Neutral Mutations Research Articles

Models of Fluctuating Selection Between Generations: A Solution for the Theoretical Inconsistency.

The theory of selection fluctuation between generations has been a topic with much activities in population genetics and molecular evolution in 1970's. Most studies suggested that, as the result of fluctuating selection between generations, the frequency of an (on average) neutral mutation may fluctuate around 0.5 during the long-term evolution before it was ultimately fixed or lost. However, this pattern can only be derived from a specific type Wright-Fisher additive model, coined by the Nei-Yokoyama puzzle. In this commentary, I revisited this issue and figured out a theoretical assumption that has never been claimed explicitly, the notion of reference phenotype. Consider one locus with two-alleles: A is the wildtype allele and A' is the mutation. The fluctuating selection model actually requires a constraint that one of three genotypes (AA, AA', or A'A') must maintain a constant fitness without fluctuating between generations. It appears that the balancing selection at a frequency of 0.5 emerges only when the heterozygote (AA') is the reference genotype. Because it is difficult to determine which genotype could be the reference genotype in a real population, a desirable population genetics model should take all three possibilities into account. To this end, I propose a mixture model, where each genotype has a certain chance to be the reference genotype. My analysis showed that the emergence of balancing selection depends on the relative proportions of three different reference genotypes.

Accumulation of Oncogenic Mutations During Progression from Healthy Tissue to Cancer

Cancers are typically fueled by sequential accumulation of driver mutations in a previously healthy cell. Some of these mutations, such as inactivation of the first copy of a tumor suppressor gene, can be neutral, and some, like those resulting in activation of oncogenes, may provide cells with a selective growth advantage. We study a multi-type branching process that starts with healthy tissue in homeostasis and models accumulation of neutral and advantageous mutations on the way to cancer. We provide results regarding the sizes of premalignant populations and the waiting times to the first cell with a particular combination of mutations, including the waiting time to malignancy. Finally, we apply our results to two specific biological settings: initiation of colorectal cancer and age incidence of chronic myeloid leukemia. Our model allows for any order of neutral and advantageous mutations and can be applied to other evolutionary settings.

Accumulating waves of random mutations before fixation

Mutations provide variation for evolution to emerge. A quantitative analysis of how mutations arising in single individuals expand and possibly fixate in a population is essential for studying evolutionary processes. While it is intuitive to expect that a continuous influx of mutations will lead to a continuous flow of mutations fixating in a stable constant population, joint fixation of multiple mutations occur frequently in stochastic simulations even under neutral selection. We quantitatively measure and analyze the distribution of joint fixation events of neutral mutations in constant populations and discussed the connection with previous results. We propose a new concept, the mutation “waves,” where multiple mutations reach given frequencies simultaneously. We show that all but the lowest frequencies of the variant allele frequency distribution are dominated by single mutation “waves,” which approximately follow an exponential distribution in terms of size. Consequently, large swaths of empty frequencies are observed in the variant allele frequency distributions, with a few frequencies having numbers of mutations far in excess of the expected average values over multiple realizations. We quantify the amount of time each frequency is empty of mutations and further show that the discrete mutation waves average out to a continuous distribution named as the wave frequency distribution, the shape of which is predictable based on few model parameters. Published by the American Physical Society 2024

Non-darwinian theories of biological evolution

The article presents material related to evolutionary theories such as Berg’s nomogenesis, Kimura’s theory of neutral evolution, and neo-Lamarckism, the content of which is opposed to Darwin’s doctrine of natural selection as a leading factor in biological evolution. Nomogenesis, a hypothesis according to which the evolution of organisms is based on internal laws. The general thesis of Berg’s worldview was that evolution occurs naturally, in accordance with a pre-existing – pre-established – law of development. The neutral theory of molecular evolution was put forward by Motoo Kimura. It argues that the vast majority of mutations at the molecular level are neutral in nature with respect to the action of natural selection. Thus, the theory of neutral evolution contradicted Darwinism, pointing to the fixation of neutral mutations in the population without the participation of natural selection. Neo-Lamarckism is a combination of three heterogeneous trends in evolutionary doctrine. Despite the fact that neo-Lamarckists have deviated from the basic postulates of Lamarck, most of their concepts contain two provisions that correspond to Lamarck's laws. The first is the recognition of adequate variability that occurs under the direct or indirect influence of the environment and creates well-acquired adaptive traits. The second is the inheritance of similar traits in individual life.

