KLF9 Antibody

What is KLF9 and why is it important to study in research settings?

KLF9 (Krüppel-like factor 9) is a transcription factor belonging to the KLF family of zinc finger transcription factors. It contains three C2H2-type zinc fingers in its carboxyterminal DNA-binding domain and functions primarily as a transcriptional repressor that binds to GC box promoter elements . KLF9 is expressed in various tissues, most abundantly in the brain, kidney, lung, and testis .

It has emerged as a significant regulatory protein in multiple contexts:

• Tumor suppression in multiple cancers including glioblastoma, colorectal cancer, and endometrial carcinoma

• Neural development and oligodendrocyte differentiation

• Transcriptional regulation of glucocorticoid responses

• Regulation of immune gene expression

• Modulation of metabolic pathways

Understanding KLF9 function provides insights into fundamental biological processes and potential therapeutic avenues for various pathologies.

What are the validated applications for KLF9 antibodies?

Based on the literature and commercial validation data, KLF9 antibodies have been successfully used in multiple applications:

When selecting antibodies for specific applications, researchers should review validation data for the specific application and experimental system planned .

How should researchers validate KLF9 antibody specificity?

Validated KLF9 antibody specificity requires a multi-step approach:

• Positive controls: Use cell lines known to express KLF9 (e.g., HeLa, A549, HepG2, U2OS cells)

• Negative controls:

  • Use KLF9 knockout/knockdown cells as negative controls

  • The GBM1a-KLF9KD cells described in the literature can serve as a model system

  • Include isotype controls in IP and ChIP experiments

• Cross-reactivity testing:

  • Test for cross-reactivity with closely related KLF family members, particularly KLF13

  • This is crucial as KLF9 and KLF13 share significant sequence homology and are often co-expressed

• Molecular weight verification:

  • Confirm band size at 27-35 kDa in Western blots

  • Note that post-translational modifications may cause slight variations in apparent molecular weight

• Application-specific validation:

  • For ChIP applications, validate enrichment at known KLF9 binding sites (e.g., fkbp5 promoter)

  • For IP, confirm with mass spectrometry when possible

An excellent approach from the literature includes performing ChIP-qPCR using primers for regions containing putative Klf9 binding motifs, comparing signals between wildtype and KLF9 knockout tissues .

How can researchers optimize ChIP-seq protocols for KLF9?

Optimizing ChIP-seq for KLF9 requires specific considerations based on published protocols:

• Cell number and crosslinking:

  • Use 5×10^6 cells minimum for robust signal

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

  • Double crosslinking with disuccinimidyl glutarate (DSG) followed by formaldehyde may improve results for KLF transcription factors

• Antibody selection and validation:

  • Use ChIP-certified antibodies specifically validated for this application

  • Test antibody specificity using KLF9 knockout cells or tissues as negative controls

  • Validate enrichment at known KLF9 binding sites before proceeding to whole-genome analysis

• Sonication optimization:

  • Aim for chromatin fragments of 200-500 bp

  • Test sonication efficiency by analyzing fragment size distribution

• Control experiments:

  • Include matched IgG control

  • Include input DNA control

  • Consider using epitope-tagged KLF9 as demonstrated in zebrafish studies using an AM epitope tag introduced into the endogenous klf9 locus

• Bioinformatic analysis:

  • Focus on GC-rich regions, particularly GC box elements (KLF9 binds to GC box promoter elements)

  • Compare with publicly available datasets for validation

Notable publication protocol: The study by Liu et al. established a genome-wide map of KLF9-regulated targets in human glioblastoma stemlike cells using ChIP-Seq and identified KLF9 as functioning primarily as a transcriptional repressor .

What are the methodological considerations when studying KLF9 in cancer stem cell research?

KLF9 has been implicated in cancer stem cell (CSC) regulation, particularly in glioblastoma stem cells. Key methodological considerations include:

• Model systems selection:

  • Use established CSC models like GBM-derived neurosphere lines (GBM1a, GBM1b, GBMKK) and low passage primary GBM-derived neurospheres

  • Consider both in vitro neurosphere cultures and in vivo xenograft models

  • Develop inducible KLF9 expression systems for temporal control (e.g., Dox-inducible systems as described in Ying et al.)

