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 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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".