Recombinant Staphylococcus aureus Succinyl-CoA ligase [ADP-forming] subunit alpha (sucD)

What is the role of sucD in Staphylococcus aureus metabolism?

The sucD gene encodes the alpha subunit of Succinyl-CoA ligase (also known as Succinyl-CoA synthetase), a key enzyme in the TCA cycle that catalyzes the conversion of succinyl-CoA to succinate while generating ATP through substrate-level phosphorylation. This enzyme functions as a heterodimer with the beta subunit encoded by sucC. Together, these proteins form a functional complex essential for energy metabolism in S. aureus. Beyond its metabolic role, sucD has been implicated in broader cellular processes, particularly in relation to antibiotic resistance mechanisms. Research has shown that mutations in sucD can significantly alter susceptibility to β-lactam antibiotics in MRSA strains, indicating its importance extends beyond primary metabolism to influence pathogenicity and antimicrobial resistance .

How does the sucD subunit interact with other components of the Succinyl-CoA ligase complex?

The sucD-encoded alpha subunit forms a heterodimeric complex with the beta subunit (encoded by sucC) to create functional Succinyl-CoA ligase. The alpha subunit contains the nucleotide-binding domain that binds ADP and is responsible for the phosphorylation aspect of the reaction, while the beta subunit contains the CoA-binding domain. These subunits must interact precisely to form the active site at their interface. The proper assembly and function of this complex are essential for TCA cycle activity. Mutations in either subunit can disrupt the formation or function of the complex, leading to accumulation of succinyl-CoA and subsequent metabolic perturbations. Studies have shown that mutations in either sucC or sucD produce similar phenotypes regarding β-lactam susceptibility, confirming their interdependent functions within the complex .

What experimental approaches are typically used to express recombinant sucD protein?

Expressing recombinant sucD protein typically employs bacterial expression systems, primarily Escherichia coli, similar to the approach used for other S. aureus proteins . The methodological approach involves:

• Gene cloning: The sucD gene is amplified from S. aureus genomic DNA using PCR with specific primers containing appropriate restriction sites.

• Vector construction: The amplified gene is cloned into an expression vector (typically pET series for E. coli) containing:

  • Strong inducible promoter (T7 or similar)

  • Affinity tag (His6, GST, or MBP) for purification

  • Appropriate antibiotic resistance marker

• Expression optimization:

  • Transform into expression strains (BL21(DE3) or derivatives)

  • Test various induction conditions (temperature: 16-37°C; IPTG concentration: 0.1-1.0 mM)

  • Optimize expression duration (4-24 hours)

  • Consider co-expression with chaperones for improved solubility

• Protein extraction and purification:

  • Cell lysis via sonication or mechanical disruption

  • Affinity chromatography using the fusion tag

  • Additional purification via ion exchange or size exclusion chromatography

  • Confirmation of purity via SDS-PAGE

• Functional verification:

  • Enzymatic activity assays measuring ADP formation or succinate production

  • Thermal shift assays to confirm proper folding

  • Co-expression with sucC to form functional heterodimeric complex

These approaches can be adapted based on specific research requirements and the intended use of the recombinant protein.

How should researchers design experiments to investigate the relationship between sucD mutations and antibiotic resistance?

Designing robust experiments to investigate the relationship between sucD mutations and antibiotic resistance requires a comprehensive approach incorporating genetic, biochemical, and phenotypic analyses . A methodologically sound experimental design should include:

• Genetic manipulation strategies:

  • Generate clean deletion mutants of sucD using allelic exchange or CRISPR-Cas9

  • Create complemented strains reintroducing wild-type sucD on plasmid or chromosome

  • Develop point mutants targeting specific functional domains of sucD

  • Construct conditional expression systems for dosage-dependent studies

• Antimicrobial susceptibility testing:

  • Standardized broth microdilution for MIC determination

  • Disk diffusion assays with multiple antibiotic classes

  • Time-kill kinetics to assess rate of bacterial killing

  • Population analysis profiles to detect heteroresistance

• Control inclusions:

