The thymine analogue uracil is one of the most frequent non-canonical bases in DNA appearing either by thymine replacing misincorporation or as a product of spontaneous or enzymatic cytosine deamination reaction (Krokan et al., 2002). Consequently, uracil in DNA is usually recognized as an error that is efficiently repaired by the multistep base excision repair (BER) pathway initiated by uracil-DNA glycosylases (UDGs) (Krokan and Bjørås, 2013; Wallace, 2014). In other respects, uracil in DNA is known to be involved in several physiological processes (e.g. antibody maturation [Liu and Schatz, 2009; Maul and Gearhart, 2010; Maul and Gearhart, 2014; Xu et al., 2012], antiviral response [Burns et al., 2015; Stenglein et al., 2010], insect development [Horváth et al., 2013; Muha et al., 2012]), however, the exact mechanism and regulation of uracil-DNA metabolism including the roles of UDGs need to be elucidated. There are four known members of the UDG family in humans: (i) the most active uracil-DNA glycosylase encoded by the ung gene (UNG1 mitochondrial and UNG2 nuclear isoform), (ii) the single-strand selective monofunctional uracil-DNA glycosylase 1 (SMUG1), (iii) thymine DNA glycosylase (TDG specialized for repair of T:G and U:G) and (iv) methyl CpG binding domain protein 4 (MBD4 repairs U:G) (Visnes et al., 2009). UNG2 removes most of the genomic uracil from both single- and double-stranded DNA regardless of the uracil originating from mutagenic cytosine deamination or thymine replacing misincorporation (Kavli et al., 2002).

Thymine replacing uracil misincorporation is normally prevented by the tight regulation of the cellular dUTP/dTTP ratio maintained by two enzymes, the dUTPase and the thymidylate synthase. The dUTPase enzyme removes dUTP from the cellular pool by catalyzing dUTP hydrolysis into dUMP and PP_i (Vértessy and Tóth, 2009). Lack or inhibition of dUTPase leads to increased dUTP levels and under such conditions, DNA polymerases readily incorporate uracil opposite to adenine. Similarly, several anticancer drugs (such as 5-fluorouracil (5-FU), 5-fluoro-2’-deoxyuridine (5FdUR), capecitabine, methotrexate, raltitrexed (RTX), pemetrexed) target the de novo thymidylate synthesis pathway via thymidylate synthase inhibition to perturb the tightly regulated dUTP/dTTP ratio, eventually triggering thymineless cell death (Blackledge, 1998; Requena et al., 2016; Wilson et al., 2014). Although the exact molecular mechanism is not yet fully understood, massive uracil misincorporation, hyperactivity of the repair process and/or stalling of the replication fork are all suggested to be involved in the process (Khodursky et al., 2015; Ostrer et al., 2015). UNG has been suggested to play a key role in this mechanism, as being responsible for the initiating step in uracil removal that may lead to futile cycles if the cellular dUTP/dTTP ratio is elevated. A quantitative insight into the magnitude and the pattern of uracil incorporation into genomic DNA as induced by these chemotherapeutic treatments is expected to contribute to a better understanding of the cell death mechanism induced by the respective drugs.

Direct observation of the uracil moieties incorporated upon drug treatments have been hampered by the efficient and fast action of UNG. To overcome this problem, we wished to counteract the action of UNG in human cells via introduction of the well characterized, specific UNG inhibitor, UGI (Luo et al., 2008; Mol et al., 1995) into the cellular milieu. It has already been shown that UGI expression does not affect either the cytotoxicity, or the DNA damage and cell cycle response upon RTX and 5FdUR treatment (Luo et al., 2008). Using UGI expressing cell lines, we aimed to reveal the nascent pattern of uracil moieties in DNA induced by perturbation of thymidylate metabolism both using genome-wide uracil-specific sequencing and in situ cellular imaging of uracils within human genomic DNA. Previously, we designed a uracil-DNA (U-DNA) sensor tailored from an inactive mutant of human UNG2 that was successfully applied in semi-quantitative dot blot analysis and direct immunocytochemistry (Róna et al., 2016). Some additional approaches have also been published to detect uracil-DNA within its genomic context such as (i) techniques focusing on specific, well-defined regions of the genome (qPCR [Horváth and Vértessy, 2010] and 3D-PCR [Suspène et al., 2005]), (ii) techniques that have been applied only to smaller sized genomes (Excision-seq [Bryan et al., 2014] and UPD-seq [Sakhtemani et al., 2019]), and (iii) techniques requiring labor-intensive isolation and multistep processing of genomic DNA samples (dU-seq [Shu et al., 2018]).

Here, we employ the U-DNA sensor in a DNA-IP-seq-like (DIP-seq-like) approach (termed as U-DNA-Seq) and develop a robust bioinformatic pipeline specifically designed for reliable interpretation of next generation sequencing (NGS) data for genome-wide distribution of uracil. We selected two drugs, RTX (raltitrexed, or tomudex) and 5FdUR that perturb thymidylate biosynthesis with different modes of action and analyzed their effects on genomic uracil distribution. These two drugs are frequently applied in treatment of colon cancers, therefore we chose a human colon carcinoma cell line, HCT116 and its mismatch repair (MMR) proficient variant as well-established and relevant cellular models (Koi et al., 1994; Meyers et al., 2001; Rashid et al., 2019). We show that drug treatment led to increased probability of uracil incorporation into more active chromatin regions in HCT116 cells expressing the UNG inhibitor protein UGI. In contrast, uracil was rather restricted to constitutive heterochromatic regions both in wild type cells and in non-treated UGI-expressing cells. Moreover, we further developed the U-DNA sensor-based staining method (Róna et al., 2016) that now uniquely allows in situ microscopic visualization of uracil in human genomic DNA. Confocal and super-resolution microscopy images and colocalization measurements strengthened the sequencing-based distribution patterns.

Here we focus on the alteration of U-DNA distribution pattern upon treatment with drugs perturbing thymidylate biosynthesis. Towards this end, we combined two new applications of further developed U-DNA sensor that was originally described in Róna et al., 2016. On one hand, using a DNA-IP-seq like application, termed U-DNA-Seq, we provided genome-wide uracil distribution data that was compared to the patterns of different genomic features. On the other hand, in immunocytochemistry, the sensor was applied to detect colocalization of U-DNA and selected histone markers.

Using U-DNA-Seq, here we demonstrate that the distribution of uracil-containing regions is altered in the drug-treated (5FdUR or RTX, in combination with UGI) as compared to the non-treated (wild type and UGI-expressing) samples. We demonstrated that UGI expression did not cause any observable change either on cell cycle progression (Figure 5, and Luo et al., 2008) or uracil distribution pattern (Figure 3). We chose HCT116 cancer cell line that is deficient in mismatch repair (MLH1^-/-), similarly to many types of cancer especially in colon cancer (Germano et al., 2018; Gupta and Heinen, 2019; Sekine et al., 2017). As its mismatch repair proficient counterpart is also available (Koi et al., 1994), we took the opportunity to address the impact of the MMR status on genomic uracil distribution. We found that the genomic uracil pattern is much more influenced by MMR proficiency in case of the 5FdUR treatment than in case of the RTX treatment (Figure 3). The genomic uracil distribution patterns either in non-treated or in drug-treated cells are found to be non-random: broad regions of uracil enriched genomic segments were detected. Within the third part of our pipeline (Figure 2—figure supplement 1 and Supplementary file 3–5), we also analyzed the distribution pattern of these broad peaks comparing them to a set of relevant and cell type-specific data of ChIP-seq experiments and other genomic features. In drug-treated cells, these broad segments showed the highest correlation with ChIP-seq-based patterns published for predominantly euchromatin and facultative heterochromatin markers (Figure 4A–B). Increasing evidence suggests that active and repressed chromatin states can be determined in a combinatorial fashion where simultaneous histone marks can efficiently shift gene expression from inactive to active states or vice versa (Gates et al., 2017; Hyun et al., 2017). Hence, it is of special interest to note that our colocalization data show similarity scores not just for one but for a variety of factors. Such combinatorial behavior was further demonstrated by the genome segmentation analysis using the Segway package (Figure 4B) that also pointed to the fact that the distribution of histone markers are not fully matched with the detected U-DNA pattern. Hence further genomic features were also studied (Figure 4C). Importantly, regarding these factors and additional features, our results are highly coherent. Namely, the outstanding correlation of uracil-DNA patterns in drug-treated samples with active promoters, CpG islands, early replicating segments and DNase hypersensitive sites, all of which are published for normally cycling cells, highly supports the above conclusion. Euchromatin was shown to imply early replicating genomic regions, whereas heterochromatin replicates in late S-phase (Black et al., 2012). Accordingly, we report that the drug treatment induced U-DNA pattern is more similar to the early replicating segments, whereas U-DNA distribution in non-treated (wild type and UGI-expressing) cells shows simultaneous association with both heterochromatin markers and late replicating regions (Figure 4C–D, also supported by Figure 4—figure supplement 3). It has to be noted that MMR proficiency leads to a major decrease in the correlation with early replication timing in case of the 5FdUR treated sample (Figure 4C–D), and a smaller decrease in the correlation with transcriptionally active regions in case of both treatments (Figure 4A–C). Still, difference between the uracil-DNA patterns of drug-treated and non-treated samples remains unambiguous, regardless the MMR status (Figures 3 and 4).

