SAE1 Human

What is SAE1 and what roles does it play in human cells?

SAE1 functions as an essential component of the heterodimeric SUMO-activating enzyme that initiates the SUMOylation cascade, a post-translational modification process critical for cellular function. SAE1 forms a thioester bond between SUMO protein and UBA2/SAE2 by regulating the activation of ATP-dependent SUMO protein, thereby participating in the process of SUMO protein modification . With a molecular weight of 110 kDa, SAE1 predominantly localizes to the nucleus and can be found in various tissues throughout the body .

The protein plays vital roles in multiple cellular processes including:

• Regulation of transcription

• Cell cycle progression and chromosome division

• Apoptosis

• Post-translational modification via SUMOylation

• Cellular responses to various stress conditions

SAE1 mainly mediates acetylation and phosphorylation while playing crucial roles in chromosome division and apoptosis . SUMOylation itself represents an important mechanism in cellular responses to stress and is frequently dysregulated in various cancer types .

How does SAE1 function in the SUMOylation pathway?

In the SUMOylation pathway, SAE1 serves as a critical component of the E1-activating enzyme. The process follows these sequential steps:

• Activation: SAE1 forms a heterodimer with UBA2/SAE2 to create the functional E1 enzyme. This complex activates the SUMO protein in an ATP-dependent manner .

• Thioester formation: SAE1/UBA2 complex facilitates the formation of a thioester bond between SUMO and the E1 enzyme .

• Transfer: The activated SUMO is subsequently transferred to the E2 conjugating enzyme (Ubc9).

• Conjugation: Finally, with the help of E3 ligases, SUMO becomes attached to target substrate proteins.

This pathway is indispensable for protein SUMOylation, which affects numerous cellular processes including nuclear transport, transcriptional regulation, chromosome segregation, and DNA repair .

What experimental approaches can reveal SAE1's molecular interactions?

When investigating SAE1 interactions, researchers should focus on its known interactions with oncogenes (PLK1, CCNB1, CDK4, CDK1) and tumor suppressors (PDK4, KLF9, FOXO1, ALDH2) . The primary interaction partner of SAE1 is UBA2/SAE2, with which it forms the functional E1 enzyme complex essential for SUMOylation .

For interaction studies, it's crucial to include appropriate controls:

• IgG controls for immunoprecipitation

• Empty vector controls for overexpression studies

• Interaction-deficient mutants to confirm specificity

What methods are most effective for measuring SAE1 expression in clinical samples?

Multiple complementary approaches can be used to accurately assess SAE1 expression in clinical samples:

RNA-level detection:

• RT-qPCR: Provides quantitative measurement of SAE1 mRNA using gene-specific primers . This method offers high sensitivity but requires carefully validated reference genes.

• RNA-seq: Enables comprehensive transcriptome analysis, allowing comparison of SAE1 expression with other genes. Several studies have utilized RNA-seq data from GTEx, TCGA, and GEO databases to analyze SAE1 expression patterns .

Protein-level detection:

• Western blot analysis: Allows semi-quantitative assessment of SAE1 protein levels and can reveal post-translational modifications .

• Immunohistochemistry (IHC): Particularly valuable for clinical samples as it preserves tissue architecture and enables visualization of SAE1 localization patterns. The reported HCC cohort study used IHC to validate SAE1 overexpression in cancerous liver tissues compared to paracancerous normal tissue .

For optimal results in clinical settings, researchers should:

• Include matched normal/tumor pairs when possible

• Establish standardized scoring systems for IHC

• Use multiple detection methods for cross-validation

• Include appropriate positive and negative controls

How should researchers analyze SAE1 expression data in relation to clinical parameters?

