozygous genotype is written “HbA HbS.” Such individuals are said to be “carriers” or to have “sickle-cell trait” their red blood cells produce about 60% normal hemoglobin and 40% mutant hemoglobin. Some of the cells will be sickled, but only 2 to 4% of the level seen in HbS homozygotes. Generally these people lead normal lives, encountering difficulty only in situations of very low oxygen tension, such as at very high altitudes or in situations of extremely strenuous exercise.

The mutation is not beneficial to those who have two copies of the mutated gene in their cells (the HbS homozygotes) they suffer greatly and often die before reaching reproductive age. But heterozygotes (HbA HbS) do receive a benefit: they are less likely to succumb to malaria. Many African infants with normal hemoglobin die of cerebral malaria, but those with sickle-cell trait have greater resistance.

The deadliest form of malaria is caused by the protist Plasmodium falciparum, which enters human blood when the person is bitten by a mosquito (genus Anopheles). The protist pathogen lodges in human red blood cells, but it decreases the pH of the cells by about 0.4 pH units, causing about 40% of infected cells to sickle. These deformed red blood cells are sequestered and phagocytized by immune system cells thus the protist is destroyed along with the sickled cells. Although this does not provide complete protection from malaria, it does lessen the severity of the disease.

Interestingly, women with sickle-cell trait appear to be more fertile than normal women. The reason for this is not known.

Does This Example Support Macroevolution?

In a limited sense, this mutation can indeed be said to be ”beneficial.” Heterozygotes (HbA HbS) are more likely to live to reproductive age and pass on their genes than are the HbA homozygotes. They receive significant protection from malaria, and in addition, heterozygote women seem to have greater fertility.

But the mutation is nonetheless a loss of information. The hemoglobin’s normal function is impaired, not improved, and the protection from malaria is simply an incidental side benefit — the pathogen happens to be destroyed along with the person’s own defective cells. This mutation does not introduce a new level of complexity there is no new functional information or novel structural feature for evolution to build on. Considered in itself, this mutation is destructive and harmful, as are so many others. It is difficult to see how any genetic change of this sort could lead to a true evolutionary advance.

Cerami, Anthony, and Charles M. Peterson. 1975. “Cyanate and Sickle-Cell Disease.” Scientific American 232(4):44-50.

Davis, Percival, and Dean H. Kenyon. 1993. Of Pandas and People: The Central Question of Biological Origins. (2nd edition). Dallas: Haughton Publishing Co.

Keeton, William T., and James L. Gould. 1986. Biological Science. (4th edition). New York: W. W. Norton & Co.

Lehninger, Albert L. 1975. Biochemistry: The Molecular Basis of Cell Structure and Function. (2nd edition). New York: Worth Publishers.

McFadden, Carol H., and William T. Keeton. 1995. Biology: An Exploration of Life. New York: W. W. Norton & Co.

McLeod, Kevin Curtis. 1982 (Jun). “The Sickle Cell Trait.” Creation Research Society Quarterly 19:19-26.

Price, Peter W. 1996. Biological Evolution. Orlando, FL: Saunders College Publishing (Harcourt Brace).

Raven, Peter H., and George B. Johnson. 1988. Understanding Biology. St. Louis: Times Mirror/Mosby College Publishing.

Starr, Cecie, and Ralph Taggart. 1989. Biology: The Unity and Diversity of Life. Belmont, CA: Wadsworth Publishing Co.


4 beneficial evolutionary mutations that humans are undergoing right now

Most random genetic changes caused by evolution are neutral, and some are harmful, but a few turn out to be positive improvements. These beneficial mutations are the raw material that may, in time, be taken up by natural selection and spread through the population. In this post, I'll list some examples of beneficial mutations that are known to exist in human beings.

Beneficial mutation #1: Apolipoprotein AI-Milano

Heart disease is one of the scourges of industrialized countries. It's the legacy of an evolutionary past which programmed us to crave energy-dense fats, once a rare and valuable source of calories, now a source of clogged arteries. But there's evidence that evolution has the potential to deal with it.

All humans have a gene for a protein called Apolipoprotein AI, which is part of the system that transports cholesterol through the bloodstream. Apo-AI is one of the HDLs, already known to be beneficial because they remove cholesterol from artery walls. But a small community in Italy is known to have a mutant version of this protein, named Apolipoprotein AI-Milano, or Apo-AIM for short. Apo-AIM is even more effective than Apo-AI at removing cholesterol from cells and dissolving arterial plaques, and additionally functions as an antioxidant, preventing some of the damage from inflammation that normally occurs in arteriosclerosis. People with the Apo-AIM gene have significantly lower levels of risk than the general population for heart attack and stroke, and pharmaceutical companies are looking into marketing an artificial version of the protein as a cardioprotective drug.

There are also drugs in the pipeline based on a different mutation, in a gene called PCSK9, which has a similar effect. People with this mutation have as much as an 88% lower risk of heart disease.

Beneficial mutation #2: Increased bone density

One of the genes that governs bone density in human beings is called low-density lipoprotein receptor-related protein 5, or LRP5 for short. Mutations which impair the function of LRP5 are known to cause osteoporosis. But a different kind of mutation can amplify its function, causing one of the most unusual human mutations known.

This mutation was first discovered fortuitously, when a young person from a Midwest family was in a serious car crash from which they walked away with no broken bones. X-rays found that they, as well as other members of the same family, had bones significantly stronger and denser than average. (One doctor who's studied the condition said, "None of those people, ranging in age from 3 to 93, had ever had a broken bone.") In fact, they seem resistant not just to injury, but to normal age-related skeletal degeneration. Some of them have benign bony growths on the roof of their mouths, but other than that, the condition has no side effects - although, as the article notes dryly, it does make it more difficult to float. As with Apo-AIM, some drug companies are researching how to use this as the basis for a therapy that could help people with osteoporosis and other skeletal diseases.

Beneficial mutation #3: Malaria resistance

The classic example of evolutionary change in humans is the hemoglobin mutation named HbS that makes red blood cells take on a curved, sickle-like shape. With one copy, it confers resistance to malaria, but with two copies, it causes the illness of sickle-cell anemia. This is not about that mutation.

As reported in 2001 (see also), Italian researchers studying the population of the African country of Burkina Faso found a protective effect associated with a different variant of hemoglobin, named HbC. People with just one copy of this gene are 29% less likely to get malaria, while people with two copies enjoy a 93% reduction in risk. And this gene variant causes, at worst, a mild anemia, nowhere near as debilitating as sickle-cell disease.

Beneficial mutation #4: Tetrachromatic vision

Most mammals have poor color vision because they have only two kinds of cones, the retinal cells that discriminate different colors of light. Humans, like other primates, have three kinds, the legacy of a past where good color vision for finding ripe, brightly colored fruit was a survival advantage.

The gene for one kind of cone, which responds most strongly to blue, is found on chromosome 7. The two other kinds, which are sensitive to red and green, are both on the X chromosome. Since men have only one X, a mutation which disables either the red or the green gene will produce red-green colorblindness, while women have a backup copy. This explains why this is almost exclusively a male condition.

But here's a question: What happens if a mutation to the red or the green gene, rather than disabling it, shifts the range of colors to which it responds? (The red and green genes arose in just this way, from duplication and divergence of a single ancestral cone gene.)

To a man, this would make no real difference. He'd still have three color receptors, just a different set than the rest of us. But if this happened to one of a woman's cone genes, she'd have the blue, the red and the green on one X chromosome, and a mutated fourth one on the other. which means she'd have four different color receptors. She would be, like birds and turtles, a natural "tetrachromat", theoretically capable of discriminating shades of color the rest of us can't tell apart. (Does this mean she'd see brand-new colors the rest of us could never experience? That's an open question.)

And we have evidence that just this has happened on rare occasions. In one study of color discrimination, at least one woman showed exactly the results we would expect from a true tetrachromat.


