Recombinant Pongo abelii Protein transport protein Sec31A (SEC31A), partial

What is SEC31A and what is its primary function in cellular transport?

SEC31A (also known as SEC31 homolog A) is a critical component of the coat protein complex II (COPII), which plays an essential role in promoting the formation of transport vesicles from the endoplasmic reticulum (ER) . The protein has dual primary functions: facilitating the physical deformation of the ER membrane into vesicles and participating in the selection of cargo molecules for transport . As a transport protein, SEC31A is conserved across multiple species, including humans, orangutans (Pongo abelii), zebrafish (Danio rerio), and other organisms, indicating its evolutionary significance in eukaryotic cellular transport mechanisms . The protein is also known by several alternative names including ABP125, ABP130, HSPC275, HSPC334, SEC31L1, and KIAA0905 .

What methodologies are commonly employed to detect and quantify SEC31A expression?

Several established methodologies can be utilized to detect and quantify SEC31A expression in experimental settings:

• Western Blot Analysis: This is the most common method, using SEC31A-specific antibodies to detect the protein (typically observed at approximately 133 kDa) . For optimal results, researchers should use appropriate antibody dilutions (e.g., 0.04 μg/mL) with whole cell lysates at multiple concentrations (5-50 μg) .

• Quantitative Real-Time PCR (qRT-PCR): This technique allows for the accurate quantification of SEC31A mRNA expression levels . Researchers typically normalize results against housekeeping genes to ensure reliable comparative analysis.

• Immunoprecipitation (IP): This technique can be used to isolate and concentrate SEC31A from complex protein mixtures prior to detection .

• Immunohistochemistry (IHC) and Immunofluorescence (IF): These methods visualize the spatial distribution of SEC31A in tissues or cells .

• Flow Cytometry: This can be used for quantitative analysis of SEC31A in cell populations .

The selection of method depends on your specific research question, available samples, and required sensitivity.

What are the key considerations when working with recombinant Pongo abelii SEC31A protein?

When working with recombinant Pongo abelii SEC31A protein, researchers should consider:

• Expression System: The protein can be expressed in various systems including E. coli, yeast, baculovirus, or mammalian cells, each offering different advantages for protein folding and post-translational modifications .

• Purity Assessment: The purity should be greater than or equal to 85% as determined by SDS-PAGE analysis . This is critical for ensuring experimental reproducibility and reliable results.

• Storage Conditions: Proper storage conditions are essential for maintaining protein stability and activity. While not explicitly stated in the search results, recombinant proteins typically require storage at -80°C with minimal freeze-thaw cycles.

• Functional Validation: Before using in complex experiments, the functionality of the recombinant protein should be verified through appropriate assays based on SEC31A's known roles in vesicular transport.

• Cross-Species Reactivity: When designing experiments, consider that Pongo abelii SEC31A shares significant homology with human SEC31A, which may affect antibody reactivity and functional conservation in experimental systems .

How does the structure of SEC31A relate to its function in vesicular transport?

SEC31A's structure is intricately linked to its function in vesicular transport:

The protein contains specific domains that facilitate its interaction with other COPII components and the ER membrane. While the search results don't provide detailed structural information, SEC31A typically contains:

• WD40 Repeats: These domains likely mediate protein-protein interactions within the COPII complex.

• Proline-Rich Domains: These regions often serve as binding sites for other transport proteins.

• Membrane-Interaction Regions: These facilitate the physical deformation of the ER membrane into vesicles .

The specific structural elements enable SEC31A to participate in both membrane deformation and cargo selection during vesicle formation. Its interaction with other COPII components creates a coat structure that drives vesicle budding from the ER membrane while simultaneously capturing appropriate cargo proteins for transport .

What is the regulatory relationship between circSEC31A, miR-376a, and SEC31A in cancer progression?

Research has uncovered a complex regulatory network involving circSEC31A, miR-376a, and SEC31A, particularly in non-small cell lung cancer (NSCLC):

• CircSEC31A Overexpression: CircSEC31A, a circular RNA derived from backsplicing of the SEC31A gene, is overexpressed in NSCLC tumor tissues compared to normal tissues .

