# Advances in Biotechnological GABA Production: Exploring Microbial Diversity, Novel Food Substrates, and Emerging Market Opportunities

## TL;DR

This paper reviews recent progress in producing GABA using microbes, focusing on improving fermentation methods and using advanced tools like AI and genomics.

## Contribution

The paper highlights novel integration of AI and bioinformatics to optimize GABA production and strain selection.

## Key findings

- Optimized fermentation strategies have achieved up to 90 mM GABA concentrations.
- Chromatography-based quantification methods dominate GABA research, with 68% usage.
- Genomic analysis reveals widespread GABA biosynthesis genes in lactic acid bacteria.

## Abstract

Gamma-aminobutyric acid (GABA) is a non-protein amino acid distributed in nature by different types of organisms and microorganisms. GABA has been widely studied for its different physiological functions and industrial applications. Its production is mainly carried out through fermentation processes using lactic acid bacteria (LAB), which are of particular interest because they are safe and possess high glutamate decarboxylase enzyme activity. However, GABA production can vary among different LAB species and is affected by culture conditions. Therefore, strain development and selection, as well as optimization of fermentation parameters, are essential to increase GABA yields and meet the needs of industrial demand. This review quantitatively analyzes recent advances in fermentative GABA production, showing a sustained increase in publications and a predominance of chromatography-based quantification methods (approximately 68%), mainly using pre-column derivatization. Optimized fermentation strategies, supported by statistical and artificial intelligence models, have achieved GABA concentrations of up to 90 mM. In parallel, in silico genomic and metabolic analyses revealed the widespread distribution of key GABA biosynthesis and transport genes among LAB, supporting their selection and engineering. Overall, the integration of advanced analytical methods, bioinformatics-guided strain selection, and computational process optimization emerges as a key strategy to enhance GABA productivity and support future industrial-scale applications.