IFNA1 Human

What is the IFNA1 gene and what protein does it encode?

IFNA1 (Interferon Alpha-1) is a protein-coding gene located on chromosome 9 in humans . It encodes Interferon alpha-1 (IFN-α1), one of the type I interferons. Notably, mature IFNA1 is identical to IFNA13, and they are considered as one gene product referred to as IFN-α1 . The mature protein spans from Cys24 to Glu189 .

IFNA1 belongs to a family of 13 homologous genes that produces 12 distinct interferon-alpha subtypes (since IFN-α1 and IFN-α13 are identical at the protein level) . These interferons function as cytokines with potent antiviral, antiproliferative, and immunomodulatory properties, classified based on their binding specificity to cell surface receptors .

How is IFNA1 related to other interferon subtypes?

IFNA1 is part of the type I interferon family, which also includes IFN-β and others. The human IFN-α subtypes share 70-80% amino acid sequence identity with each other, and about 35% identity with IFN-β . This family is distinct from type II interferon (which includes only IFN-γ) and type III interferons (IFN-λ1, -λ2, and -λ3, also known as IL-29, IL-28A, and IL-28B, respectively) .

Evolutionary studies indicate that each of the IFNA gene families among placental mammals has undergone significant gene duplication and conversion, suggesting that the various IFN-α subtypes have gained distinct functional properties over time . Despite their high homology, different IFN-α subtypes appear to have unique roles in immune responses and disease pathogenesis.

What are the primary biological functions of IFNA1?

IFNA1, like other type I interferons, exhibits several key biological activities:

• Antiviral activity: IFNA1 demonstrates potent antiviral effects. For example, recombinant human IFN-alpha-1a has shown anti-viral activity in HeLa human cervical epithelial carcinoma cells infected with encephalomyocarditis (EMC) virus, with an ED50 of 0.100-1.50 ng/mL .

• Immunomodulation: IFNA1 plays a role in regulating immune responses, potentially contributing to both protective immunity and autoimmune pathology. Single-nucleotide polymorphisms in the IFNA1 gene have been associated with systemic lupus erythematosus, suggesting a role in autoimmune disease susceptibility .

• Antiproliferative effects: Like other type I interferons, IFNA1 can inhibit cell proliferation, which may be relevant to both its antiviral functions and potential applications in cancer research.

Rather than serving merely as amplifiers of immune responses, research suggests that different IFN subtypes, including IFNA1, qualitatively modify the "antiviral state" in distinct ways .

What are the most effective methods for detecting and quantifying IFNA1 expression?

Accurately detecting and quantifying IFNA1 expression presents significant challenges due to the high homology among interferon-alpha subtypes. Researchers have developed several specialized approaches:

• Quantitative Real-Time PCR (qRT-PCR) with modified probes: To overcome the impediment of high sequence similarity, researchers have successfully employed:

  • Molecular beacon (MB) probes: These contain a hairpin loop that sequesters the fluorophore adjacent to the quencher. When binding to the specific template, the loop opens and allows fluorescence emission .

  • Locked nucleic acid (LNA) probes: These contain high-affinity nucleic acid analogues that stiffen the probe and raise its melting temperature, enhancing base mismatch discrimination .

  • LNA oligonucleotide inhibitors: In some cases, these are necessary to block cross-reactivity with similar subtypes .

• ELISA: Commercial ELISA kits are available for detecting human IFNA1/Interferon Alpha-1/13 in serum, plasma, cell culture supernatants, and urine samples . These kits typically offer:

  • Sensitivity around 0.6 ng/mL

  • Intra-assay coefficient of variation (CV) <12%

  • Inter-assay CV <10%

• Sequencing of PCR products: To confirm specificity, amplified PCR products from stimulated primary cells can be sequenced to verify that the amplicon aligns with, and includes bases unique to, the appropriate subtype template .

When reporting IFNA1 expression data, researchers should consider presenting results both as a function of housekeeping gene expression (ΔCq) and as copy number per μg RNA, as housekeeping gene expression may vary according to stimulation and cell type .

How can researchers differentiate between IFNA1 and other highly homologous interferon subtypes?

Distinguishing IFNA1 from other highly homologous interferon subtypes requires specialized techniques:

• Modified probe qRT-PCR: The combination of molecular beacon probes, LNA probes, and when necessary, LNA inhibitors allows for highly specific detection of individual IFN-α subtypes despite their high sequence similarity .

