Showing papers on "Mutation (genetic algorithm) published in 2018"
TL;DR: DeepSequence is an unsupervised deep latent-variable model that predicts the effects of mutations on the basis of evolutionary sequence information that is grounded with biologically motivated priors, reveals the latent organization of sequence families, and can be used to explore new parts of sequence space.
Abstract: The functions of proteins and RNAs are defined by the collective interactions of many residues, and yet most statistical models of biological sequences consider sites nearly independently Recent approaches have demonstrated benefits of including interactions to capture pairwise covariation, but leave higher-order dependencies out of reach Here we show how it is possible to capture higher-order, context-dependent constraints in biological sequences via latent variable models with nonlinear dependencies We found that DeepSequence ( https://githubcom/debbiemarkslab/DeepSequence ), a probabilistic model for sequence families, predicted the effects of mutations across a variety of deep mutational scanning experiments substantially better than existing methods based on the same evolutionary data The model, learned in an unsupervised manner solely on the basis of sequence information, is grounded with biologically motivated priors, reveals the latent organization of sequence families, and can be used to explore new parts of sequence space
385 citations
TL;DR: The results support the mechanism that ZIKV has accumulated mutation(s) that increases the ability to evade immune response and potentiates infection and epidemics and interferes with interferon production through interaction with TBK1.
Abstract: Virus-host interactions determine an infection outcome. The Asian lineage of Zika virus (ZIKV), responsible for the recent epidemics, has fixed a mutation in the NS1 gene after 2012 that enhances mosquito infection. Here we report that the same mutation confers NS1 to inhibit interferon-β induction. This mutation enables NS1 binding to TBK1 and reduces TBK1 phosphorylation. Engineering the mutation into a pre-epidemic ZIKV strain debilitates the virus for interferon-β induction; reversing the mutation in an epidemic ZIKV strain invigorates the virus for interferon-β induction; these mutational effects are lost in IRF3-knockout cells. Additionally, ZIKV NS2A, NS2B, NS4A, NS4B, and NS5 can also suppress interferon-β production through targeting distinct components of the RIG-I pathway; however, for these proteins, no antagonistic difference is observed among various ZIKV strains. Our results support the mechanism that ZIKV has accumulated mutation(s) that increases the ability to evade immune response and potentiates infection and epidemics.
211 citations
197 citations
TL;DR: This work sought to identify and characterize a hitherto unknown type of HAE with normal C1‐INH and without mutation in the F12 gene.
Abstract: Background
Hereditary angioedema (HAE) with normal C1-INH (HAEnCI) may be linked to specific mutations in the coagulation factor 12 (FXII) gene (HAE-FXII) or functional mutations in other genes that are still unknown. We sought to identify and characterize a hitherto unknown type of HAE with normal C1-INH and without mutation in the F12 gene.
Methods
The study comprised analysis of whole-exome sequencing, Sanger sequencing, and clinical data of patients.
Results
We detected a mutation in the plasminogen (PLG) gene in patients with HAEnCI. The mutation c.9886A>G was located in exon 9 leading to the missense mutation p.Lys330Glu (K330E) in the kringle 3 domain of the PLG protein. The mutation was identified by next-generation sequencing in 14 patients with HAEnCI belonging to 4 of 7 families. Family studies revealed that this type of HAE was transmitted as an autosomal dominant trait. The PLG gene mutation was present in all studied symptomatic patients and was also found in 9 of 38 index patients from 38 further families with HAEnCI. Most patients had swelling of face/lips (78.3%) and tongue (78.3%). A total of 331 of all 3.795 tongue swellings (8.7%) were associated with dyspnea, voice changes, and imminent asphyxiation. Two women died by asphyxiation due to a tongue swelling.
Conclusions
Hereditary angioedema with a mutation in the PLG gene is a novel type of HAE. It is associated with a high risk of tongue swellings.
192 citations
TL;DR: The data indicate that age‐corrected blood m.3243A>G heteroplasmy is the most convenient and reliable measure for routine clinical assessment, although additional factors such as mtDNA copy number may also influence disease severity.
