DNAI2 Antibody

What is DNAI2 and what is its role in ciliary function?

DNAI2 (Dynein Axonemal Intermediate Chain 2) is an essential component of the outer dynein arm (ODA) complex in respiratory cilia. It plays a crucial role in the assembly of ODA complexes that generate ciliary movement. DNAI2 is located on human chromosome 17q25 and functions as part of both ODA type 1 (in the proximal axoneme) and ODA type 2 (in the distal axoneme) . Immunofluorescence studies have shown that DNAI2 is distributed throughout the entire length of ciliary axonemes, confirming its involvement in both ODA complexes . Functionally, DNAI2 is essential for the preassembly of these ODA complexes, which serve as the driving force for generating ciliary beats .

Experimental approaches to study DNAI2 localization typically include high-resolution immunofluorescence imaging and transmission electron microscopy (TEM), which can reveal the specific distribution of DNAI2 within the ciliary axoneme and confirm its association with ODA complexes .

How do mutations in the DNAI2 gene affect ciliary structure and function?

Mutations in the DNAI2 gene cause Primary Ciliary Dyskinesia type 9 (CILD9), an autosomal recessive disorder characterized by axonemal abnormalities of motile cilia . The most common ultrastructural defect associated with DNAI2 mutations is the absence of outer dynein arms (ODAs), which can be observed using transmission electron microscopy .

Immunofluorescence analyses of respiratory cells from patients with DNAI2 mutations show:

• Complete absence of DNAI2 protein in ciliary axonemes

• Absence of other ODA components including DNAH5 and DNAI1

• Failure of both proximal (ODA type 1) and distal (ODA type 2) ODA assembly

These structural abnormalities result in insufficient mucociliary clearance of the lungs, leading to chronic respiratory infections and other PCD symptoms including randomization of left/right body asymmetry and male infertility due to impaired sperm flagella function .

How can I design experiments to investigate the role of DNAI2 in ODA assembly?

To design experiments investigating DNAI2's role in ODA assembly, consider the following methodological approaches:

Immunofluorescence co-localization studies:

• Use antibodies against DNAI2 and other ODA components (DNAH5, DNAH9, DNAI1)

• Implement high-resolution confocal microscopy to visualize subcellular localization

• Compare proximal versus distal distribution in the ciliary axoneme

• Include controls with known ODA assembly defects

Ultrastructural analysis:

• Combine immunofluorescence with transmission electron microscopy

• Verify ODA presence/absence in cross-sections of ciliary axonemes

• Correlate immunofluorescence patterns with ultrastructural findings

Genetic studies:

• Analyze cells from patients with various DNAI2 mutations

• Compare cells with mutations in different ODA components (DNAH5, DNAI1)

• Use autozygosity mapping to identify shared intervals across affected individuals

• Implement whole exome sequencing to identify specific variants

Validation experiments:

• Perform in silico analysis of variants using multiple prediction algorithms

• Validate pathogenicity through functional testing (immunofluorescence, TEM)

• Assess impact on protein-protein interactions within the ODA complex

Previous research has demonstrated that DNAI2 is essential for the assembly of both ODA type 1 and ODA type 2, as evidenced by the complete absence of DNAH5, DNAI1, and DNAI2 in axonemes of affected individuals with DNAI2 mutations .

What are the optimal protocols for detecting DNAI2 in respiratory epithelial cells using immunofluorescence?

For optimal immunofluorescence detection of DNAI2 in respiratory epithelial cells, the following protocol components are critical:

Sample preparation:

• Obtain nasal or bronchial epithelial cells via brushing

• Fix samples promptly (typically with 4% paraformaldehyde)

• Consider using acetone for enhanced epitope accessibility

• Mount on positively charged slides

Antibody selection and optimization:

• Primary antibody: Use validated antibodies (e.g., EPR11224 or HPA050565)

• Optimal dilution: Typically 1:100 for immunofluorescence (verify for specific antibody)

• Secondary antibody: Highly cross-adsorbed fluorophore-conjugated antibodies (Alexa Fluor 488 or 546)

