COTL1 Human

What is COTL1 and what are its primary cellular functions?

COTL1 is a small cytoplasmic actin-binding protein belonging to the ADF/cofilin family, which plays critical roles in regulating actin dynamics and cytoskeleton organization . It is implicated in various cellular processes including cell migration, adhesion, and intracellular signaling pathways . The protein contains specific binding domains that facilitate its interaction with F-actin, influencing cellular processes dependent on cytoskeletal integrity.

To study COTL1's functions, researchers typically employ techniques such as:

• Immunofluorescence microscopy to visualize its co-localization with actin filaments

• Co-immunoprecipitation to identify binding partners

• Live-cell imaging to observe dynamic changes in cytoskeletal organization

• Knockdown or overexpression studies to assess functional consequences

These approaches have revealed COTL1's significance beyond structural roles, with emerging evidence pointing to its involvement in cancer progression and immune regulation .

How is COTL1 expression distributed across normal and cancerous human tissues?

Pan-cancer analysis using the TIMER 2 and SangerBox databases has demonstrated significant upregulation of COTL1 expression in 21 different cancer types compared to corresponding normal tissues . Notable examples include:

• Glioblastoma multiforme (GBM, p=7.0e-84)

• Brain Lower Grade Glioma (LGG, p=4.7e-174)

• Breast invasive carcinoma (BRCA, p=1.0e-23)

• Colon adenocarcinoma (COAD, p=1.7e-52)

• Stomach and Esophageal carcinoma (STES, p=3.0e-143)

Interestingly, COTL1 is downregulated in 5 cancer types, including Lung adenocarcinoma (LUAD, p=3.0e-9), Lung squamous cell carcinoma (LUSC, p=4.1e-36), and certain leukemias . This differential expression pattern suggests tissue-specific roles in various cancer types.

Further analysis using the GSCA database has revealed significant correlations between COTL1 expression and pathological stages in Kidney Renal Clear Cell Carcinoma (KIRC) and Skin Cutaneous Melanoma (SKCM), providing additional evidence supporting COTL1's potential role in cancer progression .

What techniques are most effective for detecting and quantifying COTL1 in experimental and clinical samples?

Several validated techniques are available for COTL1 detection and quantification:

Protein-Level Detection:

• Immunohistochemistry (IHC): Successfully employed using primary antibodies against COTL1 (1:400, Proteintech, #17119-1-AP) . This technique enables visualization of COTL1 expression patterns in tissue sections and assessment of co-localization with other proteins.

• Two-dimensional electrophoresis (2-DE) and MALDI-MS: Effective for comparative proteome analysis in plasma samples, as demonstrated in studies of autoimmune disorders .

• Western blotting: Standard method for protein quantification in tissue lysates or cell lines.

Gene Expression Analysis:

• Real-time quantitative PCR (RT-qPCR): Provides sensitive quantification of COTL1 transcript levels.

• RNA-sequencing: Offers comprehensive transcriptomic profiling, enabling analysis of COTL1 expression in relation to global gene expression patterns.

When selecting detection methods, researchers should consider:

• Sample type availability (fresh tissue, FFPE samples, bodily fluids)

• Required sensitivity and specificity

• Need for spatial information versus pure quantification

• Available resources and expertise

For clinical biomarker applications, standardized protocols are essential to ensure reproducibility across different laboratories and studies.

What experimental models are available for studying COTL1 function?

Researchers have several models available for investigating COTL1 function:

In vivo models:

• Cotl1 knockout mouse (Cotl1-/-): This model has revealed insights into COTL1's unexpected role in bone metabolism. Cotl1-/- male mice exhibit increased structural bone density, bone mineral density (BMD), and improved microstructural bone properties compared to wild-type mice . This model is particularly valuable for studying systemic effects of COTL1 deficiency.

In vitro models:

• Cell lines with differential COTL1 expression: Cancer cell lines with varying levels of endogenous COTL1 expression can be used for comparative studies.

• COTL1 manipulation approaches:

  • siRNA or shRNA for transient or stable knockdown

  • CRISPR-Cas9 for precise gene editing

  • Overexpression systems for wild-type or mutant COTL1 variants

Clinical samples:

• Human tissue samples with varying COTL1 expression levels

• Patient-derived xenografts (PDXs) that maintain tumor heterogeneity

Each model offers distinct advantages depending on the research question, from molecular mechanisms to physiological outcomes. Validation of findings across multiple models strengthens the reliability and translational potential of results.

