Recombinant Rat Transmembrane protein 158 (Tmem158)

What is the molecular structure and function of rat Tmem158?

Rat Transmembrane protein 158 (Tmem158), also known as 40 kDa BINP-binding protein, Brain-specific binding protein, Ras-induced senescence protein 1 (Ris1), or p40BBP, is a multi-pass membrane protein that functions as a receptor for brain injury-derived neurotrophic peptide (BINP), a synthetic 13-mer peptide . The protein plays key roles in cellular processes including signal transduction, cell adhesion, and membrane trafficking .

The rat Tmem158 protein corresponds to UniProt ID Q91XV7 and has several synonyms in the literature . Its subcellular location is primarily in the membrane as a multi-pass membrane protein . Research indicates that Tmem158 was originally identified as an upregulated gene during Ras-induced senescence in human diploid fibroblasts infected with a RasV12-containing retrovirus .

How is Tmem158 expression regulated in different tissues and disease states?

Tmem158 expression appears to be tightly regulated in a tissue-specific manner and shows altered expression in several disease states. In cancer research, TMEM158 shows variable expression patterns depending on the cancer type:

• In ovarian cancer: TMEM158 is overexpressed compared to normal tissues, as demonstrated by RNA-sequencing data from The Cancer Genome Atlas (TCGA) project and validated by real-time PCR showing elevated expression in 84% (21/25) of tested ovarian cancer tissues .

• In prostate cancer: TMEM158 is significantly downregulated and its expression is negatively regulated by androgen receptor (AR) signaling . Expression analysis using the RNA-seq dataset from TCGA-PRAD showed differential expression between normal and tumor tissues .

• In Wilms tumors: TMEM158 was found to be overexpressed specifically in tumors with somatic CTNNB1 mutations, suggesting a relationship between Ras and Wnt signaling pathways .

The transcription of Tmem158 is specifically upregulated in response to activation of the Ras pathway, but notably not under other conditions that induce senescence, indicating a Ras-specific regulatory mechanism .

What detection methods are most effective for studying rat Tmem158 in experimental settings?

Several methods have proven effective for studying rat Tmem158 in experimental settings:

• ELISA: Sandwich ELISA assays offer quantitative measurement of Tmem158 levels in rat serum, plasma, and cell culture supernatants with high sensitivity (0.082ng/mL) and detection range (0.156-10ng/mL) .

• Real-time PCR: For mRNA expression analysis, real-time PCR has been successfully used to detect Tmem158 expression differences between normal and cancer tissues .

• RNA-sequencing: High-throughput RNA-sequencing, as used in TCGA projects, provides comprehensive expression data that can be analyzed using computational methods to identify differential expression patterns .

• Western blotting: For protein-level detection, western blot assays using specific anti-TMEM158 antibodies enable analysis of expression changes under different experimental conditions .

• Immunohistochemistry (IHC): IHC analysis using validated anti-TMEM158 antibodies (such as HPA074974) allows visualization of protein expression in tissue sections, with semi-quantitative analysis of immunosignals .

Each method offers distinct advantages depending on the specific research question, with ELISA providing precise quantification, RNA-seq offering genome-wide context, and IHC providing spatial information within tissues.

How does Tmem158 knockdown affect cellular phenotypes in different experimental models?

Knockdown studies of Tmem158 have revealed significant impacts on cellular phenotypes, particularly in cancer models:

In ovarian cancer cell lines (HO-8910 and A2780), TMEM158 knockdown using RNA interference produced the following effects:

• Cell proliferation: Significantly inhibited cell growth in TMEM158-Ri-1 virus-infected cells compared to wild-type and scramble shRNA virus-infected control cells .

• Cell cycle regulation: Resulted in G1-phase cell cycle arrest, with a higher percentage of cells in G1 phase (65.4% ± 1.5% in HO-8910 cells with TMEM158 knockdown versus 42.1% ± 1.6% in wild-type cells) .

• Cell adhesion and invasion: Notably inhibited both cellular adhesion and invasion capabilities .

