Recombinant Mannheimia succiniciproducens Succinyl-CoA ligase [ADP-forming] subunit beta (sucC)

What is the role of Succinyl-CoA ligase (sucC) in M. succiniciproducens metabolism?

Succinyl-CoA ligase [ADP-forming] subunit beta (encoded by the sucC gene) is a critical enzyme in the tricarboxylic acid (TCA) cycle of M. succiniciproducens. It catalyzes the reversible conversion of succinyl-CoA to succinate while generating ATP. In M. succiniciproducens, this enzyme operates within a branched TCA cycle, which is one of the key metabolic characteristics allowing highly efficient production of succinic acid in this organism. The branched TCA cycle, along with strong PEP carboxylation, relatively weak pyruvate formation, and the lack of a glyoxylate shunt, contributes to the organism's natural propensity for succinic acid production .

The TCA cycle in M. succiniciproducens functions differently than in many other organisms because it primarily operates in a reductive manner under anaerobic conditions, focusing on succinic acid production rather than complete oxidation of carbon sources. While the genome-scale metabolic network of M. succiniciproducens has been mapped with 686 reactions and 519 metabolites, the specific role of sucC needs to be interpreted within this unique metabolic context .

How is the sucC gene related to the major succinic acid production pathway in M. succiniciproducens?

The primary pathway for succinic acid production in M. succiniciproducens involves the reductive branch of the TCA cycle. Based on the available research, the major pathway proceeds through the following sequence:

• Phosphoenolpyruvate (PEP) is carboxylated to oxaloacetate (OAA) by PEP carboxykinase (PCKA)

• OAA is reduced to malate by malate dehydrogenase (MDH)

• Malate is converted to fumarate by fumarase (FUMC)

• Fumarate is reduced to succinate by fumarate reductase (FRD)

Although the sucC gene product (Succinyl-CoA ligase) is not directly mentioned in this main reductive pathway in the provided search results, it plays a critical role in the oxidative direction of the TCA cycle and may contribute to succinic acid metabolism through:

• Recycling of CoA molecules

• Maintenance of the energy balance in the cell

• Potential participation in alternative metabolic routes under specific conditions

Researchers investigating sucC must consider its function within this complex metabolic network that has been optimized through multiple genetic and metabolic engineering approaches .

What expression systems are most effective for producing recombinant M. succiniciproducens SucC?

The choice of expression system for recombinant M. succiniciproducens SucC depends on the research objectives. Based on methodologies employed for other M. succiniciproducens enzymes, the following approaches are recommended:

Expression System Comparison for M. succiniciproducens Enzymes:

How can protein engineering approaches improve the catalytic properties of SucC for enhanced succinic acid production?

Protein engineering of SucC can potentially enhance succinic acid production in M. succiniciproducens, drawing parallels from successful engineering of other key enzymes in the pathway. The approach should focus on:

• Structure-guided mutations: Based on the detailed structural and kinetic studies performed with malate dehydrogenase (MDH) from M. succiniciproducens, similar approaches could be applied to SucC. Structural comparison between M. succiniciproducens SucC and other bacterial SucC enzymes with higher catalytic efficiency might reveal key residues influencing specific activity and substrate affinity .

• Activity enhancement: The research on MDH showed that replacing the native M. succiniciproducens MDH (MsMDH) with the Corynebacterium glutamicum MDH (CgMDH) led to significant improvements in succinic acid production. The CgMDH showed higher specific activity and less substrate inhibition compared to MsMDH. Similar screening approaches could identify SucC variants with improved properties .

• Substrate inhibition reduction: For MDH, the structural comparison revealed differences in substrate inhibition profiles (ki of 67.4 μM for MsMDH versus 588.9 μM for CgMDH toward oxaloacetate). Identifying and modifying residues that contribute to potential substrate inhibition in SucC could significantly improve its performance .

• Methodological approach: The recommended methodology includes:

  • Homology modeling of M. succiniciproducens SucC

  • Identification of catalytic residues through sequence alignment

  • Site-directed mutagenesis of targeted residues

  • Kinetic characterization of mutant enzymes

  • In vivo testing of promising variants

Researchers should consider that alterations to SucC might have systemic effects on the metabolic network, as seen with MDH modifications, which potentially influenced other enzyme activities and corresponding reaction fluxes .

What are the optimal conditions for assaying recombinant M. succiniciproducens SucC activity?

