mtnr1al Antibody

What is MTNR1A and why is it important in research?

MTNR1A (melatonin receptor 1A) is a G-protein coupled receptor with high affinity for melatonin. In humans, the canonical protein has 350 amino acid residues with a molecular mass of approximately 39.4 kDa . Its subcellular localization is primarily in the cell membrane, and it is predominantly expressed in hypophyseal pars tuberalis and hypothalamic suprachiasmatic nuclei (SCN) . As a member of the G-protein coupled receptor 1 family, MTNR1A functions as a high-affinity receptor for melatonin, making it a crucial component in melatonin-mediated physiological processes. Understanding MTNR1A expression and function is essential for research into circadian rhythms, sleep disorders, and various pathological conditions where melatonin signaling plays a role.

What applications are MTNR1A antibodies commonly used for?

MTNR1A antibodies are primarily utilized for immunodetection of the melatonin receptor 1A protein across various experimental platforms. The most common applications include Western blotting (WB) for protein expression quantification, enzyme-linked immunosorbent assay (ELISA) for protein quantification in solution, and immunohistochemistry (IHC) for tissue localization studies . These antibodies enable researchers to investigate receptor expression patterns across different tissues, subcellular localization, and changes in expression under various physiological or pathological conditions. The selection of an appropriate antibody depends on the specific application, with considerations for species reactivity, which commonly includes human, mouse, rat, and sometimes bovine, canine, and other species .

What should researchers consider when selecting an MTNR1A antibody?

When selecting an MTNR1A antibody, researchers should consider several critical factors to ensure experimental success:

• Application compatibility: Verify the antibody has been validated for your specific application (WB, ELISA, IHC) .

• Species reactivity: Ensure compatibility with your experimental model, checking cross-reactivity with human, mouse, rat, or other relevant species .

• Epitope specificity: Consider whether you need an antibody targeting the C-terminal region, N-terminal region, or other specific domains of MTNR1A .

• Format and conjugation: Determine whether you need an unconjugated antibody or one conjugated with a detection tag depending on your experimental design .

• Validation data: Review published validation data demonstrating specificity and sensitivity in applications similar to yours.

• Post-translational modifications: Be aware that MTNR1A undergoes glycosylation, which may affect antibody recognition in certain contexts .

How can researchers validate MTNR1A antibody specificity?

Validation of MTNR1A antibody specificity is crucial for obtaining reliable research results. A comprehensive validation approach includes:

• Positive and negative control tissues/cells: Use tissues known to express or lack MTNR1A (hypophyseal pars tuberalis as positive control) .

• Blocking peptides: Perform parallel experiments with the antibody pre-incubated with the immunizing peptide to confirm signal specificity.

• Knockout/knockdown validation: Compare staining in MTNR1A knockout/knockdown samples versus wild-type.

• Multiple antibody approach: Use different antibodies targeting different epitopes of MTNR1A and compare staining patterns.

• Correlation with mRNA expression: Compare protein detection results with MTNR1A mRNA expression data to confirm biological relevance.

• Molecular weight verification: Confirm that the detected protein band matches the expected molecular weight (approximately 39.4 kDa for the canonical form, with potential variations due to glycosylation) .

How do MTNR1A variants affect G-protein signaling pathways?

MTNR1A demonstrates a complex signaling profile upon melatonin stimulation. Research has identified that the wild-type MTNR1A receptor can activate multiple G proteins, including Gαi1, Gαi2, Gαi3, GαoA, GαoB, Gα12, and Gα15, and also recruits βarrestin-2 . Through comprehensive characterization of 36 MTNR1A variants (including 34 rare variants), researchers have identified distinct clusters of functional impact:

• Cluster 1: Variants with wild-type-like signaling profiles (21 variants)

• Cluster 2: Variants with selective defects in βarrestin-2 recruitment (7 variants)

• Cluster 3: Severely defective variants affecting all signaling pathways (8 variants)

These functional clusters correlate with specific structural locations within the receptor. For example, variants in Cluster 2 (p.V52A, p.R54W, p.S87L, p.H131R, p.I257F, p.I309T, and p.C314R) specifically abolish βarrestin-2 recruitment while maintaining at least partial G protein activation capacity . This suggests that specific structural regions of MTNR1A differentially control G protein activation versus βarrestin recruitment, providing insight into biased signaling mechanisms.

What structural determinants are critical for MTNR1A function?

Based on structural analysis of MTNR1A variants, several key regions have been identified as critical for receptor function:

The selective functional impacts observed with different variants suggest that MTNR1A utilizes distinct structural elements for engaging different signaling effectors. For instance, the ICLs and Helix 8 appear particularly important for βarrestin-2 recruitment, while specific regions appear differentially important for engaging Gα12 and Gα15 versus Gαi/o proteins .

How does MTNR1A differ from MTNR1B in G-protein coupling profile?

