S100A16 Antibody

What is S100A16 and why is it significant in disease research?

S100A16 is a member of the S100 family of calcium-binding proteins that has been implicated in various disease processes. Research indicates that S100A16 is involved in cancer development, progression, and metastasis, as well as inflammatory responses and tissue damage . The protein has been found to be upregulated in multiple cancer types, including gastric cancer, pancreatic ductal adenocarcinoma (PDAC), cervical cancer, and several others .

The significance of S100A16 lies in its diverse roles in cellular processes that contribute to pathogenesis. For instance, it has been shown to expedite proliferation and invasion in gastric cancer cells, regulate tight junction proteins through interaction with ZO-2, and influence immune cell infiltration in tumors . Additionally, its differential expression across various tissues and its correlation with poor prognosis in multiple cancer types make it a promising biomarker candidate for disease diagnosis and prognosis .

What are the standard methods for detecting S100A16 expression in tissue samples?

The detection of S100A16 expression in tissue samples primarily utilizes immunohistochemistry (IHC), which can visualize protein expression patterns within tissue architecture. In standard protocols, tissue sections are stained with an S100A16 primary antibody (typically at a dilution ratio of 200:1), followed by DAB (3,3'-diaminobenzidine) staining that produces a brownish-yellow color for positive expression, while cell nuclei are counterstained blue with hematoxylin .

The evaluation of IHC results involves scoring both the staining intensity and the percentage of positive cells. Staining intensity is typically graded as: no staining (0 points), light staining (1 point), moderate staining (2 points), and strong staining (3 points). The percentage of positive cells is scored as: >10% (1 point), 11%-50% (2 points), 51%-75% (3 points), and >75% (4 points). The final IHC score is calculated by multiplying these two parameters, with scores ≥12 often classified as high expression and <12 as low expression .

Other complementary methods for detecting S100A16 expression include Western blot analysis for protein levels and quantitative PCR for mRNA expression . Cellular immunofluorescence is also utilized, particularly in cell culture models, to visualize the subcellular localization of S100A16 .

How can S100A16 be genetically manipulated for functional studies?

CRISPR/Cas9 gene editing technology has been successfully employed to knockout S100A16 in cell lines, providing a powerful tool for investigating its functional roles. Researchers have designed sgRNAs targeting exons of the S100A16 gene using online design tools such as https://www.zlab.bio/resources . The procedure involves:

• Analyzing the S100A16 gene sequence in databases like GENEBANK

• Designing multiple sgRNA pairs targeting specific exons (e.g., four sgRNAs pairs in the second S100A16 exon)

• Ligating these sgRNAs to a plasmid containing Cas9 (e.g., PX330 plasmid)

• Transfecting cells with the ligation product (S100A16-Cas9/sgRNA)

• Screening and selecting knockout cell clones

• Validating the knockout at both protein and mRNA levels using Western blot, qPCR, and immunofluorescence

For overexpression studies, researchers have constructed S100A16 expression plasmids (e.g., hS100A16-Myc) using site-directed mutagenesis kits according to manufacturer's instructions . Lentiviral vectors (lenti-S100A16) have also been used to establish stable S100A16-overexpressing cell lines for long-term studies .

What are the key molecular pathways through which S100A16 influences cancer progression?

S100A16 influences cancer progression through several molecular pathways:

• ZO-2 Regulation and EMT: In gastric cancer, S100A16 has been found to instigate invasion, migration, and epithelial-mesenchymal transition (EMT) by inhibiting ZO-2, a master regulator of cell-to-cell tight junctions. Mechanistically, S100A16 mediates ZO-2 ubiquitination and degradation, leading to tight junction disruption .

• AKT Signaling Pathway: Studies indicate that S100A16 activates the AKT signaling pathway in prostate cancer, promoting cell invasion, metastasis, and proliferation. The AKT pathway is crucial for cell survival and inhibits apoptosis .

• Apoptotic Regulation: S100A16 knockout has been shown to inhibit the upregulation of pro-apoptotic genes (BAX, Cleaved Caspase3) and prevent the downregulation of anti-apoptotic genes (Bcl-2) under stress conditions like hypoxia/reoxygenation (H/R) .

• HIF-1α and HRD1 Modulation: Evidence suggests that S100A16 regulates the expression of HIF-1α and HRD1 in renal tubular cells under H/R conditions. S100A16 knockout reversed the increased expression of these proteins during injury, while S100A16 overexpression elevated their levels .

