NFYA Antibody

What is NFYA and why is it important in research?

NFYA (Nuclear Transcription Factor Y, alpha) is a crucial component of the heteromeric transcription factor NF-Y, which is essential for binding to CCAAT sequences present in numerous eukaryotic genes. NFYA forms a stable complex with NF-YB and NF-YC subunits, which is necessary for effective DNA binding and transcriptional activation. This complex plays a fundamental role in regulating gene expression related to cellular growth, differentiation, and response to environmental signals. The high conservation of this complex across species, particularly the DNA binding domains of NF-YB and NF-YC with their unique histone-fold "handshake" motif, underscores its evolutionary significance . Recent research has highlighted NFYA's importance in cancer biology, especially in triple-negative breast cancer, making it a valuable target for oncology research .

What types of NFYA antibodies are available for research?

NFYA antibodies are available in multiple formats, including:

Researchers should select antibodies based on their specific application needs and target species .

What are the different NFYA splice variants and their significance?

NFYA exists in multiple splice variants, with NFYAv1 and NFYAv2 being the most studied. Recent research has revealed important functional differences between these variants:

• NFYAv1: This longer variant has been associated with promoting malignant behavior in triple-negative breast cancer (TNBC). Re-expression of NFYAv1 in NFYA-deficient TNBC cells significantly restores malignant phenotypes including cell growth, sphere formation, and tumor development .

• NFYAv2: This shorter variant has demonstrated limited ability to restore malignant behavior when re-expressed in NFYA-deficient cells. Studies show that NFYAv2 resulted in slight or no restoration of malignant behavior compared to NFYAv1 .

This differential functionality makes distinguishing between NFYA variants crucial for cancer research, particularly when studying TNBC pathogenesis mechanisms .

What are the recommended dilutions for NFYA antibodies in different applications?

Based on validated protocols, recommended dilutions vary by application and specific antibody. For the NFYA antibody (12981-1-AP), the following dilutions are recommended:

It is important to note that these dilutions should be optimized for each testing system to obtain optimal results, as the optimal concentration may be sample-dependent .

How should I validate the specificity of an NFYA antibody?

To validate NFYA antibody specificity, implement the following comprehensive approach:

• Positive and negative controls: Use cell lines with known NFYA expression. Based on published data, HEK-293, HeLa, HT-1080, Jurkat, and K-562 cells show positive Western blot detection for NFYA .

• Size verification: Confirm that detected bands match the expected molecular weight. NFYA typically appears at 40 kDa and 46 kDa in Western blots .

• Knockdown/knockout validation: Generate NFYA-deficient cells using CRISPR/Cas9 (as demonstrated in SUM159 cells) and verify antibody signal loss .

• Cross-reactivity testing: Evaluate antibody performance across intended species. For example, antibody 12981-1-AP shows reactivity with human and mouse samples .

• Multiple application validation: Confirm consistent results across different techniques (WB, IHC, IF) to strengthen confidence in antibody specificity .

• Immunoprecipitation followed by mass spectrometry: For highest stringency validation, perform IP with the NFYA antibody followed by protein identification via mass spectrometry .

How can I detect NFYA in different cellular compartments?

NFYA is primarily a nuclear protein, but detecting it in different cellular compartments requires specific approaches:

• Nuclear localization: Immunofluorescence studies have confirmed NFYA's predominant nuclear localization. For optimal IF detection, use protocols validated for nuclear proteins, such as those employed with HepG2 cells using NFYA antibody at 1:50-1:500 dilution .

• Subcellular fractionation: To biochemically separate nuclear and cytoplasmic fractions before Western blotting, employ established nuclear extraction protocols that preserve protein-protein interactions.

• Co-localization studies: For investigating interactions with other nuclear components, implement dual immunofluorescence staining. Research has identified interactions between NFYA and nuclear lamina proteins, specifically lamin A, which can be visualized through co-localization studies .

• Chromatin association: ChIP-qPCR experiments have demonstrated that NFYA associates with specific promoter regions containing CCAAT boxes. This technique is valuable for detecting NFYA's chromatin-bound fraction and understanding its transcriptional regulatory function .

When interpreting results, note that changes in NFYA's subcellular distribution may occur in different cellular contexts, particularly in cancer cells where altered localization might contribute to pathological processes .

How can I study the role of NFYA in cancer progression?

To investigate NFYA's role in cancer progression, particularly in triple-negative breast cancer (TNBC), implement the following research strategies:

• CRISPR/Cas9-mediated knockout: Generate NFYA-deficient cancer cell lines as demonstrated with SUM159 cells. This approach revealed that NFYA-deficient cells show suppressed carcinogenic phenotypes, including reduced cell growth capability and impaired sphere and tumor formation .

