EIF4A3 Human

What is EIF4A3 and what are its primary cellular functions?

EIF4A3 is an RNA-binding protein that serves as a core component of the exon junction complex (EJC), which plays indispensable roles in mRNA fate and function throughout eukaryotes . As part of the EJC, it primarily facilitates post-transcriptional regulation processes, including mRNA splicing, nuclear export, and nonsense-mediated decay .

Unexpectedly, recent research has discovered that EIF4A3 has additional non-canonical functions that are independent of its RNA-binding activity. Most notably, EIF4A3 directly controls microtubules, which is critical for neural wiring and axon development . This dual functionality makes EIF4A3 uniquely positioned at the intersection of gene expression regulation and cytoskeletal dynamics.

EIF4A3 belongs to the DEAD-box family of RNA helicases, possessing two RecA-like structural domains connected by a flexible hinge region that allows transitions between open and closed conformations . This structure is essential for its ATPase and RNA helicase activities.

How does EIF4A3 differ from other members of the EIF4A family?

While EIF4A3 shares structural features with other EIF4A family members, including the characteristic DEAD-box helicase domains, it is functionally distinct in several important ways . Unlike EIF4A1 and EIF4A2, which primarily function in translation initiation in the cytoplasm, EIF4A3 operates predominantly in the nucleus as part of the EJC .

These functional differences are particularly important when considering EIF4A3 as a potential therapeutic target, as selective inhibition could potentially modulate EIF4A3-specific functions without disrupting the essential translation functions of EIF4A1/2.

What methods are commonly used to study EIF4A3 expression in human tissues?

Several complementary methodologies are used to study EIF4A3 expression:

Immunohistochemistry (IHC): This is widely employed to visualize and quantify EIF4A3 protein in tissue sections. Researchers typically evaluate staining using standardized scoring systems that consider both staining intensity and positive rate. For example, staining intensity may be categorized as 0 points (negative), 1 point (1+), 2 points (2+), or 3 points (3+), while the positive rate might be classified as 0 points (negative), 1 point (1-25%), 2 points (26-50%), 3 points (51-75%), or 4 points (76-100%) . The total score is calculated by multiplying these values, with thresholds (e.g., less than 8 for low expression, 8 or greater for high expression) used to stratify samples .

Database analysis: Researchers frequently leverage public databases to examine EIF4A3 expression patterns across diverse tissue types and disease states. Common resources include:

• Oncomine for cancer vs. normal tissue comparisons

• PrognoScan for correlation with patient survival

• cBioPortal for analyzing genetic alterations and copy number variations

• TCGA datasets for comprehensive multi-omic profiling

Molecular techniques: These include RT-qPCR for mRNA quantification, Western blotting for protein-level detection, and RNA sequencing for comprehensive transcriptome analysis.

These methodological approaches collectively enable researchers to characterize EIF4A3 expression patterns and correlate them with clinical parameters such as survival outcomes.

How does EIF4A3 contribute to neuronal development?

EIF4A3 plays a critical and previously unexpected role in neuronal development through a mechanism that is distinct from its canonical function in RNA processing. Research has revealed that while neuronal survival in the developing mouse cerebral cortex depends upon an intact exon junction complex (EJC), axonal tract formation specifically requires only EIF4A3 .

This specialized function of EIF4A3 in neuronal development stems from its direct interaction with microtubules, which occurs independently of its RNA-binding activity . Through biochemistry and molecular modeling, researchers have discovered that EIF4A3 directly binds to microtubules in a manner that is mutually exclusive of its RNA-binding complex .

In growing neurons, EIF4A3 is essential for microtubule dynamics and has been shown to be sufficient to promote microtubule polymerization and stability in vitro . This represents an elegant repurposing of core gene expression machinery to directly control the cytoskeleton, which is fundamental for proper neural wiring.

The developmental significance of EIF4A3 is further supported by studies using human cortical organoids, which demonstrated that EIF4A3 disease mutations impair neuronal maturation . This highlights conserved functions that are relevant for understanding neurodevelopmental pathologies.

What is the significance of EIF4A3 in cancer progression?

EIF4A3 has emerged as a protein of significant interest in cancer research, with accumulating evidence suggesting it plays important roles in carcinogenesis and tumor progression. Analysis using the Oncomine database has shown that EIF4A3 is overexpressed in many common malignancies at the transcriptional level .

