Exploring associations between viral titer measurements and disease outcomes in ferrets inoculated with 125 contemporary influenza A viruses.

As use of the ferret model to study influenza A virus (IAV) pathogenicity increases, periodic assessment of data generated in this model is warranted, to identify features associated with virus replication throughout the respiratory tract and to refine future analyses. However, protocol-specific differences present between independent laboratories limit easy aggregation of virological data. We compiled viral titer and clinical data from >1,000 ferrets inoculated with 125 contemporary IAV under a consistent experimental protocol (including high- and low-pathogenicity avian, swine-origin, and human viruses, spanning H1, H2, H3, H5, H7, and H9 subtypes) and examined which meaningful and statistically supported associations were present among numerous quantitative measurements. Viral titers correlated positively between ferret nasal turbinate tissue, lung tissue, and nasal wash specimens, though the strength of the associations varied, notably regarding the particular nasal wash summary measure employed and properties of the virus itself. Use of correlation coefficients and mediation analyses further supported the interconnectedness of viral titer measurements taken at different sites throughout the respiratory tract. IAV possessing mammalian host adaptation markers in the HA and PB2 exhibited more rapid growth in the ferret upper respiratory tract early after infection, supported by quantities derived from infectious titer data to capture infection progression, compared with viruses bearing hallmarks of avian IAV. Collectively, this work identifies summary metrics most closely linked with virological and phenotypic outcomes in ferrets, supporting continued refinement of data analyzed from in vivo experimentation, notably from studies conducted to evaluate the public health risk posed by novel and emerging IAV.IMPORTANCEFerrets are frequently employed to study the pandemic potential of novel and emerging influenza A viruses. However, systematic retrospective analyses of data generated from these experiments are rarely performed, limiting our ability to identify trends in this data and explore how analyses can be refined. Using logarithmic viral titer and clinical data aggregated from one research group over 20 years, we assessed which meaningful and statistically supported associations were present among numerous quantitative measurements obtained from influenza A virus (IAV)-infected ferrets, including those capturing viral titers, infection progression, and disease severity. We identified numerous linear correlations between parameters assessing virus replication at discrete sites in vivo, including parameters capturing infection progression not frequently employed in the field, and sought to investigate the interconnected nature of these associations. This work supports continued refinement of data analyzed from in vivo experimentation, notably from studies which evaluate the public health risk posed by IAV.

Fatal Infection in Ferrets after Ocular Inoculation with Highly Pathogenic Avian Influenza A(H5N1) Virus.

Ocular inoculation of a clade 2.3.4.4b highly pathogenic avian influenza A(H5N1) virus caused severe and fatal infection in ferrets. Virus was transmitted to ferrets in direct contact. The results highlight the potential capacity of these viruses to cause human disease after either respiratory or ocular exposure.

Machine learning approaches for influenza A virus risk assessment identifies predictive correlates using ferret model in vivo data.

In vivo assessments of influenza A virus (IAV) pathogenicity and transmissibility in ferrets represent a crucial component of many pandemic risk assessment rubrics, but few systematic efforts to identify which data from in vivo experimentation are most useful for predicting pathogenesis and transmission outcomes have been conducted. To this aim, we aggregated viral and molecular data from 125 contemporary IAV (H1, H2, H3, H5, H7, and H9 subtypes) evaluated in ferrets under a consistent protocol. Three overarching predictive classification outcomes (lethality, morbidity, transmissibility) were constructed using machine learning (ML) techniques, employing datasets emphasizing virological and clinical parameters from inoculated ferrets, limited to viral sequence-based information, or combining both data types. Among 11 different ML algorithms tested and assessed, gradient boosting machines and random forest algorithms yielded the highest performance, with models for lethality and transmission consistently better performing than models predicting morbidity. Comparisons of feature selection among models was performed, and highest performing models were validated with results from external risk assessment studies. Our findings show that ML algorithms can be used to summarize complex in vivo experimental work into succinct summaries that inform and enhance risk assessment criteria for pandemic preparedness that take in vivo data into account.

An aggregated dataset of serial morbidity and titer measurements from influenza A virus-infected ferrets.

