Integrated multi-omics decodes the AKI-to-CKD transition: from ensemble discovery to structure-guided translational targeting

The transition from acute kidney injury (AKI) to chronic kidney disease (CKD) constitutes a major global health burden. Identifying causal drivers remains challenging due to proteomic complexity and confounding biases. We aimed to construct a discovery framework to identify actionable targets driving this pathological conversion. Among 53,014 UK Biobank participants with Olink proteomic data, 2,532 AKI patients (410 progression events) were identified and included in the subsequent predictive modeling. We developed a ‘triple-orthogonal screening strategy’, integrating an ensemble machine learning framework (six algorithms including LASSO and RF-RFE), Cox proportional hazards models, and two-sample Mendelian randomization (MR) to filter 2,923 proteins. Findings were validated via single-cell transcriptomics and a mouse model of ischemia-reperfusion injury characterized by paired bulk RNA-sequencing and TMT-based quantitative proteomics. We identified 25 key proteins possessing both statistical prognostic value and genetic support. An ensemble prediction model based on these features achieved an ROC-AUC of 0.939. Using LGALS3 as a paradigm, multi-omics validation revealed a unique ‘transcriptional-translational discordance’—characterized by sustained protein accumulation despite transcriptional silence—during the maladaptive repair phase (day 14). Furthermore, structural profiling identified a hotspot on LGALS3 (residues Glu185 and Gln187), where the binding pocket for the inhibitor olitigaltin and lactose (anhydrous) spatially converges with high-scoring B-cell epitopes. We decoded the prioritized proteomic landscape of the AKI-to-CKD transition. This study establishes a framework integrating multi-layer prioritization with structure-guided validation, identifying pathogenic drivers characterized by transcriptional-translational discordance. This workflow provides atomic-level coordinates for precision therapeutic intervention against renal fibrosis.

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Journal
Journal of Translational Medicine
Published
2026-09-18
DOI
https://doi.org/10.1186/s12967-026-08950-0
Primary Topic
Single-cell and spatial transcriptomics
Type
article
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article

Integrated multi-omics decodes the AKI-to-CKD transition: from ensemble discovery to structure-guided translational targeting

Yidong Zheng, Xiangcheng Xiao, Huipeng Ge, Qiongjing Yuan et al.
Journal of Translational Medicine
Single-cell and spatial transcriptomics
article

Integrated multi-omics decodes the AKI-to-CKD transition: from ensemble discovery to structure-guided translational targeting

Yidong Zheng, Xiangcheng Xiao, Huipeng Ge, Qiongjing Yuan, Hanwei Huang, Xin Cao, Enhui Li, Zefan Chen, Yizi Gong, Yuxi Xiao, Weimiao Kong, Jialin Li, Yiwei Wang
article en

Abstract

The transition from acute kidney injury (AKI) to chronic kidney disease (CKD) constitutes a major global health burden. Identifying causal drivers remains challenging due to proteomic complexity and confounding biases. We aimed to construct a discovery framework to identify actionable targets driving this pathological conversion. Among 53,014 UK Biobank participants with Olink proteomic data, 2,532 AKI patients (410 progression events) were identified and included in the subsequent predictive modeling. We developed a ‘triple-orthogonal screening strategy’, integrating an ensemble machine learning framework (six algorithms including LASSO and RF-RFE), Cox proportional hazards models, and two-sample Mendelian randomization (MR) to filter 2,923 proteins. Findings were validated via single-cell transcriptomics and a mouse model of ischemia-reperfusion injury characterized by paired bulk RNA-sequencing and TMT-based quantitative proteomics. We identified 25 key proteins possessing both statistical prognostic value and genetic support. An ensemble prediction model based on these features achieved an ROC-AUC of 0.939. Using LGALS3 as a paradigm, multi-omics validation revealed a unique ‘transcriptional-translational discordance’—characterized by sustained protein accumulation despite transcriptional silence—during the maladaptive repair phase (day 14). Furthermore, structural profiling identified a hotspot on LGALS3 (residues Glu185 and Gln187), where the binding pocket for the inhibitor olitigaltin and lactose (anhydrous) spatially converges with high-scoring B-cell epitopes. We decoded the prioritized proteomic landscape of the AKI-to-CKD transition. This study establishes a framework integrating multi-layer prioritization with structure-guided validation, identifying pathogenic drivers characterized by transcriptional-translational discordance. This workflow provides atomic-level coordinates for precision therapeutic intervention against renal fibrosis.

Journal of Translational Medicine
Central South University (CN), Hunan University (CN), Shenzhen University (CN), Nanfang Hospital (CN), Guangdong Academy of Medical Sciences (CN), Guangdong Provincial People's Hospital (CN), Third Xiangya Hospital (CN), Xiangya Hospital Central South University (CN), Southern Medical University (CN)
Good health and well-being
Openalex Percentile: Top 18%
Single-cell and spatial transcriptomics
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