Table 4_Integrated causal inference, kidney transcriptomics, and experimental validation identify ChREBP (MLXIPL) as a driver of maladaptive metabolic remodeling in diabetic kidney disease.docx

BackgroundDiabetic kidney disease (DKD) remains a leading cause of end-stage renal disease despite advances in glucose-, blood pressure-, and albuminuria-lowering therapies. The glucose-responsive transcription factor carbohydrate response element-binding protein (ChREBP; encoded by MLXIPL) regulates glycolytic–lipogenic programs, yet its causal contribution to renal injury is challenging to disentangle in advanced DKD, where bulk kidney transcriptomes reflect tissue remodeling and cellular compositional shifts.
MethodsWe integrated two-sample Mendelian randomization (MR), kidney transcriptomic stratification, network analyses, and experimental validation. MR used blood cis-eQTL instruments for MLXIPL to estimate causal effects on type 2 diabetes (T2D) and urinary albumin-to-creatinine ratio (UACR), including a non-diabetic UACR stratum. In kidney transcriptomics (GSE30529), we evaluated remodeling-related confounding and applied within-DKD, median-based MLXIPL-high/low stratification for GSEA/GSVA and functional/network inference. Key observations were validated in db/db mice and primary proximal tubular epithelial cells (PTECs) exposed to high glucose with matched osmotic control.
ResultsGenetically predicted higher MLXIPL expression was associated with increased T2D risk across multiple phenotype definitions and with higher UACR, including replication in non-diabetic individuals. Within DKD, MLXIPL heterogeneity tracked metabolic programs by GSEA and divergent pathway activity by GSVA, while signatures related to profibrotic and proliferative remodeling were concomitantly enriched in the low-MLXIPL subgroup. Network analyses positioned MLXIPL/ChREBP within a dense metabolic interaction and regulatory landscape. Experimentally, ChREBP and Mlxipl were increased in db/db kidneys and induced by high glucose in PTECs, accompanied by coordinated upregulation of lipogenic targets (Acly, Acaca, Fasn, Srebf1) and an inverse relationship with Ppargc1b.
ConclusionsIntegrating genetic inference, confounding-aware kidney transcriptomics, network biology, and experimental validation, our study supports MLXIPL/ChREBP as a pathogenic nutrient-sensing node linking diabetes susceptibility to renal injury and maladaptive metabolic remodeling in DKD, providing a mechanistic rationale for targeting this axis to mitigate residual renal risk.