Synthetic Biology Pathway to Nucleoside Triphosphates for Expanded Genetic Alphabets

Area of Science:

• Synthetic biology
• Genetics
• Molecular biology

Background:

• The quest for alternative DNA forms to store and evolve biological information is a key area in synthetic biology.
• Artificially expanded genetic information systems (AEGIS) have been developed, featuring 12 nucleotides forming 6 independently replicating pairs within a Watson-Crick geometry.
• AEGIS have demonstrated the capacity to support Darwinian evolution in vitro.

Purpose of the Study:

• To engineer metabolic pathways for the economical synthesis of AEGIS triphosphates from nucleosides, enabling their use in living cells.
• To create third-generation AEGIS triphosphates with enhanced stability and compatibility within biological systems.
• To investigate the performance of AEGIS triphosphates with natural DNA polymerases.

Main Methods:

• Recruitment of "polyphosphate kinases" in conjunction with natural diphosphate kinases and engineered nucleoside kinases to establish AEGIS triphosphate synthesis pathways.
• In vitro synthesis of AEGIS triphosphates, including novel third-generation variants.
• Production of α-32P-labeled AEGIS triphosphates for enzymatic studies.

Main Results:

• An in vitro metabolic pathway was established for the synthesis of AEGIS triphosphates.
• Third-generation AEGIS triphosphates exhibited improved survival characteristics in living bacterial cells compared to earlier generations.
• Studies using α-32P-labeled AEGIS triphosphates revealed instances where natural DNA polymerases performed better with third-generation AEGIS triphosphates than with second-generation ones.

Conclusions:

• The developed metabolic pathway enables the efficient in vitro production of AEGIS triphosphates, crucial for their potential in vivo applications.
• Third-generation AEGIS triphosphates represent a significant advancement, demonstrating enhanced stability in bacterial cells and improved compatibility with natural enzymatic machinery.
• This work paves the way for integrating AEGIS into living systems, expanding the possibilities for genetic information storage and evolution.