Recombinant Mouse PHD finger protein 11-like (Phf11l)

What is Mouse PHD Finger Protein 11-like (Phf11l) and how does it relate to human PHF11?

Mouse Phf11l is a homolog of the human PHF11 gene that contains a plant homeodomain (PHD) finger motif. This protein belongs to a family of transcriptional regulators that play important roles in immune response regulation. Human PHF11 has been identified through positional cloning as a modifier of serum immunoglobulin E (IgE) concentrations and is associated with asthma susceptibility . The mouse homolog shares structural similarities and conserved functional domains with the human version, though species-specific differences in regulation and function exist.

The gene is located on mouse chromosome 14 in a syntenic region corresponding to human chromosome 13q14, where human PHF11 resides. This conservation of chromosomal localization suggests evolutionary preservation of genomic organization and potentially similar functional roles between species.

What are the known functional domains of Phf11l and their significance?

The most significant functional domain in Phf11l is the PHD finger domain, characterized by a Cys4-His-Cys3 motif that coordinates two zinc ions. This domain typically:

• Functions as a protein-protein interaction module

• Recognizes and binds to specific histone modifications

• Facilitates chromatin remodeling activities

• Contributes to transcriptional regulation

Additional functional regions include nuclear localization signals and potential protein-interaction domains that facilitate its role in immune response regulation and transcriptional control. The 3' untranslated region (UTR) contains regulatory elements, with the rs1046295 SNP in human PHF11 demonstrating significant allele-specific binding by the transcription factor Oct-1, suggesting similar regulatory mechanisms may exist in the mouse homolog .

What are the optimal methods for expressing and purifying recombinant Phf11l protein?

For successful recombinant Phf11l expression and purification, the following optimized protocol is recommended:

• Expression System Selection:

  • Bacterial system (E. coli): Use BL21(DE3) strain with pET vector systems for basic structural studies

  • Mammalian system: HEK293 or CHO cells for studies requiring proper folding and post-translational modifications

  • Insect cell system: Sf9 or Hi5 cells with baculovirus expression systems for high yields of properly folded protein

• Expression Optimization:

  Parameter	Bacterial System	Mammalian System
  Temperature	16-18°C post-induction	37°C
  Induction	0.1-0.5 mM IPTG	N/A
  Duration	16-18 hours	48-72 hours
  Media	TB or LB with supplements	DMEM/F12 with 10% FBS

• Purification Strategy:

  • Immobilized metal affinity chromatography (IMAC) with 6xHis-tag

  • Size exclusion chromatography for higher purity

  • Ion exchange chromatography as a polishing step

• Buffer Optimization:

  • Maintain pH between 7.0-8.0

  • Include 150-300 mM NaCl to reduce non-specific interactions

  • Add reducing agents (5 mM DTT or 2 mM β-mercaptoethanol) to prevent disulfide bond formation

  • Consider including 5-10% glycerol for stability during storage

For zinc-finger proteins like Phf11l, inclusion of zinc salts (10-50 μM ZnCl₂) in purification buffers helps maintain structural integrity of the PHD finger domain.

How can I design an effective experimental approach to study Phf11l transcription factor binding properties?

An effective experimental approach to study Phf11l transcription factor binding would employ multiple complementary techniques:

• Electrophoretic Mobility Shift Assay (EMSA):

  • Design oligonucleotide probes containing predicted binding sites

  • Include competition assays with unlabeled probes to confirm specificity

  • Perform supershift assays with anti-Phf11l antibodies to verify protein-DNA complex formation

• Chromatin Immunoprecipitation (ChIP):

  • Use ChIP followed by sequencing (ChIP-seq) to identify genome-wide binding sites

  • Validate findings with ChIP-qPCR on selected targets

  • Include appropriate controls (IgG, input DNA)

• Reporter Gene Assays:

  • Clone predicted binding regions upstream of a luciferase reporter

  • Co-transfect with Phf11l expression vectors

  • Test multiple cell lines relevant to immune function

• DNA-Protein Interaction Analysis:
  Design a sequential experimental workflow:

