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Number of beneficial mutations cataloged?

Number of beneficial mutations cataloged?


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I can see from Wikipedia that there are possibly thousands of harmful mutations that have been cataloged and linked to disease. There are also unnumbered neutral mutations. But, does anyone know how many "beneficial" mutations have been cataloged in scientific literature?


That would be hard to say because really beneficial mutations become well distributed through the genome. Basically the differences between us and chimpanzees are a catalog of all the beneficial (or completely neutral) mutations since the ~4.7 M years since we diverged from each other.

Separating them from changes which have no special effect would be difficult too, but more to the spirit of your question, its difficult to explain what many mutations do, unless they cause significant changes in something we can observe in the individual (like height, weight, big nasty claws, etc).

For some more specific examples you can look at Online Mendelian Inheritance in Man. Some mutations which are useful sometimes might be related to skin color for instance (really helps when there is a lot of sun about) or lactose tolerance (a variation which is popular amongst europeans who have been drinking dairy milk for thousands of years).


Just continue scrolling down wikipedia: there are also listed two examples of beneficial mutations: the one conferring HIV resistance, and the one conferring malaria resistance.

Note that 'beneficial' is relative. The mutation associated to malaria resistance is actually causing sickle cell disease.


Others have posted that the term beneficial in genetics is contextual - single mutations may be harmless, unless another mutation is co-inherited; this is called epistasis (where more than a single mutation/genotype/allele is required for the phenotype).

I have not studied the list comprehensively, but there is a 'catalog' of all robust genome-wide association study results here. Depending on your outlook, all mutations associated with a disease are therefore also 'beneficial' if you have the protective allele and not the risk allele. Using this logic there have been many SNPs identified that are protective, rather than beneficial per se, and these are listed in the GWAS-catalog I linked to. As of 05/11/12, the catalog includes 1258 publications and 6400 SNPs.


Beneficial mutations: real or imaginary?&mdashpart 1

iStockphoto

Randomly occurring beneficial mutations lie at the heart of Darwinian evolution. Without them there is no mechanism by which a single originating cell could have diversified into the myriad species that we see on Earth and in the fossil record today. But according to recent reports on the human genome, mutations are being classified into just two categories&mdash&lsquodeleterious&rsquo and &lsquofunctional&rsquo. Beneficial mutations are not being catalogued. This surprising result turns out to be in accord with the history of the beneficial mutation concept. The theory was originally developed by R.A. Fisher in his 1930 book The Genetical Theory of Natural Selection in an attempt to salvage Darwinism because the only evidence he had was for deleterious mutations. Until recently genetic theorists have perpetuated his practice. Beneficial mutations are simply assumed to exist because Darwinian theory demands that they exist. The first experiments to characterize the properties of beneficial mutations were published in 2011 and the result contradicted Fisher&rsquos theory. This outcome is analyzed in part 2 of this article.

Having been a student of biology for more than 50 years I have never had a problem with the concept of beneficial mutations. I was therefore shocked to discover in recent reports on the human genome that beneficial mutations have not been found. Only &lsquodeleterious&rsquo and &lsquofunctional&rsquo mutations have been documented. On doing some research into the ways that genetic theorists have treated beneficial mutations, and the data they have worked from, I was even more shocked to discover that they have had no data to work from either.

The theory of beneficial mutations was originally developed by English statistician R.A. Fisher, the founding father of neo-Darwinism, in his 1930 book The Genetical Theory of Natural Selection. 1 But he had only deleterious mutations to work with and so he came up with his theory of beneficial mutations out of a belief that they must exist. Genetic theorists have followed his example ever since. The stranglehold that neo-Darwinian evolution has achieved over academia and the media today was thus built upon nothing more than imagination and evolutionary necessity.

Darwin&rsquos Origin of Species started the ball rolling, but while it was widely praised it met fierce opposition from professional scientists. 2 By the beginning of the 20 th century the discovery of Mendelian genes and the fact that they could mutate had largely pushed Darwin&rsquos ideas aside. By the end of the 1920s the science of genetics and the discovery that known mutations were all deleterious posed a seemingly fatal challenge to Darwinism. But in 1930 a new revolution began. Fisher published his book and he and fellow English mathematician J.B.S. Haldane, together with American geneticist Sewall Wright, then compiled during the 1930s and 1940s a body of mathematics that became known as the &lsquoModern Synthesis&rsquo, or neo-Darwinian theory.

