Reactionary fringe meets mutation-biased adaptation. 6. What "limits" adaptation?

This is part of a continuing series of posts by Arlin Stoltzfus on the role of mutation as a dispositional factor in evolution. In this post Arlin discusses the role of adaptation and what determines the pathway that it will take over time. Is it true that populations will always adapt quickly to any change in the environment? (Hint: no it isn't!) Click on the links in the box (below) to see the other posts in the series.

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Reactionary fringe meets mutation-biased adaptation.
6. What "limits" adaptation?
by Arlin Stoltzfus
According to the hatchet piece at TREE, theoretical considerations dictate that biases in variation are unlikely to influence adaptation, because this requires small population sizes and reciprocal sign epistasis.

Yet, we have established that mutation-biased adaptation is real (see The empirical case and Some objections addressed). If theoretical population genetics tells us that mutation-biased adaptation is impossible or unlikely, what is wrong with theoretical population genetics?

Adaptation, before Equilibrium Day

Reactionary fringe meets mutation-biased adaptation. 5.3. How history is distorted.

This is the ninth in a series of guest posts by Arlin Stoltzfus on the role of mutation as a dispositional factor in evolution. Click on the links in the box (below) to see the other post in the series.

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Reactionary fringe meets mutation-biased adaptation.
5.3. How history is distorted.
by Arlin Stoltzus
In his famous Materials for the Study of Variation, Bateson (1894) refers to natural selection as "obviously" a "true cause" (p. 5). Punnett (1905) explains that mutations are heritable while environmental fluctuations are not, concluding that "Evolution takes place through the action of selection on these mutations" (p. 53). De Vries begins his major 1905 English treatise by writing that ...

"Darwin discovered the great principle which rules the evolution of organisms. It is the principle of natural selection. It is the sifting out of all organisms of minor worth through the struggle for life. It is only a sieve, and not a force of nature" (p. 6)

Morgan (1916), in his closing summary, writes:

"Evolution has taken place by the incorporation into the race of those mutations that are beneficial to the life and reproduction of the organism" (p. 194)

Reactionary fringe meets mutation-biased adaptation. 5.2. The Modern Synthesis of 1959

This is the eighth in a series of guest posts by Arlin Stoltzfus on the role of mutation as a dispositional factor in evolution.

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Reactionary fringe meets mutation-biased adaptation. 5.2. The Modern Synthesis of 1959
by Arlin Stoltfus
As we learned in What makes it new?, the newness of the effect of biases in the introduction process results from a classical assumption that evolution can be understood as a process of shifting the frequencies of existing alleles. How did this position emerge? Was it a technical, mathematical issue?

Reactionary fringe meets mutation-biased adaptation. 5.1. Thinking about theories

This is the seventh in a series of guest posts by Arlin Stoltzfus on the role of mutation as a dispositional factor in evolution.

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Reactionary fringe meets mutation-biased adaptation. 5.1. Thinking about theories
by Arlin Stoltzfus
A wikipedia page disambiguating "Modern Synthesis" defines neo-Darwinism as

"the state-of-the-art in evolutionary biology, as seen at any chosen time in history from the 1890s to the present day."

Because "neo-Darwinism" and the "Synthesis" are conflated with whatever is widely accepted, they are now regularly attacked on grounds that are completely unrelated to genuine neo-Darwinism or the original Modern Synthesis, e.g., as when a network of life (rather than a tree) is invoked as a contradiction of Darwinism. The attack by Noble (2015) on the

"... conceptual framework of neo-Darwinism, including the concepts of "gene," "selfish," "code," "program," "blueprint," "book of life," "replicator" and ˜"vehicle."

is entirely a critique of late-20th-century reductionism à la Dawkins, and addresses neither neo-Darwinism (selection and variation as the potter and the clay), nor the original Modern Synthesis, which is simply not reductionistic, but positively invokes emergent phenomena (population-level forces, the gene pool as dynamic buffer) in the service of selection as a high-level governing principle.

"The state of the art" is a phrase that needs no modification. Nothing good can come from linking it to the name of a dead person.

Reactionary fringe meets mutation-biased adaptation. 5. Beyond the "Synthesis" debate

This is the sixth in a series of guest posts by Arlin Stoltzfus on the role of mutation as a dispositional factor in evolution.

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Reactionary fringe meets mutation-biased adaptation. 5. Beyond the "Synthesis" debate
by Arlin Stoltzfus
The authors of TREE's hatchet piece imply that the theory of Yampolsky and Stoltzfus (2001) is somehow not new, citing ancient work from Dobzhansky and Haldane. In Box 1, they argue that this theory is part of "standard evolutionary theory," showing a 4-step derivation ending in Eqn IV, which is Eqn 2 of Yampolsky and Stoltzfus (2001), and informing the reader that this is based on "classical" results from Fisher, Haldane and Kimura, who are named, while Yampolsky and Stoltzfus are not named.

Yet, Fisher, Haldane, and Kimura did not make the argument in Box 1, did not follow the 4 steps, and did not derive Eqn IV!

Reactionary fringe meets mutation-biased adaptation. 4. What makes this new?

This is the fifth in a series of guest posts by Arlin Stoltzfus on the role of mutation as a dispositional factor in evolution.

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Reactionary fringe meets mutation-biased adaptation. 4. What makes this new?
by Arlin Stoltzfus
Scientists value novelty because it signifies untapped potential: a new idea has not been interrogated, applied, and extended. The more novel an idea, the greater its potential to re-shape our discourse and advance our understanding beyond the well tried ideas of the past.

