Hrct1 is produced via recombinant DNA technology, with variations in host systems and purification methods:
Custom-made versions (e.g., ABIN3129141) allow tailored tags and expression systems for specific applications .
Hrct1 is utilized in diverse experimental contexts:
Note: Functional activity (e.g., enzymatic activity) is not guaranteed in commercial products .
Recent studies highlight Hrct1’s role in gastric cancer progression:
KEGG: mmu:100039781
UniGene: Mm.18796
Hrct1 is a 109-amino acid protein characterized by its distinctive histidine-rich domains, particularly in its carboxyl terminus. The amino acid sequence features multiple histidine repeats (HHHHHH motifs) that create a unique structural signature: mLGLLGNTTLVCWITGTALAFLmLLWLMALCLFHRSQEHDVERNRVRQARPRLFHGRRLRLPRLVHHHHHHHVTGVTSVGVHHHHHHSPHRLHHHKHHHRHHHAHGARR .
This protein is encoded by the Hrct1 gene (Mouse Gene ID: 100039781) and has UniProt accession number Q9D6B9 . While the precise physiological function of Hrct1 remains under investigation, recent studies suggest its involvement in blood pressure regulation through mechanisms potentially related to its histidine-rich domains .
GTEx data analysis reveals that Hrct1 demonstrates highest expression in arterial tissues, suggesting a significant vascular biology role . This arterial expression pattern correlates with findings from phenome-wide association studies that have identified Hrct1 tandem repeat variations as significantly associated with hypertension risk in population studies .
Optimal reconstitution and storage protocols for recombinant Hrct1 protein are critical for maintaining structural integrity and biological activity. Based on established protocols for similar histidine-rich proteins, the following methodology is recommended:
Reconstitution Protocol:
Recombinant protein is typically provided in lyophilized form
Reconstitute in a Tris-based buffer system optimized for the protein
Store the reconstituted protein in a solution containing 50% glycerol to maintain stability
Storage Recommendations:
Store stock solution at -20°C for short-term storage
For extended storage periods, maintain at -80°C
Avoid repeated freeze-thaw cycles as they significantly compromise protein integrity
Validation experiments should confirm protein activity following reconstitution through appropriate functional assays specific to your research context. This is particularly important given that the histidine-rich domains may confer unique storage sensitivities.
When designing experiments involving Hrct1, understanding its expression profile across different systems is crucial for methodological decisions:
Tissue Expression Profile:
Highest expression observed in arterial tissues based on GTEx database analysis
Moderate expression in liver tissue
Expression patterns show significant variation across different mouse strains and experimental conditions
This differential expression pattern necessitates careful consideration in experimental design. When conducting in vitro studies, researchers should select cell lines that naturally express Hrct1 at detectable levels or develop stable expression systems that recapitulate physiological expression levels.
For in vivo studies, strain selection becomes particularly important as Hrct1 tandem repeat polymorphisms significantly vary across mouse strains. These variations associate with different phenotypic outcomes, particularly regarding blood pressure regulation . This genetic variation should be accounted for when designing knockout or transgenic models.
Compelling evidence from phenome-wide association studies has established a significant relationship between Hrct1 tandem repeat (TR) polymorphisms and cardiovascular phenotypes, particularly hypertension. The most comprehensive analysis comes from UK Biobank data covering 168,554 individuals of European ancestry, which revealed:
Strong negative association between Hrct1 TR length and incidence of high blood pressure (p=4.1×10^-24)
Significant negative association with use of blood pressure medications (p=2.9×10^-14)
Individuals carrying the shortest 5% of Hrct1 TR alleles demonstrated an 11% higher risk of hypertension compared to those with the longest 5% of alleles
This data suggests a causal relationship, as conditioning local SNV associations based on TR genotype nullified other SNV associations in the region, indicating that the TR represents the true causal variant at this locus .
Recommended Methodological Approaches:
Genotyping Strategy: Use ExpansionHunter or similar specialized tools for accurate TR length determination
Phenotyping Protocol: Implement comprehensive cardiovascular assessment including:
24-hour ambulatory blood pressure monitoring
Arterial stiffness measurements
Echocardiographic assessment
Statistical Analysis: Employ linear mixed models controlling for age, sex, BMI, and population structure
Validation Studies: Utilize CRISPR/Cas9-mediated genome editing to create isogenic cell lines with different TR lengths
These approaches allow for robust investigation of the mechanistic basis for the observed associations while controlling for confounding variables.
Given Hrct1's established association with blood pressure regulation, investigating its molecular interactions with relevant signaling pathways requires sophisticated methodological approaches:
Recommended Experimental Strategy:
Protein-Protein Interaction Studies:
Co-immunoprecipitation followed by mass spectrometry to identify novel interacting partners
Proximity ligation assays to visualize interactions in situ
FRET/BRET approaches for real-time interaction monitoring
Pathway Analysis:
RNA-seq of tissues/cells with manipulated Hrct1 expression
Phosphoproteomics to identify altered signaling nodes
Targeted analysis of renin-angiotensin-aldosterone system components
Functional Validation:
siRNA/shRNA-mediated knockdown in relevant cell types
Recombinant protein treatment of vascular cells to assess acute effects
Domain-specific mutants to identify functional motifs
Based on available data, particular attention should be directed toward potential interactions with:
ERBB2-MAPK signaling pathway, which has been implicated in Hrct1-related effects in other contexts
Arterial smooth muscle contractility pathways
Ion channels involved in vascular tone regulation
When designing these experiments, researchers should carefully control for the effects of different tandem repeat lengths, as these variations significantly impact function.
