High resolution analysis of replication foci by conventional fluorescent microscopy. I. A study of complexity and DNA content of the foci

Newly replicated DNA segments (RDS) have been shown to form discrete foci in the mammalian nucleus. Comparison of the number of such foci in formaldehyde-fixed cell nucleus with estimated number of simultaneously active replication forks (RF) suggests that each replication focus contains a cluster of about 10 to 20 closely associated RF. That implied the cluster of synchronously activated replicons as the primary unit of mammalian DNA replication. It still remains unclear whether such clustering of RF does mean adjacency of the replicons in a genomic location (structural clustering, model 1), or it arises from transient clustering of the replicons from different DNA domains at the functioning replication machinery (functional clustering, model 2). In this study we used conventional fluorescence microscopy of the hypotonically treated nuclei preparations to investigate replication foci at the optical resolution limit. Human K562 cells were labeled with 5'-iododeoxyuridine for different time periods. We synchronized the cell culture with hydroxyurea to be able to measure an average increase in DNA content during labeling period using DNA cytometry. Under these conditions, RDS appear as multiple small foci (mini-foci, MF). Further studies revealed that most of such mini-foci of replication represent optical diffraction spots, which are standard in size and different in brightness. The number of the "spots" and variation of their brightness mostly depend on the extent of hypotonic treatment. Flow cytometry control of the synchronized cells peak movement allowed us to measure mean DNA content of the MF. In case of most effective hypotonic treatment, a MF contains about 40 Kbp of labeled DNA, and the general number of the MF approaches the number of replicons that are simultaneously active in a given moment of S-phase. Influence of the effect of hypotonic treatment on overall number of observed MF suggests that replication foci in early and mid S-phase cells do not represent stable structures, but rather arise from functional clustering of comparatively distant replicating regions, thus supporting model 2.     

Statistical models for spatial analysis in parasitology

The simplest way to study the spatial pattern of a disease is the geographical representation of its cases (or some indicators of them) over a map. Maps based on raw data are generally "wrong" since they do not take into consideration for sampling errors. Indeed, the observed differences between areas (or points in the map) are not directly interpretable, as they derive from the composition of true, structural differences and of the noise deriving from the sampling process. This problem is well known in human epidemiology, and several solutions have been proposed to filter the signal from the noise. These statistical methods are usually referred to as Disease Mapping. In geographical analysis a first goal is to evaluate the statistical significance of the heterogeneity between areas (or points). If the test indicates rejection of the hypothesis of homogeneity the following task is to study the spatial pattern of the disease. The spatial variability of risk is usually decomposed into two terms: a spatially structured (clustering) and a non spatially structured (heterogeneity) one. The heterogeneity term reflects spatial variability due to intrinsic characteristics of the sampling units (e.g. igienic conditions of farms), while the clustering term models the association due to proximity between sampling units, that usually depends on ecological conditions that vary over the study area and that affect in similar way breedings that are close to each other. Hierarchical bayesian models are the main tool to make inference over the clustering and heterogeneity components. The results are based on the marginal posterior distributions of the parameters of the model, that are approximated by Monte Carlo Markov Chain methods. Different models can be defined depending on the terms that are considered, namely a model with only the clustering term, a model with only the heterogeneity term and a model where both are included. Model selection criteria based on a compromise between degree of complexity and goodness of fit are then needed to discriminate among them, because each specification has a different biological meaning. Our aim is to demonstrate that these techniques can be used to study the geographical distribution of a parasite infection. Our analyses are based on data collected in 142 farms of the province of Latina. In each breeding a fixed number of sheeps has been sampled (20) and checked for the presence of C. daubneyi. We have specified a Binomial model for the proportion of infected animals in each breeding. The heterogeneity component is modelled in a standard way, while we have used different prior specifications for the clustering term to show how they affect the results. When we use the usual specification also for clustering, the two models show a completely different spatial pattern of infection, probably because the intrinsic spatial structure of the clustering term tend to bias our inferences. The selection criterion indicates in this case the heterogeneity model as the "best" one. However, if we modify the prior so that a lower degree of spatial interaction is assumed, the clustering model is less complex and its goodness of fit better and it should be preferred.     

