PFDN2 Human

What is PFDN2 and what is its role in the prefoldin complex?

PFDN2 (Prefoldin subunit 2) is a component of the heterohexameric prefoldin complex conserved from archaea to humans. It functions as a cochaperone during co-translational folding of proteins, particularly actin and tubulin monomers. PFDN2 binds specifically to cytosolic chaperonin (c-CPN) and transfers target proteins to it, promoting proper folding in environments with competing pathways for nonnative proteins . Notably, PFDN2 is present in both the canonical prefoldin complex and the Uri/prefoldin-like complex, distinguishing it from subunits like PFDN5 that appear only in the canonical complex .

What are the structural characteristics of human PFDN2 protein?

Human PFDN2 consists of 154 amino acids (Met1-Ser154) in its mature form. The recombinant human PFDN2 with a polyhistidine tag has 169 amino acids with a predicted molecular mass of 18.5 KDa, typically migrating as an 18-20 KDa band in SDS-PAGE under reducing conditions . The protein is encoded by the PFDN2 gene (also known as PFD2, HSPC231) and functions as part of the jellyfish-like structure of the prefoldin complex, which contains six subunits divided into α and β classes based on sequence similarity .

How is PFDN2 involved in gene expression regulation?

PFDN2 plays a significant role in transcriptional regulation through multiple mechanisms. Research has demonstrated that prefoldin perturbations cause transcriptional alterations across the human genome, with PFDN2 depletion affecting gene expression particularly under conditions of transcriptional regulation such as serum stimulation . The following table summarizes key findings regarding PFDN2's impact on gene expression:

The significant overlap and consistent direction of gene expression changes between PFDN2 and PFDN5 depletions suggest these subunits share important functions in transcriptional regulation .

What experimental methods are used to study PFDN2's role in co-transcriptional splicing?

Investigating PFDN2's role in co-transcriptional splicing requires a multi-faceted methodological approach:

• RNA-seq analysis: Used to detect changes in exon-intron ratios and alternative splicing events using computational tools like SUPPA

• RT-qPCR: For measuring pre-mRNA levels for specific introns in control versus PFDN2-depleted cells

• Chromatin immunoprecipitation (ChIP): To analyze PFDN2 binding to chromatin and its association with RNA polymerase II

• ChIP-seq analysis: For mapping genome-wide binding patterns of prefoldin components

• RNase treatment experiments: To determine if prefoldin binding to chromatin is RNA-dependent

• Phosphorylation analysis: Western blotting and ChIP to assess RNA polymerase II CTD phosphorylation status

These complementary approaches enable researchers to comprehensively characterize PFDN2's role in the complex process of co-transcriptional splicing.

How does PFDN2 depletion affect pre-mRNA splicing mechanisms?

PFDN2 depletion leads to severe pre-mRNA splicing defects, particularly affecting long genes with numerous introns. The experimental data reveals several mechanistic insights:

The mechanism involves reduced phosphorylation of RNA polymerase II's carboxy-terminal domain, which compromises the recruitment of splicing factors like PRP19 and U2AF65. This reduces co-transcriptional splicing efficiency, resulting in increased pre-mRNA retention .

What technical considerations are essential for generating PFDN2-deficient cell models?

Creating reliable PFDN2-deficient cell models requires careful technical considerations:

• siRNA-mediated knockdown: Researchers have achieved approximately 67% efficiency in reducing PFDN2 protein using specific siRNAs (sequence: CAGCCUAGUGAUCGAUACA) after 72 hours of treatment . Transfection typically uses Oligofectamine reagent (8 μl with 15 μl siRNA at 20 nM) .

• CRISPR-Cas9 gene editing: While the search results don't specifically detail CRISPR for PFDN2, the protocol described for PFDN5 can be adapted, including gRNA design, co-transfection with hCas9, clone selection by dilution, and validation through sequencing .

• Experimental timeline: For serum stimulation experiments, cells are typically transfected with siRNAs for 24 hours, serum-starved for 48 hours, then stimulated by adding serum, with samples collected before and 90 minutes after stimulation .

