S100A16 Human

What is the basic structure and molecular characteristics of human S100A16?

S100A16 is a member of the S100 protein family comprised of acidic proteins with EF-hand Ca2+ binding motifs. It is approximately 10 kDa in size as detected by Western blot analysis . The protein consists of 103 amino acids (Ser2-Ser103) as indicated by recombinant protein expression systems . As an acidic calcium-binding protein, its structure enables it to respond to calcium signaling, which is crucial for its biological functions in regulating various cellular processes including cytoskeletal reorganization .

What is the normal tissue distribution of S100A16 in human organs?

S100A16 is widely expressed in various human tissues and organs . Immunohistochemical studies have detected S100A16 in human brain cortex using specific antibodies . The protein has also been identified in renal tissues, where it plays roles in renal tubulointerstitial fibrosis . Additionally, S100A16 expression has been detected in human cancer cell lines such as Nalm-6 human Pre-B acute lymphocytic leukemia cells . This wide distribution suggests diverse physiological functions across different tissue types.

What are the most reliable methods for detecting S100A16 in clinical samples?

Several validated methods can be used for reliable detection of S100A16 in clinical samples:

• Immunohistochemistry (IHC): Effective for detecting S100A16 in paraffin-embedded tissue sections. For optimal results, use antigen affinity-purified polyclonal antibodies with appropriate concentration (e.g., 10 μg/mL) and overnight incubation at 4°C followed by HRP-DAB staining .

• Western Blotting: Reliable for quantifying S100A16 protein levels in tissue or cell lysates. For best results, use PVDF membranes probed with specific anti-S100A16 antibodies (approximately 1 μg/mL) under reducing conditions . The expected band should appear at approximately 10 kDa.

• RT-qPCR: For mRNA expression analysis, design primers that specifically target human S100A16 (e.g., forward 5′-TTG GAT CCG GAG ATG TCA GAC TGC TAC AC-3′ and reverse 5′-TTA CGC GTA AAG GGG TCT CTA GCT GCT G-3′) , with GAPDH as a reference gene. Quantify relative expression using the 2^-ΔΔCt method.

• Immunofluorescence: Useful for subcellular localization studies of S100A16 protein, particularly when investigating interactions with other proteins.

How can researchers optimize S100A16 detection in Western blot experiments?

For optimal detection of S100A16 in Western blot experiments, researchers should:

• Use appropriate lysis buffers (RIPA buffer works well for S100A16 extraction) .

• Load adequate protein amounts (typically 20-50 μg total protein per lane).

• Use reducing conditions and Immunoblot Buffer Group 8 for optimal results .

• Select a high-quality primary antibody (1 μg/mL of anti-S100A16 antibody) .

• Use HRP-conjugated secondary antibodies with appropriate specificity for the primary antibody source .

• Look for a specific band at approximately 10 kDa, which is the expected molecular weight for S100A16 .

• Include positive controls (such as Nalm-6 human Pre-B acute lymphocytic leukemia cell line) and negative controls (such as tissues or cells with verified low S100A16 expression).

• For quantification, normalize S100A16 expression to loading controls such as GAPDH .

What experimental controls are essential when studying S100A16 expression in disease models?

Essential experimental controls for studying S100A16 in disease models include:

• Tissue-matched normal controls: Always compare diseased tissues with matched normal tissues to accurately assess differential expression . For instance, when studying S100A16 in gastric cancer, adjacent normal gastric tissues serve as appropriate controls .

• Genetic manipulation controls: When using knockdown or overexpression systems, include appropriate vector controls (scrambled shRNA or empty vector) to account for non-specific effects of the manipulation process .

• Transgenic animal controls: For S100A16 transgenic mice studies, both wild-type littermates and heterozygous (S100A16+/-) mice should be included as controls to establish dose-dependent effects .

• Physiological parameter controls: Monitor relevant physiological parameters that might be affected by disease progression, such as serum creatinine and blood urea nitrogen levels in kidney disease models .

• Time course controls: Assess expression at multiple time points to understand dynamic changes, particularly in progressive disease models like unilateral ureteral obstruction (UUO) .

How does S100A16 expression vary across different cancer types?

S100A16 expression patterns and functions vary significantly across different cancer types:

• Colorectal Cancer (CRC): S100A16 suppresses the proliferation, migration, and invasion of CRC cells. It exerts this inhibitory effect partially through the JNK/p38 MAPK pathway .

• Gastric Cancer (GC): In contrast to CRC, S100A16 is significantly upregulated in GC tissues compared to adjacent normal tissues. Higher S100A16 expression correlates with poor prognosis in GC patients. Functionally, S100A16 overexpression promotes GC cell proliferation and migration both in vitro and in vivo .

• Glioma: S100A16 is markedly upregulated in glioma, and patients with higher S100A16 levels have shorter survival times. S100A16 overexpression promotes proliferation, invasion, and migration of glioma cells, as well as tumor formation in nude mice models .

• Other Cancers: Previous studies have reported that S100A16 is associated with tumor progression in bladder, lung, and breast cancer .

This variation suggests tissue-specific regulatory mechanisms and potentially distinct molecular interactions in different cellular contexts.

