Pathogen-driven CRISPR screens identify TREX1 as a regulator of DNA self-sensing during influenza virus infection

Host:pathogen interactions dictate the outcome of infection, yet the limitations of current approaches leave large regions of this interface unexplored. Here, we develop a novel fitness-based screen that queries factors important during the middle to late stages of infection. This is achieved by engineering influenza virus to direct the screen by programming dCas9 to modulate host gene expression. Our genome-wide screen for pro-viral factors identifies the cytoplasmic DNA exonuclease TREX1. TREX1 degrades cytoplasmic DNA to prevent inappropriate innate immune activation by self-DNA. We reveal that this same process aids influenza virus replication. Infection triggers release of mitochondrial DNA into the cytoplasm, activating antiviral signaling via cGAS and STING. TREX1 metabolizes the DNA, preventing its sensing. Collectively, these data show that self-DNA is deployed to amplify innate immunity, a process tempered by TREX1. Moreover, they demonstrate the power and generality of pathogen-driven fitness-based screens to pinpoint key host regulators of infection.

Identifying Protein-Protein Interactions by Proximity Biotinylation with AirID and splitAirID

Proteins frequently function in high-order complexes. Defining protein-protein interactions is essential to acquiring a full understanding of their activity and regulation. Proximity biotinylation has emerged as a highly specific approach to capture transient and stable interactions in living cells or organisms. Proximity biotinylation exploits promiscuous biotinylating enzymes fused to a bait protein, resulting in the biotinylation of adjacent endogenous proteins. Biotinylated interactors are purified under very strict conditions and identified by mass spectrometry to obtain a high-confidence list of candidate binding partners. AirID is a recently described biotin ligase specifically engineered for proximity labeling. This protocol details proximity biotinylation by AirID, using protein complexes that form during a type I interferon response as an example. It covers the construction and validation of AirID fusion proteins and the enrichment and identification of biotinylated interactors. We describe a variation on the protocol using splitAirID. In this case, AirID is split into two inactive fragments and ligase activity is only restored upon dimerization of the bait proteins. This permits selective detection of proteins that interact with homo- or heterodimeric forms of the bait. The protocol considers design strategies, optimization, and the properties of different biotin ligases to identify optimal conditions for each experimental question. We also discuss common pitfalls and how to troubleshoot them. These approaches allow proximity biotinylation to be a powerful tool for defining protein interactomes. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Construction and functional validation of AirID fusion proteins Alternate Protocol: Construction and functional validation of splitAirID fusion proteins Support Protocol: Western blot for biotinylated proteins Basic Protocol 2: Biotinylation, enrichment, and identification of protein interactors.

Pathogen-driven CRISPR screens identify TREX1 as a regulator of DNA self-sensing during influenza virus infection

Intracellular pathogens interact with host factors, exploiting those that enhance replication while countering those that suppress it. Genetic screens have begun to define the host:pathogen interface and establish a mechanistic basis for host-directed therapies. Yet, limitations of current approaches leave large regions of this interface unexplored. To uncover host factors with pro-pathogen functions, we developed a novel fitness-based screen that queries factors important during the middle-to-late stages of infection. This was achieved by engineering influenza virus to direct the screen by programing dCas9 to modulate host gene expression. A genome-wide screen identified the cytoplasmic DNA exonuclease TREX1 as a potent pro-viral factor. TREX1 normally degrades cytoplasmic DNA to prevent inappropriate innate immune activation by self DNA. Our mechanistic studies revealed that this same process functions during influenza virus infection to enhance replication. Infection triggered release of mitochondrial DNA into the cytoplasm, activating antiviral signaling via cGAS and STING. TREX1 metabolized the mitochondrial DNA preventing its sensing. Collectively, these data show that self-DNA is deployed to amplify host innate sensing during RNA virus infection, a process tempered by TREX1. Moreover, they demonstrate the power and generality of pathogen driven fitness-based screens to pinpoint key host regulators of intracellular pathogens.

Influenza A virus undergoes compartmentalized replication in vivo dominated by stochastic bottlenecks

Transmission of influenza A viruses (IAV) between hosts is subject to numerous physical and biological barriers that impose genetic bottlenecks, constraining viral diversity and adaptation. The bottlenecks within hosts and their potential impacts on evolutionary pathways taken during infection are poorly understood. To address this, we created highly diverse IAV libraries bearing molecular barcodes on two gene segments, enabling high-resolution tracking and quantification of unique virus lineages within hosts. Here we show that IAV infection in lungs is characterized by multiple within-host bottlenecks that result in “islands” of infection in lung lobes, each with genetically distinct populations. We perform site-specific inoculation of barcoded IAV in the upper respiratory tract of ferrets and track viral diversity as infection spreads to the trachea and lungs. We detect extensive compartmentalization of discrete populations within lung lobes. Bottleneck events and localized replication stochastically sample individual viruses from the upper respiratory tract or the trachea that become the dominant genotype in a particular lobe. These populations are shaped strongly by founder effects, with limited evidence for positive selection. The segregated sites of replication highlight the jackpot-style events that contribute to within-host influenza virus evolution and may account for low rates of intrahost adaptation.

Transmission of influenza A viruses (IAV) between hosts and replication within host impose genetic bottlenecks, constraining viral diversity and adaptation. Here, Amato et al. perform site-specific inoculation of barcoded IAV of ferrets and track viral diversity as infection spreads to the lower respiratory tract and conclude that narrow population bottlenecks are an important feature of the within-host infection dynamics.

