To determine the impact of accumulating Ag exposure on immunity in the aging mouse, and to develop a model more relevant to humans who are exposed to multiple pathogens during life, we sequentially infected young female mice with four distinct pathogens at 8-wk intervals: murine γ-herpesvirus 68, Sendai virus, murine CMV, and Heligmosomoides polygyrus. Mock-infected mice received PBS. After aging the sequentially infected and mock-infected mice to 18–25 mo under specific pathogen-free conditions, we analyzed multiple immune parameters. We assessed transcriptional activity in peripheral blood, T cell phenotype, the diversity of influenza epitopes recognized by CD8 T cells, and the response of the animals to infection with influenza virus and Mycobacterium tuberculosis. Our data show enhanced transcriptional activation in sequentially infected aged mice, with changes in some CD8 T cell subsets. However, there was no measurable difference in the response of mock-infected and sequentially infected aged mice to de novo infection with either influenza virus or M. tuberculosis at 18–21 mo. Unexpectedly, a single experiment in which 25-mo-old female mice were challenged with influenza virus revealed a significantly higher survival rate for sequentially infected (80%) versus mock-infected (20%) mice. These data suggest that although exposure to a variety of pathogen challenges in the mouse model does not overtly impact cellular markers of immunity in aged female mice following de novo respiratory infection, subtle changes may emerge in other compartments or with increasing age.
Immune function declines with age (1–3). Because people are living longer, it is important to understand the impact of aging on their immune system so that vaccines and therapeutic strategies tailored for the elderly can be developed. The aging mouse model has many experimental advantages in terms of dissecting mechanisms underlying impaired immunity in the elderly. However, there are acknowledged limitations to the model (4–10). One important limitation is that mice are typically maintained under specific pathogen-free (SPF) conditions, whereas humans are exposed to a variety of pathogens throughout life. Accumulating evidence has demonstrated that microbial experience impacts the immune response, leading to the growing awareness that mouse husbandry has profound impacts on immunity (10–15). The immune system in the elderly is impacted profoundly by exposure to acute and chronic pathogens, and it has been suggested that the use of SPF mice with minimal exposure to pathogens makes mice a less relevant model for aging human immunity (5–7). Indeed, two landmark studies showed that whereas SPF mice had transcriptional profiles similar to human cord blood, Ag-experienced mice, induced either by serial pathogen infection (16) or by cohousing with pathogen-exposed pet store mice (17), had transcriptional profiles characteristic of adult humans. In addition, the Ag-experienced mice showed altered responses to vaccination against yellow fever (16) and enhanced immunity following infection with Listeria monocytogenes and Plasmodium berghei (17). These early studies involved the characterization of young adult mice. The present study’s goal was to extend these findings and determine the impact of early-life sequential Ag exposure on immunity in aged mice.
To generate immunologically experienced aged mice, we adapted the strategy of Reese et al. (16) by sequentially infecting young mice with four pathogens, including viruses and worms to establish acute and chronic infections, prior to aging the mice without any further challenges. Our data reveal that sequentially infected (SI) aged mice showed overall transcriptional activation of immune markers in peripheral blood, with some alterations in CD8 T cell phenotype compared with mock-infected (MI) aged mice, but that both SI and MI aged mice at 18–21 mo responded comparably to de novo H3N2 influenza and Mycobacterium tuberculosis infection, as assessed by survival, weight loss, and induction of epitope-specific T cells.
