Biology subjects

Payne, J. L.

Publications and source records attributed to Payne, J. L..

14 recordsLinked to original sources

bioRxiv2026

Assessing Codon Language Models for Context-Aware Codon Optimization in Nucleic Acid-Based Medicines

Codon optimization uses synonymous sequence changes to improve the expression and therapeutic performance of nucleic acid-based medicines. Masked language models (MLMs) have recently been proposed as alternatives to traditional, frequency-based codon optimization approaches, yet whether they offer a meaningful advantage over such simpler methods remains unclear. Here we benchmark three prominent MLMs - CaLM, EnCodon and CodonTransformer - across backtranslation fidelity, sequence generation and nine molecular phenotype prediction tasks, and experimentally evaluate model-designed sequences using a secreted embryonic alkaline phosphatase (SEAP) reporter. The models differed markedly in amino-acid fidelity and generated distinct synonymous sequence variants. However, no single model performed best across all benchmark tasks and simple sequence features remained competitive in several settings. Our interpretability analysis revealed that the models integrate a large window of codon context for making predictions, as opposed to frequency-based approaches. Our in vitro data showed that MLM-designed variants outperformed conventional and commercial-vendor-derived sequences in both transient and stably integrated expression, supporting the models ability to capture translational context beyond codon frequency. Together, our results establish MLMs as effective and complementary tools for codon optimization and suggest that sampling across multiple models may improve the likelihood of identifying high-performing therapeutic sequences.

bioinformatics↗

bioRxiv2023

Protein evolvability under rewired genetic codes

The standard genetic code defines the rules of translation for nearly every life form on Earth. It also determines the amino acid changes accessible via single-nucleotide mutations, thus influencing protein evolvability -- the ability of mutation to bring forth adaptive variation in protein function. One of the most striking features of the standard genetic code is its robustness to mutation, yet it remains an open question whether this robustness facilitates or frustrates protein evolvability. To answer this question, we use data from massively-parallel sequence-to-function assays to construct and analyze empirical adaptive landscapes under hundreds of thousands of rewired genetic codes, including those of codon compression schemes relevant to protein engineering and synthetic biology. We find that robust genetic codes tend to enhance protein evolvability by rendering smooth adaptive landscapes with few peaks, which are readily accessible from throughout sequence space. By constructing low-dimensional visualizations of these landscapes, which each comprise more than 16 million mRNA sequences, we demonstrate that alternative genetic codes can radically alter the topological features of the network of high-fitness genotypes. Whereas the genetic codes that optimize evolvability depend to some extent on the detailed relationship between amino acid sequence and protein function, we also uncover general design principles for engineering non-standard genetic codes for enhanced and diminished evolvability, which may facilitate directed protein evolution experiments and the biocontainment of synthetic organisms, respectively. Our findings demonstrate that the standard genetic code, a critical and near-universal cellular information processing system, not only mitigates replication and translation errors as compared to most alternative genetic codes, but also facilitates predictable and directional adaptive evolution by enabling evolving populations to readily find mutational paths to adaptation.

evolutionary biology↗

bioRxiv2023

Mutation and selection induce correlations between selection coefficients and mutation rates

The joint distribution of selection coefficients and mutation rates is a key determinant of the genetic architecture of molecular adaptation. Three different distributions are of immediate interest: (1) the nominal distribution of possible changes, prior to mutation or selection, (2) the de novo distribution of realized mutations, and (3) the fixed distribution of selectively established mutations. Here, we formally characterize the relationships between these joint distributions under the strong selection, weak mutation (SSWM) regime. The de novo distribution is enriched relative to the nominal distribution for the highest rate mutations, and the fixed distribution is further enriched for the most highly beneficial mutations. Whereas mutation rates and selection coefficients are often assumed to be uncorrelated, we show that even with no correlation in the nominal distribution, the resulting de novo and fixed distributions can have correlations with any combination of signs. Nonetheless, we suggest that natural systems with a finite number of beneficial mutations will frequently have the kind of nominal distribution that induces negative correlations in the fixed distribution. We apply our mathematical framework, along with population simulations, to explore joint distributions of selection coefficients and mutation rates from deep mutational scanning and cancer informatics. Finally, we consider the evolutionary implications of these joint distributions together with two additional joint distributions relevant to parallelism and the rate of adaptation.

