Abstract

G-quadruplex (G4) is a non-canonical DNA second structure formed on specific G-rich sequences which are enriched in transcriptional regulatory regions and telomeres. This structure is involved in multiple cellular processes including transcription regulation and genome integrity. On the other hand, G-rich regions are hotspots of oxidative damage, since G has the lowest redox potential among the four nucleobases, and the 5’G of a G stretch has an even lower ionization potential. Evidence emerged recently that the most common oxidative damage, 8-oxo-7,8-dihydroguanine (OG), plays an epigenetic-like role to regulate transcription. Therefore, G4 and OG may co-localize at G-rich regions and interact with each other to regulate transcription. In this review, we summarize recent advantages about genome-wide mapping of G4 and OG, and discuss their crosstalk at genomic level.

Introduction

Human genome consists of four nucleotides, namely A, T, C, and G, which form formal Watson–Crick base pairing between A:T and C:G, respectively (Watson & Crick, 1953). The average GC content of the human genome is about 40.4%; however, GC is not uniformly distributed across the genome (Schneider et al., 2017). Indeed, GC is highly enriched in transcriptional regulatory regions, such as promoters and the 5’-untranslated regions (5’-UTR), as well as telomeres that have the “TTAGGG” repeats (Balagurumoorthy & Brahmachari, 1994; Zhang et al., 2004).

Of note, these G-rich regions have two unique properties. First, four Gs can form a square planar structure named G-tetrad through Hoogsteen hydrogen bonding, and three G-tetrads can stack to form a non-canonical DNA secondary structure called G-quadruplex (G4) (Fig. 1a) (Brooks et al., 2010; Huppert, 2010; Kwok & Merrick, 2017). Due to the relative direction of G stretches (parallel, anti-parallel, or hybrid), G4s can be formed in three conformations (Burge et al., 2006; Spiegel et al., 2020). Specific G-rich sequences having the potential to form G4 structure are known as potential quadruplex sequences (PQSs), which usually contain at least four stretches of three or more Gs (Huppert & Balasubramanian, 2005; Kwok & Merrick, 2017; Todd et al., 2005). Not surprisingly, PQSs are enriched in the GC-rich transcriptional regulatory regions and telomeres (Fleming et al., 2018; Sen & Gilbert, 1988; Xu, 2011). As a competitor of the canonical B-form double-stranded DNA, G4 is involved in multiple processes including transcription regulation, DNA replication, and genome integrity (Bryan, 2019; Cogoi & Xodo, 2006; Hansel-Hertsch et al., 2017; Rhodes & Lipps, 2015; Varshney et al., 2020). The biological roles of G4 are complicated in these processes. For transcription regulation, G4s in promoters could either inhibit transcription by blocking the transcription machinery or enhance transcription by separating two strands to facilitate the loading of transcription factors (Bochman et al., 2012). G4 can also directly recruit transcription factors (e.g., Sp1 and MAZ) and other G4-binding proteins (e.g., nucleolin and POT1) (Cogoi et al., 2014; Kwok & Merrick, 2017; Piekna-Przybylska et al., 2014; Ray et al., 2014; Santos et al., 2022) to regulate transcription. Whether promoting or suppressing transcription depends on the specific position and strand of the G4 structure. In vivo evidence of the transcription regulation role of G4 came from the observations that G-quadruplex stabilizing ligands could change the expression of many genes (e.g., c-MYC) whose promoters were enriched for PQSs (Bochman et al., 2012; Siddiqui-Jain et al., 2002).

Chemical structures of OGs and G4s. a The formation of OG by reactive oxygen species (ROS) attacking. b Structures of G-quartet and G4s. G-quartet is constituted of four Hoogsteen hydrogen-bonded guanines and central cation (colored yellow) coordinated to oxygen atoms, while G4s are formed by the stacking of three G-quartets, and are divided into three conformations (parallel, anti-parallel, and hybrid) based on the strand direction

G4 also plays important roles in DNA replication and telomere maintenance (Rhodes & Lipps, 2015). It can inhibit DNA polymerase and telomerase, and thus need to be resolved by DNA helicases, including WRN, BLM, FANCJ, and PIF1 during replication (Huber et al., 2002; Mohaghegh et al., 2001; Sanders, 2010; Sarkies et al., 2012). Lack of these enzymes results in replication pressure, telomere dysfunction, accumulation of DNA damage, and finally genome instability (Brosh, 2013; Rhodes & Lipps, 2015). On the other hand, G4 is associated with DNA replication origins, and is actually required for replication initiation in vertebrate cells (Valton et al., 2014). Moreover, G4 formed at telomeres can help telomere organization through G4-binding proteins, and protect telomeres from nuclease digestion (Bochman et al., 2012). Thus, G4 is a double-edged sword for genome integrity.

