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 in | Biophysical journal Vol. 113; no. 10; pp. 2271 - 2280 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Elsevier Inc
21.11.2017
Biophysical Society Elsevier The Biophysical Society |
Subjects | |
Online Access | Get full text |
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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. |
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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 |
Author_xml | – sequence: 1 givenname: Helgi I. 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 – sequence: 6 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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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) |
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