This body of theory remained largely academic until a convergence of three further events took place in 1953. Watson and Crick published the double-helix structure of DNA, giving biology its first ever grounding in the hard physical sciences. Bernard Kettlewell, a Research Fellow at Oxford University, began experiments on industrial melanism in the peppered moth. These produced the first ever example of natural selection in the wild 3 and it became textbook orthodoxy as &lsquoevolution in action&rsquo. And American geochemist Clair Patterson announced at a conference what was to become a &lsquouniversal constant&rsquo in the evolutionary worldview&mdashthe 4.55-billion-year &lsquoage&rsquo of the earth.

Mutations became synonymous with nucleotide changes in DNA. Natural selection re-emerged as all-conquering hero, promoting beneficial mutations, and removing deleterious ones. And the official oodles of time allowed chance to magically transform anything into anything else. Today&rsquos educated atheists grew up believing evolution as fact, the media made an industry out of it, and (almost) everybody believed it. But at the IUPS Congress in Birmingham in July 2013, the President, Oxford University Emeritus Professor Denis Noble, announced that &ldquoall the central assumptions of the Modern Synthesis &hellip have been disproven&rdquo. 4


The population genetics of beneficial mutations

The population genetic study of advantageous mutations has lagged behind that of deleterious and neutral mutations. But over the past two decades, a number of significant developments, both theoretical and empirical, have occurred. Here, I review two of these developments: the attempt to determine the distribution of fitness effects among beneficial mutations and the attempt to determine their average dominance. Considering both theory and data, I conclude that, while considerable theoretical progress has been made, we still lack sufficient data to draw confident conclusions about the distribution of effects or the dominance of beneficial mutations.

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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.


ERV wiring of developmental transcription networks

The relevance of ERVs to pathogenesis is underscored by their contributions to normal development and human biology. HERV-derived regulatory elements are bound by transcription factors and activated during preimplantation development modelled in vitro [9, 20, 81, 82] (Table 1). Presumably, this aided the retroviral ancestors of HERVs in being expressed and endogenously amplified once they had accessed the germline [33]. HERVs display distinct, embryonic stage-restricted transcriptional profiles [83] and can harness transcription start sites to adjacent genes [21, 44]. For instance, HERV-L elements are upregulated during embryonic genome activation and can be bound by the transcription factor DUX4 to serve as alternative promoters for cleavage stage genes [84]. Dux, the functional murine orthologue of DUX4, binds the related mouse ERV family, MERV-L [84, 85]. MERV-L provides numerous alternative promoters for genes expressed at the 2-cell stage, when mouse embryonic transcription begins [86,87,88]. HERVs contribute exons to long noncoding RNAs, such as ESRG, HPAT5, and linc-ROR, which are upregulated by OCT4 and other pluripotency factors and may in turn act as molecular sponges for miRNAs limiting pluripotency factor expression [7, 8, 11, 89, 90]. The trophoblast cells of the placenta also express ERVs. For example, a primate MER21A LTR inserted upstream of the CYP19A1 aromatase gene promotes transcriptional initiation of a highly abundant and placenta-specific CYP19A1 mRNA (Fig. 2a) [15, 26]. CYP19A1 generates

0.3% of the capped mRNA found in human placental tissue, and yet is not expressed in the non-primate placenta [15, 91]. ERVs in sum provide an extensive catalog of alternative and canonical transcription start sites for genes expressed in early development, many of which are human-specific [21, 44].

ERV regulatory element co-option. a An upstream MER21A LTR provides an alternative placenta-specific promoter to CYP19A1 [15]. b A HERV-K solo LTR (LTR5Hs) enhancer located in the first intron of F11R [10]. c AIM2 expression is enhanced by an adjacent MER41E LTR [16]. Note: each panel displays, from top to bottom, the first exon of a protein-coding gene, the position of an adjacent regulatory LTR, transcriptome (a) or histone modification (b,c) sequencing data, and a magnified view of relevant transcription factor binding sites in each LTR. Histone modification (H3K4me1, H3K4me3, and H3K27ac) profiles were obtained from ENCODE via the UCSC Genome Browser [2]. Transcriptome data in the form of cap analysis gene expression (CAGE) reads generated by the FANTOM consortium were visualized using the ZENBU genome browser [91]

