Cryptosporidium parvum Infection Depletes Butyrate Producer Bacteria in Goat Kid Microbiome
Bibliographic record
Abstract
Cryptosporidium parvum is an important apicomplexan parasite infecting ruminants and humans. We characterised the impact of C. parvum infection on the goat kid microbiome. C. parvum was orally administered to parasite-naïve goats and infection was monitored for 26 days in faeces samples using IFA and qPCR tests. Age-matched goats served as uninfected controls. A reduction in bodyweight gain, diarrhoea, and dehydration were observed in infected goats compared to uninfected controls. Infection decreased the bacterial diversity 5 days post infection (dpi), but this parameter recovered at 15 dpi. The infection altered the relative abundance of several taxa. A total of 38 taxa displayed significant differences in abundance between control and infected goats at both 5 and 15 dpi. Co-occurrence network analysis revealed that the infection resulted in a differential pattern of taxa interactions, and that C. parvum infection increased the relative abundance of specific taxa. The 16S dataset was used for metagenome predictions using the software package PICRUSt2. As many as 34 and 40 MetaCyc pathways (from 387 total) were significantly affected by the infection at 5 and 15 dpi, respectively. Notably, C. parvum decreased the abundance of butyrate-producing pathways in bacteria. Low levels of butyrate may increase mucosal inflammation and tissue repair. Our results suggest that the gut inflammation induced by C. parvum infection is associated with the reduction of butyrate-producing bacteria. This insight could be the basis for the development of novel control strategies to improve animal health.
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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 | 0.000 |
| 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".