Literature DB >> 27096360

Normalizing the environment recapitulates adult human immune traits in laboratory mice.

Lalit K Beura1, Sara E Hamilton2, Kevin Bi3, Jason M Schenkel1, Oludare A Odumade2, Kerry A Casey1, Emily A Thompson1, Kathryn A Fraser1, Pamela C Rosato1, Ali Filali-Mouhim4, Rafick P Sekaly4, Marc K Jenkins1, Vaiva Vezys1, W Nicholas Haining3, Stephen C Jameson2, David Masopust1.   

Abstract

Our current understanding of immunology was largely defined in laboratory mice, partly because they are inbred and genetically homogeneous, can be genetically manipulated, allow kinetic tissue analyses to be carried out from the onset of disease, and permit the use of tractable disease models. Comparably reductionist experiments are neither technically nor ethically possible in humans. However, there is growing concern that laboratory mice do not reflect relevant aspects of the human immune system, which may account for failures to translate disease treatments from bench to bedside. Laboratory mice live in abnormally hygienic specific pathogen free (SPF) barrier facilities. Here we show that standard laboratory mouse husbandry has profound effects on the immune system and that environmental changes produce mice with immune systems closer to those of adult humans. Laboratory mice--like newborn, but not adult, humans--lack effector-differentiated and mucosally distributed memory T cells. These cell populations were present in free-living barn populations of feral mice and pet store mice with diverse microbial experience, and were induced in laboratory mice after co-housing with pet store mice, suggesting that the environment is involved in the induction of these cells. Altering the living conditions of mice profoundly affected the cellular composition of the innate and adaptive immune systems, resulted in global changes in blood cell gene expression to patterns that more closely reflected the immune signatures of adult humans rather than neonates, altered resistance to infection, and influenced T-cell differentiation in response to a de novo viral infection. These data highlight the effects of environment on the basal immune state and response to infection and suggest that restoring physiological microbial exposure in laboratory mice could provide a relevant tool for modelling immunological events in free-living organisms, including humans.

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Year:  2016        PMID: 27096360      PMCID: PMC4871315          DOI: 10.1038/nature17655

Source DB:  PubMed          Journal:  Nature        ISSN: 0028-0836            Impact factor:   49.962


Given reported species-specific differences in immune responses[1-8] we wished to compare memory CD8 T cell distribution and differentiation between mice and humans. CD8 T cells are critical for adaptive immune control of intracellular infections and cancer and their distribution and differentiation relate directly to their function. We assessed nonlymphoid distribution by examining available specimens of normal cervical tissue from premenopausal adult women. We found that CD8 T cells were integrated within the mucosa, exhibited a tissue resident memory T cell (TRM) phenotype[9], and comprised ~15,000 of every million cells (Fig. 1a&b and data not shown). This agrees with previous reports that adult (unlike neonatal) human nonlymphoid tissues are abundantly populated with TRM[10-12]. In contrast, CD8 T cells were almost completely absent from cervical sections from adult inbred laboratory mice (C57BL/6 strain) housed under SPF conditions (Fig. 1a&b). We then compared major CD8 T cell lineages in blood, focusing on species-specific markers that define functionally homologous populations of naïve, central memory (TCM), and terminally differentiated effector memory CD8 T cells (TEM or TEMRA) (Fig. 1c). Relative to adult humans, memory CD8 T cells were much scarcer in laboratory mice, and almost entirely comprised of TCM rather than TEM or TEMRA. Also in contrast to humans, laboratory mice lacked CD27lo/Granzyme B+ effector differentiated memory CD8 T cells that are thought to respond most immediately to infection (Fig. 1c)[13,14]. Thus, memory CD8 T cells in laboratory mice were less frequent and strikingly different from those in adult humans, and in fact, appeared much more similar to neonates (Fig. 1c&d and extended data Fig. 1).
Figure 1

Laboratory mice lack differentiated memory CD8 T cell subsets, which is more similar to neonates than adult humans

a,b) CD8 T cell density within adult laboratory mouse (n=5) and human cervix (n=3). Representative immunofluorescence staining of frozen sections (scale bars, 50 μm) is shown. CD8β (red), DAPI (nuclei, blue). c) CD8 T cell phenotype was compared between adult human blood (n=13), adult laboratory mouse blood (n=10), and human cord blood (n=8) among 2 independent experiments by fluorescent flow cytometry (representative plots shown). Top panels gated on CD8+/CD3+ cells. Bottom panels gated on antigen experienced CD8+/CD3+ cells, as defined by conventional lineage markers in each species (red and green quadrants). d) Enumeration of granzyme B+ CD8 T cell frequencies amongst antigen experienced subsets. Significance was determined using unpaired two-sided Mann-Whitney U-test (in b) or Kruskal-wallis (ANOVA) test (in d). * p<0.05, ** p<0.01, **** p<0.0001, bars indicate mean ± S.E.M.

Extended data figure 1

Frequency of CD8 T cell subsets in neonates vs. adult humans. CD8 T cell subsets were defined in adult PBMC (n=13) and cord blood PBMC (n=8) by fluorescence flow cytometry based on the following markers: Naïve=CD45RAhi CCR7hi, TCM=CD45RAlo CCR7hi, TEM=CD45RAlo CCR7lo and TEMRA=CD45RAhi CCR7lo. Significance was determined using unpaired two-sided t-test. *** p<0.001, **** p<0.0001. Bars indicate mean ± S.E.M.

