Association between Apolipoprotein E genotype and the gut microbiome composition in humans and mice

Apolipoprotein E (APOE) genotype is the strongest prevalent genetic risk factor for Alzheimer’s disease. Numerous studies have provided insights into the pathological mechanisms. However, a comprehensive understanding of the impact of APOE genotype on microflora speciation and metabolism is completely lacking. The aim of this study was to investigate the association between APOE genotype and the gut microbiome composition in human and APOE-targeted replacement (TR, APOE3 and APOE4) transgenic mice. Faecal microbiota amplicon sequencing from matched individuals with different APOE genotypes revealed no significant differences in overall microbiota diversity (alpha or beta diversity) in group-aggregated human APOE genotypes. However, several bacterial taxa showed significantly different relative abundance between APOE genotypes. Notably, we detected an association of Prevotellaceae and Ruminococcaceae and several butyrate-producing genera abundances with APOE genotypes. These findings were confirmed by comparing the gut microbiota of APOE-TR mice. Furthermore, metabolomic analysis of faecal water from murine samples detected significant differences in microbe-associated amino acids and short-chain fatty acids between APOE genotypes. Together, the findings indicate that APOE genotype associated with specific gut microbiome profiles in both humans and in APOE-TR mice. This suggests that the gut microbiome is worth further investigation as a potential therapeutic target to mitigate the deleterious impact of the APOE4 allele on cognitive decline and the prevention and treatment of AD.


Introduction
The gut microbiome is intimately involved in numerous aspects of human physiology. Emerging evidence links perturbations in the microbiome to neurodegeneration and Alzheimer's disease (AD), with (neuro)inflammation proposed as an aetiological link (Tremlett et al., 2017).
The extent to which host genetic variation determines the microbiome composition is still currently debated. Indeed, although previous studies have reported that the microbiomes of humans and mice are associated with host genetic variation (Spor et al., 2011) and have identified several heritable bacterial taxa (Beaumont et al., 2016;Bonder et al., 2016;Turpin et al., 2016), other studies have identified a stronger environmental influence compared to host genetics in shaping human gut microbiota (Rothschild et al., 2018). Thus, the extent to which human genetics shape microbiome composition remains unclear.
Apolipoprotein E (APOE) genotype is the strongest prevalent risk factor for neuropathology and AD (Liu et al., 2013;Neu et al., 2017;Pontifex et al., 2018). ApoE was originally identified as a component of systemic circulating lipoproteins and a member of a family of apolipoprotein modulators of their metabolism. It has subsequently emerged as the almost exclusive lipid transporter in the central nervous system (Shore and Shore, 1973;Vauzour and Minihane, 2012). In humans, APOE exists in three different isoforms (apoE2, apoE3 and apoE4), arising from three different alleles (ε2, ε3 and ε4). These alleles give rise to three homozygous (APOE2/E2, APOE3/E3 and APOE4/E4) and three heterozygous (APOE3/E2, APOE4/E3 and APOE4/E2) genotypes in humans (Huang et al., 2004). Generally, 50-70% of populations present with the APOE3/E3 genotype, with the ε3 allele accounting for 70-80% of the gene pool, and the ε2 and ε4 allele accounting for 5-10% and 10-15% respectively (Huang et al., 2004). APOE4 carrier status is highly predictive of dementia and AD, with APOE3/E4 and APOE4/E4 being at 3-4 and 8-12 fold increased risk and a much earlier age of onset (Pontifex et al., 2018). Although the aetiological basis of APOE4-neuropathological associations has been widely researched and reported, the main aetiological mechanism has not been clearly defined. The ApoE protein is involved in multiple biological processes including lipoprotein metabolism (Raffai et al., 2001), intracellular cholesterol utilisation 4 (Reyland and Williams, 1991), cell growth (Ishigami et al., 1998), immunoregulation, (neuro)inflammation (Hui et al., 1980;Pepe and Curtiss, 1986) and neuroprotection (Jofre-Monseny et al., 2008). Whilst involved in the metabolism of gut-derived chylomicrons, its role in intestinal integrity and gut microbiome composition and metabolism is currently unknown.
In the present study, we explore the hypothesis that APOE variation influences the microbiome composition and its subsequent metabolism. Our experiments using human faecal samples and APOEtargeted replacement (TR) mice revealed significantly different relative abundance between bacterial taxa according to APOE genotypes. Furthermore, using a metabolomic approach, differences in microbeassociated amino acids and short-chain fatty acids according to APOE genotypes were also observed.
Taken together, our findings indicate that APOE genotype associates with specific gut microbiome profiles, which may affect the host metabolism and ultimately contribute to AD pathology.

