KIF22 Antibody, Biotin conjugated

What is KIF22 and what cellular functions does it perform?

KIF22, also known as Kinesin family member 22 or Kinesin-like DNA-binding protein, is a molecular motor protein involved in several critical cellular processes. KIF22 functions as an essential regulator of mitosis and meiosis, specifically participating in spindle formation and chromosome movement during cell division . Importantly, KIF22 possesses the unique ability to bind both microtubules and DNA, distinguishing it from many other kinesin proteins . Research has demonstrated that KIF22 plays a crucial role in the congression of laterally attached chromosomes, particularly in cells where NDC80 (a key kinetochore component) is depleted . KIF22 can be phosphorylated by CDK1, enhancing its chromosome-binding capabilities, which is essential for proper cell division .

What are the key characteristics of the KIF22 Antibody, Biotin conjugated?

The KIF22 Antibody, Biotin conjugated is a rabbit polyclonal antibody specifically developed against human KIF22 protein . Its immunogen is the recombinant human KIF22 protein fragment spanning amino acids 34-169 . This antibody has been validated for ELISA applications and demonstrates reactivity with human KIF22 . The biotin conjugation provides versatility for detection systems, as biotin can bind with high affinity to streptavidin-coupled reporter molecules. The antibody is supplied in liquid form in a diluent buffer containing 50% glycerol, 0.01M PBS (pH 7.4), and 0.03% Proclin 300 as a preservative . Proper storage at -20°C or -80°C is recommended to maintain antibody integrity and performance .

How should I optimize KIF22 antibody dilutions for ELISA assays?

For optimal ELISA performance using the KIF22 Antibody, Biotin conjugated, researchers should perform a titration experiment to determine the ideal antibody concentration. Begin with a broad range of dilutions (e.g., 1:500, 1:1000, 1:2000, 1:5000, and 1:10000) to identify the concentration that provides the best signal-to-noise ratio. When optimizing, use positive controls (such as recombinant KIF22 protein or lysates from cells known to express KIF22) and negative controls (such as KIF22 knockout cell lysates, as available from HEK-293T KIF22 knockout cell lines ). The validation data shown in search result demonstrates clear differentiation between wild-type and KIF22 knockout samples, which should be achievable in your assays as well. Remember that the assay diluent composition can significantly impact antibody performance; therefore, testing different blocking agents (BSA, milk proteins, or commercial blockers) is advisable. Document the linear range of your assay carefully, as this will be critical for quantitative applications.

What experimental controls should be included when using KIF22 antibodies in cancer research?

When investigating KIF22 in cancer research, comprehensive controls are essential for robust data interpretation. Always include:

• Positive expression controls: Tissues or cell lines known to express KIF22 at high levels, such as bladder cancer tissues which show elevated KIF22 expression compared to normal adjacent tissues .

• Negative expression controls: Use KIF22 knockout cell lines, such as the HEK-293T KIF22 knockout line validated by Western blot , or tissues with minimal KIF22 expression.

• Technical controls: Include isotype controls (non-specific IgG from the same species) to assess non-specific binding.

• Validation controls: Verify antibody specificity using alternative methods (e.g., if using the antibody for IHC, confirm findings with Western blot or RT-qPCR).

• Condition-specific controls: When examining KIF22 in specific cancer types, include samples representing various disease stages and grades, as KIF22 expression has been shown to correlate with clinical features like tumor stage (p=0.003) and recurrence (p=0.016) in bladder cancer .

The experimental design should account for the cellular localization of KIF22, which has been observed in both cytoplasm and nucleus of bladder cancer tissues , requiring appropriate subcellular fractionation protocols when relevant.

How can I design experiments to investigate KIF22's transcriptional regulatory functions?

KIF22 has demonstrated capabilities as a transcriptional regulator, making this an important avenue for investigation. To study KIF22's transcriptional regulatory functions:

• Chromatin Immunoprecipitation (ChIP) assays: KIF22 has been shown to bind promoter regions of genes like CDCA3 in bladder cancer cells . Design ChIP experiments using the KIF22 antibody to immunoprecipitate KIF22-bound DNA, followed by qPCR or sequencing to identify binding sites. In bladder cancer research, ChIP assays successfully demonstrated that promoter fragments of CDCA3 were specifically co-immunoprecipitated by KIF22 antibody but not by IgG .

• Luciferase reporter assays: Construct reporter plasmids containing promoter regions of potential KIF22 target genes (such as the pGL-CDCA3 plasmid used in bladder cancer research ). Co-transfect these with KIF22 expression vectors or siRNAs to assess transcriptional impact.

• Gene expression analysis: Perform RNA-seq or qRT-PCR after KIF22 knockdown or overexpression to identify genes regulated by KIF22. In bladder cancer studies, KIF22 silencing was found to affect the expression of proliferation markers like Ki67 .

• Protein-protein interaction studies: Investigate interactions between KIF22 and transcriptional machinery components using co-immunoprecipitation followed by mass spectrometry.