Mice are amongst the closest relatives of primates, but diverged from human ancestors 65–75 million years ago[15]. We wished to test whether the differences we observed between mice and adult humans were intrinsic to all mice (divergent immune system evolution), or unique to laboratory mice. To this end, we trapped free-living barn populations of feral mice and also procured mice from commercial pet stores. Compared to inbred SPF laboratory mice, feral and petstore mice were enriched in antigen-experienced CD8 T cells, particularly those that displayed a differentiated effector memory phenotype (Fig. 2a). More extensive phenotypic characterization of differentiation markers previously defined among CD8 T cells in laboratory mice revealed that naïve (CD44lo) cells were phenotypically identical between SPF and petstore mice (Fig. 2b). These data validated the consistency of markers used to discriminate between naïve and antigen experienced CD8 T cell populations in the different mouse cohorts. However, examination of antigen-experienced (CD44hi) CD8 T cells indicated enrichment of granzyme B+ and CD27lo cells in petstore mice, as well as other signatures of terminal effector differentiation including CD62Llo, CXCR3lo, CD127lo, and 1B11hi phenotypes. However, cell size (Fsc), proliferation (Ki67), and other measures of recent antigen exposure (CD69 and PD-1) were equivalent between both cohorts of mice, indicating that differences in CD8 T cell differentiation state were not due to an ongoing acute infection in petstore mice. Moreover, the relative density of CD8 T cells in petstore mouse nonlymphoid tissues was up to 50-fold greater than that observed in SPF laboratory mice (Fig. 2c,d).
Figure 2

CD8 T cell subsets vary between feral, petstore, and laboratory mice

a) CD8 T cell subsets were compared in PBMC between laboratory mice (n=9), feral mice that were trapped in the wild (n=10), and mice obtained from a pet store (n=6), among 2 independent experiments. b) Phenotype of CD44lo/CD62Lhi (naïve) and CD44hi (antigen-experienced) CD8+ PBMC was compared by fluorescent flow cytometry. c,d) CD8 T cell density in non-lymphoid tissues was compared between laboratory and pet store mice by quantitative immunofluorescence microscopy (QIM). Immunofluorescence staining of frozen sections of indicated tissues (n=8 animals per group, scale bars = 50 μm). CD8β (red), DAPI (nuclei, blue). Significance was determined using unpaired two-sided Mann-Whitney U-test. * p<0.05, ** p<0.01, *** p<0.001, **** p<0.0001, bars indicate mean ± S.E.M.

Petstore mice recapitulated aspects of human CD8 T cell differentiation and distribution that were absent in laboratory mice. These differences could be solely due to genetics, or dependent on environment. Inbred mice are genetically homogenous, which permits refined approaches and reductionist comparisons not possible in outbred populations, but may be accompanied by mutations that impair immune system development or function. Laboratory mice live in filtered microisolator housing that provides extreme hygiene[16]. Conditions are so clean, mice harboring fatal immunodeficiency diseases often thrive in these environments due to the absence of pathogen exposure. Thus, modern husbandry has evolved to the point in which laboratory mice, the standard model for biomedical research, exhibit far less infectious experience than free-living (i.e., dirty) humans or mice naturally acquire[17-20]. To test whether environmental conditions might impact the differentiation state and distribution of CD8 T cells, we co-housed inbred laboratory mice with petstore mice, which were not raised in ultra hygienic barrier facilities. Adult petstore mice were introduced into cages containing adult laboratory (C57BL/6 strain) inbred mice. Within four weeks of co-housing, the CD44hi cells increased from 15% to ~70% of CD8+ peripheral blood mononuclear cells (PBMC), and plateaued at ~50% after eight weeks for the duration of the study (Fig. 3a,b). 22% of laboratory mice died during the first eight weeks of co-housing, after which no further mortality was observed (Fig. 3c).
Figure 3

Co-housing with petstore mice changes the immune system of laboratory mice

a) Proportion of CD44hi (antigen-experienced) CD8+ PBMC in laboratory mice housed in either SPF conditions (blue, n=10 for each time point) or co-housed with pet store mice (green, n=9 for each time point) among 2 independent experiments. b) CD8 T cell phenotype in blood of laboratory mice after co-housing with pet store mice. Representative flow plots (n=15) are shown. c) Survival of laboratory mice after co-housing with pet store mice (n=65 mice sampled). d) Representative phenotypes of CD44hi CD8+ PBMC isolated from laboratory mice co-housed with pet store mice for 100 days (n=15) and non-cohoused age-matched controls (n=10) was compared by fluorescent flow cytometry. e) Enumeration of CD8 T cells in non-lymphoid tissues between laboratory and cohoused mice by QIM (n=8 animals per group). f) Ratio of indicated cell types isolated and enumerated by flow cytometry from cohoused (n=6) and laboratory mice (n=6) among 2 independent experiments. ILC = innate lymphoid cells, Th = CD4 T helper cells, Treg = Regulatory CD4 T cells, GC = Germinal center. Single positive staining for T-bet, Gata3, and Rorγt were used to determine Th1, Th2, and Th17 lineages, respectively. g) Serum antibody concentrations (n=7 per group). h) Principal component analysis of gene expression data from PBMC of laboratory (n=8), cohoused (n=7) and pet store mice (n=8). Significance was determined using unpaired two-sided Mann-Whitney U-test (in e) or one-way ANOVA with Bonferroni post-hoc analysis (in g). *p<0.05, ** p<0.01, ***p<0.001. Bars indicate mean ± S.E.M.