Materials and Methods
Mice, viruses, and infections
Female C57BL/6, B6.SJL-Ptprca Pepcb/BoyJ (B6.CD45.1), and B6.129P2-Tcrbtm1Mom Tcrdtm1Mom/J (TCRβδ−/−) mice were obtained from the Trudeau Institute animal facility or purchased from The Jackson Laboratory. All animal experiments were carefully evaluated and approved by the institutional animal care and use committee at Trudeau Institute. Twelve-wk-old female C57BL/6 mice were SI with 800 PFU murine γ-herpesvirus 68 (γHV68), 250 egg infectious dose 50 (EID50) Sendai virus (Enders strain), 2 × 104 PFU murine CMV (mCMV), and 200 Heligmosomoides polygyrus larvae at 8-wk intervals. The γHV68 and Sendai viruses were administered via the intranasal route, mCMV was administered via i.p. injection, and H. polygyrus larvae were administered via oral gavage. Control mice were MI with PBS. SI and MI mice were housed in separate cages and aged to 18–25 mo under SPF conditions (Fig. 1). For the influenza challenge, MI and SI aged mice were infected with 3000 EID50 A/X-31 (H3N2) via the intranasal route. For the M. tuberculosis challenge, the H37Rv strain of M. tuberculosis was grown in Proskauer and Beck medium containing 0.05% Tween 80 to midlog phase and was preserved in aliquots at −70°C. MI and SI aged mice were aerosol infected with ∼100 CFU using a Glas-Col airborne infection system, as described previously (18). Bacteria in lung, mediastinal lymph node, liver, and spleen tissues were measured by counting viable CFU in homogenized tissue as described (18).
Blood was collected from six different cohorts of 18–20-mo-old MI and SI mice, and total RNA was extracted using the RiboPure RNA purification kit following the manufacturer’s instructions (Thermo Fisher Scientific, Waltham, MA). RNA quality and concentration were assessed by NanoDrop (Thermo Fisher Scientific) and the Agilent Bioanalyzer RNA 6000 Chip (Agilent, Santa Clara, CA). Total RNA (200 ng) was labeled, purified, and quantified using the Agilent Low-Input QuickAmp Labeling Kit according to the manufacturer’s recommendations. A total of 600 ng cyanine 3–labeled cRNA was hybridized for 17 h to Agilent SurePrint G3 Mouse Gene Expression v2 8 × 60K microarrays (G4852B). Following post-hybridization rinses, arrays were scanned using an Agilent SureScan microarray scanner at 3-μm resolution, and hybridization intensity data were extracted from the scanned images using the Agilent feature extraction software.
Microarray data analysis
Raw microarray hybridization intensity data were log transformed to yield z-scores, and a Z-ratio (ZR) was calculated as the difference between the observed gene z-scores for the experimental and control comparisons and divided by the SD associated with the distribution of these differences. The false discovery rate (FDR) and p values were also calculated, both for individual cohorts (where possible) and for the combined set of SI and MI samples. Raw and z-score normalized gene expression data are deposited in the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) repository under accession number GSE199730.
Global expression changes
A volcano plot (Fig. 2) of ZR versus −log10p value illustrates a transcript-level comparison of aged SI/MI. Transcripts of absolute ZR ≥2 and p value <0.05 cutoffs are shown in red, with transcripts fulfilling only one of these criteria shown in gray (|ZR| ≥ 2) or blue (p < 0.05) and transcripts falling below these criteria labeled in black. Any p value of 10−16 or above is represented as 10−5 for clarity of the figure.
Gene ontology enrichment analysis (ShinyGO version 0.61)
Transcripts were included for gene ontology (GO) analysis if selected in a cohort (FDR, <0.05), and if the overall SI/MI comparison FDR was ≤0.1. This yielded a list of 179 upregulated transcripts (157 unique) to query which Top25 GO biological processes (Fig. 3A) and KEGG pathways (Fig. 3B) were regulated. No significant enrichment was found in either 43 downregulated transcripts with an FDR ≤0.1 or a list of 157 unique downregulated transcripts, sorted by lowest FDR. These data are summarized with fold change, ZR, and p values in Supplemental Table I. For both FDR and p values, values <10−16 are computationally returned as absolute 0 and are represented uniformly as 10−5.
