Recombinant Human Interferon omega-1 protein (IFNW1), partial (Active)

What is Interferon omega-1 (IFNW1) and how is it classified within the interferon family?

Interferon omega-1 (IFNW1) is a protein encoded by the IFNW1 gene in humans and is classified as a member of the type I interferon family, which includes IFN-α, IFN-β, and IFN-ω. This classification is based on receptor specificity, gene sequence similarity, and chromosomal location . Evolutionarily, genome sequence analysis suggests that the IFN-ω gene diverged from the IFN-α gene approximately 130 million years ago, establishing its distinct lineage within the interferon system . IFNW1 shares approximately 62% sequence similarity with IFN-α and 33% sequence similarity with IFN-β, highlighting its unique position within the interferon family while maintaining functional relatedness .

What are the primary biological activities of recombinant human IFNW1?

Recombinant human IFNW1 functions primarily as a cytokine that promotes innate immunity against viruses and cancers. It achieves this through interaction with the IFNAR-1/IFNAR-2 receptor complex, which initiates signal transduction cascades resulting in antiviral and antiproliferative actions . Experimental evidence has demonstrated that IFNW1 inhibits the proliferation of TF-1 cells induced by GM-CSF, a property that has been utilized in bioassays to measure its activity . Additionally, IFNW1 has been linked to antitumor activity and protection against bacterial and parasitic pathogens beyond its established antiviral properties . The protein contains two intramolecular disulfide bonds that are crucial for maintaining these biological activities .

How does the receptor-binding mechanism of IFNW1 differ from other type I interferons?

The IFNAR1 subunit contains an intracellular domain linked to Tyrosine kinase 2, while the IFNAR2 subunit's intracellular domain is linked to Janus kinase 1. When IFNW1 binds to these receptors, a phosphorylation cascade progresses, regulated by the STAT protein . Research indicates that the receptor can bind each type I interferon in distinct ways, creating specific downstream effects for each variant, even with shared receptor components . These structural and functional differences may explain why certain interferons are more effective against specific pathogens or cellular abnormalities.

What are the optimal expression systems for producing bioactive recombinant human IFNW1?

For producing bioactive recombinant human IFNW1, the HEK 293 expression system has demonstrated significant success. This mammalian expression system enables proper post-translational modifications, particularly the formation of crucial disulfide bonds that are essential for IFNW1 activity . The recombinant protein expressed in HEK 293 cells achieves >95% purity with endotoxin levels below 1 EU/μg, making it suitable for sensitive functional studies .

When establishing an expression system for IFNW1, researchers should consider:

• Codon optimization for the host system

• Inclusion of appropriate signal sequences for secretion

• Purification strategies that preserve disulfide bonding

• Quality control measures to ensure biological activity

The full-length protein (amino acids 1-195) includes the signal peptide and mature protein regions, though for some applications, expression of just the mature protein region may be sufficient . Validation of bioactivity should be performed using established assays such as the antiviral assay with WISH human amnion cells infected with vesicular stomatitis virus (VSV), where the ED50 typically ranges from 2-8 pg/mL .

How can researchers accurately quantify IFNW1 activity in experimental settings?

Quantifying IFNW1 activity requires specialized bioassays rather than simple protein concentration measurements. The gold standard approach utilizes the ability of IFNW1 to inhibit proliferation of specific cell lines in response to growth factors. Specifically, bioassays for IFN-ω exploit the inhibition of TF-1 cell proliferation induced by GM-CSF .

Methodology for IFNW1 activity quantification:

• Cell-based assays:

  • TF-1 cells with GM-CSF induction

  • Alternatively, assays using Epo and TF-1 cells, or Epo and Epo-transfected UT-7 cells

  • WISH human amnion cells infected with vesicular stomatitis virus for antiviral activity measurement

• Activity calculation:

  • Determine the ED50 (typically 2-8 pg/mL for antiviral effects)

  • Compare against a reference standard with known activity

  • Express results in International Units (IU) per mass of protein

• Controls to include:

  • Positive control (reference standard IFN-ω)

  • Negative control (non-interferon cytokine)

  • Cell viability controls

This methodological approach ensures accurate quantification of bioactive IFNW1, which is essential for research reproducibility and translational applications.

What are the critical considerations for designing experiments involving IFNW1 in host-pathogen interaction studies?

