Recombinant Rhodopirellula baltica Acetyl-coenzyme A carboxylase carboxyl transferase subunit beta (accD)
Product Specs
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Target Background
KEGG: rba:RB12904
STRING: 243090.RB12904
Q&A
What is the function of Acetyl-coenzyme A carboxylase carboxyl transferase subunit beta (accD) in Rhodopirellula baltica?
Acetyl-coenzyme A carboxylase carboxyl transferase subunit beta (accD) is a critical component of the multi-subunit enzyme Acetyl-coenzyme A carboxylase (ACC) in Rhodopirellula baltica. This enzyme catalyzes the first committed step in fatty acid biosynthesis by converting acetyl-coenzyme A to malonyl-coenzyme A through carboxylation. In R. baltica, the accD subunit works in conjunction with other ACC components to maintain cell membrane integrity, particularly during changes in growth phases and environmental conditions. The enzyme plays a pivotal role in R. baltica's metabolic adaptation to nutrient limitation, as evidenced by its differential expression patterns throughout the organism's life cycle . The unique cell wall composition of Planctomycetes, being peptidoglycan-free and proteinaceous, suggests that fatty acid synthesis regulated by ACC enzymes may have specialized functions in this bacterial phylum compared to other bacteria.
How is the accD gene organized in the Rhodopirellula baltica genome?
The accD gene in Rhodopirellula baltica is part of the organism's completely sequenced genome, which has revealed several interesting and surprising traits in this marine bacterium . While the search results don't provide specific information about accD gene organization, R. baltica's genome contains numerous genes involved in metabolic functions that are differentially regulated through growth phases. Based on studies of R. baltica's gene expression, the accD gene likely follows similar patterns to other metabolic genes, with expression linked to growth phase and environmental conditions. The genome annotation of R. baltica has allowed assessment of its genetic potential, revealing traits such as numerous sulfatase genes, carbohydrate-active enzymes, and distinctive metabolic pathways that may interact with or influence accD expression and function .
What expression patterns does accD show during Rhodopirellula baltica's life cycle?
The expression patterns of metabolic genes, including those involved in fatty acid synthesis like accD, vary significantly throughout R. baltica's life cycle. While specific accD expression data is not detailed in the search results, we can infer patterns based on similar metabolic genes in R. baltica:
| Growth Phase | Cell Morphology | Predicted accD Expression | Metabolic State |
|---|---|---|---|
| Early exponential | Dominated by swarmer and budding cells | Moderate to high | Active biosynthesis and growth |
| Mid-exponential | Mixed population | Moderated | Adaptation to decreasing nutrients |
| Transition | Single cells, budding cells, rosettes | Increased | Metabolic adaptation |
| Stationary | Dominated by rosette formations | Elevated | Stress response and survival |
During the transition to stationary phase, R. baltica shows upregulation of genes involved in energy production, amino acid biosynthesis, and stress response, suggesting accD would follow a similar pattern to support membrane integrity under stress conditions . The formation of rosettes in stationary phase, indicating production of holdfast substance and export of polysaccharides, correlates with changes in cell wall composition that would likely involve altered fatty acid metabolism and thus accD activity .
What is known about the regulation of accD expression in response to environmental stressors?
Rhodopirellula baltica demonstrates sophisticated gene regulation in response to environmental stressors, which likely extends to accD regulation. When transitioning to stationary phase due to nutrient limitation, R. baltica induces genes related to stress response, including glutathione peroxidase (RB2244), thioredoxin (RB12160), bacterioferritin comigratory protein (RB12362), universal stress protein (uspE, RB4742), and chaperones (e.g., RB8966) . The regulation of these stress-response genes suggests that accD may be similarly regulated to adapt fatty acid metabolism to stressed conditions.
The search results indicate that under oxygen limitation, R. baltica increases production of ubiquinone, as evidenced by the induction of genes for ubiquinone biosynthesis (RB2748, RB2749, and RB2750) in the stationary phase . This adaptation to changing oxygen availability suggests that accD expression may also be responsive to oxygen levels, potentially through similar regulatory mechanisms that control other metabolic genes during stress conditions.
What are the optimal conditions for expressing recombinant Rhodopirellula baltica accD protein?
Based on studies of R. baltica growth and metabolism, the optimal conditions for expressing recombinant R. baltica accD would likely involve careful consideration of several factors:
R. baltica naturally grows in a defined mineral medium with glucose as a sole carbon source, suggesting similar media components might benefit recombinant expression systems . The organism's salt resistance should be considered when designing expression protocols, as the native protein may require specific ionic conditions for proper folding and activity.
How does accD gene expression correlate with cell morphology changes in Rhodopirellula baltica?