Another unique property of G-rich regions is that G has the lowest redox potential among the four nucleobases, and the 5’G of a G stretch has an even lower ionization potential due to π-stacking (Genereux & Barton, 2010; Schuster, 2000; Sugiyama & Saito, 1996). Therefore, G-rich regions should be more readily oxidized. There are various G oxidation products, including two-electron oxidation products like OG (8-oxo-7,8-dihydroguanine) (Fig. 1b) and 2Ih (5-carboxamido-5-formamido-2-iminohydan-toin), and four-electron oxidation products, such as Gh (5-guanidinohydantoin), Sp (spiroiminodihydantoin) and Z (2,2,4-triamino-2H-oxazol-5-one) (Cadet et al., 2010; Delaney et al., 2012; Neeley & Essigmann, 2006; Pratviel & Meunier, 2006). Among them, OG is the most abundant and well-studied lesion (Cadet et al., 2017; Kasai, 1997; Steenken & Jovanovic, 1997). Intriguingly, OG has an even lower redox potential than the undamaged G (Steenken et al., 2000; Xue & Greenberg, 2007), and thus is easy to be further oxidized to Gh and Sp (Luo et al., 2001; Niles et al., 2001). As the most common oxidative DNA damage (Cadet et al., 2003; Steenken & Jovanovic, 1997), OG can cause misincorporation of dA on the opposite position during replication, thus may induce G to T mutation (Shibutani et al., 1991), and finally result in cancers, neurological diseases, and aging (Lodato et al., 2018; Yu et al., 2016). To avoid these deleterious consequences, OGs and the misincorporated dAs are cleared through OGG1 (8-Oxoguanine DNA Glycosylase) and MUTYH (MutY DNA Glycosylase) glycosylases in human cells, respectively, leaving apurinic sites (AP sites) which are further handled by the base excision repair pathway (Dizdaroglu et al., 2017; Gorini et al., 2021). Mutations in these repair genes will increase the risk of multiple cancers (Sakamoto et al., 2007; Xie et al., 2004). Noteworthy, the repair intermediates of OG, i.e., AP sites and single-strand DNA breaks (SSBs), are more deleterious to genome stability, since they can block DNA polymerase and be converted to double-strand DNA breaks (DSBs) during DNA replication (Gorini et al., 2021).

Although OG is a common DNA damage, it was reported that OG also plays an epigenetic-like role to regulate gene transcription through a couple of possible mechanisms [reviewed in (Fleming & Burrows, 2020)]. For instance, OGs at the promoter of estrogen-responsive genes can recruit OGG1 and topoisomerase IIβ to activate downstream transcription (Perillo et al., 2008). Another example is that an inactive form of OGG1 (OGG1ox) which has an oxidized cysteine residue would retain at the OG sites and recruite NF-κB to trigger gene expression during inflammation (Pan et al., 2016). Intriguingly, the repair intermedia of OG (i.e., AP sites) can stabilize G4 structure and regulate transcription (Fleming et al., 2017; Roychoudhury et al., 2020), which will be discussed below.

Since both G4 and OG are able to regulate gene transcription and they have crosstalk with each other in vitro (Cadet et al., 2017; Fleming & Burrows, 2020; Huppert, 2010), it is intriguing whether they co-localize in the human genome and how they affect each other in the chromatin environment. In recent years, a couple of high-throughput sequencing-based techniques were developed to detect the genomic distribution of G4s (Chambers et al., 2015; Hansel-Hertsch et al., 2016; Marsico et al., 2019; Tu et al., 2021; Zheng et al., 2020) and OGs (Amente et al., 2019; An et al., 2021; Ding et al., 2017; Fang & Zou, 2020; Poetsch et al., 2018; Wu et al., 2018), shedding light on above questions.

