Gene expression signatures in conjunctival fornix aspirates of patients with dry eye disease associated with Meibomian gland dysfunction. A proof-of-concept study

Abstract

Background: Meibomian gland dysfunction (MGD) is one of the most common conditions in ophthalmic practice and the most frequent cause of evaporative dry eye disease (DED). However, the immune mechanisms leading to this pathology are not fully understood and the diagnostic tests available are limited. Here, we used the nCounter technology to analyze immune gene expression in DED-MGD that can be used for developing diagnostic signatures for DED.

Methods: Conjunctival cell samples were obtained by aspiration from patients with DED-MGD (n = 27) and asymptomatic controls (n = 22). RNA was purified, converted to cDNA, preamplified and analyzed using the Gene Expression Human Immune V2 panel (NanoString), which includes 579 target and 15 housekeeping genes. A machine learning (ML) algorithm was applied to design a signature associated with DED-MGD.

Results: Forty-five immune genes were found upregulated in DED-MGD vs. controls, involved in eight signaling pathways, IFN I/II, MHC class I/II, immunometabolism, B cell receptor, T Cell receptor, and T helper-17 (Th-17) differentiation. Additionally, statistically significant correlations were found between 31 genes and clinical characteristics of the disease such as lid margin or tear osmolarity (Pearson's r < 0.05). ML analysis using a recursive feature elimination (RFE) algorithm selected a 4-gene mRNA signature that discriminated DED-MGD from control samples with an area under the ROC curve (AUC ROC) of 0.86 and an accuracy of 77.5%.

Conclusions: Multiplexed mRNA analysis of conjunctival cells can be used to analyze immune gene expression patterns in patients with DED-MGD and to generate diagnostic signatures.

Keywords: Dry eye disease; Gene expression; Machine learning; Meibomian gland dysfunction; Tears; nCounter.