To ensure specificity, include experimental controls such as KIF22 knockout cell lines and rescue experiments where KIF22 expression is restored following knockout or knockdown.

What approaches can resolve contradictory findings when studying KIF22 across different cancer types?

When encountering contradictory results regarding KIF22 function across different cancer types, consider these methodological approaches:

• Context-specific analysis: KIF22 displays tissue-specific functions, promoting progression in bladder cancer and multiple myeloma . Perform parallel experiments in multiple cell types using identical protocols to identify context-dependent effects.

• Signaling pathway integration: Map KIF22's interaction with different pathway components across cancer types. In bladder cancer, KIF22 transcriptionally activates CDCA3 , but may interact with different pathways in other cancers.

• Isoform-specific investigation: Examine whether different KIF22 isoforms predominate across cancer types. Design isoform-specific primers or antibodies to distinguish between variants.

• Post-translational modification profiling: Investigate whether KIF22 undergoes different modifications in various cancers. Phosphorylation by CDK1 has been shown to enhance KIF22's chromosome binding , but other modifications may occur in different contexts.

• Clinical correlation with multivariate analysis: When analyzing patient data, perform multivariate analyses that account for cancer-specific variables. In bladder cancer, KIF22 expression correlates with clinical stage and recurrence as shown in this data table:

This statistical approach revealed significant associations between KIF22 expression and both tumor stage (p=0.003) and recurrence (p=0.016) in bladder cancer patients .

How should I design experiments to investigate KIF22's role in bladder cancer progression?

Based on established research showing KIF22's significance in bladder cancer , design your experiments with these methodological considerations:

What methodological approaches can determine if KIF22 is a viable therapeutic target in multiple myeloma?

To evaluate KIF22 as a therapeutic target in multiple myeloma (MM), employ these systematic approaches:

How can I investigate the potential role of KIF22 in therapy resistance mechanisms?

To explore KIF22's involvement in therapy resistance, implement these methodological approaches:

• Paired sample analysis: Collect and analyze matched patient samples before treatment and after resistance development, quantifying KIF22 expression changes using the validated antibodies . Correlate findings with clinical outcomes and treatment response data.

• Resistance model development: Generate resistant cell lines by exposing cancer cells to escalating doses of therapeutic agents. Compare KIF22 expression and function between parental and resistant cells using techniques such as Western blot analysis with anti-KIF22 antibodies, similar to those validated in knockout systems .

• Functional modulation experiments:

  • In resistant cell lines with altered KIF22 expression, modulate KIF22 levels through knockdown or overexpression.

  • Assess whether KIF22 modulation restores drug sensitivity using proliferation, apoptosis, and cell cycle assays.

  • Investigate changes in downstream targets like CDCA3, which has been shown to be transcriptionally activated by KIF22 in bladder cancer .

• Mechanistic studies:

  • Investigate whether KIF22's involvement in mitosis and chromosome dynamics contributes to genomic instability that might drive resistance.

  • Examine KIF22's potential role in cancer stem cell maintenance, which is often associated with therapy resistance.

  • Study how KIF22 might regulate DNA damage response pathways, potentially influencing resistance to genotoxic therapies.

• Combination therapy evaluation: Test whether inhibiting KIF22 in combination with standard therapies prevents or delays resistance development in preclinical models.

What experimental designs can elucidate KIF22's role in cell cycle regulation across different cellular contexts?

To comprehensively investigate KIF22's cell cycle regulatory functions in various cellular contexts:

• Synchronized cell population analysis:

  • Implement cell synchronization protocols (double thymidine block, nocodazole treatment, or serum starvation/stimulation) in multiple cell types.

  • Analyze KIF22 expression, localization, and post-translational modifications throughout the cell cycle using the biotin-conjugated antibody with appropriate detection systems.

  • Compare patterns across normal cells, cancer cells, and cells from different tissue origins.

• Live-cell imaging approaches:

  • Generate cell lines expressing fluorescently-tagged KIF22 to track its dynamics during cell division.

  • Perform time-lapse microscopy after KIF22 manipulation (knockdown, overexpression, or mutation of key domains).

  • Quantify mitotic defects, chromosome segregation errors, and cell cycle progression times.

• Interaction partner profiling:

  • Conduct immunoprecipitation using KIF22 antibodies followed by mass spectrometry to identify cell-type-specific interaction partners.

  • Validate critical interactions using co-immunoprecipitation and proximity ligation assays.

  • Compare interactomes between normal and malignant cells of the same tissue origin.

• Domain-specific function analysis:

  • Generate constructs expressing KIF22 with mutations in motor domains versus DNA-binding domains.

  • Assess the impact of these mutations on cell cycle progression in different cell types.

  • Determine whether different cellular contexts rely on distinct KIF22 functional domains.

• Single-cell analysis:

  • Implement single-cell RNA-seq and single-cell protein analysis to capture cell-to-cell variability in KIF22 function.

  • Correlate KIF22 expression with cell cycle phase markers at the single-cell level.

  • Identify potential subpopulations with distinct KIF22-dependent regulatory mechanisms.