• Include acetylated-α-tubulin antibodies for cilia identification

Visualization and analysis:

• Use confocal laser scanning microscopy (e.g., Zeiss LSM 510)

• Counterstain DNA with Hoechst 33342

• Capture z-stack images to visualize the entire ciliary axoneme

• Analyze both proximal and distal regions of cilia

Controls and validation:

• Include healthy control samples

• Use samples with known DNAI2 mutations as negative controls

• Consider co-staining with other ODA components (DNAH5, DNAI1)

• Compare immunofluorescence findings with TEM results

Research has shown that in normal respiratory epithelial cells, DNAI2 localizes throughout the ciliary axoneme, while in patients with DNAI2 mutations, immunofluorescence shows complete absence of DNAI2 signal .

How can I differentiate between different types of ODA defects using DNAI2 antibodies?

Distinguishing between different types of ODA defects requires a strategic approach combining multiple antibodies and techniques:

Panel of ODA component antibodies:

• DNAI2: Present throughout axoneme in normal cilia

• DNAH5: Component of both ODA type 1 and type 2

• DNAH9: Component specifically of ODA type 2

• DNAI1: Particularly relevant for ODA type 2 assembly

Differential staining patterns:

• DNAI2 mutations: Complete absence of DNAI2, DNAH5, and DNAI1 throughout the axoneme

• DNAI1 mutations: DNAI2 and DNAH5 restricted to proximal axoneme, absent distally

• DNAH5 mutations: Variable patterns depending on specific mutation

Quantitative analysis:

• Measure fluorescence intensity along the ciliary axoneme

• Compare proximal vs. distal expression ratios

• Correlate with functional ciliary beat frequency measurements

• Document percentage of affected cilia in patient samples

Verification with TEM:

• Cross-sectional analysis to confirm ODA absence

• Detailed ultrastructural examination to verify specific ODA component defects

• Correlate immunofluorescence patterns with TEM findings

Research has demonstrated that DNAI2 is partially dependent on DNAI1, as DNAI2 can be assembled in proximal ODAs but is absent from distal ODAs in DNAI1-mutant respiratory epithelial cells . This pattern creates a distinctive immunofluorescence signature that helps differentiate between mutation types.

What approaches can be used to validate DNAI2 antibody specificity for research applications?

Validating DNAI2 antibody specificity is crucial for reliable research outcomes. Consider these comprehensive approaches:

Western blot validation:

• Expected molecular weight: ~69 kDa (may appear at ~50 kDa in some systems)

• Test multiple tissue samples (testis, lung) from different species

• Include positive controls (e.g., human testis lysate)

• Verify absence of non-specific bands

Immunoprecipitation:

• Confirm specificity by immunoprecipitating DNAI2 from tissue lysates (e.g., human testis)

• Verify precipitated protein by Western blot with alternative DNAI2 antibody

• Assess for co-precipitation of known interacting partners

Genetic validation:

• Test antibody in tissues/cells from DNAI2-knockout models

• Use samples from patients with confirmed DNAI2 mutations

• Compare staining patterns with wild-type controls

Multiple antibody comparison:

• Test different antibodies targeting distinct DNAI2 epitopes

• Compare staining patterns across applications

• Verify consistent results across antibody clones

Peptide competition assay:

• Pre-incubate antibody with immunizing peptide

• Confirm signal reduction/elimination

• Use as negative control in immunohistochemistry and Western blot

RNA interference validation:

• Perform siRNA knockdown of DNAI2

• Confirm reduction in antibody signal correlates with knockdown efficiency

• Include non-targeting siRNA controls

Enhanced validation of some commercial antibodies includes orthogonal validation with RNAseq, providing additional confidence in antibody specificity .

How can I design experiments to investigate interactions between DNAI2 and other ciliary proteins?

