Tissue-specific mutagenesis from endogenous guanine damage is suppressed by Polκ and DNA repair

Domain: biology
Published: 2025-12-12
Editorial outcome: major_revision

Abstract
Knowledge of mutational patterns has expanded significantly, but linking these patterns to specific molecular mechanisms or sources of endogenous DNA damage remains challenging. Translesion synthesis (TLS) is a key determinant of mutagenesis, yet the endogenous lesions that require TLS and how TLS polymerases shape mammalian mutational landscapes are unclear. Here, we characterize somatic mutational patterns across mouse tissues deficient in the TLS polymerase Polκ and find that Polκ suppresses a distinct tissue-specific mutational signature in the liver and kidney. This signature, enriched for C>A/G/T mutations with strong transcriptional-strand bias, indicates that Polκ performs error-free bypass of endogenous guanine adducts. Nucleotide excision repair (NER) acts in parallel, mitigating some of this damage. Targeted adductomics and biochemical analyses identify endogenous N2-dG lesions requiring Polκ-mediated bypass, while untargeted adductomics reveal new guanine lesions that engage NER. These findings uncover the nature of endogenous DNA damage and the coordinated roles of repair and tolerance pathways that limit mutagenesis in tissues.

Introduction
The integrity of the genome is constantly under threat. This threat comes from external agents that damage DNA (e.g., UV radiation), and from internal sources that are even more insidious. Normal cellular processes generate a range of reactive by-products that attack DNA and cause endogenous DNA damage. Known sources includes spontaneous hydrolysis, reactive oxygen species (ROS), and simple aldehydes, which produce chemically distinct DNA lesions1–3. Despite the prevalence of endogenous DNA damage, we have only a crude understanding of different DNA lesions and damage sources. This is because direct detection of endogenous DNA lesions is extremely challenging, due to their low abundance, rapid repair, and similarity to unmodified bases. The intrinsic instability of DNA led to the discovery that cells must actively counteract this damage for DNA to fulfill its function as the genetic material of the cell1.

Cells have two major pathways to deal with DNA damage, and they differ in fidelity. The first route is DNA repair, which encompasses a toolkit of efficient repair mechanisms that excise or correct DNA lesions, each tailored to specific types of damage. For example, Nucleotide excision repair (NER) is dedicated to removing bulky DNA adducts, helix-distorting and transcription-blocking lesions, like those induced by UV radiation4. However, some DNA lesions may evade repair or be encountered during DNA replication. The second pathway, known as DNA damage tolerance, allows cells to bypass DNA lesions, mostly during DNA replication to maintain fork progression5. A major route of DNA damage tolerance is translesion synthesis (TLS), which relies on diverse specialized DNA polymerases that can synthesize DNA over damaged bases. Mammals have several DNA polymerases with TLS activity (Polζ, Polκ, Polι, Polη, Rev1, Polβ, Polλ, Polμ, Polθ, Polν), and the process is tightly regulated by Rev1 and ubiquitination of the sliding clamp PCNA6. These TLS polymerases can accommodate various types of distorted DNA because they have bigger active sites. TLS polymerases can be relatively error-free depending on the lesion, but they are more often error-prone, leading to mutations. Therefore, DNA damage tolerance comes at the cost of increased mutation but protects cells from more severe genomic instability, such as DNA breaks arising from failure to replicate past DNA lesions6,7.

This interplay between DNA damage, DNA repair, and DNA damage tolerance is essential for maintaining genomic integrity and is a key determinant of mutagenesis. A classic example of this interplay is DNA damage induced by UV radiation, largely repaired by NER. In parallel, the TLS polymerase Polη performs efficient and error-free bypass (or tolerance) of UV damage8. Mutations in either NER (XPA-XPG) or Polη (XPV) both cause increased mutagenesis in response to UV, leading to skin cancer and the human syndrome Xeroderma pigmentosum9. Therefore, mutagenic outcomes depend on the nature of the DNA lesion, but more importantly, on how the cell deals with the damage.

The recent explosion of genome sequencing data has made it possible to characterize mutational patterns or ‘signatures’ across thousands of genomes10,11. Certain mutational signatures can be linked to exogenous exposures like UV radiation (single base substitution signature 7, or SBS79) or known endogenous DNA damage (e.g,. deamination, SBS112, SBS2, SBS1313; or oxidation, SBS1814, SBS3615). Interestingly, some signatures only occur in a subset of tissues, presumably due to organ-specific cellular physiology. Many mutational signatures are suspected to be caused by endogenous sources, but the identity of the lesions is unknown. For example, NER deficiency leads to SBS8 mutations in tissues not exposed to UV radiation, but the endogenous damage driving these mutations is a mystery16,17. Similarly, SBS19 mutations in blood stem cells are driven by persistent endogenous lesions of unknown origin18.

Mutational signatures are complex patterns arising from poorly understood interactions among DNA damage, repair, and damage tolerance. Here we untangle this complex interplay in mammalian tissues, with the ultimate aim of uncovering the chemical nature of novel sources of endogenous DNA damage. To tackle this fundamental question, here we characterize somatic mutations in mice lacking the Y-family TLS polymerase Polκ. Polκ is one of the most conserved TLS polymerases with orthologs in bacteria and archaea. On damaged DNA, Polκ and its E. coli ortholog DinB, are particularly efficient at bypassing DNA adducts that distort the minor groove, mainly at the N2 position of guanine in an error-free manner19–21. Polκ can bypass various bulky and non-bulky lesions, such as BPDE-N2-dG, N2-furfuryl-dG, N2-(1-carboxyethyl)-dG, N2-alkyl-dG, O2- and some O4-alkyl dT adducts, thymine glycol, DNA-peptide crosslinks, intra- and interstrand crosslinks22–27. Polκ is also particularly good at extending mispaired primer termini, therefore also playing a role during the TLS extension step28. With regards to mutagenesis, Polκ plays a dual role—both promoting29 and suppressing21,30 mutations, depending on the context and type of DNA adduct.

While considerable insight has been gained into the in vitro biochemical activity of Polκ —particularly in the context of exogenous DNA damage—its physiological role in vivo remains poorly understood. A previous study using the BigBlue lacZ reporter system, showed that Polk-/- mice exhibit a spontaneous mutator phenotype31, but these findings were limited to the small reporter assay and do not fully reflect the complexity of the genome-wide mutational processes occurring in tissues. Here, we overcome these limitations by combining state-of-the-art organoid culture systems with genome sequencing, enabling us to comprehensively map the genome-wide somatic mutational landscape across mouse tissues. We discover that Polκ suppresses a new tissue-specific genomic mutational signature, driven by endogenous guanine adducts. We then combine mouse genetics, adductomics, and biochemistry to demonstrate how both bulky and non-bulky guanine lesions contribute to mutagenesis. Finally, we use a recently developed untargeted DNA adductomics method to uncover novel endogenous lesions. Together, our findings show that Polκ and DNA repair cooperate to limit mutagenesis in tissues, and shed light on the elusive nature of endogenous DNA damage.

Results
Characterizing somatic mutations in mouse tissues
Polκ is the most conserved TLS polymerase, with orthologs in bacteria and archaea, and it is able to bypass in vitro various bulky and non-bulky lesions22. To determine the role of Polκ in shaping mutational landscapes across different organs, we comprehensively assessed somatic mutations across a panel of mammalian tissues. We analyzed tissues from aged (18-month-old) wild type and Polk-/- mice using either in vitro expansion of single cells or NanoSeq32 (Fig. 1a). We expanded single-cell clones from bone marrow progenitors, and used organoid culture to expand single stem cells from the small intestine, stomach, lung airways, and liver cholangiocytes, as done previously33,34. Clones were expanded for 3-4 weeks and subjected to whole-genome sequencing; in total 32 genomes were sequenced at around 25x depth together with germline references (Fig. 1a). We used a combination of Strelka2 and Mutect2 to call high-confidence somatic mutations, defined as clonal mutations present in the original cell that are shared by its progeny and have a variant allele frequency (VAF) centered around 0.5. Subclonal mutations that occur during in vitro culture have low allele frequencies and are removed from the analysis. Samples without a distinct peak around VAF 0.5 were considered non-clonal and excluded from downstream analysis (Supplementary Fig. 1a). Sequencing of single-cell-derived clones provides whole-genome coverage data but is limited to cell types that can be expanded in vitro. Therefore, in parallel, we used NanoSeq to interrogate wild-type and Polk-/- tissues without the need of in vitro culture. NanoSeq is a highly sensitive single-molecule technique, based on duplex sequencing, that allows detection of rare somatic mutations in a mixed population of cells32. We applied NanoSeq to bulk DNA from the kidney, adrenal gland (among the tissues with highest Polκ expression35), as well as lung and liver (to draw comparisons between the two methods). Overall, we found good correlation in mutation burden estimates for single-base substitutions (SBSs), doublet base substitutions (DBSs), and insertions/deletions (indels) between clonal expansion of single cells and NanoSeq (Supplementary Fig. 1b), with NanoSeq having a higher burden in line with previous work32.

Polκ suppresses a novel tissue-specific mutational signature
We first assessed the SBS burden in different tissues. In wild type mice, we found that the SBS burden varies across tissues, being highest in the small intestine and lowest in bone marrow progenitors (Fig. 1b). Our trends are consistent with previous observations32–34,36. However, Polk-/- mice show a strikingly different pattern (Fig. 1b). We observe a 4-fold increase in the burden of SBSs in the kidney and liver, while the SBS burden is essentially unchanged in the small intestine and bone marrow progenitors. This shows that Polκ normally suppresses point mutations and that the increased mutagenesis is tissue-specific. We find no obvious correlation between mutation burden and the expression level of Polκ or other TLS polymerases (Supplementary Fig. 1c). These results suggest that differences in TLS gene expression are unlikely to fully explain the tissue-specific mutagenesis we observe, which is more likely attributable to differences in damage burden across tissues; however, we cannot rule out the potential contribution of variation in TLS protein levels or activity over time.

To look further into the mutation patterns, we sorted SBSs into six classes, referring to the pyrimidine of the Watson-Crick base pair as the mutated base (Supplementary Fig. 2a). This analysis reveals that increased mutagenesis in Polk-/- liver cholangiocytes, bulk liver, and kidney is largely driven by C>A and C>G changes, and to a lesser extent by C>T and T>A mutations (Supplementary Fig. 2a). These six SBS classes can be further expanded by considering the nucleotide context (i.e., the bases immediately 5’ and 3’ of the mutated base). This 96-context classification allowed us to further refine mutation types and reveals considerable heterogeneity in the somatic mutational landscape of wild type mouse tissues, mirroring observations in human cancer10,11 and normal tissues37 (Supplementary Fig. 2b).

To systematically explore the differences in mutational landscapes, we performed de novo mutational signature extraction. Briefly, de novo signature extraction methods use unsupervised machine learning to identify a set of mutational signatures that explain the observed mutations and determine their activity in each sample. Using SigProfilerExtractor, a non-negative matrix factorization (NMF)-based approach, we identified three de novo signatures (SBSA-C, Supplementary Fig. 3a). We obtained comparable signatures using mSigHdp, a signature extraction method that uses a hierarchical Dirichlet process model (HDP)38,39 (Supplementary Fig. 3b). SigProfiler signatures SBSB and SBSC could be reconstructed (i.e. Cosine similarity > 0.92) by a combination of known signatures from the Catalog of Somatic Mutations in Cancer (COSMIC). These signatures included: SBS1, caused by deamination of 5-methylcytosine at CpG sites12; SBS5, a ubiquitous signature of unknown origin which accumulates over time independently of cell division32,40,41; SBS17, induced by 5-fluorouracil and an unknown endogenous driver42, and SBS18, caused by the mutagenic bypass of 8oxo-guanine, a lesion linked to reactive oxygen species14 (Fig. 1c, d).

The SBSA signature, on the other hand, is characterized by C>A, C>G and C>T changes and bears little similarity to known COSMIC signatures (Fig. 1c, e, Supplementary Fig. 3c). SBSA was reconstructed by SigProfiler with a combination of COSMIC signatures SBS4 (C>A mutations induced by tobacco smoking) and SBS39 (C>G mutations, unknown cause), albeit with low similarity (Cosine sim. 0.845) (Supplementary Fig. 3d). SBS39 is one of the few C>G rich signatures in the COSMIC database, is mostly found in medulloblastoma and breast cancer, and its driver is unknown. Upon closer inspection, we noted that SBS39 completely lacks transcriptional-strand bias, a prominent feature of both SBS4 and SBSA (Supplementary Fig. 3e), which we discuss in more detail below. Due to these differences in mutational strand-asymmetries and the fact that the mice were not exposed to tobacco, we determined SBSA should not be decomposed further into SBS4 and SBS39 but defined SBSA as a novel signature. Importantly, the SBSA signature was largely responsible for the increase of mutation in Polk-/- bulk kidney, liver and liver cholangiocytes (Fig. 1c, e). The mutational pattern was almost identical between Polk-/- kidney and liver cholangiocytes (Cosine sim. 0.93), but mutations in bulk liver had a larger C>A component compared to liver cholangiocytes (Supplementary Fig. 2a, b). As mentioned previously, NanoSeq provides somatic mutations in single DNA molecules from all cells in the tissue of interest. In the liver, around 60% of cells are hepatocytes, whereas cholangiocytes, which were grown into clones sequenced with WGS, only make up around 3%. In line with this, lung samples had even more distinct patterns, reflecting differences between clones from bronchial epithelium and bulk lung tissue (also containing alveolar epithelium and immune cells amongst other cells)43.

In summary, we characterized somatic mutations in mouse tissues using two complementary approaches. We find that the TLS polymerase Polκ suppresses a novel, tissue-specific SBS mutational signature; hereafter, we refer to SBSA as SBS-PolkKO. The signature is driven by endogenous DNA damage, and our results imply that Polκ predominantly performs error-free bypass of this damage, particularly in the liver and kidney.

The landscape of doublet base substitutions and indels in mouse tissues
Having uncovered a novel genome-wide SBS mutational signature, we next turned our attention to other types of mutations. DBSs are exceedingly rare across mouse tissues, in the range of 0-20 DBSs/genome, but we find a higher burden of DBSs in Polk-/- bulk kidney and liver (Fig. 2a). Although we can visualize DBS mutation types using the COSMIC DBS78 classification, we were unable to extract mutational signatures due to the low numbers of mutations detected. In wild-type livers, DBSs are dominated by CC>AA (or GG>TT) changes, but Polk-/- livers display a wider spectrum of changes (Fig. 2b, Supplementary Fig. 4). Because a likely cause of DBSs is the mutagenic bypass of tandem base damage (e.g., intrastrand crosslinks), our results suggest that Polκ suppresses endogenous DBSs by contributing to error-free bypass of endogenous tandem lesions. This is in line with role of Polκ facilitating extension beyond tandem lesions like UV-induced T-T dimers and cisplatin Pt-GG crosslinks44,45.

Focusing on the burden of indels, we observe similar burdens in wild type and Polk-/- mice across tissues, with the highest indel burden in the small intestine and stomach (Fig. 2c). We assessed the pattern of indels using the COSMIC ID83 classification, which considers size, nucleotides affected, and presence on repetitive and/or microhomology regions, and did not find obvious differences (Fig. 2d, Supplementary Fig. 5). Despite low numbers of indels, SigProfiler extracted two de novo indel signatures which were reconstructed by a combination of known COSMIC signatures ID1, ID2 (both polymerase slippage during replication), ID9 (unknown cause) and ID23 (aristolochic acid exposure) (Fig. 2e). The indel mutational landscape in the small intestine and stomach, both actively dividing epithelia, is dominated by ID1 and ID2, consistent with DNA replication driving these indels10. Importantly, the liver of Polk-/- mice carries a low number of indels indistinguishable from wild type controls (Fig. 2c) and with a similar pattern to wild type livers (Fig. 2d, e), indicating the mutagenesis in Polk-/- livers is restricted to substitutions.

SBS-PolkKO mutations are characterized by transcriptional-strand bias (TSB)
The most striking observation in our mutation analysis is the presence of a novel mutational signature in Polk-/- kidney and liver. To elucidate the topographical characteristics of SBS-PolkKO mutations across the mouse genome46,47, we characterized the SBS-PolkKO signature using SigProfiler Topography47. First, we considered the relationship with DNA replication and detected an enrichment of SBS-PolkKO mutations in late vs early-replicating regions (Fig. 3a). This is also observed for several other mutational signatures46–49 and may be explained in part by the preferential use of error-prone TLS over error-free template switching during late S phase50–52. Mutational processes that are coupled to replication (e.g., mismatch repair, Polδ or Polε mutations) show replication-strand bias47,53,54. In contrast, SBS-PolkKO mutations have no replication strand asymmetry (Fig. 3b), suggesting that the TLS factors responsible for SBS-PolkKO mutations are not preferentially associated with either the lagging or leading strands. As is the case with most mutational signatures, SBS-PolkKO mutations are depleted in genic regions (Fig. 3b).

Finally, the most prominent topographical feature of the SBS-PolkKO signature is its strong transcriptional-strand bias (TSB). Within genes, C>A, C>G, and C>T mutations are clearly depleted from the untranscribed strand compared to the transcribed strand (Fig. 3b, c). The two main sources of TSB are transcription-coupled repair (TCR) or poorly understood transcription-coupled damage. The hallmark of transcription-coupled damage is a higher mutation rate in genic vs intergenic regions, which increases with expression level, best illustrated by SBS16 mutations in liver cancer53,55. As we find that SBS-PolkKO mutations are depleted in genic regions (Fig. 3b), we infer that TCR is likely the main source of TSB. Indeed, when we further subdivide genic regions into expression bins, we see depletion of mutations in highly expressed genes (Fig. 3d), a known consequence of more active TCR56. These results lead to several conclusions. First, the endogenous lesions driving SBS-PolkKO mutagenesis are also subject to repair by TCR, and are possibly bulky lesions that block transcription. Second, these TCR substrate lesions are most likely adducted guanines, because we observe a depletion of cytosine mutations (C>N) from the untranscribed strand (Fig. 3e); excision of cytosine lesions would produce the opposite pattern (i.e., depletion of C>N mutations from the transcribed strand). Third, in the absence of Polκ, the guanine adducts mispair with either A, G, or T, leading to SBS-PolkKO mutations (C>A, C>G, and C>T). This is compatible with the biochemical function of Polκ, which can incorporate C opposite N2-dG adducts in vitro23,27. Together, these results reveal mechanistic details of the SBS-PolkKO mutations and underscore the fact that mutational signatures are complex patterns jointly shaped by the nature of the DNA lesion and the interplay of DNA repair and lesion bypass processes.