• Tumorigenicity: Reduced tumor formation capacity in nude mice .

• Molecular pathway effects: Knockdown of TMEM158 notably repressed cell adhesion by down-regulating the expression of intercellular adhesion molecule1 (ICAM1) and vascular cell adhesion molecule1 (VCAM1). The Transforming Growth Factor-β (TGF-β) signaling pathway was also remarkably impaired .

These findings demonstrate that Tmem158 plays critical roles in regulating multiple aspects of cancer cell behavior, though the effects may be context-dependent. For instance, while TMEM158 knockdown showed clear phenotypic changes in ovarian cancer cells, a study with TMEM158-deficient mouse embryonic fibroblasts reported no apparent changes in cellular functions such as proliferation, senescence, and oncogenic transformation, highlighting potential cell-type specificity in TMEM158 function .

What is the relationship between Tmem158 expression and clinical outcomes in different disease models?

The relationship between Tmem158 expression and clinical outcomes shows disease-specific patterns:

In prostate cancer:
TMEM158 expression correlates with several clinical parameters. Analysis of TCGA data revealed significant associations between TMEM158 expression levels and disease characteristics:

This data indicates that lower TMEM158 expression is associated with more advanced T stage (T3/T4) and higher incidence of lymph node metastasis (N1) .

In pancreatic ductal adenocarcinoma (PDAC):
Researchers have developed risk score signatures based on TMEM gene expression, including TMEM158, that correlate with clinical and pathological features. These signatures have been used to classify patients into high-risk and low-risk groups, with analyses of differences in tumor microenvironment characteristics and immune cell infiltration between these groups .

These findings suggest that TMEM158 expression may serve as a potential biomarker for disease progression and prognosis in certain cancer types, though the direction of association (whether high or low expression indicates poor prognosis) varies by cancer type.

What methodological approaches are most effective for investigating Tmem158's role in signal transduction pathways?

To effectively investigate Tmem158's role in signal transduction pathways, several complementary methodological approaches are recommended:

• Gene expression modulation techniques:

  • RNA interference (RNAi): Using shRNA or siRNA to knockdown TMEM158 expression, as demonstrated in ovarian cancer studies .

  • CRISPR-Cas9 gene editing: For complete knockout studies or specific mutations.

  • Overexpression systems: To study gain-of-function effects.

• Pathway analysis approaches:

  • Gene Set Enrichment Analysis (GSEA): As applied in ovarian cancer studies to investigate biological pathways involved in pathogenesis through TMEM158, using established databases such as KEGG .

  • Protein-protein interaction studies: To identify direct binding partners of Tmem158.

  • Phosphorylation cascade analysis: To track signaling events downstream of Tmem158 activation.

• Integrative computational methods:

  • Machine learning algorithms: Including Least Absolute Shrinkage and Selection Operator (LASSO) regression, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and random forest for survival, regression and classification (RF-SRC) to identify clinically relevant TMEM genes and their interactions .

  • Single-cell analysis: To understand cell-type specific effects of Tmem158 in heterogeneous tissues.

• Hormone and drug response studies:

  • For studying regulatory mechanisms, as shown in prostate cancer research where androgen stimulation or deprivation effects on TMEM158 expression were analyzed using public datasets (NCBI GDS2057 and GDS3358) .

  • Treatment with pathway-specific inhibitors to identify upstream regulators and downstream effectors.

How can researchers distinguish between the direct and indirect effects of Tmem158 on cellular processes?

Distinguishing between direct and indirect effects of Tmem158 on cellular processes requires sophisticated experimental designs:

• Temporal analysis of molecular events:

  • Time-course experiments following Tmem158 activation or inhibition can help establish the sequence of molecular events.

  • Rapid effects (minutes to hours) are more likely to be direct, while effects observed after longer periods may be indirect.

• Proximity-based interaction studies:

  • Proximity ligation assays (PLA) to detect protein-protein interactions in situ.

  • BioID or APEX2 proximity labeling to identify proteins in close proximity to Tmem158 in living cells.

  • Co-immunoprecipitation followed by mass spectrometry to identify direct binding partners.