• Splice variant-specific modulation: Create NFYAv1-specific deficient cells to distinguish between the functions of different NFYA variants. Research has shown that NFYAv1-specific deficient cells exhibit growth suppression similar to complete NFYA knockout, even with increased NFYAv2 expression .

• Rescue experiments: Perform complementation assays by re-expressing NFYAv1 or NFYAv2 in NFYA-deficient cells. Studies have demonstrated that NFYAv1 significantly restores malignant behavior, while NFYAv2 shows limited rescue capacity .

• Dominant-negative approaches: Overexpress dominant-negative NFYA mutants to disrupt endogenous NFYA function. Mutations within the DNA-binding domain can act as dominant repressors of NF-Y-DNA complex formation and NF-Y-dependent transcription .

• Patient sample analysis: Correlate NFYA expression and variant distribution with clinical outcomes in cancer patient samples using techniques such as IHC (recommended dilution 1:250-1:1000) .

These approaches can elucidate NFYA's mechanistic role in cancer progression and potentially identify new therapeutic targets, particularly for aggressive cancer types like TNBC .

What experimental approaches can reveal NFYA protein-protein interactions?

To investigate NFYA's protein interaction network, employ these complementary approaches:

• Co-immunoprecipitation (Co-IP): This approach has successfully identified the interaction between lamin A and NFYA. Reciprocal immunoprecipitation with anti-lamin A/C antibody validated the occurrence of endogenous lamin A/NF-YA complexes across multiple cell lines. Similarly, antibodies against NF-YB successfully immunoprecipitated lamin A, confirming that lamin A associates with the NF-Y complex rather than with the NF-YA subunit alone .

• Chromatin immunoprecipitation (ChIP): For studying NFYA interactions in the context of chromatin binding, ChIP-qPCR experiments have demonstrated that lamin A associates with several promoter regions carrying CCAAT-boxes, including CCNB2, DHFR, CCNA2, CDK1, CCNB1, CDC25C, TOPO2A, and PCNA promoters. This technique helps understand how NFYA collaborates with other proteins in transcriptional regulation .

• Affinity purification coupled with mass spectrometry: For unbiased identification of novel NFYA interactors, this approach can reveal unexpected protein associations and complex formation.

• Proximity ligation assay (PLA): This technique can visualize and quantify protein interactions in situ, providing spatial information about where NFYA interactions occur within the cell.

• FRET/BRET analysis: For studying dynamic interactions in living cells, these biophysical approaches can monitor real-time association between NFYA and partner proteins.

These methodologies collectively provide insights into NFYA's role in transcriptional regulation complexes and unexpected functions through protein-protein interactions .

How do I investigate the differential functions of NFYA splice variants?

To elucidate the distinct roles of NFYA splice variants (particularly NFYAv1 and NFYAv2), implement this systematic research strategy:

• Variant-specific expression analysis: Quantify the relative expression of different NFYA variants across cell types and disease states using variant-specific primers in qRT-PCR or RNA-seq analysis with splice junction-aware algorithms.

• Variant-selective knockdown: Design siRNAs or shRNAs targeting unique regions of specific variants. Research has demonstrated that NFYAv1-specific knockdown suppresses cell growth in TNBC models, even when NFYAv2 expression is maintained or increased .

• Variant-specific rescue experiments: In NFYA-deficient backgrounds, selectively re-express individual variants and assess phenotypic consequences. Studies show NFYAv1 significantly restores malignant behavior in NFYA-deficient TNBC cells, while NFYAv2 shows minimal rescue effects .

• Differential protein interaction analysis: Perform co-IP experiments with tagged variant-specific constructs to identify unique protein partners for each variant.

• ChIP-seq with variant-specific antibodies: If available, use variant-specific antibodies for ChIP-seq to map genome-wide binding patterns of different NFYA variants and identify variant-specific target genes.

• Domain mapping and mutagenesis: Create chimeric constructs or point mutations to identify the structural determinants responsible for the functional differences between variants.

This multi-faceted approach has revealed that NFYAv1 plays a more significant role in promoting malignant phenotypes in TNBC compared to NFYAv2, highlighting the importance of studying splice variants separately in cancer research contexts .

What are the common challenges when using NFYA antibodies and how can I overcome them?