How does S100A16 interact with the immune microenvironment in cancer?

S100A16 has significant associations with immune cell infiltration in cancer:

• Correlation with Immune Cell Populations: In pancreatic cancer, S100A16 expression has been negatively correlated with CD8+ T cells, suggesting its role in tumor immunity . Significant differences in S100A16 expression have been observed in various immune cell populations, including naive CD4 T cells, CD8 T cells, cytotoxic T cells, exhausted T cells, Tr1, nTreg, Th1, TH17, central memory cells, effector memory cells, NKT cells, MAIT cells, monocytes, and gamma delta T cells .

• Differential Gene Enrichment: Gene Set Enrichment Analysis (GSEA) has revealed that high S100A16 expression groups show differential enrichment in genes related to naive BCL6 low TFH, CRTL, and induced Treg cells. Conversely, low S100A16 expression groups show enrichment in genes related to naive CD4 T cells, naive CD8 T cells, and pro B cells .

• Copy Number Variation Effects: Changes in S100A16 copy number can significantly affect the infiltration level of immune cells in cancer, particularly in pancreatic ductal adenocarcinoma (PDAC) .

• Immune Checkpoint Response: Analysis of S100A16 expression can predict immune checkpoint treatment responses in cancer patients, suggesting its potential value in immunotherapy decision-making .

In cervical cancer, S100A16 has shown significant positive correlations with populations of resting mast cells and activated dendritic cells, while demonstrating significant negative correlations with naive B cells and resting CD4 memory T cells .

What scoring systems are used to evaluate S100A16 immunohistochemical staining?

For evaluating S100A16 immunohistochemical staining, researchers typically employ a semi-quantitative scoring system that considers both staining intensity and the percentage of positive cells:

Staining Intensity Scoring:

• No staining: 0 points

• Light staining: 1 point

• Moderate staining: 2 points

• Strong staining: 3 points

Percentage of Positive Cells Scoring:

• 10%: 1 point

• 11%-50%: 2 points

• 51%-75%: 3 points

• 75%: 4 points

The final IHC score is calculated by multiplying the staining intensity score by the percentage score. For analytical purposes, samples are typically classified into high-expression (IHC score ≥12) and low-expression (IHC score <12) groups .

It's recommended that scoring be performed blindly by multiple pathologists with independent diagnostic qualifications to ensure reliability. The procedure involves initially observing the entire tissue at 100× magnification, followed by image acquisition at 400× magnification for detailed assessment. For tissue microarrays, specimens are typically photographed at 200× magnification .

What experimental models are available for studying S100A16 function?

Several experimental models have been developed to study S100A16 function:

• Knockout Cell Lines: CRISPR/Cas9-mediated S100A16 knockout cell lines, such as S100A16 knockout rat renal tubular epithelial cells (NRK-52E cells), have been established to investigate the effects of S100A16 deletion on cellular functions . These models allow researchers to examine how S100A16 deficiency affects responses to stressors like hypoxia/reoxygenation or TGF-β1 treatment.

• Overexpression Models: Cells stably overexpressing S100A16 through lentiviral transduction (e.g., lenti-S100A16) or plasmid transfection provide models for studying the consequences of S100A16 upregulation . For instance, SGC-7901 gastric cancer cells overexpressing S100A16 have been used to investigate effects on proliferation, migration, and invasion.

• Protein Stability Assays: Cycloheximide chase assays have been employed to investigate the impact of S100A16 on protein stability. This approach has been used to demonstrate that S100A16 affects ZO-2 stability in gastric cancer cells .

• In Vivo Models: Although not extensively described in the provided search results, in vivo models have been mentioned for validating findings from in vitro studies, particularly in the context of gastric cancer research .

• Bioinformatic Analysis: Researchers have utilized public databases such as GEPIA, TCGA, ICGC, TIMER, and UALCAN to analyze S100A16 expression patterns across various cancer types and its correlations with clinical parameters and immune cell infiltration .

What are the essential controls for S100A16 antibody experiments?

When conducting experiments with S100A16 antibodies, the following controls are essential:

• Positive and Negative Tissue Controls: Include tissues known to express high levels of S100A16 (positive control) and those with minimal expression (negative control). This helps validate antibody specificity and performance.

• Adjacent Normal Tissue (ANT): When studying disease tissues, adjacent normal healthy tissue serves as a critical control for comparing expression levels. For example, in cervical cancer studies, researchers compare S100A16 expression in cancer tissues with corresponding adjacent normal cervical tissue specimens .