• Rescue experiments:

  • In knockdown studies, selective rescue of specific phenotypes by reintroducing wild-type or mutant Tmem158 can help map direct relationships between protein domains and functions.

  • For example, in ovarian cancer studies, researchers could determine if reintroducing TMEM158 in knockdown cells specifically rescues adhesion by restoring ICAM1 and VCAM1 expression .

• Pathway inhibition studies:

  • Selective inhibition of downstream pathways in Tmem158-overexpressing or knockdown models.

  • For instance, in the context of TGF-β signaling impairment observed with TMEM158 silencing , using specific TGF-β pathway inhibitors alongside TMEM158 modulation could help establish causality.

• Domain-specific mutations:

  • Creating point mutations or domain deletions to disrupt specific functions while preserving others.

  • This approach is particularly valuable for multi-functional proteins like Tmem158 that participate in multiple cellular processes.

What are the challenges in translating findings from rat Tmem158 studies to human disease models?

Translating findings from rat Tmem158 studies to human disease models presents several challenges:

• Species-specific differences in protein structure and function:

  • While rat and human TMEM158 share significant homology, subtle differences in protein structure may affect function, interaction partners, or regulatory mechanisms.

  • Researchers should validate key findings in human cell lines or tissues when possible.

• Differential expression patterns across species:

  • Expression profiles of TMEM158 may vary between rats and humans in different tissues or developmental stages.

  • For example, TMEM158 expressions in disease states like cancer show tissue-specific patterns even within human studies (overexpressed in ovarian cancer but downregulated in prostate cancer ).

• Contextual differences in signaling networks:

  • The signaling pathways that interact with TMEM158 may be organized differently between species.

  • The Ras pathway regulation of TMEM158 and its relationship with the Wnt signaling pathway may have species-specific characteristics.

• Methodological considerations:

  • Different detection methods may have varying sensitivities and specificities for rat versus human TMEM158.

  • Antibodies developed against rat Tmem158 may not cross-react with human TMEM158 with the same affinity.

• Genetic variability in humans:

  • Human TMEM158 exhibits allelic polymorphism resulting in both functional and non-functional (frameshifted) alleles , adding complexity not present in inbred rat models.

  • These polymorphisms may affect the interpretation of experimental results and clinical correlations.

To address these challenges, researchers should consider parallel studies in both rat and human systems, careful validation of reagents across species, and critical evaluation of the conservation of molecular pathways being studied.

What are the optimal conditions for expressing and purifying recombinant rat Tmem158?

The optimal conditions for expressing and purifying recombinant rat Tmem158 must account for its nature as a multi-pass membrane protein:

• Expression systems:

  • Mammalian expression systems (particularly CHO or HEK293 cells) are often preferred for transmembrane proteins to ensure proper folding and post-translational modifications.

  • For higher yield but potentially less native conformation, insect cell systems (Sf9 or Hi5) using baculovirus expression vectors are alternatives.

  • Bacterial systems (E. coli) may be suitable for expressing soluble domains but challenging for full-length Tmem158.

• Construct design considerations:

  • Including affinity tags (His, FLAG, or GST) for purification, preferably with a cleavable linker.

  • For functional studies, careful placement of tags to avoid interference with the BINP binding site or other functional domains.

  • Codon optimization for the chosen expression system.

• Solubilization and purification strategies:

  • Gentle detergents like n-dodecyl-β-D-maltoside (DDM), digitonin, or CHAPS for membrane extraction.

  • Affinity chromatography followed by size exclusion chromatography for high purity.

  • Consider using fluorescence-detection size exclusion chromatography (FSEC) to assess protein stability in different detergent conditions.

• Quality control measures:

  • Circular dichroism to verify secondary structure integrity.

  • Thermal shift assays to assess stability under different buffer conditions.

  • Functional binding assays with BINP peptide to confirm activity of the purified protein.

How can researchers address challenges in detecting low-abundance Tmem158 in tissue samples?