Increased amounts of DNA don&rsquot mean increased function

Biologists have discovered a whole range of mechanisms that can cause radical changes in the amount of DNA possessed by an organism. Gene duplication, polyploidy, insertions, etc., do not help explain evolution, however. They represent an increase in amount of DNA, but not an increase in the amount of functional genetic information&mdashthese mechanisms create nothing new. Macroevolution needs new genes (for making feathers on reptiles, for example), yet Scientific American completely misses this simple distinction:

Moreover, molecular biology has discovered mechanisms for genetic change that go beyond point mutations, and these expand the ways in which new traits can appear. Functional modules within genes can be spliced together in novel ways. Whole genes can be accidentally duplicated in an organism&rsquos DNA, and the duplicates are free to mutate into genes for new, complex features. [SA 82]

In plants, but not in animals (possibly with rare exceptions), the doubling of all the chromosomes may result in an individual which can no longer interbreed with the parent type&mdashthis is called polyploidy. Although this may technically be called a new species, because of the reproductive isolation, no new information has been produced, just repetitious doubling of existing information. If a malfunction in a printing press caused a book to be printed with every page doubled, it would not be more informative than the proper book. (Brave students of evolutionary professors might like to ask whether they would get extra marks for handing in two copies of the same assignment.)

Duplication of a single chromosome is normally harmful, as in Down&rsquos syndrome. Insertions are a very efficient way of completely destroying the functionality of existing genes. Biophysicist Dr Lee Spetner in his book Not By Chance analyzes examples of mutational changes that evolutionists have claimed to have been increases in information, and shows that they are actually examples of loss of specificity, which means they involved loss of information (which is to be expected from information theory).

The evolutionist&rsquos &lsquogene duplication idea&rsquo is that an existing gene may be doubled, and one copy does its normal work while the other copy is redundant and non-expressed. Therefore, it is free to mutate free of selection pressure (to get rid of it). However, such &lsquoneutral&rsquo mutations are powerless to produce new genuine information. Dawkins and others point out that natural selection is the only possible naturalistic explanation for the immense design in nature (not a good one, as Spetner and others have shown). Dawkins and others propose that random changes produce a new function, then this redundant gene becomes expressed somehow and is fine-tuned under the natural selective process.

This &lsquoidea&rsquo is just a lot of hand-waving. It relies on a chance copying event, genes somehow being switched off, randomly mutating to something approximating a new function, then being switched on again so natural selection can tune it.

Furthermore, mutations do not occur in just the duplicated gene they occur throughout the genome. Consequently, all the deleterious mutations in the rest of the genome have to be eliminated by the death of the unfit. Selective mutations in the target duplicate gene are extremely rare&mdashit might represent only 1 part in 30,000 of the genome of an animal. The larger the genome, the bigger the problem, because the larger the genome, the lower the mutation rate that the creature can sustain without error catastrophe as a result, it takes even longer for any mutation to occur, let alone a desirable one, in the duplicated gene. There just has not been enough time for such a naturalistic process to account for the amount of genetic information that we see in living things.

Dawkins and others have recognized that the &lsquoinformation space&rsquo possible within just one gene is so huge that random changes without some guiding force could never come up with a new function. There could never be enough &lsquoexperiments&rsquo (mutating generations of organisms) to find anything useful by such a process. Note that an average gene of 1,000 base pairs represents 4 1000 possibilities&mdashthat is 10 602 (compare this with the number of atoms in the universe estimated at &lsquoonly&rsquo 10 80 ). If every atom in the universe represented an &lsquoexperiment&rsquo every millisecond for the supposed 15 billion years of the universe, this could only try a maximum 10 100 of the possibilities for the gene. So such a &lsquoneutral&rsquo process cannot possibly find any sequence with specificity (usefulness), even allowing for the fact that more than just one sequence may be functional to some extent.

So Dawkins and company have the same problem as the advocates of neutral selection theory. Increasing knowledge of the molecular basis of biological functions has exploded the known &lsquoinformation space&rsquo so that mutations and natural selection&mdashwith or without gene duplication, or any other known natural process&mdashcannot account for the irreducibly complex nature of living systems.

Yet Scientific American has the impertinence to claim:

Comparisons of the DNA from a wide variety of organisms indicate that this [duplication of genes] is how the globin family of blood proteins evolved over millions of years. [SA 82]

This is about the vital red blood pigment hemoglobin that carries the oxygen. It has four polypeptide chains and iron. Evolutionists believe that this evolved from an oxygen-carrying iron-containing protein called myoglobin found in muscles, which has only one polypeptide chain. However, there is no demonstration that gene duplication plus natural selection turned the one-chained myoglobin into the four-chained hemoglobin. Nor is there any adequate explanation of how the hypothetical intermediates would have had selective advantages.

In fact, the proposed evolution of hemoglobin is far more complicated than Scientific American implies, though it requires a little advanced biology to understand. The &alpha- and &beta-globin chains are encoded on genes on different chromosomes, so they are expressed independently. This expression must be controlled precisely, otherwise various types of anemia called thalassemia result. Also, there is an essential protein called AHSP (alpha hemoglobin stabilizing protein) which, as the name implies, stabilizes the &alpha-chain, and also brings it to the &beta-chain. Otherwise the &alpha-chain would precipitate and damage the red blood cells.

AHSP is one of many examples of a class of protein called chaperones which govern the folding of other proteins. 3 This is yet another problem for chemical evolutionary theories&mdashhow did the first proteins fold correctly without chaperones? And since chaperones themselves are complex proteins, how did they fold? 4

Identifying information-increasing mutations may be a small part of the whole evolutionary discussion, but it is a critical &lsquoweak link&rsquo in the logical chain. PBS, Scientific American, and every other pro-evolution propaganda machine have failed to identify any evidence that might strengthen this straw link.


MODEL AND ANALYSIS

The notations used are summarized in Table 1.

The modifier of mutation rate: There is a randomly mating population of 2N haploid individuals. The population is polymorphic at a modifier locus that affects the genome-wide mutation rate. The deleterious mutation rate per genome, per generation, is U in genomes containing the Q allele and U + ΔU in genomes containing the P allele, which is rare. The mean mutation rate is Ū = U + pΔU, where q and p are the frequencies of the two alleles. The beneficial mutation rate is proportional to the deleterious mutation rate. This haploid model can be easily generalized to randomly mating diploids, because the P allele is rare and so PP homozygotes are vanishingly rare it should be noted, however, that the definition of U remains as per haploid genome.

Frequently used notations

The fitness of genomes carrying the P allele, relative to genomes carrying the Q allele, is written W. For a modifier of small effect, W is close to unity, and so ln W is approximately the effective net selection coefficient favoring the P allele. The notation of fitness is used to avoid confusion with the selection coefficients for beneficial and deleterious mutations (sb and sd, see below). The term fitness is used to describe the effect of beneficial mutations, even though they will cause p to increase and decrease in a stochastic manner. I am considering a long-term limit expectation of the change in p, such that E ( p ( t ) ) = W t p ( 0 ) for large t , where time, t, is measured in generations, and E() stands for the expectation of a random variable. Because the main interest is determining the conditions under which P will spread (i.e., when W > 1), rather than an exact description of the dynamics at the modifier locus, this definition of fitness is compatible with the restriction that p is small.

If evolutionary forces are weak then, to a good approximation, we have ln W ≈ ln W d + ln W b + ln W c , assuming that the indirect effects of deleterious mutations (Wd) and of beneficial mutations (Wb), and the direct effects on fitness (Wc or cost), act multiplicatively. This approximation holds only for a modifier of small effect because of second-order interactions between these effects. For example, the fixation probability of a beneficial mutation is reduced in the higher mutation rate background, because of its association with a greater number of deleterious mutations (C harlesworth 1994 P eck 1994 B arton 1995).

Deleterious mutations: The occurrence of deleterious mutations is assumed to be adequately described by a deterministic process. The net effect can then be represented as constant indirect selection at the modifier locus, which for a modifier of small effect will be proportional to ΔU. The precise relationship can be determined for any particular model of deleterious mutation.

For example, consider a model (K imura and M aruyama 1966) that takes the limiting case of an infinite number of unlinked loci segregating for infinitesimally rare alleles. Selection occurs before mutation, both in the haploid phase of the life cycle. In the case where each deleterious mutation has an equal, multiplicative, effect on fitness of (1 – sd), an exact expression for the reduction in log fitness experienced by a rare neutral modifier was derived by D awson (1999), ln W d = − Δ U s d 1 + s d . A similar but approximate result, for small ΔU, was obtained by K ondrashov (1995).

