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Synopsis Viral evolution unfolds across nested layers of adaptation, much like a set of Matryoshka dolls. The outermost, well-studied layer involves interactions between viruses and their hosts—where immune evasion, cross-species transmission, and long-term coevolution drive viral diversification. Yet, hidden within this framework is an often-overlooked inner layer: the coevolution of viruses with their own molecular parasites, defective interfering (DI) particles, and defective viral genomes (DVGs). These molecular parasites exploit viral replication machinery, reshaping infection dynamics and imposing selective pressures that influence viral fitness, transmission, and persistence. This perspective synthesizes evidence from experimental evolution, mathematical modeling, and molecular virology to propose a more integrated view of viral evolution. By framing host–virus interactions and virus-DI particle dynamics within a unified evolutionary framework, we highlight the underappreciated role of DI particles as evolutionary players, not just aberrant byproducts. Recognizing these internal layers of viral evolution may inform the development of antiviral strategies and broader questions in host–pathogen coevolution. 

Abstract Deletions are prevalent in the genomes of SARS-CoV-2 isolates from COVID-19 patients, but their roles in the severity, transmission, and persistence of disease are poorly understood. Millions of COVID-19 swab samples from patients have been sequenced and made available online, offering an unprecedented opportunity to study such deletions. Multiplex PCR-based amplicon sequencing (amplicon-seq) has been the most widely used method for sequencing clinical COVID-19 samples. However, existing bioinformatics methods applied to negative control samples sequenced by multiplex-PCR sequencing often yield large numbers of false-positive deletions. We found that these false positives commonly occur in short alignments, at low frequency and depth, and near primer-binding sites used for whole-genome amplification. To address this issue, we developed a filtering strategy, validated with positive control samples containing a known deletion. Our strategy accurately detected the known deletion and removed more than 99% of false positives. This method, applied to public COVID-19 swab data, revealed that deletions occurring independently of transcription regulatory sequences were about 20-fold less common than previously reported; however, they remain more frequent in symptomatic patients. Our optimized approach should enhance the reliability of SARS-CoV-2 deletion characterization from surveillance studies. Finally, our approach may guide the development of more reliable bioinformatics pipelines for genome sequence analyses of other viruses. 

Abstract Viruses persist in nature owing to their extreme genetic heterogeneity and large –population sizes, which enable them to evade host immune defenses, escape anti-viral drugs, and adapt to new hosts. The persistence of viruses is challenging to study because mutations affect multiple virus genes, interactions among genes in their impacts on virus growth are seldom known, and measures of viral fitness have yet to be standardized. To address these challenges, we employed a data-driven computational model of cell infection by a virus. The infection model accounted for the kinetics of viral gene expression, functional gene-gene interactions, genome replication, and allocation of host cellular resources to produce progeny of vesicular stomatitis virus (VSV), a prototype RNA virus. We used this model to computationally probe how interactions among genes carrying up to 11 deleterious mutations affect different measures of virus fitness: single-cycle growth yields and multi-cycle rates of infection spread. Individual mutations were implemented by perturbing biophysical parameters associated with individual gene functions of the wild-type model. Our analysis revealed synergistic epistasis among deleterious mutations in their effects on virus yield; so adverse effects of single deleterious mutations were amplified by interaction. For the same mutations, multi-cycle infection spread indicated weak or negligible epistasis, where single mutations act alone in their effects on infection spread. These results were robust to simulation under high and low host resource environments. Our work highlights how different types and magnitudes of epistasis can arise for genetically identical virus variants, depending on the fitness measure. More broadly, gene-gene interactions can differently affect how viruses grow and spread.