Performance of likelihood ratio tests of evolutionary hypotheses under inadequate substitution models.

In recent years, likelihood ratio tests (LRTs) based on DNA and protein sequence data have been proposed for testing various evolutionary hypotheses. Because conducting an LRT requires an evolutionary model of nucleotide or amino acid substitution, which is almost always unknown, it becomes important to investigate the robustness of LRTs to violations of assumptions of these evolutionary models. Computer simulation was used to examine performance of LRTs of the molecular clock, transition/transversion bias, and among-site rate variation under different substitution models. The results showed that when correct models are used, LRTs perform quite well even when the DNA sequences are as short as 300 nt. However, LRTs were found to be biased under incorrect models. The extent of bias varies considerably, depending on the hypotheses tested, the substitution models assumed, and the lengths of the sequences used, among other things. A preliminary simulation study also suggests that LRTs based on parametric bootstrapping may be more sensitive to substitution models than are standard LRTs. When an assumed substitution model is grossly wrong and a more realistic model is available, LRTs can often reject the wrong model; thus, the performance of LRTs may be improved by using a more appropriate model. On the other hand, many factors of molecular evolution have not been considered in any substitution models so far built, and the possibility of an influence of this negligence on LRTs is often overlooked. The dependence of LRTs on substitution models calls for caution in interpreting test results and highlights the importance of clarifying the substitution patterns of genes and proteins and building more realistic models.     

Model-based clustering and data transformations for gene expression data.

MOTIVATION: Clustering is a useful exploratory technique for the analysis of gene expression data. Many different heuristic clustering algorithms have been proposed in this context. Clustering algorithms based on probability models offer a principled alternative to heuristic algorithms. In particular, model-based clustering assumes that the data is generated by a finite mixture of underlying probability distributions such as multivariate normal distributions. The issues of selecting a 'good' clustering method and determining the 'correct' number of clusters are reduced to model selection problems in the probability framework. Gaussian mixture models have been shown to be a powerful tool for clustering in many applications. RESULTS: We benchmarked the performance of model-based clustering on several synthetic and real gene expression data sets for which external evaluation criteria were available. The model-based approach has superior performance on our synthetic data sets, consistently selecting the correct model and the number of clusters. On real expression data, the model-based approach produced clusters of quality comparable to a leading heuristic clustering algorithm, but with the key advantage of suggesting the number of clusters and an appropriate model. We also explored the validity of the Gaussian mixture assumption on different transformations of real data. We also assessed the degree to which these real gene expression data sets fit multivariate Gaussian distributions both before and after subjecting them to commonly used data transformations. Suitably chosen transformations seem to result in reasonable fits. AVAILABILITY: MCLUST is available at http://www.stat.washington.edu/fraley/mclust. The software for the diagonal model is under development. CONTACT: kayee@cs.washington.edu. SUPPLEMENTARY INFORMATION: http://www.cs.washington.edu/homes/kayee/model.     

Locations of radiation-produced DNA double strand breaks along chromosomes: a stochastic cluster process formalism.

Ionizing radiation produces DNA double strand breaks (DSBs) in chromosomes. For densely ionizing radiation, the DSBs are not spaced randomly along a chromosome: recent data for size distributions of DNA fragments indicate break clustering on kbp-Mbp scales. Different DSB clusters on a chromosome are typically made by different, statistically independent, stochastically structured radiation tracks, and the average number of tracks involved can be small. We therefore model DSB positions along a chromosome as a stationary Poisson cluster process, i.e. a stochastic process consisting of secondary point processes whose locations are determined by a primary point process that is Poisson. Each secondary process represents a break cluster, typically consisting of 1-10 DSBs in a comparatively localized stochastic pattern determined by chromatin geometry and radiation track structure. Using this Poisson cluster process model, which we call the randomly located clusters (RLC) formalism, theorems are derived for how the DNA fragment-size distribution depends on radiation dose. The RLC dose-response relations become non-linear when the dose becomes so high that DSB clusters from different tracks overlap or adjoin closely. The RLC formalism generalizes previous models, fits current data adequately and facilitates mechanistically based extrapolations from high-dose experiments to the much lower doses of interest for most applications.     