• Primer/probe optimization: To achieve adequate specificity (at least 512-fold, or nine PCR cycles), researchers should:

  • Adjust primer and probe concentrations

  • Edit sequences of primer/probe sets as needed

  • Include LNA competitors against nonspecific but similar sequences

• Standard curves and efficiency calculations: Due to differences in primer/probe efficiency, including a four-point standard curve allows expression to be quantified accurately. This is particularly important as small differences in efficiency amplified over multiple PCR cycles can falsely imply differences in gene expression .

• Validation through sequencing: Amplified PCR products should be sequenced to confirm specificity and verify the presence of subtype-unique bases .

The table below summarizes an example approach for differentiating between IFNA subtypes:

What are the optimal conditions for working with recombinant human IFNA1 protein?

When working with recombinant human IFNA1 protein in research applications, the following conditions are recommended:

How do expression patterns of IFNA1 differ across immune cell populations?

Different immune cell types show distinct patterns of IFNA1 expression in response to various stimuli. Based on detailed molecular analysis:

• Plasmacytoid Dendritic Cells (pDCs):

  • These are the primary producers of IFN-α subtypes, including IFNA1

  • Show robust response to CpG oligonucleotides and imiquimod (TLR9 and TLR7 ligands, respectively)

  • Express multiple IFN-α subtypes simultaneously

• Myeloid Dendritic Cells (mDCs):

  • Respond primarily to poly I:C (TLR3 ligand)

  • Notable for expressing IFN-α10 and IFN-α21 in addition to IFN-β and IFN-λ1

• Monocytes, Monocyte-Derived Macrophages (MDM), and Monocyte-Derived Dendritic Cells (MDDC):

  • Respond primarily to poly I:C and LPS (TLR3 and TLR4 ligands)

  • IFN response is dominated by IFN-β and IFN-λ1, with lower levels of IFN-α subtypes

These expression patterns indicate that both cell type and stimulatory ligand are critical determinants of interferon expression profiles. This suggests that different cell types may contribute distinct interferon signatures during various immune challenges, potentially leading to qualitatively different immune responses .

What is known about the allelic variants of IFNA1 and their functional implications?

All human IFN-α subtypes, including IFNA1, have allelic variants. While the specific allelic variants of IFNA1 aren't fully detailed in the provided search results, studies of other IFN-α subtypes provide insight into potential functional implications:

• SNPs and disease associations: Single-nucleotide polymorphisms in the IFNA1 gene have been associated with systemic lupus erythematosus, suggesting that genetic variation in IFNA1 may influence susceptibility to autoimmune diseases .

• Functional relevance of non-synonymous substitutions: The example of IFNA4, which has two allelic variants (IFNA4a and IFNA4b) with amino acid substitutions A51E and T114V, demonstrates how such variations can potentially affect protein function. These substitutions occur either proximal to or within α-helices that may affect contact points with the interferon receptor components IFNAR2 or IFNAR1, potentially altering biological activity .

• Detection methods for allelic variants: For functionally relevant allelic variants, specialized molecular methods may be needed for detection and differentiation. For example, the IFNA4 variants can be differentiated using molecular beacon primer/probe sets combined with LNA oligonucleotide competitors .

Understanding the functional implications of IFNA1 allelic variants may provide insights into individual differences in antiviral responses and susceptibility to autoimmune disorders.

How do the signaling pathways and biological effects of IFNA1 differ from other interferon subtypes?

While the search results don't provide comprehensive details on IFNA1-specific signaling, research on interferon subtypes suggests important functional distinctions:

• Receptor binding and signal transduction:

  • All type I interferons, including IFNA1, bind to the same receptor complex composed of IFNAR1 and IFNAR2

  • Despite sharing receptors, different subtypes may induce distinct signaling patterns due to:

    • Different binding affinities

    • Variations in receptor complex conformational changes

    • Distinct recruitment of intracellular signaling molecules

• Qualitative differences in gene induction:

  • Mouse models support the concept that IFN subtypes qualitatively modify the "antiviral state" rather than simply serving as amplifiers

  • Different IFN-α subtypes may induce partially overlapping but distinct sets of interferon-stimulated genes

• Cell type-specific effects:

  • The biological effects of IFNA1 and other subtypes may vary depending on the cell type

  • This is suggested by the distinct patterns of interferon subtype expression observed in different immune cell populations

• Evolutionary implications:

  • The maintenance of multiple IFN-α subtypes through evolution suggests functional specialization

  • Studies indicate that IFNA gene families have undergone significant gene duplication and conversion, suggesting functional gains for the various subtypes

These distinctions suggest that IFNA1 may have unique roles in specific immunological contexts, potentially offering tailored responses to particular pathogens or inflammatory conditions.

