A cell-based screening system for anti-influenza A virus agents

A defective interfering particle (DIP) in the context of influenza A virus is a virion with a significantly shortened RNA segment substituting one of eight full-length parent RNA segments, such that it is preferentially amplified. Hence, a cell co-infected with DIPs will produce mainly DIPs, suppressing infectious virus yields and affecting infection kinetics. Unfortunately, the quantification of DIPs contained in a sample is difficult because they are indistinguishable from standard virus (STV). Using a mathematical model, we investigated the standard experimental method for counting DIPs based on the reduction in STV yield (Bellett & Cooper, 1959, Journal of General Microbiology 21 , 498–509 ( doi:10.1099/00221287-21-3-498 )). We found the method is valid for counting DIPs provided that: (i) an STV-infected cell's co-infection window is approximately half its eclipse phase (it blocks infection by other virions before it begins producing progeny virions), (ii) a cell co-infected by STV and DIP produces less than 1 STV per 1000 DIPs and (iii) a high MOI of STV stock (more than 4 PFU per cell) is added to perform the assay. Prior work makes no mention of these criteria such that the method has been applied incorrectly in several publications discussed herein. We determined influenza A virus meets these criteria, making the method suitable for counting influenza A DIPs.

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

10.1101/752329 ◽

2019 ◽

Cited By ~ 1

Author(s):

Brigitte E. Martin ◽

Jeremy D. Harris ◽

Jiayi Sun ◽

Katia Koelle ◽

Christopher B. Brooke

Keyword(s):

Influenza A Virus ◽

Cell Lines ◽

Influenza A ◽

Mdck Cells ◽

A549 Cells ◽

Virus Production ◽

Innate Immune ◽

Type I ◽

Functional Forms ◽

A Cell

ABSTRACTDuring 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.AUTHOR SUMMARYDuring influenza A virus (IAV) infection, the number of virions to enter individual cells can be highly variable. Cellular co-infection appears to be common and plays an essential role in facilitating reassortment for IAV, yet little is known about how cellular co-infection influences infection outcomes at the cellular level. Here, we combine quantitative in vitro infection experiments with statistical model fitting to precisely define the phenotypic consequences of cellular co-infection in two cell lines. We reveal that cellular co-infection can increase and accelerate the efficiency of IAV production in a cell line-dependent fashion, identifying it as a potential determinant of viral replication kinetics. We also show that induction of type III, but not type I, interferon is highly dependent upon the number of virions that infect a given cell, implicating cellular co-infection as an important determinant of the host innate immune response to infection. Altogether, our findings show that cellular co-infection plays a crucial role in determining infection outcome. The integration of experimental and statistical modeling approaches detailed here represents a significant advance in the quantitative study of influenza virus infection and should aid ongoing efforts focused on the construction of mathematical models of IAV infection.

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Mathematical models of Influenza A. virus infections in vitro: Invesigating defective interfering particles and virus release

10.32920/ryerson.14663724 ◽

2021 ◽

Author(s):

Laura Liao

Keyword(s):

