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Exp Neurobiol 2017; 26(5): 307-317
Published online October 31, 2017
© The Korean Society for Brain and Neural Sciences
Yunjin Lee1, Jin-Young Park1, Eun-Hwa Lee1, Jinho Yang2, Bo-Ri Jeong2,Yoon-Keun Kim2, Ju-Young Seoh3, SoHyun Lee4, Pyung-Lim Han1,5* and Eui-Jung Kim6*
1Departments of Brain and Cognitive Sciences, 4Special Education, and 5Chemistry and Nano Science, Ewha Womans University, Seoul 03760, Korea, 2MD Healthcare Inc., Seoul, Korea; Departments of 3Microbiology and 6Psychiatry, College of Medicine, Ewha Womans University, Seoul 07985, Korea
Correspondence to: *To whom correspondence should be addressed.
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Individuals with autism spectrum disorder (ASD) have altered gut microbiota, which appears to regulate ASD symptoms via gut microbiota-brain interactions. Rapid assessment of gut microbiota profiles in ASD individuals in varying physiological contexts is important to understanding the role of the microbiota in regulating ASD symptoms. Microbiomes secrete extracellular membrane vesicles (EVs) to communicate with host cells and secreted EVs are widely distributed throughout the body including the blood and urine. In the present study, we investigated whether bacteria-derived EVs in urine are useful for the metagenome analysis of microbiota in ASD individuals. To address this, bacterial DNA was isolated from bacteria-derived EVs in the urine of ASD individuals. Subsequent metagenome analysis indicated markedly altered microbiota profiles at the levels of the phylum, class, order, family, and genus in ASD individuals relative to control subjects. Microbiota identified from urine EVs included gut microbiota reported in previous studies and their up- and down-regulation in ASD individuals were partially consistent with microbiota profiles previously assessed from ASD fecal samples. However, overall microbiota profiles identified in the present study represented a distinctive microbiota landscape for ASD. Particularly, the occupancy of
Keywords: Autism spectrum disorder, gut microbiota, Extracellular membrane vesicles, Bacteria-derived EVs, urine marker
Autism spectrum disorder (ASD) is a group of neurodevelopmental disabilities characterized by two domains of core symptoms, persistent social deficits and restricted repetitive patterns of behavior . Most individuals with ASD suffer from various behavioral and physical symptoms, including abnormal preferences regarding specific foods and problems in the digestive system [2,3]. Approximately 70~80% of ASD subjects have food selectivity and restricted food interests due to the texture, smell, or color of specific foods, and food intolerance [3,4]. The limited food intake behavior in ASD subjects leads to health problems including nutrition imbalance and gastrointestinal (GI) symptoms, such as diarrhea and constipation [2,5,6]. Furthermore, studies of a positive correlation between GI symptoms and ASD have been reported; the c-Met promoter variant rs1858830 is associated with ASD and GI symptoms, and the serum level of hepatocyte growth factor (HGF) that binding to the c-Met receptor, is correlated with severity of GI symptom in ASD subjects [7,8,9].
Several lines of evidence indicate that ASD patients have altered microbiota composition in the gut compared to healthy subjects [10,11,12,13,14,15,16,17,18,19,20,21]. The occupancy of the phyla
Gram-negative bacteria secrete extracellular membrane vesicles (EVs), also called nanovesicles, to communicate with host cells , and are detected in stools, and also in urine and blood serum [25,26,27]. EVs secreted by gram-negative bacteria contain DNA, RNA, proteases, phospholipases, adhesins, toxins, and immunomodulatory compounds. Bacteria-derived EVs are associated with cytotoxicity, bacterial attachment, intercellular DNA transfer, and invasion [24,28,29]. Gram-positive bacteria also produce EVs, which contain peptidoglycan, lipoteichoic acid, virulence proteins, DNA and RNA [30,31]. When bacterial EVs were intraperitoneally injected in mice, EVs were rapidly distributed throughout the body with accumulation in the liver, lung, spleen, and kidney within 3 h . Bacteria-derived EVs in the blood and urine represent the major constituents of microbes in the body, namely the gut microbiota [25,26], and indicate the microbiota that are metabolically or pathologically active [25,27].
In the present study, we investigated bodily microbiota represented by bacterial EVs in the urine of ASD individuals. The results of the present study identify markedly altered microbiota profiles in ASD relative to non-ASD healthy controls and suggest that bacterial EVs in urine can be served as a useful tool for the evaluation of microbiota composition in ASD.