Abstract: Mitochondrial disease associated with the pathogenic m.3243A>G variant is a common, clinically heterogeneous, neurogenetic disorder. Using multiple linear regression and linear mixed modelling, we evaluated which commonly assayed tissue (blood N = 231, urine N = 235, skeletal muscle N = 77) represents the m.3243A>G mutation load and mitochondrial DNA (mtDNA) copy number most strongly associated with disease burden and progression. m.3243A>G levels are correlated in blood, muscle and urine ( R 2 = 0.61–0.73). Blood heteroplasmy declines by ~2.3%/year; we have extended previously published methodology to adjust for age. In urine, males have higher mtDNA copy number and ~20% higher m.3243A>G mutation load; we present formulas to adjust for this. Blood is the most highly correlated mutation measure for disease burden and progression in m.3243A>G‐harbouring individuals; increasing age and heteroplasmy contribute ( R 2 = 0.27, P R 2 = 0.40, P G heteroplasmy is the most convenient and reliable measure for routine clinical assessment, additional factors such as mtDNA copy number may also influence disease severity.
169 citations
TL;DR: It is shown that the interplay of crossover followed by mutation may serve as a catalyst leading to a sudden burst of diversity, leading to significant improvements of the expected optimization time compared to mutation-only algorithms like the (1 + 1) evolutionary algorithm.
Abstract: Population diversity is essential for avoiding premature convergence in genetic algorithms (GAs) and for the effective use of crossover. Yet the dynamics of how diversity emerges in populations are not well understood. We use rigorous runtime analysis to gain insight into population dynamics and GA performance for the ( ${\mu +1}$ ) GA and the Jump test function. We show that the interplay of crossover followed by mutation may serve as a catalyst leading to a sudden burst of diversity. This leads to significant improvements of the expected optimization time compared to mutation-only algorithms like the (1 + 1) evolutionary algorithm. Moreover, increasing the mutation rate by an arbitrarily small constant factor can facilitate the generation of diversity, leading to even larger speedups. Experiments were conducted to complement our theoretical findings and further highlight the benefits of crossover on the function class.
152 citations
TL;DR: In this review different algorithms for the prediction of beneficial mutation sites to enhance protein stability are summarized and the advantages and disadvantages of FoldX are highlighted.
Abstract: Improving protein stability is an important goal for basic research as well as for clinical and industrial applications but no commonly accepted and widely used strategy for efficient engineering is known. Beside random approaches like error prone PCR or physical techniques to stabilize proteins, e.g. by immobilization, in silico approaches are gaining more attention to apply target-oriented mutagenesis. In this review different algorithms for the prediction of beneficial mutation sites to enhance protein stability are summarized and the advantages and disadvantages of FoldX are highlighted. The question whether the prediction of mutation sites by the algorithm FoldX is more accurate than random based approaches is addressed.
141 citations
TL;DR: Tumor mutational burden (TMB) is promising as a predictive biomarker and potentially could lead the way for immuno-oncology to enter the era of precision medicine.
Abstract: Non–small-cell lung cancer (NSCLC) exemplifies precision medicine, with multiple Food and Drug Administration–approved targeted therapies that are based on genomic biomarkers such as EGFR, ALK, ROS1, and BRAF. Recently, immunotherapy became accepted broadly as an effective treatment modality for patients with cancer. However, the ability to select patients who will benefit from immunotherapy remains limited. Tumor mutational burden (TMB) is promising as a predictive biomarker and potentially could lead the way for immuno-oncology to enter the era of precision medicine.
130 citations
TL;DR: In this paper, a Markov chain framework was devised to rigorously prove an upper bound on the runtime of standard steady state GAs to hillclimb the OneMax function.
Abstract: Explaining to what extent the real power of genetic algorithms (GAs) lies in the ability of crossover to recombine individuals into higher quality solutions is an important problem in evolutionary computation. In this paper we show how the interplay between mutation and crossover can make GAs hillclimb faster than their mutation-only counterparts. We devise a Markov chain framework that allows to rigorously prove an upper bound on the runtime of standard steady state GAs to hillclimb the OneMax function. The bound establishes that the steady-state GAs are 25% faster than all standard bit mutation-only evolutionary algorithms with static mutation rate up to lower order terms for moderate population sizes. The analysis also suggests that larger populations may be faster than populations of size 2. We present a lower bound for a greedy (2 + 1) GA that matches the upper bound for populations larger than 2, rigorously proving that two individuals cannot outperform larger population sizes under greedy selection and greedy crossover up to lower order terms. In complementary experiments the best population size is greater than 2 and the greedy GAs are faster than standard ones, further suggesting that the derived lower bound also holds for the standard steady state (2 + 1) GA.
