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Background <p>Post-weaning diarrhoea (PWD) is a major health and economic concern in intensive pig production. In this study, we hypothesized that the faecal microbiome, sampled before disease onset, could provide early prognostic markers of PWD risk and applied a machine-learning framework to identify biomarkers predictive of piglet susceptibility or resilience to PWD. At two Danish commercial farms experiencing PWD outbreaks, four pens per farm were monitored for 14 days post-weaning, with daily clinical assessments and rectal swabs collected every other day. In a nested case–control design, we profiled 140 samples from 41 piglets that developed PWD and 82 samples from 16 piglets that remained healthy by 16S rRNA sequencing. Additionally, we performed shotgun metagenomics on 56 pre-diarrhoeic samples from susceptible piglets and 47 from resilient piglets. A random-forest classifier with recursive feature elimination identified metagenome-assembled genomes (MAGs) predictive of resilience or susceptibility, trained and cross-validated independently within each farm. Negative binomial zero-inflated mixed (NBZIM) models assessed associations with known PWD risk factors (e.g. birth/weaning weights, weaning age and dam parity).</p> Results <p>Prior to diarrhoea onset, microbial community structures differed significantly between resilient and susceptible piglets at both farms (PERMANOVA, <i>p</i> &lt; 0.05). Feature-reduced models achieved high accuracy (AUC = 0.94 and 0.82 in Farm A and Farm B, respectively) and identified 10 and 13 MAGs enriched in resilient piglets, and one and two MAGs enriched in susceptible piglets from the two farms, respectively. All MAGs were farm-specific, highlighting the multifactorial aetiology of PWD. NBZIM models indicated that most predictive MAGs were independent of established PWD risk factors. Temporally, these MAGs peaked in relative abundance early after weaning (day 4 in Farm A; day 0 in Farm B). In the farm with unclear aetiology, functional analysis showed that susceptibility-associated MAGs were depleted for arginine/ornithine and vitamin (cobalamin, thiamine) biosynthesis and lactate production traits, suggesting metabolic dysbiosis.</p> Conclusions <p>Our findings indicate that pre-diarrhoeic faecal microbiome signatures predict PWD risk and provide a foundation for early prognostic tools and targeted interventions, including probiotic development, to mitigate PWD and reduce reliance on antimicrobials in pig production.</p>

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Faecal microbiome profiling uncovers putative biomarkers for piglets resilient to post-weaning diarrhoea

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月24日

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  • Mattia Pirolo,
  • Moiz Khan Sherwani,
  • Carmen Espinosa-Gongora,
  • Esben Østergaard Eriksen,
  • Clara Tassinato,
  • Antton Alberdi,
  • Luca Guardabassi

摘要

Background

Post-weaning diarrhoea (PWD) is a major health and economic concern in intensive pig production. In this study, we hypothesized that the faecal microbiome, sampled before disease onset, could provide early prognostic markers of PWD risk and applied a machine-learning framework to identify biomarkers predictive of piglet susceptibility or resilience to PWD. At two Danish commercial farms experiencing PWD outbreaks, four pens per farm were monitored for 14 days post-weaning, with daily clinical assessments and rectal swabs collected every other day. In a nested case–control design, we profiled 140 samples from 41 piglets that developed PWD and 82 samples from 16 piglets that remained healthy by 16S rRNA sequencing. Additionally, we performed shotgun metagenomics on 56 pre-diarrhoeic samples from susceptible piglets and 47 from resilient piglets. A random-forest classifier with recursive feature elimination identified metagenome-assembled genomes (MAGs) predictive of resilience or susceptibility, trained and cross-validated independently within each farm. Negative binomial zero-inflated mixed (NBZIM) models assessed associations with known PWD risk factors (e.g. birth/weaning weights, weaning age and dam parity).

Results

Prior to diarrhoea onset, microbial community structures differed significantly between resilient and susceptible piglets at both farms (PERMANOVA, p < 0.05). Feature-reduced models achieved high accuracy (AUC = 0.94 and 0.82 in Farm A and Farm B, respectively) and identified 10 and 13 MAGs enriched in resilient piglets, and one and two MAGs enriched in susceptible piglets from the two farms, respectively. All MAGs were farm-specific, highlighting the multifactorial aetiology of PWD. NBZIM models indicated that most predictive MAGs were independent of established PWD risk factors. Temporally, these MAGs peaked in relative abundance early after weaning (day 4 in Farm A; day 0 in Farm B). In the farm with unclear aetiology, functional analysis showed that susceptibility-associated MAGs were depleted for arginine/ornithine and vitamin (cobalamin, thiamine) biosynthesis and lactate production traits, suggesting metabolic dysbiosis.

Conclusions

Our findings indicate that pre-diarrhoeic faecal microbiome signatures predict PWD risk and provide a foundation for early prognostic tools and targeted interventions, including probiotic development, to mitigate PWD and reduce reliance on antimicrobials in pig production.