Recombinant Mouse Uncharacterized protein C8orf42 homolog

What is the origin and classification of uncharacterized protein C8orf42 homolog in mice?

The uncharacterized protein C8orf42 homolog in mice is derived from a gene that shares sequence similarity with a human gene located on chromosome 8. Following established nomenclature standards for mouse genes, these homologs are named according to their human counterparts' chromosomal locations .

Similar to other uncharacterized proteins (like C6H2orf81), the mouse C8orf42 homolog likely contains one or more domains of unknown function (DUF). Based on patterns observed in similar uncharacterized proteins, it may contain predominantly α-helical secondary structures with minimal β-sheets, and potentially undergo post-translational modifications including phosphorylation.

Methodologically, these homologs are identified through comparative genomics and sequence alignment tools. Researchers typically use BLAST analysis of mouse genome sequences against human reference genomes to identify orthologous genes and calculate percent identity between species.

What expression patterns are typically observed for uncharacterized protein homologs in mouse tissues?

Uncharacterized protein homologs often display tissue-specific expression patterns that can provide important clues about their potential functions. According to comprehensive gene atlas studies, the expression of novel predicted genes exhibits considerable tissue specificity rather than ubiquitous expression .

Expression data from similar uncharacterized proteins suggests that C8orf42 homolog may display:

Methodologically, researchers can determine expression patterns through:

• RNA-seq analysis of different tissues (providing RPKM values)

• Tissue-specific transcriptome profiling

• Comparison to known expression databases like the Gene Expression Omnibus

Analysis of RNA-seq data typically reveals that less than 1% of human and approximately 3% of mouse target sequences are ubiquitously expressed across all tissues, highlighting the importance of profiling multiple tissues to capture the complete expression profile .

What recombinant expression systems are available for producing mouse uncharacterized proteins?

Multiple expression systems are available for producing recombinant mouse uncharacterized proteins, each with specific advantages for different research applications:

For methodology, researchers should:

• Clone the cDNA ORF into an appropriate expression vector (e.g., pcDNA3.1+/C-(K)DYK)

• Consider adding purification tags (e.g., DYKDDDDK-tag, His-tag)

• Optimize expression conditions specific to the chosen system

• Purify using affinity chromatography methods

The choice of expression system should be guided by the intended application. For structural studies, E. coli may be sufficient, while functional studies often require mammalian expression systems to ensure proper folding and post-translational modifications .

What strategies are most effective for functional characterization of uncharacterized mouse protein homologs?

Functional characterization of uncharacterized mouse protein homologs requires an integrated multi-omics approach:

Transcriptomic Analysis:

• RNA-seq data can identify co-regulated transcripts and regions of correlated transcription (RCTs), providing functional associations

• Analysis of tissue-specific expression patterns can suggest biological context

Protein Interaction Studies:

• Identify potential binding partners through co-immunoprecipitation followed by mass spectrometry

• Yeast two-hybrid screening to detect direct protein interactions

Genetic Manipulation:

• CRISPR-Cas9 genome editing to generate knockout or knock-in models

• Phenotypic characterization of mutants using established allele nomenclature (e.g., em1.1Labcode for endonuclease-mediated mutations)

Subcellular Localization:

• Fluorescent protein tagging combined with confocal microscopy

• Subcellular fractionation followed by Western blotting

In Silico Analysis:

• Domain prediction and structural modeling

• Evolutionary conservation analysis across species to identify functional constraints

Methodologically, researchers should begin with bioinformatic analysis to generate hypotheses, then design targeted experiments based on predicted features and expression patterns. For instance, if the protein contains a predicted nuclear localization signal, subcellular localization studies should be prioritized.

How can RNA-sequencing approaches be optimized to study the expression of poorly characterized mouse genes?

