Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Advertisement

Scientific Reports
  • View all journals
  • Search
  • My Account Login
  • Content Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • RSS feed
  1. nature
  2. scientific reports
  3. articles
  4. article
Palmatine ameliorates MASLD in type 2 diabetes by modulating hepatic apoptosis and inflammation
Download PDF
Download PDF
  • Article
  • Open access
  • Published: 21 March 2026

Palmatine ameliorates MASLD in type 2 diabetes by modulating hepatic apoptosis and inflammation

  • Huasen Yang1,
  • Zhoujing Shi1,
  • Yazhi Qi1,
  • Shuchang Bao1,
  • Chaochong Li1,
  • Junhui Mei1,
  • Mingshuang Sun1,
  • Yusheng Han1 &
  • …
  • Boyan Ma1,2 

Scientific Reports , Article number:  (2026) Cite this article

  • 1205 Accesses

  • 12 Altmetric

  • Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Biochemistry
  • Diseases
  • Drug discovery
  • Molecular biology

Abstract

Type 2 diabetes mellitus (T2DM) is a major risk factor for metabolic dysfunction-associated steatotic liver disease (MASLD), and their convergence presents a significant therapeutic challenge. Palmatine, an isoquinoline alkaloid with lipid-regulating and anti-inflammatory properties, is a promising candidate; however, its multi-target mechanisms remain undefined. To address this, we employed a sequential, multi-omics bioinformatics workflow to generate a testable mechanistic hypothesis, followed by rigorous experimental validation. First, integrative analyses—including target prediction, differential gene expression, pathway enrichment, and machine learning—converged to identify five core targets: ADRB2, BCL3, EGR1, FOS, and MAP3K8. Molecular docking predicted strong binding, and single-cell sequencing contextualized their expression within specific hepatic cell types. To experimentally test this predictive framework, we evaluated palmatine in a rat model of T2DM-associated MASLD. Palmatine treatment significantly improved liver function(reduced ALT, AST), attenuated inflammation (lowered TNF-α, IL-6) and oxidative stress (increased SOD, GSH; decreased MDA), ameliorated glycolipid metabolism (reduced TC, TG, LDL-C, and GLU), and reduced hepatic steatosis and fibrosis. Mechanistically, confirming the bioinformatic prediction, palmatine downregulated the expression of the five key targets and concurrently suppressed the activation of critical apoptotic executers (Caspase-3, Caspase-8, GSDME). These findings demonstrate that palmatine alleviates MASLD by modulating a novel target network to inhibit hepatocyte apoptosis, providing a robust, hypothesis-driven preclinical foundation for its therapeutic development.

Similar content being viewed by others

A microphysiological model of human MASLD reveals paradoxical response to resmetirom

Article Open access 14 January 2026

Bioinformatic analysis of molecular expression patterns during the development and progression of metabolic dysfunction-associated steatotic liver disease (MASLD)

Article Open access 01 March 2025

Immunopathogenic mechanisms and immunoregulatory therapies in MASLD

Article Open access 10 June 2025

Data availability

Data will be available upon request from the corresponding author.

Abbreviations

ACSL1:

Acyl-CoA Synthetase Long-Chain Family Member 1

ADRB2:

Adrenoceptor Beta 2

AKT:

AKT Serine/Threonine Kinase

ALT:

Alanine Aminotransferase

AMPK:

Liver Adenosine Monophosphate-Activated Protein Kinase

ASK1:

Apoptosis Signal-regulating Kinase 1

AST:

Aspartate Aminotransferase

ATF4:

Activating Transcription Factor 4

BCA:

Bicinchoninic Acid Assay

BCL3:

BCL3 Transcription Coactivator

BSA:

Bovine Serum Albumin

BP:

BiologicalProcess

β-arrestin2:

Beta-Arrestin 2

CC:

CellularComponent

cAMP:

Cyclic Adenosine Monophosphate

CDS:

CellDynamicSimulation

C/EBP-α:

