Targeting senescence-associated fibrotic signatures identifies syringin as an effective modulator of the ATR/CHK1–p53 axis in idiopathic pulmonary fibrosis

Yuyang Wang , Guanghui Yin , Mixing Niu , Wei Wang , Zhenyun Liao , Lei Yang , Chunli Wu , Yu Cao , Kai Gao , Zhaoxing Dong

Chinese Journal of Natural Medicines ›› 2026, Vol. 24 ›› Issue (10) : 1205 -1220.

PDF (13826KB)
Chinese Journal of Natural Medicines ›› 2026, Vol. 24 ›› Issue (10) :1205 -1220. DOI: 10.1016/S1875-5364(26)61215-0
Original article
research-article
Targeting senescence-associated fibrotic signatures identifies syringin as an effective modulator of the ATR/CHK1–p53 axis in idiopathic pulmonary fibrosis
Author information +
History +
PDF (13826KB)

Abstract

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal interstitial lung disease closely associated with aging, yet the contribution of cellular senescence to fibrotic remodeling and its therapeutic implications remain incompletely understood. Syringin, a phenylpropanoid glycoside derived from Acanthopanax senticosus, has been reported to possess anti-inflammatory and antioxidant activities, but its role in IPF has not been elucidated. In this study, bulk lung transcriptomic data from the GTEx project and nine IPF cohorts were integrated with IPF scRNA-seq data to characterize senescence-associated fibrotic programs. A systems-level compound prioritization strategy incorporating reversed gene expression scoring, network proximity analysis, drug-likeness and toxicity evaluation was applied to identify candidate molecules. The results revealed a pronounced aging-associated transcriptomic profile in IPF lungs, with strong concordance between age-related and IPF-related gene programs. Single-cell analyses further identified senescent fibroblast populations as key cellular hubs characterized by elevated expression of SASP factors. Syringin was subsequently validated in TGFβ1–stimulated fibroblasts, and a murine model of bleomycin-induced pulmonary fibrosis. Syringin consistently attenuated fibroblast activation, cellular senescence markers, and pulmonary fibrosis in vivo and in vitro. Mechanistically, syringin markedly reversed the activation of replication stress–associated signaling pathways. Molecular docking and CETSA suggested a potential interaction between syringin and CHK1. Furthermore, CHK1 knockdown partially abrogated the regulatory effects of syringin on the ATR/CHK1–p53 signaling cascade, supporting CHK1 as a functional mediator of syringin activity. Collectively, these findings identify syringin as a natural small molecule with anti-senescent and anti-fibrotic properties, and may represent a potential therapeutic strategy for IPF.

Keywords

ATR/CHK1–p53 pathway / Idiopathic pulmonary fibrosis / Syringin / Cell senescence

Cite this article

Download citation ▾
Yuyang Wang, Guanghui Yin, Mixing Niu, Wei Wang, Zhenyun Liao, Lei Yang, Chunli Wu, Yu Cao, Kai Gao, Zhaoxing Dong. Targeting senescence-associated fibrotic signatures identifies syringin as an effective modulator of the ATR/CHK1–p53 axis in idiopathic pulmonary fibrosis. Chinese Journal of Natural Medicines, 2026, 24 (10) : 1205-1220 DOI:10.1016/S1875-5364(26)61215-0

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Moss BJ, Ryter SW, Rosas IO . Pathogenic mechanisms underlying idiopathic pulmonary fibrosis. Annu Rev Pathol. 2022; 17:515-546. https://doi.org/10.1146/annurev-pathol-042320-030240.

[2]

Liu GY, Budinger GRS, Dematte JE. Advances in the management of idiopathic pulmonary fibrosis and progressive pulmonary fibrosis. BMJ. 2022; 377:e066354. https://doi.org/10.1136/bmj-2021-066354.

[3]

Karampitsakos T, Juan-Guardela BM, Tzouvelekis A, et al. Precision medicine advances in idiopathic pulmonary fibrosis. EBioMedicine. 2023; 95:104766. https://doi.org/10.1016/j.ebiom.2023.104766.

[4]

Cho SJ, Stout-Delgado HW . Aging and lung disease. Annu Rev Physiol. 2020; 82:433-459. https://doi.org/10.1146/annurev-physiol-021119-034610.