Reactionary fringe meets mutation-biased adaptation. 3. The causes and consequences of biases in the introduction process

This is the fourth in a series of guest posts by Arlin Stoltzfus on the role of mutation as a dispositional factor in evolution.

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Reactionary fringe meets mutation-biased adaptation. 3. The causes and consequences of biases in the introduction process
by Arlin Stoltzfus
As discussed previously, mutation-biased adaptation occurs in the laboratory and in nature. In the cases that have been examined, modest several-fold mutational biases have modest several-fold effects on the changes involved in adaptation.

Reactionary fringe meets mutation-biased adaptation
Introduction
1. The empirical case
2. Some objections addressed
3. The causes and consequences of biases in the introduction process
4. What makes this new?
5. Beyond the "Synthesis" debate
    -Thinking about theories
    -Modern Synthesis of 1959
    -How history is distorted
    -Taking neo-Darwinism
      seriously
    -Synthesis apologetics
6. What "limits" adaptation?
7. Going forwardHow can this happen? Classical thinking says that mutation is a weak pressure easily overcome by selection. This "opposing pressures" argument was invoked by Fisher (1930), Haldane (1933) and Wright (1931), as well as Huxley, Ford, Stebbins, Simpson and others. On this basis, it is assumed that the effects of mutation bias will be seen only in neutral evolution, where the opposing pressure of selection is absent, or with unusually high mutation rates.

Reactionary fringe meets mutation-biased adaptation. 2. Some objections addressed.

This is the third in a series of guest posts by Arlin Stoltzfus on the role of mutation as a dispositional factor in evolution.

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Reactionary fringe meets mutation-biased adaptation. 2. Some objections addressed.
by Arlin Stoltzfus
In the previous post Part 1, we reviewed evidence from 8 analyses suggesting that modest several-fold biases in mutation may impose modest several-fold biases on the spectrum of changes involved in adaptation, including some legendary cases of natural adaptation.

Reactionary fringe meets mutation-biased adaptation
Introduction
1. The empirical case
2. Some objections addressed
3. The causes and consequences of biases in the introduction process
4. What makes this new?
5. Beyond the "Synthesis" debate
    -Thinking about theories
    -Modern Synthesis of 1959
    -How history is distorted
    -Taking neo-Darwinism
      seriously
    -Synthesis apologetics
6. What "limits" adaptation?
7. Going forwardIs the evidence strong enough already to conclude in favor of a bold new idea? The authors of the hatchet piece at TREE believe that nothing has been shown, arguing that the proposed effect is theoretically unlikely and is probably due to selection.

The focus of this post is on alternative hypotheses (theoretical arguments will be addressed later). For the sake of brevity, I will address just 2 of the many spurious objections offered by these authors in their quest to exemplify the Dunning-Kruger effect. For instance, they write "we stress that parallel genetic change underlying phenotypic convergence is not sufficient evidence for mutation bias being important in causing such convergence."

This is an inversion of the argument, common in the parallelism literature (see Bailey, et al. 2015), that the recurrence of exactly the same change is by itself evidence of selection.

In fact, the case for mutation-biased adaptation does not depend on such weak inferences. In the 8 analyses we reviewed, no change is designated as adaptive solely based on a pattern of recurrence. Instead, each mutational path has either (1) a genetic association with fitness or resistance, or (2) an experimentally verified molecular effect consistent with the adaptive story. Once adaptive changes have been identified, statistical tests are applied to detect an excess of changes of the mutationally favored class.

As another example, TREE's hatchet piece refers to selection as an independent force of adaptation, then attacks the strawman theory of mutation bias as an independent force of adaptation. To ensure that the reader is deceived about mutation-biased adaptation, and ill disposed toward this line of research, this strawman is repeated 5 times on the first page (figure).

Both arguments illustrate how reactionary minds fail to grasp new ideas, and see only perversions or inversions of cherished old ideas.

Now, let us set aside strawman arguments, to focus on genuine alternatives.

For instance, the authors suggest that transitions could be favored "owing to selection on genomic base composition," citing work on GC content. This hypothesis can not work. If the effect of selection is to conserve GC content, this can not explain a bias toward transitions, because the universe of GC-conserving mutations has a transition:transversion ratio of 0. Likewise, if the effect of selection is to change GC content, this can not explain the observed degree of bias, because the universe of GC-changing amino acid replacement mutations has roughly a 1:1 transition:transversion ratio, not large enough to explain results of Payne, et al. (2019) or Stoltzfus and McCandlish (2017).

A more plausible alternative raised by the authors, following Stoltzfus and Norris (2016), is that the observed evolutionary bias could be caused by a bias in protein-level fitness effects that happens to align with the mutation bias, e.g., they suggest that "selectively beneficial transitions and selectively beneficial transversions could also have different distributions of fitness effects."

Let us consider, for the 8 analyses addressed previously, the hypothesis that observed evolutionary biases are not due to mutation bias at all, but to a cryptic fitness bias that happens to align with the mutation bias.

First, in the studies by MacLean, et al. (2010), Sackman, et al. (2017) and Liu, et al. (2019), the authors measure fitness (or resistance). The data from MacLean, et al. (2010) reveal no correlation of mutation rate with fitness (figure).

In their model of effects in drug-resistant tumors, Liu, et al. (2019) find that the mutational factor (estimated mutation rate) explains more variance than the fitness-related factor (measured drug resistance). Results of one-step adaptation from Sackman, et al. (2017) are shown in the figure (left: transitions are in light gray, transversions are in dark gray; upper scale is selection coefficient, lower scale is number of evolved lineages out of 20). Here the mean selection coefficients for transitions and transversions are 0.37 (CI 0.053) and 0.40 (CI 0.18), respectively, i.e., transversions are insignificantly better (data from their Table 1).

