Transcriptome analysis of adult and fetal trabecular meshwork, cornea, and ciliary body tissues by RNA sequencing.

Published

Journal Article

PURPOSE: To characterize the transcriptional landscape of human adult and fetal trabecular meshwork (TM), cornea, and ciliary body (CB) tissues, and to evaluate the expression level of candidate genes selected from genetic association studies of primary-open angle glaucoma, central corneal thickness, intraocular pressure, vertical cup to disc ratio, and optic nerve parameters. METHODS: Deep RNA sequencing was performed on the selected human tissues. Transcriptome analyses were performed to 1) characterize the total number of expressed genes, 2) identify the most highly expressed genes, 3) estimate the number of novel transcripts, and 4) evaluate the expression of candidate genes in each tissue. Finally, a differential gene expression analysis was conducted to compare the adult and fetal ocular tissues. RESULTS: There was an average of 12,362 protein coding genes and 3725 novel transcripts expressed in each tissue. The top most expressed genes in each tissue included SPARC (fetal cornea and TM), APOD (adult TM), CLU (adult cornea), and PTGDS (adult and fetal CB). Twenty-nine candidate genes selected from genetic association studies primarily showed high expression levels in the trabecular meshwork and cornea. Comparison of adult and fetal samples identified 2012 and 1261 differentially expressed protein-coding genes within the cornea and trabecular meshwork, respectively. CONCLUSIONS: This study has provided an unbiased glimpse into the transcriptome of three essential anterior ocular tissues, resulting in the development of several novel hypotheses. These data can be used in the future to better guide ocular research questions.

Full Text

Duke Authors

Cited Authors

  • Carnes, MU; Allingham, RR; Ashley-Koch, A; Hauser, MA

Published Date

  • February 2018

Published In

Volume / Issue

  • 167 /

Start / End Page

  • 91 - 99

PubMed ID

  • 27914989

Pubmed Central ID

  • 27914989

Electronic International Standard Serial Number (EISSN)

  • 1096-0007

Digital Object Identifier (DOI)

  • 10.1016/j.exer.2016.11.021

Language

  • eng

Conference Location

  • England