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. Author manuscript; available in PMC: 2022 Jun 1.
Published in final edited form as: Am J Ophthalmol. 2021 Jan 24;226:226–234. doi: 10.1016/j.ajo.2021.01.007

Identifying RNA Biomarkers and Molecular Pathways Involved in Multiple Subtypes of Uveitis

James T Rosenbaum 1,2,3,4, Christina A Harrington 5,6, Robert P Searles 5, Suzanne S Fei 7, Amr Zaki 1, Sruthi Arepalli 1, Michael A Paley 8, Lynn M Hassman 9, Albert T Vitale 10, Christopher D Conrady 10, Puthyda Keath 1, Claire Mitchell 1, Lindsey Watson 1, Stephen R Planck 1,3, Tammy M Martin 1,11, Dongseok Choi 1,2,12,13
PMCID: PMC8286715  NIHMSID: NIHMS1668466  PMID: 33503442

Abstract

Purpose:

Uveitis is a heterogeneous collection of diseases. We tested the hypothesis that despite the diversity of uveitides, there could be common mechanisms shared by multiple subtypes, and that evidence of these common mechanisms may be detected as gene expression profiles in whole blood.

Design:

Cohort study.

Methods:

Ninety subjects with uveitis 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 controls were enrolled, predominantly at Oregon Health & Science University. RNA-Seq data generated from peripheral, whole blood identified 19859 unique transcripts. We analyzed gene expression pathways via KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology). We validated our list of upregulated genes by comparison to a previously published study on peripheral blood gene expression among 50 subjects with diverse forms of uveitis.

Results:

Both the KEGG and GO analysis identified multiple shared pathways or GO terms with a p value <0.0001. Almost all pathways related to the immune response and/or response to an infection. 119 individual transcripts were upregulated by at least 1.5 fold, false discovery rate (FDR) <0.05 and 61 were down regulated by similar criteria. Comparing mRNA from our study with an FDR <0.05 and the prior report, we identified 10 common gene transcripts: ICAM1, IL15RA, IL15, IRF1, IL10RB, GSK3A, TYK2, MEF2A, MEF2B, and MEF2D.

Conclusions:

Many forms of uveitis share overlapping mechanisms. These data support the concept that a single therapeutic approach could benefit diverse forms of this disease.

Introduction

Uveitis is a major cause of acquired visual loss1. Devising effective therapy for uveitis is complicated by the heterogeneity of this disease. Even in an analysis restricted to noninfectious uveitis, at least in theory, not all immune-mediated causes of uveitis should be treated identically. Examples of the heterogeneity of inflammation and the response to treatment are frequent outside of ophthalmology. Mycophenolate mofetil is considered an excellent drug for lupus nephritis2, but it does not fare well as a treatment for rheumatoid arthritis3. A monoclonal antibody to interleukin-17 is very effective for psoriasis4, but it worsens Crohn’s disease5 and showed disappointing efficacy for rheumatoid arthritis6. Corticosteroids are often considered to be a universal anti-inflammatory therapy. They produce dramatic improvement in joints for patients with rheumatoid arthritis, although long term usage at high doses has excessive toxicity7. On the other hand, corticosteroids are discouraged for the joint disease of spondyloarthritis8.

Despite the heterogeneity of non-infectious uveitis, several observations argue for overlapping causation among different etiologies. For example, corticosteroids are used effectively for a variety of different causes of uveitis. The monoclonal antibody that neutralizes tumor necrosis factor alpha, adalimumab, showed benefit in randomized controlled trials when tested against a collection of different forms of non-infectious uveitis9,10. Lymphocytes are an essential component of the pathogenesis of uveitis11,12. Targeting lymphocytes, especially those that migrate preferentially to the uvea, could ameliorate multiple forms of uveitis. The identification of RNA transcripts associated with specific forms of uveitis could reveal biomarkers that clarify the pathogenesis of disease and which suggest targeted therapies. These transcripts could be interrogated in the inflamed eye itself; or mRNA from peripheral blood could be used as a surrogate indicator of the activity within the eye.

While there is evidence for shared mechanisms contributing to inflammation in a variety of organs, there is also evidence for some degree of organ specificity. The vast majority of clinical trials for uveitis have been premised on the concept that different forms of ocular inflammation share overlapping causes, i.e. most clinical trials for uveitis do not target a specific cause of uveitis. In a previous study13, we compared gene transcripts between different uveitic entities to identify possible gene profiles that are unique to each. In this study, we investigated mRNA transcripts to identify those mRNAs in common among multiple forms of uveitis., We then utilized bioinformatic resources to identify the possible relationships among those identified gene products. In this effort to identify shared contributors to inflammation from various causes of uveitis, we analyzed gene expression in peripheral blood from 90 patients with uveitis including sarcoidosis, ankylosing spondylitis, inflammatory bowel disease, tubulo-interstitial nephritis with uveitis and idiopathic uveitis. Because our methodology involved multiple statistical comparisons, we sought to cross-reference our findings with those of a prior study that was based on gene expression using peripheral blood mononuclear cells from patients with a variety of uveitides14.

Methods

A prior manuscript on gene expression in idiopathic uveitis describes the methods for selection of human subjects, diagnostic criteria, data management, RNA-Seq using whole blood and statistics 13. The blood was drawn into anti-coagulated PAXgene tubes. RNA was analyzed from whole blood as we recently described15. We identified 19859 unique transcripts. The data were normalized by the weighted trimmed mean of M-values 16, and then RUVSeq (removing unwanted variation method) 17 was used with edgeR (version 3.24.3) to remove (potential) unwanted variations. Age and sex were included in the model as confounders.