How does COTL1 expression correlate with patient survival across different cancer types?

Comprehensive pan-cancer survival analysis has revealed significant correlations between COTL1 expression and patient outcomes:

• Combined Glioma and Glioblastoma multiforme (GBMLGG, p=0.01, HR=1.19)

• Other cancer types not specifically detailed in the search results

Disease-Specific Survival (DSS):
Elevated COTL1 expression correlates with poor prognosis in seven tumor types:

• GBMLGG (p=5.0e-24, HR=2.31)

• LGG (p=4.2e-10, HR=2.13)

• Kidney Renal Papillary Cell Carcinoma (KIRP, p=0.03, HR=1.85)

• Pan-kidney cohort (KIPAN, p=0.02, HR=1.21)

• Glioblastoma multiforme (GBM, p=0.03, HR=1.44)

• Uveal Melanoma (UVM, p=5.5e-5, HR=2.79)

• Adrenocortical carcinoma (ACC, p=8.2e-4, HR=1.86)

Disease-Free Interval (DFI) and Progression-Free Interval (PFI):
COTL1 functions as a high-risk factor in several cancers, including:

• Pancreatic adenocarcinoma (PAAD)

• Head and neck squamous cell carcinoma (HNSC)

• LGG

• GBMLGG

• ACC

Interestingly, COTL1 appears to be a low-risk factor in Liver Hepatocellular Carcinoma (LIHC), highlighting the context-dependent nature of its prognostic value .

These findings demonstrate that COTL1 expression may serve as a valuable prognostic biomarker across multiple tumor types, with particularly strong associations in brain and kidney cancers.

What is the relationship between COTL1 and cancer stemness?

Research has revealed significant associations between COTL1 expression and cancer stemness across multiple tumor types:

Stemness Correlations:

• COTL1 is associated with DNA stemness in 20 different tumor types

• COTL1 correlates with RNA stemness in 22 different tumor types

This consistent relationship across diverse cancers suggests COTL1 may play a fundamental role in maintaining or promoting cancer stem cell-like properties, which are crucial for tumor initiation, progression, and therapy resistance.

Methodological Approaches for Studying COTL1 in Cancer Stemness:

• Bioinformatic Analysis:

  • Correlation analysis between COTL1 expression and established stemness indices (DNAss and RNAss)

  • Gene set enrichment analysis (GSEA) to identify stemness-related pathways associated with COTL1

• Functional Assays:

  • Sphere formation assays following COTL1 manipulation

  • Analysis of stemness marker expression (e.g., SOX2, OCT4, NANOG)

  • In vivo limiting dilution assays to assess tumor-initiating capacity

• Mechanistic Studies:

  • Investigation of COTL1's impact on stem cell signaling pathways

  • Analysis of COTL1's role in stemness-related epigenetic modifications

Understanding this relationship has significant implications for targeting cancer stem cells, which often drive tumor relapse and metastasis.

How does COTL1 influence genomic instability in cancer?

Analysis using tools such as SangerBox has revealed significant associations between COTL1 expression and various genomic instability parameters across cancer types . The data shows favorable relationships between COTL1 expression and several genomic instability markers:

• Loss of Heterozygosity (LOH)

• Homologous Recombination Deficiency (HRD)

• Mutant Allele Tumor Heterogeneity (MATH)

Additionally, COTL1 shows links to other genomic features including:

• Tumor Mutation Burden (TMB)

• Microsatellite Instability (MSI)

• Neoantigen load (NEO)

Research Approaches to Study This Relationship:

• Genomic Analysis:

  • DNA sequencing to measure mutation frequencies and patterns

  • Microsatellite instability testing

  • Copy number variation analysis

• Functional Studies:

  • DNA damage response assays after COTL1 manipulation

  • Homologous recombination efficiency testing

  • Chromosomal instability assessment through metaphase spreads

• Mechanistic Investigations:

  • Analysis of COTL1's interaction with DNA repair proteins

  • Assessment of COTL1's impact on cell cycle checkpoints

  • Evaluation of cytoskeletal changes affecting chromosomal segregation

These correlations suggest that COTL1 might play a previously unrecognized role in mechanisms underlying genomic instability, a hallmark of cancer that contributes to tumor heterogeneity and therapy resistance.

How does COTL1 relate to immune cell infiltration and immunotherapy markers?

