Recombinant Human Interferon alpha-1/13  (IFNA1)  (Active)

Basic Research Questions

• What is Recombinant Human Interferon alpha-1/13 (IFNA1) and how is it produced?

  Recombinant Human Interferon alpha-1/13 (IFNA1) is a type I interferon cytokine that belongs to the alpha/beta interferon family. It is primarily produced by macrophages and possesses significant antiviral activities .

  The recombinant protein is typically expressed in either bacterial systems (E. coli) or mammalian cells (HEK 293). The choice of expression system affects post-translational modifications:

  Expression System	Molecular Weight	Post-translational Modifications	Purity Standards	Common Applications
  E. coli	19.5 kDa	Minimal	>96% by SDS-PAGE	In vitro studies
  HEK 293	Varies (24-189 aa range)	Glycosylation present	>95% purity	In vivo studies

  Production typically involves gene cloning, expression in the chosen system, followed by purification using chromatography techniques. Quality control includes tests for endotoxin levels (<1 EU/μg), purity verification by SDS-PAGE/HPLC, and biological activity assessment .

• How does IFNA1's activity compare to other interferon alpha subtypes?

  IFNA1 has distinct characteristics that differentiate it from other interferon alpha subtypes:

  • Receptor binding: IFNA1 has approximately 100-fold lower affinity for IFNAR2 compared to most other IFN-α subtypes, while maintaining relatively high affinity for IFNAR1

  • Genetic conservation: IFNA1 shows lower polymorphism frequency in human populations, suggesting evolutionary importance

  • Transcriptional regulation: Unlike most other IFN-α subtypes, IFNA1 can be induced by IRF3 activation alone, without requiring IRF7

  • Genetic redundancy: Humans have two genes (IFNA1 and IFNA13) that express identical IFNα1 proteins, suggesting important evolutionary conservation

  Key substitutions in the protein sequence affecting receptor binding include:

  • F27S: Decreases affinity for IFNAR2 by 4-fold

  • R22S: Together with S27, decreases affinity by ~14-fold

  • K31M: May contribute to decreased IFNAR2 affinity by disrupting helix structure

• What are the primary biological functions of IFNA1?

  IFNA1 exhibits several key biological functions critical to immune responses:

  1. Antiviral activity: Stimulates production of protein kinase and oligoadenylate synthetase enzymes that inhibit viral replication

  2. Immunomodulatory effects:

     • Activation of natural killer (NK) cells via STAT1 pathway

     • Regulation of monocyte and neutrophil recruitment during viral pneumonia

     • Modulation of antigen presentation pathways

  3. Cell signaling: Triggers JAK/STAT signaling pathways that induce interferon-stimulated genes (ISGs)

  4. Regulatory functions:

     • Balances immune activation and suppression during viral infections

     • May restrict type 2 immunopathology through regulation of group 2 innate lymphoid cells

  The biological activities of IFNA1 are concentration-dependent, with specific activity in antiviral assays typically measured at no less than 1.0×10^8 IU/mg .

Advanced Research Applications

• What signaling pathways are activated by IFNA1 and how do they differ from other interferons?

  IFNA1 triggers complex signaling cascades primarily through the JAK/STAT pathway:

  1. Receptor binding: IFNA1 binds to the heterodimeric receptor complex composed of IFNAR1 and IFNAR2, with uniquely higher relative affinity for IFNAR1 than other IFN-α subtypes

  2. JAK activation: Receptor engagement activates Janus kinases (TYK2 and JAK1) associated with the cytoplasmic domains of IFNAR1 and IFNAR2

  3. STAT phosphorylation: Activated JAKs phosphorylate STAT proteins, primarily STAT1 and STAT2, forming:

     • STAT1-STAT2 heterodimers

     • STAT1 homodimers

     • STAT1-STAT3 heterodimers

  4. Transcription factor complex formation: Phosphorylated STAT complexes associate with IRF9 to form ISGF3 complexes

  5. Nuclear translocation: These complexes translocate to the nucleus where they bind to specific DNA sequences:

     • ISRE (Interferon-Stimulated Response Elements)

     • GAS (Gamma-Activated Sequences)

  6. Gene induction: This leads to expression of hundreds of ISGs with antiviral, antiproliferative, and immunomodulatory functions

  Unique to IFNA1 is its distinct activation pattern of STAT proteins compared to other subtypes, leading to differential gene expression profiles in human T cells and dendritic cells .

