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Cholesterol modulates the physiological response to nanoparticles by changing the composition of protein corona

Nature Nanotechnology volume 18, pages 1067–1077 (2023)Cite this article

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Abstract

Nanoparticles (NPs) in biological fluids form a layer of biomolecules known as the protein corona. The protein corona has been shown to determine the biological identity and in vivo fate of NPs, but whether and how metabolites, especially disease-related small molecules, regulate the protein corona and subsequently impact NP fate in vivo is relatively poorly understood. Here we report on the effects of cholesterol on the generation of protein corona and subsequent effects. We find that high levels of cholesterol, as in hypercholesterolemia, result in a protein corona with enriched apolipoproteins and reduced complement proteins by altering the binding affinity of the proteins to the NPs. The cholesterol-mediated protein corona can induce stronger inflammatory responses to NPs in macrophages and promote the cellular uptake of NPs in hepatocytes by enhancing the recognition of lipoprotein receptors when compared with normal protein corona. The result of in vivo biodistribution assays shows that, compared with healthy mice, NPs in hypercholesterolemic mice were more likely to be delivered to the liver, spleen and brain, and less likely to be delivered to the lungs. Our findings reveal that the metabolome profile is an unexploited factor impacting the target efficacy and safety of nanomedicines, providing a way to develop personalized nanomedicines by harnessing disease-related metabolites.

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Fig. 1: Dissecting cholesterol-mediated PC formation.
Fig. 2: Proteomic fingerprints of PC modulated by cholesterol.
Fig. 3: Cho-PC elicited a stronger immune response of macrophages to NPs.
Fig. 4: Cho-PC enhanced cellular internalization of NPs in hepatocytes.
Fig. 5: In vivo biodistribution of NPs in normal and hypercholesterolemic mice.
Fig. 6: The proposed fates of NPs in healthy and hypercholesterolemic individuals.

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Data availability

All proteomics raw data were deposited to the ProteomeXchange (https://proteomecentral.proteomexchange.org/cgi/GetDataset) with the identifier PXD041073. Source data are provided with this paper. Additional datasets supporting our findings in this study are available from the corresponding authors upon reasonable request.

Code availability

The analysis codes for molecular dynamics simulation and bioinformatic analysis of proteomic data are preserved in the China Academy of Chinese Medical Sciences and are available from the corresponding authors upon reasonable request.

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Acknowledgements

The work was supported by grants from the National Key Research and Development Program of China (2020YFA0908000 and 2022YFC2303600); the Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine (no. ZYYCXTD-C-202002); the National Natural Science Foundation of China (32201177, 32000026, 82141001, 82274182 and 82074098); the CACMS Innovation Fund (CI2021A05101 and CI2021A05104); the Scientific and Technological Innovation Project of China Academy of Chinese Medical Sciences (CI2021B014); the Science and Technology Foundation of Shenzhen (JCYJ20210324115800001); the Science and Technology Foundation of Shenzhen (Shenzhen Clinical Medical Research Center for Geriatric Diseases); the National Key R&D Program of China Key projects for international cooperation on science, technology and innovation (2020YFE0205100); the Fundamental Research Funds for the Central Public Welfare Research Institutes (ZZ14-YQ-061, ZZ16-ND-10–24, ZZ14-YQ-057, ZZ14-YQ-050, ZZ14-YQ-051, ZZ14-FL-002, ZZ14-ND-010 and ZZ15-ND-10); GuangDong Basic and Applied Basic Research Foundation (2021A1515012164), Shenzhen Key Laboratory of Kidney Diseases (ZDSYS201504301616234); and Shenzhen Governmental Sustainable Development Fund (KCXFZ20201221173612034).