• Isotype Controls: Include appropriate isotype-matched control antibodies to identify non-specific binding.

• Genetic Models as Controls: S100A16 knockout or knockdown cells serve as excellent negative controls for antibody specificity validation. Similarly, S100A16 overexpression models provide positive controls .

• Secondary Antibody-Only Controls: These controls help identify background staining from the secondary antibody.

• Dilution Series: Testing antibodies at different dilutions helps optimize signal-to-noise ratios and ensures reliable detection.

• Multiple Detection Methods: Validating S100A16 expression using multiple methods (IHC, Western blot, qPCR) provides more robust evidence and controls for method-specific artifacts .

How does S100A16 expression correlate with clinical outcomes in different cancer types?

S100A16 expression has shown significant correlations with clinical outcomes across several cancer types:

What role does S100A16 play in drug sensitivity and potential therapeutic approaches?

S100A16 has emerging implications for drug sensitivity and therapeutic strategies:

• Chemotherapy Sensitivity: In cervical cancer, high S100A16 expression correlates with increased sensitivity to certain chemotherapeutic drugs, including gemcitabine and axitinib .

• Combination Therapies: Research suggests that targeting S100A16 could enhance the efficacy of existing therapies. For example, when used concomitantly with cisplatin, gemcitabine may be more effective in treating recurrent or advanced cervical cancer in patients with high S100A16 expression .

• Cisplatin Resistance: S100A16 has been implicated in cisplatin resistance during chemotherapy for lung cancer treatment, suggesting that modulating S100A16 expression could potentially overcome resistance mechanisms .

• Immunotherapy Implications: Given S100A16's relationship with immune cell infiltration and predicted immune checkpoint treatment responses, it may serve as a biomarker for selecting patients who would benefit from immunotherapy approaches .

• Therapeutic Target: The mechanistic studies revealing S100A16's involvement in multiple cancer-promoting pathways suggest that directly targeting S100A16 could be a novel therapeutic strategy. Particularly, disrupting S100A16's interaction with proteins like ZO-2 or its influence on the AKT signaling pathway might provide therapeutic benefits .

How can contradictory findings about S100A16 expression across different cancer types be reconciled?

The differential expression patterns of S100A16 across cancer types present an intriguing research question. While S100A16 is upregulated in many cancers including gastric, pancreatic, and cervical cancers, it is downregulated in others such as adrenocortical carcinoma, esophageal carcinoma, and prostate adenocarcinoma .

Reconciling these contradictory findings requires:

• Tissue-Specific Context: Investigating whether S100A16 functions in a tissue-specific manner, potentially interacting with different partners or affecting different pathways depending on the cellular context.

• Molecular Subtyping: Determining if S100A16 expression correlates with specific molecular subtypes within each cancer type rather than the cancer type as a whole.

• Temporal Dynamics: Examining whether S100A16 expression changes during disease progression, potentially explaining discrepancies in studies that sample at different disease stages.

• Integration with Genomic Data: Analyzing whether genetic alterations, such as copy number variations or mutations in interacting partners, influence S100A16's role across different cancer types .

• Microenvironmental Factors: Investigating how the tumor microenvironment, including immune cell infiltration patterns, affects S100A16 expression and function in different cancers .

What are the implications of S100A16 in immune regulation for cancer immunotherapy?

S100A16's relationship with immune cell populations has significant implications for cancer immunotherapy:

• Predictive Biomarker: S100A16 expression levels could potentially serve as a biomarker for predicting responses to immune checkpoint inhibitors, helping to select patients who would benefit most from immunotherapy .

• Immune Cell Infiltration: The negative correlation between S100A16 and CD8+ T cells suggests that targeting S100A16 might enhance cytotoxic T cell infiltration into tumors, potentially improving immunotherapy outcomes .

• Immune Checkpoint Modulation: Analysis of S100A16 expression and its relationship to immune checkpoint molecules could inform combination strategies that enhance the efficacy of checkpoint inhibitors .

• Tumor Microenvironment Remodeling: Given S100A16's associations with various immune cell populations, therapies targeting S100A16 might remodel the tumor immune microenvironment to favor anti-tumor responses .

• Personalized Immunotherapy Approaches: The differential effects of S100A16 on immune cell populations across cancer types suggest that tailoring immunotherapy approaches based on S100A16 expression patterns could improve treatment outcomes .