EBNA1BP2 Human

What is EBNA1BP2 and what are its basic characteristics?

EBNA1BP2 (EBNA1 Binding Protein 2), also known by synonyms NOBP and EBP2, is a protein encoded by the EBNA1BP2 gene (NCBI Gene ID: 10969) in humans . It was initially identified as a protein that interacts with Epstein-Barr virus nuclear antigen 1 (EBNA1) and plays a crucial role in EBV latent infection . This protein is predominantly involved in nucleoplasm functions and RNA binding pathways .

At the molecular level, EBNA1BP2 has 6,025 functional associations with biological entities spanning 8 categories, including molecular profiles, chemicals, functional terms, diseases, phenotypes, structural features, cell types, and genes/proteins, extracted from 98 datasets . These associations highlight its diverse biological roles beyond viral interaction.

To study this protein effectively, researchers typically use antibody-based detection methods, with recombinant EBNA1BP2 protein (such as that derived from E. coli expression systems) serving as a valuable control for validation studies .

How does EBNA1BP2 function in normal cellular processes?

In normal cellular physiology, EBNA1BP2 functions primarily in the nucleoplasm compartment where it participates in RNA binding activities . Single-cell analysis demonstrates that EBNA1BP2 expression positively correlates with cell cycle progression and DNA repair processes, suggesting a role in cellular proliferation and genomic stability maintenance .

Mechanistically, EBNA1BP2 interacts with a network of proteins involved in nucleolar functions. Gene ontology (GO) enrichment analysis of EBNA1BP2-correlated genes reveals significant associations with fundamental cellular processes including ribosome biogenesis, RNA processing, and nucleolar organization .

For experimental investigation of these normal functions, researchers should consider using techniques that preserve native protein interactions, such as proximity ligation assays or co-immunoprecipitation combined with mass spectrometry, rather than relying solely on overexpression systems that may disrupt normal stoichiometry of interacting partners.

What are the optimal methods for detecting EBNA1BP2 expression in different tissue types?

For detecting EBNA1BP2 expression in research settings, multiple complementary approaches should be employed:

• RNA-level detection: Real-time PCR represents a sensitive method for quantifying EBNA1BP2 mRNA expression. The established primer sequences used in published research are:

  • Forward: 5′-CGAAGCGACCCACTGATTAT-3′

  • Reverse: 5′-TCCATGGCAGCCTGTTTAG-3′

  • FAM-labeled probe: 5′-AGATGCAGAAGATTCGACAGAAGCTGC-3′

• Protein detection:

  • Immunohistochemistry (IHC) using validated antibodies against EBNA1BP2, with appropriate controls including recombinant protein

  • Western blotting with proper loading controls

  • Immunofluorescence for cellular localization studies

• Database integration:

  • Verification through public resources such as the Human Protein Atlas, which provides reference IHC images for comparison

  • Cross-validation with CPTAC (Clinical Proteomic Tumor Analysis Consortium) protein expression data

For tissues with dense cellular composition, consider using single-cell RNA sequencing to differentiate expression patterns among specific cell populations. When analyzing tumors, microdissection techniques may help isolate cancer cells from stromal components for more precise expression analysis.

How does EBNA1BP2 expression vary across different human tissues and cancer types?

EBNA1BP2 exhibits distinct expression patterns across normal and cancerous tissues. Comprehensive analysis using TIMER2.0 and other databases reveals:

• Bladder urothelial carcinoma (BLCA)

• Breast invasive carcinoma (BRCA)

• Cervical squamous cell carcinoma (CESC)

• Cholangiocarcinoma (CHOL)

• Colon adenocarcinoma (COAD)

• Esophageal carcinoma (ESCA)

• Head and neck squamous cell carcinoma (HNSC)

• Kidney renal clear cell carcinoma (KIRC)

• Liver hepatocellular carcinoma (LIHC)

• Lung adenocarcinoma (LUAD)

• Lung squamous cell carcinoma (LUSC)

• Prostate adenocarcinoma (PRAD)

• Rectum adenocarcinoma (READ)

• Stomach adenocarcinoma (STAD)

• Thyroid carcinoma (THCA)

• Uterine corpus endometrial carcinoma (UCEC)

Interestingly, kidney chromophobe (KICH) shows the opposite trend, with lower EBNA1BP2 expression in tumor samples compared to normal tissues . This differential expression suggests tissue-specific regulatory mechanisms that warrant further investigation using tissue-specific cell models and ChIP-seq analysis for identifying regulatory elements.