Taken together, in the non-treated cells, where the level of genomic uracil is low, we show that uracil is preferentially located in the constitutive heterochromatin, which can be explained by the fact that heterochromatin is generally highly condensed and thus less accessible for DNA repair and replicative DNA synthesis. In contrast, in the open, more frequently transcribed euchromatin, DNA repair can efficiently correct uracils in the presence of a balanced dNTP pool. The low amount of genomic uracil in non-treated cells might remain from either cytosine deamination or thymine replacing misincorporation that escaped DNA repair. However, drug (5FdUR or RTX) treatments perturb the cellular nucleotide pool, and consequently highly increase the rate of thymine replacing uracil misincorporation events overwriting the background uracil pattern of non-treated cells’ genome. Uracil appearance via thymine replacing misincorporation implies prior DNA synthesis involved in either replication, or transcription-coupled DNA repair, or epigenetic reprogramming (e. g. erasing the methyl-cytosine epigenetic mark). Importantly, we found that uracil pattern showed the highest correlation with the features (early replication, active promoters and DNase hypersensitive sites, and CpG islands) linked exactly to these processes (Figure 4C). This is further supported by the fact that in MMR proficient drug-treated samples higher U-DNA content was measured as compared to the MMR deficient ones (Figure 1—figure supplement 1C–D). This observation in MMR proficient cells might be explained by either longer segments synthesized during the MMR process (Bowen et al., 2013), or the less tight control on cell cycle arrest (Figure 5) allowing more extended replicative synthesis.

Our data showing that under normal conditions, that is in lack of drug treatment, localization of human genomic uracils can be associated with the heterochromatic regions which is in agreement with the recent study by Shu et al., 2018. We propose that this pattern may reflect less efficient DNA repair in the heterochromatin. In accordance, it was shown that mutation rate within the later replicating heterochromatin is markedly increased (Stamatoyannopoulos et al., 2009). Interestingly, uracil distribution in bacterial and yeast genomes was found to be mostly excluded from the earliest as well as from the latest replicating segments (Bryan et al., 2014), suggesting a partially different pattern as compared to what is observed in human cells. In yeast cells it was also shown that transcription coupled repair synthesis might result in elevated uracil incorporation into actively transcribed regions under normal conditions (Kim and Jinks-Robertson, 2009; Owiti et al., 2018). However, both yeast and bacteria show major differences in both mechanisms of dNTP pool regulation (Mathews, 2014) and the set of available UDGs, as they do not encode the SMUG1 enzyme which is an important backup of the UNG2 in human (Elateri et al., 2003; Kavli et al., 2002). These differences may account for the alterations found in the genomic uracil distribution patterns.The antifolate or nucleotide-based thymidylate synthase inhibitors, such as 5-FU, RTX or 5FdUR are known to lead to cell cycle arrest, as it is confirmed in our experimental system (Figure 5) and is also reflected in the detected uracil-DNA pattern that strongly correlates with the early replicating segments in case of both drug treatments. The two drugs caused similar, but not equivalent uracil-DNA pattern. On the one hand, the correlations with the H3K36me3 marker as well as with the early replicating segments are both markedly stronger with the RTX treated sample as compared to the 5FdUR treated sample (Figure 4). On the other hand, the correlation of uracil accumulation with the H3K27me3 marker and with the CpG islands is stronger in the 5FdUR treated sample. Moreover, the MMR status has markedly different influence on the resulting U-DNA pattern in case of the two drugs (Figures 3–4). Such differences might correspond to drug-specific mechanism of action, involving alterations in signaling processes, transcription regulation and the timing of cell cycle arrest (Van Triest et al., 2000). Details of these mechanisms remain obscure in the literature. Still, it is well-known that both drugs inhibit thymidylate synthase thereby facilitating dUMP incorporation into DNA, while the nucleotide analogue 5FdUR also leads to direct incorporation of 5-fluorodeoxyuridine monophosphate (FdUP) into the DNA (Longley et al., 2003; Pettersen et al., 2011). Genomic uracil and fluorouracil might have different effects on transcription and epigenetic regulation processes that could also contribute to the observed differences of the two U-DNA patterns. It should be noted that our method detects both uracil and also fluorouracil within the DNA, since the UNG enzyme binds to fluorouracil as well (Pettersen et al., 2011). Phenotypic differences in cell cycle progression upon the two drug treatments were also reported. The 5FdUR treatment was shown to cause an S-phase arrest in the second cycle (Huehls et al., 2016; Yan et al., 2016), while the actual time point of cell cycle arrest upon RTX treatment is still controversial (Blackledge, 1998; Ding et al., 2019; Zhao et al., 2016). Similarly, we also detected slightly altered cell cycle distribution patterns in case of the two drug treatments, which were differently influenced by the MMR status (Figure 5). In case of the 5FdUR treatment, MMR proficiency seems to lead to a weaker S-phase arrest. This might correspond to the observed decrease in the correlation of U-DNA pattern and early replication timing (Figure 4C). However, equally induced DNA damage response (reported by γH2AX) was detected upon both drug treatments (Figure 5—figure supplement 1). Consistently with our observations, Weeks et al recently showed that treatment with the antifolate pemetrexed in UNG -/- human colon cancer cells led to preferential enrichment of double-strand breaks (DSBs) within highly accessible euchromatic regions, like transcription factor binding sites, origins of replication, DNase hypersensitivity regions and CpG islands (Weeks et al., 2014). This study did not directly address the occurrence of uracil moieties but caught the process initiated by uracil incorporation at a later stage. Still, the distribution pattern of the resulting DSBs showed similarities to our U-DNA-Seq data.

As we demonstrated here, the genome-wide uracil distribution patterns have relevance for example in case of drug-treated cancer cells. Therefore, besides the global U-DNA quantification methods (MS based [Galashevskaya et al., 2013], and dot blot [Róna et al., 2016]), NGS-based techniques also have high impact.The presented new method, termed U-DNA-Seq is a direct, feasible alternative to the recently published UPD-Seq (Sakhtemani et al., 2019), Excision-seq (Bryan et al., 2014) or dU-seq (Shu et al., 2018) methods, all of which rely on indirect detection requiring one or more auxiliary chemical or enzymatic step(s). Only these three methods have the potential thus far to map genome-wide distribution of uracil within isolated genomic DNA based on NGS, and only dU-seq was used in the context of human genome. One advantage of our U-DNA-Seq is that it is a direct method employing U-DNA specific binding of catalytically inactive UNG-derived sensor constructs to pull-down uracil-containing genomic DNA-fragments. In terms of resolution, only the pre-digestion Excision-seq was shown to be able to provide single-base resolution in case of smaller size genomes with high uracil content (Bryan et al., 2014). The resolution of other methods including our new U-DNA-Seq is limited by the fragment size of the DNA library. Importantly, single-base resolution of uracil positions has decreased relevance in most cases, considering the basically stochastic nature of uracil appearance either by incorporation as a result of drug-treatment-induced dNTP pool perturbations during DNA synthesis due to insensitivity of the polymerases, or by spontaneous cytosine deamination. Due to the stochastic processes, the actual positions of uracils are expected to be variable in every single cell. Therefore, a statistical approach has higher descriptive value about the uracil distribution in these cases. Accordingly, we constructed a novel computational pipeline (Figure 2 and Figure 2—figure supplement 1) that is suitable for the description of this kind of uracil distribution patterns. We also demonstrated that the usual analysis methods designed for ChIP-seq experiment are suboptimal in this case (Figure 3—figure supplement 2). Moreover, re-analysis of the earlier published dU-seq data (Appendix 1—table 1) with the herein developed pipeline, showed very high correlation with our U-DNA-Seq data in case of comparable samples (non-treated K562 cells in both cases; and 5FdUR treated UGI expressing HCT116 vs 5FdUR treated UNG^-/- HEK293T cells, Appendix 1—figure 1–2) confirming robustness and reliability of our method. However, our interpretation is markedly different regarding the preferential centromeric location of uracils that has been suggested by Shu et al., 2018. We analyzed the underlying reasons of this discrepancy in Appendix 1.

