MLXIPL Antibody

What is MLXIPL and why is it important in research?

MLXIPL, also known as carbohydrate-responsive element-binding protein (ChREBP), functions as an important regulator of glucolipid metabolism and has been implicated in various pathological processes, particularly in cancer progression. It has been shown to promote malignant progression in hepatocellular carcinoma (HCC) by activating phosphorylation of mTOR, indicating its critical role in cancer metabolism and growth signaling pathways . Additionally, recent research has identified MLXIPL as a factor associated with tumor-infiltrating CD8+ T cells in prostate cancer, suggesting its involvement in immune responses within the tumor microenvironment . This multifaceted role in both metabolic regulation and cancer progression makes MLXIPL a compelling target for researchers investigating metabolic disorders, cancer development, and potential therapeutic interventions.

What are the standard applications for MLXIPL antibodies in laboratory research?

MLXIPL antibodies are employed in several standard research techniques, primarily:

• Immunohistochemistry (IHC): As demonstrated in studies of HCC tissues, where MLXIPL levels were shown to be significantly elevated compared to para-cancerous liver tissues. In typical protocols, tissue sections are deparaffinized, rehydrated, and subjected to antigen retrieval using citric acid buffer before incubation with anti-MLXIPL antibodies (commonly using dilutions around 1/500) followed by secondary antibody staining and DAB visualization .

• Western blotting: For quantitative protein expression analysis, where MLXIPL expression differences can be measured between normal and cancer cell lines. Studies have successfully used anti-MLXIPL antibodies with dilutions of approximately 1/100 for Co-IP-western blotting procedures .

• Co-immunoprecipitation (Co-IP): For investigating protein-protein interactions, particularly between MLXIPL and mTOR. This technique has been crucial in elucidating the mechanism by which MLXIPL promotes HCC progression through mTOR pathway activation .

• Immunofluorescence: For cellular localization studies to determine the subcellular distribution of MLXIPL in different cell types.

How should MLXIPL antibodies be validated before experimental use?

Thorough validation of MLXIPL antibodies is critical for ensuring reliable and reproducible research results. A comprehensive validation approach should include:

• Positive and negative controls: Use cell lines or tissues known to express high levels of MLXIPL (such as Hep3B and Huh7 hepatocellular carcinoma cells) as positive controls, and those with minimal expression (like THLE3 normal liver cells) as negative controls .

• Knockdown/knockout verification: Employ MLXIPL siRNA or CRISPR-Cas9 gene editing to create knockdown or knockout controls. This allows verification that the antibody signal decreases proportionally with reduced target protein levels.

• Western blot validation: Confirm that the antibody detects a band of the expected molecular weight (approximately 100 kDa for MLXIPL) with minimal non-specific bands.

• Cross-reactivity assessment: Test the antibody against related proteins, particularly MLX and other bHLH family transcription factors, to ensure specificity.

• Multiple antibody comparison: When possible, compare results using antibodies from different sources or those targeting different epitopes of MLXIPL.

This multi-faceted validation approach significantly enhances confidence in subsequent experimental results and interpretations.

What are the optimal conditions for using MLXIPL antibodies in immunohistochemistry?

Optimizing immunohistochemistry protocols for MLXIPL detection requires careful attention to several key parameters:

• Fixation and antigen retrieval: Formalin-fixed, paraffin-embedded tissues typically require heat-induced epitope retrieval (HIER) using citric acid buffer (pH 6.0) to unmask MLXIPL epitopes. As demonstrated in HCC tissue studies, this approach effectively exposes antigens for antibody binding .

• Antibody concentration: Titrate antibody concentrations to determine optimal dilution; previous studies have successfully used anti-MLXIPL at 1/500 dilution for IHC applications . This concentration balances specific signal detection with minimal background.

• Incubation conditions: Overnight incubation at 4°C with the primary antibody generally yields optimal results with minimal background staining, followed by 30-minute incubation at 37°C with the secondary antibody .

• Detection system: DAB (3,3′-diaminobenzidine) chromogen systems provide excellent contrast for visualizing MLXIPL expression patterns in tissue sections, allowing for detailed morphological analysis.

• Blocking optimization: Thorough blocking with normal serum matching the secondary antibody host species minimizes non-specific binding and improves signal-to-noise ratio.

