KCNN4 Antibody

What is KCNN4 and why is it important in research?

KCNN4, also known as KCa3.1, IK1, or SK4, is an intermediate conductance calcium-activated potassium channel belonging to the KCNN family. This channel is expressed in multiple cell types including T cells, mast cells, macrophages, erythrocytes, vascular smooth muscle cells, airway smooth muscle cells, and various epithelial cells . The significance of KCNN4 in research stems from its involvement in numerous physiological processes and pathological conditions, particularly its role in cancer progression, immune modulation, and as a potential therapeutic target.

Research methodologies for studying KCNN4 typically involve:

• Transcriptomic analysis (RT-PCR, RNA-seq)

• Protein detection techniques (Western blotting, immunohistochemistry)

• Functional channel assays (patch-clamp electrophysiology)

• Channel modulation through inhibitors (e.g., Senicapoc, TRAM-34)

What are the different isoforms of KCNN4 and how do they differ functionally?

Three distinct KCNN4 isoforms have been identified:

These isoforms exhibit tissue-specific expression patterns, with KCNN4a being predominantly expressed in smooth muscle, while KCNN4b and KCNN4c are primarily found in epithelial cells. The functional differences between these isoforms are significant: KCNN4c, which lacks the S2 transmembrane segment, requires coexpression of a large conductance K⁺ channel β-subunit for plasma membrane expression and shows different sensitivity to the inhibitor TRAM-34 compared to KCNN4b .

What are the optimal methods for detecting KCNN4 protein expression in tissue samples?

The detection of KCNN4 in tissue samples requires careful methodology selection based on research objectives:

Immunohistochemistry (IHC):

• Recommended dilution: Follow antibody manufacturer specifications (typically 1:200-1:800)

• Critical factors: Proper antigen retrieval, validated antibody specificity

• Advantages: Permits subcellular localization analysis, allows assessment in clinical samples

• Interpretation considerations: KCNN4 shows distinct staining patterns in different cellular compartments (membrane, cytoplasm, nuclear) that correlate with different clinical outcomes

Western Blotting:

• Recommended dilution: 1:1000-1:8000, optimized per sample type

• Expected molecular weight: 48 kDa (but isoform-dependent: 40 kDa for KCNN4b, 37 kDa for KCNN4c)

• Controls: Include both positive controls (A431, HepG2, HEK-293 cells) and negative controls

• Validation approach: Use knockdown/knockout samples to confirm antibody specificity

For robust detection of KCNN4 isoforms, researchers should consider using isoform-specific antibodies or primers. The anti-KCNN4-abc antibody has been validated to detect both apical (37 kDa) and basolateral (40 kDa) KCNN4 proteins in epithelial cells, with specificity confirmed through peptide competition assays .

How can researchers distinguish between different KCNN4 isoforms in experimental settings?

Distinguishing between KCNN4 isoforms requires specialized approaches:

At mRNA level:

• RT-PCR with isoform-specific primers:

  • KCNN4a-specific primers: sense 5′-TTGGTCTCTGTGTCCCTGTG-3′; antisense 5′-TGTCCAGAGATGGGAAGACA-3′

  • KCNN4b/c-specific primers: sense 5′-GGCCACATAGCTGCCTGTTA; antisense 5′-TCCTTGAGCTCAGTCCTTCG-3′

At protein level:

• Western blot analysis with antibodies that can detect different molecular weights:

  • KCNN4c typically appears at 37 kDa

  • KCNN4b typically appears at 40 kDa

• Subcellular fractionation to separate apical and basolateral membranes followed by immunoblotting

Functional differentiation:

• Differential sensitivity to inhibitors: KCNN4b and KCNN4c show different sensitivities to TRAM-34 (IC₅₀ of 0.6 ± 0.1 μM and 7.8 ± 0.4 μM, respectively)

• Membrane localization studies using confocal microscopy or immunogold electron microscopy can help identify the differential distribution of isoforms in polarized cells

How reliable is KCNN4 as a prognostic biomarker across different cancer types?