The new U-DNA-Seq method was shown to be reliable, robust and potent enough to gain systematic information on uracil-DNA metabolism upon drug treatments. Such information could essentially contribute to the future understanding of the mechanistic details either of cytotoxic effect induced by anti-cancer drugs, or other biological processes involving genomic uracil appearance. To this end, it is also of key importance to establish new visualization methods allowing colocalization measurements between U-DNA and other factors in highly complex eukaryotic cells.

Therefore, we further developed the U-DNA sensor to visualize genomic uracil in situ in human cells. The FLAG-ΔUNG-SNAP sensor construct and the optimized staining method presented here were successfully applied in confocal and super-resolution (STED or dSTORM) microscopies (see Figures 6–8). To our knowledge, there is no alternative technique published so far for in situ microscopic detection of mammalian genomic uracil. A recent paper was published reporting a similar approach, where uracil-DNA glycosylase UdgX was coupled to a fluorescent tag and applied for staining of uracils in E. coli DNA (Datta et al., 2019), however, in our previous study ΔUNG had already been proved to be potent for in situ uracil detection in the same organism (Róna et al., 2016). Still, the UdgX-based tool was not further extended for detection of uracils within the highly complex chromatin of human cells. Moreover, our detection method also allows simultaneous staining for other factors in colocalization experiments, potentially providing mechanistic insight into several important biological phenomena that involve uracil-DNA. For colocalization studies, two histone markers were selected based on the U-DNA-Seq results, namely H3K36me3 and H3K27me3, which were the strongest correlating factors for RTX_UGI and 5FdUR_UGI U-DNA patterns, respectively (Figure 4A–B). Using dSTORM super-resolution microscopy we could confirm significant correlation of genomic uracil with both selected histone markers in drug-treated (5FdUR or RTX), UGI-expressing cells (Figure 8). H3K36me3 was shown to associate with actively transcribed genes (Becker et al., 2017; Hyun et al., 2017; Pfister et al., 2014), while H3K27me3 is the most cited marker for facultative heterochromatin (Becker et al., 2017; Saksouk et al., 2015). Strikingly, we found that H3K27me3 shows even stronger colocalization with the U-DNA pattern in case of the RTX treated sample as compared to the 5FdUR treated one, which might be indicative for RTX treatment induced chromatin remodeling at least regarding this histone modification. It is important to note that our U-DNA-Seq was compared to published data that corresponds to ChIP-seq experiments performed in non-treated cells. However, during in situ cellular colocalization studies, the drug treatment is obviously applied to both patterns (i.e. U-DNA and histone marker). With the ChIP-seq experiment for H3K36me3 performed on both RTX treated and non-treated cells, we demonstrated that such treatment-induced chromatin remodeling is not a general phenomenon, but may rather confine to certain factors (Figure 4A–B, and Figure 4—figure supplement 1). Based on these observations, we can confirm that such in silico correlation studies has a predictive potential allowing qualitative characterization, and further independent techniques are required for detailed studies. In summary, co-staining of the selected histone markers and the genomic uracil in drug-treated cells via dSTORM reinforced the association between uracil occurrence and transcriptionally active regions.

It has been argued that uracil accumulation may play a more decisive role in genomic instability than the induced uracil-excision repair (Huehls et al., 2016; Yan et al., 2016). Uracil in DNA may therefore be used as a key marker for estimating efficiency of chemotherapeutic drugs targeting thymidylate biosynthesis. Our presented new techniques, namely the U-DNA-Seq and the related in situ U-DNA detection methods provide key insights into the mechanism of chemotherapeutic drugs. The combination of these methods might become a highly potent approach in the future, that is to investigate the complex pattern of intra-tumor heterogeneity that is closely related to cancer progression and drug-resistance (Stanta and Bonin, 2018), therefore may contribute to improving clinical practice.

Sequencing data have been deposited into the Gene Expression Omnibus (GEO) under accession number GSE126822 and GSE153407, which have been unified under SuperSeries GSE153408. In the following Genome Browser session, we included all the log2 coverage ratio (bigwig) and the derived uracil enriched interval (bed) files corresponding to this manuscript. The color code and the names are the same as used here. https://genome.ucsc.edu/s/bekesiangi/GSE126822_UCSC_Genome_Browser_session. Source data have been provided for Figure 1-figure supplement 1, Figure 2-figure supplement 2, Figure 3, Figure 3-figure supplement 4, Figure 4, Figure 4-figure supplement 3, Figure 8, Appendix 1-figure 1, Appendix 1-figure 2.

1. 1. Pálinkás HL
   2. Békési A
   3. Pongor L
   4. Holub E
   5. Papp G
   6. Gemma C
   7. Ali S
   8. Győrffy B
   9. Vértessy BG

   (2020) NCBI Gene Expression Omnibus

   ID GSE126822. Genome-wide alterations of uracil distribution patterns in human DNA upon chemotherapeutic treatments.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126822
2. 1. Pálinkás HL
   2. Békési A
   3. Vértessy BG

   (2020) NCBI Gene Expression Omnibus

   ID GSE153407. H3K36me3 ChIP-seq in non-treated and raltitrexed treated UGI-expressing HCT116 cells.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE153407

1. 1. Shu X
   2. Lu Z
   3. Yi C

   (2018) NCBI Gene Expression Omnibus

   ID GSE99011. Genome-wide mapping reveals that deoxyuridine is enriched in the human centromeric DNA.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE99011

1. 1. Amemiya HM
   2. Kundaje A
   3. Boyle AP

   (2019) The ENCODE blacklist: identification of problematic regions of the genome

   Scientific Reports 9:9354.

   https://doi.org/10.1038/s41598-019-45839-z

   • PubMed
   • Google Scholar
2. 1. Becker JS
   2. McCarthy RL
   3. Sidoli S
   4. Donahue G
   5. Kaeding KE
   6. He Z
   7. Lin S
   8. Garcia BA
   9. Zaret KS

   (2017) Genomic and proteomic resolution of heterochromatin and its restriction of alternate fate genes

   Molecular Cell 68:1023–1037.

   https://doi.org/10.1016/j.molcel.2017.11.030

   • PubMed
   • Google Scholar
3. 1. Bermudez-Hernandez K
   2. Keegan S
   3. Whelan DR
   4. Reid DA
   5. Zagelbaum J
   6. Yin Y
   7. Ma S
   8. Rothenberg E
   9. Fenyö D

   (2017) A method for quantifying molecular interactions using stochastic modelling and Super-Resolution microscopy

   Scientific Reports 7:1–13.

   https://doi.org/10.1038/s41598-017-14922-8

   • PubMed
   • Google Scholar
4. 1. Black JC
   2. Van Rechem C
   3. Whetstine JR

   (2012) Histone lysine methylation dynamics: establishment, regulation, and biological impact

   Molecular Cell 48:491–507.

   https://doi.org/10.1016/j.molcel.2012.11.006

   • PubMed
   • Google Scholar
5. 1. Blackledge G

   (1998) New developments in Cancer treatment with the novel thymidylate synthase inhibitor raltitrexed ('Tomudex')

