Recombinant Yarrowia lipolytica Acetyl-CoA acetyltransferase (PAT1)

What is the primary function of PAT1 in Yarrowia lipolytica?

PAT1 in Yarrowia lipolytica encodes peroxisomal acetoacetyl-CoA thiolase (also called Acetyl-CoA C-acetyltransferase), which plays a crucial role in the β-oxidation pathway. This enzyme catalyzes the thiolytic cleavage of 3-ketoacyl-CoA to produce acetyl-CoA and an acyl-CoA shortened by two carbon atoms. Research has demonstrated that PAT1 is essential for the utilization of n-alkanes, particularly n-decane, as a carbon source in Y. lipolytica . The enzyme functions as part of a metabolic sequence involving fatty acyl-CoA synthetase, multifunctional enzyme type 2 (MFE2), 3-ketoacyl-CoA thiolase (POT1), and PAT1 to break down fatty acids into acetyl-CoA units .

What is the significance of PAT1 in metabolic engineering applications?

PAT1 has emerged as a valuable target for metabolic engineering in Y. lipolytica, particularly for enhancing the production of high-value compounds. Studies have shown that overexpression of PAT1, along with POT1, can significantly improve the bioproduction of terpenoids such as amorphadiene by harnessing the lipogenic acetyl-CoA pathway . In engineered strains, PAT1 overexpression helps direct carbon flux toward acetyl-CoA, which serves as a key precursor for numerous valuable metabolites. The strategic manipulation of PAT1 expression represents an important approach for optimizing Y. lipolytica as a microbial factory for bioproduction of natural products and unusual lipids .

What structural features are essential for PAT1 catalytic activity?

The catalytic functionality of PAT1 in Y. lipolytica depends on specific conserved structural features typical of peroxisomal acetoacetyl-CoA thiolases. Research has identified a critical residue at position 382, where the replacement of glycine with glutamate (Gly382Glu) completely inactivates the enzyme's ability to complement defective n-decane utilization in PAT1 disruptants . This finding suggests that this glycine residue is essential for maintaining the proper structure or catalytic function of the enzyme. The exact catalytic mechanism involves the formation of a tetrahedral intermediate during the thiolytic cleavage reaction, which requires precise spatial orientation of the active site residues. When engineering recombinant PAT1, researchers must carefully consider preserving these structural elements to ensure proper enzymatic function in heterologous expression systems .

How do subcellular localization strategies enhance PAT1-dependent metabolic pathways?

Strategic subcellular localization of enzymes like PAT1 represents an advanced approach to optimize metabolic pathways in Y. lipolytica. Research has demonstrated that engineered compartmentalization can significantly improve pathway efficiency. For instance, when working with PAT1 in terpenoid production pathways, localizing downstream enzymes such as amorphadiene synthase (ADS) to the endoplasmic reticulum (ER) enhances production titers significantly . This spatial organization strategy creates microenvironments that facilitate substrate channeling and reduces metabolic crosstalk. Furthermore, enlarging the ER surface area through deletion of the PAH1 gene provides more subcellular space for enzyme action, further increasing product yields . When designing PAT1-dependent pathways, researchers should consider combining enzyme overexpression with strategic compartmentalization for maximum pathway efficiency.

What are the mechanisms of transcriptional regulation controlling PAT1 expression?

The transcriptional regulation of PAT1 in Y. lipolytica involves complex mechanisms responding to different carbon sources. The gene is subject to substrate-dependent induction by hydrophobic carbon sources and carbon catabolite repression by glycerol . Recent research using transcriptome analysis has revealed that among the 140 transcription factors (TFs) identified in Y. lipolytica, a significant subset is involved in regulating genes related to recombinant protein synthesis and fatty acid metabolism, potentially including PAT1 . The specific transcriptional activators binding to the PAT1 promoter under n-alkane induction have not been fully characterized, but they likely belong to zinc cluster protein families common in yeast. The glycerol repression mechanism appears to require glycerol phosphorylation, as evidenced by the absence of repression in GUT1 deletion mutants . Understanding these regulatory networks is crucial for designing expression systems with optimal PAT1 production.

