Recombinant Human Putative uncharacterized protein C17orf110 (C17orf110)

What is C17orf110 and what are its alternative nomenclatures?

C17orf110 (chromosome 17 open reading frame 110) is a protein-coding gene located on chromosome 17. This gene is also known as SMIM6 (small integral membrane protein 6) and ELN according to current annotations . The protein is classified among uncharacterized or poorly characterized proteins, with relatively limited functional data available in standard databases. According to Pharos classification, it belongs to the "dark genome" category of proteins about which relatively little is known .

What methods are recommended for detecting C17orf110 expression in experimental samples?

Based on the validation data available, the following methodological approach is recommended for C17orf110 detection:

• RT-qPCR methodology:

  • Use exonic primers for C17orf110 detection to avoid genomic DNA contamination

  • Implement SYBR Green-based detection with the validated primers showing 99% efficiency and 100% specificity

  • Include appropriate housekeeping genes for normalization

  • Follow standard melt curve analysis to confirm amplification specificity

• Expression profiling by microarray:
  When conducting broader expression studies:

  • Extract total RNA using TRI-REAGENT followed by RNeasy kit purification

  • Verify RNA quality using Agilent 2100 Bioanalyzer

  • Convert 10 μg of total RNA to double-stranded cDNA using oligo-dT primers containing T7 RNA polymerase promoter

  • Purify double-stranded cDNA by phenol/chloroform extraction

  • Follow standard hybridization protocols for microarray-based detection

What tissue distribution pattern has been observed for C17orf110?

Current knowledge about C17orf110/SMIM6 tissue distribution is limited, but preliminary data suggests:

• Knowledge scores by tissue relevance:

  • Cell line: 0.52

  • Tissue: 0.45

  • Transcription factor: 0.39

  • Histone modification site profile: 0.21

  • microRNA: 0.20

Researchers investigating tissue expression should implement a systematic approach using:

• Multi-tissue qPCR panels

• Immunohistochemistry (if antibodies are available)

• Mining of public RNA-seq datasets such as GTEx and TCGA

• Single-cell RNA-seq data where available to identify cell-type specific expression

What are the recommended methodologies for producing recombinant C17orf110 protein?

When producing recombinant C17orf110, researchers should consider the following methodological approach based on established protocols for small membrane proteins:

• Expression vector selection:

  • For small membrane proteins like C17orf110/SMIM6, use vectors with strong promoters (T7, CMV) for sufficient expression

  • Consider ORF vectors with restriction enzyme-independent cloning methods between appropriate cut sites (similar to approaches used for other ORF vectors)

  • Include appropriate tags for detection and purification (His-tag, FLAG-tag)

• Expression system recommendations:

  • For functional studies: Mammalian expression systems (HEK293, CHO) to ensure proper folding and post-translational modifications

  • For structural studies: E. coli systems with codon optimization or cell-free systems

  • For difficult-to-express membrane proteins: Consider insect cell (baculovirus) systems

• Purification strategy:

  • For membrane proteins: Detergent-based extraction (DDM, CHAPS) followed by affinity chromatography

  • Consider carrier-free formulations for applications where carrier proteins might interfere

  • Reconstitute in appropriate buffer systems based on downstream applications

How should researchers approach functional characterization of an uncharacterized protein like C17orf110?

A systematic approach to functional characterization should include:

• Computational prediction:

  • Perform comparative sequence analysis (BLAST, HMM)

  • Utilize protein structure prediction (AlphaFold2)

  • Identify conserved domains and motifs

  • Analyze phylogenetic relationships

• Localization studies:

  • Express fluorescently-tagged C17orf110 to determine subcellular localization

  • Verify with cell fractionation and Western blotting

  • Consider co-localization studies with organelle markers

• Interaction studies:

  • Conduct yeast two-hybrid or BioID proximity labeling

  • Perform co-immunoprecipitation followed by mass spectrometry

  • Consider FRET/BRET for dynamic interaction studies

• Loss-of-function studies:

  • Implement CRISPR-Cas9 knockout/knockdown

  • Analyze resulting phenotypes across multiple cell types

  • Perform transcriptomic and proteomic profiling of knockout models

How can researchers investigate the potential role of C17orf110 as a micropeptide?