   British Journal of Cancer 77 Suppl 2:29–37.

   https://doi.org/10.1038/bjc.1998.423

   • PubMed
   • Google Scholar
6. 1. Boissinot S
   2. Furano AV

   (2005) The recent evolution of human L1 retrotransposons

   Cytogenetic and Genome Research 110:402–406.

   https://doi.org/10.1159/000084972

   • PubMed
   • Google Scholar
7. 1. Bowen N
   2. Smith CE
   3. Srivatsan A
   4. Willcox S
   5. Griffith JD
   6. Kolodner RD

   (2013) Reconstitution of long and short patch mismatch repair reactions using Saccharomyces cerevisiae proteins

   PNAS 110:18472–18477.

   https://doi.org/10.1073/pnas.1318971110

   • PubMed
   • Google Scholar
8. 1. Bryan DS
   2. Ransom M
   3. Adane B
   4. York K
   5. Hesselberth JR

   (2014) High resolution mapping of modified DNA nucleobases using excision repair enzymes

   Genome Research 24:1534–1542.

   https://doi.org/10.1101/gr.174052.114

   • PubMed
   • Google Scholar
9. 1. Burns MB
   2. Leonard B
   3. Harris RS

   (2015) APOBEC3B: pathological consequences of an innate immune DNA mutator

   Biomedical Journal 38:102–110.

   https://doi.org/10.4103/2319-4170.148904

   • PubMed
   • Google Scholar
10. 1. Buske OJ
    2. Hoffman MM
    3. Ponts N
    4. Le Roch KG
    5. Noble WS

    (2011) Exploratory analysis of genomic segmentations with segtools

    BMC Bioinformatics 12:415.

    https://doi.org/10.1186/1471-2105-12-415

    • PubMed
    • Google Scholar
11. 1. Chan RCW
    2. Libbrecht MW
    3. Roberts EG
    4. Bilmes JA
    5. Noble WS
    6. Hoffman MM

    (2018) Segway 2.0: gaussian mixture models and minibatch training

    Bioinformatics 34:669–671.

    https://doi.org/10.1093/bioinformatics/btx603

    • PubMed
    • Google Scholar
12. 1. Coltharp C
    2. Yang X
    3. Xiao J

    (2014) Quantitative analysis of single-molecule superresolution images

    Current Opinion in Structural Biology 28:112–121.

    https://doi.org/10.1016/j.sbi.2014.08.008

    • PubMed
    • Google Scholar
13. 1. Creyghton MP
    2. Cheng AW
    3. Welstead GG
    4. Kooistra T
    5. Carey BW
    6. Steine EJ
    7. Hanna J
    8. Lodato MA
    9. Frampton GM
    10. Sharp PA
    11. Boyer LA
    12. Young RA
    13. Jaenisch R

    (2010) Histone H3K27ac separates active from poised enhancers and predicts developmental state

    PNAS 107:21931–21936.

    https://doi.org/10.1073/pnas.1016071107

    • PubMed
    • Google Scholar
14. 1. Cutler PJ
    2. Malik MD
    3. Liu S
    4. Byars JM
    5. Lidke DS
    6. Lidke KA

    (2013) Multi-color quantum dot tracking using a high-speed hyperspectral line-scanning microscope

    PLOS ONE 8:e64320.

    https://doi.org/10.1371/journal.pone.0064320

    • PubMed
    • Google Scholar
15. 1. Datta M
    2. Aroli S
    3. Karmakar K
    4. Dutta S
    5. Chakravortty D
    6. Varshney U

    (2019) Development of mCherry tagged UdgX as a highly sensitive molecular probe for specific detection of uracils in DNA

    Biochemical and Biophysical Research Communications 518:38–43.

    https://doi.org/10.1016/j.bbrc.2019.08.005

    • PubMed
    • Google Scholar
16. 1. Ding WX
    2. Liu S
    3. Ma JX
    4. Pu J
    5. Wang HJ
    6. Zhang S
    7. Sun XC

    (2019) Raltitrexed increases radiation sensitivity of esophageal squamous carcinoma cells

    Cancer Cell International 19:36.

    https://doi.org/10.1186/s12935-019-0752-y

    • PubMed
    • Google Scholar
17. 1. Doetzlhofer A
    2. Rotheneder H
    3. Lagger G
    4. Koranda M
    5. Kurtev V
    6. Brosch G
    7. Wintersberger E
    8. Seiser C

    (1999) Histone deacetylase 1 can repress transcription by binding to Sp1

    Molecular and Cellular Biology 19:5504–5511.

    https://doi.org/10.1128/MCB.19.8.5504

    • PubMed
    • Google Scholar
18. 1. Elateri I
    2. Tinkelenberg BA
    3. Hansbury M
    4. Caradonna S
    5. Muller-Weeks S
    6. Ladner RD

    (2003) hSMUG1 can functionally compensate for Ung1 in the yeast Saccharomyces cerevisiae

    DNA Repair 2:315–323.

    https://doi.org/10.1016/S1568-7864(02)00221-5

    • PubMed
    • Google Scholar
19. 1. ENCODE Project Consortium

    (2012) An integrated encyclopedia of DNA elements in the human genome

    Nature 489:57–74.

    https://doi.org/10.1038/nature11247

    • PubMed
    • Google Scholar
20. 1. Feng J
    2. Liu T
    3. Zhang Y

    (2011) Using MACS to identify peaks from ChIP-Seq data

    Current Protocols in Bioinformatics Chapter 2:2.14.1–2.14.2.

    https://doi.org/10.1002/0471250953.bi0214s34

    • PubMed
    • Google Scholar
21. 1. Galashevskaya A
    2. Sarno A
    3. Vågbø CB
    4. Aas PA
    5. Hagen L
    6. Slupphaug G
    7. Krokan HE

    (2013) A robust, sensitive assay for genomic uracil determination by LC/MS/MS reveals lower levels than previously reported

    DNA Repair 12:699–706.

    https://doi.org/10.1016/j.dnarep.2013.05.002

    • PubMed
    • Google Scholar
22. 1. Gates LA
    2. Shi J
    3. Rohira AD
    4. Feng Q
    5. Zhu B
    6. Bedford MT
    7. Sagum CA
    8. Jung SY
    9. Qin J
    10. Tsai MJ
    11. Tsai SY
    12. Li W
    13. Foulds CE
    14. O'Malley BW

    (2017) Acetylation on histone H3 lysine 9 mediates a switch from transcription initiation to elongation

    Journal of Biological Chemistry 292:14456–14472.

    https://doi.org/10.1074/jbc.M117.802074

    • PubMed
    • Google Scholar
23. 1. Germano G
    2. Amirouchene-Angelozzi N
    3. Rospo G
    4. Bardelli A

    (2018) The clinical impact of the genomic landscape of mismatch Repair-Deficient cancers

    Cancer Discovery 8:1518–1528.

    https://doi.org/10.1158/2159-8290.CD-18-0150

    • PubMed
    • Google Scholar
24. 1. Giaimo BD
    2. Ferrante F
    3. Herchenröther A
    4. Hake SB
    5. Borggrefe T

    (2019) The histone variant H2A.Z in gene regulation

    Epigenetics & Chromatin 12:37.

    https://doi.org/10.1186/s13072-019-0274-9

    • PubMed
    • Google Scholar
25. 1. Gilbert DM

    (2002) Replication timing and transcriptional control: beyond cause and effect

    Current Opinion in Cell Biology 14:377–383.

    https://doi.org/10.1016/S0955-0674(02)00326-5

    • PubMed
    • Google Scholar
26. 1. Guo Y
    2. Dai Y
    3. Yu H
    4. Zhao S
    5. Samuels DC
    6. Shyr Y

    (2017) Improvements and impacts of GRCh38 human reference on high throughput sequencing data analysis

    Genomics 109:83–90.

    https://doi.org/10.1016/j.ygeno.2017.01.005

    • PubMed
    • Google Scholar
27. 1. Gupta D
    2. Heinen CD

    (2019) The mismatch repair-dependent DNA damage response: mechanisms and implications

