TM7SF3 Antibody

What is TM7SF3 and what cellular functions has it been implicated in?

TM7SF3 (transmembrane 7 superfamily member 3) is a pro-survival factor induced by p53 that regulates protein homeostasis and attenuates cellular stress. Research has demonstrated that TM7SF3 inhibits caspase 3/7 activity while its silencing accelerates ER stress and unfolded protein responses (UPR). This involves inhibitory phosphorylation of eIF2α and increased expression of stress-response transcription factors including ATF3, ATF4, and C/EBP homologous protein (CHOP), ultimately leading to apoptosis induction . TM7SF3 also maintains cellular reducing power within physiological levels and reduces the content of pro-apoptotic proteins such as FAS, FADD, and caspase-8 . Notably, contrary to expectations for a transmembrane protein, recent studies have localized TM7SF3 to the nucleus, specifically within nuclear speckles, suggesting a role in regulating alternative splicing .

What applications are TM7SF3 antibodies validated for in research settings?

TM7SF3 antibodies have been validated for multiple research applications:

It's important to note that antibody performance is sample-dependent, and researchers should titrate the antibody in their specific testing system to obtain optimal results .

How does the molecular weight of TM7SF3 vary across detection methods?

The calculated molecular weight of TM7SF3 is 64 kDa (570 amino acids), but the observed molecular weight in experimental settings ranges from 64-80 kDa . This variation may be attributed to post-translational modifications or tissue-specific processing. In immunoblotting experiments, full-length TM7SF3 typically migrates as an approximately 80 kDa protein that co-localizes with histone H2B in the nuclear fraction . These observations highlight the importance of including appropriate positive controls when first establishing TM7SF3 detection protocols in a new experimental system.

What are the optimal conditions for TM7SF3 antibody use in immunohistochemistry?

For optimal immunohistochemistry results with TM7SF3 antibodies, the following protocol parameters are recommended:

• Antigen retrieval: Use TE buffer pH 9.0 (primary recommendation) or alternatively citrate buffer pH 6.0

• Antibody dilution: Begin with 1:50-1:500 range, with optimization based on specific tissue type

• Blocking solution: Use standard blocking with 5% normal serum from the species in which the secondary antibody was raised

• Incubation conditions: Overnight at 4°C for primary antibody is typically effective

• Visualization system: Both DAB and fluorescent secondary antibody detection systems are compatible

When validating the specificity of TM7SF3 antibodies, researchers have used purified TM7SF3 C-terminal synthetic peptides as competitive inhibitors to confirm binding specificity .

How should researchers design siRNA experiments to study TM7SF3 function?

When designing siRNA experiments to investigate TM7SF3 function, researchers should consider:

• siRNA design and validation: Target-specific siRNAs should be designed to silence TM7SF3 expression. Validation of knockdown efficiency through both protein (Western blot) and mRNA (qRT-PCR) level measurements is essential .

• Appropriate controls: Include non-targeting siRNA controls to account for non-specific effects of the transfection procedure.

• Cell type selection: Based on published research, U2OS, HepG2, MIN6, and human pancreatic islet cells have all demonstrated successful TM7SF3 silencing .

• Phenotypic readouts: When examining TM7SF3 function, measure:

  • Caspase 3/7 activity (increases approximately 2-fold after silencing)

  • Cell viability/cell number (typically reduces by ~50%)

  • ER stress markers (ATF3, ATF4, CHOP expression increases)

  • iNOS expression and NO production (increases 1.7-2 fold)

  • Alternative splicing events, particularly at the 3' end of introns

• Timing: Effects of TM7SF3 silencing should be examined both under basal conditions and following stress induction (thapsigargin, tunicamycin, or pro-inflammatory cytokines) .

What methods are effective for confirming the subcellular localization of TM7SF3?

To confirm TM7SF3's unexpected nuclear localization, researchers have employed multiple complementary approaches:

• Immunofluorescence microscopy: Using antibodies targeting different epitopes (both N- and C-terminal regions) to verify consistent nuclear localization pattern .

• Nuclear-cytoplasmic fractionation: Western blotting of subcellular fractions demonstrated that TM7SF3 co-localizes with histone H2B in the nuclear fraction rather than with plasma membrane markers .

• Peptide competition assays: Inclusion of purified TM7SF3 synthetic peptides can abolish antibody binding to nuclear TM7SF3 in both immunofluorescence and immunoblotting, confirming specificity .

• siRNA-mediated knockdown: Silencing TM7SF3 expression abolishes the nuclear immunoreactivity detected by TM7SF3 antibodies .

