Effects in the use of a genetically engineered strain of<i>Lactococcus lactis</i>delivering in situ IL-10 as a therapy to treat low-grade colon inflammation
Bibliographic record
Abstract
Irritable bowel syndrome (IBS) is a gastrointestinal disorder characterized by chronic abdominal pain, discomfort, and bloating. Interestingly, there is now evidence of the presence of a low-grade inflammatory status in many IBS patients, including histopathological and mucosal cytokine levels in the colon, as well as the presence of IBS-like symptoms in quiescent inflammatory bowel disease (IBD). The use of a genetically engineered food-grade bacterium, such as Lactococcus lactis, secreting the anti-inflammatory cytokine IL-10 has been proven by many pre-clinical studies to be a successful therapy to treat colon inflammation. In this study, we first reproduced the recovery-recurrence periods observed in IBS-patients in a new chronic model characterized by 2 episodes of DiNitro-BenzeneSulfonic-acid (DNBS)-challenge and we tested the effects of a recombinant strain of L. lactis secreting IL-10 under a Stress-Inducible Controlled Expression (SICE) system. In vivo gut permeability, colonic serotonin levels, cytokine profiles, and spleen cell populations were then measured as readouts of a low-grade inflammation. In addition, since there is increasing evidence that gut microbiota tightly regulates gut barrier function, tight junction proteins were also measured by qRT-PCR after administration of recombinant L. lactis in DNBS-treated mice. Strikingly, oral administration of L. lactis secreting active IL-10 in mice resulted in significant protective effects in terms of permeability, immune activation, and gut-function parameters. Although genetically engineered bacteria are, for now, used only as a "proof-of-concept," our study validates the interest in the use of the novel SICE system in L. lactis to express therapeutic molecules, such as IL-10, locally at mucosal surfaces.
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 | 0.000 | 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 | 0.001 |
| 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".