Skip to main content
  • AACR Publications
    • Blood Cancer Discovery
    • Cancer Discovery
    • Cancer Epidemiology, Biomarkers & Prevention
    • Cancer Immunology Research
    • Cancer Prevention Research
    • Cancer Research
    • Clinical Cancer Research
    • Molecular Cancer Research
    • Molecular Cancer Therapeutics

AACR logo

  • Register
  • Log in
  • My Cart
Advertisement

Main menu

  • Home
  • About
    • The Journal
    • AACR Journals
    • Subscriptions
    • Permissions and Reprints
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • Meeting Abstracts
    • Cancer Immunology Essentials
    • Collections
      • COVID-19 & Cancer Resource Center
      • Toolbox: Advanced Technologies for Antigen Identification
      • Toolbox: Coding and Computation
      • Toolbox: Signatures and Cells
      • "Best of" Collection
      • Editors' Picks
  • For Authors
    • Information for Authors
    • Author Services
    • Best of: Author Profiles
    • Submit
  • Alerts
    • Table of Contents
    • Editors' Picks
    • OnlineFirst
    • Citation
    • Author/Keyword
    • RSS Feeds
    • My Alert Summary & Preferences
  • News
    • Cancer Discovery News
  • COVID-19
  • Webinars
  • Search More

    Advanced Search

  • AACR Publications
    • Blood Cancer Discovery
    • Cancer Discovery
    • Cancer Epidemiology, Biomarkers & Prevention
    • Cancer Immunology Research
    • Cancer Prevention Research
    • Cancer Research
    • Clinical Cancer Research
    • Molecular Cancer Research
    • Molecular Cancer Therapeutics

User menu

  • Register
  • Log in
  • My Cart

Search

  • Advanced search
Cancer Immunology Research
Cancer Immunology Research
  • Home
  • About
    • The Journal
    • AACR Journals
    • Subscriptions
    • Permissions and Reprints
  • Articles
    • OnlineFirst
    • Current Issue
    • Past Issues
    • Meeting Abstracts
    • Cancer Immunology Essentials
    • Collections
      • COVID-19 & Cancer Resource Center
      • Toolbox: Advanced Technologies for Antigen Identification
      • Toolbox: Coding and Computation
      • Toolbox: Signatures and Cells
      • "Best of" Collection
      • Editors' Picks
  • For Authors
    • Information for Authors
    • Author Services
    • Best of: Author Profiles
    • Submit
  • Alerts
    • Table of Contents
    • Editors' Picks
    • OnlineFirst
    • Citation
    • Author/Keyword
    • RSS Feeds
    • My Alert Summary & Preferences
  • News
    • Cancer Discovery News
  • COVID-19
  • Webinars
  • Search More

    Advanced Search

Research Articles

TLR2 Promotes Glioma Immune Evasion by Downregulating MHC Class II Molecules in Microglia