  • Wild-type parental strain (positive control)

  • Complemented mutant (genetic control)

  • sucC mutant (related TCA cycle component)

  • Other TCA cycle mutants (sucA, sucB) as comparative controls

  • Standard reference strains for antibiotic testing

• Mechanistic investigations:

  • Measurement of intracellular succinyl-CoA levels

  • Analysis of protein succinylation patterns

  • Assessment of cell wall properties (thickness, cross-linking)

  • Gene expression analysis of resistance determinants

• Statistical considerations:

  • Minimum of three biological replicates per experiment

  • Appropriate statistical tests (ANOVA, t-tests) with correction for multiple comparisons

  • Power analysis to determine sample size requirements

  • Blinding procedures where applicable

This systematic approach ensures rigorous investigation of the causal relationship between sucD function and antibiotic resistance phenotypes .

What controls are essential when studying the effects of sucD mutations on protein succinylation?

• Genetic controls:

  • Wild-type parental strain

  • Clean sucD deletion mutant

  • Complemented sucD mutant

  • sucC mutant (partner subunit)

  • sucA/sucB mutants (affecting succinyl-CoA production)

  • Point mutants with altered enzyme activity but intact protein

• Biochemical controls:

  • Direct measurement of intracellular succinyl-CoA concentrations

  • Monitoring of TCA cycle metabolites

  • Assessment of other acyl-CoA species to determine specificity

  • Verification of antibody specificity using synthetic succinylated peptides

• Proteomic controls:

  • Non-enriched samples to determine total protein abundance changes

  • IgG controls for immunoprecipitation specificity

  • Label-free and labeled quantification approaches

  • Technical replicates to assess variability in sample preparation

  • Spike-in standards of known succinylated peptides

• Site-specific controls:

  • Generation of site-directed mutants (K→R) at key succinylation sites

  • In vitro succinylation assays with purified proteins

  • Comparison of multiple succinylation sites on the same protein

• Growth condition controls:

  • Standardized growth phase for harvesting

  • Consistent media composition

  • Parallel processing of all samples

  • Time-course analysis to capture dynamic changes

These comprehensive controls allow researchers to distinguish direct effects of sucD mutation on protein succinylation from indirect metabolic consequences or technical artifacts .

How can researchers optimize experimental protocols to detect and quantify changes in the S. aureus succinylome?

Optimizing experimental protocols for succinylome analysis in S. aureus requires attention to multiple methodological aspects to ensure sensitive, specific, and reproducible results . The following approach provides a comprehensive strategy:

• Sample preparation optimization:

  • Rapid cell harvesting and lysis in denaturing conditions to preserve modifications

  • Addition of deacetylase/desuccinylase inhibitors (e.g., nicotinamide, trichostatin A)

  • Protein extraction under conditions that minimize succinyl group hydrolysis

  • Stringent quality control checks for protein integrity before enrichment

• Enrichment strategy refinement:

  • Optimization of anti-succinyl-lysine antibody selection and concentration

  • Comparison of multiple commercial antibodies for coverage and specificity

  • Implementation of sequential elution strategies to reduce non-specific binding

  • Pre-clearing samples with protein A/G beads to remove interfering proteins

• Mass spectrometry method development:

  • Optimization of fragmentation parameters for succinylated peptides

  • Development of inclusion lists for known succinylation sites

  • Implementation of retention time prediction for improved identification

  • Parallel reaction monitoring (PRM) for targeted quantification of key sites

• Quantification approach enhancement:

  • Stable isotope labeling (SILAC or TMT) for accurate relative quantification

  • Development of internal standards for absolute quantification

  • Normalization strategies to account for changes in protein abundance

  • Statistical frameworks for handling missing values in succinylome datasets

• Validation procedures:

  • Orthogonal techniques (Western blotting, targeted MS) to confirm key findings

  • Site-directed mutagenesis to verify functional importance of sites

  • Correlation analysis between succinylation levels and phenotypic outcomes

  • Cross-comparison with other acylation modifications to determine specificity

This optimized workflow maximizes the detection and accurate quantification of succinylation changes in response to sucD mutations, providing insights into the functional consequences of altered TCA cycle activity on the S. aureus succinylome.

