ZBTB32 Antibody

What is ZBTB32 and what are its primary functions in the immune system?

ZBTB32 (zinc finger and BTB domain containing 32) is a transcription factor belonging to the BTB/POZ-ZF protein family. In humans, the canonical protein has 487 amino acid residues with a mass of 53 kDa and nuclear localization . ZBTB32 acts primarily as a transcriptional regulator with context-dependent functions:

• In B cells: ZBTB32 restrains the magnitude and duration of recall antibody responses, particularly during chronic viral infections like murine cytomegalovirus (MCMV)

• In T cells: ZBTB32 limits effector T cell responses during viral infections and promotes T cell exhaustion during chronic infections

• In NK cells: ZBTB32 facilitates the proliferative burst of virus-specific NK cells by antagonizing the anti-proliferative factor Blimp-1

The protein functions as a DNA-binding repressor that recognizes a 5'-TGTACAGTGT-3' core sequence . It may exert its repressive effects by preventing transcription factors like GATA3 from binding to DNA and recruiting histone deacetylases (HDACs) to target genes .

What experimental applications are ZBTB32 antibodies commonly used for?

Based on commercially available antibodies, ZBTB32 antibodies are validated for multiple applications:

ZBTB32 antibodies are primarily used to study expression patterns in immune cells, particularly following activation or during infection .

How is ZBTB32 expression regulated in immune cells?

ZBTB32 expression shows distinct regulatory patterns across immune cell types:

• B cells: Highly expressed in memory B cells (20-30 fold higher than naïve B cells), particularly in CD80+ PD-L2+ memory B cells, regardless of whether they express IgM or IgG

• T cells: Expression is induced by TCR stimulation (α-CD3/CD28) and enhanced by cytokines:

  • IL-2 induces ZBTB32 through STAT5 binding

  • IFNβ induces ZBTB32 through STAT1 binding

  • IL-12 induces ZBTB32 through STAT4 binding

• NK cells: Rapidly upregulated following viral infection due to proinflammatory cytokine signaling

In CD8+ T cells responding to LCMV infection, ZBTB32 expression peaks at day 6 post-infection, followed by a sharp decline . Later in immune responses, ZBTB32 is repressed by Blimp-1, creating a regulatory feedback loop .

What are the recommended protocols for Western blot detection of ZBTB32?

For optimal Western blot detection of ZBTB32:

• Sample preparation:

  • Use fresh spleen, lymph node, or activated immune cell samples

  • For mouse models, consider timepoint carefully (e.g., day 6 post-infection for peak expression in T cells)

• Antibody dilution and detection:

  • Recommended dilution range: 1:1000-1:4000

  • Use 53 kDa as the expected molecular weight

  • Include positive controls: mouse spleen tissue or Jurkat cells

• Optimization considerations:

  • ZBTB32 expression is highly cell-type and activation-state dependent

  • Consider using ZBTB32-deficient samples as negative controls

  • For memory B cell studies, CD80+ memory B cells show highest expression levels

How should researchers design experiments to study ZBTB32 expression during immune responses?

When designing experiments to study ZBTB32 expression dynamics:

• Timepoint selection is critical:

  • For acute viral infections: Include day 6 post-infection, when ZBTB32 expression peaks in CD8+ T cells

  • For recall responses: Include early timepoints (days 1-3) after rechallenge to capture effects on memory cells

• Cell isolation strategies:

  • For memory B cells: Sort based on CD80 and PD-L2 expression

  • For T cells: Consider both naïve and TCR-stimulated populations with/without cytokine treatments

• Induction methods:

  • TCR stimulation: α-CD3/CD28 treatment induces ZBTB32 expression

  • Cytokine treatment: IL-2, IFNβ, or IL-12 further enhances expression

• Experimental controls:

  • Include both resting and activated cells

  • Compare expression across multiple immune cell types

  • Consider using ZBTB32-deficient cells as negative controls

How can researchers confirm ZBTB32 antibody specificity?

To ensure antibody specificity for ZBTB32:

• Use genetic controls:

  • Zbtb32−/− samples: Multiple studies have generated ZBTB32-knockout mice that serve as excellent negative controls

  • CRISPR/Cas9-mediated deletion: Studies have directly targeted the Zbtb32 gene for deletion in NOD mice

• Perform validation experiments:

  • Compare results with multiple antibodies targeting different epitopes

  • Conduct siRNA or shRNA-mediated knockdown experiments

  • For immunohistochemistry, use peptide competition assays

• Consider expression patterns:

  • Verify higher expression in memory B cells compared to naïve B cells

  • Confirm induction after TCR stimulation in T cells

  • Check for nuclear localization in immunofluorescence studies

How does ZBTB32 regulate B cell responses to different types of infections?

