Differential Retention of Pfam Domains Contributes to Long-term Evolutionary Trends.

Protein domains that emerged more recently in evolution have a higher structural disorder and greater clustering of hydrophobic residues along the primary sequence. It is hard to explain how selection acting via descent with modification could act so slowly as not to saturate over the extraordinarily long timescales over which these trends persist. Here, we hypothesize that the trends were created by a higher level of selection that differentially affects the retention probabilities of protein domains with different properties. This hypothesis predicts that loss rates should depend on disorder and clustering trait values. To test this, we inferred loss rates via maximum likelihood for animal Pfam domains, after first performing a set of stringent quality control methods to reduce annotation errors. Intermediate trait values, matching those of ancient domains, are associated with the lowest loss rates, making our results difficult to explain with reference to previously described homology detection biases. Simulations confirm that effect sizes are of the right magnitude to produce the observed long-term trends. Our results support the hypothesis that differential domain loss slowly weeds out those protein domains that have nonoptimal levels of disorder and clustering. The same preferences also shape the differential diversification of Pfam domains, thereby further impacting proteome composition.

nQMaker: Estimating Time Nonreversible Amino Acid Substitution Models.

Amino acid substitution models are a key component in phylogenetic analyses of protein sequences. All commonly used amino acid models available to date are time-reversible, an assumption designed for computational convenience but not for biological reality. Another significant downside to time-reversible models is that they do not allow inference of rooted trees without outgroups. In this article, we introduce a maximum likelihood approach nQMaker, an extension of the recently published QMaker method, that allows the estimation of time nonreversible amino acid substitution models and rooted phylogenetic trees from a set of protein sequence alignments. We show that the nonreversible models estimated with nQMaker are a much better fit to empirical alignments than pre-existing reversible models, across a wide range of data sets including mammals, birds, plants, fungi, and other taxa, and that the improvements in model fit scale with the size of the data set. Notably, for the recently published plant and bird trees, these nonreversible models correctly recovered the commonly estimated root placements with very high-statistical support without the need to use an outgroup. We provide nQMaker as an easy-to-use feature in the IQ-TREE software (http://www.iqtree.org), allowing users to estimate nonreversible models and rooted phylogenies from their own protein data sets. The data sets and scripts used in this article are available at https://doi.org/10.5061/dryad.3tx95x6hx. [amino acid sequence analyses; amino acid substitution models; maximum likelihood model estimation; nonreversible models; phylogenetic inference; reversible models.].

Quantifying SARS-CoV-2 Infection Risk Within the Google/Apple Exposure Notification Framework to Inform Quarantine Recommendations.

Most early Bluetooth-based exposure notification apps use three binary classifications to recommend quarantine following SARS-CoV-2 exposure: a window of infectiousness in the transmitter, ≥15 minutes duration, and Bluetooth attenuation below a threshold. However, Bluetooth attenuation is not a reliable measure of distance, and infection risk is not a binary function of distance, nor duration, nor timing. We model uncertainty in the shape and orientation of an exhaled virus-containing plume and in inhalation parameters, and measure uncertainty in distance as a function of Bluetooth attenuation. We calculate expected dose by combining this with estimated infectiousness based on timing relative to symptom onset. We calibrate an exponential dose-response curve based on infection probabilities of household contacts. The probability of current or future infectiousness, conditioned on how long postexposure an exposed individual has been symptom-free, decreases during quarantine, with shape determined by incubation periods, proportion of asymptomatic cases, and asymptomatic shedding durations. It can be adjusted for negative test results using Bayes' theorem. We capture a 10-fold range of risk using six infectiousness values, 11-fold range using three Bluetooth attenuation bins, ∼sixfold range from exposure duration given the 30 minute duration cap imposed by the Google/Apple v1.1, and ∼11-fold between the beginning and end of 14 day quarantine. Public health authorities can either set a threshold on initial infection risk to determine 14-day quarantine onset, or on the conditional probability of current and future infectiousness conditions to determine both quarantine and duration.

Readthrough Errors Purge Deleterious Cryptic Sequences, Facilitating the Birth of Coding Sequences.

