Recombinant Pongo abelii Late secretory pathway protein AVL9 homolog (AVL9)

What is AVL9 and what is its biological function in normal cells?

AVL9, originally identified in budding yeast as an exocytosis gene, plays important roles in cell polarity, cell migration, and cell cycle progression . The protein functions in the late secretory pathway and is conserved across species, including in primates such as Pongo abelii (Sumatran orangutan). In normal cells, AVL9 participates in cellular processes including single organismal cell-cell adhesion and post-transcriptional regulation of gene expression . Research indicates that AVL9 is involved in regulating migration through various signaling pathways, as shown by GO analysis and KEGG pathway enrichment studies .

Methodologically, to study AVL9's normal function, researchers should consider:

• Cell fractionation to determine subcellular localization

• Co-immunoprecipitation to identify binding partners

• Gene knockout/knockdown experiments to observe phenotypic changes

• Fluorescent tagging to monitor intracellular trafficking

How is AVL9 expression best measured in biological samples?

AVL9 expression can be quantified at both mRNA and protein levels using several complementary techniques:

RNA-based methods:

• RT-qPCR is the most commonly used method for detecting AVL9 mRNA in tissue and plasma samples . For plasma samples, the sensitivity was 80.0% with specificity of 63.3% at a cutoff value of 0.602, with an AUC of 0.729 .

• RNA sequencing can provide comprehensive expression profiles and has been used in the TCGA database analyses to demonstrate AVL9 upregulation in cancer tissues .

Protein-based methods:

• Immunohistochemistry (IHC) can effectively detect AVL9 protein in tissue samples. In CRC studies, staining intensity analysis showed that 2 of 10 samples had strong staining while 8 of 10 were moderate .

• Western blotting provides quantitative protein expression data.

Researchers should select methods based on sample type availability and research questions. For biomarker studies, combining techniques yields more robust results.

What protein-protein interactions are known for AVL9?

Protein-protein interaction (PPI) network analysis using bioinformatics tools has identified several proteins that directly interact with AVL9:

These interactions were identified using Cytoscape 3.7.1 for PPI network analysis, and their correlations were confirmed using the GEPIA database with Pearson's correlation coefficient (P = 0) . To validate these interactions experimentally, researchers should employ co-immunoprecipitation, proximity ligation assays, or yeast two-hybrid approaches to confirm direct physical interactions.

How does AVL9 expression correlate with clinical pathological features in disease states?

AVL9 expression shows significant correlation with several clinical pathological features, particularly in colorectal cancer:

Methodologically, researchers studying these correlations should:

• Use multivariate survival analysis to control for confounding factors

• Employ Kaplan-Meier curves for survival analysis

• Apply Cox regression models to determine hazard ratios

• Establish clear cutoff values for high vs. low expression groups

AVL9 has been identified as an independent prognostic factor with a hazard ratio of 5.695 (95% CI: 1.860-17.442, P = 0.002) .

What are the optimal methods for producing recombinant Pongo abelii AVL9 for functional studies?

For producing recombinant Pongo abelii AVL9, researchers should consider:

Expression System Selection:

• Bacterial systems (E. coli): Simple and cost-effective but may not provide proper post-translational modifications

• Mammalian cell lines (HEK293, CHO): Provide proper folding and modifications but are more expensive

• Insect cell systems (Sf9, Sf21): Offer a compromise between bacterial and mammalian systems

Optimization Steps:

• Gene synthesis with codon optimization for the chosen expression system

• Selection of appropriate tags (His, GST, or FLAG) that won't interfere with protein function

• Optimization of induction conditions (temperature, time, inducer concentration)

• Purification strategy development using affinity chromatography

• Functional validation through activity assays

For AVL9 specifically, mammalian expression systems may be preferable due to the protein's involvement in complex cellular processes and potential post-translational modifications that affect function.

How should researchers investigate the role of AVL9 in cancer cell migration and metastasis?

Based on findings that AVL9 contributes to colorectal carcinoma cell migration via regulating EGFR expression , researchers investigating this function should employ:

In vitro methodologies:

• Wound healing/scratch assays to measure collective cell migration

• Transwell migration and invasion assays to quantify individual cell movement

• Live-cell imaging with fluorescently labeled AVL9 to track subcellular localization during migration

• CRISPR-Cas9 mediated knockout/knockin to create isogenic cell lines for comparative studies

In vivo approaches:

• Orthotopic xenograft models with AVL9-manipulated cells

• Metastasis tracking using bioluminescence imaging

• Circulating tumor cell isolation and characterization

Molecular mechanistic studies:

• Co-immunoprecipitation to identify AVL9-EGFR interactions

• Western blotting to assess EGFR expression and phosphorylation status

• Phosphoproteomics to identify downstream signaling changes

Since research has shown that "AVL9 promoted CRC cell migration via regulating EGFR expression" , these methods would allow researchers to further elucidate the molecular mechanisms involved.