Methods to measure genomic distribution of G4s

The positions of G4s in the genome are important for their biological roles (Bryan, 2019; Hansel-Hertsch et al., 2017; Rhodes & Lipps, 2015; Varshney et al., 2020); however, it is challenging to determine the existence and locations of G4s. On one hand, as a non-canonical structure, G4 is quite dynamic and always competing with the regular double-strand structure (Maizels, 2006; Spiegel et al., 2020). An important factor determining the balance between G4 and dsDNA is the type and concentration of monovalent cations (Sen & Gilbert, 1990). The G4 structure is stable in the presence of a low concentration of K+ or a higher concentration of Na+, while becoming unstable with Li+ (Gray & Chaires, 2008; Hud et al., 1996; Sen & Gilbert, 1990). Moreover, protein chaperones and helicases also impact the balance between G4 and dsDNA in vivo (Fang & Cech, 1993; Lerner & Sale, 2019; Rhodes & Lipps, 2015; Sauer & Paeschke, 2017). Therefore, the existence of G4 structure may change when extracting genomic DNA from cells. On the other hand, G4 is topologically polymorphic and can arise from the intra- or inter-molecular folding of various G-rich sequences (Burge et al., 2006; Patel et al., 2007; Rhodes & Lipps, 2015). This property causes two technical challenges. First, it is difficult to identify all possible sequences with the ability to form quadruplexes by theoretical analysis (Huppert & Balasubramanian, 2005; Kwok & Merrick, 2017). Second, the G4-specific antibodies cannot recognize all kinds of G4 structures (Kwok & Merrick, 2017). Nonetheless, a couple of high-throughput sequencing technology-based methods emerged in recent years to detect the genomic distribution of G4s both in vitro and in vivo (Fig. 2) (Chambers et al., 2015; Hansel-Hertsch et al., 2016; Marsico et al., 2019; Tu et al., 2021; Zheng et al., 2020).

Illustration of high-throughput sequencing-based methods for measuring G4s. a G4-seq and G4-miner detect G4s in extracted genomic DNA based on the interference of G4 on DNA replication. b G4 ChIP-seq measures in vivo G4s by immunoprecipitation with a G4-antibody after crosslinking. c G4P-ChIP-seq captures G4s in situ by expressing an artificial G4-probe protein (G4P) and enriches G4-containing fragments by pull-down G4P after crosslinking

The first breakthrough of genome-wide G4 mapping is G4-seq (Chambers et al., 2015; Marsico et al., 2019) which detects the polymerase stalling caused by G4 (Rhodes & Lipps, 2015; Weitzmann et al., 1996) with the Illumina sequencing platform (Fig. 2a, left). In the G4-seq procedure, the genomic DNA sequencing library is first sequenced in the buffer containing Na + or Li + which is suitable for dsDNA formation (Hud et al., 1996) and enables detection of the actual sequence. The same library is then sequenced again in the buffer with K + and/or the G4-stabilizing ligand pyridostatin (PDS) (Rodriguez et al., 2008) which can promote the formation and stabilization of G4 and induce polymerase stalling. By comparing the differences in sequencing quality and mismatches between two runs, G4-seq can elucidate the distribution of in vitro G4s. G4-seq was applied to 12 species including human beings, and showed that the proportion of canonical PQS (G≥3N1–7G≥3N1–7G≥3N1–7G≥3) (Huppert & Balasubramanian, 2005; Todd et al., 2005) in OG4 (observed G4 sequence) was > 70% for 10 out of 12 species, and > 50% for the other two. The remaining OG4s are non-canonical PQSs, e.g., those with long (> 7) intervals between G-tracks and/or a discontinuity in the G-tracks leading to bulges (Chambers et al., 2015; Guedin et al., 2010; Mukundan & Phan, 2013). Moreover, this study revealed that the strong enrichment of OG4s in gene promoter regions and 5’UTRs was particular to mammals among the 12 species, with human having the strongest enrichment. Of note, OG4s in human were closely associated with regulatory regions of cancer-related genes and somatic copy-number variations. Although G4-seq is a powerful approach for identifying all possible G4 sites in genomic DNA, especially various non-canonical G4 structures that are difficult to be predicted (Huppert & Balasubramanian, 2005), it still has some limitations. First, G4-seq detected G4s in vitro, which reflects the potential of particular sequences to form G4s under specific conditions instead of actual G4 structures in vivo. Second, G4-seq is inevitably biased due to the sequence mismatch analysis, and its resolution is not enough to disentangle G4s in close proximity (Marsico et al., 2019). Finally, G4-seq requires specific sequencing buffers that are not readily available, as well as the cost of two sequencing runs.

Another similar method, G4-miner (Tu et al., 2021), is indeed a bioinformatic tool to identify G4 structure from regular whole-genome sequencing data (Fig. 2a, right). The presence of G4 can cause unexpected local fluctuations of sequencing quality (Rhodes & Lipps, 2015; Weitzmann et al., 1996), which are captured by G4-miner. Therefore, G4-miner is a convenient method that needs no specific reagents or operations. Similar to G4-seq, G4-miner detected in vitro G4 structures. Furthermore, since there is no extra G4 stabilization step, G4-miner requires a high sequencing depth and cannot completely exclude the influence of other DNA secondary structures. With this method, Tu et al. identified 45.9% of all PG4s (predicted canonical G4-forming sequences) in genomic DNA from GM12878 human lymphoblast, and found a strong enrichment of OG4s in 5’-UTRs and splicing junctions in the human genome (Tu et al., 2021). Moreover, compared to G4-seq, G4-miner preferred to detect non-canonical G4s, especially two quartets (GGNxGGNxGGNxGG).

