Abstract
Small molecules that induce autophagy in specific biological contexts can provide invaluable chemical probes and potential anticancer therapeutics. Here we identified a potent autophagy inducer 3,4-diisobutyryl derivative of auxarthrol A (DAA) from an endophyte-derived small-molecule library. DAA demonstrates notable antitumor efficacy in non-small cell lung cancer (NSCLC) tumor and sensitizes tumors to anti-PD1 immunotherapy. Using a photoaffinity labeling approach, we identified light intermediate chain 1 (LIC1), a subunit of dynein, as the direct target of DAA. We found that LIC1 is overexpressed in NSCLC tumors and correlates with poor survival. Mechanistically, the targeting of LIC1 by DAA markedly disrupts the interactions between LIC1 and stress-sensing effector RuvB-like AAA ATPase 1, which in turn elevates downstream GCN2–eIF2α–ATF4 axis-mediated integrated stress response, ultimately promoting autophagic cell death. Our findings define LIC1 as a novel therapeutic target for NSCLC and highlight the potential of DAA as a promising autophagy inducer for treatment of this disease.

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Data availability
All RNA-seq data are available from the GEO under accession number GSE282502. MS proteomics data can be accessed through the ProteomeXchange Consortium from the PRIDE repository under identifiers PXD058272 and PXD058259. Crystallographic data were assigned deposition numbers 2407680 and 2407681 at the Cambridge Crystallographic Data Center. Additional supporting data are available upon reasonable request from the corresponding author. Source data are provided with this paper.
References
Mizushima, N. & Komatsu, M. Autophagy: renovation of cells and tissues. Cell 147, 728–741 (2011).
Levy, J. M. M., Towers, C. G. & Thorburn, A. Targeting autophagy in cancer. Nat. Rev. Cancer 17, 528–542 (2017).
Debnath, J., Gammoh, N. & Ryan, K. M. Autophagy and autophagy-related pathways in cancer. Nat. Rev. Mol. Cell Biol. 24, 560–575 (2023).
Yang, Y. P. et al. Application and interpretation of current autophagy inhibitors and activators. Acta Pharmacol. Sin. 34, 625–635 (2013).
Rosenfeld, M. R. et al. A phase I/II trial of hydroxychloroquine in conjunction with radiation therapy and concurrent and adjuvant temozolomide in patients with newly diagnosed glioblastoma multiforme. Autophagy 10, 1359–1368 (2014).
Benjamin, D., Colombi, M., Moroni, C. & Hall, M. N. Rapamycin passes the torch: a new generation of mTOR inhibitors. Nat. Rev. Drug Discov. 10, 868–880 (2011).
Bray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 74, 229–263 (2024).
Meyer, M. L. et al. New promises and challenges in the treatment of advanced non-small-cell lung cancer. Lancet 404, 803–822 (2024).
Rotow, J. & Bivona, T. G. Understanding and targeting resistance mechanisms in NSCLC. Nat. Rev. Cancer 17, 637–658 (2017).
Lahiri, A. et al. Lung cancer immunotherapy: progress, pitfalls, and promises. Mol. Cancer 22, 40 (2023).
Rao, S., Yang, H., Penninger, J. M. & Kroemer, G. Autophagy in non-small cell lung carcinogenesis. Autophagy 10, 529–531 (2014).
Rao, S. et al. A dual role for autophagy in a murine model of lung cancer. Nat. Commun. 5, 3056 (2014).
Cai, J. C. et al. CK1α suppresses lung tumour growth by stabilizing PTEN and inducing autophagy. Nat. Cell Biol. 20, 465–478 (2018).
Biswas, U., Roy, R., Ghosh, S. & Chakrabarti, G. The interplay between autophagy and apoptosis: its implication in lung cancer and therapeutics. Cancer Lett. 585, 216662 (2024).
Newman, D. J. & Cragg, G. M. Natural products as sources of new drugs over the nearly four decades from 01/1981 to 09/2019. J. Nat. Prod. 83, 770–803 (2020).
Cragg, G. M., Grothaus, P. G. & Newman, D. J. Impact of natural products on developing new anti-cancer agents. Chem. Rev. 109, 3012–3043 (2009).
Tan, R. X. & Zou, W. X. Endophytes: a rich source of functional metabolites. Nat. Prod. Rep. 18, 448–459 (2001).
Singh, B. N., Upreti, D. K., Gupta, V. K., Dai, X. F. & Jiang, Y. M. Endolichenic fungi: a hidden reservoir of next generation biopharmaceuticals. Trends Biotechnol. 35, 808–813 (2017).