Assessing the performance of DNA barcoding using posterior predictive simulations

Accurate estimates of biodiversity are required for research in a broad array of biological subdisciplines including ecology, evolution, systematics, conservation and biodiversity science. The use of statistical models and genetic data, particularly DNA barcoding, has been suggested as an important tool for remedying the large gaps in our current understanding of biodiversity. However, the reliability of biodiversity estimates obtained using these approaches depends on how well the statistical models that are used describe the evolutionary process underlying the genetic data. In this study, we utilize data from the Barcode of Life Database and posterior predictive simulations to assess the performance of DNA barcoding under commonly used substitution models. We demonstrate that the success of DNA barcoding varies widely across DNA substitution models and that model choice has a substantial impact on the number of operational taxonomic units identified (changing results by ~4–31%). Additionally, we demonstrate that the widely followed practice of a priori assuming the Kimura 2‐parameter model for DNA barcoding is statistically unjustified and should be avoided. Using both data‐based and inference‐based test statistics, we detect variation in model performance across taxonomic groups, clustering algorithms, genetic divergence thresholds and substitution models. Taken together, these results illustrate the importance of considering both model selection and model adequacy in studies quantifying biodiversity.     

Expression of the bacteriophage P1 cin recombinase gene from its own and heterologous promoters

The cin recombinase of bacteriophage P1, a protein that catalyses site-specific DNA inversions, has been identified and its structural gene has been cloned under the control of different promoters. One of the DNA sequences used for the site-specific recombination, cixL, overlaps with the 3' end of the gene, but we show that the presence of this site does not affect cin gene expression from strong promoters. To assay cin activity we have constructed plasmids that carry antibiotic resistance genes within the invertible segment that are transcribed from promoters outside the segment. DNA inversion switches on or off genes for chloramphenicol or kanamycin resistance. These tester plasmids are used to study cin-mediated DNA inversion both in vivo and in vitro.     

High-Copy-Number Plasmid Segregation—Single-Molecule Dynamics in Single Cells

Bacterial high-copy-number (hcn) plasmids provide an excellent model to study the underlying physical mechanisms of DNA segment segregation in an intracellular context. Using two-color fluorescent repressor-operator systems and a synthetic repressible replication origin, we tracked the motion and segregation of single hcn plasmid molecules in individual cells. The plasmid diffusion dynamics revealed between-plasmid temporal associations (clustering) as well as entropic and elastic recoiling forces in the confined intracellular spaces outside of nucleoids. These two effects could be effectively used in models to predict the heterogeneity of segregation. Additionally, the motile behaviors of hcn plasmids provide quantitative estimates of entropic exclusion strength and dynamic associations between DNA segments. Overall, this study utilizes a, to our knowledge, novel approach to predict the polymer dynamics of DNA segments in spatially confined, crowded cellular compartments as well as during bacterial chromosome segregation.     

Selecting the best-fit model of nucleotide substitution

Despite the relevant role of models of nucleotide substitution in phylogenetics, choosing among different models remains a problem. Several statistical methods for selecting the model that best fits the data at hand have been proposed, but their absolute and relative performance has not yet been characterized. In this study, we compare under various conditions the performance of different hierarchical and dynamic likelihood ratio tests, and of Akaike and Bayesian information methods, for selecting best-fit models of nucleotide substitution. We specifically examine the role of the topology used to estimate the likelihood of the different models and the importance of the order in which hypotheses are tested. We do this by simulating DNA sequences under a known model of nucleotide substitution and recording how often this true model is recovered by the different methods. Our results suggest that model selection is reasonably accurate and indicate that some likelihood ratio test methods perform overall better than the Akaike or Bayesian information criteria. The tree used to estimate the likelihood scores does not influence model selection unless it is a randomly chosen tree. The order in which hypotheses are tested, and the complexity of the initial model in the sequence of tests, influence model selection in some cases. Model fitting in phylogenetics has been suggested for many years, yet many authors still arbitrarily choose their models, often using the default models implemented in standard computer programs for phylogenetic estimation. We show here that a best-fit model can be readily identified. Consequently, given the relevance of models, model fitting should be routine in any phylogenetic analysis that uses models of evolution.     

