MRPL23 Antibody

What is MRPL23 and what cellular functions does it perform?

MRPL23 (Mitochondrial Ribosomal Protein L23) is a component of the 39S subunit of mitochondrial ribosomes. It is encoded by nuclear genes and plays a crucial role in protein synthesis within the mitochondrion. Mitochondrial ribosomes (mitoribosomes) have a distinctive composition with approximately 75% protein to rRNA ratio, which differs significantly from prokaryotic ribosomes where this ratio is reversed. MRPL23 is biallelically expressed despite its location within a region of imprinted genes on chromosome 11. Functionally, it contributes to the assembly and stability of the large mitochondrial ribosomal subunit (39S), thereby facilitating translation of proteins essential for oxidative phosphorylation and mitochondrial function .

What are the molecular characteristics of MRPL23?

MRPL23 has a molecular weight of approximately 18 kDa. Its amino acid sequence (for human MRPL23) is: MARNVVYPLYRLGGPQLRVFRTNFFIQLVRPGVAQPEDTVQFRIPMEMTRVDLRNYLEGIYNVPVAAVRTRVQHGSNKRRDHRNVRIKKPDYKVAYVQLAHGQTFTFPDLFPEKDESPEGSAADDLYSMLEEERQQRQSSDPRRGGVPSWFGL . The protein undergoes several post-translational modifications, including:

These modifications likely regulate MRPL23's function and interactions within the mitochondrial translation machinery .

What types of MRPL23 antibodies are available for research, and how do they differ?

MRPL23 antibodies are primarily available as polyclonal antibodies raised in rabbits. Based on the search results, there are several commercial options with slightly different properties:

While all identified antibodies are polyclonal and rabbit-derived, they may differ in their epitope recognition, as they are raised against different synthetic peptides derived from the MRPL23 sequence. These antibodies typically detect endogenous levels of total MRPL23 protein regardless of post-translational modifications .

What are the validated applications for MRPL23 antibodies in research?

MRPL23 antibodies have been validated for several research applications, primarily:

• Western Blotting (WB): All reported antibodies show reliable detection in WB applications with recommended dilutions of 1:1000-1:3000 .

• Immunohistochemistry (IHC): Several antibodies have been validated for detection of MRPL23 in paraffin-embedded tissues, as demonstrated in clear-cell renal cell carcinoma (ccRCC) research .

The antibodies detect MRPL23 primarily in the cytoplasmic compartment, consistent with its mitochondrial localization. Research applications should be optimized based on the specific experimental context, with appropriate controls to validate specificity .

What is the recommended protocol for immunohistochemical detection of MRPL23?

Based on published research methodologies, the following protocol has been successfully implemented for MRPL23 detection in tissue samples:

• Sample Preparation: Use formalin-fixed, paraffin-embedded (FFPE) tissue sections of approximately 4 μm thickness placed on high-adhesion glass slides (SuperFrost Plus).

• Automated Staining: Utilize automated staining platforms such as BenchMark® Ultra (Roche Diagnostics/Ventana Medical Systems) with UltraView DAB Detection Kit.

• Antibody Application: Apply rabbit polyclonal anti-MRPL23 antibody (e.g., HPA050406, Sigma-Aldrich) at a 1:100 dilution.

• Evaluation: Capture slide images using digital scanning technology (e.g., Roche Ventana DP 200 scanner) and evaluate by both image scientists and pathologists.

• Scoring System: Implement a modified Index Remmele–Stegner scale (IRS) with a range from 0 to 12, integrating:

  • Percentage of positively stained cells: 0 (no positive cells), 1 (<10%), 2 (10-50%), 3 (51-80%), and 4 (>80%)

  • Staining intensity: 0 (negative) to 3 (strong staining)

• Data Analysis: Stratify findings into low and high expression groups using statistical methods to determine optimal cutoff values .

How should Western blotting protocols be optimized for MRPL23 detection?

