How Computational Models Enable Mechanistic Insights into Virus Infection

Preexisting cell state rather than stochastic noise confers high or low infection susceptibility of human lung epithelial cells to adenovirus

Viruses display large variability across all stages of their life cycle, including entry, gene expression, replication, assembly, and egress. We previously reported that the immediate early adenovirus (AdV) E1A transcripts accumulate in human lung epithelial A549 cancer cells with high variability, mostly independent of the number of incoming viral genomes, but somewhat correlated to the cell cycle state at the time of inoculation. Here, we leveraged the classical Luria-Delbrück fluctuation analysis to address whether infection variability primarily arises from the cell state or stochastic noise. The E1A expression was measured by the expression of green fluorescent protein (GFP) from the endogenous E1A promoter in AdV-C5_E1A-FS2A-GFP and found to be highly correlated with the viral plaque formation, indicating reliability of the reporter virus. As an ensemble, randomly picked clonal A549 cell isolates displayed significantly higher coefficients of variation in the E1A expression than technical noise, indicating a phenotypic variability larger than noise. The underlying cell state determining infection variability was maintained for at least 9 weeks of cell cultivation. Our results indicate that preexisting cell states tune adenovirus infection in favor of the cell or the virus. These findings have implications for antiviral strategies and gene therapy applications.IMPORTANCEViral infections are known for their variability. Underlying mechanisms are still incompletely understood but have been associated with particular cell states, for example, the eukaryotic cell division cycle in DNA virus infections. A cell state is the collective of biochemical, morphological, and contextual features owing to particular conditions or at random. It affects how intrinsic or extrinsic cues trigger a response, such as cell division or anti-viral state. Here, we provide evidence that cell states with a built-in memory confer high or low susceptibility of clonal human epithelial cells to adenovirus infection. Results are reminiscent of the Luria-Delbrück fluctuation test with bacteriophage infections back in 1943, which demonstrated that mutations, in the absence of selective pressure prior to infection, cause infection resistance rather than being a consequence of infection. Our findings of dynamic cell states conferring adenovirus infection susceptibility uncover new challenges for the prediction and treatment of viral infections.

Machine learning for cross-scale microscopy of viruses

Despite advances in virological sciences and antiviral research, viruses continue to emerge, circulate, and threaten public health. We still lack a comprehensive understanding of how cells and individuals remain susceptible to infectious agents. This deficiency is in part due to the complexity of viruses, including the cell states controlling virus-host interactions. Microscopy samples distinct cellular infection stages in a multi-parametric, time-resolved manner at molecular resolution and is increasingly enhanced by machine learning and deep learning. Here we discuss how state-of-the-art artificial intelligence (AI) augments light and electron microscopy and advances virological research of cells. We describe current procedures for image denoising, object segmentation, tracking, classification, and super-resolution and showcase examples of how AI has improved the acquisition and analyses of microscopy data. The power of AI-enhanced microscopy will continue to help unravel virus infection mechanisms, develop antiviral agents, and improve viral vectors.

Editorial: Physical Virology and the Nature of Virus Infections

Virus particles, 'virions', range in size from nano-scale to micro-scale. They have many different shapes and are composed of proteins, sugars, nucleic acids, lipids, water and solutes. Virions are autonomous entities and affect all forms of life in a parasitic relationship. They infect prokaryotic and eukaryotic cells. The physical properties of virions are tuned to the way they interact with cells. When virions interact with cells, they gain huge complexity and give rise to an infected cell, also known as 'virus'. Virion-cell interactions entail the processes of entry, replication and assembly, as well as egress from the infected cell. Collectively, these steps can result in progeny virions, which is a productive infection, or in silencing of the virus, an abortive or latent infection. This book explores facets of the physical nature of virions and viruses and the impact of mechanical properties on infection processes at the cellular and subcellular levels.

Adenovirus Entry: From Infection to Immunity

More than 80 different adenovirus (AdV) types infect humans through the respiratory, ocular, or gastrointestinal tracts. They cause acute clinical mani-festations or persist under humoral and cell-based immunity. Immuno-suppressed individuals are at risk of death from an AdV infection. Concepts about cell entry of AdV build on strong foundations from molecular and cellular biology-and increasingly physical virology. Here, we discuss how virions enter and deliver their genome into the nucleus of epithelial cells. This process breaks open the virion at distinct sites because the particle has nonisometric mechanical strength and reacts to specific host factors along the entry pathway. We further describe how macrophages and dendritic cells resist AdV infection yet enhance productive entry into polarized epithelial cells. A deep understanding of the viral mechanisms and cell biological and biophysical principles will continue to unravel how epithelial and antigen-presenting cells respond to AdVs and control inflammation and persistence in pathology and therapy.