When designing experiments to study IFNW1 in host-pathogen interactions, researchers must consider several critical factors:

• Selection of appropriate cellular models:

  • Primary cells vs. cell lines (primary cells often provide more physiologically relevant responses)

  • Species-specific considerations (human IFNW1 may not interact with non-primate receptors optimally)

  • Cell types relevant to the pathogen of interest

• Timing of IFNW1 administration:

  • Pre-infection (to study prophylactic effects)

  • Post-infection (to study therapeutic potential)

  • Kinetic studies to determine optimal timing

• Dosage determination:

  • Establish dose-response curves (ED50 for antiviral effects typically 2-8 pg/mL)

  • Consider physiological relevance of concentrations used

• Readouts and endpoints:

  • Viral load quantification

  • Expression of interferon-stimulated genes

  • Functional assays of cellular protection

  • Cytopathic effect measurements

• Genetic approaches:

  • IFNW1 knockdown/knockout studies

  • Receptor subunit manipulation

  • Evaluation of genetic variants (e.g., rs79876898)

Evidence from HIV studies indicates that genetic variation in IFNW1 may influence viral loads in specific populations, suggesting experimental designs should account for potential genetic background effects . Additionally, since IFNW1 has evolved under strong purifying selection, it likely has non-redundant functions in immunity that should be specifically targeted in experimental designs .

How does the evolutionary conservation of IFNW1 inform its functional importance compared to other interferons?

Evolutionary genetic analyses of human interferons have revealed that IFNW1 belongs to a group of type I IFNs that have evolved under strong purifying selection, indicating essential and non-redundant functions in immunity to infection . This evolutionary constraint stands in stark contrast to certain interferon subtypes like IFN-α10 and IFN-ε, which have accumulated missense or nonsense mutations at high frequencies within human populations, suggesting functional redundancy .

The evolutionary conservation of IFNW1 has several implications for research:

• Functional importance: Strong purifying selection suggests that IFNW1 likely plays a unique role that cannot be compensated by other interferons . This challenges the notion that all type I interferons are functionally redundant.

• Cross-species considerations: IFNW1 shows conservation across mammals, though multiple subvariants have been observed in non-primate mammals with placentas . This suggests that studies in animal models should carefully consider species-specific variations.

• Receptor interaction specificity: Despite sharing the IFNAR1/IFNAR2 receptor complex with other type I interferons, the evolutionary conservation of IFNW1 suggests unique aspects of receptor engagement or downstream signaling .

• Pathogen pressure: The selective forces maintaining IFNW1 conservation likely reflect consistent pressure from specific pathogens throughout human evolutionary history.

Research approaches should leverage this evolutionary insight by examining the specific pathogen classes that might have driven IFNW1 conservation and identifying unique signaling pathways or cellular responses that distinguish IFNW1 from other type I interferons.

What are the structural determinants of IFNW1 specificity and how do they influence receptor binding dynamics?

• Disulfide bonding: IFNW1 contains two intramolecular disulfide bonds that are crucial for its biological activities . These likely contribute to the tertiary structure stability and presentation of receptor-binding epitopes.

• Sequence determinants: IFNW1 shares 62% sequence similarity with IFN-α but only 33% with IFN-β . These sequence differences, particularly in the receptor-binding regions, likely contribute to the unique binding characteristics and downstream signaling profiles.

• Receptor complex conformational changes: Different type I interferons, including IFNW1, create unique conformations when bound to the IFNAR1/IFNAR2 complex, leading to distinct downstream signaling cascades . This "conformational tuning" of the receptor complex may be a key mechanism underlying the specific biological activities of IFNW1.

• Kinetic parameters: The association and dissociation rates of IFNW1 with individual receptor subunits (IFNAR1 and IFNAR2) likely differ from other interferons, potentially affecting signal strength and duration.

To fully characterize these structural determinants, researchers should consider:

• Comparative structural studies of multiple type I interferons bound to IFNAR

• Mutagenesis studies targeting predicted interface residues

• Biophysical measurements of binding kinetics

• Computational modeling of receptor-ligand interactions

Understanding these structural determinants will facilitate rational design of IFNW1 variants with enhanced specificity or altered activity profiles for therapeutic applications.

How do single nucleotide polymorphisms in the IFNW1 gene impact antiviral responses, particularly in the context of HIV infection?

Research on single nucleotide polymorphisms (SNPs) in the IFNW1 gene has revealed intriguing associations with antiviral responses, particularly in HIV infection. A significant study involving antiretroviral-naive participants living with HIV found associations between IFNW1 genetic variation and HIV-1 viral load .

Key findings regarding IFNW1 polymorphisms include:

• Population-specific effects: The association between IFNW1 variants and HIV-1 viral load was particularly pronounced among African participants, suggesting population-specific genetic effects .

• Specific SNP identification: Among 17 SNPs within the IFNW1 region, rs79876898 (A > G) showed significant association with study entry viral load (p = 0.0020, beta = 0.32), with the G allele associated with higher viral loads compared to the A allele .