RNA-sequencing approaches for studying poorly characterized mouse genes require careful optimization:

Sample Preparation and Sequencing Strategy:

• Use polyA-selected mRNA purification for protein-coding transcripts

• Ensure high RNA integrity (RIN values >7.5)

• Utilize paired-end sequencing to improve transcript identification and quantification

Data Analysis Pipeline:

• Map reads to the mouse genome (GRCh37/mm10 or newer) using specialized tools like GEM mapper

• Quantify transcripts using Flux Capacitor or similar software

• Measure relative coverage in RPKM units (reads per kilobase of exon model per million mapped reads)

• Apply statistical methods like Fisher exact test with Benjamini-Hochberg correction for differential expression analysis

Validation Steps:

• Confirm expression patterns using qRT-PCR in independent samples

• Perform in situ hybridization to validate tissue-specific expression

Advanced Analysis:

• Examine splicing variations using splice indices (proportion between RPKM for a transcript and the sum of RPKM for all transcripts from the same gene)

• Identify regions of correlated transcription (RCTs) to find functionally related genes

For poorly characterized genes specifically, researchers should examine expression across a comprehensive panel of tissues (similar to the 79 human and 61 mouse tissues in the gene atlas study) to maximize the chance of identifying significant expression patterns.

What are the challenges in generating specific antibodies against uncharacterized mouse protein homologs?

Generating specific antibodies against uncharacterized mouse protein homologs presents several challenges:

Epitope Selection Issues:

• Limited knowledge of protein structure and accessible regions

• Potential cross-reactivity with similar proteins in the same family

• Lack of information about post-translational modifications that might mask epitopes

Production Challenges:

• Difficulty expressing full-length recombinant protein for immunization

• Potential toxicity of the protein in expression systems

• Improper folding affecting epitope presentation

Validation Complexities:

• No established positive controls for Western blotting or immunohistochemistry

• Uncertainty about endogenous expression levels and patterns

• Limited tools to confirm antibody specificity (e.g., knockout tissues)

Methodological Solutions:

• Use multiple peptide antigens from different regions of the predicted protein

• Employ parallel strategies (monoclonal and polyclonal approaches)

• Express fragments rather than full-length protein if expression proves difficult

• Validate using orthogonal techniques like RNA-seq data correlation

• Consider epitope tagging through CRISPR knock-in strategies when antibody development proves challenging

A recommended workflow includes initial bioinformatic analysis to identify unique, accessible, and immunogenic regions, followed by peptide synthesis or recombinant fragment expression for immunization.

How can chromosomal location and genomic context provide insights into the function of uncharacterized mouse proteins?

Chromosomal location and genomic context provide valuable functional insights through several analytical approaches:

Regions of Correlated Transcription (RCT) Analysis:

• Identify windows of genes with correlated expression patterns

• Analyze whether the uncharacterized gene falls within an RCT

• Determine if the RCT is driven by gene duplication or higher-order gene regulation

Synteny Analysis:

• Compare chromosomal regions across species to identify evolutionarily conserved gene clusters

• Examine if orthologous regions in humans and mice maintain similar gene organization

Regulatory Element Identification:

• Analyze promoter regions for transcription factor binding sites

• Investigate whether the gene is under the control of tissue-specific enhancers

• Determine if the gene is subject to imprinting or other epigenetic regulation

For example, a study on mouse chromosome 12 identified an RCT consisting of six adjacent genes with enriched expression in brain regions and umbilical cord, some of which were later confirmed to be imprinted genes . This approach led to the discovery of previously uncharacterized imprinted transcripts based on their shared tissue-specific expression pattern with neighboring genes.

Methodologically, researchers should:

• Map the uncharacterized gene to genome assemblies

• Scan chromosomes for windows of genes with correlated expression

• Use sequence similarity tools (e.g., tblastx) to identify potential paralogs

• Examine expression data across tissues to identify tissue-specific patterns

• Perform allele-specific expression analysis if imprinting is suspected

What bioinformatic approaches are most effective for predicting functional domains in uncharacterized mouse proteins?

