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• W4387706654 abstract "Full text Figures and data Side by side Abstract eLife assessment Introduction Results Discussion Materials and methods Data availability References Peer review Author response Article and author information Abstract The landscape of extrachromosomal circular DNA (eccDNA) during mammalian spermatogenesis, as well as the biogenesis mechanism, remains to be explored. Here, we revealed widespread eccDNA formation in human sperms and mouse spermatogenesis. We noted that germline eccDNAs are derived from oligonucleosomal DNA fragmentation in cells likely undergoing cell death, providing a potential new way for quality assessment of human sperms. Interestingly, small-sized eccDNAs are associated with euchromatin, while large-sized ones are preferentially generated from heterochromatin. By comparing sperm eccDNAs with meiotic recombination hotspots and structural variations, we found that they are barely associated with de novo germline deletions. We further developed a bioinformatics pipeline to achieve nucleotide-resolution eccDNA detection even with the presence of microhomologous sequences that interfere with precise breakpoint identification. Empowered by our method, we provided strong evidence to show that microhomology-mediated end joining is the major eccDNA biogenesis mechanism. Together, our results shed light on eccDNA biogenesis mechanism in mammalian germline cells. eLife assessment This study provides important information on the biogenesis of eccDNAs during spermatogenesis. The data presented are solid and supportive of the concussion that eccDNAs in spermatogenic cells are not derived from miotic recombination hotspots but rather represent oligonucleosomal DNA fragments from apoptotic male germ cells, whose ends are ligated through microhomology-mediated end-joining. This work is of interest to researchers working on germ cell biology and cancer biology. https://doi.org/10.7554/eLife.87115.3.sa0 About eLife assessments Introduction Apart from linear chromosome, DNA in circular form also exists in the nuclei of eukaryotes (Noer et al., 2022). Circular DNAs could be roughly classified into two groups based on their cell origins (Chiu et al., 2020). The one exclusively present in cancerous cells is usually referred to as extrachromosomal DNA (eccDNA), which is of megabase long in average and plays key roles in tumorigenesis (Wang et al., 2021b). The ecDNA biogenesis is linked to ‘episome model’ (Carroll et al., 1988), chromothripsis (Shoshani et al., 2021), breakage-fusion-bridge (Coquelle et al., 2002), or translocation-deletion-amplification model (Van Roy et al., 2006). The other class found in somatic and germ cells is usually called extrachromosomal circular DNA (eccDNA). The size of eccDNA ranges from dozens of bases to hundreds of kilobases (Paulsen et al., 2018). Contrary to ecDNAs, the biogenesis mechanisms and biological functions of eccDNAs are relatively less experimentally characterized, and current studies show inconclusive or even contradictory results. The genomic origins of eccDNAs have been extensively investigated in different cells and conditions with the application of Circle-seq and its refined derivatives (Mehta et al., 2020; Møller et al., 2015; Wang et al., 2021a), where eccDNAs are detected via rolling circle amplification and deep sequencing. While CpG islands (Shibata et al., 2012), gene-rich regions (Møller et al., 2018), and repeat elements, for example, LTR (long terminal repeat) (Møller et al., 2015), LINE-1 (Dillon et al., 2015), segmental duplication (Mouakkad-Montoya et al., 2021), or satellite DNA (Mouakkad-Montoya et al., 2021), are hotspots for eccDNA formation, others found that eccDNAs are nearly random with regard to genomic distribution (Møller et al., 2020), or even made opposite observations (Henriksen et al., 2022). Epigenomically, the overall higher GC content, the periodicities of dinucleotide, and eccDNA size convergently point to that nucleosome wrapping of DNA might contribute to the formation of small-sized eccDNAs (Shibata et al., 2012; Wang et al., 2021a), an intriguing starting point for mechanistic understanding of eccDNA origination. However, direct evidence for coincident positioning of