PrognoScan database analysis has revealed that high incidences of breast, lung, and urinary cancers are closely related to EIF4A3 expression levels and prognostic indices for survival . This suggests that EIF4A3 could potentially serve as a diagnostic and prognostic marker for certain cancer types.

In lung adenocarcinoma (LUAD) specifically, EIF4A3 expression is significantly higher in tumor tissues compared to normal tissues, and this higher expression is closely linked to poor prognosis . Functional studies have demonstrated that knockdown of EIF4A3 significantly inhibits the proliferation, invasion, and migration of LUAD cells .

Mechanistically, mass spectrometry analysis revealed that EIF4A3 can interact with Flotillin-1 (FLOT1) in LUAD cells and positively regulate FLOT1 expression at the protein level . This interaction appears to be functionally significant, as knockdown of FLOT1 reverses the increased cell proliferation and migration caused by EIF4A3 overexpression .

Recent research has also identified EIF4A3 as a potential m6A suppressor and immunotherapy biomarker through bladder cancer clinical data validation and pan-cancer analysis , further expanding its potential significance in cancer diagnostics and therapeutics.

What molecular pathways does EIF4A3 influence in cancer cells?

EIF4A3 influences several critical molecular pathways in cancer cells, with the PI3K–AKT–ERK1/2–P70S6K pathway being particularly significant. Transcriptome sequencing has shown that EIF4A3 can affect the development of lung adenocarcinoma by influencing this pathway as well as PI3K class III–mediated autophagy in the Apelin pathway .

The mechanistic link between EIF4A3 and these signaling pathways appears to involve protein-protein interactions. Mass spectrometry analysis has identified Flotillin-1 (FLOT1) as an interaction partner of EIF4A3 in lung adenocarcinoma cells . This interaction is functionally significant, as EIF4A3 positively regulates FLOT1 expression at the protein level .

Importantly, researchers have found that the activation of the PI3K–AKT–ERK1/2–P70S6K signaling pathway and PI3K class III–mediated autophagy caused by EIF4A3 overexpression can be rescued by knockdown of FLOT1 . This suggests that FLOT1 is a critical mediator of EIF4A3's effects on these pathways.

Additionally, analysis of gene co-expression networks using Coexpedia has implicated the tumor necrosis factor-α (TNF-α)/nuclear factor-κB (NF-κB) signaling pathway in EIF4A3's cancer-related functions . The Gene Ontology (GO) and pathway enrichment analyses performed using FunRich V3 further support the involvement of EIF4A3 in multiple cancer-relevant biological processes .

These findings collectively indicate that EIF4A3 influences cancer progression through multiple signaling pathways, making it a potentially valuable target for therapeutic intervention.

How can EIF4A3 be experimentally modulated in research models?

Researchers have developed several approaches to experimentally modulate EIF4A3 expression and function:

RNA interference techniques: siRNA and shRNA approaches have been successfully employed to knockdown EIF4A3 expression. In studies of lung adenocarcinoma, EIF4A3 knockdown significantly inhibited cell proliferation, invasion, and migration, demonstrating the effectiveness of this approach for functional studies .

Overexpression systems: Plasmid-based expression of wild-type or mutant EIF4A3 allows researchers to study gain-of-function effects or the consequences of specific disease-associated mutations. This approach has been valuable for investigating how EIF4A3 overexpression activates the PI3K–AKT–ERK1/2–P70S6K signaling pathway .

Mutation studies: The most prevalent mutation in EIF4A3 (E59K/Q) and other disease-associated variants can be introduced using site-directed mutagenesis to study their functional consequences . Studies using human cortical organoids have demonstrated that EIF4A3 disease mutations impair neuronal maturation, highlighting the utility of this approach .

CRISPR-Cas9 genome editing: This technology allows for precise modification of the endogenous EIF4A3 gene, including knockout, knockin of specific mutations, or introduction of tags for live imaging.

Biochemical approaches: For studying EIF4A3's direct effects on microtubules, in vitro microtubule polymerization assays have been employed to demonstrate that EIF4A3 is sufficient to promote microtubule polymerization and stability .

When designing experiments to modulate EIF4A3, researchers should consider the cell type-specific context, potential compensatory mechanisms, and the distinction between RNA-dependent and RNA-independent functions. Additionally, rescue experiments with wild-type or function-specific mutants are essential for confirming the specificity of observed phenotypes.

How might EIF4A3 serve as a biomarker in cancer diagnostics and prognostication?

