Clustering Analysis of Cecal Microbiota Dynamics in Eimeria Maxima-Infected Chickens

Sina Aghakhani (), Mohammad Fili (), Guiping Hu (), Guolong Zhang () and Lizhi Wang ()
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Sina Aghakhani: Oklahoma State University, School of Industrial Engineering and Management
Mohammad Fili: Oklahoma State University, School of Industrial Engineering and Management
Guiping Hu: Oklahoma State University, School of Industrial Engineering and Management
Guolong Zhang: Oklahoma State University, Department of Animal and Food Sciences
Lizhi Wang: Oklahoma State University, School of Industrial Engineering and Management

A chapter in AI, Society and Digital Transformation, 2026, pp 186-196 from Springer

Abstract: Abstract Understanding how intestinal microbiota responds to Eimeria maxima infection is vital for advancing microbiome-based strategies against coccidiosis. This study analyzed temporal changes in the cecal microbial community of chickens infected with E. maxima, utilizing hierarchical clustering based on cosine distance and variance ratio criterion (VRC) scores. The cecal digesta samples were collected from infected and mock-infected broiler chickens at 3, 5, 7, 14, and 21 days post-infection. After filtering amplicon sequence variants (ASVs) and normalizing abundances, optimal clustering structures were determined. Results indicated a distinct clustering pattern between infected and mock-infected groups, highlighting bacterial groups associated with infection stages. This study provides a computational perspective on the dynamic restructuring of the intestinal microbiota community following coccidiosis and emphasizes the potential for microbiome-based intervention strategies.

Keywords: Coccidiosis; Microbiota dynamics; Hierarchical clustering; Eimeria maxima; Subcommunity detection (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-13116-4_15

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