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Carbon nanotube recognition by human Siglec-14 provokes inflammation

Nature Nanotechnology volume 18, pages 628–636 (2023)Cite this article

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Abstract

For the design and development of innovative carbon nanotube (CNT)-based tools and applications, an understanding of the molecular interactions between CNTs and biological systems is essential. In this study, a three-dimensional protein-structure-based in silico screen identified the paired immune receptors, sialic acid immunoglobulin-like binding lectin-5 (Siglec-5) and Siglec-14, as CNT-recognizing receptors. Molecular dynamics simulations showed the spatiotemporally stable association of aromatic residues on the extracellular loop of Siglec-5 with CNTs. Siglec-14 mediated spleen tyrosine kinase (Syk)-dependent phagocytosis of multiwalled CNTs and the subsequent secretion of interleukin-1β from human monocytes. Ectopic in vivo expression of human Siglec-14 on mouse alveolar macrophages resulted in enhanced recognition of multiwalled CNTs and exacerbated pulmonary inflammation. Furthermore, fostamatinib, a Syk inhibitor, blocked Siglec-14-mediated proinflammatory responses. These results indicate that Siglec-14 is a human activating receptor recognizing CNTs and that blockade of Siglec-14 and the Syk pathway may overcome CNT-induced inflammation.

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Fig. 1: Identification of Siglec-5 as a CNT-recognizing receptor.
Fig. 2: MD simulations and in vitro validation to clarify the binding modes of Siglec-5 to MWCNTs.
Fig. 3: Siglec-14, but not Siglec-5, engulfs MWCNTs to induce IL-1β secretion and pulmonary inflammation.
Fig. 4: Siglec-14-mediated recognition of MWCNTs by human monocytes.
Fig. 5: Siglec-14-Syk-mediated inflammatory responses to MWCNTs are blocked by fostamatinib.

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Data availability

The data supporting the findings of this study are available within the Article, Extended Data, Supplementary Information and Source Data files. Structural data obtained from the MD simulations are available via BSM-Arc at https://bsma.pdbj.org/entry/31. Other relevant data are available for research purposes from the corresponding authors upon request. Source data are provided with this paper.

Code availability

The relevant in-house codes applied in this study are available from the corresponding authors upon request.

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Acknowledgements

We thank M. Ema (Shiga University of Medical Science) for advice and technical support on mouse experiments. We also thank the members of our laboratories for helpful suggestions. This work was supported by the Japan Science and Technology Agency (JST), PRESTO under grant number JPMJPR17H9 to M.N., JST CREST under grant number JPMJCR19H4 to S.T., the Japan Society for the Promotion of Sciences (JSPS) under grant numbers JP19H03880 and JP22H03340 to M.N., JP20K12069 and JP21K06052 to K. Kasahara, and the Uehara Memorial Foundation to M.N. This research was partially supported by the Platform Project for Supporting Drug Discovery and Life Science Research (Basis for Supporting Innovative Drug Discovery and Life Science Research (BINDS)) from AMED under grant number JP19am0101067 (support number 0314). The computational resources were provided by the HPCI System Research Project (project IDs hp200063, hp200090, hp210005 and hp210008), the NIG supercomputer at ROIS National Institute of Genetics and the Human Genome Center (University of Tokyo).

Author information

Author notes

  1. Kota Kasahara

    Present address: Central Pharmaceutical Research Institute, Japan Tobacco Inc., Takatsuki, Japan

  2. These authors contributed equally: Shin-Ichiro Yamaguchi, Qilin Xie.

Authors and Affiliations

  1. Laboratory of Immunology and Microbiology, College of Pharmaceutical Sciences, Ritsumeikan University, Kusatsu, Japan

    Shin-Ichiro Yamaguchi, Kazuki Terao, Yoshinobu Kato, Miki Kuroiwa & Masafumi Nakayama

  2. CREST, Japan Science and Technology Agency (JST), Kawaguchi, Japan

    Shin-Ichiro Yamaguchi, Qilin Xie, Fumiya Ito, Shinya Toyokuni, Kota Kasahara & Masafumi Nakayama

  3. Computational Structural Biology Laboratory, College of Life Sciences, Ritsumeikan University, Kusatsu, Japan

    Qilin Xie, Takuya Takahashi & Kota Kasahara

  4. Department of Pathology and Biological Responses, Nagoya University Graduate School of Medicine, Nagoya, Japan

    Fumiya Ito & Shinya Toyokuni

  5. Graduate School of Information Sciences, Tohoku University, Sendai, Japan

    Satoshi Omori & Kengo Kinoshita

  6. Laboratory of Neurochemistry, College of Pharmaceutical Sciences, Ritsumeikan University, Kusatsu, Japan

    Hideo Taniura

  7. Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan

    Kengo Kinoshita

  8. Advanced Research Center for Innovations in Next-Generation Medicine, Tohoku University, Sendai, Japan

    Kengo Kinoshita

  9. Department of In Silico Analyses, Institute of Development, Aging and Cancer (IDAC), Tohoku University, Sendai, Miyagi, Japan

