CES101 Antibody

What is Carboxylesterase 1 (CES1) and what is its biological significance?

Carboxylesterase 1 (CES1) is a serine hydrolase enzyme primarily expressed in the liver, located within the endoplasmic reticulum. It contains a characteristic C-terminal ER retention signal (HIEL) that maintains its localization. CES1 shares the serine hydrolase fold common to other esterases and functions in drug metabolism, detoxification, and lipid processing. This enzyme has significant clinical relevance, as CES1 deficiency has been associated with both non-Hodgkin lymphoma and B-cell lymphocytic leukemia, suggesting its potential role in cancer biology and treatment response .

What species reactivity can be expected with commercially available CES1 antibodies?

Most commercially available CES1 antibodies demonstrate cross-reactivity between human, mouse, and rat CES1 proteins. For example, the AF4920 antibody specifically recognizes CES1 in human, mouse, and rat tissue samples as demonstrated through Western blot analysis. When using this antibody, researchers can detect CES1 protein at approximately 63 kDa in liver tissue samples and hepatocellular carcinoma cell lines like HepG2 . Cross-species reactivity is valuable for translational research, allowing comparison of CES1 expression and function across different experimental models.

What are the common applications for CES1 antibodies in research?

CES1 antibodies are primarily utilized in protein detection techniques, with Western blot being the most validated application. These antibodies have been successfully employed in analyzing CES1 expression in:

• Cell lysates (including HepG2 hepatocellular carcinoma cells)

• Tissue samples (human, mouse, and rat liver)

• Drug metabolism studies

• Enzyme function investigations

When optimizing Western blot protocols, researchers should use reducing conditions and follow specific buffer group recommendations (e.g., Immunoblot Buffer Group 1 for AF4920 antibody) . Other potential applications might include immunohistochemistry, ELISA, and immunoprecipitation, though these would require separate validation.

How can researchers optimize CES1 antibody detection in challenging tissue samples?

Optimizing CES1 antibody detection in challenging samples requires attention to several methodological factors:

• Extraction buffer selection: Use specialized liver tissue extraction buffers containing protease inhibitors to prevent degradation

• Sample preparation: For membrane-associated proteins like CES1 (with ER retention signal HIEL), include mild detergents (0.1-0.5% Triton X-100) in lysis buffers

• Antibody concentration titration: Test a range of antibody concentrations (e.g., 0.1-0.5 μg/mL) to determine optimal signal-to-noise ratio

• Blocking optimization: For liver tissues with high background, extend blocking time (2+ hours) with 5% non-fat milk or BSA

• Signal amplification: Consider using HRP-polymer detection systems rather than standard secondary antibodies

For tissues with low CES1 expression, sample enrichment techniques like subcellular fractionation focusing on ER-enriched fractions may enhance detection sensitivity .

What experimental controls should be included when studying CES1 deficiency in lymphoma or leukemia models?

When investigating CES1 deficiency in lymphoma or leukemia models, the following controls are essential:

Positive controls:

• Normal human, mouse, or rat liver tissue/lysates (high CES1 expression)

• HepG2 cell lysates (confirmed CES1 expression)

Negative controls:

• Cell lines with confirmed CES1 knockout/knockdown

• Tissues from CES1-deficient animal models

• Primary antibody omission controls

Experimental validation controls:

• CES1 siRNA or shRNA knockdown efficiency verification

• CRISPR-Cas9 knockout verification via sequencing

• Rescue experiments restoring CES1 expression

• Correlation of CES1 protein levels with enzymatic activity using specific CES1 substrates

These controls help establish causality between CES1 deficiency and observed phenotypes in lymphoma or leukemia models, distinguishing specific effects from general consequences of altered esterase activity.

How should researchers design experiments to study the relationship between CES1 expression and drug metabolism?

To effectively study the relationship between CES1 expression and drug metabolism, researchers should implement a multi-faceted experimental design:

• CES1 expression modulation:

  • Establish multiple cell lines with varied CES1 expression levels (overexpression, native, knockdown, knockout)

  • Validate expression levels via Western blot using anti-CES1 antibodies

  • Confirm functional changes through CES1 activity assays

• Drug metabolism assessment:

  • Select pharmaceuticals known to be CES1 substrates

  • Perform dose-response and time-course experiments

  • Quantify parent compounds and metabolites using LC-MS/MS

  • Correlate metabolism rates with validated CES1 protein levels

• Mechanistic validation:

  • Include CES1 inhibitors as additional controls

  • Perform enzyme kinetics studies with recombinant CES1

  • Conduct molecular docking simulations to predict binding interactions

This comprehensive approach allows researchers to establish causal relationships between CES1 expression levels and metabolic outcomes for specific drugs or drug classes.

What approaches can be used to study the serine hydrolase fold structure of CES1 and its impact on enzyme function?

Studying the serine hydrolase fold structure of CES1 requires combining structural biology techniques with functional assays:

• Structural analysis methods:

  • X-ray crystallography of purified CES1 with various substrates/inhibitors

  • Cryo-electron microscopy for larger complexes

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

  • Molecular dynamics simulations to predict structural flexibility

• Structure-function correlation:

  • Site-directed mutagenesis of catalytic triad residues

  • Creation of chimeric enzymes with other esterases

  • Thermal shift assays to assess protein stability

  • Verification of structural changes via circular dichroism spectroscopy

• Antibody-based validation:

  • Epitope mapping to determine CES1 antibody binding sites

  • Conformation-specific antibodies to detect structural changes

  • Immunoprecipitation followed by mass spectrometry to identify binding partners

This multi-technique approach reveals how the serine hydrolase fold contributes to substrate specificity, catalytic efficiency, and regulatory mechanisms of CES1.

