LGALS9 Human

What is human LGALS9 and what is its molecular structure?

Human Galectin-9 (LGALS9) is a 36-kDa tandem repeat galectin containing two carbohydrate recognition domains joined by a linker peptide. These domains exhibit high affinity for β-galactoside residues. The protein can be detected in various cellular compartments including the cytoplasm, nucleus, and cell surface depending on the cell type and physiological conditions .

Methodological approach for structural studies:

• X-ray crystallography and NMR spectroscopy remain the gold standards for determining LGALS9 structure

• Computational modeling using homology-based approaches can supplement experimental data

• Recombinant expression systems (typically E. coli or mammalian cells) should be optimized for proper folding of both carbohydrate recognition domains

Which cellular receptors interact with human LGALS9?

LGALS9 interacts with multiple receptors across immune and cancer cells, with T-cell immunoglobulin and mucin-domain containing-3 (TIM-3) being the most extensively characterized . This Gal-9/TIM-3 pathway is functional in numerous human cancer cell types and plays a critical role in immune regulation .

Table 1: Primary LGALS9 Receptors and Their Cellular Distribution

How is LGALS9 expression regulated in normal versus disease states?

Methodological approaches for studying LGALS9 regulation:

• Quantitative PCR for transcript analysis

• Western blotting and flow cytometry for protein expression analysis

• Immunohistochemistry for tissue localization studies

• Use of transcription factor binding site analysis and promoter studies to identify regulatory elements

What are the optimal techniques for detecting LGALS9 expression in tissue samples?

Multiple complementary approaches should be used for comprehensive LGALS9 detection:

• Immunohistochemistry (IHC): For LGALS9 detection in formalin-fixed paraffin-embedded tissues, optimize antibody concentration and antigen retrieval methods. In pancreatic tissues, LGALS9 staining may localize differently in normal versus cancerous tissue. In pancreatic intraepithelial neoplasia (PanIN), strong LGALS9 staining is observed at the apex and nucleus of cells, with diffuse cytoplasmic staining of lower intensity .

• Flow Cytometry: For quantitative analysis of LGALS9 expression in single cell suspensions. This technique allows simultaneous analysis of LGALS9 expression in both immune (CD45+) and non-immune (CD45-) cell populations. Results should be reported as relative fluorescence intensity (RFI) in relation to isotype controls .

• RT-qPCR: For transcript level analysis, which can detect changes in expression before protein changes become apparent.

How can researchers quantify LGALS9 expression in different cellular compartments?

Subcellular fractionation combined with Western blotting or imaging techniques provides the most accurate assessment of LGALS9 distribution:

• Subcellular Fractionation Protocol:

  • Separate nuclear, cytoplasmic, membrane, and secreted fractions

  • Confirm fraction purity using compartment-specific markers

  • Quantify LGALS9 in each fraction by Western blot or ELISA

• Confocal Microscopy Approach:

  • Use fluorescently labeled antibodies against LGALS9

  • Co-stain with organelle-specific markers

  • Perform z-stack imaging for complete cellular distribution

In experimental models such as pancreatic cancer, LGALS9 localizes differently depending on the cell type. In PanIN lesions, strong nuclear and apical staining is observed, while in immune cells, the distribution pattern may differ significantly .

What is the significance of LGALS9 in pancreatic cancer progression?

LGALS9 plays a critical role in pancreatic cancer as both a biomarker and functional contributor to disease progression:

• Expression Profile: Among multiple solid tumors, pancreatic ductal adenocarcinoma (PDAC) demonstrates the highest LGALS9 expression, with mRNA levels substantially exceeding those of PD-L1 .

• Prognostic Value: High LGALS9 expression correlates with poor prognosis in PDAC and several other cancer types including renal cell carcinoma, acute myeloid leukemia, and gastric carcinoma .

• Immunosuppressive Functions: LGALS9 contributes to the immunosuppressive tumor microenvironment through:

  • Promotion of M2 macrophage polarization

  • Induction of immunosuppressive γδ T cells

  • Enhancement of regulatory T cell (Treg) activity

Experimental approaches for studying LGALS9 in pancreatic cancer include the use of genetically modified mouse models (GEMMs) such as the Pdx1-Cre; LstopL-KrasG12D (KC) model, which recapitulates the progression from early pancreatic intraepithelial neoplasia (PanIN) to adenocarcinoma .

How does LGALS9 contribute to tumor immune evasion?

LGALS9 mediates tumor immune evasion through multiple mechanisms affecting both innate and adaptive immunity:

• Regulatory T Cell Modulation: Analysis of Treg levels in KC mouse models showed significant increases in both circulating and tumor-infiltrating Tregs compared to wild-type mice, regardless of age. This indicates that Treg circulation and infiltration represent early and sustained events in pancreatic cancer development .

• T Cell Exhaustion: Through interaction with TIM-3 on effector T cells, LGALS9 can induce T cell exhaustion and apoptosis.

