RBM11 Human

What is RBM11 and what is its function in human cells?

RBM11 (RNA-Binding Motif Protein 11) is an RNA splicing factor containing an RNA Recognition Motif (RRM) at the amino terminus (N-terminal). It functions in posttranscriptional control of RNA metabolism through mechanisms like alternative splicing and RNA modification. RBM11 expression has been observed in multiple normal human tissues, including the brain, testis, and spleen . Functionally, RBM11 participates in RNA processing events that regulate gene expression, though its precise physiological function has not been fully characterized. Recent research has identified RBM11 as a potential oncogenic protein involved in cancer progression through activating signaling pathways such as Akt/mTOR .

What experimental methods are recommended for measuring RBM11 expression?

For reliable RBM11 expression analysis, researchers should employ multiple complementary techniques. In published studies, anti-RBM11 antibodies (such as 17220-1-AP from Proteintech) have been validated for both Western blot and IHC applications . When designing expression studies, include appropriate housekeeping genes or proteins (β-actin for Western blots) as loading controls and validate antibody specificity using positive and negative controls.

What methodological approaches are most effective for studying RBM11's role in cancer progression?

Studying RBM11's role in cancer progression requires a multi-faceted experimental approach combining in vitro and in vivo methodologies:

• Gene Silencing: Implement RBM11 knockdown using validated shRNAs. Published successful targeting sequences include 5′-GTT CCG AAA GTC TAA GAA GAA-3′ and 5′-CCC AGC TCA TAT AAA TGG ACT-3′ . Always validate knockdown efficiency by both qRT-PCR and Western blot.

• Overexpression Studies: Utilize flag-tagged RBM11 plasmids for ectopic expression experiments to confirm phenotypic effects are reproducible with gain-of-function .

• Functional Assays:

  • Cell proliferation: MTT/CCK-8 assays and clonogenic formation assays

  • Cell invasion: Transwell assays with Matrigel coating

  • Signaling pathway analysis: Western blots for key pathway components (e.g., phosphorylated and total Akt, mTOR)

• In Vivo Models: Xenograft models using cancer cell lines with manipulated RBM11 expression provide critical validation of in vitro findings. Measure tumor growth rates and analyze proliferation markers (e.g., Ki67) in tumor tissues by IHC .

• Patient Sample Analysis: Correlate RBM11 expression levels with clinical outcomes using tissue microarrays and survival analysis. High RBM11 expression has been associated with poor survival in ovarian cancer patients .

How can researchers effectively manipulate RBM11 expression in experimental models?

For robust manipulation of RBM11 expression, researchers should consider the following validated approaches:

For RBM11 Knockdown:

• shRNA-mediated silencing using lentiviral vectors for stable integration

• CRISPR-Cas9-mediated knockout for complete elimination of expression

• siRNA for transient knockdown in initial screening experiments

For RBM11 Overexpression:

• Transfection with flag-tagged RBM11 expression vectors using lipofectamine 3000 reagent in ovarian cancer cell lines (A2780, OVCAR-3)

• Doxycycline-inducible expression systems for controlled temporal studies

• Viral vectors for difficult-to-transfect cell types

Critical Considerations:

• Always validate expression changes at both mRNA and protein levels

• Include appropriate empty vector and scrambled sequence controls

• Optimize transfection conditions for each cell line

• Consider rescue experiments with wild-type or mutant RBM11 to confirm specificity

What mechanisms explain RBM11's contribution to the Akt/mTOR signaling pathway?

RBM11 positively regulates the Akt/mTOR signaling pathway in ovarian cancer cells through mechanisms that are still being fully characterized. Experimental data shows that:

• RBM11 knockdown significantly decreases phosphorylation of Akt (at Ser473) and mTOR (at Ser2448) without affecting total protein levels .

• Conversely, overexpression of RBM11 increases phosphorylation of both Akt and mTOR, confirming a positive regulatory relationship .

