ABSTRACT

As influenza A viruses (IAV) continue to cross species barriers and cause human infection, the establishment of risk assessment rubrics has improved pandemic preparedness efforts. In vivo pathogenicity and transmissibility evaluations in the ferret model represent a critical component of this work. As the relative contribution of in vitro experimentation to these rubrics has not been closely examined, we sought to evaluate to what extent viral titer measurements over the course of in vitro infections are predictive or correlates of nasal wash and tissue measurements for IAV infections in vivo. We compiled data from ferrets inoculated with an extensive panel of over 50 human and zoonotic IAV (inclusive of swine-origin and high- and low-pathogenicity avian influenza viruses associated with human infection) under a consistent protocol, with all viruses concurrently tested in a human bronchial epithelial cell line (Calu-3). Viral titers in ferret nasal wash specimens and nasal turbinate tissue correlated positively with peak titer in Calu-3 cells, whereas additional phenotypic and molecular determinants of influenza virus virulence and transmissibility in ferrets varied in their association with in vitro viral titer measurements. Mathematical modeling was used to estimate more generalizable key replication kinetic parameters from raw in vitro viral titers, revealing commonalities between viral infection progression in vivo and in vitro. Meta-analyses inclusive of IAV that display a diverse range of phenotypes in ferrets, interpreted with mathematical modeling of viral kinetic parameters, can provide critical information supporting a more rigorous and appropriate contextualization of in vitro experiments toward pandemic preparedness.
IMPORTANCE Both in vitro and in vivo models are employed for assessing the pandemic potential of novel and emerging influenza A viruses in laboratory settings, but systematic examinations of how well viral titer measurements obtained in vitro align with results from in vivo experimentation are not frequently performed. We show that certain viral titer measurements following infection of a human bronchial epithelial cell line are positively correlated with viral titers in specimens collected from virus-inoculated ferrets and employ mathematical modeling to identify commonalities between viral infection progression between both models. These analyses provide a necessary first step in enhanced interpretation and incorporation of in vitro-derived data in risk assessment activities and highlight the utility of employing mathematical modeling approaches to more closely examine features of virus replication not identifiable by experimental studies alone.

INTRODUCTION

Influenza A viruses (IAVs) are capable of overcoming species barriers to cause sporadic cases of human infection. While most zoonotic-to-human transmission events are self-limiting, there remains the potential during this process for the acquisition of features associated with enhanced mammalian adaptation, which could result in the emergence of a pandemic virus. Risk assessment rubrics (notably the Influenza Risk Assessment Tool [IRAT] and the World Health Organization Tool for Influenza Pandemic Risk Assessment [TIPRA]) have been established to consider virus, host, ecological, and environmental properties to facilitate the assessment of the public health risk posed by novel and emerging influenza viruses (1, 2). These models are informed by in vivo pathogenicity and transmissibility data generated in laboratory models, specifically the ferret. In vitro experimentation is frequently employed concurrently with in vivo assessments (and in the case of TIPRA, is formally considered) to provide supporting information to in vivo data. These in vitro assessments are typically conducted in cultured human respiratory epithelial cell lines but have expanded in recent years to include primary cells, tissue explants, tissue constructs, and organoid cultures (3, 4).
To assess mammalian pathogenicity of novel and emerging IAVs associated with human infection or exposure, serologically naive ferrets are typically inoculated intranasally with a high dose of virus (105 to 107 infectious units) and observed daily for clinical signs of infection, with specimens collected during the acute phase of infection from the upper respiratory tract. However, the variability of experimental conditions between laboratories performing risk assessment studies represents a challenge for contextualizing results in the field (5). Even within controlled laboratory environments, the wide heterogeneity of influenza virus subtypes associated with human infection can lead to complications for their comparative study (6). Study-to-study comparisons of viral growth kinetics in cells derived from the human respiratory tract are often similarly hindered due to the use of different cell types between studies and the limited availability of cultures that are not commercially available (4).
While previous studies have examined the association of in vivo ferret data as they pertain to human pathogenicity and transmissibility (7, 8), there remains a need to understand the strengths and limitations of in vitro characterizations performed at a fixed multiplicity of infection (MOI) to mammalian risk assessment activities. Zoonotic IAVs typically replicate to high and sustained titer at both 37°C and 40°C (the temperature of the avian enteric tract), whereas a shorter time to reach comparable titer metrics at 33°C (the temperature of the human nasal passages) could be indicative of mammalian adaptation (9, 10), although this has not been systematically examined. In vitro results are typically presented as a capacity to replicate to high titer, to indicate relative levels of host adaptation, and as a potential to replicate efficiently in human respiratory tract tissues, often contextualized with additional parameters such as elicitation of host responses (11, 12). As in vitro models become more advanced, so must our understanding of the capacity of these models to recapitulate in vivo environments. In this vein, mathematical models (MMs) of replication dynamics and their use to identify differential growth properties between human and zoonotic influenza viruses in relevant human cell types represent a sophisticated approach to improving our knowledge of host adaptation processes (13). An in vitro MM of IAVs with pandemic potential distills viral titer data into key parameters characterizing their replicative fitness, providing valuable information for both laboratory and epidemiological applications (14).
Here, we perform an exploratory analysis comparing IAV titer measurements and some MM-derived quantities in vitro and in vivo, employing data obtained in the human bronchial epithelial cell line Calu-3 with paired data obtained from virus-inoculated ferrets. Analyses were conducted using titers generated by viral titration during standard replication kinetics experiments in vitro, and MM parameters were estimated from the in vitro infections for each virus strain. The inclusion of contemporary human and zoonotic IAVs that exhibit differential mammalian pathogenicity permits the investigation and identification of features generated from in vitro assessments that are most indicative of in vivo mammalian replication fitness. Collectively, this study provides a necessary first step toward improved quantitative interpretation of in vitro data generated for risk assessment purposes.

RESULTS

Control of parameters for in vivo and in vitro assessments.