• Complex genetic architecture: When controlling for population structure using linear mixed effects models, the significance of some associations was reduced, highlighting the complex interplay between IFNW1 variants and genetic background .

• Context-dependent effects: The IFNW1 genetic associations were observed in specific cohorts with early HIV infection but were not reproduced in cohorts with advanced infection, suggesting that the impact of these variants may depend on disease stage .

These findings suggest several important considerations for researchers:

• IFNW1 genetic screening may provide valuable information for predicting antiviral responses

• Population-specific genetic backgrounds should be considered when evaluating IFNW1 function

• The effects of IFNW1 variants may be context-dependent, varying with disease stage or co-infections

• Functional validation of identified SNPs is needed to establish causality rather than mere association

Future research should focus on mechanistic studies to determine how specific IFNW1 variants alter protein function or expression, potentially leading to personalized therapeutic approaches based on genetic profiles.

What are the primary stability challenges when working with recombinant IFNW1 and how can they be addressed?

Recombinant IFNW1, like other interferon proteins, presents several stability challenges that researchers must address to maintain biological activity. Based on experimental experience with similar interferons, the following challenges and solutions are relevant:

• Disulfide bond integrity:

  • Challenge: IFNW1 contains two intramolecular disulfide bonds crucial for activity

  • Solution: Avoid reducing agents in buffers; store in non-reducing conditions; consider adding stabilizing excipients such as human serum albumin or trehalose

• Aggregation propensity:

  • Challenge: Interferons can aggregate during storage and freeze-thaw cycles

  • Solution: Limit freeze-thaw cycles; add stabilizers like polysorbates; maintain optimal pH (typically 5.5-7.5); filter solutions before storage

• Adsorption to surfaces:

  • Challenge: Low concentration IFNW1 solutions may lose activity due to adsorption to container surfaces

  • Solution: Use low-binding tubes and pipette tips; add carrier proteins (0.1-0.5% BSA); pre-saturate surfaces with carrier proteins

• Temperature sensitivity:

  • Challenge: Activity loss during temperature fluctuations

  • Solution: Store at -80°C for long-term; maintain at 4°C for short-term use; avoid ambient temperature exposure

• Proteolytic degradation:

  • Challenge: Susceptibility to proteases

  • Solution: Include protease inhibitors in working solutions; ensure high purity of recombinant preparations (>95%) ; handle samples on ice

Stability monitoring protocol:

• Establish baseline activity using standardized bioassays (e.g., WISH cell/VSV system)

• Prepare multiple single-use aliquots

• Test representative aliquots at defined intervals

• Document activity retention under various storage conditions

Following these methodological approaches will ensure maximum retention of IFNW1 bioactivity throughout experimental workflows, improving reproducibility and reliability of research outcomes.

How can researchers distinguish between the specific effects of IFNW1 and other type I interferons in experimental systems?

Distinguishing the specific effects of IFNW1 from other type I interferons presents a significant methodological challenge due to shared receptor utilization and overlapping downstream effects. Researchers can implement the following approaches to address this challenge:

• Selective neutralization strategies:

  • Use IFNW1-specific neutralizing antibodies that do not cross-react with other type I IFNs

  • Implement epitope-specific blockade of receptor interaction sites

  • Compare effects with pan-type I IFN neutralizing antibodies to determine contribution of IFNW1

• Genetic approaches:

  • CRISPR/Cas9-mediated knockout of IFNW1 specifically

  • Selective silencing using siRNA or shRNA against IFNW1

  • Receptor subunit modifications that selectively affect IFNW1 binding

• Comparative dose-response profiling:

  • Establish dose-response curves for multiple type I IFNs

  • Identify concentration ranges where IFNW1 effects diverge from other IFNs

  • Compare EC50 values across different biological readouts

• Temporal response analysis:

  • Monitor gene expression changes at multiple time points

  • Identify early versus late response genes that differ between IFNW1 and other IFNs

  • Evaluate signal duration and termination kinetics

• Receptor complex analysis:

  • Assess differential recruitment of signaling components

  • Measure STAT phosphorylation patterns (qualitative and quantitative differences)

  • Evaluate conformational changes in the receptor using FRET-based approaches

• Evolutionary considerations:

  • Leverage the knowledge that IFNW1 has evolved under strong purifying selection

  • Focus on responses that align with its evolutionary conservation

  • Compare effects across species with varying interferon repertoires

• Transcriptomic/proteomic signature identification:

  • Perform comprehensive transcriptomic or proteomic analysis following specific interferon treatments

  • Identify IFNW1-specific signature patterns

  • Validate key differentially regulated genes/proteins

By systematically implementing these approaches, researchers can delineate IFNW1-specific effects from the broader type I interferon response, advancing our understanding of its unique biological roles.

