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Background <p><i>Glaesserella parasuis</i> (GPS) is a major bacterial pathogen threatening the pig industry worldwide. It is widely prevalent in pig farms across China; however, to date, no effective strategies for its prevention and control are available. It causes divergent outcomes ranging from asymptomatic carriage to lethal disease; however, the underlying mechanisms remain unclear. In this study, we integrated bulk- and single-cell RNA-seq of over 98,000 porcine alveolar macrophages from a well-defined piglet infection model (including control, mild, and severe groups).</p> Results <p>This is the first multi-omics dissection of macrophage heterogeneity in GPS infection at single-cell resolution. We identified four transcriptionally distinct macrophage subpopulations, with two inflammation-associated subsets (Im-Mac and Inf- Mac) that expanded dramatically with increasing disease severity. Pseudotime analysis revealed that severe infection skews macrophage differentiation almost exclusively toward pro-inflammatory fates. Integrated analysis revealed two opposing cellular programs dictating clinical outcomes: a protective program (active in mild infections, marked by CXCL10, CD69, SERPING1, and KMO) balancing immune recruitment with regulation, and a pathogenic program (dominating severe infections, characterized by a 16-gene signature including AMCF-II, CCL2, SERPINB2, CD14, ECE1, and IL7R). Notably, 9 of these 16 genes overlapped with a core set of 58 genes that were positively correlated with disease progression.</p> Conclusions <p>We propose a balanced model in which the equilibrium between protective and pathogenic macrophage programs dictates the outcome of GPS infection. These findings provide novel cell-state-specific biomarkers and a molecular framework for understanding the divergent clinical outcomes of GPS infection.</p>

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Integration of bulk RNA-seq and scRNA-seq reveals cell subsets and gene signatures associated with Glaesserella parasuis infection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月7日

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  • Ke Xu,
  • Zixin Wang,
  • Ying Zhu,
  • Zhengfang Liu,
  • Min Xiang,
  • Huanhuan Zhou,
  • Ao Zhou,
  • Qing Liu,
  • Liangyu Shi,
  • Xiangwei Deng,
  • Xinqi Zeng,
  • Lei Cheng,
  • Hongbo Chen

摘要

Background

Glaesserella parasuis (GPS) is a major bacterial pathogen threatening the pig industry worldwide. It is widely prevalent in pig farms across China; however, to date, no effective strategies for its prevention and control are available. It causes divergent outcomes ranging from asymptomatic carriage to lethal disease; however, the underlying mechanisms remain unclear. In this study, we integrated bulk- and single-cell RNA-seq of over 98,000 porcine alveolar macrophages from a well-defined piglet infection model (including control, mild, and severe groups).

Results

This is the first multi-omics dissection of macrophage heterogeneity in GPS infection at single-cell resolution. We identified four transcriptionally distinct macrophage subpopulations, with two inflammation-associated subsets (Im-Mac and Inf- Mac) that expanded dramatically with increasing disease severity. Pseudotime analysis revealed that severe infection skews macrophage differentiation almost exclusively toward pro-inflammatory fates. Integrated analysis revealed two opposing cellular programs dictating clinical outcomes: a protective program (active in mild infections, marked by CXCL10, CD69, SERPING1, and KMO) balancing immune recruitment with regulation, and a pathogenic program (dominating severe infections, characterized by a 16-gene signature including AMCF-II, CCL2, SERPINB2, CD14, ECE1, and IL7R). Notably, 9 of these 16 genes overlapped with a core set of 58 genes that were positively correlated with disease progression.

Conclusions

We propose a balanced model in which the equilibrium between protective and pathogenic macrophage programs dictates the outcome of GPS infection. These findings provide novel cell-state-specific biomarkers and a molecular framework for understanding the divergent clinical outcomes of GPS infection.