CCNI2 Antibody

What is CCNI2 and what cellular functions does it regulate?

CCNI2 (Cyclin I-like or Cyclin I2) is a protein that plays a significant role in cell cycle regulation with an approximate mass of 35 kDa . As a member of the cyclin family, CCNI2 functions as a cell cycle regulator involved in critical cellular processes. Research has demonstrated that CCNI2 contributes to cancer progression through several mechanisms:

• Cell proliferation regulation: CCNI2 promotes cancer cell proliferation, with knockdown studies showing reduced proliferation rates in colorectal and gastric cancer cell lines

• Cell cycle control: CCNI2 knockdown arrests cell cycle in G2 phase in colorectal cancer cells

• Apoptosis modulation: Reduced CCNI2 expression increases susceptibility to apoptosis by affecting key apoptotic factors including BID, BIM, Caspase3, and DR6

For experimental investigations, researchers should consider using specific antibodies that target different epitopes of CCNI2, particularly those corresponding to amino acids 150 to C-terminus of the human protein, which have been validated for western blot analysis .

What are the expression patterns of CCNI2 in cancer tissues compared to normal tissues?

CCNI2 exhibits distinct expression patterns that vary between cancer and normal tissues:

Colorectal Cancer (CRC):

• Immunohistochemical staining shows significantly higher CCNI2 expression in CRC tissues compared to paracarcinoma tissues

• qPCR analysis confirms elevated CCNI2 mRNA levels in CRC cell lines (CACO2, RKO, SW480, and HCT 116) compared to normal colorectal mucosal cells (FHC)

Gastric Cancer:

• Based on TCGA database analysis of 407 gastric cancer samples, CCNI2 expression is significantly higher in tumor tissues compared to normal samples

• High expression of CCNI2 was observed in 43 of 93 tumor tissues (46.2%) and in 0 of 101 normal tissues in clinical samples

When designing experiments to evaluate CCNI2 expression, researchers should implement both protein-level detection (immunohistochemistry, western blot) and transcript-level analysis (qPCR) to obtain comprehensive expression profiles across different sample types.

What methodologies are recommended for detecting CCNI2 expression in clinical samples?

Based on published research protocols, the following methodologies are recommended for optimal CCNI2 detection:

Immunohistochemical Staining:

• Formalin-fixed paraffin-embedded samples should be cut into 5-μm sections, deparaffinized and rehydrated

• Primary antibody: Use anti-CCNI2 antibody (1:50 dilution), incubate at 4°C overnight

• Secondary antibody: HRP goat anti-rabbit IgG (1:200 dilution), incubate at room temperature for 30 min

• Signal detection: Stain with DAB and hematoxylin at room temperature

Western Blot Analysis:

• Total cellular proteins (20 μg) should be subjected to 10% SDS-PAGE

• Transfer proteins to PVDF membranes via wet transfer

• Blocking: Use TBST with 5% skim milk at 4°C for 1 hour

• Primary antibody: Anti-CCNI2 antibody (1:1000, Abcam ab97767) at 4°C overnight

• Secondary antibody: HRP-conjugated goat anti-rabbit IgG (1:3000) at room temperature for 2 hours

• Detection: Use ECL-Plus™ Western blotting system kit for visualization

qPCR Analysis:

• Design primers specific to CCNI2 gene (NM_001287253.1)

• Normalize expression data to appropriate housekeeping genes

• Validate specificity of amplification products

How can researchers effectively validate CCNI2 knockdown efficiency?

When validating CCNI2 knockdown efficiency, a multi-method approach is essential:

shRNA Design and Selection:

• Design multiple shRNA sequences targeting different regions of CCNI2 (e.g., shCCNI2-1: 5'-ATCTGCGACGCCTTCGAGGAA-3'; shCCNI2-2: 5'-TACCTGCATTGCGCCACAATT-3'; shCCNI2-3: 5'-CCTGGAAGGCGACCTGGACGA-3')

• Include appropriate negative control (e.g., shCtrl: 5'-TTCTCCGAACGTGTCACGT-3')

Transduction Efficiency Assessment:

• Use lentiviral vectors with GFP tag to visually confirm transduction (infection efficiency should exceed 80%)

• Evaluate cell fluorescence using fluorescence microscopy

Knockdown Validation:

• qPCR Validation: Quantify relative CCNI2 mRNA expression levels compared to control

  • Efficient knockdown should achieve >80% reduction in mRNA levels

• Western Blot Validation: Confirm protein level reduction

  • Use GAPDH as loading control

  • Quantify band intensity using software like Image J

• Functional Validation: Perform cellular assays to confirm phenotypic changes

  • Celigo cell counting assay can evaluate changes in cell proliferation rates

  • Compare results across multiple shRNA constructs to identify the most effective sequence

What experimental designs are most effective for studying CCNI2 function in cancer progression?