Next, consider the experimental study by Couce, et al. (2015) shown in the figure below (courtesy of Alex Couce). Among resistant mutants in PBP3, the resistant mutT isolates (blue) overwhelmingly have the kind of mutations favored by mutT (left box), and the resistant mutH isolates (red) overwhelmingly have the kind of mutations favored by mutH (center box; other types of mutations are in the right box, which includes most of the black isolates indicating a wild-type parent).

The only way to explain this as a fitness effect would be to argue that (1) the mutT and mutH genotypes have widespread, strong, and utterly distinct epistatic effects on the fitness landscape for PBP3, i.e., each mut genotype induces a distinct set of beneficial alleles, and (2) the corresponding mutations for those alleles just happen to be (overwhelmingly) the same type of mutation favored by the mutator.  This is wildly implausible because it implies that the blue-red segregation of columns in the figure above is accidental.

What about the meta-analyses of transition-transversion bias? Could there be a fitness advantage of transitions that explains this effect?

Stoltzfus and Norris (2016) analyzed data on 544 transitions and 695 transversions with experimentally measured fitness effects. Comparing various binary predictors, they considered the chance that a nominally conservative mutation is more fit than a nominally radical one, aka the AUC, which ranges from 0 to 1, with a null expectation of 0.5. Transition-transversion class is a weak predictor (AUC = 0.53, figure), out-performed by most biochemical factors, all 200 of which are out-performed by a conservative-radical distinction based on Tang's U (AUC = 0.64), an empirical measure of relative fixation probability computed from a large set of sequence alignments. Yet, the conservative-radical distinction from Tang's U corresponds to a mere 2.7-fold fixation bias in evolution. Using this relationship, Stoltzfus and Norris (2016) estimate that the transition:transversion distinction corresponds to a 1.3-fold fixation bias, with a confidence interval from 1.0 (no effect) to 1.6.

But these results use the entire distribution of mutations, including the worst ones that (in nature) would be removed by selection. Therefore, Stoltzfus and Norris (2016) truncated the data to see if a stronger benefit would emerge among benign mutations. Instead of getting stronger, the effect disappeared (their Fig. 1).

Next, Stoltzfus and Norris (2016) set aside the above data, and looked at an independent set of data from 4 studies of laboratory adaptation implicating 111 beneficial mutations with measured fitness effects. In the table below, the AUC value in the penultimate column is the chance that a transition is ranked higher than a randomly chosen transversion: the values are all < 0.5. That is, beneficial transitions rank slightly lower than beneficial transversions. The later study by Sackman, et al. (2017) (above) represents a 5th independent case in which beneficial transitions rank slightly lower than beneficial transversions.

Thus, available data, reflecting multiple lines of evidence, indicate that transitions simply do not have a fitness advantage that could explain a several-fold effect on amino acid changes in evolution.

Finally, note that Payne, et al. (2019) report evolutionary biases that cannot be explained by protein-level selection, including transition bias in non-coding changes, and the excess of Met-to-Ile transitions over Met-to-Ile transversions (which are twice as likely without mutation bias).

To summarize, in our evaluation of the cryptic-fitness-difference hypothesis, we find that: in 3 cases, the fitness effects were measured, with results that do not support the hypothesis; in 3 cases (counting 2 meta-analyses in Stoltzfus and McCandlish, 2017), the evidence indicates that the mutationally favored class (transitions) does not have a sufficient fitness advantage; in 1 case, the hypothesis is wildly implausible (Couce, et al., 2015); and in 1 remaining case, Storz, et al. (2019) invoke a mutational effect without any clear justification for assuming an absence of differential fitness effects.

Concluding thoughts

In recent years, systematic data have begun to accumulate on molecular changes implicated in phenotypic adaptation. The pattern emerging from these data is that the molecular changes implicated in adaptation are enriched for the kinds of changes that are favored by mutation, and this enrichment cannot be explained by a cryptic fitness bias that happens to align with the mutation bias.

We could treat this merely as a pattern, as a new and useful empirical generalization.

But there is much more to the story. Mutation-biased adaptation was predicted under a theory that contrasts sharply with classical thinking, which holds that internal tendencies of variation cannot cause evolutionary trends or biases, because mutation rates are too small: in order for mutation biases to be important, mutation rates must be very large, or the opposing pressure of selection must be absent, i.e., effects of biases in ordinary mutations will be limited to neutral evolution.

Yampolsky and Stoltzfus (2001) argued that this view, which derives from the mutation-selection balance model of Fisher and Haldane, assumes that evolution can be treated as a short-term process of shifting the frequencies of pre-existing alleles, without considering the (potentially biased) introduction of new alleles. Using a simple model, they showed that the efficacy of biases in introduction does not require absolute constraints, neutral evolution, or high mutation rates. They argued that this conclusion applies to developmental biases as well as mutation biases.

Thus, it is time to understand this theory, what it implies, and why it differs from classical thinking-- the topic of the next post in the series.