Detecting low-abundance Tmem158 in tissue samples presents technical challenges that can be addressed through several strategies:

• Sample enrichment techniques:

  • Membrane fraction isolation to concentrate membrane proteins like Tmem158 before analysis.

  • Immunoprecipitation to selectively enrich Tmem158 from complex tissue lysates.

  • Proximity ligation assays to amplify detection signals in tissue sections.

• High-sensitivity detection methods:

  • Using highly sensitive ELISA kits with detection limits in the pg/mL range, such as the rat Tmem158 ELISA kit with 0.082ng/mL sensitivity .

  • Digital PCR for absolute quantification of low-abundance transcripts.

  • Tyramide signal amplification for immunohistochemistry.

  • Mass spectrometry with targeted approaches like selected reaction monitoring (SRM) or parallel reaction monitoring (PRM).

• Optimization of tissue processing:

  • Minimizing time between tissue collection and processing to prevent protein degradation.

  • Optimized fixation protocols for immunohistochemistry to preserve epitopes.

  • RNase-free techniques for transcript analysis.

• Data analysis approaches:

  • Using appropriate normalization methods for comparing samples.

  • Considering statistical approaches designed for low-abundance detection.

  • Employing machine learning algorithms for pattern recognition in complex datasets .

What controls should be included when studying Tmem158 function through gene knockdown or overexpression?

Rigorous experimental controls are essential when manipulating Tmem158 expression:

• Essential controls for knockdown studies:

  • Scrambled shRNA/siRNA controls: As used in ovarian cancer studies where scramble shRNA virus-infected cells (NC) were compared with TMEM158-Ri-1 virus-infected cells .

  • Wild-type (untransfected) cells: To establish baseline expression and phenotypes .

  • Multiple shRNA/siRNA sequences targeting different regions of Tmem158 to confirm specificity and rule out off-target effects.

  • Rescue experiments: Reintroducing Tmem158 resistant to the knockdown to confirm specificity of observed phenotypes.

• Controls for overexpression studies:

  • Empty vector controls expressing the same antibiotic resistance but not the gene of interest.

  • Overexpression of an unrelated protein of similar size to control for non-specific effects of protein overproduction.

  • Dose-response studies with inducible expression systems to correlate phenotypes with expression levels.

• Validation controls:

  • qPCR and western blot to confirm successful modulation of Tmem158 at both mRNA and protein levels .

  • Functional readouts specific to Tmem158, such as BINP binding capacity.

  • Cell viability assays to ensure observed phenotypes are not due to cytotoxicity.

• Context-specific controls:

  • For studies of Ras pathway interactions, controls with and without Ras pathway activation .

  • For hormone-responsive tissues, appropriate hormone treatment controls as used in studies of androgen regulation of TMEM158 in prostate cells .

How can researchers integrate multi-omics data to better understand Tmem158 function in complex biological systems?

Integrating multi-omics data provides a comprehensive understanding of Tmem158 function:

• Data integration frameworks:

  • Network-based approaches to connect Tmem158 to its interactome across different omics layers.

  • Pathway enrichment analyses across transcriptomics, proteomics, and metabolomics data.

  • Machine learning models incorporating multiple data types, as demonstrated in PDAC studies using LASSO regression, SVM-RFE, and RF-SRC algorithms .

• Sequential analytical strategies:

  • Start with transcriptomics to identify co-expressed genes and affected pathways following Tmem158 modulation.

  • Follow with proteomics to verify translation of expression changes and identify post-translational modifications.

  • Add metabolomics to understand functional consequences on cellular metabolism.

  • Incorporate epigenomic data to understand regulatory mechanisms.

• Validation approaches:

  • Use bioinformatic predictions from integrated analyses to guide targeted experimental validation.

  • Employ CRISPR screens to systematically test predicted functional relationships.

  • Apply systems biology modeling to predict emergent properties from multi-omics datasets.

• Visualization and interpretation tools:

  • Cytoscape for network visualization and analysis.

  • Comprehensive data visualization tools like heatmaps, volcano plots, and enrichment maps.

  • Database resources like KEGG for pathway mapping and interpretation .