Exp Neurobiol 2017; 26(6): 369-379
Published online December 31, 2017
© The Korean Society for Brain and Neural Sciences
Jin-Young Park1, Juli Choi1, Yunjin Lee1, Jung-Eun Lee1, Eun-Hwa Lee1, Hye-Jin Kwon1, Jinho Yang2, Bo-Ri Jeong2, Yoon-Keun Kim2 and Pyung-Lim Han1,3*
1Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul 03760, 2MD Healthcare Inc., Seoul 03923, 3Department of Chemistry and Nano Science, Ewha Womans University, Seoul 03760, Korea
Correspondence to: *To whom correspondence should be addressed.
TEL: 82-2-3277-4130, FAX: 82-2-3277-3419
Emerging evidence has suggested that the gut microbiota contribute to brain dysfunction, including pathological symptoms of Alzheimer disease (AD). Microbiota secrete membrane vesicles, also called extracellular vesicles (EVs), which contain bacterial genomic DNA fragments and other molecules and are distributed throughout the host body, including blood. In the present study, we investigated whether bacteria-derived EVs in blood are useful for metagenome analysis in an AD mouse model. Sequence readings of variable regions of 16S rRNA genes prepared from blood EVs in Tg-APP/PS1 mice allowed us to identify over 3,200 operational taxonomic units corresponding to gut microbiota reported in previous studies. Further analysis revealed a distinctive microbiota landscape in Tg-APP/PS1 mice, with a dramatic alteration in specific microbiota at all taxonomy levels examined. Specifically, at the phylum level, the occupancy of
Alzheimer disease (AD) is a neurodegenerative disease characterized by Aβ plaque deposition and cognitive impairment. Recent studies have indicated that the gut microbiota contribute to brain dysfunction in various brain disorders [1,2,3]. The recent progress in studies of microbiota composition changes in AD has been remarkable. An analysis of selected gut microbiota from the stool of AD patients revealed an increase of
Metagenome analyses of bodily microbiota are carried out mostly with fecal samples or small-intestine fluid. Such approaches directly provide the landscape of gut microbiota profiles. Recent studies have suggested that blood serum contains bacterial genome DNA fragments. Microbiomes secrete membrane vesicles, also called extracellular vesicles (EVs) or shedding microvesicles [10,11]. Gram-negative bacterial EVs are small in size (10 to 300 nm in diameter) and have a lipopolysaccharide (LPS)-containing outer membrane and periplasmic constituents [11,12]. Gram-positive bacterial EVs are also small in size (50~150 nm in diameter) [13,14]. Bacterial EVs contain bacterial DNA fragments, RNAs, adhesins, toxins, lipoproteins, phospholipids, peptidoglycans and immunomodulatory compounds [10,13,14,15,16,17]. Due to their small size, bacterial membrane vesicles are permeable to the cellular membrane of the intestinal barrier, and are thereby distributed throughout the body including blood [18,19]. These results suggest that blood serum can be used as an alternative route for metagenome analysis of bodily microbiota.
In the present study, we investigated whether bacterial EVs in blood would be useful for the assessment of bodily microbiota in Tg-APP/PS1 mice.
Tg-APPswe/PS1dE9 (Tg-APP/PS1 for short) mice  were obtained from the Jackson Laboratories (Bar Harbor, ME, USA). Tg-APP/PS1 mice were crossed with C57BL6 mice for more than 10 generations. For genotyping, the following primer sets were used; 5′-AGGACTGACCACTCGACCAG-3′ and 5′-CGGGGGTCTAGTTCTGCAT-3′ for APP; 5′-AATAGAGAACGGCAGGAGCA-3′ and 5′-GCCATGAGGGCACTAATCAT-3′ for PSEN. Two to three mice were housed per cage under a 12 h light/dark cycle in a humidity- and temperature-controlled room, and were allowed access to a diet of lab chow and water ad libitum. All animals were handled in accordance with the animal care guidelines of the Ewha Womans University (IACUC 2013-01-007).
