Neutral Mutations Research Articles

The number of neutral mutants in an expanding Luria-Delbrück population is approximately Fréchet.

Background: A growing population of cells accumulates mutations. A single mutation early in the growth process carries forward to all descendant cells, causing the final population to have a lot of mutant cells. When the first mutation happens later in growth, the final population typically has fewer mutants. The number of mutant cells in the final population follows the Luria-Delbrück distribution. The mathematical form of the distribution is known only from its probability generating function. For larger populations of cells, one typically uses computer simulations to estimate the distribution. Methods: This article searches for a simple approximation of the Luria-Delbrück distribution, with an explicit mathematical form that can be used easily in calculations. Results: The Fréchet distribution provides a good approximation for the Luria-Delbrück distribution for neutral mutations, which do not cause a growth rate change relative to the original cells. Conclusions: The Fréchet distribution apparently provides a good match through its description of extreme value problems for multiplicative processes such as exponential growth.

Blib is a multi-module simulation platform for genetics studies and intelligent breeding

Simulation is an efficient approach for the investigation of theoretical and applied issues in population and quantitative genetics, and animal and plant breeding. In this study, we report a multi-module simulation platform called Blib, that is able to handle more complicated genetic effects and models than existing tools. Two derived data types are first defined in Blib, one to hold the required information on genetic models, and the other one to represent the genetics and breeding populations. A number of subroutines are then developed to perform specific tasks. Four case studies are present as examples to show the applications of Blib, i.e., genetic drift of multiple alleles in randomly mating populations, joint effects of neutral mutation and genetic drift, comparison of mass versus family selection, and choice of testers in hybrid breeding. Blib together with its application modules, has great potential to benefit theoretical genetic studies and intelligent breeding by simulating and predicting outcomes in a large number of scenarios, and identifying the best optimum selection and crossing schemes.

Genomic Analysis of SARS-CoV-2 Alpha, Beta and Delta Variants of Concern Uncovers Signatures of Neutral and Non-Neutral Evolution.

Due to the emergence of new variants of the SARS-CoV-2 coronavirus, the question of how the viral genomes evolved, leading to the formation of highly infectious strains, becomes particularly important. Three major emergent strains, Alpha, Beta and Delta, characterized by a significant number of missense mutations, provide a natural test field. We accumulated and aligned 4.7 million SARS-CoV-2 genomes from the GISAID database and carried out a comprehensive set of analyses. This collection covers the period until the end of October 2021, i.e., the beginnings of the Omicron variant. First, we explored combinatorial complexity of the genomic variants emerging and their timing, indicating very strong, albeit hidden, selection forces. Our analyses show that the mutations that define variants of concern did not arise gradually but rather co-evolved rapidly, leading to the emergence of the full variant strain. To explore in more detail the evolutionary forces at work, we developed time trajectories of mutations at all 29,903 sites of the SARS-CoV-2 genome, week by week, and stratified them into trends related to (i) point substitutions, (ii) deletions and (iii) non-sequenceable regions. We focused on classifying the genetic forces active at different ranges of the mutational spectrum. We observed the agreement of the lowest-frequency mutation spectrum with the Griffiths-Tavaré theory, under the Infinite Sites Model and neutrality. If we widen the frequency range, we observe the site frequency spectra much more consistently with the Tung-Durrett model assuming clone competition and selection. The coefficients of the fitting model indicate the possibility of selection acting to promote gradual growth slowdown, as observed in the history of the variants of concern. These results add up to a model of genomic evolution, which partly fits into the classical drift barrier ideas. Certain observations, such as mutation "bands" persistent over the epidemic history, suggest contribution of genetic forces different from mutation, drift and selection, including recombination or other genome transformations. In addition, we show that a "toy" mathematical model can qualitatively reproduce how new variants (clones) stem from rare advantageous driver mutations, and then acquire neutral or disadvantageous passenger mutations which gradually reduce their fitness so they can be then outcompeted by new variants due to other driver mutations.

Identification of potential driver mutations in glioblastoma using machine learning.

