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.

  • Article
  • Published:

Advanced glycation end-products as mediators of the aberrant crosslinking of extracellular matrix in scarred liver tissue

Nature Biomedical Engineering volume 7, pages 1437–1454 (2023)Cite this article

Subjects

Abstract

The extracellular matrix of cirrhotic liver tissue is highly crosslinked. Here we show that advanced glycation end-products (AGEs) mediate crosslinking in liver extracellular matrix and that high levels of crosslinking are a hallmark of cirrhosis. We used liquid chromatography–tandem mass spectrometry to quantify the degree of crosslinking of the matrix of decellularized cirrhotic liver samples from patients and from two mouse models of liver fibrosis and show that the structure, biomechanics and degree of AGE-mediated crosslinking of the matrices can be recapitulated in collagen matrix crosslinked by AGEs in vitro. Analyses via cryo-electron microscopy and optical tweezers revealed that crosslinked collagen fibrils form thick bundles with reduced stress relaxation rates; moreover, they resist remodelling by macrophages, leading to reductions in their levels of adhesion-associated proteins, altering HDAC3 expression and the organization of their cytoskeleton, and promoting a type II immune response of macrophages. We also show that rosmarinic acid inhibited AGE-mediated crosslinking and alleviated the progression of fibrosis in mice. Our findings support the development of therapeutics targeting crosslinked extracellular matrix in scarred liver tissue.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Liver cirrhosis is accompanied by ECM crosslinking.
Fig. 2: AQMC identifies AGE crosslinking of ECM as a critical feature of liver cirrhosis.
Fig. 3: In vitro reconstruction of AGE-crosslinked collagen matrix recapitulating in vivo late-fibrotic liver ECM.
Fig. 4: AGE crosslinking reduces the stress relaxation rate of collagen matrix at the single-fibril level.
Fig. 5: AGE-crosslinked fibrils show resistance to macrophage-mediated remodelling.
Fig. 6: Macrophages grown on AGE-crosslinked matrix show downregulated type I response and upregulated type II response.
Fig. 7: Schematic of AGE crosslinking effect on liver ECM during fibrosis progression.
Fig. 8: RA inhibits AGE crosslinking in liver ECM and alleviates late-stage liver fibrosis.

Similar content being viewed by others

Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The raw RNA-seq data are available at the Sequence Read Archive database via the accession numbers PRJNA852213 and PRJNA856261. Source data are provided with this paper.

References

  1. Schuppan, D. & Afdhal, N. H. Liver cirrhosis. Lancet 371, 838–851 (2008).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  2. Tsochatzis, E. A., Bosch, J. & Burroughs, A. K. Liver cirrhosis. Lancet 383, 1749–1761 (2014).

    Article  PubMed  Google Scholar 

  3. Issa, R. et al. Spontaneous recovery from micronodular cirrhosis: evidence for incomplete resolution associated with matrix cross-linking. Gastroenterology 126, 1795–1808 (2004).

    Article  CAS  PubMed  Google Scholar 

  4. Liu, S. B. et al. Lysyl oxidase activity contributes to collagen stabilization during liver fibrosis progression and limits spontaneous fibrosis reversal in mice. FASEB J. 30, 1599–1609 (2016).

    Article  CAS  PubMed  Google Scholar 

  5. Sorushanova, A. et al. The collagen suprafamily: from biosynthesis to advanced biomaterial development. Adv. Mater. 31, e1801651 (2019).

    Article  PubMed  Google Scholar 

  6. Harrison, S. A. et al. Simtuzumab is ineffective for patients with bridging fibrosis or compensated cirrhosis caused by nonalcoholic steatohepatitis. Gastroenterology 155, 1140–1153 (2018).

    Article  CAS  PubMed  Google Scholar 

  7. Fickert, P. Is this the last requiem for simtuzumab? Hepatology 69, 476–479 (2019).

    Article  PubMed  Google Scholar 

  8. Lampi, M. C. & Reinhart-King, C. A. Targeting extracellular matrix stiffness to attenuate disease: from molecular mechanisms to clinical trials. Sci. Transl. Med. 10, eaao0475 (2018).

    Article  PubMed  Google Scholar 

  9. Monnier, V. M. et al. Cross-linking of the extracellular matrix by the Maillard reaction in aging and diabetes: an update on ‘a puzzle nearing resolution’. Ann. N. Y. Acad. Sci. 1043, 533–544 (2005).