Lymphocyte isolation and flow cytometry
Mice were sacrificed at the indicated times, and their tissues were harvested. Cells from the bronchoalveolar lavage (BAL) were collected by lavage of the lungs five times with 1 ml HBSS without calcium and magnesium. Lung tissue was prepared by coarsely chopping the tissue followed by incubation in 0.5 mg/ml solution of collagenase D (Roche) and DNase (Sigma-Aldrich) for 30–45 min at 37°C. Single-cell suspensions were prepared from lung tissue, lymph nodes, or spleens by dispersing the tissues through a 70-mm nylon tissue strainer (BD Falcon). The cell suspensions were treated with buffered ammonium chloride solution to lyse erythrocytes. Lymphocytes were enriched from digested lung tissue by differential centrifugation, using a gradient of 40/80% Percoll (GE Healthcare).
Cell populations were incubated with Fc block (anti-CD16/32) for 15 min on ice, followed by staining with MHC class I or MHC class II tetramer reagents at room temperature for 1 h. Cells were then washed and stained with fluorochrome-labeled Abs to CD8, CD44, CD69, and CD127 (eBioscience); CD8, CD62L, and CD69 (BD Biosciences); CD43, CD62L, and CD4 (BioLegend); and KLRG1 (SouthernBiotech) for 30 min on ice. MHC class I peptide tetramers specific for γHV68 (ORF6487/Db), Sendai virus (NP324/Kb), mCMV (M38316/Db), influenza virus (NP366/Db, PA224/Db, PB1703/Kb, PB1-F262/Db, and NS2114/Kb), M. tuberculosis (Tb10.44-11/Kb), and an MHC class II peptide tetramer specific for M. tuberculosis (ESAT64-17/Ab) were generated by the Trudeau Institute Molecular Biology Core Facility (19). Stained samples were analyzed on a FACSCanto II or LSR II flow cytometer (BD Biosciences). Cells were gated on lymphocyte size and granularity, with doublets excluded. Data were analyzed with FlowJo software (BD Biosciences).
For analysis of resident memory T cells (Trm), intravital staining was performed immediately before mouse euthanasia and tissue harvest. To identify T cells resident in various tissues, 1.5 μg fluorophore-conjugated CD3e (145-2C11, BD Biosciences) Ab in 1× PBS was injected i.v.; 5 min after injection, mice were euthanized and exsanguinated prior to harvest of BAL, a negative control for intravital staining, and lungs. Tissues were processed and stained with MHC class I tetramers and fluorochrome-labeled Abs as described above.
H. polygyrus imaging
Mice were euthanized via CO2 narcosis and exsanguination, and the peritoneum was opened to reveal the duodenum. A Nikon SMZ800 stereo zoom microscope was used to visualize the presence of the parasitic helminth, H. polygyrus, in the duodenum. The images were collected as TIFF files using a SPOT INSIGHT complementary metal oxide semiconductor camera and SPOT 5.3 software. Brightness and contrast were adjusted using Fiji software (ImageJ, National Institutes of Health).
Total memory (CD44High) CD8 T cells from the spleens of individual SI or MI aged mice were enriched by negative selection for CD8 cells using the BD Mouse CD8 T Lymphocyte Enrichment Kit, FACS sorted, and adoptively transferred into T cell–deficient βδ−/− mice, along with CD8-depleted splenocytes from young, congenically disparate (B6.SJL-Ptprca Pepcb/BoyJ; B6.CD45.1) mice (5 × 106 cells per recipient). CD8 memory cell numbers transferred were as follows: MI range, 5.6 × 105 to 2.7 × 106 (mean, 1.4 × 106); SI range, 4.2 × 105 to 3.3 × 106 (mean, 1.6 × 106). Recipient mice were challenged intranasally with X-31 influenza virus (H3N2, 3000 EID50). BAL and lung tissue were harvested at day 12 after infection, and the epitope specificity of the influenza-specific cells from individual SI and MI aged mice and control young mice was determined using tetramers NP366/Db, PA224/Db, PB1703/Kb, PB1-F262/Db, and NS2114/Kb. Tissues were processed and stained with MHC class I tetramers and fluorochrome-labeled Abs as described above.
