Longitudinal Analysis of the Microbiome and Metabolome in the 5xfAD Mouse Model of Alzheimer's Disease.

Recent reports implicate gut microbiome dysbiosis in the onset and progression of Alzheimer's disease (AD), yet studies involving model animals overwhelmingly omit the microbial perspective. Here, we evaluate longitudinal microbiomes and metabolomes from a popular transgenic mouse model for familial AD (5xfAD). Cecal and fecal samples from 5xfAD and wild-type B6J (WT) mice from 4 to 18 months of age were subjected to shotgun Illumina sequencing. Metabolomics was performed on plasma and feces from a subset of the same animals. Significant genotype, sex, age, and cage-specific differences were observed in the microbiome, with the variance explained by genotype at 4 and 18 months of age rising from 0.9 to 9% and 0.3 to 8% for the cecal and fecal samples, respectively. Bacteria at significantly higher abundances in AD mice include multiple Alistipes spp., two Ligilactobacillus spp., and Lactobacillus sp. P38, while multiple species of Turicibacter, Lactobacillus johnsonii, and Romboutsia ilealis were less abundant. Turicibacter is similarly depleted in people with AD, and members of this genus both consume and induce the production of gut-derived serotonin. Contradicting previous findings in humans, serotonin is significantly more concentrated in the blood of older 5xfAD animals compared to their WT littermates. 5xfAD animals exhibited significantly lower plasma concentrations of carnosine and the lysophospholipid lysoPC a C18:1. Correlations between the microbiome and metabolome were also explored. Taken together, these findings strengthen the link between Turicibacter abundance and AD, provide a basis for further microbiome studies of murine models for AD, and suggest that greater control over animal model microbiomes is needed in AD research. IMPORTANCE Microorganisms residing within the gastrointestinal tract are implicated in the onset and progression of Alzheimer's disease (AD) through the mediation of inflammation, exchange of small-molecules across the blood-brain barrier, and stimulation of the vagus nerve. Unfortunately, most animal models for AD are housed under conditions that do not reflect real-world human microbial exposure and do not sufficiently account for (or meaningfully consider) variations in the microbiome. An improved understanding of AD model animal microbiomes will increase model efficacy and the translatability of research findings into humans. Here, we present the characterization of the microbiome and metabolome of the 5xfAD mouse model, which is one of the most common animal models for familial AD. The manuscript highlights the importance of considering the microbiome in study design and aims to lay the groundwork for future studies involving mouse models for AD.

PARTIE: a partition engine to separate metagenomic and amplicon projects in the Sequence Read Archive.

MOTIVATION: The Sequence Read Archive (SRA) contains raw data from many different types of sequence projects. As of 2017, the SRA contained approximately ten petabases of DNA sequence (10 16 bp). Annotations of the data are provided by the submitter, and mining the data in the SRA is complicated by both the amount of data and the detail within those annotations. Here, we introduce PARTIE, a partition engine optimized to differentiate sequence read data into metagenomic (random) and amplicon (targeted) sequence data sets. RESULTS: PARTIE subsamples reads from the sequencing file and calculates four different statistics: k -mer frequency, 16S abundance, prokaryotic- and viral-read abundance. These metrics are used to create a RandomForest decision tree to classify the sequencing data, and PARTIE provides mechanisms for both supervised and unsupervised classification. We demonstrate the accuracy of PARTIE for classifying SRA data, discuss the probable error rates in the SRA annotations and introduce a resource assessing SRA data. AVAILABILITY AND IMPLEMENTATION: PARTIE and reclassified metagenome SRA entries are available from https://github.com/linsalrob/partie. CONTACT: redwards@mail.sdsu.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.