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

2014 ◽  
Vol 4 (4) ◽  
pp. 301-306 ◽  
Author(s):  
Qian Gao ◽  
Zhen Wang ◽  
Zhenlong Liu ◽  
Xiaoyu Li ◽  
Yongxin Zhang ◽  
...  
2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Nicole B. Goecke ◽  
Maja Kobberø ◽  
Thomas K. Kusk ◽  
Charlotte K. Hjulsager ◽  
Ken Steen Pedersen ◽  
...  

Abstract Background Infectious diseases are of great economic importance in commercial pig production, causing both clinical and subclinical disease, with influence on welfare, productivity, and antibiotic use. The causes of these diseases are often multifactorial and laboratory diagnostics are seldom routinely performed. The aim of the present study was to explore the benefits of monthly pathogen monitoring in nursery and finisher herds and to examine association between laboratory results and observed clinical signs, including coughing and diarrhoea. Three monthly samplings were conducted in three different age groups in six nursery and four finisher production units. For each unit, two pens were randomly selected in each age group and evaluated for coughing and diarrhoea events. Furthermore, faecal sock and oral fluid samples were collected in the selected pens and analysed for 18 respiratory and enteric viral and bacterial pathogens using the high-throughput real-time PCR BioMark HD platform (Fluidigm, South San Francisco, USA). Results In total, 174 pens were sampled in which eight coughing events and 77 diarrhoeic events were observed. The overall findings showed that swine influenza A virus, porcine circovirus 2, porcine cytomegalovirus, Brachyspira pilosicoli, Lawsonia intracellularis, Escherichia coli fimbria types F4 and F18 were found to be prevalent in several of the herds. Association between coughing events and the presence of swine influenza A virus, porcine cytomegalovirus (Cq ≤ 20) or a combination of these were found. Furthermore, an association between diarrhoeic events and the presence of L. intracellularis (Cq ≤ 24) or B. pilosicoli (Cq ≤ 26) was found. Conclusions The use of high-throughput real-time PCR analysis for continuous monitoring of pathogens and thereby dynamics of infections in a pig herd, provided the veterinarian and farmer with an objective knowledge on the distribution of pathogens in the herd. In addition, the use of a high-throughput method in combination with information about clinical signs, productivity, health status and antibiotic consumption, presents a new and innovative way of diagnosing and monitoring pig herds and even to a lower cost than the traditional method.


2019 ◽  
Vol 14 (2) ◽  
pp. 129-141
Author(s):  
Zhu‐Nan Li ◽  
Emily Cheng ◽  
Eugenie Poirot ◽  
Kimberly M. Weber ◽  
Paul Carney ◽  
...  

2016 ◽  
Vol 13 (124) ◽  
pp. 20160412 ◽  
Author(s):  
Laura E. Liao ◽  
Shingo Iwami ◽  
Catherine A. A. Beauchemin

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.


2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Wan Ying Wong ◽  
Sheng Wei Loh ◽  
Wei Lun Ng ◽  
Ming Cheang Tan ◽  
Kok Siong Yeo ◽  
...  

2019 ◽  
Author(s):  
Brigitte E. Martin ◽  
Jeremy D. Harris ◽  
Jiayi Sun ◽  
Katia Koelle ◽  
Christopher B. Brooke

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.


2021 ◽  
Author(s):  
Laura Liao

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.


2021 ◽  
Author(s):  
Laura Liao

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.


2021 ◽  
Author(s):  
Diana Schwendener Forkel

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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