    Kengo Kinoshita

  10. Center for Low Temperature Plasma Science, Nagoya University, Nagoya, Japan

    Shinya Toyokuni

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  1. Shin-Ichiro Yamaguchi
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Contributions

K. Kasahara and M.N. designed the experiments. S.-I.Y., Q.X., F.I., K.T., Y.K., M.K., K. Kasahara and M.N. performed the experiments and analysed data. S.O., H.T., K. Kinoshita, T.T. and S.T. provided critical materials and advice. K. Kasahara and M.N. wrote the manuscript. All the authors discussed the results and assisted in the preparation of the manuscript.

Corresponding authors

Correspondence to Kota Kasahara or Masafumi Nakayama.

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Competing interests

The authors declare no competing interests. K. Kasahara is now an employee of Japan Tobacco Inc. This company is not involved in this study.

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Nature Nanotechnology thanks Yuliang Zhao and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Siglec-5, but not Siglec-3, recognizes MWCNTs.

a, Siglec-5, Siglec-3, and Tim4 expressions on NIH-3T3 cells were analyzed by flow cytometry. b, Parental NIH-3T3 cells and NIH-3T3 cells stably expressing Siglec-5, Siglec-3, or Tim4 were cultured with (shaded histograms) or without (open histograms) MWCNTs (10 or 30 μg/ml) for 30 min. Binding was analyzed by flow cytometry. Numbers indicate the median side scatter intensity (MSI). The delta median side scatter intensity (∆MSI) was calculated by subtracting MSI of MWCNT-treated cells from MSI of untreated cells in Fig. 1c. The gating strategy is shown in Supplementary Fig. 12.

Extended Data Fig. 2 Schematic diagrams of architecture of Siglec-5 with the CNT interfaces.

a, Extracellular domains of Siglec-5 are abstracted as two connected cylinders. The four interfaces (1) through (4) are placed at the extracellular bottom of the cylinder. b, The topology diagram emphasizes the positions of interfaces along the sequence.

Extended Data Fig. 3 A cluster analysis of Siglec-5-CNT binding modes observed in the MD simulations.

a, Relative contact frequency of each Siglec-5 residue in each cluster shows that modes 1 and 2 recognize CNT via V-set Ig-like domain. Mode 4 and 5 contact CNT via C2-set Ig-like domain neighboring a cytoplasmic domain. Mode 3 is the unbound form. b, Schematic diagram presents the characteristics of each mode.

Source data

Extended Data Fig. 4 Snapshots of Siglec-5 and −3 in the VcMD simulations.

a and b, Snapshots of Siglec-5 (a) and −3 (b) binding to CNT with the mode 2. All the four interfaces (labelled as (1) to (4)) directed to the CNT surface. However, Siglec-3 does not have side chains with π-electron system which attractively interact with the CNT surface. c and d, Snapshots of Siglec-5 (c) and −3 (d) binding to CNT with the mode 1*, which makes contact with CNT via the interface (2).

Extended Data Fig. 5 Multiple sequence alignments and AlphaFold-predicted structures of Siglecs.

a, Amino acid sequences of Siglec V-set Ig-like domain were aligned with CLUSTALW. Red solid squares indicate aromatic clusters. Red dot squares indicate an incomplete aromatic cluster. Numbers indicate amino acid positions of the N-terminal V-set Ig-like domain without signal sequence of Siglec-7. b, A phylogenetic tree was generated with CLUSTALW. Numbers indicate branch lengths. c, Protein structures of Siglecs were predicted by AlphaFold. The extracellular loops are surrounded by black dot squares. Aromatic residues in the extracellular loops are indicated by black arrowheads.

Extended Data Fig. 6 Generation of anti-Siglec-5/14 neutralizing mAbs.

a, Parental Jurkat.EcoR cells, and Jurkat.EcoR cells stably expressing Siglec-5 or Siglec-14 were pretreated with control mouse IgG1 (cIg), or anti-Siglec-5/14 neutralizing mAbs, SY1 and SY2 (10 μg/ml each), and then these cells were cultured with MWCNTs (30 μg/ml) for 30 min. MWCNT recognition was analyzed as in Fig. 1c. b, The indicated Jurkat.EcoR cells and NIH-3T3 cells were stained with biotinylated cIg, SY1, or SY2, followed by PE-streptavidin. Cells were analyzed by flow cytometry.