Bayesian phylogenetic model selection using reversible jump Markov chain Monte Carlo

A common problem in molecular phylogenetics is choosing a model of DNA substitution that does a good job of explaining the DNA sequence alignment without introducing superfluous parameters. A number of methods have been used to choose among a small set of candidate substitution models, such as the likelihood ratio test, the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and Bayes factors. Current implementations of any of these criteria suffer from the limitation that only a small set of models are examined, or that the test does not allow easy comparison of non-nested models. In this article, we expand the pool of candidate substitution models to include all possible time-reversible models. This set includes seven models that have already been described. We show how Bayes factors can be calculated for these models using reversible jump Markov chain Monte Carlo, and apply the method to 16 DNA sequence alignments. For each data set, we compare the model with the best Bayes factor to the best models chosen using AIC and BIC. We find that the best model under any of these criteria is not necessarily the most complicated one; models with an intermediate number of substitution types typically do best. Moreover, almost all of the models that are chosen as best do not constrain a transition rate to be the same as a transversion rate, suggesting that it is the transition/transversion rate bias that plays the largest role in determining which models are selected. Importantly, the reversible jump Markov chain Monte Carlo algorithm described here allows estimation of phylogeny (and other phylogenetic model parameters) to be performed while accounting for uncertainty in the model of DNA substitution.     

A comparison between a new model and current models for estimating trunk segment inertial parameters

Modeling of the body segments to estimate segment inertial parameters is required in the kinetic analysis of human motion. A new geometric model for the trunk has been developed that uses various cross-sectional shapes to estimate segment volume and adopts a non-uniform density function that is gender-specific. The goal of this study was to test the accuracy of the new model for estimating the trunk's inertial parameters by comparing it to the more current models used in biomechanical research. Trunk inertial parameters estimated from dual X-ray absorptiometry (DXA) were used as the standard. Twenty-five female and 24 male college-aged participants were recruited for the study. Comparisons of the new model to the accepted models were accomplished by determining the error between the models’ trunk inertial estimates and that from DXA. Results showed that the new model was more accurate across all inertial estimates than the other models. The new model had errors within 6.0% for both genders, whereas the other models had higher average errors ranging from 10% to over 50% and were much more inconsistent between the genders. In addition, there was little consistency in the level of accuracy for the other models when estimating the different inertial parameters. These results suggest that the new model provides more accurate and consistent trunk inertial estimates than the other models for both female and male college-aged individuals. However, similar studies need to be performed using other populations, such as elderly or individuals from a distinct morphology (e.g. obese). In addition, the effect of using different models on the outcome of kinetic parameters, such as joint moments and forces needs to be assessed.     

On travelling wave solutions in a model for the Belousov-Zhabotinskii reaction.