EIF4A3 shows considerable promise as a biomarker in cancer diagnostics and prognostication across multiple cancer types:

Expression-based stratification: Analysis using databases like Oncomine has shown that EIF4A3 is overexpressed in many common malignancies at the transcriptional level . This differential expression could potentially be leveraged for diagnostic purposes. In clinical applications, standardized scoring systems for immunohistochemical evaluation have been developed, allowing stratification of patients into high and low expression groups .

Prognostic value: PrognoScan database analysis has revealed that high incidences of breast, lung, and urinary cancers are closely related to EIF4A3 expression levels and prognostic indices for survival . This suggests that EIF4A3 expression levels could be used to identify patients at higher risk of poor outcomes who might benefit from more aggressive treatment or closer monitoring.

Immunotherapy response prediction: Recent research has identified EIF4A3 as a potential m6A suppressor and immunotherapy biomarker through bladder cancer clinical data validation and pan-cancer analysis . The tumor immune dysfunction and exclusion database (TIDE) has been utilized to evaluate the effectiveness of immune checkpoint blockade treatment in relation to EIF4A3 expression .

Genomic alterations: Analysis using cBioPortal has revealed patterns of EIF4A3 copy number alterations and genetic alterations in different cancers . These alterations could potentially serve as additional biomarkers, complementing expression-based approaches.

For clinical implementation, several considerations are important:

• Standardization of detection methods across laboratories

• Establishment of clear cutoff points for risk stratification

• Validation in large, diverse patient cohorts

• Integration with existing biomarker panels

• Evaluation of cost-effectiveness in various clinical settings

As research continues to elucidate the diverse roles of EIF4A3 in cancer biology, its utility as a biomarker is likely to expand, potentially informing treatment selection and monitoring disease progression.

How can researchers distinguish between EIF4A3's RNA-dependent and RNA-independent functions?

Distinguishing between EIF4A3's canonical RNA-binding functions and its newly discovered RNA-independent activities presents a significant experimental challenge. Several methodological approaches can help researchers make this distinction:

Mutation-based approaches: Generating mutant versions of EIF4A3 with selectively impaired functions is a powerful strategy. Researchers have discovered that EIF4A3 directly binds to microtubules in a manner that is mutually exclusive with its RNA-binding complex . This finding suggests that specific amino acid residues are differentially involved in these two functions, allowing for the creation of function-specific mutants.

Biochemical separation: Subcellular fractionation can help isolate pools of EIF4A3 engaged in different functions. The RNA-binding EIF4A3 complex is predominantly nuclear, while cytoskeletal interactions may be more prominent in the cytoplasm or specifically in cellular projections like axons in neuronal cells.

Functional assays:

• RNase treatment prior to co-immunoprecipitation can distinguish RNA-dependent from direct protein-protein interactions

• In vitro reconstitution assays with purified components can test direct protein interactions

• Microtubule polymerization assays in cell-free systems can assess direct effects on cytoskeletal dynamics independent of RNA processing

Imaging approaches: Co-localization studies using confocal or super-resolution microscopy can visualize where EIF4A3 associates with RNA processing machinery versus cytoskeletal components.

Computational prediction: Structural modeling approaches can predict binding interfaces for different interaction partners and guide the design of specific experimental perturbations.

The discovery that EIF4A3 directly controls microtubules independent of RNA has revealed a novel mechanism by which neurons re-utilize core gene expression machinery to rapidly and directly control the cytoskeleton . This finding underscores the importance of carefully designing experiments to distinguish between these distinct functions when studying EIF4A3 in various biological contexts.

What is known about EIF4A3's role in tumor immunity?

The relationship between EIF4A3 and tumor immunity is an emerging area of research with potentially significant implications for cancer immunotherapy. Recent studies have begun to explore this connection:

The relationship between EIF4A3 and T cell dysfunction levels has been evaluated in various cohorts, suggesting potential mechanistic links between EIF4A3 expression and anti-tumor immune responses . Additionally, analysis using cBioPortal has examined EIF4A3 copy number alterations and genetic alterations in different cancers, which may have implications for tumor immunogenicity .