• How can researchers accurately measure IFNA1 activity in experimental settings?

  Several complementary approaches can be employed to measure IFNA1 activity:

  1. Antiviral assays:

     • Human replicon assays using Huh-7 cells containing HCV replicons

     • Cytopathic effect reduction assays using susceptible cell lines

     • These typically measure EC50 values and can determine specific activity (IU/mg)

  2. Reporter gene assays:

     • Cells transfected with ISRE-luciferase reporters

     • Measures transcriptional activation of interferon-responsive elements

     • Allows high-throughput screening with quantitative readout

  3. ELISA-based detection:

     • Sandwich ELISA for protein quantification (detection range: 7.8-500pg/mL)

     • Sensitivity typically around 1.95pg/mL

     • Measures concentration but not biological activity

  4. Receptor binding assays:

     • Surface plasmon resonance to measure binding kinetics to IFNAR1/IFNAR2

     • Can determine association/dissociation constants (Ka/Kd)

  5. Phospho-STAT detection:

     • Western blotting or flow cytometry for pSTAT1/pSTAT2

     • Directly measures initial signaling events

     • Can be performed in target cells of interest

  Standard curves using reference material with known potency should be included. Intra- and inter-assay variability should be monitored (typically <4% and <7% respectively for validated assays) .

• What experimental controls should be included when studying IFNA1 effects?

  Robust experimental design for IFNA1 research should include:

  1. Positive controls:

     • Commercial recombinant IFNA1 with established activity (e.g., European Pharmacopoeia standard)

     • Other well-characterized type I interferons (IFN-β) for comparative analysis

  2. Negative controls:

     • Heat-inactivated IFNA1 (95°C for 5 minutes)

     • Buffer-only treatment

     • Isotype-matched irrelevant protein

  3. Receptor blockade controls:

     • Anti-IFNAR1/IFNAR2 neutralizing antibodies

     • JAK inhibitors (e.g., Ruxolitinib)

     • Demonstrates specificity of observed effects

  4. Dose-response relationships:

     • Minimum 5-point titration covering 2-3 logs

     • Typically ranging from 0.1-100 ng/mL

     • Establishes EC50/IC50 values

  5. Time-course experiments:

     • Early timepoints (15 min-2 hr) for signaling events

     • Intermediate timepoints (2-12 hr) for gene expression

     • Late timepoints (12-72 hr) for phenotypic changes

  6. Cell-type specific validation:

     • Test effects on multiple relevant cell types (T cells, dendritic cells, etc.)

     • Account for differential responses between cell types

  Including these controls ensures that observed effects are specific to IFNA1 activity and not due to experimental artifacts or contamination.

• How does IFNA1 contribute to antiviral immunity at the molecular level?

  IFNA1 establishes an antiviral state through multiple complementary mechanisms:

  1. Induction of restriction factors:

     • Protein kinase R (PKR): Phosphorylates eIF2α, inhibiting viral protein synthesis

     • 2'-5'-oligoadenylate synthetase (OAS): Activates RNase L, degrading viral RNA

     • Mx proteins: GTPases that trap viral ribonucleoproteins

     • IFITM proteins: Block viral cell entry and membrane fusion

  2. Cell-intrinsic immunity enhancement:

     • Upregulation of pattern recognition receptors (PRRs)

     • Amplification of downstream signaling pathways

     • Creates positive feedback loop for sustained antiviral response

  3. Regulation of cell death pathways:

     • Sensitization to apoptotic signals in infected cells

     • Upregulation of pro-apoptotic factors

     • Elimination of viral reservoirs

  4. Coordination of adaptive immunity:

     • Enhanced antigen presentation via MHC class I upregulation

     • Promotion of memory T cell generation and maintenance

     • B cell activation and antibody production

  5. Modification of cellular metabolism:

     • Shifts from oxidative phosphorylation to glycolysis

     • Alters lipid metabolism to create unfavorable environment for viral replication

  The kinetics of IFNA1 responses follow distinct patterns, with early effects on cell-intrinsic immunity followed by later coordination of adaptive responses through transcriptional and post-transcriptional regulation .