    DNA Repair 78:60–69.

    https://doi.org/10.1016/j.dnarep.2019.03.009

    • PubMed
    • Google Scholar
28. 1. Hayden KE
    2. Strome ED
    3. Merrett SL
    4. Lee HR
    5. Rudd MK
    6. Willard HF

    (2013) Sequences associated with centromere competency in the human genome

    Molecular and Cellular Biology 33:763–772.

    https://doi.org/10.1128/MCB.01198-12

    • PubMed
    • Google Scholar
29. 1. Ho TH
    2. Park IY
    3. Zhao H
    4. Tong P
    5. Champion MD
    6. Yan H
    7. Monzon FA
    8. Hoang A
    9. Tamboli P
    10. Parker AS
    11. Joseph RW
    12. Qiao W
    13. Dykema K
    14. Tannir NM
    15. Castle EP
    16. Nunez-Nateras R
    17. Teh BT
    18. Wang J
    19. Walker CL
    20. Hung MC
    21. Jonasch E

    (2016) High-resolution profiling of histone h3 lysine 36 trimethylation in metastatic renal cell carcinoma

    Oncogene 35:1565–1574.

    https://doi.org/10.1038/onc.2015.221

    • PubMed
    • Google Scholar
30. 1. Hoffman MM
    2. Buske OJ
    3. Wang J
    4. Weng Z
    5. Bilmes JA
    6. Noble WS

    (2012) Unsupervised pattern discovery in human chromatin structure through genomic segmentation

    Nature Methods 9:473–476.

    https://doi.org/10.1038/nmeth.1937

    • PubMed
    • Google Scholar
31. 1. Holmquist G
    2. Gray M
    3. Porter T
    4. Jordan J

    (1982) Characterization of giemsa dark- and light-band DNA

    Cell 31:121–129.

    https://doi.org/10.1016/0092-8674(82)90411-1

    • PubMed
    • Google Scholar
32. 1. Horváth A
    2. Békési A
    3. Muha V
    4. Erdélyi M
    5. Vértessy BG

    (2013) Expanding the DNA alphabet in the fruit fly: uracil enrichment in genomic DNA

    Fly 7:23–27.

    https://doi.org/10.4161/fly.23192

    • PubMed
    • Google Scholar
33. 1. Horváth A
    2. Vértessy BG

    (2010) A one-step method for quantitative determination of uracil in DNA by real-time PCR

    Nucleic Acids Research 38:e196.

    https://doi.org/10.1093/nar/gkq815

    • PubMed
    • Google Scholar
34. 1. Huehls AM
    2. Huntoon CJ
    3. Joshi PM
    4. Baehr CA
    5. Wagner JM
    6. Wang X
    7. Lee MY
    8. Karnitz LM

    (2016) Genomically incorporated 5-Fluorouracil that escapes UNG-Initiated base excision repair blocks DNA replication and activates homologous recombination

    Molecular Pharmacology 89:53–62.

    https://doi.org/10.1124/mol.115.100164

    • PubMed
    • Google Scholar
35. 1. Hunter JD

    (2007) Matplotlib: a 2D graphics environment

    Computing in Science & Engineering 9:90–95.

    https://doi.org/10.1109/MCSE.2007.55

    • Google Scholar
36. 1. Hyun K
    2. Jeon J
    3. Park K
    4. Kim J

    (2017) Writing, erasing and reading histone lysine methylations

    Experimental & Molecular Medicine 49:e324.

    https://doi.org/10.1038/emm.2017.11

    • PubMed
    • Google Scholar
37. 1. Jensen MA
    2. Ferretti V
    3. Grossman RL
    4. Staudt LM

    (2017) The NCI genomic data commons as an engine for precision medicine

    Blood 130:453–459.

    https://doi.org/10.1182/blood-2017-03-735654

    • PubMed
    • Google Scholar
38. 1. Karolchik D
    2. Hinrichs AS
    3. Furey TS
    4. Roskin KM
    5. Sugnet CW
    6. Haussler D
    7. Kent WJ

    (2004) The UCSC table browser data retrieval tool

    Nucleic Acids Research 32:D493–D496.

    https://doi.org/10.1093/nar/gkh103

    • PubMed
    • Google Scholar
39. 1. Kavli B
    2. Sundheim O
    3. Akbari M
    4. Otterlei M
    5. Nilsen H
    6. Skorpen F
    7. Aas PA
    8. Hagen L
    9. Krokan HE
    10. Slupphaug G

    (2002) hUNG2 is the major repair enzyme for removal of uracil from U:a matches, U:g mismatches, and U in single-stranded DNA, with hSMUG1 as a broad specificity backup

    Journal of Biological Chemistry 277:39926–39936.

    https://doi.org/10.1074/jbc.M207107200

    • PubMed
    • Google Scholar
40. 1. Keppler A
    2. Gendreizig S
    3. Gronemeyer T
    4. Pick H
    5. Vogel H
    6. Johnsson K

    (2003) A general method for the covalent labeling of fusion proteins with small molecules in vivo

    Nature Biotechnology 21:86–89.

    https://doi.org/10.1038/nbt765

    • PubMed
    • Google Scholar
41. 1. Khodursky A
    2. Guzmán EC
    3. Hanawalt PC

    (2015) Thymineless death lives on: new insights into a classic phenomenon

    Annual Review of Microbiology 69:247–263.

    https://doi.org/10.1146/annurev-micro-092412-155749

    • PubMed
    • Google Scholar
42. 1. Kim N
    2. Jinks-Robertson S

    (2009) dUTP incorporation into genomic DNA is linked to transcription in yeast

    Nature 459:1150–1153.

    https://doi.org/10.1038/nature08033

    • PubMed
    • Google Scholar
43. 1. Koi M
    2. Umar A
    3. Chauhan DP
    4. Cherian SP
    5. Carethers JM
    6. Kunkel TA
    7. Boland CR

    (1994)

    Human chromosome 3 corrects mismatch repair deficiency and microsatellite instability and reduces N-Methyl-N’-nitro-N-nitrosoguanidine Tolerance in Colon Tumor Cells with Homozygous hMLHl Mutation

    Cancer Research 54:4308–4312.

    • PubMed
    • Google Scholar
44. 1. Kovalskaya E
    2. Buzdin A
    3. Gogvadze E
    4. Vinogradova T
    5. Sverdlov E

    (2006) Functional human endogenous retroviral LTR transcription start sites are located between the R and U5 regions

    Virology 346:373–378.

    https://doi.org/10.1016/j.virol.2005.11.007

    • PubMed
    • Google Scholar
45. 1. Kramerov DA
    2. Vassetzky NS

    (2005) Short retroposons in eukaryotic genomes

    International Review of Cytology 247:165–221.

    https://doi.org/10.1016/S0074-7696(05)47004-7

    • PubMed
    • Google Scholar
46. 1. Krokan HE
    2. Drabløs F
    3. Slupphaug G

    (2002) Uracil in DNA--occurrence, consequences and repair

    Oncogene 21:8935–8948.

    https://doi.org/10.1038/sj.onc.1205996

    • PubMed
    • Google Scholar
47. 1. Krokan HE
    2. Bjørås M

    (2013) Base excision repair

    Cold Spring Harbor Perspectives in Biology 5:a012583.

    https://doi.org/10.1101/cshperspect.a012583

    • PubMed
    • Google Scholar
48. 1. Kuhn RM
    2. Haussler D
    3. Kent WJ

    (2013) The UCSC genome browser and associated tools

    Briefings in Bioinformatics 14:144–161.

    https://doi.org/10.1093/bib/bbs038

    • PubMed
    • Google Scholar
49. 1. Layer RM
    2. Pedersen BS
    3. DiSera T
    4. Marth GT
    5. Gertz J
    6. Quinlan AR

    (2018) GIGGLE: a search engine for large-scale integrated genome analysis

    Nature Methods 15:123–126.

    https://doi.org/10.1038/nmeth.4556

    • PubMed
    • Google Scholar
50. 1. Li H
    2. Handsaker B
    3. Wysoker A
    4. Fennell T
    5. Ruan J
    6. Homer N
    7. Marth G
    8. Abecasis G
    9. Durbin R
    10. 1000 Genome Project Data Processing Subgroup