When working with NFYA antibodies, researchers may encounter several challenges. Here are evidence-based solutions:

• Multiple band detection: NFYA typically appears as two bands at approximately 40 kDa and 46 kDa in Western blots . If seeing unexpected bands:

  • Verify antibody specificity using NFYA-deficient controls

  • Optimize primary antibody concentration (1:500-1:1000 for WB is recommended)

  • Adjust blocking conditions to reduce non-specific binding

• Weak or no signal in IHC:

  • Test different antigen retrieval methods (TE buffer pH 9.0 is recommended for NFYA, with citrate buffer pH 6.0 as an alternative)

  • Adjust antibody concentration (1:250-1:1000 dilution range is suggested)

  • Extend primary antibody incubation time

  • Use signal amplification systems if necessary

• Inconsistent results across applications:

  • Different applications may require different antibody concentrations (e.g., 1:50-1:500 for IF/ICC vs. 1:500-1:1000 for WB)

  • Some antibodies may perform better in certain applications than others

  • Validate each antibody separately for each intended application

• Cross-reactivity concerns:

  • Select antibodies with validated reactivity for your species of interest

  • The 12981-1-AP antibody shows validated reactivity with human and mouse samples

  • Other antibodies, like the C-Term antibody (ABIN7296436), demonstrate broader reactivity across human, mouse, rat, dog, cow, and pig samples

• Nuclear protein extraction difficulties:

  • Use specialized nuclear extraction buffers

  • Include protease inhibitors to prevent degradation

  • Consider techniques optimized for transcription factor isolation

Each experimental system may require specific optimization, so titration of the antibody is recommended to obtain optimal results .

How can I optimize ChIP experiments with NFYA antibodies?

For successful Chromatin Immunoprecipitation (ChIP) using NFYA antibodies, follow these optimization strategies based on published research:

• Antibody selection: Choose antibodies validated for ChIP applications. Research has successfully used antibodies against both NFYA and its interaction partners (such as lamin A) for ChIP experiments .

• Chromatin preparation:

  • Optimize crosslinking time (typically 10-15 minutes with 1% formaldehyde)

  • Ensure proper chromatin fragmentation to 200-500 bp fragments

  • Verify fragmentation efficiency through agarose gel electrophoresis

• Immunoprecipitation conditions:

  • Titrate antibody amount (excess antibody can increase background)

  • Include appropriate negative controls (IgG and/or NFYA-deficient samples)

  • Use protein A/G beads appropriate for your antibody host species

• Primer design for qPCR:

  • Design primers to amplify regions encompassing NF-Y consensus sites (CCAAT boxes)

  • Published research has successfully targeted promoter regions of CCNB2, DHFR, CCNA2, CDK1, CCNB1, CDC25C, TOPO2A, and PCNA

  • Include negative control primers for regions lacking CCAAT boxes (e.g., CXCR4 promoter has been used as a negative control)

• Data analysis:

  • Normalize to input chromatin

  • Compare enrichment to IgG control

  • Consider the relative enrichment across different target regions

This approach has successfully demonstrated that both NFYA and its interaction partners bind to specific promoter regions carrying CCAAT boxes, providing insights into transcriptional regulatory mechanisms .

What considerations are important when designing NFYA knockdown or knockout experiments?

When designing NFYA knockdown or knockout experiments, consider these critical factors based on published research:

• Knockout strategy selection:

  • CRISPR/Cas9 has been successfully used to generate NFYA-deficient cells (e.g., in SUM159 TNBC cells)

  • Verify knockout efficiency through multiple methods (Western blot, qRT-PCR)

  • Design guide RNAs targeting conserved regions present in all relevant NFYA variants

• Variant-specific considerations:

  • For selective NFYAv1 knockout, design guide RNAs targeting NFYAv1-specific exons

  • Confirm that other variants (e.g., NFYAv2) remain expressed using variant-specific primers

  • Consider how altered expression ratios between variants might affect interpretation

• Phenotypic assays:

  • Cell proliferation (NFYA-deficient cells show suppressed growth)

  • Sphere formation (reduced in NFYA knockouts)

  • Tumor formation in xenograft models

  • Gene expression changes in NF-Y target genes

• Rescue experiments:

  • Design expression constructs for specific NFYA variants resistant to the knockout strategy

  • Compare rescue efficiency between different variants (NFYAv1 provides significant rescue while NFYAv2 shows limited rescue capacity)

• Dominant-negative approach alternatives:

  • Consider expressing dominant-negative NFYA mutants as an alternative to knockout

  • Mutations in the DNA-binding domain can act as dominant repressors of NF-Y-dependent transcription

• Controls and validation:

  • Include proper controls (non-targeting guides, parental cells)

  • Validate knockout through sequencing, protein expression analysis, and functional assays

  • Consider compensatory mechanisms that may emerge in complete knockout models

These considerations will help ensure meaningful results when investigating NFYA's role in normal and pathological cellular processes .

How is NFYA involved in cancer metabolism reprogramming?