• What are the key methodological considerations for IFNA1 stability in laboratory experiments?

  Maintaining IFNA1 stability is critical for experimental reproducibility:

  1. Storage conditions:

     • Lyophilized: Store at -20°C to -80°C

     • Reconstituted: Add glycerol (final 30-50%) and store at -20°C or -80°C

     • Working aliquots can be stored at 4°C for up to one week

  2. Reconstitution protocols:

     • Use sterile, deionized water or manufacturer-recommended buffer

     • For lyophilized preparations, centrifuge vial before opening

     • Reconstitute to 0.1-1.0 mg/mL concentration

     • Avoid vigorous shaking or vortexing that may cause denaturation

  3. Buffer considerations:

     • Optimal pH range: 7.0-7.4

     • Common formulation: PBS with 4% mannitol and 1% HSA

     • Carrier proteins (HSA, BSA) prevent adsorption to surfaces

  4. Avoiding freeze-thaw cycles:

     • Prepare single-use aliquots to minimize freeze-thaw cycles

     • Each freeze-thaw cycle can reduce activity by 5-15%

     • Maximum recommended cycles: 3

  5. Activity monitoring:

     • Periodic testing in bioassays to confirm retained activity

     • Circular dichroism can be used to verify intact secondary structure

     • SDS-PAGE to check for aggregation or degradation

  6. Protein denaturation prevention:

     • Avoid exposure to extreme temperatures

     • Minimize exposure to air/oxidation

     • Use low-binding tubes and pipette tips

     • Keep away from strong light exposure

  Proper handling and storage procedures are essential for maintaining IFNA1 biological activity throughout the experimental timeline.

• How can pharmacokinetic-pharmacodynamic (PK-PD) modeling be applied to IFNA1 research?

  PK-PD modeling provides quantitative frameworks for understanding IFNA1 behavior in experimental systems:

  1. Key PK parameters to measure:

     • Volume of distribution (Vd)

     • Clearance rate (CL)

     • Half-life (t½)

     • Area under the curve (AUC)

     • Maximum concentration (Cmax)

  2. PD biomarkers for IFNA1 activity:

     • Phosphorylation of STAT proteins

     • Induction of interferon-stimulated genes (MX1, OAS1, etc.)

     • Antiviral activity in cellular or animal models

     • Changes in immune cell activation markers

  3. Modeling approaches:

     • Direct effect models (linking concentration to response)

     • Indirect effect models (incorporating production/degradation rates)

     • Tolerance/rebound models (accounting for adaptation)

     • Signal transduction models (capturing complex signaling networks)

  4. Implementation example from literature:
     In a study with TLR-7 agonists that induce IFNA1, researchers used:

     • A biophase distribution model to account for absorption

     • A target-mediated drug disposition model for interferon induction

     • An effect compartment to link interferon levels to antiviral activity

  5. Software tools for implementation:

     • NONMEM, Phoenix WinNonlin, or similar PK-PD modeling software

     • R packages (nlme, PKPDmodels) for statistical analysis

     • SimBiology (MATLAB) for mechanistic systems models

  This approach allows prediction of dosing regimens, understanding of inter-individual variability, and translation between experimental models and clinical applications.

Clinical Research Applications

• What is the therapeutic potential of IFNA1 compared to other interferon formulations?