For optimal Western blot detection of MRPL23:

• Sample Preparation: Prepare protein lysates ensuring preservation of mitochondrial proteins; consider using specialized mitochondrial extraction buffers if focusing specifically on mitochondrial fractions.

• Protein Loading: Load 20-30 μg of total protein per lane; MRPL23 has a predicted molecular weight of 18 kDa.

• Antibody Dilution: Use primary antibody at 1:1000 to 1:3000 dilution in 5% BSA or non-fat milk in TBST .

• Incubation Conditions: Incubate with primary antibody overnight at 4°C for optimal specific binding.

• Detection Method: Use HRP-conjugated secondary antibodies and enhanced chemiluminescence for visualization.

• Controls: Include positive controls (tissues known to express MRPL23) and negative controls (antibody diluent only) to validate specificity.

• Normalization: Use appropriate housekeeping proteins as loading controls; consider mitochondrial markers like VDAC or COX IV when specifically studying mitochondrial protein expression .

What is the significance of MRPL23 expression in cancer research, particularly in renal cell carcinoma?

MRPL23 has emerged as a protein of interest in cancer research, particularly in clear-cell renal cell carcinoma (ccRCC). Recent studies have revealed complex patterns of expression:

These findings highlight MRPL23's potential utility as a prognostic biomarker in ccRCC and suggest its possible functional role in cancer development and progression, warranting further mechanistic investigations.

How do MRPL23 expression patterns differ between normal and cancerous tissues?

Research on MRPL23 expression patterns has revealed tissue-specific variations, particularly in the context of clear-cell renal cell carcinoma:

• Cellular Localization: MRPL23 protein exhibits cytoplasmic immunoreactivity in both normal renal tubular epithelial cells and ccRCC cells, consistent with its mitochondrial function .

• Expression Level Differences: Quantitative analyses demonstrate that MRPL23 protein expression is significantly reduced in cancerous epithelial cells of ccRCC tissues compared to renal tubular epithelial cells in adjacent normal tissues .

• Expression Heterogeneity: Within ccRCC samples, approximately 50.51% of cases display high cytoplasmic MRPL23 immunoreactivity, while 49.49% show low expression, indicating substantial heterogeneity even within this cancer type .

• mRNA-Protein Correlation: Interestingly, despite reduced protein levels, MRPL23 mRNA levels are often increased in tumor tissues, suggesting complex post-transcriptional regulatory mechanisms that affect protein translation or stability in cancer cells .

These differential expression patterns may reflect altered mitochondrial function in cancer cells and could contribute to metabolic reprogramming commonly observed in malignancies.

What methodological approaches can resolve discrepancies between MRPL23 mRNA and protein expression levels?

The observed discrepancy between MRPL23 mRNA and protein levels in cancer tissues represents an intriguing research challenge. To investigate this phenomenon, several methodological approaches are recommended:

• Multi-omics Integration:

  • Combine transcriptomic data (RNA-seq) with proteomic analyses (mass spectrometry) from the same samples

  • Correlate changes in mRNA and protein levels with other parameters (e.g., patient outcome, tumor grade)

• Post-transcriptional Regulation Analysis:

  • Investigate microRNA-mediated regulation using AGO-CLIP techniques

  • Analyze RNA-binding protein interactions with MRPL23 mRNA through RIP-seq

  • Examine mRNA stability using actinomycin D chase experiments

• Translational Efficiency Assessment:

  • Implement polysome profiling to examine MRPL23 mRNA association with ribosomes

  • Use ribosome footprinting to assess translation rates

• Protein Stability Evaluation:

  • Perform cycloheximide chase experiments to measure protein half-life

  • Examine ubiquitination or other modifications affecting protein degradation

  • Investigate proteasome and/or lysosomal degradation pathways using specific inhibitors

• Subcellular Fractionation:

  • Compare whole-cell versus mitochondrial fractions to detect potential differences in protein localization or import

By systematically applying these approaches, researchers can identify key regulatory mechanisms explaining the discordance between MRPL23 transcript and protein levels, potentially revealing novel insights into cancer biology .