  Step	Technique	Purpose	Output
  1	Bioinformatic prediction	Identify potential binding sites	Candidate sequences
  2	EMSA	Confirm direct binding	Binding specificity data
  3	DNA-pulldown	Identify bound proteins	Interacting protein partners
  4	ChIP-seq	Map genome-wide binding	Comprehensive binding profile
  5	Functional validation	Assess biological relevance	Gene expression changes

When designing these experiments, ensure you include appropriate controls based on known binding interactions of related PHD finger proteins, and consider using cell lines relevant to immune function where Phf11l is naturally expressed .

What approaches are most effective for analyzing Phf11l gene expression and splicing variants?

For comprehensive analysis of Phf11l gene expression and splicing variants, a multi-tiered approach is recommended:

• RNA-Seq Analysis:

  • Perform deep sequencing across different tissues and cell types

  • Use junction-aware aligners (e.g., STAR, TopHat) for accurate splicing detection

  • Apply specialized software (e.g., rMATS, MAJIQ) to identify differential splicing events

• RT-PCR and qPCR Validation:

  • Design primers spanning exon-exon junctions for isoform-specific amplification

  • Use nested PCR for low-abundance variants

  • Employ TaqMan probes for highest specificity in quantification

• Single-cell RNA Analysis:

  • Implement scRNA-seq to characterize cell-type specific expression patterns

  • Use computational tools like Monocle or Seurat for trajectory analysis

  • Correlate expression with cellular states during immune responses

• Allele-Specific Expression Analysis:
  Implement the allelotyping method for heterozygous SNPs within Phf11l to detect preferential allelic expression:

  • Extract RNA from heterozygous samples

  • Convert to cDNA and amplify regions containing the SNP

  • Use methods like MALDI-TOF mass spectrometry or next-generation sequencing

  • Calculate allelic ratios and perform statistical analysis to identify significant biases

The above approach successfully revealed significant preferential expression of the A allele of rs1046295 in human PHF11 (P = 6.5 × 10⁻¹⁶), demonstrating the power of allele-specific expression analysis .

How do SNPs in Phf11l influence its function and what are the best methods to investigate these effects?

SNPs in Phf11l can influence protein function through multiple mechanisms, including altered transcription factor binding, modified splicing efficiency, and changes in mRNA stability. To investigate these effects:

• Functional SNP Identification:

  • Perform association studies correlating SNPs with phenotypic traits in mouse models

  • Use comparative genomics to identify conserved SNPs between human PHF11 and mouse Phf11l

  • Focus on SNPs in regulatory regions (promoters, enhancers, UTRs) and splice sites

• Transcription Factor Binding Analysis:

  • Use bioinformatic tools to predict altered transcription factor binding motifs

  • Confirm predictions with EMSA and supershift assays

  • As demonstrated for human PHF11, rs1046295 in the 3' UTR modulates binding of Oct-1 transcription factor in an allele-specific manner

• Expression Quantitative Trait Loci (eQTL) Analysis:

  • Correlate SNP genotypes with expression levels across tissues

  • Implement statistical methods to distinguish cis- and trans-effects

  • Control for population structure and environmental factors

• Methodological Approach for SNP Functional Analysis:

  Step	Method	Purpose	Example from Human PHF11
  1	In silico prediction	Identify potential regulatory SNPs	Predicted Oct-1 binding to rs1046295
  2	EMSA	Validate differential protein binding	Confirmed allele-specific Oct-1 binding
  3	Supershift assays	Identify bound transcription factors	Identified Oct-1 as the binding protein
  4	Reporter assays	Quantify functional impact	Differential expression between alleles
  5	Allele-specific expression	Validate in vivo relevance	A allele preferentially expressed (P = 6.5 × 10⁻¹⁶)

In the case of human PHF11, this approach successfully identified rs1046295 as a functional SNP that affects transcription factor binding and gene expression, providing valuable insights that could be applied to mouse Phf11l research .