CCAAT/Enhancer-Binding Protein Alpha

cGAS-STING:

cyclic GMP-AMP synthase-stimulator of interferon genes

DAMPs:

Damage-Associated Molecular Patterns

DAPI:

4’6-Diamidino-2-phenylindole

DEGs:

Differentially Expressed Genes

DNL:

De Novo Lipogenesis

DT:

Decision Tree

ECL:

Enhanced Chemiluminescence

EGR1:

Early Growth Response 1

eIF2α:

eukaryotic Initiation Factor 2 alpha

ERK:

Extracellular Signal-Regulated Kinase

FADD:

Fas Associated Via Death Domain

FBG:

Fasting Blood Glucose

FFA:

Free Fatty Acids

FAS:

FAS Cell Surface Death Receptor

FOS:

AP-1 Transcription Factor Subunit

FXR:

Farnesoid X Receptor Agonist

GF-β1:

Transforming Growth Factor-beta 1

GBM:

GradientBoostingMachine

GEO:

Gene Expression Omnibus Database

Gi:

G inhibitory protein

GLM:

GeneralizedLinearModel

GLP-1:

Glucagon-Like Peptide-1 Receptor Agonist

GLU:

Glucose

GSDME:

Gasdermin E

GSH:

glutathione

GO:

Gene Ontology

HMGB1:

High Mobility Group Box 1

HE:

Hematoxylin and Eosin Staining

HepG2:

Hepatoma Cell Line G2

HSCs:

Hepatic Stellate Cells

HRP:

Horseradish Peroxidase

IGT:

Impaired Glucose Tolerance

IL-1β:

Interleukin-1 Beta

IL-6:

Interleukin-6

IF:

Immunofluorescence Staining

IR:

Insulin Resistance

JNK:

c-Jun N-terminal Kinase

KEGG:

Kyoto Encyclopedia of Genes and Genomes

KNN:

K-NearestNeighbors

LASSO:

LeastAbsoluteShrinkageandSelectionOperator

LDL-C:

Low-Density Lipoprotein Cholesterol

mTOR:

Mechanistic Target of Rapamycin

mTORC1:

Mechanistic Target of Rapamycin Complex 1

MAP3K8:

Mitogen-Activated Protein Kinase Kinase Kinase 8

MDA:

Malondialdehyde

MF:

MolecularFunction

MyD88:

Myeloid Differentiation Primary Response 88

NAFLD:

Non-Alcoholic Fatty Liver Disease

MASLD:

Metabolic dysfunction-associated steatotic liver disease

NASH:

Non-Alcoholic Steatohepatitis

NF-κB:

Nuclear Factor kappa-light-chain-enhancer of activated B cells

NLRP3:

NACHT LRR and PYD domains-containing protein 3

NNET:

NeuralNetwork

OTCC:

Optimal Temperature Cutting Compound

Pal:

palmatine

PERK:

PKR-like Endoplasmic Reticulum Kinase

PI3K:

Phosphatidylinositol 3-Kinase

PBS:

Phosphate Buffered Saline

PKA:

cAMP-dependent Protein Kinase

PDE4:

Phosphodiesterase 4

PPAR-γ:

Peroxisome Proliferator-Activated Receptor Gamma

PUMA:

p53 Upregulated Modulator of Apoptosis

PVDF:

Polyvinylidene Fluoride

RIPA:

Radioimmunoprecipitation Assay Buffer

ROC:

Receiver Operating Characteristic Curve

RF:

RandomForest

SOD:

Superoxide Dismutase

Sem:

Semaglutide

Smad3:

Mothers Against Decapentaplegic Homolog 3

SREBP-1c:

Sterol Regulatory Element-Binding Protein 1c

STZ:

Streptozotocin

ssGSEA:

Single-Sample Gene Set Enrichment Analysis

SVM:

SupportVectorMachine

TBA:

Total Bile Acids

T-C:

Total Cholesterol

TAK1:

TGF-β-Activated Kinase 1

T-G:

Triglycerides

TLR4:

Toll-Like Receptor 4

TNF-α:

Tumor Necrosis Factor Alpha

T2DM:

Type 2 Diabetes Mellitus

WB:

Western Blot

XGB:

eXtremeGradientBoosting

References

  1. Younossi, Z. M. et al. Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology 64 (1), 73–84 (2016).

    Google Scholar 

  2. Konings, L. A. M. et al. Pharmacological treatment options for metabolic dysfunction-associated steatotic liver disease in patients with type 2 diabetes mellitus: A systematic review. Eur. J. Clin. Invest. 55(4), e70003 (2025).