Individuals who were enrolled at the Ewha Special Education Research Institute (Seoul, Republic of Korea) or Ewha Womans University MokDong Hospital (Seoul, Republic of Korea) were diagnosed according to the DSM-5 diagnostic criteria by a child and adolescent psychiatrist followed by characterization using the Korean Childhood Autism Rating Scale (K-CARS) as described previously . The K-CARS is a well-established scale for the diagnosis of ASD with good agreement with the DSM-5 diagnostic criteria . This questionnaire contained 15 items, each with 4 symptom scales, and all individual scores on each of the questions were summed to obtain the total score. When the total score was higher than 30 points, the subject was classified as autistic. Individuals who had any associated additional psychiatric and neurological diagnoses, or individuals who were on any antipsychotic medications were excluded from the present study.
Among the characterized ASD individuals, 18 male and 2 female ASD individuals (22.4+/−4.9 years) (Table 1) were joined to this study and their urine was collected during the day. The collected urine samples were frozen and stored at −20℃ until use. Age-matched normal healthy subjects (24 males and 4 females, 21.1+/−9.5 years) (Table 1) were selected from the Inje University Haeundae Paik Hospital (IRB No. 1297992-2015-064) and Seoul National University Hospital Healthcare System Gangnam Center (IRB No. 1502-034-647). The control subjects were not related to ASD and had no clinical findings suggestive of gastrointestinal problems or neuropsychiatric disorders. The control subjects of this study had not taken antibiotics, probiotics or prebiotics in the 3 months prior to the sample collection.
The experimental protocol of human subjects was reviewed and approved by the Institutional Review Board of Ewha Womans University Hospital (IRB No. 2015-08-005-002). All eligible participants had been told about the purpose, procedures, risks and benefits of the present study and informed consent was obtained from all ASD subjects.
Bacteria EVs were isolated from the urine of ASD individuals following the procedure described previously [25,26]. Briefly, each urine sample was centrifuged at 10,000 × g for 10 min at 4℃. The supernatant was taken and passed through a 0.22-µm membrane filter to eliminate foreign particles. Isolated EVs were dissolved in 100 µl PBS, and quantified on the basis of protein.
Bacterial DNA extraction from prepared EVs was performed as described previously [25,26]. Briefly, isolated EVs (1 µg by protein, each sample) were boiled at 100℃ for 40 min, centrifuged at 13,000 g for 30 min, and the supernatants were collected. Collected samples were then subjected to bacterial DNA extraction using a DNA extraction kit (PowerSoil DNA Isolation Kit, MO BIO, USA) following the manufacturer's instructions, Isolated DNA was quantified by using the QIAxpert system (QIAGEN, Germany).
Prepared bacterial DNA was used for PCR amplification of the V3-V4 hypervariable regions of the 16S ribosomal RNA genes using the primer set of 16S_V3_F (5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3′) and 16S_V4_R (5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3′). The PCR products were used for the construction of 16S rDNA gene libraries following the MiSeq System guidelines (Illumina Inc., San Diego, CA, USA). The 16S rRNA gene libraries for each sample were quantified using QIAxpert (QIAGEN, Germany), pooled at the equimolar ratio, and used for pyrosequencing with the the MiSeq System (Illumina, USA) according to manufacturer's recommendations.
Obtained raw pyrosequencing reads were filtered on the basis of the barcode and primer sequences using MiSeq Control Software version 1.1.1 (Illumina, USA). Sequence reads were taxonomically assigned using the MDx-Pro ver.1 profiling program (MD Healthcare Inc., Seoul, Korea). Briefly, the quality of sequence reads was retained by controlling an average PHRED score higher than 20 and read length of more than 300 bp. Operational taxonomic units (OTUs) were clustered using CD-HIT sequence clustering algorithms and were assigned using UCLUST  and QIIME  on the basis of the GreenGenes 8.15.13 16S rRNA sequence database . Based on the sequence similarities, taxonomic assignments were achieved at the following levels: genus, >94% similarity; family, >90% similarity; order, >85% similarity; class, >80% similarity; and phylum, >75% similarity. In cases where clustering was not possible at the genus level due to a lack of sequence information at the database or redundant sequences, the taxon was named based on the higher-level taxonomy with parentheses.
Data were normalized to have a mean of 0 and a standard deviation of 1 by linear normalization. PCA and two-dimensional scatter plots with axis of the first and second principal component were calculated and drawn using Matlab 2011a.
Two-sample comparisons were performed using Student's
Bacteria-derived EVs were isolated from the urine of 20 ASD individuals and 28 normal healthy subjects. The average age of the control and ASD subjects was 21.1+/−9.5 years and 22.4+/−4.9 years, respectively (Table 1). ASD subjects showed mild impairment of social interaction and stereotypies. The average K-CARS values of these ASD individuals was in the range between 31.5 and 33.5 and IQ values were in the range between 65 and 86. The control group was composed of healthy volunteers who had no medical problems including those related to ASD.