Comprehensive analysis has demonstrated significant relationships between COTL1 expression and immune parameters:

Immune Cell Infiltration:
COTL1 shows positive correlations with seven types of immune cells . While specific details of all cell types weren't provided in the search results, evidence confirms a positive correlation between COTL1 expression and CD8+ T cells in low-grade glioma (LGG) .

Immune Checkpoints:

• COTL1 positively correlates with immune checkpoint genes (47 were analyzed)

• A confirmed positive association exists between COTL1 expression and PD-L1 in LGG

Immunotherapy Markers:
COTL1 shows connections with key biomarkers used to predict immunotherapy response:

• Tumor Mutation Burden (TMB)

• Microsatellite Instability (MSI)

• Neoantigen load (NEO)

• Programmed death ligand 1 (PD-L1)

Research Methods to Investigate These Associations:

• Computational Approaches:

  • Correlation analysis using immune infiltration estimation algorithms

  • Gene set enrichment analysis focusing on immune-related pathways

• Experimental Validation:

  • Immunohistochemistry for COTL1 alongside immune markers

  • Flow cytometry to quantify immune cell populations in COTL1-high vs. COTL1-low tumors

  • Co-culture experiments with immune cells and cancer cells with manipulated COTL1 expression

These findings suggest COTL1 may have important implications for cancer immunotherapy, potentially serving as a biomarker for response prediction or as a novel therapeutic target to enhance immunotherapy efficacy.

What is the association between COTL1 polymorphisms and autoimmune disorders?

Research has identified significant associations between COTL1 gene polymorphisms and autoimmune disorders:

Rheumatoid Arthritis (RA) Associations:

• The genotype frequencies of two COTL1 SNPs (c.-1124G>A and c.588C>T) were significantly different between healthy controls and RA patients (p = 0.009 and 0.027, respectively)

• COTL1 is significantly associated with the levels of anti-CCP antibody (p = 0.03) in RA patients

Systemic Lupus Erythematosus (SLE) Associations:

• The genotype frequency of c.484G>A was significantly different between healthy controls and SLE patients (p = 0.025)

Haplotype Analysis:

• Five major haplotypes (>5% frequency) account for >86.1% of distributions in controls

• Four major haplotypes (~88.1%) in RA patients and three major haplotypes (~88.2%) in SLE patients were identified

• The distribution rate of the haplotype GGCA is significantly different between control group and RA or SLE patients (p = 0.021 and 0.044, respectively)

• The haplotype GGTT shows a significant association with SLE (p = 0.038)

Research Methodologies for Studying These Associations:

• Genetic Analysis:

  • Genotyping of COTL1 SNPs using PCR-based methods

  • Haplotype construction and frequency analysis

  • Genome-wide association studies (GWAS)

• Functional Studies:

  • Expression analysis of wild-type vs. variant COTL1 in immune cells

  • Cytoskeletal dynamics assessment in cells with COTL1 variants

  • Signaling pathway analysis in the context of different COTL1 polymorphisms

These findings suggest that COTL1 may play a previously unrecognized role in autoimmune pathogenesis, potentially through effects on immune cell function or structure.

What is the role of COTL1 in bone mineral density regulation?

Recent research using Cotl1 knockout mice has revealed an unexpected role for COTL1 in bone metabolism:

Key Findings in Cotl1-/- Mice:

• Increased structural bone density in femoral bones compared to wild-type male mice

• Significantly higher bone mineral density (BMD) from four weeks to 24 weeks of age

• Enhanced microstructural bone properties including:

  • Increased trabecular bone thickness (Tb.Th)

  • Greater trabecular space (Tb.sp)

  • Higher trabecular number (Tb.N)

  • Increased bone volume (BV/TV)

• Fewer TRAP-positive cells (osteoclasts) in the femur

Human Genetic Evidence:

• Analysis of UK Biobank data (application number 83990) examined 6,047 single nucleotide polymorphisms (SNPs) in the COTL1 gene for association with estimated bone mineral density (eBMD)

• This suggests potential translational relevance of COTL1's role in bone metabolism to human health

Research Methodologies:

• Animal Studies:

  • Micro-CT analysis of bone parameters

  • Histological assessment of bone cells (TRAP staining for osteoclasts)

  • Age-dependent analysis of bone development

• Genetic Association Studies:

  • SNP analysis using PLINK

  • Additive genetic model testing

  • Bonferroni correction for multiple testing

This newly discovered role suggests COTL1 may function as a negative regulator of bone density, possibly through effects on osteoclast function or development, opening new avenues for understanding and potentially treating bone disorders.