  3. EASL-EASD-EASO Clinical Practice Guidelines on the. Management of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD). Obes. Facts. 17 (4), 374–444 (2024).

    Google Scholar 

  4. Eslam, M., Sanyal, A. J. & George, J. A Consensus-Driven Proposed Nomenclature for Metabolic Associated Fatty Liver Disease. Gastroenterology 158 (7), 1999–2014e1 (2020).

    Google Scholar 

  5. Ji, Y., Wang, Q., Jiang, Y. & Liu, B. Global epidemiology of T2DM in patients with NAFLD or MAFLD: the real situation may be even more serious. BMC Med. 22 (1), 476 (2024).

    Google Scholar 

  6. Cao, L. et al. Global epidemiology of type 2 diabetes in patients with NAFLD or MAFLD: a systematic review and meta-analysis. BMC Med. 22 (1), 101 (2024).

    Google Scholar 

  7. Stefan, N. & Cusi, K. A global view of the interplay between non-alcoholic fatty liver disease and diabetes. Lancet Diabetes Endocrinol. 10 (4), 284–296 (2022).

    Google Scholar 

  8. Wang, Y. et al. Analysis of Immune and Inflammatory Microenvironment Characteristics of Noncancer End-Stage Liver Disease. J. Interferon Cytokine Res. 43 (2), 86–97 (2023).

    Google Scholar 

  9. Duell, P. B. et al. Nonalcoholic Fatty Liver Disease and Cardiovascular Risk: A Scientific Statement From the American Heart Association. Arterioscler. Thromb. Vasc Biol. 42 (6), e168–e185 (2022).

    Google Scholar 

  10. Sarker, J., Okpara, E., De Los Santos, B., Kim, M. & Kim, K. Comparative effectiveness of GLP-1 receptor agonists, SGLT2 inhibitors and DPP-4 inhibitors on liver outcomes in metabolic dysfunction-associated steatotic liver disease: A retrospective cohort study. Diabetes Obes. Metab. 28(4), 3155–3164 (2026).

  11. Tong, G. et al. The efficacy of sulodexide combined with Jinshuibao for treating early diabetic nephropathy patients. Int. J. Clin. Exp. Med. 13 (11), 8308–8317 (2020).

    Google Scholar 

  12. Cegla, J. Liraglutide safety and efficacy in patients with non-alcoholic steatohepatitis (LEAN): a multicentre, double-blind, randomized, placebo-controlled phase 2 study. Ann. Clin. Biochem. 53 (4), 518 (2016).

    Google Scholar 

  13. Li, W. et al. The signaling pathways of selected traditional Chinese medicine prescriptions and their metabolites in the treatment of diabetic cardiomyopathy: a review. Front. Pharmacol. 15, 1416403 (2024).

    Google Scholar 

  14. Choi, S. et al. Convergent metabolic pathways in MASH therapeutics: An AMPK-centric analysis. J. Cell. Mol. Med. 30(2), e71023 (2026).

  15. Tian, X. et al. Palmatine ameliorates high fat diet induced impaired glucose tolerance. Biol. Res. 53 (1), 39 (2020).

    Google Scholar 

  16. Nwabueze, O. P. et al. M. P., Comparative Studies of Palmatine with Metformin and Glimepiride on the Modulation of Insulin Dependent Signaling Pathway In Vitro, In Vivo & Ex Vivo. Pharmaceuticals (Basel) 15, (11). (2022).