Given that C17orf110/SMIM6 is annotated as a small integral membrane protein, researchers should consider methodologies specific to micropeptides:

• Ribosome profiling:

  • Implement ribosome footprinting to verify translation

  • Use harringtonine or lactimidomycin to capture translation initiation sites

  • Analyze data with specialized tools for smORF detection

• Mass spectrometry validation:

  • Employ targeted proteomics approaches (PRM/MRM)

  • Consider specialized sample preparation methods for small proteins

  • Use synthetic peptide standards for validation

• Functional characterization specific to micropeptides:

  • Investigate potential regulatory roles in protein complexes

  • Consider membrane-associated functions (similar to other characterized SMIM proteins)

  • Explore potential roles in organelle function, particularly in mitochondria (based on patterns observed in other micropeptides)

What are the best approaches for studying potential disease associations of C17orf110?

When investigating potential disease relevance:

• Gene expression analysis in disease cohorts:

  • Mine public databases (GEO, TCGA) for differential expression in disease states

  • Validate findings using qPCR in independent patient cohorts

  • Implement comprehensive bioinformatic analysis similar to approaches used for identifying biomarkers in head and neck squamous cell carcinoma

• Genetic variant analysis:

  • Analyze WES/WGS data for potentially pathogenic variants

  • Consider population-specific variant frequencies

  • Assess potential impact using predictive algorithms and functional validation

• Model systems:

  • Develop appropriate cell and animal models (knockout, knockin)

  • Consider tissue-specific conditional models if global knockout is lethal

  • Implement phenotypic analysis across multiple physiological parameters

What are the major challenges in working with uncharacterized proteins like C17orf110?

Researchers face several methodological challenges that can be addressed through specific approaches:

• Limited prior knowledge:

  • Challenge: Absence of functional annotations and validated reagents

  • Solution: Implement systematic multi-omics approaches, starting with in silico predictions followed by experimental validation

• Protein detection issues:

  • Challenge: Limited availability of specific antibodies

  • Solution: Generate epitope-tagged recombinant proteins; develop custom antibodies against synthetic peptides based on predicted epitopes

• Functional redundancy:

  • Challenge: Potential compensation by related proteins masking phenotypes

  • Solution: Implement combinatorial knockouts; use acute depletion systems (AID, dTAG)

• Publication challenges:

  • Challenge: Difficulty publishing on uncharacterized proteins

  • Solution: Focus on methodological rigor; present comprehensive characterization data; emphasize novelty aspects

How should researchers effectively visualize and present data about uncharacterized proteins?

Following best practices for scientific data presentation:

• Table design principles for uncharacterized protein research:

  • Present data in clearly organized tables with descriptive column headers

  • Ensure tables are self-explanatory without reference to the text

  • Include only results relevant to the research question

• Effective figure design:

  • Use figures to show trends, patterns, and relationships between datasets

  • Present visual explanations of experimental procedures and characteristics

  • Implement clear labeling and appropriate statistical analysis

• Data presentation decision matrix:

What emerging technologies might accelerate characterization of proteins like C17orf110?

Researchers should consider these cutting-edge approaches:

• Spatial transcriptomics/proteomics:

  • Apply single-cell spatial technologies to map expression in tissue context

  • Implement multiplexed immunofluorescence to correlate with other markers

• AlphaFold2/RoseTTAFold structure prediction:

  • Utilize AI-based structure prediction to inform functional hypotheses

  • Design validation experiments based on structural features

• High-throughput CRISPR screens:

  • Implement genome-wide or focused CRISPR screens to identify genetic interactions

  • Use CRISPRi/CRISPRa approaches for reversible modulation

• Organoid technologies:

  • Develop relevant organoid systems to study function in tissue-like context

  • Implement gene editing in organoids to assess phenotypic consequences

How can researchers contribute to community knowledge about C17orf110?

To advance collective understanding:

• Data sharing recommendations:

  • Deposit all raw data in appropriate repositories (GEO, PRIDE)

  • Contribute to protein databases with experimental evidence

  • Consider preprints for rapid dissemination of findings

• Collaborative approaches:

  • Engage with consortia focused on uncharacterized proteins

  • Implement standardized protocols for cross-laboratory validation

  • Consider multi-laboratory replication studies for key findings

• Methodological transparency:

  • Document detailed protocols in repositories like protocols.io

  • Ensure complete reporting of negative results

  • Provide comprehensive methods sections with validation metrics