ZBTB32 exhibits context-dependent regulation of B cell responses:

• Chronic viral infections (e.g., MCMV):

  • ZBTB32 significantly restrains antibody responses during chronic infection

  • Zbtb32−/− chimeras show nearly 20-fold higher antigen-specific IgG2b levels by week 9 post-MCMV infection compared to controls, despite similar viral loads

  • The effect is most pronounced after the initial acute response resolves (weeks 5-9)

• Acute infections and vaccination:

  • ZBTB32 is dispensable for primary antibody responses to T-dependent antigens

  • No significant role in restraining primary or recall antibody responses against influenza viruses

  • Similar NP-specific serum antibody titers between Zbtb32−/− and wild-type mice after NP-CGG immunization

• Mechanistic role:

  • ZBTB32 likely acts during memory B cell activation

  • May limit MHCII expression, antigen processing, and T cell help

  • Prevents secondary plasma cells from persisting too long, maintaining broader humoral immunity

This selective regulation may prevent recall responses against chronic infections from progressively overwhelming other antibody specificities.

What are the differences in ZBTB32 expression between memory B cell subsets?

Memory B cell subsets show distinct patterns of ZBTB32 expression:

• CD80+ PD-L2+ memory B cells:

  • Express 20-30-fold higher levels of ZBTB32 transcripts compared to naïve B cells

  • High expression in both IgM+ and IgG+ subsets

  • This subset preferentially differentiates into plasma cells upon rechallenge

• Other memory B cell subsets:

  • CD80- PD-L2+ and CD80- PD-L2- memory B cells express lower levels of ZBTB32

  • Expression remains higher than in naïve B cells

• Expression in human vs. mouse:

  • Similar patterns observed across species

  • Human RNA-seq analysis confirms ZBTB32 expression in both IgM+ and IgG+ memory B cells

  • Undetectable expression in naïve B cells and bone marrow plasma cells in both species

These expression patterns suggest ZBTB32 marks memory B cells poised for rapid differentiation upon reactivation, where it then functions to control the magnitude and duration of the recall response.

How can researchers design experiments to study ZBTB32's role in memory B cell responses?

When investigating ZBTB32's function in memory B cell responses:

• Experimental models:

  • Chronic infection: MCMV infection model shows pronounced ZBTB32-dependent effects

  • Acute challenge: NP-CGG immunization followed by secondary challenge

  • Bone marrow chimeras: Use mixed chimeras where B cells are derived from either Zbtb32−/− or control mice

• Readout parameters:

  • Measure antigen-specific antibody titers at multiple timepoints (weeks 1-11)

  • Assess different antibody isotypes (IgM, IgG2b, IgG2c)

  • Quantify memory B cell and plasma cell numbers

  • Analyze transcriptional signatures in secondary plasma cells

• Controls and considerations:

  • Measure viral loads to rule out differences in antigen burden

  • Include both early (acute) and late (chronic) timepoints

  • Consider potential contributions of ZBTB32 in non-B cells through appropriate chimera models

• Advanced approaches:

  • Single-cell RNA-seq to examine heterogeneity within memory B cell populations

  • ChIP-seq to identify direct ZBTB32 targets in memory B cells

  • Analysis of secondary plasma cell survival and mitochondrial function

How does ZBTB32 affect CD8+ T cell responses during viral infections?

ZBTB32 plays critical roles in regulating CD8+ T cell responses:

• Acute viral infections (LCMV-Armstrong, Vaccinia):

  • Zbtb32−/− mice generate enhanced anti-viral CD8+ T cell responses

  • Higher proportions and absolute numbers of virus-specific CD8+ T cells at days 8 and 45 post-infection

  • Increased proportions of multifunctional cells producing IFNγ, TNFα, and IL-2 simultaneously

  • Enhanced memory CD8+ T cell populations

• Chronic viral infections (LCMV clone 13):

  • Zbtb32−/− mice show approximately two-fold more virus-specific CD8+ T cells in spleens and lungs

  • Enhanced viral clearance in the spleen

  • Reduced expression of exhaustion markers (PD-1, CD160, LAG-3, 2B4)

  • These effects are T cell-intrinsic, as shown in adoptive transfer experiments

• Gain-of-function evidence:

  • Sustained ZBTB32 expression via retroviral transduction dampens T cell responses

  • ZBTB32-expressing cells show reduced IL-7R expression, a key memory cell marker

These findings indicate ZBTB32 normally functions to limit T cell responses and the generation of memory CD8+ T cells.

