Evidence map›Paper›PMID 27982133›Full record

ArticleScientific reports2016

Identifying ultrasensitive HGF dose-response functions in a 3D mammalian system for synthetic morphogenesis.

Vivek Raj Senthivel, Marc Sturrock, Gabriel Piedrafita, Mark Isalan

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.6field-weighted citation impact, top 27% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Synthetic Morphogenesis: introducing IEEE journal readers to programming living mammalian cells to make structures.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2022
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 2 countries.

Vivek Raj SenthivelDepartment of Life Sciences, Imperial College London, London SW7 2AZ, United Kingdom.
Marc SturrockDepartment of Life Sciences, Imperial College London, London SW7 2AZ, United Kingdom.
Gabriel PiedrafitaDepartment of Life Sciences, Imperial College London, London SW7 2AZ, United Kingdom.
Mark IsalanDepartment of Life Sciences, Imperial College London, London SW7 2AZ, United Kingdom.
Imperial College London · GBPompeu Fabra University · ES

Funding

Wellcome Trust 102944/Z/13/ZWellcome Trust WT102944
6 · The paper itself

Abstract

Nonlinear responses to signals are widespread natural phenomena that affect various cellular processes. Nonlinearity can be a desirable characteristic for engineering living organisms because it can lead to more switch-like responses, similar to those underlying the wiring in electronics. Steeper functions are described as ultrasensitive, and can be applied in synthetic biology by using various techniques including receptor decoys, multiple co-operative binding sites, and sequential positive feedbacks. Here, we explore the inherent non-linearity of a biological signaling system to identify functions that can potentially be exploited using cell genome engineering. For this, we performed genome-wide transcription profiling to identify genes with ultrasensitive response functions to Hepatocyte Growth Factor (HGF). We identified 3,527 genes that react to increasing concentrations of HGF, in Madin-Darby canine kidney (MDCK) cells, grown as cysts in 3D collagen cell culture. By fitting a generic Hill function to the dose-responses of these genes we obtained a measure of the ultrasensitivity of HGF-responsive genes, identifying a subset with higher apparent Hill coefficients (e.g. MMP1, TIMP1, SNORD75, SNORD86 and ERRFI1). The regulatory regions of these genes are potential candidates for future engineering of synthetic mammalian gene circuits requiring nonlinear responses to HGF signalling.

Indexed as

AnimalsCell Culture TechniquesCluster AnalysisDatabases, GeneticDogsHepatocyte Growth FactorMadin Darby Canine Kidney CellsMatrix Metalloproteinase 1MorphogenesisPrincipal Component AnalysisRNASequence Analysis, RNATranscriptomeHepatocyte Growth FactorMatrix Metalloproteinase 1RNA

Identifiers

PMID27982133
PMCPMC5159920
OpenAlexW2564269825

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.