James T Rosenbaum1, Christina A Harrington2, Robert P Searles3, Suzanne S Fei4, Amr Zaki5, Sruthi Arepalli5, Michael A Paley6, Lynn M Hassman7, Albert T Vitale8, Christopher D Conrady8, Puthyda Keath5, Claire Mitchell5, Lindsey Watson5, Stephen R Planck5, Tammy M Martin9, Dongseok Choi10. 1. Department of Ophthalmology/Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA; Department of Medicine, Oregon Health and Science University, Portland, Oregon, USA; Department of Cell Biology, Oregon Health and Science University, Portland, Oregon, USA; Legacy Devers Eye Institute, Portland, Oregon, USA. Electronic address: rosenbaj@ohsu.edu. 2. Integrated Genomics Laboratory, Oregon Health and Science University, Portland, Oregon, USA; Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, Oregon, USA. 3. Integrated Genomics Laboratory, Oregon Health and Science University, Portland, Oregon, USA. 4. Bioinformatics and Biostatistics Core, Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, Oregon, USA. 5. Department of Ophthalmology/Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA. 6. Division of Rheumatology, Department of Medicine, Washington University School of Medicine, St Louis, Missouri, USA. 7. Department of Ophthalmology, Washington University, St Louis, Missouri, USA. 8. Department of Ophthalmology and Visual Sciences, John A. Moran Eye Center, University of Utah, Salt Lake City, Utah, USA. 9. Department of Ophthalmology/Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA; Department of Molecular Microbiology and Immunology, Oregon Health and Science University, Portland, Oregon, USA. 10. Department of Ophthalmology/Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA; Department of Medicine, Oregon Health and Science University, Portland, Oregon, USA; Oregon Health and Science University-Portland State University School of Public Health, Oregon Health and Science University, Portland, Oregon, USA; Graduate School of Dentistry, Kyung Hee University, Seoul, Korea.
Abstract
PURPOSE: To test the hypothesis that idiopathic uveitis can be categorized into subtypes based on gene expression from blood. DESIGN: Case control study. METHODS: We applied RNA-Seq to peripheral blood from patients with uveitis associated with 1 of 4 systemic diseases, including axial spondyloarthritis (n = 17), sarcoidosis (n = 13), inflammatory bowel disease (n = 12), tubulo-interstitial nephritis with uveitis (n = 10), or idiopathic uveitis (n = 38) as well as 18 healthy control subjects evaluated predominantly at Oregon Health and Science University. A high-dimensional negative binomial regression model implemented in the edgeR R package compared each disease group with the control subjects. The 20 most distinctive genes for each diagnosis were extracted. Of 80 genes, there were 75 unique genes. A classification algorithm was developed by fitting a gradient boosting tree with 5-fold cross-validation. Messenger RNA from subjects with idiopathic uveitis were analyzed to see if any fit clinically and by gene expression pattern with one of the diagnosable entities. RESULTS: For uveitis associated with a diagnosable systemic disease, gene expression profiling achieved an overall accuracy of 85% (balanced average of sensitivity plus specificity, P < .001). Although most patients with idiopathic uveitis presumably have none of these 4 associated systemic diseases, gene expression profiles helped to reclassify 11 of 38 subjects. CONCLUSIONS: Peripheral blood gene expression profiling is a potential adjunct in accurate differential diagnosis of the cause of uveitis. Validation of these results and characterization of the gene expression profile from additional discrete diagnoses could enhance the value of these observations.
PURPOSE: To test the hypothesis that idiopathic uveitis can be categorized into subtypes based on gene expression from blood. DESIGN: Case control study. METHODS: We applied RNA-Seq to peripheral blood from patients with uveitis associated with 1 of 4 systemic diseases, including axial spondyloarthritis (n = 17), sarcoidosis (n = 13), inflammatory bowel disease (n = 12), tubulo-interstitial nephritis with uveitis (n = 10), or idiopathic uveitis (n = 38) as well as 18 healthy control subjects evaluated predominantly at Oregon Health and Science University. A high-dimensional negative binomial regression model implemented in the edgeR R package compared each disease group with the control subjects. The 20 most distinctive genes for each diagnosis were extracted. Of 80 genes, there were 75 unique genes. A classification algorithm was developed by fitting a gradient boosting tree with 5-fold cross-validation. Messenger RNA from subjects with idiopathic uveitis were analyzed to see if any fit clinically and by gene expression pattern with one of the diagnosable entities. RESULTS: For uveitis associated with a diagnosable systemic disease, gene expression profiling achieved an overall accuracy of 85% (balanced average of sensitivity plus specificity, P < .001). Although most patients with idiopathic uveitis presumably have none of these 4 associated systemic diseases, gene expression profiles helped to reclassify 11 of 38 subjects. CONCLUSIONS: Peripheral blood gene expression profiling is a potential adjunct in accurate differential diagnosis of the cause of uveitis. Validation of these results and characterization of the gene expression profile from additional discrete diagnoses could enhance the value of these observations.
Authors: Paul A Harris; Robert Taylor; Robert Thielke; Jonathon Payne; Nathaniel Gonzalez; Jose G Conde Journal: J Biomed Inform Date: 2008-09-30 Impact factor: 6.317
Authors: Ymkje M Hettinga; Laura M E Scheerlinck; Marc R Lilien; Aniki Rothova; Joke H de Boer Journal: JAMA Ophthalmol Date: 2015-02 Impact factor: 7.389
Authors: James T Rosenbaum; Sirichai Pasadhika; Elliott D Crouser; Dongseok Choi; Christina A Harrington; Jinnell A Lewis; Carrie R Austin; Tessa N Diebel; Emily E Vance; Rita M Braziel; Justine R Smith; Stephen R Planck Journal: Clin Immunol Date: 2009-05-22 Impact factor: 3.969
Authors: James T Rosenbaum; Christina A Harrington; Robert P Searles; Suzanne S Fei; Amr Zaki; Sruthi Arepalli; Michael A Paley; Lynn M Hassman; Albert T Vitale; Christopher D Conrady; Puthyda Keath; Claire Mitchell; Lindsey Watson; Stephen R Planck; Tammy M Martin; Dongseok Choi Journal: Am J Ophthalmol Date: 2021-01-24 Impact factor: 5.488