Cell-free expressed membraneless organelles sequester RNA in synthetic cells
Robinson, A. O.; Lee, J.; Cameron, A.; Keating, C. D.; Adamala, K. P.
Show abstract
Compartments within living cells create specialized microenvironments, allowing for multiple reactions to be carried out simultaneously and efficiently. While some organelles are bound by a lipid bilayer, others are formed by liquid-liquid phase separation, such as P-granules and nucleoli. Synthetic minimal cells have been widely used to study many natural processes, including organelle formation. Here we describe a synthetic cell expressing RGG-GFP-RGG, a phase-separating protein derived from LAF-1 RGG domains, to form artificial membraneless organelles that can sequester RNA and reduce protein expression. We create complex microenvironments within synthetic cell cytoplasm and introduce a tool to modulate protein expression in synthetic cells. Engineering of compartments within synthetic cells furthers understanding of evolution and function of natural organelles, as well as it facilitates the creation of more complex and multifaceted synthetic life-like systems.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Formation of polyphasic RNP granules by intrinsically disordered Qβ coat proteins and hairpin-containing RNA 96%
- Tuning Cell-free Composition Controls the Time-delay, Dynamics, and Productivity of TX-TL Expression 95%
- A cell-free assay for rapid screening of inhibitors of hACE2-receptor - SARS-CoV-2-Spike binding 95%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- The secreted staphylococcal biofilm protein Sbp forms biomolecular condensates in the presence of DNA 95%
- Dynamic proximity interaction profiling suggests that YPEL2 is involved in cellular stress surveillance 94%
- MemPPI platform for measuring and engineering membrane protein-protein interactions in mammalian cells via split nanoluciferase 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.