Biology subjects

Nielsen, B. F.

Publications and source records attributed to Nielsen, B. F..

9 recordsLinked to original sources

bioRxiv2026

Defective Viral Genomes Reveal History-dependent Self-regulation of Type I Interferon Dynamics

Type I interferons (IFN) help clear a viral infection, but IFN levels can then decline to reset the antiviral state and avoid innate immune dysregulation. Such dysregulation can occur following infection with highly pathogenic and pandemic RNA viruses, such as H5N1 influenza A viruses or SARS-CoV-2. In spite of the strong innate immune response that these viruses induce, pandemic viruses replicate and spread successfully, suggesting that they can circumvent the effects of IFN. Innate immune activation is triggered when host cells detect viral RNA, especially defective viral genomes (DVGs). A strong IFN response (e.g. to DVGs or a highly immunogenic wild-type infection) must then decline through negative feedback involving the IFN receptor. How DVGs modulate this feedback remains poorly understood. To quantify positive and negative IFN regulation, we first performed in vitro experiments combining different doses of viral RNA and exogenous IFN, and measured subsequent IFN expression. We next developed an in vivo Bayesian computational model that captures the impact of influenza virus DVGs on both positive and negative IFN feedback. Competition between DVGs and virus is also considered. Finally, we calibrated this model on published in vitro and in vivo influenza virus infection data. Our model suggests that the cumulative IFN exposure of a host cell, rather than the instantaneous IFN level, shapes further IFN induction; this gives DVGs a dual role: beyond triggering IFN induction, they can also lower it through negative feedback. Our model further predicts that these effects are important locally, shaping the spatial dynamics of the response, allowing suppression of innate immune responses in neighbouring, uninfected cells. Through this model, we can explain two important biological observations: i) IFN levels can decline while virus and DVG levels continue to rise in tissue culture and mouse infections; and ii) pandemic viruses that produce high levels of immunogenic RNA molecules can successfully replicate through local innate immune suppression. Overall, we propose that heterogeneity within viral populations (including DVGs and other noncanonical viral RNAs) can modulate positive and negative feedback loops and shape infection outcomes. More broadly, our work may support studies into the therapeutic use of these viral RNA populations.

microbiology↗

bioRxiv2026

Long-range seeding drives exponential growth in early respiratory viral infection

Respiratory viruses spread within the host through both local expansion and occasional long-range dissemination that seeds new infection foci. We present LEAP, an analytically tractable within-host model that captures this two-scale process by coupling local plaque growth to long-range seeding. The model reduces to an age-dependent branching process and yields a closed-form expression for the exponential growth rate during early infection. Using empirical data to parametrize the model, we find that productive dissemination requires only a small number of successful long-range seeding events per infected cell, with distinct values for SARS-CoV-2 and influenza A virus. LEAP further predicts that, in these well-adapted viruses, interferon-mediated restriction only weakly affects exponential growth, while remaining decisive for poorly adapted ones. More broadly, the model provides a flexible framework for experimentally testable predictions of early infection dynamics. Significance StatementRespiratory viral infection is an inherently spatial process, in which the virus must colonize large areas of the airways to optimize reproduction. Recent studies in animal models infected with influenza A or SARS-CoV-2 have documented long-range stochastic jumps of viral populations between distant regions of the respiratory tract. The emerging picture is one of two co-occurring spatial processes: slow, local plaque expansion and long-range seeding events that are rare but crucial to rapid colonization of the airways. We introduce LEAP (Lotka-Euler Airway Pathogen model), a simple mathematical within-host model that captures these two-scale dynamics by coupling local plaque growth to stochastic long-range seeding. Using LEAP, measurements from the petri dish can directly produce predictions of infection dynamics in the body.

immunology↗

bioRxiv2026

Stochasticity in viral infection and host response: A competition between speed and reliability

The early stages of viral infection constitute a race between viral proliferation and interferon (IFN)-mediated defenses. Recent experiments on single-cell viral kinetics have demonstrated a high degree of stochasticity in the timing of viral release, but how this shapes the competition between virus and host remains unclear. We formulate a stochastic spatial model to address the question of how variability in the release of viral progeny and IFN affect the early infection dynamics. The model distinguishes between two types of timing noise: stochasticity in the initiation of release, and variability in the secretion time of individual virions. Our key result is an asymmetry in how noise affects outcomes: For the virus, stochastic initiation accelerates expansion, while for the host, effective containment via IFN benefits from precisely timed responses. For the secreting states, we find that a broader secretion profile (higher variability in particle release times) is always advantageous. In all cases, we find that stochasticity in signal timing plays a huge/central role in the early infections states.

immunology↗

bioRxiv2025

Spatio-temporal modelling of in vitro viral infection: the impact of defective interfering particles on type I interferon response

Defective interfering particles (DIPs) are incomplete viral genomes that modulate infection by competing with wild-type viruses and activating innate immunity. How DIPs interact with type I interferon (IFN) in spatially structured environments remains unclear. Focusing initially on influenza A, we developed a spatially explicit, stochastic model of in vitro viral infection integrating virus and DIP replication, IFN signalling, and alternative dispersal modes. We find that: (1) our model captures the ring-like and patchy plaque morphologies observed experimentally; (2) IFN production peaks at an intermediate DIP ratio, reflecting a trade-off between early immune activation and sufficient co-infection; and (3) even a small fraction of long-range spread of virus and DIPs escape containment despite longer IFN ranges; this causes stronger antiviral responses but earlier peaks in lysis at similar levels of cell loss. The model is available as an interactive platform: https://shiny-spatial-infection-app-production.up.railway.app/.