Jiawen Qian, Feifei Luo, Jiao Yang, Jun Liu, Ronghua Liu, Luman Wang, Chen Wang, Yuting Deng, Zhou Lu, Yuedi Wang, Mingfang Lu, Ji-Yang Wang and Yiwei Chu
Jiawen Qian
1Department of Immunology, School of Basic Medical Sciences, and Institute of Biomedical Sciences, Fudan University, Shanghai, P.R. China.
2Biotherapy Research Center, Fudan University, Shanghai, P.R. China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Jiawen Qian
Feifei Luo
2Biotherapy Research Center, Fudan University, Shanghai, P.R. China.
3Department of Digestive Diseases, Huashan Hospital, Fudan University, Shanghai, P.R. China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jiao Yang
1Department of Immunology, School of Basic Medical Sciences, and Institute of Biomedical Sciences, Fudan University, Shanghai, P.R. China.
2Biotherapy Research Center, Fudan University, Shanghai, P.R. China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jun Liu
1Department of Immunology, School of Basic Medical Sciences, and Institute of Biomedical Sciences, Fudan University, Shanghai, P.R. China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Jun Liu
Ronghua Liu
1Department of Immunology, School of Basic Medical Sciences, and Institute of Biomedical Sciences, Fudan University, Shanghai, P.R. China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Ronghua Liu
Luman Wang
1Department of Immunology, School of Basic Medical Sciences, and Institute of Biomedical Sciences, Fudan University, Shanghai, P.R. China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Chen Wang
1Department of Immunology, School of Basic Medical Sciences, and Institute of Biomedical Sciences, Fudan University, Shanghai, P.R. China.
2Biotherapy Research Center, Fudan University, Shanghai, P.R. China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yuting Deng
1Department of Immunology, School of Basic Medical Sciences, and Institute of Biomedical Sciences, Fudan University, Shanghai, P.R. China.
2Biotherapy Research Center, Fudan University, Shanghai, P.R. China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Zhou Lu
1Department of Immunology, School of Basic Medical Sciences, and Institute of Biomedical Sciences, Fudan University, Shanghai, P.R. China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Zhou Lu
Yuedi Wang
1Department of Immunology, School of Basic Medical Sciences, and Institute of Biomedical Sciences, Fudan University, Shanghai, P.R. China.
2Biotherapy Research Center, Fudan University, Shanghai, P.R. China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mingfang Lu
1Department of Immunology, School of Basic Medical Sciences, and Institute of Biomedical Sciences, Fudan University, Shanghai, P.R. China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Mingfang Lu
Ji-Yang Wang
1Department of Immunology, School of Basic Medical Sciences, and Institute of Biomedical Sciences, Fudan University, Shanghai, P.R. China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yiwei Chu
1Department of Immunology, School of Basic Medical Sciences, and Institute of Biomedical Sciences, Fudan University, Shanghai, P.R. China.
2Biotherapy Research Center, Fudan University, Shanghai, P.R. China.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Yiwei Chu
  • For correspondence: yiwei_chu@126.com
DOI: 10.1158/2326-6066.CIR-18-0020 Published October 2018
  • Article
  • Figures & Data
  • Info & Metrics
  • PDF
Loading

Article Figures & Data

Figures

  • Additional Files
  • Figure 1.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 1.

    Microglia accumulate at tumor sites and upregulate TLR2 expression. A, Representative staining for Iba1 (green) and DAPI (blue) in the tumor-bearing brain on day 20 after GL261 inoculation. Right plots, different regions of the tumor-bearing brain. N, normal tissue; TR, tumor rim; IM, invasive margin; IT, intratumoral region. B, Iba1+ cell counts per field and percentage of Iba1 labeled area were calculated. Data were collected from 20 random fields for each region per mouse, n = 3. C, TLR expression in different cell subsets of the normal brain according to GEO dataset (GSE52564). D, qPCR analysis of TLRs’ expression pattern in FACS-sorted CD45loCD11b+ microglia isolated from glioma model (day 20) and normal brain, each sample represents 5 mice brains pooled, and expression was normalized to β-actin (n = 3). E, mRNA expression of TLR2 in human glioma samples, data from TCGA LGG/GBM datasets. N, normal tissue, n = 4; OD, oligodendroglioma, n = 191; OA, oligoastrocytoma, n = 130; AST, astrocytoma, n = 194; GBM, n = 152. F and G, Flow cytometry analysis of CD45loCD11b+ microglia for TLRs’ expression pattern, n = 5. Unpaired Student t test was performed in D and G. One-way ANOVA was performed in E. *, P < 0.05 and **, P < 0.01. All values are shown as mean ± SEM.

  • Figure 2.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 2.