KCNN4 has emerged as a significant prognostic biomarker across multiple cancer types, with substantial evidence supporting its reliability:

Kidney renal clear cell carcinoma (KIRC):

• Higher KCNN4 expression correlates with worse prognosis (validated in TCGA and GEO datasets)

• KCNN4 expression positively correlates with tumor stage and grade

• Patients with high KCNN4 levels showed significantly poorer survival outcomes

Pancreatic ductal adenocarcinoma (PDAC):

• KCNN4 overexpression correlates with poor outcomes in TCGA dataset analyses

• Functional studies confirm KCNN4 promotes PDAC cell proliferation in vitro

Thyroid cancer:

• Multivariate analysis results:

These data demonstrate that KCNN4 expression remains an independent prognostic factor even after adjusting for disease stage and T stage .

Breast cancer:

• KCNN4 protein localization patterns correlate with patient outcomes

• Membrane KCNN4 staining significantly associated with poor survival (P = 0.0005)

• Different subcellular localization patterns (nuclear, cytoplasmic, membrane) correlate with distinct survival outcomes

The reliability of KCNN4 as a prognostic biomarker is reinforced by pan-cancer analyses showing its potential utility across multiple cancer types, though the strength of association varies by cancer type .

What are the current methodologies for studying KCNN4's role in tumor progression?

Several complementary methodologies are employed to investigate KCNN4's role in tumor progression:

Gene expression manipulation:

• Gene knockdown approaches:

  • Short hairpin RNAs (shRNAs) and siRNAs targeting KCNN4

  • CRISPR-Cas9-mediated knockout of KCNN4

• Overexpression systems:

  • Transfection with KCNN4-encoding plasmids

  • Rescue experiments (re-expressing KCNN4 in knockdown cells)

Functional assays:

• Proliferation assays: CCK8 assay, trypan blue staining, colony formation assay

• Migration and invasion assays: Transwell assays, wound healing

• In vivo tumor growth: Mouse xenograft models

• Drug sensitivity testing: Response to chemotherapeutics in presence/absence of KCNN4

Pathway analysis:

Tumor microenvironment analysis:

• CIBERSORT algorithm for immune cell infiltration analysis

• Correlation with tumor-infiltrating lymphocytes (TILs)

• Association with immunotherapy response markers: TMB, MSI, immune checkpoint genes

These methodologies provide complementary data on KCNN4's functional roles in cancer progression, from molecular mechanisms to clinical outcomes.

How does KCNN4 influence the tumor microenvironment and immune response?

KCNN4 appears to significantly modulate the tumor microenvironment (TME) and immune response through several mechanisms:

Correlation with immune infiltration:
CIBERSORT analysis has revealed that KCNN4 expression correlates with multiple types of tumor-infiltrating immune cells (TICs). Specifically, in kidney renal clear cell carcinoma (KIRC):

• Negative correlation with:

  • Resting memory CD4+ T cells

  • Activated dendritic cells

  • M1 and M2 macrophages

  • Resting mast cells

  • Monocytes

  • Resting NK cells

• Positive correlation with:

  • Activated memory CD4+ T cells

  • CD8+ T cells

  • Regulatory T cells

  • Follicular helper T cells

  • Memory B cells

  • Plasma cells

These correlations suggest that KCNN4 may influence the recruitment, activation, or function of specific immune cell populations within the TME.

ImmuneScore and StromalScore correlations:
Research has found that the ImmuneScore (which quantifies immune cell infiltration) was negatively correlated with patients' prognosis in some cancers, and KCNN4 was identified among immune-related genes (IRGs) associated with these scores .

Implications for immunotherapy:
KCNN4 expression has been correlated with tumor mutational burden (TMB), microsatellite instability (MSI), and immune checkpoint genes (ICGs), suggesting its potential as a predictor of immunotherapy efficacy. Analytical methodologies include Spearman's correlation analysis visualized through radar maps using the "fmsb" R package .

The complex impact of KCNN4 on TME appears to be cancer type-specific, requiring careful assessment in each tumor context to understand the potential implications for therapeutic interventions.

What are the technical challenges in generating and validating KCNN4 knockout models?