123 citations
TL;DR: The efficiency and genetic safety of correcting a Marfan syndrome (MFS) pathogenic mutation in embryos by base editing is suggested.
118 citations
TL;DR: It is highlighted that ganglioglioma is characterized by genetic alterations that activate the MAP kinase pathway, with only a small subset of cases that harbor additional pathogenic alterations such as CDKN2A deletion.
Abstract: Ganglioglioma is the most common epilepsy-associated neoplasm that accounts for approximately 2% of all primary brain tumors. While a subset of gangliogliomas are known to harbor the activating p.V600E mutation in the BRAF oncogene, the genetic alterations responsible for the remainder are largely unknown, as is the spectrum of any additional cooperating gene mutations or copy number alterations. We performed targeted next-generation sequencing that provides comprehensive assessment of mutations, gene fusions, and copy number alterations on a cohort of 40 gangliogliomas. Thirty-six harbored mutations predicted to activate the MAP kinase signaling pathway, including 18 with BRAF p.V600E mutation, 5 with variant BRAF mutation (including 4 cases with novel in-frame insertions at p.R506 in the β3-αC loop of the kinase domain), 4 with BRAF fusion, 2 with KRAS mutation, 1 with RAF1 fusion, 1 with biallelic NF1 mutation, and 5 with FGFR1/2 alterations. Three gangliogliomas with BRAF p.V600E mutation had concurrent CDKN2A homozygous deletion and one additionally harbored a subclonal mutation in PTEN. Otherwise, no additional pathogenic mutations, fusions, amplifications, or deletions were identified in any of the other tumors. Amongst the 4 gangliogliomas without canonical MAP kinase pathway alterations identified, one epilepsy-associated tumor in the temporal lobe of a young child was found to harbor a novel ABL2-GAB2 gene fusion. The underlying genetic alterations did not show significant association with patient age or disease progression/recurrence in this cohort. Together, this study highlights that ganglioglioma is characterized by genetic alterations that activate the MAP kinase pathway, with only a small subset of cases that harbor additional pathogenic alterations such as CDKN2A deletion.
TL;DR: This paper proposes a novel algorithm, the Clustering Coefficient-based Genetic Algorithm (CC-GA), for detecting communities in social and complex networks, which is novel in terms of both the generation of the initial population and the mutation method.
TL;DR: In order to solve the minsum MTSP with multiple depots, closed path, and the requirement of minimum number of cities each salesman should visit, two partheno genetic algorithms (PGA) are proposed, one is a PGA with roulette selection and elitist selection in which four new kinds of mutation operation are proposed.
TL;DR: It is found that due to mutation at 315th position (threonine to isoleucine), original structures deviated from normal, and attained a flexible conformation, paving a clear path toward designing new inhibitors against resistant BCR‐ABL1 protein.
Abstract: BCR-ABL protein is one of the most potent target to treat chronic myeloid leukemia (CML). Apart from other mutations, T315I is especially challenging as it confers resistance to all first- and second-generation tyrosine kinase inhibitors. So, a thorough study of altered behavior upon mutation is crucially needed. To understand the resistance mechanism of mutant BCR-ABL protein, we organized a long-term molecular dynamics simulation (500 ns) and performed the detailed comparative conformational analysis. We found that due to mutation at 315th position (threonine to isoleucine), original structures deviated from normal, and attained a flexible conformation. Our observations pave a clear path toward designing new inhibitors against resistant BCR-ABL1 protein and suggest a strategy where additional flexibility governed by mutation could be given an appropriate consideration.
TL;DR: The experimental results show that the proposed improved sine-cosine algorithm can effectively avoid falling into the local optimum, and it has faster convergence speed and higher optimization accuracy.
