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Neural network and kinetic modelling of human genome replication reveal replication origin locations and strengths

In human and other metazoans, the determinants of replication origin location and strength are still elusive. Origins are licensed in G1 phase and fired in S phase of the cell cycle, respectively. It is debated which of these two temporally separate steps determines origin efficiency. Experiments ca...

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Autores principales: Arbona, Jean-Michel, Kabalane, Hadi, Barbier, Jeremy, Goldar, Arach, Hyrien, Olivier, Audit, Benjamin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256156/
https://www.ncbi.nlm.nih.gov/pubmed/37253070
http://dx.doi.org/10.1371/journal.pcbi.1011138
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author Arbona, Jean-Michel
Kabalane, Hadi
Barbier, Jeremy
Goldar, Arach
Hyrien, Olivier
Audit, Benjamin
author_facet Arbona, Jean-Michel
Kabalane, Hadi
Barbier, Jeremy
Goldar, Arach
Hyrien, Olivier
Audit, Benjamin
author_sort Arbona, Jean-Michel
collection PubMed
description In human and other metazoans, the determinants of replication origin location and strength are still elusive. Origins are licensed in G1 phase and fired in S phase of the cell cycle, respectively. It is debated which of these two temporally separate steps determines origin efficiency. Experiments can independently profile mean replication timing (MRT) and replication fork directionality (RFD) genome-wide. Such profiles contain information on multiple origins’ properties and on fork speed. Due to possible origin inactivation by passive replication, however, observed and intrinsic origin efficiencies can markedly differ. Thus, there is a need for methods to infer intrinsic from observed origin efficiency, which is context-dependent. Here, we show that MRT and RFD data are highly consistent with each other but contain information at different spatial scales. Using neural networks, we infer an origin licensing landscape that, when inserted in an appropriate simulation framework, jointly predicts MRT and RFD data with unprecedented precision and underlies the importance of dispersive origin firing. We furthermore uncover an analytical formula that predicts intrinsic from observed origin efficiency combined with MRT data. Comparison of inferred intrinsic origin efficiencies with experimental profiles of licensed origins (ORC, MCM) and actual initiation events (Bubble-seq, SNS-seq, OK-seq, ORM) show that intrinsic origin efficiency is not solely determined by licensing efficiency. Thus, human replication origin efficiency is set at both the origin licensing and firing steps.
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spelling pubmed-102561562023-06-10 Neural network and kinetic modelling of human genome replication reveal replication origin locations and strengths Arbona, Jean-Michel Kabalane, Hadi Barbier, Jeremy Goldar, Arach Hyrien, Olivier Audit, Benjamin PLoS Comput Biol Research Article In human and other metazoans, the determinants of replication origin location and strength are still elusive. Origins are licensed in G1 phase and fired in S phase of the cell cycle, respectively. It is debated which of these two temporally separate steps determines origin efficiency. Experiments can independently profile mean replication timing (MRT) and replication fork directionality (RFD) genome-wide. Such profiles contain information on multiple origins’ properties and on fork speed. Due to possible origin inactivation by passive replication, however, observed and intrinsic origin efficiencies can markedly differ. Thus, there is a need for methods to infer intrinsic from observed origin efficiency, which is context-dependent. Here, we show that MRT and RFD data are highly consistent with each other but contain information at different spatial scales. Using neural networks, we infer an origin licensing landscape that, when inserted in an appropriate simulation framework, jointly predicts MRT and RFD data with unprecedented precision and underlies the importance of dispersive origin firing. We furthermore uncover an analytical formula that predicts intrinsic from observed origin efficiency combined with MRT data. Comparison of inferred intrinsic origin efficiencies with experimental profiles of licensed origins (ORC, MCM) and actual initiation events (Bubble-seq, SNS-seq, OK-seq, ORM) show that intrinsic origin efficiency is not solely determined by licensing efficiency. Thus, human replication origin efficiency is set at both the origin licensing and firing steps. Public Library of Science 2023-05-30 /pmc/articles/PMC10256156/ /pubmed/37253070 http://dx.doi.org/10.1371/journal.pcbi.1011138 Text en © 2023 Arbona et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Arbona, Jean-Michel
Kabalane, Hadi
Barbier, Jeremy
Goldar, Arach
Hyrien, Olivier
Audit, Benjamin
Neural network and kinetic modelling of human genome replication reveal replication origin locations and strengths
title Neural network and kinetic modelling of human genome replication reveal replication origin locations and strengths
title_full Neural network and kinetic modelling of human genome replication reveal replication origin locations and strengths
title_fullStr Neural network and kinetic modelling of human genome replication reveal replication origin locations and strengths
title_full_unstemmed Neural network and kinetic modelling of human genome replication reveal replication origin locations and strengths
title_short Neural network and kinetic modelling of human genome replication reveal replication origin locations and strengths
title_sort neural network and kinetic modelling of human genome replication reveal replication origin locations and strengths
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256156/
https://www.ncbi.nlm.nih.gov/pubmed/37253070
http://dx.doi.org/10.1371/journal.pcbi.1011138
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