Based on methodologies used for studying similar enzymes in M. succiniciproducens, the following assay conditions are recommended for SucC:

Optimized Assay Parameters for SucC Activity:

For physiologically relevant results, the pH should be set to match the internal pH of M. succiniciproducens under the growth conditions of interest. Activity measurements should include controls for substrate inhibition effects, which have been observed in related enzymes like MDH in M. succiniciproducens .

How does SucC interact with other enzymes in the metabolic network of M. succiniciproducens?

The interaction of SucC with other enzymes in M. succiniciproducens should be considered within the context of its genome-scale metabolic network, which consists of 686 reactions and 519 metabolites . While specific SucC interactions are not detailed in the provided search results, we can infer potential interactions based on established metabolic pathways:

• Interaction with the reductive TCA branch: SucC likely interacts with key enzymes in the reductive branch of the TCA cycle, including malate dehydrogenase (MDH), fumarase (FUMC), and fumarate reductase (FRD) .

• Metabolic channeling: Potential metabolic channeling between SucC and other TCA cycle enzymes may enhance pathway efficiency. Similar to observed interactions in other organisms, SucC may form complexes with succinyl-CoA synthetase alpha subunit (SucD) and potentially with other enzymes.

• Regulatory interactions: Based on metabolic engineering studies of M. succiniciproducens, the activity of SucC may be affected by the same regulatory mechanisms that influence other key enzymes in the succinic acid production pathway.

• Experimental approach to study interactions:

  • Bacterial two-hybrid assays to identify protein-protein interactions

  • Co-immunoprecipitation followed by mass spectrometry

  • Metabolic flux analysis to identify functional interactions

  • Native PAGE to identify stable enzyme complexes

When designing experiments to study these interactions, researchers should consider the unique metabolic characteristics of M. succiniciproducens, including its strong PEP carboxylation, branched TCA cycle, relatively weak pyruvate formation, and non-PTS glucose uptake system .

How can genome-scale metabolic modeling be used to predict the effects of sucC modifications?

Genome-scale metabolic modeling provides a powerful framework for predicting the effects of genetic modifications, including those involving the sucC gene. Based on the established genome-scale metabolic network of M. succiniciproducens, researchers can employ the following approach:

• Constraints-based flux analysis: Utilize the existing genome-scale model consisting of 686 reactions and 519 metabolites to perform constraints-based flux analysis under various environmental and genetic conditions. This approach has been validated for M. succiniciproducens with predictions showing excellent agreement with experimental data .

• In silico knockout studies: Conduct in silico knockout or modification studies of the sucC gene to predict changes in metabolic flux distributions. Similar approaches have successfully predicted new metabolic engineering strategies for enhanced succinic acid production .

• Integration with experimental data: Combine model predictions with experimental validation, particularly focusing on:

  • Changes in growth rate

  • Alterations in substrate uptake rates

  • Shifts in byproduct formation

  • Effects on succinic acid yield and productivity

• Methodological workflow:

The genome-scale in silico model serves as an excellent platform for systematically predicting physiological responses of M. succiniciproducens to genetic perturbations involving sucC and for designing rational strategies for strain improvement .

What are the key experimental challenges in studying recombinant M. succiniciproducens SucC and how can they be addressed?

Researchers working with recombinant M. succiniciproducens SucC may encounter several challenges, particularly when attempting to maintain enzyme activity and stability. Drawing from experiences with other M. succiniciproducens enzymes, particularly MDH, the following challenges and solutions are noteworthy:

Common Challenges and Solutions:

The experience with MDH from M. succiniciproducens showed that detailed biochemical and structural analyses can lead to significant improvements in enzyme performance. Researchers observed that C. glutamicum MDH showed higher specific activity and less substrate inhibition compared to the native M. succiniciproducens MDH. Structural comparison revealed key residues influencing specific activity and susceptibility to substrate inhibition. A similar approach could be valuable for optimizing SucC performance .

What molecular biology techniques are most effective for manipulating the sucC gene in M. succiniciproducens?

