C17ORF103 Human

What is C17ORF103 and where is it located in the human genome?

C17ORF103 (Chromosome 17 Open Reading Frame 103) is a member of the GTLF3B family encoded by a gene located on human chromosome 17p11.2. This chromosomal region is particularly significant as it houses important tumor suppressor genes including BRCA1 and p53, which are crucial for cellular genetic integrity and DNA repair mechanisms. The protein is also known by several synonyms including Transcript Expressed During Hematopoiesis 2, Gene Trap Locus F3b, GTLF3B, and Protein GTLF3B .

What are the optimal storage conditions for maintaining C17ORF103 protein integrity in laboratory settings?

For short-term use (2-4 weeks), C17ORF103 should be stored at 4°C. For longer periods, the protein should be stored frozen at -20°C. For long-term storage, it is recommended to add a carrier protein such as 0.1% HSA (Human Serum Albumin) or BSA (Bovine Serum Albumin) to enhance stability. Multiple freeze-thaw cycles should be avoided as they can compromise protein integrity and functionality. The standard formulation contains 20mM Tris-HCl buffer (pH 8.0), 0.1M NaCl, 1mM DTT and 20% glycerol, which helps maintain protein stability .

How can researchers effectively design experiments to investigate the relationship between C17ORF103 and tumor suppressor genes?

To investigate the relationship between C17ORF103 and tumor suppressor genes like p53 and BRCA1, a multiple time-series design (Design 14 in Campbell & Stanley's classification) is highly recommended. This quasi-experimental approach allows researchers to:

• Establish baseline expression patterns of C17ORF103, p53, and BRCA1

• Introduce targeted manipulation of C17ORF103 expression

• Monitor changes in expression, interaction, and activity of all three proteins over time

• Compare with control groups without manipulation

This design is particularly valuable when investigating proteins in the same chromosomal region (17p11.2) that may share regulatory mechanisms or functional pathways. The time-series approach helps establish temporal relationships and potential causality between C17ORF103 and tumor suppressor activities .

What protein interaction assays are most effective for identifying binding partners of C17ORF103?

For identifying C17ORF103 binding partners, a sequential approach using complementary techniques yields the most comprehensive results:

• Initial screening: Yeast two-hybrid (Y2H) assay using the 113-amino acid core sequence of C17ORF103 as bait

• Validation: Co-immunoprecipitation (Co-IP) with the recombinant His-tagged C17ORF103

• Interaction mapping: GST pull-down assays with truncated versions of C17ORF103

• In situ confirmation: Proximity ligation assay (PLA) in relevant cell types

Given C17ORF103's potential role in tumor suppression pathways and relationship to p53 and BRCA1, particular attention should be paid to proteins involved in DNA damage response and repair mechanisms. The His-tag present in the recombinant protein (MGSSHHHHHH SSGLVPRGSH) can be leveraged for efficient pulldown experiments using nickel affinity chromatography .

How can researchers effectively measure the impact of C17ORF103 mutations on cellular function?

To assess the functional impact of C17ORF103 mutations, researchers should implement a factorial experimental design that examines multiple variables simultaneously:

This approach allows for analysis of both main effects and interaction effects between different types of mutations and cellular contexts. Statistical analysis should include multivariate ANOVA to identify significant relationships. For highest validity, the equivalent materials design (Design 9) should be employed where different mutation constructs are tested against identical cellular backgrounds in parallel .

What are the most sensitive methods for detecting low-abundance C17ORF103 protein in tissue samples?

Detection of low-abundance C17ORF103 in tissue samples requires specialized approaches:

• Sample preparation: Optimize protein extraction using a buffer containing 20mM Tris-HCl (pH 8.0), 0.1M NaCl, 1mM DTT and 20% glycerol, similar to the formulation used for the recombinant protein

• Enrichment strategies: Immunoprecipitation using high-affinity antibodies or His-tag pulldown from samples with tagged protein

• Detection methods:

  • Nano-LC-MS/MS with multiple reaction monitoring (MRM)

  • Proximity extension assay (PEA)

  • Single molecule array (Simoa) digital ELISA

When comparing detection methods, research has demonstrated the following sensitivity limits:

These approaches are particularly important when studying C17ORF103 in clinical samples where protein abundance may be limited .

How should researchers address potential confounding variables when studying C17ORF103 in relation to p53 and BRCA1?

When investigating relationships between C17ORF103 and tumor suppressors p53 and BRCA1, researchers must carefully control for potential confounding variables through both experimental design and statistical approaches:

• Experimental controls:

  • Use isogenic cell lines differing only in C17ORF103 status

  • Employ inducible expression systems with careful time-course analysis

  • Include cells with known p53/BRCA1 status variations

• Statistical approaches:

  • Implement regression-discontinuity analysis (Design 16) to identify threshold effects

  • Use multivariate regression models with interaction terms

  • Apply propensity score matching when analyzing non-randomized samples

• Validation strategies:

  • Cross-validate findings across multiple cell types

  • Confirm protein-level changes with transcriptional analysis

  • Triangulate results using in vitro, in vivo, and clinical data

This systematic approach helps distinguish the specific effects of C17ORF103 from the broader impact of chromosome 17p11.2 dysregulation, where both p53 and BRCA1 may be simultaneously affected .

What statistical approaches are most appropriate for analyzing C17ORF103 expression data across different tissue types?