Statistical analysis was performed with GraphPad Prism 5 software (GraphPad Software, La Jolla, CA). Differences were considered significant at p < 0.05.
Generation of Ag-experienced aged mice
Previous murine studies on the impact of prior pathogen exposure on immunity have used a variety of models. These include pet store mice, mice that were cohoused with either wild or pet store mice, “wilded” mice (transfer of embryos from laboratory mice into pseudo-pregnant wild dams), “rewilded” mice (laboratory mice housed in an outdoor enclosure), inbred mice exposed to dirty bedding, mice to which natural microbiota had been transferred, or mice that had been serially infected with a variety of pathogens (12, 14–17, 20–24). For our studies, to more rigorously standardize the pathogens to which the mice were exposed, we chose to serially infect mice with specific pathogens, adapting a protocol developed by the Virgin laboratory using pathogens that elicit acute and chronic infections that included mouse γHV68, mCMV, influenza virus, and H. polygyrus (16). In our studies, we infected mice with Sendai virus, a mouse parainfluenza virus, instead of influenza virus so that we could monitor the response of the Ag-experienced mice after aging to de novo influenza infection (Fig. 1). The response of SI mice to a virus-specific tetramer after each sequential infection compared with naive mice is shown (Supplemental Fig. 1). This protocol induced a well-defined immune experience in mice prior to aging. The SI mice were subsequently aged until 18–25 mo of age before they were analyzed. Control mice were MI with PBS, aged separately, and are referred to as MI aged mice.
Effects of sequential infection early in life on overall transcription in aged mice
Microarray analysis showed a significant and differential regulation of peripheral blood gene expression in SI versus MI mice aged to 20 mo. A transcript-level view is provided by the volcano plot of ZR change versus –log(p value) (Fig. 2), colorized to illustrate the significantly regulated genes in the SI aged mice. Many genes among the top 25 significantly upregulated genes represent immune response genes, including Cd8b1, Gzmk, H2-Q10, Hcst, Klrd1, Klrg1, Il18r1, and Il7r. This upregulation was also seen at the GO level, where 179 upregulated transcripts (157 unique genes) with an FDR ≤0.1 were queried to find the top 25 GO biological processes (Fig. 3A) and KEGG pathways (Fig. 3B). These almost exclusively included activation and regulation of many different innate and adaptive immune system processes and effectors, including T cell and proinflammatory disease states. In contrast, there was no significant enrichment in either 43 downregulated transcripts (FDR, ≤0.1) or in a list of 157 unique downregulated transcripts, sorted by lowest FDR.
Phenotype of CD8 memory T cells at 18 mo of age
Following the resolution of infection, effector CD8 T cells develop into short-lived effector T cells (SLECs) or memory precursor effector T cells (MPECs) (25). Two phenotypic markers that have frequently been used to identify memory T cell subsets are IL-7R (CD127) and killer cell lectin-like receptor G1 (KLRG1) (26). Using these markers, memory T cells can be categorized into short-lived effector T cells (low CD127, high KLRG1, variably termed SLECs or Tsle) and memory precursor effector T cells (high CD127 and low KLRG1, variably termed MPECs or Tmpe). Double-positive cells, variably termed double-positive effector T cells (DPECs) or Tdpe, have been shown by fate-mapping analysis to lose expression of KLRG1 and develop into a subset of CD127-positive long-term memory cells (27, 28). In the present study, we examined the impact of sequential infection on T cell phenotype in aged mice. The gating strategy to identify the subsets of CD8 T cells is shown (Fig. 4A). The distributions of distinct memory T cell subsets in the lung (Fig. 4B, 4C, 4F) and spleen (Fig. 4D, 4E, 4G) of SI and MI aged mice are shown, based on frequency and absolute numbers of the various subsets of cells from the lung and spleen of individual mice. There were more SLECs in the SI aged mice, suggesting that much of the T cell compartment is chronically activated, presumably due to multiple chronic and latent infections (γHV68, mCMV, and H. polygyrus). The chronic state of activation is also supported by the finding that within the MPECs, the SI aged mice have a preponderance of effector memory cells, whereas the MI aged mice have an abundance of central memory cells. Note that the higher frequency of cells expressing KLRG1 (SLECs and DPECs) and IL7R/CD127 (DPECs) in the SI versus MI aged mice (Fig. 4B, 4D) is consistent with data showing that Klrg1 and IL7r/CD127 gene expression is upregulated in SI aged mice (Fig. 2).