MTNR1A exhibits a broader G protein coupling profile compared to MTNR1B. While both receptors couple to the Gαi/o family of proteins, MTNR1A demonstrates additional coupling to Gα12 and Gα15 proteins . This expanded signaling repertoire suggests distinct physiological roles for the two melatonin receptor subtypes.

Importantly, MTNR1A does not couple to conventional Gαq/11 family members but specifically activates Gα15, an atypical member of this family known for its promiscuous coupling to many GPCRs . This selective coupling pattern highlights the unique signaling properties of MTNR1A and suggests potential specialized functions compared to MTNR1B.

The table below summarizes the comparative coupling profiles:

What methods are most effective for studying MTNR1A-mediated signaling?

Bioluminescence Resonance Energy Transfer (BRET)-based biosensors have proven particularly effective for comprehensively studying MTNR1A-mediated signaling. These approaches offer several advantages:

• Direct measurement of G protein activation: BRET assays can directly measure Gα protein dissociation from the Gβγ complex upon receptor activation .

• βarrestin-2 recruitment assessment: Enhanced bystander BRET (ebBRET) effectively measures βarrestin-2 recruitment to the activated receptor by detecting its proximity to the plasma membrane .

• Downstream signaling detection: BRET-based activity sensors for downstream effectors (e.g., PKC) can be used to confirm functional activation of specific G protein pathways .

• Real-time measurements: BRET assays allow for real-time monitoring of signaling events in living cells.

• Sensitivity: These assays are highly sensitive, enabling detection of subtle signaling differences between receptor variants.

When studying MTNR1A function, combining these approaches with radioligand binding assays (using 2(125I)-iodomelatonin) provides comprehensive insights into both receptor binding properties and downstream signaling capabilities .

What are the optimal conditions for immunodetection of MTNR1A?

Optimal conditions for MTNR1A immunodetection vary by application but typically include:

• For Western Blotting:

  • Sample preparation: Complete cell lysis in the presence of protease inhibitors

  • Denaturation conditions: Mild to moderate to preserve epitope structure

  • Blocking: 5% non-fat milk or BSA in TBS-T

  • Primary antibody incubation: Overnight at 4°C at dilutions typically between 1:500-1:2000

  • Detection considerations: Enhanced chemiluminescence systems are typically sufficient

• For Immunohistochemistry:

  • Fixation: Paraformaldehyde fixation (4%) is generally suitable

  • Antigen retrieval: Often necessary due to MTNR1A's membrane localization

  • Signal amplification: Consider using biotin-streptavidin systems for enhanced sensitivity

  • Controls: Include tissues known to express MTNR1A (hypophyseal pars tuberalis and hypothalamic SCN)

• For ELISA:

  • Coating concentration: Typically 1-10 μg/ml of capture antibody

  • Blocking: BSA-based blocking buffers often perform well

  • Sample preparation: May require membrane protein extraction protocols

  • Detection system: HRP-conjugated secondary antibodies with appropriate substrates

How can researchers troubleshoot common issues with MTNR1A antibodies?

When working with MTNR1A antibodies, researchers may encounter several common issues. The following troubleshooting approaches can help address these problems:

• No signal or weak signal:

  • Increase antibody concentration

  • Extend incubation time

  • Implement signal amplification techniques

  • Verify MTNR1A expression in your sample

  • Check antibody storage conditions and expiration

• Non-specific binding:

  • Optimize blocking conditions

  • Increase washing stringency

  • Try different antibody dilutions

  • Use alternative antibodies targeting different epitopes

  • Pre-absorb antibody with non-specific proteins

• Unexpected band sizes in Western blotting:

  • Consider post-translational modifications (glycosylation)

  • Check for receptor dimerization or aggregation

  • Optimize sample preparation and denaturation conditions

  • Verify using positive control samples

  • Consider potential splice variants

• High background in immunohistochemistry:

  • Optimize blocking conditions

  • Include additional blocking steps (e.g., avidin/biotin blocking)

  • Reduce primary and secondary antibody concentrations

  • Increase washing duration and frequency

  • Use more specific detection systems

What controls should be included when using MTNR1A antibodies?

Rigorous experimental controls are essential when using MTNR1A antibodies to ensure valid and interpretable results:

• Positive tissue/cell controls:

  • Hypophyseal pars tuberalis and hypothalamic suprachiasmatic nuclei (SCN) express MTNR1A and serve as excellent positive controls

  • Cell lines with confirmed MTNR1A expression

• Negative controls:

  • Tissues/cells known not to express MTNR1A

  • Primary antibody omission control

  • Isotype control antibody

• Specificity controls:

  • Peptide competition/blocking experiments

  • MTNR1A knockdown/knockout samples

  • Multiple antibodies targeting different MTNR1A epitopes

• Technical controls:

  • Loading controls for Western blotting (housekeeping proteins)

  • Internal staining controls for IHC (endogenous peroxidase blocking)

  • Parallel processing of all experimental conditions

• Functional validation:

  • Correlation with functional assays (e.g., melatonin binding, G protein activation)

  • Consistency with mRNA expression data

What are the current challenges in MTNR1A antibody research?

