RAB31 Human

What is RAB31 and what are its primary cellular functions?

RAB31 (also known as Rab22b) is a monomeric GTP-binding protein belonging to the Rab5 subfamily of small GTPases . It was first cloned from human melanocytes and subsequently from human platelets . RAB31 primarily functions in regulating intracellular membrane trafficking, particularly:

• Transport of the cation-dependent mannose 6-phosphate receptor (CD-M6PR) from the Golgi to endosomes

• Regulation of early endosome-late endosome transport, especially for the epidermal growth factor receptor (EGFR)

• Involvement in tubulovesicular carrier structures originating from the trans-Golgi network

For studying RAB31's basic functions, researchers should employ multiple complementary techniques:

• Subcellular fractionation to isolate membrane compartments

• Fluorescence microscopy with RAB31-specific antibodies or fluorescent protein fusions

• Live-cell imaging to track vesicular movement in real-time

• Co-localization studies with established organelle markers

How is RAB31 expression regulated in normal and cancer cells?

RAB31 expression is regulated through multiple mechanisms that differ between normal and cancer contexts:

Transcriptional regulation:

• RAB31 is an Estrogen Receptor α (ERα)-responsive gene, with expression regulated through the estrogen response element (ERE)

• The oncoprotein mucin1-C (MUC1-C) forms a transcriptional complex with ERα that activates RAB31 expression

Post-transcriptional regulation:

• The RNA binding protein HuR stabilizes RAB31 transcripts, contributing to elevated expression in cancer cells

• An auto-inductive loop exists where elevated RAB31 stabilizes MUC1-C levels, which further enhances RAB31 expression

For investigating RAB31 regulation, researchers should consider:

• Chromatin immunoprecipitation (ChIP) to confirm transcription factor binding

• Luciferase reporter assays with wild-type and mutated RAB31 promoter constructs

• RNA stability assays following actinomycin D treatment

• siRNA knockdown of regulatory factors to establish pathway relationships

What experimental methodologies are recommended for measuring RAB31 expression in clinical samples?

When analyzing RAB31 expression in patient samples, multiple detection methods should be employed:

When designing such studies, researchers should:

• Include appropriate reference genes/proteins for normalization

• Use RAB31-specific antibodies validated for cross-reactivity

• Include both tumor and matched normal tissue controls

• Consider correlation with clinicopathological parameters

What are the most effective experimental designs for studying RAB31's role in cancer metastasis?

Based on current research, RAB31 plays a significant role in cancer metastasis . An effective experimental design should incorporate multiple approaches:

In vitro models:

• Migration and invasion assays with RAB31 manipulation (overexpression/knockdown)

• 3D organoid cultures to better mimic the tissue microenvironment

• Analysis of epithelial-mesenchymal transition (EMT) markers

• Exosome isolation and characterization (following the approach in )

In vivo models:

• Orthotopic xenograft models that reflect the primary tumor environment

• Tail vein injection models to assess pulmonary metastasis as demonstrated in gastric cancer research

• Patient-derived xenografts for higher clinical relevance

Analysis techniques:

• Multi-omics approaches (transcriptomics, proteomics)

• Tracking of exosome production and cargo composition

• Intravital imaging to monitor cell behavior in living animals

Research has shown that cells overexpressing RAB31 demonstrate enhanced migratory ability both in vitro and in pulmonary metastatic models of gastric cancer . Additionally, exosomes derived from RAB31 overexpressing cells promoted pulmonary metastasis when injected in vivo .

How does RAB31 contribute to chemotherapy resistance in cancer cells?

RAB31 has been identified as a key factor in cisplatin resistance in stomach adenocarcinoma (STAD) . To investigate this mechanism:

Recommended experimental approaches:

• Cell viability assays following cisplatin treatment in RAB31-manipulated cells

• Analysis of apoptotic markers and DNA damage response

• Assessment of cisplatin uptake, retention, and efflux

• Investigation of downstream molecular pathways

Key molecular pathway components:

• RAB31 mediates cisplatin resistance via the epithelial-mesenchymal transition (EMT) pathway

• Both RAB31 overexpression and cisplatin treatment increase Twist1 expression

• RAB31 activates Twist1 through:

  • Activation of Stat3

  • Inhibition of Mucin 1 (MUC-1)

• Depletion of Twist1 enhances sensitivity to cisplatin in STAD cells, which cannot be fully reversed by RAB31 overexpression

This suggests a RAB31/Stat3/MUC-1/Twist1/EMT pathway in drug resistance. A comprehensive experimental design would include:

• Sequential manipulation of pathway components

• Phospho-specific detection of Stat3 activation

• Chromatin immunoprecipitation to analyze Twist1 promoter regulation

• Rescue experiments to confirm the proposed pathway

What methodological approaches should be used to investigate the relationship between RAB31 and exosome secretion?