What is the prognostic value of EBNA1BP2 expression in different cancer types?

EBNA1BP2 has emerged as a potential prognostic biomarker across multiple cancer types, with its prognostic significance varying by cancer type. Comprehensive survival analyses using GEPIA2.0 show that low EBNA1BP2 expression correlates with:

• Adrenocortical carcinoma (ACC)

• Bladder urothelial carcinoma (BLCA)

• Brain lower grade glioma (LGG)

• Liver hepatocellular carcinoma (LIHC)

• Mesothelioma (MESO)

• Sarcoma (SARC)

• Uterine carcinosarcoma (UCS)

Better disease-free survival (DFS) in:

• Adrenocortical carcinoma (ACC)

• Head and neck squamous cell carcinoma (HNSC)

• Kidney renal papillary cell carcinoma (KIRP)

• Brain lower grade glioma (LGG)

• Prostate adenocarcinoma (PRAD)

• Sarcoma (SARC)

• Uterine carcinosarcoma (UCS)

Interestingly, contrary findings were observed in certain cancer types such as kidney renal clear cell carcinoma (KIRC), ovarian cancer (OV), pheochromocytoma and paraganglioma (PCPG), stomach adenocarcinoma (STAD), and thyroid carcinoma (THCA), where higher EBNA1BP2 expression correlated with better outcomes .

To properly assess prognostic value in research settings, investigators should employ multivariate Cox regression models that account for confounding clinical variables and consider cancer molecular subtypes, which may explain these apparently contradictory findings in different tissues.

How does EBNA1BP2 contribute to tumor development and progression?

The mechanisms through which EBNA1BP2 contributes to tumor development appear multifaceted, with several key pathways identified through functional genomics and enrichment analyses:

• Cell cycle regulation: At the single-cell level, EBNA1BP2 positively correlates with cell cycle progression and DNA repair processes, potentially promoting cancer cell proliferation .

• Immune infiltration: EBNA1BP2 expression associates with immune cell infiltration patterns, including B cells, cancer-associated fibroblasts, and CD8+ T cells, suggesting involvement in tumor microenvironment modulation .

• Epigenetic regulation: Promoter methylation analysis reveals downregulated methylation levels of EBNA1BP2 in most cancer types, indicating epigenetic dysregulation as a potential mechanism of overexpression .

• Cellular adaptation: EBNA1BP2 expression negatively correlates with hypoxia response, potentially affecting tumor adaptation to microenvironmental stress .

For experimental validation of these mechanisms, CRISPR-Cas9 knockout or knockdown models combined with RNA-seq and ChIP-seq approaches would provide comprehensive insights into transcriptional networks and downstream effectors. Xenograft models with EBNA1BP2 manipulation would help validate its role in tumor growth and metastasis in vivo.

What are the key methodological considerations for studying EBNA1BP2 in cancer tissue samples?

When investigating EBNA1BP2 in cancer tissues, several methodological considerations are crucial:

• Sample selection and controls:

  • Include matched tumor and adjacent normal tissues whenever possible

  • Confirm tissue histology through pathological examination

  • Consider molecular subtypes of cancers, as EBNA1BP2's role may vary

• Expression analysis:

  • For FFPE samples, use optimized DNA extraction kits (e.g., TianGen Biochemistry FFPE DNA extraction kit)

  • Calculate relative expression using the 2^-ΔΔCt method

• Spatial context:

  • Consider intratumoral heterogeneity by analyzing multiple regions

  • Use laser capture microdissection for isolating specific cell populations

  • Employ spatial transcriptomics for mapping expression patterns within tumor architecture

• Data integration:

  • Validate findings using multiple public databases (TCGA, GEO, CPTAC)

  • Utilize tools like TIMER2.0, GEPIA2.0, and CancerSEA for comprehensive analysis

  • Apply consistent statistical approaches across datasets

• Single-cell considerations:

  • For examining EBNA1BP2 at the single-cell level, generate t-SNE plots to visualize expression distribution across tumor samples

  • Use CancerSEA or similar tools to correlate expression with functional states in cancer cells

These methodological considerations help ensure robust, reproducible results when studying EBNA1BP2 in cancer contexts.