Two DNA repair pathways protect the liver from SBS-PolkKO mutagenesis
Next, to more deeply understand the origin of SBS-PolkKO mutations, we sought to identify an in vitro experimental system where SBS-PolkKO mutations accumulate spontaneously. For this purpose, we took liver cholangiocytes (where we detected SBS-PolkKO mutations in vivo, Fig. 1e), cultured organoid clones for 4 months, and characterized the mutations that accrued specifically during in vitro culture (Supplementary Fig. 6). Interestingly, despite having a much higher mutation rate compared to mouse tissues, cultured Polk-/- cholangiocytes did not accumulate SBS-PolkKO mutations in vitro. Therefore, we conclude that the SBS-PolkKO mutational signature does not arise simply due to Polκ deficiency but depends on interactions with specific forms of endogenous DNA damage, likely driven by tissue-specific metabolism.

Therefore, we focused on the mouse liver in vivo for subsequent experiments. The considerable TSB of SBS-PolkKO mutations suggests that the endogenous lesions driving mutation in the absence of Polκ are also repaired by a transcription-coupled mechanism. This led us to hypothesize that inactivating the relevant repair pathway would lead to an increase in mutation and potentially allow us to better characterize the endogenous DNA lesions. The best characterized TCR pathway is TC-NER, where stalling of RNA polymerase II by transcription-blocking DNA lesions triggers the excision of the damage from the transcribed strand mediated by XPA and the nucleases XPF-ERCC1 and XPG. A related pathway, not coupled to transcription, is global-genome (GG)-NER, where DNA excision of bulky adducts throughout the genome is instead triggered by topological distortion of the DNA helix, detected by XPC. To test if the TSB of SBS-PolkKO mutations is caused by TC-NER, we generated mice lacking GG-NER (Xpc-/-) or both GG-NER and TC-NER (Xpa-/-) in addition to loss of Polκ. Double mutants displayed no obvious phenotypes up to the age of 7 months, after which we used clonal expansion of liver cholangiocytes to characterize mutational landscapes in vivo.

First, we examined the mutational consequence of NER deficiency alone (Supplementary Fig. 7a, b). We find that Xpa-/- and Xpc-/- mouse livers accumulate mutations which we term SBS-NER-KO, a mutational signature resembling SBS8, which has an unknown mechanism but is likely driven by endogenous NER substrates. Our results are consistent with reports from Ercc1-/Δ mouse livers16 and XPC-/- human leukemia17, with Xpc-/- mouse liver carrying a mutational signature that closely resembles that observed in XPC-/- human leukemia (Cosine similarity 0.93) (Supplementary Fig. 7d). Xpc-/- liver also displays a C>T component resembling SBS32 that is lacking from SBS8 and reduced in Xpa-/- livers. Interestingly, a side-by-side comparison reveals a significantly higher mutation burden for Xpc-/- compared to Xpa-/- mouse livers, for each class of mutation—SBS, DBS, and indels (Supplementary Fig. 7b). This is surprising given the canonical view that genetic inactivation of Xpc or Xpa should equally impair GG-NER. Importantly, a distinctive feature of mutations in Xpc-/- livers is strong TSB. Genetic disruption of Xpc selectively inactivates GG-NER but TC-NER remains active, driving TSB in transcribed regions of the genome. As expected, we find strong TSB in Xpc-/- livers, which was absent in Xpa-/- and Ercc1-/Δ livers (Supplementary Fig. 7e). Together, these results show the mutagenic consequences of NER deficiency in the liver and its distinctive features.

Next, we examined the effect of NER deficiency on the mutational landscape of Polk-/- livers (Fig. 4a). We detected significant increases in the burden of SBS, DBS and indels in the livers of Xpc-/- Polk-/- and Xpa-/- Polk-/- mutants compared to Polk-/- controls (Fig. 4b). However, the numbers alone may just reflect the additive effect of two independent mutational processes. We used two different approaches to dissect this. First, we performed de novo signature extraction as above to study the consequence of NER deficiency on the SBS-PolkKO mutational signature (Fig. 4c, d). We found a small number of SBS-PolkKO mutations in Xpa-/- livers, probably due to signature misattribution. Most importantly, the burden of SBS-PolkKO mutations was higher in NER-deficient Polk-/- mice compared to Polk-/- controls, suggesting that NER is a repair pathway that suppresses SBS-PolkKO mutagenesis, likely through excision of bulky guanine adducts (Fig. 4e). Conversely, we also looked at the effect of Polκ loss on the mutational signatures of NER deficiency (SBS-NER-KO, also split into SBS8 and SBS32-like components). We observed no difference in the burden of these mutations between Polκ-proficient and deficient samples, suggesting that the NER substrates driving these signatures require mutagenic lesion bypass by DNA polymerases other than Polκ (Fig. 4e, Supplementary Fig. 8a).

Second, we used a non-NMF approach. We hypothesized that combined loss of Polκ and NER results in an increased mutational burden beyond the additive effect of both individual gene losses, and that these mutations overlap with, but do not fully reflect, the mutation spectrum of SBS-PolkKO. To test this, we took advantage of our genetically controlled experimental setup, directly estimating the effects of each gene deficiency on the total mutational burden, using binomial regression on the mutational burdens of wild type, Polk-/-, Xpa-/- and Xpc-/- livers. To test whether the observed mutational burden can be explained just by the individual loss of each gene, or if the combined loss of Polκ and NER genes has an additional, stronger effect, we compared two statistical models. The first model included only the additive effects of each gene loss. The second model also included an additional genetic interaction effect between Polκ and NER gene loss. The model with the interaction effect fit the data significantly better, as shown by a likelihood ratio test (p = 1.5 × 10-49) and a lower Akaike Information Criterion (AIC) score (ΔAIC=220.7), indicating a better model fit. Therefore, we conclude that the interaction between Polκ and NER gene loss plays a key role in driving the observed mutational burden. This model also found a significant contribution of the combined Polκ-NER loss beyond what would be expected from losing each gene alone (p < 1 × 10–25 for both interactions) (Fig. 4f, Supplementary Table 1). We then apply the interaction model on the 6- and 96-class SBS types and reveal patterns nearly identical to the NMF signatures (cosine similarities 0.98) and, as we hypothesized, the mutation pattern induced by the Polκ-NER interaction does not exactly match the SBS-PolkKO signature (Supplementary Fig. 8b, c, Supplementary Table 2). Thus, NER loss results in a partial increase of the mutations in Polk-/- mice, rather than a uniform increase of the full SBS-PolkKO signature, in line with the limited increase in the SBS-PolkKO burden (Fig. 4e). These findings suggest that Polκ bypasses additional lesions other than those processed by NER, or that lack of NER increases the presence of only a subset of the lesions bypassed by Polκ.

Next, we looked at transcriptional-strand asymmetry in liver genomes lacking both NER and Polκ. We find that the TSB of SBS-PolkKO mutations is increased in Xpc-/- Polk-/- and decreased in Xpa-/- Polk-/- mice compared to Polk-/- controls (Fig. 4g), again supporting the model where a subset of the endogenous guanine lesions driving mutation are processed by NER. Surprisingly, and in complete contrast to SBS8 mutations (Fig. 4g, Supplementary Fig. 8d), we find that SBS-PolkKO mutations retain considerable TSB in Xpa-/- Polk-/- samples, which completely lack NER. This effect is not due to transcription-coupled damage, as the mutation rate is lower in highly expressed genes. While Xpa is considered essential for TC-NER to deal with UV damage, our data imply that residual TC-NER occurs in the absence of Xpa in response to endogenous DNA lesions. Alternatively, our findings suggest the presence of another source of TSB other than NER, potentially an alternative transcription-coupled repair pathway of endogenous guanine lesions, such as TC-BER or TC-DPC repair57–59.

Finally, we analyzed DBS and indels in double-mutant and control mice. While both Polκ and NER suppress DBSs, the low number limited the extraction of DBS signatures (Fig. 4b, Supplementary Fig. 9a). We find that indels in Xpc-/- samples are dominated by ID9 (which has an unknown mechanism) and not affected by loss of Polκ (Supplementary Fig. 9b, c). Interestingly, we detect ID6 (repair of DNA breaks), which is unique to Ercc1-/Δ livers, consistent with the function of Xpf-Ercc1 in crosslink repair. The lack of ID6 in other genotypes suggests that joint inactivation of Polκ and NER does not result in the formation of DNA breaks in the liver.

In summary, these data add to our mechanistic understanding of SBS-PolkKO mutations by showing that NER slightly suppresses SBS-PolkKO mutagenesis, implying that a subset of the endogenous guanine lesions are substrates of NER but that Polκ bypasses additional lesions other than those processed by NER. In addition, our results provide evidence that NER is not the sole repair pathway involved, with a second TCR route of guanine adducts limiting SBS-PolkKO mutations.

Polκ promotes replication-coupled bypass of endogenous dG adducts
Our results so far indicate that endogenous adducts of guanine drive tissue-specific mutation in the absence of Polκ. Uncovering the chemical nature of the DNA damage could reveal its true origins. However, we have little a priori knowledge of what the damage is, and analyzing DNA modifications directly is challenging. Endogenous DNA adducts are exceedingly rare (on the order of 10−6–10−8), undergo rapid repair, and have similar chromatographic properties to unmodified nucleosides. It is also likely that multiple lesions contribute to the SBS-PolkKO mutational signature, as our data suggest some guanine lesions are substrates of NER, while others are repaired by a transcription-coupled pathway other than NER.

To tackle this fundamental question, we undertook mass spectrometric quantification of DNA adducts that might drive mutation, we first targeted known dG modifications, followed by an untargeted screen aimed at identifying unknown dG lesions. DNA was extracted from mouse liver or kidney (where SBS-PolkKO mutations are highest), hydrolyzed, and purified as previously described60 (Fig. 5a). First, we took a candidate approach. In vitro, Polκ bypasses DNA adducts at the N2 position of guanine in a predominantly error-free manner22,26,61,62. Therefore, we quantified the abundance of three N2-dG lesions using isotopically labeled internal standards: 1,N2-ProdG (N2-propano-dG), γ-OH-Acr-dG (N2-acrolein-dG), and εdG (N2-etheno-dG). We also included 8-oxo-dG, a small and abundant oxidative lesion, as a control. We analyzed wild-type and NER-deficient tissues to test if these lesions are excised by NER in vivo. We found N2-propano-dG is significantly increased in Xpa-/- and Xpc-/- kidneys, while N2-etheno-dG is higher in NER-deficient kidney and liver (Fig. 5b). Interestingly, N2-etheno-dG is more abundant in Xpc-/- than Xpa-/- samples, suggesting differences in the excision of this lesion in vivo, which correlates with differences in mutagenic burden between these genotypes (Supplementary Fig. 7b). Therefore, we can detect endogenous N2-dG lesions, some of which are also substrates of NER in vivo.

To understand the role of Polκ in bypassing these lesions, we set out to recapitulate this process. Previous studies using primer extension assays have shown that Polκ can insert a C opposite certain N2-dG lesions in a minimal system22,26,61,62. To study bypass of these adducts in a more physiological setting with bona fide replication forks and other TLS polymerases, we used the Xenopus egg extract system. We introduced site-specific N2-propano-dG and N2-acrolein-dG adducts into plasmids and replicated them in mock and Polκ-depleted Xenopus egg extracts (Fig. 5c, d). Replication intermediates were digested and separated on a sequencing gel (Fig. 5e). In mock-depleted extracts, we find that the bottom strand that encounter the adduct shows transient stalling at 1 nucleotide (nt) before the lesion (−1 position), which is resolved after 60 min concomitant with accumulation of extension products. Replication products from the top strand, which does not encounter the lesion directly, do not show stalling. Depletion of Polκ causes severe stalling at the −1 position, until at least 180 minutes, indicative of a persistent insertion defect. This persistent stalling in the absence of Polκ is accompanied by a reduction in the molecules that reach full extension.

Finally, we investigated the miscoding properties of these adducts after replication (Fig. 5f). In mock extracts, we find that lesion bypass is largely error-free in the presence of Polκ (mutation rate 1-2%). In Polκ-depleted extracts, those products that reach full extension have an increased mutation rate (2–5%) compared to mock extracts, representing a 2- to 6-fold increase in mutation depending on the base change (Fig. 5f, g). Interestingly, we observe differences in the miscoding properties of the two structurally related lesions, with N2-propano-dG causing C>G and C>A mutations, and N2-acrolein-dG mostly miscoding C>T (Fig. 5g). These results provide biochemical confirmation that Polκ suppresses mutations opposite two N2-dG in vitro, and are in nice agreement with the mutations found in Polk-/- livers in vivo, particularly when accounting for sequence context (Fig. 5g). In summary, we used mass spectrometry to quantify candidate endogenous N2-dG lesions in vivo, and show that Polκ is critical for bypass of two of these N2-dG adducts in Xenopus egg extracts, by accomplishing TLS insertion opposite the adducts, largely in an error-free manner.

Characterization of novel endogenous guanine lesions by untargeted DNA adductomics
It is likely that several endogenous dG lesions contribute to the SBS-PolkKO signature. To further explore additional and potentially novel DNA lesions beyond the well-known adducts described above, we used our high-resolution LC/MS3 adductomics method for the characterization of endogenous DNA damage. The method monitors ions characterized by the neutral loss of the exact mass of deoxyribose (dR = 116.0474 ± 0.0006 m/z), or one of the four DNA bases (e.g., guanine + H+ = 152.0567 m/z). Importantly, this exploratory discovery experiment focused specifically on any dG adduct showing a significant increase in Xpa-/- and Xpc-/- livers, which lack damage excision and are expected to accumulate endogenous DNA lesions. Excitingly, we see seven unknown adducts which are consistently increased in NER tissues compared to wild type, with masses in the range of 580.2092–607.2926 m/z (Fig. 5h, Supplementary Figs. 10–17). The size and fragmentation spectra of these novel endogenous adducts are consistent with larger adducts of guanine, and possibly with some crosslinks, as seen for the adduct with m/z 603.1555 (Supplementary Fig. 15), where the loss of cytosine in the MS2 event triggers the appearance of guanine in the MS3 spectra. These findings are in line with the role of NER in repairing bulky lesions. While this suggests the exciting possibility that crosslinks are a prevalent endogenous DNA modification, we currently face the challenge of determining the precise molecular structure of these lesions and confirm their nature. This will require additional targeted investigations, including analysis with different hydrolysis protocols, collision energies, and chromatographic conditions to collect more detailed structural information and support the synthesis of chemical standards that will allow absolute identification and quantitation. In summary, we have applied, for the first time, an untargeted adductomics approach in a setting of DNA repair deficiency, uncovering novel endogenous DNA adducts that are likely to contribute substantially to mutagenesis.

Methods
Mice
All animal experiments were performed after institutional review by the Animal Ethics Committee of the Royal Netherlands Academy of Arts and Sciences (KNAW) with project license of AVD8010020198847. The Polktm1.1Rsky (MGI 2445458, C57BL/6J) mice were described previously and acquired from JAX75. Xpatm1Hvs (MGI 1857939, C57BL/6) and Xpctm1Ecf (MGI 1859840, C57BL/6) mice were described previously as a kind gift from G.T. van der Horst, Errol Friedberg, and Jan Hoeijmakers76,77. We used 18-month-old mice for organoid isolation and NanoSeq sequencing of wild-type and Polk-/- mice, and 7-month-old mice for cholangiocyte organoid isolation of Xpa-/- Polk-/-, Xpc-/- Polk-/- and age-matched controls. All mice were maintained and housed under standard conditions, with ambient temperature 20–23°C and humidity between 50–60%, ad libitum food and water, and on a 12-h light–dark cycle.

Organoid culture
Liver cholangiocytes were harvested and enzymatically digested as previously reported78. Briefly, minced liver was digested with 125 µg/mL collagenase (Sigma-Aldrich), 125 µg/mL dispase II (Thermo Fisher), and 0.1 mg/mL of DNAase (Sigma-Aldrich) in wash buffer. The wash buffer was based on DMEM (Thermo Fisher) supplemented with penicillin/streptomycin (Thermo Fisher) and 2% fetal bovine serum (FBS) (Sigma-Aldrich) as described by Broutier et al.79. Biliary tree fragments and associated stroma were dissociated into single cells using 7x TrypLE (Gibco), incubated in 2% FCS with antibodies anti-EpCAM/CD326 (clone G8.8, APC, eBioscience, 6:100), CD45 (clone 30-F11, PE, BioLegend, 1:50), CD31 (clone 390, PE, BioLegend, 1:50), TER-119 (clone TER-119, PE, BioLegend, 1:50) and subjected to fluorescence-activated cell sorting (FACS) based on size and granularity and singlets using a BD Influx™ Cell Sorter. Cholangiocytes were sorted as CD31/CD45/TER-119- EpCAM+. The isolated cholangiocytes were subjected to clonal expansion as previously reported80. Cells were cultured in basal medium (advanced DMEM/F12 with 10 mM HEPES, 1x Glutamax, and Penicillin/Streptomycin of 100 U/mL) supplemented with B27 (Invitrogen), 1 μM N-acetylcysteine (Sigma-Aldrich), 10 nM gastrin (Sigma-Aldrich), 50 ng/mL mEGF (PeproTech), 50 ng/mL rhHGF (Bio-Techne R&D), 100 ng/mL FGF-10 (Peprotech), 1% R-spondin3 conditioned medium, 10 mM nicotinamide (Sigma-Aldrich) and 10 µM Rho-kinase inhibitors (AbMole). Additionally, the cells were supplemented with 150 ng/mL Noggin (IPA) and 27 ng/mL Wnt surrogate (IPA) for the initial four days following seeding.