How can contradictions in reported sucD-related phenotypes be systematically analyzed and resolved?

Contradictions in reported sucD-related phenotypes are not uncommon in the scientific literature and require systematic approaches for resolution . Researchers should employ the following methodological framework to analyze and reconcile contradictory findings:

• Contradiction classification and documentation:

  • Catalog specific contradictions with detailed metadata

  • Classify contradictions (e.g., directional conflicts, magnitude differences, context-dependent variations)

  • Map contradictions to specific experimental variables

  • Apply automated contradiction detection tools to systematically identify conflicting claims in literature

• Analytical resolution approaches:

  • Meta-analysis of published data with standardized effect size calculations

  • Statistical heterogeneity assessment to quantify between-study variation

  • Subgroup analysis based on methodology, strain background, or experimental conditions

  • Sensitivity analysis excluding studies with methodological concerns

• Experimental verification strategies:

  • Direct replication studies under multiple conditions

  • Side-by-side testing of contradictory protocols

  • Development of standardized assays with positive and negative controls

  • Systematic variation of key parameters to identify interaction effects

• Systems biology integration:

  • Mathematical modeling to test whether contradictions represent alternative stable states

  • Network analysis to identify compensatory mechanisms

  • Multi-omics integration to place contradictory findings in broader context

  • Pathway flux analysis to determine metabolic consequences

• Collaborative resolution efforts:

  • Multi-laboratory standardization initiatives

  • Development of standard operating procedures

  • Pre-registered replication studies

  • Open data sharing of raw results

This systematic approach helps distinguish genuine biological complexity from methodological discrepancies, advancing understanding of sucD function while improving research reproducibility .

What methodological approaches can reveal the specific mechanisms by which sucD mutations affect β-lactam resistance?

Investigating the specific mechanisms linking sucD mutations to altered β-lactam resistance requires integrative methodological approaches that connect metabolic perturbations to antimicrobial susceptibility phenotypes . Researchers should implement:

• Genetic dissection strategies:

  • Epistasis analysis with resistance determinants (e.g., mecA, pbps)

  • Suppressor mutation screening to identify compensatory pathways

  • Conditional expression systems to establish dose-dependency

  • Allelic series of mutations targeting specific functional domains

• Metabolic profiling approaches:

  • Targeted metabolomics focusing on TCA cycle intermediates

  • Measurement of succinyl-CoA:CoA ratios under various conditions

  • Isotope tracing to track metabolic flux alterations

  • Correlation analysis between metabolite levels and resistance phenotypes

• Protein modification analysis:

  • Global succinylome profiling to identify differentially modified proteins

  • Focused analysis of cell wall-associated proteins

  • Site-directed mutagenesis of key succinylation sites

  • Activity assays of proteins with altered succinylation states

• Cell wall characterization:

  • Peptidoglycan composition analysis

  • Cell wall thickness measurement by electron microscopy

  • Quantification of crosslinking and muropeptide profiles

  • Assessment of autolytic activity in relation to succinylation changes

• Dynamic response analysis:

  • Time-course studies following β-lactam exposure

  • Transcriptional profiling during antibiotic challenge

  • Protein turnover analysis for key resistance determinants

  • Live-cell imaging to monitor morphological adaptations

• Computational integration:

  • Network analysis connecting metabolic changes to resistance mechanisms

  • Predictive modeling of resistance based on metabolic states

  • Machine learning approaches to identify patterns in multi-omics data

These approaches collectively provide mechanistic insights into how sucD mutations affect β-lactam resistance, potentially revealing novel targets for antimicrobial potentiation strategies .

How can researchers differentiate direct effects of sucD mutation from secondary metabolic adaptations?

