Comprehensive 16S rRNA and metagenomic data from the gut microbiome of aging and rejuvenation mouse models

The gut microbiota is associated with the health and longevity of the host. A few methods, such as fecal microbiota transplantation and oral administration of probiotics, have been applied to alter the gut microbiome and promote healthy aging. The changes in host microbiomes still remain poorly understood. Here, we characterized both the changes in gut microbial communities and their functional potential derived from colon samples in mouse models during aging. We achieved this through four procedures including co-housing, serum injection, parabiosis, and oral administration of Akkermansia muciniphila as probiotics using bacterial 16 S rRNA sequencing and shotgun metagenomic sequencing. The dataset comprised 16 S rRNA sequencing (36,249,200 paired-end reads, 107 sequencing data) and metagenomic sequencing data (307,194,369 paired-end reads, 109 sequencing data), characterizing the taxonomy of bacterial communities and their functional potential during aging and rejuvenation. The generated data expand the resources of the gut microbiome related to aging and rejuvenation and provide a useful dataset for research on developing therapeutic strategies to achieve healthy active aging.

www.nature.com/scientificdata www.nature.com/scientificdata/ (aged phenotype). For the co-housing model, young mice were co-caged with aged mice for six weeks in triplicate for three independent experiments. For the parabiosis model, the three parabiotic pairs were established as follows: (i) Hetero-Y (young mice from heterochronic pairs); (ii) Hetero-A (aged mice from heterochronic pairs) and Iso-Y (young mice from isochronic young pairs); and (iii) Iso-A (aged mice from isochronically aged pairs). For the serum injection model, aged (iv 8 Y → A and iv 16 Y → A) and young mice (iv 8 Y → Y and iv 16 Y → Y) were treated with serum collected from young mice by intravenous injection 8 times (for 3 weeks) or 16 times (for 6 weeks). Several blood parameters related to liver, kidney, and muscle function as well as lipids (Table 2), however, no significant changes in plasma markers were observed by parabiotic pairing. For the Akkermansia treatment model, the Aged-AK (Akkermansia-treated group) was treated daily with an oral administration of Akkermansia grown on a culture medium (BTTM) for 36 weeks and Aged-V (vehicle-treated group) was treated daily with BTTM only. These rejuvenation procedures restore age-dependent alterations in intestinal function and inflammation 20 . Furthermore, oral administration of Akkermansia led to an improvement in the frailty index (Supplementary Tables 1 and 2).
The raw sequencing data of 16 S rRNA sequencing and metagenomic sequencing comprised 36,249,200 paired-end reads generated from 107 samples and 307,194,369 paired-end reads generated from 109 samples (Supplementary Tables 3 and 4). The workflow of the data analysis is shown in Fig. 1b. The read-quality distribution of 16 S rRNA sequencing and metagenomic sequencing are shown in Fig. 2a and Fig. 3a, respectively. Operational taxonomic units (OTUs) were generated using DADA2 21 in the QIIME2 pipeline 22 . The orders Bacteroidales, Clostridiales, Enterobacteriales, Verrucomicrobiales, and Lactobacillales dominated the bacterial communities (Fig. 2b). Based on beta-diversity analyses, microbial communities of colon contents from aging,  Table 2. Blood parameters of parabiotic pairing. Levels not connected by same letter (abc) are significantly different.   The microbial profile shows dynamics with the underlying differential taxonomic abundance in the ageing and rejuvenation process. Taxonomic bar plots were generated using QIIME2. (c) Principal Coordinate Analysis (PCoA) of all datasets based on beta-diversity with Jaccard metric.
www.nature.com/scientificdata www.nature.com/scientificdata/ co-housing, parabiosis, serum injection, and AK treatment experiments clearly separated (Fig. 2c), reflecting the small intra-group variation when compared to the inter-group variation. We employed the MG-RAST pipeline and predicted function of metagenomic data containing artefacts-removed, high-quality reads, a median of 91.1% predicted features per sample (range: 55.3%-99.6%), and a median of bacteria-driven reads per sample (range: 61.6%-99.8%) (Fig. 3b,c). The microbial functional enzyme abundance is shown in Dataset 1 23 . The classified bacterial taxonomy showed a significant positive correlation (Pearson's R > 0.66, P < 0.036) between metagenomic sequencing data and 16 S rRNA sequencing data (Fig. 3d). Altogether, we enable reuse of the data and extend the collection of resources related to aging and rejuvenation related to the microbiome. The datasets described here offer an understanding of the relationship between aging and the gut microbiome and functional gene characterization of poorly known commensal bacteria related to aging. We anticipate that our data will pave the way for the development of therapeutic strategies to achieve healthy active aging.   www.nature.com/scientificdata www.nature.com/scientificdata/ model used in this study is shown in Fig. 1a. C57BL/6 J mice were obtained from the Jackson Laboratory (Bar Harbor, ME, USA). These mice were housed at the Korea Research Institute of Bioscience and Biotechnology with a 12-h light/ dark cycle with food and water ad libitum (Daejeon, Korea). For the co-housing experiment, three young mice (4 months old) were co-caged with three aged mice for six weeks (18 months old), and the co-housing experiments were performed in triplicate for three independent experiments (n = 9). For the parabiosis experiments, the were conducted as described previously 24 . The four parabiotic pairs were established as follows: (i) hetero-young (young mice from heterochronic pairs, n = 5); (ii) hetero-aged (aged mice from heterochronic pairs, n = 4); (iii) iso-young (young mice from isochronic young pairs, n = 6), and (iv) iso-aged (aged mice from isochronically aged pairs, n = 4). Effective blood circulation was assessed by intraperitoneally injecting 400 μL of 0.5% Evans blue dye into one of the parabionts 25 . For the serum injection experiments, serum was collected from young (4-5 months old) mice, pooled, and stored at −80 °C until use. Aged (20 months old; iv 8 times (iv 8 Y → A), n = 10; iv 16 times (iv 16 Y → A), n = 5) and young (5 months old; iv 8 times (iv 8 Y → Y), n = 10; iv 16 times (iv 16 Y → Y), n = 5) mice were treated with sera collected from young mice by intravenous injection 8 times (for 3 weeks) or 16 times (for 6 weeks). For AK treatment, aged mice (20-21 months old) were used. The Akkermansia-treated group (Aged-AK, n = 11) was treated daily with an oral administration of Akkermansia (4.9 × 10 8 CFU per 150 µL per day) grown on a culture medium (BTTM) and the vehicle group (Aged-V, n = 10) was treated daily with BTTM only. Treatment was continued for 36 weeks.