Distribution of circulating T cells and resident memory T cells in the lungs of sequentially infected and mock-infected aged mice
Memory T cells in mice can be divided into circulating T cell or Trm populations. Trm establish residence in tissues and serve as frontline responders to prevent reinfection (29). We enumerated CD8 T cells in the BAL and Trm and circulating CD8 T cells in the lung and spleen of SI and MI aged mice at 18 mo of age, using the gating strategy shown in Fig. 5A. There were statistically significant increased percentages and numbers of CD8 T cells in the BAL of SI aged mice (Fig. 5B, 5D); however, there were no statistically significant differences in numbers or frequency of Trm in the lungs of SI and MI aged mice (Fig. 5C, 5E). Although previous studies found that “dirty” pet store mice had increased numbers of Trm, our results differ and may reflect consequences of aging (17). We did, however, find enhanced frequencies and numbers of circulating cells in the lungs of SI aged mice (Fig. 5C, 5E).
Distribution of virtual memory and true memory cells in sequentially and mock-infected aged mice
Although memory cells are classically defined as a population of long-lasting Ag-specific T cells generated by exposure to Ag, a population of Ag-specific central memory cells has been identified in mice and humans (30–33) that develop in the absence of Ag and accumulate with age, termed virtual memory (VM) cells. It has been shown that VM cells represent the largest population of CD8 T cells in aged B6 mice (34). Consistent with this, we have previously shown a large population of VM cells in aged B6 mice that make a major contribution to the response of aged influenza-naive mice to influenza virus infection (35). Others have examined the impact of exposure to environmental Ags (36) and helminth infections (37, 38) on the population of VM cells, showing an increase in the population of VM cells due to Ag exposure. In the present studies, we investigated whether VM cells still dominated the response of Ag-experienced aged mice to de novo influenza infection. We used CD49 to distinguish classical “true” memory (TM; CD49dhigh) and VM (CD49dlow) populations among CD44highCD62Lhigh central memory cells (Fig. 6A). The data show a higher ratio of TM/VM CD8 T cells in both the lung (Fig. 6B) and spleen (Fig. 6C) of SI 11-mo-old and 20–24-mo-old mice than in MI mice of the same age. This contrasts with the previous studies cited above showing accumulation of VM cells with age and Ag exposure and supports the conclusion that repeated infection biases the memory T cell pool to TM cells and diminishes the population of VM cells.
