Gray Seal (Halichoerus grypus) Pups Fail to Mount an Inflammatory Cytokine Response to Influenza A Virus Open Access

Over the past several decades, an increasing number of disease outbreaks have been reported in coastal marine mammal species (Gulland and Hall 2007; Desforges et al. 2016). Infectious disease is a natural phenomenon that occurs in healthy ecosystems, which regulates population size and influences community composition and connectivity (Wood and Johnson 2015). Anthropogenic pressures have dramatically altered the marine environment in recent decades, leading to an increase in the intensity and spread of infectious diseases (Burge et al. 2014). In particular, climate change has been linked to an increased number of infectious disease outbreaks across marine taxa, through increasing water temperatures, acidification, precipitation, and storm intensity (Burge et al. 2014; Tracy et al. 2019). Climate-mediated disease emergence can have a detrimental impact on marine ecosystem conservation and biodiversity. To date, infectious diseases have led to declines and even extinction of critical keystone species, followed by trophic cascades that hinder species recovery and ecosystem functioning (Bossart 2011; Tracy et al. 2019).

One way to monitor ecosystem health is through the study of sentinel organisms that serve as early indicators of mid- to long-term changes in the environment that threaten animal, human, or ecosystem health (Reddy et al. 2001; Bossart 2011). Many marine mammal species are considered to be sentinels, due to their long lifespans, long-term coastal residency, position at or near the top of food chains, and extensive fat stores that accumulate environmental contaminants (Reddy et al. 2001; Bossart 2011). Since marine mammals are physiologically similar to humans, they can be used to predict health impacts from some threats that are difficult to study in humans (Fossi and Panti 2017). Additionally, marine mammals are charismatic megafauna, effective at garnering public attention to enact environmental change (Reddy et al. 2001; Bossart 2011). In coastal habitats, marine mammals contend with a multitude of stressors, such as climate change, commercial fisheries, pollution, wastewater runoff that may contain human infectious agents, and habitat encroachment and degradation that can leave populations vulnerable to disease (Reddy et al. 2001; Tracy et al. 2019; Sanderson and Alexander 2020).

At the land-sea interface, pinnipeds (seals, sea lions, fur seals, and walrus) represent a potential key pathogen host and vector. Influenza A virus (IAV), one of the few zoonotic pathogens to infect marine mammals, has been implicated as the cause of multiple pinniped mass mortality events across the North Atlantic Ocean (Runstadler and Puryear 2020). This pathogen was first isolated in 1979 during an outbreak in the northwest Atlantic associated with the mortality of ∼20% of the local harbor seal (Phoca vitulina) population (Geraci et al. 1982; Runstadler and Puryear 2020). Since then, IAV has caused epidemics of varying magnitude in both the west and east North Atlantic (Puryear et al. 2016; Runstadler and Puryear 2020). As recently as 2022, a strain of highly pathogenic avian influenza (H5N1) was detected for the first time in harbor and gray seals (Halichoerus grypus) in Maine, US, following widespread outbreaks in wild and domestic birds across the US and Canada (Puryear et al. 2023). Since then, H5N1 has caused the mortality of tens of thousands of sea lions, fur seals, and elephant seals in South America, even resulting in the death of an estimated 95% of elephant seal pups in Argentina’s 2023 pupping season (Ulloa et al. 2023; Campagna et al. 2024; Tomás et al. 2024; Uhart et al. 2024).

The recurring nature of IAV epidemics in pinnipeds suggests the presence of a reservoir host, in which the virus may evolve prior to spilling over into susceptible populations (Puryear et al. 2016). In the North Atlantic, gray seals are thought to fill this role, as they are permissive to IAV infections and remain largely asymptomatic, while harbor seal populations experience widespread morbidity and mortality (Puryear et al. 2016). Gray seals also periodically form dense aggregations and share haul-out sites on coastal beaches with harbor seals, creating favorable conditions for intra- and interspecies transmission of IAV, a virus that spreads both via aerosols and indirect environmental exposure (Fereidouni et al. 2016; Puryear et al. 2016).

