SLC16A11 Antibody, Biotin conjugated

What are the optimal applications for the biotin-conjugated SLC16A11 antibody in research settings?

The biotin-conjugated SLC16A11 antibody (such as ABIN1935529) demonstrates versatility across multiple experimental applications. This antibody has been validated for Western Blotting (WB), Enzyme-Linked Immunosorbent Assay (ELISA), Immunohistochemistry (IHC), and Flow Cytometry (FACS) . For Western Blotting applications, this antibody can detect SLC16A11 protein from human and mouse samples, making it particularly useful for comparative studies across species . The biotin conjugation enhances detection sensitivity through streptavidin-based secondary detection systems, allowing for amplified signal detection in samples where SLC16A11 may be expressed at lower levels.

What is the binding specificity of commercially available SLC16A11 antibodies?

The biotin-conjugated SLC16A11 antibody (ABIN1935529) specifically targets amino acids 48-76 located in the N-terminal region of human SLC16A11 . This region is significant as it falls within the protein's N-terminal domain, which plays a role in the protein's trafficking and localization. The antibody is generated from rabbits immunized with a KLH-conjugated synthetic peptide corresponding to this amino acid sequence and undergoes affinity purification to ensure specificity . Researchers should be aware that this specific binding region differs from other available antibodies that target the C-terminal region (AA 428-471), which may result in different detection patterns depending on protein conformation or degradation states.

How does SLC16A11's function as a transporter relate to its detection in experimental settings?

SLC16A11 functions as a proton-coupled monocarboxylate transporter belonging to category I of the SLC16 family . This classification is supported by structural analyses showing SLC16A11 contains three key charged residues (R57, D290, and R294) located in the inner pore of the protein, similar to other category I SLC16 transporters . When designing experiments using the biotin-conjugated antibody, researchers should consider that SLC16A11's transmembrane domains and conformational changes during transport cycles may affect epitope accessibility. Additionally, the protein's cell surface localization, which is critical for its transport function, may influence detection patterns in different subcellular fractionation experiments.

How can researchers effectively use SLC16A11 antibodies to investigate the two distinct mechanisms affecting SLC16A11 function in Type 2 Diabetes?

Research has identified two separate mechanisms through which SLC16A11 function is disrupted in Type 2 Diabetes: (1) decreased gene expression in liver and (2) disruption of interaction with basigin, reducing cell-surface localization . To investigate these mechanisms:

For expression studies:

• Use the biotin-conjugated antibody in Western blotting with quantitative analysis to compare SLC16A11 protein levels between samples with reference and risk haplotypes

• Combine with droplet digital PCR (ddPCR) to correlate protein levels with allele-specific expression differences

For protein-protein interaction studies:

• Employ co-immunoprecipitation experiments using the biotin-conjugated antibody to pull down SLC16A11 and detect basigin interaction

• Use immunofluorescence microscopy to examine co-localization of SLC16A11 and basigin at the cell surface

• Develop cell-surface biotinylation assays to quantify SLC16A11 membrane localization in the presence of T2D risk variants

These approaches should be conducted in relevant cellular models, particularly human hepatocytes, where SLC16A11 expression has been shown to be physiologically relevant to T2D risk .

What considerations should be made when using SLC16A11 antibodies to evaluate allelic expression imbalance in heterozygous samples?

The T2D risk haplotype in SLC16A11 has been shown to cause a significant reduction in gene expression. In heterozygous individuals, expression from the risk allele is 62% lower than from the non-risk haplotype . When designing experiments to evaluate this:

• Sample selection is critical - liver samples from heterozygous individuals allow direct comparison of expression from both alleles within the same cellular environment

• Employ allele-specific quantification methods:

  • Use the biotin-conjugated antibody in conjunction with allele-specific probes or primers

  • Combine antibody-based protein detection with techniques like droplet digital PCR (ddPCR) to distinguish expression from different alleles

• Include controls for antibody specificity to ensure equal affinity for proteins encoded by risk and non-risk alleles

• Consider the impact of post-translational modifications that might differ between proteins encoded by risk and non-risk alleles

This approach controls for inter-individual confounders that might influence total gene expression levels and provides strong evidence for cis-effects of genetic variants on SLC16A11 expression .

