Advisory Committee Chair
Roderick Fullard
Advisory Committee Members
Martin Johnson
Michael Frost
Tammy Than
Om Srivastava
Kent Keyser
Document Type
Dissertation
Date of Award
2015
Degree Name by School
Doctor of Philosophy (PhD) School of Optometry
Abstract
ABSTRACT Purpose The aim of this study was to adapt conjunctival impression cytology (CIC) and RNA isolation and processing procedures for gene expression analysis of ocular surface inflammatory biomarkers. The RNA yield and quality should be sufficient to enable quantitative real-time PCR of no less than 12 key target genes, with the goal of differentiating among dry eye groups. This study used the optimized inflammatory biomarker gene expression assay in a patient study to identify differences between dry eye and control participants. Materials and Methods CIC was used to collect conjunctival surface cells from 53 qualifying dry eye and control participants. Dry eye patients were divided into three clinical subgroups: Sjögren's syndrome, non-Sjögren's aqueous-deficient, and evaporative. After optimization of all assay steps, RNA was isolated from the samples using a Qiagen RNeasy Plus Mini Kit and qRT-PCR was able to determine gene expression of >12 genes using TaqMan Array 384-well microfluidic cards. Samples from 6 control and 6 dry eye participants were also assayed on an Illumina Human HT-12 BeadChip that analyzed gene expression using over 47,000 transcripts. Results Optimization of RNA processing procedures resulted in dramatic improvements in RNA yield and quality and allowed for an increase in the number of genes analyzed from an initial 12 genes to 96, then to the entire human transcriptome. TLDA card results revealed that MUC1, MUC4, MUC16, IL2RB, IL8, CCL2, and STAT4 were all significantly upregulated in the evaporative dry eye group compared to controls. Gene expression trends also emerged when comparing non-Sjogren's aqueous deficient dry eye and Sjögren's groups to controls. For the HT-12 BeadChip, >30 genes differed by a factor of >1.5 between the dry eye and control groups and seven genes were downregulated by a factor of >2.0 in the dry eye group: HLA-DRB5, PSCA, FOS, lysozyme, TSC22D1, CAPN13, and CXCL6. Conclusions Conjunctival impression cytology can be used to collect sufficient RNA from conjunctival surface cells to enable identification of gene expression differences between various dry eye groups and controls. However, a larger scale study will be required to produce a predictive model of sufficient precision and accuracy to differentiate between each dry eye group and controls. While successful transcriptome-wide expression analysis was also possible, an even more limited patient group precluded the development of a predictive model. Larger studies of patients with various types and severities of dry eye should reveal more significant gene expression trends that can then be targeted to improve dry eye treatment options.
Recommended Citation
Bradley, John L., "Predictive Modeling Of Ocular Surface Disease States Using Multiple Inflammatory Biomarkers" (2015). All ETDs from UAB. 1236.
https://digitalcommons.library.uab.edu/etd-collection/1236