Our currently limited understanding of pinniped immune response hinders prediction, identification, and understanding of disease outbreaks, particularly for gray seals, which largely remain asymptomatic during influenza and some other infections (e.g., phocine distemper virus [PDV] and phocine herpesvirus [PhHV]; Martina et al. 2002; Puryear et al. 2021). One way to characterize the immune response is by measuring cytokines, which are small cell-signaling proteins that initiate and regulate immune responses (Levin 2018). There is a well-known association between cytokine response and influenza, with elevated levels of some cytokines, such as IL-6, IL-10, IP-10, TNF-α, IFNγ, and/or MCP-1, marking infection in humans (Sládková and Kostolanský 2006; Betakova et al. 2017), mice (Samet and Tompkins 2017), and ferrets (Svitek et al. 2008). In some cases, levels of individual cytokines have correlated with disease severity during IAV infections (Kaiser et al. 2001; Hagau et al. 2010). However, studies of cytokines in marine mammals have primarily focused on cytokine characterization and immunoassay development (e.g., King et al. 1996; Shoda et al. 1998; St-Laurent et al. 1999; Shoji et al. 2001; Funke et al. 2003), including some studies with additional analyses into cytokine activity (King et al. 1993; Fonfara et al. 2007; Johnson et al. 2021), rather than focusing on measuring the cytokine response to infectious disease.

Our study aimed to characterize cytokine profiles and test the hypothesis that cytokine levels would be elevated in response to IAV infection in gray seal pups. In addition to IAV infection status, we aimed to determine whether variation across molt stages (as a proxy for age) and body condition (as a proxy for overall health) might influence cytokine profiles within this study system.

During annual health assessments between 2014 and 2023, gray seal pups on Monomoy Island, Muskeget Island, and Great Point, Nantucket in Massachusetts, US, were manually restrained, and separate nasal, conjunctival, and rectal swabs and blood samples were collected, as described previously (Puryear et al. 2016). The three sampling sites, within 30 km of each other off the southern coast of Cape Cod, Massachusetts, are characterized by sandy shorelines, sand dunes, and beach grass, and serve as pupping sites for a single population of gray seals in the northwest Atlantic Ocean (Wood et al. 2011). Sampling was authorized under National Marine Fisheries Service (NMFS) research permits 17670 and 21719 and National Wildlife Refuge (NWR) Research and Monitoring Special Use Permits 16-MNY-01 and 53514 and approved under NMFS Institutional Animal Care and Use Committee (IACUC) protocol Atlantic IACUC-2018-004 and Tufts University IACUC protocol G2020-108.

We determined the molt stage for each sampled gray seal pup as a proxy for age; molt stages III (molted around face and flippers), IV (molting on body), and V (completed molting) have been associated with pups that are approximately 7–18 d old, 18–25 d old, and 25–40 d old, respectively (Bowen et al. 2003; Noren et al. 2008). We measured pup girth (which reflects blubber thickness) and length to calculate a body condition index ([girth×100]/length; McLaren 1958) and used interquartile ranges to classify pup body condition (range: 70.22–110.48) as follows: “poor”≤82.96, 82.96<“average”<96.22, and “robust”>96.22. This body condition index has been used as a proxy for overall health, as pups with better health, and thus a higher body condition index, are expected to be fatter and have more blubber, while pups in poor condition (possibly due to lack of nutrition, physiological stress, etc.) are expected to be thinner (Puryear et al. 2016).

We extracted RNA from swabs that had been stored in viral transport medium using the Omega Mag-Bind Viral DNA/RNA Kit (Omega Bio-Tek, Norcross, Georgia, USA) and a KingFisher Magnetic Particle Processor (Thermo Scientific, Waltham, Massachusetts, USA), and screened for IAV using quantitative reverse transcription PCR (RT-qPCR) targeting the IAV matrix gene with qScript XLT One-Step RT-qPCR ToughMix (Quanta Biosciences, Gaithersburg, Maryland, USA), as described previously (Puryear et al. 2016). We considered pups to be IAV positive if any of the three swabs (nasal, conjunctival, or rectal) were positive for the IAV matrix gene, using a cycle threshold (Ct) of <45.

Whole blood collected in the field was centrifuged to separate the serum, which was stored at −80 C until cytokine analysis. We quantified 13 serum cytokines (IL-2, IL-6, IL-7, IL-8, IL-10, IL-15, IL-18, IFNγ, IP-10, KC-Like, MCP-1, and GM-CSF) using the commercially available MILLIPLEX Canine Cytokine/Chemokine Magnetic Bead Panel–Premixed 13 Plex (Millipore Sigma, St. Louis, Missouri, USA) and the Bio-Plex 100/200 System (Bio-Rad, Hercules, California, USA), according to the manufacturers’ instructions with appropriate quality control samples, as described previously (Levin et al. 2014). Standards were prepared using a 1/4 serial dilution to generate a standard curve, and cytokine concentrations were subsequently calculated using the Bio-Rad Manager software, accepting even values extrapolated outside of the standard curve. Cytokines were measured in three batches, and batch was accounted for as feasible in statistical analyses. Cytokines that were detected in <10% of samples across the entire dataset (n=11) were removed from statistical analyses.