    Article  CAS  PubMed  Google Scholar 

  10. Garcia-Compean, D. et al. The prevalence and clinical characteristics of glucose metabolism disorders in patients with liver cirrhosis. A prospective study. Ann. Hepatol. 11, 240–248 (2012).

    Article  CAS  PubMed  Google Scholar 

  11. Guo, C. H. et al. The investigation of glucose metabolism and insulin secretion in subjects of chronic hepatitis B with cirrhosis. Int. J. Clin. Exp. Pathol. 8, 13381–13386 (2015).

    CAS  PubMed Central  PubMed  Google Scholar 

  12. Elkrief, L. et al. Diabetes mellitus in patients with cirrhosis: clinical implications and management. Liver Int. 36, 936–948 (2016).

    Article  CAS  PubMed  Google Scholar 

  13. Nkontchou, G. et al. Insulin resistance, serum leptin, and adiponectin levels and outcomes of viral hepatitis C cirrhosis. J. Hepatol. 53, 827–833 (2010).

    Article  CAS  PubMed  Google Scholar 

  14. Brings, S. et al. Dicarbonyls and advanced glycation end-products in the development of diabetic complications and targets for intervention. Int. J. Mol. Sci. 18, 984 (2017).

    Article  PubMed Central  PubMed  Google Scholar 

  15. Snedeker, J. G. & Gautieri, A. The role of collagen crosslinks in ageing and diabetes - the good, the bad, and the ugly. Muscles 4, 303–308 (2014).

    Google Scholar 

  16. Henning, C. & Glomb, M. A. Pathways of the Maillard reaction under physiological conditions. Glycoconj. J. 33, 499–512 (2016).

    Article  CAS  PubMed  Google Scholar 

  17. Fessel, G. et al. Advanced glycation end-products reduce collagen molecular sliding to affect collagen fibril damage mechanisms but not stiffness. PLoS ONE 9, e110948 (2014).

    Article  PubMed Central  PubMed  Google Scholar 

  18. Hall, M. S. et al. Fibrous nonlinear elasticity enables positive mechanical feedback between cells and ECMs. Proc. Natl Acad. Sci. USA 113, 14043–14048 (2016).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  19. Maller, O. et al. Tumour-associated macrophages drive stromal cell-dependent collagen crosslinking and stiffening to promote breast cancer aggression. Nat. Mater. 20, 548–559 (2021).

    Article  CAS  PubMed  Google Scholar 

  20. Baker, B. M. et al. Cell-mediated fibre recruitment drives extracellular matrix mechanosensing in engineered fibrillar microenvironments. Nat. Mater. 14, 1262–1268 (2015).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  21. Liu, L. W. et al. Matrix-transmitted paratensile signaling enables myofibroblast-fibroblast cross talk in fibrosis expansion. Proc. Natl Acad. Sci. USA 117, 10832–10838 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  22. Ramachandran, P. & Iredale, J. P. Macrophages: central regulators of hepatic fibrogenesis and fibrosis resolution. J. Hepatol. 56, 1417–1419 (2012).

    Article  PubMed  Google Scholar 

  23. Klepfish, M. et al. LOXL2 inhibition paves the way for macrophage-mediated collagen degradation in liver fibrosis. Front. Immunol. 11, 480 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  24. Van Goethem, E., Poincloux, R., Gauffre, F., Maridonneau-Parini, I. & Le Cabec, V. Matrix architecture dictates three-dimensional migration modes of human macrophages: differential involvement of proteases and podosome-like structures. J. Immunol. 184, 1049–1061 (2010).

    Article  PubMed  Google Scholar 

  25. Wynn, T. A. Fibrotic disease and the T(H)1/T(H)2 paradigm. Nat. Rev. Immunol. 4, 583–594 (2004).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  26. Gordon, S. & Martinez, F. O. Alternative activation of macrophages: mechanism and functions. Immunity 32, 593–604 (2010).

    Article  CAS  PubMed  Google Scholar 

  27. Lopez-Navarrete, G. et al. Th2-associated alternative Kupffer cell activation promotes liver fibrosis without inducing local inflammation. Int. J. Biol. Sci. 7, 1273–1286 (2011).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  28. Garcia-Tsao, G., Friedman, S., Iredale, J. & Pinzani, M. Now there are many (stages) where before there was one: in search of a pathophysiological classification of cirrhosis. Hepatology 51, 1445–1449 (2010).