• Controls: Always include positive controls (HCC tissues known to express MLXIPL), negative controls (omitting primary antibody), and when possible, tissues from MLXIPL knockout models.

Following these guidelines will enhance specificity and sensitivity of MLXIPL detection in tissue sections.

How can MLXIPL antibodies be effectively used in co-immunoprecipitation experiments?

Co-immunoprecipitation (Co-IP) is crucial for investigating protein-protein interactions involving MLXIPL, particularly its interaction with mTOR. Based on published methodologies, an effective Co-IP protocol should include:

• Cell lysis optimization: Use a gentle lysis buffer (typically containing 20 mM Tris-HCl pH 7.5, 150 mM NaCl, 1% Triton X-100, and protease/phosphatase inhibitors) to preserve protein-protein interactions. Cells transfected with MLXIPL should be lysed on ice and centrifuged at 12,000×g for 10 minutes to obtain clear lysate .

• Pre-clearing step: Incubate lysates with Protein A/G agarose beads alone for 1 hour at 4°C to reduce non-specific binding.

• Antibody selection: For immunoprecipitation of MLXIPL complexes, use approximately 2-5 μg of MLXIPL antibody per 500 μg of total protein. Studies have successfully used anti-mTOR antibodies at 1/100 dilution to pull down MLXIPL-mTOR complexes .

• Incubation conditions: Allow antibody-antigen binding to occur overnight at 4°C with gentle rotation to maximize interaction while preserving complex integrity.

• Washing stringency: Perform 4-5 gentle washes with ice-cold lysis buffer containing reduced detergent concentration to remove non-specifically bound proteins while preserving specific interactions.

• Elution and analysis: Elute bound proteins by boiling in 2× SDS loading buffer and analyze by western blotting using antibodies against both MLXIPL (sc-515,922, 1/100, Santa Cruz Biotechnology) and putative interacting proteins like mTOR (ab2732, 1/100, Abcam) .

This methodical approach facilitates reliable detection of MLXIPL protein complexes and provides valuable insights into its functional interactions.

How can MLXIPL antibodies be utilized in research on cancer metabolism and progression?

MLXIPL antibodies offer powerful tools for investigating the multifaceted roles of this protein in cancer metabolism and progression. Advanced research applications include:

• Metabolic pathway analysis: MLXIPL regulates glycolysis in cancer cells, and antibodies can be used to correlate MLXIPL expression with glycolytic markers. Research has demonstrated that MLXIPL knockdown impedes glycolysis in HCC cells, which can be measured using techniques like Seahorse analysis to quantify oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) .

• Mechanistic studies of mTOR activation: MLXIPL has been shown to combine with mTOR to induce its phosphorylation, presenting a novel mechanism for mTOR pathway activation in cancer. Co-IP experiments using MLXIPL antibodies have been instrumental in characterizing this interaction . Researchers can expand on these findings by investigating how various stimuli or inhibitors affect the MLXIPL-mTOR complex formation.

• Tumor microenvironment interactions: Recent research has identified associations between MLXIPL expression and CD8+ T cell infiltration in prostate cancer, suggesting immunological implications . Multiplexed immunofluorescence using MLXIPL antibodies alongside immune cell markers can map these relationships within the tumor microenvironment.

• Prognostic biomarker validation: MLXIPL expression correlates with poor prognosis in certain cancers. IHC using validated MLXIPL antibodies on tissue microarrays can help establish its utility as a prognostic biomarker. Studies have demonstrated that patients with high levels of MLXIPL had shorter survival than those with low MLXIPL expression in HCC .

• Therapeutic response prediction: Analyzing MLXIPL expression before and after treatment with various therapeutics (particularly mTOR inhibitors like AZD2014) can provide insights into resistance mechanisms and potential combinatorial approaches .

These advanced applications highlight the versatility of MLXIPL antibodies in cancer research beyond simple protein detection.

What are the considerations when studying MLXIPL in relation to immune responses in cancer?