Analyzing SAE1 expression in relation to clinical parameters requires rigorous statistical approaches:

• Comparative analysis:

  • Compare SAE1 expression between groups (e.g., tumor vs. normal, early vs. late stage) using appropriate statistical tests based on data distribution

  • Analyze expression patterns across histological grades (research shows SAE1 expression increases significantly with higher histologic grade, p<0.0001)

  • Evaluate expression differences across tumor stages (data indicates expression pattern T1<T2<T3<T4, p=0.0009)

• Survival analysis:

  • Perform Kaplan-Meier survival analysis with log-rank tests to compare patient outcomes based on SAE1 expression levels

  • Calculate hazard ratios using Cox proportional hazards models

  • Include multivariate analysis to control for confounding factors

• Diagnostic value assessment:

  • Conduct ROC analysis to determine sensitivity and specificity (one study reported an AUC of 0.925 for SAE1 in TCGA-LIHC patients)

  • Establish optimal cutoff values for high vs. low expression

• Correlation studies:

  • Evaluate relationships between SAE1 expression and metastasis or disease progression

  • Assess correlations with established biomarkers

When conducting these analyses, researchers should use appropriate software such as GraphPad Prism, R, or specialized bioinformatics tools as mentioned in the methodology sections of published studies .

What statistical considerations are most important when establishing SAE1 as a biomarker?

When establishing SAE1 as a biomarker, several statistical considerations are crucial:

• Sample size determination:

  • Perform power analysis to ensure adequate statistical power

  • The reported TCGA-LIHC analysis included 421 patients, providing robust statistical power

  • Smaller cohorts (like the TMU-SHH HCC cohort with n=54) should be used primarily for validation

• Cutoff optimization:

  • Determine optimal thresholds for "high" vs. "low" expression

  • Use data-driven approaches like minimum p-value, X-tile, or ROC-based methods

  • Validate cutoffs in independent cohorts

• Multiple testing correction:

  • Apply appropriate corrections (Bonferroni, Benjamini-Hochberg) when performing multiple comparisons

  • Report adjusted p-values alongside nominal values

• Predictive model development:

  • Consider incorporating SAE1 into multivariate predictive models

  • Validate models using cross-validation and independent datasets

  • Assess model performance using metrics like concordance index, Brier score, and calibration plots

• Subgroup analysis:

  • Evaluate biomarker performance across different patient subgroups

  • Investigate potential interactions with other clinical factors

Researchers should be transparent about statistical methodologies and follow reporting guidelines such as REMARK for prognostic biomarker studies.

What evidence supports SAE1's role in cancer progression?

Substantial evidence from multiple studies supports SAE1's role in cancer progression:

These findings collectively establish SAE1 as a significant contributor to cancer progression and a promising biomarker.

What experimental models are most appropriate for studying SAE1 in cancer?

For SAE1 research specifically, published studies have utilized:

• Cell line manipulation:

  • CRISPR interference for SAE1 knockdown as mentioned in the methodology section

  • Real-time PCR and Western blot analysis for measuring expression changes

• Functional assays:

  • Transwell Matrigel Invasion Assay: Using cells placed in chambers with Matrigel-coated membranes and 48-hour incubation

  • Scratch-Wound Migration Assay: Monitoring cell migration at 0 and 16 hours after creating a wound in cell monolayers

• Patient samples:

  • Immunohistochemical analysis of clinical HCC samples (n=54 in the TMU-SHH cohort)

  • Correlation with clinicopathological parameters

When selecting models, researchers should consider the specific research question, available resources, and the translational relevance of their findings.

How should researchers design studies to investigate SAE1's metabolic effects?

SAE1 has been implicated in dysregulated cancer metabolic signaling, particularly affecting ROS, glycolysis, and cholesterol homeostasis pathways . To properly investigate these metabolic effects, researchers should design studies with the following components:

• Gene expression profiling:

  • Perform RNA-seq or targeted gene expression analysis after SAE1 manipulation

  • Focus on key metabolic pathway genes identified in previous studies

  • Conduct Gene Set Enrichment Analysis (GSEA) to identify affected pathways, as done in LIHC (n=371), GSE14520 (n=225), GSE36376 (n=240), and GSE64041 (n=60) datasets

• Metabolic flux analysis:

  • Use isotope-labeled glucose, glutamine, or fatty acids to trace metabolic pathways

  • Measure incorporation of labeled atoms into downstream metabolites via mass spectrometry

  • Quantify changes in flux rates through glycolysis, TCA cycle, and other relevant pathways