RAB31 plays a significant role in regulating exosomes in gastric cancer, affecting both metastasis and cell-cell communication . To investigate this mechanism:

Exosome isolation and characterization:

• Differential ultracentrifugation or commercial precipitation kits

• Nanoparticle tracking analysis (NTA) for size and concentration measurement

• Electron microscopy for morphological assessment

• Western blotting for exosomal markers (CD63, CD9, TSG101)

Functional analyses:

• Track exosome release using fluorescently labeled markers

• Assess exosome uptake by recipient cells

• Investigate exosomal cargo composition by proteomics/RNA-seq

• Perform in vivo tracking of labeled exosomes

Key experimental findings to build upon:

• Both number and size of exosomes secreted by gastric cancer cells were reduced when RAB31 expression was depleted

• Injection of exosomes derived from RAB31-overexpressing cells promoted pulmonary metastasis in vivo

• PSMA1 was identified as an exosomal protein overexpressed in gastric cancer tissue in accordance with RAB31 expression

• PSMA1 overexpression was highly associated with poor prognosis in gastric cancer patients

A comprehensive experimental approach would include controls such as:

• Comparison with other Rab proteins known to regulate exosome secretion

• Assessment of general secretory pathway function

• Validation in multiple cell lines to ensure reproducibility

How can researchers design experiments to reconcile the dual role of RAB31 in EGFR trafficking and cancer progression?

The literature presents an interesting contradiction: RAB31 enhances EGFR degradation through early endosome-late endosome transport , which would suggest a tumor-suppressive role, yet it promotes cancer progression in multiple studies . Experimental designs to address this paradox should include:

Comparative analysis across cell systems:

• Parallel studies in normal versus transformed cells

• Investigation across different cancer subtypes

• Assessment of RAB31 function in paired drug-sensitive and resistant cell lines

Mechanistic investigations:

• EGFR trafficking assays using fluorescently labeled EGFR

• Quantification of surface versus internalized EGFR

• Assessment of EGFR degradation rates with varying RAB31 levels

• Analysis of downstream EGFR signaling pathways (MAPK, PI3K/AKT)

Pathway interaction studies:

• Investigation of how the RAB31-MUC1-C auto-inductive loop interfaces with EGFR trafficking

• Assessment of whether RAB31 differentially affects trafficking of distinct receptor tyrosine kinases

• Examination of possible compensatory mechanisms in cancer cells

Experimental design considerations:

• Use of inducible expression systems to study acute versus chronic effects

• Employment of domain-specific RAB31 mutants to dissect different functions

• Development of computational models to predict the net outcome of multiple RAB31 functions

What are the most effective genetic manipulation strategies for studying RAB31 function in experimental models?

When designing experiments to manipulate RAB31 expression or function, researchers should consider these approaches:

Experimental design recommendations:

• Use multiple targeting sequences/approaches in parallel

• Include appropriate controls (non-targeting, wild-type overexpression)

• Validate manipulation at both mRNA and protein levels

• Perform rescue experiments with RNAi-resistant constructs

• Consider temporal control using inducible systems

For maximum rigor, experimental designs should incorporate both loss-of-function and gain-of-function approaches to fully characterize RAB31's role in the biological process under investigation.

What are the essential controls required for RAB31 functional studies?

Proper experimental controls are crucial for RAB31 research validity:

For expression studies:

• Positive control: Tissues/cells known to express high RAB31 levels (brain oligodendrocytes)

• Negative control: Tissues/cells with minimal RAB31 expression

• Technical controls: Loading controls, reference genes for normalization

For functional studies:

• Wild-type RAB31 expression alongside mutant constructs

• Empty vector controls for overexpression studies

• Non-targeting siRNA/shRNA for knockdown studies

• Other Rab protein manipulations (especially Rab5 subfamily members) to test specificity

For clinical correlation studies:

• Matched normal-tumor tissue pairs

• Stratification by relevant clinical parameters

• Multiple cohorts for validation

A rigorous experimental design should include time-course analyses and dose-response relationships where applicable, with appropriate statistical analyses determined during experimental planning.

How should researchers interpret conflicting data on RAB31 function across different experimental systems?

When facing contradictory results about RAB31 function, consider these methodological approaches:

Systematic comparison:

• Document key differences in experimental systems (cell types, culture conditions, etc.)