  17. Long, J. et al. Palmatine: A review of its pharmacology, toxicity and pharmacokinetics. Biochimie 162, 176–184 (2019).

    Google Scholar 

  18. Yin, X., Liu, Z. & Wang, J. Tetrahydropalmatine ameliorates hepatic steatosis in nonalcoholic fatty liver disease by switching lipid metabolism via AMPK-SREBP-1c-Sirt1 signaling axis. Phytomedicine 119, 155005 (2023).

    Google Scholar 

  19. Cheng, J. J. et al. Palmatine Protects Against MSU-Induced Gouty Arthritis via Regulating the NF-κB/NLRP3 and Nrf2 Pathways. Drug Des. Devel Ther. 16, 2119–2132 (2022).

    Google Scholar 

  20. Zhang, N. et al. Discovery and development of palmatine analogues as anti-NASH agents by activating farnesoid X receptor (FXR). Eur. J. Med. Chem. 245 (Pt 1), 114886 (2023).

    Google Scholar 

  21. Choi, J. S. et al. Coptis chinensis alkaloids exert anti-adipogenic activity on 3T3-L1 adipocytes by downregulating C/EBP-α and PPAR-γ. Fitoterapia 98, 199–208 (2014).

    Google Scholar 

  22. Zhang, S., Liu, K., Liu, Y., Hu, X. & Gu, X. The role and application of bioinformatics techniques and tools in drug discovery. Front. Pharmacol. 16, 1547131 (2025).

    Google Scholar 

  23. Altman, R. B. & Klein, T. E. Challenges for biomedical informatics and pharmacogenomics. Annu. Rev. Pharmacol. Toxicol. 42, 113–133 (2002).

    Google Scholar 

  24. Qian, Y., Wang, Q., Yin, L. & Lu, A. Y. CF-DTI: coarse-to-fine feature extraction for enhanced drug-target interaction prediction. Health Inf. Sci. Syst. 13 (1), 55 (2025).

    Google Scholar 

  25. Ru, J. et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J. Cheminform. 6, 13 (2014).

    Google Scholar 

  26. Wang, X. et al. PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Res. 45 (W1), W356–w360 (2017).

    Google Scholar 

  27. Fang, S. et al. HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine. Nucleic Acids Res. 49 (D1), D1197–d1206 (2021).

    Google Scholar 

  28. Wu, Y. et al. SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping. Nucleic Acids Res. 47 (D1), D1110–d1117 (2019).

    Google Scholar 

  29. UniProt. the Universal Protein Knowledgebase in 2025. Nucleic Acids Res. 53 (D1), D609–d617 (2025).

    Google Scholar 

  30. Cheng, M. et al. Exploring the mechanism of PPCPs on human metabolic diseases based on network toxicology and molecular docking. Environ. Int. 196, 109324 (2025).

    Google Scholar 

  31. He, Q. et al. Exploring the mechanism of curcumin in the treatment of colon cancer based on network pharmacology and molecular docking. Front. Pharmacol. 14, 1102581 (2023).

    Google Scholar 

  32. Clough, E. & Barrett, T. The Gene Expression Omnibus Database. Methods Mol. Biol. 1418, 93–110 (2016).

    Google Scholar 

  33. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43(7), e47 (2015).

  34. Rosario, S. R. et al. Pan-cancer analysis of transcriptional metabolic dysregulation using The Cancer Genome Atlas. Nat. Commun. 9 (1), 5330 (2018).

    Google Scholar 

  35. Qin, H. et al. Integrated machine learning survival framework develops a prognostic model based on inter-crosstalk definition of mitochondrial function and cell death patterns in a large multicenter cohort for lower-grade glioma. J. Transl Med. 21 (1), 588 (2023).

    Google Scholar 

  36. Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods. 12 (5), 453–457 (2015).

    Google Scholar 

  37. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177(7), 1888–1902e21 (2019).

  38. Zappia, L. & Oshlack, A. Clustering trees: a visualization for evaluating clusterings at multiple resolutions. Gigascience 7, (7). (2018).

