Tear proteomics in dry eye disease

Abstract

Dry eye disease (DED) is a multi-factorial ocular surface condition driven by compromised ocular lubrication and inflammation which leads to itching, dryness, and vision impairment. The available treatment modalities primarily target the acquired symptoms of DED including tear film supplements, anti-inflammatory drugs, mucin secretagogues, etc., However, the underlying etiology is still an area of active research, especially in regard to the diverse etiology and symptoms. Proteomics is a robust approach that has been playing major role in understanding the causative mechanism and biochemical changes in DED by identifying the changes in protein expression profile in tears. Tears are a complex fluid composed of several biomolecules such as proteins, peptides, lipids, mucins, and metabolites secreted from lacrimal gland, meibomian gland, cornea, and vascular sources. Over the past two decades, tears have emerged as a bona-fide source for biomarker identification in many ocular conditions because of the minimally invasive and simple sample collection procedure. However, the tear proteome can be altered by several factors, which increases the complexity of the approach. The recent advancements in untargeted mass spectrometry-based proteomics could overcome such shortcomings. Also, these technological advancements help to distinguish the DED profiles based on its association with other complications such as Sjogren's syndrome, rheumatoid arthritis, diabetes, and meibomian gland dysfunction. This review summarizes the important molecular profiles found in proteomics studies to be altered in DED which have added to the understanding of its pathogenesis.

Keywords: Biomarkers; dry eye disease; inflammation; molecular markers; ocular surface; proteomics.

Figure 1

Proteomics workflow for biomarker identification for DED. The tear samples can be collected using Schirmer’s strip or microcapillary tubes. The extracted proteins can be used for insolution digestion or ingel digestion after separation of proteins based on molecular weight (and pI). The isobaric labeling helps in analyzing relative quantitation of the proteins. Before MS run, the desalting/protein purification should be carried out using reverse-phase chromatography, to avoid noise and proper ionization. The MS instrument generates the m/z spectra. The generated spectra can be matched with protein database for further downstream analysis such as differential protein expression analysis, novel protein identification, pathway enrichment, and PTM analysis

Figure 2

Important proteins involved in dry eye-related biological pathways. (i) Under oxidative stress, hyperosmolarity stress, and desiccation stress, HSP family B is upregulated. It activates MAPK pathway, which in turn activates JNK, ERK, and NF-kB pathway and increases the MMP9, IL-1β, Il-6, and TNF-α. These inflammatory cytokines stimulate the innate immune system to produce antigen-presenting cells (APCs). (ii) Oxidative stress inhibits glutathione metabolism. Glutathione S transferase pi 1 protein activates the glutathione metabolism, and it can also be used as a therapeutic measure. (iii) Peroxisome proliferator-activated receptor (PPAR) pathway has a protective role against dry eye by activating several important metabolic gene expressions. (iv) But under oxidative and desiccation stress, NF-kB pathway is activated, and it blocks the PPAR signaling, and inflammatory genes are expressed and produces IL-17. Apolipoprotein A1 and A2 and phospholipid transfer proteins are downregulated in dry eye. (v) In presence of increased inflammatory cytokines, S100 calcium-binding proteins A8 and A9, APCs become matured APC (mAPC) and activates Th1 cells to release IL-17, IFN-γ, TNF-α. (vi) These cytokines stimulate the adaptive immune system, and HIF-1/2α and HIF-1β genes are transcribed to VEGF-A, VEGF-C, and other angiogenic proteins. These inflammatory pathways altogether give rise to the dry eye pathology

Figure 3

Protein–protein interaction network for the protein-based biomarkers. The biomarkers which are reported to be involved in the DED were collected, and the interaction for the shortlisted proteins was generated. The blue (downregulation) and red (upregulation) highlighted edges are reported in more than three proteomic studies and shows clinical relevance. The other reported proteins are observed under certain disease classification or reflecting the treatment modality (used in certain study)