eccDNAs and nucleosomes is still lacking, not to mention specific epigenetic marks on nucleosomes that are tightly associated with eccDNA formation. EccDNAs are increased upon DNA damages (Møller et al., 2015; Paulsen et al., 2021), suggesting them as by-products of successive DNA repairs. Among diverse repair pathways, it was reported that eccDNA levels particularly depend on resection after double-strand DNA break (DSB) and repair by microhomology-mediated end joining (MMEJ) (Paulsen et al., 2021; Wang et al., 2021a). In further support of the involvement of MMEJ, microhomology is found around eccDNA breakpoints (Lukaszewicz et al., 2021; Møller et al., 2015). However, in all studies using short-read sequencing technologies, eccDNA breakpoints are mis-annotated if microhomologous sequences are present around due to their interference to precise breakpoint detection (see our main text; Dillon et al., 2015; Henriksen et al., 2022; Kumar et al., 2017; Lv et al., 2022; Mann et al., 2022; Møller et al., 2018; Møller et al., 2020; Paulsen et al., 2019; Prada-Luengo et al., 2019; Shibata et al., 2012; Sin et al., 2020; Wang et al., 2021a; Zhang et al., 2021), or the eccDNA identification does not depend on precise breakpoint detections at all (Møller et al., 2015; Mouakkad-Montoya et al., 2021). The contribution of microhomology to eccDNA generations thus needs to be revisited with precise mapping of breakpoints. Alternative or additional mechanisms might be involved in germline eccDNA formation. During meiosis, two spatially closed break sites catalyzed by SPO11 at recombination hotspots may release eccDNAs accompanied by de novo deletions on linear chromosomes (Lukaszewicz et al., 2021). Consistently, germline microdeletions display similar sequence features with eccDNAs (Shibata et al., 2012). However, a recent study reported that the creation of germline eccDNAs negatively correlate with meiotic recombination rates (Henriksen et al., 2022). Therefore, it remains to be determined whether meiosis might significantly contribute to eccDNA biogenesis. We envision that eccDNA landscape during spermatogenesis is ideal for clarifying the abovementioned issues and so better understand the biogenesis mechanisms and biological implications of eccDNAs. Only a small fraction of histones will survive from the histone-to-protamine transition in mature sperms, allowing us to more specifically correlate eccDNA origination with histones. Studying eccDNAs in germline cells rather than somatic cells could help reveal to what extent meiosis might contribute to eccDNA generation and de novo structural variations that can be passed to offspring. Therefore, in this study, we profiled eccDNAs via Circle-seq in human sperms and different developmental stages of mouse germ cells with an improved analysis pipeline to identify eccDNAs at nucleotide resolution. We conclude that germline eccDNAs are likely formed by microhomology-mediated ligation of nucleosome-protected fragments and barely contribute to de novo genomic deletions at meiotic recombination hotspots. Results Widespread eccDNA formation in human and mouse germline cells Because it was reported that high content of eccDNAs existed in sperms (Henriksen et al., 2022), we examined the genome-wide eccDNA landscape in two human sperm samples by Circle-seq (Møller et al., 2018; Møller et al., 2015; Møller et al., 2020; see ‘Materials and methods’) and indeed found that there were widespread eccDNAs across the human genome (Figure 1A, Figure 1—figure supplement 1A). This motivated us to further investigate the biogenesis mechanism, particularly whether it might be linked to specific spermatogenesis processes. Given that it is ethically prohibited and technically challenging to collect pure spermatogenic cell types from human individuals, we therefore turned to use mouse model to study the eccDNA formation during spermatogenesis. Figure 1 with 3 supplements see all Download asset Open asset Overview of extrachromosomal circular DNA (eccDNA) formation during mouse spermatogenesis. (A) Schematic representation of Circle-seq in human sperm cells and mouse spermatocytes (SPA), round spermatids (RST), elongated spermatids (EST), and sperm cells validated with immunochemistry. SYCP3: a component of the synaptonemal