What are the most reliable approaches for determining the role of Phf11l in immune regulation and asthma models?

To reliably determine Phf11l's role in immune regulation and asthma models, implement these methodological approaches:

• Genetic Manipulation Models:

  • Generate conditional knockout mice using Cre-loxP system targeting specific immune cell populations

  • Create knock-in models with specific mutations corresponding to human disease-associated variants

  • Develop CRISPR/Cas9-mediated point mutations to study specific SNPs identified in human studies

• Asthma Model Characterization:

  • Implement established protocols for ovalbumin (OVA) or house dust mite (HDM) sensitization

  • Measure airway hyperresponsiveness using whole-body plethysmography

  • Quantify inflammatory cell infiltration in bronchoalveolar lavage (BAL) fluid

  • Analyze tissue remodeling through histopathological examination

• Immunological Parameter Assessment:

  • Measure serum IgE levels given the established association between PHF11 and IgE in humans

  • Quantify cytokine production (IL-4, IL-5, IL-13, IFN-γ) in BAL fluid and lung tissue

  • Characterize T cell differentiation into Th1/Th2/Th17/Treg subsets

  • Analyze dendritic cell maturation and antigen presentation

• Multi-parameter Dataset Collection:
  A comprehensive dataset should include:

  Parameter	Method	Typical Values in Wild-type vs. Phf11l-deficient
  Total serum IgE	ELISA	WT: 150-250 ng/ml; KO: potentially elevated
  Airway resistance	FlexiVent	WT: 2-3 cmH₂O.s/ml baseline; KO: potentially higher after challenge
  Eosinophil count	Flow cytometry	WT: 5-15% of BAL cells after challenge; KO: potentially altered
  IL-4, IL-5, IL-13	Multiplex assay	WT: low baseline, elevated after challenge; KO: potentially dysregulated
  Lung histopathology	Inflammation scoring	WT: minimal baseline inflammation; KO: potentially enhanced

This approach parallels human studies that identified PHF11 as a modifier of serum IgE concentrations and asthma susceptibility through positional cloning .

How can I effectively investigate the protein-protein interactions of Phf11l in cellular contexts?

Investigating Phf11l protein-protein interactions in cellular contexts requires a multi-faceted approach:

• Identification of Interaction Partners:

  • Immunoprecipitation-Mass Spectrometry (IP-MS): Use specific antibodies against Phf11l or epitope tags in transfected cells

  • Proximity-Dependent Biotin Identification (BioID): Fuse Phf11l with a biotin ligase to identify proteins in close proximity

  • Yeast Two-Hybrid Screening: Use Phf11l as bait to screen immune cell cDNA libraries

• Validation of Interactions:

  • Co-immunoprecipitation (Co-IP): Confirm interactions under endogenous conditions

  • Proximity Ligation Assay (PLA): Visualize interactions in situ within cells

  • Fluorescence Resonance Energy Transfer (FRET): Measure direct protein interactions in living cells

  • Split-Luciferase Complementation: Quantify interactions in various cellular compartments

• Functional Characterization of Interactions:

  • Mutational Analysis: Create domain-specific mutants to map interaction interfaces

  • Competition Assays: Use peptides to disrupt specific interactions

  • Functional Readouts: Measure transcriptional activity, chromatin modification, or immune signaling

• Systematic Interaction Mapping:

  Approach	Advantages	Limitations	Application for Phf11l
  IP-MS	Identifies complexes in native context	May lose transient interactions	Identify core Phf11l complexes
  BioID	Captures weak/transient interactions	Non-specific labeling	Map Phf11l neighborhood in nucleus
  FRET	Direct interaction confirmation	Limited to fluorescent protein pairs	Confirm specific interactions
  PLA	Single-molecule sensitivity	Antibody specificity dependent	Validate interactions in immune cells

Given that PHF11 contains a PHD finger domain known to interact with modified histones and other nuclear proteins, special attention should be given to chromatin-associated protein interactions that might reveal its role in transcriptional regulation .

