Effects of aging and environmental tobacco smoke exposure on ocular and plasma circulatory microRNAs in the Rhesus macaque

Abstract

Purpose: To identify changes induced by environmental tobacco smoke (ETS) in circulatory microRNA (miRNA) in plasma and ocular fluids of the Rhesus macaque and compare these changes to normal age-related changes. Tobacco smoke has been identified as the leading environmental risk factor for age-related macular degeneration (AMD).

Methods: All Rhesus macaques were housed at the California National Primate Research Center (CNPRC), University of California, Davis. Four groups of animals were used: Group 1 (1-3 years old), Group 2 (19-28 years old), Group 3 (10-16 years old), and Group 4 (middle aged, 9-14 years old). Group 4 was exposed to smoke for 1 month. Ocular fluids and plasma samples were collected, miRNAs isolated, and expression data obtained using Affymetrix miRNA GeneTitan Array Plates 4.0. Bioinformatics analysis was done on the Affymetrix Expression Console (EC), Transcriptome Analysis Software (TAS) using ANOVA for candidate miRNA selection, followed by Ingenuity Pathway Analysis (IPA).

Results: The expression of circulatory miRNAs showed statistically significant changes with age and ETS. In the plasma samples, 45 miRNAs were strongly upregulated (fold change >±1.5, p<0.05) upon ETS exposure. In the vitreous, three miRNAs were statistically significantly downregulated with ETS, and two of them (miR-6794 and miR-6790) were also statistically significantly downregulated with age. Some retinal layers exhibited a thinning trend measured with optical coherence tomography (OCT) imaging. The pathways activated were IL-17A, VEGF, and recruitment of eosinophils, Th2 lymphocytes, and macrophages.

Conclusions: ETS exposure of Rhesus macaques resulted in statistically significant changes in the expression of the circulatory miRNAs, distinct from those affected by aging. The pathways activated appear to be common for ETS and AMD pathogenesis. These data will be used to develop an animal model of early dry AMD.

Figure 1

Imaging of the Rhesus macaque eye pre- and post-cigarette smoke exposure. A: Hematoxylin and eosin (H&E) staining showing Rhesus macaque retina layers. B: Representative spectral domain optical coherence tomography (SD-OCT) image using the enhanced depth imaging (EDI) mode to optimize visualization of the choroid, showing comparable retinal layers. C: SD-OCT image of the Rhesus macaque fovea region before smoke exposure. D: SD-OCT of the same eye post-exposure. Digital image segmentation of the same eye preexposure performed on the line scan closest to the foveal center, defined as the center of the foveal pit with the greatest separation between the IS/OS junction and the RPE layer. E: Digital images of SD-OCT images were semiautomatically segmented using the Duke Optical Coherence Tomography Retinal Analysis Program (DOCTRAP, version 62.0), a custom image analysis software designed using MATLAB (Mathworks). F: Digital image segmentation of the same eye post-exposure. G: Fundus image of the Rhesus macaque eye. NFL, Nerve fiber layer; GCL, Ganglion cell layer; IPL, Inner plexiform layer; INL, Inner nuclear layer; OPL, Outer plexiform layer; ONL, Outer nuclear layer; IS, Photoreceptor inner segments; OS, Photoreceptor outer segments; RPE, Retinal pigment epithelium; CC, Choriocapillaris; C, Choroid.

Figure 2

Comparison of BioAnalyzer RNA profiles of circulatory microRNAs and small RNAs in Rhesus macaque ocular fluids and plasma. RNA profiles have the front labeled with a fluorescent marker (black arrow) and microRNA peak (red arrow): aqueous humor (A), vitreous humor (B), and plasma (C).

Figure 3

Differential expression of circulatory microRNAs in Rhesus macaques exposed to ETS, compared to controls. Data are presented as fold change (FC) values (−1.5 ≥ FC ≥ 1.5; p<0.05) in the plasma (ETS: n=4, control: n=4; top ten; A) and the vitreous (ETS: n=4, control: n=2; B). Error bars represent the standard deviation (SD) between the samples.