What proteins interact with EBNA1BP2 and how can these interactions be studied effectively?

EBNA1BP2 engages in multiple protein-protein interactions that influence its functional role in both normal and pathological contexts. Effective study of these interactions requires:

• Interaction identification approaches:

  • Protein-protein interaction networks can be extracted from BioGRID (Biological General Repository for Interaction Datasets)

  • Yeast two-hybrid screening for novel interactors

  • Co-immunoprecipitation followed by mass spectrometry

  • Proximity-dependent biotin identification (BioID) or APEX labeling for context-specific interactions

• Validation methods:

  • Reciprocal co-immunoprecipitation with specific antibodies

  • Fluorescence resonance energy transfer (FRET) or bimolecular fluorescence complementation (BiFC) for live-cell interaction studies

  • Surface plasmon resonance (SPR) or isothermal titration calorimetry (ITC) for determining binding kinetics and thermodynamics

• Functional characterization:

  • Mutagenesis of key residues to disrupt specific interactions

  • Domain mapping to identify interaction interfaces

  • CRISPR-Cas9 gene editing to create interaction-deficient variants

The amino acid sequence segment ESDESLVTDRELQDAFSRGLLKPGLNVVLEG has been identified as a functional region of EBNA1BP2 , which may be particularly relevant for interaction studies. For studying viral interactions specifically, reconstitution of EBNA1BP2-EBNA1 complexes in vitro using purified components can provide mechanistic insights into structural determinants of these interactions.

How does the relationship between EBNA1BP2 and immune infiltration affect research approaches?

The relationship between EBNA1BP2 and immune infiltration has significant implications for research design and interpretation:

• Characterizing immune associations:

  • EBNA1BP2 expression correlates with infiltration of specific immune cell populations, including B cells, cancer-associated fibroblasts, and CD8+ T cells

  • These associations should be analyzed using multiple computational approaches (TIMER, EPIC, TIDE, QUANTISEQ, CIBERSORT, XCELL, MCPCOUNTER) for robust results

• Methodological considerations:

  • Single-cell RNA sequencing is essential for distinguishing EBNA1BP2 expression in tumor cells versus immune infiltrates

  • Multiplex immunofluorescence or imaging mass cytometry can provide spatial context for expression relative to immune niches

  • Flow cytometry sorting of distinct cell populations followed by expression analysis helps determine cell-type-specific effects

• Functional validation:

  • Co-culture experiments with manipulated EBNA1BP2 expression can assess direct effects on immune cell recruitment and function

  • Conditional knockout models in specific compartments (tumor vs. immune) help delineate cell-autonomous versus non-cell-autonomous effects

  • Checkpoint blockade response studies in EBNA1BP2-high versus low tumors may reveal therapeutic implications

When designing studies examining this relationship, researchers should carefully account for tumor type and stage, treatment history, and baseline immune characteristics of experimental models, as these factors can significantly influence results and interpretation.

What epigenetic mechanisms regulate EBNA1BP2 expression and how can they be investigated?

Epigenetic regulation of EBNA1BP2 appears important in cancer contexts, with the promoter methylation level of EBNA1BP2 downregulated in the majority of cancer types . To investigate these mechanisms thoroughly:

• Methylation analysis approaches:

  • Bisulfite sequencing of the EBNA1BP2 promoter region for site-specific methylation patterns

  • Methylation-specific PCR for targeted assessment of key regulatory regions

  • Genome-wide methylation arrays (e.g., Illumina MethylationEPIC) for contextualizing EBNA1BP2 methylation within broader epigenetic landscapes

  • RRBS (Reduced Representation Bisulfite Sequencing) for cost-effective profiling

• Chromatin structure analysis:

  • ChIP-seq for histone modifications (H3K4me3, H3K27ac, H3K27me3) at the EBNA1BP2 locus

  • ATAC-seq to assess chromatin accessibility of the promoter and enhancer regions

  • Hi-C or similar techniques to investigate three-dimensional chromatin organization affecting EBNA1BP2 regulation

• Transcriptional regulation:

  • ChIP-seq for transcription factors binding to the EBNA1BP2 promoter

  • Reporter assays with wild-type and mutated promoter constructs

  • CRISPR interference or activation (CRISPRi/CRISPRa) targeting regulatory elements

• Integrated analysis:

  • Correlation of methylation patterns with expression levels across cancer types

  • Multi-omics approaches combining methylation, chromatin, and expression data

  • Single-cell multi-omics for cellular heterogeneity in epigenetic regulation

When investigating epigenetic mechanisms, researchers should consider developmental stages, environmental exposures, and treatment effects that might influence the epigenetic landscape around EBNA1BP2.