The preparation of mouse lung cell suspensions was conducted via collagenase digestion of the lungs, with the upper airways removed as previously described81. The dissociated cells were resuspended and incubated in 2% FCS with antibodies, including CD31 (clone 390, PE, BioLegend, 1:400), CD45 (clone 30-F11, PE, BioLegend, 1:400), TER-119 (clone TER-119, PE, BioLegend, 1:400), EpCAM/CD326 (clone G8.8, APC, eBioscience, 1:400), MHCII (clone M5/114.15.2, PE-Cy7, BioLegend, 1:100) and CD24 (clone M1/69, BV421, BioLegend, 1:100). The labeled cells were then washed and sorted using a BD Influx™ Cell Sorter. Club cells were defined as CD31/CD45/TER-119- EpCAM+ MHCII- CD24low. The sorted cells were then subjected to single-cell expansion in Matrigel (BD Biosciences) and subsequently cultured in basal medium supplemented with 50 ng/mL EGF, 100 ng/mL FGF-7, 100 ng/mL FGF-10, and 2 µM Rho-kinase inhibitors.

Gastric epithelial stem cells were harvested, and cultured as reported82. In brief, the glands were extracted from 1 cm2 of the mouse stomach using EDTA in cold PBS. The gastric glands were filtered through a 100 mm strainer and cultured in Matrigel, followed by limiting dilution for single-cell expansion. The culture medium for gastric epithelial stem cells is based on basal medium supplemented with 150 ng/mL noggin, 27 ng/mL Wnt surrogate, 2% R-spondin3 conditioned medium, 50 ng/mL EGF, 100 ng/mL FGF-10, 10 nM gastrin, 0.5 mM TGF-β inhibitor and 10 µM Rho-kinase inhibitor.

Small intestinal crypts were isolated according to the methodology previously described83. Briefly, 1 cm small intestines were incised lengthwise, chopped into pieces, and thoroughly washed with cold PBS. The tissue fragments were vigorously shaken in PBS containing 2.5 mM EDTA and incubated on ice for 30 minutes. This process was repeated once more, after which the supernatant was passed through a 70 µm cell strainer in order to remove residual villous material. Subsequently, the isolated crypts were subjected to centrifugation and resuspension for bulk culture, followed by limiting dilution for single-cell expansion. The cells were cultured in basal medium supplemented with B27, 1 mM N-acetylcysteine, 10 nM gastrin, 50 ng/mL EGF, 1% R-spondin3 conditioned medium, 10 mM nicotinamide, 10 µM Rho-kinase inhibitors, and 27 ng/mL Wnt surrogate.

Bone marrow cells were harvested and cultured as reported previously84. Briefly, the bone marrow cells were collected from both the femur and tibia of the mouse using wash media (DMEM + 2% FCS), and filtered through a 40 µm strainer. The total bone marrow was suspended and cultured in MethoCult GFM3434 (StemCell Technologies) semi-solid medium, followed by limiting dilution for single cell expansion.

Whole genome sequencing of cultured organoids and data processing
Genomic DNA was extracted from the respective organoid samples using the QIAamp DNA Mini Kit (Qiagen). The library preparation and sequencing were conducted by Novogene. The sequencing was performed using the Illumina 2×150 bp paired-end sequencing on a Novaseq 6000 platform, with a minimum coverage of 20X. The quality of sequencing reads was evaluated using FastQC (v0.11.9), and the adapter sequences, primers, poly-A tails, and other undesirable sequences were removed using cutadapt (v4.2). The sequencing reads were mapped to the mouse genome GRCm38 (mm10) using BWA-MEM with the default settings. Further mapping cleanup was conducted with Samtools (v.1.6), Picard CleanSam, Picard FixMateInformation, and Picard MarkDuplicates (v.2.18.29).

Variant calling and filtering
Somatic single and doublet base substitutions were identified using Strelka (version 2.9.10), with the corresponding tail sample serving as the normal. The quality of single-nucleotide variant (SNV) calls was evaluated using FINGs (version 1.7.2) with the default settings, with the exception of a maximum depth threshold of ≤60 and a maximum variant allele frequency (max vaf normal) of 0.01 in the normal sample (tail). A combination of Strelka and GATK Mutect2 (version 4.5.0) was employed to identify high-quality indels. Only the indels identified by both tools were subjected to further evaluation, requiring a mapping quality (MQ) >50, a read depth >10 and <60 in both tumor (organoid) and normal (tail) samples. To minimize sequencing strand bias, valid indels were required to be present on at least two forward and two reverse read strands. Small indels (<4 bp) located within tandem repeat regions of at least 9 repeats were excluded. All SNV and indel variants with an allele frequency <0.3 or >0.7 were excluded to ensure clonality and minimize sequencing artifacts. For both SNVs and indels, only positions flagged as “PASS” by Mutect2 and Strelka were considered. Furthermore, variants that were present in samples from the same parental clone, as well as in normal (tail) samples or other mice, were discarded to eliminate potential germline variants.

NanoSeq library preparation and sequencing
Restriction-enzyme NanoSeq libraries were prepared from 20 ng genomic DNA of a respective sample as input. In the case of matched normal samples, 40 ng of genomic DNA extracted from the same mouse tails was used to prepare an undiluted NanoSeq library. The preparation of all libraries was in accordance with the protocol described in the original publication of the NanoSeq method32. The NanoSeq libraries were sequenced using 2×150 bp paired-end reads on a NovaSeq 6000 platform at the Wellcome Sanger Institute. The data pre-processing was implemented in alignment with descriptions by Abascal and colleagues32. Briefly, reads were aligned to the mouse genome GRCm38 using BWA-MEM. Alignments were then sorted using biobambam2 as previously reported85.

NanoSeq variant calling
Variant calling was performed as described previously32. Briefly, the method requires a matched normal sample from the same individual to filter out germline SNPs. For this purpose, matched normal samples were generated from undiluted NanoSeq libraries of the tail of each mouse. For a mutation to be called as a variant, several criteria had to be fulfilled: (1) each read bundle had to contain a minimum of two reads from each of the two original DNA strands; (2) the consensus base quality scores needed to be ≥60; (3) the minimum difference between the primary (AS) and secondary alignment score (XS) was >50 to keep only read pairs with unambiguous mapping; (4) the average number of mismatches in a group of reads should not be >2, either in the matched normal or sample itself; (5) the maximum number of 5’ clips needed to be 0; (6) the minimum number of improper read pairs needed to be 0; (7) base calls in read ends, referring to the last 8 bp from the 5’ or 3’ ends, are discarded; (8) for SNV calling, reads in the RB are not allowed to contain indels; (9) the number of reads per strand in the matched at a given site was required to be ≥15; (10) for a given mutation, the respective base will not be seen at a frequency >0.01 in the matched normal; (11) a site should not overlap a common SNP and noise mask. We created this mask by calling variants across all matched normal samples, including variants supported by ≥2 reads and VAF ≥ 0.01. The resulting mask had a size of 79 MB in total.

Mutation burden and trinucleotide substitution profiles
Given the biases in the creation of restriction-enzyme NanoSeq libraries, resulting from trinucleotides overlapping the restriction enzyme site, a correction for sequence composition is applied to each of the 96 possible trinucleotide substitutions as detailed in the methods for the original publication32. We use corrected substitution counts to calculate the corrected mutation burden, as well as the extrapolated burden per genome/cell by multiplying the burdens by the size of a diploid mouse genome (2×2.6 Gb). Indel burdens were calculated by dividing the number of detected indels by the total number of base pairs sequenced. Confidence intervals were determined by performing an exact Poisson test in R (poisson.test).

Mutational signature analysis
Mutations were analyzed using SigProfilerMatrixGenerator86 (v. 1.2.25) to classify mutations into specific categories and generate mutational spectra plots. The resulting matrix was then utilized as the input for signature extraction. Two algorithms were employed for this purpose: SigProfilerExtractor87 (v. 1.1.23), which is based on non-negative matrix factorization (with “NMF replicates” = 100, “minimum NMF iterations” = 10,000, and “maximum NMF iterations” = 1,000,000); and mSigHdp39 (v. 2.1.2), which utilizes a Bayesian hierarchical Dirichlet process mixture model (with K.guess = 10, post.n = 1000, and high.confidence.prop = 0.8, with all other parameters set to their default values). In addition to the standard SBS96 catalog, we also extracted de novo signatures in the context of SBS288, which divides the genome into transcriptional, non-transcriptional, and intergenic regions. SBS288 considers the influence of DNA repair on mutation in gene bodies and provides further insight into the disparate mutagenic pathways that assist in differentiating de novo signatures from those that have already been identified. Where applicable, the extracted de novo signatures were decomposed using SigProfilerAssignment88 (v. 0.1.3) to the COSMIC signature database (v. 3.4), with potential artifact signatures excluded. The candidate signatures considered for decomposition were restricted to those suggested by SigProfilerExtraction. For SBS288 signatures, the aforementioned decomposition was performed subsequent to the collapsing of the 288 channels into the standard 96 catalog. A comparison of the results obtained from the two methods, as well as the decomposition of de novo signatures into COSMIC signatures, is presented in Fig. S3. The specific extraction parameters for both methods are available upon request.

Topography of mutational signatures
The analysis of mutational signatures was conducted using SigProfilerTopography (v. 1.0.85) to examine strand asymmetries and distributions of mutations related to replication time. The detailed workflow for SigProfilerTopography has been previously described47. In summary, the workflow randomly generated SBS while preserving the original somatic mutation patterns in each sample at a predetermined resolution. Mutations were retrieved from each strand/region across six mutational channels (C>A, C>G, C>T, T>A, T>C, and T>G), from real and simulated results. P-values were calculated for the odds ratio between the ratio of real mutations and the ratio of simulated mutations. Only those strand asymmetries with a corrected p-value <0.05 and odds ratios >1.10 were considered to be significant.

Transcription strand asymmetries analysis
A comparative analysis of tissue-specific transcription strand bias between genes expressed at low and high levels was conducted using R (v. 4.3.3) and RStudio. The clonal data were derived from cholangiocyte-specific RNA-seq from Aloia et al.89, and the mouse liver-specific RNA-seq from Li et al90. The mutations were classified as either transcribed or untranscribed within gene bodies, and the genes were divided into three quantiles based on their expression level. The statistical significance of each quantile was determined using unpaired t-tests. In the case of clonal samples, only genomic regions with a coverage greater than 20x were included in the analysis to calculate the mutational burden. Due to the restriction enzyme used in the NanoSeq experiment, approximately 30% of the genome was sequenced; consequently, the analysis was confined to these sequenced genic regions.

Binomial regression of mutational burdens
The effects of gene losses were estimated using two binomial general linear models, with the total number of mutated bases per covered base in mouse liver cholangiocytes as the response variable. As we expect independent mutational effects to act additively, an identity link was used for the binomial models. Each individual gene loss, as well as combined gene loss interaction effects, is modeled as a binary variable, where the intercept represents the background mutational burden. The simple additive model includes only the individual gene losses, while the full model includes both individual gene loss variables as well as interaction effects for combined gene losses. The binomial general linear model was fitted using the ‘GLM’ function from the Python statsmodels package91. To evaluate the fit of the models to the data, the difference in Akaike information criterion (AIC) was determined, and a likelihood ratio test was performed. To investigate the mutational profile of the mutations resulting from each gene deficiency, regression with the full model was repeated on each of the six substitution types as well as on the 96 trinucleotide mutation classes, using the total burden of each mutation type as the response variable. The reported p-values and confidence intervals for the estimated effects have been Bonferroni-corrected to account for multiple testing.

Replication-coupled lesion bypass in Xenopus egg extracts
All Xenopus laevis procedures were performed in accordance with national animal welfare laws, reviewed by the Animal Ethics Committee of the Royal Netherlands Academy of Arts and Sciences (KNAW) (license number AVD80100202216633). Preparation of Xenopus egg extracts and DNA replication were performed as previously described92,93. For DNA replication, plasmids were incubated in a high-speed supernatant extract (HSS) at a final concentration of 15 ng/µl for 20 min at room temperature to license the DNA. Two volumes of nucleoplasmic extract (NPE) were added to start DNA replication. To label the nascent strands, HSS was supplemented with 32P-α-dCTPs. At the indicated time, the reactions were stopped with 10 volumes of replication stop solution II (Stop II: 50 mM Tris pH 7.5, 0.5% SDS, 10 mM EDTA pH 8)., followed by Proteinase K (0.5 μg/μl) treatment for 1 h at 37°C or overnight at room temperature. DNA was phenol/chloroform extracted; ethanol precipitated with glycogen (0.3 μg/μl), and resuspended in 10 mM Tris pH 7.5 in a volume equal to the reaction sample taken.

Polk was depleted from Xenopus egg extract using an antibody raised against a C-terminal peptide (KSKPNSSKNTIDRFFK) of Xenopus laevis Polk (Biosynth). Affinity-purified Polk antibody was incubated with Dynabeads Protein A beads (Thermo Fisher Scientific) to their maximum binding capacity. Two volumes of the antibody-coated beads were then mixed with one volume of HSS or NPE and incubated for 30 minutes at 4°C. Depleted extracts were collected and immediately used for replication assays. The same antibody was used to detect loss of Polk by Western blot (1:1000).

Nascent strand analysis was performed as previously described94. In brief, DNA replication products were digested with AflIII (1 unit, NEB) to resolve stalling products, or PstI and BamHI (1 unit each, NEB) to resolve full extension products from bottom or top strands; one volume of denaturing PAGE Gel Loading Buffer II (Invitrogen™) was added, the samples were separated on a 7% polyacrylamide sequencing gel and visualized by autoradiography. The sequencing gel ladder was produced using the Thermo Sequenase Cycle Sequencing Kit (USB) and primer S (5’-CATGTTTTACTAGCCAGATTTTTCCTCCTC-3’, for analysis with AflIII digestion) and primer P (5’-GCTCGAGCGGAAGTGCAGAACCAATGCATG-3’, for analysis with PstI-BamHI digestion).

Generation of 1,N2-ProdG (N2-propano-dG), γ-OH-Acr-dG (N2-acrolein-dG) containing plasmids
Generation of the plasmid containing a site-specific 1,N2-ProdG (N2-propano-dG) was described previously95. The plasmid containing a site-specific γ-OH-Acr-dG (N2-acrolein-dG) was generated using a similar method. Specifically, a custom oligonucleotide (5’-[phos]-GCACGAAAGAAGAGC2FdI-GAAG-3’, Eurogentec) was synthesized, using a 5’-Dimethoxytrityl-2-fluoro-O6-p-nitrophenylethyl-2’-deoxyInosine, 3’-[(2-cyanoethyl)-(N,N-diisopropyl)]-phosphoramidite, and shipped on its support. The support (ca 0.25 μmol oligo) was incubated overnight with 7 mg 4-amino-1,2-butanediol (FluoroChem) in 220 μl DMSO and 110 μl TEA with agitation at RT. The support was washed three times with 200 μl DMSO and 400 μl CH3CN, followed by deprotection of the O6-p-nitrophenylethyl group with 300 μl of 1 M DBU in CH3CN at RT for 1 h. The support was washed three times with 250 μl CH3CN and treated with 500 μl aq. 28% NH4OH at 55°C for 6 h to remove the remaining protecting groups and elute the N2-(3,4-dihydroxybutyl)-guanine-modified oligo from the support. The oligo was dried using a SpeedVac, resuspended in water, applied to a MonoQ 5/50 GL column in buffer A (10 mM TRIS-HCL pH 7.5, 100 mM NaCl) and eluted in a gradient of 3% buffer B (10 mM TRIS-HCL pH 7.5, 800 mM NaCl) per CV at 4 °C. The N2-(3,4-dihydroxybutyl)-guanine-modified oligo eluted at 46.1 Ms/cm. Peak fractions were pooled and re-injected to increase purity, followed by desalting using a NAP-5 column (Cytiva). The modified oligo was reacted with 50 mM NaIO4 for 1 h at RT, and the reaction was quenched by desalting over a NAP-5 column in MQ water. The resulting γ-hydroxy-1,N2-propanoguanine-modified oligodeoxynucleotide was mixed at a 1:1 molar ratio with the complementary oligo: 5’-[phos]-CCCTCTTCCGCTCTTCTTTC-3’ in PBS and annealed at 85°C for 5 min, ramped to 25°C at −0.1°C s-1 and flash frozen immediately to prevent crosslink formation.

Mutational analysis of replicated N2-propano-dG and N2-acrolein-dG containing plasmids
To analyze mutations generated upon replication of lesion-containing plasmids in Xenopus egg extracts, 5 ng of RNase A-treated extracted DNA from a replication reaction was used. Using an equimolar amount of primer A (5’-GTTCAGACGTGTGCTCTTCCGATCTNNNNNNNNNNNNNNNNTAGGTGTTGGGGCGGGACTATGGTTGCTGACT-3’), that anneals to the lesion-containing strand (123 nt downstream of the lesion) and contains a sample-specific barcode and 16 nt UMI sequence, a linear amplification was performed with Herculase II Fusion polymerase (1 unit, Agilent Technologies). Reaction products were further amplified using a nested PCR with Primer B (5’-GACTGGAGTTCAGACGTGTGCTCTTCCGATCT-3’), and primer C (5’-ACACTCTTTCCCTACACGACGCTCTTCCGATCT CTC CTG ACT ACT CCC AGT CAT AGC TGT CCC-3’), annealing 114 nt upstream of the lesion. Illumina-compatible adapters were incorporated by subsequent PCR amplification using NEBNext Dual Index Primers, and the libraries were sequenced by Novogene. The sequencing reads were deduplicated using umi-tools (v1.1.6), mapped to the reference plasmid using BWA-MEM, and further cleaned using fgbio (v2.4.0) to remove the soft-clip sequences. Properly paired sequencing reads were merged using bedtools (v.2.31.1), and samtools mpileup (1.19.2) was used to generate per-base composition data. Downstream analysis and visualization were performed using a custom R script. Mutation rates were corrected by the degree of top/bottom strand bias, based on the radioactive intensity from the sequencing gel and cytosine content in each strand. The raw data and complete analysis script are available on GitHub.