De novo protein-coding innovations sometimes emerge from ancestrally noncoding DNA, despite the expectation that translating random sequences is overwhelmingly likely to be deleterious. The "preadapting selection" hypothesis claims that emergence is facilitated by prior, low-level translation of noncoding sequences via molecular errors. It predicts that selection on polypeptides translated only in error is strong enough to matter and is strongest when erroneous expression is high. To test this hypothesis, we examined noncoding sequences located downstream of stop codons (i.e., those potentially translated by readthrough errors) in Saccharomyces cerevisiae genes. We identified a class of "fragile" proteins under strong selection to reduce readthrough, which are unlikely substrates for co-option. Among the remainder, sequences showing evidence of readthrough translation, as assessed by ribosome profiling, encoded C-terminal extensions with higher intrinsic structural disorder, supporting the preadapting selection hypothesis. The cryptic sequences beyond the stop codon, rather than spillover effects from the regular C-termini, are primarily responsible for the higher disorder. Results are robust to controlling for the fact that stronger selection also reduces the length of C-terminal extensions. These findings indicate that selection acts on 3' UTRs in Saccharomyces cerevisiae to purge potentially deleterious variants of cryptic polypeptides, acting more strongly in genes that experience more readthrough errors.

Mutation bias can shape adaptation in large asexual populations experiencing clonal interference.

The extended evolutionary synthesis invokes a role for development in shaping adaptive evolution, which in population genetics terms corresponds to mutation-biased adaptation. Critics have claimed that clonal interference makes mutation-biased adaptation rare. We consider the behaviour of two simultaneously adapting traits, one with larger mutation rate U, the other with larger selection coefficient s, using asexual travelling wave models. We find that adaptation is dominated by whichever trait has the faster rate of adaptation v in isolation, with the other trait subject to evolutionary stalling. Reviewing empirical claims for mutation-biased adaptation, we find that not all occur in the 'origin-fixation' regime of population genetics where v is only twice as sensitive to s as to U. In some cases, differences in U are at least ten to twelve times larger than differences in s, as needed to cause mutation-biased adaptation even in the 'multiple mutations' regime. Surprisingly, when U > s in the 'diffusive-mutation' regime, the required sensitivity ratio is also only two, despite pervasive clonal interference. Given two traits with identical v, the benefit of having higher s is surprisingly small, occurring largely when one trait is at the boundary between the origin-fixation and multiple mutations regimes.

Intercellular competition and the inevitability of multicellular aging.

Current theories attribute aging to a failure of selection, due to either pleiotropic constraints or declining strength of selection after the onset of reproduction. These theories implicitly leave open the possibility that if senescence-causing alleles could be identified, or if antagonistic pleiotropy could be broken, the effects of aging might be ameliorated or delayed indefinitely. These theories are built on models of selection between multicellular organisms, but a full understanding of aging also requires examining the role of somatic selection within an organism. Selection between somatic cells (i.e., intercellular competition) can delay aging by purging nonfunctioning cells. However, the fitness of a multicellular organism depends not just on how functional its individual cells are but also on how well cells work together. While intercellular competition weeds out nonfunctional cells, it may also select for cells that do not cooperate. Thus, intercellular competition creates an inescapable double bind that makes aging inevitable in multicellular organisms.

Density-dependent selection and the limits of relative fitness.

Selection is commonly described by assigning constant relative fitness values to genotypes. Yet population density is often regulated by crowding. Relative fitness may then depend on density, and selection can change density when it acts on a density-regulating trait. When strong density-dependent selection acts on a density-regulating trait, selection is no longer describable by density-independent relative fitnesses, even in demographically stable populations. These conditions are met in most previous models of density-dependent selection (e.g. "K-selection" in the logistic and Lotka-Volterra models), suggesting that density-independent relative fitnesses must be replaced with more ecologically explicit absolute fitnesses unless selection is weak. Here we show that density-independent relative fitnesses can also accurately describe strong density-dependent selection under some conditions. We develop a novel model of density-regulated population growth with three ecologically intuitive traits: fecundity, mortality, and competitive ability. Our model, unlike the logistic or Lotka-Volterra, incorporates a density-dependent juvenile "reproductive excess", which largely decouples density-dependent selection from the regulation of density. We find that density-independent relative fitnesses accurately describe strong selection acting on any one trait, even fecundity, which is both density-regulating and subject to density-dependent selection. Pleiotropic interactions between these traits recover the familiar K-selection behavior. In such cases, or when the population is maintained far from demographic equilibrium, our model offers a possible alternative to relative fitness.

