Presentation Information
[P04-542]Identification of Membrane Curvature Sensing Proteins from the Endoplasmic Reticulum Using Spherical Supported Lipid Bilayer
○Rikuto Kawakami1, Takumi Komikawa1, Tatsuya Niwa1, Hideki Taguchi1, Masayoshi Tanaka1 (1. Institute of Science Tokyo (Japan))
Keywords:
Membrane Curvature Sensing Protein,Endoplasmic Reticulum,Proteomic Analysis
[Purpose]
The endoplasmic reticulum (ER) exhibits a complex and dynamic membrane architecture, which is thought to be shaped and maintained by membrane curvature-sensing (MCS) proteins. However, only a few ER-derived MCS proteins have been reported, and their overall landscape remains unclear. This study aimed to systematically identify novel ER-derived MCS proteins by combining spherical supported lipid bilayers (SSLBs) with comparative proteomic analysis.
[Method]
ER proteins from MDA-MB-231 cells were subjected to binding assays with SSLBs of four different sizes using our previously established method (Anal. Chem., 92:16197-16203. (2020)), and SSLB-bound proteins were identified by proteomic analysis.
[Results]
We identified 867 SSLB-binding proteins, with significant enrichment of ER proteins. Among them, 95 exhibited size-dependent binding behavior and were extracted as MCS candidates. Clustering based on SSLB binding profiles and STRING interaction analysis revealed a group of strong candidates with autonomous curvature-sensing ability independent of other factors.
[Consideration]
This study provides the first comprehensive exploration of ER-derived MCS proteins, identifying not only known proteins but also novel candidates involved in ER membrane dynamics. These findings provide new insights into ER dynamics and their potential links to disease.
[Conclusion]
We developed a systematic approach combining SSLBs and proteomics to identify ER-derived MCS proteins. In total, 867 SSLB-binding proteins were identified, including 95 candidates showing size-dependent binding behavior. Clustering and interaction analyses further highlighted a subset of proteins with potential intrinsic curvature-sensing ability. These findings provide the first comprehensive overview of ER-derived MCS proteins and reveal novel candidates that may contribute to ER membrane dynamics and related diseases.
The endoplasmic reticulum (ER) exhibits a complex and dynamic membrane architecture, which is thought to be shaped and maintained by membrane curvature-sensing (MCS) proteins. However, only a few ER-derived MCS proteins have been reported, and their overall landscape remains unclear. This study aimed to systematically identify novel ER-derived MCS proteins by combining spherical supported lipid bilayers (SSLBs) with comparative proteomic analysis.
[Method]
ER proteins from MDA-MB-231 cells were subjected to binding assays with SSLBs of four different sizes using our previously established method (Anal. Chem., 92:16197-16203. (2020)), and SSLB-bound proteins were identified by proteomic analysis.
[Results]
We identified 867 SSLB-binding proteins, with significant enrichment of ER proteins. Among them, 95 exhibited size-dependent binding behavior and were extracted as MCS candidates. Clustering based on SSLB binding profiles and STRING interaction analysis revealed a group of strong candidates with autonomous curvature-sensing ability independent of other factors.
[Consideration]
This study provides the first comprehensive exploration of ER-derived MCS proteins, identifying not only known proteins but also novel candidates involved in ER membrane dynamics. These findings provide new insights into ER dynamics and their potential links to disease.
[Conclusion]
We developed a systematic approach combining SSLBs and proteomics to identify ER-derived MCS proteins. In total, 867 SSLB-binding proteins were identified, including 95 candidates showing size-dependent binding behavior. Clustering and interaction analyses further highlighted a subset of proteins with potential intrinsic curvature-sensing ability. These findings provide the first comprehensive overview of ER-derived MCS proteins and reveal novel candidates that may contribute to ER membrane dynamics and related diseases.
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