• MiR-376a Interaction: Bioinformatic analysis and experimental validation have revealed that circSEC31A can bind to miR-376a, a microRNA with known anti-tumor roles in NSCLC .

• SEC31A Regulation: MiR-376a targets SEC31A mRNA, regulating its expression. In NSCLC, a positive correlation between circSEC31A and SEC31A expression has been observed .

• Regulatory Mechanism: The proposed mechanism suggests that circSEC31A acts as a miR-376a sponge, reducing available miR-376a that would otherwise suppress SEC31A expression. This creates a circSEC31A/miR-376a/SEC31A regulatory axis .

This regulatory network contributes to cancer progression by influencing cellular processes including migration, invasion, glycolysis, and apoptosis . Researchers interested in cancer biology should consider this complex interplay when studying SEC31A in malignancies.

What experimental approaches can be used to investigate circSEC31A function in relation to SEC31A?

To investigate the relationship between circSEC31A and SEC31A, researchers can employ several sophisticated experimental approaches:

• Subcellular Fractionation Assay: This technique separates nuclear and cytoplasmic fractions to determine the subcellular localization of circSEC31A, providing insights into its potential function .

• RNase R Assay: This method helps assess the stability of circSEC31A by treating RNA samples with RNase R, which degrades linear RNAs but not circular RNAs, thus confirming the circular nature of circSEC31A .

• Dual-Luciferase Reporter Assay: This approach can verify direct interactions between circSEC31A and miR-376a, as well as between miR-376a and SEC31A. The assay involves inserting segments of circSEC31A or SEC31A 3'UTR containing miR-376a binding sites into reporter plasmids .

• RNA Pull-Down Assay: This technique can confirm the direct binding between circSEC31A and miR-376a .

• Knockdown and Overexpression Studies: Using siRNAs or shRNAs to knockdown circSEC31A, or vectors to overexpress it, can help elucidate its role in regulating SEC31A expression and associated cellular phenotypes .

• Quantitative Analysis: qRT-PCR and Western blot can be used to measure expression levels of circSEC31A, miR-376a, and SEC31A following experimental manipulations .

How does SEC31A modulation impact cellular phenotypes in cancer models?

Modulation of SEC31A and its circular RNA form (circSEC31A) significantly impacts various cellular phenotypes in cancer models, particularly in NSCLC:

These findings indicate that SEC31A and circSEC31A play crucial roles in cancer cell metabolism and survival . The impact on glycolysis is particularly noteworthy, as cancer cells often rely on glycolytic metabolism (the Warburg effect) for energy production and biomass generation.

The correlation between circSEC31A expression and clinicopathological features (tumor size, TNM stage, and lymphatic metastasis) further underscores its importance in cancer progression . Researchers investigating SEC31A in cancer contexts should consider these phenotypic effects when designing experiments and interpreting results.

What methods can be used to distinguish between canonical SEC31A protein function and circSEC31A-specific effects?

Distinguishing between canonical SEC31A protein function and circSEC31A-specific effects requires sophisticated experimental design:

• Selective Knockdown Strategies:

  • Use siRNAs targeting the linear mRNA junction (affects only linear SEC31A)

  • Design siRNAs targeting the back-splice junction (affects only circSEC31A)

  • Compare phenotypic outcomes between these two approaches

• Rescue Experiments:

  • Knockdown endogenous SEC31A and simultaneously express an siRNA-resistant SEC31A construct

  • Similarly, knockdown circSEC31A and express a synthetic circSEC31A resistant to degradation

  • These approaches help attribute observed phenotypes to specific molecular species

• Subcellular Localization Studies:

  • CircSEC31A and SEC31A protein typically localize to different cellular compartments

  • Use subcellular fractionation followed by qRT-PCR (for circSEC31A) and immunofluorescence or cell fractionation followed by Western blot (for SEC31A protein)

• Interactome Analysis:

  • Identify protein binding partners of SEC31A using co-immunoprecipitation followed by mass spectrometry

  • Identify RNA binding partners of circSEC31A using RNA pull-down assays

  • Compare interaction networks to infer distinct functions

• Temporal Expression Analysis:

  • Monitor expression of both SEC31A and circSEC31A under various cellular conditions

  • Divergent expression patterns suggest independent regulation and possibly distinct functions

What are the key methodological considerations when studying SEC31A in different model systems?

