Inferring the DNA replication origin density landscape in human cells from replication timing and fork directionality data

The determinants of the locations and firing times of the multiple replication origins are still elusive in human and other metazoan organisms. Experiments can independently profile mean replication timing (MRT) and replication fork directionality (RFD) genome-wide. In the hypothesis of a constant replication fork speed, MRT and RFD are related to each other by an analytical formula so are a priori equivalent. However, we show here that experimental noises result in MRT and RFD profiles containing information at different spatial frequencies. We further demonstrate that one can compute an origin density landscape that, when inserted in an appropriate simulation framework, jointly predicts experimental MRT and RFD profiles with an unprecedented precision. We also extract an analytical formula linking intrinsic origin efficiency with observed origin efficiency and MRT. We then compare the computed origin density landscape with experimental distributions of potential origins (ORC, MCM) or actual initiation events (Bubble-seq, SNS-seq, OK-seq). The results indicate that MRT and RFD data are highly consistent with each other, that our simple model suffices to capture the replication dynamics during S phase given an appropriate initiation probability landscape, but that the density of potential origins is not the sole determinant of this landscape.

Determination of human DNA replication origin position and efficiency reveals principles of initiation zone organisation

Replication of the human genome initiates within broad zones of ~ 150 kb. The extent to which firing of individual DNA replication origins within initiation zones is spatially stochastic or localised at defined sites remains a matter of debate. A thorough characterisation of the dynamic activation of origins within initiation zones is hampered by the lack of a high-resolution map of both their position and efficiency. To address this shortcoming, we describe a modification of initiation site sequencing (ini-seq) based on density substitution. Newly-replicated DNA is rendered heavy-light (HL) by incorporation of BrdUTP, unreplicated DNA remaining light-light (LL). Replicated HL-DNA is separated from unreplicated LL-DNA by equilibrium density gradient centrifugation, then both fractions are subjected to massive parallel sequencing. This allows precise mapping of 23,905 replication origins simultaneously with an assignment of a replication initiation efficiency score to each. We show that origin firing within initiation zones is not randomly distributed. Rather, origins are arranged hierarchically with a set of very highly efficient origins marking zone boundaries. We propose that these origins explain much of the early firing activity arising within initiation zones, helping to unify the concept of replication initiation zones with the identification of discrete replication origin sites.

Organization of DNA Replication Origin Firing in Xenopus Egg Extracts: The Role of Intra-S Checkpoint

During cell division, the duplication of the genome starts at multiple positions called replication origins. Origin firing requires the interaction of rate-limiting factors with potential origins during the S(ynthesis)-phase of the cell cycle. Origins fire as synchronous clusters which is proposed to be regulated by the intra-S checkpoint. By modelling the unchallenged, the checkpoint-inhibited and the checkpoint protein Chk1 over-expressed replication pattern of single DNA molecules from Xenopus sperm chromatin replicated in egg extracts, we demonstrate that the quantitative modelling of data requires: (1) a segmentation of the genome into regions of low and high probability of origin firing; (2) that regions with high probability of origin firing escape intra-S checkpoint regulation and (3) the variability of the rate of DNA synthesis close to replication forks is a necessary ingredient that should be taken in to account in order to describe the dynamic of replication origin firing. This model implies that the observed origin clustering emerges from the apparent synchrony of origin firing in regions with high probability of origin firing and challenge the assumption that the intra-S checkpoint is the main regulator of origin clustering.

Quantitative Profiling of Adaptation to Cyclin E Overproduction

Cyclin E/CDK2 drives cell cycle progression from G1 to S phase. Cyclin E overproduction is toxic to mammalian cells, although the gene encoding cyclin E (CCNE1) is overexpressed in some cancers. It is not yet understood how cancer cells tolerate high levels of cyclin E. To address this question, we extensively characterized non-transformed epithelial cells subjected to chronic cyclin E overproduction. Cells overproducing human cyclin E briefly experienced truncated G1 phases, then consistently endured a transient period of DNA replication origin underlicensing, replication stress, and severely impaired proliferation. Individual cells displayed substantial intercellular heterogeneity in both cell cycle dynamics and CDK activity. Each of these phenotypes improved rapidly despite maintaining high cyclin E-associated activity. Transcriptome analysis revealed that adapted cells downregulated a cohort of G1-regulated genes. These cells also shared at least one unique change also found in breast tumors that overproduce cyclin E, expression of the cancer/testis antigen HORMAD1. Withdrawing cyclin E induction partially reversed the intermediate licensing phenotype of adapted cells indicating that adaptation is at least partly independent of genetic alterations. This study provides evidence that mammalian cyclin E/CDK inhibits origin licensing by an indirect mechanism through premature S phase onset. It serves as an example of specific oncogene adaptation that can identify key molecular changes during tumorigenesis.