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Bailey SF, Blanquart F, Bataillon T, Kassen R. (2017). What drives parallel evolution?: How population size and mutational variation contribute to repeated evolution. Bioessays 39:1-9.[doi.org/10.1002/bies.201600176]

Couce A., RodrÃ-guez-Rojas A., and Blázquez J. (2015) Bypass of genetic constraints during mutator evolution to antibiotic resistance. Proc. Biol. Sci. Apr 7;282(1804):20142698 [doi: 10.1098/rspb.2014.2698]

Liu, C., Leighow, S., Inam, H., Zhao, B., and Pritchard, J.R. (2019) Exploiting the 'survival of the likeliest' to enable evolution-guided drug design. bioRxiv 557645; [doi: 10.1101/557645] 

MacLean R.C., Perron G.G., and Gardner A. (2010) Diminishing returns from beneficial mutations and pervasive epistasis shape the fitness landscape for rifampicin resistance in Pseudomonas aeruginosa. Genetics 186: 1345-1354. [doi: 10.1534/genetics.110.123083]

Payne J.L., Menardo F., Trauner A., Borrell S., Gygli S.M., Loiseau C., et al. (2019). Transition bias influences the evolution of antibiotic resistance in Mycobacterium tuberculosis. PLoS Biol 17(5): e3000265. [doi: 10.1371/journal.pbio.3000265]

Sackman, A.M., McGee, L.W., Morrison, A.J., Pierce, J., Anisman, J., Hamilton, H., Sanderbeck, S., Newman, C., and Rokyta, D.R. (2017) Mutation-Driven Parallel Evolution during Viral Adaptation. Mol. Biol. Evol. 34:3243-3253. [doi: 10.1093/molbev/msx257]

Stoltzfus, A. and McCandlish, D.M. (2017) Mutational Biases Influence Parallel Adaptation. Molecular Biology and Evolution 34:2163–2172, [doi: 10.1093/molbev/msx180]

Stoltzfus A, Norris RW. (2016). On the Causes of Evolutionary Transition:Transversion Bias. Mol Biol Evol 33:595-602. [doi.org/10.1093/molbev/msv274]

Storz J.F., Natarajan C., Signore A.V., Witt C.C., McCandlish D.M. and Stoltzfus A. (2019) The role of mutation bias in adaptive molecular evolution: insights from convergent changes in protein function. Phil. Trans. R. Soc. B [doi: 10.1098/rstb.2018.0238]

Reactionary fringe meets mutation-biased adaptation. 1. The empirical case

This is the second in a series of guest posts by Arlin Stoltzfus on the role of mutation as a dispositional factor in evolution. The first post was: Reactionary fringe meets mutation-biased adaptation: Introduction.

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Reactionary fringe meets mutation-biased adaptation. 1. The empirical case
by Arlin Stoltzfus
Reactionary fringe meets mutation-biased adaptation
Introduction
1. The empirical case
2. Some objections addressed
3. The causes and consequences of biases in the introduction process
4. What makes this new?
5. Beyond the "Synthesis" debate
    -Thinking about theories
    -Modern Synthesis of 1959
    -How history is distorted
    -Taking neo-Darwinism
      seriously
    -Synthesis apologetics
6. What "limits" adaptation?
7. Going forwardAs noted in the intro to this series, the appearance of an opinion piece on how mutation bias affects adaptation in Trends in Ecology and Evolution (TREE) appears to be a milestone for understanding this interesting new topic. The authors misrepresent a position on dual causation stated consistently in the literature for 20 years, ignore or misinterpret important new empirical work, and blithely repeat a self-serving view of history that my colleagues and I spent years debunking with careful scholarship.

In other words, as a colleague once said, "This is what victory looks like-- everyone butchering your ideas."

But it gets better. Scientists often invoke the cliche that new truths are first ridiculed as impossible, then opposed as unlikely, then claimed as traditional.  QuoteInvestigator has a piece on this "stages of truth" meme, with the following from Dr. J. Marion Sims, 1868:

For it is ever so with any great truth. It must first be opposed, then ridiculed, after a while accepted, and then comes the time to prove that it is not new, and that the credit of it belongs to some one else.

The idea that the course of evolution reflects internal biases in variation-- or, stated differently, that the generation of variation plays a dispositional role in evolution-- has been (1) ridiculed as an appeal to "the vagueness of inherent tendencies, vital urges, or cosmic goals, without known mechanism" (Simpson, 1967), and (2) ruled out by the famous "opposing pressures" argument from Fisher and Haldane that we will address later.

So, it was exciting that the TREE authors, who represent the reactionary fringe of evolutionary biology-- dedicating to shifting the "Synthesis" goal-posts to maintain the illusion that nothing is new--, not only butcher the idea of mutation-biased adaptation, try to minimize its importance, and misrepresent the evidence: they also want to claim it! Yes, they want to appropriate this unoriginal, unimportant, unsubstantiated idea for the legacy of Ronald Fisher, the closeted mutationist!

Seriously, you could not make this stuff up!

To dig out from under this mess will take some time. Let's begin by reviewing the evidence that the changes that occur during adaptation are enriched for mutationally likely changes.

The case for mutation-biased adaptation

First, consider 4 analyses of experimental evolution.

In the first stage of the compound study by Sackman, et al. (2017), Rokyta, et al. (2005) measured fitness for the beneficial changes-- 9 of them-- implicated in 20 replicate episodes of adaptation of phiX174, aka ID11. Because the fittest variant was found only once, yet the 4th most-fit was found 6 times, the authors explored a model of mutational effects, including transition bias and the multiplicity of mutational paths to an alternative amino acid (e.g., an ATG Met codon can mutate to Ile in 3 different ways, but an Ile codon such as ATC has only one mutational path to Met). An origin-fixation model with mutation bias fit the data better than the mutational landscape model of Orr, which considers only fixation probability. Sackman, et al. (2017) carried out this same 20-replicate protocol with 3 closely related phages: the combined results how a strong bias favoring transitions, 29:5 for paths, 74:6 for events (figure below).