What are the recommended controls and validation steps for IFNW1 functional assays?

Establishing rigorous controls and validation steps is essential for ensuring reliable and reproducible results in IFNW1 functional assays. Based on established interferon research methodologies, the following framework is recommended:

Essential Controls for IFNW1 Functional Assays:

• Activity reference standards:

  • Include a well-characterized reference standard of IFNW1 with defined activity units

  • Use international reference preparations when available

  • Include dose-response curves for the reference standard

• Specificity controls:

  • Positive controls: Other type I interferons (IFN-α, IFN-β) to compare response patterns

  • Negative controls:

    • Heat-inactivated IFNW1 (95°C for 5 minutes)

    • Unrelated cytokines that do not activate interferon pathways

    • Vehicle-only treatments

• Neutralization controls:

  • Anti-IFNW1 neutralizing antibodies

  • Soluble IFNAR receptor domains to sequester IFNW1

  • JAK/STAT pathway inhibitors as downstream blockade

• Cell system controls:

  • IFNAR1/IFNAR2 knockout cells as receptor-deficient controls

  • Multiple cell types to confirm consistency across cellular contexts

  • Cell viability assessments parallel to functional readouts

Validation Steps:

• Bioactivity confirmation:

  • Antiviral assays using WISH human amnion cells with VSV challenge

  • Verification of expected ED50 (typically 2-8 pg/mL)

  • Confirmation of dose-dependent responses

• Technical validation:

  • Inter-assay reproducibility assessment (minimum 3 independent experiments)

  • Intra-assay variability determination (technical replicates)

  • Stability testing of IFNW1 under assay conditions

• Molecular signature verification:

  • Confirmation of canonical interferon-stimulated gene (ISG) induction

  • Assessment of phospho-STAT1/STAT2 activation

  • Verification of ISRE-dependent reporter activation

• Functional outcome validation:

  • Demonstration of expected biological endpoints (e.g., antiviral protection)

  • Correlation between molecular signatures and functional outcomes

  • Time-course analysis to confirm appropriate kinetics

This comprehensive approach to controls and validation will ensure that experimental findings related to IFNW1 function are robust, reproducible, and physiologically relevant.

How should researchers interpret apparent contradictions between in vitro and in vivo IFNW1 activity?

When confronted with discrepancies between in vitro and in vivo IFNW1 activity, researchers should implement a systematic analytical approach to reconcile these contradictions. Understanding these differences is crucial for accurate translation of research findings.

Methodological Framework for Resolving Contradictions:

• Pharmacokinetic considerations:

  • In vitro systems lack the clearance mechanisms present in vivo

  • Measure IFNW1 half-life in relevant in vivo models

  • Adjust dosing schedules to account for in vivo clearance rates

• Microenvironmental factors:

  • Assess the impact of tissue-specific factors on IFNW1 activity

  • Consider extracellular matrix components that may sequester or enhance IFNW1

  • Evaluate the presence of soluble inhibitors in specific tissues

• Receptor expression dynamics:

  • Quantify IFNAR1/IFNAR2 expression across relevant tissues

  • Assess receptor downregulation kinetics following IFNW1 exposure

  • Evaluate competition with endogenously produced interferons in vivo

• Cell-cell interactions:

  • Investigate how immune cell cross-talk affects IFNW1 responses

  • Develop co-culture systems that better mimic in vivo cellular interactions

  • Consider secondary cytokine cascades triggered in complex environments

• Feedback regulation mechanisms:

  • Measure expression of negative regulators (SOCS proteins, USP18) in various contexts

  • Assess the kinetics of feedback inhibition in vitro versus in vivo

  • Evaluate the impact of pre-existing activation states on responsiveness

Experimental Approaches:

• Bridging studies:

  • Implement progressively complex models (2D culture → 3D culture → ex vivo tissue → in vivo)

  • Identify the threshold of complexity at which discrepancies emerge

  • Use identical readouts across different model systems when possible

• Biomarker correlation analysis:

  • Identify molecular signatures that correlate with functional outcomes across systems

  • Validate these signatures as predictive biomarkers

  • Use these biomarkers to normalize responses across different contexts

• Genetic validation:

  • Utilize genetic models (knockouts, SNP variants) to test hypotheses about mechanistic differences

  • Compare genetic effects in vitro and in vivo to identify context-dependent mechanisms

By systematically addressing these factors and implementing the suggested experimental approaches, researchers can develop more nuanced interpretations of IFNW1 activity and improve the translational relevance of their findings.

