Recombinant Mouse Transmembrane protein 183 (Tmem183)

How is Tmem183 expression regulated in different tissues?

Expression regulation of transmembrane proteins often varies across tissues. Similar to how TMEM18 expression has been observed in multiple hypothalamic nuclei (arcuate, ventral medial, paraventricular, and dorsal medial nuclei), Tmem183 likely has tissue-specific expression patterns . To determine this, researchers should conduct RNA-Seq analysis of laser-capture microdissection (LCM)-acquired tissue from different regions of interest, followed by qRT-PCR validation. Expression may also be influenced by physiological states, as seen with other transmembrane proteins that respond to nutritional changes.

What are the predicted structural features of Tmem183?

For structural prediction of Tmem183, researchers should employ profile-to-profile homology analysis using tools like MPI Bioinformatics Toolkit software to identify remote homologies with known ion channels or other membrane proteins . Computational modeling could suggest the number of transmembrane domains present and potential 3D structure, which would need experimental verification using approaches similar to those used for TMEM18, such as epitope tagging and immunofluorescence microscopy to confirm topology .

What is the optimal expression system for producing recombinant Tmem183?

Based on experience with similar transmembrane proteins, E. coli expression systems can be effective for producing recombinant transmembrane proteins when properly optimized . For Tmem183, consider using a system similar to that employed for producing recombinant mouse IL-36 beta/IL-1F8 protein, with appropriate modifications for membrane protein expression . Key considerations include:

• Codon optimization for the expression host

• Selection of appropriate fusion tags (His, GST, or MBP) to enhance solubility

• Optimization of induction conditions (temperature, IPTG concentration)

• Addition of specific detergents during purification

Eukaryotic expression systems (mammalian or insect cells) may provide better protein folding for complex transmembrane proteins and should be considered if functional studies are planned.

How can I validate the functional activity of purified recombinant Tmem183?

Functional validation requires assays specific to the protein's biological role. For transmembrane proteins with unknown functions like Tmem183, researchers should:

• Conduct protein interaction studies using techniques like affinity purification coupled with mass spectrometry (similar to the approach used for TMEM18)

• Perform biomolecular fluorescence complementation (BiFC) assays to confirm interactions with potential partner proteins

• Develop cell-based functional assays based on predicted functions from structural homology

A table outlining suggested validation approaches is provided below:

What strategies should I use to generate Tmem183 knockout mice?

Based on approaches used for other transmembrane proteins, CRISPR/Cas9 or conditional knockout strategies are recommended. The methodology used for TMEM18 knockout provides a valuable template:

• Target specific exons that ensure complete disruption of protein expression

• Validate knockout efficiency using qRT-PCR to measure residual transcript levels (aim for <5% expression as achieved with TMEM18)

• Verify specificity by confirming that only the target transcript is affected, with no off-target effects on neighboring genes

• Generate both constitutive and conditional knockouts to address potential developmental effects

How do I interpret conflicting phenotypes in Tmem183 knockdown/knockout studies?

When facing contradictory results in knockdown versus knockout models, consider:

• Compensatory mechanisms that may emerge in complete knockout but not in transient knockdown

• Potential differences in genetic backgrounds, as seen with sex-specific differences in TMEM18 knockout mice

• Environmental factors including diet, which significantly affected the phenotype in TMEM18-deficient mice

Researchers should implement multiple approaches (siRNA, shRNA, CRISPR) across different cell lines and animal models to build a comprehensive understanding. Additionally, rescue experiments with wild-type protein expression can confirm specificity of observed phenotypes.

What statistical approaches are recommended for analyzing Tmem183 expression data?

For differential expression analysis of Tmem183:

• Use RNA-Seq analysis pipelines with tools like Salmon and DESeq2, as employed in TMEM184B studies

• Consider fold change thresholds (log2 fold change ≥ 2) to identify biologically significant changes

• Employ qPCR validation of RNA-Seq findings, as demonstrated in miRNA-183-5p research

• For single-cell RNA-Seq data, extract raw count data and evaluate expression in specific cell populations using appropriate clustering approaches

How can I investigate Tmem183's role in protein-protein interaction networks?