How should researchers interpret variations in CES1 expression levels between different tissue samples?

When analyzing variations in CES1 expression across tissue samples, researchers should consider multiple factors and follow a structured interpretation framework:

• Baseline expression analysis:

  • Establish normal expression ranges in different tissues (liver typically shows highest expression)

  • Quantify relative expression using densitometry of Western blots with CES1 antibodies

  • Normalize to appropriate housekeeping proteins specific to each tissue type

• Biological vs. technical variation assessment:

  • Calculate coefficient of variation across technical replicates

  • Compare with biological replicates to distinguish sources of variability

  • Consider using mixed-effects statistical models to account for nested variation

• Physiological and pathological context:

  • Correlate CES1 expression with tissue-specific function

  • Evaluate disease state influence (particularly in lymphoma or leukemia samples)

  • Consider genetic polymorphisms affecting expression or function

  • Assess influence of medications, diet, or environmental factors

• Multi-omics integration:

  • Correlate protein expression with mRNA levels

  • Consider epigenetic regulation of CES1 expression

  • Evaluate post-translational modifications affecting protein detection

This comprehensive analysis framework helps distinguish biologically meaningful CES1 expression differences from technical artifacts or normal physiological variation.

What statistical approaches are recommended for analyzing correlation between CES1 expression and clinical outcomes in cancer research?

When analyzing correlations between CES1 expression and clinical outcomes in cancer research (particularly for non-Hodgkin lymphoma and B-cell lymphocytic leukemia), the following statistical approaches are recommended:

• Univariate analysis:

  • Kaplan-Meier survival analysis with log-rank tests for categorical CES1 expression

  • Cox proportional hazards models for continuous CES1 expression measurements

  • Determination of optimal cutoff values using methods like CUTP or ROC curves

• Multivariate analysis:

  • Cox proportional hazards models adjusting for clinical covariates

  • Competing risk models when multiple outcome events are possible

  • Propensity score matching to control for treatment selection bias

• Advanced modeling approaches:

  • Machine learning algorithms for complex pattern recognition

  • Time-dependent coefficient models if CES1 effect varies over follow-up

  • Joint models for longitudinal CES1 measurements and survival outcomes

• Validation strategies:

  • Internal validation using bootstrapping or cross-validation

  • External validation in independent cohorts

  • Sensitivity analyses with different CES1 antibodies or detection methods

For all analyses, researchers should report hazard ratios with confidence intervals, p-values adjusted for multiple testing when applicable, and measures of model discrimination and calibration.

How might single-cell analysis techniques advance our understanding of CES1 expression heterogeneity in tissues?

Single-cell analysis techniques offer transformative potential for understanding CES1 expression heterogeneity:

• Single-cell protein detection methods:

  • Mass cytometry (CyTOF) with metal-conjugated CES1 antibodies

  • Imaging mass cytometry for spatial context

  • Single-cell Western blotting for protein isoform discrimination

  • Proximity ligation assays to detect CES1 protein interactions

• Multi-omics single-cell approaches:

  • Correlating CES1 protein expression with transcriptomics

  • Integrating with single-cell metabolomics to link expression to function

  • Spatial transcriptomics to map CES1 expression zones within tissues

• Methodological considerations:

  • Optimizing tissue dissociation protocols to preserve CES1

  • Validating antibody specificity at single-cell level

  • Developing computational methods to interpret heterogeneous expression patterns

Single-cell approaches will likely reveal previously unrecognized CES1 expression patterns in subpopulations of cells within tissues, potentially identifying specialized metabolic niches or cells particularly vulnerable to CES1 deficiency in disease states.

What are the emerging applications of mathematical modeling in predicting CES1-mediated drug interactions?

Mathematical modeling approaches are increasingly valuable for predicting CES1-mediated drug interactions:

• Physiologically-based pharmacokinetic (PBPK) models:

  • Incorporation of tissue-specific CES1 expression data from antibody studies

  • Simulation of drug-drug interactions involving CES1 substrates

  • Prediction of population variability based on CES1 polymorphisms

  • Integration of disease state effects on CES1 function

• Machine learning approaches:

  • Development of QSAR models for predicting novel CES1 substrates

  • Feature extraction from molecular structures to predict binding affinity

  • Neural networks incorporating both structural and functional data

• Systems pharmacology models:

  • Integration of CES1 within broader metabolic networks

  • Simulation of downstream effects of altered CES1 activity

  • Prediction of therapeutic windows for CES1-metabolized drugs

• Model validation approaches:

  • In vitro validation using recombinant CES1 enzymes

  • Ex vivo validation in tissue samples with quantified CES1 expression

  • Clinical validation through pharmacokinetic studies in patients with varied CES1 activity

These modeling approaches will enable more precise dosing recommendations for drugs metabolized by CES1 and better prediction of potential drug interactions, particularly important in polypharmacy situations.