When studying SEC31A across different model systems, researchers should consider several methodological factors:

• Cross-Species Homology and Antibody Selection:

  • SEC31A is conserved across species but with sequence variations

  • Antibodies should be validated for the specific species being studied

  • Available antibodies have been tested for human samples, with potential cross-reactivity with mouse and rat SEC31A

• Expression System Selection for Recombinant Protein:

  • E. coli: Simple and high-yield but lacks post-translational modifications

  • Yeast: Better for eukaryotic proteins with some post-translational modifications

  • Baculovirus: Suitable for complex eukaryotic proteins

  • Mammalian cells: Provides most authentic post-translational modifications

• Application-Specific Optimizations:

  • Western Blot: Optimal antibody concentration (~0.04 μg/mL) and protein loading amounts (5-50 μg)

  • Immunoprecipitation: Protein A affinity purification methods are effective for SEC31A

  • Immunofluorescence: Fixation and permeabilization protocols may need optimization based on cellular localization

• Model System Selection Based on Research Question:

  • Cancer studies: Human cell lines (e.g., HeLa) are well-established models

  • Developmental studies: Model organisms like zebrafish (Danio rerio) offer advantages

  • Evolutionary studies: Comparing SEC31A across species like Pongo abelii (orangutan) can provide insights

• Data Normalization and Statistical Analysis:

  • For expression studies, utilize appropriate housekeeping genes or proteins

  • Statistical methods should account for variability in biological systems (t-test for two-group comparisons, ANOVA for multiple groups, Spearman correlation for relationship analyses)

How can researchers optimize Western blot protocols for SEC31A detection?

Optimizing Western blot protocols for SEC31A detection requires attention to several key parameters:

• Sample Preparation:

  • Use appropriate lysis buffers that effectively solubilize membrane-associated proteins

  • Include protease inhibitors to prevent degradation

  • Test multiple protein concentrations (5 μg, 15 μg, and 50 μg) to determine optimal loading

• SDS-PAGE Conditions:

  • Use lower percentage gels (6-8%) to effectively resolve SEC31A's high molecular weight (133 kDa)

  • Consider gradient gels for improved resolution

  • Ensure complete protein transfer to membrane using appropriate transfer conditions (longer transfer times or semi-dry transfer systems)

• Antibody Selection and Dilution:

  • Optimal primary antibody concentration of approximately 0.04 μg/mL has been reported for some anti-SEC31A antibodies

  • Test different antibody lots and sources if inconsistent results are observed

  • Use antibodies validated for Western blot applications specifically

• Detection Method:

  • Enhanced chemiluminescence (ECL) systems provide good sensitivity

  • Adjust exposure times based on signal strength (starting with 3 seconds as reported)

  • Consider fluorescence-based detection for more quantitative analysis

• Controls:

  • Include positive control lysates (e.g., HeLa cells) where SEC31A is known to be expressed

  • Use loading controls (β-actin, GAPDH) to normalize expression levels

  • Consider using SEC31A knockdown samples as negative controls

Following these optimization steps should yield a clear band at approximately 133 kDa, corresponding to SEC31A protein .

What are common pitfalls in studying circSEC31A and how can they be addressed?

Studying circSEC31A presents several unique challenges that researchers should anticipate and address:

How can researchers effectively design experiments to investigate the SEC31A-COPII pathway?

Designing effective experiments to investigate the SEC31A-COPII pathway requires a systematic approach:

How should researchers interpret contradictory findings about SEC31A expression in different experimental systems?

When confronted with contradictory findings regarding SEC31A expression across different experimental systems, researchers should consider several factors for proper interpretation:

• Tissue and Cell Type Specificity:

  • SEC31A expression varies naturally across tissues and cell types

  • For example, SEC31A shows elevated expression in cancer tissues compared to normal controls, particularly in NSCLC

  • Different cell lines may have varying baseline expression levels

• Experimental Technique Limitations:

  • Different detection methods (qRT-PCR, Western blot, IHC) have varying sensitivities and specificities

  • Antibody selection significantly impacts results - validate antibodies in your specific system