    Article  PubMed  Google Scholar 

  29. Wei, G. et al. Comparison of murine steatohepatitis models identifies a dietary intervention with robust fibrosis, ductular reaction, and rapid progression to cirrhosis and cancer. Am. J. Physiol. Gastrointest. Liver Physiol. 318, G174–G188 (2020).

    Article  PubMed  Google Scholar 

  30. Hu, M. et al. Hepatic macrophages act as a central hub for relaxin-mediated alleviation of liver fibrosis. Nat. Nanotechnol. 16, 466–477 (2021).

    Article  CAS  PubMed  Google Scholar 

  31. Jean, D., Pouligon, M. & Dalle, C. Evaluation in vitro of AGE-crosslinks breaking ability of rosmarinic acid. Glycative Stress Res. 2, 204–207 (2015).

    Google Scholar 

  32. Ou, J. Y., Huang, J. Q., Wang, M. F. & Ou, S. Y. Effect of rosmarinic acid and carnosic acid on AGEs formation in vitro. Food Chem. 221, 1057–1061 (2017).

    Article  CAS  PubMed  Google Scholar 

  33. Gautieri, A., Redaelli, A., Buehler, M. J. & Vesentini, S. Age- and diabetes-related nonenzymatic crosslinks in collagen fibrils: candidate amino acids involved in Advanced Glycation End-products. Matrix Biol. 34, 89–95 (2014).

    Article  CAS  PubMed  Google Scholar 

  34. Cao, X. et al. Multiscale model predicts increasing focal adhesion size with decreasing stiffness in fibrous matrices. Proc. Natl Acad. Sci. USA 114, E4549–E4555 (2017).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  35. Adebowale, K. et al. Enhanced substrate stress relaxation promotes filopodia-mediated cell migration. Nat. Mater. 20, 1290–1299 (2021).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  36. Jain, N. & Vogel, V. Spatial confinement downsizes the inflammatory response of macrophages. Nat. Mater. 17, 1134–1144 (2018).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  37. Nguyen, H. C. B., Adlanmerini, M., Hauck, A. K. & Lazar, M. A. Dichotomous engagement of HDAC3 activity governs inflammatory responses. Nature 584, 286–290 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  38. Lee, H. P., Gu, L., Mooney, D. J., Levenston, M. E. & Chaudhuri, O. Mechanical confinement regulates cartilage matrix formation by chondrocytes. Nat. Mater. 16, 1243–1251 (2017).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  39. Jiang, S. et al. Cryoprotectant enables structural control of porous scaffolds for exploration of cellular mechano-responsiveness in 3D. Nat. Commun. 10, 3491 (2019).

    Article  PubMed Central  PubMed  Google Scholar 

  40. Kagan, H. M. & Li, W. D. Lysyl oxidase: properties, specificity, and biological roles inside and outside of the cell. J. Cell. Biochem. 88, 660–672 (2003).

    Article  CAS  PubMed  Google Scholar 

  41. Stammers, M. et al. Age-related changes in the physical properties, cross-linking, and glycation of collagen from mouse tail tendon. J. Biol. Chem. 295, 10562–10571 (2020).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  42. Jost, T., Zipprich, A. & Glomb, M. A. Analysis of advanced glycation endproducts in rat tail collagen and correlation to tendon stiffening. J. Agric. Food Chem. 66, 3957–3965 (2018).

    Article  CAS  PubMed  Google Scholar 

  43. Petrides, A. S., Vogt, C., Schulzeberge, D., Matthews, D. & Strohmeyer, G. Pathogenesis of glucose-intolerance and diabetes mellitus in cirrhosis. Hepatology 19, 616–627 (1994).

    Article  CAS  PubMed  Google Scholar 

  44. Nenna, A. et al. Basic and clinical research against Advanced Glycation End Products (AGEs): new compounds to tackle cardiovascular disease and diabetic complications. Recent Adv. Cardiovasc. Drug Discov. 10, 10–33 (2015).

    Article  CAS  PubMed  Google Scholar 

  45. Goodwin, M. et al. Advanced glycation end products augment experimental hepatic fibrosis. J. Gastroenterol. Hepatol. 28, 369–376 (2013).

