Viral–bacterial interactions in the respiratory tract

Bellinghausen, Carla; Rohde, Gernot; Savelkoul, Paul H. M.; Wouters, Emiel F.M.; Stassen, Frank R. M.

doi:10.1099/jgv.0.000627

Cited by 60 publications

(52 citation statements)

References 153 publications

(137 reference statements)

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“…Respiratory viruses can interact with each other or bacteria affecting predisposition to severe respiratory disease, particularly in patients with underlying immunodeficiency or chronic respiratory disease such as chronic obstructive pulmonary disease or cystic fibrosis [112,113]. The presence of influenza or other community-acquired viruses can compromise physical and immunologic barriers and increase the likelihood of secondary bacterial infection [112].…”

Section: Clinical Characteristicsmentioning

confidence: 99%

Epidemiology and Clinical Characteristics of Influenza C Virus

Sederdahl

Williams

2020

Viruses

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Influenza C virus (ICV) is a common yet under-recognized cause of acute respiratory illness. ICV seropositivity has been found to be as high as 90% by 7-10 years of age, suggesting that most people are exposed to ICV at least once during childhood. Due to difficulty detecting ICV by cell culture, epidemiologic studies of ICV likely have underestimated the burden of ICV infection and disease. Recent development of highly sensitive RT-PCR has facilitated epidemiologic studies that provide further insights into the prevalence, seasonality, and course of ICV infection. In this review, we summarize the epidemiology and clinical characteristics of ICV.

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Section: Clinical Characteristicsmentioning

confidence: 99%

Epidemiology and Clinical Characteristics of Influenza C Virus

Sederdahl

Williams

2020

Viruses

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“…Antecedent bacterial infections prior to influenza have not been well studied. However, a wealth of knowledge exists about the viral, bacterial, and host responses that affect bacterial invasion and the development of pneumonia in influenza‐infected hosts . Multiple studies indicate immune exacerbation as a key driver of coinfection severity.…”

Section: Influenza‐bacteria Coinfection Kineticsmentioning

confidence: 99%

“…Multiple studies indicate immune exacerbation as a key driver of coinfection severity. A plethora of immune responses, including MΦs, neutrophils, NK cells, T cells, B cells, and various cytokines and chemokines, are altered during influenza virus infection and/or during bacterial coinfection . The varying time scales and interconnectedness of host responses has made establishing the contribution and regulation of each factor complicated.…”

Section: Influenza‐bacteria Coinfection Kineticsmentioning

confidence: 99%

“…Throughout influenza virus infection, epithelial cells are infected and die, and inflammation accumulates as host immune responses work to halt virus spread. As lung tissue becomes injured and the host immune response weakens, bacterial pathogens readily invade and cause pneumonia . Heightened lethality occurs when bacteria invade during the virus resolution phase with the maximum synergistic effect at 7 days post‐influenza …”

Section: Influenza‐bacteria Coinfection Kineticsmentioning

confidence: 99%

“…Two or more pathogens can interact in ways that are not intuitive with numerous alterations occurring on varying time scales. Furthermore, different viral and/or bacterial strains, initial doses, timings of the secondary insult, and host immune status can result in distinct infection kinetics and disease outcomes . Examining every scenario and detailing different regulatory mechanisms is challenging even with animal models that can recapitulate many aspects of clinical diseases.…”

Section: Introductionmentioning

confidence: 99%

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Host‐pathogen kinetics during influenza infection and coinfection: insights from predictive modeling

Smith

2018

Immunological Reviews

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SummaryInfluenza virus infections are a leading cause of morbidity and mortality worldwide. This is due in part to the continual emergence of new viral variants and to synergistic interactions with other viruses and bacteria. There is a lack of understanding about how host responses work to control the infection and how other pathogens capitalize on the altered immune state. The complexity of multi‐pathogen infections makes dissecting contributing mechanisms, which may be non‐linear and occur on different time scales, challenging. Fortunately, mathematical models have been able to uncover infection control mechanisms, establish regulatory feedbacks, connect mechanisms across time scales, and determine the processes that dictate different disease outcomes. These models have tested existing hypotheses and generated new hypotheses, some of which have been subsequently tested and validated in the laboratory. They have been particularly a key in studying influenza‐bacteria coinfections and will be undoubtedly be useful in examining the interplay between influenza virus and other viruses. Here, I review recent advances in modeling influenza‐related infections, the novel biological insight that has been gained through modeling, the importance of model‐driven experimental design, and future directions of the field.

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Whole transcriptome analysis of high and low IFN‐α producers reveals differential response patterns following rhinovirus stimulation

et al. 2021

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Objectives Viral respiratory infections cause considerable morbidity and economic loss. While rhinoviruses (RV) typically cause little more than the common cold, they can produce severe infections and disease exacerbations in susceptible individuals, such as those with asthma. Variations in the regulation of key antiviral cytokines, particularly type I interferon (IFN‐α and IFN‐β), may contribute to RV susceptibility. To understand this variability, we compared the transcriptomes of high and low type I IFN producers. Methods Blood mononuclear cells from 238 individuals with or without asthma were cultured in the presence or absence of RV. Those samples demonstrating high or low RV‐stimulated IFN‐α production (N = 75) underwent RNA‐sequencing. Results Gene expression patterns were similar in samples from healthy participants and those with asthma. At baseline, the high IFN‐α producer group showed higher expression of genes associated with plasmacytoid dendritic cells, the innate immune response and vitamin D activation, but lower expression of oxidative stress pathways than the low IFN‐α producer group. After RV stimulation, the high IFN‐α producer group showed higher expression of genes found in immune response biological pathways and lower expression of genes linked to developmental and catabolic processes when compared to the low IFN‐α producer group. Conclusions These differences suggest that the high IFN‐α group has a higher level of immune system readiness, resulting in a more intense and perhaps more focussed pathogen‐specific immune response. These results contribute to a better understanding of the variability in type I IFN production between individuals.

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Viral–bacterial interactions in the respiratory tract

Cited by 60 publications

References 153 publications

Epidemiology and Clinical Characteristics of Influenza C Virus

Epidemiology and Clinical Characteristics of Influenza C Virus

Host‐pathogen kinetics during influenza infection and coinfection: insights from predictive modeling

Whole transcriptome analysis of high and low IFN‐α producers reveals differential response patterns following rhinovirus stimulation

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