Mathematical Models ◽

Influenza A Virus ◽

Release Rate ◽

Influenza A ◽

Quantitative Methods ◽

Virus Release ◽

Virus Infections ◽

Defective Interfering Particles ◽

A Cell

In this work, two studies were performed where mathematical models (MM) were used to re-examine and refine quantitative methods based on in vitro assays of influenza A virus infections. In the first study, we investigated the standard experimental method for counting defective interfering particles (DIPs) based on the reduction in standard virus (STV) yield (Bellett & Cooper, 1959). We found the method is valid for counting DIPs provided that: (1) a STV-infected cell’s co-infection window is approximately half its eclipse phase (it blocks infection by other virions before it begins producing progeny virions); (2) a cell co-infected by STV and DIP produces less than 1 STV per 1,000 DIPs; and (3) a high MOI of STV stock (>4 plaque-forming units/cell) is added to perform the assay. Prior work makes no mention of these criteria such that the counting method has been applied incorrectly in several publications discussed herein. We determined influenza A virus meets these criteria, making the method suitable for counting influenza A DIPs. In the second study, we compared a MM with an explicit representation of viral release to a simple MM without explicit release, and investigated whether parameter estimation and the estimation of neuraminidase inhibitor (NAI) efficacy were affected by the use of a simple MM. Since the release rate of influenza A virus is not well-known, a broad range of release rates were considered. If the virus release rate is greater than ∼0.1 h−1, the simple MM provides accurate estimates of infection parameters, but underestimates NAI efficacy, which could lead to underdosing and the emergence of NAI resistance. In contrast, when release is slower than ∼0.1 h−1, the simple MM accurately estimates NAI efficacy, but it can significantly overestimate the infectious lifespan (i.e., the time a cell remains infectious and producing free virus), and it will significantly underestimate the total virus yield and thus the likelihood of resistance emergence. We discuss the properties of, and a possible lower bound for, the influenza A virus release rate. Overall, MMs are a valuable tool in the exploration of the known unknowns (i.e., DIPs, virus release) of influenza A virus infection.

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Mathematical models of Influenza A. virus infections in vitro: Invesigating defective interfering particles and virus release

10.32920/ryerson.14663724.v1 ◽

2021 ◽

Author(s):

Laura Liao

Keyword(s):

Mathematical Models ◽

Influenza A Virus ◽

Release Rate ◽

Influenza A ◽

Quantitative Methods ◽

Virus Release ◽

Virus Infections ◽

Defective Interfering Particles ◽

A Cell

In this work, two studies were performed where mathematical models (MM) were used to re-examine and refine quantitative methods based on in vitro assays of influenza A virus infections. In the first study, we investigated the standard experimental method for counting defective interfering particles (DIPs) based on the reduction in standard virus (STV) yield (Bellett & Cooper, 1959). We found the method is valid for counting DIPs provided that: (1) a STV-infected cell’s co-infection window is approximately half its eclipse phase (it blocks infection by other virions before it begins producing progeny virions); (2) a cell co-infected by STV and DIP produces less than 1 STV per 1,000 DIPs; and (3) a high MOI of STV stock (>4 plaque-forming units/cell) is added to perform the assay. Prior work makes no mention of these criteria such that the counting method has been applied incorrectly in several publications discussed herein. We determined influenza A virus meets these criteria, making the method suitable for counting influenza A DIPs. In the second study, we compared a MM with an explicit representation of viral release to a simple MM without explicit release, and investigated whether parameter estimation and the estimation of neuraminidase inhibitor (NAI) efficacy were affected by the use of a simple MM. Since the release rate of influenza A virus is not well-known, a broad range of release rates were considered. If the virus release rate is greater than ∼0.1 h−1, the simple MM provides accurate estimates of infection parameters, but underestimates NAI efficacy, which could lead to underdosing and the emergence of NAI resistance. In contrast, when release is slower than ∼0.1 h−1, the simple MM accurately estimates NAI efficacy, but it can significantly overestimate the infectious lifespan (i.e., the time a cell remains infectious and producing free virus), and it will significantly underestimate the total virus yield and thus the likelihood of resistance emergence. We discuss the properties of, and a possible lower bound for, the influenza A virus release rate. Overall, MMs are a valuable tool in the exploration of the known unknowns (i.e., DIPs, virus release) of influenza A virus infection.

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Mathematical Model for the Intracellular Kinetics of Influenza a Virus Replication in Vitro

10.32920/ryerson.14648898.v1 ◽

2021 ◽

Author(s):

Diana Schwendener Forkel

Keyword(s):