    Article  CAS  PubMed  Google Scholar 

  46. Valencia, J. V., Mone, M., Koehne, C., Rediske, J. & Hughes, T. E. Binding of receptor for advanced glycation end products (RAGE) ligands is not sufficient to induce inflammatory signals: lack of activity of endotoxin-free albumin-derived advanced glycation end products. Diabetologia 47, 844–852 (2004).

    Article  CAS  PubMed  Google Scholar 

  47. Gaar, J., Naffa, R. & Brimble, M. Enzymatic and non-enzymatic crosslinks found in collagen and elastin and their chemical synthesis. Org. Chem. Front. 7, 2789–2814 (2020).

    Article  CAS  Google Scholar 

  48. Kong, W. Y., Lyu, C., Liao, H. G. & Du, Y. N. Collagen crosslinking: effect on structure, mechanics and fibrosis progression. Biomed. Mater. https://iopscience.iop.org/article/10.1088/1748-605X/ac2b79 (2021).

  49. Zhang, J. J., Wang, Y. L., Feng, X. B., Song, X. D. & Lu, W. B. Rosmarinic acid inhibits proliferation and induces apoptosis of hepatic stellate cells. Biol. Pharm. Bull. 34, 343–348 (2011).

    Article  PubMed  Google Scholar 

  50. Luo, C. et al. A review of the anti-inflammatory effects of rosmarinic acid on inflammatory diseases. Front. Pharm. 11, 153 (2020).

    Article  CAS  Google Scholar 

  51. Yang, M. D. et al. Rosmarinic acid and baicalin epigenetically derepress peroxisomal proliferator-activated receptor gamma in hepatic stellate cells for their antifibrotic effect. Hepatology 55, 1271–1281 (2012).

    Article  CAS  PubMed  Google Scholar 

  52. Domitrovic, R. et al. Rosmarinic acid ameliorates acute liver damage and fibrogenesis in carbon tetrachloride-intoxicated mice. Food Chem. Toxicol. 51, 370–378 (2013).

    Article  CAS  PubMed  Google Scholar 

  53. Levental, K. R. et al. Matrix crosslinking forces tumor progression by enhancing integrin signaling. Cell 139, 891–906 (2009).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  54. Han, Y. L. et al. Cell contraction induces long-ranged stress stiffening in the extracellular matrix. Proc. Natl Acad. Sci. USA 115, 4075–4080 (2018).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  55. Chaudhuri, O. et al. Hydrogels with tunable stress relaxation regulate stem cell fate and activity. Nat. Mater. 15, 326–334 (2016).

    Article  CAS  PubMed  Google Scholar 

  56. Rezaei, N. et al. Using optical tweezers to study mechanical properties of collagen. Proc. SPIE https://doi.org/10.1117/12.905714 (2011).

  57. Jiao, J. Y., Rebane, A. A., Ma, L. & Zhang, Y. L. Single-molecule protein folding experiments using high-precision optical tweezers. Methods Mol. Biol. 1486, 357–390 (2017).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  58. Wang, M. D., Yin, H., Landick, R., Gelles, J. & Block, S. M. Stretching DNA with optical tweezers. Biophys. J. 72, 1335–1346 (1997).

    Article  CAS  PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgements

We thank W. Wang, F. Wei and T. Yu at the Center of Pharmaceutical Technology, Tsinghua University for assistance in HPLC/MS assay; the sequencing core facility, Tsinghua University Cryo-EM Facility of the China National Center for Protein Sciences (Tsinghua University, Beijing, China) for help in Cryo-EM experiments; L. Bingyu at the Imaging Core Facility, Technology Center for Protein Sciences (Tsinghua University, Beijing, China) for assistance in operating Imaris 9.7 and Amira 20.2; J. Wang and Y. Sun at the Cell Biology Facility, Center of Biomedical Analysis (Tsinghua University, Beijing, China) for assistance with confocal microscopy; and the Laboratory Animal Resources Center (Tsinghua University, Beijing, China) for technical support. Some of the illustrations were created with reference to pictures in BioRender.com and ref. 19. This work was financially supported by the National Natural Science Foundation of China (82125018).