In a large population (i.e.,2Nsd > 1) with no recombination, any individual carrying more than the minimum number of deleterious mutations ultimately leaves no descendants (F isher 1930, p. 136), and so ln W d = − Δ U . This result was also obtained from deterministic analyses of population genetic models incorporating modifier loci (K imura 1967 L eigh 1973).

Here, I use a result derived by L eigh (1973) for a two-locus model with arbitrary linkage to estimate ln Wd for deleterious mutations randomly scattered over a genetic map of n chromosomes, each of length M morgans. By analyzing a model in which both mutation and selection are deterministic processes, L eigh (1973) obtained an equation for the strength of indirect selection on a modifier, which increases the mutation rate at a single linked locus by Δμ. His analysis of a continuous-time model assumes that the linkage disequilibrium between the modifier and the selected locus changes rapidly relative to the allele frequency of the modifier. This quasi-linkage equilibrium approach is appropriate for a modifier of small effect and yields d p d t ≈ − p ( 1 − p ) Δ μ s d s d + r .

A similar result has been derived by K imura (1967). A more general result for a deterministic multi-locus model has been derived by K. J. D awson (unpublished results). Dawson's analysis further demonstrates that, if there is no epistasis in log fitness between deleterious mutations, then linkage disequilibrium between them is only generated because a modifier segregates in the population. The linkage disequilibrium is of order (ΔU) 2 when ΔU is small, the individual effects on the modifier therefore combine multiplicatively, to a good approximation.

Now consider deleterious mutations scattered randomly over a genome of n chromosomes, each of length M morgans. A deleterious mutation is unlinked to the modifier with probability (n – 1)/n, and otherwise the map distance, z, between it, and a modifier in the middle of a chromosome is a random variable with a uniform distribution on [0, M/2]. This gives ln W d ≈ − Δ U × ( 2 n M ∫ z = 0 M ∕ 2 s d s d + r ( z ) d z + ( n − 1 ) n s d s d + 1 2 ) (1a) ≈ − Δ U 2 s d < 1 + ln ( 1 ∕ 2 s d ) n M >, (1b) where r(z) is the recombination probability obtained from z by using H aldane 's (1919) mapping function, r(z) = ½ (1 – e –2 z ). The quantity contained in braces in Equation 1b describes the increase over the free linkage (nM → ∞) case. Equation 1b is obtained from Equation 1a in the limiting case where sd ⪡ 1 and M ⪢ 1 and is surprisingly accurate for almost all plausible values of these parameters. The approximation is least accurate when n = 1, but as long as sd < 0.1, the error is <2% for M > 2, and <11% for M > 1. The error is reduced for larger n it is roughly halved for n = 4. Note that, in the case of free recombination, this result differs by a factor of two from D awson 's (1999) analysis of the infinitesimally rare alleles model, where mutation occurs after selection, and hence each deleterious mutation has a 50% chance of being separated from the modifier by recombination before selection acts on it.

Beneficial mutations: In this model, I consider only a single beneficial mutation to be segregating at any one time. However, as is seen below, in sexual populations only beneficial mutations that are tightly linked to the modifier locus and that are destined to be fixed have any role to play in the evolution of mutation rates, and so this is only a weak restriction on the total rate of beneficial mutations. Because the effect at the modifier locus depends on whether the beneficial mutation arises in the Q or the P background, which is a single random event, it is necessary to study the long-term dynamics over the course of many beneficial mutations, each sweeping through the population in turn. The approach is to calculate the expectation of the effect of a single beneficial mutation, and then to combine the individual effects to estimate the net effect.

Each beneficial mutation that is destined to be fixed is assumed to arise at a point in time such that it does not interfere with other beneficial mutations sweeping through the population. This allele, b, confers a selective advantage sb compared with the alternative allele B. It is assumed that stochastic effects are important only when b is rare (i.e.,2Nsb ⪢ 1). The probability of recombination between this locus and the modifier locus is r. For each beneficial mutation that arises, r is a random variable, and so the effect of many beneficial mutations can be found by taking the expectation of the effect of a single beneficial mutation over a distribution of values of r.

The rate of occurrence, in the whole population, of beneficial mutations that are destined to be fixed, is K per generation. K may implicitly be a function of 2N and Ū and may vary through time, depending on the model of adaptive evolution. If, for example, adaptation is limited by the rate of environmental change (as assumed by K aplan et al. 1989), then K would be independent of both 2N and Ū. Note that even if the delay between an environmental change and the ensuing beneficial mutations arising is a function of 2N and Ū, the overall rate of beneficial mutations remains independent of these parameters. The opposite extreme is a model of adaptation where there are very many loci at which beneficial mutations could potentially arise, so K would be proportional to both 2N and Ū. A model intermediate between these two extremes seems most likely to be realistic.

The hitchhiking effect is simply represented by the parameter h, which is the fraction by which the frequency of the allele not initially associated with the beneficial mutation is multiplied, as a net effect of the entire selective sweep. If, for example, b arises in the P background, then h = frequency of Q ( as Qb ) after b is fixed frequency of Q ( as QB ) before b arises . Previous work has concentrated on the effect of hitchhiking on neutral diversity. For a totally asexual population, h = 0. For sexual populations, the hitchhiking effect was first studied in by M aynard S mith and H aigh (1974), who derived an approximate expression for h. However, their analysis ignored stochastic fluctuations in the frequency of the b allele while it is rare. Taking this into account and conditioning on the ultimate fixation of b, B arton (1998) has found an exact expression for h in terms of gamma functions, 1 − h = ( 4 N e s b ) − r ∕ s b Γ ( 1 + r ∕ s b ) Γ ( 1 − r ∕ s b ) Γ ( 1 + 2 r ∕ s b ) (2) for r/sb < 1 and 4Nesb > 1. The dependence on Ne, the effective population size, arises because this is conditional on the fixation of b, which has probability 2sb (Ne/N). In sexual populations, the hitchhiking effect decreases with increasing population size, because of the greater number of generations (and hence recombination events) between a beneficial mutation arising and sweeping to fixation.

In the model studied here, the modifier allele is not neutral. However, the direct selection (ln Wc) and indirect selection due to deleterious mutations (ln Wd) are assumed to be weak relative to the selection acting on the beneficial mutation (sb), and so the result for a neutral modifier should be a sufficiently accurate approximation.

Effect of a single beneficial mutation: In this part of the analysis, q and p denote the modifier allele frequencies at the moment the beneficial allele b arises. I derive an expression for the expectation of p′, the frequency of the P allele after the b allele has swept to high frequency. Because the rate of beneficial mutation in each modifier background is proportional to the deleterious mutation rate, the probability of b arising in the Q background is qU/Ū, and in the P background is p(U + ΔU)/Ū. In the former case, p′= hp, and in the latter case p′= (1 – q′) = (1 – hq). Because h is a random variable, independent of which background the mutation arises on, E ( p ′ p ) = q U U ¯ E ( h ) + p ( U + Δ U ) U ¯ ( 1 − E ( h ) q ) p = ( 1 + Δ U U ¯ E ( 1 − h ) ( 1 − p ) ) . (3)

Net effect of a succession of beneficial mutations: Consider a series of x beneficial mutations arising at rate K over a total time t. I make use of the fact that the expectation of the product of independent random variables is the product of the expectations. While p is small, E(p/p) is independent of p, and hence of the outcome of previous events. In this case t ln W b = ln ( E ( p ′ p ) x ) . Because xKt as t → ∞, using (3) we obtain ln W b = K ln ( 1 + Δ U U ¯ E ( 1 − h ) ) ≈ K Δ U U ¯ E ( 1 − h ) . (4)

For asexual populations (h = 0), Equation 4 is identical to a result derived by L eigh (1973). Although the linkage disequilibrium is much stronger in the model analyzed here, when the consequently larger effects are averaged over the different genetic backgrounds, the net effect is the same as in Leigh's model.

