Bio-engineering a common probiotic to exploit colonic inflammation promotes reliable efficacy in translational models of colitis
Bibliographic record
Abstract
Abstract The intricate balance between the gut microbiome and host health inspires innovations in drug development. Commensal bacteria provide a multi-targeted approach ideal for treating complex medical conditions, like inflammatory bowel disease (IBD). These bacteria are self-replicating factories with broad targets that promote balanced intestinal inflammation, mucosal barrier function, and eubiosis. Yet, the lack of superiority to gold-standard treatments and their clinical inconsistency makes most probiotics unreliable for disease treatments. Intestinal inflammation, a driving factor in many diseases, often overwhelms commensal bacteria, which lack the stress-resistance mechanisms necessary to withstand host immune defenses. To address this, we introduced a persistence platform BioPersist™ into E. coli Nissle 1917. We hypothesized that a bio-engineered probiotic, or genetically engineered microbial medicine (GEMM™), designed to persist during inflammation would enhance probiotic bioavailability during colitis, leading to sustained therapeutic outcomes. We evaluated BioPersist in multiple translational colitis models such as in mice and pigs. BioPersist delayed the onset and reduced the severity of both chronic and acute colitis, proving more effective than 5-aminosalicylate. BioPersist thrived during inflammation promoting tolerogenic immune responses that limited infiltrating leukocyte activity and decreased TNF-α from resident myeloid cells in the mesentery. The persistence feature of BioPersist allowed the probiotic to overcome the damaging inflammatory response, eliciting mucosal healing evident by the increase in microbially-derived butyric acid. Based on these preclinical results, BioPersist may be a novel therapeutic option for both human and veterinary applications that sustains efficacy during colitis. One Sentence Summary Adding a persistence feature to a probiotic enhances its efficacy for colitis treatment, enhancing future human and veterinary therapeutic applications.
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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 | 0.001 | 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".