Recombinant Human Kelch domain-containing protein 7A (KLHDC7A)

What is the structural composition of human KLHDC7A?

KLHDC7A belongs to the Kelch domain-containing (KLHDC) subfamily within the evolutionary conserved Kelch protein superfamily. The Kelch superfamily contains 63 alternate protein coding members and is divided into three subfamilies: Kelch like (KLHL), Kelch-repeat and bric-a-bracs (BTB) domain containing (KBTBD), and Kelch domain containing protein (KLHDC) . The KLHDC subfamily is one of the smallest, containing 10 primary members.

Structurally, like other Kelch proteins, KLHDC7A contains the characteristic Kelch domain that typically consists of five to seven repeated motifs . Unlike other Kelch family members that contain additional domains like BTB (bric-a-bracs), KLHDC7A primarily features the Kelch domain. Bioinformatic predictions suggest KLHDC7A is likely membrane-associated .

Where is KLHDC7A located in the human genome?

KLHDC7A is located on chromosome 1p36.13. The genomic context includes:

• Chromosome: 1

• Sequence reference: NC_000001.11 (18480930..18485974)

• Number of exons: 1

This single-exon structure is notable and has implications for gene expression regulation and evolutionary conservation.

What experimental methods are most commonly used to study KLHDC7A expression?

Several methodological approaches have been established for investigating KLHDC7A expression:

• RNA expression analysis:

  • RT-qPCR (Real-Time Quantitative PCR) for quantifying KLHDC7A mRNA levels

  • RNA-seq for genome-wide expression profiling

• Protein expression analysis:

  • Western blotting with specific antibodies

  • Immunofluorescence microscopy for localization studies

• Epigenetic analysis:

  • Methylation analysis of the KLHDC7A promoter region using bisulfite sequencing

  • ChIP-seq (Chromatin Immunoprecipitation Sequencing) for analyzing transcription factor binding

• Genetic manipulation:

  • CRISPR/Cas9 system for gene knockout or activation studies

  • siRNA-mediated knockdown approaches

For example, when studying KLHDC7A's role in cellular processes, researchers have employed RNA-seq analysis followed by RT-qPCR validation to confirm expression changes. This methodological approach has been effective in correlating KLHDC7A expression with cellular phenotypes in cancer research contexts .

What is the current understanding of KLHDC7A's role in breast cancer development?

KLHDC7A has emerged as a significant gene in breast cancer research with complex expression patterns. Genome-wide association studies (GWAS) identified a SNP (rs2992756) located just 85bp from the transcription start site of KLHDC7A that showed significant association with breast cancer risk (P=1.6×10⁻¹⁵) .

Interestingly, KLHDC7A exhibits a paradoxical expression pattern in breast cancer, with hypermethylation at the promoter region but upregulated expression in tumor tissues . This contradicts the conventional understanding that promoter hypermethylation typically leads to gene silencing.

Functional studies have revealed:

• The risk T-allele of rs2992756 in the KLHDC7A promoter construct has significantly lower activity than the reference construct, as demonstrated through reporter assays .

• KLHDC7A expression is significantly associated with a long non-coding RNA, ST8SIA6-AS1 (STAR1) in breast cancer tissues (R² = 0.3466, P < 0.01) .

• Experimental manipulation of KLHDC7A in MCF-7 breast cancer cells showed that:

  • Upregulation decreased apoptosis by 49.5%

  • Downregulation increased apoptosis by 33.1%

  • Upregulation increased cell proliferation, while downregulation decreased it

These findings collectively suggest an oncogenic role for KLHDC7A in breast cancer progression.

How does the cellular localization of KLHDC7A influence its function?

While not specifically documented for KLHDC7A, insights can be drawn from studies on related Kelch domain proteins and limited data on KLHDC7B:

KLHDC7B (a related family member) has been shown to localize to both the nuclear and cytosolic compartments . This dual localization pattern suggests potential diverse functions depending on subcellular context. For instance:

• Nuclear localization: May be associated with transcriptional regulation

  • In studies of KLHDC7B, nuclear localization correlated with regulation of gene expression, particularly genes involved in the interferon signaling pathway

  • This suggests KLHDC7A might similarly function in transcriptional regulation when nuclear

• Cytoplasmic/membrane localization: May be involved in:

  • Protein-protein interactions

  • Cytoskeletal arrangement

  • Signal transduction pathways

  • Protein degradation via the ubiquitin-proteasome system

The predicted membrane localization of KLHDC7A suggests it may function at cellular interfaces, possibly in signal transduction or cell-cell communication.

