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.
- ANTINEUTROPHIL CYTOPLASMIC ANTIBODY–ASSOCIATED VASCULITIS
- VASCULITIS
- GRANULOMATOSIS WITH POLYANGIITIS
- METAANALYSIS
- MICROSCOPIC POLYANGIITIS
- GENE EXPRESSION
Granulomatosis with polyangiitis (GPA) and microscopic polyangiitis (MPA) are life-threatening multisystem vasculitides affecting small to medium-sized blood vessels. Both diseases are associated with the antineutrophil cytoplasmic antibody (ANCA) and are together referred to as ANCA-associated vasculitis (AAV). Several studies have characterized the gene expression signature of AAV in affected tissues. To date, the gene expression signatures of orbital tissue, peripheral leukocytes, and nasal sinus brushings have been reported1,2,3. The purpose of our study is to compare gene expression data across these 3 tissues to discover common AAV disease pathways and to validate reported data.
Commonalities in gene expression data can be determined by focusing on individual genes. However, the comparison of pathways can lend additional insight beyond the gene level alone. The comparative pathway analysis approach has been used in metaanalyses of cancers, systemic lupus erythematosus (SLE), rheumatoid arthritis, ankylosing spondylitis, neuropsychiatric disorders, and other diseases4,5,6,7,8,9,10. Individual genes have many functions and can affect a large number of different processes. Disease pathways involve many genes that may be used at different points in time in different tissues. In the study of SLE, for example, pathway analyses identified the importance of the interferon pathway based on interferon-inducible genes, even though the interferons themselves were not upregulated9. Through the use of a network analysis system based on knowledge of protein-protein interactions, network pathway analyses allow association of the biologic functions and pathologic mechanisms with a set of expressed genes.
In this analysis, we tested the hypothesis that there are commonly preserved pathways of disease associated with all tissues thus far characterized in AAV. The pathway analysis yielded more robust pathogenic information than the evaluation of commonly expressed individual genes alone. The identified pathways reflect known and plausible mechanisms of AAV disease.
MATERIALS AND METHODS
Study inclusion criteria
We used published gene expression data from studies of patients with AAV compared to controls. Studies were identified through a PubMed search for studies containing at least 1 AAV term (ANCA, GPA, MPA, or Wegener’s) and at least 1 gene expression term (gene expression, RNA, or microarray). Ethics and institutional review board approval was not required because our study is a metaanalysis of previously published and de-identified data.
The set of differentially upregulated and then downregulated genes from each study was analyzed by the Cytoscape software application11,12 with the Reactome Functional Interactional (FI) plugin12. This is a pathway-based network analysis system that uses expert-gathered pathways based on available knowledge of protein-protein interactions. The 2016 Reactome FI application incudes 1127 pathways collected from Reactome as well as other pathway databases including the National Cancer Institute Pathway Interactions Database, Kyoto Encyclopedia of Genes and Genomes, Panther, CellMap, and Biocarta13,14.
Pathways associated with upregulated gene lists from each tissue were collected along with (the conventional Benjamini-Hochberg global) false discovery rate (FDR) and p values15. However, because there is no existing method to combine 3 lists of FDR directly, we chose to estimate the local FDR (locfdr) for pathways of each tissue and multidimensional locfdr (md-locfdr) of common pathways of 3 tissues16. The locfdr and md-locfdr of pathway lists were estimated using a semiparametric mixture model16. A locfdr is an estimate of the probability of an individual pathway being a false discovery in that tissue, while the md-locfdr can be thought of as the probability of an individual pathway being a false discovery in all 3 tissues. The locfdr and the conventional FDR are closely related. A locfdr can be defined as locfdr(z) = Probability{null|Z = z}, where Z is a test statistic, and the conventional FDR as FDR(z) = E{fdr(Z)|Z ≤ z}. That is, FDR(z) is a (weighted) average of locfdr, locfdr(Z) for Z ≤ z. The technical details of locfdr can be found in Supplement A (available with the online version of this article) and in Jeong, et al16. While there is no widely accepted cutoff value for locfdr, Efron suggested that a locfdr of 0.2 could be used, which corresponds to the conventional FDR of 0.05 to 0.15 under some mild conditions17. In our study, an locfdr of < 0.2 was used as a cutoff. Twenty-eight pathways with an md-locfdr of < 0.2 were identified.
Downregulated genes were those with a fold change (FC) value of ≤ −1.5 in ANCA samples compared to healthy controls. A conventional FDR cutoff of < 0.25 was used to identify pathways associated with the downregulated gene lists. Because very few pathways were identified and none were associated with all 3 tissues, locfdr was not used for this analysis.
RESULTS
Three studies were identified that characterized the gene expression of AAV tissues compared to healthy controls. This included studies of peripheral leukocytes3, sinus brushings2, and orbital inflammatory disease1. All 3 studies used RNA microarray to characterize these tissues. The characteristics of each study are shown in Table 1. Differences in treatment with glucocorticoid and other immunosuppressant medications are not included in the table; however, the nasal sinus brushing and orbital inflammatory data were adjusted for medication use. Medication use in the peripheral leukocyte study is not precisely known. The nasal sinus brushing and orbital inflammatory disease papers included only patients with GPA. The peripheral leukocyte study included patients who had AAV with pauciimmune glomerulonephritis; however, the AAV subtype was not reported3.