Genetic diversity during selective sweeps in non-recombining populations.

Selective sweeps, resulting from the spread of beneficial, neutral, or deleterious mutations through a population, shape patterns of genetic variation at linked neutral sites. While many theoretical, computational, and statistical advances have been made in understanding the genomic signatures of selective sweeps in recombining populations, substantially less is understood in populations with little/no recombination. We present a mathematical framework based on diffusion theory for obtaining the site frequency spectrum (SFS) at linked neutral sites immediately post and during the fixation of moderately or strongly beneficial mutations. We find that when a single hard sweep occurs, the SFS decays as 1/ x for low derived allele frequencies ( x ), similar to the neutral SFS at equilibrium, whereas at higher derived allele frequencies, it follows a 1/ x 2 power law. These power laws are universal in the sense that they are independent of the dominance and inbreeding coefficient, and also characterize the SFS during the sweep. Additionally, we find that the derived allele frequency where the SFS shifts from the 1/ x to 1/ x 2 law, is inversely proportional to the selection strength: thus under strong selection, the SFS follows the 1/ x 2 dependence for most allele frequencies, resembling a rapidly expanding neutral population. When clonal interference is pervasive, the SFS immediately post-fixation becomes U-shaped and is better explained by the equilibrium SFS of selected sites. Our results will be important in developing statistical methods to infer the timing and strength of recent selective sweeps in asexual populations, genomic regions that lack recombination, and clonally propagating tumor populations.

PLS‐guided Mutant Recombination to Improve the Stability of Bovine Enterokinases Obtained by Directed Evolution

Activity and thermostability are critical yet challenging to improve simultaneously in enzymes. Using directed evolution, we previously identified bovine enterokinase (EKL) variants with enhanced soluble expression and thermal stability. Partial least squares (PLS) analysis of 321 EKL variants revealed the impact of individual mutations and identified neutral or detrimental mutations in top‐performing variants. Leveraging PLS rankings, we created new variants with fewer mutations and enhanced stability. Most original and PLS‐guided variants exhibited an activity‐stability trade‐off. However, two new triple‐ and quadruple‐mutants improved both activity and stability, surpassing the trade‐off limit. Recombining PLS‐guided mutations likely eliminated neutral or harmful mutations, enhancing stability. MD simulations linked residue‐specific dynamics with stability, pinpointing critical structural regions near aggregation‐prone areas. Our findings validate PLS as a potent strategy to enhance enzyme properties, complementing directed evolution.

Evolution of the substrate specificity of an RNA ligase ribozyme from phosphorimidazole to triphosphate activation

The acquisition of new RNA functions through evolutionary processes was essential for the diversification of RNA-based primordial biology and its subsequent transition to modern biology. However, the mechanisms by which RNAs access new functions remain unclear. Do RNA enzymes need completely new folds to support new but related functions, or is reoptimization of the active site sufficient? What are the roles of neutral and adaptive mutations in evolutionary innovation? Here, we address these questions experimentally by focusing on the evolution of substrate specificity in RNA-catalyzed RNA assembly. We use directed in vitro evolution to show that a ligase ribozyme that uses prebiotically relevant 5'-phosphorimidazole-activated substrates can be evolved to catalyze ligation with substrates that are 5'-activated with the biologically relevant triphosphate group. Interestingly, despite catalyzing a related reaction, the new ribozyme folds into a completely new structure and exhibits promiscuity by catalyzing RNA ligation with both triphosphate and phosphorimidazole-activated substrates. Although distinct in sequence and structure, the parent phosphorimidazolide ligase and the evolved triphosphate ligase ribozymes can be connected by a series of point mutations where the intermediate sequences retain at least some ligase activity. The existence of a quasi-neutral pathway between these distinct ligase ribozymes suggests that neutral drift is sufficient to enable the acquisition of new substrate specificity, thereby providing opportunities for subsequent adaptive optimization. The transition from RNA-catalyzed RNA assembly using phosphorimidazole-activated substrates to triphosphate-activated substrates may have foreshadowed the later evolution of the protein enzymes that use monomeric triphosphates (nucleoside triphosphates, NTPs) for RNA synthesis.