To investigate KLHDC7A localization experimentally, researchers should consider:

• Cell fractionation followed by Western blotting

• Immunofluorescence microscopy with co-localization studies

• Live-cell imaging with fluorescently-tagged KLHDC7A

What molecular pathways does KLHDC7A interact with in normal and pathological conditions?

Network analysis and expression studies indicate KLHDC7A may be significantly involved in the interferon signaling pathway . This connection is particularly relevant for understanding its role in both normal immune function and cancer.

Key findings regarding pathway interactions include:

• Interferon signaling pathway: Both KLHDC7A and the associated lncRNA STAR1 affect the expression of interferon-related genes including:

  • IFITs (Interferon-induced proteins with tetratricopeptide repeats)

  • STATs (Signal transducer and activator of transcription)

  • IL-29 (Interleukin-29)

• Potential interaction with the ubiquitin-proteasome system: Drawing from knowledge of other Kelch domain proteins like Keap1, which functions as a substrate adaptor for Cullin-3-based E3 ubiquitin ligase complexes , KLHDC7A might similarly participate in protein degradation pathways.

For a comprehensive understanding of KLHDC7A's pathway interactions, researchers should consider:

• Protein-protein interaction studies (co-immunoprecipitation, proximity labeling)

• Phosphoproteomics to identify signaling pathway alterations

• Transcriptome analysis after KLHDC7A manipulation

• Network analysis integrating multi-omics data

How can researchers address contradictory data regarding KLHDC7A's role in cancer?

The paradoxical finding that KLHDC7A is hypermethylated at the promoter yet upregulated in breast cancer exemplifies the contradictory data researchers may encounter. To address such contradictions, consider this methodological framework:

• Validate findings across multiple platforms:

  • Confirm expression changes using different techniques (RNA-seq, RT-qPCR, Western blot)

  • Analyze methylation using multiple approaches (bisulfite sequencing, methylation arrays)

  • Use diverse cell lines and patient samples to ensure reproducibility

• Integrate mixed methods approaches:

  • Combine qualitative and quantitative data analysis

  • Use triangulation to strengthen inferences when data points converge

  • Consider different categories and levels of analysis

• Explore alternative explanations:

  • Investigate presence of alternative promoters or enhancers

  • Consider post-transcriptional or post-translational regulation

  • Examine contextual factors (tissue-specific effects, cellular microenvironment)

• Time-course experiments:

  • Analyze dynamic changes in expression and methylation during cancer progression

  • Consider that contradictory observations might reflect different stages of disease

• Single-cell analysis:

  • Determine if contradictions reflect heterogeneity at the single-cell level

  • Identify specific cell populations with distinct regulatory mechanisms

As noted in research methodology literature, contradictions in mixed methods research can be particularly informative and lead to new hypotheses about underlying mechanisms .

How should researchers design CRISPR-based experiments to study KLHDC7A function?

CRISPR-based approaches offer powerful tools for studying KLHDC7A function. Based on available technologies and methodologies, researchers should consider:

For KLHDC7A knockout studies:

• Design multiple sgRNAs targeting the coding region

• Include appropriate controls (non-targeting sgRNAs)

• Validate knockout efficiency at both mRNA and protein levels

• Perform rescue experiments by reintroducing KLHDC7A to confirm specificity

For KLHDC7A activation studies:
KLHDC7A CRISPR Activation Plasmid systems are available, utilizing a deactivated Cas9 (dCas9) fused to VP64 activation domain, together with sgRNA (MS2) and MS2-P65-HSF1 fusion protein . This synergistic activation mediator (SAM) system maximizes endogenous gene expression.