To assess if analyses of IAV infection status was confounded by the batch in which cytokines were measured, molt stage, or body condition, chi-square, and Fisher’s exact tests were performed in R (R Core Team, 2024) to compare the proportions of IAV-positive (IAV+) and IAV-negative (IAV−) pups across batches, molt stages, and body condition classes. We performed pairwise Fisher’s exact tests on significant associations to determine which groups had significantly different proportions of IAV+ and IAV− pups using the R package rstatix v.0.7.2 (Kassambara 2023).

Because cytokines operate within a complex biological network, we used multivariate statistics to 1) determine if the presence and/or concentration of cytokines differed across molt stage (as a proxy for age), body condition (as a proxy for health), and IAV infection status; and 2) rank cytokines in their order of importance in explaining differences across these categorical variables. We used permutational multivariate analysis of variance (PERMANOVA) to compare the dissimilarity of cytokine presence or absence and cytokine concentration across groups defined by molt stage, body condition, or IAV infection status using the R package vegan (v.2.6-8; Oksanen et al. 2024). The nonparametric PERMANOVA statistical test uses a distance matrix to test the null hypothesis that centroids and dispersion of groups are equivalent. For cytokine presence or absence, we defined dissimilarity using the 1–Sørensen’s similarity index; for cytokine concentrations, we defined dissimilarity using the Bray-Curtis index. To eliminate the potential influence of batch on cytokine profiles (Supplementary Material Fig. S1), permutations were restricted within strata defined as batch. Group dispersions were tested for the assumption of homogeneity using an analysis of variance (ANOVA), and post-hoc Tukey honestly significant difference (HSD) tests were used to determine which groups had significantly different dispersions.

Partial-least squares discriminant analysis (PLS-DA) was used to visualize cytokine profiles between groups defined by molt stage, body condition, or IAV infection status using the R package mixOmics v.6.26.0 (Rohart et al. 2017), following removal of batch effects from the matrix of cytokine concentrations using the R package PLSDAbatch (Wang and Lê Cao 2023). The PLS-DA is a multivariate supervised dimensionality-reduction technique that is aware of group identities and attempts to create latent variables that best separate the groups, with each latent variable explaining some proportion of the variation among samples (Barker and Rayens 2003; Ruiz-Perez et al. 2020). This type of analysis is useful for datasets with correlated independent variables, such as cytokine measurements.

To rank the importance of cytokines in differentiating between groups defined by molt stage, body condition, or IAV infection status, classification and regression trees (CART) were used to assess variable (i.e., cytokine) importance using the R package rpart v.4.1.23 (Therneau et al. 2023). This analysis was considered exploratory, as batch effects could not be accounted for as they were in the PERMANOVA and PLS-DA analyses. First, the CART parameters were tuned by splitting samples into a training and validation set and conducting 10-fold cross-validation in the R package mlr3 v.0.19.0 (Lang et al. 2019) over the following parameter ranges: maxdepth=2–5, minbucket=1 to the minimum number of samples per group (molt stage=7, body condition=25, IAV=40), and minsplit=1–30. The classification error, which ranges from 0 to 1 with smaller values indicating a smaller error rate, was estimated by comparing observed and predicted classifications. Following this tuning process, the final classification trees were built; the optimal parameters for each group were as follows: for molt stage, maxdepth=2, minbucket=5, minsplit=1, and classification error=0.30; for body condition, maxdepth=4, minbucket=3, minsplit=20, and classification error=0.51; and for IAV infection status, maxdepth=5, minbucket=9, minsplit=20, and classification error=0.29.

We analyzed nasal, conjunctival, and rectal swabs, and measured 13 serum cytokines in samples collected from 116 gray seal pups from Monomoy Island (n=63), Muskeget Island (n=43), and Great Point, Nantucket (n=10) between 2014 and 2023 (Supplementary Material Table S1). These included 51 males and 62 females. They were predominately in molt stage V (n=67), but some were also in molt stages IV (n=27) or III (n=7). Most pups were classified as having “average” body condition (n=52), with similar numbers of pups classified as “poor” (n=31) or “robust” (n=25). More than half (76/116) of the gray seal pups tested positive for IAV by RT-qPCR.