A putative scenario of how de novo protein-coding genes originate in the Saccharomyces cerevisiae lineage

BackgroundNovel protein-coding genes were considered to be born by re-organization of pre-existing genes, such as gene duplication and gene fusion. However, recent progress of genome research revealed that more protein-coding genes than expected were born de novo, that is, gene origination by accumulating mutations in non-genic DNA sequences. Nonetheless, the in-depth process (scenario) for de novo origination is not well understood.ResultsWe have conceived bioinformatic analysis for sketching a scenario for de novo origination of protein-coding genes. For each de novo protein-coding gene, we firstly identified an edge of a given phylogenetic tree where the gene was born based on parsimony. Then, from a multiple sequence alignment of the de novo gene and its orthologous regions, we constructed ancestral DNA sequences of the gene corresponding to both end nodes of the edge. We finally revealed statistical features observed in evolution between the two ancestral sequences. In the analysis of the Saccharomyces cerevisiae lineage, we have successfully sketched a putative scenario for de novo origination of protein-coding genes. (1) In the beginning was GC-rich genome regions. (2) Neutral mutations were accumulated in the regions. (3) ORFs were extended/combined, and then (4) translation signature (Kozak consensus sequence) was recruited. Interestingly, as the scenario progresses from (2) to (4), the specificity of mutations increases.ConclusionTo the best of our knowledge, this is the first report outlining a scenario of de novo origination of protein-coding genes. Our bioinformatic analysis can capture events that occur during a short evolutionary time by directly observing the evolution of the ancestral sequences from non-genic to genic. This property is suitable for the analysis of fast evolving de novo genes.

Recurrent selection shapes the genomic landscape of differentiation between a pair of host-specialized haplodiploids that diverged with gene flow.

Understanding the genetics of adaptation and speciation is critical for a complete picture of how biodiversity is generated and maintained. Heterogeneous genomic differentiation between diverging taxa is commonly documented, with genomic regions of high differentiation interpreted as resulting from differential gene flow, linked selection and reduced recombination rates. Disentangling the roles of each of these non-exclusive processes in shaping genome-wide patterns of divergence is challenging but will enhance our knowledge of the repeatability of genomic landscapes across taxa. Here, we combine whole-genome resequencing and genome feature data to investigate the processes shaping the genomic landscape of differentiation for a sister-species pair of haplodiploid pine sawflies, Neodiprion lecontei and Neodiprion pinetum. We find genome-wide correlations between genome features and summary statistics are consistent with pervasive linked selection, with patterns of diversity and divergence more consistently predicted by exon density and recombination rate than the neutral mutation rate (approximated by dS). We also find that both global and local patterns of FST, dXY and π provide strong support for recurrent selection as the primary selective process shaping variation across pine sawfly genomes, with some contribution from balancing selection and lineage-specific linked selection. Because inheritance patterns for haplodiploid genomes are analogous to those of sex chromosomes, we hypothesize that haplodiploids may be especially prone to recurrent selection, even if gene flow occurred throughout divergence. Overall, our study helps fill an important taxonomic gap in the genomic landscape literature and contributes to our understanding of the processes that shape genome-wide patterns of genetic variation.

Fixation times on directed graphs.