• Replicate published protocols precisely before introducing variations

• Perform side-by-side comparisons using standardized assays

Context-dependent analysis:

• Evaluate RAB31 function across a panel of cell lines representing different tissues/cancer types

• Assess the impact of the microenvironment on RAB31 function

• Consider the influence of co-expressed proteins and signaling pathways

Validation strategies:

• Employ multiple technical approaches to measure the same endpoint

• Use both in vitro and in vivo models when possible

• Validate with patient-derived samples or public datasets

Resolution framework:

• Identify specific variables that might explain discrepancies

• Design targeted experiments to test each variable systematically

• Consider multifactorial designs to assess interaction effects

• Develop integrated models that account for context-dependent functions

What are the best approaches for validating RAB31 as a biomarker in clinical samples?

To establish RAB31 as a clinically relevant biomarker, researchers should implement:

Study design requirements:

• Prospective collection with standardized protocols

• Adequate sample size based on power calculations

• Inclusion of diverse patient populations

• Longitudinal sample collection where possible

Technical validation:

• Multiple detection methods (IHC, qRT-PCR, proteomics)

• Blinded assessment by multiple observers

• Standardized scoring systems

• Analytical validation (reproducibility, accuracy, precision)

Clinical validation:

• Correlation with established clinicopathological parameters

• Multivariate analysis controlling for confounding factors

• Assessment of sensitivity, specificity, positive/negative predictive values

• Independent validation cohorts

Current evidence supporting RAB31 as a biomarker includes:

• Association with poor survival in breast cancer patients

• Correlation with distant metastasis-free survival

• Identification as a marker of ERα-positive breast carcinomas

• Association with cisplatin resistance in stomach adenocarcinoma

How can RAB31 research findings be effectively translated into therapeutic applications?

To bridge the gap between RAB31 basic research and clinical applications:

Target validation strategies:

• Genetic manipulation in patient-derived models

• Correlation between RAB31 inhibition and clinical endpoints

• Demonstration of synthetic lethality with existing therapies

• Identification of patient subgroups most likely to benefit

Therapeutic approaches to explore:

• Small molecule inhibitors targeting RAB31 GTPase activity

• Disruption of the RAB31-MUC1-C auto-inductive loop

• Targeting downstream effectors (Twist1, Stat3)

• Exosome-targeted therapies in RAB31-high tumors

Experimental design for therapeutic development:

• High-throughput screening for RAB31 modulators

• Structure-based drug design targeting specific RAB31 domains

• Combination studies with established chemotherapeutics

• Assessment in patient-derived xenograft models

Translational considerations:

• Development of companion diagnostics for patient selection

• Biomarkers of response to RAB31-targeted therapy

• Potential resistance mechanisms

• Assessment of on-target versus off-target effects

What statistical approaches are most appropriate for analyzing RAB31 expression data in clinical studies?

Researchers should employ rigorous statistical methods when analyzing RAB31 expression data:

For expression level analysis:

• Normality testing before selecting parametric/non-parametric tests

• Appropriate paired/unpaired tests for tumor-normal comparisons

• ANOVA with post-hoc tests for multi-group comparisons

• Correction for multiple testing when performing genome-wide analyses

For survival analysis:

• Kaplan-Meier analysis with log-rank test for initial assessment

• Cox proportional hazards models for multivariate analysis

• Consideration of competing risks when appropriate

• Testing for proportional hazards assumption

For biomarker evaluation:

• ROC curve analysis for diagnostic potential

• Determination of optimal cutoff values

• Net reclassification improvement assessment

• Decision curve analysis for clinical utility

Advanced approaches:

• Machine learning algorithms for complex pattern recognition

• Nomogram development incorporating RAB31 with other factors

• Propensity score matching to reduce bias in observational studies

How should researchers integrate RAB31 findings with broader -omics datasets?

For comprehensive understanding of RAB31 biology:

Data integration strategies:

• Pathway enrichment analysis incorporating RAB31 expression data

• Correlation networks linking RAB31 to other molecular features

• Multi-omics factor analysis to identify latent factors

• Causal inference methods to establish directionality

Computational approaches:

• Gene set enrichment analysis (GSEA) with RAB31-correlated genes

• Protein-protein interaction network analysis

• Systems biology modeling of RAB31-related pathways

• Inference of master regulators controlling RAB31 expression

Visualization methods:

• Heatmaps for correlation patterns

• Volcano plots for differential expression

• Network graphs for interaction mapping

• Sankey diagrams for pathway relationships

Validation requirements:

• In silico validation using independent datasets

• Experimental validation of key predictions

• Cross-platform validation using different omics technologies