complex; γH2AX: a marker for double-strand breaks; SP56: a marker for acrosome organelle; TUBULIN: structural component of manchette in EST and flagellum axoneme in sperm cells. (B) Number of eccDNAs detected in different cell types. *Two-sided t-test p-value<0.05; **two-sided t-test p-value<0.01. (C) Size distribution of eccDNAs during mouse spermatogenesis. Dotted lines indicate multiplies of 180 bp. (D) A representative genomic locus showing the gene annotation, Circle-seq signals, detected eccDNAs, and SINE and DNA repeat elements. Highlighted in red rectangle is a large-sized eccDNA. (E) Enrichment of eccDNAs at given genomic regions relative to randomly-selected control regions. (F) Enrichment of eccDNAs at given repeat elements relative to randomly selected control regions. A series of cell divisions and morphological changes are involved in spermatogenesis, where spermatogonial stem cells develop into spermatocytes (SPA) via mitosis, and SPA then undergo meiosis to produce haploid round spermatids (RST), which will take a dramatic morphological change and chromatin compaction to produce elongated spermatids (EST) and finally matured sperms (Hess and Renato de Franca, 2008; ROOSEN-RUNGE, 1962). We isolated SPA, RST, and EST using flow cytometry and collected sperms from mouse cauda epididymis (Hayama et al., 2016) for subsequent Circle-seq (Møller et al., 2018; Møller et al., 2015; Møller et al., 2020; see ‘Materials and methods’). All four cell types were validated with known markers and cell morphology (Figure 1A). EccDNA isolation procedures were validated by a high ratio of an exogenous circular DNA (pUC19) to a linear DNA locus (H19 gene) (Figure 1—figure supplement 2A), and the low abundance of mitochondria DNA that was supposed to be cleaved by PacI and degraded by exonuclease (Figure 1—figure supplement 2B). To account for sample variations, up to five biological replicates and ~150 million reads for each cell type were sequenced for eccDNA detection. From ~1500 to ~180,000 high-confidence eccDNAs were identified, suggesting widespread circular DNA formation during mouse spermatogenesis (Figure 1B; see ‘Materials and methods’). Some randomly selected eccDNAs were validated with PCR using outward primers (Figure 1—figure supplement 2C). The reproducible rate of eccDNAs with 50% reciprocal overlap between biological replicates was only ~2.4% in average, a level comparable to previous studies (Henriksen et al., 2022; Møller et al., 2018; Figure 1—figure supplement 1B). As noted earlier (Møller et al., 2018), the detected eccDNAs seemed not saturated (Figure 1—figure supplement 1C; see ‘Discussion’ and ‘Materials and methods’), which might underlie the observed low reproducibility. Nevertheless, principal component analysis suggested that the within-group similarity was marginally higher than the between-group similarity (Figure 1—figure supplement 1D), allowing investigation of stage-specific eccDNA features during mouse spermatogenesis. The detected germline eccDNAs verified known genomic features of eccDNAs. First, the natural size distribution of eccDNA is usually distorted in Circle-seq as smaller eccDNAs tend to be overrepresented in rolling circle amplification (Mohsen and Kool, 2016; Møller et al., 2018; Møller et al., 2015). As expected, the detected eccDNA population was dominated by small-sized eccDNAs, most of which were ~180 bp or ~360 bp long (Figure 1C). However, eccDNA size could occasionally reach to several kilobases and even tens of kilobases (Figure 1D). Second, eccDNAs from different cell types were all enriched at gene-rich regions, especially 5′UTR (Figure 1E, Figure 1—figure supplement 1E), corroborating the reported association between eccDNA frequency and gene density in somatic cells (Dillon et al., 2015; Shibata et al., 2012). ccDNAs were also highly associated with SINE but not LINE elements (Figure 1F), and quantitative analysis revealed that eccDNA biogenesis was positively correlated with SINE density (Figure 1—figure supplement 1F), but negatively correlated with LINE density (Figure 1—figure supplement 1G). Given that SINE and LINE elements function to orchestrate chromosomes into gene-rich A compartment and gene-poor B compartment, respectively (Lu et al., 2021), the positive