The activation system consists of three plasmids at a 1:1:1 mass ratio:

• The CRISPR/dCas9 nuclease expression plasmid

• The MS2-P65-HSF1 fusion protein expression plasmid

• The target-specific sgRNA (MS2) expression plasmid

Experimental readouts to consider:

• Cellular phenotypes (proliferation, apoptosis, migration)

• Transcriptome analysis (RNA-seq)

• Pathway activation (phosphorylation status of interferon pathway components)

• Protein-protein interactions (immunoprecipitation followed by mass spectrometry)

What approaches are recommended for studying KLHDC7A protein interactions?

Understanding protein interactions is crucial for elucidating KLHDC7A function. Based on methodologies used for other Kelch domain proteins, researchers should consider:

• Affinity purification-mass spectrometry (AP-MS):

  • Express tagged KLHDC7A (FLAG, HA, or BioID) in relevant cell lines

  • Purify protein complexes and identify interacting partners by mass spectrometry

  • Compare interaction profiles under different conditions (normal vs. stress)

• Yeast two-hybrid screening:

  • Use the Kelch domain as bait to identify interacting proteins

  • Validate interactions in mammalian cells

• Proximity labeling approaches:

  • BioID or TurboID fusion proteins to identify proteins in proximity to KLHDC7A

  • APEX2-based proximity labeling for temporal resolution of interactions

• Co-immunoprecipitation with specific candidates:

  • Based on pathway analysis, test interaction with predicted partners

  • Particular focus on components of the interferon signaling pathway

• Structural studies:

  • X-ray crystallography or Cryo-EM of the Kelch domain

  • NMR studies for understanding dynamic interactions

For example, structural studies of the Kelch domain of Keap1 bound to a peptide from Nrf2 revealed important insights into the binding interface . Similar approaches could elucidate KLHDC7A's interaction mechanisms.

What multi-omic strategies would be most effective for characterizing KLHDC7A function?

To comprehensively characterize KLHDC7A function, integrated multi-omic approaches are recommended:

• Transcriptomics:

  • RNA-seq following KLHDC7A manipulation (knockout, knockdown, overexpression)

  • Analysis of alternative splicing events

  • Temporal gene expression changes

• Proteomics:

  • Global proteome analysis (SWATH-MS)

  • Phosphoproteomics to identify signaling pathways

  • Ubiquitin proteomics to identify potential degradation targets

• Epigenomics:

  • ChIP-seq to identify genomic binding sites if KLHDC7A has nuclear functions

  • ATAC-seq to assess chromatin accessibility changes

  • DNA methylation analysis to correlate with expression

• Metabolomics:

  • Targeted metabolomics focusing on interferon-related metabolic changes

  • Global metabolite profiling

• Integration strategies:

  • Correlation analysis across data types

  • Network analysis to identify key hubs and modules

  • Machine learning approaches to predict functional relationships

Multi-omic studies of liver tissues across different diets and ages have successfully integrated transcriptome, proteome, and metabolome data , providing a methodological framework that could be adapted for KLHDC7A research.

Beyond breast cancer, what other diseases show associations with KLHDC7A variants?

While breast cancer has the strongest documented association with KLHDC7A, evidence suggests potential links to other conditions:

• Cleft Lip with or without Cleft Palate (CL(P)):
  KLHDC7A has been mentioned in association with CL(P), though its role appears to be less direct compared to other candidates like PAX7. The association was identified in genome-wide studies, with KLHDC7A being proximal to significant signals .

• Diabetic Retinopathy:
  Genome-wide meta-analysis for severe diabetic retinopathy has identified KLHDC7A as a potentially associated gene, though detailed functional validation is still needed .

• Potential neurological connections:
  While not directly stated in the search results, the chromosome location (1p36.13) has been associated with various neurological conditions, suggesting KLHDC7A variants might contribute to these phenotypes.

To investigate disease associations, researchers should consider:

• Analyzing existing GWAS datasets for KLHDC7A variants

• Performing targeted genotyping in disease cohorts

• Using phenome-wide association studies (PheWAS) to identify novel connections

• Developing animal models with KLHDC7A mutations to observe phenotypic effects

How should researchers interpret the contradictory findings on KLHDC7A methylation and expression in cancer?