What are the current challenges in reconciling contradictory findings about Phf11l function across different experimental systems?

Reconciling contradictory findings about Phf11l function requires systematic investigation of several potential sources of variation:

• Species and Strain Differences:

  • Mouse Phf11l may have evolved distinct functions from human PHF11

  • Different mouse strains (C57BL/6, BALB/c, etc.) may show genetic background effects

  • Systematic comparison across species and strains using identical experimental protocols is needed

• Cell Type-Specific Functions:

  • Phf11l may have divergent roles in different immune cell populations

  • Expression levels and splicing variants may vary across cell types

  • Conditional knockout models targeting specific lineages can help resolve these discrepancies

• Technical Variations in Methodology:

  • Antibody specificity issues may lead to contradictory results

  • Different knockout/knockdown strategies might affect distinct functional domains

  • Variations in experimental conditions (timing, dose, readout sensitivity)

• Integrated Analysis Framework:

  Contradiction Type	Reconciliation Approach	Example Application
  Expression pattern discrepancies	Single-cell RNA-seq across tissues	Map cell-specific expression
  Phenotype differences between models	Side-by-side comparison with standardized protocols	Compare asthma phenotypes across knockout models
  Opposing regulatory effects	Context-dependent transcriptomics	Profile Phf11l effects under different stimulation conditions
  Protein interaction inconsistencies	Quantitative interaction proteomics under defined conditions	Compare interactomes in resting vs. activated states

The approach to contradictory findings should follow the principle demonstrated in human PHF11 research, where multiple methods (bioinformatics, EMSA, allele-specific expression) were used to establish the functional significance of the rs1046295 SNP, building a consistent mechanistic understanding despite initial conflicting predictions .

How can I design experiments to investigate the epigenetic regulation mediated by Phf11l's PHD finger domain?

Designing experiments to investigate epigenetic regulation by Phf11l's PHD finger domain requires specialized approaches targeting histone modifications and chromatin interactions:

• PHD Finger Binding Specificity:

  • Histone Peptide Arrays: Screen for binding to modified histone tail peptides

  • Isothermal Titration Calorimetry (ITC): Measure binding affinities to specific modifications

  • Nuclear Magnetic Resonance (NMR): Map the structural basis of histone recognition

  • Structural studies: X-ray crystallography or cryo-EM of Phf11l PHD finger with bound histone peptides

• Chromatin Association Patterns:

  • ChIP-seq for Phf11l: Map genome-wide binding sites

  • Sequential ChIP (ChIP-reChIP): Identify genomic regions where Phf11l co-localizes with specific histone marks

  • CUT&RUN or CUT&Tag: Higher resolution mapping of chromatin binding with lower background

  • HiChIP: Investigate three-dimensional chromatin interactions mediated by Phf11l

• Functional Impact on Chromatin State:

  • ATAC-seq: Compare chromatin accessibility in wild-type versus Phf11l-deficient cells

  • ChIP-seq for histone modifications: Examine how Phf11l affects histone mark distribution

  • PRO-seq or GRO-seq: Measure effects on transcriptional activity at specific loci

• Experimental Design for PHD Finger Functional Analysis:

  Experimental Approach	Controls	Expected Outcome	Interpretation
  PHD domain mutagenesis + ChIP-seq	Wild-type protein; structure-based mutations	Altered genomic binding pattern	Identifies residues critical for chromatin recognition
  Histone binding assays with peptide arrays	Unmodified peptides; other PHD fingers	Specific binding to select modifications	Defines histone modification preference
  Domain swapping experiments	Chimeric proteins with other PHD fingers	Altered target specificity	Determines domain-specific functions
  Inducible expression in Phf11l-knockout cells	Empty vector; catalytically inactive mutants	Rescue of specific phenotypes	Links chromatin binding to biological function

This systematic approach builds on methodologies similar to those used in studying transcription factor binding of human PHF11, but with specific adaptations for chromatin interactions relevant to PHD finger domains .