What controls should be included when validating MRPL23 antibodies for specific applications?

Proper validation of MRPL23 antibodies requires rigorous controls to ensure specificity and reliability of results:

• Positive Controls:

  • Cell lines/tissues with known high MRPL23 expression (based on published literature)

  • Recombinant MRPL23 protein as western blot standard

  • Mitochondrially-enriched fractions to confirm expected subcellular localization

• Negative Controls:

  • MRPL23 knockdown/knockout cell lines generated via CRISPR-Cas9 or siRNA technologies

  • Secondary antibody-only controls to assess non-specific binding

  • Isotype controls (irrelevant antibodies of the same isotype/species)

• Peptide Competition Assays:

  • Pre-incubate antibody with immunizing peptide to confirm specific epitope recognition

  • Decreasing signal intensity proportional to competing peptide concentration confirms specificity

• Cross-Reactivity Assessment:

  • Test antibody reactivity in species beyond those claimed by manufacturer

  • Verify antibody performance across multiple techniques (WB, IHC, IF) to confirm consistent recognition

• Reproducibility Verification:

  • Test multiple antibody lots to ensure consistent performance

  • Compare results with alternative MRPL23 antibodies targeting different epitopes

These validation steps help ensure experimental reliability and facilitate accurate interpretation of MRPL23 expression patterns in research contexts.

How can researchers troubleshoot weak or non-specific signals when using MRPL23 antibodies?

When encountering weak or non-specific signals with MRPL23 antibodies, consider the following troubleshooting approaches:

• For Western Blotting Issues:

  • Weak Signal: Increase antibody concentration (up to 1:500), extend incubation time (overnight at 4°C), or enhance detection system sensitivity

  • Multiple Bands: Optimize blocking conditions (try 5% BSA instead of milk), adjust washing stringency, or consider using gradient gels to better resolve the 18 kDa target

  • No Signal: Verify protein transfer efficiency with reversible stains; consider enriching mitochondrial fractions to concentrate target protein

• For Immunohistochemistry Challenges:

  • Weak Staining: Implement heat-induced epitope retrieval (HIER) with citrate buffer (pH 6.0) or EDTA buffer (pH 9.0)

  • High Background: Optimize blocking (increase time/concentration), use more dilute antibody, or implement additional washing steps

  • Variable Results: Standardize fixation protocols; tissues fixed >24 hours may require extended antigen retrieval

• Sample Preparation Considerations:

  • Ensure protein extraction methods preserve mitochondrial proteins

  • For FFPE tissues, limit storage time of cut sections before staining

  • Consider specialized mitochondrial preservation protocols if studying mitochondrial dynamics

• Antibody-Specific Optimization:

  • Test multiple dilutions to determine optimal concentration (1:100-1:3000 range)

  • Verify storage conditions (aliquot and store at -20°C, avoid repeated freeze/thaw)

  • Consider testing alternative MRPL23 antibodies targeting different epitopes

Systematic optimization of these parameters should improve detection specificity and sensitivity for MRPL23 analysis.

What quantification methods provide the most reliable assessment of MRPL23 expression in tissue samples?

For reliable quantification of MRPL23 expression in tissue samples, several complementary approaches are recommended:

• Immunohistochemistry Quantification:

  • Implement a standardized scoring system like the Index Remmele–Stegner scale (IRS, range 0-12)

  • Combine evaluation of both staining intensity (0-3) and percentage of positively stained cells (0-4)

  • Employ digital pathology platforms for automated analysis to reduce observer bias

  • Have at least two independent observers (preferably including a pathologist) score samples blindly

• Western Blot Densitometry:

  • Normalize MRPL23 band intensity to appropriate loading controls

  • For mitochondrial proteins specifically, normalize to mitochondrial markers (e.g., VDAC, COX IV)

  • Use standard curves with recombinant protein for absolute quantification

  • Employ image analysis software with background subtraction capabilities

• Multimodal Confirmation:

  • Validate protein expression findings with orthogonal methods (e.g., mass spectrometry)

