Validation of bacterial replication termination models using simulation of genomic mutations.
Ontology highlight
ABSTRACT:
SUBMITTER: Kono N
PROVIDER: S-EPMC3317982 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Spatial capture-recapture (SCR) models have improved the ability to estimate densities of rare and elusive animals. However, SCR models have seldom been validated even as model formulations diversify and expand to incorporate new sampling methods and/or additional sources of information on model parameters. Information on the relationship between encounter probabilities, sources of additional information, and the reliability of density estimates, is rare but crucial to assessing reliability of SCR-based estimates. We used a simulation-based approach that incorporated prior empirical work to assess the accuracy and precision of density estimates from SCR models using spatially unstructured sampling. To assess the consequences of sparse data and potential sources of bias, we simulated data under six scenarios corresponding to three different levels of search effort and two levels of correlation between search effort and animal density. We then estimated density for each scenario using four models that included increasing amounts of information from harvested individuals and telemetry to evaluate the impact of additional sources of information. Model results were sensitive to the quantity of available information: density estimates based on low search effort were biased high and imprecise, whereas estimates based on high search effort were unbiased and precise. A correlation between search effort and animal density resulted in a positive bias in density estimates, though the bias decreased with increasingly informative datasets. Adding information from harvested individuals and telemetered individuals improved density estimates based on low and moderate effort but had negligible impact for datasets resulting from high effort. We demonstrated that density estimates from SCR models using spatially unstructured sampling are reliable when sufficient information is provided. Accurate density estimates can result if empirical-based simulations such as those presented here are used to develop study designs with appropriate amounts of effort and information sources.
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Project description:Problems that arise during DNA replication can drive genomic alterations that are instrumental in the development of cancers and many human genetic disorders. Replication fork barriers are a commonly encountered problem, which can cause fork collapse and act as hotspots for replication termination. Collapsed forks can be rescued by homologous recombination, which restarts replication. However, replication restart is relatively slow and, therefore, replication termination may frequently occur by an active fork converging on a collapsed fork. We find that this type of non-canonical fork convergence in fission yeast is prone to trigger deletions between repetitive DNA sequences via a mechanism we call Inter-Fork Strand Annealing (IFSA) that depends on the recombination proteins Rad52, Exo1 and Mus81, and is countered by the FANCM-related DNA helicase Fml1. Based on our findings, we propose that IFSA is a potential threat to genomic stability in eukaryotes.
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Project description:Bacterial chromosomes harbour a unique origin of bidirectional replication, oriC. They are almost always circular, with replication terminating in a region diametrically opposite to oriC, the terminus. The oriC-terminus organisation is reflected by the orientation of the genes and by the disposition of DNA-binding protein motifs implicated in the coordination of chromosome replication and segregation with cell division. Correspondingly, the E. coli and B. subtilis model bacteria possess a replication fork trap system, Tus/ter and RTP/ter, respectively, which enforces replication termination in the terminus region. Here, we show that tus and rtp are restricted to four clades of bacteria, suggesting that tus was recently domesticated from a plasmid gene. We further demonstrate that there is no replication fork system in Vibrio cholerae, a bacterium closely related to E. coli. Marker frequency analysis showed that replication forks originating from ectopic origins were not blocked in the terminus region of either of the two V. cholerae chromosomes, but progressed normally until they encountered an opposite fork. As expected, termination synchrony of the two chromosomes is disrupted by these ectopic origins. Finally, we show that premature completion of the primary chromosome replication did not modify the choreography of segregation of its terminus region.