To investigate DNAI2 interactions with other ciliary proteins, consider these experimental approaches:

Co-immunoprecipitation studies:

• Use DNAI2 antibodies to pull down protein complexes

• Analyze precipitated proteins by mass spectrometry

• Confirm specific interactions by Western blot

• Compare results from normal samples versus PCD patients

Proximity ligation assay (PLA):

• Detect protein-protein interactions in situ

• Combine antibodies against DNAI2 and potential interacting partners

• Quantify interaction signals in different ciliary regions

• Compare different cell types and disease states

Yeast two-hybrid screening:

• Use DNAI2 domains as bait to identify novel interacting partners

• Focus on WD40 domains (aa208-246, aa253-294) which are functionally important

• Validate interactions with co-immunoprecipitation

• Map specific interaction domains

FRET/FLIM analysis:

• Label DNAI2 and potential partners with appropriate fluorophores

• Detect energy transfer indicating close proximity

• Perform in intact ciliary structures

• Map interactions to specific ciliary compartments

Comparative analysis in mutant samples:

• Study DNAI2 localization in samples with mutations in other ODA components

• Analyze localization of ODA components in DNAI2-mutant samples

• Create a dependency map for ODA assembly

• Document protein interactions affected by specific mutations

Research has shown that DNAI2 mutations affect the localization of multiple ODA components, including DNAH5 and DNAI1, suggesting complex interdependencies in ODA assembly . Additionally, studies in Chlamydomonas have demonstrated that the DNAI2 ortholog (IC69) interacts with other dynein components in a manner similar to human DNAI2 .

What are the best approaches for analyzing DNAI2 expression in patient samples with suspected ciliopathies?

For analyzing DNAI2 expression in patient samples with suspected ciliopathies, implement these methodological approaches:

Sample collection and processing:

• Obtain nasal or bronchial epithelial cells via minimally invasive brushing

• Process samples immediately to preserve ciliary structure

• Prepare parallel samples for immunofluorescence, TEM, and genetic analysis

• Include age-matched healthy controls

Multi-modal analysis strategy:

• Immunofluorescence panel:

  • DNAI2 antibody (primary target)

  • DNAH5 antibody (ODA marker)

  • DNAI1 antibody (ODA marker)

  • Acetylated-α-tubulin (ciliary axoneme marker)

• Ultrastructural examination:

  • TEM to visualize ODAs directly

  • Quantify percentage of cross-sections showing ODA defects

  • Document specific ultrastructural abnormalities

  • Compare with immunofluorescence findings

• Functional ciliary analysis:

  • High-speed video microscopy for ciliary beat pattern

  • Correlate beat abnormalities with DNAI2 expression

  • Measure ciliary beat frequency

  • Document specific motility defects

• Genetic analysis workflow:

  • Autozygosity mapping for consanguineous families

  • Targeted sequencing of DNAI2 and related genes

  • Whole exome sequencing for unresolved cases

  • Segregation analysis in family members

• Data integration:

  • Correlate genotype with DNAI2 expression pattern

  • Document variant-specific effects on ODA assembly

  • Classify variants using ACMG criteria with functional evidence

  • Create comprehensive patient profile

Research has demonstrated that this multi-modal approach successfully identified a founder variant (DNAI2:c.740G>A; p.Arg247Gln) in multiple PCD patients from a Kuwaiti Arabian tribe, with corresponding absence of DNAI2, DNAH5, and DNAI1 in ciliary axonemes .

What are common pitfalls when working with DNAI2 antibodies and how can they be addressed?

Researchers working with DNAI2 antibodies may encounter several challenges. Here are common pitfalls and solutions:

Inconsistent band size in Western blot:

• Expected molecular weight: 69 kDa

• Observed band often appears at ~50 kDa

• Solution: Verify specificity through knockout controls

• Confirm identity through mass spectrometry

Weak or absent signal in immunofluorescence:

• Optimize fixation (4% paraformaldehyde vs. acetone)

• Implement heat-mediated antigen retrieval (citrate buffer pH 6)

• Adjust antibody concentration (typically 1:100 dilution)

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

Non-specific staining in tissue sections:

• Implement more stringent blocking (5% BSA or 10% serum)

