<i>In Vivo</i> Selection To Identify Bacterial Strains with Enhanced Ecological Performance in Synbiotic Applications
Bibliographic record
Abstract
One strategy for enhancing the establishment of probiotic bacteria in the human intestinal tract is via the parallel administration of a prebiotic, which is referred to as a synbiotic. Here we present a novel method that allows a rational selection of putative probiotic strains to be used in synbiotic applications: in vivo selection (IVS). This method consists of isolating candidate probiotic strains from fecal samples following enrichment with the respective prebiotic. To test the potential of IVS, we isolated bifidobacteria from human subjects who consumed increasing doses of galactooligosaccharides (GOS) for 9 weeks. A retrospective analysis of the fecal microbiota of one subject revealed an 8-fold enrichment in Bifidobacterium adolescentis strain IVS-1 during GOS administration. The functionality of GOS to support the establishment of IVS-1 in the gastrointestinal tract was then evaluated in rats administered the bacterial strain alone, the prebiotic alone, or the synbiotic combination. Strain-specific quantitative real-time PCR showed that the addition of GOS increased B. adolescentis IVS-1 abundance in the distal intestine by nearly 2 logs compared to rats receiving only the probiotic. Illumina 16S rRNA sequencing not only confirmed the increased establishment of IVS-1 in the intestine but also revealed that the strain was able to outcompete the resident Bifidobacterium population when provided with GOS. In conclusion, this study demonstrated that IVS can be used to successfully formulate a synergistic synbiotic that can substantially enhance the establishment and competitiveness of a putative probiotic strain in the gastrointestinal tract.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".