Transcriptome analysis on monocytes from patients with neovascular age-related macular degeneration

Scientific Reports

Volumn 6, Issue , 2016, Pages

  Grunin, Michelle ^a   Shira Hagbi Levi, ^a   Rinsky, Batya ^a   Smith, Yoav ^b   Chowers, Itay ^a  

^a HADASSAH HEBREW UNIVERSITY MEDICAL CENTER   (Israel)

^b HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

CHEMOKINE RECEPTOR; TRANSCRIPTOME;

AGED; BLOOD; FEMALE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENETICS; HUMAN; LEUKOCYTE; MACULAR DEGENERATION; MALE; METABOLISM; MIDDLE AGED; MONOCYTE; PATHOLOGY; PATHOPHYSIOLOGY; PHAGOCYTE; RETINA; SUBRETINAL NEOVASCULARIZATION; VERY ELDERLY; WET MACULAR DEGENERATION;

AGED; AGED, 80 AND OVER; CHOROIDAL NEOVASCULARIZATION; FEMALE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; HUMANS; LEUKOCYTES; MACULAR DEGENERATION; MALE; MIDDLE AGED; MONOCYTES; PHAGOCYTES; RECEPTORS, CHEMOKINE; RETINA; TRANSCRIPTOME; WET MACULAR DEGENERATION;

EID: 84977575839     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep29046     Document Type: Article

References (76)