Beyond serving as promoters during early development, HERV-H and HERV-K sequences appear to more frequently behave as enhancers (Table 1). The genome contains an estimated 800,000 elements bearing an enhancer biochemical signature [2], although only a fraction of these have been functionally validated as bona fide enhancers [92]. Enhancers may regulate both proximal and distal genes, and as enhancer-associated noncoding RNAs are widespread, the mechanisms by which enhancers influence gene expression are often not straightforward to resolve [92]. Candidate enhancer elements are disproportionately likely to overlap HERV-H sequences (Table 1). HERV-H copies are highly transcribed in the pluripotent cells of the blastocyst [7, 9, 11] and demarcate boundaries of open chromatin [12]. CRISPR-Cas9 mediated deletion or repression of HERV-H loci can alter the expression of upstream genes in the same topologically associated domain, in line with HERV-H copies often functioning as enhancers over longer genomic distances (Table 1) [10, 12]. HERVs supply transcription factor binding sites to the network of regulatory elements governing pluripotency [9, 10, 33, 93, 94]. By example, the consensus HERV-K LTR (LTR5Hs) harbors an OCT4 binding site, and in embryonic stem cell cultures, OCT4 directly binds and transactivates DNA hypomethylated LTR5Hs sequences [9]. HERV-K transcription, in contrast to HERV-H, begins in the embryonic genome at the 8-cell stage [9, 93]. However, as per HERV-H, HERV-K can provide proximal and distal enhancers (Table 1). One such example is an intronic HERV-K LTR enhancer of the pluripotency marker gene F11R [10] (Fig. 2b). Concomitant with the global acquisition of somatic heterochromatin, HERV transcription is downregulated post embryonic implantation [12, 93]. Although further work is needed to understand the developmental function and dispensability of HERV expression in vivo, it is clear that HERV regulatory elements are intimately and reciprocally linked with early embryonic development.

Despite their global postimplantation downregulation, some ERV families are predicted to contribute to lineage-specific enhancer networks [13, 25]. In particular, ERV cis-regulatory elements facilitate the interferon gamma (IFNG) response [16, 17, 20, 95] with the primate-specific MER41 family being the best characterized example [16, 96]. MER41 can harbor binding motifs for the transcription factors IRF1 (interferon regulatory factor 1) and STAT1 (signal transducer and activator of transcription 1) [16, 96]. Upon IFNG induction, some MER41 copies are bound by STAT1 and IRF1 and exhibit H3K27ac enrichment, a property of active enhancers [2, 16, 91, 96]. In a seminal 2016 study, Chuong et al. found a MER41 element immediately adjacent to AIM2, which encodes an interferon-stimulated protein that detects double-stranded DNA (dsDNA) upon viral or bacterial infection, and one that can elicit an inflammatory response [97]. Crucially, the MER41 sequence contained the only STAT1 binding motif within 50 kbp of AIM2 (Fig. 2c). CRISPR-Cas9 deletion of this MER41 instance prevented AIM2 expression following IFNG induction and diminished the downstream inflammatory response. As well, Chuong et al. observed attenuated expression of several other interferon response genes upon deletion of nearby MER41 copies, suggesting recurrent co-option of this ERV family as IFNG-inducible proximal enhancers [16]. MER41 has also been found to participate in long-range chromatin interactions with genes involved in the IFNG response, raising the possibility that MER41 copies can act as distal enhancers [98]. The contribution of MER41 to IFNG signalling elucidated by Chuong et al. provides clear precedent for ERVs shaping somatic transcriptional networks [95] and offers a framework by which ERV regulation could be probed and manipulated elsewhere.


