Conjunctival Inflammatory Gene Expression Profiling in Dry Eye Disease: Correlations With HLA-DRA and HLA-DRB1

Abstract

Purpose: In several multicenter clinical trials, HLA-DR was found to be a potential biomarker of dry eye disease (DED)'s severity and prognosis. Given the fact that HLA-DR receptor is a heterodimer consisting in an alpha and a beta chain, we intended to investigate the correlation of inflammatory targets with the corresponding transcripts, HLA-DRA and HLA-DRB1, to characterize specific targets closely related to HLA-DR expressed in conjunctival cells from patients suffering from DED of various etiologies. Methods: A prospective study was conducted in 88 patients with different forms of DED. Ocular symptom scores, ocular-staining grades, tear breakup time (TBUT) and Schirmer test were evaluated. Superficial conjunctival cells were collected by impression cytology and total RNAs were extracted for analyses using the new NanoString^® nCounter technology based on an inflammatory human code set containing 249 inflammatory genes. Results: Two hundred transcripts were reliably detected in conjunctival specimens at various levels ranging from 1 to 222,546 RNA copies. Overall, from the 88 samples, 21 target genes showed a highly significant correlation (R > 0.8) with HLA-DRA and HLA-DRB1, HLA-DRA and B1 presenting the highest correlation (R = 0.9). These selected targets belonged to eight family groups, namely interferon and interferon-stimulated genes, tumor necrosis factor superfamily and related factors, Toll-like receptors and related factors, complement system factors, chemokines/cytokines, the RIPK enzyme family, and transduction signals such as the STAT and MAPK families. Conclusions: We have identified a profile of 21 transcripts correlated with HLA-DR expression, suggesting closely regulated signaling pathways and possible direct or indirect interactions between them. The NanoString^® nCounter technology in conjunctival imprints could constitute a reliable tool in the future for wider screening of inflammatory biomarkers in DED, usable in very small samples. Broader combinations of biomarkers associated with HLA-DR could be analyzed to develop new diagnostic approaches, identify tighter pathophysiological gene signatures and personalize DED therapies more efficiently.

Keywords: HLA-DR; NanoString® assay; conjunctival imprints; dry eye disease; inflammatory targets.

Figure 1

RNA abundance of the selected highly correlated genes with both HLA-DR transcripts according to their family. RNA abundance is represented by the detected RNA copies on the y-axis. Numbers above the hatched rectangle correspond to each target family selected: (1) IFN and ISGs, (2) TNF superfamily, (3) TLR and related factors, (4) chemokines/cytokines, (5) complement and CRP (6) RIPK enzymes, (7) STAT, (8) MAPK.

Figure 2

Correlation analysis of genes with HLA-DRA and HLA-DRB1 based on the Spearman's correlation coefficients of genes in SSDE and NSSDE patients. (A,B) Represent Heat Map of genes significantly (P < 0.05) correlated with both HLA-DR-A and HLA-DRB1, in a descending manner. (C) Venn diagram showing the number of specific and common targets identified in the two groups of patients. SSDE, syndrome Sjögren dry eye; NSSDE, non syndrome Sjögren dry eye.

Figure 3

Proposal for a pattern of signaling pathways associated with increased HLA-DR expression in conjunctival cells. This figure presents the localization of the targets identified with their possible interactions in the conjunctiva's spatial microenvironment. The four major signaling responses, in black circles, are mediated by (1) TLR responses, (2) IFN responses, (3) TNF responses, and (4) members of complement pathways. The target genes selected via their high correlation with HLA-DR are mentioned in bold red. The first line of pathogen recognition could be mediated by TLR responses via MAPK and NF-κB to induce inflammatory cytokine responses as INFs. IFNs bind to their receptors and initiate a signaling cascade, involving the JAK-STAT family of transcription factors, which leads to the transcriptional induction of the ISGs (IRF-1, IFI-44, HSH2D, Mx1, and OAS2), class II transactivator (CII-TA), and HLA-DR complex, which will migrate to the membrane. CD40, TRAF2, TRADD, and RIPK2, involved in TNF pathways, promote NF-κB and the MAPK family: MK2 and MAPK8, members of the p38 MAPK and JNK cascades, respectively. Pathogen-associated molecular patterns (PAMPs); CCAAT/enhancer binding protein beta(C/EBPβ); AP-1 transcription factor subunit(AP−1); IFN regulatory factor IRF-3 and IRF-7(IRF3/7); IFN-stimulated response element (ISRE); IFNγ-activated site (GAS); phosphate(P); class II transactivator (CIITA); MHC class II-specific regulatory module (XYS); nuclear factor kappa B subunit 2(p52); NF-κB subunit transcription factor RelB (RelB).