  39. Xiong, X. et al. Landscape of Intercellular Crosstalk in Healthy and NASH Liver Revealed by Single-Cell Secretome Gene Analysis. Mol. Cell. 75 (3), 644–660e5 (2019).

    Google Scholar 

  40. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32 (4), 381–386 (2014).

    Google Scholar 

  41. La Manno, G. et al. V., RNA velocity of single cells. Nature 560 (7719), 494–498 (2018).

    Google Scholar 

  42. Ning, Y., Xu, F., Xin, R. & Yao, F. Palmatine regulates bile acid cycle metabolism and maintains intestinal flora balance to maintain stable intestinal barrier. Life Sci. 262, 118405 (2020).

    Google Scholar 

  43. Ning, N. et al. Hypolipidemic Effect and Mechanism of Palmatine from Coptis chinensis in Hamsters Fed High-Fat diet. Phytother Res. 29 (5), 668–673 (2015).

    Google Scholar 

  44. Tong, J., Ding, Y. & Zhang, D. Mechanisms of Actinidia chinensis Planch roots in the treatment of breast cancer based on network pharmacology and molecular docking. Med. (Baltim). 104(31), e43560 (2025).

  45. de Oliveira, A. S., Muniz Seif, E. J. & da Silva Junior, P. I. In silico prospection of receptors associated with the biological activity of U1-SCTRX-lg1a: an antimicrobial peptide isolated from the venom of Loxosceles gaucho. Silico Pharmacol. 12 (1), 15 (2024).

    Google Scholar 

  46. Kanehisa, M., Furumichi, M., Sato, Y., Matsuura, Y. & Ishiguro-Watanabe, M. KEGG: biological systems database as a model of the real world. Nucleic Acids Res. 53 (D1), D672–d677 (2025).

    Google Scholar 

  47. Cheng, X. et al. Quercetin: A promising therapy for diabetic encephalopathy through inhibition of hippocampal ferroptosis. Phytomedicine 126, 154887 (2024).

    Google Scholar 

  48. Singh, A. B., Dong, B., Xu, Y., Zhang, Y. & Liu, J. Identification of a novel function of hepatic long-chain acyl-CoA synthetase-1 (ACSL1) in bile acid synthesis and its regulation by bile acid-activated farnesoid X receptor. Biochim. Biophys. Acta Mol. Cell. Biol. Lipids. 1864 (3), 358–371 (2019).

    Google Scholar 

  49. Schuster-Gaul, S. et al. ASK1 inhibition reduces cell death and hepatic fibrosis in an Nlrp3 mutant liver injury model. JCI Insight 5, (2). (2020).

  50. Hotamisligil, G. S. Endoplasmic reticulum stress and the inflammatory basis of metabolic disease. Cell 140 (6), 900–917 (2010).

    Google Scholar 

  51. Pan, J. et al. Fatty acid activates NLRP3 inflammasomes in mouse Kupffer cells through mitochondrial DNA release. Cell. Immunol. 332, 111–120 (2018).

    Google Scholar 

  52. Friedman, S. L. Mechanisms of hepatic fibrogenesis. Gastroenterology 134 (6), 1655–1669 (2008).

    Google Scholar 

  53. Wang, F. et al. Canonical Wnt signaling promotes HSC glycolysis and liver fibrosis through an LDH-A/HIF-1α transcriptional complex. Hepatology 79 (3), 606–623 (2024).

    Google Scholar 

  54. Duan, Y. et al. Crosstalk in extrahepatic and hepatic system in NAFLD/NASH. Liver Int. 44 (8), 1856–1871 (2024).

    Google Scholar 

  55. Ren, W. et al. Glutamine Metabolism in Macrophages: A Novel Target for Obesity/Type 2 Diabetes. Adv. Nutr. 10 (2), 321–330 (2019).

