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. 2017 May;187(5):589-598.
doi: 10.1667/RR4554.1. Epub 2017 Mar 20.

Late Effects of Exposure to Ionizing Radiation and Age on Human Thymus Morphology and Function

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Late Effects of Exposure to Ionizing Radiation and Age on Human Thymus Morphology and Function

Reiko Ito et al. Radiat Res. 2017 May.

Abstract

The thymus is essential for proper development and maintenance of a T-cell repertoire that can respond to newly encountered antigens, but its function can be adversely affected by internal factors such as pregnancy and normal aging or by external stimuli such as stress, infection, chemotherapy and ionizing radiation. We have utilized a unique archive of thymus tissues, obtained from 165 individuals, exposed to the 1945 atomic bomb blast in Hiroshima, to study the long-term effects of receiving up to ∼3 Gy dose of ionizing radiation on human thymus function. A detailed morphometric analysis of thymus activity and architecture in these subjects at the time of their natural deaths was performed using bright-field immunohistochemistry and dual-color immunofluorescence and compared to a separate cohort of nonexposed control subjects. After adjusting for age-related effects, increased hallmarks of thymic involution were observed histologically in individuals exposed to either low (5-200 mGy) or moderate-to-high (>200 mGy) doses of ionizing radiation compared to unirradiated individuals (<5 mGy). Sex-related differences were seen when the analysis was restricted to individuals under 60 years of attained age at sample collection, but were not observed when comparing across the entire age range. This indicates that while females undergo slower involution than males, they ultimately attain similar phenotypes. These findings suggest that even low-dose-radiation exposure can accelerate thymic aging, with decreased thymopoiesis relative to nonexposed controls evident years after exposure. These data were used to develop a model that can predict thymic function during normal aging or in individuals therapeutically or accidentally exposed to radiation.

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Figures

FIG 1
FIG 1
Representative histology of thymus tissues with mild vs. severe involution. Tissues varied widely in their percentage area containing lymphocytes (as defined by H&E stain; panels A and D), thymic epithelium (TE area, as defined by CK stain; panels B and E) and immature thymocytes (cortical area, as defined by CD1a stain; panels C and F). Mild involution is shown in thymus from a 28-year-old male who was 15 years old at time of exposure to <10 mGy (panels A–C). The thymus from a 76-year-old male who was not exposed to radiation shows severe involution with rare lymphocytes (panel D), thin strips of thymic epithelium (panel E) and complete lack of CD1a-positive immature thymocytes (panel F). Scale bar represents 0.2 mm. Cort = cortex; M = medulla; HB = Hassall body; P = perivascular space, a region outside of the thymic epithelial network that contains adipose tissue, vessels and peripheral lymphocytes rather than developing thymocytes.
FIG 2
FIG 2
Changes in thymus morphology and architecture with aging and radiation exposure. Changes in percentage lymphoid (panels A–D), percentage TE (panels E– H) and percentage cortical areas (panels I–L) are shown for the following cohorts of subjects: Panels A, E, I: Duke nonexposed; Panels B, F, J: RERF nonexposed; Panels C, G, K: RERF low dose; Panels D, H, L: RERF; moderate-to-high dose. In these scatterplots, each point represents a single subject and fitted lines are based on loess regression lines.
FIG 3
FIG 3
Predicting thymopoiesis based on age and radiation exposure. Prediction probabilities for nonzero cortical area (percentage cortical area >0) based on attained age at sample collection and radiation dose group and the interaction between these covariates. Solid lines represent predicted values for the indicated dose groups; dotted lines represent the 95% confidence intervals. The form of this model was nonzero cortical area ~β0 + β1 age + β2 dose group + β3 age × dose group (interaction) and was analyzed using a multivariable logistic regression model.

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