There was a significant difference in the proportion of IAV+ and IAV− pups across batches (Fisher’s exact test, P<0.01) and molt stages (Fisher’s exact test, P=0.04). We found a higher IAV prevalence in samples analyzed in batch 1 (22%) and 2 (49%) compared to batch 3 (4%), with a significant difference between the proportion of IAV+ pups in batches 2 and 3 (Fisher’s exact P_adj<0.01). There was a higher prevalence of IAV in molt stage III pups (57%), the molt stage with the lowest sample size, compared to molt stage IV (44%) and molt stage V (24%). However, a post-hoc Fisher’s exact test across each pairwise comparison indicated no significant pairwise differences between molt stages (III vs. IV: P_adj=0.68; III vs. V: P_adj=0.24; IV vs. V: P_adj=0.24). There was no significant difference in the proportions of IAV+ and IAV− pups across body condition classes (X²=2.3, df = 2, P=0.32) nor by location (Fisher’s exact P=0.70); therefore, all samples were pooled and batch was accounted for in subsequent analyses.

Individual cytokines ranged from being detected in a single sample (MCP-1) to being detected in all samples (IL-18; Table 1). The cytokines GM-CSF, IP-10, MCP-1, and TNF-α were all detected in <10% of samples and were therefore excluded from further analyses. Seal pups for which molt stage (n=15) and body condition (n=8) information was not available were also removed from their respective analyses of differences in cytokine profiles for the remaining nine cytokines (IFNγ, IL-2, IL-6, IL-7, IL-8, IL-10, IL-15, IL-18, KC-like).

There was no significant difference between molt stages in the profiles for cytokine presence or absence (F_[2,98]=2.9, P=0.19) or concentration (F_[2,98]=2.5, P=0.17), and neither cytokine presence or absence (F_[2,98]=2.1, P= 0.12) nor concentration (F_[2,98]=1.6, P= 0.20) profiles violated the assumption of homogeneity of dispersions. After removing batch effects, molt stages III and V appeared to be most different from each other along the first latent variable (explaining 21% of variation) from the PLS-DA, with molt stage IV in the middle (Fig. 1). Through the classification algorithm, which excluded molt stage III due to its low sample size (n=7), IL-8 was identified as the most important cytokine (variable importance=5.5) for differentiating between molt stages IV and V. We detected IL-8 more frequently in molt stage III pups (43%) compared to molt stage IV (26%) and V (4%), but concentrations were similar across molt stages (Supplementary Material Fig. S2).

There was no significant difference across body condition classes in the cytokine profiles for presence or absence (F_[2,105]=1.90, P=0.20) or concentration (F_[2,105]=1.06, P=0.52), and cytokine concentration profiles did not violate the assumption of homogeneity of dispersion (F_[2,105]=0.54, P=0.58). However, there was a significant difference in group dispersions for cytokine presence or absence data (F_[2,105]=10.6, P<0.01), specifically between pups with body conditions classified as “robust” (average distance to centroid=0.33) and “poor” (average distance to centroid=0.38; Tukey HSD P_adj<0.01), and between pups classified as “robust” and “average” (average distance to centroid=0.37; Tukey HSD P_adj<0.01), but not between pups classified as “poor” and “average” (Tukey HSD P_adj=0.99). Following batch effect removal, the PLS-DA was unable to separate groups based on body condition classes, despite the first two latent variables explaining 45% and 26% of variance, respectively, in the data (Fig. 1). Through the classification algorithm, IFNγ was determined to be the most important cytokine (variable importance=8.2) in differentiating pups across body condition classes. We detected IFNγ more frequently in pups classified as “robust” (80%) compared to those classified as “average” (60%) and “poor” (58%), but concentrations appeared to be similar across body condition classes (Supplementary Material Fig. S3).