Abstract: For the deficiency of the basic sine-cosine algorithm in dealing with global optimization problems such as the low solution precision and the slow convergence speed, a new improved sine-cosine algorithm is proposed in this paper. The improvement involves three optimization strategies. Firstly, the method of exponential decreasing conversion parameter and linear decreasing inertia weight is adopted to balance the global exploration and local development ability of the algorithm. Secondly, it uses the random individuals near the optimal individuals to replace the optimal individuals in the primary algorithm, which allows the algorithm to easily jump out of the local optimum and increases the search range effectively. Finally, the greedy Levy mutation strategy is used for the optimal individuals to enhance the local development ability of the algorithm. The experimental results show that the proposed algorithm can effectively avoid falling into the local optimum, and it has faster convergence speed and higher optimization accuracy.
27 Jun 2018
TL;DR: The logic and simple mathematics why this evolve-and-resequence approach is a powerful way to find the mutations or mutation combinations that best increase fitness in any new environment are reviewed.
Abstract: Experimental evolution is a method in which populations of organisms, often microbes, are founded by one or more ancestors of known genotype and then propagated under controlled conditions to study the evolutionary process. These evolving populations are influenced by all population genetic forces, including selection, mutation, drift, and recombination, and the relative contributions of these forces may be seen as mysterious. Here, I describe why the outcomes of experimental evolution should be viewed with greater certainty because the force of selection typically dominates. Importantly, any mutant rising rapidly to high frequency in large populations must have acquired adaptive traits in the selective environment. Sequencing the genomes of these mutants can identify genes or pathways that contribute to an adaptation. I review the logic and simple mathematics why this evolve-and-resequence approach is a powerful way to find the mutations or mutation combinations that best increase fitness in any new environment.
TL;DR: In 10 of 120 family trios (consisting of a child with de novo epileptic encephalopathy and the child’s biologic parents), one parent was found to have mosaicism for the etiologic variant.
Abstract: Risk of Recurrence of Epileptic Encephalopathies In 10 of 120 family trios (consisting of a child with de novo epileptic encephalopathy and the child’s biologic parents), one parent was found to have mosaicism for the etiologic variant. This finding has implications for determining the risk of recurrence.
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TL;DR: This paper implements the first practical bytecode-level APR technique, PraPR, and presents the first extensive study on fixing real-world bugs using JVM bytecode mutation, and demonstrates the overfitting problem of recent advanced APR tools for the first time.
Abstract: Software debugging is tedious, time-consuming, and even error-prone by itself. So, various automated debugging techniques have been proposed in the literature to facilitate the debugging process. Automated Program Repair (APR) is one of the most recent advances in automated debugging, and can directly produce patches for buggy programs with minimal human intervention. Although various advanced APR techniques (including those that are either search-based or semantic-based) have been proposed, the simplistic mutation-based APR technique, which simply uses pre-defined mutation operators (e.g., changing a>=b into a>b) to mutate programs for finding patches, has not yet been thoroughly studied. In this paper, we implement the first practical bytecode-level APR technique, PraPR, and present the first extensive study on fixing real-world bugs (e.g., Defects4J bugs) using bytecode mutation. The experimental results show that surprisingly even PraPR with only the basic traditional mutators can produce genuine patches for 18 bugs. Furthermore, with our augmented mutators, PraPR is able to produce genuine patches for 43 bugs, significantly outperforming state-of-the-art APR. It is also an order of magnitude faster, indicating a promising future for bytecode-mutation-based APR.
TL;DR: It is indicated that PIK3CA mutation induced PI3K/Akt activation contributed to CRC stem cells survival and proliferation, from which cells further resistance to chemotherapy.
Abstract: Chemotherapy represents an important treatment option for colorectal cancer (CRC), but only half of the patients benefit from these regimens. We explored the potential predicting value and mechanism of PIK3CA mutation in CRC chemotherapy. CRC specimens from 440 patients were retrospectively collected and examined with a fluorescence PCR-based method. The correlation of first-line chemotherapy response and PIK3CA mutation was evaluated according to follow-up and medical records. The underlying mechanism of PIK3CA mutation in chemotherapy resistance was assessed with CRC tumors and primary cells. The mutation frequency of the PIK3CA gene in CRC patients was 9.55%, which was correlated with late TNM staging and lower histological grade. The CRC patients with PIK3A mutation showed worse response to first-line chemotherapy than those without PIK3CA mutation. PIK3A mutation tumor cells showed poor sensitivity to first-line chemotherapy in vitro and in vivo. PIK3CA mutation induced PI3K/Akt signaling activation to increase LGR5+ CRC stem cells survival and proliferation, from which lead to chemotherapy resistance. Furthermore, PIK3CA mutation/LGR5+ expression was an independent detrimental factor for CRC patients. Our findings indicated that PIK3CA mutation induced PI3K/Akt activation contributed to CRC stem cells survival and proliferation, from which cells further resistance to chemotherapy. PIK3CA mutation/LGR5+ expression was a potential biomarker for monitoring chemotherapy resistance in CRC.