• Innate Immune Suppression: LGALS9 influences myeloid cell differentiation and function, promoting immunosuppressive phenotypes.

Methodological approaches for studying these mechanisms include:

• Flow cytometry analysis of immune cell populations in peripheral blood and tumor tissues

• Functional assays measuring T cell activation and cytokine production

• In vivo depletion or blocking studies to assess the contribution of specific immune cell populations

What preclinical models are most appropriate for studying LGALS9 function?

The selection of appropriate preclinical models is critical for LGALS9 research:

• Genetically Engineered Mouse Models (GEMMs):

  • The Pdx1-Cre; LstopL-KrasG12D (KC) model is considered optimal for studying early pancreatic pathology, as it develops the full spectrum of tumor progression from acinar to ductal metaplasia (ADM) through preneoplastic lesions (PanIN) to adenocarcinoma .

  • This model is particularly valuable as the KrasG12D mutation is found in 75%-95% of human pancreatic cancers and in precancerous PanIN lesions .

  • The model recapitulates human disease in terms of activated signaling pathways and immune response establishment, including Treg accumulation .

• Patient-Derived Xenografts (PDXs):

  • Maintain tumor heterogeneity more effectively than cell lines

  • Must be used in immunocompromised mice, limiting studies of immune interactions

  • Can be used to validate findings from GEMMs in human tumor tissues

• Organoid Models:

  • Allow examination of LGALS9 expression and function in a 3D context

  • Can be derived from both normal and tumor tissues

  • Permit genetic manipulation via CRISPR/Cas9 to study LGALS9 regulatory mechanisms

How can researchers develop effective anti-LGALS9 immunotherapies?

Development of anti-LGALS9 immunotherapies requires systematic investigation of targeting strategies:

• Antibody-Based Approaches:

  • Select antibodies that bind specific epitopes to block receptor interactions

  • Validate antibody specificity across species if testing in mouse models

  • Determine whether human-specific anti-LGALS9 antibodies cross-react with murine targets

• Small Molecule Inhibitors:

  • Target the carbohydrate recognition domains to disrupt glycan binding

  • Develop isoform-specific inhibitors to minimize off-target effects

  • Assess pharmacokinetics and tissue distribution to ensure target engagement

• Combination Strategies:

  • Test anti-LGALS9 therapies in combination with existing immunotherapies

  • Explore synergies with chemotherapy or targeted therapies

  • Measure effects on both cancer and immune cells to assess mechanism of action

Table 2: Comparison of LGALS9 Targeting Strategies

What are the technical challenges in quantifying LGALS9 expression in heterogeneous tissues?

Accurate quantification of LGALS9 in complex tissues presents several challenges:

• Tissue Heterogeneity:

  • Different cell populations within the same tissue may express varying levels of LGALS9

  • In pancreatic cancer models, LGALS9 expression varies between PanIN lesions, normal pancreatic tissue, Langerhans islets, and infiltrating immune cells

  • Solution: Combine laser capture microdissection with qPCR or use single-cell RNA sequencing

• Subcellular Localization Variations:

  • LGALS9 can localize to the cytoplasm, nucleus, cell surface, or be secreted

  • In PanIN lesions, strong LGALS9 staining is observed at the apex and in the nucleus, with diffuse cytoplasmic staining of lower intensity

  • Solution: Use confocal microscopy with appropriate subcellular markers

• Expression Dynamics:

  • LGALS9 expression changes with disease progression and immune activation

  • Different age groups of KC mice show variations in LGALS9 expression patterns

  • Solution: Perform time-course studies with consistent sampling and quantification methods

How does LGALS9 expression differ between immune and non-immune cells in the tumor microenvironment?

Understanding cell type-specific LGALS9 expression patterns is essential for targeted interventions:

• Pancreatic Cells (CD45- Population):

  • In pancreatic cancer models, non-immune pancreatic cells (CD45-) show significantly higher LGALS9 expression in KC mice (RFI: 3.03) compared to wild-type mice (RFI: 1.63)

  • This increased expression is consistent across all age subgroups

• Peripheral Immune Cells (Circulating CD45+ Cells):

  • No significant difference in LGALS9 expression is observed between KC mice (RFI: 3.44 ± 0.34) and wild-type mice (RFI: 2.86 ± 0.35)

  • A trend showing decreased LGALS9 expression with age in both KC and wild-type mice has been observed (p~0.08)

• Tumor-Infiltrating Immune Cells (Tissue CD45+ Cells):

  • Significant increase in LGALS9 expression in pancreatic infiltrating immune cells in KC mice compared to wild-type mice

  • Age-specific subgroup comparisons show a tendency for increased LGALS9-positive immune cell infiltration in KC mice (p=0.056 between WT 4-12 months and KC 4-12 months)

Methodological approach: Flow cytometry analysis after mechanical and enzymatic digestion of tissues, with appropriate gating strategies to distinguish immune (CD45+) from non-immune (CD45-) cells. Expression should be reported as relative fluorescence intensity (RFI) compared to isotype controls .