  • mRNA levels (measured by qRT-PCR) may not directly correlate with protein levels (Western blot) due to post-transcriptional regulation

• Isoform Considerations:

  • SEC31A has multiple isoforms (e.g., "protein transport protein Sec31A isoform 4")

  • Different detection methods may preferentially detect specific isoforms

  • Design primers and select antibodies that account for relevant isoforms

• Experimental Conditions Impact:

  • Cell culture conditions (confluence, passage number, medium composition)

  • Sample preparation methods (lysis buffers, protein extraction protocols)

  • Timing of analysis in dynamic processes (cell cycle stage, differentiation state)

• Statistical Analysis Approach:

  • Ensure appropriate statistical tests (t-test, ANOVA, Mann-Whitney U-test) based on data distribution

  • Consider multiple testing corrections for large-scale studies

  • Evaluate whether differences are statistically significant and biologically meaningful

When reporting contradictory findings, researchers should clearly describe all methodological details and discuss potential reasons for discrepancies.

What statistical approaches are most appropriate for analyzing SEC31A expression in clinical samples?

When analyzing SEC31A expression in clinical samples, the following statistical approaches are recommended:

• Comparison Between Groups:

  • For normally distributed data: Student's t-test (two groups) or ANOVA (multiple groups)

  • For non-parametric data: Mann-Whitney U-test (two groups) or Kruskal-Wallis test (multiple groups)

  • Present data as mean ± standard deviation with clear indication of sample sizes

• Correlation Analysis:

  • Spearman correlation test is appropriate for evaluating relationships between SEC31A expression and other variables (e.g., circSEC31A levels, clinical parameters)

  • Report correlation coefficients (r) along with p-values

  • Visualize correlations using scatter plots with regression lines

• Survival Analysis:

  • Kaplan-Meier curves with log-rank tests to assess the impact of SEC31A expression on patient outcomes

  • Cox proportional hazards regression for multivariate analysis

  • Define clear cutoff values for categorizing high versus low SEC31A expression

• Multiple Testing Correction:

  • Apply Bonferroni correction or false discovery rate (FDR) control when performing multiple comparisons

  • Clearly state which correction method was used and adjusted p-value thresholds

• Sample Size Considerations:

  • Perform power analysis to ensure adequate sample sizes

  • Report confidence intervals along with p-values

  • Acknowledge limitations when sample sizes are small

• Multivariate Analysis:

  • Adjust for confounding variables using multiple regression models

  • Consider relevant clinical covariates such as age, gender, tumor stage, and treatment history

In published studies, SEC31A expression data in NSCLC has been analyzed using t-tests and Spearman correlation tests, with significance thresholds of *P < 0.05, **P < 0.01, or ***P < 0.001 .

How can researchers integrate SEC31A functional data with broader cellular pathway analysis?

Integrating SEC31A functional data with broader pathway analysis requires a multifaceted approach:

• Pathway Mapping and Enrichment Analysis:

  • Map SEC31A-interacting proteins to known pathways using databases like KEGG, Reactome, or BioCarta

  • Perform Gene Ontology (GO) enrichment analysis on datasets from SEC31A perturbation experiments

  • Use tools like GSEA (Gene Set Enrichment Analysis) to identify pathways affected by SEC31A modulation

• Network Analysis:

  • Construct protein-protein interaction networks centered on SEC31A using databases like STRING or BioGRID

  • Identify key hub proteins and modules within the network

  • Apply graph theory metrics to quantify network properties and identify critical nodes

• Multi-omics Data Integration:

  • Correlate SEC31A expression with transcriptomic, proteomic, and metabolomic data

  • Use methods like WGCNA (Weighted Gene Co-expression Network Analysis) to identify co-regulated modules

  • Apply multi-omics integration tools like mixOmics or MOFA (Multi-Omics Factor Analysis)

• Systems Biology Modeling:

  • Develop mathematical models of vesicular transport incorporating SEC31A function

  • Use ordinary differential equations (ODEs) to simulate pathway dynamics

  • Validate models with experimental data and use them to predict system behavior

• Contextual Analysis Based on Cellular State:

  • Compare SEC31A pathway function across different cell states (normal vs. cancer, differentiated vs. stem cell)

  • Identify context-dependent interactions and functions

  • In cancer contexts, integrate with oncogenic signaling pathways based on findings that SEC31A expression correlates with tumor progression

• Visualization Strategies:

  • Create pathway diagrams highlighting SEC31A's position within the COPII complex and vesicular transport system

  • Use heatmaps to visualize expression patterns across conditions

  • Employ dimension reduction techniques (PCA, t-SNE) to visualize high-dimensional data

What are promising research directions for understanding SEC31A's role beyond vesicular transport?

