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G. Beckham

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Open access Jul 2026

Pathway selection for arabinose utilization in Pseudomonas putida reveals a rate-yield tradeoff in muconic acid production from lignocellulosic sugars

Engineering heterologous utilization of substrates requires selection of catabolic pathways that balance strain performance and product biosynthesis. Here, we compare the oxidative and isomerase arabinose utilization pathways in Pseudomonas putida strains engineered for cis,cis-muconic acid production from glucose and xylose. Based on the point of entry into central carbon metabolism, we hypothesized that the oxidative arabinose pathway would enable higher productivity while the arabinose isomerase pathway would enable higher muconate yield. In both strains, additional modifications were engineered to improve muconic acid production including sugar transporter tuning, catechol 1,2-dioxygenase overexpression, a feedback-resistant DAHP synthase, and a flux-stabilizing gltA variant. Consistent with our hypothesis, the oxidative arabinose pathway supported faster growth and higher productivity (0.58 g/L/h), whereas the arabinose isomerase pathway improved carbon efficiency, achieving muconate yields of up to 50 C-mol% in fed-batch bioreactors. Process modeling indicates that these performance metrics can reduce the minimum selling price of muconate-derived adipic acid to $2.74/kg and greenhouse gas emissions to 1.31 kg CO2e/kg, approaching cost parity and reducing emissions by 86% relative to fossil carbon-derived adipic acid. Overall, this study presents a systematic comparison of sugar catabolic pathways that enabled development of strains suited for the tradeoffs between rate and yield.

Dowan Kim, Torrey M Lind, Chen Ling et al. · 1 citation
Open access Jul 2026

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

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. Efficient co-utilization of sugars from lignocellulose is essential for microbial bioconversion. Here the authors perform laboratory evolution of P. putida to reveal how selection shapes retention or loss of catabolic pathways, offering design rules for biomanufacturing phenotypes.

Sunghwa Woo, H. Lim, B. Norton-Baker et al. · 0 citations
Open access Jul 2026

Engineered Pseudomonas putida reconfigures metabolic fluxes to support energy demands during muconate bioproduction from lignin-related aromatics

Muconic acid is a versatile platform chemical that can be biologically produced from lignocellulosic substrates, including from lignin-related aromatic compounds. Pseudomonas putida has been previously engineered to convert lignin-related aromatic compounds to muconate at quantitative molar yields. This high atom efficiency requires a supplemental carbon and energy source to support bacterial growth, and central carbon metabolic efficiency and its interaction with aromatic catabolism are underexplored. Here, we applied proteomics, metabolomics, and 13C-fluxomics to quantitatively compare central carbon and energy metabolism in wild-type P. putida KT2440 and a muconate-producing strain, P. putida CJ781. During cultivation on glucose and 4-hydroxybenzoate, CJ781 showed increased glucose uptake, reconfigured central fluxes, and increased extracellular leakage of aliphatic acids relative to wild type. These altered fluxes supported a 3-fold higher ATP pool, in excess of demand. Pyruvate and acetate secretion in CJ781 was mitigated by debottlenecking TCA-cycle entry via citrate synthase overexpression. Furthermore, tuned expression of the catechol dioxygenase and protocatechuate decarboxylase enabled the production of 36.3 g L-1 muconate at 1.1 g L-1 h-1. Overall, this work reveals how P. putida redirects carbon and energy fluxes to support aromatic bioconversion for improved bioproduction from renewable feedstocks.

R. Wilkes, P. Suthers, A. Borchert et al. · 3 citations
Open access Jul 2026

Overcoming protocatechuate and catechol accumulation in muconic acid production via adaptive laboratory evolution and metabolic engineering in Pseudomonas putida

Muconic acid is an industrially valuable molecule that can be biologically produced from diverse biogenic and waste-derived feedstocks, including sugars and lignin- and plastic-derived aromatic compounds. However, accumulation of protocatechuate (PCA) has been observed in multiple microbes engineered for muconate production when the PCA decarboxylase, AroY, is used. This raises the question of whether PCA decarboxylation represents a rate-limiting step and how this bottleneck might be alleviated, especially given the toxicity and reactivity of PCA and catechol intermediates. To address this, we performed adaptive laboratory evolution (ALE) on a strain of Pseudomonas putida originally engineered for muconate production from aromatic compounds, but with catBC restored, to select for improved conversion of PCA and, in separate lineages, 4-hydroxybenzoate. Contrary to our expectations, the predominant beneficial mutations localized to the catA1 cassette encoding catechol 1,2-dioxygenase, rather than aroY or its associated cofactor biosynthesis genes. Transcriptomic analysis revealed elevated catA1 expression in evolved isolates from ALE, and introduction of these mutations improved productivity in strains designed for muconate production from both aromatic and sugar substrates. Quantitative proteomics and biochemical assays demonstrated that the mutations also led to increased CatA1 protein abundance and modest enhancements in catalytic efficiency, respectively, with strain phenotypes largely driven by high CatA1 levels and potentially synergistic kinetic improvements. Additional reverse-engineering studies identified variants with modest effects on muconate accumulation, including those with potential to enhance biosynthesis of the prenylated FMN cofactor of AroY. Collectively, these results indicate that catechol, not PCA, is the principal bottleneck in muconate production via the PCA decarboxylation route originally demonstrated by Draths et al., refining our understanding of pathway limitations and offering new strategies for improving rate, yield, and strain resilience in muconate bioproduction. Highlights Accumulation of metabolic intermediates was alleviated by adaptive laboratory evolution Sequencing, proteomics, and enzyme kinetics revealed mechanisms for adaptation Increased CatA1 expression reduced bottlenecks and improved muconate production

Alissa C. Bleem, Tracy L. Hodges, Torrey M Lind et al. · 2 citations
Open access Jul 2026

Machine learning guided cell-free expression maps the biochemical landscape of carbonic anhydrase

This work demonstrates that integrating cell-free enzyme engineering with machine learning enables opportunities for high-throughput experimental measurements to benchmark and improve protein language models, accelerate design loops, and expand functional exploration within protein families where experimental information is limited.

J. Lazar, Evan Komp, I. Martínez et al. · 1 citation