[5]

Alder JK, Armanios M . Telomere-mediated lung disease. Physiol Rev. 2022; 102(4):1703-1720. https://doi.org/10.1152/physrev.00046.2021.

[6]

Schuliga M, Pechkovsky DV, Read J, et al. Mitochondrial dysfunction contributes to the senescent phenotype of IPF lung fibroblasts. J Cell Mol Med. 2018; 22(12):5847-5861. https://doi.org/10.1111/jcmm.13855.

[7]

Schafer MJ, White TA, Iijima K, et al. Cellular senescence mediates fibrotic pulmonary disease. Nat Commun. 2017; 8:14532. https://doi.org/10.1038/ncomms14532.

[8]

Huang J, Gacha-Garay MJ, Wang Y, et al. Alternative polyadenylation contributes to fibroblast senescence in pulmonary fibrosis. Aging Cell. 2025; 24(10):e70179. https://doi.org/10.1111/acel.70179.

[9]

Liang J, Liu N, Liu X, et al. Mitogen-activated protein kinase-activated protein kinase 2 inhibition attenuates fibroblast invasion and severe lung fibrosis. Am J Respir Cell Mol Biol. 2019; 60(1):41-48. https://doi.org/10.1165/rcmb.2018-0033OC.

[10]

Guan RJ, Yuan L, Li JP, et al. Bone morphogenetic protein 4 inhibits pulmonary fibrosis by modulating cellular senescence and mitophagy in lung fibroblasts. Eur Respir J. 2022; 60(6):2102307. https://doi.org/10.1183/13993003.02307-2021.

[11]

Schuliga M, Read J, Blokland KEC, et al. Self DNA perpetuates IPF lung fibroblast senescence in a cGAS-dependent manner. Clin Sci (Lond). 2020; 134(7):889-905. https://doi.org/10.1042/CS20191160.

[12]

GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013; 45(6):580-585. https://doi.org/10.1038/ng.2653.

[13]

Zhai ZY, Lin ZW, Meng XH, et al. DiSignAtlas: an atlas of human and mouse disease signatures based on bulk and single-cell transcriptomics. Nucleic Acids Res. 2024; 52(D1):D1236-D1245. https://doi.org/10.1093/nar/gkad961.

[14]

Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res. 2013; 41:D991-D995. https://doi.org/10.1093/nar/gks1193.

[15]

Wolock SL, Lopez R, Klein AM . Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Systems. 2019; 8(4):281-291. e9. https://doi.org/10.1016/j.cels.2018.11.005.

[16]

Wang B, Wei W, Long SR, et al. CENPA acts as a prognostic factor that relates to immune infiltrates in gliomas. Front Neurol. 2022; 13:1015221. https://doi.org/10.3389/fneur.2022.1015221.

[17]

Xu SB, Hu EQ, Cai YT, et al. Using clusterProfiler to characterize multiomics data. Nat Protoc. 2024; 19(11):3292-3320. https://doi.org/10.1038/s41596-024-01020-z.

[18]

Wang QN, Lin YS, Yu WG, et al. The core role of macrophages in hepatocellular carcinoma: the definition of molecular subtypes and the prognostic risk system. Front Pharmacol. 2023; 14:1228052. https://doi.org/10.3389/fphar.2023.1228052.

[19]

Tian SS, Zhang JB, Yuan SL, et al. Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM. Brief Bioinform. 2023; 24(2):bbad027. https://doi.org/10.1093/bib/bbad027.

[20]

Gao K, Liu L, Lei S, et al. HERB 2.0: an updated database integrating clinical and experimental evidence for traditional Chinese medicine. Nucleic Acids Res. 2025; 53 (D1):D1404-D1414. https://doi.org/10.1093/nar/gkae1037.

[21]

Samart K, Tuyishime P, Krishnan A, et al. Reconciling multiple connectivity scores for drug repurposing. Brief Bioinform. 2021; 22(6):bbab161. https://doi.org/10.1093/bib/bbab161.

[22]

Guney E, Menche J, Vidal M, et al. Network-based in silico drug efficacy screening. Nat Commun. 2016; 7:10331. https://doi.org/10.1038/ncomms10331.

[23]

Cook HV, Doncheva NT, Szklarczyk D, et al. Viruses.STRING: a virus-host protein-protein interaction database. Viruses. 2018;10(10):1-11. doi:10.3390/v10100519.

