UVA-induced DNA double-strand breaks result from the repair of clustered oxidative DNA damages

UVA (320–400 nm) represents the main spectral component of solar UV radiation, induces pre-mutagenic DNA lesions and is classified as Class I carcinogen. Recently, discussion arose whether UVA induces DNA double-strand breaks (dsbs). Only few reports link the induction of dsbs to UVA exposure and the underlying mechanisms are poorly understood. Using the Comet-assay and γH2AX as markers for dsb formation, we demonstrate the dose-dependent dsb induction by UVA in G₁-synchronized human keratinocytes (HaCaT) and primary human skin fibroblasts. The number of γH2AX foci increases when a UVA dose is applied in fractions (split dose), with a 2-h recovery period between fractions. The presence of the anti-oxidant Naringin reduces dsb formation significantly. Using an FPG-modified Comet-assay as well as warm and cold repair incubation, we show that dsbs arise partially during repair of bi-stranded, oxidative, clustered DNA lesions. We also demonstrate that on stretched chromatin fibres, 8-oxo-G and abasic sites occur in clusters. This suggests a replication-independent formation of UVA-induced dsbs through clustered single-strand breaks via locally generated reactive oxygen species. Since UVA is the main component of solar UV exposure and is used for artificial UV exposure, our results shine new light on the aetiology of skin cancer.     

The Basic Reproduction Number for Cellular SIR Networks

The basic reproduction number \(R_0\) is the average number of new infections produced by a typical infective individual in the early stage of an infectious disease, following the introduction of few infective individuals in a completely susceptible population. If \(R_0<1\) , then the disease dies, whereas for \(R_0>1\) the infection can invade the host population and persist. This threshold quantity is well studied for SIR compartmental or mean field models based on ordinary differential equations, and a general method for its computation has been proposed by van den Driessche and Watmough. We concentrate here on SIR epidemiological models that take into account the contact network N underlying the transmission of the disease. In this context, it is generally admitted that \(R_{0}\) can be approximated by the average number \(R_{2,3}\) of infective individuals of generation three produced by an infective of generation two. We give here a simple analytic formula of \(R_{2,3}\) for SIR cellular networks. Simulations on two-dimensional cellular networks with von Neumann and Moore neighborhoods show that \(R_{2,3}\) can be used to capture a threshold phenomenon related the dynamics of SIR cellular network and confirm the good quality of the simple approach proposed recently by Aparicio and Pascual for the particular case of Moore neighborhood.     

On the basic reproduction number in a random environment

The concept of basic reproduction number $R_0$ in population dynamics is studied in the case of random environments. For simplicity the dependence between successive environments is supposed to follow a Markov chain. $R_0$ is the spectral radius of a next-generation operator. Its position with respect to 1 always determines population growth or decay in simulations, unlike another parameter suggested in a recent article (Hernandez-Suarez et al., Theor Popul Biol, doi:10.1016/j.tpb.2012.05.004, 2012). The position of the latter with respect to 1 determines growth or decay of the population’s expectation. $R_0$ is easily computed in the case of scalar population models without any structure. The main emphasis is on discrete-time models but continuous-time models are also considered.     

Two types of double-strand breaks in electron and photon tracks and their relation to exchange-type chromosome aberrations

Yields of DNA double-strand breaks (dsb), i. e. the average number of dsb, N–, per relative molar mass, M _r , and dose, D, produced by electrons and photons in the energy range 50 eV – 1 MeV were calculated. The experimental data of dsb induction by ultrasoft x-rays and by photons agree well with the calculated yields of dsb as a function of photon energy. The dsb are classified into simple and complex ones. Energy transfers of less than about 200 eV producing at least two ionizations generate mainly simple dsb, while low-energy electrons with an initial energy between 200 and 500 eV induce preferentially complex dsb. Assuming that dsb is the main DNA lesion leading to exchange-type chromosome aberrations (etca), three different mechanisms have to be considered: 1) complex dsb on its own; 2) interaction between two dsb induced by the same primary particle; and 3) interaction between two dsb induced by different primary particles. Mechanisms 1) and 2) produce a linear term, whereas mechanism 3) leads to a quadratic term for the yield of etca. The sum of contributions 1) and 2) to the yield of dicentrics describes fairly well the non-trivial structure of the experimental data. The results suggest that interaction between complex dsb does not contribute significantly to the formation of dicentrics via mechanism 3). Received: 30 July 1995 / Accepted in revised form: 28 March 1996     