TL;DR: Overall, considerable, but not unlimited, evolutionary potential exists in populations facing detrimental environmental or genetic change, however, further studies with diverse methods and species are required for more robust and general insights.
Abstract: The rate of evolution of population mean fitness informs how selection acting in contemporary populations can counteract environmental change and genetic degradation (mutation, gene flow, drift, re...
TL;DR: A tool for single-cell variant calling via phylogenetic inference, and use it to analyze cancer genomics datasets, is developed and applied to different real-world datasets.
Abstract: Reconstructing the evolution of tumors is a key aspect towards the identification of appropriate cancer therapies. The task is challenging because tumors evolve as heterogeneous cell populations. Single-cell sequencing holds the promise of resolving the heterogeneity of tumors; however, it has its own challenges including elevated error rates, allelic drop-out, and uneven coverage. Here, we develop a new approach to mutation detection in individual tumor cells by leveraging the evolutionary relationship among cells. Our method, called SCIΦ, jointly calls mutations in individual cells and estimates the tumor phylogeny among these cells. Employing a Markov Chain Monte Carlo scheme enables us to reliably call mutations in each single cell even in experiments with high drop-out rates and missing data. We show that SCIΦ outperforms existing methods on simulated data and applied it to different real-world datasets, namely a whole exome breast cancer as well as a panel acute lymphoblastic leukemia dataset.
TL;DR: In this article, the purpose of the study was to collate information about information about vascular anomalies and to classify them according to their clinical and histological characteristics, and to identify mutations in most types of vascular anomalies.
Abstract: Background:Vascular anomalies currently are classified according to their clinical and histological characteristics. Recent advances in molecular genetics have enabled the identification of somatic mutations in most types of vascular anomalies. The purpose of this study was to collate information re
TL;DR: A novel index is formed to quantify the constraints driving the observed amount of repeated adaptation in pairwise contrasts based on the hypergeometric distribution and an index is formulated to quantify the effective proportion of genes in the genome that have the potential to contribute to adaptation.
Abstract: Convergent adaptation occurs at the genome scale when independently evolving lineages use the same genes to respond to similar selection pressures. These patterns of genetic repeatability provide insights into the factors that facilitate or constrain the diversity of genetic responses that contribute to adaptive evolution. A first step in studying such factors is to quantify the observed amount of repeatability relative to expectations under a null hypothesis. Here, we formulate a novel index to quantify the constraints driving the observed amount of repeated adaptation in pairwise contrasts based on the hypergeometric distribution, and then generalize this for simultaneous analysis of multiple lineages. This index is explicitly based on the probability of observing a given amount of repeatability by chance under a given null hypothesis and is readily compared among different species and types of trait. We also formulate an index to quantify the effective proportion of genes in the genome that have the potential to contribute to adaptation. As an example of how these indices can be used to draw inferences, we assess the amount of repeatability observed in existing datasets on adaptation to stress in yeast and climate in conifers. This approach provides a method to test a wide range of hypotheses about how different kinds of factors can facilitate or constrain the diversity of genetic responses observed during adaptive evolution.
TL;DR: In this paper, a machine learning-based approach was proposed to detect mutations in cancer patients. But, the method was only applied to melanoma and lung cancer patients previously treated with immune checkpoint inhibitors.