To capture in vivo G4 structures, a fixing step before DNA extraction is usually required. According to this idea, Hansel-Hertsch et al. adapted the antibody-based chromatin immunoprecipitation followed by the next-generation sequencing (ChIP-seq) approach for G4 mapping, namely G4 ChIP-seq (Hansel-Hertsch et al., 2016, 2018) (Fig. 2b). In brief, after formaldehyde crosslinking, DNA is fragmented and subjected to immunoprecipitation (IP) with an antibody (BG4) specific to G4 structures (Biffi et al., 2013) for the enrichment and subsequent sequencing. This method identified about 10,000 G4 peaks from the human genome, predominantly in nucleosome-depleted regulatory regions. Consistent with the conclusion from G4-seq, these OG4s are enriched in the promoters and 5’UTRs of highly transcribed genes, particularly cancer-related genes, as well as in regions with somatic copy-number amplifications. In contrast to G4-seq, G4 ChIP-seq detects in vivo G4s within a chromatin context. Thus, the fact that G4 ChIP-seq detected fewer OG4s than G4-seq implies a general inhibitory effect of heterochromatin on G4 formation. Moreover, the number and distribution of G4 peaks are quite different between HaCaT and NHEK cells, further suggesting the impacts of chromatin environment and other cellular factors on in vivo G4 formation (Hansel-Hertsch et al., 2016). However, G4 ChIP-seq depends on specific antibodies which may introduce nonspecific binding and biases, while cellular G4-binding proteins can interfere with binding of antibodies. In addition, since IP of G4 is carried out with isolated and segmented DNA, incidental G4 formation after DNA extraction cannot be ruled out. G4 antibodies other than BG4 were also used for G4 ChIP-seq and obtained similar results (Lam et al., 2013; Liu et al., 2016). Recently, the CUT&TAG technique was adopted to replace traditional ChIP-seq and enabled G4 detection in fewer cells with higher sensitivity (Hui et al., 2022; Li et al., 2021; Lyu et al., 2022).

To capture G4s under native conditions, Zheng et al. developed G4P-ChIP-seq method (Zheng et al., 2020) (Fig. 2c). In this assay, a small (6.7 kDa) artificial protein “G4 probe” (G4P) with high affinity and specificity to G4 structure was expressed in cells to bind G4s in vivo. After formaldehyde crosslinking, genomic G4s were determined by ChIP-seq targeting the epitope-tagged G4P protein (Zheng et al., 2020). Compared with BG4 and other G4 antibodies, G4P has a higher affinity to various G4 structures and less nonspecific binding, and the smaller size of G4P can reduce the steric hindrance to access G4s. More importantly, G4P binds to G4s in vivo, thus excluding incidental G4 formation after DNA extraction. G4P-ChIP-seq identified more than 123,000 G4P peaks in A549 cells, and the ratio of PQS-positive peaks was much higher than that in G4 ChIP-seq (89% vs 25%). The resolution and sensitivity of G4P-ChIP-seq were also obviously improved in comparison to G4 ChIP-seq. Similar to G4 ChIP-seq, the pattern of G4 distribution varied among different cell lines. Furthermore, G4P-ChIP-seq also identified gene promoters as hot spots of G4s, in accordance with other studies (Chambers et al., 2015; Hansel-Hertsch et al., 2016; Tu et al., 2021). However, the expression of G4P in cells might influence the stability of G4s and indirectly affect transcription, leading to artificial results.

In summary, it can be concluded that G4s are highly enriched in promoter regions of the human genome, consistent with the regulatory role of G4 in gene expression (Hansel-Hertsch et al., 2017; Rhodes & Lipps, 2015). However, all current methods for G4 mapping are far from perfect. None of these methods can faithfully capture all G4s under physiological conditions, nor assign a detected G4 peak to a specific PQS. Moreover, computational prediction of G4s is widely used; however, current algorithms are based on available experimental data from the above methods (Bedrat et al., 2016; Hon et al., 2017; Sahakyan et al., 2017; Xiao et al., 2014). Thus, better G4 sequencing methods as well as new bioinformatic tools and improved computational prediction algorithms are required for studying the biological functions of G4.

