MRPS17 Antibody

What is MRPS17 and what are its known cellular functions?

MRPS17 (Mitochondrial Ribosomal Protein S17) encodes a 28S protein belonging to the ribosomal protein S17P family. As revealed in multiple studies, it functions primarily as a component of the mitochondrial ribosome's small subunit .

The protein has a calculated molecular weight of approximately 15 kDa and consists of 130 amino acids . While its primary function relates to mitochondrial translation, recent research has uncovered that MRPS17 is not exclusively located in the mitochondria. Immunofluorescence analysis with AGS, SGC7901, and GSE1 cell lines has demonstrated that MRPS17 is distributed not only in the cytoplasm but also significantly present in the nucleus and cell membrane, potentially contributing to extracellular matrix interactions .

Nucleocytoplasmic separation experiments have further confirmed that MRPS17 is expressed in both nucleus and cytoplasm, with higher nuclear protein expression observed in gastric cancer cell lines compared to normal gastric cell lines .

What applications are MRPS17 antibodies typically used for in research?

MRPS17 antibodies have been validated for multiple experimental applications:

For IHC applications, antigen retrieval protocols using either TE buffer (pH 9.0) or citrate buffer (pH 6.0) have been successfully employed . The antibody has been cited in at least 13 publications for Western blot, 1 for IHC, and 1 for IF applications, demonstrating its reliability in research settings .

How should samples be prepared for optimal MRPS17 detection in Western blotting?

For optimal MRPS17 detection in Western blotting, the following protocol has demonstrated consistent results:

• Cell lysis: Wash cells with PBS, then lyse with RIPA (radioimmunoprecipitation assay) solution supplemented with protease inhibitor .

• Protein quantification: Quantify proteins using a bicinchoninic acid protein assay (BCA) kit .

• Protein separation: Load 20 μg of total protein on 8% or 10% sodium dodecyl sulfate polyacrylamide gel (SDS-PAGE) .

• Transfer: Transfer proteins onto a polyvinylidene difluoride (PVDF) membrane .

• Blocking: Block the membrane with 5% bovine serum albumin (BSA) solution for one hour .

• Antibody incubation: Incubate with primary MRPS17 antibody at a dilution of 1:1000 overnight at 4°C .

• Detection: Use appropriate secondary antibodies and detection reagents compatible with your imaging system.

The predicted band size for MRPS17 is 15 kDa, which aligns with the observed molecular weight in validated Western blots .

How does MRPS17 contribute to cancer progression through the PI3K/AKT pathway?

MRPS17 has emerged as a significant player in cancer progression through its modulation of the PI3K/AKT signaling pathway. In gastric cancer studies, MRPS17 upregulation has been linked to increased activation of this pathway, which is a key mediator of cellular proliferation, survival, and invasion .

Research has demonstrated that knocking down MRPS17 gene in AGS and SGC7901 cells significantly inhibits their proliferation and invasion capabilities . The mechanism appears to involve:

• Regulation of cell adhesion molecules (CAMs): MRPS17 expression correlates with CAMs, which are critical for cancer cell migration and invasion .

• Interaction with extracellular matrix: KEGG pathway analysis revealed that MRPS17-related genes participate in multiple cancer-related signaling pathways, particularly PI3K/AKT, and are significantly correlated with collagen-containing extracellular matrix components .

• Phosphorylation of AKT: Western blot analysis has shown that MRPS17 knockdown leads to decreased phosphorylation of AKT (P-AKT), suggesting direct involvement in PI3K/AKT pathway activation .

In non-small cell lung cancer (NSCLC), MRPS17 upregulation has similarly been linked to resistance to chemotherapy treatments, including temozolomide and nitrosoureas, potentially through PI3K/AKT signaling alterations .

What experimental approaches best evaluate MRPS17's prognostic significance in cancer research?