Organization of DNA Replication Origin Firing in Xenopus Egg Extracts : The Role of Intra-S Checkpoint

Background: During cell division, the duplication of the genome starts at multiple positions called replication origins. Origin firing requires the interaction of rate-limiting factors with potential origins during the S(ynthesis)-phase of the cell cycle. Origins fire as synchronous clusters which is proposed to be regulated by the intra-S checkpoint. Results: By modelling the unchallenged, the checkpoint-inhibited and the checkpoint protein Chk1 over-expressed replication pattern of single DNA molecules from Xenopus sperm chromatin replicated in egg extracts, we demonstrate that the quantitative modelling of data requires: 1) a segmentation of the genome into regions of low and high probability of origin firing; 2) that regions with high probability of origin firing escape intra-S checkpoint regulation and 3) the variability of the rate of DNA synthesis close to replication forks is a necessary ingredient that should be taken in to account in order to describe the dynamic of replication origin firing. Conclusions: This model implies that the observed origin clustering emerges from the apparent synchrony of origin firing in regions with high probability of origin firing and challenge the assumption that the intra-S checkpoint is the main regulator of origin clustering. Availabily: The datasets supporting the conclusions of this article are from reference [1].

MTBP phosphorylation controls DNA replication origin firing

Faithful genome duplication requires regulation of origin firing to determine loci, timing and efficiency of replisome generation. Established kinase targets for eukaryotic origin firing regulation are the Mcm2-7 helicase, Sld3/Treslin/TICRR and Sld2/RecQL4. We report that metazoan Sld7, MTBP (Mdm2 binding protein), is targeted by at least three kinase pathways. MTBP was phosphorylated at CDK consensus sites by cell cycle cyclin-dependent kinases (CDK) and Cdk8/19-cyclin C. Phospho-mimetic MTBP CDK site mutants, but not non-phosphorylatable mutants, promoted origin firing in human cells. MTBP was also phosphorylated at DNA damage checkpoint kinase consensus sites. Phospho-mimetic mutations at these sites inhibited MTBP’s origin firing capability. Whilst expressing a non-phospho MTBP mutant was insufficient to relieve the suppression of origin firing upon DNA damage, the mutant induced a genome-wide increase of origin firing in unperturbed cells. Our work establishes MTBP as a regulation platform of metazoan origin firing.

Computational prediction of species-specific yeast DNA replication origin via iterative feature representation

Deoxyribonucleic acid replication is one of the most crucial tasks taking place in the cell, and it has to be precisely regulated. This process is initiated in the replication origins (ORIs), and thus it is essential to identify such sites for a deeper understanding of the cellular processes and functions related to the regulation of gene expression. Considering the important tasks performed by ORIs, several experimental and computational approaches have been developed in the prediction of such sites. However, existing computational predictors for ORIs have certain curbs, such as building only single-feature encoding models, limited systematic feature engineering efforts and failure to validate model robustness. Hence, we developed a novel species-specific yeast predictor called yORIpred that accurately identify ORIs in the yeast genomes. To develop yORIpred, we first constructed optimal 40 baseline models by exploring eight different sequence-based encodings and five different machine learning classifiers. Subsequently, the predicted probability of 40 models was considered as the novel feature vector and carried out iterative feature learning approach independently using five different classifiers. Our systematic analysis revealed that the feature representation learned by the support vector machine algorithm (yORIpred) could well discriminate the distribution characteristics between ORIs and non-ORIs when compared with the other four algorithms. Comprehensive benchmarking experiments showed that yORIpred achieved superior and stable performance when compared with the existing predictors on the same training datasets. Furthermore, independent evaluation showcased the best and accurate performance of yORIpred thus underscoring the significance of iterative feature representation. To facilitate the users in obtaining their desired results without undergoing any mathematical, statistical or computational hassles, we developed a web server for the yORIpred predictor, which is available at: http://thegleelab.org/yORIpred.