LECT2 Antibody, Biotin conjugated

What is LECT2 and what are its main biological functions?

LECT2 is a secretory protein initially identified as a chemotactic factor for neutrophils. It is predominantly expressed in the liver and functions as a multifunctional protein associated with several diseases of global concern, including rheumatoid arthritis, hepatocellular carcinoma, and obesity . Structurally, LECT2 contains an M23 metalloendopeptidase fold but lacks catalytic activity as a metalloendopeptidase, suggesting it primarily functions as a ligand for protein receptors rather than as an enzyme .

LECT2 has neutrophil chemotactic activity and acts as a positive regulator of chondrocyte proliferation . Recent research has demonstrated its potential as a non-invasive diagnostic biomarker, particularly for alcohol-induced liver cirrhosis .

What is the principle behind LECT2 ELISA assays using biotin-conjugated antibodies?

LECT2 ELISA assays employing biotin-conjugated antibodies operate on the quantitative sandwich enzyme immunoassay technique. The process follows these specific steps:

• Microtiter plates are pre-coated with antibodies specific to LECT2

• Standards or samples are added to wells, allowing any LECT2 present to bind to the immobilized antibody

• A biotin-conjugated antibody specific for LECT2 is added to the wells

• Avidin conjugated to Horseradish Peroxidase (HRP) is added, which binds to the biotin component

• After washing, TMB substrate solution is added, producing color that develops in proportion to the amount of LECT2 bound

• The reaction is stopped with sulphuric acid solution

• Color intensity is measured spectrophotometrically at 450nm ± 10nm

• LECT2 concentration is determined by comparing sample optical density to a standard curve

What sample types can be analyzed using LECT2 antibody assays?

LECT2 antibody assays have been validated for use with multiple biological sample types. According to product specifications, these assays can reliably detect LECT2 in:

• Serum

• Plasma

• Tissue homogenates

• Cell culture supernatants

In clinical research settings, serum samples have been effectively used to measure LECT2 levels in patients with alcohol-induced liver cirrhosis and healthy controls . When working with different sample types, researchers should be aware that matrix effects may influence concentration measurements, necessitating appropriate controls and standard curves specific to each sample type.

What is the typical detection range and sensitivity for LECT2 in human samples?

According to technical specifications of commercial LECT2 ELISA kits:

• Detection range: 1.56 ng/ml to 100 ng/ml

• Minimum detectable dose (sensitivity): typically less than 0.39 ng/ml

In clinical research, typical LECT2 levels have been reported as:

• Healthy individuals: 18.99 ± 5.36 ng/mL

• Alcohol-induced liver cirrhosis (Pugh-Child A+B group): 11.06 ± 6.47 ng/mL

• Alcohol-induced liver cirrhosis (Pugh-Child C group): 8.06 ± 5.74 ng/mL

These reference ranges provide important context for researchers interpreting LECT2 measurements in experimental or clinical samples.

How does LECT2 concentration correlate with the progression of liver disease?

Research demonstrates a significant inverse correlation between LECT2 serum concentrations and the progression of alcoholic liver cirrhosis. Statistical analysis has revealed strong correlations between LECT2 levels and various clinical parameters:

A multiple regression model identified angiotensinogen, AST, total bilirubin, and age as independent LECT2-related variables:

This model was statistically significant (P < 0.0001) and explained approximately 59% of LECT2 level variability (adjusted R² = 0.59) . These findings strongly support the potential utility of LECT2 as a non-invasive diagnostic factor for monitoring alcoholic liver cirrhosis progression.

What is the relationship between LECT2 and fibroblast growth factors in liver pathology?

Clinical research has revealed an inverse relationship between LECT2 and fibroblast growth factors in alcoholic liver cirrhosis. While LECT2 levels decrease with disease progression, both FGF-1 and FGF-21 demonstrate significant increases:

• FGF-1 levels:

  • Healthy controls: 37.94 ± 40.4 pg/mL

  • Pugh-Child A+B patients: 144.77 ± 1 pg/mL

  • Pugh-Child C patients: 164.52 ± 169.46 pg/mL (P < 0.01)

• FGF-21 concentrations:

  • Controls: 13.52 ± 7.51 pg/mL

  • Pugh-Child A+B: 44.27 ± 64.19 pg/mL

  • Pugh-Child C: 45.4 ± 51.69 pg/mL (P = 0.008)

Statistical analysis confirmed significant negative correlations between LECT2 and both FGF-1 (r = -0.38; P = 0.004) and FGF-21 (r = -0.39; P = 0.004) . This opposing relationship suggests potential regulatory interactions between LECT2 and fibroblast growth factors in the pathogenesis of alcoholic liver disease.

How do the structural features of LECT2 affect antibody binding and detection?

Crystal structure analysis has revealed that human LECT2 contains an M23 metalloendopeptidase fold with a conserved Zn(II) coordination configuration . Two key structural features significantly impact antibody binding and assay design:

• LECT2 lacks a catalytic histidine residue typically found in active metalloendopeptidases

• Its potential substrate-binding groove is blocked by an additional intrachain loop at the N-terminus

Researchers should note that "antibody targets conformational epitope rather than linear epitope" may lead to situations where "some native or recombinant proteins from other manufacturers may not be recognized by our products" . Understanding these structure-function relationships is essential for optimizing LECT2 detection methods and interpreting experimental results.

What methodological considerations are important when measuring LECT2 in clinical samples?

When measuring LECT2 in clinical samples, particularly from patients with liver cirrhosis, several methodological considerations are critical:

• Patient stratification: In research settings, cirrhosis severity has been evaluated using the Pugh-Child criteria, with patients categorized into groups such as P-Ch A+B and P-Ch C . This stratification allows for meaningful comparison of LECT2 levels across disease stages.