Descriptive statistics of human APOE-genotyped cohorts.
A total of 56 fecal samples were analysed from participants of the COB (NCT01922869) and the CANN studies (NCT02525198) (Norwich Clinical Centre, UK) with the four APOE genotype groups selected to be matched for sex, age and body mass index (BMI) ( Table 1 and Table S1). (2.6±5.1%) ( Figure S1a). After rarefaction with a depth of 8,736 reads per sample, alpha diversity (net diversity within a single sample/subject) was measured by calculating three diversity indices namely 6 chao1 (richness), phylogenetic diversity, and Shannon diversity index. None of these metrics was significantly different between APOE genotypes ( Figure S2a). Similarly, there was no significant difference in any of the alpha diversity-metrics between males and females ( Figure S2b). However, the microbiota alpha diversity of obese subjects (n=3) was significantly lower than that in normal weight and overweight subjects (p < 0.05, Figure S2c), in line with previous observations (Le Chatelier et al., 2013).
Beta-diversity analysis (which measures inter-individual microbiota relatedness) was performed using Principal Coordinate Analysis (PCoA) clustering based on unweighted and weighted UniFrac distances.
PERMANOVA for testing associations between clinical parameters and microbiota composition are given in Table S2. There was no difference in beta diversity of gut microbiota composition according to APOE genotype ( Figure S3a). However, we observed a weak, but significant association between microbiota composition and gender and BMI categories ( Figure S3b and Figure S3c).
Although alpha and beta-diversity analyses of the gut microbiota did not discriminate between APOE genotypes, these are global measures that detect relatively large differences in microbiota structure.
We therefore questioned whether the relative abundance of any taxa might differ between these genotypes, using Kruskal-Wallis H test to compare all taxa at various phylogenetic assignment levels across all genotypes. We observed that the relative abundance of the phylum Firmicutes and order Clostridiales was higher in subjects of the APOE2/E3 genotype than in APOE3/E4 or APOE4/E4 (p < 0.05; Figure 1 and Table S3). Furthermore, at the bacterial family level, the abundance of Ruminococcaceae (a family of fermentative anaerobes associated with fibre degradation and short chain fatty acid production) was higher in APOE2/E3 than in APOE3/E3 (p = 0.004), APOE3/E4 (p = 0.002) or APOE4/E4 (p = 0.072). On the other hand, the abundance of Prevotellaceae was lower in APOE2/E3 than the other three APOE genotypes (APOE3/E3 p = 0.008, APOE3/E4 p = 0.085, APOE4/E4 p = 0.015) and was slightly more abundant at close to significant levels (p = 0.088) in APOE3/E4 compared to APOE4/E4 with mean of relative abundance of 1.79% versus 1.40% (Figure 1c, Table S3 and Figure S1b). Within the Ruminococcaceae family, three genera including Clostridium cluster IV, Clostridium cluster XIVa and Gemmiger were statistically significant and differentially abundant according to APOE genotypes. The 7 abundance of Clostridium cluster IV was lower in APOE3/E3 than in APOE2/E3 (p = 0.027) and APOE4/E4 (p = 0.039) while the abundance of Clostridium cluster XIVa was higher in APOE4/E4 than in APOE2/E3 (p = 0.044) and APOE3/E4 (p = 0.078). Higher presence of Gemmiger was observed in fecal samples from APOE2/E3 compared to APOE3/E3 (p = 0.0499) and APOE3/E4 (p = 0.086).
Moreover, we observed a higher abundance of Roseburia in fecal samples at close to significant levels (p < 0.1) in APOE3/E3 compared to three other APOE genotypes and in APOE3/E4 compared to APOE4/E4 ( Figure 1d, Table S3 and Figure S1c).
To determine possible associations between APOE genotypes and microbial translocation, we measured the plasma levels of two biomarkers of intestinal integrity, namely haptoglobin (Hp) and lipopolysaccharide binding protein (LBP). No significant differences were observed in the levels of Hp and LBP (Table S1) according to genotype. Furthermore, no significant correlation was observed between the clinical parameters and both the weighted and unweighted Unifrac distances (Table S2).

