Characterizing Gene Regulatory Network Ensembles in Kidney Injury and Repair

The inference of gene regulatory networks (GRNs) from single-cell RNAseq data allows for mechanistic characterization of the different cell states and their dynamics in complex biological processes. While numerous algorithms have been proposed to infer GRNs from single-cell transcriptomic data, multiple network solutions may explain the same dataset, posing a challenge for biologically meaningful interpretation. Here, we use the Reasoning Engine for Interaction Networks (RE:IN), a computational tool based on formal reasoning, to characterize GRN ensembles in the context of acute kidney injury (AKI). To this end, we applied RE:IN to a single-cell RNAseq dataset from a mouse ischemiareperfusion injury (IRI) model, focusing on distinct proximal tubule cell states related to kidney injury and repair. We first created an Abstract Boolean Net-work (ABN) model for the kidney using RE:IN and synthesized an ensemble of consistent network solutions. Then, we visualized the ensemble in latent space using Principal Components Analysis (PCA) and discovered four distinct GRN families compatible with the input gene expression and regulatory constraints. Finally, we identified two specific network substructures that discern between the four different network families. This study provides a methodology for characterizing and interpreting GRN heterogeneity in complex processes such as tissue development, disease, and repair.