Aging is a complex process that has dramatic impacts on most systems in the body (1). This is especially true of the immune system where significant age-related changes are observed in both innate and adaptive immune responses. One of the most prominent manifestations of aging is an increase in susceptibility to infections. In fact, influenza infection is one of the top killers of elderly people in the world, with the oldest being most at risk (2, 3). In aging mouse models, the clearance of influenza virus is slower and T cell responses are reduced and delayed when compared to young mice, which closely mirrors what happens during influenza infection of elderly humans (4–7). In addition, our recent study demonstrated that there are lingering inflammatory cytokines such as IL-6, IL-1α, and G-CSF in the bronchiolar lavage fluid (BAL) of aged mice during influenza infection. We have also shown that there is an increasing level of albumin in the BAL of aged mice, which is indicative of lung damage during the later stages of infection (8). These results indicate that the inability to efficiently clear virus in aged lungs corresponds to extended inflammation and lung damage.

One of the most consistent ways to delay aging in mice is via caloric restriction (CR). Indeed, CR was first shown to extend the lifespan of rodents in the 1930s (9) and has since been observed across multiple species, ranging from invertebrates to rodents to even some non-human primate studies (10–13); however, it is important to note that this is not observed in all studies (14–19) suggesting some mechanisms of longevity with CR may not be conserved among species and that details of implementation likely effect outcomes. Despite these discrepancies, and more importantly, along with extending lifespan, CR has been shown to delay age-related deficits in multiple physiological systems in mice (20–25), seemingly targeting the process of aging itself. From a translational perspective, human CR studies assessing longevity are nearly impossible, and short term studies evaluating other healthspan measures are difficult to control and criticized for practicality (please refer to Ref. (26) for a recent review of human CR trials). Regardless of these limitations, results from both population studies (27), as well as the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (28–30) and Caloric Restriction with Optimal Nutrition (31, 32) studies show benefits in some areas, albeit not in all aspects of CR in rodent trials, suggesting CR does hold value within human aging research. Moreover, the benefits to multiple different systems evident in rodent studies makes it an attractive avenue to pursue. It is important to note that CR in these cases is not malnutrition (although feed is generally reduced by 40% caloric content, it is fortified with micronutrients to prevent deficiencies), and it is normally introduced after mice have reached maturation. There are many hypotheses for the mechanism of action of CR, including modulation of glucose–insulin homeostasis, growth hormone axis, autophagy changes, and alteration of inflammatory pathways; however, it is likely many of these changes may act in concert to delay aging. Nonetheless, CR has consistently shown improvement in multiple facets of aging, including delaying immunosenescence (33–37). Most notably, CR attenuates the shift from naive to memory phenotype observed in T cells with aging (38) and maintains the proliferative capacity of T cells (39, 40). Despite these positive effects on the aged immune system, the effects of CR on immune responses to influenza with aging is not clear. Original studies by Effros et al. (41) demonstrated that CR could enhance the immune response in aged mice ameliorating age-related declines in T cell proliferation and antibody production in response to intraperitoneal immunization to influenza. Conversely, work from Gardner and colleagues (42–45) demonstrated increased severity of infection in aged CR mice with increased viral titers and mortality in response to intranasal influenza infection attributed to impaired NK cell responses (43, 44) and/or reduced energy reserves and lethal weight loss (46). However, in these studies, high doses of influenza were utilized with mortality even observed in young mice; thus, it is unclear how CR modulates the immune response to more sublethal doses of influenza.

More recently, the importance of the microbiome in regulating immune responses has been elucidated. The gut microbiota have regulatory effects on not only intestinal immunity but also systemic immune responses (47) and systemic T cell subset populations can be skewed by different microbiota predominance (48–51). Further, pulmonary immunity is directly affected by the gut microbiota with regards to both allergic airway (52) and infectious disease (53, 54) responses. Importantly, the gut microbiota plays a major role in mediating flu infection-related immune responses and is particularly crucial for respiratory tract DCs migration, T cell priming, cytokine secretion, and overall viral clearance (53). Dysbiosis of the gut microbiota induced by antibiotic administration during flu infection influences helper T cell responses and can negatively impact flu outcomes and recovery (55). In addition, influenza infection itself induces gut microbiota dysbiosis through type I interferons (IFN-I) favoring Proteobacteria overgrowth (56). Thus, the relationship between gut microbiota and influenza infection seems complex and integrated.