Methods to map OG across the genome

Although OG is the most common oxidative damage, the steady-state level of cellular OG is estimated to be as low as ~ 0.5–1 lesion per 106 nucleotides (nt) in human cells (European Standards Committee on Oxidative, 2003; Ravanat et al., 2002; Gedik et al., 2005), since it is controlled by the dynamic balance between formation and repair. These OGs are not uniformly distributed throughout the genome, and the genomic distribution is important for their negative and positive roles in human cells. Some early studies revealed the heterogeneous distributions of OGs (Ohno et al., 2006; Yoshihara et al., 2014); however, these methods could not detect the accurate sites of OGs. Noteworthy, there are several obstacles to OG sequencing. First, as mentioned above, OGs are rare in cellular DNA under physiological conditions (European Standards Committee on Oxidative, 2003; Ravanat et al., 2002), and their occurrence is relatively random. Therefore, it is challenging to distinguish OGs from bulky normal DNA. Moreover, a mass of artificial OG would be produced during sample processing, such as DNA purification, sonication, and enzymatic reaction. The incidental oxidation could greatly compromise the accuracy of OG measurement, albeit the addition of anti-oxidation agents can reduce this effect (European Standards Committee on Oxidative, 2003; Mangal et al., 2009; Badouard et al., 2008). Nevertheless, a couple of new methods were developed for OG sequencing in recent years. These methods took use of different strategies for OG capturing, involving an antibody recognizing OGs, specific glycosylases, and OG-selective chemical reaction (Fig. 3) (Amente et al., 2019; An et al., 2021; Ding et al., 2017; Fang & Zou, 2020; Poetsch et al., 2018; Wu et al., 2018).

Illustration of high-throughput sequencing-based methods for measuring OGs. These methods are different in damage recognition/labeling strategies and procedure designs. In general, OGs are recognized or labeled by an OG-antibody, specific enzymes recognizing OG (OGG1 or FpG), or a selective chemical reaction. Regarding to procedure design, except OG-AP-seq and CLAPS-seq which label OGs prior to sonication, other methods all recognize or label OGs after DNA fragmentation. In addition, CLAPS-seq and click-code-seq have extra steps to achieve single-nucleotide resolution

The strategy of immunoprecipitation with an anti-OG-antibody was utilized by OxiDIP-seq to enrich OG-containing fragments from sonicated genomic DNA (Amente et al., 2019; Gorini et al., 2020; Scala et al., 2022) (Fig. 3, “OxiDIP-seq”). This method identified tens of thousands of OG hotspots from the human genome and revealed that OGs co-localize with DNA replication origins within the body of long genes. Moreover, it also found that genes within Common Fragile Sites (CFSs), as well as a set of super-enhancers associated with fragile CTCF-mediated chromatin loops, show high levels of OG. However, due to the limitation of antibody IP strategy (Mitchell et al., 2002; Serrano et al., 1996), this method just reached a resolution of several hundred base pairs, and nonspecific binding is unavoidable. Remarkably, sonication and denaturation were required prior to IP, which would produce a substantial amount of artificial OGs and compromise the results (European Standards Committee on Oxidative, 2003; Mangal et al., 2009; Badouard et al., 2008).

OG-specific glycosidases, i.e., OGG1 and FpG (Boiteux et al., 2017), are used for OG recognition in a couple of methods (Gilat et al., 2021; Jin et al., 2022; Poetsch et al., 2018; Wu et al., 2018). Among them, click-code-seq used FpG together with APE1 to remove OGs in sonicated genomic DNA and created single-nucleotide gaps, which were filled by the propargyl-modified guanine (Wu et al., 2018) (Fig. 3, “click-code-seq”). Then an azido-modified code sequence was added through a Click reaction for sequencing. This method was the first one to achieve single-base resolution, and verified that OGs were enriched at the 5’ G of GG dinucleotide, in accordance with the theoretical calculation (Genereux & Barton, 2010; Schuster, 2000). It revealed that there were fewer OGs in transcription start sites (TSSs), DNase I hypersensitive sites (DHSs), and autonomously replicating sequences (ARSs), while higher OG enrichment appeared at telomeres, nucleosomes, and positions of low RNA pol II occupancy. However, the specificity of click-code-seq was challenged by the relatively wide substrate spectrum of FpG (David & Wiliams, 1998; Krishnamurthy et al., 2008), and depended on the efficiency of prior blocking of pre-existing AP sites and free 3’ hydroxyl groups. This method was only applied to yeast till now. Similar to OxiDIP-seq, artificial OGs would be generated by sonication and could not be distinguished by this method.