Difference in murine gut microbiota composition between APOE genotypes
We next sought to investigate if APOE genotype/gut microbiota interactions in humans were evident in human transgenic homozygous APOE3-and APOE4-TR mice at 4 months (young) and 18 months (old) of age. Their gut microbial communities were analyzed based on sequencing the V4 hypervariable region (approximately 254 bp) of the 16S rRNA gene. There was no significant difference in alpha diversity between APOE3 and APOE4 genotypes. However, in line with our previous studies of microbiota in ageing humans (O'Toole and Jeffery, 2015) and rodents (Flemer et al., 2017), both chao1 and phylogenetic diversity were much higher in young mice compared to old mice (p < 0.001; Figure   S4). Moreover, UniFrac distances (unweighted and weighted) PCoA showed that faecal microbial profiles of young mice separated significantly from those of old mice (PERMANOVA, p = 0.001; Figure 2a).
Within each age group, both UniFrac measures showed significant microbiota differences between APOE3 and APOE4 genotypes, with the p-value from PERMANOVA analysis less than 0.005 ( Figure  2a). These differences could be explained by differences detected in the relative abundance of dominant taxa, of which the most dominant were Firmicutes (62.8 ± 14.4%) and Bacteroidetes (32.3 ± 14.8%), followed by Proteobacteria, Verrucomicrobia and Deferribacteres accounting for less than 5% in total ( Figure S5a).
Analysis of differentially abundant taxa between APOE3-TR and APOE4-TR animals at the phylum level revealed that Deferribacteres in combined young and old mice were notably higher in the APOE4-TR mice compared to the APOE3-TR mice, while the opposite was true for Candidatus Saccharibacteria. In addition, lower relative abundance of Proteobacteria were seen in APOE4-TR young mice when compared to the APOE3-TR young mice (Table S4 and Figure S6). Although no significant difference was found in aggregated Firmicute or Bacteroidetes phylum abundance between APOE genotypes, we observed an increase in Firmicutes/Bacteroidetes ratio in old mice when compared to young mice (p < 0.001, Figure S7), in agreement with a previous C57BL/6N mouse study (Hoffman et al., 2017). At the order level, Deferribacterales abundance in combined age groups was significantly higher in the APOE4-TR mice compared to the APOE3-TR mice. Additionally, Clostridiales, Erysipelotrichales and Desulfovibrionales in young mice were significantly different in relative abundance between the two APOE genotypes. The increase of Lachnospiraceae, Deferribacteraceae abundance and decrease of Bacteroidaceae abundance at family level in APOE4 transgenes compared with APOE3 was detected in combined age groups. Desulfovibrionaceae, Clostridiales Incertae Sedis XIII, Rikenellaceae, Prevotellaceae, Erysipelotrichaceae were also found to be significantly different between APOE genotypes in young mice ( Figure 2b, Table S4, and Figure S6).  Table S4, and Figure S6).