What molecular mechanisms underlie S100A16's role in tumor progression?

S100A16 influences tumor progression through multiple molecular mechanisms:

• Regulation of Signaling Pathways:

  • In colorectal cancer, S100A16 suppresses tumor growth via the JNK/p38 MAPK pathway .

  • In glioma, S100A16 functions as a negative regulator of the Hippo pathway by decreasing LATS1 expression levels, promoting YAP nuclear import, and initiating downstream target genes like CYR61 .

• Protein-Protein Interactions:

  • In gastric cancer, S100A16 interacts with ZO-2 (Zonula Occludens-2), a master regulator of cell-to-cell tight junctions. S100A16 promotes ZO-2 ubiquitination and degradation, leading to enhanced invasion, migration, and epithelial-mesenchymal transition (EMT) .

  • In renal fibrosis, S100A16 responds to Ca2+ increases and interacts with myosin-9 during kidney injury or following TGF-β stimulation, promoting cytoskeleton reorganization and EMT progression .

• Cell Motility and Invasion:

  • S100A16 influences cellular cytoskeleton organization through calcium-dependent mechanisms, affecting cell motility and invasive capacity .

These diverse mechanisms highlight S100A16's context-dependent functions in cancer progression.

How can S100A16 be used as a prognostic biomarker in cancer patients?

S100A16 shows promise as a prognostic biomarker in several cancer types:

• Expression Analysis Methods: For prognostic assessment, immunohistochemistry (IHC) scoring systems can be employed. For example, in gastric cancer studies, patients were stratified into S100A16high (IHC score ≥ 2) and S100A16low (IHC score < 2) groups, with S100A16high patients showing significantly worse prognosis .

• Survival Correlation: Kaplan-Meier survival analysis can be used to correlate S100A16 expression with patient outcomes. This approach has demonstrated that high S100A16 expression correlates with poor prognosis in gastric cancer and glioma patients .

• Multivariate Analysis: To establish S100A16 as an independent prognostic factor, researchers should perform multivariate analysis controlling for established prognostic factors such as tumor stage, grade, and patient demographics.

• Combined Biomarker Panels: For improved prognostic value, consider integrating S100A16 expression with other biomarkers in a comprehensive prognostic panel specific to each cancer type.

• Early Detection Applications: In gastric cancer, S100A16 has been identified as a promising candidate biomarker for early diagnosis and prediction of metastasis .

How does calcium binding affect S100A16's molecular interactions?

Calcium binding is crucial for S100A16's molecular interactions and biological functions:

• Conformational Changes: Like other S100 proteins, S100A16 contains EF-hand Ca2+ binding motifs that undergo conformational changes upon calcium binding . This calcium-induced conformational change exposes hydrophobic residues that can then interact with target proteins.

• Target Protein Binding: The calcium-bound form of S100A16 has increased affinity for specific target proteins. For example, S100A16 responds to Ca2+ increases to interact with myosin-9 during kidney injury or following TGF-β stimulation .

• Cytoskeletal Regulation: Calcium signaling through S100A16 is involved in cytoskeleton reorganization . The calcium-dependent interaction between S100A16 and cytoskeletal proteins like myosin-9 influences cell morphology changes associated with processes such as epithelial-mesenchymal transition.

• Experimental Approaches: To study calcium-dependent interactions, researchers can use calcium chelators like BAPTA-AM to inhibit calcium binding, or calcium ionophores to enhance it . Fluorescent calcium indicators such as Rhod-2 AM can be used to monitor calcium levels during S100A16 activation .

What proteomic approaches are most effective for identifying S100A16 binding partners?

Several proteomic approaches have proven effective for identifying S100A16 binding partners:

• Immunoprecipitation (IP) coupled with Mass Spectrometry:

  • Cells overexpressing S100A16 or transfected with control vectors are lysed with RIPA buffer

  • Cell lysates are immunoprecipitated with anti-S100A16 antibody

  • Precipitated proteins are analyzed using LTQ-Orbitrap instruments connected to Nano ACQUITY UPLC systems

  • This approach successfully identified myosin-9 as an S100A16 binding partner in renal cells

• Proximity-Based Labeling Methods:

  • BioID or APEX2 fusion proteins can be used to identify proximal proteins in the cellular context

  • These methods are particularly valuable for detecting transient or weak interactions

• Pull-Down Assays with Recombinant Proteins:

  • Purified recombinant S100A16 (such as E. coli-derived recombinant human S100A16, Ser2-Ser103) can be used as bait

  • Both calcium-dependent and calcium-independent binding conditions should be tested

  • Follow with SDS-PAGE and mass spectrometry analysis

• Validation of Interactions:

  • Confirm potential interactions using reciprocal co-immunoprecipitation

  • Verify direct interaction using techniques like FRET or in vitro binding assays

  • Assess subcellular co-localization using immunofluorescence microscopy

How does S100A16 influence the epithelial-mesenchymal transition (EMT) process?

