DGKA Antibody

What is DGKA and why is it important in research?

DGKA (diacylglycerol kinase alpha) is a key enzyme that converts diacylglycerol (DAG) into phosphatidic acid (PA) and regulates the respective levels of these two bioactive lipids within cells. It functions as a central switch between signaling pathways activated by these second messengers, which have different cellular targets and often opposite effects in numerous biological processes. DGKA plays important roles in the biosynthesis of complex lipids and can also phosphorylate 1-alkyl-2-acylglycerol in vitro provided it contains an arachidonoyl group. Additionally, it's involved in the production of alkyl-lysophosphatidic acid, another bioactive lipid, through the phosphorylation of 1-alkyl-2-acetyl glycerol .

The canonical DGKA protein is 735 amino acids in length with a molecular weight of 82.6 kDa and typically exhibits cytoplasmic subcellular localization. It's a member of the eukaryotic diacylglycerol kinase protein family with functions in calcium ion binding and ATP binding, as well as roles in hemostasis and GPCR signaling . DGKA's expression occurs in various tissues with notable presence in the brain and immune system, highlighting its significance in neurological and immunological functions . Recent research has also implicated DGKA in cancer biology, with elevated expression correlating with poor prognosis in lung cancer patients .

What applications are DGKA antibodies validated for?

DGKA antibodies have been validated for multiple research applications across different experimental platforms:

Multiple antibodies have been tested in these applications with confirmed reactivity against human, mouse, and rat samples. Positive Western blot detection has been reported in various cell lines including Jurkat, HeLa, MOLT-4, and HEK-293 cells, as well as in human and mouse cerebellum tissue . For immunohistochemistry applications, DGKA antibodies have been successfully used to analyze human gliomas and brain tissue .

How should I select the appropriate DGKA antibody for my specific research?

When selecting a DGKA antibody, consider the following methodological approach:

• Application compatibility: First determine which experimental technique you'll be using. Not all antibodies work equally well across all applications. For example, antibody 11547-1-AP has been extensively validated for Western blot, immunohistochemistry, immunofluorescence, flow cytometry, and immunoprecipitation applications .

• Species reactivity: Verify that the antibody has been validated for your species of interest. Most DGKA antibodies show reactivity with human samples, while some are also validated for mouse and rat samples .

• Epitope region: Consider which domain of DGKA your research is focusing on. Some antibodies target specific regions of the protein. For example, ab64845 targets a synthetic peptide within Human DGKA aa 300-400 , while others like CAB13969 target a sequence corresponding to amino acids 1-200 .

• Validation data: Review available validation data for your specific application. Check for published literature using the antibody, as well as manufacturer-provided data. Several DGKA antibodies have been cited in multiple publications, providing additional confidence in their specificity and performance .

• Format: Consider whether you need a conjugated or unconjugated antibody based on your detection system. Most primary DGKA antibodies are available in unconjugated format and require appropriate secondary antibodies for detection .

What positive controls should I use when working with DGKA antibodies?

For reliable experimental validation, the following positive controls have been confirmed for DGKA detection:

Cell lines with documented DGKA expression include:

• Jurkat cells (human T lymphocyte cell line)

• HeLa cells (human cervical cancer cell line)

• MOLT-4 cells (human T lymphoblast cell line)

• SK-BR-3 cells (human breast cancer cell line)

Tissue samples with reliable DGKA expression include:

• Human cerebellum tissue

• Mouse cerebellum tissue

• Human brain tissue

When using these controls, employ appropriate lysis conditions to ensure optimal protein extraction. For Western blot applications, the expected molecular weight of DGKA is approximately 83 kDa . Always run both positive and negative controls to confirm antibody specificity and performance in your experimental system.

How can I effectively optimize DGKA knockdown studies?

Establishing stable DGKA-knockdown cell lines requires methodical optimization of several experimental parameters:

• shRNA design: Use validated shRNA sequences targeting DGKA. Research has successfully employed sequences such as:

  • 5'-CGGCCAGAAGACAAGTTAGAA-3' (shDGKA#1)

  • 5'-GCTCTGGAAGTTCCAGTATAT-3' (shDGKA#2)

  Always include a scrambled control sequence (e.g., 5'-TTCTCCGAACGTGTCACGT-3') .