Using a comparison involving gene expression from all samples from subjects with uveitis versus samples from healthy controls, all significant genes with false discovery rate adjusted (FDR) p-value < 0.05 were used for gene ontology (GO)18 and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses19–21. GO and KEGG are public databases with overlapping goals which include attempting to standardize designations for genes, information about their function, and descriptions of pathways which show relationships among genes, i.e. how the expression of one gene might affect the expression of another. The goana and kegga functions in the edgeR package of R statistical language (https://www.R-project.org/) were used for the analyses. The significant genes were compared to the previously published uveitis gene list14 for cross-reference.

Results

Table 1 lists the 20 most activated pathways common to the uveitic diseases which we studied using 1829 up-regulated and 1814 down-regulated genes with FDR p-value < 0.05 from differential gene expression analysis of peripheral blood and based on Gene Ontology (GO). Table 2 shows a similar analysis based on KEGG. It is obvious from both tables that there is systemic immune system activation in association with uveitis. This is an anticipated finding based on the predominant understanding of the pathogenesis of most forms of uveitis.

Table 1.

Top 20 Biological Processes in Uveitis Identified by Gene Ontology

GO Term Number of Genes Significantly Up Significantly Down P Value
GO:0006955 Immune response 1,627 395 89 <.001
GO:0002376 Immune system process 2,355 482 140 <.001
GO:0002252 Immune effector process 1,007 267 40 <.001
GO:0002274 Myeloid leukocyte activation 587 186 13 <.001
GO:0016192 Vesicle-mediated transport 1,656 354 93 <.001
GO:0002366 Leukocyte activation involved in immune response 633 190 21 <.001
GO:0002263 Cell activation involved in immune response 636 190 21 <.001
GO:0043299 Leukocyte degranulation 495 163 10 <.001
GO:0002275 Myeloid cell activation involved in immune response 502 164 10 <.001
GO:0001775 Cell activation 1,165 274 66 <.001
GO:0045321 Leukocyte activation 1,051 256 61 <.001
GO:0043312 Neutrophil degranulation 456 153 8 <.001
GO:0002444 Myeloid leukocyte mediated immunity 510 163 10 <.001
GO:0042119 Neutrophil activation 469 155 8 <.001
GO:0002283 Neutrophil activation involved in immune response 459 153 8 <.001
GO:0002446 Neutrophil mediated immunity 467 154 8 <.001
GO:0036230 Granulocyte activation 475 155 8 <.001
GO:0002443 Leukocyte mediated immunity 696 194 21 <.001
GO:0045055 Regulated exocytosis 671 189 19 <.001
GO:0046903 Secretion 1,246 279 46 <.001
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GO = gene ontology.

All the p values are <0.001. The biological processes are listed from the most statistically significant (top) to least statistically significant (bottom).

Table 2.

Top 20 Pathways in Uveitis Identified by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways

Pathway Description Number of Genes Significantly Up Significantly Down P Value
path:hsa05168 Herpes simplex virus 1 infection 445 43 105 <.001
path:hsa05152 Tuberculosis 146 47 5 <.001
path:hsa04145 Phagosome 126 42 1 <.001
path:hsa05164 Influenza A 130 42 4 <.001
path:hsa04380 Osteoclast differentiation 115 39 5 <.001
path:hsa04621 NOD-like receptor signaling pathway 146 45 5 <.001
path:hsa05167 Kaposi sarcoma-associated herpesvirus infection 153 46 4 <.001
path:hsa05130 Pathogenic Escherichia coli infection 162 47 3 <.001
path:hsa05131 Shigellosis 212 55 9 <.001
path:hsa04666 Fc gamma R-mediated phagocytosis 86 31 3 <.001
path:hsa05132 Salmonella infection 196 51 11 <.001
path:hsa04650 Natural killer cell mediated cytotoxicity 96 31 5 <.001
path:hsa05163 Human cytomegalovirus infection 181 46 5 <.001
path:hsa05140 Leishmaniasis 69 25 0 <.001
path:hsa04810 Regulation of actin cytoskeleton 163 42 3 <.001
path:hsa04062 Chemokine signaling pathway 141 38 3 <.001
path:hsa04630 JAK-STAT signaling pathway 103 31 7 <.001
path:hsa05135 Yersinia infection 111 32 4 <.001
path:hsa05150 Staphylococcus aureus infection 48 19 0 <.001
path:hsa04670 Leukocyte transendothelial migration 81 25 6 <.001
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JAK-STAT = Janus Kinase-signal Transducer and Activator, NOD = Nucleotide oligomerization domain.

All the p values are <.001. The biological processes are listed from the most statistically significant (top) to least statistically significant (bottom).

Among the 1829 up-regulated genes with FDR p-value < 0.05, 119 genes had at least a 1.5 fold increase. These 119 transcripts are common to the four discrete forms of uveitis as well as the subjects with idiopathic uveitis. These transcripts are listed in Table 3. Table 4 lists 61 down regulated genes common to various forms of uveitis using a similar stringency of reduction by at least 1.5-fold and FDR p-value < 0.05. The volcano plot shown in Figure 1 shows transcripts which are up regulated or down regulated in uveitis relative to healthy controls.

