Marcia A Friedman1,2, Dongseok Choi3,4, Stephen R Planck3,4, James T Rosenbaum3,4, Cailin H Sibley3,4. 1. From the Department of Medicine, and the Casey Eye Institute, and the OHSU-PSU (Portland State University) School of Public Health, Oregon Health & Science University (OHSU), Portland, Oregon, USA; Graduate School of Dentistry, Kyung Hee University, Seoul, Korea; Devers Eye Institute, Legacy Health Systems, Portland, Oregon, USA. friedmam@ohsu.edu. 2. M.A. Friedman, MD, Instructor of Medicine, Department of Medicine, OHSU; D. Choi, PhD, Professor, Department of Medicine, and the Casey Eye Institute, and the OHSU-PSU School of Public Health, and the Graduate School of Dentistry, Kyung Hee University; S.R. Planck, PhD, Professor of Ophthalmology, Department of Medicine, and Casey Eye Institute, OHSU; J.T. Rosenbaum, MD, Professor of Ophthalmology, Department of Medicine, and Casey Eye Institute, OHSU, and Devers Eye Institute, Legacy Health Systems; C.H. Sibley, MD, Assistant Professor of Medicine, Department of Medicine, OHSU. friedmam@ohsu.edu. 3. From the Department of Medicine, and the Casey Eye Institute, and the OHSU-PSU (Portland State University) School of Public Health, Oregon Health & Science University (OHSU), Portland, Oregon, USA; Graduate School of Dentistry, Kyung Hee University, Seoul, Korea; Devers Eye Institute, Legacy Health Systems, Portland, Oregon, USA. 4. M.A. Friedman, MD, Instructor of Medicine, Department of Medicine, OHSU; D. Choi, PhD, Professor, Department of Medicine, and the Casey Eye Institute, and the OHSU-PSU School of Public Health, and the Graduate School of Dentistry, Kyung Hee University; S.R. Planck, PhD, Professor of Ophthalmology, Department of Medicine, and Casey Eye Institute, OHSU; J.T. Rosenbaum, MD, Professor of Ophthalmology, Department of Medicine, and Casey Eye Institute, OHSU, and Devers Eye Institute, Legacy Health Systems; C.H. Sibley, MD, Assistant Professor of Medicine, Department of Medicine, OHSU.
Abstract
OBJECTIVE: To identify commonalities in gene expression data across all antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) tissues thus far characterized. METHODS: Gene expression data were collected from the 3 AAV tissues thus far characterized (orbit, peripheral leukocytes, and sinus brushings). These data were analyzed to identify commonly expressed genes and disease pathways. The pathways data were adjusted for multiple comparisons using a combined local false discovery rate, which estimates the probability of a false discovery of a given pathway in all 3 tissues analyzed. RESULTS: Only 4 genes were upregulated in all 3 tissues - IL1RN, TLR2, SLC11A1, and MMP9. After multiple comparison adjustments, the network pathway analysis revealed 28 pathways associated with all 3 tissues. The most strongly associated pathway for all 3 tissues was the neutrophil degranulation pathway [multidimensional local false discovery (md-locfdr) = 1.05 × 10-12], followed by the osteoclast differentiation (md-locfdr = 3.8 × 10-05), cell surface interactions at the vascular wall (md-locfdr = 4.2 × 10-04), signaling by interleukins (md-locfdr = 6.1 × 10-04), and phagosome (md-locfdr = 0.003) pathways. There were no downregulated genes or pathways common to all 3 tissues. CONCLUSION: This analysis identified individual genes and pathways of disease common to all AAV tissues thus far characterized. The use of a network pathway analysis allowed us to identify pathologic mechanisms that were not readily apparent in the commonly expressed genes alone. Many of these pathways are consistent with current theories about infectious drivers and the crossroads of innate and adaptive immune mechanisms. In addition, this analysis highlights novel pathways, such as vessel wall interactions and platelet activation, which require further investigation.
OBJECTIVE: To identify commonalities in gene expression data across all antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) tissues thus far characterized. METHODS: Gene expression data were collected from the 3 AAV tissues thus far characterized (orbit, peripheral leukocytes, and sinus brushings). These data were analyzed to identify commonly expressed genes and disease pathways. The pathways data were adjusted for multiple comparisons using a combined local false discovery rate, which estimates the probability of a false discovery of a given pathway in all 3 tissues analyzed. RESULTS: Only 4 genes were upregulated in all 3 tissues - IL1RN, TLR2, SLC11A1, and MMP9. After multiple comparison adjustments, the network pathway analysis revealed 28 pathways associated with all 3 tissues. The most strongly associated pathway for all 3 tissues was the neutrophil degranulation pathway [multidimensional local false discovery (md-locfdr) = 1.05 × 10-12], followed by the osteoclast differentiation (md-locfdr = 3.8 × 10-05), cell surface interactions at the vascular wall (md-locfdr = 4.2 × 10-04), signaling by interleukins (md-locfdr = 6.1 × 10-04), and phagosome (md-locfdr = 0.003) pathways. There were no downregulated genes or pathways common to all 3 tissues. CONCLUSION: This analysis identified individual genes and pathways of disease common to all AAV tissues thus far characterized. The use of a network pathway analysis allowed us to identify pathologic mechanisms that were not readily apparent in the commonly expressed genes alone. Many of these pathways are consistent with current theories about infectious drivers and the crossroads of innate and adaptive immune mechanisms. In addition, this analysis highlights novel pathways, such as vessel wall interactions and platelet activation, which require further investigation.
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