A Bayesian gene network reveals insight into the JAK-STAT pathway in systemic lupus erythematosus

• Published in: PloS one Vol. 14; no. 12; p. e0225651
• Main Authors: Li, Yupeng, Higgs, Richard E., Hoffman, Robert W., Dow, Ernst R., Liu, Xiong, Petri, Michelle, Wallace, Daniel J., Dörner, Thomas, Eastwood, Brian J., Miller, Bradley B., Liu, Yushi
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.12.2019; Public Library of Science (PLoS)
• Subjects: Abnormalities; Adaptive immunity; Algorithms; Analysis; Autoimmune diseases; Bayes Theorem; Bayesian analysis; Biology and Life Sciences; Biomarkers; Cancer; Chronic conditions; Clinical trials; Clinical Trials, Phase III as Topic; Computer and Information Sciences; Computer Simulation; Data Mining; Datasets as Topic; Drug therapy; Endoplasmic reticulum; Gene expression; Gene Expression Profiling; Gene loci; Gene Regulatory Networks - immunology; Genes; Genomics; Humans; Immune response; Inflammatory diseases; Interferon regulatory factor 1; Interferon regulatory factor 7; Janus kinase 2; Janus Kinase 2 - immunology; Janus Kinase 2 - metabolism; Linear Models; Lupus; Lupus erythematosus; Lupus Erythematosus, Systemic - drug therapy; Lupus Erythematosus, Systemic - genetics; Lupus Erythematosus, Systemic - immunology; Medicine and Health Sciences; Methods; Models, Genetic; Natural language processing; Network topologies; Oligonucleotide Array Sequence Analysis; Organs; Pathogenesis; Pharmaceutical industry; Random variables; Randomized Controlled Trials as Topic; Ribonucleic acid; RNA; Signal Transduction - drug effects; Signal Transduction - genetics; Signal Transduction - immunology; Social networks; Stat1 protein; STAT1 Transcription Factor - immunology; STAT1 Transcription Factor - metabolism; Stat2 protein; STAT2 Transcription Factor - immunology; STAT2 Transcription Factor - metabolism; Success; Systemic lupus erythematosus; Therapeutic applications; United States; Los Angeles California; California; United States > US; Indianapolis Indiana
• DOI: 10.1371/journal.pone.0225651
• ISSN: 1932-6203

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 in an attempt 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 tabalumab Phase III trials (ILLUMINATE-I & -II), the largest SLE RNA dataset to date. Further, based on the gene network, we carried out hub- and key driver-gene analyses for gene prioritization. Our analyses identified that the JAK-STAT pathway genes, including JAK2, STAT1, and STAT2, played essential roles in SLE pathogenesis, and reaffirmed the recent discovery of pathogenic relevance of JAK-STAT signaling in SLE. Additionally, we showed that other genes, such as IRF1, IRF7, PDIA4, FAM72C, TNFSF10, DHX58, SIGLEC1, and PML, may be also important in SLE and serve as potential therapeutic targets for 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 into SLE activity.