NPL Antibody

What is NPL and what is its biological function?

NPL (N-acetylneuraminate pyruvate lyase) is an enzyme that catalyzes the reversible aldol reaction of sialic acid and has been extensively used in the synthesis of sialic acids and their analogues. Human NPL (hNAL) consists of 320 amino acids with a molecular mass of 35.2 kDa. The enzyme is widely distributed in numerous prokaryotic and eukaryotic cells, playing a critical role in sialic acid metabolism .

NPL belongs to the DapA superfamily and is also known by several other names including NALase, Sialate lyase, Sialate-pyruvate lyase, N-acetylneuraminic acid aldolase, Sialic acid aldolase, and Sialic acid lyase .

What are the optimal applications for NPL antibodies?

NPL antibodies have been validated for multiple experimental applications with specific performance characteristics:

For Western blot applications, positive detection has been confirmed in mouse brain tissue, mouse kidney tissue, Raji cells, rat brain tissue, and rat kidney tissue. For immunohistochemistry, positive signals have been observed in human ovary cancer tissue. In immunofluorescence applications, positive detection has been validated in MCF-7 cells .

What is the established reactivity profile of commercial NPL antibodies?

Commercial NPL antibodies typically show reactivity against multiple species. For example, the 16715-1-AP NPL antibody demonstrates confirmed reactivity with human, mouse, and rat samples. This cross-reactivity is beneficial for comparative studies across different model organisms . Other antibodies like A03704 have been specifically validated for reactivity with mouse and rat NPL .

When selecting an antibody for your research, verify the validated species reactivity to ensure compatibility with your experimental model system.

What is the molecular weight range for NPL detection?

When detecting NPL using antibodies, researchers should expect:

The discrepancy between calculated and observed molecular weights may be due to post-translational modifications or the specific nature of the protein structure. When performing Western blot analysis, the observed band typically appears at approximately 35-36 kDa .

How do pathogenic NPL variants affect protein function and phenotype?

Recent research has identified pathogenic NPL variants that cause skeletal myopathy and cardiac edema in both humans and zebrafish. Specifically, the p.Arg63Cys and p.Asn45Asp variants were found in a compound heterozygous state in a patient presenting with cardiomyopathy, mild skeletal myopathy, and sensorineural hearing loss .

Functional studies have demonstrated that:

• The p.Arg63Cys variant causes almost complete loss of enzymatic activity

• The p.Asn45Asp variant retains approximately 30% of NPL activity

These findings were validated in both in vitro systems and animal models. Zebrafish with NPL knockdown displayed severe skeletal myopathy and cardiac edema, confirming the essential role of NPL in muscle function .

What are the tissue-specific expression patterns of NPL and how should this inform experimental design?

NPL activity varies significantly across different tissues, which should be considered when designing experiments:

When designing experiments to study NPL expression or function, consider using tissues with high natural expression (such as kidney) for positive controls. For tissues with lower expression, optimization of antibody concentration and detection methods may be necessary .

What methodology should be used to assess NPL activity in experimental models?

To assess NPL activity in experimental models, researchers can employ an enzyme activity assay using Neu5Ac (N-acetylneuraminic acid) as a substrate. This approach allows quantification of NPL activity in tissue homogenates, expressed as nmol hour mg⁻¹ .

When comparing NPL-deficient models to wild-type controls, a comprehensive approach should include:

• Enzymatic activity measurements in multiple tissues

• mRNA expression analysis via qPCR

• Protein expression confirmation via immunoblotting

• Measurement of free sialic acid levels in urine or serum as a biomarker of NPL deficiency

This multi-faceted approach provides robust validation of NPL deficiency models and allows correlation between genotype and phenotype .

How can researchers validate NPL knockout or mutation models?

Validation of NPL knockout or mutation models should employ multiple complementary approaches:

• Enzymatic activity assessment: Measure NPL activity in target tissues using enzymatic assays with Neu5Ac substrate to confirm functional deficiency

• Transcript analysis: Perform real-time qPCR to quantify NPL mRNA levels, noting that certain mutations (like R63C) may lead to transcript degradation through nonsense-mediated decay

• Protein detection: Use immunoblotting with validated NPL antibodies to confirm protein absence or reduction, selecting tissues with high endogenous expression (e.g., kidney) for optimal detection

• Metabolic biomarkers: Measure free sialic acid levels in urine, which are typically elevated in NPL-deficient models

• Functional phenotyping: Assess muscle strength using tests such as hindlimb and front limb suspension tests to detect early-onset muscle weakness, a hallmark of NPL deficiency

What are the optimal antigen retrieval methods for NPL immunohistochemistry?

For successful NPL detection in immunohistochemistry applications, antigen retrieval is critical. Based on validated protocols:

• Primary recommendation: TE buffer pH 9.0

• Alternative option: Citrate buffer pH 6.0

The choice between these methods may depend on tissue type and fixation conditions. For human ovary cancer tissue, TE buffer at pH 9.0 has been specifically validated for optimal antigen retrieval .

What are the recommended storage conditions for maintaining NPL antibody stability?

To maintain NPL antibody activity and stability:

• Store at -20°C

• Expect stability for one year after shipment under proper storage conditions

• For antibodies in PBS with 0.02% sodium azide and 50% glycerol (pH 7.3), aliquoting is unnecessary for -20°C storage

• Note that some smaller aliquots (20μl sizes) may contain 0.1% BSA as a stabilizer

Adhering to these storage recommendations ensures optimal antibody performance throughout the expected shelf life .

How can machine learning approaches enhance antibody development for NPL-related research?

Recent advancements in computational approaches demonstrate how machine learning can accelerate antibody development. While not specifically applied to NPL antibodies yet, the principles used in other contexts (such as SARS-CoV-2 antibody design) could be adapted for NPL research:

• Use of high-performance computing and machine learning to generate novel antibody sequences

• Starting with known antibody structures and applying mutation algorithms to optimize binding

• Selection of candidates from a vast sequence space based on predicted binding properties

• Iterative computational-experimental process to refine antibody designs

These approaches could potentially be applied to develop more specific and effective NPL antibodies for research purposes, especially in cases where current antibodies show limitations .