While research in this area is still in its early stages, several potential mechanisms could explain EIF4A3's influence on tumor immunity:

• EIF4A3 might affect the splicing of genes involved in immune recognition or immune checkpoint pathways

• EIF4A3-mediated regulation of the PI3K-AKT pathway could influence immune cell function in the tumor microenvironment

• EIF4A3's role in the TNF-α/NF-κB signaling pathway, suggested by gene co-expression network analysis , could affect inflammatory processes relevant to anti-tumor immunity

The identification of EIF4A3 as a potential immunotherapy biomarker through bladder cancer clinical data validation and pan-cancer analysis suggests that further investigation of its role in tumor immunity could yield valuable insights for improving cancer immunotherapy strategies.

How might therapeutic targeting of EIF4A3 be developed for cancer treatment?

Developing therapeutic approaches targeting EIF4A3 presents both opportunities and challenges. Several potential strategies could be pursued:

Function-selective targeting: The discovery that EIF4A3 has distinct RNA-binding and microtubule-binding functions opens the possibility of developing compounds that selectively disrupt one function while preserving others. This approach might reduce toxicity compared to complete inhibition of all EIF4A3 activities.

Disruption of protein-protein interactions: Targeting the interaction between EIF4A3 and specific partners like Flotillin-1 (FLOT1) could provide a more selective approach. The finding that EIF4A3 can interact with FLOT1 to activate the PI3K–AKT–ERK1/2–P70S6K pathway in lung adenocarcinoma provides a rationale for this strategy .

RNA-based therapeutics: Antisense oligonucleotides or siRNAs targeting EIF4A3 could provide an alternative approach, particularly if delivery to specific tissues can be achieved.

Combination strategies: Given EIF4A3's role in multiple pathways, combination approaches might be particularly effective. For example, combining EIF4A3 inhibition with PI3K or AKT inhibitors could yield synergistic effects based on the pathway connections identified .

Several considerations would be important for clinical development:

• Target validation: Further validation of EIF4A3's role in specific cancer types is necessary

• Biomarker development: Identifying patients most likely to benefit based on EIF4A3 expression or mutation status

• Therapeutic window: Determining whether a sufficient window exists between anti-cancer efficacy and toxicity

• Delivery systems: Developing approaches to deliver inhibitors to appropriate tissues

The finding that EIF4A3 is overexpressed in multiple cancer types and correlates with poor prognosis supports its potential as a therapeutic target, but careful development and validation will be necessary to translate these findings into clinical applications.

What are the challenges in translating EIF4A3 research findings from cell lines to in vivo models?

Translating EIF4A3 research findings from cell lines to in vivo models presents several significant challenges:

Developmental essentiality: EIF4A3 plays fundamental roles in neuronal development and survival, as evidenced by studies in the developing mouse cerebral cortex . Complete knockout of EIF4A3 is likely to be embryonically lethal, necessitating sophisticated conditional or inducible approaches to study its functions in vivo. Temporal and spatial control of EIF4A3 modulation is crucial for separating developmental versus acute functional roles.

Tissue specificity: EIF4A3 functions may vary considerably across different tissue types. For example, its direct control of microtubules is particularly important in neuronal cells for axon formation , but may have different significance in other cell types. This heterogeneity requires careful consideration when extrapolating findings from one system to another.

Compensatory mechanisms: In vivo systems often have more robust compensatory mechanisms than cell lines. Other EIF4A family members or related RNA helicases might partially compensate for EIF4A3 modulation in vivo, potentially masking phenotypes observed in vitro.

Model selection: Different model organisms offer complementary advantages for studying EIF4A3. While mouse models provide mammalian relevance, human cortical organoids have been valuable for studying EIF4A3 disease mutations and their effects on neuronal maturation . For cancer studies, patient-derived xenografts might better recapitulate tumor heterogeneity than cell lines.

Technical approaches to address these challenges:

• Tissue-specific conditional knockout/knockdown systems

• Temporal control using drug-inducible systems

• Function-specific mutations rather than complete ablation

• Combination of multiple model systems (in vitro, organoid, in vivo)

• Single-cell approaches to address cellular heterogeneity

The successful translation of cortical organoid findings to understand EIF4A3's role in human neurodevelopment demonstrates that these challenges can be overcome with appropriate model selection and experimental design. Similarly, validation of cancer-related findings across multiple experimental systems would strengthen their translational relevance.

How do EIF4A3 mutations contribute to disease pathology?