What is the relationship between LGALS9 expression and T cell populations in cancer models?

LGALS9 expression correlates with specific changes in T cell populations:

• Total CD4+ T Cells:

  • No significant differences in peripheral CD4+ T cell levels between KC and wild-type mice

  • Significant increase in pancreatic infiltrating CD4+ T cells in KC mice compared to wild-type mice

  • More than 50% of wild-type mice had no lymphocyte infiltration, whereas all KC mice had some degree of infiltration

• Regulatory T Cells (Tregs):

  • Significant increase in both circulating and tumor-infiltrating Tregs in KC mice compared to age-matched wild-type mice

  • Increased Treg levels observed regardless of age, indicating Treg circulation and infiltration as an early and sustained event in pancreatic cancer development

• Correlation Analysis:

  • High Treg percentage in circulation and tumor infiltrates predicts poor prognosis in PDAC patients

  • LGALS9 expression correlates with Treg abundance, suggesting a functional relationship

Methodological approach: Multi-parameter flow cytometry with appropriate markers for Tregs (typically CD4+CD25+FOXP3+) and analysis of both peripheral blood and tumor tissues to assess systemic versus local immune changes .

How can researchers validate the specificity of anti-LGALS9 therapeutics?

Ensuring specificity of anti-LGALS9 therapeutics requires comprehensive validation:

• In Vitro Validation:

  • Binding affinity assays using recombinant LGALS9 proteins

  • Competitive binding assays with known LGALS9 ligands

  • Cross-reactivity testing against other galectin family members

  • Functional assays measuring inhibition of LGALS9-dependent activities

• Cross-Species Reactivity:

  • When testing human-specific anti-LGALS9 antibodies in mouse models, validate cross-reactivity with murine LGALS9

  • Perform side-by-side comparisons using cells or tissues from both species

  • Consider developing species-specific reagents if cross-reactivity is insufficient

• Tissue Distribution Studies:

  • Assess binding to LGALS9 in different tissue types using immunohistochemistry

  • Validate specificity using LGALS9 knockout or knockdown controls

  • Evaluate potential off-target binding to similar proteins or glycan structures

What is the role of LGALS9 in viral infections such as SARS-CoV-2?

Recent research suggests LGALS9 may play important roles in viral infections:

• SARS-CoV-2 Interactions:

  • Human galectin-9 has been identified as potentially enhancing SARS-CoV-2 replication

  • This represents an emerging area of research with implications for understanding COVID-19 pathogenesis

• Methodological Approaches:

  • Viral replication assays in the presence/absence of LGALS9

  • Analysis of LGALS9 expression in COVID-19 patient samples

  • In vitro binding studies between LGALS9 and viral components

  • Therapeutic targeting of LGALS9 in viral infection models

Researchers investigating this area should consider both direct virus-LGALS9 interactions and indirect effects through immune modulation .

How can researchers effectively extract and analyze LGALS9-related data from public databases?

Systematic approaches to data mining can yield valuable insights:

• Public Database Resources:

  • Gene Expression Omnibus (GEO) datasets such as GSE53659 contain valuable LGALS9 expression data

  • The Cancer Genome Atlas (TCGA) provides multi-omics data across cancer types

  • Protein Data Bank (PDB) contains structural information

• Data Extraction Methods:

  • Bioinformatic tools like R/Bioconductor packages for standardized analysis

  • Google's "People Also Ask" data extraction tools can help identify trending research questions

  • Automated data extraction using spreadsheet tools like ImportFromWeb can improve efficiency

• Analysis Approaches:

  • Comparative analysis across different tissues and disease states

  • Correlation of LGALS9 expression with clinical outcomes

  • Network analysis to identify LGALS9-associated pathways

  • Meta-analysis combining multiple datasets for increased statistical power

Table 3: Key Public Databases for LGALS9 Research

What statistical approaches are most appropriate for analyzing LGALS9 expression data?

• Data Distribution Assessment:

  • Use Shapiro-Wilk normality test to determine distribution characteristics

  • For non-Gaussian distributions, apply non-parametric tests like Mann-Whitney

• Significance Testing:

  • For comparing two groups (e.g., KC vs. WT mice), use Mann-Whitney test for non-parametric data

  • Report p-values with appropriate significance thresholds: p ≤ 0.05 (), p ≤ 0.01 (), p ≤ 0.001 (), and p ≤ 0.0001 (****)

• Visualization Approaches:

  • Represent data with appropriate error bars (standard deviations for descriptive statistics)

  • Use statistical packages like GraphPad Prism for both analysis and visualization

  • Consider specialized plots for specific data types (e.g., volcano plots for differential expression)

• Correlation Analysis:

  • Assess relationships between LGALS9 expression and clinical parameters

  • Use Spearman correlation for non-parametric data

  • Consider multivariate analysis to account for confounding factors

When analyzing flow cytometry data, report relative fluorescence intensity (RFI) in relation to isotype controls to standardize results across experiments .