Abstract:Influenza A virus (IAV) and respiratory syncytial virus (RSV) are common recurrent respiratory infectants that show a relatively high coincidence. We demonstrated that preinfection with RSV partitions the cell population into a subpopulation susceptible to subsequent infection with IAV and an IAV-proof subpopulation.
“…Experiments with samples below the limit of detection as well as simulation results with no IFN are marked with crosses. For MOIs of 0.01 and 0.1 we used data from Czerkies et al (2022, see Figure 1A therein). Appendix Figure S2. Viral infection triggers an innate immune signaling cascade. A, B RSV activates multiple components of the innate immune signaling. Western blot analysis of A549 cells infected with RSV at an MOI of 0.1 (A, B) is juxtaposed with model simulations (B).…”
Section: Appendix Figure S1mentioning
confidence: 99%
“…Western blot analysis of A549 cells infected with RSV at an MOI of 0.1 (A, B) is juxtaposed with model simulations (B). Western blot data originates from Czerkies et al (2022; see Figure 1D therein). Both the IFNAR1 KO and IFNAR1–INFLR1 double KO cell lines were simulated by disabling the forward transition of . C, D Activation of STAT1/2 attenuates the spread of RSV infection.…”
Section: Appendix Figure S1mentioning
confidence: 99%
“…Western blot analysis (A, B) is juxtaposed with model simulations (B). For MOIs 0.1, 1 we use experimental data shown in Czerkies et al (2022; as a part of Figure 2C therein). C Viral load in RSV infection without or with prior IFNβ pre-stimulation (1000 U/ml). Digital PCR results for A549 cells infected with RSV at MOIs of 0.01, 0.1, and 1 (black) are juxtaposed with model predictions (red).…”
Section: Appendix Figure S1mentioning
confidence: 99%
“…Upon viral infection, these cells communicate through interferons of type I (IFNβ) and type III (IFNλ) (Czerkies et al, 2022). By analyzing results from cell population-level techniques (Western blot, ELISA, dPCR), we delineated the structure and constrained kinetic rates of the regulatory network.…”
Section: Introductionmentioning
confidence: 99%
“…The model was developed and calibrated based on an array of our experiments on epithelial cells of respiratory origin (A549 cell line) infected with RSV, the most common pathogen in severe respiratory disease in both young children and the elderly (Crowe, 2014; Falsey et al , 2005; Scheltema et al , 2017). Upon viral infection, these cells communicate through interferons of type I (IFNβ) and type III (IFNλ) (Czerkies et al , 2022). By analyzing results from cell population-level techniques (Western blot, ELISA, dPCR), we delineated the structure and constrained kinetic rates of the regulatory network.…”
When infected with a virus, cells may secrete interferons (IFNs) that prompt nearby cells to prepare for upcoming infection. Reciprocally, viral proteins often interfere with IFN synthesis and IFN-induced signaling. We modeled the crosstalk between the propagating virus and the innate immune response using an agent-based stochastic approach. By analyzing immunofluorescence microscopy images we observed that the mutual antagonism between the respiratory syncytial virus (RSV) and infected A549 cells leads to dichotomous responses at the single-cell level and complex spatial patterns of cell signaling states. Our analysis indicates that RSV blocks innate responses at three levels: by inhibition of IRF3 activation, inhibition of IFN synthesis, and inhibition of STAT1/2 activation. In turn, proteins coded by IFN-stimulated (STAT1/2-activated) genes inhibit the synthesis of viral RNA and viral proteins. The striking consequence of these inhibitions is lack of coincidence of viral proteins and IFN expression within single cells. The model enables investigation of the impact of immunostimulatory defective viral particles and signaling network perturbations that could potentially facilitate containment or clearance of the viral infection.
