Mathematical modelling of ion regulations in fungi

Intracellular ion concentration and cation transporter activities are important determinants of many fundamental physiological parameters, including cell turgor, plasma membrane potential and intracellular pH. In order to maintain these parameters within physiological ranges despite external perturbations, cells regulate their transporter activities through both post-translational modifications and gene regulation. In this thesis, the ion regulations in two model fungal species, Saccharomyces cerevisiae and Aspergillus nidulans, are investigated. We use a mathematical modelling approach to gain a quantitative understanding of the impact of collective transporter activities on the intracellular cation concentrations and the cellular adaptation processes. This thesis is mainly composed of two parts: 1) A biophysical and mathematical model is built for the cation transporters and their regulatory proteins to describe the temporal changes of cell volume, intracellular pH and cation concentrations during hyper-osmotic stress, ionic stress and alkaline pH stress in S. cerevisiae. 2) Four models are built for the activation of the alkaline pH responsive transcription factor, PacC, in A. nidulans, based on competing hypotheses. The integrated model in the first part shows that calcineurin activation in response to stress conditions results in a rapid and transient decrease of membrane potential, which we speculate is an important strategy for the cell to respond to unknown external ionic perturbations. The model also confirms the importance of Hog1p phosphorylation on Nha1p and Tok1p for immediate adaptation to salt stress and predicted that activated Hog1p down-regulates Tok1p activity. In alkaline stress conditions, the induction of Ena1p expression results in increased membrane potential. This model provides a theoretical framework for the study of ion homeostasis in stress conditions, the understanding of drug effects, such as FK506. And since the membrane potential is an important determinant of drug uptake, the model is well suited for the drug development. In the second part of the thesis, results from those competing models for PacC activation show significant difference for the pacC904 mutant strain and suggests that further experiments on this strain would be able to uncover the role of the intermediate form, PacC53, plays in the activation process

Superinfection and cure of infected cells as mechanisms for hepatitis C virus adaptation and persistence

Copyright © 2018 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).RNA viruses exist as a genetically diverse quasispecies with extraordinary ability to adapt to abrupt changes in the host environment. However, the molecular mechanisms that contribute to their rapid adaptation and persistence in vivo are not well studied. Here, we probe hepatitis C virus (HCV) persistence by analyzing clinical samples taken from subjects who were treated with a second-generation HCV protease inhibitor. Frequent longitudinal viral load determinations and large-scale single-genome sequence analyses revealed rapid antiviral resistance development, and surprisingly, dynamic turnover of dominant drug-resistant mutant populations long after treatment cessation. We fitted mathematical models to both the viral load and the viral sequencing data, and the results provided strong support for the critical roles that superinfection and cure of infected cells play in facilitating the rapid turnover and persistence of viral populations. More broadly, our results highlight the importance of considering viral dynamics and competition at the intracellular level in understanding rapid viral adaptation. Thus, we propose a theoretical framework integrating viral and molecular mechanisms to explain rapid viral evolution, resistance, and persistence despite antiviral treatment and host immune responses.info:eu-repo/semantics/publishedVersio

A quantitative model used to compare within-host SARS-CoV-2, MERS-CoV, and SARS-CoV dynamics provides insights into the pathogenesis and treatment of SARS-CoV-2

The scientific community is focused on developing antiviral therapies to mitigate the impacts of the ongoing novel coronavirus disease 2019 (COVID-19) outbreak. This will be facilitated by improved understanding of viral dynamics within infected hosts. Here, using a mathematical model in combination with published viral load data, we compare within-host viral dynamics of SARS-CoV-2 with analogous dynamics of MERS-CoV and SARS-CoV. Our quantitative analyses using a mathematical model revealed that the within-host reproduction number at symptom onset of SARS-CoV-2 was statistically significantly larger than that of MERS-CoV and similar to that of SARS-CoV. In addition, the time from symptom onset to the viral load peak for SARS-CoV-2 infection was shorter than those of MERS-CoV and SARS-CoV. These findings suggest the difficulty of controlling SARS-CoV-2 infection by antivirals. We further used the viral dynamics model to predict the efficacy of potential antiviral drugs that have different modes of action. The efficacy was measured by the reduction in the viral load area under the curve (AUC). Our results indicate that therapies that block de novo infection or virus production are likely to be effective if and only if initiated before the viral load peak (which appears 2–3 days after symptom onset), but therapies that promote cytotoxicity of infected cells are likely to have effects with less sensitivity to the timing of treatment initiation. Furthermore, combining a therapy that promotes cytotoxicity and one that blocks de novo infection or virus production synergistically reduces the AUC with early treatment. Our unique modeling approach provides insights into the pathogenesis of SARS-CoV-2 and may be useful for development of antiviral therapies