DNA extraction for mass spectrometry
Genomic DNA from liver and kidney tissues of wild-type, Xpc-/-, and Xpa-/- 10-month-old mice was extracted using Puregene Kit (Qiagen) with modifications. Briefly, tissues were minced and lysed using the Cell Lysis Solution supplemented with 1 mM glutathione, 200 mM pentostatin, 100 mM deferoxamine, 100 mM butylated hydroxytoluene, and 25 mL proteinase K. Tissue samples were lysed overnight on a rotator at 25°C, followed by RNase treatment and protein precipitation. DNA was then precipitated with isopropanol, washed, and dissolved in TE buffer containing antioxidants. The DNA solution was further purified using chloroform/isoamyl alcohol extraction to ensure removal of contaminants, followed by a final DNA precipitation, washing, and drying. Throughout the process, care is taken to degas the buffers and minimize oxidative stress on the samples, ensuring the integrity of the isolated DNA.

DNA enzymatic digestion, sample purification and enrichment
The hydrolysis and purification of the isolated DNA were performed similarly to what has been described previously96. Isolated DNA (60 µg) from the livers and kidney of mice was dissolved in 800 µL buffer of 10 mM sodium succinate, 5 mM CaCl2, and 5 mM GSH (pH 7.0). A buffer blank (800 µL of buffer) and calf thymus DNA (60 µg in 800 µL of buffer) were prepared as negative and positive controls, respectively. The DNA was then enzymatically hydrolyzed with 30 units of micrococcal nuclease and 0.18 units of phosphodiesterase II incubated at 37°C for 5 hours. Then, 60 units of alkaline phosphatase (from calf intestine) were added, and the mixture was incubated at 37°C overnight. The following day [13C]1,N2-ε-dG (N2-etheno-dG), [13C5 15N5]ɣ-OH-Acr-dG (N2-acrolein-dG), [15N5](6S,8S;6R,8R)ɣ-OH-Cro-dG (N2-propano-dG), and [13C2 15N5]−8-oxo-dG were spiked into the hydrolysate as internal standards. The samples were then added to Amicon 10K filters (Ultracel® 10K, Millipore) with centrifugal filtration performed at 14000xg for 20 min. After filtration, 10 µL aliquots were removed for dG quantitation by HPLC. The rest of the hydrolysate was purified using solid-phase extraction (SPE) with reversed-phase separation (Strata-X, 33 µm, 30 mg/1 mL (Phenomenex)). The SPE cartridges were activated with 3 mL of CH3OH and 3 mL of H2O/0.1 mM GSH. After the samples were added, the cartridges were washed with 6 mL of H2O/0.1 mM GSH, 1 mL of 3% CH3OH in H2O/0.1 mM GSH. The analytes were eluted with 1 mL 70% CH3OH in H2O/0.1 mM GSH into 1.2 mL silanized glass vials (Chrom Tech) containing 0.65 µL of 100 mM GSH. The eluted samples were then evaporated to dryness via SpeedVac and stored at −20°C until analysis.

Quantitation of dG
Quantitation of dG was conducted using a Dionex UltiMate 3000 RSLCnano HPLC system (ThermoFisher) with a UV detector set to 254 nm and equipped with a Luna C18 column (25 cm × 0.5 μm ID, 5 μm, 100 Å) (ThermoFisher). The mobile phases were H2O (A) and CH3OH (B), the flow rate was 15 µL/min, and 2 µL were injected into the system. The gradient started at 5% B for 1 min, followed by an increase to 25% in 10 min. The gradient then increased to 95% in 3 min and was maintained at those conditions for 5 min before returning to 5% B in 2 min. The instrument was re-equilibrated at 5% B for 9 min for a total run time of 30 min. A calibration curve for dG (ranging from 2 ng/µL to 32 ng/µL in H2O) was run in duplicate and used to calculate the dG content in each sample.

Quantitative Parallel Reaction Monitoring (PRM) of endogenous adducts
Samples from wild type, Xpa-/- and Xpc-/- liver and kidney DNA were reconstituted in 20 µL of H2O for LC-MS2 analysis targeting known endogenous DNA adducts. The analysis was done using an Orbitrap Exploris 480 instrument (ThermoFisher) coupled to a Vanquish™ Neo UHPLC system (ThermoFisher) using positive nanoelectrospray ionization (NSI) with a source temperature of 300°C and a spray voltage of 1900 V. The reversed-phase chromatographic separation was performed using a nanoflow column (50 cm × 75 μm, CoAnn Technologies, Richland, WA) self-packed with Luna C18 (5 μm, 100 Å, Phenomenex) stationary phase. The mobile phases consisted of 5 mM NH4OAc (A) and 95% CH3CN in H2O (B), and the injection volume was 4 µL. The gradient started with an increase from 1% to 5% B over 5 min at a flow rate of 0.3 µL/min, followed by an increase to 22% B over 30 min. The gradient was then increased to 95% B over 1 min, and the flow rate was increased to 0.9 µL/min. Finally, the gradient was maintained at 95% B for 2 min and the flow rate was increased to 1.0 µL/min to wash the system for a total runtime of 43 min. The column was re-equilibrated at the starting conditions with 5 column volumes to prepare for the next injection. This targeted method included the following precursor ions and corresponding extracted productions used for quantitation: 338.1459 m/z→222.0986 m/z for N2-propano-dG; 343.1311 m/z→227.0837 m/z for [15N5]N2-propano-dG; 324.1302 m/z→208.0829 m/z for N2-acrolein-dG; 339.1490 m/z→218.0848 m/z for [13C2 15N5]N2-acrolein-dG; 284.0989 m/z→168.0516 m/z for 8-oxo-dG; 287.0964 m/z→171.0490 m/z for [13C2 15N5]−8-oxo-dG; 292.1040 m/z→176.0567 m/z for N2-etheno-dG and 297.1208 m/z→176.0567 m/z for [13C5]N2-etheno-dG. MS2 fragmentation was performed with a quadrupole isolation width of 1.5 m/z, HCD collision energy of 20%, AGC value of 1000%, maximum injection time of 200 ms, and a resolution setting of 60,000. A 100-650 m/z full scan event with a resolution setting of 15,000 was included to monitor for any anomalies in sample composition or irregularities in the analysis. Calibration curves were prepared using standard solutions of the N2-acrolein-dG and N2-propano-dG, ranging from 2.5 to 250 amol/μL, with 100 amol/μL of the internal standards [13C2 15N5]N2-acrolein-dG and [15N5]N2-propano-dG. In a separate calibration curve, a constant amount of the internal standard [13C2 15N5]−8-oxo-dG (1 fmol/μL) was mixed with different amounts of 8-oxo-dG (10–400 fmol/μL). Utilizing these calibration curves, we were able to absolutely quantify each of our adducts except for N2-etheno-dG, which was not included in the calibration curve standard mix. Semi-quantitation of N2-etheno-dG was performed by assuming linear and equal response for N2-etheno-dG and [13C5]N2-etheno-dG in the sample data. Quantified adduct levels were all normalized to the measured dG amounts.

Parallel Reaction Monitoring of Putative Adducts
Samples from wild-type, Xpa-/-, and Xpc-/- liver DNA were prepared for LC-MS2 analysis of putative DNA adducts detected in the screening assay using an Orbitrap Lumos instrument (ThermoFisher) coupled to a UHPLC system (Ultimate 3000 RSLCnano UHPLC, ThermoFisher) using positive NSI with the source temperature of 300°C and the spray voltage set to static at 2200 V. The UHPLC was equipped with a 5 µL loop and reversed-phase chromatographic separation was performed using a nanoflow column (50 cm × 75 μm, CoAnn Technologies, Richland, WA) self-packed with Luna C18 (5 μm, 100 Å, Phenomenex). The mobile phases consisted of 5 mM NH4OAc (A) and 95% CH3CN in H2O (B), and the injection volume was 4 µL. The gradient started at 1% B for 20 min at a flow rate of 0.3 µL/min, followed by an increase to 5% over 5 min, then an increase to 22% over 35 min, followed by an increase to 95% over 1 min and held at these conditions for 2 min. The gradient was then returned to 1% B in 1 min, and the column was re-equilibrated at this mobile phase composition for 3 min at a flow rate of 0.9 µL/min before the next injection for a full runtime of 69 min. This targeted approach MS2 fragmentation (quadrupole isolation width of 1.5 m/z, HCD collision energy of 30%, AGC value of 1000%, maximum injection time of 200 ms, and Orbitrap resolution setting of 60,000) was performed on 33 m/z values: 249.093 m/z, 276.1343 m/z, 284.0338 m/z, 284.0746 m/z, 293.1167 m/z, 298.1147 m/z, 361.0637 m/z, 365.1013 m/z, 375.2234 m/z, 384.0895 m/z, 384.0975 m/z, 391.0741 m/z, 401.1119 m/z, 401.1128 m/z, 408.1085 m/z, 424.1021 m/z, 442.1132 m/z, 530.2819 m/z, 578.2565 m/z, 578.2565 m/z, 580.2092 m/z, 587.1613 m/z, 589.2773 m/z, 595.2197 m/z, 597.1568 m/z, 602.1562 m/z, 603.1555 m/z, 604.1405 m/z, 605.1959 m/z, 606.1998 m/z, 606.2908 m/z, 607.1010 m/z, and 607.2926 m/z. A 200-650 m/z full scan event (with a maximum injection time of 400 ms, an AGC value of 50%, and an Orbitrap resolution setting of 15,000) was included to monitor for any anomalies in sample composition or irregularities in the analysis.

Untargeted LC-MS2/MS3 Screening
Samples from wild-type and Xpc-/- liver DNA were prepared in triplicate as described above. The samples were reconstituted in 20 µL of H2O, then all three 20 µL aliquots of the wild-type samples were combined into one vial. The same was done for the three 20 µL aliquots of the Xpc-/- samples. The analysis was done using an Orbitrap Lumos instrument (ThermoFisher) coupled to a UHPLC system (Ultimate 3000 RSLCnano UHPLC, ThermoFisher) using positive NSI with the source temperature at 300°C and the spray voltage at 2200 V. The UHPLC was equipped with a 5 μL loop, and reversed-phase chromatographic separation was performed using a nanoflow column (50 cm × 75 μm ID, New Objective, Woburn, MA) self-packed with Luna C18 (5 μm, 100 Å, Phenomenex). The mobile phases consisted of 5 mM NH4OAc (A) and 95% CH3CN in H2O (B), and the injection volume was 4 µL. The gradient started at 1% B for 20 min at a flow rate of 0.3 µL/min, followed by an increase to 5% over 5 min, then an increase to 22% over 35 min, followed by an increase from to 95% over 1 min and held at these conditions for 2 min, the gradient was then returned to 1% B in 1 min and the column was re-equilibrated at this mobile phase composition for 3 min at a flow rate of 0.9 µL/min before the next injection for a full runtime of 69 min. The samples were injected four separate times with each injection using a different mass range (range 1: 145-288 m/z, range 2: 283-426 m/z, range 3: 421-564 m/z, and range 4: 559-702 m/z) with a maximum injection time of 250 ms, an AGC value of 1250%, and a resolution setting of 120,000. Data-dependent parameters included a mass tolerance of ±5 ppm, a repeat count of 1, a dynamic exclusion of 5 s, a minimum intensity of 5.0e3, and a cycle time of 2 s. MS2 fragmentation involved a quadrupole isolation width of 1.5 m/z, stepped HCD collision energy of 15, 30, and 45%, an AGC value of 1000%, a maximum injection time of 22 ms, and a resolution setting of 15,000. MS2 productions were isolated in the ion trap with a 2 m/z isolation window, and MS3 fragmentation was triggered upon observation of the neutral loss of 2′-deoxyribose (-dR; 116.0474 Da), the base moieties (-G; 151.0494 Da, -A; 135.0545 Da, -T; 126.0429 Da; -C; 111.0433 Da), or base moieties plus water (-G+H2O; 169.0646 Da, -A+H2O; 153.0651 Da, -T+H2O; 144.0535 Da, -C+H2O; 129.0538 Da).