systems biology↗

bioRxiv2025

Susceptible host dynamics explain pathogen resilience to perturbations

Interventions to slow the spread of SARS-CoV-2 significantly disrupted the transmission of other pathogens. As interventions lifted, whether and when human pathogens would eventually return to their pre-pandemic dynamics remains to be answered. Here, we present a framework for estimating pathogen resilience based on how fast epidemic patterns return to their pre-pandemic dynamics. By analyzing time series data from Hong Kong, Canada, Korea, and the US, we quantify the resilience of common respiratory pathogens and further predict when each pathogen will eventually return to its pre-pandemic dynamics. Our predictions are able to distinguish which pathogens should have returned already, and deviations from these predictions reveal long-term impacts of pandemic perturbations. We find a faster rate of susceptible replenishment underlies pathogen resilience and sensitivity to both large and small perturbations. Overall, our analysis highlights the persistent nature of common respiratory pathogens compared to vaccine-preventable infections, such as measles. Significance StatementCOVID-19 interventions slowed the transmission of many respiratory pathogens in different ways, raising questions about the mechanisms driving the variation in responses to interventions. To address this gap, we characterized the sensitivity of pathogen transmission to perturbations by quantifying how fast each pathogen returned to its pre-pandemic circulation patterns. We analyzed data from Hong Kong, Canada, Korea, and the US, and showed that common respiratory pathogens are far less sensitive to perturbations than measles, a vaccine-preventable infection. Finally, we showed that the speed of replenishment of the susceptible population--for example, through waning immunity--largely determines this sensitivity, suggesting that the persistence of common respiratory pathogens is likely driven by rapid susceptible replenishment.

ecology↗

bioRxiv2024

Eco-evolutionary dynamics of pathogen immune-escape: deriving a population-level phylodynamic curve

The phylodynamic curve [1] conceptualizes how immunity shapes the rate of viral adaptation in a non-monotonic fashion, through its opposing effects on viral abundance and the strength of selection. However, concrete and quantitative model realizations of this influential concept are rare. Here, we present an analytic, stochastic framework in which a population-scale phylodynamic curve emerges dynamically, allowing us to address questions regarding the risk and timing of emergence of viral immune escape variants. We explore how pathogen- and population-specific parameters such as strength of immunity, transmissibility and antigenic constraints affect the phylodynamic curve, leading to distinct phylodynamic curves for different pathogens. Motivated by the COVID-19 pandemic, we probe the likely effects of non-pharmaceutical interventions (NPIs), and the lifting thereof, on the risk of viral escape variant emergence. Looking ahead, the framework has the potential to become a useful tool for probing how natural immunity, as well as choices in vaccine design and distribution and the implementation of NPIs affect the evolution of common viral pathogens.

ecology↗

bioRxiv2024

One Hundred Years of Influenza A Evolution

Leveraging the simplicity of raw nucleotide distances, we provide an intuitive window into the evolution of the human influenza A nonstructural (NS) gene. In an analysis suggested by the eminent Danish biologist Freddy B. Christiansen, we illustrate the existence of a continuous genetic "backbone" of influenza A NS genes, steadily increasing in distance to the 1918 root over more than a century. Interestingly, the 2009 influenza pandemic represents a clear departure from this enduring genetic backbone. Utilizing nucleotide distance maps and phylogenetic analyses, we illustrate remaining uncertainties regarding the origin of the 2009 pandemic, highlighting the complexity of influenza evolution. The NS gene is interesting precisely because it experiences neutral genetic drift over long periods of time time, while sudden deviations from this drift pattern can indicate changes in other genes via the hitchhiking effect. Our approach employs two measures based on genotypic distance -- the rooted temporal Hamming map and the unrooted temporal Hamming distribution -- to analyze the evolutionary dynamics of the NS gene. The rooted Hamming map elucidates distances between a reference sequence and all other sequences over time. In contrast, the unrooted temporal Hamming distribution captures the distribution of genotypic distances between simultaneously circulating viruses, thereby revealing patterns of sequence diversity and epi-evolutionary dynamics. Our study aims to supplement traditional tree-based phylogenetic inference with these direct temporal distance-based measures, offering transparent insights into the evolution of the influenza NS gene.

evolutionary biology↗

bioRxiv2023

Self-inhibiting percolation and viral spreading in epithelial tissue

SARS-CoV-2 induces delayed type-I/III interferon production, allowing it to escape the early innate immune response. The delay has been attributed to a deficiency in the ability of cells to sense viral replication upon infection, which in turn hampers activation of the antiviral state in bystander cells. Here, we introduce a cellular automaton model to investigate the spatiotemporal spreading of viral infection as a function of virus and host-dependent parameters. The model suggests that the considerable person-to-person heterogeneity in SARS-CoV-2 infections is a consequence of high sensitivity to slight variations in biological parameters near a critical threshold. It further suggests that within-host viral proliferation can be curtailed by the presence of remarkably few cells that are primed for IFN production. Thus the observed heterogeneity in defense readiness of cells reflects a remarkably cost-efficient strategy for protection.

systems biology↗

bioRxiv2019

Model to link cell shape and polarity with organogenesis

How do tubes -- gut or neural tube -- form from flat sheets of polarized cells? The prevalent view is that it is a two-step process: first cells wedge to bend the sheet, then cells intercalate and extend the initial invagination into a tube. We computationally challenged this model by asking if one mechanism (either cell wedging or intercalation) may suffice for the entire sheet-to-tube transition. Using a physical model with epithelial cells represented by polarized point particles, we show that either cell intercalation or wedging alone can be sufficient and each can both bend the sheet and extend the tube. When working in parallel, the two mechanisms increase the robustness of the tube formation. The successful simulations of Drosophila salivary gland, Sea urchin gastrulation and mammalian neurulation support the generality of our results.

developmental biology↗