Recent advances in cancer metabolism research have revealed that NFYA plays a significant role in metabolic reprogramming, particularly in lipid metabolism regulation, which is increasingly recognized as a hallmark of cancer . While the molecular mechanisms are still being elucidated, research suggests several pathways through which NFYA influences cancer metabolism:

• Lipid metabolism regulation: NFYA has been implicated in the regulation of genes involved in lipid synthesis and metabolism, contributing to the altered lipid profiles observed in cancer cells. This is particularly relevant as reprogramming of lipid metabolism is now considered one of the hallmarks of cancer .

• Cell growth and proliferation: NFYA-deficient cancer cells show suppressed growth capabilities, suggesting that NFYA regulates metabolic pathways essential for cancer cell proliferation. NFYAv1, in particular, appears crucial for maintaining the metabolic state supporting malignant behavior .

• Variant-specific metabolic effects: The differential ability of NFYAv1 and NFYAv2 to restore malignant behavior in NFYA-deficient cells suggests variant-specific roles in metabolic regulation. NFYAv1 significantly restores malignant phenotypes, indicating it may be more effective at reprogramming metabolism to support cancer progression .

• Transcriptional regulation of metabolic enzymes: As a transcription factor binding to CCAAT boxes in numerous promoters, NFYA likely directly regulates the expression of metabolic enzymes critical for cancer-specific metabolic adaptations.

Future research directions should focus on identifying the specific metabolic pathways and enzymes regulated by different NFYA variants in cancer contexts, potentially revealing new therapeutic vulnerabilities .

What is the significance of NFYA's interaction with nuclear lamina proteins?

The discovery of NFYA's interaction with nuclear lamina proteins, particularly lamin A, reveals an unexpected connection between transcriptional regulation and nuclear architecture:

• Evidence of interaction: Reciprocal immunoprecipitation experiments have validated the occurrence of endogenous lamin A/NF-YA complexes across multiple cell lines. Antibodies against NF-YB also immunoprecipitated lamin A, confirming that lamin A associates with the NF-Y complex rather than with the NF-YA subunit alone .

• Chromatin association: ChIP-qPCR experiments have demonstrated that lamin A associates with several promoter regions carrying CCAAT-boxes, including genes involved in cell cycle regulation (CCNB2, CCNA2, CDK1, CCNB1, CDC25C) and DNA replication (DHFR, TOPO2A, PCNA). This co-localization with NFYA at specific genomic regions suggests functional cooperation in gene regulation .

• Potential mechanisms:

  • Lamin A may function as a co-regulator of NFYA-mediated transcription

  • The interaction might facilitate the organization of NFYA target genes within the nuclear architecture

  • Lamin A could influence NFYA's accessibility to specific genomic regions

• Pathological implications: Mutations in lamins are associated with a group of diseases called laminopathies, while NFYA dysregulation is linked to cancer. Their interaction suggests potential mechanistic connections between these pathological conditions that warrant further investigation .

This unexpected protein interaction reveals a novel layer of transcriptional regulation involving nuclear architecture components and opens new research directions for understanding how spatial organization within the nucleus contributes to gene expression control .

How can NFYA research contribute to developing new therapeutic strategies for cancer?

NFYA research has revealed several potential avenues for novel cancer therapeutic strategies:

• Targeting NFYA splice variants: The differential roles of NFYAv1 and NFYAv2 in cancer progression suggest that selective targeting of NFYAv1 could inhibit malignant behavior while potentially reducing off-target effects. Research has demonstrated that NFYAv1-specific deficiency suppresses cell growth in TNBC models, even when NFYAv2 expression is maintained .

• Disrupting protein-protein interactions: The identification of specific interactions between NFYA and partners like lamin A offers opportunities to develop small molecules or peptides that disrupt these interactions, potentially interfering with NFYA's oncogenic functions .

• Blocking DNA binding: Since NFYA's function depends on binding to CCAAT boxes in target gene promoters, developing compounds that prevent this DNA interaction could inhibit NFYA-dependent transcriptional programs driving cancer progression.

• Combination therapies: NFYA's role in lipid metabolism reprogramming suggests potential synergies between NFYA inhibition and drugs targeting metabolic vulnerabilities in cancer cells .

• Biomarker development: The ratio of different NFYA splice variants could serve as a prognostic or predictive biomarker in cancer, helping to stratify patients for specific treatment approaches.

• Dominant-negative approaches: Research employing dominant-negative NFYA mutants demonstrates the potential efficacy of this approach in blocking NFYA-dependent transcription. Developing deliverable dominant-negative constructs could offer another therapeutic strategy .

As research continues to elucidate NFYA's precise roles in different cancer types, these approaches may contribute to developing targeted therapies, particularly for aggressive cancers like triple-negative breast cancer that currently lack effective targeted treatment options .