For sexual populations, L eigh (1973) tabulated values of (p′–p) for a range of r/sb found by approximate solution of similar equations to those used to study hitchhiking (M aynard S mith and H aigh 1974), but assuming deterministic mutation and hence weaker linkage disequilibrium. The result obtained here is much simpler and clearly shows the relationship between the indirect selection at the mutator locus and the mean magnitude of hitchhiking events in the population in question.

Expectation of the hitchhiking effect: The results obtained above depend on the expectation of (1 – h). For no recombination, this is equal to one, and hitchhiking events have maximum effect on the frequency of the modifier. For a sexual population, E(1 – h) can be estimated by assuming that the beneficial mutations that arise are randomly scattered over n chromosomes, each M morgans long. Only a small fraction of these mutations are likely to have any effect, because (1 – h) is insignificant unless r < sb. Unless the selective advantage of the b allele is very large, r is small enough for it to be reasonable to directly equate r with map distance rather than use H aldane 's (1919) mapping function (see N ordborg et al. 1996).

When Equation 2 is averaged over a distribution of r, the gamma functions in Equation 2 can be ignored to a good approximation if Nesb is large. This is because when r/sb ⪡ 1, the gamma functions are all approximately one, and when r/sb is larger, (4Nesb) – r / s b becomes very small. In the calculation that follows, the error in making this approximation is <3% when Nesb > 10 3 , and <15% when Nesb > 10 2 .

In the same way as for deleterious mutations, the probability that the modifier and a beneficial mutation are on the same chromosome is 1/n. When the map distance between the two is chosen from a uniform distribution on [0, M/2], the probability that r < sb is simply 2sb/M. In this case, r is uniformly distributed on the interval [0, sb], and the expectation of (1 – h) according to Equation 2 without the gamma functions is given by E ( 1 − h ∣ r < s b ) ≈ 1 s b ∫ r = 0 s b ( 4 N e s b ) − r ∕ s b d r = 1 − ( 4 N e s b ) − 1 ln ( 4 N e s b ) and therefore, for beneficial mutations scattered randomly over the entire genetic map and large Nesb E ( 1 − h ) ≈ 2 s b n M ln ( 4 N e s b ) . (5)

Direct selection on the modifier: The log-fitness of the P allele relative to the Q allele is a function of both U and ΔU. The component of this, due to differences in the direct fitness effects of the Q and P alleles, is ln Wc, which is also a function of both U and ΔU. Let w(U) be the fitness of an individual with mutation rate U, carrying the B allele and no deleterious mutations. Assume that there is no epistasis between the modifier alleles and any fitness-affecting mutations. Then, for a modifier of small effect, ln Wc is linear in ΔU, as follows: ln W c ( U , Δ U ) = ln w ( U + Δ U ) − ln w ( U ) ≈ d ln w ( U ) d U Δ U for small Δ U .

Although it is widely believed that increasing the fidelity of DNA replication is costly (S turtevant 1937 L eigh 1973 K irkwood et al. 1986 K ondrashov 1995), very little is known about the nature of such a cost. Here I assume that the direct selection results only from increasing costs of higher-fidelity replication or mutation repair. This cost approaches infinity for perfect fidelity (K irkwood et al. 1986, p. 5), and therefore fitness w is zero for U = 0. If the general form of the cost is as shown in Figure 2, then it would be reasonable to assume that w(U) asymptotically approaches some maximum as U increases. In this case, the derivative of the fitness function, d ln w(U)/dU, is a strictly positive, monotonically decreasing function of U. This is important in determining the existence and uniqueness of an evolutionarily stable mutation rate (ESS see below and Figure 3). It appears that it is not possible to make such a statement if the effect of the modifier is considered in relative (ΔU/U) rather than absolute (ΔU) terms.

Asexual populations: Although the model described here is a reasonable one with which to study the evolution of mutation rates in sexual populations, it is inappropriate for asexual populations. In a totally asexual population each beneficial mutation will cause a complete clonal replacement, and hence the restriction that p should remain small would be violated. Hypermutators (modifiers) increasing the rate of certain mutations by factors of up to a thousand have been found at low frequency in natural populations of the bacteria Escherichia coli and Salmonella enterica (L e C lerc et al. 1996). The rate of mutation at modifier loci themselves would be increased in a mutator phenotype, and hence a mutator allele coupled to a beneficial mutation stands an appreciable chance of back-mutation once at high frequency. This can result in ultimate fixation of a genotype combining the low mutation rate modifier with the beneficial mutation (T addei et al. 1997). In other words, clonal replacement need not occur, and the modifier that “caused” the beneficial mutation is not fixed, so h ≠ 0. Microorganisms maintained in continuous culture show population turnovers that are too rapid to be explained by sequential fixation of unique beneficial mutations (D ykhuizen 1990). A fundamentally different model such as the one studied by T addei et al. (1997) is clearly more appropriate. However, this would not allow easy comparison with results from the model used here for sexual populations. Therefore the treatment of asexual populations in this article is better regarded as a limiting case for sexual populations, as recombination rates approach zero.

—Data obtained by B essman et al. (1974) for polymerase extracted from bacteriophage T4 strains characterized as antimutator (left two points), wild type (central point), or mutator (right two points). The assay was made in equal concentrations of adenine triphosphate and its analogue, 2-aminopurine triphosphate. A base is turned over if it is temporarily polymerized into the DNA chain and then excised again as a monophosphate. This is costly in terms of time and energy.

The evolutionarily stable mutation rate: An ESS (see M aynard S mith 1982), Û, is defined here such that, given suitable genetic variation, natural selection will always move U toward Û. In the preceding sections, I derived an expression for ln W as a function of U and ΔU. Because the modifier is of small effect, this expression is linear in ΔU, and so we need consider only d ln W/dΔU. If this derivative is positive then modifiers increasing the rate of mutation are favored, and if it is negative then modifiers decreasing the rate of mutation are favored. At the ESS it will be zero and all modifiers (of small effect) are selectively neutral. A graph of d ln W/dΔU against U will therefore cross the U-axis, with a negative gradient, at the ESS.

—The ESS is the value of U where d ln W/dΔU (solid line) passes through the U-axis. The functions from the three contributing effects act additively, d ln Wb/dΔU (dotted line) from beneficial mutations, d ln Wd/dΔU (dashed line) from deleterious mutations, and d ln Wc/dΔU (dot-dashed line) from the direct fitness effect of the modifier. K = 0.01, sb = sd = 0.01, nM = 3, 2Ne = 10 4 . A cost function of appropriate shape was invented for illustrative purposes.

If the slope of this graph is instead positive at the point it crosses the U-axis, then all modifiers of small effect are still selectively neutral, so it is an evolutionary equilibrium. However, populations even a small distance away from this equilibrium will not move toward it, and hence it is not an ESS.

Because the components of ln W combine additively, they can be differentiated individually, and a necessary condition for the ESS can be written d ln W d Δ U = d ln W d d Δ U + d ln W b d Δ U + d ln W c d Δ U = 0 ⇐ U = U ^ . (6a) This is shown graphically in Figure 3. It is also useful to determine the ESS for the nonbiological case where there is no direct selection acting on the modifier, which I call the “neutral” ESS, Ûneutral. A necessary condition for this is simply d ln W d Δ U = d ln W d d Δ U + d ln W b d Δ U = 0 ⇐ U = U ^ neutral . (6b)

A general relationship between the indirect selection pressures due to beneficial and deleterious mutations: The result derived in this section relies only on the general form of the equations derived above and should therefore be robust to many of the specific assumptions made in this article (constant sd and sb, rare modifier). It requires only that K does not depend on Û, i.e., that adaptation is not mutation limited. Equation 1, in agreement with other analyses (K imura 1967 L eigh 1973 K ondrashov 1995 D awson 1999), states that the indirect selection on a modifier due to deleterious mutations is proportional to the absolute change in the mutation rate caused by that modifier, ΔU. This is likely to be true for (at least) all cases where deleterious mutations are modeled as a deterministic process, because the number of extra deleterious mutations associated with a mutator allele will vary with ΔU. Then, using D to represent a function of any of the model parameters except U and ΔU, we can write d ln W d d Δ U = D . (7a)

