Long-read metagenomics using PromethION uncovers oral bacteriophages and their interaction with host bacteria
Bibliographic record
Abstract
Abstract Bacteriophages (phages), or bacterial viruses, are very diverse and highly abundant worldwide, including human microbiomes. Although a few metagenomic studies have focused on oral phages, they relied on short-read sequencing. Here, we conducted a long-read metagenomic study of human saliva for the first time using PromethION that requires a smaller amount of DNA than PacBio. Our analyses, which integrated both PromethION and HiSeq data of >30 Gb per sample, revealed N50 ranging from 187-345 kb and thousands of contigs with >1 kb accounting for > 99% of all contigs on which 94-96% of HiSeq reads were mapped. We identified hundreds of viral contigs (95 phages and 333 prophages on an average per sample); 0-43.8% and 12.5-56.3% of the “most confident” phages and prophages, respectively, didn’t cluster with those reported previously and were identified as novel. Our integrated analyses identified highly abundant oral phages/prophages, including a novel Streptococcus phage cluster and nine jumbo phages/prophages. Interestingly, 86% of the phage cluster and 67% of the jumbo phages/prophages contained remote homologs of antimicrobial resistance genes, suggesting their potential role as a source of recombination to generate new resistance genes. Pan-genome analysis of the phages/prophages revealed remarkable diversity, identifying 0.3% and 86.4% of the genes as core and singletons, respectively. Functional annotation revealed that the highest fraction of the core genes was enriched in phage morphogenesis, followed by the fraction enriched in host cellular processes. Furthermore, our study suggested that oral phages present in human saliva are under selective pressure for escaping CRISPR immunity. Importance Despite the abundance and grave implications oral bacterial viruses in health and disease, little is known regarding the different groups of oral bacterial viruses, their relative abundances under various conditions, and their activities. We provided answers to these questions for the first time utilizing a recently developed sequencer that can capture and sequence long DNA fragments, including viruses, and requires only a small amount of DNA input, making it suitable for analyzing human oral samples. We identified hundreds of viral sequences, including “jumbo” viruses and a distinctive group of highly abundant oral viruses, which often contained parts of antimicrobial resistance genes; the entire repertoire of these viral genes showed remarkable diversity and supported a recently proposed hypothesis that phages modulate oral microbiota through multiple mechanisms. We also revealed genomic signs of coevolution of viruses and host bacteria that have been missed in large viromic studies in humans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".