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Involvement of p38-βTrCP-Tristetraprolin-TNFα axis in radiation pneumonitis.

ABSTRACT: Early release of tumor necrosis factor-alpha (TNF-α) during radiotherapy of thoracic cancers plays an important role in radiation pneumonitis, whose inhibition may provide lung radioprotection. We previously reported radiation inactivates Tristetraprolin (TTP), a negative regulator of TNF-α synthesis, which correlated with increased TNF-α release. However, the molecular events involved in radiation-induced TTP inactivation remain unclear. To determine if eliminating Ttp in mice resulted in a phenotypic response to radiation, Ttp-null mice lungs were exposed to a single dose of 15 Gy, and TNF-α release and lung inflammation were analyzed at different time points post-irradiation. Ttp-/- mice with elevated (9.5±0.6 fold) basal TNF-α showed further increase (12.2±0.9 fold, p<0.02) in TNF-α release and acute lung inflammation within a week post-irradiation. Further studies using mouse lung macrophage (MH-S), human lung fibroblast (MRC-5), and exogenous human TTP overexpressing U2OS and HEK293 cells upon irradiation (a single dose of 4 Gy) promoted p38-mediated TTP phosphorylation at the serine 186 position, which primed it to be recognized by an ubiquitin ligase (E3), beta transducing repeat containing protein (β-TrCP), to promote polyubiquitination-mediated proteasomal degradation. Consequently, a serine 186 to alanine (SA) mutant of TTP was resistant to radiation-induced degradation. Similarly, either a p38 kinase inhibitor (SB203580), or siRNA-mediated β-TrCP knockdown, or overexpression of dominant negative Cullin1 mutants protected TTP from radiation-induced degradation. Consequently, SB203580 pretreatment blocked radiation-induced TNF-α release and radioprotected macrophages. Together, these data establish the involvement of the p38-βTrCP-TTP-TNFα signaling axis in radiation-induced lung inflammation and identified p38 inhibition as a possible lung radioprotection strategy.

SUBMITTER: Krishnamurthy PM

PROVIDER: S-EPMC5564603 | biostudies-other | 2017 Jul

REPOSITORIES: biostudies-other

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Project description:Objective The study aims to establish and validate an effective CT-based radiation pneumonitis (RP) prediction model using the multiomics method of radiomics and EQD2-based dosiomics. Materials and Methods The study performed a retrospective analysis on 91 nonsmall cell lung cancer patients who received radiotherapy from 2019 to 2021 in our hospital. The patients with RP grade ≥1 were labeled as 1, and those with RP grade < 1 were labeled as 0. The whole lung excluding clinical target volume (lung-CTV) was used as the region of interest (ROI). The radiomic and dosiomic features were extracted from the lung-CTV area's image and dose distribution. Besides, the equivalent dose of the 2 Gy fractionated radiation (EQD2) model was used to convert the physical dose to the isoeffect dose, and then, the EQD2-based dosiomic (eqd-dosiomic) features were extracted from the isoeffect dose distribution. Four machine learning (ML) models, including DVH, radiomics combined with DVH (radio + DVH), radiomics combined with dosiomics (radio + dose), and radiomics combined with eqd-dosiomics (radio + eqdose), were established to construct the prediction model via eleven different classifiers. The fivefold cross-validation was used to complete the classification experiment. The area under the curve (AUC) of the receiver operating characteristics (ROC), accuracy, precision, recall, and F1-score were calculated to assess the performance level of the prediction models. Results Compared with the DVH, radio + DVH, and radio + dose model, the value of the training AUC, accuracy, and F1-score of radio + eqdose was higher, and the difference was statistically significant (p < 0.05). Besides, the average value of the precision and recall of radio + eqdose was higher, but the difference was not statistically significant (p > 0.05). Conclusion The performance of using the ML-based multiomics method of radiomics and eqd-dosiomics to predict RP is more efficient and effective.

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Involvement of p38-βTrCP-Tristetraprolin-TNFα axis in radiation pneumonitis.

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