Genome replication in asynchronously growing microbial populations

TL;DR

This paper develops a quantitative theoretical framework to predict DNA fragment abundance in asynchronously growing microbial populations, accurately inferring replication origins and providing insights across diverse organisms.

Contribution

It introduces a stochastic modeling approach that quantitatively links DNA replication programs to sequencing data, enabling precise inference of replication origins and dynamics.

Findings

Analytical predictions match experimental sequencing data.

Method accurately infers known replication origins.

Applicable to both bacteria and eukaryotic organisms.

Abstract

Biological cells replicate their genomes in a well-planned manner. The DNA replication program of an organism determines the timing at which different genomic regions are replicated, with fundamental consequences for cell homeostasis and genome stability. Qualitatively, in a growing cell culture, one expects that genomic regions that are replicated early should be more abundant than regions that are replicated late. This abundance pattern can be experimentally measured using deep sequencing. However, a general quantitative theory to explain these data is still lacking. In this paper, we predict the abundance of DNA fragments in asynchronously growing cultures from any given stochastic model of the DNA replication program. As key examples, we present stochastic models of the DNA replication programs in Escherichia coli and in budding yeast. In both cases, our approach leads to analytical predictions that are in excellent agreement with experimental data and permit to infer key information about the replication program. In particular, our method is able to infer the locations of known replication origins in budding yeast with high accuracy. These examples demonstrate that our method can provide insight into a broad range of organisms, from bacteria to eukaryotes.