Simultaneous optimization of lignocellulosic sugar catabolism via systematic laboratory evolution under complex selection pressure

Abstract

Abstract Efficient co-utilization of hexose and pentose sugars from lignocellulose is essential for microbial bioconversion, yet engineered catabolic pathways can be unstable or suboptimal in complex resource environments. Here, we use a Pseudomonas putida strain engineered to catabolize xylose and arabinose to examine how resource abundance, temporal availability, and subculturing shape evolutionary outcomes. Using an automated adaptive laboratory evolution (ALE) platform, we evolve the strain under simple single-substrate and complex multi-substrate selection pressures. These environments drive divergence between catabolic specialists and generalists. Weak or absent selection for xylose frequently leads to loss of xylose catabolism, whereas carbon-limited mixed-sugar environments promote stable retention and coordinated optimization of multiple catabolic pathways, enhancing growth and substrate utilization. Genomic, proteomic, and biochemical analyses show that pathway-specific fitness costs determine evolutionary stability. A generalist clone also shows improved indigoidine production from mixed sugars relative to the parental strain. Together, these findings show how resource dynamics shape fitness landscapes that govern catabolic specialization, generalization, evolutionary trade-offs, and engineering of bioconversion.