• Delivery method: Lentiviral transduction has proven effective for DGKA knockdown. Infect target cells with shDGKA-plvx-puro viral particles in the presence of 10 μg/mL polybrene to enhance transduction efficiency .

• Selection process: Following transduction, select stably transduced cells using puromycin-containing medium. Titrate puromycin concentration for your specific cell line to determine the optimal concentration that kills non-transduced cells while maintaining viability of transduced populations .

• Validation: Always confirm knockdown efficiency through multiple methods:

  • Western blot using anti-DGKA antibody (recommended dilution 1:6000)

  • qRT-PCR to assess mRNA levels

  • Functional assays to confirm biological impact of DGKA depletion

• Phenotypic assessment: In cancer research models, evaluate the effects of DGKA knockdown on:

  • Cell proliferation and viability

  • Cell cycle progression (monitoring CCND3, MAD2L1, PKMYT1, CCND1, ESPL1, E2F2)

  • Migration and invasion capabilities

  • Signaling pathway alterations

The successful establishment of stable DGKA-knockdown models has enabled researchers to demonstrate DGKA's role in cancer progression, particularly in lung cancer tumorigenesis .

What are the critical considerations when studying DGKA in cancer research?

When investigating DGKA in cancer contexts, several methodological and experimental design factors should be considered:

• Expression analysis: DGKA is aberrantly elevated in several cancer types, including lung cancer, where it correlates with poor prognosis . When analyzing expression:

  • Compare tumor samples with matched normal tissue

  • Correlate expression levels with clinical outcomes

  • Examine expression across cancer subtypes and stages

• Signaling pathway interactions: DGKA functions through multiple signaling networks in cancer:

  • In ovarian cancer, DGKA provides platinum resistance through activation of c-JUN-WEE1 signaling

  • Monitor downstream effectors through phospho-specific antibodies

  • Employ pathway inhibitors to dissect specific DGKA-dependent mechanisms

• Functional studies:

  • Develop both knockdown and overexpression models to assess gain/loss of function

  • Examine effects on cell cycle regulators, including CCND3, MAD2L1, PKMYT1, CCND1, ESPL1, and E2F2

  • Assess impact on tumor growth in vivo using xenograft models

  • Investigate drug sensitivity changes following DGKA modulation

• Technical considerations:

  • Use multiple DGKA antibodies targeting different epitopes to confirm findings

  • Implement appropriate controls in each experiment

  • Confirm antibody specificity through knockout/knockdown validation

  • Consider tissue-specific expression patterns when designing experiments

The investigation of DGKA in cancer context has revealed its potential as both a biomarker and therapeutic target, particularly in lung cancer where its inhibition impairs tumorigenesis .

How do I troubleshoot non-specific binding when using DGKA antibodies in Western blot?

When encountering non-specific binding with DGKA antibodies in Western blot applications, implement the following methodological troubleshooting approach:

• Optimize blocking conditions:

  • Test different blocking agents (5% non-fat milk, 5% BSA, commercial blocking buffers)

  • Increase blocking time to 2 hours at room temperature or overnight at 4°C

  • For DGKA detection, BSA-based blocking may reduce background compared to milk-based blockers

• Antibody dilution optimization:

  • Test a dilution series ranging from 1:2000 to 1:12000 for primary antibody

  • Extend primary antibody incubation to overnight at 4°C

  • Ensure secondary antibody is used at appropriate dilution (typically 1:5000-1:10000)

• Washing protocol enhancement:

  • Increase number of washes (minimum 3-5 washes of 5-10 minutes each)

  • Add 0.1-0.3% Tween-20 to wash buffer to reduce non-specific binding

  • Consider using TBS instead of PBS if phospho-proteins are of interest

• Sample preparation improvements:

  • Include protease and phosphatase inhibitors in lysis buffers

  • Optimize protein loading (20-50 μg total protein per lane)