Virus pathogenicity and titration data were aggregated from ferrets inoculated with a wide range of 52 contemporary human and zoonotic IAVs (isolated from 1999 to 2018), employing a uniform experimental protocol for all experiments to minimize laboratory- or protocol-based confounders (Table 1) (5). Nasal wash (NW) specimens (inclusive of virus replication in multiple locations in the ferret upper respiratory tract [15]) were collected from all virus-inoculated ferrets; mean peak NW titer, reflecting the maximum viral titer days 1 to 5 postinoculation (p.i.) from n ≥ 3 ferrets, was determined for each virus (reported in Table S1 in the supplemental material). With few exceptions, mean viral titers from respiratory tract tissues collected on day 3 p.i. (nasal turbinates, trachea, and lung) were included in analyses as specified in Materials and Methods.
TABLE 1
TABLE 1 Influenza A viruses evaluated in ferrets and Calu-3 cells
Virus nameSubtypeaDescriptionbTitration unitscPB2 627d33°CeReferencesf
A/Brisbane/59/2007H1N1Human seasonalPFUKX11, 43, 44
A/California/4/2009H1N1pdm09Human pandemicPFUEX11, 43, 45
A/Mexico/4482/2009H1N1pdm09Human pandemicPFUEX11, 43
A/Texas/15/2009H1N1pdm09Human pandemicPFUEX11, 43
A/Netherlands/1132/2009H1N1pdm09Human pandemicPFUEX46, this study
A/Ohio/2/2007H1N1vVariant swinePFUEX11, 47, 48
A/Texas/14/2008H1N1vVariant swinePFUEX11, 47, 48
A/Iowa/39/2015H1N1vVariant swinePFUEX45, 48
A/Ohio/9/2015H1N1vVariant swinePFUEX45, 48
A/Hunan/42443/2015H1N1vVariant swinePFUEX49, this study
A/Minnesota/19/2011H1N2vVariant swinePFUEX48
A/Minnesota/45/2016H1N2vVariant swinePFUEX48
A/Wisconsin/71/2016H1N2vVariant swinePFUEX48
A/Panama/2007/1999H3N2Human seasonalPFU, EID50KX50, 51, this study
A/Perth/16/2009H3N2Human seasonalPFUK 52, 53
A/canine/Illinois/12191/2015H3N2 canineCanine isolateEID50EX54
A/Kansas/13/2009H3N2vVariant swinePFUE 51, 53
A/Minnesota/11/2010H3N2vVariant swinePFUE 51, 53
A/Pennsylvania/14/2010H3N2vVariant swinePFUE 51, 53
A/Indiana/8/2011H3N2vVariant swinePFUE 5153
A/Iowa/8/2011H3N2vVariant swinePFUE 51
A/Ohio/13/2012H3N2vVariant swinePFUE 51
A/Michigan/39/2015H3N2vVariant swinePFUE 51
A/Ohio/27/2016H3N2vVariant swinePFUE 51
A/Thailand/16/2004H5N1HPAI (Eurasian)EID50KX41, this study
A/Vietnam/1203/2004H5N1HPAI (Eurasian)EID50KX41, this study
A/Bangladesh/5487/2011H5N1HPAI (Eurasian)EID50KX55, this study
A/duck/Vietnam/NCVD-672/2011H5N1HPAI (Eurasian)EID50EX55, this study
A/chicken/Texas/18-007912-2/2018H7N1LPAI (N. American)EID50EX56
A/turkey/Virginia/4529/2002H7N2LPAI (N. American)EID50EX5759
A/New York/107/2003H7N2LPAI (N. American)EID50EX5759
A/New York/108/2016H7N2LPAI (N. American)EID50EX59
A/Canada/504/2004H7N3HPAI (N. American)EID50E 39, 46
A/Mexico/InDRE7218/2012H7N3HPAI (N. American)EID50EX56, 60
A/turkey/California/18-031151-4/2018H7N3LPAI (N. American)EID50EX56
A/Netherlands/219/2003H7N7HPAI (Eurasian)EID50KX5658
A/Netherlands/230/2003H7N7HPAI (Eurasian)EID50EX5658
A/Italy/3/2013H7N7HPAI (Eurasian)EID50EX56, 61
A/turkey/Indiana/1403/2016H7N8HPAI (N. American)EID50E 62
A/turkey/Indiana/1573-2/2016H7N8LPAI (N. American)EID50E 62
A/goose/Nebraska/17096-1/2011H7N9LPAI (N. American)EID50EX56, 60, 62, 63
A/chicken/Tennessee/17-007147-2/2017H7N9HPAI (N. American)EID50E 63
A/chicken/Tennessee/17-007431-3/2017H7N9LPAI (N. American)EID50E 63
A/shoveler/Egypt/00215-NAMRU3/2007H7N9LPAI (Eurasian)PFUEX64
A/Anhui/1/2013H7N9 (1)LPAI (Eurasian)PFUKX64
A/Shanghai/1/2013H7N9 (1)LPAI (Eurasian)PFUKX64
A/Taiwan/1/2013H7N9 (1)LPAI (Eurasian)PFUKX65
A/Hong Kong/5942/2013H7N9 (2)LPAI (Eurasian)PFUKX65
A/British Columbia/1/2015H7N9 (3)LPAI (Eurasian)PFUKX65
A/Hong Kong/4553/2016H7N9 (5)LPAI (Eurasian)EID50KX66, this study
A/Guangdong/17SF003/2016H7N9 (5)HPAI (Eurasian)EID50E 66
A/Taiwan/1/2017H7N9 (5)HPAI (Eurasian)EID50K 66
a
Epidemiological wave from which H7N9 viruses were isolated from humans is indicated in parentheses. v, variant virus.
b
Variant swine describes human isolates of swine origin. HPAI, highly pathogenic avian influenza virus; LPAI, low pathogenic avian influenza virus. Virus lineage for avian influenza viruses is specified in parentheses.
c
The method of titration for detection of infectious virus. EID50, 50% egg infectious dose; PFU, PFU in London-line Madin Darby Canine Kidney (MDCK) cells. For analyses split between egg and cell titration matrix, Panama/99 virus is represented in both; for combined analyses, PFU data are included.
d
Amino acid at PB2 position 627. K, lysine; E, glutamic acid.
e
Viruses for which paired Calu-3 data were generated at both 37°C and 33°C are indicated.
f
All ferret data have been published previously. Calu-3 data have either been published previously or generated for this study as indicated.
All viruses were evaluated in the human tracheal-bronchial epithelial cell line Calu-3, one of many in vitro models employed to support risk assessment work (3). Calu-3 cells were grown to confluence in either 12-mm or 24-mm Transwell inserts at a liquid-liquid interface, and standard replication kinetics evaluations were conducted at 37°C using a fixed MOI of 0.01 infectious units/cell. Comparable viral replication kinetics and mean peak titers were observed following infection with a diverse range of IAVs independent of well size (Fig. S1), permitting pooled analysis of these data. Peak viral titers (24 to 72 h p.i.) from Calu-3 cells infected with the same virus were found to be comparable between replicates from multiple independent experiments, in contrast with mean titers collected at specific time points that possessed the potential for greater variability between repeat experimentation (Table S1; Fig. S2).
Quantification of infectious virus load from samples collected in vitro or in vivo was determined by titration in either embryonated chicken eggs or Madin-Darby Canine Kidney (MDCK) cells to determine a 50% egg infectious dose (EID50) or PFU titer, respectively. The panel of viruses employed in this study included both titration methods, with a general bias toward cell titration for human H1 and H3 subtype viruses, and egg titration for avian H5 and H7 subtype viruses (Table 1), due to strain-specific viral replication capacities in each titration matrix. Because the relationship between EID50 and PFU titers can vary between viruses in a strain-specific manner (Fig. S3), only viruses for which the same matrix was uniformly employed for both in vivo and in vitro sample titration were included in this study. While the majority of analyses were performed on the entire data set, for certain comparisons (notably those that compared viral titer data with nontiter measures generated from mathematical modeling), independent analyses were performed for each titration matrix.
Using this information, we conducted statistical and other predictive analyses to examine possible correlations and associations between Calu-3 and ferret viral titer data. Pearson correlation coefficients, denoted by r, are used throughout to quantify the extent to which two measures are correlated; correlations for which zero is excluded from the 95% confidence interval (95% CI) of the estimated r value were of primary interest. Because multiple comparisons were performed, the p values computed herein do not correspond to an absolute measure of the true significance of any one comparison. Instead, we report a quantity we call the ranking statistic (RS-p), which is used only to rank each correlation from most to least significant but is not to be interpreted in absolute terms. Throughout this study, viral titer is presented as log10 titer, and all calculations were performed with the log10 of the measured virus titer and not the virus titer itself.

Correlation between ferret and Calu-3 viral titers postinfection.

We first examined to what extent Calu-3 viral titers were predictive of viral load measured in ferret NW specimens. When employing the entire data set (all viruses listed in Table 1), peak ferret NW titers were positively correlated (r = 0.34 with 95% CI [0.08, 0.56], RS-P = 0.01) with peak Calu-3 titers from infections conducted at 37°C (Fig. 1A). When viruses were separated by titration matrix, positive Pearson correlation coefficients were higher for the viral titer determined in eggs than in cells; similar results were obtained when analyses were stratified by host origin rather than titration matrix (with higher coefficients among viruses of avian origin) (Fig. 1B and C; Table S2A). Comparable trends following data stratification were maintained between peak Calu-3 titers at 37°C and NW specimens collected on individual days p.i. (Fig. S4; Table S2A). Correlation coefficients were generally higher when analyses were limited to groups of genetically related viruses (e.g., H7N9 subtype viruses only [Fig. 1G; Table S2A]). Interestingly, compared to peak titer measurements, assessments of viral titer 24 h post in vivo inoculation (day 1 p.i. NW) and in vitro infection (24-h Calu-3 titer cultured at 37°C) were more significant both for the entire data set (r = 0.44, [0.19, 0.63], RS-P = 0.001, Fig. S4) or when restricted to H7N9 subtype viruses (r = 0.96 [0.85, 0.99], RS-P = 1e-6, Table S2A).
FIG 1
FIG 1 Correlations between ferret nasal wash, ferret nasal turbinate, ferret lung, and Calu-3 viral titers following IAV infection. Best-fit line determined by least-squares regression; r values are from Pearson correlation tests (see Table S2 in the supplemental material). Viral titers correspond to the log10 of the viral titers, measured in EID50/mL (NW, NT) or EID50/g (Lg) (viral titration in eggs, circles) or PFU/mL (NW, NT) or PFU/g (Lg) (viral titration in cells, triangles); sample titration matrix in mixed data sets is specified in Table 1. (A to C) Peak ferret NW versus peak Calu-3 titer (37°C) for all viruses in data set (A), the viral titers determined in eggs only (B), or viral titers determined in cells only (C). (D to F) Day 3 p.i. NT titer versus peak Calu-3 titer (37°C) for all viruses in data set (D), the viral titers determined in eggs only (E), or viral titers determined in cells only (F). (G to I) Among H7N9 subtype viruses only, peak ferret NW versus peak Calu-3 titer (37°C) (G), day 3 p.i. NT titer versus peak Calu-3 titer (37°C) (H), and day 3 p.i. Lg titer versus peak Calu-3 titer (37°C) (I). (H to L) Day 1 p.i. ferret NW versus 24-h Calu-3 titer (33°C) for all viruses in data set (J), H7N9 subtype viruses only (K), or H1v (H1N1v and H1N2v) subtype viruses only (L).
Similar to peak NW titer, day 3 p.i. nasal turbinate (NT) titers (all samples were sampled on day 3 p.i.) were positively correlated (r = 0.46, [0.20, 0.66], RS-p = 0.001) with peak Calu-3 titers for infections conducted at 37°C, with higher positive Pearson correlation coefficients for viruses titrated in eggs or of avian origin, or when restricted to H7N9 subtype viruses (Fig. 1D to F and H; Table S2B). In contrast to NT titers, Pearson correlation coefficients were generally lower between trachea (Tr) or lung (Lg) titers and peak Calu-3 titers cultured at 37°C when employing either the full data set or when stratifying the viruses by titration matrix or host origin (Table S2B, Fig. S4). That said, compared to the entire data set, limiting the analysis to H7N9 viruses did yield higher positive correlation coefficients for both Tr (r = 0.88 [0.52, 0.97], RS-p = 0.002) and Lg (r = 0.94 [0.73, 0.99], RS-p = 1e-4) titers (Fig. 1I; Table S2B). Collectively, we found that in vitro assessments of replication ability in Calu-3 cells cultured at 37°C are capable of informing data collected from both NW specimens and discrete tissues, but as expected, individual in vitro viral titer time points only accounted for some of the variability in the discrete viral titer time points measured in vivo.
Because the temperature of the mammalian nasal airways is lower than the core body temperature (16), standard replication kinetics evaluations were conducted in Calu-3 cells with a subset of viruses (n = 36) at both 33°C and 37°C (Table 1). Pearson correlations between peak NW titer and peak Calu-3 titer following culturing at 33°C were generally weaker compared to 37°C when employing the entire data set or when stratified by titration matrix or host origin (Table S2A and C; Fig. S4). However, in contrast to peak Calu-3 titer measurements, stronger correlations were identified between day 1 p.i. NW titer and mean viral titer at 24 h p.i. in Calu-3 cells cultured at 33°C than at 37°C in either the full data set (r = 0.54 [0.26, 0.74], RS-p = 6e-4, Fig. 1J) or following data stratification (Table S2C). Restricting these analyses to H7N9 or H1 variant subtype viruses further increased correlation coefficients (Fig. 1K to L; Table S2C). Compared with day 1 p.i. NW analyses, Pearson correlation coefficients employing mean viral titer at 24 h p.i. in Calu-3 cells cultured at 33°C were weaker when employing ferret titer data collected day 3 p.i. (either NW or tissue titers, Fig. S4), supporting the utility of 33°C titer measurements in predicting data collected early during in vivo infection (e.g., within 24 h p.i.).