Threshold dynamics in an SEIRS model with latency and temporary immunity

A disease transmission model of SEIRS type with distributed delays in latent and temporary immune periods is discussed. With general/particular probability distributions in both of these periods, we address the threshold property of the basic reproduction number \(R_0\) and the dynamical properties of the disease-free/endemic equilibrium points present in the model. More specifically, we 1. show the dependence of \(R_0\) on the probability distribution in the latent period and the independence of \(R_0\) from the distribution of the temporary immunity, 2. prove that the disease free equilibrium is always globally asymptotically stable when \(R_0<1\) , and 3. according to the choice of probability functions in the latent and temporary immune periods, establish that the disease always persists when \(R_0>1\) and an endemic equilibrium exists with different stability properties. In particular, the endemic steady state is at least locally asymptotically stable if the probability distribution in the temporary immunity is a decreasing exponential function when the duration of the latency stage is fixed or exponentially decreasing. It may become oscillatory under certain conditions when there exists a constant delay in the temporary immunity period. Numerical simulations are given to verify the theoretical predictions.     

Bone refilling in cortical basic multicellular units: insights into tetracycline double labelling from a computational model

Bone remodelling is carried out by ‘bone multicellular units’ ( $\text{ BMU }$ s) in which active osteoclasts and active osteoblasts are spatially and temporally coupled. The refilling of new bone by osteoblasts towards the back of the $\text{ BMU }$ occurs at a rate that depends both on the number of osteoblasts and on their secretory activity. In cortical bone, a linear phenomenological relationship between matrix apposition rate and $\text{ BMU }$ cavity radius is found experimentally. How this relationship emerges from the combination of complex, nonlinear regulations of osteoblast number and secretory activity is unknown. Here, we extend our previous mathematical model of cell development within a single cortical $\text{ BMU }$ to investigate how osteoblast number and osteoblast secretory activity vary along the $\text{ BMU }$ ’s closing cone. The mathematical model is based on biochemical coupling between osteoclasts and osteoblasts of various maturity and includes the differentiation of osteoblasts into osteocytes and bone lining cells, as well as the influence of $\text{ BMU }$ cavity shrinkage on osteoblast development and activity. Matrix apposition rates predicted by the model are compared with data from tetracycline double labelling experiments. We find that the linear phenomenological relationship observed in these experiments between matrix apposition rate and $\text{ BMU }$ cavity radius holds for most of the refilling phase simulated by our model, but not near the start and end of refilling. This suggests that at a particular bone site undergoing remodelling, bone formation starts and ends rapidly, supporting the hypothesis that osteoblasts behave synchronously. Our model also suggests that part of the observed cross-sectional variability in tetracycline data may be due to different bone sites being refilled by $\text{ BMU }$ s at different stages of their lifetime. The different stages of a $\text{ BMU }$ ’s lifetime (such as initiation stage, progression stage, and termination stage) depend on whether the cell populations within the $\text{ BMU }$ are still developing or have reached a quasi-steady state whilst travelling through bone. We find that due to their longer lifespan, active osteoblasts reach a quasi-steady distribution more slowly than active osteoclasts. We suggest that this fact may locally enlarge the Haversian canal diameter (due to a local lack of osteoblasts compared to osteoclasts) near the $\text{ BMU }$ ’s point of origin.     