Faecal metabolite associations with APOE genotype and age
In order to improve our understanding of the relationships between metabolite and microbiota composition in the gut, we performed metabolomic analyses of faecal water prepared from caecal contents. Sparse PLS Discriminant Analysis (sPLS-DA) showed a trend for separation according to age and APOE genotypes (Fig S8). Two-way ANOVA was therefore performed to investigate interactions between age and APOE genotype. Seven metabolites, adenosine monophosphate, alpha ketoisovaleric acid, glucose, glycine, lactate, oxocaproate, and xanthine were present at significantly different levels in age-APOE genotype interaction while 39 and 19 metabolites were significantly different in age groups and APOE genotype groups, respectively ( Figure S9,  3a). In addition, Metabolite Set Enrichment Analysis (MESA) was used to identify significantly enriched pathways in metabolomics data associated with APOE genotype and age. Of the top 50 assigned pathways, the significant pathways in APOE genotype were ammonia recycling, urea cycle, and alanine metabolism (Table S6, Figure S10a) while the significant pathways in age were ammonia recycling, urea cycle, glycine and serine metabolism, glutamate metabolism and alanine metabolism (Table S7, Figure S10b).
Co-inertia analysis (COIA) was carried to explore the correlation between the composition of microbiota at OTU level and the faecal metabolome (Figure 3b). The Monte-Carlo permutation test revealed a high overall similarity in the structure between the two datasets which was statistically significant (RV co-efficient = 0.685; p = 0.01). The first four axes represented 72.5 %, 8.9%, 7.0% and 2.6% of the explained variance respectively, and so the analysis focused on the first axis. Each sample is represented by an arrow where length of arrow indicates the divergence between two datasets. We observed that the aggregate arrow length was shorter in APOE4 mice compared with that in APOE3 mice, which indicated a higher consensus between microbiota composition and metabolites of APOE4 mice compared with APOE3 mice. The metabolites and OTUs that strongly correlated in the co-inertia analysis axes were plotted in on the first two COIA axes ( Figure S11). Metabolites and bacterial OTUs were projected onto the same direction as samples, indicating that they were more abundant in those samples.
There was an agreement between the metabolite abundance and the specific taxon abundance. Notably, SCFAs including acetate, butyrate and propionate were located in the direction of butyrate-producing bacteria from Clostridium cluster IV genus and the families Ruminococcaceae and Lachnospiraceae.

Discussion
Although several recent studies have implicated a link between the gut microbiome and the development of AD (Bhattacharjee and Lukiw, 2013;Ghaisas et al., 2016;Sampson et al., 2016;Vogt et al., 2017), there is no direct study that establishes a link between gut microbiota composition and the strongest genetic risk factor for AD, APOE genotype. The current study marks the first analysis that compares gut microbiota composition in humans and transgenic mice with different APOE genotypes.
Analysis of 16S rRNA gene sequences and faecal metabolome showed that APOE genotype correlated with abundance differences of several gut bacterial taxa which may drive the difference in amino acids and SCFAs levels.
Higher levels of Prevotellaceae were evident in APOE3/E3 carriers relative to other genotype subgroups, while higher levels of Ruminococcaceae were correlated with the APOE2/E3 genotype (Figure 1 and Table S3) relative to APOE4 carriers. Similar associations were observed in APOE-TR transgenic animals with an increase in Prevotellaceae abundance in APOE3 young mice compared to APOE4 young mice in this study and an increase of Ruminococcaceae in APOE2 mice compared to APOE4 and APOE3 mice (Parikh et al., 2017). Interestingly, loss of these bacteria has been reported to negatively correlate with neurodegenerative disorders. Prevotellaceae and Ruminococcaceae were noted as being less abundant in patients with Parkinson's disease (PD) (Unger et al., 2016) and AD (Vogt et al., 2017). A reduction of Prevotellaceae influenced mucin synthesis and increased mucosal permeability, allowing local and systemic exposure to bacterial endotoxin which may lead to the accumulation of alphasynuclein (α-syn) in the colon (Forsyth et al., 2011;Scheperjans et al., 2015). Aggregation-prone proteins such as β-amyloid (Aβ) and α-syn can propagate from the gut to the brain via the vagus nerve (Holmqvist et al., 2014) and contribute to the pathogenesis of PD, AD, and other neurodegenerative disorders (Bennett, 2005;Crews et al., 2009;Jellinger, 2003;Stefanis, 2012). Ruminococcaceae are involved in the production of short chain fatty acids (SCFAs), such that their depletion is causally linked to inflammation (Larsen et al., 2010;Pryde et al., 2002;Zhang et al., 2016). These findings suggest that these bacteria might contribute to the protective effects of APOE2 and APOE3 alleles against AD relative to the APOE4 genotype (de-Almada et al., 2012;Farrer et al., 1997;Liu et al., 2013).