What comparative analysis framework should be used when evaluating IFNW1 against other type I interferons?

A robust comparative analysis framework is essential when evaluating IFNW1 against other type I interferons to accurately characterize its unique properties and potential applications. The following structured approach is recommended:

Comparative Analysis Framework:

• Sequence and structural comparison:

  • Align amino acid sequences of IFNW1 with other type I IFNs

  • Note that IFNW1 shares 62% sequence similarity with IFN-α and 33% with IFN-β

  • Compare available structural data, particularly receptor-binding interfaces

  • Identify unique structural features that may confer functional specificity

• Receptor binding parameters:

  • Compare binding affinities (KD) for IFNAR1 and IFNAR2

  • Measure association and dissociation kinetics (kon/koff)

  • Assess conformational changes induced in the receptor complex

  • Evaluate the stability of ternary complexes (IFN-IFNAR1-IFNAR2)

• Signaling activation profiles:

  Parameter	IFNW1	IFN-α	IFN-β	Notes
  STAT1 activation	+	+	++	Relative intensity
  STAT2 activation	+	+	++	Relative intensity
  STAT3 activation	?	+/-	+	Cell-type dependent
  STAT4/5 activation	?	+/-	+/-	Context-dependent
  IRF9 recruitment	+	+	+	Similar among type I IFNs
  Non-canonical pathways	?	+	++	Requires investigation

• Transcriptional response comparison:

  • Perform RNA-seq or microarray analysis after treatment with equiactive doses

  • Identify IFNW1-specific gene induction signatures

  • Compare early versus late response genes

  • Analyze pathway enrichment differences

• Functional outcome assessment:

  • Antiviral activity spectrum across different viruses

  • Antiproliferative potency in various cell types

  • Immunomodulatory effects on different immune cell populations

  • Species-specificity of activity

• Evolutionary context integration:

  • Consider that IFNW1 has evolved under strong purifying selection

  • Compare evolutionary constraints across type I IFN family members

  • Relate evolutionary conservation to functional uniqueness

• Clinical and translational relevance:

  • Compare associations of genetic variants with disease outcomes

  • Evaluate therapeutic potential for specific indications

  • Assess side effect profiles relative to other type I IFNs

This comprehensive framework enables systematic characterization of IFNW1's unique properties within the type I interferon family and provides a foundation for identifying specific research or therapeutic applications where IFNW1 may offer advantages over other interferons.

How can researchers resolve conflicting data on IFNW1 genetic associations with viral infection outcomes?

The literature presents some conflicting data regarding IFNW1 genetic associations with viral infection outcomes, particularly in HIV studies . Resolving these conflicts requires a systematic approach combining statistical rigor, biological validation, and contextual analysis.

Methodological Framework for Resolving Conflicting Genetic Associations:

• Statistical reanalysis and meta-analysis:

  • Standardize statistical approaches across studies

  • Implement statistical methods that account for population structure (e.g., linear mixed effects models)

  • Perform power calculations to determine if sample sizes are adequate

  • Conduct meta-analysis when multiple datasets are available

• Population stratification considerations:

  • Analyze associations within genetically homogeneous populations

  • Note that IFNW1 associations with HIV viral load were primarily observed in African participants

  • Consider different linkage disequilibrium patterns across populations

  • Adjust for genetic ancestry using principal component analysis

• Gene-environment interaction assessment:

  • Evaluate whether conflicting results arise from different environmental contexts

  • Consider viral strain differences across study populations

  • Assess potential interactions with other host factors

  • Investigate timing of infection relative to genetic assessment

• Biological validation of genetic associations:

  • Functionally characterize identified variants (e.g., rs79876898)

  • Determine impact on IFNW1 expression or activity

  • Develop cellular models incorporating the genetic variants

  • Measure downstream effects on antiviral pathways

• Phenotype refinement:

  • Ensure consistent definition of outcome measures across studies

  • Consider disease stage-specific effects (early vs. advanced infection)

  • Develop more precise intermediate phenotypes

  • Collect longitudinal data to assess dynamic rather than static outcomes

• Multifactorial genetic analysis:

  • Consider polygenic contributions rather than single-gene effects

  • Evaluate epistatic interactions with other interferon pathway genes

  • Assess cumulative genetic burden across the type I IFN pathway

  • Implement pathway-level analysis approaches

• Integration with evolutionary context:

  • Consider the strong purifying selection that has shaped IFNW1 evolution

  • Evaluate whether variants under study affect conserved or variable regions

  • Assess potential balancing selection in response to specific pathogens