The life cycle of R. baltica involves distinct morphological stages, transitioning from motile swarmer cells to sessile adult cells that form rosettes. This process resembles the life cycle of Caulobacter crescentus and involves significant changes in gene expression . While specific correlations between accD expression and morphological changes aren't detailed in the search results, we can develop a hypothetical model based on observed patterns:
| Cell Morphology Stage | Predicted accD Expression | Associated Cellular Processes |
|---|---|---|
| Motile swarmer cells | Moderate | Active membrane synthesis for motility |
| Budding cells | High | Increased fatty acid synthesis for new cell formation |
| Single adult cells | Moderate | Maintenance of existing membranes |
| Rosette formation | Elevated | Modified fatty acid composition for adhesion structures |
The formation of rosettes in stationary phase indicates production of holdfast substance, suggesting changes in cell wall and membrane composition that would likely involve altered fatty acid metabolism through accD activity . The expression of cell membrane-related genes (COG class M) increases during late stationary phase, supporting the hypothesis that accD expression may correlate with these morphological adaptations .
What structural features characterize the active site of Rhodopirellula baltica accD?
While the search results don't provide specific structural information about R. baltica accD, a research-based analysis of its likely structural features can be proposed based on knowledge of bacterial carboxyl transferases:
The active site of R. baltica accD likely contains conserved residues typical of bacterial carboxyl transferases, including:
-
A zinc-binding domain with coordinating cysteine and histidine residues
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Substrate-binding pocket for acetyl-coenzyme A with hydrophobic and basic amino acids
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Carboxyl-group transfer region with catalytic amino acids (typically aspartate, glutamate)
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Interface region for interaction with the alpha subunit of carboxyl transferase
The unique metabolic adaptations of R. baltica throughout its life cycle suggest that its accD protein may contain structural features that enable functionality across varying environmental conditions. The organism's adaptation to marine environments may be reflected in salt-tolerant structural features of the enzyme .
How can site-directed mutagenesis be used to study accD function in Rhodopirellula baltica?
Site-directed mutagenesis offers a powerful approach to investigate structure-function relationships in R. baltica accD:
| Target Residue Type | Expected Effect of Mutation | Functional Insight |
|---|---|---|
| Zinc-coordinating residues | Loss of catalytic activity | Confirms metal dependence |
| Substrate-binding pocket | Altered substrate specificity | Defines binding determinants |
| Catalytic residues | Reduced reaction rate | Identifies key catalytic mechanisms |
| Interface residues | Disrupted subunit interaction | Maps protein-protein interaction sites |
| Salt-bridge forming residues | Reduced salt tolerance | Identifies salt adaptation mechanisms |
A systematic mutagenesis approach would begin with sequence alignment to identify conserved residues across bacterial accD proteins, followed by targeted mutations of these residues in the recombinant R. baltica accD. Given R. baltica's unique cell biology and adaptation to marine environments, mutations targeting residues unique to this organism could reveal specialized functional adaptations .
What expression systems are most suitable for recombinant Rhodopirellula baltica accD?
Selecting an appropriate expression system for R. baltica accD requires consideration of the protein's characteristics and experimental goals:
| Expression System | Advantages | Limitations | Best Application |
|---|---|---|---|
| E. coli pET system | High yield, easy manipulation | Possible inclusion bodies | Initial expression testing |
| E. coli Arctic Express | Better folding at low temperatures | Lower yield | Improving solubility |
| Yeast (P. pastoris) | Post-translational modifications | Longer development time | Functional studies |
| Cell-free system | Rapid results, avoids toxicity | Lower yield, higher cost | Difficult-to-express constructs |
| Native R. baltica | Natural conditions | Complex growth requirements | Validation of in vitro findings |
For initial characterization, an E. coli-based system using pET vectors with T7 promoter control would provide sufficient material for biochemical studies. Based on R. baltica's growth characteristics, inclusion of salt in the media and growth at lower temperatures (20-25°C) may improve functional expression . For more advanced functional studies, particularly those examining interactions with other R. baltica proteins, expression in the native organism using its natural growth conditions in mineral medium with glucose might be necessary .
How can the enzymatic activity of recombinant accD be measured in vitro?