What are the most effective strategies for translating mouse Phf11l findings to human PHF11 in asthma and immune disorders?

Translating mouse Phf11l findings to human PHF11 requires careful consideration of species differences and methodological approaches:

• Comparative Genomics and Expression Analysis:

  • Identify conserved regulatory elements between mouse and human genes

  • Compare expression patterns across analogous cell types and tissues

  • Focus on evolutionary conserved protein domains and motifs

  • Pay special attention to the rs1046295 region in humans and equivalent regions in mice, as this SNP has demonstrated functional significance

• Cross-Species Validation Methods:

  • Humanized Mouse Models: Generate mice expressing human PHF11 variants

  • Parallel In Vitro Systems: Test equivalent mutations in both mouse and human cell lines

  • Comparative Transcriptomics: Identify conserved gene networks regulated by PHF11/Phf11l

• Clinical Correlation Design:

  • Develop biomarker panels based on mouse models for testing in human patients

  • Design human genetic studies guided by mouse phenotype observations

  • Establish patient-derived systems (PBMCs, organoids) to validate mouse findings

• Translational Pathway Development:

  Stage	Mouse Studies	Human Translation	Success Metrics
  Discovery	Identify Phf11l function in asthma models	Correlate with human genetic associations	Convergence of mechanisms
  Validation	Test specific variants/pathways	Analyze in patient samples	Consistent biomarker patterns
  Therapeutic development	Test pathway interventions	Design human-relevant compounds	Target engagement proof
  Clinical application	Predict responder populations	Stratify patients by genotype	Improved treatment outcomes

Human PHF11 was originally identified through positional cloning as affecting serum IgE levels and asthma susceptibility . The specific finding that rs1046295 affects Oct-1 transcription factor binding and shows preferential expression of the A allele provides a model for how functional genomics findings can connect molecular mechanisms to disease associations .

How can I design robust experimental controls when studying Phf11l in complex immune system models?

Designing robust controls for Phf11l studies in complex immune models requires multi-layered validation strategies:

• Genetic Control Design:

  • Littermate Controls: Use littermates from heterozygous crosses to minimize background effects

  • Multiple Knockout Strategies: Compare phenotypes from conventional, conditional, and inducible knockout models

  • Allelic Series: Generate hypomorphic and point mutation variants alongside complete knockouts

  • Rescue Experiments: Re-express Phf11l or human PHF11 in knockout backgrounds

• Experimental System Controls:

  • Cell Type-Specific Validation: Confirm findings across multiple immune cell populations

  • Stimulus Titration: Test across ranges of antigen/allergen concentrations

  • Temporal Controls: Examine acute versus chronic models

  • Environmental Standardization: Control for microbiome, housing conditions, and environmental exposures

• Technical and Analytical Controls:

  • Antibody Validation: Use knockout tissues to confirm specificity

  • Multi-method Confirmation: Validate key findings with orthogonal techniques

  • Blinding and Randomization: Implement throughout experimental workflow

  • Pre-registration: Define analysis plans before data collection

• Comprehensive Control Framework:

  Control Type	Implementation	Purpose	Example Application
  Genetic	Cre-only and floxed-only controls alongside cKO	Control for Cre toxicity and floxed allele effects	T cell-specific Phf11l deletion studies
  Cellular	Isolated cell populations vs. whole tissue	Distinguish cell-autonomous effects	Compare purified B cells to whole spleen responses
  Technical	Multiple antibody clones for key targets	Ensure detection specificity	Validate Phf11l ChIP-seq findings with different antibodies
  Biological	Multiple challenge models	Test consistency across contexts	Compare OVA, HDM, and IL-33 asthma models

This control framework follows principles exemplified in human PHF11 research, where multiple controls were implemented in EMSA experiments to confirm specific binding of Oct-1 to the rs1046295 SNP, including competitive binding assays and supershift controls .