The diversity of influenza virus epitopes recognized by CD8 memory T cells responding to de novo influenza infection is not profoundly affected by sequential infection of aged mice
As mice and humans age, the number of naive T cells decreases while memory T cell numbers increase. It has been suggested that this results in the inclusion of (cross-reactive) memory T cells into the response to new pathogens (39, 40). In support of this, we have previously shown that memory phenotype cells dominate the response to de novo influenza infection in aged animals (35). In addition, we have shown that the diversity of epitopes to which CD8 T cells from aged compared with young mice respond following influenza virus infection is dramatically reduced, particularly the frequency of CD8 T cells specific for the immunodominant influenza nucleoprotein (NP) epitope, NP366, in C57BL/6 mice (41). We attributed this to the decline in naive T cells associated with aging (42–46). In this study, we determined whether changes in the T cell makeup of SI aged mice, including the increased numbers of TM cells (as shown in Fig. 6), would impact the diversity of the epitope-specific response of aged mice to primary influenza virus infection. We transferred FACS-sorted memory (CD44high) CD8 T cells from influenza-naive SI and MI aged mice into T cell–deficient (βδ−/−) young mice. CD8 T cell–depleted splenocytes from young, congenic (B6.CD45.1) mice were cotransferred, and the mice were challenged with influenza virus (Fig. 7A). Responding transferred T cells from aged mice were identified, as shown in Fig. 7B. The responding T cells in individual mice were analyzed for repertoire diversity using T cell tetramers to five immunodominant influenza epitopes, described in the Materials and methods section, referred to as NP, PA, PB-1, PB1-F2, and NS2 (nonstructural protein 2) (Fig. 7C). The repertoire diversity within individual mice is presented as a rainbow plot (Fig. 7D), which shows the T cell repertoire in individual mice to be diverse and, as previously reported (41), showed a dramatic reduction in the frequency of CD8 T cells specific for NP in aged MI mice compared with young SPF mice (Fig. 7C, 7D). Furthermore, there was no evidence for a significant change in the frequency of NP-specific T cells in the SI aged mice (Fig. 7D).
Sequentially infected and mock-infected aged mice respond comparably to de novo infection with influenza virus
SI and MI aged mice (20–21 mo) were infected with influenza virus, a pathogen they had not previously encountered, as shown in Fig. 8A. Weight loss after infection was comparable in the SI and MI aged mice, and the rate of recovery of weight was not statistically different between the two groups (Fig. 8B). Additionally, the percentage survival in the two groups was comparable in two independent experiments and was 70–80% at 14 d after infection (Fig. 8C). Analysis of T cells specific for two immunodominant epitopes, NP and PA, showed that there were no statistically significant differences in the percentage distribution of tetramer-positive T cells or in the absolute number of tetramer-positive T cells in the BAL (Fig. 8D, 8E), lung (Fig. 8F, 8G), or spleen (Fig. 8H, 8I) in two independent experiments. A third experiment, in which the mice were older when infected with influenza virus (25 mo compared with 20–21 mo) also showed similar weight loss between SI and MI aged mice. Importantly, however, 25-mo-old MI aged mice had a significantly lower survival rate (20%) than SI aged mice (80%), consistent with the role of Ag experience in affecting immunity of aged mice to a new respiratory pathogen (Supplemental Fig. 2). This is an intriguing result that needs to be confirmed in additional experiments with older mice.
Sequentially infected and mock-infected aged mice respond comparably to de novo infection with M. tuberculosis
SPF aged (18–20 mo) MI and SI mice were infected with M. tuberculosis and analyzed for bacterial load and responding T cells (Fig. 9). No statistical differences in bacterial load (assessed by CFU) were seen at days 21, 30, and 65 after infection in the lungs, mesenteric lymph nodes, livers, or spleens of the MI compared with SI mice (Fig. 9A). Likewise, no statistical differences were seen in the numbers of tetramer-positive Tb10.4-specific CD8 T cells (Fig. 9B) or ESAT-6-specific CD4 T cells (Fig. 9C) assessed with Ag-specific tetramers.
Our results show a clear effect of sequential infection on the transcriptional profile in peripheral blood from aged (18–20 mo) mice, reflecting overall activation, particularly of genes associated with T cell function and immunity. Despite this, we identified only subtle changes in T cell phenotype, including a dominance of SLECs in SI mice and increased numbers of effector memory cells compared with central memory cells in the memory (MPEC) population. Both are probably a consequence of continual stimulation by the chronic and latent infections by the pathogens used for the sequential infection. There was also no increase in the numbers of Trm in the spleens and lungs of SI aged mice. Rather, the increased numbers of CD8 T cells in these organs were circulating, rather than resident, T cells. One clear impact of sequential infection was the increased ratio of TM to VM CD8 T cells. However, there was no discernible impact of sequential infection on the repertoire diversity of CD8 T cells responding to immunodominant influenza virus epitopes, nor was there an impact of sequential infection on the response of aged mice to primary infection with influenza virus or M. tuberculosis at 18–21 mo of age.