    Google Scholar 

  56. Liu, J., Wang, G., Jia, Y. & Xu, Y. GLP-1 receptor agonists: effects on the progression of non-alcoholic fatty liver disease. Diabetes Metab. Res. Rev. 31 (4), 329–335 (2015).

    Google Scholar 

  57. He, J. et al. Graveoline attenuates D-GalN/LPS-induced acute liver injury via inhibition of JAK1/STAT3 signaling pathway. Biomed. Pharmacother. 177, 117163 (2024).

    Google Scholar 

  58. Hindson, J. Obeticholic acid for the treatment of NASH. Nat. Rev. Gastroenterol. Hepatol. 17 (2), 66 (2020).

    Google Scholar 

  59. Guicciardi, M. E. & Gores, G. J. Bile acid-mediated hepatocyte apoptosis and cholestatic liver disease. Dig. Liver Dis. 34 (6), 387–392 (2002).

    Google Scholar 

  60. Angelim, M. & Moraes-Vieira, P. M. Glycolysis modulates efferocytosis in a noncanonical manner. Nat. Metab. 5 (3), 360–361 (2023).

    Google Scholar 

  61. Hambright, H. G., Meng, P., Kumar, A. P. & Ghosh, R. Inhibition of PI3K/AKT/mTOR axis disrupts oxidative stress-mediated survival of melanoma cells. Oncotarget 6 (9), 7195–7208 (2015).

    Google Scholar 

  62. Yu, J. et al. Extracellular vesicles derived from menstrual blood-derived mesenchymal stem cells suppress inflammatory atherosclerosis by inhibiting NF-κB signaling. BMC Med. 23 (1), 565 (2025).

    Google Scholar 

  63. Mohammadpour, H., MacDonald, C. R., McCarthy, P. L., Abrams, S. I. & Repasky, E. A. β2-adrenergic receptor signaling regulates metabolic pathways critical to myeloid-derived suppressor cell function within the TME. Cell. Rep. 37 (4), 109883 (2021).

    Google Scholar 

  64. Zhang, Q., Didonato, J. A., Karin, M. & McKeithan, T. W. BCL3 encodes a nuclear protein which can alter the subcellular location of NF-kappa B proteins. Mol. Cell. Biol. 14 (6), 3915–3926 (1994).

    Google Scholar 

  65. Wu, F., Zhang, P. & Zhou, G. The involvement of EGR1 in neuron apoptosis in the in vitro model of spinal cord injury via BTG2 up-regulation. Neurol. Res. 45 (7), 646–654 (2023).

    Google Scholar 

  66. Li, X., Meng, Y., Wu, P., Zhang, Z. & Yang, X. Angiotensin II and Aldosterone stimulating NF-kappaB and AP-1 activation in hepatic fibrosis of rat. Regul. Pept. 138 (1), 15–25 (2007).

    Google Scholar 

  67. Wu, K. C. et al. Tpl2 kinase regulates inflammation but not tumorigenesis in mice. Toxicol. Appl. Pharmacol. 418, 115494 (2021).

    Google Scholar 

  68. Fleischmann, A., Jochum, W., Eferl, R., Witowsky, J. & Wagner, E. F. Rhabdomyosarcoma development in mice lacking Trp53 and Fos: tumor suppression by the Fos protooncogene. Cancer Cell. 4 (6), 477–482 (2003).

    Google Scholar 

  69. Schonthaler, H. B., Guinea-Viniegra, J. & Wagner, E. F. Targeting inflammation by modulating the Jun/AP-1 pathway. Ann. Rheum. Dis. 70 (Suppl 1), i109–i112 (2011).

    Google Scholar 

  70. Liu, S. et al. Melatonin mitigates aflatoxin B1-induced liver injury via modulation of gut microbiota/intestinal FXR/liver TLR4 signaling axis in mice. J. Pineal Res. 73(2), e12812 (2022).

  71. Bhat, A. A. et al. The pyroptotic role of Caspase-3/GSDME signalling pathway among various cancer: A Review. Int. J. Biol. Macromol. 242 (Pt 2), 124832 (2023).

