Proteins and Peptides in Tears Indicate Eye Conditions

Tears were collected from 44 patients using surgical sponges, 16 with each disease, and 12 controls. The abundant protein immunoglobulin A was removed by immunoaffinity chromatography in case it swamped the less abundant proteins.

The remaining proteins were separated by 2D electrophoresis (PAGE) and their positions on the gel were identified by staining with a fluorescent dye. A total of 130 protein spots were resolved and image analysis was conducted to pinpoint the proteins which had different abundances between the three groups and these were identified by mass spectrometry.

Before refining the panel of proteins to a more manageable number that retained the predictive capability, they were screened by enzyme-linked immuno sorbent assay (ELISA) to identify the most predictive ones. Two validations steps followed in which the proteins were collected by sponge or capillary. The best classification method was achieved with the Random Forest and Naïve Bayes machine learning techniques which were trained using one set of results and confirmed on the others.

A network analysis of the 15 discriminatory proteins revealed that nine of them were directly or indirectly related. The major biological processes in which they are involved include inflammatory response, stress response, immune response, and oxidative stress response.

The final discriminatory panel obtained after refinement of the model consisted of five proteins: S100A6, annexin A1, annexin A11, cystatin-S and phospholipase A2-activating protein. Together they were able to discriminate between dry eye and control patients with high sensitivity and specificity. They could also distinguish between the dry eye, MGD, and control groups with good accuracy. The results were independent of the tear collection method.

The team will examine the biomarkers in a clinical setting and possibly develop a test for dry eye and meibomian gland dysfunction.

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