Author information

Author notes

  1. These authors contributed equally: Cheng Lyu, Wenyu Kong.

Authors and Affiliations

  1. Department of Biomedical Engineering, School of Medicine, Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, China

    Cheng Lyu, Wenyu Kong, Zhiqiang Liu, Peng Zhao, Kaini Liang, Yudi Niu & Yanan Du

  2. School of Life Sciences, Tsinghua University, Beijing, China

    Sihan Wang & Xueming Li

  3. Department of Hepatobiliary Surgery, Beijing Tsinghua Changgung Hospital, Beijing, China

    Wei Yang & Canhong Xiang

  4. Institute for Immunology and School of Medicine, Tsinghua University, Beijing, China

    Xiaoyu Hu

  5. Beijing Key Laboratory for Immunological Research on Chronic Diseases, Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, China

    Xiaoyu Hu

Authors
  1. Cheng Lyu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenyu Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhiqiang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sihan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Peng Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kaini Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yudi Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Wei Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Canhong Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Xiaoyu Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Xueming Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yanan Du
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.L. and Y.D. conceived and designed the research. C.L. and W.K. performed the AQMC assays, collagen matrix construction, BMDM studies and animal experiments. Z.L. performed the optical tweezer experiments. W.K. and S.W. performed the cryo-EM experiments and established 3D collagen fibril models with the guidance of X.L. W.Y. and C.X. provided the clinical tissue samples and helped with the clinical consultation. P.Z. helped with the animal studies. K.L. prepared the illustrations. Y.N. helped with the preparation of stiffened collagen matrix. X.H. provided the guidance for the BMDM studies. C.L. and Y.D. wrote the paper. Y.D. is the principal investigator of the supporting grants.

Corresponding author

Correspondence to Yanan Du.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Biomedical Engineering thanks Joshua Doloff, Shaik Rahaman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 Schematic of developing Absolute Quantification of Matrix-specific Crosslinking (AQMC) method.

a, Flow chart of quantifying crosslinking degree of liver decellularized ECM using AQMC method (detailed in Methods section). (1) Harvesting the liver decellularized ECM. Quality control is performed to verify the complete de-cell process without cellular component left. (2) Degradation of the liver ECM into fragments. (3) Complete degradation of the fragments into dipeptides and amino acids. (4) Enrichment of the crosslinking products. (5) Absolute quantification of the mole quantity of crosslinking products and hydroxyproline using LC-MS/MS. (6) Calculation of the crosslinking degree. The crosslinking degree of a certain type of crosslinking is defined as the molar ratio of crosslinking products and tropocollagen molecules. The mole quantity of triple-helix tropocollagen molecules is calculated according to the content of hydroxyproline as detailed in Methods section. b, Schematic of preparing mouse liver decellularized ECM using the whole-liver-perfusion method. The decellularization buffer is perfused into the liver from the hepatic portal vein and effused out from the inferior vena cava, as detailed in Methods section. c, Quality control of the mouse liver decellularization ECM performed by HE, Sirius Red, and cell nuclei staining. Nuclear components in liver tissue were removed by decellularization, whereas main ECM components (that is, collagen fibrils) were preserved as indicated by Sirius Red staining. Scale bars, 100 μm. d, Dry weight of liver decellularized ECM samples used for AQMC test. The mean ± s.e.m. values are marked on the plot.

Source data

Extended Data Fig. 2 MS/MS fragmentation spectra of standard crosslinking products.

a, LOX crosslinking product, pyridinoline (PYD), is marked by green colour. b, TGM crosslinking product, γ-Glutamyl-ε-Lysine (γ-GLY-ε-LYS), is marked by red colour. c-f, AGE crosslinking products are marked by blue colour. Total crosslinking degree of AGE crosslinking is quantified by the molar sum of CML (c), CEL (d), glucosepane (e), and pentosidine (f). The MH + labels indicate the protonated forms of precursor ions. Square labels above ions peaks are matched to their suggested fragment ion structures used to identify the crosslinking products.

Source data

Extended Data Fig. 3 Quantification of ECM crosslinking in livers from HFCDAA diet-induced liver fibrosis (HF fibrosis) model.

a, Schematic of preparing liver decellularized ECM from mice with HFCDAA diet-induced liver fibrosis. b, Representative images of Sirius red staining in livers from healthy mice and HF-fibrosis mice. Scale bars, 200 μm. c, Representative bright-field image of liver decellularized matrix harvested from HF-fibrosis mice. Scale bar, 1 cm. d, Representative SEM images of liver decellularized ECM from HF-fibrosis mice. Scale bars, 2 μm. e-j, Quantification of the crosslinking degree in liver ECM from HF-fibrosis mice using AQMC method. Data are shown in mole quantity of specific crosslinks normalized to mole quantity of tropocollagen molecules. e, LOX crosslinking degree quantified by pyridinoline (n ≥ 8); f, TGM crosslinking degree quantified by γ-Glutamyl-ε-Lysine (n ≥ 8); g, AGE crosslinking degree quantified by CML (h), CEL (i) and glucosepane (j) (n ≥ 8); Pentosidine is not detectable in this HF-fibrosis model. Results are presented as means ± s.e.m. The statistical analysis was performed using two-tailed unpaired t-test. All n values represent pieces of liver decellularized matrix obtained from at least 4 biologically independent mice.