Mathematical modeling of in vitro infection in Calu-3 cells.

The use of MMs is a useful and effective way to validate results from analyses based on experimentally measured titer alone, such as that presented above. It also offers enhanced analysis, between and beyond the sometimes sparsely collected time points. Data from Calu-3 infections were subjected to MM analysis to estimate key replication parameters and features of the viral titer time course for each virus (illustrated in Fig. 2 and described in Table 2). Since many parameters were not tightly constrained by the data, analyses are generally limited to looking at features of the MM-predicted time course, which were well informed by the data. A description of the MM and its associated analysis and limitations is provided in the Supplemental Methods.
FIG 2
FIG 2 Representation of the MM-derived features of an in vivo viral time course. (A) An illustration of the key MM-estimated features shown against an example MM-predicted time course for infection of Calu-3 cells with A/British Columbia/1/2015 at 37°C. (B) A representation of the MM-estimated infecting time (tinf). All MM-derived quantities are described in more details in Table 2 and the Supplemental Methods.
TABLE 2
TABLE 2 MM-derived key replication parameters and features of an in vitro viral time course
Parameter (units)aDefinitionBiological meaningb
maxlog10V (log10 viral titer)Peak log10 viral titerPeak titer present anytime 0 to 86 h; Calu-3 experimental time course calculates peak from 24 h, 48 h, or 72 h sampling times only
meanlog10V (log10 viral titer)Mean log10 viral titerSimilar to an area under the curve measurement generated from experimental time course and then divided by the duration of the infection, but with increased accuracy since the entire MM-predicted time course (nearly continuous time points from 0 to 86 h) is used. It is largest for infections that reach peak titer more rapidly and/or sustain high titer over a longer duration and/or have a higher peak.
tinf (h)Time between start of virus progeny release by newly infected cell and infection of its first cellNo close equivalent can be estimated from experimental titer measurements alone. Similar to the serial interval in epidemiology. It is shortest for viruses that cause infected cells to produce and release progeny at a higher rate and/or whose progeny are more infectious (fewer virions required to cause a cell infection).
t1%maxV (h)Time to reach 1% of peak viral titerNo good equivalent in experimental time course because experimental measurement time points are typically too sparsely distributed in time to provide an accurate estimate of timings for viral titer milestones. This is shortest for the most rapidly progressing infections, but it can also be shortest for infections with a low viral titer peak that can be more rapidly attained.
tmeanlog10V (h)Time to reach peak mean log10 viral titerLargely similar to t1%maxV but since it is a different milestone, it can occasionally capture a slightly different timing.
growth (h−1)Calu-3 titer growth rateThis is similar to calculating the slope between pairs of exptl log10 viral titer time points but provides a more accurate/reliable estimate since the MM analysis uses all exptl viral titer measurements to inform this measure. A virus with a higher growth rate claims more cells per infected cell per hour; this measure is equivalent to the basic reproductive number divided by the life span of an infected cell.
a
All parameters pertain to the MM-simulated time course (0 to 86 h p.i.) derived from Calu-3 in vitro generated data and were extracted from the MM-predicted most likely viral titer time course given the experimental data, rather from the data itself.
b
Added benefit of MM parameter relative to viral titer-only time course, both of which are inevitably measured at sparse experimental time points.
We first examined how well in vitro-generated Calu-3 data aligned with analogs from the MM-predicted values (e.g., from time points measured 24, 48, and 72 h p.i.). As expected, strong correlations were observed between the MM-predicted and experimentally measured peak titers (r = 0.99, [0.97, 0.99], RS-p = 1e-16) and 24 h p.i. titer (r = 0.95, [0.91, 0.97], RS-p = 1e-16) for infections in Calu-3 cultured at 37°C (Fig. S5A to F). Comparable correlations between MM-predicted and experimentally measured titers were observed at 33°C, and when data were stratified by titration matrix (Table S3A). Substitution of these MM-predicted values into analyses with ferret titer measures (Table S3A; Fig. S5G to I) yielded correlations generally comparable to those found with the Calu-3 experimental measurements (reported in Fig. 1 and Table S2A and B).
We next explored the utility of other MM-predicted values not obtainable by experimental in vitro study alone to offer enhanced predictive ability of in vivo-generated ferret measures. Unlike peak titer assessments, which are determined by sampling culture supernatant at specific times p.i. and therefore may miss actual titer peaks, MM can predict the maximum titer over the entirety of the time course (in this instance, 0 to 86 h). When employing the entire data set, both peak ferret NW titers (r = 0.32 [0.05, 0.54], RS-p = 0.02) and day 3 p.i. nasal turbinates (NT) titers (r = 0.52, [0.27, 0.70], RS-P = 2e-4) were positively correlated with the MM-predicted peak titer (maxlog10V, Table S3B) when cultured at 37°C, in alignment with similar correlations observed when employing peak Calu-3 titer. With few exceptions, MM-predicted and experimentally measured Calu-3 peak titer offered similar predictive benefits against all ferret parameters examined (Table S2A and B versus 3B), suggesting that collected time points during in vitro experimentation generally encompass the maximum titer possible from the culture. Comparable correlations were also observed when employing the MM-predicted mean viral titer over 0 to 86 h (meanlog10V), rather than peak titer (Table S3B).
Finally, we examined the choice of discrete time points within the replication curve. While 24 h represents the earliest time point examined in the Calu-3 infections in this study above baseline measurements, selected studies will report viral titers from the initial rounds of viral replication, such as 12 h or 16 h p.i.; MM-predicted titers at these times exhibited equivalent or weaker correlations from those at 24 h (Table S3C), at either temperature examined. Collectively, these analyses highlight the ability of the MM to inform time-point selection criteria and support the utility of peak titer and 24-h time point measurements generated in vitro to capture key aspects of viral load over the course of an infection initiated with an MOI of 0.01 infectious units/cell.

Correlations between mathematical metrics in vitro and raw titers in vivo.

After confirming that MM parameters that emulate data points collected during experimental infection offered similar predictive benefit to in vivo titer measurements as data generated experimentally, we next examined the predictive ability of parameters associated with the timing of viral growth and spread in vitro, which cannot be accurately determined without a MM. The MM can identify the time to reach intermediate (pre-peak) viral titer milestones, including the MM-predicted time for the viral titer to reach 1% of peak titer (labeled t1%maxV) or time to reach the log10 mean titer over 0 to 86 h (tmeanlog10V). For the viral titers determined in cells, negative correlations were observed between ferret NW (peak or day 1 p.i. titers) and either t1%maxV or tmeanlog10V, which were consistently stronger for infections cultured at 33°C than 37°C (Fig. 3A and B; Table S3D). This suggests that viruses that grow more rapidly in vitro (i.e., achieve these infection progression milestones more quickly) are detected at higher titer in NW specimens in vivo. These same correlations were weaker for the viral titer determined in eggs and not consistently detected for tissues collected at day 3 p.i., at either culture temperature, with few exceptions. The most notable exception was the strong negative correlation, among viruses titrated in eggs, between ferret lung (Lg) titer at day 3 p.i. and either the time to reach 1% peak titer, time to reach the mean log10 titer, or the infecting time (i.e., the time for an infectious cell to cause the infection of one another, similar to the serial interval in epidemiology, labeled tinf) in MM-predicted infections at 37°C (Fig. 3C; Table S3D). This suggests that viruses titrated in eggs that grew more rapidly (e.g., shorter time to reach pre-peak milestones) in Calu-3 were strongly associated with higher ferret Lg titers at day 3 p.i., but not (or less so) with higher peak or day 1 p.i. NW titers.
FIG 3
FIG 3 Correlations between ferret nasal wash and MM-predicted parameters. Best-fit line determined by least-squares regression; r values are from Pearson correlation tests (see Supplemental Table 3). Viral titers correspond to the log10 of the viral titers, measured in EID50/mL (NW) or EID50/g (Lg) (viral titration in eggs, circles) or PFU/mL (NW) (viral titration in cells, triangles); sample titration matrix in mixed data sets is specified in Table 1. (A to C) Peak ferret NW versus tmeanlog10V (33°C) (A) or t1%maxV (33°C) (B) for viral titers determined in cells only and day 3 p.i. lung versus tmeanlog10V (37°C) (C) for viral titers determined in eggs only. (D to F) Representative graphs of ferret NW specimens; red line presents slope1,3 generated for representative viruses showing decay (D), no change (E), or growth (F) between mean log10 NW titers day 1 and 3 p.i. Horizontal line depicts titration limit of detection for each matrix. (G to I) Pearson correlation tests of slope1,3 versus MM-predicted parameters tmeanlog10V (G), t1%maxV (H), and growth (I) at 33°C.

Correlations between mathematical metrics in vitro and titer growth/decay in vivo.