Serology was performed for common mouse pathogens, and revealed exposure to viral, bacterial, and helminth pathogens (although not murine cytomegalovirus, see extended data table 1). Co-housing resulted in a constitutive increase in highly differentiated effector memory cells that matched signatures in outbred mice and humans, including the accrual of granzyme B+ and CD27lo cells (Fig. 3d). Moreover, nonlymphoid tissues became populated by CD8 T cells expressing a TRM phenotype (Fig. 3e and extended data Fig. 2). Expanding the cellular analysis beyond CD8 T cells revealed extensive and profound changes to many innate and adaptive immune cell lineages throughout the host and increased levels of serum antibody (Fig. 3f&g and extended data Fig. 2). Furthermore, Principal Components Analysis of PBMC gene expression data in the space of all detected genes (~18000) revealed a spatial shift of cohoused samples away from laboratory samples and towards pet store along PC1 (Fig. 3h). Together, these data demonstrate that co-housing profoundly impacted the status of the immune system.
Extended data table 1

Microbial exposure in laboratory, petstore and cohoused mice.Frequency of each indicated microbial exposure within the population was evaluated by serological analysis and/or PCR among laboratory (n=4), petstore (n=15) and cohoused (n=13) mice. Each cohoused sample was collected from a different cage. Pinworm and mite specific-PCR was performed on pooled petstore and cohoused samples.

PetstoreLaboratoryCohoused

Viruses

Rotavirus (EDIM)000
Mouse Hepatitis Virus93.3061.5
Murine norovirus60038.5
Mouse parvovirus NS153.300
Mouse parvovirus type 14007.7
Mouse parvovirus type 246.700
Minute virus of mice46.700
Theiler’s murine encephalomyelitis virus60038.5
Sendai virus66.7023.1
Ectromelia virus000
Lymphocytic Choriomeningitis virus6.707.7
Mouse adenovirus 1 and 2000
Mouse cytomegalo virus000
Polyoma virus6.700
Pneumonia virus of mouse53.300
Reovirus000

Bacteria

Cillia-Associated Respiratory Bacillus000
Mycoplasma pulmonis73.3030.8
Clostridium piliforme26.700

Parasites/Protozoa/Fungi

Enchephalitozoon cuniculi4000
Pinworm1000100
Mites1000100
Extended data figure 2

Co-housing laboratory mice with petstore mice induces accumulation of TRM-phenotype CD8 T cells and other innate cells in tissues of laboratory mice

a) CD8 T cell density within the indicated tissues of adult laboratory mice (n=5) and cohoused mice (n=7). Representative immunofluorescence staining, CD8β (red), DAPI (nuclei, blue), scale bars = 50 μm. b) Phenotype of CD8 T cells was compared between laboratory mice (n=9) and age-matched laboratory mice that were co-housed (n=9, representative flow cytometry plots shown). Samples gated on CD44hi cells isolated from the indicated tissue (vasculature populations were excluded, see methods). c) Enumeration of CD11b+ granulocytes and Ly6Chi inflammatory monocytes in spleens of laboratory (n=6) and cohoused (n=6) mouse. Significance was determined using unpaired two-sided Mann-Whitney U-test. ** p<0.01, bars indicate mean ± S.E.M.

Our data suggested that laboratory mice have features in common with neonatal humans, and that altering the environment reproduced phenotypic signatures of adult humans. To test this hypothesis more broadly, we queried expression-profiling data of maternal and neonatal cord PBMC from an unaffiliated study[21] with petstore vs. laboratory and cohoused vs. laboratory signatures via gene set enrichment analysis (GSEA) (Fig. 4a and 4b). The top 400 genes up-regulated in petstore mice showed highly significant enrichment in human adult expression data, while the top 400 down-regulated genes showed enrichment in neonates. Laboratory mice acquired this gene expression program after co-housing.
Figure 4

Microbial experience matures mouse immune transcriptome from neonatal to adult human and impacts immune system function

a) Experimental design and b) GSEA plots showing the enrichment of gene signatures among the indicated mouse group comparisons relative to human adult vs. neonatal comparison. Signatures consist of top 400 significantly differentially expressed genes of laboratory (n=8), cohoused (n=7) and petstore mice (n=8). c) Pairwise overlaps of metagenes identified through leading-edge metagene analysis and corresponding GO terms. d) Bacterial load in the liver 3 days post-challenge with 8.5 × 104 CFU of Listeria monocytogenes (LM) among indicated groups (n=9 for all except laboratory mice where n=8). e) Parasitic load in peripheral blood 5 days post Plasmodium berghei ANKA parasitized RBC challenge among the indicated groups of laboratory (n=15), cohoused (n=19), and petstore mice (n=15). f) 28 days after LCMV Armstrong infection, the proportion of H-2Db/gp33-specific CD8 T cell MPECs (KLRG1-, CD127+) and SLECs (KLRG1+, CD127−) in PBMC of cohoused (n=8) and laboratory (n=9). Data are cumulative of 2 independent experiments. d–f) Data points represent individual mice. Significance was determined using Kruskal-wallis (ANOVA) test (in d), one-way ANOVA test (in e) and unpaired two-sided t-test (in f) * p<0.05, ** p<0.01, **** p<0.0001. Bars indicate mean ± S.E.M.