Generating and validating KCNN4 knockout models present several technical challenges that researchers should address methodically:

Challenges in KCNN4 knockout generation:

• Isoform complexity:

  • The existence of multiple KCNN4 isoforms (KCNN4a, KCNN4b, KCNN4c) complicates knockout design

  • Targeting shared exons is necessary for complete knockout

  • Isoform-specific knockouts require precise targeting of unique regions

• Compensatory mechanisms:

  • Other potassium channels may compensate for KCNN4 loss

  • Changes in calcium signaling pathways may mask knockout phenotypes

  • Developmental adaptation in constitutive knockouts

• Cell type-specific effects:

  • KCNN4 functions differently across cell types

  • Conditional knockout approaches may be necessary to avoid confounding results

Validation methodologies:

• Genomic validation:

  • PCR amplification and sequencing of the targeted region

  • Analysis of indels and potential frameshifts

• Transcript verification:

  • RT-PCR with primers spanning the targeted region

  • RNA-seq to confirm absence of specific transcripts and identify potential alternative splicing

• Protein validation:

  • Western blotting with verified antibodies

  • Immunofluorescence or immunohistochemistry

  • Flow cytometry for cell surface expression (for membrane-localized KCNN4)

• Functional validation:

  • Patch-clamp electrophysiology to confirm loss of KCNN4-mediated currents

  • Calcium flux assays to assess impact on calcium signaling

  • Response to KCNN4-specific inhibitors (e.g., Senicapoc, TRAM-34) should be absent

  • Rescue experiments through re-expression of wild-type KCNN4

Recent research has employed gene editing to make deletions within Kcnn4 in 4T1 cells to determine whether the KCNN4 inhibitor Senicapoc had off-target effects on tumor growth, demonstrating the importance of knockout models for validating pharmacological interventions .

How do researchers assess the efficacy of KCNN4 inhibitors in preclinical models?

Assessment of KCNN4 inhibitors in preclinical models follows several methodological approaches:

In vitro efficacy assessment:

• Electrophysiological methods:

  • Patch-clamp recordings to directly measure KCNN4 channel activity

  • Determination of IC₅₀ values (e.g., TRAM-34 shows different potency against KCNN4b [IC₅₀ = 0.6 ± 0.1 μM] and KCNN4c [IC₅₀ = 7.8 ± 0.4 μM])

• Functional readouts:

  • ⁸⁶Rb (potassium surrogate) efflux assays

  • Calcium flux measurements

  • Cell viability and proliferation assays (CCK8, trypan blue exclusion)

  • Migration and invasion assays

In vivo efficacy assessment:

• Animal models:

  • Murine mammary tumor models to evaluate Senicapoc's effect on tumor development

  • Treatment of mice bearing 4T1 mammary tumors with Senicapoc via:

    • Subcutaneous injection

    • Oral gavage

• Control methodologies:

  • Use of KCNN4 knockout cells to distinguish on-target from off-target effects

  • Comparison with standard-of-care treatments

  • Dose-response relationships

Mechanistic evaluation:

• Assessment of downstream signaling pathways

• Analysis of tumor microenvironment changes

• Evaluation of immune cell infiltration and function

• Combination with other therapies to identify synergistic effects

The research by Moulder et al. (2023) exemplifies this approach, where they systematically evaluated Senicapoc in murine mammary tumor models and complemented pharmacological studies with gene editing approaches to validate target specificity .

What are the conflicting results in KCNN4 research and how can they be reconciled?

KCNN4 research has produced several conflicting results that require careful methodological consideration:

Prognostic significance contradictions:

• While most studies show KCNN4 overexpression correlates with poor prognosis in multiple cancers , some datasets show variable associations

• The GSE29609 dataset showed a trend toward poor survival with high KCNN4 expression but did not reach statistical significance (P=0.088), possibly due to small sample size (n=39)

• Reconciliation approach: Meta-analysis of multiple datasets with adequate sample sizes and multivariate analyses adjusting for confounding factors

Subcellular localization implications:

• Conflicting data on the significance of KCNN4's subcellular localization:

  • Membrane KCNN4 staining associated with poor survival (P = 0.0005)

  • Nuclear staining without cytoplasmic or membrane staining (N1C0M0) associated with improved survival in ER+/HER2- breast cancer cases (P < 0.0001)

  • Strong cytoplasmic staining shows different associations across cancer subtypes

• Reconciliation approach: Standardized scoring systems for subcellular localization patterns and stratification by molecular subtypes

Immune correlation discrepancies:

Methodological considerations for reconciliation:

By implementing these methodological refinements, researchers can better understand the seemingly contradictory findings in KCNN4 research and develop more precise hypotheses for future studies.

