Logo image
In-depth transcriptomic analysis of human retina reveals molecular mechanisms underlying diabetic retinopathy
Journal article   Open access   Peer reviewed

In-depth transcriptomic analysis of human retina reveals molecular mechanisms underlying diabetic retinopathy

Kolja Becker, Holger Klein, Eric Simon, Coralie Viollet, Christian Haslinger, German Leparc, Christian Schultheis, Victor Chong, Markus H Kuehn, Francesc Fernandez-Albert, …
Scientific reports, Vol.11(1), pp.10494-10494
05/18/2021
DOI: 10.1038/s41598-021-88698-3
PMCID: PMC8131353
PMID: 34006945
url
https://doi.org/10.1038/s41598-021-88698-3View
Published (Version of record) Open Access

Abstract

Diabetic Retinopathy (DR) is among the major global causes for vision loss. With the rise in diabetes prevalence, an increase in DR incidence is expected. Current understanding of both the molecular etiology and pathways involved in the initiation and progression of DR is limited. Via RNA-Sequencing, we analyzed mRNA and miRNA expression profiles of 80 human post-mortem retinal samples from 43 patients diagnosed with various stages of DR. We found differentially expressed transcripts to be predominantly associated with late stage DR and pathways such as hippo and gap junction signaling. A multivariate regression model identified transcripts with progressive changes throughout disease stages, which in turn displayed significant overlap with sphingolipid and cGMP–PKG signaling. Combined analysis of miRNA and mRNA expression further uncovered disease-relevant miRNA/mRNA associations as potential mechanisms of post-transcriptional regulation. Finally, integrating human retinal single cell RNA-Sequencing data revealed a continuous loss of retinal ganglion cells, and Müller cell mediated changes in histidine and β-alanine signaling. While previously considered primarily a vascular disease, attention in DR has shifted to additional mechanisms and cell-types. Our findings offer an unprecedented and unbiased insight into molecular pathways and cell-specific changes in the development of DR, and provide potential avenues for future therapeutic intervention.
Computational models Data integration Target identification Machine learning Biomarkers Eye diseases Cardiovascular diseases Metabolic disorders

Details

Metrics

Logo image