Materials and Methods

Strains

The strains used in this experiment are derived from the base strain, DBY15084, a haploid yeast strain derived from the W303 background with genotype MATa, ade2-1, CAN1, his3-11, leu2-3,112, trp1-1, URA3, bar1Δ::ADE2, hmlαΔ::LEU2. DBY15095 and DBY15092 carry a ClonNat R -marked GPA1 allele derived from RM11-1a or BY4741, respectively this single-nucleotide change alters the amount of basal signaling through the mating pathway, thereby modulating the selective benefit conferred by sterility in these two strains (L ang et al. 2009). To allow us to use flow cytometry to detect sterility, we amplified PFUS1-yEVenus from the plasmid pNTI37 (I ngolia and M urray 2007) and integrated it at the URA3 locus of DBY15095 and DBY15092 to generate strains DBY15104 and DBY15105, respectively, using oGIL133 (5′-ACTGC ACAGA ACAAA AACCT GCAGG AAACG AAGAT AAATC CTCAC TATAG GGCGA ATTGG-3′) and oGIL134 (5′-GTGAG TTTAG TATAC ATGCA TTTAC TTATA ATACA GTTTG CAATT AACCC TCACT AAAGG-3′). Integrative transformations were performed using standard yeast procedures (S herman et al. 1974). Sterile derivatives of DBY15095 and DBY15092 were generated, targeting the STE7 gene with the KanMX cassette (DBY15106 and DBY15107, respectively), using oGIL059 (5′-AGTTC TAAGA TTGTG TTGTC C-3′) and oGIL060 (5′-GGGTT ATTAA TCGCC TTCGG-3′). To generate a reference strain for competitive growth rate assays (DBY15108), ymCherry was amplified from pJHK044 (from John Koschwanez and Andrew Murray, Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA) and integrated at the URA3 locus of DBY15095 using oGIL270 (5′-ACTGC ACAGA ACAAA AACCT GCAGG AAACG AAGAT AAATC TCATA CACAT ACGAT TTAGG-3′) and oGIL271 (5′-GTGAG TTTAG TATAC ATGCA TTTAC TTATA ATACA GTTTG GCGGC CATCA AAATG TATGG-3′).

Long-term evolution

We propagated a total of 592 parallel cultures for 1000 generations without shaking at 30°. We varied two experimental parameters: the population size and the selective advantage of sterility. The selective advantage conferred by sterility was varied by using two ancestral strain backgrounds: DBY15104 and DBY15105. In most of this article, we refer to these strains as “RM” and “BY”, respectively in the RM strain sterility confers a distribution of selective advantages centered around 0.6%, and in the BY strain it confers a distribution of advantages centered around 1.5% (see Results). For each population size and selective advantage we established 148 independent cultures of the ancestral strain distributed over two 96-well plates each plate contained a unique pattern of 22 blank wells to detect contamination/cross-contamination events and to prevent plate misidentification.

All liquid handling (the dilutions, the dispensing of solutions, and the preparation for flow cytometry) was performed using the Biomek FX (Beckman Coulter, Fullerton, CA) equipped with a 16-position deck, a multichannel pod, and a Beckman Stacker Carousel, using AP96 P20 barrier for culture transfers and AP96 P250 tips for media, glycerol, and PBST transfers. The P20 tips were washed by pipetting water, ethanol, and air and then were autoclaved tips were discarded after three uses. The big population size cultures were diluted 1:32 (4 μl into a total volume of 128 μl) every 12 hr into fresh YPD the small population size cultures were serially diluted 1:32 × 1:32 (1:1024) every 24 hr. This propagation regime corresponds to 10 generations per day. Given a saturation density of ∼10 8 cells/ml, this design corresponds to an effective population size of ∼10 5 in the small populations and 10 6 in the large populations (W ahl and G errish 2001).

Approximately every 40 generations cultures were assayed for the presence of sterile mutants (see below) and frozen down following the addition of 50 μl of 75% glycerol to each well. During the experiment we noticed several contamination events. Immediately after starting the experiment, we observed bacterial contamination. This issue was resolved by adding ampicillin (100 μg/ml) and tetracycline (25 μg/ml) to the media. Around generation 700 we observed contamination of our media source with a standard laboratory yeast strain. This contamination was detected by observing growth in the blank wells however, no laboratory yeast invaded the evolving populations as evidenced by plating each culture to media selective for the contaminating genotype. Twice the experiment was restarted from frozen stock (after generation 320, as a planned disruption, and after generation 820 following a possible second yeast contamination). Reanimated cultures were diluted 1:32 initially and propagated for 12 hr (5 generations) before resuming the standard propagation and dilution protocol.

Determining the frequency of sterile cells within a population

The ancestral strains, DBY15104 and DBY15105, contain a yEVenus reporter responsive to the yeast mating pheromone (αF). To detect the ratio of sterile to mating-competent cells, each plate was diluted 1:25 into a PCR plate containing 100 μl YPD + αF (10 μg/ml) per well. Plates were incubated at 30° for exactly 6 hr in a thermal cycler and then held at 4° for ∼4 hr. Plates were spun at 5000 rpm for 2 min in a Beckman Coulter centrifuge, and the medium was aspirated from the cell pellets. Cells were resuspended in cold PBST (0.5% Tween) and transferred to a 96-well plate. The ratio of yEVenus-positive to nonfluorescent cells was determined using an LSRII flow cytometer with a high-throughput sampler adaptor for 96-well plates. Since sterile cells continue to divide during the 6-hr incubation, while mating competent cells do not, the ratio of yEVenus-positive to nonfluorescent cells was converted to a frequency of steriles in the population using a standard curve (Figure 1B). To construct the standard curve, we determined the ratio of yEVenus-positive to nonfluorescent cells following αF induction for known frequencies of sterile (DBY15106 and DBY15107) strains seeded into a population of ancestral cells. The evolution experiment was begun before this method for determining the fraction of sterile cells was developed. Therefore, for data points after generation 270, the inductions were performed coincident with the dilutions, but for data points prior to generation 245, the inductions were performed on frozen cultures. In the latter case, the populations were propagated for 5 generations to acclimate the cells prior to induction however, we noticed than many cultures failed to induce properly, leading to some spurious measurements for these early time points (supporting information, Figure S1).