LC-MS2/MS3 data analysis using compound discoverer (CD)
The data generated from the untargeted analysis on the Orbitrap Lumos instrument was imported into CD (ThermoFisher), which provides analyte identification, characterization, and comparative analyses between sample groups. CD generated a list of all the potential compounds present in both the wild-type and Xpc-/- liver samples; the list consisted of a total of 65,108 potential compounds. Filters were then implemented based on the following criteria: peak area ratio of Xpc-/- over wild-type greater than 1.00, presence of guanine product ion in MS2 spectra (provided by the Compound Class node in CD), or neutral loss of either deoxyribose, guanine, cytosine, adenine, thymine, or any of those four base moieties plus water (provided by the Neutral Loss node in CD). With these filters implemented, the list of potential compounds decreased to 285. All 285 compounds were then manually confirmed using Xcalibur Freestyle software (ThermoFisher). The manual confirmation resulted in 33 of the 285 showing a peak area ≥ 1.5 times higher in Xpc -/- than the wild-type sample, a Gaussian-shaped peak, and similar retention times in both sample groups. The parameters used to generate the list of putative adducts are illustrated in Supplementary Fig. 10 and listed in Supplementary Methods.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Tissue-specific mutagenesis from endogenous guanine damage is suppressed by PolK and DNA repair\nSummary of research and overall impression\nThe authors expand on existing knowledge of mutational patterns by incorporating tissue-specific endogenous DNA damage (PMID: 26404825, 34433962). Additionally, they confirm prior findings regarding the mutagenesis associated with polymerase kappa using an unperturbed in vivo model (PMID: 12444249, 27308312, 23457261). Through whole genome sequencing and sensitive single-molecule sequencing techniques, they deliver a comprehensive overview of the mutational landscape in mouse somatic tissues. After assessing somatic mutations across various mouse tissues, the authors identify a tissue-specific mutational signature typically suppressed by PolK. Their method of deconvolving the mutational signature aligns with previously published literature (PMID: 23945592, 29695279). The authors utilise organoid clones to characterize the mutations accrued during in vitro culture to further elucidate the spontaneous mutations observed in murine liver tissue. This analysis reveals that the SBS-PolK mutational signature is influenced not only by PolK deficiency but also by endogenous DNA damage driven by tissue-specific metabolism. The investigation of DNA repair pathways complements the single base substitution mutational signature associated with PolK. In this context, the authors generate mouse strains with either a loss of GG-NER (Xpc -/-) or a combined loss of GG-NER and TC-NER (Xpa -/-). Following the assessment of mutational patterns linked to these deficient DNA repair pathways, they demonstrate that nucleotide excision repair reduces the mutational burden resulting from SBS-PolK mutagenesis. While translesion synthesis of dG adducts by PolK is well-documented (PMID: 23457261,12444249), the authors employ Xenopus egg extracts to quantify N2-dG lesions that are bypassed by polymerase Kappa in vivo. Furthermore, untargeted adductomics has the potential to identify endogenous DNA adducts contributing to mutagenesis. Overall, the authors implement advanced and comprehensive methodologies to test their hypotheses in a physiological setting, provide new insights into the link between tissue-specific sources of DNA damage and the critical anti-mutator role of TLS polymerases, specifically PolK, and offer substantial supplementary information to support their findings.\nMinor comments\n- The abstract should focus more on the specific findings presented in the manuscript. It feels currently like it’s trying to sell the manuscript and making rather broad / nebulous statements about what was done rather than what was found. This is a solid and interesting piece of work that does not need this sales pitch. The word ‘novel’ is not helpful.\n- The introduction should include a more detailed summary of the biological processes evaluated in this manuscript, such as background to polK, polK-dependent TLS and mutagenesis, and DNA repair, particularly emphasising the role of nucleotide excision repair (NER), which becomes important in the manuscript. Additionally, it would be helpful to discuss existing literature on these topics (some important examples of which are well over twenty years old) and explain how they connect to the authors' motivation and/or hypotheses. TLS and mutagenesis is a mature field and much has been done before. The value of the current manuscript lies in the physiological context and depth with which the authors study the problem, but it is crucial to put this work into context with earlier work.\n- Given that this is a journal aimed at a broader audience, the manuscript would greatly improve with a clearer explanation of how both NMF and non-NMF methods are used for deconvolving mutational signatures, as well as some background on these techniques.\n- I appreciate the complementary approaches taken to deconvolve the mutational patterns, but could the authors provide further explanation of their choice of using the SigProfiler signature over the alternative methods?\n- Lin 108. The high level of PolK expression in the adrenal (specifically the adrenal cortex) had been previously noted back in 2003 (PMID: 12509270) leading to speculation that PolK might be specifically required for bypass of DNA adducts created as a by-product of steroid biosynthesis. Are any of the authors’ results consistent with this conjecture?\n- Line 52: The comment regarding DNA breaks resulting from the failure to replicate previous DNA lesions would benefit from a reference.\n- Line 136: Could the authors specify the number of iterations used for NMF with SigProfilerExtractor?\n- Line 143: The authors mention SBS17, but this mutational signature is divided into SBS17a and SBS17b. I suspect the authors mean SBS17b, but could they please confirm?\n- Line 149: What does ‘limited confidence’ mean? Non–significant statistical confidence?\n- Line 180: The authors’ results suggest that PolK suppresses endogenous DBSs by contributing to an error-free bypass of lesions. Could the authors please provide perspective based on the literature?\n- The authors develop two models to reconstruct the mutational burden in relation to different gene losses. However, only the second model is utilised. Could the authors explain (and report) why the first model was inadequate?\n- The authors identify a number of dG lesions. Can the authors put their observations in context of the known structural and existing molecular biology of this family of polymerases?\n- This manuscript focuses on disentangling the different biological processes that comprise mutational patterns. The resolution of mutational signatures is constrained, as different DNA lesions can result in the same mutational outcome and vice versa (Reviewed in: PMID: 34316057). Could the authors relate their findings and interpretations of the endogenous somatic mutations they characterise to this limitation?\n- Figure 4, panel e: The y-axis scale of the bar plots makes the data points with low SBS counts per genome difficult to interpret. Are they zero or non-zero counts? Could the authors address this for better interpretation (e.g., log10 scale)?\n- The extensive supplementary figures are appreciated.\n- I could not find a link to the code used to generate the results (e.g. GitHub). This should be included and made publicly available for reproducibility.\n- Could the authors please clarify why the sequencing data was aligned to the reference genome assembly GRCm38 instead of GRCm39?\n- Line 906-907: Authors state that the specific parameters for mutational signature analysis are available upon request. This is unacceptable. Sufficient information and code used to perform the analyses must be provided to reproduce the presented results.\n- Please ensure acronyms are used minimally and that they are clearly spelled out. e.g. DBSs (p6), which is awfully close to the commonly used abbreviation for double strand breaks (DSBs). Likewise, TSB.", "reviewer_2": "This study investigates how endogenous DNA damage contributes to tissue-specific mutagenesis, particularly in the liver and kidney, focusing on the roles of DNA polymerase kappa (Polκ) and nucleotide excision repair (NER). Using mouse models deficient in Polκ and/or NER, the authors identify a novel somatic mutational signature, termed SBS-Polκ, which is enriched in C>A, C>G, and C>T substitutions and exhibits strong transcriptional strand bias. The study suggests that this signature is driven by guanine adducts that, in the absence of Polκ, mispair with other bases and escape proper repair. Key methodologies include whole-genome sequencing of organoid-derived clones, NanoSeq for single-molecule mutation detection, and untargeted high-resolution DNA adductomics. The authors show that SBS-Polκ mutations are partially suppressed by NER and propose that XPA-independent transcription-coupled repair pathways may also be involved. Untargeted adductomics reveals seven novel guanine adducts enriched in NER-deficient tissues, suggesting they may be mutagenic.\nThe significance of the study lies in three major contributions:\n1. Discovery of a novel, tissue-specific mutational signature likely arising from genetic repair deficiencies and endogenous mutagen exposure.\n2. Higher-quality data on the mutagenic effects of NER deficiency, including the unexpected observation that XPC loss causes greater mutation accumulation than XPA loss.\n3. Proof-of-concept demonstration that untargeted DNA adductomics can uncover previously uncharacterized endogenous DNA lesions relevant to mutagenesis.\nWhile the study is innovative and addresses important questions, its conclusion that endogenous guanine adducts are the primary drivers of tissue-specific mutagenesis remains debatable for several reasons:\n1. The adductomics workflow in this study employed stepped higher-energy collisional dissociation (HCD) to fragment nucleosides. However, the relatively high collision energy used in HCD precludes detection of the characteristic neutral loss of deoxyribose, a key diagnostic feature for confirming nucleoside adducts (Line 1131 and Supplementary Figures 11-17). Consequently, the analysis relied solely on the detection of protonated guanine ([G]+) as the diagnostic fragment, which markedly reduces analytical specificity. Moreover, all guanine adducts identified in this study had molecular weights exceeding 580 Da, leading the authors to speculate that they may represent crosslinks (Figure 5). However, the study did not provide supporting evidence such as the neutral loss of two deoxyribose units, which is commonly used as an indicator of crosslink structures (Chem Res Toxicol. 2022, 10;35(11):2025–2036).\n2. The suggestion that these adducts are intrastrand crosslinks is not experimentally substantiated. DNA enzymatic hydrolysis often fails to cleave intra-dimer bonds, a feature not addressed in the methodology, raising questions about whether these lesions can be reliably detected by the approach used.\n3. The seven novel DNA adducts identified by untargeted LC-HRMS were not fully characterized, as no chemical formulas or structural annotations were included, making it difficult to assess their potential mutagenic significance.\n4. The study links SBS-Polκ mutations to bulky guanine adducts based on transcriptional strand bias and mutation spectra. However, these associations are indirect. The assumption that bulky guanine adducts are the causative lesions is based primarily on observed mutation patterns and inferred repair processes (e.g., TCR and NER) rather than direct mechanistic evidence.", "reviewer_3": "In this manuscript, Jiang and collaborators characterise somatic mutations in mice lacking the TLS polymerase PolK. The authors deeply study these genomic lesions through tissue-specific mutational signature analyses in wild-type and PolK-deficient mice, finding a novel mutational signature (which they term SBS-Polk) present mostly in liver and kidney. Through careful analysis of the topography of this signature, they infer that a deficiency of transcription-coupled repair, and therefore NER, is probably contributing to the mutagenesis seen in PolK-mutant tissues. Then, through additional mouse experiments, they uncover that the mutations observed are not simply a sum of NER- and PolK-deficiency, but that NER slightly suppresses SBS-PolK, indicating that other repair pathways are involved. Then the authors perform DNA adductomics to identify specific lesions bypassed by PolK, identifying seven potentially novel adduct types.\nI found this manuscript clear, thorough, and really beautiful. I want to commend the authors for their efforts as I can appreciate the amount of work that went into this paper. I believe that addressing the comments below may provide further clarity.\nResults\n1. Line 91. Can it be explained at what age the germline reference was taken? And if it was at 18 months of age, are there mutational processes happening in the tail that are not being assessed? Can it also be specified whether it was just one mouse and several samples from this mouse's tissue or whether there were several mice? Were different mice used for the WGS and NanoSeq sequencing?\n2. Line 96. The text says that 32 genomes were sequenced but Figure 1a shows 16 whole genomes + 1 reference. Where does the 32 come from? Is it one genome from wild type and another one from PolK-/- mice, and not counting the references? Please clarify the experiment set up further. Also, please explaiin what c.f.u. means in Figure 1.\n3. Line 122. \"These results indicate that differences in TLS usage cannot explain the tissue-specific mutagenesis we observe, and that it is likely due to different damage burden across tissues.\" But I would argue that RNA expression does not necessarily translate into protein expression or usage. Is there any evidence in the literature examining the relationship between protein activity? Can we definitely throw away the hypothesis that it may be due to differential protein activity? Further, I understand expression is assessed not from the current experiment but from other data from the literature. I would recommend perhaps softening this statement.\n4. The authors show that the mutational pattern of SBS-PolK was almost identical between bulk kidney and liver cholangiocytes, but that bulk liver showed differences in proportions of C>A mutations, and ascribe this to the different cell types that compose the liver. But kidneys are also made up by different cell types - do the mutations then occur equally in the different cell types in the kidney? I suppose the tissue sections sequenced by NanoSeq also had a composition of different cell types?\n5. Line 220. The authors write \"Second, these TCR substrate lesions are most likely adducted guanines, because we observe a depletion of cytosine mutations (C>N) from the untranscribed strand (Fig. 3e).\" Can the cytosines be involved in these lesions, or why do the authors go to adducted guanines as the most likely explanation? Is it due to the function of PolK?\nDiscussion\n1. COSMIC shows possible mutation hotspots of POLK at positions 298 and 309. Does this fall into the active site? Do POLK-mutant cancers have more mutations (and potentially the novel POLK signature?)\n2. Line 423. The authors mention that \"However, unlike Poln (POLH/XPV), no germline mutations have so far been described for human POLK.\". However, there are a few germline pathogenic variants reported in GnomAD for POLK. What do the authors think would happen to these individuals? Would they be more prone to cancer?\nMethods\nMutation calling. Regarding clonal mutation calling, the authors write (line 98): \"We used a combination of Strelka2 and Mutect2 to call high-confidence somatic mutations, defined as clonal mutations present in the original cell that are shared by its progeny and have a variant allele frequency (VAF) centered around 0.5. Subclonal mutations that occur during in vitro culture have low allele frequencies and are removed from the analysis.\" - Are the clonal mutations then those present in the evaluated tissues but absent in the tail sample? And if so, why would they have a VAF around 0.5, wouldn't it be expected that they would have a VAF close to 1? Please explain the rationale behind this.\nLine 836. The authors write \"The quality of single nucleotide variant (SNV) calls was evaluated using FINGs (version 1.7.2) with the default settings, with the exception of a maximum depth threshold of ≤60 and a maximum variant allele frequency (maxvafnormal) of 0.01 in the normal sample (tail).\" - Can the authors please clarify what these filters refer to? Do they mean that somatic mutations should not have a depth higher than 60 and not be present in more than 1% of reads in the normal (effectively one read)?\nFigures and Tables\n1. Figure 4d. SBS-PolK signature is present in Xpa-/- livers, but these mice have PolK intact. What could be the source of this fraction of mutations in these samples? Apologies if this was addressed somewhere in the manuscript, but I could not find it.\n2. Please add legends for the Supplementary Tables. I found them a little difficult to follow without a brief explanation of what they represent.\nNote: Although really interesting, I do not have enough knowledge about DNA adductomics so could not evaluate that part of the paper.", "reviewer_4": "Much of our understanding of mutation accumulation and mutagenesis has come from single-cell model organisms and more recently large scale sequencing of DNA from tumors and tumor cells. Less is known about the processes that precede and drive tumor formation in general and even less is known about how these differ across different tissues. In the current study the authors set out to address these issues by performing WGS on different tissues and organoids using a mouse deleted for Pol kappa. It is known that these mice are not tumor-prone, but do accumulate mutations in the liver and kidney. The authors confirm these earlier studies and extend them significantly by analyzing mutations across the entire genomic landscape. Based on their observations that the mutations have a transcriptional bias, they crossed the Pol kappa deleted mice with both XPA- and XPC-deleted mice. Then based on their observations that the endogenous lesions responsible for their phenotype are guanine adducts, they use Xenopus extracts to show that Pol kappa-depleted extracts fail to efficiently bypass some guanine adducts, but not others. They then use mass spectrometry to identify a number of known and unknown guanine adducts from mouse liver and kidney. Overall this study addresses a highly significant topic, the experiments are well-designed and of high quality. The manuscript is written with clarity. As the authors themselves note, this study opens the door to investigate which TLS pol is bypassing which endogenous lesion in non-treated tissues.\nThe authors call their novel mutation spectrum that arises in the absence of Pol kappa as “SBS-Polk”. This is confusing and a little misleading as the increased observed mutations are by definition Pol kappa-independent. The Szüts lab has characterized a novel mutation signature that occurs in Pol kappa-deleted cell lines as \"PolK-Δ\" (Gyüre Cell Reports 2023). This is more accurate and should also be used here, since the increased mutations are likely arising from one or more of the remaining TLS Pols. Additionally, that mutation spectrum showed similar increases in C>A to the tissues here. However, they saw no increases in C>G and a large increase in T>A in (C/G/T)TT sequence contexts. This last context increases significantly when BRCA1 is also deleted. These experiments were done in cultured DT40 cells, so there are obvious differences. But these should all be commented on here, though.\nMouse livers can be up to 90% polyploid and also accumulate significant aneuploidy. This could affect both the role of Pol kappa in these tissues as well as fitness in the absence of Pol kappa. Have the authors measured ploidy in their tissues and organoids? Bypass in the absence of Pol kappa may be facilitated in tissues with increased ploidy (liver) while not tolerated in tissues that cannot alter ploidy without transformation (bone marrow).\nBecause these mice are aged out to 18- or 7-months, and mutations could have occurred at any point in the tissue development, I’m not sure how much weight can be ascribed to the expression levels of the other TLS Pols. These levels can also vary significantly among cells while remaining the same when averaged over the entire tissue.\nPol kappa has also been shown to play roles in both TCR and GGR in UV-irradiated cells (Ogi & Lehmann 2006 Nat Cell Bio). Pol kappa also plays non-catalytic structural roles in TLS, particularly in stabilizing Rev1-Pol zeta lesion bypass, that would be disrupted in the kappa deleted cells. The authors have done a very large amount of work in the current study. One does wonder, though, to what extent the increased SBS-PolkΔ, and possibly the transcription bias, would be suppressed by the addition of a catalytically dead Pol kappa.\nThe simplest explanation for the observed SBS and SBS-PolkΔ increases in the double mutant livers is that they are additive, which would suggest independent activities. The data in 4B and 4D are supportive of this simple explanation. I understand that the authors applied a binomial regression that supports a model beyond this independence, but I remain unconvinced.\nCan the authors explain why the dispersion of SBS/Mb in the 7 month old Polk-/- cholangiocyte clones is so much larger than those from 18 month old polk-/- mice? Comparing Fig 1 to Fig 4. One would have likely presumed the opposite. Particularly interesting is the single clone that is similar to wild type. One possibility is simply a larger n in the 7 month old samples and there is significant inter-clonal variation or inter-individual variation. Another possibility is that liver tissue in older mice represents clones that struck a balance between lesion abundance, mutation accumulation, and clonal outgrowth."}, "author_response": "We thank the Reviewer for the overall positive feedback regarding the advanced and comprehensive methodologies and our new findings, we are pleased that they find our conclusions are supported by our data.\nMinor comments\n- The abstract should focus more on the specific findings presented in the manuscript. It feels currently like it’s trying to sell the manuscript and making rather broad / nebulous statements about what was done rather than what was found. This is a solid and interesting piece of work that does not need this sales pitch. The word ‘novel’ is not helpful.\nWe have taken this onboard and have now rewritten the abstract to more accurately reflect our findings.