Study characteristics.
Upregulated genes in common
We identified only 4 genes that were upregulated across all 3 studies: interleukin 1 receptor antagonist (IL1RN), matrix metalloproteinase 9 (MMP9), solute carrier family 11 member 1 (SLC11A1), and Toll-like receptor 2 (TLR2). These 4 genes were identified in all 3 tissues with an FC compared to control tissues of > 1.5 and p values of < 0.05.
IL1RN is upregulated in peripheral leukocytes (FC = 1.7, p = 1.9 × 10−4), sinus tissue (FC = 2.5, p = 1.9 × 10−4), and orbital tissue (FC = 3.69 and 4.06, p = 4.68 × 10−3 and 8.97 × 10−3). The orbital study shows 2 values each for FC and p values because in our study, 2 sets of samples were processed at different timepoints. SLC11A1 encodes an iron transport membrane protein and is upregulated in peripheral leukocytes (FC = 2.3, p = 8.4 × 10−7), sinus tissue (FC = 2.5, p = 1.9 × 10−4), and orbital tissue (FC = 3.5 and 3.3, p = 3.6 × 10−4 and 1.9 × 10−2). TLR2 encodes a cell surface protein of the TLR family; it is upregulated in peripheral leukocytes (FC = 2.2, p = 2.1 × 10−6), sinus tissue (FC = 1.7, p = 2.3 × 10−3), and orbital tissue (FC = 3.1 and 2.5, p = 3.3 × 10−2 and 2.6 × 10−2)18. MMP9 is upregulated in peripheral leukocytes (FC = 4.0, p = 1.5 × 10−6), nasal tissue (FC = 2.2, p = 2.0 × 10−4), and the orbit (FC = 10.4 and 4.75, p = 2.2 × 10−5 and 1.5 × 10−2). A further 45 genes were identified that were present in at least 2 of the 3 tissues; these are listed in Table 2.
Individual genes upregulated in 2 or more AAV studies.
Associated upregulated pathways in common
Out of 1127 identifiable pathways, 28 had an md-locfdr of < 0.2 across all 3 tissues16,17. These 28 disease pathways are represented in Table 3. The individual genes associated with each pathway in each tissue are shown in the Supplementary Table 1 (available with the online version of this article).
Pathways commonly associated with all ANCA–associated vasculitis gene expression studies.
The 28 pathways discovered in this analysis can be grouped into 8 functional categories including innate immunity, adaptive immunity, vascular wall interactions, cellular signaling, complement activation, tissue damage/repair, infectious disease pathways, and platelet activation (Table 3). The most strongly associated pathway in all 3 tissues is the neutrophil degranulation pathway (locfdr = 1.05 × 10−12). After this, the most strongly associated pathways are osteoclast differentiation (locfdr = 3.8 × 10−05), cell surface interactions at the vascular wall (locfdr = 4.2 × 10−04), signaling by interleukins (locfdr = 6.1 × 10−04), and the phagosome (locfdr = 0.003).
Downregulated genes and pathways in common
There were no downregulated genes or pathways found in all tissues. Only 1 gene, the G0S2 gene, was downregulated in 2 of the 3 tissues; peripheral leukocytes (FC = −1.9, p = 0.26), and the orbit (FC = −13.7 and −24.9, p = 4.7 × 10−3 and 1.4 × 10−4). Downregulated pathways common to 2 out of 3 tissues are shown in Supplementary Table 2 (available with the online version of this article).
DISCUSSION
This is the first analysis of gene expression pathways in common across multiple tissues in AAV, to our knowledge. The use of a network analysis system has allowed us to identify biologic functions and pathologic mechanisms that were not readily apparent by analysis of upregulated genes alone. Many of the common genes and pathways identified in our analysis support current knowledge and theories about AAV pathophysiology, while other pathways have not previously been implicated in AAV, and thus they may offer novel insights into disease pathogenesis. The pathophysiology of AAV based on gene expression pathways is represented in Figure 119,20,21,22,23,24,25,26.
Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis pathophysiology revealed through gene expression pathways: 1. Infectious diseases, such as Staphylococcus aureus, activate Toll-like receptors on antigen presenting cells and macrophages19. 2. Antimicrobial peptide homology with human antigens leads to the formation of pathogenic ANCA20. 3. Macrophages secrete inflammatory cytokines that prime neutrophils to express PR3 and MPO on their cell surface, allowing ANCA to bind and activate the neutrophil. 4. Cytokines and ANCA activate endothelial cells, which express leukocyte adhesion molecules21,22,23,24. 5. ANCA-activated neutrophils migrate through the vascular wall where they degranulate, releasing reactive oxygen species and damaging the vessel wall, thus causing the characteristic necrotizing vasculitis. 6. Platelets are activated at the sites of damaged endothelium; however, their precise contribution to AAV pathophysiology is not well understood. 7. Activated neutrophils release neutrophil extracellular traps (NET); NET contain all of the intra-cellular contents of a neutrophil including MPO and PR325. 8. Exposure of MPO and PR3 to the extracellular environment during NETosis drives further antibody formation. 9. Activated neutrophils also activate the alternative complement cascade, resulting in cleavage of C5 into C5a, and C5b–C5a goes on to prime and attract more neutrophils, leading to an amplification loop during active disease26. PR3: proteinase 3; MPO: myeloperoxidase; AAV: ANCA-associated vasculitis.
