High throughput variant libraries and machine learning yield design rules for retron gene editors.

Area of Science:

• Molecular Biology
• Biotechnology
• Synthetic Biology

Background:

• Bacterial retron reverse transcriptase systems are used for intracellular single-stranded DNA production.
• Modifying retron non-coding RNA (ncRNA) enables custom DNA synthesis via reverse transcription.
• Improving reverse transcription efficiency is crucial for retron technology but lacks systematic understanding.

Purpose of the Study:

• To systematically identify modifications to Retron-Eco1 ncRNA that maintain or improve reverse transcription efficiency.
• To establish design rules for 'editrons'—retron-based DNA donors for CRISPR-Cas9 genome editing.
• To enhance retron-mediated DNA production and editron efficiency in both yeast and human cells.

Main Methods:

• High-throughput pooled variant library experiments were used to test thousands of Retron-Eco1 ncRNA modifications.
• DNA production was measured to identify tolerant and intolerant regions of the ncRNA.
• Saccharomyces cerevisiae was used to define editron design rules, which were then applied to human genome editing.

Main Results:

• Thousands of Retron-Eco1 ncRNA variants were analyzed, revealing specific regions critical for DNA production efficiency.
• Design rules for editrons were established using high-throughput libraries in yeast.
• The optimized retron system achieved the highest efficiency for Retron-Eco1 editrons in human genome editing to date.

Conclusions:

• Systematic analysis of retron ncRNA modifications provides critical insights into optimizing DNA production.
• Defined editron design rules significantly enhance the efficiency of CRISPR-Cas9-mediated genome editing.
• This work advances retron technology for efficient custom DNA synthesis and precise genome engineering applications.