Expanding the <i>Bacteroides</i> synthetic biology toolkit to develop an <i>in vivo</i> intestinal malabsorption biosensor
Bibliographic record
Abstract
Abstract The human gut is a highly dynamic physical environment where perturbations—including factors such as acidification, oxygenation, and particle concentration (osmolality)—can influence microbiota composition and contribute to disease states. Understanding gut environmental changes is essential for advancing diagnostic and therapeutic strategies for gut health. However, non-invasive methods for continuous monitoring remain limited. The bacterial gut microbiota represents a powerful platform for continuous, non-invasive biosensing technologies for the gut environment, with genetically tractable commensal species like Bacteroides thetaiotaomicron ( B. theta ) emerging as promising hosts for engineered biosensors. However, the availability of genetic tools for precise, modular environmental sensing and reporting control in B. theta remains limited. Here, we present an expanded genetic engineering toolkit for B. theta that enables precise, fluorescence-based environmental sensing of the gut environment. This toolkit includes i) three libraries of orthogonally inducible promoters capable of driving fluorescence expression, ii) a DNA-based system to tune repressor activity in B. theta , iii) a resulting modular transcriptional reporter circuit that integrates native promoter activation with fluorescent outputs, and iv) characterization of a novel plasmid integration mode in B. theta . To demonstrate its utility, we engineered biosensors for gut malabsorption, a condition characterized by increased luminal osmolality. Using identified osmolality-responsive native promoters from B. theta , we made biosensors capable of detecting changes in gut physiology through graded fluorescent outputs. These biosensors were validated both in vitro and in vivo using a murine model of laxative-induced malabsorption, where they enabled continuous, long-term, non-invasive monitoring of single-cell response from fecal samples with sensitivity to subclinical malabsorption levels. By expanding the genetic toolkit for Bacteroides and demonstrating its use in a physiologically relevant context, this approach highlights the potential of engineered gut bacteria as a monitoring platform for diverse gut health applications. This work advances strategies for microbial biosensing and positions gut commensals as key players in next-generation diagnostic methods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".