LILRA1 Antibody

What distinguishes LILRA1 from other members of the leukocyte immunoglobulin-like receptor family?

LILRA1, also known as LIR-6 or CD85i, is a glycoprotein member of the LIR family characterized by its activating function, in contrast to inhibitory receptors like LILRB1. LILRA1 contains a short cytoplasmic tail and a charged amino acid within the transmembrane domain that interacts with FcR gamma. Two structural variants exist: LIR-6a (four Ig-like domains) and LIR-6b (two Ig-like domains), both expressed by monocytes and B cells. LILRA1 is primarily expressed on monocytes, macrophages, dendritic cells, NK cells, basophils, eosinophils, neutrophils, T cells, and mast cell progenitors .

Unlike LILRB members which transmit inhibitory signals via immunoreceptor tyrosine-based inhibitory motifs (ITIMs), LILRA1 couples with molecules containing immunoreceptor tyrosine-based activation motifs (ITAMs) such as the FcRγ chain, leading to activation of immune responses rather than suppression .

How does LILRA1 expression vary across immune cell populations?

LILRA1 shows a distinctive expression pattern across immune cell types:

Flow cytometry is the primary method for detecting LILRA1 expression on immune cells. For example, human peripheral blood monocytes can be stained with anti-LILRA1 antibodies (such as Mouse Anti-Human LILRA1/LILRB1 APC-conjugated Monoclonal Antibody) and isotype control antibodies for accurate quantification of expression .

What are the optimal protocols for detecting LILRA1 expression by flow cytometry?

For optimal detection of LILRA1 expression by flow cytometry:

• Cell preparation: Isolate primary immune cells (e.g., PBMCs) using standard density gradient centrifugation methods.

• Antibody selection: Use validated monoclonal antibodies such as Mouse Anti-Human LILRA1/LILRB1 APC-conjugated Monoclonal Antibody (Clone # 586326) or unconjugated antibodies followed by appropriate secondary detection.

• Controls: Include isotype controls (e.g., Catalog # IC0041A for APC-conjugated antibodies) to establish background fluorescence levels .

• Staining procedure:

  • Resuspend 1×10^6 cells in 100 μL of flow cytometry staining buffer

  • Add recommended concentration of primary antibody (typically 5-10 μg/mL)

  • Incubate for 30 minutes at 4°C in the dark

  • Wash twice with staining buffer

  • If using unconjugated primary antibodies, add appropriate secondary antibody (e.g., Phycoerythrin-conjugated Anti-Mouse IgG) and incubate for 30 minutes at 4°C

  • Wash twice and resuspend in staining buffer for analysis

For human monocytes, this methodology has been successfully employed to detect LILRA1 expression, with positive staining appearing as a rightward shift in fluorescence intensity compared to isotype controls .

What are the optimal storage conditions for LILRA1 antibodies to maintain functionality?

Storage conditions vary based on antibody format and conjugation:

For unconjugated LILRA1 antibodies:

• Store at -20 to -70°C as supplied for up to 12 months

• After reconstitution, store at 2 to 8°C under sterile conditions for up to 1 month

• For longer storage after reconstitution, aliquot and store at -20 to -70°C for up to 6 months

• Avoid repeated freeze-thaw cycles by using a manual defrost freezer

For APC-conjugated LILRA1 antibodies:

• Store at 2 to 8°C as supplied for up to 12 months

• Important: Do not freeze conjugated antibodies

• Protect from light to prevent photobleaching of fluorophores

These storage recommendations are critical for maintaining antibody binding affinity and specificity. Improper storage can lead to degradation, resulting in decreased signal intensity, increased background staining, or complete loss of binding activity .

How does LILRA1 contribute to pathogen recognition and immune responses in infectious diseases?

LILRA1 plays complex roles in infectious disease pathogenesis and immune responses:

In bacterial infections:

• LILRA1 recognizes microbially-cleaved IgG and IgM at their N-terminus, serving as a sensor of bacterial proteolytic activity

• This recognition triggers calcium influx in monocytes, pro-inflammatory cytokine release, and granulocyte degranulation

• LILRA1 and its paired inhibitory receptor LILRB3 can recognize bacterially-infected cells, helping coordinate appropriate immune responses

In viral infections (particularly HIV):

• LILRA1 expression can be altered during HIV infection, with implications for immune cell function

• LILRA1 binds preferentially to β2 microglobulin-free HLA-C, which is associated with symptoms of HIV infection

• This interaction may contribute to the dysregulation of immune responses during HIV infection

In parasitic infections (particularly malaria):

• Recent studies have identified antibodies containing LILRA1 extracellular domains that target Plasmodium falciparum-encoded RIFINs

• These LILRA1-containing antibodies demonstrate extensive cross-reactivity with P. falciparum RIFINs, potentially contributing to antimalarial immunity

The involvement of LILRA1 in these diverse infectious contexts highlights its importance in pathogen recognition and immune response regulation, though the precise mechanisms remain under active investigation.

