REN-former prioritizes candidate regulators of kidney disease-state transitions through single-cell foundation modeling and human genetics

Background Acute kidney injury (AKI)-to-chronic kidney disease (CKD) transition is associated with dynamic changes in tubular cell state. However, conventional single-cell transcriptomic analyses primarily identify genes differentially expressed between disease states and do not directly evaluate genes associated with directional transitions between cellular states. Methods We developed REN-former by fine-tuning Geneformer using the GSE183276 single-cell RNA-sequencing dataset from 45 participants representing Normal Reference, AKI, and CKD states. In silico gene deletion and overexpression analyses were used to estimate directional transcriptomic shifts, with a focus on proximal tubular cells. Selected genes were evaluated using summary-data-based Mendelian randomization (SMR), colocalization and expression analysis in additional KPMP participants not included in GSE183276. Results REN-former achieved recall values of 0.99, 0.80, and 0.79 for Normal Reference, AKI, and CKD, respectively. In silico perturbation analyses identified distinct gene programs associated with transitions from Normal Reference to AKI, from Normal Reference to CKD, from AKI to CKD, and from CKD to Normal Reference. Conventional analysis showed metabolic suppression and increased inflammatory and stress-response activation. SMR identified IFITM3, CALR, TTR, CALM1, MUC13, and RPL13, and colocalization supported IFITM3, CALR, TTR, and CALM1. In additional KPMP data, IFITM3 was higher, whereas TTR and CALM1 were lower, in CKD proximal tubules; CALR did not differ significantly. The observed expression changes were concordant with the REN-former-predicted directions for TTR and CALM1 but discordant for IFITM3. Conclusion REN-former provides a framework for prioritizing candidate regulators of kidney disease-associated cell states by integrating predicted perturbation effects with human genetic and transcriptomic evidence.