There was no significant difference between IAV+ and IAV− pups in the cytokine profiles for presence or absence (F_[1,114]=3.97, P=0.19) or concentration (F_[1,114]=1.58, P=0.48), and neither dataset violated the assumption of homogeneity of dispersion (presence or absence F_[1,114]=0.41, P=0.52; concentration F_[1,114]=3.62, P=0.06). Following batch effect removal, the first latent variable created by PLS-DA that explained 20% of variance showed some separation between IAV+ and IAV− pups, but there was still a large degree of overlap. Through the classification algorithm, KC-like (variable importance=5.8) and IL-6 (variable importance=4.5) were the two most important cytokines in differentiating between IAV+ and IAV− pups (Supplementary Material Fig. S4). Both KC-like and IL-6 were more frequently detected in IAV-pups (KC-like: 49%; IL-6: 39%) compared to IAV+ pups (KC-like: 30%; IL-6: 10%), yet their concentrations did not appear to differ much between groups. This trend was observed across cytokines, with five additional cytokines (IFNg, IL-7, IL-8, IL-15, and IL-10) all detected less frequently in IAV+ pups (Fig. 2), albeit at similar concentrations (Supplementary Material Fig. S5).

Gray seals in the northwest Atlantic are hypothesized to be a wild reservoir for IAV, as they are largely permissive to infection without associated morbidity and mortality (Puryear et al. 2016). Despite the potential significant implications for zoonotic disease transmission at the land-sea interface, limited understanding of the gray seal immune response hinders the ability to understand and predict their role in infectious disease outbreaks. Therefore, this study assessed cytokine profiles in wild gray seal pups, providing new insights into the early development and activity of gray seal immune systems. Cytokine profiles of gray seal pups did not significantly vary across molt stages (proxy for age), body conditions (proxy for overall health), or IAV infection status. However, individual cytokines exhibited patterns that may, in part, help explain how gray seals resist morbidity and mortality during infection.

Of the 13 cytokines measured in gray seal pup serum, four (GM-CSF, IP-10, MCP-1, and TNF-α) were detected in <10% of samples and were therefore excluded from statistical analyses. Three of these cytokines, GM-CSF, MCP-1, and TNF-α, have been previously detected in gray seals using these methods (Levin et al. 2014), suggesting that the lack of detection in our study was not a result of cross-reactivity. As these four cytokines play critical roles in inflammation during an immune response (Lee et al. 2020; Jang et al. 2021; Singh et al. 2021; Madhurantakam et al. 2023), their low detection rates may indicate a lack of inflammation. However, pro-inflammatory IL-18 was detected in every pup in our study, consistent with the stimulation of both the innate and adaptive immune systems (Ihim et al. 2022). Comparisons of the remaining frequently detected cytokines across age, overall health status, and IAV infection status therefore may provide some insights into the gray seal pup immune system.

Although no significant differences were detected in cytokine profiles across molt stages for both presence or absence and concentration of cytokines, visualization through PLS-DA suggested some variation that may reflect the natural progression of immune system development (Fig. 1). Pinnipeds, like other mammals, are born with an immune system that is not yet fully developed, marked by low immunoglobulin levels and reduced phagocytic and lymphocyte activity (Ross et al. 1993, 1994; King et al. 1994, 1998; Lalancette et al. 2003; Fonfara et al. 2008). Measures of immune function such as immunoglobulin M (IgM) and total white blood cells have been found to rapidly increase during the gray seal’s short nursing period (∼15–20 d) and then remain steady or decrease (King et al. 1994; Hall et al. 2003; Watson et al. 2021); however, similar analyses of cytokines throughout gray seal early development have not been conducted. We sampled pups at the time of weaning, following the first ∼2–3 wk of rapid immune development; therefore, their immune systems should have been close to or fully developed. Indeed, IL-8, which was identified as the most important cytokine for differentiating between molt stage groups, was detected in a declining proportion of individuals across molt stages in our study, though at approximately equal concentrations (Supplementary Material Fig. S2). More robust sample sizes of seals in early molt stages and further research including older seals, such as juveniles and adults, would be necessary to fully describe gray seal immune development and variable cytokine response across age classes. For our study, because age class distribution was not skewed between health groups, all seals were retained for subsequent analyses.