Equations 4 and 5 state that the indirect selection on a modifier caused by beneficial mutations is proportional to the relative change in the mutation rate caused by that modifier, ΔU/Ū. This is likely to be true for any model where beneficial mutations arise as a stochastic process with low fixed rate. This is because, given that a beneficial mutation arises, its subsequent effect on the dynamics at the modifier locus depends only on the probability that it arose in the modifier background, which depends only on ΔU/Ū (see Equation 3). Using B to represent a function of any of the model parameters except U and ΔU, we can write d ln W b d Δ U = B 1 U ¯ . (7b)

In all models where these two conditions (7a and 7b) are satisfied, it is possible to write an exact expression for the indirect selection caused by both beneficial and deleterious mutations combined, as a fraction of the indirect selection caused by deleterious mutations alone, as follows. In terms of B and D, the condition for the neutral ESS (6b) is B 1 U ^ neutral + D = 0 . Multiplying all the terms by Ûneutral/Ū gives B 1 U ¯ + U ^ neutral U ¯ D = 0 . Referring back to the definitions of B and D in Equations 7a and 7b gives the general result d ln W b d Δ U + d ln W d d Δ U = < 1 − U ^ neutral U ¯ >d ln W d d Δ U . (7c) Equation 7c is true for all values of Ū over which K remains constant. It describes the indirect selection on a modifier caused by both deleterious and beneficial mutations (for some value of Ū), in terms of the indirect selection caused by deleterious mutations alone (at that Ū). The term in braces depends only on Ū relative to the neutral ESS, Ûneutral. This equation summarizes indirect selection on a weak modifier of mutation rates. If Ū = Ûneutral, there is no net indirect selection. As Ū increases, the effect of beneficial mutations vanishes. As Ū approaches zero, the effect of beneficial mutations becomes increasingly important, although the restriction of constant K cannot hold when this limit is reached.


Are there known chains of beneficial mutations? - Biology

Mutations can be beneficial, neutral, or harmful for the organism, but mutations do not "try" to supply what the organism "needs." Factors in the environment may influence the rate of mutation but are not generally thought to influence the direction of mutation. For example, exposure to harmful chemicals may increase the mutation rate, but will not cause more mutations that make the organism resistant to those chemicals. In this respect, mutations are random — whether a particular mutation happens or not is unrelated to how useful that mutation would be.

For example, in the U.S. where people have access to shampoos with chemicals that kill lice, we have a lot of lice that are resistant to those chemicals. There are two possible explanations for this:

Hypothesis A: Hypothesis B:
Resistant strains of lice were always there — and are just more frequent now because all the non-resistant lice died a sudsy death. Exposure to lice shampoo actually caused mutations for resistance to the shampoo.

Scientists generally think that the first explanation is the right one and that directed mutations, the second possible explanation relying on non-random mutation, is not correct.

Researchers have performed many experiments in this area. Though results can be interpreted in several ways, none unambiguously support directed mutation. Nevertheless, scientists are still doing research that provides evidence relevant to this issue.

In addition, experiments have made it clear that many mutations are in fact random, and did not occur because the organism was placed in a situation where the mutation would be useful. For example, if you expose bacteria to an antibiotic, you will likely observe an increased prevalence of antibiotic resistance. Esther and Joshua Lederberg determined that many of these mutations for antibiotic resistance existed in the population even before the population was exposed to the antibiotic — and that exposure to the antibiotic did not cause those new resistant mutants to appear.

The Lederberg experiment
In 1952, Esther and Joshua Lederberg performed an experiment that helped show that many mutations are random, not directed. In this experiment, they capitalized on the ease with which bacteria can be grown and maintained. Bacteria grow into isolated colonies on plates. These colonies can be reproduced from an original plate to new plates by "stamping" the original plate with a cloth and then stamping empty plates with the same cloth. Bacteria from each colony are picked up on the cloth and then deposited on the new plates by the cloth.

Esther and Joshua hypothesized that antibiotic resistant strains of bacteria surviving an application of antibiotics had the resistance before their exposure to the antibiotics, not as a result of the exposure. Their experimental set-up is summarized below:

When the original plate is washed with penicillin, the same colonies (those in position X and Y) live — even though these colonies on the original plate have never encountered penicillin before.


Diversifying Selection

Sometimes two or more distinct phenotypes can each have their advantages and be selected for by natural selection, while the intermediate phenotypes are, on average, less fit. Known as diversifying selection (Figure 1c), this is seen in many populations of animals that have multiple male forms. Large, dominant alpha males obtain mates by brute force, while small males can sneak in for furtive copulations with the females in an alpha male’s territory. In this case, both the alpha males and the “sneaking” males will be selected for, but medium-sized males, which can’t overtake the alpha males and are too big to sneak copulations, are selected against. Diversifying selection can also occur when environmental changes favor individuals on either end of the phenotypic spectrum. Imagine a population of mice living at the beach where there is light-colored sand interspersed with patches of tall grass. In this scenario, light-colored mice that blend in with the sand would be favored, as well as dark-colored mice that can hide in the grass. Medium-colored mice, on the other hand, would not blend in with either the grass or the sand, and would thus be more likely to be eaten by predators. The result of this type of selection is increased genetic variance as the population becomes more diverse.

Practice Question

Figure 1. Different types of natural selection can impact the distribution of phenotypes within a population. In (a) stabilizing selection, an average phenotype is favored. In (b) directional selection, a change in the environment shifts the spectrum of phenotypes observed. In (c) diversifying selection, two or more extreme phenotypes are selected for, while the average phenotype is selected against.

In recent years, factories have become cleaner, and less soot is released into the environment. What impact do you think this has had on the distribution of moth color in the population?


Are there known chains of beneficial mutations? - Biology

Figure 1. The Central Dogma of Molecular Biology.

The central dogma of molecular biology

James Crick (cofounder of DNA’s secondary structure) proposed that DNA is an informational storage molecule capable of replicating itself. Further, he proposed that the information that was transmitted had to be “read” by a manufacturing body within the cell which put amino acids together in a specific sequence ultimately synthesizing a protein. This became known as the central dogma of molecular biology.

The central dogma predicts that DNA serves as a template for the direct synthesis of a messenger RNA (mRNA) molecule, in a process known as transcription. Secondly, mRNA is “read” at a ribosome by transfer RNAs (tRNAs) , which work together to assemble a specific chain of amino acids, which collectively assemble to generate a protein, a process is known as translation.

Figure 2. Retroviruses represent an exception to the central dogma.

Messenger RNA is just one of seven major different types of RNA. Some are also involved in protein synthesis (like transfer RNA). And DNA directly codes for these RNA molecules. So the information flow in this case would be simply DNA to RNA. The other major exception to this dogma, the information flow is reversed. Some viruses for example have genes composed of DNA. When these viruses infect a cell, the viral RNA synthesizes DNA. So in this way, the information flow would be from RNA to DNA. But even though there are exception to the dogma, the central dogma of molecular biology encompasses The most important flow of information for life. DNA codes for RNA and that RNA codes proteins.

RNA replication is the copying of one RNA to another. Many viruses replicate this way. The enzymes that copy RNA to new RNA, called RNA-dependent RNA polymerases, are also found in many eukaryotes where they are involved in RNA silencing. RNA editing, in which an RNA sequence is altered by a complex of proteins and a "guide RNA", could also be considered an RNA-to-RNA transfer.

Once biologists understood the dogma, they understood the general pattern of information flow in the cell. The next challenge was understand how the sequence of bases in a strand of messenger RNA code for the sequence of amino acids in a protein. What is the genetic code? What are the rules that specify the relationship between the sequence of nucleotides in DNA and the sequences of amino acids in a protein? George Gamow suggested a code based on logic. He suggested that each code word contains three bases. His reasoning was based on the observation that there are 20 amino acids.

Since there are only four unique nucleotides in DNA and 20 amino acids, a combination of base pairs was required to code for the amino acids. If an amino acid was based on a single nucleotide, then there would only be four amino acids. With the same logic, Gamow surmised that the code could not be represented by a combination of two nucleotides, because 4x4 is 16 and there are 20 amino acids. The code must be a three base code (or triplet code), because it is the simplest code that allows for the 20 known amino acids: 4 X 4 X 4 = 64. This suggests that there could be up to 64 unique amino acids. However, there are only 20 amino acids.