Measuring the fraction of sterile cells within a population. (A) Binding of the mating pheromone (αF) is signaled through the mating pathway, ultimately resulting in a cell-cycle arrest and a transcriptional response mediated by the transcription factor Ste12. To detect mating competency, we put the fluorescent reporter yEVenus under the control of a Ste12-responsive promoter. A mutation in any one of 10 genes within the mating pathway (green) results in sterility and eliminates αF-induced expression of yEVenus. In the presence of αF, mating-competent cells arrest and induce the fluorescent reporter. Sterile cells, however, remain dark and continue dividing, thereby amplifying low-frequency steriles within a population. (B) Standard curve showing the amplification of steriles following αF inductions of 4, 6, 8, and 12 hr. For the experiments described here, we used a 6-hr induction. (C) Examples of flow cytometry profiles for cultures with no steriles (left) and with 1% sterile individuals (right) following a 6-hr αF induction.

Analyzing trajectories

We defined steriles to be “observed” if we measured a sterile frequency >0.1% in two consecutive time points. The trajectories were classified into one of the eight categories shown in Figure 3B. The initial rate of increase of sterile mutations, sup, the final rate of decrease, sdown, the generation at which sterile mutations reach 0.1%, τup, and the generation where they are forced below 0.1%, τdown, were determined by fitting the equations y = sup(t − τup) + y0 and y = sdowndownt) + y0 to the upslopes and downslopes, respectively, where y is the natural logarithm of the ratio of sterile to nonsterile and t is the generation and y0 = ln(1/999), corresponding to 0.1% sterile. Fits were performed in Matlab and the points used for fitting were determined by eye. For a given trajectory, the time over which a particular sterile mutation exists at >0.1%, τtransit, is calculated as τdown − τup. The %max for each population is the highest observed percent sterile, excluding points suspected to be spurious measurements due to the freeze–thaw process (see above). For populations with multiple mutations, we recorded multiple values for the applicable parameters. All of the extracted parameters are shown in Figure S1 and Table S1.

Fitness assays

To measure the fitness of evolved cultures from frozen stock, we compared each to a reference strain, labeled with mCherry, such that fitness is always measured against the same standard. We were careful to begin the fitness test only after both strains were growing exponentially. To this end, we thawed plates of experimental populations and mCherry-labeled reference strain (DBY15108), diluted each plate 1:32 into fresh medium, and grew them for 12 hr (5 generations) prior to mixing. The experimental and reference plates were mixed 50:50 and propagated at the appropriate population size for 30 generations. At generations 10, 20, and 30, we transferred 4 μl of saturated culture into 128 μl fresh YPD and incubated it for 3 hr. Cells were spun down and resuspended in cold PBST and the ratio of nonfluorescent (experimental) and mCherry-positive (reference) cells was determined by flow cytometry using an LSRII flow cytometer (BD Biosciences, San Jose, CA), counting 50,000 total cells for each sample. The fitness difference between the experimental and reference strain was calculated as the rate of the change in the ln ratio of experimental to reference vs. generations (H artl 2000). To measure the fitness advantage conferred by sterility alone, we derived spontaneous steriles from the ancestral strains by plating cultures of DBY15104 and DBY15105 onto YPD + αF (10 μg/ml) and picking αF-resistant mutants from 18 independent cultures each. As described above, 50:50 mixtures of growing cultures of the mutants and the mCherry-labeled reference strain (DBY15108) were propagated in both the large and the small bottleneck regimes. Sampling and flow cytometry were performed as described above.

