SIGLEC10 Human

What is the expression pattern of SIGLEC10 in human immune cells?

SIGLEC10 is broadly expressed on B cells, dendritic cells, and specific macrophage subsets. Flow cytometry analysis reveals that all B cells express SIGLEC10, along with subsets of human leukocytes including eosinophils, monocytes, and a minor population of natural killer cells. Northern blot analysis demonstrates high mRNA expression levels in peripheral blood leukocytes, spleen, and liver . Detection can be performed using anti-human SIGLEC10 antibodies with phycoerythrin-conjugated secondary antibodies in flow cytometry protocols .

What is the molecular structure of human SIGLEC10?

Human SIGLEC10 is a transmembrane protein containing an N-terminal V-set immunoglobulin-like domain responsible for sialic acid binding, followed by two C2-set immunoglobulin-like domains . The protein's structural model reveals:

• A critical arginine residue in the V-set domain mediating sialic acid recognition

• Unlike the typical 7 β-strand structure of C2-type immunoglobulin folds, SIGLEC10's second domain contains only 6 β-strands (ABE and CFG)

• The seventh β-strand (C') is replaced by a highly flexible loop between strands C and E (CE loop) that forms two one-turn helices

Structural modeling approaches using related Siglecs as templates (such as Siglec-5) can be employed to study SIGLEC10's conformation and binding interfaces.

How is SIGLEC10 expression regulated in pathological conditions?

SIGLEC10 expression is dynamically regulated during pathological states. Research shows that SIGLEC10 levels are elevated in peritoneal cells following RNA virus infections . In glioma studies, SIGLEC10 expression correlates with poor survival outcomes and acts as an immunosuppressor . Methodologically, researchers can:

• Use qPCR to quantify expression changes during disease progression

• Apply single-sample Gene Set Enrichment Analysis (ssGSEA) to identify correlations between SIGLEC10 and immune-related genes

• Utilize immunohistochemistry to visualize expression patterns in tissue sections from different pathological states

What are the binding specificities of SIGLEC10 for sialylated glycans?

SIGLEC10 demonstrates selective binding to specific sialylated structures. Methodologically, this can be investigated using:

• Cell-based glycan arrays comprised of isogenic human cells displaying diverse sialic acid configurations

• Binding assays using recombinant SIGLEC10-Fc fusion proteins

• Competitive inhibition experiments with synthetic sialylated compounds

Research shows SIGLEC10 binds strongly to CD24, a glycosyl-phosphatidylinositol-anchored sialoprotein, in a sialylation-dependent manner . This binding depends on the critical arginine residue in SIGLEC10's V-set domain that mediates sialic acid recognition .

How does SIGLEC10 interaction with VAP-1 influence leukocyte trafficking?

SIGLEC10 has been identified as a leukocyte ligand for vascular adhesion protein-1 (VAP-1), a glycoprotein expressed on inflamed endothelium with dual roles as an amine oxidase enzyme and an adhesion molecule . This interaction:

• Mediates lymphocyte adhesion to endothelium during inflammation

• Forms a transient Schiff base between SIGLEC10 and VAP-1 during catalytic reactions

• Produces hydrogen peroxide that modulates additional adhesion molecules, signaling molecules, and transcription factors

The binding mechanism involves the WVLQNRVLSSS peptide from SIGLEC10, which docks into the active site of VAP-1, with the arginine (R293) forming a covalent bond with topaquinone . This interaction can be studied using:

• Ex vivo frozen section adhesion assays with SIGLEC10-expressing lymphocytes

• Molecular docking simulations with peptides derived from the CE loop

• Hydrogen peroxide production assays to quantify enzymatic activity

How does the structural modeling of SIGLEC10-VAP-1 interaction inform therapeutic targeting?

The structural basis of SIGLEC10-VAP-1 interaction provides crucial insights for drug development. Docking studies reveal that:

• The peptide WVLQNRVLSS from SIGLEC10's CE loop fits optimally into VAP-1's active site cavity

• The arginine residue within this peptide is essential for covalent binding to topaquinone in VAP-1

• The mouse ortholog (Siglec-G) contains a glutamine instead of arginine in this critical position, preventing similar interaction with VAP-1

These findings suggest design parameters for potential therapeutic agents:

• Small molecules mimicking the WVLQNRVLSS peptide to competitively inhibit SIGLEC10-VAP-1 binding

• Agents targeting the topaquinone site in VAP-1 to prevent interaction with SIGLEC10

• Modified peptides with enhanced stability and bioavailability for anti-inflammatory applications

How does SIGLEC10 contribute to B-cell tolerance mechanisms?

