Dirty mice as an alternative for preclinical vaccine testing

Laboratory mice comprise an inexpensive and expeditious model organism for preclinical vaccine testing; however, vaccine immunogenicity often fails to adequately translate to humans. Recent reports indicate that reconstituting physiologic microbial experience to specific pathogen free (SPF) mice induces durable immunological changes that better recapitulate the human immune system. We examined the impact of microbial experience on responses to vaccination after cohousing laboratory mice with pet store mice. We demonstrate that human transcriptional responses to influenza vaccination are better recapitulated in cohoused mice. Induction of humoral responses by vaccination was dampened in cohoused mice and resulted in poor control upon challenge. Additionally, the establishment of protective heterosubtypic T cell immunity was compromised in cohoused mice. In summary, SPF mice exaggerated both humoral and T cell protection induced by influenza vaccines compared to cohoused mice, suggesting that reconstituting microbial experience in laboratory mice through cohousing may better inform preclinical vaccine testing. Overall design: SAMPLE GROUP 1: Pet store mice were purchased from various Twin Cities area pet stores. Pet store mice were cohoused with 8-week-old female C57BL/6 (The Jackson Laboratory). Cohousing occurred within a BSL-3 facility. C57BL/6 mice were cohoused for 60 days, bled for flow cytometry analysis and screened for infectious agents using EZ-spot and PCR Rodent Infections Agent (PRIA) array methods (Charles River Laboratories). Age-matched mice were maintained in SPF facilities. SAMPLE GROUP 2: For IAV infections, mice were anesthetized using a weight-based dose of ketamine/xylazine delivered intraperitoneally (i.p.). Mice were vaccinated intranasally (i.n.) with 1000 PFU of X31 or vaccinated intramuscularly (i.m.) with 180 ug of 2019-2020 seasonal quadrivalent influenza vaccine (Sanofi Pasteur) with or without 25 uL AddaVax (InvivoGen). For challenge, mice were infected i.n. with 1000 PFU of PR8. Whole lungs were harvested and RNA was extracted using AllPrep DNA/RNA kit (Qiagen). The cDNA library was prepared using strand-specific RNA-sequencing protocols. Samples were run on an Illumina NovaSeq (50 bp paired-end). We obtained an average of 27 million read pairs per sample. Sequencing reads were mapped to the mouse genome (GRCm38) using Bowtie aligner (bowtie2 version 2.3.4.1) with local mode, -L 22 and -N 1 parameters (Langmead and Salzberg, 2012). Reads were assigned to Ensembl gene models (Mus_musculus.GRCm38.87.gtf) with featureCounts of the Subread software package (version 1.5.1) (Liao et al., 2014). The reads count matrices were organized corresponding to experimental design and used for subsequent statistical analysis using the bioconductor package edgeR (version 3.24.3) (McCarthy et al., 2012; Robinson et al., 2010). The raw reads count table were normalized by using default method in the package prior to generating statistics. Multidimensional scaling was performed with edgeR using the top 500 differentially expressed genes across samples. The regression model in the edgeR package with block design and interaction model was used for statistical analysis to select the corresponding significant genes. Gene ontology analysis was performed using Panther on genes in each cluster with an adjusted p value <0.01 and LogFC >0.5. Mouse PBMCs were isolated using Ficol-Paque PREMIUM (Millipore Sigma) and RNA was extracted using RNeasy Micro Plus Kit (Qiagen). The cDNA library was prepared using strand-specific RNA-sequencing protocols. Samples were run on an Illumina NovaSeq (150 bp paired-end). We obtained an average of 35 million read pairs per sample. Sequence processing and mapping was performed as described above. The normalized reads table of mouse blood samples were reformatted to meet the requirements for the subsequent GSEA analysis (Mootha et al., 2003; Subramanian et al., 2005). The gene lists were prepared from human vaccination data and merged into an immune geneset (c7.immune.datasets.gmt) to generate a customized geneset (c7.custom.immune.datasets.gmt) (Mootha et al., 2003; Subramanian et al., 2005). The mouse datasets originated from either SPF or dirty mouse models were computed against the customized geneset by using desktop GSEA analysis engine with default parameters except the enrichment plot generation.