Effective bioinformatic approaches for functional domain prediction involve multiple complementary methods:

Sequence-Based Analysis:

• PSI-BLAST for iterative sequence similarity searches

• Hidden Markov Model (HMM) profiling using PFAM, SMART, and InterPro databases

• Identification of conserved motifs through MEME and GLAM2

Structural Prediction:

• Secondary structure prediction (JPred, PSIPRED)

• Tertiary structure modeling (AlphaFold2, I-TASSER)

• Domain boundary prediction (DomPred, DomCut)

Post-Translational Modification Site Prediction:

• Phosphorylation sites (NetPhos, GPS)

• Glycosylation sites (NetNGlyc, NetOGlyc)

• Other modifications (UbPred for ubiquitination)

Functional Association Networks:

• Analyze protein-protein interaction networks using STRING database

• Gene Ontology enrichment analysis

• Co-expression network analysis

For uncharacterized proteins, a methodological workflow should include:

• Initial sequence analysis to identify domain architectures (like the DUF4639 domain in C2orf81 homolog)

• Secondary structure prediction to identify predominant structural elements (e.g., α-helices vs. β-sheets)

• Prediction of post-translational modification sites that may regulate function

• Comparative analysis across species to identify conserved regions under evolutionary constraints

• Integration of predictions with available experimental data

For example, analysis of C2orf81 homolog revealed a Domain of Unknown Function (DUF4639) spanning residues 17-615, predominantly α-helical secondary structure, and predicted O-linked glycosylation at 3 C-terminal sites and serine phosphorylation sites.

How can CRISPR-Cas9 genome editing be optimized for studying uncharacterized mouse protein homologs?

CRISPR-Cas9 genome editing provides powerful approaches for studying uncharacterized proteins:

Knockout Strategies:

• Complete gene knockout using multiple guide RNAs

• Conditional knockout using loxP/Cre system for tissue-specific analysis

• Partial knockout of specific domains to assess domain function

Knock-in Approaches:

• Reporter gene fusion to track expression patterns

• Epitope tagging for protein localization and interaction studies

• Precise point mutations to assess functional residues

Optimization Methods:

• Guide RNA design using algorithms to minimize off-target effects

• HDR template optimization for precise editing

• Delivery methods adapted to target tissues (viral vectors, lipid nanoparticles)

• Enrichment of edited cells using selectable markers

Validation Protocol:

• Genomic PCR and sequencing to confirm edits

• qRT-PCR to assess transcript levels

• Western blotting to verify protein expression/absence

• Phenotypic characterization across multiple systems

Nomenclature Considerations:
Following established nomenclature guidelines, CRISPR-generated alleles should be designated as "em" (endonuclease-mediated) mutations, using the format Gene em#Labcode or Gene em#.#Labcode for derivative alleles . For example, the first CRISPR-induced mutation of an uncharacterized gene produced by laboratory "XYZ" would be designated as C8orf42-hom em1Xyz .

For complex genetic modifications like conditional alleles, the recommended approach involves first generating the floxed allele (e.g., C8orf42-hom em1Xyz) followed by derivation of the deleted allele through Cre-mediated recombination (e.g., C8orf42-hom em1.1Xyz) .

What methodological approaches can determine the subcellular localization and potential binding partners of uncharacterized mouse proteins?

Determining subcellular localization and binding partners requires complementary methodological approaches:

Subcellular Localization Methods:

The C2orf81 homolog, for example, showed predominantly nuclear localization with potential mitochondrial/cytoplasmic distribution, demonstrating the importance of comprehensive localization studies.

Protein Interaction Discovery:

• Affinity Purification-Mass Spectrometry (AP-MS)

  • Express tagged version of the protein

  • Purify along with interacting partners

  • Identify using mass spectrometry

• Proximity-dependent Biotin Identification (BioID)

  • Fusion with biotin ligase to biotinylate proximal proteins

  • Purify biotinylated proteins using streptavidin

  • Identify using mass spectrometry

• Co-immunoprecipitation with specific antibodies

  • Pull down endogenous protein complexes

  • Western blot or mass spectrometry analysis

• Yeast Two-Hybrid Screening

  • Systematic screening against cDNA libraries

  • Validation by reciprocal testing

For example, in sperm flagella, the C2orf81 homolog was found to co-localize with calcium signaling proteins (CaMKII, PP2B-Aγ) in quadrilateral membrane domains through co-immunoprecipitation and immunofluorescence studies, suggesting roles in calcium-dependent motility regulation.

A recommended workflow involves initial fluorescent protein tagging to determine subcellular localization, followed by proximity labeling approaches to identify potential binding partners, with subsequent validation through co-immunoprecipitation and functional assays.