  • Correlate protein levels with mRNA expression (qRT-PCR, RNA-seq)

  • Consider analyzing protein expression in subcellular fractions to assess mitochondrial enrichment

• Statistical Considerations:

  • Determine optimal expression cutoff values using statistical approaches (e.g., Evaluate Cutpoints program)

  • Stratify expression into clinically relevant groups (high vs. low) based on outcome associations

  • Apply appropriate statistical tests for comparing expression between sample groups

  • Consider multivariate analyses to account for confounding factors

These methodological approaches provide comprehensive and robust quantification of MRPL23 expression, enhancing the reliability and reproducibility of research findings.

What emerging technologies might enhance the study of MRPL23 in cellular and disease contexts?

Several cutting-edge technologies hold promise for advancing MRPL23 research:

• Spatial Transcriptomics and Proteomics:

  • Technologies like Visium, CosMx, or MERFISH could reveal spatial distribution of MRPL23 mRNA within tissues

  • CODEX or Imaging Mass Cytometry could provide spatial mapping of MRPL23 protein alongside other mitochondrial markers

  • These approaches would elucidate tissue microenvironment influences on MRPL23 expression

• Single-Cell Analysis:

  • Single-cell RNA-seq and proteomics to identify cell-specific expression patterns

  • Combined with lineage tracing to understand dynamics of MRPL23 expression during disease progression

  • Single-cell energetics measurements to correlate MRPL23 levels with mitochondrial function

• Live-Cell Imaging:

  • CRISPR-based tagging of endogenous MRPL23 with fluorescent reporters

  • Super-resolution microscopy to visualize MRPL23 in mitochondrial ribosomes

  • Correlative light and electron microscopy to precisely localize MRPL23 within mitochondrial ultrastructure

• Functional Genomics:

  • CRISPR activation/interference screens to identify regulators of MRPL23 expression

  • Massively parallel reporter assays to define regulatory elements controlling MRPL23

  • Base editing approaches for introducing specific disease-associated mutations

These technologies would provide unprecedented insights into MRPL23 function and regulation in normal physiology and disease states, particularly in cancer contexts where expression alterations have been documented .

How might understanding MRPL23's role in mitochondrial translation inform cancer therapeutic strategies?

The emerging connection between MRPL23 and cancer progression suggests several potential therapeutic strategies:

• Targeting Mitochondrial Translation:

  • Considering MRPL23's role in mitochondrial protein synthesis, selective inhibitors of mitochondrial translation could be effective in cancers with MRPL23 dysregulation

  • Cancer cells often rely on mitochondrial function despite metabolic reprogramming, making this a potential vulnerability

• Biomarker-Guided Treatment:

  • The prognostic significance of MRPL23 in ccRCC suggests its utility for patient stratification

  • High MRPL23 expression could identify patients requiring more aggressive treatment approaches or closer monitoring

  • Combining MRPL23 assessment with other molecular markers could enhance predictive accuracy

• Exploiting mRNA-Protein Discrepancies:

  • The observed discordance between MRPL23 mRNA and protein levels points to potential therapeutic opportunities

  • Targeting post-transcriptional or post-translational mechanisms regulating MRPL23 could restore normal expression patterns

  • RNA-binding proteins or microRNAs involved in MRPL23 regulation could represent novel drug targets

• Metabolic Modulation:

  • As a mitochondrial protein, MRPL23 likely influences cellular energetics

  • Therapies targeting metabolic vulnerabilities might be especially effective in tumors with altered MRPL23 expression

  • Combination approaches targeting both glycolysis and oxidative phosphorylation could prevent adaptive resistance

Future therapeutic development will require deeper mechanistic understanding of how MRPL23 alterations contribute to cancer pathogenesis and progression .

What are the key methodological challenges in studying mitochondrial ribosomal proteins like MRPL23?