Bacterial EVs were isolated from the sera as described previously [21,22,23]. Male Tg-APPswe/PS1dE9 mice were sacrificed at the age of 8 months and blood was collected from the hearts of sacrificed mice. Collected blood was centrifuged at 1,500 ×
The sera were diluted 1 in 3 with 1x PBS (pH 7.4; ML008-01, Welgene Inc., Gyeongsan, Korea) and centrifuged at 10,000 × g for 1 min at 4℃. Then, the supernatants were collected and filtered through a 0.22-µm filter to remove bacteria and foreign particles. The separated bacterial EVs were boiled at 100℃ for 40 min. They were then centrifuged at 13,000 rpm for 30 min at 4℃, and the supernatants were collected. Bacterial DNA was extracted from the boiled EVs with a PowerSoil DNA Isolation Kit (MO BIO Laboratories Inc., Carlsbad, CA USA) in accordance with the manufacturer's instructions. The DNA from the EVs in each sample was quantified with a QIAxpert system (QIAGEN, Hilden, Germany).
Bacterial DNA was extracted from isolated EVs with a genomic DNA extraction kit (Bioneer Inc., Korea) as described previously . PCR amplification of bacterial 16S ribosomal RNA genes was carried out with the primer set of 16S_V3_F (5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3′) and 16S_V4_R (5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3′), which were specific for the V3-V4 hypervariable regions of 16S rDNA.
The libraries were prepared with PCR products in accordance with the MiSeq System guide (Illumina Inc., San Diego, CA, USA), and the prepared libraries were quantified with a QIAxpert (QIAGEN, Germany). The prepared libraries were pooled at equimolar ratios, and sequenced on a MiSeq (Illumina Inc.) in accordance with the manufacturer's recommendations.
Taxonomic assignments were made by sequence reads of the 16S rRNA genes as described previously . Briefly, the pyrosequencing reads obtained were filtered bythe barcode and primer sequences on a MiSeq (Illumina, USA). Taxonomic assignment was performed with the profiling program MDx-Pro ver.1 (MD Healthcare, Seoul, Korea). Briefly, the quality of sequence reads was controlled through the inclusion of sequences with read lengths longer than 300 bp and average PHRED scores higher than 20. Operational taxonomy units (OTUs) were clustered by the sequence clustering algorithm CD-HIT. Subsequently, taxonomy assignments were achieved by means of UCLUST and QIIME on the basis of sequence similarities with the 16S rDNA sequence database of GreenGenes 8.15.13. In the event that clusters could not be assigned at the genus level due to the lack of sequences in the database or redundant sequences, the taxon was assigned at a higher level and indicated in parentheses. Taxonomic assignments were achieved on the basis of similarities at the following levels: genus, >94% similarity; family, >90% similarity; order, >85% similarity; class, >80% similarity; and phylum, >75% similarity.
Data are presented as the mean percentage±SEM. The z-score was calculated as X − µ/σ, where µ=mean; X=individual score; σ=standard deviation (SD).
Tg-APP/PS1 mice show plaque deposition in the brain starting from 6.5 months of age and severe cognitive deficits at 7~7.5 months [24,25,26]. Bacterial EVs in blood were collected from 8-month-old Tg-APP/PS1 mice and their wild-type controls, and bacterial DNA was extracted from prepared EVs as described in the Materials and Methods. PCR amplification of variable regions of 16S ribosomal RNA (rRNA) genes and subsequent sequence readings led us to identify approximately 2,500 and 3,200 OTUs for wild-type and Tg-APP/PS1 mice, respectively. Although a slightly higher number of OTUs was identified for Tg-APP/PS1 mice than for controls, the difference was not statistically significant (Fig. 1A). Among the identified OTUs, 30 OTUs at the phylum level, 72 OTUs at the class level, 133 OTUs at the order level, 263 OTUs at the family level, and 571 OTUs at the genus level were assigned to either wild-type or Tg-APP/PS1 mice. Among the identified OTUs, in the present study we focused on the OTUs with occupancies ≥0.1% at each taxon level.