  • Ensure complete protein denaturation by heating samples at 95°C for 5 minutes

• Antibody validation controls:

  • Include positive control samples (Jurkat, HeLa, or MOLT-4 cells)

  • Run negative control samples (cell lines with low/no DGKA expression)

  • Include a DGKA knockdown sample as specificity control

• Detection system considerations:

  • Try enhanced chemiluminescence with different exposure times

  • For weak signals, consider more sensitive detection systems or signal enhancers

  • For strong background, reduce antibody concentrations and exposure time

The expected molecular weight for DGKA is 83 kDa . If bands appear at unexpected sizes, they may represent splice variants, post-translationally modified forms, or non-specific binding.

What are the optimal protocols for DGKA detection in immunohistochemistry applications?

For optimal DGKA detection in immunohistochemistry applications, follow this detailed methodological protocol:

• Tissue preparation:

  • Fix tissues in 10% neutral buffered formalin for 24-48 hours

  • Process and embed in paraffin following standard histological procedures

  • Section tissues at 4-5 μm thickness onto positively charged slides

• Antigen retrieval optimization:

  • Primary recommendation: Use TE buffer pH 9.0 for antigen retrieval

  • Alternative method: Citrate buffer pH 6.0 may also be effective

  • Heat-induced epitope retrieval using pressure cooker or microwave for 15-20 minutes

• Blocking and antibody incubation:

  • Block endogenous peroxidase activity with 3% H₂O₂ in methanol for 15 minutes

  • Block non-specific binding with 5-10% normal serum from secondary antibody host species

  • Apply primary DGKA antibody at dilutions ranging from 1:50 to 1:500

  • Incubate primary antibody overnight at 4°C in a humidified chamber

• Detection system:

  • Use biotin-streptavidin or polymer-based detection systems

  • Apply appropriate species-specific secondary antibody

  • Develop with DAB (3,3'-diaminobenzidine) for 5-10 minutes

  • Counterstain with hematoxylin, dehydrate, and mount

• Controls and validation:

  • Include positive control tissues (human brain tissue or gliomas)

  • Run negative controls (primary antibody omission, isotype control)

  • Consider running DGKA-knockdown or knockout tissue sections if available

• Evaluation parameters:

  • Assess both intensity and distribution of DGKA staining

  • Document subcellular localization (primarily cytoplasmic)

  • Consider quantitative scoring systems for comparative studies

This protocol has been effectively used to detect DGKA in human gliomas and brain tissue samples , providing valuable insights into its expression patterns in normal and pathological contexts.

How can I quantitatively measure DGKA levels in biological samples?

For precise quantitative measurement of DGKA levels in biological samples, ELISA (Enzyme-Linked Immunosorbent Assay) offers the most reliable approach. The following methodological guidelines ensure accurate results:

• Sample preparation:

  • Serum/plasma: Collect samples using standard protocols, avoiding hemolysis

  • Cell culture supernatants: Collect media after appropriate treatment periods

  • Tissue homogenates: Homogenize in compatible buffer with protease inhibitors

  • Optimal sample dilutions should be determined empirically for each sample type

• ELISA protocol optimization:

  • Use validated sandwich ELISA kits specific for DGKA detection

  • Follow the standard sandwich enzyme immunoassay technique with pre-coated microplates containing antibodies specific for DGKA

  • Standards and samples are added to wells where DGKA binds to immobilized antibody

  • After washing, add detection antibody specific for DGKA

  • Following wash steps, add enzyme conjugate and substrate solution

  • Measure absorbance, which develops proportionally to DGKA concentration

• Standard curve preparation:

  • Prepare fresh standards for each assay

  • Use at least 6-8 concentration points for accurate curve fitting

  • Include blank controls (zero standard)

  • Perform duplicate or triplicate measurements for each standard and sample

• Data analysis and interpretation:

  • Calculate DGKA concentration using four-parameter logistic curve fitting

  • Account for any sample dilution factors in final calculations

  • Express results in ng/mL or pg/mL depending on detection range

  • Assess intra- and inter-assay coefficients of variation (CV) to ensure reliability

• Validation and quality control:

  • Include known positive controls (e.g., recombinant DGKA protein)

  • Run samples with established DGKA levels as references

  • Perform spike-and-recovery experiments to confirm accuracy

  • Ensure parallelism between standard curve and serial sample dilutions

ELISA kits have been successfully employed for quantitative measurement of DGKA in mouse serum, plasma, cell culture supernatants, tissue homogenates, and other biological fluids , providing valuable tools for comparative studies and biomarker research.