Gan, L. et al. A natural small molecule alleviates liver fibrosis by targeting apolipoprotein L2. Nat. Chem. Biol. 21, 80–90 (2025).
Yan, X. L. et al. Discovery of the first Raptor (regulatory-associated protein of mTOR) inhibitor as a new type of antiadipogenic agent. J. Med. Chem. 66, 5839–5858 (2023).
Huang, J. L. et al. Discovery of highly potent daphnane diterpenoids uncovers importin-β1 as a druggable vulnerability in castration-resistant prostate cancer. J. Am. Chem. Soc. 66, 17522–17532 (2022).
Huang, J. L. et al. Discovery of a highly potent and orally available importin-β1 inhibitor that overcomes enzalutamide-resistance in advanced prostate cancer. Acta Pharm. Sin. B 13, 4934–4944 (2023).
Maiuri, M. C., Zalckvar, E., Kimchi, A. & Kroemer, G. Self-eating and self-killing: crosstalk between autophagy and apoptosis. Nat. Rev. Mol. Cell Biol. 8, 741–752 (2007).
Isaka, M. et al. Cytotoxic hydroanthraquinones from the mangrove-derived fungus Paradictyoarthrinium diffractum BCC 8704. J. Antibiot. 68, 334–338 (2015).
Pan, S. J., Zhang, H. L., Wang, C. Y., Yao, S. C. L. & Yao, S. Q. Target identification of natural products and bioactive compounds using affinity-based probes. Nat. Prod. Rep. 33, 612–620 (2016).
Lee, I. G. et al. A conserved interaction of the dynein light intermediate chain with dynein-dynactin effectors necessary for processivity. Nat. Commun. 9, 986 (2018).
Hetz, C., Chevet, E. & Harding, H. P. Targeting the unfolded protein response in disease. Nat. Rev. Drug Discov. 12, 703–719 (2013).
Costa-Mattioli, M. & Walter, P. The integrated stress response: from mechanism to disease. Science 368, eaat5314 (2020).
Donnelly, N., Gorman, A. M., Gupta, S. & Samali, A. The eIF2α kinases: their structures and functions. Cell. Mol. Life Sci. 70, 3493–3511 (2013).
Kroemer, G., Mariño, G. & Levine, B. Autophagy and the integrated stress response. Mol. Cell 40, 280–293 (2010).
B'chir, W. et al. The eIF2α/ATF4 pathway is essential for stress-induced autophagy gene expression. Nucleic Acids Res. 41, 7683–7699 (2013).
Cloutier, P. et al. R2TP/Prefoldin-like component RUVBL1/RUVBL2 directly interacts with ZNHIT2 to regulate assembly of U5 small nuclear ribonucleoprotein. Nat. Commun. 8, 15615 (2017).
Jha, S. & Dutta, A. RVB1/RVB2: running rings around molecular biology. Mol. Cell 34, 521–533 (2009).
Izumi, N. et al. AAA+ proteins RUVBL1 and RUVBL2 coordinate PIKK activity and function in nonsense-mediated mRNA decay. Sci. Signal. 3, ra27 (2010).
McKeegan, K. S., Debieux, C. M. & Watkins, N. J. Evidence that the AAA proteins TIP48 and TIP49 bridge interactions between 15.5K and the related NOP56 and NOP58 proteins during box C/D snoRNP biogenesis. Mol. Cell. Biol. 29, 4971–4981 (2009).
Wu, C. C. C., Peterson, A., Zinshteyn, B., Regot, S. & Green, R. Ribosome collisions trigger general stress responses to regulate cell fate. Cell 182, 404–416 (2020).
Xia, H. J., Green, D. R. & Zou, W. P. Autophagy in tumour immunity and therapy. Nat. Rev. Cancer 21, 281–297 (2021).
Michaud, M. et al. Autophagy-dependent anticancer immune responses induced by chemotherapeutic agents in mice. Science 334, 1573–1577 (2011).
Tiwari, P. & Bae, H. Endophytic fungi: key insights, emerging prospects, and challenges in natural product drug discovery. Microorganisms 10, 360 (2022).
Zhan, Z. J., Li, S., Chu, W. & Yin, S. Diterpenoids: isolation, structure, bioactivity, biosynthesis, and synthesis (2013−2021). Nat. Prod. Rep. 39, 2132–2174 (2022).
Even, I. et al. DLIC1, but not DLIC2, is upregulated in colon cancer and this contributes to proliferative overgrowth and migratory characteristics of cancer cells. FEBS J. 286, 803–820 (2019).
Virard, F. et al. Targeting ERK−MYD88 interaction leads to ERK dysregulation and immunogenic cancer cell death. Nat. Commun. 15, 7037 (2024).