These data contribute an aging perspective to the literature comparing the immune response of “clean,” SPF-maintained laboratory mice and “dirty” mice, generated by a variety of methods, including sequential infection, cohousing with pet store or wild mice, or transfer of microbiota from wild mice (14–17, 20–24, 47). The published literature on immunity in dirty mice has shown variable results, with some studies showing enhanced immunity (17, 47, 48) and others showing depressed immunity (15, 49) or no effect (48). Variations in these results likely reflect differences in the manner of inducing antigenic experience, the pathogen used to assess immunity, the type of immune responses analyzed, and the time points and/or age at which the assessments were undertaken.
The most important distinction in our studies from those previously published is that we evaluated the difference between clean and dirty aged mice to reflect the immune experiences that accumulate in elderly humans. An important variable for our studies may be the age at which the mice were analyzed. With the exception of the data presented in Supplemental Fig. 2, all of our analysis was performed on SI or MI mice that had been aged for 18–21 mo. This age in C57BL/6 mice corresponds to a human age of 56–69 y and is a typical age of mice used for aging studies (50). When 18–21-mo-old mice were challenged with influenza virus or M. tuberculosis in this study, there was no discernible difference in the response of MI and SI aged mice. However, in a single experiment in which we challenged 25-mo-old MI and SI mice with influenza virus, the SI aged mice exhibited a significantly enhanced protection of 80% survival rate versus 20% in MI aged mice. The finding that sequential infection protects older mice at 25 mo of age from influenza infection would need to be confirmed with additional studies, and characterization of the other immune parameters described in this study would need to be repeated with mice 25 mo of age or older. These studies are beyond the scope of the present article. However, 18–21-mo-old mice are typically used for aging studies, and it is unclear why we would only see an effect in 25-mo-old mice.
What is the significance of our findings for the validity of SI aged mice as models for elderly humans? There are two possibilities. One is that, even though the aged mice are maintained in SPF conditions, they are not germ-free, so there is still exposure over time to antigenic stimulation. Perhaps this exposure is sufficient to represent infections throughout life and it is not necessary to deliberately infect mice as they age to mimic the human situation. However, transcriptional analysis showed that the SI aged mice were more generally activated, and CD8 T cells differed subtly in their CD8 T cell phenotype, indicating that sequential infection caused immune activation beyond that of SPF-maintained mice. This might be a consequence of the fact that the sequential infection included chronic pathogens. A second possibility, consistent with studies in young “dirty” mice, is that there is little effect of antigenic history on the primary immune response to influenza virus, as shown in one (15, 49) but not all (47) studies. Because an effect of prior Ag exposure was seen on vaccination, resulting in compromised responses to heterosubtypic challenge of young mice (15), additional studies in “dirty” aged mice are needed to assess the innate, humoral, and cellular responses to primary infection and vaccination.
Our experiments were designed to test whether Ag experience would impact the T cell repertoire in aged mice. It has been shown that there is a dramatic reduction of the naive T cell repertoire with aging in mice and humans (42, 51–54). We previously found a dramatic decline in the proportion of naive T cells in aged mice that resulted in a “hole in the repertoire,” manifested by the reduced response of CD8 T cells specific for a normally immunodominant influenza virus epitope in the influenza NP, NP366-374 (41). We have hypothesized that because of this decline in naive T cell repertoire diversity, responses to new Ags would be significantly mediated by memory T cells generated during the lifespan that are fortuitously cross-reactive with the new Ags (39). In support of this hypothesis, our previous data using dual adoptive transfer of memory and naive CD8 T cells from influenza-naive aged mice showed that aged memory CD8 T cells outcompete the response of aged naive CD8 T cells to a de novo influenza virus infection and further that these cells were of the VM phenotype rather than TM T cells (35). In this study, we demonstrate a clear shift in the memory CD8 T cell phenotype in SI aged mice to a TM phenotype. Despite this, and in contrast to evidence showing that specific priming with influenza virus early in life enhanced the anti-NP response of aged mice (55), memory CD8 T cells from SI aged mice did not reconstitute the strong response to influenza NP that is characteristic of young mice. This suggests that the memory repertoire generated by sequential infection cannot compensate for the diversity of the naive repertoire in young mice.
