Evolution of foot-and-mouth disease virus intra-sample sequence diversity during serial transmission in bovine hosts

RNA virus populations within samples are highly heterogeneous, containing a large number of minority sequence variants which can potentially be transmitted to other susceptible hosts. Consequently, consensus genome sequences provide an incomplete picture of the within- and between-host viral evolutionary dynamics during transmission. Foot-and-mouth disease virus (FMDV) is an RNA virus that can spread from primary sites of replication, via the systemic circulation, to found distinct sites of local infection at epithelial surfaces. Viral evolution in these different tissues occurs independently, each of them potentially providing a source of virus to seed subsequent transmission events. This study employed the Illumina Genome Analyzer platform to sequence 18 FMDV samples collected from a chain of sequentially infected cattle. These data generated snap-shots of the evolving viral population structures within different animals and tissues. Analyses of the mutation spectra revealed polymorphisms at frequencies >0.5% at between 21 and 146 sites across the genome for these samples, while 13 sites acquired mutations in excess of consensus frequency (50%). Analysis of polymorphism frequency revealed that a number of minority variants were transmitted during host-to-host infection events, while the size of the intra-host founder populations appeared to be smaller. These data indicate that viral population complexity is influenced by small intra-host bottlenecks and relatively large inter-host bottlenecks. The dynamics of minority variants are consistent with the actions of genetic drift rather than strong selection. These results provide novel insights into the evolution of FMDV that can be applied to reconstruct both intra- and inter-host transmission routes

Coevolutionary immune system dynamics driving pathogen speciation

We introduce and analyze a within-host dynamical model of the coevolution between rapidly mutating pathogens and the adaptive immune response. Pathogen mutation and a homeostatic constraint on lymphocytes both play a role in allowing the development of chronic infection, rather than quick pathogen clearance. The dynamics of these chronic infections display emergent structure, including branching patterns corresponding to asexual pathogen speciation, which is fundamentally driven by the coevolutionary interaction. Over time, continued branching creates an increasingly fragile immune system, and leads to the eventual catastrophic loss of immune control.Comment: main article: 16 pages, 5 figures; supporting information: 3 page

HIV-1 Polymerase Inhibition by Nucleoside Analogs: Cellular- and Kinetic Parameters of Efficacy, Susceptibility and Resistance Selection

Nucleoside analogs (NAs) are used to treat numerous viral infections and cancer. They compete with endogenous nucleotides (dNTP/NTP) for incorporation into nascent DNA/RNA and inhibit replication by preventing subsequent primer extension. To date, an integrated mathematical model that could allow the analysis of their mechanism of action, of the various resistance mechanisms, and their effect on viral fitness is still lacking. We present the first mechanistic mathematical model of polymerase inhibition by NAs that takes into account the reversibility of polymerase inhibition. Analytical solutions for the model point out the cellular- and kinetic aspects of inhibition. Our model correctly predicts for HIV-1 that resistance against nucleoside analog reverse transcriptase inhibitors (NRTIs) can be conferred by decreasing their incorporation rate, increasing their excision rate, or decreasing their affinity for the polymerase enzyme. For all analyzed NRTIs and their combinations, model-predicted macroscopic parameters (efficacy, fitness and toxicity) were consistent with observations. NRTI efficacy was found to greatly vary between distinct target cells. Surprisingly, target cells with low dNTP/NTP levels may not confer hyper-susceptibility to inhibition, whereas cells with high dNTP/NTP contents are likely to confer natural resistance. Our model also allows quantification of the selective advantage of mutations by integrating their effects on viral fitness and drug susceptibility. For zidovudine triphosphate (AZT-TP), we predict that this selective advantage, as well as the minimal concentration required to select thymidine-associated mutations (TAMs) are highly cell-dependent. The developed model allows studying various resistance mechanisms, inherent fitness effects, selection forces and epistasis based on microscopic kinetic data. It can readily be embedded in extended models of the complete HIV-1 reverse transcription process, or analogous processes in other viruses and help to guide drug development and improve our understanding of the mechanisms of resistance development during treatment