Author information

Author notes

  1. These authors contributed equally: Huan Tang, Ying Zhang.

Authors and Affiliations

  1. State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Artemisinin Research Center, and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China

    Huan Tang, Ying Zhang, Tong Yang, Chen Wang, Yinhua Zhu, Liangjia Qiu, Yang Song, Lirue Zhou, Junzhe Zhang, Chengchao Xu & Jigang Wang

  2. Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, China

    Jiahui Liu & Yuanfang Liu

  3. Department of Biological Sciences, National University of Singapore, Singapore, Singapore

    Yin Kwan Wong

  4. Institute of Nanochemistry and Nanobiology, Shanghai University, Shanghai, China

    Yuanfang Liu & Haifang Wang

  5. Department of Nephrology, Shenzhen Key Laboratory of Kidney Diseases, and Shenzhen Clinical Research Centre for Geriatrics, Shenzhen People’s Hospital, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China

    Chengchao Xu & Jigang Wang

  6. Guangdong Provincial Key Laboratory of New Drug Screening, School of Pharmaceutical Sciences, and School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China

    Jigang Wang

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Contributions

J.W., C.X. and H.T. conceived the project and supervised the study. H.T. and Y. Zhang designed and performed all experiments with the help from the other authors. T.Y., C.W., L.Q. and J.Z. assisted in the collection and analysis of proteomic data. Y. Zhu, C.W. and Y.K.W. assisted in the molecular dynamics simulation. J.L., Y.S. and L.Z assisted in the animal experiment and inductively coupled plasma mass spectrometry measurement. Y.L. and H.W. guided and assisted in the experiment design. H.T., Y. Zhang, C.X. and J.W. analysed data and wrote the paper. J.W., C.X. and Y.K.W. edited the paper. All authors approved the paper.

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Correspondence to Chengchao Xu or Jigang Wang.

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Extended data

Extended Data Fig. 1 Characterization of cholesterol-mediated protein corona on NPs.

(a) Coomassie-stained SDS-PAGE gel of PC on SNPs under different concentrations of cholesterol. (b) The total protein amounts of PC on SNPs under different concentrations of cholesterol. The data were presented as mean ± standard deviation with n = 3 (biologically independent experiments). Statistical significance was tested with a two-tailed, unpaired Student’s t-test. (c) Densitometric analysis of Extended Data Fig. 1a. (d) Densitometric analysis of Fig. 1a. (e) Coomassie-stained SDS-PAGE gel of human serum samples with different concentrations of cholesterol.

Source data

Extended Data Fig. 2 Characterization of NPs-Nor-PC and NPs-Cho-PC.

(a) Cryo-TEM images of NPs, NPs-Nor-PC, and NPs-Cho-PC. Cryo-TEM imaging showed that cholesterol did not affect the possible protein contamination in the background. (b) Hydrodynamic (HD) size distribution AuNPs, AuNPs-Nor-PC, and AuNPs-Cho-PC. (c) HD size distribution of SNPs, SNPs-Nor-PC, and SNPs-Cho-PC. (d) Summary of physicochemical parameters including TEM size, HD, polydispersity index (PDI), and zeta potential of naked NPs, NPs-Nor-PC, and NPs-Cho-PC for AuNPs and SNPs. (e) The loading amount of cholesterol on SNPs under different concentrations of cholesterol. The data were presented as mean ± standard deviation with n = 3 (biologically independent experiments).

Source data

Extended Data Fig. 3 Schematic illustration for the mechanistic formation of Cho-PC and proteomic fingerprints identification.

(a) Schematic process of PC on SNPs under different conditions for elucidating the mechanism of Cho-PC formation. PC-I refers to the normal PC formed in FBS. PC-II refers to the PC formed in FBS after SNPs were pre-incubated with the cholesterol solution. PC-III refers to the final PC formed in FBS containing cholesterol after SNPs were pre-incubated with FBS. PC-IV refers to the Cho-PC formed in FBS containing cholesterol. (b) Schematic diagram of identification and quantification of Cho-PC and Nor-PC at the proteomic level.

Extended Data Fig. 4 Proteomic fingerprints identification and quantification of Cho-PC.