Source data

Extended Data Fig. 4 Reagent residuals in the reconstructed AGE-crosslinked collagen matrix have negligible effects on macrophages’ response.

a, Characterization of reagent residuals in collagen matrix. b, Macrophages’ response to endotoxin of different concentrations. Verified by relative mRNA expression of Nos2 (b), Il6 (c), and Il1b (d). The detailed results in upper panels within the endotoxin concentration range of 0-0.05 EU·ml−1 are shown in corresponding panels below (n = 3, biologically independent samples per group). The blue dashed lines indicate the endotoxin residue levels in the reconstructed collagen matrix (~0.03 EU·ml−1). These results indicate that endotoxin residues in the in vitro collagen matrix keep at very low levels and cannot induce the unexpected response of macrophages. The statistical analysis was performed using a one-way ANOVA with Turkey test. Results are presented as mean ± s.d.

Source data

Extended Data Fig. 5 Decreased expression of HDAC3 in macrophages is observed when inhibiting F-actin organization by applying increased osmotic pressure.

a-e, Characterization of F-actin organization and HDAC3 expression in macrophages grown on 2D substrate. Cells were untreated or treated by PEG-induced osmotic pressure for 24 h (as detailed in Methods section). a, Representative images of F-actin (red) and G-actin (green) in macrophages with or without the treatment by the increased osmotic pressure. Scar bars, 10 μm. b, Statistical analysis of F/G-actin ratio in macrophages as shown in (a) (n ≥ 14, number of cells analysed per condition). c, Representative images of HDAC3 staining in macrophages. Top panel, HDAC3 (yellow), F-actin (green), cell nuclear (blue). Bottom panels, Colour-coded images of HDAC3. Colour bar indicates pixel intensity values. Scale bars, 10 μm. d, Statistical analysis of total HDAC3 intensity per cell as shown in (c) (n = 10, number of cells analysed per condition). e, Western blot analysis of HDAC3 expression in macrophages. Data are representative of three independent experiments. f-i, Characterization of F-actin organization and HDAC3 expression in macrophages grown on collagen matrix. NC (grey): macrophages grown on non-crosslinked collagen matrix; NC + Pressure (cyan): macrophages grown on non-crosslinked collagen matrix with treatment by PEG-induced osmotic pressure for 24 h; AGE (blue): macrophages grown on AGE-crosslinked collagen matrix. f, Representative images of F-actin (red) and G-actin (green) in macrophages grown on collagen matrix with or without the treatment by increased osmotic pressure. Scar bars, 4 μm. g, Representative images of HDAC3 staining in macrophages. Top panel, HDAC3 (yellow), F-actin (green), cell nuclear (blue). Bottom panels, Colour-coded images of HDAC3. Colour bar indicates pixel intensity values. Scale bars, 4 μm. h, Statistical analysis of F/G-actin ratio in macrophages as shown in (f) (n = 11, number of cells analysed per condition). i, Statistical analysis of total HDAC3 intensity per cell as shown in (c) (n = 11, number of cells analysed per condition). The statistical analysis was performed using two-tailed unpaired t-test in (b, d) and using a one-way ANOVA with Turkey test in (h, i). Results are presented as mean ± s.e.m.

Source data

Extended Data Fig. 6 Quantification of AGE crosslinking degree in liver ECM from mice with HFCDAA diet-induced liver fibrosis (HF fibrosis) after RA treatment.

a, Schematic of quantifying AGE crosslinking in liver decellularized ECM from HF-fibrotic mice after RA treatment. b, Representative bright-field images of liver decellularized ECM. Scale bars, 1 cm. c, Statistical analysis of the dry weight of entire liver decellularized ECM (n ≥ 4, biologically independent mice per group). d-g, Quantification of AGE crosslinking degree of liver decellularized ECM using AQMC method. d, Total AGE crosslinking degree; e, CML; f, CEL; g, Glucosepane (n = 6, pieces of liver decellularized ECM derived from at least 3 biologically independent mice). Results are presented as mean ± s.e.m. The statistical analysis was performed using two-tailed unpaired t-test.

