The mechanisms and consequences of defective interfering particle (DIP) formation during influenza virus infection remain poorly understood. The development of next-generation sequencing (NGS) technologies has made it possible to identify large numbers of DIP-associated sequences, providing a powerful tool to better understand their biological relevance. However, NGS approaches pose numerous technical challenges, including the precise identification and mapping of deletion junctions in the presence of frequent mutation and base-calling errors, and the potential for numerous experimental and computational artifacts. Here, we detail an Illumina-based sequencing framework and bioinformatics pipeline capable of generating highly accurate and reproducible profiles of DIP-associated junction sequences. We use a combination of simulated and experimental control data sets to optimize pipeline performance and demonstrate the absence of significant artifacts. Finally, we use this optimized pipeline to reveal how the patterns of DIP-associated junction formation differ between different strains and subtypes of influenza A and B viruses and to demonstrate how these data can provide insight into mechanisms of DIP formation. Overall, this work provides a detailed roadmap for high-resolution profiling and analysis of DIP-associated sequences within influenza virus populations. IMPORTANCE Influenza virus defective interfering particles (DIPs) that harbor internal deletions within their genomes occur naturally during infection in humans and during cell culture. They have been hypothesized to influence the pathogenicity of the virus; however, their specific function remains elusive. The accurate detection of DIP-associated deletion junctions is crucial for understanding DIP biology but is complicated by an array of technical issues that can bias or confound results. Here, we demonstrate a combined experimental and computational framework for detecting DIP-associated deletion junctions using next-generation sequencing (NGS). We detail how to validate pipeline performance and provide the bioinformatics pipeline for groups interested in using it. Using this optimized pipeline, we detect hundreds of distinct deletion junctions generated during infection with a diverse panel of influenza viruses and use these data to test a long-standing hypothesis concerning the molecular details of DIP formation.

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Influenza A viruses (IAVs) in swine can cause sporadic infections and pandemic outbreaks among humans, but how avian IAV emerges in swine is still unclear. Unlike domestic swine, feral swine are free ranging and have many opportunities for IAV exposure through contacts with various habitats and animals, including migratory waterfowl, a natural reservoir for IAVs. During the period from 2010 to 2013, 8,239 serum samples were collected from feral swine across 35 U.S. states and tested against 45 contemporary antigenic variants of avian, swine, and human IAVs; of these, 406 (4.9%) samples were IAV antibody positive. Among 294 serum samples selected for antigenic characterization, 271 cross-reacted with ≥1 tested virus, whereas the other 23 did not cross-react with any tested virus. Of the 271 IAV-positive samples, 236 cross-reacted with swine IAVs, 1 with avian IAVs, and 16 with avian and swine IAVs, indicating that feral swine had been exposed to both swine and avian IAVs but predominantly to swine IAVs. Our findings suggest that feral swine could potentially be infected with both avian and swine IAVs, generating novel IAVs by hosting and reassorting IAVs from wild birds and domestic swine and facilitating adaptation of avian IAVs to other hosts, including humans, before their spillover. Continued surveillance to monitor the distribution and antigenic diversities of IAVs in feral swine is necessary to increase our understanding of the natural history of IAVs. There are more than 5 million feral swine distributed across at least 35 states in the United States. In contrast to domestic swine, feral swine are free ranging and have unique opportunities for contact with wildlife, livestock, and their habitats. Our serological results indicate that feral swine in the United States have been exposed to influenza A viruses (IAVs) consistent with those found in both domestic swine and wild birds, with the predominant infections consisting of swine-adapted IAVs. Our findings suggest that feral swine have been infected with IAVs at low levels and could serve as hosts for the generation of novel IAVs at the interface of feral swine, wild birds, domestic swine, and humans.