Figure 4

Differential expression of circulatory microRNAs in old compared to young Rhesus macaques. Data are presented as fold change (FC) values (−1.5 ≥ FC ≥ 1.5; p<0.05) of the old animals compared to young animals in the plasma (young: n=4, old: n=4; A), vitreous (young: n=4, old: n=3; B), and aqueous (young: n=4, old: n=4; C). Error bars represent the standard deviation (SD) between the replicate samples.

Figure 5

Pathway Analysis of the smoke-induced microRNAs and their target genes. A: MicroRNAs and gene network targeting IL-17A: AGO2 (argonaute 2, RISC catalytic component); BCL6 (B-cell CLL/lymphoma 6); BRAF (B-Raf proto-oncogene, serine/threonine kinase); DICER1 (dicer 1, RNase III); IL-17A (interleukin-17A); LMNA (lamin A/C); miR-106b-3p (miRNAs w/seed CGCACUG); miR-1180-3p (miRNAs w/seed UUCCGGC); mir-126; miR-127-3p (miRNAs w/seed CGGAUCC); mir-130; miR-151-3p (and other miRNAs w/seed UAGACUG); miR-151-5p (and other miRNAs w/seed CGAGGAG); mir-17; mir-187; miR-187-3p (miRNAs w/seed CGUGUCU); miR-197-3p (and other miRNAs w/seed UCACCAC); mir-221; miR-221-3p (and other miRNAs w/seed GCUACAU); mir-26; mir-28; mir-339; miR-339-3p (miRNAs w/seed GAGCGCC); miR-339-5p (and other miRNAs w/seed CCCUGUC); mir-361; miR-361-5p (miRNAs w/seed UAUCAGA); miR-409-3p (miRNAs w/seed AAUGUUG); miR-487b-3p (miRNAs w/seed AUCGUAC); miR-494-3p (miRNAs w/seed GAAACAU); miR-93-3p (miRNAs w/seed CUGCUGA); NR0B2 (nuclear receptor subfamily 0 group B member 2); RPS15 (ribosomal protein S15); TRIM71 (tripartite motif containing 71); XBP1 (X-box binding protein 1). B: MicroRNAs and gene network targeting VEGF: ADGRG1 (adhesion G protein-coupled receptor G1); calcifediol; FADS2 (fatty acid desaturase 2); FNDC3A (fibronectin type III domain containing 3A); FSH (follicle-stimulating hormone); Insulin; let-7a-5p (and other miRNAs w/seed GAGGUAG); MAP2K1/2; MEK1/2; MKK1/2; miR-125b-5p (and other miRNAs w/seed CCCUGAG); miR-126a-3p (and other miRNAs w/seed CGUACCG); mir-130; miR-155-5p (miRNAs w/seed UAAUGCU); miR-17-5p (and other miRNAs w/seed AAAGUGC); miR-185-5p (and other miRNAs w/seed GGAGAGA); miR-191-5p (and other miRNAs w/seed AACGGAA); miR-210-3p (miRNAs w/seed UGUGCGU); miR-23a-3p (and other miRNAs w/seed UCACAUU), miR-130a*; miR-26a-5p (and other miRNAs w/seed UCAAGUA); miR-1297; miR-30c-5p (and other miRNAs w/seed GUAAACA); mir-363; mir-423; miR-423-3p (miRNAs w/seed GCUCGGU); miR-432 (and other miRNAs w/seed CUUGGAG); miR-532-5p (and other miRNAs w/seed AUGCCUU), miR-6339; miR-92a-3p (and other miRNAs w/seed AUUGCAC), miR-25, miR-32, miR-363, miR-367; SLC38A1 (solute carrier family 38 member 1); Smad2/3 (TGF beta signaling proteins); SMAD6/7 (TGF beta signaling proteins); SYPL1 (synaptophysin like 1); TP53I11 (tumor protein p53 inducible protein 11); TUSC2 (tumor suppressor candidate 2); Vegf (VEGF mRNA). C: Inflammatory pathway - Recruitment and activation of eosinophils, Th2 lymphocytes, and macrophages: Histamine; IL-13 (interleukin-13); IL-4 (interleukin-4); IL-5 (interleukin-5); IL17A (interleukin-17A, CTLA-8); IL17B (interleukin-17B); Leukotrienes; TNFα (tumor necrosis factor α); VEGFA (vascular endothelial growth factor A).