Glioblastoma is a fast and aggressively growing tumor in the brain and spinal cord. Mutation of amino acid residues in targets proteins, which are involved in glioblastoma, alters the structure and function and may lead to disease. In this study, we collected a set of 9386 disease-causing (drivers) mutations based on the recurrence in patient samples and experimentally annotated as pathogenic and 8728 as neutral (passenger) mutations. We observed that Arg is highly preferred at the mutant sites of drivers, whereas Met and Ile showed preferences in passengers. Inspecting neighboring residues at the mutant sites revealed that the motifs YP, CP and GRH, are preferred in drivers, whereas SI, IQ and TVI are dominant in neutral. In addition, we have computed other sequence-based features such as conservation scores, Position Specific Scoring Matrices (PSSM) and physicochemical properties, and developed a machine learning-based method, GBMDriver (GlioBlastoma Multiforme Drivers), for distinguishing between driver and passenger mutations. Our method showed an accuracy and AUC of 73.59% and 0.82, respectively, on 10-fold cross-validation and 81.99% and 0.87 in a blind set of 1809 mutants. The tool is available at https://web.iitm.ac.in/bioinfo2/GBMDriver/index.html. We envisage that the present method is helpful to prioritize driver mutations in glioblastoma and assist in identifying therapeutic targets.

The impact of neutral mutations on genome evolvability

Beneficial mutations with strong effects are rare and deleterious mutations are purged by natural selection. Therefore, the majority of mutations that accumulate in genomes have very weak or no selective effects, being then called neutral mutations. Over the last two decades, it has been shown that mutations, even when they are neutral, affect evolvability by providing access to new phenotypes through later-occurring mutations that would not have been available otherwise. We propose here that in addition to this effect, many mutations -independent of their selective effects- can affect the mutability of neighboring DNA sequences and modulate the efficiency of homologous recombination. Such mutations do not alter the spectrum of accessible phenotypes, but rather the rate at which new phenotypes will be produced, a process that has long-term but also potentially short-term consequences for cancer emergence.

Evolution dynamic of intelligent construction strategy of coal mine enterprises in China

The intelligent construction of coal mining enterprises is the fundamental strategy to prevent and curb major coal mine accidents, and the core technical support to realize the high-quality development of the coal industry. Considering the intelligent construction of coal mining enterprises enabled by emerging information technologies such as 5G and artificial intelligence, this paper constructs a Moran process stochastic evolutionary game model of intelligent construction of coal mining enterprises. Based on the fixed point probability of the Moran process, the probability of successful invasion of the "intelligent construction" strategy and the "traditional production" strategy in the limited coal mining enterprise group is calculated. By comparing the probability of individual fixed point and the probability of neutral mutation, the dominant condition of the strategy under strong selection and weak selection is obtained. The research shows that external stochastic factors, the number and scale of enterprises, the intensity of capacity replacement and the cost-benefit of intelligent construction are the main factors affecting the intelligent construction behavior of coal mining enterprises. When the intelligent construction of coal mining enterprises is in the cultivation period, the intelligent construction of underground coal mines dominated by stochastic factors can be effectively promoted by increasing the intensity of capacity reduction replacement. For the open-pit coal mine dominated by expected payoffs, reducing the number of mining rights and improving the concentration of open-pit coal mining industry will have a better effect on promoting its intelligent cultivation process. When the intelligent construction of coal mining enterprises is in the mature stage, with the improvement of the cost and benefits of intelligent construction, "intelligent construction" strategy will become a general consensus of coal mining enterprises. In addition, this paper analyzes the relevant parameters through specific examples to verify the effectiveness of the conclusions, which provides a scientific basis for effectively accelerating the intelligent construction of coal mining enterprises and promoting the transformation and upgrading of the coal industry.

Identification of key somatic oncogenic mutation based on a confounder-free causal inference model.