Abstract: Variability in the accuracy of somatic mutation detection may affect the discovery of alterations and the therapeutic management of cancer patients. To address this issue, we developed a somatic mutation discovery approach based on machine learning that outperformed existing methods in identifying experimentally validated tumor alterations (sensitivity of 97% versus 90 to 99%; positive predictive value of 98% versus 34 to 92%). Analysis of paired tumor-normal exome data from 1368 TCGA (The Cancer Genome Atlas) samples using this method revealed concordance for 74% of mutation calls but also identified likely false-positive and false-negative changes in TCGA data, including in clinically actionable genes. Determination of high-quality somatic mutation calls improved tumor mutation load–based predictions of clinical outcome for melanoma and lung cancer patients previously treated with immune checkpoint inhibitors. Integration of high-quality machine learning mutation detection in clinical next-generation sequencing (NGS) analyses increased the accuracy of test results compared to other clinical sequencing analyses. These analyses provide an approach for improved identification of tumor-specific mutations and have important implications for research and clinical management of cancer patients.
TL;DR: An enhanced structure for Differential Evolution algorithm with less control parameters to be tuned is proposed and an enhanced mutation strategy with time stamp mechanism is advanced in this paper.
Abstract: Optimization demands are ubiquitous in science and engineering. The key point is that the approach to tackle a complex optimization problem should not itself be difficult. Differential Evolution (DE) is such a simple method, and it is arguably a very powerful stochastic real-parameter algorithm for single-objective optimization. However, the performance of DE is highly dependent on control parameters and mutation strategies. Both tuning the control parameters and selecting the proper mutation strategy are still tedious but important tasks for users. In this paper, we proposed an enhanced structure for DE algorithm with less control parameters to be tuned. The crossover rate control parameter Cr is replaced by an automatically generated evolution matrix and the control parameter F can be renewed in an adaptive manner during the whole evolution. Moreover, an enhanced mutation strategy with time stamp mechanism is advanced as well in this paper. CEC2013 test suite for real-parameter single objective optimization is employed in the verification of the proposed algorithm. Experiment results show that our proposed algorithm is competitive with several well-known DE variants.
University of Ulm1, University Hospital Bonn2, Humboldt University of Berlin3, Hannover Medical School4, University of Hamburg5, Ludwig Maximilian University of Munich6, University of Würzburg7, University of Duisburg-Essen8, RWTH Aachen University9, Katholieke Universiteit Leuven10, Martin Luther University of Halle-Wittenberg11, Technische Universität München12, Umeå University13
TL;DR: A comprehensive genetic characterisation of German familial ALS is presented and several previously unreported rare variants are identified and demonstrated the absence of likely pathogenic variants in some of the recently described ALS disease genes.
Abstract: Objectives Recent advances in amyotrophic lateral sclerosis (ALS) genetics have revealed that mutations in any of more than 25 genes can cause ALS, mostly as an autosomal-dominant Mendelian trait. ...
TL;DR: Transformations of the scaling factors that make the expectation vectors and covariance matrices of the expected mutants' distributions equal to the respective statistics of DE/best/1 or DE/rand/1 establish a framework for a synthetic investigation of various differential mutation operators and for generalizing the results of parameter tuning.
Abstract: Differential Evolution (DE) is a state-of-the art evolutionary algorithm that solves global optimization problems in a real domain. The algorithm adapts the mutation range and direction by basing these on the differences between individuals in the current population. In this paper, we provide formulas for the expectation vectors and covariance matrices of the mutants' distribution for several operators of differential mutation. The covariance matrices are proportional to each other, which means that the main difference between the analyzed DE operators is the mutation range. This can be conveniently described using a generalized scaling factor g ( F ) , introduced in this paper. Next, we propose transformations of the scaling factors that make the expectation vectors and covariance matrices of the expected mutants' distributions equal to the respective statistics of DE/best/1 or DE/rand/1. These transformations establish a framework for a synthetic investigation of various differential mutation operators and for generalizing the results of parameter tuning. A simulation study based on the CEC′13 benchmark in 10, 30 and 50 dimensions confirms that the transformations do not influence the performance much, especially in the case of DE operators with two difference vectors.
TL;DR: It is demonstrated that variation in the mutation rate does not generate large-scale variation in GC-content, and hence that mutation bias does not maintain the isochore structure of the human genome.