What are the emerging methodologies for studying KCNN4 in clinical samples?

Several innovative methodologies are emerging for KCNN4 research in clinical samples:

Single-cell analysis approaches:

• Single-cell RNA sequencing to identify cell type-specific expression patterns

• Mass cytometry (CyTOF) for simultaneous detection of KCNN4 and multiple cellular markers

• Single-cell patch-clamp recordings from patient-derived cells to assess functional channel activity

Advanced imaging techniques:

• Multiplexed immunofluorescence to simultaneously visualize KCNN4 and TME components

• Super-resolution microscopy for detailed subcellular localization

• Intravital imaging in patient-derived xenograft models

Integrative multi-omics analysis:

• Combined analysis of KCNN4 expression with:

  • Genomic alterations (mutations, copy number variations)

  • Epigenetic modifications (methylation, histone modifications)

  • Proteomic profiles

  • Metabolomic signatures

Liquid biopsy approaches:

• Detection of KCNN4 in circulating tumor cells

• Analysis of KCNN4 expression in extracellular vesicles

• Cell-free DNA methylation status of the KCNN4 promoter

Computational and AI-based methods:

• Machine learning algorithms to identify KCNN4-associated gene signatures

• Predictive models for therapy response based on KCNN4 expression patterns

• Network analysis to identify context-dependent KCNN4 functions

These emerging methodologies offer opportunities for more precise characterization of KCNN4's role in human cancers and may lead to improved patient stratification and personalized therapeutic approaches.

How should researchers design experiments to investigate the causal relationship between KCNN4 expression and cancer progression?

Designing experiments to establish causality between KCNN4 expression and cancer progression requires rigorous methodological approaches:

Genetic manipulation studies:

• Loss-of-function approaches:

  • CRISPR-Cas9 knockout models with complete deletion of KCNN4

  • Inducible shRNA systems for temporal control of KCNN4 knockdown

  • Isoform-specific targeting to determine which variant drives progression

• Gain-of-function approaches:

  • Stable overexpression of KCNN4 in cell lines with low endogenous expression

  • Inducible expression systems to control timing and level of expression

  • Mutation studies to identify critical functional domains

• Rescue experiments:

  • Re-expression of KCNN4 in knockout cells to confirm phenotype specificity

  • Structure-function analysis with domain mutants

  • Isoform-specific rescue to determine functional equivalence

Pharmacological inhibitor studies:

• Target validation:

  • Use of multiple structurally distinct KCNN4 inhibitors (e.g., Senicapoc, TRAM-34)

  • Dose-response relationships

  • Comparison of genetic and pharmacological inhibition

• Timing considerations:

  • Treatment at different stages of cancer development

  • Intermittent vs. continuous dosing

  • Combination with standard therapies

In vivo models:

• Genetically engineered mouse models (GEMMs):

  • Tissue-specific KCNN4 knockout or overexpression

  • Inducible systems for temporal control

  • Combination with oncogene expression or tumor suppressor deletion

• Orthotopic and patient-derived xenograft models:

  • Implantation of genetically modified cells

  • Treatment with KCNN4 inhibitors

  • Analysis of metastatic potential and tumor microenvironment

• Experimental metastasis models:

  • Tail vein injection to assess extravasation and colonization

  • Intracardiac injection to evaluate multi-organ metastasis

  • In vivo imaging to track KCNN4-modified cells

Mechanistic studies:

• Signaling pathway analysis:

  • Investigation of Ca²⁺/MET/AKT axis in PDAC

  • Epithelial-mesenchymal transition (EMT) markers

  • Cell cycle regulators

• Tumor microenvironment interactions:

  • Co-culture systems with immune cells

  • Organoid models incorporating stromal components

  • Analysis of secreted factors

By implementing these complementary approaches, researchers can establish more robust causal relationships between KCNN4 expression and cancer progression, potentially identifying context-specific mechanisms and therapeutic vulnerabilities.