<i>In Vivo</i>Analysis of the Viable Microbiota and Helicobacter pylori Transcriptome in Gastric Infection and Early Stages of Carcinogenesis
Bibliographic record
Abstract
ABSTRACT Emerging evidence shows that the human microbiota plays a larger role in disease progression and health than previously anticipated. Helicobacter pylori , the causative agent of gastric cancer and duodenal and gastric ulcers, was early associated with gastric disease, but it has also been proposed that the accompanying microbiota in Helicobacter pylori -infected individuals might affect disease progression and gastric cancer development. In this study, the composition of the transcriptionally active microbial community and H. pylori gene expression were determined using metatranscriptomic RNA sequencing of stomach biopsy specimens from individuals with different H. pylori infection statuses and premalignant tissue changes. The results show that H. pylori completely dominates the microbiota not only in infected individuals but also in most individuals classified as H. pylori uninfected using conventional methods. Furthermore, H. pylori abundance is positively correlated with the presence of Campylobacter , Deinococcus , and Sulfurospirillum . Finally, we quantified the expression of a large number of Helicobacter pylori genes and found high expression of genes involved in pH regulation and nickel transport. Our study is the first to dissect the viable microbiota of the human stomach by metatranscriptomic analysis, and it shows that metatranscriptomic analysis of the gastric microbiota is feasible and can provide new insights into how bacteria respondin vivoto variations in the stomach microenvironment and at different stages of disease progression.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".