Computational Lipidomics of the Neuronal Plasma Membrane

Membrane lipid composition varies greatly within submembrane compartments, different organelle membranes, and also between cells of different cell stage, cell and tissue types, and organisms. Environmental factors (such as diet) also influence membrane composition. The membrane lipid composition is...

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Published inBiophysical journal Vol. 113; no. 10; pp. 2271 - 2280
Main Authors Ingólfsson, Helgi I., Carpenter, Timothy S., Bhatia, Harsh, Bremer, Peer-Timo, Marrink, Siewert J., Lightstone, Felice C.
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 21.11.2017
Biophysical Society
Elsevier
The Biophysical Society
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Abstract Membrane lipid composition varies greatly within submembrane compartments, different organelle membranes, and also between cells of different cell stage, cell and tissue types, and organisms. Environmental factors (such as diet) also influence membrane composition. The membrane lipid composition is tightly regulated by the cell, maintaining a homeostasis that, if disrupted, can impair cell function and lead to disease. This is especially pronounced in the brain, where defects in lipid regulation are linked to various neurological diseases. The tightly regulated diversity raises questions on how complex changes in composition affect overall bilayer properties, dynamics, and lipid organization of cellular membranes. Here, we utilize recent advances in computational power and molecular dynamics force fields to develop and test a realistically complex human brain plasma membrane (PM) lipid model and extend previous work on an idealized, “average” mammalian PM. The PMs showed both striking similarities, despite significantly different lipid composition, and interesting differences. The main differences in composition (higher cholesterol concentration and increased tail unsaturation in brain PM) appear to have opposite, yet complementary, influences on many bilayer properties. Both mixtures exhibit a range of dynamic lipid lateral inhomogeneities (“domains”). The domains can be small and transient or larger and more persistent and can correlate between the leaflets depending on lipid mixture, Brain or Average, as well as on the extent of bilayer undulations.
AbstractList Membrane lipid composition varies greatly within submembrane compartments, different organelle membranes, and also between cells of different cell stage, cell and tissue types, and organisms. Environmental factors (such as diet) also influence membrane composition. The membrane lipid composition is tightly regulated by the cell, maintaining a homeostasis that, if disrupted, can impair cell function and lead to disease. This is especially pronounced in the brain, where defects in lipid regulation are linked to various neurological diseases. The tightly regulated diversity raises questions on how complex changes in composition affect overall bilayer properties, dynamics, and lipid organization of cellular membranes. We utilize recent advances in computational power and molecular dynamics force fields to develop and test a realistically complex human brain plasma membrane (PM) lipid model and extend previous work on an idealized, “average” mammalian PM. The PMs showed both striking similarities, despite significantly different lipid composition, and interesting differences. The main differences in composition (higher cholesterol concentration and increased tail unsaturation in brain PM) appear to have opposite, yet complementary, influences on many bilayer properties. Both mixtures exhibit a range of dynamic lipid lateral inhomogeneities (“domains”). The domains can be small and transient or larger and more persistent and can correlate between the leaflets depending on lipid mixture, Brain or Average, as well as on the extent of bilayer undulations.
Membrane lipid composition varies greatly within submembrane compartments, different organelle membranes, and also between cells of different cell stage, cell and tissue types, and organisms. Environmental factors (such as diet) also influence membrane composition. The membrane lipid composition is tightly regulated by the cell, maintaining a homeostasis that, if disrupted, can impair cell function and lead to disease. This is especially pronounced in the brain, where defects in lipid regulation are linked to various neurological diseases. The tightly regulated diversity raises questions on how complex changes in composition affect overall bilayer properties, dynamics, and lipid organization of cellular membranes. Here, we utilize recent advances in computational power and molecular dynamics force fields to develop and test a realistically complex human brain plasma membrane (PM) lipid model and extend previous work on an idealized, "average" mammalian PM. The PMs showed both striking similarities, despite significantly different lipid composition, and interesting differences. The main differences in composition (higher cholesterol concentration and increased tail unsaturation in brain PM) appear to have opposite, yet complementary, influences on many bilayer properties. Both mixtures exhibit a range of dynamic lipid lateral inhomogeneities ("domains"). The domains can be small and transient or larger and more persistent and can correlate between the leaflets depending on lipid mixture, Brain or Average, as well as on the extent of bilayer undulations.