418. Comparison of the Abbott SARS-CoV-2 IgG and DiaSorin LIASON SARS-CoV-2 S1/S2 IgG Antibody Assays

Background

The Abbott Laboratories SARS-CoV-2 IgG assay and the DiaSorin LIASON SARS-CoV-2 S1/S2 IgG assay are both chemiluminescent immunoassays that qualitatively detect IgG antibodies against SARS-CoV-2 antigens. The Abbott assay detects IgG against the viral nucleocapsid (N) protein, while the DiaSorin assay uses antigen derived from the viral spike (S) protein. Here we evaluate the performance of these two assays at our institution.

Methods

45 patient samples (serum or plasma) were tested for anti-SARS-CoV-2 IgG by both the Abbott and DiaSorin assays. The samples were previously characterized at a national reference laboratory using the Abbott assay or by an in-house PCR-based test for SARS-CoV-2 RNA. Samples yielding discordant results across platforms were further tested using the EUROIMMUN Anti-SARS-CoV-2 ELISA (IgG) assay at the reference laboratory.

Results

22 samples tested negative for SARS-CoV-2 by the reference lab Abbott assay, and 23 tested positive by the same reference lab test (n = 13) or by an in-house PCR-based test (n = 10). The 22 samples characterized as negative again tested negative by both the Abbott (in-house) and DiaSorin assays (100% NPA). Among the 23 samples characterized as positive, all 23 tested positive by the Abbott assay (100% PPA), while only 15 tested positive by the DiaSorin assay (65% PPA). For each of the 8 discordant cases, samples were further tested by EUROIMMUN assay, which targets the S protein; 7 of the 8 samples tested negative by this assay, in agreement with the DiaSorin test results. Thus, for the discordant cases, testing for IgG against N (in-house and reference lab Abbott assays) gave positive results, while testing for IgG against S (DiaSorin and EUROIMMUN assays) mostly gave negative results.

Conclusion

These findings highlight the importance of the differences between various SARS-CoV-2 antibody tests, and providers should be aware of the specific antigenic target(s) in each test. Selection of a specific assay may depend on the need to assess past exposure to SARS-CoV-2 (for which a nucleocapsid target may be more sensitive) or to detect neutralizing antibodies (for which a spike target may be more relevant). This also has implications for disease surveillance as reliance on anti-spike antibodies alone may underestimate infection prevalence.

Disclosures

All Authors: No reported disclosures

689. Nasal versus Nasopharyngeal Sample Collection for Diagnostic Nucleic Acid Amplification Testing for Influenza A, Influenza B, and Respiratory Syncytial Viruses

Background

Nucleic acid amplification testing (NAAT) for influenza A virus (IAV), influenza B virus (IBV), and respiratory syncytial virus (RSV) is a standard component of diagnosis of infections with these pathogens. At our institution, current standard of practice is to collect nasopharyngeal (NP) samples for such NAAT. In an effort to provide clinicians and patients a simpler, more comfortable sample collection option, we evaluated the use of nasal samples for NAAT, compared to NP samples.

Methods

Both nasal and NP specimens were collected from each of 58 patients seen in our emergency department (January – March 2020). NP samples were collected using minitip FLOQswabs; nasal samples were collected using regular or minitip FLOQswabs. Nasal and NP samples were processed using the same protocol and tested for influenza viruses and RSV using the Cepheid GeneXpert (Xpress Flu/RSV) platform.

Results

In total, 20 NP samples tested negative for virus and 38 tested positive (16 IAV-positive, 14 IBV-positive, 8 RSV-positive). There were 3 cases (5% of total cases) in which qualitative (positive/negative) results from the corresponding nasal samples were not in agreement with results derived from NP samples. These were considered false-negative results; one such discrepancy was resolved upon re-testing the same samples. Overall positive percent agreement between nasal- and NP-derived results was 92% (35/38), and negative percent agreement was 100% (20/20). Among samples testing positive for virus by both NP and nasal sampling methods, we found that the average cycle threshold (Ct) value for IAV detection was 5.1 cycles (n = 16, SEM = 0.83) higher for nasal samples than for NP samples. The average Ct for IBV detection was 3.3 cycles (n = 12, SEM = 1.87) higher for nasal than for NP samples. The average Ct for RSV detection was 1.9 cycles (n = 7, SEM = 1.57) higher for nasal than for NP samples.

Conclusion

These results suggest that recovery of viral RNA from nasal samples is lower than that from nasopharyngeal samples, particularly for influenza viruses. This decreased detection of viral RNA from nasal samples may explain the false-negative results seen in our discrepant cases. These data suggest that a decrease in recovery of viral RNA by nasal sampling may translate to decreased diagnostic accuracy.

Disclosures

All Authors: No reported disclosures

Experimental Approaches to Identify Host Factors Important for Influenza Virus

An ever-expanding toolkit of experimental methods provides the means to discover and characterize host factors important for influenza virus. Here, we describe common methods for investigating genetic relationships and physical interactions between virus and host. A comprehensive knowledge of host:virus interactions is key to understanding how influenza virus exploits the host cell and to potentially identify vulnerabilities that may be manipulated to prevent or treat disease.