Recombinant Human Uncharacterized protein C4orf3 (C4orf3)

What is the C4orf3 protein and what cellular functions has it been associated with?

C4orf3 (Chromosome 4 Open Reading Frame 3) is a largely uncharacterized human protein believed to participate in various cellular activities including protein synthesis, cellular signaling, and stress response pathways . As an uncharacterized protein, its complete functional profile remains under investigation, though knockout studies have begun to elucidate its potential roles. Current research indicates it may be involved in fundamental cellular mechanisms that impact proliferation, metabolic activity, and cell survival pathways . The protein is encoded by a gene located on chromosome 4 and expressed in multiple human tissue types, suggesting potential tissue-specific functions that warrant further investigation.

What experimental systems are available for studying C4orf3 function?

Several experimental systems have been developed to study C4orf3 function, with the most well-characterized being the C4orf3 Knockout cell line derived from HEK293 cells . This system allows for loss-of-function studies through complete genetic deletion of the C4orf3 gene. Additional experimental approaches include:

The choice of experimental system depends on the specific research question being addressed. The HEK293 knockout line offers particular advantages due to its ease of use and high transfection efficiency compared to other human cell lines, enabling greater consistency in experimental outcomes .

How do I validate C4orf3 knockout efficiency in experimental models?

Validating C4orf3 knockout efficiency requires a multi-faceted approach to ensure complete absence of functional protein. Recommended validation procedures include:

• Genomic validation: PCR amplification and sequencing of the targeted region to confirm successful deletion or mutation of the C4orf3 gene.

• Transcriptional validation: RT-qPCR using primers specific to C4orf3 mRNA to verify absence of transcript. Reference genes such as β-actin should be used as internal controls for normalization .

• Protein validation: Western blot analysis using validated antibodies against C4orf3, though this may be challenging due to the uncharacterized nature of the protein and potential lack of highly specific antibodies.

• Functional validation: Assessing phenotypic changes consistent with C4orf3 absence, such as altered proliferation rates, metabolic activity, or stress responses .

A combined approach using at least two different validation methods is recommended to confidently establish knockout efficiency in any experimental model used for C4orf3 research.

What signaling pathways interact with or are regulated by C4orf3?

Current research suggests C4orf3 may participate in multiple cellular signaling pathways, though specific interactions remain under investigation. Based on knockout studies, C4orf3 appears to influence biochemical pathways related to:

• Cellular stress response: C4orf3 knockout cells show altered responses to stress conditions, suggesting involvement in stress-adaptive signaling mechanisms .

• Cellular proliferation pathways: Loss-of-function studies indicate changes in proliferation rates when C4orf3 is absent, potentially implicating it in cell cycle regulation or growth factor signaling .

• Metabolic signaling: Preliminary evidence suggests C4orf3 may influence cellular metabolic activity, though specific pathway interactions require further characterization .

Researchers investigating C4orf3 pathway interactions should consider employing phosphoproteomics, interactome analysis through co-immunoprecipitation studies, and targeted pathway inhibition experiments to better elucidate the signaling networks connected to this protein. Computational approaches using consensus independent component analysis (c-ICA) may also help identify transcriptional components associated with C4orf3 function in large datasets .

How do I design experiments to identify C4orf3 binding partners and interaction networks?

Designing experiments to identify C4orf3 binding partners requires multiple complementary approaches:

Proteomic Approaches:

• Immunoprecipitation followed by mass spectrometry (IP-MS): Using antibodies against C4orf3 or epitope-tagged recombinant C4orf3 to pull down the protein along with its binding partners.

• Proximity labeling methods: BioID or APEX2 techniques can be employed by fusing C4orf3 to biotin ligase, which biotinylates proximal proteins for subsequent purification and identification.

• Yeast two-hybrid screening: Though prone to false positives, this can serve as an initial screen for potential interactors.

Validation Methods:

• Co-immunoprecipitation experiments: To confirm direct interactions between C4orf3 and candidate binding partners.

• Fluorescence resonance energy transfer (FRET) or bimolecular fluorescence complementation (BiFC): To visualize protein-protein interactions in living cells.

• Surface plasmon resonance (SPR) or isothermal titration calorimetry (ITC): For in vitro binding studies using purified proteins.