The high abundance of Ruminococcaceae in subjects of the APOE2/E3 genotype was reflected by
Gemmiger at the genus level. Gemmiger are strictly anaerobic bacteria which ferment a variety of carbohydrates to produce formic and n-butyric acids, often with small amounts of acetic, lactic, succinic, malonic, and pyruvic acids (Gossling and Moore, 1975). In addition, we observed differences between APOE genotypes in Clostridium cluster IV, Roseburia and Clostridium cluster XIVa which are able to convert dietary fibres to SCFAs Van den Abbeele et al., 2013). Although the butyrate-producing bacteria Clostridium cluster IV were modestly less abundant in human APOE3/E3 individuals, they could be substituted by Roseburia with an increased abundance of these bacteria in the APOE3/E3. A slight increase of Clostridium cluster XIVa from Lachnospiraceae was seen in the human APOE4/E4 genotype which is consistent with the murine data. However, the increase of this genus in APOE4/E4 may not substitute for the reduction of other butyrate-producing bacteria. Additionally, several genera, which were not correlated with APOE genotype in human gut microbiomes, were significantly different between APOE3 and APOE4 genotypes in murine gut microbiomes. These taxa could not be detected in human data due to: (i) absence of some mouse gut microbiota in human such as Mucispirillum, (ii) differences in relative abundance of each individual taxa, and (iii) complexity in interactions of human gut microbiota with genetics, diet, and other environmental factors.
Incorporating the gut microbiota data with the corresponding metabolites, faecal samples between APOE genotype groups were clearly discriminated based on the metabolomic profiles of faecal water extracts. We detected a higher level of SCFAs including acetate, lactate, propionate, and pyruvate in APOE3 mice which might be the result of a high abundance of butyrate-producing bacteria. Several SCFAs have been shown to inhibit the formation of toxic soluble Aβ aggregates in vitro (Ho et al., 2018).
Interestingly, co-segregation of the faecal metabolomic profiles and the gut microbiome profiles as revealed by the co-inertia analysis suggests that the differences in gut microbiota associated with APOE 13 genotype and age in APOE-TR mice are reflected in the segregation of metabolites, which may be clinically relevant.
In conclusion, the collective evidence here suggests a link between APOE genotypes and gut microbiome composition. Loss of butyrate-producing bacteria and SCFAs in APOE4 carriers might drive the impact of the APOE4 allele on neuropathology. Our findings suggest a possible role of gut microbiota, especially butyrate-producing bacteria, as an intervention point to mitigate the impact of APOE genotype in the development of AD.

Participants, sample collection, APOE genotyping and biochemical analysis
Fifty-six healthy participants, aged between 56 and 78 years, were prospectively selected according to APOE genotype from the COB (NCT01922869) and the CANN (NCT02525198) studies for the analysis of their gut microbiota speciation. Participants were provided with faecal collection kits, which included a stool collection bag and an ice pack. They were asked to defecate directly into the bag, which was secured and placed with the ice pack into an insulated container and delivered to the study scientist. The samples were then homogenised by physical manipulation before aliquots were taken and stored at −80°C. The study protocols were approved by the National Research Ethics Service (NRES) Committee (13/EE/0066 (COB) and 14/EE/0189 (CANN) studies), and all participants consented to provide stool samples, and to the use of the stored samples for research purposes.
Briefly, DNA was isolated from the buffy coat layer of 8 mL of blood collected into sodium heparin

Human faecal bacterial DNA extraction and 16S rRNA amplicon sequencing
Total genomic DNA were isolated from human faecal samples using DNeasy Blood and Tissue kit (Qiagen, UK) following the manufacturer's instructions with some modifications following the repeated bead-beating method (Yu and Morrison, 2004). The V3-V4 hypervariable region of the16S rRNA gene was amplified to generate a fragment of 460 bp using the forward primer 5′TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG and reverse primer 5′GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC (Klindworth et al., 2013). and 72 o C for 5 minutes for final elongation. The SPRI select reagent kit (Beckman Coulter, UK) was used to purify the amplicons. The Qubit ® dsDNA HS Assay Kit (Life Technologies) was followed for quantification and pooling. Library preparation was carried out by Teagasc, Fermoy, Ireland on the Illumina MiSeq platform using paired-end Illumina sequencing run (2 × 250 bp).

