journal article·2024-10

M2 Macrophage‐Derived Exosomes Inhibiting Neutrophil Extracellular Traps for Ischemic Stroke Therapy

Zhenhua Wang, Hongrui Qing, Ran Li, Xue Li, Xing Guo, Shaobing Zhou

Advanced Functional Materials· Volume 34· Issue 42
Publisher page

Abstract

Abstract Neutrophil extracellular traps (NETs) are highly associated with inflammatory response and vascular injury after ischemic stroke. As the primary degrader of NETs, DNase 1 is limited by easy deactivation and low efficiency of crossing the blood‐brain barrier (BBB). Here, CD206 + M2‐like macrophages‐derived exosomal (M2exo) system is developed for DNase 1 delivery to achieve enhanced ischemic stroke therapy. The nanoplatform can cross the BBB through transcytosis of exosomes, subsequently clearing NETs by DNase 1 to inhibit inflammatory factors release and prevent vascular injury. Moreover, M2exo induces the polarization of M1 microglia to M2 phenotype, alleviating neuroinflammation via producing anti‐inflammatory cytokines. This nanoplatform exhibits significant efficiency in reducing brain infarct area, improving long‐term neurologic outcomes, and promoting BBB remodeling. The mechanism for the synergistic effect from M2exo and DNase 1 is unveiled at the genetic level through transcriptome analysis. This work provides a paradigm for improving the effectiveness of ischemic stroke therapy.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Neutrophil, Myeloperoxidase and Oxidative Mechanisms · Neuroinflammation and Neurodegeneration Mechanisms · Immune cells in cancer

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Institute of Biomedical Engineering College of Medicine Southwest Jiaotong University Chengdu 610031 P. R. China

Key Laboratory of Advanced Technologies of Materials Ministry of Education School of Materials Science and Engineering Southwest Jiaotong University Chengdu 610031 P. R. China

Funding

National Key Research and Development Program of China · National Natural Science Foundation of China · Natural Science Foundation of Sichuan Province · Fundamental Research Funds for the Central Universities

References (40)

Show source-provided references
1. 10.1016/S0140-6736(20)31179-X10.1016/S0140-6736(20)31179-X ↗
2. 10.1038/nm.239910.1038/nm.2399 ↗
3. 10.1038/s41569-019-0326-710.1038/s41569-019-0326-7 ↗
4. 10.1038/nri.2017.10510.1038/nri.2017.105 ↗
5. 10.1126/sciimmunol.aaw033610.1126/sciimmunol.aaw0336 ↗
6. 10.1126/sciadv.aax884710.1126/sciadv.aax8847 ↗
7. 10.1002/hep.2784110.1002/hep.27841 ↗
8. 10.1038/s41467-020-16191-y10.1038/s41467-020-16191-y ↗
9. 10.1126/science.aar242810.1126/science.aar2428 ↗
10. 10.1172/JCI15422510.1172/JCI154225 ↗
11. 10.1007/s12035-021-02635-z10.1007/s12035-021-02635-z ↗
12. 10.1038/nrneurol.2017.6910.1038/nrneurol.2017.69 ↗
13. 10.1126/sciadv.abq780610.1126/sciadv.abq7806 ↗
14. 10.14336/AD.2020.070110.14336/AD.2020.0701 ↗
15. 10.1016/j.biomaterials.2021.12087310.1016/j.biomaterials.2021.120873 ↗
16. 10.1021/acsnano.7b0449510.1021/acsnano.7b04495 ↗
17. 10.1016/j.biomaterials.2020.11994210.1016/j.biomaterials.2020.119942 ↗
18. 10.1080/08830185.2020.180068710.1080/08830185.2020.1800687 ↗
19. 10.1016/j.jconrel.2021.11.01910.1016/j.jconrel.2021.11.019 ↗
20. 10.1002/anie.20191370010.1002/anie.201913700 ↗
21. 10.1016/j.canlet.2021.10.04510.1016/j.canlet.2021.10.045 ↗
22. 10.1038/s41564-019-0405-x10.1038/s41564-019-0405-x ↗
23. 10.1016/j.neuroimage.2008.10.04910.1016/j.neuroimage.2008.10.049 ↗
24. 10.7150/thno.3087910.7150/thno.30879 ↗
25. 10.7150/thno.4876110.7150/thno.48761 ↗
26. 10.1182/blood.202000891310.1182/blood.2020008913 ↗
27. 10.1172/JCI15422510.1172/JCI154225 ↗
28. 10.1083/jcb.20021002010.1083/jcb.200210020 ↗
29. 10.1038/nsmb.166910.1038/nsmb.1669 ↗
30. 10.1073/pnas.070255310410.1073/pnas.0702553104 ↗
31. 10.1084/jem.2016093510.1084/jem.20160935 ↗
32. 10.1093/cvr/cvz23210.1093/cvr/cvz232 ↗
33. 10.1016/S0016-5085(20)34095-610.1016/S0016-5085(20)34095-6 ↗
34. 10.1182/blood-2020-14338410.1182/blood-2020-143384 ↗
35. 10.1182/blood.V108.11.3434.343410.1182/blood.V108.11.3434.3434 ↗
36. 10.1002/ajh.2497010.1002/ajh.24970 ↗
37. 10.1021/acsnano.0c0797310.1021/acsnano.0c07973 ↗
38. 10.1021/acsnano.1c0067210.1021/acsnano.1c00672 ↗
39. 10.1182/blood-2016-10-74708910.1182/blood-2016-10-747089 ↗
40. 10.1093/bioinformatics/btu17010.1093/bioinformatics/btu170 ↗