SIGLEC10 plays a critical role in B-cell tolerance and self-nonself discrimination of the immune system . This function is based on the recognition of sialylated glycans, which are:

• Ubiquitously expressed on host tissues

• Largely absent from most microbes (except some pathogenic strains)

• Key molecular patterns distinguishing self from nonself

Experimentally, this can be investigated by:

• Testing B cell responses to synthetic T-independent type 2 (TI-2) antigens harboring sialic acid motifs

• Comparing binding of sialylated antigens between wild-type and Siglec-modified B cells

• Analyzing B cell activation thresholds in the presence of sialylated self-antigens

What is the relationship between SIGLEC10 and immune checkpoint molecules in cancer?

SIGLEC10 exhibits significant correlations with immune checkpoint molecules in cancer contexts. Analysis of glioma datasets reveals that SIGLEC10 expression positively correlates with:

• Programmed cell death 1 (PDCD1)

• Cytotoxic T-lymphocyte-associated protein 4 (CTLA4)

• Lymphocyte activating 3 (LAG3)

• T-cell immunoreceptor with Ig and ITIM domains (TIGIT)

These correlations suggest SIGLEC10 may function as part of the immunosuppressive network in tumors. Methodologically, researchers can:

• Perform correlation analyses between SIGLEC10 and immune checkpoint gene expression in cancer datasets

• Conduct ssGSEA to identify relationships with immune-related gene sets

• Investigate combinatorial blockade of SIGLEC10 and established checkpoint molecules in cancer models

What are optimal techniques for examining SIGLEC10 binding partners?

Several complementary approaches can be used to identify and characterize SIGLEC10 binding partners:

• Cell-based glycan arrays: Using isogenic human cell libraries with combinatorial loss/gain of sialyltransferase genes to present diverse sialoglycans in their natural context

• Protein capture assays: Testing if recombinant SIGLEC10-Fc fusion proteins can capture potential binding partners (e.g., CD24) expressed on various cell types

• Docking simulations: Generating structural models of SIGLEC10 domains based on crystal structures of related Siglecs and performing in silico docking with potential ligands

• Ex vivo binding assays: Using frozen tissue sections to assess binding of SIGLEC10-expressing cells under conditions that mimic physiological interactions

How can functional consequences of SIGLEC10 engagement be measured?

To assess the functional outcomes of SIGLEC10 binding to its ligands, researchers can employ:

• Hydrogen peroxide production assays: Measuring H₂O₂ generation following SIGLEC10-VAP-1 interaction to quantify enzymatic activity

• B cell tolerance assays: Analyzing B cell activation thresholds in response to sialylated versus non-sialylated antigens

• Gene expression profiling: Performing RNA-seq or microarray analysis following SIGLEC10 engagement to identify downstream signaling pathways

• Functional immune assays: Measuring cytokine production, cellular proliferation, or cytotoxic activity following manipulation of SIGLEC10 signaling

What approaches can be used to target SIGLEC10 for therapeutic purposes?

Based on current understanding, several strategies for therapeutic targeting of SIGLEC10 show promise:

• Blocking antibodies: Developing antibodies that specifically interfere with SIGLEC10 binding to its ligands

• Competitive inhibitors: Designing peptides or small molecules based on the WVLQNRVLSS sequence that competes for binding to VAP-1

• Enzymatic modification: Altering the sialylation patterns of SIGLEC10 ligands to modulate binding strength

• Combination therapies: Co-targeting SIGLEC10 with established immune checkpoint inhibitors (anti-PD1, anti-CTLA4) based on their correlated expression patterns

How does SIGLEC10 expression correlate with clinical outcomes in cancer patients?

SIGLEC10 expression has been identified as a negative predictor of survival in glioma patients . Comprehensive analysis strategies include:

• Survival analysis: Kaplan-Meier plots stratifying patients by SIGLEC10 expression levels

• Multivariate Cox regression: Determining whether SIGLEC10 is an independent prognostic factor when controlling for other clinical variables

• Immune infiltration correlation: Using ssGSEA to assess relationships between SIGLEC10 expression and immune cell infiltration patterns

• Pathway enrichment analysis: Performing GO and KEGG analyses to identify biological processes and pathways associated with SIGLEC10 expression in tumors