Membrane lipid composition varies greatly within submembrane compartments, different organelle membranes, and also between cells of different cell stage, cell and tissue types, and organisms. Environmental factors (such as diet) also influence membrane composition. The membrane lipid composition is tightly regulated by the cell, maintaining a homeostasis that, if disrupted, can impair cell function and lead to disease. This is especially pronounced in the brain, where defects in lipid regulation are linked to various neurological diseases. The tightly regulated diversity raises questions on how complex changes in composition affect overall bilayer properties, dynamics, and lipid organization of cellular membranes. Here, we utilize recent advances in computational power and molecular dynamics force fields to develop and test a realistically complex human brain plasma membrane (PM) lipid model and extend previous work on an idealized, "average" mammalian PM. The PMs showed both striking similarities, despite significantly different lipid composition, and interesting differences. The main differences in composition (higher cholesterol concentration and increased tail unsaturation in brain PM) appear to have opposite, yet complementary, influences on many bilayer properties. Both mixtures exhibit a range of dynamic lipid lateral inhomogeneities ("domains"). The domains can be small and transient or larger and more persistent and can correlate between the leaflets depending on lipid mixture, Brain or Average, as well as on the extent of bilayer undulations.Membrane lipid composition varies greatly within submembrane compartments, different organelle membranes, and also between cells of different cell stage, cell and tissue types, and organisms. Environmental factors (such as diet) also influence membrane composition. The membrane lipid composition is tightly regulated by the cell, maintaining a homeostasis that, if disrupted, can impair cell function and lead to disease. This is especially pronounced in the brain, where defects in lipid regulation are linked to various neurological diseases. The tightly regulated diversity raises questions on how complex changes in composition affect overall bilayer properties, dynamics, and lipid organization of cellular membranes. Here, we utilize recent advances in computational power and molecular dynamics force fields to develop and test a realistically complex human brain plasma membrane (PM) lipid model and extend previous work on an idealized, "average" mammalian PM. The PMs showed both striking similarities, despite significantly different lipid composition, and interesting differences. The main differences in composition (higher cholesterol concentration and increased tail unsaturation in brain PM) appear to have opposite, yet complementary, influences on many bilayer properties. Both mixtures exhibit a range of dynamic lipid lateral inhomogeneities ("domains"). The domains can be small and transient or larger and more persistent and can correlate between the leaflets depending on lipid mixture, Brain or Average, as well as on the extent of bilayer undulations.
During cytokinesis in Saccharomyces cerevisiae, damaged proteins are distributed unequally between the daughter and mother cells. The retention of these proteins is correlated with yeast aging. Even though evidence suggests that aggregates are retained due to an underlying molecular mechanism, the debate on whether an active mechanism is necessary for this asymmetry remains unsolved. In particular, passive diffusion and a bud-specific dilution remain as possible explanations. Here, a computational and mathematical model is provided to test whether passive mechanisms alone are sufficient to account for the aggregate distribution patterns and the aggregate kinetics observed in living cells. To this author's knowledge, this is the most comprehensive model available on this subject and the only one combining key potentially essential passive-only mechanisms proposed in existing bibliography -- namely, the geometrical effect of the dividing yeast cell on the diffusion of protein aggregates, and the possibility of aggregate binding and aggregate formation at different rates. Results suggest that although passive processes alone can reproduce certain averaged observables from experimental bibliography, they are insufficient to vindicate aggregate activity observed in living budding yeast cells. These results are complemented by showing that under basic forms of active quality control, discrepancies between the outputs of the model and experimental bibliography are reduced.
Author Carpenter, Timothy S.
Bremer, Peer-Timo
Ingólfsson, Helgi I.
Marrink, Siewert J.
Bhatia, Harsh
Lightstone, Felice C.