Table 3.

Up-regulated genes in uveitis with at least 1.5 fold change and FDR p-value < 0.05

Ensembl* Identification Gene Symbol Fold change FDR p-value
ENSG00000231535 LINC00278 8.22 0.024
ENSG00000183878 UTY 8.07 0.033
ENSG00000012817 KDM5D 7.71 0.026
ENSG00000123838 C4BPA 7.54 0.000
ENSG00000280800 FP671120.6 7.30 0.042
ENSG00000099725 PRKY 7.26 0.021
ENSG00000067048 DDX3Y 7.15 0.025
ENSG00000131002 TXLNGY 6.64 0.045
ENSG00000176728 TTTY14 6.33 0.017
ENSG00000198692 EIF1AY 6.33 0.027
ENSG00000114374 USP9Y 6.23 0.034
ENSG00000129824 RPS4Y1 6.14 0.033
ENSG00000067646 ZFY 5.30 0.037
ENSG00000215580 BCORP1 4.38 0.038
ENSG00000159189 C1QC 3.84 0.000
ENSG00000241859 ANOS2P 3.83 0.037
ENSG00000225231 LINC02470 3.10 0.011
ENSG00000234665 LERFS 2.92 0.032
ENSG00000239265 CLRN1-AS1 2.72 0.004
ENSG00000124785 NRN1 2.57 0.000
ENSG00000225492 GBP1P1 2.51 0.014
ENSG00000172967 XKR3 2.49 0.006
ENSG00000149131 SERPING1 2.39 0.002
ENSG00000100336 APOL4 2.35 0.007
ENSG00000036448 MYOM2 2.35 0.036
ENSG00000197646 PDCD1LG2 2.35 0.001
ENSG00000274173 AL035661.1 2.31 0.000
ENSG00000152766 ANKRD22 2.30 0.001
ENSG00000165949 IFI27 2.26 0.025
ENSG00000168062 BATF2 2.26 0.002
ENSG00000150337 FCGR1A 2.25 0.000
ENSG00000204936 CD177 2.24 0.015
ENSG00000239887 C1orf226 2.24 0.022
ENSG00000152463 OLAH 2.22 0.023
ENSG00000198929 NOS1AP 2.20 0.019
ENSG00000243273 AC020636.1 2.17 0.000
ENSG00000115414 FN1 2.13 0.023
ENSG00000265531 FCGR1CP 2.09 0.000
ENSG00000173369 C1QB 2.09 0.010
ENSG00000173372 C1QA 2.09 0.001
ENSG00000183347 GBP6 2.06 0.003
ENSG00000271304 DPRXP2 1.97 0.000
ENSG00000197272 IL27 1.97 0.002
ENSG00000010030 ETV7 1.96 0.022
ENSG00000256988 AC005833.2 1.96 0.010
ENSG00000224789 AC012363.1 1.92 0.003
ENSG00000255221 CARD17 1.92 0.001
ENSG00000253139 AC013644.1 1.86 0.000
ENSG00000270393 AC000095.1 1.85 0.000
ENSG00000257017 HP 1.85 0.001
ENSG00000177294 FBXO39 1.85 0.012
ENSG00000154451 GBP5 1.84 0.002
ENSG00000120217 CD274 1.84 0.001
ENSG00000105707 HPN 1.84 0.005
ENSG00000207357 RNU6-2 1.83 0.026
ENSG00000145244 CORIN 1.82 0.027
ENSG00000264400 RN7SL491P 1.81 0.004
ENSG00000112053 SLC26A8 1.81 0.000
ENSG00000183762 KREMEN1 1.76 0.000
ENSG00000215156 AC138409.1 1.76 0.008
ENSG00000260943 LINC02555 1.75 0.001
ENSG00000011201 ANOS1 1.75 0.037
ENSG00000117228 GBP1 1.75 0.004
ENSG00000251139 AC084871.1 1.75 0.004
ENSG00000279633 AL137918.1 1.75 0.006
ENSG00000234436 AC245884.2 1.72 0.000
ENSG00000198019 FCGR1B 1.71 0.000
ENSG00000138772 ANXA3 1.70 0.000
ENSG00000284642 AL139424.2 1.70 0.000
ENSG00000185897 FFAR3 1.68 0.025
ENSG00000110203 FOLR3 1.68 0.005
ENSG00000198216 CACNA1E 1.67 0.000
ENSG00000115507 OTX1 1.66 0.001
ENSG00000203797 DDO 1.66 0.003
ENSG00000157152 SYN2 1.66 0.042
ENSG00000183160 TMEM119 1.66 0.005
ENSG00000162772 ATF3 1.65 0.028
ENSG00000137757 CASP5 1.65 0.000
ENSG00000123342 MMP19 1.64 0.013
ENSG00000255089 AC136475.4 1.64 0.014
ENSG00000222179 RN7SKP26 1.64 0.002
ENSG00000286273 AC078816.1 1.62 0.026
ENSG00000214772 AC010168.1 1.62 0.003
ENSG00000129682 FGF13 1.61 0.040
ENSG00000079215 SLC1A3 1.61 0.005
ENSG00000283633 AP000547.3 1.61 0.002
ENSG00000204767 INSYN2B 1.60 0.020
ENSG00000142621 FHAD1 1.60 0.024
ENSG00000254288 AC087672.2 1.59 0.000
ENSG00000158714 SLAMF8 1.59 0.027
ENSG00000176834 VSIG10 1.59 0.004
ENSG00000274525 AC008443.7 1.59 0.001
ENSG00000105948 TTC26 1.58 0.001
ENSG00000267065 LINC02080 1.58 0.041
ENSG00000226239 AL031658.1 1.58 0.013
ENSG00000133106 EPSTI1 1.57 0.047
ENSG00000198785 GRIN3A 1.57 0.038
ENSG00000166265 CYYR1 1.57 0.023
ENSG00000112984 KIF20A 1.56 0.046
ENSG00000228526 MIR34AHG 1.56 0.034
ENSG00000287167 AL133313.1 1.55 0.019
ENSG00000273076 AL021707.7 1.55 0.000
ENSG00000108387 SEPTIN4 1.55 0.027
ENSG00000224606 TGFA-IT1 1.54 0.000
ENSG00000196935 SRGAP1 1.54 0.025
ENSG00000286076 AC005050.3 1.54 0.000
ENSG00000183318 SPDYE4 1.53 0.016
ENSG00000233287 AC009362.1 1.53 0.008
ENSG00000184838 PRR16 1.53 0.031
ENSG00000139572 GPR84 1.53 0.010
ENSG00000287338 AL929091.1 1.52 0.005
ENSG00000253983 AC087627.1 1.52 0.015
ENSG00000239466 RN7SL552P 1.51 0.000
ENSG00000140839 CLEC18B 1.51 0.004
ENSG00000250138 AC139495.3 1.51 0.007
ENSG00000235321 AC007556.1 1.51 0.035
ENSG00000233030 AC243772.2 1.51 0.012
ENSG00000161643 SIGLEC16 1.50 0.005
ENSG00000249437 NAIP 1.50 0.001
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*