    TLR2 activation downregulates MHC class II in microglia. A, The Kaplan–Meier survival curves represent the cumulative survival of WT and TLR2−/− mice after GL261 inoculation, n = 8. B, H&E staining for sections of tumor-bearing WT and TLR2−/− mice (day 20). Scale bar, 500 μm. Right plot, tumor area was calculated, n = 5–15. C, Flow cytometry analysis of the glioma model (day 20) derived CD45loCD11b+ microglia for antigen-presenting function-related markers. Right plot, summary of the data, n = 8–10. Data are representative of three independent experiments. D, Representative staining for Iba1 (green), MHC class II (red), and DAPI (blue) in tumor-bearing brains on day 20 after GL261 inoculation. Dashed line, tumor boundary; IM, invasive margin; T, tumor; N, normal tissue. Bottom plots, higher magnification of the regions indicated in the dashed boxes. E, Representative staining for Iba1 (green), MHC class II (red), and DAPI (blue) in primary microglia. Control, microglia treated with IFNγ. GCM, microglia treated with GCM and followed with IFNγ. F, Flow cytometry analysis of primary microglia for MHC class II expression. Right plot, summary of the data, n = 3. G, Flow cytometry analysis of primary microglia (treated with the indicated dose of Pam3CSK4 and followed with IFNγ) for MHC class II expression. Right plot, the summary of the data: mean fluorescence intensity (MFI) values were normalized against respective control groups, n = 3. Data are representative of three independent experiments. H, Schematic figure of the Pam3CSK4 administration experiments in vivo. I, Flow cytometry analysis of CD45loCD11b+ microglia that isolated from mice described in H; the MFI values of MHC class II were normalized against corresponding normal brain samples (N), pooled data from two separate experiments. Ctr, mice treated with PBS; 1st, 2nd, and 3rd, mice treated with Pam3CSK4 (5 μg) for 1, 2, and 3 times at indicated time points. The log-rank (Mantel–Cox) test was performed in A. One-way ANOVA was performed in G and I. Two-way ANOVA was performed in F. Unpaired Student t test was performed in C. *, P < 0.05 and **, P < 0.01. All values are shown as mean ± SEM.

  • Figure 3.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 3.

    TLR2-induced downregulation of MHC class II suppresses the antigen-presenting function of microglia. A, Timescales of the microglia-induced OT II CD4+ T-cell proliferation assay. B, Flow cytometry analysis of the cocultured microglia for MHC class II level. Histogram numbers indicated the mean fluorescence intensity (MFI) values of MHC class II for each group, representative data of two experiments. C, Flow cytometry analysis of cocultured OT II CD4+ T cells for proliferation, CD69, and PD-1 expression. Right plots, the summary of the data, n = 3, representative data of three independent experiments. D, Representative staining for CD4 (green) and DAPI (blue) in the tumor-bearing brain on day 20 after GL261 inoculation; right panel, CD4+ cell counts per field were calculated. Data were collected from 16 to 20 random fields per mouse, n = 3. Scale bar, 50 μm. E, Representative staining for CD4 (green), Iba1 (red), MHC class II (white), and DAPI (blue) in the tumor-bearing brain on day 20 after GL261 inoculation. Scale bar, 100 μm. F, Flow cytometry analysis of tumor-infiltrated CD4+ T cells for IFNγ, TNFα, Foxp3, and PD-1. Right plots, the summary of the data, n = 4–5, representative data of two independent experiments. One-way ANOVA was performed in C. Unpaired Student t test was performed in D and F. *, P < 0.05 and **, P < 0.01. All values are shown as mean ± SEM.

  • Figure 4.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 4.

    RNA-seq analysis reveals the inhibition of MHC class II–related genes in TLR2-activated microglia. Primary microglia treated with or without Pam3CSK4 and followed with IFNγ. Pam3, microglia with Pam3CSK4 treatment; Control, microglia without Pam3CSK4 treatment. A, Scatterplot of FPKM values for all genes in both groups. The differentially expressed genes were selected using the following filter criteria: FDR ≤ 0.05 and fold change ≥ 2. B, Heat map of the most upregulated and downregulated genes (based on P value) in Pam3 group compared with Control group. Genes in red, MHC class II–related genes. C and D, KEGG pathway (C) and Gene Ontology enrichment analysis (D) were performed using the differentially expressed genes. The details of the enriched Gene Ontology terms were listed in Supplementary Table S3.

  • Figure 5.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 5.