Cytokine concentrations have often been linked to health conditions, yet our study found no significant differences in cytokine profiles between gray seal pups of differing body condition classes (as a proxy intended to represent overall pup health). Body condition indices are commonly used across wild and domestic species and may represent differences in nutritional or disease status (McLaren 1958). The body condition classifications established for our study were generally consistent with those from McLaren et al. (1958), who found that the body condition of >300 ringed seals (Phoca hispida) ranged from 70 to 105, with the lower end (body condition <82) corresponding to seals that were wounded or stressed and the higher end (body condition >93) corresponding to seals that had been feeding heavily and were in good condition. Body condition has been associated with differences in cytokine profiles in several studies of domestic animals (e.g., cows, Harrison et al. 2023; dogs, Frank et al. 2015). Among marine mammals, positive associations between body condition metrics and cytokine concentrations have been found in adult female northern elephant seals (Mirounga angustirostris) during energy-demanding periods of breeding and molting (Peck et al. 2016). The lack of association between gray seal pup body condition and cytokines reported in our study may suggest that the pups were not energy limited, and thus they did not have to reduce the energy allocated toward an immune response, or that the pups were not experiencing immune challenges. However, energy-demanding growth is critical for early survival of gray seal pups (Hall et al. 2002), and disease monitoring suggests high levels of pathogen exposure (Puryear et al. 2016), suggesting there should be a relationship between body condition and immune response. Instead, we detected a significant difference in group dispersions of cytokine presence or absence between pups with a body condition classified as “robust” and those classified as “poor” or “average,” suggesting there were different levels of cytokine variation across body condition classes. Body condition, in general, may be difficult to interpret with weaned grey seal pups as they can spend >4 wk fasting post-lactation, using internal fat stores until they can forage independently (Coulson and Hickling 1964), which may confound the definition of a “healthy” gray seal pup body condition. Other metrics, such as those derived from hematology and blood chemistry, may be more indicative of pup health status (Hall 1998) and yield more insights into how body condition influences pinniped cytokine profiles.

Naturally occurring IAV infection of gray seal pups provided us with an opportunity to test the effect of a more direct health challenge on cytokine profiles, and to infer if the cytokine response to IAV infection was consistent with observed resistance to morbidity and mortality in gray seals. Gray seals in the northwest Atlantic are exposed to a variety of influenza subtypes (Puryear et al. 2016), across which cytokine responses may vary (Sládková and Kostolanský 2006). However, our mode of disease screening, RT-qPCR, is unable to distinguish between subtypes, and therefore we consider our results to reflect responses to the dominant IAV subtype present in pups or responses shared across multiple subtypes. Broadly, this study found no significant differences in the presence or concentration of cytokines between IAV+ and IAV− pups, though we observed that seven of nine cytokines measured in our study were detected less frequently in IAV+ gray seal pups (Fig. 2; Supplementary Material Fig. S5). These findings suggest that gray seals infected with IAV do not exhibit hypercytokinemia, or the “cytokine storm,” that is typically associated with symptom onset and disease severity in influenza patients (de Jong et al. 2006). Research in laboratory-adapted strains of mice has shown that a hyper-inflammatory response to influenza infection can increase morbidity and mortality (Alberts et al. 2010; Samet and Tompkins 2017).

In contrast, the observed decreases in detection of several cytokines in infected gray seal pups, as well as the absence (e.g., TNF-α) or lack of increase (e.g., IL-18) of other proinflammatory cytokines associated with the cytokine storm, may help explain the gray seals’ lack of symptoms and apparent resistance to disease.

The cytokines KC-like and IL-6 were most important for differentiating between IAV+ and IAV− gray seal pups, and both were detected less frequently in IAV+ gray seal pups (Supplementary Material Fig. S4). The pleiotropic pro-inflammatory cytokine IL-6 is typically produced by macrophages, monocytes, and T helper 1 lymphocytes at the beginning of the immune response to stimulate T- and B-lymphocytes and antibody production (Levin 2018), and increases in this cytokine have been associated with host tissue damage, morbidity, and mortality during influenza infections (Guo and Thomas 2017). In IAV-infected human patients, increases in IL-6 occur early on, coinciding with the onset of symptoms, and IL-6 levels correlate with disease severity (Kaiser et al. 2001; Hagau et al. 2010). In complement to these primary cytokines, KC is a chemokine responsible for recruiting neutrophils to the site of infection during the immune response (Sawant et al. 2016). Among mice, an established model for influenza virus infection, greater increases in KC levels during early influenza infection have been observed in susceptible mice strains, which experience greater disease severity as compared to resistant mice strains (Samet and Tompkins 2017).