Among the identified OTUs at the phylum level, 14 phylum members had occupancies ≥0.1% in either wild-type or Tg-APP/PS1 mice (Fig. 1B). The top 5 phylum members (
The microbiota for which occupancies in Tg-APP/PS1 mice were altered at the class, order, and family levels are further specified and summarized in Table 1. Among
We continued to analyze the occupancies of specific genus members in Tg-APP/PS1 mice relative to wild-type controls. Of the 130 genus members with relative occupancies ≥0.1% in either wild type or Tg-APP/PS1 mice, 39 members were upregulated, and 16 members were downregulated in Tg-APP/PS1 mice, by more than one SD relative to wild-type mice, while the remaining 70 members were unchanged in Tg-APP/PS1 mice. Of these altered genus members, 17 were newly detected in Tg-APP/PS1 mice, but not in wild-type mice, while 12 were detected in wild-type mice, but lost in Tg-APP/PS1 mice (Table 2). The heat-map analysis in Fig. 2A represents these changes pictorially (Fig. 2A). The total relative occupancy of these 55 (39+16) altered genus members was 29.9% in wild-type mice and 52.2% in Tg-APP/PS1 mice (Table 2). Of the 55 altered genus members, 9 had occupancies ≥0.1% in either wild-type or Tg-APP/PS1 mice. Specifically,
We demonstrated in the present study that bodily microbiota compositions were altered in Tg-APP/PS1 mice compared to those of non-transgenic control. Of the alterations in phylum members, it is worth noting that the occupancy of
The total occupancy of
As summarized with the results of the heat-map analysis, certain genus members were newly detected in Tg-APP/PS1 mice, but not in wild-type mice, while other members were detected in wild-type mice, but lost in Tg-APP/PS1 mice. Although the relative occupancy of each of these genus members was not high, determining whether these genus members could can serve as microbiome markers for specific AD pathology will require further investigation.
In comparing the results of the present study with those published in the literature, it may be worth noting that the altered occupancy of
At the genus level, Tg-APP/PS1 mice exhibited decreases in occupancy of
Although physiological significance of these changes are not known, it will be worthy of note the following genus members. A recent study reported that the occupancy of
Overall, these results suggest that the microbiota composition of AD patients and AD animal models is altered, although detailed microbiota profiles are not consistent. These results imply that the microbiota composition of AD or AD models in different physiological contexts is highly complex and dynamic. Considering the importance of understanding the interaction between the gut microbiota and the brain in AD, more systematic and comprehensive analyses of the microbiota compositions in AD are needed.
The results of the present study raise some important issues regarding bodily microbiota in Tg-APP/PS1 mice, which are summarized as follows. First, it is worth noting that Tg-APP/PS1 mice and their wild-type controls were cultured in the same animal room environment with the same food from gestation and birth. Nonetheless, microbiota compositions of Tg-APP/PS1 mice were altered. This result emphasizes the importance of the relationship between microbiota and AD pathology. Second, detailed analysis of microbiome profiles indicated that the microbiota represented in blood EVs matched the gut microbiota reported in previous studies [34,35,36,37,38]. This is not surprising, because the main source of bodily microbiota is in the gut/gastrointestinal tract [39,40,41]. Microbiota that are metabolically active  or exist under pathogenic conditions [12,15] produce EVs. It will be worth investigating what proportions of bacteria-derived EVs in blood in Tg-APP/PS1 mice are metabolically active or exist under pathogenic conditions. Third, the specific microbiota identified to have altered occupancies in the present study were partly consistent with those assessed on the basis of fecal microbiomes, although the relative proportions of certain taxa differed from those in fecal samples (Table 3). As summarized in Table 3, the relative proportions of specific taxa identified from fecal samples have varied across different studies (eg., APPPS1 mice of  vs. 3xTg mice of ). Given the considerable variation among AD animal models, which are cultured in relatively defined environments, more comprehensive and systematic studies are necessary in AD patients to elaborate whether and how various genetic, ethnic, environmental and regional factors affect microbiota profiles. Fourth, to the best of our knowledge, this is the first study characterizing bodily microbiota on the basis of blood EVs. Unlike feces, blood is collected during normal medical examination. Therefore, the ability to use blood as a sample source will facilitate the rapid assessment of the microbiomes of AD patients in various physiological contexts.