What is the significance of LILRA1 in cancer immunology and how might it be targeted therapeutically?

LILRA1's role in cancer immunology is emerging as a significant area of research:

• Expression patterns: LILRA1 expression can be altered in the tumor microenvironment, affecting immune cell function. For example, LILRA1 expression has been observed in oestrogen receptor-positive breast cancer .

• Immunoregulatory effects: As an activating receptor, LILRA1 can potentially enhance anti-tumor immune responses by promoting:

  • Pro-inflammatory cytokine production by myeloid cells

  • Enhanced phagocytic activity

  • Improved antigen presentation to T cells

• Therapeutic approaches:
  While most current immunotherapeutic development focuses on inhibitory receptors like LILRB1, activating receptors like LILRA1 represent potential complementary targets. Strategies may include:

  • Agonistic antibodies to enhance LILRA1 signaling

  • Combination approaches with inhibitory receptor blockade (e.g., anti-LILRB1/B2)

  • Cell therapy approaches incorporating LILRA1 stimulation

For example, IOMX-0675, an antibody that targets both LILRB1 and LILRB2 while preserving LILRA1 function, has shown promise in promoting macrophage repolarization, T cell activation, and phagocytosis, resulting in significant inhibition of tumor growth in melanoma xenograft models .

How do LILRA1-containing antibodies form during malaria infection, and what are the mechanisms underlying this unique antibody diversification process?

Recent discoveries have revealed a fascinating mechanism of antibody diversity generation involving LILRA1:

The formation of LILRA1-containing antibodies represents a novel mechanism of antibody diversification. In a study of 672 plasma samples from donors in Mali, researchers identified individuals with IgG antibodies that incorporated LILRA1 domains. These antibodies were produced by B cell clones that exhibited substantial DNA insertions in the switch region, specifically encoding for non-apical extracellular domains (D3D4 or just D3) of LILRA1 within the variable-constant (VH-CH1) elbow region .

Mechanism of formation:

• DNA insertion event occurs in the switch region of antibody genes

• The insertion encodes LILRA1 extracellular domains (primarily D3 or D3D4)

• This creates a unique triangular antibody structure where LILRA1 domains expand the VH-CH1 elbow without affecting VH-VL or CH1-CL pairings

• Unlike LAIR1-containing antibodies (which often undergo somatic hypermutation to reduce self-reactivity), LILRA1 inserts generally do not display mutations, consistent with their non-self-reactive nature

The resulting LILRA1-containing antibodies can recognize multiple RIFINs (Plasmodium falciparum-encoded repetitive interspersed families of polypeptides) expressed on infected erythrocytes, potentially contributing to antimalarial immunity. This discovery represents a significant advance in understanding antibody diversification and opens new possibilities for generating multispecific antibodies for therapeutic applications .

What are the methodological considerations when developing LILRA1-targeting therapeutic antibodies?

Development of LILRA1-targeting therapeutic antibodies involves several critical methodological considerations:

• Epitope selection and binding characterization:

  • Target selection should consider the four extracellular immunoglobulin-like domains of LILRA1

  • Binding kinetics should be characterized using methods like biolayer interferometry (BLI) on systems such as Octet Red96e with a 1:1 binding model

  • Affinity measurements (Kd values) should typically aim for sub-nanomolar range (0.5-1.5 nM) for optimal efficacy

• Cross-reactivity assessment:

  • Evaluate binding to closely related LILR family members, particularly LILRB1 and other activating LILRs

  • Consider the impact of potential cross-reactivity on therapeutic effects and safety

  • For example, when developing antibodies targeting inhibitory receptors like LILRB1/B2, ensure they don't interfere with activating receptors like LILRA1/A3

• Functional characterization:

  • For agonistic LILRA1 antibodies: assess calcium mobilization, cytokine production, and activation marker expression

  • For applications involving LILRA1 detection: validate specificity using knockout controls and isotype controls

  • Functional assessments should include primary immune cells from multiple donors to account for genetic variability

• Antibody format optimization:

  • Consider various formats (whole IgG, Fab, F(ab')2) depending on the intended application

  • Evaluate Fc-dependent effects when relevant

  • For therapeutic applications, consider humanization strategies to reduce immunogenicity

• In vivo model selection:

  • Given that LILRA1 lacks non-primate orthologs with high sequence identity, transgenic mouse models expressing human LILRA1 may be required for in vivo testing

  • Consider humanized mouse models for evaluation of human immune cell function in response to LILRA1-targeting strategies

How do LILRA1 and LILRB1 interact in regulating immune responses, and what are the implications for targeting either receptor therapeutically?