Distinguishing direct effects of sucD mutation from secondary metabolic adaptations represents a significant challenge in S. aureus research. To address this challenge, researchers should implement a multi-faceted methodological approach:

• Temporal analysis strategies:

  • Time-course experiments immediately following gene disruption

  • Inducible expression systems for controlled temporal modulation

  • Metabolic pulse-chase experiments to track adaptive responses

  • Progressive phenotypic characterization following mutation introduction

• Genetic manipulation approaches:

  • Construction of catalytically inactive point mutants

  • Development of sucD variants with altered substrate specificity

  • Tunable expression systems to create enzymatic activity gradients

  • Complementation with heterologous sucD genes from related organisms

• Metabolic rescue experiments:

  • Supplementation with TCA cycle intermediates

  • Metabolic bypass engineering to restore metabolic balance

  • Genetic suppression screens to identify compensatory pathways

  • Controlled environmental manipulations to normalize metabolism

• Multi-omics integration:

  • Sequential analysis of transcriptome, proteome, and metabolome changes

  • Network modeling to identify primary and secondary response nodes

  • Comparison with other TCA cycle mutants to identify common response patterns

  • Correlation analysis between different molecular changes and phenotypes

• In situ proximal labeling techniques:

  • BioID or APEX2 fusion proteins to identify proximal interacting partners

  • Spatially-resolved metabolomics to detect local metabolite changes

  • Fluorescent reporters for real-time metabolic sensing

  • Crosslinking mass spectrometry to capture transient interactions

This methodological framework enables researchers to temporally and mechanistically separate immediate consequences of sucD dysfunction from downstream adaptive responses, providing a clearer understanding of the primary role of sucD in antibiotic resistance and other phenotypes .

What statistical approaches are most appropriate for analyzing succinylome data in relation to sucD function?

Analyzing succinylome data in relation to sucD function requires specialized statistical approaches that account for the complexities of post-translational modification data . Researchers should consider implementing:

• Differential modification analysis:

  • Empirical Bayes methods adapted for modification site quantification

  • Linear mixed effects models accounting for protein abundance changes

  • Site-specific analysis using specialized software (e.g., PTM-SEA, PECA-pSILAC)

  • Multiple testing correction appropriate for large-scale data (e.g., Benjamini-Hochberg)

• Normalization strategies:

  • Global median normalization adjusted for modification enrichment

  • Normalization to unmodified peptides from the same protein

  • Housekeeping modification sites as internal controls

  • Intensity-dependent normalization methods

• Missing value handling:

  • Specialized imputation methods for modification data

  • Left-censoring models for below-detection values

  • Peptide-level versus site-level aggregation approaches

  • Sensitivity analysis with and without imputation

• Pattern recognition techniques:

  • Clustering approaches (hierarchical, k-means) for modification patterns

  • Principal component analysis for dimensionality reduction

  • Self-organizing maps to identify co-regulated modification sites

  • Motif analysis around modified sites for sequence preferences

• Functional enrichment methods:

  • Modified protein set enrichment analysis

  • Topology-based pathway analysis incorporating modification data

  • Domain enrichment analysis for structural distribution of modifications

  • Protein interaction network analysis with modification overlay

• Causal inference techniques:

  • Mediation analysis to identify modification-dependent phenotypes

  • Instrumental variable approaches using genetic variation

  • Directed acyclic graphs for causal hypothesis testing

  • Time-series analysis for temporal ordering of events

These statistical approaches provide rigorous frameworks for interpreting succinylome changes in response to sucD mutation, enabling researchers to identify biologically meaningful patterns amid the complexity of large-scale modification data .

What computational tools and databases are valuable for interpreting sucD-related experimental data?