When identifying C4orf3 interaction networks, consider employing both forward and reverse approaches (i.e., pulling down C4orf3 to identify partners and pulling down suspected partners to confirm C4orf3 interaction). Additionally, conducting experiments under different cellular conditions (normal vs. stress) may reveal context-dependent interactions that provide insight into the protein's functional roles.

What cellular phenotypes are altered in C4orf3 knockout models, and how do I quantify them?

C4orf3 knockout models exhibit several altered cellular phenotypes that can be systematically quantified:

Proliferation Alterations:

• Quantification method: Cell Counting Kit-8 (CCK-8) assays can measure proliferation over 24h, 48h, 72h, and 96h intervals .

• Alternative method: 5-ethynyl-2'-deoxyuridine (EdU) incorporation assays can measure DNA synthesis rates as an indicator of proliferation .

Colony Formation Capacity:

• Quantification method: Crystal violet staining of colonies formed after 2 weeks of culture, followed by colony counting and measurement .

Cellular Migration and Invasion:

• Quantification method: Transwell migration assays using chambers with or without Matrigel coating; cells are counted after 24-hour migration through membrane pores .

Metabolic Activity:

• Quantification method: Seahorse XF analysis to measure oxygen consumption rate (OCR) and extracellular acidification rate (ECAR).

Response to Cellular Stress:

• Quantification method: Measure viability and apoptotic markers after exposure to various stressors (oxidative, heat, nutrient deprivation).

When designing phenotypic analysis experiments, it's recommended to employ multiple independent methods to confirm observed changes and to conduct time-course studies to capture dynamic phenotypic alterations. Additionally, rescue experiments, where wild-type C4orf3 is reintroduced into knockout cells, should be performed to confirm phenotypic changes are directly attributable to C4orf3 absence rather than off-target effects or cellular adaptations.

How can I assess the contribution of C4orf3 to in vivo phenotypes using animal models?

Assessing C4orf3's contribution to in vivo phenotypes requires careful experimental design using appropriate animal models:

Animal Model Selection:

• Knockout mouse models: Complete or conditional C4orf3 knockout mice can be generated using CRISPR-Cas9 technology.

• Xenograft models: Similar to methods used for other genes, where C4orf3 knockout cell lines (e.g., modified HEK293) are injected subcutaneously into immunocompromised mice to assess tumor growth and metastatic potential .

Experimental Approaches:

• Subcutaneous tumor xenograft model:

  • Inject 1×10^6 cells with C4orf3 knockout and control cells into the upper-right flank of BALB/c nude mice.

  • Monitor tumor volume weekly using the formula: volume (mm^3) = (Length×Width^2)/2.

  • After 5 weeks, collect tumor tissues and perform immunohistochemical staining for proliferation markers like KI67 and PCNA .

• Metastasis model:

  • Inject 1×10^7 C4orf3 knockout and control cells into the spleen capsule of BALB/c mice.

  • Monitor animal health and behavior daily.

  • Upon sacrifice, count metastatic foci in target organs and perform H&E staining to confirm metastatic status .

• Tissue-specific conditional knockout:

  • Use Cre-loxP system to delete C4orf3 in specific tissues of interest.

  • Analyze tissue-specific phenotypes through histology, immunohistochemistry, and functional tests.

When designing in vivo experiments, ensure appropriate sample sizes (minimum n=5 per group) for statistical power, implement proper randomization and blinding procedures, and obtain all necessary ethical approvals for animal research. Additionally, consider using various doses of injected cells and different genetic backgrounds to assess the robustness of observed phenotypes across experimental conditions.

What are the optimal conditions for expressing and purifying recombinant C4orf3 protein?

Optimizing the expression and purification of recombinant C4orf3 requires careful consideration of expression systems, tags, and purification strategies:

Expression Systems:

Expression Optimization:

• Codon optimization: Adapt the C4orf3 sequence to the codon usage bias of the expression host.

• Fusion tags: Consider using solubility-enhancing tags (MBP, SUMO, Thioredoxin) if protein solubility is an issue.

• Induction conditions: For bacterial systems, optimize IPTG concentration (typically 0.1-1.0 mM) and induction temperature (16-37°C).

• Growth media: Test enriched media formulations to improve protein yield.