evolutionary biology↗

bioRxiv2022

Environment-dependent epistasis increases phenotypic diversity in gene regulatory networks

Mutations to gene regulatory networks can be maladaptive or a source of evolutionary novelty. Epistasis confounds our understanding of how mutations impact the expression patterns of gene regulatory networks, because such nonlinearities make it difficult to predict the combined phenotypic effects of mutations based on knowledge of the mutations individual effects. This challenge is exacerbated by the dependence of epistasis on the environment, which is particularly germane to gene regulatory networks that interpret signals in space or time. To help fill this knowledge gap, we used the toolkit of synthetic biology to systematically assay the effects of pairwise and triplet combinations of mutant genotypes on the expression pattern of a gene regulatory network expressed in Escherichia coli that interprets an inducer gradient across a spatial domain. We uncovered a preponderance of epistasis in both pairwise and triplet combinations that can switch in magnitude and sign across the inducer gradient to produce a greater diversity of expression pattern phenotypes than would be possible in the absence of such environment-dependent epistasis. We discuss our findings in the context of the evolution of hybrid incompatibilities and evolutionary novelties, arguing that environment-dependent epistasis is likely an important cause of both phenomena in gene regulatory networks.

evolutionary biology↗

bioRxiv2022

The transformability of genotype-phenotype landscapes

The mapping from genotype to phenotype to fitness typically involves multiple nonlinearities that can transform the effects of mutations. For example, mutations may contribute additively to a phenotype, but their effects on fitness may combine non-additively because selection favors a low or intermediate value of that phenotype. This can cause incongruence between the topographical properties of a fitness landscape and its underlying genotype-phenotype landscape. Yet, genotype-phenotype landscapes are often used as a proxy for fitness landscapes to study the dynamics and predictability of evolution. Here, we use theoretical models and empirical data on transcription factor-DNA interactions to systematically study the incongruence of genotype-phenotype and fitness landscapes when selection favors a low or intermediate phenotypic value. Using the theoretical models, we prove a number of fundamental results. For example, selection for low or intermediate phenotypic values does not change simple sign epistasis into reciprocal sign epistasis, yet it changes reciprocal sign epistasis into simple sign epistasis and no sign epistasis with equal probability. More broadly, we show that such selection tends to create fitness landscapes that are more rugged than the underlying genotype-phenotype landscape, but this increased ruggedness typically does not frustrate adaptive evolution because the local adaptive peaks in the fitness landscape tend to be nearly as tall as the global peak. Many of these results carry forward to the empirical genotype-phenotype landscapes, which may help to explain why low- and intermediate-affinity transcription factor-DNA interactions are so prevalent in eukaryotic gene regulation. AUTHOR SUMMARYHow do mutations change phenotypic traits and organismal fitness? This question is often addressed in the context of a classic metaphor of evolutionary theory -- the fitness landscape. A fitness landscape is akin to a physical landscape, in which genotypes define spatial coordinates, and fitness defines the elevation of each coordinate. Evolution then acts like a hill-climbing process, in which populations ascend fitness peaks as a consequence of mutation and selection. It is becoming increasingly common to construct such landscapes using experimental data from high-throughput sequencing technologies and phenotypic assays, in systems such as macromolecules and gene regulatory circuits. Although these landscapes are typically defined by molecular phenotypes, and are therefore more appropriately referred to as genotype-phenotype landscapes, they are often used to study evolutionary dynamics. This requires the assumption that the molecular phenotype is a reasonable proxy for fitness, which need not be the case. For example, selection may favor a low or intermediate phenotypic value, causing incongruence between a fitness landscape and its underlying genotype-phenotype landscape. Here, we study such incongruence using a diversity of theoretical models and experimental data from gene regulatory systems. We regularly find incongruence, in that fitness landscapes tend to comprise more peaks than their underlying genotype-phenotype landscapes. However, using evolutionary simulations, we show that this increased ruggedness need not impede adaptation.

