Mathematical modeling of Plus-strand RNA virus replication

Medientyp: E-Book; Hochschulschrift

Titel: Mathematical modeling of Plus-strand RNA virus replication

Beteiligte: Zitzmann, Carolin [VerfasserIn]; Kaderali, Lars [AkademischeR BetreuerIn]; Figge, Marc Thilo Günter [AkademischeR BetreuerIn]

Körperschaft: Universität Greifswald

Erschienen: Greifswald, 2023

Umfang: 1 Online-Ressource (PDF-Datei: 173 Seiten, 28972 Kilobyte); Illustrationen (farbig), Diagramme (farbig)

Sprache: Englisch

Identifikator:

Schlagwörter: Hepatitis-C-Virus > Denguefieber > Enteroviren > Infektionskrankheit > Immunreaktion > Mathematische Modellierung > Viren

Entstehung:

Hochschulschrift: Dissertation, Universitätsmedizin der Universität Greifswald, 2023

Anmerkungen: Literaturverzeichnis: Seite 145-155. - Literaturangaben

Beschreibung: Coxsackievirus B3, Dengue virus, Enterovirus, Hepatitis C virus, Mathematical modeling, Modeling of immune response, Modeling of infectious diseases, Viral dynamics, Virus-host interaction

Plus‐strand RNA [(+)RNA] viruses are the largest group of viruses, medically highly relevant human pathogens, and are a socio‐economic burden. The current global pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) shows how a virus has been rapidly spreading around the globe and that– without an antiviral treatment– virus trans mission is solely dependent on human behavior. However, other (+)RNA viruses such as rhino‐, noro‐, dengue‐ (DENV), Zika, and hepatitis C virus (HCV) are constantly spreading and expanding geographically. As in the case of hepatitis C, since its first identification in the 1970s, it took more than 30 years to understand the HCV structure, genome organiza t ion, life cycle, and virus‐host interplay leading to the cure of a chronic and life‐threatening disease. However, no vaccination or antiviral treatment exists for most (+)RNA viruses. Con sequently, a precise and comprehensive analysis of the viruses, their life cycles, and parasitic interactions with their hosts remains an important field of research. In the presented thesis, we use mathematical modeling to study the life cycles of (+)RNA viruses. We analyze replication strategies of closely related (+)RNA viruses, namely HCV, DENV, and coxsackievirus B3 (CVB3), to compare their life cycles in the presence and ab sence of the host’s immune response and antiviral drug treatment and consider different viral spreading mechanisms. Host dependency factors shape the viral life cycle, contribut ing to permissiveness and replication efficiency. Our mathematical models predicted that host dependency factors, such as ribosomes, and thus the virus’ ability to hijack the host cell’s translation machinery play an essential role in the viral genome replication efficiency. Furthermore, our mathematical model suggested that the availability of ribosomes in the vi ral life cycle is a crucial factor in disease outcome: the development of an acute or chronic disease. Even though the host developed strategies to attack the virus, e.g., by degrading the viral genome, blocking the viral protein production, and preventing viral spread, viruses found strategies to countermeasure those so‐called host restriction factors derived from the immune system. Our mathematical models predicted that DENV might be highly effective in blocking the cell’s attempts to recognize the invader. Moreover, we found ongoing HCV RNAreplication even with highly effective antiviral drugs that block processes in the viral life cycle. Furthermore, we found alternative pathways of infection spread, e.g., by HCV RNA carrying exosomes, which may be a possible explanation for reported plasma HCV RNA at the end of treatment, found in a subset of patients. Hence, the mathematical models presented in this thesis provide valuable tools to study the viral replication mechanism in detail. Even though being a simplification of reality, our model predictions confirm and explain known and suggest novel biological mechanisms. In the pre sented thesis, I will summarize and discuss key findings and contextualize model predictions in the broader scientific literature to improve our understanding of the viral dynamics and the virus‐host interplay.

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