Note the terminology. A "path" specifies the starting and ending genotype, e.g., "gene F, site 3665, C → T", and an event is an occurrence of change along a path-- in this case, a replicate culture in which a phage genome changes. Events along the same paths are parallel events.

MacLean, et al. (2010) repeatedly evolved Rifampicin-resistant Pseudomonas aeruginosa, with results showing a significant correlation between the chance of evolving and the measured mutation rate for 11 changes in rpoB (center panel below). All of these changes are nucleotide substitutions with mutation rates that differ due to unexamined context effects. The lack of correlation in the left panel is not too surprising, given that the calculated probability of fixation ranges only from 0.47 to 0.84, because s is so large (meanwhile, the mutation rate varies 50-fold).

Couce, et al. (2015) evolved cefotaxime resistance repeatedly in 2 different Escherichia coli mutator strains with distinctly different mutation spectra, resulting in two distinct distributions of changes among resistant strains, each with a strong correspondence to the respective parental mutation spectrum.

McCandlish and Stoltzfus (2017) gathered a large set of published cases of laboratory parallel adaptation due to recurrent amino acid replacements (389 events on 63 paths), and found that these data exhibit a substantial excess of transitions, relative to the null expectation for no mutation bias (a 1:2 ratio). Note that this study integrates data from MacLean, et al. (2010) and Rokyta, et al. (2005), but (1) this is a minority of the data (22 %), and (2) the test for transition bias is an independent result from the correlation shown earlier in data from MacLean, et al. (2010).

Next, consider 4 analyses of natural evolution. McCandlish and Stoltzfus (2017), in the same paper just mentioned, gathered data on natural parallelisms from 10 different study systems (231 events on 55 paths), and showed the same kind of transition bias. The summary table below shows that they draw from some famous adaptive stories in molecular evolution, including spectral tuning, resistance to cardiac glycosides (e.g., bird vs. monarch vs. milkweed), foregut fermentation, and echolocation.

The table above represents natural cases from Stoltzfus and McCandlish (2017). Cases 1, 4, 8 and 10 represent recent local adaptation of sub-populations (total 11:10 paths, 69:48 events), while the others represent species divergence (17:17 paths, 63:51 events).

Payne, et al. (2019) examined effects of transition bias in two different curated databases of causative mutations in antibiotic-resistant isolates of Mycobacterium tuberculosis, finding an excess of transition mutations. For instance, they take advantage of the unusual case of Met-to-Ile replacements, which can take place by 1 transition (ATG to ATA) or 2 different transversions (ATG to ATT or ATC). Instead of this 1:2 ratio of possibilities, they see a ratio of 88:49 (Basel dataset) or 96:39 (Manson dataset), roughly 4-fold above null expectations. Because all the replacements are the same type, the bias can not be due to selection preferring some replacements over others.

Storz, et al. (2019) examined changes in hemoglobins using 35 phylogenetically independent comparisons of low- and high-altitude bird populations. They identified adaptive changes in 20 comparisons, implicating 10 different paths and 22 events. (See Table below: Asterisks indicate CpG mutations.) The observation of 6 paths and 10 events associated with CpG mutations was about 6-fold over the null expectation, a statistically significant excess.

Finally, in a completely different type of study, Liu, et al. (2019) explore the emergence of imatinib resistance in leukemia patients, combining clinical data on the frequency of various resistant mutants (of the BCR-ABL oncogene) across 4 continents over 17 years, with laboratory characterization of engineered mutants. In a model for clinical frequency based on drug resistance (measured) and mutation biases (inferred from comparative data), they found that both factors were important, but the mutational factor was more important.

I mention Liu, et al. (2019) to draw attention to a fascinating, biomedically important study. However, I won't include it in future discussions about biological significance, because typically we do not include resistant tumor outgrowths in the category of natural adaptation (and I don't want to confuse people or invite distracting criticisms).

To summarize, various recent studies suggest that the changes that occur during adaptation are enriched for the kinds of changes that are mutationally likely. The spectrum of adaptive changes shows modest biases with the same orientation and magnitude as modest mutation biases (either known or suspected) that range in magnitude from a few-fold (transition bias) to 10-fold (CpG bias) to as large as 50-fold (the range of mutation rates measured by MacLean, et al., 2010).

Going further

Considered on their own, these results are perhaps uninteresting. Mutation biases are secretly influencing the details underlying adaptation. Perhaps this was not expected on classical grounds, but why should we care?

These results are important because they demonstrate a principle not previously accepted: modest quantitative biases in the generation of variation may impose predictable biases on evolution, without a requirement for absolute constraints, neutral evolution or high mutation rates, contradicting the classic logic of the opposing-pressures argument.

If this new principle is general, it would apply to other kinds of mutational biases, as well as to other types of biases, e.g., developmental biases induced by the structure of a genotype-phenotype map. That is, these results provide proof-of-principle for ideas long discussed in evo-devo. As argued by Stoltzfus (2019), the same results add plausibility to key ideas in the self-organization literature, regarding what Cowperthwaite and Ancel (2007) call "the large-scale patterns of mutational connectivity within genotype spaces."

Before exploring these implications in future posts, we need to take a more critical look at the evidence. The authors at TREE claim that nothing has been shown, and that the results are more likely to be due to selection. What is the status of this alternative hypothesis?