Based on successful genetic engineering approaches used with M. succiniciproducens, the following molecular biology techniques are recommended for manipulating the sucC gene:

• Gene replacement strategy: For chromosomal integration or replacement of the native sucC gene, researchers can adopt the strategy used for replacing the native mdh gene with the C. glutamicum mdh gene. This approach involved:

  • Construction of a gene integration vector containing homologous regions flanking the target gene

  • Inclusion of a selectable marker (e.g., the cat gene conferring chloramphenicol resistance)

  • Use of a counterselection marker (e.g., sacB gene) to facilitate the selection of double-crossover events

• Plasmid-based expression: For overexpression or complementation studies, plasmid-based expression systems can be used. The construction of expression vectors with appropriate promoters (such as the frd promoter) has proven effective for expressing heterologous genes in M. succiniciproducens .

• CRISPR-Cas9 system: While not explicitly mentioned in the provided search results for M. succiniciproducens, CRISPR-Cas9 systems have been adapted for related organisms and could provide more efficient gene editing capabilities.

• Practical workflow:

The successful construction of the PALKcgmdh strain by replacing the native mdh gene with the C. glutamicum mdh gene demonstrates the feasibility of this approach for genetic manipulation in M. succiniciproducens .

How can metabolic flux analysis be applied to understand the role of SucC in M. succiniciproducens metabolism?

Metabolic flux analysis (MFA) provides valuable insights into intracellular reaction rates and can be particularly useful for understanding the role of SucC in M. succiniciproducens metabolism. Based on previous studies with M. succiniciproducens, the following approach is recommended:

• 13C-based metabolic flux analysis: Use 13C-labeled substrates (typically glucose) to trace carbon flow through metabolic pathways. This technique has been successfully applied to study the metabolic network of M. succiniciproducens .

• Experimental design considerations:

  • Culture M. succiniciproducens under controlled conditions (anaerobic, with CO2 supplementation)

  • Use defined media with 13C-labeled glucose or other carbon sources

  • Collect samples at appropriate time points during steady-state growth

  • Measure extracellular metabolite concentrations and biomass formation

  • Extract and analyze intracellular metabolites using LC-MS/MS

• Data analysis workflow:

The genome-scale metabolic model of M. succiniciproducens, validated through constraints-based flux analysis, provides an excellent foundation for integrating experimental MFA data to understand the specific role of SucC in the metabolic network .

What proteomics approaches can help identify SucC interaction partners in M. succiniciproducens?

To comprehensively identify protein-protein interactions involving SucC in M. succiniciproducens, several complementary proteomics approaches can be employed:

• Affinity purification-mass spectrometry (AP-MS):

  • Express SucC with an affinity tag (His-tag, FLAG-tag, or TAP-tag)

  • Perform gentle cell lysis to preserve protein-protein interactions

  • Capture SucC and associated proteins using affinity chromatography

  • Identify interaction partners by LC-MS/MS

  • Validate interactions with reciprocal pull-downs

• Proximity-dependent biotin identification (BioID):

  • Fuse SucC to a promiscuous biotin ligase (BirA*)

  • Express the fusion protein in M. succiniciproducens

  • Allow biotinylation of proteins in close proximity to SucC

  • Purify biotinylated proteins and identify by MS

  • This approach captures both stable and transient interactions

• Crosslinking-MS approaches:

  • Treat cells with chemical crosslinkers to stabilize protein-protein interactions

  • Purify SucC complexes under denaturing conditions

  • Identify crosslinked peptides by specialized MS methods

  • Map interaction interfaces at amino acid resolution

• Experimental workflow:

These proteomics approaches would help identify potential interactions between SucC and other enzymes in the TCA cycle, particularly those involved in the reductive branch leading to succinic acid production (MDH, FUMC, FRD) . Understanding these interactions could provide insights into potential metabolic channeling or regulatory mechanisms affecting succinic acid production.

How does the kinetic behavior of wild-type versus recombinant SucC differ in M. succiniciproducens?

Understanding the kinetic differences between wild-type and recombinant SucC is crucial for optimizing enzyme performance. Drawing from detailed kinetic studies of other M. succiniciproducens enzymes like MDH, the following approach is recommended:

• Comprehensive kinetic parameter determination:

  • Measure Km, kcat, and kcat/Km for both wild-type and recombinant SucC

  • Determine substrate inhibition constants (Ki) for key substrates

  • Assess the effects of allosteric regulators

  • Evaluate pH and temperature dependence of activity

• Comparative kinetic analysis:

• Structure-function analysis:

  • Compare protein stability using thermal shift assays

  • Assess oligomerization state by size exclusion chromatography

  • Analyze potential structural differences using CD spectroscopy

  • Consider targeted mutagenesis to understand key residues affecting kinetics

What computational approaches can predict the impact of specific SucC mutations on M. succiniciproducens metabolism?