What molecular mechanisms underlie ZBTB32's function in T cells?

ZBTB32 employs several molecular mechanisms to regulate T cell responses:

• Chromatin modification:

  • ZBTB32 promotes repressive chromatin modifications by recruiting HDAC1 and HDAC2 to target genes

  • ChIP experiments show ZBTB32 binding to regulatory regions of genes including Eomes and Cd27

  • These target genes normally promote memory T cell persistence and survival

• Interaction with Blimp-1:

  • ZBTB32 and Blimp-1 act cooperatively to mediate repressive chromatin modifications

  • Co-immunoprecipitation experiments show physical interaction between the two repressors

  • In the absence of ZBTB32, Blimp-1 fails to bind to regulatory regions of target genes

• Target gene regulation:

  • Eomes and Cd27 mRNA and protein expression are significantly enhanced in the absence of ZBTB32

  • ChIP for Pol II, p300, and histone modifications at these loci reveals altered chromatin states

  • This leads to premature upregulation of memory cell genes that promote long-term cell survival

This molecular activity dictates the magnitude of the T cell response and the numbers of memory T cells generated.

How does ZBTB32 deficiency differently affect T cells versus B cells?

ZBTB32 deficiency reveals distinct functional roles across lymphocyte lineages:

These differences highlight how ZBTB32 has evolved specialized functions in different lymphocyte lineages, though generally serving as a negative regulator of excessive immune responses.

How do transcription factors and cytokine signaling regulate ZBTB32 expression?

ZBTB32 expression is regulated through multiple signaling pathways:

• TCR and cytokine-induced STATs:

  • IL-2 induces STAT5 binding to the ZBTB32 promoter and 5' UTR

  • IFNβ induces STAT1 binding to the same regions

  • IL-12 induces STAT4 binding to these regulatory regions

• Chromatin changes:

  • ChIP-seq analysis shows that both dimeric and tetrameric forms of STAT5A and STAT5B bind to ZBTB32 regulatory regions upon IL-2 stimulation

  • STAT binding correlates with increased H3-Ac modification

  • Active transcription is associated with increased RNA polymerase II binding, high permissive H3-Ac and H3K4me3, and low repressive H3K27me3 modifications

• Negative regulation:

  • Blimp-1 represses ZBTB32 expression later in immune responses

  • This creates a regulatory feedback loop where ZBTB32 initially cooperates with Blimp-1 for target gene repression, but is later suppressed by Blimp-1

This complex regulation allows for precise temporal control of ZBTB32 expression during immune responses.

What is the relationship between ZBTB32 and Blimp-1 in regulating immune responses?

ZBTB32 and Blimp-1 (encoded by Prdm1) exhibit complex interactions:

• Cooperative repression:

  • In CD8+ T cells, they act cooperatively to mediate repressive chromatin modifications

  • ChIP experiments show that in the absence of ZBTB32, Blimp-1 fails to bind to the proximal regulatory regions of target genes like Eomes and Cd27

  • Co-immunoprecipitation experiments demonstrate physical interaction between the two repressors

• Distinct target genes:

  • Some genes are co-regulated by both ZBTB32 and Blimp-1 (Eomes, Cd27)

  • Others are Blimp-1 specific targets (Il2ra)

  • This explains why Blimp-1-deficient CD8+ T cells have reduced expression of cytolytic molecules, while ZBTB32-deficient cells do not

• Antagonistic relationship:

  • In NK cells, ZBTB32 antagonizes the anti-proliferative function of Blimp-1

  • This facilitates NK cell proliferation during infection

• Temporal regulation:

  • Blimp-1 represses ZBTB32 expression later in immune responses

  • This creates a regulatory feedback loop that helps terminate ZBTB32's effects

This multilayered relationship allows for precise coordination of immune cell responses during infections.

How might ZBTB32 research inform therapeutic approaches for chronic infections?