DNA fragments partially digested by a 3′- or 5′-specific nuclease to produce single chain ends of opposite polarity will form a ring if the ends contain complementary sequences and are allowed to anneal. The frequency of rings can then be used as an assay to determine where and how identical repetitious sequences are arranged in the DNA. Thomas et al. (1973b) showed that all eucaryote chromosomes studied contain similar if not identical repetitious sequences clustered into regions called g-regions. To account for the observed ring frequency under different experimental conditions Thomas, Zimm &; Dancis (1973c) derived equations for two possible models of g-region organization. In the pure tandem model, the repetitious sequences are contiguous and occupy the entire g-region. In the intermittent repetition model, the repetitious sequences are simple copolymers and are irregularly arranged among non-repetitious sequences which are heterogenous in length. In the present paper, the results of Thomas et al. (1973c) are extended to cover the fractional tandem model. In this model, adjacent repetitious sequences are separated by non-repetitious sequences of uniform length. In addition, the equations for both the pure tandem and intermittent repetition models are shown to be special cases of the fractional tandem model but not vice versa.The capabilities and limitations of an analysis of ring formation are demonstrated using data from Drosophila. Although it is not possible to rule out any of the three models, the analysis can limit the ranges of the parameters describing each of the models that are consistent with the data. Previous conclusions that the data could only be explained by a pure tandem model which lacks any intervening unique sequences (Bick, Huang &; Thomas, 1973; Thomas et al., 1973b), are shown to be incorrect, in part because the equations for the fractional tandem model had not then been derived. Thus ring theory equations can be used to show the presence of clusters of similar if not identical sequences from ring-forming experiments, but they may not be able to determine the exact spacing and arrangement of these sequences within the clusters.     

Mutation detection using nucleotide analogs that alter electrophoretic mobility.

A simple primer extension assay has been developed to distinguish homologous DNA segments differing by as little as a single nucleotide. DNA strands are synthesized with one of the four natural nucleotides replaced with an analog that affects electrophoretic mobility. DNAs that are the same length but differ in the number of analog molecules per strand exhibit different mobilities on a sequencing gel. In combination with the polymerase chain reaction (PCR; 1, 2), this method has been used to distinguish mutant and normal alleles of the human insulin receptor gene that differ by a single-base substitution. The method appears to be generally applicable to the detection of any nucleotide polymorphism in any segment of DNA.     

Stable transformation of the cyanobacterium Synechocystis sp. PCC 6803 induced by UV irradiation.

Irradiation of the photoheterotrophic cyanobacterium Synechocystis sp. PCC 6803 with low levels of UV light allows for stable, integrative transformation of these cells by heterologous DNA. In this system, transformation does not rely on an autonomously replicating plasmid and is independent of homologous recombination. Cells treated with UV light in the absence of DNA and cells given DNA but not exposed to UV do not yield antibiotic-resistant colonies in platings of up to 2 X 10(8) cells. Optimal conditions for this UV-induced transformation are described. Analysis of the transformants indicates that (i) only a segment of the introduced plasmid is found in the DNA of the transformed cells; (ii) in independently isolated clones, DNA insertion apparently occurs at different sites in the chromosome; and (iii) hybridization data suggest that insertion in one of the transformants may have occurred into a region of the chromosome that is repeated or that integration of plasmid DNA may have been accompanied by a rearrangement or duplication of DNA sequences near the insertion site. DNA isolated from the primary transformants as well as a cloned fragment containing the UV-inserted plasmid sequence and flanking cyanobacterial DNA transform wild-type cells at a high frequency (5.0 X 10(-4) and 1.5 X 10(-5), respectively). Possible mechanisms of this transformation system are discussed, as are the potential uses of this system as an integrative cloning-complementation vector and as a mutagenic agent in which the genetic lesion is already tagged with a selectable marker.     