Investigating mitochondrial ribosomal proteins presents several unique methodological challenges:

• Protein Abundance and Detection Limitations:

  • Mitochondrial ribosomal proteins like MRPL23 are typically expressed at lower levels than cytosolic counterparts

  • Their detection requires sensitive antibodies and optimized protocols

  • Mitochondrial enrichment procedures are often necessary to achieve adequate signal

• Subcellular Localization Complexities:

  • Distinguishing newly synthesized MRPL23 in the cytosol from mature mitochondrially-imported protein

  • Tracking dynamic changes in localization during cellular stress or disease states

  • Developing techniques that preserve mitochondrial architecture while enabling protein detection

• Functional Redundancy and Compensation:

  • Understanding potential compensatory mechanisms when MRPL23 is depleted

  • Distinguishing between direct functional consequences of MRPL23 alterations versus secondary adaptations

  • Developing models that accurately reflect the impact of MRPL23 disruption on mitochondrial translation

• Translating Between Model Systems:

  • Ensuring antibody cross-reactivity across species used in research

  • Accounting for potential species-specific differences in MRPL23 function

  • Validating findings from cell lines in primary tissues and in vivo models

• Technical Considerations in Post-Translational Modification Analysis:

  • Detecting and quantifying methylation at specific arginine residues (R3, R11, R54)

  • Measuring phosphorylation dynamics at tyrosine sites (Y56, Y61)

  • Understanding the functional significance of acetylation at K89

Addressing these challenges requires interdisciplinary approaches combining biochemistry, cell biology, advanced imaging, and systems biology methodologies .

How should researchers design experiments to investigate the relationship between MRPL23 and cancer progression?

Robust experimental design for investigating MRPL23's role in cancer progression should include:

This comprehensive approach would establish causal relationships between MRPL23 alterations and cancer phenotypes while providing clinically relevant insights.

What considerations are important when selecting cell lines or animal models for MRPL23 research?

Selecting appropriate experimental models for MRPL23 research requires careful consideration of several factors:

These considerations ensure selection of experimental systems that will yield physiologically relevant and translatable insights into MRPL23 function.

What statistical approaches are most appropriate for analyzing MRPL23 expression data in clinical samples?

Robust statistical analysis of MRPL23 expression in clinical contexts requires careful methodological consideration:

• Expression Cutoff Determination:

  • Implement data-driven approaches like receiver operating characteristic (ROC) curve analysis to identify optimal expression thresholds

  • Use the "Evaluate Cutpoints" statistical tool to determine clinically relevant cutoffs based on survival outcomes

  • Consider multiple cutoff approaches (median, quartiles, continuous variable) to ensure robustness of findings

• Survival Analysis:

  • Apply Kaplan-Meier methodology with log-rank tests for initial survival comparisons between expression groups

  • Conduct univariate Cox proportional hazards regression to quantify hazard ratios

  • Perform multivariate Cox regression to adjust for confounding variables (age, gender, tumor grade, stage)

  • Assess proportional hazards assumptions and implement time-dependent coefficients if violated

  • Consider competing risk analysis when appropriate

• Correlation with Clinicopathological Features:

  • Use chi-square or Fisher's exact tests for categorical variables

  • Apply Mann-Whitney U or Kruskal-Wallis tests for non-normally distributed continuous variables

  • Conduct Spearman or Pearson correlation analysis as appropriate for continuous variables

  • Implement multiple testing corrections (Bonferroni, Benjamini-Hochberg) to control false discovery rate

• Multi-omics Integration:

  • Utilize principal component analysis or t-SNE for dimension reduction and pattern identification

  • Apply hierarchical clustering to identify patient subgroups based on MRPL23 and related markers

  • Consider machine learning approaches for developing integrated prognostic models

  • Implement mediation analysis to evaluate relationships between MRPL23 mRNA, protein, and clinical outcomes

• Sample Size and Power Considerations:

  • Conduct a priori power calculations based on expected effect sizes

  • Consider bootstrapping approaches for validation in smaller cohorts

  • Implement external validation in independent patient cohorts

  • Report confidence intervals alongside point estimates