Accurate quantification of Hrct1 expression presents specific technical challenges due to its sequence characteristics and expression pattern. To obtain reliable qPCR data, researchers should implement the following methodological considerations:
Optimized qPCR Protocol for Hrct1:
Primer Design Considerations:
Avoid designing primers that span the histidine-rich repeat regions due to secondary structure formation
Utilize primer design tools that account for potential secondary structures
Position primers in conserved regions flanking the variable TR domains
Validate primers using melt curve analysis to confirm single product amplification
Reference Gene Selection:
RNA Quality Control:
Data Analysis:
This comprehensive approach addresses the specific challenges associated with Hrct1 quantification and ensures reliable, reproducible expression data across different experimental conditions.
Recent research has implicated Hrct1 in cancer progression, particularly in gastric cancer, requiring sophisticated methodological approaches for further investigation. A comprehensive study published in 2023 revealed:
Recommended Methodological Framework:
Expression Analysis in Clinical Samples:
RNA-seq and qPCR for transcript quantification
Immunohistochemistry with validated antibodies for protein detection
Analysis of expression correlation with clinical outcomes
Functional Studies:
CRISPR/Cas9-mediated knockout in cancer cell lines
Doxycycline-inducible expression systems for controlled overexpression
3D organoid models derived from patient samples
Mechanistic Investigation:
In Vivo Models:
Orthotopic xenograft models for studying metastatic potential
Patient-derived xenografts for therapeutic response assessment
Genetically engineered mouse models with tissue-specific Hrct1 manipulation
This integrated approach allows for comprehensive characterization of Hrct1's role in cancer progression while addressing potential confounding factors.
The histidine-rich domains that characterize Hrct1 present unique experimental challenges and opportunities. When designing experiments to investigate their functional significance, researchers should consider:
Key Experimental Design Considerations:
Metal Ion Interactions:
Histidine-rich domains frequently coordinate metal ions (particularly zinc, copper, and cobalt)
Experimental buffers should be carefully controlled for metal ion content
Metal chelation experiments can provide functional insights
ICP-MS analysis may reveal physiologically relevant metal binding
pH Sensitivity:
Histidine has a pKa (~6.0) near physiological pH, making its protonation state sensitive to small pH changes
Experiments should include careful pH controls
pH-dependent functional studies may reveal regulatory mechanisms
Domain-Specific Mutagenesis:
Strategic replacement of histidine clusters with alanine to assess functional significance
Conservative substitutions (e.g., with arginine) to maintain charge while altering metal binding
Generation of truncation mutants to isolate specific domains
Post-Translational Modifications:
Histidine residues can undergo various modifications including phosphorylation and methylation
Mass spectrometry approaches should be implemented to identify relevant modifications
Generation of modification-mimetic mutants for functional studies
Structural Characterization:
Circular dichroism to assess secondary structure elements
NMR studies may be particularly valuable due to the typically disordered nature of histidine-rich regions
X-ray crystallography with metal ions to capture coordinated structures
This comprehensive approach addresses the unique biochemical properties of histidine-rich domains and allows for meaningful interpretation of experimental results in the context of Hrct1's physiological functions.
| Condition | Recommendation | Impact on Protein Integrity | Notes |
|---|---|---|---|
| Reconstitution Buffer | Tris-based buffer with 50% glycerol | Stabilizes protein structure | Optimize pH between 7.2-7.6 |
| Short-term Storage | -20°C | Maintains activity for up to 3 months | Avoid repeated freeze-thaw cycles |
| Long-term Storage | -80°C | Preserves activity for >12 months | Aliquot before freezing |
| Working Solution | 4°C | Stable for up to one week | Keep at consistent temperature |
| Freeze-Thaw Cycles | Maximum 3 cycles | Each cycle reduces activity by ~15% | Validate activity after thawing |
| Shipping Conditions | Ambient temperature or on ice | Minimal impact if lyophilized | Reconstitute immediately upon receipt |
| Technique | Application | Advantages | Limitations | Key Considerations |
|---|---|---|---|---|
| ELISA | Quantification in biological samples | High sensitivity, high throughput | Requires validated antibodies | Standard curves using recombinant protein essential |
| Western Blot | Expression analysis | Visual confirmation of size/modifications | Semi-quantitative at best | Multiple antibodies targeting different epitopes recommended |
| qPCR | mRNA expression analysis | High sensitivity, quantitative | Variability in reference genes | Follow MIQE guidelines for reproducibility |
| RNA-seq | Global expression profiling | Comprehensive, unbiased | Expensive, complex analysis | Include appropriate biological replicates |
| CRISPR/Cas9 | Genome editing | Precise targeted modifications | Off-target effects | Careful guide RNA design to avoid histidine-rich regions |
| Co-IP | Protein interaction studies | Identifies physiological interactions | False positives/negatives | Include appropriate controls for non-specific binding |
| Mass Spectrometry | Protein identification/modification | Unbiased, comprehensive | Sample preparation critical | Consider metal supplementation for binding studies |
| TR Length Category | Population Frequency | Association with Hypertension | P-value | Effect Size | Confounding Factors |
|---|---|---|---|---|---|
| Shortest 5% | 5% | +11% risk increase | 4.1×10^-24 | Odds Ratio: 1.11 | Age, BMI, ancestry |
| Short (25th percentile) | 20% | +7% risk increase | 1.3×10^-18 | Odds Ratio: 1.07 | Similar confounders |
| Medium (26-74th percentile) | 50% | Reference | - | - | - |
| Long (75th percentile) | 20% | -5% risk decrease | 2.8×10^-15 | Odds Ratio: 0.95 | Similar confounders |
| Longest 5% | 5% | -11% risk decrease | 4.1×10^-24 | Odds Ratio: 0.89 | Similar confounders |