The results of our analysis support the central roles of an infectious trigger, molecular mimicry, and the dysregulation of both innate and adaptive immune mechanisms in AAV (Figure 1). Infections are thought to drive AAV through molecular mimicry. This is supported by the antimicrobial peptide pathway (locfdr = 8.2 × 10−03), infectious disease pathways, and the clinical finding that nasal carriage of Staphylococcus aureus, which shares peptide homology with human proteinase 3 (PR3) antigen, increases the risk of relapse in GPA20,27. Three of the four genes expressed in common, IL1RN, SLC11A1, and TLR2, are also important innate immune genes. TLR2 is particularly interesting because it is the predominant TLR recognizing S. aureus ligands (Figure 1).
In addition to innate and infectious disease mechanisms, AAV is also an adaptive immune disease associated with pathogenic antibodies. Myeloperoxidase (MPO) antibodies have been found to be pathogenic in murine transfer models and in 1 transplacental human transfer case report28,29. However, perhaps the strongest evidence for ANCA pathogenicity is the success of rituximab treatment, which is increasingly becoming the standard of care in severe pathology30.
ANCA have diverse functions in this disease; however, their main function is activating primed neutrophils (Figure 1)23,31,32. All 3 tissues’ gene lists were most strongly associated with the neutrophil degranulation pathway (locfdr = 1.05 × 10−12). ANCA-activated neutrophils may degranulate or extrude their intracellular contents in the form of a neutrophil extracellular trap (NET)25,33. AAV neutrophils also activate the alternative complement cascade — consistent with the complement and coagulation cascade pathway (locfdr = 0.044) in our analysis (Figure 1)26. A C5a inhibitor is currently in clinical trials as a steroid-sparing agent in AAV26.
Platelet activation and vascular wall interaction pathways are not well understood regarding their role in AAV and require further study. Patients with AAV are at increased risk for venous thrombosis, ischemic heart disease, and cardiovascular (CV) events34,35. Platelets are acute-phase reactants, but can also secrete cytokines and mediate inflammatory responses36,37. Despite the increased CV risk associated with AAV, to date there are no specific recommendations for CV risk reduction in these patients.
The interaction of the peripheral immune mechanisms with the vascular wall represents an important gap in our understanding of this disease. Endothelial cells lining the vessel wall are heterogeneous and immunologically active cells, yet our understanding of how these cells function in AAV remains limited38. Enhancing our understanding of the vascular wall in AAV may allow us to understand patterns of organ involvement and to identify novel therapeutic targets.
Our analysis has limitations. Network-based pathway analyses are based on current knowledge of protein interactions, and some pathways may be associated owing to diverse functions of genes rather than specific AAV physiology. For example, while infections are likely important, pathogens such as malaria, amebiasis, and measles (Table 3) are unlikely to be the inciting pathogens. This analysis is also limited by variability in methodologies. Each study has a different number of samples and used a microarray with different numbers of probes, which may prevent the detection of more subtle pathways of interest. The control groups studied for these tissues were also different, which may result in variability in which genes are considered differentially regulated when compared to controls. Finally, comparing peripheral leukocytes to end-organ tissue is problematic, because end-organ tissue includes a large number of other cell types that are not represented in the leukocyte study. Vascular wall interaction and tissue damage/repair pathways are more strongly associated with the orbital and nasal studies, which is most likely due to the inclusion of end-organ tissue in these studies.
Despite these limitations, our pathway analysis has successfully identified pathways of disease common to all AAV tissues thus far characterized. Many of these pathways are consistent with current theories about infectious drivers and the crossroads of innate and adaptive immune mechanisms. In addition, our analysis highlights novel pathways that require further investigation, such as vessel wall interactions and platelet activation. Further study of these pathways may lead to novel biomarkers and therapeutic strategies that improve the care of patients with this disease.
ONLINE SUPPLEMENT
Supplementary material accompanies the online version of this article.
Footnotes
This research was supported by the US National Institutes of Health (NIH) National Institute of Arthritis and Musculoskeletal and Skin Diseases: 5K23AR068440; the NIH National Heart, Lung, and Blood Institute: 3T32HL094294-08S1; the NIH National Eye Institute: EY010572, EY020249, and EY265722; Research to Prevent Blindness; the Stan and Madelle Rosenfeld Family Trust; the William and Mary Bauman Foundation.
- Accepted for publication October 11, 2018.
REFERENCES
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