We next explored whether measures of infection progression in ferrets, rather than direct titer measurements used thus far, would provide novel or more robust comparison to the MM-predicted metrics. Unfortunately, due to various limitations inherent to both the shape of the viral titer time course and the sparsity of data collected over the course of infection in ferrets (discussed in Supplemental Methods), we were not able to apply the MM analysis performed in Calu-3 cells to the ferret viral titer time courses. As an intermediate analysis, between raw titer and full MM-estimated infection time courses, we estimated the rate of decay (negative slope) or growth (positive slope) of the NW titer in ferrets for every sampling interval between days 1 to 7 p.i., as described in the Supplemental Methods (shown therein and in Fig. 3D to F). The MM-predicted measures of infection progression in vitro were highly correlated with the rate of growth or decay of the log10 NW titer between day 1 and 3 p.i. (labeled slope1,3 to indicate the slope between these sampling points). Specifically, viruses with a shorter time to reach pre-peak milestones (t1%maxV, tmeanlog10V, tinf) and a more rapid initial growth rate (labeled growth) in Calu-3 cells were all associated with the greatest decay in ferret NW titer between day 1 and 3 p.i., for viral titer determined in both eggs and cells, at either temperature (Fig. 3G to I). In other words, viruses whose infections progressed most rapidly in Calu-3 cells also progressed most rapidly within ferrets, so much so that viral titer had already peaked and was on its way down soon after day 1 p.i. in ferrets. Significant correlations were maintained among viruses of avian, but not mammalian, origin (Table S3E). Of note, among the mammalian viruses in this study, 21/24 had negative slope1,3 values, indicating NW titer decay between day 1 and 3 p.i., while viruses of avian origin were equally split between positive and negative slope1,3 values (n = 14 each), indicating greater uniformity in this parameter among mammalian viruses (RS-p = 0.007 by Fisher’s exact test). In contrast, instantaneous growth/decay rates between other pairs of time points within these data set (e.g., between day 3 and 5 p.i., slope3,5) provided poorer correlations than slope1,3 (data not shown). Taken together, these findings support the utility of employing infection progression parameters to link virus replication in vitro and in vivo but highlight the challenges in data interpretation when employing heterogenous viruses possessing differing levels of mammalian adaptation.

Exploring virus behavior under different temperature conditions.

Slower infection progression or lower viral titer yield at 33°C than at 37°C could be indicative of poor adaptation of a virus to the cooler temperature of the upper mammalian airways. To investigate this possibility, we used the MM-predicted infection time course and parameters of 36 viruses for which replication kinetics were assessed in Calu-3 cells at both 37°C and 33°C (Fig. 4A to D; Supplemental Methods). The difference in each MM-predicted quantity at 37°C minus that at 33°C was computed across the 36 viruses and then ordered (1st to 36th) from smallest (most negative, indicating that the quantity is lower) to greatest (most positive, indicating that the quantity is larger) at 37°C compared to that at 33°C (Fig. 4E to H; Supplemental Methods).
FIG 4
FIG 4 Comparison of the effect of temperature sensitivity on MM-predicted parameters. (A to D) Time course of experimental and MM-simulated in vitro IAV infections in Calu-3 cells. Cells were infected in triplicate with the viruses shown at either 37°C (orange, stars) or 33°C (blue, circles). Experimental measurements are shown as symbols. The MM-predicted time course is shown as a solid (titer determined in cells) or dashed (titer determined in eggs) line with 95-percentile bounds at each time point (shaded regions). Horizontal and vertical solid lines represent 1% of the MM-predicted peak viral titer and the time at which it occurs (t1%maxV), respectively. The dashed diagonal lines in the bottom right of each graph are a visual representation of the growth rate of the virus (growth, see Table 2). Time courses for all viruses are shown in the Supplemental Methods. (E to H) Difference in MM-derived quantity at 37°C minus that at 33°C, computed for all viral titers determined (n = 36) in eggs (circles) or cells (triangles), ordered from smallest (or most negative) to largest (or most positive). The horizontal dashed line corresponds to zero (no difference), and the horizontal solid line corresponds to the median difference over the 36 viruses, also indicated in parenthesis [e.g., “Greater (+1.8)]”. (I to L) Correlations are between the indicated ferret titer measurement (y axis) against the difference in MM-derived quantity at 37°C minus that at 33°C (x axis). Best-fit line determined by least-squares regression; r values are from Pearson correlation tests.
The MM-predicted titer at 24 h and 36 h p.i., during the intermediate, pre-peak growth phase of infection in Calu-3, and the mean log10 titer were larger, and the infecting time (tinf) was shorter for all 36 viruses at 37°C compared to 33°C. Similarly, the MM-predicted titer at 12 h and 16 h p.i. and the Calu-3 titer growth rate were higher, and the time to reach mean log10 titer or 1% peak titer was shorter at 37°C compared to 33°C, in all but at most 3 out of 36 viruses (Fig. S3). In many cases, the differences were quite striking and are most likely biologically significant. For example, for 18 out of the 36 viruses, the MM-predicted titers at 24 h p.i. were at least 63 times higher (10+1.8 times greater) and titer grew at least 15% more rapidly (10+0.062 times greater), such that the 1% of peak titer was reached at least 13 h earlier (t1%maxV) at 37°C compared to 33°C (Fig. 4FH; Supplemental Methods). In contrast, the MM-predicted and experimentally measured peak Calu-3 titer and titer at 72 h p.i., the typical time of titer peak, were lower for 10 versus higher for 26 out of 36 viruses, at 37°C compared to 33°C, with a likely experimentally unmeasurable median difference of 1.5-fold (a log10 titer difference of 0.1), with the interesting exception of 4 viruses (e.g., A/Turkey/VA/4529/2002, Fig. 4D) for which peak titer at 37°C was more than 100-fold higher than at 33°C, whereas the exponential titer growth rate was comparable at either temperature. Taken together, these observations suggest rather strongly that while peak titer is generally unaffected by the culture temperature, higher temperature generally hastens infection progression.
As the temperature of the ferret respiratory tract is inclusive of both 33°C and 37°C, we next investigated if differences in temperature sensitivity in vitro could serve as predictive measures of in vivo replicative fitness (Table S3F; Fig. 4I to L). Interestingly, the difference in MM-predicted peak titer in Calu-3 (maxlog10V) at 37°C minus that at 33°C, a quantity that was not consistently and generally only weakly affected by temperature (Fig. 4E), correlated most strongly with slope1,3 (r = 0.59 [0.28, 0.80], RS-p = 0.2e-4), ferret NT titer at day 3 p.i. (r = 0.54 [0.35, 0.76], RS-p = 0.001), and ferret NW titer at day 1 p.i. (r = −0.43 [−0.66, −0.10], RS-p = 0.003) (Fig. 4I to J; Table S3F). There were also strong correlations between the difference in the MM-predicted mean log10 viral titer in Calu-3 (meanlog10V) at 37°C minus that at 33°C, a quantity that was significantly affected by temperature (Supplemental Methods), and both the NT titer at day 3 p.i. (r = 0.65 [0.5, 0.77], RS-p = 3e-5) and slope1,3 (r = 0.61 [0.22,0.78], RS-p = 7e-5) (Fig. 4K to L). Together, these correlations suggest that viruses that experience the largest decrease in their peak titer or total titer yield (mean log10 titer) in vitro in Calu-3 cells as a result of the decreased temperature from 37°C down to 33°C grow the slowest in ferrets, resulting in lower NW titer at day 1 p.i., with continued growth to day 3 p.i. (more positive slope1,3), leading to higher, detectable titer in the NT by day 3 p.i. Collectively, these findings support the use of a MM toward understanding the behavior of IAV strains in vitro, which can subsequently inform in vivo phenotypes of each virus, and underscore the utility of assessing IAV replicative ability at multiple physiologically relevant temperatures.

Correlation with nonviral titer contributing factors.

The capacity for high-titer virus replication in mammals represents just one of many features that contribute to zoonotic viruses overcoming host range restrictions; these can include molecular determinants of virulence with known roles in modulating virus replication, receptor binding, pH fusion, and other critical properties (17). While the scope and number of viruses in this study prohibited an exhaustive examination of all features likely to contribute, and analyses were limited to data stratified by titration matrix, we employed representative examples of key features often evaluated during in vivo risk assessments in the context of in vitro study.
Measures of virus pathogenicity in ferrets (such as weight loss and lethality) are considered in risk assessment rubrics, but it was unclear if Calu-3 IAV replication data could inform or otherwise contribute to our understanding of these properties. Peak mean weight loss in ferrets was more highly correlated with in vitro titer measurements collected at 24 h rather than with peak Calu-3 titer (Table S2D), with stronger correlations observed following culturing at 33°C compared with 37°C. At the lower temperature, the 24-h Calu-3 titer for the viral titer determined in eggs (r = 0.59 [0.16, 0.84], RS-P = 0.01) and cells (r = 0.46 [0.02, 0.75], RS-p = 0.04) was both correlated with peak mean weight loss in ferrets (Fig. 5A and B). Assessments correlating the magnitude of IAV titers in vitro to a lethal phenotype in vivo were limited, because only six strains exhibited >50% lethality (all viruses titrated in eggs). However, compared with 18 nonlethal viruses titrated in eggs, viruses exhibiting >50% lethality in ferrets replicated to marginally higher peak titer in Calu-3 cells (median [95% CI] 109.4 [108.1, 1010] EID50/mL versus 108.7 [104.6, 1010] EID50/mL) and higher titer at 24 h p.i. (median [95% CI] 107.3 [105.0, 108.7] EID50/mL versus 105.3 [102.2, 107.9], fold difference 102.1 [101.5, 105.8] EID50/mL) at 37°C.
FIG 5
FIG 5 Correlations between ferret nasal wash, PB2 627, weight loss, and 33°C Calu-3 viral titers following IAV infection. Best-fit line determined by least-squares regression; r values are from Pearson correlation tests (see Table S2). Viral titers correspond to the log10 of the viral titers, measured in EID50/mL (viral titration in eggs, circles) or PFU/mL (viral titration in cells, triangles); sample titration matrix is specified in Table 1. (A and B) Percent mean maximum ferret weight loss versus 24-h Calu-3 titer (33°C) for viral titers determined in eggs (A) or cells (B). (C) Box and whiskers plot of peak Calu-3 titer (37°C) among viruses exhibiting transmission in 0/3 pairs in a respiratory droplet transmission (RDT) ferret model (none), transmission in 1/3 or 2/3 pairs (inefficient), or transmission in 3/3 pairs (efficient) among viral titers determined in cells. (D) Box and whiskers plot of peak Calu-3 titer (37°C) among viruses exhibiting transmission in 0/3 pairs in a direct contact transmission (DCT) ferret model (none), or transmission in at least one ferret pair (some). (E and F) Box plot of 24-h Calu-3 titer at 33°C (E) and 37°C (F) for viruses bearing an E or K at PB2 627; data points represent individual viral titers determined in eggs or cells as specified in Table 1. Red dot in C to F plots signifies mean.
Virus transmissibility cannot be modeled outside a living host, and both transmissible and nontransmissible IAV replicate robustly in Calu-3 cells (18). Prior studies have identified links between donor viral load and transmissibility (19) but have not examined the utility of in vitro measures in this context. Correlations were not detected between virus transmissibility (in either a respiratory droplet or direct contact transmission model) and absolute Calu-3 viral titer (peak titer or 24-h titer cultured at 37°C, or 24-h titer cultured at 33°C) (Fig. 5C and D).
Residue 627 in PB2 has long been recognized as a critical determinant of host range for IAVs (20), with known roles in modulating mammalian pathogenicity and transmissibility, polymerase activity, and temperature sensitivity (21). For infections of Calu-3 cells, the viral titer determined in eggs with a lysine (K) at this position reached higher titer by 24 h p.i. than viruses bearing a glutamic acid (E) at both culture temperatures: a median [95% CI] 24-h titer of 105.2 [104.1, 105.9] EID50/mL versus 102.9 [102.2, 104.0] EID50/mL at 33°C (fold difference 101.9 [100.33, 103.5] EID50/mL) and 106.8 [105.3, 108.7] EID50/mL versus 105.0 [102.1, 108.0] EID50/mL at 37°C (fold difference 102.1 [10−1.6, 105.8] EID50/mL) (Fig. 5E and F). This was not the case for viruses titrated in cells where no meaningful difference was observed. While viruses bearing E627K did reach higher peak titers in vitro, the presence of the E627K mutation alone was not associated with significantly higher titers in ferret NW (peak or day 1 p.i.) or day 3 p.i. in NT (Table S2D).

