A Bayesian Gene Network Reveals Insight into the JAK-STAT Pathway in Systemic Lupus Erythematosus

• Activity Number: 508 - Innovative Statistical Methods for Preclinical to Clinical Translatability in Drug Development
• Type: Topic Contributed
• Date/Time: Thursday, August 11, 2022 : 8:30 AM to 10:20 AM
• Sponsor: Biopharmaceutical Section
• Author(s): Yushi Liu*
• Companies: Eli Lilly and Company
• Keywords: Bayesian network; RNA Seq; causal inference; literature prior

Abstract

Systemic lupus erythematosus (SLE) is a chronic, remitting, and relapsing, inflammatory disease involving multiple organs, which exhibits abnormalities of both the innate and adaptive immune responses. A limited number of transcriptomic studies have characterized the gene pathways involved in SLE to identify the key pathogenic drivers of the disease. In order to further advance our understanding of the pathogenesis of SLE, we used a novel Bayesian network algorithm to hybridize knowledge- and data-driven methods, and then applied the algorithm to build an SLE gene network using transcriptomic data from 1,760 SLE patients’ RNA from the two trials. Further, based on the gene network, we carried out key driver-gene analyses for gene prioritization. Our analyses identified that the JAK-STAT pathway genes played essential roles in SLE pathogenesis and reaffirmed the recent discovery of pathogenic relevance of JAK-STAT signaling in SLE. In summary, using a hybridized network construction approach, we systematically investigated gene-gene interactions based on their transcriptomic profiles, prioritized genes based on their importance in the network structure, and revealed new insights