The absence of the typical hyperinflammatory response to IAV could be due to IAV-induced host shutoff, a viral strategy to inhibit host gene expression and redirect host cellular machinery to viral replication (Rivas et al. 2016). Consistent with this hypothesis, recent whole blood transcriptome sequencing suggests a general down-regulation of immune response genes in gray seal pups infected with IAV (C.M.M. pers. comm.). By interfering with the host’s cellular machinery, the host-shutoff strategy can benefit the host by preventing overstimulation of the immune system. In the case of gray seals, the absence of a hyperinflammatory response could also be a mechanism by which these seals have adapted to resist IAV-associated morbidity and mortality, thus allowing them to serve as a wild disease reservoir (Puryear et al. 2016). How gray seals clear IAV infection without inducing these cytokines, some of which have been reported as critical to surviving IAV infection (Dienz et al. 2012), and the interplay of cytokine response with IAV-induced host shutoff are interesting avenues for future research across other components of the immune system. As gray seals are less susceptible to morbidity and mortality associated with various viruses (e.g., IAV, PDV, PhHV) compared to sympatric harbor seals (Martina et al. 2002; Puryear et al. 2016, 2021), future studies exploring additional pathogens and multiple host species would contribute to a more complete understanding of the evolution of differential host response. In validating the commercial canine cytokine kit for three pinniped species, Levin et al. (2014) noted that concentrations of most mitogen-stimulated cytokines were higher in harbor seals compared to gray and harp seals (Pagophilus groenlandicus), which could suggest harbor seals do exhibit increased cytokine production that leads to detrimental health effects; however, this hypothesis requires further investigation. Future studies could also target additional tissues, including the primary IAV infection site (i.e., lungs), as Kollmus et al. (2018) and Leist et al. (2016) have reported variable blood and lung transcriptome responses to influenza virus infections in mice.

Beyond pinnipeds, interesting contrasts can be made with other aquatic and terrestrial relatives. In apparent contrast to gray seals, canines exhibit severe pathogenesis during IAV infections as a result of an overactive immune response (Su et al. 2015). Among mustelids, IAV causes illness and mortality in mink and skunks, but not sea otters (Englund et al. 1986; Britton et al. 2010; White et al. 2013). These studies, among others, provide evidence of differential host immune response to IAV within and across families that may be explained by the evolutionary trajectory of host immune genetics. As new IAV strains emerge and spread, understanding the basis of the mammalian immune response will be beneficial for targeted intervention and mitigation during outbreaks.

Overall, this research provides novel insights into the immune response of gray seals to IAV. Such insights into how gray seals have evolved to resist morbidity and mortality associated with viral outbreaks across the North Atlantic may shed light on their capacity to contend with the expected climate-mediated increases in disease outbreaks (Sanderson and Alexander 2020). Knowledge on the evolution of pinniped immune response to infectious disease is increasingly important as populations around the globe are exposed to novel diseases such as highly pathogenic avian influenza in South America (Tomás et al. 2024) and PDV in the North Pacific (Goldstein et al. 2009; VanWormer et al. 2019). Future research should incorporate comparative studies of pinnipeds with varying susceptibility to infectious diseases, such as harbor seals in the context of IAV and PDV, and be conducted at varying biological scales, from cellular response to genomic underpinnings, to predict how the increased emergence and spread of infectious disease will impact global pinniped populations.

We thank Gordon Waring, Elizabeth Josephson, Frederick Wenzel, and the rest of the team at the NMFS Northeast Fisheries Science Center who provided expertise, permitting, and logistical support for this study. We are grateful to Alexa Simulynas for logistical support and sample processing, and we thank anonymous reviewers for their helpful comments. We thank Monomoy National Wildlife Refuge, the US National Park Service, the US Fish and Wildlife Service, the Trustees of Reservations, Marine Mammal Alliance Nantucket, and Crocker Snow for access or critical assistance in field operations. We are forever grateful to the many members of the field teams who made it possible to obtain samples from the rookeries. All gray seal rookery sampling was conducted under NMFS permits 17670 and 21719. Work on Monomoy Island was performed under NWR Special Use Permits 16-MNY-01 and 53514. This research was supported by the American Association of Zoo Veterinarians’ Wild Animal Health Fund, the University of Maine Office of the Vice President for Research, the Centers of Excellence for Influenza Research and Response (funded by the National Institutes of Health’s National Institute of Allergy and Infectious Disease; grants no. HHSN272201400008C, HHSN266200700010C, and 75N93021C00014), and the US National Science Foundation One Health and the Environment (OH&E): Convergence of Social and Biological Sciences NRT program grant DGE-1922560.

Supplementary material for this article is online at http://dx.doi.org/10.7589/JWD-D-24-00166.