(a-c) Comparison of the number of PC identified under different concentrations of cholesterol according to their functional classification (a), isoelectric point (b), and molecular weight (c). (d-f) The biological function (d), cell component (e), and molecular function (f) category of the significantly changed proteins in Cho-PC compared with Nor-PC according to the GO classification. (g) The enriched KEGG pathway of the significantly changed proteins in Cho-PC compared with Nor-PC. (h) Heat map of the top 25 abundance proteins in Nor-PC and Cho-PC with the clustering analysis. (i-j) Relative abundances of the identified apolipoprotein proteins (i) and complement proteins (j) in Nor-PC and Cho-PC. The data were presented as mean ± standard deviation with n = 3 (biologically independent experiments).

Source data

Extended Data Fig. 5 Proteomic analysis of Cho-PC in human serum and adsorption of LDL and HDL on NPs.

(a)The heatmap of apolipoproteins and complement proteins identified in PC when bare SNPs and cholesterol-precoated SNPs were incubated with the normal FBS. Cholesterol precoated SNPs were prepared by incubating bare SNPs in 5.1 mM cholesterol solution and subsequent three PBS washing. (b-e) The heatmaps (b, d) and relative protein abundance (c, e) of apolipoproteins (b, c) and complement proteins (d, e) identified in neat serum and protein corona on SNPs in human serum with different concentrations of cholesterol. (f-g) The amount of LDL (f) and HDL (g) adsorbed on SNPs by measuring the concentrations in supernatants before and after SNPs addition. The data are presented as mean ± standard deviation with n = 3 (biologically independent experiments).

Source data

Extended Data Fig. 6 Circular dichroism analysis of the interactions of proteins with SNPs in the absence or presence of cholesterol.

(a-h) Circular dichroism (a, c, e, g) and secondary structure (b, d, f, h) of APOE (a-b), C1Q (c-d), LDL (e-f) and HDL (h-g) in the absence and presence of cholesterol or SNPs. The conformation of APOE and C1Q changed after interacting with SNPs. Even though cholesterol itself did not induce conformational changes, it further enhanced the conformational change of APOE elicited by SNPs. The conformation of LDL and HDL could be altered by both cholesterol and SNPs, and the change became more distinct in the presence of both cholesterol and SNPs.

Source data

Extended Data Fig. 7 The effects of Nor-PC and Cho-PC on cytotoxicity and immune response of macrophages to NPs.

(a-b) Cytotoxicity of AuNPs (a) and SNPs (b) with various coatings on Raw264.7 cells at 24 h. The data are presented as mean ± standard deviation with n = 3 (biologically independent experiments). Statistical significance was tested with a two-tailed, unpaired Student’s t-test. (c-d) Volcano plots of the protein expression change for the Nor-PC group (c) and Nor-PC group (d) versus the control group. (e) The overlap of the significantly changed proteins in the Cho-PC group and Nor-PC group versus the control group. (f) The enriched biological pathway of down-regulated proteins in the Nor-PC group and Cho-PC group by GO analysis.

Source data

Extended Data Fig. 8 The effects of Cho-PC on cellular internalization of NPs in hepatocytes.