evolutionary biology↗

bioRxiv2021

Linking host plants to damage types in the fossil record of insect herbivory

Studies of insect herbivory on fossilized leaves tend to focus on a few, relatively simple metrics that are agnostic to the distribution of insect damage types among host plants. More complex metrics that link particular damage types to particular host plants have the potential to address additional ecological questions, but such metrics can be biased by sampling incompleteness due to the difficulty of distinguishing the true absence of a particular interaction from the failure to detect it--a challenge that has been raised in the ecological literature. We evaluate a range of methods for characterizing the relationships between damage types and host plants by performing resampling and subsampling exercises on a variety of datasets. We found that the components of beta diversity provide a more valid, reliable, and interpretable method for comparing component communities than do bipartite network metrics. We found the rarefaction of interactions to be a valid, reliable, and interpretable method for comparing compound communities. Both beta diversity and rarefaction of interactions avoid the potential pitfalls of multiple comparisons. Lastly, we found that the host specificity of individual damage types is challenging to assess. Whereas some previously used methods are sufficiently biased by sampling incompleteness to be inappropriate for fossil herbivory data, alternatives exist that are perfectly suitable for fossil datasets with sufficient sample coverage.

paleontology↗

bioRxiv2021

Early life stress-induced vulnerability to postpartum mental disturbance: prolonged dysregulation of the HPA axis and behavior

Stress during childhood and adolescence increases the risk for postpartum depression (PPD). Patients with depression who have experienced adverse life events tend to be treatment refractory. However, the mechanism by which stress during childhood and adolescence are involved in the pathophysiology of PPD remains unclear. We investigated the longitudinal effects of adolescent stress on the hypothalamic-pituitary-adrenal (HPA) axis and behaviors in the postpartum period through mouse and human studies. We observed that adolescent social isolation caused an aberrantly sustained elevation of glucocorticoids via dysregulation of the HPA axis, leading to long-lasting postpartum behavioral changes in female mice. The postpartum behavioral changes elicited by this adolescent stress were not ameliorated by the medicines currently used for PPD treatment. However, a post-delivery treatment with a glucocorticoid receptor antagonist effectively ameliorated the behavioral changes in mice. We also demonstrated a significant impact of stress during childhood and adolescence on the HPA axis dysregulation and PPD in women. We provide experimental evidence that suggests a mechanism-driven therapeutic strategy (repurposing a GR antagonist) for at least some cases of treatment refractory PPD.

molecular biology↗

bioRxiv2021

Generating and testing hypotheses about the fossil record of insect herbivory with a theoretical ecospace

A typical fossil flora examined for insect herbivory contains a few hundred leaves and a dozen or two insect damage types. Paleontologists employ a wide variety of metrics to assess differences in herbivory among assemblages: damage type diversity, intensity (the proportion of leaves, or of leaf surface area, with insect damage), the evenness of diversity, and comparisons of the evenness and diversity of the flora to the evenness and diversity of damage types. Although the number of metrics calculated is quite large, given the amount of data that is usually available, the study of insect herbivory in the fossil record still lacks a quantitative framework that can be used to distinguish among different causes of increased insect herbivory and to generate null hypotheses of the magnitude of changes in insect herbivory over time. Moreover, estimates of damage type diversity, the most common metric, are generated with inconsistent sampling standardization routines. Here we demonstrate that coverage-based rarefaction yields valid, reliable estimates of damage type diversity that are robust to differences among floral assemblages in the number of leaves examined, average leaf surface area, and the inclusion of plant organs other than leaves such as seeds and axes. We outline the potential of a theoretical ecospace that combines various metrics to distinguish between potential causes of increased herbivory. We close with a discussion of the most appropriate uses of a theoretical ecospace for insect herbivory, with the overlapping damage type diversities of Paleozoic gymnosperms and Cenozoic angiosperms as a brief case study.