correlation between eccDNAs and SINE elements might further support that eccDNAs are overall highly associated with the gene-rich regions. Interestingly, we also noticed a strong association between eccDNAs and DNA transposons (Figure 1F), suggesting that DNA transposons might get circularized rather than or in addition to reintegrated into the genome, an interesting possibility awaiting further investigations. Altogether, the genome-wide eccDNA landscape during mouse spermatogenesis allows us to further study the biogenesis mechanism and function of eccDNAs. High eccDNA load and periodic eccDNA size distribution in mouse sperm cells Notably, sperm cells had 97,372 eccDNAs detected in average, a number significantly higher than those in SPA (15,246), RST (18,426), and EST (3591) cells (Figure 1B). SPA cells did not show higher eccDNA numbers (Figure 1B), suggesting that meiosis does not seem to contribute significantly to eccDNA biogenesis. Since the same amount of eccDNAs (10 ng) was used for library construction and all samples were sequenced in comparable and sufficiently-deep depth, it suggests that eccDNA species in sperm cells has higher complexity. However, the higher starting cell number for sperm cells might account for the larger diversity of sperm eccDNA species (see ‘Discussion’ and ‘Materials and methods’); otherwise, it would be interesting to explore any specific features of sperm cells underlying the higher load of eccDNAs. In contrast to SPA, EST, and RST eccDNAs showing the unimodal distribution that was centered at ~180 bp, sperm-derived eccDNAs showed a multimodal distribution with a pronounced periodicity of ~180 bp (Figure 1C), which was readily seen in individual samples (Figure 1—figure supplement 3). Given that each nucleosome consists of 147 bp DNA wrapping itself around a histone core, the ~180-bp-long fragments likely corresponded to histone core region plus ~20–30 bp linker regions, as observed in apoptotic cells (Matassov et al., 2004). Although the identified eccDNAs in all spermatogenesis stages were likely related to nucleosomes, the different modes of size distribution might be due to distinct nucleosome compositions and structures between sperm and other spermatogenic cells. Mouse sperm eccDNAs come from DNA fragments protected by histones Only a small fraction of histones will be retained in mouse sperm cells after histone-to-protamine transition (Torres-Flores and Hernández-Hernández, 2020), permitting us to more specifically correlate eccDNAs with histones. We were therefore motivated to see whether the detected eccDNAs were derived from the retained histones in mature sperm cells. We noted that sperm eccDNAs had higher GC content than surrounding regions as well as control regions randomly selected across the genome (Figure 2A), resembling the sequence feature of nucleosome-protected DNA fragments. Consistently, sequence-based prediction revealed significantly higher nucleosome occupancy probability for ~180 bp (from 175 bp to 185 bp) and ~360 bp (from 355 bp to 365 bp) sperm eccDNA regions (Figure 2B; see ‘Materials and methods’). A small dip was observed at the center of ~360 bp eccDNA regions, which likely corresponded to the linker region between two nucleosomes (Figure 2B, right). Figure 2 Download asset Open asset Association between sperm extrachromosomal circular DNAs (eccDNAs) and nucleosome positioning. (A) GC contents of sperm eccDNAs, regions upstream and downstream of eccDNAs, and randomly selected length-matched control regions. ***Two-sided Wilcoxon test p-value<0.001. (B) Predicted probability of nucleosome occupancy for eccDNA and randomly selected length-matched control regions (highlighted by red-shaded area), and surrounding regions. Boxplots showing the probability distribution of individual eccDNAs and control regions. ***Two-sided Wilcoxon test p-value<0.001. (C) Enrichment of eccDNAs at different histones and histone modifications. (D, E) ChIP-seq signal distribution at [–1.8 kb, +1.8 kb] of the centers of ~180 bp (D) and ~360 bp (E) eccDNAs. ChIP-seq signals quantified as reads density are color-coded below heatmaps. It is a common practice to reuse publicly available genomics data generated in the same cell types for integrative analysis. Taking advantage of public