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Biology subjects

Chhajer, H.

Publications and source records attributed to Chhajer, H..

3 recordsLinked to original sources

Integrative Modelling of Innate Immune Response Dynamics during Virus Infection

Positive-sense RNA viruses that constitute a large class of human pathogens employ various strategies to suppress and evade host immune defenses. Understanding the dynamic interaction between the viral life cycle and immune signaling is crucial to designing effective antiviral strategies. Although significant progress has been made, quantitative models that can accurately capture the intricate interactions and the intertwined dynamics during viral infection of cells remain missing. In this study, we develop a comprehensive mathematical model that integrates the intracellular viral life cycle with key cellular innate immune pathways, including RIG-I-mediated detection and JAK-STAT signaling. The model provides mechanistic insights into long-standing observations, capturing both virus-specific dynamics and innate immune response, and the key components driving their coupled dynamics. For example, a comparison of viruses shows how the Japanese Encephalitis virus undergoes a dramatic reduction in viral load in cells, due to its rapid replication that robustly activates the RIG-I pathway, in contrast to the poor immune control of HCV. More importantly, our model demonstrates how virus-host interactions exhibit a sharp bifurcation behavior, where minor differences in immune strength or viral suppression capacity can determine whether infections resolve or persist. We propose that ISG mRNA translation and viral replication predominantly dictate these bimodal infection outcomes. Additionally, the model not only recapitulates but also highlights molecular players involved in IFN desensitization. We demonstrate how our models ability to capture IFN dynamics allows us to predict optimal timing and dosing strategies for interferon-based prophylactic therapies. Together, our approach reveals fundamental features that govern the delicate balance between the establishment of infection and immune control in RNA virus infections. Author SummaryViruses responsible for diseases like hepatitis and dengue rapidly proliferate by invading host cells, which in turn activate immune responses to counteract the infection. Viruses have also developed a variety of mechanisms to interfere with this immune response, thereby complicating the process of effectively treating infections. Here, we simulate the comprehensive sequence of molecular-level actions and the corresponding counteractions that occur between the virus and the host cell components as the infection proceeds. We find that the battle between the virus and the immune system behaves like a seesaw, and small changes can dramatically tip the balance. Sometimes, a tiny boost to our immune response or a small weakness in the virus can mean the difference between cells succumbing to infection or recovering completely. Similarly, it is now possible to recognize why specific viruses are more effectively controlled by the immune system compared to others. We can now explain the decline in the immune response to interferon therapy with prolonged use and suggest the optimal timing and dosing for such treatments. For example, we found that interferon treatment administered before infection, even in small doses, can block the virus more effectively than treatment after infection begins. In addition, we propose better ways to inhibit the virus by effectively combining complementary approaches, such as blocking its replication or its ability to hide from the immune system, which can boost the effectiveness of interferon treatment. This work provides a roadmap for developing quantitative and more effective antiviral approaches by understanding the precise biological mechanisms that determine how hosts can overcome virus infection.

microbiology↗

Rationalised experiment design for parameter estimation with sensitivity clustering

Quantitative experiments are essential for investigating, uncovering and confirming our understanding of complex systems, necessitating the use of effective and robust experimental designs. Despite generally outperforming other approaches, the broader adoption of model-based design of experiments (MBDoE) has been hindered by oversimplified assumptions and computational overhead. To address this, we present PARameter SEnsitivity Clustering (PARSEC), an MBDoE framework that identifies informative measurable combinations through parameter sensitivity (PS) clustering. We combined PARSEC with a new variant of Approximate Bayesian Computation for rapid, automated assessment and ranking of designs. By inherent design, PARSEC can take into account experimental restrictions and parameter variability. We show that PARSEC improves parameter estimation for two different types of biological models. Importantly, PARSEC can determine the optimal sample size for information gain, which we show correlates well with the optimal number of PS clusters. This supports our rationale for PARSEC and demonstrates the potential to harness both model structure and system behaviour to efficiently navigate the experiment design space.

bioinformatics↗

Life cycle process dependencies of positive-sense RNA viruses suggest strategies for inhibiting productive cellular infection

Life cycle processes of positive-strand (+)RNA viruses are broadly conserved across families, yet they employ different life cycle strategies to grow in the cell. Using a generalized dynamical model for intracellular (+)ssRNA virus growth, we decipher these life cycle determinants and their dependencies for several viruses and parse the effect of viral mutations and host cell permissivity. We show that Poliovirus employs rapid replication and virus assembly whereas Japanese Encephalitis virus leverages its higher rate of translation and efficient cellular reorganization compared to Hepatitis C virus. Stochastic simulations of the model demonstrate infection extinction if all seeding viral RNA degrade before establishing robust replication. The probability of productive cellular infection is affected by virus-host processes, defined by early life cycle events and viral seeding. Synergy among these parameters in limiting infection suggests new avenues for inhibiting viral infections by targeting early life cycle bottlenecks.

microbiology↗