<i>In vivo</i> screening of multiple bacterial strains identifies <i>Lactobacillus rhamnosus</i> Lb102 and <i>Bifidobacterium animalis</i> ssp. <i>lactis</i> Bf141 as probiotics that improve metabolic disorders in a mouse model of obesity
Bibliographic record
Abstract
ABSTRACT Given the growing evidence that gut dysfunction, including changes in gut microbiota composition, plays a critical role in the development of inflammation and metabolic diseases, the identification of novel probiotic bacteria with immunometabolic properties has recently attracted more attention. Herein, bacterial strains were first isolated from dairy products and human feces and then screened in vitro for their immunomodulatory activity. Five selected strains were further analyzed in vivo , using a mouse model of diet‐induced obesity. C57BL/6 mice were fed a high‐fat high‐sucrose diet, in combination with 1 of 3 Lactobacillus strains (Lb38, L. plantarum; L79, L. paracaseil casei; Lbl02, L. rhamnosus ) or Bifidobacterium strains (Bf26, Bfl41, 2 different strains of B. animalis ssp. lactis species) administered for 8 wk at 10 9 colony‐forming units/d. Whereas 3 strains showed only modest (Lb38, Bf26) or no (L79) effects, Lbl02 and Bfl41 reduced diet‐induced obesity, visceral fat accretion, and inflammation, concomitant with improvement of glucose tolerance and insulin sensitivity. Further analysis revealed that Lbl02 and Bf141 enhanced intestinal integrity markers in association with selective changes in gut microbiota composition. We have thus identified 2 new potential probiotic bacterial strains with immunometabolic properties to alleviate obesity development and associated metabolic disturbances.—Le Barz, M., Daniel, N., Varin, T. V., Naimi, S., Demers‐Mathieu, V., Pilon, G., Audy, J., Laurin, E., Roy, D., Urdaci, M. C., St‐Gelais, D., Fliss, I, Marette, A. In vivo screening of multiple bacterial strains identifies Lactobacillus rhamnosus Lbl02 and Bifidobacterium animalis ssp. lactis Bf 141 as probiotics that improve metabolic disorders in a mouse model of obesity. FASEB J. 33, 4921—4935 (2019). www.fasebj.org
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".