• Increase washing steps and duration

• Reduce primary antibody concentration

• Use highly cross-adsorbed secondary antibodies

Inconsistent results between applications:

• Select antibodies validated for specific applications

• EPR11224 performs well in IP, WB, and IHC-P

• PACO08869 is optimized for ELISA and WB

• HPA050565 works effectively for IHC and Western blot

Cross-reactivity concerns:

• Validate with tissue from DNAI2 knockout models

• Perform peptide competition assays

• Compare results with multiple antibody clones

• Include appropriate negative controls

Tissue-specific considerations:

• Testis tissue shows strong DNAI2 expression (useful positive control)

• Respiratory epithelium requires careful preparation to preserve cilia

• Fetal lung tissue shows developmental expression patterns

• Consider tissue-specific optimization protocols

How can I optimize protocols when comparing DNAI2 expression across different experimental models?

When comparing DNAI2 expression across different experimental models, methodological consistency is crucial:

Standardized sample preparation:

• Implement identical fixation protocols across all samples

• Process all samples simultaneously when possible

• Maintain consistent time intervals between collection and processing

• Use standardized buffers and reagents

Antibody optimization for cross-species studies:

• Validate antibody reactivity in each species (human, mouse, rat)

• Optimize dilution for each species independently

• Select antibodies with confirmed cross-reactivity (e.g., EPR11224)

• Include species-specific positive controls

Quantitative analysis framework:

• Implement identical image acquisition parameters

• Use automated analysis workflows to reduce bias

• Incorporate internal reference standards

• Normalize to appropriate housekeeping proteins/structures

Comparative experimental design:

• Include wild-type controls for each model system

• Process mutant and control samples in parallel

• Blind analysis to prevent observer bias

• Use multiple technical and biological replicates

Data integration strategy:

• Create standardized scoring systems for each technique

• Document model-specific differences in expression patterns

• Correlate findings across techniques (IF, WB, TEM)

• Develop normalized metrics for cross-model comparison

Validation across techniques:

• Western blot: Quantify relative expression levels

• Immunofluorescence: Document subcellular localization

• TEM: Verify ultrastructural consequences

• Functional assays: Correlate with phenotypic outcomes

Research has shown that DNAI2 expression patterns can vary significantly between wild-type and mutant samples, requiring careful optimization of detection protocols to capture these differences accurately .

What are the considerations for using DNAI2 antibodies in multi-color immunofluorescence studies?

For successful multi-color immunofluorescence studies involving DNAI2 antibodies, consider these critical factors:

Antibody compatibility planning:

• Select primary antibodies from different host species (e.g., rabbit anti-DNAI2 with mouse anti-tubulin)

• If using same-species antibodies, implement sequential staining with direct conjugates

• Verify absence of cross-reactivity between antibodies

• Test each antibody individually before combining

Fluorophore selection strategy:

• Choose fluorophores with minimal spectral overlap

• Consider brightness hierarchy (assign brightest fluorophores to least abundant targets)

• For DNAI2 co-localization studies, optimal combinations include:

  • DNAI2 (Alexa Fluor 488) + DNAH5 (Alexa Fluor 546) + DNA (Hoechst 33342)

  • DNAI2 (Alexa Fluor 488) + DNAI1 (Alexa Fluor 647) + acetylated-α-tubulin (Alexa Fluor 546)

Image acquisition optimization:

• Use sequential scanning to minimize bleed-through

• Implement spectral unmixing for closely overlapping fluorophores

• Optimize laser power and detector gain for each channel

• Include single-stained controls for compensation settings

Analysis considerations:

• Employ co-localization analysis software (e.g., JACoP in ImageJ)

• Quantify Pearson's correlation coefficients along ciliary axonemes

• Create intensity profiles along ciliary length

• Differentiate proximal versus distal ciliary compartments

Technical validation:

• Include positive controls showing known co-localization patterns

• Use negative controls with proteins known not to co-localize

• Implement antibody competition controls

• Consider super-resolution techniques for detailed co-localization

Research has demonstrated that multi-color immunofluorescence with DNAI2 antibodies can effectively distinguish between different patterns of ODA defects, revealing specific dependencies in the assembly pathway of these complex structures .