Fluctuation assays

Fluctuation assays (L uria and Delbrück 1943) were performed on five clones of DBY15104 and DBY15105 to determine the mutation rate to α-factor resistance as described previously (L ang and M urray 2008). Briefly, each clone was grown overnight to saturation in minimal medium supplemented with histidine, tryptophan, and uracil. Overnight cultures were diluted 1:10,000 into low-glucose minimal medium (0.1% glucose), 10 μl was dispensed into each well of a 96-well plate, and the plates were sealed with an aluminum plate seal to prevent evaporation. Cultures were grown for 36 hr at 30° without shaking, and sterile water was added to bring the volume up to 70 μl. Twenty-four cultures were pooled, diluted, and counted in triplicate using a particle counter (Beckman Coulter) to determine the number of cells per culture. The remaining 72 cultures were spot plated onto overdried YPD + αF (10 μg/ml) plates to select for mutants (9 cultures per plate) as described previously (L ang and M urray 2008). Colonies were counted after 24 hr growth at 30°. Fluctuation data were analyzed by the Ma–Sandri–Sarkar maximum-likelihood method (S arkar et al. 1992 F oster 2006). Ninety-five percent confidence intervals were determined using Equations 24 and 25 from R osche and F oster (2000).

Notebook

The complete laboratory notebook describing these experiments is available at http://www.genomics.princeton.edu/glang/notebooks.htm.


The importance of BRCA1 and BRCA2 genes mutations in breast cancer development

Many factors including genetic, environmental, and acquired are involved in breast cancer development across various societies. Among all of these factors in families with a history of breast cancer throughout several generations, genetics, like predisposing genes to develop this disease, should be considered more. Early detection of mutation carriers in these genes, in turn, can play an important role in its prevention. Because this disease has a high prevalence in half of the global population, female screening of reported mutations in predisposing genes, which have been seen in breast cancer patients, seems necessary. In this review, a number of mutations in two predisposing genes (BRCA1 and BRCA2) that occurred in patients with a family history was investigated. We studied published articles about mutations in genes predisposed to breast cancer between 2000 and 2015. We then summarized and classified reported mutations in these two genes to recommend some exons which have a high potential to mutate. According to previous studies, exons have been reported as most mutated exons presented in this article. Considering the large size and high cost of screening all exons in these two genes in patients with a family history, especially in developing countries, the results of this review article can be beneficial and helpful in the selection of exon to screen for patients with this disease.

Keywords: BRCA1 gene BRCA2 gene Breast cancer Mutations.


Are There Beneficial Mutations?

What are beneficial mutations? Does AiG need to change its stance regarding them? Dr. Georgia Purdom, AiG–U.S., clarifies this often-contentious area.

Thank you for contacting Answers in Genesis. I welcome your questions to help clarify this often-contentious area of “beneficial mutations.” I’d like to comment first on your last statement.

Truth in Admission

Just read [a] recent commentary on how the Smithsonian is actively speaking against Genesis , and [I] remember how when I was a child, I cannot remember any museum (like Chicago’s Field that I haunted as a young ’un) would have [admitted] that there was evidence for a worldwide Flood 25 years ago.

Ironically, the very act of speaking out against the Flood story is the Smithsonian’s admission that the anthropological evidence for a Flood has become so strong that even they cannot ignore it. It’s great news!

I also enjoyed the claim that a Flood would leave a uniform stratum from what I learned (at the Field and elsewhere) about geology, this is the very last thing any competent geologist would expect—that the reality of masses of material in any flood creates a number of strata of rock, sand, clay, and organic materials, not just one.

What about you?

Let us know how AiG has impacted your life.

I cannot find anywhere on our website or in our publications where we make the claim that all mutations are bad. On the contrary, we do believe that certain mutations can have beneficial outcomes, as experimental science has shown (see Are Mutations Part of the “Engine” of Evolution? and Ancon Sheep: Just Another Loss Mutation). Hopefully, this will become clear in the examples that follow.

It is true that the majority of mutations fall into the categories of either nearly neutral or harmful. Silent (neutral) mutations alter the DNA sequence but do not alter the amino acids encoded by the DNA sequence. This is due to built-in redundancy in the code (also referred to as degeneracy). For example, CCC, CCT, CCA, and CCG in the DNA all code for the amino acid glycine. In a hypothetical DNA sequence that has the sequence CCC if the last base is changed to any of the remaining DNA bases (T, A, or G), it will still code for the amino acid glycine. (However, there is some evidence that indicates that even these changes may not be completely neutral and may alter the stability of the mRNA, which serves as the intermediate between DNA and proteins).

