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Precision of Readout at the hunchback Gene: Analyzing Short Transcription Time Traces in Living Fly Embryos
- Source :
- PLoS Computational Biology, PLoS Computational Biology, Public Library of Science, 2016, 12 (12), pp.e1005256. ⟨10.1371/journal.pcbi.1005256⟩, PLoS Computational Biology, Vol 12, Iss 12, p e1005256 (2016), PLoS Computational Biology, 2016, 12 (12), pp.e1005256. ⟨10.1371/journal.pcbi.1005256⟩
- Publication Year :
- 2016
- Publisher :
- HAL CCSD, 2016.
-
Abstract
- The simultaneous expression of the hunchback gene in the numerous nuclei of the developing fly embryo gives us a unique opportunity to study how transcription is regulated in living organisms. A recently developed MS2-MCP technique for imaging nascent messenger RNA in living Drosophila embryos allows us to quantify the dynamics of the developmental transcription process. The initial measurement of the morphogens by the hunchback promoter takes place during very short cell cycles, not only giving each nucleus little time for a precise readout, but also resulting in short time traces of transcription. Additionally, the relationship between the measured signal and the promoter state depends on the molecular design of the reporting probe. We develop an analysis approach based on tailor made autocorrelation functions that overcomes the short trace problems and quantifies the dynamics of transcription initiation. Based on live imaging data, we identify signatures of bursty transcription initiation from the hunchback promoter. We show that the precision of the expression of the hunchback gene to measure its position along the anterior-posterior axis is low both at the boundary and in the anterior even at cycle 13, suggesting additional post-transcriptional averaging mechanisms to provide the precision observed in fixed embryos.<br />Author Summary The fly embryo provides a natural laboratory to study the dynamics of transcription and its implications for the developing organism. Using live imaging experiments we investigate the nature of transcription regulation of the hunchback gene—the first to read out the maternal Bicoid gradient. While traditional time trace analysis methods based on OFF time distributions or autocorrelation functions fail for short signals, our tailored autocorrelation function overcomes these limitations revealing bursty dynamics that is reproducible between cell cycles and embryos. The inferred rates result in a lot of variability in the readout of nuclei sensing similar Bicoid concentrations, suggesting additional readout mechanisms than a one-to-one mapping of the input onto the output.
- Subjects :
- 0301 basic medicine
Embryology
Embryo, Nonmammalian
Time Factors
Transcription, Genetic
Molecular Networks (q-bio.MN)
[SDV]Life Sciences [q-bio]
Biochemistry
Polymerases
Mathematical and Statistical Techniques
Transcription (biology)
Gene expression
Transcriptional regulation
Drosophila Proteins
Quantitative Biology - Molecular Networks
Cell Cycle and Cell Division
lcsh:QH301-705.5
Genetics
Ecology
Messenger RNA
Cell Cycle
Embryo
Cell biology
Nucleic acids
Computational Theory and Mathematics
Cell Processes
Modeling and Simulation
Autocorrelation
Physical Sciences
embryonic structures
Engineering and Technology
Drosophila
Statistics (Mathematics)
Research Article
animal structures
DNA transcription
Embryonic Development
Biology
Research and Analysis Methods
Transcription initiation
03 medical and health sciences
Cellular and Molecular Neuroscience
Live cell imaging
DNA-binding proteins
Animals
Gene Regulation
Statistical Methods
Molecular Biology
Gene
Ecology, Evolution, Behavior and Systematics
Models, Genetic
Embryos
Computational Biology
Biology and Life Sciences
Proteins
Cell Biology
030104 developmental biology
lcsh:Biology (General)
FOS: Biological sciences
Signal Processing
RNA
Mathematics
Transcription Factors
Developmental Biology
Subjects
Details
- Language :
- English
- ISSN :
- 1553734X and 15537358
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology, PLoS Computational Biology, Public Library of Science, 2016, 12 (12), pp.e1005256. ⟨10.1371/journal.pcbi.1005256⟩, PLoS Computational Biology, Vol 12, Iss 12, p e1005256 (2016), PLoS Computational Biology, 2016, 12 (12), pp.e1005256. ⟨10.1371/journal.pcbi.1005256⟩
- Accession number :
- edsair.doi.dedup.....9566b38eafe3697efd87779f854f6506
- Full Text :
- https://doi.org/10.1371/journal.pcbi.1005256⟩