• Functional assessment of stemness:

  • Neurosphere formation assays to assess self-renewal

  • Differentiation assays (e.g., using retinoic acid or serum to induce differentiation)

  • In vivo tumor initiation assays using limiting dilution of cells

  • Gene expression analysis of stemness markers before and after KLF9 modulation

• KLF9 manipulation strategies:

  • Generate KLF9 knockdown models using validated shRNAs

  • Create conditional/inducible KLF9 overexpression models

  • Consider CRISPR/Cas9 genome editing for complete knockout models

• Pathway analysis approaches:

  • Assess effects on Notch signaling, a KLF9 target pathway in GSCs

  • Integrate RNA-seq with ChIP-seq data to identify direct transcriptional targets

  • Examine interactions with critical CSC regulatory pathways (e.g., integrin signaling)

• Translational relevance:

  • Correlate findings with patient specimens and clinical outcomes

  • Test combinatorial approaches with standard-of-care therapies

The research by Man et al. demonstrated that KLF9 inhibits glioblastoma stemness and tumorigenicity through direct repression of genes including ITGA6, providing a methodological framework for similar studies .

How can researchers address potential cross-reactivity between KLF9 and KLF13 antibodies?

Cross-reactivity between KLF9 and KLF13 is a significant concern since:

• They share high sequence homology, particularly in the zinc finger domains

• They are often co-expressed in the same tissues

• Both bind similar DNA motifs and may have overlapping functions

Methodological approaches to address this issue:

• Epitope selection:

  • Select antibodies raised against N-terminal regions where KLF9 and KLF13 diverge

  • Avoid antibodies recognizing the highly conserved C-terminal zinc finger domains

  • Review the immunogen information carefully before antibody selection

• Validation with genetic models:

  • Test antibodies on KLF9 knockout tissues/cells

  • Test on KLF13 knockout tissues/cells to ensure no cross-reactivity

  • Use siRNA-mediated knockdown of each factor individually as control

• Epitope tagging approach:

  • Generate cell lines expressing epitope-tagged versions of KLF9 (e.g., FLAG-tagged, AM-tagged)

  • Use tag-specific antibodies for detection

  • This approach was successfully employed in a zebrafish model where researchers introduced a C-terminal AM epitope tag into the endogenous klf9 locus

• Western blot discrimination:

  • KLF9 typically resolves at 27-35 kDa

  • KLF13 typically resolves at a slightly different molecular weight

  • Run both recombinant proteins as controls on the same gel

• Antibody pre-absorption:

  • Pre-absorb antibodies with recombinant protein of the potentially cross-reactive factor

  • Test residual reactivity against both proteins

The research by Speksnijder et al. demonstrated that both KLF9 and KLF13 are co-expressed in differentiating oligodendrocytes and required the development of specific antibodies to distinguish between them .

What are the optimal experimental designs for studying KLF9 in relation to glucocorticoid signaling?

KLF9 plays important roles in glucocorticoid signaling pathways. Research approaches should consider:

• Temporal dynamics analysis:

  • Use time-course experiments as KLF9 and fkbp5 show distinct temporal expression patterns in response to glucocorticoids

  • Implement frequent sampling (e.g., every 4-8 hours) to capture oscillatory dynamics

  • Combine with chronobiology approaches to account for circadian influences

• Cell/tissue selection:

  • Choose appropriate model systems (zebrafish, cultured cells, mouse models)

  • Consider tissue-specific effects as glucocorticoid responses vary widely

  • For in vivo studies, control for endogenous glucocorticoid fluctuations

• Genetic manipulation approaches:

  • Generate KLF9 knockout/knockdown models

  • Create glucocorticoid receptor (GR) mutant models

  • Develop double KLF9/FKBP5 knockout models to study interactions

• Molecular interaction studies:

  • Perform ChIP-seq for both KLF9 and GR to identify shared and unique binding sites

  • Use sequential ChIP (Re-ChIP) to identify genomic loci bound by both factors

  • Analyze KLF9 binding to FKBP5 promoter regions containing putative KLF9 binding motifs

• Pharmacological approaches:

  • Use FK506 to inhibit FKBP5 activity

  • Compare chronic vs. acute glucocorticoid treatments

  • Implement washout studies to assess reversibility

• Read-out systems:

  • qRT-PCR for key GR target genes

  • Luciferase reporter assays with promoters containing GREs

  • RNA-seq to identify genome-wide effects

  • Metabolic assessments (e.g., oxygen consumption rate)

The study by Gans et al. provides an excellent methodology template, demonstrating that KLF9 and FKBP5 are synchronously expressed with GR-dependent dynamics that differ from those of other GC-responsive genes .

How should researchers design experiments to study KLF9's role in transcriptional repression?