What is the optimal strategy for PAT1 gene isolation and characterization from Y. lipolytica?

The optimal strategy for PAT1 isolation involves a systematic functional complementation approach. Begin by identifying a Y. lipolytica strain with defective n-decane utilization. For gene isolation, construct a genomic DNA library from wild-type Y. lipolytica in a suitable shuttle vector. Transform the library into the mutant strain and select transformants on minimal media with n-decane as the sole carbon source . Colonies that regain the ability to grow on n-decane likely contain plasmids carrying functional PAT1. Recover these plasmids and sequence the inserts to identify the PAT1 gene.

For detailed characterization, perform bioinformatic analysis to identify conserved domains characteristic of acetoacetyl-CoA thiolases. Confirm the gene's identity through targeted gene disruption using homologous recombination. Create a PAT1 disruptant by replacing the gene with a selectable marker and verify the phenotype by testing growth on different carbon sources, particularly n-decane . Complement the disruptant with the cloned PAT1 gene to restore function. Finally, express the recombinant protein, purify it using affinity chromatography, and perform enzymatic assays to measure acetoacetyl-CoA thiolase activity under various conditions.

How should researchers design experiments to evaluate PAT1 overexpression effects on terpenoid production?

To evaluate PAT1 overexpression effects on terpenoid production, researchers should implement a comprehensive experimental design with appropriate controls and analytics. Begin by constructing expression vectors containing PAT1 under the control of strong constitutive promoters (such as TEF or YLEXP) or inducible promoters (such as POX2) using Gibson assembly or conventional cloning methods . Develop multiple strains with varying PAT1 expression levels, alongside control strains containing empty vectors.

The experimental design should include:

• Growth characterization in various media compositions (YPD, YNB with different carbon sources)

• Quantification of PAT1 expression levels using RT-qPCR and Western blotting

• Measurement of acetoacetyl-CoA thiolase enzymatic activity in cell extracts

• Analysis of terpenoid production (e.g., amorphadiene) using GC-MS or HPLC methods

• Metabolomic analysis of key intermediates in the terpenoid pathway

• Carbon flux analysis using 13C-labeled substrates

For maximized terpenoid production, consider co-expressing PAT1 with complementary enzymes like POT1 and implementing a modular co-culture approach with specialized strains (e.g., Po1g/PPtM and Po1f/AaADSER) . Monitor production over time in batch or fed-batch cultures, and optimize fermentation conditions including temperature, pH, and feeding strategies to maximize product titers.

What methods are recommended for analyzing PAT1 enzymatic activity in recombinant Y. lipolytica strains?

For robust analysis of PAT1 enzymatic activity in recombinant Y. lipolytica strains, researchers should employ a multi-faceted approach combining spectrophotometric assays, chromatographic methods, and in vivo functional tests.

The standard spectrophotometric assay for acetoacetyl-CoA thiolase activity involves measuring the thiolytic cleavage of acetoacetyl-CoA in the presence of CoA. Cell extracts should be prepared by mechanical disruption (glass beads or French press) in a suitable buffer (typically 50 mM potassium phosphate, pH 7.5, with protease inhibitors) . The reaction mixture should contain acetoacetyl-CoA substrate, free CoA, and cell extract. Monitor the decrease in absorbance at 303 nm, which corresponds to the disappearance of the magnesium-enolate complex of acetoacetyl-CoA. Calculate specific activity using the extinction coefficient of 16.9 mM−1 cm−1.

For direct measurement of acetyl-CoA production, employ HPLC or LC-MS/MS methods with appropriate standards. Additionally, assess the in vivo functionality of PAT1 by measuring growth rates on media containing n-decane or fatty acids as sole carbon sources . Compare growth curves of wild-type, PAT1-overexpressing, and PAT1-disrupted strains to correlate enzymatic activity with physiological function.

To ensure assay specificity, include controls with specific thiolase inhibitors and perform kinetic analyses to determine Km and Vmax values for the recombinant enzyme compared to the native form.

How can researchers overcome challenges in expressing functional recombinant PAT1?

