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  • It is well known that the tumor suppressor p


    It is well known that the tumor suppressor p53 plays an important role in regulating the AM251 and cellular senescence [43,46,48]. For example, FOXO4 could interact with P53, which selectively induced apoptosis in senescent cells [48]. In addition, loss of MECP2 was found to lead to the induction of P53 and senescence [49]. Cell cycle arrest is a typical feature of cellular senescence, which is a consequence of cross-talk between cyclins and CDKs, and especially, Cyclin D1 and CDK4 are required for the G1/S transition. Our data showed that CAE could dose-dependently down-regulate P53, and its inhibitor PTF-α exhibited an anti-senescence effect based on decreasing SA-β-gal activity, inhibiting P16INK4a and P21, as well as promoting the expression of Cyclin D1 and CDK4. Furthermore, the anti-senescence effect was enhanced when PTF-α was added with CAE together. Therefore, our results indicated that the inhibition of fibroblast senescence by P53 might be a therapeutic target for the inactivation of fibroblasts by CAE. Cellular senescence mediated by P53 involves multiple signaling pathways. However, how to regulate the expression of P53 by CAE in fibroblasts was previously unexplored. Studies have shown that COX-2 was down-regulated in IPF patients and that its activity was required to exert an anti-fibrotic effect on plasminogen activation [26]. In addition, an increase in COX-2 was also required to sensitize fibroblasts to Fas/FasL-induced apoptosis [50]. However, reports regarding the cross-talk of COX-2 and P53 in cellular senescence are conflicting. On the one side, COX-2 is upregulated in fibroblast senescence and P53 is shown to mediate COX-2 overexpression in premature senescence [51,52]. And more, fibroblasts from COX-2 knocked out mice exhibits cellular senescence and COX-2 inhibits senescence via binding P53 and inactivating P53 function [44]. In this study, our results showed that CAE suppressed the expression of P53 through activation of COX-2 in lung fibroblasts, and more CAE activated COX-2 in a dose- and time-dependent manner. Nevertheless, COX-2 RNAi or the COX-2 inhibitors NS-398 and indomethacin promoted P53 expression and diminished the anti-senescence effect of CAE in both PMLFs and MRC-5 cells. Moreover, to assess the activation of COX-2 during lung fibroblast senescence and its crosstalk with P53 in vivo, mice were subjected to intratracheal instillation with LV-COX-2-RNAi. We found that the effect of CAE on mouse pulmonary fibrosis model and its anti-senescence effect were partially abolished, as well as the expression of P53 in lung tissue, suggesting that CAE suppress the expression of P53 through activation of COX-2 in lung fibroblasts. Collectively, our findings elucidated that activation of COX-2 by CAE causally resulted in inhibition of P53, which most likely regulated the cellular senescence both in vitro and in vivo.
    Conclusions To conclude, we explored the effects of CAE on the senescent fibroblasts, and elucidate the underlying mechanism to ameliorate pulmonary fibrosis. Our findings illustrated that CAE regulates lung fibroblast senescence, which is dependent on the COX-2/P53 pathway, suggesting that inhibition of cellular senescence might represent an approach to control pro-fibrotic lung fibroblasts (Fig. 8). These findings also provided evidence that CAE might be potentially developed as a new agent for the treatment of IPF.
    Author contributions
    Funding This work was finically supported by the National Nature Science Foundation of China (grant number 81673936) and Postgraduate Research & Practice Innovation Program of Jiangsu Province (grant number KYCX17_1311).
    Conflict of interest
    Introduction The proportional hazards models, also known as the Cox regression models (Cox, 1972, Cox, 1975), are widely used to identify the risk factors given time to event data. Optimal model selection and estimation procedures are desired to correctly select the true risk factors and yield efficient estimators for regression coefficients. The model selection techniques for linear regression models, such as best-subset selection, stepwise deletion, Akaike information criterion (AIC) and Bayesian information criterion (BIC), can be easily extended for the Cox models. Faraggi and Simon (1998) and Ibrahim et al. (1999) discussed Bayesian variable selection and Sauerbrei and Schumacher (1992) proposed a bootstrap procedure for the Cox model. From the vantage point of penalized regression, Tibshirani (1997) and Fan and Li (2002) extended the least absolute shrinkage and selection operator (LASSO) and the smoothly clipped absolute deviation (SCAD) methods, firstly proposed in Tibshirani (1996) and Fan and Li (2001), respectively. Zhang and Lu (2007) investigated the adaptive LASSO method, where the penalty associated with each coefficient is weighted by its magnitude. Both the LASSO and the adaptive LASSO methods have a convex form for the ease of global optimization, but the adaptive LASSO also enjoys the oracle property as the SCAD method did. All the above mentioned penalized likelihoods for the sparse Cox models are defined upon the partial likelihood function. Many other studies were related to penalized likelihood estimations, including Zucker and Karr (1990), Gu (1996) and Goeman (2010). More recently with the motivation to handle ultra high-dimensional data, novel statistical methods for censored data have been developed from a variety of perspectives, such as quantile regression by He et al. (2013), accelerated failure time models by Xia et al. (2016) and survival impact index by Li et al. (2016).