BKPyVN-associated macrophage subpopulations and diagnostic biomarker identification - combine machine learning with single-cell analysis
- 1Organ Transplant Center, The First Affiliated Hospital of Sun Yat-Sen University
Abstract
Background: As pivotal effector cells of innate immunity, macrophages may play a critical role in immune regulation and fibrotic remodeling in BK polyomavirus nephropathy (BKPyVN) following kidney transplantation. However, their subpopulation heterogeneity, molecular characteristics, and functional mechanisms remain poorly understood.
Methods: We integrated single-cell RNA sequencing (scRNA-seq) data from BKPyVN and stable transplant kidney (STK) biopsies. Pseudotime trajectory, CellChat, and high-dimensional weighted gene co-expression network analysis (hdWGCNA) were used to characterize BKPyVN-associated macrophages (BKPyVN-Mac) and identify related gene modules. Characteristic genes from these modules were used to construct diagnostic models from an integrated renal allograft microarray dataset, and nine machine-learning algorithms were compared. Key genes were validated by RT-qPCR and immunohistochemistry in human biopsies and by time-course transcript and protein analyses in a persistent mouse polyomavirus (MPyV) infection model.
Results: BKPyVN-Mac occupied a disease-associated macrophage state and may serve as a central communication hub within the allograft microenvironment. The support vector machine model exhibited the best diagnostic performance, with the highest area under the receiver operating characteristic curve (AUC = 0.923). ATF4 and MCL1 were identified as key feature genes, and their upregulation was confirmed in human BKPyVN biopsies and, through their mouse orthologs Atf4 and Mcl1, in the persistent MPyV infection model. Functional analysis suggested that these genes are associated with inflammation, fibrotic remodeling, and metabolic regulation.
Conclusion: This study characterizes the molecular phenotype and regulatory network of a BKPyVN-Mac subpopulation, providing new insights into BKPyVN pathophysiology and identifying candidate biomarkers for clinical diagnosis.
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BibTeX
@article{xu2026bkpyvnassociated,
title = {{BKPyVN-associated macrophage subpopulations and diagnostic biomarker identification - combine machine learning with single-cell analysis}},
author = {Shengxian Xu and Guodong Zhao and Yingzhen He and Junying Mo and Gang Huang},
journal = {Frontiers in Immunology},
year = {2026},
month = oct,
doi = {10.3389/fimmu.2026.1881621},
url = {https://www.frontiersin.org/articles/10.3389/fimmu.2026.1881621/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - BKPyVN-associated macrophage subpopulations and diagnostic biomarker identification - combine machine learning with single-cell analysis
AU - Shengxian Xu
AU - Guodong Zhao
AU - Yingzhen He
AU - Junying Mo
AU - Gang Huang
PY - 2026
DA - 2026/10/14
JO - Frontiers in Immunology
DO - 10.3389/fimmu.2026.1881621
UR - https://www.frontiersin.org/articles/10.3389/fimmu.2026.1881621/full
AB - Background: As pivotal effector cells of innate immunity, macrophages may play a critical role in immune regulation and fibrotic remodeling in BK polyomavirus nephropathy (BKPyVN) following kidney transplantation. However, their subpopulation heterogeneity, molecular characteristics, and functional mechanisms remain poorly understood. Methods: We integrated single-cell RNA sequencing (scRNA-seq) data from BKPyVN and stable transplant kidney (STK) biopsies. Pseudotime trajectory, CellChat, and high-dimensional weighted gene co-expression network analysis (hdWGCNA) were used to characterize BKPyVN-associated macrophages (BKPyVN-Mac) and identify related gene modules. Characteristic genes from these modules were used to construct diagnostic models from an integrated renal allograft microarray dataset, and nine machine-learning algorithms were compared. Key genes were validated by RT-qPCR and immunohistochemistry in human biopsies and by time-course transcript and protein analyses in a persistent mouse polyomavirus (MPyV) infection model. Results: BKPyVN-Mac occupied a disease-associated macrophage state and may serve as a central communication hub within the allograft microenvironment. The support vector machine model exhibited the best diagnostic performance, with the highest area under the receiver operating characteristic curve (AUC = 0.923). ATF4 and MCL1 were identified as key feature genes, and their upregulation was confirmed in human BKPyVN biopsies and, through their mouse orthologs Atf4 and Mcl1, in the persistent MPyV infection model. Functional analysis suggested that these genes are associated with inflammation, fibrotic remodeling, and metabolic regulation. Conclusion: This study characterizes the molecular phenotype and regulatory network of a BKPyVN-Mac subpopulation, providing new insights into BKPyVN pathophysiology and identifying candidate biomarkers for clinical diagnosis.
SN - 1664-3224
ER -