Figure 3. The genetic code. The triplet code mRNA directly codes for the assembly of amino acids that make up a protein. To identify the amino acid coded by the mRNA sequence, locate the mRNA triplet code (codon), the grey box to its right represents the corresponding amino acid. For example, CCC indicates the amino acid Proline (Pro).

There are far more possibilities of amino acids provided by a triplet code, than the number of amino acids (20) we see in nature. Therefore, it is said that the code is redundant, meaning that amino acids can be coded by more than one triplet code. For example, the triplet codes of CCU and CCC of mRNA code for the same amino acid: proline. In fact, all amino acids are coded by more than one triplet code except for methionine and tryptophan. Further investigations indicated that a specific triplet code always coded for the same amino acid. In other words, the code is unambiguous. For example, the triplet code of AUG in mRNA always codes for methionine. Amazingly, the code works exactly the same for all living organisms, from bacteria to plants and animals! While there are very few exceptions to this, the consistency of the code across widely variable organisms hints that we all stem from a single common ancestor. The code is universal. Lastly, the code is conservative. If the first two base pairs of the mRNA are the same but the third is different, there is a high likelihood (but not an absolute certainty), that the will code for the same amino acid.

The group of the three bases that species a particular amino acid is called a codon. And according to Gamow’s triplet hypothesis, each codon is made of three nucleotides. And each gene is defined by a start codon and a stop codon. The start codon has been identified, and it is the same start codon for every single gene of every single organism on Earth. In contrast, there are three stop codons.

Figure 4. Predicting polypeptide chains from DNA. In this example, the template strand of DNA (the strand that transcribes into RNA) is: 3' - TAC GTC TAG TCC ATC - 5'. This is transcribed into the mRNA strand: 5' - AUG CAG AUC AGG UAG- 3'. Consulting the amino acid chart (Fig. 4), we can predict the sequence of amino acids for this protein: methionine(START CODON)-glutamic acid-isoleucine-arginine-STOP.

Predicting proteins from DNA

Once the code was deciphered, any sequence of DNA can be read in order to determine the sequence of amino acids, or a polypeptide chain (Fig. 4). In eukaryotes, DNA is composed of many chromosomes. Each chromosome is made up of multiple genes (along with other non-transcribing regions). Each gene synthesizes an mRNA, which is then transcribed into a protein. For the segment of DNA that makes up a gene, only one strand synthesizes the mRNA, known as the template stand. The other strand of DNA doesn't synthesize mRNA is called the non-template strand, or more commonly the coding strand. The beginning of a gene is defined by the three bases of the template strand, TAC, which is transcribed into the start codon, AUG. So far as we know, all living organisms have the same start codon for every protein created. The next three deoxyribonucleic acids are transcribed into the next codon. In Fig. 4, the deoxyribonucleic acids (GTC) are transcribed into the mRNA codon (CAG), which is eventually translated into the amino acid, Glutamic acid. Repeat this sequence until you reach one of the three STOP codons which, as you will see below, does not code for an amino acid. Rather it codes for a termination factor which ends the process of translation, resulting in a protein.

Figure 5. Point mutations. Point mutations are a change to a single deoxyribonucleotide, which happens during a mismatch during DNA replication. Point mutations can affect the eventual protein by changing an amino acid in the polypeptide chain.

Mutations are permanent changes in an organism’s DNA, a modification in the cell’s information archive. Mutations are important in evolution, because they are the only know mechanism that actually creates new alleles. New alleles can create different proteins and consequently different cellular functionality, and serve as the origin of biodiversity. Mutations can either affect an organism’s fitness, an organism's ability to survive and reproduce, or not. Mutations increase fitness are termed beneficial, whereas those that decrease fitness are said to be deleterious. Mutations that have no affect on an organism’s fitness are said to be silent mutations. Most mutations are neutral or slightly deleterious. Mutations can be put into two categories. Point mutations are when a single nucleotide changes and chromosome-level mutations occur with the addition, deletion or modification of chromosome.

Figure 6. Genotypes determine phenotypes. A change in a single deoxyribonucleotide can change the sequence of amino acids, which can have an effect on the organism's phenotype.

Point mutations occur when DNA’s proofreading mechanism (DNA polymerase) fails to correct a mismatched base pair before the finalization of DNA replication, the process in which DNA copies itself. This results in a single base change in one of the newly synthesized DNA strands (Fig. 5). There are two resultant consequences of point mutations. Point mutations that result in change in the amino acid sequence are known as replacement mutations or missense mutations (Fig. 6). A change in the amino acid can (and often does) change the functionality of the protein it codes for, which can change the organisms fitness: either positively, negatively or not at all. Whereas, silent mutations are point changes that don’t change the amino acid sequence, because the DNA transcribes an mRNA which codes for the same amino acid as the original DNA strand. For example, if there was a change in the template strand of DNA from TAA (transcribed to the codon AUU) to TAT (transcribed as AUA), the resultant amino acid following translation would be isoleucine for both. Silent are most common when the third nucleotide in a codon is altered, highlighting the conservative property of the code.

In a species of mouse (Fig. 6), a point mutation occurred in the past at a single base pair, changing the final product of the protein resulting in a different phenotype of fur color, a missense mutation. Where the dark mouse has an arginine amino acid in its protein at a specific location, the white mouse has a cysteine. This single change in a DNA molecule is enough to cause a change in the phenotype of the mouse, and has caused members of the same species to live in different environments, a first step to becoming different species.

Figure 7. Chromosome-level mutations.

Chromosome-level mutations are major changes to the DNA of eukaryotes, with the addition, deletion or movement of segments of chromosomes or even entire chromosomes. Nearly all of these mutations occur as a mistake during nuclear division (either during meiosis or mitosis) and nearly all of them negatively affect an organism's fitness. Inversion is an alteration of a single chromosome's structure, when a segment of the chromosome is detached, inverted and reattached to the same chromosomes. All genes in the inverted section are no longer serve as a template for the original proteins they transcribed for. This is due to the sequence of nucleotides are complete reverse of the original DNA, and transcription only happens in a single direction. Translocation occurs when a segment of one chromosome is removed and reattached to another chromosomes. Potentially, the translocated genes can adequately transcribe so long as no inversion has occurred.

During the cell cycle, the chromosomes replicate and are separated into different nuclei during mitosis. Following mitosis the duplicate nuclei are separated into two cells. Sometimes mistakes happen during this process. Occasionally, the duplicated chromosomes never separate, leaving an duplicate copy of chromosomes in the cell. This is known as polyploidy, and is very rare in animals. However, it is relatively common in plants and can form the origin of a new species as polyploidy plants are reproductively isolated from diploid plants. Typically during meiosis each set of replicated chromosomes is split in half, eventually producing gametes. Occasionally, an one of the replicated chromosomes doesn't properly segregate during meiosis. Once fertilized, the zygote (and eventual organism) has an additional chromosome in its cells. In humans, Down's syndrome is cause by the presence of an extra copy of chromosome 21. Two chromosome 21s came from one parent, while one came from the other. Most humans have two copies of each chromosome, one from their mother and one from their father, known as homologous chromosomes. Homologous chromosomes are similar is size and have the same sequence of genes, but can differ in the alleles they carry. Humans have 23 pairs of homologous chromosomes, 23 from the mother and 23 from the father for a total of 46 chromosomes.

Transcription

Figure 8. Transcription creates a transcript, or mRNA, according to complementary base pairing of the template strand of DNA.

In transcription, a segment of DNA (known as a gene) synthesizes mRNA. RNA are polymers composed of a chain of ribonucleotides. Ribonucleotides contain the sugar ribose whereas deoxyribonucleotides (of DNA) contain the sugar deoxyribose. While DNA is double stranded and RNA is single-stranded, RNA contains the nitrogenous base uracil (U) where DNA would have of thymine (T). For a specific gene only one of the DNA strands, the template strand, actively synthesizes a strand mRNA, known as a transcript. The other strand of DNA, the coding strand, is not involved in transcription. However, the coding strand of DNA is more similar to the mRNA since both the coding strand and the transcript are complimentary of the template strand. However, it doesn’t match it exactly. The ribonucleotides of the transcript have the sugar ribose, and where the coding strand would have the nitrogenous base, thymine, the transcript has uracil.