How can researchers utilize SLC16A11 antibodies to investigate the relationship between SLC16A11 transport activity and fatty acid metabolism?

Studies have shown that genetic perturbation of SLC16A11 induces changes in fatty acid and lipid metabolism associated with increased T2D risk . To investigate this relationship:

• Combine antibody-based detection with metabolic profiling:

  • Use the biotin-conjugated SLC16A11 antibody to quantify protein levels in cellular or tissue samples

  • Correlate protein levels with comprehensive lipidomic and metabolomic analyses

• Develop functional transport assays:

  • Utilize the antibody to confirm SLC16A11 expression in cellular models before measuring transport of potential substrates

  • Compare transport activity in cells expressing normal versus mutant SLC16A11 proteins

• Investigate downstream metabolic pathways:

  • Use immunoprecipitation with the biotin-conjugated antibody to identify protein-protein interactions with metabolic enzymes

  • Employ proximity labeling techniques with the antibody to identify proteins in close proximity to SLC16A11 in relevant cellular compartments

This integrated approach can help elucidate how SLC16A11 function impacts specific metabolic pathways related to T2D pathophysiology.

What are the common pitfalls when using SLC16A11 antibodies in subcellular localization studies?

SLC16A11 subcellular localization is critically important for its function, with disease-associated variants affecting its presence at the cell surface . Common challenges researchers face include:

• Detection sensitivity issues:

  • SLC16A11 may be expressed at relatively low levels, requiring signal amplification

  • The biotin-conjugated antibody can enhance detection through streptavidin-based amplification systems

• Distinguishing between intracellular and membrane-localized protein:

  • Use cell surface biotinylation followed by streptavidin pulldown and detection with the SLC16A11 antibody

  • Employ membrane fractionation protocols before Western blotting

• Artifact prevention in immunofluorescence:

  • Optimize fixation methods to preserve membrane integrity while allowing antibody access

  • Use confocal microscopy with membrane markers to confirm genuine surface localization

• Basigin co-localization assessment:

  • Employ dual-labeling approaches with carefully validated antibodies for both SLC16A11 and basigin

  • Consider using live-cell imaging with tagged versions of proteins to monitor trafficking dynamics

Researchers should include appropriate positive and negative controls in subcellular localization studies to accurately interpret results.

How can researchers address variability in antibody performance across different experimental systems?

Variability in antibody performance can significantly impact experimental reproducibility. For SLC16A11 antibodies:

• Systematic validation approach:

  • Test antibody performance in systems with confirmed SLC16A11 expression versus knockout/knockdown controls

  • Validate across multiple experimental platforms (Western blot, IHC, flow cytometry) as applications may vary in sensitivity

• Sample preparation considerations:

  • Optimize protein extraction methods for membrane proteins, which may require specialized detergents

  • Consider native versus denaturing conditions, as epitope accessibility may differ

• Species cross-reactivity assessment:

  • While the biotin-conjugated SLC16A11 antibody (ABIN1935529) is reported to react with both human and mouse samples , validate specificity in your specific model system

  • Consider species-specific amino acid differences within the epitope region (AA 48-76)

• Batch-to-batch consistency:

  • Document lot numbers and perform comparative validation when using new antibody batches

  • Maintain positive control samples that have previously demonstrated reliable results

Establishing a standardized protocol for each experimental application will help minimize variability.

How can SLC16A11 antibodies be utilized to investigate the genetic risk haplotype associated with Type 2 Diabetes?