Answering evolutionary questions: A guide for mechanistic biologists.

The questions and methods of molecular biology and evolutionary biology are clearly distinct, yet a unified approach can lead to deep insights. Unfortunately, attempts to unify these approaches are fraught with pitfalls. In this informal series of questions and answers, we offer the mechanistically oriented biologist a set of steps to come up with evolutionarily reasonable and meaningful hypotheses. We emphasize the critical power and importance of carefully constructed null hypotheses, and we illustrate our ideas with examples representing a range of topics, from the biology of aging, to protein structure, to speciation, and more. We also stress the importance of mathematics as the lingua franca for biologists of all stripes, and encourage mechanistic biologists to seek out quantitative collaborators to build explicit mathematical models, making their assumptions explicit, and their logic clear and testable. Biologists in all realms of inquiry stand to gain from strong bridges between our disciplines.

Outcome Orientation: A Misconception of Probability That Harms Medical Research and Practice.

Human understanding of randomness and variation is shaped by a number of cognitive biases. In this article, we relate a lesser-known cognitive bias, the "outcome orientation," to medical questions and describe the harm it can do to medical research and practice. An outcome orientation means predicting outcomes one at a time, neglecting the fact that each event may be a member of a group of comparable events. People who reason according to an outcome orientation assign a subjective degree of belief to an outcome, but do so in a way that is incompatible with Bayesian reasoning or any other standard laws of probability. Instead of accepting that uncertainty is inevitable and generalizing from the frequency of similar events, the outcome orientation prefers one-off causal narratives. In medicine, the outcome orientation therefore erodes support for randomized controlled trials in favor of reductionist approaches. The rhetoric of personalized medicine resonates with, and can promote, the outcome orientation by emphasizing how the measurable attributes of individual patients, rather than chance or unknowable factors, causally produce each particular patient's outcome.

Rethinking Hardy-Weinberg and genetic drift in undergraduate biology.

Population genetics is often taught in introductory biology classes, starting with the Hardy-Weinberg principle (HWP) and genetic drift. Here I argue that teaching these two topics first aligns neither with current expert knowledge, nor with good pedagogy. Student difficulties with mathematics in general, and probability in particular, make population genetics difficult to teach and learn. I recommend an alternative, historically inspired ordering of population genetics topics, based on progressively increasing mathematical difficulty. This progression can facilitate just-in-time math instruction. This alternative ordering includes, but does not privilege, the HWP and genetic drift. Stochastic events whose consequences are felt within a single generation, and the deterministic accumulation of the effects of selection across multiple generations, are both taught before tackling the stochastic accumulation of the effects of accidents of sampling.

Evolution of molecular error rates and the consequences for evolvability.

Making genes into gene products is subject to predictable errors, each with a phenotypic effect that depends on a normally cryptic sequence. Many cryptic sequences have strongly deleterious effects, for example when they cause protein misfolding. Strongly deleterious effects can be avoided globally by avoiding making errors (e.g., via proofreading machinery) or locally by ensuring that each error has a relatively benign effect. The local solution requires powerful selection acting on every cryptic site and so evolves only in large populations. Small populations with less effective selection evolve global solutions. Here we show that for a large range of realistic intermediate population sizes, the evolutionary dynamics are bistable and either solution may result. The local solution facilitates the genetic assimilation of cryptic genetic variation and therefore substantially increases evolvability.

Background Selection From Unlinked Sites Causes Nonindependent Evolution of Deleterious Mutations.