What approaches can validate AVL9 as a biomarker for early cancer detection?

To validate AVL9 as a biomarker for early cancer detection, researchers should follow a structured validation pathway:

Phase 1: Discovery Validation

• Expand tissue-based studies beyond the current findings (n=50) to larger cohorts

• Validate plasma AVL9 expression differences between cancer patients and healthy controls in diverse populations

• Determine sensitivity and specificity in early-stage (I+II) versus late-stage (III+IV) patients

Phase 2: Clinical Validation

• Prospective studies in at-risk populations

• Comparison with established biomarkers (e.g., CEA, CA19-9)

• Development of standardized detection protocols

Phase 3: Implementation

• Development of clinical-grade assays

• Establishment of reference ranges and cutoff values

• Integration into screening algorithms

How can researchers investigate the relationship between AVL9 and other genes in signaling pathways?

To investigate AVL9's role in signaling pathways, researchers should employ a multi-omics approach:

Transcriptomic approaches:

• RNA-seq following AVL9 manipulation to identify gene expression changes

• ChIP-seq to determine if AVL9 affects transcription factor binding

• Single-cell RNA-seq to capture heterogeneous responses

Proteomic approaches:

• Mass spectrometry-based proteomics after AVL9 knockdown/overexpression

• Phosphoproteomic analysis to identify altered signaling cascades

• Proximity-dependent biotinylation (BioID or APEX) to identify near-neighbors

Pathway analysis:

• GO analysis and KEGG pathway enrichment as performed in existing research

• Network analysis tools to identify key nodes and hubs

Current research has identified several pathways associated with AVL9, including:

• Progesterone-mediated oocyte maturation

• Axon guidance

• Insulin signaling pathway

• Ubiquitin-mediated proteolysis signaling pathways

Additionally, researchers should investigate the molecular mechanisms connecting AVL9 to EGFR regulation, as this has been identified as a key pathway in CRC migration .

How should contradictory findings about AVL9's role in different cancer types be reconciled?

When facing contradictory findings about AVL9 across different cancer types, researchers should:

Methodological approach to reconciliation:

• Context-dependent analysis:

  • Compare expression levels, mutations, and splice variants across cancer types

  • Analyze tissue-specific co-factors that might modulate AVL9 function

  • Investigate cell-type specific effects through single-cell approaches

• Molecular mechanism dissection:

  • Determine if AVL9 functions through different pathways in different cancers

  • Investigate if post-translational modifications differ by cancer type

  • Examine genetic and epigenetic regulation of AVL9 across cancers

• Experimental validation:

  • Use consistent methodologies across cancer types for direct comparison

  • Develop isogenic models expressing tissue-specific factors

  • Create patient-derived organoids to preserve tissue context

Current research has identified AVL9 upregulation in colorectal cancer , clear cell renal carcinomas , and non-small cell lung cancer , with each potentially involving different mechanisms. For instance, in non-small cell lung cancer, AVL9 was identified as a direct target of miR-203a-3p , while in CRC, it was shown to be regulated by the linc00662/miR-497-5p axis .

What are the best experimental controls when studying AVL9 function?

When designing experiments to study AVL9 function, appropriate controls are essential for result validity:

For gene expression manipulation:

• Empty vector controls for overexpression studies

• Non-targeting siRNA/shRNA controls for knockdown experiments

• Wild-type cells alongside CRISPR-modified lines

• Rescue experiments to confirm specificity of observed effects

For protein interaction studies:

• IgG controls for immunoprecipitation

• GST-only controls for GST-pulldown assays

• Competition assays with excess untagged protein

For functional assays:

• Positive controls using known regulators of the pathway

• Time-course experiments to establish temporal relationships

• Dose-response analyses for pharmacological studies

For clinical specimen analysis:

• Matched normal and tumor tissues from the same patient

• Age and gender-matched controls for plasma studies

• Technical replicates to ensure method reliability

In existing AVL9 research, matched paired tissues were used (50 paired CRC tissues and adjacent normal tissues) , and plasma samples from 60 CRC patients were compared with healthy control plasma .

How can researchers analyze the prognostic value of AVL9 in combination with other biomarkers?

To analyze AVL9's prognostic value in combination with other biomarkers, researchers should employ these methodological approaches:

Statistical methods:

• Multivariate Cox regression models to adjust for confounding factors

• Nomogram construction to visualize combined predictive power

• Decision tree analysis to identify optimal marker combinations

• Net reclassification improvement (NRI) to quantify added value

Machine learning approaches:

• Random forest algorithms to identify optimal marker combinations

• Support vector machines for classification

• Deep learning models for complex pattern recognition

Validation strategies:

• Training and validation cohorts to test predictive models

• Cross-validation techniques to assess model stability

• External validation in independent patient populations

Current research has established AVL9 as an independent prognostic factor with a hazard ratio of 5.695 , but its performance in combination with established biomarkers like CEA, CA19-9, and CA72-4 should be evaluated, particularly given their reported low specificity and sensitivity for early-stage detection .