To evaluate MRPS17's prognostic significance in cancer, researchers have employed multiple complementary approaches:

Database analysis:

• Analysis of TCGA and GEO databases to correlate MRPS17 expression with clinical outcomes

• Kaplan-Meier survival analysis to assess the relationship between MRPS17 expression and patient survival

• Univariate and multivariate Cox regression analyses to determine if MRPS17 is an independent prognostic factor (p<0.001)

Tissue analysis:

• Immunohistochemistry scoring of patient samples, evaluated by independent pathologists using standardized protocols

• Tissue microarray analysis from databases like Human Protein Atlas (HPA)

• Stratification of patients based on MRPS17 expression levels (positive vs. negative or high vs. low)

Statistical approaches:

• Wilcox test for comparing differences between two groups

• Kruskal test for comparisons among multiple groups

• Log-rank tests for comparing survival curves

• Correlation analysis between MRPS17 expression and clinicopathological variables (age, gender, TNM stage)

A comprehensive study of 100 gastric cancer patients found that MRPS17-positive patients had significantly worse prognosis than MRPS17-negative patients (p=0.022), which was further verified using GEO database for Kaplan-Meier Plotter survival analysis (p<0.01) .

How can researchers validate differential expression of MRPS17 across cancer subtypes?

Validating differential expression of MRPS17 across cancer subtypes requires a multi-faceted approach:

• Transcriptomic analysis:

  • Analyze RNA-Seq data from databases like TCGA and GEO

  • Use differentially expressed gene (DEG) analysis with appropriate statistical thresholds (P<0.05 corrected by Benjamini-Hochberg False Discovery Rate)

  • Calculate Z-scores to classify genes as upregulated (Z score>1.96) or downregulated (Z score<-1.96)

• Protein-level validation:

  • Western blot analysis across different cancer cell lines (e.g., AGS, SGC7901 for gastric cancer)

  • Immunohistochemistry of tissue samples with proper scoring systems

  • Proteomic analysis with appropriate controls

• Functional verification:

  • siRNA knockdown experiments to assess MRPS17's role in different cancer subtypes

  • Cell proliferation and invasion assays following MRPS17 modulation

  • Pathway analysis to identify subtype-specific mechanism differences

For example, research has shown that MRPS17 is significantly upregulated in gastric cancer tissue compared to normal gastric tissue through IHC analysis of samples from the Human Protein Atlas database . In NSCLC, MRPS17 upregulation was identified through meta-analysis of microarray datasets and validated through multiple additional analyses .

What are the optimal conditions for immunohistochemical detection of MRPS17?

For optimal immunohistochemical detection of MRPS17, the following protocol has been validated:

• Sample preparation:

  • Use formalin-fixed and paraffin-embedded tissue sections

  • Deparaffinize with xylene and rehydrate sections

• Antigen retrieval:

  • Method 1: Use Tris/EDTA buffer pH 9.0 for 20 minutes at 95°C

  • Method 2: Use citrate buffer pH 6.0 with heat-mediated antigen retrieval

• Antibody incubation:

  • Primary antibody dilution: 1:50 to 1:500 depending on antibody source and tissue type

  • Incubate with MRPS17 antibody overnight at 4°C

• Detection system:

  • Use Dako EnVision-HRP or equivalent detection system

  • Follow manufacturer's protocol for development and counterstaining

• Evaluation:

  • Have two independent pathologists review and score each slide

  • Use standardized scoring criteria based on staining intensity and percentage of positive cells

Validated antibody sources include Proteintech Group (1:300 dilution) and recombinant monoclonal antibodies like Abcam's EPR12583 (1:50 dilution) .

How can researchers distinguish between non-specific binding and true MRPS17 signal?

Distinguishing between non-specific binding and true MRPS17 signal requires implementation of rigorous controls and validation steps:

• Negative controls:

  • Omit primary antibody but include all other reagents

  • Use isotype control antibodies (e.g., normal rabbit IgG for rabbit-derived MRPS17 antibodies)

  • Include tissues known to have low or no MRPS17 expression

• Positive controls:

  • Include tissues or cell lines with confirmed MRPS17 expression (e.g., HepG2, HeLa cells)

  • Use recombinant MRPS17 protein as a control in Western blot applications

• Knockdown validation:

  • Compare results between cells with normal MRPS17 expression and those with MRPS17 knockdown via siRNA

  • The absence or significant reduction of signal in knockdown samples confirms antibody specificity

• Multiple antibodies approach:

  • Validate findings using different antibodies targeting distinct epitopes of MRPS17

  • Similar patterns with different antibodies increase confidence in signal specificity

• Multiple detection methods:

  • Cross-validate expression using different techniques (e.g., IHC, IF, Western blot)

  • Consistent results across different methods strongly support true signal detection

For Western blot applications, the expected MRPS17 band at 15 kDa should be clearly distinguishable, and the absence of this band in negative controls helps confirm specificity .