ENSEMBL is a database that endeavors to standardize designations for genes and provides annotations about their functions and relationships.

Table 4.

Down-regulated genes in uveitis with at least 1.5 fold change and FDR p-value < 0.05

Ensembl* Identification Gene Symbol Fold changea FDR p-value
ENSG00000264940 SNORD3C −2.90 0.017
ENSG00000123201 GUCY1B2 −2.79 0.024
ENSG00000183117 CSMD1 −2.59 0.022
ENSG00000145362 ANK2 −2.33 0.000
ENSG00000225746 MEG8 −2.14 0.006
ENSG00000259692 AC104041.1 −2.13 0.032
ENSG00000264057 AC103810.1 −2.08 0.003
ENSG00000255595 AC007368.1 −2.00 0.006
ENSG00000239855 IGKV1–6 −1.97 0.003
ENSG00000214548 MEG3 −1.93 0.023
ENSG00000233236 LINC02573 −1.90 0.045
ENSG00000228696 ARL17B −1.85 0.028
ENSG00000150556 LYPD6B −1.83 0.008
ENSG00000260423 LINC02367 −1.81 0.006
ENSG00000261786 AC006058.1 −1.81 0.001
ENSG00000258927 AL133467.2 −1.78 0.025
ENSG00000128253 RFPL2 −1.76 0.000
ENSG00000258168 AC025569.1 −1.74 0.012
ENSG00000169031 COL4A3 −1.73 0.000
ENSG00000197549 PRAMENP −1.73 0.000
ENSG00000002745 WNT16 −1.71 0.002
ENSG00000124780 KCNK17 −1.70 0.046
ENSG00000100473 COCH −1.68 0.003
ENSG00000248673 LINC01331 −1.65 0.028
ENSG00000185483 ROR1 −1.65 0.025
ENSG00000242082 SLC5A4-AS1 −1.64 0.009
ENSG00000081052 COL4A4 −1.64 0.000
ENSG00000279024 AC112255.1 −1.63 0.009
ENSG00000155974 GRIP1 −1.62 0.000
ENSG00000128438 TBC1D27P −1.62 0.000
ENSG00000165810 BTNL9 −1.61 0.028
ENSG00000258116 PPIAP45 −1.61 0.040
ENSG00000102934 PLLP −1.60 0.001
ENSG00000168702 LRP1B −1.59 0.005
ENSG00000211669 IGLV3–10 −1.59 0.029
ENSG00000249236 AC008892.1 −1.59 0.002
ENSG00000211957 IGHV3–35 −1.58 0.001
ENSG00000147642 SYBU −1.58 0.004
ENSG00000196167 COLCA1 −1.57 0.015
ENSG00000211892 IGHG4 −1.57 0.043
ENSG00000197702 PARVA −1.56 0.000
ENSG00000263417 GTSCR1 −1.56 0.036
ENSG00000214401 KANSL1-AS1 −1.55 0.004
ENSG00000258878 AL355076.3 −1.55 0.015
ENSG00000079931 MOXD1 −1.55 0.003
ENSG00000234184 LINC01781 −1.55 0.008
ENSG00000134532 SOX5 −1.55 0.003
ENSG00000165300 SLITRK5 −1.54 0.011
ENSG00000232732 AC097717.1 −1.54 0.002
ENSG00000143869 GDF7 −1.54 0.028
ENSG00000164542 KIAA0895 −1.54 0.000
ENSG00000278196 IGLV2–8 −1.54 0.044
ENSG00000187510 C12orf74 −1.53 0.005
ENSG00000100376 FAM118A −1.53 0.003
ENSG00000204677 FAM153CP −1.53 0.019
ENSG00000206077 ZDHHC11B −1.52 0.006
ENSG00000091129 NRCAM −1.52 0.035
ENSG00000150627 WDR17 −1.51 0.000
ENSG00000124615 MOCS1 −1.51 0.027
ENSG00000123095 BHLHE41 −1.51 0.003
ENSG00000135549 PKIB −1.51 0.004
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*

ENSEMBL is a database that endeavors to standardize designations for genes and provides annotations about their functions and relationships.

a

A negative fold change indicates that the gene is down-regulated in uveitis.