• Functional metabolic assays:

  • Seahorse XF analysis to measure oxygen consumption rate and extracellular acidification rate

  • Glucose uptake assays using fluorescent glucose analogs

  • ROS measurement using fluorescent probes

  • Lipid droplet staining to assess changes in lipid metabolism

• Metabolomics:

  • Perform untargeted metabolomics to identify global metabolic changes

  • Follow with targeted metabolomics focusing on pathways identified in GSEA (glycolysis, nucleotide metabolism)

  • Compare metabolic profiles between SAE1-high and SAE1-low conditions

• Integration with clinical data:

  • Correlate metabolic signatures with SAE1 expression in patient samples

  • Analyze associations between metabolic markers and clinical outcomes

This multi-faceted approach will provide comprehensive insights into how SAE1 influences cancer metabolism.

What knockdown and knockout strategies are most effective for SAE1 functional studies?

Several genetic manipulation techniques can be employed for SAE1 functional studies, each with specific advantages:

• CRISPR-based approaches:

  • CRISPR interference (CRISPRi): Used successfully in published SAE1 studies , involves dCas9-KRAB to repress transcription without DNA cleavage

  • CRISPR-Cas9 knockout: For complete gene inactivation, though may be lethal if SAE1 is essential

  • CRISPR activation: To model SAE1 overexpression as seen in cancer contexts

  Implementation protocol:

  • Design gRNAs targeting SAE1 promoter or early exons

  • Optimize delivery method (lentiviral vectors recommended for stable expression)

  • Validate knockdown efficiency at both mRNA and protein levels

  • Include non-targeting controls and rescue experiments

• RNA interference:

  • siRNA: For transient knockdown in initial screening studies

  • shRNA: For stable knockdown via lentiviral delivery, useful in long-term experiments

• Inducible systems:

  • Tet-On/Off: Allows temporal control of SAE1 expression

  • Conditional alleles: For tissue-specific or temporal deletion in animal models

For validation of knockdown efficacy, researchers should:

• Perform RT-qPCR to verify mRNA reduction

• Conduct Western blot analysis to confirm protein depletion

• Assess functional consequences using invasion and migration assays as described in the methodology of published studies

How can researchers differentiate between SAE1-specific effects and general SUMOylation disruption?

Distinguishing SAE1-specific effects from general SUMOylation disruption presents a significant challenge in research. These methodological approaches can help address this issue:

• Comparative knockdown studies:

  • Perform parallel knockdown of SAE1, UBA2/SAE2, and downstream SUMOylation components

  • Compare phenotypes to identify effects unique to SAE1 depletion

  • Quantify global SUMOylation levels in each condition using anti-SUMO antibodies

• Domain-specific mutations:

  • Generate SAE1 constructs with mutations in different functional domains

  • Express these constructs in SAE1-depleted backgrounds

  • Identify which domains are required for specific phenotypes

• SUMOylation-independent function analysis:

  • Compare effects of SAE1 manipulation with chemical inhibitors of SUMOylation

  • Look for SAE1 effects that persist despite restored SUMOylation

  • Investigate SAE1 binding partners beyond the SUMOylation machinery

• SUMOylome analysis:

  • Perform proteome-wide analysis of SUMOylated proteins after SAE1 manipulation

  • Identify specific substrates affected by SAE1 but not other SUMOylation components

  • Use techniques like SUMO-remnant immunoprecipitation followed by mass spectrometry

• Rescue experiments:

  • Attempt rescue with catalytically inactive SAE1 mutants

  • Compare with rescue using downstream SUMOylation components

  • Assess which phenotypes can be rescued by which approach

These approaches should be applied systematically to build a comprehensive understanding of SAE1-specific functions beyond its role in general SUMOylation.

What assays best capture SAE1's effects on invasion and migration?