Investigating MLXIPL's role in cancer immunology presents unique challenges and opportunities that require careful experimental design:

• Context-dependent expression: MLXIPL expression appears to correlate with CD8+ T cell infiltration in certain cancers, with research showing elevated expression in high CD8+ T cell groups in prostate cancer (p = 2.06E-4) . When designing studies, it's important to consider this context-dependency by:

  • Performing parallel analyses of MLXIPL and immune cell markers

  • Stratifying samples based on immune infiltration levels

  • Using multiple cancer types to identify common versus tissue-specific patterns

• Pathway interaction analysis: MLXIPL may influence immune checkpoint pathways. Research has shown associations between MLXIPL expression and immune checkpoint molecules such as ITGAL, CD74, and TIGIT . When investigating these relationships:

  • Use multiparameter flow cytometry or multiplexed immunofluorescence to simultaneously detect MLXIPL and immune checkpoint molecules

  • Perform pathway enrichment analyses focused on cell communication and immune response pathways

  • Consider the effects of metabolic alterations induced by MLXIPL on immune cell function

• Immune cell subset considerations: MLXIPL expression has been linked to increased infiltration of immunosuppressive regulatory T cells . Experimental designs should:

  • Include comprehensive immune cell profiling beyond just CD8+ T cells

  • Assess functional states of immune cells in relation to MLXIPL expression

  • Consider single-cell approaches to resolve heterogeneity within immune populations

• Therapeutic implications: As research suggests, inhibiting MLXIPL may improve responses to immunotherapy treatments in certain cancers like prostate cancer . Investigations should:

  • Combine MLXIPL inhibition with immune checkpoint blockade in preclinical models

  • Monitor both metabolic and immunological parameters

  • Develop readouts for potential synergistic effects

These considerations highlight the complexity of studying MLXIPL in the context of cancer immunology and provide a framework for robust experimental design.

How can I troubleshoot non-specific binding when using MLXIPL antibodies?

Non-specific binding is a common challenge when working with MLXIPL antibodies. Systematic troubleshooting approaches include:

• Antibody concentration optimization: Titrate antibody concentrations carefully. While previous studies have used 1/500 dilution for IHC and 1/100 for western blotting and Co-IP , optimal concentrations may vary by application and sample type. Test a range of dilutions to find the balance between specific signal and background.

• Blocking optimization:

  • Increase blocking time (2 hours instead of 1 hour)

  • Try alternative blocking agents (BSA, normal serum, commercial blockers)

  • Consider adding 0.1-0.3% Triton X-100 to blocking solutions for membrane permeabilization

  • Use blocking agent from the same species as the secondary antibody

• Cross-reactivity assessment: MLXIPL belongs to the basic helix-loop-helix leucine zipper (bHLH-Zip) family, which has structural similarities to other transcription factors. Validate antibody specificity by:

  • Testing on MLXIPL-knockout or knockdown samples

  • Performing peptide competition assays

  • Comparing results with multiple antibodies targeting different MLXIPL epitopes

• Sample preparation improvements:

  • For IHC, optimize fixation time and antigen retrieval conditions

  • For western blotting, ensure complete protein denaturation and separation

  • For Co-IP, increase washing stringency gradually while monitoring specific signal

• Secondary antibody considerations:

  • Use highly cross-adsorbed secondary antibodies

  • Consider fluorescent secondaries for clearer signal-to-noise assessment

  • Test secondary alone controls to identify non-specific binding

By systematically addressing these factors, researchers can significantly improve the specificity of MLXIPL detection across various applications.

How should I interpret conflicting results from different MLXIPL antibodies?

When faced with conflicting results from different MLXIPL antibodies, a methodical analysis approach is essential:

• Epitope mapping comparison:

  • Determine which epitopes each antibody targets within the MLXIPL protein

  • Consider whether conflicting results might reflect detection of different MLXIPL isoforms

  • Antibodies targeting different domains (DNA-binding, glucose-sensing, or transactivation domains) may yield different results depending on protein conformation or interaction status

• Validation status assessment:

  • Evaluate the validation evidence for each antibody

  • Prioritize antibodies validated in multiple ways (western blot, IHC, knockout controls)

  • Consider the relevance of validation to your specific experimental system and cancer type

• Technical cause investigation:

  • Compare experimental conditions (fixation, antigen retrieval, blocking, detection systems)

  • Test antibodies side-by-side under identical conditions

  • Consider whether different antibody formats (polyclonal vs. monoclonal) might explain discrepancies

• Biological interpretation:

  • Assess whether differences might reflect biologically relevant states (post-translational modifications, protein-protein interactions)

  • Particularly for MLXIPL, which interacts with proteins like mTOR , certain antibodies may have reduced accessibility to epitopes in protein complexes

• Orthogonal validation:

  • Use complementary techniques (e.g., mRNA analysis, CRISPR-based tagging) to resolve discrepancies

  • Consider mass spectrometry-based confirmation of MLXIPL presence and quantity

Studies of MLXIPL in different cancer contexts have sometimes shown seemingly contradictory results, which may reflect genuine biological differences rather than technical artifacts. For example, while some studies found MLXIPL acts as a tumor promoter in HCC , contradictions in findings across different cancer cell lines highlight the importance of careful antibody selection and validation.