Extended Data Fig. 7 Effect of sialidase on Siglec-14 responses to MWCNTs.

a, Siglec-14/THP-1 cells were treated with the indicated activity of sialidase for 1 hr and were then stained with indicated mAbs. Desialylation was assessed by flow cytometry using anti-sialylated CD43 mAb (clone 1G10). b, Cells and MWCNTs (30 μg/ml) were each pretreated with sialidase as in a, and then were combined and cultured for 30 min. MWCNT binding was analyzed by flow cytometry. c, MWCNTs (30 μg/ml) and PMA-primed cells were each pretreated with sialidase as in a, and then were combined and cultured for 5 hr in serum-free medium containing 1% BSA. IL-1β secretion was analyzed by ELISA. Data are shown as mean ± SD (n = 3). ***p < 0.001, two-way ANOVA with Tukey-Kramer test.

Source data

Extended Data Fig. 8 Siglec-14 recognize SWCNTs resulting in induction of IL-8, but not IL-1β, secretion.

a, b, Size and shape of MWCNTs and SWCNTs were analyzed by transmission electron microscopy (a), and by light microscopy in 0.5% BSA/PBS (b). c, MWCNTs and SWCNTs were stained with cIg (black lines) or Siglec-14-Ig (blue lines), followed by AF647-anti-mouse IgG in 0.5% BSA/PBS. Siglec-14-Ig binding was analyzed by flow cytometry. d, Indicated THP-1 cells were primed with PMA (0.5 μM) for 12 hr and were treated with the indicated dose of MWCNTs or SWCNTs for 5 hr. IL-1β secretion was analyzed by ELISA. Data are shown as mean ± SD (n = 3). e, Indicated THP-1 cells were stimulated with the indicated dose of MWCNTs or SWCNTs for 5 hr. IL-8 secretion was analyzed by ELISA. See also Method section. Data are shown as mean ± SD (n = 3). **p = 0.0364, ***p < 0.01, two-way ANOVA with Tukey-Kramer test to compare each mean with every other mean.

Source data

Extended Data Fig. 9 Galectin-3-fluorescent reporter system for detection of lysosomal damage.

a, Galectin-3 is normally distributed throughout cytoplasm and nucleus. Upon lysosomal membrane rupture, Galectin-3 is rapidly recruited lysosomes to access the luminal β-galactoside sugar-containing carbohydrates. Galectin-3 fused with monomeric azami-green fluorescent protein (mAG-Gal3) allows visualization of galectin-3 re-localization and is used as a tool to monitor vesicle rupture. Figure was drawn with BioRender.com. b, THP-1 cells stably expressing Siglec-14 and mAG-Gal3 were stimulated with MWCNTs, SWCNTs (100 μg/ml each), or a lysosomotropic compound L-Leucyl-L-leucine methy ester (LLOMe; 3 mM) for 3 hr. Cells were stained with DAPI and were analyzed by fluorescence microcopy.

Extended Data Fig. 10 Model of Siglec-14-mediated pro-inflammatory responses to MWCNTs and SWCNTs.

a, Upon MWCNT recognition, Siglec-14 transmits activation signals leading to phagocytosis and IL-8 induction. Phagocytosed MWCNTs cause phagosomal rupture, resulting in NLRP3 inflammasome activation and caspase-1 activation leading to IL-1β secretion. b, Upon SWCNT recognition, Siglec-14 transmits activation signals leading to phagocytosis and IL-8 induction. Since phagocytosed SWCNTs do not cause phagosomal rupture, IL-1β is not secreted. Figure was drawn with BioRender.com.

Supplementary information

Supplementary Information

Supplementary Note and Figs. 1-13.

Reporting Summary

Supplementary Video 1

A side-view movie of an MD simulation trajectory of the WT model. One of ten 300 ns trajectories is shown as a side-view movie.

Supplementary Video 2

A long shot movie of an MD simulation trajectory of the WT model. The same trajectory as Supplementary Movie 1 is presented from a different perspective.

Supplementary Video 3

A side-view movie of an MD simulation trajectory of the WY50/51AA model. One of ten 300 ns trajectories is shown as a side-view movie.

Supplementary Video 4

A long shot movie of an MD simulation trajectory of the WY50/51AA model. The same trajectory as Supplementary Movie 3 is presented from a different perspective.

Supplementary Video 5

A side-view movie of an MD simulation trajectory of the WYYY50/51/68/69AAAA model. One of ten 300 ns trajectories is shown as a side-view movie.

Supplementary Video 6

A long shot movie of an MD simulation trajectory of the WYYY50/51/68/69AAAA model. The same trajectory as Supplementary Movie 5 is presented from a different perspective.

Source data

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Source Data Supplementary Fig. 10

Statistical Source Data, Uncropped western blots

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Yamaguchi, SI., Xie, Q., Ito, F. et al. Carbon nanotube recognition by human Siglec-14 provokes inflammation. Nat. Nanotechnol. 18, 628–636 (2023). https://doi.org/10.1038/s41565-023-01363-w

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