159 Impact of dietary fiber and carbohydrases on intestinal gene transcription in enterotoxigenic E. coli challenged pigs
Bibliographic record
Abstract
Abstract This study aimed to evaluate if soluble (10% sugar beet pulp) or insoluble dietary fiber (15% corn DDGS) with or without exogenous carbohydrases protect piglets against enterotoxigenic E. coli (ETEC)-induced changes in gene transcription of markers of intestinal inflammation and barrier integrity. Sixty newly-weaned piglets (BW=6.9 ± 0.1 kg; L337 X Camborough, PIC, Hendersonville, TN) were blocked by initial BW, preselected for F18 ETEC sensitivity and placed in individual pens. Pens were randomly assigned to 1 of 6 treatments (n = 10/trt): non-challenged (NC), ETEC-challenged (PC), PC-fed a soluble fiber diet without (SF-) or with (SF+) carbohydrases (xylanase, β-glucanase, and pectinase), or PC fed an insoluble fiber diet without (IF-) or with carbohydrases (IF+). Pigs were orally inoculated with ETEC or PBS on d 7 post-weaning. Intestinal tissues were collected on d 14 or 15. Data were analyzed using PROC GLIMMIX (SAS 9.4); treatment was a fixed effect and block a random effect. The NC decreased interleukin (IL)-8 and increased claudin-1 (CLDN1; 2.32 vs. 0.96) mRNA abundance in the ileum compared to PC (P < 0.05). The SF- increased ileal CLDN1 mRNA compared to PC (P < 0.05). A trend (P < 0.10) for lower levels of tumor necrosis factor alpha (TNFα) and greater occludin (OCLN) mRNA in the ileum was observed in SF+ compared with PC. The IF+ decreased ileal cluster of differentiation 14 (CD14) mRNA abundance compared to PC (P < 0.05). Greater colonic abundance of zonula occludens (ZO-1) mRNA was observed in NC than PC (P < 0.05). Pigs fed SF+ tended (P = 0.051) to have greater ZO-1 and greater OCLN mRNA abundance than pigs on PC (P < 0.05). Collectively, these data suggest that an ETEC challenge increased markers of gut inflammation and reduced mRNA levels of tight junction proteins. Soluble fiber, regardless of enzyme supplementation, may help improve gut barrier integrity during such a challenge.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".