Purification Strategy:

• Affinity chromatography: Use histidine or other affinity tags (HIS, GST, FLAG) for initial capture.

• Size exclusion chromatography: Remove aggregates and purify by molecular size.

• Ion exchange chromatography: Further purify based on protein charge.

Quality Control:

• SDS-PAGE and Western blot to confirm identity and purity.

• Mass spectrometry for accurate mass determination and sequence verification.

• Circular dichroism spectroscopy to assess secondary structure and proper folding.

• Dynamic light scattering to evaluate homogeneity and aggregation state.

When working with C4orf3, initial small-scale expression trials are recommended to determine optimal conditions before scaling up production. Additionally, if the protein proves difficult to produce in soluble form, consider structural predictions to identify and potentially remove problematic regions while maintaining functional domains.

How can I assess post-translational modifications of C4orf3 and their functional significance?

Assessing post-translational modifications (PTMs) of C4orf3 and determining their functional significance requires a multi-step approach:

PTM Identification Methods:

• Mass spectrometry-based approaches:

  • Bottom-up proteomics: Digest C4orf3 with proteases and analyze peptides by LC-MS/MS.

  • Top-down proteomics: Analyze intact C4orf3 protein to preserve PTM combinations.

  • Enrichment strategies: Use specific antibodies or chemical approaches to enrich for phosphorylated, glycosylated, or ubiquitinated forms.

• Western blotting:

  • Use PTM-specific antibodies (anti-phospho, anti-ubiquitin, etc.).

  • Mobility shift assays to detect changes in migration patterns due to PTMs.

• Specific PTM detection assays:

  • Pro-Q Diamond staining for phosphorylation.

  • Periodic acid-Schiff staining for glycosylation.

Functional Significance Assessment:

• Site-directed mutagenesis:

  • Mutate identified PTM sites (e.g., S/T→A for phosphorylation sites).

  • Express mutant proteins in C4orf3 knockout cells to assess rescue capabilities.

• Temporal dynamics:

  • Monitor PTM changes in response to cellular stimuli or stress conditions.

  • Use pulse-chase labeling to determine PTM turnover rates.

• Interactome changes:

  • Compare binding partners between wild-type C4orf3 and PTM-deficient mutants.

  • Use proximity labeling methods to identify proteins that interact with C4orf3 in a PTM-dependent manner.

• Localization effects:

  • Assess whether PTMs alter C4orf3 subcellular localization using immunofluorescence or fractionation approaches.

When studying C4orf3 PTMs, consider examining multiple cell types and conditions, as PTM patterns may vary contextually. Additionally, computational prediction tools can help prioritize potential modification sites for experimental validation, especially for this relatively uncharacterized protein.

What approaches can I use to study the transcriptional regulation of C4orf3?

Studying the transcriptional regulation of C4orf3 requires comprehensive analysis of its promoter region, transcription factors, and regulatory elements:

Promoter Characterization:

• Bioinformatic analysis:

  • Identify the core promoter region and potential transcription factor binding sites using tools like JASPAR, TRANSFAC, or ENCODE ChIP-seq datasets.

  • Analyze CpG islands and potential epigenetic regulatory elements.

• Promoter reporter assays:

  • Clone various lengths of the C4orf3 promoter region upstream of a luciferase reporter gene.

  • Transfect constructs into relevant cell lines and measure luciferase activity to identify key regulatory regions.

  • Create truncation and mutation constructs to pinpoint essential regulatory elements.

Transcription Factor Identification:

• Chromatin Immunoprecipitation (ChIP):

  • Perform ChIP using antibodies against predicted transcription factors.

  • Analyze enrichment at the C4orf3 promoter by qPCR or sequencing (ChIP-seq).

• DNA affinity precipitation (DAPA):

  • Use biotinylated C4orf3 promoter fragments to capture binding proteins.

  • Identify bound proteins by mass spectrometry.

• CRISPR activation/interference:

  • Use dCas9 fused to activators or repressors to target the C4orf3 promoter region.

  • Measure effects on C4orf3 expression to identify functionally important regulatory regions.

Epigenetic Regulation:

• Bisulfite sequencing to assess DNA methylation patterns at the C4orf3 promoter.

• ChIP for histone modifications (H3K4me3, H3K27ac, H3K27me3) to characterize chromatin state.

