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Record W2969025717 · doi:10.1101/738039

Optogenetic inhibition of Delta reveals digital Notch signaling output during tissue differentiation

2019· preprint· en· W2969025717 on OpenAlexaff
Ranjith Viswanathan, Aleksandar Necakov, Mateusz Trylinski, Rohit Krishnan Harish, Daniel Krueger, Emilia Esposito, François Schweisguth, Pierre Neveu, Stefano De Renzis

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDevelopmental Biology and Gene Regulation
Canadian institutionsBrock University
Fundersnot available
KeywordsNotch signaling pathwayMulticellular organismOptogeneticsBiologyDrosophila melanogasterCell biologyHes3 signaling axisSignallingSignal transductionGeneticsCellNeuroscienceGene

Abstract

fetched live from OpenAlex

Abstract Spatio-temporal regulation of signalling pathways plays a key role in generating diverse responses during the development of multicellular organisms. The role of signal dynamics in transferring signalling information in vivo is incompletely understood. Here we employ genome engineering in Drosophila melanogaster to generate a functional optogenetic allele of the Notch ligand Delta (opto-Delta), which replaces both copies of the endogenous wild type locus. Using clonal analysis, we show that optogenetic activation blocks Notch activation through cis-inhibition in signal-receiving cells. Signal perturbation in combination with quantitative analysis of a live transcriptional reporter of Notch pathway activity reveals differential tissue- and cell-scale regulatory modes. While at the tissue-level the duration of Notch signalling determines the probability with which a cellular response will occur, in individual cells Notch activation acts through a switch-like mechanism. Thus, time confers regulatory properties to Notch signalling that exhibit integrative digital behaviours during tissue differentiation.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score1.000

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 integrity0.0010.000
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 head0.008
GPT teacher head0.210
Teacher spread0.202 · 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.

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

Citations6
Published2019
Admission routes1
Has abstractyes

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