Authors
Hao Zhang, Zongxi Han, Chuangchao Zou, Yihui Huang, Shiping Sun, Ouyang Peng, Usama Ashraf, Qiuping Xu, Yao Ge, Qixiang Kang, Xiuli Ma, Xinheng Zhang, Ye Zhao, Xin Yang, Hongning Wang, Guozhong Zhang, Qingmei Xie, Mang Shi, Yongsen Ruan, Min Liao, Shengwang Liu, Yongchang Cao
Published in
iMetaOmics. Volume 3. Issue 1. Pages e70077. Epub Jan 19, 2026.
Abstract
Avian infectious bronchitis virus (IBV), a gammacoronavirus with substantial agricultural impact, offers a tractable model for dissecting coronavirus evolution. Here, we integrated 20 years of epidemiological surveillance with whole-genome sequence analysis of 624 IBV strains, including 136 newly isolated field samples, to investigate the evolutionary and structural dynamics of N-linked glycosylation at the spike protein. We identified three dominant glycosylation haplotypes defined by residues 51 and 77 of spike protein, which correlate with receptor-binding interfaces, clinical phenotypes, and spatiotemporal transmission patterns. Molecular modeling and docking analyses provided insights into potential mechanistic links between glycan positioning and Neu5Acα2-3Galβ1-3GlcNAc receptor engagement. Complementing these findings, we developed a proof-of-concept machine learning model that shows potential for predicting clinical serotypes directly from the spike protein sequence, achieving high accuracy on a preliminary independent validation set. These findings support the use of glycosylation motifs as structural-genomic markers and highlight the potential of sequence-based serotype prediction. Our work establishes a scalable genomic-structural framework that leverages glycosylation motifs and sequence features as evolutionary markers, providing a powerful approach for forecasting coronavirus adaptation and informing vaccine design and outbreak preparedness.
PMID:
42891327
Bibliographic data and abstract were imported from PubMed on 18 Oct 2026.
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