The SLC16A11 risk haplotype explains approximately 20% of the increased T2D prevalence in Mexico and is associated with a ~30% increase in T2D risk . Researchers can use SLC16A11 antibodies to:

• Quantify haplotype-specific effects on protein expression:

  • Compare SLC16A11 protein levels in liver samples from individuals with different haplotypes

  • Correlate protein levels with genetic data to establish genotype-phenotype relationships

• Investigate the molecular consequences of coding variants:

  • Use the antibody to examine protein-protein interactions, particularly with basigin

  • Assess membrane localization differences between wild-type and variant proteins

• Develop cellular models for functional studies:

  • Generate cell lines expressing different SLC16A11 haplotypes

  • Use the biotin-conjugated antibody to confirm expression and localization before functional assays

• Tissue-specific expression analysis:

  • The liver appears to be a key site where SLC16A11 variants exert their effects

  • Use immunohistochemistry with the biotin-conjugated antibody to examine expression patterns across different tissues relevant to diabetes pathophysiology

These approaches can help elucidate how genetic variation in SLC16A11 contributes to T2D risk.

What methodological approaches can researchers use to investigate the interaction between SLC16A11 and basigin using available antibodies?

The interaction between SLC16A11 and basigin is critical for proper cell surface localization, and this interaction is disrupted by T2D-associated coding variants . To investigate this:

• Co-immunoprecipitation strategies:

  • Use the biotin-conjugated SLC16A11 antibody with streptavidin beads to pull down SLC16A11

  • Probe for basigin in the immunoprecipitated material

  • Perform reciprocal experiments using basigin antibodies followed by SLC16A11 detection

• Proximity ligation assays:

  • Utilize the SLC16A11 antibody in conjunction with basigin-specific antibodies

  • This technique allows visualization of protein-protein interactions within intact cells

• FRET/BRET approaches:

  • When combined with fluorescently tagged proteins, antibodies can be used to validate energy transfer results

  • Use the biotin-conjugated antibody with fluorescently labeled streptavidin as one component of the FRET pair

• Structure-function analysis:

  • Generate SLC16A11 constructs with mutations in potential basigin interaction domains

  • Use the antibody to confirm expression before testing for basigin interaction

These methods can provide complementary evidence for the physical interaction between SLC16A11 and basigin and how disease-associated variants disrupt this interaction.

How can researchers effectively design control experiments when using SLC16A11 antibodies in functional studies?

Proper controls are essential for interpreting results from antibody-based experiments investigating SLC16A11 function:

• Genetic controls:

  • Include SLC16A11 knockout or knockdown samples to confirm antibody specificity

  • When possible, use cells expressing known SLC16A11 variants as comparative controls

• Peptide competition assays:

  • Pre-incubate the biotin-conjugated antibody with the immunizing peptide (AA 48-76)

  • This should abolish specific binding and confirm signal specificity

• Isotype controls:

  • Include rabbit IgG conjugated to biotin at the same concentration as the SLC16A11 antibody

  • This controls for non-specific binding of antibodies of the same isotype class

• Cross-validation with multiple antibodies:

  • When possible, confirm key findings using antibodies targeting different epitopes of SLC16A11

  • This helps rule out epitope-specific artifacts

• Functional rescue experiments:

  • After knockdown or knockout of endogenous SLC16A11, reintroduce wild-type or variant SLC16A11

  • Use the antibody to confirm expression of the introduced protein before functional assays

These control strategies help ensure that observed effects are specifically related to SLC16A11 and not experimental artifacts.

How might SLC16A11 antibodies contribute to investigating potential therapeutic approaches for Type 2 Diabetes?