To characterize Tmem183's interactome:

• Perform affinity purification followed by mass spectrometry analysis, similar to the approach used for TMEM18 that identified 244 unique interacting proteins

• Confirm key interactions using orthogonal methods such as BiFC assays

• Map the interaction domains through mutagenesis studies

• Conduct functional validation of interactions through co-localization studies and functional assays

The TMEM18 studies provide an excellent template, where interactions with nuclear pore complex proteins NDC1, AAAS, and NUP35/53 were identified through mass spectrometry and subsequently confirmed through BiFC assays .

What approaches are recommended for investigating potential roles of Tmem183 in disease models?

Based on research with other transmembrane proteins:

• Generate tissue-specific conditional knockout models using Cre-lox systems

• Perform comprehensive phenotyping including metabolic, behavioral, and physiological parameters

• Challenge models with appropriate stressors (e.g., high-fat diet as used in TMEM18 studies)

• Analyze disease-relevant pathways, such as inflammation or oxidative stress markers

For example, TMEM18 knockout mice showed increased body weight on normal chow by 14 weeks of age due to increases in both fat and lean mass, with further exacerbation on high-fat diet, revealing its role in metabolic regulation .

How can I identify potential miRNA regulators of Tmem183 expression?

To investigate miRNA regulation:

• Use bioinformatic prediction tools such as miRanda (as used in miRNA-183-5p studies)

• Validate predicted interactions using luciferase reporter assays with wild-type and mutated binding sites

• Perform functional studies by modulating miRNA levels with agomirs or antagomirs and measuring effects on Tmem183 expression

• Assess physiological relevance by examining correlation between miRNA and Tmem183 expression levels in relevant tissues/conditions

The approach used to verify HO-1 as a direct target of miR-183-5p through dual-luciferase reporter assays provides a methodological framework .

What strategies help overcome solubility issues with recombinant Tmem183?

Membrane proteins frequently present solubility challenges. Recommended approaches include:

• Optimize detergent selection for extraction and purification (test a panel including DDM, LDAO, and CHS)

• Engineer fusion constructs with solubility-enhancing tags

• Consider expressing only specific domains rather than the full-length protein

• Modify buffer conditions (pH, salt, additives) during purification steps

The reconstitution calculator approach used for IL-36 beta/IL-1F8 protein can be adapted for optimal buffer selection .

How can I improve antibody specificity for detecting endogenous Tmem183?

To generate highly specific antibodies:

• Design immunogens based on unique, accessible epitopes identified through structural modeling

• Validate antibody specificity using knockout/knockdown controls

• Perform epitope mapping to confirm binding sites

• Use multiple antibodies targeting different regions to cross-validate findings

Include rigorous controls in immunodetection experiments, including pre-absorption with immunizing peptides and validation in tissues from knockout animals.

What bioinformatic resources are most valuable for Tmem183 research?

Researchers should utilize:

• Protein structure prediction tools that can model membrane proteins (AlphaFold, Rosetta)

• Phylogenetic analysis tools for identifying evolutionary relationships and conserved domains

• Transcriptomic databases to explore expression patterns across tissues and conditions

• Interactome databases to identify potential binding partners

The phylogenetic approach used for TMEM18, which revealed homology to ion channels from Pfam families of fungal transient receptor potential channels (PF06011) and bacterial mechanosensitive ion channels (PF12794), exemplifies the value of comprehensive bioinformatic analysis .

What are the key considerations for designing experiments to study Tmem183 in different mouse disease models?

When designing disease model studies:

• Select appropriate genetic backgrounds based on disease relevance

• Consider sex-specific differences, as observed with TMEM18 knockout mice where phenotypes were more pronounced in males

• Design comprehensive phenotyping protocols including metabolic cages, energy expenditure measurements, and tissue-specific analyses

• Include age-matched controls and sufficient sample sizes for statistical power

Data from TMEM18 studies showed that male knockout mice had significantly increased body weight due to increased fat and lean mass, while female knockouts showed no significant differences, highlighting the importance of sex-specific analysis .