Oxford Nanopore 16S Full-Length Method

Version: Petsci_2502

Sample collection and preparation

Sequential process from DNA samples to final data analysis encompasses sample testing, PCR, Nanopore library preparation, and sequencing to examine environmental microbial diversity and community differences. The workflow proceeds as follows:

  1. Extraction of genome DNA
    Genomic DNA from samples was extracted using a column-based method (QIAamp PowerFecal DNA Kit, Qiagen). DNA concentration was measured using a Qubit 4.0 Fluorometer (Thermo Scientific) and diluted to 1 ng/µl for subsequent processing.
  2. PCR Amplification and Purification
    The full-length 16S rRNA genes (V1–V9 regions) were amplified using barcoded 16S gene-specific primers. Following the Oxford Nanopore Technologies (ONT) protocol for full-length 16S rRNA sequencing, each primer contained a 5′ buffer sequence (GCATC) with a 5′ phosphate modification, a 16-base barcode, and the degenerate 16S gene-specific forward or reverse primer sequences. The primers used were as follows:

    Forward: 5′Phos/GCATC-16-base barcode-GC ATC AGR GTT YGA TYMT GGC TCA G-3′
    Reverse: 5′Phos/GCATC-16-base barcode-GC ATC RGY TAC CTT GTT ACG ACT T-3′

    Degenerate base identities are: R = A, G; Y = C, T; M = A, C. For PCR amplification, 2 ng of genomic DNA (gDNA) served as the template. Using KAPA HiFi HotStart ReadyMix (Roche), PCR was performed under these conditions: initial denaturation at 95°C for 5 minutes, followed by 30 cycles (sample dependent) of 95°C for 30 seconds, 57.5°C for 30 seconds, and 72°C for 90 seconds, with a final extension at 72°C for 15 minutes, and hold at 4°C. PCR products were visualized on a 1% agarose gel. We selected samples showing a bright main band at approximately 1500 bp for further processing. These products were purified using AMPure XP beads (Beckman Coulter) and quantified with a Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific) before library preparation.
  3. Library construction and Sequencing
    For Nanopore sequencing, we processed the purified amplicons using the Native Barcoding Kit (SQK-NBD114, Oxford Nanopore Technologies). We performed DNA repair and end-prep using the NEBNext Ultra II End Repair/dA-Tailing Module (New England Biolabs, NEB), then conducted adapter ligation using the ONT Adapter Mix and Quick T4 DNA Ligase (NEB). The final library was loaded onto a MinION/GridION flow cell (FLO-MIN114 or equivalent) and sequenced using MinKNOW software (Oxford Nanopore Technologies) with live basecalling enabled. After sequencing, we processed the reads using Guppy basecaller (Oxford Nanopore Technologies) and performed quality filtering with Filtlong or similar tools to retain high-quality full-length 16S reads for downstream data analysis.

Data analysis

Raw sequencing data were processed using QIIME2 (v2024.5.0) [1], where demultiplexed reads were dereplicated using qiime vsearch dereplicate-sequences, and chimeric sequences were removed via qiime vsearch uchime-ref against a reference database. Open-reference clustering (qiime vsearch cluster-features-open-reference, 95% identity) was applied to group similar sequences, ensuring robust microbial community profiling. Multiple sequence alignment was performed using QIIME2 alignment MAFFT, and phylogenetic inference was conducted via QIIME2 phylogeny fasttree. Taxonomic classification was performed using the QIIME2 feature-classifier with the classify-consensus-blast method, referencing the NCBI database. Further refinement at the species level was conducted using the SPINGO classifier (v1.2) following the standard workflow [2, 3] with the ITGDB database [4].

Microbial diversity was assessed using alpha diversity metrics (Observed-species, Chao1, ACE, Shannon, Simpson, and Pielou) and beta diversity indices (Euclidean, Jaccard, and Bray-Curtis). Principal Coordinate Analysis (PCoA) and Principal Component Analysis (PCA, Hellinger transformation) were performed using the MicrobiotaProcess (v1.14.0) [5] and phyloseq (v1.46.0) [6] packages in R (v4.3.3).

Differential abundance analysis was conducted using Welch’s t-test (STAMP, v2.1.3) [7] and LEfSe [8] with taxa displaying LDA scores (log10) > 2 considered significantly enriched. To further investigate microbial community structure variations, ANOSIM and PERMANOVA tests were performed to assess group-level differences in microbial composition. Hierarchical clustering was implemented using the single-euclidean method to visualize sample relationships.

Functional prediction was performed using PICRUSt2 (v2.5.1) [9] to infer metagenomic functional pathways based on 16S rRNA sequencing data, predicting the presence of key microbial enzymes and metabolic pathways.

以下段落為人類糞便檢體進行營養代謝物分析適用之說明：
Additionally, NURECON [3], a microbiome-based nutrient recommendation framework, was applied to analyze microbial metabolic potential and its impact on host nutrient interactions, providing insights into microbial contributions to dietary metabolism.

Reference

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  • [2] Allard, G., Ryan, F. J., Jeffery, I. B., & Claesson, M. J. (2015). SPINGO: a rapid species-classifier for microbial amplicon sequences. BMC bioinformatics, 16, 1-8.
  • [3] Hu, Z. Q., Hung, Y. M., Chen, L. H., Lai, L. C., Pan, M. H., Chuang, E. Y., & Tsai, M. H. (2024). NURECON: A Novel Online System for Determining Nutrition Requirements Based on Microbial Composition. IEEE/ACM Transactions on Computational Biology and Bioinformatics.
  • [4] Hsieh, Y. P., Hung, Y. M., Tsai, M. H., Lai, L. C., & Chuang, E. Y. (2022). 16S-ITGDB: an integrated database for improving species classification of prokaryotic 16S ribosomal RNA sequences. Frontiers in Bioinformatics, 2, 905489.
  • [5] Xu, S., Zhan, L., Tang, W., Wang, Q., Dai, Z., Zhou, L., … & Yu, G. (2023). MicrobiotaProcess: A comprehensive R package for deep mining microbiome. The Innovation, 4(2).
  • [6] McMurdie, P. J., & Holmes, S. (2013). phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PloS one, 8(4), e61217.
  • [7] Parks, D. H., Tyson, G. W., Hugenholtz, P., & Beiko, R. G. (2014). STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics, 30(21), 3123-3124.
  • [8] Segata, N., Izard, J., Waldron, L., Gevers, D., Miropolsky, L., Garrett, W. S., & Huttenhower, C. (2011). Metagenomic biomarker discovery and explanation. Genome biology, 12, 1-18.
  • [9] Langille, M. G., Zaneveld, J., Caporaso, J. G., McDonald, D., Knights, D., Reyes, J. A., … & Huttenhower, C. (2013). Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nature biotechnology, 31(9), 814-821.