ArticleFrontiers in immunology2026
Identification of lactylation and its hub genes in contributing immune activation and renal allograft fibrosis by integrative bioinformatics and machine learning.
Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- The Lactate-lactylation circuitry in kidney fibrosis: cellular crosstalk from tubular metabolic reprogramming to macrophage effector functions.Seminars in immunopathology · 2026Review
- Lactylation in kidney diseases: a review of regulatory mechanisms and therapeutic prospects.Frontiers in cell and developmental biology · 2026Review
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Authors and funding
6 authors.
Funding
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Abstract
Background: Late graft loss due to chronic renal allograft fibrosis remains a major challenge after kidney transplantation. Excessive immune-cell activation is a key driver of allograft fibrosis; however, the underlying mechanisms remain incompletely understood. Recent studies have implicated lactylation, a post-translational protein modification derived from lactate, in immune activation. Nonetheless, the role of lactylation in renal allograft fibrosis has not been systematically explored. Methods: Transcriptomic datasets from kidney transplant recipients with and without interstitial fibrosis/tubular atrophy (IFTA) were obtained from the GEO database. Differentially expressed genes were intersected with lactylation-related genes (LRGs) to identify differentially expressed LRGs (DELRGs). Functional enrichment analyses were performed to explore associated biological processes and pathways. Weighted gene co-expression network analysis (WGCNA) combined with multiple machine-learning algorithms was used to screen for hub genes. A lactylation-related risk score (LRS) was constructed and validated across independent cohorts, and its predictive performance was evaluated by receiver operating characteristic (ROC) analysis. Single-nucleus RNA sequencing (snRNA-seq) data from allograft biopsies (GSE195718) were processed with Seurat and Harmony for clustering and annotation; cell type-specific hub LRG expression and lactylation scores were profiled. Two murine renal fibrosis models were established to validate the expression of hub genes and to assess their associations with immune-cell infiltration. Results: We identified five hub LRGs- Conclusion: This study identified five lactylation-related hub genes that are closely associated with immune-cell infiltration and exhibit strong predictive performance, suggesting their potential as diagnostic biomarkers and therapeutic targets in renal allograft fibrosis.
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