paleontology↗

bioRxiv2021

The adaptive potential of non-heritable somatic mutations

Non-heritable somatic mutations are typically associated with deleterious effects such as in cancer and senescence, so their role in adaptive evolution has received little attention. However, most somatic mutations are harmless and some even confer a fitness advantage to the organism carrying them. We hypothesized that heritable, germline genotypes that are likely to express an advantageous phenotype via non-heritable somatic mutation will have a selective advantage over other germline genotypes, and this advantage will channel evolving populations toward more fit germline genotypes, thus promoting adaptation. We tested this hypothesis by simulating evolving populations of developing organisms with an impermeable germline-soma separation navigating a minimal fitness landscape. The simulations revealed the conditions under which non-heritable somatic mutations promote adaptation. Specifically, this can occur when the somatic mutation supply is high, when only very few cells with the advantageous somatic mutation are required to increase organismal fitness, and when the somatic mutation also confers a selective advantage to cells with that mutation. We therefore provide proof-of-principle that non-heritable somatic mutations can promote adaptive evolution via a process we call somatic genotypic exploration. We discuss the biological plausibility of this phenomenon, as well as its evolutionary implications. SignificanceThe immensity of non-heritable genetic diversity arising in the soma has been largely disregarded in evolutionary theory as a source of adaptation. Here, we introduce a model in which non-heritable somatic mutations arising during development confer an organismal fitness advantage. Analysis of this model shows how such mutations channel evolving populations toward adaptive germline genotypes. This is most likely to occur when somatic mutations confer a fitness benefit at both the cellular and organismal levels, evoking a synergistic form of multi-level selection that contrasts with the antagonistic forms typically associated with somatic mutations in cancer and senescence. As such, our study invites a new view of somatic genetic diversity in evolutionary theory as a potential source of adaptation.

evolutionary biology↗

bioRxiv2021

Mutation bias shapes the spectrum of adaptive substitutions

Evolutionary adaptation often occurs via the fixation of beneficial point mutations, but different types of mutation may differ in their relative frequencies within the collection of substitutions contributing to adaptation in any given species. Recent studies have established that this spectrum of adaptive substitutions is enriched for classes of mutations that occur at higher rates. Yet, little is known at a quantitative level about the precise extent of this enrichment, or its dependence on other factors such as the beneficial mutation supply or demographic conditions. Here we address the extent to which the mutation spectrum shapes the spectrum of adaptive amino acid substitutions by applying a codon-based negative binomial regression model to three large data sets that include thousands of amino acid changes identified in natural and experimental adaptation in S. cerevisiae, E. coli, and M. tuberculosis. We find that the mutation spectrum has a strong and roughly proportional influence on the spectrum of adaptive substitutions in all three species. In fact, we find that by inferring the mutation rates that best explain the spectrum of adaptive substitutions, we can accurately recover species-specific mutational spectra obtained via mutation accumulation experiments. We complement this empirical analysis with simulations to determine the factors that influence how closely the spectrum of adaptive substitutions mirrors the spectrum of amino acid variants introduced by mutation, and find that the predictive power of mutation depends on multiple factors including population size and the breadth of the mutational target for adaptation. SIGNIFICANCE STATEMENTHow do mutational biases influence the process of adaptation? Classical neo-Darwinian thinking assumes that selection alone determines the course of adaptation from abundant pre-existing variation. Yet, theoretical work shows that under some circumstances the mutation rate to a given variant may have a strong impact on the probability of that variant contributing to adaptation. Here we introduce a statistical approach to analyzing how mutation shapes protein sequence adaptation, and show that the mutation spectrum has a proportional influence on the changes fixed in adaptation observed in three large data sets. We also show via computer simulations that a variety of factors can influence how closely the spectrum of adaptive substitutions mirrors the spectrum of variants introduced by mutation.

evolutionary biology↗

bioRxiv2021

Little evidence the standard genetic code is optimized for resource conservation

Selection for resource conservation can shape the coding sequences of organisms living in nutrient-limited environments. Recently, it was proposed that selection for resource conservation, specifically for nitrogen and carbon content, has also shaped the structure of the standard genetic code, such that the missense mutations it allows tend to cause small increases in the number of nitrogen and carbon atoms in amino acids. Moreover, it was proposed that this optimization is not confounded by known optimizations of the standard genetic code, such as for polar requirement or hydropathy. We challenge these claims. We show the proposed optimization for nitrogen conservation is highly sensitive to choice of null model and the proposed optimization for carbon conservation is confounded by the known conservative nature of the standard genetic code with respect to the molecular volume of amino acids. There is therefore little evidence the standard genetic code is optimized for resource conservation.

evolutionary biology↗

bioRxiv2020

SWATH-MS co-expression profiles reveal paralogue interference in protein complex evolution