My graduate work focused on studying mice that were missing three bases in their DNA and, thus, one amino acid from one protein. The mice were blind (no eyes), deaf, albino, had deficient immune systems, suffered osteopetrosis, had no teeth, and died upon weaning without supplemental nutrition. Talk about a harmful mutation! Even a small change in the DNA can cause large detrimental effects to the overall development and health of an organism.

But are there such things as beneficial mutations? In short, no, but let me explain. While I have yet to see evidence of a truly beneficial mutation, I have seen evidence of mutations with beneficial outcomes in restricted environments. Mutations are context dependent, meaning their environment determines whether the outcome of the mutation is beneficial. One well-known example is antibiotic resistance in bacteria. In an environment where antibiotics are present, mutations in the bacterial DNA that alter the target of the antibiotic allow the bacteria to survive (the bacteria are faced with a “live or die” situation). However, these same mutations come at the cost of altering a protein or system that is important for the normal functioning of the bacteria (such as nutrient acquisition). If the antibiotics are removed, typically the antibiotic resistant bacteria do not fare as well as the normal (or wild-type) bacteria whose proteins and systems are not affected by mutations (see also Is Bacterial Resistance to Antibiotics an Appropriate Example of Evolutionary Change?). There are numerous other examples as well. Thus, the benefit of any given mutation is not an independent quality, but rather a dependent quality based on the environment.

It is true that there are people who have mutations with beneficial outcomes. For example, individuals with the CCR5 mutation who are exposed to HIV are not likely to develop an infection and subsequently AIDS. Individuals who develop cancer but have certain mutations can be effectively treated with a certain class of cancer drugs. However, there may be currently unknown detrimental effects from these mutations as well.

For example, studies have shown that people with the CCR5 mutation may be at a higher risk of developing West Nile Virus illness and hepatitis C. In addition, the detrimental effects may not be detrimental enough to affect the overall fitness of the individual, and thus, the ability of the individuals to survive in most environments does not differ from those without the mutations. These mutations are not selected against by natural selection, and so, they remain in the population. In humans, determining the beneficial or detrimental outcomes of mutations is many times difficult to assess since the mutations do not result in a “live or die” situation as is often the case for bacteria (i.e. upon exposure to antibiotics).

Again, the mutations only improve a person’s chance for survival in a given environment (external or internal), such as if the person is exposed to HIV or cancer develops within a person’s body. It is possible that the mutations would not be beneficial in other environments (i.e. if the person is exposed to West Nile Virus).

Keep in mind that beneficial, information-gaining mutations are a necessary mechanism of molecules-to-man evolution, so focusing on any potential for this is essential for evolutionists. What doesn’t seem to be often addressed is the vast amount of data to the contrary. But even if there were a clearly beneficial mutation, this would by no means “prove” the mechanism for evolution (for one thing, beneficial, information-gaining mutations would have to be a regularly occurring phenomenon and would have to “build” on previous mutations so as not to be “undone” and to keep the evolution going “uphill”), nor negate the truth of God’s revelation of His Creation in Genesis .

I hope this has helped clarify our stance on mutations and their effects.


Introduction

Evolutionary dynamics of a population of nucleic acid sequences is controlled by several acting forces, including random mutation, natural selection, genetic drift, and linkage opposed by recombination. Of central interest is the adaptation of an organism to a new environment, which occurs due to the fixation in a population of rare mutations that increase the fitness of the organism [1–5]. The existing models with directional selection and adaptation in a multi-site population demonstrate that only those beneficial mutations that are established in a population, as opposed to those becoming extinct, contribute to the average speed of adaptation in the long term. The advantage of each favorable mutation is measured by the relative change it causes in genome fitness (average progeny number). Thus, the knowledge of fitness effects for different mutations is essential for predicting the evolutionary trajectory of a population, for example, during the development of resistance of a pathogen to treatment or the immune response. Therefore, a great effort has been invested in their estimation.

In the HIV genome, the average-over-genome fitness effect of a beneficial mutation,

1%, was estimated using genetic samples from infected patients [6]. Finding out the Distribution of Fitness Effects of new mutations (DFE) over genomic sites in viruses and bacteria requires specially designed and elaborate experiments [1–5]. Selection coefficients for different sites of the hemagglutinin gene of human influenza A/H3N2 were estimated by fitting the deterministic one-locus model and its approximate extension for two-loci [7], where the model was fit to time-series data on allele frequencies. Another group [8] proposed a method of DFE estimation for deleterious mutations in mutation-selection-drift equilibrium based on the assumption that DFE has the form of the gamma distribution. These efforts emphasize the need for a more general approach based on evolutionary dynamics and not restricted to a one-locus model [9].