“…Experiments with samples below the limit of detection as well as simulation results with no IFN are marked with crosses. For MOIs of 0.01 and 0.1 we used data from Czerkies et al (2022, see Figure 1A therein). Appendix Figure S2. Viral infection triggers an innate immune signaling cascade. A, B RSV activates multiple components of the innate immune signaling. Western blot analysis of A549 cells infected with RSV at an MOI of 0.1 (A, B) is juxtaposed with model simulations (B).…”
Section: Appendix Figure S1mentioning
confidence: 99%
“…Western blot analysis of A549 cells infected with RSV at an MOI of 0.1 (A, B) is juxtaposed with model simulations (B). Western blot data originates from Czerkies et al (2022; see Figure 1D therein). Both the IFNAR1 KO and IFNAR1–INFLR1 double KO cell lines were simulated by disabling the forward transition of . C, D Activation of STAT1/2 attenuates the spread of RSV infection.…”
Section: Appendix Figure S1mentioning
confidence: 99%
“…Western blot analysis (A, B) is juxtaposed with model simulations (B). For MOIs 0.1, 1 we use experimental data shown in Czerkies et al (2022; as a part of Figure 2C therein). C Viral load in RSV infection without or with prior IFNβ pre-stimulation (1000 U/ml). Digital PCR results for A549 cells infected with RSV at MOIs of 0.01, 0.1, and 1 (black) are juxtaposed with model predictions (red).…”
Section: Appendix Figure S1mentioning
confidence: 99%
“…Upon viral infection, these cells communicate through interferons of type I (IFNβ) and type III (IFNλ) (Czerkies et al, 2022). By analyzing results from cell population-level techniques (Western blot, ELISA, dPCR), we delineated the structure and constrained kinetic rates of the regulatory network.…”
Section: Introductionmentioning
confidence: 99%
“…The model was developed and calibrated based on an array of our experiments on epithelial cells of respiratory origin (A549 cell line) infected with RSV, the most common pathogen in severe respiratory disease in both young children and the elderly (Crowe, 2014; Falsey et al , 2005; Scheltema et al , 2017). Upon viral infection, these cells communicate through interferons of type I (IFNβ) and type III (IFNλ) (Czerkies et al , 2022). By analyzing results from cell population-level techniques (Western blot, ELISA, dPCR), we delineated the structure and constrained kinetic rates of the regulatory network.…”
When infected with a virus, cells may secrete interferons (IFNs) that prompt nearby cells to prepare for upcoming infection. Reciprocally, viral proteins often interfere with IFN synthesis and IFN-induced signaling. We modeled the crosstalk between the propagating virus and the innate immune response using an agent-based stochastic approach. By analyzing immunofluorescence microscopy images we observed that the mutual antagonism between the respiratory syncytial virus (RSV) and infected A549 cells leads to dichotomous responses at the single-cell level and complex spatial patterns of cell signaling states. Our analysis indicates that RSV blocks innate responses at three levels: by inhibition of IRF3 activation, inhibition of IFN synthesis, and inhibition of STAT1/2 activation. In turn, proteins coded by IFN-stimulated (STAT1/2-activated) genes inhibit the synthesis of viral RNA and viral proteins. The striking consequence of these inhibitions is lack of coincidence of viral proteins and IFN expression within single cells. The model enables investigation of the impact of immunostimulatory defective viral particles and signaling network perturbations that could potentially facilitate containment or clearance of the viral infection.
Objectives
The concurrent impact of COVID-19 and influenza on disease burden is a topic of great concern. This discussion delves into the epidemiological characteristics of seasonal influenza activity in Shanghai within the context of the SARS-CoV-2 epidemic.
Methods
From 2017 to 2023, a total of 11,081 patients having influenza-like illness (ILI) were included in this study for influenza virus detection. Reverse transcription polymerase chain reaction (RT-PCR) assays were conducted according to standardised protocols to identify the types and subtypes of influenza viruses. The positivity rate of the influenza virus among the sampled ILI cases served as a surrogate measure for estimating various influenza seasonal characteristics, such as periodicity, duration, peak occurrences, and the prevalent subtypes or lineages. Epidemiological aspects across different years and age groups were subjected to comprehensive analysis. For categorical variables, the Chi-square test or Fisher's exact test was employed, as deemed appropriate.
Results
A total of 1553 (14.0%) tested positive for influenza virus pathogens. The highest positivity rate for influenza was observed in adults aged 25–59 years (18.8%), while the lowest rate was recorded in children under 5 years (3.8%). The influenza circulation patterns in Shanghai were characterised: (1) 2 years exhibited semiannual periodicity (2017–2018, 2022–2023); (2) 3 years displayed annual periodicity (2018–2019, 2019–2020, and 2021–2022); and (3) during 2020–2021, epidemic periodicities of seasonal influenza viruses disappeared. In terms of influenza subtypes, four subtypes were identified during 2017–2018. In 2018–2019 and 2019–2020, A/H3N2, A/H1N1, and B/Victoria were circulating. Notably, one case of B/Victoria was detected in 2020–2021. The epidemic period of 2021–2022 was attributed to B/Victoria, and during 2022–2023, the influenza A virus was the dominant circulating strain.
Conclusions
The seasonal epidemic period and the predominant subtype/lineage of influenza viruses around the SARS-CoV-2 epidemic period in Shanghai city are complex. This underscores the necessity for vigilant influenza control strategies amidst the backdrop of other respiratory virus pandemics.
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