Background selection describes the reduction in neutral diversity caused by selection against deleterious alleles at other loci. It is typically assumed that the purging of deleterious alleles affects linked neutral variants, and indeed simulations typically only treat a genomic window. However, background selection at unlinked loci also depresses neutral diversity. In agreement with previous analytical approximations, in our simulations of a human-like genome with a realistically high genome-wide deleterious mutation rate, the effects of unlinked background selection exceed those of linked background selection. Background selection reduces neutral genetic diversity by a factor that is independent of census population size. Outside of genic regions, the strength of background selection increases with the mean selection coefficient, contradicting the linked theory but in agreement with the unlinked theory. Neutral diversity within genic regions is fairly independent of the strength of selection. Deleterious genetic load among haploid individuals is underdispersed, indicating nonindependent evolution of deleterious mutations. Empirical evidence for underdispersion was previously interpreted as evidence for global epistasis, but we recover it from a non-epistatic model.

The protein domains of vertebrate species in which selection is more effective have greater intrinsic structural disorder.

The nearly neutral theory of molecular evolution posits variation among species in the effectiveness of selection. In an idealized model, the census population size determines both this minimum magnitude of the selection coefficient required for deleterious variants to be reliably purged, and the amount of neutral diversity. Empirically, an 'effective population size' is often estimated from the amount of putatively neutral genetic diversity and is assumed to also capture a species' effectiveness of selection. A potentially more direct measure of the effectiveness of selection is the degree to which selection maintains preferred codons. However, past metrics that compare codon bias across species are confounded by among-species variation in %GC content and/or amino acid composition. Here, we propose a new Codon Adaptation Index of Species (CAIS), based on Kullback-Leibler divergence, that corrects for both confounders. We demonstrate the use of CAIS correlations, as well as the Effective Number of Codons, to show that the protein domains of more highly adapted vertebrate species evolve higher intrinsic structural disorder.

Evolution is the process through which populations change over time, starting with mutations in the genetic sequence of an organism. Many of these mutations harm the survival and reproduction of an organism, but only by a very small amount. Some species, especially those with large populations, can purge these slightly harmful mutations more effectively than other species. This fact has been used by the 'drift barrier theory' to explain various profound differences amongst species, including differences in biological complexity. In this theory, the effectiveness of eliminating slightly harmful mutations is specified by an 'effective' population size, which depends on factors beyond just the number of individuals in the population. Effective population size is normally calculated from the amount of time a 'neutral' mutation (one with no effect at all) stays in the population before becoming lost or taking over. Estimating this time requires both representative data for genetic diversity and knowledge of the mutation rate. A major limitation is that these data are unavailable for most species. A second limitation is that a brief, temporary reduction in the number of individuals has an oversized impact on the metric, relative to its impact on the number of slighly harmful mutations accumulated. Weibel, Wheeler et al. developed a new metric to more directly determine how effectively a species purges slightly harmful mutations. Their approach is based on the fact that the genetic code has 'synonymous' sequences. These sequences code for the same amino acid building block, with one of these sequences being only slightly preferred over others. The metric by Weibel, Wheeler et al. quantifies the proportion of the genome from which less preferred synonymous sequences have been effectively purged. It judges a population to have a higher effective population size when the usage of synonymous sequences departs further from the usage predicted from mutational processes. The researchers expected that natural selection would favour 'ordered' proteins with robust three-dimensional structures, i.e., that species with a higher effective population size would tend to have more ordered versions of a protein. Instead, they found the opposite: species with a higher effective population size tend to have more disordered versions of the same protein. This changes our view of how natural selection acts on proteins. Why species are so different remains a fundamental question in biology. Weibel, Wheeler et al. provide a useful tool for future applications of drift barrier theory to a broad range of ways that species differ.

The protein domains of vertebrate species in which selection is more effective have greater intrinsic structural disorder.

The nearly neutral theory of molecular evolution posits variation among species in the effectiveness of selection. In an idealized model, the census population size determines both this minimum magnitude of the selection coefficient required for deleterious variants to be reliably purged, and the amount of neutral diversity. Empirically, an "effective population size" is often estimated from the amount of putatively neutral genetic diversity and is assumed to also capture a species' effectiveness of selection. A potentially more direct measure of the effectiveness of selection is the degree to which selection maintains preferred codons. However, past metrics that compare codon bias across species are confounded by among-species variation in %GC content and/or amino acid composition. Here we propose a new Codon Adaptation Index of Species (CAIS), based on Kullback-Leibler divergence, that corrects for both confounders. We demonstrate the use of CAIS correlations, as well as the Effective Number of Codons, to show that the protein domains of more highly adapted vertebrate species evolve higher intrinsic structural disorder.