DISCUSSION

Pandemic preparedness efforts encompass a diverse array of approaches to understand not only the properties of novel influenza viruses but also the host and environmental contexts in which these viruses emerge (1, 2). As such, in vitro and in vivo assessments presented in this study represent just one of a multitude of concurrent activities performed when assessing the pandemic potential of influenza viruses. As laboratory assessments of novel and emerging influenza viruses are conducted worldwide, it is critical to understand how different experimental protocols can inform and improve our ability to contextualize results, to achieve the highest confidence possible in conclusions drawn from concurrent in vitro and in vivo evaluations. Here, we use viral titer measurements from IAV infections in vitro to assess the extent to which they correlate with in vivo viral titer measurements in the respiratory tract of ferrets following infection and employ mathematical modeling to examine in finer detail the replication parameters not apparent by traditional virology analyses alone.
The data presented in this study represents an analysis of in vitro and in vivo data generated under consistent laboratory experimental protocols representative of those employed in risk assessment activities, an approach that helps reduce the effect of confounders when comparing data between different laboratory groups (7, 22). Due to extensive heterogeneity in both in vitro (3) and in vivo (5) protocols associated with influenza virus risk assessment activities, analyses investigating different cell types (such as A549 or NHBE cells), titration methods (such as TCID50), and ferret specimens (such as nose/throat swabs) were not included in this study. Calu-3 cells are highly permissive to IAV infection and support high-titer replication of a range of human and zoonotic IAVs without the addition of exogeneous trypsin, making them well suited to evaluate the heterogenous panel of viruses in this study (18). Both titer and infection progression MM-predicted parameters reported here are specific to Calu-3 cells grown under Transwell conditions, and would likely vary should the culture method or cell type be changed. Despite these limitations on the in vitro culture condition and the MM analysis, the utility of MM to validate time point selection during experimentation and to identify features of the replication curve (with regard to both viral titer and infection progression kinetics) that offer the highest predictive value to in vivo-derived measurements highlights the benefit that this analysis approach can contribute to the interpretation of data generated for risk assessment purposes.
Our data set included 52 contemporary IAV evaluated in both in vivo and in vitro models via consistent experimental protocols. An important limitation within this data set was the use of two different titration matrices (eggs and cells). There were numerous instances where high-ranking correlations were identified between in vitro and in vivo measures for viruses titrated in one titration matrix that were low ranking in the other matrix (notably with regard to measures of infection progression). We suspect this is because zoonotic viruses of avian origin tend to be titrated in eggs while mammalian-origin viruses tend to be titrated by plaque assay, as eggs are more sensitive in detecting the presence of avian-origin viruses, which may not grow well in mammalian cells. In contrast, many human and swine influenza viruses are preferentially titrated in mammalian cells (typically MDCK cells), due to robust growth in this matrix and to avoid the potential for egg-adapted mutations (23). In support of this, stratifying the data set by virus origin rather than titration matrix when possible resulted in generally comparable correlation coefficients and maintenance of statistically significant findings, although this was not possible in all analyses. As such, we cannot fully separate the contributing role of titration matrix versus host origin when reporting observed correlations. Nonetheless, considering the dual use of both egg and cell titration of IAV in the field, approaches such as this to better control for and interpret data generated from multiple titration matrices represent a critical and overdue effort.
The virologic and phenotypic heterogeneity among viruses in these data sets represented an additional challenge for comparative study. Striking differences in exponential growth or decay of replicating virus in ferret NW specimens depending on virus host origin (as supported by slope1,3 measurements) further support the idea that mixed data sets inclusive of both mammalian- and zoonotic-origin IAV may exhibit host-specific features during replication in vivo. Of note, even the contribution of specific molecular determinants of virulence can be challenging to assess in data sets with viruses containing substantial genetic diversity. For example, when cultured at 33°C, the PB2 627K mutation was associated with a higher growth rate in Calu-3 among viruses titrated in eggs (0.13 [0.11, 0.17] h−1 627K versus 0.095 [0.045, 0.11] h−1 627E) but not among viruses titrated in cells (0.16 [0.12, 0.2] h−1 627K versus 0.15 [0.08, 0.19] h−1 627E); other MM-predicted parameters (e.g., t1%maxV) exhibited similar, though less striking, trends. In the case of PB2 E627K, we examined one mutation in isolation (Table S2D); it is known that many amino acid substitutions can functionally compensate for one another (24) and that polygenic traits such as pathogenicity and transmissibility are often a reflection of specific mutations present at multiple amino acid residues (25). Limiting analyses to genetically related viruses (e.g., H7N9) often yielded higher correlation coefficients, although this could be at least in part due to the smaller number of viruses in these analyses; nonetheless, this study provides a framework for further investigation of predictive roles for specific molecular features in vitro in predicting in vivo behavior should viruses that possess reduced genetic diversity be employed.
As similar analyses have not previously been conducted, we designed this work to be exploratory in nature, intended to identify avenues and analysis strategies for further investigation rather than to provide definitive findings. Considering the substantial number of metrics evaluated throughout this study (from both raw Calu-3 data and MM derived, many of which were ultimately not included in this report), we do not report the absolute statistical significance of any correlation to avoid overinflation of reported significance. Rather, we sought to focus on biologically meaningful relationships present between parameters of the viral replication curve in vitro and multiple sample types in vivo. Linear associations (identified via Pearson correlations) between viral titers obtained in vitro and in vivo were frequently present, though correlations rarely exceeded r = 0.5 (Table S2), even among our comparisons with the highest statistical significance. Of note, analyses repeated using rank correlations were found to have relative significance (RS-p) and strength (r) largely similar to those obtained with the linear correlations (see Supplemental Methods). Calculation of predictive power scores (PPS) was employed as a complementary analysis approach to Pearson correlations, as this asymmetric nonlinear index can explore predictive nonlinear and asymmetric relationships between in vitro and in vivo data not possible by linear correlation analyses alone (26, 27). PPS analysis of viral titer data in vitro generally agreed with the strength of correlations obtained with Pearson correlations (Table S4). Collectively, these analyses demonstrated that viral titer outcomes in vitro are associated with viral titers from both nasal wash specimens and discrete tissues collected in vivo during the acute phase of infection, even when working with heterogeneous viruses, but can be dependent on the IAV under investigation, culture temperature in vitro, and sampling time and specimen type in vivo. Furthermore, these analyses identified novel areas of investigation to pursue metrics associated with infection progression in both in vitro and in vivo settings.
A MM, by its structure and the biological assumptions it encodes, constrains what range of viral titer measurements are possible over the course of an infection. Experimental measurements then provide further constraints on the shape of the MM-predicted curves, which the MM translates into constraints on its parameters (e.g., rate of virus production by each infected cell, life span of virus-producing cells, etc.). This allows a MM that is well informed by experimental measurements to successfully interpolate and predict unobserved quantities, smooth over experimental variability, and isolate different facets of the replicative fitness of a virus (13, 28). The in vitro data in the analyses considered herein were sparser (titers collected only at 1 to 2 h, 24 h, 48 h, and 72 h) than what is ideally required for MM analysis and often did not include data points during the viral decay phase following titer peak. As such, the MM-predicted viral titer curves were generally well constrained, but less so for the MM parameters.
Mathematical modeling provides an analytical and quantitative approach to examine the magnitude and impact of IAV replication efficiency relative to infection temperature in a more analytical and quantitative way than is possible with standard experimental measures. While experiments only allow us to compare the difference in titers at specific times, MM-derived quantities such as the infection growth rate or the time to reach 1% of peak titer suggested that higher temperatures generally increased the progression rate of the infection (how quickly peak titer is reached) more so than it increased the peak titer (Supplemental Methods). Interestingly, with regard to temperature sensitivity, viruses for which MM-derived peak and mean log10 Calu-3 titers decreased the most when the infection temperature was decreased from 37°C to 33°C were generally found at higher titers at day 3 p.i. In ferret NT, the tissue specimen exposed to the coolest temperatures (Fig. 4I and K) (16, 29). This same drop in peak and mean log10 Calu-3 titers at lower infection temperature was also associated with lower day 1 p.i. NW titers (Table S3F) and a larger (more positive) slope1,3 (Fig. 4J and L). Taken together, these findings suggest that viruses whose yield is most compromised at lower temperatures in vitro tend to grow more slowly in vivo, peaking at day 3 p.i., the day tissue titers were collected, rather than day 1 p.i. It is also of interest that this was more pronounced among viruses titrated in eggs (largely comprised of avian-origin strains) and not viruses titrated in cells (predominantly mammalian-origin strains), suggesting a potential difference in host adaptation captured by these analyses. To our knowledge, this is the first time a MM has been used to analyze infections conducted at different temperatures and identifies several potential areas of future inquiry. Unfortunately, because individual MM parameters were not well constrained by the analysis, we were not able to ascribe the effect of lower temperature to one or more of the virus replication steps captured by the MM parameters. Despite this, identification of heightened predictive power of 33°C Calu-3 data for upper respiratory tract specimens collected in vivo highlights the utility of employing culture temperatures emulative of the human upper respiratory tract when conducting risk assessment evaluations in vitro.
While it would have been highly desirable, we were not able to expand the same MM analysis to the in vivo infection data. This is in part because most of the ferret infection time course data considered herein either were already at peak titer at the first NW sample collected and decreased thereafter or had a single NW sample measurement collected before reaching peak titer (see Supplemental Methods). While the timing of this sampling is a reflection of appropriate anesthesia schedules for laboratory animals (30), these data lack key kinetic information, specifically the rate at which infection expanded in the host, i.e., the viral titer up-slope, which is required by the MM to robustly extract the replication efficacy of each strain. Furthermore, administration of a high-titer inoculum is necessary when evaluating pathogenicity of IAV with unknown 50% ferret infectious doses, but collection of the first NW specimen 24 h p.i. means that the presence of residual inoculum in this sample cannot be fully ruled out. Despite these challenges, we calculated the growth or decay rate of the ferret NW titer between pairs of measurement time points (see Materials and Methods) and identified one (slope1,3), which showed strong correlations with mathematical quantities characterizing infection kinetics in vitro. Our findings indicate that viruses exhibiting the most rapid growth in vitro were associated with the greatest decay between NW specimens collected day 1 and 3 p.i., likely driven by mammalian-origin viruses exhibiting robust replication in a mammalian cell line and productively infecting the ferret upper respiratory tract early after infection due to strong binding to α2,6 linked sialic acid receptors. Poorer correlation with measures of titer growth or decay in ferret NW beyond day 3 p.i. may be attributed to elicitation of diverse innate immune responses and other confounders (31); it should be noted that NW specimens are inclusive of multiple independent sites of virus replication throughout the ferret upper respiratory tract, where infection likely proceeds at different rates, peaking at different times (32). MMs incorporating experimental data from in vitro experimentation have been used to inform refinement of in vivo models and kinetics of host response elicited following viral infection, most frequently in mouse models (33, 34), highlighting the need to perform similar analyses in the ferret. As public health efforts increasingly rely on data generated from the ferret model to prevent and mitigate influenza virus infection in humans, it is prudent to similarly explore the use of MMs for data generated in this species, especially as in vitro assessments are often employed before in vivo experimentation in adherence to the 3Rs (replacement, reduction, and refinement) of animal research when employing small mammalian models (35).
This study emphasizes the need for in vivo assessments so long as in vitro experimentation cannot fully recapitulate or predict in vivo virulence phenotypes. That said, it is possible that, under yet-to-be-identified protocols for both in vitro and in vivo experimentation, additional correlates between these experiments could be identified and the predictive power of in vitro data could be significantly increased. For example, the adoption of a more accurate measure of virus sample infectivity (36) or identifying and then better managing in vitro factors that undermine reproducibility across in vitro infection experiments (37) could improve correlations between within-host and in vitro infections (38). Ultimately, this study identifies previously unrecognized correlates between data generated in vitro and in vivo for the purpose of influenza A virus risk assessment and highlights potential future areas of investigation to more quantifiably apply in vitro data toward pandemic preparedness efforts.

