# Microbial Production of N-Acetylneuraminic Acid Using Metabolically Engineered Escherichia coli and Bacillus subtilis: Advances and Perspectives

## TL;DR

This paper reviews how E. coli and B. subtilis can be engineered to produce sialic acid, a valuable compound used in food, pharmaceuticals, and cosmetics.

## Contribution

The paper systematically summarizes strategies for engineering microbial cell factories to improve Neu5Ac production efficiency.

## Key findings

- E. coli and B. subtilis are key organisms for Neu5Ac biosynthesis due to their well-characterized genetics.
- Rational and irrational strategies, including metabolic engineering and directed evolution, are used to enhance Neu5Ac production.
- Integrating machine learning with metabolic engineering is proposed to overcome current bioproduction challenges.

## Abstract

N-Acetylneuraminic acid (Neu5Ac), the predominant form of sialic acids (Sias), is extensively utilized in the food, pharmaceutical, and cosmetic industries. Microbial fermentation serves as a critical production method for its economical, eco-friendly, and scalable production. Escherichia coli and Bacillus subtilis, as primary industrial workhorses for Neu5Ac production, have been extensively investigated owing to their well-characterized genetic frameworks and mature molecular toolkits. Nevertheless, the intricate regulatory networks inherent to microbial systems present formidable obstacles to the high-efficiency biosynthesis of Neu5Ac. This review delineates the genetic and molecular mechanisms underlying Neu5Ac biosynthesis in both E. coli and B. subtilis. Furthermore, the rational and irrational strategies for constructing Neu5Ac microbial cell factories are systematically summarized, including the application of rational metabolic engineering to relieve feedback regulation, reconfigure metabolic networks, implement dynamic regulation, and optimize carbon sources; as well as the use of irrational strategies including directed evolution of key enzymes and high-throughput screening based on biosensors. Finally, this review addresses current challenges in Neu5Ac bioproduction and proposes integrative solutions combining machine learning with systems metabolic engineering to advance the construction of high-titer Neu5Ac microbial cell factory and the refinement of advanced fermentation technologies.