Computing the rate of evolution in spatially structured populations is difficult. A key quantity is the fixation time of a single mutant with relative reproduction rate r which invades a population of residents. We say that the fixation time is "fast" if it is at most a polynomial function in terms of the population size N. Here we study fixation times of advantageous mutants (r > 1) and neutral mutants (r = 1) on directed graphs, which are those graphs that have at least some one-way connections. We obtain three main results. First, we prove that for any directed graph the fixation time is fast, provided that r is sufficiently large. Second, we construct an efficient algorithm that gives an upper bound for the fixation time for any graph and any r ≥ 1. Third, we identify a broad class of directed graphs with fast fixation times for any r ≥ 1. This class includes previously studied amplifiers of selection, such as Superstars and Metafunnels. We also show that on some graphs the fixation time is not a monotonically declining function of r; in particular, neutral fixation can occur faster than fixation for small selective advantages.

Synergistic effects of global exclusion and mutation on replicator dynamics of public cooperation

Global exclusion represents an effective mechanism for the evolution of cooperation, even within an infinitely well-mixed population. However, it remains unknown how global exclusion performs when faced with the evolutionary challenges posed by both defection and neutral mutation in the public goods game. Here, we report that global exclusion is able to resist or even have a positive interplay with unbiased mutations in the replicator dynamics of public cooperation. In the limit of an infinite population size, we find that the replicator-mutation dynamics can result in either a global stable coexistence or two local stable coexistences, whose attraction basins are separated by an unstable fixed point, between global exclusion and defection, as well as several types of bifurcations. Interestingly, there is an optimal mutation rate that leads to the largest enhancement of the emergent level for cooperation by global exclusion when the exclusion cost is reasonably low. Our results thus indicate that random exploration of strategies by mutation can enhance the beneficial effects of global exclusion on the evolution of public cooperation.

Are Next-Generation Pathogenicity Predictors Applicable to Cancer?

Next-generation pathogenicity predictors are designed to identify pathogenic mutations in genetic disorders but are increasingly used to detect driver mutations in cancer. Despite this, their suitability for cancer is not fully established. Here we have assessed the effectiveness of next-generation pathogenicity predictors when applied to cancer by using a comprehensive experimental benchmark of cancer driver and neutral mutations. Our findings indicate that state-of-the-art methods AlphaMissense and VARITY demonstrate commendable performance despite generally underperforming compared to cancer-specific methods. This is notable considering that these methods do not explicitly incorporate cancer-related data in their training and have made concerted efforts to prevent data leakage from the human-curated training and test sets. Nevertheless, it should be mentioned that a significant limitation of using pathogenicity predictors for cancer arises from their inability to detect cancer potential driver mutations specific for a particular cancer type.

Method for Predicting the Oncogenicity of Mutant Proteins Using the Example of p53 Protein

This paper presents computational methods for separating oncogenic mutations from neutral mutations of the p53 protein using mathematical processing of the following calculated values for each sample: [Formula: see text], machine learning methods were used to determine the main physical quantities for predicting experimental values [Urea]50%, Kd, [Formula: see text], which determine the stability of mutant monomers of the p53 protein in various denatured alcohol concentrations and when bound to the p53-Bcl-Xl biocomplex, as well as RNA expression; machine learning methods are presented to determine key calculated quantities and dependencies, which will thus help predict experimental results. The description of the software package that implements the presented algorithm is given on the website https://binomlabs.com/.

Emergence failure of early epidemics: A mathematical modeling approach.

Epidemic or pathogen emergence is the phenomenon by which a poorly transmissible pathogen finds its evolutionary pathway to become a mutant that can cause an epidemic. Many mathematical models of pathogen emergence rely on branching processes. Here, we discuss pathogen emergence using Markov chains, for a more tractable analysis, generalizing previous work by Kendall and Bartlett about disease invasion. We discuss the probability of emergence failure for early epidemics, when the number of infected individuals is small and the number of the susceptible individuals is virtually unlimited. Our formalism addresses both directly transmitted and vector-borne diseases, in the cases where the original pathogen is 1) one step-mutation away from the epidemic strain, and 2) undergoing a long chain of neutral mutations that do not change the epidemiology. We obtain analytic results for the probabilities of emergence failure and two features transcending the transmission mechanism. First, the reproduction number of the original pathogen is determinant for the probability of pathogen emergence, more important than the mutation rate or the transmissibility of the emerged pathogen. Second, the probability of mutation within infected individuals must be sufficiently high for the pathogen undergoing neutral mutations to start an epidemic, the mutation threshold depending again on the basic reproduction number of the original pathogen. Finally, we discuss the parameterization of models of pathogen emergence, using SARS-CoV1 as an example of zoonotic emergence and HIV as an example for the emergence of drug resistance. We also discuss assumptions of our models and implications for epidemiology.