ChIP-seq data for histones and their modifications in mouse sperm cells (Jung et al., 2019; Jung et al., 2017; Singh and Parte, 2021), we found that eccDNAs were significantly enriched with certain histone variants and modifications (Figure 2C), and 7.46% of sperm eccDNAs in total were intersected with at least one ChIP-seq peaks. Considering that histones occasionally retained in sperms might not generate strong ChIP-seq signals exceeding the peak calling cutoff, a meta-gene analysis of ChIP-seq signals at and around sperm eccDNA regions will likely provide more insights. Interestingly, enrichment of H3 histone and H2A.Z, TH2A, and TH2B histone variants but depletion of H3.3 variant was observed at ~180 bp sperm eccDNA regions (Figure 2D). These eccDNAs also showed strong associations with H3K27ac, H3K4me1, H3K9ac, and H3K27me3 modifications; however, no enrichment was seen for H3K4me3, and H3K36me3 and H3K9me3 signals were comparable with or even lower than randomly selected regions as control (Figure 2D). We next examined ~360 bp sperm eccDNAs, which supposedly correspond to two nucleosomes and made similar observations. Centers of ~360 bp eccDNAs were well positioned between two adjacent nucleosomes consisting of H3 histone and H2A.Z histone variants, and H3K27ac, H3K4me1, H3K9ac, and H3K27me3 histone modifications (Figure 2E). Similar to ~180 bp eccDNAs, ~360 bp eccDNAs did not show association with H3.3 or H3K4me3, or stronger association than randomly selected regions with H3K36me3 and H3K9me3 either (Figure 2E). Although H3.3 variant coincides with active transcription, it is also well known for its localization at heterochromatin region and its roles in promoting heterochromatin formation by inhibiting H3K9/K36 histone demethylase (Udugama et al., 2022). Together, euchromatin is generally more preferred than heterochromatin for eccDNA biogenesis, which is consistent with the enrichment of sperm eccDNAs at gene-rich regions (Figure 1D). Large-sized eccDNAs are preferentially generated from heterochromatin regions Intriguingly, periodic distribution of nucleosomes, for example, those marked with H3K27me3, was observed for ~360 bp but not for ~180 bp eccDNAs, indicating that eccDNAs from di-nucleosomes but not mono-nucleosomes preferentially originate from well-positioned nucleosome arrays (Figure 2E). We were further prompted to ask whether eccDNAs of different sizes are originated from different genomic regions. Indeed, small-sized eccDNAs (<3 kb) were more enriched at H3K27ac-marked euchromatin regions, while large-sized ones (≥3 kb) at H3K9me3-marked heterochromatin regions (Figure 3A). Accordingly, small-sized eccDNAs were generally more associated with genic regions, while large-sized ones with non-genic regions (Figure 3B). Since sperm eccDNAs in this study were dominantly small-sized ones (Figure 1C), strong enrichment of eccDNAs at genic regions was observed (Figure 1E). However, strong depletion at genic regions was reported for human sperm eccDNAs in a recent study (Henriksen et al., 2022). Close inspection suggests that the discrepancy is partially reconciled in the light of two eccDNA groups of different sizes. Henriksen et al. studied eccDNAs with the size largely ranging from ~3 kb to 50 kb (Henriksen et al., 2022), rather than small-sized ones reported by us and many others (Dillon et al., 2015; Møller et al., 2018; Møller et al., 2020; Paulsen et al., 2019; Shibata et al., 2012; Wang et al., 2021a). This was why we chose 3 kb as the cutoff to separate eccDNAs into small- and large-sized categories. In support of this notion, the large-sized sperm eccDNAs detected in this study displayed a weak negative correlation with gene density or Alu elements (Figure 3C and D). Altogether, compared to euchromatin regions, heterochromatin regions are probably too condensed to be fragmented into smaller pieces for small-sized eccDNA formation. Figure 3 with 1 supplement see all Download asset Open asset Large-sized extrachromosomal circular DNAs (eccDNAs) are preferentially generated from heterochromatin regions. (A) Distribution at H3K27ac- and H3K9me3-marked regions for eccDNAs of different sizes. (B) Distribution at different genomic regions for eccDNAs of different sizes. (C) Number of small (<3 kb) vs. large (≥3 