\n- The introduction should include a more detailed summary of the biological processes evaluated in this manuscript, such as background to polK, polK-dependent TLS and mutagenesis, and DNA repair, particularly emphasising the role of nucleotide excision repair (NER), which becomes important in the manuscript. Additionally, it would be helpful to discuss existing literature on these topics (some important examples of which are well over twenty years old) and explain how they connect to the authors' motivation and/or hypotheses. TLS and mutagenesis is a mature field and much has been done before. The value of the current manuscript lies in the physiological context and depth with which the authors study the problem, but it is crucial to put this work into context with earlier work.\nWe apologize for the omissions, but we needed to introduce several concepts including endogenous DNA damage, DNA repair, TLS, Polk and how these are interconnected to shape mutagenesis and mutational signatures. We have taken this onboard and now expanded the background on Polk and\n- Given that this is a journal aimed at a broader audience, the manuscript would greatly improve with a clearer explanation of how both NMF and non-NMF methods are used for deconvolving mutational signatures, as well as some background on these techniques.\nWe thank the Reviewer for their suggestion. We agree that it would improve clarity to include a brief description of signature extraction methods, and have adjusted the manuscript accordingly (lines 157-159). We feel however that a technical explanation on how these methods work would break the flow of the Results section, especially as both methods extract the same SBS_Polk signature. Instead, we refer the reader to the relevant publications.\n- I appreciate the complementary approaches taken to deconvolve the mutational patterns, but could the authors provide further explanation of their choice of using the SigProfiler signature over the alternative methods?\nSigProfilerExtractor outperforms other signature extraction methods (see benchmarking PMID: 36388765) and it is integrated into a suite of bioinformatic tools for mutational signature analysis (https://cancer.sanger.ac.uk/signatures/tools/).\n- Lin 108. The high level of PolK expression in the adrenal (specifically the adrenal cortex) had been previously noted back in 2003 (PMID: 12509270) leading to speculation that PolK might be specifically required for bypass of DNA adducts created as a by-product of steroid biosynthesis. Are any of the authors’ results consistent with this conjecture?\nWe were aware of this work which was the reason we included adrenal gland in the analysis. We have now added the reference to line 130 (Velasco-Miguel et al 2003), thanks for pointing out this omission.\nThere seems to be a trend towards higher mutation burden in the Polk-/- adrenal gland, but we have limited number of samples and low mutation burden make a strong claim about this. However, our results with NER would argue against by-product of steroid biosynthesis being the main mutagen, as these are bulky lesions and our data points towards smaller (non-NER) substrates as the main drivers of SBS-Polk mutagenesis. We have added this to the Discussion (lines 495-498).\n- Line 52: The comment regarding DNA breaks resulting from the failure to replicate previous DNA lesions would benefit from a reference.\nWe now reference PMID: 18498753.\n- Line 136: Could the authors specify the number of iterations used for NMF with SigProfilerExtractor?\nFor SigProfilerExtracto, we used “NMF replicates” = 100, “minimum NMF iterations” = 10,000, and “maximum NMF iterations” = 1,000,000 for signature extractions. For mSigHdp, we used K.guess = 10, post.n = 1000, and high.confidence.prop = 0.8, with all other parameters set to their default values (now added to Methods, lines 1005-1008).\n- Line 143: The authors mention SBS17, but this mutational signature is divided into SBS17a and SBS17b. I suspect the authors mean SBS17b, but could they please confirm?\nIn our samples we see both SBS17a and SBS17b (Fig. 1) but in the text we refer to them collectively as SBS17.\n- Line 149: What does ‘limited confidence’ mean? Non–significant statistical confidence?\nIn the context of mutational signature extraction, cosine similarity is a widely used metric to compare mutational signatures, which are typically represented as vectors of mutation frequencies across categories (e.g. 96 SBS classes). Usually, cosine similarity should be higher than 0.9 to confidently say two signatures are similar. In this case, the cosine similarity between SBS-Polk vs a reconstruction with a combination of SBS4 and SBS39 is below 0.9, therefore these signatures are not similar.\n2\n- Line 180: The authors’ results suggest that PolK suppresses endogenous DBSs by contributing to an error-free bypass of lesions. Could the authors please provide perspective based on the literature?\nPolk is known to mediate bypass of certain types of tandem lesions, for example extension beyond UV TT dimers or cisplatin Pt-GG crosslinks, by mediating extension rather than insertion (PMID: 29715472). We have added these references to lines 203-205.\n- The authors develop two models to reconstruct the mutational burden in relation to different gene losses. However, only the second model is utilised. Could the authors explain (and report) why the first model was inadequate?\nWe thank the reviewer for pointing out that the purpose of the simpler model is not clear from the manuscript text. We have now rewritten this section to improve clarity (lines 312-321). To first establish that the data is better represented by a model including a combined gene loss effect, we compare it to a simpler model containing only the additive effects. To illustrate that the data supports the model with a combined gene loss effect, we report a reduced AIC and a significant likelihood ratio test. A reduction of Akaike information criterion (AIC) of 220.7 for the more complex model compared to the simple additive model provides strong evidence in favour of the interaction model (https://doi.org/10.1177/0049124104268644). This is further supported by a significant likelihood ratio test, rejecting the null hypothesis that the added interaction term is redundant. Having established that the interaction model is a better representation of the observed data, we report the estimates of this model in the manuscript, including a significant increase of the mutational burden upon combined Polk and NER gene loss. To clarify this in the manuscript text, we have revised the relevant section, explaining more clearly the purpose of the simpler model and that the observed mutational burdens support the second model (See Results, lines 312-321).\n- The authors identify a number of dG lesions. Can the authors put their observations in context of the known structural and existing molecular biology of this family of polymerases?\nWe were able to study the bypass of two (known) dG lesions which we can also detect in vivo (N2-propano-dG and N2-acrolein-dG), and our results are consistent with previous reports on Polk bypassing this type of N2-dG minor-groove lesions (e.g. Discussed lines 370, and 383).\nWith regards to the novel dG adducts, we know they are endogenous NER substrates as we find them to be increased in NER KO tissues. While we know their exact molecular mass, much work is still needed to identify the structure of these lesions, undertake organic synthesis and study their bypass in vitro as we have done for N2-propano-dG and N2-acrolein-dG. Given the high molecular weight of the novel adducts and bulky nature, we can predict TLS is likely needed to bypass these lesions but at present we are not able to predict which polymerase is involved solely based on the molecular mass.\n- This manuscript focuses on disentangling the different biological processes that comprise mutational patterns. The resolution of mutational signatures is constrained, as different DNA lesions can result in the same mutational outcome and vice versa (Reviewed in: PMID: 34316057). Could the authors relate their findings and interpretations of the endogenous somatic mutations they characterise to this limitation?\nWe thank the reviewer for raising this important point. We agree that the mapping between DNA lesions and observed mutational outcomes is often degenerate (there are many more DNA adducts than there are classes of mutations), this poses a fundamental challenge in assigning specific mutational processes or DNA damage to observed signatures. We have tried to tackle this challenge with genetics (by inactivating DNA repair) and DNA adductomics, and our data so far shows that 1) one N2-dG adduct can lead to C>A, C>G and C>T mutations, 2) there are likely NER and non-NER substrates contributing to mutagenesis, 3) there are several endogenous dG adducts whose consequence on mutagenesis are unknown. It is highly likely that the SBS-Polk signature is the cumulative effect of several endogenous dG adducts. We have now added a discussion of this limitation and its implications in the revised manuscript (see Discussion, lines 482-487). We are currently investigating potential sources of endogenous DNA damage, but this lies outside the scope of the current manuscript.\n3\n- Figure 4, panel e: The y-axis scale of the bar plots makes the data points with low SBS counts per genome difficult to interpret. Are they zero or non-zero counts? Could the authors address this for better interpretation (e.g., log10 scale)?\nThese low points represent 0 counts as no mutations have been attributed to the SBS-Polk or SBS8 signature in these samples. As a logarithmic scale would remove these datapoints, we kept the linear scale.\n- The extensive supplementary figures are appreciated.\nWe thank the Reviewer for this comment.\n- I could not find a link to the code used to generate the results (e.g. GitHub). This should be included and made publicly available for reproducibility.\nWe apologise for the omission. The code is now available here https://github.com/GaraycoecheaGroup/MuFASA and we have updated the manuscript to indicate this (lines 1276).\n- Could the authors please clarify why the sequencing data was aligned to the reference genome assembly GRCm38 instead of GRCm39?\nThis study was initiated few years ago, at a time when the GRCm39 was not widely adopted. By the time GRCm39 became more established, we had already generated a substantial amount of data aligned to GRCm38, including sequencing samples and the panel of normals. To maintain consistency across datasets and to avoid the significant effort and potential complications associated with reprocessing all existing data, we decided to continue using GRCm38 for the duration of this study.\n- Line 906-907: Authors state that the specific parameters for mutational signature analysis are available upon request. This is unacceptable. Sufficient information and code used to perform the analyses must be provided to reproduce the presented results.\nAll data underlying the figures, codes, scripts, parameter settings, and demo files that necessary to reproduce the analyses, are available on https://github.com/GaraycoecheaGroup/SBS-Polk. We have also updated the manuscript to clearly indicate this (lines 1269).\n- Please ensure acronyms are used minimally and that they are clearly spelled out. e.g. DBSs (p6), which is awfully close to the commonly used abbreviation for double strand breaks (DSBs). Likewise, TSB.\nWe were careful not to use the acronym DSBs (DNA double strand breaks) precisely for this reason, instead we refer to DNA breaks throughout the manuscript. For Doublet-base substitutions we think it is important to use DBS as it is used in the field (https://cancer.sanger.ac.uk/signatures/dbs/).\nWe have removed TCD (transcription-coupled repair) but kept TSB (transcription-strand bias) as it is used extensively throughout the manuscript, and it is spelled out in the heading (line 220).\nThis study investigates how endogenous DNA damage contributes to tissue-specific mutagenesis, particularly in the liver and kidney, focusing on the roles of DNA polymerase kappa (Polκ) and nucleotide excision repair (NER). Using mouse models deficient in Polκ and/or NER, the authors identify a novel somatic mutational signature, termed SBS-Polκ, which is enriched in C>A, C>G, and C>T substitutions and exhibits strong transcriptional strand bias. The study suggests that this signature is driven by guanine adducts that, in the absence of Polκ, mispair with other bases and escape proper repair. Key methodologies include whole-genome sequencing of organoid-derived clones, NanoSeq for single-molecule mutation detection, and untargeted high-resolution DNA adductomics. The authors show that SBS-Polκ mutations are partially suppressed by NER and propose that XPA-independent transcription-coupled repair pathways may also be involved. Untargeted adductomics reveals seven novel guanine adducts enriched in NER-deficient tissues, suggesting they may be mutagenic.\nThe significance of the study lies in three major contributions:\n1. Discovery of a novel, tissue-specific mutational signature likely arising from genetic repair deficiencies and endogenous mutagen exposure.\n2. Higher-quality data on the mutagenic effects of NER deficiency, including the unexpected observation that XPC loss causes greater mutation accumulation than XPA loss.\n3. Proof-of-concept demonstration that untargeted DNA adductomics can uncover previously uncharacterized endogenous DNA lesions relevant to mutagenesis.\nWhile the study is innovative and addresses important questions, its conclusion that endogenous guanine adducts are the primary drivers of tissue-specific mutagenesis remains debatable for several reasons:\nWe thank the reviewer for their thoughtful and constructive feedback, as well as for recognizing the significance of our study. We greatly appreciate the acknowledgement of our contributions, particularly the discovery of a novel tissue-specific mutational signature, the improved insights into the mutagenic consequences of NER deficiency, and the proof-of-concept use of untargeted DNA adductomics, however we would like to point out we have yet to characterize the mutagenic impact of these seven novel adducts.\nRegarding the reviewer’s concern that the conclusion about endogenous guanine adducts being primary drivers of tissue-specific mutagenesis remains debatable, we respectfully disagree. In the points below, we provide clarification and new supporting evidence to explain why we believe our data support this conclusion.\n1. The adductomics workflow in this study employed stepped higher-energy collisional dissociation (HCD) to fragment nucleosides. However, the relatively high collision energy used in HCD precludes detection of the characteristic neutral loss of deoxyribose, a key diagnostic feature for confirming nucleoside adducts (Line 1131 and Supplementary Figures 11-17). Consequently, the analysis relied solely on the detection of protonated guanine ([G]+) as the diagnostic fragment, which markedly reduces analytical specificity. Moreover, all guanine adducts identified in this study had molecular weights exceeding 580 Da, leading the authors to speculate that they may represent crosslinks (Figure 5). However, the study did not provide supporting evidence such as the neutral loss of two deoxyribose units, which is commonly used as an indicator of crosslink structures (Chem Res Toxicol. 2022, 10;35(11):2025–2036).\nIn order to perform the most comprehensive analysis of DNA adducts, a stepped collision energy and the monitoring of different diagnostic fragments (the neutral loss of the deoxyribose or of one of the 4 nucelobases) were included in our method. The stepped (15, 30, 45%) higher-energy collisional dissociation (HCD) was chosen to provide for both the lower energy fragmentation producing the neutral loss of deoxyribose for DNA nucleoside adducts, as well as higher energy fragmentation required for the neutral loss of the bases and production of the base ions for DNA nucleobase adducts. The percentages used correspond to voltage potentials in the 15 - 45 V range, with the 15 V value similar to that used in Q-TOF and triple quadrupole detection of DNA nucleoside adducts. Fragmentation spectra using these stepped HCD values and the method described in this work, have been collected for a variety of DNA nucleoside adduct synthetic standards (N6-ethyl-dA, N2-ethyl-dG, OH-Acr-dG, N6-methyl-dA, O2-PHB-dT, N2-propano-dG and N1-POB-dI) and the neutral loss of deoxyribose was either the dominant or a prominent fragmentation event (Rebuttal Figures 1-7 below). Additionally, 8-oxo-dG (Rebuttal Figure 8) and the internal standard [13C 15N ]γ-OH-Acr-dG (Rebuttal Figure 9) were observed in the\n10 5\nexperimental data based upon the observation of the neutral loss of deoxyribose. These results confirm that the stepped collision energy used allows for detection of the neutral loss of the deoxyribose.\nWhile the neutral loss of two deoxyribose can be an indicator of crosslink adducts, modifications at the N7 position of guanine and the N3 and N7 positions of adenine result in depurination upon DNA hydrolysis and therefore cannot be analysed relying on the loss of the deoxyribose but on the loss of the nucleobases. The discovery of the colibactin adduct (PMID: 30765538) is an example of an N3-N3 crosslinking adduct, which was observed by the neutral loss of adenine. These results and observations upon analysis of different types of adducts led us to the addition of the observation of nucleobase ions and the neutral losses of the nucleobases upon fragmentation, to our method in order to avoid having to rely on the solely observation of one type of fragment or neutral loss and expand the comprehensive nature of the DNA adductomic approach used in this study.\n2. The suggestion that these adducts are intrastrand crosslinks is not experimentally substantiated. DNA enzymatic hydrolysis often fails to cleave intra-dimer bonds, a feature not addressed in the methodology, raising questions about whether these lesions can be reliably detected by the approach used.\nWe only suggest that some of the novel ‘endogenous adducts are consistent with larger adducts of guanine, and possibly with some crosslinks (line 393)’ based on strong MS2 and MS3 data showing that the 603.1555 m/z adduct contains both guanine and cytosine (see Supplementary Figure 15). This said, and in agreement with the Reviewer, we currently do not have a precise method, based solely on the analysis of the hydrolysed DNA, to determine whether any crosslinks are intrastrand or interstrand. Consequently, we did not comment on the fact that the adduct identified is an intrastrand crosslink.\nOur method has been used to identify previously unknown DNA-DNA and DNA-RNA crosslinks (then confirmed with synthetic standards) originating from exposures to several alkylating agents including formaldehyde (PMID: 36356054), colibactin (PMID: 30765538), busulfan (PMID: 36820303) and cyclophosphamide (PMID: 37490747). Therefore, we are confident that our DNA hydrolysis protocols, which include the use of a DNA phosphatase, enable the release and detection of these modifications.\nAlso, we note that our experiment is genetically controlled as we have focused on adducts enriched in DNA repair-deficient samples (ie putative NER substrates), any potential artefacts resulting from the incomplete enzymatic digestion should not be linked to genotype, would have been observed in control samples and therefore excluded from the analysis.\n3. The seven novel DNA adducts identified by untargeted LC-HRMS were not fully characterized, as no chemical formulas or structural annotations were included, making it difficult to assess their potential mutagenic significance.\nWe thank the reviewer for this observation. We acknowledged the limitation regarding the structural characterization of the seven novel DNA adducts in the manuscript (Results lines 423-430; Discussion, lines 555-562). An example of the extent of the identification of the adducts, possible with our method, is provided for the adduct with m/z 603.1555, and shown in Supplementary Figure 15. This exhibits the neutral losses of G and C, resulting in the identification to be restricted to the remaining linker (m/z 341.0625), for which a chemical formula can be predicted (C H O N ). Full structural elucidation\n13 13 9 2\nwould require additional MS experiments followed by organic synthesis of the candidate adducts.\nAdditionally, elucidation of their mutagenic impact would require exposure of cells to candidate mutagens or replication of the experiment in Figure 5 using site-specific lesions. While we agree that this is a valuable direction, such an undertaking is beyond the scope of the current manuscript and will\n6\nbe a focus of future studies aimed at defining the mutagenic potential and physiological relevance of these novel guanine adducts.\nHowever, in Figure 5c-g we show that the bypass of two other endogenous guanine adducts (N2-propano-dG and N2-acrolein-dG) produces mutations compatible with the SBS-Polk signature and requires Polk.\n4. The study links SBS-Polκ mutations to bulky guanine adducts based on transcriptional strand bias and mutation spectra. However, these associations are indirect. The assumption that bulky guanine adducts are the causative lesions is based primarily on observed mutation patterns and inferred repair processes (e.g., TCR and NER) rather than direct mechanistic evidence.\nWe appreciate the Reviewer’s comment and agree that the relationship between mutational signatures and underlying DNA lesions can often be indirect. However, we respectfully note that our conclusions are based on a combination of established published literature, direct genetic evidence, and mechanistic biochemical data. Specifically:\n1. Transcriptional Strand Bias as a proxy for TCR Activity: While transcriptional strand bias does not directly identify the adduct, it is widely used in the field as an indicator of transcription-coupled repair (TCR) acting on DNA lesions. Multiple COSMIC mutational signatures display this characteristic pattern of transcriptional strand bias, including SBS4 (tobacco-induced bulky adducts), SBS7 (UV-induced pyrimidine dimers), and SBS22 (aristolochic acid–derived adducts). Our use of this bias is consistent with these widely accepted precedents. See also Haradhvala et al Cell, 2016 (PMID: 26806129) - Mutational Strand Asymmetries in Cancer Genomes Reveal Mechanisms of DNA Damage and Repair.\n2. Published biochemical evidence on Polκ bypass specificity: a substantial body of literature shows that Polk bypasses N²-dG adducts — such as those formed by benzo(a)pyrene and multiple alkylating agents— largely in an error-free manner. In contrast, there is little to no evidence that Polk bypasses dC adducts (the other potential alternative source of C>N mutations). Relevant studies:\n- Zhang et al., PNAS, 2000 (PMID: 11058110) - biochemical evidence for accurate and mutagenic bypass of acetylaminofluorene (AAF)-N²-dG adducts by Polκ.\n- Vaisman et al., J Biol Chem, 2002 (PMID: 12509229) – biochemical evidence for accurate bypass of BPDE-N²-dG adducts by Polκ.\n- Choi et al., J Biol Chem, 2006 (PMID: 16751196) - biochemical evidence for accurate bypass of eight N²-alkyl-dG adducts by Polκ, including N²-Me-dG, N²-Et-dG, N²,N²-diMe-dG, N²-Ib-dG, N²-Bz-dG, N²-Naph-dG, N²-Anth-dG, or N²-BP-dG\n- Yuan et al., PNAS, 2008 (PMID: 18562283) – biochemical evidence for accurate bypass of N²-CE-dG adducts by Polκ.\n- Minko et al., Chem Res Toxicol (PMID: 18788757) - biochemical evidence for accurate bypass of N²-propano-dG and N²-peptide-dG crosslinks by Polκ.\n3. Genetic evidence from NER-deficient mice: To move beyond inference, we performed a genetic experiment in mice lacking GG-NER and TC-NER (Xpc-/- or Xpa-/-). Double mutant animals show a significant increase in mutation burden, with increase strand-biased mutations at guanine positions in the case of Xpc-/-Polk-/- and reduced bias for Xpa-/-Polk-/- (Fig. 4). These results are consistent with the accumulation of unrepaired bulky guanine adducts. This supports the model that NER acts on guanine lesions, and in its absence, these adducts become substrates for error-prone TLS.\n4. New biochemical evidence from Xenopus extracts: We had shown that Polk is required for insertion opposite N²-propano-dG and N²-acrolein-dG. To address the Reviewer’s concern, we have now repeated and extended our Xenopus egg extract experiments to quantify mutagenesis during bypass in the presence and absence of Polk (lines, 390-406, and revised Figure 5). We find that in the absence of Polk, there is increased mutagenesis in those molecules that reach full extension (Fig. 5f) and the resulting mutations show a spectrum similar to that observed in Polk-deficient mice (Fig. 5g). The results strengthen the mechanistic link between loss of Polk activity, guanine adduct bypass, and the observed SBS-Polk signature.\nWe believe these multiple lines of evidence collectively support our conclusion that endogenous guanine adducts are the causative DNA lesions underlying the observed SBS-Polk mutation signature.\nSpectrometry data for N6-\nethyl-dA (m/z = 280.1404).\na) Extracted chromatogram for\nthe adduct in the full scan and\nthe co-eluting peak\ncorresponding to the fragment\ndetected in the MS2\nfragmentation event.\nb) Spectra corresponding to\nthe MS2 fragmentation event\nwith the mass corresponding\nto the loss of deoxyribose\nhighlighted.\nRebuttal Figure 2. Mass\nSpectrometry data for N2-\nethyl-dG (m/z = 296.1353).\na) Extracted chromatogram for\nthe adduct in the full scan and\nthe co-eluting peak\ncorresponding to the fragment\ndetected in the MS2\nfragmentation event.\nb) Spectra corresponding to\nthe MS2 fragmentation event\nwith the mass corresponding\nto the loss of deoxyribose\nhighlighted.\nRebuttal Figure 4. Mass\nSpectrometry data for N6-\nmethyl-dA (m/z = 266.1247).\na) Extracted chromatogram for\nthe adduct in the full scan and\nthe co-eluting peak\ncorresponding to the fragment\ndetected in the MS2\nfragmentation event.\nb) Spectra corresponding to\nthe MS2 fragmentation event\nwith the mass corresponding\nto the loss of deoxyribose\nhighlighted.\nSpectrometry data for O2-\nPHB-dT (m/z = 392.1816).\na) Extracted chromatogram for\nthe adduct in the full scan and\nthe co-eluting peak\ncorresponding to the fragment\ndetected in the MS2\nfragmentation event.\nb) Spectra corresponding to\nthe MS2 fragmentation event\nwith the mass corresponding\nto the loss of deoxyribose\nhighlighted.\nRebuttal Figure 6.\nMass Spectrometry\ndata for (S;R)N2-\npropano-dG (m/z =\n338.1459).\na) Extracted\nchromatogram for the\nadducts in the full\nscan and the co-\neluting peaks\ncorresponding to the\nfragments detected in\nthe MS2 fragmentation\nevent.\nb) Spectra\ncorresponding to the\nMS2 fragmentation\nevent with the masses\ncorresponding to the\nlosses of deoxyribose\nhighlighted.\nSpectrometry data for N1-POB-dI\n(m/z = 400.1615).\na) Extracted chromatogram for the\nadduct in the full scan and the co-\neluting peak corresponding to the\nfragment detected in the MS2\nfragmentation event.\nb) Spectra corresponding to the\nMS2 fragmentation event with the mass\ncorresponding to the loss of\ndeoxyribose highlighted.\nRebuttal Figure 8. Mass\nSpectrometry data for 8-oxo-dG\n(m/z 284.0989).\na) Extracted chromatogram for the\nadduct in the full scan and the co-\neluting peak corresponding to the\nfragment detected in the MS2\nfragmentation event.\nb) Spectra corresponding to the\nMS2 fragmentation event with the mass\ncorresponding to the loss of\ndeoxyribose highlighted.\nRebuttal Figure 9. Mass\nSpectrometry data for [13C 15N]γ-\n10 5\nOH-Acr-dG (m/z = 339.1490).\na) Extracted chromatogram for the\nadduct in the full scan and the co-\neluting peak corresponding to the\nfragment detected in the MS2\nfragmentation event.\nb) Spectra corresponding to the\nMS2 fragmentation event with the mass\ncorresponding to the loss of\ndeoxyribose highlighted.\nIn this manuscript, Jiang and collaborators characterise somatic mutations in mice lacking the TLS\npolymerase PolK. The authors deeply study these genomic lesions through tissue-specific mutational\nsignature analyses in wild-type and PolK-deficient mice, finding a novel mutational signature (which\nthey term SBS-Polk) present mostly in liver and kidney. Through careful analysis of the topography of this\nsignature, they infer that a deficiency of transcription-coupled repair, and therefore NER, is probably\ncontributing to the mutagenesis seen in PolK-mutant tissues. Then, through additional mouse\nexperiments, they uncover that the mutations observed are not simply a sum of NER- and PolK-\ndeficiency, but that NER slightly suppresses SBS-Polk, indicating that other repair pathways are\ninvolved. Then the authors perform DNA adductomics to identify specific lesions bypassed by PolK,\nidentifying seven potentially novel adduct types.\nI found this manuscript clear, thorough, and really beautiful. I want to commend the authors for their\nefforts as I can appreciate the amount of work that went into this paper. I believe that addressing the\ncomments below may provide further clarity.\nWe sincerely thank the reviewer for their thoughtful and generous comments. We are especially grateful\nfor the kind words describing the manuscript as \"clear, thorough, and really beautiful”. We have carefully\naddressed the specific comments below and hope the revisions further enhance the clarity and strength\nof the paper.\nResults\n1. Line 91. Can it be explained at what age the germline reference was taken? And if it was at 18 months\nof age, are there mutational processes happening in the tail that are not being assessed? Can it also be\nspecified whether it was just one mouse and several samples from this mouse's tissue or whether there\nwere several mice? Were different mice used for the WGS and NanoSeq sequencing?\nThe germline reference was taken at 18 months with the rest of the tissues, if any mutations\naccumulated in the tail after birth, they would be considered germline variants, and these mutations\nare not seen in our analysis. We use somatic callers which look for variants present in tissue of interest\n(or ‘tumour’) and absent from the tail (or ‘normal’).\nDifferent mice were used for WGS (Fig. 1), NanoSeq (Fig.1) and WGS (Fig. 4), but we consistently find\nthe SBS-Polk signature. In each figure, each dot represents a clone or bulk sample from the same\nmouse. We have now added this information to the Figure legend.\n2. Line 96. The text says that 32 genomes were sequenced but Figure 1a shows 16 whole genomes + 1\nreference. Where does the 32 come from? Is it one genome from wild type and another one from PolK-\n/- mice, and not counting the references? Please clarify the experiment set up further. Also, please\nexplain what c.f.u. means in Figure 1.\nFor Figure 1, we sequenced 16 clones and 1 reference for wild type and 16 clones and 1 reference for\nPolk-/-, so line 96 (now line 119) should read ‘in total 32 genomes were sequenced at around 25x depth\ntogether with germline references’. From the 32 clones, 2 samples were not clonal and removed from\nthe analysis, this was shown in Supplementary Figure 1a. c.f.u. stands for colony forming units, we have\nnow added this to the legend.\n3. Line 122. \"These results indicate that differences in TLS usage cannot explain the tissue-specific\nmutagenesis we observe, and that it is likely due to different damage burden across tissues.\" But I would\nargue that RNA expression does not necessarily translate into protein expression or usage. Is there any\nevidence in the literature examining the relationship between protein activity? Can we definitely throw\naway the hypothesis that it may be due to differential protein activity? Further, I understand expression\nis assessed not from the current experiment but from other data from the literature. I would recommend\nperhaps softening this statement.\nWe thank the reviewer for this comment. We agree that mRNA levels do not always correlate with protein\nabundance or enzymatic activity, particularly for pathways like TLS that may be tightly regulated at the\npost-transcriptional level. While we used RNA expression data as a proxy due to the lack of available\n12\ntissue-matched activity data, we acknowledge that this is an indirect measure and may not fully capture\ndifferences in TLS function. To address this, we have now softened our conclusion in Line 122 (now line\n145) to reflect this uncertainty: \"These results suggest that differences in TLS gene expression are\nunlikely to fully explain the tissue-specific mutagenesis we observe, which is more likely attributable to\ndifferences in damage burden across tissues; however, we cannot rule out the potential contribution of\nvariation in TLS protein levels or activity over time” (lines 145-148).\n4. The authors show that the mutational pattern of SBS-PolK was almost identical between bulk kidney\nand liver cholangiocytes, but that bulk liver showed differences in proportions of C>A mutations, and\nascribe this to the different cell types that compose the liver. But kidneys are also made up by different\ncell types - do the mutations then occur equally in the different cell types in the kidney? I suppose the\ntissue sections sequenced by NanoSeq also had a composition of different cell types?\nWe should first clarify NanoSeq was performed on bulk genomic DNA and not tissue sections. So, the\nNanoSeq data also reflects the cellular composition of the tissue. As the bulk of the liver is made up of\nhepatocytes (60-70%) it is easier to compare this data to liver cholangiocyte clones.\nIndeed, the Reviewer is correct to point out the complex cellular composition of the kidney, we would\nneed to perform NanosSeq on isolated populations to better define mutation burden and pattern across\nkidney cell types.\n5. Line 220. The authors write \"Second, these TCR substrate lesions are most likely adducted guanines,\nbecause we observe a depletion of cytosine mutations (C>N) from the untranscribed strand (Fig. 3e).\"\nCan the cytosines be involved in these lesions, or why do the authors go to adducted guanines as the\nmost likely explanation? Is it due to the function of PolK?\nWe thank the reviewer for this insightful question. It is indeed true that C>A, G, T (or G>T, C, A) mutations\ncould arise from damage to either cytosines or guanines. However, the observed strand bias in our data\nsuggests that the damage likely occurs at guanines, which are then preferentially repaired by\ntranscription-coupled repair (TCR) from the transcribed strand. This would lead to a depletion of C>N\nmutations on the untranscribed strand, as observed in our data and depicted schematically in Figure\n3e. In contrast, if cytosines were the primary lesion, TCR removal of damaged cytosines from the\ntranscribed strand would lead to depletion of C>N mutations from the transcribed strand, the opposite\nstrand bias. Furthermore, this interpretation is supported by the known biochemical activity of Polk,\nwhich is capable of bypassing bulky guanine adducts (refs. 23, 27). We have now clarified this rationale\nin the revised manuscript text (line 245).\nDiscussion\n1. COSMIC shows possible mutation hotspots of POLK at positions 298 and 309. Does this fall into the\nactive site? Do POLK-mutant cancers have more mutations (and potentially the novel POLK signature?).\nWe have investigated the presence of the novel SBS-Polk mutational signature in publicly available\ncancer genome datasets but have not identified clear evidence of the signature to date. However, it is\nimportant to note that, based on the available data, we have not found any liver or kidney cancer\ngenomes with confirmed biallelic loss of Polk. As such, the absence of the signature in current datasets\nmay reflect the limited representation of relevant tissue types and genotypes. We have added a brief\nclarification on this point in the Discussion section of the revised manuscript (line 455-456).\n2. Line 423. The authors mention that \"However, unlike Poln (POLH/XPV), no germline mutations have\nso far been described for human POLK.\". However, there are a few germline pathogenic variants reported\nin GnomAD for POLK. What do the authors think would happen to these individuals? Would they be more\nprone to cancer?\nWe agree that some potentially deleterious germline variants in POLK are reported in gnomAD. Our\nstatement was intended to highlight that, to date, no causative germline mutations in POLK have been\nlinked to a defined genetic syndrome (such as xeroderma pigmentosum-V for POLH) or conclusively\nassociated with elevated cancer risk in humans (we have adjusted the text in line 453 accordingly). The\nfunctional and clinical significance of these rare POLK variants remains largely unknown. Although Polk\n/- mice have no obvious phenotypes, it is plausible that individuals carrying germline biallelic POLK\n13\nmutations could exhibit mutagenesis and potentially increased susceptibility to cancer, due to their\nincreased lifespan compared to mice.\nMethods\nMutation calling. Regarding clonal mutation calling, the authors write (line 98): \"We used a combination\nof Strelka2 and Mutect2 to call high-confidence somatic mutations, defined as clonal mutations\npresent in the original cell that are shared by its progeny and have a variant allele frequency (VAF)\ncentered around 0.5. Subclonal mutations that occur during in vitro culture have low allele frequencies\nand are removed from the analysis.\" - Are the clonal mutations then those present in the evaluated\ntissues but absent in the tail sample? And if so, why would they have a VAF around 0.5, wouldn't it be\nexpected that they would have a VAF close to 1? Please explain the rationale behind this.\nAs the cells that give rise to the clones are diploid and mutations fall in one of two alleles, roughly 50%\nof the reads will support the variant, therefore VAF close to 0.5. VAF close to 1 is expected for mutations\nin haploid cells or regions with loss of heterozygosity.\nLine 836. The authors write \"The quality of single nucleotide variant (SNV) calls was evaluated using\nFINGs (version 1.7.2) with the default settings, with the exception of a maximum depth threshold of ≤60\nand a maximum variant allele frequency (maxvafnormal) of 0.01 in the normal sample (tail).\" - Can the authors please clarify what these filters refer to? Do they mean that somatic mutations should not have\na depth higher than 60 and not be present in more than 1% of reads in the normal (effectively one read)?\nThis is correct. With a mean coverage of 25X, we exclude regions with coverage >60X as these regions\nusually represent repetitive sequences and carry mapping artefacts. We also exclude somatic variants\nif there is at least one read supporting the same variant in the tail (normal) sample.\nFigures and Tables\n1. Figure 4d. SBS-PolK signature is present in Xpa-/- livers, but these mice have PolK intact. What could\nbe the source of this fraction of mutations in these samples? Apologies if this was addressed\nsomewhere in the manuscript, but I could not find it.\nThe apparent presence of the SBS-PolK signature in Xpa-/- livers, despite intact Polk, is likely due to the\nrelatively low number of mutations in these samples (in the order of 500 mutations/genome), which\nincreases the risk of signature misattribution during deconvolution. This issue is particularly common\nwith ‘flat’ signatures like SBS-Polk, SBS8 and SBS5.\n2. Please add legends for the Supplementary Tables. I found them a little difficult to follow without a\nbrief explanation of what they represent.\nWe thank the Reviewer for pointing out this omission. We have now added legends to Supplementary\nTables (lines 1772-1785).\nNote: Although really interesting, I do not have enough knowledge about DNA adductomics so could not\nevaluate that part of the paper.\nMuch of our understanding of mutation accumulation and mutagenesis has come from single-cell\nmodel organisms and more recently large scale sequencing of DNA from tumors and tumor cells. Less\nis known about the processes that precede and drive tumor formation in general and even less is known\nabout how these differ across different tissues. In the current study the authors set out to address these\nissues by performing WGS on different tissues and organoids using a mouse deleted for Pol kappa. It is\nknown that these mice are not tumor-prone, but do accumulate mutations in the liver and kidney. The\nauthors confirm these earlier studies and extend them significantly by analyzing mutations across the\nentire genomic landscape. Based on their observations that the mutations have a transcriptional bias,\nthey crossed the Pol kappa deleted mice with both XPA- and XPC-deleted mice. Then based on their\nobservations that the endogenous lesions responsible for their phenotype are guanine adducts, they\nuse Xenopus extracts to show that Pol kappa-depleted extracts fail to efficiently bypass some guanine\nadducts, but not others. They then use mass spectrometry to identify a number of known and unknown\nguanine adducts from mouse liver and kidney. Overall this study addresses a highly significant topic, the\nexperiments are well-designed and of high quality. The manuscript is written with clarity. As the authors\nthemselves note, this study opens the door to investigate which TLS pol is bypassing which endogenous\nlesion in non-treated tissues.\nWe sincerely thank the reviewer for their positive and encouraging feedback. We greatly appreciate the\nrecognition of the significance of our study, the quality of the experimental design, and the clarity of the\nmanuscript. We also value the reviewer’s comment regarding the potential of this work to pave the way\nfor future investigations into the role of other TLS in shaping mutation in tissues.\nThe authors call their novel mutation spectrum that arises in the absence of Pol kappa as “SBS-Polk”.\nThis is confusing and a little misleading as the increased observed mutations are by definition Pol\nkappa-independent. The Szüts lab has characterized a novel mutation signature that occurs in Pol\nkappa-deleted cell lines as \"PolK-Δ\" (Gyüre Cell Reports 2023). This is more accurate and should also\nbe used here, since the increased mutations are likely arising from one or more of the remaining TLS\nPols. Additionally, that mutation spectrum showed similar increases in C>A to the tissues here.\nHowever, they saw no increases in C>G and a large increase in T>A in (C/G/T)TT sequence contexts. This\nlast context increases significantly when BRCA1 is also deleted. These experiments were done in\ncultured DT40 cells, so there are obvious differences. But these should all be commented on here,\nthough.\nWe thank the Reviewer for this comment, although we think the Reviewer is referring to Chen et al,\nnature Communications 2022, also from the Szüts lab (https://www.nature.com/articles/s41467-021-\n27872-7). Indeed, it would be more appropriate to name our novel signature SBS-PolkKO, and we have\nchanged the nomenclature throughout the manuscript.\nWith regards to the SBS.PolKΔ signature reported by Chen et al., we note this signature differs\nsignificantly from the one reported here (cosine similarity 0.35) and does not share a C>A component\n(Rebuttal Figure 10). These differences are probably due to the differences in the type of DNA damage\nfound in vitro and in vivo, and HR deficiency. We have added this to the Discussion (lines 457-460).\nRebuttal Figure 10.\nComparison between the SBS-PolkKO signature\nreported here and a signature previously found in\nBRCA1-/-POLK-/- DT40 cells (Chen et al, 2022, PMID:\n35017534). These signatures differ significantly (cosine\nsimilarity 0.35).\nboth the role of Pol kappa in these tissues as well as fitness in the absence of Pol kappa. Have the\nauthors measured ploidy in their tissues and organoids? Bypass in the absence of Pol kappa may be\nfacilitated in tissues with increased ploidy (liver) while not tolerated in tissues that cannot alter ploidy\nwithout transformation (bone marrow).\nThe Reviewer raises the interesting possibility that SBS-PolkKO is modulated by ploidy levels. However,\nwe would like to point out that the SBS-PolkKO signature is present in liver cholangiocytes; unlike\nhepatocytes, cholangiocytes are diploid which in our data is supported by a Variant Allele Frequency\n(VAF) distribution centred around 0,5 (Supplementary Figure 1a). Nonetheless, as suggested by the\nReviewer, we have measured ploidy levels in hepatocytes in aged (18 month) wild type and Polk-/- mice.\nWe found no significant differences in the distribution of 2N, 4N and 8N hepatocytes (Rebuttal Figure\n11), therefore we conclude SBS-PolkKO mutations are not linked to polyploidy.\nRebuttal Figure 11.\nAnalysis of ploidy content\nin 18-month-old livers.\nBecause these mice are aged out to 18- or 7-months, and mutations could have occurred at any point\nin the tissue development, I’m not sure how much weight can be ascribed to the expression levels of the\nother TLS Pols. These levels can also vary significantly among cells while remaining the same when\naveraged over the entire tissue.\nWe thank the reviewer for this comment. While we used RNA expression data as a proxy due to the lack\nof available TLS activity data in tissues, we acknowledge that this is an indirect measure and may not\nfully capture differences in TLS function, and function over time. To address this, we have now softened\nour conclusion in Line 122 to reflect this uncertainty: \"These results suggest that differences in TLS gene\nexpression are unlikely to fully explain the tissue-specific mutagenesis we observe, which is more likely\nattributable to differences in damage burden across tissues; however, we cannot rule out the potential\ncontribution of variation in TLS protein levels or activity over time” (lines 145-148).\nPol kappa has also been shown to play roles in both TCR and GGR in UV-irradiated cells (Ogi & Lehmann\nRev1-Pol zeta lesion bypass, that would be disrupted in the kappa deleted cells. The authors have done\na very large amount of work in the current study. One does wonder, though, to what extent the increased\nSBS-PolkΔ, and possibly the transcription bias, would be suppressed by the addition of a catalytically\ndead Pol kappa.