The interplay between activating LILRA1 and inhibitory LILRB1 represents a complex regulatory network in immune function:

Shared ligands but opposing functions:

• Both LILRA1 and LILRB1 can bind certain HLA class I molecules, including HLA-B27 (LILRA1) and HLA-A, HLA-B, HLA-C, and HLA-G (LILRB1)

• LILRA1 transduces activating signals through association with FcRγ chain containing ITAMs

• LILRB1 delivers inhibitory signals through ITIMs that recruit phosphatases like SHP1 and SHP2

Expression patterns and regulation:

• LILRA1 and LILRB1 show partially overlapping expression patterns on immune cells

• Their expression can be differentially regulated during infection, inflammation, and in the tumor microenvironment

• For example, LILRB1 expression increases on immune cells from individuals with CMV infection, rheumatoid arthritis, and late-stage tumors

Therapeutic targeting considerations:

• Selective targeting:

  • Antagonistic anti-LILRB1 antibodies can enhance NK cell cytotoxicity against multiple cancer types, including multiple myeloma, leukemia, lymphoma, and solid tumors

  • Agonistic LILRA1 antibodies could potentially synergize with LILRB1 blockade

• Dual targeting:

  • Antibodies like IOMX-0675 target both LILRB1 and LILRB2 while sparing LILRA1/A3, promoting macrophage repolarization and T cell activation

• Balancing immune activation and overactivation:

  • While LILRB1 blockade enhances immune responses, complete removal of inhibitory signals could potentially lead to autoimmunity

  • The balance between LILRA1 activation and LILRB1 inhibition may be crucial for therapeutic efficacy and safety

• Context-dependent effects:

  • In infectious diseases, pathogens like cytomegalovirus, dengue virus, and Plasmodium falciparum exploit LILRB1 to evade immune recognition

  • Therapeutic blockade of LILRB1 with monoclonal antibodies could counteract these evasion mechanisms, allowing host immune responses to clear infectious pathogens

What validation steps are critical when selecting LILRA1 antibodies for specific research applications?

Rigorous validation of LILRA1 antibodies is essential for generating reliable research data:

• Application-specific validation:

  • For flow cytometry: Verify staining patterns on known LILRA1-positive cells (monocytes, B cells) compared to isotype controls

  • For Western blotting: Confirm detection of appropriately sized bands (~60-70 kDa for full-length LILRA1) and absence of non-specific bands

  • For immunoprecipitation: Demonstrate successful pulldown of LILRA1 with verification by mass spectrometry or Western blot

  • For immunohistochemistry: Establish staining pattern consistency with known LILRA1 expression and absence of staining in negative controls

• Clone selection considerations:

  • Clone #586326 has been widely validated for multiple applications including flow cytometry and can detect LILRA1/LILRB1 in human blood monocytes

  • Clone 4A1E10A6 targets LILRA1 (AA: 322-461) and has been validated for ELISA and flow cytometry

• Epitope mapping:

  • Antibodies targeting different domains of LILRA1 may yield different results

  • For example, some antibodies target N-terminal regions while others target internal regions (AA 322-461) or C-terminal regions (AA 401-489)

  • Understanding the target epitope is crucial for interpreting results, especially when studying domain-specific functions

• Cross-reactivity testing:

  • Test for potential cross-reactivity with closely related LILR family members

  • Some antibodies may detect both LILRA1 and LILRB1 due to sequence similarity

  • When specificity for LILRA1 alone is required, extensive validation against other LILR family members is essential

• Functional validation:

  • For blocking antibodies: Demonstrate inhibition of ligand binding using competition assays

  • For agonistic antibodies: Show induction of appropriate downstream signaling events (calcium flux, cytokine production)

How can researchers optimize LILRA1 antibody-based detection in complex samples with variable expression levels?

Optimizing LILRA1 detection in complex biological samples requires careful methodology:

• Signal amplification strategies:

  • For flow cytometry in samples with low expression: Consider using biotin-streptavidin systems or tyramide signal amplification

  • For IHC/ICC: Use polymer-based detection systems or amplification steps for enhanced sensitivity

  • For Western blotting: ECL substrate selection should match anticipated expression levels

• Background reduction approaches:

  • Optimize blocking conditions (5% BSA or serum from the same species as the secondary antibody)

  • Include Fc receptor blocking reagents when working with samples containing Fc receptor-expressing cells

  • Consider cell-specific markers for co-staining to identify LILRA1+ subpopulations

• Sample enrichment techniques:

  • For rare LILRA1+ populations, consider magnetic bead-based enrichment prior to analysis

  • Cell sorting may be employed to isolate specific LILRA1+ populations for downstream analysis

  • Density gradient centrifugation can enrich for monocytes and other LILRA1-expressing cells

• Quantification methods:

  • Use quantitative flow cytometry with calibration beads to determine absolute receptor numbers

  • For Western blots, include recombinant LILRA1 protein standards for quantitative analysis

  • Consider digital PCR or quantitative RT-PCR for LILRA1 mRNA quantification in parallel with protein detection

• Comparative analysis approaches:

  • Include both positive controls (known LILRA1-high cells like monocytes) and negative controls

  • When comparing samples (e.g., healthy vs. disease), process and analyze them simultaneously under identical conditions

  • Use consistent gating strategies in flow cytometry and standardized exposure settings for imaging applications