Sayers, C. M. et al. Identification and characterization of a potent activator of p53-independent cellular senescence via a small-molecule screen for modifiers of the integrated stress response. Mol. Pharmacol. 83, 594–604 (2013).
Thomson, C. G. et al. Discovery of HC-7366: an orally bioavailable and efficacious GCN2 kinase activator. J. Med. Chem. 67, 5259–5271 (2024).
Fu, Y. et al. Discovery of a small molecule targeting autophagy via ATG4B inhibition and celldeath of colorectal cancer cells in vitro and in vivo. Autophagy 15, 295–311 (2019).
Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).
Zhou, Y. et al. Self-assembled glycopeptide as a biocompatible mRNA vaccine platform elicits robust antitumor immunity. ACS Nano 19, 14727–14741 (2025).
Acknowledgements
This work was supported by supported by the National Natural Science Foundation of China (82273804 to S.Y., 82304322 to J.H., 82404454 to F.Y. and 22407144 to D.H.) and Natural Science Foundation of Guangdong Province (2024A1515010169 to S.Y.). Graphical abstract created with BioRender.com.
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S.Y. and J.H. conceptualized and designed the study. J.H., L.W., S.W., F.Y., H.W. and L.G. performed the experiments. J.H., L.W., S.W. and D.H. analyzed the results. S.Y., S.C. and G.T. provided technical and material support. S.Y., J.H., L.W. and S.W. wrote the paper. S.Y., J.H. and G.T. were responsible for research supervision. All authors approved the paper.
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Nature Chemical Biology thanks Sung Hee Baek, Seung-Yong Seo and Zhe Wang the for their contribution to the peer review of this work.
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Extended data
Extended Data Fig. 1 Construction of the tetrahydroanthraquinones compound library and evaluation of the anti-NSCLC activity.
a, Structures of 1–20 and 18 derivatives 21–44. b, IC50s for tetrahydroanthraquinones in H1975 cells treated for 72 h. c, SARs of tetrahydroanthraquinones on anti-proliferative activity.
Extended Data Fig. 2 DAA induces autophagy-mediated cell death.
a, Representative western blot for cycle-related proteins in H1975 and HCC827 cells treated with indicated concentrations of DAA for 24 h. b, H1975 cells were exposed to indicated concentrations of DAA with or without 3-MA (60 μM) for 48 h. Then the cell viability was assayed. c, H1975 cells and HCC827 cells were exposed to DAA (1.25 μM for H1975 cells or 2.5 μM for HCC827 cells) alone or in combination with Autophinib (50 nM) and SBI-0206965 (0.5 μM), respectively. After 48 h of treatment, the cell viability was assayed. Right panels show the corresponding immunoblots. The ratio of LC3-II: GAPDH was calculated based on the band density. d, Representative western blot for the indicated proteins in H1975 and HCC827 cells treated with DAA (2.5 μM) for indicated time points. e, H1975 cells were treated with DAA (2.5 μM), Baf (0.5 μM), or Rap (1 μM) for 4 h, followed by staining with LysoTracker Red (LTR, 50 ng/mL) or acridine orange (AO, 0.5 μg/mL) for 30 min. Fluorescence images of live cells were recorded without fixation. Scale bars, 10 μm. Data in b and c are shown as mean ± s.d. Statistical significance was determined by two-tailed unpaired Student’s t-test. For a–e, n = 3 independent experiments.
Extended Data Fig. 3 DAA directly targets LIC1.
a, Cell viability curves measured by CCK-8 for H1975 cells treated with probes for 72 h. b, Western blot analysis on endogenous LIC1 and LIC2 protein after DAA-PT pull-down in HCC827 cells. c, Western blot analysis on exogenous LIC1 protein after DAA-PT pull-down in 293 T cells. 293 T cells were transfected with V5-tagged LIC1 for 6 h. After 24 h, the cells were incubated with indicated concentrations of DAA-PT or IN-PT in the presence or absence of DAA for 3 h. After photocross-linking, the probe-bound protein was precipitated by streptavidin beads and was immunoblotted with an antibody against V5. d, Representative western blot for LIC1 in indicated cells treated with indicated concentrations of DAA for 24 h or treated with DAA (2.5 μM) for indicated time points. e, Quantification for LIC1 protein levels in the cellular thermal shift assay. Statistical significance was determined by two-tailed unpaired Student’s t-test compared with vehicle group. f, SPR analysis of interactions between 20 and LIC1. Data in a and e are shown as mean ± s.d. For a–f, n = 3 independent experiments.