Gold nanostars, functionalized with thiolated DNA hairpins bearing a Raman-active fluorescent dye at the 3′ terminus, were engineered to identify and quantify RNA mutations in the influenza A virus (IAV) genome employing surface enhanced Raman spectroscopy (SERS). The DNA hairpin structure was designed to selectively extend/fold in the absence/presence of the viral RNA targets, resulting in the fluorophore being brought away from or close to the gold nanostar surface, leading to an "OFF-ON" switching of the SERS signal. Validation of the switchable SERS nanostar probes was first carried out in buffer, showing that the detection is sequencespecific and that the high sensitivity provided by these SERS probes allows target detection at the single particle level. We also demonstrate that the degree of signal recovery can be closely correlated with the number of genetic mutations. Further experiments carried out with HeLa cell lysate spiked with RNA oligonucleotides demonstrate that the functionality of these nanoprobes were not detrimentally affected by the complex matrix. As a proof of concept, we also tested these nanoparticle probes in vitro by specifically targeting the hemagglutinin (HA) segment in live HeLa cells transfected with plasmids coding for either HA or two other IAV segments, PB1 and PB2, as negative controls. The intracellular SERS response in individual transfected HeLa cells demonstrates high sequenceselectivity of the probes for the HA segment, suggesting the applicability of these probes for multiplexed detection and quantification of viral RNAs in individual cells with an approach that can account for the viral population diversity. This also represents the first time that molecular beacon-based SERS probes have been employed to detect viral RNA target in intact individual cells.

During viral infection, the numbers of virions infecting individual cells can vary significantly over time and space. The functional consequences of this variation in cellular multiplicity of infection (MOI) remain poorly understood. Here, we rigorously quantify the phenotypic consequences of cellular MOI during influenza A virus (IAV) infection over a single round of replication in terms of cell death rates, viral output kinetics, interferon and antiviral effector gene transcription, and superinfection potential. By statistically fitting mathematical models to our data, we precisely define specific functional forms that quantitatively describe the modulation of these phenotypes by MOI at the single cell level. To determine the generality of these functional forms, we compare two distinct cell lines (MDCK cells and A549 cells), both infected with the H1N1 strain A/Puerto Rico/8/1934 (PR8). We find that a model assuming that infected cell death rates are independent of cellular MOI best fits the experimental data in both cell lines. We further observe that a model in which the rate and efficiency of virus production increase with cellular co-infection best fits our observations in MDCK cells, but not in A549 cells. In A549 cells, we also find that induction of type III interferon, but not type I interferon, is highly dependent on cellular MOI, especially at early timepoints. This finding identifies a role for cellular co-infection in shaping the innate immune response to IAV infection. Finally, we show that higher cellular MOI is associated with more potent superinfection exclusion, thus limiting the total number of virions capable of infecting a cell. Overall, this study suggests that the extent of cellular co-infection by influenza viruses may be a critical determinant of both viral production kinetics and cellular infection outcomes in a host cell type-dependent manner.

Influenza A viruses can infect a wide variety of animal species and, occasionally, humans. Infection occurs through the binding formed by viral surface glycoprotein hemagglutinin and certain types of glycan receptors on host cell membranes. Studies have shown that the α2,3-linked sialic acid motif (SA2,3Gal) in avian, equine, and canine species; the α2,6-linked sialic acid motif (SA2,6Gal) in humans; and SA2,3Gal and SA2,6Gal in swine are responsible for the corresponding host tropisms. However, more detailed and refined substructures that determine host tropisms are still not clear. Thus, in this study, we applied association mining on a set of glycan microarray data for 211 influenza viruses from five host groups: humans, swine, canine, migratory waterfowl, and terrestrial birds. The results suggest that besides Neu5Acα2–6Galβ, human-origin viruses could bind glycans with Neu5Acα2–8Neu5Acα2–8Neu5Ac and Neu5Gcα2–6Galβ1–4GlcNAc substructures; Galβ and GlcNAcβ terminal substructures, without sialic acid branches, were associated with the binding of human-, swine-, and avian-origin viruses; sulfated Neu5Acα2–3 substructures were associated with the binding of human- and swine-origin viruses. Finally, through three-dimensional structure characterization, we revealed that the role of glycan chain shapes is more important than that of torsion angles or of overall structural similarities in virus host tropisms.

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