Construction of Out-of-Equilibrium Metabolic Networks in Nano- and Micrometer-Sized Vesicles

Jelmer Coenradij, Eleonora Bailoni, Bert Poolman*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

We present a method to incorporate into vesicles complex protein networks, involving integral membrane proteins, enzymes, and fluorescence-based sensors, using purified components. This method is relevant for the design and construction of bioreactors and the study of complex out-of-equilibrium metabolic reaction networks. We start by reconstituting (multiple) membrane proteins into large unilamellar vesicles (LUVs) according to a previously developed protocol. We then encapsulate a mixture of purified enzymes, metabolites, and fluorescence-based sensors (fluorescent proteins or dyes) via freeze-thaw-extrusion and remove non-incorporated components by centrifugation and/or size-exclusion chromatography. The performance of the metabolic networks is measured in real time by monitoring the ATP/ADP ratio, metabolite concentration, internal pH, or other parameters by fluorescence readout. Our membrane protein-containing vesicles of 100-400 nm diameter can be converted into giant-unilamellar vesicles (GUVs), using existing but optimized procedures. The approach enables the inclusion of soluble components (enzymes, metabolites, sensors) into micrometer-size vesicles, thus upscaling the volume of the bioreactors by orders of magnitude. The metabolic network containing GUVs are trapped in microfluidic devices for analysis by optical microscopy.

Original languageEnglish
Article number66627
JournalJournal of visualized experiments : JoVE
Issue number206
DOIs
Publication statusPublished - 12-Apr-2024

Keywords

  • Unilamellar Liposomes/metabolism
  • Metabolic Networks and Pathways
  • Membrane Proteins/metabolism

Fingerprint

Dive into the research topics of 'Construction of Out-of-Equilibrium Metabolic Networks in Nano- and Micrometer-Sized Vesicles'. Together they form a unique fingerprint.

Cite this