Another method, OGG1-AP-seq, utilized OGG1 to convert OGs to AP sites, which were then labeled with biotin by the aldehyde reactive probe (ARP) (Poetsch et al., 2018) (Fig. 3, “OGG1-AP-seq”). Genomic DNA was sonicated after labeling, and biotinylated fragments were pulled down with streptavidin beads for sequencing. Therefore, sonication-produced OGs would not interfere with the final results. Like OxiDIP-seq, the resolution of OGG1-AP-seq was hundreds of base pairs. OGG1-AP-seq identified fewer OGs at promoters, exons, and transcription termination sites. Likewise, other sequencing methods for AP sites or free 3’ hydroxyl groups, e.g., snAP-seq (Liu et al., 2019), Endo-seq (Ding et al., 2015), Nick-seq (Cao et al., 2020), and circle-damage-seq (Jin et al., 2022), can also be or have already been used for OG mapping with the help of OG-specific glycosidases (and APE1 for the latter three). These methods could reach single-nucleotide resolution; however, the nonspecific activity of the enzymes and pre-existing AP sites and/or free 3’ hydroxyl groups would influence the specificity of these methods.

Unlike the above methods, enTRAP-seq made use of an engineered OGG1 which removed the oxidized base and formed a covalent bond with the repair intermediate to “trap” OGs (Fang & Zou, 2020) (Fig. 3, “enTRAP-seq”). This method had a resolution of hundreds of base pairs, and only identified ~ 1500 FpG-sensitive OG peaks from the mouse genome. Of note, this method used nucleases to perform DNA fragmentation, and thus should produce fewer OGs than sonication (Costello et al., 2013). However, the specificity and efficiency of the engineered OGG1 might not be satisfactory, since only a small fraction of the identified peaks were sensitive to FpG-treatment. This study concluded that OG peaks were significantly enriched in open chromatin regions and slightly decreased in closed heterochromatin regions. This conclusion was contrary to OGG1-AP-seq (Poetsch et al., 2018) and click-code-seq (Wu et al., 2018). In addition, it reported that more than 80% of OG peaks possessed PQSs, and OG peaks frequently occurred in regulatory elements such as promoter and 5’UTR, consistent with the results of OG-seq (see below) (Ding et al., 2017) and OxiDIP-seq (Amente et al., 2019).

OGs can also be labeled by a selective oxidation reaction due to their lower redox potential than G and other bases (Steenken et al., 2000; Xue & Greenberg, 2007). OG-seq took advantage of this reaction and labeled OGs with biotin for subsequent purification and sequencing (Ding et al., 2017) (Fig. 3, “OG-seq”). However, it had the same issue of artificial OGs as genomic DNA was sonicated before the labeling reaction, and possessed a resolution of several hundred base pairs. This method identified ~ 10,000 OG-enriched regions in WT mouse cells and ~ 18,000 in Ogg1−/− mouse cells. This study reported that promotors and UTRs had more OG-enriched sites; however, reanalyses of OG-seq data by other groups lead to different conclusions (Gorini et al., 2020; Poetsch et al., 2018).

CLAPS-seq (Chemical Labeling And Polymerase Stalling Sequencing) was a single-nucleotide resolution OG sequencing method based on above chemical labeling reaction (An et al., 2021) (Fig. 3, “CLAPS-seq”). In contrast to OG-seq, CLAPS-seq carried out the chemical labeling reaction before sonication to minimize incidental oxidation. After sonication, end-repair and adaptor ligation, a primer extension reaction was performed with a high-fidelity DNA polymerase to determine the exact position of OGs, since the polymerase could be blocked by biotin-labeled OGs. The extension products were ligated to the second adaptor and sequenced. The specificity of this method was confirmed by the high ratio of G residues (≥ 70%) at sequenced damage sites for both endogenous and exogenous OGs in human cells. It is worth noting that the first base resolution OG sequencing method, click-code-seq, incorporated a modified G base to enzyme-generated single-nucleotide gaps (Wu et al., 2018), and thus, the ratio of G at sequenced damage sites could not reflect the specificity of this method. Consistent with click-code-seq, CLAPS-seq revealed the enrichment of OGs at the 5’G of GG, as well as relative lower OG level in promoter regions which have the highest GC ratio in the human genome. Further analysis showed that the reduced occurrence of OGs at promoters is, at least partly, due to the reduction of OGs at G4s, whereas non-G4-PQSs are hot spots of OGs (An et al., 2021).

Moreover, single-molecular sequencing technologies, or the third-generation sequencing, have the potential to directly identify DNA modifications. Two major routes of the third-generation sequencing, namely single-molecule sequencing in real time (SMRT) and nanopore sequencing, are both used to detect OGs at base resolution, either directly or after labeling (An et al., 2015; Schadt et al., 2013; Zatopek et al., 2019). However, these methods are not mature and have only been applied in samples with simple context, including synthesized templates, plasmid DNA, and mitochondrial DNA.

As discussed above, each method has its own benefits and drawbacks, and led to conflicting conclusions. They differed in specificity, resolution and incidental oxidation, which are three major concerns to evaluate OG sequencing methods. Nonetheless, more data from different methods, especially those with distinct OG recognition mechanisms, are required for the clarification of opposite views.

The crosstalk between OG and G4 at genomic level

As both OGs and G4s might be enriched in the same G-rich regions, it is an intriguing question how they crosstalk with each other. Theoretically, OG could interrupt the G–G Hoogsteen hydrogen bond, while just having a weak impact on the canonical G–C Watson–Crick base pairing (Bielskute et al., 2019; Fleming et al., 2017; Lee et al., 2020). Thus, OG would change the balance between G4 and dsDNA, and is unfavorable for the formation of G4. In contrast, the repair intermedia of OG (i.e., AP sites) could disrupt the Watson–Crick base pairing, and is preferable for G4 (Fleming et al., 2017). Reporter gene assays demonstrated that OGs in PQS-containing promoter sequences could activate downstream transcription (Fleming et al., 2017, 2019; Pastukh et al., 2015; Cogoi et al., 2018). This effect depended on the activity of OGG1 to convert OGs to AP sites, and also required APE1 to be loaded onto the AP sites (Fleming et al., 2017, 2019). Thus, PQSs or G4s are thought to act as a sensor to sense oxidative stress and regulate transcription (Fleming & Burrows, 2020; Linke et al., 2021).

On the other hand, the effects of G4 on OG are complicated. First, since a G4 contains at least four stretches of Gs and the 5’ G of a G stretch is more readily oxidized (Genereux & Barton, 2010; Schuster, 2000; Sugiyama & Saito, 1996), G4s or PQSs were supposed to be hot spots of OGs (Fleming & Burrows, 2020; Linke et al., 2021). In addition to the G track sequence, the structure of G4 would also influence the formation and repair of G oxidation products (Dizdaroglu et al., 2017; Linke et al., 2021). Compared with dsDNA, the formation of G4 would change the types, distribution, and frequencies of G oxidation products along a PQS, as shown by in vitro experiments (Fleming & Burrows, 2013). However, these results were highly dependent on the reaction conditions, e.g., the specific oxidant and its concentration. Second, OGG1, the main glycosylase to repair OGs in human cells, can only recognize OGs in dsDNA but not in G4s or ssDNA (Dizdaroglu et al., 2017). This property conflicts with the role of OG in transcription regulation which need OGG1-generated AP sites to stabilize G4 structure. Moreover, unrepaired OGs in G4s would increase the risk of mutagenesis (Shibutani et al., 1991). To resolve this paradox, the “spare tire” model was proposed based on the fact that ~ 40% of all PQSs had five or more G-tracks (Fleming & Burrows, 2020; Fleming et al., 2018). In this model, when a G in the G4 core is oxidized, the fifth G track can replace the OG-bearing G track and preserve the G4 fold (Fleming et al., 2015, 2017). However, it is not clear whether OGs in the loop regions of G4s can be efficiently repaired by OGG1. Therefore, as a mechanism of oxidation response, the G4–OG model seems to be ineffective and has a high risk of genome instability.

The above results were mainly obtained from synthesized specific PQSs. Recent advantages in G4 and OG sequencing techniques unveiled in vivo connection between them. First, as expected, both G4s and OGs were highly enriched in PQSs (Amente et al., 2019; Ding et al., 2017; Fang & Zou, 2020; Hansel-Hertsch et al., 2016; Marsico et al., 2019; Poetsch et al., 2018; Tu et al., 2021; Zheng et al., 2020). As regions with the highest GC ratio, the TSS-proximal regions were hotspots for both PQSs and G4s (Huppert, 2010; Kwok & Merrick, 2017). However, there seem to be fewer OGs in promoter G-rich regions, as revealed by different studies (Amente et al., 2019; An et al., 2021; Poetsch et al., 2018; Wu et al., 2018). This apparent paradox was resolved by comprehensive analyses of G4P-ChIP-seq (Zheng et al., 2020) and CLAPS-seq (An et al., 2021) data from the same cell line (HeLa cells). The genomic maps of cellular G4s and OGs uncovered reduced OGs in not only TSS-proximal G4s but also TSS-distal G4s, while there were fewer OGs around TSSs with G4P peaks than those without G4P peaks. Moreover, in vitro oxidized genomic DNA showed similar OG distribution, indicating that this pattern was due to the DNA itself, i.e., specific sequences or secondary structures. In sharp contrast to G4s, PQSs without G4P peaks were hotspots of OGs, indicating that specific sequences were not responsible for reduced OG occurrence. Taken together, these results suggested that although the G-rich PQSs are hotspots of OGs, the G4 structure can strongly inhibit the formation of OGs. Therefore, a new model about G4–OG crosstalk was proposed (Fig. 4) (An et al., 2021). The PQSs in dsDNA can be easily oxidized to OGs which are then excised by OGG1. The resulting AP sites can recruit APE1 and facilitate the formation of G4s to regulate downstream transcription. On the other hand, pre-existing G4s are protected from oxidation, avoiding OG-related mutagenesis. Thus, the non-G4 PQSs, but not the pre-existing G4s, may play a role in sensing oxidative stress and regulating gene expression. This model resolves the conflict of the biological roles of OG as a source of genome instability and a gene regulator in the same G-rich regions.