How can pathway analysis of EBNA1BP2-correlated genes inform therapeutic targeting approaches?

Pathway analysis of EBNA1BP2-correlated genes provides valuable insights for therapeutic targeting strategies:

• Identification of relevant pathways:

  • Gene enrichment analysis indicates EBNA1BP2 is primarily involved in nucleoplasm functions and RNA binding pathways

  • KEGG pathway analysis of the top 100 EBNA1BP2-correlated genes from TCGA data reveals associations with specific cellular processes

• Network-based approaches:

  • Generate protein-protein interaction networks using BioGRID and similar databases

  • Identify hub proteins or signaling nodes that connect EBNA1BP2 to critical cellular functions

  • Perform gene set enrichment analysis (GSEA) to identify enriched pathways in EBNA1BP2-high versus low tumors

• Therapeutic implications:

  • Target synthetic lethal interactions by identifying genes whose inhibition is selectively toxic in EBNA1BP2-high cells

  • Explore vulnerability to RNA metabolism inhibitors, given EBNA1BP2's association with RNA binding pathways

  • Investigate combinatorial approaches targeting both EBNA1BP2-dependent pathways and immune checkpoints, based on immune infiltration correlations

• Experimental validation:

  • High-throughput drug screening in isogenic cell lines with EBNA1BP2 knockdown/overexpression

  • CRISPR-Cas9 screening to identify synthetic lethal interactions

  • Patient-derived xenograft models stratified by EBNA1BP2 expression for preclinical therapeutic evaluation

The correlation between EBNA1BP2 and cell cycle/DNA repair processes suggests potential synergy with existing therapies targeting these pathways, such as PARP inhibitors or cell cycle checkpoint inhibitors, which should be systematically evaluated.

What are the most significant contradictions or knowledge gaps in current EBNA1BP2 research?

Several important contradictions and knowledge gaps exist in current EBNA1BP2 research:

• Cancer-type specific effects:

  • While low EBNA1BP2 expression correlates with better prognosis in most cancers (ACC, BLCA, LGG, LIHC, MESO, SARC, UCS), the opposite trend is observed in others (KIRC, OV, PCPG, STAD, THCA)

  • The molecular basis for these differential effects remains unexplained and requires tissue-specific mechanistic studies

• Causality versus correlation:

  • Current evidence primarily establishes correlations between EBNA1BP2 expression and cancer outcomes

  • Causal relationships and mechanisms by which EBNA1BP2 influences tumor biology need further investigation through interventional studies

• EBV-dependent versus independent functions:

  • While EBNA1BP2 was initially identified through its interaction with the Epstein-Barr virus, its broader roles independent of viral infection remain poorly characterized

  • The extent to which viral interaction influences EBNA1BP2's cellular functions requires clarification

• Regulatory mechanisms:

  • While promoter methylation changes have been observed, the comprehensive regulatory network controlling EBNA1BP2 expression, including transcription factors and non-coding RNAs, remains incomplete

  • The stimuli and signaling pathways that modulate EBNA1BP2 expression under physiological and pathological conditions are not well defined

• Protein structure-function relationships:

  • Detailed structural information about EBNA1BP2 and how it relates to functional interactions is limited

  • The specific domains mediating protein-protein interactions and their conformational dynamics need characterization

Addressing these gaps requires integrative approaches combining structural biology, systems biology, and functional genomics in relevant model systems.

How do single-cell technologies advance our understanding of EBNA1BP2 function in heterogeneous tumor environments?