    (2009) The sequence alignment/Map format and SAMtools

    Bioinformatics 25:2078–2079.

    https://doi.org/10.1093/bioinformatics/btp352

    • PubMed
    • Google Scholar
51. 1. Li H
    2. Durbin R

    (2010) Fast and accurate long-read alignment with Burrows-Wheeler transform

    Bioinformatics 26:589–595.

    https://doi.org/10.1093/bioinformatics/btp698

    • PubMed
    • Google Scholar
52. 1. Liu M
    2. Schatz DG

    (2009) Balancing AID and DNA repair during somatic hypermutation

    Trends in Immunology 30:173–181.

    https://doi.org/10.1016/j.it.2009.01.007

    • PubMed
    • Google Scholar
53. 1. Longley DB
    2. Harkin DP
    3. Johnston PG

    (2003) 5-fluorouracil: mechanisms of action and clinical strategies

    Nature Reviews Cancer 3:330–338.

    https://doi.org/10.1038/nrc1074

    • PubMed
    • Google Scholar
54. 1. López-Flores I
    2. Garrido-Ramos MA

    (2012) The repetitive DNA content of eukaryotic genomes

    Genome Dynamics 7:1–28.

    https://doi.org/10.1159/000337118

    • PubMed
    • Google Scholar
55. 1. Luo Y
    2. Walla M
    3. Wyatt MD

    (2008) Uracil incorporation into genomic DNA does not predict toxicity caused by chemotherapeutic inhibition of thymidylate synthase

    DNA Repair 7:162–169.

    https://doi.org/10.1016/j.dnarep.2007.09.001

    • PubMed
    • Google Scholar
56. 1. Marchal C
    2. Sasaki T
    3. Vera D
    4. Wilson K
    5. Sima J
    6. Rivera-Mulia JC
    7. Trevilla-García C
    8. Nogues C
    9. Nafie E
    10. Gilbert DM

    (2018) Genome-wide analysis of replication timing by next-generation sequencing with E/L Repli-seq

    Nature Protocols 13:819–839.

    https://doi.org/10.1038/nprot.2017.148

    • PubMed
    • Google Scholar
57. 1. Mathews CK

    (2014) Deoxyribonucleotides as genetic and metabolic regulators

    The FASEB Journal 28:3832–3840.

    https://doi.org/10.1096/fj.14-251249

    • PubMed
    • Google Scholar
58. 1. Maul RW
    2. Gearhart PJ

    (2010) AID and somatic hypermutation

    Advances in Immunology 105:159–191.

    https://doi.org/10.1016/S0065-2776(10)05006-6

    • PubMed
    • Google Scholar
59. 1. Maul RW
    2. Gearhart PJ

    (2014) Refining the neuberger model: uracil processing by activated B cells

    European Journal of Immunology 44:1913–1916.

    https://doi.org/10.1002/eji.201444813

    • PubMed
    • Google Scholar
60. 1. Mei S
    2. Qin Q
    3. Wu Q
    4. Sun H
    5. Zheng R
    6. Zang C
    7. Zhu M
    8. Wu J
    9. Shi X
    10. Taing L
    11. Liu T
    12. Brown M
    13. Meyer CA
    14. Liu XS

    (2017) Cistrome data browser: a data portal for ChIP-Seq and chromatin accessibility data in human and mouse

    Nucleic Acids Research 45:D658–D662.

    https://doi.org/10.1093/nar/gkw983

    • PubMed
    • Google Scholar
61. 1. Meyers M
    2. Wagner MW
    3. Hwang HS
    4. Kinsella TJ
    5. Boothman DA

    (2001)

    Role of the hMLH1 DNA mismatch repair protein in fluoropyrimidine-mediated cell death and cell cycle responses

    Cancer Research 61:5193–5201.

    • PubMed
    • Google Scholar
62. 1. Miga KH
    2. Newton Y
    3. Jain M
    4. Altemose N
    5. Willard HF
    6. Kent WJ

    (2014) Centromere reference models for human chromosomes X and Y satellite arrays

    Genome Research 24:697–707.

    https://doi.org/10.1101/gr.159624.113

    • PubMed
    • Google Scholar
63. 1. Mol CD
    2. Arvai AS
    3. Sanderson RJ
    4. Slupphaug G
    5. Kavli B
    6. Krokan HE
    7. Mosbaugh DW
    8. Tainer JA

    (1995) Crystal structure of human uracil-DNA glycosylase in complex with a protein inhibitor: protein mimicry of DNA

    Cell 82:701–708.

    https://doi.org/10.1016/0092-8674(95)90467-0

    • PubMed
    • Google Scholar
64. 1. Molnár P
    2. Marton L
    3. Izrael R
    4. Pálinkás HL
    5. Vértessy BG

    (2018) Uracil moieties in Plasmodium falciparum genomic DNA

    FEBS Open Bio 8:1763–1772.

    https://doi.org/10.1002/2211-5463.12458

    • PubMed
    • Google Scholar
65. 1. Muha V
    2. Horváth A
    3. Békési A
    4. Pukáncsik M
    5. Hodoscsek B
    6. Merényi G
    7. Róna G
    8. Batki J
    9. Kiss I
    10. Jankovics F
    11. Vilmos P
    12. Erdélyi M
    13. Vértessy BG

    (2012) Uracil-containing DNA in Drosophila: stability, stage-specific accumulation, and developmental involvement

    PLOS Genetics 8:e1002738.

    https://doi.org/10.1371/journal.pgen.1002738

    • PubMed
    • Google Scholar
66. 1. Ostrer L
    2. Hamann BL
    3. Khodursky A

    (2015) Perturbed states of the bacterial chromosome: a thymineless death case study

    Frontiers in Microbiology 6:363.

    https://doi.org/10.3389/fmicb.2015.00363

    • PubMed
    • Google Scholar
67. 1. Owiti N
    2. Wei S
    3. Bhagwat AS
    4. Kim N

    (2018) Unscheduled DNA synthesis leads to elevated uracil residues at highly transcribed genomic loci in Saccharomyces cerevisiae

    PLOS Genetics 14:e1007516.

    https://doi.org/10.1371/journal.pgen.1007516

    • PubMed
    • Google Scholar
68. 1. Pettersen HS
    2. Visnes T
    3. Vågbø CB
    4. Svaasand EK
    5. Doseth B
    6. Slupphaug G
    7. Kavli B
    8. Krokan HE

    (2011) UNG-initiated base excision repair is the major repair route for 5-fluorouracil in DNA, but 5-fluorouracil cytotoxicity depends mainly on RNA incorporation

    Nucleic Acids Research 39:8430–8444.

    https://doi.org/10.1093/nar/gkr563

    • PubMed
    • Google Scholar
69. 1. Pfister SX
    2. Ahrabi S
    3. Zalmas LP
    4. Sarkar S
    5. Aymard F
    6. Bachrati CZ
    7. Helleday T
    8. Legube G
    9. La Thangue NB
    10. Porter AC
    11. Humphrey TC

    (2014) SETD2-dependent histone H3K36 trimethylation is required for homologous recombination repair and genome stability

    Cell Reports 7:2006–2018.

    https://doi.org/10.1016/j.celrep.2014.05.026

    • PubMed
    • Google Scholar
70. 1. Qin Q
    2. Mei S
    3. Wu Q
    4. Sun H
    5. Li L
    6. Taing L
    7. Chen S
    8. Li F
    9. Liu T
    10. Zang C
    11. Xu H
    12. Chen Y
    13. Meyer CA
    14. Zhang Y
    15. Brown M
    16. Long HW
    17. Liu XS

    (2016) ChiLin: a comprehensive ChIP-seq and DNase-seq quality control and analysis pipeline

    BMC Bioinformatics 17:404.

    https://doi.org/10.1186/s12859-016-1274-4

    • PubMed
    • Google Scholar
71. 1. Quinlan AR
    2. Hall IM

    (2010) BEDTools: a flexible suite of utilities for comparing genomic features

    Bioinformatics 26:841–842.