Researchers investigating sucD function in S. aureus can leverage various computational tools and databases to enhance data interpretation and generate new hypotheses. Key resources include:

• Protein sequence and structure databases:

  • UniProt for sequence information and functional annotation

  • Protein Data Bank (PDB) for structural information on sucD homologs

  • SWISS-MODEL for homology modeling of S. aureus sucD

  • AlphaFold DB for AI-predicted protein structures

  • Pfam and InterPro for domain identification and functional prediction

• Metabolic pathway resources:

  • KEGG for TCA cycle pathway mapping

  • BioCyc/StaphyCyc for S. aureus-specific metabolic information

  • MetaCyc for detailed enzymatic reaction information

  • BRENDA for enzyme kinetic parameters

  • Reactome for interaction with connected pathways

• Post-translational modification tools:

  • dbPTM for known modification sites

  • PLMD (Protein Lysine Modification Database) for succinylation sites

  • GPS-PAIL for prediction of succinylation sites

  • PTMfunc for functional impact prediction of modifications

  • PTM-X for cross-talk analysis between different modifications

• Antimicrobial resistance resources:

  • CARD (Comprehensive Antibiotic Resistance Database)

  • MEGARes for antimicrobial resistance gene annotation

  • PATRIC for pathogen-specific genomic data and AMR information

  • EUCAST database for clinical breakpoint information

  • ResistomeDB for resistome analysis

• Integrated analysis platforms:

  • STRING for protein-protein interaction networks

  • Cytoscape for network visualization and analysis

  • Perseus for proteomics data analysis

  • MetaboAnalyst for metabolomics data interpretation

  • Galaxy for workflow development and sharing

• S. aureus-specific resources:

  • AureoWiki for S. aureus gene annotation

  • SAMMD (S. aureus Microarray Meta-Database)

  • StaphDB for comparative genomics

  • NARSA repository information for strain resources

  • VirulenceFinder for virulence factor identification

These computational resources provide essential context for interpreting experimental data, facilitating the connection between sucD function and broader cellular processes in S. aureus.

How should researchers approach the integration of metabolomic, proteomic, and phenotypic data when studying sucD?

Integrating metabolomic, proteomic, and phenotypic data in sucD research requires a systematic multi-omics approach to uncover the complex relationships between metabolism, protein function, and bacterial physiology . Researchers should implement the following methodological framework:

• Experimental design for integration:

  • Synchronize sampling across all data types

  • Include shared controls across experiments

  • Apply consistent growth conditions and perturbations

  • Design time-course studies for temporal integration

  • Consider technical variation through replicate analyses

• Data preprocessing strategies:

  • Normalize each data type appropriately

  • Harmonize identifiers across platforms

  • Apply quality control metrics consistently

  • Address missing values with platform-appropriate methods

  • Scale data to enable cross-platform comparison

• Correlation analysis approaches:

  • Calculate pairwise correlations between features across datasets

  • Develop multi-block correlation methods

  • Implement canonical correlation analysis for dimension reduction

  • Apply partial correlation to account for confounding factors

  • Generate correlation networks for visual interpretation

• Pathway-based integration:

  • Map metabolites and proteins to common pathways

  • Calculate pathway enrichment scores across data types

  • Implement flux balance analysis incorporating multiple data types

  • Identify regulatory modules connecting metabolites to proteins

  • Develop pathway visualization incorporating multiple data types

• Machine learning methods:

  • Implement supervised learning to predict phenotypes from molecular data

  • Apply multi-view clustering to identify integrated patterns

  • Develop feature selection approaches to identify key drivers

  • Use deep learning for complex pattern recognition

  • Implement transfer learning between data types

• Causal network modeling:

  • Construct directed graphical models across data types

  • Test specific causal hypotheses about sucD function

  • Apply Bayesian network inference methods

  • Develop mathematical models of sucD-dependent processes

  • Validate model predictions with targeted experiments

This integrated approach enables researchers to construct a comprehensive understanding of how sucD mutations affect S. aureus physiology across multiple biological layers, providing insights that would not be apparent from any single data type alone .

How does research on sucD contribute to our understanding of antibiotic resistance mechanisms?