Understanding ZBTB32 function provides several potential therapeutic avenues:

• Enhancing anti-viral immunity:

  • Temporary inhibition of ZBTB32 could boost T cell responses against chronic viral infections

  • This approach might reduce T cell exhaustion and enhance viral clearance

  • Most applicable to cytomegalovirus infections, where ZBTB32 strongly limits antibody responses

• Vaccine design implications:

  • Modulating ZBTB32 activity might enhance vaccine efficacy against chronic infections

  • Transient ZBTB32 inhibition during vaccination could increase memory T and B cell generation

  • Particularly relevant for pathogens that establish chronic infections

• Potential risks and considerations:

  • Prolonged ZBTB32 inhibition might lead to immunopathology

  • Enhanced responses against chronic infections might reduce breadth of immunity

  • Timing of intervention would be critical to balance benefits and risks

• Autoimmunity applications:

  • Surprisingly, studies in NOD mice showed that ZBTB32 deficiency did not significantly alter T cell activation or diabetes incidence

  • This suggests potential compensatory mechanisms exist in autoimmune contexts

  • More research is needed to clarify ZBTB32's role in autoimmune settings

These therapeutic possibilities highlight the importance of understanding ZBTB32's context-dependent functions in immune regulation.

What factors should researchers consider when detecting ZBTB32 in different cell types?

When detecting ZBTB32 across different cell populations:

• Expression timing:

  • In B cells: Highest in memory B cells, particularly CD80+ PD-L2+ subsets

  • In T cells: Peaks at day 6 post-infection during acute viral infections

  • In NK cells: Rapidly upregulated following viral infection

• Cell-type specific considerations:

  • For Western blot: Spleen tissue contains multiple ZBTB32-expressing cell types

  • For flow cytometry: Additional markers needed to identify specific immune subsets

  • For IHC: Highest reported expression in testis tissue

• Activation state:

  • Resting T cells show minimal ZBTB32 expression

  • TCR stimulation (α-CD3/CD28) significantly upregulates expression

  • Cytokine treatment (IL-2, IFNβ, IL-12) further enhances expression

• Background concerns:

  • When staining tissues with multiple cell types, consider potential non-specific binding

  • For IHC, antigen retrieval with TE buffer pH 9.0 is recommended for optimal results

How can researchers resolve discrepancies between protein and mRNA detection of ZBTB32?

When facing discrepancies between ZBTB32 protein and mRNA detection:

• Timing considerations:

  • ZBTB32 mRNA expression is highly dynamic during immune responses

  • Protein detection may lag behind mRNA upregulation

  • Consider assessing multiple timepoints following activation

• Technical approaches:

  • For protein detection: Try multiple antibodies targeting different epitopes

  • For mRNA quantification: RT-qPCR specifically targeting Zbtb32 exon junctions

  • Use ZBTB32-deficient samples as negative controls for both approaches

• Sample preparation:

  • For protein: Nuclear extraction protocols may improve detection of this nuclear transcription factor

  • For mRNA: Ensure RNA quality with appropriate controls

  • Consider cell sorting to enrich for populations with highest expression

• Validation strategies:

  • Perform parallel protein and mRNA detection on the same samples

  • Include positive controls (e.g., activated CD8+ T cells at day 6 post-infection)

  • Confirm specificity using genetic approaches (knockdown, knockout)

What are the key experimental controls needed when studying ZBTB32 function?

Critical controls for ZBTB32 functional studies include:

• Genetic controls:

  • Zbtb32−/− mice or cells as negative controls

  • Matched wild-type or heterozygous littermates as positive controls

  • For chimera studies, include controls for potential effects of radiation sensitivity

• Expression validation:

  • Confirm ZBTB32 protein expression by Western blot or flow cytometry

  • Verify mRNA expression by RT-qPCR or RNA-seq

  • Include both resting and activated cells to capture induction

• Experimental design controls:

  • For viral infection studies: Measure viral loads to rule out differences in antigen burden

  • For adoptive transfer experiments: Mix wild-type and knockout cells at 1:1 ratio and transfer into same recipient

  • For ChIP studies: Include isotype control antibodies and non-specific genomic regions

• Cell-type specific considerations:

  • For B cell studies: Include multiple antibody isotypes and timepoints

  • For T cell studies: Assess both effector functions and memory formation

  • For in vitro studies: Compare multiple stimulation conditions

Implementing these controls will ensure robust and reproducible findings when investigating ZBTB32 function.