Figure 1.

Figure 1.

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Volcano plot of significant genes. Square (red) points are the: genes with at least 1.5-fold change and FDR p < 0.05 listed in Tables 3 and 4. Triangle (blue) points 10 genes listed in Table 5. Two triangles, those farthest to the left, overlap so it appears that only 9 genes are represented by triangles.

The mRNAs identified in Table 3 result from multiple statistical comparisons. The predominance of transcripts related to inflammation suggests that the list is not merely an artefact resulting from the multiple statistical tests. However, to validate the list we compared our results to a previous National Eye Institute (NEI) study that relied on a somewhat similar methodology14. That study analyzed 50 subjects with multiple forms of uveitis. Peripheral blood mononuclear cells were used rather than whole blood. A microarray limited to 400 transcripts related to inflammation was employed rather than the broader RNA-Seq methodology which we used. Table 5 lists the 10 transcripts which had an FDR p-value < 0.05 in our study and in the report from the NEI.

Table 5.

Common significant genes in uveitis based on the present study and that of Li et al.17

Ensembl* Identification Gene Symbol Fold change FDR p-value Fold changes reported in Li et al.
ENSG00000213999 MEF2B 1.35 0.006 3.4
ENSG00000090339 ICAM1 1.28 0.000 2.1
ENSG00000134470 IL15RA 1.23 0.011 4.4
ENSG00000125347 IRF1 1.21 0.001 2.3
ENSG00000164136 IL15 1.21 0.048 7.6
ENSG00000243646 IL10RB 1.19 0.000 2.5
ENSG00000068305 MEF2A 1.10 0.037 2.1
ENSG00000105723 GSK3A 1.09 0.025 2.6
ENSG00000105397 TYK2 1.07 0.015 2.9
ENSG00000116604 MEF2D 1.06 0.017 2.6
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*

ENSEMBL is a database that endeavors to standardize designations for genes and provides annotations about their functions and relationships.

The relative expression of mRNAs for specific subsets of uveitis was presented in a prior manuscript13. These data are displayed as a heat map in supplementary material (available at www.AJO.com).

Discussion

In this report we identify 10 transcripts that are up regulated in the blood of subjects with a variety of forms of uveitis based on concordance of two studies published more than a decade apart. Each of the 10 transcripts is known to play a role in inflammation and each is a plausible contributor to intraocular inflammation. For example, intercellular adhesion molecule-1 (ICAM-1) is expressed on endothelial cells and plays an important role in leukocyte migration. Polymorphisms in ICAM-1 affect susceptibility to Behcet’s disease22. Blocking ICAM-1 results in a reduction of leukocyte migration into the eye in animal models of uveitis23. ICAM-1 also serves as an accessory molecule expressed by T cells and binds to leukocyte functional antigen-1 (LFA-1) on macrophages and dendritic cells. A monoclonal antibody to LFA-1, efalizumab, was effective in psoriasis and tested for its ability to reduce uveitic macular edema24. This antibody can no longer be prescribed because of infectious complications such as progressive multifocal leukoencephalopathy25. Soluble ICAM-1 is an important biomarker in other forms of systemic inflammation such as rheumatoid arthritis26 and psoriatic arthritis27. Interleukin-15 is a growth factor for T cells. It has homology to IL-2. Its role is especially recognized in mast cells and natural killer (NK) cells, both of which have been implicated in uveitis. Interferon regulatory factor-1 (IRF-1) is induced by gamma interferon. It is known to be expressed by both the retina and retinal pigment epithelium28. Polymorphisms of IRF-1 are implicated in Behcet’s disease29. Interleukin-10 signals through the IL-10 receptor, plays a critical role in regulating immune responses, but also has immune stimulatory effects30. Our own studies were among the first to demonstrate the potential role of IL-10 in uveitis31. Glucose synthase kinase 3A (GSK3A) is a major enzyme in glucose metabolism. Inhibition of glucose metabolism is being studied as a selective approach to inhibit T lymphocytes32. TYK2 is one of four members of the janus kinase family of signaling molecules. It is a major contributor to the function of many cells in the immune system including natural killer cells and dendritic cells33. Selective TYK2 inhibition is a potential target for several forms of inflammation including rheumatoid arthritis34. Finally, the four members of the myocyte enhancing factor 2 (MEF2) protein family (A, B, C, and D) found in vertebrates are transcription factors with known roles in various biological processes including neural and heart development, and carcinogenesis35. Our results found that three of these four MEF2 genes were upregulated in blood from uveitis subjects. MEF2A and MEF2D are ubiquitously expressed in most tissues while MEF2B expression is enriched in lymphoid tissues, especially germinal center B cells (https://www.proteinatlas.org/). Regarding ocular tissues, MEF2A and MEF2D are expressed at levels in the human adult retina similar to levels in whole blood (https://eyeintegration.nei.nih.gov/#). Furthermore, in murine knockout models, Mef2d has been shown to be required for the development and function of photoreceptors and bipolar cells36,37 and endothelial expression of Mef2a and Mef2c play an important role in retinal angiogenesis38.