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Cowperthwaite, M.C., Meyers, L.A. (2007) How Mutational Networks Shape Evolution: Lessons from RNA Models. Annual Review of Ecology, Evolution, and Systematics 38:203-230.[doi.org/10.1146/annurev.ecolsys.38.091206.095507]

Couce A., Rodríguez-Rojas A., and Blázquez J. (2015) Bypass of genetic constraints during mutator evolution to antibiotic resistance. Proc. Biol. Sci. Apr 7;282(1804):20142698 [doi: 10.1098/rspb.2014.2698]

Liu, C., Leighow, S., Inam, H., Zhao, B., and Pritchard, J.R. (2019) Exploiting the 'survival of the likeliest' to enable evolution-guided drug design. bioRxiv 557645; [doi: 10.1101/557645]

MacLean R.C., Perron G.G., and Gardner A. (2010) Diminishing returns from beneficial mutations and pervasive epistasis shape the fitness landscape for rifampicin resistance in Pseudomonas aeruginosa. Genetics 186: 1345-1354. [doi: 10.1534/genetics.110.123083]

Payne J.L., Menardo F., Trauner A., Borrell S., Gygli S.M., Loiseau C., et al. (2019) Transition bias influences the evolution of antibiotic resistance in Mycobacterium tuberculosis. PLoS Biol 17(5): e3000265. [doi: 10.1371/journal.pbio.3000265]

Rokyta DR, Joyce P, Caudle SB, Wichman HA. (2005) An empirical test of the mutational landscape model of adaptation using a single-stranded DNA virus. Nat Genet 37:441-444. [https://doi.org/10.1038/ng1535]

Sackman, A.M., McGee, L.W., Morrison, A.J., Pierce, J., Anisman, J., Hamilton, H., Sanderbeck, S., Newman, C., and Rokyta, D.R. (2017) Mutation-Driven Parallel Evolution during Viral Adaptation. Mol. Biol. Evol. 34:3243-3253. [doi: 10.1093/molbev/msx257]

Simpson GG. (1967) The Meaning of Evolution. New Haven, Conn.: Yale University Press.

Stoltzfus, A. and McCandlish, D.M. (2017) Mutational Biases Influence Parallel Adaptation, Molecular Biology and Evolution 34:2163–2172, [doi: 10.1093/molbev/msx180]

Stoltzfus, A. (2019) Understanding bias in the introduction of variation as an evolutionary cause. [https://arxiv.org/abs/1805.06067]

Storz J.F., Natarajan C., Signore A.V., Witt C.C., McCandlish D.M. and Stoltzfus A. (2019) The role of mutation bias in adaptive molecular evolution: insights from convergent changes in protein function. Phil. Trans. R. Soc. B [doi: 10.1098/rstb.2018.0238]

Reactionary fringe meets mutation-biased adaptation: Introduction

This is the first of a series of guest posts by Arlin Stoltzfus on the role of mutation as a dispositional factor in evolution.

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Reactionary fringe meets mutation-biased adaptation: Introduction
by Arlin Stoltzfus
Theoreticians often formulate mathematical or computational models with the aim of exploring (or justifying) behavior anticipated from pre-existing verbal theories.  Yet, the resulting formalisms may exhibit behavior that was not expected.  Indeed, sometimes the model breaks the theory.

Reactionary fringe meets mutation-biased adaptation
Introduction
1. The empirical case
2. Some objections addressed
3. The causes and consequences of biases in the introduction process
4. What makes this new?
5. Beyond the "Synthesis" debate
    -Thinking about theories
    -Modern Synthesis of 1959
    -How history is distorted
    -Taking neo-Darwinism
      seriously
    -Synthesis apologetics
6. What "limits" adaptation?
7. Going forwardIn the 1970s, deterministic chaos was recognized in a number of dynamic models, including the Lotka-Volterra model used by ecologists for decades to illustrate notions of control via feedbacks between predator and prey abundance.  Depending on the delay in feedback, the system either oscillates predictably, crashes, or becomes chaotic.

Before the chaotic realm was named, characterized, and publicized, surely many researchers stumbled upon it, either when looking at data, or while working out numeric examples.  However, this did not elicit the phrase "Eureka!  I have discovered deterministic chaos!"  Chaotic dynamics did not fit with ideas about "control" or "feedback."  It existed in the world of nature but not in the world of science, even though it involves no new underlying processes.

In evolutionary genetics, the breeder's equation for change in a quantitative trait under selection once justified neo-Darwinism: invoking selection as the creative principle and source of direction in evolution had a rigorous mathematical basis, given abundant infinitesimal variation, and assuming that everything is a continuous trait (see Gould's excellent, well documented analysis in Ch. 4 of Ever Since Darwin, or ca. p. 140 of his 2002 book The Structure of Evolutionary Theory).

What Darwinism means for Ernst Mayr (Mayr and Provine 1980 p. 3).



Yet, after Lande and Arnold (1983) derived the multivariate generalization of quantitative genetics for the simultaneous change in multiple traits, Δz = Gβ, quantitative geneticists began to acknowledge that, in the words of Steppan, Phillips and Houle (2002) "Together with natural selection (the adaptive landscape), [the G matrix] determines the direction and rate of evolution." This new verbal theory, in which the rate and direction of evolution are jointly attributed to selection and standing variation, does not correspond to the old verbal theory, even though quantitative genetics is the branch of mathematical theory most closely aligned with neo-Darwinism, literally assuming abundant infinitesimal variation in every trait.

That is, Darwin's verbal theory led to Fisher's mathematical theory, then further developments along with empirical results (e.g., Schluter, 1996)) led to conflicts with the original verbal theory. As a result, quantitative genetics now tells us something different: the verbal theory of selection as a governing principle or independent shaping force, invoked by generations of neo-Darwinians, is mathematically impossible.