Abstract: It has long been suspected that the rate of mutation varies across the human genome at a large scale based on the divergence between humans and other species. However, it is now possible to directly investigate this question using the large number of de novo mutations (DNMs) that have been discovered in humans through the sequencing of trios. We investigate a number of questions pertaining to the distribution of mutations using more than 130,000 DNMs from three large datasets. We demonstrate that the amount and pattern of variation differs between datasets at the 1MB and 100KB scales probably as a consequence of differences in sequencing technology and processing. In particular, datasets show different patterns of correlation to genomic variables such as replication time. Never-the-less there are many commonalities between datasets, which likely represent true patterns. We show that there is variation in the mutation rate at the 100KB, 1MB and 10MB scale that cannot be explained by variation at smaller scales, however the level of this variation is modest at large scales–at the 1MB scale we infer that ~90% of regions have a mutation rate within 50% of the mean. Different types of mutation show similar levels of variation and appear to vary in concert which suggests the pattern of mutation is relatively constant across the genome. We demonstrate that variation in the mutation rate does not generate large-scale variation in GC-content, and hence that mutation bias does not maintain the isochore structure of the human genome. We find that genomic features explain less than 40% of the explainable variance in the rate of DNM. As expected the rate of divergence between species is correlated to the rate of DNM. However, the correlations are weaker than expected if all the variation in divergence was due to variation in the mutation rate. We provide evidence that this is due the effect of biased gene conversion on the probability that a mutation will become fixed. In contrast to divergence, we find that most of the variation in diversity can be explained by variation in the mutation rate. Finally, we show that the correlation between divergence and DNM density declines as increasingly divergent species are considered.
TL;DR: This work demonstrates that the mutation rate changes the global balance between deleterious and beneficial mutational effects on fitness, and suggests that this tipping point already occurs at the modest mutation rates that are found in the wild.
Abstract: Mutation is fundamental to evolution, because it generates the genetic variation on which selection can act. In nature, genetic changes often increase the mutation rate in systems that range from viruses and bacteria to human tumors. Such an increase promotes the accumulation of frequent deleterious or neutral alleles, but it can also increase the chances that a population acquires rare beneficial alleles. Here, we study how up to 100-fold increases in Escherichia coli’s genomic mutation rate affect adaptive evolution. To do so, we evolved multiple replicate populations of asexual E. coli strains engineered to have four different mutation rates for 3000 generations in the laboratory. We measured the ability of evolved populations to grow in their original environment and in more than 90 novel chemical environments. In addition, we subjected the populations to whole genome population sequencing. Although populations with higher mutation rates accumulated greater genetic diversity, this diversity conveyed benefits only for modestly increased mutation rates, where populations adapted faster and also thrived better than their ancestors in some novel environments. In contrast, some populations at the highest mutation rates showed reduced adaptation during evolution, and failed to thrive in all of the 90 alternative environments. In addition, they experienced a dramatic decrease in mutation rate. Our work demonstrates that the mutation rate changes the global balance between deleterious and beneficial mutational effects on fitness. In contrast to most theoretical models, our experiments suggest that this tipping point already occurs at the modest mutation rates that are found in the wild.
TL;DR: PIK3CA mutation correlates with poor prostate cancer prognosis and causes prostate cancer in mice, and PIK3ca mutation and PTEN loss coexist in prostate cancer and can cooperate in vivo to accelerate tumorigenesis and facilitate CRPC.
Abstract: Genetic alterations that potentiate PI3K signalling are frequent in prostate cancer, yet how different genetic drivers of the PI3K cascade contribute to prostate cancer is unclear. Here, we report PIK3CA mutation/amplification correlates with poor prostate cancer patient survival. To interrogate the requirement of different PI3K genetic drivers in prostate cancer, we employed a genetic approach to mutate Pik3ca in mouse prostate epithelium. We show Pik3caH1047R mutation causes p110α-dependent invasive prostate carcinoma in-vivo. Furthermore, we report PIK3CA mutation and PTEN loss co-exist in prostate cancer patients, and can cooperate in-vivo to accelerate disease progression via AKT-mTORC1/2 hyperactivation. Contrasting single mutants that slowly acquire castration-resistant prostate cancer (CRPC), concomitant Pik3ca mutation and Pten loss caused de-novo CRPC. Thus, Pik3ca mutation and Pten deletion are not functionally redundant. Our findings indicate that PIK3CA mutation is an attractive prognostic indicator for prostate cancer that may cooperate with PTEN loss to facilitate CRPC in patients.