NeMu: a comprehensive pipeline for accurate reconstruction of neutral mutation spectra from evolutionary data.

The recognized importance of mutational spectra in molecular evolution is yet to be fully exploited beyond human cancer studies and model organisms. The wealth of intraspecific polymorphism data in the GenBank repository, covering a broad spectrum of genes and species, presents an untapped opportunity for detailed mutational spectrum analysis. Existing methods fall short by ignoring intermediate substitutions on the inner branches of phylogenetic trees and lacking the capability for cross-species mutational comparisons. To address these challenges, we present the NeMu pipeline, available at https://nemu-pipeline.com, a tool grounded in phylogenetic principles designed to provide comprehensive and scalable analysis of mutational spectra. Utilizing extensive sequence data from numerous available genome projects, NeMu rapidly and accurately reconstructs the neutral mutational spectrum. This tool, facilitating the reconstruction of gene- and species-specific mutational spectra, contributes to a deeper understanding of evolutionary mechanisms across the broad spectrum of known species.

A Synergistic, Cultivator Model of De Novo Gene Origination.

The origin and fixation of evolutionarily young genes is a fundamental question in evolutionary biology. However, understanding the origins of newly evolved genes arising de novo from noncoding genomic sequences is challenging. This is partly due to the low likelihood that several neutral or nearly neutral mutations fix prior to the appearance of an important novel molecular function. This issue is particularly exacerbated in large effective population sizes where the effect of drift is small. To address this problem, we propose a regulation-focused, cultivator model for de novo gene evolution. This cultivator-focused model posits that each step in a novel variant's evolutionary trajectory is driven by well-defined, selectively advantageous functions for the cultivator genes, rather than solely by the de novo genes, emphasizing the critical role of genome organization in the evolution of new genes.

In silico energetic and molecular dynamic simulations studies demonstrate potential effect of the point mutations with implications for protein engineering in BDNF

Protein engineering by directed evolution is time-consuming. Hence, in silico techniques like FoldX-Yasara for ∆∆G calculation, and SNPeffect for predicting propensity for aggregation, amyloid formation, and chaperone binding are employed to design proteins. Here, we used in silico techniques to engineer BDNF-NTF3 interaction and validated it using mutations with known functional implications for NGF dimer. The structures of three mutants representing a positive, negative, or neutral ∆∆G involving two interface residues in BDNF and two mutations representing a neutral and positive ∆∆G in NGF, which is aligned with BDNF, were selected for molecular dynamics (MD) simulation. Our MD results conclude that the secondary structure of individual protomers of the positive and negative mutants displayed a similar or different conformation from the NTF3 monomer, respectively. The positive mutants showed fewer hydrophobic interactions and higher hydrogen bonds compared to the wild-type, negative, and neutral mutants with similar SASA, suggesting solvent-mediated disruption of hydrogen-bonded interactions. Similar results were obtained for mutations with known functional implications for NGF and BDNF. The results suggest that mutations with known effects in homologous proteins could help in validation, and in silico directed evolution experiments could be a viable alternative to the experimental technique used for protein engineering.