Data from influenza A virus (IAV) infected ferrets provides invaluable information towards the study of novel and emerging viruses that pose a threat to human health. This gold standard model can recapitulate many clinical signs of infection present in IAV-infected humans, support virus replication of human, avian, swine, and other zoonotic strains without prior adaptation, and permit evaluation of virus transmissibility by multiple modes. While ferrets have been employed in risk assessment settings for >20 years, results from this work are typically reported in discrete stand-alone publications, making aggregation of raw data from this work over time nearly impossible. Here, we describe a dataset of 728 ferrets inoculated with 126 unique IAV, conducted by a single research group under a uniform experimental protocol. This collection of morbidity, mortality, and viral titer data represents the largest publicly available dataset to date of in vivo-generated IAV infection outcomes on a per-ferret level.

Key considerations to improve the normalization, interpretation and reproducibility of morbidity data in mammalian models of viral disease.

Viral pathogenesis and therapeutic screening studies that utilize small mammalian models rely on the accurate quantification and interpretation of morbidity measurements, such as weight and body temperature, which can vary depending on the model, agent and/or experimental design used. As a result, morbidity-related data are frequently normalized within and across screening studies to aid with their interpretation. However, such data normalization can be performed in a variety of ways, leading to differences in conclusions drawn and making comparisons between studies challenging. Here, we discuss variability in the normalization, interpretation, and presentation of morbidity measurements for four model species frequently used to study a diverse range of human viral pathogens - mice, hamsters, guinea pigs and ferrets. We also analyze findings aggregated from influenza A virus-infected ferrets to contextualize this discussion. We focus on serially collected weight and temperature data to illustrate how the conclusions drawn from this information can vary depending on how raw data are collected, normalized and measured. Taken together, this work supports continued efforts in understanding how normalization affects the interpretation of morbidity data and highlights best practices to improve the interpretation and utility of these findings for extrapolation to public health contexts.

A naturally occurring HA-stabilizing amino acid (HA1-Y17) in an A(H9N2) low-pathogenic influenza virus contributes to airborne transmission.

IMPORTANCE: Despite the accumulation of evidence showing that airborne transmissible influenza A virus (IAV) typically has a lower pH threshold for hemagglutinin (HA) fusion activation, the underlying mechanism for such a link remains unclear. In our study, by using a pair of isogenic recombinant A(H9N2) viruses with a phenotypical difference in virus airborne transmission in a ferret model due to an acid-destabilizing mutation (HA1-Y17H) in the HA, we demonstrate that an acid-stable A(H9N2) virus possesses a multitude of advantages over its less stable counterpart, including better fitness in the ferret respiratory tract, more effective aerosol emission from infected animals, and improved host susceptibility. Our study provides supporting evidence for the requirement of acid stability in efficient airborne transmission of IAV and sheds light on fundamental mechanisms for virus airborne transmission.

Highly pathogenic avian influenza A(H5N1) virus of clade 2.3.4.4b isolated from a human case in Chile causes fatal disease and transmits between co-housed ferrets.

Clade 2.3.4.4b highly pathogenic avian influenza A(H5N1) viruses have caused large outbreaks within avian populations on five continents, with concurrent spillover into a variety of mammalian species. Mutations associated with mammalian adaptation have been sporadically identified in avian isolates, and more frequently among mammalian isolates following infection. Reports of human infection with A(H5N1) viruses following contact with infected wildlife have been reported on multiple continents, highlighting the need for pandemic risk assessment of these viruses. In this study, the pathogenicity and transmissibility of A/Chile/25945/2023 HPAI A(H5N1) virus, a novel reassortant with four gene segments (PB1, PB2, NP, MP) from North American lineage, isolated from a severe human case in Chile, was evaluated in vitro and using the ferret model. This virus possessed a high capacity to cause fatal disease, characterized by high morbidity and extrapulmonary spread in virus-inoculated ferrets. The virus was capable of transmission to naïve contacts in a direct contact setting, with contact animals similarly exhibiting severe disease, but did not exhibit productive transmission in respiratory droplet or fomite transmission models. Our results indicate that the virus would need to acquire an airborne transmissible phenotype in mammals to potentially cause a pandemic. Nonetheless, this work warrants continuous monitoring of mammalian adaptations in avian viruses, especially in strains isolated from humans, to aid pandemic preparedness efforts.