To more deeply interrogate transcriptional concordances between petstore mice, cohoused mice, and adult humans relative to laboratory mice and neonatal humans, we applied GSEA with the ImmuneSigDB database of immunological signatures[22]. We then used leading edge metagene analysis of GSEA results to identify modules of coregulated genes upregulated in human adult vs. cord PBMC, and those found in pairwise comparisons of laboratory, petstore, and co-housed mice. Overlap between the resulting metagenes was used to identify global similarities between each dataset. We observed highly significant overlaps between metagenes upregulated in adult vs. cord human blood and metagenes upregulated in petstore or co-housed vs. laboratory mouse blood. These included numerous pathways related to innate and adaptive immune functions (Fig. 4c, extended data Fig. 3 and supplemental data Tables 1&2). Conversely, metagenes upregulated in human cord blood overlapped with those upregulated in laboratory mice. Thus, these functional modules represent a major axis of similarity in immune status between petstore mice and adult humans relative to laboratory mice and neonatal humans, and can be conferred to laboratory mice via cohousing with petstore mice.
Extended data figure 3

LEM metagene analysis. For each comparison, standard GSEA was performed using the ImmSigDB database of gene-sets. Genes in the top 150 enriched sets (FDR<0.001, ranked by P-value) were filtered to only leading edge genes and subsequently clustered into groups (metagenes) using an NMF algorithm. Hierarchical clustering of genes within individual metagenes was performed to obtain the final heatmap. Metagenes with qualitatively discernible ‘blocks’ of gene-set membership were annotated according to the identity of corresponding enriched gene-sets. Heatmaps for Adult vs. Neonatal, Petstore vs. Laboratory, Cohoused vs. Laboratory, Neonatal vs. Adult, Laboratory vs. Petstore, and Laboratory vs. Cohoused comparisons are shown. Individual genes within each metagene are listed in Supplemental table 1. Pairwise overlaps between metagenes from different comparisons are visualized in Figure 4c.

We next questioned whether mouse husbandry affected immune responses. We performed challenges with the intracellular pathogen Listeria monocytogenes (LM), which is a bacterial infection often used to gauge immune function in laboratory mice. Compared to laboratory mice, both petstore and co-housed mice exhibited a >10,000 fold reduction in bacterial burden three days after challenge, which actually matched bacterial control in laboratory mice that were previously vaccinated against LM (Fig. 4d and extended data Fig. 4a). Hence, physiological exposure to environmental microbes revealed that C57BL/6 mice exhibit considerably more innate resistance to LM infection than indicated from studies using SPF animals. The impact of mouse husbandry on infection control extended to a cerebral malaria model (Plasmodium berghei ANKA, Fig. 4e, extended data Fig. 4b). Lymphocytic choriomeningitis virus infection is an oft-used model to examine critical questions of adaptive T cell differentiation, including the regulation of memory precursor vs. short lived effector cell (MPEC vs. SLEC) development[23,24]. In this case, we observed that the proportion of MPECs and SLECs were significantly impacted by mouse husbandry (Fig. 4f and extended data Fig. 4c).
Extended data figure 4

Environment altered antimicrobial resistance and CD8 T cell differentiation. Laboratory mice were co-housed with petstore mice as described in figure 3. a) Bacterial load in the spleen 3 days post-challenge with 8.5 × 104 CFU of Listeria monocytogenes (LM) among laboratory (n=8), LM-immune (n=9), cohoused (n=9) and petstore mice (n=9) among 2 independent experiments. b) Survival of laboratory mice (n=15), cohoused mice (n=19) and petstore mice (n=15) after challenge with 106 Plasmodium berghei ANKA parasitized RBCs among 2 independent experiments. c) Laboratory (n=9) and cohoused (n=8) mice were infected with LCMV. Four weeks later, LCMV-specific CD8 T cells (identified with H-2Db/gp33 MHC I tetramers) were evaluated for expression of the indicated markers. Top row, gated on live CD8α+ T cells. Bottom 3 rows, gated on live CD8α+ H-2Db-gp33+ T cells. Significance was determined using Kruskal-wallis (ANOVA) test (in a) and log-rank (Mantel-Cox) test (in b). * p<0.05, *** p<0.001, **** p<0.0001. Bars indicate mean ± S.E.M.

Experiments in mice have informed much of our understanding of immune regulation, and have directly informed the development of life-saving clinical therapies. However, our study reveals an unanticipated impact of SPF husbandry on the immune system. Our study does not advocate for an end to SPF studies. But for studies of the immune system it is ironic that such an immunologically inexperienced organism has become de rigueur, as our data show that this compromises development of an adult-like human immune system. For maximizing opportunities for bench to bedside translational medicine, it may be opportune to add dirty mice to our repertoire of investigative tools. Much like the analysis of truly sterile “germ-free” mice has revealed how influential the commensal flora is on “normal” physiology and immune system function, our study suggests that the immune system in mice may not be fully “normalized” without more complete microbial exposure[10]. Indeed, just as many autoimmune diseases do not manifest in genetically predisposed mice in the absence of commensal flora, certain infectious experiences have been shown to induce heterologous and innate immune memory, trigger autoimmune disease, and affect transplantation tolerance [25-30]. Forward genetic screens are ongoing in mice to reveal the function of immunological genes, and it might be beneficial to conduct these screens in a “dirty” mouse model. More generally, dirty mice might be valuable for interrogating aspects of the hygiene hypothesis, immune function and treatment from disease in the settings of transplantation, allergy, autoimmunity, and vaccination, and perhaps in disparate diseases that may have an immune system or inflammatory component (such as cardiovascular disease and cancer)[4]. Such mice could supplement current models to either increase translation potential for human disease or to better inform efficacy of preclinical prophylactic and therapeutic modalities, without sacrificing powerful experimental tools and approaches that are unavailable to human studies.