EIF4A3 mutations have been implicated in both cancer and neurodevelopmental disorders, with distinct pathological mechanisms:

Cancer-associated mutations: Analysis of cancer databases has revealed that E59K/Q is the most prevalent mutation in EIF4A3 across various cancers . These mutations likely affect EIF4A3's function, potentially altering its interactions with RNA or protein partners. The functional consequences could include dysregulated splicing of cancer-relevant genes or aberrant activation of signaling pathways such as PI3K–AKT–ERK1/2–P70S6K .

Neurodevelopmental mutations: Research using human cortical organoids has demonstrated that EIF4A3 disease mutations impair neuronal maturation . This finding highlights conserved functions of EIF4A3 that are relevant for neurodevelopmental pathology. The mechanism likely involves disruption of EIF4A3's direct interaction with microtubules, which is critical for axon development and neural wiring .

Dual-function disruption: Given EIF4A3's multiple cellular roles, mutations may have pleiotropic effects. Some mutations might predominantly affect RNA binding and processing, while others might primarily impact cytoskeletal interactions. This functional separation could explain why certain mutations lead to specific disease phenotypes.

Methodological approaches to study mutation effects:

• Structural modeling to predict functional consequences

• Creation of mutation-specific constructs for functional assays

• Patient-derived iPSC models (such as cortical organoids)

• In vitro biochemical assays to assess specific activities (RNA binding, ATPase activity, microtubule binding)

Understanding how specific mutations affect EIF4A3's diverse functions is crucial for developing targeted therapeutic approaches. The finding that EIF4A3 has separable functions in RNA processing versus cytoskeletal regulation suggests that mutation-specific interventions might be possible, potentially allowing for correction of pathological effects while preserving essential functions.

How should researchers interpret contradictory findings regarding EIF4A3's role in different cancer types?

Researchers face apparent contradictions in the literature regarding EIF4A3's roles in different cancer contexts. For example, while most studies indicate that high EIF4A3 expression correlates with poor prognosis in cancers like lung adenocarcinoma , some data from blood cancers suggest a different relationship . Several approaches can help researchers interpret these seemingly contradictory findings:

Contextual analysis:

• Recognize that molecular pathways are highly context-dependent

• Consider cancer-specific molecular backgrounds (mutational landscapes)

• Analyze cancer subtypes separately rather than broadly grouping cancer types

• Evaluate the tumor microenvironment's influence on EIF4A3 function

Methodological considerations:

• Standardize quantification methods for EIF4A3 expression

• Establish consistent cutoff points for "high" versus "low" expression

• Account for confounding variables through multivariate analyses

• Consider technical differences between studies (antibodies, scoring systems)

Mechanistic investigations:

• Determine cancer-specific interaction partners that might alter EIF4A3 function

• Identify downstream targets that differ between cancer types

• Evaluate post-translational modifications that might alter function in specific contexts

Data integration approaches:

• Perform meta-analyses across independent cohorts

• Use discovery and validation cohorts to confirm findings

• Consider publication bias when reviewing contradictory literature

• Employ systems biology approaches to model complex interactions

The observation that EIF4A3 can interact with different partners (such as FLOT1 in lung adenocarcinoma ) and influence multiple signaling pathways provides a mechanistic basis for context-dependent functions. By carefully considering cellular context and employing rigorous methodology, researchers can develop more nuanced models of EIF4A3's roles across different cancer types.

What statistical considerations are important when analyzing EIF4A3 expression in patient samples?

When analyzing EIF4A3 expression in patient samples, several important statistical considerations should be addressed:

Sample stratification:

• Standardized approaches for processing original data are essential. For example, in immunohistochemical studies, consistent scoring systems should be employed. One approach classifies staining intensity into four levels (0-3 points) and positive rate into five levels (0-4 points), with the total score calculated by multiplication .

• Threshold selection for "high" versus "low" expression groups is critical. Some studies assign patients with a total score less than 8 to the low expression group and those with a score greater than or equal to 8 to the high expression group . The rationale for specific cutoff points should be clearly stated and validated.

Confounding variables:

• Patient demographics (age, sex, ethnicity)

• Tumor characteristics (stage, grade, molecular subtype)

• Treatment history

• Comorbidities

Statistical methods:

• Multivariate analyses (Cox regression) to account for confounding factors

• Kaplan-Meier survival analysis with appropriate statistical tests (log-rank)

• Correction for multiple hypothesis testing when examining multiple outcomes

• Power calculations to ensure adequate sample size

Validation approaches:

• Independent validation cohorts to confirm findings

• Cross-validation techniques for predictive modeling

• Assessment of reproducibility across different analytical platforms

Reporting standards:

• Clear documentation of all analytical steps

• Transparent reporting of all statistical tests performed

• Inclusion of effect sizes and confidence intervals, not just p-values

• Discussion of limitations and potential biases

When examining EIF4A3's relationship with patient outcomes, researchers should be particularly careful to distinguish prognostic value (correlation with outcome regardless of treatment) from predictive value (ability to predict response to specific therapies). This distinction is especially important given emerging evidence of EIF4A3's potential role as an immunotherapy biomarker .

