CCS Human
Description
Mechanism of Action
CCS Human enables SOD1 activation through two key steps:
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Cu Transfer: Domain I acquires Cu, which is transferred to SOD1 via Domain III.
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Disulfide Oxidation: CCS catalyzes the oxidation of SOD1’s disulfide bond (Cys57–Cys146) in an oxygen-dependent process .
In copper-rich conditions, CCS interacts with the 26S proteasome to degrade excess Cu-bound complexes, maintaining cellular homeostasis . Knockout models (Δccs) show 70–90% reduced SOD1 activity and compensatory upregulation of other Cu-binding proteins (e.g., COX17, ATP7A) .
Clinical and Pathological Significance
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ALS Link: Mutations disrupting CCS-SOD1 interactions cause SOD1 misfolding and aggregation, mimicking amyotrophic lateral sclerosis (ALS) pathology . A missense mutation (Arg163Trp) in CCS Domain II impairs Cu delivery, highlighting its role in neurodegeneration .
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Biomarker Potential: CCS levels inversely correlate with cellular Cu status, making it a candidate biomarker for Cu deficiency or toxicity .
Research Tools and Databases
CCS values (collision cross sections) are critical for identifying compounds in metabolomics and lipidomics. Key resources include:
Table 1: CCS Databases and Prediction Tools
Applications in Omics Studies
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Metabolomics: CCS values improve identification accuracy by distinguishing isomers (e.g., 3,5-Diiodothyronine [CCS = 195.5 Ų] ).
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Drug Screening: CCS databases aid in characterizing veterinary drugs (e.g., benzimidazoles, quinolones) in biological matrices .
Recombinant CCS Human Production
Recombinant CCS (31.2 kDa) is expressed in E. coli with an N-terminal His-tag, purified via chromatography, and stabilized in Tris-HCl buffer (pH 8.0) . It is used for studying SOD1 maturation and metal homeostasis.
Product Specs
Superoxide dismutase copper chaperone, Copper chaperone for superoxide dismutase.
MGSSHHHHHH SSGLVPRGSH MASDSGNQGT LCTLEFAVQM TCQSCVDAVR KSLQGVAGVQ DVEVHLEDQM VLVHTTLPSQ EVQALLEGTG RQAVLKGMGS GQLQNLGAAV AILGGPGTVQ GVVRFLQLTP ERCLIEGTID GLEPGLHGLH VHQYGDLTNN CNSCGNHFNP DGASHGGPQD SDRHRGDLGN VRADADGRAI FRMEDEQLKV WDVIGRSLII DEGEDDLGRG GHPLSKITGN SGERLACGII ARSAGLFQNP KQICSCDGLT IWEERGRPIA GKGRKESAQP PAHL.
Q&A
What is CCS in the context of human research?
CCS (Collision Cross-Section) represents a critical measurement in ion mobility-mass spectrometry that captures the effective area of an ion interacting with a buffer gas. In human research applications, CCS values provide valuable structural information about molecules including drugs, metabolites, and other biological compounds. These measurements enable researchers to relate structure and function of drugs, characterize multiple structural configurations of compounds, and identify bioactive molecules in formulations. CCS values have emerged as an essential parameter for large-scale structural characterization of drug and drug-like compounds in human studies .
How does CCS measurement enhance compound identification in human studies?
CCS measurement adds a crucial dimension of analytical information beyond mass and retention time, significantly improving identification confidence. When combined with accurate mass, retention time, and fragmentation data, CCS values create a multi-dimensional identification approach that substantially reduces false positives in complex biological matrices. In toxicology studies, applying a CCS and retention time index filter of mean error ± 2 standard deviations has demonstrated remarkable efficacy in removing misidentifications, reducing potential false hits by up to 76% in studies of human exposure to tobacco cigarettes .
What types of compounds can be characterized using CCS measurements in human research?
CCS measurements in human research can characterize diverse chemical classes essential to human metabolism and drug studies, including:
| Chemical Class | Relevance to Human Research | CCS Application |
|---|---|---|
| Carbohydrates | Energy metabolism, glycemic regulation | Structural differentiation of isomers |
| Carboxylic acids | TCA cycle intermediates, fatty acid metabolism | Identification in complex matrices |
| Lipids | Membrane structure, signaling molecules | Classification of lipid families |
| Nucleotides | Genetic materials, energy transfer | Distinguishing modifications |
| Organoheterocyclic compounds | Drug molecules, cofactors | Structural characterization |
These compounds reflect the metabolic status in biological matrices of interest and are involved in numerous pathways critical to human health and disease .
What are the essential components of a well-designed CCS measurement experiment?
A robust experimental design for CCS studies requires careful consideration of several critical elements:
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Variable definition: Clearly identify independent variables (e.g., compound concentration, matrix effects), dependent variables (CCS values, mass accuracy), and control variables that must be standardized.
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Hypothesis formulation: Develop specific, testable hypotheses addressing the research question, such as "Increased air temperature leads to increased soil respiration" .