Material and Methods

Mice, Cohousing and Infections

Petstore mice were purchased from a nation-wide retailer. Feral mice were trapped on a horse farm or rural outdoor petting zoo in Minnesota or Georgia, USA. Male or female petstore mice were introduced into the cages of six to eight week old C57BL/6 mice of the same sex purchased from the National Cancer Institute. Co-housing occurred within a BSL-3 facility. Age-matched C57BL/6 laboratory mice maintained in SPF facilities served as controls. Number of animals was determined based on previous experience in order to reach statistical significance. All animals surviving the experimental treatment were included in the final analysis. No method of randomization was used to allocate animals to experimental groups. Investigators were not blinded to the group allocation during experiments. Listeria monocytogenes (LM) was grown in tryptic soy broth containing streptomycin to log phase growth. The indicated groups of mice were infected i.v. with 8.5 × 104 CFU of wild type LM (provided by John Harty, University of Iowa). Bacterial load in the spleen and liver was determined 3 days post-challenge as previously described[31,32]. LM immune mice were generated by primary infection with LM-OVA, (provided by Hao Shen, University of Pennsylvania School of Medicine)[33] 5 months prior to secondary challenge. Plasmodium berghei ANKA (provided by Susan K. Pierce, NIH/NIAID) was propagated by passage in mice and blood collection. A million parasitized RBCs were injected i.p. into the indicated mice. Parasitemia was determined by flow cytometry on peripheral blood[34]. All mice were used in accordance with the Institutional Animal Care and Use Committees guidelines at the University of Minnesota.

Human specimens

Adult PBMC samples were collected from healthy volunteers at the University of Minnesota[35]. Fresh cord blood samples were acquired from the Clinical Cell Therapy Laboratory at the University of Minnesota Medical Center. The PBMC isolation has been described in detail elsewhere[36]. After isolation, cells were frozen in 107cells/ml aliquots in a cryopreservative solution (Sigma-Aldrich) for future phenotyping. Premenopausal, human cervical tissue was obtained from the Tissue Procurement Facility (BioNet, University of Minnesota). Cervical samples were frozen roughly 1–2 hours after surgical resection. Informed consent was obtained from all subjects. The University of Minnesota Institutional Review Board approved all protocols used.

Intravascular staining, leukocyte isolation and phenotyping

An intravascular staining method was used to discriminate between cells present in the vasculature from cells in the tissue parenchyma[37]. Briefly, animals were injected i.v. with biotin/fluorochrome-conjugated anti-CD45 through the tail vein. Three minutes post-injection, animals were sacrificed, and tissues were harvested as described before[38]. Isolated mouse cells were surface-stained with antibodies against CD3 (145-2C11), CD45 (30F-11), CD11b (M1/70), CD11c (N418), NKp46 (29A1.4), Ly6G (1A8), MHC II (Ia-Ie) (M5/114.15.2), CD8α (53-6.7), CD45.2 (104), CD4 (RM4-5), CD62L (MEL-14), CD44 (IM7), CD69 (H1.2F3), CD103 (M290), Ly6C (AL21), CD43 (1B11), CD43 (S7), CD27 (LG.3A10), PD-1 (RMP1-30 and J43), KLRG1 (2F1), CXCR3 (CXCR3-173), CD127 (SB/199), α4β7 (DATK32), F4/80 (Cl-A3-1), CXCR5 (2G8), CD38 (90), IgM (RMM-1), IgD (11-26c.2a), GL7 (GL7), CD19 (6D5), B220 (RA3-6B2). Isolated human cells were surface-stained with antibodies against CD8α (3B5), CD45RA (HI100), CCR7 (G043H7), CD27 (O323), and CD3 (SK7). All of the above antibodies were purchased from BD Biosciences, Biolegend or Affymetrix eBiosciences. Cell viability was determined using Ghost Dye 780 (Tonbo Biosciences). Phycoerythrin (PE)-conjugated granzyme B (Invitrogen), Fluorescein isothiocyanate (FITC)-conjugated Ki67 (Invitrogen) and AF488 conjugated goat anti-mouse IgG (H+L) antibody intracellular staining was performed using the Cytofix/Cytoperm kit (BD Pharmingen) following manufacturer’s instructions. Intracellular staining for transcription factors was performed using Transcription factor staining buffer set (Affymetrix eBiosciences) with antibodies against Foxp3 (FJK-16s), T-bet (4B10), Eomes (Dan11mag), Gata3 (L50-823), Rorγt (Q31-378) following manufacturer’s guidelines. Single positive staining for T-bet, Gata3, and Rorγt were used to determine Th1, Th2, and Th17 lineages, respectively. FITC-conjugated mouse lineage cocktail (Tonbo Biosciences) was used in combination with other recommended lineage markers to identify various innate lymphoid cell subsets. The stained samples were acquired using LSRII or LSR Fortessa flow cytometers (BD) and analyzed with FlowJo software (Tree Star, Inc.).

Infectious agent screening

Laboratory mice, cohoused laboratory mice (after at least 30 days of cohousing) and petstore mice were screened using EZ-spot and PCR Rodent Infectious Agent (PRIA) array methods (Charles River Laboratories). Dried whole blood, feces, oral swab and body swabs were collected as per the sample submission guidelines of Charles River Laboratories.

Tissue freezing, immunofluorescence and microscopy

Harvested murine tissues were fixed in 2% Paraformaldehyde for 2hrs before being treated with 30% sucrose overnight for cryoprotection. The sucrose treated tissue is embedded in tissue freezing medium OCT and frozen in an isopentane liquid bath. Human cervix specimens were embedded in tissue freezing medium OCT and snap frozen in an isopentane liquid bath. Frozen blocks were processed, stained, imaged, and enumerated by quantitative immunofluorescence microscopy as described[39,40] and included staining with the following antibodies: anti-mouse CD8β (YTS156.7.7), anti-human CD8β (SIDI8BEE, eBioscience), anti-mouse CD4 (RM4-5), anti-mouse CD11b (M1/70), and counterstaining with DAPI (mouse) or Cytox Green (human) to detect nuclei.