What is the recommended approach for quantifying MRPS17 expression in immunohistochemistry studies?

For reliable quantification of MRPS17 expression in immunohistochemistry studies, researchers should follow these methodological approaches:

• Standardized scoring system:

  • Develop a scoring system based on both staining intensity and percentage of positive cells

  • Typical scoring: 0 (negative), 1+ (weak), 2+ (moderate), 3+ (strong) for intensity

  • Calculate H-score = Σ(intensity × percentage) ranging from 0-300

• Independent evaluation:

  • Have two or more pathologists independently score the samples

  • Calculate inter-observer agreement (kappa statistic)

  • Resolve discrepancies through consensus review

• Digital image analysis:

  • Use digital pathology software for more objective quantification

  • Calibrate software using control samples with known expression levels

  • Define parameters for cell recognition, background subtraction, and positive signal thresholds

• Statistical analysis of IHC data:

  • Categorize samples as "high" vs. "low" expression using median values as cutoff

  • For survival analysis, use Kaplan-Meier method and log-rank tests to compare groups

  • For correlation with clinicopathological variables, use appropriate statistical tests (Wilcox test, Kruskal test)

• Reporting standards:

  • Clearly document antibody source, dilution, incubation conditions, and scoring criteria

  • Include representative images of different staining intensities

  • Report both raw data and statistical analyses with appropriate p-values

In published studies, MRPS17 expression has been successfully quantified in gastric cancer tissues, with scores grouped according to independent pathologists' evaluations, revealing significant prognostic differences between positive and negative patients (p=0.022) .

How can MRPS17 expression data be integrated with other molecular markers for improved prognostic value?

Integration of MRPS17 expression data with other molecular markers requires sophisticated bioinformatic approaches:

• Multivariate statistical modeling:

  • Perform multivariate Cox regression analyses incorporating MRPS17 and other established biomarkers

  • Include relevant clinical variables (age, gender, tumor stage, etc.) in the model

  • Calculate hazard ratios to determine the independent prognostic value of each marker

• Gene correlation analysis:

  • Identify genes highly correlated with MRPS17 expression (e.g., the 474 genes identified in previous research)

  • Categorize genes as positively or negatively correlated based on expression patterns

  • Develop gene signatures that combine MRPS17 with other correlated genes

• Pathway integration:

  • Perform GO enrichment and KEGG pathway analysis to identify biological processes and signaling pathways associated with MRPS17

  • Focus on pathways like PI3K/AKT and cell adhesion molecules (CAMs) that have demonstrated relationships with MRPS17

  • Develop integrated pathway scores that capture activation status of relevant pathways

• Protein-protein interaction network analysis:

  • Use tools like STRING and Molecular Complex Detection to identify protein interactions

  • Map MRPS17 and related proteins in interaction networks

  • Identify central hub proteins that may serve as critical nodes in disease progression

• Machine learning approaches:

  • Develop machine learning models that incorporate MRPS17 expression with other molecular features

  • Use methods like random forest or neural networks for predictive modeling

  • Validate models using independent datasets to ensure generalizability

Research has shown that MRPS17 relates to PI3K/AKT pathway and cell adhesion molecules, with its function mediated by collagen-containing extracellular matrix and receptor ligand/regulator activity . This provides a foundation for integrating MRPS17 with markers from these pathways for enhanced prognostic value.

What methodologies are best suited for studying MRPS17's subcellular localization and its functional implications?

