Study subjects
Fecal samples were collected from 10 middle-aged Japanese subjects (five men and five women) from January 2013 to March 2014. The subjects were systematically recruited (based on age, gender, residence, and non-severe diseases such as heart and cerebrovascular diseases, and cancer) as volunteers from participants in the Sakura Diet Study in Shizuoka, located in central Japan. They had lived in the area for at least 1 year. Briefly, we orally explained to them the purpose of the study and obtained their signed informed consent for participation in this study. Lifestyle information, including dietary habits, was collected using a scientifically validated questionnaire (Tokudome et al. 2005). During 1-year interval between the first and the second administration of the food frequency questionnaires, the following samples were systematically collected each season: a 3-day dietary record, blood, urine, saliva, feces, and green tea samples as well as blood pressure, height, and body weight. The study was approved by the Ethics Committee, University of Shizuoka (No. 24-24).
Questionnaires on defecation and collection of fecal samples
We asked the subjects to collect fecal samples and fill out questionnaires on defecation times in every quarter (four times, representing seasonal samples), and at all times of defecation on 7 continuous days after the fourth season (more than three times in 1 week, considering ones with constipation). For both sets of samples, soon after defecation, each fecal sample was individually suspended by the study subjects in 100 mM Tris–HCl (pH 9), 40 mM EDTA, 4 M guanidine thiocyanate (protein denaturant to inhibit bacterial growth), and 0.001 % bromothymol, as previously described (Fukuda and Fujita 2014; Shiozaki et al. 2014). To clarify inter- and intra-individual variations of human gut microbiota, we collected seasonal and daily fecal sample sets from the same subjects.
The questionnaire on defecation included questions on defecation clock time, stool volume, stool shape/consistency, and stool color, along with questions regarding dietary intake of fermented milk (i.e., yoghurt and fermented milk beverages), antibiotics, other medication, probiotics, and prebiotics within 1 week or 1 day before fecal collection. Stool volume was recorded in terms of the number (e.g., 0.5, 1 and 2) of Japanese standard chicken egg size “S” (i.e., 46–52 g). Color was selected from the following categorical variables; “yellow,” “green ocher,” “brown,” “blackish brown,” or “slightly blackish,” and shape/consistency was selected from the following categorical variables: “watery,” “muddy,” “soft,” “banana shape,” “hard,” or “very hard” (Nakamura and Oku 2002).
DNA extraction from fecal samples
Fecal solids in the suspension were broken down by using FastPrep 24 Instrument (MP Biomedicals, Santa Ana, CA, USA) with zirconia beads at 5 m/s for 2 min. Bacterial DNA extraction from 200 μL of the suspension was performed by using a Magtration System 12GC (Precision System Science, Japan), with MagDEA DNA 200 (Precision System Science) as a reagent for the automatic nucleic acid extraction.
NGS analysis of bacterial community structure in feces
In this study, a series of representative bacteria in the human gut microbiota was analyzed by previously described NGS of 16S rDNA methods (Takahashi et al. 2014) using the following primers (for the V3–V4 region of 16S rDNA of prokaryotes): forward primer 5′-AATGATACGGCGACCACCGAGATCTACACXXXXXXXXACACTCTTTCCCTACACGACGCTCTTCCGATCTCCTACGGGNBGCASCAG-3′, where Xs represent the sample-specific 8-bp barcode sequences (CTCTCTAT, TATCCTCT, GTAAGGAG, ACTGCATA, AAGGAGTA, CTAAGCCT, CGTCTAAT, TCTCTCCG, TCGACTAG, and TTCTAGCT) and reverse primer 5′-CAAGCAGAAGACGGCATACGAGATZZZZZZZZGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGACTACNVGGGTATCTAATCC-3′, where Zs represent the sample-specific 8-bp barcode sequences (TCGCCTTA, CTAGTACG, TTCTGCCT, GCTCAGGA, AGGAGTCC, CATGCCTA, GTAGAGAG, CAGCCTCG, TGCCTCTT, TCCTCTAC, TCATGAGC, and CCTGAGAT); the underlined sequences represent the PCR primer region (Pro341F and Pro805R). Sequencing was conducted using a paired-end and modified to 2 × 300-bp cycle run on an Illumina MiSeq sequencing system (Illumina, San Diego, CA, USA) and MiSeq Reagent Kit version 3 (600 Cycle) chemistry. Paired-end sequencing with read lengths of 301 bp was performed. After demultiplexing, a clear overlap in the paired-end reads was observed. The method of quality filtering of sequences was as follows: only reads that had quality value (QV) scores of ≥20 for more than 99 % of the sequence were extracted for further analysis.