    TLR2-induced MAPK Erk1/2 signaling pathway inhibits Ciita expression, leading to the downregulation of MHC class II in microglia. qPCR analysis of primary microglia for MHC class II (H2-Ab1) mRNA (A) and Ciita mRNA (B), n = 3. The data presented represent one of three individual experiments. C, BV2 microglia were treated with GCM or different TLRs’ ligands and IFNγ. Ciita mRNA was measured by qPCR, and expression was normalized to β-actin and subsequently to control group, n = 3. D, BV2 microglia were treated with IFNγ and followed with Pam3CSK4 for indicated time points, Ciita mRNA was measured by qPCR, and expression was normalized to β-actin and subsequently to control group, n = 3. E and F, BV2 microglia were pretreated with the PI3K (E) or MAPK (F) inhibitors and treated with Pam3CSK4,for 2 hours followed with IFNγ for 18 hours. Ciita mRNA was measured by qPCR, and expression was normalized to β-actin and subsequently to control group, n = 3. Bottom plot in E, phosphorylated Akt of the corresponding samples was analyzed by Western blotting. Bottom plot in F, phosphorylated p65, phosphorylated ERK1/2, phosphorylated p38, and phosphorylated JNK of the corresponding samples were analyzed by Western blotting. G and H, Primary microglia were pretreated with U0126 and treated with Pam3CSK4 for 2 hours, followed by IFNγ for 18 hours, and MHC class II was quantified by flow cytometry (G). Summary of the MHC class II quantification (H), n = 3. Bottom plot, phosphorylated ERK1/2, total ERK1/2, phosphorylated p65, and phosphorylated Akt of the corresponding samples were analyzed by Western blotting. β-Actin was used as an internal control. All experiments were repeated 3 times. One-way ANOVA was performed in C, D, E, F, and H. Unpaired Student t test was performed in A and B. *, P < 0.05 and **, P < 0.01. All values are shown as mean ± SEM.

  • Figure 6.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 6.

    Histone acetylation at Ciita promoters was reduced in TLR2-activated microglia. A, The modular structure of the regulatory region of Ciita. Ciita isoforms 1, 2, and 3 are shown as typical mRNA isoforms that derive from promoter pI, pIII, and pIV, respectively. For gene structure visualization, SnapGene software was used. B, TLR2 activation impairs the expression of Ciita mRNA derived from promoters pI and pIV. Primary microglia treated with or without Pam3CSK4 and followed by IFNγ, qPCR analysis of the microglia for the isoforms of Ciita mRNA. Expression was normalized to β-actin, n = 3. C, Primary microglia were pretreated with epigenetic inhibitors, and then treated with or without Pam3CSK, followed with IFNγ. qPCR analysis of the microglia for MHC class II (H2-Ab1), Total Ciita, pI Ciita, and pIV Ciita mRNA. Expression was normalized to β-actin, n = 3. D, ChIP qPCR was applied to quantify H3K9ac and H3K4me2 in the promoter region of Ciita. The fold enrichment method was used for data normalization. Shown is one of two individual experiments. One-way ANOVA was performed in C. Unpaired Student t test was performed in B and D. *, P < 0.05 and **, P < 0.01. All values are shown as mean ± SEM.

  • Figure 7.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 7.

    HSPs are enriched in GBM samples and correlate with CIITA inhibition. A, Expression of endogenous TLR2 ligand mRNAs in human GBM samples, data from TCGA database. Normal, n = 4; GBM, n = 156. Underlined ligands are detectable in GL261 glioma supernatants. Unpaired Student t test was performed. **, P < 0.01. Values are shown as mean ± SEM. B, Immunohistochemistry staining for TLR2 ligands in human GBM samples; image credit, Human Protein Atlas. Scale bar, 200 μm. The information details (including URL links) of the GBM samples were listed in Supplementary Table S4. C, The RNA-seq profiles of laser-microdissected structures of GBM samples. Expression from genes encoding TLR2 ligands and MHC class II–related molecules is indicated as heat maps. The data were derived from the Ivy Glioblastoma Atlas Project; image credit, Allen Institute. D, Correlation analysis between CIITA and genes encoding endogenous TLR2 ligands was based on the pool of samples from immune cells’ infiltrated PCAN and MvP regions. The data were derived from the Ivy Glioblastoma Atlas Project. Pearson correlation coefficient was calculated to analyze the correlation between gene expression levels.

