Recombinant Human Transmembrane protein 213 (TMEM213)

What is Transmembrane Protein 213 (TMEM213) and its basic structural characteristics?

TMEM213 belongs to the transmembrane protein family and is characterized by its membrane-bound structure. The protein has been confirmed to have a transmembrane topology with the N-terminus exposed to the cytoplasm . Structurally, TMEM213 is identified in UniProt under the primary accession A2RRL7, with secondary accessions including A4D1R3, C9JH49, C9JX41, and C9K0P0 . The protein is encoded by the TMEM213 gene (Gene ID: 155006) . Despite its identification and classification, TMEM213 notably lacks substantial protein and transcript evidence in UniProt and currently has no conclusively suggested function .

Where is TMEM213 primarily expressed in human tissues?

TMEM213 demonstrates a highly specific expression pattern primarily limited to kidney and salivary gland tissues. According to the Human Protein Atlas, TMEM213 is classified as one of only eight genes showing group-enriched expression in both kidney and salivary gland . More specifically, both bulk and single-cell RNA sequencing data reveal TMEM213 expression in:

• Kidney: Distal tubular cells and collecting duct cells

• Salivary gland: Salivary duct cells, serous glandular cells, and ionocytes

• Large ducts in the salivary gland

These findings have been validated at the protein level through immunohistochemistry, which confirms staining exclusively in salivary gland and tubular cells of the kidney .

What are the known cellular localizations of TMEM213?

Experimental studies have confirmed the membrane-bound status of TMEM213, specifically assigning it to early endosomes within the cellular compartment . The protein demonstrates a specific orientation within the membrane where the N-terminus is exposed to the cytoplasm . This topological orientation provides important structural information that can guide potential functional studies and protein interaction analyses.

How can researchers effectively use antibodies against TMEM213 in laboratory experiments?

When working with TMEM213 antibodies, researchers should consider the following experimental parameters based on validated protocols:

For optimal results with polyclonal rabbit anti-human TMEM213 antibodies:

• Use antibodies with confirmed specificity against human TMEM213

• Store antibody aliquots at -20°C and avoid repeated freeze/thaw cycles

• When conducting immunostaining experiments, use appropriate buffer systems (e.g., 0.01 M PBS, pH 7.4)

• Validate antibody specificity using recombinant TMEM213 protein as a positive control

• Consider using protein G-purified antibodies with >95% purity for reduced background

What experimental approaches are recommended for studying TMEM213 expression patterns?

For comprehensive analysis of TMEM213 expression patterns, a multi-platform approach is recommended:

• RNA-level analysis:

  • Bulk RNA sequencing for tissue-level expression profiling

  • Single-cell RNA sequencing for cellular resolution of expression patterns

  • Correlation analyses with functionally related genes (clustering with genes related to transmembrane transport)

• Protein-level confirmation:

  • Immunohistochemistry for tissue localization (focus on kidney and salivary gland)

  • Multiplex immunofluorescence for subcellular localization, particularly effective for visualizing TMEM213 in ionocytes and large ducts of salivary glands

  • Western blotting for protein size confirmation and semi-quantitative analysis

• Functional confirmation:

  • Cloning and overexpression systems in relevant cell lines (HEK293 and HK-2 have been successfully used)

  • Co-localization studies with established endosomal markers to confirm subcellular distribution

This integrated approach provides complementary data that can validate expression findings across multiple biological levels.

How does TMEM213 expression correlate with clinical parameters in cancer studies?

TMEM213 expression has shown significant correlations with clinical parameters, particularly in lung adenocarcinoma:

These correlations suggest potential clinical utility of TMEM213 as both a prognostic and predictive biomarker in lung adenocarcinoma.

What are the current methodologies for investigating TMEM213's role in cancer progression?

Contemporary research into TMEM213's role in cancer progression employs several advanced methodologies:

• Transcriptomic screening approaches:

  • Max stat package analysis to identify prognostic genes from large datasets (e.g., TCGA database)

  • Univariate Cox analysis combined with Kaplan-Meier curves (log-rank test) to screen for genes with prognostic value

  • Subgroup Treatment Effect Pattern Plot (STEPP) analysis to evaluate treatment effects across different expression levels

• Mechanistic investigations:

  • Cloning and overexpression of full-length TMEM213 in appropriate cell lines (e.g., HEK293 and HK-2)

  • Gene set enrichment analysis (GSEA) to investigate biological characteristics associated with different TMEM213 expression levels

  • Analysis of KEGG pathways, particularly KEGG_DRUG_METABOLISM_CYTOCHROME_P450 and KEGG_ABC_TRANSPORTERS, which have shown association with TMEM213 expression

• Genetic and structural analyses:

  • Mutation screening in cancer tissues to identify potentially damaging mutations

  • Chromosomal aberration analysis using cytogenetic and molecular techniques

  • Isoform expression profiling in tumor tissues

• Validation techniques:

  • Internal validation using bootstrapping methods (1,000 replications)

  • External validation using public databases like Kaplan-Meier plotter

  • Multivariate Cox regression models adjusted for standard prognostic factors

How can researchers investigate the potential function of TMEM213 in kidney and salivary gland physiology?