Edge removal in random contact networks and the basic reproduction number

Understanding the effect of edge removal on the basic reproduction number ${\mathcal{R}_0}$ for disease spread on contact networks is important for disease management. The formula for the basic reproduction number ${\mathcal{R}_0}$ in random network SIR models of configuration type suggests that for degree distributions with large variance, a reduction of the average degree may actually increase ${\mathcal{R}_0}$ . To understand this phenomenon, we develop a dynamical model for the evolution of the degree distribution under random edge removal, and show that truly random removal always reduces ${\mathcal{R}_0}$ . The discrepancy implies that any increase in ${\mathcal{R}_0}$ must result from edge removal changing the network type, invalidating the use of the basic reproduction number formula for a random contact network. We further develop an epidemic model incorporating a contact network consisting of two groups of nodes with random intra- and inter-group connections, and derive its basic reproduction number. We then prove that random edge removal within either group, and between groups, always decreases the appropriately defined ${\mathcal{R}_0}$ . Our models also allow an estimation of the number of edges that need to be removed in order to curtail an epidemic.     

Approximation of sojourn-times via maximal couplings: motif frequency distributions

Sojourn-times provide a versatile framework to assess the statistical significance of motifs in genome-wide searches even under non-Markovian background models. However, the large state spaces encountered in genomic sequence analyses make the exact calculation of sojourn-time distributions computationally intractable in long sequences. Here, we use coupling and analytic combinatoric techniques to approximate these distributions in the general setting of Polish state spaces, which encompass discrete state spaces. Our approximations are accompanied with explicit, easy to compute, error bounds for total variation distance. Broadly speaking, if \({\mathsf{T}}_n\) is the random number of times a Markov chain visits a certain subset \({\mathsf{T}}\) of states in its first \(n\) transitions, then we can usually approximate the distribution of \({\mathsf{T}}_n\) for \(n\) of order \((1-\alpha )^{-m}\) , where \(m\) is the largest integer for which the exact distribution of \({\mathsf{T}}_m\) is accessible and \(0\le \alpha \le 1\) is an ergodicity coefficient associated with the probability transition kernel of the chain. This gives access to approximations of sojourn-times in the intermediate regime where \(n\) is perhaps too large for exact calculations, but too small to rely on Normal approximations or stationarity assumptions underlying Poisson and compound Poisson approximations. As proof of concept, we approximate the distribution of the number of matches with a motif in promoter regions of C. elegans. Mathematical properties of the proposed ergodicity coefficients and connections with additive functionals of homogeneous Markov chains as well as ergodicity of non-homogeneous Markov chains are also explored.     

PENDISC: A Simple Method for Constructing a Mathematical Model from Time-Series Data of Metabolite Concentrations

The availability of large-scale datasets has led to more effort being made to understand characteristics of metabolic reaction networks. However, because the large-scale data are semi-quantitative, and may contain biological variations and/or analytical errors, it remains a challenge to construct a mathematical model with precise parameters using only these data. The present work proposes a simple method, referred to as PENDISC ( arameter stimation in a on- mensionalized -system with onstraints), to assist the complex process of parameter estimation in the construction of a mathematical model for a given metabolic reaction system. The PENDISC method was evaluated using two simple mathematical models: a linear metabolic pathway model with inhibition and a branched metabolic pathway model with inhibition and activation. The results indicate that a smaller number of data points and rate constant parameters enhances the agreement between calculated values and time-series data of metabolite concentrations, and leads to faster convergence when the same initial estimates are used for the fitting. This method is also shown to be applicable to noisy time-series data and to unmeasurable metabolite concentrations in a network, and to have a potential to handle metabolome data of a relatively large-scale metabolic reaction system. Furthermore, it was applied to aspartate-derived amino acid biosynthesis in Arabidopsis thaliana plant. The result provides confirmation that the mathematical model constructed satisfactorily agrees with the time-series datasets of seven metabolite concentrations.     

Random generation of RNA secondary structures according to native distributions

Background

Random biological sequences are a topic of great interest in genome analysis since, according to a powerful paradigm, they represent the background noise from which the actual biological information must differentiate. Accordingly, the generation of random sequences has been investigated for a long time. Similarly, random object of a more complicated structure like RNA molecules or proteins are of interest.