S100A16 plays significant roles in the epithelial-mesenchymal transition through several mechanisms:

• Regulation of Tight Junction Proteins:

  • In gastric cancer, S100A16 promotes EMT by targeting ZO-2, a key tight junction protein

  • S100A16 mediates ZO-2 ubiquitination and degradation, leading to disruption of cell-to-cell junctions and increased invasiveness

• Cytoskeletal Reorganization:

  • S100A16 interacts with myosin-9 in a calcium-dependent manner during kidney injury

  • This interaction promotes cytoskeleton reorganization, which is essential for the morphological changes associated with EMT

  • The interaction facilitates the transition from epithelial to mesenchymal phenotype in renal tubular cells

• EMT Marker Expression:

  • S100A16 influences the expression of classic EMT markers including:

    • Decreased E-cadherin (epithelial marker)

    • Increased N-cadherin, vimentin, fibronectin, collagen I, and α-SMA (mesenchymal markers)

• TGF-β Pathway Interaction:

  • S100A16 expression increases in response to TGF-β stimulation

  • S100A16 appears to function downstream of TGF-β in promoting EMT during renal fibrosis

What transgenic models are available for studying S100A16 function in vivo?

Several transgenic models have been developed for studying S100A16 function:

• S100A16 Transgenic (Tg) Mice:

  • Overexpress S100A16 under the control of the PCAG promoter

  • Used to study the effects of S100A16 overexpression in various disease models

  • Particularly useful in renal fibrosis studies using the unilateral ureteral obstruction (UUO) model

• S100A16 Heterozygous Mice (S100A16+/-):

  • Express intermediate levels of S100A16

  • Allow for the study of dose-dependent effects of S100A16

  • Demonstrate distinct physiological responses compared to wild-type and transgenic mice

• Tissue-Specific S100A16 Knockout/Overexpression Models:

  • Various conditional expression systems can be used to study tissue-specific functions

  • Particularly valuable for resolving contradictory roles of S100A16 in different tissues

• Xenograft Models with Modified S100A16 Expression:

  • Human cancer cells with S100A16 overexpression or knockdown can be injected into nude mice

  • Used to study the effects of S100A16 on tumor formation, growth, and metastasis in vivo

  • Effective for translating in vitro findings to more physiologically relevant contexts

These models have revealed that S100A16 overexpression can promote tumor formation in nude mice models of glioma and contribute to renal tubulointerstitial fibrosis in the UUO model .

How can CRISPR-Cas9 technology be optimized for S100A16 functional studies?

CRISPR-Cas9 technology offers powerful approaches for S100A16 functional studies:

• Gene Knockout Strategies:

  • Design multiple sgRNAs targeting early exons of S100A16

  • Focus on conserved regions encoding calcium-binding domains

  • Screen clones using both genomic PCR with sequencing and Western blot verification for complete protein loss

• Knock-in Applications:

  • Create fusion proteins (e.g., S100A16-GFP) for live cell imaging

  • Introduce specific mutations in calcium-binding domains to study structure-function relationships

  • Develop reporter systems by knocking in fluorescent proteins under the endogenous S100A16 promoter

• Transcriptional Modulation:

  • Use CRISPR activation (CRISPRa) to upregulate endogenous S100A16

  • Use CRISPR interference (CRISPRi) for partial and reversible knockdown

  • These approaches maintain natural regulatory elements and avoid overexpression artifacts

• Validation Protocols:

  • Perform off-target analysis using whole-genome sequencing

  • Include rescue experiments by re-expressing S100A16 in knockout cells

  • Use multiple independent clones to ensure phenotype consistency

• Time-Resolved Studies:

  • Implement inducible CRISPR systems to study the temporal aspects of S100A16 function

  • Particularly valuable for developmental studies or acute vs. chronic effects

How should researchers address contradictory findings regarding S100A16's role in different cancer types?

Researchers should employ several strategies to address contradictory findings regarding S100A16's role in different cancers:

• Systematic Comparative Studies:

  • Directly compare S100A16 function in multiple cell lines from different cancer types under identical experimental conditions

  • Use both overexpression and knockdown approaches in each cell type

  • Analyze the same functional endpoints (proliferation, migration, invasion) using identical methodologies

• Context-Dependent Interaction Mapping:

  • Identify tissue-specific binding partners through proteomic approaches

  • Compare S100A16 interactomes across different cancer types

  • This may reveal why S100A16 suppresses proliferation in colorectal cancer but promotes it in gastric cancer and glioma

• Signaling Pathway Analysis:

  • Comprehensively evaluate how S100A16 affects key cancer-related pathways in different contexts

  • For example, determine whether S100A16 acts through the JNK/p38 MAPK pathway in cancers besides colorectal cancer

  • Investigate how S100A16 interacts with the Hippo pathway in different cancer types beyond glioma

• Consideration of Genetic Background:

  • Analyze how mutations or alterations in key cancer genes modify S100A16 function

  • This may explain tissue-specific effects based on the genetic landscape of each cancer type

• Integration of Clinical Data:

  • Correlate S100A16 expression with clinical outcomes across multiple cancer types

  • Stratify patients based on genetic profiles to identify subgroups where S100A16 may have differential effects

This comprehensive approach will help resolve apparent contradictions and establish a unified understanding of S100A16's context-dependent functions in cancer biology.