• Confounding variables: The multiple regression model identified angiotensinogen, alanine aminotransferase, total bilirubin, and age as independent LECT2-related variables . These factors should be considered when interpreting LECT2 measurements.

• Antibody selection: The sandwich ELISA format used in LECT2 detection employs a capture antibody and a biotin-conjugated detection antibody . Antibody pairs should target non-overlapping epitopes that remain accessible in native LECT2 conformation.

• Sample preparation: For serum and plasma, proper collection and processing procedures are essential—excessive hemolysis or lipemia can interfere with accurate detection. For tissue homogenates, standardized homogenization methods help maintain consistent results.

• Standard curve optimization: Commercial LECT2 ELISA kits typically cover a detection range of 1.56-100 ng/ml , but researchers may need to adjust dilution factors for samples expected to contain concentrations outside this range.

How can researchers validate the specificity of LECT2 antibodies?

Validating the specificity of LECT2 antibodies requires a multi-faceted approach:

• Cross-reactivity testing: Commercial LECT2 antibodies should be evaluated for specificity against closely related proteins. Manufacturers typically perform initial testing, but researchers should note that "Limited by current skills and knowledge, it is impossible for us to complete the cross-reactivity detection between human LECT2 and all the analogues, therefore, cross reaction may still exist" .

• Blocking experiments: Use recombinant LECT2 protein to confirm signal specificity.

• Multiple epitope targeting: Compare results from antibodies targeting different epitopes of LECT2.

• Known concentration testing: Validate accuracy using samples with established LECT2 concentrations.

• Genetic validation: For advanced confirmation, consider using LECT2 knockdown/knockout models.

Researchers should be aware that conformational epitopes may be affected by sample preparation methods. Some native or recombinant proteins may not be recognized by certain antibodies if they target conformational rather than linear epitopes .

What are recommended normalization approaches when comparing LECT2 levels across patient groups?

When comparing LECT2 levels across different patient groups, implementing appropriate normalization strategies is essential:

• Demographic normalization: Age has been identified as an independent LECT2-related variable (P = 0.016) . LECT2 shows a negative correlation with age (r = -0.29; P = 0.048) , suggesting age-matched controls or statistical adjustment for age differences is advisable.

• Liver function parameter normalization: Consider the strong correlations between LECT2 and liver markers such as total bilirubin (r = -0.59; P < 0.0001) and alanine transaminase (r = -0.43; P = 0.003) .

• Multivariate normalization: Use regression models to account for multiple influencing factors simultaneously. The multiple regression model including angiotensinogen, AST, total bilirubin, and age explained approximately 59% of LECT2 level variability .

• Disease stage stratification: Categorizing patients according to established staging systems (e.g., Pugh-Child criteria) allows for meaningful comparison of LECT2 levels across disease progression .

• Inflammatory status consideration: LECT2 correlates with C-reactive protein (r = -0.4; P = 0.008) . Account for or stratify by inflammatory markers to distinguish disease-specific changes from those related to general inflammatory processes.

What is the recommended protocol for optimizing signal-to-noise ratio in LECT2 assays?

For optimal signal-to-noise ratio in LECT2 immunoassays:

• Antibody selection: The sandwich ELISA format requires careful selection of antibody pairs. The capture antibody pre-coated on microplates and the biotin-conjugated detection antibody should have high specificity for LECT2 with minimal cross-reactivity.

• Blocking optimization: Proper blocking is essential to prevent non-specific binding to the microplate surface. Most commercial kits include optimized blocking reagents.

• Washing procedures: Thorough washing between steps is critical to remove unbound materials. After addition of the TMB substrate, only wells containing LECT2, biotin-conjugated antibody, and enzyme-conjugated Avidin will exhibit a change in color .

• Sample dilution optimization: Determine optimal sample dilutions to ensure measurements fall within the assay's linear range (typically 1.56-100 ng/ml) .

• Incubation conditions: Follow manufacturer recommendations for temperature and duration of each incubation step to ensure optimal binding kinetics while minimizing background.

The proper execution of these technical aspects ensures that "only those wells that contain LECT2, biotin-conjugated antibody, and enzyme-conjugated Avidin will exhibit a change in color" , maximizing signal specificity and assay sensitivity.

What are promising research applications for LECT2 measurement in disease monitoring?

Based on current evidence, several promising research applications for LECT2 measurement warrant further investigation:

• Non-invasive diagnostic biomarker: The consistent finding that LECT2 levels decline with progression of alcohol-induced liver cirrhosis suggests its potential as a non-invasive diagnostic factor . The strong correlations with established liver function tests strengthen this application.

• Therapeutic target identification: The structural analysis revealing LECT2's interaction with the c-Met receptor suggests potential therapeutic targeting opportunities. Monitoring LECT2 levels could help evaluate treatment efficacy.

• Disease progression monitoring: The progressive decline in LECT2 with advancing cirrhosis severity (from 18.99 ± 5.36 ng/mL in controls to 8.06 ± 5.74 ng/mL in severe cirrhosis) indicates its potential utility in longitudinal monitoring.

• LECT2/FGF axis in liver pathology: The inverse correlation between LECT2 and fibroblast growth factors (FGF-1 and FGF-21) suggests a regulatory relationship that could be exploited for therapeutic intervention.

• Multi-marker panels: Including LECT2 in panels with other biomarkers may improve diagnostic accuracy. The multiple regression model identified several independent variables related to LECT2 levels that could be combined into comprehensive assessment tools .

Future research should explore whether the inverse correlation of LECT2 and FGF-21 is specific to alcoholic liver disease or represents a broader mechanism in liver pathology . Additionally, investigating LECT2's role in other diseases where it has been implicated, including rheumatoid arthritis, hepatocellular carcinoma, and obesity , could expand its clinical utility.