A complementary panel approach might overcome the limitations of single biomarkers, as suggested by the research noting that "the most commonly used diagnostic markers, namely, carcinoembryonic antigen, carbohydrate antigen19-9, and carbohydrate antigen 72–4, exhibit low specificity and sensitivity, particularly in early-stage CRC" .

What are the challenges in developing antibodies specific to Pongo abelii AVL9 for research applications?

Developing specific antibodies against Pongo abelii AVL9 presents several challenges:

Sequence homology considerations:

• High sequence similarity between human and Pongo abelii AVL9 requires careful epitope selection

• Cross-reactivity testing against human AVL9 is essential

• Unique epitope identification may require extensive sequence analysis

Production strategies:

• Monoclonal antibodies offer higher specificity but require more resources

• Polyclonal antibodies provide better coverage but may have batch-to-batch variability

• Recombinant antibodies (such as single-chain variable fragments) may offer advantages for certain applications

Validation requirements:

• Knockout/knockdown controls to confirm specificity

• Western blot analysis to verify molecular weight

• Immunoprecipitation to confirm native protein recognition

• Competition assays with recombinant protein

Application-specific optimization:

• Different fixation methods for IHC applications

• Buffer optimization for immunoprecipitation

• Epitope accessibility assessment for different applications

While current research has successfully used immunohistochemistry to detect AVL9 in CRC samples , developing antibodies specifically for the Pongo abelii homolog would require additional considerations of cross-species reactivity and epitope conservation.

How should researchers optimize isolation of AVL9 from clinical samples for biomarker studies?

Optimizing AVL9 isolation from clinical samples requires careful consideration of pre-analytical, analytical, and post-analytical variables:

Pre-analytical considerations:

• Standardized collection protocols to minimize variability

• Appropriate preservation methods (FFPE vs. frozen tissue)

• Sample processing time minimization to prevent degradation

• Consistent handling procedures across all samples

Analytical optimization:

• For tissue samples: Optimized tissue disruption and lysis buffers

• For plasma samples: Specialized extraction protocols for circulating proteins

• For RNA analysis: RNase-free conditions and stabilization reagents

Quantification methods:

• RT-qPCR with validated primers for mRNA detection

• ELISA development for protein quantification

• Digital PCR for absolute quantification

• Mass spectrometry for protein identification and quantification

Quality control measures:

• Internal standards for normalization

• Standard curves for absolute quantification

• Replicate analysis to ensure reproducibility

For AVL9 specifically, existing research used RT-qPCR for detection in both tissue and plasma samples , but methodological details for optimal extraction were not fully specified in the search results. Researchers should consider adapting protocols used for similar secretory pathway proteins.

What are the key unanswered questions about AVL9's molecular mechanisms?

Despite progress in understanding AVL9's role in cancer, several fundamental questions remain unanswered:

Molecular function questions:

• What are the precise molecular mechanisms by which AVL9 regulates EGFR expression?

• How does AVL9 interact with the cellular migration machinery?

• What post-translational modifications regulate AVL9 activity?

• How does AVL9 function in the context of the secretory pathway?

Regulatory questions:

• What transcription factors control AVL9 expression?

• Besides miR-497-5p , what other microRNAs regulate AVL9?

• Are there tissue-specific enhancers or repressors of AVL9?

Structural questions:

• What are the critical domains for AVL9's various functions?

• How does the protein structure influence its binding partners?

• Are there functional differences between AVL9 isoforms?

Evolutionary questions:

• How conserved is AVL9 function across species?

• What are the functional differences between human AVL9 and Pongo abelii AVL9?

To address these questions, researchers should employ interdisciplinary approaches including structural biology, systems biology, and comparative genomics alongside traditional molecular biology techniques.

How can high-throughput screening be used to identify modulators of AVL9 function?

High-throughput screening (HTS) offers powerful approaches to identify modulators of AVL9 function:

Screening approaches:

• Small molecule libraries to identify chemical modulators

• CRISPR-Cas9 libraries for genetic interaction mapping

• siRNA/shRNA libraries for knockdown phenotyping

• cDNA overexpression libraries to identify synthetic interactions

Assay development considerations:

• Reporter systems based on AVL9-dependent phenotypes

• Cell migration assays amenable to high-throughput formats

• EGFR expression levels as a readout for AVL9 activity

• Split-reporter systems for protein-protein interactions

Analysis strategies:

• Z-factor determination for assay quality assessment

• Hit validation cascades with orthogonal assays

• Structure-activity relationship studies for chemical hits

• Network analysis of genetic interaction data

Translation to biological insights:

• Target deconvolution for chemical modulators

• Pathway enrichment for genetic modulators

• Validation in diverse cellular contexts

• Integration with existing knowledge of AVL9 biology

Given AVL9's role in regulating EGFR expression and cancer cell migration , high-throughput screens focusing on these phenotypes could identify potential therapeutic targets or diagnostic markers for further development.