MATERIALS AND METHODS

Viruses.

Influenza A viruses evaluated in vivo and in vitro and included in this study are listed in Table 1. Virus stocks were either propagated in the allantoic cavity of 10- to 11-day-old embryonated chicken eggs, or MDCK cells, as described in the provided references. Infectivity titers following in vivo or in vitro inoculation were generated by determination of the EID50 or PFU titer in MDCK cells (London line) by standard plaque assay, as indicated in Table 1. As specified in the references, zoonotic influenza viruses were manipulated under biosafety level 3 containment, including enhancements as required by the U.S. Department of Agriculture and the National Select Agent Program (39).

Ferret data.

All in vivo data presented here were performed under the guidance of the Centers for Disease Control and Prevention’s Institutional Animal Care and Use Committee and were conducted in an Association for Assessment and Accreditation of Laboratory Animal Care International-accredited animal facility. Ferret pathotyping data were published previously as indicated by references in Table 1. Briefly, ferrets (5 to 12 months of age, Triple F Farms, Sayre, PA), serologically negative to circulating influenza A and B viruses, were inoculated intranasally with 105 to 107 infectious units (PFU or EID50) of virus in a 1 mL volume. Ferrets were observed daily for clinical signs of infection; ferrets that lost >25% of preinoculation body weight or exhibited signs of neurological involvement were humanely euthanized. Nasal wash specimens were collected under anesthesia on alternate days 1 to 7 p.i. as described previously (40). Necropsy of virus-inoculated ferrets was conducted day 3 p.i. for collection and titration of nasal turbinates, trachea, and lung tissue as described previously (40, 41). Nasal wash and nasal turbinates titers are reported per milliliter, whereas trachea and lung tissue are reported per gram of tissue. All samples were immediately frozen at −70°C after collection and the viral titers were determined in either eggs or MDCK cells as specified. The limit of detection was 101.5 EID50/mL or 10 PFU/mL. The growth or decay rates of ferret NW titer between a pair of measurement times, e.g., slope1,3 between day 1 and 3 p.i., were computed as the mean of the log10 NW titer measurements on day 3 minus that on day 1, divided by the time interval, e.g., 48 h.

Standard time courses in Calu-3 cells.

The human bronchial epithelial cell line Calu-3 (ATCC) was employed for all in vitro experiments. Calu-3 cells were cultured on 24-mm or 12-mm semipermeable membrane inserts with a 0.4-μm pore size (Transwell, Corning) until transepithelial resistance >1,000 Ω·cm2 was achieved as described previously (18). Calu-3 cells were infected in at least triplicate with each virus at a multiplicity of infection (MOI) of 0.01 EID50 or PFU per cell and cultured at either 37°C or 33°C, as described previously (18). The ratio of apical media volume to surface area of the Transwell was generally the same in both insert sizes (4.67 cm2/2 mL and 1.12 cm2/0.5 mL for 6-well and 12-well plate formats, respectively). Cells were collected at 2, 24, 48, and 72 h p.i., frozen at −70°C, and subsequently titers of the viruses were determined in either eggs or MDCK cells as specified. Data were collected from previously published experiments or were generated for this study as indicated.

Statistical and correlation analysis.

Peak log10 titers employed in all statistical analyses were determined for each well of Calu-3 cells or for each ferret individually and then averaged to yield mean log10 peak titer values (n ≥ 3 virus-infected Calu-3 wells or ferrets). As such, peak log10 titer averages may be inclusive of multiple time points or collection days. For tissues collected day 3 p.i. at necropsy, only viruses for which >50% of all inoculated ferrets had detectable infectious virus in each tissue were included in analyses (see Table S1 for tissue exclusion criteria). Pearson product-moment correlations were calculated to describe the relationship between in vitro and in vivo titer or weight loss data without adjustment for multiple comparisons. Statistical tests between categorical data (PB2, weight loss, lethality, transmission) were Pearson product-moment correlations. Predictive Power Scores were calculated using the R package ppsr v0.0.2 (42). All analyses were performed in R v4.0.3 (R Core Team 2020).

Modeling analyses.

Modeling analyses are described in the Supplemental Methods.

ACKNOWLEDGMENTS

We thank the international influenza community for facilitating access to many of the viruses employed in this study.
This project was supported in part by an appointment to the Research Participation Program at the Centers for Disease Control and Prevention (CDC) administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the US Department of Energy and the CDC (to H.M.C. and T.J.K.). This work was supported in part by Discovery Grants 355837-2013 and 2022-03744 (to C.A.A.B.) from the Natural Sciences and Engineering Research Council of Canada (www.nserc-crsng.gc.ca) and by the Interdisciplinary Theoretical and Mathematical Sciences program (iTHEMS, https://ithems.riken.jp/en) at RIKEN (to C.A.A.B.).
The findings and conclusions are those of the authors and do not necessarily reflect the views of the Agency for Toxic Substances and Disease Registry/CDC.

