Global metabolomics reveals metabolic dysregulation in ischemic retinopathy

Metabolomics

Volumn 12, Issue 1, 2016, Pages 1-10

  Paris, Liliana P ^a   Johnson, Caroline H ^a   Aguilar, Edith ^a   Usui, Yoshihiko ^a,b   Cho, Kevin ^c   Hoang, Lihn T ^a   Feitelberg, Daniel ^a   Benton, H Paul ^a   Westenskow, Peter D ^a,d   Kurihara, Toshihide ^a   Trombley, Jennifer ^a,d   Tsubota, Kinya ^b   Ueda, Shunichiro ^b   Wakabayashi, Yoshihiro ^b   Patti, Gary J ^c   Ivanisevic, Julijana ^a   Siuzdak, Gary ^a   Friedlander, Martin ^a,d  

^a SCRIPPS RESEARCH INSTITUTE   (United States)

^b TOKYO MEDICAL UNIVERSITY   (Japan)

^c WASHINGTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d Division of Basic Science   (United States)

Author keywords

Arginine metabolism; Pathway enrichment analysis; Proliferative diabetic retinopathy; Untargeted metabolomics

Indexed keywords

ACYLCARNITINE; ARGININE;

ANIMAL EXPERIMENT; ANIMAL MODEL; ANIMAL TISSUE; ARTICLE; CLINICAL ARTICLE; CONTROLLED STUDY; DOWN REGULATION; HUMAN; METABOLIC REGULATION; METABOLOMICS; MOUSE; NONHUMAN; OXYGEN-INDUCED RETINOPATHY; PHACOEMULSIFICATION; PROLIFERATIVE DIABETIC RETINOPATHY; PURINE METABOLISM; REVERSED PHASE HIGH PERFORMANCE LIQUID CHROMATOGRAPHY; TIME OF FLIGHT MASS SPECTROMETRY; VITRECTOMY; VITREOUS BODY;

EID: 84947432371     PISSN: 15733882     EISSN: 15733890     Source Type: Journal    
DOI: 10.1007/s11306-015-0877-5     Document Type: Article

References (24)

1. Bringmann, A., et al. (2006). Muller cells in the healthy and diseased retina. Progress Retinal Eye Research,25, 397–424. doi:10.1016/j.preteyeres.2006.05.003.
2. Cheung, N., Wong, I. Y., & Wong, T. Y. (2014). Ocular anti-VEGF therapy for diabetic retinopathy: overview of clinical efficacy and evolving applications. Diabetes Care,37, 900–905. doi:10.2337/dc13-1990.
3. Das, A., Stroud, S., Mehta, A., & Rangasamy, S. (2015). New treatments for diabetic retinopathy. Diabetes, Obesity & Metabolism,17, 219–230. doi:10.1111/dom.12384.
4. Ding, J., & Wong, T. Y. (2012). Current epidemiology of diabetic retinopathy and diabetic macular edema. Current Diabetic Reports,12, 346–354. doi:10.1007/s11892-012-0283-6.
5. Fong, D. S., Ferris, F. L, 3rd, Davis, M. D., & Chew, E. Y. (1999). Causes of severe visual loss in the early treatment diabetic retinopathy study: ETDRS report no. 24. Early treatment diabetic retinopathy study research group. American Journal of Ophthalmology,127, 137–141.
6. Georgalas, I., Papaconstantinou, D., Papadopoulos, K., Pagoulatos, D., Karagiannis, D., & Koutsandrea, C. (2014). Renal injury following intravitreal anti-VEGF administration in diabetic patients with proliferative diabetic retinopathy and chronic kidney disease-a possible side effect? Current Drug Safety,9(2), 156–158.
7. Ivanisevic, J., et al. (2013). Toward ‘omic scale metabolite profiling: a dual separation-mass spectrometry approach for coverage of lipid and central carbon metabolism. Analytical Chemistry,85, 6876–6884. doi:10.1021/ac401140h.
8. Lains, I., et al. (2014). Choroidal thickness in diabetic retinopathy: the influence of antiangiogenic therapy. Retina,34, 1199–1207. doi:10.1097/iae.0000000000000053.
9. Metea, M. R., & Newman, E. A. (2006). Glial cells dilate and constrict blood vessels: a mechanism of neurovascular coupling. Journal of Neuroscience,26, 2862–2870. doi:10.1523/jneurosci.4048-05.2006.
10. Narayanan, S. P., Rojas, M., Suwanpradid, J., Toque, H. A., Caldwell, R. W., & Caldwell, R. B. (2013). Arginase in retinopathy. Progress Retinal Eye Research,36, 260–280. doi:10.1016/j.preteyeres.2013.06.002.
11. Narayanan, S. P., et al. (2011). Arginase 2 deletion reduces neuro-glial injury and improves retinal function in a model of retinopathy of prematurity. PLoS One,6, e22460. doi:10.1371/journal.pone.0022460.
12. Narayanan, S. P., et al. (2014). Arginase 2 deficiency reduces hyperoxia-mediated retinal neurodegeneration through the regulation of polyamine metabolism. Cell Death and Disease,5, e1075. doi:10.1038/cddis.2014.23.
13. Patel, C., et al. (2013). Arginase as a mediator of diabetic retinopathy. Frontiers in Immunology,4, 173. doi:10.3389/fimmu.2013.00173.
14. Patti, G. J., et al. (2012). Metabolomics implicates altered sphingolipids in chronic pain of neuropathic origin. Nature Chemical Biology,8, 232–234. doi:10.1038/nchembio.767.
15. Patti, G. J., et al. (2013). A view from above: Cloud plots to visualize global metabolomic data. Analytical Chemistry,85, 798–804. doi:10.1021/Ac3029745.
16. Roberts, L. D., Koulman, A., & Griffin, J. L. (2014). Towards metabolic biomarkers of insulin resistance and type 2 diabetes: progress from the metabolome. Lancet Diabetes Endocrinology,2, 65–75. doi:10.1016/s2213-8587(13)70143-8.
17. Robinson, R., Barathi, V. A., Chaurasia, S. S., Wong, T. Y., & Kern, T. S. (2012). Update on animal models of diabetic retinopathy: from molecular approaches to mice and higher mammals. Disease Model Mechanisms,5, 444–456. doi:10.1242/dmm.009597.
18. Smith, L. E., et al. (1994). Oxygen-induced retinopathy in the mouse. Investigative Ophthalmology & Visual Science,35, 101–111.
19. Stahl, A., et al. (2010). The mouse retina as an angiogenesis model. Investigative Ophthalmology & Visual Science,51, 2813–2826. doi:10.1167/iovs.10-5176.
20. Storey, J. D. (2002). A direct approach to false discovery rates. Journal of the Royal Statistical Society Series B-Statistical Methodology,64, 479–498.
21. Storey, J. D., & Tibshirani, R. (2003). Statistical significance for genomewide studies. Proceedings National Academy Science USA,100, 9440–9445. doi:10.1073/pnas.1530509100.
22. Tautenhahn, R., Patti, G. J., Rinehart, D., & Siuzdak, G. (2012). XCMS online: a web-based platform to process untargeted metabolomic data. Analytical Chemistry,84, 5035–5039. doi:10.1021/ac300698c.
23. Xia, J. G., Psychogios, N., Young, N., & Wishart, D. S. (2009). MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Research,37, W652–W660. doi:10.1093/Nar/Gkp356.
24. Zheng, Y., He, M., & Congdon, N. (2012). The worldwide epidemic of diabetic retinopathy. Indian Journal of Ophthalmology,60, 428–431. doi:10.4103/0301-4738.100542.