    https://doi.org/10.1093/bioinformatics/btq033

    • PubMed
    • Google Scholar
72. 1. Ramírez F
    2. Ryan DP
    3. Grüning B
    4. Bhardwaj V
    5. Kilpert F
    6. Richter AS
    7. Heyne S
    8. Dündar F
    9. Manke T

    (2016) deepTools2: a next generation web server for deep-sequencing data analysis

    Nucleic Acids Research 44:W160–W165.

    https://doi.org/10.1093/nar/gkw257

    • PubMed
    • Google Scholar
73. 1. Rashid S
    2. Freitas MO
    3. Cucchi D
    4. Bridge G
    5. Yao Z
    6. Gay L
    7. Williams M
    8. Wang J
    9. Suraweera N
    10. Silver A
    11. McDonald SAC
    12. Chelala C
    13. Szabadkai G
    14. Martin SA

    (2019) MLH1 deficiency leads to deregulated mitochondrial metabolism

    Cell Death & Disease 10:795.

    https://doi.org/10.1038/s41419-019-2018-y

    • PubMed
    • Google Scholar
74. 1. Requena CE
    2. Pérez-Moreno G
    3. Horváth A
    4. Vértessy BG
    5. Ruiz-Pérez LM
    6. González-Pacanowska D
    7. Vidal AE

    (2016) The nucleotidohydrolases DCTPP1 and dUTPase are involved in the cellular response to decitabine

    The Biochemical Journal 473:2635–2643.

    https://doi.org/10.1042/BCJ20160302

    • PubMed
    • Google Scholar
75. 1. Róna G
    2. Scheer I
    3. Nagy K
    4. Pálinkás HL
    5. Tihanyi G
    6. Borsos M
    7. Békési A
    8. Vértessy BG

    (2016) Detection of uracil within DNA using a sensitive labeling method for in vitro and cellular applications

    Nucleic Acids Research 44:e28.

    https://doi.org/10.1093/nar/gkv977

    • PubMed
    • Google Scholar
76. 1. Rona G
    2. Roberti D
    3. Yin Y
    4. Pagan JK
    5. Homer H
    6. Sassani E
    7. Zeke A
    8. Busino L
    9. Rothenberg E
    10. Pagano M

    (2018) PARP1-dependent recruitment of the FBXL10-RNF68-RNF2 ubiquitin ligase to sites of DNA damage controls H2A.Z loading

    eLife 7:e38771.

    https://doi.org/10.7554/eLife.38771

    • PubMed
    • Google Scholar
77. 1. Sakhtemani R
    2. Senevirathne V
    3. Stewart J
    4. Perera MLW
    5. Pique-Regi R
    6. Lawrence MS
    7. Bhagwat AS

    (2019) Genome-wide mapping of regions preferentially targeted by the human DNA-cytosine deaminase APOBEC3A using uracil-DNA pulldown and sequencing

    Journal of Biological Chemistry 294:15037–15051.

    https://doi.org/10.1074/jbc.RA119.008053

    • PubMed
    • Google Scholar
78. 1. Saksouk N
    2. Simboeck E
    3. Déjardin J

    (2015) Constitutive heterochromatin formation and transcription in mammals

    Epigenetics & Chromatin 8:3.

    https://doi.org/10.1186/1756-8935-8-3

    • PubMed
    • Google Scholar
79. 1. Sekine S
    2. Mori T
    3. Ogawa R
    4. Tanaka M
    5. Yoshida H
    6. Taniguchi H
    7. Nakajima T
    8. Sugano K
    9. Yoshida T
    10. Kato M
    11. Furukawa E
    12. Ochiai A
    13. Hiraoka N

    (2017) Mismatch repair deficiency commonly precedes adenoma formation in lynch Syndrome-Associated colorectal tumorigenesis

    Modern Pathology 30:1144–1151.

    https://doi.org/10.1038/modpathol.2017.39

    • PubMed
    • Google Scholar
80. 1. Shu X
    2. Liu M
    3. Lu Z
    4. Zhu C
    5. Meng H
    6. Huang S
    7. Zhang X
    8. Yi C

    (2018) Genome-wide mapping reveals that deoxyuridine is enriched in the human centromeric DNA

    Nature Chemical Biology 14:680–687.

    https://doi.org/10.1038/s41589-018-0065-9

    • PubMed
    • Google Scholar
81. 1. Stamatoyannopoulos JA
    2. Adzhubei I
    3. Thurman RE
    4. Kryukov GV
    5. Mirkin SM
    6. Sunyaev SR

    (2009) Human mutation rate associated with DNA replication timing

    Nature Genetics 41:393–395.

    https://doi.org/10.1038/ng.363

    • PubMed
    • Google Scholar
82. 1. Stanta G
    2. Bonin S

    (2018) Overview on clinical relevance of Intra-Tumor heterogeneity

    Frontiers in Medicine 5:85.

    https://doi.org/10.3389/fmed.2018.00085

    • PubMed
    • Google Scholar
83. 1. Stenglein MD
    2. Burns MB
    3. Li M
    4. Lengyel J
    5. Harris RS

    (2010) APOBEC3 proteins mediate the clearance of foreign DNA from human cells

    Nature Structural & Molecular Biology 17:222–229.

    https://doi.org/10.1038/nsmb.1744

    • PubMed
    • Google Scholar
84. 1. Suspène R
    2. Henry M
    3. Guillot S
    4. Wain-Hobson S
    5. Vartanian JP

    (2005) Recovery of APOBEC3-edited human immunodeficiency virus G->A hypermutants by differential DNA denaturation PCR

    Journal of General Virology 86:125–129.

    https://doi.org/10.1099/vir.0.80426-0

    • PubMed
    • Google Scholar
85. 1. Thorvaldsdóttir H
    2. Robinson JT
    3. Mesirov JP

    (2013) Integrative genomics viewer (IGV): high-performance genomics data visualization and exploration

    Briefings in Bioinformatics 14:178–192.

    https://doi.org/10.1093/bib/bbs017

    • PubMed
    • Google Scholar
86. 1. Van Triest B
    2. Pinedo HM
    3. Giaccone G
    4. Peters GJ

    (2000) Downstream molecular determinants of response to 5-fluorouracil and antifolate thymidylate synthase inhibitors

    Annals of Oncology 11:385–391.

    https://doi.org/10.1023/A:1008351221345

    • PubMed
    • Google Scholar
87. 1. Veatch SL
    2. Machta BB
    3. Shelby SA
    4. Chiang EN
    5. Holowka DA
    6. Baird BA

    (2012) Correlation functions quantify super-resolution images and estimate apparent clustering due to over-counting

    PLOS ONE 7:e31457.

    https://doi.org/10.1371/journal.pone.0031457

    • PubMed
    • Google Scholar
88. 1. Vértessy BG
    2. Tóth J

    (2009) Keeping uracil out of DNA: physiological role, structure and catalytic mechanism of dUTPases

    Accounts of Chemical Research 42:97–106.

    https://doi.org/10.1021/ar800114w

    • PubMed
    • Google Scholar
89. 1. Visnes T
    2. Doseth B
    3. Pettersen HS
    4. Hagen L
    5. Sousa MM
    6. Akbari M
    7. Otterlei M
    8. Kavli B
    9. Slupphaug G
    10. Krokan HE

    (2009) Uracil in DNA and its processing by different DNA glycosylases

    Philosophical Transactions of the Royal Society B: Biological Sciences 364:563–568.

    https://doi.org/10.1098/rstb.2008.0186

    • PubMed
    • Google Scholar
90. 1. Wallace SS

    (2014) Base excision repair: a critical player in many games

    DNA Repair 19:14–26.

    https://doi.org/10.1016/j.dnarep.2014.03.030

    • PubMed
    • Google Scholar
91. 1. Weddington N
    2. Stuy A
    3. Hiratani I
    4. Ryba T
    5. Yokochi T
    6. Gilbert DM

    (2008) ReplicationDomain: a visualization tool and comparative database for genome-wide replication timing data

    BMC Bioinformatics 9:530.

    https://doi.org/10.1186/1471-2105-9-530

    • PubMed
    • Google Scholar
92. 1. Weeks LD
    2. Zentner GE
    3. Scacheri PC
    4. Gerson SL