16S rDNA-based taxonomic analysis
Bacterial identification from sequences was performed using the Metagenome@KIN analysis software (World Fusion, Japan) and the TechnoSuruga Lab Microbial Identification database DB-BA 9.0 (TechnoSuruga Laboratory, Japan). Regarding the composition (%) of human intestinal bacteria, we focused on genera representing >0.1 % of the total human gut microbiota, considering the measurement precision, which was approximately >85 % in this study. Based on the analysis of a series of 20 pooled standard samples (i.e., a mixture of our study samples) measured within one run (for 2 days), intra-assay coefficients of variation were <5.0, <10.0, and <15.0 % for 6, 7, and 9 genera, respectively, out of the 22 selected human intestinal bacteria (>0.2 % of the total human gut microbiota), except for Odoribacter. Inter-assay coefficients of variation based on replicate analyses of a total of 60 pooled standard samples over three runs were <7.0 % (<5.0 % for 13 genera out of these), except for Bifidobacterium and Akkermansia. For the remaining genera (including Collinsella, 0.1–0.2 % of the total human gut microbiota), however, the intra- and inter-assay coefficients were >15.0, and 2.3–14.4 %, respectively.
Statistical analyses
Considering the measurement precision, in the stability analyses, hierarchical clustering for the selected 17 human intestinal bacterium (>1.0 % of the total human gut microbiota) was performed using GeneMaths software (Applied Maths, Belgium) to obtain an overview of the similarity between 16S rDNA genomic profiles in the subjects. For clustering, the similarity between the profiles was calculated using Pearson’s correlation coefficient (r
p), while linkage was calculated using the Ward method. Each variation in the profiles was visualized by different lengths of branches (i.e., r
p) in the clustering tree for seasonal and daily fecal sample sets. For mean values of each human intestinal bacterium over four seasons (v_four seasons %), r
p (only for >1.0 % of the total human gut microbiota, considering r
p based on hierarchical clustering analysis) and Spearman’s correlation coefficients (r
s) are shown with each genus for 1–3 continuous days (v_1d, v_2d, and v_3d %, in the order). However, regarding r
p and r
s for daily fecal sample set, the two mean values of 1–3 and 7 days could not be used as independent variables.
Cochran’s Q-test was used to assess changes in fermented milk intake. For the selected 39 human intestinal bacteria, the inter- and intra-individual differences from each composition of the human intestinal microbiota were analyzed by one-way repeated measures ANOVA. Using a single imputation method, each mean value was used to replace missing values for subjects who could not provide their fecal samples because of constipation. In case of subjects with two or more defecations in a day, mean value of all defecations in the day was used. Friedman test, as a nonparametric method, was also performed, considering the small sample size, i.e., 40 seasonal and 72 daily fecal samples obtained from the 10 subjects. The total variance was partitioned by ANOVA into the following two sources: (1) inter-individual variance in the subject’s stationary composition and (2) residual variance, which measured intra-individual variance in either the seasonal or daily fecal sample set (Ogawa et al. 1999). We also calculated the number of days needed to estimate the true value with 95 % confidence intervals within 10 and 20 % of their true mean (Beaton et al. 1979). These analyses were performed with SPSS version 18 (IBM Corporation, Chicago, IL, USA).