Expressing functional recombinant PAT1 in Y. lipolytica or heterologous hosts presents several challenges that can be addressed through systematic optimization strategies. One common issue is protein misfolding or formation of inclusion bodies, especially when expressing at high levels. To overcome this:

• Optimize codon usage based on the expression host's preference to improve translation efficiency

• Use inducible promoter systems with tunable expression levels to prevent overwhelming the cell's folding machinery

• Co-express molecular chaperones (such as Hsp70 or Hsp90 family proteins) to assist proper folding

• Lower cultivation temperature (20-25°C) during induction phase to slow protein synthesis and allow proper folding

• Add fusion tags that enhance solubility (such as MBP, SUMO, or Thioredoxin) with appropriate protease cleavage sites

If targeting peroxisomal localization is necessary for proper function, ensure the construct includes the C-terminal peroxisomal targeting signal (PTS1) or N-terminal PTS2 sequence. For cases where protein activity is suboptimal, site-directed mutagenesis can be employed to improve catalytic efficiency, focusing on conserved residues. Remember that the Gly382 residue is critical, as its mutation to glutamate abolishes enzyme function . Finally, when expressing in heterologous hosts, consider the appropriate redox environment and cofactor availability for proper enzyme function.

What strategies can address carbon source repression of PAT1 expression?

Carbon source repression, particularly glycerol-mediated repression of PAT1 expression, presents a significant challenge in Y. lipolytica engineering. To overcome this regulatory constraint, researchers can implement several strategic approaches:

• Promoter engineering: Replace the native PAT1 promoter with constitutive promoters like TEF or hybrid promoters that are insensitive to carbon catabolite repression. This decouples PAT1 expression from the natural regulatory mechanisms.

• Deletion of repressor elements: Identify and remove repressor binding sites in the PAT1 promoter region through targeted mutagenesis while preserving activator binding sites.

• Regulatory cascade modification: As glycerol repression requires glycerol phosphorylation by GUT1 (glycerol kinase) , consider using gut1 deletion strains when glycerol is present in the medium but not needed as a carbon source.

• Transcription factor engineering: Overexpress positive transcriptional regulators that activate PAT1 transcription or implement CRISPR interference (CRISPRi) to repress negative regulators.

• Controlled feeding strategies: In fermentation processes, implement carefully timed feeding strategies that separate growth and production phases, adding glycerol for biomass accumulation followed by hydrophobic substrate addition for PAT1 induction after glycerol depletion.

• Dual-promoter systems: Design expression cassettes containing the PAT1 coding sequence under both constitutive and native promoters to ensure baseline expression while maintaining responsiveness to natural inducers.

How can researchers optimize PAT1 activity for enhanced bioproduction of terpenoids?

Optimizing PAT1 activity for enhanced terpenoid bioproduction requires a multi-faceted approach addressing enzyme expression, pathway balancing, and cellular physiology. Researchers should consider implementing the following strategies:

• Enzyme engineering: Conduct rational design or directed evolution of PAT1 to improve its kinetic properties. Focus on enhancing substrate affinity, catalytic efficiency, and thermostability without compromising specificity.

• Pathway compartmentalization: Co-localize PAT1 with other enzymes in the mevalonate pathway to facilitate substrate channeling. Recent research demonstrates that localizing amorphadiene synthase (ADS) to the endoplasmic reticulum significantly improves terpenoid production .

• ER expansion strategy: Implement deletion of the PAH1 gene to enlarge the endoplasmic reticulum surface area, providing more subcellular space for enzyme action. This approach has been shown to increase amorphadiene production to 71.74 mg/L .

• Modular co-culture engineering: Develop specialized Y. lipolytica strains with complementary functions. For example, use Po1g strains to provide substrates for Po1f strains in a commensalism interaction, dividing the mevalonate pathway between strains to optimize conversion efficiency .

• Carbon source optimization: Implement mixed carbon feeding strategies. The co-utilization of 5 μM sodium acetate with 20 g/L glucose in YPD media has been shown to increase amorphadiene titers significantly .

• Redox balance management: Ensure sufficient NADPH supply for terpenoid biosynthesis by overexpressing NADPH-generating enzymes or implementing pathways with improved cofactor usage.