kb) eccDNAs per Mb as a function of gene number per Mb. Pearson correlation coefficients and two-sided t-test p-values are indicated. (D) Number of small (<3 kb) vs. large (≥3 kb) eccDNAs per Mb as a function of Alu number per Mb. Pearson correlation coefficients and two-sided t-test p-values are indicated. Germline eccDNAs as apoptotic products are not associated with meiotic recombination hotspots The observed association between eccDNA and oligonucleosomal DNA fragmentation (Figure 2) is a typical feature of cell death. The spontaneous death of germ cells has been observed during the normal spermatogenesis (Liu et al., 2017; Shaha et al., 2010; Weinbauer et al., 2001; Young et al., 2001); however, it is still debatable whether spermatids and sperm can undergo apoptosis (Lachaud et al., 2004). Thus, sperm-derived eccDNAs might be associated with apoptosis (if exists) or unprogrammed cell death of germ cells during the spermatogenesis (see also ‘Discussion’). In support of this hypothesis, all features associated with mouse germline eccDNAs identified in this study (Figure 1C, E, and F) closely matched with those of eccDNAs whose generation is dependent on apoptotic DNA fragmentation (Figure 4—figure supplement 1; Wang et al., 2021a). During meiosis, two spatially closed cleavage sites catalyzed by SPO11 at recombination hotspots could release eccDNAs and generate de novo genomic deletions (Lukaszewicz et al., 2021), which, if transmitted to offspring, might contribute to structural variations within population. Since most sperm eccDNAs likely result from oligonucleosomal DNA fragments in sperm cells undergoing cell death (Figures 2 and 3) and SPA cells undergoing meiosis does give rise to more eccDNAs than other cells (Figure 1B), meiotic recombination is unlikely the major mechanism for germline eccDNA generation. To test this hypothesis, we first investigated to what extent eccDNA breakpoints well correspond to recombination hotspots defined as SPO11 or PRDM9 binding sites (Alleva et al., 2021; Lange et al., 2016). We noted that there was only a small number of eccDNAs with both breakpoints located in one recombination hotspot or two different hotspots (Figure 4A). These eccDNAs only constituted <0.15% (or <350) of mouse germline eccDNAs, suggesting a very low level of coincidence between eccDNA generation and meiotic recombination (Figure 4A). Consistently, only dozens of, or a few hundred eccDNAs in mouse germline cells coincided with known genomic deletions within mouse population (Figure 4B). Altogether, germline eccDNAs are likely apoptotic products that are not associated with meiotic recombination hotspots and heritable genomic deletions. Figure 4 with 2 supplements see all Download asset Open asset Microhomology-directed ligation accounts for emergence of most extrachromosomal circular DNAs (eccDNAs). (A) Numbers of eccDNAs or randomly selected control regions overlapped with recombination hotspots in mouse. (B) Shown are numbers of mouse sperm eccDNAs or randomly selected control regions having 95% reciprocal overlap with different types of structural variations. (C) Illustrated are how an eccDNA with homologous sequences (CGA) at two ends is identified from short-read sequencing data by our methods vs. other methods. (D) Percentages of homologous sequences of different lengths (coded by different color saturation levels) are shown for eccDNAs and randomly selected control regions. (E) GC content of homologous sequences and randomly selected control regions. (F) Percentages of homologous sequences of different lengths (coded by different color saturation levels) are shown for small-sized (<3 kb) and large-sized (≥3 kb) eccDNAs. (G) Length of microhomologous sequences as a function of the eccDNA size. Data points are shown as median plus lower (25%) and upper (75%) quartiles. The shaded area is 95% confidence interval of linear regression line. Pearson correlation coefficient and two-sided t-test p-value are indicated. (H) Sequencing motif analysis for ±10 bp leftmost left ends and ±10 bp leftmost right ends of eccDNAs with no perfectly matched homologous sequences observed. (I) Model for microhomology-mediated end joining (MMEJ)-directed eccDNA biogenesis. Microhomology-directed ligation is the major biogenesis mechanism