For comprehensive investigation of CCNI2 function in cancer progression, researchers should implement the following experimental design strategy:

In Vitro Studies:

• Cell Proliferation Assessment:

  • MTT assay: Seed 2000 cells/well in 96-well plates after CCNI2 knockdown/overexpression

  • Measure absorbance at 490 nm over multiple time points (typically days 1-5)

  • Calculate proliferation curves to quantify growth rate differences

• Colony Formation Analysis:

  • Seed 500 cells/well in 6-well plates and culture for 8 days

  • Fix with 4% paraformaldehyde for 60 min

  • Stain with GIEMSA for 15 min

  • Count colonies (defined as >50 cells) to assess clonogenic potential

• Cell Cycle Analysis:

  • Utilize fluorescence-activated cell sorting (FACS)

  • Apply proper gating strategy as demonstrated in published protocols (reference Figure S5A-B)

  • Quantify cell distribution across G0/G1, S, and G2/M phases

• Apoptosis Assessment:

  • Conduct FACS analysis with appropriate apoptosis markers

  • Use human apoptosis antibody arrays to identify differential expression of apoptosis-related proteins

  • Key proteins to monitor include BID, BIM, Caspase3, DR6, and IGF-II

In Vivo Studies:

• Mouse Xenograft Models:

  • Inject CCNI2-knockdown and control cancer cells subcutaneously into immunodeficient mice

  • Monitor tumor growth by measuring dimensions periodically

  • Use fluorescence imaging to visualize tumor development

• Tumor Analysis:

  • Measure tumor volume, weight, and fluorescence intensity

  • Perform immunohistochemical staining for proliferation markers (e.g., Ki-67)

  • Analyze tumor microenvironment components

Mechanism Investigation:

• Pathway Analysis:

  • RNA sequencing to identify differentially expressed genes

  • Western blot analysis of key signaling molecules (e.g., AKT phosphorylation, CCND1, CDK1, MAPK9)

  • Co-immunoprecipitation to identify binding partners

• Correlation with Clinical Data:

  • Perform Kaplan-Meier survival analysis to correlate CCNI2 expression with patient outcomes

  • Conduct multivariate analysis to account for confounding factors

How can researchers distinguish between the effects of CCNI2 and other cyclins in cancer progression studies?

Distinguishing the specific effects of CCNI2 from other cyclins requires careful experimental design:

Specificity Controls:

• Expression Correlation Analysis:

  • Measure expression levels of multiple cyclins (CCNI, CCND2, CCNB2) alongside CCNI2

  • Calculate correlation coefficients between expression patterns

  • Identify unique expression patterns specific to CCNI2

• Selective Knockdown and Rescue Experiments:

  • Design highly specific shRNAs targeting unique regions of CCNI2

  • Confirm specificity by measuring expression levels of other cyclin family members

  • Perform rescue experiments with shRNA-resistant CCNI2 constructs to confirm phenotype specificity

Functional Discrimination:

• Cell Cycle Phase Analysis:

  • CCNI2 knockdown arrests cell cycle in G2 phase

  • Compare with effects of other cyclins knockdown on cell cycle distribution

  • Use synchronized cell populations to identify phase-specific effects

• Protein Interaction Network Analysis:

  • Identify CCNI2-specific binding partners via co-immunoprecipitation followed by mass spectrometry

  • Compare with interaction networks of other cyclins

  • Focus on unique interactions as potential mediators of CCNI2-specific effects

Data Integration Approach:

This comprehensive comparative approach allows researchers to isolate CCNI2-specific effects from those of other cyclins.

What molecular mechanisms underlie CCNI2-mediated cancer progression?

Based on current research findings, several molecular mechanisms have been implicated in CCNI2-mediated cancer progression:

Apoptosis Regulation:

CCNI2 knockdown in cancer cells modulates key apoptotic factors:

• Upregulates pro-apoptotic factors: BID, BIM, and Caspase3

• Downregulates anti-apoptotic factors: IGF-II

• May affect cytochrome c release, a critical step in the intrinsic apoptotic pathway

When investigating this mechanism, researchers should:

• Examine mitochondrial membrane potential changes

• Monitor caspase activation cascade

• Assess cytochrome c translocation from mitochondria to cytosol

Cell Cycle Regulation:

CCNI2 impacts cell cycle progression with knockdown resulting in:

• G2 phase arrest in colorectal cancer cells

• Potential CDK5 interaction similar to its homolog CCNI

• Downregulation of CCND1 and CDK1 expression in gastric cancer cells

Research approaches should include:

• CDK activity assays to identify CCNI2-regulated CDKs

• Phosphorylation analysis of cell cycle checkpoint proteins

• Time-lapse imaging to visualize cell cycle progression defects

Signaling Pathway Modulation:

Evidence suggests CCNI2 influences multiple signaling pathways:

• AKT phosphorylation is reduced following CCNI2 knockdown

• MAPK9 expression is upregulated upon CCNI2 silencing

• In gastric cancer, CCNI2 potentially interacts with HDGF (Hepatoma-derived growth factor)

To elucidate these mechanisms, researchers should:

• Perform phosphoproteomic analysis to identify signaling alterations

• Use pathway-specific inhibitors to determine functional relevance

• Apply CRISPR-Cas9 screening to identify synthetic lethal interactions

Transcriptional Regulation:

CCNI2 may influence gene expression programs:

• RNA sequencing following CCNI2 knockdown reveals differentially expressed genes

• Potential involvement in transcriptional regulation through interaction with HDGF

Comprehensive analysis should include:

• ChIP-seq to identify genomic binding sites of CCNI2-associated factors

• RNA-seq time course following CCNI2 modulation

• ATAC-seq to assess chromatin accessibility changes

How can researchers address contradictory data regarding CCNI2 function across different cancer types?