• Co-localization studies: Quantitative analysis of co-localization with nuclear speckle markers like SC-35 (SRSF2) using tools such as the 'Squassh' plugin in Fiji software .

These approaches collectively provide strong evidence for the nuclear localization of TM7SF3, challenging earlier assumptions about its subcellular distribution.

How can researchers reconcile the transmembrane structure prediction of TM7SF3 with its observed nuclear localization?

The unexpected nuclear localization of TM7SF3 despite its classification as a seven-transmembrane protein presents an interesting scientific paradox. Researchers can approach this discrepancy through:

• Structure analysis: AlphaFold2 neural network-based modeling predicts an extended N-terminal domain for TM7SF3 that somewhat resembles Class B GPCRs but with unique barrel-like β-pleated sheets that could serve as a ligand-binding domain . This distinct structure may explain its non-canonical localization.

• Alternative splicing consideration: Determine if nuclear TM7SF3 represents a specific splice variant lacking transmembrane domains. RT-PCR analysis of TM7SF3 variants should be performed using primers that can distinguish potential isoforms .

• Post-translational modifications: Investigate whether nuclear localization is regulated by specific post-translational modifications that might redirect the protein from membranes to the nucleus.

• Domain mapping: Generate deletion constructs to identify which regions of TM7SF3 are necessary and sufficient for nuclear localization.

• Evolutionary analysis: Compare TM7SF3 sequences across species to identify conserved features that might explain its dual classification as both a transmembrane and nuclear protein.

This reconciliation process exemplifies how structural predictions must sometimes be re-evaluated in light of experimental localization data .

What potential artifacts should researchers be aware of when interpreting TM7SF3 antibody staining patterns?

When interpreting TM7SF3 antibody staining patterns, researchers should consider several potential artifacts:

• Antibody cross-reactivity: Validate specificity using:

  • Peptide competition assays with purified TM7SF3 peptides

  • siRNA-mediated knockdown of TM7SF3

  • Multiple antibodies targeting different epitopes of TM7SF3

• Fixation-dependent artifacts: Different fixation methods (paraformaldehyde vs. methanol) can affect epitope accessibility, especially for membrane or nuclear proteins. Compare staining patterns across fixation methods.

• Cell-type dependent expression: TM7SF3 expression and localization may vary across cell types. The protein has been successfully detected in U2OS, HepG2, MIN6, U-251, and human pancreatic islet cells .

• Stress-induced relocalization: Consider that cellular stress conditions (thapsigargin, tunicamycin, cytokines) might alter TM7SF3 localization or expression levels .

• Overexpression artifacts: Overexpressed tagged constructs may display different localization patterns than endogenous protein. Compare endogenous staining with that of tagged constructs.

These considerations are particularly important given the unexpected nuclear localization of TM7SF3 that contradicts its classification as a transmembrane protein .

How should conflicting data on TM7SF3 function be reconciled across different experimental systems?

When faced with conflicting data on TM7SF3 function across different experimental systems, researchers should systematically:

• Consider cell type-specific effects: While TM7SF3 appears to function as a pro-survival factor across multiple cell types (U2OS, HepG2, MIN6, HEK293, human pancreatic islets), the magnitude of effects varies. For example, silencing TM7SF3 increased iNOS mRNA levels 1.7-fold in MIN6 cells but 4-12 fold in HepG2 and U2OS cells .

• Evaluate stress context dependence: TM7SF3's effects may differ depending on the stressor. For instance, certain inducers like doxorubicin and etoposide increased TM7SF3 expression, while thapsigargin, tunicamycin, and cytokines failed to increase or even decreased TM7SF3 mRNA levels .

• Examine p53 status: Given the regulatory feedback loop between p53 and TM7SF3, the p53 status of experimental systems should be considered when interpreting TM7SF3 function. Silencing of p53 significantly reduced the mRNA levels of TM7SF3 stimulated by different stress inducers .

• Address temporal dynamics: Time-dependent effects should be considered. Nutlin-3a treatment increased TM7SF3 mRNA in a time-dependent manner in human islets and HepG2 cells .

• Integrate multiple readouts: Combining measurements of cell viability, caspase activity, ER stress markers, and alternative splicing events provides a more comprehensive understanding of TM7SF3 function than any single readout .

This approach recognizes that apparent conflicts may reflect biological complexity rather than experimental error.

How might researchers investigate the role of TM7SF3 in alternative splicing regulation?

To investigate TM7SF3's role in alternative splicing regulation, researchers could implement the following advanced approaches:

• RNA-Seq after TM7SF3 manipulation: Perform RNA-Seq following TM7SF3 knockdown or overexpression to identify global changes in alternative splicing patterns. Analysis revealed enrichment of specific motifs in the last 100 nucleotides before splice acceptors that exhibited increased usage upon TM7SF3 knockdown .