Serum antibody quantification

Mouse serum antibody titers were quantified using Ready-Set-Go! ELISA kits (Affymetrix eBioscience) following manufacturer’s instructions.

RNA isolation and Microarray hybridization

For each sample, 1–3×106 PMBCs were used for each RNA extraction. Cells were first homogenized using QIAshredder columns (Qiagen) then RNA was then extracted using RNeasy kit (Qiagen) as per manufacturer’s instructions. Following quality control, total RNA samples were processed using the Illumina TotalPrep-96 RNA Amplification Kit for High-Throughput RNA Amplification for Array Analysis. Samples were loaded onto the MouseRef-8 v2.0 Expression BeadChip (Illumina) and hybridized Beadchips were scanned using the Illumina iScan Beadarray Reader. Basic quality metrics were checked using Illumina Genomestudio.

Bioinformatics analysis

Prior to analysis, mouse microarray data were quantile normalized using preprocessCore (Bioconductor) and batch correction was performed using the ComBat algorithm. Principal components analysis was performed in R. Raw human adult and neonatal cord PBMC microarray data were obtained from a previous unaffiliated study profiling the peripheral blood of 72 smoking or non-smoking women and the cord blood of their neonates (Gene Expression Omnibus, accession code GSE27272)[21]. Human microarray data were quantile normalized as previously described. To obtain lists of genes upregulated or downregulated between Petstore, Cohoused, and Laboratory cohorts, differential expression analysis was performed using the linear modeling and empirical Bayesian method implemented in limma (Bioconductor). Gene set enrichment analysis (GSEA) was performed as described previously[41]. LEM (Leading Edge Metagene) analysis was performed downstream of GSEA to yield groups of genes, termed metagenes, which are coordinately upregulated in a given phenotypic comparison and common to multiple enriched gene sets. Briefly, for a given phenotypic comparison, gene set enrichment analysis was performed using ImmuneSigDB, a curated compendium of 4872 gene sets describing a wide range of cell states and experimental perturbations from immunology literature[22]. The top 150 significantly enriched gene sets, as restricted by an FDR < 0.25 and ranked by P < 0.05, were subsetted for their leading edge genes. These genes were then clustered into metagenes using non-negative matrix factorization. The significance of overlap between pairs of metagenes was determined using a Fisher exact test (P < 1e-05). Metagenes were functionally annotated based on the significance of overlap between member genes and Gene Ontology terms [41], as measured by hypergeometric test using the GOrilla enrichment analysis tool[42].

Statistics

Data were subjected to D’Agostino & Pearson omnibus normality test to determine if they are sampled from a Gaussian distribution. If a Gaussian model of sampling was satisfied, parametric tests (unpaired two-tailed Student’s t-test for two groups and one-way analysis of variance (ANOVA) with Bonferroni multiple comparison test for more than two groups) were used. If the samples deviated from Gaussian distribution, non-parametric tests (Mann-Whitney U test for two groups, Kruskal-Wallis with Dunn’s multiple comparison test for more than two groups) were used unless otherwise stated. Variances between groups were compared using F test and found to be equal. All statistical analysis was done in GraphPad Prism (GraphPad Software Inc.). P-values <0.05 are considered significant and are indicated in figure legends. Frequency of CD8 T cell subsets in neonates vs. adult humans. CD8 T cell subsets were defined in adult PBMC (n=13) and cord blood PBMC (n=8) by fluorescence flow cytometry based on the following markers: Naïve=CD45RAhi CCR7hi, TCM=CD45RAlo CCR7hi, TEM=CD45RAlo CCR7lo and TEMRA=CD45RAhi CCR7lo. Significance was determined using unpaired two-sided t-test. *** p<0.001, **** p<0.0001. Bars indicate mean ± S.E.M.

Co-housing laboratory mice with petstore mice induces accumulation of TRM-phenotype CD8 T cells and other innate cells in tissues of laboratory mice

a) CD8 T cell density within the indicated tissues of adult laboratory mice (n=5) and cohoused mice (n=7). Representative immunofluorescence staining, CD8β (red), DAPI (nuclei, blue), scale bars = 50 μm. b) Phenotype of CD8 T cells was compared between laboratory mice (n=9) and age-matched laboratory mice that were co-housed (n=9, representative flow cytometry plots shown). Samples gated on CD44hi cells isolated from the indicated tissue (vasculature populations were excluded, see methods). c) Enumeration of CD11b+ granulocytes and Ly6Chi inflammatory monocytes in spleens of laboratory (n=6) and cohoused (n=6) mouse. Significance was determined using unpaired two-sided Mann-Whitney U-test. ** p<0.01, bars indicate mean ± S.E.M. LEM metagene analysis. For each comparison, standard GSEA was performed using the ImmSigDB database of gene-sets. Genes in the top 150 enriched sets (FDR<0.001, ranked by P-value) were filtered to only leading edge genes and subsequently clustered into groups (metagenes) using an NMF algorithm. Hierarchical clustering of genes within individual metagenes was performed to obtain the final heatmap. Metagenes with qualitatively discernible ‘blocks’ of gene-set membership were annotated according to the identity of corresponding enriched gene-sets. Heatmaps for Adult vs. Neonatal, Petstore vs. Laboratory, Cohoused vs. Laboratory, Neonatal vs. Adult, Laboratory vs. Petstore, and Laboratory vs. Cohoused comparisons are shown. Individual genes within each metagene are listed in Supplemental table 1. Pairwise overlaps between metagenes from different comparisons are visualized in Figure 4c. Environment altered antimicrobial resistance and CD8 T cell differentiation. Laboratory mice were co-housed with petstore mice as described in figure 3. a) Bacterial load in the spleen 3 days post-challenge with 8.5 × 104 CFU of Listeria monocytogenes (LM) among laboratory (n=8), LM-immune (n=9), cohoused (n=9) and petstore mice (n=9) among 2 independent experiments. b) Survival of laboratory mice (n=15), cohoused mice (n=19) and petstore mice (n=15) after challenge with 106 Plasmodium berghei ANKA parasitized RBCs among 2 independent experiments. c) Laboratory (n=9) and cohoused (n=8) mice were infected with LCMV. Four weeks later, LCMV-specific CD8 T cells (identified with H-2Db/gp33 MHC I tetramers) were evaluated for expression of the indicated markers. Top row, gated on live CD8α+ T cells. Bottom 3 rows, gated on live CD8α+ H-2Db-gp33+ T cells. Significance was determined using Kruskal-wallis (ANOVA) test (in a) and log-rank (Mantel-Cox) test (in b). * p<0.05, *** p<0.001, **** p<0.0001. Bars indicate mean ± S.E.M. Microbial exposure in laboratory, petstore and cohoused mice.Frequency of each indicated microbial exposure within the population was evaluated by serological analysis and/or PCR among laboratory (n=4), petstore (n=15) and cohoused (n=13) mice. Each cohoused sample was collected from a different cage. Pinworm and mite specific-PCR was performed on pooled petstore and cohoused samples.
  42 in total