• Genetic stability enhancement: Use genome integration techniques to ensure stable long-term expression of PAT1 and other pathway genes, avoiding the instability issues associated with plasmid-based expression.

By combining these approaches and fine-tuning based on metabolomic and flux analysis data, researchers can develop robust Y. lipolytica strains with optimized PAT1 activity for efficient terpenoid production.

How should researchers interpret conflicting results between in vitro PAT1 activity and in vivo metabolic outcomes?

Discrepancies between in vitro PAT1 enzymatic measurements and in vivo metabolic outcomes are common challenges requiring careful analysis and interpretation. When faced with such conflicts, researchers should systematically evaluate several factors:

• Enzyme microenvironment differences: The cellular environment differs significantly from in vitro assay conditions. Factors such as pH, ion concentrations, metabolite levels, and molecular crowding can drastically affect enzyme kinetics. Consider performing in vitro assays under conditions that better mimic the intracellular environment, including physiologically relevant concentrations of substrates, products, and potential inhibitors.

• Post-translational modifications: PAT1 may undergo regulatory modifications in vivo that are absent in purified enzyme preparations. Similar enzymes in related systems, such as PatZ in E. coli, are known to be regulated by acetylation . Investigate potential modifications through mass spectrometry analysis of PAT1 isolated directly from Y. lipolytica cells.

• Metabolic flux constraints: High in vitro enzyme activity doesn't guarantee proportional metabolic flux in vivo, where upstream substrate availability or downstream product removal may be rate-limiting. Perform metabolomic analysis and 13C flux analysis to identify potential bottlenecks.

• Enzyme localization effects: Proper subcellular localization is crucial for function. Verify that recombinant PAT1 correctly localizes to peroxisomes using fluorescent tagging and microscopy, as mislocalization could explain activity differences.

• Protein-protein interactions: PAT1 may function within enzyme complexes or require interaction partners present in vivo but absent in purified preparations. Consider pull-down experiments to identify potential interaction partners.

To resolve discrepancies, implement a comprehensive approach combining enzyme characterization under various conditions, subcellular fractionation studies, and system-level metabolic analysis to build a more complete understanding of PAT1's role in the cellular context.

What statistical approaches are most appropriate for analyzing PAT1 variant performance in metabolic engineering studies?

• Experimental design considerations:

  • Implement factorial designs to evaluate interactions between PAT1 variants and other genetic or environmental factors

  • Use randomized complete block designs to account for batch effects in fermentation studies

  • Include biological replicates (minimum n=3) and technical replicates for robust analysis

• Comparative performance analysis:

  • For comparing multiple PAT1 variants, use one-way ANOVA followed by appropriate post-hoc tests (Tukey's HSD for all pairwise comparisons or Dunnett's test when comparing to a control)

  • When assumptions of normality are violated, implement non-parametric alternatives such as Kruskal-Wallis followed by Dunn's test

  • For time-course experiments, apply repeated measures ANOVA or mixed-effects models

• Multivariate analysis techniques:

  • Principal Component Analysis (PCA) to identify patterns in metabolomic data associated with different PAT1 variants

  • Partial Least Squares Discriminant Analysis (PLS-DA) to correlate PAT1 enzyme parameters with production outcomes

  • Hierarchical clustering to group PAT1 variants based on multiple performance metrics

• Regression modeling:

  • Multiple linear regression to identify key PAT1 kinetic parameters influencing product titers

  • Response surface methodology to optimize expression conditions for the best-performing PAT1 variants

  • Machine learning approaches (random forests, support vector machines) for predicting variant performance from sequence or structural features

• Robust outlier identification:

  • Apply Grubbs' test or Dixon's Q test to identify significant outliers before statistical analysis

  • Consider bootstrap resampling methods when dealing with small sample sizes

When reporting results, include effect sizes alongside p-values, and clearly state the statistical methods and software packages used. This comprehensive statistical approach enables robust identification of superior PAT1 variants and provides insights into structure-function relationships guiding further engineering efforts.

How can researchers effectively integrate PAT1 enzymatic data with other omics datasets for systems-level understanding?

