Cystic fibrosis is caused by mutations, or errors, in the cystic fibrosis transmembrane conductance regulator (CFTR) gene, which result in either no CFTR protein being made or a malformed CFTR protein that can't perform its key function in the cell. Show Over the years, scientists have used several different ways of grouping these mutations into different classes. The most recent classification system groups mutations by the problems that they cause in the production of the CFTR protein:
Protein Production MutationsProtein production mutations, which include nonsense and splice mutations, interfere with the production of the CFTR protein. All proteins, including CFTR, are made of building blocks called amino acids that are linked together into a long chain. The protein-building instructions spelled out in the CFTR gene tell the cell which of the 20 available amino acids to use at each position in the chain. The letters in the gene also spell out a “stop” signal that lets the cell know that it has reached the end of the instructions and can stop making the protein. If the CFTR gene has a nonsense mutation, the protein-building instructions contain an early stop signal that causes the production of the CFTR protein to stop prematurely. Therefore, the cell begins to build the CFTR protein normally until it reaches the early stop signal. The cell “thinks” that it has reached the end of the instructions and stops production too soon. Because the cell stops reading the instructions before it finishes making the protein, no functional CFTR protein is produced. Watch this webcast (starting at 3:03) to see how a nonsense mutation affects production of the CFTR protein and how the mutation might be corrected to make normal CFTR protein. Splice mutations interfere with the ability of the cell to correctly read the instructions for making the CFTR protein. In a healthy person, the instructions spelled out in a gene are interrupted by stretches of DNA letters that do not code for protein, like an article in a magazine might be interrupted by ads. The beginning and end of these stretches of irrelevant letters are marked with a special signal. In order to make the protein, the cell copies the DNA letters into a similar alphabet called ribonucleic acid (RNA), and then follows the signals to clip out all of the irrelevant letters — as you might clip out the ads. That way, the instructions can be read straight through from start to finish. A splice mutation changes the signal that tells the cell where the irrelevant letters in the instructions begin or end. When the cell tries to read its RNA copy of the instructions, it no longer can tell where to begin and end reading. As a result, the cell will either leave in some irrelevant letters, or remove some relevant ones. When the cell tries to follow the RNA instructions containing the irrelevant letters, or missing relevant ones, it will be unable to build a correct CFTR protein. Protein Processing MutationsThe CFTR protein is made up of 1,480 amino acids. When the CFTR protein is made using all of the correct amino acids, it forms a stable 3-D shape. It has to be the right shape to transport chloride. When a mutation causes an amino acid to be deleted or an incorrect amino acid to be added, the CFTR protein cannot form its correct 3-D shape and function properly. These mutations are considered to be protein processing mutations. The most common CF mutation, F508del, is primarily considered to be a processing mutation. The F508del mutation removes a single amino acid from the CFTR protein. Without this building block, the CFTR protein cannot stay in the correct 3-D shape. The cell recognizes that the protein isn't the right shape and disposes of it. The drug combination Trikafta® (elexacaftor/tezacaftor/ivacaftor) works by enabling CFTR protein with an F508del mutation to fold in a more correct shape, and then activates the protein to allow more chloride to pass through. Although this drug combination is not a perfect fix, it helps the mutant CFTR protein to move some chloride. This movement of chloride reduces the symptoms of CF. Watch the webcast (starting at 3:02) to learn more about CF protein processing mutations and how drugs such as CFTR modulators can help a person with one of these mutations. Researchers are working on more effective drugs that can fold the protein into a more normal shape, move more chloride out of the cell, and reduce symptoms even further. In addition to F508del, missense mutations can sometimes cause processing problems and therefore can be considered processing mutations in those cases. Missense mutations occur when a change in DNA letters causes an incorrect amino acid to be incorporated into the CFTR protein. This leads to either a decrease in the quantity of the protein at the cell surface (defective processing) and/or a decrease in the function of the protein (defective gating or conduction). Gating MutationsThe CFTR protein is shaped like a tunnel, or channel, with a gate. The cell can open the gate when chloride needs to flow through the channel. Otherwise, the gate stays closed. Gating mutations lock the gate in the closed position so that chloride cannot get through. The drug Kalydeco® (ivacaftor) helps people with gating mutations by forcing the gate on the CFTR channel to stay open. This enables chloride to move through the channel and reduces the symptoms of CF. Watch the webcast (starting