Supplemental Material

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File (jvi.01536-22-s0002.xlsx)
File (jvi.01536-22-s0003.xlsx)
File (jvi.01536-22-s0004.xlsx)
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REFERENCES

1.
Cox NJ, Trock SC, Burke SA. 2014. Pandemic preparedness and the Influenza Risk Assessment Tool (IRAT). Curr Top Microbiol Immunol 385:119–136.
2.
WHO. 2016. Tool for Influenza Pandemic Risk Assessment (TIPRA). World Health Organization, Geneva, Switzerland.
3.
Belser JA. 2018. Cell culture keeps pace with influenza virus. Lancet Respir Med 6:805–806.
4.
Hui KPY, Ching RHH, Chan SKH, Nicholls JM, Sachs N, Clevers H, Peiris JSM, Chan MCW. 2018. Tropism, replication competence, and innate immune responses of influenza virus: an analysis of human airway organoids and ex-vivo bronchus cultures. Lancet Respir Med 6:846–854.
5.
Belser JA, Barclay W, Barr I, Fouchier RAM, Matsuyama R, Nishiura H, Peiris M, Russell CJ, Subbarao K, Zhu H, Yen HL. 2018. Ferrets as models for influenza virus transmission studies and pandemic risk assessments. Emerg Infect Dis 24:965–971.
6.
Uyeki TM, Katz JM, Jernigan DB. 2017. Novel influenza A viruses and pandemic threats. Lancet 389:2172–2174.
7.
Buhnerkempe MG, Gostic K, Park M, Ahsan P, Belser JA, Lloyd-Smith JO. 2015. Mapping influenza transmission in the ferret model to transmission in humans. Elife 4:e07969.
8.
Stark GV, Long JP, Ortiz DI, Gainey M, Carper BA, Feng J, Miller SM, Bigger JE, Vela EM. 2013. Clinical profiles associated with influenza disease in the ferret model. PLoS One 8:e58337.
9.
Scull MA, Gillim-Ross L, Santos C, Roberts KL, Bordonali E, Subbarao K, Barclay WS, Pickles RJ. 2009. Avian Influenza virus glycoproteins restrict virus replication and spread through human airway epithelium at temperatures of the proximal airways. PLoS Pathog 5:e1000424.
10.
Massin P, Kuntz-Simon G, Barbezange C, Deblanc C, Oger A, Marquet-Blouin E, Bougeard S, van der Werf S, Jestin V. 2010. Temperature sensitivity on growth and/or replication of H1N1, H1N2 and H3N2 influenza A viruses isolated from pigs and birds in mammalian cells. Vet Microbiol 142:232–241.
11.
Zeng H, Pappas C, Katz JM, Tumpey TM. 2011. The 2009 pandemic H1N1 and triple-reassortant swine H1N1 influenza viruses replicate efficiently but elicit an attenuated inflammatory response in polarized human bronchial epithelial cells. J Virol 85:686–696.
12.
Chan MC, Chan RW, Chan LL, Mok CK, Hui KP, Fong JH, Tao KP, Poon LL, Nicholls JM, Guan Y, Peiris JS. 2013. Tropism and innate host responses of a novel avian influenza A H7N9 virus: an analysis of ex-vivo and in-vitro cultures of the human respiratory tract. Lancet Respir Med 1:534–542.
13.
Simon PF, de La Vega MA, Paradis E, Mendoza E, Coombs KM, Kobasa D, Beauchemin CA. 2016. Avian influenza viruses that cause highly virulent infections in humans exhibit distinct replicative properties in contrast to human H1N1 viruses. Sci Rep 6:24154.
14.
Beauchemin CA, Handel A. 2011. A review of mathematical models of influenza A infections within a host or cell culture: lessons learned and challenges ahead. BMC Public Health 11:S7.
15.
Belser JA, Eckert AM, Tumpey TM, Maines TR. 2016. Complexities in ferret influenza virus pathogenesis and transmission models. Microbiol Mol Biol Rev 80:733–744.
16.
Keck T, Leiacker R, Riechelmann H, Rettinger G. 2000. Temperature profile in the nasal cavity. Laryngoscope 110:651–654.
17.
Long JS, Mistry B, Haslam SM, Barclay WS. 2019. Host and viral determinants of influenza A virus species specificity. Nat Rev Microbiol 17:67–81.
18.
Zeng H, Goldsmith C, Thawatsupha P, Chittaganpitch M, Waicharoen S, Zaki S, Tumpey TM, Katz JM. 2007. Highly pathogenic avian influenza H5N1 viruses elicit an attenuated type I interferon response in polarized human bronchial epithelial cells. J Virol 81:12439–12449.
19.
Danzy S, Lowen AC, Steel J. 2021. A quantitative approach to assess influenza A virus fitness and transmission in guinea pigs. J Virol 95:e02320-20.
20.
Subbarao EK, London W, Murphy BR. 1993. A single amino acid in the PB2 gene of influenza A virus is a determinant of host range. J Virol 67:1761–1764.
21.
Cauldwell AV, Long JS, Moncorge O, Barclay WS. 2014. Viral determinants of influenza A virus host range. J Gen Virol 95:1193–1210.
22.
Pulit-Penaloza JA, Belser JA, Tumpey TM, Maines TR. 2019. Sowing the seeds of a pandemic? Mammalian pathogenicity and transmissibility of H1 variant influenza viruses from the swine reservoir. Trop Med Infect Dis 4:41.
23.
Szretter KJ, Balish AL, Katz JM. 2006. Influenza: propagation, quantification, and storage. Curr Protoc Microbiol 15:Unit 15G.1.
24.
Manz B, de Graaf M, Mogling R, Richard M, Bestebroer TM, Rimmelzwaan GF, Fouchier RAM. 2016. Multiple natural substitutions in avian influenza A virus PB2 facilitate efficient replication in human cells. J Virol 90:5928–5938.
25.
Steel J, Lowen AC, Mubareka S, Palese P. 2009. Transmission of influenza virus in a mammalian host is increased by PB2 amino acids 627K or 627E/701N. PLoS Pathog 5:e1000252.
26.
Gribova V, Navalikhina A, Lysenko O, Calligaro C, Lebaudy E, Deiber L, Senger B, Lavalle P, Vrana NE. 2021. Prediction of coating thickness for polyelectrolyte multilayers via machine learning. Sci Rep 11:18702.
27.
Wetschoreck F. 2020. RIP correlation. Introducing the Predictive Power Score. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9649467. Accessed 19 December 2022.
28.
Yan AWC, Zhou J, Beauchemin CAA, Russell CA, Barclay WS, Riley S. 2020. Quantifying mechanistic traits of influenza viral dynamics using in vitro data. Epidemics 33:100406.
29.
Lindemann J, Leiacker R, Rettinger G, Keck T. 2002. Nasal mucosal temperature during respiration. Clin Otolaryngol Allied Sci 27:135–139.
30.
National Research Council. 1996. Guide for the care and use of laboratory animals. National Academy Press, Washington, DC.
31.
Maines TR, Belser JA, Gustin KM, van Hoeven N, Zeng H, Svitek N, von Messling V, Katz JM, Tumpey TM. 2012. Local innate immune responses and influenza virus transmission and virulence in ferrets. J Infect Dis 205:474–485.
32.
Quirouette C, Younis NP, Reddy MB, Beauchemin CAA. 2020. A mathematical model describing the localization and spread of influenza A virus infection within the human respiratory tract. PLoS Comput Biol 16:e1007705.
33.
Pawelek KA, Dor D, Jr, Salmeron C, Handel A. 2016. Within-host models of high and low pathogenic influenza virus infections: the role of macrophages. PLoS One 11:e0150568.
34.
Handel A, Li Y, McKay B, Pawelek KA, Zarnitsyna V, Antia R. 2018. Exploring the impact of inoculum dose on host immunity and morbidity to inform model-based vaccine design. PLoS Comput Biol 14:e1006505.
35.
Russell WMS, Burch RL. 1959. The principles of humane experimental technique. Methuen & Co. Ltd., London.
36.
Cresta D, Warren DC, Quirouette C, Smith AP, Lane LC, Smith AM, Beauchemin CAA. 2021. Time to revisit the endpoint dilution assay and to replace the TCID50 as a measure of a virus sample's infection concentration. PLoS Comput Biol 17:e1009480.
37.
Paradis EG, Pinilla LT, Holder BP, Abed Y, Boivin G, Beauchemin CA. 2015. Impact of the H275Y and I223V mutations in the neuraminidase of the 2009 pandemic influenza virus in vitro and evaluating experimental reproducibility. PLoS One 10:e0126115.
38.
Pinilla LT, Holder BP, Abed Y, Boivin G, Beauchemin CA. 2012. The H275Y neuraminidase mutation of the pandemic A/H1N1 influenza virus lengthens the eclipse phase and reduces viral output of infected cells, potentially compromising fitness in ferrets. J Virol 86:10651–10660.
39.
Meechan PJ, Potts J, U.S. Dept. of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Institutes of Health. 2020. Biosafety in microbiological and biomedical laboratories, 6th ed. U.S. Dept. of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Institutes of Health, Washington, DC.
40.
Zitzow LA, Rowe T, Morken T, Shieh WJ, Zaki S, Katz JM. 2002. Pathogenesis of avian influenza A (H5N1) viruses in ferrets. J Virol 76:4420–4429.
41.
Maines TR, Lu XH, Erb SM, Edwards L, Guarner J, Greer PW, Nguyen DC, Szretter KJ, Chen LM, Thawatsupha P, Chittaganpitch M, Waicharoen S, Nguyen DT, Nguyen T, Nguyen HH, Kim JH, Hoang LT, Kang C, Phuong LS, Lim W, Zaki S, Donis RO, Cox NJ, Katz JM, Tumpey TM. 2005. Avian influenza (H5N1) viruses isolated from humans in Asia in 2004 exhibit increased virulence in mammals. J Virol 79:11788–11800.