Space precludes a discussion of all the mRNAs which we detected as listed in Table 3, and which were not detected in the report by Li et.al. 14 using older methodology. Restricting comments to the most differentially expressed transcripts, LINC00278 is a long, noncoding RNA which is implicated in CD8 T cell responses to active tuberculosis 39, a potential cause of uveitis. UTY codes for H-Y, a male specific minor histocompatibility complex antigen 40. It has been implicated in the immune response as in graft versus host disease 41. Subretinal injection of this antigen is immunogenic 42.

Table 2 based on KEGG pathways shows that many pathways relate to the response to infection. This results from the overlap between the immune response to an infection or to antigen that is not necessarily derived from an infection such as ovalbumin. The data do not necessarily indicate that an infection is a likely cause of uveitis.

This study has a variety of limitations. By applying stringencies, i.e. FDR p-value < 0.05, our list of transcripts of interest undoubtedly omits additional molecules that contribute to eye inflammation. While we have studied an array of forms of uveitis, many subtypes of noninfectious uveitis have been omitted. Conversely, by identifying factors that seem to be contributing to multiple forms of uveitis, we could easily miss an essential contributor to inflammation in a specific form of uveitis. In addition, by interrogating peripheral blood we may overlook critical mediators which are restricted to the site of inflammation. Our analysis omits extracellular mRNA. And not all RNAs are translated into protein; or the level of protein might not correlate well with the level of mRNA.

The study, however, also has a number of strengths. The clinical success of medications such as prednisone and adalimumab indicates that indeed different subsets of uveitis share mediators in common. Proteins encoded by several of the transcripts that we identified such as ICAM-1, IL-10, IL-15, IRF-1, and Tyk2 are highly likely to contribute to ocular inflammation and in the case of ICAM-1, its ligand has already been targeted in a clinical trial for macular edema associated with uveitis24. It seems unlikely that any of the transcripts identified in this study contribute to uveitis without affecting inflammation in other organ systems. Adhesion molecules expressed on a subset of leukocytes or preferentially in a vascular bed in the eye would be candidates for this type of selectivity. While ICAM-1 is an adhesion molecule, its expression is not specific to the eye. It is also possible that changes in mRNA which are statistically significant do not result in changes in proteins which are biologically significant.

Based on a pubmed computerized literature search on December 20, 2020 using the terms “RNA seq” and “uveitis”, this study along with our prior report 13 are the first human studies to use RNA-Seq to analyze gene expression in peripheral blood to understand intraocular inflammation. The validity of our approach is supported by the current understanding of inflammation, by some genomic polymorphisms, and by a prior study using microarray and a much more limited set of measured transcripts. Single cell RNA Seq allows the analysis of transcripts from an individual cell, in contrast to the methodology used in this report in which we measured gene expression from a pool of cells in blood. Single cell RNA-Seq reveals heterogeneity in a cell population such as CD4 lymphocytes which appear identical using routine histology. Single cell RNA Seq can identify changes in an immunologic response, changes that would be missed by bulk RNA-Sea. Single cell RNA Seq is an additional methodology that promises to clarify further the pathogenesis of uveitis. Our study identifies several mediators such as IL-15 and Tyk2 for which pharmacotherapies are being evaluated. If these products demonstrate safety, they are excellent candidates to explore for the treatment of uveitis.

Table of Content statement

Uveitis is associated with changes in mRNA transcripts which can be detected in peripheral blood. The authors report that different forms of uveitis share common transcripts and RNA pathways. A previous report detected that similar transcripts were present in leukocytes from patients with uveitis. The research suggests biomarkers and approaches to therapy that could be applied to uveitis from disparate causes.

Supplementary Material

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Highlights.

  • Uveitis is a heterogeneous collection of diseases.

  • Despite the heterogeneity of uveitis, specific RNA transcripts expressed by cells in peripheral blood as well as pathways involving multiple genes are common to different forms of uveitis.

  • The shared expression of mRNA from different forms of uveitis is consistent with a prior publication based on peripheral blood and thus independently validated.

  • Transcripts that are implicated in multiple forms of uveitis could suggest therapeutic targets that could benefit multiple forms of uveitis.

Acknowledgments

This research was supported by NIH NEI Grant EY026572 and Core Grant NIH NEI P30 EY010572. JTR receives support from the Grandmaison Fund for Autoimmunity Research, the William and Mary Bauman Foundation, the Stan and Madelle Rosenfeld Family Trust. The Casey Eye Institute receives support from Research to Prevent Blindness. CDC received support from the Heed Ophthalmic Fellowship.

RNA isolation and RNA-Seq were performed in the OHSU Gene Profiling Shared Resource and Massively Parallel Sequencing Shared Resource, respectively. Data processing was performed in the Oregon National Primate Research Center’s Bioinformatics & Biostatistics Core, which is funded in part by NIH grant OD P51 OD011092.

We gratefully acknowledge Drs. Phoebe Lin, Eric Suhler and Russell Van Gelder for assistance in identifying study subjects. We are grateful to Kimberly Ogle for assistance with the IRB.

