Shotgun metagenome sequencing identification of a set of genes encoded by Actinomyces associated with medication-related osteonecrosis of the jaw
Bibliographic record
Abstract
Medication-related osteonecrosis of the jaw (MRONJ) is intractable and severely affects a patient's quality of life. Although many cases of MRONJ have been reported in the past decade, the disease pathophysiology is unclear and there are no evidence-based therapeutic strategies. MRONJ usually features bone inflammation and infection. Prior studies that explored the association between MRONJ and microbial infection used the culture-based approach, which is not applicable to hundreds of unculturable taxa in the human oral microbiome, or 16S ribosomal RNA gene sequencing, which does not provide quantitative information of the abundance of specific taxa, and information of the presence, abundance, and function of specific genes in the microbiome. Here, deep shotgun metagenome sequencing (>10 Gb per sample) of bulk DNA extracted from saliva of MRONJ patients and healthy controls was performed to overcome these limitations. Comparative quantitative analyses of taxonomic and functional composition of these deep metagenomes (initially of 5 patients and 5 healthy controls) revealed an average 10.1% increase of genus Actinomyces and a 33.2% decrease in genus Streptococcus normally predominant in the human oral microbiota. Pan-genome analysis identified genes present exclusively in the MRONJ samples. Further analysis of the reads mapping to the genes in the extended dataset comprising five additional MRONJ samples and publicly available dataset of nine healthy controls resulted in the identification of 31 genes significantly associated with MRONJ. All these genes were encoded by Actinomyces genomic regions. Of these, the top two abundant genes were almost exclusively encoded by Actinomyces among usual taxa in the human oral microbiota. The potential relationships of these key genes with the disease are discussed at molecular level based on the literature. Although the sample size was small, this study will aid future studies to verify the data and characterize these genes in vitro and in vivo to understand the disease mechanisms, develop molecular targeted drugs, and for early stage screening and prognosis prediction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".