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Encapsulation of ribozymes inside model protocells leads to faster evolutionary adaptation

Citation

Lai, Yei-Chen; Liu, Ziwei; Chen, Irene (2021), Encapsulation of ribozymes inside model protocells leads to faster evolutionary adaptation, Dryad, Dataset, https://doi.org/10.5068/D1PX0K

Abstract

Functional biomolecules, such as RNA, encapsulated inside a protocellular membrane are believed to have comprised a very early, critical stage in the evolution of life, since membrane vesicles allow selective permeability and create a unit of selection enabling cooperative phenotypes. The biophysical environment inside a protocell would differ fundamentally from bulk solution, due to the microscopic confinement. However, the effect of the encapsulated environment on ribozyme evolution has not been previously studied experimentally. Here we examine the effect of encapsulation inside model protocells on the self-aminoacylation activity of tens of thousands of RNA sequences, using a high-throughput sequencing assay. We find that encapsulation of these ribozymes generally increases their activity, giving encapsulated sequences an advantage over non-encapsulated sequences in an amphiphile-rich environment. In addition, highly active ribozymes benefit disproportionately more from encapsulation. The asymmetry in fitness gain broadens the distribution of fitness in the system. Consistent with Fisher’s Fundamental Theorem of Natural Selection, encapsulation therefore leads to faster adaptation when the RNAs are encapsulated inside a protocell during in vitro selection. Thus, protocells would not only provide a compartmentalization function, but also promote activity and evolutionary adaptation during the origin of life.

Usage Notes

File name explanation

The file contains the archived raw data from the high-throughput sequencing assay in the study.

The "InV" and "OutV" indicate the encapsulated and unencapsulated conditions, respectively.

The "kSeq" indicates the data for k-Seq experiments of doped RNA pool.

The "selection" indicates the data for the in-vitro selection experiments starting from a random sequence pool.

The *-fastqs.zip contains the raw data. The dereplicated list created by the EasyDIVER pipeline (https://github.com/ichen-lab-ucsb/EasyDIVER) is archived in the *-counts.zip files. For the k-Seq data analysis, the achieved MATLAB workspace, scripts, and Readme files are included in the *-matlab.zip file.

Funding

Simons Foundation, Award: 290356FY18

Camille and Henry Dreyfus Foundation

Ministry of Science and Technology, Taiwan (MOST), Award: 107-2917-I564-002