7.4 Metabolic engineering of microorganisms

Metabolic engineering tweaks microbes' internal chemistry to make useful stuff. Scientists add, remove, or modify genes to optimize pathways, balancing enzyme levels for maximum output. It's like tuning a car engine, but for tiny living factories.

Advanced techniques include synthetic biology, creating new pathways from scratch. Researchers also use flux analysis and metabolomics to spot bottlenecks and fine-tune their microbial workforce. It's a powerful toolbox for harnessing microbes in biotech.

Metabolic Engineering Techniques

Modifying Metabolic Pathways

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• Metabolic pathways consist of a series of enzymatic reactions that convert a starting compound into a desired product
• Metabolic engineering involves modifying these pathways to optimize production of a specific metabolite or compound
• Can involve adding, removing, or modifying genes encoding enzymes in the pathway
• Pathway optimization often requires balancing enzyme expression levels to prevent accumulation of intermediates or byproducts (pyruvate, acetyl-CoA)

Gene Manipulation Strategies

• Gene knockout involves deleting or inactivating genes to eliminate competing pathways or redirect metabolic flux
  • Commonly targets genes for enzymes that divert intermediates away from the desired product
  • Can be achieved through homologous recombination or CRISPR-Cas9 gene editing (E. coli, S. cerevisiae)
• Gene overexpression increases the activity of enzymes in the desired pathway to enhance product formation
  • Often targets rate-limiting steps or enzymes with low natural expression levels
  • Achieved by introducing strong promoters, increasing gene copy number, or optimizing codon usage (G3P dehydrogenase, pyruvate decarboxylase)

Advanced Metabolic Engineering Approaches

• Synthetic biology involves designing and constructing novel metabolic pathways or enzymes with improved properties
  • Can introduce genes from other organisms or engineer entirely new enzymes through directed evolution
  • Enables production of non-native compounds or optimization of pathway efficiency (artemisinic acid, 1,3-propanediol)
• Strain improvement through adaptive laboratory evolution or mutagenesis can enhance overall cellular performance
  • Selects for beneficial mutations that improve growth, stress tolerance, or product formation
  • Can be combined with rational engineering strategies for optimal results (high-temperature tolerance, increased product yield)

Metabolic Analysis Methods

Quantifying Metabolic Fluxes

• Flux analysis measures the rate of metabolite flow through a metabolic network
  • Uses isotope labeling and mass spectrometry to track the fate of individual atoms
  • Provides a quantitative understanding of pathway usage and identifies targets for engineering (13C-glucose, 15N-ammonia)
• Metabolic flux represents the rate of turnover of metabolites within a specific pathway or reaction
  • Determined by the concentration of metabolites and the activity of enzymes in the pathway
  • Can be manipulated by changing enzyme expression levels or environmental conditions (oxygen availability, nutrient limitation)

Identifying Metabolic Limitations

• Metabolic bottlenecks are reactions or pathways that limit the overall flux through a metabolic network
  • Often caused by insufficient enzyme activity, feedback inhibition, or accumulation of intermediates
  • Identifying and alleviating bottlenecks is a key goal of metabolic engineering (acetyl-CoA carboxylase, malonyl-CoA)
• Metabolomics involves the comprehensive analysis of all metabolites within a cell or organism
  • Uses high-throughput analytical techniques like mass spectrometry and NMR spectroscopy
  • Provides a snapshot of the metabolic state and can reveal changes in response to genetic or environmental perturbations (heat stress, nutrient starvation)

Systems-Level Analysis

• Systems biology integrates data from multiple omics technologies to build comprehensive models of cellular metabolism
  • Incorporates genomics, transcriptomics, proteomics, and metabolomics data
  • Enables prediction of metabolic behavior and identification of targets for engineering (genome-scale metabolic models, flux balance analysis)
• Aims to understand the complex interactions and regulation of metabolic networks at a global level
  • Considers the effects of gene expression, protein-protein interactions, and metabolite concentrations on metabolic flux
  • Facilitates rational design of metabolic engineering strategies and strain optimization (metabolic control analysis, metabolic pathway analysis)