APOE Targeted Replacement mice
All experimental procedures and protocols used in this study were reviewed and approved by the include recognition sequences that enable a second nested PCR, using the N501f and N701r primers, to incorporate Illumina adapter sequences and barcode sequences. The use of these primers enables efficient community clustering for the length of reads obtained through Illumina sequencing, and this method also allows for high-throughput sequencing. Sequencing data were supplied in FASTQ format with adaptors already trimmed.
High resolution 1 H NMR spectra were recorded on a 600 MHz Bruker Avance spectrometer fitted with a 5 mm TCI cryoprobe and a 60 slot autosampler (Bruker, Rheinstetten, Germany). Sample

Analysis of 16S amplicon sequencing data from humans and mice
Bioinformatics analysis of 16S amplicon sequencing data from humans and mice were performed using the Quantitative Insights Into Microbial Ecology (QIIME) v1.9.1 (Caporaso et al., 2010) and usearch v8.1 (Edgar, 2010) software and the following procedure. First, the paired-end reads were merged using FLASH v1.2.8 (Magoc and Salzberg, 2011), then adaptors were removed from reads using cutadapt v1.8.3 (Martin, 2011). The sequences were demultiplexed and filtered using QIIME with the split_libraries_fastq.py script, all reads with a quality score below 19 were removed. Reverse primers were removed using QIIME with truncate_reverse_primer.py. An operational taxonomic unit (OTU) table was obtained using usearch. Unique sequences were filtered (derep_fulllength) and sorted by length (sortbylength) with a length of 373-473 nt for V3-V4 region and a length of 237-271 nt for V4 region.
After singleton removal (sortbysize), the remaining sequences were clustered into OTUs at a default 97% sequence identity (cluster_otus) and filtered for chimeras against the ChimeraSlayer reference database (uchime_ref) (Haas et al., 2011). All sequences were mapped against this database (usearch_global) to generate an OTU Benjamini-Hochberg correction to control false discovery rate. Differences in beta diversity were determined using permutational multivariate analysis of variance (PERMANOVA) (R package vegan).
Multivariate statistical analysis (Sparse PLS Discriminant Analysis (sPLS-DA) and MSEA) of the least 50% of the samples were used in COIA analysis. Overall similarity in the structure between two datasets were measured by RV-coefficient. The significant of the RV-coefficient was tested using the Monte-Carlo permutation test (Moonseong and Ruben Gabriel, 1998    Principle Coordinates Analysis based on unweighted and weighted UniFrac distances of partial sequences of bacterial 16S rRNA genes showing gut microbiota beta diversity grouped by age and APOE genotypes. Significant differences between groups were calculated by PERMANOVA tests. (b) Comparison of relative abundance taxa between APOE3 and APOE4 in young mice samples, old mice samples and both age groups combined were represented by log 2 fold changes. Statistical significances were determined by the Mann-Whitney U test and were corrected for the multiple comparison using the Benjamini-Hochberg adjustment, *p < 0.05. E3Y, APOE3 young mice; E4Y, APOE4 young mice; E3O, APOE3 old mice; E4O, APOE4 old mice. (a) Heatmap and cluster analysis of two-way ANOVA of significantly differentially abundant metabolites grouped by age and APOE genotype. Clustering was obtained following similarity analysis using the Ward hierarchical algorithm and Euclidean distance metrics. (b) Co-inertia analysis (COIA) of the association between metabolites and microbiota composition in the gut. The left panel shows the COIA of the microbiota principal component analysis (PCA) (solid circle) at OTU level and the metabolomics PCA (empty circle); length of arrow indicates the divergence between two datasets. The right panel shows co-inertia of metabolome and microbiota data, represented by arrow length between the two data points per sample, grouped according to APOE genotype or age. Length of arrow was estimated using Euclidean distance measurement. *p < 0.05.