GPIHBP1 expression in gliomas promotes utilization of lipoprotein-derived nutrients
Abstract
GPIHBP1, a GPI-anchored protein of capillary endothelial cells, binds lipoprotein lipase (LPL) within the subendothelial spaces and shuttles it to the capillary lumen. The GPIHBP1-bound LPL is essential for the margination of triglyceride-rich lipoproteins (TRLs) along capillaries, allowing the lipolytic processing of TRLs to proceed. In peripheral tissues, the intravascular processing of TRLs by the GPIHBP1–LPL complex is crucial for generating lipid nutrients for adjacent parenchymal cells. GPIHBP1 is absent in capillaries of the brain, which uses glucose for fuel; however, GPIHBP1 is expressed in capillaries of mouse and human gliomas. Importantly, the GPIHBP1 in glioma capillaries captures locally produced LPL. We document, by NanoSIMS imaging, that TRLs marginate along glioma capillaries and that there is uptake of TRL-derived lipid nutrients by surrounding glioma cells. Thus, GPIHBP1 expression in gliomas facilitates TRL processing and provides a source of lipid nutrients for glioma cells.
Data availability
All data generated during this study are included in the manuscript and supporting files.
Article and author information
Author details
Funding
National Heart, Lung, and Blood Institute (HL090553)
- Stephen G Young
National Heart, Lung, and Blood Institute (HL087228)
- Stephen G Young
National Heart, Lung, and Blood Institute (HL125335)
- Stephen G Young
Foundation Leduq (12CVD04)
- Stephen G Young
Ruth L. Kirschstein National Research Service Award (T32HL69766)
- Xuchen Hu
National Institute of General Medical Sciences (GM008042)
- Xuchen Hu
NCI Brain Tumor SPORE (P50-CA211015)
- Linda M Liau
Stichting Tegen Kanker (2012‐181)
- Holger Gerhardt
Stichting Tegen Kanker (2018-074)
- Holger Gerhardt
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: Animal housing and experimental protocols were approved by UCLA's Animal Research Committee (ARC; 2004-125-51, 2016-005) and the Institutional Animal Care and Research Advisory Committee of the KU Leuven (085/2016). The animals were housed in an AAALAC (Association for Assessment and Accreditation of Laboratory Animal Care International)-approved facility and cared for according to guidelines established by UCLA's Animal Research Committee.
Human subjects: All tissue samples from patients were obtained after informed consent and with approval from the UCLA Institutional Review Board (IRB; protocol 10-000655).
Copyright
© 2019, Hu et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,645
- views
-
- 255
- downloads
-
- 9
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Biochemistry and Chemical Biology
- Structural Biology and Molecular Biophysics
Nature has inspired the design of improved inhibitors for cancer-causing proteins.
-
- Biochemistry and Chemical Biology
Mutations in the kinase and juxtamembrane domains of the MET Receptor Tyrosine Kinase are responsible for oncogenesis in various cancers and can drive resistance to MET-directed treatments. Determining the most effective inhibitor for each mutational profile is a major challenge for MET-driven cancer treatment in precision medicine. Here, we used a deep mutational scan (DMS) of ~5764 MET kinase domain variants to profile the growth of each mutation against a panel of 11 inhibitors that are reported to target the MET kinase domain. We validate previously identified resistance mutations, pinpoint common resistance sites across type I, type II, and type I ½ inhibitors, unveil unique resistance and sensitizing mutations for each inhibitor, and verify non-cross-resistant sensitivities for type I and type II inhibitor pairs. We augment a protein language model with biophysical and chemical features to improve the predictive performance for inhibitor-treated datasets. Together, our study demonstrates a pooled experimental pipeline for identifying resistance mutations, provides a reference dictionary for mutations that are sensitized to specific therapies, and offers insights for future drug development.