Abnormal cell proliferation and epithelial-mesenchymal transition (EMT) are the essential events that induce cancer initiation and progression. A fundamental goal in cancer research is to develop an efficient method to detect mutational genes capable of driving cancer. Although several computational methods have been proposed to identify these key mutations, many of them focus on the association between genetic mutations and functional changes in relevant biological processes, but not their real causality. Causal effect inference provides a way to estimate the real induce effect of a certain mutation on vital biological processes of cancer initiation and progression, through addressing the confounder bias due to neutral mutations and unobserved latent variables. In this study, integrating genomic and transcriptomic data, we construct a novel causal inference model based on a deep variational autoencoder to identify key oncogenic somatic mutations. Applied to 10 cancer types, our method quantifies the causal effect of genetic mutations on cell proliferation and EMT by reducing both observed and unobserved confounding biases. The experimental results indicate that genes with higher mutation frequency do not necessarily mean they are more potent in inducing cancer and promoting cancer development. Moreover, our study fills a gap in the use of machine learning for causal inference to identify oncogenic mutations.

Uncovering the extensive trade-off between adaptive evolution and disease susceptibility.

Favored mutations in the human genome may make the carriers adapt to changing environments and lifestyles but also susceptible to specific diseases. The scale and details of the trade-off between adaptive evolution and disease susceptibility are unclear because most favored mutations in different populations remain unidentified. As no statistical test can discriminate favored mutations from nearby hitchhiking neutral ones, we report a deep-learning network (DeepFavored) to integrate multiple statistical tests and divide identifying favored mutations into two subtasks. We identify favored mutations in three human populations and analyzed the correlation between favored/hitchhiking mutations and genome-wide association study (GWAS) sites. Both favored and hitchhiking neutral mutations are enriched in GWAS sites with population-specific features, and the enrichment and population specificity are prominent in genes in specific Gene Ontology (GO) terms. These provide evidence for extensive and population-specific trade-offs between adaptive evolution and disease susceptibility. The unveiled scale helps understand and investigate differences and diseases of humans.

AI-based search for convergently expanding, advantageous mutations in SARS-CoV-2 by focusing on oligonucleotide frequencies.

Among mutations that occur in SARS-CoV-2, efficient identification of mutations advantageous for viral replication and transmission is important to characterize and defeat this rampant virus. Mutations rapidly expanding frequency in a viral population are candidates for advantageous mutations, but neutral mutations hitchhiking with advantageous mutations are also likely to be included. To distinguish these, we focus on mutations that appear to occur independently in different lineages and expand in frequency in a convergent evolutionary manner. Batch-learning SOM (BLSOM) can separate SARS-CoV-2 genome sequences according by lineage from only providing the oligonucleotide composition. Focusing on remarkably expanding 20-mers, each of which is only represented by one copy in the viral genome, allows us to correlate the expanding 20-mers to mutations. Using visualization functions in BLSOM, we can efficiently identify mutations that have expanded remarkably both in the Omicron lineage, which is phylogenetically distinct from other lineages, and in other lineages. Most of these mutations involved changes in amino acids, but there were a few that did not, such as an intergenic mutation.

Massive Loss of Transcription Factors Promotes the Initial Diversification of Placental Mammals.

As one of the most successful group of organisms, mammals occupy a variety of niches on Earth as a result of macroevolution. Transcription factors (TFs), the fundamental regulators of gene expression, may also have evolved. To examine the relationship between TFs and mammalian macroevolution, we analyzed 140,821 de novo-identified TFs and their birth and death histories from 96 mammalian species. Gene tree vs. species tree reconciliation revealed that placental mammals experienced an upsurge in TF losses around 100 million years ago (Mya) and also near the Cretaceous–Paleogene boundary (K–Pg boundary, 66 Mya). Early Euarchontoglires, Laurasiatheria and marsupials appeared between 100 and 95 Mya and underwent initial diversification. The K-Pg boundary was associated with the massive extinction of dinosaurs, which lead to adaptive radiation of mammals. Surprisingly, TF loss decelerated, rather than accelerated, molecular evolutionary rates of their target genes. As the rate of molecular evolution is affected by the mutation rate, the proportion of neutral mutations and the population size, the decrease in molecular evolution may reflect increased functional constraints to survive target genes.

