Search bioRxiv⌕ Search

bioRxiv · 10.1101/2023.12.05.570138

Pipeline for species-resolved full-length16S rRNA amplicon nanopore sequencing analysis of low-complexity bacterial microbiota

Abstract

16S rRNA amplicon sequencing is a fundamental tool for characterizing prokaryotic microbial communities. While short-read 16S rRNA sequencing is a proven standard for examining complex microbiomes, it cannot achieve taxonomic classification beyond genus level. Third-generation sequencing technologies, particularly nanopore sequencing, have allowed for full-length 16S rRNA gene sequencing enabling enhanced taxonomic resolution to species and strain levels. Precise species-to-strain level classification is crucial in investigating low-complexity microbiota. This research presents an efficient pipeline using full-length 16S rRNA amplicon nanopore sequencing, spanning library prep to computational analysis for low-complexity microbiota composition analysis. We applied this pipeline to a defined intestinal bacterial community in gnotobiotic mice to evaluate different methods. Our findings revealed that the proprietary barcoded universal primers 27F-1492R from Oxford Nanopore Technologies(ONT) 16S amplicon sequencing kit did not efficiently amplify the 16S rRNA gene of Bifidobacterium species. Addressing this constraint, we designed degenerate primers and employed ONTs native barcoding kit for library preparation. We developed a customized wet lab and bioinformatics pipeline for processing and classifying amplicon reads at the species level. Validation of the protocol using a mock community DNA sample with known composition confirmed a reduced analytical bias. Additionally, our method surpassed Illumina short-read V3-V4 amplicon sequencing, achieving accurate species-level classification compared to Illuminas genus-level accuracy. This pipeline is tailored for analyzing the composition of low-complexity microbiota from natural ecosystems and synthetic/gnotobiotic communities. It is cost- and time-effective and therefore accessible for small-scale studies that would otherwise be hindered by the typically long turnaround times of NGS services. Importance16S rRNA amplicon sequencing is conventionally used to identify microbes and determine their composition in microbial communities. Deep amplicon sequencing of complex microbiomes is well established using short-read sequencing targeting variable regions of the 16S rRNA gene. Short reads enable the classification of bacteria until the genus level in the taxa hierarchy, whereas long reads provide better chances of identifying bacteria to species and even strain levels. This study introduces a streamlined approach for analyzing simple microbial communities using full-length 16S rRNA amplicon nanopore long read sequencing. This approach surpasses Illumina sequencing in species accuracy, is cost-effective and time-efficient. Tailored for low-complexity microbiota, it facilitates studies in natural or synthetic communities, especially beneficial for smaller-scale projects with limited resources.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Tandon, D., Dong, Y., Hapfelmeier, S.. 2023-12-05. Pipeline for species-resolved full-length16S rRNA amplicon nanopore sequencing analysis of low-complexity bacterial microbiota. https://doi.org/10.1101/2023.12.05.570138

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Comparative functional genomics reveals distributed SERPINE1 like characteristics among Drosophila serpins

Serpins (serine protease inhibitors) regulate proteolytic activity through a suicide-substrate inhibitory mechanism, whereby the serpin covalently traps and inactivates their target protease. Serpins are broadly conserved across eukaryotes, but the evolutionary and functional relationships between human SERPINE1, and Drosophila Serpins in incompletely defined. To identify candidate Drosophila functional analogs of SERPINE1/PAI-1, a multi-omics framework was applied to thoroughly compare the serpins across species. The multi-level analysis integrated orthology prediction, protein sequence and structural characterization, phylogenetic reconstruction, bulk tissue transcriptomics, single-cell transcriptomics, and aging-associated expression analysis. Candidate serpins (Spn42Da, Spn42Db, Spn42Dc, Spn42Dd, Spn28B, and Spn28F) were evaluated using DIOPT, AlphaFold/ChimeraX structural comparisons, reactive center loop analyses, phylogenetic distances, GTEx, Human Protein Atlas, FlyAtlas2, Fly Cell Atlas, and Aging Fly Cell Atlas datasets. Comparative analyses demonstrated full-length sequence and reactive center loop (RCL) divergence despite preservation of the canonical serpin fold. Tissue, cell-type, and age-associated expression analysis revealed distinct candidate-specific patterns. Spn42Dd comparisons uncovered similarities to human SERPINE1 in sequence/structural and broad expression but did not represent the strongest overall cross-species expression correlations. Spn42Da and Spn42Db received the highest cross species integrated support. Quality check of the ranking confirmed that Spn42Da and Spn42Db were stable top candidates across orthology, sequence alignment, and expression domains. The multi-domain analysis was unable to identify a single definitive Drosophila serpin as the closest counterpart to human SERPINE1, indicating that cross-species similarity is distributed across multiple candidate serpins. This study therefore provides a quantitative method for prioritizing potential evolutionary and functional analogs across highly divergent species.