Furthering this line of thought, the gut microbiome also changes with age [recently reviewed in Ref. (57, 58)]. More specifically, there seems to be a general decrease in microbiota diversity with aging (59, 60), as well as an increase in Proteobacteria abundance and lower levels of Firmicutes (59–63). Also, a shift toward a more Bacteroidetes dominated microbiome was associated with frailty (61). It is unknown how these changes to gut microbiota with aging may influence immune responses. CR also impacts the microbiome with greater levels of Lactobacillus and other potential probiotics associated with longevity (64). But importantly, the influence of gut bacteria microbiota changes on age-related pathologies has yet to be determined. It is possible that gut microbiota changes with CR may be a potential mechanism of longevity and/or related to some of the “antiaging” effects evident in many murine studies.

It is known that different components of nutrition can affect the aged immune system [reviewed elsewhere (65)] and that prebiotic/probiotic supplementation may decrease the severity of infection in the elderly (66–69). Moreover, small-scale studies have indicated that specific probiotics and prebiotics may improve flu vaccine response in hospitalized elderly (70–73) suggesting that age-related alterations in gut microbiota may precipitate reduced flu responses in the elderly, and that this dysbiosis can be treated to improve immune responses. Indeed, depletion of specific gut microbiota through antibiotic treatment can negatively affect both DC (53) and T cell (53, 55) influenza immune responses, while the gut microbiota itself is affected by influenza infection through type I interferons (IFN-I) (56), thus the relationship between gut microbiota and influenza infection is bidirectional and warrants further investigation. Here, in this exploratory study, we sought to examine how CR, a known modulator of aging and gut microbiota, can influence influenza-induced gut microbiota changes and immune responses during acute influenza infection in young, middle, and aged C57BL/6 mice. We hypothesized that CR would protect aged mice from age-related gut microbiota changes and thus mitigate influenza-induced changes to gut microbiota and improve immune responses.

The goal of this study was to explore the interaction of aging, diet, and influenza infection with the gut microbiome. We aimed to gain preliminary insight into how aging may impact influenza-induced microbiome changes, as well as how age-related microbiome changes may impact influenza responses. The first part of the study examined how age and CR impact the response to flu, while the second part examined the effect on the composition of gut microbiome. For these studies, we employed a sublethal infection dose and examined immune parameters at 7 days postinfection. At this time point, influenza-induced weight loss becomes more evident, however, is not different between ages (80) or CR and AL groups (unpublished experiments from the Haynes lab). Thus, percent weight loss should not be a confounding factor between groups and should not put CR mice at greater risk to succumb to infection (45, 46). While this limits the observed differences between groups due to infection, it allows us to assess early time points where the gut microbiome may play a role. Additionally, since it is known that weight loss itself affects gut microbiota (81–83), this control was necessary to gain preliminary insight into influenza-induced alterations.

In this exploratory study, we sought to obtain preliminary information as to how CR, a known modulator of aging and gut microbiota, could influence influenza-induced gut microbiota changes and immune responses during acute influenza infection in young, middle aged, and aged mice. Different outcomes of influenza infection, due to the influence of aging and CR, could be mediated by modulation of the gut microbiota by these factors with subsequent effects of infection due to the different microbial communities. We observed that CR affected the gut microbial communities (Figure 2), as found in previous studies, and that age also has an effect (the different patterns in Figure 2B). However, no obvious effect of infection was observed (Figure 2). Unlike previous studies (41, 45), we did not observe significant differences in weight loss or viral burden following influenza infection in CR aged animals. However, our study differed in that we utilized a sublethal dose of influenza and sacrificed mice at a relatively early point of infection, and both of these could contribute to a lessening of the effect of infection on the microbiome.

Although there was not a large effect on microbial community structure following infection, we did observe an increase in the proportion of Proteobacteria, which was more significant in CR mice but independent of age (Figure 2A). The increase in the proportion of Proteobacteria was correlated with weight loss (Figure 3), taken as a marker of infection severity. This again was independent of age. Further, multiple OTUs correlated with weight loss from the bacteroidetes phyla. This connection raises the possibility that a change in the microbiome has a connection with infection outcome, as hypothesized above; however, the relationship does not seem to be straight forward and members of the same phyla have differential relationships. Future research can examine if elimination or transfer of these specific bacteria can impact flu responses. Indeed, others have shown antibiotic treatment is detrimental to flu immune responses, specifically oral neomycin eliminated Gram-positive bacteria and impaired immune responses (53). Here, we identify Gram-negative bacteria that may also be crucial for immune responses.