Results

In this article, we present a new general framework for deriving algorithms for the non-uniform random generation of combinatorial objects according to the encoding and probability distribution implied by a stochastic context-free grammar. Briefly, the framework extends on the well-known recursive method for (uniform) random generation and uses the popular framework of admissible specifications of combinatorial classes, introducing weighted combinatorial classes to allow for the non-uniform generation by means of unranking. This framework is used to derive an algorithm for the generation of RNA secondary structures of a given fixed size. We address the random generation of these structures according to a realistic distribution obtained from real-life data by using a very detailed context-free grammar (that models the class of RNA secondary structures by distinguishing between all known motifs in RNA structure). Compared to well-known sampling approaches used in several structure prediction tools (such as SFold) ours has two major advantages: Firstly, after a preprocessing step in time for the computation of all weighted class sizes needed, with our approach a set of m random secondary structures of a given structure size n can be computed in worst-case time complexity while other algorithms typically have a runtime in . Secondly, our approach works with integer arithmetic only which is faster and saves us from all the discomforting details of using floating point arithmetic with logarithmized probabilities.

Conclusion

A number of experimental results shows that our random generation method produces realistic output, at least with respect to the appearance of the different structural motifs. The algorithm is available as a webservice at http://wwwagak.cs.uni-kl.de/NonUniRandGen and can be used for generating random secondary structures of any specified RNA type. A link to download an implementation of our method (in Wolfram Mathematica) can be found there, too.     

A time since onset of injection model for hepatitis C spread amongst injecting drug users

Studies of hepatitis C virus (HCV) infection amongst injecting drug users (IDUs) have suggested that this population can be separated into two risk groups (naive and experienced) with different injecting risk behaviours. Understanding the differences between these two groups and how they interact could lead to a better allocation of prevention measures designed to reduce the burden of HCV in this population. In this paper we develop a deterministic, compartmental mathematical model for the spread of HCV in an IDU population that has been separated into two groups (naive and experienced) by time since onset of injection. We will first describe the model. After deriving the system of governing equations, we will examine the basic reproductive number $R_0$ , the existence and uniqueness of equilibrium solutions and the global stability of the disease free equilibrium (DFE) solution. The model behaviour is determined by the basic reproductive number, with $R_0=1$ a critical threshold for endemic HCV prevalence. We will show that when $R_0\le 1$ , and HCV is initially present in the population, the system will tend towards the globally asymptotically stable DFE where HCV has been eliminated from the population. We also show that when $R_0>1$ there exists a unique non-zero equilibrium solution. Then we estimate the value of $R_0$ from epidemiological data for Glasgow and verify our theoretical results using simulations with realistic parameter values. The numerical results suggest that if $R_0>1$ and the disease is initially present then the system will tend to the unique endemic equilibrium.     

Interdependency and hierarchy of exact and approximate epidemic models on networks

Over the years numerous models of \(SIS\) (susceptible \(\rightarrow \) infected \(\rightarrow \) susceptible) disease dynamics unfolding on networks have been proposed. Here, we discuss the links between many of these models and how they can be viewed as more general motif-based models. We illustrate how the different models can be derived from one another and, where this is not possible, discuss extensions to established models that enables this derivation. We also derive a general result for the exact differential equations for the expected number of an arbitrary motif directly from the Kolmogorov/master equations and conclude with a comparison of the performance of the different closed systems of equations on networks of varying structure.     