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Project description:In the two decades of continuous development of genomic selection, a great variety of models have been proposed to make predictions from the information available in dense marker panels. Besides deciding which particular model to use, practitioners also need to make many minor choices for those parameters in the model which are not typically estimated by the data (so called "hyper-parameters"). When the focus is placed on predictions, most of these decisions are made in a direction sought to optimize predictive accuracy. Here we discuss and illustrate using publicly available crop datasets the use of cross validation to make many such decisions. In particular, we emphasize the importance of paired comparisons to achieve high power in the comparison between candidate models, as well as the need to define notions of relevance in the difference between their performances. Regarding the latter, we borrow the idea of equivalence margins from clinical research and introduce new statistical tests. We conclude that most hyper-parameters can be learnt from the data by either minimizing REML or by using weakly-informative priors, with good predictive results. In particular, the default options in a popular software are generally competitive with the optimal values. With regard to the performance assessments themselves, we conclude that the paired k-fold cross validation is a generally applicable and statistically powerful methodology to assess differences in model accuracies. Coupled with the definition of equivalence margins based on expected genetic gain, it becomes a useful tool for breeders.
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Project description:The outer membrane (OM) of Gram-negative bacteria is an asymmetric bilayer having phospholipids in the inner leaflet and lipopolysaccharides in the outer leaflet. This unique asymmetry and the complex carbohydrates in lipopolysaccharides make it a daunting task to study the asymmetrical OM structure and dynamics, its interactions with OM proteins, and its roles in translocation of substrates, including antibiotics. In this study, we combine neutron reflectometry and molecular simulation to explore the physical properties of OM mimetics. There is excellent agreement between experiment and simulation, allowing experimental testing of the conclusions from simulations studies and also atomistic interpretation of the behavior of experimental model systems, such as the degree of lipid asymmetry, the lipid component (tail, head, and sugar) profiles along the bilayer normal, and lateral packing (i.e., average surface area per lipid). Therefore, the combination of both approaches provides a powerful new means to explore the biological and biophysical behavior of the bacterial OM.
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Project description:Chromosome replication initiates at multiple replicons and terminates when forks converge. In E. coli, the Tus-TER complex mediates polar fork converging at the terminator region, and aberrant termination events challenge chromosome integrity and segregation. Since in eukaryotes, termination is less characterized, we used budding yeast to identify the factors assisting fork fusion at replicating chromosomes. Using genomic and mechanistic studies, we have identified and characterized 71 chromosomal termination regions (TERs). TERs contain fork pausing elements that influence fork progression and merging. The Rrm3 DNA helicase assists fork progression across TERs, counteracting the accumulation of X-shaped structures. The Top2 DNA topoisomerase associates at TERs in S phase, and G2/M facilitates fork fusion and prevents DNA breaks and genome rearrangements at TERs. We propose that in eukaryotes, replication fork barriers, Rrm3, and Top2 coordinate replication fork progression and fusion at TERs, thus counteracting abnormal genomic transitions.
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Project description:Understanding the genetic underpinnings of complex traits requires knowledge of the genetic variants that contribute to phenotypic variability. Reliable statistical approaches are needed to obtain such knowledge. In genome-wide association studies, variants are tested for association with trait variability to pinpoint loci that contribute to the quantitative trait. Because stringent genome-wide significance thresholds are applied to control the false positive rate, many true causal variants can remain undetected. To ameliorate this problem, many alternative approaches have been developed, such as genomic feature models (GFM). The GFM approach tests for association of set of genomic markers, and predicts genomic values from genomic data utilizing prior biological knowledge. We investigated to what degree the findings from GFM have biological relevance. We used the Drosophila Genetic Reference Panel to investigate locomotor activity, and applied genomic feature prediction models to identify gene ontology (GO) categories predictive of this phenotype. Next, we applied the covariance association test to partition the genomic variance of the predictive GO terms to the genes within these terms. We then functionally assessed whether the identified candidate genes affected locomotor activity by reducing gene expression using RNA interference. In five of the seven candidate genes tested, reduced gene expression altered the phenotype. The ranking of genes within the predictive GO term was highly correlated with the magnitude of the phenotypic consequence of gene knockdown. This study provides evidence for five new candidate genes for locomotor activity, and provides support for the reliability of the GFM approach.
