The effect of antibiotics and propionate treatment on astrocyte transcription in APPPS1-21 mice

Previously, we have shown that broad spectrum antibiotic treatment reduces reactive astrocyte phenotypes in the APPPS1-21 model of AD-related amyloidosis. We have also found that antibiotics selectively increases propionate levels and exogenous propionate treatment recapitulates phenotypes observed in antibiotic treated mice. In the current study, we wanted to assess astrocyte transcriptional state using bulk RNA sequencing. To accomplish this we used translating ribosome affinity purification (TRAP) sequencing, a ribosomal protein L10a is fused to eGFP under the control of a cell type specific promoter in a transgenic mouse model. We crossed APPPS1-21 mice to the Aldh1l1-eGFP/Rpl10a bacTRAP mouse model and progeny were treated with antibiotics, propionate, or VHL and performed bulk TRAPseq. Overall design: APPPS1-21 male were treated with broad-spectrum antibiotics, propionate, or water vehicle (N=5/group). Mice were perfused at 3 months of age. The cortex was collected and flash frozen in liquid nitrogen. Cortex was processed for TRAPSeq as previously described by Heiman et al 2014. Quality of RNA isolated from TRAP purifications was assessed using Agilent Bioanalyzer. All RNA used for downstream sequencing had a RIN score above 7. mRNA libraries were generated from TRAP-purified RNA using the Illumina Stranded mRNA prep kit. Prepared libraries were sequenced on an Illumina HighSeq 4000. The quality of reads, in FASTQ format, was evaluated using FastQC. Reads were trimmed to remove Illumina adapters from the 3' ends using cutadapt (Martin, 2011). Trimmed reads were aligned to the Mus musculus genome (mm39) using STAR (Dobin et al, 2013). Read counts for each gene were calculated using htseq-count (Anders et al, 2015) in conjunction with a gene annotation file for mm39 obtained from Ensembl (http://useast.ensembl.org/index.html). Normalization and differential expression were calculated using DESeq2 that employs the Wald test (Love et al, 2014). The cutoff for determining significant DEGs was an FDR-adjusted p-value less than 0.1 using the Benjami-Hochberg method. Pathway analysis was performed using Metascape.