• Splicing reporter assays: Develop minigene constructs containing exons with their flanking intronic regions to monitor how TM7SF3 affects specific splicing events in a controlled system.

• RNA immunoprecipitation (RIP): Determine if TM7SF3 directly interacts with specific pre-mRNAs or splicing-related RNAs by performing RIP followed by sequencing or qPCR for candidate targets.

• Protein interaction studies: Investigate TM7SF3's interaction with splicing regulators identified as being enriched near splice sites affected by TM7SF3 knockdown, such as SRSF1, HNRNPC, PCBP1, PCBP2, HEN1, SRSF5, SFPQ, and SF1 .

• Single-molecule RNA imaging: Track splicing dynamics in real-time using MS2-tagging systems in the presence or absence of TM7SF3.

• Structure-function analysis: Generate domain deletion mutants of TM7SF3 to identify which regions are required for its effects on alternative splicing.

The unexpected nuclear speckle localization of TM7SF3 provides strong support for its role in splicing regulation, fundamentally changing our understanding of this protein's cellular function .

What is the significance of the p53-TM7SF3 regulatory feedback loop in cellular stress responses?

The p53-TM7SF3 regulatory feedback loop represents a sophisticated cellular stress response mechanism with several significant implications:

• Homeostatic control: p53 directly promotes TM7SF3 expression by binding to the TM7SF3 gene (particularly at site-3, located within the first intron) . TM7SF3 then attenuates ER stress, providing a mechanism for p53 to mitigate cellular stress and prevent premature cell death.

• Negative feedback regulation: TM7SF3 inhibits p53 activity, creating a negative feedback loop that prevents excessive p53 activation . This was demonstrated by the observation that silencing TM7SF3 markedly potentiated the stimulatory effect of stress inducers on p53 activity, as measured by increased p21 mRNA levels .

• Stress-specific response patterns: Different stress inducers show distinct patterns of activating this pathway. Genotoxic stressors like doxorubicin and etoposide increase TM7SF3 expression, while ER stressors like thapsigargin and tunicamycin do not .

• Therapeutic implications: The feedback loop suggests potential therapeutic strategies for conditions where either excessive ER stress or p53 dysregulation contributes to pathology. Modulating TM7SF3 could provide a means to fine-tune p53 activity without directly targeting this critical tumor suppressor.

• Connection to alternative splicing: Given TM7SF3's role in alternative splicing regulation, this feedback loop may also influence stress-induced splicing changes, potentially affecting a broad range of cellular processes .

Understanding this regulatory circuit provides insight into how cells balance stress responses with survival signals.

What methodological approaches could reveal the role of TM7SF3 in pancreatic β-cell function and diabetes?

To investigate TM7SF3's role in pancreatic β-cell function and diabetes, several advanced methodological approaches are warranted:

• Conditional knockout models: Generate pancreatic β-cell-specific TM7SF3 knockout mice to assess effects on:

  • Glucose-stimulated insulin secretion

  • β-cell mass and turnover during aging and metabolic stress

  • Susceptibility to diabetes in models of type 1 and type 2 diabetes

• Single-cell RNA-Seq: Perform single-cell transcriptomics on islets from control and TM7SF3-deficient mice to identify cell-type specific alterations in gene expression and alternative splicing patterns.

• Human islet studies: Correlate TM7SF3 expression/splicing patterns with donor characteristics (age, BMI, diabetes status) in human islets, and perform ex vivo functional studies after TM7SF3 manipulation.

• Splicing-specific analyses: Given TM7SF3's role in alternative splicing and its residence in nuclear speckles , examine β-cell-specific splicing events affected by TM7SF3 manipulation, particularly focusing on genes involved in ER stress responses and insulin production.

• Idd6.3 locus investigation: Since TM7SF3 is one of ten genes found in the 700 kb murine type 1 diabetes locus Idd6.3 , examine potential genetic variants affecting TM7SF3 expression or function in human diabetes cohorts.

• Stress-response profiling: Examine how TM7SF3 influences β-cell responses to diabetogenic stressors (cytokines, fatty acids, ER stress inducers) by measuring ER stress markers, alternative splicing of XBP1, and apoptosis rates in the presence or absence of TM7SF3 .

These approaches would address the significance of TM7SF3 in the context of diabetes pathophysiology, particularly given its role in inhibiting cytokine-induced death and promoting insulin secretion from pancreatic β-cells .

