Mattia Pirolo, Moiz Khan Sherwani, Carmen Espinosa-Gongora, Esben Østergaard Eriksen, Clara Tassinato, Antton Alberdi, Luca Guardabassi
{"title":"Faecal microbiome profiling uncovers putative biomarkers for piglets resilient to post-weaning diarrhoea.","authors":"Mattia Pirolo, Moiz Khan Sherwani, Carmen Espinosa-Gongora, Esben Østergaard Eriksen, Clara Tassinato, Antton Alberdi, Luca Guardabassi","doi":"10.1186/s42523-026-00522-3","DOIUrl":null,"url":null,"abstract":"<p><p>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. </p>","PeriodicalId":72201,"journal":{"name":"Animal microbiome","volume":" ","pages":""},"PeriodicalIF":4.5000,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13040825/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Animal microbiome","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s42523-026-00522-3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
背景:断奶后腹泻(PWD)是集约化养猪生产中一个主要的健康和经济问题。在这项研究中,我们假设在疾病发作前取样的粪便微生物组可以提供PWD风险的早期预后标志物,并应用机器学习框架来识别预测仔猪对PWD易感性或恢复力的生物标志物。在两个出现PWD疫情的丹麦商业养殖场,对每个养殖场的4只猪圈进行了断奶后14天的监测,每天进行临床评估,每隔一天收集一次直肠拭子。在巢式病例对照设计中,我们通过16S rRNA测序分析了来自41头PWD仔猪的140个样本和来自16头健康仔猪的82个样本。此外,我们对56个易感仔猪腹泻前样本和47个弹性仔猪腹泻前样本进行了散弹枪宏基因组学分析。随机森林分类器具有递归特征消除识别宏基因组组装基因组(MAGs)预测弹性或易感性,并在每个农场独立训练和交叉验证。负二项零膨胀混合(NBZIM)模型评估了与已知PWD危险因素(如出生/断奶体重、断奶年龄和胎次)的关系。结果:在腹泻发病之前,两个猪场的弹性仔猪和易感仔猪之间的微生物群落结构存在显著差异(PERMANOVA, p