To accurately study MRPS17's subcellular localization and understand its functional implications, researchers should employ multiple complementary techniques:

• Immunofluorescence microscopy:

  • Use confocal microscopy for high-resolution localization studies

  • Employ co-staining with organelle-specific markers (mitochondria, nucleus, membrane)

  • Recommended antibody dilution: 1:200-1:800 for optimal results

  • Include z-stack imaging to capture three-dimensional distribution

• Subcellular fractionation:

  • Perform nucleocytoplasmic separation and protein determination

  • Use differential centrifugation to isolate mitochondria, nuclear, and cytoplasmic fractions

  • Analyze MRPS17 distribution across fractions via Western blot

  • Compare expression patterns between normal and cancer cell lines

• Proximity ligation assays:

  • Identify protein-protein interactions in situ

  • Map MRPS17 interactions with components of PI3K/AKT pathway and cell adhesion molecules

  • Quantify interaction signals in different subcellular compartments

• Live-cell imaging:

  • Use GFP-tagged MRPS17 for real-time localization studies

  • Track dynamic changes in localization under different cellular conditions

  • Correlate localization patterns with cellular functions

Research has revealed that MRPS17 is not only located in the cytoplasm (its expected mitochondrial location) but also significantly located in the nucleus and cell membrane of cancer cells, which may contribute to its interaction with cell adhesion molecules and extracellular matrix . This unexpected localization pattern suggests broader functional roles beyond mitochondrial translation.

How do experimental results with MRPS17 in cell lines compare with findings in patient tissue samples?

Research comparing MRPS17 expression in cell lines versus patient tissues reveals important consistencies and differences:

Similarities:

• Expression patterns:

  • Both cell lines and patient samples show elevated MRPS17 expression in cancer compared to normal controls

  • Western blot analysis of cancer cell lines (AGS, SGC7901) shows upregulation similar to that seen in cancer patient tissues

• Functional implications:

  • Knockdown experiments in cell lines (showing reduced proliferation and invasion) align with clinical observations where higher MRPS17 expression correlates with more aggressive disease

  • PI3K/AKT pathway involvement observed in cell lines mirrors pathway alterations in patient samples

Differences:

• Expression heterogeneity:

  • Patient samples show greater heterogeneity in MRPS17 expression compared to cell lines

  • Tissue microenvironmental factors present in patients but absent in cell culture may influence expression patterns

• Clinical correlations:

  • Patient data allows correlation with survival outcomes and clinicopathological features

  • Cell line data provides mechanistic insights but lacks direct clinical correlation

Methodological considerations for comparing results:

• Validation approach:

  • Use matched cell lines and patient-derived samples when possible

  • Confirm cell line findings in patient-derived xenografts as an intermediate step

  • Validate mechanistic findings from cell lines in patient tissues using techniques like laser capture microdissection followed by expression analysis

• Quantitative comparison:

  • For gastric cancer, MRPS17 mRNA levels were significantly elevated in multiple GC cell lines, particularly AGS and SGC7901, as measured by QT-PCR

  • These findings paralleled observations in patient cohorts, where MRPS17 positivity correlated with worse prognosis (p=0.022)

• Translational relevance:

  • Findings from cell line experiments (e.g., MRPS17 knockdown inhibiting proliferation and invasion) provide potential therapeutic strategies that can be explored in clinical settings

  • Patient data validates the clinical significance of these mechanistic insights

Analysis of 100 gastric cancer patients showed that patients with T3-4 gastric cancer had significantly higher expression of MRPS17 compared to those with T1-2 disease (p<0.001), suggesting MRPS17 is more highly expressed in advanced or aggressive cancers , which aligns with the functional observations in cell line experiments.

What are the current technical limitations in MRPS17 research and potential solutions?

Current technical limitations in MRPS17 research and their potential solutions include:

1. Antibody specificity challenges:

• Limitation: Cross-reactivity with related ribosomal proteins may confound results

• Solution: Validate antibody specificity using knockout/knockdown controls, multiple antibodies targeting different epitopes, and peptide competition assays

2. Subcellular localization complexity:

• Limitation: MRPS17's presence in multiple cellular compartments (mitochondria, nucleus, membrane) complicates functional studies

• Solution: Use super-resolution microscopy techniques combined with compartment-specific markers; employ proximity labeling techniques like BioID or APEX to map compartment-specific interactors

3. Mechanistic understanding gaps:

• Limitation: Precise molecular mechanisms linking MRPS17 to PI3K/AKT pathway activation remain incompletely understood

• Solution: Employ phosphoproteomics, protein-protein interaction studies, and targeted mutagenesis to delineate signaling pathways; use CRISPR/Cas9 to create specific domain mutations

4. Integration of multi-omics data:

• Limitation: Connecting MRPS17 expression to broader molecular landscapes is challenging