Computational approaches offer powerful tools for predicting how specific mutations in SucC might impact M. succiniciproducens metabolism. Based on successful modeling approaches used for this organism, the following computational strategies are recommended:

• Molecular dynamics simulations:

  • Generate homology models of wild-type and mutant SucC

  • Perform molecular dynamics simulations to assess structural stability and substrate binding

  • Calculate binding free energies for substrates and cofactors

  • Identify potential allosteric sites and conformational changes

• Enzyme kinetics prediction:

  • Use machine learning approaches trained on existing enzyme kinetics data

  • Apply quantitative structure-activity relationship (QSAR) models

  • Predict changes in Km, kcat, and substrate inhibition parameters

• Genome-scale metabolic modeling:

  • Integrate predicted kinetic changes into the existing genome-scale model of M. succiniciproducens

  • Perform flux balance analysis with the modified parameters

  • Predict changes in growth rate, substrate utilization, and product formation

  • Identify potential metabolic bottlenecks or unexpected pathway activations

• Integrated computational workflow:

What are the key considerations for expressing recombinant SucC in different host organisms for structural studies?

For structural studies of recombinant M. succiniciproducens SucC, the choice of expression host and conditions can significantly impact protein quality and crystallization success. Based on experiences with related enzymes, consider the following:

• Host organism selection:

• Construct design for structural studies:

  • Include affinity tags (His6, Strep-tag II) for purification

  • Consider tag position (N- or C-terminal) based on structural predictions

  • Include protease cleavage sites for tag removal

  • Co-express with the alpha subunit (SucD) for proper complex formation

  • Consider fusion proteins (MBP, SUMO) to enhance solubility

• Purification strategy:

  • Implement multi-step purification (affinity, ion exchange, size exclusion)

  • Optimize buffer conditions for stability (pH, salt, additives)

  • Consider on-column refolding for inclusion body recovery

  • Utilize thermal shift assays to identify stabilizing conditions

  • Assess protein quality by dynamic light scattering

• Crystallization considerations:

  • Screen protein with and without ligands/substrates

  • Test both apo and holo enzyme forms

  • Consider surface entropy reduction mutations

  • Evaluate complex formation with interaction partners

  • Implement high-throughput crystallization screening

• Alternative structural approaches:

  • Cryo-EM for larger complexes

  • NMR for dynamic regions

  • Small-angle X-ray scattering (SAXS) for solution structure

  • Hydrogen-deuterium exchange mass spectrometry (HDX-MS) for conformational dynamics

The detailed structural and biochemical analyses performed for M. succiniciproducens MDH led to significant insights about key residues influencing activity and substrate inhibition . A similar approach for SucC could reveal important structure-function relationships that could be exploited for enzyme engineering to enhance succinic acid production.

How can systems biology approaches integrate SucC function with the broader metabolic network of M. succiniciproducens?

Systems biology offers powerful frameworks for understanding how SucC functions within the complex metabolic network of M. succiniciproducens. Based on the genome-scale metabolic studies of this organism, the following integrated approaches are recommended:

• Multi-omics data integration:

  • Combine transcriptomic, proteomic, and metabolomic data

  • Correlate SucC expression/activity with global metabolic patterns

  • Identify regulatory networks affecting SucC expression

  • Map metabolic flux changes resulting from SucC perturbations

• Network analysis approaches:

  • Apply elementary mode analysis to identify minimal functional pathways involving SucC

  • Use metabolic control analysis to quantify SucC's control over succinic acid flux

  • Implement extreme pathway analysis to understand theoretical yield limits

  • Employ network topology analysis to identify key nodes interacting with SucC

• Integration with genome-scale modeling:

  • Update the existing M. succiniciproducens model (686 reactions, 519 metabolites)

  • Incorporate enzyme kinetic information for SucC and other key enzymes

  • Implement dynamic flux balance analysis to capture temporal responses

  • Use ensemble modeling to account for biological variability

• Visualization and analysis workflow:

The genome-scale metabolic model of M. succiniciproducens has already been successfully used to decipher key metabolic characteristics allowing highly efficient production of succinic acid, including strong PEP carboxylation, branched TCA cycle, relatively weak pyruvate formation, and non-PTS glucose uptake . Integrating detailed information about SucC into this framework would provide a more comprehensive understanding of its role in the metabolic network.