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Project description:The molecular mechanisms mediating eukaryotic replication termination and pausing remain largely unknown. Here we present the molecular characterization of Rtf1 that mediates site-specific replication termination at the polar Schizosaccharomyces pombe barrier RTS1. We show that Rtf1 possesses two chimeric myb/SANT domains: one is able to interact with the repeated motifs encoded by the RTS1 element as well as the elements enhancer region, while the other shows only a weak DNA binding activity. In addition we show that the C-terminal tail of Rtf1 mediates self-interaction, and deletion of this tail has a dominant phenotype. Finally, we identify a point mutation in Rtf1 domain I that converts the RTS1 element into a replication barrier of the opposite polarity. Together our data establish that multiple protein DNA and protein-protein interactions between Rtf1 molecules and both the repeated motifs and the enhancer region of RTS1 are required for site-specific termination at the RTS1 element.
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Project description:The genomic prediction of phenotypes and breeding values in animals and plants has developed rapidly into its own research field. Results of genomic prediction studies are often difficult to compare because data simulation varies, real or simulated data are not fully described, and not all relevant results are reported. In addition, some new methods have been compared only in limited genetic architectures, leading to potentially misleading conclusions. In this article we review simulation procedures, discuss validation and reporting of results, and apply benchmark procedures for a variety of genomic prediction methods in simulated and real example data. Plant and animal breeding programs are being transformed by the use of genomic data, which are becoming widely available and cost-effective to predict genetic merit. A large number of genomic prediction studies have been published using both simulated and real data. The relative novelty of this area of research has made the development of scientific conventions difficult with regard to description of the real data, simulation of genomes, validation and reporting of results, and forward in time methods. In this review article we discuss the generation of simulated genotype and phenotype data, using approaches such as the coalescent and forward in time simulation. We outline ways to validate simulated data and genomic prediction results, including cross-validation. The accuracy and bias of genomic prediction are highlighted as performance indicators that should be reported. We suggest that a measure of relatedness between the reference and validation individuals be reported, as its impact on the accuracy of genomic prediction is substantial. A large number of methods were compared in example simulated and real (pine and wheat) data sets, all of which are publicly available. In our limited simulations, most methods performed similarly in traits with a large number of quantitative trait loci (QTL), whereas in traits with fewer QTL variable selection did have some advantages. In the real data sets examined here all methods had very similar accuracies. We conclude that no single method can serve as a benchmark for genomic prediction. We recommend comparing accuracy and bias of new methods to results from genomic best linear prediction and a variable selection approach (e.g., BayesB), because, together, these methods are appropriate for a range of genetic architectures. An accompanying article in this issue provides a comprehensive review of genomic prediction methods and discusses a selection of topics related to application of genomic prediction in plants and animals.
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Project description:BackgroundComputer simulation models are used increasingly to support public health research and policy, but questions about their quality persist. The purpose of this article is to review the principles and methods for validation of population-based disease simulation models.MethodsWe developed a comprehensive framework for validating population-based chronic disease simulation models and used this framework in a review of published model validation guidelines. Based on the review, we formulated a set of recommendations for gathering evidence of model credibility.ResultsEvidence of model credibility derives from examining: 1) the process of model development, 2) the performance of a model, and 3) the quality of decisions based on the model. Many important issues in model validation are insufficiently addressed by current guidelines. These issues include a detailed evaluation of different data sources, graphical representation of models, computer programming, model calibration, between-model comparisons, sensitivity analysis, and predictive validity. The role of external data in model validation depends on the purpose of the model (e.g., decision analysis versus prediction). More research is needed on the methods of comparing the quality of decisions based on different models.ConclusionAs the role of simulation modeling in population health is increasing and models are becoming more complex, there is a need for further improvements in model validation methodology and common standards for evaluating model credibility.
| S-EPMC3001435 | biostudies-literature