To accurately assess SAE1's effects on cancer cell invasion and migration, researchers should employ multiple complementary assays:

• Transwell Matrigel Invasion Assay:

  • As described in the methodology, cells are placed in chambers with Matrigel-coated membranes

  • Medium with 1% FBS is added to the upper chamber and 10% FBS to the lower chamber as chemoattractant

  • After 48 hours of incubation, invaded cells are fixed, stained with crystal violet, and counted in five random fields

  • This assay specifically measures the capacity of cells to degrade and migrate through an extracellular matrix

• Scratch-Wound Migration Assay:

  • Cells are cultured until 95-100% confluence in media containing 0.2% FBS

  • A wound is created in the monolayer using a pipette tip

  • Migration is monitored at 0 and 16 hours post-wounding

  • Analysis is performed using NIH ImageJ software

  • This assay primarily assesses collective cell migration in 2D

• 3D Spheroid Invasion Assay:

  • Form spheroids using SAE1-manipulated cells

  • Embed spheroids in 3D matrices (Matrigel, collagen)

  • Monitor invasion into surrounding matrix over time

  • This provides a more physiologically relevant model than 2D assays

• Live-cell imaging:

  • Track individual cell movement in real-time

  • Calculate parameters like velocity, persistence, and directionality

  • Correlate migration patterns with SAE1 expression levels

For optimal results, researchers should:

• Include appropriate controls (SAE1 knockdown, overexpression, rescue)

• Standardize cell density and experimental conditions

• Quantify results using automated image analysis when possible

• Validate findings across multiple cell lines

These assays collectively provide a comprehensive assessment of how SAE1 affects the invasive and migratory capacity of cancer cells.

How can researchers optimize immunohistochemical detection of SAE1 in tissue samples?

Optimizing immunohistochemical detection of SAE1 requires attention to multiple technical aspects:

• Tissue preparation:

  • Use formalin-fixed, paraffin-embedded (FFPE) tissue sections of 4-5μm thickness

  • Consider heat-induced epitope retrieval methods to expose SAE1 antigenic sites

  • Test multiple antigen retrieval conditions (pH, buffer composition)

• Antibody selection and validation:

  • Choose antibodies with validated specificity for SAE1

  • Perform preliminary testing using positive control tissues (e.g., tissues known to express SAE1)

  • Include negative controls (primary antibody omission, isotype controls)

  • Validate antibody specificity using tissues from SAE1 knockdown models

• Staining protocol optimization:

  • Determine optimal antibody dilution through titration experiments

  • Optimize incubation times and temperatures

  • Select appropriate detection systems (DAB vs. fluorescent, amplification methods)

  • Consider automated staining platforms for consistency

• Scoring and quantification:

  • Develop a standardized scoring system (e.g., H-score, Allred score)

  • Consider both staining intensity and percentage of positive cells

  • Use digital pathology and image analysis software for objective quantification

  • Ensure multiple pathologists score independently to assess inter-observer reliability

• Correlation with other methods:

  • Validate IHC findings with orthogonal techniques (Western blot, RT-qPCR)

  • Compare staining patterns with mRNA expression data from the same samples when possible

Published studies have successfully used IHC to detect SAE1 in HCC samples and demonstrated its correlation with clinicopathological parameters , suggesting that optimized protocols for SAE1 detection are achievable.

Which signaling pathways are most significantly affected by SAE1 expression?

SAE1 expression influences multiple signaling pathways critical to cellular function and cancer biology:

Gene Set Enrichment Analysis (GSEA) of multiple HCC datasets (LIHC n=371, GSE14520 n=225, GSE36376 n=240, and GSE64041 n=60) revealed significant positive correlations between high SAE1 expression and dysregulated metabolic pathways . The oncogenic effects of SAE1 appear to be mediated through both direct interactions with cell cycle regulators and broader effects on cellular metabolism.

For researchers investigating these pathways, recommended approaches include:

• Pathway-specific reporter assays

• Phosphorylation state analysis of key signaling nodes

• Transcriptional profiling after SAE1 manipulation

• Metabolic flux analysis for metabolic pathways

How does SAE1 influence the cell cycle and proliferation?