The PR8 strain of influenza virus used for these studies will not directly infect gut tissue of B6 mice (84), raising the question of how a respiratory infection can affect the gut microbiome. The mucosal surfaces in lung and gut are considered a common mucosal surface that share immunological signals (85), so inflammation from one site is likely to affect the other site. Since Proteobacteria are generally observed during gut inflammation (86), the more severe infection in the lung, the greater the effect on the gut, and this would result in the observed correlation between Proteobacteria (a measure of inflammation in the gut) and weight loss (a measure of infection). Finally, we note that overgrowth of Proteobacteria can be impaired by blocking the type I interferon (IFN-I) signal (56), suggesting this gut microbiota change correlated with lung inflammation is mediated by an IFN-I-related immune response.

The gut is believed to be the largest immunological compartment in the body (87) and thus signaling from the gut microbiome may play an important role in viral infections. For example, mouse mammary tumor virus (88) and Enteric virus (89) require intestinal bacterial flora to establish effective infection. Lymphocytic choriomeningitis virus and influenza virus (90), conversely, will trigger a more effective immune response if intestinal bacterial flora is present. Also, germ-free mice (91), mice treated with an antibiotic cocktail (53), or TLR5 KO mice (with impaired function in sensing bacterial flagellin) (92) will not generate adequate immune response to influenza viral infection. Thus, the microbiome helps protect during flu infection. Although here we show a correlation between Proteobacteria abundance and infection, each member of the microbiota may signal the immune system in a different manner (93). For example, a previous report (53) showed that a host with antibiotic treatment to largely deplete Lachnospiraceae would not generate a good antibody response against trivalent inactivated influenza vaccine. Similarly, we noticed that higher relative abundance of genus Lachnospiraceae OTU_12 and OTU_32 are correlated with higher amount of CXCL1 and IL-1α, respectively. More generally, we show here that cytokine production associated with flu infection in our study correlates differently for each of 22 OTUs (Figure 4). This suggests that members of the microbiome regulate the immune response in different ways.

These preliminary findings contribute to the understanding of dynamics and complexity of gut bacterial microbiota and influenza infection. We are the first to show the early changes (Day 4 post influenza infection) of gut bacterial microbiota composition in both young, middle, and aged mice on both AL or CR diet. Interestingly, though CR had a great impact on gut microbiota, it did not seem to affect flu-induced immune responses or flu-induced alterations in the gut microbiota at these early time points. It is possible that these alterations, such as increased proteobacteria compared to AL mice, may have effects at later time points in flu responses and recovery. More research is necessary to determine if CR modulation of gut microbiota with aging is beneficial to flu immune responses. Cytokine production associated with influenza infection in our study correlates differently with each OTUs. Although the data we present do not allow causal relations between bacteria and cytokine production to be determined, they do provide hypotheses for virus-bacterial interactions through the immune system. There is evidence that the genus Lactobacillus can improve the immune response to the PR8 strain of influenza (94, 95) and respiratory syncytial virus (96) infection. Here, we find multiple members of the Bacteroidetes phyla to be correlated with immune parameters and flu pathogenicity. Thus, while influenza infection promotes Proteobacteria overgrowth through IFN-I (56), members of the Bacteroidetes phyla are also affected and likely in turn affect immune parameters, both positively and negatively. Interestingly, a Bacteroidetes dominated microbiome was associated with increased frailty among the elderly (61), perhaps suggesting a microbiota link associated with the increased disability observed following influenza infection in the elderly (97). Future research should explore manipulation of bacterial species from this phyla to modulate flu immune responses. Thus, we believe this exploratory study can provide some additional guidance to the use of microbiota to facilitate virus-specific immune responses, especially for elderly whose immune responses are known to be deficient.

This study was carried out in accordance with all federal, state, and institutional laws, policies, and guidelines. The protocol was approved by the UConn Health Institutional Animal Care and Use Committee.

JB. and LH designed the study; JB carried out the influenza infection and analysis of responses; XZ and GW performed the microbiome analysis; JB, LH, GK, XZ, and GW participated in preparation of the figures and manuscript.

The authors declare that this research was conducted in the absence of any commercial or financial relationships that could be considered a potential conflict of interest.