How can MLXIPL antibodies be utilized in biomarker development for cancer prognosis?

MLXIPL shows considerable promise as a prognostic biomarker in multiple cancer types, with research demonstrating its potential for clinical application:

• Survival correlation studies: Research has demonstrated that patients with high levels of MLXIPL had shorter survival than those with low MLXIPL expression in HCC . Similarly, in prostate cancer, elevated MLXIPL expression was associated with an unfavorable prognosis, as evidenced by hazard ratios of 2.57 (95% CI: 1.42-4.65, p = 1.76E-03) in an independent cohort study . When developing MLXIPL as a prognostic biomarker:

  • Standardize IHC protocols for consistent assessment across laboratories

  • Establish clear cutoff values for "high" versus "low" expression

  • Validate in multiple independent cohorts with diverse patient populations

• Multimarker panel integration: MLXIPL may provide additional prognostic value when combined with established markers:

  • For HCC, integrate with AFP, tumor stage, and differentiation markers

  • For prostate cancer, combine with PSA levels, Gleason score, and immune infiltration markers

  • Develop scoring algorithms that weight markers appropriately based on their predictive strength

• Predictive biomarker potential: Beyond prognosis, MLXIPL may predict response to specific therapies:

  • mTOR inhibitors: Given MLXIPL's interaction with mTOR , expression levels might predict response to rapalogs or newer mTOR inhibitors

  • Immunotherapies: The association between MLXIPL and immune cell infiltration suggests potential value in predicting immunotherapy response

• Technical standardization for clinical implementation:

  • Develop reference standards for antibody performance

  • Establish digital pathology parameters for quantitative assessment

  • Create quality control procedures suitable for clinical laboratory adoption

• Combination with molecular profiling:

  • Correlate MLXIPL protein expression with genomic alterations

  • Integrate with tumor mutational burden (TMB) assessment, noting that reduced mutation frequency and TMB were observed in high MLXIPL expression groups

These approaches can accelerate the translation of MLXIPL from a research target to a clinically useful biomarker.

What role might MLXIPL antibodies play in research on neurodevelopmental disorders?

While MLXIPL has been primarily studied in cancer contexts, emerging evidence suggests potential roles in neurodevelopmental conditions:

• Association with autism spectrum disorders: Genetic studies have identified potential links between MLXIPL and autism spectrum disorders/neurodevelopmental disorders . Research antibodies can be employed to:

  • Compare MLXIPL expression patterns in neuronal tissues from animal models of autism

  • Investigate MLXIPL's role in brain energy metabolism, given its central role in metabolic regulation

  • Explore potential interactions with pathways known to be dysregulated in autism

• Methodological considerations for neurological research:

  • Brain tissue requires specialized fixation and antigen retrieval protocols

  • Antibody validation should include appropriate neuronal and glial cell types

  • Co-localization studies with neuronal markers can help identify cell-type specific expression patterns

• Developmental expression analysis:

  • Track MLXIPL expression across neurodevelopmental stages

  • Compare expression patterns between normal and pathological development

  • Investigate potential roles in neuronal metabolism during critical developmental periods

• Potential mechanistic insights:

  • MLXIPL's role in metabolic regulation may connect to the growing understanding of metabolic abnormalities in neurodevelopmental disorders

  • The protein's interaction with signaling pathways like mTOR is particularly relevant, as mTOR dysregulation has been implicated in various neurodevelopmental conditions

• Translational research directions:

  • Develop methodologies to assess MLXIPL in accessible patient samples

  • Investigate whether peripheral MLXIPL expression correlates with neurological phenotypes

  • Explore potential as a biomarker for metabolic interventions in neurodevelopmental disorders

This emerging area represents an opportunity to expand MLXIPL research beyond cancer into neurological conditions where metabolic dysregulation may play a role.