1. Hearps, A. C., et al. Aging is associated with chronic innate immune activation and dysregulation of monocyte phenotype and function. Aging Cell 11, 867-875 (2012)
2. Grunin, M., Burstyn-Cohen, T., Hagbi-Levi, S., Peled, A., & Chowers, I. Chemokine receptor expression in peripheral blood monocytes from patients with neovascular age-related macular degeneration. Invest. Ophthalmol. Vis. Sci. 53, 5292-300 (2012)
3. Kramer, M., et al. Monocyte chemoattractant protein-1 in the aqueous humour of patients with age-related macular degeneration. Clin Exp. Ophthalmol 40, 617-625 (2012)
4. Sene, A., et al. Impaired cholesterol efflux in senescent macrophages promotes age-related macular degeneration. Cell Metab 17, 549-561 (2013)
5. Ambati, J., et al. An animal model of age-related macular degeneration in senescent Ccl-2- or Ccr-2-deficient mice. Nat Med 9, 1390-1397 (2003)
6. Espinosa-Heidmann, D. G., et al. Macrophage depletion diminishes lesion size and severity in experimental choroidal neovascularization. Invest Ophthalmol Vis Sci 44, 3586-3592 (2003)
7. Thornton, M. R., Mantovani, C., Birchall, M. A., & Terenghi, G. Quantification of N-CAM and N-cadherin expression in axotomized and crushed rat sciatic nerve. J Anat 206, 69-78 (2005)
8. Sennlaub, F., et al. CCR2+ monocytes infiltrate atrophic lesions in age-related macular disease and mediate photoreceptor degeneration in experimental subretinal inflammation in Cx3cr1 deficient mice. EMBO Mol. Med. 5, 1775-1793 (2013)
9. Sene, A., et al. Impaired cholesterol efflux in senescent macrophages promotes age-related macular degeneration. Cell Metab. 17, 549-561 (2013)
10. Guymer, R. H., et al. Identification of urinary biomarkers for age-related macular degeneration. Invest. Ophthalmol. Vis. Sci. 52, 4639-4644 (2011)
11. Kyger, M., Worley, A., & Adamus, G. Autoimmune responses against photoreceptor antigens during retinal degeneration and their role in macrophage recruitment into retinas of RCS rats. J Neuroimmunol 254, 91-100 (2013)
12. Seidler, S., Zimmermann, H. W., Bartneck, M., Trautwein, C., & Tacke, F. Age-dependent alterations of monocyte subsets and monocyte-related chemokine pathways in healthy adults. BMC Immunol 11, 30 (2010)
13. Strohacker, K., Breslin, W. L., Carpenter, K. C., & McFarlin, B. K. Aged mice have increased inflammatory monocyte concentration and altered expression of cell-surface functional receptors. J. Biosci. 37, 55-62 (2012)
14. Fritsche, L. G., et al. Seven new loci associated with age-related macular degeneration. Nat. Genet. 45, 433-9, 439e1-2 (2013)
15. Francis, P. J., Hamon, S. C., Ott, J., Weleber, R. G., & Klein, M. L. Polymorphisms in C2, CFB and C3 are associated with progression to advanced age related macular degeneration associated with visual loss. J Med Genet 46, 300-307 (2009)
16. Seddon, J. M., et al. Rare variants in CFI, C3 and C9 are associated with high risk of advanced age-related macular degeneration. Nat. Genet. 45, 1366-1370 (2013)
17. Singh, A., et al. Altered expression of CD46 and CD59 on leukocytes in neovascular age-related macular degeneration. Am J Ophthalmol 154, 193-199 e2 (2012)
18. Rutar, M., Natoli, R., Chia, R., Valter, K., & Provis, J. M. Chemokine-mediated inflammation in the degenerating retina is coordinated by Müller cells, activated microglia, and retinal pigment epithelium. J. Neuroinflammation 12, 1-15 (2015)
19. Geissmann, F., Jung, S., & Littman, D. R. Blood monocytes consist of two principal subsets with distinct migratory properties. Immunity 19, 71-82 (2003)
20. Ziegler-Heitbrock, H. W., & Ziegler-Heitbrock, H. W. Definition of human blood monocytes. J Leukoc Biol 67, 603-606 (2000)
21. Ziegler-Heitbrock, L. Monocyte subsets in man and other species. Cell. Immunol. 289, 135-139 (2014)
22. Ziegler-Heitbrock, L. The CD14+ CD16+ blood monocytes: their role in infection and inflammation. J Leukoc Biol 81, 584-592 (2007)
23. Frankenberger, M., et al. Transcript profiling of CD16-positive monocytes reveals a unique molecular fingerprint. Eur J Immunol 42, 957-974 (2012)
24. Lederman, M., Weiss, A., & Chowers, I. Association of neovascular age-related macular degeneration with specific gene expression patterns in peripheral white blood cells. Invest. Ophthalmol. Vis. Sci. 51, 53-58 (2010)
25. Singh, A., Falk, M. K., Hviid, T. V. F., & Sørensen, T. L. Increased expression of cd200 on circulating cd11b+ monocytes in patients with neovascular age-related macular degeneration. Ophthalmology 120, 1029-1037 (2013)
26. Benjamini, Y., & Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Ser. B 57, 289-300 (1995)
27. Shamir, R., et al. EXPANDER-An integrative program suite for microarray data analysis. BMC Bioinformatics 6, 232 (2005)
28. Shi, J., & Walker, M. G. Gene Set Enrichment Analysis (GSEA) for Interpreting Gene Expression Profiles. Current Bioinformatics 2, 133-137 (2007)
29. Dennis, G., et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 4, P3 (2003)
30. Chen, E. Y., et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013)
31. Shay, T., & Kang, J. Immunological Genome Project and systems immunology. Trends in Immunology 34, 602-609 (2013)
32. Rickman, C. B., et al. Defining the human macula transcriptome and candidate retinal disease genes using EyeSAGE. Investig. Ophthalmol. Vis. Sci. 47, 2305-2316 (2006)
33. Liu, Y., et al. Serial analysis of gene expression (SAGE) in normal human trabecular meshwork. Mol. Vis. 17, 885-893 (2011)
34. Karolchik, D., et al. The UCSC Table Browser data retrieval tool. Nucleic Acids Res 32, D493-6 (2004)
35. Balwierz, P. J., et al. ISMARA: automated modeling of genomic signals as a democracy of regulatory motifs. Genome Res. 24, 869-884 (2014)
36. Kent, W. J., et al. The human genome browser at UCSC. Genome Res 12, 996-1006 (2002)
37. Bailey, T. L., et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res 37, W202-8 (2009)
38. Wrigley, B. J., Lip, G. Y., & Shantsila, E. The role of monocytes and inflammation in the pathophysiology of heart failure. Eur J Hear. Fail 13, 1161-1171 (2011)