Research on sucD provides crucial insights into non-conventional antibiotic resistance mechanisms in S. aureus, highlighting the complex interplay between bacterial metabolism and antimicrobial susceptibility . The significance of this research extends across multiple dimensions:

• Metabolic regulation of resistance:

  • Demonstrates how TCA cycle function influences β-lactam resistance

  • Reveals that mutations in sucD increase antibiotic susceptibility in MRSA

  • Establishes connections between central metabolism and cell wall homeostasis

  • Challenges traditional views that resistance is primarily mediated by specific resistance genes

  • Provides evidence for metabolic adaptation as an adjunct resistance mechanism

• Post-translational modification mechanisms:

  • Identifies protein succinylation as a regulatory mechanism affecting resistance

  • Shows how accumulation of succinyl-CoA alters the bacterial succinylome

  • Demonstrates that key cell wall proteins are targets for succinylation

  • Reveals how metabolic perturbations can affect protein function through PTMs

  • Establishes a mechanistic link between TCA cycle activity and cell wall processes

• Therapeutic implications:

  • Suggests TCA cycle enzymes as potential targets for antibiotic potentiation

  • Identifies metabolic vulnerabilities that could be exploited therapeutically

  • Provides rationale for combination therapies targeting both conventional resistance mechanisms and metabolism

  • Explains why certain metabolic conditions might enhance antibiotic efficacy

  • Offers new approaches to resensitize resistant bacteria to existing antibiotics

• Evolutionary perspectives:

  • Demonstrates how metabolic adaptation contributes to resistance phenotypes

  • Suggests how environmental or host factors affecting metabolism might influence resistance

  • Provides insights into potential fitness costs of resistance mechanisms

  • Explains aspects of heterogeneity in resistance within bacterial populations

  • Reveals potential metabolic biomarkers for predicting resistance development

This research represents a paradigm shift in understanding antibiotic resistance, moving beyond traditional resistance determinants to consider the broader metabolic context in which resistance operates .

What are the methodological approaches for investigating the impact of sucD on S. aureus virulence?

Investigating how sucD affects S. aureus virulence requires comprehensive methodological approaches spanning in vitro, ex vivo, and in vivo systems . Researchers should implement:

• In vitro virulence factor assessment:

  • Quantification of toxin production (α-toxin, PVL, enterotoxins)

  • Measurement of enzyme secretion (proteases, lipases, nucleases)

  • Assessment of biofilm formation capacity

  • Evaluation of pigment production (staphyloxanthin)

  • Analysis of quorum sensing molecule production

• Host-pathogen interaction models:

  • Macrophage survival and replication assays

  • Neutrophil killing resistance assessment

  • Epithelial cell adhesion and invasion studies

  • Human serum survival analysis

  • Ex vivo tissue infection models

• In vivo infection models:

  • Murine systemic infection models with survival endpoints

  • Skin and soft tissue infection models with lesion measurement

  • Endocarditis models assessing vegetation formation

  • Osteomyelitis models evaluating bone destruction

  • Pneumonia models assessing pulmonary damage and bacterial burden

• Virulence gene expression analysis:

  • RNA-Seq during infection conditions

  • qRT-PCR validation of key virulence genes

  • Reporter constructs for in vivo expression monitoring

  • Single-cell analysis of virulence gene expression

  • Correlation of expression with metabolic state

• Comparative approaches:

  • Isogenic mutant comparison (sucD vs. wild-type)

  • Complemented strain validation

  • Dose-dependent phenotype assessment with controlled expression

  • Cross-comparison with other TCA cycle mutants

  • Analysis across multiple clinical isolate backgrounds

• Systems biology integration:

  • Multi-omics profiling under infection-relevant conditions

  • Network analysis connecting metabolism to virulence regulation

  • Mathematical modeling of metabolic state and virulence expression

  • Host response analysis to identify differential immune activation

  • Metabolic requirement analysis during infection progression

These methodological approaches collectively provide a comprehensive assessment of how sucD impacts S. aureus virulence across multiple dimensions, connecting metabolic function to pathogenic potential in clinically relevant contexts.

How can contradictions in published data about sucD function be reconciled through improved experimental design?