(a) Cell viability assessment of AuNPs, AuNPs-Nor-PC, and AuNPs-Cho-PC on HepG2 cells. The data are presented as mean ± standard deviation with n = 3 (biologically independent experiments). Statistical significance was tested with a two-tailed, unpaired Student’s t-test. (b) Cell viability assessment of SNPs, SNPs-Nor-PC, and SNPs-Cho-PC on HepG2 cells. The data are presented as mean ± standard deviation with n = 3 (biologically independent experiments). Statistical significance was tested with a two-tailed, unpaired Student’s t-test. (c) Uptake kinetics of SNPs-Nor-PC and SNPs-Cho-PC in the media with or without FBS. (d-e) Cellular uptake of 100 µg ml−1 of FITC labelled SNPs in HepG2 cells under different concentrations of cholesterol in the medium with FBS quantified by flow cytometry. The average and standard deviation represented three replicates of the median cell fluorescence intensities obtained. The data are presented as mean ± standard deviation with n = 3 (biologically independent experiments). Statistical significance was tested with a two-tailed, unpaired Student’s t-test. (f-g) Cellular uptake of 100 µg ml−1 of bare SNPs, SNPs-Nor-PC and SNPs-Cho-PC in HepG2 cells in serum-free medium in the presence and absence of cholesterol. The average and standard deviation represented three replicates of the median cell fluorescence intensities obtained. The data are presented as mean ± standard deviation with n = 3 (biologically independent experiments). Statistical significance was tested with a two-tailed, unpaired Student’s t-test. (h-m) The uptake kinetics of SNPs-Nor-PC and SNPs-Cho-PC in the presence or absence of 10 μg/mL chlorpromazine (h), 2.5 mg/mL methyl-β-cyclodextrin (i), 25 μg/mL dynasore (j), 100 μM EIPA (k), 2.5 μg/mL cytochalasin D (l), and 5 μM nocodazole (m). The data are presented as mean ± standard deviation with n = 3 (biologically independent experiments).

Source data

Extended Data Fig. 9 Characterization of Cho-PC in normal and hypercholesterolemia mice.

(a-b) SDS-PAGE characterization of PC formed on AuNPs immersed in plasmas (a) and neat plasmas (b) from the normal and hypercholesterolemia mice. (c-d) SDS-PAGE characterization of PC formed on AuNPs immersed in serums (c) and neat serums (d) from the normal and hypercholesterolemia mice. (e) The heatmap of apolipoproteins and complement proteins identified in neat serum and protein corona on AuNPs after incubation with the serum from normal and hypercholesterolemic mice. (f) The heatmap of apolipoproteins and complement proteins identified in PC when SNPs were incubated with the serums from normal and hypercholesterolemic mice.

Source data

Extended Data Fig. 10 In vivo biodistribution of NPs in normal and hypercholesterolemia mice.

(a-b) The concentrations of Au in blood and tissues from the normal and hyperlipidemia mice at 1 h (a) and 24 h (b) after intravenous injection of AuNPs at doses of 0.1 g per kg body weight. The data are presented as mean ± standard deviation with n = 5 (biologically independent mice). Statistical significance was tested with a two-tailed, unpaired Student’s t-test. (c, e) Tissue distribution estimated by the fluorescence intensity at 1 h (c) and 12 h (e) after normal and hypercholesterolemic mice were intravenously injected with Cy3-labelled SNPs at doses of 20 mg per kg body weight. The data are presented as mean ± standard deviation with n = 4 (biologically independent mice). Statistical significance was tested with a two-tailed, unpaired Student’s t test. (d, f) Ex vivo fluorescence images of major organs showing the distribution of SNPs in normal and hypercholesterolemic mice at 1 h (d) and 12 h (f). (Bl, blood; Lu, lungs; St, stomach; Te, testis; Li, liver; Sp, spleen; Bo, bone; Br, brain; Ki, kidneys; He, heart; In, intestinal).

Source data

Supplementary information

Supplementary Information

Supplementary Methods, Figs. 1–7, Table 1 and References.

Reporting Summary

Supplementary Video 1

Molecular dynamics trajectory of apolipoprotein E with SNPs in the absence of cholesterol.

Supplementary Video 2

Molecular dynamics trajectory of apolipoprotein E with SNPs in the presence of cholesterol.

Source data

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Numerical source data and unprocessed western blots.

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Tang, H., Zhang, Y., Yang, T. et al. Cholesterol modulates the physiological response to nanoparticles by changing the composition of protein corona. Nat. Nanotechnol. 18, 1067–1077 (2023). https://doi.org/10.1038/s41565-023-01455-7

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