[24]

Xiong GL, Wu ZX, Yi JC, et al. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res. 2021; 49(W1):W5-W14. https://doi.org/10.1093/nar/gkab255.

[25]

Tashiro J, Rubio GA, Limper AH, et al. Exploring animal models that resemble idiopathic pulmonary fibrosis. Front Med (Lausanne). 2017; 4:118. https://doi.org/10.3389/fmed.2017.00118.

[26]

Berman HM, Westbrook J, Feng Z, et al. The protein data bank. Nucleic Acids Res. 2000; 28(1):235-242. https://doi.org/10.1093/nar/28.1.235.

[27]

Morris GM, Huey R, Lindstrom W, et al. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem. 2009; 30(16):2785-2791. https://doi.org/10.1002/jcc.21256.

[28]

Trott O, Olson AJ . AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J Comput Chem. 2010; 31(2):455-461. https://doi.org/10.1002/jcc.21334.

[29]

Laskowski RA, Swindells MB . LigPlot + : multiple ligand-protein interaction diagrams for drug discovery. J Chem Inf Model. 2011; 51(10):2778-2786. https://doi.org/10.1021/ci200227u.

[30]

Gul A, Yang FY, Xie C, et al. Pulmonary fibrosis model of mice induced by different administration methods of bleomycin. BMC Pulm Med. 2023; 23(1):91. https://doi.org/10.1186/s12890-023-02349-z.

[31]

Hu SD, He WL, Wu GY . Hydroxyproline in animal metabolism, nutrition, and cell signaling. Amino Acids. 2022; 54(4):513-528. https://doi.org/10.1007/s00726-021-03056-x.

[32]

Mora AL, Rojas M, Pardo A, et al. Emerging therapies for idiopathic pulmonary fibrosis, a progressive age-related disease. Nat Rev Drug Discov. 2017; 16(11):755-772. https://doi.org/10.1038/nrd.2017.170.

[33]

Leung J, Cho Y, Lockey RF, et al. The role of aging in idiopathic pulmonary fibrosis. Lung. 2015; 193(4):605-610. https://doi.org/10.1007/s00408-015-9729-3.

[34]

Zhong C, Lei YqQ, Zhang JY, et al. Prognostic function and immunologic landscape of a predictive model based on five senescence-related genes in IPF bronchoalveolar lavage fluid. Biomedicines. 2024; 12(6):1246. https://doi.org/10.3390/biomedicines12061246.

[35]

Arbatskiy M, Balandin D. Universal markers of cellular and replicative senescence. Probl Gerosci. 2024; 14:121-130. https://doi.org/10.37586/2949-4745-3-2024-121-130.

[36]

Yan S, Sorrell M, Berman Z . Functional interplay between ATM/ATR-mediated DNA damage response and DNA repair pathways in oxidative stress. Cell Mol Life Sci. 2014; 71(20):3951-3967. https://doi.org/10.1007/s00018-014-1666-4.

[37]

Rundle S, Bradbury A, Drew Y, et al. Targeting the ATR-CHK1 axis in cancer therapy. Cancers (Basel). 2017; 9(5):41. https://doi.org/10.3390/cancers9050041.

[38]

Baker JR, Daly L, Hassibi S, et al. Senolytic therapy reduces inflammation in epithelial cells from COPD patients and in smoke-exposure mice. Front Med (Lausanne). 2025; 12:1451056. https://doi.org/10.3389/fmed.2025.1451056.

[39]

Ovadya Y, Krizhanovsky V . Strategies targeting cellular senescence. J Clin Invest. 2018; 128(4):1247-1254. https://doi.org/10.1172/JCI95149.

[40]

Lelarge V, Capelle R, Oger F, et al. Senolytics: from pharmacological inhibitors to immunotherapies, a promising future for patients’ treatment. NPJ Aging. 2024; 10(1):12. https://doi.org/10.1038/s41514-024-00138-4.

[41]

Liu F, Bayliss G, Zhuang SG . Application of nintedanib and other potential anti-fibrotic agents in fibrotic diseases. Clin Sci (Lond). 2019; 133(12):1309-1320. https://doi.org/10.1042/CS20190249.

PDF (13826KB)

0

Accesses

0

Citation

Detail

Sections
Recommended

/

〈 〉