Measuring the enzymatic activity of recombinant R. baltica accD requires appropriate assays that reflect its biological function:
| Assay Method | Measurement Principle | Advantages | Considerations |
|---|---|---|---|
| Coupled spectrophotometric assay | NADH oxidation coupled to malonyl-CoA formation | Real-time monitoring | Requires additional coupling enzymes |
| Radioisotope incorporation | 14C-bicarbonate incorporation into malonyl-CoA | High sensitivity | Requires radioisotope handling |
| LC-MS/MS | Direct measurement of acetyl-CoA and malonyl-CoA | Precise quantification | Expensive equipment required |
| Circular dichroism | Monitoring structural changes upon substrate binding | Provides conformational insights | Not a direct activity measurement |
| Isothermal titration calorimetry | Heat changes during catalysis | Thermodynamic parameters | Low throughput |
When designing activity assays, it's important to consider R. baltica's natural growth conditions, including salt concentration and pH . The activity measurements should include controls that account for the transition between different growth phases, as R. baltica shows significant metabolic adaptations during these transitions that might affect enzyme function .
What purification strategies yield the highest purity of recombinant accD protein?
Effective purification of recombinant R. baltica accD requires a tailored approach:
| Purification Step | Method | Critical Parameters | Expected Result |
|---|---|---|---|
| Initial capture | Immobilized metal affinity chromatography (IMAC) | Buffer salt concentration 1-2% NaCl | 70-80% purity |
| Intermediate purification | Ion exchange chromatography | pH based on theoretical pI of accD | 85-90% purity |
| Polishing | Size exclusion chromatography | Flow rate and sample volume | >95% purity |
| Buffer optimization | Differential scanning fluorimetry | Temperature range 4-95°C | Optimal stability buffer |
| Quality control | SDS-PAGE and Western blotting | Antibody specificity | Confirmation of identity |
R. baltica's adaptation to salt conditions should be considered during purification, maintaining appropriate salt concentrations in buffers to preserve protein stability . The enzyme's activity may be sensitive to the physiological state, as R. baltica shows significant metabolic shifts during growth phase transitions, suggesting that care should be taken to minimize exposure to conditions that might trigger conformational changes or activity loss .
How can contradictory results in accD activity assays be reconciled?
Researchers working with R. baltica accD may encounter contradictory results due to several factors that can be systematically addressed:
The life cycle of R. baltica involves significant morphological and metabolic changes that affect gene expression patterns . Seemingly contradictory results may reflect these natural variations rather than experimental errors. Researchers should use a temporally ordered table approach, as described in qualitative research methodologies, to track changes in accD activity across different growth phases and conditions .
What statistical approaches are most appropriate for analyzing accD expression data?
Analyzing accD expression data from R. baltica requires statistical approaches that account for the organism's complex life cycle and gene regulation:
| Statistical Method | Application | Benefits | Implementation |
|---|---|---|---|
| ANOVA with post-hoc tests | Comparing expression across multiple growth phases | Identifies significant phase-specific changes | R packages like 'stats' |
| Principal Component Analysis | Identifying co-regulated genes | Reveals functional relationships | R packages like 'FactoMineR' |
| Time-series analysis | Tracking expression through growth curve | Captures temporal patterns | R packages like 'forecast' |
| Bayesian network analysis | Modeling regulatory relationships | Accounts for uncertainty in regulatory networks | R packages like 'bnlearn' |
| Cluster analysis | Grouping genes with similar expression patterns | Identifies functional modules | R packages like 'cluster' |
When analyzing accD expression data, researchers should consider the assignment of differentially expressed genes to functional cluster of orthologous group (COG) classes, as this approach has been effective in understanding R. baltica's metabolic activity through different growth stages . Comparative analysis with other metabolic genes, such as those involved in energy production, amino acid metabolism, and stress response, can provide context for interpreting accD expression patterns .
How can changes in accD expression be correlated with other metabolic pathways?
Correlating accD expression with other metabolic pathways in R. baltica requires integrative analysis approaches:
| Correlation Method | Data Requirements | Insights Provided |
|---|---|---|
| Co-expression network analysis | Transcriptome data across conditions | Identifies functionally related genes |
| Metabolic flux analysis | Labeled substrate tracing data | Quantifies pathway activities |
| Pathway enrichment analysis | Expression data with pathway annotations | Identifies coordinated pathway regulation |
| Protein-protein interaction mapping | Affinity purification-mass spectrometry data | Reveals physical interactions with other enzymes |
| Conditional knockout phenotyping | Growth and metabolite profiles of mutants | Demonstrates pathway dependencies |
Based on R. baltica gene expression studies, accD expression likely correlates with other metabolic genes, particularly those involved in cell wall composition adaptation during growth phase transitions . The upregulation of glutamate dehydrogenase (RB6930) during the stationary phase suggests potential correlation between amino acid metabolism and fatty acid synthesis pathways, as both contribute to cell wall components . Similarly, the regulation of stress response genes during nutrient limitation may correlate with changes in accD expression as part of the cellular adaptation strategy .