Prior infection with pathogens elicits a variety of Ag-specific immune responses and induces generalized activation, inflammation, and broad nonspecific effects on innate immunity (48, 56–58), and our data suggest that neither Ag-specific nor nonspecific effects of sequential infection were sufficient to overtly change the course of primary immunity to influenza virus or M. tuberculosis.
We chose to make our mice “dirty” by sequential infection rather than cohousing with pet store mice to control the type and duration of pathogen exposure. We essentially followed a previous protocol and sequentially infected our mice with four distinct pathogens (16), although practical considerations caused us to change the order of infection slightly and to substitute Sendai virus for influenza virus. We acknowledge that the order of infection may have an impact, but these studies are beyond the scope of the present study. Importantly, we monitored the response to each subsequent infection in SI mice compared with naive mice to confirm adequate infection in all cases. An obvious limitation of this approach is the small number of pathogens used. A better strategy may be to transfer the pooled microbiome of pet store or wild-caught mice to laboratory mice prior to aging, because several studies have underscored the importance of the microbiome in response to infection. For example, studies in which antibiotics disrupted the microbiome demonstrated the important role of the microbiome in regulating cellular and humoral immunity to influenza virus (59). More recently, transfer of microbiota from wild mice into laboratory mice was shown to greatly enhance survival to an otherwise lethal infection with heterosubtypic influenza virus (47). Another study failed to find an impact on primary influenza virus infection but described a depressed response after heterosubtypic influenza challenge (15). Transferring microbiota from wild mice into genetically defined laboratory mice prior to aging would combine the advantages of well-defined genetics of inbred mice with identical microbiota reflecting enhanced Ag experience.
Our studies are the first to assess the impact of Ag experience in the aging mouse model. The goal was to develop a “better” aging mouse model, reflective of antigenic experience characteristic of aged humans. Our key finding was that sequential infection did not have a profound impact on primary immunity in aged mice in response to de novo influenza or M. tuberculosis infections in terms of weight loss, survival, or the numbers of tetramer-positive CD8 T cells elicited. Clearly, more extensive studies are necessary, including the generation of “dirty” aged mice by different means, assessing immunity at different ages, and more rigorously assessing immunity in both the adaptive and innate immune systems.
The authors have no financial conflicts of interest.
We thank Dr. Ray Welsh for viral stocks of mCMV and advice and Mike Tighe, director of the Imaging Core Facility at Trudeau Institute, for help with the imaging of worms after H. polygyrus infection. We are grateful to Dr. David Woodland for critical discussions and evaluation of the manuscript and to Dr. In-Jeong Kim for help with figures. We acknowledge the important contribution of the Trudeau Institute animal facility for dedicated care of the mice.
This work was supported by the National Institutes of Health Grants 5R21 AG054973-02 and 3R21 AG054973-02S1 (to M.A.B., W.W.R.); the Trudeau Institute; and the Intramural Research Program of the National Institutes of Health, National Institute on Aging.
The online version of this article contains supplemental material.
The gene expression data presented in this article have been submitted to the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE199730) under accession number GSE199730.
murine γ-herpesvirus 68
double-positive effector T cell
egg infectious dose 50
false discovery rate
killer cell lectin-like receptor G1
memory precursor effector T cell
nonstructural protein 2
short-lived effector T cell
classical true memory T cell
resident memory T cell
virtual memory T cell