This series of posts relates to another unexpected twist that arises from another archaic ruling-principle theory: the view that the course of evolution largely reflects innate tendencies that shape variation, with selection playing only a minor role, traditionally known as orthogenesis.

A classic argument from Fisher and Haldane (based on their mutation-selection balance equation) says that variation-induced trends cannot be a cause of direction: for mutation to overcome the opposing force of selection would require abnormally high mutation rates. Mutation biases can influence neutral evolution, but otherwise, the only kind of bias that could possibly be influential is an absolute constraint distinguishing possible from impossible variants.

Yet Yampolsky and Stoltzfus (2001) used computer simulations of a simple 2-locus model, along with mathematical formulas based on origin-fixation dynamics, to show how parallelisms and trends may arise from mutational and developmental biases in variation, without requiring neutral evolution, absolute constraints, or high mutation rates.

We will delve into this theory later. For now, the important thing to note is the crucial prediction that the changes involved in adaptation may show the effects of modest quantitative biases in mutations with ordinary rates (e.g., transition-transversion bias in nucleotide substitution mutations).

When this theory was proposed, data on molecular changes involved in adaptation were rare—not sufficient to support statistical hypothesis-testing. In recent years, the data have become much more abundant, and we are seeing the predicted effect in both experimental adaptation (e.g. MacLean, et al. (2010), Couce, et al. (2015), Sackman, et al. (2017)), and more importantly, in retrospective analyses of natural adaptation (e.g., Payne, et al. (2019), Storz, et al. (2019), Liu, et al. (2019), and Stoltzfus and McCandlish (2017)).

That is, we are witnessing the establishment of a fundamental new principle of evolution, a principle that was not just unexpected, but rejected by the architects of the Modern Synthesis.

Thus, it was an odd choice for Trends in Ecology and Evolution (TREE) to publish a deeply deceptive article that attacks this new idea, from a pair of authors so unfamiliar with the topic that they literally mis-define "mutation bias." According to the authors, there are no new principles here, only "standard evolutionary theory" from Fisher, Haldane and Kimura. The appearance of mutation-biased adaptation is illusory, they argue, claiming that the evolutionary biases are due to selection.

What motivated such a gratuitous attack? A colleague who described the paper as "an abomination" assigned it to one of his advisees to study as an example of what a really bad paper looks like. How did it get published? Why didn't the editor get critical reviews?

The answers relate to a dispute between advocates of an "Extended Evolutionary Synthesis" or EES, and defenders of a re-branded version of the Modern Synthesis called SET (Standard Evolutionary Theory). The authors of the hatchet piece are members of a fringe movement dedicated to arguing that (1) SET automagically updates itself to include valid new thinking, and (2) there is no valid new thinking, because anything that seems new actually traces back to important dead people. Thus, these guardians of orthodoxy were obliged to undermine the novelty and importance of mutation-biased adaptation, while at the same time claiming it for SET.

This peculiar set of circumstances—an unorthodox theory, powerful new results, and the backlash from reactionaries—defines a series of posts that Larry has offered to host here on SandWalk. The plan for the series is as follows:

1. The empirical case. Results from adaptation in the lab, and retrospective analyses of adaptation in nature, show that the changes involved in adaptation are enriched for mutationally-favored changes.
2. Some objections addressed. The evidence now available rules out the possibility that the observed evolutionary biases are due to a cryptic bias in fitness that happens to align with the mutational bias.
3. The causes and consequences of biases in the introduction process. The theory of Yampolsky and Stoltzfus (2001) addresses mutation biases, developmental biases, and effects of connectivity of genetic networks (invoked in the self-organization literature)
4. What makes this theory new. For a very long time, mutation-biased adaptation was not anticipated, due to the "gene pool" assumption that evolution begins with standing variation.
5. A diversion into the EES-SET culture war. Issues relevant to navigating high-level disputes in evolutionary biology are addressed in a series of 4 posts.
   5.1. Thinking about theories.
   5.2. The Modern Synthesis (1959 - 1969).
   5.3. The abuse of history.
   5.4. Synthesis sophistry.
6. What "limits" adaptation? What makes adaptation something other than an ideal process in which the best genotype arises in infinite time after all possibilities have been tested?
7. Future directions. Abundant opportunities exist to build a broader empirical and theoretical understanding of the evolutionary role of biases in the introduction of variation, and to leverage this role in evolutionary inference.

The posts will be released every few days, and will be linked in to this Introduction page (so you can bookmark this).

So, please join in the discussion, and invite your colleagues to do the same.

Next post: Reactionary fringe meets mutation-biased adaptation. 1. The empirical case.

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Couce A., Rodríguez-Rojas A., and Blázquez J. (2015) Bypass of genetic constraints during mutator evolution to antibiotic resistance. Proc. Biol. Sci. Apr 7;282(1804):20142698 [doi: 10.1098/rspb.2014.2698]

Lande, R., and Arnold, S.J. (1983) The measurement of selection on correlated characters. Evolution, 37:1210-1226. [doi: 10.1111/j.1558-5646.1983.tb00236.x]

Liu, C., Leighow, S., Inam, H., Zhao, B., and Pritchard, J.R. (2019) Exploiting the 'survival of the likeliest' to enable evolution-guided drug design. bioRxiv 557645; [doi: 10.1101/557645]

MacLean R.C., Perron G.G., and Gardner A. (2010) Diminishing returns from beneficial mutations and pervasive epistasis shape the fitness landscape for rifampicin resistance in Pseudomonas aeruginosa. Genetics 186: 1345-1354. [doi: 10.1534/genetics.110.123083]

