Single cell heterogeneity in influenza A virus gene expression shapes the innate antiviral response to infection

Sun, Jiayi; Vera, J. Cristobal; Drnevich, Jenny; Lin, Yen Ting; Ke, Ruian; Brooke, Christopher B.

doi:10.1371/journal.ppat.1008671

Cited by 60 publications

(108 citation statements)

References 59 publications

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“…At the single-cell level, we document extreme heterogeneity in viral mRNA and protein expression, extending the observation of high cell-to-cell variation in IAV replication to circulating human mononuclear phagocytes (Cao et al, 2020;Kudo et al, 2019;Ramos et al, 2019;Russell et al, 2019;Russell et al, 2018;Steuerman et al, 2018;Sun et al, 2020;Wang et al, 2020). We also show, as documented in other cell types, that bystander cells display increased mRNA expression of IFN-stimulated genes relative to infected cells; yet, we additionally observe both an induction of ribosomal gene mRNA expression in IAV-transcribing cells and a down regulation of these genes in bystander cells at later time points.…”

Section: Discussionsupporting

confidence: 64%

“…In the context of influenza, single-cell RNAsequencing (scRNA-seq) allows for the simultaneous capture and characterization of both host and viral transcriptomes as IAV mRNAs are polyadenylated. Using this technology, extreme variation in viral replication has been reported for epithelial cell lines (Russell et al, 2018), a phenomenon that has been replicated in other systems including animal models and patient samples (Cao et al, 2020;Kudo et al, 2019;Ramos et al, 2019;Russell et al, 2019;Russell et al, 2018;Steuerman et al, 2018;Sun et al, 2020;Wang et al, 2020). Importantly, single-cell analyses can distinguish between bystander (exposed but uninfected) and infected cells, allowing the characterization of antiviral states.…”

Section: Introductionmentioning

confidence: 93%

“…Importantly, single-cell analyses can distinguish between bystander (exposed but uninfected) and infected cells, allowing the characterization of antiviral states. Although viral contributions to variation in cellular responses to infection have begun to be probed with this technology (Russell et al, 2019;Sun et al, 2020), how host responses vary between individuals and populations remains unexplored.…”

Section: Introductionmentioning

confidence: 99%

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Heterogeneity of monocyte subsets and susceptibility to influenza virus contribute to inter-population variability of protective immunity

O’Neill

Quach

Pothlichet³

et al. 2020

Preprint

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SUMMARYThere is considerable inter-individual immunological and clinical variability upon influenza A virus (IAV) infection in humans; yet, the factors underlying such heterogeneity remain elusive. Here, using an ex vivo cellular model that captures natural human variation in the transcriptional responses of monocytes to IAV, we find significant differences in viral mRNA levels between individuals of African and European ancestry. Using single cell analyses, we show that the overall number of cells that will ultimately become infected, rather than the amount of viral transcript expression per cell, is the main driver of the higher IAV mRNA levels detected in European cells. Finally, we identify 135 genes, including the interferon-stimulated genes IFITM3, MX1, and OAS3, for which basal mRNA expression levels associate with IAV mRNA levels post infection. Our findings reveal that basal differences in activation of IRF/STAT-induced antiviral mechanisms may contribute to individual and population disparities in susceptibility to IAV infection.

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Section: Discussionsupporting

confidence: 64%

Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Heterogeneity of monocyte subsets and susceptibility to influenza virus contribute to inter-population variability of protective immunity

O’Neill

Quach

Pothlichet³

et al. 2020

Preprint

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“…It is important to point out that the experimental data described here were all collected from aggregate populations of cells and that our models ignore single cell heterogeneity between cells with the same cellular MOI. There is substantial heterogeneity in viral gene expression and progeny production between individual cells infected under similar conditions (24)(25)(26)(27). Timecourse experiments from single poliovirus-infected cells have demonstrated that bulk measurements like those used here can obscure more complex, heterogeneous dynamics that occur at the single cell level (28).…”