How does COTL1 influence RNA methylation processes?

Recent investigations have uncovered a previously unknown relationship between COTL1 and RNA methylation:

Key Findings:

• Strong positive correlation between COTL1 expression and most genes involved in modifying RNA methylation

• The analysis specifically examined three types of RNA methylation modifications:

  • m1A (N1-methyladenosine)

  • m5C (5-methylcytosine)

  • m6A (N6-methyladenosine)

Potential Functional Implications:
This correlation suggests COTL1 may influence post-transcriptional modifications that regulate:

• RNA stability and degradation

• Protein translation efficiency

• Alternative splicing patterns

• RNA localization

Research Approaches to Study This Relationship:

• Correlation Analysis:

  • Expression correlation between COTL1 and RNA methylation enzymes

  • Integration with transcriptomic and proteomic datasets

• Functional Validation:

  • RNA methylation profiling after COTL1 manipulation

  • RNA immunoprecipitation with methylation-specific antibodies

  • Mass spectrometry to quantify methylation changes

• Mechanistic Studies:

  • Protein interaction studies between COTL1 and methylation enzymes

  • Subcellular localization analysis of COTL1 and RNA processing machinery

  • Analysis of cytoskeletal influence on RNA methylation processes

This relationship represents a novel intersection between cytoskeletal proteins and epigenetic regulation, potentially revealing new therapeutic targets that disrupt cancer-specific RNA processing mechanisms.

What bioinformatic approaches are most promising for COTL1 research?

Based on successful applications in current COTL1 research, several bioinformatic approaches show particular promise:

Database and Platform Utilization:

• GSCA, TIMER, and SangerBox databases for expression analysis across tumor types

• TCGA Pan-Atlas Cancer Genomics Dataset via cBioportal for genetic alterations analysis

• PLINK for genetic association studies

Statistical and Analytical Methods:

• Wilcoxon test for differential expression analyses

• Log rank tests for survival curve comparisons and hazard ratio calculations

• Spearman correlation for gene expression associations

• ANOVA for comparing multiple experimental groups

Integrative Approaches with Promising Applications:

• Multi-omics Integration:

  • Combining transcriptomics, proteomics, and epigenomics data

  • Correlation of COTL1 with genomic instability parameters

  • Integration with clinical outcome data

• Network Analysis:

  • Protein-protein interaction networks centered on COTL1

  • Pathway enrichment analysis to identify COTL1-associated biological processes

  • Co-expression network analysis to identify functional modules

• Advanced Machine Learning:

  • Predictive modeling of COTL1's role in disease progression

  • Patient stratification based on COTL1 and associated signatures

  • Drug response prediction models incorporating COTL1 status

For researchers beginning COTL1 studies, a multi-faceted bioinformatic approach that combines multiple data types and analytical methods will likely yield the most comprehensive insights into this protein's diverse functions.

What are the most promising therapeutic applications targeting COTL1?

Based on current evidence, several therapeutic strategies targeting COTL1 show promise:

Cancer Immunotherapy Applications:

• COTL1's positive correlations with immune checkpoints and immune cell infiltration suggest it could enhance immunotherapy efficacy

• Connection with PD-L1 expression indicates potential for combination with existing checkpoint inhibitors

• Association with tumor mutation burden and neoantigen load suggests relevance for personalized neoantigen vaccines

Potential Therapeutic Strategies:

• Direct COTL1 Targeting:

  • Small molecule inhibitors disrupting COTL1-actin interaction

  • RNA interference approaches (siRNA, antisense oligonucleotides)

  • Blocking antibodies against accessible COTL1 domains

• Combination Approaches:

  • COTL1 inhibition plus immune checkpoint blockade

  • COTL1 targeting combined with DNA damage response inhibitors

  • COTL1 modulation to enhance conventional therapies

• Biomarker-Driven Applications:

  • Patient stratification for immunotherapy based on COTL1 expression

  • Monitoring COTL1 levels during treatment to assess response

  • Targeting COTL1 in minimal residual disease settings

Development Considerations:

• Cancer-type specificity, given differential expression and prognostic value

• Potential off-target effects on normal cytoskeletal functions

• Delivery methods to reach specific tumor types (e.g., brain-penetrant for gliomas)

The strongest therapeutic potential currently appears to be in brain cancers and kidney cancers, where COTL1 shows both high expression and strong prognostic value .