Potential mechanistic explanations include:

• RBM11 may regulate alternative splicing or stability of mRNAs encoding upstream regulators of the Akt pathway

• RBM11 could affect translation efficiency of key pathway components

• RBM11 might directly interact with pathway components through protein-protein interactions

Research methodologies to further elucidate these mechanisms should include:

• RNA immunoprecipitation (RIP) followed by sequencing to identify RBM11-bound transcripts

• RNA splicing analysis using RT-PCR or RNA-seq to detect alternative splicing events

• Immunoprecipitation followed by mass spectrometry to identify protein interaction partners

• Phosphoproteomics to identify changes in the broader signaling network

How can researchers design experiments to address conflicting data on RBM11 function?

When facing conflicting data regarding RBM11 function across different studies or cancer types, researchers should implement the following experimental design strategies:

• Systematic Replication: Reproduce key experiments using multiple cell lines representing different cancer subtypes or tissues.

• Context-Dependent Analysis: Design experiments that directly compare RBM11 function across different contexts:

  • Cell line panels representing multiple cancer types

  • Normal vs. cancerous cells from the same tissue origin

  • Different stages of cancer progression

• Comprehensive Pathway Analysis: Examine RBM11's effect on multiple signaling pathways simultaneously:

  • Use phospho-kinase arrays to screen for differential effects

  • Implement RNA-seq and proteomic approaches to capture global changes

  • Validate key findings with targeted assays

• Controlled Microenvironment: Test whether RBM11 function varies under different conditions:

  • Hypoxia vs. normoxia

  • Different growth factor stimulation

  • 2D vs. 3D culture systems

• Genetic Background Consideration: Introduce RBM11 manipulations in isogenic cell lines with defined genetic alterations to identify potential interactions with other cancer-related genes.

What considerations are essential when designing RBM11 knockdown experiments?

When designing RBM11 knockdown experiments, researchers should implement these critical considerations:

• Target Selection and Validation:

  • Design at least two independent shRNAs/siRNAs targeting different regions of RBM11

  • Validate knockdown efficiency at both mRNA (qRT-PCR) and protein levels (Western blot)

  • Include appropriate non-targeting controls

• Cell Line Selection:

  • Choose cell lines with confirmed endogenous RBM11 expression

  • Include multiple cell lines to ensure findings aren't cell line-specific

  • Consider using paired isogenic cell lines when possible

• Phenotypic Assays:

  • Assess proliferation using multiple time points and methods (MTT/CCK-8 and colony formation)

  • Measure invasion using standardized transwell assays with appropriate controls

  • Examine effects on apoptosis and cell cycle progression

• Signaling Pathway Analysis:

  • Monitor phosphorylation status of key Akt/mTOR pathway components (pAkt S473, pmTOR S2448)

  • Assess total protein levels to rule out degradation effects

  • Include downstream effectors to confirm pathway inhibition

• Rescue Experiments:

  • Reintroduce shRNA-resistant RBM11 constructs to confirm specificity

  • Consider using domain-specific mutants to identify functional regions

What are the most reliable in vivo models for studying RBM11 function?

For robust in vivo evaluation of RBM11 function, researchers should consider these validated models:

• Xenograft Models:

  • Subcutaneous implantation of cancer cells with manipulated RBM11 expression

  • A2780 ovarian cancer xenograft model has successfully demonstrated RBM11's role in tumor growth

  • Monitor tumor volume over time and harvest for histological analysis

• Patient-Derived Xenografts (PDXs):

  • Maintain tumor heterogeneity and microenvironment

  • Allow for studying RBM11 in different genetic backgrounds

  • Require RBM11 manipulation through viral delivery or pharmacological approaches

• Orthotopic Models:

  • Better recapitulate the native tumor microenvironment

  • For ovarian cancer, intraperitoneal injection allows assessment of metastatic potential

  • Requires specialized imaging techniques for longitudinal monitoring

• Genetic Mouse Models:

  • Consider conditional RBM11 knockout/transgenic models for tissue-specific studies

  • Can evaluate developmental and tissue-specific functions

  • May require extensive breeding and characterization

• Analysis Parameters:

  • Tumor growth rate (volume measurements)

  • Immunohistochemistry for proliferation markers (Ki67)

  • Assessment of Akt/mTOR pathway activation in tumor tissues

  • Metastatic burden evaluation when applicable

How can researchers effectively analyze RBM11's impact on RNA processing?