SAE1 exerts significant influence on cell cycle progression and proliferation through multiple mechanisms:

• Direct activation of cell cycle regulators:

  • SAE1 upregulates key oncogenic cell cycle proteins including PLK1, CCNB1, CDK4, and CDK1

  • These proteins are critical drivers of G1/S and G2/M transitions

• Suppression of cell cycle checkpoints:

  • By inhibiting tumor suppressors like FOXO1 , SAE1 removes important cell cycle checkpoint controls

  • This inhibition allows cells to bypass normal growth restrictions

• SUMOylation-dependent effects:

  • As a key component of the SUMOylation machinery, SAE1 mediates post-translational modification of numerous cell cycle proteins

  • SUMOylation can alter protein stability, localization, and activity of cell cycle regulators

• Metabolic support for proliferation:

  • SAE1 positively correlates with glycolysis and nucleotide metabolism pathways

  • These metabolic effects provide necessary building blocks and energy for rapid cell division

For experimental validation of SAE1's effects on cell cycle:

• Flow cytometry analysis of cell cycle distribution after SAE1 manipulation

• EdU incorporation assays to measure DNA synthesis

• Time-lapse microscopy to track mitotic progression

• Western blot analysis of cyclins and CDK activity

• Chromatin immunoprecipitation to identify transcriptional targets

The combination of direct effects on cell cycle regulators and broader metabolic reprogramming suggests SAE1 as a master regulator of proliferative capacity in cancer cells.

What techniques best reveal SAE1's role in metabolic reprogramming?

To comprehensively investigate SAE1's role in metabolic reprogramming, researchers should employ a multi-omics approach:

• Transcriptomic analysis:

  • RNA-seq to identify differentially expressed metabolic genes after SAE1 manipulation

  • GSEA to identify enriched metabolic pathways, as demonstrated in previous studies showing SAE1 association with ROS, glycolysis, and cholesterol homeostasis pathways

  • RT-qPCR validation of key metabolic genes

• Metabolic flux analysis:

  • 13C-glucose or 13C-glutamine tracing followed by mass spectrometry

  • Measure incorporation of labeled carbons into glycolytic intermediates, TCA cycle metabolites, and biosynthetic precursors

  • Compare flux patterns between SAE1-high and SAE1-low conditions

• Bioenergetic profiling:

  • Seahorse XF analysis to measure:

    • Oxygen consumption rate (OCR) for mitochondrial respiration

    • Extracellular acidification rate (ECAR) for glycolytic activity

  • Stress tests with inhibitors of specific pathways

• Metabolomics:

  • Global untargeted metabolomics to identify metabolic signatures

  • Targeted analysis of specific pathways identified in GSEA (glucose metabolism, pyrimidine metabolism, purine metabolism)

  • Stable isotope-resolved metabolomics for pathway elucidation

• In situ metabolic analysis:

  • Fluorescent glucose analogs to measure uptake

  • ROS-sensitive probes to assess oxidative stress

  • Lipid droplet staining to evaluate lipid metabolism

  • Metabolic biosensors for real-time monitoring

• Integration with protein analysis:

  • Identify SUMOylated metabolic enzymes using proteomics

  • Measure activity of key metabolic enzymes after SAE1 manipulation

  • Analyze phosphorylation status of metabolic regulators

These complementary approaches will provide a comprehensive understanding of how SAE1 contributes to metabolic reprogramming in cancer.

How does SAE1 expression correlate with patient prognosis?

SAE1 expression demonstrates strong prognostic significance across multiple studies:

These findings collectively establish SAE1 as a clinically relevant prognostic marker, particularly in hepatocellular carcinoma. For clinical implementation, researchers should focus on standardizing assessment methods and establishing validated cutoff values for "high" versus "low" expression.

What is the diagnostic potential of SAE1 in cancer detection?