    Google Scholar 

  72. Li, L. et al. Photodynamic therapy induces human esophageal carcinoma cell pyroptosis by targeting the PKM2/caspase-8/caspase-3/GSDME axis. Cancer Lett. 520, 143–159 (2021).

    Google Scholar 

  73. Li, R. Y. et al. Cisplatin-induced pyroptosis is mediated via the CAPN1/CAPN2-BAK/BAX-caspase-9-caspase-3-GSDME axis in esophageal cancer. Chem. Biol. Interact. 361, 109967 (2022).

    Google Scholar 

  74. Li, Y. et al. PD-L1 Regulates Platelet Activation and Thrombosis via Caspase-3/GSDME Pathway. Front. Pharmacol. 13, 921414 (2022).

    Google Scholar 

  75. Mai, F. Y. et al. Caspase-3-mediated GSDME activation contributes to cisplatin- and doxorubicin-induced secondary necrosis in mouse macrophages. Cell. Prolif. 52(5), e12663 (2019).

  76. Mouasni, S. & Tourneur, L. FADD at the Crossroads between Cancer and Inflammation. Trends Immunol. 39 (12), 1036–1053 (2018).

    Google Scholar 

  77. Werner, M. H., Wu, C. & Walsh, C. M. Emerging roles for the death adaptor FADD in death receptor avidity and cell cycle regulation. Cell. Cycle. 5 (20), 2332–2338 (2006).

    Google Scholar 

  78. Scott, F. L. et al. The Fas-FADD death domain complex structure unravels signalling by receptor clustering. Nature 457 (7232), 1019–1022 (2009).

    Google Scholar 

  79. Lavrik, I. et al. The active caspase-8 heterotetramer is formed at the CD95 DISC. Cell. Death Differ. 10 (1), 144–145 (2003).

    Google Scholar 

  80. Li, J. et al. Integrating transcriptomics and network pharmacology to reveal the mechanisms of total Rhizoma Coptidis alkaloids against nonalcoholic steatohepatitis. J. Ethnopharmacol. 322, 117600 (2024).

    Google Scholar 

  81. Lin, G. S. et al. Palmatine attenuates hepatocyte injury by promoting autophagy via the AMPK/mTOR pathway after alcoholic liver disease. Drug Dev. Res. 83 (7), 1613–1622 (2022).

    Google Scholar 

  82. Ai, X. et al. Berberis dictyophylla F. inhibits angiogenesis and apoptosis of diabetic retinopathy via suppressing HIF-1α/VEGF/DLL-4/Notch-1 pathway. J. Ethnopharmacol. 296, 115453 (2022).

    Google Scholar 

  83. Liu, Z. et al. Palmatine ameliorates cisplatin-induced acute kidney injury through regulating Akt and NF-κB/MAPK pathways. Arab. J. Chem. 17 (5), 105731 (2024).

    Google Scholar 

  84. Zhang, W. et al. Antiviral effect of palmatine against infectious bronchitis virus through regulation of NF-κB/IRF7/JAK-STAT signalling pathway and apoptosis. Br. Poult. Sci. 65 (2), 119–128 (2024).

    Google Scholar 

  85. Selase, A., Cynthia, D. A. & Newman, O. et al. Palmatine modulates triple negative mammary carcinoma by regulating the endogenous function of P53, P21 and Mdm2. Biomed. Pharmacol. J. 14(2), 943–954 (2021).

Download references

Acknowledgements

Not applicable.

Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was funded by the National Natural Science Foundation of China (grant numbers 81873231 and 82474381).