KLF9 functions predominantly as a transcriptional repressor . To study this regulatory mechanism:

• Target gene identification approaches:

  • Combine ChIP-seq and RNA-seq after KLF9 manipulation

  • Focus on genes upregulated in KLF9 knockout/knockdown models

  • Analyze enrichment of KLF binding motifs (GC-box elements) in regulatory regions of affected genes

• Protein domain analysis:

  • Study the role of KLF9's SID domain that mediates interaction with Sin3A transcriptional corepressor

  • Create domain-specific mutations to dissect functional regions

  • Perform interaction studies with known corepressors

• Chromatin modification studies:

  • Analyze histone acetylation status at KLF9-bound promoters

  • Study recruitment of histone deacetylases (HDACs) to KLF9 target sites

  • Implement ChIP-seq for repressive histone marks (H3K27me3, H3K9me3)

• Mechanistic analysis of specific targets:

  • Focus on well-characterized targets like FKBP5 or Notch1

  • Use luciferase reporter assays with wild-type and mutated KLF9 binding sites

  • Implement CRISPR activation/interference at KLF9 binding sites

• Contextual analysis:

  • Compare repression mechanisms across different cell types

  • Study how cellular state affects KLF9-mediated repression

  • Analyze repression in normal versus disease states

The research by Bagamasbad et al. demonstrated that KLF9 interacts physically with the FKBP5 promoter region, which becomes hyperacetylated in KLF9 knockout mutants, suggesting direct transcriptional repression .

How can researchers resolve weak or non-specific KLF9 antibody signals in Western blots?

When facing challenges with KLF9 antibody performance in Western blots:

• Sample preparation optimization:

  • Use nuclear extracts rather than whole cell lysates when possible

  • HeLa nuclear extracts have been validated as a positive control

  • Implement protease and phosphatase inhibitors during extraction

  • Use fresh samples when possible

• Antibody dilution optimization:

  • Test wide dilution ranges (1:1000 to 1:50000 has been reported effective)

  • Perform titration experiments to determine optimal concentration

  • Extend primary antibody incubation time (overnight at 4°C)

• Blocking optimization:

  • Test different blocking agents (BSA vs. non-fat milk)

  • For some antibodies, milk-based blocking may interfere with detection

  • Optimize blocking time and temperature

• Detection system enhancement:

  • Use high-sensitivity ECL substrates for chemiluminescent detection

  • Consider secondary antibody optimization (highly cross-adsorbed versions)

  • Try fluorescent Western detection systems for greater linear range

• Gel percentage and transfer conditions:

  • Use 12% SDS-PAGE gels for optimal resolution of the 27-35 kDa range

  • Optimize transfer conditions (time, voltage, buffer composition)

  • Consider using PVDF membranes instead of nitrocellulose for stronger binding

If continued problems occur, consider alternative antibody clones or custom antibody production against unique KLF9 epitopes.

What are the critical factors for successful KLF9 chromatin immunoprecipitation (ChIP)?

Successful KLF9 ChIP requires attention to several critical factors:

• Optimal starting material:

  • Use at least 5×10^6 cells per ChIP reaction

  • Ensure high cell viability before crosslinking

  • For tissue samples, optimize homogenization and fixation protocols

• Crosslinking optimization:

  • Test multiple formaldehyde concentrations (typically 0.75-1%)

  • Optimize crosslinking time (10-15 minutes optimal for most applications)

  • Consider dual crosslinking for improved efficiency

• Chromatin preparation:

  • Optimize sonication conditions for each cell type

  • Verify fragment size distribution (200-500 bp ideal)

  • Ensure complete nuclear lysis before sonication

• Antibody selection and validation:

  • Use ChIP-certified antibodies specifically validated for this application

  • Test antibody specificity using IgG controls

  • Consider epitope-tagged KLF9 approaches with tag-specific antibodies

• Washing conditions:

  • Optimize wash stringency to reduce background

  • Include high salt washes to reduce non-specific binding

  • Test detergent concentrations in wash buffers

• Elution and reversal of crosslinks:

  • Optimize elution conditions (temperature, buffer composition)

  • Ensure complete reversal of crosslinks

  • Include RNase and proteinase K treatments

• qPCR primer design:

  • Design primers for regions containing putative KLF9 binding motifs

  • Include positive control regions (known KLF9 binding sites)

  • Include negative control regions (gene deserts)

The research by Bagamasbad et al. describes successful ChIP-qPCR using primers encompassing putative Klf9 binding motifs identified via JASPAR in the FKBP5 promoter region .

How can KLF9 antibodies be utilized in research on metabolism regulation?