Transmission dynamics for vector-borne diseases in a patchy environment

In this paper, a mathematical model is derived to describe the transmission and spread of vector-borne diseases over a patchy environment. The model incorporates into the classic Ross–MacDonald model two factors: disease latencies in both hosts and vectors, and dispersal of hosts between patches. The basic reproduction number \(\mathcal{R }_0\) is identified by the theory of the next generation operator for structured disease models. The dynamics of the model is investigated in terms of \(\mathcal{R }_0\) . It is shown that the disease free equilibrium is asymptotically stable if \(\mathcal{R }_0<1\) , and it is unstable if \(\mathcal{R }_0>1\) ; in the latter case, the disease is endemic in the sense that the variables for the infected compartments are uniformly persistent. For the case of two patches, more explicit formulas for \(\mathcal{R }_0\) are derived by which, impacts of the dispersal rates on disease dynamics are also explored. Some numerical computations for \(\mathcal{R }_0\) in terms of dispersal rates are performed which show visually that the impacts could be very complicated: in certain range of the parameters, \(\mathcal{R }_0\) is increasing with respect to a dispersal rate while in some other range, it can be decreasing with respect to the same dispersal rate. The results can be useful to health organizations at various levels for setting guidelines or making policies for travels, as far as malaria epidemics is concerned.     

Threshold dynamics of an infective disease model with a fixed latent period and non-local infections

In this paper, we derive and analyze an infectious disease model containing a fixed latency and non-local infection caused by the mobility of the latent individuals in a continuous bounded domain. The model is given by a spatially non-local reaction–diffusion system carrying a discrete delay associated with the zero-flux condition on the boundary. By applying some existing abstract results in dynamical systems theory, we prove the existence of a global attractor for the model system. By appealing to the theory of monotone dynamical systems and uniform persistence, we show that the model has the global threshold dynamics which can be described either by the principal eigenvalue of a linear non-local scalar reaction diffusion equation or equivalently by the basic reproduction number ${\mathcal{R}_0}$ for the model. Such threshold dynamics predicts whether the disease will die out or persist. We identify the next generation operator, the spectral radius of which defines basic reproduction number. When all model parameters are constants, we are able to find explicitly the principal eigenvalue and ${\mathcal{R}_0}$ . In addition to computing the spectral radius of the next generation operator, we also discuss an alternative way to compute ${\mathcal{R}_0}$ .     

Recombination-dependent DNA replication stimulated by double-strand breaks in bacteriophage T4.

We analyzed the mechanism of recombination-dependent DNA replication in bacteriophage T4-infected Escherichia coli using plasmids that have sequence homology to the infecting phage chromosome. Consistent with prior studies, a pBR322 plasmid, initially resident in the infected host cell, does not replicate following infection by T4. However, the resident plasmid can be induced to replicate when an integrated copy of pBR322 vector is present in the phage chromosome. As expected for recombination-dependent DNA replication, the induced replication of pBR322 required the phage-encoded UvsY protein. Therefore, recombination-dependent plasmid replication requires homology between the plasmid and phage genomes but does not depend on the presence of any particular T4 DNA sequence on the test plasmid. We next asked whether T4 recombination-dependent DNA replication can be triggered by a double-strand break (dsb). For these experiments, we generated a novel phage strain that cleaves its own genome within the nonessential frd gene by means of the I-TevI endonuclease (encoded within the intron of the wild-type td gene). The dsb within the phage chromosome substantially increased the replication of plasmids that carry T4 inserts homologous to the region of the dsb (the plasmids are not themselves cleaved by the endonuclease). The dsb stimulated replication when the plasmid was homologous to either or both sides of the break but did not stimulate the replication of plasmids with homology to distant regions of the phage chromosome. As expected for recombination-dependent replication, plasmid replication triggered by dsbs was dependent on T4-encoded recombination proteins. These results confirm two important predictions of the model for T4-encoded recombination-dependent DNA replication proposed by Gisela Mosig (p. 120-130, in C. K. Mathews, E. M. Kutter, G. Mosig, and P. B. Berget (ed.), Bacteriophage T4, 1983). In addition, replication stimulated by dsbs provides a site-specific version of the process, which should be very useful for mechanistic studies.     