    (2014) Uracil DNA glycosylase (UNG) loss enhances DNA double strand break formation in human Cancer cells exposed to pemetrexed

    Cell Death & Disease 5:e1045.

    https://doi.org/10.1038/cddis.2013.477

    • PubMed
    • Google Scholar
93. 1. Whelan DR
    2. Lee WTC
    3. Yin Y
    4. Ofri DM
    5. Bermudez-Hernandez K
    6. Keegan S
    7. Fenyo D
    8. Rothenberg E

    (2018) Spatiotemporal dynamics of homologous recombination repair at single collapsed replication forks

    Nature Communications 9:3882.

    https://doi.org/10.1038/s41467-018-06435-3

    • PubMed
    • Google Scholar
94. 1. Wilson PM
    2. Danenberg PV
    3. Johnston PG
    4. Lenz HJ
    5. Ladner RD

    (2014) Standing the test of time: targeting thymidylate biosynthesis in Cancer therapy

    Nature Reviews Clinical Oncology 11:282–298.

    https://doi.org/10.1038/nrclinonc.2014.51

    • PubMed
    • Google Scholar
95. 1. Xu Z
    2. Zan H
    3. Pone EJ
    4. Mai T
    5. Casali P

    (2012) Immunoglobulin class-switch DNA recombination: induction, targeting and beyond

    Nature Reviews Immunology 12:517–531.

    https://doi.org/10.1038/nri3216

    • PubMed
    • Google Scholar
96. 1. Yan Y
    2. Han X
    3. Qing Y
    4. Condie AG
    5. Gorityala S
    6. Yang S
    7. Xu Y
    8. Zhang Y
    9. Gerson SL

    (2016) Inhibition of uracil DNA glycosylase sensitizes Cancer cells to 5-fluorodeoxyuridine through replication fork collapse-induced DNA damage

    Oncotarget 7:59299–59313.

    https://doi.org/10.18632/oncotarget.11151

    • PubMed
    • Google Scholar
97. 1. Yin Y
    2. Rothenberg E

    (2016) Probing the spatial organization of molecular complexes using Triple-Pair-Correlation

    Scientific Reports 6:30819.

    https://doi.org/10.1038/srep30819

    • PubMed
    • Google Scholar
98. 1. Zerbino DR
    2. Achuthan P
    3. Akanni W
    4. Amode MR
    5. Barrell D
    6. Bhai J
    7. Billis K
    8. Cummins C
    9. Gall A
    10. Girón CG
    11. Gil L
    12. Gordon L
    13. Haggerty L
    14. Haskell E
    15. Hourlier T
    16. Izuogu OG
    17. Janacek SH
    18. Juettemann T
    19. To JK
    20. Laird MR
    21. Lavidas I
    22. Liu Z
    23. Loveland JE
    24. Maurel T
    25. McLaren W
    26. Moore B
    27. Mudge J
    28. Murphy DN
    29. Newman V
    30. Nuhn M
    31. Ogeh D
    32. Ong CK
    33. Parker A
    34. Patricio M
    35. Riat HS
    36. Schuilenburg H
    37. Sheppard D
    38. Sparrow H
    39. Taylor K
    40. Thormann A
    41. Vullo A
    42. Walts B
    43. Zadissa A
    44. Frankish A
    45. Hunt SE
    46. Kostadima M
    47. Langridge N
    48. Martin FJ
    49. Muffato M
    50. Perry E
    51. Ruffier M
    52. Staines DM
    53. Trevanion SJ
    54. Aken BL
    55. Cunningham F
    56. Yates A
    57. Flicek P

    (2018) Ensembl 2018

    Nucleic Acids Research 46:D754–D761.

    https://doi.org/10.1093/nar/gkx1098

    • PubMed
    • Google Scholar
99. 1. Zhang Y
    2. Liu T
    3. Meyer CA
    4. Eeckhoute J
    5. Johnson DS
    6. Bernstein BE
    7. Nusbaum C
    8. Myers RM
    9. Brown M
    10. Li W
    11. Liu XS

    (2008) Model-based analysis of ChIP-Seq (MACS)

    Genome Biology 9:R137.

    https://doi.org/10.1186/gb-2008-9-9-r137

    • PubMed
    • Google Scholar
100. 1. Zhao H
     2. Zhang Y
     3. Sun J
     4. Zhan C
     5. Zhao L

     (2016) Raltitrexed inhibits HepG2 cell proliferation via G0/G1 cell cycle arrest

     Oncology Research 23:237–248.

     https://doi.org/10.3727/096504016X14562725373671

     • PubMed
     • Google Scholar

Author details

1. Hajnalka L Pálinkás

   1. Genome Metabolism Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Hungary
   2. Department of Applied Biotechnology and Food Sciences, Budapest University of Technology and Economics, Budapest, Hungary
   3. Doctoral School of Multidisciplinary Medical Science, University of Szeged, Szeged, Hungary

   Contribution

   Conceptualization, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   palinkas.hajnalka@ttk.hu

   Competing interests

   No competing interests declared

2. Angéla Békési

   1. Genome Metabolism Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Hungary
   2. Department of Applied Biotechnology and Food Sciences, Budapest University of Technology and Economics, Budapest, Hungary

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2294-3002

3. Gergely Róna

   1. Department of Applied Biotechnology and Food Sciences, Budapest University of Technology and Economics, Budapest, Hungary
   2. Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, United States
   3. Perlmutter Cancer Center, New York University School of Medicine, New York, United States
   4. Howard Hughes Medical Institute, New York University School of Medicine, New York, United States

   Contribution

   Conceptualization, Investigation, Visualization, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

4. Lőrinc Pongor

   1. Cancer Biomarker Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Hungary
   2. Department of Bioinformatics and 2nd Department of Pediatrics, Semmelweis University, Budapest, Hungary

   Contribution

   Data curation, Software, Formal analysis

   Competing interests

   No competing interests declared

5. Gábor Papp

   Department of Applied Biotechnology and Food Sciences, Budapest University of Technology and Economics, Budapest, Hungary

   Contribution

   Data curation, Software, Formal analysis, Visualization

   Competing interests

   No competing interests declared

6. Gergely Tihanyi

   1. Genome Metabolism Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Hungary
   2. Department of Applied Biotechnology and Food Sciences, Budapest University of Technology and Economics, Budapest, Hungary

   Contribution

   Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3729-6709

7. Eszter Holub

   Department of Applied Biotechnology and Food Sciences, Budapest University of Technology and Economics, Budapest, Hungary

   Contribution

   Formal analysis

   Competing interests

   No competing interests declared

8. Ádám Póti

   Genome Stability Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Hungary

   Contribution

   Formal analysis, Visualization

   Competing interests

   No competing interests declared

9. Carolina Gemma

   Department of Surgery and Cancer, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom

   Contribution

   Resources

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4890-9972

10. Simak Ali

    Department of Surgery and Cancer, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom

    Contribution

    Resources

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-1320-0816

11. Michael J Morten

    Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, United States

    Contribution

    Investigation

    Competing interests

    No competing interests declared

12. Eli Rothenberg

    Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-1382-1380

13. Michele Pagano

    1. Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, United States
    2. Perlmutter Cancer Center, New York University School of Medicine, New York, United States
    3. Howard Hughes Medical Institute, New York University School of Medicine, New York, United States

    Contribution

    Supervision

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3210-2442

14. Dávid Szűts

    Genome Stability Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Hungary

    Contribution

    Supervision, Writing - review and editing

    Competing interests

    No competing interests declared

15. Balázs Győrffy

    1. Cancer Biomarker Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Hungary
    2. Department of Bioinformatics and 2nd Department of Pediatrics, Semmelweis University, Budapest, Hungary

    Contribution

    Supervision, Writing - review and editing

    Competing interests

    No competing interests declared

16. Beáta G Vértessy

    1. Genome Metabolism Research Group, Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Hungary
    2. Department of Applied Biotechnology and Food Sciences, Budapest University of Technology and Economics, Budapest, Hungary

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Writing - original draft, Project administration, Writing - review and editing

    For correspondence

    vertessy@mail.bme.hu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-1288-2982

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