KLF9 has been implicated in metabolic regulation, particularly in gluconeogenesis and glycolysis. Methodological approaches include:

• Metabolic pathway analysis:

  • Use KLF9 antibodies for ChIP-seq to identify direct binding to promoters of metabolic genes

  • Combine with RNA-seq in KLF9 knockout/overexpression models

  • The Bagamasbad et al. study identified KLF9 binding to glycolytic genes, suggesting it functions as a repressor of glycolysis

• Functional metabolic assays:

  • Measure oxygen consumption rate (OCR) in wildtype vs. KLF9 mutant cells

  • Analyze glycolytic flux using extracellular acidification rate (ECAR)

  • Perform glucose uptake and lactate production assays

• Pathway integration studies:

  • Investigate KLF9's role in glucocorticoid-regulated metabolism

  • Study interaction with insulin signaling

  • Examine cross-talk with other metabolic transcription factors

• In vivo metabolic phenotyping:

  • Characterize metabolic parameters in KLF9 knockout animal models

  • Perform glucose and insulin tolerance tests

  • Analyze tissue-specific metabolic effects

• Disease model applications:

  • Study KLF9's role in metabolic diseases (diabetes, fatty liver disease)

  • Analyze KLF9 expression in patient samples with metabolic disorders

  • Investigate potential as therapeutic target

The research indicates that Klf9 may function predominantly as a repressor, regulating metabolism in part by repressing glycolytic genes with a predicted effect of shunting flux through the pentose phosphate pathway .

What methodological approaches can be used to study KLF9 in relation to osteoarthritis pathogenesis?

Recent evidence has identified KLF9 as potentially important in osteoarthritis (OA) pathogenesis. Key methodological approaches include:

• Expression analysis in disease models:

  • Analyze KLF9 expression in cartilage tissues from OA patients

  • Study expression in experimental OA models (e.g., medial meniscotibial ligament-induced OA rats)

  • Compare with IL-1β-treated chondrocytes in vitro

• Functional manipulation approaches:

  • Perform KLF9 knockdown in chondrocyte cultures

  • Create conditional KLF9 knockout mice in cartilage

  • Develop KLF9 overexpression models

• Phenotypic readouts:

  • Assess extracellular matrix (ECM) degradation

  • Measure chondrocyte viability and apoptosis

  • Analyze cartilage-specific gene expression

• Mechanistic studies:

  • ChIP assays to identify direct KLF9 targets in chondrocytes

  • Study the KLF9-GRK5-HDAC6 signaling axis

  • Analyze KLF9 binding to the GRK5 promoter

• Therapeutic intervention studies:

  • Test HDAC6 inhibitors (e.g., TubastatinA) in KLF9-overexpressing models

  • Develop methods to modulate KLF9 activity in cartilage

  • Investigate combination approaches targeting multiple points in the pathway

The research by Zhang et al. demonstrated that the KLF9-GRK5-HDAC6 axis plays a crucial role in promoting OA progression, with KLF9 mediating the transcription of GRK5 by directly targeting its promoter .

How can researchers investigate the relationship between KLF9 and KLF13 in redundant biological functions?

KLF9 and KLF13 show functional redundancy in certain contexts, particularly in oligodendrocyte differentiation . Methodological approaches to study this relationship include:

• Co-expression analysis:

  • Use dual immunofluorescence with specific antibodies

  • Perform single-cell RNA-seq to identify co-expressing cells

  • Analyze temporal expression patterns during differentiation processes

• Genetic models:

  • Generate single KLF9 and KLF13 knockout models

  • Create double KLF9/KLF13 knockout models

  • Develop conditional and inducible knockout systems

• Binding site analysis:

  • Perform comparative ChIP-seq for both factors

  • Identify shared and unique binding sites

  • Analyze enrichment of binding motifs

• Protein interaction studies:

  • Investigate physical interaction between KLF9 and KLF13 using co-immunoprecipitation

  • Perform proximity ligation assays in intact cells

  • Study cooperative binding to regulatory regions

• Functional redundancy assessment:

  • Rescue experiments with one factor in the absence of the other

  • Structure-function analyses with chimeric proteins

  • Domain swap experiments to identify critical regions

The research by Speksnijder et al. showed that KLF9 and KLF13 physically interact, synergistically activate oligodendrocyte-specific regulatory regions with SOX10 and MYRF, and exhibit functional redundancy in promoting oligodendrocyte differentiation and myelination .

Validated KLF9 Antibody Applications and Experimental Conditions

Known KLF9 Target Genes and Binding Motifs

KLF9 Knockout/Knockdown Phenotypes in Different Model Systems