Dynamics of replication origin over-activation

Safeguards against excess DNA replication are often dysregulated in cancer, and driving cancer cells towards over-replication is a promising therapeutic strategy. We determined DNA synthesis patterns in cancer cells undergoing partial genome re-replication due to perturbed regulatory interactions (re-replicating cells). These cells exhibited slow replication, increased frequency of replication initiation events, and a skewed initiation pattern that preferentially reactivated early-replicating origins. Unlike in cells exposed to replication stress, which activated a novel group of hitherto unutilized (dormant) replication origins, the preferred re-replicating origins arose from the same pool of potential origins as those activated during normal growth. Mechanistically, the skewed initiation pattern reflected a disproportionate distribution of pre-replication complexes on distinct regions of licensed chromatin prior to replication. This distinct pattern suggests that circumventing the strong inhibitory interactions that normally prevent excess DNA synthesis can occur via at least two pathways, each activating a distinct set of replication origins.

The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly The statistical test(s) used AND whether they are one-or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.
A description of all covariates tested A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals) For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted Give P values as exact values whenever suitable.
For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code
Policy information about availability of computer code Data collection 1, Flow cytometry data were collected with BD LSR Fortessa cell analyzer with FACSDiva software (version 6.2).
For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Research guidelines for submitting code & software for further information.

April 2020
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability All the sequencing data were deposited in GEO (GSE172417) . Source data are provided with this paper: Histone modification ChIP-seq data from ENCODE database and Hi-C data from GEO (GSM2795535). The source data underlying Figs. 1g, 1h; 2e; 4c and 5c and Supplementary Figs. 3e, 3f; 4a; 5a, 5c; 9c and 10c, 10d are provided as Source Data files. All data within the manuscript are available from the authors upon request.

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Life sciences study design
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Sample size
For DNA combing analysis and microscopy cell counting, we analyze 50 to 300 signals/cells that gave sufficient statistics for the effect sizes of interest.
Data exclusions No data were excluded from analysis.

Replication
For all the experiments, we did at least three independent biological replicates except for sequencing experiments, which have at least 2 independent biological replicates. Results were consistently replicated across multiple experiments with all replicates generating similar results.
Randomization For DNA fiber analysis, fibers were random selected by the FiberStudio software. For manually counting of IdU and CldU overlapping signals, 3-6 images were randomly selected. We used cell lines for all the experiments, no human or animal subjects were used in the study. Randomization is not generally used for other experiments.

Blinding
Blinding is also not necessary because the results are quantitative and did not require subjective judgment or interpretation.

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Validation
All antibodies except antibodies for DNA combing were validated using immunoblots based on the molecular weight of the target. GammaH2AX, Phospho-RPA, PCNA Histone H3 and Phospho-Chk1 antibodies also have been used by many people with many publications. BrdU (IgG1, Becton Dickinson, 347580), BrdU (Accurate chemical, OBT0030) and single-stranded DNA antibodies have been used for combing by many scientists. Phospho-MCM2 (Ser139) was further validated using phosphatase and flow cytometry that bound to the right cell cycle stage (Fig.1e). We validated CDT1 for ChIP-seq by comparing G1 cells and S cells, which have very high CDT1 and very low CDT1 levels, respectively ( Supplementary Fig. 9a).

Eukaryotic cell lines Policy information about cell lines
Cell line source(s) HCT116(CCL247) and U2OS (HTB96) cell lines, both arefrom ATCC.

Authentication
Doxycycline inducible CDT1 over-expression U2OS cells were validated by western blot and flow cytometry.

Mycoplasma contamination
Mycoplasma tested negative with both cell lines.
Commonly misidentified lines (See ICLAC register) No commonly misidentified cell lines were used.

ChIP-seq Data deposition
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nature research | reporting summary
April 2020 Flow Cytometry Plots Confirm that: The axis labels state the marker and fluorochrome used (e.g. CD4-FITC).
The axis scales are clearly visible. Include numbers along axes only for bottom left plot of group (a 'group' is an analysis of identical markers).
All plots are contour plots with outliers or pseudocolor plots.
A numerical value for number of cells or percentage (with statistics) is provided.

Methodology
Sample preparation Cancer cell lines were processed according to the EdU kit. For CDT1 and pMCM2 staining, cells were permeabilized before PFA fixation.

Software
FACSDiva software for collecting samples and Flowjo 10.6. for analysis.

Cell population abundance
Since we have background information for the parameter detected, antibodies are very good, there are always both negative and positive populations in the same sample, it's pretty straightforward to gate. Since it's pretty straightforward, to avoid too crowd graph, we did not induce axis scales for all the graphs.

Gating strategy
Single cells gated according to DAPI-H DAPI-A were analyzed as the gates shown on each graphs.
Tick this box to confirm that a figure exemplifying the gating strategy is provided in the Supplementary Information.