JTR receives clinical trial support from Pfizer. JTR receives consulting fees from Abbvie, Gilead, Novartis, Santen, Horizon, UCB, Roche, Corvus, and Eyevensys. He receives royalties from UpToDate. ATV receives consulting fees from Roche.

Footnotes

Conflicts of interest: No other authors report a conflict of interest.

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References

  • 1.Nussenblatt RB. The natural history of uveitis. Int Ophthalmol. 1990;14(5–6):303–308. [DOI] [PubMed] [Google Scholar]
  • 2.Fanouriakis A, Kostopoulou M, Cheema K, et al. 2019 Update of the Joint European League Against Rheumatism and European Renal Association-European Dialysis and Transplant Association (EULAR/ERA-EDTA) recommendations for the management of lupus nephritis. Ann Rheum Dis. 2020;79(6):713–723. [DOI] [PubMed] [Google Scholar]
  • 3.Schiff M, Beaulieu A, Scott DL, Rashford M. Mycophenolate mofetil in the treatment of adults with advanced rheumatoid arthritis: three 24-week, randomized, double-blind, placebo- or ciclosporin-controlled trials. Clin drug investig. 2010;30(9):613–624. [DOI] [PubMed] [Google Scholar]
  • 4.Bilal J, Berlinberg A, Bhattacharjee S, Trost J, Riaz IB, Kurtzman DJB. A systematic review and meta-analysis of the efficacy and safety of the interleukin (IL)-12/23 and IL-17 inhibitors ustekinumab, secukinumab, ixekizumab, brodalumab, guselkumab and tildrakizumab for the treatment of moderate to severe plaque psoriasis. J Dermatolog Treat. 2018;29(6):569–578. [DOI] [PubMed] [Google Scholar]
  • 5.Hueber W, Sands BE, Lewitzky S, et al. Secukinumab, a human anti-IL-17A monoclonal antibody, for moderate to severe Crohn’s disease: unexpected results of a randomised, double-blind placebo-controlled trial. Gut. 2012;61(12):1693–1700. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Dokoupilova E, Aelion J, Takeuchi T, et al. Secukinumab after anti-tumour necrosis factor-alpha therapy: a phase III study in active rheumatoid arthritis. Scand J Rheumatol. 2018;47(4):276–281. [DOI] [PubMed] [Google Scholar]
  • 7.Miloslavsky EM, Naden RP, Bijlsma JW, et al. Development of a Glucocorticoid Toxicity Index (GTI) using multicriteria decision analysis. Ann Rheum Dis. 2017;76(3):543–546. [DOI] [PubMed] [Google Scholar]
  • 8.Ward MM, Deodhar A, Akl EA, et al. American College of Rheumatology/Spondylitis Association of America/Spondyloarthritis Research and Treatment Network 2015 Recommendations for the Treatment of Ankylosing Spondylitis and Nonradiographic Axial Spondyloarthritis. Arthritis rheumatol. 2016;68(2):282–298. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Jaffe GJ, Dick AD, Brezin AP, et al. Adalimumab in Patients with Active Noninfectious Uveitis. N Engl J Med. 2016;375(10):932–943. [DOI] [PubMed] [Google Scholar]
  • 10.Nguyen QD, Merrill PT, Jaffe GJ, et al. Adalimumab for prevention of uveitic flare in patients with inactive non-infectious uveitis controlled by corticosteroids (VISUAL II): a multicentre, double-masked, randomised, placebo-controlled phase 3 trial. Lancet. 2016;388(10050):1183–1192. [DOI] [PubMed] [Google Scholar]
  • 11.Kaplan HJ, Waldrep JC, Nicholson JK, Gordon D. Immunologic analysis of intraocular mononuclear cell infiltrates in uveitis. Arch Ophthalmol. 1984;102(4):572–575. [DOI] [PubMed] [Google Scholar]
  • 12.Pulido JS, Canal I, Salomao D, Kravitz D, Bradley E, Vile R. Histological findings of birdshot chorioretinopathy in an eye with ciliochoroidal melanoma. Eye (Lond). 2012;26(6):862–865. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Rosenbaum JT, Harrington CA, Searles RP, et al. Revising the Diagnosis of Idiopathic Uveitis by Peripheral Blood Transcriptomics. Am J Ophthalmol. 2020;222:15–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Li Z, Liu B, Maminishkis A, et al. Gene expression profiling in autoimmune noninfectious uveitis disease. J Immunol. 2008;181(7):5147–5157. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Harrington CA, Fei SS, Minnier J, et al. RNA-Seq of human whole blood: Evaluation of globin RNA depletion on Ribo-Zero library method. Sci Rep. 2020;10(1):6271. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11(3):R25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Risso D, Ngai J, Speed TP, Dudoit S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat Biotechnol. 2014;32(9):896–902. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010;11(2):R14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Kanehisa M, Sato Y, Furumichi M, Morishima K, Tanabe M. New approach for understanding genome variations in KEGG. Nucleic Acids Res. 2019;47(D1):D590–D595. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Kanehisa M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 2019;28(11):1947–1951. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Zou J, Guan JL. Intercellular adhesion molecule-1 polymorphisms in patients with Behcet disease: a meta-analysis. Mod Rheumatol. 2014;24(3):481–486. [DOI] [PubMed] [Google Scholar]
