Dextran sodium sulfate-induced colitis alters the proportion and composition of replicating gut bacteria
Bibliographic record
Abstract
Abstract The bacteria living in the human gut are essential for host health. Though the composition and metabolism of these bacteria is well described in both healthy hosts and those with intestinal disease, less is known about the activity of the gut bacteria prior to, and during, disease development – especially regarding gut bacterial replication. Here, we use a recently developed single-cell technique alongside existing metagenomics-based tools to identify, track, and quantify the replicating gut bacteria and their replication dynamics in the dextran sodium sulfate mouse model of colitis. We show that the proportion of replicating gut bacteria decreases when mice have the highest levels of inflammation and returns to baseline levels as mice begin recovering. We additionally report significant alterations in the composition of the total replicating gut bacterial community during colitis development. On the taxa level, we observe significant changes in the abundance of taxa such as the mucus-degrading Akkermansia muciniphila and the poorly described Erysipelatoclostridium genus. We further demonstrate that many taxa exhibit variable replication rates during colitis, including A. muciniphila . Lastly, we show that colitis development is positively correlated with increases in the presence and abundance of bacteria predicted to be fast replicators, suggesting that taxa with the potential to replicate quickly may have an advantage during intestinal inflammation. These data support the need for additional research using activity-based approaches to further characterize the gut bacterial response to intestinal inflammation and its consequences for both the host and the gut microbial community at large. Importance It is well known that the bacteria living inside the gut are important for human health. Indeed, the type of bacteria which are present and their metabolism is different in healthy people versus those with intestinal disease. However, less is known about how these gut bacteria are replicating, especially as someone begins to develop intestinal disease. This is especially important as it is thought that the active gut bacteria may be more relevant to health. Here, we begin addressing this gap by using several complementary approaches to characterize the replicating gut bacteria in a mouse model of intestinal inflammation. We reveal which gut bacteria are replicating, and how quickly, as mice develop and recover from inflammation. This work can serve as a model for future research to identify how the active gut bacteria may be impacting health, or why these particular bacteria tend to thrive during intestinal inflammation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".