at 3:02) to learn more about CF gating mutations and how drugs, such as CFTR modulators, can help a person with one of these mutations. Conduction MutationsSometimes, a change in one of the amino acids of CFTR means that even though the protein makes the right 3-D shape, it doesn't function as well as it should. In order for CFTR to work correctly, chloride has to be able to move quickly and smoothly through the protein's channel. Some mutations change the shape of the inside of the channel so that chloride cannot move through as easily as it should. This kind of mutation is called a conduction mutation. Watch the webcast (starting at 3:00) to learn more about CF conduction mutations and how a drug such as a CFTR modulator might help a person with one of these mutations. Insufficient Protein MutationsInsufficient protein mutations result in a reduced amount of normal CFTR protein at the cell surface. This occurs for several reasons: a limited amount of CFTR protein is produced; only a small number of protein at the cell surface works correctly; or normal protein at the cell surface degrades too quickly, leaving small numbers of protein behind. In each case, insufficient functional proteins at the cell surface produce only some, or residual, function of the chloride channel. Insufficient protein can be caused by several mutations, including missense and splice mutations. As mentioned above, some splice mutations interfere with the way the cell reads the DNA instructions for making a protein. This can result in a limited quantity of normal CFTR protein reaching the cell surface, which results in residual function. The FDA approved Kalydeco for five splice mutations in 2017 [and later Symdeko® (tezacaftor/ivacaftor) in 2020]. People with these mutations make a small amount of normal CFTR. Ivacaftor in both Kalydeco and Symdeko can force the gate on the normal CFTR protein to stay open for longer to compensate for the insufficient protein numbers on the surface of the cell. By staying open longer, more chloride can flow through the channel, which may reduce the symptoms of CF. Resources on CF Mutations*** Reference to any specific product, process, or service does not necessarily constitute or imply its endorsement, recommendation or favoring by the Cystic Fibrosis Foundation. The appearance of external hyperlinks does not constitute endorsement by the Cystic Fibrosis Foundation of the linked websites, or the information, products, or services contained therein. Information contained on this site does not cover all possible uses, actions, precautions, side effects, or interactions. This site is not intended as a substitute for treatment advice from a medical professional. Consult your doctor before making any changes to your treatment. FDA-approved drug information is available at dailymed.nlm.nih.gov/dailymed.
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Citation: Duffy S (2018) Why are RNA virus mutation rates so damn high? PLoS Biol 16(8): e3000003. https://doi.org/10.1371/journal.pbio.3000003 Published: August 13, 2018 Copyright: © 2018 Siobain Duffy. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: SD was funded by the US National Science Foundation 1453241. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The author has declared that no competing interests exist. Provenance: Commissioned; externally peer reviewed RNA viruses have high mutation rates—up to a million times higher than their hosts—and these high rates are correlated with enhanced virulence and evolvability, traits considered beneficial for viruses. However, their mutation rates are almost disastrously high, and a small increase in mutation rate can cause RNA viruses to go locally extinct. Researchers often assume that natural selection has optimized the mutation rate of RNA viruses, but new data shows that, in poliovirus, selection for faster replication is stronger and faster polymerases make more mistakes. The fabled mutation rates of RNA viruses appear to be partially a consequence of selection on another trait, not because such a high mutation rate is optimal in and of itself. Mutations are the building blocks of most of evolution—they are the variation upon which natural selection can act, and they are the cause of much of the novelty we see occur in evolution [1]. However, most mutations are not beneficial for the organisms with them. Many mutations cause organisms to leave fewer descendants over time, so the action of natural selection on these mutations is to purge them from the population. While a small percentage of mutations are helpful and some are inconsequential (neutral or nearly neutral in effect), a large portion of mutations are harmful [2]. While the fraction of mutations that are harmful versus beneficial may change in different organisms, in different environments, and over time, deleterious mutations are thought to always outnumber beneficial mutations [2]. That remains true whether an organism has a low mutation rate or a high mutation rate, and biological entities differ dramatically in their per-nucleotide mutation rate (over eight orders of magnitude, Fig 1). Download: Fig 1. Biological mutation rates summarized from fastest to slowest: Viroid (RNA elements that cause some plant disease without encoding any genes), viruses (RNA shown as Ebola, single-stranded DNA shown as an icosohedron, and double-stranded DNA shown as a myophage), prokaryotes (rod-shaped bacteria), and eukaryotes (rodent). Icons are roughly the size of the range of mutation rates and genome sizes of measured organisms within that group. Axes are log-transformed, data as in [3]. Images are in the public domain except viroid [4], single-stranded DNA virus (icon made by Pixel perfect, www.flaticon.com), and rodent (icon made by Freepik, www.flaticon.com). https://doi.org/10.1371/journal.pbio.3000003.g001 In some cases, there is no benefit to mutation at all. At an extreme, an organism that’s “perfectly” adapted to its constant environment would do best to reduce its mutation rate to zero—there are no more beneficial mutations, so all mutations are likely worse than the current genotype (see C in Fig 2). In a constant environment (one where the fitness landscape does not change), it would be best for the optimal genotype to not mutate at all. At another extreme, if an organism is suddenly thrust into an environment that it’s not well adapted to (akin to being at A in Fig 2), there is a larger fraction of potentially beneficial mutations available and having a nonzero mutation rate would be preferable to all descendants always staying exactly the same. The more variable the environments an organism experiences and the lower fitness the organism is in those environments, the more an increased mutation rate would be favored since there is a greater chance per mutation of a mutation being beneficial. Download: A fitness landscape showing three genotypes on different places on the landscape (A, B, and C) and a schematic pie chart of the distribution of mutations available to each genotype. The genotype at A is not well adapted to the environment (far from a fitness peak) so has a larger fraction of mutations that would be beneficial. The genotype at B is more fit than A and is closer to a fitness peak, so it has a smaller fraction of beneficial mutations than that at A. The genotype at the fitness peak C does not have any way to become more fit on this landscape and thus has no beneficial mutations available to it. The allocations of mutations as beneficial, neutral, and deleterious is for representational purposes only (not based on actual data), and the proportion of neutral mutations was held constant for all three genotypes. Figure includes a fitness landscape from the public domain, originally created by C. Wilke. https://doi.org/10.1371/journal.pbio.3000003.g002 Organisms may not be able to change the fraction of mutations that are deleterious, but they do have some control over their mutation rates, which can limit the number of deleterious mutations that will plague their descendants. Of course, a lower mutation rate comes with the tradeoff that it will also limit the smaller fraction of beneficial mutations—alleles that are beneficial in the current environment and that will help an organism leave more descendants over time. It would also limit the accumulation of neutral (or nearly neutral) variation in populations that might be beneficial if circumstances change, alleles that could be beneficial in a new environment or after climactic change [5]. The mutation rate of all cellular life is under selection, and cells have evolved many ways of tweaking their mutation rates—largely to lower the mutation rate inherent in a fast-moving, processive polymerase replicating their large genomes. These involve proofreading components of the polymerases themselves and a variety of other proteins and systems to check for errors in DNA and to repair common kinds of DNA damage [6]. Some DNA viruses with larger genomes also have DNA repair proteins, and the very largest RNA viruses have some ability to proofread and correct replication errors [7]. Mutant viruses and cells with lowered mutation rates can be isolated by exposing cells or viruses to mutagens, but just as there are proteins and alleles that decrease mutation rates, there are mutations to break those proteins and other alleles that increase mutation rates, which are beneficial in some environments [8]. RNA viruses are perhaps the most intriguing biological entities in which to study mutation rates. They encode their replication machinery, and thus their mutation rates can be optimized for their fitness (in comparison to small DNA viruses that use the polymerases of their host cells). Their inherently high mutation rates yield offspring that differ by 1–2 mutations each from their parent [9], producing a mutant cloud of descendants that complicates our conception of a genotype’s fitness. Their ability to rapidly change their genome underlies their ability to emerge in novel hosts, escape vaccine-induced immunity, and evolve to circumvent disease resistance engineered or bred into our crops [10, 11]. On the other hand, their mutation rates are an exploitable Achilles’ heel: researchers and clinicians can increase RNA virus mutation rates using nucleoside analogues, and a 3–5-fold increase in mutation rate causes lethal mutagenesis in human-infecting viruses like poliovirus and influenza [12, 13]. The exogenous mutagen causes enough additional mutations, which are often deleterious, so that the progeny RNA viruses are of lower fitness, eventually leading to ecological collapse of the population [14]. Another way in which researchers have seen the constraints imposed by the high mutation rate of RNA viruses is in their limited genome size—the mutation rates per nucleotide are too high