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Treatment design: Determine both the breadth and granularity of independent variable manipulation, balancing practical constraints with the need for comprehensive data.
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Subject assignment: Implement appropriate randomization strategies, choosing between completely randomized designs and randomized block designs based on study requirements.
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Measurement protocols: Establish standardized procedures for CCS data collection, ensuring consistency across operators and instruments .
How should researchers approach multi-site studies involving CCS measurements?
Multi-site CCS studies present unique challenges that require rigorous standardization and quality control. Based on established multi-site research protocols, researchers should implement:
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Methodological standardization: Develop thoroughly manualized methods, supported by video documentation and in-person training sessions to ensure consistency.
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Equipment harmonization: Acquire and program all equipment at a centralized "CCS site" to minimize instrumental variations.
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Quality assurance protocols: Implement stringent QA procedures to identify non-standardized practices and procedural "drift" that inevitably emerges over time.
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Variability assessment: Recognize that significant variability may exist across sites in some but not all primary measures, and design statistical analyses accordingly.
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Influencing factor identification: Determine factors within both the testing process and subject characteristics that may influence measurements .
What randomization strategies are most appropriate for CCS human studies?
The choice of randomization strategy significantly impacts experimental validity in CCS studies:
| Design Approach | Description | Appropriate Applications |
|---|---|---|
| Completely randomized design | Subjects assigned to treatment groups entirely at random | Studies with homogeneous subject populations |
| Randomized block design | Subjects grouped by shared characteristics before randomization within blocks | Studies where subject characteristics (age, sex, etc.) may influence outcomes |
| Between-subjects design | Individuals receive only one level of experimental treatment | Cross-sectional studies comparing different groups |
| Within-subjects design | Every individual receives each experimental treatment consecutively | Longitudinal studies tracking changes over time |
When implementing within-subjects designs, counterbalancing (randomizing treatment order) is essential to prevent order effects from influencing results .
How can machine learning improve CCS prediction in human metabolomics?
Machine learning approaches have demonstrated remarkable efficacy in predicting CCS values for large-scale metabolomic databases. Recent research has achieved prediction models with R² scores of 0.938 between predicted and measured CCS values, with a mean absolute error of only 3.94 Ų and a standard deviation of 6.11 Ų (3.36%). The predictive relationship follows the equation:
Predicted CCS = 0.95 × Measured CCS + 7.92
These models have enabled the prediction of CCS values for nearly 114,000 metabolites from the HMDB v4.0 database, generating 916,104 CCS adduct values across different ionization modes. Such comprehensive predictive databases substantially enhance compound identification confidence in complex human samples .
What methodological approaches can resolve contradictory CCS measurements across research sites?
When faced with contradictory CCS measurements from different research sites, researchers should employ a systematic troubleshooting approach:
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Equipment calibration verification: Examine calibration methods and reference standards used at each site, ensuring calibration curves encompass the relevant CCS range.
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Procedural audit: Review testing procedures through video analysis to identify subtle methodological differences not captured in written protocols.
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Sample preparation comparison: Standardize sample preparation methods, as minor variations can significantly impact CCS measurements.
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Staff training assessment: Evaluate operator technique through blinded proficiency testing to identify potential sources of human error.
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Statistical correction models: Develop site-specific correction factors based on measurements of common standard compounds when systematic differences are identified .
What advances in CCS measurement technology are most relevant to human research applications?
Recent technological advances have significantly enhanced the applicability of CCS measurements in human research:
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Nitrogen-based CCS measurements: Development of rapid workflows for measuring CCS values of drug or drug-like molecules in nitrogen has expanded accessibility of this technology.
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Enhanced prediction algorithms: Improved computational methods now allow researchers to calculate theoretical CCS values with greater accuracy, serving as benchmarks for experimental validation.
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Integration with multi-omics platforms: Coupling CCS measurements with other analytical techniques creates multi-dimensional identification protocols with substantially improved specificity.
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Standardized reporting formats: Establishment of community-wide standards for reporting CCS values facilitates cross-study comparisons and database development .
How should researchers evaluate the reliability of CCS measurements?
Evaluating CCS measurement reliability requires consideration of multiple quality indicators:
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Internal validation: Compare measured CCS values of known standards against literature values, calculating relative standard deviations to assess precision.
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Calibration quality: Examine calibration curves for linearity (R² > 0.95) and stability across the experimental time frame.
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Reproducibility assessment: Analyze technical replicates to determine measurement variability, with RSDs < 2% generally indicating acceptable precision.
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Cross-platform validation: When possible, verify measurements using different ion mobility technologies to identify platform-specific biases.
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Outlier analysis: Systematically evaluate outlier measurements to determine whether they represent measurement errors or genuine structural variations .
What statistical approaches are most effective for analyzing CCS data in human studies?
Statistical analysis of CCS data in human studies benefits from several specialized approaches:
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Linear regression models: Evaluate relationships between predicted and measured CCS values, with R² scores above 0.9 indicating strong predictive power.