Homeostasis limits keratinocyte evolution

Recent studies have revealed that normal human tissues accumulate many somatic mutations. In particular, human skin is riddled with mutations, with multiple subclones of variable sizes. Driver mutations are frequent and tend to have larger subclone sizes, suggesting selection. To begin to understand the histories encoded by these complex somatic mutations, we incorporated genomes into a simple agent-based skin-cell model whose prime directive is homeostasis. In this model, stem-cell survival is random and dependent on proximity to the basement membrane. This simple homeostatic skin model recapitulates the observed log-linear distributions of somatic mutations, where most mutations are found in increasingly smaller subclones that are typically lost with time. Hence, neutral mutations are "passengers" whose fates depend on the random survival of their stem cells, where a rarer larger subclone reflects the survival and spread of mutations acquired earlier in life. The model can also maintain homeostasis and accumulate more frequent and larger driver subclones if these mutations (NOTCH1 and TP53) confer relatively higher persistence in normal skin or during tissue damage (sunlight). Therefore, a relatively simple model of epithelial turnover indicates how observed passenger and driver somatic mutations could accumulate without violating the prime directive of homeostasis in normal human tissues.

Genetic structure of some candidate genes of repeat breeder syndrome in Egyptian buffaloes

BackgroundThis study aimed to explore the association between polymorphisms in three genes: leptin (LEP), leptin receptor (LEPR), and BMP4, and incidence of repeat breeding in Egyptian buffaloes. MethodsDNA was extracted from 160 female buffaloes, involving 108 fertile and 52 repeat breeders. Genotyping was performed by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Sequence analysis and alignment were performed by employing NCBI/BLAST/blastn suite, to identify SNPs among different patterns and alleles. We utilized PredictSNP software to predict the non-synonymous SNPs influences on protein function. Moreover, the conservation score of the amino acids within the target proteins was computed by ConSurf server. ResultsThe genotyping results showed that LEP and BMP4 genes were monomorphic (CC, GG) in all tested fertile and repeat breeder buffaloes. Leptin gene sequencing showed a non-synonymous C73T SNP, replacing R to C at position 25 within the leptin polypeptide (position 4 in the mature form; R4C) which is a neutral mutation, not affecting function or structure of LEP protein. For LEPR, one synonymous SNP (T102C) and two non-synonymous SNPs (A106G and C146A), triggering V967A and G954C replacements, respectively in LEPR protein. Moreover, they are neutral mutations. Sequencing results of BMP4 showed HinfI restriction site indicate fixed GG genotype (CC genotype in the anti-sense strand) in all sequenced samples. No SNPs were observed within the amplified region. ConclusionGenotyping and sequencing results of the surveyed three genes revealed that there is no association between these genes mutations and the incidence of repeat breeding in Egyptian buffaloes.

Laws of Spatially Structured Population Dynamics on a Lattice

We consider spatial population dynamics on a lattice, following a type of a contact (birth–death) stochastic process. We show that simple mathematical approximations for the density of cells can be obtained in a variety of scenarios. In the case of a homogeneous cell population, we derive the cellular density for a two-dimensional (2D) spatial lattice with an arbitrary number of neighbors, including the von Neumann, Moore, and hexagonal lattice. We then turn our attention to evolutionary dynamics, where mutant cells of different properties can be generated. For disadvantageous mutants, we derive an approximation for the equilibrium density representing the selection–mutation balance. For neutral and advantageous mutants, we show that simple scaling (power) laws for the numbers of mutants in expanding populations hold in 2D and 3D, under both flat (planar) and range population expansion. These models have relevance for studies in ecology and evolutionary biology, as well as biomedical applications including the dynamics of drug-resistant mutants in cancer and bacterial biofilms.

Longitudinal dynamics of clonal hematopoiesis identifies gene-specific fitness effects

Clonal hematopoiesis of indeterminate potential (CHIP) increases rapidly in prevalence beyond age 60 and has been associated with increased risk for malignancy, heart disease and ischemic stroke. CHIP is driven by somatic mutations in hematopoietic stem and progenitor cells (HSPCs). Because mutations in HSPCs often drive leukemia, we hypothesized that HSPC fitness substantially contributes to transformation from CHIP to leukemia. HSPC fitness is defined as the proliferative advantage over cells carrying no or only neutral mutations. If mutations in different genes lead to distinct fitness advantages, this could enable patient stratification. We quantified the fitness effects of mutations over 12 years in older age using longitudinal sequencing and developed a filtering method that considers individual mutational context alongside mutation co-occurrence to quantify the growth potential of variants within individuals. We found that gene-specific fitness differences can outweigh inter-individual variation and, therefore, could form the basis for personalized clinical management.