• ATAC-seq to determine chromatin accessibility at the C4orf3 locus.

Expression Analysis:

• RT-qPCR to quantify C4orf3 transcript levels under various conditions and treatments.

• RNA-seq to identify co-regulated genes that may share regulatory mechanisms with C4orf3.

• Single-cell RNA-seq to examine cell-to-cell variation in C4orf3 expression.

For comprehensive transcriptional regulation studies, consider employing consensus independent component analysis (c-ICA) as described in the literature to identify transcriptional components that include C4orf3 and co-regulated genes , which may provide insight into broader regulatory networks controlling C4orf3 expression.

How is C4orf3 expression altered in disease states, and what methodologies best capture these changes?

Current research on C4orf3 expression in disease states is limited, but researchers can employ several approaches to investigate potential alterations:

Expression Analysis Methodologies:

• Mining public databases:

  • Query GEO, TCGA, and other repositories for C4orf3 expression patterns across disease datasets .

  • Use consensus independent component analysis (c-ICA) to detect subtle transcriptional footprints of C4orf3 in complex datasets .

• Quantitative expression assessment:

  • RT-qPCR using SYBR Green PCR Master Mix with β-actin as internal control .

  • RNA-seq for genome-wide expression profiling that includes C4orf3.

  • In situ hybridization to examine spatial expression patterns in tissue samples.

• Protein-level analysis:

  • Immunohistochemistry of tissue microarrays to assess C4orf3 protein expression across multiple disease and normal samples.

  • Western blotting for quantitative protein expression comparison.

  • Proteomics approaches for unbiased protein quantification.

Disease-Specific Considerations:
While specific disease associations with C4orf3 are not well-established in the current literature, researchers should consider examining:

• Cancer contexts: Given the potential role of C4orf3 in cellular proliferation, investigating expression patterns across cancer types using approaches similar to those used for other genes in pancreatic cancer research may be valuable .

• Stress-response related pathologies: Since C4orf3 is implicated in cellular stress responses , examining expression changes in conditions characterized by cellular stress (ischemia, neurodegeneration, inflammatory disorders) may reveal relevant patterns.

• Metabolic disorders: The potential involvement of C4orf3 in metabolic pathways suggests examining expression changes in diabetes, obesity, and related conditions.

When investigating C4orf3 in disease contexts, researchers should employ multiple detection methods and consider both spatial and temporal dimensions of expression changes, as these may provide complementary insights into the protein's role in pathological processes.

What therapeutic approaches could target C4orf3 or its pathways in disease?

Though therapeutic targeting of C4orf3 is in early exploratory stages, several approaches could be considered based on its cellular functions:

Potential Therapeutic Strategies:

• Direct protein targeting:

  • Small molecule inhibitors: Structure-based or high-throughput screening to identify compounds that bind to functional domains of C4orf3.

  • Peptide inhibitors: Design of peptides that mimic binding interfaces to disrupt C4orf3 interactions.

  • Proteolysis targeting chimeras (PROTACs): Bifunctional molecules that bind C4orf3 and recruit E3 ubiquitin ligases to promote its degradation.

• Genetic approaches:

  • Antisense oligonucleotides or RNAi to reduce C4orf3 expression at the mRNA level.

  • CRISPR-based approaches for selective gene editing in appropriate therapeutic contexts.

• Pathway-based approaches:

  • Targeting upstream regulators of C4orf3 expression.

  • Modulating downstream effectors of C4orf3-mediated pathways.

Therapeutic Evaluation Methodologies:

• In vitro screening:

  • C4orf3 knockout cell lines provide platforms for evaluating therapeutic candidates .

  • High-throughput proliferation, apoptosis, and metabolic assays can assess functional impacts.

• Ex vivo models:

  • Patient-derived organoids or tissue explants to test interventions in more complex systems.

• In vivo models:

  • Xenograft models using C4orf3-modified cell lines to assess tumor growth inhibition .

  • Genetic mouse models with C4orf3 alterations to evaluate systemic effects of targeted therapies.

• Target validation:

  • Biomarker development to identify patients likely to respond to C4orf3-targeted therapies.

  • Combination strategy testing to identify synergistic therapeutic approaches.