Source data

Extended Data Fig. 7 Comparative analysis of ECM crosslinking degree from cirrhotic patients’ liver samples with different disease history.

a, Results from cirrhotic patients with (n = 9, pieces of liver ECM obtained from 5 independent patients) or without (n = 9, pieces of liver ECM obtained from 4 independent patients) viral hepatitis. b, Results from cirrhotic patients with (n = 12, pieces of liver ECM obtained from 6 independent patients) or without (n = 6, pieces of liver ECM obtained from 3 independent patients) carcinoma. The statistical analysis was performed using two-tailed unpaired t-test between the groups with or without viral hepatitis (a), and with or without carcinoma (b). Exact P values are marked on the plots. Results are presented as mean ± s.e.m.

Source data

Extended Data Fig. 8 TGM crosslinking affects structural and mechanical properties of collagen matrix in a way different from AGE crosslinking.

a, Schematic of reconstructing TGM-crosslinked collagen matrix in vitro. NC, Non-crosslinked collagen matrix; TGMlow, TGM-crosslinked collagen matrix with a low crosslinking degree; TGMhi, TGM-crosslinked collagen matrix with a high crosslinking degree; TGMhi + cys, collagen matrix with additional cystamine to inhibit TGMhi treatment. b, Quantification of TGM crosslinking degree of reconstructed collagen matrix using AQMC method. Data are shown in mole quantity of γ-Glutamyl-ε-Lysine normalized to mole quantity of tropocollagen molecules (n ≥ 6, independent collagen matrix samples). TGMhi collagen matrix is selected for the following assays in c-g (shown as TGM-crosslinked). c, Representative SEM images of non-crosslinked and TGM-crosslinked collagen matrix. Scale bars, 2 μm. d, Representative SEM images of macrophages grown on non-crosslinked and TGM-crosslinked collagen matrix. The bottom panels show protrusions of macrophages binding adjacent collagen fibrils. Scale bars, 5 μm. e, Statistical analysis of fibril diameter (n ≥ 123, number of fibrils randomly selected from at least 5 fields of SEM images). f, Statistical analysis of the Young’s modulus of non-crosslinked and TGM-crosslinked collagen matrix measured by AFM (n ≥ 171, points of measurement randomly selected from at least 10 fields of samples). g, Statistical analysis of the roundness of macrophages grown on non-crosslinked and TGM-crosslinked collagen matrix (n = 6, number of cells analysed per condition). The statistical analysis was performed using a one-way ANOVA with Turkey test in (b) and using two-tailed unpaired t-test in (e-g). Results are presented as mean ± s.e.m.

Source data

Supplementary information

Supplementary Information

Supplementary discussion, methods, figures, tables, references and video captions.

Reporting Summary

Supplementary Video 1

3D reconstruction model of a non-crosslinked collagen fibril.

Supplementary Video 2

3D reconstruction model of an AGE-crosslinked collagen fibril.

Supplementary Video 3

3D reconstruction model of collagen fibril where AGE crosslinking was inhibited by RA (AGE + RA).

Supplementary Video 4

3D view of F-actin in a macrophage that co-localized with collagen fibrils in the non-crosslinked matrix.

Supplementary Video 5

3D view of F-actin in a macrophage that co-localized with collagen fibrils in the AGE-crosslinked matrix.

Supplementary Video 6

Time-lapse imaging showing the displacement of non-crosslinked collagen fibrils caused by force applied by an optical trap.

Supplementary Video 7

Time-lapse imaging showing the displacement of AGE-crosslinked collagen fibrils caused by force applied by an optical trap.

Supplementary Video 8

Time-lapse imaging showing the displacement of AGE + RA collagen fibrils caused by force applied by an optical trap.

Source data

Source Data for Figs. 1–8 and Extended Data Figs. 1–8

Source data for all main figures and Extended Data figures, and unprocessed gels for Supplementary Fig. 14.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lyu, C., Kong, W., Liu, Z. et al. Advanced glycation end-products as mediators of the aberrant crosslinking of extracellular matrix in scarred liver tissue. Nat. Biomed. Eng 7, 1437–1454 (2023). https://doi.org/10.1038/s41551-023-01019-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41551-023-01019-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing