ATG101 Antibody, Biotin conjugated

What is ATG101 and why is it significant in cellular research?

ATG101 is an autophagy-related gene that plays a critical role in the autophagy pathway, a fundamental cellular process for degrading and recycling cellular components. It forms a complex with ATG13 and is essential for proper autophagy functioning. Research indicates that ATG101 is significantly overexpressed in various tumor types compared to normal tissues, suggesting its important role in cancer development . The protein interacts with other autophagy components through its HORMA domain, which facilitates protein-protein interactions in the autophagy initiation complex .

What is the critical distinction between ATG101 and ATG-101?

Despite the similar nomenclature, these are entirely different entities:

• ATG101: An autophagy-related protein involved in the cellular autophagy pathway that interacts with ATG13

• ATG-101: A tetravalent "2+2" PD-L1×4-1BB bispecific antibody engineered for cancer immunotherapy that simultaneously binds to PD-L1 and 4-1BB

This distinction is crucial as confusion between these entities can lead to misinterpretation of research findings and experimental designs.

How does ATG101 interact with other autophagy proteins in the cellular context?

ATG101 primarily interacts with ATG13 through the HORMA domain of ATG13. This interaction is independent of ULK1 binding, as demonstrated in experiments where ATG13 ΔHORMA (lacking the HORMA domain) failed to recover ATG101 binding, while ULK1 binding-defective ATG13 Δ2AA completely restored interaction with ATG101 . Furthermore, BioID experiments revealed that in HA-ATG9A-BirA* expressing cells, the loss of ATG13 completely abrogated the capture of ATG101, indicating that ATG13 is required for the interaction between ATG9A and ATG101 .

How can BioID technology be applied to study ATG101 interactions in autophagy research?

BioID (proximity-dependent biotin identification) is a powerful approach for studying protein-protein interactions in their native cellular context. For ATG101 research, this technique involves:

• Creating fusion proteins with a modified biotin ligase (BirA*) attached to proteins of interest (like ATG9A in search result )

• The BirA* enzyme biotinylates proteins in close proximity (within ~10nm)

• Biotinylated proteins can then be isolated using streptavidin capture and identified by mass spectrometry

This approach revealed that ATG9A interacts with the ATG13-ATG101 complex, with ATG13 serving as a bridge between ATG9A and ATG101 . When designing BioID experiments for ATG101 research, it's crucial to verify that the fusion of BirA* doesn't impair the function of the bait protein, as demonstrated by rescue experiments in knockout cells .

What controls should be included when using biotin-based proximity labeling to study ATG101?

When employing BioID or similar biotin-based techniques to study ATG101 interactions, several critical controls should be included:

• Functional validation: Verify that the BirA* fusion protein maintains normal function and localization, as demonstrated in the ATG9A-BirA* construct which fully rescued p62/SQSTM1 degradation and LC3B lipidation in ATG9A KO cells

• Localization controls: Confirm that the biotin signal overlaps with the BirA*-tagged protein of interest

• Genetic controls: Use knockout cell lines (e.g., ATG13 KO) to validate specific interactions, as seen in experiments where ATG13 deletion abrogated ATG101 capture

• Reconstitution experiments: Employ wild-type and mutant reconstitution (e.g., ATG13 WT vs. ΔHORMA) to map interaction domains

• Non-relevant protein controls: Include BirA* fusions to proteins not expected to interact with ATG101 to identify non-specific biotinylation

How does ATG101 expression correlate with tumor prognosis across different cancer types?

Comprehensive analysis of The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) data revealed that ATG101 is overexpressed in various tumor types compared to normal tissues . Kaplan-Meier survival analysis specifically found that higher ATG101 expression was associated with poor prognosis in cholangiocarcinoma (CHOL) and liver hepatocellular carcinoma (LIHC) . This suggests that ATG101 expression levels could serve as a prognostic biomarker in certain cancers, particularly hepatobiliary malignancies.

What is the relationship between ATG101 expression and tumor immune microenvironment?

Research has demonstrated significant correlations between ATG101 expression and various immune parameters:

• Immune cell infiltration: ATG101 expression shows relationships with six types of immune cells in the tumor microenvironment

• Immune checkpoint correlation: ATG101 expression correlates with various immune checkpoint genes, suggesting its potential role in modulating anti-tumor immune responses

• DNA methylation: ATG101 may regulate DNA methylation, a process that influences gene expression patterns in cancer. Analysis revealed correlations between ATG101 expression and four key methyltransferases (DNMT1, DNMT2, DNMT3A, and DNMT3B)

These correlations indicate that ATG101 might serve as a target and prognostic marker for tumor immunotherapy across different cancer types .

How might ATG101 influence response to photodynamic therapy in cancer?

Validation tests have shown that ATG101 is upregulated following photodynamic therapy (PDT) treatment, and this upregulation appears to inhibit apoptosis in cholangiocarcinoma cells . This suggests that high expression of ATG101 may contribute to resistance mechanisms against PDT. Further research revealed that the upregulation of ATG101 after PDT might be induced by the early stress gene EGR2 . These findings indicate that ATG101 expression levels could potentially serve as a resistance marker for photodynamic therapy, and targeting ATG101 might enhance PDT efficacy.

What methodological approaches are recommended for studying the relationship between ATG101 and immune checkpoint genes?

Based on research methodologies described in the literature, the following approaches are recommended:

• Correlation analysis: Use Spearman correlation tests to analyze relationships between ATG101 expression and immune checkpoint target genes (47 genes have been examined in previous studies)

• Heat map visualization: Employ the reshape2 software package to establish related heat maps that visually represent these correlations

• Functional validation: After identifying correlations, validate functional relationships using techniques such as siRNA knockdown, lentivirus transfection, and ChIP-qPCR

• Multiparametric analysis: Simultaneously evaluate the relationship between ATG101 expression and tumor mutational burden (TMB), microsatellite instability (MSI), and neoantigens using Spearman correlation tests

This comprehensive approach allows researchers to thoroughly characterize the immunological significance of ATG101 in the tumor microenvironment.

What experimental systems are appropriate for investigating ATG101 function in autophagy?

Several experimental systems have proven valuable for ATG101 research:

• Knockout cell lines: Generate ATG101 knockout cells using CRISPR-Cas9 to assess the functional consequences of ATG101 loss on autophagy markers (p62/SQSTM1 accumulation, LC3 lipidation)

• Reconstitution models: Complementing knockout cells with wild-type or mutant ATG101 to map functional domains

• Proximity labeling: BioID approaches where interacting proteins (like ATG9A) are fused with BirA* to identify proximity partners of ATG101

• Co-immunoprecipitation: Reciprocal co-IP experiments to validate protein-protein interactions involving ATG101

• Fluorescence microscopy: To monitor co-localization of ATG101 with other autophagy proteins during different stages of the autophagy process

How can researchers differentiate between ATG101's role in normal autophagy versus cancer-promoting functions?

To distinguish between ATG101's physiological role in autophagy and its potential cancer-promoting functions, researchers should consider:

• Tissue-specific expression analysis: Compare ATG101 expression levels between normal and malignant tissues across multiple cancer types

• Conditional knockout models: Generate tissue-specific or inducible ATG101 knockout models to separate developmental from tumor-specific functions

• Functional genomics: Perform Gene Set Enrichment Analysis (GSEA) to identify the distinct pathways and biological processes associated with ATG101 in normal versus cancer cells

• Correlation studies: Analyze correlations between ATG101 expression and various cancer hallmarks, including immune cell infiltration, tumor mutation burden, and patient survival

• Mechanistic studies: Investigate whether ATG101 promotes cancer through autophagy-dependent mechanisms or through autophagy-independent functions by using autophagy inhibitors alongside ATG101 manipulation

How does the design of ATG-101 bispecific antibody enable its selective activity?

ATG-101 is engineered as a tetravalent "2+2" PD-L1×4-1BB bispecific antibody with several key design features:

• Dual binding capacity: ATG-101 simultaneously binds to PD-L1 and 4-1BB, with a higher affinity for PD-L1 than for 4-1BB

• Crosslinking-dependent activation: ATG-101 activates 4-1BB+ T cells only when cross-linked with PD-L1-positive cells, which limits systemic activation and associated toxicity

• Fc modifications: The N297A mutation on CH2 abolishes binding to most FcγRs while retaining binding to FcγRn, which helps optimize the antibody's pharmacokinetic properties while reducing Fc-mediated toxicity

This design allows ATG-101 to localize to PD-L1-rich tumor microenvironments and selectively activate 4-1BB+ immune cells in a PD-L1 cross-linking-dependent manner, minimizing off-tumor toxicity issues that have hampered previous 4-1BB agonist development .

What methodological approaches have been used to evaluate ATG-101 efficacy in preclinical models?

Researchers have employed diverse methodologies to assess ATG-101 efficacy:

• In vitro activation assays: Measuring T cell activation when ATG-101 is cross-linked by PD-L1 positive cells

• Exhausted T cell studies: Evaluating ATG-101's ability to activate PD1+TIM3+ exhausted T cells upon PD-L1 binding

• Humanized mouse models: Testing in h4-1BB humanized mice bearing MC38 colon cancer to assess efficacy in models with human 4-1BB

• Resistance models: Evaluating efficacy in PD(L)1 blockade insensitive models like B16F10 melanoma and EL4 lymphoma

• Acquired resistance models: Assessing activity in tumors progressing after anti-PD(L)1 treatment by switching to ATG-101 upon progression

• Flow cytometry analysis: Analyzing changes in immune cell populations and activation status in the tumor microenvironment

• Single-cell RNA sequencing: Comprehensively characterizing changes in the tumor microenvironment after ATG-101 treatment

These diverse approaches provide a comprehensive understanding of ATG-101's mechanism of action and therapeutic potential.

What bioinformatic approaches are recommended for analyzing ATG101 expression across cancer datasets?

For comprehensive analysis of ATG101 expression patterns in cancer:

• Integrated multi-database analysis: Combine data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) portals to compare expression levels between tumor and normal tissues

• Survival analysis: Apply Kaplan-Meier curves to evaluate the relationship between ATG101 expression levels and patient outcomes in specific cancer types

• Correlation analyses: Use Spearman correlation tests to analyze relationships between ATG101 expression and:

  • Immune cell infiltration markers

  • Immune checkpoint genes

  • Methyltransferase expression

  • Tumor mutational burden (TMB)

  • Microsatellite instability (MSI)

  • Neoantigens

• Pathway enrichment: Perform Gene Set Enrichment Analysis (GSEA) to identify functional pathways associated with ATG101 expression in different cancer types

• Heat map visualization: Generate heat maps using packages like reshape2 to represent complex correlation patterns between ATG101 and other genes of interest

What statistical methods should be employed when correlating ATG101 expression with clinical outcomes?

Based on published research methodologies, the following statistical approaches are recommended:

• Kaplan-Meier survival analysis: To evaluate the relationship between ATG101 expression levels (typically dichotomized as high vs. low expression) and patient survival outcomes

• Spearman correlation: For analyzing relationships between continuous variables such as ATG101 expression and immune cell infiltration levels

• Multivariate Cox regression: To assess whether ATG101 expression is an independent prognostic factor after adjusting for clinical variables

• Stratified analyses: To determine whether the prognostic value of ATG101 varies across different patient subgroups defined by clinical or molecular characteristics

• Meta-analysis: When combining data from multiple cohorts to increase statistical power and generalizability of findings

These statistical approaches help ensure robust and clinically meaningful interpretation of ATG101 expression data in relation to patient outcomes.