To comprehensively analyze RBM11's impact on RNA processing, researchers should implement these methodological approaches:

• RNA-Binding Profiling:

  • CLIP-seq (Cross-linking and immunoprecipitation followed by sequencing) to identify direct RNA targets

  • RIP-seq (RNA immunoprecipitation sequencing) to capture RBM11-associated transcripts

  • PAR-CLIP for enhanced crosslinking efficiency and precise binding site identification

• Splicing Analysis:

  • RT-PCR with exon-spanning primers to detect alternative splicing events

  • RNA-seq with specialized computational pipelines for global splicing analysis

  • Minigene assays to validate specific splicing events in reporter systems

• RNA Stability Assessment:

  • Actinomycin D chase experiments to measure half-life of candidate transcripts

  • Pulse-chase labeling with modified nucleosides to track newly synthesized RNA

  • Polysome profiling to assess translation efficiency

• Functional Validation:

  • Rescue experiments using wild-type and mutant versions of identified targets

  • CRISPR/Cas9 editing of RBM11 binding sites in target RNAs

  • Structural analysis of RBM11-RNA complexes

• Data Analysis Pipeline:

  • Motif discovery algorithms to identify consensus binding sequences

  • Integration with proteomic data to correlate RNA changes with protein outcomes

  • Pathway enrichment analysis of affected transcripts

How should researchers interpret contradictory results between in vitro and in vivo RBM11 studies?

When facing contradictions between in vitro and in vivo RBM11 studies, researchers should implement this analytical framework:

• Systematic Comparison:

  • Create a detailed comparison table of experimental conditions, cell types, and endpoints

  • Identify specific variables that differ between systems (growth factors, oxygen levels, etc.)

  • Determine whether contradictions are complete or context-dependent

• Biological Context Considerations:

  • Tumor microenvironment influence (absent in vitro)

  • Duration of experiments (acute vs. chronic effects)

  • Systemic factors present only in vivo (hormones, immune components)

  • Three-dimensional architecture and cell-cell interactions

• Technical Validation:

  • Verify antibody specificity in both systems

  • Confirm knockdown/overexpression efficiency is comparable

  • Assess for compensatory mechanisms that might emerge in vivo

• Reconciliation Strategies:

  • Develop intermediate models (3D organoids, co-culture systems)

  • Manipulate specific microenvironmental factors in vitro

  • Conduct time-course studies to capture dynamic effects

  • Implement more sophisticated in vivo models (orthotopic vs. subcutaneous)

• Interpretation Framework:

  • Consider that contradictions may reveal context-dependent functions

  • Evaluate whether differences reflect technical limitations or biological reality

  • Develop integrated models that accommodate conditional functions

What statistical approaches are recommended for analyzing RBM11 expression data in patient cohorts?

For robust statistical analysis of RBM11 expression in patient cohorts, researchers should implement these approaches:

• Expression Analysis:

  • Normalize RBM11 expression against validated housekeeping genes

  • Use box plots or violin plots to visualize distribution across groups

  • Apply appropriate parametric (t-test, ANOVA) or non-parametric tests (Mann-Whitney, Kruskal-Wallis) based on data distribution

• Survival Analysis:

  • Kaplan-Meier curves stratified by RBM11 expression levels

  • Cox proportional hazards models for multivariate analysis

  • Determine optimal cutoff values using ROC curve analysis or quartile distribution

• Correlation Studies:

  • Spearman or Pearson correlation with clinical parameters

  • Multiple testing correction (Bonferroni, FDR) for genome-wide analyses

  • Multivariate regression to account for confounding variables

• Cohort Considerations:

  • Power analysis to determine adequate sample size

  • Stratification by cancer subtype, stage, and treatment history

  • Independent validation cohorts to confirm findings

• Visualization and Reporting:

  • Forest plots for hazard ratios across subgroups

  • Heatmaps for correlation with other molecular markers

  • Transparent reporting of all statistical parameters (sample sizes, p-values, confidence intervals)

How can researchers differentiate between direct and indirect effects of RBM11 on cellular phenotypes?

