What is it about?

As DNA encodes our very identity, it has been subject to a plethora of studies over the last century. The advent of new technologies that permit rapid sequencing of large DNA and RNA samples opened doors to before unknown mechanisms and interactions on a genomic scale. This led to an in-depth analysis of several nuclear processes, including transcription of genes and lesion repair. However, the applied protocols do not allow a high temporal resolution. Quite the contrary, the experiments yield often only a few data signals over several hours. The details of the dynamics between time points are chiefly ignored, implicitly assuming that they straightforwardly transition from one to another. Here, we show that such an understanding can be flawed. We use the repair process of UV-induced DNA damage as an example to present a quantitative analysis framework that permits the representation of the entire temporal process. We subsequently describe how they can be linked to other heterogeneous data sets. Consequently, we evaluate a correlation to the whole kinetic process rather than to a single time point. Although the approach is exemplified using DNA repair, it can be readily applied to any other mechanism and sequencing data that represent a transition between two states, such as damaged and repaired.

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Why is it important?

Molecular integrity of the DNA is vital for cell functionality and survival. Because virtually everything in the environment (and even cell-internal processes) can alter the chemical structure, repair mechanisms are of paramount importance. Malfunctioning DNA repair can lead to severe phenotypes, including cancer as well as neurodegenerative and ageing disorders. The kinetics are orchestrated together with other nuclear processes, such as gene expression and DNA folding. It is therefore impossible to single out DNA lesion removal from other pivotal nuclear mechanisms to truly understand repair. The development of high-throughput sequencing methods permitted the in-depth assessment of the genome-wide dynamics. However, the data analysis is not straightforward, as there are commonly only a few time points over several hours available, and the comparison with other processes is difficult. An improved interpretation of the data can ultimately lead to a better understanding of repair-related disorders, and therefore can contribute to the development of improved treatments.


Despite our great advances in measuring DNA processes and the large body of studies, I am still surprised how little we actually know. This is largely due to the immense complexity of the strongly entangled nuclear mechanisms, such as transcription, replication, and chromatin organisation. Unfortunately, it is not possible to directly observe these processes, and we rely on auxiliary methods. By simply re-interpreting the data, we can obtain different angles. This is what we did in this project. By understanding the data as the superposition of independent cells rather than as an average, we can make new conclusions about the repair process itself. Although we present the minimal model in the context of DNA lesion removal, it can be similarly applied to other processes that can be understood as a state transition similar to the change from damaged to repaired.

Leo Zeitler
Commissariat à l'énergie atomique et aux énergies alternatives

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This page is a summary of: A quantitative modelling approach for DNA repair on a population scale, PLoS Computational Biology, September 2022, PLOS,
DOI: 10.1371/journal.pcbi.1010488.
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