After the extraction of bacterial genomic DNA from the isolated EVs, variable regions of the 16S rRNA genes were amplified by PCR, and the libraries were constructed, as described previously [25,26]. Subsequent DNA sequencing analyses led us to identify over 2,000 operational taxonomic units (OTUs) for ASD and normal individuals. There was no significant difference in the alpha diversity between the two groups (Fig. 1A). Among the identified OTUs, we assigned 30 OTUs at the phylum level, 75 OTUs at the class level, 141 OTUs at the order level, 279 OTUs at the family level, and 619 OTUs at the genus level. Among these OTUs, we primarily focused on OTUs that occupied more than 0.1% of the identified taxons in the following analyses.
Sequence readings of EVs-based 16S rDNA indicated that the top five members of the phyla
The microbiota whose occupancy decreased or increased in ASD individuals were further analyzed at the class, order and family levels (Table 2, Supplemental Fig. S1). The decrease of
The members of the genus occupied by more than 0.1% in either control or ASD individuals are summarized in Table 3 and Supplemental Table 1. Overall, 14 members at the genus level were down-regulated in ASD and their total occupancy in ASD dropped from 34.77 to 14.06%. On the contrary, 17 genus members were up-regulated in ASD and their total occupancy in ASD increased from 6.47 to 22.58%.
More specifically, an unclassified member of
On the other hand,
In the present study, we demonstrated that bacteria-derived EVs in urine were useful for the rapid assessment of microbiota profiles in ASD. The metagenome analysis of urine EVs indicated that
At the genus level, the present study identified
The EV levels of
Oral treatment with
The microbiota profile assessed from urine EVs might reflect a large part of the gut microbiota. Nonetheless, we do believe that the microbiota profile assessed from urine EVs is not likely a simple alternative for microbiota profile assessed from stool. Possible sources for metagenome analysis of bodily microbiota may include stool bacteria, stool EVs, gut (ex, stomach and/or specific regions of the small and large intestines) bacteria, respiratory exhale EVs, oral/nasal bacteria and EVs, urinary system bacteria and EVs, and blood EVs. Generally speaking, microbiota in stool represents the intestinal compartment, whereas microbiota in urine or blood reflects the whole body including the intestinal compartments, oral system, respiratory system, and urinary system. Nonetheless, among the body parts, the gut is the major source of bodily microbiota. It was reported that the metabolites of intestinal microbiota activities, including phenyllactate, p-cresol sulfate, concentrations, and serotonin in urine, plasma, and stool of mouse pups undernourished by timed separation from lactating dams, then resumed ad libitum nursing, were different from each other, although they had some correlations [44,45]. Similar to metabolite profiles of intestinal microbiota activities, available information suggests that metagenome analysis assessed from these sources might be closely related, but represent some distinct landscapes. For an example, metagenome analysis of bacteria and bacteria-derived EV in stool of inflammatory bowel disease model mice indicated that the EV composition in stool was more drastically altered compared to that of bacterial composition in stool . Considering that bacteria-derived EV indicates the metabolically or pathologically activated microbiota [25,27], urine EV may be more representative of the host's microbiota activities than stool bacteria.
To the best of our knowledge, this is the first report characterizing microbiota in ASD individuals on the basis of urine EVs. Compared to blood and feces, urine is easily obtained in large volume and is readily available via a non-invasive method. Considering the general difficulties in repeated sampling microbiota sources from ASD individuals, particularly low functioning individuals with ASD or toddlers with ASD, using urine as a sample source would be a great advantage for rapid and repeated assessments of microbiota changes under varying physiological contexts compared to the use of blood and feces. Comparative analyses of EV profiles from urine, blood and stool of ASD individuals will be valuable. Also it will be worth to understand EV profiles of ASD with diverse factors including age, sex, familial history, genetics, and ethnics.
Overall, the present study assessed urine EVs from individuals with mildly autistic subjects. We believe that further systematic and unbiased analyses of male and female subjects with broad ASD spectrums are necessary. This study focused on young adult subjects. Considering that ASD should be diagnosed in young children as early as 1.5~3 yr of age, this analysis should be expanded to toddlers and infants.