42.
van der Laken P. 2021. ppsr: Predictive Power Score, v0.0.2. https://CRAN.R-project.org/package=ppsr. Accessed 19 December 2022.
43.
Maines TR, Jayaraman A, Belser JA, Wadford DA, Pappas C, Zeng H, Gustin KM, Pearce MB, Viswanathan K, Shriver ZH, Raman R, Cox NJ, Sasisekharan R, Katz JM, Tumpey TM. 2009. Transmission and pathogenesis of swine-origin 2009 A(H1N1) influenza viruses in ferrets and mice. Science 325:484–487.
44.
Pulit-Penaloza JA, Sun X, Creager HM, Zeng H, Belser JA, Maines TR, Tumpey TM. 2015. Pathogenesis and transmission of novel highly pathogenic avian influenza H5N2 and H5N8 viruses in ferrets and mice. J Virol 89:10286–10293.
45.
Pulit-Penaloza JA, Jones J, Sun X, Jang Y, Thor S, Belser JA, Zanders N, Creager HM, Ridenour C, Wang L, Stark TJ, Garten R, Chen LM, Barnes J, Tumpey TM, Wentworth DE, Maines TR, Davis CT. 2018. Antigenically diverse swine origin H1N1 variant influenza viruses exhibit differential ferret pathogenesis and transmission phenotypes. J Virol 92:e00095-18.
46.
Pearce MB, Belser JA, Houser KV, Katz JM, Tumpey TM. 2011. Efficacy of seasonal live attenuated influenza vaccine against virus replication and transmission of a pandemic 2009 H1N1 virus in ferrets. Vaccine 29:2887–2894.
47.
Belser JA, Gustin KM, Maines TR, Blau DM, Zaki SR, Katz JM, Tumpey TM. 2011. Pathogenesis and transmission of triple-reassortant swine H1N1 influenza viruses isolated before the 2009 H1N1 pandemic. J Virol 85:1563–1572.
48.
Pulit-Penaloza JA, Pappas C, Belser JA, Sun X, Brock N, Zeng H, Tumpey TM, Maines TR. 2018. Comparative in vitro and in vivo analysis of H1N1 and H1N2 variant influenza viruses isolated from humans between 2011 and 2016. J Virol 92:e01444-18.
49.
Pulit-Penaloza JA, Belser JA, Tumpey TM, Maines TR. 2019. Mammalian pathogenicity and transmissibility of a reassortant Eurasian avian-like A(H1N1v) influenza virus associated with human infection in China (2015). Virology 537:31–35.
50.
Maines TR, Chen LM, Matsuoka Y, Chen H, Rowe T, Ortin J, Falcon A, Nguyen TH, Mai Le Q, Sedyaningsih ER, Harun S, Tumpey TM, Donis RO, Cox NJ, Subbarao K, Katz JM. 2006. Lack of transmission of H5N1 avian-human reassortant influenza viruses in a ferret model. Proc Natl Acad Sci USA 103:12121–12126.
51.
Sun X, Pulit-Penaloza JA, Belser JA, Pappas C, Pearce MB, Brock N, Zeng H, Creager HM, Zanders N, Jang Y, Tumpey TM, Davis CT, Maines TR. 2018. Pathogenesis and transmission of genetically diverse swine-origin H3N2 variant influenza A viruses from multiple lineages isolated in the United States, 2011–2016. J Virol 92:e00665-18.
52.
Houser KV, Pearce MB, Katz JM, Tumpey TM. 2013. Impact of prior seasonal H3N2 influenza vaccination or infection on protection and transmission of emerging variants of influenza A(H3N2)v virus in ferrets. J Virol 87:13480–13489.
53.
Pearce MB, Jayaraman A, Pappas C, Belser JA, Zeng H, Gustin KM, Maines TR, Sun X, Raman R, Cox NJ, Sasisekharan R, Katz JM, Tumpey TM. 2012. Pathogenesis and transmission of swine origin A(H3N2)v influenza viruses in ferrets. Proc Natl Acad Sci USA 109:3944–3949.
54.
Pulit-Penaloza JA, Simpson N, Yang H, Creager HM, Jones J, Carney P, Belser JA, Yang G, Chang J, Zeng H, Thor S, Jang Y, Killian ML, Jenkins-Moore M, Janas-Martindale A, Dubovi E, Wentworth DE, Stevens J, Tumpey TM, Davis CT, Maines TR. 2017. Assessment of molecular, antigenic, and pathological features of canine influenza A(H3N2) viruses that emerged in the United States. J Infect Dis 216:S499–S507.
55.
Pearce MB, Pappas C, Gustin KM, Davis CT, Pantin-Jackwood MJ, Swayne DE, Maines TR, Belser JA, Tumpey TM. 2017. Enhanced virulence of clade 2.3.2.1 highly pathogenic avian influenza A H5N1 viruses in ferrets. Virology 502:114–122.
56.
Belser JA, Sun X, Brock N, Pulit-Penaloza JA, Jones J, Zanders N, Davis CT, Tumpey TM, Maines TR. 2020. Mammalian pathogenicity and transmissibility of low pathogenic avian influenza H7N1 and H7N3 viruses isolated from North America in 2018. Emerg Microbes Infect 9:1037–1045.
57.
Belser JA, Blixt O, Chen LM, Pappas C, Maines TR, Van Hoeven N, Donis R, Busch J, McBride R, Paulson JC, Katz JM, Tumpey TM. 2008. Contemporary North American influenza H7 viruses possess human receptor specificity: implications for virus transmissibility. Proc Natl Acad Sci USA 105:7558–7563.
58.
Belser JA, Lu X, Maines TR, Smith C, Li Y, Donis RO, Katz JM, Tumpey TM. 2007. Pathogenesis of avian influenza (H7) virus infection in mice and ferrets: enhanced virulence of Eurasian H7N7 viruses isolated from humans. J Virol 81:11139–11147.
59.
Belser JA, Pulit-Penaloza JA, Sun X, Brock N, Pappas C, Creager HM, Zeng H, Tumpey TM, Maines TR. 2017. A novel A(H7N2) influenza virus isolated from a veterinarian caring for cats in a New York City animal shelter causes mild disease and transmits poorly in the ferret model. J Virol 91:e00672-17.
60.
Belser JA, Davis CT, Balish A, Edwards LE, Zeng H, Maines TR, Gustin KM, Martinez IL, Fasce R, Cox NJ, Katz JM, Tumpey TM. 2013. Pathogenesis, transmissibility, and ocular tropism of a highly pathogenic avian influenza A (H7N3) virus associated with human conjunctivitis. J Virol 87:5746–5754.
61.
Belser JA, Creager HM, Zeng H, Maines TR, Tumpey TM. 2017. Pathogenesis, transmissibility, and tropism of a highly pathogenic avian influenza A(H7N7) virus associated with human conjunctivitis in Italy, 2013. J Infect Dis 216:S508–S511.
62.
Sun X, Belser JA, Pulit-Penaloza JA, Zeng H, Lewis A, Shieh WJ, Tumpey TM, Maines TR. 2016. Pathogenesis and transmission assessments of two H7N8 influenza A viruses recently isolated from turkey farms in indiana using mouse and ferret models. J Virol 90:10936–10944.
63.
Belser JA, Brock N, Sun X, Jones J, Zanders N, Hodges E, Pulit-Penaloza JA, Wentworth D, Tumpey TM, Davis T, Maines TR. 2018. Mammalian pathogenesis and transmission of avian influenza A(H7N9) Viruses, Tennessee, USA, 2017. Emerg Infect Dis 24:149–152.
64.
Belser JA, Gustin KM, Pearce MB, Maines TR, Zeng H, Pappas C, Sun X, Carney PJ, Villanueva JM, Stevens J, Katz JM, Tumpey TM. 2013. Pathogenesis and transmission of avian influenza A (H7N9) virus in ferrets and mice. Nature 501:556–559.
65.
Belser JA, Creager HM, Sun X, Gustin KM, Jones T, Shieh WJ, Maines TR, Tumpey TM. 2016. Mammalian pathogenesis and transmission of H7N9 influenza viruses from three waves, 2013–2015. J Virol 90:4647–4657.
66.
Sun X, Belser JA, Pappas C, Pulit-Penaloza JA, Brock N, Zeng H, Creager HM, Le S, Wilson M, Lewis A, Stark TJ, Shieh WJ, Barnes J, Tumpey TM, Maines TR. 2019. Risk assessment of fifth-wave H7N9 influenza A viruses in mammalian models. J Virol 93:e01740-18.

Information & Contributors

Information

Published In

cover image Journal of Virology
Journal of Virology
Volume 97Number 131 January 2023
eLocator: e01536-22
Editor: Anice C. Lowen, Emory University School of Medicine
PubMed: 36602361

History

Received: 5 October 2022
Accepted: 1 December 2022
Published online: 5 January 2023

Keywords

  1. epithelial cells
  2. ferret
  3. influenza
  4. risk assessment
  5. virus replication

Contributors

Authors

Hannah M. Creager
Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
Present address: Hannah M. Creager, University of Pittsburgh, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA.
Troy J. Kieran
Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
Hui Zeng
Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
Xiangjie Sun
Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
Joanna A. Pulit-Penaloza
Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
Katie E. Holmes
Emory University, Atlanta, Georgia, USA
Anders F. Johnson
Emory University, Atlanta, Georgia, USA
Terrence M. Tumpey
Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
Department of Physics, Ryerson University, Toronto, Canada
Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS) at RIKEN, Wako, Japan
Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, USA

Editor

Anice C. Lowen
Editor
Emory University School of Medicine

Notes

Hannah M. Creager and Troy J. Kieran contributed equally to this article. Author order reflects relative involvement in project conception.
The authors declare no conflict of interest.

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