DBD-fish on neutral comets: simultaneous analysis of DNA single- and double-strand breaks in individual cells

Humanblood leukocytes exposed to X-rays were immersed in an agarose microgel on a slide, extensively deproteinized, and electrophoresed under neutral conditions. Following this single-cell gel electrophoresis assay, characteristics of DNA migration (i.e., area of the comet) are related to the DNA double-strand breaks (dsbs) yield. After electrophoresis, comets were briefly incubated in an alkaline unwinding solution, transforming DNA breaks and alkali-labile sites into restricted single-stranded DNA (ssDNA) motifs. These motifs behave as target sites for hybridization with a whole genome probe, following the DNA breakage detection-fluorescence in situ hybridization (DBD-FISH) procedure. As DNA breakage increases with dose, more ssDNA is produced in the comet by the alkali and more DNA probe hybridizes, resulting in an increase in the mean fluorescence intensity. Since radiation-induced DNA single-strand breaks (ssbs) are far more frequent than dsbs, the mean fluorescence intensity of the DBD-FISH signal from the comet is related to the ssb level, whereas the surface area of the same comet signal is indicative of the dsb yield. Thus, both DNA break types may be simultaneously analyzed in the same cell. This was confirmed in a repair assay performing the DBD-FISH on neutral comets from a human cell line defective in the repair of dsbs. Otherwise, treatment with hydrogen peroxide, a main inducer of ssbs, increased the mean fluorescence intensity, but not the surface, of X-ray-exposed human leukocytes.     

Viral dynamics model with CTL immune response incorporating antiretroviral therapy

We present two HIV models that include the CTL immune response, antiretroviral therapy and a full logistic growth term for uninfected $\text{ CD4}^+$ T-cells. The difference between the two models lies in the inclusion or omission of a loss term in the free virus equation. We obtain critical conditions for the existence of one, two or three steady states, and analyze the stability of these steady states. Through numerical simulation we find substantial differences in the reproduction numbers and the behaviour at the infected steady state between the two models, for certain parameter sets. We explore the effect of varying the combination drug efficacy on model behaviour, and the possibility of reconstituting the CTL immune response through antiretroviral therapy. Furthermore, we employ Latin hypercube sampling to investigate the existence of multiple infected equilibria.     

When does pathogen evolution maximize the basic reproductive number in well-mixed host–pathogen systems?

Pathogen evolution towards the largest basic reproductive number, $\mathcal R _0$ , has been observed in many theoretical models, but this conclusion does not hold universally. Previous studies of host–pathogen systems have defined general conditions under which $\mathcal R _0$ maximization occurs in terms of $\mathcal R _0$ itself. However, it is unclear what constraints these conditions impose on the functional forms of pathogen related processes (e.g. transmission, recover, or mortality) and how those constraints relate to the characteristics of natural systems. Here we focus on well-mixed SIR-type host–pathogen systems and, via a synthesis of results from the literature, we present a set of sufficient mathematical conditions under which evolution maximizes $\mathcal R _0$ . Our conditions are in terms of the functional responses of the system and yield three general biological constraints on when $\mathcal R _0$ maximization will occur. First, there are no genotype-by-environment interactions. Second, the pathogen utilizes a single transmission pathway (i.e. either horizontal, vertical, or vector transmission). Third, when mortality is density dependent: (i) there is a single infectious class that individuals cannot recover from, (ii) mortality in the infectious class is entirely density dependent, and (iii) the rates of recovery, infection progression, and mortality in the exposed classes are independent of the pathogen trait. We discuss how this approach identifies the biological mechanisms that increase the dimension of the environmental feedback and prevent $\mathcal R _0$ maximization.     

Cherry Picking: A Characterization of the Temporal Hybridization Number for a Set of Phylogenies

Recently, we have shown that calculating the minimum–temporal-hybridization number for a set ${\mathcal{P}}$ of rooted binary phylogenetic trees is NP-hard and have characterized this minimum number when ${\mathcal{P}}$ consists of exactly two trees. In this paper, we give the first characterization of the problem for ${\mathcal{P}}$ being arbitrarily large. The characterization is in terms of cherries and the existence of a particular type of sequence. Furthermore, in an online appendix to the paper, we show that this new characterization can be used to show that computing the minimum–temporal hybridization number for two trees is fixed-parameter tractable.