to increase their genome size without having a higher per-genome accumulation of mutations [9, 15]. Researchers have suggested that RNA virus mutation rates have evolved to be just under the threshold for lethal mutagenesis (sometimes referred to as error threshold [16]) but that selection for genetic diversity and other consequences of a high mutation rate push RNA viruses to near their catastrophic limits. It has been hard to assess this assumption and verify that RNA viruses have their optimal mutation rates due to natural selection on mutation rate. One of the best-studied systems for RNA virus mutation is poliovirus, in which a now frequently used lower mutation rate mutant (G64S in the 3D RNA-dependent RNA polymerase, 3D:G64S) was characterized, simultaneously, by virologists working at two locations in the San Francisco Bay Area [17, 18]. The 3D:G64S strains not only have a lower mutation rate than wild-type polio but also are less fit in several ways: in one-step growth curves, in cell culture passaging, and in mice, in which they have reduced virulence (the 3D:G64S strains more slowly invade the central nervous system). They are more fit than wild-type poliovirus only in the presence of mutagens, in which their lower mutation rate reduces the inherent number of mutations in each progeny genome, so more exogenous mutations can be tolerated. The 3D:G64S strain also has measurably less genetic diversity during infections, which has suggested a link between population diversity and virulence as well as the adaptability that is conferred by having more standing genetic variation and being able to more rapidly create more variation. However, these conclusions are largely correlational and theoretical, as it has been difficult to conduct experiments to definitively prove that it is indeed the reduced mutation rate of 3D:G64S and not other effects of this mutation causing the reduced virulence and fitness observed in experiments. In this issue of PLOS Biology, Fitzsimmons and colleagues show that reduced replication speed explains more of the effects of the 3D:G64S than its reduced mutation rate per se [19]. There is an intuitive link between replication speed and mutational fidelity [15, 20]—it’s easier for anyone or anything to complete a repetitive task if one can tolerate a certain level of mistakes. If a task is critical to do without any errors at all, it will likely need to be done more slowly with more care and attention. That slower/more accurate relationship has been suggested by previous, less sequencing-intensive work [21], but not all mutations in poliovirus obligately affect both replication speed and mutational fidelity. Fitzsimmons and colleagues demonstrate that a compensatory mutation in 3D:G64S can restore replication speed but not affect the lower mutation rate of 3D:G64S, and this increases viral fitness (2C:V127L). This key experiment teased apart two highly correlated traits to reveal that replication rate affects fitness more than mutation rate. Further, Fitzsimmons and colleagues cast doubt on the wild type’s advantage of genetic diversity for virulence. The process of entering the mouse central nervous system is a severe bottleneck and is dominated by drift compared to selection—both the wild type and 3D:G64S polioviruses have similar diversities in the mouse central nervous system [19]. Deep sequencing of cell culture-passaged wild type and 3D:G64S populations revealed that both lacked genetic diversity at a meaningful level (SNPS at 0.1%). Finally, the wild type and 3D:G64S increased fitness by identical amounts after passaging in cell culture, refuting that the lower mutation rate of the 3D:G64S strain reduces adaptability. Altogether, this new work suggests that the 3D:G64S strain has a lower fitness because of slower replication, not its reduced mutation rate. RNA viruses like poliovirus likely have higher mutation rates than what would be optimal for the organism because higher mutation rates are, in part, a byproduct of selection for faster genomic replication. This deeper dive into RNA virus replication fidelity will focus researchers on the consequences of RNA viruses coping with higher than desired mutation rates. This makes the clinical uses of lethal mutagenesis easier to understand—the small increases in mutation rate are not knocking RNA viruses off an optimal peak but are a further insult to an already nearly intolerable mutation rate. Also, just as bacterial populations are known to house mutation rate polymorphisms [22], this work should strengthen the nascent field of understanding mutation rate variation within RNA viral populations [23]. Additionally, replication time (generation time) may be a larger component of understanding virus evolvability than it has been given credit for—likely undervalued because of the difficulties in measuring that trait in multicellular organisms [24, 25]. RNA viruses have high mutation rates, but they may tolerate them rather than revel in them. That they were optimized for genetic variation alone is a “just so story” that should be skeptically re-examined as the more complicated biological reality is revealed [26].
I thank Lele Zhao and Lisa Nigro for helpful discussions.
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Why natural selection sometimes Cannot get rid of a lethal mutant allele like CF in a certain population?
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