A proposed model of the crosstalk between OG and G4. On one hand, G4s can protect genome integrity by preventing OG formation. On the other hand, non-G4-PQSs are prone to form OGs which can promote the formation of G4 through OGG1 and APE1, and thus regulate downstream transcription in response to oxidative stress. This figure is taken from (An et al., 2021)

Perspectives

Mapping G4s and OGs at the genomic level opens the door to investigate how G4s and OGs coordinate at G-rich regions in vivo to regulate transcription and genome integrity. However, there are many limitations in current studies. First, as discussed above, all these sequencing techniques have their own pitfalls, especially artifacts and biases which may compromise the conclusion. Further improvements of sequencing techniques are required to generate more accurate data, and results from different approaches are desired to obtain a comprehensive view for the genomic distributions of G4s and OGs. Second, the repair intermedia of OG, i.e., AP site, as well as the OG oxidation products, namely Gh and Sp, are not included in the analysis. Alternatively, the decrease of OGs in G4s might be attributed to increased oxidation of OG to generate Gh and Sp (Luo et al., 2001; Niles et al., 2001), since G4 can affect the oxidation of G and OG (Fleming & Burrows, 2013). Nonetheless, as Gh and Sp in G4s can be repaired by NEIL1 and NEIL2 (Zhou et al., 2015), oxidation of OGs in G4s should also be beneficial for genome stability. Currently, there are a couple of methods to map AP sites in the genome (Liu et al., 2019; Poetsch et al., 2018), while sequencing Gh or Sp is still unavailable. Integrating information of these OG-derivates is important for elucidating the mechanism of G4–OG crosstalk. Finally, as four human telomeric repeats (TTAGGG) can form a canonical PQS, the existence and functions of G4 and OG in telomere are an attractive question (An et al., 2015; Merta et al., 2019; Poetsch et al., 2018; Rodriguez et al., 2008; Wu et al., 2018). However, it is challenging to perform bioinformatic analysis in the highly repeated telomeric regions. Single-molecular sequencing technology-based methods and/or new bioinformatic tools may help overcome this obstacle in the future.

The genomics of G4 and OG is a fast-growing field. More studies with current approaches and new methods will emerge in the next few years to profile G4s and OGs in the genome, and uncover the mechanism and consequence of G4–OG crosstalk in G-rich regions.

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Funding

This study is supported by the National Natural Science Foundation of China (NSFC) [31870804, 21807013], Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning [2019], Shanghai Outstanding Young Talent Program [2019], innovative research team of high-level local university in Shanghai, Shanghai Municipal Natural Science Foundation [22ZR1413900] to J.H.

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1. Jiao An and Mengdie Yin wish it to be known that, in their opinion, the authors should be regarded as joint first authors.

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1. Shanghai Fifth People’s Hospital, Fudan University, Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China

   Jiao An, Mengdie Yin & Jinchuan Hu

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Correspondence to Jinchuan Hu.

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An, J., Yin, M. & Hu, J. G-quadruplex and 8-oxo-7,8-dihydroguanine across the genome: methodologies and crosstalk. GENOME INSTAB. DIS. 3, 241–254 (2022). https://doi.org/10.1007/s42764-022-00082-1

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• Received26 July 2022

• Revised05 September 2022

• Accepted06 September 2022

• Published17 September 2022

• Issue DateOctober 2022

• DOIhttps://doi.org/10.1007/s42764-022-00082-1

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Keywords

• G-quadruplex (G4)

• 8-Oxo-7,8-dihydroguanine (OG)

• Genome-wide mapping