What are the key considerations for designing experiments to study IFNA1 in primary human cells?

When designing experiments to study IFNA1 in primary human cells, researchers should consider:

• Cell type selection:

  • Different cell types show distinct patterns of interferon expression

  • Plasmacytoid dendritic cells (pDCs) are the primary producers of IFN-α subtypes and may be preferred for studying IFNA1 production

  • Consider both natural producers (pDCs, mDCs) and responding cells (various tissue cells) depending on whether production or response is being studied

• Stimulation conditions:

  • Select appropriate TLR ligands or pathogen stimuli:

    • pDCs respond strongly to CpG oligonucleotides (TLR9) and imiquimod (TLR7)

    • mDCs, monocytes, MDM, and MDDC respond to poly I:C (TLR3) and LPS (TLR4)

  • Optimize concentration and timing of stimulation

  • Consider physiologically relevant pathogen models (e.g., viral infections) in addition to TLR ligands

• Cell purification and characterization:

  • Ensure high purity of isolated cell populations (>95% recommended)

  • Verify purity by flow cytometry before proceeding with experiments

  • For derived cells (e.g., MDM, MDDC), validate differentiation markers

• Detection methodology:

  • Use specific molecular approaches to distinguish IFNA1 from other subtypes

  • Include appropriate controls to verify specificity

  • Consider both mRNA expression (using specialized qRT-PCR) and protein production (using specific ELISA)

• Time course considerations:

  • Include multiple time points to capture both early and late responses

  • Standard measurement at 4 hours post-stimulation captures peak expression for many interferon responses

• Donor variability:

  • Include multiple donors to account for genetic and epigenetic variations

  • Consider potential influence of allelic variants

  • Report results as geometric means when aggregating data from multiple donors

How can researchers effectively analyze conflicting data about IFNA1 function from different experimental systems?

When facing conflicting data about IFNA1 function from different experimental systems, researchers should apply a systematic analytical approach:

• Methodological differences assessment:

  • Detection specificity: Evaluate whether the methods used truly distinguished IFNA1 from other IFN-α subtypes. Different detection methods vary in specificity and sensitivity .

  • Quantification approach: Consider whether results were reported as ΔCq versus copy number, as these can lead to different interpretations due to variations in housekeeping gene expression or primer efficiency .

  • Standardization: Check if standard curves were used to account for differences in primer/probe efficiency.

• Biological system variations:

  • Cell type differences: The cell types used (e.g., primary cells vs. cell lines, different immune cell subsets) significantly affect interferon responses .

  • Stimulation conditions: The type, concentration, and duration of stimuli can dramatically alter interferon expression patterns.

  • Donor/genetic background: Consider potential differences in allelic variants or other genetic factors between experimental systems.

• Integration strategies:

  • Meta-analysis approach: Systematically compare methodologies and results across studies.

  • Validation experiments: Design experiments specifically to address contradictions using standardized methods across different systems.

  • Computational modeling: Use in silico approaches to integrate diverse datasets and generate testable hypotheses.

• Contextual interpretation:

  • Physiological relevance: Consider which experimental system more closely mimics the relevant in vivo context.

  • Functional readouts: Give greater weight to studies measuring functional outcomes rather than just expression levels.

  • Evolutionary conservation: Consider whether findings are consistent with the evolutionary conservation patterns of interferon subtypes .

What clinical and translational applications of IFNA1 research are currently being pursued?