When developing therapeutic approaches targeting C4orf3, careful consideration of tissue specificity, potential off-target effects, and delivery methods is essential. Additionally, given the uncharacterized nature of C4orf3, therapeutic development should proceed alongside continued basic research into the protein's fundamental biology and disease relevance.

How can I integrate multi-omics data to better understand C4orf3 function in cellular networks?

Integrating multi-omics data provides a comprehensive view of C4orf3 function within cellular networks. Researchers can employ the following approaches:

Data Integration Methodologies:

• Consensus Independent Component Analysis (c-ICA):

  • Apply c-ICA to separate gene expression profiles into transcriptional components, potentially revealing C4orf3's participation in specific cellular programs .

  • This approach can be used with large datasets (>30,000 profiles) to detect both pronounced and subtle transcriptional footprints .

• Network-based integration:

  • Construct protein-protein interaction networks incorporating C4orf3.

  • Overlay transcriptomic, proteomic, and metabolomic data to identify functional modules.

  • Apply algorithms like WGCNA (Weighted Gene Co-expression Network Analysis) to identify co-regulated gene modules.

• Pathway enrichment analysis:

  • Perform GSEA (Gene Set Enrichment Analysis) on multi-omics data to identify pathways enriched in conjunction with C4orf3 .

  • Use consensus clustering with a Pearson correlation distance metric and hierarchical clustering to reduce redundancy in gene sets from different collections .

• Causal network modeling:

  • Apply Bayesian network approaches to infer causal relationships between C4orf3 and other cellular components.

  • Use time-course data to establish temporal sequence of events following C4orf3 perturbation.

Implementation Strategy:

When implementing multi-omics integration for C4orf3 research, consider using consensus clustering with a maximum number of clusters (maxK) of 150 and 2000 resamplings for optimal results . Additionally, Spearman correlation analysis can be valuable for determining the significance of gene overlap between different metabolic transcriptional components associated with C4orf3 .

What computational approaches can predict C4orf3 structure and functional domains in the absence of crystallographic data?

In the absence of crystallographic data, several computational approaches can help predict C4orf3 structure and functional domains:

Structural Prediction Approaches:

• Homology modeling:

  • Identify structural homologs of C4orf3 using sequence alignment tools like BLAST, HHpred, or SWISS-MODEL.

  • Build structural models based on templates with the highest sequence similarity.

  • Assess model quality using metrics like QMEAN, DOPE score, or Ramachandran plot analysis.

• Ab initio modeling:

  • Use physics-based approaches like Rosetta or QUARK to predict structure from sequence alone.

  • Apply fragment-based methods that assemble structures from short segments with known conformations.

• Deep learning approaches:

  • Leverage AI-based tools like AlphaFold2 or RoseTTAFold that have demonstrated near-experimental accuracy for many proteins.

  • Generate multiple models and assess confidence scores for different regions.

Functional Domain Prediction:

• Sequence-based domain prediction:

  • Use tools like SMART, Pfam, InterPro, or PROSITE to identify conserved domains.

  • Apply disorder prediction algorithms (DISOPRED, IUPred) to identify structured versus unstructured regions.

• Function prediction:

  • Employ tools like DeepFRI or COFACTOR to predict protein function from structure.

  • Use ConSurf to identify evolutionarily conserved residues that may be functionally important.

• Binding site prediction:

  • Apply tools like CASTp, COACH, or FTSite to identify potential ligand binding pockets.

  • Use PPiPP or PRISM to predict protein-protein interaction interfaces.

Validation Approaches:

• Experimental validation of predictions:

  • Design mutations targeting predicted functional residues and assess impact on C4orf3 function.

  • Use limited proteolysis to experimentally map domain boundaries and compare with predictions.

  • Apply crosslinking mass spectrometry to validate predicted structural features.

• Molecular dynamics simulations:

  • Assess stability of predicted structures through MD simulations.

  • Identify conformational changes that may relate to function.

When applying computational approaches to C4orf3 structure prediction, researchers should generate multiple models using different methods and look for consensus features. Additionally, incorporating evolutionary information through multiple sequence alignments can significantly improve prediction accuracy, especially for functional residues. Given the uncharacterized nature of C4orf3, structural predictions should be treated as hypotheses to guide experimental design rather than definitive representations.