Several promising research directions could expand our understanding of SEC31A beyond its canonical role in vesicular transport:

• Cancer Biology and Therapeutic Targeting:

  • Further investigate the circSEC31A/miR-376a/SEC31A regulatory axis in different cancer types beyond NSCLC

  • Explore SEC31A as a potential therapeutic target, particularly in cancers where its expression correlates with poor clinical outcomes

  • Develop small molecule inhibitors or peptide-based approaches to modulate SEC31A function

• Non-canonical Functions:

  • Investigate potential moonlighting functions of SEC31A outside the COPII complex

  • Explore nuclear roles of SEC31A, if any, as suggested by subcellular localization studies

  • Examine potential interactions with transcription factors or chromatin remodeling complexes

• Stress Response Mechanisms:

  • Study SEC31A's involvement in cellular stress responses, particularly ER stress

  • Investigate the relationship between SEC31A function and the unfolded protein response (UPR)

  • Examine how SEC31A adaptation contributes to cellular resilience under stress conditions

• Post-translational Modifications:

  • Comprehensively map post-translational modifications of SEC31A (phosphorylation, ubiquitination, etc.)

  • Identify enzymes responsible for these modifications

  • Determine how these modifications regulate SEC31A function in health and disease

• Evolutionary Perspectives:

  • Comparative analysis of SEC31A across species (humans, Pongo abelii, Danio rerio, etc.)

  • Identify conserved and divergent domains that might reveal functional specialization

  • Study how SEC31A function has adapted across evolutionary lineages

• Emerging RNA Biology:

  • Further characterize the biogenesis and regulation of circSEC31A

  • Identify additional RNA species (lncRNAs, miRNAs) that interact with SEC31A mRNA or protein

  • Explore the broader implications of the circRNA-miRNA-mRNA network in cellular homeostasis

What technological advances would enhance SEC31A research?

Emerging technologies that could significantly advance SEC31A research include:

• Advanced Imaging Techniques:

  • Super-resolution microscopy (STORM, PALM, SIM) to visualize SEC31A in COPII complexes at nanoscale resolution

  • Live-cell imaging with faster acquisition rates to capture dynamic vesicle formation events

  • Correlative light and electron microscopy (CLEM) to link protein localization with ultrastructural features

• Protein Structure Determination:

  • Cryo-electron microscopy to resolve the structure of SEC31A within the COPII coat

  • X-ray crystallography of specific SEC31A domains to guide structure-based drug design

  • NMR spectroscopy to study dynamic protein-protein interactions

• CRISPR-Based Technologies:

  • CRISPR interference (CRISPRi) for precise temporal control of SEC31A expression

  • CRISPR activation (CRISPRa) to upregulate SEC31A in specific contexts

  • Base editing or prime editing to introduce specific mutations to study structure-function relationships

  • CRISPR screens to identify genetic interactors of SEC31A

• Single-Cell Technologies:

  • Single-cell RNA-seq to examine cell-to-cell variability in SEC31A and circSEC31A expression

  • Single-cell proteomics to correlate protein levels with cellular phenotypes

  • Spatial transcriptomics to map SEC31A expression patterns within tissues

• Protein Engineering Approaches:

  • Split protein complementation assays for detecting SEC31A interactions in living cells

  • Optogenetic tools to achieve spatiotemporal control of SEC31A function

  • Engineered SEC31A variants with bioorthogonal chemistry handles for selective labeling

• Computational Advances:

  • Molecular dynamics simulations to study SEC31A conformational changes

  • Machine learning approaches to predict functional consequences of SEC31A variants

  • Systems biology models incorporating SEC31A within vesicular transport pathways

These technological advances would provide deeper insights into SEC31A's structure, function, and role in both normal physiology and disease states.