Year: 2026
Type: preprint
Abstract: VEXAS syndrome is an adult-onset severe autoinflammatory disease caused by somatic mutations in UBA1 , yet the cascade mechanisms linking primitive hematopoietic abnormalities to mature myeloid dysfunctions remain largely unknown. Identifying master regulators of a progressive disease, a black-box process, from complex transcriptomic data also remains challenging. To address this challenge, we developed CauNagi, a computational framework for prioritizing cascade candidate regulators (CCRs). CauNagi integrates a causal representation learning module derived from CausCell with an iterative deep learning backbone adapted from UNAGI; in addition, CauNagi extends these two components with a unique downstream module for CCRs analysis designed to characterize regulatory propagation across hierarchical cellular states. Mechanistically, CauNagi iteratively integrates causal disentangled representation learning with (1) disease-stage cell-state trajectory reconstruction and (2) dynamic regulatory analysis. Benchmarking on single-cell transcriptomic datasets showed that CauNagi preserved cell-type structure in idiopathic pulmonary fibrosis (IPF) and enriched known acute myeloid leukemia (AML)- associated genes among its top-ranked global regulators. When applied to VEXAS syndrome, CauNagi readily revealed inflammatory responses, endoplasmic reticulum stress, and myeloid bias, consistent with the disease features. Furthermore, the CCRs analysis module of CauNagi assisted us in identifying 36 causal drivers, with SPI1, NFKB1, STAT3 , and FOS prioritized as high-confidence regulatory hubs linking aberrant myeloid differentiation and inflammatory programs. These findings were further supported by an independent single-cell transcriptomic dataset from a murine VEXAS model. Overall, CauNagi provides a computationally efficient and systematic framework for identifying candidate causal regulators. Beyond hematopoietic diseases, CauNagi may also be applicable to other progressive disorders for which multistage single-cell transcriptomic datasets are available. CauNagi is available at https://github.com/steamed-stuffedbun/CauNagi . Highlights CauNagi identifies disease-progression regulators from multistage single-cell transcriptomes and prioritizes cascade candidate regulators (CCRs) by comparing them along hematopoietic myeloid hierarchy; Benchmark studies demonstrate strong performance of CauNagi in both latent representation learning and identification of global candidate regulators (GCRs) To apply CauNagi for understanding VEXAS syndrome, we constructed a single-cell transcriptomic atlas of VEXAS syndrome, comprising 290,441 cells from 38 samples; Cascade-based prioritization identified 36 core CCRs across myeloid differentiation trajectories, with supports from the VEXAS-like murine models developed in house;
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: G5339496806, G7507784590
OpenAlex ID: W7214799024