Single-cell technologies provide transformative insights into EBNA1BP2 function within heterogeneous tumor environments:

• Cellular heterogeneity mapping:

  • Single-cell RNA sequencing reveals cell-type-specific expression patterns of EBNA1BP2 across tumor ecosystems

  • t-SNE visualization of EBNA1BP2 expression across tumor samples helps identify distinct cellular clusters with differential expression

  • CancerSEA analysis at the single-cell level reveals correlations between EBNA1BP2 expression and functional states such as cell cycle progression, DNA repair, and hypoxia response

• Methodological approaches:

  • scRNA-seq coupled with computational lineage inference to track EBNA1BP2 expression changes during tumor evolution

  • CITE-seq or REAP-seq for simultaneous profiling of EBNA1BP2 mRNA and surface proteins on immune cells

  • Spatial transcriptomics to map EBNA1BP2 expression within architectural features of tumors

  • Single-cell ATAC-seq to correlate chromatin accessibility with EBNA1BP2 expression

• Functional relationships:

  • Integration of single-cell data with spatial information to understand EBNA1BP2's role in tumor-stroma-immune interactions

  • Correlation of EBNA1BP2 expression with markers of stemness, differentiation, and drug resistance at single-cell resolution

  • Trajectory analysis to identify cellular states where EBNA1BP2 expression changes dynamically

• Therapeutic implications:

  • Identification of rare cell populations with extreme EBNA1BP2 expression that may drive therapeutic resistance

  • Determination of optimal cellular targets based on co-expression patterns with druggable pathways

  • Prediction of treatment response heterogeneity based on EBNA1BP2-associated cellular states

For effective single-cell studies of EBNA1BP2, researchers should consider technical factors such as dissociation protocols that preserve cellular states, depth of sequencing adequate for detecting moderately expressed genes, and computational approaches that account for technical artifacts while preserving biological variation.

What are the most promising future directions for EBNA1BP2 research in cancer biology?

Based on current evidence, several high-priority research directions for EBNA1BP2 in cancer biology include:

• Mechanistic dissection of tissue-specific effects: Investigating why EBNA1BP2 has opposing prognostic significance in different cancer types through tissue-specific knockout models and transcriptomic profiling.

• Therapeutic targeting: Developing approaches to modulate EBNA1BP2 function or expression, potentially through:

  • Small molecule inhibitors targeting protein-protein interactions

  • Degrader technologies (PROTACs) targeting EBNA1BP2 for ubiquitin-mediated degradation

  • RNA-based therapeutics to modulate expression levels

• Biomarker development: Validating EBNA1BP2 as a prognostic or predictive biomarker in prospective clinical trials, particularly in cancer types where it shows strong survival correlations.

• Immune modulation: Exploring how EBNA1BP2 influences the tumor immune microenvironment and response to immunotherapies, given its correlations with immune cell infiltration.

• Structural biology: Determining the three-dimensional structure of EBNA1BP2 and its complexes to enable structure-based drug design and mechanistic insights.

For effective advancement of these research directions, collaborative approaches combining clinical samples, diverse model systems, and integrative multi-omics analyses will be essential to develop a comprehensive understanding of EBNA1BP2's role in cancer biology and its potential as a therapeutic target.

How should researchers design experiments to resolve contradictory findings about EBNA1BP2?

To address contradictory findings in EBNA1BP2 research, investigators should implement the following experimental design principles:

• Standardized methodology:

  • Establish consensus protocols for EBNA1BP2 detection and quantification

  • Use multiple antibodies or detection methods to validate findings

  • Include appropriate positive and negative controls, including recombinant EBNA1BP2 protein

• Context-specific investigation:

  • Study EBNA1BP2 in multiple cell types and cancer models simultaneously

  • Analyze effects within specific molecular subtypes of cancers

  • Consider microenvironmental factors that may influence EBNA1BP2 function

• Multi-level analysis:

  • Integrate genomic, transcriptomic, proteomic, and functional data

  • Correlate EBNA1BP2 genetic alterations with expression and function

  • Employ both in vitro and in vivo models for validation

• Mechanistic focus:

  • Use CRISPR-Cas9 for precise genetic manipulation rather than relying solely on overexpression or siRNA

  • Employ domain-specific mutations to dissect functional regions

  • Utilize inducible systems to study temporal aspects of EBNA1BP2 function

• Transparent reporting:

  • Clearly document experimental conditions and cellular context

  • Report all outcomes, including negative results

  • Share raw data through appropriate repositories