SAE1 demonstrates considerable diagnostic potential, particularly for hepatocellular carcinoma:

• Diagnostic accuracy:

  • Receiver operating characteristic analysis of SAE1 in TCGA-LIHC patients (n=421) showed an AUC of 0.925

  • This exceptionally high AUC indicates excellent diagnostic value for distinguishing HCC from normal liver tissue

• Expression differential:

  • SAE1 is consistently overexpressed in HCC samples compared to normal liver tissue

  • This clear differential expression facilitates diagnostic application

• Stage independence:

  • While expression increases with stage, SAE1 overexpression is detectable even in early-stage disease

  • This suggests potential utility for early detection

• Multi-cancer applicability:

  • Evidence suggests SAE1 overexpression in multiple cancer types including glioma and gastric cancer

  • This indicates potential broader diagnostic applications beyond HCC

For clinical implementation as a diagnostic biomarker, researchers should focus on:

• Establishing standardized detection methods

• Determining optimal cutoff values

• Comparing performance with existing diagnostic markers

• Evaluating cost-effectiveness and accessibility of testing

• Developing specific applications (screening, differential diagnosis, etc.)

The documented high AUC value suggests SAE1 could potentially become a valuable addition to the diagnostic toolkit for HCC and possibly other cancer types.

What therapeutic strategies could target SAE1 or SAE1-dependent pathways?

Based on the current understanding of SAE1 biology, several therapeutic approaches could be developed:

• Direct inhibition strategies:

  • Small molecule inhibitors targeting SAE1 enzymatic activity

  • Disruptors of SAE1-UBA2 protein interaction

  • Degraders (PROTACs) targeting SAE1 for proteasomal degradation

  • Antisense oligonucleotides or siRNA-based approaches to reduce SAE1 expression

• Synthetic lethality approaches:

  • Identify contexts where SAE1 inhibition would be selectively lethal to cancer cells

  • Combine SAE1 targeting with inhibitors of complementary pathways

  • Explore vulnerabilities created by SAE1 overexpression

• Targeting downstream effects:

  • Inhibit the cell cycle regulators activated by SAE1 (PLK1, CDK4, CDK1)

  • Target the metabolic pathways dysregulated by SAE1 (glycolysis, nucleotide metabolism)

  • Restore activity of tumor suppressors inhibited by SAE1 (FOXO1)

• SUMOylation modulation:

  • Develop selective inhibitors of the SUMOylation pathway

  • Target specific SUMOylation events affected by SAE1 overexpression

For clinical development, researchers should address:

• Potential toxicity due to SAE1's role in normal cells

• Mechanisms of resistance that might emerge

• Biomarkers to identify patients most likely to respond

• Rational combination strategies

The statement that "SAE1 is a targetable cancer metabolic biomarker with high potential diagnostic and prognostic implications" suggests researchers already recognize its therapeutic potential, though specific inhibitors have not yet been described in the provided search results.

How can SAE1 research be translated into clinical applications?

Translating SAE1 research into clinical applications requires a structured pathway from basic science to clinical implementation:

• Diagnostic development:

  • Standardize detection methods (IHC scoring systems, ELISA, qPCR)

  • Validate in large, diverse patient cohorts

  • Compare with and integrate into existing diagnostic algorithms

  • Develop companion diagnostics for potential SAE1-targeted therapies

  • The excellent AUC value (0.925) in HCC diagnosis provides strong foundation

• Prognostic application:

  • Establish consensus cutoff values for risk stratification

  • Develop integrated prognostic models incorporating SAE1 with other markers

  • Validate in prospective studies

  • Create clinical decision tools to guide treatment selection

• Therapeutic development:

  • Conduct high-throughput screens for SAE1 inhibitors

  • Develop targeted degradation approaches

  • Identify synthetic lethal interactions

  • Establish appropriate model systems for preclinical testing

  • Design early-phase clinical trials with appropriate biomarkers

• Research infrastructure needs:

  • Tissue biobanks with comprehensive clinical annotation

  • Multi-institutional collaborations for validation

  • Standardized reporting of SAE1-related findings

  • Public-private partnerships to accelerate development

• Regulatory considerations:

  • Engage with regulatory authorities early in development

  • Define clear intended use claims for diagnostic applications

  • Address analytical and clinical validation requirements

  • Consider companion diagnostic pathways if linked to therapeutics

The translation of SAE1 research into clinical applications represents a promising opportunity, particularly given its documented roles in cancer progression and its potential as both a biomarker and therapeutic target.