Stochastic and State Space Models of Carcinogenesis Under Complex Situation

With more and more biological mechanisms of cancer development being discovered, in order to improve cancer control and prevention, it becomes necessary to develop effective and efficient mathematical and statistical models and methods to incorporate the biological information, and to identify critical events in the process of carcinogenesis. In this dissertation, the complex nature of carcinogenesis has been represented by stochastic system model; combining this model with information from observations and prior knowledge, we have developed state space models to evaluate cancer gene mutations and cell proliferation at different cancer development stages. Also, we have proposed a generalized Bayesian method via multi-level Gibbs sampling procedure to predict state (stage) variables of the models. In this dissertation, stochastic models have been proposed for initiation, promotion and complete carcinomas experiments; these experiments are most commonly performed in cancer risk assessment of environmental agents. These stochastic models are simple multi-pathway models which are constructed based on biological mechanisms. The estimates we obtained from the models have provided quantitative evaluation of dose related mutation rates of major genes and cells proliferation rates; these results could be used to assess the risk of developing malignant tumor in the environment we live. More complicated stochastic and state space models have been developed for sporadic human colon cancer and for hereditary and non-hereditary human liver cancer. We have utilized the proposed models to fit to Surveillance Epidemiology and End Results (SEER) data. The results imply that our models have effectively incorporated biological information and observations; these models fitted the data very well and the inferences based on estimate were very consistent with biological findings. Furthermore, the models reflected the complex nature of carcinogenesis. We notice that many cancers are developed through multiple-stage multiple-pathway. Our analyses of colon cancer and liver cancer have showed that some pathways are more devastated than others. This suggests thus it would be more efficient to intervene or treat the critical events in the more devastated pathways

Mechanisms of Substrate Recognition by HCV NS3/4A Protease Provide Insights Into Drug Resistance: A Dissertation

HCV afflicts many millions of people globally, and antiviral therapies are often ineffective and intolerable. The Food and Drug Administration approved the HCV protease inhibitors telaprevir and boceprevir in May 2011, marking an important milestone in anti-HCV research over the past two decades. Nevertheless, severe drug side effects of combination therapy – flu-like symptoms, depression and anemia – limit patient adherence to treatment regimens. The acquisition of resistance challenges the long-term efficacy of antiviral therapies, including protease inhibitors, as suboptimal dosing allows for the selection of drug resistant viral variants. A better understanding of the molecular basis of drug resistance is therefore central to developing future generation protease inhibitors that retain potency against a broader spectrum of HCV strains. To this end, my research characterizes the molecular basis of drug resistance against HCV protease inhibitors. Chapter II defines the mode of substrate recognition by the common volume shared by NS3/4A substrate products – the substrate envelope. Chapter III then correlates patterns of drug resistance to regions where drugs protrude from the substrate envelope. Lastly, Chapter IV elucidates the molecular underpinnings of resistance against four leading protease inhibitors – telaprevir, danoprevir, vaniprevir and MK-5172 – and provides practical approaches to designing novel drugs that are less susceptible to resistance. I ultimately hope my work appeals to the broader biomedical community of virologists, medicinal chemists and clinicians, who struggle to understand HCV and other human pathogens in the face of rapid disease evolution

The survival probability of beneficial de novo mutations in budding viruses, with an emphasis on influenza A viral dynamics

A deterministic model is developed of the within-host dynamics of a budding virus, and coupled with a detailed life-history model using a branching process approach to follow the fate of de novo beneficial mutations affecting five life-history traits: clearance, attachment, eclipse, budding, and cell death. Although the model can be generalized for any given budding virus, our work was done with a major emphasis on the early stages of infection with influenza A virus in human populations. The branching process was then interleaved with a stochastic process describing the disease transmission of this virus. These techniques allowed us to predict that mutations affecting clearance and cell death rate, two adaptive changes in influenza A\u27s life-history traits, are most likely to persist for small selective advantage (s\u3c0.08) when rare. These results also show that the overall adaptability of the virus is much higher than classically predicted, and that the period of growth between bottlenecks has a greater impact on increasing survival probability relative to the impact of bottlenecks, which is consistent with previous work