Extended Data Fig. 4 LIC1 is associated with the progression of NSCLC.
a, Pull-down experiments of DAA-PT (5 μM) on different LIC1 mutants in 293 T cells. 3 A: Tyr93, Lys94 and Glu100 mutate into Ala; 5 A: Gly96, Arg97, Leu99, Glu100, Tyr101 and Trp120 mutate into Ala. b, Cell counting assay after LIC1 overexpression in H1975 and HCC827 cells. c, Immunoblotting analysis of the indicated proteins in H1975 and HCC827 cells overexpressing LIC1 or corresponding vector. d,e, H1975 and HCC827 cells stably overexpressing LIC1 or corresponding vector were subcutaneously transplanted in nude mice (n = 6 biologically independent mice per group). Tumor growth curves, subcutaneous tumors at the experimental endpoint and tumor weight are shown as indicated. Tumor volume is presented as mean values ± s.e.m., tumor weight is shown as mean ± s.d. Statistical significance was determined by two-tailed unpaired Student’s t-test, and representative tumor images are shown. f, Immunohistochemistry images and quantification of Ki67 expression in tumors from d and e. Scale bars, 100 μm. g, Immunoblot of V5 in H1975 cells infected by PLX304-LIC12A overexpressing or control PXL304 lentiviruses after 72 h. Data in b and f are shown as mean ± s.d. Statistical significance was determined by two-tailed unpaired Student’s t-test. For a–c, f and g, n = 3 independent experiments.
Extended Data Fig. 5 DAA induces autophagic cell death via ISR.
a, qRT-PCR analysis of the indicated genes in HCC827 and H1975 cells treated with DAA for 24 h or knockdown of LIC1 for 48 h. b, Correlation between LIC1 and DDIT3, TRIB3, or CHAC1 gene expression levels in public available clinical NSCLC databases (accession numbers GSE33532). Correlation was estimated with Pearson correlation coefficient (r) and significance was calculated by a two-tailed t-test. c, Immunoblot analysis of UPR-related proteins in H1975 cells treated with DAA (2.5 μM) at instructed times. d, Immunoblot analysis of ISR-related proteins in H1975 and HCC827 cells transfected with LIC1 or control siRNA and incubated for 72 h. e, Anti-proliferation assay of DAA after eIF2α or ATF4 knockdown. H1975 cells were transfected with eIF2α or ATF4 siRNA for 48 h and then treated with vehicle or DAA at indicated concentrations for 48 h. Viable cells were counted. Left panels show the corresponding immunoblots. f, HCC827 cells were exposed to DAA (5 μM) with or without 4-PBA (500 μM) for 48 h. Then the cell viability was assayed. Left panels show the corresponding immunoblots. g, qRT-PCR analysis of the autophagy-related genes in H1975 cells treated with DAA (2.5 μM) at indicated time points. Student’s t-test compared with vehicle group. Data in a and e–g are shown as mean ± s.d. Statistical significance was determined by two-tailed unpaired Student’s t-test. For a and c–g, n = 3 independent experiments.
Extended Data Fig. 6 DAA disrupts the interaction of LIC1 with RUVBL1.
a, Coomassie brilliant blue stained protein co-precipitated with LIC1. b, Venn diagram of the MS data from two repeated experiments of LIC1-binding proteins production in H1975-V5-LIC1 and HCC827-V5-LIC1 cells. The potential LIC1-interacting proteins are listed based on the abundance ratio. c, Co-IP experiments of HCC827-V5-LIC1 cells, using an anti-V5 antibody for IP and anti-RUVBL1 antibody for immunoblotting. d, Co-IP experiments of H1975 cells or HCC827 cells, using an anti-RUVBL1 antibody for IP and anti-LIC1 antibody for immunoblotting. e, Co-IP experiment of Flag-RUVBL1 and indicated fragment proteins of GFP-LIC1 in 293 T cells. f, Co-IP experiments of H1975-LIC12A and HCC827-LIC12A cells, using an anti-V5 antibody for IP and anti-RUVBL1 antibody for immunoblotting. g, Western blot for PIKK-related proteins in HCC827 cells treated with indicated concentrations of DAA for 12 h. h,i, Immunoblotting analysis of the indicated proteins in HCC827 cells transfected with LIC1 or control siRNA and incubated for 3 d. j, Immunoblotting analysis of the indicated proteins in HCC827 cells treated with indicated concentrations of CB-6644 for 12 h. For c–j, n = 3 independent experiments.