Effective integration of PAT1 enzymatic data with other omics datasets requires a systematic multi-layered approach to achieve comprehensive systems-level understanding. Researchers should implement the following integration strategies:

• Data preprocessing and normalization:

  • Standardize data across platforms (enzymatic, transcriptomic, proteomic, metabolomic)

  • Apply appropriate transformation methods (log transformation, z-score normalization)

  • Perform quality control procedures specific to each data type

• Multi-omics correlation analysis:

  • Calculate Pearson or Spearman correlation coefficients between PAT1 enzymatic activities and expression levels

  • Develop correlation networks linking PAT1 activity to metabolite concentrations in related pathways

  • Apply canonical correlation analysis (CCA) to identify relationships between PAT1-related parameters and broader metabolic patterns

• Pathway enrichment analysis:

  • Map differentially expressed genes/proteins in PAT1-engineered strains to metabolic pathways

  • Identify significantly enriched pathways using tools like KEGG, BioCyc, or Gene Ontology

  • Perform reporter metabolite analysis to identify metabolic hotspots affected by PAT1 engineering

• Genome-scale metabolic modeling:

  • Incorporate PAT1 kinetic parameters into constraint-based models (FBA, DFBA)

  • Perform sensitivity analysis to identify how changes in PAT1 activity propagate through the metabolic network

  • Use metabolic control analysis (MCA) to quantify PAT1's control coefficient on terpenoid flux

• Network inference and visualization:

  • Construct gene regulatory networks involving PAT1 and other transcription factors

  • Develop integrative visualizations linking enzymatic activities, gene expression, and metabolite levels

  • Use Cytoscape or similar tools for interactive exploration of multi-omics relationships

• Machine learning integration:

  • Apply supervised learning methods to predict metabolic outcomes from multi-omics signatures

  • Use unsupervised learning to identify patterns across datasets that reveal emergent properties

  • Implement deep learning approaches for complex pattern recognition across heterogeneous data types

This integrative approach has been successfully applied in recent studies of Y. lipolytica, where transcriptome analysis revealed that 87 of 140 transcription factor-encoding genes were significantly deregulated in engineered strains , providing insights into the regulatory networks governing PAT1 and related enzymes in recombinant protein production contexts.

What are the most promising unexplored aspects of PAT1 biology in Y. lipolytica?

Several unexplored aspects of PAT1 biology in Y. lipolytica offer promising avenues for future research:

• Post-translational regulatory mechanisms: While transcriptional regulation of PAT1 has been studied in response to carbon sources , potential post-translational modifications regulating enzyme activity remain largely unexplored. By analogy to PatZ in E. coli, which undergoes regulatory acetylation , Y. lipolytica PAT1 may be subject to similar modifications affecting its activity, stability, or interactions. Proteomics approaches focusing on phosphorylation, acetylation, or other modifications could reveal novel regulatory mechanisms.

• Enzyme evolution and substrate specificity: Comparative analysis of PAT1 across Yarrowia species and related oleaginous yeasts could provide insights into the evolution of substrate specificity and catalytic efficiency. This evolutionary perspective might guide rational engineering of PAT1 variants with enhanced activity toward specific substrates.

• Protein-protein interaction networks: PAT1 likely functions within a complex network of protein interactions influencing its localization, stability, and activity. Interactome analysis using approaches like proximity labeling (BioID, APEX) could identify novel interaction partners and reveal unexpected functional connections.

• Moonlighting functions: Beyond its canonical role in β-oxidation, PAT1 might serve additional cellular functions. Investigation of potential moonlighting roles in cellular stress responses, metabolism of unusual lipids, or interaction with signaling pathways could uncover novel biological functions.

• Synthetic biology applications beyond terpenoids: While PAT1 has been leveraged for terpenoid production , its potential in other synthetic biology applications remains underexplored. Integration of PAT1 into artificial metabolic pathways for production of biofuels, bioplastics, or pharmaceutical precursors represents a promising direction.

Exploring these aspects would not only deepen our understanding of PAT1 biology but also expand the biotechnological applications of this important enzyme in Y. lipolytica.

How might emerging genetic tools advance PAT1 engineering for novel applications?