  IFNA1 has distinct properties relevant to its therapeutic applications:

  1. Comparative receptor binding profile:

     • Lower affinity for IFNAR2 but higher relative affinity for IFNAR1 compared to other subtypes

     • May result in different signaling cascade activation patterns

     • Potentially different side effect profiles due to differential ISG induction

  2. Applications in viral infections:

     • Hepatitis B and C virus infections: Established efficacy of type I interferons

     • COVID-19: Potential to normalize dysregulated innate immunity

     • Other viral infections: Human papillomavirus, Kaposi's sarcoma associated with HIV

  3. Administration routes and formulations:

     • Inhalation/nebulization: Commonly used for respiratory infections like COVID-19

     • Subcutaneous: Standard for systemic delivery

     • Pegylated formulations: Extended half-life

  4. Comparative efficacy data:

     Interferon Type	Common Applications	Relative Potency	Half-life	Main Advantages
     IFNA1	Viral infections	Variable by subtype	Short	Specific receptor binding profile
     Pegylated IFN-α	Hepatitis B/C	High	Extended	Longer duration of action
     IFN-β	Multiple sclerosis	Different ISG profile	Intermediate	Auto-immune regulation

  5. Safety considerations:

     • Inhalation of IFNA1 showed no association with delayed-phase thrombocytopenia

     • May have hepatoprotective effects during COVID-19 infection

     • Lower risk of elevated alanine aminotransferase in patients receiving inhaled IFNA1

  The therapeutic application of IFNA1 requires balancing its antiviral efficacy with potential immunomodulatory effects, considering the context-specific benefits and risks.

• How can researchers investigate IFNA1 resistance mechanisms?

  Investigating IFNA1 resistance requires multi-level analytical approaches:

  1. Receptor expression and signaling analysis:

     • Flow cytometry for IFNAR1/IFNAR2 surface expression

     • Phospho-flow for STAT1/STAT2 activation

     • Western blot for total and phosphorylated signaling proteins

     • RNA-seq for JAK-STAT pathway component expression

  2. Genetic analysis techniques:

     • Targeted sequencing of IFNAR1/IFNAR2 and downstream effectors

     • CRISPR-Cas9 screening to identify resistance mediators

     • Single-cell analysis to identify resistant subpopulations

  3. Assessment of viral evasion strategies:

     • Viral protein expression that antagonizes IFNA1 signaling

     • Measuring viral protein interactions with IFNAR components

     • Evaluation of viral mutations correlating with treatment failure

  4. Auto-antibody screening:

     • ELISA to detect anti-IFNA1 autoantibodies

     • Neutralization assays to assess functional impact

     • Particularly important in diseases like COVID-19 where autoantibodies against type I IFNs correlate with disease severity

  5. Epigenetic regulation analysis:

     • Chromatin immunoprecipitation (ChIP) for histone modifications at ISG promoters

     • Methylation analysis of CpG islands in IFN pathway genes

     • ATAC-seq to assess chromatin accessibility changes

  6. Longitudinal monitoring protocols:

     • Serial sampling before, during, and after IFNA1 treatment

     • Correlation of molecular markers with clinical/experimental outcomes

     • Development of predictive biomarkers for resistance

  This comprehensive approach helps differentiate between intrinsic resistance mechanisms, acquired resistance, and viral evasion strategies.

• What methodologies are most appropriate for studying IFNA1's role in autoimmune conditions?

  Investigating IFNA1 in autoimmune contexts requires specialized methodologies:

