Exploring the predictive power of jejunal microbiome composition in clinical and subclinical necrotic enteritis caused by Clostridium perfringens: insights from a broiler chicken model
Bibliographic record
Abstract
BACKGROUND: Necrotic enteritis (NE) is a severe intestinal infection that affects both humans and poultry. It is caused by the bacterium Clostridium perfringens (CP), but the precise mechanisms underlying the disease pathogenesis remain elusive. This study aims to develop an NE broiler chicken model, explore the impact of the microbiome on NE pathogenesis, and study the virulence of CP isolates with different toxin gene combinations. METHODS: This study established an animal disease model for NE in broiler chickens. The methodology encompassed inducing abrupt protein changes and immunosuppression in the first experiment, and in the second, challenging chickens with CP isolates containing various toxin genes. NE was evaluated through gross and histopathological scoring of the jejunum. Subsequently, jejunal contents were collected from these birds for microbiome analysis via 16S rRNA amplicon sequencing, followed by sequence analysis to investigate microbial diversity and abundance, employing different bioinformatic approaches. RESULTS: Our findings reveal that CP infection, combined with an abrupt increase in dietary protein concentration and/or infection with the immunosuppressive variant infectious bursal disease virus (vIBDV), predisposed birds to NE development. We observed a significant decrease (p < 0.0001) in the abundance of Lactobacillus and Romboutsia genera in the jejunum, accompanied by a notable increase (p < 0.0001) in Clostridium and Escherichia. Jejunal microbial dysbiosis and severe NE lesions were particularly evident in birds infected with CP isolates containing cpa, netB, tpeL, and cpb2 toxin genes, compared to CP isolates with other toxin gene combinations. Notably, birds that did not develop clinical or subclinical NE following CP infection exhibited a significantly higher (p < 0.0001) level of Romboutsia. These findings shed light on the complex interplay between CP infection, the gut microbiome, and NE pathogenesis in broiler chickens. CONCLUSION: Our study establishes that dysbiosis within the jejunal microbiome serves as a reliable biomarker for detecting subclinical and clinical NE in broiler chicken models. Additionally, we identify the potential of the genera Romboutsia and Lactobacillus as promising candidates for probiotic development, offering effective alternatives to antibiotics in NE prevention and control.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".