Contradictions in published data regarding sucD function can be reconciled through improved experimental design strategies that address variability and enhance reproducibility . Researchers should implement:

• Standardization of experimental protocols:

  • Develop consensus methods for key assays

  • Establish minimum reporting standards for experimental conditions

  • Create reference strains with well-defined genotypes

  • Standardize growth media composition and preparation

  • Define precise conditions for phenotypic assays

• Comprehensive genetic controls:

  • Use whole genome sequencing to identify background mutations

  • Construct multiple independent mutants to rule out off-target effects

  • Always include complemented strains with wild-type gene restoration

  • Verify gene expression levels in complemented strains

  • Create allelic series to distinguish partial from complete loss of function

• Environmental variable assessment:

  • Systematically test multiple growth conditions

  • Evaluate oxygen availability effects (aerobic vs. microaerobic)

  • Assess carbon source influence on observed phenotypes

  • Measure growth phase-dependent effects

  • Determine the impact of pH and osmolarity

• Enhanced statistical approaches:

  • Implement power analysis to determine appropriate sample sizes

  • Utilize blind assessment where feasible

  • Apply appropriate statistical tests with correction for multiple comparisons

  • Report effect sizes along with p-values

  • Share raw data to enable reanalysis

• Contradiction resolution framework:

  • Explicitly test contradictory findings side-by-side

  • Identify specific variables that might explain discrepancies

  • Develop unifying hypotheses that accommodate apparently contradictory results

  • Design experiments specifically targeting the source of contradiction

  • Utilize automated contradiction detection tools to systematically identify conflicts in literature

• Collaborative validation approaches:

  • Engage in multi-laboratory validation studies

  • Implement ring testing of key protocols

  • Share materials and reagents to minimize variability

  • Develop community resources for consistent experimentation

  • Create repositories for experimental workflows and raw data

What are the current technical limitations in studying the succinylome of S. aureus with sucD mutations?

Studying the succinylome of S. aureus with sucD mutations presents several technical challenges that limit comprehensive analysis . Current limitations include:

• Enrichment and detection challenges:

  • Limited specificity of anti-succinyl-lysine antibodies

  • Cross-reactivity with other acyl modifications (acetylation, malonylation)

  • Low stoichiometry of succinylation requiring sensitive detection methods

  • Incomplete coverage of the succinylome due to enrichment biases

  • Difficulty detecting dynamics of succinylation/desuccinylation

• Quantification limitations:

  • Challenges in normalizing for protein abundance changes

  • Variability in enrichment efficiency between experiments

  • Limited dynamic range for detecting subtle changes

  • Difficulty establishing absolute stoichiometry of modification

  • Technical variation masking biologically significant changes

• Metabolic state assessment:

  • Challenges in accurately measuring succinyl-CoA levels

  • Rapid metabolic changes during sample preparation

  • Difficulty correlating metabolite levels with modification patterns

  • Limited temporal resolution of metabolic flux

  • Compartmentalization effects not captured in whole-cell analyses

• Functional interpretation barriers:

  • Difficulty distinguishing regulatory from non-functional modifications

  • Limited tools for site-specific functional analysis

  • Incomplete knowledge of enzymes mediating succinylation/desuccinylation

  • Challenges in connecting specific modifications to phenotypes

  • Complexity of interaction with other post-translational modifications

• Technical considerations for S. aureus:

  • Thick peptidoglycan layer complicating efficient protein extraction

  • Limited genetic tools compared to model organisms

  • Strain-specific variations affecting reproducibility

  • Biosafety requirements limiting certain experimental approaches

  • Challenges working with clinically relevant strains

Future technical developments needed include improved antibody specificity, enhanced mass spectrometry methods optimized for succinylation, development of site-specific genetic tools, and computational approaches for integrating succinylome data with other omics datasets to provide more comprehensive insights into how sucD mutations affect protein succinylation and bacterial physiology .

What experimental approaches can address the question of whether sucD is a potential drug target for antibiotic potentiation?