During transcription, ribonucleotides bond to the template strand based on complementary base pairing via hydrogen bonds. The ribonucleotides then bond together with a phosphodiester bond just like DNA is bonded.

Initiation of Transcription

In prokaryotes, transcription is initiated by the attachment of a protein known as a sigma. The sigma attaches to one strand of the DNA (the template strand) at a very specific location. In bacteria, several sigmas exists and each one initiates the transcription of a specific sequence of DNA (or gene). Once this sigma protein attaches to the DNA molecule, it serves to guide the RNA polymerase down the template strand. The sigma protein recognizes and binds to what is deemed the promoter sequence. The promoter sequence is a specific group of base pairs. Once the sigma binds to the DNA, transcription begins. There are several different sigmas. Each one is unique and initiates the synthesis of a specific gene, or in some cases several different genes. While there are several sigmas, each for different gene complexes, RNA Polymerase is the same molecule that connects to all the different sigmas. RNA Polymerase adds ribonucleotides to the template strand based on complementary base pairing, generating an mRNA.

Figure 9. Steps of transcription in prokaryotes.

The sigma protein first opens DNA’s double helix at the promoter section of the DNA strand. Then the template strand of the DNA is threaded through the RNA polymerase. Incoming RNA nucleotides come through a channel in the sigma protein and pairs with the complementary bases of the DNA’s template strand. At this point the RNA polymerase is functional and the begins to work. And once that happens the sigma disconnects from the DNA chain. This defines the beginning of the elongation phase of transcription.

Once the appropriate sigma is attached, RNA Polymerase attaches to the sigma protein. After successful attachment, the sigma guides the DNA into place inside of the RNA Polymerase. As the DNA is thread through the RNA Polymerase, hydrogen bonds are split between the the DNA molecule, by a zipper. Once DNA is inserted in to RNA Polymerase, ribonucleotides enter an entrance portal into the RNA Polymerase and match up with the D-nucleotides based on complementary base pairing. Similar to DNA base pairing, cytosine-containing deoxyribonucleotides (D-cytosine) pair with guanine containing ribonucleotides (R-guanine), D-guanine pairs with R-cytosine, and D-thymine pairs with R-adenine. Different from DNA base pairing, D-adenine pairs with R-uracil. Through another portal in the RNA Polymerase, emerges the developing mRNA. Once a few ribonucleotides are synthesized by RNA Polymerase, the sigma protein is removed. Once the sigma is removed, it can be reused to initiate transcription.

Elongation of Transcription

Elongation in transcription is fairly straight forward. The RNA polymerase zips along the open DNA molecule matching up complementary ribonucleotide base pairs from the template strand of the open DNA (A-U, T-A, C-G, and G-C). After the sigma is removed, RNA Polymerase continues to unzip template and coding strands of the the DNA, and ribonucleotides are bonded via phosphodiester linkages based on complementary based pairing determined by the template strand of DNA. The incoming DNA enters into an intake portal and the strands are separated by an internal zipper. As the DNA passes the zipper, the hydrogen bonds reattach between the coding and template strand and the DNA double helix leaves through an exit portal. Ribonucleotides enter in through another intake portal and are combined via complementary base pairing to the template strand of DNA. The ribonucleotides are bonded to each other via phosphodiester linkages, forming an emerging . Ribonucleotides are continuously added to the 3’ end of the developing RNA strand. The 5’ end of the RNA strand leaves through another exit portal of the RNA Polymerase.

Termination of Transcription

In bacteria, once RNA Polymerase transcribes a specific sequence of ribonucleotides from the DNA template strand, transcription ends (or terminates). When this sequence is synthesized, a section of the RNA bends back on itself forms a short double helix based on complementary base pairing. This forms a RNA hairpin. This hairpin forces the RNA to separate from the DNA and the RNA Polymerase detaches and the opened DNA reattaches based on complementary base pairing

Figure 10. Steps of transcription in eukaryotes and RNA splicing.

Transcription in Eukaryotes

Fundamentally, transcription in eukaryotes is similar to transcription in prokaryotes with a few exceptions. In bacteria, RNA Polymerase can synthesize any RNA molecule. In eukaryotes, there are three different RNA Polymerases (I, II, and III). RNA Polymerase I is primarily responsible for the synthesis of ribosomal RNA (rRNA), the molecule that makes up ribosomes. Most eukaryotic RNA Polymerase are RNA Polymerase II. RNA Polymerase II is responsible for synthesizing mRNA, making it the only RNA Polymerase capable of transcribing protein-coding genes. RNA Polymerase III is responsible for synthesizing transfer RNA (tRNA). During translation, tRNAs read the messages from the mRNA and link a specific amino acid sequence generating proteins.

Where bacterial transcription is initiated by a sigma protein, RNA Polymerases in eukaryotes require a group of proteins known as basal transcription factors. Like sigma in prokaryotes, once the basal transcription factors attach to the DNA, its respective RNA Polymerase attaches and transcription begins. The elongation process is virtually identical in prokaryotes and eukaryotes. However, termination of transcription differs between prokaryotes and eukaryotes. In eukaryotes, a short sequence in the DNA signals the attachment of an enzyme downstream of active transcription. This enzyme cuts the emerging RNA, leaving the RNA Polymerase.

In eukaryotes, pre-RNA is made up of regions of mRNA that code for amino acids (known as exons) and regions of mRNA that don’t code for amino acids. Before the mRNA can be functional the introns must be removed in a process known as RNA splicing, or post-transcriptional modification.

Post-transcriptional modification of mRNA in eukaryotes

In bacteria, transcription from DNA to mRNA is a direct pathway. However in eukaryotes once mRNA is synthesized by RNA Polymerase II, the mRNA goes through further modification (Fig. 11). The product following transcription is known as a primary transcript (or pre-mRNA). Before mRNA travels outside the nucleus, the mRNA is shortened by cutting out specific sections of mRNA and reattaching the remaining sections back together. This process is known as RNA splicing and the resulting, modified mRNA is known as mature mRNA. Segments of the mRNA that are respliced back together are known as exons (because they exit the nucleus) while the segments of mRNA that are removed from the pre-mRNA are known as introns. The exons (which collectively make up the mature mRNA) leave the nucleus through a nuclear pore and travel to a ribosome in the cytosol and begin the process of translation.

RNA splicing is processed by hybrid protein-RNA complexes known as small nuclear ribonucleoproteins (or snRNPs). RNA splicing begins when a primary snRNP binds to a guanine R-nucleotide (G) adjacent to an uracil R-nucleotide (U) at the 5’ end of the pre-mRNA. This marks the exon-intron boundary. Another secondary snRNP reads from 5’ to 3’ down the mRNA and when it comes in contact with an adenine (A), and it attaches at that point. This point represents the intron-exon boundary. Once the primary and secondary snRNPs are attached other snRNPS attach to those, in a complex known as a spliceosome. Collectively the spliceosome breaks the G-U bond of the primary snRNP and the bond between the adenine (A) of the secondary snRNP and its adjacent R-nucleotide. Since U and A are complementary bases, the spliceosomes places them in close contact with each other, generating an intron loop. Nucleotides of the intron loop are disassembled into their monomers, ribonucleotides, and are recycled for future transcriptional events. Exons are spliced back together generating a mature mRNA.

Translation in prokaryotes

Figure 11. Contrast of transcription and translation in prokaryotes and eukaryotes.

Transcription is the process of creating mRNA from DNA translation is the process of converting the genetic information of mRNA into proteins. Since prokaryotic DNA is not bounded by a nucleus, translation in prokaryotes (i.e. bacteria) occurs before transcription is complete. Translation and transcription happen simultaneously. Ribosomes are adjacent to transcribing DNA, allowing the ribosomes begin translation before transcription is terminated. This allows for translation of proteins to be more efficient in prokaryotes than eukaryotes.