• Cell viability considerations: Researchers should monitor potential cytotoxicity, as prefoldin depletion may affect cell viability, though serum starvation appears to mitigate some viability issues with PFDN5 siRNA .

How can researchers analyze PFDN2's genome-wide binding patterns?

Analysis of PFDN2's genome-wide binding employs several sophisticated approaches:

• ChIP-seq protocol: After crosslinking with formaldehyde, cells are sonicated and immunoprecipitated with PFDN2-specific antibodies. The DNA is purified, sequenced, and mapped to the reference genome .

• Data processing workflow:

  • Filter ChIP-seq data using FASTQ Toolkit

  • Align reads to the reference genome (hg19) using Rsubread

  • Remove duplicates with samtools rmdup

  • Create bigwig files comparing IP signal to control

  • Generate density plots and heatmaps using deeptools and EnrichedHeatmap packages

• Binding pattern analysis: Prefoldin binding correlates with RNA polymerase II occupancy, showing the highest signals around transcription start sites .

• Functional correlation: A key finding is the negative correlation between PFDN2 binding intensity and the effect of its depletion on gene expression - genes with higher PFDN2 occupancy show greater dependence on PFDN2 for their expression .

How does PFDN2 influence alternative pre-mRNA processing events?

PFDN2 significantly impacts alternative pre-mRNA processing through its role in co-transcriptional splicing:

• Approximately 20% of genes show altered alternative processing events upon prefoldin depletion

• These events include both suppressed (≥10% less frequent) and enhanced (≥10% more frequent) processing compared to control cells

• No specific class of alternative processing event is particularly affected, suggesting a broad influence on splicing mechanisms

• Analysis uses computational tools like SUPPA to identify and classify alternative processing events from RNA-seq data

• The exon ratio (exonic reads/total reads) calculation provides a quantitative measure of splicing efficiency before and after stimulation

The systematic analysis of these events reveals PFDN2's broad impact on the splicing machinery rather than affecting specific types of alternative processing.

What are the recommended protocols for studying PFDN2-RNA polymerase II interactions?

Studying PFDN2-RNA polymerase II interactions requires precise methodology:

• Chromatin Immunoprecipitation (ChIP):

  • Crosslink cells with 1% formaldehyde for 10 minutes at room temperature

  • Sonicate chromatin to 200-500 bp fragments

  • Immunoprecipitate with antibodies against PFDN2 and RNA polymerase II

  • Perform qPCR with primers targeting specific genomic regions

• Co-immunoprecipitation:

  • Prepare nuclear extracts under non-denaturing conditions

  • Immunoprecipitate with anti-PFDN2 antibodies

  • Perform Western blotting for RNA polymerase II and phosphorylated forms (Ser2P, Ser5P)

  • Include RNase treatment controls to distinguish RNA-dependent interactions

• Phosphorylation analysis:

  • Use phospho-specific antibodies against Ser2P and Ser5P forms of RNA polymerase II CTD

  • Normalize to total RNA polymerase II levels

  • Compare phosphorylation levels between control and PFDN2-depleted conditions

These approaches have revealed that PFDN2 depletion reduces CTD phosphorylation and CDK9 recruitment, providing mechanistic insight into how PFDN2 influences transcription and splicing.

How should researchers interpret discrepancies between PFDN2 and PFDN5 depletion effects?

When analyzing differences between PFDN2 and PFDN5 depletion effects, researchers should consider:

• Complex-specific functions: PFDN2 exists in both canonical prefoldin and Uri/prefoldin-like complexes, while PFDN5 is only in the canonical complex. Effects seen with PFDN2 depletion but not with PFDN5 depletion likely reflect Uri/prefoldin-like complex functions .

• Depletion efficiency: PFDN2 depletion (67% reduction) versus PFDN5 depletion (55% reduction) may account for some differences in experimental outcomes .