The effectiveness of selection in a species affects the direction of amino acid frequency evolution.

Nearly neutral theory predicts that species with higher effective population size (N e ) are better able to purge slightly deleterious mutations. We compare evolution in high-N e vs. low-N e vertebrates to reveal which amino acid frequencies are subject to subtle selective preferences. We take three complementary approaches, two measuring flux and one measuring outcomes. First, we fit non-stationary substitution models of amino acid flux using maximum likelihood, comparing the high-N e clade of rodents and lagomorphs to its low-N e sister clade of primates and colugos. Second, we compare evolutionary outcomes across a wider range of vertebrates, via correlations between amino acid frequencies and N e . Third, we dissect the details of flux in human, chimpanzee, mouse, and rat, as scored by parsimony - this also enables comparison to a historical paper. All three methods agree on which amino acids are preferred under more effective selection. Preferred amino acids tend to be smaller, less costly to synthesize, and to promote intrinsic structural disorder. Parsimony-induced bias in the historical study produces an apparent reduction in structural disorder, perhaps driven by slightly deleterious substitutions. Within highly exchangeable pairs of amino acids, arginine is strongly preferred over lysine, and valine over isoleucine, consistent with more effective selection preferring a marginally larger free energy of folding. These two preferences match differences between thermophiles and mesophilic relatives. These results reveal the biophysical consequences of mutation-selection-drift balance, and demonstrate the utility of nearly neutral theory for understanding protein evolution.

Order of amino acid recruitment into the genetic code resolved by Last Universal Common Ancestor's protein domains.

The current "consensus" order in which amino acids were added to the genetic code is based on potentially biased criteria such as absence of sulfur-containing amino acids from the Urey-Miller experiment which lacked sulfur. Even if inferred perfectly, abiotic abundance might not reflect abundance in the organisms in which the genetic code evolved. Here, we instead exploit the fact that proteins that emerged prior to the genetic code's completion are likely enriched in early amino acids and depleted in late amino acids. We identify the most ancient protein-coding sequences born prior to the archaeal-bacterial split. Amino acid usage in protein sequences whose ancestors date back to a single homolog in the Last Universal Common Ancestor (LUCA) largely matches the consensus order. However, our findings indicate that metal-binding (cysteine and histidine) and sulfur-containing (cysteine and methionine) amino acids were added to the genetic code much earlier than previously thought. Surprisingly, even more ancient protein sequences - those that had already diversified into multiple distinct copies in LUCA - show a different pattern to single copy LUCA sequences: significantly less depleted in the late amino acids tryptophan and tyrosine, and enriched rather than depleted in phenylalanine. This is compatible with at least some of these sequences predating the current genetic code. Their distinct enrichment patterns thus provide hints about earlier, alternative genetic codes.

Robustness and evolvability.

Why isn't random variation always deleterious? Are there factors that sometimes make adaptation easier? Biological systems are extraordinarily robust to perturbation by mutations, recombination and the environment. It has been proposed that this robustness might make them more evolvable. Robustness to mutation allows genetic variation to accumulate in a cryptic state. Switching mechanisms known as evolutionary capacitors mean that the amount of heritable phenotypic variation available can be correlated to the degree of stress and hence to the novelty of the environment and remaining potential for adaptation. There have been two somewhat separate literatures relating robustness to evolvability. One has focused on molecular phenotypes and new mutations, the other on morphology and cryptic genetic variation. Here, we review both literatures, and show that the true distinction is whether recombination rates are high or low. In both cases, the evidence supports the claim that robustness promotes evolvability.

Hsp90 depletion goes wild.

Hsp90 reveals phenotypic variation in the laboratory, but is Hsp90 depletion important in the wild? Recent work from Chen and Wagner in BMC Evolutionary Biology has discovered a naturally occurring Drosophila allele that downregulates Hsp90, creating sensitivity to cryptic genetic variation. Laboratory studies suggest that the exact magnitude of Hsp90 downregulation is important. Extreme Hsp90 depletion might reactivate transposable elements and/or induce aneuploidy, in addition to revealing cryptic genetic variation. See research article http://wwww.biomedcentral.com/1471-2148/12/25.