How can DNAI2 antibodies be used to advance understanding of ciliopathy pathogenesis?

DNAI2 antibodies offer powerful tools for advancing our understanding of ciliopathy pathogenesis through several research applications:

Genotype-phenotype correlation studies:

• Screen large cohorts of PCD patients for DNAI2 expression patterns

• Correlate specific DNAI2 variants with protein expression profiles

• Document variant-specific effects on ODA assembly

• Connect molecular findings with clinical severity

Developmental biology applications:

• Track DNAI2 expression during ciliogenesis

• Study temporal aspects of ODA assembly during development

• Investigate tissue-specific DNAI2 expression patterns

• Examine DNAI2 role in embryonic left-right patterning

Therapeutic development platforms:

• Screen compounds for restoration of DNAI2 expression

• Assess correction of ODA assembly in patient-derived cells

• Validate gene therapy approaches targeting DNAI2

• Evaluate read-through compounds for nonsense mutations

Mechanistic investigations:

• Elucidate the precise role of DNAI2 in ODA assembly

• Map protein-protein interactions within the dynein complex

• Investigate how specific domains contribute to function

• Study evolutionary conservation of DNAI2 function (compared to Chlamydomonas ortholog)

Translational research applications:

• Develop diagnostic algorithms incorporating DNAI2 testing

• Create immunofluorescence-based rapid diagnostic assays

• Establish biomarkers for disease progression

• Implement precision medicine approaches for PCD subtypes

Current research has identified DNAI2 mutations in approximately 2% of all PCD families and 4% of PCD families with documented ODA defects . The identification of founder mutations, such as DNAI2:c.740G>A in Kuwaiti patients, highlights the potential for targeted genetic testing in specific populations .

What emerging techniques might enhance the utility of DNAI2 antibodies in ciliary research?

Several emerging techniques promise to enhance the utility of DNAI2 antibodies in ciliary research:

Super-resolution microscopy:

• Implement STED, STORM, or PALM for nanoscale resolution

• Resolve precise DNAI2 localization within ODA structure

• Visualize dynamic assembly processes

• Map protein proximity relationships at molecular scale

Live-cell imaging approaches:

• Develop cell-permeable DNAI2 antibody fragments

• Combine with genetically encoded fluorescent ODA components

• Track dynamic assembly processes in real time

• Correlate with functional ciliary movement

CRISPR-based genome editing:

• Generate isogenic cell lines with specific DNAI2 variants

• Create reporter systems for DNAI2 expression/localization

• Develop conditional knockout models

• Study domain-specific functions through precise mutations

Single-cell proteomics:

• Profile DNAI2 expression in heterogeneous ciliated tissues

• Identify cell-type specific expression patterns

• Correlate with other ciliary proteins at single-cell level

• Discover novel regulatory relationships

Cryo-electron tomography:

• Visualize 3D structure of ODA complexes

• Locate DNAI2 within intact axonemal complexes

• Compare wild-type and mutant structures

• Combine with immunogold labeling for precise localization

Antibody engineering technologies:

• Develop smaller antibody fragments for improved penetration

• Create bifunctional antibodies for proximity studies

• Implement directly conjugated nanobodies for live imaging

• Enhance specificity through affinity maturation

These emerging techniques will help address current knowledge gaps regarding the precise assembly mechanism of ODA complexes and how DNAI2 mutations disrupt this process at the molecular level .

How can in silico analysis complement experimental approaches when studying DNAI2 variants?

