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Record W4221109056 · doi:10.1038/s41467-022-28623-y

Protease-controlled secretion and display of intercellular signals

2022· article· en· W4221109056 on OpenAlexfundno aff
Alexander E. Vlahos, Jeewoo Kang, Carlos A. Aldrete, Ronghui Zhu, Lucy S. Chong, Michael B. Elowitz, Xiaojing Gao

Bibliographic record

VenueNature Communications · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular transport and secretion
Canadian institutionsnot available
FundersStanford Cancer InstituteNational Institute of Biomedical Imaging and BioengineeringNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthHuman Frontier Science ProgramGovernment of CanadaStanford SystemX AllianceU.S. Department of Health and Human Services
KeywordsSecretionProteasesCell biologyIntracellularEndoplasmic reticulumBiologyComputational biologyBiochemistryEnzyme

Abstract

fetched live from OpenAlex

To program intercellular communication for biomedicine, it is crucial to regulate the secretion and surface display of signaling proteins. If such regulations are at the protein level, there are additional advantages, including compact delivery and direct interactions with endogenous signaling pathways. Here we create a modular, generalizable design called Retained Endoplasmic Cleavable Secretion (RELEASE), with engineered proteins retained in the endoplasmic reticulum and displayed/secreted in response to specific proteases. The design allows functional regulation of multiple synthetic and natural proteins by synthetic protease circuits to realize diverse signal processing capabilities, including logic operation and threshold tuning. By linking RELEASE to additional sensing and processing circuits, we can achieve elevated protein secretion in response to "undruggable" oncogene KRAS mutants. RELEASE should enable the local, programmable delivery of intercellular cues for a broad variety of fields such as neurobiology, cancer immunotherapy and cell transplantation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.803
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.251
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations58
Published2022
Admission routes1
Has abstractyes

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