Additional Files

  • Figures
  • Supplementary Data

    • Table S1-S4 - Supplementary Table S1 Primer sequences for qPCR used in our study. Supplementary Table S2 Technical specifications of antibodies used in our study. Supplementary Table S3 The detail information of the enriched Gene Ontology Terms. Supplementary Table S4 The information of GBM samples from the Human Protein Atlas.
    • Supplementary Figure Legends - Supplementary Figure Legends for Figure S1-S6
    • Figure S1-S6 - Supplementary Figure S1. GL261 glioma model establishment and immune pattern evaluation. Supplementary Figure S2. TLR2 expression of cell subsets in GL261 glioma model. Supplementary Figure S3. The gating strategies of flow-cytometric experiments. Supplementary Figure S4. The culture and identification of murine adult microglia. Supplementary Figure S5. TLR2-mediated down-regulation of MHC-II in bone marrow-derived macrophages and tumor infiltrated peripheral myeloid cells. Supplementary Figure S6. TLR2-induced down-regulation of MHC-II suppresses the antigen-presenting function of microglia.
PreviousNext
Back to top
Cancer Immunology Research: 6 (10)
October 2018
Volume 6, Issue 10
  • Table of Contents
  • Table of Contents (PDF)
  • About the Cover
  • Editorial Board (PDF)

Sign up for alerts

View this article with LENS

Open full page PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for sharing this Cancer Immunology Research article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
TLR2 Promotes Glioma Immune Evasion by Downregulating MHC Class II Molecules in Microglia
(Your Name) has forwarded a page to you from Cancer Immunology Research
(Your Name) thought you would be interested in this article in Cancer Immunology Research.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
TLR2 Promotes Glioma Immune Evasion by Downregulating MHC Class II Molecules in Microglia
Jiawen Qian, Feifei Luo, Jiao Yang, Jun Liu, Ronghua Liu, Luman Wang, Chen Wang, Yuting Deng, Zhou Lu, Yuedi Wang, Mingfang Lu, Ji-Yang Wang and Yiwei Chu
Cancer Immunol Res October 1 2018 (6) (10) 1220-1233; DOI: 10.1158/2326-6066.CIR-18-0020

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
TLR2 Promotes Glioma Immune Evasion by Downregulating MHC Class II Molecules in Microglia
Jiawen Qian, Feifei Luo, Jiao Yang, Jun Liu, Ronghua Liu, Luman Wang, Chen Wang, Yuting Deng, Zhou Lu, Yuedi Wang, Mingfang Lu, Ji-Yang Wang and Yiwei Chu
Cancer Immunol Res October 1 2018 (6) (10) 1220-1233; DOI: 10.1158/2326-6066.CIR-18-0020
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Introduction
    • Materials and Methods
    • Results
    • Discussion
    • Disclosure of Potential Conflicts of Interest
    • Authors' Contributions
    • Acknowledgments
    • Footnotes
    • References
  • Figures & Data
  • Info & Metrics
  • PDF
Advertisement

Related Articles

Cited By...

More in this TOC Section

  • CAR Tonic Signaling
  • DGKA Mediates Resistance to Anti–PD-1 Therapy
  • Boosting Eomes Expression Augments Antitumor Immunity
Show more Research Articles
  • Home
  • Alerts
  • Feedback
  • Privacy Policy
Facebook   Twitter   LinkedIn   YouTube   RSS

Articles

  • Online First
  • Current Issue
  • Past Issues
  • Cancer Immunology Essentials

Info for

  • Authors
  • Subscribers
  • Advertisers
  • Librarians

About Cancer Immunology Research

  • About the Journal
  • Editorial Board
  • Permissions
  • Submit a Manuscript
AACR logo

Copyright © 2021 by the American Association for Cancer Research.

Cancer Immunology Research
eISSN: 2326-6074
ISSN: 2326-6066

Advertisement