Payne J.L., Menardo F., Trauner A., Borrell S., Gygli S.M., Loiseau C., et al. (2019) Transition bias influences the evolution of antibiotic resistance in Mycobacterium tuberculosis. PLoS Biol 17(5): e3000265. [doi: 10.1371/journal.pbio.3000265]

Sackman, A.M., McGee, L.W., Morrison, A.J., Pierce, J., Anisman, J., Hamilton, H., Sanderbeck, S., Newman, C., and Rokyta, D.R. (2017) Mutation-Driven Parallel Evolution during Viral Adaptation. Mol. Biol. Evol. 34:3243-3253. [doi: 10.1093/molbev/msx257]

Schluter, D. (1996) Adaptive radiation along genetic lines of least resistance. Evolution, 50:1766-1774. [doi: 10.1111/j.1558-5646.1996.tb03563.x]

Steppan, S.J., Phillips, P.C., and Houle, D. (2002) Comparative quantitative genetics: evolution of the G matrix. Trends in Ecology & Evolution, 17:320-327. [doi: 10.1016/S0169-5347(02)02505-3]

Stoltzfus, A. and McCandlish, D.M. (2017) Mutational Biases Influence Parallel Adaptation, Molecular Biology and Evolution 34:2163–2172, [doi: 10.1093/molbev/msx180]

Storz J.F., Natarajan C., Signore A.V., Witt C.C., McCandlish D.M. and Stoltzfus A. (2019) The role of mutation bias in adaptive molecular evolution: insights from convergent changes in protein function. Phil. Trans. R. Soc. B [doi: 10.1098/rstb.2018.0238]

The Evolutionary Synthesis: Perspectives on the Unification of Biology E. Mayr and W.B. Provine eds Harvard University Press, Cambridge MA, USA (1980)

Yampolsky, L.Y., and Stoltzfus, A. (2001) Bias in the introduction of variation as an orienting factor in evolution. Evolution & development, 3:73-83. [doi: 10.1046/j.1525-142x.2001.003002073.x]

My father on D-Day: 75 years ago

Today is the 75th anniversary of D-Day—the day British, Canadian, and American troops landed on the beaches of Normandy.¹

For us baby boomers it always meant a day of special significance for our parents. In my case, it was my father who took part in the invasions. That's him on the right as he looked in 1944. He was an RAF pilot flying rocket-firing typhoons in close support of the ground troops. His missions were limited to quick strikes and reconnaissance during the first few days of the invasion because Normandy was at the limit of their range from southern England. During the second week of the invasion (June 14th) his squadron landed in Crepon, Normandy and things became very hectic from then on with several close support missions every day [see Hawker Hurricanes and Typhoons in World War II].

The frequency of splicing errors reflects the balance between selection and drift

Splice variants are very common in eukaryotes. We know that it's possible to detect dozens of different splice variants for each gene with multiple introns. In the past, these variants were thought to be examples of differential regulation by alternative spicing but we now know that most of them are due to splicing errors. Most of the variants have been removed from the sequence databases but many remain and they are annotated as examples of alternative splicing, which implies that they have a biological function.

I have blogged about splice variants many times, noting that alternative splicing is a very real phenomenon but it's probably restricted to just a small percentage of genes. Most of splice variants that remain in the databases are probably due to splicing errors. They are junk RNA [The persistent myth of alternative splicing].

The ongoing controversy over the origin of splice variants is beginning to attract attention in the scientific literature although it's fair to say that most scientists are still unaware of the controversy. They continue to believe that abundant alternative splicing is a real phenomenon and they don't realize that the data is more compatible with abundant splicing errors.

Some molecular evolution labs have become interested in the controversy and have devised tests of the two possibilities. I draw your attention to a paper that was published 18 months ago.

Are multiple transcription start sites functional or mistakes?

If you look in the various databases you'll see that most human genes have multiple transcription start sites. The evidence for the existence of these variants is solid—they exist—but it's not clear whether the minor start sites are truly functional or whether they are just due to mistakes in transcription initiation. They are included in the databases because annotators are unable to distinguish between these possibilities.

Let's look at the entry for the human triosephosphate isomerase gene (TPI1; Gene ID 7167).

The correct mRNA is NM_0003655, third from the top. (Trust me on this!). The three other variants have different transcription start sites: two of them are upstream and one is downstream of the major site. Are these variants functional or are they simply transcription initiation errors? This is the same problem that we dealt with when we looked at splice variants. In that case I concluded that most splice variants are due to splicing errors and true alternative splicing is rare.

What is the dominant view of junk DNA?

I think that about 90% of our genome is junk and I know lots of other scientists who feel the same way. I'm pretty sure that this view is not shared by the majority of scientists but I don't know whether they are convinced that most of our genome is functional or whether they just think the question is unanswerable at the present time. I suspect that the latter view is more common but I'd like to hear your opinion.

Yeast loses its introns

Baker’s yeast (Saccharomyces cerevisiae) is one of the best studied eukaryotes. Its genome is just slightly larger than the largest bacterial genome and it was the first eukaryotic genome to be sequenced (Mewes at al., 1997). It has about 7000 genes in total and 6,604 of these genes are protein-coding genes but only 280 of these genes contain introns.¹ The rest have lost their introns over the course of several hundred million years of evolution (Hooks et al., 2014).

We know that introns have been lost in yeast because the genes of related species have lots of introns. The common ancestor of all fungi undoubtedly had genes with multiple introns because the available evidence indicates that introns invaded eukarotic genes very early in the evolution of eukaryotes. The fact that most introns have been purged from the yeast genome suggests that introns are not essential for gene function. In other words, introns are mostly junk.²