In summary, we identified a number of microbiota from blood EVs whose composition was altered in an AD mouse model. Among the altered microbiota, a number of genus members were detected in Tg-APP/PS1 mice, but not in wild-type mice, while other genus members were identified in wild-type mice, but not in Tg-APP/PS1 mice. The results of the present study support that bacterial EVs in blood represent a new opportunity for metagenome analysis of microbiota in AD models and AD patients.
|34.7||57.5||↑ 3.4||18.1||25.9||↑ 1.4||18.1||25.9||↑ 1.4||4.7||7||↑ 1.1|
|16||30.3||↑ 5.4||8.3||17||↑ 3.2||5.7||3.1||↓ −1.7|
|7.3||12.9||↑ 1.1||6.4||12||↑ 1|
|0||0.3||↑ 27.8||0||0.3||↑ 27.8|
|30.5||20.7||↓ −1.6||14.4||13||9.1||8.8||3.1||4.5||↑ 1.4|
|0||0.1||↑ 5.6||0||0.1||↑ 5.6|
|10.2||3.7||3.8||0.7||↓ −1.1||3.8||0.7||↓ −1.1|
|0.2||0.9||↑ 2.1||0.2||0.9||↑ 2.1|
|0.2||0||↓ >>||0.2||0||↓ >>|
|0||0.2||↑ <<||0||0.2||↑ <<||0||0.2||↑ <<|
|0||0.2||↑ 2.6||0||0.2||↑ 2.6||0||0.2||↑ 2.6|
|3.8||2.6||3.8||2.6||0||0.2||↑ <<||0||0.2||↑ <<|
|0.9||0.3||↓ −1.1||0.9||0.3||↓ −1.1||0.9||0.3||↓ −1.1||0.9||0.3||↓ −1.1|
|0.9||0.5||0||0.2||↑ 1.4||0||0.2||↑ 1.4||0||0.2||↑ 1.4|
|0.2||0||0.2||0||↓ >>||0.1||0||↓ >>||0.1||0||↓ >>|
|0.1||0||↓ >>||0.1||0||↓ >>|
|0||0.2||↑ <<||0||0.2||↑ <<||0||0.2||↑ <<||0||0.2||↑ <<|
|0||0.1||↑ <<||0||0.1||↑ <<||0||0.1||↑ <<||0||0.1||↑ <<|
The family members of microbiomes for which occupancy differed by more than one standard deviation (SD; one z) in Tg-APP/PS1 mice from that of wild-type mice are presented, along with the higher taxonomy levels. Those occupying 0.1% and higher in either wild-type or Tg-APP/PS1 mice were included..
Data are presented as the mean percentages and z-scores. The z-score is X − µ/σ, where µ=mean; X=individual score; σ=standard deviation (SD). Increases and decreases in the mean occupancy by more than one z in Tg-APP/PS1 are indicated by ↑ and ↓, respectively. « and » denote infinite increases and decreases, respectively..
The percent compositions of microbiomes at the genus level for which occupancy differed by more than one SD (one z) in Tg-APP/PS1 mice. Microbiomes with occupancies higher than 0.1% in either wild-type or Tg-APP/PS1 mice were included..
Data are presented as the mean percentage +/− SEM. The z-score is X − µ/σ, where µ=mean; X=individual score; σ=standard deviation (SD). The increase and decrease of the mean occupancy by more than one z in Tg-APP/PS1 are indicated by ↑ and ↓, respectively. « and » denote infinite increases and decreases, respectively..
|APP/PS1a) (serum)||APP/PS1b) (fecal)||3xTg (fecal)|
|(Harach et al., 2017)||(Bonfili et al., 2017)|
The microbiomes for which the percent composition increased or decreased by more than one z score in Tg-APP/PS1 are marked by ↑ and ↓, respectively. « and » denote infinite increases and decreases, respectively. The increases and decreases of the indicated microbiota in the previous studies are also marked by ↑ and ↓, respectively..
The z-score is expressed as X − µ/σ, where µ=mean; X=individual score; σ=standard deviation (SD). a)APPswe/PSEN1dE9; b)APPswe/PSEN1-L166P..