Influenza A Virus ◽

Virus Replication ◽

Influenza A ◽

Infection Process ◽

Treatment Modalities ◽

Viral Production ◽

A Cell ◽

Basic Biology ◽

Kinetics Of

In the last twenty years, mathematical modelling (MM) has been notably used to capture the infection kinetics of many infectious diseases as it allows insights into the basic biology, infection kinetics, and the mechanisms and efficacy of treatment modalities. MMs of influenza A virus (IAV) infection usually represent the process of virus replication within a cell as a ‘black box’ term for viral production. The simplification is appropriate when we are not interested in the microscopic details of infection. Recently though, MMs have begun to account for the kinetics of intracellular IAV replication. Herein, we examine the MM by Heldt et al., which is able to capture kinetics of IAV infection. It however, does so by adjusting parameters of the MM to various events in the infection process. We developed a robust, yet concise, MM for the intracellular kinetics of influenza A virus infection in vitro with a consistent set of parameters. We use attachment, fusion and RNA data gathered from literature sources to validate our simplified MM and match known infection kinetics consistent throughout infection.

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Influenza A Virus Superinfection Potential Is Regulated by Viral Genomic Heterogeneity

mBio ◽

10.1128/mbio.01761-18 ◽

2018 ◽

Vol 9 (5) ◽

Cited By ~ 3

Author(s):

Jiayi Sun ◽

Christopher B. Brooke

Keyword(s):

Single Cell ◽

Influenza A Virus ◽

Influenza A ◽

Natural Infection ◽

Viral Gene ◽

Evolutionary Potential ◽

Viral Genes ◽

A Cell ◽

Complete Set ◽

Sequential Infection

ABSTRACTDefining the specific factors that govern the evolution and transmission of influenza A virus (IAV) populations is of critical importance for designing more-effective prediction and control strategies. Superinfection, the sequential infection of a single cell by two or more virions, plays an important role in determining the replicative and evolutionary potential of IAV populations. The prevalence of superinfection during natural infection and the specific mechanisms that regulate it remain poorly understood. Here, we used a novel single virion infection approach to directly assess the effects of individual IAV genes on superinfection efficiency. Rather than implicating a specific viral gene, this approach revealed that superinfection susceptibility is determined by the total number of viral gene segments expressed within a cell. IAV particles that express a complete set of viral genes potently inhibit superinfection, while semi-infectious particles (SIPs) that express incomplete subsets of viral genes do not. As a result, virus populations that contain more SIPs undergo more-frequent superinfection. We further demonstrate that viral replicase activity is responsible for inhibiting subsequent infection. These findings identify both a major determinant of IAV superinfection potential and a prominent role for SIPs in promoting viral coinfection.IMPORTANCESuperinfection, the sequential infection of a single cell by two or more virions, plays an important role in determining the replicative and evolutionary potential of influenza A virus (IAV) populations. The specific mechanisms that regulate superinfection during natural infection remain poorly understood. Here, we show that superinfection susceptibility is determined by the total number of viral genes expressed within a cell and is independent of their specific identity. Virions that express a complete set of viral genes potently inhibit superinfection, while the semi-infectious particles (SIPs) that make up the bulk of IAV populations and express incomplete subsets of viral genes do not. As a result, viral populations with more SIPs undergo more-frequent superinfection. These findings identify both the primary determinant of IAV superinfection potential and a prominent role for SIPs in promoting coinfection.

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A cell-based high-throughput approach to identify inhibitors of influenza A virus

Acta Pharmaceutica Sinica B ◽

10.1016/j.apsb.2014.06.005 ◽

2014 ◽

Vol 4 (4) ◽

pp. 301-306 ◽

Cited By ~ 16

Author(s):

Qian Gao ◽

Zhen Wang ◽

Zhenlong Liu ◽

Xiaoyu Li ◽

Yongxin Zhang ◽

...