How do I investigate DGKA's role in signaling pathways?

To effectively investigate DGKA's role in signaling pathways, employ these methodological approaches:

• Lipid signaling analysis:

  • Measure DAG and PA levels using mass spectrometry or specific reporter assays

  • Track conversion rates between these lipid mediators using radioactive or fluorescent labeling

  • Assess pathway activation following DGKA modulation (knockdown/overexpression)

  • Analyze effects of DGKA inhibition on downstream effectors

• Protein-protein interaction studies:

  • Perform co-immunoprecipitation using optimized anti-DGKA antibodies (0.5-4.0 μg for 1.0-3.0 mg of total protein lysate)

  • Use proximity ligation assays to detect interactions in intact cells

  • Consider yeast two-hybrid or mammalian two-hybrid systems for interaction screening

  • Validate interactions with reciprocal co-immunoprecipitation or pull-down assays

• Downstream pathway monitoring:

  • In cancer contexts, monitor c-JUN-WEE1 signaling activation

  • Assess cell cycle regulators including CCND3, MAD2L1, PKMYT1, CCND1, ESPL1, and E2F2

  • Use phospho-specific antibodies to track pathway activation

  • Implement pathway inhibitors to dissect specific mechanisms

• Functional output assessment:

  • Design experiments to measure cellular outcomes (proliferation, migration, etc.)

  • Connect pathway activation to functional consequences

  • Use rescue experiments to confirm specificity of observed effects

  • Correlate pathway activation with biological endpoints

DGKA functions as a central switch between signaling pathways activated by different second messengers with diverse cellular targets and often opposite effects in numerous biological processes . This signaling role has been particularly significant in cancer research, where DGKA provides platinum resistance in ovarian cancer through activation of c-JUN-WEE1 signaling .

What approaches should I use to study DGKA in specific disease models?

When investigating DGKA in disease-specific contexts, tailor your experimental approach according to these methodological guidelines:

• Cancer research models:

  • Lung cancer: DGKA is aberrantly elevated in patients with lung cancer and correlates with poor prognosis

  • Ovarian cancer: Study platinum resistance mechanisms involving DGKA and c-JUN-WEE1 signaling

  • Establish stable DGKA-knockdown cell lines using validated shRNA sequences

  • Assess tumor growth, drug resistance, and metastatic potential in vivo

• Neurological disease models:

  • Given DGKA's notable expression in brain tissue , investigate:

  • Neuronal cultures (primary or cell lines) with DGKA modulation

  • Brain-specific conditional knockout mouse models

  • Analyze lipid composition changes in neural membranes

  • Assess synaptic function and neuronal signaling

• Immunological disorders:

  • DGKA's presence in the immune system suggests roles in:

  • T-cell activation and signaling (using Jurkat cells as models)

  • Immune cell migration and inflammatory responses

  • Cytokine production and release

  • Study models of autoimmunity or immunodeficiency

• Methodological considerations across disease models:

  • Use tissue-specific or inducible knockout/knockdown systems

  • Implement relevant primary cell cultures when possible

  • Design experiments comparing disease vs. normal tissue/cells

  • Validate findings in multiple model systems

  • Consider patient-derived xenografts or organoids for translational relevance

• Translational approaches:

  • Correlate DGKA expression with clinical outcomes

  • Identify potential biomarker applications

  • Explore therapeutic targeting strategies

  • Connect basic mechanisms to clinical observations

By tailoring experimental approaches to specific disease contexts, researchers can uncover the diverse roles of DGKA in pathological processes, potentially leading to new diagnostic or therapeutic strategies.