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Filtering algorithms: Apply CCS filters of mean error ± 2 standard deviations to remove likely misidentifications in complex samples.
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Principal component analysis: Use multivariate statistics to identify patterns in CCS data across different compound classes or experimental conditions.
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Variance component analysis: In multi-site studies, quantify the contribution of different factors (site, operator, instrument) to measurement variability.
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Machine learning classification: Apply supervised learning algorithms to categorize compounds based on their CCS characteristics and predict bioactivity .
How can researchers integrate CCS data with other analytical parameters to enhance biological interpretation?
Effective integration of CCS data with other analytical parameters creates powerful multi-dimensional approaches to biological interpretation:
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Multi-parameter identification workflows: Combine CCS values with accurate mass, retention time, and fragmentation patterns to create identification protocols with significantly improved specificity.
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Pathway enrichment analysis: Use improved compound identification from CCS filtering to enhance the accuracy of metabolic pathway mapping.
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Structure-activity relationship studies: Correlate CCS values with biological activities to identify structural features associated with specific effects.
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Temporal profiling: Track changes in CCS profiles over time to identify dynamic metabolic responses to interventions or disease progression.
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Cross-omics integration: Combine CCS-enhanced metabolite identification with transcriptomic or proteomic data to develop comprehensive biological models .
What emerging applications of CCS have the greatest potential impact on human research?
Several emerging applications of CCS technology show particular promise for advancing human research:
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Personalized medicine: CCS profiles of patient samples may reveal individual metabolic phenotypes relevant to treatment response.
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Environmental exposure assessment: Improved identification of xenobiotic compounds and their metabolites can enhance understanding of environmental exposures.
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Microbiome-host interactions: CCS measurements can help distinguish between host and microbial metabolites in complex biological samples.
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Longitudinal monitoring: Standardized CCS measurements facilitate reliable tracking of metabolic changes over time in clinical studies.
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Drug development acceleration: CCS databases of drug candidates can expedite lead optimization by predicting pharmacokinetic properties .
How can researchers contribute to community-wide CCS databases?
Contributing to community-wide CCS databases requires attention to several key factors:
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Standardized measurement protocols: Follow established guidelines for CCS determination, including instrument parameters and calibration procedures.
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Comprehensive metadata reporting: Document experimental conditions, sample preparation methods, and instrument specifications.
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Quality control metrics: Report statistical measures of data quality, including calibration R² values and measurement precision.
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Chemical diversity: Prioritize measurements of novel compound classes or structures underrepresented in existing databases.
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Validation across platforms: When possible, verify measurements using multiple ion mobility technologies to ensure transferability .
Product Science Overview
Copper Chaperone for Superoxide Dismutase (CCS) is a crucial protein involved in the activation of the enzyme superoxide dismutase (SOD). SOD plays a vital role in protecting cells from oxidative damage by catalyzing the dismutation of superoxide radicals into oxygen and hydrogen peroxide. The human recombinant form of CCS is a synthesized version of this protein, designed to mimic its natural counterpart in the human body.
CCS is a copper-binding protein that facilitates the delivery of copper ions to SOD1, the cytosolic form of superoxide dismutase. The protein is composed of three distinct domains:
- Domain I: This domain contains a copper-binding motif and is responsible for the initial binding of copper ions.
- Domain II: This domain is structurally similar to SOD1 and is involved in the transfer of copper ions from CCS to SOD1.
- Domain III: This domain contains a cysteine-rich motif that is essential for the release of copper ions to SOD1.
The interaction between CCS and SOD1 is critical for the proper functioning of SOD1. Without the assistance of CCS, SOD1 would remain in an inactive apo-form, unable to perform its antioxidant functions.
The primary role of CCS is to ensure the proper activation of SOD1, which is essential for maintaining cellular redox balance. SOD1 is a key component of the cellular defense mechanism against oxidative stress, which can cause significant damage to cellular components, including DNA, proteins, and lipids. By facilitating the activation of SOD1, CCS helps protect cells from oxidative damage and contributes to overall cellular health.
Mutations in the CCS gene or disruptions in its function can lead to various health issues. For instance, impaired CCS function has been linked to neurodegenerative diseases such as Amyotrophic Lateral Sclerosis (ALS). In ALS, the accumulation of misfolded SOD1 proteins can lead to motor neuron degeneration. Understanding the role of CCS in SOD1 activation has provided valuable insights into the pathogenesis of such diseases and has opened up potential avenues for therapeutic interventions.
Research on human recombinant CCS has been extensive, with studies focusing on its structure, function, and role in disease. The recombinant form of CCS is used in various experimental setups to study its interaction with SOD1 and to explore potential therapeutic applications. For example, recombinant CCS can be used to investigate the effects of specific mutations on its function and to screen for compounds that can enhance its activity.
In addition to its role in neurodegenerative diseases, CCS has also been studied in the context of cancer. Elevated levels of CCS have been observed in certain types of cancer, suggesting that it may play a role in tumor progression. Further research is needed to fully understand the implications of CCS in cancer biology and to explore its potential as a therapeutic target.