The diversity of microbiota at the class, order, family, and genus levels in control vs. ASD subjects. (A~D) Principal component analysis (PCA) of microbiota diversity at the class (A), order (B), family (C), and genus (D) levels based on the weighted UniFrac distance and Bray-Curtis dissimilarity. Data were normalized to have a mean of 0 and a standard deviation of 1. Control (blue) and ASD (red).en-26-307-s001.pdf
The percent composition of microbiota at the genus level in control and ASD subjectsen-26-307-s002.pdf
|Taxon||Mean (%)||Fold change||p-value||Taxon||Mean (%)||Fold change||p-value||Taxon||Mean (%)||Fold change||p-value|
Microbiota at the family level whose occupancy was significantly different in ASD subjects are presented with associated higher taxonomy levels. Those occupying 0.1% or higher in either normal healthy or ASD subjects were included. ↑ and ↓ denote an increase and decrease in the percent composition, respectively. Data are the mean percentages. * and ** denote significant differences between the indicated groups at p<0.05 and p<0.01, respectively (Student's
|Class||Order||Family||Taxon||Mean±SEM (%)||Fold change||p-value|
|Unclassified 1||0.63±0.17||0.07±0.03||↓ 0.11||0.00**|
|Unclassified 2||0.11±0.03||0.00±0.00||↓ 0.00||0.00*|
Microbiota at the genus level whose occupancy was significantly different in ASD subjects are presented with associated higher taxonomy levels. Microbiota with occupancy 0.1% or higher in either normal healthy or ASD subjects were considered. ↑ and ↓ denote an increase and decrease in the percent composition, respectively.
Data are the mean percentage±SEM. * and ** denote significant differences between the indicated groups at p<0.05 and p<0.01, respectively (Student's
|Taxons||Mean (%)||Fold change||p-value||Literatures|
|Phylum||Proteobacteria||49.12||35.30||↓ 0.72||0.01**||↓ ; ↑ |
|Firmicutes||24.96||33.07||↑ 1.33||0.03*||↓ ; ↑ |
|Actinobacteria||10.91||11.74||↑ 1.08||0.56||↓ |
|Bacteroidetes||5.85||8.62||↑ 1.47||0.11||↑ ; ↓ ; ↓ |
|Verrucomicrobia||0.58||2.37||↑ 4.12||0.02*||↓ |
|Class||Betaproteobacteria||10.09||6.26||↓ 0.62||0.10||↑ |
|Order||Clostridiales||11.59||15.38||↑ 1.33||0.18||↑ |
|Family||Ruminococcaceae||5.46||6.11||↑ 1.12||0.68||↑ |
|Lachnospiraceae||3.12||2.57||↓ 0.82||0.54||↑ |
|Corynebacterium||2.60||3.38||↑ 1.3||0.34||↑ |
|Alcaligenaceae||2.53||0.12||↓ 0.05||0.00**||↑ |
|Pseudomonas||7.48||5.10||↓ 0.68||0.03*||↑ |
|Lactobacillus||2.56||5.45||↑ 2.13||0.08||↑ ; ↑ |
|Bacteroides||2.48||2.93||↑ 1.18||0.65||↑ ; ↑ |
|Staphylococcus||2.23||2.57||↑ 1.15||0.66||↓ |
|Faecalibacterium||2.14||2.05||↓ 0.96||0.90||↑ |
|Bifidobacterium||1.90||0.80||↓ 0.42||0.06||↓ ; ↓ ; ↓ ; ↓ |
|Streptococcus||1.58||4.77||↑ 3.03||0.02*||↓ ; ↓ |
|Akkermansia||0.52||2.35||↑ 4.52||0.02*||↓ ; ↓ |
|Blautia||0.47||0.28||↓ 0.59||0.38||↓ |
|Enterococcus||0.40||0.78||↑ 1.92||0.06||↑ ; ↓ |
|Collinsella||0.37||0.11||↓ 0.29||0.16||↓ ; ↑ |
|Veillonella||0.37||0.50||↑ 1.34||0.43||↓ |
|Lactococcus||0.35||0.11||↓ 0.3||0.16||↓ ; ↓ |
|[Ruminococcus]||0.34||0.15||↓ 0.45||0.16||↓ |
|Coprococcus||0.28||0.27||↓ 0.94||0.90||↓ |
|Leuconostoc||0.27||0.01||↓ 0.02||0.14||↓ |
|Dialister||0.23||1.39||↑ 6.16||0.18||↓ ; ↓ |
|Parabacteroides||0.21||0.18||↓ 0.83||0.76||↓ ; ↑ |
|Weissella||0.21||0.07||↓ 0.31||0.33||↓ |
|Turicibacter||0.19||0.02||↓ 0.12||0.07||↓ |
|Dorea||0.17||0.10||↓ 0.6||0.44||↑ |
|Clostridium||0.11||0.31||↑ 2.72||0.09||↑ ; ↓ |
|[Prevotella]||0.09||0.12||↑ 1.34||0.74||↑ ; ↓ |
|Desulfovibrio||0.04||0.48||↑ 10.88||0.00**||↑ ; ↓ |
|Genus||0.03||0.37||↑ 11.53||0.08||↑ |
The microbiota whose percent composition were significantly different in ASD subjects as characterized in the present study were compared with those identified from fecal samples in previous studies. ↑ and ↓ denote an increase and decrease in the percent composition, respectively. * and ** denote significant differences between the indicated groups at p<0.05 and p<0.01, respectively (Student's