The paradox of KLHDC7A showing promoter hypermethylation yet increased expression in breast cancer represents a complex regulatory mechanism that requires careful interpretation:

Methodological approaches for resolving this contradiction:

• Comprehensive epigenetic profiling:

  • Map all CpG sites across the KLHDC7A locus, not just promoter regions

  • Analyze histone modifications that might override DNA methylation effects

  • Investigate enhancer regions that could drive expression despite promoter methylation

• Alternative promoter analysis:

  • Test for the presence of alternative, unmethylated promoters

  • Perform 5' RACE to identify all transcription start sites

• Transcription factor binding studies:

  • Identify transcription factors that can overcome methylation-induced repression

  • Analyze accessibility of transcription factor binding sites

• Mechanistic experiments:

  • Use methylation inhibitors to test causality between methylation and expression

  • Perform site-specific methylation using CRISPR-dCas9-DNMT3A to test effects of specific methylated regions

• Clinical correlation studies:

  • Stratify patient samples based on both methylation and expression

  • Correlate patterns with clinical outcomes to identify functional relevance

This contradiction highlights the complexity of epigenetic regulation in cancer and suggests KLHDC7A may be subject to unique regulatory mechanisms that warrant further investigation.

What are the current limitations in KLHDC7A research and how might they be addressed?

Several technical and conceptual limitations currently hamper KLHDC7A research:

• Limited antibody availability and specificity:

  • Solution: Develop and rigorously validate new antibodies

  • Alternative approach: Use epitope tagging in model systems

• Single-exon structure complicating genetic manipulation:

  • Solution: Design CRISPR strategies that avoid complete gene deletion

  • Utilize knockdown approaches with careful control validation

• Limited knowledge of physiological function:

  • Solution: Generate and characterize knockout mouse models

  • Perform tissue-specific and inducible knockouts to bypass potential developmental effects

• Contradictory data on expression and regulation:

  • Solution: Integrated multi-omic approaches (see question 3.3)

  • Single-cell analysis to address cellular heterogeneity

• Unknown three-dimensional structure:

  • Solution: Structural biology approaches (X-ray crystallography, Cryo-EM)

  • Computational modeling based on other Kelch domain proteins

• Limited cell line models expressing endogenous KLHDC7A at detectable levels:

  • Solution: Screen diverse cell line panels to identify suitable models

  • Use physiologically relevant primary cell cultures when possible

How can researchers effectively use CRISPR activation systems to study KLHDC7A function?

CRISPR activation (CRISPRa) systems offer powerful tools for studying KLHDC7A gain-of-function:

Detailed methodological approach:

• System selection:

  • The synergistic activation mediator (SAM) system is recommended, consisting of:

    • dCas9-VP64 fusion

    • MS2-P65-HSF1 fusion protein

    • sgRNA with MS2-binding loops

• Guide RNA design:

  • Target sequences 50-200bp upstream of the transcription start site

  • Design multiple guides and test their efficiency

  • Ensure minimal off-target effects using prediction tools

• Delivery methods:

  • Transient transfection for short-term studies

  • Lentiviral transduction for stable expression and long-term studies

  • Inducible systems (e.g., Tet-On) for temporal control

• Validation steps:

  • Confirm activation at mRNA level (RT-qPCR)

  • Verify protein upregulation (Western blot)

  • Include appropriate controls (non-targeting guides)

  • Monitor potential off-target effects

• Functional readouts:

  • Proliferation assays

  • Apoptosis assessment

  • Migration and invasion assays for cancer studies

  • Gene expression analysis (targeted or genome-wide)

  • Protein-protein interaction changes

The KLHDC7A CRISPR Activation Plasmid system available from commercial sources typically includes three plasmids that must be co-transfected at specific ratios for optimal activation .

What statistical approaches are most appropriate for analyzing KLHDC7A expression data in heterogeneous samples?