Research suggests that increasing SLC16A11 function could be therapeutically beneficial for T2D . The biotin-conjugated SLC16A11 antibody can facilitate several approaches in therapeutic research:

• Drug screening platforms:

  • Use the antibody to develop assays that measure SLC16A11 cellular localization or expression

  • Screen compounds for their ability to increase SLC16A11 membrane localization or prevent its internalization

• Assessment of therapeutic interventions:

  • Evaluate whether experimental compounds restore the SLC16A11-basigin interaction

  • Monitor changes in SLC16A11 expression levels in response to treatment

• Therapeutic antibody development:

  • Use the epitope information from existing antibodies to guide development of therapeutic antibodies

  • Design antibodies that might stabilize SLC16A11-basigin interaction or enhance SLC16A11 trafficking

• Biomarker development:

  • Investigate whether SLC16A11 protein levels or localization patterns could serve as biomarkers for treatment response

  • Develop assays to monitor changes in SLC16A11 function during therapeutic interventions

These approaches utilize antibody tools to advance understanding of SLC16A11 as a potential therapeutic target for T2D.

What are the methodological considerations when using SLC16A11 antibodies to investigate its transport substrate specificity?

SLC16A11 functions as a proton-coupled monocarboxylate transporter, but its specific physiological substrates remain under investigation . When using antibodies to support transport studies:

• Expression confirmation before transport assays:

  • Use the biotin-conjugated antibody to verify SLC16A11 expression in experimental systems

  • Confirm proper membrane localization before interpreting transport results

• Structure-function correlation:

  • Combine site-directed mutagenesis of key residues (R57, D290, R294) with antibody detection

  • Assess how mutations that alter transport function affect protein expression and localization

• Metabolomic integration:

  • Correlate SLC16A11 expression levels determined by antibody-based methods with metabolite profiles

  • This may reveal associations between transporter abundance and specific substrate levels

• Comparative transport studies:

  • Use the antibody to quantify relative expression levels of SLC16A11 versus other monocarboxylate transporters

  • This information is crucial for interpreting transport data in systems with multiple transporters

These methodological approaches can help uncover the physiological substrates of SLC16A11 and how they relate to T2D pathophysiology.

What statistical considerations should researchers apply when quantifying SLC16A11 expression using antibody-based methods?

• Sample size determination:

  • Given the observed 62% reduction in expression from the risk allele compared to the non-risk haplotype , researchers should perform power calculations

  • Studies comparing expression between genotypes typically require larger sample sizes than those examining gross knockdown effects

• Normalization strategies:

  • Select appropriate housekeeping proteins for normalization in Western blots

  • For membrane proteins like SLC16A11, consider using membrane-specific loading controls

• Accounting for technical variability:

  • Include technical replicates to assess assay performance

  • Consider batch effects when processing multiple samples

• Statistical tests for different experimental designs:

  • For comparing two groups (e.g., risk vs. non-risk haplotypes): t-tests or non-parametric alternatives

  • For multiple conditions: ANOVA with appropriate post-hoc tests

  • For correlation between SLC16A11 levels and metabolic parameters: regression analyses

• Data presentation:

  • Include both representative images and quantification data

  • Present normalized data with appropriate measures of central tendency and dispersion

These considerations help ensure robust and reproducible quantification of SLC16A11 expression patterns.

How can researchers integrate antibody-derived SLC16A11 data with other omics approaches in Type 2 Diabetes research?

Multi-omics integration provides a comprehensive view of SLC16A11's role in T2D pathophysiology:

• Integration with genomics:

  • Correlate SLC16A11 protein levels determined by antibody-based methods with specific genetic variants

  • Stratify samples by genetic haplotypes when analyzing protein expression patterns

• Proteomics integration:

  • Use antibody-based pulldown followed by mass spectrometry to identify SLC16A11 interaction partners

  • Compare interaction networks between wild-type and variant SLC16A11 proteins

• Metabolomics correlation:

  • Relate SLC16A11 abundance or localization to changes in metabolite profiles

  • Focus on lipid species and fatty acids, which have been associated with SLC16A11 function

• Transcriptomics complementation:

  • Compare protein levels determined by antibody-based methods with mRNA expression

  • Identify potential post-transcriptional regulatory mechanisms affecting SLC16A11

• Data visualization and modeling:

  • Develop integrated models that incorporate antibody-based protein quantification with other omics data

  • Use these models to generate testable hypotheses about SLC16A11 function in metabolic regulation