Exploring sucD as a potential drug target for antibiotic potentiation requires robust experimental approaches that establish target validation, druggability assessment, and therapeutic potential . Researchers should implement:

• Target validation methodologies:

  • Generation of conditional sucD mutants to verify essentiality

  • Testing of sucD inhibition across diverse clinical isolates

  • Evaluation of sucD contribution to virulence in infection models

  • Assessment of metabolic bypass mechanisms and resistance potential

  • Determination of human homolog selectivity concerns

• Chemical biology approaches:

  • Development of selective chemical probes targeting sucD

  • Activity-based protein profiling for engagement confirmation

  • Fragment-based screening to identify binding scaffolds

  • Structure-based design utilizing crystal structures or homology models

  • Allosteric modulator screening for non-competitive inhibition

• Combination therapy assessment:

  • Checkerboard assays with clinical antibiotics

  • Time-kill studies to determine synergistic potential

  • Resistance development monitoring in combination settings

  • In vivo efficacy studies of combination approaches

  • Pharmacokinetic/pharmacodynamic modeling of combination effects

• Cellular and biochemical assays:

  • Development of high-throughput sucD activity assays

  • Thermal shift assays for binding confirmation

  • Cellular reporter systems for TCA cycle perturbation

  • Bacterial cytological profiling to characterize inhibition phenotypes

  • Metabolomic profiling to confirm on-target effects

• Translational research approaches:

  • Testing in diverse infection models relevant to S. aureus disease

  • Evaluation of efficacy against biofilm-embedded bacteria

  • Assessment of activity against persister cell populations

  • Host toxicity and off-target effect evaluation

  • Resistance frequency determination and mechanism characterization

This comprehensive approach provides a methodological framework for evaluating sucD as a potential antibiotic potentiation target, advancing from basic mechanistic understanding to translational application in addressing antibiotic resistance in S. aureus infections.

How can systems biology approaches enhance our understanding of sucD function in the context of S. aureus metabolism and pathogenesis?

Systems biology approaches offer powerful frameworks for understanding sucD function within the complex network of S. aureus metabolism and pathogenesis . Researchers should implement:

• Multi-omics integration strategies:

  • Concurrent analysis of transcriptome, proteome, metabolome, and succinylome

  • Temporal profiling to capture dynamic responses to sucD perturbation

  • Spatial organization analysis to identify subcellular impact

  • Integration of host response data in infection models

  • Development of computational workflows for multi-dimensional data integration

• Network-based approaches:

  • Construction of genome-scale metabolic models incorporating sucD function

  • Protein-protein interaction network analysis centered on sucD

  • Regulatory network mapping connecting metabolism to virulence

  • Bayesian network inference to identify causal relationships

  • Network perturbation analysis to predict system-wide effects

• Constraint-based modeling:

  • Flux balance analysis with sucD constraints

  • Metabolic control analysis to quantify sucD influence

  • Simulation of growth and energy production under various conditions

  • Integration of thermodynamic constraints for realistic flux predictions

  • Dynamic modeling of TCA cycle function with variable sucD activity

• Machine learning applications:

  • Pattern recognition in multi-omics data related to sucD function

  • Predictive modeling of phenotypic outcomes based on metabolic state

  • Feature selection to identify key determinants of sucD-dependent phenotypes

  • Classification of clinical isolates based on metabolic signatures

  • Deep learning for complex phenotype prediction from genotype

• Comparative systems approaches:

  • Cross-species comparison of sucD function and regulation

  • Analysis across diverse S. aureus lineages and clinical isolates

  • Evolutionary analysis of sucD conservation and adaptation

  • Host-pathogen interface modeling in different infection contexts

  • Ecological modeling of metabolic adaptation in different environments

• Model-driven experimentation:

  • Generation of testable hypotheses from computational models

  • Design of targeted experiments to resolve model uncertainties

  • Iterative refinement of models based on experimental validation

  • Development of minimal models capturing essential sucD-dependent behaviors

  • In silico prediction of intervention strategies for experimental testing

These systems biology approaches collectively provide a comprehensive framework for understanding sucD function beyond individual pathways, revealing emergent properties and context-dependent behaviors that explain its complex role in S. aureus metabolism and pathogenesis .