Abstract
Understanding the cellular pathways activated by elevated intraocular pressure (IOP) is crucial for the development of more effective glaucoma treatments. Microarray studies have previously been used to identify several key gene expression changes in early and extensively injured ONH, as well as in the retina. Limitations of microarrays include that they can only be used to detect transcripts that correspond to existing genomic sequencing information and their narrower dynamic range. However, RNA sequencing (RNA-seq) is a powerful tool for investigating known transcripts, as well as for exploring new ones (including noncoding RNAs and small RNAs), is more quantitative, and has the added benefit that the data can be re-analyzed as new sequencing information becomes available. Here, we describe an RNA-seq method specifically developed for identifying differentially expressed genes in optic nerve heads of eyes exposed to elevated intraocular pressure. The methods described here could also be applied to small tissue samples (less than 100 ng in total RNA yield) from retina, optic nerve, or other regions of the central nervous system.
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References
Howell GR, Macalinao DG, Sousa GL, Walden M, Soto I, Kneeland SC, Barbay JM, King BL, Marchant JK, Hibbs M, Stevens B, Barres BA, Clark AF, Libby RT, John SW (2011) Molecular clustering identifies complement and endothelin induction as early events in a mouse model of glaucoma. J Clin Invest 121(4):1429–1444. https://doi.org/10.1172/JCI44646
Johnson EC, Doser TA, Cepurna WO, Dyck JA, Jia L, Guo Y, Lambert WS, Morrison JC (2011) Cell proliferation and interleukin-6-type cytokine signaling are implicated by gene expression responses in early optic nerve head injury in rat glaucoma. Invest Ophthalmol Vis Sci 52(1):504–518. https://doi.org/10.1167/iovs.10-5317
Johnson EC, Jia L, Cepurna WO, Doser TA, Morrison JC (2007) Global changes in optic nerve head gene expression after exposure to elevated intraocular pressure in a rat glaucoma model. Invest Ophthalmol Vis Sci 48(7):3161–3177. https://doi.org/10.1167/iovs.06-1282
Ahmed F, Brown KM, Stephan DA, Morrison JC, Johnson EC, Tomarev SI (2004) Microarray analysis of changes in mRNA levels in the rat retina after experimental elevation of intraocular pressure. Invest Ophthalmol Vis Sci 45(4):1247–1258
Andreeva K, Zhang M, Fan W, Li X, Chen Y, Rebolledo-Mendez JD, Cooper NG (2014) Time-dependent gene profiling indicates the presence of different phases for ischemia/reperfusion injury in retina. Ophthalmol Eye Dis 6:43–54. https://doi.org/10.4137/OED.S17671
Guo Y, Cepurna WO, Dyck JA, Doser TA, Johnson EC, Morrison JC (2010) Retinal cell responses to elevated intraocular pressure: a gene array comparison between the whole retina and retinal ganglion cell layer. Invest Ophthalmol Vis Sci 51(6):3003–3018. https://doi.org/10.1167/iovs.09-4663
Panagis L, Zhao X, Ge Y, Ren L, Mittag TW, Danias J (2011) Retinal gene expression changes related to IOP exposure and axonal loss in DBA/2J mice. Invest Ophthalmol Vis Sci 52(11):7807–7816. https://doi.org/10.1167/iovs.10-7063
Steele MR, Inman DM, Calkins DJ, Horner PJ, Vetter ML (2006) Microarray analysis of retinal gene expression in the DBA/2J model of glaucoma. Invest Ophthalmol Vis Sci 47(3):977–985. https://doi.org/10.1167/iovs.05-0865
Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, Szczesniak MW, Gaffney DJ, Elo LL, Zhang X, Mortazavi A (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17:13. https://doi.org/10.1186/s13059-016-0881-8
Korpelainen E, Tuimala J, Somervuo P, Huss M, Wong G (2015) RNA-seq data analysis: a practical approach. Chapman & Hall/CRC mathematical and computational biology. CRC Press, Boca Raton
Huang da W, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4(1):44–57. https://doi.org/10.1038/nprot.2008.211
Huang da W, Sherman BT, Lempicki RA (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37(1):1–13. https://doi.org/10.1093/nar/gkn923
Robinson MD, Oshlack A (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11(3):R25. https://doi.org/10.1186/gb-2010-11-3-r25
Acknowledgments
The National Institutes of Health Grants: 3 R01EY010145-17S1 (DCL); The US-UK Fulbright Commission in conjunction with Fight for Sight; The Special Trustees of Moorfields Eye Hospital (in conjunction with the National Institute for Health Research award to Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology for a Biomedical Research Centre for Ophthalmology) (HJ); R01EY010145 (JCM); P30EY010572 (OHSU Core Grant); and an unrestricted grant from Research to Prevent Blindness (RPB), Inc. JCM is a past RPB Senior Investigator. Short read sequencing assays were performed by the Oregon Health & Science University Massively Parallel Sequencing Shared Resource.
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Lozano, D.C., Choi, D., Jayaram, H., Morrison, J.C., Johnson, E.C. (2018). Utilizing RNA-Seq to Identify Differentially Expressed Genes in Glaucoma Model Tissues, Such as the Rodent Optic Nerve Head. In: Jakobs, T. (eds) Glaucoma. Methods in Molecular Biology, vol 1695. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7407-8_20
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DOI: https://doi.org/10.1007/978-1-4939-7407-8_20
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