AuthorAffiliation 1 Biosciences and Biotechnology Division, Physical and Life Sciences Directorate
3 Groningen Biomolecular Science and Biotechnology Institute and the Zernike Institute for Advanced Materials, University of Groningen, Groningen, the Netherlands
2 Center for Applied Scientific Computing (CASC), Computational Directorate, Lawrence Livermore National Laboratory, Livermore, California
AuthorAffiliation_xml – name: 3 Groningen Biomolecular Science and Biotechnology Institute and the Zernike Institute for Advanced Materials, University of Groningen, Groningen, the Netherlands
– name: 2 Center for Applied Scientific Computing (CASC), Computational Directorate, Lawrence Livermore National Laboratory, Livermore, California
– name: 1 Biosciences and Biotechnology Division, Physical and Life Sciences Directorate
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  surname: Ingólfsson
  fullname: Ingólfsson, Helgi I.
  organization: Biosciences and Biotechnology Division, Physical and Life Sciences Directorate
– sequence: 2
  givenname: Timothy S.
  surname: Carpenter
  fullname: Carpenter, Timothy S.
  organization: Biosciences and Biotechnology Division, Physical and Life Sciences Directorate
– sequence: 3
  givenname: Harsh
  surname: Bhatia
  fullname: Bhatia, Harsh
  organization: Center for Applied Scientific Computing (CASC), Computational Directorate, Lawrence Livermore National Laboratory, Livermore, California
– sequence: 4
  givenname: Peer-Timo
  surname: Bremer
  fullname: Bremer, Peer-Timo
  organization: Center for Applied Scientific Computing (CASC), Computational Directorate, Lawrence Livermore National Laboratory, Livermore, California
– sequence: 5
  givenname: Siewert J.
  surname: Marrink
  fullname: Marrink, Siewert J.
  organization: Groningen Biomolecular Science and Biotechnology Institute and the Zernike Institute for Advanced Materials, University of Groningen, Groningen, the Netherlands
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  givenname: Felice C.
  surname: Lightstone
  fullname: Lightstone, Felice C.
  email: lightstone1@llnl.gov
  organization: Biosciences and Biotechnology Division, Physical and Life Sciences Directorate
BackLink https://www.ncbi.nlm.nih.gov/pubmed/29113676$$D View this record in MEDLINE/PubMed
https://www.osti.gov/biblio/1408151$$D View this record in Osti.gov
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ContentType Journal Article
Copyright 2017 Biophysical Society
Copyright © 2017 Biophysical Society. All rights reserved.
Copyright Biophysical Society Nov 21, 2017
2017 Biophysical Society. 2017 Biophysical Society
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– notice: Copyright © 2017 Biophysical Society. All rights reserved.
– notice: Copyright Biophysical Society Nov 21, 2017
– notice: 2017 Biophysical Society. 2017 Biophysical Society
CorporateAuthor Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Frederick National Lab. for Cancer Research (FNLCR), Frederick, MD (United States)
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
CorporateAuthor_xml – name: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
– name: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
– name: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
– name: Frederick National Lab. for Cancer Research (FNLCR), Frederick, MD (United States)
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DOI 10.1016/j.bpj.2017.10.017
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Snippet Membrane lipid composition varies greatly within submembrane compartments, different organelle membranes, and also between cells of different cell stage, cell...
During cytokinesis in Saccharomyces cerevisiae, damaged proteins are distributed unequally between the daughter and mother cells. The retention of these...
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SubjectTerms Active control
Aggregates
Aging
Baking yeast
BASIC BIOLOGICAL SCIENCES
Bibliographies
Cell Membrane - metabolism
Cells
Computation
Cytokinesis
Dilution
Humans
Kinetics
Mathematical models
MATHEMATICS AND COMPUTING
Membrane Lipids - chemistry
Membrane Lipids - metabolism
Models, Molecular
Molecular Conformation
Neurons - cytology
Proteins
Quality control
Saccharomyces cerevisiae
Yeast
Title Computational Lipidomics of the Neuronal Plasma Membrane
URI https://dx.doi.org/10.1016/j.bpj.2017.10.017
https://www.ncbi.nlm.nih.gov/pubmed/29113676
https://www.proquest.com/docview/1985138248
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https://www.osti.gov/biblio/1408151
https://pubmed.ncbi.nlm.nih.gov/PMC5700369
Volume 113
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