  1. Quantification of IFNA1 in patient samples:

     • Digital ELISA (Simoa) for ultrasensitive detection

     • IFNA1-specific assays to distinguish from other subtypes

     • Standardization against international reference materials

  2. Gene expression profiling:

     • IFN signature scoring using validated gene sets

     • NanoString technology for targeted quantification

     • Single-cell RNA-seq to identify cellular sources and responders

  3. Functional assays:

     • Reporter cells expressing ISRE-luciferase constructs

     • Ex vivo stimulation of patient PBMCs

     • Serum/plasma transfer experiments

  4. Animal models:

     • Conditional knockout of IFNAR in specific cell types

     • Inducible expression systems for temporal control

     • Humanized mice expressing human IFNAR components

  5. Therapeutic intervention studies:

     • IFNAR-blocking antibodies

     • JAK inhibitors as downstream signaling blockers

     • Correlation of IFNA1 levels with treatment responses

  6. Genetic association studies:

     • SNP analysis in IFNA1 and pathway genes

     • Correlation with disease severity and progression

     • Functional validation of identified variants

  These approaches are particularly relevant for studying conditions like systemic lupus erythematosus (SLE), which has a high type I IFN signature, while considering the dual nature of IFNs in promoting or ameliorating autoimmunity in different contexts.

• How can researchers distinguish between direct and indirect effects of IFNA1 treatment in complex biological systems?

  Differentiating direct from indirect IFNA1 effects requires careful experimental design:

  1. Cell-specific receptor knockout/knockdown:

     • CRISPR-Cas9 deletion of IFNAR1/IFNAR2

     • siRNA knockdown in specific cell populations

     • Conditional knockout mouse models with cell-specific Cre expression

  2. Timing analysis:

     • Early transcriptomic responses (1-4 hours): Likely direct effects

     • Late responses (12-24+ hours): Potential secondary effects

     • Pulse-chase experiments with actinomycin D to block secondary transcription

  3. Direct target identification:

     • ChIP-seq for STAT1/STAT2 binding sites

     • Motif analysis for ISRE/GAS elements

     • Integration with expression data to confirm functional relevance

  4. Secondary mediator blockade:

     • Neutralizing antibodies against induced cytokines

     • Receptor antagonists for secondary signaling pathways

     • Small molecule inhibitors of downstream effectors

  5. In vitro vs. in vivo comparison:

     • Isolated cell systems to establish direct effects

     • Co-culture systems to identify cell-cell interactions

     • In vivo models to capture full complexity

  6. Systems biology approaches:

     • Network analysis to identify signaling hubs

     • Mathematical modeling of signaling cascades

     • Multi-omics integration (transcriptomics, proteomics, metabolomics)

  These strategies help create a comprehensive map of IFNA1's effects, distinguishing between primary receptor-mediated responses and secondary effects mediated by induced factors.

• What are the sex-specific considerations when studying IFNA1 responses in research models?

  Sex differences significantly impact IFNA1 biology and should be addressed methodologically:

  1. Prevalence of autoantibodies:

     • Studies show markedly higher prevalence of anti-type I IFN autoantibodies in males (12.5%) compared to females (2.6%) with severe COVID-19

     • Odds ratio of 5.22 [95% CI: 2.27-14.80] for males vs. females

     • May influence experimental outcomes if not controlled for

  2. X-chromosome influences:

     • IFNA1 signaling components may be differentially expressed due to X-chromosome inactivation

     • Evidence from patients with X-linked incontinentia pigmenti suggests X-chromosome linkage of autoantibody production

     • Consider X-chromosome inactivation status in female subjects

  3. Experimental design considerations:

     Consideration	Methodology	Rationale
     Sex-matched controls	Use same-sex controls	Prevents confounding by sex-specific responses
     Hormonal influences	Control for estrous/menstrual cycle	Hormones modulate IFN responses
     Sample size calculations	Power for sex-stratified analysis	May require larger total sample size
     Cell sources	Document donor sex for in vitro studies	Cell intrinsic sex differences exist
     Data reporting	Always report subject sex	Enables meta-analysis and replication

  4. Age interactions with sex:

     • Studies indicate age-dependent increases in anti-IFN autoantibodies

     • Age-sex interactions may confound results if not accounted for

     • Age-matching should accompany sex-matching in study design

  5. Applications in drug development:

     • Different dosing requirements potentially needed by sex

     • Sex-specific adverse effect profiles may emerge

     • Pharmacokinetic differences may affect drug exposure

  Incorporating sex as a biological variable in IFNA1 research is essential for valid, reproducible, and translatable findings.