Section: Cellular Co-infection Enhances Type III (But Not Type I) Ifnmentioning

confidence: 88%

Cellular co-infection can modulate the efficiency of influenza A virus production and shape the interferon response

Martin

Harris

Sun

et al. 2019

Preprint

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15 During viral infection, the numbers of virions infecting individual cells can vary significantly over 16 time and space. The functional consequences of this variation in cellular multiplicity of infection 49 Cellular co-infection plays an important, yet poorly defined role in shaping the outcome of 50 influenza A virus (IAV) infection. By facilitating reassortment between incoming viral genomes, 51 cellular co-infection can give rise to new viral genotypes with increased fitness or emergence 52 potential (1). Cellular co-infection can also enhance the replicative potential of the virus by 53promoting the complementation and multiplicity reactivation of the semi-infectious particles that 54 constitute the bulk of influenza virus populations (2-4). Despite its clear importance, the 55 prevalence and the specific functional consequences of cellular co-infection during IAV infection 56 remain largely unknown. 57 58We and others have previously shown that cellular co-infection can be common in vivo (5,6). 59There is growing evidence that IAV replication and spread is focal and thus that the distribution 60 of individual virions across cells and tissues is highly spatially structured, resulting in foci of high 61 cellular multiplicity of infection (MOI) (7-10). Given the dynamic distribution of virions over time 62and space during infection, it appears likely that the MOIs of individual infected cells can vary 63 significantly. This raises the question whether this variation in the number of virions that infect a 64 cell has distinct phenotypic consequences. If so, it could have significant implications for 65understanding IAV infection dynamics as two viral populations of identical size and genome 66 sequence could give rise to divergent infection outcomes if the dispersal patterns of virions (and 67 thus the MOI distribution across cells) differs. 68 69Several previous studies have suggested that the number of virions that enter a given cell 70 (referred to throughout as "cellular MOI" or "viral input") may affect replication kinetics and 71 interferon (IFN) induction (11-14); however, the phenotypic consequences of cellular MOI during 72 IAV infection have not yet been rigorously or comprehensively quantified. Here, we combine 73 precise single-cycle infection experiments with statistical model fitting to reveal that cellular MOI 74 significantly alters virus production rates, the host response to infection, and the potential for 75 further superinfection. In doing so, we precisely define the functional forms that govern the 76 relationships between the amount of viral input and the phenotypes of infected cells, information 77 that will aid future efforts to quantitatively model IAV infection. Altogether, these results reveal 78and define an unappreciated role for cellular co-infection in shaping the outcome of IAV infection. 79 80 RESULTS 81To define how variation in cellular MOI affects viral replication dynamics and the host response 82to infection, we infected either MDCK or A549 cells with A/Puerto Rico/8...

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“…It is important to point out that the experimental data described here were all collected from aggregate populations of cells and that our models ignore single cell heterogeneity between cells with the same cellular MOI. There is substantial heterogeneity in viral gene expression and progeny production between individual cells infected under similar conditions [ 24 – 27 ]. Time-course experiments from single poliovirus-infected cells have demonstrated that bulk measurements like those used here can obscure more complex, heterogeneous dynamics that occur at the single cell level [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

Cellular co-infection can modulate the efficiency of influenza A virus production and shape the interferon response

et al. 2020

Self Cite

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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.

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Single cell heterogeneity in influenza A virus gene expression shapes the innate antiviral response to infection

Cited by 60 publications

References 59 publications

Heterogeneity of monocyte subsets and susceptibility to influenza virus contribute to inter-population variability of protective immunity

Heterogeneity of monocyte subsets and susceptibility to influenza virus contribute to inter-population variability of protective immunity

Cellular co-infection can modulate the efficiency of influenza A virus production and shape the interferon response

Cellular co-infection can modulate the efficiency of influenza A virus production and shape the interferon response

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