1.  IL-2 complex treatment can protect naive mice from bacterial and viral infection.

Authors:  Sara E Hamilton; Jason M Schenkel; Adovi D Akue; Stephen C Jameson
Journal:  J Immunol       Date:  2010-10-29       Impact factor: 5.422

Review 2.  Immunological memory within the innate immune system.

Authors:  Joseph C Sun; Sophie Ugolini; Eric Vivier
Journal:  EMBO J       Date:  2014-03-27       Impact factor: 11.598

3.  Transcriptome alterations in maternal and fetal cells induced by tobacco smoke.

Authors:  H Votavova; M Dostalova Merkerova; K Fejglova; A Vasikova; Z Krejcik; A Pastorkova; N Tabashidze; J Topinka; M Veleminsky; R J Sram; R Brdicka
Journal:  Placenta       Date:  2011-07-30       Impact factor: 3.481

4.  Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.

Authors:  Aravind Subramanian; Pablo Tamayo; Vamsi K Mootha; Sayan Mukherjee; Benjamin L Ebert; Michael A Gillette; Amanda Paulovich; Scott L Pomeroy; Todd R Golub; Eric S Lander; Jill P Mesirov
Journal:  Proc Natl Acad Sci U S A       Date:  2005-09-30       Impact factor: 11.205

5.  The generation of protective memory-like CD8+ T cells during homeostatic proliferation requires CD4+ T cells.

Authors:  Sara E Hamilton; Monika C Wolkers; Stephen P Schoenberger; Stephen C Jameson
Journal:  Nat Immunol       Date:  2006-04-09       Impact factor: 25.606

6.  Genomic responses in mouse models greatly mimic human inflammatory diseases.

Authors:  Keizo Takao; Tsuyoshi Miyakawa
Journal:  Proc Natl Acad Sci U S A       Date:  2014-08-04       Impact factor: 11.205

7.  Compendium of Immune Signatures Identifies Conserved and Species-Specific Biology in Response to Inflammation.

Authors:  Jernej Godec; Yan Tan; Arthur Liberzon; Pablo Tamayo; Sanchita Bhattacharya; Atul J Butte; Jill P Mesirov; W Nicholas Haining
Journal:  Immunity       Date:  2016-01-12       Impact factor: 31.745

8.  Immune-cell lineage commitment: translation from mice to humans.

Authors:  Kimberly J Payne; Gay M Crooks
Journal:  Immunity       Date:  2007-06       Impact factor: 31.745

9.  Inhibiting the Mammalian target of rapamycin blocks the development of experimental cerebral malaria.

Authors:  Emile B Gordon; Geoffrey T Hart; Tuan M Tran; Michael Waisberg; Munir Akkaya; Jeff Skinner; Severin Zinöcker; Mirna Pena; Takele Yazew; Chen-Feng Qi; Louis H Miller; Susan K Pierce
Journal:  MBio       Date:  2015-06-02       Impact factor: 7.867

10.  Early-life compartmentalization of human T cell differentiation and regulatory function in mucosal and lymphoid tissues.

Authors:  Joseph J C Thome; Kara L Bickham; Yoshiaki Ohmura; Masaru Kubota; Nobuhide Matsuoka; Claire Gordon; Tomer Granot; Adam Griesemer; Harvey Lerner; Tomoaki Kato; Donna L Farber
Journal:  Nat Med       Date:  2015-12-14       Impact factor: 53.440

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  393 in total

1.  Topical Antimicrobial Treatments Can Elicit Shifts to Resident Skin Bacterial Communities and Reduce Colonization by Staphylococcus aureus Competitors.

Authors:  Adam J SanMiguel; Jacquelyn S Meisel; Joseph Horwinski; Qi Zheng; Elizabeth A Grice
Journal:  Antimicrob Agents Chemother       Date:  2017-08-24       Impact factor: 5.191

Review 2.  The Pediatric Cell Atlas: Defining the Growth Phase of Human Development at Single-Cell Resolution.