Extended Data Fig. 7 Toxicity evaluation on DAA.
a, Body weight of mice from different treatment groups (n = 7 mice per group). Data are shown as mean ± s.d. b,c, Mice bearing HCC827 xenografts treated with vehicle or with DAA (5 or 10 mg/kg i.p. five times per week). After 27 days, mice were sacrificed and organs were removed. Representative photographs b and H&E images c were shown. Scale bars, 100 μm. n = 3 independent experiments. d, Clinical signs of KM mice in acute toxicity analysis of DAA (n = 4 mice per group). e, Toxicology analysis of complete blood count, differential, and blood chemistry in KM mice treated with vehicle, or with DAA (10 mg/kg i.p. once per day for two weeks, n = 5 mice per group).
Extended Data Fig. 8 DAA induces immunogenic cell death and enhances antigen presentation.
a, Detection of high mobility group protein B1 (HMGB1) release by ELISA kit. HCC827 cells were treated with vehicle or with DAA (2.5 μM or 5 μM) for 24 h. b, Detection of adenosine triphosphate (ATP) secretion by luciferin-based ATP assay kit. HCC827 cells were treated with vehicle or with DAA (2.5 μM or 5 μM) for 24 h. c,d, Representative and statistic flow cytometry results of antigen presentation on DC2.4 cells treated with vehicle or with DAA (1.25 μM, 2.5 μM or 5 μM) for 24 h. Data in a–c are shown as mean ± s.d. Statistical significance was determined by two-tailed unpaired Student’s t-test, n = 3 independent experiments.
Extended Data Fig. 9 DAA sensitizes tumor to anti-PD1.
a, CD8+ and CD4+ T cells detected by Flow cytometry (FCM) with different treatments in LLC1-bearing mice. Detailed in vivo experimental settings please refer to the Fig. 6f legends of main text. b, Quantification of tumor-infiltrating lymphocyte CD4+ T-cell or IFN-γ+ CD4+ T-cell numbers in tumors, as determined by flow cytometry. Data are shown as mean ± s.d. Statistical significance was determined by two-tailed unpaired Student’s t-test, n.s., no significance. n = 4 independent mice. c, Body weight of mice from different treatment groups (n = 7 mice per group). Data are shown as mean ± s.d. d,e, Mice bearing LLC1 syngeneic tumor with indicated treatments (DAA, i.p., 10 mg/kg; anti-PD1, i.p., 10 mg/kg; DAA and anti-PD1 combined). After 13 days, mice were sacrificed and organs were removed. Representative H&E images d and photographs e were shown. Scale bars, 100 μm. n = 3 independent experiments.
Extended Data Fig. 10 CD8⁺ T cells and CD4⁺ T cells are essential for the efficacy of DAA/anti-PD1 combination therapy in vivo.
a,b, Effects of the indicated treatments (DAA, i.p., 10 mg/kg; anti-PD1, i.p., 10 mg/kg; DAA and anti-PD1 combined, n = 6 mice per group) on the tumor growth of LLC1 syngeneic tumor model upon depletion of CD4+ T cells (a) or CD8+ T cells (b). Statistical significance was determined by two-tailed unpaired Student’s t-test at post-treatment day 12. n.s., no significance. Tumor volume was monitored and tumor weight was measured on the last day. Tumor volume is presented as mean values ± s.e.m., tumor weight is shown as mean ± s.d., and representative tumor images are shown. c, Changes in immune cell populations in blood on the last day following administration of either anti-CD4 or anti-CD8 in LLC1 tumor-bearing mice. Data are shown as mean ± s.d.; Statistical significance was determined by two-tailed unpaired Student’s t-test compared with vehicle group, n = 6 mice per group.
Supplementary information
Supplementary Information
Chemistry experimental procedures and data and supplementary figures for spectra and chromatogram of compounds.
Supplementary Table 1
The 53 endophytic strains isolated from Euphorbia plants and their inhibition of the NSCLC cell line H1975 at 100 μg ml−1.
Supplementary Table 2
Antibodies for immunoblotting.
Supplementary Table 3
Primers for qPCR.
Supplementary Table 4
List of quantified proteins with abundance ratio from H1975 and HCC827 cells.
Supplementary Table 5
RNA-seq data in H1975 and HCC827 cells treated with different concentrations of DAA or knockdown of LIC1.
Supplementary Table 6
List of proteins that interact with LIC1 in H1975-V5-LIC1 and HCC827-V5-LIC1 cells.
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Huang, JL., Wu, LM., Wu, SQ. et al. A small molecule targets LIC1 to suppress lung tumor growth by inducing autophagy. Nat Chem Biol (2025). https://doi.org/10.1038/s41589-025-02040-w
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DOI: https://doi.org/10.1038/s41589-025-02040-w