Keyword(s):

Influenza A Virus ◽

High Throughput ◽

Influenza A ◽

A Cell

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A high-throughput screening system targeting the nuclear export pathway via the third nuclear export signal of influenza A virus nucleoprotein

Virus Research ◽

10.1016/j.virusres.2016.02.007 ◽

2016 ◽

Vol 217 ◽

pp. 23-31 ◽

Cited By ~ 6

Author(s):

Michinori Kakisaka ◽

Takafumi Mano ◽

Yoko Aida

Keyword(s):

Influenza A Virus ◽

High Throughput ◽

Nuclear Export ◽

High Throughput Screening ◽

Influenza A ◽

Nuclear Export Signal ◽

Screening System ◽

The Third ◽

Export Pathway ◽

Export Signal

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Mathematical Model for the Intracellular Kinetics of Influenza a Virus Replication in Vitro

10.32920/ryerson.14648898 ◽

2021 ◽

Author(s):

Diana Schwendener Forkel

Keyword(s):

Influenza A Virus ◽

Virus Replication ◽

Influenza A ◽

Infection Process ◽

Treatment Modalities ◽

Viral Production ◽

A Cell ◽

Basic Biology ◽

Kinetics Of

In the last twenty years, mathematical modelling (MM) has been notably used to capture the infection kinetics of many infectious diseases as it allows insights into the basic biology, infection kinetics, and the mechanisms and efficacy of treatment modalities. MMs of influenza A virus (IAV) infection usually represent the process of virus replication within a cell as a ‘black box’ term for viral production. The simplification is appropriate when we are not interested in the microscopic details of infection. Recently though, MMs have begun to account for the kinetics of intracellular IAV replication. Herein, we examine the MM by Heldt et al., which is able to capture kinetics of IAV infection. It however, does so by adjusting parameters of the MM to various events in the infection process. We developed a robust, yet concise, MM for the intracellular kinetics of influenza A virus infection in vitro with a consistent set of parameters. We use attachment, fusion and RNA data gathered from literature sources to validate our simplified MM and match known infection kinetics consistent throughout infection.

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The Nucleolar Protein LYAR Facilitates Ribonucleoprotein Assembly of Influenza A Virus

Journal of Virology ◽

10.1128/jvi.01042-18 ◽

2018 ◽

Vol 92 (23) ◽

Cited By ~ 7

Author(s):

Cha Yang ◽

Xiaokun Liu ◽

Qingxia Gao ◽

Tailang Cheng ◽

Rong Xiao ◽

...

Keyword(s):

Cell Growth ◽

Influenza A Virus ◽

Virus Replication ◽

Influenza A ◽

Rna Synthesis ◽

Nucleolar Protein ◽

Host Factors ◽

Viral Rna ◽

A Cell ◽

Transcription And Replication

ABSTRACTInfluenza A viral ribonucleoprotein (vRNP) is responsible for transcription and replication of the viral genome in infected cells and depends on host factors for its functions. Identification of the host factors interacting with vRNP not only improves understanding of virus-host interactions but also provides insights into novel mechanisms of viral pathogenicity and the development of new antiviral strategies. Here, we have identified 80 host factors that copurified with vRNP using affinity purification followed by mass spectrometry. LYAR, a cell growth-regulating nucleolar protein, has been shown to be important for influenza A virus replication. During influenza A virus infection, LYAR expression is increased and partly translocates from the nucleolus to the nucleoplasm and cytoplasm. Furthermore, LYAR interacts with RNP subunits, resulting in enhancing viral RNP assembly, thereby facilitating viral RNA synthesis. Taken together, our studies identify a novel vRNP binding host partner important for influenza A virus replication and further reveal the mechanism of LYAR regulating influenza A viral RNA synthesis by facilitating viral RNP assembly.IMPORTANCEInfluenza A virus (IAV) must utilize the host cell machinery to replicate, but many of the mechanisms of IAV-host interaction remain poorly understood. Improved understanding of interactions between host factors and vRNP not only increases our basic knowledge of the molecular mechanisms of virus replication and pathogenicity but also provides insights into possible novel antiviral targets that are necessary due to the widespread emergence of drug-resistant IAV strains. Here, we have identified LYAR, a cell growth-regulating nucleolar protein, which interacts with viral RNP components and is important for efficient replication of IAVs and whose role in the IAV life cycle has never been reported. In addition, we further reveal the role of LYAR in viral RNA synthesis. Our results extend and improve current knowledge on the mechanisms of IAV transcription and replication.

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