When analyzing KLHDC7A expression in heterogeneous samples (e.g., tumor tissues, mixed cell populations), specialized statistical approaches are needed:

• Deconvolution methods:

  • Computational techniques to estimate cell-type proportions and cell type-specific expression

  • Examples: CIBERSORT, DeconRNASeq, or MuSiC for RNA-seq data

• Batch effect correction:

  • Combat, Surrogate Variable Analysis (SVA), or Removing Unwanted Variation (RUV)

  • Critical for integrating datasets from different sources

• Differential expression analysis:

  • Linear models with empirical Bayes moderation (limma)

  • Negative binomial models for count data (DESeq2, edgeR)

  • Include relevant covariates (age, sex, stage, treatment)

• Correlation analysis with clinical variables:

  • Spearman or Pearson correlation for continuous variables

  • ANOVA or Kruskal-Wallis for categorical variables

  • Adjust for multiple testing (Benjamini-Hochberg procedure)

• Survival analysis:

  • Cox proportional hazards regression

  • Kaplan-Meier curves with log-rank tests

  • Consider KLHDC7A as both continuous and dichotomized variable

• Network-based approaches:

  • Weighted Gene Co-expression Network Analysis (WGCNA)

  • Incorporate prior knowledge about pathways

• Single-cell analysis (when applicable):

  • Specialized tools for single-cell RNA-seq (Seurat, Scanpy)

  • Trajectory analysis to identify cell state transitions

These approaches help account for the complexity and heterogeneity in biological samples and provide more robust insights into KLHDC7A's role in normal and disease states.

What emerging technologies might advance our understanding of KLHDC7A function?

Several cutting-edge technologies hold promise for elucidating KLHDC7A function:

• Single-cell multi-omics:

  • Simultaneous profiling of transcriptome, proteome, and epigenome at single-cell resolution

  • Reveals cell-specific regulation and heterogeneity in KLHDC7A expression patterns

• Spatial transcriptomics:

  • Maps KLHDC7A expression in tissue context

  • Identifies spatial relationships with other genes and cell types

• Organoid models:

  • Three-dimensional tissue cultures that better recapitulate in vivo conditions

  • Allows functional studies in physiologically relevant systems

• CRISPR base editing and prime editing:

  • Precise modification of specific nucleotides without double-strand breaks

  • Creation of specific disease-associated variants

• Protein structure prediction using AI:

  • AlphaFold and similar tools for predicting KLHDC7A structure

  • Insights into potential binding interfaces and functional domains

• Live-cell imaging with advanced microscopy:

  • Lattice light-sheet microscopy for high-resolution imaging

  • Optogenetic tools to control KLHDC7A activity with spatial and temporal precision

• Interactome mapping technologies:

  • Improved proximity labeling methods (TurboID, APEX2)

  • Cross-linking mass spectrometry for capturing transient interactions

These technologies, applied systematically to KLHDC7A research, could resolve current contradictions and provide comprehensive insights into its molecular and cellular functions.

How might understanding KLHDC7A function lead to new therapeutic strategies?

Insights into KLHDC7A function could translate to therapeutic applications in several ways:

• In breast cancer:

  • If confirmed as an oncogenic factor, KLHDC7A inhibition could represent a therapeutic strategy

  • Small molecule inhibitors targeting the Kelch domain-substrate interface

  • Degrader approaches (PROTACs) to selectively eliminate KLHDC7A protein

• Interferon pathway modulation:

  • KLHDC7A manipulation to enhance interferon responses in immunotherapy-resistant tumors

  • Potential application in viral infections or autoimmune conditions

• Biomarker development:

  • KLHDC7A expression or methylation status as prognostic/predictive biomarkers

  • Liquid biopsy approaches detecting KLHDC7A alterations

• Targeted delivery strategies:

  • Nanoparticle-based delivery of KLHDC7A modulators to specific tissues

  • Antibody-drug conjugates if KLHDC7A is confirmed to have membrane expression

• Combination therapy approaches:

  • Synthetic lethality screens to identify drugs that specifically kill cells with altered KLHDC7A

  • Rational combinations targeting parallel survival pathways

Research priorities should include comprehensive validation of KLHDC7A's role in specific diseases, development of selective modulators, and preclinical testing in relevant model systems.