39. Zuccolo, J., et al. Phylogenetic analysis of the MS4A and TMEM176 gene families. PLoS One 5, 1-10 (2010)
40. Condamine, T., et al. Tmem176B and Tmem176A are associated with the immature state of dendritic cells. J. Leukoc. Biol. 88, 507-515 (2010)
41. Cuajungco, M. P., et al. Abnormal accumulation of human transmembrane (TMEM)-176A and 176B proteins is associated with cancer pathology. Acta Histochem. 114, 705-712 (2012)
42. Segovia, M., et al. Autologous dendritic cells prolong allograft survival through Tmem176b-dependent antigen cross-presentation. Am. J. Transplant. 14, 1021-1031 (2014)
43. Melchior, B., et al. Dual induction of TREM2 and tolerance-related transcript, Tmem176b, in amyloid transgenic mice: implications for vaccine-based therapies for Alzheimer's disease. ASN Neuro 2, e00037 (2010)
44. Maeda, Y., et al. Role of Clast1 in development of cerebellar granule cells. Brain Res. 1104, 18-26 (2006)
45. Reese, B. E. Development of the Retina and Optic Pathway. Vision Res. 51, 613-632 (2010)
46. Kasukawa, T., et al. Quantitative expression profile of distinct functional regions in the adult mouse brain. PLoS One 6, e23228 (2011)
47. Lein, E. S., et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168-176 (2007)
48. Rutella, S., et al. Hepatocyte growth factor favors monocyte differentiation into regulatory interleukin (IL)-10+ + IL-12low/neg accessory cells with dendritic-cell features. Blood 108, 218-227 (2006)
49. Cavigelli, M., et al. The tumor promoter arsenite stimulates AP-1 activity by inhibiting a JNK phosphatase. EMBO J. 15, 6269-6279 (1996)
50. Hong, S., Skaist, A. M., Wheelan, S. J., & Friedman, A. D. AP-1 protein induction during monopoiesis favors C/EBP: AP-1 heterodimers over C/EBP homodimerization and stimulates FosB transcription. Journal of Leukocyte Biology 90, 643-651 (2011)
51. Wenzel, A., Grimm, C., Samardzija, M., & Remé, C. E. The genetic modifier Rpe65Leu450: Effect on light damage susceptibility in c-Fos-deficient mice. Investig. Ophthalmol. Vis. Sci. 44, 2798-2802 (2003).
52. Chaum, E., Yin, J., Yang, H., Thomas, F., & Lang, J. C. Quantitative AP-1 gene regulation by oxidative stress in the human retinal pigment epithelium. J. Cell. Biochem. 108, 1280-1291 (2009)
53. Syrovets, T., Jendrach, M., Rohwedder, A., Schüle, A., & Simmet, T. Plasmin-induced expression of cytokines and tissue factor in human monocytes involves AP-1 and IKKβ -mediated NF-? B activation. Blood 97, 3941-3950 (2001)
54. Nakamura, R., et al. IL10-driven STAT3 signalling in senescent macrophages promotes pathological eye angiogenesis. Nat. Commun. 6, 7847 (2015)
55. Strunnikova, N. V., et al. Transcriptome analysis and molecular signature of human retinal pigment epithelium. Hum. Mol. Genet. 19, 2468-2486 (2010)
56. Kozhevnikova, O. S., Korbolina, E. E., Ershov, N. I., & Kolosova, N. G. Rat retinal transcriptome: Effects of aging and AMD-like retinopathy. Cell Cycle 12, 1745-1761 (2013)
57. Huang, E., & Wells, C. A. The Ground State of Innate Immune Responsiveness Is Determined at the Interface of Genetic, Epigenetic, and Environmental Influences. J. Immunol. 193, 13-19 (2014)
58. Merino, A., et al. Senescent CD14+ CD16+ monocytes exhibit proinflammatory and proatherosclerotic activity. J Immunol 186, 1809-1815 (2011)
59. Luhmann, U. F., et al. The drusenlike phenotype in aging Ccl2-knockout mice is caused by an accelerated accumulation of swollen autofluorescent subretinal macrophages. Invest Ophthalmol Vis Sci 50, 5934-5943 (2009)
60. Italiani, P., et al. Transcriptomic profiling of the development of the inflammatory response in human monocytes in vitro e87680. PLoS One 9, (2014)
61. Age-Related Eye Disease Study Research, G. The Age-Related Eye Disease Study (AREDS): design implications. AREDS report no. 1. Control Clin Trials 20, 573-600 (1999)
62. Bustin, S. A., et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem 55, 611-622 (2009)
63. Gautier, L., Cope, L., Bolstad, B. M., & Irizarry, R. A. Affy-Analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 307-315 (2004)
64. Carvalho, B S., & Irizarry, R. A. A framework for oligonucleotide microarray preprocessing. Bioinformatics 26 2363-2367 (2010)
65. Smyth, G In Bioinformatics and Computational Biology Solutions Using R and Bioconductor 397-420doi: citeulike-Article-id: 5722720 (2005)
66. Wilson, C. L., & Miller, C. J. Simpleaffy: A BioConductor package for Affymetrix Quality Control and data analysis. Bioinformatics 21, 3683-3685 (2005)
67. Sanges, R., Cordero, F., & Calogero, R. A. oneChannelGUI: A graphical interface to Bioconductor tools, designed for life scientists who are not familiar with R language. Bioinformatics 23, 3406-3408 (2007)
68. Quackenbush, J. Microarray data normalization and transformation. Nat. Genet. 32 Suppl, 496-501 (2002)
69. Irizarry, R. A., et al. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31, e15 (2003)
70. Ashburner, M., et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25-29 (2000)
71. Liberzon, A., et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739-1740 (2011)
72. Bryne, J. C., et al. JASPAR, the open access database of transcription factor-binding profiles: new content and tools in the 2008 update. Nucleic Acids Res 36, D102-6 (2008)
73. Kozomara, A., & Griffiths-Jones, S. miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res 39, D152-7 (2011)
74. Vergoulis, T., et al. TarBase 6.0: capturing the exponential growth of miRNA targets with experimental support. Nucleic Acids Res 40, D222-9 (2012)
75. Riggins, G. J., & Strausberg, R. L. Genome and genetic resources from the Cancer Genome Anatomy Project. Hum. Mol. Genet. 10, 663-667 (2001)
76. Farkas, M. H., et al. Transcriptome analyses of the human retina identify unprecedented transcript diversity and 3.5 Mb of novel transcribed sequence via significant alternative splicing and novel genes. BMC Genomics 14, 486 (2013)