Polyploidization as an opportunistic mutation: The role of unreduced gametes formation and genetic drift in polyploid establishment.

It is broadly assumed that polyploidy success reflects an increase in fitness associated with whole-genome duplication (WGD), due to higher tolerance to stressful conditions. Nevertheless, WGD also arises with several costs in neo-polyploid lineages, like genomic instability, or cellular mis-management. In addition to these costs, neo-polyploid individuals also face frequency dependent selection because of frequent low-fitness triploids formed by cross-ploidy pollinations when tetraploids are primarily rare in the population. Interestingly, the idea that polyploidy can be fixed by genetic drift as a neutral or deleterious mutation is currently underexplored in the literature. To test how and when polyploidy can fix in a population by chance, we built a theoretical model in which autopolyploidization occurs through the production of unreduced gametes, a trait modelled as a quantitative trait that is allowed to vary through time. We found that when tetraploid individuals are less or as fit as their diploid progenitors, fixation of polyploidy is only possible when genetic drift is stronger than natural selection. The necessity of drift for tetraploid fixation holds even when polyploidy confers a selective advantage, except for scenarios where tetraploids are much fitter than diploids. Finally, we found that self-fertilization is less beneficial for tetraploid establishment than previously thought, notably when polyploids harbour an initial decrease in fitness. Our results bring a novel, non-exclusive explanation for the unequal temporal and spatial distribution of polyploid species.

The stationary and quasi-stationary properties of neutral multi-type branching process diffusions

The stationary asymptotic properties of the diffusion limit of a multi-type branching process with neutral mutations are studied. For the critical and subcritical processes the interesting limits are those of quasi-stationary distributions conditioned on non-extinction. Pedagogical derivations are given for known results that the limiting distributions for supercritical and critical processes are found to collapse onto rays aligned with stationary eigenvectors of the mutation rate matrix, in agreement with discrete multi-type branching processes. For the sub-critical process the previously unsolved quasi-stationary distribution is obtained to first order in the overall mutation rate, which is assumed to be small. The sampling distribution over allele types for a sample of given finite size is found to agree to first order in mutation rates with the analogous sampling distribution for a Wright-Fisher diffusion with constant population size.

Co-evolution of interacting proteins through non-contacting and non-specific mutations.

SummaryProteins often accumulate neutral mutations that do not affect current functions but can profoundly influence future mutational possibilities and functions. Understanding such hidden potential has major implications for protein design and evolutionary forecasting, but has been limited by a lack of systematic efforts to identify potentiating mutations. Here, through the comprehensive analysis of a bacterial toxin-antitoxin system, we identified all possible single substitutions in the toxin that enable it to tolerate otherwise interface-disrupting mutations in its antitoxin. Strikingly, the majority of enabling mutations in the toxin do not contact, and promote tolerance non-specifically to, many different antitoxin mutations, despite covariation in homologs occurring primarily between specific pairs of contacting residues across the interface. In addition, the enabling mutations we identified expand future mutational paths that both maintain old toxin-antitoxin interactions and form new ones. These non-specific mutations are missed by widely used covariation and machine learning methods. Identifying such enabling mutations will be critical for ensuring continued binding of therapeutically relevant proteins, such as antibodies, aimed at evolving targets.

The genomic signatures of natural selection in admixed human populations

Admixture has been a pervasive phenomenon in human history, extensively shaping the patterns of population genetic diversity. There is increasing evidence to suggest that admixture can also facilitate genetic adaptation to local environments, i.e., admixed populations acquire beneficial mutations from source populations, a process that we refer to as "adaptive admixture." However, the role of adaptive admixture in human evolution and the power to detect it remain poorly characterized. Here, we use extensive computer simulations to evaluate the power of several neutrality statistics to detect natural selection in the admixed population, assuming multiple admixture scenarios. We show that statistics based on admixture proportions, Fadm and LAD, show high power to detect mutations that are beneficial in the admixed population, whereas other statistics, including iHS and FST, falsely detect neutral mutations that have been selected in the source populations only. By combining Fadm and LAD into a single, powerful statistic, we scanned the genomes of 15 worldwide, admixed populations for signatures of adaptive admixture. We confirm that lactase persistence and resistance to malaria have been under adaptive admixture in West Africans and in Malagasy, North Africans, and South Asians, respectively. Our approach also uncovers other cases of adaptive admixture, including APOL1 in Fulani nomads and PKN2 in East Indonesians, involved in resistance to infection and metabolism, respectively. Collectively, our study provides evidence that adaptive admixture has occurred in human populations whose genetic history is characterized by periods of isolation and spatial expansions resulting in increased gene flow.