In silico analysis provides valuable complementary information to experimental approaches when studying DNAI2 variants:

Comprehensive variant pathogenicity prediction:

• Implement multiple prediction algorithms:

  • MetaLR, MetaSVM, MetaRNN

  • BayesDel (noAF and addAF)

  • REVEL, DANN, MutPred

  • Mutation Assessor, MutationTaster

  • Provean, PolyPhen-2

Structural impact assessment:

• Model effects on protein domains (particularly WD40 domains)

• Analyze conservation scores (Rejected Substitutions score)

• Evaluate impact on protein-protein interaction interfaces

• Model effects on protein stability and folding

Evolutionary conservation analysis:

• Compare with DNAI2 orthologs across species

• Focus on functionally characterized orthologs (e.g., Chlamydomonas IC69)

• Identify evolutionary constraints on specific domains

• Map conserved functional motifs

Integration with experimental data:

• Correlate in silico predictions with immunofluorescence patterns

• Compare structural predictions with TEM findings

• Establish predictive frameworks for variant classification

• Validate computational models with functional data

Population genetics analysis:

• Evaluate variant frequency across populations

• Identify potential founder mutations

• Calculate mutational burden in specific populations

• Assess autozygosity patterns in consanguineous families

Research has demonstrated the value of combining multiple in silico approaches with experimental validation. For example, the DNAI2:c.740G>A variant was predicted to be pathogenic by 16 out of 18 individual prediction programs, which correlated with experimental findings showing complete absence of DNAI2 protein and ODA defects in patient samples .

How should researchers interpret conflicting data when studying DNAI2 expression?

When faced with conflicting data regarding DNAI2 expression, researchers should implement a systematic approach to resolution:

Technical vs. biological variability assessment:

• Evaluate technical factors:

  • Antibody lot-to-lot variation

  • Sample preparation differences

  • Detection method sensitivity

  • Image acquisition parameters

• Consider biological factors:

  • Cell type heterogeneity

  • Developmental stage differences

  • Species-specific expression patterns

  • Disease-related alterations

Multi-technique validation strategy:

Antibody validation checklist:

• Verify specificity through:

  • Genetic knockout controls

  • Peptide competition assays

  • Multiple antibodies targeting different epitopes

  • Correlation with genetic data

• Document antibody performance characteristics

Experimental design evaluation:

• Assess sample size and statistical power

• Review blinding procedures and controls

• Check for appropriate normalization

• Evaluate reproducibility across replicates

• Consider influence of experimental conditions

Integrated data analysis framework:

• Implement transparent reporting of all results

• Conduct meta-analysis of available data

• Consider weight of evidence approach

• Acknowledge limitations and contradictions

• Design critical experiments to resolve discrepancies

Research on DNAI2 has shown that apparent discrepancies can arise from technical factors such as antibody selection or from biological complexity. For example, the observed molecular weight of DNAI2 in Western blots (~50 kDa) often differs from the predicted size (69 kDa), which may reflect post-translational modifications or alternative splicing .

What reference datasets should researchers consult when interpreting DNAI2 antibody results?

For comprehensive interpretation of DNAI2 antibody results, researchers should consult these reference datasets:

Protein expression and localization resources:

• Human Protein Atlas (HPA): Contains validated DNAI2 antibody data

• UniProt: Provides detailed annotation of DNAI2 protein structure and domains

• CilDB: Comprehensive database of ciliary proteins across species

• GeneCards: Integrated database of human genes with expression data

Genetic and variant databases:

• gnomAD: Population frequency data for DNAI2 variants

• ClinVar: Clinically interpreted DNAI2 variants

• LOVD: Locus-specific database for ciliary genes

• dbSNP: Repository of DNAI2 variants (e.g., rs755060592)

Disease-specific resources:

• CILD (Ciliary Dyskinesia) database: Collects PCD-associated variants

• PCD Foundation Genetic Mutation Panel: Compilation of disease-causing mutations

• Ciliopathy Alliance registry: Clinical and genetic data on ciliopathies

Antibody validation resources:

• Antibodypedia: Independent validation data for antibodies

• Biocompare: Comparison of commercial antibody performance

• Enhanced validation data from vendors (orthogonal RNAseq validation)

• Literature compilation of validated antibodies

Comparative datasets:

• Model organism databases (mouse, zebrafish, Chlamydomonas)

• Developmental expression atlases

• Single-cell RNA sequencing databases

• Tissue-specific proteomics datasets