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Scientific result | DNA | Cellular mechanisms
The protocols applied for in vivo mapping of nuclear processes, on a genome-wide scale and at very high spatial resolution do not allow for high temporal resolution. Thus, data sets usually contain only a few samples. A team from I2BC has shown that such an understanding can be wrong, using a mathematical model. Their study published in PLoS Computational Biology analyzes sequencing signals as the superposition of signals from independent cells. Their model can be applied to any process that can be modeled by a transition between two states.
New technologies for rapid sequencing of large samples of DNA and RNA (NGS - Next Generation Sequencing), have made it possible to map nuclear processes in vivo, on a genome-wide scale and at very high spatial resolution. This is particularly the case for gene transcription and lesion repair.
While the protocols applied are efficient, they do not allow for high temporal resolution. Thus, the data sets generally contain only a few samples over several hours, and often the transition from one point to another is implicitly considered as direct. The "Transcriptional regulation of the genome" team of the I2BC therefore wanted to demonstrate that such an understanding could be wrong.
The team decided to submit previous sequencing data of cyclobutanic pyrimidine dimers (CPD) 1 obtained in the yeast Saccharomyces cerevisiae to a mathematical model. CPDs are one of the DNA damages induced by UVs, so their mapping provides information on the repair process. The model applied is a probabilistic model, which comes from physics 2, established in the 1940s to describe changes of state in solids.
Compared to other studies that consider sequencing signals as an average behavior, their study, published in PLoS Computational Biology, analyzes them as the superposition of signals from independent cells. Their repair model thus constructed leads to consider that the change observed in the sequencing signals of CPDs is not constant over time. Moreover, it allowed them to establish correlations with other nuclear processes/parameters such as transcription rate and nucleosomal density.
With this model, repair positions on the genome appear as "patterns" that develop within a population of cells. It thus allows a complete analysis of nuclear processes at the population level. As their model is validated by comparing their repair dynamics with independent sequencing data, it can be applied to any process that can be modeled by a transition between two states, such as repaired and damaged.
Leo Zeitler,Cyril DenbyWilkes,Arach Goldar,Julie Soutourina.
A quantitative modelling approach for DNA repair on a population scale.
PLoS Computational Biology 2022. https://doi.org/10.1371/journal.pcbi.1010488
CEA is a French government-funded technological research organisation in four main areas: low-carbon energies, defense and security, information technologies and health technologies. A prominent player in the European Research Area, it is involved in setting up collaborative projects with many partners around the world.