Authors:  Deanne M Taylor; Bruce J Aronow; Kai Tan; Kathrin Bernt; Nathan Salomonis; Casey S Greene; Alina Frolova; Sarah E Henrickson; Andrew Wells; Liming Pei; Jyoti K Jaiswal; Jeffrey Whitsett; Kathryn E Hamilton; Sonya A MacParland; Judith Kelsen; Robert O Heuckeroth; S Steven Potter; Laura A Vella; Natalie A Terry; Louis R Ghanem; Benjamin C Kennedy; Ingo Helbig; Kathleen E Sullivan; Leslie Castelo-Soccio; Arnold Kreigstein; Florian Herse; Martijn C Nawijn; Gerard H Koppelman; Melissa Haendel; Nomi L Harris; Jo Lynne Rokita; Yuanchao Zhang; Aviv Regev; Orit Rozenblatt-Rosen; Jennifer E Rood; Timothy L Tickle; Roser Vento-Tormo; Saif Alimohamed; Monkol Lek; Jessica C Mar; Kathleen M Loomes; David M Barrett; Prech Uapinyoying; Alan H Beggs; Pankaj B Agrawal; Yi-Wen Chen; Amanda B Muir; Lana X Garmire; Scott B Snapper; Javad Nazarian; Steven H Seeholzer; Hossein Fazelinia; Larry N Singh; Robert B Faryabi; Pichai Raman; Noor Dawany; Hongbo Michael Xie; Batsal Devkota; Sharon J Diskin; Stewart A Anderson; Eric F Rappaport; William Peranteau; Kathryn A Wikenheiser-Brokamp; Sarah Teichmann; Douglas Wallace; Tao Peng; Yang-Yang Ding; Man S Kim; Yi Xing; Sek Won Kong; Carsten G Bönnemann; Kenneth D Mandl; Peter S White
Journal:  Dev Cell       Date:  2019-03-28       Impact factor: 12.270

3.  Colonization with Helicobacter is concomitant with modified gut microbiota and drastic failure of the immune control of Mycobacterium tuberculosis.

Authors:  L Majlessi; F Sayes; J-F Bureau; A Pawlik; V Michel; G Jouvion; M Huerre; M Severgnini; C Consolandi; C Peano; R Brosch; E Touati; C Leclerc
Journal:  Mucosal Immunol       Date:  2017-02-01       Impact factor: 7.313

4.  Multi-color flow cytometry for evaluating age-related changes in memory lymphocyte subsets in dogs.

Authors:  Sita S Withers; Peter F Moore; Hong Chang; Jin W Choi; Stephen J McSorley; Michael S Kent; Arta M Monjazeb; Robert J Canter; William J Murphy; Ellen E Sparger; Robert B Rebhun
Journal:  Dev Comp Immunol       Date:  2018-05-31       Impact factor: 3.636

5.  Sepsis erodes CD8+ memory T cell-protective immunity against an EBV homolog in a 2B4-dependent manner.

Authors:  Jianfeng Xie; Rebecca L Crepeau; Ching-Wen Chen; Wenxiao Zhang; Shunsuke Otani; Craig M Coopersmith; Mandy L Ford
Journal:  J Leukoc Biol       Date:  2019-01-09       Impact factor: 4.962

6.  Immunity to commensal skin fungi promotes psoriasiform skin inflammation.

Authors:  Charlotte Hurabielle; Verena M Link; Nicolas Bouladoux; Seong-Ji Han; Eric Dean Merrill; Yaima L Lightfoot; Nickie Seto; Christopher K E Bleck; Margery Smelkinson; Oliver J Harrison; Jonathan L Linehan; Samira Tamoutounour; Michail S Lionakis; Mariana J Kaplan; Saeko Nakajima; Yasmine Belkaid
Journal:  Proc Natl Acad Sci U S A       Date:  2020-06-29       Impact factor: 11.205

Review 7.  The initiation of metabolic inflammation in childhood obesity.

Authors:  Kanakadurga Singer; Carey N Lumeng
Journal:  J Clin Invest       Date:  2017-01-03       Impact factor: 14.808

Review 8.  Impact of gut microbiota on gut-distal autoimmunity: a focus on T cells.

Authors:  Maran L Sprouse; Nicholas A Bates; Krysta M Felix; Hsin-Jung Joyce Wu
Journal:  Immunology       Date:  2019-01-21       Impact factor: 7.397

9.  "The Impact of Mycobacterium tuberculosis Immune Evasion on Protective Immunity: Implications for TB Vaccine Design" - Meeting report.

Authors:  Cesar Boggiano; Katrin Eichelberg; Lakshmi Ramachandra; Jaqueline Shea; Lalita Ramakrishnan; Samuel Behar; Joel D Ernst; Steven A Porcelli; Markus Maeurer; Hardy Kornfeld
Journal:  Vaccine       Date:  2017-05-02       Impact factor: 3.641

10.  Laboratory mice born to wild mice have natural microbiota and model human immune responses.

Authors:  Jasmin Herz; Brian G Vassallo; Stephan P Rosshart; Ashli Hunter; Morgan K Wall; Jonathan H Badger; John A McCulloch; Dimitrios G Anastasakis; Aishe A Sarshad; Irina Leonardi; Nicholas Collins; Joshua A Blatter; Seong-Ji Han; Samira Tamoutounour; Svetlana Potapova; Mark B Foster St Claire; Wuxing Yuan; Shurjo K Sen; Matthew S Dreier; Benedikt Hild; Markus Hafner; David Wang; Iliyan D Iliev; Yasmine Belkaid; Giorgio Trinchieri; Barbara Rehermann
Journal:  Science       Date:  2019-08-01       Impact factor: 47.728

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