Machine learning assisted prediction of pulcherrimin production through pul gene expression in Metschnikowia pulcherrima

Metschnikowia pulcherrima is a widespread non- Saccharomyces yeast whose ability to produce pulcherrimin, an iron-chelating red pigment, contributes to biocontrol activity. In this study, we investigated pulcherrimin production in 49 M. pulcherrima strains isolated from different ecological niches. Pulcherrimin production was evaluated, revealing a pronounced strain-dependent variability that enabled classification of isolates into low, intermediate, high and very high producers. The expression of key genes involved in pulcherrimin biosynthesis and transport ( pul 1 , pul 2 , pul 3 , and pul 4) was evaluated by RT-qPCR, highlighting heterogeneous transcriptional profiles that were not strictly associated with strain origin. Notably, pul 2 showed the widest range of expression and was consistently elevated in high-producing strains. In order to better capture genotype–phenotype relationships, machine learning-based regression of pulcherrimin production relying on gene expression was implemented. A feature importance analysis demonstrated that the pul 2 gene was the key transcriptional predictor to the regression model for pulcherrimin production. A 1D cluster analysis through Kernel Density Estimation showed that pulcherrimin production is highly variable, a strain-specific trait shaped by regulatory diversity. The combined experimental and machine-learning framework proposed could be useful for the rational selection of strains with enhanced pulcherrimin-producing potential for biocontrol applications.