The translational potential of IFNA1 research spans several clinical domains:

• Autoimmune disease connections:

  • The association between IFNA1 SNPs and systemic lupus erythematosus suggests potential therapeutic targets

  • Modulating IFNA1 activity might help manage autoimmune conditions characterized by interferon signatures

• Antiviral applications:

  • The potent antiviral activity of IFNA1 against viruses like encephalomyocarditis virus (ED50 of 0.100-1.50 ng/mL) indicates potential applications in viral infections

  • Understanding the unique properties of IFNA1 compared to other interferon subtypes may lead to more targeted antiviral therapies

• Diagnostic developments:

  • ELISA kits for IFNA1/Interferon Alpha-1/13 detection in serum, plasma, cell culture supernatants, and urine enable clinical research applications

  • Measuring IFNA1 levels may provide biomarkers for disease states or treatment responses

• Cancer immunotherapy:

  • The antiproliferative and immunomodulatory properties of interferons, including IFNA1, continue to be explored in cancer treatment

  • Subtype-specific approaches may overcome limitations of broader interferon therapies

• Personalized medicine approaches:

  • Understanding the functional implications of IFNA1 allelic variants could inform personalized treatment strategies

  • Genetic testing for relevant IFNA1 variants might help predict disease susceptibility or treatment responses

While the search results don't provide complete details on current clinical trials or approved therapies specifically targeting IFNA1, the foundational research on its biology, detection methods, and genetic associations provides crucial groundwork for these translational applications.

What are the most promising approaches for understanding IFNA1-specific functions in complex immune responses?

Several cutting-edge approaches hold promise for elucidating IFNA1-specific functions:

• CRISPR-based gene editing:

  • Precise knockout or modification of IFNA1 while leaving other interferon subtypes intact

  • Generation of cell lines or animal models expressing tagged versions of IFNA1 for tracking

  • Introduction of specific allelic variants to study their functional consequences

• Single-cell analysis techniques:

  • Single-cell RNA sequencing to identify cell populations producing IFNA1 in various contexts

  • Combined protein and RNA detection at single-cell resolution to correlate IFNA1 expression with cellular phenotypes

  • Spatial transcriptomics to map IFNA1 expression patterns within tissues

• Systems biology approaches:

  • Integrative analysis of transcriptomic, proteomic, and metabolomic data to define IFNA1-specific signatures

  • Network analysis to identify unique signaling pathways or gene modules associated with IFNA1 activity

  • Computational modeling of interferon responses incorporating subtype-specific parameters

• Structural biology insights:

  • High-resolution structures of IFNA1 in complex with its receptors

  • Comparison with other interferon subtypes to identify structural determinants of functional specificity

  • Structure-guided development of subtype-selective agonists or antagonists

• Humanized mouse models:

  • Development of models expressing the human interferon gene cluster

  • Conditional expression systems for IFNA1 in specific cell types

  • Models mimicking human IFNA1 genetic variants associated with disease

These approaches, particularly when used in combination, may reveal the unique contributions of IFNA1 to immune protection and disease pathogenesis, potentially leading to more targeted therapeutic strategies.

How might emerging technologies improve our ability to study IFNA1 expression and function?

Emerging technologies are poised to revolutionize IFNA1 research:

• Advanced nucleic acid detection methods:

  • Digital PCR for absolute quantification of low-abundance IFNA1 transcripts

  • CRISPR-based nucleic acid detection systems with enhanced specificity

  • Long-read sequencing to analyze complex interferon gene loci and transcript variants

• Protein analysis innovations:

  • Mass cytometry (CyTOF) for simultaneous detection of multiple interferons and downstream signaling events

  • Proximity ligation assays for visualizing IFNA1 interactions with receptors and signaling components

  • Advanced proteomics to characterize post-translational modifications of IFNA1

• Imaging technologies:

  • Super-resolution microscopy to visualize IFNA1 secretion and receptor engagement

  • Intravital imaging to track IFNA1 production and effects in living tissues

  • Multiplexed ion beam imaging (MIBI) to simultaneously detect multiple proteins in tissue sections

• Artificial intelligence applications:

  • Machine learning algorithms to identify patterns in complex interferon response data

  • Predictive modeling of IFNA1-specific effects based on sequence and structural features

  • Automated analysis of imaging data to quantify spatial and temporal aspects of IFNA1 signaling

• Organoid and microphysiological systems:

  • Human organoids to study tissue-specific IFNA1 functions in a physiologically relevant context

  • Organ-on-chip models incorporating multiple cell types to assess complex IFNA1-mediated interactions

  • Microfluidic systems for high-throughput analysis of IFNA1 responses

These technological advances promise to overcome current limitations in specificity, sensitivity, and physiological relevance, enabling researchers to better understand the unique contributions of IFNA1 to immune function and disease.