What controls are essential when studying EIF4A3's dual roles in RNA processing and cytoskeletal regulation?

Given EIF4A3's multiple cellular functions, carefully designed controls are essential to distinguish between its roles in RNA processing and cytoskeletal regulation:

For RNA processing studies:

• Positive controls: Include known EIF4A3-dependent splicing events or mRNA targets

• Negative controls: Examine transcripts that are processed independently of EIF4A3

• Functional validation: Confirm altered splicing patterns using minigene assays

• Domain mutants: Use mutants specifically defective in RNA binding or ATPase activity

• RNase treatment: Determine whether observed interactions are RNA-dependent

For cytoskeletal studies:

• Visualization controls: Include established microtubule markers in co-localization studies

• Functional validation: Assess microtubule dynamics using live imaging of fluorescently tagged tubulin

• In vitro systems: Use purified components to test direct effects on microtubule polymerization

• Domain mutants: Employ mutants specifically defective in microtubule binding but preserving RNA functions

• Competitive binding: Test whether RNA and microtubule binding are mutually exclusive as reported

For distinguishing between functions:

• Rescue experiments: Attempt to rescue phenotypes with wild-type versus function-specific mutants

• Subcellular localization: Determine where different functions predominate through fractionation or imaging

• Temporal analysis: Establish the sequence of events when multiple functions might be involved

• Chemical inhibitors: Use RNA processing inhibitors versus cytoskeleton-disrupting agents

For validation across systems:

• Multiple cell types: Test whether findings are consistent in different cellular contexts

• In vitro to in vivo translation: Validate key findings in progressively more complex systems

• Human relevance: Confirm findings in human samples or models (e.g., cortical organoids)

The discovery that EIF4A3 directly controls microtubules independent of RNA highlights the importance of these controls . Without proper experimental design, this non-canonical function might have been overlooked or misattributed to indirect effects of altered RNA processing.

What approaches can resolve discrepancies between predicted and observed functions of EIF4A3 in experimental systems?

Discrepancies between predicted and observed functions of EIF4A3 across different experimental systems can be addressed through several methodological approaches:

Model system considerations:

• Side-by-side comparison: Directly compare multiple model systems using identical methodologies

• Species-specific analysis: Assess whether discrepancies relate to species-specific variations in EIF4A3 sequence or regulation

• Cell type context: Evaluate expression of known interaction partners across different systems

• Developmental stage: Consider temporal aspects, as EIF4A3 functions may vary during development

Technical approaches:

• Dose-response relationships: Test whether discrepancies relate to expression levels or activity thresholds

• Temporal analysis: Distinguish immediate versus adaptive responses through time-course studies

• Acute versus chronic modulation: Compare short-term versus long-term effects of EIF4A3 perturbation

• Partial versus complete inhibition: Use graded levels of knockdown to identify threshold effects

Functional validation:

• Rescue experiments: Confirm specificity through restoration with wild-type EIF4A3

• Domain-specific mutants: Use targeted mutations to dissect structure-function relationships

• Orthogonal methods: Validate key findings using complementary technical approaches

• Gain-of-function and loss-of-function: Compare phenotypes from overexpression versus knockdown

Integrative analysis:

• Multi-omics integration: Combine transcriptomic, proteomic, and functional data

• Network analysis: Map EIF4A3 into context-specific interaction networks

• Computational modeling: Develop predictive models incorporating system-specific variables

• Machine learning approaches: Identify patterns that might explain context-dependent functions

The discovery that EIF4A3 directly binds to microtubules, mutually exclusive of its RNA-binding complex , exemplifies how experimental approaches can reveal unexpected functions that might explain discrepancies between predicted and observed phenotypes. This finding demonstrates that EIF4A3 can "moonlight" in roles beyond its canonical function in the exon junction complex, providing a mechanistic basis for seemingly contradictory observations across different experimental systems.