• Solution: Develop integrated bioinformatic pipelines combining transcriptomics, proteomics, and functional data; employ machine learning approaches to identify patterns across datasets

5. Translation from model systems to patients:

• Limitation: Findings in cell lines may not fully recapitulate patient tumor biology

• Solution: Utilize patient-derived organoids and xenografts; validate findings across multiple patient cohorts; conduct prospective biomarker studies

6. Technical variability in quantification:

• Limitation: Variable scoring methods for IHC and inconsistent normalization in expression studies limit cross-study comparisons

• Solution: Adopt standardized reporting guidelines; use digital pathology quantification; implement batch correction algorithms for expression data analysis

Addressing these limitations through methodological innovations will accelerate understanding of MRPS17's role in normal physiology and disease, potentially leading to new diagnostic and therapeutic approaches.

What emerging technologies could advance our understanding of MRPS17 function?

Several cutting-edge technologies hold promise for deepening our understanding of MRPS17's functions:

• CRISPR-based technologies:

  • CRISPR/Cas9 for precise genetic manipulation of MRPS17

  • CRISPR interference/activation (CRISPRi/CRISPRa) for modulating MRPS17 expression without genetic modification

  • CRISPR screens to identify synthetic lethal interactions with MRPS17 in cancer contexts

• Advanced imaging approaches:

  • Super-resolution microscopy for detailed visualization of MRPS17's subcellular localization

  • Live-cell tracking of fluorescently tagged MRPS17 to monitor dynamic localization

  • Correlative light and electron microscopy (CLEM) to connect MRPS17 localization with ultrastructural features

• Proximity labeling techniques:

  • BioID or APEX2 fusion proteins to identify proteins that interact with MRPS17 in living cells

  • Compartment-specific proximity labeling to map interactions in different subcellular locations

  • Temporal proximity labeling to capture dynamic interaction changes

• Single-cell technologies:

  • Single-cell RNA-seq to analyze MRPS17 expression heterogeneity within tumors

  • Single-cell proteomics to assess protein-level variation

  • Spatial transcriptomics to preserve tissue context while analyzing expression patterns

• Structural biology approaches:

  • Cryo-EM studies of MRPS17 within the mitochondrial ribosome

  • Structural analysis of MRPS17 interactions with components of the PI3K/AKT pathway

  • Protein-protein docking simulations to predict interaction interfaces

These technologies could help resolve outstanding questions about MRPS17's unexpected roles beyond the mitochondrial ribosome, including its nuclear and membrane localization and its connections to cancer-related signaling pathways .

How might our understanding of MRPS17's role in cancer influence development of novel therapeutic approaches?

Understanding MRPS17's role in cancer could inform novel therapeutic strategies through several avenues:

• Targeting MRPS17 directly:

  • Development of small molecule inhibitors that disrupt MRPS17's non-canonical functions

  • Antisense oligonucleotides or siRNA-based therapies to downregulate MRPS17 expression

  • Peptide-based approaches to interfere with specific protein-protein interactions

• Exploiting MRPS17-dependent vulnerabilities:

  • Research has demonstrated that knocking down MRPS17 gene in AGS and SGC7901 cells significantly inhibits proliferation and invasion capabilities

  • This suggests that MRPS17 inhibition could sensitize cancer cells to existing therapies

  • Screening for synthetic lethal interactions with MRPS17 could identify combination therapy opportunities

• Biomarker-guided treatment strategies:

  • MRPS17 expression levels could guide therapy selection

  • Patients with high MRPS17 expression (associated with poor prognosis) might benefit from more aggressive treatment approaches

  • MRPS17 status could predict response to PI3K/AKT pathway inhibitors, given the established connection between MRPS17 and this pathway

• Addressing therapy resistance:

  • MRPS17 upregulation has been linked with resistance to chemotherapy treatments, including temozolomide and nitrosoureas

  • Combination approaches targeting both MRPS17 and its downstream effectors could overcome resistance mechanisms

  • Monitoring MRPS17 expression during treatment could help detect emerging resistance

• Immunotherapy considerations:

  • Investigation of relationships between MRPS17 expression and tumor immune microenvironment

  • Potential for combining MRPS17-targeted approaches with immunotherapy

  • Exploration of MRPS17's influence on cancer cell recognition by immune cells