of germline eccDNAs We therefore further explored how nucleosome-protected DNA fragments get circularized into eccDNAs. As suggested by previous studies, MMEJ is implicated in eccDNA biogenesis (Lukaszewicz et al., 2021; Møller et al., 2015; Paulsen et al., 2021; Wang et al., 2021a). The precise distribution of microhomologous sequences relative to eccDNA breakpoints will help better understand how and to what extent MMEJ might contribute to eccDNA biogenesis. However, we noted that the presence of microhomologous sequences will hinder precision eccDNA breakpoint identification (Figure 4C), which is not well dealt with by existing methods for eccDNA detection, including ECCsplorer (Mann et al., 2022), Circle_finder (Kumar et al., 2017), Circle_Map (Prada-Luengo et al., 2019), and ecc_finder (Zhang et al., 2021; Figure 4—figure supplement 2A). Short sequencing reads spanning eccDNA breakpoints will be mapped to the genome as split reads, with its first part mapped to the right end of eccDNA, and the second part to the left end. If the sequence in front of the left eccDNA end is homologous to the right eccDNA end, or if the sequence following the right eccDNA end is homologous to the left eccDNA end, the homologous regions will be included in both parts of split reads to reach to a maximal length of matches, and many existing methods will mistake the eccDNA plus two homologous regions as the whole eccDNA region (Figure 4C). Being aware of it, we developed a base-resolution method for eccDNA identification on the basis of previous efforts (Figure 4—figure supplement 2B; Kumar et al., 2017; Møller et al., 2018). When homologous sequences are present, we record the coordinates of the leftmost form of eccDNA and an offset corresponding to the length of homologous sequences to represent all possible eccDNA variants (Figure 4C). Similar to ECCsplorer (Mann et al., 2022), Circle_finder (Kumar et al., 2017), Circle_Map (Prada-Luengo et al., 2019), and ecc_finder (Zhang et al., 2021), our method was not designed to identity eccDNAs that encompass multiple gene loci. We evaluated the performance of our method in comparison with existing methods. Firstly, we simulated paired-end reads derived from a set of eccDNAs with homologous sequences around breakpoints and employed all methods for eccDNA identification (see ‘Materials and methods’). In total, 97.9, 97.9, 97.4, 95.3, and 91.1% eccDNA regions could be detected by our method, Circle_Map, Circle_finder, ecc_finder, and ECCsplorer, respectively (Figure 4—figure supplement 2C). This result suggests that our method has comparable performance with existing methods in detecting eccDNA regions. Moreover, our method could faithfully assign breakpoints with 97.4% accuracy, in contrast to no more than 15% by other methods (Figure 4—figure supplement 2D). Secondly, we applied all methods on one dataset generated in this study. Again, our method had comparable sensitivity and specificity with existing methods (especially Circle_finder and Circle_Map) in detecting eccDNA regions (Figure 4—figure supplement 2E). At least 60% of eccDNAs with homologous sequences were misannotated by ECCsplorer, ecc_finder, Circle_finder, and Circle_Map, respectively (Figure 4—figure supplement 2A and F). Overall, our method shows a high efficiency and accuracy in precise eccDNA detection. In contrast to simulated controls (15%), more than one-third of eccDNAs had ≥1 bp homologous sequences, most of which were shorter than 5 bp (Figure 4D), suggesting the involvement of MMEJ in eccDNA biogenesis. The GC content of homologous sequences was higher than that of simulated control regions, permitting stronger base-pairing for efficient MMEJ (Figure 4E). Considering that two free-ends of long DNA fragments might be not as spatially close as those of short DNA fragments, formation of longer eccDNA should more rely on longer homologous sequences for stable base-pairing. Indeed, large-sized eccDNAs in SPA, RST, and EST cells did show higher percentage of ≥2 bp" @default.
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• W4387706654 title "Joint Public Review:: Microhomology-mediated circular DNA formation from oligonucleosomal fragments during spermatogenesis" @default.
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