For analyzing C17ORF103 expression patterns across diverse tissue types, a combination of parametric and non-parametric approaches is recommended:

• For normally distributed data:

  • One-way ANOVA with post-hoc Tukey tests for multiple tissue comparisons

  • Mixed-effects models when incorporating both fixed factors (tissue type) and random factors (individual samples)

• For non-normally distributed data:

  • Kruskal-Wallis test followed by Dunn's test for multiple comparisons

  • Permutation-based methods for small sample sizes

• For complex patterns analysis:

  • Principal Component Analysis (PCA) to identify tissue-specific expression patterns

  • Hierarchical clustering to identify tissues with similar C17ORF103 expression profiles

When reporting results, include both the statistical significance (p-value) and effect size measurements (Cohen's d or partial η²) to provide complete information about the magnitude of tissue-specific differences .

How can researchers resolve contradictory findings regarding C17ORF103 function in different experimental models?

To address contradictory findings across different experimental models of C17ORF103 function, researchers should implement a systematic meta-analytical approach:

• Standardize experimental parameters:

  • Use consistent protein preparations (>95% purity by SDS-PAGE)

  • Maintain uniform buffer conditions (20mM Tris-HCl pH 8.0, 0.1M NaCl, 1mM DTT, 20% glycerol)

  • Employ standardized assay conditions across laboratories

• Implement factorial design:

  • Test multiple hypotheses simultaneously

  • Examine interaction effects between experimental variables

  • Use the Solomon Four-Group Design to control for pretesting effects

• Conduct comparative analysis:

  • Create standardized effect size measurements across studies

  • Implement forest plots to visualize consistency/inconsistency

  • Use funnel plots to check for potential publication bias

• Moderator analysis:

  • Identify variables that explain differences between experimental models

  • Test for interaction effects between protein characteristics and cellular contexts

  • Conduct sensitivity analyses by systematically excluding specific studies

This structured approach helps determine whether contradictions represent true biological variability in C17ORF103 function or stem from methodological differences between experimental systems .

What are the most promising approaches for investigating C17ORF103's potential role in dysregulated Schwann cell growth in neurofibromatosis?

Given C17ORF103's relationship to neurofibromatosis, characterized by dysregulated Schwann cell growth, several promising research approaches emerge:

• Cellular models:

  • CRISPR-engineered Schwann cell lines with C17ORF103 modifications

  • Patient-derived Schwann cells with varying neurofibromatosis severity

  • 3D organoid models incorporating multiple neural cell types

• Signaling pathway analysis:

  • Phosphoproteomic profiling before and after C17ORF103 manipulation

  • Network analysis linking C17ORF103 to established NF1/NF2 pathways

  • Investigation of crosstalk with Merlin-Hippo signaling

• In vivo approaches:

  • Conditional C17ORF103 knockout in Schwann cell lineage in mouse models

  • Xenograft models with C17ORF103-modified Schwann cells

  • Therapeutic testing using compounds targeting identified pathways

These approaches should implement a time-series experimental design (Design 7) to capture temporal dynamics of Schwann cell growth regulation and the progression of pathological changes following C17ORF103 disruption .

How might researchers develop targeted therapeutic approaches based on C17ORF103 function?

Development of targeted therapeutics based on C17ORF103 function requires a systematic research pipeline:

• Target validation phase:

  • Confirm essential functions through knockout/knockdown studies

  • Identify synthetic lethal interactions in disease contexts

  • Map binding pockets and interaction surfaces

• Therapeutic development strategies:

  • Small molecule screening targeting functional domains

  • Protein-protein interaction disruptors if functioning as part of a complex

  • Proteolysis-targeting chimeras (PROTACs) for controlled degradation

• Evaluation methodology:

  • Implement factorial design with multiple concentrations and cell types

  • Use the equivalent time-samples design (Design 8) for pharmacodynamic studies

  • Apply the multiple time-series design (Design 14) for long-term efficacy studies

When conducting these studies, researchers should pay particular attention to the relationship between C17ORF103 and the tumor suppressor pathways linked to p53 and BRCA1, as these connections may provide therapeutic vulnerabilities in cancers with chromosome 17p11.2 aberrations .

What are the critical considerations for translational research integrating C17ORF103 findings from laboratory models to clinical applications?

Translating C17ORF103 research from laboratory to clinic requires careful consideration of:

• Model validity assessment:

  • Evaluate how well in vitro findings reproduce in complex in vivo systems

  • Compare protein interactions identified in recombinant systems vs. native contexts

  • Assess clinical relevance of identified C17ORF103 mechanisms

• Biomarker development pipeline:

  • Standardize C17ORF103 detection methods for clinical samples

  • Establish reference ranges across diverse populations

  • Determine specificity/sensitivity for relevant pathological conditions

• Translational study design:

  • Implement the recurrent institutional cycle design (Design 15) for clinical validation

  • Use regression-discontinuity analysis (Design 16) to identify clinically relevant thresholds

  • Apply nonequivalent control group design (Design 10) when randomization isn't feasible

• Clinical correlation strategy:

  • Collect C17ORF103 data alongside standard clinical parameters

  • Analyze prognostic/predictive value through longitudinal studies

  • Integrate findings with broader -omics data through systems biology approaches

This translational framework helps maintain scientific rigor while navigating the complex path from fundamental C17ORF103 biology to clinical applications in neurofibromatosis and potentially cancer contexts .