When faced with contradictory data on CCNI2 function across cancer types, researchers should implement the following systematic approach:

1. Context-Dependent Analysis Framework:

Establish a standardized experimental design that accounts for:

• Cancer type-specific genetic backgrounds

• Tissue microenvironment factors

• Patient characteristics (age, gender, tumor stage)

2. Meta-Analysis of Expression Data:

Compare CCNI2 expression patterns across cancer types:

• In colorectal cancer, high CCNI2 expression correlates with:

  • Poor prognosis (Kaplan-Meier analysis)

  • Pathological grade (Spearman rank correlation coefficient: 0.257, p=0.011)

• In gastric cancer, CCNI2 expression correlates with:

  • Pathological stage (Pearson correlation: 0.242, p=0.020)

  • VEGF expression (p=0.025)

3. Functional Validation Across Multiple Models:

4. Reconciliation Strategies:

When contradictory results are observed:

• Evaluate differences in experimental methodologies

• Consider genetic heterogeneity within cancer subtypes

• Examine post-translational modifications affecting CCNI2 function

• Assess differences in binding partners across tissue types

5. Integrated Mechanism Model:

Develop a comprehensive model that incorporates cancer type-specific variations:

• Core conserved functions (proliferation, apoptosis regulation)

• Tissue-specific pathways and interactions

• Microenvironment-dependent effects

• Patient-specific factors (genetic background, comorbidities)

By implementing this systematic approach, researchers can develop a nuanced understanding of CCNI2 function that accounts for context-dependent variations across cancer types.

What considerations should be addressed when designing in vivo experiments to study CCNI2 function?

When designing in vivo experiments to study CCNI2 function, researchers should address several critical considerations:

1. Animal Model Selection:

• Xenograft Models:

  • Use immunodeficient mice (e.g., BALB/c nude) to prevent rejection of human cancer cells

  • Consider orthotopic implantation for more physiologically relevant tumor microenvironments

  • Validate cell lines for stable CCNI2 knockdown before implantation

• Genetically Engineered Mouse Models (GEMMs):

  • Consider tissue-specific CCNI2 knockout or overexpression using Cre-loxP systems

  • Evaluate tissue-specific promoters for targeted expression (e.g., Villin for intestinal epithelium)

  • Account for potential developmental effects of CCNI2 modulation

2. CCNI2 Modulation Strategies:

• Stable Knockdown Approach:

  • Use lentiviral vectors with validated shRNA sequences (e.g., shCCNI2-2: 5'-TACCTGCATTGCGCCACAATT-3')

  • Include fluorescent reporters (e.g., GFP) for tracking transduced cells in vivo

  • Validate knockdown stability over experimental timeframe

• Inducible Systems:

  • Consider doxycycline-inducible shRNA systems for temporal control

  • Implement CRISPR-Cas9 approaches for complete gene knockout

  • Evaluate pharmacological approaches if available

3. Experimental Design Parameters:

• Sample Size Determination:

  • Perform power analysis based on expected effect sizes

  • Include adequate biological replicates (minimum n=5 per group based on published studies)

  • Account for potential animal loss during experiments

• Timing Considerations:

  • Determine optimal endpoint based on tumor growth kinetics (typically 2-4 weeks post-implantation)

  • Implement intermediate measurements for longitudinal analysis

  • Consider multiple timepoints for mechanistic studies

4. Analysis Methods:

• Tumor Growth Assessment:

  • Measure tumor dimensions regularly (2-3 times/week)

  • Calculate tumor volume using formula: V = (length × width²)/2

  • Use fluorescence imaging for accurate visualization of tumors

• Molecular and Histological Analysis:

  • Perform immunohistochemical staining for proliferation markers (Ki-67)

  • Assess apoptosis using TUNEL assay

  • Analyze CCNI2 expression in tumor tissues to confirm maintained knockdown

  • Consider tissue microarray analysis for comprehensive protein profiling

5. Ethical and Regulatory Considerations:

• Follow institutional animal care guidelines and obtain proper approvals

• Implement humane endpoints based on tumor burden and animal welfare

• Consider the 3Rs principle (Replacement, Reduction, Refinement)

• Ensure proper reporting of animal experiments following the ARRIVE guidelines

6. Translational Relevance:

• Correlate findings with clinical data from patient samples

• Consider patient-derived xenograft models for higher clinical relevance

• Evaluate potential therapeutic implications through combination studies