What techniques could help elucidate the structural basis for TM7SF3's dual transmembrane/nuclear functionality?

Advanced structural biology techniques could resolve the paradox of TM7SF3's classification as a transmembrane protein versus its observed nuclear localization:

• Cryo-electron microscopy: Determine the full-length structure of TM7SF3 at high resolution, potentially revealing domains responsible for its unusual subcellular targeting.

• Hydrogen-deuterium exchange mass spectrometry (HDX-MS): Map solvent-exposed regions of TM7SF3 in different cellular compartments to understand conformational changes associated with localization.

• Live-cell super-resolution microscopy: Track TM7SF3 trafficking between cellular compartments in real-time using techniques like PALM or STORM with fluorescently tagged TM7SF3.

• In-cell NMR spectroscopy: Examine the structural dynamics of TM7SF3 within intact cells to understand how its conformation might differ between membrane and nuclear environments.

• Cross-linking mass spectrometry: Identify TM7SF3 interaction partners in different subcellular compartments that might explain its localization pattern.

• AlphaFold2 modeling with experimental validation: Refine structural predictions using experimental constraints derived from limited proteolysis, cross-linking, or spectroscopic data .

These approaches would help reconcile the apparent contradiction between TM7SF3's predicted transmembrane structure and its experimental localization to nuclear speckles.

How can researchers evaluate the therapeutic potential of targeting TM7SF3 in diseases involving ER stress?

To evaluate TM7SF3 as a therapeutic target in diseases involving ER stress, researchers should:

• Disease model validation:

  • Test TM7SF3 manipulation in disease models where ER stress plays a key role, including:

    • Diabetes (particularly β-cell stress)

    • Neurodegenerative disorders (Alzheimer's, Parkinson's)

    • Inflammatory conditions

    • Cancer (considering the p53-TM7SF3 feedback loop)

• Target validation approaches:

  • Genetic approaches: Conditional knockout/overexpression in disease-relevant tissues

  • Pharmacological approaches: Screen for small molecule modulators of TM7SF3 expression or function

  • RNA therapeutics: Test siRNA or antisense oligonucleotides targeting TM7SF3 in disease models

• Biomarker development:

  • Establish whether TM7SF3 expression or specific splicing events regulated by TM7SF3 could serve as biomarkers for ER stress severity or therapeutic response

  • Develop assays to monitor TM7SF3 activity in patient samples

• Safety assessment:

  • Evaluate the consequences of long-term TM7SF3 modulation on cellular homeostasis across multiple tissues

  • Assess potential on-target and off-target effects of TM7SF3 manipulation

  • Determine whether context-specific targeting could minimize adverse effects

• Combination approaches:

  • Test TM7SF3 modulation in combination with other ER stress modulators (PERK inhibitors, ATF6 activators, etc.)

  • Investigate synergistic effects with p53 modulators given the feedback relationship

These approaches would help determine whether targeting TM7SF3 represents a viable therapeutic strategy for ER stress-mediated pathologies.

What genome-wide approaches could reveal the complete spectrum of TM7SF3-regulated alternative splicing events?

To comprehensively characterize TM7SF3-regulated alternative splicing events, several genome-wide approaches should be employed:

• RNA-Seq with junction analysis:

  • Perform deep RNA sequencing after TM7SF3 knockdown/overexpression

  • Use specialized computational tools (rMATS, MAJIQ, LeafCutter) to identify differential splicing events

  • Focus particularly on the 3' end of introns where significant enrichment of specific motifs has been observed upon TM7SF3 knockdown

• Crosslinking immunoprecipitation sequencing (CLIP-seq):

  • Map direct RNA-protein interactions between TM7SF3 and pre-mRNAs

  • Identify sequence motifs preferentially bound by TM7SF3

  • Compare binding patterns to splicing outcomes

• Long-read sequencing:

  • Employ Oxford Nanopore or PacBio sequencing to capture full-length transcripts

  • Identify complex splicing events and coupling between distant exons

  • Characterize complete isoform differences rather than isolated splicing events

• Splicing reporter screens:

  • Develop high-throughput splicing reporter libraries

  • Screen for TM7SF3-responsive splicing events in a controlled context

  • Validate hits with endogenous transcript analysis

• Single-cell splicing analysis:

  • Apply single-cell RNA-seq with computational approaches to detect alternative splicing

  • Identify cell-type specific TM7SF3-dependent splicing regulation

  • Correlate with cellular phenotypes

• Comparative analysis across stress conditions:

  • Profile splicing changes under different stress conditions (ER stress, genotoxic stress)

  • Determine how the p53-TM7SF3 axis modulates stress-specific splicing patterns