Abstract:The lipid composition of cellular membranes is dynamic and undergoes remodelling affecting biophysical properties, such as membrane fluidity, which are critical to biological function. Here, we introduce an optical approach to manipulate membrane fluidity based on exogenous synthetic fatty acid with an azobenzene photoswitch, termed FAAzo4. Cells rapidly incorporate FAAzo4 into phosphatidylcholine (PC), the major phospholipid in mammalian cells, in a concentration- and cell type-dependent manner. This generate… Show more
“…Eukaryotic cells face the challenge of maintaining the properties of not just a single plasma membrane but that of several coexisting organellar membranes each with unique lipid compositions and each exchanging membrane material with other organelles via vesicular carriers and/or lipid transfer proteins. Despite recent advances to manipulate and follow membrane properties (John Peter et al , 2022; Renne et al , 2022; preprint: Jiménez-Rojo et al , 2022; preprint: Tsuchiya et al , 2022), we know little about how stressed cells coordinate membrane adaptation between organelles whilst maintaining organelle identity and functions.…”
Biological membranes have a stunning ability to adapt their composition in response to physiological stress and metabolic challenges. Little is known how such perturbations affect individual organelles in eukaryotic cells. Pioneering work provided insights into the subcellular distribution of lipids, but the composition of the endoplasmic reticulum (ER) membrane, which also crucially regulates lipid metabolism and the unfolded protein response, remained insufficiently characterized. Here we describe a method for purifying organellar membranes from yeast, MemPrep. We demonstrate the purity of our ER preparations by quantitative proteomics and document the general utility of MemPrep by isolating vacuolar membranes. Quantitative lipidomics establishes the lipid composition of the ER and the vacuolar membrane. Our findings have important implications for understanding the role of lipids in membrane protein insertion, folding, and their sorting along the secretory pathway. Application of the combined preparative and analytical platform to acutely stressed cells reveals dynamic ER membrane remodeling and establishes molecular fingerprints of lipid bilayer stress.
“…Eukaryotic cells face the challenge of maintaining the properties of not just a single plasma membrane but that of several coexisting organellar membranes each with unique lipid compositions and each exchanging membrane material with other organelles via vesicular carriers and/or lipid transfer proteins. Despite recent advances to manipulate and follow membrane properties (John Peter et al , 2022; Renne et al , 2022; preprint: Jiménez-Rojo et al , 2022; preprint: Tsuchiya et al , 2022), we know little about how stressed cells coordinate membrane adaptation between organelles whilst maintaining organelle identity and functions.…”
Biological membranes have a stunning ability to adapt their composition in response to physiological stress and metabolic challenges. Little is known how such perturbations affect individual organelles in eukaryotic cells. Pioneering work provided insights into the subcellular distribution of lipids, but the composition of the endoplasmic reticulum (ER) membrane, which also crucially regulates lipid metabolism and the unfolded protein response, remained insufficiently characterized. Here we describe a method for purifying organellar membranes from yeast, MemPrep. We demonstrate the purity of our ER preparations by quantitative proteomics and document the general utility of MemPrep by isolating vacuolar membranes. Quantitative lipidomics establishes the lipid composition of the ER and the vacuolar membrane. Our findings have important implications for understanding the role of lipids in membrane protein insertion, folding, and their sorting along the secretory pathway. Application of the combined preparative and analytical platform to acutely stressed cells reveals dynamic ER membrane remodeling and establishes molecular fingerprints of lipid bilayer stress.
“…12 These photolipids have been used to control biological targets of signaling lipids, including GPCRs, 13−15 ion channels, 16−18 enzymes, 19−22 nuclear hormone receptors, 23,24 and immunoreceptors, 25 and as a means to control membrane biophysics in model membranes 26−28 and cells. 29 However, to date, this approach has not been extended to other important classes of lipids, such as steroids or isoprenoids. The development of photoswitchable isoprenoid lipids was further motivated by previously reported arene-rich analogs that proved to be efficient substrates for FTase (Figure 1A).…”
Section: ■ Introductionmentioning
confidence: 99%
“…In recent years, this approach has been extensively explored for photoswitchable sphingolipids and glycerolipids . These photolipids have been used to control biological targets of signaling lipids, including GPCRs, − ion channels, − enzymes, − nuclear hormone receptors, , and immunoreceptors, and as a means to control membrane biophysics in model membranes − and cells . However, to date, this approach has not been extended to other important classes of lipids, such as steroids or isoprenoids.…”
Photoswitchable
lipids have emerged as attractive tools for the
optical control of lipid bioactivity, metabolism, and biophysical
properties. Their design is typically based on the incorporation of
an azobenzene photoswitch into the hydrophobic lipid tail, which can
be switched between its trans- and cis-form using two different wavelengths of light. While glycero- and
sphingolipids have been successfully designed to be photoswitchable,
isoprenoid lipids have not yet been investigated. Herein, we describe
the development of photoswitchable analogs of an isoprenoid lipid
and systematically assess their potential for the optical control
of various steps in the isoprenylation processing pathway of CaaX
proteins in Saccharomyces cerevisiae. One photoswitchable
analog of farnesyl diphosphate (AzoFPP-1) allowed effective
optical control of substrate prenylation by farnesyltransferase. The
subsequent steps of isoprenylation processing (proteolysis by either
Ste24 or Rce1 and carboxyl methylation by Ste14) were less affected
by photoisomerization of the group introduced into the lipid moiety
of the substrate a-factor, a mating pheromone from yeast. We assessed
both proteolysis and methylation of the a-factor analogs in
vitro and the bioactivity of a fully processed a-factor analog
containing the photoswitch, exogenously added to cognate yeast cells.
Combined, these data describe the first successful conversion of an
isoprenoid lipid into a photolipid and suggest the utility of this
approach for the optical control of protein prenylation.
“…In this regard, synthetic photoswitchable phospholipids, or photolipids, have emerged as a research tool to reversibly alter and control a variety of SLB properties, such as fluidity and thickness, lipid order and domain formation, − protein molecular dynamics, and photoactivation of mechanosensitive channels by photoisomerization. Recent studies have further shown the potential of photolipids to trigger the release of molecular cargo from liposomes and lipid nanoparticles, and to control protein secretion in living cells by means of light …”
Nanophotonic devices excel at confining light into intense hot spots of electromagnetic near fields, creating exceptional opportunities for light−matter coupling and surface-enhanced sensing. Recently, all-dielectric metasurfaces with ultrasharp resonances enabled by photonic bound states in the continuum (BICs) have unlocked additional functionalities for surfaceenhanced biospectroscopy by precisely targeting and reading out the molecular absorption signatures of diverse molecular systems. However, BIC-driven molecular spectroscopy has so far focused on end point measurements in dry conditions, neglecting the crucial interaction dynamics of biological systems. Here, we combine the advantages of pixelated all-dielectric metasurfaces with deep learning-enabled feature extraction and prediction to realize an integrated optofluidic platform for time-resolved in situ biospectroscopy. Our approach harnesses high-Q metasurfaces specifically designed for operation in a lossy aqueous environment together with advanced spectral sampling techniques to temporally resolve the dynamic behavior of photoswitchable lipid membranes. Enabled by a software convolutional neural network, we further demonstrate the real-time classification of the characteristic cis and trans membrane conformations with 98% accuracy. Our synergistic sensing platform incorporating metasurfaces, optofluidics, and deep learning reveals exciting possibilities for studying multimolecular biological systems, ranging from the behavior of transmembrane proteins to the dynamic processes associated with cellular communication.
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