Molecular and biophysical mechanism of plasma membrane internalization during nonclathrin endocytosis
Molecular and biophysical mechanism of plasma membrane internalization during nonclathrin endocytosis
批准号:
10728441
负责人:
Matthew Sataro Akamatsu
金额:
$24.9万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2019
资助国家:
美国
项目状态:
未结题
起止时间:
2019-09-01 至 2025-11-30
关键词:
3-DimensionalActinsApicalBindingBiological ModelsBiophysical ProcessBiophysicsBullaCalibrationCell PolarityCell membraneCell physiologyCellsCellular MembraneCharacteristicsChimeric ProteinsClassificationClassification SchemeClathrinCollaborationsComplexComputer ModelsCytoskeletonDevelopment PolarityDiploidyDisseminated Malignant NeoplasmEndocytosisEventExtracellular FluidFeedbackFluorescenceFluorescence MicroscopyFoundationsGatekeepingGenerationsHumanImageLeadLightMachine LearningMeasuresMechanicsMembraneMethodsMicrofilamentsMicroscopyModelingMolecularMonitorNeoplasm MetastasisOsmosisPhysiologicalPlasma CellsPolymersPostdoctoral FellowProcessProteinsResearchRoleSiteStressSurfaceTertiary Protein StructureTestingTrainingWorkanalytical toolbasecancer cellcell motilitycellular imagingepithelial to mesenchymal transitionexperimental studyextracellularfluorescence imagingglycosylationinduced pluripotent stem cellinhibitorinterestlaboratory experimentlive cell imagingmachine learning methodmathematical modelmodels and simulationmulti-scale modelingnovelphysical modelpolarized cellpolymerizationpost-doctoral trainingpredictive modelingprotein biomarkersprotein functionreceptorreceptor internalizationrecruitsimulationskillsspatiotemporalstem cellstemporal measurementtheories
中文摘要
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英文摘要
During endocytosis, the cell's plasma membrane is deformed and internalized to bring in extracellular
cargo and transmembrane receptors. Nonclathrin/noncaveolar (CLIC/GEEC) endocytosis internalizes
glycosylated receptors and extracellular fluid, and is connected to cell polarity development, blebbing, and the
epithelial-to-mesenchymal transition during cancer metastasis. A molecular and mechanical understanding of
CLIC is necessary to understand how CLIC coordinates membrane curvature and actin polymerization to
internalize the plasma membrane against membrane tension.
The primary limitation in studying CLIC has been the lack of unambiguous markers for the process. A
machine learning approach used in Dr. Akamatsu's postdoctoral lab to classify bona fide endocytic events will
be adapted to disambiguate markers for CLIC endocytosis relative to other types of endocytosis. This will
define the first unique markers for CLIC and will reveal the order of protein assembly during CLIC membrane
internalization, which is essential for understanding the function of each protein. To test the hypothesis that
membrane tension controls CLIC progression, Dr. Akamatsu will combine lattice light-sheet microscopy with a
calibration method he developed during his postdoctoral research to convert fluorescence intensity to numbers
of molecules in live cells. With this new method, molecule-counting lattice light-sheet microscopy, he will
measure the numbers of molecules of CLIC endocytic proteins at both the apical and basolateral surfaces of
polarized iPS cells, which differ in their membrane tension. He will image the cells under osmotic stress to
increase cellular membrane tension. Finally, to understand the feedback relationship between membrane
curvature-sensing BAR proteins and actin polymerization during CLIC membrane internalization, he will
incorporate membrane tubulation by BAR proteins and their reciprocal interactions with actin filament
nucleation proteins into a multi-scale mathematical model developed during his postdoctoral work. Simulations
of this model will predict the critical feedback relationships between plasma membrane curvature, tension and
actin polymerization necessary for the timely completion of CLIC endocytosis. Predictions from the model will
be tested in his own lab by imaging cells endogenously expressing protein domain truncations in the presence
of inhibitors of actin nucleation and polymerization.
Dr. Akamatsu has a longstanding interest in combining physical modeling with live-cell quantitative
experiments. One to two years of additional postdoctoral training will allow him to fully develop both skills in
order to effectively implement a highly synergistic feedback loop in his own lab. Co-advising in experimental
approaches by David Drubin and in computational modeling by Padmini Rangamani at UCSD have given him
the foundation for this integrated approach. Additional training in theory from Padmini Rangamani and Hernan
Garcia, and in quantitative experimental methods from Eric Betzig, Matt Welch, and Dan Fletcher will fully
prepare him to lead an integrated modeling and experimental lab of his own.
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