Translation in eukaryotes

In eukaryotes, transcription and modification of mRNA happens exclusively in the nucleus. After mRNA processing, the mature mRNA travels out of the nucleus through a nuclear pore. In the cytosol, the body of the cell outside the nucleus, the mature mRNA attaches to a ribosome and goes through translation, eventually producing a protein. Most ribosomes are attached to the rough endoplasmic reticulum. However there are several ribosomes within the cytosol itself, as well.

This separation of transcription and translation provides a greater control over gene regulation, specifically by the removal of introns from the pre-mRNA. It is hypothesized that the removal of introns is a defense against expressing ancient retroviral genes, a key field of study in HIV research. It is also hypothesized that eukaryotic DNA is less susceptible to mutations than prokaryotes, due to the physical barrier of the nuclear envelope between the DNA and the cytosol.

In addition to mRNA, another important RNA molecule is the transfer RNA, known as tRNA, tRNA is the molecule that bridges the genetic code with the a specific protein. Each transfer molecule is attached to a specific amino acid. And each amino acid has three base pairs attached to the opposite end of it. At the ribosome, the three base pairs of the tRNA join up with the complement of the three base pairs of the mRNA. So the three complimentary base pairs of the transfer RNA are known as an anticodon whereas the triplet code of the messenger RNA is known as a codon. Each anticodon of tRNA links with a specific amino acid is combined to it complementary codon of mRNA at the ribosome. And then the amino acids are linked together by peptide bonds into a growing peptide chain.

Figure 12. Steps of translation.

The ribosomal complex is made of another RNA (ribosomal RNA) and proteins. These make up two structure. The small subunit holds the mRNA in place during translation while the large subunit is where the peptide bonds form. And the large subunit has three distinct chambers: A, P and E. In general, the function of the ribosome is to synthesize proteins. First, the amino acid connected to a tRNA enters the ribosome at the A site. As another tRNA molecule comes into the A site, the other tRNA molecule moves over three base pairs and a peptide bond is formed between the two amino acids. As another tRNA molecule comes into the ribosomal complex, the other two tRNAs move 3 bases, and the oldest tRNA exits the ribosome at the E site. Just remember APE.

Initiation of Translation

Let’s take a closer look at the process translation (Fig. 12). The mRNA has a special section of it right before the start codon that the ribosome recognizes, known as the ribosome binding site. Translation begins with the start codon on the mRNA. Connected to the tRNA of the start codon is an amino acid, called f-Met. Once the f-Met tRNA binds to the small subunit of the ribosome, the large subunit of ribosome binds to the small unit, and translation is ready to begin.

Translation begins when an mRNA connects to the small subunit of a ribosome. Ribosomes are made up of proteins and another type of RNA, ribosomal RNA (or rRNA). Initiation of translation begins when rRNA binds to a specific sequence of the mRNA, known as the ribosome binding site. This connection is based on complementary base pairing of adjacent ribonucleotides of rRNA and and mRNA, which is guided into place by special proteins known as initiation factors. One of the initiation factors also serves as a docking station for the first tRNA to connect to the start codon of mRNA, which is AUG for the synthesis of all proteins. tRNAs have a complementary triplet code that connects to the codon of the mRNA, known as an anticodon (Fig. 12). The anticodon of the initial tRNA is UAC. Attached to the initial tRNA is the amino acid, methionine (met). Once this tRNA is attached to the small subunit, the large subunit of the ribosome attaches to it and elongation of translation begins.

Elongation of Translation

The next tRNA enters the A site due to complementary base pairing of the codon of the mRNA and the anticodon of the tRNA. Once the codon-anticodon pairing is successful, the new tRNA in the A site is positioned such that the amino acid it is carrying is adjacent to the amino acid already present in the P site. This proximity encourages a peptide bond to form between the two adjacent amino acids, the beginning of a polypeptide chain.

attaches to its complementary codon in the A site of the large subunit of the ribosome.

Second, a peptide bonds forms at the P site. Next the whole ribosome complex moves down three base pairs. The tRNA in the A site moves to the P site and the tRNA of the P site move to the E site, and the tRNA at the E site leaves the ribosomal complex. This process is known as translocation.

Termination of Translation

Translation is terminated by one of three stop codons. Once the ribosome encounters one of these stop codons, it causes a specific protein, known as a release factor, to enter the ribosome and it causes the release of the release of the polypeptide chain. Also at this time, the large ribosome separates from the small subunit.


10 Unusual Genetic Mutations in Humans

No two people are alike, due to the subtly different ways our genomes are expressed. But sometimes these biological differences lead to genetic mutations that are extremely rare, and sometimes debilitating. Historically, many people suffering from these mutations were labeled monsters or freaks — but today, we know they are simply part of the broad spectrum of genetic variations in our species. Here are 10 of the most unusual genetic mutations we've identified in humans.

1. Progeria

This genetic disorder is as rare as it is severe. The classic form of the disease, called Hutchinson-Gilford Progeria, causes accelerated aging.

Most children who have progeria essentially die of age-related diseases around the age of 13 , but some can live into their 20s. Death is typically caused by a heart attack or stroke. It affects as few as one per eight million live births .

The disease is caused by a mutation in the LMNA gene, a protein that provides support to the cell nucleus. Other symptoms of progeria include rigid (sclerotic) skin, full body baldness (alopecia), bone abnormalities, growth impairment, and a characteristic “sculptured” nasal tip.

Progeria is of great interest to gerontologists who hope connect genetic factors to the aging process. Image: HBO.

2. Uner Tan Syndrome

Uner Tan syndrome is a somewhat controversial condition, whose most obvious property is that people who suffer from it walk on all fours. UTS is a syndrome that was proposed by the Turkish evolutionary biologist Üner Tan after studying five members of the Ulaş family in rural Turkey. These individuals walk with a quadrupedal locomotion, use primitive speech, and have a congenital brain impairment (including “disturbed conscious experience”). The family was featured in a 2006 BBC2 documentary called, " The Family That Walks On All Fours ." Tan describes it like this:

The genetic nature of this syndrome suggests a backward stage in human evolution, which is most probably caused by a genetic mutation, rendering, in turn, the transition from quadrupedality to bipedality. This would then be consistent with theories of punctuated evolution.

The new syndrome, says Tan, “may be used as a live model for human evolution.” Some experts think this is bunk, and that genetics may have very little to do with it.

3. Hypertrichosis

Hypertrichosi s is also called “werewolf syndrome” or Ambras syndrome, and it affects as few as one in a billion people and in fact, only 50 cases have been documented since the Middle Ages.

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People with hypertrichosis have excessive hair on the shoulders, face, and ears. Studies have implicated it to a rearrangement of chromosome 8. It happens due to a disruption of the “crosstalk” between the epidermis and the dermis as hair follicles form in the 3-month fetus at the eyebrows and down to the toes. Normally, signals from the dermis send the messages to form follicles. As a follicle forms, it sends signals to prevent the area around it from also becoming a follicle, which results in the equal spacing of our five million or so follicles. Most of our body parts ignore the messages to form follicles, which explains why most of us are relatively hairless.

4. Epidermodysplasia Verruciformis

Epidermodysplasia verruciformis is an extremely rare disorder that makes people prone to widespread human papillomavirus (HPV) infection. This infection causes scaly macules and papules ( cutaneous squamous cell carcinomas ) to grow on the hands, feet, and even face. These skin “eruptions” appear as wart-like lesions — and even wood-like and horn-like growths — with reddish-brown pigmented plaques. Typically, the skin tumors start to emerge in people between the age of 20 and 40, and the growths tend to appear on areas exposed to the sun. Also called Lewandowsky-Lutz dysplasia, there is no known cure, though treatments to scale back the growths are possible.

The disorder was brought to the public’s attention in November 2007 when a video of a 34-year-old Indonesian man named Dede Koswara appeared on the internet . In 2008, he underwent surgery to have 13 pounds (6 kg) of the warts removed . After the lesions and horns were extracted from his hands, head, torso, and feet, his hands were grafted with new skin. In all, about 95% of the warts were removed.


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