To explore TMEM213's function in kidney and salivary gland physiology, researchers should consider:

• Comparative co-expression analysis:

  • TMEM213 clusters with genes related to transmembrane transport in kidney and salivary secretion

  • Compare with V-ATPase subunits (also enriched in kidney and salivary gland) that maintain pH of intracellular compartments and mediate proton secretion from renal intercalated cells

• Cell-specific functional studies:

  • Target experiments to specific cell types: distal tubular cells, collecting duct cells, salivary duct cells, and serous glandular cells

  • Employ cell-specific isolation techniques (laser capture microdissection or FACS sorting) followed by molecular characterization

• Physiological function investigation:

  • Assess pH regulation capabilities in relevant cell types

  • Measure ion transport activities in overexpression and knockdown models

  • Evaluate interactions with known ion channels and transporters

• Advanced imaging approaches:

  • Multiplex immunofluorescence to visualize TMEM213 alongside functional markers

  • Live-cell imaging to track dynamic localization during cellular processes

  • Super-resolution microscopy to determine precise membrane positioning

• Molecular interaction studies:

  • Proximity labeling techniques to identify protein interaction partners

  • Co-immunoprecipitation followed by mass spectrometry

  • Yeast two-hybrid screening for direct protein interactions

What experimental design considerations are important when studying TMEM213 mutations and their impact?

When investigating TMEM213 mutations, consider these experimental design principles:

• Mutation identification and classification:

  • Screen for naturally occurring mutations in patient samples, particularly from kidney cancers and lung adenocarcinomas

  • Focus on potentially damaging mutations, as these have been identified in TMEM213 in ccRCC tumors

  • Classify mutations based on their predicted impact (e.g., missense, nonsense, frameshift)

• Functional validation approaches:

  • Site-directed mutagenesis to introduce specific mutations into expression constructs

  • Stable and transient transfection experiments in relevant cell lines

  • Comparison of wild-type and mutant protein localization, stability, and function

• Expression system selection:

  • HEK293 and HK-2 cell lines have been successfully used for TMEM213 overexpression studies

  • Consider tissue-specific cell models when available (e.g., salivary gland or kidney tubular cell lines)

  • Primary cells may provide more physiologically relevant contexts

• Phenotypic readouts:

  • Assess subcellular localization changes using immunofluorescence microscopy

  • Measure protein stability through cycloheximide chase assays

  • Evaluate functional consequences through appropriate physiological assays

• Data collection and analysis:

  • Define steady-state experimental conditions clearly

  • Generate comprehensive expression matrices as shown in the inference methodology

  • Consider Boolean network approaches for analyzing gene interaction effects

  • Apply statistical methods like Predictor method to determine minimum sets of experimental variables

How can conflicting data about TMEM213 expression in different cancer types be reconciled?

Researchers facing conflicting TMEM213 expression data across cancer types should consider:

• Tissue-specific context evaluation:

  • TMEM213 shows downregulation in clear cell Renal Cell Carcinoma (ccRCC)

  • In lung adenocarcinoma, high expression correlates with better prognosis

  • These apparently contradictory findings may reflect tissue-specific functions

• Methodological standardization:

  • Ensure consistent measurement techniques across studies (qPCR, RNA-seq, protein detection)

  • Apply uniform cutoff criteria for defining "high" versus "low" expression

  • Document specific isoforms being measured in each study

• Subtype stratification:

  • Analyze cancer subtypes separately (e.g., adenocarcinoma vs. squamous cell carcinoma)

  • Consider molecular subtypes defined by comprehensive genomic analyses

  • Correlate with specific driver mutations or signaling pathway alterations

• Integrative analysis approaches:

  • Perform meta-analyses across multiple datasets with appropriate statistical correction

  • Apply machine learning techniques to identify patterns across heterogeneous datasets

  • Integrate multi-omics data (transcriptomics, proteomics, epigenomics)

• Validation in larger cohorts:

  • Confirm findings using larger, independent patient cohorts

  • Utilize public databases like TCGA and the Kaplan-Meier plotter for external validation

  • Apply bootstrapping or similar resampling techniques to assess reliability of findings