  • 23.Becker MD, Garman K, Whitcup SM, Planck SR, Rosenbaum JT. Inhibition of leukocyte sticking and infiltration, but not rolling, by antibodies to ICAM-1 and LFA-1 in murine endotoxin-induced uveitis. Invest Ophthalmol Vis Sci. 2001;42(11):2563–2566. [PubMed] [Google Scholar]
  • 24.Faia LJ, Sen HN, Li Z, Yeh S, Wroblewski KJ, Nussenblatt RB. Treatment of inflammatory macular edema with humanized anti-CD11a antibody therapy. Invest Ophthalmol Vis Sci. 2011;52(9):6919–6924. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Schwab N, Ulzheimer JC, Fox RJ, et al. Fatal PML associated with efalizumab therapy: insights into integrin alphaLbeta2 in JC virus control. Neurology. 2012;78(7):458–467; discussion 465. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Zhao J, Ye X, Zhang Z. The predictive value of serum soluble ICAM-1 and CXCL13 in the therapeutic response to TNF inhibitor in rheumatoid arthritis patients who are refractory to csDMARDs. Clin Rheumatol. 2020;39(9):2573–2581. [DOI] [PubMed] [Google Scholar]
  • 27.Boyd TA, Eastman PS, Huynh DH, et al. Correlation of serum protein biomarkers with disease activity in psoriatic arthritis. Expert rev clin immunol. 2020;16(3):335–341. [DOI] [PubMed] [Google Scholar]
  • 28.Amadi-Obi A, Yu CR, Dambuza I, Kim SH, Marrero B, Egwuagu CE. Interleukin 27 induces the expression of complement factor H (CFH) in the retina. PLoS One. 2012;7(9):e45801. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Lee YJ, Kang SW, Song JK, et al. Associations between interferon regulatory factor-1 polymorphisms and Behcet’s disease. Hum Immunol. 2007;68(9):770–778. [DOI] [PubMed] [Google Scholar]
  • 30.Ouyang W, O’Garra A. IL-10 Family Cytokines IL-10 and IL-22: from Basic Science to Clinical Translation. Immunity. 2019;50(4):871–891. [DOI] [PubMed] [Google Scholar]
  • 31.Rosenbaum JT, Angell EM. Paradoxical effects of interleukin-10 in endotoxin-induced uveitis. J Immunol. 1995;155:4090–4094. [PubMed] [Google Scholar]
  • 32.Petrasca A, Phelan JJ, Ansboro S, Veale DJ, Fearon U, Fletcher JM. Targeting bioenergetics prevents CD4 T cell-mediated activation of synovial fibroblasts in rheumatoid arthritis. Rheumatology (Oxford, England). 2020;59(10):2816–2828. [DOI] [PubMed] [Google Scholar]
  • 33.Simonovic N, Witalisz-Siepracka A, Meissl K, et al. NK Cells Require Cell-Extrinsic and - Intrinsic TYK2 for Full Functionality in Tumor Surveillance and Antibacterial Immunity. J Immunol. 2019;202(6):1724–1734. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.He X, Chen X, Zhang H, Xie T, Ye XY. Selective Tyk2 inhibitors as potential therapeutic agents: a patent review (2015–2018). Expert Opin Ther Pat. 2019;29(2):137–149. [DOI] [PubMed] [Google Scholar]
  • 35.Chen X, Gao B, Ponnusamy M, Lin Z, Liu J. MEF2 signaling and human diseases. Oncotarget. 2017;8(67):112152–112165. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Nagar S, Trudler D, McKercher SR, et al. Molecular Pathway to Protection From Age-Dependent Photoreceptor Degeneration in Mef2 Deficiency. Invest Ophthalmol Vis Sci. 2017;58(9):3741–3749. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Omori Y, Kitamura T, Yoshida S, et al. Mef2d is essential for the maturation and integrity of retinal photoreceptor and bipolar cells. Genes Cells. 2015;20(5):408–426. [DOI] [PubMed] [Google Scholar]
  • 38.Sacilotto N, Chouliaras KM, Nikitenko LL, et al. MEF2 transcription factors are key regulators of sprouting angiogenesis. Genes Dev. 2016;30(20):2297–2309. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Fu Y, Gao K, Tao E, Li R, Yi Z. Aberrantly Expressed Long Non-Coding RNAs In CD8(+) T Cells Response to Active Tuberculosis. J Cell Biochem. 2017;118(12):4275–4284. [DOI] [PubMed] [Google Scholar]
  • 40.Warren EH, Gavin MA, Simpson E, et al. The human UTY gene encodes a novel HLA-B8-restricted H-Y antigen. J Immunol. 2000;164(5):2807–2814. [DOI] [PubMed] [Google Scholar]
  • 41.Bund D, Buhmann R, Gokmen F, Zorn J, Kolb HJ, Schmetzer HM. Minor histocompatibility antigen UTY as target for graft-versus-leukemia and graft-versus-haematopoiesis in the canine model. Scand J Immunol. 2013;77(1):39–53. [DOI] [PubMed] [Google Scholar]
  • 42.Vendomele J, Dehmani S, Khebizi Q, Galy A, Fisson S. Subretinal Injection of HY Peptides Induces Systemic Antigen-Specific Inhibition of Effector CD4(+) and CD8(+) T-Cell Responses. Front Immunol. 2018;9:504. [DOI] [PMC free article] [PubMed] [Google Scholar]

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