Understanding the conservation and evolution of protein complexes is of critical value to decode their function in physiological and pathological processes. One prominent proposal posits gene duplication as a potential mechanism for protein complex evolution. In this study we take advantage of large-scale proteome expression datasets to systematically investigate the role of paralogues, and specifically self-interacting paralogues, in shaping the evolutionary trajectories of protein complexes. First, we show that protein co-expression derived from quantitative proteomic matrices is a good indicator for complex membership and is conserved across species. Second, we suggest that paralogues are commonly strongly co-expressed and that for the subset of paralogues that show diverging co-expression patterns, the divergent co-expression patterns reflect both sequence and functional divergence. Finally, on this basis, we show that homomeric paralogues known to be part of protein complexes display a unique co-expression pattern distribution, with a subset of them being highly diverging. These findings support the idea that homomeric paralogues can avoid cross-interference by diversifying their expression patterns, and corroborates the role of this mechanism as a force shaping protein complex evolution and specialization.

evolutionary biology↗

bioRxiv2020

Mutation bias interacts with composition bias to influence adaptive evolution

Mutation is a biased stochastic process, with some types of mutations occurring more frequently than others. Previous work has used synthetic genotype-phenotype landscapes to study how such mutation bias affects adaptive evolution. Here, we consider 746 empirical genotype-phenotype landscapes, each of which describes the binding affinity of target DNA sequences to a transcription factor, to study the influence of mutation bias on adaptive evolution of increased binding affinity. By using empirical genotype-phenotype landscapes, we need to make only few assumptions about landscape topography and about the DNA sequences that each landscape contains. The latter is particularly important because the set of sequences that a landscape contains determines the types of mutations that can occur along a mutational path to an adaptive peak. That is, landscapes can exhibit a composition bias -- a statistical enrichment of a particular type of mutation relative to a null expectation, throughout an entire landscape or along particular mutational paths -- that is independent of any bias in the mutation process. Our results reveal the way in which composition bias interacts with biases in the mutation process under different population genetic conditions, and how such interaction impacts fundamental properties of adaptive evolution, such as its predictability, as well as the evolution of genetic diversity and mutational robustness. AUTHOR SUMMARYMutation is often depicted as a random process due its unpredictable nature. However, such randomness does not imply uniformly distributed outcomes, because some DNA sequence changes happen more frequently than others. Such mutation bias can be an orienting factor in adaptive evolution, influencing the mutational trajectories populations follow toward higher-fitness genotypes. Because these trajectories are typically just a small subset of all possible mutational trajectories, they can exhibit composition bias - an enrichment of a particular kind of DNA sequence change, such as transition or transversion mutations. Here, we use empirical data from eukaryotic transcriptional regulation to study how mutation bias and composition bias interact to influence adaptive evolution.

evolutionary biology↗

bioRxiv2018

Transition bias influences the evolution of antibiotic resistance in Mycobacterium tuberculosis

Transition bias, an overabundance of transitions relative to transversions, has been widely reported among studies of mutations spreading under relaxed selection. However, demonstrating the role of transition bias in adaptive evolution remains challenging. We addressed this challenge by analyzing adaptive antibiotic-resistance mutations in the major human pathogen Mycobacterium tuberculosis. We found strong evidence for transition bias in two independently curated datasets comprising 152 and 208 antibiotic resistance mutations. This was true at the level of mutational paths (distinct, adaptive DNA sequence changes) and events (individual instances of the adaptive DNA sequence changes), and across different genes and gene promoters conferring resistance to a diversity of antibiotics. It was also true for mutations that do not code for amino acid changes (in gene promoters and the ribosmal gene rrs), and for mutations that are synonymous to each other and are therefore likely to have similar fitness effects, suggesting that transition bias can be caused by a bias in mutation supply. These results point to a central role for transition bias in determining which mutations drive adaptive antibiotic resistance evolution in a key pathogen.\n\nSignificance statementWhether and how transition bias influences adaptive evolution remain open questions. We studied 296 DNA mutations that confer antibiotic resistance to the human pathogen Mycobacterium tuberculosis. We uncovered strong transition bias among these mutations and also among the number of times each mutation has evolved in different strains or geographic locations, demonstrating that transition bias can influence adaptive evolution. For a subset of mutations, we were able to rule out an alternative selection-based hypothesis for this bias, indicating that transition bias can be caused by a biased mutation supply. By revealing this bias among M. Tuberculosis resistance mutations, our findings improve our ability to predict the mutational pathways by which pathogens overcome treatment.

evolutionary biology↗