Author information

Authors and Affiliations

  1. School of Basic Medicine, Heilongjiang University of Chinese Medicine, Harbin, 150040, China

    Huasen Yang, Zhoujing Shi, Yazhi Qi, Shuchang Bao, Chaochong Li, Junhui Mei, Mingshuang Sun, Yusheng Han & Boyan Ma

  2. Heilongjiang University of Chinese Medicine, Harbin, 150040, China

    Boyan Ma

Authors
  1. Huasen Yang
    View author publications

    Search author on:PubMed Google Scholar

  2. Zhoujing Shi
    View author publications

    Search author on:PubMed Google Scholar

  3. Yazhi Qi
    View author publications

    Search author on:PubMed Google Scholar

  4. Shuchang Bao
    View author publications

    Search author on:PubMed Google Scholar

  5. Chaochong Li
    View author publications

    Search author on:PubMed Google Scholar

  6. Junhui Mei
    View author publications

    Search author on:PubMed Google Scholar

  7. Mingshuang Sun
    View author publications

    Search author on:PubMed Google Scholar

  8. Yusheng Han
    View author publications

    Search author on:PubMed Google Scholar

  9. Boyan Ma
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Huasen Yang: Writing–original draft, Formal analysis, Visualization. Zhoujing Shi: Software, Validation. Yazhi Qi: Investigation, Validation. Shuchang Bao: Investigation, Validation. Chaochong Li: Software, Conceptualization. Junhui Mei: Software, Conceptualization. Mingshuang Sun: Software, Conceptualization. Yusheng Han: Supervision, Writing–review and editing. Boyan Ma: Funding acquisition, Project administration. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Boyan Ma.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics statement

This study is reported in accordance with the ARRIVE guidelines and was approved by the Ethics Committee of Heilongjiang University of Traditional Chinese Medicine (Institutional Animal Use License: SYXK-2020-004; ethical approval number: 2023113008).

Euthanasia method

Intravenous injection of Zoletil™50, followed by cervical dislocation to induce death after anesthesia.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (download JPG )

Supplementary Material 2 (download TIF )

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, H., Shi, Z., Qi, Y. et al. Palmatine ameliorates MASLD in type 2 diabetes by modulating hepatic apoptosis and inflammation. Sci Rep (2026). https://doi.org/10.1038/s41598-026-45476-3

Download citation

  • Received: 18 October 2025

  • Accepted: 19 March 2026

  • Published: 21 March 2026

  • DOI: https://doi.org/10.1038/s41598-026-45476-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

  • Palmatine
  • T2DM
  • MASLD
  • Apoptosis
  • Single-cell sequencing
  • Bioinformatics
  • Experimental validation
Download PDF

Advertisement

Explore content

  • Research articles
  • News & Comment
  • Collections
  • Subjects
  • Follow us on Facebook
  • Follow us on X
  • Sign up for alerts
  • RSS feed

About the journal

  • About Scientific Reports
  • Contact
  • Journal policies
  • Guide to referees
  • Calls for Papers
  • Editor's Choice
  • Journal highlights
  • Open Access Fees and Funding

Publish with us

  • For authors
  • Language editing services
  • Open access funding
  • Submit manuscript

Search

Advanced search

Quick links

  • Explore articles by subject
  • Find a job
  • Guide to authors
  • Editorial policies

Scientific Reports (Sci Rep)

ISSN 2045-2322 (online)

nature.com footer links

About Nature Portfolio

  • About us
  • Press releases
  • Press office
  • Contact us

Discover content

  • Journals A-Z
  • Articles by subject
  • protocols.io
  • Nature Index

Publishing policies

  • Nature portfolio policies
  • Open access

Author & Researcher services

  • Reprints & permissions
  • Research data
  • Language editing
  • Scientific editing
  • Nature Masterclasses
  • Research Solutions

Libraries & institutions

  • Librarian service & tools
  • Librarian portal
  • Open research
  • Recommend to library

Advertising & partnerships

  • Advertising
  • Partnerships & Services
  • Media kits
  • Branded content

Professional development

  • Nature Awards
  • Nature Careers
  • Nature Conferences

Regional websites

  • Nature Africa
  • Nature China
  • Nature India
  • Nature Japan
  • Nature Middle East
  • Privacy Policy
  • Use of cookies
  • Legal notice
  • Accessibility statement
  • Terms & Conditions
  • Your US state privacy rights
Springer Nature

© 2026 Springer Nature Limited

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research