Methods
Akkermansia muciniphila DSM 22959 T was obtained from the German Collection of Microorganisms and Cell Cultures (Leibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH, Germany). A. muciniphila was grown anaerobically at 37 °C.
Sample collection and DNA extraction. Mouse colon contents were collected, immediately frozen in liquid nitrogen, and stored at −80 °C. Total DNA from the snap-frozen colon content samples was extracted using the DNeasy PowerSoil DNA Pro Kit (Qiagen, Hilden, Germany) with a repeated bead-beating step for complete DNA extraction, as previously described 26 . The quantity and quality of the purified DNA were measured for 16 S rRNA and metagenomic sequencing using a Nanodrop spectrophotometer (Thermo Fisher Scientific, Massachusetts, USA), gel electrophoresis, and Qubit dsDNA HS Assay Kit (Thermo Scientific, Rockford, IL, USA).
16S ribosomal RNA (rRNA) and shotgun metagenomic sequencing. Targeted  Microbial community profiling. Raw paired-end reads were processed using the QIIME 2 pipeline (ver. 2018.2.0) according to a previously described workflow 22 . Paired-end reads were trimmed and merged, and chimeric sequences were removed using the DADA2 plugin in QIIME2 (qiime dada2 denoise-paired) 21 . OTUs were clustered at 100% sequence similarity (amplicon sequence variants) using DADA2 and classified using the QIIME2 pre-trained naïve Bayes classifier trained on the Greengenes database (13_8). For phylogenetic diversity analyses, a phylogenetic tree was constructed (qiime phylogeny fasttree) by using an alignment of OTU sequence (qiime alignment "mafft" and "mask"). For beta diversity, pairwise permutational analysis of variance (PERMANOVA) statistics were calculated based on Jaccard using QIIME2 (qiime diversity core-metrics-phylogenetic). Principal coordinate analysis (PCoA) plots for beta diversity metrics were generated using the QIIME1 package (make_2d_plots.py).
Metabolic function profiling. Using the CLC Genomics Workbench 6.5.1 (Qiagen), the raw reads were filtered and trimmed to remove low-quality bases and adaptor sequences. The FASTQ files of metagenomic data were uploaded to the Metagenomics Rapid Annotation using Subsystems Technology (MG-RAST) server version 4.0.3 27,28 . The resulting reads were trimmed and normalized, and sequencing artifacts were removed using the MG-RAST pipeline. Taxonomic assignment and annotation of metabolic functions of the metagenomic reads were performed using the non-redundant M5NR 29 with a BLAT 30 in the MG-RAST pipeline. Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology 31 , RefSeq, and SEED subsystem database tools 32 were also employed for the alignment of functional genes, with default parameters 23

Technical Validation
The 16 S rRNA libraries were checked by reviewing the absence of contamination by agarose gel electrophoresis and Agilent 2200 TapeStation. The 16 S rRNA sequencing and metagenomic raw reads were evaluated for their read qualities using the MultiQC 35 wrapper for FastQC (Figs. 2a and 3a). The observed quality distribution showed that most of the reads scored Q25 or higher, indicating no quality problems. Quality control and denoising of 16 S rRNA sequencing reads were performed using the DADA2 plugin 21 in the QIIME2 pipeline 22 . A clear difference in the microbial community structure was observed in the 16 S rRNA sequencing data from aging and four rejuvenation samples.
Raw DNA sequences from colon content for metagenome analysis showed sufficient bacterial sequences with any host DNA contamination (a median of 1.2% eukaryota reads), except for Week 1 samples in aging experiments (a median of 35.2% eukaryota reads) (Fig. 3c). All the metagenomic sequencing samples underwent data quality control to remove contaminants and trim reads to a Phred quality below 15. The bacterial taxonomy comparison between 16 S rRNA sequencing and metagenomic sequencing data demonstrated the strong consistency of the bacterial microbiome structure on aging and four rejuvenation experiments (Fig. 3d).

Code availability
In this analysis, default parameters or parameters recommended by the developer were used. The options used for the processing of the 16 S rRNA sequencing were as follows: qiime dada2 denoise-paired --i-demultiplexed-seqs demux-paired-end.qza --o -table table --o-representative-sequences rep-seqs --p-trim-left-f 6 --p-trim-left-r 6 --p-trunc-len-f 250 --p-trunc-lenr 250. The options used for processing the metagenomic data in the CLC Genomics Workbench (ver. 6.5.1) is as follows: trimming: limit = 0.05, maximum two ambiguous nucleotides allowed