Activin E is a TGFβ ligand that signals specifically through activin receptor-like kinase 7
Bibliographic record
Abstract
Abstract Activins are one of the three distinct subclasses within the greater Transforming Growth Factor β (TGFβ) superfamily. First discovered for their critical roles in reproductive biology, activins have since been shown to alter cellular differentiation and proliferation. At present, members of the activin subclass include activin A (ActA), ActB, ActC, ActE, and the more distant members myostatin and GDF11. While the biological roles and signaling mechanisms of most activins class members have been well-studied, the signaling potential of ActE has remained largely unknown. Here, we characterized the signaling capacity of homodimeric ActE. Molecular modeling of the ligand:receptor complexes showed that ActC and ActE shared high similarity in both the type I and type II receptor binding epitopes. ActE signaled specifically through ALK7, utilized the canonical activin type II receptors, ActRIIA and ActRIIB, and was resistant to the extracellular antagonists follistatin and WFIKKN. In mature murine adipocytes, ActE invoked a SMAD2/3 response via ALK7, similar to ActC. Collectively, our results establish ActE as an ALK7 ligand, thereby providing a link between genetic and in vivo studies of ActE as a regulator of adipose tissue. Significance Activin E is a homodimeric member of the TGFβ family belonging to the activin subclass. Currently, the signaling capacity of ActE is unknown due to a lack of reliable reagents to study the protein. Here, we demonstrate that ActE acts as a canonical TGFβ ligand that signals through SMAD2/3 in an ALK7-dependent manner, similar to ActC. ActE also utilizes the activin type II receptors, ActRIIA and ActRIIB, to signal and is unable to be antagonized by FS288 and WFIKKN2. This study shows that ActE is a signaling ligand and provides a connection between genetic and in vivo studies that links ActE to adiposity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".