Intraspecific genetic diversity and coexistence in phytoplankton populations

AbstractThe past two decades have seen a drastic increase in the availability and use of genetic techniques to study phytoplankton communities. As a result, it is now well documented that phytoplankton populations are genetically diverse, despite predominantly asexual reproduction and minute morphological variation. Genetic variation can lead to variation also in phenotype, and some traits vary more among genotypes than between species. Trait‐based approaches tackle this by focusing on traits rather than on species. However, trait‐based models often have difficulty predicting and explaining the huge trait‐diversity among coexisting individuals competing for the same few resources. Thus, we ask the question: How do hundreds, if not thousands, of genotypes coexist in a highly competitive environment? In this review, we gather information on genetic and phenotypic variations in coexisting genotypes and elaborate on three mechanisms by which broad intraspecific genetic diversity may be possible: neutral mutations, environmental fluctuations, and trade‐offs among traits. These have all been applied on an interspecies level, and we discuss their use also among coexisting genotypes. We find that genetic diversity to be almost exclusively studied in blooming species and that clonal diversity frequently measure above 0.95 (i.e., 95% of individuals sampled are genetically different). Genetic diversity seems stable throughout blooms, suggesting that competitive exclusion is low or that new genetic material is frequently being introduced into populations. Further, we find high intraspecific trait‐variation in several key traits among coexisting strains but also that trait‐variation is often neglected in studies on phytoplankton, making coexistence difficult to predict.

The speed of neutral evolution on graphs.

The speed of evolution on structured populations is crucial for biological and social systems. The likelihood of invasion is key for evolutionary stability. But it makes little sense if it takes long. It is far from known what population structure slows down evolution. We investigate the absorption time of a single neutral mutant for all the 112 non-isomorphic undirected graphs of size 6. We find that about three-quarters of the graphs have an absorption time close to that of the complete graph, less than one-third are accelerators, and more than two-thirds are decelerators. Surprisingly, determining whether a graph has a long absorption time is too complicated to be captured by the joint degree distribution. Via the largest sojourn time, we find that echo-chamber-like graphs, which consist of two homogeneous graphs connected by few sparse links, are likely to slow down absorption. These results are robust for large graphs, mutation patterns as well as evolutionary processes. This work serves as a benchmark for timing evolution with complex interactions, and fosters the understanding of polarization in opinion formation.

Uganda chicken genetic resources: II. genetic diversity and population demographic history inferred from mitochondrial DNA D-loop sequences.

The genetic diversity of indigenous chickens, which comprise over 80% of the chicken resources in Uganda, is largely not well-characterized for their genetic contribution. This study assessed the genetic diversity and population structure of the indigenous chicken population in Uganda to serve as an essential component for improvement and conservation strategies. A set of 344 mitochondrial DNA (mtDNA) D-loop sequences among 12 Ugandan chicken populations was evaluated. Twenty-eight polymorphic sites, accounting for 4.26% of the total analyzed loci of 658bp, defined 32 haplotypes. The haplotype diversity (Hd) was 0.437, with a nucleotide diversity (π) of 0.0169, while the average number of nucleotide differences (k) was 0.576, indicating a population that is moderately genetically diverse. Analysis of molecular variance found 98.39% (ρ < 0.01) of the total sequence variation among the chicken haplotypes within populations, 1.08% (ρ < 0.05) among populations, and 0.75% (ρ > 0.05) among populations within regions. This revealed subtle genetic differentiation among the populations, which appeared to be influenced by population fragmentation, probably due to neutral mutation, random genetic drift, and/or balancing selection. All the haplotypes showed affinity exclusively to the haplogroup-E mtDNA phylogeny, with haplotype UGA01 signaling an ancestral haplotype in Uganda. Neutrality tests Tajima's D (-2.320) and Fu's Fs (-51.369), augmented with mismatch distribution to measure signatures of recent historical demographic events, supported a population expansion across the chicken populations. The results show one matrilineal ancestry of Ugandan chickens from a lineage widespread throughout the world that began in the Indian subcontinent. The lack of phylogeographic signals is consistent with recent expansion events with extensive within-country genetic intermixing among haplotypes. Thus, the findings in this study hold the potential to guide conservation strategies and breeding programs in Uganda, given that higher genetic diversity comes from within the chicken population.