\nWe thank the Reviewer for pointing out roles of Polk other than its traditional TLS bypass activity. If SBS-\nPolkKO mutations were driven by Polk’s role during fill-in synthesis in NER (Ogi & Lehmann 2006 Nat Cell\nBio), we would expect SBS-PolkKO mutations to decrease by inactivating NER excision upstream of fill-\nin synthesis. Instead, we find SBS-PolkKO mutations are slightly but significantly increased in Xpc-/-\nPolk-/- and Xpa-/-Polk-/- double mutants (Figures 4e, 4f).\nWith regards to the non-catalytic structural role of Polk in TLS, the Reviewer is referring to recent work\nby the Duxin lab in Xenopus extracts (PMID: 39300172) that shows that the catalytic activity of Polk is\nrequired to replicate across a minor groove DNA lesions; while Polk also interacts with Rev1,\nubiquitylated PCNA and Polζ to extend past major groove DNA lesions and abasic sites, in a process\nindependent of its catalytic activity. First, catalytic-dead PolkD197A-E198A mice have been generated and\nthey have a spontaneous increase in C>A and C>G mutations in the liver at 52 weeks as judged by the\ngpt reporter assay (Masumura et al, 2017, PMID: 29076178). Characterizing genome-wide signatures in\n16\nthese mice is beyond the scope of the current study, but this mutation pattern is comparable to that of\nthe Polk-/- null mice used here (Rebuttal Figure 12).\nSecond, here we show preliminary data from ongoing work where we are using Nanoseq to determine\nthe role of core TLS factors in shaping mutation in mouse tissues. While the data is too preliminary in\nterms of sequencing depth and number of mutations to be included in the current manuscript, it clearly\ndemonstrates that mice lacking Rev1, Polζ (Rev7-/-) or PCNA ubiquitination (Pcna K164 r/r) lack the\nincrease in C>N mutations characteristic of the SBS-PolkKO signature (Rebuttal Figure 12).\nTherefore, both our own data and a previous report suggest Polk’s catalytic activity rather than the Rev1-\nPolζ interaction suppresses the SBS-PolkKO mutational signature.\nRebuttal Figure 12.\nComparison of 6 mutation classes\nbetween Polk-/- mice (this\nmanuscript), PolkD197A-E198A\ncatalytic-dead mice (PMID:\n29076178), and TLS-deficient mice\n(unpublished data). Data is shown\nas fold-increase compared to\nrelevant wild type controls.\nThe simplest explanation for the observed SBS and SBS-PolkΔ increases in the double mutant livers is\nthat they are additive, which would suggest independent activities. The data in 4B and 4D are supportive\nof this simple explanation. I understand that the authors applied a binomial regression that supports a\nmodel beyond this independence, but I remain unconvinced.\nThe Reviewer comments that the burdens shown in Figure 4b are supportive of the simpler additive\nexplanation. However, we argue that, after accounting for the presence of the background mutational\nburden observed in WT livers, the observed burdens actually suggest that the mutational burden double\nknockout livers are higher than expected based on the additive effect of the individual knockouts.\nTo illustrate this for the Xpa-/-Polk-/- double mutant; the mean observed burdens for Xpa-/- and Polk-/-\n, after subtracting the mean background mutational burden of 0.0508 SBS per Mb, are 0.0512 and\n0.1192 SBS per Mb, respectively. The mean observed burden in Xpa-/-Polk-/- double mutant is 0.2856\nSBS per Mb. After accounting for the background mutational burden and the individual Xpa and PolK\nand effects this still leaves around 0.0644 SBS per Mb unexplained. The same approach applied to the\nXpc-PolK double knockout leaves 0.078 SBS per Mb unexplained by the additive effects.\nThus, we argue that the double knockout burdens shown in Figure 4b cannot be explained by the additive\neffects of the individual gene deficiencies. The binomial regression models provide statistical support\nfor this observation. Firstly, through showing that the model including the interaction term is a\nsignificantly better representation of the data (illustrated by a strongly reduced AIC and significant\nlikelihood ratio test). Secondly, by assigning a statistically significant contribution of the interaction\neffect to the mutational burdens on top of the additive effects. This is illustrated in Figure 4f, where the\nobserved mutational burdens from Figure 4b are reconstructed well by the interaction model estimates.\nTo more clearly illustrate that the additive model does not as adequately explain the burdens in double\nknockout livers, below we also show the reconstruction of the observed burdens as in Figure 4f using\nthe estimates from the simple additive model (Rebuttal Figure 13).\nFinally, a second line of evidence unrelated to the binomial model is provided by de novo signature\nextraction that attributes significantly higher SBS-PolkKO mutations to NER-deficient Polk-/- mice\ncompared to Polk-/- mice (Figure 4e). We believe both sets of data do not support a simple additive\nmodel and suggest a genetic interaction.\nComparison of two binomial\nregression models to interrogate\nthe effect of combined gene\ndeficiency on the observed\nmutational burden. The second\nmodel fits the data better as\njudged by a reduced Akaike\ninformation criterion (AIC) value.\nCan the authors explain why the dispersion of SBS/Mb in the 7-month-old Polk-/- cholangiocyte clones\nis so much larger than those from 18-month-old polk-/- mice? Comparing Fig 1 to Fig 4. One would have\nlikely presumed the opposite. Particularly interesting is the single clone that is similar to wild type.\nOne possibility is simply a larger n in the 7-month-old samples and there is significant inter-clonal\nvariation or inter-individual variation. Another possibility is that liver tissue in older mice represents\nclones that struck a balance between lesion abundance, mutation accumulation, and clonal outgrowth.\nThe coefficient of variation (ratio of standard deviation to mean) for the 18-month Polk-/- clones is 0.255,\nwhich is lower than the 7-month clones, which is 0.344. Notably, within the 7-month samples, there is\na single data point that visually appears as an outlier. Removing this sample reduces the coefficient of\nvariation for the 7-month samples to 0.214, indicating that the 7-month clones are generally more\nconsistent than the 18-month clones, aside from this one potential outlier.\nHowever, this sample was not statistically identified as an outlier (Grubbs’ test, ⍺= 0.05). this may be\ndue to the relatively small sample size (n = 8) that limits the power for outlier identification, or as the\nreviewer suggested, it reflect the inter-clonal variations. While it is hard to draw a conclusion from a\nsingle potential outlier, this variation could arise due to fewer proliferation cycles of the cell, or reduced\nexposure to endogenous DNA damaging agents.\nCoefficient of variation:\nPolk-/- 18m = 0.07301/0.2864 = 0.255\nPolk-/- 7m = 0.05851/0.1700 = 0.344\nPolk-/- 7m_without_outlier = 0.03987/0.1861 = 0.214\n18"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have substantially improved the writing and accessibility of the manuscript, and I believe have carefully and satisfactorily addressed our comments and those of the other reviewers. The abstract is much clearer and reflects the key findings of the paper. Polk is more clearly introduced for a general audience, and the method referencing is improved. The reasoning for choosing the second model to reconstruct the mutational burden in relation to the different gene losses has been significantly improved in the manuscript and I thank the authors for including the limitations of signature analysis in the Discussion. The authors have now included a link to a repository containing code used for the manuscript and I appreciate the extra effort to include demo files.\nOne minor point: I appreciate the authors' answer outlining their interpretation of cosine similarity in the context of signature analysis. However, the term 'confidence' typically refers to statistical certainty, whereas cosine similarity only reports the (dis)similarity between two vectors.\n(Remarks on code availability)\nI have looked at the code and the read me files, but not re-run it. It appears to be adequately commented and should be useable by others wanting to run it.", "reviewer_2": "Thank you for providing Rebuttal Figures 1–9. I remain concerned about the strength of the analytical evidence in your DNA adductomics analysis. If the seven newly proposed DNA adducts cannot be convincingly supported, the statement “Using untargeted DNA adductomics, we then reveal new endogenous guanine lesions that serve as substrates for NER” may not be sufficiently substantiated as a major finding.\n1. By your own data, stepped HCD enables detection of the neutral loss of deoxyribose in multiple adduct standards. This raises the question: why do none of the seven newly proposed DNA adducts in your study show either the neutral loss of one or two deoxyribose units? Without such evidence, it is difficult to see how they can be confidently assigned as DNA adducts.\n2. You argue that depurination at N7-G and N3/N7-A explains the absence of deoxyribose losses. However, it seems unlikely that all seven new adducts would undergo complete depurination. If depurination occurs very rapidly, these adducts might not survive DNA extraction and thus would not be present in your samples. If depurination is slower, then after hydrolysis one would expect to detect at least some intact nucleoside precursors (before depurination) in LC-HRMS. It would strengthen your case if you could provide evidence such as extracted ion chromatograms or spectra showing co-existing non-depurinated precursors.\n3. If these DNA adducts are intrastrand crosslinks, then indeed neutral loss of deoxyribose may not be observed after hydrolysis because the products retain phosphate groups. However, such structures usually generate characteristic fragment ions (PMCID: 3625460). It would therefore be important to check whether these expected fragments are present in your data.\n4. You state that “we are confident that our DNA hydrolysis protocols, which include the use of a DNA phosphatase, enable the release and detection of these modifications.” However, the literature (DOI: 10.1021/acs.chemrestox.2c00308) suggests that current enzymatic digestion protocols may not always release crosslinked DNA in the expected form. I encourage you to provide additional supporting data to demonstrate that your protocol is indeed effective for the modifications described.\n(Remarks on code availability)", "reviewer_3": "In this revised manuscript, Jiang and collaborators have addressed a number of comments from the reviewers that centered on clarifying certain aspects of their experiment, explaining further the evidence behind the claim that guanine adducts are behind the now named SBS-PolkKO signature (I agree this is a better name), and putting some previous work in perspective. I do believe that the rewritten abstract and introduction help clarify the work and make the manuscript easier to understand and interpret.\nI think the authors have done a very thorough job in replying to the reviewers and have satisfactorily answered all my comments. I would just insist on clarifying somewhere that the increase of PolKKO-associated mutations in Xpa-/- livers is due to a potential artifact in assigning signatures and not a \"true\" increase - this would in my opinion help clarify the significance of the results.\n(Remarks on code availability)\nI have reviewed the github page where the code is hosted, but I have not tried installing it as it would be quite difficult in my personal computer. The README is clear, with a thorough explanation on how to run the code, and it is organised appropriately.", "reviewer_4": "The authors have done a thorough and commendable job responding to reviewer comments. This is greatly appreciated.\nThe manuscript is significantly strengthened due to their efforts.\n(Remarks on code availability)"}, "author_response": "We thank the reviewer for this comment and we agree with the valid observation about the uncertainty in the definitive assignment of the identified analytes as DNA adducts. While we believe that we have identified DNA adducts containing guanine, it is true that without a synthetic standard for comparison, uncertainty remains in the assignments. Taking this into consideration, we address each of the reviewer’s specific comments below while also changing the sentence highlighted, in the effort of increasing accuracy and addressing the lack of certainty of DNA adduct identification.\nThe sentence was changed from “Using untargeted DNA adductomics, we then reveal new endogenous guanine lesions that serve as substrates for NER” to “Using untargeted DNA adductomics, we then reveal potentially new endogenous guanine lesions serving as substrates for NER” (line 26).\n1.By your own data, stepped HCD enables detection of the neutral loss of deoxyribose in multiple adduct standards. This raises the question: why do none of the seven newly proposed DNA adducts in your study show either the neutral loss of one or two deoxyribose units? Without such evidence, it is difficult to see how they can be confidently assigned as DNA adducts.\nIn other studies, we have been able to assign the identity of DNA nucleobase adducts, which lack the deoxyribose/s of their corresponding nucleoside adducts, through the observation of nucleobase fragmentation ions and product ions corresponding to the neutral loss of the nucleobases. This is described in Figure 2 of our investigation of the genotoxin colibactin and reported here for [REDACTED] convenience (DOI: 10,1126/science.aar7785). The structural confirmation of these proposed nucleobase adducts was achieved through chemical synthesis and comparison of the LC-MS spectra of the synthetic standard and the experimental sample.\nIn that work and a subsequent study (DOI: 10.1021/jacs.9b02453), the presence of the versions of the adducts carrying a dR or 2dRs was investigated and no observable amounts were observed.\nSimilarly, a crosslink adduct was observed in a study characterizing and quantifying busulfan DNA adducts (Figure 2 of DOI: 10.1016/j.omto.2023.01.005 - see below) and it was identified by its MS2 [REDACTED] fragmentation spectrum which is dominated by the loss of guanine. Its structure was confirmed by comparison with a synthetic standard (The papers discussing the detection of alkylating agents-derived DNA adducts are listed as references 72 and 73 in our manuscript).\nIn the experiments described in this current manuscript, we were not expecting larger DNA adducts and therefore the highest scanned mass was 700 m/z. This upper mass limit precluded the ability to detect the deoxyribose-containing nucleoside adduct versions of the reported nucleobase adducts. Following the reviewer’s observation and suggestion, we have now repeated the analysis with a broader scanning range and included in our analysis the targeted detection of analytes corresponding to the reported adducts with one dR or 2dRs bound to them. No signal attributable to these hypothetical adducts was observed.\nIn the case of the colibactin adduct, mentioned above (DOI:10,1126/science.aar7785), after the discovery of the DNA adduct using enzymatic hydrolysis we confirmed that the adduct was also observable after neutral thermal hydrolysis of the DNA. This observation is consistent with the adduct being bound to the depurinating N3 or N7 position of adenine, and consistent with the lack of the neutral loss of deoxyribose and observation of adenine in the MS2 spectra. Following this observation we performed neutral thermal hydrolysis of the samples used for the present study and confirmed that, for example, the m/z 603 analyte, with a spectrum consistent with a cross-link and with clear fragments corresponding to guanine and cytosine, was observed also in these conditions. This observation is consistent with our assignment of this putative DNA adduct as being released upon depurination/depyrimidation and suggests that the modifications are at the N7 position of the guanine and/or the O2 position of cytosine.\nAdditionally, for the m/z 603 analyte for which the appearance of protonated guanine and cytosine and neutral loss of guanine and cytosine dominate its MS2 spectrum we collected MS3 fragmentation data on the m/z 152 and 112 product ions from the MS2 fragmentation. A comparison of experimental data with database spectra confirmed that they are guanine and cytosine, respectively. Taking all of these new observations into account we believe that we can confidently say that the m/z 603 analyte is guanine covalently linked to cytosine, and is observed upon enzymatic and neutral thermal hydrolysis of DNA supporting that it is presumably originates from chemically-modified DNA (i.e. a DNA adduct).\n2.You argue that depurination at N7-G and N3/N7-A explains the absence of deoxyribose losses. However, it seems unlikely that all seven new adducts would undergo complete depurination. If depurination occurs very rapidly, these adducts might not survive DNA extraction and thus would not be present in your samples. If depurination is slower, then after hydrolysis one would expect to detect at least some intact nucleoside precursors (before depurination) in LC-HRMS. It would strengthen your case if you could provide evidence such as extracted ion chromatograms or spectra showing co-existing non-depurinated precursors.\nWe have reanalyzed the samples of this study with targeted detection of the hypothetical versions of the reported putative DNA adducts with one and two dR moieties bound to them. No signals corresponding to these non-depurinated precursors were observed. We believe the observed DNA adducts are relatively stable in intact DNA but completely depurinate/depyrimidate upon enzymatic hydrolysis of DNA, as was observed in our colibactin study (mentioned above) where the presence of a deoxyribose bound to the DNA adduct was also investigated and not observed.\n3. If these DNA adducts are intrastrand crosslinks, then indeed neutral loss of deoxyribose may not be observed after hydrolysis because the products retain phosphate groups. However, such structures usually generate characteristic fragment ions (PMCID: 3625460). It would therefore be important to check whether these expected fragments are present in your data.\nWe thank the reviewer for this valuable insight and suggestion. We have further scrutinized the spectra of our unknown DNA adducts and did not detect any fragments consistent with those reported in the paper (PMCID: 3625460) mentioned by the reviewer in Point 4.\nAdditionally, we currently do not have an established and universal fragmentation pattern for the detection of intrastrand crosslinking adducts for which the DNA phosphatase does not cleave the phosphate (DOI: 10.1021/acs.chemrestox.2c00308). The fragmentation behavior of cisplatin intrastrand crosslink adducts is a good starting point for proposing a generalized fragmentation behavior of these adducts, however this needs to be extended beyond cisplatin adducts for us to be comfortable reporting the discovery of novel intrastrand crosslinks. Any DNA adducts attributed to an intrastrand crosslink based solely on the fragmentation behavior of cisplatin adducts would be speculative. We plan to investigate the fragmentation behavior of additional intrastrand crosslinks to establish fragmentation patterns to be used to identify these crosslinks with our DNA adductomic methodology. Unfortunately, in the present study, we cannot rule in or out the presence of such intrastrand DNA adducts.\n4. You state that “we are confident that our DNA hydrolysis protocols, which include the use of a DNA phosphatase, enable the release and detection of these modifications.” However, the literature (DOI: 10.1021/acs.chemrestox.2c00308) suggests that current enzymatic digestion protocols may not always release crosslinked DNA in the expected form. I encourage you to provide additional supporting data to demonstrate that your protocol is indeed effective for the modifications described.\nOur workflow is designed to be untargeted, with the goal of detecting as many DNA modifications as possible in an unbiased manner. By definition, this approach prioritizes breadth of detection over optimization for any one specific adduct type. We agree with the reviewer that enzymatic hydrolysis protocols may not always be fully optimized for specific modifications, such as intrastrand crosslinks, and we have clarified this point in the manuscript (line 426). While our use of DNA phosphatase is intended to maximize release of nucleoside adducts, we acknowledge that additional optimization would be needed to ensure quantitative recovery of specific crosslinked structures.\nThe assertion made in the sentence quoted by the reviewer comes from other investigations (colibactin and busulfan mentioned previously) using the same enzymatic digestion protocol where we were able to detect depurinated crosslink DNA adducts, consistent with the G-C crosslink (m/z 603) proposed here. In these other studies, the identity of the depurinated crosslink adducts were confirmed by comparison with synthetic standards. The efficiency of the effects of the phosphatase enzyme on the release of these adducts may be adduct specific and requires dedicated experiments which are beyond the scope of this work. However, the work cited above on a number of different initially unknown adducts then confirmed synthetically, supports our ability to see these modifications when using the same enzymatic protocol.\nWe thank the reviewer for their feedback, and helping us improve the manuscript.\nOne minor point: I appreciate the authors' answer outlining their interpretation of cosine similarity in the context of signature analysis. However, the term 'confidence' typically refers to statistical certainty, whereas cosine similarity only reports the (dis)similarity between two vectors.\nGood point, we have rephrased this to ‘low similarity’ (line 172).\nWe also find the suggested changes have imporved the manuscript significantly and thank the Reviewer for their feedback.\nI would just insist on clarifying somewhere that the increase of PolKKO-associated mutations in Xpa-/- livers is due to a potential artifact in assigning signatures and not a \"true\" increase - this would in my opinion help clarify the significance of the results.\nYes, we have clarified the increase of PolKKO-associated mutations in Xpa-/- livers is potentially an artifact of signature misattribution (line 299).\nWe sincerely thank the reviewer for these kind words and for their thoughtful feedback throughout the review process."}, {"round": 3, "reviewer_comments": {"reviewer_2": "Although the authors could not provide additional evidence to further confirm that the newly observed endogenous lesions are guanine adducts or to clarify their binding structures, the study is well-conducted and provides meaningful insights. I believe the manuscript is suitable for publication.\n(Remarks on code availability)"}, "author_response": ""}]

Decision letter
[Editorial Note: See attachment]