Researchers face several significant challenges when working with MTNR1A antibodies:

• Antibody specificity: Ensuring absolute specificity for MTNR1A versus MTNR1B and other GPCRs remains challenging due to sequence homology.

• Low expression levels: MTNR1A is often expressed at relatively low levels in native tissues, necessitating sensitive detection methods.

• Post-translational modifications: Glycosylation and other modifications can affect antibody binding and create variability in detected molecular weights .

• Receptor conformation: Different functional states of the receptor may expose different epitopes, affecting antibody recognition.

• Cross-reactivity issues: Many commercially available antibodies demonstrate cross-reactivity with unrelated proteins in certain contexts.

• Limited validation data: Comprehensive validation data across multiple applications and species is often lacking for commercial antibodies.

• Reproducibility concerns: Batch-to-batch variability in antibody production can lead to inconsistent results.

• Detection in complex samples: Detecting MTNR1A in complex tissue samples with potential interfering substances presents technical challenges.

How can BRET-based assays be optimized for studying MTNR1A signaling?

BRET-based assays have emerged as powerful tools for studying MTNR1A signaling dynamics. To optimize these assays, researchers should consider:

• Expression level optimization:

  • Titrate MTNR1A and biosensor expression levels to achieve optimal signal-to-noise ratios

  • Use inducible expression systems to control receptor levels precisely

• Sensor selection:

  • For G protein activation, use sensors measuring Gα dissociation from Gβγ

  • For βarrestin recruitment, enhanced bystander BRET (ebBRET) measuring βarrestin proximity to the plasma membrane is effective

  • For downstream signaling, specific effector sensors (e.g., PKC activity sensors) can confirm pathway activation

• Control experiments:

  • Include untagged receptor controls to account for bystander BRET

  • Use pathway-specific positive controls to validate assay functionality

  • Include response saturation controls

• Data analysis considerations:

  • Apply appropriate normalization strategies

  • Consider kinetic measurements for dynamic signaling events

  • Use concentration-response curves to determine potency (EC50) and efficacy (Emax) parameters

• Assay conditions optimization:

  • Buffer composition may affect coupling efficiency

  • Temperature stability is crucial for reliable measurements

  • Optimize incubation times for different signaling events

Utilizing these optimized BRET approaches enables comprehensive profiling of MTNR1A signaling, allowing detection of subtle differences between receptor variants and providing insights into biased signaling mechanisms.

How might structural biology approaches enhance MTNR1A antibody development?

Recent advances in structural biology, particularly cryo-electron microscopy and X-ray crystallography of GPCRs, open new avenues for MTNR1A antibody development:

• Structure-guided epitope selection: Detailed structural models of MTNR1A can identify unique, accessible epitopes for more specific antibody generation .

• Conformation-specific antibodies: Structural insights into different activation states of MTNR1A could enable development of antibodies that selectively recognize active or inactive receptor conformations.

• Nanobody development: Single-domain antibodies (nanobodies) designed based on structural information could provide tools for studying MTNR1A conformational dynamics.

• Improved validation: Structural data permits more informed validation strategies by identifying critical residues for antibody binding.

• Antibody engineering: Structure-guided antibody engineering could enhance affinity, specificity, and performance in specific applications.

The computational analysis of experimental data combined with three-dimensional structural modeling has already provided insights into MTNR1A function, identifying key regions for βarrestin-2 recruitment and G protein activation . Building on these approaches could revolutionize MTNR1A antibody development.

What emerging technologies might advance MTNR1A research?

Several cutting-edge technologies show promise for advancing MTNR1A research:

• CRISPR-based approaches:

  • Base editing and prime editing for precise introduction of MTNR1A variants

  • CRISPRi/CRISPRa for endogenous receptor modulation

  • Knock-in reporters for visualizing native MTNR1A expression and trafficking

• Advanced imaging techniques:

  • Super-resolution microscopy for detailed receptor localization studies

  • Single-molecule imaging for receptor dynamics

  • Multiplexed imaging with other signaling components

• Proteomics approaches:

  • Proximity labeling to identify MTNR1A interaction partners

  • Phosphoproteomics to characterize downstream signaling events

  • Cross-linking mass spectrometry for structural insights

• Organoid and advanced cell models:

  • Patient-derived organoids for studying MTNR1A function in disease-relevant contexts

  • Microfluidic systems for spatiotemporal control of receptor stimulation

  • Bioengineered cellular microenvironments mimicking native receptor context

• In silico approaches:

  • Molecular dynamics simulations to predict variant effects on receptor structure

  • Machine learning for predicting antibody performance in different applications

  • Network modeling of MTNR1A signaling in complex physiological systems

These emerging technologies, combined with the comprehensive variant profiling approaches already demonstrated , could substantially advance our understanding of MTNR1A biology and improve the tools available for its study.