bioinformatics↗

CellChat Hotspot: A Targeted Approach to Cell Cell Communication Analysis With Spatial transcriptomics Validation.

Abstract. In recent years, single cell RNA sequencing (scRNA seq) has enabled the investigation of intercellular communication through Cell Cell Communication (CCC) tools such as CellChat. Despite their potential, these approaches remain challenging to interpret, particularly when global analyses generate complex interaction networks that may include biologically irrelevant signals. To address this limitation, we developed a framework termed CellChat Hotspot, designed to improve the biological interpretability of inferred communication networks. This approach integrates gene set enrichment analysis with a context driven filtering strategy, selecting both genes and cells based on their functional relevance prior to communication inference. Applied to stage II colon cancer data, CellChat Hotspot enabled the identification of condition-specific interaction patterns associated with recurrence. Non recurrent patients showed enrichment of immune related processes, whereas recurrent cases were characterized by pathways associated with invasion and metastasis. Notably, a loss of interaction in the HSPG pathway between CALB1+ and stromal cells was observed in recurrent patients, suggesting a potential disruption of cell matrix adhesion mechanisms. To verify the reliability of this finding, we applied the framework to spatial transcriptomics data, which confirmed the presence of the HSPG2 DAG1 interaction in non-recurrent tumors and its near absence in recurrent ones. Overall, this framework provides a biologically guided strategy for studying intercellular communication in complex systems such as the tumor microenvironment.

bioinformatics↗

DiffTernary: a Diffusion Model for Molecular Glue Ternary Complex Docking

Molecular glues (MGs) modulate protein function by inducing or stabilizing protein interactions. However, accurately modeling MG ternary complexes remains challenging. Here, we present DiffTernary, a diffusion-based docking method for the joint prediction of relative protein arrangement and MG binding pose. We introduce Ternary-PDB, a dataset of 76,702 experimentally determined ternary complexes curated from the Protein Data Bank using structural filters and a release cutoff aligned with AlphaFold 3 (AF3). When trained on this dataset, DiffTernary achieved acceptable protein-protein interaction (PPI) predictions (DockQ > 0.23) for 50.0% of MGBench complexes with a single prediction, compared with 51.1% for AF3. In comparisons with AF3, OpenFold3, ESMFold2, Protenix-v1 and Protenix-v2, DiffTernary ranked second with a best-of-25 PPI prediction success rate of 78.4%, behind ESMFold2 (90.9%) and ahead of AF3 (72.7%). A separate DiffTernary model trained on TernaryDB achieved a mean DockQ of 0.306 on the 94 complexes in the TernaryDB MGD test set with a single prediction, compared with 0.228 for DeepTernary. Confidence-based selection among 40 samples yielded acceptable PPI predictions for 41.5% of complexes, compared with 30.9% for DeepTernary. An E2Former variant increased training and sampling throughput 6.90-fold and 4.37-fold, respectively, under the evaluated configurations. Together, DiffTernary and Ternary-PDB provide an efficient docking approach for MG complex prediction and a valuable structural resource for MG discovery and design.

bioinformatics↗