Emerging genetic tools offer unprecedented opportunities to advance PAT1 engineering for novel applications in Y. lipolytica. The following cutting-edge approaches show particular promise:

• CRISPR-Cas systems optimized for Y. lipolytica: Advanced CRISPR tools enable precise genomic modifications with enhanced efficiency. Beyond simple knockouts, CRISPR base editing and prime editing allow for scarless, single-nucleotide modifications of PAT1 without double-strand breaks. This precision enables subtle tuning of enzyme properties through targeted amino acid substitutions, introduction of regulatory elements, or modification of substrate binding sites.

• High-throughput mutagenesis coupled with biosensors: Creating comprehensive PAT1 variant libraries using saturation mutagenesis, combined with acetyl-CoA or fatty acid biosensors, enables rapid screening of thousands of variants simultaneously. This approach accelerates the discovery of PAT1 variants with enhanced activity, altered substrate specificity, or improved stability.

• Synthetic regulatory circuits: Implementing synthetic biology principles allows for the development of dynamic control systems for PAT1 expression. These include feedback-responsive promoters, riboswitches, or degron-based systems that modulate PAT1 levels in response to metabolic signals, optimizing pathway flux and reducing metabolic burden.

• Multiplexed genome engineering: Combinatorial approaches targeting PAT1 alongside other pathway genes enable the exploration of synergistic effects. CRISPRa (activation) and CRISPRi (interference) systems allow simultaneous modulation of multiple genes affecting PAT1 function or related pathways.

• In vivo directed evolution systems: Continuous evolution systems such as PACE (Phage-Assisted Continuous Evolution) adapted for yeast could dramatically accelerate PAT1 engineering by coupling improved enzyme function directly to cell fitness.

• Protein structure prediction and design: AI-powered tools like AlphaFold2 enable accurate prediction of PAT1 structure, while computational protein design tools help rationalize engineering strategies for improved performance in specific applications.

These emerging tools will facilitate the development of PAT1 variants optimized for novel applications such as the production of unusual lipids , bioremediation of recalcitrant pollutants, or synthesis of high-value chemicals through non-native metabolic pathways.

What potential role might PAT1 play in engineering Y. lipolytica for sustainable bioprocesses?

PAT1 is poised to play a pivotal role in engineering Y. lipolytica for sustainable bioprocesses across multiple applications:

• Valorization of waste streams: Engineered PAT1 variants with expanded substrate specificity could enable Y. lipolytica to efficiently metabolize fatty components in industrial and agricultural waste streams. Optimized PAT1 expression could enhance the yeast's ability to convert waste cooking oils, fatty acid-rich effluents, or agricultural residues into high-value products, advancing circular bioeconomy principles.

• Carbon-negative bioproduction: PAT1's central role in acetyl-CoA metabolism positions it as a key enzyme for developing carbon-negative bioproduction systems. By engineering PAT1 alongside carbon fixation or carbon concentration mechanisms, Y. lipolytica could be developed as a platform for converting atmospheric CO2 into valuable compounds while reducing greenhouse gas levels.

• Biodegradation of persistent pollutants: Modified PAT1 variants could potentially expand Y. lipolytica's capacity to metabolize recalcitrant compounds with structures similar to fatty acids, including certain plasticizers, surfactants, or petroleum derivatives. Such applications would leverage PAT1's thiolase activity while engineering substrate specificity for novel compounds.

• Production of biodegradable materials: PAT1 engineering could redirect acetyl-CoA flux toward pathways for producing biodegradable polymers or polymer precursors. By balancing PAT1 activity with downstream enzymes, Y. lipolytica could be optimized for producing polyhydroxyalkanoates (PHAs) or other biodegradable materials from renewable feedstocks.

• Multi-product biorefinery development: Strategic regulation of PAT1 activity could enable dynamic switching between different product profiles in Y. lipolytica biorefineries. Controlled expression or activity modulation could direct carbon flux between lipid accumulation, terpenoid synthesis, or other valuable product pathways depending on market demands and economic factors.

• Strain robustness in industrial conditions: Engineering PAT1 for enhanced stability under industrial bioprocess conditions (elevated temperatures, pH fluctuations, presence of inhibitors) would improve the robustness of Y. lipolytica in scaled-up sustainable bioprocesses.