To distinguish direct from indirect effects of RBM11 on cellular phenotypes, implement these methodological approaches:

• Temporal Analysis:

  • Time-course experiments after RBM11 manipulation

  • Pulse-induction systems (e.g., doxycycline-inducible) to capture immediate effects

  • Monitor sequential activation of signaling events

• Direct Target Identification:

  • CLIP-seq to identify directly bound RNA targets

  • Structure-function analysis using RBM11 mutants lacking RNA-binding capacity

  • In vitro binding assays with purified components

• Pathway Dissection:

  • Selective inhibitors of downstream pathways (e.g., Akt/mTOR inhibitors)

  • Genetic manipulation of pathway components in combination with RBM11

  • Phosphoproteomics to map signaling cascades

• Rescue Experiments:

  • Restore expression of specific RBM11 targets to reverse phenotypes

  • Express constitutively active downstream effectors

  • Use domain-specific RBM11 mutants to dissect functional regions

• System-Level Analysis:

  • Integrate transcriptomic, proteomic, and phenotypic data

  • Network analysis to identify direct regulatory relationships

  • Mathematical modeling of signaling dynamics

What emerging technologies could advance understanding of RBM11 function?

Several cutting-edge technologies hold promise for elucidating RBM11 function:

• CRISPR Screening Approaches:

  • Genome-wide CRISPR screens to identify synthetic lethal interactions with RBM11

  • CRISPRi/CRISPRa for fine-tuned modulation of RBM11 expression

  • Base editing of endogenous RBM11 regulatory elements

• Single-Cell Technologies:

  • scRNA-seq to capture heterogeneous responses to RBM11 manipulation

  • Spatial transcriptomics to map RBM11 activity in tissue context

  • Single-cell proteomics to correlate RNA changes with protein outcomes

• Advanced Imaging:

  • Live-cell imaging of RBM11-RNA interactions using MS2 systems

  • Super-resolution microscopy to visualize RBM11 in subnuclear structures

  • FRET-based sensors to monitor RBM11 activity in real-time

• Structural Biology:

  • Cryo-EM of RBM11-RNA complexes

  • Hydrogen-deuterium exchange mass spectrometry for conformational dynamics

  • Integrative structural biology combining multiple techniques

• Translational Approaches:

  • Development of small molecule inhibitors targeting RBM11-RNA interactions

  • RNA therapeutics to modulate RBM11 activity

  • Biomarker development for patient stratification

What are the methodological challenges in studying RBM11's role across different cancer types?

Researchers face several methodological challenges when studying RBM11 across cancer types:

• Tissue-Specific Functions:

  • RBM11 may regulate different RNA targets in different tissues

  • Cell type-specific protein interaction networks may alter function

  • Baseline expression levels vary across tissues, affecting experimental design

• Technical Considerations:

  • Antibody validation across multiple tissue types

  • Optimization of transfection/transduction for diverse cell lines

  • Suitable in vivo models for each cancer type

• Context-Dependent Regulation:

  • Tumor microenvironment varies between cancer types

  • Genetic background effects on RBM11 function

  • Treatment history may affect RBM11 activity and dependency

• Standardization Challenges:

  • Consistent measurement methods across studies

  • Appropriate control selection for each cancer type

  • Normalization strategies for cross-cancer comparisons

• Experimental Design Solutions:

  • Pan-cancer cell line panels with standardized protocols

  • Tissue-specific conditional knockout models

  • Comprehensive multi-omics profiling across cancer types

  • Meta-analysis frameworks to integrate heterogeneous datasets