A major complication in predicting an evolutionary trajectory and estimating mutational effects is that the fates of individual alleles at different genomic sites are not independent due to clonal interference and linkage effects [10,11]. These effects increase with the number of linked variable sites. Another factor creating site-site interference is epistasis [12,13]. Recent advances in theoretical population genetics provide accurate and general expressions for the average speed of adaptation of an asexual population, as well as well for other observable parameters, such as genetic diversity, the probability of allele fixation, and phylogenetic properties. The technique is the traveling wave theory [14–28]. These models show that the evolution of a multi-site genome can be described by a narrow distribution of genomes in fitness, which slowly moves towards higher or lower fitness. The speed and direction depend on the interplay between selection, mutation, random drift, and linkage effects and recombination. The traveling wave was observed experimentally in yeast [29]. In all these models, the distribution of fitness effects across mutation sites (DFE) serves as an important input parameter, in addition to the population size, mutation rate, and recombination rate.

In the present work, we propose a rather general approach to measure selection coefficients for specific sites that applies in the presence of multi-site linkage, both within and outside of the traveling wave regime. The key to the method is the intriguing fact that DFE for beneficial mutations has frequently an exponential form, which was observed for E. coli, Pseudomonas aeruginosa, Pseudomonas fluorescence, and poliovirus (Fig 1) [1–5]. We offer a simple interpretation of this phenomenon. We demonstrate that, regardless of the initial distribution of fitness effects across genomic sites, an exponential DFE emerges naturally, as a consequence of the evolutionary process of slow adaptation. However, the prediction is not completely universal. When the population approaches equilibrium, this result ceases to apply. Based on these findings, we develop a method of estimating the fitness effect of mutation for each variable site in the genome.

Y-axis: Frequency of beneficial alleles (arbitrary units), DFE(s,t)π(s) in Eq 1. X-axis: Mutation gain in fitness due to a beneficial mutation (selection coefficient). Symbols represent results obtained for different sites of the genome in experiments on Escherichia coli [1], Pseudomonas fluorescens [2], poliovirus synonymous mutations, poliovirus non-synonymous mutations [3], poliovirus low MOI [4], E. coli acetamide (ACT), propionamide (PR), and isobutyramide (IB) [5].

In the existing literature on DFE, two different distributions are referred as DFE. The first is the inherent distribution of selection coefficients of a genome, which represents the genome site density with respect to the values of their selection coefficient. This distribution can be observed in a site-directed mutagenesis experiment, where the fitness difference between alleles for each site is measured [30,31]. We will refer to it as "intrinsic DFE" to emphasize the fact that it is the property of the pathogen/environment and does not depend on the state of population. Another distribution is the distribution of new beneficial mutations arising naturally in an evolution experiment, which depends on the state of adapting population (Fig 1). We will use term "DFE" to denote the second distribution. We demonstrate below that these two distributions are different from each other [32], and that only one of them is close to the exponential. We focus on beneficial mutations only.


Conclusion

Bacteria frequently develop mutations that enable them to survive and adapt to a variety of environmental conditions. These mutations are generated by many different mechanisms, and provide a wide range of phenotypic modifications. However, most of these mutations can be classified as a form of antagonistic pleiotropy. Some existing systems are sacrificed as a means for surviving certain environments.

Antagonistic pleiotropy is a useful feature of a creation model. Bacteria face a variety of environmental conditions and stressful situations. However, in order to survive, they must contend with any environmental condition that confronts them. Antagonistic pleiotropy provides them genetic mechanisms where they can make specific (and potentially detrimental) genetic changes that will then serve in a particular environment. If the environmental conditions change, the mutation usually becomes less beneficial and perhaps even detrimental. Hence, these mutations do not provide a genetic mechanism that accounts for the origin of biological systems or functions. Rather, they require the prior existence of the targeted cellular systems. As such, beneficial mutations of bacteria fit concisely within a creation model where (a) biological systems and functions were fully formed at creation , (b) subsequent mutations can provide conditional benefits that enable the organism to survive harsh conditions even though the mutation is generally degenerative, and (c) most bacteria need the ability to rapidly adapt to ever changing environments and food sources.


Watch the video: Beneficial Mutations? Yes! Evolution? Nope! (May 2022).