Western diet-based NutriCol medium: A high-pectin, low-inulin culture medium promoted gut microbiota stability and diversity in PolyFermS and M-ARCOL continuous in vitro models
Bibliographic record
Abstract
Optimizing fermentation media to accurately reflect the colonic environment remains a challenge in developing in vitro models that simulate the human colon. This study aimed to develop a fermentation medium, Nutritive Colonic (NutriCol), which mimics colonic chyme with fiber content reflective of a typical Western diet and compared to the widely used MacFarlane medium. MacFarlane/NutriCol media contained the following fiber (g/L): potato starch (5/0.1), pectin (2/5.6), xylan (2/4.4), arabinogalactan (2/1.8), guar gum (1/0.4), glucomannan (0/0.8), and inulin (1/0.2). The performance of NutriCol was evaluated using two in vitro models: PolyFermS, which simulates the human proximal colon, and M-ARCOL, which mimics both the lumen and mucosa of the human colon. In the PolyFermS model, findings revealed that NutriCol maintained microbiota α-diversity closer to the donor fecal samples and significantly higher than MacFarlane (Shannon's p ≤ 0.01; Simpson's p ≤ 0.001). In contrast, no significant differences in α-diversity were observed between NutriCol and MacFarlane in the M-ARCOL model, likely due to differences in model design and donor microbiome composition. Microbial community structure, assessed by Bray-Curtis distance and A Permutational multivariate analysis of variance (PERMANOVA), revealed significant variations between the two media in both models (PolyFermS: p = 0.02; M-ARCOL: p = 0.01). Additionally, NutriCol demonstrated a higher capacity to cultivate gut microbes, with increased ASV numbers compared to MacFarlane across PolyFermS and M-ARCOL. SCFAs production was influenced by media composition, individual microbiome structure, and the colonic model used. In the M-ARCOL, NutriCol significantly increased acetate ( p = 0.0006) and butyrate ( p = 0.02) levels compared to MacFarlane. While a similar trend was observed with the PolyFermS, the differences were not statistically significant ( p > 0.05). This increase is attributed to the enrichment of SCFA-producing bacteria, such as Butyricicoccus , Lachnospira , Oscillospiraceae UCG-003, Clostridium butyricum , and Lachnospiraceae NK4A136-group. Additionally, NutriCol generated lower levels of intestinal gases (H 2 , O 2 , CO 2 , and CH 4 ) than MacFarlane in the M-ARCOL model. In conclusion, our study demonstrates that NutriCol, a growth medium specifically designed to replicate the typical fiber content of a Western diet, supports gut microbiota diversity and structure better than the established MacFarlane medium. NutriCol's impact was model- and donor-dependent, enhancing microbiota diversity in PolyFermS, while promoting SCFA production and reducing gas levels in M-ARCOL. • The nutritive medium is a key parameter of in vitro gut models. • Nutritive Colonic (NutriCol) medium aims to better mimic fiber content of a Western diet. • NutriCol efficiency was evaluated in two colonic models compared to MacFarlane. • A higher bacterial diversity was retained with NutriCol compared to MacFarlane. • Short-chain fatty acids levels increased with NutriCol due to enrichment in producing bacteria.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".