• Protein abundance considerations: The higher baseline levels of PFDN2 compared to PFDN5 in human cells may explain the lower impact of PFDN2 depletion on some phenotypes .

• Redundancy mechanisms: The prefoldin complex architecture may allow some functional redundancy between subunits, potentially masking certain phenotypes.

• Experimental validation: Key findings should be validated using multiple approaches:

  • Compare siRNA knockdown with CRISPR knockout models

  • Perform rescue experiments with wild-type protein expression

  • Use gene-specific assays (RT-qPCR) to validate RNA-seq findings

Understanding these factors helps researchers distinguish technical artifacts from biologically meaningful differences in subunit functions.

How can PFDN2 research inform understanding of splicing-related diseases?

PFDN2's role in co-transcriptional splicing has significant implications for understanding splicing-related diseases:

• Mechanistic insights: The finding that PFDN2 influences RNA polymerase II CTD phosphorylation and subsequent splicing factor recruitment provides a molecular mechanism that may be dysregulated in disease contexts .

• Long gene susceptibility: The observation that PFDN2 depletion particularly affects long genes with numerous introns suggests a potential vulnerability relevant to neurodevelopmental disorders, where long genes are often affected .

• Alternative splicing regulation: PFDN2's broad impact on alternative processing events (affecting ~20% of genes) indicates it may contribute to splicing dysregulation in diseases characterized by aberrant splicing patterns .

• Experimental approaches: Researchers investigating splicing-related diseases could:

  • Analyze PFDN2 expression or mutations in patient samples

  • Examine PFDN2 binding patterns at disease-relevant genomic loci

  • Test whether PFDN2 overexpression can rescue splicing defects in disease models

What are the optimal experimental designs for studying PFDN2's dual roles in protein folding and transcriptional regulation?

To dissect PFDN2's dual roles, researchers should consider:

• Compartment-specific analysis:

  • Nuclear versus cytoplasmic fractionation to separate transcriptional from protein folding functions

  • Fluorescent tagging of PFDN2 to track localization during different cellular processes

• Domain-specific mutations:

  • Engineer PFDN2 variants with mutations in domains required for:

    • Chaperonin binding (protein folding function)

    • Chromatin association (transcriptional function)

  • Test these variants in rescue experiments

• Temporal dynamics:

  • Synchronized cell studies to examine PFDN2 function during different cell cycle phases

  • Pulse-chase experiments to distinguish immediate versus sustained effects

• Interaction partner analysis:

  • BioID or proximity labeling to identify compartment-specific interaction partners

  • Compare interaction networks under conditions that primarily engage one function versus the other

• Integrated multi-omics approach:

  • Combine proteomics (for folding function) with transcriptomics and genomics (for transcriptional function)

  • Correlate protein folding defects with transcriptional alterations in the same experimental system

This comprehensive approach would help delineate PFDN2's functions in different cellular contexts and reveal potential crosstalk between its roles.

What quality control measures are essential when working with recombinant PFDN2 protein?

When working with recombinant human PFDN2 protein, implement these quality control measures:

• Purity verification: Confirm >95% purity via SDS-PAGE analysis

• Molecular weight confirmation: Verify migration at 18-20 KDa on SDS-PAGE, consistent with its predicted 18.5 KDa mass

• Functional assays:

  • Test binding to cytosolic chaperonin

  • Verify ability to assist in protein folding of model substrates

  • Assess complex formation with other prefoldin subunits

• Storage optimization:

  • Determine optimal buffer conditions to prevent aggregation

  • Validate protein stability after freeze-thaw cycles

  • Monitor activity retention during storage

• Batch consistency:

  • Compare lot-to-lot variation in activity assays

  • Ensure reproducible results across different preparations

• Expression system considerations:

  • For E. coli-expressed PFDN2, confirm proper folding and absence of inclusion bodies

  • Validate that the polyhistidine tag doesn't interfere with function

These measures ensure that experimental outcomes reflect genuine PFDN2 biology rather than artifacts from improperly prepared protein.