Myocilin Mutations in a Zambian Population Attending Selected Referral Eye Health Facilities

Purpose: Research has shown that primary open-angle glaucoma (POAG) in Zambia has an earlier age of onset and is more clinically severe than in Europe and the United States of America. Mutations of Myocilin have been reported to be associated with POAG in multiple populations. We therefore, investigated the role of myocilin gene mutations in Primary Open Angle Glaucoma in a Zambian population. Methods: The unrelated POAG patients and unaffected controls seen at the University Teaching Hospitals Eye Hospital, Kitwe Teaching Eye Hospital and Lusaka Eye Hospital were recruited for this study. Glaucoma specialists from the participating institutions ascertained all POAG and control patients. Age, sex and ethnicity matched unaffected controls were obtained in patients with an IOP < 22 mm Hg, clear ocular media and normal-appearing optic nerve heads. A complete eye examination, including visual field assessment, was performed in all cases. Genomic DNA was extracted from whole peripheral blood, then subjected to polymerase chain reaction to amplify exons, flanking introns and promoter regions of the myocilin gene. The amplified products were screened for base mutations by autosequence based on the Sanger method. The study used the chi square test and conditional logistic regression to compare the cases and controls. Identified mutations were compared to known myocilin mutations. Results: Unrelated 165 POAG patients and unaffected 173 controls enrolled for the study. The analysis revealed four variants of myocilin mutations in 49 participants which included one synonymous (silent) mutation (Thr474Thr; 45/338) and three missense mutations (Ala446Thr; 16/338), (Leu158Arg; 4/338) and (Arg342Lys; 1/338). The prevalence of myocilin (MYOC) gene mutations in this study was 14.5% (49/338). The study observed two previously reported mutations, Ala446Thr and Arg342Lys, as glaucoma causing mutations. The variant (Lys158Arg) observed in the study was a novel finding. These mutations were detected in age, sex and ethnically matched controls. The missense mutation, Ala446Thr, was found in eight cases and eight controls. Twenty (20) controls and 25 cases had the synonymous or silent (neutral) mutation, (Thr474Thr). Conclusions: The myocilin mutations represent a prevalence of 14.5 in a Zambian population. The Leu158Arg seems to represent novel glaucoma, causing missense mutation. Mutations in myocilin appear to play a big role in the pathogenesis of POAG in a Zambian population.

LIM domain-wide comprehensive virtual mutagenesis provides structural rationale for cardiomyopathy mutations in CSRP3

Cardiomyopathies are a severe and chronic cardiovascular burden worldwide, affecting a large cohort in the general population. Cysteine and glycine-rich protein 3 (CSRP3) is one of key proteins implicated in dominant dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM). In this study, we device a rapid in silico screening protocol that creates a mutational landscape map for all possible allowed and disallowed substitutions in the protein of interest. This map provides the structural and functional insights on the stability of LIM domains of CSRP3. Further, the sequence analysis delineates the eukaryotic CSRP3 protein orthologs which complements the mutational map, but provide limited information of amino acid exchanges. Next, we also evaluated the effect of HCM/DCM mutations on these domains. One of highly destabilising mutations—L44P (also disease causing) and a neutral mutation—L44M were further subjected to molecular dynamics (MD) simulations. The results establish that L44P substitution affects the LIM domain structure by altering secondary structure and due to loss of hydrophobic interaction with Phenylananine 35. The present study provides a useful perspective to our understanding of the role of mutations in the CSRP3 LIM domains and their evolution. This study provides a novel computational screening method for quick identification of key mutation sites for specific protein structures that can reduce the burden on experimental research.