Tests of spool models for DNA packaging in phage lambda

Experiments are reported which bear on two spool models proposed for packaging the DNA of phage lambda. Both spool models fill an assumed spherical cavity with DNA wrapped in cylindrical or quasi-cylindrical layers composed of adjacent circular turns. In the curved-spool model, a single continuous segment of DNA, about 20% of the DNA length and probably located near the left end of the DNA, is in contact with the coat protein of the phage capsid. In the straight spool model, there are several DNA segments in contact with the capsid; they are concentrated in one half (probably the left half) of lambda DNA. We have identified the loci on the DNA which are in contact with the capsid by chemical crosslinking, induced by ultraviolet-irradiation of phage containing 5-bromodeoxyuridine in place of thymine.In an electron microscope experiment, phage are first lysed with EDTA, and then spread in a cytochrome c film by the formamide method. The disrupted capsid, which has the appearance of a phage ghost, serves as a marker showing where the DNA is crosslinked to the coat. The left end of the DNA is not distinguished from the right end, and so the map of DNA-capsid contacts is folded over on itself. Contacts are found nearly randomly over the entire map.In a second experiment, DNA from lysed, crosslinked phage is cut either with EcoRI or HindIII restriction endonucleases and the cut restriction fragments are labeled at their ends with ³²P. Density centrifugation in a CsCl gradient separates free DNA from restriction fragments crosslinked to protein. After digestion with proteinase k, the DNA fragments previously crosslinked to protein are identified by size after agarose gel electrophoresis. DNA fragments from all parts of the genome are found.These two experiments show that, if the DNA of each phage is packaged identically, then the curved-spool model is ruled out and the straight spool model is unlikely. Alternatively, the manner of packaging the DNA may vary from one phage to the next. These results agree with other recent experiments on λ DNA packaging by Hall & Schellman (1982a,b), and by Haas et al. (1982).A different experiment is also reported. The psoralen derivative aminomethyltrioxalen (AMT) is allowed to intercalate into λ phage and then the DNA strands are crosslinked by ultraviolet-irradiation after the rapid phase of AMT intercalation is complete. The DNA is subsequently denatured by glyoxal modification and spread for electron microscopy in a cytochrome c film by the formamide method. Sites of AMT crosslinking appear duplex; uncrosslinked regions appear as single-stranded loops. AMT is found to intercalate throughout the λ DNA. Patterns of reacted sites appear different from one DNA molecule to the next, and no consistent pattern can be found. More extensive intercalation occurs with the deletion mutant λb221 than with phage of wild-type DNA length, and free DNA shows much more reaction than the DNA inside either phage type. In order for intercalation to occur, the DNA helix must unwind and become further extended. This experiment shows that regions throughout the entire DNA molecule can unwind and be extended by intercalation, which is not confined to a single DNA segment or to segments in one half of the DNA molecule, as would be expected for the two spool models if only the DNA in contact with the capsid were accessible to the dye.     

Double-stranded DNA organization in bacteriophage heads: an alternative toroid-based model.

Studies of the organization of double-stranded DNA within bacteriophage heads during the past four decades have produced a wealth of data. However, despite the presentation of numerous models, the true organization of DNA within phage heads remains unresolved. The observations of toroidal DNA structures in electron micrographs of phage lysates have long been cited as support for the organization of DNA in a spool-like fashion. This particular model, like all other models, has not been found to be consistent will all available data. Recently we proposed that DNA within toroidal condensates produced in vitro is organized in a manner significantly different from that suggested by the spool model. This new toroid model has allowed the development of an alternative model for DNA organization within bacteriophage heads that is consistent with a wide range of biophysical data. Here we propose that bacteriophage DNA is packaged in a toroid that is folded into a highly compact structure.     

The split-end model for homologous recombination at double-strand breaks and at Chi

In recent years two different styles of model for homologous recombination have been discussed, depending on whether or not the recombination event occurs in the vicinity of a double-strand break in DNA. The models of Holliday and Meselson and Radding exemplify those that do not involve a break whereas the model of Szostak et al is taken as an example of those that do. Recent advances in understanding a prototypic recombination system thought to promote exchange distant from DNA ends, at Chi sites, suggest a mechanism of initiation neither like Holliday/Meselson-Radding nor like Szostak et al. In those models, only one strand of DNA may invade a homologous DNA molecule. We propose a model for Chi in which exonuclease degrades DNA from a double-strand break to the Chi site; the exonuclease is converted into a helicase upon interaction with Chi; unwinding produces a recombinagenic split-end, and both 3'- and 5'-ending strands at the split-end are capable of invading a homologue. Different genetic consequences are proposed to result from invasion by each. We review evidence supporting the split-end model and suggest its application in at least some cases previously considered to proceed via the Meselson/Radding model and by the double-strand-break repair model of Szostak et al.