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Retinal transcriptome profiling at transcription start sites: a cap analysis of gene expression early after axonal injury

BACKGROUND: Glaucoma is characterized by progressive loss of the visual field and death of retinal ganglion cells (RGCs), a process that is mediated, in part, by axonal injury. However, the molecular pathomechanisms linking RGC death and axonal injury remain largely unknown. Here, we examined these...

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Detalles Bibliográficos
Autores principales: Yasuda, Masayuki, Tanaka, Yuji, Nishiguchi, Koji M, Ryu, Morin, Tsuda, Satoru, Maruyama, Kazuichi, Nakazawa, Toru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4246558/
https://www.ncbi.nlm.nih.gov/pubmed/25407019
http://dx.doi.org/10.1186/1471-2164-15-982
Descripción
Sumario:BACKGROUND: Glaucoma is characterized by progressive loss of the visual field and death of retinal ganglion cells (RGCs), a process that is mediated, in part, by axonal injury. However, the molecular pathomechanisms linking RGC death and axonal injury remain largely unknown. Here, we examined these mechanisms with a cap analysis of gene expression (CAGE), which allows the comprehensive quantification of transcription initiation across the entire genome. We aimed to identify changes in gene expression patterns and to predict the resulting alterations in the protein network in the early phases of axonal injury in mice. RESULTS: We performed optic nerve crush (ONC) in mice to model axonal injury. Two days after ONC, the retinas were isolated, RNA was extracted, and a CAGE library was constructed and sequenced. CAGE data for ONC eyes and sham-treated eyes was compared, revealing 180 differentially expressed genes. Among them, the Bcat1 gene, involved in the catabolism of branched-chain amino acid transaminase, showed the largest change in expression (log2 fold-change = 6.70). In some differentially expressed genes, alternative transcription start sites were observed in the ONC eyes, highlighting the dynamism of transcription initiation in a state of disease. In silico pathway analysis predicted that ATF4 was the most significant upstream regulator orchestrating pathological processes after ONC. Its downstream candidate targets included Ddit3, which is known to induce cell death under endoplasmic reticulum stress. In addition, a regulatory network comprising IFNG, P38 MAPK, and TP53 was predicted to be involved in the induction of cell death. CONCLUSION: Through CAGE, we have identified differentially expressed genes that may account for the link between axonal injury and RGC death. Furthermore, an in silico pathway analysis provided a global view of alterations in the networks of key regulators of biological pathways that presumably take place in ONC. We thus believe that our study serves as a valuable resource to understand the molecular processes that define axonal injury-driven RGC death. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-982) contains supplementary material, which is available to authorized users.